From 7ff89f4fd2ba67fda82b6229d29eaf12e967761d Mon Sep 17 00:00:00 2001 From: JimmyYang20 Date: Wed, 24 Aug 2022 23:00:19 +0800 Subject: [PATCH 01/22] Add lifelong learning feature Signed-off-by: JimmyYang20 --- core/common/constant.py | 21 ++ core/storymanager/rank/rank.py | 5 +- .../testcasecontroller/algorithm/algorithm.py | 37 ++- .../algorithm/module/module.py | 83 +++---- .../algorithm/paradigm/__init__.py | 1 + .../algorithm/paradigm/base.py | 46 +++- .../paradigm/lifelong_learning/__init__.py | 16 ++ .../lifelong_learning/lifelong_learning.py | 226 ++++++++++++++++++ core/testcasecontroller/metrics/metrics.py | 2 +- .../third_party/sedna-0.4.5-py3-none-any.whl | Bin 124813 -> 158785 bytes 10 files changed, 371 insertions(+), 66 deletions(-) create mode 100644 core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py create mode 100644 core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py diff --git a/core/common/constant.py b/core/common/constant.py index d6bdc3b6..16df05fd 100644 --- a/core/common/constant.py +++ b/core/common/constant.py @@ -32,6 +32,7 @@ class ParadigmType(Enum): """ SINGLE_TASK_LEARNING = "singletasklearning" INCREMENTAL_LEARNING = "incrementallearning" + LIFELONG_LEARNING = "lifelonglearning" class ModuleType(Enum): @@ -39,8 +40,27 @@ class ModuleType(Enum): Algorithm module type. """ BASEMODEL = "basemodel" + + # HEM HARD_EXAMPLE_MINING = "hard_example_mining" + # STP + TASK_DEFINITION = "task_definition" + TASK_RELATIONSHIP_DISCOVERY = "task_relationship_discovery" + TASK_ALLOCATION = "task_allocation" + TASK_REMODELING = "task_remodeling" + INFERENCE_INTEGRATE = "inference_integrate" + + # KM + TASK_UPDATE_DECISION = "task_update_decision" + + # UTP + UNSEEN_TASK_ALLOCATION = "unseen_task_allocation" + + # UTD + UNSEEN_SAMPLE_RECOGNITION = "unseen_sample_recognition" + UNSEEN_SAMPLE_RE_RECOGNITION = "unseen_sample_re_recognition" + class SystemMetricType(Enum): """ @@ -48,6 +68,7 @@ class SystemMetricType(Enum): """ SAMPLES_TRANSFER_RATIO = "samples_transfer_ratio" + class TestObjectType(Enum): """ Test object type of ianvs. diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py index ac30c7a9..7614d3bd 100644 --- a/core/storymanager/rank/rank.py +++ b/core/storymanager/rank/rank.py @@ -107,7 +107,10 @@ def _get_all_module_types(cls, test_cases) -> list: def _get_algorithm_hyperparameters(cls, algorithm): hps = {} for module in algorithm.modules.values(): - hps.update(**module.hyperparameters) + for name, value in module.hyperparameters.items(): + name = f"{module.type}-{name}" + value = str(value) + hps.update({name: value}) return hps def _get_all_hps_names(self, test_cases) -> list: diff --git a/core/testcasecontroller/algorithm/algorithm.py b/core/testcasecontroller/algorithm/algorithm.py index 9a4311c2..f064d7bf 100644 --- a/core/testcasecontroller/algorithm/algorithm.py +++ b/core/testcasecontroller/algorithm/algorithm.py @@ -17,13 +17,15 @@ import copy from core.common.constant import ParadigmType +from core.common.utils import load_module from core.testcasecontroller.algorithm.module import Module -from core.testcasecontroller.algorithm.paradigm import SingleTaskLearning, IncrementalLearning +from core.testcasecontroller.algorithm.paradigm import SingleTaskLearning, IncrementalLearning, \ + LifelongLearning from core.testcasecontroller.generation_assistant import get_full_combinations - -# pylint: disable=too-few-public-methods class Algorithm: + # pylint: disable=too-many-instance-attributes + # pylint: disable=too-few-public-methods """ Algorithm: typical distributed-synergy AI algorithm paradigm. Notes: @@ -49,14 +51,20 @@ class Algorithm: def __init__(self, name, config): self.name = name self.paradigm_type: str = "" + self.third_party_packages: list = [] self.incremental_learning_data_setting: dict = { "train_ratio": 0.8, "splitting_method": "default" } + self.lifelong_learning_data_setting: dict = { + "train_ratio": 0.8, + "splitting_method": "default" + } self.initial_model_url: str = "" self.modules: list = [] self.modules_list = None self._parse_config(config) + self._load_third_party_packages() def paradigm(self, workspace: str, **kwargs): """ @@ -86,6 +94,9 @@ def paradigm(self, workspace: str, **kwargs): if self.paradigm_type == ParadigmType.INCREMENTAL_LEARNING.value: return IncrementalLearning(workspace, **config) + if self.paradigm_type == ParadigmType.LIFELONG_LEARNING.value: + return LifelongLearning(workspace, **config) + return None def _check_fields(self): @@ -106,6 +117,11 @@ def _check_fields(self): f"algorithm incremental_learning_data_setting" f"({self.incremental_learning_data_setting} must be dictionary type.") + if not isinstance(self.lifelong_learning_data_setting, dict): + raise ValueError( + f"algorithm lifelong_learning_data_setting" + f"({self.lifelong_learning_data_setting} must be dictionary type.") + if not isinstance(self.initial_model_url, str): raise ValueError( f"algorithm initial_model_url({self.initial_model_url}) must be string type.") @@ -131,7 +147,7 @@ def _parse_modules_config(cls, config): for module in modules: hps_list = module.hyperparameters_list if not hps_list: - modules_list.append((module.type, None)) + modules_list.append((module.type, [module])) continue module_list = [] @@ -145,3 +161,16 @@ def _parse_modules_config(cls, config): module_combinations_list = get_full_combinations(modules_list) return module_combinations_list + + def _load_third_party_packages(self): + if len(self.third_party_packages) == 0: + return + + for package in self.third_party_packages: + name = package["name"] + url = package["url"] + try: + load_module(url) + except Exception as err: + raise Exception(f"load third party packages(name={name}, url={url}) failed," + f" error: {err}.") from err diff --git a/core/testcasecontroller/algorithm/module/module.py b/core/testcasecontroller/algorithm/module/module.py index 7298b18d..04ee6527 100644 --- a/core/testcasecontroller/algorithm/module/module.py +++ b/core/testcasecontroller/algorithm/module/module.py @@ -23,6 +23,7 @@ from core.testcasecontroller.generation_assistant import get_full_combinations +# pylint: disable=too-few-public-methods class Module: """ Algorithm Module: @@ -52,8 +53,8 @@ def __init__(self, config): self.type: str = "" self.name: str = "" self.url: str = "" - self.hyperparameters = None - self.hyperparameters_list = None + self.hyperparameters = {} + self.hyperparameters_list = [] self._parse_config(config) def _check_fields(self): @@ -71,75 +72,59 @@ def _check_fields(self): if not isinstance(self.url, str): raise ValueError(f"module url({self.url}) must be string type.") - def basemodel_func(self): + def get_module_instance(self, module_type): """ - get basemodel module function of the module. + get function of algorithm module by using module type + + Parameters + --------- + module_type: string + module type, e.g.: basemodel, hard_example_mining, etc. Returns - -------- + ------ function """ + class_factory_type = ClassType.GENERAL + if module_type in [ModuleType.HARD_EXAMPLE_MINING.value]: + class_factory_type = ClassType.HEM - if not self.url: - raise ValueError(f"url({self.url}) of basemodel module must be provided.") + elif module_type in [ModuleType.TASK_DEFINITION.value, + ModuleType.TASK_RELATIONSHIP_DISCOVERY.value, + ModuleType.TASK_REMODELING.value, + ModuleType.TASK_ALLOCATION.value, + ModuleType.INFERENCE_INTEGRATE.value]: + class_factory_type = ClassType.STP - try: - utils.load_module(self.url) - # pylint: disable=E1134 - basemodel = ClassFactory.get_cls(type_name=ClassType.GENERAL, - t_cls_name=self.name)(**self.hyperparameters) - except Exception as err: - raise Exception(f"basemodel module loads class(name={self.name}) failed, " - f"error: {err}.") from err + elif module_type in [ModuleType.TASK_UPDATE_DECISION.value]: + class_factory_type = ClassType.KM - return basemodel + elif module_type in [ModuleType.UNSEEN_TASK_ALLOCATION.value]: + class_factory_type = ClassType.UTP - def hard_example_mining_func(self): - """ - get hard example mining function of the module. - - Returns: - -------- - function - - """ + elif module_type in [ModuleType.UNSEEN_SAMPLE_RECOGNITION.value, + ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value]: + class_factory_type = ClassType.UTD if self.url: try: utils.load_module(self.url) # pylint: disable=E1134 func = ClassFactory.get_cls( - type_name=ClassType.HEM, t_cls_name=self.name)(**self.hyperparameters) + type_name=class_factory_type, t_cls_name=self.name)(**self.hyperparameters) return func except Exception as err: - raise Exception(f"hard_example_mining module loads class" - f"(name={self.name}) failed, error: {err}.") from err + raise Exception(f"module(type={module_type} loads class(name={self.name}) " + f"failed, error: {err}.") from err - # call built-in hard example mining function - hard_example_mining = {"method": self.name} + # call lib built-in module function + module_func = {"method": self.name} if self.hyperparameters: - hard_example_mining["param"] = self.hyperparameters - - return hard_example_mining + module_func["param"] = self.hyperparameters - def get_module_func(self, module_type): - """ - get function of algorithm module by using module type - - Parameters - --------- - module_type: string - module type, e.g.: basemodel, hard_example_mining, etc. - - Returns - ------ - function - - """ - func_name = f"{module_type}_func" - return getattr(self, func_name) + return module_func def _parse_config(self, config): # pylint: disable=C0103 diff --git a/core/testcasecontroller/algorithm/paradigm/__init__.py b/core/testcasecontroller/algorithm/paradigm/__init__.py index b5804704..7e5e0d84 100644 --- a/core/testcasecontroller/algorithm/paradigm/__init__.py +++ b/core/testcasecontroller/algorithm/paradigm/__init__.py @@ -15,3 +15,4 @@ # pylint: disable=missing-module-docstring from .incremental_learning import IncrementalLearning from .singletask_learning import SingleTaskLearning +from .lifelong_learning import LifelongLearning diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py index 726f4bb5..67cca5e4 100644 --- a/core/testcasecontroller/algorithm/paradigm/base.py +++ b/core/testcasecontroller/algorithm/paradigm/base.py @@ -17,6 +17,7 @@ import os from sedna.core.incremental_learning import IncrementalLearning +from sedna.core.lifelong_learning import LifelongLearning from core.common.constant import ModuleType, ParadigmType @@ -51,7 +52,7 @@ def __init__(self, workspace, **kwargs): self.dataset = kwargs.get("dataset") self.workspace = workspace self.system_metric_info = {} - self.modules_funcs = self._get_module_funcs() + self.module_instances = self._get_module_instances() os.environ["LOCAL_TEST"] = "TRUE" def dataset_output_dir(self): @@ -68,13 +69,12 @@ def dataset_output_dir(self): os.makedirs(output_dir) return output_dir - def _get_module_funcs(self): - module_funcs = {} + def _get_module_instances(self): + module_instances = {} for module_type, module in self.modules.items(): - func = module.get_module_func(module_type) - if callable(func): - module_funcs.update({module_type: func}) - return module_funcs + func = module.get_module_instance(module_type) + module_instances.update({module_type: func}) + return module_instances def build_paradigm_job(self, paradigm_type): """ @@ -91,12 +91,36 @@ def build_paradigm_job(self, paradigm_type): """ if paradigm_type == ParadigmType.SINGLE_TASK_LEARNING.value: - return self.modules_funcs.get(ModuleType.BASEMODEL.value)() + return self.module_instances.get(ModuleType.BASEMODEL.value) if paradigm_type == ParadigmType.INCREMENTAL_LEARNING.value: return IncrementalLearning( - estimator=self.modules_funcs.get(ModuleType.BASEMODEL.value)(), - hard_example_mining=self.modules_funcs.get( - ModuleType.HARD_EXAMPLE_MINING.value)()) + estimator=self.module_instances.get(ModuleType.BASEMODEL.value), + hard_example_mining=self.module_instances.get( + ModuleType.HARD_EXAMPLE_MINING.value)) + + if paradigm_type == ParadigmType.LIFELONG_LEARNING.value: + return LifelongLearning( + estimator=self.module_instances.get( + ModuleType.BASEMODEL.value), + task_definition=self.module_instances.get( + ModuleType.TASK_DEFINITION.value), + task_relationship_discovery=self.module_instances.get( + ModuleType.TASK_DEFINITION.value), + task_allocation=self.module_instances.get( + ModuleType.TASK_ALLOCATION.value), + task_remodeling=self.module_instances.get( + ModuleType.TASK_REMODELING.value), + inference_integrate=self.module_instances.get( + ModuleType.INFERENCE_INTEGRATE.value), + task_update_decision=self.module_instances.get( + ModuleType.TASK_UPDATE_DECISION.value), + unseen_task_allocation=self.module_instances.get( + ModuleType.UNSEEN_TASK_ALLOCATION.value), + unseen_sample_recognition=self.module_instances.get( + ModuleType.UNSEEN_SAMPLE_RECOGNITION.value), + unseen_sample_re_recognition=self.module_instances.get( + ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value) + ) return None diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py new file mode 100644 index 00000000..001fc95e --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# pylint: disable=missing-module-docstring +from .lifelong_learning import LifelongLearning diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py new file mode 100644 index 00000000..2b035706 --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -0,0 +1,226 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Lifelong Learning Paradigm""" + +import os +import shutil + +import numpy as np +from sedna.datasources import BaseDataSource + +from core.common.constant import ParadigmType, SystemMetricType +from core.testcasecontroller.algorithm.paradigm.base import ParadigmBase +from core.testcasecontroller.metrics import get_metric_func +from core.common.utils import get_file_format, is_local_dir + + +class LifelongLearning(ParadigmBase): + # pylint: disable=too-many-locals + """ + LifelongLearning + provide the flow of lifelong learning paradigm. + Notes: + 1. Ianvs serves as testing tools for test objects, e.g., algorithms. + 2. Ianvs does NOT include code directly on test object. + 3. Algorithms serve as typical test objects in Ianvs + and detailed algorithms are thus NOT included in this Ianvs python file. + 4. As for the details of example test objects, e.g., algorithms, + please refer to third party packages in Ianvs example. + For example, AI workflow and interface pls refer to sedna + (sedna docs: https://sedna.readthedocs.io/en/latest/api/lib/index.html), + and module implementation pls refer to `examples' test algorithms`, + e.g., basemodel.py, hard_example_mining.py. + + Parameters + --------- + workspace: string + the output required for lifelong learning paradigm. + kwargs: dict + config required for the test process of lifelong learning paradigm, + e.g.: algorithm modules, dataset, initial model, incremental rounds, + model eval config, etc. + + """ + + def __init__(self, workspace, **kwargs): + ParadigmBase.__init__(self, workspace, **kwargs) + + self.incremental_learning_data_setting = kwargs.get("lifelong_learning_data_setting") + self.initial_model = kwargs.get("initial_model_url") + + self.incremental_rounds = kwargs.get("incremental_rounds", 1) + self.model_eval_config = kwargs.get("model_eval") + + self.cloud_task_index = '/tmp/cloud_task/index.pkl' + self.edge_task_index = '/tmp/edge_task/index.pkl' + + self.system_metric_info = {SystemMetricType.SAMPLES_TRANSFER_RATIO.value: []} + + def run(self): + # pylint:disable=duplicate-code + """ + run the test flow of incremental learning paradigm. + + Returns + ------ + test result: numpy.ndarray + system metric info: dict + information needed to compute system metrics. + + """ + + rounds = self.incremental_rounds + samples_transfer_ratio_info = self.system_metric_info.get( + SystemMetricType.SAMPLES_TRANSFER_RATIO.value) + dataset_files = self._split_dataset(splitting_dataset_times=rounds) + + # pylint: disable=C0103 + for r in range(1, rounds + 1): + if r == 1: + train_dataset_file, eval_dataset_file = dataset_files[r - 1] + self.cloud_task_index = self._train(self.cloud_task_index, train_dataset_file, r) + self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + + else: + infer_dataset_file, eval_dataset_file = dataset_files[r - 1] + + inference_results, unseen_task_train_samples = self._inference(self.edge_task_index, + infer_dataset_file, + r) + samples_transfer_ratio_info.append((inference_results, unseen_task_train_samples.x)) + + # If no unseen task samples in the this round, starting the next round + if len(unseen_task_train_samples.x) <= 0: + continue + + self.cloud_task_index = self._train(self.cloud_task_index, + unseen_task_train_samples, + r) + self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + + test_res, unseen_task_train_samples = self._inference(self.edge_task_index, + self.dataset.test_url, + "test") + + samples_transfer_ratio_info.append((test_res, unseen_task_train_samples.x)) + + return test_res, self.system_metric_info + + def _inference(self, edge_task_index, data_index_file, rounds): + # pylint:disable=duplicate-code + output_dir = os.path.join(self.workspace, + f"output/inference/results/{rounds}") + if not is_local_dir(output_dir): + os.makedirs(output_dir) + + unseen_task_saved_dir = os.path.join(self.workspace, + f"output/inference/unseen_task_samples/{rounds}") + if not is_local_dir(unseen_task_saved_dir): + os.makedirs(unseen_task_saved_dir) + + os.environ["INFERENCE_RESULT_DIR"] = output_dir + os.environ["MODEL_URLS"] = f"{edge_task_index}" + + inference_dataset = self.dataset.load_data(data_index_file, "eval", + feature_process=_data_feature_process) + + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + + inference_results = [] + unseen_tasks = [] + unseen_task_labels = [] + for i, _ in enumerate(inference_dataset.x): + data = BaseDataSource(data_type="test") + data.x = inference_dataset.x[i:(i + 1)] + res, is_unseen_task, _ = job.inference(data) + + inference_results.extend(res) + if is_unseen_task: + unseen_tasks.append(inference_dataset.x[i]) + unseen_task_labels.append(inference_dataset.y[i]) + for infer_data in inference_dataset.x[i]: + shutil.copy(infer_data, unseen_task_saved_dir) + + del job + + unseen_task_train_samples = BaseDataSource(data_type="train") + unseen_task_train_samples.x = np.array(unseen_tasks) + unseen_task_train_samples.y = np.array(unseen_task_labels) + + return inference_results, unseen_task_train_samples + + def _train(self, cloud_task_index, train_dataset, rounds): + train_output_dir = os.path.join(self.workspace, f"output/train/{rounds}") + if not is_local_dir(train_output_dir): + os.makedirs(train_output_dir) + + os.environ["CLOUD_KB_INDEX"] = cloud_task_index + os.environ["OUTPUT_URL"] = train_output_dir + if rounds == 1: + os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'False' + else: + os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'True' + + if isinstance(train_dataset, str): + train_dataset = self.dataset.load_data(train_dataset, "train", + feature_process=_data_feature_process) + + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + cloud_task_index = job.train(train_dataset) + del job + + return cloud_task_index + + def _eval(self, cloud_task_index, data_index_file, rounds): + eval_output_dir = os.path.join(self.workspace, f"output/eval/{rounds}") + if not is_local_dir(eval_output_dir): + os.makedirs(eval_output_dir) + + model_eval_info = self.model_eval_config + model_metric = model_eval_info.get("model_metric") + + os.environ["OUTPUT_URL"] = eval_output_dir + os.environ["model_threshold"] = str(model_eval_info.get("threshold")) + os.environ["operator"] = model_eval_info.get("operator") + os.environ["MODEL_URLS"] = f"{cloud_task_index}" + + eval_dataset = self.dataset.load_data(data_index_file, "eval", + feature_process=_data_feature_process) + + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + _, metric_func = get_metric_func(model_metric) + edge_task_index = job.evaluate(eval_dataset, metrics=metric_func) + + del job + + return edge_task_index + + def _split_dataset(self, splitting_dataset_times=1): + # pylint:disable=duplicate-code + train_dataset_ratio = self.incremental_learning_data_setting.get("train_ratio") + splitting_dataset_method = self.incremental_learning_data_setting.get("splitting_method") + + return self.dataset.split_dataset(self.dataset.train_url, + get_file_format(self.dataset.train_url), + train_dataset_ratio, + method=splitting_dataset_method, + dataset_types=("model_train", "model_eval"), + output_dir=self.dataset_output_dir(), + times=splitting_dataset_times) + + +def _data_feature_process(line: str): + res = line.strip().split() + return res[:-1], res[-1] diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py index 8ad0d2d5..8a8481a4 100644 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -67,8 +67,8 @@ def get_metric_func(metric_dict: dict): name = metric_dict.get("name") url = metric_dict.get("url") if url: - load_module(url) try: + load_module(url) metric_func = ClassFactory.get_cls(type_name=ClassType.GENERAL, t_cls_name=name) return name, metric_func except Exception as err: diff --git a/examples/resources/third_party/sedna-0.4.5-py3-none-any.whl b/examples/resources/third_party/sedna-0.4.5-py3-none-any.whl index 2d5de75353e154b96a864868c1124f8cfa74502d..f438bec77f5ffee067293191e870b9b733f032e3 100644 GIT binary patch delta 55959 zcmagFV{~TCw)Y*|wr$(#*tTu6lU#Afw(WG%v28mY+qRRZ?|bj}*=L`#&+~q$G1jV4 zt5#j3<~3){`TwoxX~^E+&s@PAzmBr$d_00RN} zCw9S;0-}|B?K6dtIwi&gqq1aZTis7hz8Az)7JSoWDN>TRga?fRXfC`?8X~}7gzrCff55JT*y-BZE2%;*$idxv-+et5*Z0f=X&grJ)++$T zuryU8&Mjy8k$cRC4PQA(?*rw(qMG6Rjzbkw0{9`1FN_5saG;l)398;un*gUpRYFm%mO$rYu z3JHRnV{0fERcvx;E*JVA{m-7v&CUGWU7Z`qdIhD1tL`^Z5nm2tMB5a zgUFh$_%F3;j3DDe^U4xco*mwMG7x$OYGzZewNUlZCQ{M`6~m$9%7wb=d&1$86TC2} zjKq3dEC*HZGMfd?SHj-uk*~qj@xQiZf<uaYq4@lQE@zdXMHkGvey z!b|BQ?ySlm!2it*Di9BW5&qhIMLnSieQ+S41;hkP%+#7OV7U5TWOwj?mQ1C=d{HVm>k&z{cLe!`95y!py+V$ll1p z%+Ac-mC?Y!%HGP=z<|Ng%Qs=xA(IJdXp8m%z*WqcnH0|uyM`*m3SUgBoLBjZhVA38 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z$s+`zWgU11p^|jeLr+{_=gF=fFO&_ou=Bou&|*=D*zVXF20K{H4?w}TAUkAX2T~q( z^*UO77KB#qKtZV1?q~%saeEsb3@86KJn)iOex4>nL)9<=iK@-bn1ff|1$eDj#zM`| z9umq$A*i6=jz<$&aHhDj3&z*Ncrh%z0P7)sR7ydVY-? z*`rC2P=yF$%nF84cv~3`*uV-MaEf9pAe4_3GA>@?dl=VrN7&#MzeDlP{J%4JM|zI)HB7Sur>5xNc%fIY&jE*cBPWmo zlMY%ncvll(zv5uOSh1iz!3gLrF-Q$Pb~;K2ni8q+9B`g>!y6@b>STu=VdtGddJHet z-5e}lyo4jHtTRY)*cBsZI2AHdj^r%(@Nbk4oR4Gv1l@@D7}LlJai|>U&n{YSawr#u zG+mA+M3H@&r!~A66v2B2_8KIWdBlbh-BP@LFhY;F<$q!G)h+Sc$ zBSi>^#0{i@lw4t=<6|^ym;n97fJHQ0eT0p11y3JxJVA5#*Br@vT|qhw$3JIn@NK4l ZalA$}A7Z&k%mH(Nlo Date: Thu, 25 Aug 2022 00:27:29 +0800 Subject: [PATCH 02/22] Add lifelong learning example Signed-off-by: JimmyYang20 --- .../benchmarkingjob.yaml | 72 ++++ .../testalgorithms/rfnet/RFNet/accuracy.py | 38 ++ .../testalgorithms/rfnet/RFNet/basemodel.py | 315 ++++++++++++++ .../rfnet/RFNet/dataloaders/__init__.py | 119 ++++++ .../RFNet/dataloaders/custom_transforms.py | 240 +++++++++++ .../dataloaders/custom_transforms_rgb.py | 230 ++++++++++ .../RFNet/dataloaders/datasets/__init__.py | 0 .../dataloaders/datasets/citylostfound.py | 276 ++++++++++++ .../RFNet/dataloaders/datasets/cityrand.py | 151 +++++++ .../RFNet/dataloaders/datasets/cityscapes.py | 152 +++++++ .../rfnet/RFNet/dataloaders/datasets/e1.py | 151 +++++++ .../RFNet/dataloaders/datasets/mapillary.py | 152 +++++++ .../RFNet/dataloaders/datasets/target.py | 152 +++++++ .../rfnet/RFNet/dataloaders/datasets/temp.txt | 38 ++ .../rfnet/RFNet/dataloaders/datasets/xrlab.py | 152 +++++++ .../rfnet/RFNet/dataloaders/utils.py | 244 +++++++++++ .../testalgorithms/rfnet/RFNet/eval.py | 247 +++++++++++ .../rfnet/RFNet/models/replicate.py | 88 ++++ .../rfnet/RFNet/models/resnet/__init__.py | 0 .../resnet_single_scale_single_attention.py | 391 +++++++++++++++++ ...et_single_scale_single_attention_unseen.py | 396 ++++++++++++++++++ .../rfnet/RFNet/models/rfnet.py | 27 ++ .../rfnet/RFNet/models/rfnet_for_unseen.py | 33 ++ .../testalgorithms/rfnet/RFNet/models/util.py | 99 +++++ .../testalgorithms/rfnet/RFNet/mypath.py | 20 + .../testalgorithms/rfnet/RFNet/predict.py | 98 +++++ .../testalgorithms/rfnet/RFNet/run_server.py | 252 +++++++++++ .../rfnet/RFNet/sedna_evaluate.py | 50 +++ .../rfnet/RFNet/sedna_predict.py | 132 ++++++ .../testalgorithms/rfnet/RFNet/sedna_train.py | 78 ++++ .../testalgorithms/rfnet/RFNet/test.py | 52 +++ .../testalgorithms/rfnet/RFNet/train.py | 211 ++++++++++ .../rfnet/RFNet/utils/__init__.py | 0 .../testalgorithms/rfnet/RFNet/utils/args.py | 52 +++ .../rfnet/RFNet/utils/calculate_weights.py | 29 ++ .../rfnet/RFNet/utils/iouEval.py | 142 +++++++ .../testalgorithms/rfnet/RFNet/utils/loss.py | 64 +++ .../rfnet/RFNet/utils/lr_scheduler.py | 70 ++++ .../rfnet/RFNet/utils/metrics.py | 146 +++++++ .../testalgorithms/rfnet/RFNet/utils/saver.py | 68 +++ .../rfnet/RFNet/utils/summaries.py | 39 ++ .../testalgorithms/rfnet/basemodel.py | 290 +++++++++++++ .../testalgorithms/rfnet/rfnet_algorithm.yaml | 59 +++ .../rfnet/task_allocation_by_origin.py | 64 +++ .../rfnet/task_definition_by_origin.py | 81 ++++ .../testenv/accuracy.py | 55 +++ .../testenv/testenv.yaml | 34 ++ 47 files changed, 5849 insertions(+) create mode 100644 examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py create mode 100644 examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml diff --git a/examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml b/examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml new file mode 100644 index 00000000..7e787e16 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,72 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py new file mode 100644 index 00000000..8d356fed --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py @@ -0,0 +1,38 @@ +from basemodel import val_args +from utils.metrics import Evaluator +from tqdm import tqdm +from dataloaders import make_data_loader +from sedna.common.class_factory import ClassType, ClassFactory + +__all__ = ('accuracy') + +@ClassFactory.register(ClassType.GENERAL) +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py new file mode 100644 index 00000000..dba4cfdf --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py @@ -0,0 +1,315 @@ +import os +import numpy as np +import torch +from PIL import Image +import argparse +from train import Trainer +from eval import Validator +from tqdm import tqdm +from eval import load_my_state_dict +from utils.metrics import Evaluator +from dataloaders import make_data_loader +from dataloaders import custom_transforms as tr +from torchvision import transforms +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.datasources import TxtDataParse +from torch.utils.data import DataLoader +from sedna.common.file_ops import FileOps +from utils.lr_scheduler import LR_Scheduler + +def preprocess(image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images + +class Model: + def __init__(self, **kwargs): + self.val_args = val_args() + self.train_args = train_args() + + self.train_args.lr = kwargs.get("learning_rate", 1e-4) + self.train_args.epochs = kwargs.get("epochs", 2) + self.train_args.eval_interval = kwargs.get("eval_interval", 2) + self.train_args.no_val = kwargs.get("no_val", True) + # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) + self.trainer = None + + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range(self.trainer.args.start_epoch, self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and \ + (epoch % self.trainer.args.eval_interval == (self.trainer.args.eval_interval - 1) + or epoch == self.trainer.args.epochs - 1): + # save checkpoint when it meets eval_interval or the training finished + is_best = False + checkpoint_path = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + # if not self.trainer.args.no_val and \ + # epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \ + # and self.trainer.val_loader: + # self.trainer.validation(epoch) + + self.trainer.writer.close() + + return checkpoint_path + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = preprocess(data.x) + predictions = self.predict(samples) + return accuracy(data.y, predictions) + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: how to save unstructured data model + pass + +def train_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet Training") + parser.add_argument('--depth', action="store_true", default=False, + help='training with depth image or not (default: False)') + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--loss-type', type=str, default='ce', + choices=['ce', 'focal'], + help='loss func type (default: ce)') + # training hyper params + # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # help='number of epochs to train (default: auto)') + parser.add_argument('--epochs', type=int, default=None, metavar='N', + help='number of epochs to train (default: auto)') + parser.add_argument('--start_epoch', type=int, default=0, + metavar='N', help='start epochs (default:0)') + parser.add_argument('--batch-size', type=int, default=None, + metavar='N', help='input batch size for \ + training (default: auto)') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--use-balanced-weights', action='store_true', default=False, + help='whether to use balanced weights (default: True)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + # optimizer params + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help='learning rate (default: auto)') + parser.add_argument('--lr-scheduler', type=str, default='cos', + choices=['poly', 'step', 'cos', 'inv'], + help='lr scheduler mode: (default: cos)') + parser.add_argument('--momentum', type=float, default=0.9, + metavar='M', help='momentum (default: 0.9)') + parser.add_argument('--weight-decay', type=float, default=2.5e-5, + metavar='M', help='w-decay (default: 5e-4)') + # cuda, seed and logging + parser.add_argument('--no-cuda', action='store_true', default= + False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') + # checking point + parser.add_argument('--resume', type=str, + default=None, + help='put the path to resuming file if needed') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + # finetuning pre-trained models + parser.add_argument('--ft', action='store_true', default=True, + help='finetuning on a different dataset') + # evaluation option + parser.add_argument('--eval-interval', type=int, default=1, + help='evaluation interval (default: 1)') + parser.add_argument('--no-val', action='store_true', default=False, + help='skip validation during training') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + print(torch.cuda.is_available()) + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + if args.epochs is None: + epoches = { + 'cityscapes': 200, + 'citylostfound': 200, + } + args.epochs = epoches[args.dataset.lower()] + + if args.batch_size is None: + args.batch_size = 4 * len(args.gpu_ids) + + if args.test_batch_size is None: + args.test_batch_size = args.batch_size + + if args.lr is None: + lrs = { + 'cityscapes': 0.0001, + 'citylostfound': 0.0001, + 'cityrand': 0.0001 + } + args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size + + if args.checkname is None: + args.checkname = 'RFNet' + print(args) + torch.manual_seed(args.seed) + + return args + +def val_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet validation") + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--batch-size', type=int, default=6, + help='batch size for training') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + validating (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", + help='enter your path of the weight') + parser.add_argument('--save-predicted-image', action='store_true', default=False, + help='save predicted images') + parser.add_argument('--color-label-save-path', type=str, + default='./test/color/', + help='path to save label') + parser.add_argument('--merge-label-save-path', type=str, + default='./test/merge/', + help='path to save merged label') + parser.add_argument('--label-save-path', type=str, default='./test/label/', + help='path to save merged label') + parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') + parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + return args + +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA + +if __name__ == '__main__': + model_path = "/tmp/RFNet/" + if not os.path.exists(model_path): + os.makedirs(model_path) + + p1 = Process(target=exp_train, args=(10,)) + p1.start() + p1.join() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py new file mode 100644 index 00000000..2c71f7b1 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py @@ -0,0 +1,119 @@ +from dataloaders.datasets import cityscapes, citylostfound, cityrand, target, xrlab, e1, mapillary +from torch.utils.data import DataLoader + +def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **kwargs): + + if args.dataset == 'cityscapes': + if train_data is not None: + train_set = cityscapes.CityscapesSegmentation(args, data=train_data, split='train') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + else: + train_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if valid_data is not None: + val_set = cityscapes.CityscapesSegmentation(args, data=valid_data, split='val') + num_class = val_set.NUM_CLASSES + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + else: + val_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if test_data is not None: + test_set = cityscapes.CityscapesSegmentation(args, data=test_data, split='test') + num_class = test_set.NUM_CLASSES + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + test_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + # custom_set = cityscapes.CityscapesSegmentation(args, split='custom_resize') + # custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + # return train_loader, val_loader, test_loader, custom_loader, num_class + return train_loader, val_loader, test_loader, num_class + + if args.dataset == 'citylostfound': + if args.depth: + train_set = citylostfound.CitylostfoundSegmentation(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + train_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, num_class + if args.dataset == 'cityrand': + train_set = cityrand.CityscapesSegmentation(args, split='train') + val_set = cityrand.CityscapesSegmentation(args, split='val') + test_set = cityrand.CityscapesSegmentation(args, split='test') + custom_set = cityrand.CityscapesSegmentation(args, split='custom_resize') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'target': + train_set = target.CityscapesSegmentation(args, split='train') + val_set = target.CityscapesSegmentation(args, split='val') + test_set = target.CityscapesSegmentation(args, split='test') + custom_set = target.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'xrlab': + train_set = xrlab.CityscapesSegmentation(args, split='train') + val_set = xrlab.CityscapesSegmentation(args, split='val') + test_set = xrlab.CityscapesSegmentation(args, split='test') + custom_set = xrlab.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'e1': + train_set = e1.CityscapesSegmentation(args, split='train') + val_set = e1.CityscapesSegmentation(args, split='val') + test_set = e1.CityscapesSegmentation(args, split='test') + custom_set = e1.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'mapillary': + train_set = mapillary.CityscapesSegmentation(args, split='train') + val_set = mapillary.CityscapesSegmentation(args, split='val') + test_set = mapillary.CityscapesSegmentation(args, split='test') + custom_set = mapillary.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + else: + raise NotImplementedError + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py new file mode 100644 index 00000000..d63f200a --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py @@ -0,0 +1,240 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + # mean and std for original depth images + mean_depth = 0.12176 + std_depth = 0.09752 + + depth /= 255.0 + depth -= mean_depth + depth /= std_depth + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + depth = torch.from_numpy(depth).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'depth': depth, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + depth = depth.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + depth = depth.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # depth = depth.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + depth = depth.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + depth = depth.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + depth = ImageOps.expand(depth, border=(0, 0, padw, padh), fill=0) # depth多余的部分填0 + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + + assert img.size == depth.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + depth = depth.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py new file mode 100644 index 00000000..e04ef5a3 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py @@ -0,0 +1,230 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return {'image': img, + 'label': mask} + + +class Normalize_test(object): + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample + img = np.array(img).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return img + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + print(img.size) + + return {'image': img, + 'label': mask} + +class ToTensor_test(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + + img = torch.from_numpy(img).float() + + return img + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + + assert img.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py new file mode 100644 index 00000000..6ffd0a4b --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py @@ -0,0 +1,276 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr +from dataloaders import custom_transforms_rgb as tr_rgb + +class CitylostfoundSegmentation(data.Dataset): + NUM_CLASSES = 20 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root,'disparity',self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix= '.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix= '.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, + suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'depth': _depth, 'label': _target} + + # data augment + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) # background->255 ignore + input = tr.Relabel(1, 0)(input) # road 1->0 + # input = Relabel(255, 20)(input) # unlabel 20 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=None): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + if isinstance(suffix, str): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + elif isinstance(suffix, list): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for x in suffix for filename in filenames if filename.startswith(x)] + + + def transform_tr(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + +class CitylostfoundSegmentation_rgb(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.files = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.files[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.files[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.files[split]: + raise Exception("No files for split=[%s] found in %s" % (split, self.images_base)) + + print("Found %d %s images" % (len(self.files[split]), split)) + + def __len__(self): + return len(self.files[self.split]) + + def __getitem__(self, index): + + img_path = self.files[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'label': _target} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) + input = tr.Relabel(1, 0)(input) # road 1->0 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.RandomHorizontalFlip(), + tr_rgb.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.FixedResize(size=self.args.crop_size), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CitylostfoundSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py new file mode 100644 index 00000000..74eddb67 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('cityrand'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py new file mode 100644 index 00000000..66676745 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -0,0 +1,152 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 # 25 + + def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"): + + # self.root = root + self.root = "/home/lsq/Dataset/" + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.disparities_base = os.path.join(self.root, self.split, "depth", "cityscapes_real") + self.images[split] = [img[0] for img in data.x] if hasattr(data, "x") else data + + + if hasattr(data, "x") and len(data.x[0]) == 1: + # TODO: fit the case that depth images don't exist. + self.disparities[split] = self.images[split] + elif hasattr(data, "x") and len(data.x[0]) == 2: + self.disparities[split] = [img[1] for img in data.x] + else: + self.disparities[split] = data + + self.labels[split] = data.y if hasattr(data, "y") else data + + self.ignore_index = 255 + + if len(self.images[split]) == 0: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if len(self.disparities[split]) == 0: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py new file mode 100644 index 00000000..40e06e98 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('e1'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py new file mode 100644 index 00000000..d665649b --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('mapillary'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 768 + args.crop_size = 768 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py new file mode 100644 index 00000000..739e85f8 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('target'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt new file mode 100644 index 00000000..3c81afef --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt @@ -0,0 +1,38 @@ +for i in range(len(nam_label)): + img_label = cv2.imread(label_ori_path+nam_label[i], -1)[:,:,2] + img_label_temp = img_label.copy() + img_label_temp[img_label == 0] = 22 + img_label_temp[img_label == 1] = 10 + img_label_temp[img_label == 2] = 2 + img_label_temp[img_label == 3] = 0 + img_label_temp[img_label == 4] = 1 + img_label_temp[img_label == 5] = 4 + img_label_temp[img_label == 6] = 8 + img_label_temp[img_label == 7] = 5 + img_label_temp[img_label == 8] = 13 + img_label_temp[img_label == 9] = 7 + img_label_temp[img_label == 10] = 11 + img_label_temp[img_label == 11] = 18 + img_label_temp[img_label == 12] = 17 + img_label_temp[img_label == 13] = 21 + img_label_temp[img_label == 14] = 20 + img_label_temp[img_label == 15] = 6 + img_label_temp[img_label == 16] = 9 + img_label_temp[img_label == 17] = 12 + img_label_temp[img_label == 18] = 14 + img_label_temp[img_label == 19] = 15 + img_label_temp[img_label == 20] = 16 + img_label_temp[img_label == 21] = 3 + img_label_temp[img_label == 22] = 19 + #print(img_label) + #img_label[img_label == 0] = 10 + #img_label[img_label == 6] = 0 + #img_label[img_label == 5] = 11 + #img_label[img_label == 1] = 5 + #img_label[img_label == 2] = 1 + #img_label[img_label == 4] = 9 + #img_label[img_label == 3] = 4 + #img_label[img_label == 7] = 8 + #img_label[img_label == 11] = 2 + img_resize_lab = cv2.resize(img_label_temp, (2048,1024), interpolation=cv2.INTER_NEAREST) + cv2.imwrite(label_save_path+str(i)+'TrainIds.png', img_resize_lab.astype(np.uint16)) \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py new file mode 100644 index 00000000..4b261fcd --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 25 + + def __init__(self, args, root=Path.db_root_dir('xrlab'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py new file mode 100644 index 00000000..ef572332 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py @@ -0,0 +1,244 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +def decode_seg_map_sequence(label_masks, dataset='pascal'): + rgb_masks = [] + for label_mask in label_masks: + rgb_mask = decode_segmap(label_mask, dataset) + rgb_masks.append(rgb_mask) + rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2])) # change for val + return rgb_masks + + +def decode_segmap(label_mask, dataset, plot=False): + """Decode segmentation class labels into a color image + Args: + label_mask (np.ndarray): an (M,N) array of integer values denoting + the class label at each spatial location. + plot (bool, optional): whether to show the resulting color image + in a figure. + Returns: + (np.ndarray, optional): the resulting decoded color image. + """ + if dataset == 'pascal' or dataset == 'coco': + n_classes = 21 + label_colours = get_pascal_labels() + elif dataset == 'cityscapes': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'target': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'cityrand': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'citylostfound': + n_classes = 20 + label_colours = get_citylostfound_labels() + elif dataset == 'xrlab': + n_classes = 25 + label_colours = get_cityscapes_labels() + elif dataset == 'e1': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'mapillary': + n_classes = 24 + label_colours = get_cityscapes_labels() + else: + raise NotImplementedError + + r = label_mask.copy() + g = label_mask.copy() + b = label_mask.copy() + for ll in range(0, n_classes): + r[label_mask == ll] = label_colours[ll, 0] + g[label_mask == ll] = label_colours[ll, 1] + b[label_mask == ll] = label_colours[ll, 2] + rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3)) # change for val + # rgb = torch.ByteTensor(3, label_mask.shape[0], label_mask.shape[1]).fill_(0) + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + # r = torch.from_numpy(r) + # g = torch.from_numpy(g) + # b = torch.from_numpy(b) + + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + if plot: + plt.imshow(rgb) + plt.show() + else: + return rgb + + +def encode_segmap(mask): + """Encode segmentation label images as pascal classes + Args: + mask (np.ndarray): raw segmentation label image of dimension + (M, N, 3), in which the Pascal classes are encoded as colours. + Returns: + (np.ndarray): class map with dimensions (M,N), where the value at + a given location is the integer denoting the class index. + """ + mask = mask.astype(int) + label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16) + for ii, label in enumerate(get_pascal_labels()): + label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii + label_mask = label_mask.astype(int) + return label_mask + + +def get_cityscapes_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [119, 11, 119], + [128, 64, 64], + [102, 10, 156], + [102, 102, 15], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156]]) + +def get_citylostfound_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [111, 74, 0]]) + + +def get_pascal_labels(): + """Load the mapping that associates pascal classes with label colors + Returns: + np.ndarray with dimensions (21, 3) + """ + return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], + [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], + [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], + [0, 64, 128]]) + + +def colormap_bdd(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([128, 64, 128]) + cmap[1,:] = np.array([244, 35, 232]) + cmap[2,:] = np.array([ 70, 70, 70]) + cmap[3,:] = np.array([102, 102, 156]) + cmap[4,:] = np.array([190, 153, 153]) + cmap[5,:] = np.array([153, 153, 153]) + + cmap[6,:] = np.array([250, 170, 30]) + cmap[7,:] = np.array([220, 220, 0]) + cmap[8,:] = np.array([107, 142, 35]) + cmap[9,:] = np.array([152, 251, 152]) + cmap[10,:]= np.array([70, 130, 180]) + + cmap[11,:]= np.array([220, 20, 60]) + cmap[12,:]= np.array([255, 0, 0]) + cmap[13,:]= np.array([0, 0, 142]) + cmap[14,:]= np.array([0, 0, 70]) + cmap[15,:]= np.array([0, 60, 100]) + + cmap[16,:]= np.array([0, 80, 100]) + cmap[17,:]= np.array([0, 0, 230]) + cmap[18,:]= np.array([119, 11, 32]) + cmap[19,:]= np.array([111, 74, 0]) #多加了一类small obstacle + + return cmap + +def colormap_bdd0(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([0, 0, 0]) + cmap[1,:] = np.array([70, 130, 180]) + cmap[2,:] = np.array([70, 70, 70]) + cmap[3,:] = np.array([128, 64, 128]) + cmap[4,:] = np.array([244, 35, 232]) + cmap[5,:] = np.array([64, 64, 128]) + + cmap[6,:] = np.array([107, 142, 35]) + cmap[7,:] = np.array([153, 153, 153]) + cmap[8,:] = np.array([0, 0, 142]) + cmap[9,:] = np.array([220, 220, 0]) + cmap[10,:]= np.array([220, 20, 60]) + + cmap[11,:]= np.array([119, 11, 32]) + cmap[12,:]= np.array([0, 0, 230]) + cmap[13,:]= np.array([250, 170, 160]) + cmap[14,:]= np.array([128, 64, 64]) + cmap[15,:]= np.array([250, 170, 30]) + + cmap[16,:]= np.array([152, 251, 152]) + cmap[17,:]= np.array([255, 0, 0]) + cmap[18,:]= np.array([0, 0, 70]) + cmap[19,:]= np.array([0, 60, 100]) #small obstacle + cmap[20,:]= np.array([0, 80, 100]) + cmap[21,:]= np.array([102, 102, 156]) + cmap[22,:]= np.array([102, 102, 156]) + + return cmap + +class Colorize: + + def __init__(self, n=24): # n = nClasses + # self.cmap = colormap(256) + self.cmap = colormap_bdd(256) + self.cmap[n] = self.cmap[-1] + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + # print(size) + color_images = torch.ByteTensor(size[0], 3, size[1], size[2]).fill_(0) + # color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0) + + # for label in range(1, len(self.cmap)): + for i in range(color_images.shape[0]): + for label in range(0, len(self.cmap)): + mask = gray_image[0] == label + # mask = gray_image == label + + color_images[i][0][mask] = self.cmap[label][0] + color_images[i][1][mask] = self.cmap[label][1] + color_images[i][2][mask] = self.cmap[label][2] + + return color_images diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py new file mode 100644 index 00000000..247fe71a --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py @@ -0,0 +1,247 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import time +import torch +from torchvision.transforms import ToPILImage +from PIL import Image + +from dataloaders import make_data_loader +from dataloaders.utils import decode_seg_map_sequence, Colorize +from utils.metrics import Evaluator +from models.rfnet import RFNet +from models import rfnet_for_unseen +from models.resnet.resnet_single_scale_single_attention import * +from models.resnet import resnet_single_scale_single_attention_unseen +import torch.backends.cudnn as cudnn + +class Validator(object): + def __init__(self, args, data=None, unseen_detection=False): + self.args = args + self.time_train = [] + self.num_class = args.num_class + + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + # _, self.val_loader, _, self.custom_loader, self.num_class = make_data_loader(args, **kwargs) + _, _, self.test_loader, _ = make_data_loader(args, test_data=data, **kwargs) + print('un_classes:'+str(self.num_class)) + + # Define evaluator + self.evaluator = Evaluator(self.num_class) + + # Define network + if unseen_detection: + self.resnet = resnet_single_scale_single_attention_unseen.\ + resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = rfnet_for_unseen.RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + else: + self.resnet = resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + + if args.cuda: + self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + self.model = self.model.cuda() + cudnn.benchmark = True # accelarate speed + print('Model loaded successfully!') + + # # Load weights + # assert os.path.exists(args.weight_path), 'weight-path:{} doesn\'t exit!'.format(args.weight_path) + # self.new_state_dict = torch.load(args.weight_path, map_location=torch.device("cpu")) + # self.model = load_my_state_dict(self.model, self.new_state_dict['state_dict']) + + def validate(self): + self.model.eval() + self.evaluator.reset() + # tbar = tqdm(self.test_loader, desc='\r') + predictions = [] + for sample, image_name in self.test_loader: + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + # spec = time.time() + image, target = sample['image'], sample['label'] + + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + + if self.args.cuda: + torch.cuda.synchronize() + + pred = output.data.cpu().numpy() + # todo + pred = np.argmax(pred, axis=1) + predictions.append(pred) + + if not self.args.save_predicted_image: + continue + + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + print(pre_labels.shape) + # save + for i in range(pre_colors.shape[0]): + print(image_name[0]) + + if not image_name[0]: + img_name = "test.png" + else: + img_name = os.path.basename(image_name[0]) + + color_label_name = os.path.join(self.args.color_label_save_path, img_name) + label_name = os.path.join(self.args.label_save_path, img_name) + merge_label_name = os.path.join(self.args.merge_label_save_path, img_name) + + os.makedirs(os.path.dirname(color_label_name), exist_ok=True) + os.makedirs(os.path.dirname(merge_label_name), exist_ok=True) + os.makedirs(os.path.dirname(label_name), exist_ok=True) + + pre_color_image = ToPILImage()(pre_colors[i]) # pre_colors.dtype = float64 + pre_color_image.save(color_label_name) + + pre_label_image = ToPILImage()(pre_labels[i]) + pre_label_image.save(label_name) + + if (self.args.merge): + image_merge(image[i], pre_color_image, merge_label_name) + print('save image: {}'.format(merge_label_name)) + + return predictions + + def task_divide(self): + seen_task_samples, unseen_task_samples = [], [] + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + for i, (sample, image_name) in enumerate(tbar): + + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + start_time = time.time() + with torch.no_grad(): + if self.args.depth: + output_, output, _ = self.model(image, depth) + else: + output_, output, _ = self.model(image) + if self.args.cuda: + torch.cuda.synchronize() + if i != 0: + fwt = time.time() - start_time + self.time_train.append(fwt) + print("Forward time per img (bath size=%d): %.3f (Mean: %.3f)" % ( + self.args.val_batch_size, fwt / self.args.val_batch_size, + sum(self.time_train) / len(self.time_train) / self.args.val_batch_size)) + time.sleep(0.1) # to avoid overheating the GPU too much + + # pred colorize + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + for i in range(pre_colors.shape[0]): + task_sample = dict() + task_sample.update(image=sample["image"][i]) + task_sample.update(label=sample["label"][i]) + if self.args.depth: + task_sample.update(depth=sample["depth"][i]) + + if torch.max(pre_labels) == output.shape[1] - 1: + unseen_task_samples.append((task_sample, image_name[i])) + else: + seen_task_samples.append((task_sample, image_name[i])) + + return seen_task_samples, unseen_task_samples + +def image_merge(image, label, save_name): + image = ToPILImage()(image.detach().cpu().byte()) + # width, height = image.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + image = image.crop((left, top, right, bottom)) + # resize + image = image.resize(label.size, Image.BILINEAR) + + image = image.convert('RGBA') + label = label.convert('RGBA') + image = Image.blend(image, label, 0.6) + image.save(save_name) + +def load_my_state_dict(model, state_dict): # custom function to load model when not all dict elements + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + print('{} not in model_state'.format(name)) + continue + else: + own_state[name].copy_(param) + + return model + +# def main(): +# parser = argparse.ArgumentParser(description="PyTorch RFNet validation") +# parser.add_argument('--dataset', type=str, default='cityscapes', +# choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], +# help='dataset name (default: cityscapes)') +# parser.add_argument('--workers', type=int, default=4, +# metavar='N', help='dataloader threads') +# parser.add_argument('--base-size', type=int, default=1024, +# help='base image size') +# parser.add_argument('--crop-size', type=int, default=768, +# help='crop image size') +# parser.add_argument('--batch-size', type=int, default=6, +# help='batch size for training') +# parser.add_argument('--val-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# validating (default: auto)') +# parser.add_argument('--test-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# testing (default: auto)') +# parser.add_argument('--no-cuda', action='store_true', default= +# False, help='disables CUDA training') +# parser.add_argument('--gpu-ids', type=str, default='0', +# help='use which gpu to train, must be a \ +# comma-separated list of integers only (default=0)') +# parser.add_argument('--checkname', type=str, default=None, +# help='set the checkpoint name') +# parser.add_argument('--weight-path', type=str, default=None, +# help='enter your path of the weight') +# parser.add_argument('--color-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/color/', +# help='path to save label') +# parser.add_argument('--merge-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/merge/', +# help='path to save merged label') +# parser.add_argument('--label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/label/', +# help='path to save merged label') +# parser.add_argument('--merge', action='store_true', default=False, help='merge image and label') +# parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') +# +# +# args = parser.parse_args() +# args.cuda = not args.no_cuda and torch.cuda.is_available() +# if args.cuda: +# try: +# args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] +# except ValueError: +# raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') +# +# validator = Validator(args) +# validator.validate() +# +# +# if __name__ == "__main__": +# main() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py new file mode 100644 index 00000000..3734266e --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py new file mode 100644 index 00000000..63d81991 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py @@ -0,0 +1,391 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = []#[ self.spp, self.upsample] + self.fine_tune += [self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x.detach(), self.layer4) + features += [self.spp.forward(skip)] + return features + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer1) + y, skip_depth = self.forward_resblock(y.detach(), self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 2 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer2) + y, skip_depth = self.forward_resblock(y.detach(), self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 3 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer3) + y, skip_depth = self.forward_resblock(y.detach(), self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 4 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer4) + y, skip_depth = self.forward_resblock(y.detach(), self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + return features + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + i = 0 + for skip, up in zip(features[1:], self.upsample): + i += 1 + #print(len(self.upsample)) + if i < len(self.upsample): + x = up(x, skip) + else: + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + return self.forward_up(self.forward_down(rgb)) + else: + return self.forward_up(self.forward_down_fusion(rgb, depth)) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py new file mode 100644 index 00000000..9668734e --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py @@ -0,0 +1,396 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp +import cv2 +import numpy as np + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = [ self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def output_num(self): + return self.__in_features + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x, self.layer4) + features += [self.spp.forward(skip)] + features_da = self.spp.forward(skip) + return features, features_da + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x, self.layer1) + y, skip_depth = self.forward_resblock(y, self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 2 + x, skip_rgb = self.forward_resblock(x, self.layer2) + y, skip_depth = self.forward_resblock(y, self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 3 + x, skip_rgb = self.forward_resblock(x, self.layer3) + y, skip_depth = self.forward_resblock(y, self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 4 + x, skip_rgb = self.forward_resblock(x, self.layer4) + y, skip_depth = self.forward_resblock(y, self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + features_da = self.spp.forward(x) + return features, features_da + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + for skip, up in zip(features[1:], self.upsample): + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + down_features, da_features = self.forward_down(rgb) + x, additional = self.forward_up(down_features) + return x, additional, da_features#self.forward_up(self.forward_down(rgb)), self.forward_down(rgb) + else: + down_features, da_features = self.forward_down_fusion(rgb, depth) + x, additional = self.forward_up(down_features) + #print(down_features.shape) + return x, additional, da_features#self.forward_up(self.forward_down_fusion(rgb, depth)), self.forward_down_fusion(rgb, depth) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py new file mode 100644 index 00000000..87f02863 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py @@ -0,0 +1,27 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + print(self.backbone.num_features) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional = self.backbone(rgb_inputs, depth_inputs) + logits = self.logits.forward(x) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + #print(logits_upsample.size) + return logits_upsample + + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py new file mode 100644 index 00000000..f61eb1ce --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py @@ -0,0 +1,33 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + #self.bottleneck = _BNReluConv(self.backbone.num_features, 128, k = 3, batch_norm=use_bn) + #self.logits = _BNReluConv(128, self.num_classes+1, k = 1, batch_norm=use_bn) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + #self.logits_target = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + self.logits_aux = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional, da_features = self.backbone(rgb_inputs, depth_inputs) + #print(additional['features'][0].shape) + #bottleneck = self.bottleneck(x) + logits = self.logits.forward(x) + logits_aux = self.logits_aux.forward(x) + #print(logits_aux.shape) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + logits_aux_upsample = upsample(logits_aux, rgb_inputs.shape[2:]) + return logits_upsample, logits_aux_upsample, da_features + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.logits_aux.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py new file mode 100644 index 00000000..5c86e759 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False) +batchnorm_momentum = 0.01 / 2 + + +def get_n_params(parameters): + pp = 0 + for p in parameters: + nn = 1 + for s in list(p.size()): + nn = nn * s + pp += nn + return pp + + +class _BNReluConv(nn.Sequential): + def __init__(self, num_maps_in, num_maps_out, k=3, batch_norm=True, bn_momentum=0.1, bias=False, dilation=1): + super(_BNReluConv, self).__init__() + if batch_norm: + self.add_module('norm', nn.BatchNorm2d(num_maps_in, momentum=bn_momentum)) + self.add_module('relu', nn.ReLU(inplace=batch_norm is True)) + padding = k // 2 # same conv + self.add_module('conv', nn.Conv2d(num_maps_in, num_maps_out, + kernel_size=k, padding=padding, bias=bias, dilation=dilation)) + + +class _Upsample(nn.Module): + def __init__(self, num_maps_in, skip_maps_in, num_maps_out, use_bn=True, k=3): + super(_Upsample, self).__init__() + print(f'Upsample layer: in = {num_maps_in}, skip = {skip_maps_in}, out = {num_maps_out}') + self.bottleneck = _BNReluConv(skip_maps_in, num_maps_in, k=1, batch_norm=use_bn) + self.blend_conv = _BNReluConv(num_maps_in, num_maps_out, k=k, batch_norm=use_bn) + + def forward(self, x, skip): + skip = self.bottleneck.forward(skip) + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x + + +class SpatialPyramidPooling(nn.Module): + def __init__(self, num_maps_in, num_levels, bt_size=512, level_size=128, out_size=128, + grids=(6, 3, 2, 1), square_grid=False, bn_momentum=0.1, use_bn=True): + super(SpatialPyramidPooling, self).__init__() + self.grids = grids + self.square_grid = square_grid + self.spp = nn.Sequential() + self.spp.add_module('spp_bn', + _BNReluConv(num_maps_in, bt_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + num_features = bt_size + final_size = num_features + for i in range(num_levels): + final_size += level_size + self.spp.add_module('spp' + str(i), + _BNReluConv(num_features, level_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + self.spp.add_module('spp_fuse', + _BNReluConv(final_size, out_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + + def forward(self, x): + levels = [] + target_size = x.size()[2:4] + + ar = target_size[1] / target_size[0] + + x = self.spp[0].forward(x) + levels.append(x) + num = len(self.spp) - 1 + + for i in range(1, num): + if not self.square_grid: + grid_size = (self.grids[i - 1], max(1, round(ar * self.grids[i - 1]))) + x_pooled = F.adaptive_avg_pool2d(x, grid_size) + else: + x_pooled = F.adaptive_avg_pool2d(x, self.grids[i - 1]) + level = self.spp[i].forward(x_pooled) + + level = upsample(level, target_size) + levels.append(level) + x = torch.cat(levels, 1) + x = self.spp[-1].forward(x) + return x + + +class _UpsampleBlend(nn.Module): + def __init__(self, num_features, use_bn=True): + super(_UpsampleBlend, self).__init__() + self.blend_conv = _BNReluConv(num_features, num_features, k=3, batch_norm=use_bn) + + def forward(self, x, skip): + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py new file mode 100644 index 00000000..640544e7 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py @@ -0,0 +1,20 @@ +class Path(object): + @staticmethod + def db_root_dir(dataset): + if dataset == 'cityscapes': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapes/' # folder that contains leftImg8bit/ + elif dataset == 'citylostfound': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapesandlostandfound/' # folder that mixes Cityscapes and Lost and Found + elif dataset == 'cityrand': + return '/home/robo/m0063/project/RFNet-master/Data/cityrand/' + elif dataset == 'target': + return '/home/robo/m0063/project/RFNet-master/Data/target/' + elif dataset == 'xrlab': + return '/home/robo/m0063/project/RFNet-master/Data/xrlab/' + elif dataset == 'e1': + return '/home/robo/m0063/project/RFNet-master/Data/e1/' + elif dataset == 'mapillary': + return '/home/robo/m0063/project/RFNet-master/Data/mapillary/' + else: + print('Dataset {} not available.'.format(dataset)) + raise NotImplementedError diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py new file mode 100644 index 00000000..82b527a2 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py @@ -0,0 +1,98 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +# os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +# os.environ["video_url"] = "./video/radio.mp4" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" + + +import cv2 +import time +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +import warnings +from io import BytesIO + +from sedna.datasources import BaseDataSource +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from dataloaders import custom_transforms as tr +from torchvision import transforms + +from accuracy import accuracy +from basemodel import preprocess, val_args, Model + +def preprocess(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def init_ll_job(): + estimator = Model() + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"], + "default": "real" + } + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None) + + return ll_job + +def predict(): + ll_job = init_ll_job() + + camera_address = Context.get_parameters('video_url') + # use video streams for testing + camera = cv2.VideoCapture(camera_address) + fps = 10 + nframe = 0 + while 1: + ret, input_yuv = camera.read() + if not ret: + time.sleep(5) + camera = cv2.VideoCapture(camera_address) + continue + + if nframe % fps: + nframe += 1 + continue + + img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_BGR2RGB) + nframe += 1 + if nframe % 1000 == 1: # logs every 1000 frames + warnings.warn(f"camera is open, current frame index is {nframe}") + + img_rgb = cv2.resize(np.array(img_rgb), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + sample = {'image': img_rgb, "depth": img_rgb, "label": img_rgb} + data = preprocess(sample) + print("Inference results:", ll_job.inference(data=data)) + +if __name__ == '__main__': + predict() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py new file mode 100644 index 00000000..438cd70a --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py @@ -0,0 +1,252 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from io import BytesIO +from typing import Optional, Any + +import cv2 +import numpy as np +from PIL import Image +import uvicorn +import time +from pydantic import BaseModel +from fastapi import FastAPI, UploadFile, File +from fastapi.routing import APIRoute +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +import sedna_predict +from sedna.common.utils import get_host_ip +from dataloaders.datasets.cityscapes import CityscapesSegmentation + + +class ImagePayload(BaseModel): + image: UploadFile = File(...) + depth: Optional[UploadFile] = None + + +class ResultModel(BaseModel): + type: int = 0 + box: Any = None + curr: str = None + future: str = None + img: str = None + + +class ResultResponse(BaseModel): + msg: str = "" + result: Optional[ResultModel] = None + code: int + + +class BaseServer: + # pylint: disable=too-many-instance-attributes,too-many-arguments + DEBUG = True + WAIT_TIME = 15 + + def __init__( + self, + servername: str, + host: str, + http_port: int = 8080, + grpc_port: int = 8081, + workers: int = 1, + ws_size: int = 16 * 1024 * 1024, + ssl_key=None, + ssl_cert=None, + timeout=300): + self.server_name = servername + self.app = None + self.host = host or '0.0.0.0' + self.http_port = http_port or 80 + self.grpc_port = grpc_port + self.workers = workers + self.keyfile = ssl_key + self.certfile = ssl_cert + self.ws_size = int(ws_size) + self.timeout = int(timeout) + protocal = "https" if self.certfile else "http" + self.url = f"{protocal}://{self.host}:{self.http_port}" + + def run(self, app, **kwargs): + if hasattr(app, "add_middleware"): + app.add_middleware( + CORSMiddleware, allow_origins=["*"], allow_credentials=True, + allow_methods=["*"], allow_headers=["*"], + ) + + uvicorn.run( + app, + host=self.host, + port=self.http_port, + ssl_keyfile=self.keyfile, + ssl_certfile=self.certfile, + workers=self.workers, + timeout_keep_alive=self.timeout, + **kwargs) + + def get_all_urls(self): + url_list = [{"path": route.path, "name": route.name} + for route in getattr(self.app, 'routes', [])] + return url_list + + +class InferenceServer(BaseServer): # pylint: disable=too-many-arguments + """ + rest api server for inference + """ + + def __init__( + self, + servername, + host: str, + http_port: int = 5000, + max_buffer_size: int = 104857600, + workers: int = 1): + super( + InferenceServer, + self).__init__( + servername=servername, + host=host, + http_port=http_port, + workers=workers) + + self.job, self.detection_validator = sedna_predict.init_ll_job() + + self.max_buffer_size = max_buffer_size + self.app = FastAPI( + routes=[ + APIRoute( + f"/{servername}", + self.model_info, + methods=["GET"], + ), + APIRoute( + f"/{servername}/predict", + self.predict, + methods=["POST"], + response_model=ResultResponse + ), + ], + log_level="trace", + timeout=600, + ) + self.index_frame = 0 + + def start(self): + return self.run(self.app) + + @staticmethod + def model_info(): + return HTMLResponse( + """

Welcome to the RestNet API!

+

To use this service, send a POST HTTP request to {this-url}/predict

+

The JSON payload has the following format: {"image": "BASE64_STRING_OF_IMAGE", + "depth": "BASE64_STRING_OF_DEPTH"}

+ """) + + async def predict(self, image: UploadFile = File(...), depth: Optional[UploadFile] = None) -> ResultResponse: + contents = await image.read() + recieve_img_time = time.time() + print("Recieve image from the robo:", recieve_img_time) + + image = Image.open(BytesIO(contents)).convert('RGB') + + img_dep = None + self.index_frame = self.index_frame + 1 + + if depth: + depth_contents = await depth.read() + depth = Image.open(BytesIO(depth_contents)).convert('RGB') + img_dep = cv2.resize(np.array(depth), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_dep = Image.fromarray(img_dep) + + img_rgb = cv2.resize(np.array(image), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + + sample = {'image': img_rgb, "depth": img_dep, "label": img_rgb} + results = sedna_predict.predict(self.job, data=sample, validator=self.detection_validator) + + predict_finish_time = time.time() + print(f"Prediction costs {predict_finish_time - recieve_img_time} seconds") + + post_process = True + if results["result"]["box"] is None: + results["result"]["curr"] = None + results["result"]["future"] = None + elif post_process: + curr, future = get_curb(results["result"]["box"]) + results["result"]["curr"] = curr + results["result"]["future"] = future + results["result"]["box"] = None + print("Post process cost at worker:", (time.time()-predict_finish_time)) + else: + results["result"]["curr"] = None + results["result"]["future"] = None + + print("Result transmit to robo time:", time.time()) + return results + +def parse_result(label, count): + label_map = ['road', 'sidewalk', ] + count_d = dict(zip(label, count)) + curb_count = count_d.get(19, 0) + if curb_count / np.sum(count) > 0.3: + return "curb" + r = sorted(label, key=count_d.get, reverse=True)[0] + try: + c = label_map[r] + except: + c = "other" + + return c + +def get_curb(results): + results = np.array(results[0]) + input_height, input_width = results.shape + + closest = np.array([ + [0, int(input_height)], + [int(input_width), + int(input_height)], + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + ]) + + future = np.array([ + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + [int(.765 * input_width + .5), + int(.66 * input_height + .5)], + [int(.235 * input_width + .5), + int(.66 * input_height + .5)] + ]) + + mask = np.zeros((input_height, input_width), dtype=np.uint8) + mask = cv2.fillPoly(mask, [closest], 1) + mask = cv2.fillPoly(mask, [future], 2) + d1, c1 = np.unique(results[mask == 1], return_counts=True) + d2, c2 = np.unique(results[mask == 2], return_counts=True) + c = parse_result(d1, c1) + f = parse_result(d2, c2) + + return c, f + +if __name__ == '__main__': + web_app = InferenceServer("lifelong-learning-robo", host=get_host_ip()) + web_app.start() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py new file mode 100644 index 00000000..56633347 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py @@ -0,0 +1,50 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +# os.environ["test_dataset_url"] = "./data_txt/sedna_data.txt" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" +# os.environ["operator"] = "<" +# os.environ["model_threshold"] = "0" + +from sedna.core.lifelong_learning import LifelongLearning +from sedna.datasources import IndexDataParse +from sedna.common.config import Context + +from accuracy import accuracy +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def eval(): + estimator = Model() + eval_dataset_url = Context.get_parameters("test_dataset_url") + eval_data = IndexDataParse(data_type="eval", func=_load_txt_dataset) + eval_data.parse(eval_dataset_url, use_raw=False) + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.evaluate(eval_data, metrics=accuracy) + + +if __name__ == '__main__': + print(eval()) diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py new file mode 100644 index 00000000..b32c01d2 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py @@ -0,0 +1,132 @@ +import os + +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["UNSEEN_SAVE_URL"] = "s3://kubeedge/sedna-robo/unseen_samples/" +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt" +os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +os.environ["ORIGINAL_DATASET_URL"] = "/tmp" + +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +from io import BytesIO +from torchvision.transforms import ToPILImage +from torchvision import transforms +from torch.utils.data import DataLoader + +from sedna.datasources import IndexDataParse +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from eval import Validator +from accuracy import accuracy +from basemodel import preprocess, val_args, Model +from dataloaders.utils import Colorize +from dataloaders import custom_transforms as tr +from dataloaders.datasets.cityscapes import CityscapesSegmentation + +def _load_txt_dataset(dataset_url): + # use original dataset url, + # see https://github.com/kubeedge/sedna/issues/35 + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def fetch_data(): + test_dataset_url = Context.get_parameters("test_dataset_url") + test_data = IndexDataParse(data_type="test", func=_load_txt_dataset) + test_data.parse(test_dataset_url, use_raw=False) + return test_data + +def pre_data_process(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def post_process(res, is_unseen_task): + if is_unseen_task: + res, base64_string = None, None + else: + res = res[0].tolist() + + type = 0 if not is_unseen_task else 1 + mesg = { + "msg": "", + "result": { + "type": type, + "box": res + }, + "code": 0 + } + return mesg + +def image_merge(raw_img, result): + raw_img = ToPILImage()(raw_img) + + pre_colors = Colorize()(torch.from_numpy(result)) + pre_color_image = ToPILImage()(pre_colors[0]) # pre_colors.dtype = float64 + + image = raw_img.resize(pre_color_image.size, Image.BILINEAR) + image = image.convert('RGBA') + label = pre_color_image.convert('RGBA') + image = Image.blend(image, label, 0.6) + with tempfile.NamedTemporaryFile(suffix='.png') as f: + image.save(f.name) + + with open(f.name, 'rb') as open_file: + byte_content = open_file.read() + base64_bytes = base64.b64encode(byte_content) + base64_string = base64_bytes.decode('utf-8') + return base64_string + +def init_ll_job(): + estimator = Model() + inference_integrate = { + "method": "BBoxInferenceIntegrate" + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + unseen_sample_recognition = { + "method": "SampleRegonitionByRFNet" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=inference_integrate, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=unseen_sample_recognition, + unseen_sample_re_recognition=None) + + args = val_args() + args.weight_path = "./models/detection_model.pth" + args.num_class = 31 + + return ll_job, Validator(args, unseen_detection=True) + +def predict(ll_job, data=None, validator=None): + if data: + data = pre_data_process(data) + else: + data = fetch_data() + data.x = preprocess(data.x) + + res, is_unseen_task, _ = ll_job.inference( + data, validator=validator, initial=False) + return post_process(res, is_unseen_task) + +if __name__ == '__main__': + ll_job, validator = init_ll_job() + print("Inference result:", predict(ll_job, validator=validator)) diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py new file mode 100644 index 00000000..1c99361a --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py @@ -0,0 +1,78 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +os.environ["OUTPUT_URL"] = "./cloud_kb/" +# os.environ['CLOUD_KB_INDEX'] = "./cloud_kb/index.pkl" +os.environ["TRAIN_DATASET_URL"] = "./data_txt/sedna_data.txt" +os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = "false" + +from sedna.common.file_ops import FileOps +from sedna.datasources import IndexDataParse +from sedna.common.config import Context, BaseConfig +from sedna.core.lifelong_learning import LifelongLearning + +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def train(estimator, train_data): + task_definition = { + "method": "TaskDefinitionByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=task_definition, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.train(train_data) + +def update(estimator, train_data): + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.update(train_data) + +def run(): + estimator = Model() + train_dataset_url = BaseConfig.train_dataset_url + train_data = IndexDataParse(data_type="train") + train_data.parse(train_dataset_url, use_raw=False) + + is_completed_initilization = str(Context.get_parameters("HAS_COMPLETED_INITIAL_TRAINING", "false")).lower() + if is_completed_initilization == "false": + train(estimator, train_data) + else: + update(estimator, train_data) + +if __name__ == '__main__': + run() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py new file mode 100644 index 00000000..fd9cd657 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py @@ -0,0 +1,52 @@ +import numpy as np +import seaborn as sns +import pandas as pd +import matplotlib.pyplot as plt + +CPA_results = np.load("./cpa_results.npy").T +ratios = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9] +ratio_counts = np.zeros((len(CPA_results), len(ratios)), dtype=float) + +for i in range(len(CPA_results)): + for j in range(len(ratios)): + result = CPA_results[i] + result = result[result <= ratios[j]] + + ratio_counts[i][j] = len(result) / 275 + +plt.figure(figsize=(45, 10)) +ratio_counts = pd.DataFrame(data=ratio_counts.T, index=ratios) +sns.heatmap(data=ratio_counts, annot=True, cmap="YlGnBu", annot_kws={'fontsize': 15}) +plt.xticks(fontsize=20) +plt.yticks(fontsize=25) +plt.xlabel("Test images", fontsize=25) +plt.ylabel("Ratio of PA ranges", fontsize=25) +plt.savefig("./figs/ratio_count.png") +plt.show() + + +# data = pd.DataFrame(CPA_results.T) +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[:15]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/door_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[15:31]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden1_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[31:]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden2_heatmap_pa.png") +# plt.show() diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py new file mode 100644 index 00000000..d7d01e7f --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py @@ -0,0 +1,211 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import torch + +from mypath import Path +from dataloaders import make_data_loader +from models.rfnet import RFNet +from models.resnet.resnet_single_scale_single_attention import * +from utils.loss import SegmentationLosses +from models.replicate import patch_replication_callback +from utils.calculate_weights import calculate_weigths_labels +from utils.lr_scheduler import LR_Scheduler +from utils.saver import Saver +from utils.summaries import TensorboardSummary +from utils.metrics import Evaluator +from sedna.datasources import BaseDataSource + +class Trainer(object): + def __init__(self, args, train_data=None, valid_data=None): + self.args = args + # Define Saver + self.saver = Saver(args) + self.saver.save_experiment_config() + # Define Tensorboard Summary + self.summary = TensorboardSummary(self.saver.experiment_dir) + self.writer = self.summary.create_summary() + # denormalize for detph image + self.mean_depth = torch.as_tensor(0.12176, dtype=torch.float32, device='cpu') + self.std_depth = torch.as_tensor(0.09752, dtype=torch.float32, device='cpu') + self.nclass = args.num_class + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + self.train_loader, self.val_loader, self.test_loader, _ = make_data_loader(args, train_data=train_data, + valid_data=valid_data, **kwargs) + + # Define network + resnet = resnet18(pretrained=True, efficient=False, use_bn=True) + model = RFNet(resnet, num_classes=self.nclass, use_bn=True) + train_params = [{'params': model.random_init_params(), 'lr': args.lr}, + {'params': model.fine_tune_params(), 'lr': 0.1*args.lr, 'weight_decay':args.weight_decay}] + # Define Optimizer + optimizer = torch.optim.Adam(train_params, lr=args.lr, + weight_decay=args.weight_decay) + # Define Criterion + # whether to use class balanced weights + if args.use_balanced_weights: + classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy') + if os.path.isfile(classes_weights_path): + weight = np.load(classes_weights_path) + else: + weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass) + weight = torch.from_numpy(weight.astype(np.float32)) + else: + weight = None + # Define loss function + self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type) + self.model, self.optimizer = model, optimizer + # Define Evaluator + self.evaluator = Evaluator(self.nclass) + # # Define lr scheduler + self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr, args.epochs, len(self.train_loader)) + # Using cuda + if args.cuda: + self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + patch_replication_callback(self.model) + self.model = self.model.cuda() + + # Resuming checkpoint + self.best_pred = 0.0 + if args.resume is not None: + if not os.path.isfile(args.resume): + raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume)) + print(f"Training: load model from {args.resume}") + checkpoint = torch.load(args.resume, map_location=torch.device("cpu")) + args.start_epoch = checkpoint['epoch'] + # if args.cuda: + # self.model.load_state_dict(checkpoint['state_dict']) + # else: + # self.model.load_state_dict(checkpoint['state_dict']) + self.model.load_state_dict(checkpoint['state_dict']) + if not args.ft: + self.optimizer.load_state_dict(checkpoint['optimizer']) + self.best_pred = checkpoint['best_pred'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + + # Clear start epoch if fine-tuning + if args.ft: + args.start_epoch = 0 + + def training(self, epoch): + train_loss = 0.0 + print(self.optimizer.state_dict()['param_groups'][0]['lr']) + self.model.train() + tbar = tqdm(self.train_loader) + num_img_tr = len(self.train_loader) + + for i, sample in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + #print(target.shape) + else: + image, target = sample['image'], sample['label'] + print(image.shape) + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + self.scheduler(self.optimizer, i, epoch, self.best_pred) + self.optimizer.zero_grad() + + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + #print(target.max()) + #print(output.shape) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + loss.backward() + self.optimizer.step() + #print(self.optimizer.state_dict()['param_groups'][0]['lr']) + train_loss += loss.item() + tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) + self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) + # Show 10 * 3 inference results each epoch + if i % (num_img_tr // 10) == 0: + global_step = i + num_img_tr * epoch + if self.args.depth: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + depth_display = depth[0].cpu().unsqueeze(0) + depth_display = depth_display.mul_(self.std_depth).add_(self.mean_depth) + depth_display = depth_display.numpy() + depth_display = depth_display*255 + depth_display = depth_display.astype(np.uint8) + self.writer.add_image('Depth', depth_display, global_step) + + else: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch) + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print('Loss: %.3f' % train_loss) + + # if self.args.no_val: + # # save checkpoint every epoch + # is_best = False + # checkpoint_path = self.saver.save_checkpoint({ + # 'epoch': epoch + 1, + # 'state_dict': self.model.state_dict(), + # 'optimizer': self.optimizer.state_dict(), + # 'best_pred': self.best_pred, + # }, is_best) + + def validation(self, epoch): + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.val_loader, desc='\r') + test_loss = 0.0 + for i, (sample, img_path) in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + # print(f"val image is {image}") + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + test_loss += loss.item() + tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1))) + pred = output.data.cpu().numpy() + target = target.cpu().numpy() + pred = np.argmax(pred, axis=1) + # Add batch sample into evaluator + self.evaluator.add_batch(target, pred) + + # Fast test during the training + Acc = self.evaluator.Pixel_Accuracy() + Acc_class = self.evaluator.Pixel_Accuracy_Class() + mIoU = self.evaluator.Mean_Intersection_over_Union() + FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union() + self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch) + self.writer.add_scalar('val/mIoU', mIoU, epoch) + self.writer.add_scalar('val/Acc', Acc, epoch) + self.writer.add_scalar('val/Acc_class', Acc_class, epoch) + self.writer.add_scalar('val/fwIoU', FWIoU, epoch) + print('Validation:') + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU)) + print('Loss: %.3f' % test_loss) + + new_pred = mIoU + if new_pred > self.best_pred: + is_best = True + self.best_pred = new_pred + self.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.model.state_dict(), + 'optimizer': self.optimizer.state_dict(), + 'best_pred': self.best_pred, + }, is_best) \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py new file mode 100644 index 00000000..f3d54437 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -0,0 +1,52 @@ +class TrainArgs: + def __init__(self, **kwargs): + self.depth = False + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.loss_type = 'ce' + self.epochs = kwargs.get("epochs", 2) + self.start_epoch = 0 + + self.batch_size = 4 + self.val_batch_size = 1 + self.use_balanced_weights = False + self.num_class = 24 + self.lr = kwargs.get("learning_rate", 1e-4) + self.lr_scheduler = 'cos' + self.momentum = 0.9 + self.weight_decay = 2.5e-5 + self.no_cuda = False + self.gpu_ids = '0' + + self.seed = 1 + self.resume = None + self.checkname = 'RFNet' + self.ft = True + self.eval_interval = kwargs.get("eval_interval", 2) + self.no_val = kwargs.get("no_val", True) + self.cuda = False + + +class ValArgs: + def __init__(self, **kwargs): + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.batch_size = 6 + self.val_batch_size = 1 + self.test_batch_size = 1 + self.num_class = 24 + self.no_cuda = False + self.gpu_ids = '0' + self.checkname = None + self.weight_path = "./models/530_exp3_2.pth" + self.save_predicted_image = False + self.color_label_save_path = './test/color' + self.merge_label_save_path = './test/merge' + self.label_save_path = './test/label' + self.merge = True + self.depth = False + self.cuda = False diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py new file mode 100644 index 00000000..2c2c9821 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py @@ -0,0 +1,29 @@ +import os +from tqdm import tqdm +import numpy as np +from mypath import Path + +def calculate_weigths_labels(dataset, dataloader, num_classes): + # Create an instance from the data loader + z = np.zeros((num_classes,)) + # Initialize tqdm + tqdm_batch = tqdm(dataloader) + print('Calculating classes weights') + for sample in tqdm_batch: + y = sample['label'] + y = y.detach().cpu().numpy() + mask = (y >= 0) & (y < num_classes) + labels = y[mask].astype(np.uint8) + count_l = np.bincount(labels, minlength=num_classes) + z += count_l + tqdm_batch.close() + total_frequency = np.sum(z) + class_weights = [] + for frequency in z: + class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) + class_weights.append(class_weight) + ret = np.array(class_weights) + classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy') + np.save(classes_weights_path, ret) + + return ret \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py new file mode 100644 index 00000000..de955825 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py @@ -0,0 +1,142 @@ +import torch + + +class iouEval: + + def __init__(self, nClasses, ignoreIndex=20): + + self.nClasses = nClasses + self.ignoreIndex = ignoreIndex if nClasses > ignoreIndex else -1 # if ignoreIndex is larger than nClasses, consider no ignoreIndex + self.reset() + + def reset(self): + classes = self.nClasses if self.ignoreIndex == -1 else self.nClasses - 1 + self.tp = torch.zeros(classes).double() + self.fp = torch.zeros(classes).double() + self.fn = torch.zeros(classes).double() + self.cdp_obstacle = torch.zeros(1).double() + self.tp_obstacle = torch.zeros(1).double() + self.idp_obstacle = torch.zeros(1).double() + self.tp_nonobstacle = torch.zeros(1).double() + # self.cdi = torch.zeros(1).double() + + def addBatch(self, x, y): # x=preds, y=targets + # sizes should be "batch_size x nClasses x H x W" + # cdi = 0 + + # print ("X is cuda: ", x.is_cuda) + # print ("Y is cuda: ", y.is_cuda) + + if (x.is_cuda or y.is_cuda): + x = x.cuda() + y = y.cuda() + + # if size is "batch_size x 1 x H x W" scatter to onehot + if (x.size(1) == 1): + x_onehot = torch.zeros(x.size(0), self.nClasses, x.size(2), x.size(3)) + if x.is_cuda: + x_onehot = x_onehot.cuda() + x_onehot.scatter_(1, x, 1).float() # dim index src 按照列用1替换0，索引为x + else: + x_onehot = x.float() + + if (y.size(1) == 1): + y_onehot = torch.zeros(y.size(0), self.nClasses, y.size(2), y.size(3)) + if y.is_cuda: + y_onehot = y_onehot.cuda() + y_onehot.scatter_(1, y, 1).float() + else: + y_onehot = y.float() + + if (self.ignoreIndex != -1): + ignores = y_onehot[:, self.ignoreIndex].unsqueeze(1) # 加一维 + x_onehot = x_onehot[:, :self.ignoreIndex] # ignoreIndex后的都不要 + y_onehot = y_onehot[:, :self.ignoreIndex] + else: + ignores = 0 + + + tpmult = x_onehot * y_onehot # times prediction and gt coincide is 1 + tp = torch.sum(torch.sum(torch.sum(tpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fpmult = x_onehot * ( + 1 - y_onehot - ignores) # times prediction says its that class and gt says its not (subtracting cases when its ignore label!) + fp = torch.sum(torch.sum(torch.sum(fpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fnmult = (1 - x_onehot) * (y_onehot) # times prediction says its not that class and gt says it is + fn = torch.sum(torch.sum(torch.sum(fnmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + + self.tp += tp.double().cpu() + self.fp += fp.double().cpu() + self.fn += fn.double().cpu() + + cdp_obstacle = tpmult[:, 19].sum() # obstacle index 19 + tp_obstacle = y_onehot[:, 19].sum() + + idp_obstacle = (x_onehot[:, 19] - tpmult[:, 19]).sum() + tp_nonobstacle = (-1*y_onehot+1).sum() + + # for i in range(0, x.size(0)): + # if tpmult[i].sum()/(y_onehot[i].sum() + 1e-15) >= 0.5: + # cdi += 1 + + + self.cdp_obstacle += cdp_obstacle.double().cpu() + self.tp_obstacle += tp_obstacle.double().cpu() + self.idp_obstacle += idp_obstacle.double().cpu() + self.tp_nonobstacle += tp_nonobstacle.double().cpu() + # self.cdi += cdi.double().cpu() + + + + def getIoU(self): + num = self.tp + den = self.tp + self.fp + self.fn + 1e-15 + iou = num / den + iou_not_zero = list(filter(lambda x: x != 0, iou)) + # print(len(iou_not_zero)) + iou_mean = sum(iou_not_zero) / len(iou_not_zero) + tfp = self.tp + self.fp + 1e-15 + acc = num / tfp + acc_not_zero = list(filter(lambda x: x != 0, acc)) + acc_mean = sum(acc_not_zero) / len(acc_not_zero) + + return iou_mean, iou, acc_mean, acc # returns "iou mean", "iou per class" + + def getObstacleEval(self): + + pdr_obstacle = self.cdp_obstacle / (self.tp_obstacle+1e-15) + pfp_obstacle = self.idp_obstacle / (self.tp_nonobstacle+1e-15) + + return pdr_obstacle, pfp_obstacle + + +# Class for colors +class colors: + RED = '\033[31;1m' + GREEN = '\033[32;1m' + YELLOW = '\033[33;1m' + BLUE = '\033[34;1m' + MAGENTA = '\033[35;1m' + CYAN = '\033[36;1m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + ENDC = '\033[0m' + + +# Colored value output if colorized flag is activated. +def getColorEntry(val): + if not isinstance(val, float): + return colors.ENDC + if (val < .20): + return colors.RED + elif (val < .40): + return colors.YELLOW + elif (val < .60): + return colors.BLUE + elif (val < .80): + return colors.CYAN + else: + return colors.GREEN + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py new file mode 100644 index 00000000..6cde9a17 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py @@ -0,0 +1,64 @@ +import torch +import torch.nn as nn + +class SegmentationLosses(object): + def __init__(self, weight=None, size_average=True, batch_average=True, ignore_index=255, cuda=False): # ignore_index=255 + self.ignore_index = ignore_index + self.weight = weight + self.size_average = size_average + self.batch_average = batch_average + self.cuda = cuda + + def build_loss(self, mode='ce'): + """Choices: ['ce' or 'focal']""" + if mode == 'ce': + return self.CrossEntropyLoss + elif mode == 'focal': + return self.FocalLoss + else: + raise NotImplementedError + + def CrossEntropyLoss(self, logit, target): + n, c, h, w = logit.size() + #criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + #size_average=self.size_average) + criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=self.ignore_index) + if self.cuda: + criterion = criterion.cuda() + + loss = criterion(logit, target.long()) + + if self.batch_average: + loss /= n + + return loss + + def FocalLoss(self, logit, target, gamma=2, alpha=0.5): + n, c, h, w = logit.size() + criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + size_average=self.size_average) + if self.cuda: + criterion = criterion.cuda() + + logpt = -criterion(logit, target.long()) + pt = torch.exp(logpt) + if alpha is not None: + logpt *= alpha + loss = -((1 - pt) ** gamma) * logpt + + if self.batch_average: + loss /= n + + return loss + +if __name__ == "__main__": + loss = SegmentationLosses(cuda=True) + a = torch.rand(1, 3, 7, 7).cuda() + b = torch.rand(1, 7, 7).cuda() + print(loss.CrossEntropyLoss(a, b).item()) + print(loss.FocalLoss(a, b, gamma=0, alpha=None).item()) + print(loss.FocalLoss(a, b, gamma=2, alpha=0.5).item()) + + + + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py new file mode 100644 index 00000000..47124028 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py @@ -0,0 +1,70 @@ +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +## Created by: Hang Zhang +## ECE Department, Rutgers University +## Email: zhang.hang@rutgers.edu +## Copyright (c) 2017 +## +## This source code is licensed under the MIT-style license found in the +## LICENSE file in the root directory of this source tree +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +import math + +class LR_Scheduler(object): + """Learning Rate Scheduler + + Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}`` + + Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))`` + + Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9`` + + Args: + args: + :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`), + :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs, + :attr:`args.lr_step` + + iters_per_epoch: number of iterations per epoch + """ + def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, + lr_step=0, warmup_epochs=0): + self.mode = mode + print('Using {} LR Scheduler!'.format(self.mode)) + self.lr = base_lr + if mode == 'step': + assert lr_step + self.lr_step = lr_step + self.iters_per_epoch = iters_per_epoch + self.N = num_epochs * iters_per_epoch + self.epoch = -1 + self.warmup_iters = warmup_epochs * iters_per_epoch + + def __call__(self, optimizer, i, epoch, best_pred): + T = epoch * self.iters_per_epoch + i + if self.mode == 'cos': + lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) + elif self.mode == 'poly': + lr = self.lr * pow((1 - 1.0 * T / self.N), 2) + elif self.mode == 'step': + lr = self.lr * (0.1 ** (epoch // self.lr_step)) + else: + raise NotImplemented + # warm up lr schedule + if self.warmup_iters > 0 and T < self.warmup_iters: + lr = lr * 1.0 * T / self.warmup_iters + if epoch > self.epoch: + print('\n=>Epoches %i, learning rate = %.4f, \ + previous best = %.4f' % (epoch, lr, best_pred)) + self.epoch = epoch + assert lr >= 0 + self._adjust_learning_rate(optimizer, lr) + + def _adjust_learning_rate(self, optimizer, lr): + if len(optimizer.param_groups) == 1: + optimizer.param_groups[0]['lr'] = lr * 4 + else: + # enlarge the lr at the head + optimizer.param_groups[0]['lr'] = lr * 4 + for i in range(1, len(optimizer.param_groups)): + optimizer.param_groups[i]['lr'] = lr diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py new file mode 100644 index 00000000..551bc0d1 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py @@ -0,0 +1,146 @@ +import numpy as np + + +class Evaluator(object): + def __init__(self, num_class): + self.num_class = num_class + self.confusion_matrix = np.zeros((self.num_class,)*2) # shape:(num_class, num_class) + + def Pixel_Accuracy(self): + Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum() + return Acc + + def Pixel_Accuracy_Class_Curb(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + Acc = np.nanmean(Acc[:2]) + return Acc + + + def Pixel_Accuracy_Class(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + print("building : %.6f" % (Acc[2] * 100.0), "%\t") + print("wall : %.6f" % (Acc[3] * 100.0), "%\t") + print("fence : %.6f" % (Acc[4] * 100.0), "%\t") + print("pole : %.6f" % (Acc[5] * 100.0), "%\t") + print("traffic light: %.6f" % (Acc[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (Acc[7] * 100.0), "%\t") + print("vegetation : %.6f" % (Acc[8] * 100.0), "%\t") + print("terrain : %.6f" % (Acc[9] * 100.0), "%\t") + print("sky : %.6f" % (Acc[10] * 100.0), "%\t") + print("person : %.6f" % (Acc[11] * 100.0), "%\t") + print("rider : %.6f" % (Acc[12] * 100.0), "%\t") + print("car : %.6f" % (Acc[13] * 100.0), "%\t") + print("truck : %.6f" % (Acc[14] * 100.0), "%\t") + print("bus : %.6f" % (Acc[15] * 100.0), "%\t") + print("train : %.6f" % (Acc[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (Acc[17] * 100.0), "%\t") + print("bicycle : %.6f" % (Acc[18] * 100.0), "%\t") + print("dynamic : %.6f" % (Acc[19] * 100.0), "%\t") + print("stair : %.6f" % (Acc[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (Acc[19] * 100.0), "%\t") + Acc = np.nanmean(Acc) + return Acc + + def Mean_Intersection_over_Union(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + print("building : %.6f" % (MIoU[2] * 100.0), "%\t") + print("wall : %.6f" % (MIoU[3] * 100.0), "%\t") + print("fence : %.6f" % (MIoU[4] * 100.0), "%\t") + print("pole : %.6f" % (MIoU[5] * 100.0), "%\t") + print("traffic light: %.6f" % (MIoU[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (MIoU[7] * 100.0), "%\t") + print("vegetation : %.6f" % (MIoU[8] * 100.0), "%\t") + print("terrain : %.6f" % (MIoU[9] * 100.0), "%\t") + print("sky : %.6f" % (MIoU[10] * 100.0), "%\t") + print("person : %.6f" % (MIoU[11] * 100.0), "%\t") + print("rider : %.6f" % (MIoU[12] * 100.0), "%\t") + print("car : %.6f" % (MIoU[13] * 100.0), "%\t") + print("truck : %.6f" % (MIoU[14] * 100.0), "%\t") + print("bus : %.6f" % (MIoU[15] * 100.0), "%\t") + print("train : %.6f" % (MIoU[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (MIoU[17] * 100.0), "%\t") + print("bicycle : %.6f" % (MIoU[18] * 100.0), "%\t") + print("dynamic : %.6f" % (MIoU[19] * 100.0), "%\t") + print("stair : %.6f" % (MIoU[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU) + return MIoU + + def Mean_Intersection_over_Union_Curb(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU[:2]) + return MIoU + + def Frequency_Weighted_Intersection_over_Union(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return FWIoU + + def Frequency_Weighted_Intersection_over_Union_Curb(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return np.nanmean(CFWIoU[:2]) + + def _generate_matrix(self, gt_image, pre_image): + mask = (gt_image >= 0) & (gt_image < self.num_class) + label = self.num_class * gt_image[mask].astype('int') + pre_image[mask] + count = np.bincount(label, minlength=self.num_class**2) + confusion_matrix = count.reshape(self.num_class, self.num_class) + return confusion_matrix + + def add_batch(self, gt_image, pre_image): + print(gt_image.shape, pre_image.shape) + assert gt_image.shape == pre_image.shape + self.confusion_matrix += self._generate_matrix(gt_image, pre_image) + + def reset(self): + self.confusion_matrix = np.zeros((self.num_class,) * 2) + + + + diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py new file mode 100644 index 00000000..03866432 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py @@ -0,0 +1,68 @@ +import os +import time +import shutil +import tempfile +import torch +from collections import OrderedDict +import glob + +class Saver(object): + + def __init__(self, args): + self.args = args + self.directory = os.path.join('/tmp', args.dataset, args.checkname) + self.runs = sorted(glob.glob(os.path.join(self.directory, 'experiment_*'))) + run_id = int(self.runs[-1].split('_')[-1]) + 1 if self.runs else 0 + + self.experiment_dir = os.path.join(self.directory, 'experiment_{}'.format(str(run_id))) + if not os.path.exists(self.experiment_dir): + os.makedirs(self.experiment_dir) + + def save_checkpoint(self, state, is_best): # filename from .pth.tar change to .pth? + """Saves checkpoint to disk""" + filename = f'checkpoint_{time.time()}.pth' + checkpoint_path = os.path.join(self.experiment_dir, filename) + torch.save(state, checkpoint_path) + if is_best: + best_pred = state['best_pred'] + with open(os.path.join(self.experiment_dir, 'best_pred.txt'), 'w') as f: + f.write(str(best_pred)) + if self.runs: + previous_miou = [0.0] + for run in self.runs: + run_id = run.split('_')[-1] + path = os.path.join(self.directory, 'experiment_{}'.format(str(run_id)), 'best_pred.txt') + if os.path.exists(path): + with open(path, 'r') as f: + miou = float(f.readline()) + previous_miou.append(miou) + else: + continue + max_miou = max(previous_miou) + if best_pred > max_miou: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + else: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + + return checkpoint_path + + def save_experiment_config(self): + logfile = os.path.join(self.experiment_dir, 'parameters.txt') + log_file = open(logfile, 'w') + p = OrderedDict() + p['datset'] = self.args.dataset + # p['out_stride'] = self.args.out_stride + p['lr'] = self.args.lr + p['lr_scheduler'] = self.args.lr_scheduler + p['loss_type'] = self.args.loss_type + p['epoch'] = self.args.epochs + p['base_size'] = self.args.base_size + p['crop_size'] = self.args.crop_size + + for key, val in p.items(): + log_file.write(key + ':' + str(val) + '\n') + log_file.close() \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py new file mode 100644 index 00000000..04bcdb82 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py @@ -0,0 +1,39 @@ +import os +import torch +from torchvision.utils import make_grid +# from tensorboardX import SummaryWriter +from torch.utils.tensorboard import SummaryWriter +from dataloaders.utils import decode_seg_map_sequence + +class TensorboardSummary(object): + def __init__(self, directory): + self.directory = directory + + def create_summary(self): + writer = SummaryWriter(log_dir=os.path.join(self.directory)) + return writer + + def visualize_image(self, writer, dataset, image, target, output, global_step, depth=None): + if depth is None: + grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) + else: + grid_image = make_grid(image[:3].clone().cpu().data, 4, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(depth[:3].clone().cpu().data, 4, normalize=True) # normalize=False? + writer.add_image('Depth', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py new file mode 100644 index 00000000..5c3f07de --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py @@ -0,0 +1,290 @@ +import os + +from PIL import Image +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from torchvision import transforms +import numpy as np +import torch +from torch.utils.data import DataLoader +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context + +from RFNet.train import Trainer +from RFNet.eval import Validator, load_my_state_dict +from RFNet.dataloaders import custom_transforms as tr +from RFNet.utils.args import TrainArgs, ValArgs + + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.train_args = TrainArgs(**kwargs) + # self.val_args = self._val_args() + # self.train_args = self._train_args() + + # self.train_args.lr = kwargs.get("learning_rate", 1e-4) + # self.train_args.epochs = kwargs.get("epochs", 2) + # self.train_args.eval_interval = kwargs.get("eval_interval", 2) + # self.train_args.no_val = kwargs.get("no_val", True) + # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) + self.trainer = None + + self.val_args = ValArgs(**kwargs) + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range( + self.trainer.args.start_epoch, + self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and ( + epoch % + self.trainer.args.eval_interval == ( + self.trainer.args.eval_interval - + 1) or epoch == self.trainer.args.epochs - + 1): + # save checkpoint when it meets eval_interval or the training + # finished + is_best = False + self.train_model_url = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + self.trainer.writer.close() + + return self.train_model_url + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = self._preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: save unstructured data model + if not model_path: + LOGGER.warning(f"Not specify model path.") + return self.train_model_url + + return FileOps.upload(self.train_model_url, model_path) + + def _preprocess(self, image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images + + # def _train_args(self): + # parser = argparse.ArgumentParser(description="PyTorch RFNet Training") + # parser.add_argument('--depth', action="store_true", default=False, + # help='training with depth image or not (default: False)') + # parser.add_argument('--dataset', type=str, default='cityscapes', + # choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], + # help='dataset name (default: cityscapes)') + # parser.add_argument('--workers', type=int, default=4, + # metavar='N', help='dataloader threads') + # parser.add_argument('--base-size', type=int, default=1024, + # help='base image size') + # parser.add_argument('--crop-size', type=int, default=768, + # help='crop image size') + # parser.add_argument('--loss-type', type=str, default='ce', + # choices=['ce', 'focal'], + # help='loss func type (default: ce)') + # # training hyper params + # # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # # help='number of epochs to train (default: auto)') + # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # help='number of epochs to train (default: auto)') + # parser.add_argument('--start_epoch', type=int, default=0, + # metavar='N', help='start epochs (default:0)') + # parser.add_argument('--batch-size', type=int, default=None, + # metavar='N', help='input batch size for \ + # training (default: auto)') + # parser.add_argument('--val-batch-size', type=int, default=1, + # metavar='N', help='input batch size for \ + # testing (default: auto)') + # parser.add_argument('--test-batch-size', type=int, default=1, + # metavar='N', help='input batch size for \ + # testing (default: auto)') + # parser.add_argument('--use-balanced-weights', action='store_true', default=False, + # help='whether to use balanced weights (default: True)') + # parser.add_argument('--num-class', type=int, default=24, + # help='number of training classes (default: 24') + # # optimizer params + # parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + # help='learning rate (default: auto)') + # parser.add_argument('--lr-scheduler', type=str, default='cos', + # choices=['poly', 'step', 'cos', 'inv'], + # help='lr scheduler mode: (default: cos)') + # parser.add_argument('--momentum', type=float, default=0.9, + # metavar='M', help='momentum (default: 0.9)') + # parser.add_argument('--weight-decay', type=float, default=2.5e-5, + # metavar='M', help='w-decay (default: 5e-4)') + # # cuda, seed and logging + # parser.add_argument('--no-cuda', action='store_true', default= + # False, help='disables CUDA training') + # parser.add_argument('--gpu-ids', type=str, default='0', + # help='use which gpu to train, must be a \ + # comma-separated list of integers only (default=0)') + # parser.add_argument('--seed', type=int, default=1, metavar='S', + # help='random seed (default: 1)') + # # checking point + # parser.add_argument('--resume', type=str, + # default=None, + # help='put the path to resuming file if needed') + # parser.add_argument('--checkname', type=str, default=None, + # help='set the checkpoint name') + # # finetuning pre-trained models + # parser.add_argument('--ft', action='store_true', default=True, + # help='finetuning on a different dataset') + # # evaluation option + # parser.add_argument('--eval-interval', type=int, default=1, + # help='evaluation interval (default: 1)') + # parser.add_argument('--no-val', action='store_true', default=False, + # help='skip validation during training') + # + # args = parser.parse_args() + # args.cuda = not args.no_cuda and torch.cuda.is_available() + # print(torch.cuda.is_available()) + # if args.cuda: + # try: + # args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + # except ValueError: + # raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + # + # if args.epochs is None: + # epoches = { + # 'cityscapes': 200, + # 'citylostfound': 200, + # } + # args.epochs = epoches[args.dataset.lower()] + # + # if args.batch_size is None: + # args.batch_size = 4 * len(args.gpu_ids) + # + # if args.test_batch_size is None: + # args.test_batch_size = args.batch_size + # + # if args.lr is None: + # lrs = { + # 'cityscapes': 0.0001, + # 'citylostfound': 0.0001, + # 'cityrand': 0.0001 + # } + # args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size + # + # if args.checkname is None: + # args.checkname = 'RFNet' + # print(args) + # torch.manual_seed(args.seed) + # + # return args + + # def _val_args(self): + # parser = argparse.ArgumentParser(description="PyTorch RFNet validation") + # parser.add_argument('--dataset', type=str, default='cityscapes', + # choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], + # help='dataset name (default: cityscapes)') + # parser.add_argument('--workers', type=int, default=4, + # metavar='N', help='dataloader threads') + # parser.add_argument('--base-size', type=int, default=1024, + # help='base image size') + # parser.add_argument('--crop-size', type=int, default=768, + # help='crop image size') + # parser.add_argument('--batch-size', type=int, default=6, + # help='batch size for training') + # parser.add_argument('--val-batch-size', type=int, default=1, + # metavar='N', help='input batch size for \ + # validating (default: auto)') + # parser.add_argument('--test-batch-size', type=int, default=1, + # metavar='N', help='input batch size for \ + # testing (default: auto)') + # parser.add_argument('--num-class', type=int, default=24, + # help='number of training classes (default: 24') + # parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') + # parser.add_argument('--gpu-ids', type=str, default='0', + # help='use which gpu to train, must be a \ + # comma-separated list of integers only (default=0)') + # parser.add_argument('--checkname', type=str, default=None, + # help='set the checkpoint name') + # parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", + # help='enter your path of the weight') + # parser.add_argument('--save-predicted-image', action='store_true', default=False, + # help='save predicted images') + # parser.add_argument('--color-label-save-path', type=str, + # default='./test/color/', + # help='path to save label') + # parser.add_argument('--merge-label-save-path', type=str, + # default='./test/merge/', + # help='path to save merged label') + # parser.add_argument('--label-save-path', type=str, default='./test/label/', + # help='path to save merged label') + # parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') + # parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') + # + # args = parser.parse_args() + # args.cuda = not args.no_cuda and torch.cuda.is_available() + # if args.cuda: + # try: + # args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + # except ValueError: + # raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + # + # return args diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml new file mode 100644 index 00000000..efd0fbfb --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -0,0 +1,59 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.8 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + splitting_method: "default" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py new file mode 100644 index 00000000..c0c28398 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py @@ -0,0 +1,64 @@ +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('TaskAllocationByOrigin') + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + self.task_extractor = task_extractor + if self.default_origin: + return samples, [int(self.task_extractor.get( + self.default_origin))] * len(samples.x) + + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + sample_origins = [] + for _x in samples.x: + is_real = False + for city in cities: + if city in _x[0]: + is_real = True + sample_origins.append("real") + break + if not is_real: + sample_origins.append("sim") + + allocations = [int(self.task_extractor.get(sample_origin)) + for sample_origin in sample_origins] + + return samples, allocations \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py new file mode 100644 index 00000000..847db8ed --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py @@ -0,0 +1,81 @@ +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + + +__all__ = ('TaskDefinitionByOrigin') + +@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") +class TaskDefinitionByOrigin: + """ + Dividing datasets based on the their origins. + + Parameters + ---------- + origins： List[Metadata] + metadata is usually a class feature label with a finite values. + """ + + def __init__(self, **kwargs): + self.origins = kwargs.get("origins", ["real", "sim"]) + + def __call__(self, + samples: BaseDataSource, **kwargs) -> Tuple[List[Task], + Any, + BaseDataSource]: + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + tasks = [] + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + + task_index = dict(zip(self.origins, range(len(self.origins)))) + + real_df = BaseDataSource(data_type=d_type) + real_df.x, real_df.y = [], [] + sim_df = BaseDataSource(data_type=d_type) + sim_df.x, sim_df.y = [], [] + + for i in range(samples.num_examples()): + is_real = False + for city in cities: + if city in x_data[i][0]: + is_real = True + real_df.x.append(x_data[i]) + real_df.y.append(y_data[i]) + break + if not is_real: + sim_df.x.append(x_data[i]) + sim_df.y.append(y_data[i]) + + g_attr = "real_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=real_df, meta_attr="real") + tasks.append(task_obj) + + g_attr = "sim_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="sim") + tasks.append(task_obj) + + return tasks, task_index, samples \ No newline at end of file diff --git a/examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py b/examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py new file mode 100644 index 00000000..7826d897 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py @@ -0,0 +1,55 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm import tqdm + +from sedna.common.class_factory import ClassType, ClassFactory + +from RFNet.dataloaders import make_data_loader +from RFNet.utils.metrics import Evaluator +from RFNet.utils.args import ValArgs + +__all__ = ('accuracy') + + +@ClassFactory.register(ClassType.GENERAL, alias="accuracy") +def accuracy(y_true, y_pred, **kwargs): + args = ValArgs() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml b/examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml new file mode 100644 index 00000000..6956e973 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml @@ -0,0 +1,34 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/ianvs/dataset/curb-detection/train_data/index.txt" + # the url address of test dataset index; string type; + test_url: "/ianvs/dataset/curb-detection/test_data/index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/curb-detection/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 2 \ No newline at end of file From 961b7959f85b35457e0249abb37319684600ef6c Mon Sep 17 00:00:00 2001 From: JimmyYang20 Date: Thu, 25 Aug 2022 16:27:11 +0800 Subject: [PATCH 03/22] Optimized example file format Signed-off-by: JimmyYang20 --- .../testalgorithms/rfnet/basemodel.py | 175 +----------------- .../rfnet/task_allocation_by_origin.py | 5 +- .../rfnet/task_definition_by_origin.py | 4 +- 3 files changed, 9 insertions(+), 175 deletions(-) diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py index 5c3f07de..9a9b6a17 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py @@ -1,21 +1,20 @@ import os -from PIL import Image -from sedna.common.file_ops import FileOps -from sedna.common.log import LOGGER -from torchvision import transforms import numpy as np import torch from torch.utils.data import DataLoader from sedna.common.class_factory import ClassType, ClassFactory from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from PIL import Image +from torchvision import transforms from RFNet.train import Trainer from RFNet.eval import Validator, load_my_state_dict from RFNet.dataloaders import custom_transforms as tr from RFNet.utils.args import TrainArgs, ValArgs - # set backend os.environ['BACKEND_TYPE'] = 'PYTORCH' @@ -24,14 +23,6 @@ class BaseModel: def __init__(self, **kwargs): self.train_args = TrainArgs(**kwargs) - # self.val_args = self._val_args() - # self.train_args = self._train_args() - - # self.train_args.lr = kwargs.get("learning_rate", 1e-4) - # self.train_args.epochs = kwargs.get("epochs", 2) - # self.train_args.eval_interval = kwargs.get("eval_interval", 2) - # self.train_args.no_val = kwargs.get("no_val", True) - # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) self.trainer = None self.val_args = ValArgs(**kwargs) @@ -130,161 +121,3 @@ def _preprocess(self, image_urls): transformed_images.append((composed_transforms(sample), img_path)) return transformed_images - - # def _train_args(self): - # parser = argparse.ArgumentParser(description="PyTorch RFNet Training") - # parser.add_argument('--depth', action="store_true", default=False, - # help='training with depth image or not (default: False)') - # parser.add_argument('--dataset', type=str, default='cityscapes', - # choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], - # help='dataset name (default: cityscapes)') - # parser.add_argument('--workers', type=int, default=4, - # metavar='N', help='dataloader threads') - # parser.add_argument('--base-size', type=int, default=1024, - # help='base image size') - # parser.add_argument('--crop-size', type=int, default=768, - # help='crop image size') - # parser.add_argument('--loss-type', type=str, default='ce', - # choices=['ce', 'focal'], - # help='loss func type (default: ce)') - # # training hyper params - # # parser.add_argument('--epochs', type=int, default=None, metavar='N', - # # help='number of epochs to train (default: auto)') - # parser.add_argument('--epochs', type=int, default=None, metavar='N', - # help='number of epochs to train (default: auto)') - # parser.add_argument('--start_epoch', type=int, default=0, - # metavar='N', help='start epochs (default:0)') - # parser.add_argument('--batch-size', type=int, default=None, - # metavar='N', help='input batch size for \ - # training (default: auto)') - # parser.add_argument('--val-batch-size', type=int, default=1, - # metavar='N', help='input batch size for \ - # testing (default: auto)') - # parser.add_argument('--test-batch-size', type=int, default=1, - # metavar='N', help='input batch size for \ - # testing (default: auto)') - # parser.add_argument('--use-balanced-weights', action='store_true', default=False, - # help='whether to use balanced weights (default: True)') - # parser.add_argument('--num-class', type=int, default=24, - # help='number of training classes (default: 24') - # # optimizer params - # parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', - # help='learning rate (default: auto)') - # parser.add_argument('--lr-scheduler', type=str, default='cos', - # choices=['poly', 'step', 'cos', 'inv'], - # help='lr scheduler mode: (default: cos)') - # parser.add_argument('--momentum', type=float, default=0.9, - # metavar='M', help='momentum (default: 0.9)') - # parser.add_argument('--weight-decay', type=float, default=2.5e-5, - # metavar='M', help='w-decay (default: 5e-4)') - # # cuda, seed and logging - # parser.add_argument('--no-cuda', action='store_true', default= - # False, help='disables CUDA training') - # parser.add_argument('--gpu-ids', type=str, default='0', - # help='use which gpu to train, must be a \ - # comma-separated list of integers only (default=0)') - # parser.add_argument('--seed', type=int, default=1, metavar='S', - # help='random seed (default: 1)') - # # checking point - # parser.add_argument('--resume', type=str, - # default=None, - # help='put the path to resuming file if needed') - # parser.add_argument('--checkname', type=str, default=None, - # help='set the checkpoint name') - # # finetuning pre-trained models - # parser.add_argument('--ft', action='store_true', default=True, - # help='finetuning on a different dataset') - # # evaluation option - # parser.add_argument('--eval-interval', type=int, default=1, - # help='evaluation interval (default: 1)') - # parser.add_argument('--no-val', action='store_true', default=False, - # help='skip validation during training') - # - # args = parser.parse_args() - # args.cuda = not args.no_cuda and torch.cuda.is_available() - # print(torch.cuda.is_available()) - # if args.cuda: - # try: - # args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] - # except ValueError: - # raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') - # - # if args.epochs is None: - # epoches = { - # 'cityscapes': 200, - # 'citylostfound': 200, - # } - # args.epochs = epoches[args.dataset.lower()] - # - # if args.batch_size is None: - # args.batch_size = 4 * len(args.gpu_ids) - # - # if args.test_batch_size is None: - # args.test_batch_size = args.batch_size - # - # if args.lr is None: - # lrs = { - # 'cityscapes': 0.0001, - # 'citylostfound': 0.0001, - # 'cityrand': 0.0001 - # } - # args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size - # - # if args.checkname is None: - # args.checkname = 'RFNet' - # print(args) - # torch.manual_seed(args.seed) - # - # return args - - # def _val_args(self): - # parser = argparse.ArgumentParser(description="PyTorch RFNet validation") - # parser.add_argument('--dataset', type=str, default='cityscapes', - # choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], - # help='dataset name (default: cityscapes)') - # parser.add_argument('--workers', type=int, default=4, - # metavar='N', help='dataloader threads') - # parser.add_argument('--base-size', type=int, default=1024, - # help='base image size') - # parser.add_argument('--crop-size', type=int, default=768, - # help='crop image size') - # parser.add_argument('--batch-size', type=int, default=6, - # help='batch size for training') - # parser.add_argument('--val-batch-size', type=int, default=1, - # metavar='N', help='input batch size for \ - # validating (default: auto)') - # parser.add_argument('--test-batch-size', type=int, default=1, - # metavar='N', help='input batch size for \ - # testing (default: auto)') - # parser.add_argument('--num-class', type=int, default=24, - # help='number of training classes (default: 24') - # parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') - # parser.add_argument('--gpu-ids', type=str, default='0', - # help='use which gpu to train, must be a \ - # comma-separated list of integers only (default=0)') - # parser.add_argument('--checkname', type=str, default=None, - # help='set the checkpoint name') - # parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", - # help='enter your path of the weight') - # parser.add_argument('--save-predicted-image', action='store_true', default=False, - # help='save predicted images') - # parser.add_argument('--color-label-save-path', type=str, - # default='./test/color/', - # help='path to save label') - # parser.add_argument('--merge-label-save-path', type=str, - # default='./test/merge/', - # help='path to save merged label') - # parser.add_argument('--label-save-path', type=str, default='./test/label/', - # help='path to save merged label') - # parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') - # parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') - # - # args = parser.parse_args() - # args.cuda = not args.no_cuda and torch.cuda.is_available() - # if args.cuda: - # try: - # args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] - # except ValueError: - # raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') - # - # return args diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py index c0c28398..dbdecc7c 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py @@ -1,7 +1,8 @@ from sedna.datasources import BaseDataSource from sedna.common.class_factory import ClassFactory, ClassType -__all__ = ('TaskAllocationByOrigin') +__all__ = ('TaskAllocationByOrigin',) + @ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") class TaskAllocationByOrigin: @@ -61,4 +62,4 @@ def __call__(self, task_extractor, samples: BaseDataSource): allocations = [int(self.task_extractor.get(sample_origin)) for sample_origin in sample_origins] - return samples, allocations \ No newline at end of file + return samples, allocations diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py index 847db8ed..4c8dc264 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py @@ -4,8 +4,8 @@ from sedna.common.class_factory import ClassType, ClassFactory from sedna.algorithms.seen_task_learning.artifact import Task +__all__ = ('TaskDefinitionByOrigin',) -__all__ = ('TaskDefinitionByOrigin') @ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") class TaskDefinitionByOrigin: @@ -78,4 +78,4 @@ def __call__(self, task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="sim") tasks.append(task_obj) - return tasks, task_index, samples \ No newline at end of file + return tasks, task_index, samples From 6a487b6f1565ea9f362eeb298126eec25d9eaa87 Mon Sep 17 00:00:00 2001 From: JimmyYang20 Date: Fri, 26 Aug 2022 10:21:22 +0800 Subject: [PATCH 04/22] Add readme for lifelong learning example Signed-off-by: JimmyYang20 --- .../lifelong_learning_bench/README.md | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 examples/curb-detection/lifelong_learning_bench/README.md diff --git a/examples/curb-detection/lifelong_learning_bench/README.md b/examples/curb-detection/lifelong_learning_bench/README.md new file mode 100644 index 00000000..4c633768 --- /dev/null +++ b/examples/curb-detection/lifelong_learning_bench/README.md @@ -0,0 +1,103 @@ +# Quick Start + +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, +in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs +with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can +build and start your distributed synergy AI solution development within minutes. + +Before using Ianvs, you might want to have the device ready: +- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary +- 2 CPUs or more +- 4GB+ free memory, depends on algorithm and simulation setting +- 10GB+ free disk space +- Internet connection for GitHub and pip, etc +- Python 3.6+ installed + +In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. + +## Step 1. Ianvs Preparation + +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` +as: + +``` shell +mkdir /ianvs +cd /ianvs #One might use another path preferred + +mkdir project +cd project +git clone https://github.com/kubeedge/ianvs.git +``` + + +Then, we install third-party dependencies for ianvs. +``` shell +sudo apt-get update +sudo apt-get install libgl1-mesa-glx -y +python -m pip install --upgrade pip + +cd ianvs +python -m pip install ./examples/resources/third_party/* +python -m pip install -r requirements.txt +``` + +We are now ready to install Ianvs. +``` shell +python setup.py install +``` + +## Step 2. Dataset Preparation + +Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does +not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. + +``` shell +cd /ianvs #One might use another path preferred +mkdir dataset +cd dataset +wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip +unzip dataset.zip +``` + +The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, +we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + + + + +Related algorithm is also ready in this quick start. +``` shell +export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet +``` + +The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick +start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +``` shell +cd /ianvs/project +ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( +e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( +e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers +can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +The final output might look like this: + +|rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | +|:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| +|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | + + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! \ No newline at end of file From 300d38e6f83b64044201c77dcc29a6ab65de4d8e Mon Sep 17 00:00:00 2001 From: JimmyYang20 Date: Mon, 29 Aug 2022 09:48:45 +0800 Subject: [PATCH 05/22] Fix a bug: change lifelonglearning module type Signed-off-by: JimmyYang20 --- core/testcasecontroller/algorithm/paradigm/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py index 67cca5e4..0d773980 100644 --- a/core/testcasecontroller/algorithm/paradigm/base.py +++ b/core/testcasecontroller/algorithm/paradigm/base.py @@ -106,7 +106,7 @@ def build_paradigm_job(self, paradigm_type): task_definition=self.module_instances.get( ModuleType.TASK_DEFINITION.value), task_relationship_discovery=self.module_instances.get( - ModuleType.TASK_DEFINITION.value), + ModuleType.TASK_RELATIONSHIP_DISCOVERY.value), task_allocation=self.module_instances.get( ModuleType.TASK_ALLOCATION.value), task_remodeling=self.module_instances.get( From 6220a5b1bacbccb43bba0f14f1f1be4eea920fbf Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 12:27:46 +0800 Subject: [PATCH 06/22] add the multi-task inference in object detection based yolo Signed-off-by: shifan__z --- .../lifelong_learning/lifelong_learning.py | 58 +- core/testenvmanager/testenv/testenv.py | 5 + .../bdd/lifelong_learning_bench/README.md | 103 ++++ .../benchmarkingjob.yaml | 74 +++ .../testalgorithms/yolo/basemodel.py | 45 ++ .../yolo/inference_integrate.py | 99 +++ .../yolo/model_selector/choose_net_b64.py | 106 ++++ .../yolo/task_allocation_by_origin.py | 84 +++ .../testalgorithms/yolo/task_remodeling.py | 72 +++ .../testalgorithms/yolo/yolo_algorithm.yaml | 53 ++ .../lifelong_learning_bench/testenv/map.py | 567 ++++++++++++++++++ .../testenv/testenv.yaml | 41 ++ examples/resources/sedna.zip | Bin 0 -> 837831 bytes 13 files changed, 1278 insertions(+), 29 deletions(-) create mode 100644 examples/bdd/lifelong_learning_bench/README.md create mode 100644 examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/basemodel.py create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/inference_integrate.py create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py create mode 100644 examples/bdd/lifelong_learning_bench/testalgorithms/yolo/yolo_algorithm.yaml create mode 100644 examples/bdd/lifelong_learning_bench/testenv/map.py create mode 100644 examples/bdd/lifelong_learning_bench/testenv/testenv.yaml create mode 100644 examples/resources/sedna.zip diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index 2b035706..b980d200 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -59,6 +59,7 @@ def __init__(self, workspace, **kwargs): self.incremental_learning_data_setting = kwargs.get("lifelong_learning_data_setting") self.initial_model = kwargs.get("initial_model_url") + self.mode = kwargs.get("mode") self.incremental_rounds = kwargs.get("incremental_rounds", 1) self.model_eval_config = kwargs.get("model_eval") @@ -84,32 +85,31 @@ def run(self): rounds = self.incremental_rounds samples_transfer_ratio_info = self.system_metric_info.get( SystemMetricType.SAMPLES_TRANSFER_RATIO.value) - dataset_files = self._split_dataset(splitting_dataset_times=rounds) - - # pylint: disable=C0103 - for r in range(1, rounds + 1): - if r == 1: - train_dataset_file, eval_dataset_file = dataset_files[r - 1] - self.cloud_task_index = self._train(self.cloud_task_index, train_dataset_file, r) - self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) - - else: - infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - - inference_results, unseen_task_train_samples = self._inference(self.edge_task_index, - infer_dataset_file, - r) - samples_transfer_ratio_info.append((inference_results, unseen_task_train_samples.x)) - - # If no unseen task samples in the this round, starting the next round - if len(unseen_task_train_samples.x) <= 0: - continue - - self.cloud_task_index = self._train(self.cloud_task_index, - unseen_task_train_samples, - r) - self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) - + if self.mode != 'multi-inference': + dataset_files = self._split_dataset(splitting_dataset_times=rounds) + # pylint: disable=C0103 + for r in range(1, rounds + 1): + if r == 1: + train_dataset_file, eval_dataset_file = dataset_files[r - 1] + self.cloud_task_index = self._train(self.cloud_task_index, train_dataset_file, r) + self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + + else: + infer_dataset_file, eval_dataset_file = dataset_files[r - 1] + + inference_results, unseen_task_train_samples = self._inference(self.edge_task_index, + infer_dataset_file, + r) + samples_transfer_ratio_info.append((inference_results, unseen_task_train_samples.x)) + + # If no unseen task samples in the this round, starting the next round + if len(unseen_task_train_samples.x) <= 0: + continue + + self.cloud_task_index = self._train(self.cloud_task_index, + unseen_task_train_samples, + r) + self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) test_res, unseen_task_train_samples = self._inference(self.edge_task_index, self.dataset.test_url, "test") @@ -141,12 +141,12 @@ def _inference(self, edge_task_index, data_index_file, rounds): inference_results = [] unseen_tasks = [] unseen_task_labels = [] + kwargs = {"mode": self.mode} for i, _ in enumerate(inference_dataset.x): data = BaseDataSource(data_type="test") data.x = inference_dataset.x[i:(i + 1)] - res, is_unseen_task, _ = job.inference(data) - - inference_results.extend(res) + res, is_unseen_task, _ = job.inference(data, **kwargs) + inference_results.append(res) if is_unseen_task: unseen_tasks.append(inference_dataset.x[i]) unseen_task_labels.append(inference_dataset.y[i]) diff --git a/core/testenvmanager/testenv/testenv.py b/core/testenvmanager/testenv/testenv.py index 266491ef..70d8971d 100644 --- a/core/testenvmanager/testenv/testenv.py +++ b/core/testenvmanager/testenv/testenv.py @@ -43,6 +43,7 @@ def __init__(self, config): self.metrics = [] self.incremental_rounds = 2 self.dataset = None + self.mode = '' self._parse_config(config) def _check_fields(self): @@ -52,6 +53,10 @@ def _check_fields(self): if not isinstance(self.incremental_rounds, int) or self.incremental_rounds < 2: raise ValueError(f"testenv incremental_rounds(value={self.incremental_rounds})" f" must be int type and not less than 2.") + + if not isinstance(self.mode, str): + raise ValueError(f"testenv mode(value={self.mode})" + f" must be str type.") def _parse_config(self, config): config_dict = config[str.lower(TestEnv.__name__)] diff --git a/examples/bdd/lifelong_learning_bench/README.md b/examples/bdd/lifelong_learning_bench/README.md new file mode 100644 index 00000000..4c633768 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/README.md @@ -0,0 +1,103 @@ +# Quick Start + +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, +in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs +with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can +build and start your distributed synergy AI solution development within minutes. + +Before using Ianvs, you might want to have the device ready: +- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary +- 2 CPUs or more +- 4GB+ free memory, depends on algorithm and simulation setting +- 10GB+ free disk space +- Internet connection for GitHub and pip, etc +- Python 3.6+ installed + +In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. + +## Step 1. Ianvs Preparation + +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` +as: + +``` shell +mkdir /ianvs +cd /ianvs #One might use another path preferred + +mkdir project +cd project +git clone https://github.com/kubeedge/ianvs.git +``` + + +Then, we install third-party dependencies for ianvs. +``` shell +sudo apt-get update +sudo apt-get install libgl1-mesa-glx -y +python -m pip install --upgrade pip + +cd ianvs +python -m pip install ./examples/resources/third_party/* +python -m pip install -r requirements.txt +``` + +We are now ready to install Ianvs. +``` shell +python setup.py install +``` + +## Step 2. Dataset Preparation + +Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does +not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. + +``` shell +cd /ianvs #One might use another path preferred +mkdir dataset +cd dataset +wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip +unzip dataset.zip +``` + +The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, +we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + + + + +Related algorithm is also ready in this quick start. +``` shell +export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet +``` + +The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick +start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +``` shell +cd /ianvs/project +ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( +e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( +e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers +can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +The final output might look like this: + +|rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | +|:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| +|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | + + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml b/examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml new file mode 100644 index 00000000..c9438550 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,74 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "yolo_lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/bdd/lifelong_learning_bench/testenv/testenv.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "yolo_lifelong_learning_five_model_750data" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/bdd/lifelong_learning_bench/testalgorithms/yolo/yolo_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + # sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + sort_by: [ { "map": "descend" }] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + # metrics: [ "accuracy", "samples_transfer_ratio" ] + metrics: [ "map" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/basemodel.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/basemodel.py new file mode 100644 index 00000000..dc2b5627 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/basemodel.py @@ -0,0 +1,45 @@ +import os +import numpy as np +import torch +from sedna.common.class_factory import ClassType, ClassFactory + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + +yolo_hub_path = '/home/shifan/.cache/torch/hub/ultralytics_yolov5_master' + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.model = None + + def load(self, model_url, **kwargs): + if model_url: + self.model = torch.hub.load(yolo_hub_path, 'custom', path=model_url, source='local') + else: + raise Exception("model url does not exist.") + + def predict(self, data, **kwargs): + if type(data) is np.ndarray: + data = data.tolist() + with_nms, model_forward_result = kwargs.get("with_nms"), kwargs.get("model_forward_result") + only_nms, conf = kwargs.get("only_nms"),kwargs.get("conf") + self.model.eval() + predictions = [] + if not with_nms: + result = self.model(data, with_nms=with_nms, size=640) + return result + else: + result = self.model(data, model_forward_result=model_forward_result, only_nms=only_nms, conf=conf) + predictions.append(np.array(result.pandas().xywhn[0])) + return predictions + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/inference_integrate.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/inference_integrate.py new file mode 100644 index 00000000..4204ddcf --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/inference_integrate.py @@ -0,0 +1,99 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Integrate the inference results of all related tasks +""" + +from re import L +from typing import List + +import numpy as np + +from sedna.common.class_factory import ClassFactory, ClassType +import torch + +class Task: + def __init__(self, entry, samples, meta_attr=None): + self.entry = entry + self.samples = samples + self.meta_attr = meta_attr + self.test_samples = None # assign on task definition and use in TRD + self.model = None # assign on running + self.result = None # assign on running + +__all__ = ('InferenceIntegrate', ) + +yolo_hub_path = '/home/shifan/.cache/torch/hub/ultralytics_yolov5_master' + +@ClassFactory.register(ClassType.STP) +class InferenceIntegrate: + """ + Default calculation algorithm for inference integration + + Parameters + ---------- + models: All models used for sample inference + """ + + def __init__(self, **kwargs): + self.model = None + + def load(self, model_url, **kwargs): + if model_url: + self.model = torch.hub.load(yolo_hub_path, 'custom', path=model_url, source='local') + else: + raise Exception("model url does not exist.") + + def predict(self, data, **kwargs): + if type(data) is np.ndarray: + data = data.tolist() + with_nms, model_forward_result = kwargs.get("with_nms"), kwargs.get("model_forward_result") + only_nms, conf = kwargs.get("only_nms"),kwargs.get("conf") + self.model.eval() + predictions = [] + if not with_nms: + result = self.model(data, with_nms=with_nms, size=640) + return result + else: + result = self.model(data, model_forward_result=model_forward_result, only_nms=only_nms, conf=conf) + predictions.append(np.array(result.pandas().xywhn[0])) + return predictions + + + def __call__(self, tasks: List[Task]): + """ + Parameters + ---------- + tasks: All tasks with sample result + + Returns + ------- + result: minimum result + """ + pred_l = [] + for task in tasks: + pred_l.append(task.result[0]) + pred_l = torch.stack(pred_l) + pred_l = pred_l.unsqueeze(0) + kwargs = {"with_nms": True, "model_forward_result": pred_l, "only_nms": True, "conf": 0.6} + self.load(task.model.model) + res = self.predict(task.samples,**kwargs) + return res[0] + + + + + + diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py new file mode 100644 index 00000000..286748f2 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py @@ -0,0 +1,106 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='MultiLabelLinearClsHead', + num_classes=20, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True))) +dataset_type = 'BDD_Performance' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type='BDD_Performance', + data_prefix='', + ann_file= + '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_train.txt', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ]), + val=dict( + type='BDD_Performance', + data_prefix='', + ann_file= + '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]), + test=dict( + type='BDD_Performance', + data_prefix='', + ann_file= + '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ])) +evaluation = dict(interval=1, metric='mAP') +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='step', step=[30, 60, 90]) +runner = dict(type='EpochBasedRunner', max_epochs=100) +checkpoint_config = dict(interval=1) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +work_dir = 'work_dirs/220208-bdd-best' +gpu_ids = range(0, 1) \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py new file mode 100644 index 00000000..8be8229c --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py @@ -0,0 +1,84 @@ +from curses import raw +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType +from mmcls.apis import init_model +from mmcv.parallel import collate, scatter +from mmcls.datasets.pipelines import Compose +from mmcls.apis import set_random_seed +set_random_seed(0, deterministic=True) +import torch +import numpy as np +import heapq +import yaml + +__all__ = ('TaskAllocationByOrigin',) +config_file = 'examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py'# 神经网络配置文件 +checkpoint_file = 'examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/adaptive_selector_7w5_6w.pth' # 神经网络权重参数 +device = 'cuda:0' # 推理设备 +weight_list = ['all.pt', 'bdd.pt', 'traffic_0.pt', 'bdd_street.pt', 'bdd_clear.pt', 'bdd_daytime.pt', + 'bdd_highway.pt', 'traffic_2.pt', 'bdd_overcast.pt', 'bdd_residential.pt', 'traffic_1.pt', + 'bdd_snowy.pt', 'bdd_rainy.pt', 'bdd_night.pt', 'soda.pt', 'bdd_cloudy.pt', 'bdd_cloudy_night.pt', + 'bdd_highway_residential.pt', 'bdd_snowy_rainy.pt', 'soda_t1.pt'] +model_selector = init_model(config_file, checkpoint_file, device=device) +def inference_model(model, img): + """Inference image(s) with the classifier. + + Args: + model (nn.Module): The loaded classifier. + img (str/ndarray): The image filename or loaded image. + + Returns: + result (dict): The classification results that contains + `class_name`, `pred_label` and `pred_score`. + """ + cfg = model.cfg + device = next(model.parameters()).device # model device + # build the data pipeline + if isinstance(img, str): + if cfg.data.test.pipeline[0]['type'] != 'LoadImageFromFile': + cfg.data.test.pipeline.insert(0, dict(type='LoadImageFromFile')) + data = dict(img_info=dict(filename=img), img_prefix=None) + else: + if cfg.data.test.pipeline[0]['type'] == 'LoadImageFromFile': + cfg.data.test.pipeline.pop(0) + data = dict(img=img) + test_pipeline = Compose(cfg.data.test.pipeline) + data = test_pipeline(data) + data = collate([data], samples_per_gpu=1) + if next(model.parameters()).is_cuda: + data = scatter(data, [device])[0] + + with torch.no_grad(): + scores = model(return_loss=False, **data) + scores = np.array(scores) + pred_score = np.max(scores[0]) + pred_label = np.argmax(scores[0]) + top5_index = heapq.nlargest(5, range(len(scores[0])), scores[0].__getitem__) + top15_index = heapq.nlargest(len(weight_list), range(len(scores[0])), scores[0].__getitem__) + result = {'pred_label': pred_label, 'pred_score': float(pred_score), 'scores': scores[0], 'top5':top5_index, 'top15':top15_index} + + return result + + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + result = inference_model(model_selector, samples.x[0][0]) # 默认传入数据地址，传入数据的值也可以 + allocations = result['top5'] + + return samples, allocations \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py new file mode 100644 index 00000000..c2940db0 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py @@ -0,0 +1,72 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Remodeling tasks based on their relationships + +Parameters +---------- +mappings ：all assigned tasks get from the `task_mining` +samples : input samples + +Returns +------- +models : List of groups which including at least 1 task. +""" + +from typing import List +import numpy as np + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType +import yaml + +__all__ = ('TaskRemodeling',) + +selected_model_path = "examples/resources/yolo_model/" +weight_list = ['all.pt', 'bdd.pt', 'traffic_0.pt', 'bdd_street.pt', 'bdd_clear.pt', 'bdd_daytime.pt', + 'bdd_highway.pt', 'traffic_2.pt', 'bdd_overcast.pt', 'bdd_residential.pt', 'traffic_1.pt', + 'bdd_snowy.pt', 'bdd_rainy.pt', 'bdd_night.pt', 'soda.pt', 'bdd_cloudy.pt', 'bdd_cloudy_night.pt', + 'bdd_highway_residential.pt', 'bdd_snowy_rainy.pt', 'soda_t1.pt'] + + +@ClassFactory.register(ClassType.STP) +class TaskRemodeling: + """ + Assume that each task is independent of each other + """ + + def __init__(self, **kwargs): + self.models = [] + for i in range(len(weight_list)): + self.models.append([i, weight_list[i][:-3], selected_model_path + weight_list[i]]) + + + def __call__(self, samples: BaseDataSource, mappings: List): + """ + Grouping based on assigned tasks + """ + mappings = np.array(mappings) + data, models = samples.x[0], [] + for m in mappings: + try: + model = self.models[m] + except Exception as err: + print(f"self.models[{m}] not exists. {err}") + model = self.models[0] + models.append(model) + return data, models + + + diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/yolo_algorithm.yaml b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/yolo_algorithm.yaml new file mode 100644 index 00000000..f8a19fa1 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/yolo_algorithm.yaml @@ -0,0 +1,53 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.8 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + splitting_method: "default" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/bdd/lifelong_learning_bench/testalgorithms/yolo/basemodel.py" + # hyperparameters configuration for the python module; list type; + + # 2> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py" + # config_file: "/mnt/disk/shifan/ianvs/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py" + # checkpoint_file: "/mnt/disk/shifan/ianvs/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/adaptive_selector_7w5_6w.pth" + + # 3> "task_remodeling" + - type: "task_remodeling" + # name of python module; string type; + name: "TaskRemodeling" + # the url address of python module; string type; + url: "./examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py" + # hyperparameters configuration for the python module; list type; + + # 4> "inference_integrate" + - type: "inference_integrate" + # name of python module; string type; + name: "InferenceIntegrate" + # the url address of python module; string type; + url: "./examples/bdd/lifelong_learning_bench/testalgorithms/yolo/inference_integrate.py" + # hyperparameters configuration for the python module; list typ \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/testenv/map.py b/examples/bdd/lifelong_learning_bench/testenv/map.py new file mode 100644 index 00000000..a5d3642e --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testenv/map.py @@ -0,0 +1,567 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from hashlib import new +from tqdm import tqdm + +from sedna.common.class_factory import ClassType, ClassFactory + + +__all__ = ('map') + +import sys +import torch +import random + +from collections import Counter +from enum import Enum +from typing import List, Dict + +import numpy as np + +class MethodAveragePrecision(Enum): + """ + Class representing if the coordinates are relative to the + image size or are absolute values. + + Developed by: Rafael Padilla + Last modification: Apr 28 2018 + """ + AllPointsInterpolation = 1 + ElevenPointsInterpolation = 2 + + +class Box: + def __init__(self, xtl: float, ytl: float, xbr: float, ybr: float): + """ + 0,0 ------> x (width) + | + | (Left,Top) + | *_________ + | | | + | | + y |_________| + (height) * + (Right,Bottom) + + Args: + xtl: Float value representing the X top-left coordinate of the bounding box. + ytl: Float value representing the Y top-left coordinate of the bounding box. + xbr: Float value representing the X bottom-right coordinate of the bounding box. + ybr: Float value representing the Y bottom-right coordinate of the bounding box. + """ + self.xtl = xtl + self.ytl = ytl + self.xbr = xbr + self.ybr = ybr + + @property + def width(self) -> float: + return self.xbr - self.xtl + + @property + def height(self) -> float: + return self.ybr - self.ytl + + @property + def area(self) -> float: + return (self.xbr - self.xtl + 1) * (self.ybr - self.ytl + 1) + + @classmethod + def intersection_over_union(cls, box1: 'Box', box2: 'Box') -> float: + """ + Intersection Over Union (IOU) is measure based on Jaccard Index that evaluates the overlap between + two bounding boxes. + """ + # if boxes dont intersect + if not Box.is_intersecting(box1, box2): + return 0 + intersection = Box.get_intersection_area(box1, box2) + union = Box.get_union_areas(box1, box2, intersection_area=intersection) + # intersection over union + iou = intersection / union + assert iou >= 0, '{} = {} / {}, {}, {}'.format(iou, intersection, union, box1, box2) + return iou + + @classmethod + def is_intersecting(cls, box1: 'Box', box2: 'Box') -> bool: + if box1.xtl > box2.xbr: + return False # boxA is right of boxB + if box2.xtl > box1.xbr: + return False # boxA is left of boxB + if box1.ybr < box2.ytl: + return False # boxA is above boxB + if box1.ytl > box2.ybr: + return False # boxA is below boxB + return True + + @classmethod + def get_intersection_area(cls, box1: 'Box', box2: 'Box') -> float: + xtl = max(box1.xtl, box2.xtl) + ytl = max(box1.ytl, box2.ytl) + xbr = min(box1.xbr, box2.xbr) + ybr = min(box1.ybr, box2.ybr) + # intersection area + return (xbr - xtl + 1) * (ybr - ytl + 1) + + @staticmethod + def get_union_areas(box1: 'Box', box2: 'Box', intersection_area: float = None) -> float: + if intersection_area is None: + intersection_area = Box.get_intersection_area(box1, box2) + return float(box1.area + box2.area - intersection_area) + + +class BoundingBox(Box): + def __init__(self, image_name: str, label: str, xtl: float, ytl: float, xbr: float, ybr: float, + score=None): + """Constructor. + Args: + image_name: String representing the image name. + label: String value representing class id. + xtl: Float value representing the X top-left coordinate of the bounding box. + ytl: Float value representing the Y top-left coordinate of the bounding box. + xbr: Float value representing the X bottom-right coordinate of the bounding box. + ybr: Float value representing the Y bottom-right coordinate of the bounding box. + score: (optional) Float value representing the confidence of the detected + class. If detectionType is Detection, classConfidence needs to be informed. + """ + super().__init__(xtl, ytl, xbr, ybr) + self.image_name = image_name + self.score = score + self.label = label + + +class MetricPerClass: + def __init__(self): + self.label = None + self.precision = None + self.recall = None + self.ap = None + self.interpolated_precision = None + self.interpolated_recall = None + self.num_groundtruth = None + self.num_detection = None + self.tp = None + self.fp = None + + @staticmethod + def get_mAP(results: Dict[str, 'MetricPerClass']): + return np.average([m.ap for m in results.values() if m.num_groundtruth > 0]) + + +def get_pascal_voc_metrics(gold_standard: List[BoundingBox], + predictions: List[BoundingBox], + iou_threshold: float = 0.45, + method: MethodAveragePrecision = MethodAveragePrecision.AllPointsInterpolation + ) -> Dict[str, MetricPerClass]: + """Get the metrics used by the VOC Pascal 2012 challenge. + + Args: + gold_standard: Object of the class BoundingBoxes representing ground truth bounding boxes; + predictions: Object of the class BoundingBoxes representing detected bounding boxes; + iou_threshold: IOU threshold indicating which detections will be considered TP or FP (default value = 0.5); + method: It can be calculated as the implementation + in the official PASCAL VOC toolkit (EveryPointInterpolation), or applying the 11-point + interpolation as described in the paper "The PASCAL Visual Object Classes(VOC) Challenge" + or EveryPointInterpolation" (ElevenPointInterpolation); + Returns: + A list of dictionaries. Each dictionary contains information and metrics of each class. + The keys of each dictionary are: + dict['class']: class representing the current dictionary; + dict['precision']: array with the precision values; + dict['recall']: array with the recall values; + dict['AP']: average precision; + dict['interpolated precision']: interpolated precision values; + dict['interpolated recall']: interpolated recall values; + dict['total positives']: total number of ground truth positives; + dict['total TP']: total number of True Positive detections; + dict['total FP']: total number of False Negative detections; + """ + ret = {} # list containing metrics (precision, recall, average precision) of each class + + # Get all classes + classes = sorted(set(b.label for b in gold_standard + predictions)) + + # Precision x Recall is obtained individually by each class + # Loop through by classes + for c in classes: + preds = [b for b in predictions if b.label == c] # type: List[BoundingBox] + golds = [b for b in gold_standard if b.label == c] # type: List[BoundingBox] + npos = len(golds) + # print('npos', npos) + + # sort detections by decreasing confidence + preds = sorted(preds, key=lambda b: b.score, reverse=True) + tps = np.zeros(len(preds)) + fps = np.zeros(len(preds)) + + # create dictionary with amount of gts for each image + counter = Counter([cc.image_name for cc in golds]) + for key, val in counter.items(): + counter[key] = np.zeros(val) + + # Loop through detections + for i in range(len(preds)): + # Find ground truth image + gt = [b for b in golds if b.image_name == preds[i].image_name] + max_iou = sys.float_info.min + mas_idx = -1 + for j in range(len(gt)): + iou = Box.intersection_over_union(preds[i], gt[j]) + if iou > max_iou: + max_iou = iou + mas_idx = j + # Assign detection as true positive/don't care/false positive + # print('max iou', max_iou) + if max_iou >= iou_threshold: + if counter[preds[i].image_name][mas_idx] == 0: + tps[i] = 1 # count as true positive + counter[preds[i].image_name][mas_idx] = 1 # flag as already 'seen' + else: + # - A detected "cat" is overlaped with a GT "cat" with IOU >= IOUThreshold. + fps[i] = 1 # count as false positive + else: + fps[i] = 1 # count as false positive + # compute precision, recall and average precision + # print('fps', fps) + # print('tps', tps) + cumulative_fps = np.cumsum(fps) + cumulative_tps = np.cumsum(tps) + # print('cfps', cumulative_fps) + # print('ctps', cumulative_tps) + recalls = np.divide(cumulative_tps, npos, out=np.full_like(cumulative_tps, np.nan), where=npos != 0) + precisions = np.divide(cumulative_tps, (cumulative_fps + cumulative_tps)) + # Depending on the method, call the right implementation + if method == MethodAveragePrecision.AllPointsInterpolation: + ap, mpre, mrec, _ = calculate_all_points_average_precision(recalls, precisions) + else: + ap, mpre, mrec = calculate_11_points_average_precision(recalls, precisions) + # print('ap', ap) + # add class result in the dictionary to be returned + r = MetricPerClass() + r.label = c + r.precision = precisions + r.recall = recalls + r.ap = ap + r.interpolated_recall = mrec + r.interpolated_precision = mpre + r.tp = np.sum(tps) + r.fp = np.sum(fps) + r.num_groundtruth = len(golds) + r.num_detection = len(preds) + ret[c] = r + return ret + + +def calculate_all_points_average_precision(recall, precision): + """ + All points interpolated average precision + + Returns: + average precision + interpolated precision + interpolated recall + interpolated points + """ + mrec = [0] + [e for e in recall] + [1] + mpre = [0] + [e for e in precision] + [0] + for i in range(len(mpre) - 1, 0, -1): + mpre[i - 1] = max(mpre[i - 1], mpre[i]) + ii = [] + for i in range(len(mrec) - 1): + if mrec[1:][i] != mrec[0:-1][i]: + ii.append(i + 1) + ap = 0 + for i in ii: + ap = ap + np.sum((mrec[i] - mrec[i - 1]) * mpre[i]) + return ap, mpre[0:len(mpre) - 1], mrec[0:len(mpre) - 1], ii + + +def calculate_11_points_average_precision(recall, precision): + """ + 11-point interpolated average precision + + Returns: + average precision + interpolated precision + interpolated recall + """ + mrec = [e for e in recall] + mpre = [e for e in precision] + recall_values = np.linspace(0, 1, 11) + recall_values = list(recall_values[::-1]) + rho_interp = [] + recall_valid = [] + # For each recallValues (0, 0.1, 0.2, ... , 1) + for r in recall_values: + # Obtain all recall values higher or equal than r + arg_greater_recalls = np.argwhere(mrec[:] >= r) + pmax = 0 + # If there are recalls above r + if arg_greater_recalls.size != 0: + pmax = max(mpre[arg_greater_recalls.min():]) + recall_valid.append(r) + rho_interp.append(pmax) + # By definition AP = sum(max(precision whose recall is above r))/11 + ap = sum(rho_interp) / 11 + # Generating values for the plot + rvals = [recall_valid[0]] + [e for e in recall_valid] + [0] + pvals = [0] + [e for e in rho_interp] + [0] + # rhoInterp = rhoInterp[::-1] + cc = [] + for i in range(len(rvals)): + p = (rvals[i], pvals[i - 1]) + if p not in cc: + cc.append(p) + p = (rvals[i], pvals[i]) + if p not in cc: + cc.append(p) + recall_values = [i[0] for i in cc] + rho_interp = [i[1] for i in cc] + return ap, rho_interp, recall_values + + +def get_map(ret, class_tea_list, class_pred_list): + ap_list = [0 for _ in range(2)] + for class_index in range(2): + if (class_index in class_tea_list) and (class_index in class_pred_list): + # case 1: both prediction and teacher detect the same class, which should be the main case + ap_list[class_index] = ret[class_index].ap + elif (class_index not in class_tea_list) and (class_index not in class_pred_list): + # case 2: both prediction and teacher not detect the class, which is correct and the ap should be 1 + ap_list[class_index] = 1. + elif ((class_index not in class_tea_list) and (class_index in class_pred_list)) or ((class_index in class_tea_list) and (class_index not in class_pred_list)): + # case 3: only one detect the class, in all the cases it is not correct + ap_list[class_index] = 0. + m_ap = np.mean(ap_list) + return m_ap + + +def performance_evaluation_map_teacher_feedback(img, teacher, model, conf_thre=0.6): + + teacher_boxes = [] + teacher_results = teacher(img, conf=conf_thre) + teacher_results = np.array(teacher_results.pandas().xyxy[0]) + + box_tea_list = teacher_results[:, 0:4] + confidence_tea_list = teacher_results[:, 4] + class_tea_list = teacher_results[:, 5] + + for bb_index in range(len(teacher_results)): + bb = BoundingBox('filename', class_tea_list[bb_index], box_tea_list[bb_index][0], box_tea_list[bb_index][1], box_tea_list[bb_index][2], box_tea_list[bb_index][3], confidence_tea_list[bb_index]) + teacher_boxes.append(bb) + + results = model(img, conf=conf_thre) + results = np.array(results.pandas().xyxy[0]) + + inference_boxs = [] + + # print(results) + + box_pred_list = results[:, 0:4] + confidence_list = results[:, 4] + class_pred_list = results[:, 5] + + for bb_index in range(len(results)): + bb = BoundingBox('filename', class_pred_list[bb_index], box_pred_list[bb_index][0], box_pred_list[bb_index][1], box_pred_list[bb_index][2], box_pred_list[bb_index][3], confidence_list[bb_index]) + inference_boxs.append(bb) + + ret = get_pascal_voc_metrics(teacher_boxes, inference_boxs) + + m_ap = get_map(ret, class_tea_list, class_pred_list) + + return m_ap, results + + +def get_boxes_with_results(results, from_numpy=False): + # if from_numpy is False: + # results = np.array(results.pandas().xyxy[0]) + boundingbox_list = [] + box_list = results[:, 0:4] + confidence_list = results[:, 4] + class_list = results[:, 5] + + for bb_index in range(len(results)): + bb = BoundingBox('filename', class_list[bb_index], box_list[bb_index][0], box_list[bb_index][1], box_list[bb_index][2], box_list[bb_index][3], confidence_list[bb_index]) + boundingbox_list.append(bb) + return boundingbox_list, class_list + + +def get_mAP_with_results(results1, results2, from_numpy=False): + boxes1, class_list1 = get_boxes_with_results(results1, from_numpy=from_numpy) + boxes2, class_list2 = get_boxes_with_results(results2, from_numpy=from_numpy) + ret = get_pascal_voc_metrics(boxes1, boxes2) + m_ap = get_map(ret, class_list1, class_list2) + return m_ap + + +def performance_evaluation_map_teacher(img, teacher, model, conf_thre=0.6): + + teacher_boxes = [] + teacher_results = teacher(img, conf=conf_thre) + + teacher_results = np.array(teacher_results.pandas().xyxy[0]) + + box_tea_list = teacher_results[:, 0:4] + confidence_tea_list = teacher_results[:, 4] + class_tea_list = teacher_results[:, 5] + + for bb_index in range(len(teacher_results)): + bb = BoundingBox('filename', class_tea_list[bb_index], box_tea_list[bb_index][0], box_tea_list[bb_index][1], box_tea_list[bb_index][2], box_tea_list[bb_index][3], confidence_tea_list[bb_index]) + teacher_boxes.append(bb) + + results = model(img, conf=conf_thre) + + results = np.array(results.pandas().xyxy[0]) + + inference_boxs = [] + + # print(results) + + box_pred_list = results[:, 0:4] + confidence_list = results[:, 4] + class_pred_list = results[:, 5] + + for bb_index in range(len(results)): + bb = BoundingBox('filename', class_pred_list[bb_index], box_pred_list[bb_index][0], box_pred_list[bb_index][1], box_pred_list[bb_index][2], box_pred_list[bb_index][3], confidence_list[bb_index]) + inference_boxs.append(bb) + + ret = get_pascal_voc_metrics(teacher_boxes, inference_boxs) + + m_ap = get_map(ret, class_tea_list, class_pred_list) + return m_ap + + +def performance_fusion_evaluation_map_teacher(img, teacher, model_list, conf_thre=0.6): + + teacher_boxes = [] + teacher_results = teacher(img, conf=conf_thre) + + teacher_results = np.array(teacher_results.pandas().xyxy[0]) + + box_tea_list = teacher_results[:, 0:4] + confidence_tea_list = teacher_results[:, 4] + class_tea_list = teacher_results[:, 5] + + for bb_index in range(len(teacher_results)): + bb = BoundingBox('filename', class_tea_list[bb_index], box_tea_list[bb_index][0], box_tea_list[bb_index][1], box_tea_list[bb_index][2], box_tea_list[bb_index][3], confidence_tea_list[bb_index]) + teacher_boxes.append(bb) + + results = model_inference_fusion(img, model_list) + + results = np.array(results.pandas().xyxy[0]) + + inference_boxs = [] + + # print(results) + + box_pred_list = results[:, 0:4] + confidence_list = results[:, 4] + class_pred_list = results[:, 5] + + for bb_index in range(len(results)): + bb = BoundingBox('filename', class_pred_list[bb_index], box_pred_list[bb_index][0], box_pred_list[bb_index][1], box_pred_list[bb_index][2], box_pred_list[bb_index][3], confidence_list[bb_index]) + inference_boxs.append(bb) + + ret = get_pascal_voc_metrics(teacher_boxes, inference_boxs) + + m_ap = get_map(ret, class_tea_list, class_pred_list) + return m_ap, results + + +def get_box(img, teacher, model, img_name): + + teacher_boxes_each = [] + inference_boxes_each = [] + + teacher_results = teacher(img) + teacher_results = np.array(teacher_results.pandas().xyxy[0]) + + box_tea_list = teacher_results[:, 0:4] + confidence_tea_list = teacher_results[:, 4] + class_tea_list = teacher_results[:, 5] + + for bb_index in range(len(teacher_results)): + tea_bb = BoundingBox(img_name, class_tea_list[bb_index], box_tea_list[bb_index][0], box_tea_list[bb_index][1], box_tea_list[bb_index][2], box_tea_list[bb_index][3], confidence_tea_list[bb_index]) # for one single img, there is only one result + teacher_boxes_each.append(tea_bb) + + results = model(img) + results = np.array(results.pandas().xyxy[0]) + + box_pred_list = results[:, 0:4] + confidence_list = results[:, 4] + class_pred_list = results[:, 5] + + for bb_index in range(len(results)): + infer_bb = BoundingBox(img_name, class_pred_list[bb_index], box_pred_list[bb_index][0], box_pred_list[bb_index][1], box_pred_list[bb_index][2], box_pred_list[bb_index][3], confidence_list[bb_index]) # for one single img, there is only one result + inference_boxes_each.append(infer_bb) + + return teacher_boxes_each, inference_boxes_each, class_tea_list, class_pred_list, results + + +def get_boxes(imgs, teacher, models): + tea_class_all_img_list = [] + pred_class_all_img_list = [] + + teacher_boxes = [] + inference_boxes = [] + + img_name_list = random.sample(range(1, 20), 10) # generation of 10 names for the image naming + + for img_index in range(len(imgs)): + img = imgs[img_index] + model = models[img_index] # model index is the same of img_index + + tea_bb_list, infer_bb_list, class_tea_list, class_pred_list, _ = get_box(img, teacher, model, str(img_name_list[img_index])) + + teacher_boxes = list(np.concatenate((teacher_boxes, tea_bb_list), axis=0)) + inference_boxes = list(np.concatenate((inference_boxes, infer_bb_list), axis=0)) + + tea_class_all_img_list = np.concatenate((tea_class_all_img_list, class_tea_list), axis=0) + pred_class_all_img_list = np.concatenate((pred_class_all_img_list, class_pred_list), axis=0) + + # print(len(inference_boxes[0])) + tea_class = sorted(list(set(tea_class_all_img_list))) + pred_class = sorted(list(set(pred_class_all_img_list))) + + return inference_boxes, teacher_boxes, pred_class, tea_class + + +@ClassFactory.register(ClassType.GENERAL, alias="map") +def map(y_true, y_pred, **kwargs): + map = [] + assert len(y_pred) == len(y_true) + space = [] + for i in range(0, len(y_pred)): + with open(y_true[i], "r")as f: + text = f.readlines() + new_l = [] + for item in text: + new_item= [] + item_split = [] + item_split = item.split("\n")[:-1][0].split(" ") + for j in range(1,5): + new_item.append(float(item_split[j])) + new_item.append(float(1)) + new_item.append(int(float(item_split[0]))) + new_l.append(np.array(new_item)) + if not new_l: + space.append(i) + continue + # y_pred_i = np.array([item.cpu().detach().numpy() for item in y_pred[i]]) + # map.append(get_mAP_with_results(np.array(new_l), np.array(y_pred_i))) + map.append(get_mAP_with_results(np.array(new_l), np.array(y_pred[i]))) + # with open(y_true, "r")as f: + # text = f.readlines() + return np.mean(map) \ No newline at end of file diff --git a/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml b/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml new file mode 100644 index 00000000..4152c8e3 --- /dev/null +++ b/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml @@ -0,0 +1,41 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + # train_url: "dataset/curb-detection/train_data/index.txt" + train_url: "dataset/bdd/bdd_compress_train_label.txt" + # the url address of test dataset index; string type; + # test_url: "dataset/curb-detection/test_data/index.txt" + test_url: "dataset/bdd/bdd_compress_test_label.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/bdd/lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + # - name: "accuracy" + # # the url address of python file + # url: "./examples/bdd/lifelong_learning_bench/testenv/accuracy.py" + # - name: "samples_transfer_ratio" + - name: "map" + # the url address of python file + url: "./examples/bdd/lifelong_learning_bench/testenv/map.py" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 2 + + mode: "multi-inference" \ No newline at end of file diff --git a/examples/resources/sedna.zip b/examples/resources/sedna.zip new file mode 100644 index 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scence based unknown task regconition algorithm update algorithms delete annotated codes refer the origin reproduced p add class documentation upload Readme Modify folder name add an example modify readme Supplementary curb-detection cases Signed-off-by: Frank-lilinjie --- .../lifelong_learning_bench/Readme.md | 100 +++++ .../benchmarkingjob.yaml | 72 ++++ .../testalgorithms/rfnet/RFNet/accuracy.py | 38 ++ .../testalgorithms/rfnet/RFNet/basemodel.py | 315 ++++++++++++++ .../rfnet/RFNet/dataloaders/__init__.py | 119 ++++++ .../RFNet/dataloaders/custom_transforms.py | 240 +++++++++++ .../dataloaders/custom_transforms_rgb.py | 230 ++++++++++ .../RFNet/dataloaders/datasets/__init__.py | 0 .../dataloaders/datasets/citylostfound.py | 276 ++++++++++++ .../RFNet/dataloaders/datasets/cityrand.py | 151 +++++++ .../RFNet/dataloaders/datasets/cityscapes.py | 152 +++++++ .../rfnet/RFNet/dataloaders/datasets/e1.py | 151 +++++++ .../RFNet/dataloaders/datasets/mapillary.py | 152 +++++++ .../RFNet/dataloaders/datasets/target.py | 152 +++++++ .../rfnet/RFNet/dataloaders/datasets/temp.txt | 38 ++ .../rfnet/RFNet/dataloaders/datasets/xrlab.py | 152 +++++++ .../rfnet/RFNet/dataloaders/utils.py | 244 +++++++++++ .../testalgorithms/rfnet/RFNet/eval.py | 247 +++++++++++ .../rfnet/RFNet/models/replicate.py | 88 ++++ .../rfnet/RFNet/models/resnet/__init__.py | 0 .../resnet_single_scale_single_attention.py | 391 +++++++++++++++++ ...et_single_scale_single_attention_unseen.py | 396 ++++++++++++++++++ .../rfnet/RFNet/models/rfnet.py | 27 ++ .../rfnet/RFNet/models/rfnet_for_unseen.py | 33 ++ .../testalgorithms/rfnet/RFNet/models/util.py | 99 +++++ .../testalgorithms/rfnet/RFNet/mypath.py | 20 + .../testalgorithms/rfnet/RFNet/predict.py | 98 +++++ .../testalgorithms/rfnet/RFNet/run_server.py | 252 +++++++++++ .../rfnet/RFNet/sedna_evaluate.py | 50 +++ .../rfnet/RFNet/sedna_predict.py | 132 ++++++ .../testalgorithms/rfnet/RFNet/sedna_train.py | 78 ++++ .../testalgorithms/rfnet/RFNet/test.py | 52 +++ .../testalgorithms/rfnet/RFNet/train.py | 211 ++++++++++ .../rfnet/RFNet/utils/__init__.py | 0 .../testalgorithms/rfnet/RFNet/utils/args.py | 52 +++ .../rfnet/RFNet/utils/calculate_weights.py | 29 ++ .../rfnet/RFNet/utils/iouEval.py | 142 +++++++ .../testalgorithms/rfnet/RFNet/utils/loss.py | 64 +++ .../rfnet/RFNet/utils/lr_scheduler.py | 70 ++++ .../rfnet/RFNet/utils/metrics.py | 146 +++++++ .../testalgorithms/rfnet/RFNet/utils/saver.py | 68 +++ .../rfnet/RFNet/utils/summaries.py | 39 ++ .../testalgorithms/rfnet/basemodel.py | 123 ++++++ .../testalgorithms/rfnet/models/_init_.py | 1 + .../testalgorithms/rfnet/models/utils.py | 149 +++++++ .../rfnet/models/wide_resnet_embedding.py | 94 +++++ .../testalgorithms/rfnet/rfnet_algorithm.yaml | 69 +++ .../rfnet/task_allocation_by_origin.py | 65 +++ .../rfnet/task_definition_by_origin.py | 81 ++++ .../unseen_sample_recognition_by_scene.py | 61 +++ .../testenv/accuracy.py | 55 +++ .../testenv/testenv.yaml | 34 ++ 52 files changed, 6098 insertions(+) create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/Readme.md create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/_init_.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/utils.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/wide_resnet_embedding.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/unseen_sample_recognition_by_scene.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/accuracy.py create mode 100644 examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/testenv.yaml diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/Readme.md b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/Readme.md new file mode 100644 index 00000000..5649bb74 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/Readme.md @@ -0,0 +1,100 @@ +# Quick Start about Unknown task recognition + +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can build and start your distributed synergy AI solution development within minutes. + +Before using Ianvs, you might want to have the device ready: + +- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary +- 2 CPUs or more +- 4GB+ free memory depends on the algorithm and simulation setting +- 10GB+ free disk space +- Internet connection for GitHub and pip, etc +- Python 3.6+ installed + +In this example, we are using the Linux platform with Python 3.7.1. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. + +## Step 1. Ianvs Preparation + +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` as: + +```shell +mkdir /ianvs +cd /ianvs #One might use another path preferred + +mkdir project +cd project +git clone https://github.com/kubeedge/ianvs.git +``` + +Then, we install third-party dependencies for ianvs. + +```shell +sudo apt-get update +sudo apt-get install libgl1-mesa-glx -y +python -m pip install --upgrade pip + +cd ianvs +python -m pip install ./examples/resources/third_party/* +python -m pip install -r requirements.txt +``` + +We are now ready to install Ianvs. + +```shell +python setup.py install +``` + +## Step 2. Dataset and Model Preparation + +Datasets and models can be large. To avoid over-size projects in the GitHub repository of Ianvs, the Ianvs code base does not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. + +```shell +cd /ianvs #One might use another path preferred +mkdir dataset +cd dataset +wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip +unzip dataset.zip +``` + +The URL address of this dataset then should be filled in the configuration file `testenv.yaml`. In this quick start, we have done that for you and interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +```shell +cd /ianvs/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet +mkdir results +``` + +Put the model to results.Download [model](https://pan.baidu.com/s/18MA8Gaw7ptpipfLD6Hz6SA) *access code*: 37ff. + +The related algorithm is also ready for this quick start. + +```shell +export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet +export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/ +``` + +The URL address of this algorithm then should be filled in the configuration file `algorithm.yaml`. In this quick start, we have done that for you and interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +```shell +cd /ianvs/project +ianvs -f examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +The final output might look like this: + +| rank | algorithm | accuracy | samples_transfer_ratio | paradigm | basemodel | task_definition | task_allocation | unseen_sample_recognition | basemodel-learning_rate | task_definition-origins | task_allocation-origins | unseen_sample_recognition-model_path | time | url | +| ---- | ----------------------- | ------------------- | ---------------------- | ---------------- | --------- | ---------------------- | ---------------------- | ------------------------------ | ----------------------- | ----------------------- | ----------------------- | :----------------------------------------------------------- | ------------------- | ------------------------------------------------------------ | +| 1 | rfnet_lifelong_learning | 0.30090234155994056 | 0.4535 | lifelonglearning | BaseModel | TaskDefinitionByOrigin | TaskAllocationByOrigin | UnseenSampleRecognitionByScene | 0.0001 | ['real', 'sim'] | ['real', 'sim'] | /examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/results/Epochofprose17.pth | 2022-10-25 14:50:01 | /ianvs/lifelong_learning_bench/workspace/benchmarkingjob/rfnet_lifelong_learning/1dfff552-542f-11ed-b875-b07b25dd6922 | + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml new file mode 100644 index 00000000..8907ee4e --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,72 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testenv/testenv.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py new file mode 100644 index 00000000..8d356fed --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py @@ -0,0 +1,38 @@ +from basemodel import val_args +from utils.metrics import Evaluator +from tqdm import tqdm +from dataloaders import make_data_loader +from sedna.common.class_factory import ClassType, ClassFactory + +__all__ = ('accuracy') + +@ClassFactory.register(ClassType.GENERAL) +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py new file mode 100644 index 00000000..dba4cfdf --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py @@ -0,0 +1,315 @@ +import os +import numpy as np +import torch +from PIL import Image +import argparse +from train import Trainer +from eval import Validator +from tqdm import tqdm +from eval import load_my_state_dict +from utils.metrics import Evaluator +from dataloaders import make_data_loader +from dataloaders import custom_transforms as tr +from torchvision import transforms +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.datasources import TxtDataParse +from torch.utils.data import DataLoader +from sedna.common.file_ops import FileOps +from utils.lr_scheduler import LR_Scheduler + +def preprocess(image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images + +class Model: + def __init__(self, **kwargs): + self.val_args = val_args() + self.train_args = train_args() + + self.train_args.lr = kwargs.get("learning_rate", 1e-4) + self.train_args.epochs = kwargs.get("epochs", 2) + self.train_args.eval_interval = kwargs.get("eval_interval", 2) + self.train_args.no_val = kwargs.get("no_val", True) + # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) + self.trainer = None + + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range(self.trainer.args.start_epoch, self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and \ + (epoch % self.trainer.args.eval_interval == (self.trainer.args.eval_interval - 1) + or epoch == self.trainer.args.epochs - 1): + # save checkpoint when it meets eval_interval or the training finished + is_best = False + checkpoint_path = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + # if not self.trainer.args.no_val and \ + # epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \ + # and self.trainer.val_loader: + # self.trainer.validation(epoch) + + self.trainer.writer.close() + + return checkpoint_path + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = preprocess(data.x) + predictions = self.predict(samples) + return accuracy(data.y, predictions) + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: how to save unstructured data model + pass + +def train_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet Training") + parser.add_argument('--depth', action="store_true", default=False, + help='training with depth image or not (default: False)') + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--loss-type', type=str, default='ce', + choices=['ce', 'focal'], + help='loss func type (default: ce)') + # training hyper params + # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # help='number of epochs to train (default: auto)') + parser.add_argument('--epochs', type=int, default=None, metavar='N', + help='number of epochs to train (default: auto)') + parser.add_argument('--start_epoch', type=int, default=0, + metavar='N', help='start epochs (default:0)') + parser.add_argument('--batch-size', type=int, default=None, + metavar='N', help='input batch size for \ + training (default: auto)') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--use-balanced-weights', action='store_true', default=False, + help='whether to use balanced weights (default: True)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + # optimizer params + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help='learning rate (default: auto)') + parser.add_argument('--lr-scheduler', type=str, default='cos', + choices=['poly', 'step', 'cos', 'inv'], + help='lr scheduler mode: (default: cos)') + parser.add_argument('--momentum', type=float, default=0.9, + metavar='M', help='momentum (default: 0.9)') + parser.add_argument('--weight-decay', type=float, default=2.5e-5, + metavar='M', help='w-decay (default: 5e-4)') + # cuda, seed and logging + parser.add_argument('--no-cuda', action='store_true', default= + False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') + # checking point + parser.add_argument('--resume', type=str, + default=None, + help='put the path to resuming file if needed') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + # finetuning pre-trained models + parser.add_argument('--ft', action='store_true', default=True, + help='finetuning on a different dataset') + # evaluation option + parser.add_argument('--eval-interval', type=int, default=1, + help='evaluation interval (default: 1)') + parser.add_argument('--no-val', action='store_true', default=False, + help='skip validation during training') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + print(torch.cuda.is_available()) + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + if args.epochs is None: + epoches = { + 'cityscapes': 200, + 'citylostfound': 200, + } + args.epochs = epoches[args.dataset.lower()] + + if args.batch_size is None: + args.batch_size = 4 * len(args.gpu_ids) + + if args.test_batch_size is None: + args.test_batch_size = args.batch_size + + if args.lr is None: + lrs = { + 'cityscapes': 0.0001, + 'citylostfound': 0.0001, + 'cityrand': 0.0001 + } + args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size + + if args.checkname is None: + args.checkname = 'RFNet' + print(args) + torch.manual_seed(args.seed) + + return args + +def val_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet validation") + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--batch-size', type=int, default=6, + help='batch size for training') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + validating (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", + help='enter your path of the weight') + parser.add_argument('--save-predicted-image', action='store_true', default=False, + help='save predicted images') + parser.add_argument('--color-label-save-path', type=str, + default='./test/color/', + help='path to save label') + parser.add_argument('--merge-label-save-path', type=str, + default='./test/merge/', + help='path to save merged label') + parser.add_argument('--label-save-path', type=str, default='./test/label/', + help='path to save merged label') + parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') + parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + return args + +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA + +if __name__ == '__main__': + model_path = "/tmp/RFNet/" + if not os.path.exists(model_path): + os.makedirs(model_path) + + p1 = Process(target=exp_train, args=(10,)) + p1.start() + p1.join() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py new file mode 100644 index 00000000..2c71f7b1 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py @@ -0,0 +1,119 @@ +from dataloaders.datasets import cityscapes, citylostfound, cityrand, target, xrlab, e1, mapillary +from torch.utils.data import DataLoader + +def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **kwargs): + + if args.dataset == 'cityscapes': + if train_data is not None: + train_set = cityscapes.CityscapesSegmentation(args, data=train_data, split='train') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + else: + train_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if valid_data is not None: + val_set = cityscapes.CityscapesSegmentation(args, data=valid_data, split='val') + num_class = val_set.NUM_CLASSES + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + else: + val_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if test_data is not None: + test_set = cityscapes.CityscapesSegmentation(args, data=test_data, split='test') + num_class = test_set.NUM_CLASSES + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + test_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + # custom_set = cityscapes.CityscapesSegmentation(args, split='custom_resize') + # custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + # return train_loader, val_loader, test_loader, custom_loader, num_class + return train_loader, val_loader, test_loader, num_class + + if args.dataset == 'citylostfound': + if args.depth: + train_set = citylostfound.CitylostfoundSegmentation(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + train_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, num_class + if args.dataset == 'cityrand': + train_set = cityrand.CityscapesSegmentation(args, split='train') + val_set = cityrand.CityscapesSegmentation(args, split='val') + test_set = cityrand.CityscapesSegmentation(args, split='test') + custom_set = cityrand.CityscapesSegmentation(args, split='custom_resize') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'target': + train_set = target.CityscapesSegmentation(args, split='train') + val_set = target.CityscapesSegmentation(args, split='val') + test_set = target.CityscapesSegmentation(args, split='test') + custom_set = target.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'xrlab': + train_set = xrlab.CityscapesSegmentation(args, split='train') + val_set = xrlab.CityscapesSegmentation(args, split='val') + test_set = xrlab.CityscapesSegmentation(args, split='test') + custom_set = xrlab.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'e1': + train_set = e1.CityscapesSegmentation(args, split='train') + val_set = e1.CityscapesSegmentation(args, split='val') + test_set = e1.CityscapesSegmentation(args, split='test') + custom_set = e1.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'mapillary': + train_set = mapillary.CityscapesSegmentation(args, split='train') + val_set = mapillary.CityscapesSegmentation(args, split='val') + test_set = mapillary.CityscapesSegmentation(args, split='test') + custom_set = mapillary.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + else: + raise NotImplementedError + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py new file mode 100644 index 00000000..d63f200a --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py @@ -0,0 +1,240 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + # mean and std for original depth images + mean_depth = 0.12176 + std_depth = 0.09752 + + depth /= 255.0 + depth -= mean_depth + depth /= std_depth + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + depth = torch.from_numpy(depth).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'depth': depth, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + depth = depth.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + depth = depth.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # depth = depth.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + depth = depth.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + depth = depth.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + depth = ImageOps.expand(depth, border=(0, 0, padw, padh), fill=0) # depth多余的部分填0 + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + + assert img.size == depth.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + depth = depth.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py new file mode 100644 index 00000000..e04ef5a3 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py @@ -0,0 +1,230 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return {'image': img, + 'label': mask} + + +class Normalize_test(object): + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample + img = np.array(img).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return img + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + print(img.size) + + return {'image': img, + 'label': mask} + +class ToTensor_test(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + + img = torch.from_numpy(img).float() + + return img + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + + assert img.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py new file mode 100644 index 00000000..6ffd0a4b --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py @@ -0,0 +1,276 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr +from dataloaders import custom_transforms_rgb as tr_rgb + +class CitylostfoundSegmentation(data.Dataset): + NUM_CLASSES = 20 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root,'disparity',self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix= '.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix= '.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, + suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'depth': _depth, 'label': _target} + + # data augment + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) # background->255 ignore + input = tr.Relabel(1, 0)(input) # road 1->0 + # input = Relabel(255, 20)(input) # unlabel 20 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=None): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + if isinstance(suffix, str): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + elif isinstance(suffix, list): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for x in suffix for filename in filenames if filename.startswith(x)] + + + def transform_tr(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + +class CitylostfoundSegmentation_rgb(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.files = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.files[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.files[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.files[split]: + raise Exception("No files for split=[%s] found in %s" % (split, self.images_base)) + + print("Found %d %s images" % (len(self.files[split]), split)) + + def __len__(self): + return len(self.files[self.split]) + + def __getitem__(self, index): + + img_path = self.files[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'label': _target} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) + input = tr.Relabel(1, 0)(input) # road 1->0 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.RandomHorizontalFlip(), + tr_rgb.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.FixedResize(size=self.args.crop_size), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CitylostfoundSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py new file mode 100644 index 00000000..74eddb67 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('cityrand'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py new file mode 100644 index 00000000..66676745 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -0,0 +1,152 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 # 25 + + def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"): + + # self.root = root + self.root = "/home/lsq/Dataset/" + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.disparities_base = os.path.join(self.root, self.split, "depth", "cityscapes_real") + self.images[split] = [img[0] for img in data.x] if hasattr(data, "x") else data + + + if hasattr(data, "x") and len(data.x[0]) == 1: + # TODO: fit the case that depth images don't exist. + self.disparities[split] = self.images[split] + elif hasattr(data, "x") and len(data.x[0]) == 2: + self.disparities[split] = [img[1] for img in data.x] + else: + self.disparities[split] = data + + self.labels[split] = data.y if hasattr(data, "y") else data + + self.ignore_index = 255 + + if len(self.images[split]) == 0: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if len(self.disparities[split]) == 0: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py new file mode 100644 index 00000000..40e06e98 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('e1'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py new file mode 100644 index 00000000..d665649b --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('mapillary'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 768 + args.crop_size = 768 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py new file mode 100644 index 00000000..739e85f8 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('target'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt new file mode 100644 index 00000000..3c81afef --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt @@ -0,0 +1,38 @@ +for i in range(len(nam_label)): + img_label = cv2.imread(label_ori_path+nam_label[i], -1)[:,:,2] + img_label_temp = img_label.copy() + img_label_temp[img_label == 0] = 22 + img_label_temp[img_label == 1] = 10 + img_label_temp[img_label == 2] = 2 + img_label_temp[img_label == 3] = 0 + img_label_temp[img_label == 4] = 1 + img_label_temp[img_label == 5] = 4 + img_label_temp[img_label == 6] = 8 + img_label_temp[img_label == 7] = 5 + img_label_temp[img_label == 8] = 13 + img_label_temp[img_label == 9] = 7 + img_label_temp[img_label == 10] = 11 + img_label_temp[img_label == 11] = 18 + img_label_temp[img_label == 12] = 17 + img_label_temp[img_label == 13] = 21 + img_label_temp[img_label == 14] = 20 + img_label_temp[img_label == 15] = 6 + img_label_temp[img_label == 16] = 9 + img_label_temp[img_label == 17] = 12 + img_label_temp[img_label == 18] = 14 + img_label_temp[img_label == 19] = 15 + img_label_temp[img_label == 20] = 16 + img_label_temp[img_label == 21] = 3 + img_label_temp[img_label == 22] = 19 + #print(img_label) + #img_label[img_label == 0] = 10 + #img_label[img_label == 6] = 0 + #img_label[img_label == 5] = 11 + #img_label[img_label == 1] = 5 + #img_label[img_label == 2] = 1 + #img_label[img_label == 4] = 9 + #img_label[img_label == 3] = 4 + #img_label[img_label == 7] = 8 + #img_label[img_label == 11] = 2 + img_resize_lab = cv2.resize(img_label_temp, (2048,1024), interpolation=cv2.INTER_NEAREST) + cv2.imwrite(label_save_path+str(i)+'TrainIds.png', img_resize_lab.astype(np.uint16)) \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py new file mode 100644 index 00000000..4b261fcd --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 25 + + def __init__(self, args, root=Path.db_root_dir('xrlab'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py new file mode 100644 index 00000000..ef572332 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py @@ -0,0 +1,244 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +def decode_seg_map_sequence(label_masks, dataset='pascal'): + rgb_masks = [] + for label_mask in label_masks: + rgb_mask = decode_segmap(label_mask, dataset) + rgb_masks.append(rgb_mask) + rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2])) # change for val + return rgb_masks + + +def decode_segmap(label_mask, dataset, plot=False): + """Decode segmentation class labels into a color image + Args: + label_mask (np.ndarray): an (M,N) array of integer values denoting + the class label at each spatial location. + plot (bool, optional): whether to show the resulting color image + in a figure. + Returns: + (np.ndarray, optional): the resulting decoded color image. + """ + if dataset == 'pascal' or dataset == 'coco': + n_classes = 21 + label_colours = get_pascal_labels() + elif dataset == 'cityscapes': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'target': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'cityrand': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'citylostfound': + n_classes = 20 + label_colours = get_citylostfound_labels() + elif dataset == 'xrlab': + n_classes = 25 + label_colours = get_cityscapes_labels() + elif dataset == 'e1': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'mapillary': + n_classes = 24 + label_colours = get_cityscapes_labels() + else: + raise NotImplementedError + + r = label_mask.copy() + g = label_mask.copy() + b = label_mask.copy() + for ll in range(0, n_classes): + r[label_mask == ll] = label_colours[ll, 0] + g[label_mask == ll] = label_colours[ll, 1] + b[label_mask == ll] = label_colours[ll, 2] + rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3)) # change for val + # rgb = torch.ByteTensor(3, label_mask.shape[0], label_mask.shape[1]).fill_(0) + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + # r = torch.from_numpy(r) + # g = torch.from_numpy(g) + # b = torch.from_numpy(b) + + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + if plot: + plt.imshow(rgb) + plt.show() + else: + return rgb + + +def encode_segmap(mask): + """Encode segmentation label images as pascal classes + Args: + mask (np.ndarray): raw segmentation label image of dimension + (M, N, 3), in which the Pascal classes are encoded as colours. + Returns: + (np.ndarray): class map with dimensions (M,N), where the value at + a given location is the integer denoting the class index. + """ + mask = mask.astype(int) + label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16) + for ii, label in enumerate(get_pascal_labels()): + label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii + label_mask = label_mask.astype(int) + return label_mask + + +def get_cityscapes_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [119, 11, 119], + [128, 64, 64], + [102, 10, 156], + [102, 102, 15], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156]]) + +def get_citylostfound_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [111, 74, 0]]) + + +def get_pascal_labels(): + """Load the mapping that associates pascal classes with label colors + Returns: + np.ndarray with dimensions (21, 3) + """ + return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], + [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], + [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], + [0, 64, 128]]) + + +def colormap_bdd(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([128, 64, 128]) + cmap[1,:] = np.array([244, 35, 232]) + cmap[2,:] = np.array([ 70, 70, 70]) + cmap[3,:] = np.array([102, 102, 156]) + cmap[4,:] = np.array([190, 153, 153]) + cmap[5,:] = np.array([153, 153, 153]) + + cmap[6,:] = np.array([250, 170, 30]) + cmap[7,:] = np.array([220, 220, 0]) + cmap[8,:] = np.array([107, 142, 35]) + cmap[9,:] = np.array([152, 251, 152]) + cmap[10,:]= np.array([70, 130, 180]) + + cmap[11,:]= np.array([220, 20, 60]) + cmap[12,:]= np.array([255, 0, 0]) + cmap[13,:]= np.array([0, 0, 142]) + cmap[14,:]= np.array([0, 0, 70]) + cmap[15,:]= np.array([0, 60, 100]) + + cmap[16,:]= np.array([0, 80, 100]) + cmap[17,:]= np.array([0, 0, 230]) + cmap[18,:]= np.array([119, 11, 32]) + cmap[19,:]= np.array([111, 74, 0]) #多加了一类small obstacle + + return cmap + +def colormap_bdd0(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([0, 0, 0]) + cmap[1,:] = np.array([70, 130, 180]) + cmap[2,:] = np.array([70, 70, 70]) + cmap[3,:] = np.array([128, 64, 128]) + cmap[4,:] = np.array([244, 35, 232]) + cmap[5,:] = np.array([64, 64, 128]) + + cmap[6,:] = np.array([107, 142, 35]) + cmap[7,:] = np.array([153, 153, 153]) + cmap[8,:] = np.array([0, 0, 142]) + cmap[9,:] = np.array([220, 220, 0]) + cmap[10,:]= np.array([220, 20, 60]) + + cmap[11,:]= np.array([119, 11, 32]) + cmap[12,:]= np.array([0, 0, 230]) + cmap[13,:]= np.array([250, 170, 160]) + cmap[14,:]= np.array([128, 64, 64]) + cmap[15,:]= np.array([250, 170, 30]) + + cmap[16,:]= np.array([152, 251, 152]) + cmap[17,:]= np.array([255, 0, 0]) + cmap[18,:]= np.array([0, 0, 70]) + cmap[19,:]= np.array([0, 60, 100]) #small obstacle + cmap[20,:]= np.array([0, 80, 100]) + cmap[21,:]= np.array([102, 102, 156]) + cmap[22,:]= np.array([102, 102, 156]) + + return cmap + +class Colorize: + + def __init__(self, n=24): # n = nClasses + # self.cmap = colormap(256) + self.cmap = colormap_bdd(256) + self.cmap[n] = self.cmap[-1] + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + # print(size) + color_images = torch.ByteTensor(size[0], 3, size[1], size[2]).fill_(0) + # color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0) + + # for label in range(1, len(self.cmap)): + for i in range(color_images.shape[0]): + for label in range(0, len(self.cmap)): + mask = gray_image[0] == label + # mask = gray_image == label + + color_images[i][0][mask] = self.cmap[label][0] + color_images[i][1][mask] = self.cmap[label][1] + color_images[i][2][mask] = self.cmap[label][2] + + return color_images diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py new file mode 100644 index 00000000..247fe71a --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py @@ -0,0 +1,247 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import time +import torch +from torchvision.transforms import ToPILImage +from PIL import Image + +from dataloaders import make_data_loader +from dataloaders.utils import decode_seg_map_sequence, Colorize +from utils.metrics import Evaluator +from models.rfnet import RFNet +from models import rfnet_for_unseen +from models.resnet.resnet_single_scale_single_attention import * +from models.resnet import resnet_single_scale_single_attention_unseen +import torch.backends.cudnn as cudnn + +class Validator(object): + def __init__(self, args, data=None, unseen_detection=False): + self.args = args + self.time_train = [] + self.num_class = args.num_class + + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + # _, self.val_loader, _, self.custom_loader, self.num_class = make_data_loader(args, **kwargs) + _, _, self.test_loader, _ = make_data_loader(args, test_data=data, **kwargs) + print('un_classes:'+str(self.num_class)) + + # Define evaluator + self.evaluator = Evaluator(self.num_class) + + # Define network + if unseen_detection: + self.resnet = resnet_single_scale_single_attention_unseen.\ + resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = rfnet_for_unseen.RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + else: + self.resnet = resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + + if args.cuda: + self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + self.model = self.model.cuda() + cudnn.benchmark = True # accelarate speed + print('Model loaded successfully!') + + # # Load weights + # assert os.path.exists(args.weight_path), 'weight-path:{} doesn\'t exit!'.format(args.weight_path) + # self.new_state_dict = torch.load(args.weight_path, map_location=torch.device("cpu")) + # self.model = load_my_state_dict(self.model, self.new_state_dict['state_dict']) + + def validate(self): + self.model.eval() + self.evaluator.reset() + # tbar = tqdm(self.test_loader, desc='\r') + predictions = [] + for sample, image_name in self.test_loader: + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + # spec = time.time() + image, target = sample['image'], sample['label'] + + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + + if self.args.cuda: + torch.cuda.synchronize() + + pred = output.data.cpu().numpy() + # todo + pred = np.argmax(pred, axis=1) + predictions.append(pred) + + if not self.args.save_predicted_image: + continue + + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + print(pre_labels.shape) + # save + for i in range(pre_colors.shape[0]): + print(image_name[0]) + + if not image_name[0]: + img_name = "test.png" + else: + img_name = os.path.basename(image_name[0]) + + color_label_name = os.path.join(self.args.color_label_save_path, img_name) + label_name = os.path.join(self.args.label_save_path, img_name) + merge_label_name = os.path.join(self.args.merge_label_save_path, img_name) + + os.makedirs(os.path.dirname(color_label_name), exist_ok=True) + os.makedirs(os.path.dirname(merge_label_name), exist_ok=True) + os.makedirs(os.path.dirname(label_name), exist_ok=True) + + pre_color_image = ToPILImage()(pre_colors[i]) # pre_colors.dtype = float64 + pre_color_image.save(color_label_name) + + pre_label_image = ToPILImage()(pre_labels[i]) + pre_label_image.save(label_name) + + if (self.args.merge): + image_merge(image[i], pre_color_image, merge_label_name) + print('save image: {}'.format(merge_label_name)) + + return predictions + + def task_divide(self): + seen_task_samples, unseen_task_samples = [], [] + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + for i, (sample, image_name) in enumerate(tbar): + + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + start_time = time.time() + with torch.no_grad(): + if self.args.depth: + output_, output, _ = self.model(image, depth) + else: + output_, output, _ = self.model(image) + if self.args.cuda: + torch.cuda.synchronize() + if i != 0: + fwt = time.time() - start_time + self.time_train.append(fwt) + print("Forward time per img (bath size=%d): %.3f (Mean: %.3f)" % ( + self.args.val_batch_size, fwt / self.args.val_batch_size, + sum(self.time_train) / len(self.time_train) / self.args.val_batch_size)) + time.sleep(0.1) # to avoid overheating the GPU too much + + # pred colorize + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + for i in range(pre_colors.shape[0]): + task_sample = dict() + task_sample.update(image=sample["image"][i]) + task_sample.update(label=sample["label"][i]) + if self.args.depth: + task_sample.update(depth=sample["depth"][i]) + + if torch.max(pre_labels) == output.shape[1] - 1: + unseen_task_samples.append((task_sample, image_name[i])) + else: + seen_task_samples.append((task_sample, image_name[i])) + + return seen_task_samples, unseen_task_samples + +def image_merge(image, label, save_name): + image = ToPILImage()(image.detach().cpu().byte()) + # width, height = image.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + image = image.crop((left, top, right, bottom)) + # resize + image = image.resize(label.size, Image.BILINEAR) + + image = image.convert('RGBA') + label = label.convert('RGBA') + image = Image.blend(image, label, 0.6) + image.save(save_name) + +def load_my_state_dict(model, state_dict): # custom function to load model when not all dict elements + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + print('{} not in model_state'.format(name)) + continue + else: + own_state[name].copy_(param) + + return model + +# def main(): +# parser = argparse.ArgumentParser(description="PyTorch RFNet validation") +# parser.add_argument('--dataset', type=str, default='cityscapes', +# choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], +# help='dataset name (default: cityscapes)') +# parser.add_argument('--workers', type=int, default=4, +# metavar='N', help='dataloader threads') +# parser.add_argument('--base-size', type=int, default=1024, +# help='base image size') +# parser.add_argument('--crop-size', type=int, default=768, +# help='crop image size') +# parser.add_argument('--batch-size', type=int, default=6, +# help='batch size for training') +# parser.add_argument('--val-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# validating (default: auto)') +# parser.add_argument('--test-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# testing (default: auto)') +# parser.add_argument('--no-cuda', action='store_true', default= +# False, help='disables CUDA training') +# parser.add_argument('--gpu-ids', type=str, default='0', +# help='use which gpu to train, must be a \ +# comma-separated list of integers only (default=0)') +# parser.add_argument('--checkname', type=str, default=None, +# help='set the checkpoint name') +# parser.add_argument('--weight-path', type=str, default=None, +# help='enter your path of the weight') +# parser.add_argument('--color-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/color/', +# help='path to save label') +# parser.add_argument('--merge-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/merge/', +# help='path to save merged label') +# parser.add_argument('--label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/label/', +# help='path to save merged label') +# parser.add_argument('--merge', action='store_true', default=False, help='merge image and label') +# parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') +# +# +# args = parser.parse_args() +# args.cuda = not args.no_cuda and torch.cuda.is_available() +# if args.cuda: +# try: +# args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] +# except ValueError: +# raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') +# +# validator = Validator(args) +# validator.validate() +# +# +# if __name__ == "__main__": +# main() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py new file mode 100644 index 00000000..3734266e --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py new file mode 100644 index 00000000..63d81991 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py @@ -0,0 +1,391 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = []#[ self.spp, self.upsample] + self.fine_tune += [self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x.detach(), self.layer4) + features += [self.spp.forward(skip)] + return features + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer1) + y, skip_depth = self.forward_resblock(y.detach(), self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 2 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer2) + y, skip_depth = self.forward_resblock(y.detach(), self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 3 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer3) + y, skip_depth = self.forward_resblock(y.detach(), self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 4 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer4) + y, skip_depth = self.forward_resblock(y.detach(), self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + return features + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + i = 0 + for skip, up in zip(features[1:], self.upsample): + i += 1 + #print(len(self.upsample)) + if i < len(self.upsample): + x = up(x, skip) + else: + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + return self.forward_up(self.forward_down(rgb)) + else: + return self.forward_up(self.forward_down_fusion(rgb, depth)) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py new file mode 100644 index 00000000..9668734e --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py @@ -0,0 +1,396 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp +import cv2 +import numpy as np + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = [ self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def output_num(self): + return self.__in_features + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x, self.layer4) + features += [self.spp.forward(skip)] + features_da = self.spp.forward(skip) + return features, features_da + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x, self.layer1) + y, skip_depth = self.forward_resblock(y, self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 2 + x, skip_rgb = self.forward_resblock(x, self.layer2) + y, skip_depth = self.forward_resblock(y, self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 3 + x, skip_rgb = self.forward_resblock(x, self.layer3) + y, skip_depth = self.forward_resblock(y, self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 4 + x, skip_rgb = self.forward_resblock(x, self.layer4) + y, skip_depth = self.forward_resblock(y, self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + features_da = self.spp.forward(x) + return features, features_da + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + for skip, up in zip(features[1:], self.upsample): + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + down_features, da_features = self.forward_down(rgb) + x, additional = self.forward_up(down_features) + return x, additional, da_features#self.forward_up(self.forward_down(rgb)), self.forward_down(rgb) + else: + down_features, da_features = self.forward_down_fusion(rgb, depth) + x, additional = self.forward_up(down_features) + #print(down_features.shape) + return x, additional, da_features#self.forward_up(self.forward_down_fusion(rgb, depth)), self.forward_down_fusion(rgb, depth) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py new file mode 100644 index 00000000..87f02863 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py @@ -0,0 +1,27 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + print(self.backbone.num_features) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional = self.backbone(rgb_inputs, depth_inputs) + logits = self.logits.forward(x) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + #print(logits_upsample.size) + return logits_upsample + + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py new file mode 100644 index 00000000..f61eb1ce --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py @@ -0,0 +1,33 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + #self.bottleneck = _BNReluConv(self.backbone.num_features, 128, k = 3, batch_norm=use_bn) + #self.logits = _BNReluConv(128, self.num_classes+1, k = 1, batch_norm=use_bn) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + #self.logits_target = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + self.logits_aux = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional, da_features = self.backbone(rgb_inputs, depth_inputs) + #print(additional['features'][0].shape) + #bottleneck = self.bottleneck(x) + logits = self.logits.forward(x) + logits_aux = self.logits_aux.forward(x) + #print(logits_aux.shape) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + logits_aux_upsample = upsample(logits_aux, rgb_inputs.shape[2:]) + return logits_upsample, logits_aux_upsample, da_features + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.logits_aux.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py new file mode 100644 index 00000000..5c86e759 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False) +batchnorm_momentum = 0.01 / 2 + + +def get_n_params(parameters): + pp = 0 + for p in parameters: + nn = 1 + for s in list(p.size()): + nn = nn * s + pp += nn + return pp + + +class _BNReluConv(nn.Sequential): + def __init__(self, num_maps_in, num_maps_out, k=3, batch_norm=True, bn_momentum=0.1, bias=False, dilation=1): + super(_BNReluConv, self).__init__() + if batch_norm: + self.add_module('norm', nn.BatchNorm2d(num_maps_in, momentum=bn_momentum)) + self.add_module('relu', nn.ReLU(inplace=batch_norm is True)) + padding = k // 2 # same conv + self.add_module('conv', nn.Conv2d(num_maps_in, num_maps_out, + kernel_size=k, padding=padding, bias=bias, dilation=dilation)) + + +class _Upsample(nn.Module): + def __init__(self, num_maps_in, skip_maps_in, num_maps_out, use_bn=True, k=3): + super(_Upsample, self).__init__() + print(f'Upsample layer: in = {num_maps_in}, skip = {skip_maps_in}, out = {num_maps_out}') + self.bottleneck = _BNReluConv(skip_maps_in, num_maps_in, k=1, batch_norm=use_bn) + self.blend_conv = _BNReluConv(num_maps_in, num_maps_out, k=k, batch_norm=use_bn) + + def forward(self, x, skip): + skip = self.bottleneck.forward(skip) + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x + + +class SpatialPyramidPooling(nn.Module): + def __init__(self, num_maps_in, num_levels, bt_size=512, level_size=128, out_size=128, + grids=(6, 3, 2, 1), square_grid=False, bn_momentum=0.1, use_bn=True): + super(SpatialPyramidPooling, self).__init__() + self.grids = grids + self.square_grid = square_grid + self.spp = nn.Sequential() + self.spp.add_module('spp_bn', + _BNReluConv(num_maps_in, bt_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + num_features = bt_size + final_size = num_features + for i in range(num_levels): + final_size += level_size + self.spp.add_module('spp' + str(i), + _BNReluConv(num_features, level_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + self.spp.add_module('spp_fuse', + _BNReluConv(final_size, out_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + + def forward(self, x): + levels = [] + target_size = x.size()[2:4] + + ar = target_size[1] / target_size[0] + + x = self.spp[0].forward(x) + levels.append(x) + num = len(self.spp) - 1 + + for i in range(1, num): + if not self.square_grid: + grid_size = (self.grids[i - 1], max(1, round(ar * self.grids[i - 1]))) + x_pooled = F.adaptive_avg_pool2d(x, grid_size) + else: + x_pooled = F.adaptive_avg_pool2d(x, self.grids[i - 1]) + level = self.spp[i].forward(x_pooled) + + level = upsample(level, target_size) + levels.append(level) + x = torch.cat(levels, 1) + x = self.spp[-1].forward(x) + return x + + +class _UpsampleBlend(nn.Module): + def __init__(self, num_features, use_bn=True): + super(_UpsampleBlend, self).__init__() + self.blend_conv = _BNReluConv(num_features, num_features, k=3, batch_norm=use_bn) + + def forward(self, x, skip): + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py new file mode 100644 index 00000000..640544e7 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py @@ -0,0 +1,20 @@ +class Path(object): + @staticmethod + def db_root_dir(dataset): + if dataset == 'cityscapes': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapes/' # folder that contains leftImg8bit/ + elif dataset == 'citylostfound': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapesandlostandfound/' # folder that mixes Cityscapes and Lost and Found + elif dataset == 'cityrand': + return '/home/robo/m0063/project/RFNet-master/Data/cityrand/' + elif dataset == 'target': + return '/home/robo/m0063/project/RFNet-master/Data/target/' + elif dataset == 'xrlab': + return '/home/robo/m0063/project/RFNet-master/Data/xrlab/' + elif dataset == 'e1': + return '/home/robo/m0063/project/RFNet-master/Data/e1/' + elif dataset == 'mapillary': + return '/home/robo/m0063/project/RFNet-master/Data/mapillary/' + else: + print('Dataset {} not available.'.format(dataset)) + raise NotImplementedError diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py new file mode 100644 index 00000000..82b527a2 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py @@ -0,0 +1,98 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +# os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +# os.environ["video_url"] = "./video/radio.mp4" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" + + +import cv2 +import time +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +import warnings +from io import BytesIO + +from sedna.datasources import BaseDataSource +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from dataloaders import custom_transforms as tr +from torchvision import transforms + +from accuracy import accuracy +from basemodel import preprocess, val_args, Model + +def preprocess(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def init_ll_job(): + estimator = Model() + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"], + "default": "real" + } + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None) + + return ll_job + +def predict(): + ll_job = init_ll_job() + + camera_address = Context.get_parameters('video_url') + # use video streams for testing + camera = cv2.VideoCapture(camera_address) + fps = 10 + nframe = 0 + while 1: + ret, input_yuv = camera.read() + if not ret: + time.sleep(5) + camera = cv2.VideoCapture(camera_address) + continue + + if nframe % fps: + nframe += 1 + continue + + img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_BGR2RGB) + nframe += 1 + if nframe % 1000 == 1: # logs every 1000 frames + warnings.warn(f"camera is open, current frame index is {nframe}") + + img_rgb = cv2.resize(np.array(img_rgb), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + sample = {'image': img_rgb, "depth": img_rgb, "label": img_rgb} + data = preprocess(sample) + print("Inference results:", ll_job.inference(data=data)) + +if __name__ == '__main__': + predict() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py new file mode 100644 index 00000000..438cd70a --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py @@ -0,0 +1,252 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from io import BytesIO +from typing import Optional, Any + +import cv2 +import numpy as np +from PIL import Image +import uvicorn +import time +from pydantic import BaseModel +from fastapi import FastAPI, UploadFile, File +from fastapi.routing import APIRoute +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +import sedna_predict +from sedna.common.utils import get_host_ip +from dataloaders.datasets.cityscapes import CityscapesSegmentation + + +class ImagePayload(BaseModel): + image: UploadFile = File(...) + depth: Optional[UploadFile] = None + + +class ResultModel(BaseModel): + type: int = 0 + box: Any = None + curr: str = None + future: str = None + img: str = None + + +class ResultResponse(BaseModel): + msg: str = "" + result: Optional[ResultModel] = None + code: int + + +class BaseServer: + # pylint: disable=too-many-instance-attributes,too-many-arguments + DEBUG = True + WAIT_TIME = 15 + + def __init__( + self, + servername: str, + host: str, + http_port: int = 8080, + grpc_port: int = 8081, + workers: int = 1, + ws_size: int = 16 * 1024 * 1024, + ssl_key=None, + ssl_cert=None, + timeout=300): + self.server_name = servername + self.app = None + self.host = host or '0.0.0.0' + self.http_port = http_port or 80 + self.grpc_port = grpc_port + self.workers = workers + self.keyfile = ssl_key + self.certfile = ssl_cert + self.ws_size = int(ws_size) + self.timeout = int(timeout) + protocal = "https" if self.certfile else "http" + self.url = f"{protocal}://{self.host}:{self.http_port}" + + def run(self, app, **kwargs): + if hasattr(app, "add_middleware"): + app.add_middleware( + CORSMiddleware, allow_origins=["*"], allow_credentials=True, + allow_methods=["*"], allow_headers=["*"], + ) + + uvicorn.run( + app, + host=self.host, + port=self.http_port, + ssl_keyfile=self.keyfile, + ssl_certfile=self.certfile, + workers=self.workers, + timeout_keep_alive=self.timeout, + **kwargs) + + def get_all_urls(self): + url_list = [{"path": route.path, "name": route.name} + for route in getattr(self.app, 'routes', [])] + return url_list + + +class InferenceServer(BaseServer): # pylint: disable=too-many-arguments + """ + rest api server for inference + """ + + def __init__( + self, + servername, + host: str, + http_port: int = 5000, + max_buffer_size: int = 104857600, + workers: int = 1): + super( + InferenceServer, + self).__init__( + servername=servername, + host=host, + http_port=http_port, + workers=workers) + + self.job, self.detection_validator = sedna_predict.init_ll_job() + + self.max_buffer_size = max_buffer_size + self.app = FastAPI( + routes=[ + APIRoute( + f"/{servername}", + self.model_info, + methods=["GET"], + ), + APIRoute( + f"/{servername}/predict", + self.predict, + methods=["POST"], + response_model=ResultResponse + ), + ], + log_level="trace", + timeout=600, + ) + self.index_frame = 0 + + def start(self): + return self.run(self.app) + + @staticmethod + def model_info(): + return HTMLResponse( + """

Welcome to the RestNet API!

+

To use this service, send a POST HTTP request to {this-url}/predict

+

The JSON payload has the following format: {"image": "BASE64_STRING_OF_IMAGE", + "depth": "BASE64_STRING_OF_DEPTH"}

+ """) + + async def predict(self, image: UploadFile = File(...), depth: Optional[UploadFile] = None) -> ResultResponse: + contents = await image.read() + recieve_img_time = time.time() + print("Recieve image from the robo:", recieve_img_time) + + image = Image.open(BytesIO(contents)).convert('RGB') + + img_dep = None + self.index_frame = self.index_frame + 1 + + if depth: + depth_contents = await depth.read() + depth = Image.open(BytesIO(depth_contents)).convert('RGB') + img_dep = cv2.resize(np.array(depth), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_dep = Image.fromarray(img_dep) + + img_rgb = cv2.resize(np.array(image), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + + sample = {'image': img_rgb, "depth": img_dep, "label": img_rgb} + results = sedna_predict.predict(self.job, data=sample, validator=self.detection_validator) + + predict_finish_time = time.time() + print(f"Prediction costs {predict_finish_time - recieve_img_time} seconds") + + post_process = True + if results["result"]["box"] is None: + results["result"]["curr"] = None + results["result"]["future"] = None + elif post_process: + curr, future = get_curb(results["result"]["box"]) + results["result"]["curr"] = curr + results["result"]["future"] = future + results["result"]["box"] = None + print("Post process cost at worker:", (time.time()-predict_finish_time)) + else: + results["result"]["curr"] = None + results["result"]["future"] = None + + print("Result transmit to robo time:", time.time()) + return results + +def parse_result(label, count): + label_map = ['road', 'sidewalk', ] + count_d = dict(zip(label, count)) + curb_count = count_d.get(19, 0) + if curb_count / np.sum(count) > 0.3: + return "curb" + r = sorted(label, key=count_d.get, reverse=True)[0] + try: + c = label_map[r] + except: + c = "other" + + return c + +def get_curb(results): + results = np.array(results[0]) + input_height, input_width = results.shape + + closest = np.array([ + [0, int(input_height)], + [int(input_width), + int(input_height)], + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + ]) + + future = np.array([ + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + [int(.765 * input_width + .5), + int(.66 * input_height + .5)], + [int(.235 * input_width + .5), + int(.66 * input_height + .5)] + ]) + + mask = np.zeros((input_height, input_width), dtype=np.uint8) + mask = cv2.fillPoly(mask, [closest], 1) + mask = cv2.fillPoly(mask, [future], 2) + d1, c1 = np.unique(results[mask == 1], return_counts=True) + d2, c2 = np.unique(results[mask == 2], return_counts=True) + c = parse_result(d1, c1) + f = parse_result(d2, c2) + + return c, f + +if __name__ == '__main__': + web_app = InferenceServer("lifelong-learning-robo", host=get_host_ip()) + web_app.start() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py new file mode 100644 index 00000000..56633347 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py @@ -0,0 +1,50 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +# os.environ["test_dataset_url"] = "./data_txt/sedna_data.txt" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" +# os.environ["operator"] = "<" +# os.environ["model_threshold"] = "0" + +from sedna.core.lifelong_learning import LifelongLearning +from sedna.datasources import IndexDataParse +from sedna.common.config import Context + +from accuracy import accuracy +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def eval(): + estimator = Model() + eval_dataset_url = Context.get_parameters("test_dataset_url") + eval_data = IndexDataParse(data_type="eval", func=_load_txt_dataset) + eval_data.parse(eval_dataset_url, use_raw=False) + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.evaluate(eval_data, metrics=accuracy) + + +if __name__ == '__main__': + print(eval()) diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py new file mode 100644 index 00000000..b32c01d2 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py @@ -0,0 +1,132 @@ +import os + +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["UNSEEN_SAVE_URL"] = "s3://kubeedge/sedna-robo/unseen_samples/" +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt" +os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +os.environ["ORIGINAL_DATASET_URL"] = "/tmp" + +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +from io import BytesIO +from torchvision.transforms import ToPILImage +from torchvision import transforms +from torch.utils.data import DataLoader + +from sedna.datasources import IndexDataParse +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from eval import Validator +from accuracy import accuracy +from basemodel import preprocess, val_args, Model +from dataloaders.utils import Colorize +from dataloaders import custom_transforms as tr +from dataloaders.datasets.cityscapes import CityscapesSegmentation + +def _load_txt_dataset(dataset_url): + # use original dataset url, + # see https://github.com/kubeedge/sedna/issues/35 + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def fetch_data(): + test_dataset_url = Context.get_parameters("test_dataset_url") + test_data = IndexDataParse(data_type="test", func=_load_txt_dataset) + test_data.parse(test_dataset_url, use_raw=False) + return test_data + +def pre_data_process(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def post_process(res, is_unseen_task): + if is_unseen_task: + res, base64_string = None, None + else: + res = res[0].tolist() + + type = 0 if not is_unseen_task else 1 + mesg = { + "msg": "", + "result": { + "type": type, + "box": res + }, + "code": 0 + } + return mesg + +def image_merge(raw_img, result): + raw_img = ToPILImage()(raw_img) + + pre_colors = Colorize()(torch.from_numpy(result)) + pre_color_image = ToPILImage()(pre_colors[0]) # pre_colors.dtype = float64 + + image = raw_img.resize(pre_color_image.size, Image.BILINEAR) + image = image.convert('RGBA') + label = pre_color_image.convert('RGBA') + image = Image.blend(image, label, 0.6) + with tempfile.NamedTemporaryFile(suffix='.png') as f: + image.save(f.name) + + with open(f.name, 'rb') as open_file: + byte_content = open_file.read() + base64_bytes = base64.b64encode(byte_content) + base64_string = base64_bytes.decode('utf-8') + return base64_string + +def init_ll_job(): + estimator = Model() + inference_integrate = { + "method": "BBoxInferenceIntegrate" + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + unseen_sample_recognition = { + "method": "SampleRegonitionByRFNet" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=inference_integrate, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=unseen_sample_recognition, + unseen_sample_re_recognition=None) + + args = val_args() + args.weight_path = "./models/detection_model.pth" + args.num_class = 31 + + return ll_job, Validator(args, unseen_detection=True) + +def predict(ll_job, data=None, validator=None): + if data: + data = pre_data_process(data) + else: + data = fetch_data() + data.x = preprocess(data.x) + + res, is_unseen_task, _ = ll_job.inference( + data, validator=validator, initial=False) + return post_process(res, is_unseen_task) + +if __name__ == '__main__': + ll_job, validator = init_ll_job() + print("Inference result:", predict(ll_job, validator=validator)) diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py new file mode 100644 index 00000000..1c99361a --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py @@ -0,0 +1,78 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +os.environ["OUTPUT_URL"] = "./cloud_kb/" +# os.environ['CLOUD_KB_INDEX'] = "./cloud_kb/index.pkl" +os.environ["TRAIN_DATASET_URL"] = "./data_txt/sedna_data.txt" +os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = "false" + +from sedna.common.file_ops import FileOps +from sedna.datasources import IndexDataParse +from sedna.common.config import Context, BaseConfig +from sedna.core.lifelong_learning import LifelongLearning + +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def train(estimator, train_data): + task_definition = { + "method": "TaskDefinitionByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=task_definition, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.train(train_data) + +def update(estimator, train_data): + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.update(train_data) + +def run(): + estimator = Model() + train_dataset_url = BaseConfig.train_dataset_url + train_data = IndexDataParse(data_type="train") + train_data.parse(train_dataset_url, use_raw=False) + + is_completed_initilization = str(Context.get_parameters("HAS_COMPLETED_INITIAL_TRAINING", "false")).lower() + if is_completed_initilization == "false": + train(estimator, train_data) + else: + update(estimator, train_data) + +if __name__ == '__main__': + run() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py new file mode 100644 index 00000000..fd9cd657 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py @@ -0,0 +1,52 @@ +import numpy as np +import seaborn as sns +import pandas as pd +import matplotlib.pyplot as plt + +CPA_results = np.load("./cpa_results.npy").T +ratios = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9] +ratio_counts = np.zeros((len(CPA_results), len(ratios)), dtype=float) + +for i in range(len(CPA_results)): + for j in range(len(ratios)): + result = CPA_results[i] + result = result[result <= ratios[j]] + + ratio_counts[i][j] = len(result) / 275 + +plt.figure(figsize=(45, 10)) +ratio_counts = pd.DataFrame(data=ratio_counts.T, index=ratios) +sns.heatmap(data=ratio_counts, annot=True, cmap="YlGnBu", annot_kws={'fontsize': 15}) +plt.xticks(fontsize=20) +plt.yticks(fontsize=25) +plt.xlabel("Test images", fontsize=25) +plt.ylabel("Ratio of PA ranges", fontsize=25) +plt.savefig("./figs/ratio_count.png") +plt.show() + + +# data = pd.DataFrame(CPA_results.T) +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[:15]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/door_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[15:31]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden1_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[31:]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden2_heatmap_pa.png") +# plt.show() diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py new file mode 100644 index 00000000..d7d01e7f --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py @@ -0,0 +1,211 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import torch + +from mypath import Path +from dataloaders import make_data_loader +from models.rfnet import RFNet +from models.resnet.resnet_single_scale_single_attention import * +from utils.loss import SegmentationLosses +from models.replicate import patch_replication_callback +from utils.calculate_weights import calculate_weigths_labels +from utils.lr_scheduler import LR_Scheduler +from utils.saver import Saver +from utils.summaries import TensorboardSummary +from utils.metrics import Evaluator +from sedna.datasources import BaseDataSource + +class Trainer(object): + def __init__(self, args, train_data=None, valid_data=None): + self.args = args + # Define Saver + self.saver = Saver(args) + self.saver.save_experiment_config() + # Define Tensorboard Summary + self.summary = TensorboardSummary(self.saver.experiment_dir) + self.writer = self.summary.create_summary() + # denormalize for detph image + self.mean_depth = torch.as_tensor(0.12176, dtype=torch.float32, device='cpu') + self.std_depth = torch.as_tensor(0.09752, dtype=torch.float32, device='cpu') + self.nclass = args.num_class + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + self.train_loader, self.val_loader, self.test_loader, _ = make_data_loader(args, train_data=train_data, + valid_data=valid_data, **kwargs) + + # Define network + resnet = resnet18(pretrained=True, efficient=False, use_bn=True) + model = RFNet(resnet, num_classes=self.nclass, use_bn=True) + train_params = [{'params': model.random_init_params(), 'lr': args.lr}, + {'params': model.fine_tune_params(), 'lr': 0.1*args.lr, 'weight_decay':args.weight_decay}] + # Define Optimizer + optimizer = torch.optim.Adam(train_params, lr=args.lr, + weight_decay=args.weight_decay) + # Define Criterion + # whether to use class balanced weights + if args.use_balanced_weights: + classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy') + if os.path.isfile(classes_weights_path): + weight = np.load(classes_weights_path) + else: + weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass) + weight = torch.from_numpy(weight.astype(np.float32)) + else: + weight = None + # Define loss function + self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type) + self.model, self.optimizer = model, optimizer + # Define Evaluator + self.evaluator = Evaluator(self.nclass) + # # Define lr scheduler + self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr, args.epochs, len(self.train_loader)) + # Using cuda + if args.cuda: + self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + patch_replication_callback(self.model) + self.model = self.model.cuda() + + # Resuming checkpoint + self.best_pred = 0.0 + if args.resume is not None: + if not os.path.isfile(args.resume): + raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume)) + print(f"Training: load model from {args.resume}") + checkpoint = torch.load(args.resume, map_location=torch.device("cpu")) + args.start_epoch = checkpoint['epoch'] + # if args.cuda: + # self.model.load_state_dict(checkpoint['state_dict']) + # else: + # self.model.load_state_dict(checkpoint['state_dict']) + self.model.load_state_dict(checkpoint['state_dict']) + if not args.ft: + self.optimizer.load_state_dict(checkpoint['optimizer']) + self.best_pred = checkpoint['best_pred'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + + # Clear start epoch if fine-tuning + if args.ft: + args.start_epoch = 0 + + def training(self, epoch): + train_loss = 0.0 + print(self.optimizer.state_dict()['param_groups'][0]['lr']) + self.model.train() + tbar = tqdm(self.train_loader) + num_img_tr = len(self.train_loader) + + for i, sample in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + #print(target.shape) + else: + image, target = sample['image'], sample['label'] + print(image.shape) + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + self.scheduler(self.optimizer, i, epoch, self.best_pred) + self.optimizer.zero_grad() + + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + #print(target.max()) + #print(output.shape) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + loss.backward() + self.optimizer.step() + #print(self.optimizer.state_dict()['param_groups'][0]['lr']) + train_loss += loss.item() + tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) + self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) + # Show 10 * 3 inference results each epoch + if i % (num_img_tr // 10) == 0: + global_step = i + num_img_tr * epoch + if self.args.depth: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + depth_display = depth[0].cpu().unsqueeze(0) + depth_display = depth_display.mul_(self.std_depth).add_(self.mean_depth) + depth_display = depth_display.numpy() + depth_display = depth_display*255 + depth_display = depth_display.astype(np.uint8) + self.writer.add_image('Depth', depth_display, global_step) + + else: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch) + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print('Loss: %.3f' % train_loss) + + # if self.args.no_val: + # # save checkpoint every epoch + # is_best = False + # checkpoint_path = self.saver.save_checkpoint({ + # 'epoch': epoch + 1, + # 'state_dict': self.model.state_dict(), + # 'optimizer': self.optimizer.state_dict(), + # 'best_pred': self.best_pred, + # }, is_best) + + def validation(self, epoch): + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.val_loader, desc='\r') + test_loss = 0.0 + for i, (sample, img_path) in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + # print(f"val image is {image}") + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + test_loss += loss.item() + tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1))) + pred = output.data.cpu().numpy() + target = target.cpu().numpy() + pred = np.argmax(pred, axis=1) + # Add batch sample into evaluator + self.evaluator.add_batch(target, pred) + + # Fast test during the training + Acc = self.evaluator.Pixel_Accuracy() + Acc_class = self.evaluator.Pixel_Accuracy_Class() + mIoU = self.evaluator.Mean_Intersection_over_Union() + FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union() + self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch) + self.writer.add_scalar('val/mIoU', mIoU, epoch) + self.writer.add_scalar('val/Acc', Acc, epoch) + self.writer.add_scalar('val/Acc_class', Acc_class, epoch) + self.writer.add_scalar('val/fwIoU', FWIoU, epoch) + print('Validation:') + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU)) + print('Loss: %.3f' % test_loss) + + new_pred = mIoU + if new_pred > self.best_pred: + is_best = True + self.best_pred = new_pred + self.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.model.state_dict(), + 'optimizer': self.optimizer.state_dict(), + 'best_pred': self.best_pred, + }, is_best) \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py new file mode 100644 index 00000000..f3d54437 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -0,0 +1,52 @@ +class TrainArgs: + def __init__(self, **kwargs): + self.depth = False + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.loss_type = 'ce' + self.epochs = kwargs.get("epochs", 2) + self.start_epoch = 0 + + self.batch_size = 4 + self.val_batch_size = 1 + self.use_balanced_weights = False + self.num_class = 24 + self.lr = kwargs.get("learning_rate", 1e-4) + self.lr_scheduler = 'cos' + self.momentum = 0.9 + self.weight_decay = 2.5e-5 + self.no_cuda = False + self.gpu_ids = '0' + + self.seed = 1 + self.resume = None + self.checkname = 'RFNet' + self.ft = True + self.eval_interval = kwargs.get("eval_interval", 2) + self.no_val = kwargs.get("no_val", True) + self.cuda = False + + +class ValArgs: + def __init__(self, **kwargs): + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.batch_size = 6 + self.val_batch_size = 1 + self.test_batch_size = 1 + self.num_class = 24 + self.no_cuda = False + self.gpu_ids = '0' + self.checkname = None + self.weight_path = "./models/530_exp3_2.pth" + self.save_predicted_image = False + self.color_label_save_path = './test/color' + self.merge_label_save_path = './test/merge' + self.label_save_path = './test/label' + self.merge = True + self.depth = False + self.cuda = False diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py new file mode 100644 index 00000000..2c2c9821 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py @@ -0,0 +1,29 @@ +import os +from tqdm import tqdm +import numpy as np +from mypath import Path + +def calculate_weigths_labels(dataset, dataloader, num_classes): + # Create an instance from the data loader + z = np.zeros((num_classes,)) + # Initialize tqdm + tqdm_batch = tqdm(dataloader) + print('Calculating classes weights') + for sample in tqdm_batch: + y = sample['label'] + y = y.detach().cpu().numpy() + mask = (y >= 0) & (y < num_classes) + labels = y[mask].astype(np.uint8) + count_l = np.bincount(labels, minlength=num_classes) + z += count_l + tqdm_batch.close() + total_frequency = np.sum(z) + class_weights = [] + for frequency in z: + class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) + class_weights.append(class_weight) + ret = np.array(class_weights) + classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy') + np.save(classes_weights_path, ret) + + return ret \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py new file mode 100644 index 00000000..de955825 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py @@ -0,0 +1,142 @@ +import torch + + +class iouEval: + + def __init__(self, nClasses, ignoreIndex=20): + + self.nClasses = nClasses + self.ignoreIndex = ignoreIndex if nClasses > ignoreIndex else -1 # if ignoreIndex is larger than nClasses, consider no ignoreIndex + self.reset() + + def reset(self): + classes = self.nClasses if self.ignoreIndex == -1 else self.nClasses - 1 + self.tp = torch.zeros(classes).double() + self.fp = torch.zeros(classes).double() + self.fn = torch.zeros(classes).double() + self.cdp_obstacle = torch.zeros(1).double() + self.tp_obstacle = torch.zeros(1).double() + self.idp_obstacle = torch.zeros(1).double() + self.tp_nonobstacle = torch.zeros(1).double() + # self.cdi = torch.zeros(1).double() + + def addBatch(self, x, y): # x=preds, y=targets + # sizes should be "batch_size x nClasses x H x W" + # cdi = 0 + + # print ("X is cuda: ", x.is_cuda) + # print ("Y is cuda: ", y.is_cuda) + + if (x.is_cuda or y.is_cuda): + x = x.cuda() + y = y.cuda() + + # if size is "batch_size x 1 x H x W" scatter to onehot + if (x.size(1) == 1): + x_onehot = torch.zeros(x.size(0), self.nClasses, x.size(2), x.size(3)) + if x.is_cuda: + x_onehot = x_onehot.cuda() + x_onehot.scatter_(1, x, 1).float() # dim index src 按照列用1替换0，索引为x + else: + x_onehot = x.float() + + if (y.size(1) == 1): + y_onehot = torch.zeros(y.size(0), self.nClasses, y.size(2), y.size(3)) + if y.is_cuda: + y_onehot = y_onehot.cuda() + y_onehot.scatter_(1, y, 1).float() + else: + y_onehot = y.float() + + if (self.ignoreIndex != -1): + ignores = y_onehot[:, self.ignoreIndex].unsqueeze(1) # 加一维 + x_onehot = x_onehot[:, :self.ignoreIndex] # ignoreIndex后的都不要 + y_onehot = y_onehot[:, :self.ignoreIndex] + else: + ignores = 0 + + + tpmult = x_onehot * y_onehot # times prediction and gt coincide is 1 + tp = torch.sum(torch.sum(torch.sum(tpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fpmult = x_onehot * ( + 1 - y_onehot - ignores) # times prediction says its that class and gt says its not (subtracting cases when its ignore label!) + fp = torch.sum(torch.sum(torch.sum(fpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fnmult = (1 - x_onehot) * (y_onehot) # times prediction says its not that class and gt says it is + fn = torch.sum(torch.sum(torch.sum(fnmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + + self.tp += tp.double().cpu() + self.fp += fp.double().cpu() + self.fn += fn.double().cpu() + + cdp_obstacle = tpmult[:, 19].sum() # obstacle index 19 + tp_obstacle = y_onehot[:, 19].sum() + + idp_obstacle = (x_onehot[:, 19] - tpmult[:, 19]).sum() + tp_nonobstacle = (-1*y_onehot+1).sum() + + # for i in range(0, x.size(0)): + # if tpmult[i].sum()/(y_onehot[i].sum() + 1e-15) >= 0.5: + # cdi += 1 + + + self.cdp_obstacle += cdp_obstacle.double().cpu() + self.tp_obstacle += tp_obstacle.double().cpu() + self.idp_obstacle += idp_obstacle.double().cpu() + self.tp_nonobstacle += tp_nonobstacle.double().cpu() + # self.cdi += cdi.double().cpu() + + + + def getIoU(self): + num = self.tp + den = self.tp + self.fp + self.fn + 1e-15 + iou = num / den + iou_not_zero = list(filter(lambda x: x != 0, iou)) + # print(len(iou_not_zero)) + iou_mean = sum(iou_not_zero) / len(iou_not_zero) + tfp = self.tp + self.fp + 1e-15 + acc = num / tfp + acc_not_zero = list(filter(lambda x: x != 0, acc)) + acc_mean = sum(acc_not_zero) / len(acc_not_zero) + + return iou_mean, iou, acc_mean, acc # returns "iou mean", "iou per class" + + def getObstacleEval(self): + + pdr_obstacle = self.cdp_obstacle / (self.tp_obstacle+1e-15) + pfp_obstacle = self.idp_obstacle / (self.tp_nonobstacle+1e-15) + + return pdr_obstacle, pfp_obstacle + + +# Class for colors +class colors: + RED = '\033[31;1m' + GREEN = '\033[32;1m' + YELLOW = '\033[33;1m' + BLUE = '\033[34;1m' + MAGENTA = '\033[35;1m' + CYAN = '\033[36;1m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + ENDC = '\033[0m' + + +# Colored value output if colorized flag is activated. +def getColorEntry(val): + if not isinstance(val, float): + return colors.ENDC + if (val < .20): + return colors.RED + elif (val < .40): + return colors.YELLOW + elif (val < .60): + return colors.BLUE + elif (val < .80): + return colors.CYAN + else: + return colors.GREEN + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py new file mode 100644 index 00000000..6cde9a17 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py @@ -0,0 +1,64 @@ +import torch +import torch.nn as nn + +class SegmentationLosses(object): + def __init__(self, weight=None, size_average=True, batch_average=True, ignore_index=255, cuda=False): # ignore_index=255 + self.ignore_index = ignore_index + self.weight = weight + self.size_average = size_average + self.batch_average = batch_average + self.cuda = cuda + + def build_loss(self, mode='ce'): + """Choices: ['ce' or 'focal']""" + if mode == 'ce': + return self.CrossEntropyLoss + elif mode == 'focal': + return self.FocalLoss + else: + raise NotImplementedError + + def CrossEntropyLoss(self, logit, target): + n, c, h, w = logit.size() + #criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + #size_average=self.size_average) + criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=self.ignore_index) + if self.cuda: + criterion = criterion.cuda() + + loss = criterion(logit, target.long()) + + if self.batch_average: + loss /= n + + return loss + + def FocalLoss(self, logit, target, gamma=2, alpha=0.5): + n, c, h, w = logit.size() + criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + size_average=self.size_average) + if self.cuda: + criterion = criterion.cuda() + + logpt = -criterion(logit, target.long()) + pt = torch.exp(logpt) + if alpha is not None: + logpt *= alpha + loss = -((1 - pt) ** gamma) * logpt + + if self.batch_average: + loss /= n + + return loss + +if __name__ == "__main__": + loss = SegmentationLosses(cuda=True) + a = torch.rand(1, 3, 7, 7).cuda() + b = torch.rand(1, 7, 7).cuda() + print(loss.CrossEntropyLoss(a, b).item()) + print(loss.FocalLoss(a, b, gamma=0, alpha=None).item()) + print(loss.FocalLoss(a, b, gamma=2, alpha=0.5).item()) + + + + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py new file mode 100644 index 00000000..47124028 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py @@ -0,0 +1,70 @@ +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +## Created by: Hang Zhang +## ECE Department, Rutgers University +## Email: zhang.hang@rutgers.edu +## Copyright (c) 2017 +## +## This source code is licensed under the MIT-style license found in the +## LICENSE file in the root directory of this source tree +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +import math + +class LR_Scheduler(object): + """Learning Rate Scheduler + + Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}`` + + Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))`` + + Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9`` + + Args: + args: + :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`), + :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs, + :attr:`args.lr_step` + + iters_per_epoch: number of iterations per epoch + """ + def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, + lr_step=0, warmup_epochs=0): + self.mode = mode + print('Using {} LR Scheduler!'.format(self.mode)) + self.lr = base_lr + if mode == 'step': + assert lr_step + self.lr_step = lr_step + self.iters_per_epoch = iters_per_epoch + self.N = num_epochs * iters_per_epoch + self.epoch = -1 + self.warmup_iters = warmup_epochs * iters_per_epoch + + def __call__(self, optimizer, i, epoch, best_pred): + T = epoch * self.iters_per_epoch + i + if self.mode == 'cos': + lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) + elif self.mode == 'poly': + lr = self.lr * pow((1 - 1.0 * T / self.N), 2) + elif self.mode == 'step': + lr = self.lr * (0.1 ** (epoch // self.lr_step)) + else: + raise NotImplemented + # warm up lr schedule + if self.warmup_iters > 0 and T < self.warmup_iters: + lr = lr * 1.0 * T / self.warmup_iters + if epoch > self.epoch: + print('\n=>Epoches %i, learning rate = %.4f, \ + previous best = %.4f' % (epoch, lr, best_pred)) + self.epoch = epoch + assert lr >= 0 + self._adjust_learning_rate(optimizer, lr) + + def _adjust_learning_rate(self, optimizer, lr): + if len(optimizer.param_groups) == 1: + optimizer.param_groups[0]['lr'] = lr * 4 + else: + # enlarge the lr at the head + optimizer.param_groups[0]['lr'] = lr * 4 + for i in range(1, len(optimizer.param_groups)): + optimizer.param_groups[i]['lr'] = lr diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py new file mode 100644 index 00000000..551bc0d1 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py @@ -0,0 +1,146 @@ +import numpy as np + + +class Evaluator(object): + def __init__(self, num_class): + self.num_class = num_class + self.confusion_matrix = np.zeros((self.num_class,)*2) # shape:(num_class, num_class) + + def Pixel_Accuracy(self): + Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum() + return Acc + + def Pixel_Accuracy_Class_Curb(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + Acc = np.nanmean(Acc[:2]) + return Acc + + + def Pixel_Accuracy_Class(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + print("building : %.6f" % (Acc[2] * 100.0), "%\t") + print("wall : %.6f" % (Acc[3] * 100.0), "%\t") + print("fence : %.6f" % (Acc[4] * 100.0), "%\t") + print("pole : %.6f" % (Acc[5] * 100.0), "%\t") + print("traffic light: %.6f" % (Acc[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (Acc[7] * 100.0), "%\t") + print("vegetation : %.6f" % (Acc[8] * 100.0), "%\t") + print("terrain : %.6f" % (Acc[9] * 100.0), "%\t") + print("sky : %.6f" % (Acc[10] * 100.0), "%\t") + print("person : %.6f" % (Acc[11] * 100.0), "%\t") + print("rider : %.6f" % (Acc[12] * 100.0), "%\t") + print("car : %.6f" % (Acc[13] * 100.0), "%\t") + print("truck : %.6f" % (Acc[14] * 100.0), "%\t") + print("bus : %.6f" % (Acc[15] * 100.0), "%\t") + print("train : %.6f" % (Acc[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (Acc[17] * 100.0), "%\t") + print("bicycle : %.6f" % (Acc[18] * 100.0), "%\t") + print("dynamic : %.6f" % (Acc[19] * 100.0), "%\t") + print("stair : %.6f" % (Acc[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (Acc[19] * 100.0), "%\t") + Acc = np.nanmean(Acc) + return Acc + + def Mean_Intersection_over_Union(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + print("building : %.6f" % (MIoU[2] * 100.0), "%\t") + print("wall : %.6f" % (MIoU[3] * 100.0), "%\t") + print("fence : %.6f" % (MIoU[4] * 100.0), "%\t") + print("pole : %.6f" % (MIoU[5] * 100.0), "%\t") + print("traffic light: %.6f" % (MIoU[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (MIoU[7] * 100.0), "%\t") + print("vegetation : %.6f" % (MIoU[8] * 100.0), "%\t") + print("terrain : %.6f" % (MIoU[9] * 100.0), "%\t") + print("sky : %.6f" % (MIoU[10] * 100.0), "%\t") + print("person : %.6f" % (MIoU[11] * 100.0), "%\t") + print("rider : %.6f" % (MIoU[12] * 100.0), "%\t") + print("car : %.6f" % (MIoU[13] * 100.0), "%\t") + print("truck : %.6f" % (MIoU[14] * 100.0), "%\t") + print("bus : %.6f" % (MIoU[15] * 100.0), "%\t") + print("train : %.6f" % (MIoU[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (MIoU[17] * 100.0), "%\t") + print("bicycle : %.6f" % (MIoU[18] * 100.0), "%\t") + print("dynamic : %.6f" % (MIoU[19] * 100.0), "%\t") + print("stair : %.6f" % (MIoU[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU) + return MIoU + + def Mean_Intersection_over_Union_Curb(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU[:2]) + return MIoU + + def Frequency_Weighted_Intersection_over_Union(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return FWIoU + + def Frequency_Weighted_Intersection_over_Union_Curb(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return np.nanmean(CFWIoU[:2]) + + def _generate_matrix(self, gt_image, pre_image): + mask = (gt_image >= 0) & (gt_image < self.num_class) + label = self.num_class * gt_image[mask].astype('int') + pre_image[mask] + count = np.bincount(label, minlength=self.num_class**2) + confusion_matrix = count.reshape(self.num_class, self.num_class) + return confusion_matrix + + def add_batch(self, gt_image, pre_image): + print(gt_image.shape, pre_image.shape) + assert gt_image.shape == pre_image.shape + self.confusion_matrix += self._generate_matrix(gt_image, pre_image) + + def reset(self): + self.confusion_matrix = np.zeros((self.num_class,) * 2) + + + + diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py new file mode 100644 index 00000000..03866432 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py @@ -0,0 +1,68 @@ +import os +import time +import shutil +import tempfile +import torch +from collections import OrderedDict +import glob + +class Saver(object): + + def __init__(self, args): + self.args = args + self.directory = os.path.join('/tmp', args.dataset, args.checkname) + self.runs = sorted(glob.glob(os.path.join(self.directory, 'experiment_*'))) + run_id = int(self.runs[-1].split('_')[-1]) + 1 if self.runs else 0 + + self.experiment_dir = os.path.join(self.directory, 'experiment_{}'.format(str(run_id))) + if not os.path.exists(self.experiment_dir): + os.makedirs(self.experiment_dir) + + def save_checkpoint(self, state, is_best): # filename from .pth.tar change to .pth? + """Saves checkpoint to disk""" + filename = f'checkpoint_{time.time()}.pth' + checkpoint_path = os.path.join(self.experiment_dir, filename) + torch.save(state, checkpoint_path) + if is_best: + best_pred = state['best_pred'] + with open(os.path.join(self.experiment_dir, 'best_pred.txt'), 'w') as f: + f.write(str(best_pred)) + if self.runs: + previous_miou = [0.0] + for run in self.runs: + run_id = run.split('_')[-1] + path = os.path.join(self.directory, 'experiment_{}'.format(str(run_id)), 'best_pred.txt') + if os.path.exists(path): + with open(path, 'r') as f: + miou = float(f.readline()) + previous_miou.append(miou) + else: + continue + max_miou = max(previous_miou) + if best_pred > max_miou: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + else: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + + return checkpoint_path + + def save_experiment_config(self): + logfile = os.path.join(self.experiment_dir, 'parameters.txt') + log_file = open(logfile, 'w') + p = OrderedDict() + p['datset'] = self.args.dataset + # p['out_stride'] = self.args.out_stride + p['lr'] = self.args.lr + p['lr_scheduler'] = self.args.lr_scheduler + p['loss_type'] = self.args.loss_type + p['epoch'] = self.args.epochs + p['base_size'] = self.args.base_size + p['crop_size'] = self.args.crop_size + + for key, val in p.items(): + log_file.write(key + ':' + str(val) + '\n') + log_file.close() \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py new file mode 100644 index 00000000..04bcdb82 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py @@ -0,0 +1,39 @@ +import os +import torch +from torchvision.utils import make_grid +# from tensorboardX import SummaryWriter +from torch.utils.tensorboard import SummaryWriter +from dataloaders.utils import decode_seg_map_sequence + +class TensorboardSummary(object): + def __init__(self, directory): + self.directory = directory + + def create_summary(self): + writer = SummaryWriter(log_dir=os.path.join(self.directory)) + return writer + + def visualize_image(self, writer, dataset, image, target, output, global_step, depth=None): + if depth is None: + grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) + else: + grid_image = make_grid(image[:3].clone().cpu().data, 4, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(depth[:3].clone().cpu().data, 4, normalize=True) # normalize=False? + writer.add_image('Depth', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py new file mode 100644 index 00000000..9a9b6a17 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py @@ -0,0 +1,123 @@ +import os + +import numpy as np +import torch +from torch.utils.data import DataLoader +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from PIL import Image +from torchvision import transforms + +from RFNet.train import Trainer +from RFNet.eval import Validator, load_my_state_dict +from RFNet.dataloaders import custom_transforms as tr +from RFNet.utils.args import TrainArgs, ValArgs + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.train_args = TrainArgs(**kwargs) + self.trainer = None + + self.val_args = ValArgs(**kwargs) + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range( + self.trainer.args.start_epoch, + self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and ( + epoch % + self.trainer.args.eval_interval == ( + self.trainer.args.eval_interval - + 1) or epoch == self.trainer.args.epochs - + 1): + # save checkpoint when it meets eval_interval or the training + # finished + is_best = False + self.train_model_url = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + self.trainer.writer.close() + + return self.train_model_url + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = self._preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: save unstructured data model + if not model_path: + LOGGER.warning(f"Not specify model path.") + return self.train_model_url + + return FileOps.upload(self.train_model_url, model_path) + + def _preprocess(self, image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/_init_.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/_init_.py new file mode 100644 index 00000000..1272169d --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/_init_.py @@ -0,0 +1 @@ +from models.wide_resnet_embedding import * \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/utils.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/utils.py new file mode 100644 index 00000000..ddb8fdf6 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/utils.py @@ -0,0 +1,149 @@ +import os +import shutil +import time +import pprint +import torch +import numpy as np +import torch.nn as nn +# --- functional helper --- +def category_mean(data, label, label_max): + '''compute mean for each category''' + one_hot_label = one_hot(label, label_max) + class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15 + one_hot_label = one_hot_label / class_num + return torch.mm(data.view(1, -1), one_hot_label).squeeze(0) + +def category_mean1(data, label, label_max): + '''compute mean for each category + only return centers for given categories''' + labelset = torch.unique(label, sorted=True) + one_hot_label = one_hot(label, label_max) + class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15 + one_hot_label = one_hot_label / class_num + output = torch.mm(data.view(1, -1), one_hot_label).squeeze(0) + return output[labelset] + +def category_mean2(data, label, label_max): + '''compute mean for each category, based on a matrix''' + one_hot_label = one_hot(label, label_max) + data = torch.gather(data, 1, label.unsqueeze(1)) + class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15 + one_hot_label = one_hot_label / class_num + return torch.mm(data.view(1, -1), one_hot_label).squeeze(0) + +def category_mean3(data, label, label_max): + '''compute mean for each category, each row corresponds to an elements''' + one_hot_label = one_hot(label, label_max) + class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15 + one_hot_label = one_hot_label / class_num + return torch.mm(one_hot_label.t(), data) + +def category_mean4(data, label, label_max): + '''compute mean for each category, each row corresponds to an elements + only return centers for given categories''' + labelset = torch.unique(label, sorted=True) + one_hot_label = one_hot(label, label_max) + class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15 + one_hot_label = one_hot_label / class_num + pre_center = torch.mm(one_hot_label.t(), data) + return pre_center[labelset, :] + +def one_hot(indices, depth): + """ + Returns a one-hot tensor. + This is a PyTorch equivalent of Tensorflow's tf.one_hot. + + Parameters: + indices: a (n_batch, m) Tensor or (m) Tensor. + depth: a scalar. Represents the depth of the one hot dimension. + Returns: a (n_batch, m, depth) Tensor or (m, depth) Tensor. + """ + + encoded_indicies = torch.zeros(indices.size() + torch.Size([depth])) + if indices.is_cuda: + encoded_indicies = encoded_indicies.cuda() + index = indices.view(indices.size()+torch.Size([1])) + encoded_indicies = encoded_indicies.scatter_(1,index,1) + + return encoded_indicies + +def set_gpu(x): + os.environ['CUDA_VISIBLE_DEVICES'] = x + print('using gpu:', x) + + +def ensure_path(path, remove=True): + if os.path.exists(path): + if remove: + if input('{} exists, remove? ([y]/n)'.format(path)) != 'n': + shutil.rmtree(path) + os.mkdir(path) + else: + os.mkdir(path) + +class Averager(): + + def __init__(self): + self.n = 0 + self.v = 0 + + def add(self, x): + self.v = (self.v * self.n + x) / (self.n + 1) + self.n += 1 + + def item(self): + return self.v + + +def count_acc(logits, label): + pred = torch.argmax(logits, dim=1) + if torch.cuda.is_available(): + return (pred == label).type(torch.cuda.FloatTensor).mean().item() + else: + return (pred == label).type(torch.FloatTensor).mean().item() + +def euclidean_metric(a, b): + n = a.shape[0] + m = b.shape[0] + a = a.unsqueeze(1).expand(n, m, -1) + b = b.unsqueeze(0).expand(n, m, -1) + logits = -((a - b)**2).sum(dim=2) + return logits + +class Timer(): + + def __init__(self): + self.o = time.time() + + def measure(self, p=1): + x = (time.time() - self.o) / p + x = int(x) + if x >= 3600: + return '{:.1f}h'.format(x / 3600) + if x >= 60: + return '{}m'.format(round(x / 60)) + return '{}s'.format(x) + +_utils_pp = pprint.PrettyPrinter() +def pprint(x): + _utils_pp.pprint(x) + +def compute_confidence_interval(data): + """ + Compute 95% confidence interval + :param data: An array of mean accuracy (or mAP) across a number of sampled episodes. + :return: the 95% confidence interval for this data. + """ + a = 1.0 * np.array(data) + m = np.mean(a) + std = np.std(a) + pm = 1.96 * (std / np.sqrt(len(a))) + return m, pm + + +class Identity(nn.Module): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return x diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/wide_resnet_embedding.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/wide_resnet_embedding.py new file mode 100644 index 00000000..10194611 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/wide_resnet_embedding.py @@ -0,0 +1,94 @@ +import torch +import torch.nn as nn +import torch.nn.init as init +import torch.nn.functional as F +from torch.autograd import Variable + +import sys +import numpy as np + +def conv3x3(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True) + +def conv_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + init.xavier_uniform_(m.weight, gain=np.sqrt(2)) + init.constant_(m.bias, 0) + elif classname.find('BatchNorm') != -1: + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + +class wide_basic(nn.Module): + def __init__(self, in_planes, planes, dropout_rate, stride=1): + super(wide_basic, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True) + self.dropout = nn.Dropout(p=dropout_rate) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True), + ) + + def forward(self, x): + out = self.dropout(self.conv1(F.relu(self.bn1(x)))) + out = self.conv2(F.relu(self.bn2(out))) + out += self.shortcut(x) + + return out + +class Wide_ResNet(nn.Module): + def __init__(self, depth, widen_factor, dropout_rate, num_classes): + super(Wide_ResNet, self).__init__() + self.in_planes = 16 + + assert ((depth-4)%6 ==0), 'Wide-resnet depth should be 6n+4' + n = (depth-4)/6 + k = widen_factor + + print('| Wide-Resnet %dx%d' %(depth, k)) + nStages = [16, 16*k, 32*k, 64*k] + + self.conv1 = conv3x3(3,nStages[0]) + self.layer1 = self._wide_layer(wide_basic, nStages[1], n, dropout_rate, stride=1) + self.layer2 = self._wide_layer(wide_basic, nStages[2], n, dropout_rate, stride=2) + self.layer3 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2) + self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9) + + #self.linear = nn.Linear(nStages[3], num_classes) + self.linear = nn.Sequential( + nn.Linear(nStages[3], 50), + nn.ReLU(), + nn.Linear(50, num_classes), + ) + + def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride): + strides = [stride] + [1]*(int(num_blocks)-1) + layers = [] + + for stride in strides: + layers.append(block(self.in_planes, planes, dropout_rate, stride)) + self.in_planes = planes + + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv1(x) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = F.relu(self.bn1(out)) + out = F.avg_pool2d(out, 8) + out = out.view(out.size(0), -1) + out = self.linear(out) + + return out + +if __name__ == '__main__': + net = Wide_ResNet(28, 10, 0.3, 10) + y = net.forward(Variable(torch.randn(5,3,32,32))) + print(y.size()) \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml new file mode 100644 index 00000000..1321883e --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -0,0 +1,69 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.8 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + splitting_method: "default" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/basemodel.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + - type: "unseen_sample_recognition" + # name of python module; string type; + name: "UnseenSampleRecognitionByScene" + # the url address of python module; string type; + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/unseen_sample_recognition_by_scene.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + - model_path: + values: + - "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/results/Epochofprose17.pth" \ No newline at end of file diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py new file mode 100644 index 00000000..dbdecc7c --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py @@ -0,0 +1,65 @@ +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('TaskAllocationByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + self.task_extractor = task_extractor + if self.default_origin: + return samples, [int(self.task_extractor.get( + self.default_origin))] * len(samples.x) + + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + sample_origins = [] + for _x in samples.x: + is_real = False + for city in cities: + if city in _x[0]: + is_real = True + sample_origins.append("real") + break + if not is_real: + sample_origins.append("sim") + + allocations = [int(self.task_extractor.get(sample_origin)) + for sample_origin in sample_origins] + + return samples, allocations diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py new file mode 100644 index 00000000..4c8dc264 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py @@ -0,0 +1,81 @@ +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + +__all__ = ('TaskDefinitionByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") +class TaskDefinitionByOrigin: + """ + Dividing datasets based on the their origins. + + Parameters + ---------- + origins： List[Metadata] + metadata is usually a class feature label with a finite values. + """ + + def __init__(self, **kwargs): + self.origins = kwargs.get("origins", ["real", "sim"]) + + def __call__(self, + samples: BaseDataSource, **kwargs) -> Tuple[List[Task], + Any, + BaseDataSource]: + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + tasks = [] + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + + task_index = dict(zip(self.origins, range(len(self.origins)))) + + real_df = BaseDataSource(data_type=d_type) + real_df.x, real_df.y = [], [] + sim_df = BaseDataSource(data_type=d_type) + sim_df.x, sim_df.y = [], [] + + for i in range(samples.num_examples()): + is_real = False + for city in cities: + if city in x_data[i][0]: + is_real = True + real_df.x.append(x_data[i]) + real_df.y.append(y_data[i]) + break + if not is_real: + sim_df.x.append(x_data[i]) + sim_df.y.append(y_data[i]) + + g_attr = "real_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=real_df, meta_attr="real") + tasks.append(task_obj) + + g_attr = "sim_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="sim") + tasks.append(task_obj) + + return tasks, task_index, samples diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/unseen_sample_recognition_by_scene.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/unseen_sample_recognition_by_scene.py new file mode 100644 index 00000000..01614427 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/unseen_sample_recognition_by_scene.py @@ -0,0 +1,61 @@ +# @inproceedings{zhou2021learning, +# author = {Zhou, Da-Wei and Ye, Han-Jia and Zhan, De-Chuan}, +# title = {Learning Placeholders for Open-Set Recognition}, +# booktitle = {CVPR}, +# pages = {4401-4410}, +# year = {2021} +# } +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + +__all__ = ('UnseenSampleRecognitionByScene',) + +@ClassFactory.register(ClassType.UTD, alias="UnseenSampleRecognitionByScene") +class UnseenSampleRecognitionByScene: + """ + Dividing the data set based on whether the sample is a known class of data. + + Parameters + ---------- + model_path: string + Path of the model + """ + def __init__(self, **kwargs): + self.model_path = kwargs.get("model_path") + + def __call__(self, + samples: BaseDataSource, **kwargs): + from torch.utils.data import DataLoader + import torch + import torchvision + from torchvision import transforms + from PIL import Image + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + data_transforms = transforms.Compose([ + transforms.Resize([32,32]), + transforms.ToTensor() + ]) + InferenceData=data_transforms(Image.open(x_data[0][0])) + device = 'cuda' if torch.cuda.is_available() else 'cpu' + pthfile = self.model_path + state_dict = torch.load(pthfile) + unseen_image = BaseDataSource(data_type=d_type) + unseen_image.x, unseen_image.y = [], [] + seen_image = BaseDataSource(data_type=d_type) + seen_image.x, seen_image.y = [], [] + InferenceData = InferenceData.to(device) + InferenceData = torch.unsqueeze(InferenceData, 0) + outputs = state_dict(InferenceData) + a, predicted = outputs.max(1) + predicted = predicted.cpu().numpy() + if predicted[0] == 1: + seen_image.x.append(x_data[0]) + if predicted[0] == 0: + unseen_image.x.append(x_data[0]) + + return seen_image, unseen_image diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/accuracy.py b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/accuracy.py new file mode 100644 index 00000000..7826d897 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/accuracy.py @@ -0,0 +1,55 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm import tqdm + +from sedna.common.class_factory import ClassType, ClassFactory + +from RFNet.dataloaders import make_data_loader +from RFNet.utils.metrics import Evaluator +from RFNet.utils.args import ValArgs + +__all__ = ('accuracy') + + +@ClassFactory.register(ClassType.GENERAL, alias="accuracy") +def accuracy(y_true, y_pred, **kwargs): + args = ValArgs() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/testenv.yaml b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/testenv.yaml new file mode 100644 index 00000000..c9d83a27 --- /dev/null +++ b/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/testenv.yaml @@ -0,0 +1,34 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/ianvs/dataset/curb-detection/train_data/index.txt" + # the url address of test dataset index; string type; + test_url: "/ianvs/dataset/curb-detection/test_data/index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 2 \ No newline at end of file From afcabfd03f7089a251295e38a979ecaaaef72539 Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 16:57:42 +0800 Subject: [PATCH 08/22] add a quick start for multi-task joint inference Signed-off-by: shifan__z --- .../bdd/lifelong_learning_bench/README.md | 69 +- .../yolo/task_allocation_by_origin.py | 2 - .../testalgorithms/yolo/task_remodeling.py | 1 - examples/resources/algorithms/common.py | 691 ++++++++++++++++++ requirements.txt | 6 +- 5 files changed, 748 insertions(+), 21 deletions(-) create mode 100644 examples/resources/algorithms/common.py diff --git a/examples/bdd/lifelong_learning_bench/README.md b/examples/bdd/lifelong_learning_bench/README.md index 4c633768..2dd19866 100644 --- a/examples/bdd/lifelong_learning_bench/README.md +++ b/examples/bdd/lifelong_learning_bench/README.md @@ -1,9 +1,6 @@ -# Quick Start +# Quick Start for multi-task inference -Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, -in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs -with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can -build and start your distributed synergy AI solution development within minutes. +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can build and start your distributed synergy AI solution development within minutes. Before using Ianvs, you might want to have the device ready: - One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary @@ -13,12 +10,12 @@ Before using Ianvs, you might want to have the device ready: - Internet connection for GitHub and pip, etc - Python 3.6+ installed + In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. ## Step 1. Ianvs Preparation -First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` -as: +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` as: ``` shell mkdir /ianvs @@ -48,17 +45,17 @@ python setup.py install ## Step 2. Dataset Preparation -Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does -not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. +Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. ``` shell cd /ianvs #One might use another path preferred mkdir dataset cd dataset -wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip -unzip dataset.zip +mkdir bdd ``` +The url of bdd is BDD: https://bdd-data.berkeley.edu/, we download the dataset and put it in bdd + The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. @@ -73,13 +70,52 @@ export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_le The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. -## Step 3. Ianvs Execution and Presentation +## Step 3. Model Preparation + +### Yolov5 model Preparation + +1. We install mmcls: https://mmclassification.readthedocs.io/zh_CN/latest/install.html +2. We download the code of yolov5: + +``` +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +3. We modify the yolo_hub_path in `basemodel.py` + +``` +yolo_hub_path= '/home/yourname/.cache/torch/hub/ultralytics_yolov5_master' +``` + +4. We replace the `model/commom.py ` under yolo_hub_path with `examples/resources/algorithms/common.py` + +### Selector Model Preparation + +First, we config the model to select which models will be chose + +- We download the checkpoint_file from [百度网盘](https://pan.baidu.com/s/1hCbjrSW7A0J8tgfc-s5R1g), 提取码:d1b5, which is the weight file for model selector +- The checkpoint_file need to be put under `bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/` + +Then, we config the models which could be selected to realize multi-joint inference + +- We download the models from [百度网盘](https://pan.baidu.com/s/1HE10JVbQgnam264f4m57Nw), 提取码：p21x +- The file called `yolo_model` need to be put under `examples/resources/` + +### Sedna preparation + +We replace the file in `yourpath/anaconda3/envs/ianvs/lib/python3.7/site-packages/sedna` with `examples/resources/sedna.zip` + + + +## Step 4. Ianvs **Execution and Presentation** We are now ready to run the ianvs for benchmarking. ``` shell cd /ianvs/project -ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml +ianvs -f examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml ``` Finally, the user can check the result of benchmarking on the console and also in the output path( @@ -89,10 +125,9 @@ can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guide The final output might look like this: -|rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | -|:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| -|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | - +| rank | algorithm | map | paradigm | basemodel | task_allocation | task_remodeling | inference_integrate | +| :--: | :---------------------------------------: | :--: | :--------------: | :-------: | :--------------------: | --------------- | :-----------------: | +| 1 | yolo_lifelong_learning_five_model_750data | 0.5 | lifelonglearning | BaseModel | TaskAllocationByOrigin | TaskRemodeling | InferenceIntegrate | This ends the quick start experiment. diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py index 8be8229c..7c1e4841 100644 --- a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_allocation_by_origin.py @@ -1,4 +1,3 @@ -from curses import raw from sedna.datasources import BaseDataSource from sedna.common.class_factory import ClassFactory, ClassType from mmcls.apis import init_model @@ -9,7 +8,6 @@ import torch import numpy as np import heapq -import yaml __all__ = ('TaskAllocationByOrigin',) config_file = 'examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py'# 神经网络配置文件 diff --git a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py index c2940db0..8e6128c8 100644 --- a/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py +++ b/examples/bdd/lifelong_learning_bench/testalgorithms/yolo/task_remodeling.py @@ -30,7 +30,6 @@ from sedna.datasources import BaseDataSource from sedna.common.class_factory import ClassFactory, ClassType -import yaml __all__ = ('TaskRemodeling',) diff --git a/examples/resources/algorithms/common.py b/examples/resources/algorithms/common.py new file mode 100644 index 00000000..d62f9875 --- /dev/null +++ b/examples/resources/algorithms/common.py @@ -0,0 +1,691 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Common modules +""" + +import json +import math +import platform +import warnings +from collections import OrderedDict, namedtuple +from copy import copy +from pathlib import Path + +import cv2 +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +import yaml +from PIL import Image +from torch.cuda import amp + +from utils.datasets import exif_transpose, letterbox +from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path, + make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh) +from utils.plots import Annotator, colors, save_one_box +from utils.torch_utils import copy_attr, time_sync + + +def autopad(k, p=None): # kernel, padding + # Pad to 'same' + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class Conv(nn.Module): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + return self.act(self.conv(x)) + + +class DWConv(Conv): + # Depth-wise convolution class + def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2).permute(2, 0, 1) + return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class BottleneckCSP(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.SiLU() + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) + + +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + +class C3SPP(C3): + # C3 module with SPP() + def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = SPP(c_, c_, k) + + +class C3Ghost(C3): + # C3 module with GhostBottleneck() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) + + +class SPP(nn.Module): + # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 + def __init__(self, c1, c2, k=(5, 9, 13)): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act) + # self.contract = Contract(gain=2) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) + # return self.conv(self.contract(x)) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat([y, self.cv2(y)], 1) + + +class GhostBottleneck(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False)) # pw-linear + self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), + Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) + + +class Concat(nn.Module): + # Concatenate a list of tensors along dimension + def __init__(self, dimension=1): + super().__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class DetectMultiBackend(nn.Module): + # YOLOv5 MultiBackend class for python inference on various backends + def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None): + # Usage: + # PyTorch: weights = *.pt + # TorchScript: *.torchscript + # CoreML: *.mlmodel + # OpenVINO: *.xml + # TensorFlow: *_saved_model + # TensorFlow: *.pb + # TensorFlow Lite: *.tflite + # TensorFlow Edge TPU: *_edgetpu.tflite + # ONNX Runtime: *.onnx + # OpenCV DNN: *.onnx with dnn=True + # TensorRT: *.engine + from models.experimental import attempt_download, attempt_load # scoped to avoid circular import + + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w) # get backend + stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults + w = attempt_download(w) # download if not local + if data: # data.yaml path (optional) + with open(data, errors='ignore') as f: + names = yaml.safe_load(f)['names'] # class names + + if pt: # PyTorch + model = attempt_load(weights if isinstance(weights, list) else w, map_location=device) + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, 'module') else model.names # get class names + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + elif jit: # TorchScript + LOGGER.info(f'Loading {w} for TorchScript inference...') + extra_files = {'config.txt': ''} # model metadata + model = torch.jit.load(w, _extra_files=extra_files) + if extra_files['config.txt']: + d = json.loads(extra_files['config.txt']) # extra_files dict + stride, names = int(d['stride']), d['names'] + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') + check_requirements(('opencv-python>=4.5.4',)) + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f'Loading {w} for ONNX Runtime inference...') + cuda = torch.cuda.is_available() + check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) + import onnxruntime + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] + session = onnxruntime.InferenceSession(w, providers=providers) + elif xml: # OpenVINO + LOGGER.info(f'Loading {w} for OpenVINO inference...') + check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/ + import openvino.inference_engine as ie + core = ie.IECore() + if not Path(w).is_file(): # if not *.xml + w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir + network = core.read_network(model=w, weights=Path(w).with_suffix('.bin')) # *.xml, *.bin paths + executable_network = core.load_network(network, device_name='CPU', num_requests=1) + elif engine: # TensorRT + LOGGER.info(f'Loading {w} for TensorRT inference...') + import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download + check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 + Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) + logger = trt.Logger(trt.Logger.INFO) + with open(w, 'rb') as f, trt.Runtime(logger) as runtime: + model = runtime.deserialize_cuda_engine(f.read()) + bindings = OrderedDict() + for index in range(model.num_bindings): + name = model.get_binding_name(index) + dtype = trt.nptype(model.get_binding_dtype(index)) + shape = tuple(model.get_binding_shape(index)) + data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device) + bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + context = model.create_execution_context() + batch_size = bindings['images'].shape[0] + elif coreml: # CoreML + LOGGER.info(f'Loading {w} for CoreML inference...') + import coremltools as ct + model = ct.models.MLModel(w) + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + if saved_model: # SavedModel + LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') + import tensorflow as tf + model = tf.keras.models.load_model(w) + elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') + import tensorflow as tf + + def wrap_frozen_graph(gd, inputs, outputs): + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped + return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), + tf.nest.map_structure(x.graph.as_graph_element, outputs)) + + graph_def = tf.Graph().as_graph_def() + graph_def.ParseFromString(open(w, 'rb').read()) + frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0") + elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, + if 'edgetpu' in w.lower(): # Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') + delegate = {'Linux': 'libedgetpu.so.1', + 'Darwin': 'libedgetpu.1.dylib', + 'Windows': 'edgetpu.dll'}[platform.system()] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # Lite + LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False, val=False): + # YOLOv5 MultiBackend inference + b, ch, h, w = im.shape # batch, channel, height, width + if self.pt or self.jit: # PyTorch + y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize) + return y if val else y[0] + elif self.dnn: # ONNX OpenCV DNN + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + elif self.onnx: # ONNX Runtime + im = im.cpu().numpy() # torch to numpy + y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0] + elif self.xml: # OpenVINO + im = im.cpu().numpy() # FP32 + desc = self.ie.TensorDesc(precision='FP32', dims=im.shape, layout='NCHW') # Tensor Description + request = self.executable_network.requests[0] # inference request + request.set_blob(blob_name='images', blob=self.ie.Blob(desc, im)) # name=next(iter(request.input_blobs)) + request.infer() + y = request.output_blobs['output'].buffer # name=next(iter(request.output_blobs)) + elif self.engine: # TensorRT + assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape) + self.binding_addrs['images'] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = self.bindings['output'].data + elif self.coreml: # CoreML + im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) + im = Image.fromarray((im[0] * 255).astype('uint8')) + # im = im.resize((192, 320), Image.ANTIALIAS) + y = self.model.predict({'image': im}) # coordinates are xywh normalized + if 'confidence' in y: + box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + else: + y = y[sorted(y)[-1]] # last output + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) + if self.saved_model: # SavedModel + y = self.model(im, training=False).numpy() + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)).numpy() + elif self.tflite: # Lite + input, output = self.input_details[0], self.output_details[0] + int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input['quantization'] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input['index'], im) + self.interpreter.invoke() + y = self.interpreter.get_tensor(output['index']) + if int8: + scale, zero_point = output['quantization'] + y = (y.astype(np.float32) - zero_point) * scale # re-scale + y[..., :4] *= [w, h, w, h] # xywh normalized to pixels + + y = torch.tensor(y) if isinstance(y, np.ndarray) else y + return (y, []) if val else y + + def warmup(self, imgsz=(1, 3, 640, 640), half=False): + # Warmup model by running inference once + if self.pt or self.jit or self.onnx or self.engine: # warmup types + if isinstance(self.device, torch.device) and self.device.type != 'cpu': # only warmup GPU models + im = torch.zeros(*imgsz).to(self.device).type(torch.half if half else torch.float) # input image + self.forward(im) # warmup + + @staticmethod + def model_type(p='path/to/model.pt'): + # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx + from export import export_formats + suffixes = list(export_formats().Suffix) + ['.xml'] # export suffixes + check_suffix(p, suffixes) # checks + p = Path(p).name # eliminate trailing separators + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes) + xml |= xml2 # *_openvino_model or *.xml + tflite &= not edgetpu # *.tflite + return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs + + +class AutoShape(nn.Module): + # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + agnostic = False # NMS class-agnostic + multi_label = False # NMS multiple labels per box + classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs + max_det = 1000 # maximum number of detections per image + amp = False # Automatic Mixed Precision (AMP) inference + + def __init__(self, model): + super().__init__() + LOGGER.info('Adding AutoShape... ') + copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes + self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance + self.pt = not self.dmb or model.pt # PyTorch model + self.model = model.eval() + + def _apply(self, fn): + # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers + self = super()._apply(fn) + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + @torch.no_grad() + def forward(self, imgs, size=640, augment=False, profile=False, conf = 0.25, iou = 0.45, with_nms=True, only_nms=False, model_forward_result=None): + # Inference from various sources. For height=640, width=1280, RGB images example inputs are: + # file: imgs = 'data/images/zidane.jpg' # str or PosixPath + # URI: = 'https://ultralytics.com/images/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + t = [time_sync()] + p = next(self.model.parameters()) if self.pt else torch.zeros(1) # for device and type + autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference + if isinstance(imgs, torch.Tensor): # torch + with amp.autocast(enabled=autocast): + return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference + + # Pre-process + n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(imgs): + f = f'image{i}' # filename + if isinstance(im, (str, Path)): # filename or uri + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im + im = np.asarray(exif_transpose(im)) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = (size / max(s)) # gain + shape1.append([y * g for y in s]) + imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update + shape1 = [make_divisible(x, self.stride) for x in np.stack(shape1, 0).max(0)] # inference shape + x = [letterbox(im, new_shape=shape1 if self.pt else size, auto=False)[0] for im in imgs] # pad + x = np.stack(x, 0) if n > 1 else x[0][None] # stack + x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 + t.append(time_sync()) + + self.conf = conf # set the conf from input + self.iou = iou # set the iou from input + + with amp.autocast(enabled=autocast): + + if only_nms is False: + # Inference + y = self.model(x, augment, profile) # forward + + if with_nms is True: + t.append(time_sync()) + + # Post-process + y = non_max_suppression(y if self.dmb else y[0], self.conf, iou_thres=self.iou, classes=self.classes, + agnostic=self.agnostic, multi_label=self.multi_label, max_det=self.max_det) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) + + t.append(time_sync()) + return Detections(imgs, y, files, t, self.names, x.shape) + else: + return y + else: + # Post-process + y = non_max_suppression(model_forward_result, self.conf, iou_thres=self.iou, classes=self.classes, + agnostic=self.agnostic, multi_label=self.multi_label, max_det=self.max_det) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) + + t.append(time_sync()) + return Detections(imgs, y, files, t, self.names, x.shape) + + +class Detections: + # YOLOv5 detections class for inference results + def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None): + super().__init__() + d = pred[0].device # device + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations + self.imgs = imgs # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.times = times # profiling times + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + # self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) + self.s = shape # inference BCHW shape + + def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): + crops = [] + for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): + s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string + if pred.shape[0]: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + if show or save or render or crop: + annotator = Annotator(im, example=str(self.names)) + for *box, conf, cls in reversed(pred): # xyxy, confidence, class + label = f'{self.names[int(cls)]} {conf:.2f}' + if crop: + file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None + crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label, + 'im': save_one_box(box, im, file=file, save=save)}) + else: # all others + annotator.box_label(box, label, color=colors(cls)) + im = annotator.im + else: + s += '(no detections)' + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np + if pprint: + LOGGER.info(s.rstrip(', ')) + if show: + im.show(self.files[i]) # show + if save: + f = self.files[i] + im.save(save_dir / f) # save + if i == self.n - 1: + LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") + if render: + self.imgs[i] = np.asarray(im) + if crop: + if save: + LOGGER.info(f'Saved results to {save_dir}\n') + return crops + + def print(self): + self.display(pprint=True) # print results + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % + self.t) + + def show(self): + self.display(show=True) # show results + + def save(self, save_dir='runs/detect/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir + self.display(save=True, save_dir=save_dir) # save results + + def crop(self, save=True, save_dir='runs/detect/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None + return self.display(crop=True, save=save, save_dir=save_dir) # crop results + + def render(self): + self.display(render=True) # render results + return self.imgs + + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + r = range(self.n) # iterable + x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] + # for d in x: + # for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: + # setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + def __len__(self): + return self.n + + +class Classify(nn.Module): + # Classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) + self.flat = nn.Flatten() + + def forward(self, x): + z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list + return self.flat(self.conv(z)) # flatten to x(b,c2) diff --git a/requirements.txt b/requirements.txt index 9f7cec56..21bfbc35 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,5 @@ -prettytable~=2.5.0 # BSD \ No newline at end of file +prettytable~=2.5.0 # BSD +torch +numpy +heapq +yaml \ No newline at end of file From 09809c40a8489306f8ff05fa0f2ffb5909663be8 Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 21:58:48 +0800 Subject: [PATCH 09/22] change the requirenments Signed-off-by: shifan__z --- requirements.txt | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/requirements.txt b/requirements.txt index 21bfbc35..9f7cec56 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1 @@ -prettytable~=2.5.0 # BSD -torch -numpy -heapq -yaml \ No newline at end of file +prettytable~=2.5.0 # BSD \ No newline at end of file From 523904eae7680d7b6a0b7664df0afa49ee0284be Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 22:43:08 +0800 Subject: [PATCH 10/22] modift the pylint Signed-off-by: shifan__z --- .../lifelong_learning/lifelong_learning.py | 36 +++++++++++-------- core/testenvmanager/testenv/testenv.py | 6 +--- .../testenv/testenv.yaml | 5 ++- 3 files changed, 24 insertions(+), 23 deletions(-) diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index b980d200..a5e59dbb 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -57,16 +57,13 @@ class LifelongLearning(ParadigmBase): def __init__(self, workspace, **kwargs): ParadigmBase.__init__(self, workspace, **kwargs) - self.incremental_learning_data_setting = kwargs.get("lifelong_learning_data_setting") + self.incremental_learning_data_setting = + kwargs.get("lifelong_learning_data_setting") self.initial_model = kwargs.get("initial_model_url") - self.mode = kwargs.get("mode") - self.incremental_rounds = kwargs.get("incremental_rounds", 1) self.model_eval_config = kwargs.get("model_eval") - self.cloud_task_index = '/tmp/cloud_task/index.pkl' self.edge_task_index = '/tmp/edge_task/index.pkl' - self.system_metric_info = {SystemMetricType.SAMPLES_TRANSFER_RATIO.value: []} def run(self): @@ -85,22 +82,29 @@ def run(self): rounds = self.incremental_rounds samples_transfer_ratio_info = self.system_metric_info.get( SystemMetricType.SAMPLES_TRANSFER_RATIO.value) - if self.mode != 'multi-inference': + mode = self.model_eval_config.get("model_metric").get("mode") + if mode != 'multi-inference': dataset_files = self._split_dataset(splitting_dataset_times=rounds) # pylint: disable=C0103 for r in range(1, rounds + 1): if r == 1: train_dataset_file, eval_dataset_file = dataset_files[r - 1] - self.cloud_task_index = self._train(self.cloud_task_index, train_dataset_file, r) - self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + self.cloud_task_index = self._train(self.cloud_task_index, + train_dataset_file, + r) + self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, + r) else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - inference_results, unseen_task_train_samples = self._inference(self.edge_task_index, - infer_dataset_file, - r) - samples_transfer_ratio_info.append((inference_results, unseen_task_train_samples.x)) + inference_results, unseen_task_train_samples = + self._inference(self.edge_task_index, + infer_dataset_file, + r) + samples_transfer_ratio_info.append((inference_results, + unseen_task_train_samples.x)) # If no unseen task samples in the this round, starting the next round if len(unseen_task_train_samples.x) <= 0: @@ -109,7 +113,9 @@ def run(self): self.cloud_task_index = self._train(self.cloud_task_index, unseen_task_train_samples, r) - self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, + r) test_res, unseen_task_train_samples = self._inference(self.edge_task_index, self.dataset.test_url, "test") @@ -126,7 +132,7 @@ def _inference(self, edge_task_index, data_index_file, rounds): os.makedirs(output_dir) unseen_task_saved_dir = os.path.join(self.workspace, - f"output/inference/unseen_task_samples/{rounds}") + f"output/inference/unseen_task_samples/{rounds}") if not is_local_dir(unseen_task_saved_dir): os.makedirs(unseen_task_saved_dir) @@ -141,7 +147,7 @@ def _inference(self, edge_task_index, data_index_file, rounds): inference_results = [] unseen_tasks = [] unseen_task_labels = [] - kwargs = {"mode": self.mode} + kwargs = {"mode": mode} for i, _ in enumerate(inference_dataset.x): data = BaseDataSource(data_type="test") data.x = inference_dataset.x[i:(i + 1)] diff --git a/core/testenvmanager/testenv/testenv.py b/core/testenvmanager/testenv/testenv.py index 70d8971d..6682a832 100644 --- a/core/testenvmanager/testenv/testenv.py +++ b/core/testenvmanager/testenv/testenv.py @@ -34,6 +34,7 @@ class TestEnv: def __init__(self, config): self.model_eval = { "model_metric": { + "mode": "", "name": "", "url": "", }, @@ -43,7 +44,6 @@ def __init__(self, config): self.metrics = [] self.incremental_rounds = 2 self.dataset = None - self.mode = '' self._parse_config(config) def _check_fields(self): @@ -53,10 +53,6 @@ def _check_fields(self): if not isinstance(self.incremental_rounds, int) or self.incremental_rounds < 2: raise ValueError(f"testenv incremental_rounds(value={self.incremental_rounds})" f" must be int type and not less than 2.") - - if not isinstance(self.mode, str): - raise ValueError(f"testenv mode(value={self.mode})" - f" must be str type.") def _parse_config(self, config): config_dict = config[str.lower(TestEnv.__name__)] diff --git a/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml b/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml index 4152c8e3..64d55c1c 100644 --- a/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml +++ b/examples/bdd/lifelong_learning_bench/testenv/testenv.yaml @@ -13,6 +13,7 @@ testenv: # metric used for model evaluation model_metric: # metric name; string type; + mode: "multi-inference" name: "accuracy" # the url address of python file url: "./examples/bdd/lifelong_learning_bench/testenv/accuracy.py" @@ -36,6 +37,4 @@ testenv: url: "./examples/bdd/lifelong_learning_bench/testenv/map.py" # incremental rounds setting; int type; default value is 2; - incremental_rounds: 2 - - mode: "multi-inference" \ No newline at end of file + incremental_rounds: 2 \ No newline at end of file From d9a3bed56b362ac7e2a0cb967525073dd30cc9e8 Mon Sep 17 00:00:00 2001 From: nailtu Date: Mon, 31 Oct 2022 22:46:36 +0800 Subject: [PATCH 11/22] combine GAN and self-taught learning Signed-off-by: nailtu --- README_ospp.md | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 README_ospp.md diff --git a/README_ospp.md b/README_ospp.md new file mode 100644 index 00000000..93a32638 --- /dev/null +++ b/README_ospp.md @@ -0,0 +1,3 @@ +# OSPP +I changed the sedna source code to implement my algorithm. +Please turn to https://github.com/kubeedge/sedna/pull/378 and https://github.com/nailtu30/sedna/blob/ospp-final/README_ospp.md for more information. \ No newline at end of file From 0b505537425f0a7d97fbd019d2d7c0605dbb9c36 Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 23:13:25 +0800 Subject: [PATCH 12/22] modift the pylint Signed-off-by: shifan__z --- .../paradigm/lifelong_learning/lifelong_learning.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index a5e59dbb..2210883c 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -57,8 +57,7 @@ class LifelongLearning(ParadigmBase): def __init__(self, workspace, **kwargs): ParadigmBase.__init__(self, workspace, **kwargs) - self.incremental_learning_data_setting = - kwargs.get("lifelong_learning_data_setting") + self.incremental_learning_data_setting = kwargs.get("lifelong_learning_data_setting") self.initial_model = kwargs.get("initial_model_url") self.incremental_rounds = kwargs.get("incremental_rounds", 1) self.model_eval_config = kwargs.get("model_eval") @@ -99,8 +98,8 @@ def run(self): else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - inference_results, unseen_task_train_samples = - self._inference(self.edge_task_index, + inference_results, unseen_task_train_samples = self._inference( + self.edge_task_index, infer_dataset_file, r) samples_transfer_ratio_info.append((inference_results, From 2fbeb03eb09478272e5b80f86c755cf487507b55 Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 23:21:55 +0800 Subject: [PATCH 13/22] modift the pylint Signed-off-by: shifan__z --- .../lifelong_learning/lifelong_learning.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index 2210883c..97c11490 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -88,13 +88,12 @@ def run(self): for r in range(1, rounds + 1): if r == 1: train_dataset_file, eval_dataset_file = dataset_files[r - 1] - self.cloud_task_index = self._train(self.cloud_task_index, - train_dataset_file, + self.cloud_task_index = self._train(self.cloud_task_index, + train_dataset_file, r) - self.edge_task_index = self._eval(self.cloud_task_index, - eval_dataset_file, + self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, r) - else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] @@ -102,7 +101,7 @@ def run(self): self.edge_task_index, infer_dataset_file, r) - samples_transfer_ratio_info.append((inference_results, + samples_transfer_ratio_info.append((inference_results, unseen_task_train_samples.x)) # If no unseen task samples in the this round, starting the next round @@ -112,7 +111,7 @@ def run(self): self.cloud_task_index = self._train(self.cloud_task_index, unseen_task_train_samples, r) - self.edge_task_index = self._eval(self.cloud_task_index, + self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) test_res, unseen_task_train_samples = self._inference(self.edge_task_index, @@ -146,6 +145,7 @@ def _inference(self, edge_task_index, data_index_file, rounds): inference_results = [] unseen_tasks = [] unseen_task_labels = [] + mode = self.model_eval_config.get("model_metric").get("mode") kwargs = {"mode": mode} for i, _ in enumerate(inference_dataset.x): data = BaseDataSource(data_type="test") From 1a5a85727733cdba32318f77d90ee68cb7c4d6b8 Mon Sep 17 00:00:00 2001 From: shifan__z Date: Mon, 31 Oct 2022 23:25:12 +0800 Subject: [PATCH 14/22] modift the pylint Signed-off-by: shifan__z --- .../algorithm/paradigm/lifelong_learning/lifelong_learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index 97c11490..bb290b2b 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -112,7 +112,7 @@ def run(self): unseen_task_train_samples, r) self.edge_task_index = self._eval(self.cloud_task_index, - eval_dataset_file, + eval_dataset_file, r) test_res, unseen_task_train_samples = self._inference(self.edge_task_index, self.dataset.test_url, From 682ef32502352dc1bea2a7511c1dc503a969b6d7 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Thu, 16 Mar 2023 10:55:24 +0800 Subject: [PATCH 15/22] fix the bug of issue#57 Signed-off-by: hsj576 --- .../paradigm/lifelong_learning/lifelong_learning.py | 5 ++++- examples/curb-detection/lifelong_learning_bench/README.md | 8 ++++---- .../testalgorithms/rfnet/RFNet/train.py | 2 +- .../testalgorithms/rfnet/RFNet/utils/metrics.py | 4 ++++ .../testalgorithms/rfnet/rfnet_algorithm.yaml | 3 +++ 5 files changed, 16 insertions(+), 6 deletions(-) diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index bb290b2b..f7ad9117 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -146,7 +146,10 @@ def _inference(self, edge_task_index, data_index_file, rounds): unseen_tasks = [] unseen_task_labels = [] mode = self.model_eval_config.get("model_metric").get("mode") - kwargs = {"mode": mode} + if mode == None: + kwargs = {} # fix the bug of "TypeError: call() got an unexpected keyword argument 'mode'" + else: + kwargs = {"mode": mode} for i, _ in enumerate(inference_dataset.x): data = BaseDataSource(data_type="test") data.x = inference_dataset.x[i:(i + 1)] diff --git a/examples/curb-detection/lifelong_learning_bench/README.md b/examples/curb-detection/lifelong_learning_bench/README.md index 4c633768..46366074 100644 --- a/examples/curb-detection/lifelong_learning_bench/README.md +++ b/examples/curb-detection/lifelong_learning_bench/README.md @@ -13,7 +13,7 @@ Before using Ianvs, you might want to have the device ready: - Internet connection for GitHub and pip, etc - Python 3.6+ installed -In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. +In this example, we are using the Linux platform with Python 3.8.5. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. ## Step 1. Ianvs Preparation @@ -26,7 +26,7 @@ cd /ianvs #One might use another path preferred mkdir project cd project -git clone https://github.com/kubeedge/ianvs.git +git clone -b feature-lifelong-n https://github.com/kubeedge/ianvs.git ``` @@ -67,7 +67,7 @@ we have done that for you and the interested readers can refer to [testenv.yaml] Related algorithm is also ready in this quick start. ``` shell -export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet +export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet ``` The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick @@ -78,7 +78,7 @@ start, we have done that for you and the interested readers can refer to [algori We are now ready to run the ianvs for benchmarking. ``` shell -cd /ianvs/project +cd /ianvs/project/ianvs ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml ``` diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py index d7d01e7f..6075bbb8 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py @@ -125,7 +125,7 @@ def training(self, epoch): tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) # Show 10 * 3 inference results each epoch - if i % (num_img_tr // 10) == 0: + if i % (num_img_tr // 10 + 1) == 0: global_step = i + num_img_tr * epoch if self.args.depth: self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py index 551bc0d1..eb820691 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py @@ -134,7 +134,11 @@ def _generate_matrix(self, gt_image, pre_image): return confusion_matrix def add_batch(self, gt_image, pre_image): + gt_image = np.array(gt_image) + pre_image = np.array(pre_image) print(gt_image.shape, pre_image.shape) + if gt_image.shape != pre_image.shape: + pre_image = pre_image[0] assert gt_image.shape == pre_image.shape self.confusion_matrix += self._generate_matrix(gt_image, pre_image) diff --git a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml index efd0fbfb..45f3eded 100644 --- a/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml +++ b/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -31,6 +31,9 @@ algorithm: - learning_rate: values: - 0.0001 + - epochs: + values: + - 1 # 2> "task_definition": define lifelong task ; optional module; - type: "task_definition" # name of python module; string type; From 2082e2dfe1f0de0e137a323cf26cadea7c3ead8b Mon Sep 17 00:00:00 2001 From: hsj576 Date: Thu, 16 Mar 2023 11:14:36 +0800 Subject: [PATCH 16/22] initial commit: add example for semantic_segmentation Signed-off-by: hsj576 add example for semantic segmentation Signed-off-by: hsj576 add example for semantic segmentation Signed-off-by: hsj576 fix some bug Signed-off-by: hsj576 fix the bug of memory leakage Signed-off-by: hsj576 fix the bug of inference Signed-off-by: hsj576 fix some bug Signed-off-by: hsj576 fix the bug of test accuracy Signed-off-by: hsj576 edit sedna package for lifelong learning Signed-off-by: hsj576 edit sedna package Signed-off-by: hsj576 implement dataset split method for lifelong learning Signed-off-by: hsj576 add test examples Signed-off-by: hsj576 --- .../lifelong_learning/lifelong_learning.py | 27 +- core/testcasecontroller/metrics/metrics.py | 2 +- core/testcasecontroller/testcase/testcase.py | 1 + core/testenvmanager/dataset/dataset.py | 77 +- examples/resources/sedna.zip | Bin 837831 -> 864900 bytes .../sedna-0.4.5-py3-none-any.zip} | Bin .../sedna-0.4.5.dist-info/METADATA | 274 + .../sedna-0.4.5.dist-info/RECORD | 120 + .../sedna-0.4.5.dist-info/WHEEL | 5 + .../sedna-0.4.5.dist-info/top_level.txt | 1 + .../third_party/sedna-0.4.1-py3-none-any.whl | Bin 0 -> 158812 bytes .../lifelong_learning_bench/README.md | 103 + .../benchmarkingjob.yaml | 74 + .../test-index-sort.txt | 500 + .../lifelong_learning_bench/test-index.txt | 1330 ++ .../testalgorithms/rfnet/RFNet/accuracy.py | 38 + .../testalgorithms/rfnet/RFNet/basemodel.py | 315 + .../rfnet/RFNet/dataloaders/__init__.py | 119 + .../RFNet/dataloaders/custom_transforms.py | 240 + .../dataloaders/custom_transforms_rgb.py | 230 + .../RFNet/dataloaders/datasets/__init__.py | 0 .../dataloaders/datasets/citylostfound.py | 276 + .../RFNet/dataloaders/datasets/cityrand.py | 151 + .../RFNet/dataloaders/datasets/cityscapes.py | 162 + .../rfnet/RFNet/dataloaders/datasets/e1.py | 151 + .../RFNet/dataloaders/datasets/mapillary.py | 152 + .../RFNet/dataloaders/datasets/target.py | 152 + .../rfnet/RFNet/dataloaders/datasets/temp.txt | 38 + .../rfnet/RFNet/dataloaders/datasets/xrlab.py | 152 + .../rfnet/RFNet/dataloaders/utils.py | 244 + .../testalgorithms/rfnet/RFNet/eval.py | 248 + .../rfnet/RFNet/models/replicate.py | 88 + .../rfnet/RFNet/models/resnet/__init__.py | 0 .../resnet_single_scale_single_attention.py | 391 + ...et_single_scale_single_attention_unseen.py | 396 + .../rfnet/RFNet/models/rfnet.py | 27 + .../rfnet/RFNet/models/rfnet_for_unseen.py | 33 + .../testalgorithms/rfnet/RFNet/models/util.py | 99 + .../testalgorithms/rfnet/RFNet/mypath.py | 20 + .../testalgorithms/rfnet/RFNet/predict.py | 98 + .../testalgorithms/rfnet/RFNet/run_server.py | 252 + .../rfnet/RFNet/sedna_evaluate.py | 50 + .../rfnet/RFNet/sedna_predict.py | 132 + .../testalgorithms/rfnet/RFNet/sedna_train.py | 78 + .../testalgorithms/rfnet/RFNet/test.py | 52 + .../testalgorithms/rfnet/RFNet/train.py | 211 + .../rfnet/RFNet/utils/__init__.py | 0 .../testalgorithms/rfnet/RFNet/utils/args.py | 52 + .../rfnet/RFNet/utils/calculate_weights.py | 29 + .../rfnet/RFNet/utils/iouEval.py | 142 + .../testalgorithms/rfnet/RFNet/utils/loss.py | 64 + .../rfnet/RFNet/utils/lr_scheduler.py | 70 + .../rfnet/RFNet/utils/metrics.py | 151 + .../testalgorithms/rfnet/RFNet/utils/saver.py | 68 + .../rfnet/RFNet/utils/summaries.py | 39 + .../testalgorithms/rfnet/basemodel.py | 138 + .../testalgorithms/rfnet/rfnet_algorithm.yaml | 63 + .../rfnet/task_allocation_by_origin.py | 65 + .../rfnet/task_definition_by_origin.py | 81 + .../testenv/accuracy.py | 59 + .../testenv/testenv-full.yaml | 36 + .../testenv/testenv-small.yaml | 36 + .../testenv/testenv.yaml | 36 + .../train-index-sort.txt | 11976 ++++++++++++++++ .../lifelong_learning_bench/train-index.txt | 11976 ++++++++++++++++ 65 files changed, 32182 insertions(+), 8 deletions(-) rename examples/resources/{third_party/sedna-0.4.5-py3-none-any.whl => third_party-bk/sedna-0.4.5-py3-none-any.zip} (100%) create mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/METADATA create mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD create mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/WHEEL create mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/top_level.txt create mode 100644 examples/resources/third_party/sedna-0.4.1-py3-none-any.whl create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/README.md create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/test-index.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/train-index-sort.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/train-index.txt diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index f7ad9117..e2b5820c 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -96,7 +96,7 @@ def run(self): r) else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - + ''' inference_results, unseen_task_train_samples = self._inference( self.edge_task_index, infer_dataset_file, @@ -111,19 +111,36 @@ def run(self): self.cloud_task_index = self._train(self.cloud_task_index, unseen_task_train_samples, r) + ''' + self.cloud_task_index = self._train(self.cloud_task_index, + infer_dataset_file, + r) self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) + ''' test_res, unseen_task_train_samples = self._inference(self.edge_task_index, self.dataset.test_url, "test") samples_transfer_ratio_info.append((test_res, unseen_task_train_samples.x)) - + ''' + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + inference_dataset = self.dataset.load_data(self.dataset.test_url, "eval", + feature_process=_data_feature_process) + kwargs = {} + model_eval_info = self.model_eval_config + model_metric = model_eval_info.get("model_metric") + _, metric_func = get_metric_func(model_metric) + #_ = job.evaluate(inference_dataset, metrics=metric_func) + test_res = job.my_inference(inference_dataset, **kwargs) + del job + return test_res, self.system_metric_info def _inference(self, edge_task_index, data_index_file, rounds): # pylint:disable=duplicate-code + print("start inference") output_dir = os.path.join(self.workspace, f"output/inference/results/{rounds}") if not is_local_dir(output_dir): @@ -150,7 +167,9 @@ def _inference(self, edge_task_index, data_index_file, rounds): kwargs = {} # fix the bug of "TypeError: call() got an unexpected keyword argument 'mode'" else: kwargs = {"mode": mode} + print(len(inference_dataset.x)) for i, _ in enumerate(inference_dataset.x): + print(i) data = BaseDataSource(data_type="test") data.x = inference_dataset.x[i:(i + 1)] res, is_unseen_task, _ = job.inference(data, **kwargs) @@ -158,9 +177,11 @@ def _inference(self, edge_task_index, data_index_file, rounds): if is_unseen_task: unseen_tasks.append(inference_dataset.x[i]) unseen_task_labels.append(inference_dataset.y[i]) + ''' for infer_data in inference_dataset.x[i]: shutil.copy(infer_data, unseen_task_saved_dir) - + ''' + print("inference 168") del job unseen_task_train_samples = BaseDataSource(data_type="train") diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py index 8a8481a4..5b825c05 100644 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -45,7 +45,7 @@ def samples_transfer_ratio_func(system_metric_info: dict): for inference_data, transfer_data in info: inference_num += len(inference_data) transfer_num += len(transfer_data) - return round(float(transfer_num) / inference_num, 4) + return round(float(transfer_num) / (inference_num + 1), 4) def get_metric_func(metric_dict: dict): diff --git a/core/testcasecontroller/testcase/testcase.py b/core/testcasecontroller/testcase/testcase.py index 596f9017..93e9878f 100644 --- a/core/testcasecontroller/testcase/testcase.py +++ b/core/testcasecontroller/testcase/testcase.py @@ -111,6 +111,7 @@ def compute_metrics(self, paradigm_result, dataset, **kwargs): metric_res = {} system_metric_types = [e.value for e in SystemMetricType.__members__.values()] for metric_name, metric_func in metric_funcs.items(): + print(metric_name) if metric_name in system_metric_types: metric_res[metric_name] = metric_func(kwargs) else: diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py index 1d0e9b7b..2d945ee2 100644 --- a/core/testenvmanager/dataset/dataset.py +++ b/core/testenvmanager/dataset/dataset.py @@ -79,10 +79,25 @@ def _process_txt_index_file(cls, file_url): tmp_file = os.path.join(tempfile.mkdtemp(), "index.txt") with open(tmp_file, "w", encoding="utf-8") as file: for line in lines: - front, back = line.split(" ") - file.writelines( - f"{os.path.abspath(os.path.join(root, front))} " - f"{os.path.abspath(os.path.join(root, back))}") + #front, back = line.split(" ") + + words = line.split(" ") + if len(words) == 2: + front = words[0] + back = words[1] + file.writelines( + f"{os.path.abspath(os.path.join(root, front))} " + f"{os.path.abspath(os.path.join(root, back))}") + else: + front = words[0] + depth = words[1] + back = words[2] + if back[-1] != '\n': + back = back + '\n' + file.writelines( + f"{os.path.abspath(os.path.join(root, front))} " + f"{os.path.abspath(os.path.join(root, depth))} " + f"{os.path.abspath(os.path.join(root, back))}") new_file = tmp_file @@ -148,6 +163,11 @@ def split_dataset(self, dataset_url, dataset_format, ratio, method="default", data_types=dataset_types, output_dir=output_dir, times=times) + if method == "my_splitting": + return self._my_splitting(dataset_url, dataset_format, ratio, + data_types=dataset_types, + output_dir=output_dir, + times=times) raise ValueError(f"dataset splitting method({method}) is not supported," f"currently, method supports 'default'.") @@ -217,6 +237,54 @@ def _splitting_more_times(self, data_file, data_format, ratio, index += 1 return data_files + + def _my_splitting(self, data_file, data_format, ratio, + data_types=None, output_dir=None, times=1): + if not data_types: + data_types = ("train", "eval") + + if not output_dir: + output_dir = tempfile.mkdtemp() + + all_data = self._read_data_file(data_file, data_format) + + data_files = [] + + all_num = len(all_data) + index0 = 0 + for i in range(all_num): + if 'synthia_sim' in all_data[i]: + continue + else: + index0 = i + break + new_dataset = all_data[:index0] + new_num = len(new_dataset) + data_files.append(( + self._get_dataset_file(new_dataset[:int(new_num * ratio)], output_dir, + data_types[0], 1, data_format), + self._get_dataset_file(new_dataset[int(new_num * ratio):], output_dir, + data_types[1], 1, data_format))) + times = times - 1 + step = int((all_num-index0) / times) + index = 1 + while index <= times: + if index == times: + new_dataset = all_data[index0 + step * (index - 1):] + else: + new_dataset = all_data[index0 + step * (index - 1):index0 + step * index] + + new_num = len(new_dataset) + + data_files.append(( + self._get_dataset_file(new_dataset[:int(new_num * ratio)], output_dir, + data_types[0], index+1, data_format), + self._get_dataset_file(new_dataset[int(new_num * ratio):], output_dir, + data_types[1], index+1, data_format))) + + index += 1 + + return data_files @classmethod def load_data(cls, file: str, data_type: str, label=None, use_raw=False, feature_process=None): @@ -251,6 +319,7 @@ def load_data(cls, file: str, data_type: str, label=None, use_raw=False, feature if data_format == DatasetFormat.TXT.value: data = TxtDataParse(data_type=data_type, func=feature_process) + #print(file) data.parse(file, use_raw=use_raw) return data diff --git a/examples/resources/sedna.zip b/examples/resources/sedna.zip index 675b075e1e907b056f2b66d95673627aace3af31..23d3e384ffe52a162a855a8274573afd9a5ef02c 100644 GIT binary patch delta 389998 zcmZ6x18^rnxA2{8Y}>YN+qP{R|JZgmb~d)1Y;4=Mjd!1W>wZeL{SC=6cy-y4tEdr|GoL&68zuA#mv;+DE%%U948T53<3Io9&8yJsKrn@ z!Z0P;lLG7+)Yg$tpHgR|Mc!qkk=)q&8+*4Dwq$kmL{?mxBvTAJCqK>uHV zkU;;Db1_RSHuX=c4=@lA(*O0xKyP8?%3$JPZ*FA~8z*Ny$cPxavP>0|P8Hh$|47VM z5>uT>mcVK*4&MeSyJ;gPjiznz?do;Nj=bA&jiHgM8tSB&R;(RG{_~jk6@KO`n=w{U zF^==w{uh5pIB5kkT>^5^jyeR|5ju{R4%$Z`Cw)DvccYUs$709$-*qzep$FMD=?MJs zk6JWWut%Eruc{V*lbm4pZC3+ZK}TbSbimy4-xe<)=7S+&MOHr3geYI!mY8RqQCBQ4 zuE#-LnH?t&=|>i>7gSE$1(Lh;SjC!q<_kL=s~@2M9z91i1c6ZU959i%+(bQG03$4D zUoKUT%*e0FejrGkJLJ{GtFWs;`lmDX#w&Fp%NJ8GUKmx)^5n?i^uowv{7bRcFz3$$ z7rc4i?*9mePERZXCr#G=1rH3JUI&1|N)GyE1&o$_`HKaxB|pdr8+!AAN?u8hSd7vt zDR~RuCAi!!*Yru{z6QR(<)l*`tn=~UA>Yuxapkw3sDjfR_yX$LQV$B6{(w|o*~91< 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; extra == 'fl' +Provides-Extra: il +Provides-Extra: ji +Provides-Extra: ll +Requires-Dist: scikit-learn (~=0.24.1) ; extra == 'll' +Provides-Extra: me + + +# Sedna Python SDK + +The Sedna Python Software Development Kit (SDK) aims to provide developers with a convenient yet flexible tool to write the Sedna applications. + +This document introduces how to obtain and call Sedna Python SDK. + + +## Introduction + +Expose the Edge AI features to applications, i.e. training or inference programs. + +## Requirements and Installation +The build process is tested with Python 3.6, Ubuntu 18.04.5 LTS + +```bash +# Clone the repo +git clone --recursive https://github.com/kubeedge/sedna.git +cd sedna/lib + +# Build the pip package +python setup.py bdist_wheel + +# Install the pip package +pip install dist/sedna*.whl + +``` + +Install via Setuptools + +```bash +# Install dependence +pip install -r requirements.txt + +# Install sedna +python setup.py install --user +``` + +## Use Python SDK + +0. (optional) Check `Sedna` version + ```bash + $ python -c "import sedna; print(sedna.__version__)" + ``` + +1. Import the required modules as follows: + + ```python + from sedna.core.joint_inference import JointInference, BigModelService + from sedna.core.federated_learning import FederatedLearning + from sedna.core.incremental_learning import IncrementalLearning + from sedna.core.lifelong_learning import LifelongLearning + + ``` + +2. Define an `Estimator`: + + ```python + + import os + + # Keras + import keras + from keras.layers import Dense, MaxPooling2D, Conv2D, Flatten, Dropout + from keras.models import Sequential + + os.environ['BACKEND_TYPE'] = 'KERAS' + + def KerasEstimator(): + model = Sequential() + model.add(Conv2D(64, kernel_size=(3, 3), + activation="relu", strides=(2, 2), + input_shape=(128, 128, 3))) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Conv2D(32, kernel_size=(3, 3), activation="relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Flatten()) + model.add(Dropout(0.25)) + model.add(Dense(64, activation="relu")) + model.add(Dense(32, activation="relu")) + model.add(Dropout(0.5)) + model.add(Dense(2, activation="softmax")) + + model.compile(loss="categorical_crossentropy", + optimizer="adam", + metrics=["accuracy"]) + loss = keras.losses.CategoricalCrossentropy(from_logits=True) + metrics = [keras.metrics.categorical_accuracy] + optimizer = keras.optimizers.Adam(learning_rate=0.1) + model.compile(loss=loss, optimizer=optimizer, metrics=metrics) + return model + ``` + + ```python + # XGBOOST + + import os + import xgboost + + os.environ['BACKEND_TYPE'] = 'SKLEARN' + + XGBEstimator = xgboost.XGBClassifier( + learning_rate=0.1, + n_estimators=600, + max_depth=2, + min_child_weight=1, + gamma=0, + subsample=0.8, + colsample_bytree=0.8, + objective="multi:softmax", + num_class=3, + nthread=4, + seed=27 + ) + + ``` + + ```python + + # Customize + + class Estimator: + + def __init__(self, **kwargs): + ... + + def load(self, model_url=""): + ... + + def save(self, model_path=None): + ... + + def predict(self, data, **kwargs): + ... + + def evaluate(self, valid_data, **kwargs): + ... + + def train(self, train_data, valid_data=None, **kwargs): + ... + + ``` + > **Notes**: Estimator is a high-level API that greatly simplifies machine learning programming. Estimators encapsulate training, evaluation, prediction, and exporting for your model. + + +3. Initialize a Incremental Learning Job: + + ```python + + # get hard exmaple mining algorithm from config + hard_example_mining = IncrementalLearning.get_hem_algorithm_from_config( + threshold_img=0.9 + ) + + # create Incremental Learning infernece instance + il_job = IncrementalLearning( + estimator=Estimator, + hard_example_mining=hard_example_mining + ) + + ``` + + where: + + - `IncrementalLearning` is the Cloud-edge job you want to access. + - `Estimator` is the base model for your ML job. + - `hard_example_mining` is the parameters of incremental learning job. + + Inference + --------- + + > **Note:** The `job parameters` of each feature are different. + +4. Running Job - training / inference / evaluation. + + ```python + results, final_res, is_hard_example = il_job.inference( + img_rgb, + post_process=deal_infer_rsl, + input_shape=input_shape + ) + + ``` + + where: + + - `img_rgb` is the sample used to inference + - `deal_infer_rsl` is a function used to process result after model predict + - `input_shape` is the parameters of `Estimator` in inference + - `results` is the result predicted by model + - `final_res` is the result after process by `deal_infer_rsl` + - `is_hard_example` tells if the sample is hard sample or not + +## Customize algorithm + +Sedna provides a class called `class_factory.py` in `common` package, in which only a few lines of changes are required to become a module of sedna. + +Two classes are defined in `class_factory.py`, namely `ClassType` and `ClassFactory`. + +`ClassFactory` can register the modules you want to reuse through decorators. For example, in the following code example, you have customized an **hard_example_mining algorithm**, you only need to add a line of `ClassFactory.register(ClassType.HEM)` to complete the registration. + +```python + +@ClassFactory.register(ClassType.HEM, alias="Threshold") +class ThresholdFilter(BaseFilter, abc.ABC): + def __init__(self, threshold=0.5, **kwargs): + self.threshold = float(threshold) + + def __call__(self, infer_result=None): + # if invalid input, return False + if not (infer_result + and all(map(lambda x: len(x) > 4, infer_result))): + return False + + image_score = 0 + + for bbox in infer_result: + image_score += bbox[4] + + average_score = image_score / (len(infer_result) or 1) + return average_score < self.threshold +``` + +After registration, you only need to change the name of the hem and parameters in the yaml file, and then the corresponding class will be automatically called according to the name. + +```yaml +deploySpec: + hardExampleMining: + name: "Threshold" + parameters: + - key: "threshold" + value: "0.9" +``` + + diff --git a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD b/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD new file mode 100644 index 00000000..cc2345ae --- /dev/null +++ b/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD @@ -0,0 +1,120 @@ +sedna/README.md,sha256=RV3cDgUX94RS0Szr_U2Kg4bTpU0oQsGsLAAJjbCp73g,6781 +sedna/VERSION,sha256=IacfbrT6zX2RguVpt8lA4z-AIbXIOsk3lnRlhxAOFKs,7 +sedna/__init__.py,sha256=d5xfiCchgxh3dnNGiWIOmAEoOFj95972kWrsUYhdXgY,664 +sedna/__version__.py,sha256=Q2qTzZw791xAanAtj1z64AHzQwnrI5KduOvS6JN_Tkw,657 +sedna/algorithms/__init__.py,sha256=LHALMySPKI8OMmk8wSc7lMd9XlAB1QDzRnbIJF1QUrE,896 +sedna/algorithms/aggregation/__init__.py,sha256=HF83IxUpNlQR7jpoQyr5PUpCR144Dtu7tDRs_ql_ooM,690 +sedna/algorithms/aggregation/aggregation.py,sha256=qgtzP0Ww1efHyWD47ZbH768dCUlZtJON9pkzEL82ilU,4052 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Welcome to the RestNet API!

+

To use this service, send a POST HTTP request to {this-url}/predict

+

The JSON payload has the following format: {"image": "BASE64_STRING_OF_IMAGE", + "depth": "BASE64_STRING_OF_DEPTH"}

+ """) + + async def predict(self, image: UploadFile = File(...), depth: Optional[UploadFile] = None) -> ResultResponse: + contents = await image.read() + recieve_img_time = time.time() + print("Recieve image from the robo:", recieve_img_time) + + image = Image.open(BytesIO(contents)).convert('RGB') + + img_dep = None + self.index_frame = self.index_frame + 1 + + if depth: + depth_contents = await depth.read() + depth = Image.open(BytesIO(depth_contents)).convert('RGB') + img_dep = cv2.resize(np.array(depth), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_dep = Image.fromarray(img_dep) + + img_rgb = cv2.resize(np.array(image), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + + sample = {'image': img_rgb, "depth": img_dep, "label": img_rgb} + results = sedna_predict.predict(self.job, data=sample, validator=self.detection_validator) + + predict_finish_time = time.time() + print(f"Prediction costs {predict_finish_time - recieve_img_time} seconds") + + post_process = True + if results["result"]["box"] is None: + results["result"]["curr"] = None + results["result"]["future"] = None + elif post_process: + curr, future = get_curb(results["result"]["box"]) + results["result"]["curr"] = curr + results["result"]["future"] = future + results["result"]["box"] = None + print("Post process cost at worker:", (time.time()-predict_finish_time)) + else: + results["result"]["curr"] = None + results["result"]["future"] = None + + print("Result transmit to robo time:", time.time()) + return results + +def parse_result(label, count): + label_map = ['road', 'sidewalk', ] + count_d = dict(zip(label, count)) + curb_count = count_d.get(19, 0) + if curb_count / np.sum(count) > 0.3: + return "curb" + r = sorted(label, key=count_d.get, reverse=True)[0] + try: + c = label_map[r] + except: + c = "other" + + return c + +def get_curb(results): + results = np.array(results[0]) + input_height, input_width = results.shape + + closest = np.array([ + [0, int(input_height)], + [int(input_width), + int(input_height)], + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + ]) + + future = np.array([ + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + [int(.765 * input_width + .5), + int(.66 * input_height + .5)], + [int(.235 * input_width + .5), + int(.66 * input_height + .5)] + ]) + + mask = np.zeros((input_height, input_width), dtype=np.uint8) + mask = cv2.fillPoly(mask, [closest], 1) + mask = cv2.fillPoly(mask, [future], 2) + d1, c1 = np.unique(results[mask == 1], return_counts=True) + d2, c2 = np.unique(results[mask == 2], return_counts=True) + c = parse_result(d1, c1) + f = parse_result(d2, c2) + + return c, f + +if __name__ == '__main__': + web_app = InferenceServer("lifelong-learning-robo", host=get_host_ip()) + web_app.start() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py new file mode 100644 index 00000000..56633347 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py @@ -0,0 +1,50 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +# os.environ["test_dataset_url"] = "./data_txt/sedna_data.txt" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" +# os.environ["operator"] = "<" +# os.environ["model_threshold"] = "0" + +from sedna.core.lifelong_learning import LifelongLearning +from sedna.datasources import IndexDataParse +from sedna.common.config import Context + +from accuracy import accuracy +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def eval(): + estimator = Model() + eval_dataset_url = Context.get_parameters("test_dataset_url") + eval_data = IndexDataParse(data_type="eval", func=_load_txt_dataset) + eval_data.parse(eval_dataset_url, use_raw=False) + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.evaluate(eval_data, metrics=accuracy) + + +if __name__ == '__main__': + print(eval()) diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py new file mode 100644 index 00000000..b32c01d2 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py @@ -0,0 +1,132 @@ +import os + +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["UNSEEN_SAVE_URL"] = "s3://kubeedge/sedna-robo/unseen_samples/" +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt" +os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +os.environ["ORIGINAL_DATASET_URL"] = "/tmp" + +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +from io import BytesIO +from torchvision.transforms import ToPILImage +from torchvision import transforms +from torch.utils.data import DataLoader + +from sedna.datasources import IndexDataParse +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from eval import Validator +from accuracy import accuracy +from basemodel import preprocess, val_args, Model +from dataloaders.utils import Colorize +from dataloaders import custom_transforms as tr +from dataloaders.datasets.cityscapes import CityscapesSegmentation + +def _load_txt_dataset(dataset_url): + # use original dataset url, + # see https://github.com/kubeedge/sedna/issues/35 + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def fetch_data(): + test_dataset_url = Context.get_parameters("test_dataset_url") + test_data = IndexDataParse(data_type="test", func=_load_txt_dataset) + test_data.parse(test_dataset_url, use_raw=False) + return test_data + +def pre_data_process(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def post_process(res, is_unseen_task): + if is_unseen_task: + res, base64_string = None, None + else: + res = res[0].tolist() + + type = 0 if not is_unseen_task else 1 + mesg = { + "msg": "", + "result": { + "type": type, + "box": res + }, + "code": 0 + } + return mesg + +def image_merge(raw_img, result): + raw_img = ToPILImage()(raw_img) + + pre_colors = Colorize()(torch.from_numpy(result)) + pre_color_image = ToPILImage()(pre_colors[0]) # pre_colors.dtype = float64 + + image = raw_img.resize(pre_color_image.size, Image.BILINEAR) + image = image.convert('RGBA') + label = pre_color_image.convert('RGBA') + image = Image.blend(image, label, 0.6) + with tempfile.NamedTemporaryFile(suffix='.png') as f: + image.save(f.name) + + with open(f.name, 'rb') as open_file: + byte_content = open_file.read() + base64_bytes = base64.b64encode(byte_content) + base64_string = base64_bytes.decode('utf-8') + return base64_string + +def init_ll_job(): + estimator = Model() + inference_integrate = { + "method": "BBoxInferenceIntegrate" + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + unseen_sample_recognition = { + "method": "SampleRegonitionByRFNet" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=inference_integrate, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=unseen_sample_recognition, + unseen_sample_re_recognition=None) + + args = val_args() + args.weight_path = "./models/detection_model.pth" + args.num_class = 31 + + return ll_job, Validator(args, unseen_detection=True) + +def predict(ll_job, data=None, validator=None): + if data: + data = pre_data_process(data) + else: + data = fetch_data() + data.x = preprocess(data.x) + + res, is_unseen_task, _ = ll_job.inference( + data, validator=validator, initial=False) + return post_process(res, is_unseen_task) + +if __name__ == '__main__': + ll_job, validator = init_ll_job() + print("Inference result:", predict(ll_job, validator=validator)) diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py new file mode 100644 index 00000000..1c99361a --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py @@ -0,0 +1,78 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +os.environ["OUTPUT_URL"] = "./cloud_kb/" +# os.environ['CLOUD_KB_INDEX'] = "./cloud_kb/index.pkl" +os.environ["TRAIN_DATASET_URL"] = "./data_txt/sedna_data.txt" +os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = "false" + +from sedna.common.file_ops import FileOps +from sedna.datasources import IndexDataParse +from sedna.common.config import Context, BaseConfig +from sedna.core.lifelong_learning import LifelongLearning + +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def train(estimator, train_data): + task_definition = { + "method": "TaskDefinitionByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=task_definition, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.train(train_data) + +def update(estimator, train_data): + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.update(train_data) + +def run(): + estimator = Model() + train_dataset_url = BaseConfig.train_dataset_url + train_data = IndexDataParse(data_type="train") + train_data.parse(train_dataset_url, use_raw=False) + + is_completed_initilization = str(Context.get_parameters("HAS_COMPLETED_INITIAL_TRAINING", "false")).lower() + if is_completed_initilization == "false": + train(estimator, train_data) + else: + update(estimator, train_data) + +if __name__ == '__main__': + run() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py new file mode 100644 index 00000000..fd9cd657 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py @@ -0,0 +1,52 @@ +import numpy as np +import seaborn as sns +import pandas as pd +import matplotlib.pyplot as plt + +CPA_results = np.load("./cpa_results.npy").T +ratios = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9] +ratio_counts = np.zeros((len(CPA_results), len(ratios)), dtype=float) + +for i in range(len(CPA_results)): + for j in range(len(ratios)): + result = CPA_results[i] + result = result[result <= ratios[j]] + + ratio_counts[i][j] = len(result) / 275 + +plt.figure(figsize=(45, 10)) +ratio_counts = pd.DataFrame(data=ratio_counts.T, index=ratios) +sns.heatmap(data=ratio_counts, annot=True, cmap="YlGnBu", annot_kws={'fontsize': 15}) +plt.xticks(fontsize=20) +plt.yticks(fontsize=25) +plt.xlabel("Test images", fontsize=25) +plt.ylabel("Ratio of PA ranges", fontsize=25) +plt.savefig("./figs/ratio_count.png") +plt.show() + + +# data = pd.DataFrame(CPA_results.T) +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[:15]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/door_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[15:31]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden1_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[31:]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden2_heatmap_pa.png") +# plt.show() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py new file mode 100644 index 00000000..8a76c4d1 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py @@ -0,0 +1,211 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import torch + +from mypath import Path +from dataloaders import make_data_loader +from models.rfnet import RFNet +from models.resnet.resnet_single_scale_single_attention import * +from utils.loss import SegmentationLosses +from models.replicate import patch_replication_callback +from utils.calculate_weights import calculate_weigths_labels +from utils.lr_scheduler import LR_Scheduler +from utils.saver import Saver +from utils.summaries import TensorboardSummary +from utils.metrics import Evaluator +from sedna.datasources import BaseDataSource + +class Trainer(object): + def __init__(self, args, train_data=None, valid_data=None): + self.args = args + # Define Saver + self.saver = Saver(args) + self.saver.save_experiment_config() + # Define Tensorboard Summary + self.summary = TensorboardSummary(self.saver.experiment_dir) + self.writer = self.summary.create_summary() + # denormalize for detph image + self.mean_depth = torch.as_tensor(0.12176, dtype=torch.float32, device='cpu') + self.std_depth = torch.as_tensor(0.09752, dtype=torch.float32, device='cpu') + self.nclass = args.num_class + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + self.train_loader, self.val_loader, self.test_loader, _ = make_data_loader(args, train_data=train_data, + valid_data=valid_data, **kwargs) + + # Define network + resnet = resnet18(pretrained=True, efficient=False, use_bn=True) + model = RFNet(resnet, num_classes=self.nclass, use_bn=True) + train_params = [{'params': model.random_init_params(), 'lr': args.lr}, + {'params': model.fine_tune_params(), 'lr': 0.1*args.lr, 'weight_decay':args.weight_decay}] + # Define Optimizer + optimizer = torch.optim.Adam(train_params, lr=args.lr, + weight_decay=args.weight_decay) + # Define Criterion + # whether to use class balanced weights + if args.use_balanced_weights: + classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy') + if os.path.isfile(classes_weights_path): + weight = np.load(classes_weights_path) + else: + weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass) + weight = torch.from_numpy(weight.astype(np.float32)) + else: + weight = None + # Define loss function + self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type) + self.model, self.optimizer = model, optimizer + # Define Evaluator + self.evaluator = Evaluator(self.nclass) + # # Define lr scheduler + self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr, args.epochs, len(self.train_loader)) + # Using cuda + if args.cuda: + #self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + #patch_replication_callback(self.model) + self.model = self.model.cuda() + + # Resuming checkpoint + self.best_pred = 0.0 + if args.resume is not None: + if not os.path.isfile(args.resume): + raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume)) + print(f"Training: load model from {args.resume}") + checkpoint = torch.load(args.resume, map_location=torch.device("cpu")) + args.start_epoch = checkpoint['epoch'] + # if args.cuda: + # self.model.load_state_dict(checkpoint['state_dict']) + # else: + # self.model.load_state_dict(checkpoint['state_dict']) + self.model.load_state_dict(checkpoint['state_dict']) + if not args.ft: + self.optimizer.load_state_dict(checkpoint['optimizer']) + self.best_pred = checkpoint['best_pred'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + + # Clear start epoch if fine-tuning + if args.ft: + args.start_epoch = 0 + + def training(self, epoch): + train_loss = 0.0 + print(self.optimizer.state_dict()['param_groups'][0]['lr']) + self.model.train() + tbar = tqdm(self.train_loader) + num_img_tr = len(self.train_loader) + + for i, sample in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + #print(target.shape) + else: + image, target = sample['image'], sample['label'] + print(image.shape) + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + self.scheduler(self.optimizer, i, epoch, self.best_pred) + self.optimizer.zero_grad() + + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + #print(target.max()) + #print(output.shape) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + loss.backward() + self.optimizer.step() + #print(self.optimizer.state_dict()['param_groups'][0]['lr']) + train_loss += loss.item() + tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) + self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) + # Show 10 * 3 inference results each epoch + if i % (num_img_tr // 10 + 1) == 0: + global_step = i + num_img_tr * epoch + if self.args.depth: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + depth_display = depth[0].cpu().unsqueeze(0) + depth_display = depth_display.mul_(self.std_depth).add_(self.mean_depth) + depth_display = depth_display.numpy() + depth_display = depth_display*255 + depth_display = depth_display.astype(np.uint8) + self.writer.add_image('Depth', depth_display, global_step) + + else: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch) + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print('Loss: %.3f' % train_loss) + + # if self.args.no_val: + # # save checkpoint every epoch + # is_best = False + # checkpoint_path = self.saver.save_checkpoint({ + # 'epoch': epoch + 1, + # 'state_dict': self.model.state_dict(), + # 'optimizer': self.optimizer.state_dict(), + # 'best_pred': self.best_pred, + # }, is_best) + + def validation(self, epoch): + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.val_loader, desc='\r') + test_loss = 0.0 + for i, (sample, img_path) in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + # print(f"val image is {image}") + if self.args.cuda: + image, target = image.cuda(), target.cuda() + if self.args.depth: + depth = depth.cuda() + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + test_loss += loss.item() + tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1))) + pred = output.data.cpu().numpy() + target = target.cpu().numpy() + pred = np.argmax(pred, axis=1) + # Add batch sample into evaluator + self.evaluator.add_batch(target, pred) + + # Fast test during the training + Acc = self.evaluator.Pixel_Accuracy() + Acc_class = self.evaluator.Pixel_Accuracy_Class() + mIoU = self.evaluator.Mean_Intersection_over_Union() + FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union() + self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch) + self.writer.add_scalar('val/mIoU', mIoU, epoch) + self.writer.add_scalar('val/Acc', Acc, epoch) + self.writer.add_scalar('val/Acc_class', Acc_class, epoch) + self.writer.add_scalar('val/fwIoU', FWIoU, epoch) + print('Validation:') + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU)) + print('Loss: %.3f' % test_loss) + + new_pred = mIoU + if new_pred > self.best_pred: + is_best = True + self.best_pred = new_pred + self.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.model.state_dict(), + 'optimizer': self.optimizer.state_dict(), + 'best_pred': self.best_pred, + }, is_best) \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py new file mode 100644 index 00000000..0bb0895e --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -0,0 +1,52 @@ +class TrainArgs: + def __init__(self, **kwargs): + self.depth = False + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.loss_type = 'ce' + self.epochs = kwargs.get("epochs", 2) + self.start_epoch = 0 + + self.batch_size = 4 + self.val_batch_size = 1 + self.use_balanced_weights = False + self.num_class = 24 + self.lr = kwargs.get("learning_rate", 1e-4) + self.lr_scheduler = 'cos' + self.momentum = 0.9 + self.weight_decay = 2.5e-5 + self.no_cuda = False + self.gpu_ids = '0' + + self.seed = 1 + self.resume = None + self.checkname = 'RFNet' + self.ft = True + self.eval_interval = kwargs.get("eval_interval", 2) + self.no_val = kwargs.get("no_val", True) + self.cuda = True + + +class ValArgs: + def __init__(self, **kwargs): + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.batch_size = 6 + self.val_batch_size = 1 + self.test_batch_size = 1 + self.num_class = 24 + self.no_cuda = False + self.gpu_ids = '0' + self.checkname = None + self.weight_path = "./models/530_exp3_2.pth" + self.save_predicted_image = False + self.color_label_save_path = './test/color' + self.merge_label_save_path = './test/merge' + self.label_save_path = './test/label' + self.merge = True + self.depth = False + self.cuda = True diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py new file mode 100644 index 00000000..2c2c9821 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py @@ -0,0 +1,29 @@ +import os +from tqdm import tqdm +import numpy as np +from mypath import Path + +def calculate_weigths_labels(dataset, dataloader, num_classes): + # Create an instance from the data loader + z = np.zeros((num_classes,)) + # Initialize tqdm + tqdm_batch = tqdm(dataloader) + print('Calculating classes weights') + for sample in tqdm_batch: + y = sample['label'] + y = y.detach().cpu().numpy() + mask = (y >= 0) & (y < num_classes) + labels = y[mask].astype(np.uint8) + count_l = np.bincount(labels, minlength=num_classes) + z += count_l + tqdm_batch.close() + total_frequency = np.sum(z) + class_weights = [] + for frequency in z: + class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) + class_weights.append(class_weight) + ret = np.array(class_weights) + classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy') + np.save(classes_weights_path, ret) + + return ret \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py new file mode 100644 index 00000000..de955825 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py @@ -0,0 +1,142 @@ +import torch + + +class iouEval: + + def __init__(self, nClasses, ignoreIndex=20): + + self.nClasses = nClasses + self.ignoreIndex = ignoreIndex if nClasses > ignoreIndex else -1 # if ignoreIndex is larger than nClasses, consider no ignoreIndex + self.reset() + + def reset(self): + classes = self.nClasses if self.ignoreIndex == -1 else self.nClasses - 1 + self.tp = torch.zeros(classes).double() + self.fp = torch.zeros(classes).double() + self.fn = torch.zeros(classes).double() + self.cdp_obstacle = torch.zeros(1).double() + self.tp_obstacle = torch.zeros(1).double() + self.idp_obstacle = torch.zeros(1).double() + self.tp_nonobstacle = torch.zeros(1).double() + # self.cdi = torch.zeros(1).double() + + def addBatch(self, x, y): # x=preds, y=targets + # sizes should be "batch_size x nClasses x H x W" + # cdi = 0 + + # print ("X is cuda: ", x.is_cuda) + # print ("Y is cuda: ", y.is_cuda) + + if (x.is_cuda or y.is_cuda): + x = x.cuda() + y = y.cuda() + + # if size is "batch_size x 1 x H x W" scatter to onehot + if (x.size(1) == 1): + x_onehot = torch.zeros(x.size(0), self.nClasses, x.size(2), x.size(3)) + if x.is_cuda: + x_onehot = x_onehot.cuda() + x_onehot.scatter_(1, x, 1).float() # dim index src 按照列用1替换0，索引为x + else: + x_onehot = x.float() + + if (y.size(1) == 1): + y_onehot = torch.zeros(y.size(0), self.nClasses, y.size(2), y.size(3)) + if y.is_cuda: + y_onehot = y_onehot.cuda() + y_onehot.scatter_(1, y, 1).float() + else: + y_onehot = y.float() + + if (self.ignoreIndex != -1): + ignores = y_onehot[:, self.ignoreIndex].unsqueeze(1) # 加一维 + x_onehot = x_onehot[:, :self.ignoreIndex] # ignoreIndex后的都不要 + y_onehot = y_onehot[:, :self.ignoreIndex] + else: + ignores = 0 + + + tpmult = x_onehot * y_onehot # times prediction and gt coincide is 1 + tp = torch.sum(torch.sum(torch.sum(tpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fpmult = x_onehot * ( + 1 - y_onehot - ignores) # times prediction says its that class and gt says its not (subtracting cases when its ignore label!) + fp = torch.sum(torch.sum(torch.sum(fpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fnmult = (1 - x_onehot) * (y_onehot) # times prediction says its not that class and gt says it is + fn = torch.sum(torch.sum(torch.sum(fnmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + + self.tp += tp.double().cpu() + self.fp += fp.double().cpu() + self.fn += fn.double().cpu() + + cdp_obstacle = tpmult[:, 19].sum() # obstacle index 19 + tp_obstacle = y_onehot[:, 19].sum() + + idp_obstacle = (x_onehot[:, 19] - tpmult[:, 19]).sum() + tp_nonobstacle = (-1*y_onehot+1).sum() + + # for i in range(0, x.size(0)): + # if tpmult[i].sum()/(y_onehot[i].sum() + 1e-15) >= 0.5: + # cdi += 1 + + + self.cdp_obstacle += cdp_obstacle.double().cpu() + self.tp_obstacle += tp_obstacle.double().cpu() + self.idp_obstacle += idp_obstacle.double().cpu() + self.tp_nonobstacle += tp_nonobstacle.double().cpu() + # self.cdi += cdi.double().cpu() + + + + def getIoU(self): + num = self.tp + den = self.tp + self.fp + self.fn + 1e-15 + iou = num / den + iou_not_zero = list(filter(lambda x: x != 0, iou)) + # print(len(iou_not_zero)) + iou_mean = sum(iou_not_zero) / len(iou_not_zero) + tfp = self.tp + self.fp + 1e-15 + acc = num / tfp + acc_not_zero = list(filter(lambda x: x != 0, acc)) + acc_mean = sum(acc_not_zero) / len(acc_not_zero) + + return iou_mean, iou, acc_mean, acc # returns "iou mean", "iou per class" + + def getObstacleEval(self): + + pdr_obstacle = self.cdp_obstacle / (self.tp_obstacle+1e-15) + pfp_obstacle = self.idp_obstacle / (self.tp_nonobstacle+1e-15) + + return pdr_obstacle, pfp_obstacle + + +# Class for colors +class colors: + RED = '\033[31;1m' + GREEN = '\033[32;1m' + YELLOW = '\033[33;1m' + BLUE = '\033[34;1m' + MAGENTA = '\033[35;1m' + CYAN = '\033[36;1m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + ENDC = '\033[0m' + + +# Colored value output if colorized flag is activated. +def getColorEntry(val): + if not isinstance(val, float): + return colors.ENDC + if (val < .20): + return colors.RED + elif (val < .40): + return colors.YELLOW + elif (val < .60): + return colors.BLUE + elif (val < .80): + return colors.CYAN + else: + return colors.GREEN + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py new file mode 100644 index 00000000..6cde9a17 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py @@ -0,0 +1,64 @@ +import torch +import torch.nn as nn + +class SegmentationLosses(object): + def __init__(self, weight=None, size_average=True, batch_average=True, ignore_index=255, cuda=False): # ignore_index=255 + self.ignore_index = ignore_index + self.weight = weight + self.size_average = size_average + self.batch_average = batch_average + self.cuda = cuda + + def build_loss(self, mode='ce'): + """Choices: ['ce' or 'focal']""" + if mode == 'ce': + return self.CrossEntropyLoss + elif mode == 'focal': + return self.FocalLoss + else: + raise NotImplementedError + + def CrossEntropyLoss(self, logit, target): + n, c, h, w = logit.size() + #criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + #size_average=self.size_average) + criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=self.ignore_index) + if self.cuda: + criterion = criterion.cuda() + + loss = criterion(logit, target.long()) + + if self.batch_average: + loss /= n + + return loss + + def FocalLoss(self, logit, target, gamma=2, alpha=0.5): + n, c, h, w = logit.size() + criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + size_average=self.size_average) + if self.cuda: + criterion = criterion.cuda() + + logpt = -criterion(logit, target.long()) + pt = torch.exp(logpt) + if alpha is not None: + logpt *= alpha + loss = -((1 - pt) ** gamma) * logpt + + if self.batch_average: + loss /= n + + return loss + +if __name__ == "__main__": + loss = SegmentationLosses(cuda=True) + a = torch.rand(1, 3, 7, 7).cuda() + b = torch.rand(1, 7, 7).cuda() + print(loss.CrossEntropyLoss(a, b).item()) + print(loss.FocalLoss(a, b, gamma=0, alpha=None).item()) + print(loss.FocalLoss(a, b, gamma=2, alpha=0.5).item()) + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py new file mode 100644 index 00000000..47124028 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py @@ -0,0 +1,70 @@ +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +## Created by: Hang Zhang +## ECE Department, Rutgers University +## Email: zhang.hang@rutgers.edu +## Copyright (c) 2017 +## +## This source code is licensed under the MIT-style license found in the +## LICENSE file in the root directory of this source tree +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +import math + +class LR_Scheduler(object): + """Learning Rate Scheduler + + Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}`` + + Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))`` + + Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9`` + + Args: + args: + :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`), + :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs, + :attr:`args.lr_step` + + iters_per_epoch: number of iterations per epoch + """ + def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, + lr_step=0, warmup_epochs=0): + self.mode = mode + print('Using {} LR Scheduler!'.format(self.mode)) + self.lr = base_lr + if mode == 'step': + assert lr_step + self.lr_step = lr_step + self.iters_per_epoch = iters_per_epoch + self.N = num_epochs * iters_per_epoch + self.epoch = -1 + self.warmup_iters = warmup_epochs * iters_per_epoch + + def __call__(self, optimizer, i, epoch, best_pred): + T = epoch * self.iters_per_epoch + i + if self.mode == 'cos': + lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) + elif self.mode == 'poly': + lr = self.lr * pow((1 - 1.0 * T / self.N), 2) + elif self.mode == 'step': + lr = self.lr * (0.1 ** (epoch // self.lr_step)) + else: + raise NotImplemented + # warm up lr schedule + if self.warmup_iters > 0 and T < self.warmup_iters: + lr = lr * 1.0 * T / self.warmup_iters + if epoch > self.epoch: + print('\n=>Epoches %i, learning rate = %.4f, \ + previous best = %.4f' % (epoch, lr, best_pred)) + self.epoch = epoch + assert lr >= 0 + self._adjust_learning_rate(optimizer, lr) + + def _adjust_learning_rate(self, optimizer, lr): + if len(optimizer.param_groups) == 1: + optimizer.param_groups[0]['lr'] = lr * 4 + else: + # enlarge the lr at the head + optimizer.param_groups[0]['lr'] = lr * 4 + for i in range(1, len(optimizer.param_groups)): + optimizer.param_groups[i]['lr'] = lr diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py new file mode 100644 index 00000000..d16f4099 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py @@ -0,0 +1,151 @@ +import numpy as np + + +class Evaluator(object): + def __init__(self, num_class): + self.num_class = num_class + self.confusion_matrix = np.zeros((self.num_class,)*2) # shape:(num_class, num_class) + + def Pixel_Accuracy(self): + Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum() + return Acc + + def Pixel_Accuracy_Class_Curb(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + Acc = np.nanmean(Acc[:2]) + return Acc + + + def Pixel_Accuracy_Class(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + print("building : %.6f" % (Acc[2] * 100.0), "%\t") + print("wall : %.6f" % (Acc[3] * 100.0), "%\t") + print("fence : %.6f" % (Acc[4] * 100.0), "%\t") + print("pole : %.6f" % (Acc[5] * 100.0), "%\t") + print("traffic light: %.6f" % (Acc[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (Acc[7] * 100.0), "%\t") + print("vegetation : %.6f" % (Acc[8] * 100.0), "%\t") + print("terrain : %.6f" % (Acc[9] * 100.0), "%\t") + print("sky : %.6f" % (Acc[10] * 100.0), "%\t") + print("person : %.6f" % (Acc[11] * 100.0), "%\t") + print("rider : %.6f" % (Acc[12] * 100.0), "%\t") + print("car : %.6f" % (Acc[13] * 100.0), "%\t") + print("truck : %.6f" % (Acc[14] * 100.0), "%\t") + print("bus : %.6f" % (Acc[15] * 100.0), "%\t") + print("train : %.6f" % (Acc[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (Acc[17] * 100.0), "%\t") + print("bicycle : %.6f" % (Acc[18] * 100.0), "%\t") + print("dynamic : %.6f" % (Acc[19] * 100.0), "%\t") + print("stair : %.6f" % (Acc[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (Acc[19] * 100.0), "%\t") + Acc = np.nanmean(Acc) + return Acc + + def Mean_Intersection_over_Union(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + print("building : %.6f" % (MIoU[2] * 100.0), "%\t") + print("wall : %.6f" % (MIoU[3] * 100.0), "%\t") + print("fence : %.6f" % (MIoU[4] * 100.0), "%\t") + print("pole : %.6f" % (MIoU[5] * 100.0), "%\t") + print("traffic light: %.6f" % (MIoU[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (MIoU[7] * 100.0), "%\t") + print("vegetation : %.6f" % (MIoU[8] * 100.0), "%\t") + print("terrain : %.6f" % (MIoU[9] * 100.0), "%\t") + print("sky : %.6f" % (MIoU[10] * 100.0), "%\t") + print("person : %.6f" % (MIoU[11] * 100.0), "%\t") + print("rider : %.6f" % (MIoU[12] * 100.0), "%\t") + print("car : %.6f" % (MIoU[13] * 100.0), "%\t") + print("truck : %.6f" % (MIoU[14] * 100.0), "%\t") + print("bus : %.6f" % (MIoU[15] * 100.0), "%\t") + print("train : %.6f" % (MIoU[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (MIoU[17] * 100.0), "%\t") + print("bicycle : %.6f" % (MIoU[18] * 100.0), "%\t") + print("dynamic : %.6f" % (MIoU[19] * 100.0), "%\t") + print("stair : %.6f" % (MIoU[20] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU) + return MIoU + + def Mean_Intersection_over_Union_Curb(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU[:2]) + return MIoU + + def Frequency_Weighted_Intersection_over_Union(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + if len(CFWIoU) > 1: + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return FWIoU + + def Frequency_Weighted_Intersection_over_Union_Curb(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return np.nanmean(CFWIoU[:2]) + + def _generate_matrix(self, gt_image, pre_image): + mask = (gt_image >= 0) & (gt_image < self.num_class) + label = self.num_class * gt_image[mask].astype('int') + pre_image[mask] + count = np.bincount(label, minlength=self.num_class**2) + confusion_matrix = count.reshape(self.num_class, self.num_class) + return confusion_matrix + + def add_batch(self, gt_image, pre_image): + gt_image = np.array(gt_image) + pre_image = np.array(pre_image) + print(gt_image.shape, pre_image.shape) + if gt_image.shape != pre_image.shape: + pre_image = pre_image[0] + assert gt_image.shape == pre_image.shape + self.confusion_matrix += self._generate_matrix(gt_image, pre_image) + + def reset(self): + self.confusion_matrix = np.zeros((self.num_class,) * 2) + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py new file mode 100644 index 00000000..03866432 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py @@ -0,0 +1,68 @@ +import os +import time +import shutil +import tempfile +import torch +from collections import OrderedDict +import glob + +class Saver(object): + + def __init__(self, args): + self.args = args + self.directory = os.path.join('/tmp', args.dataset, args.checkname) + self.runs = sorted(glob.glob(os.path.join(self.directory, 'experiment_*'))) + run_id = int(self.runs[-1].split('_')[-1]) + 1 if self.runs else 0 + + self.experiment_dir = os.path.join(self.directory, 'experiment_{}'.format(str(run_id))) + if not os.path.exists(self.experiment_dir): + os.makedirs(self.experiment_dir) + + def save_checkpoint(self, state, is_best): # filename from .pth.tar change to .pth? + """Saves checkpoint to disk""" + filename = f'checkpoint_{time.time()}.pth' + checkpoint_path = os.path.join(self.experiment_dir, filename) + torch.save(state, checkpoint_path) + if is_best: + best_pred = state['best_pred'] + with open(os.path.join(self.experiment_dir, 'best_pred.txt'), 'w') as f: + f.write(str(best_pred)) + if self.runs: + previous_miou = [0.0] + for run in self.runs: + run_id = run.split('_')[-1] + path = os.path.join(self.directory, 'experiment_{}'.format(str(run_id)), 'best_pred.txt') + if os.path.exists(path): + with open(path, 'r') as f: + miou = float(f.readline()) + previous_miou.append(miou) + else: + continue + max_miou = max(previous_miou) + if best_pred > max_miou: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + else: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + + return checkpoint_path + + def save_experiment_config(self): + logfile = os.path.join(self.experiment_dir, 'parameters.txt') + log_file = open(logfile, 'w') + p = OrderedDict() + p['datset'] = self.args.dataset + # p['out_stride'] = self.args.out_stride + p['lr'] = self.args.lr + p['lr_scheduler'] = self.args.lr_scheduler + p['loss_type'] = self.args.loss_type + p['epoch'] = self.args.epochs + p['base_size'] = self.args.base_size + p['crop_size'] = self.args.crop_size + + for key, val in p.items(): + log_file.write(key + ':' + str(val) + '\n') + log_file.close() \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py new file mode 100644 index 00000000..04bcdb82 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py @@ -0,0 +1,39 @@ +import os +import torch +from torchvision.utils import make_grid +# from tensorboardX import SummaryWriter +from torch.utils.tensorboard import SummaryWriter +from dataloaders.utils import decode_seg_map_sequence + +class TensorboardSummary(object): + def __init__(self, directory): + self.directory = directory + + def create_summary(self): + writer = SummaryWriter(log_dir=os.path.join(self.directory)) + return writer + + def visualize_image(self, writer, dataset, image, target, output, global_step, depth=None): + if depth is None: + grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) + else: + grid_image = make_grid(image[:3].clone().cpu().data, 4, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(depth[:3].clone().cpu().data, 4, normalize=True) # normalize=False? + writer.add_image('Depth', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py new file mode 100644 index 00000000..a60a060f --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py @@ -0,0 +1,138 @@ +import os +import gc +import numpy as np +import torch +from torch.utils.data import DataLoader +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from PIL import Image +from torchvision import transforms + +from RFNet.train import Trainer +from RFNet.eval import Validator, load_my_state_dict +from RFNet.dataloaders import custom_transforms as tr +from RFNet.dataloaders import make_data_loader +from RFNet.utils.args import TrainArgs, ValArgs + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.train_args = TrainArgs(**kwargs) + self.trainer = None + + self.val_args = ValArgs(**kwargs) + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range( + self.trainer.args.start_epoch, + self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and ( + epoch % + self.trainer.args.eval_interval == ( + self.trainer.args.eval_interval - + 1) or epoch == self.trainer.args.epochs - + 1): + # save checkpoint when it meets eval_interval or the training + # finished + is_best = False + self.train_model_url = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + self.trainer.writer.close() + + return self.train_model_url + + def predict(self, data, **kwargs): + if len(data) > 10: + print("predict start for big data") + my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': False} + _, _, self.validator.test_loader, _ = make_data_loader(self.val_args, test_data=data, **my_kwargs) + else: + print("predict start for small data") + if not isinstance(data[0][0], dict): + data = self._preprocess(data) + #print("predict starting 69") + if type(data) is np.ndarray: + data = data.tolist() + #print("predict starting 72") + #print("predict starting 73") + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=False) + + #print("predict starting 75") + return self.validator.validate() + + def evaluate(self, data, **kwargs): + print("evaluate starting 77") + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: save unstructured data model + if not model_path: + LOGGER.warning(f"Not specify model path.") + return self.train_model_url + + return FileOps.upload(self.train_model_url, model_path) + + def _preprocess(self, image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + #print(img_path, depth_path) + else: + img_path = paths[0] + #print(img_path) + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + del _img + gc.collect() + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml new file mode 100644 index 00000000..4a89c5d6 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -0,0 +1,63 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.95 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + #splitting_method: "default" + splitting_method: "my_splitting" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + - epochs: + values: + - 3 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py new file mode 100644 index 00000000..dbdecc7c --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py @@ -0,0 +1,65 @@ +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('TaskAllocationByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + self.task_extractor = task_extractor + if self.default_origin: + return samples, [int(self.task_extractor.get( + self.default_origin))] * len(samples.x) + + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + sample_origins = [] + for _x in samples.x: + is_real = False + for city in cities: + if city in _x[0]: + is_real = True + sample_origins.append("real") + break + if not is_real: + sample_origins.append("sim") + + allocations = [int(self.task_extractor.get(sample_origin)) + for sample_origin in sample_origins] + + return samples, allocations diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py new file mode 100644 index 00000000..4c8dc264 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py @@ -0,0 +1,81 @@ +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + +__all__ = ('TaskDefinitionByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") +class TaskDefinitionByOrigin: + """ + Dividing datasets based on the their origins. + + Parameters + ---------- + origins： List[Metadata] + metadata is usually a class feature label with a finite values. + """ + + def __init__(self, **kwargs): + self.origins = kwargs.get("origins", ["real", "sim"]) + + def __call__(self, + samples: BaseDataSource, **kwargs) -> Tuple[List[Task], + Any, + BaseDataSource]: + cities = [ + "aachen", + "berlin", + "bochum", + "bremen", + "cologne", + "darmstadt", + "dusseldorf", + "erfurt", + "hamburg", + "hanover", + "jena", + "krefeld", + "monchengladbach", + "strasbourg", + "stuttgart", + "tubingen", + "ulm", + "weimar", + "zurich"] + + tasks = [] + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + + task_index = dict(zip(self.origins, range(len(self.origins)))) + + real_df = BaseDataSource(data_type=d_type) + real_df.x, real_df.y = [], [] + sim_df = BaseDataSource(data_type=d_type) + sim_df.x, sim_df.y = [], [] + + for i in range(samples.num_examples()): + is_real = False + for city in cities: + if city in x_data[i][0]: + is_real = True + real_df.x.append(x_data[i]) + real_df.y.append(y_data[i]) + break + if not is_real: + sim_df.x.append(x_data[i]) + sim_df.y.append(y_data[i]) + + g_attr = "real_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=real_df, meta_attr="real") + tasks.append(task_obj) + + g_attr = "sim_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="sim") + tasks.append(task_obj) + + return tasks, task_index, samples diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py b/examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py new file mode 100644 index 00000000..45db88f2 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py @@ -0,0 +1,59 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm import tqdm + +from sedna.common.class_factory import ClassType, ClassFactory + +from RFNet.dataloaders import make_data_loader +from RFNet.utils.metrics import Evaluator +from RFNet.utils.args import ValArgs + +__all__ = ('accuracy') + + +@ClassFactory.register(ClassType.GENERAL, alias="accuracy") +def accuracy(y_true, y_pred, **kwargs): + print("start accuracy 28") + args = ValArgs() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + #print(y_true) + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + #if i% 20 == 0: + # print(img_path) + # print(image, target, y_pred[i]) + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml new file mode 100644 index 00000000..45698513 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml @@ -0,0 +1,36 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + #train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" + train_url: "/root/data/semantic_segmentation_dataset/train-index.txt" + # the url address of test dataset index; string type; + #test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 2 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml new file mode 100644 index 00000000..ec98db3e --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml @@ -0,0 +1,36 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" + #train_url: "/root/data/semantic_segmentation_dataset/train-index.txt" + # the url address of test dataset index; string type; + test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" + #test_url: "/root/data/semantic_segmentation_dataset/test-index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 3 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml new file mode 100644 index 00000000..d726c693 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml @@ -0,0 +1,36 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + #train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" + train_url: "/root/data/semantic_segmentation_dataset/train-index-sort.txt" + # the url address of test dataset index; string type; + #test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 3 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/train-index-sort.txt b/examples/semantic_segmentation/lifelong_learning_bench/train-index-sort.txt new file mode 100644 index 00000000..2beacfb1 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/train-index-sort.txt @@ -0,0 +1,11976 @@ +./train/leftImg8bit/synthia_sim/1769.png ./train/depth/synthia_sim/1769.png ./train/gtFine/synthia_sim/1769TrainIds.png +./train/leftImg8bit/synthia_sim/1770.png ./train/depth/synthia_sim/1770.png ./train/gtFine/synthia_sim/1770TrainIds.png +./train/leftImg8bit/synthia_sim/1771.png ./train/depth/synthia_sim/1771.png ./train/gtFine/synthia_sim/1771TrainIds.png +./train/leftImg8bit/synthia_sim/1772.png ./train/depth/synthia_sim/1772.png ./train/gtFine/synthia_sim/1772TrainIds.png +./train/leftImg8bit/synthia_sim/1773.png ./train/depth/synthia_sim/1773.png ./train/gtFine/synthia_sim/1773TrainIds.png +./train/leftImg8bit/synthia_sim/1775.png ./train/depth/synthia_sim/1775.png ./train/gtFine/synthia_sim/1775TrainIds.png +./train/leftImg8bit/synthia_sim/1777.png ./train/depth/synthia_sim/1777.png ./train/gtFine/synthia_sim/1777TrainIds.png +./train/leftImg8bit/synthia_sim/1779.png ./train/depth/synthia_sim/1779.png ./train/gtFine/synthia_sim/1779TrainIds.png +./train/leftImg8bit/synthia_sim/1780.png ./train/depth/synthia_sim/1780.png ./train/gtFine/synthia_sim/1780TrainIds.png +./train/leftImg8bit/synthia_sim/1781.png ./train/depth/synthia_sim/1781.png ./train/gtFine/synthia_sim/1781TrainIds.png +./train/leftImg8bit/synthia_sim/1782.png ./train/depth/synthia_sim/1782.png ./train/gtFine/synthia_sim/1782TrainIds.png +./train/leftImg8bit/synthia_sim/1785.png ./train/depth/synthia_sim/1785.png ./train/gtFine/synthia_sim/1785TrainIds.png +./train/leftImg8bit/synthia_sim/1786.png ./train/depth/synthia_sim/1786.png ./train/gtFine/synthia_sim/1786TrainIds.png +./train/leftImg8bit/synthia_sim/1787.png ./train/depth/synthia_sim/1787.png ./train/gtFine/synthia_sim/1787TrainIds.png +./train/leftImg8bit/synthia_sim/1789.png ./train/depth/synthia_sim/1789.png ./train/gtFine/synthia_sim/1789TrainIds.png +./train/leftImg8bit/synthia_sim/1790.png ./train/depth/synthia_sim/1790.png ./train/gtFine/synthia_sim/1790TrainIds.png +./train/leftImg8bit/synthia_sim/1791.png ./train/depth/synthia_sim/1791.png ./train/gtFine/synthia_sim/1791TrainIds.png +./train/leftImg8bit/synthia_sim/1794.png ./train/depth/synthia_sim/1794.png ./train/gtFine/synthia_sim/1794TrainIds.png +./train/leftImg8bit/synthia_sim/1795.png ./train/depth/synthia_sim/1795.png ./train/gtFine/synthia_sim/1795TrainIds.png +./train/leftImg8bit/synthia_sim/1796.png ./train/depth/synthia_sim/1796.png ./train/gtFine/synthia_sim/1796TrainIds.png +./train/leftImg8bit/synthia_sim/1797.png ./train/depth/synthia_sim/1797.png ./train/gtFine/synthia_sim/1797TrainIds.png +./train/leftImg8bit/synthia_sim/1799.png ./train/depth/synthia_sim/1799.png ./train/gtFine/synthia_sim/1799TrainIds.png +./train/leftImg8bit/synthia_sim/1801.png ./train/depth/synthia_sim/1801.png ./train/gtFine/synthia_sim/1801TrainIds.png +./train/leftImg8bit/synthia_sim/1803.png 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./val/gtFine/synthia_sim/975TrainIds.png +./val/leftImg8bit/synthia_sim/976.png ./val/depth/synthia_sim/976.png ./val/gtFine/synthia_sim/976TrainIds.png +./val/leftImg8bit/synthia_sim/977.png ./val/depth/synthia_sim/977.png ./val/gtFine/synthia_sim/977TrainIds.png +./val/leftImg8bit/synthia_sim/978.png ./val/depth/synthia_sim/978.png ./val/gtFine/synthia_sim/978TrainIds.png +./val/leftImg8bit/synthia_sim/979.png ./val/depth/synthia_sim/979.png ./val/gtFine/synthia_sim/979TrainIds.png +./val/leftImg8bit/synthia_sim/980.png ./val/depth/synthia_sim/980.png ./val/gtFine/synthia_sim/980TrainIds.png +./val/leftImg8bit/synthia_sim/981.png ./val/depth/synthia_sim/981.png ./val/gtFine/synthia_sim/981TrainIds.png +./val/leftImg8bit/synthia_sim/982.png ./val/depth/synthia_sim/982.png ./val/gtFine/synthia_sim/982TrainIds.png +./val/leftImg8bit/synthia_sim/984.png ./val/depth/synthia_sim/984.png ./val/gtFine/synthia_sim/984TrainIds.png +./val/leftImg8bit/synthia_sim/985.png ./val/depth/synthia_sim/985.png ./val/gtFine/synthia_sim/985TrainIds.png +./val/leftImg8bit/synthia_sim/986.png ./val/depth/synthia_sim/986.png ./val/gtFine/synthia_sim/986TrainIds.png +./val/leftImg8bit/synthia_sim/987.png ./val/depth/synthia_sim/987.png ./val/gtFine/synthia_sim/987TrainIds.png +./val/leftImg8bit/synthia_sim/988.png ./val/depth/synthia_sim/988.png ./val/gtFine/synthia_sim/988TrainIds.png +./val/leftImg8bit/synthia_sim/989.png ./val/depth/synthia_sim/989.png ./val/gtFine/synthia_sim/989TrainIds.png +./val/leftImg8bit/synthia_sim/990.png ./val/depth/synthia_sim/990.png ./val/gtFine/synthia_sim/990TrainIds.png +./val/leftImg8bit/synthia_sim/991.png ./val/depth/synthia_sim/991.png ./val/gtFine/synthia_sim/991TrainIds.png +./val/leftImg8bit/synthia_sim/992.png ./val/depth/synthia_sim/992.png ./val/gtFine/synthia_sim/992TrainIds.png +./val/leftImg8bit/synthia_sim/993.png ./val/depth/synthia_sim/993.png ./val/gtFine/synthia_sim/993TrainIds.png +./val/leftImg8bit/synthia_sim/995.png ./val/depth/synthia_sim/995.png ./val/gtFine/synthia_sim/995TrainIds.png +./val/leftImg8bit/synthia_sim/996.png ./val/depth/synthia_sim/996.png ./val/gtFine/synthia_sim/996TrainIds.png +./val/leftImg8bit/synthia_sim/997.png ./val/depth/synthia_sim/997.png ./val/gtFine/synthia_sim/997TrainIds.png +./val/leftImg8bit/synthia_sim/998.png ./val/depth/synthia_sim/998.png ./val/gtFine/synthia_sim/998TrainIds.png +./val/leftImg8bit/synthia_sim/999.png ./val/depth/synthia_sim/999.png ./val/gtFine/synthia_sim/999TrainIds.png \ No newline at end of file From a74a843ca49b7f53348d818483d6a86205410b10 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Tue, 28 Mar 2023 16:57:37 +0800 Subject: [PATCH 17/22] add no-inference mode for lifelong learning Signed-off-by: hsj576 write quick start Signed-off-by: hsj576 remote useless file Signed-off-by: hsj576 revise the git command Signed-off-by: husj remove annotations and change the implisit function name Signed-off-by: husj modify the code to meet the coding standard Signed-off-by: husj modify the code to meet the coding standard Signed-off-by: husj modify the code to meet the coding standard Signed-off-by: husj --- .github/workflows/main.yaml | 4 +- core/cmd/benchmarking.py | 2 +- core/cmd/obj/benchmarkingjob.py | 5 +- core/common/utils.py | 6 +- core/storymanager/rank/rank.py | 2 +- .../testcasecontroller/algorithm/algorithm.py | 2 +- .../algorithm/module/module.py | 6 +- .../incremental_learning.py | 2 +- .../lifelong_learning/lifelong_learning.py | 67 +- core/testcasecontroller/metrics/metrics.py | 2 +- core/testcasecontroller/testcase/testcase.py | 2 +- core/testcasecontroller/testcasecontroller.py | 6 +- core/testenvmanager/dataset/dataset.py | 59 +- core/testenvmanager/testenv/testenv.py | 2 +- .../lifelong_learning_bench/README.md | 3 +- ...e-any.zip => sedna-0.4.5-py3-none-any.whl} | Bin .../sedna-0.4.5.dist-info/METADATA | 274 -------- .../sedna-0.4.5.dist-info/RECORD | 120 ---- .../sedna-0.4.5.dist-info/WHEEL | 5 - .../sedna-0.4.5.dist-info/top_level.txt | 1 - .../lifelong_learning_bench/README.md | 24 +- .../benchmarkingjob-fulltest.yaml | 74 +++ .../benchmarkingjob-smalltest.yaml | 74 +++ .../benchmarkingjob-traveltest.yaml | 74 +++ .../{ => indexes}/test-index-sort.txt | 0 .../{ => indexes}/test-index.txt | 0 .../indexes/test-small-3.txt | 66 ++ .../indexes/test-small.txt | 66 ++ .../{ => indexes}/train-index-sort.txt | 0 .../{ => indexes}/train-index.txt | 0 .../indexes/train-small-3.txt | 598 ++++++++++++++++++ .../indexes/train-small.txt | 598 ++++++++++++++++++ .../rfnet/RFNet/dataloaders/__init__.py | 3 - .../RFNet/dataloaders/datasets/cityscapes.py | 4 - .../testalgorithms/rfnet/RFNet/eval.py | 53 -- .../rfnet/rfnet_algorithm-new-split.yaml | 63 ++ .../testalgorithms/rfnet/rfnet_algorithm.yaml | 6 +- .../testenv/testenv-full.yaml | 9 +- .../testenv/testenv-small.yaml | 7 +- .../testenv/testenv-travel.yaml | 37 ++ .../testenv/testenv.yaml | 11 +- 41 files changed, 1767 insertions(+), 570 deletions(-) rename examples/resources/third_party-bk/{sedna-0.4.5-py3-none-any.zip => sedna-0.4.5-py3-none-any.whl} (100%) delete mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/METADATA delete mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD delete mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/WHEEL delete mode 100644 examples/resources/third_party-bk/sedna-0.4.5.dist-info/top_level.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-fulltest.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-traveltest.yaml rename examples/semantic_segmentation/lifelong_learning_bench/{ => indexes}/test-index-sort.txt (100%) rename examples/semantic_segmentation/lifelong_learning_bench/{ => indexes}/test-index.txt (100%) create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small-3.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small.txt rename examples/semantic_segmentation/lifelong_learning_bench/{ => indexes}/train-index-sort.txt (100%) rename examples/semantic_segmentation/lifelong_learning_bench/{ => indexes}/train-index.txt (100%) create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/indexes/train-small-3.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/indexes/train-small.txt create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-new-split.yaml create mode 100644 examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-travel.yaml diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 3eb20d07..2544cec0 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -9,11 +9,11 @@ on: jobs: pylint: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 name: pylint strategy: matrix: - python-version: [ "3.6", "3.7", "3.8", "3.9" ] + python-version: [ "3.7", "3.8", "3.9" ] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} diff --git a/core/cmd/benchmarking.py b/core/cmd/benchmarking.py index 8af41b9b..ba8c9315 100644 --- a/core/cmd/benchmarking.py +++ b/core/cmd/benchmarking.py @@ -38,7 +38,7 @@ def main(): LOGGER.info("benchmarkingjob runs successfully.") except Exception as err: - raise Exception(f"benchmarkingjob runs failed, error: {err}.") from err + raise RuntimeError(f"benchmarkingjob runs failed, error: {err}.") from err def _generate_parser(): diff --git a/core/cmd/obj/benchmarkingjob.py b/core/cmd/obj/benchmarkingjob.py index d6cfe1fe..7e733990 100644 --- a/core/cmd/obj/benchmarkingjob.py +++ b/core/cmd/obj/benchmarkingjob.py @@ -48,7 +48,6 @@ def __init__(self, config): def _check_fields(self): if not self.name and not isinstance(self.name, str): - ValueError(f"algorithm name({self.name}) must be provided and be string type.") raise ValueError(f"benchmarkingjob's name({self.name}) must be provided" f" and be string type.") @@ -106,13 +105,13 @@ def _parse_config(self, config: dict): def _parse_testenv_config(self, config_file): if not utils.is_local_file(config_file): - raise Exception(f"not found testenv config file({config_file}) in local") + raise RuntimeError(f"not found testenv config file({config_file}) in local") try: config = utils.yaml2dict(config_file) self.test_env = TestEnv(config) except Exception as err: - raise Exception(f"testenv config file({config_file}) is not supported, " + raise RuntimeError(f"testenv config file({config_file}) is not supported, " f"error: {err}") from err def _parse_rank_config(self, config): diff --git a/core/common/utils.py b/core/common/utils.py index 92b587b1..38b60526 100644 --- a/core/common/utils.py +++ b/core/common/utils.py @@ -71,7 +71,7 @@ def py2dict(url): return raw_dict - raise Exception('config file must be the py format') + raise RuntimeError('config file must be the py format') def yaml2dict(url): @@ -82,7 +82,7 @@ def yaml2dict(url): return raw_dict - raise Exception('config file must be the yaml format') + raise RuntimeError('config file must be the yaml format') def load_module(url): @@ -96,4 +96,4 @@ def load_module(url): importlib.import_module(module_name) sys.path.pop(0) except Exception as err: - raise Exception(f"load module(url={url}) failed, error: {err}") from err + raise RuntimeError(f"load module(url={url}) failed, error: {err}") from err diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py index 7614d3bd..8312ec16 100644 --- a/core/storymanager/rank/rank.py +++ b/core/storymanager/rank/rank.py @@ -257,6 +257,6 @@ def plot(self): func = get_visualization_func(method) func(self.selected_rank_file) except Exception as err: - raise Exception( + raise RuntimeError( f"process visualization(method={method}) of " f"rank file({self.selected_rank_file}) failed, error: {err}.") from err diff --git a/core/testcasecontroller/algorithm/algorithm.py b/core/testcasecontroller/algorithm/algorithm.py index f064d7bf..2d1b6a54 100644 --- a/core/testcasecontroller/algorithm/algorithm.py +++ b/core/testcasecontroller/algorithm/algorithm.py @@ -172,5 +172,5 @@ def _load_third_party_packages(self): try: load_module(url) except Exception as err: - raise Exception(f"load third party packages(name={name}, url={url}) failed," + raise RuntimeError(f"load third party packages(name={name}, url={url}) failed," f" error: {err}.") from err diff --git a/core/testcasecontroller/algorithm/module/module.py b/core/testcasecontroller/algorithm/module/module.py index 04ee6527..6d487d97 100644 --- a/core/testcasecontroller/algorithm/module/module.py +++ b/core/testcasecontroller/algorithm/module/module.py @@ -116,7 +116,7 @@ def get_module_instance(self, module_type): return func except Exception as err: - raise Exception(f"module(type={module_type} loads class(name={self.name}) " + raise RuntimeError(f"module(type={module_type} loads class(name={self.name}) " f"failed, error: {err}.") from err # call lib built-in module function @@ -164,14 +164,14 @@ def _parse_other_hyperparameters(cls, config_files): base_hps = {} for hp_config_file in config_files: if not utils.is_local_file(hp_config_file): - raise Exception(f"not found other hyperparameters config file" + raise RuntimeError(f"not found other hyperparameters config file" f"({hp_config_file}) in local") try: other_hps = utils.yaml2dict(hp_config_file) base_hps.update(**other_hps) except Exception as err: - raise Exception( + raise RuntimeError( f"other hyperparameters config file({hp_config_file}) is unvild, " f"error: {err}") from err return base_hps diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py index 3cbf40df..356f8a0e 100644 --- a/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py @@ -207,7 +207,7 @@ def _trigger_model_update(self, eval_results): operator_func = operator_map[operator] if len(eval_results) != 2: - raise Exception(f"two models of evaluation should have two results." + raise RuntimeError(f"two models of evaluation should have two results." f" the eval results: {eval_results}") metric_values = [0, 0] diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index e2b5820c..69251d78 100644 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -82,7 +82,9 @@ def run(self): samples_transfer_ratio_info = self.system_metric_info.get( SystemMetricType.SAMPLES_TRANSFER_RATIO.value) mode = self.model_eval_config.get("model_metric").get("mode") - if mode != 'multi-inference': + + # in this mode, the inference period is skipped to accelerate training speed + if mode == 'no-inference': dataset_files = self._split_dataset(splitting_dataset_times=rounds) # pylint: disable=C0103 for r in range(1, rounds + 1): @@ -96,7 +98,34 @@ def run(self): r) else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - ''' + self.cloud_task_index = self._train(self.cloud_task_index, + infer_dataset_file, + r) + self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, + r) + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + inference_dataset = self.dataset.load_data(self.dataset.test_url, "eval", + feature_process=_data_feature_process) + kwargs = {} + test_res = job.my_inference(inference_dataset, **kwargs) + del job + + elif mode != 'multi-inference': + dataset_files = self._split_dataset(splitting_dataset_times=rounds) + # pylint: disable=C0103 + for r in range(1, rounds + 1): + if r == 1: + train_dataset_file, eval_dataset_file = dataset_files[r - 1] + self.cloud_task_index = self._train(self.cloud_task_index, + train_dataset_file, + r) + self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, + r) + else: + infer_dataset_file, eval_dataset_file = dataset_files[r - 1] + inference_results, unseen_task_train_samples = self._inference( self.edge_task_index, infer_dataset_file, @@ -111,36 +140,18 @@ def run(self): self.cloud_task_index = self._train(self.cloud_task_index, unseen_task_train_samples, r) - ''' - self.cloud_task_index = self._train(self.cloud_task_index, - infer_dataset_file, - r) self.edge_task_index = self._eval(self.cloud_task_index, eval_dataset_file, r) - ''' - test_res, unseen_task_train_samples = self._inference(self.edge_task_index, + test_res, unseen_task_train_samples = self._inference(self.edge_task_index, self.dataset.test_url, "test") - samples_transfer_ratio_info.append((test_res, unseen_task_train_samples.x)) - ''' - job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) - inference_dataset = self.dataset.load_data(self.dataset.test_url, "eval", - feature_process=_data_feature_process) - kwargs = {} - model_eval_info = self.model_eval_config - model_metric = model_eval_info.get("model_metric") - _, metric_func = get_metric_func(model_metric) - #_ = job.evaluate(inference_dataset, metrics=metric_func) - test_res = job.my_inference(inference_dataset, **kwargs) - del job - return test_res, self.system_metric_info def _inference(self, edge_task_index, data_index_file, rounds): # pylint:disable=duplicate-code - print("start inference") + #print("start inference") output_dir = os.path.join(self.workspace, f"output/inference/results/{rounds}") if not is_local_dir(output_dir): @@ -163,13 +174,13 @@ def _inference(self, edge_task_index, data_index_file, rounds): unseen_tasks = [] unseen_task_labels = [] mode = self.model_eval_config.get("model_metric").get("mode") - if mode == None: - kwargs = {} # fix the bug of "TypeError: call() got an unexpected keyword argument 'mode'" + if mode is None: + kwargs = {} + # fix the bug of "TypeError: call() got an unexpected keyword argument 'mode'" else: kwargs = {"mode": mode} - print(len(inference_dataset.x)) + #print(len(inference_dataset.x)) for i, _ in enumerate(inference_dataset.x): - print(i) data = BaseDataSource(data_type="test") data.x = inference_dataset.x[i:(i + 1)] res, is_unseen_task, _ = job.inference(data, **kwargs) @@ -177,11 +188,9 @@ def _inference(self, edge_task_index, data_index_file, rounds): if is_unseen_task: unseen_tasks.append(inference_dataset.x[i]) unseen_task_labels.append(inference_dataset.y[i]) - ''' for infer_data in inference_dataset.x[i]: shutil.copy(infer_data, unseen_task_saved_dir) - ''' - print("inference 168") + del job unseen_task_train_samples = BaseDataSource(data_type="train") diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py index 5b825c05..99ebca4f 100644 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -72,6 +72,6 @@ def get_metric_func(metric_dict: dict): metric_func = ClassFactory.get_cls(type_name=ClassType.GENERAL, t_cls_name=name) return name, metric_func except Exception as err: - raise Exception(f"get metric func(url={url}) failed, error: {err}.") from err + raise RuntimeError(f"get metric func(url={url}) failed, error: {err}.") from err return name, getattr(sys.modules[__name__], str.lower(name) + "_func") diff --git a/core/testcasecontroller/testcase/testcase.py b/core/testcasecontroller/testcase/testcase.py index 93e9878f..168ad359 100644 --- a/core/testcasecontroller/testcase/testcase.py +++ b/core/testcasecontroller/testcase/testcase.py @@ -76,7 +76,7 @@ def run(self, workspace): except Exception as err: paradigm_type = self.algorithm.paradigm_type - raise Exception( + raise RuntimeError( f"(paradigm={paradigm_type}) pipeline runs failed, error: {err}") from err return test_result diff --git a/core/testcasecontroller/testcasecontroller.py b/core/testcasecontroller/testcasecontroller.py index 5c90681f..d2d082ce 100644 --- a/core/testcasecontroller/testcasecontroller.py +++ b/core/testcasecontroller/testcasecontroller.py @@ -53,7 +53,7 @@ def run_testcases(self, workspace): try: res, time = (testcase.run(workspace), utils.get_local_time()) except Exception as err: - raise Exception(f"testcase(id={testcase.id}) runs failed, error: {err}") from err + raise RuntimeError(f"testcase(id={testcase.id}) runs failed, error: {err}") from err succeed_results[testcase.id] = (res, time) succeed_testcases.append(testcase) @@ -67,14 +67,14 @@ def _parse_algorithms_config(cls, config): name = algorithm_config.get("name") config_file = algorithm_config.get("url") if not utils.is_local_file(config_file): - raise Exception(f"not found algorithm config file({config_file}) in local") + raise RuntimeError(f"not found algorithm config file({config_file}) in local") try: config = utils.yaml2dict(config_file) algorithm = Algorithm(name, config) algorithms.append(algorithm) except Exception as err: - raise Exception(f"algorithm config file({config_file} is not supported, " + raise RuntimeError(f"algorithm config file({config_file} is not supported, " f"error: {err}") from err new_algorithms = [] diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py index 2d945ee2..3852cdbc 100644 --- a/core/testenvmanager/dataset/dataset.py +++ b/core/testenvmanager/dataset/dataset.py @@ -79,25 +79,16 @@ def _process_txt_index_file(cls, file_url): tmp_file = os.path.join(tempfile.mkdtemp(), "index.txt") with open(tmp_file, "w", encoding="utf-8") as file: for line in lines: - #front, back = line.split(" ") - + #copy all the files in the line + line = line.strip() words = line.split(" ") - if len(words) == 2: - front = words[0] - back = words[1] + length = len(words) + words[-1] = words[-1] + '\n' + for i in range(length): file.writelines( - f"{os.path.abspath(os.path.join(root, front))} " - f"{os.path.abspath(os.path.join(root, back))}") - else: - front = words[0] - depth = words[1] - back = words[2] - if back[-1] != '\n': - back = back + '\n' - file.writelines( - f"{os.path.abspath(os.path.join(root, front))} " - f"{os.path.abspath(os.path.join(root, depth))} " - f"{os.path.abspath(os.path.join(root, back))}") + f"{os.path.abspath(os.path.join(root, words[i]))}") + if i < length-1: + file.writelines(" ") new_file = tmp_file @@ -163,8 +154,9 @@ def split_dataset(self, dataset_url, dataset_format, ratio, method="default", data_types=dataset_types, output_dir=output_dir, times=times) - if method == "my_splitting": - return self._my_splitting(dataset_url, dataset_format, ratio, + # add new splitting method for semantic segmantation + if method == "city_splitting": + return self._city_splitting(dataset_url, dataset_format, ratio, data_types=dataset_types, output_dir=output_dir, times=times) @@ -237,8 +229,9 @@ def _splitting_more_times(self, data_file, data_format, ratio, index += 1 return data_files - - def _my_splitting(self, data_file, data_format, ratio, + + # add new splitting method for semantic segmentation + def _city_splitting(self, data_file, data_format, ratio, data_types=None, output_dir=None, times=1): if not data_types: data_types = ("train", "eval") @@ -250,23 +243,21 @@ def _my_splitting(self, data_file, data_format, ratio, data_files = [] - all_num = len(all_data) index0 = 0 - for i in range(all_num): - if 'synthia_sim' in all_data[i]: + for i, data in enumerate(all_data): + if 'synthia_sim' in data: continue - else: - index0 = i - break + index0 = i + break + new_dataset = all_data[:index0] - new_num = len(new_dataset) data_files.append(( - self._get_dataset_file(new_dataset[:int(new_num * ratio)], output_dir, + self._get_dataset_file(new_dataset[:int(len(new_dataset) * ratio)], output_dir, data_types[0], 1, data_format), - self._get_dataset_file(new_dataset[int(new_num * ratio):], output_dir, + self._get_dataset_file(new_dataset[int(len(new_dataset) * ratio):], output_dir, data_types[1], 1, data_format))) times = times - 1 - step = int((all_num-index0) / times) + step = int((len(all_data)-index0) / times) index = 1 while index <= times: if index == times: @@ -274,12 +265,10 @@ def _my_splitting(self, data_file, data_format, ratio, else: new_dataset = all_data[index0 + step * (index - 1):index0 + step * index] - new_num = len(new_dataset) - data_files.append(( - self._get_dataset_file(new_dataset[:int(new_num * ratio)], output_dir, + self._get_dataset_file(new_dataset[:int(len(new_dataset) * ratio)], output_dir, data_types[0], index+1, data_format), - self._get_dataset_file(new_dataset[int(new_num * ratio):], output_dir, + self._get_dataset_file(new_dataset[int(len(new_dataset) * ratio):], output_dir, data_types[1], index+1, data_format))) index += 1 diff --git a/core/testenvmanager/testenv/testenv.py b/core/testenvmanager/testenv/testenv.py index 6682a832..4a4fef4d 100644 --- a/core/testenvmanager/testenv/testenv.py +++ b/core/testenvmanager/testenv/testenv.py @@ -71,4 +71,4 @@ def prepare(self): try: self.dataset.process_dataset() except Exception as err: - raise Exception(f"prepare dataset failed, error: {err}.") from err + raise RuntimeError(f"prepare dataset failed, error: {err}.") from err diff --git a/examples/curb-detection/lifelong_learning_bench/README.md b/examples/curb-detection/lifelong_learning_bench/README.md index 46366074..24598174 100644 --- a/examples/curb-detection/lifelong_learning_bench/README.md +++ b/examples/curb-detection/lifelong_learning_bench/README.md @@ -13,6 +13,7 @@ Before using Ianvs, you might want to have the device ready: - Internet connection for GitHub and pip, etc - Python 3.6+ installed + In this example, we are using the Linux platform with Python 3.8.5. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. ## Step 1. Ianvs Preparation @@ -26,7 +27,7 @@ cd /ianvs #One might use another path preferred mkdir project cd project -git clone -b feature-lifelong-n https://github.com/kubeedge/ianvs.git +git clone https://github.com/kubeedge/ianvs.git ``` diff --git a/examples/resources/third_party-bk/sedna-0.4.5-py3-none-any.zip b/examples/resources/third_party-bk/sedna-0.4.5-py3-none-any.whl similarity index 100% rename from examples/resources/third_party-bk/sedna-0.4.5-py3-none-any.zip rename to examples/resources/third_party-bk/sedna-0.4.5-py3-none-any.whl diff --git a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/METADATA b/examples/resources/third_party-bk/sedna-0.4.5.dist-info/METADATA deleted file mode 100644 index 0d62b91b..00000000 --- a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/METADATA +++ /dev/null @@ -1,274 +0,0 @@ -Metadata-Version: 2.1 -Name: sedna -Version: 0.4.5 -Summary: The sedna package is designed to help developers better use open source frameworks such as tensorflow on Sedna project -Home-page: https://github.com/kubeedge/sedna -Author: jaypume -Author-email: pujie2@huawei.com -Maintainer: jaypume -Maintainer-email: -License: Apache License 2.0 -Platform: UNKNOWN -Classifier: Programming Language :: Python :: 3 -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Operating System :: POSIX :: Linux -Requires-Python: >=3.6 -Description-Content-Type: text/markdown -Requires-Dist: numpy (>=1.13.3) -Requires-Dist: colorlog (~=4.7.2) -Requires-Dist: websockets (~=9.1) -Requires-Dist: requests (>=2.24.0) -Requires-Dist: PyYAML (~=5.4.1) -Requires-Dist: setuptools (~=54.2.0) -Requires-Dist: fastapi (~=0.68.1) -Requires-Dist: pydantic (>=1.8.1) -Requires-Dist: tenacity (~=8.0.1) -Requires-Dist: joblib (~=1.0.1) -Requires-Dist: pandas (~=1.1.5) -Requires-Dist: six (~=1.15.0) -Requires-Dist: minio (~=7.0.3) -Requires-Dist: uvicorn (~=0.14.0) -Requires-Dist: opencv-python (~=4.5.5) -Provides-Extra: fl -Requires-Dist: plato-learn (~=0.26) ; extra == 'fl' -Provides-Extra: il -Provides-Extra: ji -Provides-Extra: ll -Requires-Dist: scikit-learn (~=0.24.1) ; extra == 'll' -Provides-Extra: me - - -# Sedna Python SDK - -The Sedna Python Software Development Kit (SDK) aims to provide developers with a convenient yet flexible tool to write the Sedna applications. - -This document introduces how to obtain and call Sedna Python SDK. - - -## Introduction - -Expose the Edge AI features to applications, i.e. training or inference programs. - -## Requirements and Installation -The build process is tested with Python 3.6, Ubuntu 18.04.5 LTS - -```bash -# Clone the repo -git clone --recursive https://github.com/kubeedge/sedna.git -cd sedna/lib - -# Build the pip package -python setup.py bdist_wheel - -# Install the pip package -pip install dist/sedna*.whl - -``` - -Install via Setuptools - -```bash -# Install dependence -pip install -r requirements.txt - -# Install sedna -python setup.py install --user -``` - -## Use Python SDK - -0. (optional) Check `Sedna` version - ```bash - $ python -c "import sedna; print(sedna.__version__)" - ``` - -1. Import the required modules as follows: - - ```python - from sedna.core.joint_inference import JointInference, BigModelService - from sedna.core.federated_learning import FederatedLearning - from sedna.core.incremental_learning import IncrementalLearning - from sedna.core.lifelong_learning import LifelongLearning - - ``` - -2. Define an `Estimator`: - - ```python - - import os - - # Keras - import keras - from keras.layers import Dense, MaxPooling2D, Conv2D, Flatten, Dropout - from keras.models import Sequential - - os.environ['BACKEND_TYPE'] = 'KERAS' - - def KerasEstimator(): - model = Sequential() - model.add(Conv2D(64, kernel_size=(3, 3), - activation="relu", strides=(2, 2), - input_shape=(128, 128, 3))) - model.add(MaxPooling2D(pool_size=(2, 2))) - model.add(Conv2D(32, kernel_size=(3, 3), activation="relu")) - model.add(MaxPooling2D(pool_size=(2, 2))) - model.add(Flatten()) - model.add(Dropout(0.25)) - model.add(Dense(64, activation="relu")) - model.add(Dense(32, activation="relu")) - model.add(Dropout(0.5)) - model.add(Dense(2, activation="softmax")) - - model.compile(loss="categorical_crossentropy", - optimizer="adam", - metrics=["accuracy"]) - loss = keras.losses.CategoricalCrossentropy(from_logits=True) - metrics = [keras.metrics.categorical_accuracy] - optimizer = keras.optimizers.Adam(learning_rate=0.1) - model.compile(loss=loss, optimizer=optimizer, metrics=metrics) - return model - ``` - - ```python - # XGBOOST - - import os - import xgboost - - os.environ['BACKEND_TYPE'] = 'SKLEARN' - - XGBEstimator = xgboost.XGBClassifier( - learning_rate=0.1, - n_estimators=600, - max_depth=2, - min_child_weight=1, - gamma=0, - subsample=0.8, - colsample_bytree=0.8, - objective="multi:softmax", - num_class=3, - nthread=4, - seed=27 - ) - - ``` - - ```python - - # Customize - - class Estimator: - - def __init__(self, **kwargs): - ... - - def load(self, model_url=""): - ... - - def save(self, model_path=None): - ... - - def predict(self, data, **kwargs): - ... - - def evaluate(self, valid_data, **kwargs): - ... - - def train(self, train_data, valid_data=None, **kwargs): - ... - - ``` - > **Notes**: Estimator is a high-level API that greatly simplifies machine learning programming. Estimators encapsulate training, evaluation, prediction, and exporting for your model. - - -3. Initialize a Incremental Learning Job: - - ```python - - # get hard exmaple mining algorithm from config - hard_example_mining = IncrementalLearning.get_hem_algorithm_from_config( - threshold_img=0.9 - ) - - # create Incremental Learning infernece instance - il_job = IncrementalLearning( - estimator=Estimator, - hard_example_mining=hard_example_mining - ) - - ``` - - where: - - - `IncrementalLearning` is the Cloud-edge job you want to access. - - `Estimator` is the base model for your ML job. - - `hard_example_mining` is the parameters of incremental learning job. - - Inference - --------- - - > **Note:** The `job parameters` of each feature are different. - -4. Running Job - training / inference / evaluation. - - ```python - results, final_res, is_hard_example = il_job.inference( - img_rgb, - post_process=deal_infer_rsl, - input_shape=input_shape - ) - - ``` - - where: - - - `img_rgb` is the sample used to inference - - `deal_infer_rsl` is a function used to process result after model predict - - `input_shape` is the parameters of `Estimator` in inference - - `results` is the result predicted by model - - `final_res` is the result after process by `deal_infer_rsl` - - `is_hard_example` tells if the sample is hard sample or not - -## Customize algorithm - -Sedna provides a class called `class_factory.py` in `common` package, in which only a few lines of changes are required to become a module of sedna. - -Two classes are defined in `class_factory.py`, namely `ClassType` and `ClassFactory`. - -`ClassFactory` can register the modules you want to reuse through decorators. For example, in the following code example, you have customized an **hard_example_mining algorithm**, you only need to add a line of `ClassFactory.register(ClassType.HEM)` to complete the registration. - -```python - -@ClassFactory.register(ClassType.HEM, alias="Threshold") -class ThresholdFilter(BaseFilter, abc.ABC): - def __init__(self, threshold=0.5, **kwargs): - self.threshold = float(threshold) - - def __call__(self, infer_result=None): - # if invalid input, return False - if not (infer_result - and all(map(lambda x: len(x) > 4, infer_result))): - return False - - image_score = 0 - - for bbox in infer_result: - image_score += bbox[4] - - average_score = image_score / (len(infer_result) or 1) - return average_score < self.threshold -``` - -After registration, you only need to change the name of the hem and parameters in the yaml file, and then the corresponding class will be automatically called according to the name. - -```yaml -deploySpec: - hardExampleMining: - name: "Threshold" - parameters: - - key: "threshold" - value: "0.9" -``` - - diff --git a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD b/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD deleted file mode 100644 index cc2345ae..00000000 --- a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/RECORD +++ /dev/null @@ -1,120 +0,0 @@ -sedna/README.md,sha256=RV3cDgUX94RS0Szr_U2Kg4bTpU0oQsGsLAAJjbCp73g,6781 -sedna/VERSION,sha256=IacfbrT6zX2RguVpt8lA4z-AIbXIOsk3lnRlhxAOFKs,7 -sedna/__init__.py,sha256=d5xfiCchgxh3dnNGiWIOmAEoOFj95972kWrsUYhdXgY,664 -sedna/__version__.py,sha256=Q2qTzZw791xAanAtj1z64AHzQwnrI5KduOvS6JN_Tkw,657 -sedna/algorithms/__init__.py,sha256=LHALMySPKI8OMmk8wSc7lMd9XlAB1QDzRnbIJF1QUrE,896 -sedna/algorithms/aggregation/__init__.py,sha256=HF83IxUpNlQR7jpoQyr5PUpCR144Dtu7tDRs_ql_ooM,690 -sedna/algorithms/aggregation/aggregation.py,sha256=qgtzP0Ww1efHyWD47ZbH768dCUlZtJON9pkzEL82ilU,4052 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-Wheel-Version: 1.0 -Generator: bdist_wheel (0.34.2) -Root-Is-Purelib: true -Tag: py3-none-any - diff --git a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/top_level.txt b/examples/resources/third_party-bk/sedna-0.4.5.dist-info/top_level.txt deleted file mode 100644 index 2b4e7692..00000000 --- a/examples/resources/third_party-bk/sedna-0.4.5.dist-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -sedna diff --git a/examples/semantic_segmentation/lifelong_learning_bench/README.md b/examples/semantic_segmentation/lifelong_learning_bench/README.md index 46366074..26ec4c44 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/README.md +++ b/examples/semantic_segmentation/lifelong_learning_bench/README.md @@ -26,7 +26,7 @@ cd /ianvs #One might use another path preferred mkdir project cd project -git clone -b feature-lifelong-n https://github.com/kubeedge/ianvs.git +git clone https://github.com/kubeedge/ianvs.git ``` @@ -52,22 +52,28 @@ Datasets and models can be large. To avoid over-size projects in the Github repo not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. ``` shell -cd /ianvs #One might use another path preferred -mkdir dataset -cd dataset -wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip -unzip dataset.zip +cd /root #One might use another path preferred +mkdir data +cd data +wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/sedna-robo/semantic_segmentation_dataset.zip +unzip semantic_segmentation_dataset.zip ``` The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. +Copy the index files of dataset. + +``` shell +cp /ianvs/project/ianvs/examples/semantic_segmentation/lifelong_learning_bench/indexes/* /root/data/semantic_segmentation_dataset/ +``` + Related algorithm is also ready in this quick start. ``` shell -export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet +export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet ``` The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick @@ -79,7 +85,7 @@ We are now ready to run the ianvs for benchmarking. ``` shell cd /ianvs/project/ianvs -ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml +ianvs -f examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml ``` Finally, the user can check the result of benchmarking on the console and also in the output path( @@ -91,7 +97,7 @@ The final output might look like this: |rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | |:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| -|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | +|1 |rfnet_lifelong_learning for small test | 0.3009 |0.4807 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | This ends the quick start experiment. diff --git a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-fulltest.yaml b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-fulltest.yaml new file mode 100644 index 00000000..df3ebbc2 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-fulltest.yaml @@ -0,0 +1,74 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml" + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml" + testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning for full test" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml new file mode 100644 index 00000000..5a3e6c43 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml @@ -0,0 +1,74 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml" + testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml" + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning for small test" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-traveltest.yaml b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-traveltest.yaml new file mode 100644 index 00000000..1b3d11d7 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-traveltest.yaml @@ -0,0 +1,74 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml" + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml" + testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-travel.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning for travel mode" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-new-split.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-index-sort.txt similarity index 100% rename from examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt rename to examples/semantic_segmentation/lifelong_learning_bench/indexes/test-index-sort.txt diff --git a/examples/semantic_segmentation/lifelong_learning_bench/test-index.txt b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-index.txt similarity index 100% rename from examples/semantic_segmentation/lifelong_learning_bench/test-index.txt rename to examples/semantic_segmentation/lifelong_learning_bench/indexes/test-index.txt diff --git a/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small-3.txt b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small-3.txt new file mode 100644 index 00000000..fae2a558 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small-3.txt @@ -0,0 +1,66 @@ +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_009688_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_009688_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_009688_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_017228_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_017228_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_017228_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_004859_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_004859_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_004859_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_011715_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_011715_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_011715_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_017101_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_017101_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_017101_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_028232_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_028232_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_028232_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_035864_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_035864_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_035864_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_044525_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_044525_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_044525_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_051737_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_051737_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_051737_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_057954_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_057954_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_057954_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_063045_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_063045_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_063045_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_068682_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_068682_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_068682_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_080091_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000001_080091_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_080091_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000012_000019_leftImg8bit.png ./test/depth/cityscapes_real/lindau/lindau_000012_000019_disparity.png ./test/gtFine/cityscapes_real/lindau/lindau_000012_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000032_000019_leftImg8bit.png ./test/depth/cityscapes_real/lindau/lindau_000032_000019_disparity.png ./test/gtFine/cityscapes_real/lindau/lindau_000032_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000052_000019_leftImg8bit.png ./test/depth/cityscapes_real/lindau/lindau_000052_000019_disparity.png ./test/gtFine/cityscapes_real/lindau/lindau_000052_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000013_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000013_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000013_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000033_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000033_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000033_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000053_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000053_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000053_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000073_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000073_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000073_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000093_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000093_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000093_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000113_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000113_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000113_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000133_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000133_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000133_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000153_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000153_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000153_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000173_000019_leftImg8bit.png ./test/depth/cityscapes_real/munster/munster_000173_000019_disparity.png ./test/gtFine/cityscapes_real/munster/munster_000173_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/synthia_sim/1109.png ./test/depth/synthia_sim/1109.png ./test/gtFine/synthia_sim/1109TrainIds.png +./test/leftImg8bit/synthia_sim/122.png ./test/depth/synthia_sim/122.png ./test/gtFine/synthia_sim/122TrainIds.png +./test/leftImg8bit/synthia_sim/1329.png ./test/depth/synthia_sim/1329.png ./test/gtFine/synthia_sim/1329TrainIds.png +./test/leftImg8bit/synthia_sim/1433.png ./test/depth/synthia_sim/1433.png ./test/gtFine/synthia_sim/1433TrainIds.png +./test/leftImg8bit/synthia_sim/1548.png ./test/depth/synthia_sim/1548.png ./test/gtFine/synthia_sim/1548TrainIds.png +./test/leftImg8bit/synthia_sim/1662.png ./test/depth/synthia_sim/1662.png ./test/gtFine/synthia_sim/1662TrainIds.png +./test/leftImg8bit/synthia_sim/1778.png ./test/depth/synthia_sim/1778.png ./test/gtFine/synthia_sim/1778TrainIds.png +./test/leftImg8bit/synthia_sim/1882.png ./test/depth/synthia_sim/1882.png ./test/gtFine/synthia_sim/1882TrainIds.png +./test/leftImg8bit/synthia_sim/1997.png ./test/depth/synthia_sim/1997.png ./test/gtFine/synthia_sim/1997TrainIds.png +./test/leftImg8bit/synthia_sim/212.png ./test/depth/synthia_sim/212.png ./test/gtFine/synthia_sim/212TrainIds.png +./test/leftImg8bit/synthia_sim/235.png ./test/depth/synthia_sim/235.png ./test/gtFine/synthia_sim/235TrainIds.png +./test/leftImg8bit/synthia_sim/26.png ./test/depth/synthia_sim/26.png ./test/gtFine/synthia_sim/26TrainIds.png +./test/leftImg8bit/synthia_sim/283.png ./test/depth/synthia_sim/283.png ./test/gtFine/synthia_sim/283TrainIds.png +./test/leftImg8bit/synthia_sim/306.png ./test/depth/synthia_sim/306.png ./test/gtFine/synthia_sim/306TrainIds.png +./test/leftImg8bit/synthia_sim/33.png ./test/depth/synthia_sim/33.png ./test/gtFine/synthia_sim/33TrainIds.png +./test/leftImg8bit/synthia_sim/353.png ./test/depth/synthia_sim/353.png ./test/gtFine/synthia_sim/353TrainIds.png +./test/leftImg8bit/synthia_sim/378.png ./test/depth/synthia_sim/378.png ./test/gtFine/synthia_sim/378TrainIds.png +./test/leftImg8bit/synthia_sim/4.png ./test/depth/synthia_sim/4.png ./test/gtFine/synthia_sim/4TrainIds.png +./test/leftImg8bit/synthia_sim/423.png ./test/depth/synthia_sim/423.png ./test/gtFine/synthia_sim/423TrainIds.png +./test/leftImg8bit/synthia_sim/446.png ./test/depth/synthia_sim/446.png ./test/gtFine/synthia_sim/446TrainIds.png +./test/leftImg8bit/synthia_sim/470.png ./test/depth/synthia_sim/470.png ./test/gtFine/synthia_sim/470TrainIds.png +./test/leftImg8bit/synthia_sim/495.png ./test/depth/synthia_sim/495.png ./test/gtFine/synthia_sim/495TrainIds.png +./test/leftImg8bit/synthia_sim/517.png ./test/depth/synthia_sim/517.png ./test/gtFine/synthia_sim/517TrainIds.png +./test/leftImg8bit/synthia_sim/54.png ./test/depth/synthia_sim/54.png ./test/gtFine/synthia_sim/54TrainIds.png +./test/leftImg8bit/synthia_sim/565.png ./test/depth/synthia_sim/565.png ./test/gtFine/synthia_sim/565TrainIds.png +./test/leftImg8bit/synthia_sim/589.png ./test/depth/synthia_sim/589.png ./test/gtFine/synthia_sim/589TrainIds.png +./test/leftImg8bit/synthia_sim/610.png ./test/depth/synthia_sim/610.png ./test/gtFine/synthia_sim/610TrainIds.png +./test/leftImg8bit/synthia_sim/634.png ./test/depth/synthia_sim/634.png ./test/gtFine/synthia_sim/634TrainIds.png +./test/leftImg8bit/synthia_sim/66.png ./test/depth/synthia_sim/66.png ./test/gtFine/synthia_sim/66TrainIds.png +./test/leftImg8bit/synthia_sim/682.png ./test/depth/synthia_sim/682.png ./test/gtFine/synthia_sim/682TrainIds.png +./test/leftImg8bit/synthia_sim/703.png ./test/depth/synthia_sim/703.png ./test/gtFine/synthia_sim/703TrainIds.png +./test/leftImg8bit/synthia_sim/729.png ./test/depth/synthia_sim/729.png ./test/gtFine/synthia_sim/729TrainIds.png +./test/leftImg8bit/synthia_sim/753.png ./test/depth/synthia_sim/753.png ./test/gtFine/synthia_sim/753TrainIds.png +./test/leftImg8bit/synthia_sim/775.png ./test/depth/synthia_sim/775.png ./test/gtFine/synthia_sim/775TrainIds.png +./test/leftImg8bit/synthia_sim/799.png ./test/depth/synthia_sim/799.png ./test/gtFine/synthia_sim/799TrainIds.png +./test/leftImg8bit/synthia_sim/822.png ./test/depth/synthia_sim/822.png ./test/gtFine/synthia_sim/822TrainIds.png +./test/leftImg8bit/synthia_sim/847.png ./test/depth/synthia_sim/847.png ./test/gtFine/synthia_sim/847TrainIds.png +./test/leftImg8bit/synthia_sim/870.png ./test/depth/synthia_sim/870.png ./test/gtFine/synthia_sim/870TrainIds.png +./test/leftImg8bit/synthia_sim/894.png ./test/depth/synthia_sim/894.png ./test/gtFine/synthia_sim/894TrainIds.png +./test/leftImg8bit/synthia_sim/917.png ./test/depth/synthia_sim/917.png ./test/gtFine/synthia_sim/917TrainIds.png +./test/leftImg8bit/synthia_sim/940.png ./test/depth/synthia_sim/940.png ./test/gtFine/synthia_sim/940TrainIds.png diff --git a/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small.txt b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small.txt new file mode 100644 index 00000000..192575b6 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/indexes/test-small.txt @@ -0,0 +1,66 @@ +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_009688_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_009688_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_017228_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_017228_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_004859_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_004859_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_011715_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_011715_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_017101_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_017101_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_028232_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_028232_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_035864_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_035864_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_044525_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_044525_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_051737_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_051737_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_057954_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_057954_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_063045_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_063045_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_068682_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_068682_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000001_080091_leftImg8bit.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000001_080091_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000012_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/lindau/lindau_000012_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000032_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/lindau/lindau_000032_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/lindau/lindau_000052_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/lindau/lindau_000052_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000013_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000013_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000033_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000033_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000053_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000053_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000073_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000073_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000093_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000093_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000113_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000113_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000133_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000133_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000153_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000153_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/munster/munster_000173_000019_leftImg8bit.png ./test/gtFine/cityscapes_real/munster/munster_000173_000019_gtFine_labelTrainIds.png +./test/leftImg8bit/synthia_sim/1109.png ./test/gtFine/synthia_sim/1109TrainIds.png +./test/leftImg8bit/synthia_sim/122.png ./test/gtFine/synthia_sim/122TrainIds.png 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./val/gtFine/synthia_sim/549TrainIds.png +./val/leftImg8bit/synthia_sim/624.png ./val/gtFine/synthia_sim/624TrainIds.png +./val/leftImg8bit/synthia_sim/704.png ./val/gtFine/synthia_sim/704TrainIds.png +./val/leftImg8bit/synthia_sim/778.png ./val/gtFine/synthia_sim/778TrainIds.png +./val/leftImg8bit/synthia_sim/851.png ./val/gtFine/synthia_sim/851TrainIds.png +./val/leftImg8bit/synthia_sim/928.png ./val/gtFine/synthia_sim/928TrainIds.png +./val/leftImg8bit/synthia_sim/96.png ./val/gtFine/synthia_sim/96TrainIds.png +./val/leftImg8bit/synthia_sim/981.png ./val/gtFine/synthia_sim/981TrainIds.png diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py index 2c71f7b1..ec1f25bf 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py @@ -25,9 +25,6 @@ def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **k else: test_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES - # custom_set = cityscapes.CityscapesSegmentation(args, split='custom_resize') - # custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) - # return train_loader, val_loader, test_loader, custom_loader, num_class return train_loader, val_loader, test_loader, num_class if args.dataset == 'citylostfound': diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py index 74be3296..43743145 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -56,12 +56,8 @@ def __len__(self): return len(self.images[self.split]) def __getitem__(self, index): - #print(type(self.images[self.split][index])) - #print(self.images[self.split][index]) img_path = self.images[self.split][index].rstrip() disp_path = self.disparities[self.split][index].rstrip() - #print(img_path) - #print(index) try: lbl_path = self.labels[self.split][index].rstrip() _img = Image.open(img_path).convert('RGB') diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py index dfcb2f0c..43f87a3a 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py @@ -193,56 +193,3 @@ def load_my_state_dict(model, state_dict): # custom function to load model when own_state[name].copy_(param) return model - -# def main(): -# parser = argparse.ArgumentParser(description="PyTorch RFNet validation") -# parser.add_argument('--dataset', type=str, default='cityscapes', -# choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], -# help='dataset name (default: cityscapes)') -# parser.add_argument('--workers', type=int, default=4, -# metavar='N', help='dataloader threads') -# parser.add_argument('--base-size', type=int, default=1024, -# help='base image size') -# parser.add_argument('--crop-size', type=int, default=768, -# help='crop image size') -# parser.add_argument('--batch-size', type=int, default=6, -# help='batch size for training') -# parser.add_argument('--val-batch-size', type=int, default=1, -# metavar='N', help='input batch size for \ -# validating (default: auto)') -# parser.add_argument('--test-batch-size', type=int, default=1, -# metavar='N', help='input batch size for \ -# testing (default: auto)') -# parser.add_argument('--no-cuda', action='store_true', default= -# False, help='disables CUDA training') -# parser.add_argument('--gpu-ids', type=str, default='0', -# help='use which gpu to train, must be a \ -# comma-separated list of integers only (default=0)') -# parser.add_argument('--checkname', type=str, default=None, -# help='set the checkpoint name') -# parser.add_argument('--weight-path', type=str, default=None, -# help='enter your path of the weight') -# parser.add_argument('--color-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/color/', -# help='path to save label') -# parser.add_argument('--merge-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/merge/', -# help='path to save merged label') -# parser.add_argument('--label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/label/', -# help='path to save merged label') -# parser.add_argument('--merge', action='store_true', default=False, help='merge image and label') -# parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') -# -# -# args = parser.parse_args() -# args.cuda = not args.no_cuda and torch.cuda.is_available() -# if args.cuda: -# try: -# args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] -# except ValueError: -# raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') -# -# validator = Validator(args) -# validator.validate() -# -# -# if __name__ == "__main__": -# main() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-new-split.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-new-split.yaml new file mode 100644 index 00000000..6a5e5641 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-new-split.yaml @@ -0,0 +1,63 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.95 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + #splitting_method: "default" + splitting_method: "city_splitting" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + - epochs: + values: + - 1 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml index 4a89c5d6..9267d96c 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -12,8 +12,8 @@ algorithm: # the method of splitting dataset; string type; optional; # currently the options of value are as follows: # 1> "default": the dataset is evenly divided based train_ratio; - #splitting_method: "default" - splitting_method: "my_splitting" + splitting_method: "default" + #splitting_method: "my_splitting" # algorithm module configuration in the paradigm; list type; modules: @@ -34,7 +34,7 @@ algorithm: - 0.0001 - epochs: values: - - 3 + - 1 # 2> "task_definition": define lifelong task ; optional module; - type: "task_definition" # name of python module; string type; diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml index 45698513..d822f2ce 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml @@ -2,11 +2,11 @@ testenv: # dataset configuration dataset: # the url address of train dataset index; string type; - #train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" + #train_url: "/root/data/semantic_segmentation_dataset/train-small-3.txt" train_url: "/root/data/semantic_segmentation_dataset/train-index.txt" # the url address of test dataset index; string type; - #test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" - test_url: "/root/data/semantic_segmentation_dataset/test-index.txt" + #test_url: "/root/data/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" # model eval configuration of incremental learning; model_eval: @@ -16,6 +16,7 @@ testenv: name: "accuracy" # the url address of python file url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" # condition of triggering inference model to update # threshold of the condition; types are float/int @@ -33,4 +34,4 @@ testenv: - name: "samples_transfer_ratio" # incremental rounds setting; int type; default value is 2; - incremental_rounds: 2 \ No newline at end of file + incremental_rounds: 3 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml index ec98db3e..94e97f58 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml @@ -2,10 +2,10 @@ testenv: # dataset configuration dataset: # the url address of train dataset index; string type; - train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" + train_url: "/root/data/semantic_segmentation_dataset/train-small-3.txt" #train_url: "/root/data/semantic_segmentation_dataset/train-index.txt" # the url address of test dataset index; string type; - test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-small-3.txt" #test_url: "/root/data/semantic_segmentation_dataset/test-index.txt" # model eval configuration of incremental learning; @@ -16,6 +16,7 @@ testenv: name: "accuracy" # the url address of python file url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" # condition of triggering inference model to update # threshold of the condition; types are float/int @@ -33,4 +34,4 @@ testenv: - name: "samples_transfer_ratio" # incremental rounds setting; int type; default value is 2; - incremental_rounds: 3 \ No newline at end of file + incremental_rounds: 2 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-travel.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-travel.yaml new file mode 100644 index 00000000..ac3fc760 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-travel.yaml @@ -0,0 +1,37 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + #train_url: "/root/data/semantic_segmentation_dataset/train-small-3.txt" + train_url: "/root/data/semantic_segmentation_dataset/train-index-sort.txt" + # the url address of test dataset index; string type; + #test_url: "/root/data/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 3 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml index d726c693..2ed864af 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml @@ -2,11 +2,11 @@ testenv: # dataset configuration dataset: # the url address of train dataset index; string type; - #train_url: "/ianvs/dataset/semantic_segmentation_dataset/train-small-3.txt" - train_url: "/root/data/semantic_segmentation_dataset/train-index-sort.txt" + train_url: "/root/data/semantic_segmentation_dataset/train-small-3.txt" + #train_url: "/root/data/semantic_segmentation_dataset/train-index-sort.txt" # the url address of test dataset index; string type; - #test_url: "/ianvs/dataset/semantic_segmentation_dataset/test-small-3.txt" - test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" + test_url: "/root/data/semantic_segmentation_dataset/test-small-3.txt" + #test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" # model eval configuration of incremental learning; model_eval: @@ -16,6 +16,7 @@ testenv: name: "accuracy" # the url address of python file url: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" # condition of triggering inference model to update # threshold of the condition; types are float/int @@ -33,4 +34,4 @@ testenv: - name: "samples_transfer_ratio" # incremental rounds setting; int type; default value is 2; - incremental_rounds: 3 \ No newline at end of file + incremental_rounds: 2 \ No newline at end of file From 778db3927ff4c9c56e47d519fda257a3be7ac023 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Sat, 20 May 2023 01:26:52 +0800 Subject: [PATCH 18/22] update BWT and FWT Signed-off-by: hsj576 update some changes Signed-off-by: hsj576 update some changes Signed-off-by: hsj576 fix pylint issues Signed-off-by: hsj576 update ReadMe Signed-off-by: hsj576 update ReadMe 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a/core/cmd/__init__.py b/core/cmd/__init__.py old mode 100644 new mode 100755 diff --git a/core/cmd/benchmarking.py b/core/cmd/benchmarking.py old mode 100644 new mode 100755 diff --git a/core/cmd/obj/__init__.py b/core/cmd/obj/__init__.py old mode 100644 new mode 100755 diff --git a/core/cmd/obj/benchmarkingjob.py b/core/cmd/obj/benchmarkingjob.py old mode 100644 new mode 100755 diff --git a/core/common/__init__.py b/core/common/__init__.py old mode 100644 new mode 100755 diff --git a/core/common/constant.py b/core/common/constant.py old mode 100644 new mode 100755 index 16df05fd..f1c74891 --- a/core/common/constant.py +++ b/core/common/constant.py @@ -67,6 +67,8 @@ class SystemMetricType(Enum): System metric type of ianvs. """ SAMPLES_TRANSFER_RATIO = "samples_transfer_ratio" + FWT = "FWT" + BWT = "BWT" class TestObjectType(Enum): diff --git a/core/common/log.py b/core/common/log.py old mode 100644 new mode 100755 diff --git a/core/common/utils.py b/core/common/utils.py old mode 100644 new mode 100755 diff --git a/core/storymanager/__init__.py b/core/storymanager/__init__.py old mode 100644 new mode 100755 diff --git a/core/storymanager/rank/__init__.py b/core/storymanager/rank/__init__.py old mode 100644 new mode 100755 diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py old mode 100644 new mode 100755 index 8312ec16..c23d0c63 --- a/core/storymanager/rank/rank.py +++ b/core/storymanager/rank/rank.py @@ -173,6 +173,7 @@ def _get_all(self, test_cases, test_results) -> pd.DataFrame: return self._sort_all_df(all_df, self._get_all_metric_names(test_results)) def _save_all(self): + # pylint: disable=E1101 all_df = copy.deepcopy(self.all_df) all_df.index = pd.np.arange(1, len(all_df) + 1) all_df.to_csv(self.all_rank_file, index_label="rank", encoding="utf-8", sep=" ") @@ -202,6 +203,7 @@ def _get_selected(self, test_cases, test_results) -> pd.DataFrame: return selected_df def _save_selected(self, test_cases, test_results): + # pylint: disable=E1101 selected_df = self._get_selected(test_cases, test_results) selected_df.index = pd.np.arange(1, len(selected_df) + 1) selected_df.to_csv(self.selected_rank_file, index_label="rank", encoding="utf-8", sep=" ") diff --git a/core/storymanager/visualization/__init__.py b/core/storymanager/visualization/__init__.py old mode 100644 new mode 100755 diff --git a/core/storymanager/visualization/visualization.py b/core/storymanager/visualization/visualization.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/__init__.py b/core/testcasecontroller/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/__init__.py b/core/testcasecontroller/algorithm/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/algorithm.py b/core/testcasecontroller/algorithm/algorithm.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/module/__init__.py b/core/testcasecontroller/algorithm/module/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/module/module.py b/core/testcasecontroller/algorithm/module/module.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/__init__.py b/core/testcasecontroller/algorithm/paradigm/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py old mode 100644 new mode 100755 index 69251d78..9fd6c380 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -16,7 +16,8 @@ import os import shutil - +import pickle +import matplotlib.pyplot as plt import numpy as np from sedna.datasources import BaseDataSource @@ -25,6 +26,7 @@ from core.testcasecontroller.metrics import get_metric_func from core.common.utils import get_file_format, is_local_dir +os.environ['CUDA_VISIBLE_DEVICES'] = '0' class LifelongLearning(ParadigmBase): # pylint: disable=too-many-locals @@ -63,10 +65,13 @@ def __init__(self, workspace, **kwargs): self.model_eval_config = kwargs.get("model_eval") self.cloud_task_index = '/tmp/cloud_task/index.pkl' self.edge_task_index = '/tmp/edge_task/index.pkl' - self.system_metric_info = {SystemMetricType.SAMPLES_TRANSFER_RATIO.value: []} + self.system_metric_info = {SystemMetricType.SAMPLES_TRANSFER_RATIO.value: [], + SystemMetricType.BWT.value : {}, SystemMetricType.FWT.value: {}} def run(self): # pylint:disable=duplicate-code + # pylint: disable=R0912 + # pylint: disable=R0915 """ run the test flow of incremental learning paradigm. @@ -87,29 +92,94 @@ def run(self): if mode == 'no-inference': dataset_files = self._split_dataset(splitting_dataset_times=rounds) # pylint: disable=C0103 + # pylint: disable=C0206 + # pylint: disable=C0201 + my_dict = {} for r in range(1, rounds + 1): if r == 1: train_dataset_file, eval_dataset_file = dataset_files[r - 1] self.cloud_task_index = self._train(self.cloud_task_index, train_dataset_file, r) - self.edge_task_index = self._eval(self.cloud_task_index, + tmp_dict = {} + for j in range(rounds): + _, eval_dataset_file = dataset_files[j] + self.edge_task_index, tasks_detail, res = self.my_eval( + self.cloud_task_index, eval_dataset_file, r) + print("train from round ", r) + print("test round ", j+1) + print(f"all scores: {res}") + score_list = tmp_dict.get("all", []) + score_list.append(res) + tmp_dict["all"] = score_list + for detail in tasks_detail: + scores = detail.scores + entry = detail.entry + print(f"{entry} scores: {scores}") + score_list = tmp_dict.get(entry, []) + score_list.append(scores) + tmp_dict[entry] = score_list + for key in tmp_dict.keys(): + scores_list = my_dict.get(key, []) + scores_list.append(tmp_dict[key]) + my_dict[key] = scores_list + print(f"{key} scores: {scores_list}") + else: infer_dataset_file, eval_dataset_file = dataset_files[r - 1] self.cloud_task_index = self._train(self.cloud_task_index, infer_dataset_file, r) - self.edge_task_index = self._eval(self.cloud_task_index, + tmp_dict = {} + for j in range(rounds): + _, eval_dataset_file = dataset_files[j] + self.edge_task_index, tasks_detail, res = self.my_eval( + self.cloud_task_index, eval_dataset_file, r) + print("train from round ", r) + print("test round ", j+1) + print(f"all scores: {res}") + score_list = tmp_dict.get("all", []) + score_list.append(res) + tmp_dict["all"] = score_list + for detail in tasks_detail: + scores = detail.scores + entry = detail.entry + print(f"{entry} scores: {scores}") + score_list = tmp_dict.get(entry, []) + score_list.append(scores) + tmp_dict[entry] = score_list + for key in tmp_dict.keys(): + scores_list = my_dict.get(key, []) + scores_list.append(tmp_dict[key]) + my_dict[key] = scores_list + print(f"{key} scores: {scores_list}") + + self.edge_task_index = self._eval(self.cloud_task_index, + self.dataset.test_url, + r+1) job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) inference_dataset = self.dataset.load_data(self.dataset.test_url, "eval", feature_process=_data_feature_process) kwargs = {} test_res = job.my_inference(inference_dataset, **kwargs) del job + output_dir = os.path.join(self.workspace, + "output/result.pkl") + for key in my_dict.keys(): + print(f"{key} scores: {my_dict[key]}") + with open(output_dir,'wb') as file: + pickle.dump(my_dict,file) + for key in my_dict.keys(): + matrix = my_dict[key] + BWT, FWT = self.compute(key, matrix) + self.system_metric_info[SystemMetricType.BWT.value][key] = BWT + self.system_metric_info[SystemMetricType.FWT.value][key] = FWT + + file.close() elif mode != 'multi-inference': dataset_files = self._split_dataset(splitting_dataset_times=rounds) @@ -149,6 +219,63 @@ def run(self): return test_res, self.system_metric_info + def compute(self, key, matrix): + """ + compute BWT and FWT + """ + # pylint: disable=C0103 + length = len(matrix) + accuracy = 0.0 + BWT_score = 0.0 + FWT_score = 0.0 + flag = True + for i in range(length): + if len(matrix[i]) != length: + flag = False + break + if flag is False: + BWT_score = np.nan + FWT_score = np.nan + return BWT_score, FWT_score + + for i in range(length): + accuracy += matrix[length-1][i]['accuracy'] + BWT_score += matrix[length-1][i]['accuracy'] - matrix[i][i]['accuracy'] + for i in range(1,length): + FWT_score += matrix[i-1][i]['accuracy'] - matrix[0][i]['accuracy'] + accuracy = accuracy/(length) + BWT_score = BWT_score/(length-1) + FWT_score = FWT_score/(length-1) + #print(f"{key} accuracy: ", accuracy) + print(f"{key} BWT_score: ", BWT_score) + print(f"{key} FWT_score: ", FWT_score) + my_matrix = [] + for i in range(length): + my_matrix.append([]) + for i in range(length): + for j in range(length): + my_matrix[i].append(matrix[j][i]['accuracy']) + self.draw_picture(key,my_matrix) + return BWT_score, FWT_score + + def draw_picture(self, title, matrix): + """ + draw hot image for results + """ + plt.figure(figsize=(10,8), dpi=80) + plt.imshow(matrix, cmap='bwr', extent=(0.5,len(matrix)+0.5,0.5,len(matrix)+0.5), + origin='lower') + plt.xticks(fontsize=15) + plt.yticks(fontsize=15) + plt.xlabel('task round', fontsize=15) + plt.ylabel('task', fontsize=15) + plt.title(title, fontsize=15) + plt.colorbar(format='%.2f') + output_dir = os.path.join(self.workspace, + f"output/{title}-heatmap.png") + plt.savefig(output_dir) + plt.show() + def _inference(self, edge_task_index, data_index_file, rounds): # pylint:disable=duplicate-code #print("start inference") @@ -245,6 +372,33 @@ def _eval(self, cloud_task_index, data_index_file, rounds): return edge_task_index + def my_eval(self, cloud_task_index, data_index_file, rounds): + """ + evaluate models + """ + eval_output_dir = os.path.join(self.workspace, f"output/eval/{rounds}") + if not is_local_dir(eval_output_dir): + os.makedirs(eval_output_dir) + + model_eval_info = self.model_eval_config + model_metric = model_eval_info.get("model_metric") + + os.environ["OUTPUT_URL"] = eval_output_dir + os.environ["model_threshold"] = str(model_eval_info.get("threshold")) + os.environ["operator"] = model_eval_info.get("operator") + os.environ["MODEL_URLS"] = f"{cloud_task_index}" + + eval_dataset = self.dataset.load_data(data_index_file, "eval", + feature_process=_data_feature_process) + + job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) + _, metric_func = get_metric_func(model_metric) + edge_task_index, tasks_detail, res = job.my_evaluate(eval_dataset, metrics=metric_func) + + del job + + return edge_task_index, tasks_detail, res + def _split_dataset(self, splitting_dataset_times=1): # pylint:disable=duplicate-code train_dataset_ratio = self.incremental_learning_data_setting.get("train_ratio") diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/generation_assistant/__init__.py b/core/testcasecontroller/generation_assistant/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/generation_assistant/generation_assistant.py b/core/testcasecontroller/generation_assistant/generation_assistant.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/metrics/__init__.py b/core/testcasecontroller/metrics/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py old mode 100644 new mode 100755 index 99ebca4f..4da01191 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -47,6 +47,19 @@ def samples_transfer_ratio_func(system_metric_info: dict): transfer_num += len(transfer_data) return round(float(transfer_num) / (inference_num + 1), 4) +def bwt_func(system_metric_info: dict): + """ + compute BWT + """ + info = system_metric_info.get(SystemMetricType.BWT.value) + return info["all"] + +def fwt_func(system_metric_info: dict): + """ + compute FWT + """ + info = system_metric_info.get(SystemMetricType.FWT.value) + return info["all"] def get_metric_func(metric_dict: dict): """ diff --git a/core/testcasecontroller/testcase/__init__.py b/core/testcasecontroller/testcase/__init__.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/testcase/testcase.py b/core/testcasecontroller/testcase/testcase.py old mode 100644 new mode 100755 diff --git a/core/testcasecontroller/testcasecontroller.py b/core/testcasecontroller/testcasecontroller.py old mode 100644 new mode 100755 diff --git a/core/testenvmanager/__init__.py b/core/testenvmanager/__init__.py old mode 100644 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zLq&~Wq(@0`cvi1HXRww*A%h!!&O^wtP+m}h5+MI4k@bfz9R(o + + +Related algorithm is also ready in this quick start. +``` shell +export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet +``` + +The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick +start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +``` shell +cd /ianvs/project/ianvs +ianvs -f examples/robot/lifelong_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( +e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( +e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers +can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +The final output might look like this: + + +| rank | algorithm | accuracy | BWT | FWT | paradigm | basemodel | task_definition | task_allocation | basemodel-learning_rate | basemodel-epochs | task_definition-origins | task_allocation-origins | time | url | +|:------:|:-------------------------:|:--------------------:|:---------------------:|:---------------------:|:------------------:|:-----------:|:------------------------:|:------------------------:|:-------------------------:|:------------------:|:-------------------------:|:-------------------------:|:---------------------:|:---------------------------------------------------------------------------------------------------------------------------------:| +| 1 | rfnet_lifelong_learning | 0.2970033189775575 | 0.04239649121511442 | 0.02299711942108413 | lifelonglearning | BaseModel | TaskDefinitionByOrigin | TaskAllocationByOrigin | 0.0001 | 1 | ['front', 'garden'] | ['front', 'garden'] | 2023-05-24 15:07:57 | /ianvs/lifelong_learning_bench/robot-workspace-bwt/benchmarkingjob/rfnet_lifelong_learning/efdc47a2-f9fb-11ed-8f8b-0242ac110007 | + + + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml new file mode 100755 index 00000000..fced6b6d --- /dev/null +++ b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,72 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/robot-workspace-bwt" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "BWT": "descend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "BWT", "FWT"] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py new file mode 100755 index 00000000..4fb068f1 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py @@ -0,0 +1,38 @@ +from basemodel import val_args +from utils.metrics import Evaluator +from tqdm import tqdm +from dataloaders import make_data_loader +from sedna.common.class_factory import ClassType, ClassFactory + +__all__ = ('accuracy') + +@ClassFactory.register(ClassType.GENERAL) +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(args.gpu_ids), target.cuda(args.gpu_ids) + if args.depth: + depth = depth.cuda(args.gpu_ids) + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py new file mode 100755 index 00000000..2684a997 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py @@ -0,0 +1,315 @@ +import os +import numpy as np +import torch +from PIL import Image +import argparse +from train import Trainer +from eval import Validator +from tqdm import tqdm +from eval import load_my_state_dict +from utils.metrics import Evaluator +from dataloaders import make_data_loader +from dataloaders import custom_transforms as tr +from torchvision import transforms +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.datasources import TxtDataParse +from torch.utils.data import DataLoader +from sedna.common.file_ops import FileOps +from utils.lr_scheduler import LR_Scheduler + +def preprocess(image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images + +class Model: + def __init__(self, **kwargs): + self.val_args = val_args() + self.train_args = train_args() + + self.train_args.lr = kwargs.get("learning_rate", 1e-4) + self.train_args.epochs = kwargs.get("epochs", 2) + self.train_args.eval_interval = kwargs.get("eval_interval", 2) + self.train_args.no_val = kwargs.get("no_val", True) + # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) + self.trainer = None + + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range(self.trainer.args.start_epoch, self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and \ + (epoch % self.trainer.args.eval_interval == (self.trainer.args.eval_interval - 1) + or epoch == self.trainer.args.epochs - 1): + # save checkpoint when it meets eval_interval or the training finished + is_best = False + checkpoint_path = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + # if not self.trainer.args.no_val and \ + # epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \ + # and self.trainer.val_loader: + # self.trainer.validation(epoch) + + self.trainer.writer.close() + + return checkpoint_path + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = preprocess(data.x) + predictions = self.predict(samples) + return accuracy(data.y, predictions) + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: how to save unstructured data model + pass + +def train_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet Training") + parser.add_argument('--depth', action="store_true", default=False, + help='training with depth image or not (default: False)') + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--loss-type', type=str, default='ce', + choices=['ce', 'focal'], + help='loss func type (default: ce)') + # training hyper params + # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # help='number of epochs to train (default: auto)') + parser.add_argument('--epochs', type=int, default=None, metavar='N', + help='number of epochs to train (default: auto)') + parser.add_argument('--start_epoch', type=int, default=0, + metavar='N', help='start epochs (default:0)') + parser.add_argument('--batch-size', type=int, default=None, + metavar='N', help='input batch size for \ + training (default: auto)') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--use-balanced-weights', action='store_true', default=False, + help='whether to use balanced weights (default: True)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + # optimizer params + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help='learning rate (default: auto)') + parser.add_argument('--lr-scheduler', type=str, default='cos', + choices=['poly', 'step', 'cos', 'inv'], + help='lr scheduler mode: (default: cos)') + parser.add_argument('--momentum', type=float, default=0.9, + metavar='M', help='momentum (default: 0.9)') + parser.add_argument('--weight-decay', type=float, default=2.5e-5, + metavar='M', help='w-decay (default: 5e-4)') + # cuda, seed and logging + parser.add_argument('--no-cuda', action='store_true', default= + False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') + # checking point + parser.add_argument('--resume', type=str, + default=None, + help='put the path to resuming file if needed') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + # finetuning pre-trained models + parser.add_argument('--ft', action='store_true', default=True, + help='finetuning on a different dataset') + # evaluation option + parser.add_argument('--eval-interval', type=int, default=1, + help='evaluation interval (default: 1)') + parser.add_argument('--no-val', action='store_true', default=False, + help='skip validation during training') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + print(torch.cuda.is_available()) + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + if args.epochs is None: + epoches = { + 'cityscapes': 200, + 'citylostfound': 200, + } + args.epochs = epoches[args.dataset.lower()] + + if args.batch_size is None: + args.batch_size = 4 * len(args.gpu_ids) + + if args.test_batch_size is None: + args.test_batch_size = args.batch_size + + if args.lr is None: + lrs = { + 'cityscapes': 0.0001, + 'citylostfound': 0.0001, + 'cityrand': 0.0001 + } + args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size + + if args.checkname is None: + args.checkname = 'RFNet' + print(args) + torch.manual_seed(args.seed) + + return args + +def val_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet validation") + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--batch-size', type=int, default=6, + help='batch size for training') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + validating (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", + help='enter your path of the weight') + parser.add_argument('--save-predicted-image', action='store_true', default=False, + help='save predicted images') + parser.add_argument('--color-label-save-path', type=str, + default='./test/color/', + help='path to save label') + parser.add_argument('--merge-label-save-path', type=str, + default='./test/merge/', + help='path to save merged label') + parser.add_argument('--label-save-path', type=str, default='./test/label/', + help='path to save merged label') + parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') + parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + return args + +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(args.gpu_ids), target.cuda(args.gpu_ids) + if args.depth: + depth = depth.cuda(args.gpu_ids) + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA + +if __name__ == '__main__': + model_path = "/tmp/RFNet/" + if not os.path.exists(model_path): + os.makedirs(model_path) + + p1 = Process(target=exp_train, args=(10,)) + p1.start() + p1.join() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py new file mode 100755 index 00000000..ec1f25bf --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py @@ -0,0 +1,116 @@ +from dataloaders.datasets import cityscapes, citylostfound, cityrand, target, xrlab, e1, mapillary +from torch.utils.data import DataLoader + +def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **kwargs): + + if args.dataset == 'cityscapes': + if train_data is not None: + train_set = cityscapes.CityscapesSegmentation(args, data=train_data, split='train') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + else: + train_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if valid_data is not None: + val_set = cityscapes.CityscapesSegmentation(args, data=valid_data, split='val') + num_class = val_set.NUM_CLASSES + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + else: + val_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if test_data is not None: + test_set = cityscapes.CityscapesSegmentation(args, data=test_data, split='test') + num_class = test_set.NUM_CLASSES + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + test_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + return train_loader, val_loader, test_loader, num_class + + if args.dataset == 'citylostfound': + if args.depth: + train_set = citylostfound.CitylostfoundSegmentation(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + train_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, num_class + if args.dataset == 'cityrand': + train_set = cityrand.CityscapesSegmentation(args, split='train') + val_set = cityrand.CityscapesSegmentation(args, split='val') + test_set = cityrand.CityscapesSegmentation(args, split='test') + custom_set = cityrand.CityscapesSegmentation(args, split='custom_resize') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'target': + train_set = target.CityscapesSegmentation(args, split='train') + val_set = target.CityscapesSegmentation(args, split='val') + test_set = target.CityscapesSegmentation(args, split='test') + custom_set = target.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'xrlab': + train_set = xrlab.CityscapesSegmentation(args, split='train') + val_set = xrlab.CityscapesSegmentation(args, split='val') + test_set = xrlab.CityscapesSegmentation(args, split='test') + custom_set = xrlab.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'e1': + train_set = e1.CityscapesSegmentation(args, split='train') + val_set = e1.CityscapesSegmentation(args, split='val') + test_set = e1.CityscapesSegmentation(args, split='test') + custom_set = e1.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'mapillary': + train_set = mapillary.CityscapesSegmentation(args, split='train') + val_set = mapillary.CityscapesSegmentation(args, split='val') + test_set = mapillary.CityscapesSegmentation(args, split='test') + custom_set = mapillary.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + else: + raise NotImplementedError + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py new file mode 100755 index 00000000..d63f200a --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py @@ -0,0 +1,240 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + # mean and std for original depth images + mean_depth = 0.12176 + std_depth = 0.09752 + + depth /= 255.0 + depth -= mean_depth + depth /= std_depth + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + depth = torch.from_numpy(depth).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'depth': depth, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + depth = depth.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + depth = depth.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # depth = depth.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + depth = depth.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + depth = depth.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + depth = ImageOps.expand(depth, border=(0, 0, padw, padh), fill=0) # depth多余的部分填0 + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + + assert img.size == depth.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + depth = depth.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py new file mode 100755 index 00000000..e04ef5a3 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py @@ -0,0 +1,230 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return {'image': img, + 'label': mask} + + +class Normalize_test(object): + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample + img = np.array(img).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return img + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + print(img.size) + + return {'image': img, + 'label': mask} + +class ToTensor_test(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + + img = torch.from_numpy(img).float() + + return img + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + + assert img.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py new file mode 100755 index 00000000..e69de29b diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py new file mode 100755 index 00000000..6ffd0a4b --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py @@ -0,0 +1,276 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr +from dataloaders import custom_transforms_rgb as tr_rgb + +class CitylostfoundSegmentation(data.Dataset): + NUM_CLASSES = 20 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root,'disparity',self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix= '.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix= '.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, + suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'depth': _depth, 'label': _target} + + # data augment + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) # background->255 ignore + input = tr.Relabel(1, 0)(input) # road 1->0 + # input = Relabel(255, 20)(input) # unlabel 20 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=None): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + if isinstance(suffix, str): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + elif isinstance(suffix, list): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for x in suffix for filename in filenames if filename.startswith(x)] + + + def transform_tr(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + +class CitylostfoundSegmentation_rgb(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.files = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.files[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.files[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.files[split]: + raise Exception("No files for split=[%s] found in %s" % (split, self.images_base)) + + print("Found %d %s images" % (len(self.files[split]), split)) + + def __len__(self): + return len(self.files[self.split]) + + def __getitem__(self, index): + + img_path = self.files[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'label': _target} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) + input = tr.Relabel(1, 0)(input) # road 1->0 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.RandomHorizontalFlip(), + tr_rgb.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.FixedResize(size=self.args.crop_size), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CitylostfoundSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py new file mode 100755 index 00000000..74eddb67 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('cityrand'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py new file mode 100755 index 00000000..4396f5f4 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -0,0 +1,160 @@ +import os +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 30 # 25 + + def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"): + + # self.root = root + self.root = "/home/lsq/Dataset/" + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.disparities_base = os.path.join(self.root, self.split, "depth", "cityscapes_real") + self.images[split] = [img[0] for img in data.x] if hasattr(data, "x") else data + + + if hasattr(data, "x") and len(data.x[0]) == 1: + # TODO: fit the case that depth images don't exist. + self.disparities[split] = self.images[split] + elif hasattr(data, "x") and len(data.x[0]) == 2: + self.disparities[split] = [img[1] for img in data.x] + else: + if len(data[0]) == 2: + self.images[split] = [img[0] for img in data] + self.disparities[split] = [img[1] for img in data] + elif len(data[0]) == 1: + self.images[split] = [img[0] for img in data] + self.disparities[split] = [img[0] for img in data] + else: + self.images[split] = data + self.disparities[split] = data + + self.labels[split] = data.y if hasattr(data, "y") else data + + self.ignore_index = 255 + + if len(self.images[split]) == 0: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if len(self.disparities[split]) == 0: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py new file mode 100755 index 00000000..40e06e98 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('e1'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py new file mode 100755 index 00000000..d665649b --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('mapillary'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 768 + args.crop_size = 768 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py new file mode 100755 index 00000000..739e85f8 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('target'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt new file mode 100755 index 00000000..3c81afef --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt @@ -0,0 +1,38 @@ +for i in range(len(nam_label)): + img_label = cv2.imread(label_ori_path+nam_label[i], -1)[:,:,2] + img_label_temp = img_label.copy() + img_label_temp[img_label == 0] = 22 + img_label_temp[img_label == 1] = 10 + img_label_temp[img_label == 2] = 2 + img_label_temp[img_label == 3] = 0 + img_label_temp[img_label == 4] = 1 + img_label_temp[img_label == 5] = 4 + img_label_temp[img_label == 6] = 8 + img_label_temp[img_label == 7] = 5 + img_label_temp[img_label == 8] = 13 + img_label_temp[img_label == 9] = 7 + img_label_temp[img_label == 10] = 11 + img_label_temp[img_label == 11] = 18 + img_label_temp[img_label == 12] = 17 + img_label_temp[img_label == 13] = 21 + img_label_temp[img_label == 14] = 20 + img_label_temp[img_label == 15] = 6 + img_label_temp[img_label == 16] = 9 + img_label_temp[img_label == 17] = 12 + img_label_temp[img_label == 18] = 14 + img_label_temp[img_label == 19] = 15 + img_label_temp[img_label == 20] = 16 + img_label_temp[img_label == 21] = 3 + img_label_temp[img_label == 22] = 19 + #print(img_label) + #img_label[img_label == 0] = 10 + #img_label[img_label == 6] = 0 + #img_label[img_label == 5] = 11 + #img_label[img_label == 1] = 5 + #img_label[img_label == 2] = 1 + #img_label[img_label == 4] = 9 + #img_label[img_label == 3] = 4 + #img_label[img_label == 7] = 8 + #img_label[img_label == 11] = 2 + img_resize_lab = cv2.resize(img_label_temp, (2048,1024), interpolation=cv2.INTER_NEAREST) + cv2.imwrite(label_save_path+str(i)+'TrainIds.png', img_resize_lab.astype(np.uint16)) \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py new file mode 100755 index 00000000..4b261fcd --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 25 + + def __init__(self, args, root=Path.db_root_dir('xrlab'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py new file mode 100755 index 00000000..ef572332 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py @@ -0,0 +1,244 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +def decode_seg_map_sequence(label_masks, dataset='pascal'): + rgb_masks = [] + for label_mask in label_masks: + rgb_mask = decode_segmap(label_mask, dataset) + rgb_masks.append(rgb_mask) + rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2])) # change for val + return rgb_masks + + +def decode_segmap(label_mask, dataset, plot=False): + """Decode segmentation class labels into a color image + Args: + label_mask (np.ndarray): an (M,N) array of integer values denoting + the class label at each spatial location. + plot (bool, optional): whether to show the resulting color image + in a figure. + Returns: + (np.ndarray, optional): the resulting decoded color image. + """ + if dataset == 'pascal' or dataset == 'coco': + n_classes = 21 + label_colours = get_pascal_labels() + elif dataset == 'cityscapes': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'target': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'cityrand': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'citylostfound': + n_classes = 20 + label_colours = get_citylostfound_labels() + elif dataset == 'xrlab': + n_classes = 25 + label_colours = get_cityscapes_labels() + elif dataset == 'e1': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'mapillary': + n_classes = 24 + label_colours = get_cityscapes_labels() + else: + raise NotImplementedError + + r = label_mask.copy() + g = label_mask.copy() + b = label_mask.copy() + for ll in range(0, n_classes): + r[label_mask == ll] = label_colours[ll, 0] + g[label_mask == ll] = label_colours[ll, 1] + b[label_mask == ll] = label_colours[ll, 2] + rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3)) # change for val + # rgb = torch.ByteTensor(3, label_mask.shape[0], label_mask.shape[1]).fill_(0) + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + # r = torch.from_numpy(r) + # g = torch.from_numpy(g) + # b = torch.from_numpy(b) + + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + if plot: + plt.imshow(rgb) + plt.show() + else: + return rgb + + +def encode_segmap(mask): + """Encode segmentation label images as pascal classes + Args: + mask (np.ndarray): raw segmentation label image of dimension + (M, N, 3), in which the Pascal classes are encoded as colours. + Returns: + (np.ndarray): class map with dimensions (M,N), where the value at + a given location is the integer denoting the class index. + """ + mask = mask.astype(int) + label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16) + for ii, label in enumerate(get_pascal_labels()): + label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii + label_mask = label_mask.astype(int) + return label_mask + + +def get_cityscapes_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [119, 11, 119], + [128, 64, 64], + [102, 10, 156], + [102, 102, 15], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156]]) + +def get_citylostfound_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [111, 74, 0]]) + + +def get_pascal_labels(): + """Load the mapping that associates pascal classes with label colors + Returns: + np.ndarray with dimensions (21, 3) + """ + return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], + [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], + [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], + [0, 64, 128]]) + + +def colormap_bdd(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([128, 64, 128]) + cmap[1,:] = np.array([244, 35, 232]) + cmap[2,:] = np.array([ 70, 70, 70]) + cmap[3,:] = np.array([102, 102, 156]) + cmap[4,:] = np.array([190, 153, 153]) + cmap[5,:] = np.array([153, 153, 153]) + + cmap[6,:] = np.array([250, 170, 30]) + cmap[7,:] = np.array([220, 220, 0]) + cmap[8,:] = np.array([107, 142, 35]) + cmap[9,:] = np.array([152, 251, 152]) + cmap[10,:]= np.array([70, 130, 180]) + + cmap[11,:]= np.array([220, 20, 60]) + cmap[12,:]= np.array([255, 0, 0]) + cmap[13,:]= np.array([0, 0, 142]) + cmap[14,:]= np.array([0, 0, 70]) + cmap[15,:]= np.array([0, 60, 100]) + + cmap[16,:]= np.array([0, 80, 100]) + cmap[17,:]= np.array([0, 0, 230]) + cmap[18,:]= np.array([119, 11, 32]) + cmap[19,:]= np.array([111, 74, 0]) #多加了一类small obstacle + + return cmap + +def colormap_bdd0(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([0, 0, 0]) + cmap[1,:] = np.array([70, 130, 180]) + cmap[2,:] = np.array([70, 70, 70]) + cmap[3,:] = np.array([128, 64, 128]) + cmap[4,:] = np.array([244, 35, 232]) + cmap[5,:] = np.array([64, 64, 128]) + + cmap[6,:] = np.array([107, 142, 35]) + cmap[7,:] = np.array([153, 153, 153]) + cmap[8,:] = np.array([0, 0, 142]) + cmap[9,:] = np.array([220, 220, 0]) + cmap[10,:]= np.array([220, 20, 60]) + + cmap[11,:]= np.array([119, 11, 32]) + cmap[12,:]= np.array([0, 0, 230]) + cmap[13,:]= np.array([250, 170, 160]) + cmap[14,:]= np.array([128, 64, 64]) + cmap[15,:]= np.array([250, 170, 30]) + + cmap[16,:]= np.array([152, 251, 152]) + cmap[17,:]= np.array([255, 0, 0]) + cmap[18,:]= np.array([0, 0, 70]) + cmap[19,:]= np.array([0, 60, 100]) #small obstacle + cmap[20,:]= np.array([0, 80, 100]) + cmap[21,:]= np.array([102, 102, 156]) + cmap[22,:]= np.array([102, 102, 156]) + + return cmap + +class Colorize: + + def __init__(self, n=24): # n = nClasses + # self.cmap = colormap(256) + self.cmap = colormap_bdd(256) + self.cmap[n] = self.cmap[-1] + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + # print(size) + color_images = torch.ByteTensor(size[0], 3, size[1], size[2]).fill_(0) + # color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0) + + # for label in range(1, len(self.cmap)): + for i in range(color_images.shape[0]): + for label in range(0, len(self.cmap)): + mask = gray_image[0] == label + # mask = gray_image == label + + color_images[i][0][mask] = self.cmap[label][0] + color_images[i][1][mask] = self.cmap[label][1] + color_images[i][2][mask] = self.cmap[label][2] + + return color_images diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py new file mode 100755 index 00000000..2d303429 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py @@ -0,0 +1,198 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import time +import torch +from torchvision.transforms import ToPILImage +from PIL import Image + +from dataloaders import make_data_loader +from dataloaders.utils import decode_seg_map_sequence, Colorize +from utils.metrics import Evaluator +from models.rfnet import RFNet +from models import rfnet_for_unseen +from models.resnet.resnet_single_scale_single_attention import * +from models.resnet import resnet_single_scale_single_attention_unseen +import torch.backends.cudnn as cudnn + +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +class Validator(object): + def __init__(self, args, data=None, unseen_detection=False): + self.args = args + self.time_train = [] + self.num_class = args.num_class + + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + # _, self.val_loader, _, self.custom_loader, self.num_class = make_data_loader(args, **kwargs) + _, _, self.test_loader, _ = make_data_loader(args, test_data=data, **kwargs) + print('un_classes:'+str(self.num_class)) + + # Define evaluator + self.evaluator = Evaluator(self.num_class) + + # Define network + if unseen_detection: + self.resnet = resnet_single_scale_single_attention_unseen.\ + resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = rfnet_for_unseen.RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + else: + self.resnet = resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + + if args.cuda: + #self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + self.model = self.model.cuda(args.gpu_ids) + cudnn.benchmark = True # accelarate speed + print('Model loaded successfully!') + + # # Load weights + # assert os.path.exists(args.weight_path), 'weight-path:{} doesn\'t exit!'.format(args.weight_path) + # self.new_state_dict = torch.load(args.weight_path, map_location=torch.device("cpu")) + # self.model = load_my_state_dict(self.model, self.new_state_dict['state_dict']) + + def validate(self): + #print("start validating 55") + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + predictions = [] + for i, (sample, image_name) in enumerate(tbar):#self.test_loader: + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + # spec = time.time() + image, target = sample['image'], sample['label'] + #print(self.args.cuda, self.args.gpu_ids) + if self.args.cuda: + image = image.cuda(self.args.gpu_ids) + if self.args.depth: + depth = depth.cuda(self.args.gpu_ids) + + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + + if self.args.cuda: + torch.cuda.synchronize() + + pred = output.data.cpu().numpy() + # todo + pred = np.argmax(pred, axis=1) + predictions.append(pred) + + if not self.args.save_predicted_image: + continue + + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + print(pre_labels.shape) + # save + for i in range(pre_colors.shape[0]): + print(image_name[0]) + + if not image_name[0]: + img_name = "test.png" + else: + img_name = os.path.basename(image_name[0]) + + color_label_name = os.path.join(self.args.color_label_save_path, img_name) + label_name = os.path.join(self.args.label_save_path, img_name) + merge_label_name = os.path.join(self.args.merge_label_save_path, img_name) + + os.makedirs(os.path.dirname(color_label_name), exist_ok=True) + os.makedirs(os.path.dirname(merge_label_name), exist_ok=True) + os.makedirs(os.path.dirname(label_name), exist_ok=True) + + pre_color_image = ToPILImage()(pre_colors[i]) # pre_colors.dtype = float64 + pre_color_image.save(color_label_name) + + pre_label_image = ToPILImage()(pre_labels[i]) + pre_label_image.save(label_name) + + if (self.args.merge): + image_merge(image[i], pre_color_image, merge_label_name) + print('save image: {}'.format(merge_label_name)) + #print("start validating 120") + return predictions + + def task_divide(self): + seen_task_samples, unseen_task_samples = [], [] + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + for i, (sample, image_name) in enumerate(tbar): + + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if self.args.cuda: + image = image.cuda(self.args.gpu_ids) + if self.args.depth: + depth = depth.cuda(self.args.gpu_ids) + start_time = time.time() + with torch.no_grad(): + if self.args.depth: + output_, output, _ = self.model(image, depth) + else: + output_, output, _ = self.model(image) + if self.args.cuda: + torch.cuda.synchronize() + if i != 0: + fwt = time.time() - start_time + self.time_train.append(fwt) + print("Forward time per img (bath size=%d): %.3f (Mean: %.3f)" % ( + self.args.val_batch_size, fwt / self.args.val_batch_size, + sum(self.time_train) / len(self.time_train) / self.args.val_batch_size)) + time.sleep(0.1) # to avoid overheating the GPU too much + + # pred colorize + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + for i in range(pre_colors.shape[0]): + task_sample = dict() + task_sample.update(image=sample["image"][i]) + task_sample.update(label=sample["label"][i]) + if self.args.depth: + task_sample.update(depth=sample["depth"][i]) + + if torch.max(pre_labels) == output.shape[1] - 1: + unseen_task_samples.append((task_sample, image_name[i])) + else: + seen_task_samples.append((task_sample, image_name[i])) + + return seen_task_samples, unseen_task_samples + +def image_merge(image, label, save_name): + image = ToPILImage()(image.detach().cpu().byte()) + # width, height = image.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + image = image.crop((left, top, right, bottom)) + # resize + image = image.resize(label.size, Image.BILINEAR) + + image = image.convert('RGBA') + label = label.convert('RGBA') + image = Image.blend(image, label, 0.6) + image.save(save_name) + +def load_my_state_dict(model, state_dict): # custom function to load model when not all dict elements + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + print('{} not in model_state'.format(name)) + continue + else: + own_state[name].copy_(param) + + return model diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py new file mode 100755 index 00000000..3734266e --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py new file mode 100755 index 00000000..e69de29b diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py new file mode 100755 index 00000000..63d81991 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py @@ -0,0 +1,391 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = []#[ self.spp, self.upsample] + self.fine_tune += [self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x.detach(), self.layer4) + features += [self.spp.forward(skip)] + return features + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer1) + y, skip_depth = self.forward_resblock(y.detach(), self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 2 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer2) + y, skip_depth = self.forward_resblock(y.detach(), self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 3 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer3) + y, skip_depth = self.forward_resblock(y.detach(), self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 4 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer4) + y, skip_depth = self.forward_resblock(y.detach(), self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + return features + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + i = 0 + for skip, up in zip(features[1:], self.upsample): + i += 1 + #print(len(self.upsample)) + if i < len(self.upsample): + x = up(x, skip) + else: + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + return self.forward_up(self.forward_down(rgb)) + else: + return self.forward_up(self.forward_down_fusion(rgb, depth)) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py new file mode 100755 index 00000000..9668734e --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py @@ -0,0 +1,396 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp +import cv2 +import numpy as np + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = [ self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def output_num(self): + return self.__in_features + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x, self.layer4) + features += [self.spp.forward(skip)] + features_da = self.spp.forward(skip) + return features, features_da + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x, self.layer1) + y, skip_depth = self.forward_resblock(y, self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 2 + x, skip_rgb = self.forward_resblock(x, self.layer2) + y, skip_depth = self.forward_resblock(y, self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 3 + x, skip_rgb = self.forward_resblock(x, self.layer3) + y, skip_depth = self.forward_resblock(y, self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 4 + x, skip_rgb = self.forward_resblock(x, self.layer4) + y, skip_depth = self.forward_resblock(y, self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + features_da = self.spp.forward(x) + return features, features_da + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + for skip, up in zip(features[1:], self.upsample): + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + down_features, da_features = self.forward_down(rgb) + x, additional = self.forward_up(down_features) + return x, additional, da_features#self.forward_up(self.forward_down(rgb)), self.forward_down(rgb) + else: + down_features, da_features = self.forward_down_fusion(rgb, depth) + x, additional = self.forward_up(down_features) + #print(down_features.shape) + return x, additional, da_features#self.forward_up(self.forward_down_fusion(rgb, depth)), self.forward_down_fusion(rgb, depth) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py new file mode 100755 index 00000000..87f02863 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py @@ -0,0 +1,27 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + print(self.backbone.num_features) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional = self.backbone(rgb_inputs, depth_inputs) + logits = self.logits.forward(x) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + #print(logits_upsample.size) + return logits_upsample + + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py new file mode 100755 index 00000000..f61eb1ce --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py @@ -0,0 +1,33 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + #self.bottleneck = _BNReluConv(self.backbone.num_features, 128, k = 3, batch_norm=use_bn) + #self.logits = _BNReluConv(128, self.num_classes+1, k = 1, batch_norm=use_bn) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + #self.logits_target = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + self.logits_aux = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional, da_features = self.backbone(rgb_inputs, depth_inputs) + #print(additional['features'][0].shape) + #bottleneck = self.bottleneck(x) + logits = self.logits.forward(x) + logits_aux = self.logits_aux.forward(x) + #print(logits_aux.shape) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + logits_aux_upsample = upsample(logits_aux, rgb_inputs.shape[2:]) + return logits_upsample, logits_aux_upsample, da_features + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.logits_aux.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py new file mode 100755 index 00000000..5c86e759 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False) +batchnorm_momentum = 0.01 / 2 + + +def get_n_params(parameters): + pp = 0 + for p in parameters: + nn = 1 + for s in list(p.size()): + nn = nn * s + pp += nn + return pp + + +class _BNReluConv(nn.Sequential): + def __init__(self, num_maps_in, num_maps_out, k=3, batch_norm=True, bn_momentum=0.1, bias=False, dilation=1): + super(_BNReluConv, self).__init__() + if batch_norm: + self.add_module('norm', nn.BatchNorm2d(num_maps_in, momentum=bn_momentum)) + self.add_module('relu', nn.ReLU(inplace=batch_norm is True)) + padding = k // 2 # same conv + self.add_module('conv', nn.Conv2d(num_maps_in, num_maps_out, + kernel_size=k, padding=padding, bias=bias, dilation=dilation)) + + +class _Upsample(nn.Module): + def __init__(self, num_maps_in, skip_maps_in, num_maps_out, use_bn=True, k=3): + super(_Upsample, self).__init__() + print(f'Upsample layer: in = {num_maps_in}, skip = {skip_maps_in}, out = {num_maps_out}') + self.bottleneck = _BNReluConv(skip_maps_in, num_maps_in, k=1, batch_norm=use_bn) + self.blend_conv = _BNReluConv(num_maps_in, num_maps_out, k=k, batch_norm=use_bn) + + def forward(self, x, skip): + skip = self.bottleneck.forward(skip) + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x + + +class SpatialPyramidPooling(nn.Module): + def __init__(self, num_maps_in, num_levels, bt_size=512, level_size=128, out_size=128, + grids=(6, 3, 2, 1), square_grid=False, bn_momentum=0.1, use_bn=True): + super(SpatialPyramidPooling, self).__init__() + self.grids = grids + self.square_grid = square_grid + self.spp = nn.Sequential() + self.spp.add_module('spp_bn', + _BNReluConv(num_maps_in, bt_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + num_features = bt_size + final_size = num_features + for i in range(num_levels): + final_size += level_size + self.spp.add_module('spp' + str(i), + _BNReluConv(num_features, level_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + self.spp.add_module('spp_fuse', + _BNReluConv(final_size, out_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + + def forward(self, x): + levels = [] + target_size = x.size()[2:4] + + ar = target_size[1] / target_size[0] + + x = self.spp[0].forward(x) + levels.append(x) + num = len(self.spp) - 1 + + for i in range(1, num): + if not self.square_grid: + grid_size = (self.grids[i - 1], max(1, round(ar * self.grids[i - 1]))) + x_pooled = F.adaptive_avg_pool2d(x, grid_size) + else: + x_pooled = F.adaptive_avg_pool2d(x, self.grids[i - 1]) + level = self.spp[i].forward(x_pooled) + + level = upsample(level, target_size) + levels.append(level) + x = torch.cat(levels, 1) + x = self.spp[-1].forward(x) + return x + + +class _UpsampleBlend(nn.Module): + def __init__(self, num_features, use_bn=True): + super(_UpsampleBlend, self).__init__() + self.blend_conv = _BNReluConv(num_features, num_features, k=3, batch_norm=use_bn) + + def forward(self, x, skip): + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py new file mode 100755 index 00000000..640544e7 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py @@ -0,0 +1,20 @@ +class Path(object): + @staticmethod + def db_root_dir(dataset): + if dataset == 'cityscapes': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapes/' # folder that contains leftImg8bit/ + elif dataset == 'citylostfound': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapesandlostandfound/' # folder that mixes Cityscapes and Lost and Found + elif dataset == 'cityrand': + return '/home/robo/m0063/project/RFNet-master/Data/cityrand/' + elif dataset == 'target': + return '/home/robo/m0063/project/RFNet-master/Data/target/' + elif dataset == 'xrlab': + return '/home/robo/m0063/project/RFNet-master/Data/xrlab/' + elif dataset == 'e1': + return '/home/robo/m0063/project/RFNet-master/Data/e1/' + elif dataset == 'mapillary': + return '/home/robo/m0063/project/RFNet-master/Data/mapillary/' + else: + print('Dataset {} not available.'.format(dataset)) + raise NotImplementedError diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py new file mode 100755 index 00000000..ed56fffd --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py @@ -0,0 +1,100 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +# os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +# os.environ["video_url"] = "./video/radio.mp4" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" + + +import cv2 +cv2.setNumThreads(0) +cv2.ocl.setUseOpenCL(False) +import time +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +import warnings +from io import BytesIO + +from sedna.datasources import BaseDataSource +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from dataloaders import custom_transforms as tr +from torchvision import transforms + +from accuracy import accuracy +from basemodel import preprocess, val_args, Model + +def preprocess(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def init_ll_job(): + estimator = Model() + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"], + "default": "real" + } + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None) + + return ll_job + +def predict(): + ll_job = init_ll_job() + + camera_address = Context.get_parameters('video_url') + # use video streams for testing + camera = cv2.VideoCapture(camera_address) + fps = 10 + nframe = 0 + while 1: + ret, input_yuv = camera.read() + if not ret: + time.sleep(5) + camera = cv2.VideoCapture(camera_address) + continue + + if nframe % fps: + nframe += 1 + continue + + img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_BGR2RGB) + nframe += 1 + if nframe % 1000 == 1: # logs every 1000 frames + warnings.warn(f"camera is open, current frame index is {nframe}") + + img_rgb = cv2.resize(np.array(img_rgb), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + sample = {'image': img_rgb, "depth": img_rgb, "label": img_rgb} + data = preprocess(sample) + print("Inference results:", ll_job.inference(data=data)) + +if __name__ == '__main__': + predict() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py new file mode 100755 index 00000000..0f0b2c88 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py @@ -0,0 +1,254 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from io import BytesIO +from typing import Optional, Any + +import cv2 +cv2.setNumThreads(0) +cv2.ocl.setUseOpenCL(False) +import numpy as np +from PIL import Image +import uvicorn +import time +from pydantic import BaseModel +from fastapi import FastAPI, UploadFile, File +from fastapi.routing import APIRoute +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +import sedna_predict +from sedna.common.utils import get_host_ip +from dataloaders.datasets.cityscapes import CityscapesSegmentation + + +class ImagePayload(BaseModel): + image: UploadFile = File(...) + depth: Optional[UploadFile] = None + + +class ResultModel(BaseModel): + type: int = 0 + box: Any = None + curr: str = None + future: str = None + img: str = None + + +class ResultResponse(BaseModel): + msg: str = "" + result: Optional[ResultModel] = None + code: int + + +class BaseServer: + # pylint: disable=too-many-instance-attributes,too-many-arguments + DEBUG = True + WAIT_TIME = 15 + + def __init__( + self, + servername: str, + host: str, + http_port: int = 8080, + grpc_port: int = 8081, + workers: int = 1, + ws_size: int = 16 * 1024 * 1024, + ssl_key=None, + ssl_cert=None, + timeout=300): + self.server_name = servername + self.app = None + self.host = host or '0.0.0.0' + self.http_port = http_port or 80 + self.grpc_port = grpc_port + self.workers = workers + self.keyfile = ssl_key + self.certfile = ssl_cert + self.ws_size = int(ws_size) + self.timeout = int(timeout) + protocal = "https" if self.certfile else "http" + self.url = f"{protocal}://{self.host}:{self.http_port}" + + def run(self, app, **kwargs): + if hasattr(app, "add_middleware"): + app.add_middleware( + CORSMiddleware, allow_origins=["*"], allow_credentials=True, + allow_methods=["*"], allow_headers=["*"], + ) + + uvicorn.run( + app, + host=self.host, + port=self.http_port, + ssl_keyfile=self.keyfile, + ssl_certfile=self.certfile, + workers=self.workers, + timeout_keep_alive=self.timeout, + **kwargs) + + def get_all_urls(self): + url_list = [{"path": route.path, "name": route.name} + for route in getattr(self.app, 'routes', [])] + return url_list + + +class InferenceServer(BaseServer): # pylint: disable=too-many-arguments + """ + rest api server for inference + """ + + def __init__( + self, + servername, + host: str, + http_port: int = 5000, + max_buffer_size: int = 104857600, + workers: int = 1): + super( + InferenceServer, + self).__init__( + servername=servername, + host=host, + http_port=http_port, + workers=workers) + + self.job, self.detection_validator = sedna_predict.init_ll_job() + + self.max_buffer_size = max_buffer_size + self.app = FastAPI( + routes=[ + APIRoute( + f"/{servername}", + self.model_info, + methods=["GET"], + ), + APIRoute( + f"/{servername}/predict", + self.predict, + methods=["POST"], + response_model=ResultResponse + ), + ], + log_level="trace", + timeout=600, + ) + self.index_frame = 0 + + def start(self): + return self.run(self.app) + + @staticmethod + def model_info(): + return HTMLResponse( + """

Welcome to the RestNet API!

+

To use this service, send a POST HTTP request to {this-url}/predict

+

The JSON payload has the following format: {"image": "BASE64_STRING_OF_IMAGE", + "depth": "BASE64_STRING_OF_DEPTH"}

+ """) + + async def predict(self, image: UploadFile = File(...), depth: Optional[UploadFile] = None) -> ResultResponse: + contents = await image.read() + recieve_img_time = time.time() + print("Recieve image from the robo:", recieve_img_time) + + image = Image.open(BytesIO(contents)).convert('RGB') + + img_dep = None + self.index_frame = self.index_frame + 1 + + if depth: + depth_contents = await depth.read() + depth = Image.open(BytesIO(depth_contents)).convert('RGB') + img_dep = cv2.resize(np.array(depth), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_dep = Image.fromarray(img_dep) + + img_rgb = cv2.resize(np.array(image), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + + sample = {'image': img_rgb, "depth": img_dep, "label": img_rgb} + results = sedna_predict.predict(self.job, data=sample, validator=self.detection_validator) + + predict_finish_time = time.time() + print(f"Prediction costs {predict_finish_time - recieve_img_time} seconds") + + post_process = True + if results["result"]["box"] is None: + results["result"]["curr"] = None + results["result"]["future"] = None + elif post_process: + curr, future = get_curb(results["result"]["box"]) + results["result"]["curr"] = curr + results["result"]["future"] = future + results["result"]["box"] = None + print("Post process cost at worker:", (time.time()-predict_finish_time)) + else: + results["result"]["curr"] = None + results["result"]["future"] = None + + print("Result transmit to robo time:", time.time()) + return results + +def parse_result(label, count): + label_map = ['road', 'sidewalk', ] + count_d = dict(zip(label, count)) + curb_count = count_d.get(19, 0) + if curb_count / np.sum(count) > 0.3: + return "curb" + r = sorted(label, key=count_d.get, reverse=True)[0] + try: + c = label_map[r] + except: + c = "other" + + return c + +def get_curb(results): + results = np.array(results[0]) + input_height, input_width = results.shape + + closest = np.array([ + [0, int(input_height)], + [int(input_width), + int(input_height)], + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + ]) + + future = np.array([ + [int(0.118 * input_width + .5), + int(.8 * input_height + .5)], + [int(0.882 * input_width + .5), + int(.8 * input_height + .5)], + [int(.765 * input_width + .5), + int(.66 * input_height + .5)], + [int(.235 * input_width + .5), + int(.66 * input_height + .5)] + ]) + + mask = np.zeros((input_height, input_width), dtype=np.uint8) + mask = cv2.fillPoly(mask, [closest], 1) + mask = cv2.fillPoly(mask, [future], 2) + d1, c1 = np.unique(results[mask == 1], return_counts=True) + d2, c2 = np.unique(results[mask == 2], return_counts=True) + c = parse_result(d1, c1) + f = parse_result(d2, c2) + + return c, f + +if __name__ == '__main__': + web_app = InferenceServer("lifelong-learning-robo", host=get_host_ip()) + web_app.start() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py new file mode 100755 index 00000000..56633347 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_evaluate.py @@ -0,0 +1,50 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +# os.environ["test_dataset_url"] = "./data_txt/sedna_data.txt" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" +# os.environ["operator"] = "<" +# os.environ["model_threshold"] = "0" + +from sedna.core.lifelong_learning import LifelongLearning +from sedna.datasources import IndexDataParse +from sedna.common.config import Context + +from accuracy import accuracy +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def eval(): + estimator = Model() + eval_dataset_url = Context.get_parameters("test_dataset_url") + eval_data = IndexDataParse(data_type="eval", func=_load_txt_dataset) + eval_data.parse(eval_dataset_url, use_raw=False) + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.evaluate(eval_data, metrics=accuracy) + + +if __name__ == '__main__': + print(eval()) diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py new file mode 100755 index 00000000..b32c01d2 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_predict.py @@ -0,0 +1,132 @@ +import os + +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# os.environ["UNSEEN_SAVE_URL"] = "s3://kubeedge/sedna-robo/unseen_samples/" +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt" +os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +os.environ["ORIGINAL_DATASET_URL"] = "/tmp" + +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +from io import BytesIO +from torchvision.transforms import ToPILImage +from torchvision import transforms +from torch.utils.data import DataLoader + +from sedna.datasources import IndexDataParse +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from eval import Validator +from accuracy import accuracy +from basemodel import preprocess, val_args, Model +from dataloaders.utils import Colorize +from dataloaders import custom_transforms as tr +from dataloaders.datasets.cityscapes import CityscapesSegmentation + +def _load_txt_dataset(dataset_url): + # use original dataset url, + # see https://github.com/kubeedge/sedna/issues/35 + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def fetch_data(): + test_dataset_url = Context.get_parameters("test_dataset_url") + test_data = IndexDataParse(data_type="test", func=_load_txt_dataset) + test_data.parse(test_dataset_url, use_raw=False) + return test_data + +def pre_data_process(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def post_process(res, is_unseen_task): + if is_unseen_task: + res, base64_string = None, None + else: + res = res[0].tolist() + + type = 0 if not is_unseen_task else 1 + mesg = { + "msg": "", + "result": { + "type": type, + "box": res + }, + "code": 0 + } + return mesg + +def image_merge(raw_img, result): + raw_img = ToPILImage()(raw_img) + + pre_colors = Colorize()(torch.from_numpy(result)) + pre_color_image = ToPILImage()(pre_colors[0]) # pre_colors.dtype = float64 + + image = raw_img.resize(pre_color_image.size, Image.BILINEAR) + image = image.convert('RGBA') + label = pre_color_image.convert('RGBA') + image = Image.blend(image, label, 0.6) + with tempfile.NamedTemporaryFile(suffix='.png') as f: + image.save(f.name) + + with open(f.name, 'rb') as open_file: + byte_content = open_file.read() + base64_bytes = base64.b64encode(byte_content) + base64_string = base64_bytes.decode('utf-8') + return base64_string + +def init_ll_job(): + estimator = Model() + inference_integrate = { + "method": "BBoxInferenceIntegrate" + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + unseen_sample_recognition = { + "method": "SampleRegonitionByRFNet" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=inference_integrate, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=unseen_sample_recognition, + unseen_sample_re_recognition=None) + + args = val_args() + args.weight_path = "./models/detection_model.pth" + args.num_class = 31 + + return ll_job, Validator(args, unseen_detection=True) + +def predict(ll_job, data=None, validator=None): + if data: + data = pre_data_process(data) + else: + data = fetch_data() + data.x = preprocess(data.x) + + res, is_unseen_task, _ = ll_job.inference( + data, validator=validator, initial=False) + return post_process(res, is_unseen_task) + +if __name__ == '__main__': + ll_job, validator = init_ll_job() + print("Inference result:", predict(ll_job, validator=validator)) diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py new file mode 100755 index 00000000..1c99361a --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/sedna_train.py @@ -0,0 +1,78 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +os.environ["OUTPUT_URL"] = "./cloud_kb/" +# os.environ['CLOUD_KB_INDEX'] = "./cloud_kb/index.pkl" +os.environ["TRAIN_DATASET_URL"] = "./data_txt/sedna_data.txt" +os.environ["KB_SERVER"] = "http://0.0.0.0:9020" +os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = "false" + +from sedna.common.file_ops import FileOps +from sedna.datasources import IndexDataParse +from sedna.common.config import Context, BaseConfig +from sedna.core.lifelong_learning import LifelongLearning + +from basemodel import Model + +def _load_txt_dataset(dataset_url): + # use original dataset url + original_dataset_url = Context.get_parameters('original_dataset_url') + return os.path.join(os.path.dirname(original_dataset_url), dataset_url) + +def train(estimator, train_data): + task_definition = { + "method": "TaskDefinitionByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"] + } + } + + ll_job = LifelongLearning(estimator, + task_definition=task_definition, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.train(train_data) + +def update(estimator, train_data): + ll_job = LifelongLearning(estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=None, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=None, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None + ) + + ll_job.update(train_data) + +def run(): + estimator = Model() + train_dataset_url = BaseConfig.train_dataset_url + train_data = IndexDataParse(data_type="train") + train_data.parse(train_dataset_url, use_raw=False) + + is_completed_initilization = str(Context.get_parameters("HAS_COMPLETED_INITIAL_TRAINING", "false")).lower() + if is_completed_initilization == "false": + train(estimator, train_data) + else: + update(estimator, train_data) + +if __name__ == '__main__': + run() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py new file mode 100755 index 00000000..fd9cd657 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/test.py @@ -0,0 +1,52 @@ +import numpy as np +import seaborn as sns +import pandas as pd +import matplotlib.pyplot as plt + +CPA_results = np.load("./cpa_results.npy").T +ratios = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9] +ratio_counts = np.zeros((len(CPA_results), len(ratios)), dtype=float) + +for i in range(len(CPA_results)): + for j in range(len(ratios)): + result = CPA_results[i] + result = result[result <= ratios[j]] + + ratio_counts[i][j] = len(result) / 275 + +plt.figure(figsize=(45, 10)) +ratio_counts = pd.DataFrame(data=ratio_counts.T, index=ratios) +sns.heatmap(data=ratio_counts, annot=True, cmap="YlGnBu", annot_kws={'fontsize': 15}) +plt.xticks(fontsize=20) +plt.yticks(fontsize=25) +plt.xlabel("Test images", fontsize=25) +plt.ylabel("Ratio of PA ranges", fontsize=25) +plt.savefig("./figs/ratio_count.png") +plt.show() + + +# data = pd.DataFrame(CPA_results.T) +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[:15]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/door_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[15:31]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden1_heatmap_pa.png") +# plt.show() +# +# plt.figure(figsize=(30, 15)) +# cpa_result = pd.DataFrame(data=data[31:]) +# sns.heatmap(data=cpa_result) +# plt.savefig("./figs/garden2_heatmap_pa.png") +# plt.show() diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py new file mode 100755 index 00000000..4e673f85 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/train.py @@ -0,0 +1,310 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import torch +import copy + +from mypath import Path +from dataloaders import make_data_loader +from models.rfnet import RFNet +from models.resnet.resnet_single_scale_single_attention import * +from utils.loss import SegmentationLosses +from models.replicate import patch_replication_callback +from utils.calculate_weights import calculate_weigths_labels +from utils.lr_scheduler import LR_Scheduler +from utils.saver import Saver +from utils.summaries import TensorboardSummary +from utils.metrics import Evaluator +from sedna.datasources import BaseDataSource + +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +class Trainer(object): + def __init__(self, args, train_data=None, valid_data=None): + self.args = args + # Define Saver + self.saver = Saver(args) + self.saver.save_experiment_config() + # Define Tensorboard Summary + self.summary = TensorboardSummary(self.saver.experiment_dir) + self.writer = self.summary.create_summary() + # denormalize for detph image + self.mean_depth = torch.as_tensor(0.12176, dtype=torch.float32, device='cpu') + self.std_depth = torch.as_tensor(0.09752, dtype=torch.float32, device='cpu') + self.nclass = args.num_class + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': True} + self.train_loader, self.val_loader, self.test_loader, _ = make_data_loader(args, train_data=train_data, + valid_data=valid_data, **kwargs) + + # Define network + resnet = resnet18(pretrained=True, efficient=False, use_bn=True) + model = RFNet(resnet, num_classes=self.nclass, use_bn=True) + train_params = [{'params': model.random_init_params(), 'lr': args.lr}, + {'params': model.fine_tune_params(), 'lr': 0.1*args.lr, 'weight_decay':args.weight_decay}] + # Define Optimizer + optimizer = torch.optim.Adam(train_params, lr=args.lr, + weight_decay=args.weight_decay) + # Define Criterion + # whether to use class balanced weights + if args.use_balanced_weights: + classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy') + if os.path.isfile(classes_weights_path): + weight = np.load(classes_weights_path) + else: + weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass) + weight = torch.from_numpy(weight.astype(np.float32)) + else: + weight = None + # Define loss function + self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda, gpu_ids=args.gpu_ids).build_loss(mode=args.loss_type) + self.model, self.optimizer = model, optimizer + # Define Evaluator + self.evaluator = Evaluator(self.nclass) + # # Define lr scheduler + self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr, args.epochs, len(self.train_loader)) + # Using cuda + if args.cuda: + #self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + #patch_replication_callback(self.model) + self.model = self.model.cuda(args.gpu_ids) + + # Resuming checkpoint + self.best_pred = 0.0 + if args.resume is not None: + if not os.path.isfile(args.resume): + raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume)) + print(f"Training: load model from {args.resume}") + checkpoint = torch.load(args.resume, map_location=torch.device("cpu")) + args.start_epoch = checkpoint['epoch'] + # if args.cuda: + # self.model.load_state_dict(checkpoint['state_dict']) + # else: + # self.model.load_state_dict(checkpoint['state_dict']) + self.model.load_state_dict(checkpoint['state_dict']) + if not args.ft: + self.optimizer.load_state_dict(checkpoint['optimizer']) + self.best_pred = checkpoint['best_pred'] + print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) + + # Clear start epoch if fine-tuning + if args.ft: + args.start_epoch = 0 + + def get_weight(self): + print("get weight") + current_model = copy.deepcopy(self.model) + return current_model.parameters() + + def set_weight(self, weights): + length = len(weights) + print("set weight", length) + print("model:", self.args.resume) + tau = 0.2 + if length == 1: + for param, target_param in zip(weights[0], self.model.parameters()): + target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) + elif length == 2: + for param1, param2, target_param in zip(weights[0], weights[1], self.model.parameters()): + target_param.data.copy_(0.5 * tau * param1.data + 0.5 * tau * param2.data + (1 - tau) * target_param.data) + + + + def my_training(self, epoch): + train_loss = 0.0 + print(self.optimizer.state_dict()['param_groups'][0]['lr']) + current_model = copy.deepcopy(self.model) + self.model.train() + tbar = tqdm(self.train_loader) + num_img_tr = len(self.train_loader) + + for i, sample in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + #print(target.shape) + else: + image, target = sample['image'], sample['label'] + print(image.shape) + if self.args.cuda: + image, target = image.cuda(self.args.gpu_ids), target.cuda(self.args.gpu_ids) + if self.args.depth: + depth = depth.cuda(self.args.gpu_ids) + self.scheduler(self.optimizer, i, epoch, self.best_pred) + self.optimizer.zero_grad() + + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + #print(target.max()) + #print(output.shape) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + loss.backward() + self.optimizer.step() + #print(self.optimizer.state_dict()['param_groups'][0]['lr']) + train_loss += loss.item() + tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) + self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) + # Show 10 * 3 inference results each epoch + if i % (num_img_tr // 10 + 1) == 0: + global_step = i + num_img_tr * epoch + if self.args.depth: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + depth_display = depth[0].cpu().unsqueeze(0) + depth_display = depth_display.mul_(self.std_depth).add_(self.mean_depth) + depth_display = depth_display.numpy() + depth_display = depth_display*255 + depth_display = depth_display.astype(np.uint8) + self.writer.add_image('Depth', depth_display, global_step) + + else: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch) + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print('Loss: %.3f' % train_loss) + tau = 0.3 + flag = True + for param, target_param in zip(current_model.parameters(), self.model.parameters()): + if flag: + #print(param[0]-target_param[0]) + flag = False + target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) + + del current_model + # if self.args.no_val: + # # save checkpoint every epoch + # is_best = False + # checkpoint_path = self.saver.save_checkpoint({ + # 'epoch': epoch + 1, + # 'state_dict': self.model.state_dict(), + # 'optimizer': self.optimizer.state_dict(), + # 'best_pred': self.best_pred, + # }, is_best) + return train_loss + + def training(self, epoch): + train_loss = 0.0 + print(self.optimizer.state_dict()['param_groups'][0]['lr']) + self.model.train() + tbar = tqdm(self.train_loader) + num_img_tr = len(self.train_loader) + + for i, sample in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + #print(target.shape) + else: + image, target = sample['image'], sample['label'] + print(image.shape) + if self.args.cuda: + image, target = image.cuda(self.args.gpu_ids), target.cuda(self.args.gpu_ids) + if self.args.depth: + depth = depth.cuda(self.args.gpu_ids) + self.scheduler(self.optimizer, i, epoch, self.best_pred) + self.optimizer.zero_grad() + + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + #print(target.max()) + #print(output.shape) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + loss.backward() + self.optimizer.step() + #print(self.optimizer.state_dict()['param_groups'][0]['lr']) + train_loss += loss.item() + tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1))) + self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch) + # Show 10 * 3 inference results each epoch + if i % (num_img_tr // 10 + 1) == 0: + global_step = i + num_img_tr * epoch + if self.args.depth: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + depth_display = depth[0].cpu().unsqueeze(0) + depth_display = depth_display.mul_(self.std_depth).add_(self.mean_depth) + depth_display = depth_display.numpy() + depth_display = depth_display*255 + depth_display = depth_display.astype(np.uint8) + self.writer.add_image('Depth', depth_display, global_step) + + else: + self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step) + + self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch) + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print('Loss: %.3f' % train_loss) + + # if self.args.no_val: + # # save checkpoint every epoch + # is_best = False + # checkpoint_path = self.saver.save_checkpoint({ + # 'epoch': epoch + 1, + # 'state_dict': self.model.state_dict(), + # 'optimizer': self.optimizer.state_dict(), + # 'best_pred': self.best_pred, + # }, is_best) + return train_loss + + def validation(self, epoch): + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.val_loader, desc='\r') + test_loss = 0.0 + for i, (sample, img_path) in enumerate(tbar): + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + # print(f"val image is {image}") + if self.args.cuda: + image, target = image.cuda(self.args.gpu_ids), target.cuda(self.args.gpu_ids) + if self.args.depth: + depth = depth.cuda(self.args.gpu_ids) + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + target[target > self.nclass-1] = 255 + loss = self.criterion(output, target) + test_loss += loss.item() + tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1))) + pred = output.data.cpu().numpy() + target = target.cpu().numpy() + pred = np.argmax(pred, axis=1) + # Add batch sample into evaluator + self.evaluator.add_batch(target, pred) + + # Fast test during the training + Acc = self.evaluator.Pixel_Accuracy() + Acc_class = self.evaluator.Pixel_Accuracy_Class() + mIoU = self.evaluator.Mean_Intersection_over_Union() + FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union() + self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch) + self.writer.add_scalar('val/mIoU', mIoU, epoch) + self.writer.add_scalar('val/Acc', Acc, epoch) + self.writer.add_scalar('val/Acc_class', Acc_class, epoch) + self.writer.add_scalar('val/fwIoU', FWIoU, epoch) + print('Validation:') + print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0])) + print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU)) + print('Loss: %.3f' % test_loss) + + new_pred = mIoU + if new_pred > self.best_pred: + is_best = True + self.best_pred = new_pred + self.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.model.state_dict(), + 'optimizer': self.optimizer.state_dict(), + 'best_pred': self.best_pred, + }, is_best) \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/__init__.py new file mode 100755 index 00000000..e69de29b diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py new file mode 100755 index 00000000..8a56dd74 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -0,0 +1,52 @@ +class TrainArgs: + def __init__(self, **kwargs): + self.depth = False + self.dataset = 'cityscapes' + self.workers = 4 + self.base_size = 1024 + self.crop_size = 768 + self.loss_type = 'ce' + self.epochs = kwargs.get("epochs", 2) + self.start_epoch = 0 + + self.batch_size = 4 + self.val_batch_size = 1 + self.use_balanced_weights = False + self.num_class = 30 + self.lr = kwargs.get("learning_rate", 1e-4) + self.lr_scheduler = 'cos' + self.momentum = 0.9 + self.weight_decay = 2.5e-5 + self.no_cuda = False + self.gpu_ids = 0 + + self.seed = 1 + self.resume = None + self.checkname = 'RFNet' + self.ft = True + self.eval_interval = kwargs.get("eval_interval", 50) + self.no_val = kwargs.get("no_val", True) + self.cuda = True + + +class ValArgs: + def __init__(self, **kwargs): + self.dataset = 'cityscapes' + self.workers = 0 + self.base_size = 1024 + self.crop_size = 768 + self.batch_size = 6 + self.val_batch_size = 1 + self.test_batch_size = 1 + self.num_class = 30 + self.no_cuda = False + self.gpu_ids = 0 + self.checkname = None + self.weight_path = "./models/530_exp3_2.pth" + self.save_predicted_image = False + self.color_label_save_path = './test/color' + self.merge_label_save_path = './test/merge' + self.label_save_path = './test/label' + self.merge = True + self.depth = False + self.cuda = True diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py new file mode 100755 index 00000000..2c2c9821 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/calculate_weights.py @@ -0,0 +1,29 @@ +import os +from tqdm import tqdm +import numpy as np +from mypath import Path + +def calculate_weigths_labels(dataset, dataloader, num_classes): + # Create an instance from the data loader + z = np.zeros((num_classes,)) + # Initialize tqdm + tqdm_batch = tqdm(dataloader) + print('Calculating classes weights') + for sample in tqdm_batch: + y = sample['label'] + y = y.detach().cpu().numpy() + mask = (y >= 0) & (y < num_classes) + labels = y[mask].astype(np.uint8) + count_l = np.bincount(labels, minlength=num_classes) + z += count_l + tqdm_batch.close() + total_frequency = np.sum(z) + class_weights = [] + for frequency in z: + class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) + class_weights.append(class_weight) + ret = np.array(class_weights) + classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy') + np.save(classes_weights_path, ret) + + return ret \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py new file mode 100755 index 00000000..d3801a9e --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/iouEval.py @@ -0,0 +1,141 @@ +import torch + +class iouEval: + + def __init__(self, nClasses, ignoreIndex=20): + + self.nClasses = nClasses + self.ignoreIndex = ignoreIndex if nClasses > ignoreIndex else -1 # if ignoreIndex is larger than nClasses, consider no ignoreIndex + self.reset() + + def reset(self): + classes = self.nClasses if self.ignoreIndex == -1 else self.nClasses - 1 + self.tp = torch.zeros(classes).double() + self.fp = torch.zeros(classes).double() + self.fn = torch.zeros(classes).double() + self.cdp_obstacle = torch.zeros(1).double() + self.tp_obstacle = torch.zeros(1).double() + self.idp_obstacle = torch.zeros(1).double() + self.tp_nonobstacle = torch.zeros(1).double() + # self.cdi = torch.zeros(1).double() + + def addBatch(self, x, y): # x=preds, y=targets + # sizes should be "batch_size x nClasses x H x W" + # cdi = 0 + + # print ("X is cuda: ", x.is_cuda) + # print ("Y is cuda: ", y.is_cuda) + + if (x.is_cuda or y.is_cuda): + x = x.cuda() + y = y.cuda() + + # if size is "batch_size x 1 x H x W" scatter to onehot + if (x.size(1) == 1): + x_onehot = torch.zeros(x.size(0), self.nClasses, x.size(2), x.size(3)) + if x.is_cuda: + x_onehot = x_onehot.cuda() + x_onehot.scatter_(1, x, 1).float() # dim index src 按照列用1替换0，索引为x + else: + x_onehot = x.float() + + if (y.size(1) == 1): + y_onehot = torch.zeros(y.size(0), self.nClasses, y.size(2), y.size(3)) + if y.is_cuda: + y_onehot = y_onehot.cuda() + y_onehot.scatter_(1, y, 1).float() + else: + y_onehot = y.float() + + if (self.ignoreIndex != -1): + ignores = y_onehot[:, self.ignoreIndex].unsqueeze(1) # 加一维 + x_onehot = x_onehot[:, :self.ignoreIndex] # ignoreIndex后的都不要 + y_onehot = y_onehot[:, :self.ignoreIndex] + else: + ignores = 0 + + + tpmult = x_onehot * y_onehot # times prediction and gt coincide is 1 + tp = torch.sum(torch.sum(torch.sum(tpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fpmult = x_onehot * ( + 1 - y_onehot - ignores) # times prediction says its that class and gt says its not (subtracting cases when its ignore label!) + fp = torch.sum(torch.sum(torch.sum(fpmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + fnmult = (1 - x_onehot) * (y_onehot) # times prediction says its not that class and gt says it is + fn = torch.sum(torch.sum(torch.sum(fnmult, dim=0, keepdim=True), dim=2, keepdim=True), dim=3, + keepdim=True).squeeze() + + self.tp += tp.double().cpu() + self.fp += fp.double().cpu() + self.fn += fn.double().cpu() + + cdp_obstacle = tpmult[:, 19].sum() # obstacle index 19 + tp_obstacle = y_onehot[:, 19].sum() + + idp_obstacle = (x_onehot[:, 19] - tpmult[:, 19]).sum() + tp_nonobstacle = (-1*y_onehot+1).sum() + + # for i in range(0, x.size(0)): + # if tpmult[i].sum()/(y_onehot[i].sum() + 1e-15) >= 0.5: + # cdi += 1 + + + self.cdp_obstacle += cdp_obstacle.double().cpu() + self.tp_obstacle += tp_obstacle.double().cpu() + self.idp_obstacle += idp_obstacle.double().cpu() + self.tp_nonobstacle += tp_nonobstacle.double().cpu() + # self.cdi += cdi.double().cpu() + + + + def getIoU(self): + num = self.tp + den = self.tp + self.fp + self.fn + 1e-15 + iou = num / den + iou_not_zero = list(filter(lambda x: x != 0, iou)) + # print(len(iou_not_zero)) + iou_mean = sum(iou_not_zero) / len(iou_not_zero) + tfp = self.tp + self.fp + 1e-15 + acc = num / tfp + acc_not_zero = list(filter(lambda x: x != 0, acc)) + acc_mean = sum(acc_not_zero) / len(acc_not_zero) + + return iou_mean, iou, acc_mean, acc # returns "iou mean", "iou per class" + + def getObstacleEval(self): + + pdr_obstacle = self.cdp_obstacle / (self.tp_obstacle+1e-15) + pfp_obstacle = self.idp_obstacle / (self.tp_nonobstacle+1e-15) + + return pdr_obstacle, pfp_obstacle + + +# Class for colors +class colors: + RED = '\033[31;1m' + GREEN = '\033[32;1m' + YELLOW = '\033[33;1m' + BLUE = '\033[34;1m' + MAGENTA = '\033[35;1m' + CYAN = '\033[36;1m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + ENDC = '\033[0m' + + +# Colored value output if colorized flag is activated. +def getColorEntry(val): + if not isinstance(val, float): + return colors.ENDC + if (val < .20): + return colors.RED + elif (val < .40): + return colors.YELLOW + elif (val < .60): + return colors.BLUE + elif (val < .80): + return colors.CYAN + else: + return colors.GREEN + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py new file mode 100755 index 00000000..33f19042 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/loss.py @@ -0,0 +1,65 @@ +import torch +import torch.nn as nn + +class SegmentationLosses(object): + def __init__(self, weight=None, size_average=True, batch_average=True, ignore_index=255, cuda=False, gpu_ids=0): # ignore_index=255 + self.ignore_index = ignore_index + self.weight = weight + self.size_average = size_average + self.batch_average = batch_average + self.cuda = cuda + self.gpu_ids = gpu_ids + + def build_loss(self, mode='ce'): + """Choices: ['ce' or 'focal']""" + if mode == 'ce': + return self.CrossEntropyLoss + elif mode == 'focal': + return self.FocalLoss + else: + raise NotImplementedError + + def CrossEntropyLoss(self, logit, target): + n, c, h, w = logit.size() + #criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + #size_average=self.size_average) + criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=self.ignore_index) + if self.cuda: + criterion = criterion.cuda(self.gpu_ids) + + loss = criterion(logit, target.long()) + + if self.batch_average: + loss /= n + + return loss + + def FocalLoss(self, logit, target, gamma=2, alpha=0.5): + n, c, h, w = logit.size() + criterion = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index, + size_average=self.size_average) + if self.cuda: + criterion = criterion.cuda(self.gpu_ids) + + logpt = -criterion(logit, target.long()) + pt = torch.exp(logpt) + if alpha is not None: + logpt *= alpha + loss = -((1 - pt) ** gamma) * logpt + + if self.batch_average: + loss /= n + + return loss + +if __name__ == "__main__": + loss = SegmentationLosses(cuda=True) + a = torch.rand(1, 3, 7, 7).cuda() + b = torch.rand(1, 7, 7).cuda() + print(loss.CrossEntropyLoss(a, b).item()) + print(loss.FocalLoss(a, b, gamma=0, alpha=None).item()) + print(loss.FocalLoss(a, b, gamma=2, alpha=0.5).item()) + + + + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py new file mode 100755 index 00000000..47124028 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/lr_scheduler.py @@ -0,0 +1,70 @@ +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +## Created by: Hang Zhang +## ECE Department, Rutgers University +## Email: zhang.hang@rutgers.edu +## Copyright (c) 2017 +## +## This source code is licensed under the MIT-style license found in the +## LICENSE file in the root directory of this source tree +##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +import math + +class LR_Scheduler(object): + """Learning Rate Scheduler + + Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}`` + + Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))`` + + Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9`` + + Args: + args: + :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`), + :attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs, + :attr:`args.lr_step` + + iters_per_epoch: number of iterations per epoch + """ + def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, + lr_step=0, warmup_epochs=0): + self.mode = mode + print('Using {} LR Scheduler!'.format(self.mode)) + self.lr = base_lr + if mode == 'step': + assert lr_step + self.lr_step = lr_step + self.iters_per_epoch = iters_per_epoch + self.N = num_epochs * iters_per_epoch + self.epoch = -1 + self.warmup_iters = warmup_epochs * iters_per_epoch + + def __call__(self, optimizer, i, epoch, best_pred): + T = epoch * self.iters_per_epoch + i + if self.mode == 'cos': + lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) + elif self.mode == 'poly': + lr = self.lr * pow((1 - 1.0 * T / self.N), 2) + elif self.mode == 'step': + lr = self.lr * (0.1 ** (epoch // self.lr_step)) + else: + raise NotImplemented + # warm up lr schedule + if self.warmup_iters > 0 and T < self.warmup_iters: + lr = lr * 1.0 * T / self.warmup_iters + if epoch > self.epoch: + print('\n=>Epoches %i, learning rate = %.4f, \ + previous best = %.4f' % (epoch, lr, best_pred)) + self.epoch = epoch + assert lr >= 0 + self._adjust_learning_rate(optimizer, lr) + + def _adjust_learning_rate(self, optimizer, lr): + if len(optimizer.param_groups) == 1: + optimizer.param_groups[0]['lr'] = lr * 4 + else: + # enlarge the lr at the head + optimizer.param_groups[0]['lr'] = lr * 4 + for i in range(1, len(optimizer.param_groups)): + optimizer.param_groups[i]['lr'] = lr diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py new file mode 100755 index 00000000..b2e93683 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/metrics.py @@ -0,0 +1,201 @@ +import numpy as np + + +class Evaluator(object): + def __init__(self, num_class): + self.num_class = num_class + self.confusion_matrix = np.zeros((self.num_class,)*2) # shape:(num_class, num_class) + + def Pixel_Accuracy(self): + Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum() + return Acc + + def Pixel_Accuracy_Class_Curb(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + Acc = np.nanmean(Acc[:2]) + return Acc + + + def Pixel_Accuracy_Class(self): + Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) + print('-----------Acc of each classes-----------') + print("road : %.6f" % (Acc[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (Acc[1] * 100.0), "%\t") + print("building : %.6f" % (Acc[2] * 100.0), "%\t") + print("wall : %.6f" % (Acc[3] * 100.0), "%\t") + print("fence : %.6f" % (Acc[4] * 100.0), "%\t") + print("pole : %.6f" % (Acc[5] * 100.0), "%\t") + print("traffic light: %.6f" % (Acc[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (Acc[7] * 100.0), "%\t") + print("vegetation : %.6f" % (Acc[8] * 100.0), "%\t") + print("terrain : %.6f" % (Acc[9] * 100.0), "%\t") + print("sky : %.6f" % (Acc[10] * 100.0), "%\t") + print("person : %.6f" % (Acc[11] * 100.0), "%\t") + print("rider : %.6f" % (Acc[12] * 100.0), "%\t") + print("car : %.6f" % (Acc[13] * 100.0), "%\t") + print("truck : %.6f" % (Acc[14] * 100.0), "%\t") + print("bus : %.6f" % (Acc[15] * 100.0), "%\t") + print("train : %.6f" % (Acc[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (Acc[17] * 100.0), "%\t") + print("bicycle : %.6f" % (Acc[18] * 100.0), "%\t") + print("stair : %.6f" % (Acc[19] * 100.0), "%\t") + print("curb : %.6f" % (Acc[20] * 100.0), "%\t") + print("ramp : %.6f" % (Acc[21] * 100.0), "%\t") + print("runway : %.6f" % (Acc[22] * 100.0), "%\t") + print("flowerbed : %.6f" % (Acc[23] * 100.0), "%\t") + print("door : %.6f" % (Acc[24] * 100.0), "%\t") + print("CCTV camera : %.6f" % (Acc[25] * 100.0), "%\t") + print("Manhole : %.6f" % (Acc[26] * 100.0), "%\t") + print("hydrant : %.6f" % (Acc[27] * 100.0), "%\t") + print("belt : %.6f" % (Acc[28] * 100.0), "%\t") + print("dustbin : %.6f" % (Acc[29] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (Acc[19] * 100.0), "%\t") + Acc = np.nanmean(Acc) + return Acc + + def Mean_Intersection_over_Union(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + print("building : %.6f" % (MIoU[2] * 100.0), "%\t") + print("wall : %.6f" % (MIoU[3] * 100.0), "%\t") + print("fence : %.6f" % (MIoU[4] * 100.0), "%\t") + print("pole : %.6f" % (MIoU[5] * 100.0), "%\t") + print("traffic light: %.6f" % (MIoU[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (MIoU[7] * 100.0), "%\t") + print("vegetation : %.6f" % (MIoU[8] * 100.0), "%\t") + print("terrain : %.6f" % (MIoU[9] * 100.0), "%\t") + print("sky : %.6f" % (MIoU[10] * 100.0), "%\t") + print("person : %.6f" % (MIoU[11] * 100.0), "%\t") + print("rider : %.6f" % (MIoU[12] * 100.0), "%\t") + print("car : %.6f" % (MIoU[13] * 100.0), "%\t") + print("truck : %.6f" % (MIoU[14] * 100.0), "%\t") + print("bus : %.6f" % (MIoU[15] * 100.0), "%\t") + print("train : %.6f" % (MIoU[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (MIoU[17] * 100.0), "%\t") + print("bicycle : %.6f" % (MIoU[18] * 100.0), "%\t") + print("stair : %.6f" % (MIoU[19] * 100.0), "%\t") + print("curb : %.6f" % (MIoU[20] * 100.0), "%\t") + print("ramp : %.6f" % (MIoU[21] * 100.0), "%\t") + print("runway : %.6f" % (MIoU[22] * 100.0), "%\t") + print("flowerbed : %.6f" % (MIoU[23] * 100.0), "%\t") + print("door : %.6f" % (MIoU[24] * 100.0), "%\t") + print("CCTV camera : %.6f" % (MIoU[25] * 100.0), "%\t") + print("Manhole : %.6f" % (MIoU[26] * 100.0), "%\t") + print("hydrant : %.6f" % (MIoU[27] * 100.0), "%\t") + print("belt : %.6f" % (MIoU[28] * 100.0), "%\t") + print("dustbin : %.6f" % (MIoU[29] * 100.0), "%\t") + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU) + return MIoU + + def Mean_Intersection_over_Union_Curb(self): + MIoU = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # print MIoU of each class + print('-----------IoU of each classes-----------') + print("road : %.6f" % (MIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (MIoU[1] * 100.0), "%\t") + + if self.num_class == 20: + print("small obstacles: %.6f" % (MIoU[19] * 100.0), "%\t") + + MIoU = np.nanmean(MIoU[:2]) + return MIoU + + def Frequency_Weighted_Intersection_over_Union(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + if len(CFWIoU) > 1: + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + print('-----------freq of each classes-----------') + print("road : %.6f" % (freq[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (freq[1] * 100.0), "%\t") + print("building : %.6f" % (freq[2] * 100.0), "%\t") + print("wall : %.6f" % (freq[3] * 100.0), "%\t") + print("fence : %.6f" % (freq[4] * 100.0), "%\t") + print("pole : %.6f" % (freq[5] * 100.0), "%\t") + print("traffic light: %.6f" % (freq[6] * 100.0), "%\t") + print("traffic sign : %.6f" % (freq[7] * 100.0), "%\t") + print("vegetation : %.6f" % (freq[8] * 100.0), "%\t") + print("terrain : %.6f" % (freq[9] * 100.0), "%\t") + print("sky : %.6f" % (freq[10] * 100.0), "%\t") + print("person : %.6f" % (freq[11] * 100.0), "%\t") + print("rider : %.6f" % (freq[12] * 100.0), "%\t") + print("car : %.6f" % (freq[13] * 100.0), "%\t") + print("truck : %.6f" % (freq[14] * 100.0), "%\t") + print("bus : %.6f" % (freq[15] * 100.0), "%\t") + print("train : %.6f" % (freq[16] * 100.0), "%\t") + print("motorcycle : %.6f" % (freq[17] * 100.0), "%\t") + print("bicycle : %.6f" % (freq[18] * 100.0), "%\t") + print("stair : %.6f" % (freq[19] * 100.0), "%\t") + print("curb : %.6f" % (freq[20] * 100.0), "%\t") + print("ramp : %.6f" % (freq[21] * 100.0), "%\t") + print("runway : %.6f" % (freq[22] * 100.0), "%\t") + print("flowerbed : %.6f" % (freq[23] * 100.0), "%\t") + print("door : %.6f" % (freq[24] * 100.0), "%\t") + print("CCTV camera : %.6f" % (freq[25] * 100.0), "%\t") + print("Manhole : %.6f" % (freq[26] * 100.0), "%\t") + print("hydrant : %.6f" % (freq[27] * 100.0), "%\t") + print("belt : %.6f" % (freq[28] * 100.0), "%\t") + print("dustbin : %.6f" % (freq[29] * 100.0), "%\t") + + return FWIoU + + def Frequency_Weighted_Intersection_over_Union_Curb(self): + freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix) + iu = np.diag(self.confusion_matrix) / ( + np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) - + np.diag(self.confusion_matrix)) + + # FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() + CFWIoU = freq[freq > 0] * iu[freq > 0] + print('-----------FWIoU of each classes-----------') + print("road : %.6f" % (CFWIoU[0] * 100.0), "%\t") + print("sidewalk : %.6f" % (CFWIoU[1] * 100.0), "%\t") + + return np.nanmean(CFWIoU[:2]) + + def _generate_matrix(self, gt_image, pre_image): + mask = (gt_image >= 0) & (gt_image < self.num_class) + label = self.num_class * gt_image[mask].astype('int') + pre_image[mask] + count = np.bincount(label, minlength=self.num_class**2) + confusion_matrix = count.reshape(self.num_class, self.num_class) + return confusion_matrix + + def add_batch(self, gt_image, pre_image): + gt_image = np.array(gt_image) + pre_image = np.array(pre_image) + print(gt_image.shape, pre_image.shape) + if gt_image.shape != pre_image.shape: + pre_image = pre_image[0] + assert gt_image.shape == pre_image.shape + self.confusion_matrix += self._generate_matrix(gt_image, pre_image) + + def reset(self): + self.confusion_matrix = np.zeros((self.num_class,) * 2) + + + + diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py new file mode 100755 index 00000000..03866432 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/saver.py @@ -0,0 +1,68 @@ +import os +import time +import shutil +import tempfile +import torch +from collections import OrderedDict +import glob + +class Saver(object): + + def __init__(self, args): + self.args = args + self.directory = os.path.join('/tmp', args.dataset, args.checkname) + self.runs = sorted(glob.glob(os.path.join(self.directory, 'experiment_*'))) + run_id = int(self.runs[-1].split('_')[-1]) + 1 if self.runs else 0 + + self.experiment_dir = os.path.join(self.directory, 'experiment_{}'.format(str(run_id))) + if not os.path.exists(self.experiment_dir): + os.makedirs(self.experiment_dir) + + def save_checkpoint(self, state, is_best): # filename from .pth.tar change to .pth? + """Saves checkpoint to disk""" + filename = f'checkpoint_{time.time()}.pth' + checkpoint_path = os.path.join(self.experiment_dir, filename) + torch.save(state, checkpoint_path) + if is_best: + best_pred = state['best_pred'] + with open(os.path.join(self.experiment_dir, 'best_pred.txt'), 'w') as f: + f.write(str(best_pred)) + if self.runs: + previous_miou = [0.0] + for run in self.runs: + run_id = run.split('_')[-1] + path = os.path.join(self.directory, 'experiment_{}'.format(str(run_id)), 'best_pred.txt') + if os.path.exists(path): + with open(path, 'r') as f: + miou = float(f.readline()) + previous_miou.append(miou) + else: + continue + max_miou = max(previous_miou) + if best_pred > max_miou: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + else: + checkpoint_path_best = os.path.join(self.directory, 'model_best.pth') + shutil.copyfile(checkpoint_path, checkpoint_path_best) + checkpoint_path = checkpoint_path_best + + return checkpoint_path + + def save_experiment_config(self): + logfile = os.path.join(self.experiment_dir, 'parameters.txt') + log_file = open(logfile, 'w') + p = OrderedDict() + p['datset'] = self.args.dataset + # p['out_stride'] = self.args.out_stride + p['lr'] = self.args.lr + p['lr_scheduler'] = self.args.lr_scheduler + p['loss_type'] = self.args.loss_type + p['epoch'] = self.args.epochs + p['base_size'] = self.args.base_size + p['crop_size'] = self.args.crop_size + + for key, val in p.items(): + log_file.write(key + ':' + str(val) + '\n') + log_file.close() \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py new file mode 100755 index 00000000..04bcdb82 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/summaries.py @@ -0,0 +1,39 @@ +import os +import torch +from torchvision.utils import make_grid +# from tensorboardX import SummaryWriter +from torch.utils.tensorboard import SummaryWriter +from dataloaders.utils import decode_seg_map_sequence + +class TensorboardSummary(object): + def __init__(self, directory): + self.directory = directory + + def create_summary(self): + writer = SummaryWriter(log_dir=os.path.join(self.directory)) + return writer + + def visualize_image(self, writer, dataset, image, target, output, global_step, depth=None): + if depth is None: + grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 3, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) + else: + grid_image = make_grid(image[:3].clone().cpu().data, 4, normalize=True) + writer.add_image('Image', grid_image, global_step) + + grid_image = make_grid(depth[:3].clone().cpu().data, 4, normalize=True) # normalize=False? + writer.add_image('Depth', grid_image, global_step) + + grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Predicted label', grid_image, global_step) + grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(), + dataset=dataset), 4, normalize=False, range=(0, 255)) + writer.add_image('Groundtruth label', grid_image, global_step) \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py new file mode 100755 index 00000000..343f58c5 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py @@ -0,0 +1,160 @@ +import os +import gc +import numpy as np +import torch +from torch.utils.data import DataLoader +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from PIL import Image +from torchvision import transforms + +from RFNet.train import Trainer +from RFNet.eval import Validator, load_my_state_dict +from RFNet.dataloaders import custom_transforms as tr +from RFNet.dataloaders import make_data_loader +from RFNet.utils.args import TrainArgs, ValArgs + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.train_args = TrainArgs(**kwargs) + self.trainer = None + + self.val_args = ValArgs(**kwargs) + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def get_weights(self): + return self.trainer.get_weight() + + def set_weights(self, weights): + self.trainer.set_weight(weights) + + epoch_num = 0 + print("Total epoch: ", epoch_num) + loss_all = [] + for epoch in range(epoch_num): + train_loss = self.trainer.my_training(epoch) + loss_all.append(train_loss) + with open('/home/shijing.hu/ianvs/project/ianvs/train_loss_2.txt', 'a+') as file: + np.savetxt(file, loss_all) + file.close + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + loss_all = [] + for epoch in range( + self.trainer.args.start_epoch, + self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + loss = self.trainer.training(epoch) + loss_all.append(loss) + if self.trainer.args.no_val and ( + epoch % + self.trainer.args.eval_interval == ( + self.trainer.args.eval_interval - + 1) or epoch == self.trainer.args.epochs - + 1): + # save checkpoint when it meets eval_interval or the training + # finished + is_best = False + self.train_model_url = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + self.trainer.writer.close() + with open('/ianvs/project/ianvs/train_loss.txt', 'a+') as file: + np.savetxt(file, loss_all) + file.close + return self.train_model_url + + def predict(self, data, **kwargs): + if len(data) > 10: + print("predict start for big data") + my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True} + _, _, self.validator.test_loader, _ = make_data_loader(self.val_args, test_data=data, **my_kwargs) + else: + print("predict start for small data") + if not isinstance(data[0][0], dict): + data = self._preprocess(data) + #print("predict starting 69") + if type(data) is np.ndarray: + data = data.tolist() + #print("predict starting 72") + #print("predict starting 73") + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + + #print("predict starting 75") + return self.validator.validate() + + def evaluate(self, data, **kwargs): + #print("evaluate starting 77") + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") + + def load(self, model_url, **kwargs): + if model_url: + print("load model url: ",model_url) + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: save unstructured data model + if not model_path: + LOGGER.warning(f"Not specify model path.") + return self.train_model_url + + return FileOps.upload(self.train_model_url, model_path) + + def _preprocess(self, image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + #print(img_path, depth_path) + else: + img_path = paths[0] + #print(img_path) + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + del _img + gc.collect() + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py new file mode 100755 index 00000000..37e3fbd9 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py @@ -0,0 +1,164 @@ +import os +import gc +import numpy as np +import torch +from torch.utils.data import DataLoader +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.common.file_ops import FileOps +from sedna.common.log import LOGGER +from PIL import Image +from torchvision import transforms + +from RFNet.train import Trainer +from RFNet.eval import Validator, load_my_state_dict +from RFNet.dataloaders import custom_transforms as tr +from RFNet.dataloaders import make_data_loader +from RFNet.utils.args import TrainArgs, ValArgs + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="BaseModel") +class BaseModel: + def __init__(self, **kwargs): + self.train_args = TrainArgs(**kwargs) + self.trainer = None + + self.val_args = ValArgs(**kwargs) + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def get_weights(self): + return self.trainer.get_weight() + + def set_weights(self, weights): + self.trainer.set_weight(weights) + + epoch_num = 0 + print("Total epoch: ", epoch_num) + loss_all = [] + for epoch in range(epoch_num): + train_loss = self.trainer.my_training(epoch) + #train_loss = self.trainer.training(epoch) + loss_all.append(train_loss) + with open('/home/shijing.hu/ianvs/project/ianvs/train_loss_2.txt', 'a+') as file: + np.savetxt(file, loss_all) + file.close + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + loss_all = [] + for epoch in range( + self.trainer.args.start_epoch, + self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + if self.train_args.resume: + print("my_training:", self.train_args.resume) + loss = self.trainer.my_training(epoch) + #loss = self.trainer.training(epoch) + else: + loss = self.trainer.training(epoch) + loss_all.append(loss) + if self.trainer.args.no_val and ( + epoch % + self.trainer.args.eval_interval == ( + self.trainer.args.eval_interval - + 1) or epoch == self.trainer.args.epochs - + 1): + # save checkpoint when it meets eval_interval or the training + # finished + is_best = False + self.train_model_url = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + self.trainer.writer.close() + with open('/home/shijing.hu/ianvs/project/ianvs/train_loss.txt', 'a+') as file: + np.savetxt(file, loss_all) + file.close + return self.train_model_url + + def predict(self, data, **kwargs): + if len(data) > 10: + print("predict start for big data") + my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True} + _, _, self.validator.test_loader, _ = make_data_loader(self.val_args, test_data=data, **my_kwargs) + else: + print("predict start for small data") + if not isinstance(data[0][0], dict): + data = self._preprocess(data) + #print("predict starting 69") + if type(data) is np.ndarray: + data = data.tolist() + #print("predict starting 72") + #print("predict starting 73") + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + + #print("predict starting 75") + return self.validator.validate() + + def evaluate(self, data, **kwargs): + print("evaluate starting 77") + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = self._preprocess(data.x) + predictions = self.predict(samples) + metric_name, metric_func = kwargs.get("metric") + if callable(metric_func): + return metric_func(data.y, predictions) + else: + raise Exception(f"not found model metric func(name={metric_name}) in model eval phase") + + def load(self, model_url, **kwargs): + if model_url: + print("load model url: ",model_url) + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: save unstructured data model + if not model_path: + LOGGER.warning(f"Not specify model path.") + return self.train_model_url + + return FileOps.upload(self.train_model_url, model_path) + + def _preprocess(self, image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + #print(img_path, depth_path) + else: + img_path = paths[0] + #print(img_path) + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + del _img + gc.collect() + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml new file mode 100755 index 00000000..cd7cbbaa --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml @@ -0,0 +1,63 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.9 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + splitting_method: "default" + #splitting_method: "my_splitting" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + - epochs: + values: + - 1 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin-simple.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "front", "garden" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin-simple.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "front", "garden" ] \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml new file mode 100755 index 00000000..628ceb9a --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -0,0 +1,63 @@ +algorithm: + # paradigm type; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + # 3> "lifelonglearning" + paradigm_type: "lifelonglearning" + lifelong_learning_data_setting: + # ratio of training dataset; float type; + # the default value is 0.8. + train_ratio: 0.9 + # the method of splitting dataset; string type; optional; + # currently the options of value are as follows: + # 1> "default": the dataset is evenly divided based train_ratio; + splitting_method: "default" + #splitting_method: "my_splitting" + + # algorithm module configuration in the paradigm; list type; + modules: + # type of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel": contains important interfaces such as train、 eval、 predict and more; required module; + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "BaseModel" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - learning_rate: + values: + - 0.0001 + - epochs: + values: + - 1 + # 2> "task_definition": define lifelong task ; optional module; + - type: "task_definition" + # name of python module; string type; + name: "TaskDefinitionByOrigin" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] + # 3> "task_allocation": allocate lifelong task ; optional module; + - type: "task_allocation" + # name of python module; string type; + name: "TaskAllocationByOrigin" + # the url address of python module; string type; + url: "./examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py" + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # origins of data; value is ["real", "sim"], this means that data from real camera and simulator. + - origins: + values: + - [ "real", "sim" ] \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin-simple.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin-simple.py new file mode 100755 index 00000000..4fa0f136 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin-simple.py @@ -0,0 +1,49 @@ +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('TaskAllocationByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + #self.task_extractor = task_extractor + self.task_extractor = {"front": 0, "garden": 1} + if self.default_origin: + return samples, [int(self.task_extractor.get( + self.default_origin))] * len(samples.x) + + cities = [ + "front" + ] + + sample_origins = [] + for _x in samples.x: + is_real = False + for city in cities: + if city in _x[0]: + is_real = True + sample_origins.append("front") + break + if not is_real: + sample_origins.append("garden") + + allocations = [int(self.task_extractor.get(sample_origin)) + for sample_origin in sample_origins] + + return samples, allocations diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py new file mode 100755 index 00000000..9ce030b1 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_allocation_by_origin.py @@ -0,0 +1,49 @@ +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassFactory, ClassType + +__all__ = ('TaskAllocationByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskAllocationByOrigin") +class TaskAllocationByOrigin: + """ + Corresponding to `TaskDefinitionByOrigin` + + Parameters + ---------- + task_extractor : Dict + used to match target tasks + origins: List[Metadata] + metadata is usually a class feature + label with a finite values. + """ + + def __init__(self, **kwargs): + self.default_origin = kwargs.get("default", None) + + def __call__(self, task_extractor, samples: BaseDataSource): + self.task_extractor = task_extractor + if self.default_origin: + return samples, [int(self.task_extractor.get( + self.default_origin))] * len(samples.x) + + cities = [ + "front", + "garden" + ] + + sample_origins = [] + for _x in samples.x: + is_real = False + for city in cities: + if city in _x[0]: + is_real = True + sample_origins.append("real") + break + if not is_real: + sample_origins.append("sim") + + allocations = [int(self.task_extractor.get(sample_origin)) + for sample_origin in sample_origins] + + return samples, allocations diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin-simple.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin-simple.py new file mode 100755 index 00000000..8aaa4c35 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin-simple.py @@ -0,0 +1,64 @@ +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + +__all__ = ('TaskDefinitionByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") +class TaskDefinitionByOrigin: + """ + Dividing datasets based on the their origins. + + Parameters + ---------- + origins： List[Metadata] + metadata is usually a class feature label with a finite values. + """ + + def __init__(self, **kwargs): + self.origins = kwargs.get("origins", ["front", "garden"]) + + def __call__(self, + samples: BaseDataSource, **kwargs) -> Tuple[List[Task], + Any, + BaseDataSource]: + cities = [ + "front" + ] + + tasks = [] + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + + task_index = dict(zip(self.origins, range(len(self.origins)))) + + real_df = BaseDataSource(data_type=d_type) + real_df.x, real_df.y = [], [] + sim_df = BaseDataSource(data_type=d_type) + sim_df.x, sim_df.y = [], [] + + for i in range(samples.num_examples()): + is_real = False + for city in cities: + if city in x_data[i][0]: + is_real = True + real_df.x.append(x_data[i]) + real_df.y.append(y_data[i]) + break + if not is_real: + sim_df.x.append(x_data[i]) + sim_df.y.append(y_data[i]) + + g_attr = "front_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=real_df, meta_attr="front") + tasks.append(task_obj) + + g_attr = "garden_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="garden") + tasks.append(task_obj) + + return tasks, task_index, samples diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py new file mode 100755 index 00000000..6c59cecb --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/task_definition_by_origin.py @@ -0,0 +1,65 @@ +from typing import List, Any, Tuple + +from sedna.datasources import BaseDataSource +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.algorithms.seen_task_learning.artifact import Task + +__all__ = ('TaskDefinitionByOrigin',) + + +@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByOrigin") +class TaskDefinitionByOrigin: + """ + Dividing datasets based on the their origins. + + Parameters + ---------- + origins： List[Metadata] + metadata is usually a class feature label with a finite values. + """ + + def __init__(self, **kwargs): + self.origins = kwargs.get("origins", ["real", "sim"]) + + def __call__(self, + samples: BaseDataSource, **kwargs) -> Tuple[List[Task], + Any, + BaseDataSource]: + cities = [ + "front", + "garden" + ] + + tasks = [] + d_type = samples.data_type + x_data = samples.x + y_data = samples.y + + task_index = dict(zip(self.origins, range(len(self.origins)))) + + real_df = BaseDataSource(data_type=d_type) + real_df.x, real_df.y = [], [] + sim_df = BaseDataSource(data_type=d_type) + sim_df.x, sim_df.y = [], [] + + for i in range(samples.num_examples()): + is_real = False + for city in cities: + if city in x_data[i][0]: + is_real = True + real_df.x.append(x_data[i]) + real_df.y.append(y_data[i]) + break + if not is_real: + sim_df.x.append(x_data[i]) + sim_df.y.append(y_data[i]) + + g_attr = "real_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=real_df, meta_attr="real") + tasks.append(task_obj) + + g_attr = "sim_semantic_segamentation_model" + task_obj = Task(entry=g_attr, samples=sim_df, meta_attr="sim") + tasks.append(task_obj) + + return tasks, task_index, samples diff --git a/examples/robot/lifelong_learning_bench/testenv/accuracy.py b/examples/robot/lifelong_learning_bench/testenv/accuracy.py new file mode 100755 index 00000000..7e6fcb2a --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/accuracy.py @@ -0,0 +1,58 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm import tqdm + +from sedna.common.class_factory import ClassType, ClassFactory + +from RFNet.dataloaders import make_data_loader +from RFNet.utils.metrics import Evaluator +from RFNet.utils.args import ValArgs + +__all__ = ('accuracy') + + +@ClassFactory.register(ClassType.GENERAL, alias="accuracy") +def accuracy(y_true, y_pred, **kwargs): + args = ValArgs() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + #print(y_true) + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(args.gpu_ids), target.cuda(args.gpu_ids) + if args.depth: + depth = depth.cuda(args.gpu_ids) + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + #if i% 20 == 0: + # print(img_path) + # print(image, target, y_pred[i]) + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return mIoU diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-all.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-all.yaml new file mode 100755 index 00000000..2a7021f0 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-all.yaml @@ -0,0 +1,35 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/home/shijing.hu/ianvs/dataset/all-train-index.txt" + # the url address of test dataset index; string type; + test_url: "/home/shijing.hu/ianvs/dataset/all-test-index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 5 \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-city.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-city.yaml new file mode 100755 index 00000000..7c118f0f --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-city.yaml @@ -0,0 +1,35 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/home/shijing.hu/ianvs/dataset/semantic_segmentation_dataset/city-train-index-2.txt" + # the url address of test dataset index; string type; + test_url: "/home/shijing.hu/ianvs/dataset/semantic_segmentation_dataset/city-test-index-2.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 5 \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml new file mode 100755 index 00000000..3dd8712c --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml @@ -0,0 +1,37 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/data/datasets/robot_dataset/train-index-small.txt" + # the url address of test dataset index; string type; + test_url: "/data/datasets/robot_dataset/test-index-small.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + - name: "BWT" + - name: "FWT" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 3 \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml new file mode 100755 index 00000000..2cf1e42c --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml @@ -0,0 +1,37 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/data/datasets/robot_dataset/train-index-mix.txt" + # the url address of test dataset index; string type; + test_url: "/data/datasets/robot_dataset/test-index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + - name: "BWT" + - name: "FWT" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 5 \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv.yaml new file mode 100755 index 00000000..a6151b99 --- /dev/null +++ b/examples/robot/lifelong_learning_bench/testenv/testenv.yaml @@ -0,0 +1,35 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "/home/shijing.hu/ianvs/dataset/robot_dataset/train-index.txt" + # the url address of test dataset index; string type; + test_url: "/home/shijing.hu/ianvs/dataset/robot_dataset/test-index.txt" + + # model eval configuration of incremental learning; + model_eval: + # metric used for model evaluation + model_metric: + # metric name; string type; + name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + mode: "no-inference" + + # condition of triggering inference model to update + # threshold of the condition; types are float/int + threshold: 0 + # operator of the condition; string type; + # values are ">=", ">", "<=", "<" and "="; + operator: "<" + + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "accuracy" + # the url address of python file + url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" + - name: "samples_transfer_ratio" + + # incremental rounds setting; int type; default value is 2; + incremental_rounds: 5 \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml index 7252afc5..4904f1bb 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml @@ -2,7 +2,7 @@ benchmarkingjob: # job name of bechmarking; string type; name: "benchmarkingjob" # the url address of job workspace that will reserve the output of tests; string type; - workspace: "/ianvs/lifelong_learning_bench/workspace" + workspace: "/home/shijing.hu/ianvs/lifelong_learning_bench/workspace" # the url address of test environment configuration file; string type; # the file format supports yaml/yml; diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py index 43743145..a914bccc 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -7,7 +7,7 @@ from dataloaders import custom_transforms as tr class CityscapesSegmentation(data.Dataset): - NUM_CLASSES = 24 # 25 + NUM_CLASSES = 30 # 25 def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"): diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml index 9267d96c..95a7aae1 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml @@ -34,7 +34,7 @@ algorithm: - 0.0001 - epochs: values: - - 1 + - 50 # 2> "task_definition": define lifelong task ; optional module; - type: "task_definition" # name of python module; string type; diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml index 2ed864af..3728b8b5 100644 --- a/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml +++ b/examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml @@ -2,10 +2,10 @@ testenv: # dataset configuration dataset: # the url address of train dataset index; string type; - train_url: "/root/data/semantic_segmentation_dataset/train-small-3.txt" + train_url: "/home/shijing.hu/ianvs/dataset/semantic_segmentation_dataset/train-small.txt" #train_url: "/root/data/semantic_segmentation_dataset/train-index-sort.txt" # the url address of test dataset index; string type; - test_url: "/root/data/semantic_segmentation_dataset/test-small-3.txt" + test_url: "/home/shijing.hu/ianvs/dataset/semantic_segmentation_dataset/test-index-sort.txt" #test_url: "/root/data/semantic_segmentation_dataset/test-index-sort.txt" # model eval configuration of incremental learning; From 7bb2d8a05eabe10fb65251191f974c1b55d0e716 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Tue, 20 Jun 2023 20:17:54 +0800 Subject: [PATCH 19/22] optimize codes Signed-off-by: hsj576 solve pylint issue Signed-off-by: hsj576 solve pylint issue Signed-off-by: hsj576 solve pylint issue Signed-off-by: hsj576 solve pylint issue Signed-off-by: hsj576 --- core/common/constant.py | 4 +- core/storymanager/rank/rank.py | 17 +- core/storymanager/visualization/__init__.py | 2 +- .../visualization/visualization.py | 20 +- .../lifelong_learning/lifelong_learning.py | 198 ++++++------------ core/testcasecontroller/metrics/metrics.py | 75 ++++++- core/testcasecontroller/testcase/testcase.py | 2 +- core/testenvmanager/dataset/dataset.py | 43 ++++ .../benchmarkingjob.yaml | 6 +- .../testalgorithms/rfnet/RFNet/utils/args.py | 2 +- .../testalgorithms/rfnet/basemodel-simple.py | 2 +- .../rfnet/rfnet_algorithm-simple.yaml | 4 +- .../testenv/testenv-robot-small.yaml | 2 + .../testenv/testenv-robot.yaml | 2 + 14 files changed, 225 insertions(+), 154 deletions(-) diff --git a/core/common/constant.py b/core/common/constant.py index f1c74891..ee8b9029 100755 --- a/core/common/constant.py +++ b/core/common/constant.py @@ -66,10 +66,12 @@ class SystemMetricType(Enum): """ System metric type of ianvs. """ + # pylint: disable=C0103 SAMPLES_TRANSFER_RATIO = "samples_transfer_ratio" FWT = "FWT" BWT = "BWT" - + Task_Avg_Acc = "Task_Avg_Acc" + Matrix = "Matrix" class TestObjectType(Enum): """ diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py index c23d0c63..403fa79e 100755 --- a/core/storymanager/rank/rank.py +++ b/core/storymanager/rank/rank.py @@ -21,7 +21,7 @@ import pandas as pd from core.common import utils -from core.storymanager.visualization import get_visualization_func +from core.storymanager.visualization import get_visualization_func, draw_heatmap_picture # pylint: disable=R0902 @@ -208,6 +208,16 @@ def _save_selected(self, test_cases, test_results): selected_df.index = pd.np.arange(1, len(selected_df) + 1) selected_df.to_csv(self.selected_rank_file, index_label="rank", encoding="utf-8", sep=" ") + def _draw_pictures(self, test_cases, test_results): + # pylint: disable=E1101 + for test_case in test_cases: + out_put = test_case.output_dir + test_result = test_results[test_case.id][0] + matrix = test_result.get('Matrix') + #print(out_put) + for key in matrix.keys(): + draw_heatmap_picture(out_put, key, matrix[key]) + def _prepare(self, test_cases, test_results, output_dir): all_metric_names = self._get_all_metric_names(test_results) all_hps_names = self._get_all_hps_names(test_cases) @@ -246,6 +256,11 @@ def save(self, test_cases, test_results, output_dir): if self.save_mode == "selected_only": self._save_selected(test_cases, test_results) + if self.save_mode == "selected_and_all_and_picture": + self._save_all() + self._save_selected(test_cases, test_results) + self._draw_pictures(test_cases, test_results) + def plot(self): """ plot rank according to the visual method, include diff --git a/core/storymanager/visualization/__init__.py b/core/storymanager/visualization/__init__.py index abf528b5..6f1efe7d 100755 --- a/core/storymanager/visualization/__init__.py +++ b/core/storymanager/visualization/__init__.py @@ -13,4 +13,4 @@ # limitations under the License. # pylint: disable=missing-module-docstring -from .visualization import get_visualization_func +from .visualization import get_visualization_func, draw_heatmap_picture diff --git a/core/storymanager/visualization/visualization.py b/core/storymanager/visualization/visualization.py index fab23e2d..74cfa683 100755 --- a/core/storymanager/visualization/visualization.py +++ b/core/storymanager/visualization/visualization.py @@ -15,7 +15,8 @@ """Visualization""" import sys - +import os +import matplotlib.pyplot as plt from prettytable import from_csv @@ -25,6 +26,23 @@ def print_table(rank_file): table = from_csv(file) print(table) +def draw_heatmap_picture(output, title, matrix): + """ + draw heatmap for results + """ + plt.figure(figsize=(10, 8), dpi=80) + plt.imshow(matrix, cmap='bwr', extent=(0.5, len(matrix)+0.5, 0.5, len(matrix)+0.5), + origin='lower') + plt.xticks(fontsize=15) + plt.yticks(fontsize=15) + plt.xlabel('task round', fontsize=15) + plt.ylabel('task', fontsize=15) + plt.title(title, fontsize=15) + plt.colorbar(format='%.2f') + output_dir = os.path.join(output, f"output/{title}-heatmap.png") + #print(output_dir) + plt.savefig(output_dir) + plt.show() def get_visualization_func(mode): """ get visualization func """ diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py index 9fd6c380..6e226788 100755 --- a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py +++ b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py @@ -13,14 +13,12 @@ # limitations under the License. """Lifelong Learning Paradigm""" - +# pylint: disable=C0412 import os import shutil -import pickle -import matplotlib.pyplot as plt import numpy as np from sedna.datasources import BaseDataSource - +from core.common.log import LOGGER from core.common.constant import ParadigmType, SystemMetricType from core.testcasecontroller.algorithm.paradigm.base import ParadigmBase from core.testcasecontroller.metrics import get_metric_func @@ -66,7 +64,8 @@ def __init__(self, workspace, **kwargs): self.cloud_task_index = '/tmp/cloud_task/index.pkl' self.edge_task_index = '/tmp/edge_task/index.pkl' self.system_metric_info = {SystemMetricType.SAMPLES_TRANSFER_RATIO.value: [], - SystemMetricType.BWT.value : {}, SystemMetricType.FWT.value: {}} + SystemMetricType.Matrix.value : {}, + SystemMetricType.Task_Avg_Acc.value: {}} def run(self): # pylint:disable=duplicate-code @@ -94,92 +93,74 @@ def run(self): # pylint: disable=C0103 # pylint: disable=C0206 # pylint: disable=C0201 + # pylint: disable=W1203 my_dict = {} - for r in range(1, rounds + 1): - if r == 1: - train_dataset_file, eval_dataset_file = dataset_files[r - 1] - self.cloud_task_index = self._train(self.cloud_task_index, - train_dataset_file, - r) - tmp_dict = {} - for j in range(rounds): - _, eval_dataset_file = dataset_files[j] - self.edge_task_index, tasks_detail, res = self.my_eval( - self.cloud_task_index, - eval_dataset_file, - r) - print("train from round ", r) - print("test round ", j+1) - print(f"all scores: {res}") - score_list = tmp_dict.get("all", []) - score_list.append(res) - tmp_dict["all"] = score_list - for detail in tasks_detail: - scores = detail.scores - entry = detail.entry - print(f"{entry} scores: {scores}") - score_list = tmp_dict.get(entry, []) - score_list.append(scores) - tmp_dict[entry] = score_list - for key in tmp_dict.keys(): - scores_list = my_dict.get(key, []) - scores_list.append(tmp_dict[key]) - my_dict[key] = scores_list - print(f"{key} scores: {scores_list}") - - else: - infer_dataset_file, eval_dataset_file = dataset_files[r - 1] - self.cloud_task_index = self._train(self.cloud_task_index, - infer_dataset_file, - r) - tmp_dict = {} - for j in range(rounds): - _, eval_dataset_file = dataset_files[j] - self.edge_task_index, tasks_detail, res = self.my_eval( + for r in range(rounds + 1): + train_dataset_file, eval_dataset_file = dataset_files[r] + self.cloud_task_index = self._train(self.cloud_task_index, + train_dataset_file, + r) + tmp_dict = {} + for j in range(1, rounds+1): + _, eval_dataset_file = dataset_files[j] + self.edge_task_index, tasks_detail, res = self.my_eval( self.cloud_task_index, - eval_dataset_file, - r) - print("train from round ", r) - print("test round ", j+1) - print(f"all scores: {res}") - score_list = tmp_dict.get("all", []) - score_list.append(res) - tmp_dict["all"] = score_list - for detail in tasks_detail: - scores = detail.scores - entry = detail.entry - print(f"{entry} scores: {scores}") - score_list = tmp_dict.get(entry, []) - score_list.append(scores) - tmp_dict[entry] = score_list - for key in tmp_dict.keys(): - scores_list = my_dict.get(key, []) - scores_list.append(tmp_dict[key]) - my_dict[key] = scores_list - print(f"{key} scores: {scores_list}") - - self.edge_task_index = self._eval(self.cloud_task_index, + eval_dataset_file, + r) + LOGGER.info(f"train from round {r}") + LOGGER.info(f"test round {j}") + LOGGER.info(f"all scores: {res}") + score_list = tmp_dict.get("all", ['' for i in range(rounds)]) + score_list[j-1] = res + tmp_dict["all"] = score_list + task_avg_score = {'accuracy':0.0} + i = 0 + for detail in tasks_detail: + i += 1 + scores = detail.scores + entry = detail.entry + LOGGER.info(f"{entry} scores: {scores}") + task_avg_score['accuracy'] += scores['accuracy'] + score_list = tmp_dict.get(entry, ['' for i in range(rounds)]) + score_list[j-1] = scores + tmp_dict[entry] = score_list + task_avg_score['accuracy'] = task_avg_score['accuracy']/i + score_list = tmp_dict.get("task_avg", [{'accuracy':0.0} for i in range(rounds)]) + score_list[j-1] = task_avg_score + tmp_dict["task_avg"] = score_list + + for key in tmp_dict.keys(): + scores_list = my_dict.get(key, []) + scores_list.append(tmp_dict[key]) + my_dict[key] = scores_list + LOGGER.info(f"{key} scores: {scores_list}") + + self.edge_task_index, tasks_detail, res = self.my_eval(self.cloud_task_index, self.dataset.test_url, - r+1) + r) + task_avg_score = {'accuracy':0.0} + i = 0 + for detail in tasks_detail: + i += 1 + scores = detail.scores + entry = detail.entry + LOGGER.info(f"{entry} scores: {scores}") + task_avg_score['accuracy'] += scores['accuracy'] + task_avg_score['accuracy'] = task_avg_score['accuracy']/i + self.system_metric_info[SystemMetricType.Task_Avg_Acc.value] = task_avg_score + LOGGER.info(task_avg_score) job = self.build_paradigm_job(ParadigmType.LIFELONG_LEARNING.value) inference_dataset = self.dataset.load_data(self.dataset.test_url, "eval", feature_process=_data_feature_process) kwargs = {} test_res = job.my_inference(inference_dataset, **kwargs) del job - output_dir = os.path.join(self.workspace, - "output/result.pkl") for key in my_dict.keys(): - print(f"{key} scores: {my_dict[key]}") - with open(output_dir,'wb') as file: - pickle.dump(my_dict,file) + LOGGER.info(f"{key} scores: {my_dict[key]}") for key in my_dict.keys(): matrix = my_dict[key] - BWT, FWT = self.compute(key, matrix) - self.system_metric_info[SystemMetricType.BWT.value][key] = BWT - self.system_metric_info[SystemMetricType.FWT.value][key] = FWT - - file.close() + #BWT, FWT = self.compute(key, matrix) + self.system_metric_info[SystemMetricType.Matrix.value][key] = matrix elif mode != 'multi-inference': dataset_files = self._split_dataset(splitting_dataset_times=rounds) @@ -219,63 +200,6 @@ def run(self): return test_res, self.system_metric_info - def compute(self, key, matrix): - """ - compute BWT and FWT - """ - # pylint: disable=C0103 - length = len(matrix) - accuracy = 0.0 - BWT_score = 0.0 - FWT_score = 0.0 - flag = True - for i in range(length): - if len(matrix[i]) != length: - flag = False - break - if flag is False: - BWT_score = np.nan - FWT_score = np.nan - return BWT_score, FWT_score - - for i in range(length): - accuracy += matrix[length-1][i]['accuracy'] - BWT_score += matrix[length-1][i]['accuracy'] - matrix[i][i]['accuracy'] - for i in range(1,length): - FWT_score += matrix[i-1][i]['accuracy'] - matrix[0][i]['accuracy'] - accuracy = accuracy/(length) - BWT_score = BWT_score/(length-1) - FWT_score = FWT_score/(length-1) - #print(f"{key} accuracy: ", accuracy) - print(f"{key} BWT_score: ", BWT_score) - print(f"{key} FWT_score: ", FWT_score) - my_matrix = [] - for i in range(length): - my_matrix.append([]) - for i in range(length): - for j in range(length): - my_matrix[i].append(matrix[j][i]['accuracy']) - self.draw_picture(key,my_matrix) - return BWT_score, FWT_score - - def draw_picture(self, title, matrix): - """ - draw hot image for results - """ - plt.figure(figsize=(10,8), dpi=80) - plt.imshow(matrix, cmap='bwr', extent=(0.5,len(matrix)+0.5,0.5,len(matrix)+0.5), - origin='lower') - plt.xticks(fontsize=15) - plt.yticks(fontsize=15) - plt.xlabel('task round', fontsize=15) - plt.ylabel('task', fontsize=15) - plt.title(title, fontsize=15) - plt.colorbar(format='%.2f') - output_dir = os.path.join(self.workspace, - f"output/{title}-heatmap.png") - plt.savefig(output_dir) - plt.show() - def _inference(self, edge_task_index, data_index_file, rounds): # pylint:disable=duplicate-code #print("start inference") @@ -333,7 +257,7 @@ def _train(self, cloud_task_index, train_dataset, rounds): os.environ["CLOUD_KB_INDEX"] = cloud_task_index os.environ["OUTPUT_URL"] = train_output_dir - if rounds == 1: + if rounds <= 1: os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'False' else: os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = 'True' diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py index 4da01191..112dc691 100755 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -15,7 +15,7 @@ """Base Metrics""" import sys - +import numpy as np from sedna.common.class_factory import ClassFactory, ClassType from core.common.constant import SystemMetricType @@ -47,19 +47,84 @@ def samples_transfer_ratio_func(system_metric_info: dict): transfer_num += len(transfer_data) return round(float(transfer_num) / (inference_num + 1), 4) +def compute(key, matrix): + """ + compute BWT and FWT + """ + # pylint: disable=C0103 + length = len(matrix) + accuracy = 0.0 + BWT_score = 0.0 + FWT_score = 0.0 + flag = True + for i in range(length): + if len(matrix[i]) != length-1: + flag = False + break + if flag is False: + BWT_score = np.nan + FWT_score = np.nan + return BWT_score, FWT_score + + for i in range(length-1): + accuracy += matrix[length-1][i]['accuracy'] + BWT_score += matrix[length-1][i]['accuracy'] - matrix[i+1][i]['accuracy'] + for i in range(0,length-1): + FWT_score += matrix[i][i]['accuracy'] - matrix[0][i]['accuracy'] + accuracy = accuracy/(length) + BWT_score = BWT_score/(length-1) + FWT_score = FWT_score/(length-1) + #print(f"{key} accuracy: ", accuracy) + print(f"{key} BWT_score: ", BWT_score) + print(f"{key} FWT_score: ", FWT_score) + my_matrix = [] + for i in range(length-1): + my_matrix.append([]) + for i in range(length-1): + for j in range(length-1): + my_matrix[i].append(matrix[i+1][j]['accuracy']) + #self.draw_picture(key,my_matrix) + return my_matrix, BWT_score, FWT_score + def bwt_func(system_metric_info: dict): """ compute BWT """ - info = system_metric_info.get(SystemMetricType.BWT.value) - return info["all"] + # pylint: disable=C0103 + # pylint: disable=W0632 + info = system_metric_info.get(SystemMetricType.Matrix.value) + _, BWT_score, _ = compute("all", info["all"]) + return BWT_score def fwt_func(system_metric_info: dict): """ compute FWT """ - info = system_metric_info.get(SystemMetricType.FWT.value) - return info["all"] + # pylint: disable=C0103 + # pylint: disable=W0632 + info = system_metric_info.get(SystemMetricType.Matrix.value) + _, _, FWT_score = compute("all", info["all"]) + return FWT_score + +def matrix_func(system_metric_info: dict): + """ + compute FWT + """ + # pylint: disable=C0103 + # pylint: disable=W0632 + info = system_metric_info.get(SystemMetricType.Matrix.value) + my_dict = {} + for key in info.keys(): + my_matrix, _, _ = compute(key, info[key]) + my_dict[key] = my_matrix + return my_dict + +def task_avg_acc_func(system_metric_info: dict): + """ + compute Task_Avg_Acc + """ + info = system_metric_info.get(SystemMetricType.Task_Avg_Acc.value) + return info["accuracy"] def get_metric_func(metric_dict: dict): """ diff --git a/core/testcasecontroller/testcase/testcase.py b/core/testcasecontroller/testcase/testcase.py index 168ad359..7775316d 100755 --- a/core/testcasecontroller/testcase/testcase.py +++ b/core/testcasecontroller/testcase/testcase.py @@ -111,7 +111,7 @@ def compute_metrics(self, paradigm_result, dataset, **kwargs): metric_res = {} system_metric_types = [e.value for e in SystemMetricType.__members__.values()] for metric_name, metric_func in metric_funcs.items(): - print(metric_name) + #print(metric_name) if metric_name in system_metric_types: metric_res[metric_name] = metric_func(kwargs) else: diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py index 3852cdbc..8d39faab 100755 --- a/core/testenvmanager/dataset/dataset.py +++ b/core/testenvmanager/dataset/dataset.py @@ -160,6 +160,11 @@ def split_dataset(self, dataset_url, dataset_format, ratio, method="default", data_types=dataset_types, output_dir=output_dir, times=times) + if method == "fwt_splitting": + return self._fwt_splitting(dataset_url, dataset_format, ratio, + data_types=dataset_types, + output_dir=output_dir, + times=times) raise ValueError(f"dataset splitting method({method}) is not supported," f"currently, method supports 'default'.") @@ -230,6 +235,44 @@ def _splitting_more_times(self, data_file, data_format, ratio, return data_files + def _fwt_splitting(self, data_file, data_format, ratio, + data_types=None, output_dir=None, times=1): + if not data_types: + data_types = ("train", "eval") + + if not output_dir: + output_dir = tempfile.mkdtemp() + + all_data = self._read_data_file(data_file, data_format) + + data_files = [] + + all_num = len(all_data) + step = int(all_num / times) + data_files.append(( + self._get_dataset_file(all_data[:1], output_dir, + data_types[0], 0, data_format), + self._get_dataset_file(all_data[:1], output_dir, + data_types[1], 0, data_format))) + index = 1 + while index <= times: + if index == times: + new_dataset = all_data[step * (index - 1):] + else: + new_dataset = all_data[step * (index - 1):step * index] + + new_num = len(new_dataset) + + data_files.append(( + self._get_dataset_file(new_dataset[:int(new_num * ratio)], output_dir, + data_types[0], index, data_format), + self._get_dataset_file(new_dataset[int(new_num * ratio):], output_dir, + data_types[1], index, data_format))) + + index += 1 + + return data_files + # add new splitting method for semantic segmentation def _city_splitting(self, data_file, data_format, ratio, data_types=None, output_dir=None, times=1): diff --git a/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml index fced6b6d..d11ae789 100755 --- a/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml +++ b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml @@ -2,7 +2,7 @@ benchmarkingjob: # job name of bechmarking; string type; name: "benchmarkingjob" # the url address of job workspace that will reserve the output of tests; string type; - workspace: "/ianvs/lifelong_learning_bench/robot-workspace-bwt" + workspace: "/home/hsj/ianvs/lifelong_learning_bench/robot-workspace-test" # the url address of test environment configuration file; string type; # the file format supports yaml/yml; @@ -57,13 +57,13 @@ benchmarkingjob: # currently the options of value are as follows: # 1> "all": select all metrics in the leaderboard; # 2> metrics in the leaderboard, e.g., "F1_SCORE" - metrics: [ "accuracy", "BWT", "FWT"] + metrics: [ "accuracy", "Task_Avg_Acc", "BWT", "FWT"] # model of save selected and all dataitems in workspace `./rank` ; string type; # currently the options of value are as follows: # 1> "selected_and_all": save selected and all dataitems; # 2> "selected_only": save selected dataitems; - save_mode: "selected_and_all" + save_mode: "selected_and_all_and_picture" diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py index 8a56dd74..c19d7c9e 100755 --- a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -2,7 +2,7 @@ class TrainArgs: def __init__(self, **kwargs): self.depth = False self.dataset = 'cityscapes' - self.workers = 4 + self.workers = 0 self.base_size = 1024 self.crop_size = 768 self.loss_type = 'ce' diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py index 343f58c5..afcd4911 100755 --- a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py @@ -79,7 +79,7 @@ def train(self, train_data, valid_data=None, **kwargs): }, is_best) self.trainer.writer.close() - with open('/ianvs/project/ianvs/train_loss.txt', 'a+') as file: + with open('/home/hsj/ianvs/project/ianvs/train_loss.txt', 'a+') as file: np.savetxt(file, loss_all) file.close return self.train_model_url diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml index cd7cbbaa..6eaafad3 100755 --- a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm-simple.yaml @@ -12,8 +12,8 @@ algorithm: # the method of splitting dataset; string type; optional; # currently the options of value are as follows: # 1> "default": the dataset is evenly divided based train_ratio; - splitting_method: "default" - #splitting_method: "my_splitting" + #splitting_method: "default" + splitting_method: "fwt_splitting" # algorithm module configuration in the paradigm; list type; modules: diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml index 3dd8712c..092abcb8 100755 --- a/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-robot-small.yaml @@ -30,8 +30,10 @@ testenv: # the url address of python file url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" - name: "samples_transfer_ratio" + - name: "Task_Avg_Acc" - name: "BWT" - name: "FWT" + - name: "Matrix" # incremental rounds setting; int type; default value is 2; incremental_rounds: 3 \ No newline at end of file diff --git a/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml b/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml index 2cf1e42c..d224c3e6 100755 --- a/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml +++ b/examples/robot/lifelong_learning_bench/testenv/testenv-robot.yaml @@ -30,8 +30,10 @@ testenv: # the url address of python file url: "./examples/robot/lifelong_learning_bench/testenv/accuracy.py" - name: "samples_transfer_ratio" + - name: "Task_Avg_Acc" - name: "BWT" - name: "FWT" + - name: "Matrix" # incremental rounds setting; int type; default value is 2; incremental_rounds: 5 \ No newline at end of file From 7a38ccd8d243843c3f2e71a90d72e55d157cd43c Mon Sep 17 00:00:00 2001 From: hsj576 Date: Wed, 12 Jul 2023 19:29:20 +0800 Subject: [PATCH 20/22] recover permission Signed-off-by: hsj576 --- core/__init__.py | 0 core/__version__.py | 0 core/cmd/__init__.py | 0 core/cmd/benchmarking.py | 0 core/cmd/obj/__init__.py | 0 core/cmd/obj/benchmarkingjob.py | 0 core/common/__init__.py | 0 core/common/constant.py | 0 core/common/log.py | 0 core/common/utils.py | 0 core/storymanager/__init__.py | 0 core/storymanager/rank/__init__.py | 0 core/storymanager/rank/rank.py | 0 core/storymanager/visualization/__init__.py | 0 core/storymanager/visualization/visualization.py | 0 core/testcasecontroller/__init__.py | 0 core/testcasecontroller/algorithm/__init__.py | 0 core/testcasecontroller/algorithm/algorithm.py | 0 core/testcasecontroller/algorithm/module/__init__.py | 0 core/testcasecontroller/algorithm/module/module.py | 0 core/testcasecontroller/algorithm/paradigm/__init__.py | 0 core/testcasecontroller/algorithm/paradigm/base.py | 0 .../algorithm/paradigm/incremental_learning/__init__.py | 0 .../paradigm/incremental_learning/incremental_learning.py | 0 .../algorithm/paradigm/lifelong_learning/__init__.py | 0 .../algorithm/paradigm/lifelong_learning/lifelong_learning.py | 0 .../algorithm/paradigm/singletask_learning/__init__.py | 0 .../algorithm/paradigm/singletask_learning/singletask_learning.py | 0 core/testcasecontroller/generation_assistant/__init__.py | 0 .../generation_assistant/generation_assistant.py | 0 core/testcasecontroller/metrics/__init__.py | 0 core/testcasecontroller/metrics/metrics.py | 0 core/testcasecontroller/testcase/__init__.py | 0 core/testcasecontroller/testcase/testcase.py | 0 core/testcasecontroller/testcasecontroller.py | 0 core/testenvmanager/__init__.py | 0 core/testenvmanager/dataset/__init__.py | 0 core/testenvmanager/dataset/dataset.py | 0 core/testenvmanager/testenv/__init__.py | 0 core/testenvmanager/testenv/testenv.py | 0 40 files changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 core/__init__.py mode change 100755 => 100644 core/__version__.py mode change 100755 => 100644 core/cmd/__init__.py mode change 100755 => 100644 core/cmd/benchmarking.py mode change 100755 => 100644 core/cmd/obj/__init__.py mode change 100755 => 100644 core/cmd/obj/benchmarkingjob.py mode change 100755 => 100644 core/common/__init__.py mode change 100755 => 100644 core/common/constant.py mode change 100755 => 100644 core/common/log.py mode change 100755 => 100644 core/common/utils.py mode change 100755 => 100644 core/storymanager/__init__.py mode change 100755 => 100644 core/storymanager/rank/__init__.py mode change 100755 => 100644 core/storymanager/rank/rank.py mode change 100755 => 100644 core/storymanager/visualization/__init__.py mode change 100755 => 100644 core/storymanager/visualization/visualization.py mode change 100755 => 100644 core/testcasecontroller/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/algorithm.py mode change 100755 => 100644 core/testcasecontroller/algorithm/module/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/module/module.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/base.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/incremental_learning/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/singletask_learning/__init__.py mode change 100755 => 100644 core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py mode change 100755 => 100644 core/testcasecontroller/generation_assistant/__init__.py mode change 100755 => 100644 core/testcasecontroller/generation_assistant/generation_assistant.py mode change 100755 => 100644 core/testcasecontroller/metrics/__init__.py mode change 100755 => 100644 core/testcasecontroller/metrics/metrics.py mode change 100755 => 100644 core/testcasecontroller/testcase/__init__.py mode change 100755 => 100644 core/testcasecontroller/testcase/testcase.py mode change 100755 => 100644 core/testcasecontroller/testcasecontroller.py mode change 100755 => 100644 core/testenvmanager/__init__.py mode change 100755 => 100644 core/testenvmanager/dataset/__init__.py mode change 100755 => 100644 core/testenvmanager/dataset/dataset.py mode change 100755 => 100644 core/testenvmanager/testenv/__init__.py mode change 100755 => 100644 core/testenvmanager/testenv/testenv.py diff --git a/core/__init__.py b/core/__init__.py old mode 100755 new mode 100644 diff --git a/core/__version__.py b/core/__version__.py old mode 100755 new mode 100644 diff --git a/core/cmd/__init__.py b/core/cmd/__init__.py old mode 100755 new mode 100644 diff --git a/core/cmd/benchmarking.py b/core/cmd/benchmarking.py old mode 100755 new mode 100644 diff --git a/core/cmd/obj/__init__.py b/core/cmd/obj/__init__.py old mode 100755 new mode 100644 diff --git a/core/cmd/obj/benchmarkingjob.py b/core/cmd/obj/benchmarkingjob.py old mode 100755 new mode 100644 diff --git a/core/common/__init__.py b/core/common/__init__.py old mode 100755 new mode 100644 diff --git a/core/common/constant.py b/core/common/constant.py old mode 100755 new mode 100644 diff --git a/core/common/log.py b/core/common/log.py old mode 100755 new mode 100644 diff --git a/core/common/utils.py b/core/common/utils.py old mode 100755 new mode 100644 diff --git a/core/storymanager/__init__.py b/core/storymanager/__init__.py old mode 100755 new mode 100644 diff --git a/core/storymanager/rank/__init__.py b/core/storymanager/rank/__init__.py old mode 100755 new mode 100644 diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py old mode 100755 new mode 100644 diff --git a/core/storymanager/visualization/__init__.py b/core/storymanager/visualization/__init__.py old mode 100755 new mode 100644 diff --git a/core/storymanager/visualization/visualization.py b/core/storymanager/visualization/visualization.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/__init__.py b/core/testcasecontroller/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/__init__.py b/core/testcasecontroller/algorithm/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/algorithm.py b/core/testcasecontroller/algorithm/algorithm.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/module/__init__.py b/core/testcasecontroller/algorithm/module/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/module/module.py b/core/testcasecontroller/algorithm/module/module.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/__init__.py b/core/testcasecontroller/algorithm/paradigm/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/__init__.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/generation_assistant/__init__.py b/core/testcasecontroller/generation_assistant/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/generation_assistant/generation_assistant.py b/core/testcasecontroller/generation_assistant/generation_assistant.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/metrics/__init__.py b/core/testcasecontroller/metrics/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/testcase/__init__.py b/core/testcasecontroller/testcase/__init__.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/testcase/testcase.py b/core/testcasecontroller/testcase/testcase.py old mode 100755 new mode 100644 diff --git a/core/testcasecontroller/testcasecontroller.py b/core/testcasecontroller/testcasecontroller.py old mode 100755 new mode 100644 diff --git a/core/testenvmanager/__init__.py b/core/testenvmanager/__init__.py old mode 100755 new mode 100644 diff --git a/core/testenvmanager/dataset/__init__.py b/core/testenvmanager/dataset/__init__.py old mode 100755 new mode 100644 diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py old mode 100755 new mode 100644 diff --git a/core/testenvmanager/testenv/__init__.py b/core/testenvmanager/testenv/__init__.py old mode 100755 new mode 100644 diff --git a/core/testenvmanager/testenv/testenv.py b/core/testenvmanager/testenv/testenv.py old mode 100755 new mode 100644 From be421cc8063b39834101483ced5d44ab6153c1d4 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Mon, 24 Jul 2023 19:39:13 +0800 Subject: [PATCH 21/22] fix conflict error Signed-off-by: hsj576 --- .../algorithm/module/module.py | 8 -------- .../third_party/sedna-0.4.1-py3-none-any.whl | Bin 159450 -> 160062 bytes .../benchmarkingjob.yaml | 2 +- .../testalgorithms/rfnet/RFNet/utils/args.py | 2 +- .../testalgorithms/rfnet/basemodel-simple.py | 3 --- 5 files changed, 2 insertions(+), 13 deletions(-) diff --git a/core/testcasecontroller/algorithm/module/module.py b/core/testcasecontroller/algorithm/module/module.py index f9517d16..6d487d97 100644 --- a/core/testcasecontroller/algorithm/module/module.py +++ b/core/testcasecontroller/algorithm/module/module.py @@ -107,14 +107,6 @@ def get_module_instance(self, module_type): ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value]: class_factory_type = ClassType.UTD - try: - utils.load_module(self.url) - # pylint: disable=E1134 - basemodel = ClassFactory.get_cls(type_name=ClassType.GENERAL, - t_cls_name=self.name)(**self.hyperparameters) - except Exception as err: - raise RuntimeError(f"basemodel module loads class(name={self.name}) failed, " - f"error: {err}.") from err if self.url: try: utils.load_module(self.url) diff --git 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qJKBatv|y|tKn#w1K(U^9`|DWKzbTM%7ckyfZv)i0!3t3O5&s8}ftXqV diff --git a/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml index d11ae789..3e6b5a49 100755 --- a/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml +++ b/examples/robot/lifelong_learning_bench/benchmarkingjob.yaml @@ -2,7 +2,7 @@ benchmarkingjob: # job name of bechmarking; string type; name: "benchmarkingjob" # the url address of job workspace that will reserve the output of tests; string type; - workspace: "/home/hsj/ianvs/lifelong_learning_bench/robot-workspace-test" + workspace: "/home/vipuser/ianvs/lifelong_learning_bench/robot-workspace-test" # the url address of test environment configuration file; string type; # the file format supports yaml/yml; diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py index c19d7c9e..8a56dd74 100755 --- a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/RFNet/utils/args.py @@ -2,7 +2,7 @@ class TrainArgs: def __init__(self, **kwargs): self.depth = False self.dataset = 'cityscapes' - self.workers = 0 + self.workers = 4 self.base_size = 1024 self.crop_size = 768 self.loss_type = 'ce' diff --git a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py index afcd4911..c2ad091e 100755 --- a/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py +++ b/examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py @@ -79,9 +79,6 @@ def train(self, train_data, valid_data=None, **kwargs): }, is_best) self.trainer.writer.close() - with open('/home/hsj/ianvs/project/ianvs/train_loss.txt', 'a+') as file: - np.savetxt(file, loss_all) - file.close return self.train_model_url def predict(self, data, **kwargs): From 8d832a8adae88821ebcd422b337e3fed5c658ea4 Mon Sep 17 00:00:00 2001 From: hsj576 Date: Mon, 24 Jul 2023 20:10:44 +0800 Subject: [PATCH 22/22] fix conflict error Signed-off-by: hsj576 --- core/testcasecontroller/algorithm/paradigm/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/testcasecontroller/algorithm/paradigm/base.py b/core/testcasecontroller/algorithm/paradigm/base.py index 8e242b64..cf36cd4e 100644 --- a/core/testcasecontroller/algorithm/paradigm/base.py +++ b/core/testcasecontroller/algorithm/paradigm/base.py @@ -122,7 +122,7 @@ def build_paradigm_job(self, paradigm_type): unseen_sample_re_recognition=self.module_instances.get( ModuleType.UNSEEN_SAMPLE_RE_RECOGNITION.value) ) - + # pylint: disable=E1101 if paradigm_type == ParadigmType.MULTIEDGE_INFERENCE.value: return self.modules_funcs.get(ModuleType.BASEMODEL.value)()

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b/examples/semantic_segmentation/lifelong_learning_bench/README.md @@ -0,0 +1,103 @@ +# Quick Start + +Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, +in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs +with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can +build and start your distributed synergy AI solution development within minutes. + +Before using Ianvs, you might want to have the device ready: +- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary +- 2 CPUs or more +- 4GB+ free memory, depends on algorithm and simulation setting +- 10GB+ free disk space +- Internet connection for GitHub and pip, etc +- Python 3.6+ installed + +In this example, we are using the Linux platform with Python 3.8.5. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. + +## Step 1. Ianvs Preparation + +First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` +as: + +``` shell +mkdir /ianvs +cd /ianvs #One might use another path preferred + +mkdir project +cd project +git clone -b feature-lifelong-n https://github.com/kubeedge/ianvs.git +``` + + +Then, we install third-party dependencies for ianvs. +``` shell +sudo apt-get update +sudo apt-get install libgl1-mesa-glx -y +python -m pip install --upgrade pip + +cd ianvs +python -m pip install ./examples/resources/third_party/* +python -m pip install -r requirements.txt +``` + +We are now ready to install Ianvs. +``` shell +python setup.py install +``` + +## Step 2. Dataset Preparation + +Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does +not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. + +``` shell +cd /ianvs #One might use another path preferred +mkdir dataset +cd dataset +wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip +unzip dataset.zip +``` + +The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, +we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + + + + +Related algorithm is also ready in this quick start. +``` shell +export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet +``` + +The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick +start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +## Step 3. Ianvs Execution and Presentation + +We are now ready to run the ianvs for benchmarking. + +``` shell +cd /ianvs/project/ianvs +ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml +``` + +Finally, the user can check the result of benchmarking on the console and also in the output path( +e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( +e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers +can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. + +The final output might look like this: + +|rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | +|:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| +|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | + + +This ends the quick start experiment. + +# What is next + +If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. + +Enjoy your journey on Ianvs! \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml new file mode 100644 index 00000000..7252afc5 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml @@ -0,0 +1,74 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "/ianvs/lifelong_learning_bench/workspace" + + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml" + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-small.yaml" + #testenv: "./examples/semantic_segmentation/lifelong_learning_bench/testenv/testenv-full.yaml" + + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "rfnet_lifelong_learning" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml + url: "./examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "accuracy": "descend" }, { "samples_transfer_ratio": "ascend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "F1_SCORE" + metrics: [ "accuracy", "samples_transfer_ratio" ] + + # model of save selected and all dataitems in workspace `./rank` ; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt b/examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt new file mode 100644 index 00000000..1cd068f1 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/test-index-sort.txt @@ -0,0 +1,500 @@ +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_000294_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_000294_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_000576_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_000576_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_000576_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_001016_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_001016_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_001016_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_001236_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_001236_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_001236_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_001751_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_001751_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_001751_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_002196_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_002196_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_002196_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_002963_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_002963_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_002963_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_003025_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_003025_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_003025_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_003357_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_003357_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_003357_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_003920_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_003920_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_003920_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_004617_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_004617_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_004617_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_005543_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_005543_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_005543_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_005898_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_005898_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_005898_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_006589_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_006589_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_006589_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_007365_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_007365_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_007365_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_008206_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_008206_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_008206_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_008451_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_008451_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_008451_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_009291_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_009291_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_009291_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_009561_leftImg8bit.png 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a/examples/semantic_segmentation/lifelong_learning_bench/test-index.txt b/examples/semantic_segmentation/lifelong_learning_bench/test-index.txt new file mode 100644 index 00000000..9e396939 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/test-index.txt @@ -0,0 +1,1330 @@ +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_000294_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_000294_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_000576_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_000576_disparity.png ./test/gtFine/cityscapes_real/frankfurt/frankfurt_000000_000576_gtFine_labelTrainIds.png +./test/leftImg8bit/cityscapes_real/frankfurt/frankfurt_000000_001016_leftImg8bit.png ./test/depth/cityscapes_real/frankfurt/frankfurt_000000_001016_disparity.png 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+./test/leftImg8bit/synthia_sim/870.png ./test/depth/synthia_sim/870.png ./test/gtFine/synthia_sim/870TrainIds.png +./test/leftImg8bit/synthia_sim/871.png ./test/depth/synthia_sim/871.png ./test/gtFine/synthia_sim/871TrainIds.png +./test/leftImg8bit/synthia_sim/873.png ./test/depth/synthia_sim/873.png ./test/gtFine/synthia_sim/873TrainIds.png +./test/leftImg8bit/synthia_sim/874.png ./test/depth/synthia_sim/874.png ./test/gtFine/synthia_sim/874TrainIds.png +./test/leftImg8bit/synthia_sim/875.png ./test/depth/synthia_sim/875.png ./test/gtFine/synthia_sim/875TrainIds.png +./test/leftImg8bit/synthia_sim/876.png ./test/depth/synthia_sim/876.png ./test/gtFine/synthia_sim/876TrainIds.png +./test/leftImg8bit/synthia_sim/877.png ./test/depth/synthia_sim/877.png ./test/gtFine/synthia_sim/877TrainIds.png +./test/leftImg8bit/synthia_sim/879.png ./test/depth/synthia_sim/879.png ./test/gtFine/synthia_sim/879TrainIds.png +./test/leftImg8bit/synthia_sim/881.png ./test/depth/synthia_sim/881.png 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+./test/leftImg8bit/synthia_sim/900.png ./test/depth/synthia_sim/900.png ./test/gtFine/synthia_sim/900TrainIds.png +./test/leftImg8bit/synthia_sim/901.png ./test/depth/synthia_sim/901.png ./test/gtFine/synthia_sim/901TrainIds.png +./test/leftImg8bit/synthia_sim/902.png ./test/depth/synthia_sim/902.png ./test/gtFine/synthia_sim/902TrainIds.png +./test/leftImg8bit/synthia_sim/903.png ./test/depth/synthia_sim/903.png ./test/gtFine/synthia_sim/903TrainIds.png +./test/leftImg8bit/synthia_sim/905.png ./test/depth/synthia_sim/905.png ./test/gtFine/synthia_sim/905TrainIds.png +./test/leftImg8bit/synthia_sim/907.png ./test/depth/synthia_sim/907.png ./test/gtFine/synthia_sim/907TrainIds.png +./test/leftImg8bit/synthia_sim/908.png ./test/depth/synthia_sim/908.png ./test/gtFine/synthia_sim/908TrainIds.png +./test/leftImg8bit/synthia_sim/909.png ./test/depth/synthia_sim/909.png ./test/gtFine/synthia_sim/909TrainIds.png +./test/leftImg8bit/synthia_sim/91.png ./test/depth/synthia_sim/91.png ./test/gtFine/synthia_sim/91TrainIds.png +./test/leftImg8bit/synthia_sim/910.png ./test/depth/synthia_sim/910.png ./test/gtFine/synthia_sim/910TrainIds.png +./test/leftImg8bit/synthia_sim/911.png ./test/depth/synthia_sim/911.png ./test/gtFine/synthia_sim/911TrainIds.png +./test/leftImg8bit/synthia_sim/913.png ./test/depth/synthia_sim/913.png ./test/gtFine/synthia_sim/913TrainIds.png +./test/leftImg8bit/synthia_sim/914.png ./test/depth/synthia_sim/914.png ./test/gtFine/synthia_sim/914TrainIds.png +./test/leftImg8bit/synthia_sim/916.png ./test/depth/synthia_sim/916.png ./test/gtFine/synthia_sim/916TrainIds.png +./test/leftImg8bit/synthia_sim/917.png ./test/depth/synthia_sim/917.png ./test/gtFine/synthia_sim/917TrainIds.png +./test/leftImg8bit/synthia_sim/918.png ./test/depth/synthia_sim/918.png ./test/gtFine/synthia_sim/918TrainIds.png +./test/leftImg8bit/synthia_sim/919.png ./test/depth/synthia_sim/919.png ./test/gtFine/synthia_sim/919TrainIds.png +./test/leftImg8bit/synthia_sim/921.png ./test/depth/synthia_sim/921.png ./test/gtFine/synthia_sim/921TrainIds.png +./test/leftImg8bit/synthia_sim/922.png ./test/depth/synthia_sim/922.png ./test/gtFine/synthia_sim/922TrainIds.png +./test/leftImg8bit/synthia_sim/923.png ./test/depth/synthia_sim/923.png ./test/gtFine/synthia_sim/923TrainIds.png +./test/leftImg8bit/synthia_sim/925.png ./test/depth/synthia_sim/925.png ./test/gtFine/synthia_sim/925TrainIds.png +./test/leftImg8bit/synthia_sim/926.png ./test/depth/synthia_sim/926.png ./test/gtFine/synthia_sim/926TrainIds.png +./test/leftImg8bit/synthia_sim/927.png ./test/depth/synthia_sim/927.png ./test/gtFine/synthia_sim/927TrainIds.png +./test/leftImg8bit/synthia_sim/929.png ./test/depth/synthia_sim/929.png ./test/gtFine/synthia_sim/929TrainIds.png +./test/leftImg8bit/synthia_sim/93.png ./test/depth/synthia_sim/93.png ./test/gtFine/synthia_sim/93TrainIds.png +./test/leftImg8bit/synthia_sim/930.png ./test/depth/synthia_sim/930.png ./test/gtFine/synthia_sim/930TrainIds.png +./test/leftImg8bit/synthia_sim/931.png ./test/depth/synthia_sim/931.png ./test/gtFine/synthia_sim/931TrainIds.png +./test/leftImg8bit/synthia_sim/932.png ./test/depth/synthia_sim/932.png ./test/gtFine/synthia_sim/932TrainIds.png +./test/leftImg8bit/synthia_sim/934.png ./test/depth/synthia_sim/934.png ./test/gtFine/synthia_sim/934TrainIds.png +./test/leftImg8bit/synthia_sim/935.png ./test/depth/synthia_sim/935.png ./test/gtFine/synthia_sim/935TrainIds.png +./test/leftImg8bit/synthia_sim/937.png ./test/depth/synthia_sim/937.png ./test/gtFine/synthia_sim/937TrainIds.png +./test/leftImg8bit/synthia_sim/938.png ./test/depth/synthia_sim/938.png ./test/gtFine/synthia_sim/938TrainIds.png +./test/leftImg8bit/synthia_sim/939.png ./test/depth/synthia_sim/939.png ./test/gtFine/synthia_sim/939TrainIds.png +./test/leftImg8bit/synthia_sim/94.png ./test/depth/synthia_sim/94.png ./test/gtFine/synthia_sim/94TrainIds.png +./test/leftImg8bit/synthia_sim/940.png ./test/depth/synthia_sim/940.png ./test/gtFine/synthia_sim/940TrainIds.png +./test/leftImg8bit/synthia_sim/941.png ./test/depth/synthia_sim/941.png ./test/gtFine/synthia_sim/941TrainIds.png +./test/leftImg8bit/synthia_sim/95.png ./test/depth/synthia_sim/95.png ./test/gtFine/synthia_sim/95TrainIds.png +./test/leftImg8bit/synthia_sim/953.png ./test/depth/synthia_sim/953.png ./test/gtFine/synthia_sim/953TrainIds.png +./test/leftImg8bit/synthia_sim/963.png ./test/depth/synthia_sim/963.png ./test/gtFine/synthia_sim/963TrainIds.png +./test/leftImg8bit/synthia_sim/97.png ./test/depth/synthia_sim/97.png ./test/gtFine/synthia_sim/97TrainIds.png +./test/leftImg8bit/synthia_sim/973.png ./test/depth/synthia_sim/973.png ./test/gtFine/synthia_sim/973TrainIds.png +./test/leftImg8bit/synthia_sim/98.png ./test/depth/synthia_sim/98.png ./test/gtFine/synthia_sim/98TrainIds.png +./test/leftImg8bit/synthia_sim/983.png ./test/depth/synthia_sim/983.png ./test/gtFine/synthia_sim/983TrainIds.png +./test/leftImg8bit/synthia_sim/99.png ./test/depth/synthia_sim/99.png ./test/gtFine/synthia_sim/99TrainIds.png +./test/leftImg8bit/synthia_sim/994.png ./test/depth/synthia_sim/994.png ./test/gtFine/synthia_sim/994TrainIds.png \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py new file mode 100644 index 00000000..8d356fed --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/accuracy.py @@ -0,0 +1,38 @@ +from basemodel import val_args +from utils.metrics import Evaluator +from tqdm import tqdm +from dataloaders import make_data_loader +from sedna.common.class_factory import ClassType, ClassFactory + +__all__ = ('accuracy') + +@ClassFactory.register(ClassType.GENERAL) +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py new file mode 100644 index 00000000..dba4cfdf --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/basemodel.py @@ -0,0 +1,315 @@ +import os +import numpy as np +import torch +from PIL import Image +import argparse +from train import Trainer +from eval import Validator +from tqdm import tqdm +from eval import load_my_state_dict +from utils.metrics import Evaluator +from dataloaders import make_data_loader +from dataloaders import custom_transforms as tr +from torchvision import transforms +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.common.config import Context +from sedna.datasources import TxtDataParse +from torch.utils.data import DataLoader +from sedna.common.file_ops import FileOps +from utils.lr_scheduler import LR_Scheduler + +def preprocess(image_urls): + transformed_images = [] + for paths in image_urls: + if len(paths) == 2: + img_path, depth_path = paths + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(depth_path) + else: + img_path = paths[0] + _img = Image.open(img_path).convert('RGB') + _depth = _img + + sample = {'image': _img, 'depth': _depth, 'label': _img} + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + # tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + transformed_images.append((composed_transforms(sample), img_path)) + + return transformed_images + +class Model: + def __init__(self, **kwargs): + self.val_args = val_args() + self.train_args = train_args() + + self.train_args.lr = kwargs.get("learning_rate", 1e-4) + self.train_args.epochs = kwargs.get("epochs", 2) + self.train_args.eval_interval = kwargs.get("eval_interval", 2) + self.train_args.no_val = kwargs.get("no_val", True) + # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None) + self.trainer = None + + label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results") + self.val_args.color_label_save_path = os.path.join(label_save_dir, "color") + self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge") + self.val_args.label_save_path = os.path.join(label_save_dir, "label") + self.validator = Validator(self.val_args) + + def train(self, train_data, valid_data=None, **kwargs): + self.trainer = Trainer(self.train_args, train_data=train_data) + print("Total epoches:", self.trainer.args.epochs) + for epoch in range(self.trainer.args.start_epoch, self.trainer.args.epochs): + if epoch == 0 and self.trainer.val_loader: + self.trainer.validation(epoch) + self.trainer.training(epoch) + + if self.trainer.args.no_val and \ + (epoch % self.trainer.args.eval_interval == (self.trainer.args.eval_interval - 1) + or epoch == self.trainer.args.epochs - 1): + # save checkpoint when it meets eval_interval or the training finished + is_best = False + checkpoint_path = self.trainer.saver.save_checkpoint({ + 'epoch': epoch + 1, + 'state_dict': self.trainer.model.state_dict(), + 'optimizer': self.trainer.optimizer.state_dict(), + 'best_pred': self.trainer.best_pred, + }, is_best) + + # if not self.trainer.args.no_val and \ + # epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \ + # and self.trainer.val_loader: + # self.trainer.validation(epoch) + + self.trainer.writer.close() + + return checkpoint_path + + def predict(self, data, **kwargs): + if not isinstance(data[0][0], dict): + data = preprocess(data) + + if type(data) is np.ndarray: + data = data.tolist() + + self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False, + pin_memory=True) + return self.validator.validate() + + def evaluate(self, data, **kwargs): + self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True) + samples = preprocess(data.x) + predictions = self.predict(samples) + return accuracy(data.y, predictions) + + def load(self, model_url, **kwargs): + if model_url: + self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu")) + self.train_args.resume = model_url + else: + raise Exception("model url does not exist.") + self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict']) + + def save(self, model_path=None): + # TODO: how to save unstructured data model + pass + +def train_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet Training") + parser.add_argument('--depth', action="store_true", default=False, + help='training with depth image or not (default: False)') + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--loss-type', type=str, default='ce', + choices=['ce', 'focal'], + help='loss func type (default: ce)') + # training hyper params + # parser.add_argument('--epochs', type=int, default=None, metavar='N', + # help='number of epochs to train (default: auto)') + parser.add_argument('--epochs', type=int, default=None, metavar='N', + help='number of epochs to train (default: auto)') + parser.add_argument('--start_epoch', type=int, default=0, + metavar='N', help='start epochs (default:0)') + parser.add_argument('--batch-size', type=int, default=None, + metavar='N', help='input batch size for \ + training (default: auto)') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--use-balanced-weights', action='store_true', default=False, + help='whether to use balanced weights (default: True)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + # optimizer params + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help='learning rate (default: auto)') + parser.add_argument('--lr-scheduler', type=str, default='cos', + choices=['poly', 'step', 'cos', 'inv'], + help='lr scheduler mode: (default: cos)') + parser.add_argument('--momentum', type=float, default=0.9, + metavar='M', help='momentum (default: 0.9)') + parser.add_argument('--weight-decay', type=float, default=2.5e-5, + metavar='M', help='w-decay (default: 5e-4)') + # cuda, seed and logging + parser.add_argument('--no-cuda', action='store_true', default= + False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') + # checking point + parser.add_argument('--resume', type=str, + default=None, + help='put the path to resuming file if needed') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + # finetuning pre-trained models + parser.add_argument('--ft', action='store_true', default=True, + help='finetuning on a different dataset') + # evaluation option + parser.add_argument('--eval-interval', type=int, default=1, + help='evaluation interval (default: 1)') + parser.add_argument('--no-val', action='store_true', default=False, + help='skip validation during training') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + print(torch.cuda.is_available()) + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + if args.epochs is None: + epoches = { + 'cityscapes': 200, + 'citylostfound': 200, + } + args.epochs = epoches[args.dataset.lower()] + + if args.batch_size is None: + args.batch_size = 4 * len(args.gpu_ids) + + if args.test_batch_size is None: + args.test_batch_size = args.batch_size + + if args.lr is None: + lrs = { + 'cityscapes': 0.0001, + 'citylostfound': 0.0001, + 'cityrand': 0.0001 + } + args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size + + if args.checkname is None: + args.checkname = 'RFNet' + print(args) + torch.manual_seed(args.seed) + + return args + +def val_args(): + parser = argparse.ArgumentParser(description="PyTorch RFNet validation") + parser.add_argument('--dataset', type=str, default='cityscapes', + choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], + help='dataset name (default: cityscapes)') + parser.add_argument('--workers', type=int, default=4, + metavar='N', help='dataloader threads') + parser.add_argument('--base-size', type=int, default=1024, + help='base image size') + parser.add_argument('--crop-size', type=int, default=768, + help='crop image size') + parser.add_argument('--batch-size', type=int, default=6, + help='batch size for training') + parser.add_argument('--val-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + validating (default: auto)') + parser.add_argument('--test-batch-size', type=int, default=1, + metavar='N', help='input batch size for \ + testing (default: auto)') + parser.add_argument('--num-class', type=int, default=24, + help='number of training classes (default: 24') + parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') + parser.add_argument('--gpu-ids', type=str, default='0', + help='use which gpu to train, must be a \ + comma-separated list of integers only (default=0)') + parser.add_argument('--checkname', type=str, default=None, + help='set the checkpoint name') + parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth", + help='enter your path of the weight') + parser.add_argument('--save-predicted-image', action='store_true', default=False, + help='save predicted images') + parser.add_argument('--color-label-save-path', type=str, + default='./test/color/', + help='path to save label') + parser.add_argument('--merge-label-save-path', type=str, + default='./test/merge/', + help='path to save merged label') + parser.add_argument('--label-save-path', type=str, default='./test/label/', + help='path to save merged label') + parser.add_argument('--merge', action='store_true', default=True, help='merge image and label') + parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') + + args = parser.parse_args() + args.cuda = not args.no_cuda and torch.cuda.is_available() + if args.cuda: + try: + args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] + except ValueError: + raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') + + return args + +def accuracy(y_true, y_pred, **kwargs): + args = val_args() + _, _, test_loader, num_class = make_data_loader(args, test_data=y_true) + evaluator = Evaluator(num_class) + + tbar = tqdm(test_loader, desc='\r') + for i, (sample, img_path) in enumerate(tbar): + if args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if args.cuda: + image, target = image.cuda(), target.cuda() + if args.depth: + depth = depth.cuda() + + target[target > evaluator.num_class-1] = 255 + target = target.cpu().numpy() + # Add batch sample into evaluator + evaluator.add_batch(target, y_pred[i]) + + # Test during the training + # Acc = evaluator.Pixel_Accuracy() + CPA = evaluator.Pixel_Accuracy_Class() + mIoU = evaluator.Mean_Intersection_over_Union() + FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() + + print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU)) + return CPA + +if __name__ == '__main__': + model_path = "/tmp/RFNet/" + if not os.path.exists(model_path): + os.makedirs(model_path) + + p1 = Process(target=exp_train, args=(10,)) + p1.start() + p1.join() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py new file mode 100644 index 00000000..2c71f7b1 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/__init__.py @@ -0,0 +1,119 @@ +from dataloaders.datasets import cityscapes, citylostfound, cityrand, target, xrlab, e1, mapillary +from torch.utils.data import DataLoader + +def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **kwargs): + + if args.dataset == 'cityscapes': + if train_data is not None: + train_set = cityscapes.CityscapesSegmentation(args, data=train_data, split='train') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + else: + train_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if valid_data is not None: + val_set = cityscapes.CityscapesSegmentation(args, data=valid_data, split='val') + num_class = val_set.NUM_CLASSES + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + else: + val_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + if test_data is not None: + test_set = cityscapes.CityscapesSegmentation(args, data=test_data, split='test') + num_class = test_set.NUM_CLASSES + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + test_loader, num_class = None, cityscapes.CityscapesSegmentation.NUM_CLASSES + + # custom_set = cityscapes.CityscapesSegmentation(args, split='custom_resize') + # custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + # return train_loader, val_loader, test_loader, custom_loader, num_class + return train_loader, val_loader, test_loader, num_class + + if args.dataset == 'citylostfound': + if args.depth: + train_set = citylostfound.CitylostfoundSegmentation(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + else: + train_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='train') + val_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='val') + test_set = citylostfound.CitylostfoundSegmentation_rgb(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, num_class + if args.dataset == 'cityrand': + train_set = cityrand.CityscapesSegmentation(args, split='train') + val_set = cityrand.CityscapesSegmentation(args, split='val') + test_set = cityrand.CityscapesSegmentation(args, split='test') + custom_set = cityrand.CityscapesSegmentation(args, split='custom_resize') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'target': + train_set = target.CityscapesSegmentation(args, split='train') + val_set = target.CityscapesSegmentation(args, split='val') + test_set = target.CityscapesSegmentation(args, split='test') + custom_set = target.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'xrlab': + train_set = xrlab.CityscapesSegmentation(args, split='train') + val_set = xrlab.CityscapesSegmentation(args, split='val') + test_set = xrlab.CityscapesSegmentation(args, split='test') + custom_set = xrlab.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'e1': + train_set = e1.CityscapesSegmentation(args, split='train') + val_set = e1.CityscapesSegmentation(args, split='val') + test_set = e1.CityscapesSegmentation(args, split='test') + custom_set = e1.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + if args.dataset == 'mapillary': + train_set = mapillary.CityscapesSegmentation(args, split='train') + val_set = mapillary.CityscapesSegmentation(args, split='val') + test_set = mapillary.CityscapesSegmentation(args, split='test') + custom_set = mapillary.CityscapesSegmentation(args, split='test') + num_class = train_set.NUM_CLASSES + train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs) + val_loader = DataLoader(val_set, batch_size=args.val_batch_size, shuffle=False, **kwargs) + test_loader = DataLoader(test_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + custom_loader = DataLoader(custom_set, batch_size=args.test_batch_size, shuffle=False, **kwargs) + + return train_loader, val_loader, test_loader, custom_loader, num_class + + else: + raise NotImplementedError + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py new file mode 100644 index 00000000..d63f200a --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms.py @@ -0,0 +1,240 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + # mean and std for original depth images + mean_depth = 0.12176 + std_depth = 0.09752 + + depth /= 255.0 + depth -= mean_depth + depth /= std_depth + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + depth = np.array(depth).astype(np.float32) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + depth = torch.from_numpy(depth).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'depth': depth, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + depth = depth.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + depth = depth.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # depth = depth.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + depth = depth.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + depth = depth.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + depth = ImageOps.expand(depth, border=(0, 0, padw, padh), fill=0) # depth多余的部分填0 + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + depth = depth.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + depth = depth.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + depth = sample['depth'] + mask = sample['label'] + + assert img.size == depth.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + depth = depth.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'depth': depth, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py new file mode 100644 index 00000000..e04ef5a3 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/custom_transforms_rgb.py @@ -0,0 +1,230 @@ +import torch +import random +import numpy as np + +from PIL import Image, ImageOps, ImageFilter + +class Normalize(object): + """Normalize a tensor image with mean and standard deviation. + Args: + mean (tuple): means for each channel. + std (tuple): standard deviations for each channel. + """ + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32) + mask = np.array(mask).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return {'image': img, + 'label': mask} + + +class Normalize_test(object): + def __init__(self, mean=(0., 0., 0.), std=(1., 1., 1.)): + self.mean = mean + self.std = std + + def __call__(self, sample): + img = sample + img = np.array(img).astype(np.float32) + img /= 255.0 + img -= self.mean + img /= self.std + + return img + + +class ToTensor(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample['image'] + mask = sample['label'] + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + mask = np.array(mask).astype(np.float32) + + img = torch.from_numpy(img).float() + mask = torch.from_numpy(mask).float() + + return {'image': img, + 'label': mask} + +class CropBlackArea(object): + """ + crop black area for depth image + """ + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + width, height = img.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + img = img.crop((left, top, right, bottom)) + mask = mask.crop((left, top, right, bottom)) + # resize + img = img.resize((width,height), Image.BILINEAR) + mask = mask.resize((width,height), Image.NEAREST) + # img = img.resize((512,1024), Image.BILINEAR) + # mask = mask.resize((512,1024), Image.NEAREST) + print(img.size) + + return {'image': img, + 'label': mask} + +class ToTensor_test(object): + """Convert Image object in sample to Tensors.""" + + def __call__(self, sample): + # swap color axis because + # numpy image: H x W x C + # torch image: C X H X W + img = sample + img = np.array(img).astype(np.float32).transpose((2, 0, 1)) + + img = torch.from_numpy(img).float() + + return img + + +class RandomHorizontalFlip(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.transpose(Image.FLIP_LEFT_RIGHT) + mask = mask.transpose(Image.FLIP_LEFT_RIGHT) + + return {'image': img, + 'label': mask} + + +class RandomRotate(object): + def __init__(self, degree): + self.degree = degree + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + rotate_degree = random.uniform(-1*self.degree, self.degree) + img = img.rotate(rotate_degree, Image.BILINEAR) + mask = mask.rotate(rotate_degree, Image.NEAREST) + + return {'image': img, + 'label': mask} + + +class RandomGaussianBlur(object): + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + if random.random() < 0.5: + img = img.filter(ImageFilter.GaussianBlur( + radius=random.random())) + + return {'image': img, + 'label': mask} + + +class RandomScaleCrop(object): + def __init__(self, base_size, crop_size, fill=0): + self.base_size = base_size + self.crop_size = crop_size + self.fill = fill + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + # random scale (short edge) + short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0)) + w, h = img.size + if h > w: + ow = short_size + oh = int(1.0 * h * ow / w) + else: + oh = short_size + ow = int(1.0 * w * oh / h) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # pad crop + if short_size < self.crop_size: + padh = self.crop_size - oh if oh < self.crop_size else 0 + padw = self.crop_size - ow if ow < self.crop_size else 0 + img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0) + mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill) + # random crop crop_size + w, h = img.size + x1 = random.randint(0, w - self.crop_size) + y1 = random.randint(0, h - self.crop_size) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + + +class FixScaleCrop(object): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + w, h = img.size + if w > h: + oh = self.crop_size + ow = int(1.0 * w * oh / h) + else: + ow = self.crop_size + oh = int(1.0 * h * ow / w) + img = img.resize((ow, oh), Image.BILINEAR) + mask = mask.resize((ow, oh), Image.NEAREST) + # center crop + w, h = img.size + x1 = int(round((w - self.crop_size) / 2.)) + y1 = int(round((h - self.crop_size) / 2.)) + img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) + + return {'image': img, + 'label': mask} + +class FixedResize(object): + def __init__(self, size): + self.size = (size, size) # size: (h, w) + + def __call__(self, sample): + img = sample['image'] + mask = sample['label'] + + assert img.size == mask.size + + img = img.resize(self.size, Image.BILINEAR) + mask = mask.resize(self.size, Image.NEAREST) + + return {'image': img, + 'label': mask} + +class Relabel(object): + def __init__(self, olabel, nlabel): # change trainid label from olabel to nlabel + self.olabel = olabel + self.nlabel = nlabel + + def __call__(self, tensor): + # assert (isinstance(tensor, torch.LongTensor) or isinstance(tensor, + # torch.ByteTensor)), 'tensor needs to be LongTensor' + tensor[tensor == self.olabel] = self.nlabel + return tensor \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py new file mode 100644 index 00000000..6ffd0a4b --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/citylostfound.py @@ -0,0 +1,276 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr +from dataloaders import custom_transforms_rgb as tr_rgb + +class CitylostfoundSegmentation(data.Dataset): + NUM_CLASSES = 20 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root,'disparity',self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix= '.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix= '.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, + suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'depth': _depth, 'label': _target} + + # data augment + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) # background->255 ignore + input = tr.Relabel(1, 0)(input) # road 1->0 + # input = Relabel(255, 20)(input) # unlabel 20 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=None): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + if isinstance(suffix, str): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + elif isinstance(suffix, list): + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for x in suffix for filename in filenames if filename.startswith(x)] + + + def transform_tr(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + # tr.CropBlackArea(), + tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + +class CitylostfoundSegmentation_rgb(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('citylostfound'), split="train"): + + self.root = root + self.split = split + self.args = args + self.files = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.files[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.files[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='labelTrainIds.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.files[split]: + raise Exception("No files for split=[%s] found in %s" % (split, self.images_base)) + + print("Found %d %s images" % (len(self.files[split]), split)) + + def __len__(self): + return len(self.files[self.split]) + + def __getitem__(self, index): + + img_path = self.files[self.split][index].rstrip() + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _tmp = np.array(Image.open(lbl_path), dtype=np.uint8) + if self.split == 'train': + if index < 1036: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + elif self.split == 'val': + if index < 1203: # lostandfound + _tmp = self.relabel_lostandfound(_tmp) + else: # cityscapes + pass + _target = Image.fromarray(_tmp) + + sample = {'image': _img, 'label': _target} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample) + + + def relabel_lostandfound(self, input): + input = tr.Relabel(0, self.ignore_index)(input) + input = tr.Relabel(1, 0)(input) # road 1->0 + input = tr.Relabel(2, 19)(input) # obstacle 19 + return input + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.RandomHorizontalFlip(), + tr_rgb.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.CropBlackArea(), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr_rgb.FixedResize(size=self.args.crop_size), + tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr_rgb.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CitylostfoundSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py new file mode 100644 index 00000000..74eddb67 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityrand.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 19 + + def __init__(self, args, root=Path.db_root_dir('cityrand'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py new file mode 100644 index 00000000..74be3296 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/cityscapes.py @@ -0,0 +1,162 @@ +import os +import numpy as np +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 # 25 + + def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"): + + # self.root = root + self.root = "/home/lsq/Dataset/" + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.disparities_base = os.path.join(self.root, self.split, "depth", "cityscapes_real") + self.images[split] = [img[0] for img in data.x] if hasattr(data, "x") else data + + + if hasattr(data, "x") and len(data.x[0]) == 1: + # TODO: fit the case that depth images don't exist. + self.disparities[split] = self.images[split] + elif hasattr(data, "x") and len(data.x[0]) == 2: + self.disparities[split] = [img[1] for img in data.x] + else: + if len(data[0]) == 2: + self.images[split] = [img[0] for img in data] + self.disparities[split] = [img[1] for img in data] + elif len(data[0]) == 1: + self.images[split] = [img[0] for img in data] + self.disparities[split] = [img[0] for img in data] + else: + self.images[split] = data + self.disparities[split] = data + + self.labels[split] = data.y if hasattr(data, "y") else data + + self.ignore_index = 255 + + if len(self.images[split]) == 0: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if len(self.disparities[split]) == 0: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + #print(type(self.images[self.split][index])) + #print(self.images[self.split][index]) + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(img_path) + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py new file mode 100644 index 00000000..40e06e98 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/e1.py @@ -0,0 +1,151 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('e1'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + #tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py new file mode 100644 index 00000000..d665649b --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/mapillary.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('mapillary'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 768 + args.crop_size = 768 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py new file mode 100644 index 00000000..739e85f8 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/target.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 24 + + def __init__(self, args, root=Path.db_root_dir('target'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='TrainIds.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt new file mode 100644 index 00000000..3c81afef --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/temp.txt @@ -0,0 +1,38 @@ +for i in range(len(nam_label)): + img_label = cv2.imread(label_ori_path+nam_label[i], -1)[:,:,2] + img_label_temp = img_label.copy() + img_label_temp[img_label == 0] = 22 + img_label_temp[img_label == 1] = 10 + img_label_temp[img_label == 2] = 2 + img_label_temp[img_label == 3] = 0 + img_label_temp[img_label == 4] = 1 + img_label_temp[img_label == 5] = 4 + img_label_temp[img_label == 6] = 8 + img_label_temp[img_label == 7] = 5 + img_label_temp[img_label == 8] = 13 + img_label_temp[img_label == 9] = 7 + img_label_temp[img_label == 10] = 11 + img_label_temp[img_label == 11] = 18 + img_label_temp[img_label == 12] = 17 + img_label_temp[img_label == 13] = 21 + img_label_temp[img_label == 14] = 20 + img_label_temp[img_label == 15] = 6 + img_label_temp[img_label == 16] = 9 + img_label_temp[img_label == 17] = 12 + img_label_temp[img_label == 18] = 14 + img_label_temp[img_label == 19] = 15 + img_label_temp[img_label == 20] = 16 + img_label_temp[img_label == 21] = 3 + img_label_temp[img_label == 22] = 19 + #print(img_label) + #img_label[img_label == 0] = 10 + #img_label[img_label == 6] = 0 + #img_label[img_label == 5] = 11 + #img_label[img_label == 1] = 5 + #img_label[img_label == 2] = 1 + #img_label[img_label == 4] = 9 + #img_label[img_label == 3] = 4 + #img_label[img_label == 7] = 8 + #img_label[img_label == 11] = 2 + img_resize_lab = cv2.resize(img_label_temp, (2048,1024), interpolation=cv2.INTER_NEAREST) + cv2.imwrite(label_save_path+str(i)+'TrainIds.png', img_resize_lab.astype(np.uint16)) \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py new file mode 100644 index 00000000..4b261fcd --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/datasets/xrlab.py @@ -0,0 +1,152 @@ +import os +import numpy as np +import scipy.misc as m +from PIL import Image +from torch.utils import data +from mypath import Path +from torchvision import transforms +from dataloaders import custom_transforms as tr + +class CityscapesSegmentation(data.Dataset): + NUM_CLASSES = 25 + + def __init__(self, args, root=Path.db_root_dir('xrlab'), split="train"): + + self.root = root + self.split = split + self.args = args + self.images = {} + self.disparities = {} + self.labels = {} + + self.images_base = os.path.join(self.root, 'leftImg8bit', self.split) + self.disparities_base = os.path.join(self.root, 'disparity', self.split) + self.annotations_base = os.path.join(self.root, 'gtFine', self.split) + + self.images[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png') + self.images[split].sort() + + self.disparities[split] = self.recursive_glob(rootdir=self.disparities_base, suffix='.png') + self.disparities[split].sort() + + self.labels[split] = self.recursive_glob(rootdir=self.annotations_base, suffix='.png') + self.labels[split].sort() + + + self.ignore_index = 255 + + if not self.images[split]: + raise Exception("No RGB images for split=[%s] found in %s" % (split, self.images_base)) + if not self.disparities[split]: + raise Exception("No depth images for split=[%s] found in %s" % (split, self.disparities_base)) + + print("Found %d %s RGB images" % (len(self.images[split]), split)) + print("Found %d %s disparity images" % (len(self.disparities[split]), split)) + + + def __len__(self): + return len(self.images[self.split]) + + def __getitem__(self, index): + + img_path = self.images[self.split][index].rstrip() + disp_path = self.disparities[self.split][index].rstrip() + #print(index) + try: + lbl_path = self.labels[self.split][index].rstrip() + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + _target = Image.open(lbl_path) + sample = {'image': _img,'depth':_depth, 'label': _target} + except: + _img = Image.open(img_path).convert('RGB') + _depth = Image.open(disp_path) + sample = {'image': _img,'depth':_depth, 'label': _img} + + if self.split == 'train': + return self.transform_tr(sample) + elif self.split == 'val': + return self.transform_val(sample), img_path + elif self.split == 'test': + return self.transform_ts(sample), img_path + elif self.split == 'custom_resize': + return self.transform_ts(sample), img_path + + + def recursive_glob(self, rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + + def transform_tr(self, sample): + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.RandomHorizontalFlip(), + tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255), + # tr.RandomGaussianBlur(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_val(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + + def transform_ts(self, sample): + + composed_transforms = transforms.Compose([ + tr.CropBlackArea(), + #tr.FixedResize(size=self.args.crop_size), + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + return composed_transforms(sample) + +if __name__ == '__main__': + from dataloaders.utils import decode_segmap + from torch.utils.data import DataLoader + import matplotlib.pyplot as plt + import argparse + + parser = argparse.ArgumentParser() + args = parser.parse_args() + args.base_size = 513 + args.crop_size = 513 + + cityscapes_train = CityscapesSegmentation(args, split='train') + + dataloader = DataLoader(cityscapes_train, batch_size=2, shuffle=True, num_workers=2) + + for ii, sample in enumerate(dataloader): + for jj in range(sample["image"].size()[0]): + img = sample['image'].numpy() + gt = sample['label'].numpy() + tmp = np.array(gt[jj]).astype(np.uint8) + segmap = decode_segmap(tmp, dataset='cityscapes') + img_tmp = np.transpose(img[jj], axes=[1, 2, 0]) + img_tmp *= (0.229, 0.224, 0.225) + img_tmp += (0.485, 0.456, 0.406) + img_tmp *= 255.0 + img_tmp = img_tmp.astype(np.uint8) + plt.figure() + plt.title('display') + plt.subplot(211) + plt.imshow(img_tmp) + plt.subplot(212) + plt.imshow(segmap) + + if ii == 1: + break + + plt.show(block=True) + diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py new file mode 100644 index 00000000..ef572332 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/dataloaders/utils.py @@ -0,0 +1,244 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +def decode_seg_map_sequence(label_masks, dataset='pascal'): + rgb_masks = [] + for label_mask in label_masks: + rgb_mask = decode_segmap(label_mask, dataset) + rgb_masks.append(rgb_mask) + rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2])) # change for val + return rgb_masks + + +def decode_segmap(label_mask, dataset, plot=False): + """Decode segmentation class labels into a color image + Args: + label_mask (np.ndarray): an (M,N) array of integer values denoting + the class label at each spatial location. + plot (bool, optional): whether to show the resulting color image + in a figure. + Returns: + (np.ndarray, optional): the resulting decoded color image. + """ + if dataset == 'pascal' or dataset == 'coco': + n_classes = 21 + label_colours = get_pascal_labels() + elif dataset == 'cityscapes': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'target': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'cityrand': + n_classes = 19 + label_colours = get_cityscapes_labels() + elif dataset == 'citylostfound': + n_classes = 20 + label_colours = get_citylostfound_labels() + elif dataset == 'xrlab': + n_classes = 25 + label_colours = get_cityscapes_labels() + elif dataset == 'e1': + n_classes = 24 + label_colours = get_cityscapes_labels() + elif dataset == 'mapillary': + n_classes = 24 + label_colours = get_cityscapes_labels() + else: + raise NotImplementedError + + r = label_mask.copy() + g = label_mask.copy() + b = label_mask.copy() + for ll in range(0, n_classes): + r[label_mask == ll] = label_colours[ll, 0] + g[label_mask == ll] = label_colours[ll, 1] + b[label_mask == ll] = label_colours[ll, 2] + rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3)) # change for val + # rgb = torch.ByteTensor(3, label_mask.shape[0], label_mask.shape[1]).fill_(0) + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + # r = torch.from_numpy(r) + # g = torch.from_numpy(g) + # b = torch.from_numpy(b) + + rgb[:, :, 0] = r / 255.0 + rgb[:, :, 1] = g / 255.0 + rgb[:, :, 2] = b / 255.0 + if plot: + plt.imshow(rgb) + plt.show() + else: + return rgb + + +def encode_segmap(mask): + """Encode segmentation label images as pascal classes + Args: + mask (np.ndarray): raw segmentation label image of dimension + (M, N, 3), in which the Pascal classes are encoded as colours. + Returns: + (np.ndarray): class map with dimensions (M,N), where the value at + a given location is the integer denoting the class index. + """ + mask = mask.astype(int) + label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16) + for ii, label in enumerate(get_pascal_labels()): + label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii + label_mask = label_mask.astype(int) + return label_mask + + +def get_cityscapes_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [119, 11, 119], + [128, 64, 64], + [102, 10, 156], + [102, 102, 15], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156], + [10, 102, 156]]) + +def get_citylostfound_labels(): + return np.array([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [0, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + [111, 74, 0]]) + + +def get_pascal_labels(): + """Load the mapping that associates pascal classes with label colors + Returns: + np.ndarray with dimensions (21, 3) + """ + return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], + [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], + [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], + [0, 64, 128]]) + + +def colormap_bdd(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([128, 64, 128]) + cmap[1,:] = np.array([244, 35, 232]) + cmap[2,:] = np.array([ 70, 70, 70]) + cmap[3,:] = np.array([102, 102, 156]) + cmap[4,:] = np.array([190, 153, 153]) + cmap[5,:] = np.array([153, 153, 153]) + + cmap[6,:] = np.array([250, 170, 30]) + cmap[7,:] = np.array([220, 220, 0]) + cmap[8,:] = np.array([107, 142, 35]) + cmap[9,:] = np.array([152, 251, 152]) + cmap[10,:]= np.array([70, 130, 180]) + + cmap[11,:]= np.array([220, 20, 60]) + cmap[12,:]= np.array([255, 0, 0]) + cmap[13,:]= np.array([0, 0, 142]) + cmap[14,:]= np.array([0, 0, 70]) + cmap[15,:]= np.array([0, 60, 100]) + + cmap[16,:]= np.array([0, 80, 100]) + cmap[17,:]= np.array([0, 0, 230]) + cmap[18,:]= np.array([119, 11, 32]) + cmap[19,:]= np.array([111, 74, 0]) #多加了一类small obstacle + + return cmap + +def colormap_bdd0(n): + cmap=np.zeros([n, 3]).astype(np.uint8) + cmap[0,:] = np.array([0, 0, 0]) + cmap[1,:] = np.array([70, 130, 180]) + cmap[2,:] = np.array([70, 70, 70]) + cmap[3,:] = np.array([128, 64, 128]) + cmap[4,:] = np.array([244, 35, 232]) + cmap[5,:] = np.array([64, 64, 128]) + + cmap[6,:] = np.array([107, 142, 35]) + cmap[7,:] = np.array([153, 153, 153]) + cmap[8,:] = np.array([0, 0, 142]) + cmap[9,:] = np.array([220, 220, 0]) + cmap[10,:]= np.array([220, 20, 60]) + + cmap[11,:]= np.array([119, 11, 32]) + cmap[12,:]= np.array([0, 0, 230]) + cmap[13,:]= np.array([250, 170, 160]) + cmap[14,:]= np.array([128, 64, 64]) + cmap[15,:]= np.array([250, 170, 30]) + + cmap[16,:]= np.array([152, 251, 152]) + cmap[17,:]= np.array([255, 0, 0]) + cmap[18,:]= np.array([0, 0, 70]) + cmap[19,:]= np.array([0, 60, 100]) #small obstacle + cmap[20,:]= np.array([0, 80, 100]) + cmap[21,:]= np.array([102, 102, 156]) + cmap[22,:]= np.array([102, 102, 156]) + + return cmap + +class Colorize: + + def __init__(self, n=24): # n = nClasses + # self.cmap = colormap(256) + self.cmap = colormap_bdd(256) + self.cmap[n] = self.cmap[-1] + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + # print(size) + color_images = torch.ByteTensor(size[0], 3, size[1], size[2]).fill_(0) + # color_image = torch.ByteTensor(3, size[0], size[1]).fill_(0) + + # for label in range(1, len(self.cmap)): + for i in range(color_images.shape[0]): + for label in range(0, len(self.cmap)): + mask = gray_image[0] == label + # mask = gray_image == label + + color_images[i][0][mask] = self.cmap[label][0] + color_images[i][1][mask] = self.cmap[label][1] + color_images[i][2][mask] = self.cmap[label][2] + + return color_images diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py new file mode 100644 index 00000000..dfcb2f0c --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/eval.py @@ -0,0 +1,248 @@ +import argparse +import os +import numpy as np +from tqdm import tqdm +import time +import torch +from torchvision.transforms import ToPILImage +from PIL import Image + +from dataloaders import make_data_loader +from dataloaders.utils import decode_seg_map_sequence, Colorize +from utils.metrics import Evaluator +from models.rfnet import RFNet +from models import rfnet_for_unseen +from models.resnet.resnet_single_scale_single_attention import * +from models.resnet import resnet_single_scale_single_attention_unseen +import torch.backends.cudnn as cudnn + +class Validator(object): + def __init__(self, args, data=None, unseen_detection=False): + self.args = args + self.time_train = [] + self.num_class = args.num_class + + # Define Dataloader + kwargs = {'num_workers': args.workers, 'pin_memory': False} + # _, self.val_loader, _, self.custom_loader, self.num_class = make_data_loader(args, **kwargs) + _, _, self.test_loader, _ = make_data_loader(args, test_data=data, **kwargs) + print('un_classes:'+str(self.num_class)) + + # Define evaluator + self.evaluator = Evaluator(self.num_class) + + # Define network + if unseen_detection: + self.resnet = resnet_single_scale_single_attention_unseen.\ + resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = rfnet_for_unseen.RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + else: + self.resnet = resnet18(pretrained=False, efficient=False, use_bn=True) + self.model = RFNet(self.resnet, num_classes=self.num_class, use_bn=True) + + if args.cuda: + #self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids) + self.model = self.model.cuda() + cudnn.benchmark = True # accelarate speed + print('Model loaded successfully!') + + # # Load weights + # assert os.path.exists(args.weight_path), 'weight-path:{} doesn\'t exit!'.format(args.weight_path) + # self.new_state_dict = torch.load(args.weight_path, map_location=torch.device("cpu")) + # self.model = load_my_state_dict(self.model, self.new_state_dict['state_dict']) + + def validate(self): + #print("start validating 55") + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + predictions = [] + for i, (sample, image_name) in enumerate(tbar):#self.test_loader: + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + # spec = time.time() + image, target = sample['image'], sample['label'] + + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + + with torch.no_grad(): + if self.args.depth: + output = self.model(image, depth) + else: + output = self.model(image) + + if self.args.cuda: + torch.cuda.synchronize() + + pred = output.data.cpu().numpy() + # todo + pred = np.argmax(pred, axis=1) + predictions.append(pred) + + if not self.args.save_predicted_image: + continue + + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + print(pre_labels.shape) + # save + for i in range(pre_colors.shape[0]): + print(image_name[0]) + + if not image_name[0]: + img_name = "test.png" + else: + img_name = os.path.basename(image_name[0]) + + color_label_name = os.path.join(self.args.color_label_save_path, img_name) + label_name = os.path.join(self.args.label_save_path, img_name) + merge_label_name = os.path.join(self.args.merge_label_save_path, img_name) + + os.makedirs(os.path.dirname(color_label_name), exist_ok=True) + os.makedirs(os.path.dirname(merge_label_name), exist_ok=True) + os.makedirs(os.path.dirname(label_name), exist_ok=True) + + pre_color_image = ToPILImage()(pre_colors[i]) # pre_colors.dtype = float64 + pre_color_image.save(color_label_name) + + pre_label_image = ToPILImage()(pre_labels[i]) + pre_label_image.save(label_name) + + if (self.args.merge): + image_merge(image[i], pre_color_image, merge_label_name) + print('save image: {}'.format(merge_label_name)) + #print("start validating 120") + return predictions + + def task_divide(self): + seen_task_samples, unseen_task_samples = [], [] + self.model.eval() + self.evaluator.reset() + tbar = tqdm(self.test_loader, desc='\r') + for i, (sample, image_name) in enumerate(tbar): + + if self.args.depth: + image, depth, target = sample['image'], sample['depth'], sample['label'] + else: + image, target = sample['image'], sample['label'] + if self.args.cuda: + image = image.cuda() + if self.args.depth: + depth = depth.cuda() + start_time = time.time() + with torch.no_grad(): + if self.args.depth: + output_, output, _ = self.model(image, depth) + else: + output_, output, _ = self.model(image) + if self.args.cuda: + torch.cuda.synchronize() + if i != 0: + fwt = time.time() - start_time + self.time_train.append(fwt) + print("Forward time per img (bath size=%d): %.3f (Mean: %.3f)" % ( + self.args.val_batch_size, fwt / self.args.val_batch_size, + sum(self.time_train) / len(self.time_train) / self.args.val_batch_size)) + time.sleep(0.1) # to avoid overheating the GPU too much + + # pred colorize + pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte()) + pre_labels = torch.max(output, 1)[1].detach().cpu().byte() + for i in range(pre_colors.shape[0]): + task_sample = dict() + task_sample.update(image=sample["image"][i]) + task_sample.update(label=sample["label"][i]) + if self.args.depth: + task_sample.update(depth=sample["depth"][i]) + + if torch.max(pre_labels) == output.shape[1] - 1: + unseen_task_samples.append((task_sample, image_name[i])) + else: + seen_task_samples.append((task_sample, image_name[i])) + + return seen_task_samples, unseen_task_samples + +def image_merge(image, label, save_name): + image = ToPILImage()(image.detach().cpu().byte()) + # width, height = image.size + left = 140 + top = 30 + right = 2030 + bottom = 900 + # crop + image = image.crop((left, top, right, bottom)) + # resize + image = image.resize(label.size, Image.BILINEAR) + + image = image.convert('RGBA') + label = label.convert('RGBA') + image = Image.blend(image, label, 0.6) + image.save(save_name) + +def load_my_state_dict(model, state_dict): # custom function to load model when not all dict elements + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + print('{} not in model_state'.format(name)) + continue + else: + own_state[name].copy_(param) + + return model + +# def main(): +# parser = argparse.ArgumentParser(description="PyTorch RFNet validation") +# parser.add_argument('--dataset', type=str, default='cityscapes', +# choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'], +# help='dataset name (default: cityscapes)') +# parser.add_argument('--workers', type=int, default=4, +# metavar='N', help='dataloader threads') +# parser.add_argument('--base-size', type=int, default=1024, +# help='base image size') +# parser.add_argument('--crop-size', type=int, default=768, +# help='crop image size') +# parser.add_argument('--batch-size', type=int, default=6, +# help='batch size for training') +# parser.add_argument('--val-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# validating (default: auto)') +# parser.add_argument('--test-batch-size', type=int, default=1, +# metavar='N', help='input batch size for \ +# testing (default: auto)') +# parser.add_argument('--no-cuda', action='store_true', default= +# False, help='disables CUDA training') +# parser.add_argument('--gpu-ids', type=str, default='0', +# help='use which gpu to train, must be a \ +# comma-separated list of integers only (default=0)') +# parser.add_argument('--checkname', type=str, default=None, +# help='set the checkpoint name') +# parser.add_argument('--weight-path', type=str, default=None, +# help='enter your path of the weight') +# parser.add_argument('--color-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/color/', +# help='path to save label') +# parser.add_argument('--merge-label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/merge/', +# help='path to save merged label') +# parser.add_argument('--label-save-path', type=str, default='D:/m0063/project/RFNet-master/test/label/', +# help='path to save merged label') +# parser.add_argument('--merge', action='store_true', default=False, help='merge image and label') +# parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not') +# +# +# args = parser.parse_args() +# args.cuda = not args.no_cuda and torch.cuda.is_available() +# if args.cuda: +# try: +# args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')] +# except ValueError: +# raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only') +# +# validator = Validator(args) +# validator.validate() +# +# +# if __name__ == "__main__": +# main() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py new file mode 100644 index 00000000..3734266e --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/replicate.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py new file mode 100644 index 00000000..63d81991 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention.py @@ -0,0 +1,391 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = []#[ self.spp, self.upsample] + self.fine_tune += [self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x.detach(), self.layer4) + features += [self.spp.forward(skip)] + return features + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer1) + y, skip_depth = self.forward_resblock(y.detach(), self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 2 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer2) + y, skip_depth = self.forward_resblock(y.detach(), self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 3 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer3) + y, skip_depth = self.forward_resblock(y.detach(), self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb.detach()] + # block 4 + x, skip_rgb = self.forward_resblock(x.detach(), self.layer4) + y, skip_depth = self.forward_resblock(y.detach(), self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + return features + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + i = 0 + for skip, up in zip(features[1:], self.upsample): + i += 1 + #print(len(self.upsample)) + if i < len(self.upsample): + x = up(x, skip) + else: + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + return self.forward_up(self.forward_down(rgb)) + else: + return self.forward_up(self.forward_down_fusion(rgb, depth)) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py new file mode 100644 index 00000000..9668734e --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/resnet/resnet_single_scale_single_attention_unseen.py @@ -0,0 +1,396 @@ +import torch +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from itertools import chain +import torch.utils.checkpoint as cp +import cv2 +import numpy as np + +from ..util import _Upsample, SpatialPyramidPooling + +__all__ = ['ResNet', 'resnet18'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv1x1(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) + +def _bn_function_factory(conv, norm, relu=None): + """return a conv-bn-relu function""" + def bn_function(x): + x = conv(x) + if norm is not None: + x = norm(x) + if relu is not None: + x = relu(x) + return x + + return bn_function + + +def do_efficient_fwd(block, x, efficient): + if efficient and x.requires_grad: + return cp.checkpoint(block, x) + else: + return block(x) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, + channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + + return x + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=False, use_bn=True): + super(BasicBlock, self).__init__() + self.use_bn = use_bn + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, efficient=True, use_bn=True): + super(Bottleneck, self).__init__() + self.use_bn = use_bn + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) if self.use_bn else None + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) if self.use_bn else None + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.efficient = efficient + + def forward(self, x): + residual = x + + bn_1 = _bn_function_factory(self.conv1, self.bn1, self.relu) + bn_2 = _bn_function_factory(self.conv2, self.bn2, self.relu) + bn_3 = _bn_function_factory(self.conv3, self.bn3, self.relu) + + out = do_efficient_fwd(bn_1, x, self.efficient) + out = do_efficient_fwd(bn_2, out, self.efficient) + out = do_efficient_fwd(bn_3, out, self.efficient) + + if self.downsample is not None: + residual = self.downsample(x) + + out = out + residual + relu = self.relu(out) + + return relu, out + + +class ResNet(nn.Module): + def __init__(self, block, layers, *, num_features=128, k_up=3, efficient=True, use_bn=True, + spp_grids=(8, 4, 2, 1), spp_square_grid=False, **kwargs): + super(ResNet, self).__init__() + self.inplanes = 64 + self.efficient = efficient + self.use_bn = use_bn + + # rgb branch + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # depth branch + self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False) + self.bn1_d = nn.BatchNorm2d(64) if self.use_bn else lambda x: x + self.relu_d = nn.ReLU(inplace=True) + self.maxpool_d = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + upsamples = [] + # 修改 _make_layer_rgb _make_layer + self.layer1 = self._make_layer_rgb(block, 64, 64, layers[0]) + self.layer1_d = self._make_layer_d(block, 64, 64, layers[0]) + self.attention_1 = self.attention(64) + self.attention_1_d = self.attention(64) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] # num_maps_in, skip_maps_in, num_maps_out, k: kernel size of blend conv + + self.layer2 = self._make_layer_rgb(block, 64, 128, layers[1], stride=2) + self.layer2_d = self._make_layer_d(block, 64, 128, layers[1], stride=2) + self.attention_2 = self.attention(128) + self.attention_2_d = self.attention(128) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer3 = self._make_layer_rgb(block, 128, 256, layers[2], stride=2) + self.layer3_d = self._make_layer_d(block, 128, 256, layers[2], stride=2) + self.attention_3 = self.attention(256) + self.attention_3_d = self.attention(256) + upsamples += [_Upsample(num_features, self.inplanes, num_features, use_bn=self.use_bn, k=k_up)] + + self.layer4 = self._make_layer_rgb(block, 256, 512, layers[3], stride=2) + self.layer4_d = self._make_layer_d(block, 256, 512, layers[3], stride=2) + self.attention_4 = self.attention(512) + self.attention_4_d = self.attention(512) + + self.fine_tune = [self.conv1, self.maxpool, self.layer1, self.layer2, self.layer3, self.layer4, + self.conv1_d, self.maxpool_d, self.layer1_d, self.layer2_d, self.layer3_d, self.layer4_d] + if self.use_bn: + self.fine_tune += [self.bn1, self.bn1_d, self.attention_1, self.attention_1_d, self.attention_2, self.attention_2_d, + self.attention_3, self.attention_3_d, self.attention_4, self.attention_4_d] + + num_levels = 3 + self.spp_size = num_features + bt_size = self.spp_size + + level_size = self.spp_size // num_levels + + self.spp = SpatialPyramidPooling(self.inplanes, num_levels, bt_size=bt_size, level_size=level_size, + out_size=self.spp_size, grids=spp_grids, square_grid=spp_square_grid, + bn_momentum=0.01 / 2, use_bn=self.use_bn) + self.upsample = nn.ModuleList(list(reversed(upsamples))) + + self.random_init = [ self.spp, self.upsample] + + self.num_features = num_features + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def output_num(self): + return self.__in_features + + def _make_layer_rgb(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def _make_layer_d(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + layers = [nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False)] + if self.use_bn: + layers += [nn.BatchNorm2d(planes * block.expansion)] + downsample = nn.Sequential(*layers) + layers = [block(inplanes, planes, stride, downsample, efficient=self.efficient, use_bn=self.use_bn)] + inplanes = planes * block.expansion + self.inplanes = inplanes + for i in range(1, blocks): + layers += [block(inplanes, planes, efficient=self.efficient, use_bn=self.use_bn)] + + return nn.Sequential(*layers) + + def channel_attention(self, rgb_skip, depth_skip, attention): + assert rgb_skip.shape == depth_skip.shape, 'rgb skip shape:{} != depth skip shape:{}'.format(rgb_skip.shape, depth_skip.shape) + # single_attenton + rgb_attention = attention(rgb_skip) + depth_attention = attention(depth_skip) + rgb_after_attention = torch.mul(rgb_skip, rgb_attention) + depth_after_attention = torch.mul(depth_skip, depth_attention) + skip_after_attention = rgb_after_attention + depth_after_attention + return skip_after_attention + + def attention(self, num_channels): + pool_attention = nn.AdaptiveAvgPool2d(1) + conv_attention = nn.Conv2d(num_channels, num_channels, kernel_size=1) + activate = nn.Sigmoid() + + return nn.Sequential(pool_attention, conv_attention, activate) + + + def random_init_params(self): + return chain(*[f.parameters() for f in self.random_init]) + + def fine_tune_params(self): + return chain(*[f.parameters() for f in self.fine_tune]) + + def forward_resblock(self, x, layers): + skip = None + for l in layers: + x = l(x) + if isinstance(x, tuple): + x, skip = x + return x, skip + + def forward_down(self, rgb): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + features = [] + x, skip = self.forward_resblock(x, self.layer1) + features += [skip] + x, skip = self.forward_resblock(x, self.layer2) + features += [skip] + x, skip = self.forward_resblock(x, self.layer3) + features += [skip] + x, skip = self.forward_resblock(x, self.layer4) + features += [self.spp.forward(skip)] + features_da = self.spp.forward(skip) + return features, features_da + + def forward_down_fusion(self, rgb, depth): + x = self.conv1(rgb) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + depth = depth.unsqueeze(1) + y = self.conv1_d(depth) + y = self.bn1_d(y) + y = self.relu_d(y) + y = self.maxpool_d(y) + + features = [] + # block 1 + x, skip_rgb = self.forward_resblock(x, self.layer1) + y, skip_depth = self.forward_resblock(y, self.layer1_d) + x_attention = self.attention_1(x) + y_attention = self.attention_1_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 2 + x, skip_rgb = self.forward_resblock(x, self.layer2) + y, skip_depth = self.forward_resblock(y, self.layer2_d) + x_attention = self.attention_2(x) + y_attention = self.attention_2_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 3 + x, skip_rgb = self.forward_resblock(x, self.layer3) + y, skip_depth = self.forward_resblock(y, self.layer3_d) + x_attention = self.attention_3(x) + y_attention = self.attention_3_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [skip_rgb] + # block 4 + x, skip_rgb = self.forward_resblock(x, self.layer4) + y, skip_depth = self.forward_resblock(y, self.layer4_d) + x_attention = self.attention_4(x) + y_attention = self.attention_4_d(y) + x = torch.mul(x, x_attention) + y = torch.mul(y, y_attention) + x = x + y + features += [self.spp.forward(x)] + features_da = self.spp.forward(x) + return features, features_da + + + def forward_up(self, features): + features = features[::-1] + + x = features[0] + + upsamples = [] + for skip, up in zip(features[1:], self.upsample): + x = up(x, skip) + upsamples += [x] + return x, {'features': features, 'upsamples': upsamples} + + def forward(self, rgb, depth = None): + if depth is None: + down_features, da_features = self.forward_down(rgb) + x, additional = self.forward_up(down_features) + return x, additional, da_features#self.forward_up(self.forward_down(rgb)), self.forward_down(rgb) + else: + down_features, da_features = self.forward_down_fusion(rgb, depth) + x, additional = self.forward_up(down_features) + #print(down_features.shape) + return x, additional, da_features#self.forward_up(self.forward_down_fusion(rgb, depth)), self.forward_down_fusion(rgb, depth) + + def _load_resnet_pretrained(self, url): + pretrain_dict = model_zoo.load_url(model_urls[url]) + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + # print('%%%%% ', k) + if k in state_dict: + if k.startswith('conv1'): + model_dict[k] = v + # print('##### ', k) + model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \ + view_as(state_dict[k.replace('conv1', 'conv1_d')]) + + elif k.startswith('bn1'): + model_dict[k] = v + model_dict[k.replace('bn1', 'bn1_d')] = v + elif k.startswith('layer'): + model_dict[k] = v + model_dict[k[:6]+'_d'+k[6:]] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + +def resnet18(pretrained=True, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) + print('pretrained dict loaded sucessfully') + return model \ No newline at end of file diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py new file mode 100644 index 00000000..87f02863 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet.py @@ -0,0 +1,27 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + print(self.backbone.num_features) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional = self.backbone(rgb_inputs, depth_inputs) + logits = self.logits.forward(x) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + #print(logits_upsample.size) + return logits_upsample + + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py new file mode 100644 index 00000000..f61eb1ce --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/rfnet_for_unseen.py @@ -0,0 +1,33 @@ +import torch.nn as nn +from itertools import chain # 串联多个迭代对象 + +from .util import _BNReluConv, upsample + + +class RFNet(nn.Module): + def __init__(self, backbone, num_classes, use_bn=True): + super(RFNet, self).__init__() + self.backbone = backbone + self.num_classes = num_classes + #self.bottleneck = _BNReluConv(self.backbone.num_features, 128, k = 3, batch_norm=use_bn) + #self.logits = _BNReluConv(128, self.num_classes+1, k = 1, batch_norm=use_bn) + self.logits = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + #self.logits_target = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + self.logits_aux = _BNReluConv(self.backbone.num_features, self.num_classes, batch_norm=use_bn) + + def forward(self, rgb_inputs, depth_inputs = None): + x, additional, da_features = self.backbone(rgb_inputs, depth_inputs) + #print(additional['features'][0].shape) + #bottleneck = self.bottleneck(x) + logits = self.logits.forward(x) + logits_aux = self.logits_aux.forward(x) + #print(logits_aux.shape) + logits_upsample = upsample(logits, rgb_inputs.shape[2:]) + logits_aux_upsample = upsample(logits_aux, rgb_inputs.shape[2:]) + return logits_upsample, logits_aux_upsample, da_features + + def random_init_params(self): + return chain(*([self.logits.parameters(), self.logits_aux.parameters(), self.backbone.random_init_params()])) + + def fine_tune_params(self): + return self.backbone.fine_tune_params() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py new file mode 100644 index 00000000..5c86e759 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/models/util.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False) +batchnorm_momentum = 0.01 / 2 + + +def get_n_params(parameters): + pp = 0 + for p in parameters: + nn = 1 + for s in list(p.size()): + nn = nn * s + pp += nn + return pp + + +class _BNReluConv(nn.Sequential): + def __init__(self, num_maps_in, num_maps_out, k=3, batch_norm=True, bn_momentum=0.1, bias=False, dilation=1): + super(_BNReluConv, self).__init__() + if batch_norm: + self.add_module('norm', nn.BatchNorm2d(num_maps_in, momentum=bn_momentum)) + self.add_module('relu', nn.ReLU(inplace=batch_norm is True)) + padding = k // 2 # same conv + self.add_module('conv', nn.Conv2d(num_maps_in, num_maps_out, + kernel_size=k, padding=padding, bias=bias, dilation=dilation)) + + +class _Upsample(nn.Module): + def __init__(self, num_maps_in, skip_maps_in, num_maps_out, use_bn=True, k=3): + super(_Upsample, self).__init__() + print(f'Upsample layer: in = {num_maps_in}, skip = {skip_maps_in}, out = {num_maps_out}') + self.bottleneck = _BNReluConv(skip_maps_in, num_maps_in, k=1, batch_norm=use_bn) + self.blend_conv = _BNReluConv(num_maps_in, num_maps_out, k=k, batch_norm=use_bn) + + def forward(self, x, skip): + skip = self.bottleneck.forward(skip) + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x + + +class SpatialPyramidPooling(nn.Module): + def __init__(self, num_maps_in, num_levels, bt_size=512, level_size=128, out_size=128, + grids=(6, 3, 2, 1), square_grid=False, bn_momentum=0.1, use_bn=True): + super(SpatialPyramidPooling, self).__init__() + self.grids = grids + self.square_grid = square_grid + self.spp = nn.Sequential() + self.spp.add_module('spp_bn', + _BNReluConv(num_maps_in, bt_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + num_features = bt_size + final_size = num_features + for i in range(num_levels): + final_size += level_size + self.spp.add_module('spp' + str(i), + _BNReluConv(num_features, level_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + self.spp.add_module('spp_fuse', + _BNReluConv(final_size, out_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn)) + + def forward(self, x): + levels = [] + target_size = x.size()[2:4] + + ar = target_size[1] / target_size[0] + + x = self.spp[0].forward(x) + levels.append(x) + num = len(self.spp) - 1 + + for i in range(1, num): + if not self.square_grid: + grid_size = (self.grids[i - 1], max(1, round(ar * self.grids[i - 1]))) + x_pooled = F.adaptive_avg_pool2d(x, grid_size) + else: + x_pooled = F.adaptive_avg_pool2d(x, self.grids[i - 1]) + level = self.spp[i].forward(x_pooled) + + level = upsample(level, target_size) + levels.append(level) + x = torch.cat(levels, 1) + x = self.spp[-1].forward(x) + return x + + +class _UpsampleBlend(nn.Module): + def __init__(self, num_features, use_bn=True): + super(_UpsampleBlend, self).__init__() + self.blend_conv = _BNReluConv(num_features, num_features, k=3, batch_norm=use_bn) + + def forward(self, x, skip): + skip_size = skip.size()[2:4] + x = upsample(x, skip_size) + x = x + skip + x = self.blend_conv.forward(x) + return x diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py new file mode 100644 index 00000000..640544e7 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/mypath.py @@ -0,0 +1,20 @@ +class Path(object): + @staticmethod + def db_root_dir(dataset): + if dataset == 'cityscapes': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapes/' # folder that contains leftImg8bit/ + elif dataset == 'citylostfound': + return '/home/robo/m0063/project/RFNet-master/Data/cityscapesandlostandfound/' # folder that mixes Cityscapes and Lost and Found + elif dataset == 'cityrand': + return '/home/robo/m0063/project/RFNet-master/Data/cityrand/' + elif dataset == 'target': + return '/home/robo/m0063/project/RFNet-master/Data/target/' + elif dataset == 'xrlab': + return '/home/robo/m0063/project/RFNet-master/Data/xrlab/' + elif dataset == 'e1': + return '/home/robo/m0063/project/RFNet-master/Data/e1/' + elif dataset == 'mapillary': + return '/home/robo/m0063/project/RFNet-master/Data/mapillary/' + else: + print('Dataset {} not available.'.format(dataset)) + raise NotImplementedError diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py new file mode 100644 index 00000000..82b527a2 --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/predict.py @@ -0,0 +1,98 @@ +import os +os.environ['BACKEND_TYPE'] = 'PYTORCH' +# set at yaml +# os.environ["PREDICT_RESULT_DIR"] = "./inference_results" +# os.environ["EDGE_OUTPUT_URL"] = "./edge_kb" +# os.environ["video_url"] = "./video/radio.mp4" +# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl" + + +import cv2 +import time +import torch +import numpy as np +from PIL import Image +import base64 +import tempfile +import warnings +from io import BytesIO + +from sedna.datasources import BaseDataSource +from sedna.core.lifelong_learning import LifelongLearning +from sedna.common.config import Context + +from dataloaders import custom_transforms as tr +from torchvision import transforms + +from accuracy import accuracy +from basemodel import preprocess, val_args, Model + +def preprocess(samples): + composed_transforms = transforms.Compose([ + tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + tr.ToTensor()]) + + data = BaseDataSource(data_type="test") + data.x = [(composed_transforms(samples), "")] + return data + +def init_ll_job(): + estimator = Model() + + task_allocation = { + "method": "TaskAllocationByOrigin", + "param": { + "origins": ["real", "sim"], + "default": "real" + } + } + unseen_task_allocation = { + "method": "UnseenTaskAllocationDefault" + } + + ll_job = LifelongLearning( + estimator, + task_definition=None, + task_relationship_discovery=None, + task_allocation=task_allocation, + task_remodeling=None, + inference_integrate=None, + task_update_decision=None, + unseen_task_allocation=unseen_task_allocation, + unseen_sample_recognition=None, + unseen_sample_re_recognition=None) + + return ll_job + +def predict(): + ll_job = init_ll_job() + + camera_address = Context.get_parameters('video_url') + # use video streams for testing + camera = cv2.VideoCapture(camera_address) + fps = 10 + nframe = 0 + while 1: + ret, input_yuv = camera.read() + if not ret: + time.sleep(5) + camera = cv2.VideoCapture(camera_address) + continue + + if nframe % fps: + nframe += 1 + continue + + img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_BGR2RGB) + nframe += 1 + if nframe % 1000 == 1: # logs every 1000 frames + warnings.warn(f"camera is open, current frame index is {nframe}") + + img_rgb = cv2.resize(np.array(img_rgb), (2048, 1024), interpolation=cv2.INTER_CUBIC) + img_rgb = Image.fromarray(img_rgb) + sample = {'image': img_rgb, "depth": img_rgb, "label": img_rgb} + data = preprocess(sample) + print("Inference results:", ll_job.inference(data=data)) + +if __name__ == '__main__': + predict() diff --git a/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py new file mode 100644 index 00000000..438cd70a --- /dev/null +++ b/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet/run_server.py @@ -0,0 +1,252 @@ +# Copyright 2021 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from io import BytesIO +from typing import Optional, Any + +import cv2 +import numpy as np +from PIL import Image +import uvicorn +import time +from pydantic import BaseModel +from fastapi import FastAPI, UploadFile, File +from fastapi.routing import APIRoute +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import HTMLResponse +import sedna_predict +from sedna.common.utils import get_host_ip +from dataloaders.datasets.cityscapes import CityscapesSegmentation + + +class ImagePayload(BaseModel): + image: UploadFile = File(...) + depth: Optional[UploadFile] = None + + +class ResultModel(BaseModel): + type: int = 0 + box: Any = None + curr: str = None + future: str = None + img: str = None + + +class ResultResponse(BaseModel): + msg: str = "" + result: Optional[ResultModel] = None + code: int + + +class BaseServer: + # pylint: disable=too-many-instance-attributes,too-many-arguments + DEBUG = True + WAIT_TIME = 15 + + def __init__( + self, + servername: str, + host: str, + http_port: int = 8080, + grpc_port: int = 8081, + workers: int = 1, + ws_size: int = 16 * 1024 * 1024, + ssl_key=None, + ssl_cert=None, + timeout=300): + self.server_name = servername + self.app = None + self.host = host or '0.0.0.0' + self.http_port = http_port or 80 + self.grpc_port = grpc_port + self.workers = workers + self.keyfile = ssl_key + self.certfile = ssl_cert + self.ws_size = int(ws_size) + self.timeout = int(timeout) + protocal = "https" if self.certfile else "http" + self.url = f"{protocal}://{self.host}:{self.http_port}" + + def run(self, app, **kwargs): + if hasattr(app, "add_middleware"): + app.add_middleware( + CORSMiddleware, allow_origins=["*"], allow_credentials=True, + allow_methods=["*"], allow_headers=["*"], + ) + + uvicorn.run( + app, + host=self.host, + port=self.http_port, + ssl_keyfile=self.keyfile, + ssl_certfile=self.certfile, + workers=self.workers, + timeout_keep_alive=self.timeout, + **kwargs) + + def get_all_urls(self): + url_list = [{"path": route.path, "name": route.name} + for route in getattr(self.app, 'routes', [])] + return url_list + + +class InferenceServer(BaseServer): # pylint: disable=too-many-arguments + """ + rest api server for inference + """ + + def __init__( + self, + servername, + host: str, + http_port: int = 5000, + max_buffer_size: int = 104857600, + workers: int = 1): + super( + InferenceServer, + self).__init__( + servername=servername, + host=host, + http_port=http_port, + workers=workers) + + self.job, self.detection_validator = sedna_predict.init_ll_job() + + self.max_buffer_size = max_buffer_size + self.app = FastAPI( + routes=[ + APIRoute( + f"/{servername}", + self.model_info, + methods=["GET"], + ), + APIRoute( + f"/{servername}/predict", + self.predict, + methods=["POST"], + response_model=ResultResponse + ), + ], + log_level="trace", + timeout=600, + ) + self.index_frame = 0 + + def start(self): + return self.run(self.app) + + @staticmethod + def model_info(): + return HTMLResponse( + """