Pruned

.gitignore

@@ -0,0 +1 @@
+.idea

pointpillar_inference_README.md

@@ -0,0 +1,38 @@
+# 本地inference操作步骤
+## 1.远程连接服务器
+```
+ssh -p 8851 [email protected]
+```
+
+## 2. 进入工程路径
+```
+ cd Projects/pointpillars2/second/pytorch/
+```
+
+## 3. 激活虚拟环境
+```
+source activate pointpillar
+```
+
+## 4. 添加PYTHONPATH路径
+```
+export PYTHONPATH=~/Projects/pointpillars2:$PYTHONPATH
+```
+
+## 5. 运行evaluate程序
+```
+python train.py evaluate --config_path=../configs/pointpillars/xyres_16_4cls.proto --model_dir=/nfs/nas/model/songhongli/ppbaidusecond_pretrained_3cls/
+```
+
+
+## 6. 运行的点云文件修改方法
+```
+vim /home/songhongli/Projects/pointpillars2/second/data/preprocess.py
+```
+
+line 329:
+
+```
+points = np.fromfile("/home/songhongli/000000.bin", dtype=np.float32, co    unt=-1).reshape([-1, num_point_features])
+```
+将这里的fromfile打开的文件路径修改为要运行的一帧点云文件

second/builder/dataset_builder.py

@@ -24,7 +24,7 @@
 
 from second.protos import input_reader_pb2
 from second.data.dataset import KittiDataset
-from second.data.preprocess import prep_pointcloud
+from second.data.preprocess import prep_pointcloud, prep_pointcloud_inference
 import numpy as np
 from second.builder import dbsampler_builder
 from functools import partial
@@ -34,7 +34,10 @@ def build(input_reader_config,
           model_config,
           training,
           voxel_generator,
-          target_assigner=None):
+          target_assigner=None,
+          multi_gpu=False,
+          inference=False,
+          points=None):
     """Builds a tensor dictionary based on the InputReader config.
 
     Args:
@@ -99,13 +102,49 @@ def build(input_reader_config,
         remove_points_after_sample=cfg.remove_points_after_sample,
         remove_environment=cfg.remove_environment,
         use_group_id=cfg.use_group_id,
-        out_size_factor=out_size_factor)
+        out_size_factor=out_size_factor,
+        multi_gpu=multi_gpu)
+    prep_func_inference = partial(
+        prep_pointcloud_inference,
+        root_path=cfg.kitti_root_path,
+        class_names=list(cfg.class_names),
+        voxel_generator=voxel_generator,
+        target_assigner=target_assigner,
+        training=training,
+        max_voxels=cfg.max_number_of_voxels,
+        remove_outside_points=False,
+        remove_unknown=cfg.remove_unknown_examples,
+        create_targets=training,
+        shuffle_points=cfg.shuffle_points,
+        gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
+        gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
+        global_rotation_noise=list(cfg.global_rotation_uniform_noise),
+        global_scaling_noise=list(cfg.global_scaling_uniform_noise),
+        global_loc_noise_std=(0.2, 0.2, 0.2),
+        global_random_rot_range=list(
+            cfg.global_random_rotation_range_per_object),
+        db_sampler=db_sampler,
+        unlabeled_db_sampler=u_db_sampler,
+        generate_bev=generate_bev,
+        without_reflectivity=without_reflectivity,
+        num_point_features=num_point_features,
+        anchor_area_threshold=cfg.anchor_area_threshold,
+        gt_points_drop=cfg.groundtruth_points_drop_percentage,
+        gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
+        remove_points_after_sample=cfg.remove_points_after_sample,
+        remove_environment=cfg.remove_environment,
+        use_group_id=cfg.use_group_id,
+        out_size_factor=out_size_factor,
+        multi_gpu=multi_gpu)
     dataset = KittiDataset(
         info_path=cfg.kitti_info_path,
         root_path=cfg.kitti_root_path,
         num_point_features=num_point_features,
         target_assigner=target_assigner,
         feature_map_size=feature_map_size,
-        prep_func=prep_func)
+        prep_func=prep_func,
+        inference=inference,
+        points=points,
+        prep_func_inference=prep_func_inference)
 
     return dataset

second/builder_pruned/anchor_generator_builder.py

@@ -0,0 +1,44 @@
+import numpy as np
+
+from second.protos import box_coder_pb2
+from second.core.anchor_generator import (
+    AnchorGeneratorStride, AnchorGeneratorRange)
+
+
+def build(anchor_config):
+    """Create optimizer based on config.
+
+  Args:
+    optimizer_config: A Optimizer proto message.
+
+  Returns:
+    An optimizer and a list of variables for summary.
+
+  Raises:
+    ValueError: when using an unsupported input data type.
+  """
+    ag_type = anchor_config.WhichOneof('anchor_generator')
+
+    if ag_type == 'anchor_generator_stride':
+        config = anchor_config.anchor_generator_stride
+        ag = AnchorGeneratorStride(
+            sizes=list(config.sizes),
+            anchor_strides=list(config.strides),
+            anchor_offsets=list(config.offsets),
+            rotations=list(config.rotations),
+            match_threshold=config.matched_threshold,
+            unmatch_threshold=config.unmatched_threshold,
+            class_id=config.class_name)
+        return ag
+    elif ag_type == 'anchor_generator_range':
+        config = anchor_config.anchor_generator_range
+        ag = AnchorGeneratorRange(
+            sizes=list(config.sizes),
+            anchor_ranges=list(config.anchor_ranges),
+            rotations=list(config.rotations),
+            match_threshold=config.matched_threshold,
+            unmatch_threshold=config.unmatched_threshold,
+            class_id=config.class_name)
+        return ag
+    else:
+        raise ValueError(" unknown anchor generator type")

second/builder_pruned/dataset_builder.py

@@ -0,0 +1,150 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Input reader builder.
+
+Creates data sources for DetectionModels from an InputReader config. See
+input_reader.proto for options.
+
+Note: If users wishes to also use their own InputReaders with the Object
+Detection configuration framework, they should define their own builder function
+that wraps the build function.
+"""
+
+from second.protos import input_reader_pb2
+from second.data.dataset import KittiDataset
+from second.data.preprocess import prep_pointcloud, prep_pointcloud_inference
+import numpy as np
+from second.builder import dbsampler_builder
+from functools import partial
+
+
+def build(input_reader_config,
+          model_config,
+          training,
+          voxel_generator,
+          target_assigner=None,
+          multi_gpu=False,
+          inference=False,
+          points=None):
+    """Builds a tensor dictionary based on the InputReader config.
+
+    Args:
+        input_reader_config: A input_reader_pb2.InputReader object.
+
+    Returns:
+        A tensor dict based on the input_reader_config.
+
+    Raises:
+        ValueError: On invalid input reader proto.
+        ValueError: If no input paths are specified.
+    """
+    if not isinstance(input_reader_config, input_reader_pb2.InputReader):
+        raise ValueError('input_reader_config not of type '
+                         'input_reader_pb2.InputReader.')
+    generate_bev = model_config.use_bev
+    without_reflectivity = model_config.without_reflectivity
+    num_point_features = model_config.num_point_features
+    out_size_factor = model_config.rpn.layer_strides[0] // model_config.rpn.upsample_strides[0]
+
+    cfg = input_reader_config
+    db_sampler_cfg = input_reader_config.database_sampler
+    db_sampler = None
+    if len(db_sampler_cfg.sample_groups) > 0:  # enable sample
+        db_sampler = dbsampler_builder.build(db_sampler_cfg)
+    u_db_sampler_cfg = input_reader_config.unlabeled_database_sampler
+    u_db_sampler = None
+    if len(u_db_sampler_cfg.sample_groups) > 0:  # enable sample
+        u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg)
+    grid_size = voxel_generator.grid_size
+    # [352, 400]
+    feature_map_size = grid_size[:2] // out_size_factor
+    feature_map_size = [*feature_map_size, 1][::-1]
+
+    prep_func = partial(
+        prep_pointcloud,
+        root_path=cfg.kitti_root_path,
+        class_names=list(cfg.class_names),
+        voxel_generator=voxel_generator,
+        target_assigner=target_assigner,
+        training=training,
+        max_voxels=cfg.max_number_of_voxels,
+        remove_outside_points=False,
+        remove_unknown=cfg.remove_unknown_examples,
+        create_targets=training,
+        shuffle_points=cfg.shuffle_points,
+        gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
+        gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
+        global_rotation_noise=list(cfg.global_rotation_uniform_noise),
+        global_scaling_noise=list(cfg.global_scaling_uniform_noise),
+        global_loc_noise_std=(0.2, 0.2, 0.2),
+        global_random_rot_range=list(
+            cfg.global_random_rotation_range_per_object),
+        db_sampler=db_sampler,
+        unlabeled_db_sampler=u_db_sampler,
+        generate_bev=generate_bev,
+        without_reflectivity=without_reflectivity,
+        num_point_features=num_point_features,
+        anchor_area_threshold=cfg.anchor_area_threshold,
+        gt_points_drop=cfg.groundtruth_points_drop_percentage,
+        gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
+        remove_points_after_sample=cfg.remove_points_after_sample,
+        remove_environment=cfg.remove_environment,
+        use_group_id=cfg.use_group_id,
+        out_size_factor=out_size_factor,
+        multi_gpu=multi_gpu)
+    prep_func_inference = partial(
+        prep_pointcloud_inference,
+        root_path=cfg.kitti_root_path,
+        class_names=list(cfg.class_names),
+        voxel_generator=voxel_generator,
+        target_assigner=target_assigner,
+        training=training,
+        max_voxels=cfg.max_number_of_voxels,
+        remove_outside_points=False,
+        remove_unknown=cfg.remove_unknown_examples,
+        create_targets=training,
+        shuffle_points=cfg.shuffle_points,
+        gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
+        gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
+        global_rotation_noise=list(cfg.global_rotation_uniform_noise),
+        global_scaling_noise=list(cfg.global_scaling_uniform_noise),
+        global_loc_noise_std=(0.2, 0.2, 0.2),
+        global_random_rot_range=list(
+            cfg.global_random_rotation_range_per_object),
+        db_sampler=db_sampler,
+        unlabeled_db_sampler=u_db_sampler,
+        generate_bev=generate_bev,
+        without_reflectivity=without_reflectivity,
+        num_point_features=num_point_features,
+        anchor_area_threshold=cfg.anchor_area_threshold,
+        gt_points_drop=cfg.groundtruth_points_drop_percentage,
+        gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
+        remove_points_after_sample=cfg.remove_points_after_sample,
+        remove_environment=cfg.remove_environment,
+        use_group_id=cfg.use_group_id,
+        out_size_factor=out_size_factor,
+        multi_gpu=multi_gpu)
+    dataset = KittiDataset(
+        info_path=cfg.kitti_info_path,
+        root_path=cfg.kitti_root_path,
+        num_point_features=num_point_features,
+        target_assigner=target_assigner,
+        feature_map_size=feature_map_size,
+        prep_func=prep_func,
+        inference=inference,
+        points=points,
+        prep_func_inference=prep_func_inference)
+
+    return dataset

second/builder_pruned/dbsampler_builder.py

@@ -0,0 +1,27 @@
+import pickle
+
+import second.core.preprocess as prep
+from second.builder import preprocess_builder
+from second.core.preprocess import DataBasePreprocessor
+from second.core.sample_ops import DataBaseSamplerV2
+
+
+def build(sampler_config):
+    cfg = sampler_config
+    groups = list(cfg.sample_groups)
+    prepors = [
+        preprocess_builder.build_db_preprocess(c)
+        for c in cfg.database_prep_steps
+    ]
+    db_prepor = DataBasePreprocessor(prepors)
+    rate = cfg.rate
+    grot_range = cfg.global_random_rotation_range_per_object
+    groups = [dict(g.name_to_max_num) for g in groups]
+    info_path = cfg.database_info_path
+    with open(info_path, 'rb') as f:
+        db_infos = pickle.load(f)
+    grot_range = list(grot_range)
+    if len(grot_range) == 0:
+        grot_range = None
+    sampler = DataBaseSamplerV2(db_infos, groups, db_prepor, rate, grot_range)
+    return sampler

second/builder_pruned/preprocess_builder.py

@@ -0,0 +1,14 @@
+import second.core.preprocess as prep
+
+def build_db_preprocess(db_prep_config):
+    prep_type = db_prep_config.WhichOneof('database_preprocessing_step')
+
+    if prep_type == 'filter_by_difficulty':
+        cfg = db_prep_config.filter_by_difficulty
+        return prep.DBFilterByDifficulty(list(cfg.removed_difficulties))
+    elif prep_type == 'filter_by_min_num_points':
+        cfg = db_prep_config.filter_by_min_num_points
+        return prep.DBFilterByMinNumPoint(dict(cfg.min_num_point_pairs))
+    else:
+        raise ValueError("unknown database prep type")
+

second/builder_pruned/similarity_calculator_builder.py

@@ -0,0 +1,31 @@
+import numpy as np
+
+from second.core import region_similarity
+from second.protos import similarity_pb2
+
+
+def build(similarity_config):
+    """Create optimizer based on config.
+
+    Args:
+        optimizer_config: A Optimizer proto message.
+
+    Returns:
+        An optimizer and a list of variables for summary.
+
+    Raises:
+        ValueError: when using an unsupported input data type.
+    """
+    similarity_type = similarity_config.WhichOneof('region_similarity')
+    if similarity_type == 'rotate_iou_similarity':
+        return region_similarity.RotateIouSimilarity()
+    elif similarity_type == 'nearest_iou_similarity':
+        return region_similarity.NearestIouSimilarity()
+    elif similarity_type == 'distance_similarity':
+        cfg = similarity_config.distance_similarity
+        return region_similarity.DistanceSimilarity(
+            distance_norm=cfg.distance_norm,
+            with_rotation=cfg.with_rotation,
+            rotation_alpha=cfg.rotation_alpha)
+    else:
+        raise ValueError("unknown similarity type")