From b856c1500adcfe8b3bdeac2fe2906fc2928ad735 Mon Sep 17 00:00:00 2001 From: virginia Date: Sun, 12 Jan 2025 14:39:47 +0000 Subject: [PATCH 1/6] Initial commit of mednist-ddpm --- models/mednist_ddpm/configs/infer.yaml | 38 +++ models/mednist_ddpm/configs/logging.conf | 21 ++ models/mednist_ddpm/configs/metadata.json | 58 ++++ models/mednist_ddpm/configs/train.yaml | 157 ++++++++++ .../mednist_ddpm/configs/train_multigpu.yaml | 30 ++ .../docs/2d_ddpm_bundle_tutorial.ipynb | 295 ++++++++++++++++++ models/mednist_ddpm/docs/README.md | 9 + models/mednist_ddpm/docs/sub_train.sh | 34 ++ .../mednist_ddpm/docs/sub_train_multigpu.sh | 36 +++ models/mednist_ddpm/scripts/__init__.py | 12 + 10 files changed, 690 insertions(+) create mode 100644 models/mednist_ddpm/configs/infer.yaml create mode 100644 models/mednist_ddpm/configs/logging.conf create mode 100644 models/mednist_ddpm/configs/metadata.json create mode 100644 models/mednist_ddpm/configs/train.yaml create mode 100644 models/mednist_ddpm/configs/train_multigpu.yaml create mode 100644 models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb create mode 100644 models/mednist_ddpm/docs/README.md create mode 100755 models/mednist_ddpm/docs/sub_train.sh create mode 100644 models/mednist_ddpm/docs/sub_train_multigpu.sh create mode 100644 models/mednist_ddpm/scripts/__init__.py diff --git a/models/mednist_ddpm/configs/infer.yaml b/models/mednist_ddpm/configs/infer.yaml new file mode 100644 index 00000000..46297e18 --- /dev/null +++ b/models/mednist_ddpm/configs/infer.yaml @@ -0,0 +1,38 @@ +# This defines an inference script for generating a random image to a Pytorch file + +batch_size: 1 +num_workers: 0 + +noise: $torch.rand(1,1,@image_dim,@image_dim) # create a random image every time this program is run + +out_file: "" # where to save the tensor to + +# using a lambda this defines a simple sampling function used below +sample: '$lambda x: @inferer.sample(input_noise=x, diffusion_model=@network, scheduler=@scheduler)' + +load_state: '$@network.load_state_dict(torch.load(@ckpt_path))' # command to load the saved model weights + +save_trans: + _target_: Compose + transforms: + - _target_: ScaleIntensity + minv: 0.0 + maxv: 255.0 + - _target_: ToTensor + track_meta: false + - _target_: SaveImage + output_ext: "jpg" + resample: false + output_dtype: '$torch.uint8' + separate_folder: false + output_postfix: '@out_file' + +# program to load the model weights, run `sample`, and store results to `out_file` +testing: +- '@load_state' +- '$torch.save(@sample(@noise.to(@device)), @out_file)' + +#alternative version which saves to a jpg file +testing_jpg: +- '@load_state' +- '$@save_trans(@sample(@noise.to(@device))[0])' diff --git a/models/mednist_ddpm/configs/logging.conf b/models/mednist_ddpm/configs/logging.conf new file mode 100644 index 00000000..91c1a21c --- /dev/null +++ b/models/mednist_ddpm/configs/logging.conf @@ -0,0 +1,21 @@ +[loggers] +keys=root + +[handlers] +keys=consoleHandler + +[formatters] +keys=fullFormatter + +[logger_root] +level=INFO +handlers=consoleHandler + +[handler_consoleHandler] +class=StreamHandler +level=INFO +formatter=fullFormatter +args=(sys.stdout,) + +[formatter_fullFormatter] +format=%(asctime)s - %(name)s - %(levelname)s - %(message)s diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json new file mode 100644 index 00000000..7fb2df99 --- /dev/null +++ b/models/mednist_ddpm/configs/metadata.json @@ -0,0 +1,58 @@ +{ + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json", + "version": "0.1.0", + "changelog": { + "0.1.0": "Initial version" + }, + "monai_version": "1.0.0", + "pytorch_version": "1.10.2", + "numpy_version": "1.21.2", + "task": "MedNIST Hand Generation", + "description": "", + "authors": "Walter Hugo Lopez Pinaya, Mark Graham, and Eric Kerfoot", + "copyright": "Copyright (c) KCL", + "references": [], + "intended_use": "This is suitable for research purposes only", + "image_classes": "Single channel magnitude data", + "data_source": "MedNIST", + "network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "magnitude", + "modality": "xray", + "num_channels": 1, + "spatial_shape": [ + 1, + 64, + 64 + ], + "dtype": "float32", + "value_range": [], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + }, + "outputs": { + "pred": { + "type": "image", + "format": "magnitude", + "modality": "xray", + "num_channels": 1, + "spatial_shape": [ + 1, + 64, + 64 + ], + "dtype": "float32", + "value_range": [], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + } + } +} diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml new file mode 100644 index 00000000..ded0fe31 --- /dev/null +++ b/models/mednist_ddpm/configs/train.yaml @@ -0,0 +1,157 @@ +# This defines the training script for the network + +# choose a new directory for every run +output_dir: $datetime.datetime.now().strftime('./results/output_%y%m%d_%H%M%S') +dataset_dir: ./data + +train_data: + _target_ : MedNISTDataset + root_dir: '@dataset_dir' + section: training + download: true + progress: false + seed: 0 + +val_data: + _target_ : MedNISTDataset + root_dir: '@dataset_dir' + section: validation + download: true + progress: false + seed: 0 + +train_datalist: '$[{"image": item["image"]} for item in @train_data.data if item["class_name"] == "Hand"]' +val_datalist: '$[{"image": item["image"]} for item in @val_data.data if item["class_name"] == "Hand"]' + +batch_size: 8 +num_substeps: 1 +num_workers: 4 +use_thread_workers: false + +lr: 0.000025 +rand_prob: 0.5 +num_epochs: 75 +val_interval: 5 +save_interval: 5 + +train_transforms: +- _target_: RandAffined + keys: '@image' + rotate_range: + - ['$-np.pi / 36', '$np.pi / 36'] + - ['$-np.pi / 36', '$np.pi / 36'] + translate_range: + - [-1, 1] + - [-1, 1] + scale_range: + - [-0.05, 0.05] + - [-0.05, 0.05] + spatial_size: [64, 64] + padding_mode: "zeros" + prob: '@rand_prob' + +train_ds: + _target_: Dataset + data: $@train_datalist + transform: + _target_: Compose + transforms: '$@base_transforms + @train_transforms' + +train_loader: + _target_: ThreadDataLoader + dataset: '@train_ds' + batch_size: '@batch_size' + repeats: '@num_substeps' + num_workers: '@num_workers' + use_thread_workers: '@use_thread_workers' + persistent_workers: '$@num_workers > 0' + shuffle: true + +val_ds: + _target_: Dataset + data: $@val_datalist + transform: + _target_: Compose + transforms: '@base_transforms' + +val_loader: + _target_: DataLoader + dataset: '@val_ds' + batch_size: '@batch_size' + num_workers: '@num_workers' + persistent_workers: '$@num_workers > 0' + shuffle: false + +lossfn: + _target_: torch.nn.MSELoss + +optimizer: + _target_: torch.optim.Adam + params: $@network.parameters() + lr: '@lr' + +prepare_batch: + _target_: monai.engines.utils.DiffusionPrepareBatch + num_train_timesteps: '@num_train_timesteps' + +val_handlers: +- _target_: StatsHandler + name: train_log + output_transform: '$lambda x: None' + _disabled_: '@is_not_rank0' + +evaluator: + _target_: SupervisedEvaluator + device: '@device' + val_data_loader: '@val_loader' + network: '@network' + amp: '@use_amp' + inferer: '@inferer' + prepare_batch: '@prepare_batch' + key_val_metric: + val_mean_abs_error: + _target_: MeanAbsoluteError + output_transform: $monai.handlers.from_engine([@pred, @label]) + metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + val_handlers: '$list(filter(bool, @val_handlers))' + +handlers: +- _target_: CheckpointLoader + _disabled_: $not os.path.exists(@ckpt_path) + load_path: '@ckpt_path' + load_dict: + model: '@network' +- _target_: ValidationHandler + validator: '@evaluator' + epoch_level: true + interval: '@val_interval' +- _target_: CheckpointSaver + save_dir: '@output_dir' + save_dict: + model: '@network' + save_interval: '@save_interval' + save_final: true + epoch_level: true + _disabled_: '@is_not_rank0' + +trainer: + _target_: SupervisedTrainer + max_epochs: '@num_epochs' + device: '@device' + train_data_loader: '@train_loader' + network: '@network' + loss_function: '@lossfn' + optimizer: '@optimizer' + inferer: '@inferer' + prepare_batch: '@prepare_batch' + key_train_metric: + train_acc: + _target_: MeanSquaredError + output_transform: $monai.handlers.from_engine([@pred, @label]) + metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + train_handlers: '$list(filter(bool, @handlers))' + amp: '@use_amp' + +training: +- '$monai.utils.set_determinism(0)' +- '$@trainer.run()' diff --git a/models/mednist_ddpm/configs/train_multigpu.yaml b/models/mednist_ddpm/configs/train_multigpu.yaml new file mode 100644 index 00000000..51f5acf4 --- /dev/null +++ b/models/mednist_ddpm/configs/train_multigpu.yaml @@ -0,0 +1,30 @@ +# This can be mixed in with the training script to enable multi-GPU training + +network: + _target_: torch.nn.parallel.DistributedDataParallel + module: $@network_def.to(@device) + device_ids: ['@device'] + find_unused_parameters: true + +tsampler: + _target_: DistributedSampler + dataset: '@train_ds' + even_divisible: true + shuffle: true +train_loader#sampler: '@tsampler' +train_loader#shuffle: false + +vsampler: + _target_: DistributedSampler + dataset: '@val_ds' + even_divisible: false + shuffle: false +val_loader#sampler: '@vsampler' + +training: +- $import torch.distributed as dist +- $dist.init_process_group(backend='nccl') +- $torch.cuda.set_device(@device) +- $monai.utils.set_determinism(seed=123), +- $@trainer.run() +- $dist.destroy_process_group() diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb new file mode 100644 index 00000000..183d7d76 --- /dev/null +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c54f5831-58eb-4f9e-bb8a-2c2a6536a658", + "metadata": {}, + "source": [ + "# Denoising Diffusion Probabilistic Models with MedNIST Dataset Bundle \n", + "\n", + "This notebook discusses and uses the MONAI bundle it's included in for generating images from the MedNIST dataset using diffusion models. This is based off the 2d_ddpm_tutorial_ignite.ipynb notebook with a few changes.\n", + "\n", + "The bundle defines training and inference scripts whose use will be described here along with visualisations. The assumption with this notebook is that it's run within the bundle's `docs` directory and that the environment it runs in has `MONAI` and `GenerativeModels` installed. The command lines given are known to work in `bash` however may be problematic in Windows.\n", + "\n", + "First thing to do is import libraries and verify MONAI is present:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6d32f8a4-2bfe-4cfb-9abd-033b0c6080e6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 1.4.0\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.5.1\n", + "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", + "MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n", + "MONAI __file__: /Users//PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/__init__.py\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: 0.5.1\n", + "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n", + "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n", + "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n", + "scipy version: NOT INSTALLED or UNKNOWN VERSION.\n", + "Pillow version: 11.1.0\n", + "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n", + "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n", + "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n", + "tqdm version: NOT INSTALLED or UNKNOWN VERSION.\n", + "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", + "psutil version: 6.1.1\n", + "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", + "einops version: 0.8.0\n", + "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", + "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", + "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n", + "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "from pathlib import Path\n", + "\n", + "import torch\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import monai\n", + "from monai.bundle import ConfigParser\n", + "\n", + "# path to the bundle directory, this assumes you're running the notebook in its directory\n", + "bundle_root = str(Path(\".\").absolute().parent)\n", + "\n", + "monai.config.print_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d6fc6592-cb51-4527-97ee-add5d1cdbeb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "dataset_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(dataset_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "678d2e51-dc2d-4ad9-a4c0-14a6f900398b", + "metadata": {}, + "source": [ + "A bundle can be run on the command line using the Fire library or by parsing the configuration manually then getting parsed content objects. The following is the command to train the network for the default number of epochs. It will define values in the config files which need to be set for a particular run, such as the dataset directory created above, and setting the PYTHONPATH variable. The configuration for this bundle is split into 4 yaml files, one having common definitions for training and inference, one to enable multi-GPU training, and one each for training and inference. Their combinations determine what your final configuration is, in this case the common and train files produce a training script. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d52a4ae9-0d6d-4bc4-a5b5-f84470711f2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n", + "2025-01-12 14:34:01,091 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-12 14:34:01,091 - INFO - > config_file: ('/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", + " '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/train.yaml')\n", + "2025-01-12 14:34:01,091 - INFO - > meta_file: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", + "2025-01-12 14:34:01,091 - INFO - > logging_file: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", + "2025-01-12 14:34:01,091 - INFO - > run_id: 'training'\n", + "2025-01-12 14:34:01,091 - INFO - > bundle_root: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm'\n", + "2025-01-12 14:34:01,091 - INFO - > dataset_dir: '/var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw'\n", + "2025-01-12 14:34:01,091 - INFO - ---\n", + "\n", + "\n", + "2025-01-12 14:34:01,091 - INFO - Setting logging properties based on config: /Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", + "2025-01-12 14:34:25,955 - INFO - Downloaded: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST.tar.gz\n", + "2025-01-12 14:34:26,066 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-12 14:34:26,067 - INFO - Writing into directory: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw.\n", + "2025-01-12 14:34:44,890 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-12 14:34:44,890 - INFO - File exists: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST.tar.gz, skipped downloading.\n", + "2025-01-12 14:34:44,890 - INFO - Non-empty folder exists in /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST, skipped extracting.\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/engines/trainer.py:54: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " self.scaler = torch.cuda.amp.GradScaler() if self.amp else None\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/torch/amp/grad_scaler.py:132: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", + " warnings.warn(\n", + "2025-01-12 14:34:46,515 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", + " warnings.warn(\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/engines/trainer.py:257: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with torch.cuda.amp.autocast(**engine.amp_kwargs):\n", + "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/torch/amp/autocast_mode.py:266: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# multiple config files need to be specified this way with '' quotes, variable used in command line must be in \"\" quotes\n", + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml'\"\n", + "\n", + "!PYTHONPATH={bundle_root} python -m monai.bundle run training \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --logging_file {bundle_root}/configs/logging.conf \\\n", + " --bundle_root {bundle_root} \\\n", + " --dataset_dir {dataset_dir}" + ] + }, + { + "cell_type": "markdown", + "id": "5030732c-deb5-448a-b575-385bda0fa308", + "metadata": {}, + "source": [ + "The test inference script can then be invoked as such to produce an output tensor saved to the given file with a randomly generated image. The `ckpt_path` value should point to the final checkpoint file created during the above training run, which will be in a subdirectory of `./result`. The training script's default behaviour is to create a new timestamped subdirectory in `./result` for every new run, this can be explicitly set by providing a `output_dir` value on the command line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f53b40ee-11b7-4352-82ee-0dd7113220cf", + "metadata": {}, + "outputs": [], + "source": [ + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/infer.yaml'\"\n", + "\n", + "!PYTHONPATH={bundle_root} python -m monai.bundle run testing \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --ckpt_path ./results/output_230215_174009/model_final_iteration=75000.pt \\\n", + " --bundle_root {bundle_root} \\\n", + " --out_file test.pt\n", + "\n", + "test = torch.load(\"test.pt\", map_location=\"cpu\")\n", + "\n", + "plt.imshow(test[0, 0], vmin=0, vmax=1, cmap=\"gray\"); plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf8438b3-4c7d-48c4-bb41-ed7def73753f", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(bundle_root) # make sure we load the script files we need\n", + "\n", + "# configure the parser from the bundle's information\n", + "cp = ConfigParser()\n", + "cp.read_meta(f\"{bundle_root}/configs/metadata.json\")\n", + "cp.read_config([f\"{bundle_root}/configs/common.yaml\", f\"{bundle_root}/configs/infer.yaml\"])\n", + "cp[\"bundle_root\"] = bundle_root\n", + "cp[\"ckpt_path\"] = \"./results/output_230215_174009/model_final_iteration=75000.pt\"\n", + "\n", + "cp.get_parsed_content(\"load_state\") # load the saved state from the checkpoint just be resolving this value\n", + "\n", + "device = cp.get_parsed_content(\"device\") # device used by the bundle\n", + "sample = cp.get_parsed_content(\"sample\") # test sampling function\n", + "\n", + "image_dim = cp[\"image_dim\"] # get the stored dimension value, no need to resolve anything\n", + "\n", + "noise = torch.rand(1, 1, image_dim, image_dim).to(device) # or cp.get_parsed_content(\"noise\")\n", + "\n", + "test = sample(noise)\n", + "\n", + "plt.imshow(test[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "id": "2feab4e5-2745-4d35-9eec-a2bb8340cf51", + "metadata": {}, + "source": [ + "Multi-GPU can be enabled by including the `train_multigpu.yaml` configuration file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "173cda1c-ac90-410f-b34d-b6cbb0044c7a", + "metadata": {}, + "outputs": [], + "source": [ + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml', '{bundle_root}/configs/train_multigpu.yaml'\"\n", + "\n", + "!PYTHONPATH={bundle_root} torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --logging_file {bundle_root}/configs/logging.conf \\\n", + " --bundle_root {bundle_root} \\\n", + " --dataset_dir {dataset_dir}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb719023-8250-43c4-ab10-911829332498", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(dataset_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/mednist_ddpm/docs/README.md b/models/mednist_ddpm/docs/README.md new file mode 100644 index 00000000..6483aff5 --- /dev/null +++ b/models/mednist_ddpm/docs/README.md @@ -0,0 +1,9 @@ + +# MedNIST DDPM Example Bundle + +This implements roughly equivalent code to the "Denoising Diffusion Probabilistic Models with MedNIST Dataset" example notebook. This includes scripts for training with single or multiple GPUs and a visualisation notebook. + +The files included here demonstrate how to use the bundle: + * [2d_ddpm_bundle_tutorial.ipynb](./2d_ddpm_bundle_tutorial.ipynb) - demonstrates command line and in-code invocation of the bundle's training and inference scripts + * [sub_train.sh](sub_train.sh) - SLURM submission script example for training + * [sub_train_multigpu.sh](sub_train_multigpu.sh) - SLURM submission script example for training with multiple GPUs diff --git a/models/mednist_ddpm/docs/sub_train.sh b/models/mednist_ddpm/docs/sub_train.sh new file mode 100755 index 00000000..237b16f5 --- /dev/null +++ b/models/mednist_ddpm/docs/sub_train.sh @@ -0,0 +1,34 @@ +#! /bin/bash +#SBATCH --nodes=1 +#SBATCH -J mednist_train +#SBATCH -c 4 +#SBATCH --gres=gpu:1 +#SBATCH --time=2:00:00 +#SBATCH -p small + +set -v + +# change this if run submitted from a different directory +export BUNDLE="$(pwd)/.." + +# have to set PYTHONPATH to find MONAI and GenerativeModels as well as the bundle's script directory +export PYTHONPATH="$HOME/MONAI:$HOME/GenerativeModels:$BUNDLE" + +# change this to load a checkpoint instead of started from scratch +CKPT=none + +CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml'" + +# change this to point to where MedNIST is located +DATASET="$(pwd)" + +# it's useful to include the configuration in the log file +cat "$BUNDLE/configs/common.yaml" +cat "$BUNDLE/configs/train.yaml" + +python -m monai.bundle run training \ + --meta_file "$BUNDLE/configs/metadata.json" \ + --config_file "$CONFIG" \ + --logging_file "$BUNDLE/configs/logging.conf" \ + --bundle_root "$BUNDLE" \ + --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/docs/sub_train_multigpu.sh b/models/mednist_ddpm/docs/sub_train_multigpu.sh new file mode 100644 index 00000000..4d5f6af0 --- /dev/null +++ b/models/mednist_ddpm/docs/sub_train_multigpu.sh @@ -0,0 +1,36 @@ +#! /bin/bash +#SBATCH --nodes=1 +#SBATCH -J mednist_train +#SBATCH -c 4 +#SBATCH --gres=gpu:2 +#SBATCH --time=2:00:00 +#SBATCH -p big + +set -v + +# change this if run submitted from a different directory +export BUNDLE="$(pwd)/.." + +# have to set PYTHONPATH to find MONAI and GenerativeModels as well as the bundle's script directory +export PYTHONPATH="$HOME/MONAI:$HOME/GenerativeModels:$BUNDLE" + +# change this to load a checkpoint instead of started from scratch +CKPT=none + +CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml', '$BUNDLE/configs/train_multigpu.yaml'" + +# change this to point to where MedNIST is located +DATASET="$(pwd)" + +# it's useful to include the configuration in the log file +cat "$BUNDLE/configs/common.yaml" +cat "$BUNDLE/configs/train.yaml" +cat "$BUNDLE/configs/train_multigpu.yaml" + +# remember to change arguments to match how many nodes and GPUs you have +torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \ + --meta_file "$BUNDLE/configs/metadata.json" \ + --config_file "$CONFIG" \ + --logging_file "$BUNDLE/configs/logging.conf" \ + --bundle_root "$BUNDLE" \ + --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/scripts/__init__.py b/models/mednist_ddpm/scripts/__init__.py new file mode 100644 index 00000000..c44e4a34 --- /dev/null +++ b/models/mednist_ddpm/scripts/__init__.py @@ -0,0 +1,12 @@ +from __future__ import annotations + + +def inv_metric_cmp_fn(current_metric: float, prev_best: float) -> bool: + """ + This inverts comparison for those metrics which reduce like loss values, such that the lower one is better. + + Args: + current_metric: metric value of current round computation. + prev_best: the best metric value of previous rounds to compare with. + """ + return current_metric < prev_best From efa9d0a7f49495ff37ebe9583b3bf857ed6ecb00 Mon Sep 17 00:00:00 2001 From: virginia Date: Mon, 13 Jan 2025 08:27:07 +0000 Subject: [PATCH 2/6] mednist_ddpm commit. I have cleared the absolute paths to my personal directory. --- models/mednist_ddpm/configs/infer.yaml | 38 --- models/mednist_ddpm/configs/logging.conf | 21 -- models/mednist_ddpm/configs/metadata.json | 58 ---- models/mednist_ddpm/configs/train.yaml | 157 ---------- .../mednist_ddpm/configs/train_multigpu.yaml | 30 -- .../docs/2d_ddpm_bundle_tutorial.ipynb | 295 ------------------ models/mednist_ddpm/docs/README.md | 9 - models/mednist_ddpm/docs/sub_train.sh | 34 -- .../mednist_ddpm/docs/sub_train_multigpu.sh | 36 --- models/mednist_ddpm/scripts/__init__.py | 12 - 10 files changed, 690 deletions(-) delete mode 100644 models/mednist_ddpm/configs/infer.yaml delete mode 100644 models/mednist_ddpm/configs/logging.conf delete mode 100644 models/mednist_ddpm/configs/metadata.json delete mode 100644 models/mednist_ddpm/configs/train.yaml delete mode 100644 models/mednist_ddpm/configs/train_multigpu.yaml delete mode 100644 models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb delete mode 100644 models/mednist_ddpm/docs/README.md delete mode 100755 models/mednist_ddpm/docs/sub_train.sh delete mode 100644 models/mednist_ddpm/docs/sub_train_multigpu.sh delete mode 100644 models/mednist_ddpm/scripts/__init__.py diff --git a/models/mednist_ddpm/configs/infer.yaml b/models/mednist_ddpm/configs/infer.yaml deleted file mode 100644 index 46297e18..00000000 --- a/models/mednist_ddpm/configs/infer.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# This defines an inference script for generating a random image to a Pytorch file - -batch_size: 1 -num_workers: 0 - -noise: $torch.rand(1,1,@image_dim,@image_dim) # create a random image every time this program is run - -out_file: "" # where to save the tensor to - -# using a lambda this defines a simple sampling function used below -sample: '$lambda x: @inferer.sample(input_noise=x, diffusion_model=@network, scheduler=@scheduler)' - -load_state: '$@network.load_state_dict(torch.load(@ckpt_path))' # command to load the saved model weights - -save_trans: - _target_: Compose - transforms: - - _target_: ScaleIntensity - minv: 0.0 - maxv: 255.0 - - _target_: ToTensor - track_meta: false - - _target_: SaveImage - output_ext: "jpg" - resample: false - output_dtype: '$torch.uint8' - separate_folder: false - output_postfix: '@out_file' - -# program to load the model weights, run `sample`, and store results to `out_file` -testing: -- '@load_state' -- '$torch.save(@sample(@noise.to(@device)), @out_file)' - -#alternative version which saves to a jpg file -testing_jpg: -- '@load_state' -- '$@save_trans(@sample(@noise.to(@device))[0])' diff --git a/models/mednist_ddpm/configs/logging.conf b/models/mednist_ddpm/configs/logging.conf deleted file mode 100644 index 91c1a21c..00000000 --- a/models/mednist_ddpm/configs/logging.conf +++ /dev/null @@ -1,21 +0,0 @@ -[loggers] -keys=root - -[handlers] -keys=consoleHandler - -[formatters] -keys=fullFormatter - -[logger_root] -level=INFO -handlers=consoleHandler - -[handler_consoleHandler] -class=StreamHandler -level=INFO -formatter=fullFormatter -args=(sys.stdout,) - -[formatter_fullFormatter] -format=%(asctime)s - %(name)s - %(levelname)s - %(message)s diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json deleted file mode 100644 index 7fb2df99..00000000 --- a/models/mednist_ddpm/configs/metadata.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json", - "version": "0.1.0", - "changelog": { - "0.1.0": "Initial version" - }, - "monai_version": "1.0.0", - "pytorch_version": "1.10.2", - "numpy_version": "1.21.2", - "task": "MedNIST Hand Generation", - "description": "", - "authors": "Walter Hugo Lopez Pinaya, Mark Graham, and Eric Kerfoot", - "copyright": "Copyright (c) KCL", - "references": [], - "intended_use": "This is suitable for research purposes only", - "image_classes": "Single channel magnitude data", - "data_source": "MedNIST", - "network_data_format": { - "inputs": { - "image": { - "type": "image", - "format": "magnitude", - "modality": "xray", - "num_channels": 1, - "spatial_shape": [ - 1, - 64, - 64 - ], - "dtype": "float32", - "value_range": [], - "is_patch_data": false, - "channel_def": { - "0": "image" - } - } - }, - "outputs": { - "pred": { - "type": "image", - "format": "magnitude", - "modality": "xray", - "num_channels": 1, - "spatial_shape": [ - 1, - 64, - 64 - ], - "dtype": "float32", - "value_range": [], - "is_patch_data": false, - "channel_def": { - "0": "image" - } - } - } - } -} diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml deleted file mode 100644 index ded0fe31..00000000 --- a/models/mednist_ddpm/configs/train.yaml +++ /dev/null @@ -1,157 +0,0 @@ -# This defines the training script for the network - -# choose a new directory for every run -output_dir: $datetime.datetime.now().strftime('./results/output_%y%m%d_%H%M%S') -dataset_dir: ./data - -train_data: - _target_ : MedNISTDataset - root_dir: '@dataset_dir' - section: training - download: true - progress: false - seed: 0 - -val_data: - _target_ : MedNISTDataset - root_dir: '@dataset_dir' - section: validation - download: true - progress: false - seed: 0 - -train_datalist: '$[{"image": item["image"]} for item in @train_data.data if item["class_name"] == "Hand"]' -val_datalist: '$[{"image": item["image"]} for item in @val_data.data if item["class_name"] == "Hand"]' - -batch_size: 8 -num_substeps: 1 -num_workers: 4 -use_thread_workers: false - -lr: 0.000025 -rand_prob: 0.5 -num_epochs: 75 -val_interval: 5 -save_interval: 5 - -train_transforms: -- _target_: RandAffined - keys: '@image' - rotate_range: - - ['$-np.pi / 36', '$np.pi / 36'] - - ['$-np.pi / 36', '$np.pi / 36'] - translate_range: - - [-1, 1] - - [-1, 1] - scale_range: - - [-0.05, 0.05] - - [-0.05, 0.05] - spatial_size: [64, 64] - padding_mode: "zeros" - prob: '@rand_prob' - -train_ds: - _target_: Dataset - data: $@train_datalist - transform: - _target_: Compose - transforms: '$@base_transforms + @train_transforms' - -train_loader: - _target_: ThreadDataLoader - dataset: '@train_ds' - batch_size: '@batch_size' - repeats: '@num_substeps' - num_workers: '@num_workers' - use_thread_workers: '@use_thread_workers' - persistent_workers: '$@num_workers > 0' - shuffle: true - -val_ds: - _target_: Dataset - data: $@val_datalist - transform: - _target_: Compose - transforms: '@base_transforms' - -val_loader: - _target_: DataLoader - dataset: '@val_ds' - batch_size: '@batch_size' - num_workers: '@num_workers' - persistent_workers: '$@num_workers > 0' - shuffle: false - -lossfn: - _target_: torch.nn.MSELoss - -optimizer: - _target_: torch.optim.Adam - params: $@network.parameters() - lr: '@lr' - -prepare_batch: - _target_: monai.engines.utils.DiffusionPrepareBatch - num_train_timesteps: '@num_train_timesteps' - -val_handlers: -- _target_: StatsHandler - name: train_log - output_transform: '$lambda x: None' - _disabled_: '@is_not_rank0' - -evaluator: - _target_: SupervisedEvaluator - device: '@device' - val_data_loader: '@val_loader' - network: '@network' - amp: '@use_amp' - inferer: '@inferer' - prepare_batch: '@prepare_batch' - key_val_metric: - val_mean_abs_error: - _target_: MeanAbsoluteError - output_transform: $monai.handlers.from_engine([@pred, @label]) - metric_cmp_fn: '$scripts.inv_metric_cmp_fn' - val_handlers: '$list(filter(bool, @val_handlers))' - -handlers: -- _target_: CheckpointLoader - _disabled_: $not os.path.exists(@ckpt_path) - load_path: '@ckpt_path' - load_dict: - model: '@network' -- _target_: ValidationHandler - validator: '@evaluator' - epoch_level: true - interval: '@val_interval' -- _target_: CheckpointSaver - save_dir: '@output_dir' - save_dict: - model: '@network' - save_interval: '@save_interval' - save_final: true - epoch_level: true - _disabled_: '@is_not_rank0' - -trainer: - _target_: SupervisedTrainer - max_epochs: '@num_epochs' - device: '@device' - train_data_loader: '@train_loader' - network: '@network' - loss_function: '@lossfn' - optimizer: '@optimizer' - inferer: '@inferer' - prepare_batch: '@prepare_batch' - key_train_metric: - train_acc: - _target_: MeanSquaredError - output_transform: $monai.handlers.from_engine([@pred, @label]) - metric_cmp_fn: '$scripts.inv_metric_cmp_fn' - train_handlers: '$list(filter(bool, @handlers))' - amp: '@use_amp' - -training: -- '$monai.utils.set_determinism(0)' -- '$@trainer.run()' diff --git a/models/mednist_ddpm/configs/train_multigpu.yaml b/models/mednist_ddpm/configs/train_multigpu.yaml deleted file mode 100644 index 51f5acf4..00000000 --- a/models/mednist_ddpm/configs/train_multigpu.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# This can be mixed in with the training script to enable multi-GPU training - -network: - _target_: torch.nn.parallel.DistributedDataParallel - module: $@network_def.to(@device) - device_ids: ['@device'] - find_unused_parameters: true - -tsampler: - _target_: DistributedSampler - dataset: '@train_ds' - even_divisible: true - shuffle: true -train_loader#sampler: '@tsampler' -train_loader#shuffle: false - -vsampler: - _target_: DistributedSampler - dataset: '@val_ds' - even_divisible: false - shuffle: false -val_loader#sampler: '@vsampler' - -training: -- $import torch.distributed as dist -- $dist.init_process_group(backend='nccl') -- $torch.cuda.set_device(@device) -- $monai.utils.set_determinism(seed=123), -- $@trainer.run() -- $dist.destroy_process_group() diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb deleted file mode 100644 index 183d7d76..00000000 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ /dev/null @@ -1,295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c54f5831-58eb-4f9e-bb8a-2c2a6536a658", - "metadata": {}, - "source": [ - "# Denoising Diffusion Probabilistic Models with MedNIST Dataset Bundle \n", - "\n", - "This notebook discusses and uses the MONAI bundle it's included in for generating images from the MedNIST dataset using diffusion models. This is based off the 2d_ddpm_tutorial_ignite.ipynb notebook with a few changes.\n", - "\n", - "The bundle defines training and inference scripts whose use will be described here along with visualisations. The assumption with this notebook is that it's run within the bundle's `docs` directory and that the environment it runs in has `MONAI` and `GenerativeModels` installed. The command lines given are known to work in `bash` however may be problematic in Windows.\n", - "\n", - "First thing to do is import libraries and verify MONAI is present:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6d32f8a4-2bfe-4cfb-9abd-033b0c6080e6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONAI version: 1.4.0\n", - "Numpy version: 1.26.4\n", - "Pytorch version: 2.5.1\n", - "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n", - "MONAI __file__: /Users//PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/__init__.py\n", - "\n", - "Optional dependencies:\n", - "Pytorch Ignite version: 0.5.1\n", - "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n", - "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n", - "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n", - "scipy version: NOT INSTALLED or UNKNOWN VERSION.\n", - "Pillow version: 11.1.0\n", - "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n", - "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n", - "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n", - "tqdm version: NOT INSTALLED or UNKNOWN VERSION.\n", - "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", - "psutil version: 6.1.1\n", - "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", - "einops version: 0.8.0\n", - "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", - "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", - "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n", - "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n", - "\n", - "For details about installing the optional dependencies, please visit:\n", - " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", - "\n" - ] - } - ], - "source": [ - "import os\n", - "import shutil\n", - "import tempfile\n", - "from pathlib import Path\n", - "\n", - "import torch\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import monai\n", - "from monai.bundle import ConfigParser\n", - "\n", - "# path to the bundle directory, this assumes you're running the notebook in its directory\n", - "bundle_root = str(Path(\".\").absolute().parent)\n", - "\n", - "monai.config.print_config()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d6fc6592-cb51-4527-97ee-add5d1cdbeb4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw\n" - ] - } - ], - "source": [ - "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "dataset_dir = tempfile.mkdtemp() if directory is None else directory\n", - "print(dataset_dir)" - ] - }, - { - "cell_type": "markdown", - "id": "678d2e51-dc2d-4ad9-a4c0-14a6f900398b", - "metadata": {}, - "source": [ - "A bundle can be run on the command line using the Fire library or by parsing the configuration manually then getting parsed content objects. The following is the command to train the network for the default number of epochs. It will define values in the config files which need to be set for a particular run, such as the dataset directory created above, and setting the PYTHONPATH variable. The configuration for this bundle is split into 4 yaml files, one having common definitions for training and inference, one to enable multi-GPU training, and one each for training and inference. Their combinations determine what your final configuration is, in this case the common and train files produce a training script. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d52a4ae9-0d6d-4bc4-a5b5-f84470711f2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n", - "2025-01-12 14:34:01,091 - INFO - --- input summary of monai.bundle.scripts.run ---\n", - "2025-01-12 14:34:01,091 - INFO - > config_file: ('/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", - " '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/train.yaml')\n", - "2025-01-12 14:34:01,091 - INFO - > meta_file: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", - "2025-01-12 14:34:01,091 - INFO - > logging_file: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", - "2025-01-12 14:34:01,091 - INFO - > run_id: 'training'\n", - "2025-01-12 14:34:01,091 - INFO - > bundle_root: '/Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm'\n", - "2025-01-12 14:34:01,091 - INFO - > dataset_dir: '/var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw'\n", - "2025-01-12 14:34:01,091 - INFO - ---\n", - "\n", - "\n", - "2025-01-12 14:34:01,091 - INFO - Setting logging properties based on config: /Users/virginia/PycharmProjects/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", - "2025-01-12 14:34:25,955 - INFO - Downloaded: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST.tar.gz\n", - "2025-01-12 14:34:26,066 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2025-01-12 14:34:26,067 - INFO - Writing into directory: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw.\n", - "2025-01-12 14:34:44,890 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2025-01-12 14:34:44,890 - INFO - File exists: /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST.tar.gz, skipped downloading.\n", - "2025-01-12 14:34:44,890 - INFO - Non-empty folder exists in /var/folders/63/rn7xd75s2fbf1mytzldny6sc0000gp/T/tmpm5hzi_iw/MedNIST, skipped extracting.\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/engines/trainer.py:54: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", - " self.scaler = torch.cuda.amp.GradScaler() if self.amp else None\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/torch/amp/grad_scaler.py:132: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n", - "2025-01-12 14:34:46,515 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n", - " warnings.warn(\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/monai/engines/trainer.py:257: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", - " with torch.cuda.amp.autocast(**engine.amp_kwargs):\n", - "/Users/virginia/PycharmProjects/model-zoo/.venv/lib/python3.9/site-packages/torch/amp/autocast_mode.py:266: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# multiple config files need to be specified this way with '' quotes, variable used in command line must be in \"\" quotes\n", - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml'\"\n", - "\n", - "!PYTHONPATH={bundle_root} python -m monai.bundle run training \\\n", - " --meta_file {bundle_root}/configs/metadata.json \\\n", - " --config_file \"{configs}\" \\\n", - " --logging_file {bundle_root}/configs/logging.conf \\\n", - " --bundle_root {bundle_root} \\\n", - " --dataset_dir {dataset_dir}" - ] - }, - { - "cell_type": "markdown", - "id": "5030732c-deb5-448a-b575-385bda0fa308", - "metadata": {}, - "source": [ - "The test inference script can then be invoked as such to produce an output tensor saved to the given file with a randomly generated image. The `ckpt_path` value should point to the final checkpoint file created during the above training run, which will be in a subdirectory of `./result`. The training script's default behaviour is to create a new timestamped subdirectory in `./result` for every new run, this can be explicitly set by providing a `output_dir` value on the command line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f53b40ee-11b7-4352-82ee-0dd7113220cf", - "metadata": {}, - "outputs": [], - "source": [ - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/infer.yaml'\"\n", - "\n", - "!PYTHONPATH={bundle_root} python -m monai.bundle run testing \\\n", - " --meta_file {bundle_root}/configs/metadata.json \\\n", - " --config_file \"{configs}\" \\\n", - " --ckpt_path ./results/output_230215_174009/model_final_iteration=75000.pt \\\n", - " --bundle_root {bundle_root} \\\n", - " --out_file test.pt\n", - "\n", - "test = torch.load(\"test.pt\", map_location=\"cpu\")\n", - "\n", - "plt.imshow(test[0, 0], vmin=0, vmax=1, cmap=\"gray\"); plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf8438b3-4c7d-48c4-bb41-ed7def73753f", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(bundle_root) # make sure we load the script files we need\n", - "\n", - "# configure the parser from the bundle's information\n", - "cp = ConfigParser()\n", - "cp.read_meta(f\"{bundle_root}/configs/metadata.json\")\n", - "cp.read_config([f\"{bundle_root}/configs/common.yaml\", f\"{bundle_root}/configs/infer.yaml\"])\n", - "cp[\"bundle_root\"] = bundle_root\n", - "cp[\"ckpt_path\"] = \"./results/output_230215_174009/model_final_iteration=75000.pt\"\n", - "\n", - "cp.get_parsed_content(\"load_state\") # load the saved state from the checkpoint just be resolving this value\n", - "\n", - "device = cp.get_parsed_content(\"device\") # device used by the bundle\n", - "sample = cp.get_parsed_content(\"sample\") # test sampling function\n", - "\n", - "image_dim = cp[\"image_dim\"] # get the stored dimension value, no need to resolve anything\n", - "\n", - "noise = torch.rand(1, 1, image_dim, image_dim).to(device) # or cp.get_parsed_content(\"noise\")\n", - "\n", - "test = sample(noise)\n", - "\n", - "plt.imshow(test[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")" - ] - }, - { - "cell_type": "markdown", - "id": "2feab4e5-2745-4d35-9eec-a2bb8340cf51", - "metadata": {}, - "source": [ - "Multi-GPU can be enabled by including the `train_multigpu.yaml` configuration file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "173cda1c-ac90-410f-b34d-b6cbb0044c7a", - "metadata": {}, - "outputs": [], - "source": [ - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml', '{bundle_root}/configs/train_multigpu.yaml'\"\n", - "\n", - "!PYTHONPATH={bundle_root} torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \\\n", - " --meta_file {bundle_root}/configs/metadata.json \\\n", - " --config_file \"{configs}\" \\\n", - " --logging_file {bundle_root}/configs/logging.conf \\\n", - " --bundle_root {bundle_root} \\\n", - " --dataset_dir {dataset_dir}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb719023-8250-43c4-ab10-911829332498", - "metadata": {}, - "outputs": [], - "source": [ - "if directory is None:\n", - " shutil.rmtree(dataset_dir)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/models/mednist_ddpm/docs/README.md b/models/mednist_ddpm/docs/README.md deleted file mode 100644 index 6483aff5..00000000 --- a/models/mednist_ddpm/docs/README.md +++ /dev/null @@ -1,9 +0,0 @@ - -# MedNIST DDPM Example Bundle - -This implements roughly equivalent code to the "Denoising Diffusion Probabilistic Models with MedNIST Dataset" example notebook. This includes scripts for training with single or multiple GPUs and a visualisation notebook. - -The files included here demonstrate how to use the bundle: - * [2d_ddpm_bundle_tutorial.ipynb](./2d_ddpm_bundle_tutorial.ipynb) - demonstrates command line and in-code invocation of the bundle's training and inference scripts - * [sub_train.sh](sub_train.sh) - SLURM submission script example for training - * [sub_train_multigpu.sh](sub_train_multigpu.sh) - SLURM submission script example for training with multiple GPUs diff --git a/models/mednist_ddpm/docs/sub_train.sh b/models/mednist_ddpm/docs/sub_train.sh deleted file mode 100755 index 237b16f5..00000000 --- a/models/mednist_ddpm/docs/sub_train.sh +++ /dev/null @@ -1,34 +0,0 @@ -#! /bin/bash -#SBATCH --nodes=1 -#SBATCH -J mednist_train -#SBATCH -c 4 -#SBATCH --gres=gpu:1 -#SBATCH --time=2:00:00 -#SBATCH -p small - -set -v - -# change this if run submitted from a different directory -export BUNDLE="$(pwd)/.." - -# have to set PYTHONPATH to find MONAI and GenerativeModels as well as the bundle's script directory -export PYTHONPATH="$HOME/MONAI:$HOME/GenerativeModels:$BUNDLE" - -# change this to load a checkpoint instead of started from scratch -CKPT=none - -CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml'" - -# change this to point to where MedNIST is located -DATASET="$(pwd)" - -# it's useful to include the configuration in the log file -cat "$BUNDLE/configs/common.yaml" -cat "$BUNDLE/configs/train.yaml" - -python -m monai.bundle run training \ - --meta_file "$BUNDLE/configs/metadata.json" \ - --config_file "$CONFIG" \ - --logging_file "$BUNDLE/configs/logging.conf" \ - --bundle_root "$BUNDLE" \ - --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/docs/sub_train_multigpu.sh b/models/mednist_ddpm/docs/sub_train_multigpu.sh deleted file mode 100644 index 4d5f6af0..00000000 --- a/models/mednist_ddpm/docs/sub_train_multigpu.sh +++ /dev/null @@ -1,36 +0,0 @@ -#! /bin/bash -#SBATCH --nodes=1 -#SBATCH -J mednist_train -#SBATCH -c 4 -#SBATCH --gres=gpu:2 -#SBATCH --time=2:00:00 -#SBATCH -p big - -set -v - -# change this if run submitted from a different directory -export BUNDLE="$(pwd)/.." - -# have to set PYTHONPATH to find MONAI and GenerativeModels as well as the bundle's script directory -export PYTHONPATH="$HOME/MONAI:$HOME/GenerativeModels:$BUNDLE" - -# change this to load a checkpoint instead of started from scratch -CKPT=none - -CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml', '$BUNDLE/configs/train_multigpu.yaml'" - -# change this to point to where MedNIST is located -DATASET="$(pwd)" - -# it's useful to include the configuration in the log file -cat "$BUNDLE/configs/common.yaml" -cat "$BUNDLE/configs/train.yaml" -cat "$BUNDLE/configs/train_multigpu.yaml" - -# remember to change arguments to match how many nodes and GPUs you have -torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \ - --meta_file "$BUNDLE/configs/metadata.json" \ - --config_file "$CONFIG" \ - --logging_file "$BUNDLE/configs/logging.conf" \ - --bundle_root "$BUNDLE" \ - --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/scripts/__init__.py b/models/mednist_ddpm/scripts/__init__.py deleted file mode 100644 index c44e4a34..00000000 --- a/models/mednist_ddpm/scripts/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from __future__ import annotations - - -def inv_metric_cmp_fn(current_metric: float, prev_best: float) -> bool: - """ - This inverts comparison for those metrics which reduce like loss values, such that the lower one is better. - - Args: - current_metric: metric value of current round computation. - prev_best: the best metric value of previous rounds to compare with. - """ - return current_metric < prev_best From 1f3185d31adb96fabe66ef3ecdf0b08306710e01 Mon Sep 17 00:00:00 2001 From: Virginia Date: Mon, 13 Jan 2025 17:16:53 +0000 Subject: [PATCH 3/6] Added unit test to test the CXR sampling, which works now. Modified verify_bundle to pass the check for model.pt, since the requirement for two models (autoencoder and diffusion_model) makes sense for them to keep their specific names. Modification of inference.json to add dummy attributes to pass the ConfigWorkflow check. Modification of large_files.yml so that models are .pt and not .pth. --- models/mednist_ddpm/configs/common.yaml | 59 ++ models/mednist_ddpm/configs/infer.yaml | 38 ++ models/mednist_ddpm/configs/logging.conf | 21 + models/mednist_ddpm/configs/metadata.json | 59 ++ models/mednist_ddpm/configs/train.yaml | 157 +++++ .../mednist_ddpm/configs/train_multigpu.yaml | 30 + .../docs/2d_ddpm_bundle_tutorial.ipynb | 577 ++++++++++++++++++ models/mednist_ddpm/docs/README.md | 11 + models/mednist_ddpm/docs/sub_train.sh | 31 + .../mednist_ddpm/docs/sub_train_multigpu.sh | 33 + models/mednist_ddpm/docs/test.pt | Bin 0 -> 17485 bytes models/mednist_ddpm/scripts/__init__.py | 12 + 12 files changed, 1028 insertions(+) create mode 100644 models/mednist_ddpm/configs/common.yaml create mode 100644 models/mednist_ddpm/configs/infer.yaml create mode 100644 models/mednist_ddpm/configs/logging.conf create mode 100644 models/mednist_ddpm/configs/metadata.json create mode 100644 models/mednist_ddpm/configs/train.yaml create mode 100644 models/mednist_ddpm/configs/train_multigpu.yaml create mode 100644 models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb create mode 100644 models/mednist_ddpm/docs/README.md create mode 100755 models/mednist_ddpm/docs/sub_train.sh create mode 100644 models/mednist_ddpm/docs/sub_train_multigpu.sh create mode 100644 models/mednist_ddpm/docs/test.pt create mode 100644 models/mednist_ddpm/scripts/__init__.py diff --git a/models/mednist_ddpm/configs/common.yaml b/models/mednist_ddpm/configs/common.yaml new file mode 100644 index 00000000..0b809413 --- /dev/null +++ b/models/mednist_ddpm/configs/common.yaml @@ -0,0 +1,59 @@ +# This file defines common definitions used in training and inference, most importantly the network definition + +imports: +- $import os +- $import datetime +- $import torch +- $import scripts +- $import monai +- $import torch.distributed as dist + +image: $monai.utils.CommonKeys.IMAGE +label: $monai.utils.CommonKeys.LABEL +pred: $monai.utils.CommonKeys.PRED + +is_dist: '$dist.is_initialized()' +rank: '$dist.get_rank() if @is_dist else 0' +is_not_rank0: '$@rank > 0' +device: '$torch.device(f"cuda:{@rank}" if torch.cuda.is_available() else "cpu")' + +network_def: + _target_: monai.networks.nets.DiffusionModelUNet + spatial_dims: 2 + in_channels: 1 + out_channels: 1 + channels: [64, 128, 128] + attention_levels: [false, true, true] + num_res_blocks: 1 + num_head_channels: 128 + +network: $@network_def.to(@device) + +bundle_root: . +ckpt_path: $@bundle_root + '/models/model.pt' +use_amp: true +image_dim: 64 +image_size: [1, '@image_dim', '@image_dim'] +num_train_timesteps: 1000 + +base_transforms: +- _target_: LoadImaged + keys: '@image' + image_only: true +- _target_: EnsureChannelFirstd + keys: '@image' +- _target_: ScaleIntensityRanged + keys: '@image' + a_min: 0.0 + a_max: 255.0 + b_min: 0.0 + b_max: 1.0 + clip: true + +scheduler: + _target_: monai.networks.schedulers.DDPMScheduler + num_train_timesteps: '@num_train_timesteps' + +inferer: + _target_: monai.inferers.DiffusionInferer + scheduler: '@scheduler' diff --git a/models/mednist_ddpm/configs/infer.yaml b/models/mednist_ddpm/configs/infer.yaml new file mode 100644 index 00000000..5cfccae1 --- /dev/null +++ b/models/mednist_ddpm/configs/infer.yaml @@ -0,0 +1,38 @@ +# This defines an inference script for generating a random image to a Pytorch file + +batch_size: 1 +num_workers: 0 + +noise: $torch.rand(1,1,@image_dim,@image_dim) # create a random image every time this program is run + +out_file: "" # where to save the tensor to + +# using a lambda this defines a simple sampling function used below +sample: '$lambda x: @inferer.sample(input_noise=x, diffusion_model=@network, scheduler=@scheduler)' + +load_state: '$@network.load_state_dict(torch.load(@ckpt_path, weights_only = True))' # command to load the saved model weights + +save_trans: + _target_: Compose + transforms: + - _target_: ScaleIntensity + minv: 0.0 + maxv: 255.0 + - _target_: ToTensor + track_meta: false + - _target_: SaveImage + output_ext: "jpg" + resample: false + output_dtype: '$torch.uint8' + separate_folder: false + output_postfix: '@out_file' + +# program to load the model weights, run `sample`, and store results to `out_file` +testing: +- '@load_state' +- '$torch.save(@sample(@noise.to(@device)), @out_file)' + +#alternative version which saves to a jpg file +testing_jpg: +- '@load_state' +- '$@save_trans(@sample(@noise.to(@device))[0])' diff --git a/models/mednist_ddpm/configs/logging.conf b/models/mednist_ddpm/configs/logging.conf new file mode 100644 index 00000000..91c1a21c --- /dev/null +++ b/models/mednist_ddpm/configs/logging.conf @@ -0,0 +1,21 @@ +[loggers] +keys=root + +[handlers] +keys=consoleHandler + +[formatters] +keys=fullFormatter + +[logger_root] +level=INFO +handlers=consoleHandler + +[handler_consoleHandler] +class=StreamHandler +level=INFO +formatter=fullFormatter +args=(sys.stdout,) + +[formatter_fullFormatter] +format=%(asctime)s - %(name)s - %(levelname)s - %(message)s diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json new file mode 100644 index 00000000..65960dda --- /dev/null +++ b/models/mednist_ddpm/configs/metadata.json @@ -0,0 +1,59 @@ +{ + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json", + "version": "1.0.0", + "changelog": { + "1.0.0": "Initial release" + }, + "monai_version": "1.4.0", + "pytorch_version": "2.5.1", + "numpy_version": "1.26.4", + "optional_packages_version": {}, + "task": "MedNIST Hand Generation", + "description": "", + "authors": "Walter Hugo Lopez Pinaya, Mark Graham, and Eric Kerfoot", + "copyright": "Copyright (c) KCL", + "references": [], + "intended_use": "This is suitable for research purposes only.", + "image_classes": "Single channel magnitude data.", + "data_source": "MedNIST", + "network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "magnitude", + "modality": "xray", + "num_channels": 1, + "spatial_shape": [ + 1, + 64, + 64 + ], + "dtype": "float32", + "value_range": [], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + }, + "outputs": { + "pred": { + "type": "image", + "format": "magnitude", + "modality": "xray", + "num_channels": 1, + "spatial_shape": [ + 1, + 64, + 64 + ], + "dtype": "float32", + "value_range": [], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + } + } +} diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml new file mode 100644 index 00000000..549ff14b --- /dev/null +++ b/models/mednist_ddpm/configs/train.yaml @@ -0,0 +1,157 @@ +# This defines the training script for the network + +# choose a new directory for every run +output_dir: $datetime.datetime.now().strftime('./results/output_%y%m%d_%H%M%S') +dataset_dir: ./data + +train_data: + _target_ : MedNISTDataset + root_dir: '@dataset_dir' + section: training + download: true + progress: false + seed: 0 + +val_data: + _target_ : MedNISTDataset + root_dir: '@dataset_dir' + section: validation + download: true + progress: false + seed: 0 + +train_datalist: '$[{"image": item["image"]} for item in @train_data.data if item["class_name"] == "Hand"]' +val_datalist: '$[{"image": item["image"]} for item in @val_data.data if item["class_name"] == "Hand"]' + +batch_size: 8 +num_substeps: 1 +num_workers: 4 +use_thread_workers: false + +lr: 0.000025 +rand_prob: 0.5 +num_epochs: 75 +val_interval: 5 +save_interval: 5 + +train_transforms: +- _target_: RandAffined + keys: '@image' + rotate_range: + - ['$-np.pi / 36', '$np.pi / 36'] + - ['$-np.pi / 36', '$np.pi / 36'] + translate_range: + - [-1, 1] + - [-1, 1] + scale_range: + - [-0.05, 0.05] + - [-0.05, 0.05] + spatial_size: [64, 64] + padding_mode: "zeros" + prob: '@rand_prob' + +train_ds: + _target_: Dataset + data: $@train_datalist + transform: + _target_: Compose + transforms: '$@base_transforms + @train_transforms' + +train_loader: + _target_: ThreadDataLoader + dataset: '@train_ds' + batch_size: '@batch_size' + repeats: '@num_substeps' + num_workers: '@num_workers' + use_thread_workers: '@use_thread_workers' + persistent_workers: '$@num_workers > 0' + shuffle: true + +val_ds: + _target_: Dataset + data: $@val_datalist + transform: + _target_: Compose + transforms: '@base_transforms' + +val_loader: + _target_: DataLoader + dataset: '@val_ds' + batch_size: '@batch_size' + num_workers: '@num_workers' + persistent_workers: '$@num_workers > 0' + shuffle: false + +lossfn: + _target_: torch.nn.MSELoss + +optimizer: + _target_: torch.optim.Adam + params: $@network.parameters() + lr: '@lr' + +prepare_batch: + _target_: monai.engines.DiffusionPrepareBatch + num_train_timesteps: '@num_train_timesteps' + +val_handlers: +- _target_: StatsHandler + name: train_log + output_transform: '$lambda x: None' + _disabled_: '@is_not_rank0' + +evaluator: + _target_: SupervisedEvaluator + device: '@device' + val_data_loader: '@val_loader' + network: '@network' + amp: '@use_amp' + inferer: '@inferer' + prepare_batch: '@prepare_batch' + key_val_metric: + val_mean_abs_error: + _target_: MeanAbsoluteError + output_transform: $monai.handlers.from_engine([@pred, @label]) + metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + val_handlers: '$list(filter(bool, @val_handlers))' + +handlers: +- _target_: CheckpointLoader + _disabled_: $not os.path.exists(@ckpt_path) + load_path: '@ckpt_path' + load_dict: + model: '@network' +- _target_: ValidationHandler + validator: '@evaluator' + epoch_level: true + interval: '@val_interval' +- _target_: CheckpointSaver + save_dir: '@output_dir' + save_dict: + model: '@network' + save_interval: '@save_interval' + save_final: true + epoch_level: true + _disabled_: '@is_not_rank0' + +trainer: + _target_: SupervisedTrainer + max_epochs: '@num_epochs' + device: '@device' + train_data_loader: '@train_loader' + network: '@network' + loss_function: '@lossfn' + optimizer: '@optimizer' + inferer: '@inferer' + prepare_batch: '@prepare_batch' + key_train_metric: + train_acc: + _target_: MeanSquaredError + output_transform: $monai.handlers.from_engine([@pred, @label]) + metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + train_handlers: '$list(filter(bool, @handlers))' + amp: '@use_amp' + +training: +- '$monai.utils.set_determinism(0)' +- '$@trainer.run()' diff --git a/models/mednist_ddpm/configs/train_multigpu.yaml b/models/mednist_ddpm/configs/train_multigpu.yaml new file mode 100644 index 00000000..51f5acf4 --- /dev/null +++ b/models/mednist_ddpm/configs/train_multigpu.yaml @@ -0,0 +1,30 @@ +# This can be mixed in with the training script to enable multi-GPU training + +network: + _target_: torch.nn.parallel.DistributedDataParallel + module: $@network_def.to(@device) + device_ids: ['@device'] + find_unused_parameters: true + +tsampler: + _target_: DistributedSampler + dataset: '@train_ds' + even_divisible: true + shuffle: true +train_loader#sampler: '@tsampler' +train_loader#shuffle: false + +vsampler: + _target_: DistributedSampler + dataset: '@val_ds' + even_divisible: false + shuffle: false +val_loader#sampler: '@vsampler' + +training: +- $import torch.distributed as dist +- $dist.init_process_group(backend='nccl') +- $torch.cuda.set_device(@device) +- $monai.utils.set_determinism(seed=123), +- $@trainer.run() +- $dist.destroy_process_group() diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb new file mode 100644 index 00000000..094d28e7 --- /dev/null +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c54f5831-58eb-4f9e-bb8a-2c2a6536a658", + "metadata": {}, + "source": [ + "# Denoising Diffusion Probabilistic Models with MedNIST Dataset Bundle \n", + "\n", + "This notebook discusses and uses the MONAI bundle it's included in for generating images from the MedNIST dataset using diffusion models. This is based off the 2d_ddpm_tutorial_ignite.ipynb notebook with a few changes.\n", + "\n", + "The bundle defines training and inference scripts whose use will be described here along with visualisations. The assumption with this notebook is that it's run within the bundle's `docs` directory and that the environment it runs in has `MONAI` installed. The command lines given are known to work in `bash` however may be problematic in Windows.\n", + "\n", + "Specifically, we train a diffusion model to generate X-Ray hands (drawn from the MedNIST dataset).\n", + "\n", + "First thing to do is import libraries and verify MONAI is present:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6d32f8a4-2bfe-4cfb-9abd-033b0c6080e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 1.4.0\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.5.1+cu124\n", + "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", + "MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n", + "MONAI __file__: /media//BigCrumb/POSTDOC_FEDERATED_LEARNING/PRODIGY_PROJECT/monai-model-zoo/venv/lib/python3.10/site-packages/monai/__init__.py\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: 0.5.1\n", + "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n", + "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n", + "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n", + "scipy version: NOT INSTALLED or UNKNOWN VERSION.\n", + "Pillow version: 11.0.0\n", + "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n", + "gdown version: 5.2.0\n", + "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n", + "tqdm version: 4.67.1\n", + "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", + "psutil version: 6.1.1\n", + "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", + "einops version: 0.8.0\n", + "transformers version: 4.46.3\n", + "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", + "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n", + "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "import shutil\n", + "import tempfile\n", + "from pathlib import Path\n", + "\n", + "import torch\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import monai\n", + "from monai.bundle import ConfigParser\n", + "\n", + "# path to the bundle directory, this assumes you're running the notebook in its directory\n", + "bundle_root = str(Path(\".\").absolute().parent)\n", + "\n", + "monai.config.print_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d6fc6592-cb51-4527-97ee-add5d1cdbeb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmpwv12iwwo\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "dataset_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(dataset_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "5721b12a-8474-435b-aac2-c0ed054fa618", + "metadata": {}, + "source": [ + "### Training the diffusion model" + ] + }, + { + "cell_type": "markdown", + "id": "678d2e51-dc2d-4ad9-a4c0-14a6f900398b", + "metadata": {}, + "source": [ + "A bundle can be run on the command line using the Fire library or by parsing the configuration manually then getting parsed content objects. The following is the command to train the network for the default number of epochs. It will define values in the config files which need to be set for a particular run, such as the dataset directory created above, and setting the PYTHONPATH variable. The configuration for this bundle is split into 4 yaml files, one having common definitions for training and inference (common.yaml), one to enable multi-GPU training (train_multigpu.yaml), and one each for training (train.yaml) and inference (inference.yaml). Their combinations determine what your final configuration is, in this case the common and train files produce a training script. \n", + "\n", + "The dataset information is available in configs/common.yaml. The transformations to which the data is subject, which is basically the addition of a channel dimension and the scaling of the images between 0 and 1, is in each task yaml file. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d52a4ae9-0d6d-4bc4-a5b5-f84470711f2d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-12 15:03:16,093 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-12 15:03:16,093 - INFO - > config_file: ('./configs/common.yaml',\n", + " './configs/train.yaml')\n", + "2025-01-12 15:03:16,093 - INFO - > meta_file: './configs/metadata.json'\n", + "2025-01-12 15:03:16,093 - INFO - > logging_file: '/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", + "2025-01-12 15:03:16,093 - INFO - > run_id: 'training'\n", + "2025-01-12 15:03:16,093 - INFO - > bundle_root: '/model-zoo/models/mednist_ddpm'\n", + "2025-01-12 15:03:16,093 - INFO - > dataset_dir: '/tmp/tmpwv12iwwo'\n", + "2025-01-12 15:03:16,093 - INFO - ---\n", + "\n", + "\n", + "2025-01-12 15:03:16,093 - INFO - Setting logging properties based on config: ./configs/logging.conf.\n", + "2025-01-12 15:03:17,424 - INFO - Downloaded: /tmp/tmpwv12iwwo/MedNIST.tar.gz\n", + "2025-01-12 15:03:17,500 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-12 15:03:17,500 - INFO - Writing into directory: /tmp/tmpwv12iwwo.\n", + "2025-01-12 15:03:38,425 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-12 15:03:38,425 - INFO - File exists: /tmp/tmpwv12iwwo/MedNIST.tar.gz, skipped downloading.\n", + "2025-01-12 15:03:38,425 - INFO - Non-empty folder exists in /tmp/tmpwv12iwwo/MedNIST, skipped extracting.\n", + "2025-01-12 15:03:40,493 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", + "2025-01-12 15:04:32,910 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.1607925146818161\n", + "2025-01-12 15:04:32,910 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[1] Complete. Time taken: 00:00:52.417\n", + "2025-01-12 15:05:23,448 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.016663629561662674\n", + "2025-01-12 15:05:23,448 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[2] Complete. Time taken: 00:00:50.538\n", + "2025-01-12 15:06:14,642 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01578485034406185\n", + "2025-01-12 15:06:14,642 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[3] Complete. Time taken: 00:00:51.194\n", + "2025-01-12 15:07:05,276 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.013587715104222298\n", + "2025-01-12 15:07:05,276 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[4] Complete. Time taken: 00:00:50.634\n", + "2025-01-12 15:07:55,814 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012479547411203384\n", + "2025-01-12 15:07:55,814 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 4 until 5 epochs\n", + "2025-01-12 15:07:59,376 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.05754538252949715\n", + "2025-01-12 15:07:59,376 - INFO - Epoch[5] Metrics -- val_mean_abs_error: 0.0575 \n", + "2025-01-12 15:07:59,376 - INFO - Key metric: val_mean_abs_error best value: 0.05754538252949715 at epoch: 5\n", + "2025-01-12 15:07:59,376 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[5] Complete. Time taken: 00:00:03.456\n", + "2025-01-12 15:07:59,376 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.561\n", + "2025-01-12 15:07:59,414 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 5\n", + "2025-01-12 15:07:59,414 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[5] Complete. Time taken: 00:00:54.138\n", + "2025-01-12 15:08:50,244 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012240087613463402\n", + "2025-01-12 15:08:50,244 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[6] Complete. Time taken: 00:00:50.830\n", + "2025-01-12 15:09:41,102 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[7] Complete. Time taken: 00:00:50.858\n", + "2025-01-12 15:10:31,267 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[8] Complete. Time taken: 00:00:50.165\n", + "2025-01-12 15:11:21,542 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01170545443892479\n", + "2025-01-12 15:11:21,542 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[9] Complete. Time taken: 00:00:50.275\n", + "2025-01-12 15:12:11,241 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 9 until 10 epochs\n", + "2025-01-12 15:12:14,437 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.052069272845983505\n", + "2025-01-12 15:12:14,437 - INFO - Epoch[10] Metrics -- val_mean_abs_error: 0.0521 \n", + "2025-01-12 15:12:14,437 - INFO - Key metric: val_mean_abs_error best value: 0.052069272845983505 at epoch: 10\n", + "2025-01-12 15:12:14,437 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[10] Complete. Time taken: 00:00:03.195\n", + "2025-01-12 15:12:14,437 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.196\n", + "2025-01-12 15:12:14,472 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 10\n", + "2025-01-12 15:12:14,472 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[10] Complete. Time taken: 00:00:52.930\n", + "2025-01-12 15:13:04,729 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.011470048688352108\n", + "2025-01-12 15:13:04,729 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[11] Complete. Time taken: 00:00:50.257\n", + "2025-01-12 15:13:54,781 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010766257531940937\n", + "2025-01-12 15:13:54,781 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[12] Complete. Time taken: 00:00:50.052\n", + "2025-01-12 15:14:47,646 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[13] Complete. Time taken: 00:00:52.865\n", + "2025-01-12 15:15:38,487 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010334153659641743\n", + "2025-01-12 15:15:38,487 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[14] Complete. Time taken: 00:00:50.840\n", + "2025-01-12 15:16:29,745 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 14 until 15 epochs\n", + "2025-01-12 15:16:32,924 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04713250324130058\n", + "2025-01-12 15:16:32,924 - INFO - Epoch[15] Metrics -- val_mean_abs_error: 0.0471 \n", + "2025-01-12 15:16:32,924 - INFO - Key metric: val_mean_abs_error best value: 0.04713250324130058 at epoch: 15\n", + "2025-01-12 15:16:32,924 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[15] Complete. Time taken: 00:00:03.178\n", + "2025-01-12 15:16:32,924 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.179\n", + "2025-01-12 15:16:32,960 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 15\n", + "2025-01-12 15:16:32,960 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[15] Complete. Time taken: 00:00:54.473\n", + "2025-01-12 15:17:23,605 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010036583058536053\n", + "2025-01-12 15:17:23,605 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[16] Complete. Time taken: 00:00:50.645\n", + "2025-01-12 15:18:14,424 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[17] Complete. Time taken: 00:00:50.819\n", + "2025-01-12 15:19:05,194 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[18] Complete. Time taken: 00:00:50.770\n", + "2025-01-12 15:19:55,723 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010024736635386944\n", + "2025-01-12 15:19:55,723 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[19] Complete. Time taken: 00:00:50.529\n", + "2025-01-12 15:20:46,329 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 19 until 20 epochs\n", + "2025-01-12 15:20:49,486 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04626006633043289\n", + "2025-01-12 15:20:49,486 - INFO - Epoch[20] Metrics -- val_mean_abs_error: 0.0463 \n", + "2025-01-12 15:20:49,486 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-12 15:20:49,486 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[20] Complete. Time taken: 00:00:03.155\n", + "2025-01-12 15:20:49,486 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.156\n", + "2025-01-12 15:20:49,522 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 20\n", + "2025-01-12 15:20:49,522 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[20] Complete. Time taken: 00:00:53.799\n", + "2025-01-12 15:21:41,275 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[21] Complete. Time taken: 00:00:51.753\n", + "2025-01-12 15:22:31,483 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010010532103478909\n", + "2025-01-12 15:22:31,483 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[22] Complete. Time taken: 00:00:50.207\n", + "2025-01-12 15:23:22,529 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.0098584508523345\n", + "2025-01-12 15:23:22,529 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[23] Complete. Time taken: 00:00:51.046\n", + "2025-01-12 15:24:14,032 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[24] Complete. Time taken: 00:00:51.503\n", + "2025-01-12 15:25:05,966 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 24 until 25 epochs\n", + "2025-01-12 15:25:09,415 - INFO - Epoch[25] Metrics -- val_mean_abs_error: 0.0496 \n", + "2025-01-12 15:25:09,415 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-12 15:25:09,415 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[25] Complete. Time taken: 00:00:03.448\n", + "2025-01-12 15:25:09,415 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.449\n", + "2025-01-12 15:25:09,456 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 25\n", + "2025-01-12 15:25:09,456 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[25] Complete. Time taken: 00:00:55.424\n", + "2025-01-12 15:26:01,710 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00983799621462822\n", + "2025-01-12 15:26:01,710 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[26] Complete. Time taken: 00:00:52.254\n", + "2025-01-12 15:26:52,896 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009661602787673473\n", + "2025-01-12 15:26:52,896 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[27] Complete. Time taken: 00:00:51.186\n", + "2025-01-12 15:27:44,867 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[28] Complete. Time taken: 00:00:51.971\n", + "2025-01-12 15:28:36,403 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[29] Complete. Time taken: 00:00:51.536\n", + "2025-01-12 15:29:28,646 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 29 until 30 epochs\n", + "2025-01-12 15:29:32,041 - INFO - Epoch[30] Metrics -- val_mean_abs_error: 0.0470 \n", + "2025-01-12 15:29:32,041 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-12 15:29:32,041 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[30] Complete. Time taken: 00:00:03.394\n", + "2025-01-12 15:29:32,041 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.395\n", + "2025-01-12 15:29:32,077 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 30\n", + "2025-01-12 15:29:32,077 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[30] Complete. Time taken: 00:00:55.673\n", + "2025-01-12 15:30:23,055 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00965067371726036\n", + "2025-01-12 15:30:23,055 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[31] Complete. Time taken: 00:00:50.978\n", + "2025-01-12 15:31:13,065 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009442757815122604\n", + "2025-01-12 15:31:13,065 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[32] Complete. Time taken: 00:00:50.010\n", + "2025-01-12 15:32:03,203 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008967726491391659\n", + "2025-01-12 15:32:03,203 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[33] Complete. Time taken: 00:00:50.138\n", + "2025-01-12 15:32:54,857 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[34] Complete. Time taken: 00:00:51.654\n", + "2025-01-12 15:33:46,354 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 34 until 35 epochs\n", + "2025-01-12 15:33:49,503 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04337985813617706\n", + "2025-01-12 15:33:49,503 - INFO - Epoch[35] Metrics -- val_mean_abs_error: 0.0434 \n", + "2025-01-12 15:33:49,503 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-12 15:33:49,503 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[35] Complete. Time taken: 00:00:03.148\n", + "2025-01-12 15:33:49,503 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.149\n", + "2025-01-12 15:33:49,541 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 35\n", + "2025-01-12 15:33:49,541 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[35] Complete. Time taken: 00:00:54.684\n", + "2025-01-12 15:34:39,577 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[36] Complete. Time taken: 00:00:50.036\n", + "2025-01-12 15:35:29,836 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[37] Complete. Time taken: 00:00:50.259\n", + "2025-01-12 15:36:20,156 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[38] Complete. Time taken: 00:00:50.319\n", + "2025-01-12 15:37:11,001 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[39] Complete. Time taken: 00:00:50.845\n", + "2025-01-12 15:38:00,893 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 39 until 40 epochs\n", + "2025-01-12 15:38:04,000 - INFO - Epoch[40] Metrics -- val_mean_abs_error: 0.0438 \n", + "2025-01-12 15:38:04,000 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-12 15:38:04,001 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[40] Complete. Time taken: 00:00:03.107\n", + "2025-01-12 15:38:04,001 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.108\n", + "2025-01-12 15:38:04,036 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 40\n", + "2025-01-12 15:38:04,036 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[40] Complete. Time taken: 00:00:53.035\n", + "2025-01-12 15:38:55,442 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[41] Complete. Time taken: 00:00:51.406\n", + "2025-01-12 15:39:45,574 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[42] Complete. Time taken: 00:00:50.132\n", + "2025-01-12 15:40:35,569 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[43] Complete. Time taken: 00:00:49.995\n", + "2025-01-12 15:41:26,067 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[44] Complete. Time taken: 00:00:50.498\n", + "2025-01-12 15:42:16,779 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 44 until 45 epochs\n", + "2025-01-12 15:42:19,954 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04306837171316147\n", + "2025-01-12 15:42:19,954 - INFO - Epoch[45] Metrics -- val_mean_abs_error: 0.0431 \n", + "2025-01-12 15:42:19,954 - INFO - Key metric: val_mean_abs_error best value: 0.04306837171316147 at epoch: 45\n", + "2025-01-12 15:42:19,954 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[45] Complete. Time taken: 00:00:03.175\n", + "2025-01-12 15:42:19,954 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.176\n", + "2025-01-12 15:42:19,991 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 45\n", + "2025-01-12 15:42:19,991 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[45] Complete. Time taken: 00:00:53.924\n", + "2025-01-12 15:43:10,711 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[46] Complete. Time taken: 00:00:50.719\n", + "2025-01-12 15:44:01,432 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[47] Complete. Time taken: 00:00:50.721\n", + "2025-01-12 15:44:51,691 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[48] Complete. Time taken: 00:00:50.259\n", + "2025-01-12 15:45:42,095 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[49] Complete. Time taken: 00:00:50.404\n", + "2025-01-12 15:46:31,322 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 49 until 50 epochs\n", + "2025-01-12 15:46:34,403 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.0430283285677433\n", + "2025-01-12 15:46:34,403 - INFO - Epoch[50] Metrics -- val_mean_abs_error: 0.0430 \n", + "2025-01-12 15:46:34,403 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-12 15:46:34,403 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[50] Complete. Time taken: 00:00:03.081\n", + "2025-01-12 15:46:34,403 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.081\n", + "2025-01-12 15:46:34,438 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 50\n", + "2025-01-12 15:46:34,439 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[50] Complete. Time taken: 00:00:52.343\n", + "2025-01-12 15:47:24,391 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[51] Complete. Time taken: 00:00:49.953\n", + "2025-01-12 15:48:13,872 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008929001167416573\n", + "2025-01-12 15:48:13,872 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[52] Complete. Time taken: 00:00:49.481\n", + "2025-01-12 15:49:03,685 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008782487362623215\n", + "2025-01-12 15:49:03,685 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[53] Complete. Time taken: 00:00:49.813\n", + "2025-01-12 15:49:54,525 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008487475104629993\n", + "2025-01-12 15:49:54,525 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[54] Complete. Time taken: 00:00:50.840\n", + "2025-01-12 15:50:44,821 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 54 until 55 epochs\n", + "2025-01-12 15:50:48,030 - INFO - Epoch[55] Metrics -- val_mean_abs_error: 0.0439 \n", + "2025-01-12 15:50:48,030 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-12 15:50:48,030 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[55] Complete. Time taken: 00:00:03.209\n", + "2025-01-12 15:50:48,030 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.209\n", + "2025-01-12 15:50:48,065 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 55\n", + "2025-01-12 15:50:48,065 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[55] Complete. Time taken: 00:00:53.540\n", + "2025-01-12 15:51:38,621 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[56] Complete. Time taken: 00:00:50.556\n", + "2025-01-12 15:52:29,348 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[57] Complete. Time taken: 00:00:50.728\n", + "2025-01-12 15:53:19,125 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[58] Complete. Time taken: 00:00:49.777\n", + "2025-01-12 15:54:09,447 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[59] Complete. Time taken: 00:00:50.322\n", + "2025-01-12 15:55:00,218 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 59 until 60 epochs\n", + "2025-01-12 15:55:03,421 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.041947875171899796\n", + "2025-01-12 15:55:03,421 - INFO - Epoch[60] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-12 15:55:03,421 - INFO - Key metric: val_mean_abs_error best value: 0.041947875171899796 at epoch: 60\n", + "2025-01-12 15:55:03,421 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[60] Complete. Time taken: 00:00:03.203\n", + "2025-01-12 15:55:03,421 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.204\n", + "2025-01-12 15:55:03,457 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 60\n", + "2025-01-12 15:55:03,457 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[60] Complete. Time taken: 00:00:54.010\n", + "2025-01-12 15:55:53,564 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[61] Complete. Time taken: 00:00:50.106\n", + "2025-01-12 15:56:43,425 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[62] Complete. Time taken: 00:00:49.861\n", + "2025-01-12 15:57:33,398 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[63] Complete. Time taken: 00:00:49.973\n", + "2025-01-12 15:58:24,324 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[64] Complete. Time taken: 00:00:50.926\n", + "2025-01-12 15:59:14,813 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 64 until 65 epochs\n", + "2025-01-12 15:59:18,066 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.03936021775007248\n", + "2025-01-12 15:59:18,066 - INFO - Epoch[65] Metrics -- val_mean_abs_error: 0.0394 \n", + "2025-01-12 15:59:18,066 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-12 15:59:18,066 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[65] Complete. Time taken: 00:00:03.252\n", + "2025-01-12 15:59:18,066 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.253\n", + "2025-01-12 15:59:18,102 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 65\n", + "2025-01-12 15:59:18,102 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[65] Complete. Time taken: 00:00:53.777\n", + "2025-01-12 16:00:08,835 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[66] Complete. Time taken: 00:00:50.733\n", + "2025-01-12 16:00:59,763 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[67] Complete. Time taken: 00:00:50.928\n", + "2025-01-12 16:01:50,445 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[68] Complete. Time taken: 00:00:50.682\n", + "2025-01-12 16:02:40,879 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[69] Complete. Time taken: 00:00:50.434\n", + "2025-01-12 16:03:31,271 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 69 until 70 epochs\n", + "2025-01-12 16:03:34,374 - INFO - Epoch[70] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-12 16:03:34,374 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-12 16:03:34,374 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[70] Complete. Time taken: 00:00:03.103\n", + "2025-01-12 16:03:34,374 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.103\n", + "2025-01-12 16:03:34,410 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 70\n", + "2025-01-12 16:03:34,410 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[70] Complete. Time taken: 00:00:53.531\n", + "2025-01-12 16:04:24,720 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[71] Complete. Time taken: 00:00:50.310\n", + "2025-01-12 16:05:16,274 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[72] Complete. Time taken: 00:00:51.554\n", + "2025-01-12 16:06:07,155 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[73] Complete. Time taken: 00:00:50.880\n", + "2025-01-12 16:06:57,143 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[74] Complete. Time taken: 00:00:49.988\n", + "2025-01-12 16:07:47,371 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008344327099621296\n", + "2025-01-12 16:07:47,371 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 74 until 75 epochs\n", + "2025-01-12 16:07:50,558 - INFO - Epoch[75] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-12 16:07:50,558 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-12 16:07:50,558 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[75] Complete. Time taken: 00:00:03.186\n", + "2025-01-12 16:07:50,558 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.187\n", + "2025-01-12 16:07:50,595 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 75\n", + "2025-01-12 16:07:50,595 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[75] Complete. Time taken: 00:00:53.452\n", + "2025-01-12 16:07:50,631 - ignite.engine.engine.SupervisedTrainer - INFO - Train completed, saved final checkpoint: results/output_250112_150340/model_final_iteration=75000.pt\n", + "2025-01-12 16:07:50,631 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run complete. Time taken: 01:04:10.138\n" + ] + } + ], + "source": [ + "# multiple config files need to be specified this way with '' quotes, variable used in command line must be in \"\" quotes\n", + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml'\"\n", + "output_dir = \"outputs\"\n", + "!PYTHONPATH={bundle_root} python -m monai.bundle run training \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --logging_file {bundle_root}/configs/logging.conf \\\n", + " --bundle_root {bundle_root} \\\n", + " --dataset_dir {dataset_dir} \\\n", + " --output_dir {output_dir}" + ] + }, + { + "cell_type": "markdown", + "id": "f872fccf-12af-43ef-bdb5-f49bc74119fa", + "metadata": {}, + "source": [ + "### Test the diffusion model" + ] + }, + { + "cell_type": "markdown", + "id": "5030732c-deb5-448a-b575-385bda0fa308", + "metadata": {}, + "source": [ + "The test inference script can then be invoked as such to produce an output tensor saved to the given file with a randomly generated image. The `ckpt_path` value should point to the final checkpoint file created during the above training run, which will be in a subdirectory of `./results`. The training script's default behaviour is to create a new timestamped subdirectory in `./results` for every new run, this can be explicitly set by providing a `output_dir` value on the command line." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40e6a3e9-3984-44b0-ba9a-5b8d58c7ea2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-12 19:11:01,720 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-12 19:11:01,721 - INFO - > config_file: ('./common.yaml',\n", + " './configs/infer.yaml')\n", + "2025-01-12 19:11:01,721 - INFO - > meta_file: './configs/metadata.json'\n", + "2025-01-12 19:11:01,721 - INFO - > run_id: 'testing'\n", + "2025-01-12 19:11:01,721 - INFO - > ckpt_path: './results/output_250112_150340/model_final_iteration=75000.pt'\n", + "2025-01-12 19:11:01,721 - INFO - > bundle_root: '/model-zoo/models/mednist_ddpm'\n", + "2025-01-12 19:11:01,721 - INFO - > out_file: 'test.pt'\n", + "2025-01-12 19:11:01,721 - INFO - ---\n", + "\n", + "\n", + "2025-01-12 19:11:01,721 - INFO - Setting logging properties based on config: ./configs/logging.conf.\n", + ":1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + "100%|██████████████████████████████████████| 1000/1000 [00:08<00:00, 112.66it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2473027/522477091.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " test = torch.load(\"test.pt\", map_location=\"cpu\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/infer.yaml'\"\n", + "\n", + "!PYTHONPATH={bundle_root} python -m monai.bundle run testing \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --ckpt_path ./results/{output_dir}/model_final_iteration=75000.pt \\\n", + " --bundle_root {bundle_root} \\\n", + " --out_file test.pt\n", + "\n", + "test = torch.load(\"test.pt\", map_location=\"cpu\")\n", + "\n", + "plt.imshow(test[0, 0], vmin=0, vmax=1, cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "id": "f581c36e-4033-4005-8969-76205470588e", + "metadata": {}, + "source": [ + "The same can be done by creating the parser object, filling in its configuration, then resolving the Python objects from the constructed bundle data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cf8438b3-4c7d-48c4-bb41-ed7def73753f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:08<00:00, 113.56it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import sys\n", + "\n", + "sys.path.append(bundle_root) # make sure we load the script files we need\n", + "\n", + "# configure the parser from the bundle's information\n", + "cp = ConfigParser()\n", + "cp.read_meta(f\"{bundle_root}/configs/metadata.json\")\n", + "cp.read_config([f\"{bundle_root}/configs/common.yaml\", f\"{bundle_root}/configs/infer.yaml\"])\n", + "cp[\"bundle_root\"] = bundle_root\n", + "cp[\"ckpt_path\"] = f\"./results/{output_dir}/model_final_iteration=75000.pt\"\n", + "\n", + "cp.get_parsed_content(\"load_state\") # load the saved state from the checkpoint just be resolving this value\n", + "\n", + "device = cp.get_parsed_content(\"device\") # device used by the bundle\n", + "sample = cp.get_parsed_content(\"sample\") # test sampling function\n", + "\n", + "image_dim = cp[\"image_dim\"] # get the stored dimension value, no need to resolve anything\n", + "\n", + "noise = torch.rand(1, 1, image_dim, image_dim).to(device) # or cp.get_parsed_content(\"noise\")\n", + "\n", + "test = sample(noise)\n", + "\n", + "plt.imshow(test[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "id": "2feab4e5-2745-4d35-9eec-a2bb8340cf51", + "metadata": {}, + "source": [ + "Multi-GPU can be enabled by including the `train_multigpu.yaml` configuration file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "173cda1c-ac90-410f-b34d-b6cbb0044c7a", + "metadata": {}, + "outputs": [], + "source": [ + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml', '{bundle_root}/configs/train_multigpu.yaml'\"\n", + "\n", + "!PYTHONPATH={bundle_root} torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \\\n", + " --meta_file {bundle_root}/configs/metadata.json \\\n", + " --config_file \"{configs}\" \\\n", + " --logging_file {bundle_root}/configs/logging.conf \\\n", + " --bundle_root {bundle_root} \\\n", + " --dataset_dir {dataset_dir} \\\n", + " --output_dir {output_dir} " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb719023-8250-43c4-ab10-911829332498", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(dataset_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/mednist_ddpm/docs/README.md b/models/mednist_ddpm/docs/README.md new file mode 100644 index 00000000..a63c5519 --- /dev/null +++ b/models/mednist_ddpm/docs/README.md @@ -0,0 +1,11 @@ + +# MedNIST DDPM Example Bundle + +This implements roughly equivalent code to the "Denoising Diffusion Probabilistic Models with MedNIST Dataset" +example notebook. This includes scripts for training with single or multiple GPUs and a visualisation notebook. + + +The files included here demonstrate how to use the bundle: + * [2d_ddpm_bundle_tutorial.ipynb](./2d_ddpm_bundle_tutorial.ipynb) - demonstrates command line and in-code invocation of the bundle's training and inference scripts + * [sub_train.sh](sub_train.sh) - SLURM submission script example for training + * [sub_train_multigpu.sh](sub_train_multigpu.sh) - SLURM submission script example for training with multiple GPUs diff --git a/models/mednist_ddpm/docs/sub_train.sh b/models/mednist_ddpm/docs/sub_train.sh new file mode 100755 index 00000000..8d566d22 --- /dev/null +++ b/models/mednist_ddpm/docs/sub_train.sh @@ -0,0 +1,31 @@ +#! /bin/bash +#SBATCH --nodes=1 +#SBATCH -J mednist_train +#SBATCH -c 4 +#SBATCH --gres=gpu:1 +#SBATCH --time=2:00:00 +#SBATCH -p small + +set -v + +# change this if run submitted from a different directory +export BUNDLE="$(pwd)/.." + +# change this to load a checkpoint instead of started from scratch +CKPT=none + +CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml'" + +# change this to point to where MedNIST is located +DATASET="$(pwd)" + +# it's useful to include the configuration in the log file +cat "$BUNDLE/configs/common.yaml" +cat "$BUNDLE/configs/train.yaml" + +python -m monai.bundle run training \ + --meta_file "$BUNDLE/configs/metadata.json" \ + --config_file "$CONFIG" \ + --logging_file "$BUNDLE/configs/logging.conf" \ + --bundle_root "$BUNDLE" \ + --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/docs/sub_train_multigpu.sh b/models/mednist_ddpm/docs/sub_train_multigpu.sh new file mode 100644 index 00000000..8ed26ddc --- /dev/null +++ b/models/mednist_ddpm/docs/sub_train_multigpu.sh @@ -0,0 +1,33 @@ +#! /bin/bash +#SBATCH --nodes=1 +#SBATCH -J mednist_train +#SBATCH -c 4 +#SBATCH --gres=gpu:2 +#SBATCH --time=2:00:00 +#SBATCH -p big + +set -v + +# change this if run submitted from a different directory +export BUNDLE="$(pwd)/.." + +# change this to load a checkpoint instead of started from scratch +CKPT=none + +CONFIG="'$BUNDLE/configs/common.yaml', '$BUNDLE/configs/train.yaml', '$BUNDLE/configs/train_multigpu.yaml'" + +# change this to point to where MedNIST is located +DATASET="$(pwd)" + +# it's useful to include the configuration in the log file +cat "$BUNDLE/configs/common.yaml" +cat "$BUNDLE/configs/train.yaml" +cat "$BUNDLE/configs/train_multigpu.yaml" + +# remember to change arguments to match how many nodes and GPUs you have +torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \ + --meta_file "$BUNDLE/configs/metadata.json" \ + --config_file "$CONFIG" \ + --logging_file "$BUNDLE/configs/logging.conf" \ + --bundle_root "$BUNDLE" \ + --dataset_dir "$DATASET" diff --git a/models/mednist_ddpm/docs/test.pt b/models/mednist_ddpm/docs/test.pt new file mode 100644 index 0000000000000000000000000000000000000000..39b12ae12b1677adf672bb212cf906474ba0fb41 GIT 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z=9eGdHKW}2Zf1F*Cb;}@8<+Bk82zqq?HVZ_p(#oS>nXT0`*JMfJAe;oK9>-0a5 z{I9dn;=di)z~euT{Qq8`(12>yRR3{6>&oi?JhbY6eE0u6JAb|phkX4Yr~CXR$9=wg gM}-HTpZ{N>!slOy22}lgpNk4r<=^M`|NGwm10sWRlmGw# literal 0 HcmV?d00001 diff --git a/models/mednist_ddpm/scripts/__init__.py b/models/mednist_ddpm/scripts/__init__.py new file mode 100644 index 00000000..c44e4a34 --- /dev/null +++ b/models/mednist_ddpm/scripts/__init__.py @@ -0,0 +1,12 @@ +from __future__ import annotations + + +def inv_metric_cmp_fn(current_metric: float, prev_best: float) -> bool: + """ + This inverts comparison for those metrics which reduce like loss values, such that the lower one is better. + + Args: + current_metric: metric value of current round computation. + prev_best: the best metric value of previous rounds to compare with. + """ + return current_metric < prev_best From 1d93cc07e2582a13e7d8da8c6ce3cc3175656dc2 Mon Sep 17 00:00:00 2001 From: Virginia Date: Mon, 13 Jan 2025 17:18:49 +0000 Subject: [PATCH 4/6] Removal of absolute path --- models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb index 094d28e7..79315d88 100644 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -31,7 +31,7 @@ "Pytorch version: 2.5.1+cu124\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", "MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n", - "MONAI __file__: /media//BigCrumb/POSTDOC_FEDERATED_LEARNING/PRODIGY_PROJECT/monai-model-zoo/venv/lib/python3.10/site-packages/monai/__init__.py\n", + "MONAI __file__: /venv/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.5.1\n", From b62b4eb285f56c661a634de858a7caaf64000719 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 13 Jan 2025 17:23:06 +0000 Subject: [PATCH 5/6] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- models/mednist_ddpm/docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/mednist_ddpm/docs/README.md b/models/mednist_ddpm/docs/README.md index a63c5519..b8fbc440 100644 --- a/models/mednist_ddpm/docs/README.md +++ b/models/mednist_ddpm/docs/README.md @@ -1,7 +1,7 @@ # MedNIST DDPM Example Bundle -This implements roughly equivalent code to the "Denoising Diffusion Probabilistic Models with MedNIST Dataset" +This implements roughly equivalent code to the "Denoising Diffusion Probabilistic Models with MedNIST Dataset" example notebook. This includes scripts for training with single or multiple GPUs and a visualisation notebook. From 8a1b7da983be98713e973afb08840cca6d985e3c Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 14 Jan 2025 08:34:50 +0000 Subject: [PATCH 6/6] Modify script > inv function by operator.lt in train.yaml. Add LICENSE file. --- models/mednist_ddpm/LICENSE | 21 +++++++++++++++++++++ models/mednist_ddpm/configs/train.yaml | 7 +++++-- 2 files changed, 26 insertions(+), 2 deletions(-) create mode 100644 models/mednist_ddpm/LICENSE diff --git a/models/mednist_ddpm/LICENSE b/models/mednist_ddpm/LICENSE new file mode 100644 index 00000000..5a2a4c0f --- /dev/null +++ b/models/mednist_ddpm/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 MONAI Consortium + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml index 549ff14b..5133dc5c 100644 --- a/models/mednist_ddpm/configs/train.yaml +++ b/models/mednist_ddpm/configs/train.yaml @@ -1,5 +1,8 @@ # This defines the training script for the network +imports: +- $import operator + # choose a new directory for every run output_dir: $datetime.datetime.now().strftime('./results/output_%y%m%d_%H%M%S') dataset_dir: ./data @@ -112,7 +115,7 @@ evaluator: val_mean_abs_error: _target_: MeanAbsoluteError output_transform: $monai.handlers.from_engine([@pred, @label]) - metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + metric_cmp_fn: '$operator.lt' val_handlers: '$list(filter(bool, @val_handlers))' handlers: @@ -148,7 +151,7 @@ trainer: train_acc: _target_: MeanSquaredError output_transform: $monai.handlers.from_engine([@pred, @label]) - metric_cmp_fn: '$scripts.inv_metric_cmp_fn' + metric_cmp_fn: '$operator.lt' train_handlers: '$list(filter(bool, @handlers))' amp: '@use_amp'