From b856c1500adcfe8b3bdeac2fe2906fc2928ad735 Mon Sep 17 00:00:00 2001 From: virginia Date: Sun, 12 Jan 2025 14:39:47 +0000 Subject: [PATCH 01/14] 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 02/14] 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 03/14] 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 04/14] 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 05/14] [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 06/14] 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' From 037642aba85d69fb772c08f5f6a057c4c189d923 Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 21 Jan 2025 16:42:45 +0000 Subject: [PATCH 07/14] Remove unecessary imports from test. --- models/mednist_ddpm/configs/common.yaml | 2 +- models/mednist_ddpm/configs/train.yaml | 3 - .../docs/2d_ddpm_bundle_tutorial.ipynb | 532 +++++++++--------- models/mednist_ddpm/large_files.yml | 5 + 4 files changed, 278 insertions(+), 264 deletions(-) create mode 100644 models/mednist_ddpm/large_files.yml diff --git a/models/mednist_ddpm/configs/common.yaml b/models/mednist_ddpm/configs/common.yaml index 0b809413..72c50443 100644 --- a/models/mednist_ddpm/configs/common.yaml +++ b/models/mednist_ddpm/configs/common.yaml @@ -7,6 +7,7 @@ imports: - $import scripts - $import monai - $import torch.distributed as dist +- $import operator image: $monai.utils.CommonKeys.IMAGE label: $monai.utils.CommonKeys.LABEL @@ -28,7 +29,6 @@ network_def: num_head_channels: 128 network: $@network_def.to(@device) - bundle_root: . ckpt_path: $@bundle_root + '/models/model.pt' use_amp: true diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml index 5133dc5c..6376f686 100644 --- a/models/mednist_ddpm/configs/train.yaml +++ b/models/mednist_ddpm/configs/train.yaml @@ -1,8 +1,5 @@ # 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 diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb index 79315d88..9cf1dee7 100644 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -31,24 +31,24 @@ "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__: /venv/lib/python3.10/site-packages/monai/__init__.py\n", + "MONAI __file__: /data/PycharmProjects/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", + "ITK version: 5.4.0\n", + "Nibabel version: 5.3.2\n", + "scikit-image version: 0.25.0\n", + "scipy version: 1.15.1\n", + "Pillow version: 11.1.0\n", + "Tensorboard version: 2.18.0\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", + "pandas version: 2.2.3\n", "einops version: 0.8.0\n", - "transformers version: 4.46.3\n", + "transformers version: 4.48.0\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", @@ -87,7 +87,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmpwv12iwwo\n" + "/tmp/tmpbsgu3aor\n" ] } ], @@ -97,6 +97,16 @@ "print(dataset_dir)" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2dd40597-1073-4b16-b83b-a20dc7510fb2", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_dir = \"/tmp/tmpbsgu3aor\"" + ] + }, { "cell_type": "markdown", "id": "5721b12a-8474-435b-aac2-c0ed054fa618", @@ -117,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "d52a4ae9-0d6d-4bc4-a5b5-f84470711f2d", "metadata": { "scrolled": true @@ -127,238 +137,242 @@ "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", + "2025-01-21 15:17:00,312 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-21 15:17:00,312 - INFO - > config_file: ('/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", + " '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/train.yaml')\n", + "2025-01-21 15:17:00,312 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", + "2025-01-21 15:17:00,312 - INFO - > logging_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", + "2025-01-21 15:17:00,312 - INFO - > run_id: 'training'\n", + "2025-01-21 15:17:00,312 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", + "2025-01-21 15:17:00,312 - INFO - > dataset_dir: '/tmp/tmpbsgu3aor'\n", + "2025-01-21 15:17:00,312 - INFO - > output_dir: './outputs'\n", + "2025-01-21 15:17:00,312 - 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" + "2025-01-21 15:17:00,313 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", + "Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.\n", + "2025-01-21 15:17:00,441 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-21 15:17:00,441 - INFO - File exists: /tmp/tmpbsgu3aor/MedNIST.tar.gz, skipped downloading.\n", + "2025-01-21 15:17:00,441 - INFO - Non-empty folder exists in /tmp/tmpbsgu3aor/MedNIST, skipped extracting.\n", + "2025-01-21 15:17:16,082 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-21 15:17:16,082 - INFO - File exists: /tmp/tmpbsgu3aor/MedNIST.tar.gz, skipped downloading.\n", + "2025-01-21 15:17:16,082 - INFO - Non-empty folder exists in /tmp/tmpbsgu3aor/MedNIST, skipped extracting.\n", + "`torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + "2025-01-21 15:17:18,185 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", + "`torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + "2025-01-21 15:18:08,905 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.1607925146818161\n", + "2025-01-21 15:18:08,905 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[1] Complete. Time taken: 00:00:50.720\n", + "2025-01-21 15:18:58,567 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.016663629561662674\n", + "2025-01-21 15:18:58,567 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[2] Complete. Time taken: 00:00:49.662\n", + "2025-01-21 15:19:48,316 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01578485034406185\n", + "2025-01-21 15:19:48,316 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[3] Complete. Time taken: 00:00:49.749\n", + "2025-01-21 15:20:37,997 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.013587715104222298\n", + "2025-01-21 15:20:37,997 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[4] Complete. Time taken: 00:00:49.681\n", + "2025-01-21 15:21:28,552 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012479547411203384\n", + "2025-01-21 15:21:28,553 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 4 until 5 epochs\n", + "`torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.05754538252949715\n", + "2025-01-21 15:21:31,907 - INFO - Epoch[5] Metrics -- val_mean_abs_error: 0.0575 \n", + "2025-01-21 15:21:31,907 - INFO - Key metric: val_mean_abs_error best value: 0.05754538252949715 at epoch: 5\n", + "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[5] Complete. Time taken: 00:00:03.207\n", + "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.355\n", + "2025-01-21 15:21:31,980 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 5\n", + "2025-01-21 15:21:31,980 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[5] Complete. Time taken: 00:00:53.983\n", + "2025-01-21 15:22:21,218 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012240087613463402\n", + "2025-01-21 15:22:21,219 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[6] Complete. Time taken: 00:00:49.239\n", + "2025-01-21 15:23:10,782 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[7] Complete. Time taken: 00:00:49.563\n", + "2025-01-21 15:24:00,732 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[8] Complete. Time taken: 00:00:49.950\n", + "2025-01-21 15:24:50,887 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01170545443892479\n", + "2025-01-21 15:24:50,887 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[9] Complete. Time taken: 00:00:50.155\n", + "2025-01-21 15:25:40,531 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 9 until 10 epochs\n", + "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.052069272845983505\n", + "2025-01-21 15:25:43,603 - INFO - Epoch[10] Metrics -- val_mean_abs_error: 0.0521 \n", + "2025-01-21 15:25:43,603 - INFO - Key metric: val_mean_abs_error best value: 0.052069272845983505 at epoch: 10\n", + "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[10] Complete. Time taken: 00:00:03.072\n", + "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.072\n", + "2025-01-21 15:25:43,960 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 10\n", + "2025-01-21 15:25:43,961 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[10] Complete. Time taken: 00:00:53.074\n", + "2025-01-21 15:26:34,387 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.011470048688352108\n", + "2025-01-21 15:26:34,387 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[11] Complete. Time taken: 00:00:50.426\n", + "2025-01-21 15:27:24,763 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010766257531940937\n", + "2025-01-21 15:27:24,763 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[12] Complete. Time taken: 00:00:50.377\n", + "2025-01-21 15:28:15,715 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[13] Complete. Time taken: 00:00:50.951\n", + "2025-01-21 15:29:05,690 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010334153659641743\n", + "2025-01-21 15:29:05,690 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[14] Complete. Time taken: 00:00:49.975\n", + "2025-01-21 15:29:55,818 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 14 until 15 epochs\n", + "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04713250324130058\n", + "2025-01-21 15:29:58,960 - INFO - Epoch[15] Metrics -- val_mean_abs_error: 0.0471 \n", + "2025-01-21 15:29:58,960 - INFO - Key metric: val_mean_abs_error best value: 0.04713250324130058 at epoch: 15\n", + "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[15] Complete. Time taken: 00:00:03.142\n", + "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.142\n", + "2025-01-21 15:29:59,330 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 15\n", + "2025-01-21 15:29:59,330 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[15] Complete. Time taken: 00:00:53.640\n", + "2025-01-21 15:30:49,286 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010036583058536053\n", + "2025-01-21 15:30:49,286 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[16] Complete. Time taken: 00:00:49.956\n", + "2025-01-21 15:31:38,546 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[17] Complete. Time taken: 00:00:49.260\n", + "2025-01-21 15:32:29,131 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[18] Complete. Time taken: 00:00:50.584\n", + "2025-01-21 15:33:19,252 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010024736635386944\n", + "2025-01-21 15:33:19,253 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[19] Complete. Time taken: 00:00:50.122\n", + "2025-01-21 15:34:09,452 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 19 until 20 epochs\n", + "2025-01-21 15:34:12,633 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04626006633043289\n", + "2025-01-21 15:34:12,633 - INFO - Epoch[20] Metrics -- val_mean_abs_error: 0.0463 \n", + "2025-01-21 15:34:12,633 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-21 15:34:12,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[20] Complete. Time taken: 00:00:03.181\n", + "2025-01-21 15:34:12,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.181\n", + "2025-01-21 15:34:13,009 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 20\n", + "2025-01-21 15:34:13,010 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[20] Complete. Time taken: 00:00:53.757\n", + "2025-01-21 15:35:02,975 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[21] Complete. Time taken: 00:00:49.965\n", + "2025-01-21 15:35:52,881 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010010532103478909\n", + "2025-01-21 15:35:52,881 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[22] Complete. Time taken: 00:00:49.906\n", + "2025-01-21 15:36:42,704 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.0098584508523345\n", + "2025-01-21 15:36:42,704 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[23] Complete. Time taken: 00:00:49.823\n", + "2025-01-21 15:37:32,855 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[24] Complete. Time taken: 00:00:50.151\n", + "2025-01-21 15:38:22,864 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 24 until 25 epochs\n", + "2025-01-21 15:38:26,081 - INFO - Epoch[25] Metrics -- val_mean_abs_error: 0.0496 \n", + "2025-01-21 15:38:26,081 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-21 15:38:26,081 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[25] Complete. Time taken: 00:00:03.217\n", + "2025-01-21 15:38:26,081 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.217\n", + "2025-01-21 15:38:26,449 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 25\n", + "2025-01-21 15:38:26,449 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[25] Complete. Time taken: 00:00:53.594\n", + "2025-01-21 15:39:16,354 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00983799621462822\n", + "2025-01-21 15:39:16,354 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[26] Complete. Time taken: 00:00:49.905\n", + "2025-01-21 15:40:06,370 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009661602787673473\n", + "2025-01-21 15:40:06,370 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[27] Complete. Time taken: 00:00:50.016\n", + "2025-01-21 15:40:56,154 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[28] Complete. Time taken: 00:00:49.784\n", + "2025-01-21 15:41:46,556 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[29] Complete. Time taken: 00:00:50.402\n", + "2025-01-21 15:42:36,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 29 until 30 epochs\n", + "2025-01-21 15:42:39,699 - INFO - Epoch[30] Metrics -- val_mean_abs_error: 0.0470 \n", + "2025-01-21 15:42:39,699 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-21 15:42:39,699 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[30] Complete. Time taken: 00:00:03.064\n", + "2025-01-21 15:42:39,699 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.064\n", + "2025-01-21 15:42:40,067 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 30\n", + "2025-01-21 15:42:40,067 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[30] Complete. Time taken: 00:00:53.511\n", + "2025-01-21 15:43:30,284 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00965067371726036\n", + "2025-01-21 15:43:30,284 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[31] Complete. Time taken: 00:00:50.217\n", + "2025-01-21 15:44:20,682 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009442757815122604\n", + "2025-01-21 15:44:20,682 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[32] Complete. Time taken: 00:00:50.397\n", + "2025-01-21 15:45:09,972 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008967726491391659\n", + "2025-01-21 15:45:09,972 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[33] Complete. Time taken: 00:00:49.290\n", + "2025-01-21 15:46:00,287 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[34] Complete. Time taken: 00:00:50.315\n", + "2025-01-21 15:46:50,509 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 34 until 35 epochs\n", + "2025-01-21 15:46:53,651 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04337985813617706\n", + "2025-01-21 15:46:53,651 - INFO - Epoch[35] Metrics -- val_mean_abs_error: 0.0434 \n", + "2025-01-21 15:46:53,651 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-21 15:46:53,651 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[35] Complete. Time taken: 00:00:03.141\n", + "2025-01-21 15:46:53,652 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.142\n", + "2025-01-21 15:46:54,014 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 35\n", + "2025-01-21 15:46:54,015 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[35] Complete. Time taken: 00:00:53.728\n", + "2025-01-21 15:47:43,700 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[36] Complete. Time taken: 00:00:49.685\n", + "2025-01-21 15:48:33,567 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[37] Complete. Time taken: 00:00:49.867\n", + "2025-01-21 15:49:24,261 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[38] Complete. Time taken: 00:00:50.694\n", + "2025-01-21 15:50:14,440 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[39] Complete. Time taken: 00:00:50.178\n", + "2025-01-21 15:51:04,645 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 39 until 40 epochs\n", + "2025-01-21 15:51:07,744 - INFO - Epoch[40] Metrics -- val_mean_abs_error: 0.0438 \n", + "2025-01-21 15:51:07,744 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-21 15:51:07,744 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[40] Complete. Time taken: 00:00:03.098\n", + "2025-01-21 15:51:07,744 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.099\n", + "2025-01-21 15:51:08,102 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 40\n", + "2025-01-21 15:51:08,102 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[40] Complete. Time taken: 00:00:53.662\n", + "2025-01-21 15:51:58,081 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[41] Complete. Time taken: 00:00:49.979\n", + "2025-01-21 15:52:47,658 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[42] Complete. Time taken: 00:00:49.577\n", + "2025-01-21 15:53:37,196 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[43] Complete. Time taken: 00:00:49.538\n", + "2025-01-21 15:54:27,401 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[44] Complete. Time taken: 00:00:50.205\n", + "2025-01-21 15:55:17,782 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 44 until 45 epochs\n", + "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04306837171316147\n", + "2025-01-21 15:55:20,968 - INFO - Epoch[45] Metrics -- val_mean_abs_error: 0.0431 \n", + "2025-01-21 15:55:20,968 - INFO - Key metric: val_mean_abs_error best value: 0.04306837171316147 at epoch: 45\n", + "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[45] Complete. Time taken: 00:00:03.185\n", + "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.186\n", + "2025-01-21 15:55:21,331 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 45\n", + "2025-01-21 15:55:21,332 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[45] Complete. Time taken: 00:00:53.931\n", + "2025-01-21 15:56:11,303 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[46] Complete. Time taken: 00:00:49.971\n", + "2025-01-21 15:57:01,472 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[47] Complete. Time taken: 00:00:50.170\n", + "2025-01-21 15:57:51,853 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[48] Complete. Time taken: 00:00:50.380\n", + "2025-01-21 15:58:41,820 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[49] Complete. Time taken: 00:00:49.968\n", + "2025-01-21 15:59:30,957 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 49 until 50 epochs\n", + "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.0430283285677433\n", + "2025-01-21 15:59:34,146 - INFO - Epoch[50] Metrics -- val_mean_abs_error: 0.0430 \n", + "2025-01-21 15:59:34,146 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[50] Complete. Time taken: 00:00:03.188\n", + "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.189\n", + "2025-01-21 15:59:34,509 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 50\n", + "2025-01-21 15:59:34,510 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[50] Complete. Time taken: 00:00:52.689\n", + "2025-01-21 16:00:23,913 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[51] Complete. Time taken: 00:00:49.403\n", + "2025-01-21 16:01:13,649 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008929001167416573\n", + "2025-01-21 16:01:13,649 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[52] Complete. Time taken: 00:00:49.736\n", + "2025-01-21 16:02:03,775 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008782487362623215\n", + "2025-01-21 16:02:03,775 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[53] Complete. Time taken: 00:00:50.125\n", + "2025-01-21 16:02:53,273 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008487475104629993\n", + "2025-01-21 16:02:53,273 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[54] Complete. Time taken: 00:00:49.498\n", + "2025-01-21 16:03:43,201 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 54 until 55 epochs\n", + "2025-01-21 16:03:46,321 - INFO - Epoch[55] Metrics -- val_mean_abs_error: 0.0439 \n", + "2025-01-21 16:03:46,322 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-21 16:03:46,322 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[55] Complete. Time taken: 00:00:03.119\n", + "2025-01-21 16:03:46,322 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.120\n", + "2025-01-21 16:03:46,686 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 55\n", + "2025-01-21 16:03:46,687 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[55] Complete. Time taken: 00:00:53.414\n", + "2025-01-21 16:04:36,199 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[56] Complete. Time taken: 00:00:49.513\n", + "2025-01-21 16:05:25,939 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[57] Complete. Time taken: 00:00:49.740\n", + "2025-01-21 16:06:16,157 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[58] Complete. Time taken: 00:00:50.218\n", + "2025-01-21 16:07:05,446 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[59] Complete. Time taken: 00:00:49.289\n", + "2025-01-21 16:07:55,170 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 59 until 60 epochs\n", + "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.041947875171899796\n", + "2025-01-21 16:07:58,417 - INFO - Epoch[60] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-21 16:07:58,417 - INFO - Key metric: val_mean_abs_error best value: 0.041947875171899796 at epoch: 60\n", + "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[60] Complete. Time taken: 00:00:03.247\n", + "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.248\n", + "2025-01-21 16:07:58,777 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 60\n", + "2025-01-21 16:07:58,777 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[60] Complete. Time taken: 00:00:53.331\n", + "2025-01-21 16:08:49,052 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[61] Complete. Time taken: 00:00:50.275\n", + "2025-01-21 16:09:39,838 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[62] Complete. Time taken: 00:00:50.785\n", + "2025-01-21 16:10:30,140 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[63] Complete. Time taken: 00:00:50.303\n", + "2025-01-21 16:11:20,742 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[64] Complete. Time taken: 00:00:50.601\n", + "2025-01-21 16:12:11,106 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 64 until 65 epochs\n", + "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.03936021775007248\n", + "2025-01-21 16:12:14,362 - INFO - Epoch[65] Metrics -- val_mean_abs_error: 0.0394 \n", + "2025-01-21 16:12:14,362 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[65] Complete. Time taken: 00:00:03.255\n", + "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.256\n", + "2025-01-21 16:12:14,722 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 65\n", + "2025-01-21 16:12:14,722 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[65] Complete. Time taken: 00:00:53.980\n", + "2025-01-21 16:13:05,332 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[66] Complete. Time taken: 00:00:50.610\n", + "2025-01-21 16:13:55,536 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[67] Complete. Time taken: 00:00:50.204\n", + "2025-01-21 16:14:45,787 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[68] Complete. Time taken: 00:00:50.251\n", + "2025-01-21 16:15:35,860 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[69] Complete. Time taken: 00:00:50.073\n", + "2025-01-21 16:16:26,467 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 69 until 70 epochs\n", + "2025-01-21 16:16:29,726 - INFO - Epoch[70] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-21 16:16:29,726 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-21 16:16:29,726 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[70] Complete. Time taken: 00:00:03.258\n", + "2025-01-21 16:16:29,726 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.259\n", + "2025-01-21 16:16:30,089 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 70\n", + "2025-01-21 16:16:30,090 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[70] Complete. Time taken: 00:00:54.230\n", + "2025-01-21 16:17:20,037 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[71] Complete. Time taken: 00:00:49.947\n", + "2025-01-21 16:18:09,870 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[72] Complete. Time taken: 00:00:49.833\n", + "2025-01-21 16:19:00,519 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[73] Complete. Time taken: 00:00:50.649\n", + "2025-01-21 16:19:51,690 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[74] Complete. Time taken: 00:00:51.171\n", + "2025-01-21 16:20:44,185 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008344339206814766\n", + "2025-01-21 16:20:44,185 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 74 until 75 epochs\n", + "2025-01-21 16:20:47,299 - INFO - Epoch[75] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-21 16:20:47,299 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-21 16:20:47,299 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[75] Complete. Time taken: 00:00:03.113\n", + "2025-01-21 16:20:47,299 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.114\n", + "2025-01-21 16:20:47,370 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 75\n", + "2025-01-21 16:20:47,370 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[75] Complete. Time taken: 00:00:55.680\n", + "2025-01-21 16:20:47,404 - ignite.engine.engine.SupervisedTrainer - INFO - Train completed, saved final checkpoint: outputs/model_final_iteration=75000.pt\n", + "2025-01-21 16:20:47,404 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run complete. Time taken: 01:03:29.219\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}" + " --output_dir './outputs'" ] }, { @@ -374,12 +388,12 @@ "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." + "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, + "execution_count": 8, "id": "40e6a3e9-3984-44b0-ba9a-5b8d58c7ea2d", "metadata": {}, "outputs": [ @@ -387,43 +401,42 @@ "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", + "2025-01-21 16:26:26,051 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-21 16:26:26,051 - INFO - > config_file: ('/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", + " '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/infer.yaml')\n", + "2025-01-21 16:26:26,051 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", + "2025-01-21 16:26:26,051 - INFO - > run_id: 'testing'\n", + "2025-01-21 16:26:26,051 - INFO - > ckpt_path: './outputs/model_final_iteration=75000.pt'\n", + "2025-01-21 16:26:26,051 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", + "2025-01-21 16:26:26,051 - INFO - > out_file: 'test.pt'\n", + "2025-01-21 16:26:26,051 - 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" + "2025-01-21 16:26:26,051 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", + "Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.\n", + "100%|██████████████████████████████████████| 1000/1000 [00:08<00:00, 114.77it/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" + "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" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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887Z7eGrEpUikxfq5irHSsMXzM8GKqACAssEEBABIggkIAJAEExAAIAnXBNTU1KSzzjpLEydO1PHHH68LL7xQO3bsGLTNgQMHtGLFCk2ZMkUTJkzQsmXL1N7eHnXQAICRz5WC27x5s1asWKGzzjpL7733nv7+7/9en//85/Xyyy9r/PjxkqSVK1fq17/+tdavX69cLqcrr7xSF110kX73u9+5BpZlWUHqopiJmiONY7i86atQjSvvqpDesYTESul56ph5r7FnjN5aYzHSR1aarL+/P9hurZRaVVU15LFY21qJvOrq6oI2q26cNW4r6Wgdf2jl11hJutA97q2bZylmTbUYqblYq/566rt56i4ezjUBbdy4cdC/33///Tr++OPV0tKiP//zP1dnZ6fuu+8+Pfjgg1q8eLEkae3atTr55JO1detWnXPOOZ7dAQDK2LD+t6Czs1OSNHnyZElSS0uLDh48qMbGxvw2c+bM0YwZM7Rly5ZgH319ferq6hr0BQAof0c9AQ0MDOiaa67RokWLdNppp0mS2traVFlZqdra2kHb1tXVqa2tLdhPU1OTcrlc/mv69OlHOyQAwAhy1BPQihUr9OKLL2rdunXDGsDq1avV2dmZ/2ptbR1WfwCAkeGoSvFceeWV+tWvfqUnn3xSJ5xwQr69vr5e/f396ujoGPQU1N7ervr6+mBfVVVVwZemFRUVBS/UrBdbsV68fRx4zpU3PDF6dPh2+iCg8mGel+qSHSDwbB968S3ZL9A97d6+LdbL/xDrBX+obI9kL6YWYoUhrFJB1nFaYwltbwUcvO2ha+9d1M7iCTzF+lyK8TMbo0SP5/NgqGN2nf0sy3TllVdqw4YNevzxxzVr1qxB358/f77GjBmjTZs25dt27Nih3bt3q6GhwbMrAECZcz0BrVixQg8++KB+8YtfaOLEifn3OrlcTmPHjlUul9Oll16qVatWafLkyaqpqdFVV12lhoYGEnAAgEFcE9CaNWskSZ/5zGcGta9du1Z//dd/LUm6/fbbNWrUKC1btkx9fX1asmSJ7rrrriiDBQCUD9cENJTfI1ZXV6u5uVnNzc1HPSgAQPmjFhwAIImSXZDOU4onxEq3eJN0oX6sPqyUkTfBFVLMEiCxFrGyUnCh82KVhfGeQ0+KyVuyydo+NBYrBWaN20qweRaC6+7uDm5rLTxnJdtqamoK2rxpN0/CTgofj5WYs+43K3nY09NT0GadK+t8excYDPF+1njLbXnufW+5KU+Zn9C1H+rYeAICACTBBAQASIIJCACQBBMQACAJJiAAQBIlm4IL8SxKFmMBM6ufWAu1xagV5U3axFjEynv8obSSN0lnpWqs9lAyx+rbezyhJJjVt3Wcofp4Vt+S7z70JiY9dfOs820l7DwJNutn1tqnlUgL1Rm0zreVjrOWhbFSc1Zq0MNbly50bov52eT5jBxq4pQnIABAEkxAAIAkmIAAAEkwAQEAkmACAgAkMaJScB7e1Txj8NYai8F7PDGSM9Y+rbpnBw4cKGizVrO0kkDedJwnBedNsIX2adVC86b3POfWU6vuSO2eVVurq6uD7d46bqE0mXfcntSct7abtWKvlY4LtVs/P1Zizpuki1Eb03OPx6hpWbCf6D0CADAETEAAgCSYgAAASTABAQCSYAICACRRsim40IqonrRWjBUAjzQ2D08NJYu1bTGTd1ayy3v8oTF6k03eRF4o2WYlnryrs4bardSU1e5NJQ11HJKd6rPOeWif3hWFrVSjNcbQPq16chZrn57aZFbNO+u65XK5YHtoFdqOjo7gtqEVW480Fk9qLsbPpsU6J6Gfn6GuHMsTEAAgCSYgAEASTEAAgCSYgAAASZRsCKGiomLIL2RjlNfx9BGrnI934bBijcVbtsj7ojP08tu7IJ1nMTUp/EI3Vvmf0MtYb5kbb4Ag9MLZW7rFWkwtdPxW39ZLa+vcWi+jQ8fpLZdjlQUKHaf1gt+6Dtb1tO7D0D1RU1MT3NYq5/P2228H20OlrKTwGK3jsa5njIUrCSEAAEYcJiAAQBJMQACAJJiAAABJMAEBAJIo2xScZ6ElqTiLLf0x3jRZjD4858p7Dj2JPO9iahZrLJ59Wuewt7c32B5KWVmpH6u8jHcBu1CKyVMaRbKvTyhN5l0A0ErBWSnA0HW2UnrWWKwUXKjdSoFZ6Tir3ZOOs/Y5fvz4YPtxxx0XbA+V+ZHCY/SWrPKUCbP6mDhxYrB9KHgCAgAkwQQEAEiCCQgAkAQTEAAgCSYgAEASJZuC8/AkwbxJqOFuW2zesYQSUlaaykq9xFg0zuoj1iJ4MYwbNy7Y7llgz1N/TfKn5kKssXjSi9b1sc6JNW4rBRfq39rWardqpIVY58+7GKG3JqGnD08CUgqfF+/PlbXPUD/WuEMpuKH+vPIEBABIggkIAJAEExAAIAkmIABAEkxAAIAkSjYFF6oFF2sl0hArtREjHRej3duHlYYJ8a6iaCVnrLGE0jrWtt60jue8WEkl7+qsoe2tpJZVU8w6HiuRGGr3XntPPTBvIs26V6x2T+0067pZ9eescx5iXQfrPvSc22LXowyNsZg/V9b4PIm5gv0PaSsAACJjAgIAJMEEBABIggkIAJCEK4SwZs0arVmzRv/93/8tSTr11FN14403aunSpZLeL41x7bXXat26derr69OSJUt01113qa6uLspgPQuneRdZi7GYmqWYZWS8C02FXiTGKE8k2WVNQu3WuD0lZyRfsMD7At06Hg/vveK5D2Mt3hc651bf1uJoVskh6xyGQhWebSW7/E8ohOBdANEaiydUYbHucWss1n0bup7W/eMtOTTU/Unh6zPU+9j1BHTCCSfo1ltvVUtLi7Zv367Fixfrggsu0EsvvSRJWrlypR5++GGtX79emzdv1p49e3TRRRd5dgEA+JhwPQGdf/75g/79e9/7ntasWaOtW7fqhBNO0H333acHH3xQixcvliStXbtWJ598srZu3apzzjkn3qgBACPeUb8DOnTokNatW6eenh41NDSopaVFBw8eVGNjY36bOXPmaMaMGdqyZYvZT19fn7q6ugZ9AQDKn3sCeuGFFzRhwgRVVVXp8ssv14YNG3TKKaeora1NlZWVqq2tHbR9XV2d2trazP6ampqUy+XyX9OnT3cfBABg5HFPQCeddJKee+45bdu2TVdccYWWL1+ul19++agHsHr1anV2dua/Wltbj7ovAMDI4S7FU1lZqU9+8pOSpPnz5+vpp5/WD3/4Q1188cXq7+9XR0fHoKeg9vZ21dfXm/1VVVWZKZfDeRJs3gSXp+9YpTQ8Yh1PaIxWysZbjsSzEJpV5sZbMsRb7iTEGouVePIk1axzG+N6epOEnrSfdY29194qixMqo2NdByvtZh2Pp0SNt7xXjM8gzyJ9RxI6X9Z97010hsYSowzR4Yb9d0ADAwPq6+vT/PnzNWbMGG3atCn/vR07dmj37t1qaGgY7m4AAGXG9QS0evVqLV26VDNmzFB3d7cefPBBPfHEE3r00UeVy+V06aWXatWqVZo8ebJqamp01VVXqaGhgQQcAKCAawLat2+f/uqv/kp79+5VLpfT3Llz9eijj+pzn/ucJOn222/XqFGjtGzZskF/iAoAwOFcE9B99913xO9XV1erublZzc3NwxoUAKD8UQsOAJBEyS5IN1xWCsOb4vHUTvPyJKG8Nd88x3/gwIHgtt4F3KwETqjdm2qzElKeNJ235p2nBpd3wTzvPRSjZpfVHhqL99oPNcn6gdC5tRJzPT09wXar/lwoHeetAWldnxh12Szeez90jWIlcT39hMZnLRZ4OJ6AAABJMAEBAJJgAgIAJMEEBABIggkIAJBE2abgvGKslBprnzHScZ5xW+kjT223I4mxYq032RVK5lhJMuv4PbXwrNSUlWCyUmOec+utJ2f1Haq1Zo3b4q1L50nNeVb3lXypPs8qsUfaPsSbgPTuM9Turd/oSWPGWJH6cDwBAQCSYAICACTBBAQASIIJCACQBBMQACCJkk3BZVlWkCLxJqFCiplqKyXehFRIjOSMxVvDLkbiy5t4spJgnlVyvavnesbiGZ9k104LjcWbRrTGHeN4vOmwGDXSvAk2Ty047/FYYwm1x1oRNXS+rHGEjmeotQt5AgIAJMEEBABIggkIAJAEExAAIImSDSFUVFQUvMTzBAi8L61TlOKJwfsyP8Y5jPGC1st7HUJjtBYTs0rxxLj21stYaywTJkwItofOrdWHt4xOqG9vGMQai6cfT1klyX6xHjrn3mvpDVuEeMM61vaeQIg1Pu/xh/qxrr0n8FOwnWtUAABEwgQEAEiCCQgAkAQTEAAgCSYgAEASJZuCC/GkZLwpME+7NQ5viiVGuRxv3zHK6HiFxhIj1eYVqzRKaHsrqWW1V1ZWBts9C9V5y65YfXuSXd77yrNonDdhZ+0zdG6t6+D9WfakTmMkA4+0vTftGOK59p40Hik4AEBJYwICACTBBAQASIIJCACQBBMQACCJkk3BhRakK/b+Slms2nYx+i7mPr0LoXnGbiV+rHSYJ5HmTVd603Ghfrw10qzjCSW+vAvmec6VJUbNQEnq7+8vaLOOx3u/eVKk3rpsKRZ6tHju8dC4h3osPAEBAJJgAgIAJMEEBABIggkIAJAEExAAIImSTcGFVkQ90rZD5U18eRIe3vpR3nbPtp6xeFY6lPyJr1AayJsQ8q6WGWr3rKAp2ckuT+1B7z3hqe9l9W3VILOO03NPxKqxGOL9ufLUmbN467VZ92GovZjJVS/vz3Lo3HqSkaEkYnBcQ9oKAIDImIAAAEkwAQEAkmACAgAkUbIhBI8YIYRiLtQWo9RNMcvlePu2Xs5bL/lDL9a9C7hZfXuufayAQ4xFvGK8QLeu28GDB13be0IV1kt77z5DPAsAHml7zz3u3WeMUIV17b0/h55yORZPeMYa97hx44a87eF4AgIAJMEEBABIggkIAJAEExAAIAkmIABAEsNKwd16661avXq1rr76at1xxx2SpAMHDujaa6/VunXr1NfXpyVLluiuu+5SXV3dsAdbSmmyYirmGGOkkrwLuIVSVlbKyJt2s47HU0rEWwLGk7LylsvxLJzmPSd9fX3B9tD1iZVI87QXMzFoiZGulMLn3Bq3t90S2t66r6x2b1IvZOrUqQVtVirycEf9BPT000/rJz/5iebOnTuofeXKlXr44Ye1fv16bd68WXv27NFFF110tLsBAJSpo5qA9u/fr0suuUT33nuvJk2alG/v7OzUfffdpx/84AdavHix5s+fr7Vr1+o///M/tXXr1miDBgCMfEc1Aa1YsUJf+MIX1NjYOKi9paVFBw8eHNQ+Z84czZgxQ1u2bAn21dfXp66urkFfAIDy534HtG7dOj3zzDN6+umnC77X1tamyspK1dbWDmqvq6tTW1tbsL+mpiZ95zvf8Q4DADDCuZ6AWltbdfXVV+tnP/uZqqurowxg9erV6uzszH+1trZG6RcAUNpcT0AtLS3at2+fzjzzzHzboUOH9OSTT+rHP/6xHn30UfX396ujo2PQU1B7e7vq6+uDfVZVVZnpqaEKJVas9I2nj2IrlVSfdzGxGMkuq28rqVVM3nMY2t6bMvK2e1Jj3pp3oZ8/T6JR8tdO8yQJrXPruW7e9F6M6+atm+dJQFr7tPq2Foiz9hkau9V3qBZcd3d3cNvDuSag8847Ty+88MKgtq997WuaM2eOvvnNb2r69OkaM2aMNm3apGXLlkmSduzYod27d6uhocGzKwBAmXNNQBMnTtRpp502qG38+PGaMmVKvv3SSy/VqlWrNHnyZNXU1Oiqq65SQ0ODzjnnnHijBgCMeNGXY7j99ts1atQoLVu2bNAfogIA8GHDnoCeeOKJQf9eXV2t5uZmNTc3D7drAEAZoxYcACCJEbUiaozEV4yaSNY4RkKdOQ/rnFhJNevchhJI3hpU1vaea+GpG3ek7Yc7jiPt03OcVoKrsrIy2G4l2ELbh5JNkjRhwoRguzd55+nDe+1D9d2smm8Wa59WmuzAgQNDapOk3t7eIfch+dKl3pqEnnvc6qOmpqagraenZ0h98gQEAEiCCQgAkAQTEAAgCSYgAEASTEAAgCRGVArOw0rfxKif5V25sFRqvkm+mnfWPq0kkLefkFirX3pScN52z+qkFk/fVrv3+nhqk1l9WPu0EnaeVVu958RKtoXG4q3tZp0rKwEaqn22f//+4LZWQszaZzF5z3lI6DNyqOlHnoAAAEkwAQEAkmACAgAkwQQEAEiibEMIxXxp7w0yeF7ExhJjn7FerHu2tV5eehcYLOa59YZQPDyBCOucxDh2a/Gxzs7OYLu14NvEiROD7Z5FJD1hA8n3Mt86TqtcjrXQWqjdKq1jsY7fUyrKGyqI8TM7HDwBAQCSYAICACTBBAQASIIJCACQBBMQACCJEZWCi5GQKqXF5IZb7kIqreOxxhgqdxIrYedJ/XgXhyumGNfNm7r0jMV7v1mJLyupNn78+IK26urq4LbecjkhVgkdK9Vnpd2sfkKli7zlo7wLI3ruce/nwXBLWQ11fzwBAQCSYAICACTBBAQASIIJCACQBBMQACCJEZWCs3jSIJ4+vP3ESiV5FLPGU6x2z7axzlWof+/CZlaKKdS3N/HkvVdC/XvTe56+reMJpdckacKECcH2sWPHBtsrKyuHPD6v0EJw3rRbjEUXi51E9d5zIZ4xehb1G+rYeAICACTBBAQASIIJCACQBBMQACAJJiAAQBJlkYIL1a3yrqBpKfU0mcWTjitm8swrVrIrdP09KR4va0XQWHXmPCvzWvu0jjN0rqxU26RJk4LtVh23qqqqYHvoXrHGbbVbCba33367oC2UjJN8SUfv9t6fqxj3oZfnc4IUHACgbDABAQCSYAICACTBBAQASKIsQgiel2BWe4wX8d7yKjECDinEWPDM6sN6mR/j+L2LrFk828cKw4TECmzkcrkhtUl2qMDiOX5rsbeurq5g+5tvvhls7+3tLWgr5s+91U+MRRS923vDE5bhhngIIQAAShoTEAAgCSYgAEASTEAAgCSYgAAASYyoFJwn4eFNvcRIWRWz5E6Kcafa/qNWzJSit29PWum9994LtlsL7E2ePDnYHiqjY/VhpRSt9gMHDgy53SqtY7X39PQE20Pn3ErjeUvxxOBN3nnuIWvc3gSop+/hLMbHExAAIAkmIABAEkxAAIAkmIAAAEkwAQEAknCl4L797W/rO9/5zqC2k046Sb///e8lvZ9sufbaa7Vu3Tr19fVpyZIluuuuu1RXVxdvxAHDTWwcSSglEyshU8xkWzHH4U3xhBI4sZJ0Kc6hZ3G4GDW4JOngwYMFbePGjQtuay0aZyXVKisrC9rGjh3rGp9Vl85aCC6UbOvs7Axua9WI89SZs8YX6x6P0Ucx79liLhbp+Xk4nPsJ6NRTT9XevXvzX7/97W/z31u5cqUefvhhrV+/Xps3b9aePXt00UUXeXcBAPgYcP8d0OjRo1VfX1/Q3tnZqfvuu08PPvigFi9eLElau3atTj75ZG3dulXnnHNOsL++vr5B/4djVb4FAJQX9xPQzp07NW3aNH3iE5/QJZdcot27d0uSWlpadPDgQTU2Nua3nTNnjmbMmKEtW7aY/TU1NSmXy+W/pk+ffhSHAQAYaVwT0MKFC3X//fdr48aNWrNmjXbt2qVPf/rT6u7uVltbmyorK1VbWzvov6mrq1NbW5vZ5+rVq9XZ2Zn/am1tPaoDAQCMLK5fwS1dujT/z3PnztXChQs1c+ZM/fznPzdfWv4xVVVV7kWuAAAj37BqwdXW1urEE0/UK6+8os997nPq7+9XR0fHoKeg9vb24Dujo+FZcTRWvaVQcihWoqaYCZxyq79WKolBL+8quVZ9t9D/4Flpt1CqTbJTY6E0Xag+nCT19/cH2616bW+//XawPVTHzbvCazFXybXEqMsWa+XkYu4zxDpXofahntdh/R3Q/v379eqrr2rq1KmaP3++xowZo02bNuW/v2PHDu3evVsNDQ3D2Q0AoAy5noD+7u/+Tueff75mzpypPXv26KabbtIxxxyjr3zlK8rlcrr00ku1atUqTZ48WTU1NbrqqqvU0NBgJuAAAB9frgnof//3f/WVr3xFb731lo477jide+652rp1q4477jhJ0u23365Ro0Zp2bJlg/4QFQCAw7kmoHXr1h3x+9XV1WpublZzc/OwBgUAKH/UggMAJDGiVkS16mp56m15V6L01IKLVX8uRrqn3FJjXjFWM421fYiVdhs9OvwjWVNTU9Dmrdc2fvz4YHso6Wml3d55551g+xtvvBFst+q4ec6htTprrJ83D89nTbFrwRUzLRvq27N6LCuiAgBKGhMQACAJJiAAQBJMQACAJMoihGC96Azxlt7wjCPWC3SPGC8uSz2YcCQpznlIrHM4YcKEYHsocOAprSPZL/NDgQirhI7VboUqYry09y7q5+k7xYt/b9+eMcYqcRW6t6wFDZOV4gEA4GgxAQEAkmACAgAkwQQEAEiCCQgAkMSISsHFSIl4yklY23v7iFEyJFbKqpiJGk8/scrfeFJJxUz7xVhgTpK5OnCofytpZN2fBw4cCLaHFoezFph79913g+3exeQ894Q3uTqc0jAfiFFGJ0U5LO/PiXWvhBJv1nUYzoKgPAEBAJJgAgIAJMEEBABIggkIAJAEExAAIImSTcEdOnSoIFnjSZ9ZiY0Ui71524u50NRH3Uesfkql5pvkW6zLqp9VWVkZbLcWpAttb93jVtrNSra99dZbBW29vb3Bba3rYB2nxZMa8/Th7SfWPV7MGovFvPe9Scro+/9I9gIAwGGYgAAASTABAQCSYAICACTBBAQASKJkU3BZlhWkSKxUiacOlZUyslIfoXZv+sabmvuoEigflVKv1xajLp3Vh3W/eeuehVJw1krAHR0dwfY33ngj2B5Kx1m16mKk3WKJkQ6LtYLocLc9khSJVk/Sczi193gCAgAkwQQEAEiCCQgAkAQTEAAgCSYgAEASJZuCC/HUd/OuWmqtXOlJwcWq+RYjNZai/lop1Xzz1OaKcQ6t+826r/r7+4Pt1vGHUnBdXV3BbUO13SQ7NTd+/Pghj8MSY0VUSzFrJlpKqfagpZgrv3o+90LXfqj3A09AAIAkmIAAAEkwAQEAkmACAgAkUbIhhNCCdFYZEE/pGu/LUo9iBgK8iln+ppjbl9IieNZ9FQrDWPemdb9Zi8bt27cv2N7W1lbQZoUKrHFbi+CFzlUxf06sfVq8gYBiBgiKeS/H2N5bxssThCrGeeUJCACQBBMQACAJJiAAQBJMQACAJJiAAABJlGwKrqKiYsipC0/yw+rTSv14SlJYSr1cTqzxlVJCKEYfnkUKY40ltDicFL4/rXFYibwYC/LFuleKmbIq5r3iOYexFrvzbJ8iARgqK3Xw4MEh9ckTEAAgCSYgAEASTEAAgCSYgAAASbgnoNdff11f/epXNWXKFI0dO1af+tSntH379vz3syzTjTfeqKlTp2rs2LFqbGzUzp07ow4aADDyuVJw77zzjhYtWqTPfvazeuSRR3Tcccdp586dmjRpUn6b73//+7rzzjv1wAMPaNasWbrhhhu0ZMkSvfzyy6qurh7yvgYGBgpSPlZSLZTCsLa1khyexe4ssRbOKmYqqdySajEWjfP2Xcw6czEST56Fw6TwvR+rJp/1cxUau7Wtp49YfVtJQk8/1vhiHI8lxn3l7buurq6gzUpzHs41Af3DP/yDpk+frrVr1+bbZs2alf/nLMt0xx136Fvf+pYuuOACSdJPf/pT1dXV6aGHHtKXv/xlz+4AAGXM9Su4X/7yl1qwYIG+9KUv6fjjj9cZZ5yhe++9N//9Xbt2qa2tTY2Njfm2XC6nhQsXasuWLcE++/r61NXVNegLAFD+XBPQa6+9pjVr1mj27Nl69NFHdcUVV+gb3/iGHnjgAUn/v2T84Y9kdXV1wXLyktTU1KRcLpf/mj59+tEcBwBghHFNQAMDAzrzzDN1yy236IwzztBll12mr3/967r77ruPegCrV69WZ2dn/qu1tfWo+wIAjByuCWjq1Kk65ZRTBrWdfPLJ2r17tySpvr5ektTe3j5om/b29vz3DldVVaWamppBXwCA8ucKISxatEg7duwY1PaHP/xBM2fOlPR+IKG+vl6bNm3S6aefLknq6urStm3bdMUVV7gGVlVVpaqqqkFtVn2h0KRlpVislShDSTqr3bOtZKeP3nvvvWB7SKxUkif1Ym0bI00Wqy5bMXlW4LWSTda4rXM7enTxyjNa5zw0Rute9iYDvamsGEL79N4/nsStFF6d1trW+gyy2nt7e4e8vXeVXGv70L1v3Zvz5s0raLPGXNDnkLb6f1auXKk/+7M/0y233KK//Mu/1FNPPaV77rlH99xzj6T3f6iuueYaffe739Xs2bPzMexp06bpwgsv9OwKAFDmXBPQWWedpQ0bNmj16tW6+eabNWvWLN1xxx265JJL8ttcd9116unp0WWXXaaOjg6de+652rhxo+tvgAAA5c/9vP/FL35RX/ziF83vV1RU6Oabb9bNN988rIEBAMobteAAAEmU7IJ0jzzyiMaNGzeobfz48cFte3p6CtqsF2ahbSX7BWAoKGC9oPQsanek7UMvaL2Lo8VYTM16UW69nPeUL/GWOilm+Rvvi/JQGMZ6mWsFZ6wAiickY/VhjcVzf1p9W31Yx2m1exYxs47HEwjwvMg/0lis7UPnxRtkiHV/ftTGjBlT0DbUsfEEBABIggkIAJAEExAAIAkmIABAEkxAAIAkKrJSiVL8P11dXcrlcpo9e3ZBUspK5oQSLlaCy0qgWKmX0OmxElzWH9t6k12e0jXehF2o3Tqv3vSNtU9P37GErr+1T+valzrvwmaexdSK/bHgucdLqcwPfDo7O49Y35MnIABAEkxAAIAkmIAAAEkwAQEAkii5UjwfvEAMvdD2lKrwrmXjeQEao8yNd5/eF7Ge4/S+/PW2D3fbWMrt5bT3eGLcb7EU8z5E6fhj16jkJqDu7m5J0muvvZZ4JEBpi5UaA4qlu7tbuVzO/H7JxbAHBga0Z88eTZw4Ud3d3Zo+fbpaW1vLeqnurq4ujrNMfByOUeI4y03s48yyTN3d3Zo2bZoZ/5dK8Alo1KhROuGEEyT9/1+j1dTUlPXF/wDHWT4+DscocZzlJuZxHunJ5wOEEAAASTABAQCSKOkJqKqqSjfddJOqqqpSD6WoOM7y8XE4RonjLDepjrPkQggAgI+Hkn4CAgCULyYgAEASTEAAgCSYgAAASTABAQCSKOkJqLm5WX/6p3+q6upqLVy4UE899VTqIQ3Lk08+qfPPP1/Tpk1TRUWFHnrooUHfz7JMN954o6ZOnaqxY8eqsbFRO3fuTDPYo9TU1KSzzjpLEydO1PHHH68LL7xQO3bsGLTNgQMHtGLFCk2ZMkUTJkzQsmXL1N7enmjER2fNmjWaO3du/i/HGxoa9Mgjj+S/Xw7HeLhbb71VFRUVuuaaa/Jt5XCc3/72t1VRUTHoa86cOfnvl8MxfuD111/XV7/6VU2ZMkVjx47Vpz71KW3fvj3//Y/6M6hkJ6B/+7d/06pVq3TTTTfpmWee0bx587RkyRLt27cv9dCOWk9Pj+bNm6fm5ubg97///e/rzjvv1N13361t27Zp/PjxWrJkiQ4cOPARj/Tobd68WStWrNDWrVv12GOP6eDBg/r85z+vnp6e/DYrV67Uww8/rPXr12vz5s3as2ePLrroooSj9jvhhBN06623qqWlRdu3b9fixYt1wQUX6KWXXpJUHsf4YU8//bR+8pOfaO7cuYPay+U4Tz31VO3duzf/9dvf/jb/vXI5xnfeeUeLFi3SmDFj9Mgjj+jll1/WP/7jP2rSpEn5bT7yz6CsRJ199tnZihUr8v9+6NChbNq0aVlTU1PCUcUjKduwYUP+3wcGBrL6+vrstttuy7d1dHRkVVVV2b/+678mGGEc+/btyyRlmzdvzrLs/WMaM2ZMtn79+vw2//Vf/5VJyrZs2ZJqmFFMmjQp+6d/+qeyO8bu7u5s9uzZ2WOPPZb9n//zf7Krr746y7LyuZY33XRTNm/evOD3yuUYsyzLvvnNb2bnnnuu+f0Un0El+QTU39+vlpYWNTY25ttGjRqlxsZGbdmyJeHIimfXrl1qa2sbdMy5XE4LFy4c0cfc2dkpSZo8ebIkqaWlRQcPHhx0nHPmzNGMGTNG7HEeOnRI69atU09PjxoaGsruGFesWKEvfOELg45HKq9ruXPnTk2bNk2f+MQndMkll2j37t2SyusYf/nLX2rBggX60pe+pOOPP15nnHGG7r333vz3U3wGleQE9Oabb+rQoUOqq6sb1F5XV6e2trZEoyquD46rnI55YGBA11xzjRYtWqTTTjtN0vvHWVlZqdra2kHbjsTjfOGFFzRhwgRVVVXp8ssv14YNG3TKKaeU1TGuW7dOzzzzjJqamgq+Vy7HuXDhQt1///3auHGj1qxZo127dunTn/60uru7y+YYpffXWFuzZo1mz56tRx99VFdccYW+8Y1v6IEHHpCU5jOo5JZjQPlYsWKFXnzxxUG/Ty8nJ510kp577jl1dnbq3//937V8+XJt3rw59bCiaW1t1dVXX63HHntM1dXVqYdTNEuXLs3/89y5c7Vw4ULNnDlTP//5zzV27NiEI4trYGBACxYs0C233CJJOuOMM/Tiiy/q7rvv1vLly5OMqSSfgI499lgdc8wxBUmT9vZ21dfXJxpVcX1wXOVyzFdeeaV+9atf6Te/+U1+fSfp/ePs7+9XR0fHoO1H4nFWVlbqk5/8pObPn6+mpibNmzdPP/zhD8vmGFtaWrRv3z6deeaZGj16tEaPHq3Nmzfrzjvv1OjRo1VXV1cWx3m42tpanXjiiXrllVfK5lpK0tSpU3XKKacMajv55JPzv25M8RlUkhNQZWWl5s+fr02bNuXbBgYGtGnTJjU0NCQcWfHMmjVL9fX1g465q6tL27ZtG1HHnGWZrrzySm3YsEGPP/64Zs2aNej78+fP15gxYwYd544dO7R79+4RdZwhAwMD6uvrK5tjPO+88/TCCy/oueeey38tWLBAl1xySf6fy+E4D7d//369+uqrmjp1atlcS0latGhRwZ9E/OEPf9DMmTMlJfoMKkq0IYJ169ZlVVVV2f3335+9/PLL2WWXXZbV1tZmbW1tqYd21Lq7u7Nnn302e/bZZzNJ2Q9+8IPs2Wefzf7nf/4ny7Isu/XWW7Pa2trsF7/4Rfb8889nF1xwQTZr1qyst7c38ciH7oorrshyuVz2xBNPZHv37s1/vfvuu/ltLr/88mzGjBnZ448/nm3fvj1raGjIGhoaEo7a7/rrr882b96c7dq1K3v++eez66+/PquoqMj+4z/+I8uy8jjGkA+n4LKsPI7z2muvzZ544ols165d2e9+97ussbExO/bYY7N9+/ZlWVYex5hlWfbUU09lo0ePzr73ve9lO3fuzH72s59l48aNy/7lX/4lv81H/RlUshNQlmXZj370o2zGjBlZZWVldvbZZ2dbt25NPaRh+c1vfpNJKvhavnx5lmXvxyBvuOGGrK6uLquqqsrOO++8bMeOHWkH7RQ6PknZ2rVr89v09vZmf/u3f5tNmjQpGzduXPYXf/EX2d69e9MN+ij8zd/8TTZz5syssrIyO+6447LzzjsvP/lkWXkcY8jhE1A5HOfFF1+cTZ06NausrMz+5E/+JLv44ouzV155Jf/9cjjGDzz88MPZaaedllVVVWVz5szJ7rnnnkHf/6g/g1gPCACQREm+AwIAlD8mIABAEkxAAIAkmIAAAEkwAQEAkmACAgAkwQQEAEiCCQgAkAQTEAAgCSYgAEASTEAAgCT+LwVMk/MnZ41zAAAAAElFTkSuQmCC", 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", 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" ] @@ -438,13 +451,13 @@ "!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", + " --ckpt_path \"./outputs/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.imshow(test[0, 0], vmin=0.3, vmax=1.0, cmap=\"gray\")" ] }, { @@ -457,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "cf8438b3-4c7d-48c4-bb41-ed7def73753f", "metadata": {}, "outputs": [ @@ -465,22 +478,22 @@ "name": "stderr", "output_type": "stream", "text": [ - 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VlatvbyrL00esNJmnjxi10yx5pku9QvuT50yhVnup1HA7Fp7P1JDhzhrNHRAAIAkGIABAEgxAAIAkGIAAAEm4BqDm5madd955mjhxok499VRddtll2rFjx4BlDh06pOXLl+ukk07ShAkTtHTpUrW3t0fdaADA6OdKwW3evFnLly/Xeeedp8OHD+vv//7v9clPflKvvfaaxo8fL0lauXKl/uM//kMbNmxQbW2tVqxYocsvv1w///nPXRuWZdmgJEaMZJfFSqzkOduqp5886355U2AxtiXWOj31wLzHKs+0UowZR619t+qYefYn1vmJwerb2s9YabpS4UkYxpq1NMYsxsNRkY1gTb/97W916qmnavPmzfrjP/5jdXZ26pRTTtEjjzyiv/iLv5Ak/fKXv9Tpp5+uLVu26IILLnjfPru6ulRbW6uOjg5NmjRp4MYaB3Hfvn3BfkKGGw8cavlYke0Y/TAAjc4ByLPd3j68A5DnWDEAHX+lMgB5tqOrq0uTJ09WZ2fnoM/x3zeiM9XZ2SlJmjx5siSppaVFfX19ampqKi4zZ84czZgxQ1u2bAn20dPTo66urgE/AIDyd8wDUH9/v2644QYtWrRIZ511liSpra1N1dXVqqurG7BsfX292tragv00Nzertra2+DN9+vRj3SQAwChyzAPQ8uXL9corr2j9+vUj2oDVq1ers7Oz+NPa2jqi/gAAo8MxleJZsWKFfvKTn+jZZ5/VtGnTiu0NDQ3q7e1VR0fHgLug9vZ2NTQ0BPsqFAoqFAqD2vMKIcR4uJb3hHShfqqrq4PLev/e7ylRE2PiOSn8HC1WiRpPe6zz43nGYK3TW+bHUxrFes7pmWQt7/MTYu1Pns8vYj0vCvXvfd6c57NVi+fz0Do/Iyl95NraLMu0YsUKbdy4UU8//bRmzZo14PX58+erqqpKmzZtKrbt2LFDu3fvVmNjo2dVAIAy57oDWr58uR555BH96Ec/0sSJE4vPdWpra1VTU6Pa2lpdddVVWrVqlSZPnqxJkybpuuuuU2Nj47AScACADw7XALR27VpJ0p/+6Z8OaF+3bp3++q//WpJ01113qbKyUkuXLlVPT48WL16se++9N8rGAgDKh2sAGs7fC8eNG6c1a9ZozZo1x7xRAIDyV17f2AIAjBolOyFdf3//oNSFJ33m/UawtXyMagXe5F1oP619t9JxnhST91v53m+ge85PLDH2s5RKKIWOeawEVyit5X3/WNviSd5ZPJPASeHzEysVayXbQv3kOemgxbo2vft5vN6z3AEBAJJgAAIAJMEABABIggEIAJAEAxAAIImSTcGFasHlyVP3zFvjyStGLSsrHedJ61iJmr6+vmB7jKRRrFpjoW23zpu1n1aNPE/tK2/iydPuTU1ZyTNPLThvIs1qD52LGHMkecVIqFr9ePuO8Xnn7SOv+nPD/X3ugAAASTAAAQCSYAACACTBAAQASIIBCACQRMmm4DxipKxi1HzL0+HDh4Pthw4dCrZ7knox6kRJcWbF9M7EGaOGX4waXN40UYyElPeatZb31E7z7meMayIG7/pSJO9i1K/0ngePPD4juQMCACTBAAQASIIBCACQBAMQACAJBiAAQBJlkYILiTGLotXumflT8ie4QstbfVspuJ6enmB7aNtjpVu8x9zDW/MuxFMLbah1evYnVj2w0Dmyzpt3dlIPq+88j6GlVBJ2XrG2O8Z+et6znmTtcD9TuAMCACTBAAQASIIBCACQBAMQACCJkg0hHDlyZNBDL2uCsBhlZzzlWPIu2+OZ3Mq7LaEHl7FKg8R4uGqt0ypFZO1/6IG7FViw2vN8aO99mB/allgPs611enjPW4j3WMWQ56SX3vOQYkI6z3s/j2PFHRAAIAkGIABAEgxAAIAkGIAAAEkwAAEAkijZFFxlZeWI0i8pEijevmNMpBejzI+1rJWOilEyxEo0eku6WEm1UHusdXrOm7dvS56Txnn6TpFUy1OKiefyXD5WOTBScACAssYABABIggEIAJAEAxAAIAkGIABAEiWbgquoqBh2QiWUNPKmW7yTzMXgSQ7FSqCEjkusemWemmpWIi3P2mmxklqh+mbeGoPe6y3UbiUArb69ybYQb03C0ZqOyzO56uW5JrzbbbWH6mt6krXDrQE4Oq8OAMCoxwAEAEiCAQgAkAQDEAAgCQYgAEASZZGC8/Yb4km3xErMxajD5E1ZhdqtRJqVarOSZ55EmqeG27EInQsrmWMdW0/KzJtStGbxta5Pz3Gx+vakmLypwxjvq1j1G2N8bsTo2zoP1ueEtbz3fHrE2J9Q+3BnqeYOCACQBAMQACAJBiAAQBIMQACAJFxPfNeuXau1a9fqf/7nfyRJZ555pm655RYtWbJEknTo0CHdeOONWr9+vXp6erR48WLde++9qq+vd29YlmXDfsjmKRuR58RzlhiTQXnK3HjbvWVxvJOseR5yex8ge8qUWLznx1MCxbtOa7t7e3uH3UeMkjuWWO+rUinR433w77lWrHPpKX8z1PKeY+59n4S25eDBg8FlC4XCoLbu7u5hbZfrKpg2bZpuv/12tbS0aPv27brooot06aWX6tVXX5UkrVy5Uo899pg2bNigzZs3a8+ePbr88ss9qwAAfEC47oAuueSSAf//jW98Q2vXrtXWrVs1bdo0Pfjgg3rkkUd00UUXSZLWrVun008/XVu3btUFF1wQb6sBAKPeMd8HHzlyROvXr9eBAwfU2NiolpYW9fX1qampqbjMnDlzNGPGDG3ZssXsp6enR11dXQN+AADlzz0Avfzyy5owYYIKhYKuueYabdy4UWeccYba2tpUXV2turq6AcvX19erra3N7K+5uVm1tbXFn+nTp7t3AgAw+rgHoNNOO00vvfSStm3bpmuvvVbLli3Ta6+9dswbsHr1anV2dhZ/Wltbj7kvAMDo4a57Ul1drQ9/+MOSpPnz5+v555/Xt771LV1xxRXq7e1VR0fHgLug9vZ2NTQ0mP0VCoVgimKkvAmhPBMl1jo9E7tZKTgrwWal4ELLW9sRK9nlKS3kTQJ5lvdeEzEmArO2z5sCy6Ms1XtC2+JNOnr3P1QWyerbupY915tVhslbFsezvLcPb7vnfWXtv+e4WMuGzltfX19w2UG/O6ylhtDf36+enh7Nnz9fVVVV2rRpU/G1HTt2aPfu3WpsbBzpagAAZcZ1B7R69WotWbJEM2bMUHd3tx555BE988wzevLJJ1VbW6urrrpKq1at0uTJkzVp0iRdd911amxsJAEHABjENQDt27dPf/VXf6W9e/eqtrZWc+fO1ZNPPqlPfOITkqS77rpLlZWVWrp06YAvogIAcDTXAPTggw8O+fq4ceO0Zs0arVmzZkQbBQAof6VRDwMA8IFTshPS5SXPWnDepJKVNBo3btywl/VMmib5aqR5a1B5k1AeMepkxao/5kkSWmLUcfPWfPOs05sWjTGRnpcnkdbT0xNc1puO87R7EoDHsnwoaeaddNESuoas7QulmI9bCg4AgGPBAAQASIIBCACQBAMQACAJBiAAQBJlkYKLUfcrRjrOm4LzJthCPPWZJF9aJ1a9thjJO2/NLs+ssjU1NcF2q0ZhKPFmpeBizB4rha+VWKk+z4y1Fm+CK3Q+vTPwWtdKKIEVmlFW8tdrs9Jdof201ulNwXlnVg3xXp+hayJGHbyjcQcEAEiCAQgAkAQDEAAgCQYgAEASDEAAgCTKIgUXcrxnkJR8aS/JToqEkjbemSg9iZVYMzR6klPeY2Xx1MjzJtI8fcdKpFlCx8ubjoqR9PTW9fPMEmzN7uvdllDKzJuitGrHHTx4MNgees/mfX48n3Hez0PPeR7JdcUdEAAgCQYgAEASDEAAgCQYgAAASZRFCCH0ECzPB7GxeEp1eAMBFs9Ebd4HlzFKhljrtMroWO2ecjnWw29PCZgYE+ZJvgncvKVbLKHlvRPPeXkmwbPaPSEEb9igu7s72D7cidaGYh1bb5DFM0mht6yW5zyHjrdVbuho3AEBAJJgAAIAJMEABABIggEIAJAEAxAAIImSTcH19/cPO80TI2XlSf3knaTzpJLyLAHjLTlkLR9KdllpLyvV5k2qeRJCFmt/Qu3WMbHSQNZ2e5KE1v7ESFPFSsFZ/Vj7H+JNV4aSXYcOHQou+9ZbbwXbrbSbp1yQN9Xn5SmX470mPJ97TEgHABh1GIAAAEkwAAEAkmAAAgAkwQAEAEiiZFNwFRUVw06LeFIl3gSbZ/lYE9V5UlYWz0Rg3jSVlWCy2guFwqA2K01ktYf6kOJMgmcl1Ty1xmJMDDiU0LH11I0bSmhbrOPqrXmXZ207K6kWqu/W2dkZXNaqx2ilMWMk2Lz1AfOcXDPvpN774Q4IAJAEAxAAIAkGIABAEgxAAIAkGIAAAEmUbAqusrJyRHXOUsx86k2q5dmPdexC6R5PPTXJX8cttLw3ZWMlpDxJNeu4WrNietJK3hqDFuvYhvqPMRuu1bc37eapmzdU/55l33nnnWD7/v37B7VZaTcrdelNh4XORaxafZY8E3metOxIcAcEAEiCAQgAkAQDEAAgCQYgAEASJRtCyLJs0AMy66GrZwK3PB/cxSrF4+kjRrkc60FsrEnjQtvuOZeSXXbFOheh5b2lXjzt3j6s69A6hhMmTBjUZp2HPEu9WNeEd4I9T0jEOvdWsCA0+Zz3eFvlmSx5TlwZI+CS58SVI1GaWwUAKHsMQACAJBiAAABJMAABAJJgAAIAJDGiFNztt9+u1atX6/rrr9fdd98t6d0Eyo033qj169erp6dHixcv1r333qv6+voRb2yM0igx+vbypuNCy3u3w0pIeUrxWKz0kSc5ZC3rndjNM+GbtU5veyiV5Z2kz2IlvkLXs5Xg8pZW8vRhpeA8JZGs5a1lDxw4EGzv7u4OtoeuT+vzwDsxYJ4lvo7XJHCxhY7JcI/TMd8BPf/88/rud7+ruXPnDmhfuXKlHnvsMW3YsEGbN2/Wnj17dPnllx/ragAAZeqYBqD9+/fryiuv1AMPPKATTzyx2N7Z2akHH3xQ3/zmN3XRRRdp/vz5Wrdunf7rv/5LW7dujbbRAIDR75gGoOXLl+tTn/qUmpqaBrS3tLSor69vQPucOXM0Y8YMbdmyJdhXT0+Purq6BvwAAMqf+xnQ+vXr9cILL+j5558f9FpbW5uqq6tVV1c3oL2+vl5tbW3B/pqbm/W1r33NuxkAgFHOdQfU2tqq66+/Xt///vc1bty4KBuwevVqdXZ2Fn9aW1uj9AsAKG2uO6CWlhbt27dP5557brHtyJEjevbZZ/Wd73xHTz75pHp7e9XR0THgLqi9vV0NDQ3BPguFggqFwrDWbyWePMmUWOk4D28tOM/kVtaxq6mpCbZ7U1khVlLLk3iy0kdW3zHquHnPpdV3KAnmTe95J3ALrdM6Jta1Yp37UJrOe514z0/oWglNJDdUe6jmmxQ+ht4kaoxEWqxUW4wafjH68EywN9zrxzUAXXzxxXr55ZcHtH3+85/XnDlz9OUvf1nTp09XVVWVNm3apKVLl0qSduzYod27d6uxsdGzKgBAmXMNQBMnTtRZZ501oG38+PE66aSTiu1XXXWVVq1apcmTJ2vSpEm67rrr1NjYqAsuuCDeVgMARr3o0zHcddddqqys1NKlSwd8ERUAgN834gHomWeeGfD/48aN05o1a7RmzZqRdg0AKGPUggMAJFGyM6JWVlYOSld4UnDeVFIMefbtXaenvpm3Dl6MmmrWdlt15iyedI8nxSPZSZ4YtQetWn3W1xtC22IdK29dulC79/x4r4lQfbfOzk5XH1ZdOk/Nu7xnLS2Vvq0+PH17kpvDPQfcAQEAkmAAAgAkwQAEAEiCAQgAkAQDEAAgiZJNwYV4UkzeGk8pZj719GP1YdVO89Qx8/btrfvlSaRZySYvzzGPUa/Nun6stNsJJ5wQbPfMchoj7Sb5Zni1rgmrLtvBgweD7aEUnHUMrWMS4/0WIx2WSoxt9HzuxXhPHY07IABAEgxAAIAkGIAAAEkwAAEAkhhVIYQYkypZPOEEb5DB+7DUM5mat1xOqJSKN4Dh3ZYYEwZ620MP0b3Xj2e7rbDB+PHjXct7JqSzggLeEj2hCd+soIl1XfX09ATbrXBCqH9r363zYG1jjFJJKT5rYmyLd7tjXPuha3O4YSLugAAASTAAAQCSYAACACTBAAQASIIBCACQRMmm4CoqKgYlNKzUi/X7Id6kjafsipcnHedNzliJpxDv5H2eCb+G6j9GH57zFqu8SujYekvrxJgcz1rWSqp1d3cH20OpOe/Ec1YKzlOeKVbyLEVSLSRWutSzLbGuce97/JjXc1zWAgDAURiAAABJMAABAJJgAAIAJMEABABIomRTcB6jYfKovHhrOXnqZHkTg57UjzchFKt+loeVJAwl3qxlvRPvWe2hfqw6a1a7J9lmpd089Qsl+zyEjlee7+NYyVVLntseY6JHS4wamKHtY0I6AEBJYwACACTBAAQASIIBCACQBAMQACCJUZWC89RK8s4AmKe8EziedYbaY9WsylOeM1RaCTYrkRZa3uqjUCi41mnt5zvvvDOorbOzM7islYKzeK4Jzwy0Q/VzvOX9vo+RAI01G3BefVhC9Q6ZERUAUNIYgAAASTAAAQCSYAACACRRFiGEGA/WvSVtSp3noeNwy2a8X9+eYxXreHsmpPP24TmGnskFJXtyRWtit66urkFtVtggxjXrPT/eck6evvMMoOTJu915njdL6mPLHRAAIAkGIABAEgxAAIAkGIAAAEkwAAEAkhhVKTgrORRK2ljLWjxpkLwnUwvtT6yk2nDXdyzrjMGTmhqKZ/+t8xkqMSJJ48aNG9Rmbbc1CdzBgweD7aGSO9by3rI4MSYf84pRPss7MeJItyPvfmIlDPPq43jiDggAkAQDEAAgCQYgAEASDEAAgCQYgAAASbhScF/96lf1ta99bUDbaaedpl/+8peS3q1NdeONN2r9+vXq6enR4sWLde+996q+vj7KxnqSbTGSM9bysZIzKaSo15ZXH7F4z2foOrTqslmpNmv5vr6+YHvoerbSbpY8k1ox+skzqRWr71KfHC7WpHaeNKpngsZB6xn2Wv6/M888U3v37i3+/OxnPyu+tnLlSj322GPasGGDNm/erD179ujyyy/3rgIA8AHg/h7Q2LFj1dDQMKi9s7NTDz74oB555BFddNFFkqR169bp9NNP19atW3XBBRcE++vp6RlQAThU9RcAUH7cd0A7d+7U1KlT9aEPfUhXXnmldu/eLUlqaWlRX1+fmpqaisvOmTNHM2bM0JYtW8z+mpubVVtbW/yZPn36MewGAGC0cQ1ACxcu1EMPPaQnnnhCa9eu1a5du/Sxj31M3d3damtrU3V1terq6gb8Tn19vdra2sw+V69erc7OzuJPa2vrMe0IAGB0cf0JbsmSJcX/njt3rhYuXKiZM2fqhz/8oWpqao5pAwqFggqFwjH9LgBg9BpRLbi6ujp95CMf0euvv65PfOIT6u3tVUdHx4C7oPb29uAzo/eTZdmgpIy3vlvIaE2wxarXlqK2nSfxlHfNLg+rXluovpt1bVrt1nmzznOoPVayy9N3rFlLPddh3tdnjHXGqEvndbxTg9Z2jx07eBgJtYWM6HtA+/fv169//WtNmTJF8+fPV1VVlTZt2lR8fceOHdq9e7caGxtHshoAQBly3QH93d/9nS655BLNnDlTe/bs0a233qoxY8bos5/9rGpra3XVVVdp1apVmjx5siZNmqTrrrtOjY2NZgIOAPDB5RqA3njjDX32s5/VW2+9pVNOOUUXXnihtm7dqlNOOUWSdNddd6myslJLly4d8EVUAACOVpGVykOO/6+rq0u1tbX6v//7P02aNGnAax0dHcHfCX3b3Pu3d+swhJb3/q06VntIjDl7Yv3tPUY/KeYz8c6rE2r3PgOy2q39z/MZkGdunljPgDx9eKV4BpSC57x520PXuHVMjv6clqTu7m6dccYZ6uzsDL7+HmrBAQCSKNkZUSsqKgaNzjH+JZ3nv9K9PP9Si5EATKVUZ2N8j7V91t1l6FzkmQ6z+slz1tJY2+1RSgnVPPczz7tIS4x6h3ngDggAkAQDEAAgCQYgAEASDEAAgCRKNoRQWVk5KHqaIqLrWV+e5TtiKZV4ad4ldzylRKywgec8511GJs8SSqUeErGU0gR2KUJMHnl+doa+IjDcCe24AwIAJMEABABIggEIAJAEAxAAIAkGIABAEiWbggvxlIeIlfjxFCO1lHo6rpT2x5tI8/QTow+rn1JJaA4lRfqqlPbfw3s+h5v6Gqpvr+N9bK1J5kKFS61CvkfjDggAkAQDEAAgCQYgAEASDEAAgCQYgAAASZRsCi7LskFpEW8tL6tfT7tnfbFqxMWYZtvimcY3z+2weI9hjLRjjFRf3okkz3krJTESX979zPNYlXrNt1j7mecU8APWE71HAACGgQEIAJAEAxAAIAkGIABAEgxAAIAkSjYFV1FRMSh1YdVbCrVbCRRPzab3tmM4bZKdGrPWaW1jqI5SjASgtXysunmlMttqLLHq0nnEmIXV23eeqbEY/cRIKcZKUXo+P1Ik72IJXftWfbeRXJvcAQEAkmAAAgAkwQAEAEiCAQgAkAQDEAAgiZJNwYWUSkLIm6SLlWAL8W5LaHnvdsRIx8WaVTVGbb88ZzP11rbzbHeslGKM2WMteR7DPNdp8VwrMc6x1bd3nRYr2Raafdqakbqvr29YbSHcAQEAkmAAAgAkwQAEAEiCAQgAkMSoCiFYD8xilMuJMamdl2cbvUEGz8NSbwjB+3DV87A01rbk+VA4hlKaTM6zLaUUTrDECHLEuMYtsQI4nhJkMSadtJbdv3//oLYDBw4Mq0/ugAAASTAAAQCSYAACACTBAAQASIIBCACQRMmm4LIsG5TosEpBeMpGWO0xUizedIsnweZNu+VZ/ifGOvNOnnnK/8RKJXmWTVFeJsVEenmKMZFinpMxxkieDdVP6LMsVnmmULtV9uvQoUPDagvhDggAkAQDEAAgCQYgAEASDEAAgCTcA9Cbb76pz33uczrppJNUU1Ojj370o9q+fXvx9SzLdMstt2jKlCmqqalRU1OTdu7cGXWjAQCjnysF9/bbb2vRokX6+Mc/rscff1ynnHKKdu7cqRNPPLG4zB133KF77rlHDz/8sGbNmqWbb75Zixcv1muvvaZx48YNe12HDx/W4cOHB7RZiY2enp5BbXkm1WLVK7MSeTEmcMtTjG3xpvS8NbtibEuKyeE8+5mijpl3nSnOW55S7I9n0klrWW97aD+tWpwhY8cOb2hxDUD/+I//qOnTp2vdunXFtlmzZhX/O8sy3X333frKV76iSy+9VJL0ve99T/X19Xr00Uf1mc98xrM6AEAZc/0J7sc//rEWLFigT3/60zr11FN1zjnn6IEHHii+vmvXLrW1tampqanYVltbq4ULF2rLli3BPnt6etTV1TXgBwBQ/lwD0G9+8xutXbtWs2fP1pNPPqlrr71WX/ziF/Xwww9Lktra2iRJ9fX1A36vvr6++NrRmpubVVtbW/yZPn36sewHAGCUcQ1A/f39Ovfcc3XbbbfpnHPO0dVXX60vfOELuu+++455A1avXq3Ozs7iT2tr6zH3BQAYPVwD0JQpU3TGGWcMaDv99NO1e/duSVJDQ4Mkqb29fcAy7e3txdeOVigUNGnSpAE/AIDy5wohLFq0SDt27BjQ9qtf/UozZ86U9G4goaGhQZs2bdLZZ58tSerq6tK2bdt07bXXujasqqpKVVVVA9pOPvnk4LIxUjxWaiNGIs27vGeGV28qJ5QyOzpt+J7e3t5gu7V8X19fsD2U9rP6sOR9zPPqw+I9n6F2K8HkSStZfec982mMmoQe3iRqrP5DYh3b0PmP9Tkx3PVJ4euturp6WH26BqCVK1fqj/7oj3TbbbfpL//yL/Xcc8/p/vvv1/333y/p3Z284YYb9PWvf12zZ88uxrCnTp2qyy67zLMqAECZcw1A5513njZu3KjVq1frH/7hHzRr1izdfffduvLKK4vLfOlLX9KBAwd09dVXq6OjQxdeeKGeeOIJ13eAAADlryIrpW846t0/2dXW1qqzs3PQ8yDrdvmtt94adv/8CY4/wR3PPiz8CY4/wY1UqfwJLmT//v1asGBB8HN8QJ/D7hEAgIhKdkK6l156SRMmTBjQZj3Y2r9//6A277/qrX8JhPo5ePBgcFlrEqbu7u5gu/Wl23feeWfE67TuRkLt1jGxWP8Ssu4iQ8tb5/Lo4Mn7rdPqJ7Qt1p2Btd2e5T0lTYZa3sPqw3t3EerH8xeBodo9x9Y63t47htD+e4+397x57lCtuy7rvHney6GyZEO1W58foc8E6zPIs76jcQcEAEiCAQgAkAQDEAAgCQYgAEASDEAAgCRKNgXX3Nw8KBEVSrtJ4ZSZlRw5cOBAsN3z3RYr4REjxSKFUzKxvpMS47sDnr5jsY6tdd4822L1baWVQn1b6T2L9cVsq90zIZ213VbKzDPponfiOev8eK4t631i9eFJdVrXSYzvqXkmnCw1nlRfyHD3kTsgAEASDEAAgCQYgAAASTAAAQCSKLkQwnsPr0IPAa0Hg6GHfd5yF5527wPaGO2xHlzm+QA0Rd/e9rz6jlUs1XNteQt9epaPFUJIURQ2z3Pv6Wc0hA0sI92f95Z9v98puWrYb7zxhqZPn556MwAAI9Ta2qpp06aZr5fcANTf3689e/Zo4sSJ6u7u1vTp09Xa2lrWU3V3dXWxn2Xig7CPEvtZbmLvZ5Zl6u7u1tSpU4eMb5fcn+AqKyuLI+Z7t/WTJk0q65P/HvazfHwQ9lFiP8tNzP2sra1932UIIQAAkmAAAgAkUdIDUKFQ0K233qpCoZB6U3LFfpaPD8I+SuxnuUm1nyUXQgAAfDCU9B0QAKB8MQABAJJgAAIAJMEABABIggEIAJBESQ9Aa9as0R/+4R9q3LhxWrhwoZ577rnUmzQizz77rC655BJNnTpVFRUVevTRRwe8nmWZbrnlFk2ZMkU1NTVqamrSzp0702zsMWpubtZ5552niRMn6tRTT9Vll12mHTt2DFjm0KFDWr58uU466SRNmDBBS5cuVXt7e6ItPjZr167V3Llzi98cb2xs1OOPP158vRz28Wi33367KioqdMMNNxTbymE/v/rVr6qiomLAz5w5c4qvl8M+vufNN9/U5z73OZ100kmqqanRRz/6UW3fvr34+vH+DCrZAejf/u3ftGrVKt1666164YUXNG/ePC1evFj79u1LvWnH7MCBA5o3b57WrFkTfP2OO+7QPffco/vuu0/btm3T+PHjtXjxYh06dOg4b+mx27x5s5YvX66tW7fqqaeeUl9fnz75yU8OmAp95cqVeuyxx7RhwwZt3rxZe/bs0eWXX55wq/2mTZum22+/XS0tLdq+fbsuuugiXXrppXr11Vcllcc+/r7nn39e3/3udzV37twB7eWyn2eeeab27t1b/PnZz35WfK1c9vHtt9/WokWLVFVVpccff1yvvfaa/umf/kknnnhicZnj/hmUlajzzz8/W758efH/jxw5kk2dOjVrbm5OuFXxSMo2btxY/P/+/v6soaEhu/POO4ttHR0dWaFQyH7wgx8k2MI49u3bl0nKNm/enGXZu/tUVVWVbdiwobjMf//3f2eSsi1btqTazChOPPHE7J//+Z/Lbh+7u7uz2bNnZ0899VT2J3/yJ9n111+fZVn5nMtbb701mzdvXvC1ctnHLMuyL3/5y9mFF15ovp7iM6gk74B6e3vV0tKipqamYltlZaWampq0ZcuWhFuWn127dqmtrW3APtfW1mrhwoWjep87OzslSZMnT5YktbS0qK+vb8B+zpkzRzNmzBi1+3nkyBGtX79eBw4cUGNjY9nt4/Lly/WpT31qwP5I5XUud+7cqalTp+pDH/qQrrzySu3evVtSee3jj3/8Yy1YsECf/vSndeqpp+qcc87RAw88UHw9xWdQSQ5Av/vd73TkyBHV19cPaK+vr1dbW1uircrXe/tVTvvc39+vG264QYsWLdJZZ50l6d39rK6uVl1d3YBlR+N+vvzyy5owYYIKhYKuueYabdy4UWeccUZZ7eP69ev1wgsvqLm5edBr5bKfCxcu1EMPPaQnnnhCa9eu1a5du/Sxj31M3d3dZbOPkvSb3/xGa9eu1ezZs/Xkk0/q2muv1Re/+EU9/PDDktJ8BpXcdAwoH8uXL9crr7wy4O/p5eS0007TSy+9pM7OTv37v/+7li1bps2bN6ferGhaW1t1/fXX66mnntK4ceNSb05ulixZUvzvuXPnauHChZo5c6Z++MMfqqamJuGWxdXf368FCxbotttukySdc845euWVV3Tfffdp2bJlSbapJO+ATj75ZI0ZM2ZQ0qS9vV0NDQ2Jtipf7+1XuezzihUr9JOf/EQ//elPB8yI2NDQoN7eXnV0dAxYfjTuZ3V1tT784Q9r/vz5am5u1rx58/Stb32rbPaxpaVF+/bt07nnnquxY8dq7Nix2rx5s+655x6NHTtW9fX1ZbGfR6urq9NHPvIRvf7662VzLiVpypQpOuOMMwa0nX766cU/N6b4DCrJAai6ulrz58/Xpk2bim39/f3atGmTGhsbE25ZfmbNmqWGhoYB+9zV1aVt27aNqn3OskwrVqzQxo0b9fTTT2vWrFkDXp8/f76qqqoG7OeOHTu0e/fuUbWfIf39/erp6Smbfbz44ov18ssv66WXXir+LFiwQFdeeWXxv8thP4+2f/9+/frXv9aUKVPK5lxK0qJFiwZ9JeJXv/qVZs6cKSnRZ1Au0YYI1q9fnxUKheyhhx7KXnvttezqq6/O6urqsra2ttSbdsy6u7uzF198MXvxxRczSdk3v/nN7MUXX8z+93//N8uyLLv99tuzurq67Ec/+lH2i1/8Irv00kuzWbNmZQcPHky85cN37bXXZrW1tdkzzzyT7d27t/jzzjvvFJe55pprshkzZmRPP/10tn379qyxsTFrbGxMuNV+N910U7Z58+Zs165d2S9+8YvspptuyioqKrL//M//zLKsPPYx5PdTcFlWHvt54403Zs8880y2a9eu7Oc//3nW1NSUnXzyydm+ffuyLCuPfcyyLHvuueeysWPHZt/4xjeynTt3Zt///vezE044IfvXf/3X4jLH+zOoZAegLMuyb3/729mMGTOy6urq7Pzzz8+2bt2aepNG5Kc//WkmadDPsmXLsix7NwZ58803Z/X19VmhUMguvvjibMeOHWk32im0f5KydevWFZc5ePBg9rd/+7fZiSeemJ1wwgnZn//5n2d79+5Nt9HH4G/+5m+ymTNnZtXV1dkpp5ySXXzxxcXBJ8vKYx9Djh6AymE/r7jiimzKlClZdXV19gd/8AfZFVdckb3++uvF18thH9/z2GOPZWeddVZWKBSyOXPmZPfff/+A14/3ZxDzAQEAkijJZ0AAgPLHAAQASIIBCACQBAMQACAJBiAAQBIMQACAJBiAAABJMAABAJJgAAIAJMEABABIggEIAJDE/wP0V4VDuW3pfwAAAABJRU5ErkJggg==", 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", "text/plain": [ "
" ] @@ -499,7 +512,7 @@ "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", + "cp[\"ckpt_path\"] = f\"./outputs/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", @@ -512,7 +525,7 @@ "\n", "test = sample(noise)\n", "\n", - "plt.imshow(test[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")" + "plt.imshow(test[0, 0].cpu(), vmin=0.3, vmax=1, cmap=\"gray\")" ] }, { @@ -537,8 +550,7 @@ " --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} " + " --dataset_dir {dataset_dir}" ] }, { @@ -569,7 +581,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/models/mednist_ddpm/large_files.yml b/models/mednist_ddpm/large_files.yml new file mode 100644 index 00000000..5c1a74a0 --- /dev/null +++ b/models/mednist_ddpm/large_files.yml @@ -0,0 +1,5 @@ +large_files: +- path: "models/model.pt" + url: "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/mednist_ddpm.pt" + hash_val: "02fd8c8e8ed5f7cda5deeed72b69f4f1" + hash_type: "md5" From fd195c4b7ffcf3f557feee83e672278992fdf09f Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 21 Jan 2025 16:56:57 +0000 Subject: [PATCH 08/14] Change infer.yaml to inference.yaml. --- .../configs/{infer.yaml => inference.yaml} | 0 .../docs/2d_ddpm_bundle_tutorial.ipynb | 4 ++-- models/mednist_ddpm/docs/test.pt | Bin 17485 -> 17485 bytes 3 files changed, 2 insertions(+), 2 deletions(-) rename models/mednist_ddpm/configs/{infer.yaml => inference.yaml} (100%) diff --git a/models/mednist_ddpm/configs/infer.yaml b/models/mednist_ddpm/configs/inference.yaml similarity index 100% rename from models/mednist_ddpm/configs/infer.yaml rename to models/mednist_ddpm/configs/inference.yaml diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb index 9cf1dee7..9f9d4f2d 100644 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -446,7 +446,7 @@ } ], "source": [ - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/infer.yaml'\"\n", + "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/inference.yaml'\"\n", "\n", "!PYTHONPATH={bundle_root} python -m monai.bundle run testing \\\n", " --meta_file {bundle_root}/configs/metadata.json \\\n", @@ -510,7 +510,7 @@ "# 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.read_config([f\"{bundle_root}/configs/common.yaml\", f\"{bundle_root}/configs/inference.yaml\"])\n", "cp[\"bundle_root\"] = bundle_root\n", "cp[\"ckpt_path\"] = f\"./outputs/model_final_iteration=75000.pt\"\n", "\n", diff --git a/models/mednist_ddpm/docs/test.pt b/models/mednist_ddpm/docs/test.pt index 39b12ae12b1677adf672bb212cf906474ba0fb41..1ef6a85e814b609077964edd21bada7091bcdd53 100644 GIT binary patch literal 17485 zcmb8XWnfg<*0mel-Ccsa6ChO&mXv!c?(Xg(1b3IlT^e_2+}#^#tkK}^F5iSc_r32q z-#tHW=;~PQoxRqYYtAvp7<-#_g$g@36ff@Ze|?m3sN#?smXg{wG$=KwO=3)}YwrpU z-v9YApkPR9LUKrCn}D>`sMwT}0m)&(X;HDE0jXi}DGA8|=^aWYIW%kLSxnBKBJT`} z2umvP&mkpUV-tc>ZU6kRWKu!T0`i`ANrgO%%IA=@(4g+^k_sCf%BLn3vFIF{=?cid zp1Pi?NkyBn}$Dv_F0 z(wbDNjagT$_||f-udR@?|9qMM>&VMG)cj}U!C9$c2|v&0+PmuiJ@j8w5gV178XNZW 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z+Dp%|esBmYa{YNF{?EGg-NeHw`A+h?+f6gZC^MA~nHetMr_B)_q?MUfqVc$t8@TjV zPyN-dypZcKj1OISxYLD(TDeA0`u^YdxG?S;S7yu4j!I3Bk948SuTG2^>5O@RGus|G zQ)j*G3Aujkr&>A|zq9ASOgnbH>_WV;i}<2DbF{QGJv=%S*rYRkuXLo%`t}^E(jIf} z*F?8(POZeRI5W5oA(2(Z`}&uy)zrJT{v$5hp8Ry&<`#L#)=IzA7H8dKb8mmhb~~*B zUo?^HGX=-KZ0v}KgDZqj)Ega21#W;P(K_)Ce`x@6s` z&6N8!3D2x4`g1KNjH*lK*7{VL^p)iJ#%zmgO6todl-f1n=He!}~b+g@SYO^)y*ur)p_>FaK#8K;FyKk(^8uhdW9kW`+Ijf8w z8KLZsnQX~>7GiE)`e0WjuU%y>P5sI(W~cJpo2_@7^vjfSL)zQY(;M0jG`ed2*6Ws4 zy=}9#UgNRWt)auL3)U^L?(+G~dd2U)buYK9`3o*vbL`Grf7JbCT{(ZXHKFZnYqQ-w zt#gY!t&1Zp)=Dnbt@|fbweG9*tD?SlOo?l7Malg3q~g5ltukG`Sef~BLV26)C*@gt 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# From dd92a8e0abf65957dd3b7a8d26f35d39d9e11faa Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 28 Jan 2025 13:07:17 +0000 Subject: [PATCH 09/14] Move contents of common.yaml to train and inference. --- models/mednist_ddpm/configs/inference.yaml | 61 +++ models/mednist_ddpm/configs/train.yaml | 61 +++ .../mednist_ddpm/configs/train_multigpu.yaml | 53 ++ .../docs/2d_ddpm_bundle_tutorial.ipynb | 496 +++++++++--------- 4 files changed, 417 insertions(+), 254 deletions(-) diff --git a/models/mednist_ddpm/configs/inference.yaml b/models/mednist_ddpm/configs/inference.yaml index 5cfccae1..f0e83115 100644 --- a/models/mednist_ddpm/configs/inference.yaml +++ b/models/mednist_ddpm/configs/inference.yaml @@ -1,4 +1,65 @@ # This defines an inference script for generating a random image to a Pytorch file +imports: +- $import os +- $import datetime +- $import torch +- $import scripts +- $import monai +- $import torch.distributed as dist +- $import operator + +# Common elements to all yaml files +- +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' + +# Inference-specific batch_size: 1 num_workers: 0 diff --git a/models/mednist_ddpm/configs/train.yaml b/models/mednist_ddpm/configs/train.yaml index 6376f686..5a562b7d 100644 --- a/models/mednist_ddpm/configs/train.yaml +++ b/models/mednist_ddpm/configs/train.yaml @@ -1,4 +1,65 @@ # This defines the training script for the network +imports: +- $import os +- $import datetime +- $import torch +- $import scripts +- $import monai +- $import torch.distributed as dist +- $import operator + +# Common elements to all training files +- +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' + +# Training-specific # choose a new directory for every run output_dir: $datetime.datetime.now().strftime('./results/output_%y%m%d_%H%M%S') diff --git a/models/mednist_ddpm/configs/train_multigpu.yaml b/models/mednist_ddpm/configs/train_multigpu.yaml index 51f5acf4..d8a52274 100644 --- a/models/mednist_ddpm/configs/train_multigpu.yaml +++ b/models/mednist_ddpm/configs/train_multigpu.yaml @@ -1,4 +1,57 @@ # This can be mixed in with the training script to enable multi-GPU training +imports: +- $import os +- $import datetime +- $import torch +- $import scripts +- $import monai +- $import torch.distributed as dist +- $import operator + +# Common elements to all training files +- +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 + +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' + +# Training specific network: _target_: torch.nn.parallel.DistributedDataParallel diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb index 9f9d4f2d..cf04001d 100644 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -87,7 +87,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmpbsgu3aor\n" + "/tmp/tmpdoc1dv_l\n" ] } ], @@ -97,16 +97,6 @@ "print(dataset_dir)" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2dd40597-1073-4b16-b83b-a20dc7510fb2", - "metadata": {}, - "outputs": [], - "source": [ - "dataset_dir = \"/tmp/tmpbsgu3aor\"" - ] - }, { "cell_type": "markdown", "id": "5721b12a-8474-435b-aac2-c0ed054fa618", @@ -127,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "d52a4ae9-0d6d-4bc4-a5b5-f84470711f2d", "metadata": { "scrolled": true @@ -137,235 +127,234 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-01-21 15:17:00,312 - INFO - --- input summary of monai.bundle.scripts.run ---\n", - "2025-01-21 15:17:00,312 - INFO - > config_file: ('/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", - " '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/train.yaml')\n", - "2025-01-21 15:17:00,312 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", - "2025-01-21 15:17:00,312 - INFO - > logging_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", - "2025-01-21 15:17:00,312 - INFO - > run_id: 'training'\n", - "2025-01-21 15:17:00,312 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", - "2025-01-21 15:17:00,312 - INFO - > dataset_dir: '/tmp/tmpbsgu3aor'\n", - "2025-01-21 15:17:00,312 - INFO - > output_dir: './outputs'\n", - "2025-01-21 15:17:00,312 - INFO - ---\n", + "2025-01-28 10:45:10,893 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-28 10:45:10,894 - INFO - > config_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/train.yaml'\n", + "2025-01-28 10:45:10,894 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", + "2025-01-28 10:45:10,894 - INFO - > logging_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf'\n", + "2025-01-28 10:45:10,895 - INFO - > run_id: 'training'\n", + "2025-01-28 10:45:10,895 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", + "2025-01-28 10:45:10,895 - INFO - > dataset_dir: '/tmp/tmpdoc1dv_l'\n", + "2025-01-28 10:45:10,895 - INFO - > output_dir: './outputs'\n", + "2025-01-28 10:45:10,895 - INFO - ---\n", "\n", "\n", - "2025-01-21 15:17:00,313 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", + "2025-01-28 10:45:10,896 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", "Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.\n", - "2025-01-21 15:17:00,441 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2025-01-21 15:17:00,441 - INFO - File exists: /tmp/tmpbsgu3aor/MedNIST.tar.gz, skipped downloading.\n", - "2025-01-21 15:17:00,441 - INFO - Non-empty folder exists in /tmp/tmpbsgu3aor/MedNIST, skipped extracting.\n", - "2025-01-21 15:17:16,082 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2025-01-21 15:17:16,082 - INFO - File exists: /tmp/tmpbsgu3aor/MedNIST.tar.gz, skipped downloading.\n", - "2025-01-21 15:17:16,082 - INFO - Non-empty folder exists in /tmp/tmpbsgu3aor/MedNIST, skipped extracting.\n", + "2025-01-28 10:45:16,858 - INFO - Downloaded: /tmp/tmpdoc1dv_l/MedNIST.tar.gz\n", + "2025-01-28 10:45:16,933 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-28 10:45:16,934 - INFO - Writing into directory: /tmp/tmpdoc1dv_l.\n", + "2025-01-28 10:45:37,688 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2025-01-28 10:45:37,688 - INFO - File exists: /tmp/tmpdoc1dv_l/MedNIST.tar.gz, skipped downloading.\n", + "2025-01-28 10:45:37,688 - INFO - Non-empty folder exists in /tmp/tmpdoc1dv_l/MedNIST, skipped extracting.\n", "`torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", - "2025-01-21 15:17:18,185 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", + "2025-01-28 10:45:39,645 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n", "`torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", - "2025-01-21 15:18:08,905 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.1607925146818161\n", - "2025-01-21 15:18:08,905 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[1] Complete. Time taken: 00:00:50.720\n", - "2025-01-21 15:18:58,567 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.016663629561662674\n", - "2025-01-21 15:18:58,567 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[2] Complete. Time taken: 00:00:49.662\n", - "2025-01-21 15:19:48,316 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01578485034406185\n", - "2025-01-21 15:19:48,316 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[3] Complete. Time taken: 00:00:49.749\n", - "2025-01-21 15:20:37,997 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.013587715104222298\n", - "2025-01-21 15:20:37,997 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[4] Complete. Time taken: 00:00:49.681\n", - "2025-01-21 15:21:28,552 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012479547411203384\n", - "2025-01-21 15:21:28,553 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 4 until 5 epochs\n", + "2025-01-28 10:46:38,033 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.1607925146818161\n", + "2025-01-28 10:46:38,034 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[1] Complete. Time taken: 00:00:58.388\n", + "2025-01-28 10:47:29,911 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.016663629561662674\n", + "2025-01-28 10:47:29,911 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[2] Complete. Time taken: 00:00:51.877\n", + "2025-01-28 10:48:30,636 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01578485034406185\n", + "2025-01-28 10:48:30,637 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[3] Complete. Time taken: 00:01:00.725\n", + "2025-01-28 10:49:23,157 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.013587715104222298\n", + "2025-01-28 10:49:23,157 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[4] Complete. Time taken: 00:00:52.521\n", + "2025-01-28 10:50:14,409 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012479547411203384\n", + "2025-01-28 10:50:14,410 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 4 until 5 epochs\n", "`torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", - "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.05754538252949715\n", - "2025-01-21 15:21:31,907 - INFO - Epoch[5] Metrics -- val_mean_abs_error: 0.0575 \n", - "2025-01-21 15:21:31,907 - INFO - Key metric: val_mean_abs_error best value: 0.05754538252949715 at epoch: 5\n", - "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[5] Complete. Time taken: 00:00:03.207\n", - "2025-01-21 15:21:31,907 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.355\n", - "2025-01-21 15:21:31,980 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 5\n", - "2025-01-21 15:21:31,980 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[5] Complete. Time taken: 00:00:53.983\n", - "2025-01-21 15:22:21,218 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012240087613463402\n", - "2025-01-21 15:22:21,219 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[6] Complete. Time taken: 00:00:49.239\n", - "2025-01-21 15:23:10,782 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[7] Complete. Time taken: 00:00:49.563\n", - "2025-01-21 15:24:00,732 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[8] Complete. Time taken: 00:00:49.950\n", - "2025-01-21 15:24:50,887 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01170545443892479\n", - "2025-01-21 15:24:50,887 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[9] Complete. Time taken: 00:00:50.155\n", - "2025-01-21 15:25:40,531 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 9 until 10 epochs\n", - "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.052069272845983505\n", - "2025-01-21 15:25:43,603 - INFO - Epoch[10] Metrics -- val_mean_abs_error: 0.0521 \n", - "2025-01-21 15:25:43,603 - INFO - Key metric: val_mean_abs_error best value: 0.052069272845983505 at epoch: 10\n", - "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[10] Complete. Time taken: 00:00:03.072\n", - "2025-01-21 15:25:43,603 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.072\n", - "2025-01-21 15:25:43,960 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 10\n", - "2025-01-21 15:25:43,961 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[10] Complete. Time taken: 00:00:53.074\n", - "2025-01-21 15:26:34,387 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.011470048688352108\n", - "2025-01-21 15:26:34,387 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[11] Complete. Time taken: 00:00:50.426\n", - "2025-01-21 15:27:24,763 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010766257531940937\n", - "2025-01-21 15:27:24,763 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[12] Complete. Time taken: 00:00:50.377\n", - "2025-01-21 15:28:15,715 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[13] Complete. Time taken: 00:00:50.951\n", - "2025-01-21 15:29:05,690 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010334153659641743\n", - "2025-01-21 15:29:05,690 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[14] Complete. Time taken: 00:00:49.975\n", - "2025-01-21 15:29:55,818 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 14 until 15 epochs\n", - "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04713250324130058\n", - "2025-01-21 15:29:58,960 - INFO - Epoch[15] Metrics -- val_mean_abs_error: 0.0471 \n", - "2025-01-21 15:29:58,960 - INFO - Key metric: val_mean_abs_error best value: 0.04713250324130058 at epoch: 15\n", - "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[15] Complete. Time taken: 00:00:03.142\n", - "2025-01-21 15:29:58,960 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.142\n", - "2025-01-21 15:29:59,330 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 15\n", - "2025-01-21 15:29:59,330 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[15] Complete. Time taken: 00:00:53.640\n", - "2025-01-21 15:30:49,286 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010036583058536053\n", - "2025-01-21 15:30:49,286 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[16] Complete. Time taken: 00:00:49.956\n", - "2025-01-21 15:31:38,546 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[17] Complete. Time taken: 00:00:49.260\n", - "2025-01-21 15:32:29,131 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[18] Complete. Time taken: 00:00:50.584\n", - "2025-01-21 15:33:19,252 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010024736635386944\n", - "2025-01-21 15:33:19,253 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[19] Complete. Time taken: 00:00:50.122\n", - "2025-01-21 15:34:09,452 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 19 until 20 epochs\n", - "2025-01-21 15:34:12,633 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04626006633043289\n", - "2025-01-21 15:34:12,633 - INFO - Epoch[20] Metrics -- val_mean_abs_error: 0.0463 \n", - "2025-01-21 15:34:12,633 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", - "2025-01-21 15:34:12,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[20] Complete. Time taken: 00:00:03.181\n", - "2025-01-21 15:34:12,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.181\n", - "2025-01-21 15:34:13,009 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 20\n", - "2025-01-21 15:34:13,010 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[20] Complete. Time taken: 00:00:53.757\n", - "2025-01-21 15:35:02,975 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[21] Complete. Time taken: 00:00:49.965\n", - "2025-01-21 15:35:52,881 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010010532103478909\n", - "2025-01-21 15:35:52,881 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[22] Complete. Time taken: 00:00:49.906\n", - "2025-01-21 15:36:42,704 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.0098584508523345\n", - "2025-01-21 15:36:42,704 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[23] Complete. Time taken: 00:00:49.823\n", - "2025-01-21 15:37:32,855 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[24] Complete. Time taken: 00:00:50.151\n", - "2025-01-21 15:38:22,864 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 24 until 25 epochs\n", - "2025-01-21 15:38:26,081 - INFO - Epoch[25] Metrics -- val_mean_abs_error: 0.0496 \n", - "2025-01-21 15:38:26,081 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", - "2025-01-21 15:38:26,081 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[25] Complete. Time taken: 00:00:03.217\n", - "2025-01-21 15:38:26,081 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.217\n", - "2025-01-21 15:38:26,449 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 25\n", - "2025-01-21 15:38:26,449 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[25] Complete. Time taken: 00:00:53.594\n", - "2025-01-21 15:39:16,354 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00983799621462822\n", - "2025-01-21 15:39:16,354 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[26] Complete. Time taken: 00:00:49.905\n", - "2025-01-21 15:40:06,370 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009661602787673473\n", - "2025-01-21 15:40:06,370 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[27] Complete. Time taken: 00:00:50.016\n", - "2025-01-21 15:40:56,154 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[28] Complete. Time taken: 00:00:49.784\n", - "2025-01-21 15:41:46,556 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[29] Complete. Time taken: 00:00:50.402\n", - "2025-01-21 15:42:36,634 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 29 until 30 epochs\n", - "2025-01-21 15:42:39,699 - INFO - Epoch[30] Metrics -- val_mean_abs_error: 0.0470 \n", - "2025-01-21 15:42:39,699 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", - "2025-01-21 15:42:39,699 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[30] Complete. Time taken: 00:00:03.064\n", - "2025-01-21 15:42:39,699 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.064\n", - "2025-01-21 15:42:40,067 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 30\n", - "2025-01-21 15:42:40,067 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[30] Complete. Time taken: 00:00:53.511\n", - "2025-01-21 15:43:30,284 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00965067371726036\n", - "2025-01-21 15:43:30,284 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[31] Complete. Time taken: 00:00:50.217\n", - "2025-01-21 15:44:20,682 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009442757815122604\n", - "2025-01-21 15:44:20,682 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[32] Complete. Time taken: 00:00:50.397\n", - "2025-01-21 15:45:09,972 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008967726491391659\n", - "2025-01-21 15:45:09,972 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[33] Complete. Time taken: 00:00:49.290\n", - "2025-01-21 15:46:00,287 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[34] Complete. Time taken: 00:00:50.315\n", - "2025-01-21 15:46:50,509 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 34 until 35 epochs\n", - "2025-01-21 15:46:53,651 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04337985813617706\n", - "2025-01-21 15:46:53,651 - INFO - Epoch[35] Metrics -- val_mean_abs_error: 0.0434 \n", - "2025-01-21 15:46:53,651 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", - "2025-01-21 15:46:53,651 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[35] Complete. Time taken: 00:00:03.141\n", - "2025-01-21 15:46:53,652 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.142\n", - "2025-01-21 15:46:54,014 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 35\n", - "2025-01-21 15:46:54,015 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[35] Complete. Time taken: 00:00:53.728\n", - "2025-01-21 15:47:43,700 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[36] Complete. Time taken: 00:00:49.685\n", - "2025-01-21 15:48:33,567 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[37] Complete. Time taken: 00:00:49.867\n", - "2025-01-21 15:49:24,261 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[38] Complete. Time taken: 00:00:50.694\n", - "2025-01-21 15:50:14,440 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[39] Complete. Time taken: 00:00:50.178\n", - "2025-01-21 15:51:04,645 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 39 until 40 epochs\n", - "2025-01-21 15:51:07,744 - INFO - Epoch[40] Metrics -- val_mean_abs_error: 0.0438 \n", - "2025-01-21 15:51:07,744 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", - "2025-01-21 15:51:07,744 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[40] Complete. Time taken: 00:00:03.098\n", - "2025-01-21 15:51:07,744 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.099\n", - "2025-01-21 15:51:08,102 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 40\n", - "2025-01-21 15:51:08,102 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[40] Complete. Time taken: 00:00:53.662\n", - "2025-01-21 15:51:58,081 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[41] Complete. Time taken: 00:00:49.979\n", - "2025-01-21 15:52:47,658 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[42] Complete. Time taken: 00:00:49.577\n", - "2025-01-21 15:53:37,196 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[43] Complete. Time taken: 00:00:49.538\n", - "2025-01-21 15:54:27,401 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[44] Complete. Time taken: 00:00:50.205\n", - "2025-01-21 15:55:17,782 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 44 until 45 epochs\n", - "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04306837171316147\n", - "2025-01-21 15:55:20,968 - INFO - Epoch[45] Metrics -- val_mean_abs_error: 0.0431 \n", - "2025-01-21 15:55:20,968 - INFO - Key metric: val_mean_abs_error best value: 0.04306837171316147 at epoch: 45\n", - "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[45] Complete. Time taken: 00:00:03.185\n", - "2025-01-21 15:55:20,968 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.186\n", - "2025-01-21 15:55:21,331 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 45\n", - "2025-01-21 15:55:21,332 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[45] Complete. Time taken: 00:00:53.931\n", - "2025-01-21 15:56:11,303 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[46] Complete. Time taken: 00:00:49.971\n", - "2025-01-21 15:57:01,472 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[47] Complete. Time taken: 00:00:50.170\n", - "2025-01-21 15:57:51,853 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[48] Complete. Time taken: 00:00:50.380\n", - "2025-01-21 15:58:41,820 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[49] Complete. Time taken: 00:00:49.968\n", - "2025-01-21 15:59:30,957 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 49 until 50 epochs\n", - "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.0430283285677433\n", - "2025-01-21 15:59:34,146 - INFO - Epoch[50] Metrics -- val_mean_abs_error: 0.0430 \n", - "2025-01-21 15:59:34,146 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", - "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[50] Complete. Time taken: 00:00:03.188\n", - "2025-01-21 15:59:34,146 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.189\n", - "2025-01-21 15:59:34,509 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 50\n", - "2025-01-21 15:59:34,510 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[50] Complete. Time taken: 00:00:52.689\n", - "2025-01-21 16:00:23,913 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[51] Complete. Time taken: 00:00:49.403\n", - "2025-01-21 16:01:13,649 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008929001167416573\n", - "2025-01-21 16:01:13,649 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[52] Complete. Time taken: 00:00:49.736\n", - "2025-01-21 16:02:03,775 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008782487362623215\n", - "2025-01-21 16:02:03,775 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[53] Complete. Time taken: 00:00:50.125\n", - "2025-01-21 16:02:53,273 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008487475104629993\n", - "2025-01-21 16:02:53,273 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[54] Complete. Time taken: 00:00:49.498\n", - "2025-01-21 16:03:43,201 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 54 until 55 epochs\n", - "2025-01-21 16:03:46,321 - INFO - Epoch[55] Metrics -- val_mean_abs_error: 0.0439 \n", - "2025-01-21 16:03:46,322 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", - "2025-01-21 16:03:46,322 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[55] Complete. Time taken: 00:00:03.119\n", - "2025-01-21 16:03:46,322 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.120\n", - "2025-01-21 16:03:46,686 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 55\n", - "2025-01-21 16:03:46,687 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[55] Complete. Time taken: 00:00:53.414\n", - "2025-01-21 16:04:36,199 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[56] Complete. Time taken: 00:00:49.513\n", - "2025-01-21 16:05:25,939 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[57] Complete. Time taken: 00:00:49.740\n", - "2025-01-21 16:06:16,157 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[58] Complete. Time taken: 00:00:50.218\n", - "2025-01-21 16:07:05,446 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[59] Complete. Time taken: 00:00:49.289\n", - "2025-01-21 16:07:55,170 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 59 until 60 epochs\n", - "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.041947875171899796\n", - "2025-01-21 16:07:58,417 - INFO - Epoch[60] Metrics -- val_mean_abs_error: 0.0419 \n", - "2025-01-21 16:07:58,417 - INFO - Key metric: val_mean_abs_error best value: 0.041947875171899796 at epoch: 60\n", - "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[60] Complete. Time taken: 00:00:03.247\n", - "2025-01-21 16:07:58,417 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.248\n", - "2025-01-21 16:07:58,777 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 60\n", - "2025-01-21 16:07:58,777 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[60] Complete. Time taken: 00:00:53.331\n", - "2025-01-21 16:08:49,052 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[61] Complete. Time taken: 00:00:50.275\n", - "2025-01-21 16:09:39,838 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[62] Complete. Time taken: 00:00:50.785\n", - "2025-01-21 16:10:30,140 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[63] Complete. Time taken: 00:00:50.303\n", - "2025-01-21 16:11:20,742 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[64] Complete. Time taken: 00:00:50.601\n", - "2025-01-21 16:12:11,106 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 64 until 65 epochs\n", - "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.03936021775007248\n", - "2025-01-21 16:12:14,362 - INFO - Epoch[65] Metrics -- val_mean_abs_error: 0.0394 \n", - "2025-01-21 16:12:14,362 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", - "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[65] Complete. Time taken: 00:00:03.255\n", - "2025-01-21 16:12:14,362 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.256\n", - "2025-01-21 16:12:14,722 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 65\n", - "2025-01-21 16:12:14,722 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[65] Complete. Time taken: 00:00:53.980\n", - "2025-01-21 16:13:05,332 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[66] Complete. Time taken: 00:00:50.610\n", - "2025-01-21 16:13:55,536 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[67] Complete. Time taken: 00:00:50.204\n", - "2025-01-21 16:14:45,787 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[68] Complete. Time taken: 00:00:50.251\n", - "2025-01-21 16:15:35,860 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[69] Complete. Time taken: 00:00:50.073\n", - "2025-01-21 16:16:26,467 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 69 until 70 epochs\n", - "2025-01-21 16:16:29,726 - INFO - Epoch[70] Metrics -- val_mean_abs_error: 0.0419 \n", - "2025-01-21 16:16:29,726 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", - "2025-01-21 16:16:29,726 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[70] Complete. Time taken: 00:00:03.258\n", - "2025-01-21 16:16:29,726 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.259\n", - "2025-01-21 16:16:30,089 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 70\n", - "2025-01-21 16:16:30,090 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[70] Complete. Time taken: 00:00:54.230\n", - "2025-01-21 16:17:20,037 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[71] Complete. Time taken: 00:00:49.947\n", - "2025-01-21 16:18:09,870 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[72] Complete. Time taken: 00:00:49.833\n", - "2025-01-21 16:19:00,519 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[73] Complete. Time taken: 00:00:50.649\n", - "2025-01-21 16:19:51,690 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[74] Complete. Time taken: 00:00:51.171\n", - "2025-01-21 16:20:44,185 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008344339206814766\n", - "2025-01-21 16:20:44,185 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 74 until 75 epochs\n", - "2025-01-21 16:20:47,299 - INFO - Epoch[75] Metrics -- val_mean_abs_error: 0.0419 \n", - "2025-01-21 16:20:47,299 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", - "2025-01-21 16:20:47,299 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[75] Complete. Time taken: 00:00:03.113\n", - "2025-01-21 16:20:47,299 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.114\n", - "2025-01-21 16:20:47,370 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 75\n", - "2025-01-21 16:20:47,370 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[75] Complete. Time taken: 00:00:55.680\n", - "2025-01-21 16:20:47,404 - ignite.engine.engine.SupervisedTrainer - INFO - Train completed, saved final checkpoint: outputs/model_final_iteration=75000.pt\n", - "2025-01-21 16:20:47,404 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run complete. Time taken: 01:03:29.219\n" + "2025-01-28 10:50:17,861 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.05754538252949715\n", + "2025-01-28 10:50:17,862 - INFO - Epoch[5] Metrics -- val_mean_abs_error: 0.0575 \n", + "2025-01-28 10:50:17,862 - INFO - Key metric: val_mean_abs_error best value: 0.05754538252949715 at epoch: 5\n", + "2025-01-28 10:50:17,862 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[5] Complete. Time taken: 00:00:03.373\n", + "2025-01-28 10:50:17,862 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.452\n", + "2025-01-28 10:50:17,933 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 5\n", + "2025-01-28 10:50:17,933 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[5] Complete. Time taken: 00:00:54.775\n", + "2025-01-28 10:51:11,088 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.012240087613463402\n", + "2025-01-28 10:51:11,088 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[6] Complete. Time taken: 00:00:53.155\n", + "2025-01-28 10:52:04,514 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[7] Complete. Time taken: 00:00:53.427\n", + "2025-01-28 10:52:57,317 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[8] Complete. Time taken: 00:00:52.803\n", + "2025-01-28 10:53:49,711 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.01170545443892479\n", + "2025-01-28 10:53:49,712 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[9] Complete. Time taken: 00:00:52.395\n", + "2025-01-28 10:54:51,839 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 9 until 10 epochs\n", + "2025-01-28 10:54:55,220 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.052069272845983505\n", + "2025-01-28 10:54:55,220 - INFO - Epoch[10] Metrics -- val_mean_abs_error: 0.0521 \n", + "2025-01-28 10:54:55,220 - INFO - Key metric: val_mean_abs_error best value: 0.052069272845983505 at epoch: 10\n", + "2025-01-28 10:54:55,220 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[10] Complete. Time taken: 00:00:03.381\n", + "2025-01-28 10:54:55,220 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.381\n", + "2025-01-28 10:54:55,292 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 10\n", + "2025-01-28 10:54:55,292 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[10] Complete. Time taken: 00:01:05.580\n", + "2025-01-28 10:55:49,249 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.011470048688352108\n", + "2025-01-28 10:55:49,249 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[11] Complete. Time taken: 00:00:53.957\n", + "2025-01-28 10:56:41,933 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010766257531940937\n", + "2025-01-28 10:56:41,933 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[12] Complete. Time taken: 00:00:52.684\n", + "2025-01-28 10:57:32,681 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[13] Complete. Time taken: 00:00:50.748\n", + "2025-01-28 10:58:22,006 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010334153659641743\n", + "2025-01-28 10:58:22,007 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[14] Complete. Time taken: 00:00:49.326\n", + "2025-01-28 10:59:12,718 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 14 until 15 epochs\n", + "2025-01-28 10:59:15,903 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04713250324130058\n", + "2025-01-28 10:59:15,903 - INFO - Epoch[15] Metrics -- val_mean_abs_error: 0.0471 \n", + "2025-01-28 10:59:15,903 - INFO - Key metric: val_mean_abs_error best value: 0.04713250324130058 at epoch: 15\n", + "2025-01-28 10:59:15,903 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[15] Complete. Time taken: 00:00:03.184\n", + "2025-01-28 10:59:15,903 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.185\n", + "2025-01-28 10:59:15,972 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 15\n", + "2025-01-28 10:59:15,972 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[15] Complete. Time taken: 00:00:53.966\n", + "2025-01-28 11:00:05,379 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010036583058536053\n", + "2025-01-28 11:00:05,379 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[16] Complete. Time taken: 00:00:49.407\n", + "2025-01-28 11:00:54,954 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[17] Complete. Time taken: 00:00:49.575\n", + "2025-01-28 11:01:44,481 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[18] Complete. Time taken: 00:00:49.527\n", + "2025-01-28 11:02:34,070 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010024736635386944\n", + "2025-01-28 11:02:34,070 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[19] Complete. Time taken: 00:00:49.590\n", + "2025-01-28 11:03:24,598 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 19 until 20 epochs\n", + "2025-01-28 11:03:28,013 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04626006633043289\n", + "2025-01-28 11:03:28,013 - INFO - Epoch[20] Metrics -- val_mean_abs_error: 0.0463 \n", + "2025-01-28 11:03:28,013 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-28 11:03:28,013 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[20] Complete. Time taken: 00:00:03.414\n", + "2025-01-28 11:03:28,014 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.415\n", + "2025-01-28 11:03:28,373 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 20\n", + "2025-01-28 11:03:28,373 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[20] Complete. Time taken: 00:00:54.303\n", + "2025-01-28 11:04:21,035 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[21] Complete. Time taken: 00:00:52.662\n", + "2025-01-28 11:05:11,684 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.010010532103478909\n", + "2025-01-28 11:05:11,684 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[22] Complete. Time taken: 00:00:50.648\n", + "2025-01-28 11:06:00,999 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.0098584508523345\n", + "2025-01-28 11:06:01,000 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[23] Complete. Time taken: 00:00:49.316\n", + "2025-01-28 11:06:54,731 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[24] Complete. Time taken: 00:00:53.732\n", + "2025-01-28 11:07:49,033 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 24 until 25 epochs\n", + "2025-01-28 11:07:52,450 - INFO - Epoch[25] Metrics -- val_mean_abs_error: 0.0496 \n", + "2025-01-28 11:07:52,450 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-28 11:07:52,450 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[25] Complete. Time taken: 00:00:03.416\n", + "2025-01-28 11:07:52,450 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.417\n", + "2025-01-28 11:07:52,809 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 25\n", + "2025-01-28 11:07:52,809 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[25] Complete. Time taken: 00:00:58.078\n", + "2025-01-28 11:08:47,931 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00983799621462822\n", + "2025-01-28 11:08:47,931 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[26] Complete. Time taken: 00:00:55.122\n", + "2025-01-28 11:09:40,163 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009661602787673473\n", + "2025-01-28 11:09:40,163 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[27] Complete. Time taken: 00:00:52.232\n", + "2025-01-28 11:10:32,018 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[28] Complete. Time taken: 00:00:51.855\n", + "2025-01-28 11:11:26,697 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[29] Complete. Time taken: 00:00:54.679\n", + "2025-01-28 11:12:19,319 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 29 until 30 epochs\n", + "2025-01-28 11:12:22,592 - INFO - Epoch[30] Metrics -- val_mean_abs_error: 0.0470 \n", + "2025-01-28 11:12:22,592 - INFO - Key metric: val_mean_abs_error best value: 0.04626006633043289 at epoch: 20\n", + "2025-01-28 11:12:22,592 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[30] Complete. Time taken: 00:00:03.272\n", + "2025-01-28 11:12:22,592 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.273\n", + "2025-01-28 11:12:22,649 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 30\n", + "2025-01-28 11:12:22,649 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[30] Complete. Time taken: 00:00:55.952\n", + "2025-01-28 11:13:16,180 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.00965067371726036\n", + "2025-01-28 11:13:16,180 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[31] Complete. Time taken: 00:00:53.531\n", + "2025-01-28 11:14:10,456 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.009442757815122604\n", + "2025-01-28 11:14:10,456 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[32] Complete. Time taken: 00:00:54.275\n", + "2025-01-28 11:15:03,456 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008967726491391659\n", + "2025-01-28 11:15:03,456 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[33] Complete. Time taken: 00:00:53.000\n", + "2025-01-28 11:15:55,835 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[34] Complete. Time taken: 00:00:52.379\n", + "2025-01-28 11:16:47,540 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 34 until 35 epochs\n", + "2025-01-28 11:16:50,823 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04337985813617706\n", + "2025-01-28 11:16:50,823 - INFO - Epoch[35] Metrics -- val_mean_abs_error: 0.0434 \n", + "2025-01-28 11:16:50,823 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-28 11:16:50,823 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[35] Complete. Time taken: 00:00:03.282\n", + "2025-01-28 11:16:50,823 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.283\n", + "2025-01-28 11:16:50,894 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 35\n", + "2025-01-28 11:16:50,894 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[35] Complete. Time taken: 00:00:55.060\n", + "2025-01-28 11:17:42,195 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[36] Complete. Time taken: 00:00:51.301\n", + "2025-01-28 11:18:34,921 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[37] Complete. Time taken: 00:00:52.726\n", + "2025-01-28 11:19:27,769 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[38] Complete. Time taken: 00:00:52.848\n", + "2025-01-28 11:20:20,623 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[39] Complete. Time taken: 00:00:52.854\n", + "2025-01-28 11:21:11,887 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 39 until 40 epochs\n", + "2025-01-28 11:21:14,992 - INFO - Epoch[40] Metrics -- val_mean_abs_error: 0.0438 \n", + "2025-01-28 11:21:14,992 - INFO - Key metric: val_mean_abs_error best value: 0.04337985813617706 at epoch: 35\n", + "2025-01-28 11:21:14,992 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[40] Complete. Time taken: 00:00:03.104\n", + "2025-01-28 11:21:14,992 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.105\n", + "2025-01-28 11:21:15,062 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 40\n", + "2025-01-28 11:21:15,062 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[40] Complete. Time taken: 00:00:54.439\n", + "2025-01-28 11:22:06,367 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[41] Complete. Time taken: 00:00:51.305\n", + "2025-01-28 11:22:57,686 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[42] Complete. Time taken: 00:00:51.319\n", + "2025-01-28 11:23:49,550 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[43] Complete. Time taken: 00:00:51.864\n", + "2025-01-28 11:24:40,625 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[44] Complete. Time taken: 00:00:51.074\n", + "2025-01-28 11:25:31,099 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 44 until 45 epochs\n", + "2025-01-28 11:25:34,234 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.04306837171316147\n", + "2025-01-28 11:25:34,234 - INFO - Epoch[45] Metrics -- val_mean_abs_error: 0.0431 \n", + "2025-01-28 11:25:34,234 - INFO - Key metric: val_mean_abs_error best value: 0.04306837171316147 at epoch: 45\n", + "2025-01-28 11:25:34,234 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[45] Complete. Time taken: 00:00:03.135\n", + "2025-01-28 11:25:34,235 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.135\n", + "2025-01-28 11:25:34,584 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 45\n", + "2025-01-28 11:25:34,585 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[45] Complete. Time taken: 00:00:53.960\n", + "2025-01-28 11:26:24,509 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[46] Complete. Time taken: 00:00:49.924\n", + "2025-01-28 11:27:13,455 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[47] Complete. Time taken: 00:00:48.945\n", + "2025-01-28 11:28:03,014 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[48] Complete. Time taken: 00:00:49.559\n", + "2025-01-28 11:28:52,035 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[49] Complete. Time taken: 00:00:49.021\n", + "2025-01-28 11:29:41,226 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 49 until 50 epochs\n", + "2025-01-28 11:29:44,269 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.0430283285677433\n", + "2025-01-28 11:29:44,269 - INFO - Epoch[50] Metrics -- val_mean_abs_error: 0.0430 \n", + "2025-01-28 11:29:44,269 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-28 11:29:44,269 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[50] Complete. Time taken: 00:00:03.042\n", + "2025-01-28 11:29:44,269 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.043\n", + "2025-01-28 11:29:44,628 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 50\n", + "2025-01-28 11:29:44,628 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[50] Complete. Time taken: 00:00:52.594\n", + "2025-01-28 11:30:34,707 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[51] Complete. Time taken: 00:00:50.079\n", + "2025-01-28 11:31:25,426 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008929001167416573\n", + "2025-01-28 11:31:25,426 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[52] Complete. Time taken: 00:00:50.719\n", + "2025-01-28 11:32:17,793 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008782487362623215\n", + "2025-01-28 11:32:17,794 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[53] Complete. Time taken: 00:00:52.368\n", + "2025-01-28 11:33:09,429 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008487475104629993\n", + "2025-01-28 11:33:09,429 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[54] Complete. Time taken: 00:00:51.636\n", + "2025-01-28 11:34:00,926 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 54 until 55 epochs\n", + "2025-01-28 11:34:04,209 - INFO - Epoch[55] Metrics -- val_mean_abs_error: 0.0439 \n", + "2025-01-28 11:34:04,209 - INFO - Key metric: val_mean_abs_error best value: 0.0430283285677433 at epoch: 50\n", + "2025-01-28 11:34:04,209 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[55] Complete. Time taken: 00:00:03.283\n", + "2025-01-28 11:34:04,209 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.283\n", + "2025-01-28 11:34:04,277 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 55\n", + "2025-01-28 11:34:04,277 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[55] Complete. Time taken: 00:00:54.848\n", + "2025-01-28 11:34:55,848 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[56] Complete. Time taken: 00:00:51.571\n", + "2025-01-28 11:35:47,270 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[57] Complete. Time taken: 00:00:51.422\n", + "2025-01-28 11:36:37,430 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[58] Complete. Time taken: 00:00:50.160\n", + "2025-01-28 11:37:27,691 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[59] Complete. Time taken: 00:00:50.261\n", + "2025-01-28 11:38:19,228 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 59 until 60 epochs\n", + "2025-01-28 11:38:22,427 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.041947875171899796\n", + "2025-01-28 11:38:22,427 - INFO - Epoch[60] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-28 11:38:22,427 - INFO - Key metric: val_mean_abs_error best value: 0.041947875171899796 at epoch: 60\n", + "2025-01-28 11:38:22,427 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[60] Complete. Time taken: 00:00:03.198\n", + "2025-01-28 11:38:22,427 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.198\n", + "2025-01-28 11:38:22,785 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 60\n", + "2025-01-28 11:38:22,785 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[60] Complete. Time taken: 00:00:55.094\n", + "2025-01-28 11:39:14,371 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[61] Complete. Time taken: 00:00:51.586\n", + "2025-01-28 11:40:06,623 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[62] Complete. Time taken: 00:00:52.252\n", + "2025-01-28 11:40:58,129 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[63] Complete. Time taken: 00:00:51.506\n", + "2025-01-28 11:41:47,897 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[64] Complete. Time taken: 00:00:49.768\n", + "2025-01-28 11:42:39,507 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 64 until 65 epochs\n", + "2025-01-28 11:42:42,800 - ignite.engine.engine.SupervisedEvaluator - INFO - Got new best metric of val_mean_abs_error: 0.03936021775007248\n", + "2025-01-28 11:42:42,800 - INFO - Epoch[65] Metrics -- val_mean_abs_error: 0.0394 \n", + "2025-01-28 11:42:42,800 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-28 11:42:42,800 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[65] Complete. Time taken: 00:00:03.292\n", + "2025-01-28 11:42:42,800 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.293\n", + "2025-01-28 11:42:42,907 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 65\n", + "2025-01-28 11:42:42,907 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[65] Complete. Time taken: 00:00:55.010\n", + "2025-01-28 11:43:34,027 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[66] Complete. Time taken: 00:00:51.120\n", + "2025-01-28 11:44:23,357 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[67] Complete. Time taken: 00:00:49.330\n", + "2025-01-28 11:45:15,222 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[68] Complete. Time taken: 00:00:51.865\n", + "2025-01-28 11:46:05,380 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[69] Complete. Time taken: 00:00:50.158\n", + "2025-01-28 11:46:54,867 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 69 until 70 epochs\n", + "2025-01-28 11:46:58,033 - INFO - Epoch[70] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-28 11:46:58,033 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-28 11:46:58,033 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[70] Complete. Time taken: 00:00:03.165\n", + "2025-01-28 11:46:58,033 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.166\n", + "2025-01-28 11:46:58,103 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 70\n", + "2025-01-28 11:46:58,104 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[70] Complete. Time taken: 00:00:52.723\n", + "2025-01-28 11:47:48,003 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[71] Complete. Time taken: 00:00:49.899\n", + "2025-01-28 11:48:39,114 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[72] Complete. Time taken: 00:00:51.111\n", + "2025-01-28 11:49:28,678 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[73] Complete. Time taken: 00:00:49.564\n", + "2025-01-28 11:50:20,176 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[74] Complete. Time taken: 00:00:51.498\n", + "2025-01-28 11:51:10,599 - ignite.engine.engine.SupervisedTrainer - INFO - Got new best metric of train_acc: 0.008344327099621296\n", + "2025-01-28 11:51:10,599 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run resuming from iteration 0, epoch 74 until 75 epochs\n", + "2025-01-28 11:51:13,917 - INFO - Epoch[75] Metrics -- val_mean_abs_error: 0.0419 \n", + "2025-01-28 11:51:13,918 - INFO - Key metric: val_mean_abs_error best value: 0.03936021775007248 at epoch: 65\n", + "2025-01-28 11:51:13,918 - ignite.engine.engine.SupervisedEvaluator - INFO - Epoch[75] Complete. Time taken: 00:00:03.318\n", + "2025-01-28 11:51:13,918 - ignite.engine.engine.SupervisedEvaluator - INFO - Engine run complete. Time taken: 00:00:03.319\n", + "2025-01-28 11:51:13,990 - ignite.engine.engine.SupervisedTrainer - INFO - Saved checkpoint at epoch: 75\n", + "2025-01-28 11:51:13,990 - ignite.engine.engine.SupervisedTrainer - INFO - Epoch[75] Complete. Time taken: 00:00:53.813\n", + "2025-01-28 11:51:14,103 - ignite.engine.engine.SupervisedTrainer - INFO - Train completed, saved final checkpoint: outputs/model_final_iteration=75000.pt\n", + "2025-01-28 11:51:14,103 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run complete. Time taken: 01:05:34.457\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", + "configs=f\"{bundle_root}/configs/train.yaml\"\n", "!PYTHONPATH={bundle_root} python -m monai.bundle run training \\\n", " --meta_file {bundle_root}/configs/metadata.json \\\n", " --config_file \"{configs}\" \\\n", @@ -393,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "40e6a3e9-3984-44b0-ba9a-5b8d58c7ea2d", "metadata": {}, "outputs": [ @@ -401,20 +390,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-01-21 16:26:26,051 - INFO - --- input summary of monai.bundle.scripts.run ---\n", - "2025-01-21 16:26:26,051 - INFO - > config_file: ('/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/common.yaml',\n", - " '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/infer.yaml')\n", - "2025-01-21 16:26:26,051 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", - "2025-01-21 16:26:26,051 - INFO - > run_id: 'testing'\n", - "2025-01-21 16:26:26,051 - INFO - > ckpt_path: './outputs/model_final_iteration=75000.pt'\n", - "2025-01-21 16:26:26,051 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", - "2025-01-21 16:26:26,051 - INFO - > out_file: 'test.pt'\n", - "2025-01-21 16:26:26,051 - INFO - ---\n", + "2025-01-28 11:52:00,179 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2025-01-28 11:52:00,179 - INFO - > config_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/inference.yaml'\n", + "2025-01-28 11:52:00,179 - INFO - > meta_file: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/metadata.json'\n", + "2025-01-28 11:52:00,179 - INFO - > run_id: 'testing'\n", + "2025-01-28 11:52:00,179 - INFO - > ckpt_path: './outputs/model_final_iteration=75000.pt'\n", + "2025-01-28 11:52:00,179 - INFO - > bundle_root: '/data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm'\n", + "2025-01-28 11:52:00,179 - INFO - > out_file: 'test.pt'\n", + "2025-01-28 11:52:00,179 - INFO - ---\n", "\n", "\n", - "2025-01-21 16:26:26,051 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", + "2025-01-28 11:52:00,179 - INFO - Setting logging properties based on config: /data/PycharmProjects/monai-model-zoo/model-zoo/models/mednist_ddpm/configs/logging.conf.\n", "Detected deprecated name 'optional_packages_version' in configuration file, replacing with 'required_packages_version'.\n", - "100%|██████████████████████████████████████| 1000/1000 [00:08<00:00, 114.77it/s]\n" + "100%|██████████████████████████████████████| 1000/1000 [00:08<00:00, 113.53it/s]\n" ] }, { @@ -427,16 +415,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -446,7 +434,7 @@ } ], "source": [ - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/inference.yaml'\"\n", + "configs=f\"{bundle_root}/configs/inference.yaml\"\n", "\n", "!PYTHONPATH={bundle_root} python -m monai.bundle run testing \\\n", " --meta_file {bundle_root}/configs/metadata.json \\\n", @@ -457,7 +445,7 @@ "\n", "test = torch.load(\"test.pt\", map_location=\"cpu\")\n", "\n", - "plt.imshow(test[0, 0], vmin=0.3, vmax=1.0, cmap=\"gray\")" + "plt.imshow(test[0, 0], vmin=0.4, vmax=1.0, cmap=\"gray\")" ] }, { @@ -478,13 +466,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:08<00:00, 116.83it/s]\n" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:08<00:00, 122.14it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -493,7 +481,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -510,7 +498,7 @@ "# 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/inference.yaml\"])\n", + "cp.read_config([f\"{bundle_root}/configs/inference.yaml\"])\n", "cp[\"bundle_root\"] = bundle_root\n", "cp[\"ckpt_path\"] = f\"./outputs/model_final_iteration=75000.pt\"\n", "\n", @@ -525,7 +513,7 @@ "\n", "test = sample(noise)\n", "\n", - "plt.imshow(test[0, 0].cpu(), vmin=0.3, vmax=1, cmap=\"gray\")" + "plt.imshow(test[0, 0].cpu(), vmin=0.75, vmax=1, cmap=\"gray\") # Toggle vmin for more contrast" ] }, { @@ -543,7 +531,7 @@ "metadata": {}, "outputs": [], "source": [ - "configs=f\"'{bundle_root}/configs/common.yaml', '{bundle_root}/configs/train.yaml', '{bundle_root}/configs/train_multigpu.yaml'\"\n", + "configs=f\"'{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", @@ -581,7 +569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.16" } }, "nbformat": 4, From 31552e9aba5896ace88b808ce79c1a3bff455b1e Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 28 Jan 2025 13:22:32 +0000 Subject: [PATCH 10/14] Bypass bundle_custom_data.py and change metadata. --- ci/bundle_custom_data.py | 2 ++ models/mednist_ddpm/configs/metadata.json | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 92613b97..8894136a 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -15,6 +15,7 @@ # If a bundle does not need to be tested, please add the bundle name into the list. exclude_verify_shape_list = [ "mednist_gan", + "mednist_ddpm", "lung_nodule_ct_detection", "pathology_nuclei_segmentation_classification", "brats_mri_generative_diffusion", @@ -41,6 +42,7 @@ "vista3d", "maisi_ct_generative", "vista2d", + "mednist_ddpm" ] # This list is used for our CI tests to determine whether a bundle needs to be tested after downloading diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json index 65960dda..af994be0 100644 --- a/models/mednist_ddpm/configs/metadata.json +++ b/models/mednist_ddpm/configs/metadata.json @@ -7,7 +7,7 @@ "monai_version": "1.4.0", "pytorch_version": "2.5.1", "numpy_version": "1.26.4", - "optional_packages_version": {}, + "required_packages_version": {}, "task": "MedNIST Hand Generation", "description": "", "authors": "Walter Hugo Lopez Pinaya, Mark Graham, and Eric Kerfoot", From 7400ce1a1c30da1fa4dc55f8a660583e5c1c4b4f Mon Sep 17 00:00:00 2001 From: Virginia Date: Tue, 28 Jan 2025 14:33:31 +0000 Subject: [PATCH 11/14] Change schema. --- models/mednist_ddpm/configs/metadata.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/mednist_ddpm/configs/metadata.json b/models/mednist_ddpm/configs/metadata.json index af994be0..54645008 100644 --- a/models/mednist_ddpm/configs/metadata.json +++ b/models/mednist_ddpm/configs/metadata.json @@ -1,5 +1,5 @@ { - "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json", + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json", "version": "1.0.0", "changelog": { "1.0.0": "Initial release" From a66c53de4958e7691851c01e5a4eba0f0921eaaf Mon Sep 17 00:00:00 2001 From: virginia Date: Tue, 28 Jan 2025 15:05:19 +0000 Subject: [PATCH 12/14] Autofix --- ci/bundle_custom_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 8894136a..51e6ead8 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -42,7 +42,7 @@ "vista3d", "maisi_ct_generative", "vista2d", - "mednist_ddpm" + "mednist_ddpm", ] # This list is used for our CI tests to determine whether a bundle needs to be tested after downloading From bb36f4e50492e4177312709718fb28800dadfdba Mon Sep 17 00:00:00 2001 From: Virginia Date: Wed, 5 Feb 2025 09:02:05 +0000 Subject: [PATCH 13/14] Remove common.yaml and test.pt. --- models/mednist_ddpm/configs/common.yaml | 59 ------------------------ models/mednist_ddpm/docs/test.pt | Bin 17485 -> 0 bytes 2 files changed, 59 deletions(-) delete mode 100644 models/mednist_ddpm/configs/common.yaml delete mode 100644 models/mednist_ddpm/docs/test.pt diff --git a/models/mednist_ddpm/configs/common.yaml b/models/mednist_ddpm/configs/common.yaml deleted file mode 100644 index 72c50443..00000000 --- a/models/mednist_ddpm/configs/common.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# 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 -- $import operator - -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: $@bundle_root + '/models/model.pt' -use_amp: true -image_dim: 64 -image_size: [1, '@image_dim', '@image_dim'] -num_train_timesteps: 1000 - -base_transforms: -- _target_: LoadImaged - keys: 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z^AMgza7MQW5_2&ojtf6__PXp9@u#;FePg-K#%$s0%4~8c!|F+J4=;|d_9p6v7dFvc zyE^-@bFwd1;iYGM4PtHOa279&qUEDlk}N^uZRkgn@_8)k5KoVJ;bcz@B>t5<#l)vm zX0ZpwN7%`$B=-X~bYnd*t4aUL93z33Z|A%-%dUIJ4|VH$HvtglTq1+^@D1+|iaYacu~UY=iM*YaBN; zrE00h9Q@FbX5Z?QE3aua>I>&mk7_0A^JGqa-qvlvFsFuiUv0pTrjE3z)QGvN#w@ls zru~>E)Xr%_>ru_P(y=)!<@3s(O}I9*5fk4wr0~Xi+>3GK%+`7g%IQI;yI+(8hJR8z zyM9x8nZ78Wb$F}1(&UM_Rnsw&fz4V8uJl~dMTRzO+q`(;I2mMGfQSg5Gq zd$^)$`05WSYVG?&@#)+S#h_O^6{8ByQEciwN#WVTqS)3gU6C*$NHO%SU9sh3AH|_P zofVxjEs7FT$0}ZZoTXUvW2U0dqg4uJhi!^ewyg^N>P?E@66Yy;7|%L?9$a1Vbzr1o zy?4CA9DTONbj&t1ul@ya!A8=kXV2kry)g9*u)*at5K>e`C zy>SP3`V?xovsn32JKGQavZHf3_Z`Lep%c1i>XK3^Dkq~WD!jhwcVxzKyem;01AS%?gcl-bU&oXG&xoa1v z&Q4C9yLNQy+^t)e4qcr(w3CnaU7Wgf=+?eNmu~Gkb#Us??dMfitF`J|Gr7W_FSD+| z&x`$^Cs+LMCp(n+`HO%1__ya+{O6(n`@s!m|Lw5P^4+gP|K~vv|9R~Hp88V%cC5QR z&EkLlY} Date: Wed, 5 Feb 2025 15:20:23 +0000 Subject: [PATCH 14/14] Removing references to common.yaml Signed-off-by: Eric Kerfoot --- models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb | 4 +--- models/mednist_ddpm/docs/sub_train.sh | 3 +-- models/mednist_ddpm/docs/sub_train_multigpu.sh | 3 +-- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb index cf04001d..e203d599 100644 --- a/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb +++ b/models/mednist_ddpm/docs/2d_ddpm_bundle_tutorial.ipynb @@ -110,9 +110,7 @@ "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. " + "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 3 yaml files, one each for training (train.yaml) and inference (inference.yaml), and one to enable multi-GPU training (train_multigpu.yaml) which can be combined with the others to enable distributed training." ] }, { diff --git a/models/mednist_ddpm/docs/sub_train.sh b/models/mednist_ddpm/docs/sub_train.sh index 8d566d22..5f4a6a30 100755 --- a/models/mednist_ddpm/docs/sub_train.sh +++ b/models/mednist_ddpm/docs/sub_train.sh @@ -14,13 +14,12 @@ 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'" +CONFIG="$BUNDLE/configs/train.yaml" # change this to point to where MedNIST is located DATASET="$(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 \ diff --git a/models/mednist_ddpm/docs/sub_train_multigpu.sh b/models/mednist_ddpm/docs/sub_train_multigpu.sh index 8ed26ddc..15322e3d 100644 --- a/models/mednist_ddpm/docs/sub_train_multigpu.sh +++ b/models/mednist_ddpm/docs/sub_train_multigpu.sh @@ -14,13 +14,12 @@ 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'" +CONFIG="'$BUNDLE/configs/train.yaml', '$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"