Add DeepEdit Bundle

deepedit/deepedit_bundle/configs/inference.json

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+{
+    "imports": [
+        "$import glob",
+        "$import os",
+        "$import ignite"
+    ],
+    "bundle_root": "/workspace/tutorials/deepedit/deepedit_bundle",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/Datasets/MSD_datasets/Task09_Spleen",
+    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
+    "label_names": {"spleen": 1, "background": 0},
+    "spatial_size": [128, 128, 128],
+    "number_intensity_ch": 1,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "DynUNet",
+        "spatial_dims": 3,
+        "in_channels": "$len(@label_names) + @number_intensity_ch",
+        "out_channels": "$len(@label_names)",
+        "kernel_size": [3, 3, 3, 3, 3, 3],
+        "strides": [1, 2, 2, 2, 2, [2, 2, 1]],
+        "upsample_kernel_size": [2, 2, 2, 2, [2, 2, 1]],
+        "norm_name": "instance",
+        "deep_supervision": false,
+        "res_block": true
+    },
+    "network": "$@network_def.to(@device)",
+    "pre_transforms": {
+        "_target_": "Compose",
+        "transforms": [
+              {
+                  "_target_": "LoadImaged",
+                  "keys": "image",
+                  "reader": "ITKReader"
+              },
+              {
+                  "_target_": "EnsureChannelFirstd",
+                  "keys": "image"
+              },
+              {
+                  "_target_": "Orientationd",
+                  "keys": "image",
+                  "axcodes": "RAS"
+              },
+              {
+                  "_target_": "ScaleIntensityRanged",
+                  "keys": "image",
+                  "a_min":-175,
+                  "a_max": 250,
+                  "b_min": 0.0,
+                  "b_max": 1.0,
+                  "clip": true
+              },
+              {
+                  "_target_": "Resized",
+                  "keys":  "image",
+                  "spatial_size": "@spatial_size",
+                  "mode": "area"
+              },
+              {
+                  "_target_": "DiscardAddGuidanced",
+                  "keys": "image",
+                  "label_names": "@label_names",
+                  "number_intensity_ch": "@number_intensity_ch"
+              },
+              {
+                  "_target_": "EnsureTyped",
+                  "keys": "image"
+              }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[{'image': i} for i in @datalist]",
+        "transform": "@pre_transforms"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 2
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "post_transforms": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "EnsureTyped",
+                "keys": "pred"
+            },
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "Invertd",
+                "keys": "pred",
+                "transform": "@pre_transforms",
+                "orig_keys": "image",
+                "meta_key_postfix": "meta_dict",
+                "nearest_interp": false,
+                "to_tensor": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "argmax": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/model.pt'",
+            "load_dict": {"model": "@network"}
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@post_transforms",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "[email protected]()"
+    ]
+}

deepedit/deepedit_bundle/configs/logging.conf

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+[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

deepedit/deepedit_bundle/configs/metadata.json

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+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.1.0",
+    "changelog": {
+        "0.1.0": "complete the model package",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "0.9.0",
+    "pytorch_version": "1.10.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "nibabel": "3.2.1"
+    },
+    "task": "Decathlon spleen segmentation",
+    "description": "This is a pre-trained model for 3D segmentation of the spleen organ from CT images using DeepEdit.",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
+    "data_type": "dicom",
+    "image_classes": "single channel data, intensity scaled to [0, 1]",
+    "label_classes": "single channel data, 1 is spleen, 0 is background",
+    "pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
+    "eval_metrics": {
+        "mean_dice": 0.92
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Sakinis, Tomas, et al. 'Interactive segmentation of medical images through fully convolutional neural networks.' arXiv preprint arXiv:1903.08205 (2019)"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "magnitude",
+                "num_channels": 3,
+                "spatial_shape": [
+                    128,
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 2,
+                "spatial_shape": [
+                    128,
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "background",
+                    "1": "spleen"
+                }
+            }
+        }
+    }
+}

deepedit/deepedit_bundle/configs/multi_gpu_train.json

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+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": ["@device"]
+    },
+    "train#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@train#dataset",
+        "even_divisible": true,
+        "shuffle": true
+    },
+    "train#dataloader#sampler": "@train#sampler",
+    "train#dataloader#shuffle": false,
+    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
+    "training": [
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}