Mednist GAN Bundle

ci/bundle_custom_data.py

@@ -13,7 +13,7 @@
 # This list is used for our CI tests to determine whether a bundle needs to be tested with
 # the `verify_data_shape` function in `verify_bundle.py`.
 # If a bundle does not need to be tested, please add the bundle name into the list.
-exclude_verify_shape_list = []
+exclude_verify_shape_list = ["mednist_gan"]
 
 # This list is used for our CI tests to determine whether a bundle needs to be tested with
 # the `verify_export_torchscript` function in `verify_bundle.py`.

models/mednist_gan/configs/inference.json

@@ -0,0 +1,88 @@
+{
+    "imports": [
+        "$import glob"
+    ],
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "ckpt_path": "$@bundle_root + '/models/model.pt'",
+    "output_dir": "./outputs",
+    "latent_size": 64,
+    "num_samples": 10,
+    "network_def": {
+        "_target_": "Generator",
+        "latent_shape": "@latent_size",
+        "start_shape": [
+            64,
+            8,
+            8
+        ],
+        "channels": [
+            32,
+            16,
+            8,
+            1
+        ],
+        "strides": [
+            2,
+            2,
+            2,
+            1
+        ]
+    },
+    "network": "$@network_def.to(@device)",
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[torch.rand(@latent_size) for i in range(@num_samples)]"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 0
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "sigmoid": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "output_dir": "@output_dir",
+                "output_ext": "png",
+                "separate_folder": false,
+                "scale": 255,
+                "output_dtype": "$np.uint8",
+                "meta_key_postfix": null
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "@ckpt_path",
+            "load_dict": {
+                "model": "@network"
+            }
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "prepare_batch": "$lambda batchdata, *_,**__: (batchdata.to(@device),None,(),{})",
+        "val_handlers": "@handlers"
+    },
+    "inferring": [
+        "$@evaluator.run()"
+    ]
+}

models/mednist_gan/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

models/mednist_gan/configs/metadata.json

@@ -0,0 +1,60 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_generator_20220718.json",
+    "version": "0.1.0",
+    "changelog": {
+        "0.1.0": "Initial version"
+    },
+    "monai_version": "0.9.0",
+    "pytorch_version": "1.10.0",
+    "numpy_version": "1.21.0",
+    "optional_packages_version": {
+        "pytorch-ignite": "0.4.8",
+        "pillow": "8.4.0"
+    },
+    "task": "Generate random hand images from the MedNIST dataset",
+    "description": "This example of a GAN generator produces hand xray images like those in the MedNIST dataset",
+    "authors": "MONAI Team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "intended_use": "This is an example of a GAN with generator discriminator networks using MONAI, suitable for demonstration purposes only.",
+    "data_source": "MedNIST dataset kindly made available by Dr. Bradley J. Erickson M.D., Ph.D. (Department of Radiology, Mayo Clinic)",
+    "data_type": "jpeg",
+    "network_data_format": {
+        "inputs": {
+            "latent": {
+                "type": "tuples",
+                "format": "latent",
+                "num_channels": 0,
+                "spatial_shape": [
+                    64
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {}
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "magnitude",
+                "num_channels": 1,
+                "spatial_shape": [
+                    64,
+                    64
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        }
+    }
+}

models/mednist_gan/configs/train.json

@@ -0,0 +1,164 @@
+{
+    "imports": [
+        "$from functools import partial",
+        "$import glob",
+        "$from losses import discriminator_loss",
+        "$from losses import generator_loss"
+    ],
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "ckpt_path": "$@bundle_root + '/models/model.pt'",
+    "dataset_dir": "./MedNIST/Hand",
+    "datalist": "$list(sorted(glob.glob(@dataset_dir + '/*.jpeg')))",
+    "latent_size": 64,
+    "discriminator": {
+        "_target_": "Discriminator",
+        "in_shape": [
+            1,
+            64,
+            64
+        ],
+        "channels": [
+            8,
+            16,
+            32,
+            64,
+            1
+        ],
+        "strides": [
+            2,
+            2,
+            2,
+            2,
+            1
+        ],
+        "num_res_units": 1,
+        "kernel_size": 5
+    },
+    "generator": {
+        "_target_": "Generator",
+        "latent_shape": "@latent_size",
+        "start_shape": [
+            64,
+            8,
+            8
+        ],
+        "channels": [
+            32,
+            16,
+            8,
+            1
+        ],
+        "strides": [
+            2,
+            2,
+            2,
+            1
+        ]
+    },
+    "dnetwork": "$@discriminator.apply(monai.networks.normal_init).to(@device)",
+    "gnetwork": "$@generator.apply(monai.networks.normal_init).to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "reals"
+            },
+            {
+                "_target_": "AddChanneld",
+                "keys": "reals"
+            },
+            {
+                "_target_": "ScaleIntensityd",
+                "keys": "reals"
+            },
+            {
+                "_target_": "RandRotated",
+                "keys": "reals",
+                "range_x": "$np.pi/12",
+                "prob": 0.5,
+                "keep_size": true
+            },
+            {
+                "_target_": "RandFlipd",
+                "keys": "reals",
+                "spatial_axis": 0,
+                "prob": 0.5
+            },
+            {
+                "_target_": "RandZoomd",
+                "keys": "reals",
+                "min_zoom": 0.9,
+                "max_zoom": 1.1,
+                "prob": 0.5
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "reals"
+            }
+        ]
+    },
+    "real_dataset": {
+        "_target_": "CacheDataset",
+        "data": "$[{'reals': i} for i in @datalist]",
+        "transform": "@preprocessing"
+    },
+    "real_dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@real_dataset",
+        "batch_size": 600,
+        "shuffle": true,
+        "num_workers": 12
+    },
+    "doptimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@dnetwork.parameters()",
+        "lr": 0.0002,
+        "betas": [
+            0.5,
+            0.999
+        ]
+    },
+    "goptimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@gnetwork.parameters()",
+        "lr": 0.0002,
+        "betas": [
+            0.5,
+            0.999
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointSaver",
+            "save_dir": "$@bundle_root + '/models'",
+            "save_dict": {
+                "model": "@gnetwork"
+            },
+            "save_interval": 0,
+            "save_final": true,
+            "epoch_level": true
+        }
+    ],
+    "trainer": {
+        "_target_": "GanTrainer",
+        "device": "@device",
+        "max_epochs": 50,
+        "train_data_loader": "@real_dataloader",
+        "g_network": "@gnetwork",
+        "g_optimizer": "@goptimizer",
+        "g_loss_function": "$partial(generator_loss, disc_net=@dnetwork)",
+        "d_network": "@dnetwork",
+        "d_optimizer": "@doptimizer",
+        "d_loss_function": "$partial(discriminator_loss, disc_net=@dnetwork)",
+        "d_train_steps": 5,
+        "g_update_latents": true,
+        "latent_shape": "@latent_size",
+        "key_train_metric": "$None",
+        "train_handlers": "@handlers"
+    },
+    "training": [
+        "$@gnetwork.conv.add_module('activation', torch.nn.Sigmoid())",
+        "$@trainer.run()"
+    ]
+}