[WIP] 486 Add example model package

modules/model_package/spleen_segmentation/commands/export.sh

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+python ../programs/export.py
+    --weights ../models/model.pt
+    --config ../configs/inference.json
+    --meta ../configs/metadata.json

modules/model_package/spleen_segmentation/commands/inference.sh

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+python ../programs/inference.py
+    --config ../configs/inference.json
+    --meta ../configs/metadata.json

modules/model_package/spleen_segmentation/configs/inference.json

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+{
+    "multi_gpu": false,
+    "amp": true,
+    "model": "monai.data.load_net_with_metadata('../models/model.ts')[0]",
+    "network": {
+        "name": "UNet",
+        "args": {
+            "spatial_dims": 3,
+            "in_channels": 1,
+            "out_channels": 2,
+            "channels": [16, 32, 64, 128, 256],
+            "strides": [2, 2, 2, 2],
+            "num_res_units": 2,
+            "norm": "batch"
+        }
+    },
+    "preprocessing": [
+        {
+            "name": "LoadImaged",
+            "args": {
+                "keys": "image"
+            }
+        },
+        {
+            "name": "Spacingd",
+            "args": {
+                "keys": "image",
+                "pixdim": [1.5, 1.5, 2],
+                "mode": "bilinear"
+            }
+        },
+        {
+            "name": "Orientationd",
+            "args": {
+                "keys": "image",
+                "axcodes": "RAS"
+            }
+        },
+        {
+            "name": "EnsureChannelFirstd",
+            "args": {
+                "keys": "image"
+            }
+        },
+        {
+            "name": "ScaleIntensityRanged",
+            "args": {
+                "keys": "image",
+                "a_min": -57,
+                "a_max": 164,
+                "b_min": 0,
+                "b_max": 1,
+                "clip": true
+            }
+        },
+        {
+            "name": "CropForegroundd",
+            "args": {
+                "keys": "image",
+                "source_key": "image"
+            }
+        },
+        {
+            "name": "ToTensord",
+            "args": {
+                "keys": "image"
+            }
+        }
+    ],
+    "datalist": {
+        "name": "DatasetFunc",
+        "args": {
+            "data": "#@dataset_dir + '/dataset.json'",
+            "func": "monai.data.load_decathlon_datalist",
+            "is_segmentation": true,
+            "data_list_key": "test",
+            "base_dir": "@dataset_dir"
+        }
+    },
+    "dataset": {
+        "name": "Dataset",
+        "args": {
+            "data": "@datalist",
+            "transform": "@preprocessing"
+        }
+    },
+    "dataloader": {
+        "name": "DataLoader",
+        "args": {
+            "dataset": "@dataset",
+            "batch_size": 1,
+            "shuffle": false,
+            "num_workers": 4
+        }
+    },
+    "inferer": {
+        "name": "SlidingWindowInferer",
+        "args": {
+            "roi_size": [160, 160, 160],
+            "sw_batch_size": 4,
+            "overlap": 0.5
+        }
+    },
+    "postprocessing": [
+        {
+            "name": "Activationsd",
+            "args": {
+                "keys": "pred",
+                "softmax": true
+            }
+        },
+        {
+            "name": "Invertd",
+            "args": {
+                "keys": "pred",
+                "transform": "@preprocessing",
+                "orig_keys": "image",
+                "meta_keys": "pred_meta_dict",
+                "nearest_interp": false,
+                "to_tensor": true,
+                "device": "cuda"
+            }
+        },
+        {
+            "name": "AsDiscreted",
+            "args": {
+                "keys": "pred",
+                "argmax": true
+            }
+        },
+        {
+            "name": "SaveImaged",
+            "args": {
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "{MMAR_EVAL_OUTPUT_PATH}",
+                "resample": false,
+                "squeeze_end_dims": true
+            }
+        }
+    ]
+}

modules/model_package/spleen_segmentation/configs/metadata.json

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+{
+    "version": "0.1.0",
+    "changelog": {
+        "0.1.0": "complete the model package",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "0.8.0",
+    "pytorch_version": "1.10.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {"nibabel": "3.2.1"},
+    "task": "Decathlon spleen segmentation",
+    "description": "A pre-trained model for volumetric (3D) segmentation of the spleen from CT image",
+    "authorship": "MONAI team",
+    "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
+    "dataset_dir": "/workspace/data/Task09_Spleen",
+    "image_classes": "single channel data, intensity scaled to [0, 1]",
+    "label_classes": "single channel data, 1 is spleen, 0 is everything else",
+    "pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
+    "image_shape": [1, 160, 160, 160],
+    "label_shape": [1, 160, 160, 160],
+    "pred_shape": [2, 160, 160, 160],
+    "eval_metrics": {
+        "mean_dice": 0.96
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Xia, Yingda, et al. '3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training.' arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.",
+        "Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40"
+    ]
+}

modules/model_package/spleen_segmentation/docs/README.md

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+# Description
+A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
+
+# Model Overview
+This model is trained using the runnerup [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
+
+## Data
+The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
+
+## Training configuration
+The training was performed with command train.sh, which required 12GB-memory GPUs.
+
+Actual Model Input: 96 x 96 x 96
+
+## Input and output formats
+Input: 1 channel CT image
+
+Output: 2 channels: Label 1: spleen; Label 0: everything else
+
+## Scores
+This model achieve the following Dice score on the validation data (our own split from the training dataset):
+
+Mean dice = 0.96
+
+# Disclaimer
+This is an example, not to be used for diagnostic purposes.
+
+# References
+[1] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.
+
+[2] Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40

modules/model_package/spleen_segmentation/docs/license.txt

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+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. Medical Segmentation Decathlon
+   http://medicaldecathlon.com/

modules/model_package/spleen_segmentation/programs/__init__.py

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+
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

modules/model_package/spleen_segmentation/programs/export.py

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+
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+
+import torch
+from ignite.handlers import Checkpoint
+from monai.data import save_net_with_metadata
+from monai.networks import convert_to_torchscript
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', '-w', type=str, help='file path of the trained model weights', required=True)
+    parser.add_argument('--config', '-c', type=str, help='file path of config file that defines network', required=True)
+    parser.add_argument('--meta', '-e', type=str, help='file path of the meta data')
+    args = parser.parse_args()
+
+    # load config file
+    with open(args.config, "r") as f:
+        cofnig_dict = json.load(f)
+    # load meta data
+    with open(args.meta, "r") as f:
+        meta_dict = json.load(f)
+
+    net: torch.nn.Module = None
+    # TODO: parse network definiftion from config file and construct network instance
+    # config_parser = ConfigParser(config_dict, meta_dict)
+    # net = config_parser.get_component("network")
+
+    checkpoint = torch.load(args.weights)
+    # here we use ignite Checkpoint to support nested weights and be compatible with MONAI CheckpointSaver
+    Checkpoint.load_objects(to_load={"model": net}, checkpoint=checkpoint)
+
+    # convert to TorchScript model and save with meta data
+    net = convert_to_torchscript(model=net)
+
+    save_net_with_metadata(
+        jit_obj=net,
+        filename_prefix_or_stream="model.ts",
+        include_config_vals=False,
+        append_timestamp=False,
+        meta_values=meta_dict,
+        more_extra_files={args.config: json.dumps(cofnig_dict).encode()},
+    )
+
+
+if __name__ == '__main__':
+    main()

modules/model_package/spleen_segmentation/programs/inference.py

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+
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+
+import torch
+from monai.data import decollate_batch
+from monai.inferers import Inferer
+from monai.transforms import Transform
+from monai.utils.enums import CommonKeys
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', '-c', type=str, help='file path of config file that defines network', required=True)
+    parser.add_argument('--meta', '-e', type=str, help='file path of the meta data')
+    args = parser.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # load config file
+    with open(args.config, "r") as f:
+        cofnig_dict = json.load(f)
+    # load meta data
+    with open(args.meta, "r") as f:
+        meta_dict = json.load(f)
+
+    net: torch.nn.Module = None
+    dataloader: torch.utils.data.DataLoader = None
+    inferer: Inferer = None
+    postprocessing: Transform = None
+    # TODO: parse inference config file and construct instances
+    # config_parser = ConfigParser(config_dict, meta_dict)
+    # net = config_parser.get_component("model").to(device)
+    # dataloader = config_parser.get_component("dataloader")
+    # inferer = config_parser.get_component("inferer")
+    # postprocessing = config_parser.get_component("postprocessing")
+
+    net.eval()
+    with torch.no_grad():
+        for d in dataloader:
+            images = d[CommonKeys.IMAGE].to(device)
+            # define sliding window size and batch size for windows inference
+            d[CommonKeys.PRED] = inferer(inputs=images, predictor=net)
+            # decollate the batch data into a list of dictionaries, then execute postprocessing transforms
+            d = [postprocessing(i) for i in decollate_batch(d)]
+
+
+if __name__ == '__main__':
+    main()