[WIP] 486 Add example model package

modules/model_package/spleen_segmentation/commands/export.sh

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+python -m monai.apps.mmar.export
+    --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 -m monai.apps.mmar.inference.py
+    --config ../configs/inference.json
+    --meta ../configs/metadata.json

modules/model_package/spleen_segmentation/commands/schema_check.sh

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+python -m monai.apps.mmar.verify_json.py
+    --schema_id metadata
+    --config ../configs/metadata.json

modules/model_package/spleen_segmentation/commands/verify_network.sh

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+python -m monai.apps.mmar.verify_network.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": "$monai.data.load_net_with_metadata('../models/model.ts')[0]",
+    "network": {
+        "<name>": "UNet",
+        "<args>": {
+            "spatial_dims": 3,
+            "in_channels": "@network_data_format#inputs#image#num_channels",
+            "out_channels": "@network_data_format#outputs#pred#num_channels",
+            "channels": [16, 32, 64, 128, 256],
+            "strides": [2, 2, 2, 2],
+            "num_res_units": 2,
+            "norm": "batch"
+        }
+    },
+    "preprocessing": [
+        {
+            "<name>": "LoadImaged",
+            "<args>": {
+                "keys": "image"
+            }
+        },
+        {
+            "<name>": "Spacingd",
+            "<args>": {
+                "keys": "image",
+                "pixdim": [1.5, 1.5, 2],
+                "mode": "bilinear"
+            }
+        },
+        {
+            "<name>": "Orientationd",
+            "<args>": {
+                "keys": "image",
+                "axcodes": "RAS"
+            }
+        },
+        {
+            "<name>": "EnsureChannelFirstd",
+            "<args>": {
+                "keys": "image"
+            }
+        },
+        {
+            "<name>": "ScaleIntensityRanged",
+            "<args>": {
+                "keys": "image",
+                "a_min": -57,
+                "a_max": 164,
+                "b_min": 0,
+                "b_max": 1,
+                "clip": true
+            }
+        },
+        {
+            "<name>": "CropForegroundd",
+            "<args>": {
+                "keys": "image",
+                "source_key": "image"
+            }
+        },
+        {
+            "<name>": "ToTensord",
+            "<args>": {
+                "keys": "image"
+            }
+        }
+    ],
+    "datalist": {
+        "<name>": "DatasetFunc",
+        "<args>": {
+            "data": "$@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": "@network_data_format#inputs#image#spatial_shape",
+            "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
+            }
+        }
+    ],
+    "handlers": [
+        {
+            "<name>": "StatsHandler",
+            "<args>": {
+                "output_transform": "lambda x: None"
+            }
+        }
+      ],
+    "evaluator": {
+        "<name>": "SupervisedEvaluator",
+        "<args>": {
+            "device": "cuda",
+            "val_data_loader": "@dataloader",
+            "network": "@model",
+            "inferer": "@inferer",
+            "postprocessing": "@postprocessing",
+            "val_handlers": "@handlers",
+            "amp": "@amp"
+        }
+    }
+}

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",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
+    "data_type": "dicom",
+    "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",
+    "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"
+    ],
+    "network_data_format":{
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "magnitude",
+                "num_channels": 1,
+                "spatial_shape": [160, 160, 160],
+                "dtype": "float32",
+                "value_range": [0, 1]
+            },
+            "label": {
+                "type": "label",
+                "format": "segmentation",
+                "num_channels": 1,
+                "spatial_shape": [160, 160, 160],
+                "dtype": "float32",
+                "value_range": [0, 1]
+            }
+        },
+        "outputs":{
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 2,
+                "spatial_shape": [160, 160, 160],
+                "dtype": "float32",
+                "value_range": [0, 1]
+            }
+        },
+        "patch_inference": false
+    }
+}

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_a100/commands/inference.sh

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+python -m monai.apps.mmar.inference.py
+    --config ../../spleen_segmentation/configs/inference.json
+    --meta ../configs/metadata.json
+    --override ../configs/inference.json

modules/model_package/spleen_segmentation_a100/configs/inference.json

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+{
+    "preprocessing#1#<args>#pixdim": [1.0, 1.0, 1.0],
+    "inferer": {
+        "<path>": "custom_inferer.TensorRTInferer",
+        "<args>": {
+            "roi_size": [200, 200, 200],
+            "sw_batch_size": 4,
+            "overlap": 0.6
+        }
+    }
+}

modules/model_package/spleen_segmentation_a100/scripts/__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_a100/scripts/custom_inferer.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.
+
+from monai.inferers import SlidingWindowInferer
+
+
+class TensorRTInferer(SlidingWindowInferer):
+    def __init__(self, **kwargs) -> None:
+        print("fake code to show the customize logic.")
+        super().__init__(**kwargs)