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endoscopic_inbody_classification

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endoscopic_inbody_classification
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{
    "imports": [
        "$import json",
        "$import os",
        "$import numpy",
        "$import torch"
    ],
    "bundle_root": ".",
    "image_key": "image",
    "load_pretrain": true,
    "output_dir": "$@bundle_root + '/eval'",
    "output_filename": "predictions.csv",
    "dataset_dir": "/workspace/data/endoscopic_inbody_classification",
    "test_json": "$@bundle_root+'/label/test_samples.json'",
    "test_fp": "$open(@test_json,'r', encoding='utf8')",
    "test_dict": "$json.load(@test_fp)",
    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
    "network_def": {
        "_target_": "SEResNet50",
        "spatial_dims": 2,
        "in_channels": 3,
        "num_classes": 2
    },
    "network": "$@network_def.to(@device)",
    "preprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "LoadImaged",
                "keys": "@image_key",
                "image_only": false
            },
            {
                "_target_": "EnsureChannelFirstd",
                "keys": "@image_key",
                "channel_dim": -1
            },
            {
                "_target_": "Resized",
                "keys": "@image_key",
                "spatial_size": [
                    256,
                    256
                ],
                "mode": "bilinear"
            },
            {
                "_target_": "CastToTyped",
                "dtype": "$torch.float32",
                "keys": "@image_key"
            },
            {
                "_target_": "NormalizeIntensityd",
                "nonzero": true,
                "channel_wise": true,
                "keys": "@image_key"
            },
            {
                "_target_": "EnsureTyped",
                "keys": "@image_key"
            }
        ]
    },
    "dataset": {
        "_target_": "Dataset",
        "data": "@test_dict",
        "transform": "@preprocessing"
    },
    "dataloader": {
        "_target_": "DataLoader",
        "dataset": "@dataset",
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 4
    },
    "inferer": {
        "_target_": "SimpleInferer"
    },
    "postprocessing": {
        "_target_": "Compose",
        "transforms": [
            {
                "_target_": "AsDiscreted",
                "argmax": true,
                "to_onehot": 2,
                "keys": [
                    "pred"
                ]
            },
            {
                "_target_": "SaveClassificationd",
                "keys": "pred",
                "meta_keys": "$@image_key + '_meta_dict'",
                "output_dir": "@output_dir",
                "filename": "@output_filename"
            }
        ]
    },
    "handlers": [
        {
            "_target_": "StatsHandler",
            "iteration_log": true
        }
    ],
    "evaluator": {
        "_target_": "SupervisedEvaluator",
        "device": "@device",
        "val_data_loader": "@dataloader",
        "network": "@network",
        "inferer": "@inferer",
        "postprocessing": "@postprocessing",
        "val_handlers": "@handlers"
    },
    "checkpointloader": {
        "_target_": "CheckpointLoader",
        "load_path": "$@bundle_root + '/models/model.pt'",
        "load_dict": {
            "model": "@network"
        }
    },
    "initialize": [
        "$monai.utils.set_determinism(seed=123)",
        "$@checkpointloader(@evaluator) if @load_pretrain else None"
    ],
    "run": [
        "$@evaluator.run()"
    ],
    "finalize": [
        "$@test_fp.close()"
    ]
}