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retrain using an internal pretrained ResNet18
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{
"imports": [
"$import glob",
"$import os"
],
"bundle_root": ".",
"output_dir": "$os.path.join(@bundle_root, 'eval')",
"dataset_dir": "/workspace/data/medical/pathology",
"testing_file": "$os.path.join(@bundle_root, 'testing.csv')",
"wsi_reader": "cuCIM",
"patch_size": [
224,
224
],
"number_intensity_ch": 3,
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
"network_def": {
"_target_": "TorchVisionFCModel",
"model_name": "resnet18",
"num_classes": 1,
"use_conv": true,
"pretrained": true
},
"network": "$@network_def.to(@device)",
"preprocessing": {
"_target_": "Compose",
"transforms": [
{
"_target_": "CastToTyped",
"keys": "image",
"dtype": "float32"
},
{
"_target_": "ScaleIntensityRanged",
"keys": "image",
"a_min": 0.0,
"a_max": 255.0,
"b_min": -1.0,
"b_max": 1.0
},
{
"_target_": "ToTensord",
"keys": "image"
}
]
},
"datalist": {
"_target_": "CSVDataset",
"src": "@testing_file",
"kwargs_read_csv": {
"names": [
"image"
],
"header": null
},
"transform": {
"_target_": "Lambdad",
"keys": "image",
"func": "$lambda x: os.path.join(@dataset_dir, 'testing/images', x + '.tif')"
}
},
"dataset": {
"_target_": "MaskedPatchWSIDataset",
"data": "@datalist",
"mask_level": 6,
"patch_size": "@patch_size",
"transform": "@preprocessing",
"reader": "@wsi_reader"
},
"dataloader": {
"_target_": "DataLoader",
"dataset": "@dataset",
"batch_size": 400,
"shuffle": false,
"num_workers": 8
},
"inferer": {
"_target_": "SimpleInferer"
},
"postprocessing": {
"_target_": "Compose",
"transforms": [
{
"_target_": "EnsureTyped",
"keys": "pred"
},
{
"_target_": "Activationsd",
"keys": "pred",
"sigmoid": true
},
{
"_target_": "ToNumpyd",
"keys": "pred"
}
]
},
"handlers": [
{
"_target_": "CheckpointLoader",
"load_path": "$@bundle_root + '/models/model.pt'",
"load_dict": {
"model": "@network"
}
},
{
"_target_": "StatsHandler",
"tag_name": "progress",
"iteration_print_logger": "$lambda engine: print(f'image: \"{engine.state.batch[\"image\"].meta[\"name\"][0]}\", iter: {engine.state.iteration}/{engine.state.epoch_length}') if engine.state.iteration % 100 == 0 else None",
"output_transform": "$lambda x: None"
},
{
"_target_": "monai.handlers.ProbMapProducer",
"output_dir": "@output_dir"
}
],
"evaluator": {
"_target_": "SupervisedEvaluator",
"device": "@device",
"val_data_loader": "@dataloader",
"network": "@network",
"inferer": "@inferer",
"postprocessing": "@postprocessing",
"val_handlers": "@handlers",
"amp": true,
"decollate": false
},
"run": [
"[email protected]()"
]
}