monai
medical
katielink's picture
update license files
a23faaa
raw
history blame
3.09 kB
{
"imports": [
"$import json",
"$import os",
"$import torch"
],
"bundle_root": "/workspace/bundle/endoscopic_inbody_classification",
"output_dir": "$@bundle_root + '/eval'",
"dataset_dir": "/workspace/data/endoscopic_inbody_classification",
"test_json": "$@dataset_dir+'/test.json'",
"test_fp": "$open(@test_json,'r', encoding='utf8')",
"test_dict": "$json.load(@test_fp)",
"test_close": "$@test_fp.close()",
"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"
},
{
"_target_": "AsChannelFirstd",
"keys": "image"
},
{
"_target_": "Resized",
"keys": "image",
"spatial_size": [
256,
256
],
"mode": "bilinear"
},
{
"_target_": "CastToTyped",
"dtype": "$torch.float32",
"keys": "image"
},
{
"_target_": "NormalizeIntensityd",
"nonzero": true,
"channel_wise": true,
"keys": "image"
},
{
"_target_": "EnsureTyped",
"keys": "image"
}
]
},
"dataset": {
"_target_": "Dataset",
"data": "@test_dict",
"transform": "@preprocessing"
},
"dataloader": {
"_target_": "DataLoader",
"dataset": "@dataset",
"batch_size": 1,
"shuffle": false,
"num_workers": 4
},
"inferer": {
"_target_": "SimpleInferer"
},
"postprocessing": {
"_target_": "Compose",
"transforms": [
{
"_target_": "AsDiscreted",
"argmax": true,
"to_onehot": 2,
"keys": [
"pred"
]
}
]
},
"handlers": [
{
"_target_": "CheckpointLoader",
"load_path": "$@bundle_root + '/models/model.pt'",
"load_dict": {
"model": "@network"
}
},
{
"_target_": "StatsHandler",
"iteration_log": true
}
],
"evaluator": {
"_target_": "SupervisedEvaluator",
"device": "@device",
"val_data_loader": "@dataloader",
"network": "@network",
"inferer": "@inferer",
"postprocessing": "@postprocessing",
"val_handlers": "@handlers"
},
"evaluating": [
"$setattr(torch.backends.cudnn, 'benchmark', True)",
"$@evaluator.run()"
]
}