{ "imports": [ "$import glob", "$import os" ], "bundle_root": ".", "output_dir": "$@bundle_root + '/eval'", "dataset_dir": "$@bundle_root + '/dataset/images'", "datalist": "$list(sorted(glob.glob(@dataset_dir + '/*.nii.gz')))", "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')", "network_def": { "_target_": "scripts.networks.unest_base_patch_4.UNesT", "in_channels": 1, "out_channels": 133, "patch_size": 4, "depths": [ 2, 2, 8 ], "embed_dim": [ 128, 256, 512 ], "num_heads": [ 4, 8, 16 ] }, "network": "$@network_def.to(@device)", "preprocessing": { "_target_": "Compose", "transforms": [ { "_target_": "LoadImaged", "keys": "image" }, { "_target_": "EnsureChannelFirstd", "keys": "image" }, { "_target_": "NormalizeIntensityd", "keys": "image", "nonzero": "True", "channel_wise": "True" }, { "_target_": "EnsureTyped", "keys": "image" } ] }, "dataset": { "_target_": "Dataset", "data": "$[{'image': i} for i in @datalist]", "transform": "@preprocessing" }, "dataloader": { "_target_": "DataLoader", "dataset": "@dataset", "batch_size": 1, "shuffle": false, "num_workers": 4 }, "inferer": { "_target_": "SlidingWindowInferer", "roi_size": [ 96, 96, 96 ], "sw_batch_size": 4, "overlap": 0.7 }, "postprocessing": { "_target_": "Compose", "transforms": [ { "_target_": "Activationsd", "keys": "pred", "softmax": true }, { "_target_": "Invertd", "keys": "pred", "transform": "@preprocessing", "orig_keys": "image", "meta_key_postfix": "meta_dict", "nearest_interp": false, "to_tensor": true }, { "_target_": "AsDiscreted", "keys": "pred", "argmax": true }, { "_target_": "SaveImaged", "keys": "pred", "meta_keys": "pred_meta_dict", "output_dir": "@output_dir" } ] }, "handlers": [ { "_target_": "CheckpointLoader", "load_path": "$@bundle_root + '/models/model.pt'", "load_dict": { "model": "@network" }, "strict": "True" }, { "_target_": "StatsHandler", "iteration_log": false } ], "evaluator": { "_target_": "SupervisedEvaluator", "device": "@device", "val_data_loader": "@dataloader", "network": "@network", "inferer": "@inferer", "postprocessing": "@postprocessing", "val_handlers": "@handlers", "amp": false }, "evaluating": [ "$setattr(torch.backends.cudnn, 'benchmark', True)", "$@evaluator.run()" ] }