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--- |
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imports: |
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- "$import glob" |
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- "$import json" |
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- "$import os" |
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- "$import ignite" |
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- "$from scipy import ndimage" |
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input_channels: 1 |
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output_classes: 3 |
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arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'" |
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arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))" |
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bundle_root: "." |
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ckpt_dir: "$@bundle_root + '/models'" |
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output_dir: "$@bundle_root + '/eval'" |
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dataset_dir: "/workspace/data/msd/Task07_Pancreas" |
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data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'" |
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train_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='training', |
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base_dir=@dataset_dir)" |
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val_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation', |
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base_dir=@dataset_dir)" |
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device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')" |
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dints_space: |
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_target_: monai.networks.nets.TopologyInstance |
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channel_mul: 1 |
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num_blocks: 12 |
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num_depths: 4 |
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use_downsample: true |
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arch_code: |
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- "$@arch_ckpt['arch_code_a']" |
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- "$@arch_ckpt['arch_code_c']" |
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device: "$torch.device('cuda')" |
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network_def: |
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_target_: monai.networks.nets.DiNTS |
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dints_space: "@dints_space" |
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in_channels: "@input_channels" |
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num_classes: "@output_classes" |
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use_downsample: true |
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node_a: "$@arch_ckpt['node_a']" |
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network: "$@network_def.to(@device)" |
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loss: |
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_target_: DiceCELoss |
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include_background: false |
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to_onehot_y: true |
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softmax: true |
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squared_pred: true |
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batch: true |
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smooth_nr: 1.0e-05 |
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smooth_dr: 1.0e-05 |
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optimizer: |
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_target_: torch.optim.SGD |
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params: "$@network.parameters()" |
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momentum: 0.9 |
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weight_decay: 4.0e-05 |
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lr: 0.025 |
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lr_scheduler: |
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_target_: torch.optim.lr_scheduler.StepLR |
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optimizer: "@optimizer" |
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step_size: 80 |
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gamma: 0.5 |
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image_key: image |
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label_key: label |
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val_interval: 10 |
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train: |
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deterministic_transforms: |
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- _target_: LoadImaged |
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keys: |
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- "@image_key" |
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- "@label_key" |
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- _target_: EnsureChannelFirstd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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- _target_: Orientationd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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axcodes: RAS |
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- _target_: Spacingd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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pixdim: |
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- 1 |
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- 1 |
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- 1 |
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mode: |
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- bilinear |
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- nearest |
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align_corners: |
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- true |
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- true |
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- _target_: CastToTyped |
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keys: "@image_key" |
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dtype: "$torch.float32" |
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- _target_: ScaleIntensityRanged |
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keys: "@image_key" |
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a_min: -87 |
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a_max: 199 |
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b_min: 0 |
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b_max: 1 |
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clip: true |
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- _target_: CastToTyped |
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keys: |
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- "@image_key" |
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- "@label_key" |
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dtype: |
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- "$np.float16" |
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- "$np.uint8" |
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- _target_: CopyItemsd |
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keys: "@label_key" |
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times: 1 |
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names: |
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- label4crop |
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- _target_: Lambdad |
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keys: label4crop |
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func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype), |
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iterations=48).astype(float) for _k in range(s)]), axis=0)" |
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overwrite: true |
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- _target_: EnsureTyped |
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keys: |
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- "@image_key" |
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- "@label_key" |
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- _target_: CastToTyped |
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keys: "@image_key" |
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dtype: "$torch.float32" |
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- _target_: SpatialPadd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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- label4crop |
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spatial_size: |
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- 96 |
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- 96 |
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- 96 |
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mode: |
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- reflect |
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- constant |
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- constant |
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random_transforms: |
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- _target_: RandCropByLabelClassesd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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label_key: label4crop |
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num_classes: "@output_classes" |
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ratios: "$[1,] * @output_classes" |
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spatial_size: |
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- 96 |
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- 96 |
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- 96 |
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num_samples: 1 |
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- _target_: Lambdad |
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keys: label4crop |
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func: "$lambda x: 0" |
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- _target_: RandRotated |
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keys: |
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- "@image_key" |
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- "@label_key" |
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range_x: 0.3 |
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range_y: 0.3 |
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range_z: 0.3 |
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mode: |
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- bilinear |
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- nearest |
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prob: 0.2 |
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- _target_: RandZoomd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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min_zoom: 0.8 |
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max_zoom: 1.2 |
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mode: |
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- trilinear |
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- nearest |
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align_corners: |
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- true |
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- |
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prob: 0.16 |
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- _target_: RandGaussianSmoothd |
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keys: "@image_key" |
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sigma_x: |
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- 0.5 |
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- 1.15 |
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sigma_y: |
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- 0.5 |
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- 1.15 |
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sigma_z: |
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- 0.5 |
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- 1.15 |
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prob: 0.15 |
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- _target_: RandScaleIntensityd |
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keys: "@image_key" |
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factors: 0.3 |
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prob: 0.5 |
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- _target_: RandShiftIntensityd |
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keys: "@image_key" |
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offsets: 0.1 |
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prob: 0.5 |
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- _target_: RandGaussianNoised |
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keys: "@image_key" |
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std: 0.01 |
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prob: 0.15 |
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- _target_: RandFlipd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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spatial_axis: 0 |
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prob: 0.5 |
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- _target_: RandFlipd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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spatial_axis: 1 |
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prob: 0.5 |
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- _target_: RandFlipd |
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keys: |
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- "@image_key" |
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- "@label_key" |
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spatial_axis: 2 |
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prob: 0.5 |
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- _target_: CastToTyped |
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keys: |
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- "@image_key" |
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- "@label_key" |
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dtype: |
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- "$torch.float32" |
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- "$torch.uint8" |
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- _target_: ToTensord |
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keys: |
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- "@image_key" |
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- "@label_key" |
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preprocessing: |
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_target_: Compose |
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transforms: "$@train#deterministic_transforms + @train#random_transforms" |
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dataset: |
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_target_: CacheDataset |
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data: "@train_datalist" |
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transform: "@train#preprocessing" |
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cache_rate: 0.125 |
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num_workers: 4 |
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dataloader: |
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_target_: DataLoader |
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dataset: "@train#dataset" |
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batch_size: 2 |
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shuffle: true |
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num_workers: 4 |
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inferer: |
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_target_: SimpleInferer |
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postprocessing: |
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_target_: Compose |
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transforms: |
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- _target_: Activationsd |
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keys: pred |
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softmax: true |
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- _target_: AsDiscreted |
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keys: |
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- pred |
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- label |
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argmax: |
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- true |
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- false |
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to_onehot: "@output_classes" |
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handlers: |
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- _target_: LrScheduleHandler |
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lr_scheduler: "@lr_scheduler" |
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print_lr: true |
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- _target_: ValidationHandler |
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validator: "@validate#evaluator" |
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epoch_level: true |
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interval: "@val_interval" |
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- _target_: StatsHandler |
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tag_name: train_loss |
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output_transform: "$monai.handlers.from_engine(['loss'], first=True)" |
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- _target_: TensorBoardStatsHandler |
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log_dir: "@output_dir" |
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tag_name: train_loss |
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output_transform: "$monai.handlers.from_engine(['loss'], first=True)" |
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key_metric: |
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train_accuracy: |
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_target_: ignite.metrics.Accuracy |
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output_transform: "$monai.handlers.from_engine(['pred', 'label'])" |
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trainer: |
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_target_: SupervisedTrainer |
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max_epochs: 400 |
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device: "@device" |
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train_data_loader: "@train#dataloader" |
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network: "@network" |
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loss_function: "@loss" |
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optimizer: "@optimizer" |
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inferer: "@train#inferer" |
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postprocessing: "@train#postprocessing" |
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key_train_metric: "@train#key_metric" |
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train_handlers: "@train#handlers" |
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amp: true |
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validate: |
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preprocessing: |
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_target_: Compose |
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transforms: "%train#deterministic_transforms" |
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dataset: |
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_target_: CacheDataset |
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data: "@val_datalist" |
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transform: "@validate#preprocessing" |
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cache_rate: 0.125 |
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dataloader: |
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_target_: DataLoader |
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dataset: "@validate#dataset" |
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batch_size: 1 |
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shuffle: false |
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num_workers: 4 |
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inferer: |
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_target_: SlidingWindowInferer |
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roi_size: |
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- 96 |
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- 96 |
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- 96 |
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sw_batch_size: 6 |
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overlap: 0.625 |
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postprocessing: "%train#postprocessing" |
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handlers: |
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- _target_: StatsHandler |
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iteration_log: false |
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- _target_: TensorBoardStatsHandler |
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log_dir: "@output_dir" |
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iteration_log: false |
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- _target_: CheckpointSaver |
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save_dir: "@ckpt_dir" |
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save_dict: |
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model: "@network" |
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save_key_metric: true |
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key_metric_filename: model.pt |
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key_metric: |
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val_mean_dice: |
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_target_: MeanDice |
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include_background: false |
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output_transform: "$monai.handlers.from_engine(['pred', 'label'])" |
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additional_metrics: |
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val_accuracy: |
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_target_: ignite.metrics.Accuracy |
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output_transform: "$monai.handlers.from_engine(['pred', 'label'])" |
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evaluator: |
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_target_: SupervisedEvaluator |
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device: "@device" |
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val_data_loader: "@validate#dataloader" |
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network: "@network" |
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inferer: "@validate#inferer" |
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postprocessing: "@validate#postprocessing" |
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key_val_metric: "@validate#key_metric" |
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additional_metrics: "@validate#additional_metrics" |
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val_handlers: "@validate#handlers" |
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amp: true |
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initialize: |
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- "$monai.utils.set_determinism(seed=123)" |
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run: |
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- "$@train#trainer.run()" |
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