add weights

nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

@@ -0,0 +1,15 @@
+{
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+        "1": "nonorm"
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+        "background": 0,
+        "GTVp": 1,
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+    "numTraining": 150,
+    "file_ending": ".nii.gz",
+    "name": "Dataset504_midRT_geodist",
+    "description": "Dataset 504 - midRT with Gradient Map"
+}

nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json

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+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 224, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [3, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f38942dff10>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f38942de9e0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [Convert3DTo2DTransform( apply_to_keys = ('data', 'seg') ), SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [224, 192], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-3.141592653589793, 3.141592653589793), angle_y = (0, 0), angle_z = (0, 0), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), Convert2DTo3DTransform( apply_to_keys = ('data', 'seg') ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = (0,) ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [1.0, 0.5, 0.5], [0.5, 0.25, 0.25], [0.25, 0.125, 0.125], [0.125, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
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+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f38942dee60>",
+    "dataloader_val.num_processes": "6",
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+    "dataset_json": "{'channel_names': {'0': 'T2', '1': 'nonorm'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 150, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_geodist', 'description': 'Dataset 504 - midRT with Gradient Map'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA RTX A6000",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f3894803a90>",
+    "hostname": "hermes",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f3894803fd0>",
+    "local_rank": "0",
+    "log_file": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2024_8_22_22_53_03.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f3894803a00>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f38942de110>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset504_midRT_geodist', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.0, 0.5, 0.5], 'original_median_shape_after_transp': [76, 511, 511], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 511.0], 'spacing': [0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [7, 7], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 192, 192], 'median_image_size_in_voxels': [92, 310, 309], 'spacing': [1.6126957141129536, 0.826423816135876, 0.826423816135876], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 224, 192], 'median_image_size_in_voxels': [123.0, 512.0, 511.0], 'spacing': [1.199997067451477, 0.5, 0.5], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [3, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 4889.44140625, 'mean': 381.5381327568607, 'median': 146.0, 'min': 0.0, 'percentile_00_5': 24.0, 'percentile_99_5': 2863.040283203125, 'std': 531.4364174220877}, '1': {'max': 1.0, 'mean': 0.3983462524159885, 'median': 0.32261767983436584, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 1.0, 'std': 0.27251933209306795}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'T2', '1': 'nonorm'}, 'labels': {'background': 0, 'GTVp': 1, 'GTVn': 2}, 'numTraining': 150, 'file_ending': '.nii.gz', 'name': 'Dataset504_midRT_geodist', 'description': 'Dataset 504 - midRT with Gradient Map'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
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+    "output_folder": "/data/jintao/nnUNet/nnUNet_results/Dataset504_midRT_geodist/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0",
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nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

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