Delete Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres
Browse files- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json +0 -18
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json +0 -1426
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/.ipynb_checkpoints/progress-checkpoint.png +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_latest.pth +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/debug.json +0 -52
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/progress.png +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2023_7_24_00_01_52.txt +0 -1194
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/.ipynb_checkpoints/Untitled-checkpoint.ipynb +0 -6
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/.ipynb_checkpoints/progress-checkpoint.png +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/Untitled.ipynb +0 -112
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/checkpoint_best.pth +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/debug.json +0 -52
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/progress.png +0 -3
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/training_log_2023_7_24_09_31_46.txt +0 -170
- Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json +0 -342
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json
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{
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"name": "Prostate158",
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"description": "Prostate cancer segmentation dataset",
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"channel_names": {
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json
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|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/.ipynb_checkpoints/progress-checkpoint.png
DELETED
Git LFS Details
|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth
DELETED
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
|
| 2 |
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| 3 |
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_latest.pth
DELETED
|
@@ -1,3 +0,0 @@
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|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/debug.json
DELETED
|
@@ -1,52 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"_best_ema": "None",
|
| 3 |
-
"batch_size": "2",
|
| 4 |
-
"configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}",
|
| 5 |
-
"configuration_name": "3d_fullres",
|
| 6 |
-
"cudnn_version": 8500,
|
| 7 |
-
"current_epoch": "0",
|
| 8 |
-
"dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fe17c7ca020>",
|
| 9 |
-
"dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fe17c7c8c40>",
|
| 10 |
-
"dataloader_train.num_processes": "4",
|
| 11 |
-
"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 = [256, 256], 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], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
|
| 12 |
-
"dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fe17c7c90f0>",
|
| 13 |
-
"dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fe17c7c90c0>",
|
| 14 |
-
"dataloader_val.num_processes": "2",
|
| 15 |
-
"dataloader_val.transform": "Compose ( [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], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
|
| 16 |
-
"dataset_json": "{'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}",
|
| 17 |
-
"device": "cuda:0",
|
| 18 |
-
"disable_checkpointing": "False",
|
| 19 |
-
"fold": "0",
|
| 20 |
-
"folder_with_segs_from_previous_stage": "None",
|
| 21 |
-
"gpu_name": "NVIDIA A10G",
|
| 22 |
-
"grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fe17d646560>",
|
| 23 |
-
"hostname": "s-osbm-jupyter-f0b83-8689bbb555-5t6kn",
|
| 24 |
-
"inference_allowed_mirroring_axes": "(0, 1, 2)",
|
| 25 |
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"initial_lr": "0.01",
|
| 26 |
-
"is_cascaded": "False",
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| 27 |
-
"is_ddp": "False",
|
| 28 |
-
"label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fe17d6464a0>",
|
| 29 |
-
"local_rank": "0",
|
| 30 |
-
"log_file": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2023_7_24_00_01_52.txt",
|
| 31 |
-
"logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fe17d646410>",
|
| 32 |
-
"loss": "DeepSupervisionWrapper(\n (loss): DC_and_CE_loss(\n (ce): RobustCrossEntropyLoss()\n (dc): MemoryEfficientSoftDiceLoss()\n )\n)",
|
| 33 |
-
"lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fe17d646470>",
|
| 34 |
-
"my_init_kwargs": "{'plans': {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [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]], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
|
| 35 |
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"network": "PlainConvUNet",
|
| 36 |
-
"num_epochs": "1000",
|
| 37 |
-
"num_input_channels": "3",
|
| 38 |
-
"num_iterations_per_epoch": "250",
|
| 39 |
-
"num_val_iterations_per_epoch": "50",
|
| 40 |
-
"optimizer": "SGD (\nParameter Group 0\n dampening: 0\n differentiable: False\n foreach: None\n initial_lr: 0.01\n lr: 0.01\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)",
|
| 41 |
-
"output_folder": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0",
|
| 42 |
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"output_folder_base": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres",
|
| 43 |
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"oversample_foreground_percent": "0.33",
|
| 44 |
-
"plans_manager": "{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [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]], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}",
|
| 45 |
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"preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset001_Prostate158/nnUNetPlans_3d_fullres",
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| 46 |
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"preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset001_Prostate158",
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| 47 |
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"save_every": "50",
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| 48 |
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"torch_version": "2.0.1+cu117",
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| 49 |
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"unpack_dataset": "True",
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| 50 |
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"was_initialized": "True",
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| 51 |
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"weight_decay": "3e-05"
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| 52 |
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}
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/progress.png
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Git LFS Details
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2023_7_24_00_01_52.txt
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#######################################################################
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| 3 |
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Please cite the following paper when using nnU-Net:
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| 4 |
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Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
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#######################################################################
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This is the configuration used by this training:
|
| 9 |
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Configuration name: 3d_fullres
|
| 10 |
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{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}
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These are the global plan.json settings:
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{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}
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2023-07-24 00:01:53.902022: unpacking dataset...
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2023-07-24 00:02:07.139120: unpacking done...
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2023-07-24 00:02:07.196625: do_dummy_2d_data_aug: True
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2023-07-24 00:02:07.197410: Creating new 5-fold cross-validation split...
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2023-07-24 00:02:07.198499: Desired fold for training: 0
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2023-07-24 00:02:07.198552: This split has 111 training and 28 validation cases.
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2023-07-24 00:02:14.074938: Unable to plot network architecture:
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2023-07-24 00:02:14.075182: module 'torch.onnx' has no attribute '_optimize_trace'
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2023-07-24 00:02:14.123964:
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2023-07-24 00:02:14.124048: Epoch 0
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| 25 |
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2023-07-24 00:02:14.124163: Current learning rate: 0.01
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| 26 |
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2023-07-24 00:06:16.568555: train_loss -0.0413
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2023-07-24 00:06:16.568763: val_loss -0.1561
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| 28 |
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2023-07-24 00:06:16.568854: Pseudo dice [0.6633, 0.3715, 0.0]
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2023-07-24 00:06:16.568946: Epoch time: 242.45 s
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2023-07-24 00:06:16.569015: Yayy! New best EMA pseudo Dice: 0.3449
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2023-07-24 00:06:19.487634:
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2023-07-24 00:06:19.487758: Epoch 1
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2023-07-24 00:06:19.487858: Current learning rate: 0.00999
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2023-07-24 00:09:51.638274: train_loss -0.2546
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| 35 |
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2023-07-24 00:09:51.638539: val_loss -0.3228
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2023-07-24 00:09:51.638626: Pseudo dice [0.7605, 0.5865, 0.0]
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2023-07-24 00:09:51.638798: Epoch time: 212.15 s
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| 38 |
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2023-07-24 00:09:51.638938: Yayy! New best EMA pseudo Dice: 0.3553
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2023-07-24 00:09:54.319529:
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2023-07-24 00:09:54.319663: Epoch 2
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2023-07-24 00:09:54.319769: Current learning rate: 0.00998
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| 42 |
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2023-07-24 00:13:25.194643: train_loss -0.324
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| 43 |
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2023-07-24 00:13:25.194925: val_loss -0.3233
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2023-07-24 00:13:25.195090: Pseudo dice [0.7874, 0.5763, 0.0]
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2023-07-24 00:13:25.195179: Epoch time: 210.88 s
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2023-07-24 00:13:25.195318: Yayy! New best EMA pseudo Dice: 0.3652
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2023-07-24 00:13:27.399833:
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2023-07-24 00:13:27.399949: Epoch 3
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2023-07-24 00:13:27.400062: Current learning rate: 0.00997
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2023-07-24 00:17:09.770445: train_loss -0.3629
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2023-07-24 00:17:09.770754: val_loss -0.3386
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| 52 |
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2023-07-24 00:17:09.771257: Pseudo dice [0.7865, 0.6213, 0.0]
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2023-07-24 00:17:09.771582: Epoch time: 222.37 s
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2023-07-24 00:17:09.771953: Yayy! New best EMA pseudo Dice: 0.3756
|
| 55 |
-
2023-07-24 00:17:13.443594:
|
| 56 |
-
2023-07-24 00:17:13.443887: Epoch 4
|
| 57 |
-
2023-07-24 00:17:13.444007: Current learning rate: 0.00996
|
| 58 |
-
2023-07-24 00:20:25.372916: train_loss -0.4038
|
| 59 |
-
2023-07-24 00:20:25.373092: val_loss -0.4029
|
| 60 |
-
2023-07-24 00:20:25.373185: Pseudo dice [0.821, 0.6488, 0.2694]
|
| 61 |
-
2023-07-24 00:20:25.373266: Epoch time: 191.93 s
|
| 62 |
-
2023-07-24 00:20:25.373332: Yayy! New best EMA pseudo Dice: 0.3961
|
| 63 |
-
2023-07-24 00:20:27.527769:
|
| 64 |
-
2023-07-24 00:20:27.527886: Epoch 5
|
| 65 |
-
2023-07-24 00:20:27.527997: Current learning rate: 0.00995
|
| 66 |
-
2023-07-24 00:23:59.957678: train_loss -0.4395
|
| 67 |
-
2023-07-24 00:23:59.957844: val_loss -0.4287
|
| 68 |
-
2023-07-24 00:23:59.957929: Pseudo dice [0.8103, 0.634, 0.4383]
|
| 69 |
-
2023-07-24 00:23:59.958011: Epoch time: 212.43 s
|
| 70 |
-
2023-07-24 00:23:59.958077: Yayy! New best EMA pseudo Dice: 0.4192
|
| 71 |
-
2023-07-24 00:24:01.987802:
|
| 72 |
-
2023-07-24 00:24:01.987912: Epoch 6
|
| 73 |
-
2023-07-24 00:24:01.988024: Current learning rate: 0.00995
|
| 74 |
-
2023-07-24 00:27:44.002411: train_loss -0.4486
|
| 75 |
-
2023-07-24 00:27:44.002615: val_loss -0.4226
|
| 76 |
-
2023-07-24 00:27:44.002702: Pseudo dice [0.8368, 0.6463, 0.3067]
|
| 77 |
-
2023-07-24 00:27:44.002805: Epoch time: 222.02 s
|
| 78 |
-
2023-07-24 00:27:44.002879: Yayy! New best EMA pseudo Dice: 0.4369
|
| 79 |
-
2023-07-24 00:27:46.502084:
|
| 80 |
-
2023-07-24 00:27:46.502206: Epoch 7
|
| 81 |
-
2023-07-24 00:27:46.502326: Current learning rate: 0.00994
|
| 82 |
-
2023-07-24 00:31:13.044165: train_loss -0.4879
|
| 83 |
-
2023-07-24 00:31:13.044357: val_loss -0.4522
|
| 84 |
-
2023-07-24 00:31:13.044453: Pseudo dice [0.8328, 0.6665, 0.389]
|
| 85 |
-
2023-07-24 00:31:13.044543: Epoch time: 206.54 s
|
| 86 |
-
2023-07-24 00:31:13.050315: Yayy! New best EMA pseudo Dice: 0.4562
|
| 87 |
-
2023-07-24 00:31:15.979445:
|
| 88 |
-
2023-07-24 00:31:15.979643: Epoch 8
|
| 89 |
-
2023-07-24 00:31:15.979780: Current learning rate: 0.00993
|
| 90 |
-
2023-07-24 00:34:55.354722: train_loss -0.5049
|
| 91 |
-
2023-07-24 00:34:55.355004: val_loss -0.4647
|
| 92 |
-
2023-07-24 00:34:55.355092: Pseudo dice [0.8501, 0.6851, 0.4456]
|
| 93 |
-
2023-07-24 00:34:55.355233: Epoch time: 219.38 s
|
| 94 |
-
2023-07-24 00:34:55.355299: Yayy! New best EMA pseudo Dice: 0.4766
|
| 95 |
-
2023-07-24 00:34:57.734633:
|
| 96 |
-
2023-07-24 00:34:57.734877: Epoch 9
|
| 97 |
-
2023-07-24 00:34:57.734988: Current learning rate: 0.00992
|
| 98 |
-
2023-07-24 00:38:36.038373: train_loss -0.5101
|
| 99 |
-
2023-07-24 00:38:36.038563: val_loss -0.4518
|
| 100 |
-
2023-07-24 00:38:36.038657: Pseudo dice [0.8332, 0.6664, 0.4652]
|
| 101 |
-
2023-07-24 00:38:36.038745: Epoch time: 218.3 s
|
| 102 |
-
2023-07-24 00:38:36.038840: Yayy! New best EMA pseudo Dice: 0.4944
|
| 103 |
-
2023-07-24 00:38:39.940725:
|
| 104 |
-
2023-07-24 00:38:39.941038: Epoch 10
|
| 105 |
-
2023-07-24 00:38:39.941158: Current learning rate: 0.00991
|
| 106 |
-
2023-07-24 00:42:04.394748: train_loss -0.5153
|
| 107 |
-
2023-07-24 00:42:04.398857: val_loss -0.4822
|
| 108 |
-
2023-07-24 00:42:04.399046: Pseudo dice [0.8432, 0.6794, 0.4922]
|
| 109 |
-
2023-07-24 00:42:04.399148: Epoch time: 204.46 s
|
| 110 |
-
2023-07-24 00:42:04.399216: Yayy! New best EMA pseudo Dice: 0.5121
|
| 111 |
-
2023-07-24 00:42:08.116886:
|
| 112 |
-
2023-07-24 00:42:08.117016: Epoch 11
|
| 113 |
-
2023-07-24 00:42:08.117131: Current learning rate: 0.0099
|
| 114 |
-
2023-07-24 00:45:37.933030: train_loss -0.5128
|
| 115 |
-
2023-07-24 00:45:37.933209: val_loss -0.4819
|
| 116 |
-
2023-07-24 00:45:37.933295: Pseudo dice [0.8511, 0.6729, 0.4875]
|
| 117 |
-
2023-07-24 00:45:37.933378: Epoch time: 209.82 s
|
| 118 |
-
2023-07-24 00:45:37.933442: Yayy! New best EMA pseudo Dice: 0.528
|
| 119 |
-
2023-07-24 00:45:41.223381:
|
| 120 |
-
2023-07-24 00:45:41.223522: Epoch 12
|
| 121 |
-
2023-07-24 00:45:41.223654: Current learning rate: 0.00989
|
| 122 |
-
2023-07-24 00:49:18.457412: train_loss -0.5139
|
| 123 |
-
2023-07-24 00:49:18.457591: val_loss -0.4663
|
| 124 |
-
2023-07-24 00:49:18.457679: Pseudo dice [0.8304, 0.6664, 0.4751]
|
| 125 |
-
2023-07-24 00:49:18.457762: Epoch time: 217.24 s
|
| 126 |
-
2023-07-24 00:49:18.457826: Yayy! New best EMA pseudo Dice: 0.5409
|
| 127 |
-
2023-07-24 00:49:21.452374:
|
| 128 |
-
2023-07-24 00:49:21.452548: Epoch 13
|
| 129 |
-
2023-07-24 00:49:21.452664: Current learning rate: 0.00988
|
| 130 |
-
2023-07-24 00:52:53.617019: train_loss -0.5306
|
| 131 |
-
2023-07-24 00:52:53.617260: val_loss -0.4947
|
| 132 |
-
2023-07-24 00:52:53.617345: Pseudo dice [0.8434, 0.6944, 0.4883]
|
| 133 |
-
2023-07-24 00:52:53.617487: Epoch time: 212.17 s
|
| 134 |
-
2023-07-24 00:52:53.617552: Yayy! New best EMA pseudo Dice: 0.5544
|
| 135 |
-
2023-07-24 00:52:55.726403:
|
| 136 |
-
2023-07-24 00:52:55.726518: Epoch 14
|
| 137 |
-
2023-07-24 00:52:55.726615: Current learning rate: 0.00987
|
| 138 |
-
2023-07-24 00:56:22.506680: train_loss -0.5446
|
| 139 |
-
2023-07-24 00:56:22.506907: val_loss -0.4714
|
| 140 |
-
2023-07-24 00:56:22.507013: Pseudo dice [0.8411, 0.671, 0.4914]
|
| 141 |
-
2023-07-24 00:56:22.507121: Epoch time: 206.78 s
|
| 142 |
-
2023-07-24 00:56:22.507219: Yayy! New best EMA pseudo Dice: 0.5657
|
| 143 |
-
2023-07-24 00:56:24.667025:
|
| 144 |
-
2023-07-24 00:56:24.667140: Epoch 15
|
| 145 |
-
2023-07-24 00:56:24.667252: Current learning rate: 0.00986
|
| 146 |
-
2023-07-24 00:59:50.747144: train_loss -0.5394
|
| 147 |
-
2023-07-24 00:59:50.747421: val_loss -0.4625
|
| 148 |
-
2023-07-24 00:59:50.747515: Pseudo dice [0.8447, 0.654, 0.5712]
|
| 149 |
-
2023-07-24 00:59:50.747690: Epoch time: 206.08 s
|
| 150 |
-
2023-07-24 00:59:50.747818: Yayy! New best EMA pseudo Dice: 0.5781
|
| 151 |
-
2023-07-24 00:59:53.732963:
|
| 152 |
-
2023-07-24 00:59:53.733104: Epoch 16
|
| 153 |
-
2023-07-24 00:59:53.733224: Current learning rate: 0.00986
|
| 154 |
-
2023-07-24 01:03:24.895722: train_loss -0.5551
|
| 155 |
-
2023-07-24 01:03:24.895922: val_loss -0.496
|
| 156 |
-
2023-07-24 01:03:24.896017: Pseudo dice [0.8705, 0.6946, 0.4975]
|
| 157 |
-
2023-07-24 01:03:24.896102: Epoch time: 211.16 s
|
| 158 |
-
2023-07-24 01:03:24.896200: Yayy! New best EMA pseudo Dice: 0.5891
|
| 159 |
-
2023-07-24 01:03:27.655639:
|
| 160 |
-
2023-07-24 01:03:27.655761: Epoch 17
|
| 161 |
-
2023-07-24 01:03:27.655877: Current learning rate: 0.00985
|
| 162 |
-
2023-07-24 01:07:09.366258: train_loss -0.5496
|
| 163 |
-
2023-07-24 01:07:09.371549: val_loss -0.4745
|
| 164 |
-
2023-07-24 01:07:09.371795: Pseudo dice [0.8679, 0.6897, 0.514]
|
| 165 |
-
2023-07-24 01:07:09.371953: Epoch time: 221.71 s
|
| 166 |
-
2023-07-24 01:07:09.372037: Yayy! New best EMA pseudo Dice: 0.5992
|
| 167 |
-
2023-07-24 01:07:12.715889:
|
| 168 |
-
2023-07-24 01:07:12.716015: Epoch 18
|
| 169 |
-
2023-07-24 01:07:12.716132: Current learning rate: 0.00984
|
| 170 |
-
2023-07-24 01:10:59.095254: train_loss -0.5406
|
| 171 |
-
2023-07-24 01:10:59.095483: val_loss -0.483
|
| 172 |
-
2023-07-24 01:10:59.095583: Pseudo dice [0.8634, 0.6795, 0.5385]
|
| 173 |
-
2023-07-24 01:10:59.095723: Epoch time: 226.38 s
|
| 174 |
-
2023-07-24 01:10:59.095788: Yayy! New best EMA pseudo Dice: 0.6087
|
| 175 |
-
2023-07-24 01:11:01.356395:
|
| 176 |
-
2023-07-24 01:11:01.356525: Epoch 19
|
| 177 |
-
2023-07-24 01:11:01.356640: Current learning rate: 0.00983
|
| 178 |
-
2023-07-24 01:14:19.901330: train_loss -0.5567
|
| 179 |
-
2023-07-24 01:14:19.901525: val_loss -0.4783
|
| 180 |
-
2023-07-24 01:14:19.901613: Pseudo dice [0.8492, 0.6553, 0.535]
|
| 181 |
-
2023-07-24 01:14:19.901693: Epoch time: 198.55 s
|
| 182 |
-
2023-07-24 01:14:19.901747: Yayy! New best EMA pseudo Dice: 0.6158
|
| 183 |
-
2023-07-24 01:14:22.511663:
|
| 184 |
-
2023-07-24 01:14:22.511957: Epoch 20
|
| 185 |
-
2023-07-24 01:14:22.512064: Current learning rate: 0.00982
|
| 186 |
-
2023-07-24 01:17:55.925512: train_loss -0.5745
|
| 187 |
-
2023-07-24 01:17:55.925694: val_loss -0.4715
|
| 188 |
-
2023-07-24 01:17:55.925791: Pseudo dice [0.8548, 0.653, 0.5559]
|
| 189 |
-
2023-07-24 01:17:55.925879: Epoch time: 213.41 s
|
| 190 |
-
2023-07-24 01:17:55.925951: Yayy! New best EMA pseudo Dice: 0.623
|
| 191 |
-
2023-07-24 01:17:58.448946:
|
| 192 |
-
2023-07-24 01:17:58.449071: Epoch 21
|
| 193 |
-
2023-07-24 01:17:58.449180: Current learning rate: 0.00981
|
| 194 |
-
2023-07-24 01:21:29.617508: train_loss -0.5761
|
| 195 |
-
2023-07-24 01:21:29.617684: val_loss -0.4919
|
| 196 |
-
2023-07-24 01:21:29.617774: Pseudo dice [0.8628, 0.7072, 0.4682]
|
| 197 |
-
2023-07-24 01:21:29.617859: Epoch time: 211.17 s
|
| 198 |
-
2023-07-24 01:21:29.617927: Yayy! New best EMA pseudo Dice: 0.6286
|
| 199 |
-
2023-07-24 01:21:32.232873:
|
| 200 |
-
2023-07-24 01:21:32.232998: Epoch 22
|
| 201 |
-
2023-07-24 01:21:32.233115: Current learning rate: 0.0098
|
| 202 |
-
2023-07-24 01:24:54.282193: train_loss -0.5831
|
| 203 |
-
2023-07-24 01:24:54.282391: val_loss -0.5023
|
| 204 |
-
2023-07-24 01:24:54.282501: Pseudo dice [0.8587, 0.6942, 0.4825]
|
| 205 |
-
2023-07-24 01:24:54.282606: Epoch time: 202.05 s
|
| 206 |
-
2023-07-24 01:24:54.282692: Yayy! New best EMA pseudo Dice: 0.6336
|
| 207 |
-
2023-07-24 01:24:57.618637:
|
| 208 |
-
2023-07-24 01:24:57.618829: Epoch 23
|
| 209 |
-
2023-07-24 01:24:57.618946: Current learning rate: 0.00979
|
| 210 |
-
2023-07-24 01:28:25.124920: train_loss -0.5864
|
| 211 |
-
2023-07-24 01:28:25.125104: val_loss -0.5029
|
| 212 |
-
2023-07-24 01:28:25.125192: Pseudo dice [0.8623, 0.7014, 0.5049]
|
| 213 |
-
2023-07-24 01:28:25.125279: Epoch time: 207.51 s
|
| 214 |
-
2023-07-24 01:28:25.125346: Yayy! New best EMA pseudo Dice: 0.6392
|
| 215 |
-
2023-07-24 01:28:29.015366:
|
| 216 |
-
2023-07-24 01:28:29.015487: Epoch 24
|
| 217 |
-
2023-07-24 01:28:29.015594: Current learning rate: 0.00978
|
| 218 |
-
2023-07-24 01:32:03.197192: train_loss -0.5843
|
| 219 |
-
2023-07-24 01:32:03.197452: val_loss -0.478
|
| 220 |
-
2023-07-24 01:32:03.197542: Pseudo dice [0.8586, 0.6702, 0.5603]
|
| 221 |
-
2023-07-24 01:32:03.197691: Epoch time: 214.18 s
|
| 222 |
-
2023-07-24 01:32:03.197757: Yayy! New best EMA pseudo Dice: 0.6449
|
| 223 |
-
2023-07-24 01:32:05.726707:
|
| 224 |
-
2023-07-24 01:32:05.726868: Epoch 25
|
| 225 |
-
2023-07-24 01:32:05.726998: Current learning rate: 0.00977
|
| 226 |
-
2023-07-24 01:35:41.851906: train_loss -0.5882
|
| 227 |
-
2023-07-24 01:35:41.852082: val_loss -0.4719
|
| 228 |
-
2023-07-24 01:35:41.852175: Pseudo dice [0.862, 0.6743, 0.3966]
|
| 229 |
-
2023-07-24 01:35:41.852259: Epoch time: 216.13 s
|
| 230 |
-
2023-07-24 01:35:43.534244:
|
| 231 |
-
2023-07-24 01:35:43.534373: Epoch 26
|
| 232 |
-
2023-07-24 01:35:43.534485: Current learning rate: 0.00977
|
| 233 |
-
2023-07-24 01:39:02.738217: train_loss -0.6018
|
| 234 |
-
2023-07-24 01:39:02.743619: val_loss -0.4994
|
| 235 |
-
2023-07-24 01:39:02.743780: Pseudo dice [0.8766, 0.733, 0.4566]
|
| 236 |
-
2023-07-24 01:39:02.743932: Epoch time: 199.21 s
|
| 237 |
-
2023-07-24 01:39:02.744023: Yayy! New best EMA pseudo Dice: 0.6493
|
| 238 |
-
2023-07-24 01:39:05.142646:
|
| 239 |
-
2023-07-24 01:39:05.142778: Epoch 27
|
| 240 |
-
2023-07-24 01:39:05.142897: Current learning rate: 0.00976
|
| 241 |
-
2023-07-24 01:42:29.737434: train_loss -0.5995
|
| 242 |
-
2023-07-24 01:42:29.737619: val_loss -0.4867
|
| 243 |
-
2023-07-24 01:42:29.737704: Pseudo dice [0.8689, 0.689, 0.4957]
|
| 244 |
-
2023-07-24 01:42:29.737784: Epoch time: 204.6 s
|
| 245 |
-
2023-07-24 01:42:29.737848: Yayy! New best EMA pseudo Dice: 0.6528
|
| 246 |
-
2023-07-24 01:42:32.621387:
|
| 247 |
-
2023-07-24 01:42:32.621503: Epoch 28
|
| 248 |
-
2023-07-24 01:42:32.621617: Current learning rate: 0.00975
|
| 249 |
-
2023-07-24 01:46:07.936492: train_loss -0.5998
|
| 250 |
-
2023-07-24 01:46:07.936731: val_loss -0.4998
|
| 251 |
-
2023-07-24 01:46:07.943375: Pseudo dice [0.8649, 0.6936, 0.5489]
|
| 252 |
-
2023-07-24 01:46:07.943589: Epoch time: 215.32 s
|
| 253 |
-
2023-07-24 01:46:07.943666: Yayy! New best EMA pseudo Dice: 0.6578
|
| 254 |
-
2023-07-24 01:46:12.240499:
|
| 255 |
-
2023-07-24 01:46:12.240789: Epoch 29
|
| 256 |
-
2023-07-24 01:46:12.240907: Current learning rate: 0.00974
|
| 257 |
-
2023-07-24 01:49:49.765490: train_loss -0.6192
|
| 258 |
-
2023-07-24 01:49:49.765681: val_loss -0.4884
|
| 259 |
-
2023-07-24 01:49:49.765781: Pseudo dice [0.8677, 0.6745, 0.5335]
|
| 260 |
-
2023-07-24 01:49:49.765875: Epoch time: 217.53 s
|
| 261 |
-
2023-07-24 01:49:49.765948: Yayy! New best EMA pseudo Dice: 0.6612
|
| 262 |
-
2023-07-24 01:49:54.074142:
|
| 263 |
-
2023-07-24 01:49:54.074317: Epoch 30
|
| 264 |
-
2023-07-24 01:49:54.074434: Current learning rate: 0.00973
|
| 265 |
-
2023-07-24 01:53:33.757728: train_loss -0.6115
|
| 266 |
-
2023-07-24 01:53:33.758031: val_loss -0.4838
|
| 267 |
-
2023-07-24 01:53:33.758127: Pseudo dice [0.8786, 0.6666, 0.5728]
|
| 268 |
-
2023-07-24 01:53:33.758209: Epoch time: 219.69 s
|
| 269 |
-
2023-07-24 01:53:33.758281: Yayy! New best EMA pseudo Dice: 0.6656
|
| 270 |
-
2023-07-24 01:53:37.008034:
|
| 271 |
-
2023-07-24 01:53:37.008158: Epoch 31
|
| 272 |
-
2023-07-24 01:53:37.008272: Current learning rate: 0.00972
|
| 273 |
-
2023-07-24 01:57:03.417002: train_loss -0.625
|
| 274 |
-
2023-07-24 01:57:03.417286: val_loss -0.5044
|
| 275 |
-
2023-07-24 01:57:03.417374: Pseudo dice [0.8686, 0.7071, 0.5431]
|
| 276 |
-
2023-07-24 01:57:03.417525: Epoch time: 206.41 s
|
| 277 |
-
2023-07-24 01:57:03.417588: Yayy! New best EMA pseudo Dice: 0.6697
|
| 278 |
-
2023-07-24 01:57:05.575807:
|
| 279 |
-
2023-07-24 01:57:05.575927: Epoch 32
|
| 280 |
-
2023-07-24 01:57:05.576024: Current learning rate: 0.00971
|
| 281 |
-
2023-07-24 02:00:42.226546: train_loss -0.6184
|
| 282 |
-
2023-07-24 02:00:42.232471: val_loss -0.4894
|
| 283 |
-
2023-07-24 02:00:42.232700: Pseudo dice [0.8702, 0.7033, 0.4937]
|
| 284 |
-
2023-07-24 02:00:42.232790: Epoch time: 216.65 s
|
| 285 |
-
2023-07-24 02:00:42.232924: Yayy! New best EMA pseudo Dice: 0.6716
|
| 286 |
-
2023-07-24 02:00:45.014342:
|
| 287 |
-
2023-07-24 02:00:45.014468: Epoch 33
|
| 288 |
-
2023-07-24 02:00:45.014586: Current learning rate: 0.0097
|
| 289 |
-
2023-07-24 02:04:19.757532: train_loss -0.6217
|
| 290 |
-
2023-07-24 02:04:19.757771: val_loss -0.4804
|
| 291 |
-
2023-07-24 02:04:19.757856: Pseudo dice [0.8748, 0.693, 0.5362]
|
| 292 |
-
2023-07-24 02:04:19.758005: Epoch time: 214.74 s
|
| 293 |
-
2023-07-24 02:04:19.758069: Yayy! New best EMA pseudo Dice: 0.6746
|
| 294 |
-
2023-07-24 02:04:24.430918:
|
| 295 |
-
2023-07-24 02:04:24.431120: Epoch 34
|
| 296 |
-
2023-07-24 02:04:24.431232: Current learning rate: 0.00969
|
| 297 |
-
2023-07-24 02:08:00.604949: train_loss -0.6087
|
| 298 |
-
2023-07-24 02:08:00.605173: val_loss -0.4999
|
| 299 |
-
2023-07-24 02:08:00.605262: Pseudo dice [0.8614, 0.7024, 0.5684]
|
| 300 |
-
2023-07-24 02:08:00.605341: Epoch time: 216.18 s
|
| 301 |
-
2023-07-24 02:08:00.605400: Yayy! New best EMA pseudo Dice: 0.6782
|
| 302 |
-
2023-07-24 02:08:05.228923:
|
| 303 |
-
2023-07-24 02:08:05.229156: Epoch 35
|
| 304 |
-
2023-07-24 02:08:05.229259: Current learning rate: 0.00968
|
| 305 |
-
2023-07-24 02:11:46.128471: train_loss -0.6278
|
| 306 |
-
2023-07-24 02:11:46.128723: val_loss -0.4975
|
| 307 |
-
2023-07-24 02:11:46.128807: Pseudo dice [0.8697, 0.6883, 0.5635]
|
| 308 |
-
2023-07-24 02:11:46.128886: Epoch time: 220.9 s
|
| 309 |
-
2023-07-24 02:11:46.128947: Yayy! New best EMA pseudo Dice: 0.6811
|
| 310 |
-
2023-07-24 02:11:48.352022:
|
| 311 |
-
2023-07-24 02:11:48.352140: Epoch 36
|
| 312 |
-
2023-07-24 02:11:48.352254: Current learning rate: 0.00968
|
| 313 |
-
2023-07-24 02:15:18.130978: train_loss -0.6341
|
| 314 |
-
2023-07-24 02:15:18.131156: val_loss -0.514
|
| 315 |
-
2023-07-24 02:15:18.131239: Pseudo dice [0.8717, 0.7153, 0.5774]
|
| 316 |
-
2023-07-24 02:15:18.131315: Epoch time: 209.78 s
|
| 317 |
-
2023-07-24 02:15:18.131555: Yayy! New best EMA pseudo Dice: 0.6852
|
| 318 |
-
2023-07-24 02:15:20.282215:
|
| 319 |
-
2023-07-24 02:15:20.282343: Epoch 37
|
| 320 |
-
2023-07-24 02:15:20.282458: Current learning rate: 0.00967
|
| 321 |
-
2023-07-24 02:18:53.374968: train_loss -0.6275
|
| 322 |
-
2023-07-24 02:18:53.375212: val_loss -0.5119
|
| 323 |
-
2023-07-24 02:18:53.375304: Pseudo dice [0.867, 0.6981, 0.5894]
|
| 324 |
-
2023-07-24 02:18:53.375387: Epoch time: 213.09 s
|
| 325 |
-
2023-07-24 02:18:53.375454: Yayy! New best EMA pseudo Dice: 0.6885
|
| 326 |
-
2023-07-24 02:18:56.726088:
|
| 327 |
-
2023-07-24 02:18:56.726211: Epoch 38
|
| 328 |
-
2023-07-24 02:18:56.726327: Current learning rate: 0.00966
|
| 329 |
-
2023-07-24 02:22:22.025652: train_loss -0.6328
|
| 330 |
-
2023-07-24 02:22:22.031740: val_loss -0.503
|
| 331 |
-
2023-07-24 02:22:22.032003: Pseudo dice [0.8649, 0.7161, 0.5203]
|
| 332 |
-
2023-07-24 02:22:22.032098: Epoch time: 205.3 s
|
| 333 |
-
2023-07-24 02:22:22.032236: Yayy! New best EMA pseudo Dice: 0.6897
|
| 334 |
-
2023-07-24 02:22:25.923321:
|
| 335 |
-
2023-07-24 02:22:25.923668: Epoch 39
|
| 336 |
-
2023-07-24 02:22:25.923775: Current learning rate: 0.00965
|
| 337 |
-
2023-07-24 02:25:55.050353: train_loss -0.6391
|
| 338 |
-
2023-07-24 02:25:55.050546: val_loss -0.5015
|
| 339 |
-
2023-07-24 02:25:55.050636: Pseudo dice [0.8618, 0.6972, 0.566]
|
| 340 |
-
2023-07-24 02:25:55.050725: Epoch time: 209.13 s
|
| 341 |
-
2023-07-24 02:25:55.050815: Yayy! New best EMA pseudo Dice: 0.6915
|
| 342 |
-
2023-07-24 02:25:57.131437:
|
| 343 |
-
2023-07-24 02:25:57.131554: Epoch 40
|
| 344 |
-
2023-07-24 02:25:57.131677: Current learning rate: 0.00964
|
| 345 |
-
2023-07-24 02:29:24.101611: train_loss -0.6453
|
| 346 |
-
2023-07-24 02:29:24.101807: val_loss -0.4857
|
| 347 |
-
2023-07-24 02:29:24.101905: Pseudo dice [0.8591, 0.6952, 0.5601]
|
| 348 |
-
2023-07-24 02:29:24.102006: Epoch time: 206.97 s
|
| 349 |
-
2023-07-24 02:29:24.102089: Yayy! New best EMA pseudo Dice: 0.6929
|
| 350 |
-
2023-07-24 02:29:26.722635:
|
| 351 |
-
2023-07-24 02:29:26.722777: Epoch 41
|
| 352 |
-
2023-07-24 02:29:26.722900: Current learning rate: 0.00963
|
| 353 |
-
2023-07-24 02:33:01.503387: train_loss -0.6384
|
| 354 |
-
2023-07-24 02:33:01.503580: val_loss -0.495
|
| 355 |
-
2023-07-24 02:33:01.503671: Pseudo dice [0.8646, 0.7111, 0.4851]
|
| 356 |
-
2023-07-24 02:33:01.503757: Epoch time: 214.78 s
|
| 357 |
-
2023-07-24 02:33:03.298025:
|
| 358 |
-
2023-07-24 02:33:03.298147: Epoch 42
|
| 359 |
-
2023-07-24 02:33:03.298252: Current learning rate: 0.00962
|
| 360 |
-
2023-07-24 02:36:28.531214: train_loss -0.6454
|
| 361 |
-
2023-07-24 02:36:28.531463: val_loss -0.4999
|
| 362 |
-
2023-07-24 02:36:28.531549: Pseudo dice [0.8693, 0.691, 0.6198]
|
| 363 |
-
2023-07-24 02:36:28.531719: Epoch time: 205.23 s
|
| 364 |
-
2023-07-24 02:36:28.531906: Yayy! New best EMA pseudo Dice: 0.6957
|
| 365 |
-
2023-07-24 02:36:30.675420:
|
| 366 |
-
2023-07-24 02:36:30.675563: Epoch 43
|
| 367 |
-
2023-07-24 02:36:30.675693: Current learning rate: 0.00961
|
| 368 |
-
2023-07-24 02:39:57.254115: train_loss -0.6449
|
| 369 |
-
2023-07-24 02:39:57.254383: val_loss -0.5029
|
| 370 |
-
2023-07-24 02:39:57.254475: Pseudo dice [0.8713, 0.6894, 0.6393]
|
| 371 |
-
2023-07-24 02:39:57.254557: Epoch time: 206.58 s
|
| 372 |
-
2023-07-24 02:39:57.254622: Yayy! New best EMA pseudo Dice: 0.6995
|
| 373 |
-
2023-07-24 02:39:59.978240:
|
| 374 |
-
2023-07-24 02:39:59.978516: Epoch 44
|
| 375 |
-
2023-07-24 02:39:59.978635: Current learning rate: 0.0096
|
| 376 |
-
2023-07-24 02:43:33.330351: train_loss -0.6418
|
| 377 |
-
2023-07-24 02:43:33.330585: val_loss -0.4937
|
| 378 |
-
2023-07-24 02:43:33.330672: Pseudo dice [0.8727, 0.6956, 0.5291]
|
| 379 |
-
2023-07-24 02:43:33.330842: Epoch time: 213.35 s
|
| 380 |
-
2023-07-24 02:43:35.411677:
|
| 381 |
-
2023-07-24 02:43:35.411899: Epoch 45
|
| 382 |
-
2023-07-24 02:43:35.412007: Current learning rate: 0.00959
|
| 383 |
-
2023-07-24 02:47:16.638136: train_loss -0.6484
|
| 384 |
-
2023-07-24 02:47:16.638322: val_loss -0.5065
|
| 385 |
-
2023-07-24 02:47:16.638409: Pseudo dice [0.8781, 0.7181, 0.5566]
|
| 386 |
-
2023-07-24 02:47:16.638503: Epoch time: 221.23 s
|
| 387 |
-
2023-07-24 02:47:16.638575: Yayy! New best EMA pseudo Dice: 0.7013
|
| 388 |
-
2023-07-24 02:47:18.875849:
|
| 389 |
-
2023-07-24 02:47:18.876126: Epoch 46
|
| 390 |
-
2023-07-24 02:47:18.876246: Current learning rate: 0.00959
|
| 391 |
-
2023-07-24 02:50:43.664102: train_loss -0.6594
|
| 392 |
-
2023-07-24 02:50:43.664368: val_loss -0.5021
|
| 393 |
-
2023-07-24 02:50:43.664471: Pseudo dice [0.8832, 0.7081, 0.5237]
|
| 394 |
-
2023-07-24 02:50:43.664641: Epoch time: 204.79 s
|
| 395 |
-
2023-07-24 02:50:43.664771: Yayy! New best EMA pseudo Dice: 0.7016
|
| 396 |
-
2023-07-24 02:50:45.913680:
|
| 397 |
-
2023-07-24 02:50:45.913988: Epoch 47
|
| 398 |
-
2023-07-24 02:50:45.914099: Current learning rate: 0.00958
|
| 399 |
-
2023-07-24 02:54:09.407397: train_loss -0.6559
|
| 400 |
-
2023-07-24 02:54:09.407646: val_loss -0.4876
|
| 401 |
-
2023-07-24 02:54:09.407736: Pseudo dice [0.8737, 0.6605, 0.6181]
|
| 402 |
-
2023-07-24 02:54:09.407889: Epoch time: 203.49 s
|
| 403 |
-
2023-07-24 02:54:09.407957: Yayy! New best EMA pseudo Dice: 0.7032
|
| 404 |
-
2023-07-24 02:54:12.936934:
|
| 405 |
-
2023-07-24 02:54:12.937068: Epoch 48
|
| 406 |
-
2023-07-24 02:54:12.937182: Current learning rate: 0.00957
|
| 407 |
-
2023-07-24 02:57:47.539666: train_loss -0.6583
|
| 408 |
-
2023-07-24 02:57:47.539865: val_loss -0.5048
|
| 409 |
-
2023-07-24 02:57:47.540035: Pseudo dice [0.8792, 0.7214, 0.4783]
|
| 410 |
-
2023-07-24 02:57:47.540195: Epoch time: 214.6 s
|
| 411 |
-
2023-07-24 02:57:49.232162:
|
| 412 |
-
2023-07-24 02:57:49.232288: Epoch 49
|
| 413 |
-
2023-07-24 02:57:49.232400: Current learning rate: 0.00956
|
| 414 |
-
2023-07-24 03:01:31.963185: train_loss -0.6562
|
| 415 |
-
2023-07-24 03:01:31.963373: val_loss -0.5071
|
| 416 |
-
2023-07-24 03:01:31.963463: Pseudo dice [0.8811, 0.7189, 0.5632]
|
| 417 |
-
2023-07-24 03:01:31.963546: Epoch time: 222.73 s
|
| 418 |
-
2023-07-24 03:01:32.441789: Yayy! New best EMA pseudo Dice: 0.7041
|
| 419 |
-
2023-07-24 03:01:34.494628:
|
| 420 |
-
2023-07-24 03:01:34.494856: Epoch 50
|
| 421 |
-
2023-07-24 03:01:34.494962: Current learning rate: 0.00955
|
| 422 |
-
2023-07-24 03:05:00.456790: train_loss -0.66
|
| 423 |
-
2023-07-24 03:05:00.457008: val_loss -0.4885
|
| 424 |
-
2023-07-24 03:05:00.457094: Pseudo dice [0.8722, 0.7199, 0.3882]
|
| 425 |
-
2023-07-24 03:05:00.457232: Epoch time: 205.96 s
|
| 426 |
-
2023-07-24 03:05:01.920852:
|
| 427 |
-
2023-07-24 03:05:01.921130: Epoch 51
|
| 428 |
-
2023-07-24 03:05:01.921252: Current learning rate: 0.00954
|
| 429 |
-
2023-07-24 03:08:21.863105: train_loss -0.6615
|
| 430 |
-
2023-07-24 03:08:21.863394: val_loss -0.5063
|
| 431 |
-
2023-07-24 03:08:21.863482: Pseudo dice [0.8652, 0.6923, 0.5877]
|
| 432 |
-
2023-07-24 03:08:21.863767: Epoch time: 199.94 s
|
| 433 |
-
2023-07-24 03:08:23.565506:
|
| 434 |
-
2023-07-24 03:08:23.565624: Epoch 52
|
| 435 |
-
2023-07-24 03:08:23.565736: Current learning rate: 0.00953
|
| 436 |
-
2023-07-24 03:11:58.618505: train_loss -0.6541
|
| 437 |
-
2023-07-24 03:11:58.618745: val_loss -0.5208
|
| 438 |
-
2023-07-24 03:11:58.618862: Pseudo dice [0.8769, 0.7111, 0.5391]
|
| 439 |
-
2023-07-24 03:11:58.619015: Epoch time: 215.05 s
|
| 440 |
-
2023-07-24 03:12:00.422971:
|
| 441 |
-
2023-07-24 03:12:00.423093: Epoch 53
|
| 442 |
-
2023-07-24 03:12:00.423211: Current learning rate: 0.00952
|
| 443 |
-
2023-07-24 03:15:34.016732: train_loss -0.6717
|
| 444 |
-
2023-07-24 03:15:34.016971: val_loss -0.4904
|
| 445 |
-
2023-07-24 03:15:34.017070: Pseudo dice [0.8808, 0.7097, 0.4256]
|
| 446 |
-
2023-07-24 03:15:34.017220: Epoch time: 213.59 s
|
| 447 |
-
2023-07-24 03:15:36.404579:
|
| 448 |
-
2023-07-24 03:15:36.404704: Epoch 54
|
| 449 |
-
2023-07-24 03:15:36.404819: Current learning rate: 0.00951
|
| 450 |
-
2023-07-24 03:19:13.253185: train_loss -0.677
|
| 451 |
-
2023-07-24 03:19:13.253387: val_loss -0.4902
|
| 452 |
-
2023-07-24 03:19:13.253475: Pseudo dice [0.8606, 0.7035, 0.5965]
|
| 453 |
-
2023-07-24 03:19:13.253560: Epoch time: 216.85 s
|
| 454 |
-
2023-07-24 03:19:14.880905:
|
| 455 |
-
2023-07-24 03:19:14.881205: Epoch 55
|
| 456 |
-
2023-07-24 03:19:14.881327: Current learning rate: 0.0095
|
| 457 |
-
2023-07-24 03:22:41.606614: train_loss -0.6675
|
| 458 |
-
2023-07-24 03:22:41.606848: val_loss -0.4941
|
| 459 |
-
2023-07-24 03:22:41.606937: Pseudo dice [0.873, 0.7194, 0.3798]
|
| 460 |
-
2023-07-24 03:22:41.607017: Epoch time: 206.73 s
|
| 461 |
-
2023-07-24 03:22:45.052177:
|
| 462 |
-
2023-07-24 03:22:45.052385: Epoch 56
|
| 463 |
-
2023-07-24 03:22:45.052505: Current learning rate: 0.00949
|
| 464 |
-
2023-07-24 03:26:17.637499: train_loss -0.6827
|
| 465 |
-
2023-07-24 03:26:17.637738: val_loss -0.5062
|
| 466 |
-
2023-07-24 03:26:17.637826: Pseudo dice [0.8737, 0.71, 0.6042]
|
| 467 |
-
2023-07-24 03:26:17.637978: Epoch time: 212.59 s
|
| 468 |
-
2023-07-24 03:26:21.340287:
|
| 469 |
-
2023-07-24 03:26:21.340517: Epoch 57
|
| 470 |
-
2023-07-24 03:26:21.340631: Current learning rate: 0.00949
|
| 471 |
-
2023-07-24 03:29:47.100365: train_loss -0.6838
|
| 472 |
-
2023-07-24 03:29:47.100567: val_loss -0.4964
|
| 473 |
-
2023-07-24 03:29:47.100666: Pseudo dice [0.8891, 0.7083, 0.4048]
|
| 474 |
-
2023-07-24 03:29:47.100763: Epoch time: 205.76 s
|
| 475 |
-
2023-07-24 03:29:48.555212:
|
| 476 |
-
2023-07-24 03:29:48.555346: Epoch 58
|
| 477 |
-
2023-07-24 03:29:48.555466: Current learning rate: 0.00948
|
| 478 |
-
2023-07-24 03:33:20.722145: train_loss -0.6697
|
| 479 |
-
2023-07-24 03:33:20.722324: val_loss -0.5062
|
| 480 |
-
2023-07-24 03:33:20.722407: Pseudo dice [0.8666, 0.6973, 0.5627]
|
| 481 |
-
2023-07-24 03:33:20.722488: Epoch time: 212.17 s
|
| 482 |
-
2023-07-24 03:33:23.647374:
|
| 483 |
-
2023-07-24 03:33:23.647504: Epoch 59
|
| 484 |
-
2023-07-24 03:33:23.647635: Current learning rate: 0.00947
|
| 485 |
-
2023-07-24 03:36:54.070232: train_loss -0.6887
|
| 486 |
-
2023-07-24 03:36:54.070504: val_loss -0.5134
|
| 487 |
-
2023-07-24 03:36:54.070593: Pseudo dice [0.8732, 0.7361, 0.4845]
|
| 488 |
-
2023-07-24 03:36:54.070743: Epoch time: 210.42 s
|
| 489 |
-
2023-07-24 03:36:56.444733:
|
| 490 |
-
2023-07-24 03:36:56.444867: Epoch 60
|
| 491 |
-
2023-07-24 03:36:56.444985: Current learning rate: 0.00946
|
| 492 |
-
2023-07-24 03:40:22.488861: train_loss -0.6985
|
| 493 |
-
2023-07-24 03:40:22.498857: val_loss -0.4992
|
| 494 |
-
2023-07-24 03:40:22.499064: Pseudo dice [0.8846, 0.7076, 0.5453]
|
| 495 |
-
2023-07-24 03:40:22.499147: Epoch time: 206.05 s
|
| 496 |
-
2023-07-24 03:40:25.415841:
|
| 497 |
-
2023-07-24 03:40:25.415988: Epoch 61
|
| 498 |
-
2023-07-24 03:40:25.416104: Current learning rate: 0.00945
|
| 499 |
-
2023-07-24 03:43:58.801075: train_loss -0.6876
|
| 500 |
-
2023-07-24 03:43:58.801260: val_loss -0.5122
|
| 501 |
-
2023-07-24 03:43:58.801349: Pseudo dice [0.8586, 0.716, 0.5498]
|
| 502 |
-
2023-07-24 03:43:58.801434: Epoch time: 213.39 s
|
| 503 |
-
2023-07-24 03:44:01.958030:
|
| 504 |
-
2023-07-24 03:44:01.970955: Epoch 62
|
| 505 |
-
2023-07-24 03:44:01.971086: Current learning rate: 0.00944
|
| 506 |
-
2023-07-24 03:47:44.738477: train_loss -0.6943
|
| 507 |
-
2023-07-24 03:47:44.738670: val_loss -0.5106
|
| 508 |
-
2023-07-24 03:47:44.738753: Pseudo dice [0.882, 0.7326, 0.544]
|
| 509 |
-
2023-07-24 03:47:44.738875: Epoch time: 222.78 s
|
| 510 |
-
2023-07-24 03:47:48.039309:
|
| 511 |
-
2023-07-24 03:47:48.039480: Epoch 63
|
| 512 |
-
2023-07-24 03:47:48.039597: Current learning rate: 0.00943
|
| 513 |
-
2023-07-24 03:51:16.951300: train_loss -0.6731
|
| 514 |
-
2023-07-24 03:51:16.951505: val_loss -0.4852
|
| 515 |
-
2023-07-24 03:51:16.951604: Pseudo dice [0.8708, 0.7122, 0.4841]
|
| 516 |
-
2023-07-24 03:51:16.951797: Epoch time: 208.91 s
|
| 517 |
-
2023-07-24 03:51:18.621170:
|
| 518 |
-
2023-07-24 03:51:18.621321: Epoch 64
|
| 519 |
-
2023-07-24 03:51:18.621441: Current learning rate: 0.00942
|
| 520 |
-
2023-07-24 03:54:44.957338: train_loss -0.6761
|
| 521 |
-
2023-07-24 03:54:44.957540: val_loss -0.4724
|
| 522 |
-
2023-07-24 03:54:44.957629: Pseudo dice [0.8598, 0.6921, 0.5167]
|
| 523 |
-
2023-07-24 03:54:44.957714: Epoch time: 206.34 s
|
| 524 |
-
2023-07-24 03:54:46.812274:
|
| 525 |
-
2023-07-24 03:54:46.812412: Epoch 65
|
| 526 |
-
2023-07-24 03:54:46.812517: Current learning rate: 0.00941
|
| 527 |
-
2023-07-24 03:58:23.950075: train_loss -0.6927
|
| 528 |
-
2023-07-24 03:58:23.950717: val_loss -0.5027
|
| 529 |
-
2023-07-24 03:58:23.951065: Pseudo dice [0.8701, 0.7152, 0.4785]
|
| 530 |
-
2023-07-24 03:58:23.951216: Epoch time: 217.14 s
|
| 531 |
-
2023-07-24 03:58:25.802012:
|
| 532 |
-
2023-07-24 03:58:25.802195: Epoch 66
|
| 533 |
-
2023-07-24 03:58:25.802299: Current learning rate: 0.0094
|
| 534 |
-
2023-07-24 04:01:56.722209: train_loss -0.6963
|
| 535 |
-
2023-07-24 04:01:56.722415: val_loss -0.5062
|
| 536 |
-
2023-07-24 04:01:56.722507: Pseudo dice [0.8726, 0.7042, 0.5969]
|
| 537 |
-
2023-07-24 04:01:56.722586: Epoch time: 210.92 s
|
| 538 |
-
2023-07-24 04:01:59.771096:
|
| 539 |
-
2023-07-24 04:01:59.771243: Epoch 67
|
| 540 |
-
2023-07-24 04:01:59.771358: Current learning rate: 0.00939
|
| 541 |
-
2023-07-24 04:05:40.925208: train_loss -0.6952
|
| 542 |
-
2023-07-24 04:05:40.925490: val_loss -0.5088
|
| 543 |
-
2023-07-24 04:05:40.925585: Pseudo dice [0.8802, 0.729, 0.5521]
|
| 544 |
-
2023-07-24 04:05:40.925745: Epoch time: 221.16 s
|
| 545 |
-
2023-07-24 04:05:43.897002:
|
| 546 |
-
2023-07-24 04:05:43.898616: Epoch 68
|
| 547 |
-
2023-07-24 04:05:43.898738: Current learning rate: 0.00939
|
| 548 |
-
2023-07-24 04:09:15.657769: train_loss -0.6889
|
| 549 |
-
2023-07-24 04:09:15.658007: val_loss -0.516
|
| 550 |
-
2023-07-24 04:09:15.658100: Pseudo dice [0.8782, 0.7278, 0.5643]
|
| 551 |
-
2023-07-24 04:09:15.658307: Epoch time: 211.76 s
|
| 552 |
-
2023-07-24 04:09:15.658453: Yayy! New best EMA pseudo Dice: 0.7051
|
| 553 |
-
2023-07-24 04:09:18.116199:
|
| 554 |
-
2023-07-24 04:09:18.116321: Epoch 69
|
| 555 |
-
2023-07-24 04:09:18.116441: Current learning rate: 0.00938
|
| 556 |
-
2023-07-24 04:12:42.753379: train_loss -0.6904
|
| 557 |
-
2023-07-24 04:12:42.753681: val_loss -0.4671
|
| 558 |
-
2023-07-24 04:12:42.753862: Pseudo dice [0.8749, 0.7138, 0.3681]
|
| 559 |
-
2023-07-24 04:12:42.753966: Epoch time: 204.64 s
|
| 560 |
-
2023-07-24 04:12:44.327600:
|
| 561 |
-
2023-07-24 04:12:44.327721: Epoch 70
|
| 562 |
-
2023-07-24 04:12:44.327840: Current learning rate: 0.00937
|
| 563 |
-
2023-07-24 04:16:22.761208: train_loss -0.6958
|
| 564 |
-
2023-07-24 04:16:22.761458: val_loss -0.5012
|
| 565 |
-
2023-07-24 04:16:22.761544: Pseudo dice [0.8879, 0.7126, 0.5304]
|
| 566 |
-
2023-07-24 04:16:22.761694: Epoch time: 218.43 s
|
| 567 |
-
2023-07-24 04:16:24.628846:
|
| 568 |
-
2023-07-24 04:16:24.629004: Epoch 71
|
| 569 |
-
2023-07-24 04:16:24.629131: Current learning rate: 0.00936
|
| 570 |
-
2023-07-24 04:19:50.557256: train_loss -0.6919
|
| 571 |
-
2023-07-24 04:19:50.557556: val_loss -0.4675
|
| 572 |
-
2023-07-24 04:19:50.557651: Pseudo dice [0.8698, 0.6855, 0.5056]
|
| 573 |
-
2023-07-24 04:19:50.557837: Epoch time: 205.93 s
|
| 574 |
-
2023-07-24 04:19:53.142438:
|
| 575 |
-
2023-07-24 04:19:53.142620: Epoch 72
|
| 576 |
-
2023-07-24 04:19:53.142735: Current learning rate: 0.00935
|
| 577 |
-
2023-07-24 04:23:22.464141: train_loss -0.6977
|
| 578 |
-
2023-07-24 04:23:22.464365: val_loss -0.5149
|
| 579 |
-
2023-07-24 04:23:22.464458: Pseudo dice [0.8859, 0.7116, 0.5175]
|
| 580 |
-
2023-07-24 04:23:22.464544: Epoch time: 209.32 s
|
| 581 |
-
2023-07-24 04:23:24.605744:
|
| 582 |
-
2023-07-24 04:23:24.605880: Epoch 73
|
| 583 |
-
2023-07-24 04:23:24.605996: Current learning rate: 0.00934
|
| 584 |
-
2023-07-24 04:26:50.364227: train_loss -0.6983
|
| 585 |
-
2023-07-24 04:26:50.364492: val_loss -0.4938
|
| 586 |
-
2023-07-24 04:26:50.364594: Pseudo dice [0.8637, 0.7213, 0.5451]
|
| 587 |
-
2023-07-24 04:26:50.364779: Epoch time: 205.76 s
|
| 588 |
-
2023-07-24 04:26:52.800122:
|
| 589 |
-
2023-07-24 04:26:52.800305: Epoch 74
|
| 590 |
-
2023-07-24 04:26:52.800421: Current learning rate: 0.00933
|
| 591 |
-
2023-07-24 04:30:26.828684: train_loss -0.6956
|
| 592 |
-
2023-07-24 04:30:26.828918: val_loss -0.4781
|
| 593 |
-
2023-07-24 04:30:26.829004: Pseudo dice [0.8634, 0.6982, 0.4993]
|
| 594 |
-
2023-07-24 04:30:26.829283: Epoch time: 214.03 s
|
| 595 |
-
2023-07-24 04:30:28.715835:
|
| 596 |
-
2023-07-24 04:30:28.716055: Epoch 75
|
| 597 |
-
2023-07-24 04:30:28.716175: Current learning rate: 0.00932
|
| 598 |
-
2023-07-24 04:33:56.996001: train_loss -0.6978
|
| 599 |
-
2023-07-24 04:33:56.996192: val_loss -0.5014
|
| 600 |
-
2023-07-24 04:33:56.996281: Pseudo dice [0.8721, 0.6953, 0.5428]
|
| 601 |
-
2023-07-24 04:33:56.996363: Epoch time: 208.28 s
|
| 602 |
-
2023-07-24 04:34:01.112788:
|
| 603 |
-
2023-07-24 04:34:01.112929: Epoch 76
|
| 604 |
-
2023-07-24 04:34:01.113038: Current learning rate: 0.00931
|
| 605 |
-
2023-07-24 04:37:36.118190: train_loss -0.6992
|
| 606 |
-
2023-07-24 04:37:36.118396: val_loss -0.5014
|
| 607 |
-
2023-07-24 04:37:36.118491: Pseudo dice [0.8768, 0.7055, 0.5652]
|
| 608 |
-
2023-07-24 04:37:36.118583: Epoch time: 215.01 s
|
| 609 |
-
2023-07-24 04:37:39.211814:
|
| 610 |
-
2023-07-24 04:37:39.211969: Epoch 77
|
| 611 |
-
2023-07-24 04:37:39.212115: Current learning rate: 0.0093
|
| 612 |
-
2023-07-24 04:41:10.694273: train_loss -0.7058
|
| 613 |
-
2023-07-24 04:41:10.694464: val_loss -0.4895
|
| 614 |
-
2023-07-24 04:41:10.694552: Pseudo dice [0.8602, 0.7049, 0.5613]
|
| 615 |
-
2023-07-24 04:41:10.694637: Epoch time: 211.48 s
|
| 616 |
-
2023-07-24 04:41:13.013998:
|
| 617 |
-
2023-07-24 04:41:13.014146: Epoch 78
|
| 618 |
-
2023-07-24 04:41:13.014262: Current learning rate: 0.0093
|
| 619 |
-
2023-07-24 04:44:39.537027: train_loss -0.7016
|
| 620 |
-
2023-07-24 04:44:39.537224: val_loss -0.4884
|
| 621 |
-
2023-07-24 04:44:39.537313: Pseudo dice [0.8797, 0.7206, 0.4807]
|
| 622 |
-
2023-07-24 04:44:39.537394: Epoch time: 206.52 s
|
| 623 |
-
2023-07-24 04:44:41.752397:
|
| 624 |
-
2023-07-24 04:44:41.752547: Epoch 79
|
| 625 |
-
2023-07-24 04:44:41.752668: Current learning rate: 0.00929
|
| 626 |
-
2023-07-24 04:48:09.494605: train_loss -0.6964
|
| 627 |
-
2023-07-24 04:48:09.494830: val_loss -0.5074
|
| 628 |
-
2023-07-24 04:48:09.494932: Pseudo dice [0.8874, 0.7203, 0.6069]
|
| 629 |
-
2023-07-24 04:48:09.495023: Epoch time: 207.74 s
|
| 630 |
-
2023-07-24 04:48:11.746876:
|
| 631 |
-
2023-07-24 04:48:11.747036: Epoch 80
|
| 632 |
-
2023-07-24 04:48:11.747147: Current learning rate: 0.00928
|
| 633 |
-
2023-07-24 04:51:42.348668: train_loss -0.7065
|
| 634 |
-
2023-07-24 04:51:42.348896: val_loss -0.4837
|
| 635 |
-
2023-07-24 04:51:42.348983: Pseudo dice [0.8784, 0.7032, 0.5621]
|
| 636 |
-
2023-07-24 04:51:42.349133: Epoch time: 210.6 s
|
| 637 |
-
2023-07-24 04:51:42.349199: Yayy! New best EMA pseudo Dice: 0.7061
|
| 638 |
-
2023-07-24 04:51:44.484021:
|
| 639 |
-
2023-07-24 04:51:44.484140: Epoch 81
|
| 640 |
-
2023-07-24 04:51:44.484257: Current learning rate: 0.00927
|
| 641 |
-
2023-07-24 04:55:09.696577: train_loss -0.714
|
| 642 |
-
2023-07-24 04:55:09.696886: val_loss -0.491
|
| 643 |
-
2023-07-24 04:55:09.696984: Pseudo dice [0.8814, 0.7251, 0.4554]
|
| 644 |
-
2023-07-24 04:55:09.697072: Epoch time: 205.21 s
|
| 645 |
-
2023-07-24 04:55:11.259889:
|
| 646 |
-
2023-07-24 04:55:11.260105: Epoch 82
|
| 647 |
-
2023-07-24 04:55:11.260218: Current learning rate: 0.00926
|
| 648 |
-
2023-07-24 04:58:42.167684: train_loss -0.7151
|
| 649 |
-
2023-07-24 04:58:42.167853: val_loss -0.5149
|
| 650 |
-
2023-07-24 04:58:42.167938: Pseudo dice [0.8747, 0.6999, 0.632]
|
| 651 |
-
2023-07-24 04:58:42.168024: Epoch time: 210.91 s
|
| 652 |
-
2023-07-24 04:58:42.168089: Yayy! New best EMA pseudo Dice: 0.7073
|
| 653 |
-
2023-07-24 04:58:46.756355:
|
| 654 |
-
2023-07-24 04:58:46.756490: Epoch 83
|
| 655 |
-
2023-07-24 04:58:46.756597: Current learning rate: 0.00925
|
| 656 |
-
2023-07-24 05:02:22.008022: train_loss -0.7232
|
| 657 |
-
2023-07-24 05:02:22.008212: val_loss -0.5229
|
| 658 |
-
2023-07-24 05:02:22.008301: Pseudo dice [0.8914, 0.7236, 0.6229]
|
| 659 |
-
2023-07-24 05:02:22.008386: Epoch time: 215.25 s
|
| 660 |
-
2023-07-24 05:02:22.008452: Yayy! New best EMA pseudo Dice: 0.7112
|
| 661 |
-
2023-07-24 05:02:24.066390:
|
| 662 |
-
2023-07-24 05:02:24.066524: Epoch 84
|
| 663 |
-
2023-07-24 05:02:24.066619: Current learning rate: 0.00924
|
| 664 |
-
2023-07-24 05:05:50.327326: train_loss -0.725
|
| 665 |
-
2023-07-24 05:05:50.327611: val_loss -0.5159
|
| 666 |
-
2023-07-24 05:05:50.327698: Pseudo dice [0.8756, 0.7106, 0.5817]
|
| 667 |
-
2023-07-24 05:05:50.327851: Epoch time: 206.26 s
|
| 668 |
-
2023-07-24 05:05:50.327918: Yayy! New best EMA pseudo Dice: 0.7123
|
| 669 |
-
2023-07-24 05:05:54.485906:
|
| 670 |
-
2023-07-24 05:05:54.486033: Epoch 85
|
| 671 |
-
2023-07-24 05:05:54.486146: Current learning rate: 0.00923
|
| 672 |
-
2023-07-24 05:09:37.322994: train_loss -0.723
|
| 673 |
-
2023-07-24 05:09:37.323187: val_loss -0.5029
|
| 674 |
-
2023-07-24 05:09:37.323277: Pseudo dice [0.8783, 0.7171, 0.4587]
|
| 675 |
-
2023-07-24 05:09:37.323362: Epoch time: 222.84 s
|
| 676 |
-
2023-07-24 05:09:38.723594:
|
| 677 |
-
2023-07-24 05:09:38.723815: Epoch 86
|
| 678 |
-
2023-07-24 05:09:38.723936: Current learning rate: 0.00922
|
| 679 |
-
2023-07-24 05:13:09.316705: train_loss -0.7182
|
| 680 |
-
2023-07-24 05:13:09.316886: val_loss -0.4834
|
| 681 |
-
2023-07-24 05:13:09.316982: Pseudo dice [0.8828, 0.7206, 0.4715]
|
| 682 |
-
2023-07-24 05:13:09.317072: Epoch time: 210.59 s
|
| 683 |
-
2023-07-24 05:13:12.422567:
|
| 684 |
-
2023-07-24 05:13:12.422688: Epoch 87
|
| 685 |
-
2023-07-24 05:13:12.422828: Current learning rate: 0.00921
|
| 686 |
-
2023-07-24 05:16:46.310607: train_loss -0.7222
|
| 687 |
-
2023-07-24 05:16:46.310821: val_loss -0.502
|
| 688 |
-
2023-07-24 05:16:46.310914: Pseudo dice [0.8901, 0.6998, 0.6513]
|
| 689 |
-
2023-07-24 05:16:46.310997: Epoch time: 213.89 s
|
| 690 |
-
2023-07-24 05:16:48.065801:
|
| 691 |
-
2023-07-24 05:16:48.065928: Epoch 88
|
| 692 |
-
2023-07-24 05:16:48.066038: Current learning rate: 0.0092
|
| 693 |
-
2023-07-24 05:20:26.412961: train_loss -0.7227
|
| 694 |
-
2023-07-24 05:20:26.413243: val_loss -0.5113
|
| 695 |
-
2023-07-24 05:20:26.413336: Pseudo dice [0.885, 0.7263, 0.6205]
|
| 696 |
-
2023-07-24 05:20:26.413419: Epoch time: 218.35 s
|
| 697 |
-
2023-07-24 05:20:26.413479: Yayy! New best EMA pseudo Dice: 0.7149
|
| 698 |
-
2023-07-24 05:20:28.466174:
|
| 699 |
-
2023-07-24 05:20:28.466462: Epoch 89
|
| 700 |
-
2023-07-24 05:20:28.466586: Current learning rate: 0.0092
|
| 701 |
-
2023-07-24 05:23:53.870799: train_loss -0.7281
|
| 702 |
-
2023-07-24 05:23:53.871042: val_loss -0.5177
|
| 703 |
-
2023-07-24 05:23:53.871127: Pseudo dice [0.8774, 0.7281, 0.5178]
|
| 704 |
-
2023-07-24 05:23:53.871280: Epoch time: 205.41 s
|
| 705 |
-
2023-07-24 05:23:56.666574:
|
| 706 |
-
2023-07-24 05:23:56.666709: Epoch 90
|
| 707 |
-
2023-07-24 05:23:56.666846: Current learning rate: 0.00919
|
| 708 |
-
2023-07-24 05:27:24.511108: train_loss -0.7268
|
| 709 |
-
2023-07-24 05:27:24.511282: val_loss -0.4883
|
| 710 |
-
2023-07-24 05:27:24.511378: Pseudo dice [0.8808, 0.7023, 0.5429]
|
| 711 |
-
2023-07-24 05:27:24.511463: Epoch time: 207.85 s
|
| 712 |
-
2023-07-24 05:27:26.253584:
|
| 713 |
-
2023-07-24 05:27:26.253713: Epoch 91
|
| 714 |
-
2023-07-24 05:27:26.253826: Current learning rate: 0.00918
|
| 715 |
-
2023-07-24 05:31:00.680078: train_loss -0.7187
|
| 716 |
-
2023-07-24 05:31:00.680262: val_loss -0.5126
|
| 717 |
-
2023-07-24 05:31:00.680357: Pseudo dice [0.8854, 0.734, 0.5313]
|
| 718 |
-
2023-07-24 05:31:00.680444: Epoch time: 214.43 s
|
| 719 |
-
2023-07-24 05:31:03.875111:
|
| 720 |
-
2023-07-24 05:31:03.875342: Epoch 92
|
| 721 |
-
2023-07-24 05:31:03.875453: Current learning rate: 0.00917
|
| 722 |
-
2023-07-24 05:34:36.700777: train_loss -0.7261
|
| 723 |
-
2023-07-24 05:34:36.701067: val_loss -0.4824
|
| 724 |
-
2023-07-24 05:34:36.701155: Pseudo dice [0.8677, 0.7068, 0.5625]
|
| 725 |
-
2023-07-24 05:34:36.701310: Epoch time: 212.83 s
|
| 726 |
-
2023-07-24 05:34:38.725615:
|
| 727 |
-
2023-07-24 05:34:38.725760: Epoch 93
|
| 728 |
-
2023-07-24 05:34:38.725878: Current learning rate: 0.00916
|
| 729 |
-
2023-07-24 05:38:14.723124: train_loss -0.7303
|
| 730 |
-
2023-07-24 05:38:14.723329: val_loss -0.4918
|
| 731 |
-
2023-07-24 05:38:14.723435: Pseudo dice [0.8725, 0.7201, 0.5379]
|
| 732 |
-
2023-07-24 05:38:14.723536: Epoch time: 216.0 s
|
| 733 |
-
2023-07-24 05:38:18.938028:
|
| 734 |
-
2023-07-24 05:38:18.938344: Epoch 94
|
| 735 |
-
2023-07-24 05:38:18.938459: Current learning rate: 0.00915
|
| 736 |
-
2023-07-24 05:41:49.722474: train_loss -0.7321
|
| 737 |
-
2023-07-24 05:41:49.722725: val_loss -0.5192
|
| 738 |
-
2023-07-24 05:41:49.722838: Pseudo dice [0.8869, 0.73, 0.5719]
|
| 739 |
-
2023-07-24 05:41:49.722987: Epoch time: 210.79 s
|
| 740 |
-
2023-07-24 05:41:49.723047: Yayy! New best EMA pseudo Dice: 0.7151
|
| 741 |
-
2023-07-24 05:41:52.238441:
|
| 742 |
-
2023-07-24 05:41:52.238770: Epoch 95
|
| 743 |
-
2023-07-24 05:41:52.238896: Current learning rate: 0.00914
|
| 744 |
-
2023-07-24 05:45:28.216917: train_loss -0.7294
|
| 745 |
-
2023-07-24 05:45:28.217144: val_loss -0.4859
|
| 746 |
-
2023-07-24 05:45:28.217231: Pseudo dice [0.8868, 0.7071, 0.502]
|
| 747 |
-
2023-07-24 05:45:28.217372: Epoch time: 215.98 s
|
| 748 |
-
2023-07-24 05:45:30.203134:
|
| 749 |
-
2023-07-24 05:45:30.203313: Epoch 96
|
| 750 |
-
2023-07-24 05:45:30.203428: Current learning rate: 0.00913
|
| 751 |
-
2023-07-24 05:49:07.118325: train_loss -0.7275
|
| 752 |
-
2023-07-24 05:49:07.130988: val_loss -0.4979
|
| 753 |
-
2023-07-24 05:49:07.131268: Pseudo dice [0.8742, 0.7169, 0.5796]
|
| 754 |
-
2023-07-24 05:49:07.131369: Epoch time: 216.92 s
|
| 755 |
-
2023-07-24 05:49:10.592634:
|
| 756 |
-
2023-07-24 05:49:10.592791: Epoch 97
|
| 757 |
-
2023-07-24 05:49:10.592889: Current learning rate: 0.00912
|
| 758 |
-
2023-07-24 05:52:37.449055: train_loss -0.7377
|
| 759 |
-
2023-07-24 05:52:37.449250: val_loss -0.4932
|
| 760 |
-
2023-07-24 05:52:37.449338: Pseudo dice [0.878, 0.7154, 0.5691]
|
| 761 |
-
2023-07-24 05:52:37.449421: Epoch time: 206.86 s
|
| 762 |
-
2023-07-24 05:52:37.449489: Yayy! New best EMA pseudo Dice: 0.7151
|
| 763 |
-
2023-07-24 05:52:39.613982:
|
| 764 |
-
2023-07-24 05:52:39.614139: Epoch 98
|
| 765 |
-
2023-07-24 05:52:39.614302: Current learning rate: 0.00911
|
| 766 |
-
2023-07-24 05:56:12.741833: train_loss -0.725
|
| 767 |
-
2023-07-24 05:56:12.742075: val_loss -0.5039
|
| 768 |
-
2023-07-24 05:56:12.742163: Pseudo dice [0.8862, 0.711, 0.6208]
|
| 769 |
-
2023-07-24 05:56:12.742243: Epoch time: 213.13 s
|
| 770 |
-
2023-07-24 05:56:12.742309: Yayy! New best EMA pseudo Dice: 0.7175
|
| 771 |
-
2023-07-24 05:56:16.737276:
|
| 772 |
-
2023-07-24 05:56:16.737576: Epoch 99
|
| 773 |
-
2023-07-24 05:56:16.737698: Current learning rate: 0.0091
|
| 774 |
-
2023-07-24 05:59:45.462096: train_loss -0.7306
|
| 775 |
-
2023-07-24 05:59:45.462285: val_loss -0.5266
|
| 776 |
-
2023-07-24 05:59:45.462388: Pseudo dice [0.8861, 0.7386, 0.513]
|
| 777 |
-
2023-07-24 05:59:45.462479: Epoch time: 208.73 s
|
| 778 |
-
2023-07-24 05:59:50.668950:
|
| 779 |
-
2023-07-24 05:59:50.669163: Epoch 100
|
| 780 |
-
2023-07-24 05:59:50.669280: Current learning rate: 0.0091
|
| 781 |
-
2023-07-24 06:03:33.285699: train_loss -0.7382
|
| 782 |
-
2023-07-24 06:03:33.285881: val_loss -0.5096
|
| 783 |
-
2023-07-24 06:03:33.285975: Pseudo dice [0.8703, 0.712, 0.5639]
|
| 784 |
-
2023-07-24 06:03:33.286058: Epoch time: 222.62 s
|
| 785 |
-
2023-07-24 06:03:35.851426:
|
| 786 |
-
2023-07-24 06:03:35.851692: Epoch 101
|
| 787 |
-
2023-07-24 06:03:35.851813: Current learning rate: 0.00909
|
| 788 |
-
2023-07-24 06:07:07.771703: train_loss -0.7398
|
| 789 |
-
2023-07-24 06:07:07.771932: val_loss -0.5006
|
| 790 |
-
2023-07-24 06:07:07.772034: Pseudo dice [0.8863, 0.723, 0.5644]
|
| 791 |
-
2023-07-24 06:07:07.772126: Epoch time: 211.92 s
|
| 792 |
-
2023-07-24 06:07:07.772207: Yayy! New best EMA pseudo Dice: 0.7176
|
| 793 |
-
2023-07-24 06:07:09.785565:
|
| 794 |
-
2023-07-24 06:07:09.785925: Epoch 102
|
| 795 |
-
2023-07-24 06:07:09.786036: Current learning rate: 0.00908
|
| 796 |
-
2023-07-24 06:10:43.597014: train_loss -0.7306
|
| 797 |
-
2023-07-24 06:10:43.597273: val_loss -0.5015
|
| 798 |
-
2023-07-24 06:10:43.599555: Pseudo dice [0.8817, 0.7251, 0.5061]
|
| 799 |
-
2023-07-24 06:10:43.599782: Epoch time: 213.81 s
|
| 800 |
-
2023-07-24 06:10:45.487350:
|
| 801 |
-
2023-07-24 06:10:45.487549: Epoch 103
|
| 802 |
-
2023-07-24 06:10:45.487668: Current learning rate: 0.00907
|
| 803 |
-
2023-07-24 06:14:14.858136: train_loss -0.7477
|
| 804 |
-
2023-07-24 06:14:14.858318: val_loss -0.4896
|
| 805 |
-
2023-07-24 06:14:14.858405: Pseudo dice [0.8898, 0.7226, 0.5152]
|
| 806 |
-
2023-07-24 06:14:14.858487: Epoch time: 209.37 s
|
| 807 |
-
2023-07-24 06:14:18.636439:
|
| 808 |
-
2023-07-24 06:14:18.636607: Epoch 104
|
| 809 |
-
2023-07-24 06:14:18.636723: Current learning rate: 0.00906
|
| 810 |
-
2023-07-24 06:17:56.407210: train_loss -0.7288
|
| 811 |
-
2023-07-24 06:17:56.407393: val_loss -0.4908
|
| 812 |
-
2023-07-24 06:17:56.407491: Pseudo dice [0.8777, 0.7242, 0.4401]
|
| 813 |
-
2023-07-24 06:17:56.407592: Epoch time: 217.77 s
|
| 814 |
-
2023-07-24 06:17:59.395841:
|
| 815 |
-
2023-07-24 06:17:59.406929: Epoch 105
|
| 816 |
-
2023-07-24 06:17:59.407068: Current learning rate: 0.00905
|
| 817 |
-
2023-07-24 06:21:34.379379: train_loss -0.7297
|
| 818 |
-
2023-07-24 06:21:34.379545: val_loss -0.5001
|
| 819 |
-
2023-07-24 06:21:34.379629: Pseudo dice [0.8812, 0.7174, 0.5466]
|
| 820 |
-
2023-07-24 06:21:34.379707: Epoch time: 215.0 s
|
| 821 |
-
2023-07-24 06:21:36.480978:
|
| 822 |
-
2023-07-24 06:21:36.481113: Epoch 106
|
| 823 |
-
2023-07-24 06:21:36.481229: Current learning rate: 0.00904
|
| 824 |
-
2023-07-24 06:24:59.495173: train_loss -0.7319
|
| 825 |
-
2023-07-24 06:24:59.495373: val_loss -0.4684
|
| 826 |
-
2023-07-24 06:24:59.495463: Pseudo dice [0.8719, 0.699, 0.4954]
|
| 827 |
-
2023-07-24 06:24:59.495545: Epoch time: 203.02 s
|
| 828 |
-
2023-07-24 06:25:01.416497:
|
| 829 |
-
2023-07-24 06:25:01.416672: Epoch 107
|
| 830 |
-
2023-07-24 06:25:01.416785: Current learning rate: 0.00903
|
| 831 |
-
2023-07-24 06:28:38.100931: train_loss -0.7425
|
| 832 |
-
2023-07-24 06:28:38.101100: val_loss -0.5066
|
| 833 |
-
2023-07-24 06:28:38.101199: Pseudo dice [0.8828, 0.7349, 0.551]
|
| 834 |
-
2023-07-24 06:28:38.101286: Epoch time: 216.69 s
|
| 835 |
-
2023-07-24 06:28:39.838402:
|
| 836 |
-
2023-07-24 06:28:39.838579: Epoch 108
|
| 837 |
-
2023-07-24 06:28:39.838694: Current learning rate: 0.00902
|
| 838 |
-
2023-07-24 06:32:19.977618: train_loss -0.7335
|
| 839 |
-
2023-07-24 06:32:19.990841: val_loss -0.4957
|
| 840 |
-
2023-07-24 06:32:19.990945: Pseudo dice [0.8713, 0.7344, 0.4371]
|
| 841 |
-
2023-07-24 06:32:19.991140: Epoch time: 220.14 s
|
| 842 |
-
2023-07-24 06:32:22.127137:
|
| 843 |
-
2023-07-24 06:32:22.127293: Epoch 109
|
| 844 |
-
2023-07-24 06:32:22.127414: Current learning rate: 0.00901
|
| 845 |
-
2023-07-24 06:35:56.862900: train_loss -0.7247
|
| 846 |
-
2023-07-24 06:35:56.863097: val_loss -0.4827
|
| 847 |
-
2023-07-24 06:35:56.863201: Pseudo dice [0.8801, 0.6793, 0.5831]
|
| 848 |
-
2023-07-24 06:35:56.863297: Epoch time: 214.74 s
|
| 849 |
-
2023-07-24 06:36:00.448839:
|
| 850 |
-
2023-07-24 06:36:00.449008: Epoch 110
|
| 851 |
-
2023-07-24 06:36:00.449116: Current learning rate: 0.009
|
| 852 |
-
2023-07-24 06:39:25.954977: train_loss -0.7307
|
| 853 |
-
2023-07-24 06:39:25.955158: val_loss -0.4855
|
| 854 |
-
2023-07-24 06:39:25.955249: Pseudo dice [0.8795, 0.6975, 0.5762]
|
| 855 |
-
2023-07-24 06:39:25.955334: Epoch time: 205.51 s
|
| 856 |
-
2023-07-24 06:39:28.934479:
|
| 857 |
-
2023-07-24 06:39:28.934601: Epoch 111
|
| 858 |
-
2023-07-24 06:39:28.934713: Current learning rate: 0.009
|
| 859 |
-
2023-07-24 06:42:53.226294: train_loss -0.7364
|
| 860 |
-
2023-07-24 06:42:53.226513: val_loss -0.4781
|
| 861 |
-
2023-07-24 06:42:53.226613: Pseudo dice [0.8844, 0.7035, 0.4998]
|
| 862 |
-
2023-07-24 06:42:53.226707: Epoch time: 204.29 s
|
| 863 |
-
2023-07-24 06:42:57.119614:
|
| 864 |
-
2023-07-24 06:42:57.119749: Epoch 112
|
| 865 |
-
2023-07-24 06:42:57.119859: Current learning rate: 0.00899
|
| 866 |
-
2023-07-24 06:46:27.868778: train_loss -0.7428
|
| 867 |
-
2023-07-24 06:46:27.868973: val_loss -0.5043
|
| 868 |
-
2023-07-24 06:46:27.869063: Pseudo dice [0.8869, 0.6979, 0.6164]
|
| 869 |
-
2023-07-24 06:46:27.869147: Epoch time: 210.75 s
|
| 870 |
-
2023-07-24 06:46:29.738551:
|
| 871 |
-
2023-07-24 06:46:29.738678: Epoch 113
|
| 872 |
-
2023-07-24 06:46:29.738820: Current learning rate: 0.00898
|
| 873 |
-
2023-07-24 06:49:47.016800: train_loss -0.7404
|
| 874 |
-
2023-07-24 06:49:47.017059: val_loss -0.484
|
| 875 |
-
2023-07-24 06:49:47.017146: Pseudo dice [0.8878, 0.7123, 0.5259]
|
| 876 |
-
2023-07-24 06:49:47.017297: Epoch time: 197.28 s
|
| 877 |
-
2023-07-24 06:49:49.028085:
|
| 878 |
-
2023-07-24 06:49:49.028212: Epoch 114
|
| 879 |
-
2023-07-24 06:49:49.028322: Current learning rate: 0.00897
|
| 880 |
-
2023-07-24 06:53:22.595026: train_loss -0.7403
|
| 881 |
-
2023-07-24 06:53:22.595240: val_loss -0.4923
|
| 882 |
-
2023-07-24 06:53:22.595333: Pseudo dice [0.8744, 0.7065, 0.5055]
|
| 883 |
-
2023-07-24 06:53:22.595417: Epoch time: 213.57 s
|
| 884 |
-
2023-07-24 06:53:25.630225:
|
| 885 |
-
2023-07-24 06:53:25.630350: Epoch 115
|
| 886 |
-
2023-07-24 06:53:25.630466: Current learning rate: 0.00896
|
| 887 |
-
2023-07-24 06:56:54.846274: train_loss -0.7407
|
| 888 |
-
2023-07-24 06:56:54.846479: val_loss -0.5206
|
| 889 |
-
2023-07-24 06:56:54.846571: Pseudo dice [0.8874, 0.7354, 0.4706]
|
| 890 |
-
2023-07-24 06:56:54.846656: Epoch time: 209.22 s
|
| 891 |
-
2023-07-24 06:56:58.530087:
|
| 892 |
-
2023-07-24 06:56:58.530329: Epoch 116
|
| 893 |
-
2023-07-24 06:56:58.530463: Current learning rate: 0.00895
|
| 894 |
-
2023-07-24 07:00:33.757893: train_loss -0.7397
|
| 895 |
-
2023-07-24 07:00:33.758083: val_loss -0.4963
|
| 896 |
-
2023-07-24 07:00:33.758173: Pseudo dice [0.8784, 0.7196, 0.5139]
|
| 897 |
-
2023-07-24 07:00:33.758259: Epoch time: 215.23 s
|
| 898 |
-
2023-07-24 07:00:36.559549:
|
| 899 |
-
2023-07-24 07:00:36.559805: Epoch 117
|
| 900 |
-
2023-07-24 07:00:36.559918: Current learning rate: 0.00894
|
| 901 |
-
2023-07-24 07:04:09.798198: train_loss -0.737
|
| 902 |
-
2023-07-24 07:04:09.798404: val_loss -0.4986
|
| 903 |
-
2023-07-24 07:04:09.809834: Pseudo dice [0.879, 0.706, 0.5266]
|
| 904 |
-
2023-07-24 07:04:09.810041: Epoch time: 213.24 s
|
| 905 |
-
2023-07-24 07:04:13.458022:
|
| 906 |
-
2023-07-24 07:04:13.458441: Epoch 118
|
| 907 |
-
2023-07-24 07:04:13.458563: Current learning rate: 0.00893
|
| 908 |
-
2023-07-24 07:07:55.203243: train_loss -0.7376
|
| 909 |
-
2023-07-24 07:07:55.203503: val_loss -0.5011
|
| 910 |
-
2023-07-24 07:07:55.203593: Pseudo dice [0.8852, 0.7273, 0.5817]
|
| 911 |
-
2023-07-24 07:07:55.203745: Epoch time: 221.75 s
|
| 912 |
-
2023-07-24 07:07:57.539746:
|
| 913 |
-
2023-07-24 07:07:57.539875: Epoch 119
|
| 914 |
-
2023-07-24 07:07:57.539999: Current learning rate: 0.00892
|
| 915 |
-
2023-07-24 07:11:23.694811: train_loss -0.7511
|
| 916 |
-
2023-07-24 07:11:23.706887: val_loss -0.5173
|
| 917 |
-
2023-07-24 07:11:23.707143: Pseudo dice [0.8818, 0.7369, 0.559]
|
| 918 |
-
2023-07-24 07:11:23.707327: Epoch time: 206.16 s
|
| 919 |
-
2023-07-24 07:11:26.298614:
|
| 920 |
-
2023-07-24 07:11:26.298933: Epoch 120
|
| 921 |
-
2023-07-24 07:11:26.299044: Current learning rate: 0.00891
|
| 922 |
-
2023-07-24 07:14:58.226911: train_loss -0.7376
|
| 923 |
-
2023-07-24 07:14:58.227126: val_loss -0.4984
|
| 924 |
-
2023-07-24 07:14:58.227228: Pseudo dice [0.8841, 0.7261, 0.4873]
|
| 925 |
-
2023-07-24 07:14:58.227322: Epoch time: 211.93 s
|
| 926 |
-
2023-07-24 07:15:00.409765:
|
| 927 |
-
2023-07-24 07:15:00.409939: Epoch 121
|
| 928 |
-
2023-07-24 07:15:00.410051: Current learning rate: 0.0089
|
| 929 |
-
2023-07-24 07:18:26.223230: train_loss -0.7331
|
| 930 |
-
2023-07-24 07:18:26.223434: val_loss -0.4727
|
| 931 |
-
2023-07-24 07:18:26.223520: Pseudo dice [0.8632, 0.6888, 0.5576]
|
| 932 |
-
2023-07-24 07:18:26.223612: Epoch time: 205.81 s
|
| 933 |
-
2023-07-24 07:18:28.232954:
|
| 934 |
-
2023-07-24 07:18:28.233098: Epoch 122
|
| 935 |
-
2023-07-24 07:18:28.233214: Current learning rate: 0.00889
|
| 936 |
-
2023-07-24 07:22:00.937126: train_loss -0.739
|
| 937 |
-
2023-07-24 07:22:00.937339: val_loss -0.4894
|
| 938 |
-
2023-07-24 07:22:00.937483: Pseudo dice [0.8769, 0.7199, 0.4559]
|
| 939 |
-
2023-07-24 07:22:00.937608: Epoch time: 212.71 s
|
| 940 |
-
2023-07-24 07:22:04.448639:
|
| 941 |
-
2023-07-24 07:22:04.448863: Epoch 123
|
| 942 |
-
2023-07-24 07:22:04.448967: Current learning rate: 0.00889
|
| 943 |
-
2023-07-24 07:25:39.226343: train_loss -0.7513
|
| 944 |
-
2023-07-24 07:25:39.226538: val_loss -0.4662
|
| 945 |
-
2023-07-24 07:25:39.226624: Pseudo dice [0.8728, 0.7032, 0.4345]
|
| 946 |
-
2023-07-24 07:25:39.226705: Epoch time: 214.78 s
|
| 947 |
-
2023-07-24 07:25:41.940106:
|
| 948 |
-
2023-07-24 07:25:41.940430: Epoch 124
|
| 949 |
-
2023-07-24 07:25:41.940552: Current learning rate: 0.00888
|
| 950 |
-
2023-07-24 07:29:31.867979: train_loss -0.7385
|
| 951 |
-
2023-07-24 07:29:31.868199: val_loss -0.4822
|
| 952 |
-
2023-07-24 07:29:31.868285: Pseudo dice [0.8879, 0.7072, 0.4662]
|
| 953 |
-
2023-07-24 07:29:31.868367: Epoch time: 229.93 s
|
| 954 |
-
2023-07-24 07:29:33.666378:
|
| 955 |
-
2023-07-24 07:29:33.666722: Epoch 125
|
| 956 |
-
2023-07-24 07:29:33.666862: Current learning rate: 0.00887
|
| 957 |
-
2023-07-24 07:33:13.897824: train_loss -0.735
|
| 958 |
-
2023-07-24 07:33:13.898085: val_loss -0.5056
|
| 959 |
-
2023-07-24 07:33:13.898172: Pseudo dice [0.8899, 0.7388, 0.4273]
|
| 960 |
-
2023-07-24 07:33:13.898322: Epoch time: 220.23 s
|
| 961 |
-
2023-07-24 07:33:15.701629:
|
| 962 |
-
2023-07-24 07:33:15.701919: Epoch 126
|
| 963 |
-
2023-07-24 07:33:15.702039: Current learning rate: 0.00886
|
| 964 |
-
2023-07-24 07:36:46.580624: train_loss -0.7412
|
| 965 |
-
2023-07-24 07:36:46.590938: val_loss -0.4682
|
| 966 |
-
2023-07-24 07:36:46.591583: Pseudo dice [0.8875, 0.707, 0.464]
|
| 967 |
-
2023-07-24 07:36:46.591727: Epoch time: 210.88 s
|
| 968 |
-
2023-07-24 07:36:49.059450:
|
| 969 |
-
2023-07-24 07:36:49.059957: Epoch 127
|
| 970 |
-
2023-07-24 07:36:49.060068: Current learning rate: 0.00885
|
| 971 |
-
2023-07-24 07:40:16.085344: train_loss -0.7467
|
| 972 |
-
2023-07-24 07:40:16.085670: val_loss -0.5007
|
| 973 |
-
2023-07-24 07:40:16.085762: Pseudo dice [0.8923, 0.7309, 0.4699]
|
| 974 |
-
2023-07-24 07:40:16.085928: Epoch time: 207.03 s
|
| 975 |
-
2023-07-24 07:40:18.069315:
|
| 976 |
-
2023-07-24 07:40:18.069484: Epoch 128
|
| 977 |
-
2023-07-24 07:40:18.069586: Current learning rate: 0.00884
|
| 978 |
-
2023-07-24 07:43:48.285688: train_loss -0.7613
|
| 979 |
-
2023-07-24 07:43:48.285896: val_loss -0.5026
|
| 980 |
-
2023-07-24 07:43:48.285983: Pseudo dice [0.8685, 0.7138, 0.5262]
|
| 981 |
-
2023-07-24 07:43:48.286072: Epoch time: 210.22 s
|
| 982 |
-
2023-07-24 07:43:50.040725:
|
| 983 |
-
2023-07-24 07:43:50.040847: Epoch 129
|
| 984 |
-
2023-07-24 07:43:50.040961: Current learning rate: 0.00883
|
| 985 |
-
2023-07-24 07:47:20.600014: train_loss -0.749
|
| 986 |
-
2023-07-24 07:47:20.600197: val_loss -0.4715
|
| 987 |
-
2023-07-24 07:47:20.600303: Pseudo dice [0.8669, 0.7066, 0.5142]
|
| 988 |
-
2023-07-24 07:47:20.600403: Epoch time: 210.56 s
|
| 989 |
-
2023-07-24 07:47:22.825845:
|
| 990 |
-
2023-07-24 07:47:22.825982: Epoch 130
|
| 991 |
-
2023-07-24 07:47:22.826100: Current learning rate: 0.00882
|
| 992 |
-
2023-07-24 07:51:01.366344: train_loss -0.7542
|
| 993 |
-
2023-07-24 07:51:01.372711: val_loss -0.4867
|
| 994 |
-
2023-07-24 07:51:01.373079: Pseudo dice [0.8709, 0.7212, 0.5275]
|
| 995 |
-
2023-07-24 07:51:01.373174: Epoch time: 218.54 s
|
| 996 |
-
2023-07-24 07:51:04.639824:
|
| 997 |
-
2023-07-24 07:51:04.640065: Epoch 131
|
| 998 |
-
2023-07-24 07:51:04.640186: Current learning rate: 0.00881
|
| 999 |
-
2023-07-24 07:54:25.996458: train_loss -0.7539
|
| 1000 |
-
2023-07-24 07:54:25.996660: val_loss -0.493
|
| 1001 |
-
2023-07-24 07:54:25.996764: Pseudo dice [0.879, 0.7073, 0.5086]
|
| 1002 |
-
2023-07-24 07:54:25.996862: Epoch time: 201.36 s
|
| 1003 |
-
2023-07-24 07:54:27.833304:
|
| 1004 |
-
2023-07-24 07:54:27.833434: Epoch 132
|
| 1005 |
-
2023-07-24 07:54:27.833548: Current learning rate: 0.0088
|
| 1006 |
-
2023-07-24 07:58:06.308678: train_loss -0.7424
|
| 1007 |
-
2023-07-24 07:58:06.308868: val_loss -0.4781
|
| 1008 |
-
2023-07-24 07:58:06.308955: Pseudo dice [0.8749, 0.7132, 0.4636]
|
| 1009 |
-
2023-07-24 07:58:06.309040: Epoch time: 218.48 s
|
| 1010 |
-
2023-07-24 07:58:07.995495:
|
| 1011 |
-
2023-07-24 07:58:07.995820: Epoch 133
|
| 1012 |
-
2023-07-24 07:58:07.995934: Current learning rate: 0.00879
|
| 1013 |
-
2023-07-24 08:01:39.319222: train_loss -0.7583
|
| 1014 |
-
2023-07-24 08:01:39.319408: val_loss -0.4887
|
| 1015 |
-
2023-07-24 08:01:39.319496: Pseudo dice [0.8776, 0.6941, 0.6211]
|
| 1016 |
-
2023-07-24 08:01:39.319587: Epoch time: 211.32 s
|
| 1017 |
-
2023-07-24 08:01:42.469862:
|
| 1018 |
-
2023-07-24 08:01:42.470145: Epoch 134
|
| 1019 |
-
2023-07-24 08:01:42.470260: Current learning rate: 0.00879
|
| 1020 |
-
2023-07-24 08:05:12.546540: train_loss -0.7622
|
| 1021 |
-
2023-07-24 08:05:12.546793: val_loss -0.487
|
| 1022 |
-
2023-07-24 08:05:12.546890: Pseudo dice [0.877, 0.6954, 0.6036]
|
| 1023 |
-
2023-07-24 08:05:12.547057: Epoch time: 210.08 s
|
| 1024 |
-
2023-07-24 08:05:15.318394:
|
| 1025 |
-
2023-07-24 08:05:15.318724: Epoch 135
|
| 1026 |
-
2023-07-24 08:05:15.318935: Current learning rate: 0.00878
|
| 1027 |
-
2023-07-24 08:08:54.008573: train_loss -0.757
|
| 1028 |
-
2023-07-24 08:08:54.008773: val_loss -0.4737
|
| 1029 |
-
2023-07-24 08:08:54.008857: Pseudo dice [0.8687, 0.6997, 0.5345]
|
| 1030 |
-
2023-07-24 08:08:54.008935: Epoch time: 218.69 s
|
| 1031 |
-
2023-07-24 08:08:57.009470:
|
| 1032 |
-
2023-07-24 08:08:57.009609: Epoch 136
|
| 1033 |
-
2023-07-24 08:08:57.009723: Current learning rate: 0.00877
|
| 1034 |
-
2023-07-24 08:12:31.627689: train_loss -0.7468
|
| 1035 |
-
2023-07-24 08:12:31.628009: val_loss -0.4989
|
| 1036 |
-
2023-07-24 08:12:31.628209: Pseudo dice [0.8809, 0.7079, 0.5713]
|
| 1037 |
-
2023-07-24 08:12:31.628382: Epoch time: 214.62 s
|
| 1038 |
-
2023-07-24 08:12:34.747013:
|
| 1039 |
-
2023-07-24 08:12:34.747181: Epoch 137
|
| 1040 |
-
2023-07-24 08:12:34.747300: Current learning rate: 0.00876
|
| 1041 |
-
2023-07-24 08:16:01.029350: train_loss -0.7588
|
| 1042 |
-
2023-07-24 08:16:01.029534: val_loss -0.4855
|
| 1043 |
-
2023-07-24 08:16:01.029621: Pseudo dice [0.8791, 0.6898, 0.6106]
|
| 1044 |
-
2023-07-24 08:16:01.029705: Epoch time: 206.28 s
|
| 1045 |
-
2023-07-24 08:16:02.771766:
|
| 1046 |
-
2023-07-24 08:16:02.771899: Epoch 138
|
| 1047 |
-
2023-07-24 08:16:02.772012: Current learning rate: 0.00875
|
| 1048 |
-
2023-07-24 08:19:44.406471: train_loss -0.747
|
| 1049 |
-
2023-07-24 08:19:44.406666: val_loss -0.5084
|
| 1050 |
-
2023-07-24 08:19:44.406777: Pseudo dice [0.8869, 0.7204, 0.5542]
|
| 1051 |
-
2023-07-24 08:19:44.406874: Epoch time: 221.64 s
|
| 1052 |
-
2023-07-24 08:19:47.105821:
|
| 1053 |
-
2023-07-24 08:19:47.105972: Epoch 139
|
| 1054 |
-
2023-07-24 08:19:47.106068: Current learning rate: 0.00874
|
| 1055 |
-
2023-07-24 08:23:18.237113: train_loss -0.7528
|
| 1056 |
-
2023-07-24 08:23:18.237421: val_loss -0.4871
|
| 1057 |
-
2023-07-24 08:23:18.237525: Pseudo dice [0.8776, 0.706, 0.5431]
|
| 1058 |
-
2023-07-24 08:23:18.237715: Epoch time: 211.13 s
|
| 1059 |
-
2023-07-24 08:23:20.150906:
|
| 1060 |
-
2023-07-24 08:23:20.151026: Epoch 140
|
| 1061 |
-
2023-07-24 08:23:20.151122: Current learning rate: 0.00873
|
| 1062 |
-
2023-07-24 08:26:46.623374: train_loss -0.7637
|
| 1063 |
-
2023-07-24 08:26:46.623651: val_loss -0.4917
|
| 1064 |
-
2023-07-24 08:26:46.623740: Pseudo dice [0.8785, 0.6989, 0.6665]
|
| 1065 |
-
2023-07-24 08:26:46.623897: Epoch time: 206.47 s
|
| 1066 |
-
2023-07-24 08:26:48.784806:
|
| 1067 |
-
2023-07-24 08:26:48.785008: Epoch 141
|
| 1068 |
-
2023-07-24 08:26:48.785114: Current learning rate: 0.00872
|
| 1069 |
-
2023-07-24 08:30:30.098001: train_loss -0.7605
|
| 1070 |
-
2023-07-24 08:30:30.098207: val_loss -0.499
|
| 1071 |
-
2023-07-24 08:30:30.098298: Pseudo dice [0.8795, 0.7093, 0.5429]
|
| 1072 |
-
2023-07-24 08:30:30.098378: Epoch time: 221.31 s
|
| 1073 |
-
2023-07-24 08:30:32.113620:
|
| 1074 |
-
2023-07-24 08:30:32.113780: Epoch 142
|
| 1075 |
-
2023-07-24 08:30:32.113915: Current learning rate: 0.00871
|
| 1076 |
-
2023-07-24 08:33:58.876559: train_loss -0.7434
|
| 1077 |
-
2023-07-24 08:33:58.876756: val_loss -0.4936
|
| 1078 |
-
2023-07-24 08:33:58.876848: Pseudo dice [0.8844, 0.7121, 0.5416]
|
| 1079 |
-
2023-07-24 08:33:58.876932: Epoch time: 206.76 s
|
| 1080 |
-
2023-07-24 08:34:01.494818:
|
| 1081 |
-
2023-07-24 08:34:01.495366: Epoch 143
|
| 1082 |
-
2023-07-24 08:34:01.495486: Current learning rate: 0.0087
|
| 1083 |
-
2023-07-24 08:37:26.398371: train_loss -0.7416
|
| 1084 |
-
2023-07-24 08:37:26.398579: val_loss -0.5108
|
| 1085 |
-
2023-07-24 08:37:26.398680: Pseudo dice [0.8855, 0.7182, 0.5946]
|
| 1086 |
-
2023-07-24 08:37:26.398790: Epoch time: 204.91 s
|
| 1087 |
-
2023-07-24 08:37:30.655529:
|
| 1088 |
-
2023-07-24 08:37:30.655930: Epoch 144
|
| 1089 |
-
2023-07-24 08:37:30.656043: Current learning rate: 0.00869
|
| 1090 |
-
2023-07-24 08:40:55.134679: train_loss -0.7574
|
| 1091 |
-
2023-07-24 08:40:55.134889: val_loss -0.4785
|
| 1092 |
-
2023-07-24 08:40:55.134991: Pseudo dice [0.8803, 0.7162, 0.3907]
|
| 1093 |
-
2023-07-24 08:40:55.135085: Epoch time: 204.48 s
|
| 1094 |
-
2023-07-24 08:40:57.261309:
|
| 1095 |
-
2023-07-24 08:40:57.261564: Epoch 145
|
| 1096 |
-
2023-07-24 08:40:57.261678: Current learning rate: 0.00868
|
| 1097 |
-
2023-07-24 08:44:23.101116: train_loss -0.7516
|
| 1098 |
-
2023-07-24 08:44:23.106944: val_loss -0.4902
|
| 1099 |
-
2023-07-24 08:44:23.107199: Pseudo dice [0.8784, 0.7066, 0.5606]
|
| 1100 |
-
2023-07-24 08:44:23.107291: Epoch time: 205.84 s
|
| 1101 |
-
2023-07-24 08:44:25.099223:
|
| 1102 |
-
2023-07-24 08:44:25.099430: Epoch 146
|
| 1103 |
-
2023-07-24 08:44:25.099553: Current learning rate: 0.00868
|
| 1104 |
-
2023-07-24 08:47:56.039377: train_loss -0.759
|
| 1105 |
-
2023-07-24 08:47:56.039689: val_loss -0.5251
|
| 1106 |
-
2023-07-24 08:47:56.039789: Pseudo dice [0.8786, 0.7236, 0.5975]
|
| 1107 |
-
2023-07-24 08:47:56.039928: Epoch time: 210.94 s
|
| 1108 |
-
2023-07-24 08:47:58.670197:
|
| 1109 |
-
2023-07-24 08:47:58.670312: Epoch 147
|
| 1110 |
-
2023-07-24 08:47:58.670412: Current learning rate: 0.00867
|
| 1111 |
-
2023-07-24 08:51:23.394095: train_loss -0.7657
|
| 1112 |
-
2023-07-24 08:51:23.394388: val_loss -0.4863
|
| 1113 |
-
2023-07-24 08:51:23.394477: Pseudo dice [0.8613, 0.7275, 0.4779]
|
| 1114 |
-
2023-07-24 08:51:23.394634: Epoch time: 204.72 s
|
| 1115 |
-
2023-07-24 08:51:25.151827:
|
| 1116 |
-
2023-07-24 08:51:25.152007: Epoch 148
|
| 1117 |
-
2023-07-24 08:51:25.152129: Current learning rate: 0.00866
|
| 1118 |
-
2023-07-24 08:55:00.305970: train_loss -0.759
|
| 1119 |
-
2023-07-24 08:55:00.306156: val_loss -0.4877
|
| 1120 |
-
2023-07-24 08:55:00.306260: Pseudo dice [0.8708, 0.7051, 0.5229]
|
| 1121 |
-
2023-07-24 08:55:00.306348: Epoch time: 215.16 s
|
| 1122 |
-
2023-07-24 08:55:03.166358:
|
| 1123 |
-
2023-07-24 08:55:03.166571: Epoch 149
|
| 1124 |
-
2023-07-24 08:55:03.166703: Current learning rate: 0.00865
|
| 1125 |
-
2023-07-24 08:58:36.158555: train_loss -0.7584
|
| 1126 |
-
2023-07-24 08:58:36.163698: val_loss -0.5075
|
| 1127 |
-
2023-07-24 08:58:36.163839: Pseudo dice [0.8796, 0.7186, 0.6129]
|
| 1128 |
-
2023-07-24 08:58:36.163924: Epoch time: 212.99 s
|
| 1129 |
-
2023-07-24 08:58:39.431387:
|
| 1130 |
-
2023-07-24 08:58:39.431509: Epoch 150
|
| 1131 |
-
2023-07-24 08:58:39.431633: Current learning rate: 0.00864
|
| 1132 |
-
2023-07-24 09:02:08.121246: train_loss -0.7586
|
| 1133 |
-
2023-07-24 09:02:08.121416: val_loss -0.5081
|
| 1134 |
-
2023-07-24 09:02:08.121502: Pseudo dice [0.8847, 0.727, 0.6269]
|
| 1135 |
-
2023-07-24 09:02:08.121582: Epoch time: 208.69 s
|
| 1136 |
-
2023-07-24 09:02:10.686369:
|
| 1137 |
-
2023-07-24 09:02:10.686602: Epoch 151
|
| 1138 |
-
2023-07-24 09:02:10.686719: Current learning rate: 0.00863
|
| 1139 |
-
2023-07-24 09:05:44.706747: train_loss -0.7629
|
| 1140 |
-
2023-07-24 09:05:44.706952: val_loss -0.4625
|
| 1141 |
-
2023-07-24 09:05:44.707042: Pseudo dice [0.8698, 0.6773, 0.509]
|
| 1142 |
-
2023-07-24 09:05:44.707127: Epoch time: 214.02 s
|
| 1143 |
-
2023-07-24 09:05:48.504527:
|
| 1144 |
-
2023-07-24 09:05:48.504706: Epoch 152
|
| 1145 |
-
2023-07-24 09:05:48.504832: Current learning rate: 0.00862
|
| 1146 |
-
2023-07-24 09:09:19.069351: train_loss -0.7578
|
| 1147 |
-
2023-07-24 09:09:19.069588: val_loss -0.5
|
| 1148 |
-
2023-07-24 09:09:19.069682: Pseudo dice [0.8778, 0.7217, 0.5765]
|
| 1149 |
-
2023-07-24 09:09:19.069833: Epoch time: 210.57 s
|
| 1150 |
-
2023-07-24 09:09:21.075281:
|
| 1151 |
-
2023-07-24 09:09:21.075402: Epoch 153
|
| 1152 |
-
2023-07-24 09:09:21.075518: Current learning rate: 0.00861
|
| 1153 |
-
2023-07-24 09:12:58.820027: train_loss -0.7531
|
| 1154 |
-
2023-07-24 09:12:58.826884: val_loss -0.4773
|
| 1155 |
-
2023-07-24 09:12:58.827152: Pseudo dice [0.8738, 0.7111, 0.5466]
|
| 1156 |
-
2023-07-24 09:12:58.827244: Epoch time: 217.75 s
|
| 1157 |
-
2023-07-24 09:13:02.862645:
|
| 1158 |
-
2023-07-24 09:13:02.862877: Epoch 154
|
| 1159 |
-
2023-07-24 09:13:02.862992: Current learning rate: 0.0086
|
| 1160 |
-
2023-07-24 09:16:28.130270: train_loss -0.7474
|
| 1161 |
-
2023-07-24 09:16:28.130463: val_loss -0.4756
|
| 1162 |
-
2023-07-24 09:16:28.130564: Pseudo dice [0.8795, 0.7165, 0.489]
|
| 1163 |
-
2023-07-24 09:16:28.130655: Epoch time: 205.27 s
|
| 1164 |
-
2023-07-24 09:16:30.896615:
|
| 1165 |
-
2023-07-24 09:16:30.896794: Epoch 155
|
| 1166 |
-
2023-07-24 09:16:30.896921: Current learning rate: 0.00859
|
| 1167 |
-
2023-07-24 09:20:10.606297: train_loss -0.7662
|
| 1168 |
-
2023-07-24 09:20:10.609706: val_loss -0.4819
|
| 1169 |
-
2023-07-24 09:20:10.609968: Pseudo dice [0.8773, 0.711, 0.5174]
|
| 1170 |
-
2023-07-24 09:20:10.610058: Epoch time: 219.71 s
|
| 1171 |
-
2023-07-24 09:20:13.662477:
|
| 1172 |
-
2023-07-24 09:20:13.662828: Epoch 156
|
| 1173 |
-
2023-07-24 09:20:13.662953: Current learning rate: 0.00858
|
| 1174 |
-
2023-07-24 09:23:55.274102: train_loss -0.7626
|
| 1175 |
-
2023-07-24 09:23:55.274285: val_loss -0.4917
|
| 1176 |
-
2023-07-24 09:23:55.274372: Pseudo dice [0.8786, 0.7163, 0.5242]
|
| 1177 |
-
2023-07-24 09:23:55.274455: Epoch time: 221.61 s
|
| 1178 |
-
2023-07-24 09:23:56.839917:
|
| 1179 |
-
2023-07-24 09:23:56.840061: Epoch 157
|
| 1180 |
-
2023-07-24 09:23:56.840175: Current learning rate: 0.00858
|
| 1181 |
-
2023-07-24 09:27:20.148015: train_loss -0.7542
|
| 1182 |
-
2023-07-24 09:27:20.148191: val_loss -0.5016
|
| 1183 |
-
2023-07-24 09:27:20.148277: Pseudo dice [0.8869, 0.7229, 0.5561]
|
| 1184 |
-
2023-07-24 09:27:20.148360: Epoch time: 203.31 s
|
| 1185 |
-
2023-07-24 09:27:21.869529:
|
| 1186 |
-
2023-07-24 09:27:21.869649: Epoch 158
|
| 1187 |
-
2023-07-24 09:27:21.869765: Current learning rate: 0.00857
|
| 1188 |
-
2023-07-24 09:30:58.994606: train_loss -0.7622
|
| 1189 |
-
2023-07-24 09:30:58.994871: val_loss -0.4855
|
| 1190 |
-
2023-07-24 09:30:58.994982: Pseudo dice [0.8842, 0.7165, 0.4867]
|
| 1191 |
-
2023-07-24 09:30:58.995074: Epoch time: 217.13 s
|
| 1192 |
-
2023-07-24 09:31:03.135907:
|
| 1193 |
-
2023-07-24 09:31:03.136067: Epoch 159
|
| 1194 |
-
2023-07-24 09:31:03.136214: Current learning rate: 0.00856
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|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/.ipynb_checkpoints/Untitled-checkpoint.ipynb
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
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{
|
| 2 |
-
"cells": [],
|
| 3 |
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"metadata": {},
|
| 4 |
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"nbformat": 4,
|
| 5 |
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"nbformat_minor": 5
|
| 6 |
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}
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|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/.ipynb_checkpoints/progress-checkpoint.png
DELETED
Git LFS Details
|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/Untitled.ipynb
DELETED
|
@@ -1,112 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"cells": [
|
| 3 |
-
{
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 7,
|
| 6 |
-
"id": "f69d6289-cca0-4546-baf8-c3e0bd052370",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [
|
| 9 |
-
{
|
| 10 |
-
"name": "stdout",
|
| 11 |
-
"output_type": "stream",
|
| 12 |
-
"text": [
|
| 13 |
-
" \u001b[0m\u001b[01;34mProstate158\u001b[0m/ create_nnunet_dataset.py \u001b[01;34mnnUNet_results\u001b[0m/\n",
|
| 14 |
-
"'Untitled (2) (1) (2).ipynb' \u001b[01;34mnnUNet_preprocessed\u001b[0m/\n",
|
| 15 |
-
" Untitled.ipynb \u001b[01;34mnnUNet_raw\u001b[0m/\n"
|
| 16 |
-
]
|
| 17 |
-
}
|
| 18 |
-
],
|
| 19 |
-
"source": [
|
| 20 |
-
"ls"
|
| 21 |
-
]
|
| 22 |
-
},
|
| 23 |
-
{
|
| 24 |
-
"cell_type": "code",
|
| 25 |
-
"execution_count": 5,
|
| 26 |
-
"id": "d358c520-7bfb-4846-a9a7-e01a151b6912",
|
| 27 |
-
"metadata": {},
|
| 28 |
-
"outputs": [
|
| 29 |
-
{
|
| 30 |
-
"name": "stderr",
|
| 31 |
-
"output_type": "stream",
|
| 32 |
-
"text": [
|
| 33 |
-
"/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
| 34 |
-
" from .autonotebook import tqdm as notebook_tqdm\n"
|
| 35 |
-
]
|
| 36 |
-
},
|
| 37 |
-
{
|
| 38 |
-
"ename": "RepositoryNotFoundError",
|
| 39 |
-
"evalue": "404 Client Error. (Request ID: Root=1-64be540a-13608ebb4f5e72723d9907a8;bec748ef-4d79-4099-914c-5e806f83f5e0)\n\nRepository Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case.",
|
| 40 |
-
"output_type": "error",
|
| 41 |
-
"traceback": [
|
| 42 |
-
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 43 |
-
"\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)",
|
| 44 |
-
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py:261\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 261\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
|
| 45 |
-
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
|
| 46 |
-
"\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main",
|
| 47 |
-
"\nThe above exception was the direct cause of the following exception:\n",
|
| 48 |
-
"\u001b[0;31mRepositoryNotFoundError\u001b[0m Traceback (most recent call last)",
|
| 49 |
-
"Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m api \u001b[38;5;241m=\u001b[39m HfApi()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Upload all the content from the local folder to your remote Space.\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# By default, files are uploaded at the root of the repo\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupload_folder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43musername/my-cool-space\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mspace\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m)\u001b[49m\n",
|
| 50 |
-
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
| 51 |
-
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 827\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
| 52 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:3509\u001b[0m, in \u001b[0;36mHfApi.upload_folder\u001b[0;34m(self, repo_id, folder_path, path_in_repo, commit_message, commit_description, token, repo_type, revision, create_pr, parent_commit, allow_patterns, ignore_patterns, delete_patterns, multi_commits, multi_commits_verbose, run_as_future)\u001b[0m\n\u001b[1;32m 3497\u001b[0m pr_url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_commits_on_pr(\n\u001b[1;32m 3498\u001b[0m repo_id\u001b[38;5;241m=\u001b[39mrepo_id,\n\u001b[1;32m 3499\u001b[0m repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3506\u001b[0m verbose\u001b[38;5;241m=\u001b[39mmulti_commits_verbose,\n\u001b[1;32m 3507\u001b[0m )\n\u001b[1;32m 3508\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3509\u001b[0m commit_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_commit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3510\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3511\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3512\u001b[0m \u001b[43m \u001b[49m\u001b[43moperations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_operations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3513\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_message\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3514\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommit_description\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3515\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3516\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3517\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3518\u001b[0m \u001b[43m \u001b[49m\u001b[43mparent_commit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent_commit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3519\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3520\u001b[0m pr_url \u001b[38;5;241m=\u001b[39m commit_info\u001b[38;5;241m.\u001b[39mpr_url\n\u001b[1;32m 3522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;129;01mand\u001b[39;00m pr_url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
| 53 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
| 54 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 827\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
| 55 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py:2675\u001b[0m, in \u001b[0;36mHfApi.create_commit\u001b[0;34m(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)\u001b[0m\n\u001b[1;32m 2672\u001b[0m warn_on_overwriting_operations(operations)\n\u001b[1;32m 2674\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2675\u001b[0m upload_modes \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_upload_modes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2676\u001b[0m \u001b[43m \u001b[49m\u001b[43madditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madditions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2677\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2678\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2679\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2680\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2681\u001b[0m \u001b[43m \u001b[49m\u001b[43mendpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2682\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2683\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2684\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 2685\u001b[0m e\u001b[38;5;241m.\u001b[39mappend_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)\n",
|
| 56 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 116\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
| 57 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/_commit_api.py:483\u001b[0m, in \u001b[0;36mfetch_upload_modes\u001b[0;34m(additions, repo_type, repo_id, token, revision, endpoint, create_pr)\u001b[0m\n\u001b[1;32m 465\u001b[0m payload \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m 467\u001b[0m {\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 474\u001b[0m ]\n\u001b[1;32m 475\u001b[0m }\n\u001b[1;32m 477\u001b[0m resp \u001b[38;5;241m=\u001b[39m get_session()\u001b[38;5;241m.\u001b[39mpost(\n\u001b[1;32m 478\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/api/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/preupload/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrevision\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 479\u001b[0m json\u001b[38;5;241m=\u001b[39mpayload,\n\u001b[1;32m 480\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m 481\u001b[0m params\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreate_pr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1\u001b[39m\u001b[38;5;124m\"\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 482\u001b[0m )\n\u001b[0;32m--> 483\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 484\u001b[0m preupload_info \u001b[38;5;241m=\u001b[39m _validate_preupload_info(resp\u001b[38;5;241m.\u001b[39mjson())\n\u001b[1;32m 485\u001b[0m upload_modes\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m]: file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muploadMode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m preupload_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles\u001b[39m\u001b[38;5;124m\"\u001b[39m]})\n",
|
| 58 |
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"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py:293\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepoNotFound\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m401\u001b[39m:\n\u001b[1;32m 280\u001b[0m \u001b[38;5;66;03m# 401 is misleading as it is returned for:\u001b[39;00m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;66;03m# - private and gated repos if user is not authenticated\u001b[39;00m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;66;03m# - missing repos\u001b[39;00m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;66;03m# => for now, we process them as `RepoNotFound` anyway.\u001b[39;00m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;66;03m# See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9\u001b[39;00m\n\u001b[1;32m 285\u001b[0m message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m make sure you are authenticated.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 292\u001b[0m )\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RepositoryNotFoundError(message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m400\u001b[39m:\n\u001b[1;32m 296\u001b[0m message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 297\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m endpoint:\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m endpoint_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 298\u001b[0m )\n",
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| 59 |
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"\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-64be540a-13608ebb4f5e72723d9907a8;bec748ef-4d79-4099-914c-5e806f83f5e0)\n\nRepository Not Found for url: https://huggingface.co/api/spaces/username/my-cool-space/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case."
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| 60 |
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]
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| 61 |
-
}
|
| 62 |
-
],
|
| 63 |
-
"source": [
|
| 64 |
-
"from huggingface_hub import HfApi\n",
|
| 65 |
-
"api = HfApi()\n",
|
| 66 |
-
"\n",
|
| 67 |
-
"api.create_repo(\n",
|
| 68 |
-
" repo_id=\"osbm/prostate158_nnUNet_results_3d_fullres2\",\n",
|
| 69 |
-
" repo_type=\"dataset\",\n",
|
| 70 |
-
" exist_ok=True,\n",
|
| 71 |
-
" private=False,\n",
|
| 72 |
-
")\n",
|
| 73 |
-
"# Upload all the content from the local folder to your remote Space.\n",
|
| 74 |
-
"# By default, files are uploaded at the root of the repo\n",
|
| 75 |
-
"api.upload_folder(\n",
|
| 76 |
-
" folder_path=\"\",\n",
|
| 77 |
-
" repo_id=\"username/my-cool-space\",\n",
|
| 78 |
-
" repo_type=\"space\",\n",
|
| 79 |
-
")"
|
| 80 |
-
]
|
| 81 |
-
},
|
| 82 |
-
{
|
| 83 |
-
"cell_type": "code",
|
| 84 |
-
"execution_count": null,
|
| 85 |
-
"id": "9dc0670f-31b2-4b0d-b756-043b798d865e",
|
| 86 |
-
"metadata": {},
|
| 87 |
-
"outputs": [],
|
| 88 |
-
"source": []
|
| 89 |
-
}
|
| 90 |
-
],
|
| 91 |
-
"metadata": {
|
| 92 |
-
"kernelspec": {
|
| 93 |
-
"display_name": "Python 3 (ipykernel)",
|
| 94 |
-
"language": "python",
|
| 95 |
-
"name": "python3"
|
| 96 |
-
},
|
| 97 |
-
"language_info": {
|
| 98 |
-
"codemirror_mode": {
|
| 99 |
-
"name": "ipython",
|
| 100 |
-
"version": 3
|
| 101 |
-
},
|
| 102 |
-
"file_extension": ".py",
|
| 103 |
-
"mimetype": "text/x-python",
|
| 104 |
-
"name": "python",
|
| 105 |
-
"nbconvert_exporter": "python",
|
| 106 |
-
"pygments_lexer": "ipython3",
|
| 107 |
-
"version": "3.10.6"
|
| 108 |
-
}
|
| 109 |
-
},
|
| 110 |
-
"nbformat": 4,
|
| 111 |
-
"nbformat_minor": 5
|
| 112 |
-
}
|
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/checkpoint_best.pth
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:3b1c91cbd76cbc97c1a5c93420cf3496051fab1e0972623ab6a21416aab02550
|
| 3 |
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size 356790417
|
|
|
|
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|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/debug.json
DELETED
|
@@ -1,52 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"_best_ema": "None",
|
| 3 |
-
"batch_size": "2",
|
| 4 |
-
"configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}",
|
| 5 |
-
"configuration_name": "3d_fullres",
|
| 6 |
-
"cudnn_version": 8500,
|
| 7 |
-
"current_epoch": "0",
|
| 8 |
-
"dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f974c2f5780>",
|
| 9 |
-
"dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f974c2f5090>",
|
| 10 |
-
"dataloader_train.num_processes": "4",
|
| 11 |
-
"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 = [256, 256], 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], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
|
| 12 |
-
"dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f974c2f4880>",
|
| 13 |
-
"dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f974c2f4be0>",
|
| 14 |
-
"dataloader_val.num_processes": "2",
|
| 15 |
-
"dataloader_val.transform": "Compose ( [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], [1.0, 0.25, 0.25], [0.5, 0.125, 0.125], [0.25, 0.0625, 0.0625], [0.25, 0.03125, 0.03125]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
|
| 16 |
-
"dataset_json": "{'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}",
|
| 17 |
-
"device": "cuda:0",
|
| 18 |
-
"disable_checkpointing": "False",
|
| 19 |
-
"fold": "1",
|
| 20 |
-
"folder_with_segs_from_previous_stage": "None",
|
| 21 |
-
"gpu_name": "NVIDIA A10G",
|
| 22 |
-
"grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f974d1d1a50>",
|
| 23 |
-
"hostname": "s-osbm-jupyter-f0b83-8689bbb555-5t6kn",
|
| 24 |
-
"inference_allowed_mirroring_axes": "(0, 1, 2)",
|
| 25 |
-
"initial_lr": "0.01",
|
| 26 |
-
"is_cascaded": "False",
|
| 27 |
-
"is_ddp": "False",
|
| 28 |
-
"label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f974d1d1c00>",
|
| 29 |
-
"local_rank": "0",
|
| 30 |
-
"log_file": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/training_log_2023_7_24_09_31_46.txt",
|
| 31 |
-
"logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f974d1d1ae0>",
|
| 32 |
-
"loss": "DeepSupervisionWrapper(\n (loss): DC_and_CE_loss(\n (ce): RobustCrossEntropyLoss()\n (dc): MemoryEfficientSoftDiceLoss()\n )\n)",
|
| 33 |
-
"lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f974d1d1b40>",
|
| 34 |
-
"my_init_kwargs": "{'plans': {'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [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]], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}, 'configuration': '3d_fullres', 'fold': 1, 'dataset_json': {'name': 'Prostate158', 'description': 'Prostate cancer segmentation dataset', 'channel_names': {'0': 'T2', '1': 'ADC', '2': 'DFI'}, 'labels': {'background': 0, 'prostate_inner': 1, 'prostate_outer': 2, 'tumor': 3}, 'numTraining': 139, 'numTest': 19, 'file_ending': '.nii.gz'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
|
| 35 |
-
"network": "PlainConvUNet",
|
| 36 |
-
"num_epochs": "1000",
|
| 37 |
-
"num_input_channels": "3",
|
| 38 |
-
"num_iterations_per_epoch": "250",
|
| 39 |
-
"num_val_iterations_per_epoch": "50",
|
| 40 |
-
"optimizer": "SGD (\nParameter Group 0\n dampening: 0\n differentiable: False\n foreach: None\n initial_lr: 0.01\n lr: 0.01\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)",
|
| 41 |
-
"output_folder": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1",
|
| 42 |
-
"output_folder_base": "nnUNet_results/Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres",
|
| 43 |
-
"oversample_foreground_percent": "0.33",
|
| 44 |
-
"plans_manager": "{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], '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': 31, 'patch_size': [320, 320], 'median_image_size_in_voxels': [270.0, 270.0], 'spacing': [0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [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]], '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_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}",
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"preprocessed_dataset_folder": "nnUNet_preprocessed/Dataset001_Prostate158/nnUNetPlans_3d_fullres",
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"preprocessed_dataset_folder_base": "nnUNet_preprocessed/Dataset001_Prostate158",
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"save_every": "50",
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"torch_version": "2.0.1+cu117",
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| 49 |
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"unpack_dataset": "True",
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"was_initialized": "True",
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"weight_decay": "3e-05"
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}
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/progress.png
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Git LFS Details
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Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_1/training_log_2023_7_24_09_31_46.txt
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#######################################################################
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Please cite the following paper when using nnU-Net:
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Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
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#######################################################################
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This is the configuration used by this training:
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Configuration name: 3d_fullres
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{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': False}
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| 12 |
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These are the global plan.json settings:
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{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}
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| 15 |
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2023-07-24 09:31:48.662707: unpacking dataset...
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| 16 |
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2023-07-24 09:31:51.254056: unpacking done...
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2023-07-24 09:31:51.254646: do_dummy_2d_data_aug: True
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2023-07-24 09:31:51.255391: Using splits from existing split file: nnUNet_preprocessed/Dataset001_Prostate158/splits_final.json
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2023-07-24 09:31:51.255736: The split file contains 5 splits.
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2023-07-24 09:31:51.255795: Desired fold for training: 1
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2023-07-24 09:31:51.255847: This split has 111 training and 28 validation cases.
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2023-07-24 09:31:55.429484: Unable to plot network architecture:
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| 23 |
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2023-07-24 09:31:55.429709: module 'torch.onnx' has no attribute '_optimize_trace'
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2023-07-24 09:31:55.479973:
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| 25 |
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2023-07-24 09:31:55.480082: Epoch 0
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| 26 |
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2023-07-24 09:31:55.480217: Current learning rate: 0.01
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| 27 |
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2023-07-24 09:35:51.149318: train_loss -0.0686
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| 28 |
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2023-07-24 09:35:51.149595: val_loss -0.1977
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| 29 |
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2023-07-24 09:35:51.149694: Pseudo dice [0.6772, 0.3986, 0.0]
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| 30 |
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2023-07-24 09:35:51.149858: Epoch time: 235.67 s
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| 31 |
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2023-07-24 09:35:51.149975: Yayy! New best EMA pseudo Dice: 0.3586
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| 32 |
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2023-07-24 09:35:52.944725:
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2023-07-24 09:35:52.944855: Epoch 1
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2023-07-24 09:35:52.945019: Current learning rate: 0.00999
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| 35 |
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2023-07-24 09:39:26.457915: train_loss -0.2509
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| 36 |
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2023-07-24 09:39:26.458209: val_loss -0.2695
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| 37 |
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2023-07-24 09:39:26.458309: Pseudo dice [0.7352, 0.5579, 0.0]
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| 38 |
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2023-07-24 09:39:26.458386: Epoch time: 213.51 s
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| 39 |
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2023-07-24 09:39:26.458445: Yayy! New best EMA pseudo Dice: 0.3658
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| 40 |
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2023-07-24 09:39:30.325983:
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2023-07-24 09:39:30.326120: Epoch 2
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| 42 |
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2023-07-24 09:39:30.326235: Current learning rate: 0.00998
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| 43 |
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2023-07-24 09:42:59.369360: train_loss -0.3283
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| 44 |
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2023-07-24 09:42:59.369581: val_loss -0.2917
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| 45 |
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2023-07-24 09:42:59.369678: Pseudo dice [0.7702, 0.5453, 0.0]
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| 46 |
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2023-07-24 09:42:59.369768: Epoch time: 209.04 s
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| 47 |
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2023-07-24 09:42:59.369841: Yayy! New best EMA pseudo Dice: 0.3731
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| 48 |
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2023-07-24 09:43:02.898393:
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| 49 |
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2023-07-24 09:43:02.898701: Epoch 3
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| 50 |
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2023-07-24 09:43:02.898844: Current learning rate: 0.00997
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| 51 |
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2023-07-24 09:46:30.423032: train_loss -0.3623
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| 52 |
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2023-07-24 09:46:30.423313: val_loss -0.362
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| 53 |
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2023-07-24 09:46:30.423406: Pseudo dice [0.8084, 0.6354, 0.0]
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| 54 |
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2023-07-24 09:46:30.423570: Epoch time: 207.53 s
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| 55 |
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2023-07-24 09:46:30.423706: Yayy! New best EMA pseudo Dice: 0.3839
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| 56 |
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2023-07-24 09:46:32.679859:
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| 57 |
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2023-07-24 09:46:32.679986: Epoch 4
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| 58 |
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2023-07-24 09:46:32.680083: Current learning rate: 0.00996
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| 59 |
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2023-07-24 09:50:16.112560: train_loss -0.4055
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| 60 |
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2023-07-24 09:50:16.112745: val_loss -0.3814
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2023-07-24 09:50:16.112836: Pseudo dice [0.8423, 0.6152, 0.3433]
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| 62 |
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2023-07-24 09:50:16.112920: Epoch time: 223.43 s
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| 63 |
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2023-07-24 09:50:16.112987: Yayy! New best EMA pseudo Dice: 0.4056
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| 64 |
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2023-07-24 09:50:18.759251:
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| 65 |
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2023-07-24 09:50:18.759383: Epoch 5
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| 66 |
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2023-07-24 09:50:18.759503: Current learning rate: 0.00995
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| 67 |
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2023-07-24 09:53:41.007815: train_loss -0.4426
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| 68 |
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2023-07-24 09:53:41.007999: val_loss -0.4302
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| 69 |
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2023-07-24 09:53:41.008113: Pseudo dice [0.8359, 0.6439, 0.4462]
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| 70 |
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2023-07-24 09:53:41.008215: Epoch time: 202.25 s
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| 71 |
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2023-07-24 09:53:41.008303: Yayy! New best EMA pseudo Dice: 0.4292
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| 72 |
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2023-07-24 09:53:43.767199:
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| 73 |
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2023-07-24 09:53:43.767324: Epoch 6
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| 74 |
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2023-07-24 09:53:43.767419: Current learning rate: 0.00995
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| 75 |
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2023-07-24 09:57:16.562120: train_loss -0.4301
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| 76 |
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2023-07-24 09:57:16.562339: val_loss -0.4084
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| 77 |
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2023-07-24 09:57:16.562424: Pseudo dice [0.8011, 0.6376, 0.48]
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| 78 |
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2023-07-24 09:57:16.562501: Epoch time: 212.8 s
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| 79 |
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2023-07-24 09:57:16.562591: Yayy! New best EMA pseudo Dice: 0.4502
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| 80 |
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2023-07-24 09:57:19.469062:
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2023-07-24 09:57:19.469288: Epoch 7
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| 82 |
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2023-07-24 09:57:19.469407: Current learning rate: 0.00994
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2023-07-24 10:00:48.194506: train_loss -0.4578
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2023-07-24 10:00:48.194689: val_loss -0.4189
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| 85 |
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2023-07-24 10:00:48.194808: Pseudo dice [0.8137, 0.6457, 0.4479]
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| 86 |
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2023-07-24 10:00:48.194900: Epoch time: 208.73 s
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| 87 |
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2023-07-24 10:00:48.194967: Yayy! New best EMA pseudo Dice: 0.4688
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2023-07-24 10:00:52.373474:
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2023-07-24 10:00:52.373721: Epoch 8
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| 90 |
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2023-07-24 10:00:52.373865: Current learning rate: 0.00993
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| 91 |
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2023-07-24 10:04:19.517230: train_loss -0.498
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| 92 |
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2023-07-24 10:04:19.517421: val_loss -0.4187
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| 93 |
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2023-07-24 10:04:19.517504: Pseudo dice [0.8555, 0.6358, 0.3124]
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| 94 |
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2023-07-24 10:04:19.517583: Epoch time: 207.15 s
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| 95 |
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2023-07-24 10:04:19.517648: Yayy! New best EMA pseudo Dice: 0.482
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| 96 |
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2023-07-24 10:04:21.806987:
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| 97 |
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2023-07-24 10:04:21.807117: Epoch 9
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| 98 |
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2023-07-24 10:04:21.807218: Current learning rate: 0.00992
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| 99 |
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2023-07-24 10:07:51.359195: train_loss -0.5027
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| 100 |
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2023-07-24 10:07:51.370858: val_loss -0.4306
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| 101 |
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2023-07-24 10:07:51.371073: Pseudo dice [0.8521, 0.6531, 0.3801]
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| 102 |
-
2023-07-24 10:07:51.371159: Epoch time: 209.55 s
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| 103 |
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2023-07-24 10:07:51.371223: Yayy! New best EMA pseudo Dice: 0.4967
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| 104 |
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2023-07-24 10:07:54.287527:
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| 105 |
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2023-07-24 10:07:54.287659: Epoch 10
|
| 106 |
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2023-07-24 10:07:54.287776: Current learning rate: 0.00991
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| 107 |
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2023-07-24 10:11:21.020047: train_loss -0.5007
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| 108 |
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2023-07-24 10:11:21.020299: val_loss -0.4555
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| 109 |
-
2023-07-24 10:11:21.020390: Pseudo dice [0.8525, 0.6574, 0.5038]
|
| 110 |
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2023-07-24 10:11:21.020547: Epoch time: 206.73 s
|
| 111 |
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2023-07-24 10:11:21.020615: Yayy! New best EMA pseudo Dice: 0.5141
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| 112 |
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2023-07-24 10:11:23.595996:
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| 113 |
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2023-07-24 10:11:23.596125: Epoch 11
|
| 114 |
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2023-07-24 10:11:23.596240: Current learning rate: 0.0099
|
| 115 |
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2023-07-24 10:14:50.060935: train_loss -0.5114
|
| 116 |
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2023-07-24 10:14:50.061202: val_loss -0.4435
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| 117 |
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2023-07-24 10:14:50.061292: Pseudo dice [0.8367, 0.6786, 0.3972]
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| 118 |
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2023-07-24 10:14:50.061454: Epoch time: 206.47 s
|
| 119 |
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2023-07-24 10:14:50.061521: Yayy! New best EMA pseudo Dice: 0.5265
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| 120 |
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2023-07-24 10:14:54.044929:
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| 121 |
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2023-07-24 10:14:54.045076: Epoch 12
|
| 122 |
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2023-07-24 10:14:54.045194: Current learning rate: 0.00989
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| 123 |
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2023-07-24 10:18:35.033546: train_loss -0.5262
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| 124 |
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2023-07-24 10:18:35.033730: val_loss -0.4482
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| 125 |
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2023-07-24 10:18:35.033818: Pseudo dice [0.8485, 0.6596, 0.5738]
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| 126 |
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2023-07-24 10:18:35.033900: Epoch time: 220.99 s
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| 127 |
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2023-07-24 10:18:35.033965: Yayy! New best EMA pseudo Dice: 0.5432
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| 128 |
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2023-07-24 10:18:37.229037:
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| 129 |
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2023-07-24 10:18:37.229155: Epoch 13
|
| 130 |
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2023-07-24 10:18:37.229270: Current learning rate: 0.00988
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| 131 |
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2023-07-24 10:22:07.718349: train_loss -0.5297
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| 132 |
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2023-07-24 10:22:07.718728: val_loss -0.4473
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| 133 |
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2023-07-24 10:22:07.718979: Pseudo dice [0.8557, 0.6552, 0.5333]
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| 134 |
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2023-07-24 10:22:07.719130: Epoch time: 210.49 s
|
| 135 |
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2023-07-24 10:22:07.719215: Yayy! New best EMA pseudo Dice: 0.557
|
| 136 |
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2023-07-24 10:22:12.377048:
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| 137 |
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2023-07-24 10:22:12.377682: Epoch 14
|
| 138 |
-
2023-07-24 10:22:12.377826: Current learning rate: 0.00987
|
| 139 |
-
2023-07-24 10:25:35.042624: train_loss -0.544
|
| 140 |
-
2023-07-24 10:25:35.042880: val_loss -0.4672
|
| 141 |
-
2023-07-24 10:25:35.042980: Pseudo dice [0.8415, 0.6909, 0.5861]
|
| 142 |
-
2023-07-24 10:25:35.043072: Epoch time: 202.67 s
|
| 143 |
-
2023-07-24 10:25:35.043142: Yayy! New best EMA pseudo Dice: 0.5719
|
| 144 |
-
2023-07-24 10:25:39.141425:
|
| 145 |
-
2023-07-24 10:25:39.141566: Epoch 15
|
| 146 |
-
2023-07-24 10:25:39.141699: Current learning rate: 0.00986
|
| 147 |
-
2023-07-24 10:29:17.006086: train_loss -0.536
|
| 148 |
-
2023-07-24 10:29:17.006276: val_loss -0.458
|
| 149 |
-
2023-07-24 10:29:17.006367: Pseudo dice [0.8684, 0.6802, 0.4725]
|
| 150 |
-
2023-07-24 10:29:17.006453: Epoch time: 217.87 s
|
| 151 |
-
2023-07-24 10:29:17.006521: Yayy! New best EMA pseudo Dice: 0.5821
|
| 152 |
-
2023-07-24 10:29:19.086107:
|
| 153 |
-
2023-07-24 10:29:19.086326: Epoch 16
|
| 154 |
-
2023-07-24 10:29:19.086447: Current learning rate: 0.00986
|
| 155 |
-
2023-07-24 10:32:52.937337: train_loss -0.5573
|
| 156 |
-
2023-07-24 10:32:52.937526: val_loss -0.4805
|
| 157 |
-
2023-07-24 10:32:52.937625: Pseudo dice [0.8545, 0.6796, 0.6108]
|
| 158 |
-
2023-07-24 10:32:52.937717: Epoch time: 213.85 s
|
| 159 |
-
2023-07-24 10:32:52.937808: Yayy! New best EMA pseudo Dice: 0.5954
|
| 160 |
-
2023-07-24 10:32:55.486784:
|
| 161 |
-
2023-07-24 10:32:55.486909: Epoch 17
|
| 162 |
-
2023-07-24 10:32:55.487025: Current learning rate: 0.00985
|
| 163 |
-
2023-07-24 10:36:29.203138: train_loss -0.5585
|
| 164 |
-
2023-07-24 10:36:29.203351: val_loss -0.4583
|
| 165 |
-
2023-07-24 10:36:29.203443: Pseudo dice [0.8662, 0.6791, 0.4972]
|
| 166 |
-
2023-07-24 10:36:29.203535: Epoch time: 213.72 s
|
| 167 |
-
2023-07-24 10:36:29.203619: Yayy! New best EMA pseudo Dice: 0.604
|
| 168 |
-
2023-07-24 10:36:34.055434:
|
| 169 |
-
2023-07-24 10:36:34.055742: Epoch 18
|
| 170 |
-
2023-07-24 10:36:34.055855: Current learning rate: 0.00984
|
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|
Dataset001_Prostate158/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json
DELETED
|
@@ -1,342 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"dataset_name": "Dataset001_Prostate158",
|
| 3 |
-
"plans_name": "nnUNetPlans",
|
| 4 |
-
"original_median_spacing_after_transp": [
|
| 5 |
-
3.0,
|
| 6 |
-
0.4017857015132904,
|
| 7 |
-
0.4017857015132904
|
| 8 |
-
],
|
| 9 |
-
"original_median_shape_after_transp": [
|
| 10 |
-
25,
|
| 11 |
-
270,
|
| 12 |
-
270
|
| 13 |
-
],
|
| 14 |
-
"image_reader_writer": "SimpleITKIO",
|
| 15 |
-
"transpose_forward": [
|
| 16 |
-
0,
|
| 17 |
-
1,
|
| 18 |
-
2
|
| 19 |
-
],
|
| 20 |
-
"transpose_backward": [
|
| 21 |
-
0,
|
| 22 |
-
1,
|
| 23 |
-
2
|
| 24 |
-
],
|
| 25 |
-
"configurations": {
|
| 26 |
-
"2d": {
|
| 27 |
-
"data_identifier": "nnUNetPlans_2d",
|
| 28 |
-
"preprocessor_name": "DefaultPreprocessor",
|
| 29 |
-
"batch_size": 31,
|
| 30 |
-
"patch_size": [
|
| 31 |
-
320,
|
| 32 |
-
320
|
| 33 |
-
],
|
| 34 |
-
"median_image_size_in_voxels": [
|
| 35 |
-
270.0,
|
| 36 |
-
270.0
|
| 37 |
-
],
|
| 38 |
-
"spacing": [
|
| 39 |
-
0.4017857015132904,
|
| 40 |
-
0.4017857015132904
|
| 41 |
-
],
|
| 42 |
-
"normalization_schemes": [
|
| 43 |
-
"ZScoreNormalization",
|
| 44 |
-
"ZScoreNormalization",
|
| 45 |
-
"ZScoreNormalization"
|
| 46 |
-
],
|
| 47 |
-
"use_mask_for_norm": [
|
| 48 |
-
false,
|
| 49 |
-
false,
|
| 50 |
-
false
|
| 51 |
-
],
|
| 52 |
-
"UNet_class_name": "PlainConvUNet",
|
| 53 |
-
"UNet_base_num_features": 32,
|
| 54 |
-
"n_conv_per_stage_encoder": [
|
| 55 |
-
2,
|
| 56 |
-
2,
|
| 57 |
-
2,
|
| 58 |
-
2,
|
| 59 |
-
2,
|
| 60 |
-
2,
|
| 61 |
-
2
|
| 62 |
-
],
|
| 63 |
-
"n_conv_per_stage_decoder": [
|
| 64 |
-
2,
|
| 65 |
-
2,
|
| 66 |
-
2,
|
| 67 |
-
2,
|
| 68 |
-
2,
|
| 69 |
-
2
|
| 70 |
-
],
|
| 71 |
-
"num_pool_per_axis": [
|
| 72 |
-
6,
|
| 73 |
-
6
|
| 74 |
-
],
|
| 75 |
-
"pool_op_kernel_sizes": [
|
| 76 |
-
[
|
| 77 |
-
1,
|
| 78 |
-
1
|
| 79 |
-
],
|
| 80 |
-
[
|
| 81 |
-
2,
|
| 82 |
-
2
|
| 83 |
-
],
|
| 84 |
-
[
|
| 85 |
-
2,
|
| 86 |
-
2
|
| 87 |
-
],
|
| 88 |
-
[
|
| 89 |
-
2,
|
| 90 |
-
2
|
| 91 |
-
],
|
| 92 |
-
[
|
| 93 |
-
2,
|
| 94 |
-
2
|
| 95 |
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],
|
| 96 |
-
[
|
| 97 |
-
2,
|
| 98 |
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2
|
| 99 |
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],
|
| 100 |
-
[
|
| 101 |
-
2,
|
| 102 |
-
2
|
| 103 |
-
]
|
| 104 |
-
],
|
| 105 |
-
"conv_kernel_sizes": [
|
| 106 |
-
[
|
| 107 |
-
3,
|
| 108 |
-
3
|
| 109 |
-
],
|
| 110 |
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[
|
| 111 |
-
3,
|
| 112 |
-
3
|
| 113 |
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],
|
| 114 |
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[
|
| 115 |
-
3,
|
| 116 |
-
3
|
| 117 |
-
],
|
| 118 |
-
[
|
| 119 |
-
3,
|
| 120 |
-
3
|
| 121 |
-
],
|
| 122 |
-
[
|
| 123 |
-
3,
|
| 124 |
-
3
|
| 125 |
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],
|
| 126 |
-
[
|
| 127 |
-
3,
|
| 128 |
-
3
|
| 129 |
-
],
|
| 130 |
-
[
|
| 131 |
-
3,
|
| 132 |
-
3
|
| 133 |
-
]
|
| 134 |
-
],
|
| 135 |
-
"unet_max_num_features": 512,
|
| 136 |
-
"resampling_fn_data": "resample_data_or_seg_to_shape",
|
| 137 |
-
"resampling_fn_seg": "resample_data_or_seg_to_shape",
|
| 138 |
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"resampling_fn_data_kwargs": {
|
| 139 |
-
"is_seg": false,
|
| 140 |
-
"order": 3,
|
| 141 |
-
"order_z": 0,
|
| 142 |
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"force_separate_z": null
|
| 143 |
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},
|
| 144 |
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"resampling_fn_seg_kwargs": {
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| 145 |
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"is_seg": true,
|
| 146 |
-
"order": 1,
|
| 147 |
-
"order_z": 0,
|
| 148 |
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"force_separate_z": null
|
| 149 |
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},
|
| 150 |
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"resampling_fn_probabilities": "resample_data_or_seg_to_shape",
|
| 151 |
-
"resampling_fn_probabilities_kwargs": {
|
| 152 |
-
"is_seg": false,
|
| 153 |
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"order": 1,
|
| 154 |
-
"order_z": 0,
|
| 155 |
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"force_separate_z": null
|
| 156 |
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},
|
| 157 |
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"batch_dice": true
|
| 158 |
-
},
|
| 159 |
-
"3d_fullres": {
|
| 160 |
-
"data_identifier": "nnUNetPlans_3d_fullres",
|
| 161 |
-
"preprocessor_name": "DefaultPreprocessor",
|
| 162 |
-
"batch_size": 2,
|
| 163 |
-
"patch_size": [
|
| 164 |
-
28,
|
| 165 |
-
256,
|
| 166 |
-
256
|
| 167 |
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],
|
| 168 |
-
"median_image_size_in_voxels": [
|
| 169 |
-
25.0,
|
| 170 |
-
270.0,
|
| 171 |
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270.0
|
| 172 |
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],
|
| 173 |
-
"spacing": [
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| 174 |
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2.999998998641968,
|
| 175 |
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| 176 |
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0.4017857015132904
|
| 177 |
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],
|
| 178 |
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"normalization_schemes": [
|
| 179 |
-
"ZScoreNormalization",
|
| 180 |
-
"ZScoreNormalization",
|
| 181 |
-
"ZScoreNormalization"
|
| 182 |
-
],
|
| 183 |
-
"use_mask_for_norm": [
|
| 184 |
-
false,
|
| 185 |
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false,
|
| 186 |
-
false
|
| 187 |
-
],
|
| 188 |
-
"UNet_class_name": "PlainConvUNet",
|
| 189 |
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"UNet_base_num_features": 32,
|
| 190 |
-
"n_conv_per_stage_encoder": [
|
| 191 |
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2,
|
| 192 |
-
2,
|
| 193 |
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2,
|
| 194 |
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2,
|
| 195 |
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2,
|
| 196 |
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2,
|
| 197 |
-
2
|
| 198 |
-
],
|
| 199 |
-
"n_conv_per_stage_decoder": [
|
| 200 |
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2,
|
| 201 |
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2,
|
| 202 |
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2,
|
| 203 |
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2,
|
| 204 |
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2,
|
| 205 |
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2
|
| 206 |
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],
|
| 207 |
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"num_pool_per_axis": [
|
| 208 |
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2,
|
| 209 |
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6,
|
| 210 |
-
6
|
| 211 |
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],
|
| 212 |
-
"pool_op_kernel_sizes": [
|
| 213 |
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[
|
| 214 |
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1,
|
| 215 |
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1,
|
| 216 |
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1
|
| 217 |
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],
|
| 218 |
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[
|
| 219 |
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1,
|
| 220 |
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2,
|
| 221 |
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2
|
| 222 |
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],
|
| 223 |
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[
|
| 224 |
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1,
|
| 225 |
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2,
|
| 226 |
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2
|
| 227 |
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],
|
| 228 |
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[
|
| 229 |
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2,
|
| 230 |
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|
| 231 |
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2
|
| 232 |
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],
|
| 233 |
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[
|
| 234 |
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2,
|
| 235 |
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2,
|
| 236 |
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2
|
| 237 |
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],
|
| 238 |
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[
|
| 239 |
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1,
|
| 240 |
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2,
|
| 241 |
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2
|
| 242 |
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],
|
| 243 |
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[
|
| 244 |
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1,
|
| 245 |
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2,
|
| 246 |
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2
|
| 247 |
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]
|
| 248 |
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],
|
| 249 |
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"conv_kernel_sizes": [
|
| 250 |
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[
|
| 251 |
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1,
|
| 252 |
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3,
|
| 253 |
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3
|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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3
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| 259 |
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|
| 260 |
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[
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|
| 265 |
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[
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| 266 |
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| 269 |
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| 270 |
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|
| 271 |
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| 272 |
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3,
|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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"unet_max_num_features": 320,
|
| 287 |
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"resampling_fn_data": "resample_data_or_seg_to_shape",
|
| 288 |
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"resampling_fn_seg": "resample_data_or_seg_to_shape",
|
| 289 |
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"resampling_fn_data_kwargs": {
|
| 290 |
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|
| 291 |
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|
| 292 |
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| 294 |
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|
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|
| 298 |
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|
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|
| 300 |
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|
| 301 |
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| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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|
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|
| 307 |
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|
| 308 |
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"batch_dice": false
|
| 309 |
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}
|
| 310 |
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},
|
| 311 |
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"experiment_planner_used": "ExperimentPlanner",
|
| 312 |
-
"label_manager": "LabelManager",
|
| 313 |
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"foreground_intensity_properties_per_channel": {
|
| 314 |
-
"0": {
|
| 315 |
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"max": 1694.0,
|
| 316 |
-
"mean": 267.35308837890625,
|
| 317 |
-
"median": 242.0,
|
| 318 |
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"min": 0.0,
|
| 319 |
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"percentile_00_5": 36.0,
|
| 320 |
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"percentile_99_5": 768.0,
|
| 321 |
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"std": 136.11251831054688
|
| 322 |
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},
|
| 323 |
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"1": {
|
| 324 |
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"max": 3557.286865234375,
|
| 325 |
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"mean": 1215.81591796875,
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| 326 |
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"median": 1203.8331298828125,
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| 327 |
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"min": 0.0,
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| 328 |
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|
| 329 |
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"percentile_99_5": 2259.82861328125,
|
| 330 |
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"std": 338.6748352050781
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| 331 |
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},
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| 332 |
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"2": {
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| 333 |
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| 334 |
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"mean": 72.26309204101562,
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| 335 |
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"min": 0.0,
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| 338 |
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| 339 |
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"std": 18.909290313720703
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| 340 |
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}
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| 341 |
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| 342 |
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