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Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json

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Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth

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Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json

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+    "cudnn_version": 8500,
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+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fb9a6db8890>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [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 = [80, 192, 160], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), 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 ), 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 = None ), 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 ), 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], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
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+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fb9a74e2ad0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [7, 7], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [130, 275, 275], 'spacing': [4.650736429273743, 2.361701649461784, 2.361701649461784], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1620.0, 'mean': -38.229164123535156, 'median': -54.0, 'min': -1000.0, 'percentile_00_5': -941.0, 'percentile_99_5': 897.0, 'std': 192.37086486816406}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'Bladder': 1, 'Anorectum': 2, 'Bag_Bowel': 3, 'Femur_Head_L': 4, 'Femur_Head_R': 5, 'Penilebulb': 6}, 'numTraining': 60, 'file_ending': '.nii.gz', 'numTest': 0}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    initial_lr: 0.01\n    lr: 0.003897412779133726\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "./data/nnUNet_results/Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "./data/nnUNet_results/Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [7, 7], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [130, 275, 275], 'spacing': [4.650736429273743, 2.361701649461784, 2.361701649461784], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1620.0, 'mean': -38.229164123535156, 'median': -54.0, 'min': -1000.0, 'percentile_00_5': -941.0, 'percentile_99_5': 897.0, 'std': 192.37086486816406}}}",
+    "preprocessed_dataset_folder": "./data/nnUNet_preprocessed/Dataset720_TSPrime/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "./data/nnUNet_preprocessed/Dataset720_TSPrime",
+    "save_every": "50",
+    "torch_version": "2.0.1+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/network_architecture

@@ -0,0 +1,171 @@
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+	"/outputs/110" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/111" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/112" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/113" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/114" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/115" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/116" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/117" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/118" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/119" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/120" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/121" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/122" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/123" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/124" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/125" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/126" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/127" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/128" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/129" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/130" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/131" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/132" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/133" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/134" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/135" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/136" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/137" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/138" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/139" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 2, 2]</td></tr></table>>]
+	"/outputs/140" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/141" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/142" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/143" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/144" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/145" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [1, 2, 2], stride: [1, 2, 2]</td></tr></table>>]
+	"/outputs/146" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/147" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/148" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/149" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/150" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/151" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/152" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/153" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/154" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/155" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/156" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/157" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/158" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/159" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/160" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/161" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/162" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/163" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/164" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/165" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/166" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/167" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/168" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/169" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/170" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/171" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/172" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/173" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/174" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/175" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/176" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/177" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/178" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/179" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/180" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/181" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/182" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/183" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/184" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/185" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/186" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/187" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/188" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/189" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/109" -> "/outputs/110" [label="1x32x80x192x160"]
+	"/outputs/110" -> "/outputs/111" [label="1x32x80x192x160"]
+	"/outputs/111" -> "/outputs/112" [label="1x32x80x192x160"]
+	"/outputs/112" -> "/outputs/113" [label="1x32x80x192x160"]
+	"/outputs/113" -> "/outputs/114" [label="1x32x80x192x160"]
+	"/outputs/114" -> "/outputs/115" [label="1x32x80x192x160"]
+	"/outputs/114" -> "/outputs/182" [label="1x32x80x192x160"]
+	"/outputs/115" -> "/outputs/116" [label="1x64x40x96x80"]
+	"/outputs/116" -> "/outputs/117" [label="1x64x40x96x80"]
+	"/outputs/117" -> "/outputs/118" [label="1x64x40x96x80"]
+	"/outputs/118" -> "/outputs/119" [label="1x64x40x96x80"]
+	"/outputs/119" -> "/outputs/120" [label="1x64x40x96x80"]
+	"/outputs/120" -> "/outputs/121" [label="1x64x40x96x80"]
+	"/outputs/120" -> "/outputs/173" [label="1x64x40x96x80"]
+	"/outputs/121" -> "/outputs/122" [label="1x128x20x48x40"]
+	"/outputs/122" -> "/outputs/123" [label="1x128x20x48x40"]
+	"/outputs/123" -> "/outputs/124" [label="1x128x20x48x40"]
+	"/outputs/124" -> "/outputs/125" [label="1x128x20x48x40"]
+	"/outputs/125" -> "/outputs/126" [label="1x128x20x48x40"]
+	"/outputs/126" -> "/outputs/127" [label="1x128x20x48x40"]
+	"/outputs/126" -> "/outputs/164" [label="1x128x20x48x40"]
+	"/outputs/127" -> "/outputs/128" [label="1x256x10x24x20"]
+	"/outputs/128" -> "/outputs/129" [label="1x256x10x24x20"]
+	"/outputs/129" -> "/outputs/130" [label="1x256x10x24x20"]
+	"/outputs/130" -> "/outputs/131" [label="1x256x10x24x20"]
+	"/outputs/131" -> "/outputs/132" [label="1x256x10x24x20"]
+	"/outputs/132" -> "/outputs/133" [label="1x256x10x24x20"]
+	"/outputs/132" -> "/outputs/155" [label="1x256x10x24x20"]
+	"/outputs/133" -> "/outputs/134" [label="1x320x5x12x10"]
+	"/outputs/134" -> "/outputs/135" [label="1x320x5x12x10"]
+	"/outputs/135" -> "/outputs/136" [label="1x320x5x12x10"]
+	"/outputs/136" -> "/outputs/137" [label="1x320x5x12x10"]
+	"/outputs/137" -> "/outputs/138" [label="1x320x5x12x10"]
+	"/outputs/138" -> "/outputs/139" [label="1x320x5x12x10"]
+	"/outputs/138" -> "/outputs/146" [label="1x320x5x12x10"]
+	"/outputs/139" -> "/outputs/140" [label="1x320x5x6x5"]
+	"/outputs/140" -> "/outputs/141" [label="1x320x5x6x5"]
+	"/outputs/141" -> "/outputs/142" [label="1x320x5x6x5"]
+	"/outputs/142" -> "/outputs/143" [label="1x320x5x6x5"]
+	"/outputs/143" -> "/outputs/144" [label="1x320x5x6x5"]
+	"/outputs/144" -> "/outputs/145" [label="1x320x5x6x5"]
+	"/outputs/145" -> "/outputs/146" [label="1x320x5x12x10"]
+	"/outputs/146" -> "/outputs/147" [label="1x640x5x12x10"]
+	"/outputs/147" -> "/outputs/148" [label="1x320x5x12x10"]
+	"/outputs/148" -> "/outputs/149" [label="1x320x5x12x10"]
+	"/outputs/149" -> "/outputs/150" [label="1x320x5x12x10"]
+	"/outputs/150" -> "/outputs/151" [label="1x320x5x12x10"]
+	"/outputs/151" -> "/outputs/152" [label="1x320x5x12x10"]
+	"/outputs/152" -> "/outputs/153" [label="1x320x5x12x10"]
+	"/outputs/152" -> "/outputs/154" [label="1x320x5x12x10"]
+	"/outputs/154" -> "/outputs/155" [label="1x256x10x24x20"]
+	"/outputs/155" -> "/outputs/156" [label="1x512x10x24x20"]
+	"/outputs/156" -> "/outputs/157" [label="1x256x10x24x20"]
+	"/outputs/157" -> "/outputs/158" [label="1x256x10x24x20"]
+	"/outputs/158" -> "/outputs/159" [label="1x256x10x24x20"]
+	"/outputs/159" -> "/outputs/160" [label="1x256x10x24x20"]
+	"/outputs/160" -> "/outputs/161" [label="1x256x10x24x20"]
+	"/outputs/161" -> "/outputs/162" [label="1x256x10x24x20"]
+	"/outputs/161" -> "/outputs/163" [label="1x256x10x24x20"]
+	"/outputs/163" -> "/outputs/164" [label="1x128x20x48x40"]
+	"/outputs/164" -> "/outputs/165" [label="1x256x20x48x40"]
+	"/outputs/165" -> "/outputs/166" [label="1x128x20x48x40"]
+	"/outputs/166" -> "/outputs/167" [label="1x128x20x48x40"]
+	"/outputs/167" -> "/outputs/168" [label="1x128x20x48x40"]
+	"/outputs/168" -> "/outputs/169" [label="1x128x20x48x40"]
+	"/outputs/169" -> "/outputs/170" [label="1x128x20x48x40"]
+	"/outputs/170" -> "/outputs/171" [label="1x128x20x48x40"]
+	"/outputs/170" -> "/outputs/172" [label="1x128x20x48x40"]
+	"/outputs/172" -> "/outputs/173" [label="1x64x40x96x80"]
+	"/outputs/173" -> "/outputs/174" [label="1x128x40x96x80"]
+	"/outputs/174" -> "/outputs/175" [label="1x64x40x96x80"]
+	"/outputs/175" -> "/outputs/176" [label="1x64x40x96x80"]
+	"/outputs/176" -> "/outputs/177" [label="1x64x40x96x80"]
+	"/outputs/177" -> "/outputs/178" [label="1x64x40x96x80"]
+	"/outputs/178" -> "/outputs/179" [label="1x64x40x96x80"]
+	"/outputs/179" -> "/outputs/180" [label="1x64x40x96x80"]
+	"/outputs/179" -> "/outputs/181" [label="1x64x40x96x80"]
+	"/outputs/181" -> "/outputs/182" [label="1x32x80x192x160"]
+	"/outputs/182" -> "/outputs/183" [label="1x64x80x192x160"]
+	"/outputs/183" -> "/outputs/184" [label="1x32x80x192x160"]
+	"/outputs/184" -> "/outputs/185" [label="1x32x80x192x160"]
+	"/outputs/185" -> "/outputs/186" [label="1x32x80x192x160"]
+	"/outputs/186" -> "/outputs/187" [label="1x32x80x192x160"]
+	"/outputs/187" -> "/outputs/188" [label="1x32x80x192x160"]
+	"/outputs/188" -> "/outputs/189" [label="1x32x80x192x160"]
+}

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_10_11_21_50_08.txt

@@ -0,0 +1,26 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [240.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [240, 512, 512], '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': 3071.0, 'mean': -12.232562065124512, 'median': -21.0, 'min': -1000.0, 'percentile_00_5': -793.0, 'percentile_99_5': 974.0, 'std': 168.24203491210938}}} 
+ 
+2023-10-11 21:50:09.984604: unpacking dataset... 
+2023-10-11 21:51:22.972373: unpacking done... 
+2023-10-11 21:51:23.374131: do_dummy_2d_data_aug: False 
+2023-10-11 21:51:23.375596: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset720_TSPrime/splits_final.json 
+2023-10-11 21:51:23.541955: The split file contains 5 splits. 
+2023-10-11 21:51:23.542130: Desired fold for training: 0 
+2023-10-11 21:51:23.542256: This split has 20 training and 5 validation cases. 
+2023-10-11 21:52:06.109724: Unable to plot network architecture: 
+2023-10-11 21:52:06.109830: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH 
+2023-10-11 21:52:06.212852:  
+2023-10-11 21:52:06.212922: Epoch 0 
+2023-10-11 21:52:06.213036: Current learning rate: 0.01 

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_10_11_21_56_01.txt

@@ -0,0 +1,26 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 3, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [240.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [240, 512, 512], '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': 3071.0, 'mean': -12.232562065124512, 'median': -21.0, 'min': -1000.0, 'percentile_00_5': -793.0, 'percentile_99_5': 974.0, 'std': 168.24203491210938}}} 
+ 
+2023-10-11 21:56:02.926755: unpacking dataset... 
+2023-10-11 21:56:06.110042: unpacking done... 
+2023-10-11 21:56:06.110639: do_dummy_2d_data_aug: False 
+2023-10-11 21:56:06.111117: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset720_TSPrime/splits_final.json 
+2023-10-11 21:56:06.111239: The split file contains 5 splits. 
+2023-10-11 21:56:06.111284: Desired fold for training: 0 
+2023-10-11 21:56:06.111325: This split has 20 training and 5 validation cases. 
+2023-10-11 21:56:27.774188: Unable to plot network architecture: 
+2023-10-11 21:56:27.774289: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH 
+2023-10-11 21:56:27.879786:  
+2023-10-11 21:56:27.879845: Epoch 0 
+2023-10-11 21:56:27.879956: Current learning rate: 0.01 

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_10_14_08_44_57.txt

@@ -0,0 +1,782 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], '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': 1620.0, 'mean': -38.229164123535156, 'median': -54.0, 'min': -1000.0, 'percentile_00_5': -941.0, 'percentile_99_5': 897.0, 'std': 192.37086486816406}}} 
+ 
+2023-10-14 08:44:59.641317: unpacking dataset... 
+2023-10-14 08:45:03.933325: unpacking done... 
+2023-10-14 08:45:03.934265: do_dummy_2d_data_aug: False 
+2023-10-14 08:45:03.934828: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset720_TSPrime/splits_final.json 
+2023-10-14 08:45:03.955630: The split file contains 5 splits. 
+2023-10-14 08:45:03.955693: Desired fold for training: 0 
+2023-10-14 08:45:03.955763: This split has 48 training and 12 validation cases. 
+2023-10-14 08:45:26.437840: Unable to plot network architecture: 
+2023-10-14 08:45:26.438034: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH 
+2023-10-14 08:45:26.536218:  
+2023-10-14 08:45:26.536288: Epoch 550 
+2023-10-14 08:45:26.536392: Current learning rate: 0.00487 
+2023-10-14 08:53:03.674403: train_loss -0.7726 
+2023-10-14 08:53:03.700099: val_loss -0.6799 
+2023-10-14 08:53:03.700228: Pseudo dice [0.971, 0.8644, 0.908, 0.8821, 0.8677, 0.666] 
+2023-10-14 08:53:03.700332: Epoch time: 457.14 s 
+2023-10-14 08:53:05.057412:  
+2023-10-14 08:53:05.057607: Epoch 551 
+2023-10-14 08:53:05.057753: Current learning rate: 0.00486 
+2023-10-14 08:58:50.064842: train_loss -0.758 
+2023-10-14 08:58:50.064996: val_loss -0.682 
+2023-10-14 08:58:50.065178: Pseudo dice [0.9698, 0.87, 0.9137, 0.9011, 0.8763, 0.7155] 
+2023-10-14 08:58:50.065330: Epoch time: 345.01 s 
+2023-10-14 08:58:51.281017:  
+2023-10-14 08:58:51.281138: Epoch 552 
+2023-10-14 08:58:51.281244: Current learning rate: 0.00485 
+2023-10-14 09:04:36.496282: train_loss -0.7495 
+2023-10-14 09:04:36.498847: val_loss -0.6299 
+2023-10-14 09:04:36.498962: Pseudo dice [0.9742, 0.8785, 0.8972, 0.8699, 0.8523, 0.6534] 
+2023-10-14 09:04:36.499053: Epoch time: 345.22 s 
+2023-10-14 09:04:37.717736:  
+2023-10-14 09:04:37.717844: Epoch 553 
+2023-10-14 09:04:37.717949: Current learning rate: 0.00484 
+2023-10-14 09:10:22.975977: train_loss -0.7464 
+2023-10-14 09:10:22.976139: val_loss -0.6956 
+2023-10-14 09:10:22.976246: Pseudo dice [0.9605, 0.8878, 0.9084, 0.9079, 0.8974, 0.6374] 
+2023-10-14 09:10:22.976335: Epoch time: 345.26 s 
+2023-10-14 09:10:24.198349:  
+2023-10-14 09:10:24.198598: Epoch 554 
+2023-10-14 09:10:24.198841: Current learning rate: 0.00484 
+2023-10-14 09:16:09.415752: train_loss -0.7741 
+2023-10-14 09:16:09.415916: val_loss -0.6677 
+2023-10-14 09:16:09.416036: Pseudo dice [0.9697, 0.8769, 0.8922, 0.8684, 0.9019, 0.6411] 
+2023-10-14 09:16:09.416137: Epoch time: 345.22 s 
+2023-10-14 09:16:10.620770:  
+2023-10-14 09:16:10.620962: Epoch 555 
+2023-10-14 09:16:10.621224: Current learning rate: 0.00483 
+2023-10-14 09:21:55.950917: train_loss -0.7982 
+2023-10-14 09:21:55.951100: val_loss -0.6904 
+2023-10-14 09:21:55.951208: Pseudo dice [0.9724, 0.8793, 0.9091, 0.8707, 0.878, 0.6579] 
+2023-10-14 09:21:55.951298: Epoch time: 345.33 s 
+2023-10-14 09:21:57.192797:  
+2023-10-14 09:21:57.192911: Epoch 556 
+2023-10-14 09:21:57.193016: Current learning rate: 0.00482 
+2023-10-14 09:27:42.440943: train_loss -0.7506 
+2023-10-14 09:27:42.441109: val_loss -0.6581 
+2023-10-14 09:27:42.441239: Pseudo dice [0.9699, 0.8746, 0.9064, 0.9102, 0.8858, 0.6497] 
+2023-10-14 09:27:42.441343: Epoch time: 345.25 s 
+2023-10-14 09:27:43.650142:  
+2023-10-14 09:27:43.650428: Epoch 557 
+2023-10-14 09:27:43.650620: Current learning rate: 0.00481 
+2023-10-14 09:33:28.773947: train_loss -0.7664 
+2023-10-14 09:33:28.774114: val_loss -0.6489 
+2023-10-14 09:33:28.774237: Pseudo dice [0.9673, 0.8709, 0.8961, 0.8558, 0.8833, 0.668] 
+2023-10-14 09:33:28.774338: Epoch time: 345.12 s 
+2023-10-14 09:33:30.001891:  
+2023-10-14 09:33:30.002107: Epoch 558 
+2023-10-14 09:33:30.002374: Current learning rate: 0.0048 
+2023-10-14 09:39:15.284474: train_loss -0.7648 
+2023-10-14 09:39:15.284639: val_loss -0.667 
+2023-10-14 09:39:15.284746: Pseudo dice [0.9739, 0.8739, 0.9031, 0.9063, 0.8898, 0.6799] 
+2023-10-14 09:39:15.284836: Epoch time: 345.28 s 
+2023-10-14 09:39:16.520813:  
+2023-10-14 09:39:16.520922: Epoch 559 
+2023-10-14 09:39:16.521024: Current learning rate: 0.00479 
+2023-10-14 09:45:01.838422: train_loss -0.7745 
+2023-10-14 09:45:01.838580: val_loss -0.7023 
+2023-10-14 09:45:01.838696: Pseudo dice [0.9725, 0.8726, 0.8938, 0.9313, 0.8859, 0.6822] 
+2023-10-14 09:45:01.838784: Epoch time: 345.32 s 
+2023-10-14 09:45:03.072559:  
+2023-10-14 09:45:03.072677: Epoch 560 
+2023-10-14 09:45:03.072782: Current learning rate: 0.00478 
+2023-10-14 09:50:48.482734: train_loss -0.7542 
+2023-10-14 09:50:48.482884: val_loss -0.6193 
+2023-10-14 09:50:48.483016: Pseudo dice [0.9709, 0.8778, 0.8924, 0.8774, 0.9118, 0.6265] 
+2023-10-14 09:50:48.483118: Epoch time: 345.41 s 
+2023-10-14 09:50:49.873660:  
+2023-10-14 09:50:49.873856: Epoch 561 
+2023-10-14 09:50:49.874034: Current learning rate: 0.00477 
+2023-10-14 09:56:35.256728: train_loss -0.7759 
+2023-10-14 09:56:35.256931: val_loss -0.6749 
+2023-10-14 09:56:35.257066: Pseudo dice [0.9724, 0.8705, 0.8941, 0.8893, 0.8904, 0.6678] 
+2023-10-14 09:56:35.257167: Epoch time: 345.38 s 
+2023-10-14 09:56:36.487928:  
+2023-10-14 09:56:36.488053: Epoch 562 
+2023-10-14 09:56:36.488159: Current learning rate: 0.00476 
+2023-10-14 10:02:21.787369: train_loss -0.7461 
+2023-10-14 10:02:21.787533: val_loss -0.6248 
+2023-10-14 10:02:21.788099: Pseudo dice [0.9743, 0.8629, 0.8877, 0.8809, 0.8345, 0.6541] 
+2023-10-14 10:02:21.788211: Epoch time: 345.3 s 
+2023-10-14 10:02:23.005492:  
+2023-10-14 10:02:23.005673: Epoch 563 
+2023-10-14 10:02:23.005828: Current learning rate: 0.00475 
+2023-10-14 10:08:08.231398: train_loss -0.7442 
+2023-10-14 10:08:08.231559: val_loss -0.6617 
+2023-10-14 10:08:08.231754: Pseudo dice [0.9706, 0.8615, 0.9109, 0.8754, 0.8734, 0.6588] 
+2023-10-14 10:08:08.231893: Epoch time: 345.23 s 
+2023-10-14 10:08:09.463090:  
+2023-10-14 10:08:09.463255: Epoch 564 
+2023-10-14 10:08:09.463451: Current learning rate: 0.00474 
+2023-10-14 10:13:54.732506: train_loss -0.7684 
+2023-10-14 10:13:54.732661: val_loss -0.6995 
+2023-10-14 10:13:54.732769: Pseudo dice [0.9735, 0.8821, 0.9084, 0.8772, 0.9093, 0.6494] 
+2023-10-14 10:13:54.732857: Epoch time: 345.27 s 
+2023-10-14 10:13:55.953084:  
+2023-10-14 10:13:55.953255: Epoch 565 
+2023-10-14 10:13:55.953446: Current learning rate: 0.00473 
+2023-10-14 10:19:41.225027: train_loss -0.7761 
+2023-10-14 10:19:41.225191: val_loss -0.683 
+2023-10-14 10:19:41.225320: Pseudo dice [0.971, 0.8826, 0.91, 0.9083, 0.9002, 0.7113] 
+2023-10-14 10:19:41.225426: Epoch time: 345.27 s 
+2023-10-14 10:19:42.462402:  
+2023-10-14 10:19:42.462528: Epoch 566 
+2023-10-14 10:19:42.462646: Current learning rate: 0.00472 
+2023-10-14 10:25:27.782266: train_loss -0.7355 
+2023-10-14 10:25:27.782458: val_loss -0.7007 
+2023-10-14 10:25:27.782598: Pseudo dice [0.9663, 0.8682, 0.9063, 0.8794, 0.8787, 0.6951] 
+2023-10-14 10:25:27.782697: Epoch time: 345.32 s 
+2023-10-14 10:25:28.998262:  
+2023-10-14 10:25:28.998374: Epoch 567 
+2023-10-14 10:25:28.998532: Current learning rate: 0.00471 
+2023-10-14 10:31:14.337671: train_loss -0.7735 
+2023-10-14 10:31:14.337815: val_loss -0.7058 
+2023-10-14 10:31:14.337922: Pseudo dice [0.9742, 0.879, 0.9126, 0.883, 0.8763, 0.6931] 
+2023-10-14 10:31:14.338011: Epoch time: 345.34 s 
+2023-10-14 10:31:15.750798:  
+2023-10-14 10:31:15.750981: Epoch 568 
+2023-10-14 10:31:15.751223: Current learning rate: 0.0047 
+2023-10-14 10:37:01.195973: train_loss -0.7652 
+2023-10-14 10:37:01.196126: val_loss -0.7085 
+2023-10-14 10:37:01.196294: Pseudo dice [0.9726, 0.8601, 0.9137, 0.8982, 0.8773, 0.6364] 
+2023-10-14 10:37:01.196413: Epoch time: 345.45 s 
+2023-10-14 10:37:02.437130:  
+2023-10-14 10:37:02.437374: Epoch 569 
+2023-10-14 10:37:02.437515: Current learning rate: 0.00469 
+2023-10-14 10:42:47.777742: train_loss -0.7729 
+2023-10-14 10:42:47.777902: val_loss -0.6988 
+2023-10-14 10:42:47.778010: Pseudo dice [0.9685, 0.8696, 0.9031, 0.8781, 0.8985, 0.6406] 
+2023-10-14 10:42:47.778097: Epoch time: 345.34 s 
+2023-10-14 10:42:49.021302:  
+2023-10-14 10:42:49.021410: Epoch 570 
+2023-10-14 10:42:49.021516: Current learning rate: 0.00468 
+2023-10-14 10:48:34.305914: train_loss -0.7413 
+2023-10-14 10:48:34.306067: val_loss -0.7214 
+2023-10-14 10:48:34.306176: Pseudo dice [0.9726, 0.8777, 0.9129, 0.9223, 0.8883, 0.6428] 
+2023-10-14 10:48:34.306266: Epoch time: 345.29 s 
+2023-10-14 10:48:35.529996:  
+2023-10-14 10:48:35.530115: Epoch 571 
+2023-10-14 10:48:35.530219: Current learning rate: 0.00467 
+2023-10-14 10:54:20.924050: train_loss -0.7771 
+2023-10-14 10:54:20.924205: val_loss -0.6726 
+2023-10-14 10:54:20.924314: Pseudo dice [0.972, 0.871, 0.9062, 0.9067, 0.8683, 0.6494] 
+2023-10-14 10:54:20.924403: Epoch time: 345.39 s 
+2023-10-14 10:54:22.148553:  
+2023-10-14 10:54:22.148817: Epoch 572 
+2023-10-14 10:54:22.149071: Current learning rate: 0.00466 
+2023-10-14 11:00:07.467053: train_loss -0.7492 
+2023-10-14 11:00:07.467210: val_loss -0.6786 
+2023-10-14 11:00:07.467318: Pseudo dice [0.9718, 0.8647, 0.9095, 0.8812, 0.8904, 0.6684] 
+2023-10-14 11:00:07.467407: Epoch time: 345.32 s 
+2023-10-14 11:00:09.043088:  
+2023-10-14 11:00:09.043208: Epoch 573 
+2023-10-14 11:00:09.043312: Current learning rate: 0.00465 
+2023-10-14 11:05:54.443021: train_loss -0.7607 
+2023-10-14 11:05:54.443180: val_loss -0.7221 
+2023-10-14 11:05:54.443288: Pseudo dice [0.9718, 0.8747, 0.9108, 0.8771, 0.8841, 0.6492] 
+2023-10-14 11:05:54.443378: Epoch time: 345.4 s 
+2023-10-14 11:05:55.779569:  
+2023-10-14 11:05:55.779737: Epoch 574 
+2023-10-14 11:05:55.779896: Current learning rate: 0.00464 
+2023-10-14 11:11:41.234959: train_loss -0.7702 
+2023-10-14 11:11:41.235169: val_loss -0.7058 
+2023-10-14 11:11:41.235277: Pseudo dice [0.9711, 0.8677, 0.9159, 0.8803, 0.8847, 0.6971] 
+2023-10-14 11:11:41.235377: Epoch time: 345.46 s 
+2023-10-14 11:11:42.492971:  
+2023-10-14 11:11:42.493136: Epoch 575 
+2023-10-14 11:11:42.493296: Current learning rate: 0.00463 
+2023-10-14 11:17:27.932884: train_loss -0.7933 
+2023-10-14 11:17:27.933038: val_loss -0.6719 
+2023-10-14 11:17:27.933154: Pseudo dice [0.9695, 0.8583, 0.9105, 0.8701, 0.896, 0.6404] 
+2023-10-14 11:17:27.933241: Epoch time: 345.44 s 
+2023-10-14 11:17:29.175566:  
+2023-10-14 11:17:29.175782: Epoch 576 
+2023-10-14 11:17:29.175936: Current learning rate: 0.00462 
+2023-10-14 11:23:14.508476: train_loss -0.7557 
+2023-10-14 11:23:14.508634: val_loss -0.678 
+2023-10-14 11:23:14.508741: Pseudo dice [0.9744, 0.8663, 0.9076, 0.8893, 0.9128, 0.6918] 
+2023-10-14 11:23:14.508830: Epoch time: 345.33 s 
+2023-10-14 11:23:15.748343:  
+2023-10-14 11:23:15.750563: Epoch 577 
+2023-10-14 11:23:15.750682: Current learning rate: 0.00461 
+2023-10-14 11:29:01.167244: train_loss -0.7662 
+2023-10-14 11:29:01.167400: val_loss -0.6888 
+2023-10-14 11:29:01.167508: Pseudo dice [0.9724, 0.8683, 0.9002, 0.8689, 0.8669, 0.6766] 
+2023-10-14 11:29:01.167598: Epoch time: 345.42 s 
+2023-10-14 11:29:02.412012:  
+2023-10-14 11:29:02.412133: Epoch 578 
+2023-10-14 11:29:02.412236: Current learning rate: 0.0046 
+2023-10-14 11:34:47.791409: train_loss -0.7781 
+2023-10-14 11:34:47.791618: val_loss -0.6522 
+2023-10-14 11:34:47.791725: Pseudo dice [0.9755, 0.8814, 0.8988, 0.8996, 0.8768, 0.6853] 
+2023-10-14 11:34:47.791815: Epoch time: 345.38 s 
+2023-10-14 11:34:49.055247:  
+2023-10-14 11:34:49.055444: Epoch 579 
+2023-10-14 11:34:49.055591: Current learning rate: 0.00459 
+2023-10-14 11:40:34.502784: train_loss -0.7883 
+2023-10-14 11:40:34.502934: val_loss -0.6966 
+2023-10-14 11:40:34.503041: Pseudo dice [0.9716, 0.8709, 0.9169, 0.8648, 0.8968, 0.6263] 
+2023-10-14 11:40:34.503128: Epoch time: 345.45 s 
+2023-10-14 11:40:35.741698:  
+2023-10-14 11:40:35.741818: Epoch 580 
+2023-10-14 11:40:35.741922: Current learning rate: 0.00458 
+2023-10-14 11:46:21.154979: train_loss -0.776 
+2023-10-14 11:46:21.155136: val_loss -0.6672 
+2023-10-14 11:46:21.155242: Pseudo dice [0.9714, 0.8654, 0.9134, 0.832, 0.8995, 0.6908] 
+2023-10-14 11:46:21.155330: Epoch time: 345.41 s 
+2023-10-14 11:46:22.391256:  
+2023-10-14 11:46:22.391378: Epoch 581 
+2023-10-14 11:46:22.391483: Current learning rate: 0.00457 
+2023-10-14 11:52:07.812470: train_loss -0.7667 
+2023-10-14 11:52:07.812626: val_loss -0.6644 
+2023-10-14 11:52:07.812735: Pseudo dice [0.9703, 0.8694, 0.9021, 0.9013, 0.9243, 0.6478] 
+2023-10-14 11:52:07.812823: Epoch time: 345.42 s 
+2023-10-14 11:52:09.212539:  
+2023-10-14 11:52:09.212670: Epoch 582 
+2023-10-14 11:52:09.212773: Current learning rate: 0.00456 
+2023-10-14 11:57:54.671791: train_loss -0.7335 
+2023-10-14 11:57:54.671950: val_loss -0.674 
+2023-10-14 11:57:54.672076: Pseudo dice [0.9707, 0.8749, 0.9071, 0.8735, 0.8597, 0.6389] 
+2023-10-14 11:57:54.672175: Epoch time: 345.46 s 
+2023-10-14 11:57:55.910544:  
+2023-10-14 11:57:55.910660: Epoch 583 
+2023-10-14 11:57:55.910774: Current learning rate: 0.00455 
+2023-10-14 12:03:41.224444: train_loss -0.7809 
+2023-10-14 12:03:41.224600: val_loss -0.6982 
+2023-10-14 12:03:41.224707: Pseudo dice [0.9737, 0.8681, 0.91, 0.8818, 0.8758, 0.6505] 
+2023-10-14 12:03:41.224799: Epoch time: 345.31 s 
+2023-10-14 12:03:42.465361:  
+2023-10-14 12:03:42.465484: Epoch 584 
+2023-10-14 12:03:42.465588: Current learning rate: 0.00454 
+2023-10-14 12:09:27.768269: train_loss -0.7659 
+2023-10-14 12:09:27.768430: val_loss -0.6903 
+2023-10-14 12:09:27.768552: Pseudo dice [0.9717, 0.8757, 0.9116, 0.8827, 0.8703, 0.6597] 
+2023-10-14 12:09:27.768653: Epoch time: 345.3 s 
+2023-10-14 12:09:29.019685:  
+2023-10-14 12:09:29.019790: Epoch 585 
+2023-10-14 12:09:29.019923: Current learning rate: 0.00453 
+2023-10-14 12:15:14.337044: train_loss -0.78 
+2023-10-14 12:15:14.337203: val_loss -0.6906 
+2023-10-14 12:15:14.337310: Pseudo dice [0.9702, 0.8768, 0.8986, 0.8672, 0.8984, 0.6971] 
+2023-10-14 12:15:14.337398: Epoch time: 345.32 s 
+2023-10-14 12:15:15.580557:  
+2023-10-14 12:15:15.580729: Epoch 586 
+2023-10-14 12:15:15.580879: Current learning rate: 0.00452 
+2023-10-14 12:21:00.794588: train_loss -0.7613 
+2023-10-14 12:21:00.794739: val_loss -0.6561 
+2023-10-14 12:21:00.794865: Pseudo dice [0.9704, 0.869, 0.8913, 0.86, 0.8707, 0.6004] 
+2023-10-14 12:21:00.794974: Epoch time: 345.21 s 
+2023-10-14 12:21:02.047369:  
+2023-10-14 12:21:02.047487: Epoch 587 
+2023-10-14 12:21:02.047603: Current learning rate: 0.00451 
+2023-10-14 12:26:47.170517: train_loss -0.7592 
+2023-10-14 12:26:47.170678: val_loss -0.7093 
+2023-10-14 12:26:47.170804: Pseudo dice [0.9738, 0.8839, 0.908, 0.8469, 0.8882, 0.6799] 
+2023-10-14 12:26:47.170903: Epoch time: 345.12 s 
+2023-10-14 12:26:48.454413:  
+2023-10-14 12:26:48.454673: Epoch 588 
+2023-10-14 12:26:48.454905: Current learning rate: 0.0045 
+2023-10-14 12:32:33.824881: train_loss -0.7433 
+2023-10-14 12:32:33.825027: val_loss -0.7018 
+2023-10-14 12:32:33.825155: Pseudo dice [0.9762, 0.8921, 0.9013, 0.8873, 0.8627, 0.6741] 
+2023-10-14 12:32:33.825258: Epoch time: 345.37 s 
+2023-10-14 12:32:35.246654:  
+2023-10-14 12:32:35.246777: Epoch 589 
+2023-10-14 12:32:35.246901: Current learning rate: 0.00449 
+2023-10-14 12:38:20.571609: train_loss -0.7588 
+2023-10-14 12:38:20.571753: val_loss -0.6777 
+2023-10-14 12:38:20.571877: Pseudo dice [0.9714, 0.8796, 0.9018, 0.9064, 0.8735, 0.662] 
+2023-10-14 12:38:20.571964: Epoch time: 345.33 s 
+2023-10-14 12:38:21.826131:  
+2023-10-14 12:38:21.826255: Epoch 590 
+2023-10-14 12:38:21.826372: Current learning rate: 0.00448 
+2023-10-14 12:44:07.192964: train_loss -0.7657 
+2023-10-14 12:44:07.193155: val_loss -0.6732 
+2023-10-14 12:44:07.193263: Pseudo dice [0.9743, 0.8746, 0.9044, 0.879, 0.9048, 0.6682] 
+2023-10-14 12:44:07.193352: Epoch time: 345.37 s 
+2023-10-14 12:44:08.441660:  
+2023-10-14 12:44:08.441845: Epoch 591 
+2023-10-14 12:44:08.441998: Current learning rate: 0.00447 
+2023-10-14 12:49:53.791086: train_loss -0.7813 
+2023-10-14 12:49:53.791235: val_loss -0.6501 
+2023-10-14 12:49:53.791344: Pseudo dice [0.9734, 0.8784, 0.8948, 0.8317, 0.8887, 0.6691] 
+2023-10-14 12:49:53.791434: Epoch time: 345.35 s 
+2023-10-14 12:49:55.040131:  
+2023-10-14 12:49:55.040316: Epoch 592 
+2023-10-14 12:49:55.040468: Current learning rate: 0.00446 
+2023-10-14 12:55:40.360149: train_loss -0.7668 
+2023-10-14 12:55:40.360301: val_loss -0.71 
+2023-10-14 12:55:40.360408: Pseudo dice [0.9715, 0.8831, 0.9131, 0.89, 0.9071, 0.6549] 
+2023-10-14 12:55:40.360495: Epoch time: 345.32 s 
+2023-10-14 12:55:41.608411:  
+2023-10-14 12:55:41.608523: Epoch 593 
+2023-10-14 12:55:41.608628: Current learning rate: 0.00445 
+2023-10-14 13:01:27.035468: train_loss -0.7672 
+2023-10-14 13:01:27.035642: val_loss -0.695 
+2023-10-14 13:01:27.035750: Pseudo dice [0.9729, 0.8742, 0.9165, 0.8934, 0.8787, 0.6867] 
+2023-10-14 13:01:27.035839: Epoch time: 345.43 s 
+2023-10-14 13:01:28.273444:  
+2023-10-14 13:01:28.273566: Epoch 594 
+2023-10-14 13:01:28.273671: Current learning rate: 0.00444 
+2023-10-14 13:07:13.489238: train_loss -0.7572 
+2023-10-14 13:07:13.489397: val_loss -0.6996 
+2023-10-14 13:07:13.489504: Pseudo dice [0.973, 0.8829, 0.8863, 0.9056, 0.8796, 0.6745] 
+2023-10-14 13:07:13.489593: Epoch time: 345.22 s 
+2023-10-14 13:07:14.721650:  
+2023-10-14 13:07:14.721843: Epoch 595 
+2023-10-14 13:07:14.722018: Current learning rate: 0.00443 
+2023-10-14 13:12:59.954200: train_loss -0.77 
+2023-10-14 13:12:59.954342: val_loss -0.6637 
+2023-10-14 13:12:59.954451: Pseudo dice [0.9695, 0.8752, 0.9112, 0.9069, 0.8798, 0.6646] 
+2023-10-14 13:12:59.954548: Epoch time: 345.23 s 
+2023-10-14 13:13:01.391377:  
+2023-10-14 13:13:01.391502: Epoch 596 
+2023-10-14 13:13:01.391612: Current learning rate: 0.00442 
+2023-10-14 13:18:46.692570: train_loss -0.7654 
+2023-10-14 13:18:46.692728: val_loss -0.7086 
+2023-10-14 13:18:46.692838: Pseudo dice [0.9725, 0.8777, 0.9106, 0.8923, 0.8924, 0.6852] 
+2023-10-14 13:18:46.692928: Epoch time: 345.3 s 
+2023-10-14 13:18:47.935062:  
+2023-10-14 13:18:47.935189: Epoch 597 
+2023-10-14 13:18:47.935295: Current learning rate: 0.00441 
+2023-10-14 13:24:33.156263: train_loss -0.7385 
+2023-10-14 13:24:33.156409: val_loss -0.6835 
+2023-10-14 13:24:33.156517: Pseudo dice [0.974, 0.8899, 0.8993, 0.8955, 0.8543, 0.6984] 
+2023-10-14 13:24:33.156605: Epoch time: 345.22 s 
+2023-10-14 13:24:34.406686:  
+2023-10-14 13:24:34.406871: Epoch 598 
+2023-10-14 13:24:34.407012: Current learning rate: 0.0044 
+2023-10-14 13:30:19.651484: train_loss -0.7959 
+2023-10-14 13:30:19.651639: val_loss -0.6565 
+2023-10-14 13:30:19.651747: Pseudo dice [0.9677, 0.8802, 0.9159, 0.8923, 0.8631, 0.689] 
+2023-10-14 13:30:19.651837: Epoch time: 345.25 s 
+2023-10-14 13:30:20.914225:  
+2023-10-14 13:30:20.914349: Epoch 599 
+2023-10-14 13:30:20.914452: Current learning rate: 0.00439 
+2023-10-14 13:36:06.187173: train_loss -0.7701 
+2023-10-14 13:36:06.187314: val_loss -0.7048 
+2023-10-14 13:36:06.187428: Pseudo dice [0.9732, 0.8707, 0.9143, 0.8791, 0.8855, 0.6655] 
+2023-10-14 13:36:06.187528: Epoch time: 345.27 s 
+2023-10-14 13:36:09.221221:  
+2023-10-14 13:36:09.221345: Epoch 600 
+2023-10-14 13:36:09.221469: Current learning rate: 0.00438 
+2023-10-14 13:41:54.536702: train_loss -0.758 
+2023-10-14 13:41:54.562621: val_loss -0.6929 
+2023-10-14 13:41:54.562764: Pseudo dice [0.9703, 0.8739, 0.8939, 0.904, 0.9011, 0.673] 
+2023-10-14 13:41:54.562858: Epoch time: 345.32 s 
+2023-10-14 13:41:55.812133:  
+2023-10-14 13:41:55.812340: Epoch 601 
+2023-10-14 13:41:55.812508: Current learning rate: 0.00437 
+2023-10-14 13:47:41.026841: train_loss -0.7647 
+2023-10-14 13:47:41.027003: val_loss -0.7232 
+2023-10-14 13:47:41.027112: Pseudo dice [0.9738, 0.8782, 0.9069, 0.8824, 0.8664, 0.6583] 
+2023-10-14 13:47:41.027201: Epoch time: 345.22 s 
+2023-10-14 13:47:42.263849:  
+2023-10-14 13:47:42.264035: Epoch 602 
+2023-10-14 13:47:42.264179: Current learning rate: 0.00436 
+2023-10-14 13:53:27.466546: train_loss -0.7432 
+2023-10-14 13:53:27.466691: val_loss -0.7264 
+2023-10-14 13:53:27.466818: Pseudo dice [0.972, 0.8614, 0.9016, 0.8538, 0.8984, 0.6688] 
+2023-10-14 13:53:27.466918: Epoch time: 345.2 s 
+2023-10-14 13:53:28.878375:  
+2023-10-14 13:53:28.878511: Epoch 603 
+2023-10-14 13:53:28.878630: Current learning rate: 0.00435 
+2023-10-14 13:59:14.072809: train_loss -0.762 
+2023-10-14 13:59:14.072963: val_loss -0.691 
+2023-10-14 13:59:14.073070: Pseudo dice [0.9733, 0.8664, 0.911, 0.8664, 0.9055, 0.6559] 
+2023-10-14 13:59:14.073159: Epoch time: 345.2 s 
+2023-10-14 13:59:15.308695:  
+2023-10-14 13:59:15.308889: Epoch 604 
+2023-10-14 13:59:15.309061: Current learning rate: 0.00434 
+2023-10-14 14:05:00.432752: train_loss -0.7551 
+2023-10-14 14:05:00.432909: val_loss -0.6737 
+2023-10-14 14:05:00.433038: Pseudo dice [0.9732, 0.8841, 0.8865, 0.8843, 0.8986, 0.6903] 
+2023-10-14 14:05:00.433140: Epoch time: 345.12 s 
+2023-10-14 14:05:01.668171:  
+2023-10-14 14:05:01.668294: Epoch 605 
+2023-10-14 14:05:01.668412: Current learning rate: 0.00433 
+2023-10-14 14:10:46.701825: train_loss -0.7394 
+2023-10-14 14:10:46.701970: val_loss -0.6639 
+2023-10-14 14:10:46.702087: Pseudo dice [0.9726, 0.8663, 0.9001, 0.882, 0.81, 0.6324] 
+2023-10-14 14:10:46.702175: Epoch time: 345.03 s 
+2023-10-14 14:10:47.937988:  
+2023-10-14 14:10:47.938114: Epoch 606 
+2023-10-14 14:10:47.938219: Current learning rate: 0.00432 
+2023-10-14 14:16:33.035287: train_loss -0.7721 
+2023-10-14 14:16:33.035452: val_loss -0.7028 
+2023-10-14 14:16:33.035562: Pseudo dice [0.9675, 0.8681, 0.9084, 0.8931, 0.8772, 0.7123] 
+2023-10-14 14:16:33.035650: Epoch time: 345.1 s 
+2023-10-14 14:16:34.287815:  
+2023-10-14 14:16:34.287935: Epoch 607 
+2023-10-14 14:16:34.288050: Current learning rate: 0.00431 
+2023-10-14 14:22:19.361040: train_loss -0.7542 
+2023-10-14 14:22:19.361205: val_loss -0.6814 
+2023-10-14 14:22:19.361313: Pseudo dice [0.9678, 0.8706, 0.8949, 0.8421, 0.8567, 0.639] 
+2023-10-14 14:22:19.361403: Epoch time: 345.07 s 
+2023-10-14 14:22:20.597335:  
+2023-10-14 14:22:20.597454: Epoch 608 
+2023-10-14 14:22:20.597558: Current learning rate: 0.0043 
+2023-10-14 14:28:05.865697: train_loss -0.7874 
+2023-10-14 14:28:05.865849: val_loss -0.689 
+2023-10-14 14:28:05.865958: Pseudo dice [0.9689, 0.8736, 0.8951, 0.8791, 0.9071, 0.6724] 
+2023-10-14 14:28:05.866059: Epoch time: 345.27 s 
+2023-10-14 14:28:07.096414:  
+2023-10-14 14:28:07.096534: Epoch 609 
+2023-10-14 14:28:07.096638: Current learning rate: 0.00429 
+2023-10-14 14:33:52.213276: train_loss -0.7678 
+2023-10-14 14:33:52.213439: val_loss -0.6511 
+2023-10-14 14:33:52.213578: Pseudo dice [0.9682, 0.8618, 0.8946, 0.8588, 0.852, 0.6899] 
+2023-10-14 14:33:52.213680: Epoch time: 345.12 s 
+2023-10-14 14:33:53.617540:  
+2023-10-14 14:33:53.617666: Epoch 610 
+2023-10-14 14:33:53.617785: Current learning rate: 0.00429 
+2023-10-14 14:39:38.881116: train_loss -0.7615 
+2023-10-14 14:39:38.881281: val_loss -0.685 
+2023-10-14 14:39:38.881410: Pseudo dice [0.9724, 0.875, 0.9019, 0.9056, 0.913, 0.686] 
+2023-10-14 14:39:38.881510: Epoch time: 345.26 s 
+2023-10-14 14:39:40.115401:  
+2023-10-14 14:39:40.115527: Epoch 611 
+2023-10-14 14:39:40.115643: Current learning rate: 0.00428 
+2023-10-14 14:45:25.545999: train_loss -0.7808 
+2023-10-14 14:45:25.546160: val_loss -0.7257 
+2023-10-14 14:45:25.546287: Pseudo dice [0.973, 0.8702, 0.9215, 0.8727, 0.8979, 0.638] 
+2023-10-14 14:45:25.546388: Epoch time: 345.43 s 
+2023-10-14 14:45:26.776140:  
+2023-10-14 14:45:26.776267: Epoch 612 
+2023-10-14 14:45:26.776371: Current learning rate: 0.00427 
+2023-10-14 14:51:12.205135: train_loss -0.779 
+2023-10-14 14:51:12.205280: val_loss -0.7399 
+2023-10-14 14:51:12.205399: Pseudo dice [0.9719, 0.8736, 0.9185, 0.8698, 0.8706, 0.7204] 
+2023-10-14 14:51:12.205492: Epoch time: 345.43 s 
+2023-10-14 14:51:13.434371:  
+2023-10-14 14:51:13.434646: Epoch 613 
+2023-10-14 14:51:13.434908: Current learning rate: 0.00426 
+2023-10-14 14:56:58.723003: train_loss -0.755 
+2023-10-14 14:56:58.723155: val_loss -0.687 
+2023-10-14 14:56:58.723260: Pseudo dice [0.9701, 0.8712, 0.9066, 0.897, 0.8999, 0.6792] 
+2023-10-14 14:56:58.723349: Epoch time: 345.29 s 
+2023-10-14 14:56:59.948792:  
+2023-10-14 14:56:59.948977: Epoch 614 
+2023-10-14 14:56:59.949131: Current learning rate: 0.00425 
+2023-10-14 15:02:45.222680: train_loss -0.7638 
+2023-10-14 15:02:45.222840: val_loss -0.678 
+2023-10-14 15:02:45.222947: Pseudo dice [0.9662, 0.8619, 0.9091, 0.878, 0.9066, 0.6529] 
+2023-10-14 15:02:45.223037: Epoch time: 345.27 s 
+2023-10-14 15:02:46.451826:  
+2023-10-14 15:02:46.452006: Epoch 615 
+2023-10-14 15:02:46.452170: Current learning rate: 0.00424 
+2023-10-14 15:08:31.715137: train_loss -0.7744 
+2023-10-14 15:08:31.715300: val_loss -0.649 
+2023-10-14 15:08:31.715408: Pseudo dice [0.9734, 0.8757, 0.9048, 0.8251, 0.8372, 0.6787] 
+2023-10-14 15:08:31.715497: Epoch time: 345.26 s 
+2023-10-14 15:08:32.946714:  
+2023-10-14 15:08:32.946826: Epoch 616 
+2023-10-14 15:08:32.946929: Current learning rate: 0.00423 
+2023-10-14 15:14:18.370008: train_loss -0.7348 
+2023-10-14 15:14:18.370180: val_loss -0.7027 
+2023-10-14 15:14:18.370288: Pseudo dice [0.9722, 0.8598, 0.9081, 0.9042, 0.8572, 0.6507] 
+2023-10-14 15:14:18.370378: Epoch time: 345.42 s 
+2023-10-14 15:14:19.774088:  
+2023-10-14 15:14:19.774227: Epoch 617 
+2023-10-14 15:14:19.774343: Current learning rate: 0.00422 
+2023-10-14 15:20:05.151113: train_loss -0.7712 
+2023-10-14 15:20:05.151275: val_loss -0.6953 
+2023-10-14 15:20:05.151402: Pseudo dice [0.9739, 0.8773, 0.9011, 0.8979, 0.8697, 0.6598] 
+2023-10-14 15:20:05.151501: Epoch time: 345.38 s 
+2023-10-14 15:20:06.379423:  
+2023-10-14 15:20:06.379615: Epoch 618 
+2023-10-14 15:20:06.379761: Current learning rate: 0.00421 
+2023-10-14 15:25:51.732977: train_loss -0.7603 
+2023-10-14 15:25:51.733143: val_loss -0.6633 
+2023-10-14 15:25:51.733252: Pseudo dice [0.973, 0.8762, 0.8955, 0.8469, 0.8547, 0.6434] 
+2023-10-14 15:25:51.733340: Epoch time: 345.35 s 
+2023-10-14 15:25:52.956948:  
+2023-10-14 15:25:52.957223: Epoch 619 
+2023-10-14 15:25:52.957383: Current learning rate: 0.0042 
+2023-10-14 15:31:38.207267: train_loss -0.7639 
+2023-10-14 15:31:38.207419: val_loss -0.6473 
+2023-10-14 15:31:38.207534: Pseudo dice [0.9733, 0.8753, 0.9119, 0.9126, 0.8255, 0.6438] 
+2023-10-14 15:31:38.207627: Epoch time: 345.25 s 
+2023-10-14 15:31:39.433022:  
+2023-10-14 15:31:39.433202: Epoch 620 
+2023-10-14 15:31:39.433369: Current learning rate: 0.00419 
+2023-10-14 15:37:24.750193: train_loss -0.7808 
+2023-10-14 15:37:24.750353: val_loss -0.6758 
+2023-10-14 15:37:24.750461: Pseudo dice [0.9724, 0.8676, 0.9047, 0.8751, 0.8983, 0.6504] 
+2023-10-14 15:37:24.750569: Epoch time: 345.32 s 
+2023-10-14 15:37:26.024541:  
+2023-10-14 15:37:26.024662: Epoch 621 
+2023-10-14 15:37:26.024769: Current learning rate: 0.00418 
+2023-10-14 15:43:11.284546: train_loss -0.7552 
+2023-10-14 15:43:11.284713: val_loss -0.6358 
+2023-10-14 15:43:11.284839: Pseudo dice [0.9726, 0.8642, 0.8908, 0.8684, 0.9013, 0.6421] 
+2023-10-14 15:43:11.284938: Epoch time: 345.26 s 
+2023-10-14 15:43:12.513359:  
+2023-10-14 15:43:12.513530: Epoch 622 
+2023-10-14 15:43:12.513690: Current learning rate: 0.00417 
+2023-10-14 15:48:57.758520: train_loss -0.771 
+2023-10-14 15:48:57.758674: val_loss -0.6537 
+2023-10-14 15:48:57.758806: Pseudo dice [0.9727, 0.8635, 0.9034, 0.8372, 0.8479, 0.6164] 
+2023-10-14 15:48:57.758916: Epoch time: 345.25 s 
+2023-10-14 15:48:58.997432:  
+2023-10-14 15:48:58.997557: Epoch 623 
+2023-10-14 15:48:58.997694: Current learning rate: 0.00416 
+2023-10-14 15:54:44.139509: train_loss -0.792 
+2023-10-14 15:54:44.139672: val_loss -0.6581 
+2023-10-14 15:54:44.139781: Pseudo dice [0.9724, 0.8767, 0.9003, 0.8994, 0.8994, 0.6691] 
+2023-10-14 15:54:44.139871: Epoch time: 345.14 s 
+2023-10-14 15:54:45.546206:  
+2023-10-14 15:54:45.546422: Epoch 624 
+2023-10-14 15:54:45.546611: Current learning rate: 0.00415 
+2023-10-14 16:00:30.779316: train_loss -0.7774 
+2023-10-14 16:00:30.779478: val_loss -0.6956 
+2023-10-14 16:00:30.779586: Pseudo dice [0.9714, 0.875, 0.9005, 0.8732, 0.8709, 0.6667] 
+2023-10-14 16:00:30.779676: Epoch time: 345.23 s 
+2023-10-14 16:00:32.006842:  
+2023-10-14 16:00:32.006967: Epoch 625 
+2023-10-14 16:00:32.007073: Current learning rate: 0.00414 
+2023-10-14 16:06:17.287866: train_loss -0.7442 
+2023-10-14 16:06:17.288028: val_loss -0.6678 
+2023-10-14 16:06:17.288138: Pseudo dice [0.9715, 0.8641, 0.9098, 0.7926, 0.8992, 0.6277] 
+2023-10-14 16:06:17.288226: Epoch time: 345.28 s 
+2023-10-14 16:06:18.518711:  
+2023-10-14 16:06:18.518934: Epoch 626 
+2023-10-14 16:06:18.519116: Current learning rate: 0.00413 
+2023-10-14 16:12:03.838896: train_loss -0.7532 
+2023-10-14 16:12:03.839072: val_loss -0.6503 
+2023-10-14 16:12:03.839226: Pseudo dice [0.9714, 0.8681, 0.9088, 0.8553, 0.8588, 0.6468] 
+2023-10-14 16:12:03.839345: Epoch time: 345.32 s 
+2023-10-14 16:12:05.069823:  
+2023-10-14 16:12:05.069941: Epoch 627 
+2023-10-14 16:12:05.070088: Current learning rate: 0.00412 
+2023-10-14 16:17:50.271927: train_loss -0.7608 
+2023-10-14 16:17:50.272089: val_loss -0.7213 
+2023-10-14 16:17:50.272195: Pseudo dice [0.9718, 0.8799, 0.9169, 0.8719, 0.8759, 0.6543] 
+2023-10-14 16:17:50.272283: Epoch time: 345.2 s 
+2023-10-14 16:17:51.503624:  
+2023-10-14 16:17:51.503731: Epoch 628 
+2023-10-14 16:17:51.503853: Current learning rate: 0.00411 
+2023-10-14 16:23:36.594076: train_loss -0.7626 
+2023-10-14 16:23:36.594239: val_loss -0.6653 
+2023-10-14 16:23:36.594346: Pseudo dice [0.9741, 0.8788, 0.9138, 0.8925, 0.8815, 0.6214] 
+2023-10-14 16:23:36.594435: Epoch time: 345.09 s 
+2023-10-14 16:23:37.819789:  
+2023-10-14 16:23:37.819902: Epoch 629 
+2023-10-14 16:23:37.820030: Current learning rate: 0.0041 
+2023-10-14 16:29:22.992501: train_loss -0.7483 
+2023-10-14 16:29:22.992672: val_loss -0.6787 
+2023-10-14 16:29:22.992779: Pseudo dice [0.9755, 0.8821, 0.8981, 0.8838, 0.8583, 0.7084] 
+2023-10-14 16:29:22.992866: Epoch time: 345.17 s 
+2023-10-14 16:29:24.221108:  
+2023-10-14 16:29:24.221229: Epoch 630 
+2023-10-14 16:29:24.221332: Current learning rate: 0.00409 
+2023-10-14 16:35:09.353429: train_loss -0.7734 
+2023-10-14 16:35:09.353594: val_loss -0.6573 
+2023-10-14 16:35:09.353700: Pseudo dice [0.9677, 0.8737, 0.8995, 0.9009, 0.9125, 0.6357] 
+2023-10-14 16:35:09.353797: Epoch time: 345.13 s 
+2023-10-14 16:35:10.758466:  
+2023-10-14 16:35:10.758607: Epoch 631 
+2023-10-14 16:35:10.758712: Current learning rate: 0.00408 
+2023-10-14 16:40:55.876935: train_loss -0.7641 
+2023-10-14 16:40:55.877102: val_loss -0.6507 
+2023-10-14 16:40:55.877269: Pseudo dice [0.9708, 0.8651, 0.8982, 0.8435, 0.911, 0.6598] 
+2023-10-14 16:40:55.877389: Epoch time: 345.12 s 
+2023-10-14 16:40:57.107229:  
+2023-10-14 16:40:57.107358: Epoch 632 
+2023-10-14 16:40:57.107470: Current learning rate: 0.00407 
+2023-10-14 16:46:42.215181: train_loss -0.7705 
+2023-10-14 16:46:42.215334: val_loss -0.6972 
+2023-10-14 16:46:42.215441: Pseudo dice [0.971, 0.8804, 0.8964, 0.8674, 0.9125, 0.6708] 
+2023-10-14 16:46:42.215530: Epoch time: 345.11 s 
+2023-10-14 16:46:43.440230:  
+2023-10-14 16:46:43.440356: Epoch 633 
+2023-10-14 16:46:43.440461: Current learning rate: 0.00406 
+2023-10-14 16:52:28.556360: train_loss -0.7688 
+2023-10-14 16:52:28.556517: val_loss -0.6371 
+2023-10-14 16:52:28.556624: Pseudo dice [0.9736, 0.8787, 0.8855, 0.893, 0.8857, 0.6842] 
+2023-10-14 16:52:28.556714: Epoch time: 345.12 s 
+2023-10-14 16:52:29.781759:  
+2023-10-14 16:52:29.781941: Epoch 634 
+2023-10-14 16:52:29.782093: Current learning rate: 0.00405 
+2023-10-14 16:58:14.835334: train_loss -0.7634 
+2023-10-14 16:58:14.835504: val_loss -0.7116 
+2023-10-14 16:58:14.835631: Pseudo dice [0.9733, 0.8843, 0.8985, 0.8945, 0.8925, 0.6721] 
+2023-10-14 16:58:14.835730: Epoch time: 345.05 s 
+2023-10-14 16:58:16.061664:  
+2023-10-14 16:58:16.061890: Epoch 635 
+2023-10-14 16:58:16.062093: Current learning rate: 0.00404 
+2023-10-14 17:04:01.232498: train_loss -0.7566 
+2023-10-14 17:04:01.232659: val_loss -0.6579 
+2023-10-14 17:04:01.232765: Pseudo dice [0.9712, 0.8645, 0.8988, 0.9094, 0.9212, 0.6647] 
+2023-10-14 17:04:01.232853: Epoch time: 345.17 s 
+2023-10-14 17:04:02.460922:  
+2023-10-14 17:04:02.461122: Epoch 636 
+2023-10-14 17:04:02.461257: Current learning rate: 0.00403 
+2023-10-14 17:09:47.632440: train_loss -0.7576 
+2023-10-14 17:09:47.632618: val_loss -0.6736 
+2023-10-14 17:09:47.632725: Pseudo dice [0.9737, 0.8742, 0.9068, 0.8537, 0.9097, 0.671] 
+2023-10-14 17:09:47.632813: Epoch time: 345.17 s 
+2023-10-14 17:09:49.027920:  
+2023-10-14 17:09:49.028105: Epoch 637 
+2023-10-14 17:09:49.028272: Current learning rate: 0.00402 
+2023-10-14 17:15:34.278707: train_loss -0.7735 
+2023-10-14 17:15:34.278879: val_loss -0.6382 
+2023-10-14 17:15:34.278991: Pseudo dice [0.9737, 0.8726, 0.9154, 0.8873, 0.8475, 0.6396] 
+2023-10-14 17:15:34.279081: Epoch time: 345.25 s 
+2023-10-14 17:15:35.508776:  
+2023-10-14 17:15:35.508887: Epoch 638 
+2023-10-14 17:15:35.508996: Current learning rate: 0.00401 
+2023-10-14 17:21:20.734416: train_loss -0.7648 
+2023-10-14 17:21:20.734588: val_loss -0.7062 
+2023-10-14 17:21:20.734701: Pseudo dice [0.9703, 0.8699, 0.9038, 0.905, 0.9083, 0.6299] 
+2023-10-14 17:21:20.734789: Epoch time: 345.23 s 
+2023-10-14 17:21:21.964436:  
+2023-10-14 17:21:21.964605: Epoch 639 
+2023-10-14 17:21:21.964769: Current learning rate: 0.004 
+2023-10-14 17:27:07.221666: train_loss -0.7648 
+2023-10-14 17:27:07.221833: val_loss -0.6922 
+2023-10-14 17:27:07.221941: Pseudo dice [0.9724, 0.8682, 0.9092, 0.8575, 0.9191, 0.6126] 
+2023-10-14 17:27:07.222028: Epoch time: 345.26 s 
+2023-10-14 17:27:08.456499:  
+2023-10-14 17:27:08.456623: Epoch 640 
+2023-10-14 17:27:08.456729: Current learning rate: 0.00399 
+2023-10-14 17:32:53.649395: train_loss -0.7576 
+2023-10-14 17:32:53.649562: val_loss -0.6936 
+2023-10-14 17:32:53.649688: Pseudo dice [0.9735, 0.8827, 0.9038, 0.8838, 0.8844, 0.6632] 
+2023-10-14 17:32:53.649786: Epoch time: 345.19 s 
+2023-10-14 17:32:54.871769:  
+2023-10-14 17:32:54.871948: Epoch 641 
+2023-10-14 17:32:54.872139: Current learning rate: 0.00398 
+2023-10-14 17:38:39.993190: train_loss -0.752 
+2023-10-14 17:38:39.993353: val_loss -0.656 
+2023-10-14 17:38:39.993459: Pseudo dice [0.9706, 0.873, 0.9123, 0.9178, 0.9002, 0.678] 
+2023-10-14 17:38:39.993546: Epoch time: 345.12 s 
+2023-10-14 17:38:41.223131:  
+2023-10-14 17:38:41.223406: Epoch 642 
+2023-10-14 17:38:41.223625: Current learning rate: 0.00397 
+2023-10-14 17:44:26.419851: train_loss -0.7631 
+2023-10-14 17:44:26.420016: val_loss -0.6677 
+2023-10-14 17:44:26.420125: Pseudo dice [0.9716, 0.8758, 0.9048, 0.8369, 0.8966, 0.5712] 
+2023-10-14 17:44:26.420213: Epoch time: 345.2 s 
+2023-10-14 17:44:27.645399:  
+2023-10-14 17:44:27.645557: Epoch 643 
+2023-10-14 17:44:27.645711: Current learning rate: 0.00396 
+2023-10-14 17:50:12.877445: train_loss -0.7848 
+2023-10-14 17:50:12.877609: val_loss -0.6909 
+2023-10-14 17:50:12.877716: Pseudo dice [0.9734, 0.8767, 0.8875, 0.9047, 0.8963, 0.6348] 
+2023-10-14 17:50:12.877804: Epoch time: 345.23 s 
+2023-10-14 17:50:14.322228:  
+2023-10-14 17:50:14.322353: Epoch 644 
+2023-10-14 17:50:14.322461: Current learning rate: 0.00395 
+2023-10-14 17:55:59.667900: train_loss -0.7964 
+2023-10-14 17:55:59.668062: val_loss -0.6591 
+2023-10-14 17:55:59.668168: Pseudo dice [0.9715, 0.8696, 0.9139, 0.8615, 0.8939, 0.6804] 
+2023-10-14 17:55:59.668256: Epoch time: 345.35 s 
+2023-10-14 17:56:00.962695:  
+2023-10-14 17:56:00.963004: Epoch 645 
+2023-10-14 17:56:00.963206: Current learning rate: 0.00394 
+2023-10-14 18:01:46.228445: train_loss -0.7786 
+2023-10-14 18:01:46.272744: val_loss -0.6726 
+2023-10-14 18:01:46.273038: Pseudo dice [0.9693, 0.8762, 0.9047, 0.8573, 0.889, 0.665] 
+2023-10-14 18:01:46.273135: Epoch time: 345.27 s 
+2023-10-14 18:01:47.589313:  
+2023-10-14 18:01:47.589441: Epoch 646 
+2023-10-14 18:01:47.589548: Current learning rate: 0.00393 
+2023-10-14 18:07:32.782410: train_loss -0.7808 
+2023-10-14 18:07:32.806095: val_loss -0.665 
+2023-10-14 18:07:32.806214: Pseudo dice [0.9722, 0.8793, 0.9051, 0.8769, 0.888, 0.6735] 
+2023-10-14 18:07:32.806305: Epoch time: 345.19 s 
+2023-10-14 18:07:34.099209:  
+2023-10-14 18:07:34.099390: Epoch 647 
+2023-10-14 18:07:34.099658: Current learning rate: 0.00392 
+2023-10-14 18:13:19.320418: train_loss -0.7735 
+2023-10-14 18:13:19.338535: val_loss -0.72 
+2023-10-14 18:13:19.338682: Pseudo dice [0.9723, 0.8818, 0.9029, 0.9075, 0.9213, 0.6385] 
+2023-10-14 18:13:19.338782: Epoch time: 345.22 s 
+2023-10-14 18:13:20.618846:  
+2023-10-14 18:13:20.618957: Epoch 648 
+2023-10-14 18:13:20.619078: Current learning rate: 0.00391 
+2023-10-14 18:19:05.833261: train_loss -0.7531 
+2023-10-14 18:19:05.854315: val_loss -0.7067 
+2023-10-14 18:19:05.854431: Pseudo dice [0.9717, 0.8727, 0.9134, 0.8782, 0.9091, 0.6161] 
+2023-10-14 18:19:05.854533: Epoch time: 345.22 s 
+2023-10-14 18:19:07.083223:  
+2023-10-14 18:19:07.083346: Epoch 649 
+2023-10-14 18:19:07.083539: Current learning rate: 0.0039 
+2023-10-14 18:24:52.414732: train_loss -0.7412 
+2023-10-14 18:24:52.414915: val_loss -0.6814 
+2023-10-14 18:24:52.415040: Pseudo dice [0.9731, 0.8838, 0.9099, 0.8508, 0.9159, 0.6808] 
+2023-10-14 18:24:52.415140: Epoch time: 345.33 s 
+2023-10-14 18:24:55.490115:  
+2023-10-14 18:24:55.490302: Epoch 650 
+2023-10-14 18:24:55.490507: Current learning rate: 0.00389 
+2023-10-14 18:30:40.699064: train_loss -0.7826 
+2023-10-14 18:30:40.699532: val_loss -0.6758 
+2023-10-14 18:30:40.699661: Pseudo dice [0.9737, 0.868, 0.8963, 0.8625, 0.8257, 0.6266] 
+2023-10-14 18:30:40.699759: Epoch time: 345.21 s 
+2023-10-14 18:30:42.128822:  
+2023-10-14 18:30:42.129018: Epoch 651 
+2023-10-14 18:30:42.129209: Current learning rate: 0.00388 
+2023-10-14 18:36:27.414385: train_loss -0.7857 
+2023-10-14 18:36:27.414559: val_loss -0.6537 
+2023-10-14 18:36:27.414689: Pseudo dice [0.9745, 0.8709, 0.9013, 0.8435, 0.87, 0.6651] 
+2023-10-14 18:36:27.414789: Epoch time: 345.29 s 
+2023-10-14 18:36:28.638571:  
+2023-10-14 18:36:28.638687: Epoch 652 
+2023-10-14 18:36:28.638800: Current learning rate: 0.00387 
+2023-10-14 18:42:13.898769: train_loss -0.7566 
+2023-10-14 18:42:13.898952: val_loss -0.6672 
+2023-10-14 18:42:13.899078: Pseudo dice [0.9721, 0.8805, 0.905, 0.8985, 0.8761, 0.6544] 
+2023-10-14 18:42:13.899177: Epoch time: 345.26 s 
+2023-10-14 18:42:15.124807:  
+2023-10-14 18:42:15.125002: Epoch 653 
+2023-10-14 18:42:15.125160: Current learning rate: 0.00386 
+2023-10-14 18:48:00.439467: train_loss -0.7553 
+2023-10-14 18:48:00.439612: val_loss -0.6673 
+2023-10-14 18:48:00.439730: Pseudo dice [0.9724, 0.8704, 0.8984, 0.8762, 0.8767, 0.6696] 
+2023-10-14 18:48:00.439824: Epoch time: 345.32 s 
+2023-10-14 18:48:01.674665:  
+2023-10-14 18:48:01.674914: Epoch 654 
+2023-10-14 18:48:01.675084: Current learning rate: 0.00385 
+2023-10-14 18:53:46.911062: train_loss -0.7787 
+2023-10-14 18:53:46.911207: val_loss -0.6667 
+2023-10-14 18:53:46.911326: Pseudo dice [0.9731, 0.8704, 0.9041, 0.8151, 0.8472, 0.6625] 
+2023-10-14 18:53:46.911428: Epoch time: 345.24 s 
+2023-10-14 18:53:48.138912:  
+2023-10-14 18:53:48.139034: Epoch 655 
+2023-10-14 18:53:48.139151: Current learning rate: 0.00384 
+2023-10-14 18:59:33.442257: train_loss -0.7725 
+2023-10-14 18:59:33.442420: val_loss -0.6797 
+2023-10-14 18:59:33.442545: Pseudo dice [0.9744, 0.8791, 0.9072, 0.8569, 0.8972, 0.6969] 
+2023-10-14 18:59:33.442636: Epoch time: 345.3 s 
+2023-10-14 18:59:34.668129:  
+2023-10-14 18:59:34.668350: Epoch 656 
+2023-10-14 18:59:34.668544: Current learning rate: 0.00383 
+2023-10-14 19:05:19.916166: train_loss -0.7891 
+2023-10-14 19:05:19.916329: val_loss -0.6891 
+2023-10-14 19:05:19.916438: Pseudo dice [0.9716, 0.8722, 0.8968, 0.8606, 0.8814, 0.6483] 
+2023-10-14 19:05:19.916527: Epoch time: 345.25 s 
+2023-10-14 19:05:21.143101:  
+2023-10-14 19:05:21.143220: Epoch 657 
+2023-10-14 19:05:21.143395: Current learning rate: 0.00382 
+2023-10-14 19:11:06.492922: train_loss -0.748 
+2023-10-14 19:11:06.493066: val_loss -0.6908 
+2023-10-14 19:11:06.493173: Pseudo dice [0.9607, 0.8645, 0.9061, 0.8701, 0.8748, 0.654] 
+2023-10-14 19:11:06.493262: Epoch time: 345.35 s 
+2023-10-14 19:11:08.721048:  
+2023-10-14 19:11:08.721277: Epoch 658 
+2023-10-14 19:11:08.721462: Current learning rate: 0.00381 

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_10_16_11_52_25.txt

@@ -0,0 +1,887 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_fullres
+ {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset720_TSPrime', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], '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': 1620.0, 'mean': -38.229164123535156, 'median': -54.0, 'min': -1000.0, 'percentile_00_5': -941.0, 'percentile_99_5': 897.0, 'std': 192.37086486816406}}} 
+ 
+2023-10-16 11:52:36.021841: unpacking dataset... 
+2023-10-16 11:52:39.807831: unpacking done... 
+2023-10-16 11:52:39.809407: do_dummy_2d_data_aug: False 
+2023-10-16 11:52:39.809969: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset720_TSPrime/splits_final.json 
+2023-10-16 11:52:39.826854: The split file contains 5 splits. 
+2023-10-16 11:52:39.826942: Desired fold for training: 0 
+2023-10-16 11:52:39.827013: This split has 48 training and 12 validation cases. 
+2023-10-16 11:53:10.871201: Unable to plot network architecture: 
+2023-10-16 11:53:10.871277: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH 
+2023-10-16 11:53:10.957982:  
+2023-10-16 11:53:10.958041: Epoch 650 
+2023-10-16 11:53:10.958157: Current learning rate: 0.00389 
+2023-10-16 12:01:33.061845: train_loss -0.7657 
+2023-10-16 12:01:33.126682: val_loss -0.6653 
+2023-10-16 12:01:33.126856: Pseudo dice [0.9702, 0.8726, 0.8798, 0.9072, 0.9047, 0.6282] 
+2023-10-16 12:01:33.126974: Epoch time: 502.1 s 
+2023-10-16 12:01:35.984811:  
+2023-10-16 12:01:35.985008: Epoch 651 
+2023-10-16 12:01:35.985184: Current learning rate: 0.00388 
+2023-10-16 12:07:22.139421: train_loss -0.773 
+2023-10-16 12:07:22.139565: val_loss -0.6575 
+2023-10-16 12:07:22.139684: Pseudo dice [0.9719, 0.8755, 0.8979, 0.8985, 0.9027, 0.6282] 
+2023-10-16 12:07:22.139770: Epoch time: 346.16 s 
+2023-10-16 12:07:23.367573:  
+2023-10-16 12:07:23.367742: Epoch 652 
+2023-10-16 12:07:23.367918: Current learning rate: 0.00387 
+2023-10-16 12:13:09.660832: train_loss -0.7574 
+2023-10-16 12:13:09.660987: val_loss -0.68 
+2023-10-16 12:13:09.661093: Pseudo dice [0.9742, 0.8747, 0.9106, 0.8856, 0.8901, 0.6318] 
+2023-10-16 12:13:09.661180: Epoch time: 346.29 s 
+2023-10-16 12:13:10.920336:  
+2023-10-16 12:13:10.920450: Epoch 653 
+2023-10-16 12:13:10.920563: Current learning rate: 0.00386 
+2023-10-16 12:18:57.220682: train_loss -0.7666 
+2023-10-16 12:18:57.220850: val_loss -0.6945 
+2023-10-16 12:18:57.220954: Pseudo dice [0.9703, 0.8777, 0.9185, 0.8674, 0.9026, 0.646] 
+2023-10-16 12:18:57.221044: Epoch time: 346.3 s 
+2023-10-16 12:18:58.643119:  
+2023-10-16 12:18:58.643296: Epoch 654 
+2023-10-16 12:18:58.643456: Current learning rate: 0.00385 
+2023-10-16 12:24:45.117256: train_loss -0.7659 
+2023-10-16 12:24:45.117415: val_loss -0.7027 
+2023-10-16 12:24:45.117518: Pseudo dice [0.9738, 0.8922, 0.9142, 0.8803, 0.8772, 0.685] 
+2023-10-16 12:24:45.117602: Epoch time: 346.47 s 
+2023-10-16 12:24:46.332685:  
+2023-10-16 12:24:46.332862: Epoch 655 
+2023-10-16 12:24:46.333028: Current learning rate: 0.00384 
+2023-10-16 12:30:32.836236: train_loss -0.7869 
+2023-10-16 12:30:32.836398: val_loss -0.6938 
+2023-10-16 12:30:32.836502: Pseudo dice [0.9713, 0.8664, 0.8991, 0.8783, 0.8994, 0.6656] 
+2023-10-16 12:30:32.836591: Epoch time: 346.5 s 
+2023-10-16 12:30:34.058037:  
+2023-10-16 12:30:34.058140: Epoch 656 
+2023-10-16 12:30:34.058258: Current learning rate: 0.00383 
+2023-10-16 12:36:20.418155: train_loss -0.7692 
+2023-10-16 12:36:20.418319: val_loss -0.6525 
+2023-10-16 12:36:20.418422: Pseudo dice [0.9751, 0.8812, 0.9, 0.924, 0.8911, 0.6772] 
+2023-10-16 12:36:20.418515: Epoch time: 346.36 s 
+2023-10-16 12:36:21.640579:  
+2023-10-16 12:36:21.640756: Epoch 657 
+2023-10-16 12:36:21.640867: Current learning rate: 0.00382 
+2023-10-16 12:42:08.193615: train_loss -0.7616 
+2023-10-16 12:42:08.193762: val_loss -0.6783 
+2023-10-16 12:42:08.193863: Pseudo dice [0.9695, 0.8775, 0.9076, 0.8641, 0.8654, 0.647] 
+2023-10-16 12:42:08.193949: Epoch time: 346.55 s 
+2023-10-16 12:42:09.417391:  
+2023-10-16 12:42:09.417494: Epoch 658 
+2023-10-16 12:42:09.417598: Current learning rate: 0.00381 
+2023-10-16 12:47:55.878795: train_loss -0.75 
+2023-10-16 12:47:55.878945: val_loss -0.7167 
+2023-10-16 12:47:55.879050: Pseudo dice [0.971, 0.8696, 0.8976, 0.8648, 0.9152, 0.6271] 
+2023-10-16 12:47:55.879137: Epoch time: 346.46 s 
+2023-10-16 12:47:57.113813:  
+2023-10-16 12:47:57.113915: Epoch 659 
+2023-10-16 12:47:57.114023: Current learning rate: 0.0038 
+2023-10-16 12:53:43.509501: train_loss -0.7638 
+2023-10-16 12:53:43.509643: val_loss -0.6401 
+2023-10-16 12:53:43.509757: Pseudo dice [0.9735, 0.8785, 0.9114, 0.8061, 0.8515, 0.6451] 
+2023-10-16 12:53:43.509848: Epoch time: 346.4 s 
+2023-10-16 12:53:44.733188:  
+2023-10-16 12:53:44.733356: Epoch 660 
+2023-10-16 12:53:44.733542: Current learning rate: 0.00379 
+2023-10-16 12:59:31.136624: train_loss -0.7907 
+2023-10-16 12:59:31.136807: val_loss -0.6695 
+2023-10-16 12:59:31.136927: Pseudo dice [0.9751, 0.8736, 0.9003, 0.8785, 0.9003, 0.6579] 
+2023-10-16 12:59:31.137013: Epoch time: 346.4 s 
+2023-10-16 12:59:32.528379:  
+2023-10-16 12:59:32.528518: Epoch 661 
+2023-10-16 12:59:32.528620: Current learning rate: 0.00378 
+2023-10-16 13:05:19.065362: train_loss -0.7425 
+2023-10-16 13:05:19.065505: val_loss -0.6758 
+2023-10-16 13:05:19.065624: Pseudo dice [0.9717, 0.8713, 0.8965, 0.8627, 0.9088, 0.7013] 
+2023-10-16 13:05:19.065720: Epoch time: 346.54 s 
+2023-10-16 13:05:20.282246:  
+2023-10-16 13:05:20.282420: Epoch 662 
+2023-10-16 13:05:20.282581: Current learning rate: 0.00377 
+2023-10-16 13:11:06.956717: train_loss -0.7424 
+2023-10-16 13:11:06.956860: val_loss -0.656 
+2023-10-16 13:11:06.956975: Pseudo dice [0.9719, 0.8685, 0.8859, 0.8873, 0.9081, 0.6718] 
+2023-10-16 13:11:06.957066: Epoch time: 346.68 s 
+2023-10-16 13:11:08.177942:  
+2023-10-16 13:11:08.178053: Epoch 663 
+2023-10-16 13:11:08.178155: Current learning rate: 0.00376 
+2023-10-16 13:16:55.040390: train_loss -0.7843 
+2023-10-16 13:16:55.040555: val_loss -0.6718 
+2023-10-16 13:16:55.040658: Pseudo dice [0.9739, 0.8831, 0.9084, 0.8481, 0.9041, 0.7072] 
+2023-10-16 13:16:55.040744: Epoch time: 346.86 s 
+2023-10-16 13:16:56.278773:  
+2023-10-16 13:16:56.279001: Epoch 664 
+2023-10-16 13:16:56.279125: Current learning rate: 0.00375 
+2023-10-16 13:22:43.218038: train_loss -0.7699 
+2023-10-16 13:22:43.218202: val_loss -0.6593 
+2023-10-16 13:22:43.218335: Pseudo dice [0.9702, 0.8744, 0.9099, 0.8694, 0.9013, 0.6695] 
+2023-10-16 13:22:43.218433: Epoch time: 346.94 s 
+2023-10-16 13:22:44.439598:  
+2023-10-16 13:22:44.439700: Epoch 665 
+2023-10-16 13:22:44.439813: Current learning rate: 0.00374 
+2023-10-16 13:28:31.328723: train_loss -0.7893 
+2023-10-16 13:28:31.328860: val_loss -0.6805 
+2023-10-16 13:28:31.328979: Pseudo dice [0.9727, 0.8743, 0.9098, 0.8652, 0.8763, 0.6501] 
+2023-10-16 13:28:31.329065: Epoch time: 346.89 s 
+2023-10-16 13:28:32.552882:  
+2023-10-16 13:28:32.553001: Epoch 666 
+2023-10-16 13:28:32.553102: Current learning rate: 0.00373 
+2023-10-16 13:34:19.444855: train_loss -0.8055 
+2023-10-16 13:34:19.444997: val_loss -0.714 
+2023-10-16 13:34:19.445113: Pseudo dice [0.9722, 0.8727, 0.9127, 0.8944, 0.8632, 0.6484] 
+2023-10-16 13:34:19.445198: Epoch time: 346.89 s 
+2023-10-16 13:34:20.666053:  
+2023-10-16 13:34:20.666156: Epoch 667 
+2023-10-16 13:34:20.666275: Current learning rate: 0.00372 
+2023-10-16 13:40:07.663087: train_loss -0.7773 
+2023-10-16 13:40:07.663230: val_loss -0.6389 
+2023-10-16 13:40:07.663347: Pseudo dice [0.9724, 0.8718, 0.8939, 0.8236, 0.8816, 0.6543] 
+2023-10-16 13:40:07.663431: Epoch time: 347.0 s 
+2023-10-16 13:40:09.075113:  
+2023-10-16 13:40:09.075330: Epoch 668 
+2023-10-16 13:40:09.075481: Current learning rate: 0.00371 
+2023-10-16 13:45:55.930044: train_loss -0.7507 
+2023-10-16 13:45:55.930204: val_loss -0.6692 
+2023-10-16 13:45:55.930308: Pseudo dice [0.9716, 0.8709, 0.8977, 0.8937, 0.8658, 0.6703] 
+2023-10-16 13:45:55.930395: Epoch time: 346.86 s 
+2023-10-16 13:45:57.174367:  
+2023-10-16 13:45:57.174487: Epoch 669 
+2023-10-16 13:45:57.174600: Current learning rate: 0.0037 
+2023-10-16 13:51:44.146317: train_loss -0.7798 
+2023-10-16 13:51:44.146501: val_loss -0.6813 
+2023-10-16 13:51:44.146618: Pseudo dice [0.9723, 0.8781, 0.8978, 0.9021, 0.919, 0.7104] 
+2023-10-16 13:51:44.146704: Epoch time: 346.97 s 
+2023-10-16 13:51:45.386658:  
+2023-10-16 13:51:45.386854: Epoch 670 
+2023-10-16 13:51:45.387032: Current learning rate: 0.00369 
+2023-10-16 13:57:32.357284: train_loss -0.7779 
+2023-10-16 13:57:32.357433: val_loss -0.6658 
+2023-10-16 13:57:32.357549: Pseudo dice [0.9709, 0.8515, 0.905, 0.8648, 0.8305, 0.6363] 
+2023-10-16 13:57:32.357636: Epoch time: 346.97 s 
+2023-10-16 13:57:33.598728:  
+2023-10-16 13:57:33.598853: Epoch 671 
+2023-10-16 13:57:33.598966: Current learning rate: 0.00368 
+2023-10-16 14:03:20.613798: train_loss -0.7601 
+2023-10-16 14:03:20.613941: val_loss -0.6489 
+2023-10-16 14:03:20.614055: Pseudo dice [0.975, 0.8824, 0.899, 0.8721, 0.8874, 0.65] 
+2023-10-16 14:03:20.614146: Epoch time: 347.02 s 
+2023-10-16 14:03:21.852867:  
+2023-10-16 14:03:21.853044: Epoch 672 
+2023-10-16 14:03:21.853190: Current learning rate: 0.00367 
+2023-10-16 14:09:08.883238: train_loss -0.7634 
+2023-10-16 14:09:08.883381: val_loss -0.706 
+2023-10-16 14:09:08.883495: Pseudo dice [0.9726, 0.8749, 0.9215, 0.864, 0.9013, 0.6659] 
+2023-10-16 14:09:08.883586: Epoch time: 347.03 s 
+2023-10-16 14:09:10.117944:  
+2023-10-16 14:09:10.118047: Epoch 673 
+2023-10-16 14:09:10.118160: Current learning rate: 0.00366 
+2023-10-16 14:14:57.067062: train_loss -0.7912 
+2023-10-16 14:14:57.067231: val_loss -0.6643 
+2023-10-16 14:14:57.067334: Pseudo dice [0.9719, 0.8677, 0.9038, 0.8649, 0.879, 0.6476] 
+2023-10-16 14:14:57.067421: Epoch time: 346.95 s 
+2023-10-16 14:14:58.304619:  
+2023-10-16 14:14:58.304735: Epoch 674 
+2023-10-16 14:14:58.304837: Current learning rate: 0.00365 
+2023-10-16 14:20:45.279370: train_loss -0.7615 
+2023-10-16 14:20:45.279514: val_loss -0.6747 
+2023-10-16 14:20:45.279631: Pseudo dice [0.9713, 0.8788, 0.9164, 0.88, 0.9019, 0.6342] 
+2023-10-16 14:20:45.279728: Epoch time: 346.98 s 
+2023-10-16 14:20:46.698153:  
+2023-10-16 14:20:46.698259: Epoch 675 
+2023-10-16 14:20:46.698378: Current learning rate: 0.00364 
+2023-10-16 14:26:33.759420: train_loss -0.7831 
+2023-10-16 14:26:33.759569: val_loss -0.6407 
+2023-10-16 14:26:33.759697: Pseudo dice [0.972, 0.8794, 0.8897, 0.905, 0.8675, 0.6662] 
+2023-10-16 14:26:33.759787: Epoch time: 347.06 s 
+2023-10-16 14:26:35.012318:  
+2023-10-16 14:26:35.012434: Epoch 676 
+2023-10-16 14:26:35.012539: Current learning rate: 0.00363 
+2023-10-16 14:32:22.167936: train_loss -0.76 
+2023-10-16 14:32:22.168096: val_loss -0.6501 
+2023-10-16 14:32:22.168233: Pseudo dice [0.9728, 0.874, 0.8996, 0.8746, 0.8343, 0.639] 
+2023-10-16 14:32:22.168343: Epoch time: 347.16 s 
+2023-10-16 14:32:23.423537:  
+2023-10-16 14:32:23.423660: Epoch 677 
+2023-10-16 14:32:23.423763: Current learning rate: 0.00362 
+2023-10-16 14:38:10.591294: train_loss -0.773 
+2023-10-16 14:38:10.591457: val_loss -0.6392 
+2023-10-16 14:38:10.591580: Pseudo dice [0.9732, 0.8753, 0.8913, 0.8908, 0.8786, 0.6712] 
+2023-10-16 14:38:10.591680: Epoch time: 347.17 s 
+2023-10-16 14:38:11.866211:  
+2023-10-16 14:38:11.866316: Epoch 678 
+2023-10-16 14:38:11.866436: Current learning rate: 0.00361 
+2023-10-16 14:43:58.890313: train_loss -0.7868 
+2023-10-16 14:43:58.890458: val_loss -0.6914 
+2023-10-16 14:43:58.890584: Pseudo dice [0.9726, 0.8752, 0.9006, 0.8883, 0.8705, 0.6923] 
+2023-10-16 14:43:58.890673: Epoch time: 347.02 s 
+2023-10-16 14:44:00.139863:  
+2023-10-16 14:44:00.139964: Epoch 679 
+2023-10-16 14:44:00.140081: Current learning rate: 0.0036 
+2023-10-16 14:49:47.252280: train_loss -0.7583 
+2023-10-16 14:49:47.252435: val_loss -0.6891 
+2023-10-16 14:49:47.252540: Pseudo dice [0.9735, 0.8717, 0.9093, 0.8589, 0.9095, 0.6843] 
+2023-10-16 14:49:47.252628: Epoch time: 347.11 s 
+2023-10-16 14:49:48.496274:  
+2023-10-16 14:49:48.496376: Epoch 680 
+2023-10-16 14:49:48.496490: Current learning rate: 0.00359 
+2023-10-16 14:55:35.546312: train_loss -0.7443 
+2023-10-16 14:55:35.546449: val_loss -0.6779 
+2023-10-16 14:55:35.546575: Pseudo dice [0.972, 0.8794, 0.906, 0.8671, 0.8294, 0.6668] 
+2023-10-16 14:55:35.546662: Epoch time: 347.05 s 
+2023-10-16 14:55:36.785294:  
+2023-10-16 14:55:36.785476: Epoch 681 
+2023-10-16 14:55:36.785625: Current learning rate: 0.00358 
+2023-10-16 15:01:23.742646: train_loss -0.7571 
+2023-10-16 15:01:23.742798: val_loss -0.7238 
+2023-10-16 15:01:23.742901: Pseudo dice [0.9724, 0.8781, 0.9204, 0.8934, 0.8916, 0.6764] 
+2023-10-16 15:01:23.742988: Epoch time: 346.96 s 
+2023-10-16 15:01:25.167395:  
+2023-10-16 15:01:25.167524: Epoch 682 
+2023-10-16 15:01:25.167639: Current learning rate: 0.00357 
+2023-10-16 15:07:12.223466: train_loss -0.782 
+2023-10-16 15:07:12.223630: val_loss -0.6878 
+2023-10-16 15:07:12.223733: Pseudo dice [0.9723, 0.8679, 0.9029, 0.8468, 0.8487, 0.6375] 
+2023-10-16 15:07:12.223820: Epoch time: 347.06 s 
+2023-10-16 15:07:13.468253:  
+2023-10-16 15:07:13.468360: Epoch 683 
+2023-10-16 15:07:13.468473: Current learning rate: 0.00356 
+2023-10-16 15:13:00.568304: train_loss -0.7683 
+2023-10-16 15:13:00.568466: val_loss -0.7197 
+2023-10-16 15:13:00.568593: Pseudo dice [0.9729, 0.888, 0.9113, 0.9009, 0.9092, 0.7284] 
+2023-10-16 15:13:00.568692: Epoch time: 347.1 s 
+2023-10-16 15:13:01.855891:  
+2023-10-16 15:13:01.855999: Epoch 684 
+2023-10-16 15:13:01.856115: Current learning rate: 0.00355 
+2023-10-16 15:18:48.879060: train_loss -0.7536 
+2023-10-16 15:18:48.879210: val_loss -0.6543 
+2023-10-16 15:18:48.879330: Pseudo dice [0.9701, 0.8749, 0.9048, 0.851, 0.9098, 0.6551] 
+2023-10-16 15:18:48.879416: Epoch time: 347.02 s 
+2023-10-16 15:18:50.125212:  
+2023-10-16 15:18:50.125395: Epoch 685 
+2023-10-16 15:18:50.125538: Current learning rate: 0.00354 
+2023-10-16 15:24:37.183900: train_loss -0.7768 
+2023-10-16 15:24:37.184041: val_loss -0.7177 
+2023-10-16 15:24:37.184159: Pseudo dice [0.9731, 0.8867, 0.9078, 0.8753, 0.8575, 0.6952] 
+2023-10-16 15:24:37.184245: Epoch time: 347.06 s 
+2023-10-16 15:24:38.425323:  
+2023-10-16 15:24:38.425500: Epoch 686 
+2023-10-16 15:24:38.425670: Current learning rate: 0.00353 
+2023-10-16 15:30:25.450144: train_loss -0.7616 
+2023-10-16 15:30:25.450284: val_loss -0.6811 
+2023-10-16 15:30:25.450403: Pseudo dice [0.9726, 0.8773, 0.9077, 0.9011, 0.8914, 0.6842] 
+2023-10-16 15:30:25.450490: Epoch time: 347.03 s 
+2023-10-16 15:30:26.691565:  
+2023-10-16 15:30:26.691684: Epoch 687 
+2023-10-16 15:30:26.691785: Current learning rate: 0.00352 
+2023-10-16 15:36:13.898718: train_loss -0.746 
+2023-10-16 15:36:13.898895: val_loss -0.6756 
+2023-10-16 15:36:13.899005: Pseudo dice [0.9733, 0.8742, 0.9142, 0.8642, 0.8651, 0.6719] 
+2023-10-16 15:36:13.899095: Epoch time: 347.21 s 
+2023-10-16 15:36:15.144222:  
+2023-10-16 15:36:15.144325: Epoch 688 
+2023-10-16 15:36:15.144439: Current learning rate: 0.00351 
+2023-10-16 15:42:02.385429: train_loss -0.755 
+2023-10-16 15:42:02.385591: val_loss -0.6726 
+2023-10-16 15:42:02.385713: Pseudo dice [0.9694, 0.8737, 0.9169, 0.8593, 0.8885, 0.6376] 
+2023-10-16 15:42:02.385812: Epoch time: 347.24 s 
+2023-10-16 15:42:03.798766:  
+2023-10-16 15:42:03.799072: Epoch 689 
+2023-10-16 15:42:03.799268: Current learning rate: 0.0035 
+2023-10-16 15:47:50.885578: train_loss -0.7973 
+2023-10-16 15:47:50.885720: val_loss -0.7042 
+2023-10-16 15:47:50.885838: Pseudo dice [0.9577, 0.8764, 0.9045, 0.9065, 0.9145, 0.6357] 
+2023-10-16 15:47:50.885922: Epoch time: 347.09 s 
+2023-10-16 15:47:52.128037:  
+2023-10-16 15:47:52.128145: Epoch 690 
+2023-10-16 15:47:52.128262: Current learning rate: 0.00349 
+2023-10-16 15:53:39.254061: train_loss -0.7614 
+2023-10-16 15:53:39.254211: val_loss -0.7025 
+2023-10-16 15:53:39.254324: Pseudo dice [0.9724, 0.8791, 0.9141, 0.853, 0.8714, 0.6621] 
+2023-10-16 15:53:39.254413: Epoch time: 347.13 s 
+2023-10-16 15:53:40.498606:  
+2023-10-16 15:53:40.498716: Epoch 691 
+2023-10-16 15:53:40.498834: Current learning rate: 0.00348 
+2023-10-16 15:59:27.492378: train_loss -0.8142 
+2023-10-16 15:59:27.492520: val_loss -0.6976 
+2023-10-16 15:59:27.492631: Pseudo dice [0.9733, 0.8819, 0.9122, 0.8859, 0.9243, 0.6735] 
+2023-10-16 15:59:27.492722: Epoch time: 346.99 s 
+2023-10-16 15:59:28.744697:  
+2023-10-16 15:59:28.744801: Epoch 692 
+2023-10-16 15:59:28.744918: Current learning rate: 0.00346 
+2023-10-16 16:05:15.697318: train_loss -0.7856 
+2023-10-16 16:05:15.697461: val_loss -0.695 
+2023-10-16 16:05:15.697591: Pseudo dice [0.9567, 0.8851, 0.9146, 0.869, 0.8951, 0.6365] 
+2023-10-16 16:05:15.697677: Epoch time: 346.95 s 
+2023-10-16 16:05:16.930659:  
+2023-10-16 16:05:16.930859: Epoch 693 
+2023-10-16 16:05:16.930986: Current learning rate: 0.00345 
+2023-10-16 16:11:03.948810: train_loss -0.7754 
+2023-10-16 16:11:03.948950: val_loss -0.718 
+2023-10-16 16:11:03.949069: Pseudo dice [0.9714, 0.8689, 0.8983, 0.8828, 0.8668, 0.6395] 
+2023-10-16 16:11:03.949156: Epoch time: 347.02 s 
+2023-10-16 16:11:05.185005:  
+2023-10-16 16:11:05.185197: Epoch 694 
+2023-10-16 16:11:05.185301: Current learning rate: 0.00344 
+2023-10-16 16:16:52.270051: train_loss -0.7802 
+2023-10-16 16:16:52.270246: val_loss -0.628 
+2023-10-16 16:16:52.270350: Pseudo dice [0.97, 0.8711, 0.9118, 0.9183, 0.8963, 0.622] 
+2023-10-16 16:16:52.270437: Epoch time: 347.09 s 
+2023-10-16 16:16:53.686908:  
+2023-10-16 16:16:53.687034: Epoch 695 
+2023-10-16 16:16:53.687136: Current learning rate: 0.00343 
+2023-10-16 16:22:40.835341: train_loss -0.7637 
+2023-10-16 16:22:40.835490: val_loss -0.6938 
+2023-10-16 16:22:40.835609: Pseudo dice [0.9748, 0.8836, 0.8923, 0.8937, 0.9091, 0.6934] 
+2023-10-16 16:22:40.835694: Epoch time: 347.15 s 
+2023-10-16 16:22:42.072929:  
+2023-10-16 16:22:42.073036: Epoch 696 
+2023-10-16 16:22:42.073146: Current learning rate: 0.00342 
+2023-10-16 16:28:29.189310: train_loss -0.7534 
+2023-10-16 16:28:29.189445: val_loss -0.673 
+2023-10-16 16:28:29.189578: Pseudo dice [0.9704, 0.8706, 0.8972, 0.9129, 0.9055, 0.697] 
+2023-10-16 16:28:29.189677: Epoch time: 347.12 s 
+2023-10-16 16:28:30.433985:  
+2023-10-16 16:28:30.434089: Epoch 697 
+2023-10-16 16:28:30.434215: Current learning rate: 0.00341 
+2023-10-16 16:34:17.422712: train_loss -0.7803 
+2023-10-16 16:34:17.422876: val_loss -0.6951 
+2023-10-16 16:34:17.422993: Pseudo dice [0.9715, 0.8701, 0.9001, 0.8812, 0.886, 0.6462] 
+2023-10-16 16:34:17.423078: Epoch time: 346.99 s 
+2023-10-16 16:34:18.685070:  
+2023-10-16 16:34:18.685174: Epoch 698 
+2023-10-16 16:34:18.685283: Current learning rate: 0.0034 
+2023-10-16 16:40:05.730069: train_loss -0.7805 
+2023-10-16 16:40:05.730230: val_loss -0.625 
+2023-10-16 16:40:05.730333: Pseudo dice [0.9709, 0.8713, 0.9143, 0.803, 0.9116, 0.6199] 
+2023-10-16 16:40:05.730420: Epoch time: 347.05 s 
+2023-10-16 16:40:06.970120:  
+2023-10-16 16:40:06.970280: Epoch 699 
+2023-10-16 16:40:06.970449: Current learning rate: 0.00339 
+2023-10-16 16:45:54.016073: train_loss -0.7806 
+2023-10-16 16:45:54.016217: val_loss -0.7162 
+2023-10-16 16:45:54.016339: Pseudo dice [0.9718, 0.8703, 0.9138, 0.8974, 0.8785, 0.6422] 
+2023-10-16 16:45:54.016433: Epoch time: 347.05 s 
+2023-10-16 16:45:56.997331:  
+2023-10-16 16:45:56.997594: Epoch 700 
+2023-10-16 16:45:56.997781: Current learning rate: 0.00338 
+2023-10-16 16:51:44.045771: train_loss -0.7833 
+2023-10-16 16:51:44.045931: val_loss -0.6933 
+2023-10-16 16:51:44.046034: Pseudo dice [0.9723, 0.865, 0.9224, 0.8523, 0.8763, 0.6381] 
+2023-10-16 16:51:44.046121: Epoch time: 347.05 s 
+2023-10-16 16:51:45.448343:  
+2023-10-16 16:51:45.448452: Epoch 701 
+2023-10-16 16:51:45.448575: Current learning rate: 0.00337 
+2023-10-16 16:57:32.561426: train_loss -0.7571 
+2023-10-16 16:57:32.561591: val_loss -0.6655 
+2023-10-16 16:57:32.561729: Pseudo dice [0.9711, 0.8732, 0.9, 0.8923, 0.9004, 0.6384] 
+2023-10-16 16:57:32.561820: Epoch time: 347.11 s 
+2023-10-16 16:57:33.801441:  
+2023-10-16 16:57:33.801694: Epoch 702 
+2023-10-16 16:57:33.801955: Current learning rate: 0.00336 
+2023-10-16 17:03:20.859094: train_loss -0.7672 
+2023-10-16 17:03:20.859270: val_loss -0.7034 
+2023-10-16 17:03:20.859374: Pseudo dice [0.9721, 0.8795, 0.898, 0.9042, 0.9137, 0.6659] 
+2023-10-16 17:03:20.859462: Epoch time: 347.06 s 
+2023-10-16 17:03:22.101246:  
+2023-10-16 17:03:22.101372: Epoch 703 
+2023-10-16 17:03:22.101486: Current learning rate: 0.00335 
+2023-10-16 17:09:09.126226: train_loss -0.783 
+2023-10-16 17:09:09.126378: val_loss -0.6845 
+2023-10-16 17:09:09.126510: Pseudo dice [0.9726, 0.8791, 0.9095, 0.8866, 0.8608, 0.6806] 
+2023-10-16 17:09:09.126611: Epoch time: 347.03 s 
+2023-10-16 17:09:10.386247:  
+2023-10-16 17:09:10.386356: Epoch 704 
+2023-10-16 17:09:10.386464: Current learning rate: 0.00334 
+2023-10-16 17:14:57.403064: train_loss -0.7716 
+2023-10-16 17:14:57.403202: val_loss -0.698 
+2023-10-16 17:14:57.403321: Pseudo dice [0.9718, 0.8811, 0.9088, 0.9002, 0.8809, 0.6618] 
+2023-10-16 17:14:57.403420: Epoch time: 347.02 s 
+2023-10-16 17:14:58.645834:  
+2023-10-16 17:14:58.646003: Epoch 705 
+2023-10-16 17:14:58.646159: Current learning rate: 0.00333 
+2023-10-16 17:20:45.704206: train_loss -0.7503 
+2023-10-16 17:20:45.704348: val_loss -0.6847 
+2023-10-16 17:20:45.704459: Pseudo dice [0.9698, 0.873, 0.8917, 0.8889, 0.8997, 0.6743] 
+2023-10-16 17:20:45.704549: Epoch time: 347.06 s 
+2023-10-16 17:20:46.957640:  
+2023-10-16 17:20:46.957807: Epoch 706 
+2023-10-16 17:20:46.958007: Current learning rate: 0.00332 
+2023-10-16 17:26:33.958879: train_loss -0.7752 
+2023-10-16 17:26:33.959020: val_loss -0.7045 
+2023-10-16 17:26:33.959134: Pseudo dice [0.9722, 0.8739, 0.9183, 0.8454, 0.9018, 0.6716] 
+2023-10-16 17:26:33.959222: Epoch time: 347.0 s 
+2023-10-16 17:26:35.206671:  
+2023-10-16 17:26:35.206788: Epoch 707 
+2023-10-16 17:26:35.206922: Current learning rate: 0.00331 
+2023-10-16 17:32:22.138206: train_loss -0.7669 
+2023-10-16 17:32:22.138348: val_loss -0.7071 
+2023-10-16 17:32:22.138467: Pseudo dice [0.9745, 0.8816, 0.8954, 0.8867, 0.8916, 0.6833] 
+2023-10-16 17:32:22.138563: Epoch time: 346.93 s 
+2023-10-16 17:32:23.563481:  
+2023-10-16 17:32:23.563607: Epoch 708 
+2023-10-16 17:32:23.563741: Current learning rate: 0.0033 
+2023-10-16 17:38:10.497249: train_loss -0.7673 
+2023-10-16 17:38:10.497402: val_loss -0.7156 
+2023-10-16 17:38:10.497540: Pseudo dice [0.9729, 0.8705, 0.9078, 0.8799, 0.8892, 0.6529] 
+2023-10-16 17:38:10.497638: Epoch time: 346.93 s 
+2023-10-16 17:38:11.745688:  
+2023-10-16 17:38:11.745798: Epoch 709 
+2023-10-16 17:38:11.745906: Current learning rate: 0.00329 
+2023-10-16 17:43:58.635768: train_loss -0.7809 
+2023-10-16 17:43:58.635956: val_loss -0.6491 
+2023-10-16 17:43:58.636094: Pseudo dice [0.9702, 0.8701, 0.8895, 0.8521, 0.8554, 0.6343] 
+2023-10-16 17:43:58.636192: Epoch time: 346.89 s 
+2023-10-16 17:43:59.878851:  
+2023-10-16 17:43:59.878956: Epoch 710 
+2023-10-16 17:43:59.879080: Current learning rate: 0.00328 
+2023-10-16 17:49:46.728398: train_loss -0.7643 
+2023-10-16 17:49:46.728543: val_loss -0.7095 
+2023-10-16 17:49:46.728656: Pseudo dice [0.9692, 0.8689, 0.9171, 0.8931, 0.9119, 0.6732] 
+2023-10-16 17:49:46.728746: Epoch time: 346.85 s 
+2023-10-16 17:49:47.973462:  
+2023-10-16 17:49:47.973571: Epoch 711 
+2023-10-16 17:49:47.973685: Current learning rate: 0.00327 
+2023-10-16 17:55:34.924966: train_loss -0.7672 
+2023-10-16 17:55:34.925110: val_loss -0.7094 
+2023-10-16 17:55:34.925245: Pseudo dice [0.9737, 0.8833, 0.9068, 0.917, 0.8682, 0.6801] 
+2023-10-16 17:55:34.925343: Epoch time: 346.95 s 
+2023-10-16 17:55:36.177106:  
+2023-10-16 17:55:36.177264: Epoch 712 
+2023-10-16 17:55:36.177436: Current learning rate: 0.00326 
+2023-10-16 18:01:22.954619: train_loss -0.7593 
+2023-10-16 18:01:22.954769: val_loss -0.6383 
+2023-10-16 18:01:22.954875: Pseudo dice [0.9728, 0.8743, 0.9015, 0.8736, 0.8608, 0.6368] 
+2023-10-16 18:01:22.954967: Epoch time: 346.78 s 
+2023-10-16 18:01:24.252601:  
+2023-10-16 18:01:24.252719: Epoch 713 
+2023-10-16 18:01:24.252821: Current learning rate: 0.00325 
+2023-10-16 18:07:11.209710: train_loss -0.7669 
+2023-10-16 18:07:11.209862: val_loss -0.6773 
+2023-10-16 18:07:11.209980: Pseudo dice [0.9705, 0.876, 0.9022, 0.9134, 0.895, 0.6792] 
+2023-10-16 18:07:11.210068: Epoch time: 346.96 s 
+2023-10-16 18:07:12.472027:  
+2023-10-16 18:07:12.472130: Epoch 714 
+2023-10-16 18:07:12.472245: Current learning rate: 0.00324 
+2023-10-16 18:12:59.421642: train_loss -0.7703 
+2023-10-16 18:12:59.421783: val_loss -0.6806 
+2023-10-16 18:12:59.421922: Pseudo dice [0.9727, 0.875, 0.9039, 0.8918, 0.8685, 0.6262] 
+2023-10-16 18:12:59.422021: Epoch time: 346.95 s 
+2023-10-16 18:13:00.847863:  
+2023-10-16 18:13:00.847979: Epoch 715 
+2023-10-16 18:13:00.848104: Current learning rate: 0.00323 
+2023-10-16 18:18:47.766829: train_loss -0.7554 
+2023-10-16 18:18:47.766988: val_loss -0.629 
+2023-10-16 18:18:47.767092: Pseudo dice [0.9728, 0.8666, 0.8996, 0.8588, 0.8688, 0.6596] 
+2023-10-16 18:18:47.767176: Epoch time: 346.92 s 
+2023-10-16 18:18:49.017198:  
+2023-10-16 18:18:49.017307: Epoch 716 
+2023-10-16 18:18:49.017418: Current learning rate: 0.00322 
+2023-10-16 18:24:35.870587: train_loss -0.7733 
+2023-10-16 18:24:35.870733: val_loss -0.6543 
+2023-10-16 18:24:35.870835: Pseudo dice [0.9709, 0.8719, 0.9111, 0.8689, 0.9133, 0.6523] 
+2023-10-16 18:24:35.870922: Epoch time: 346.85 s 
+2023-10-16 18:24:37.124043:  
+2023-10-16 18:24:37.124146: Epoch 717 
+2023-10-16 18:24:37.124261: Current learning rate: 0.00321 
+2023-10-16 18:30:23.957323: train_loss -0.77 
+2023-10-16 18:30:23.957465: val_loss -0.6474 
+2023-10-16 18:30:23.957584: Pseudo dice [0.9735, 0.8619, 0.9143, 0.8353, 0.8954, 0.6592] 
+2023-10-16 18:30:23.957669: Epoch time: 346.83 s 
+2023-10-16 18:30:25.211150:  
+2023-10-16 18:30:25.211277: Epoch 718 
+2023-10-16 18:30:25.211446: Current learning rate: 0.0032 
+2023-10-16 18:36:12.018801: train_loss -0.7782 
+2023-10-16 18:36:12.018966: val_loss -0.6482 
+2023-10-16 18:36:12.019079: Pseudo dice [0.9726, 0.8791, 0.8996, 0.884, 0.9021, 0.7036] 
+2023-10-16 18:36:12.019172: Epoch time: 346.81 s 
+2023-10-16 18:36:13.263700:  
+2023-10-16 18:36:13.263804: Epoch 719 
+2023-10-16 18:36:13.263930: Current learning rate: 0.00319 
+2023-10-16 18:42:00.188163: train_loss -0.7888 
+2023-10-16 18:42:00.188304: val_loss -0.7018 
+2023-10-16 18:42:00.188424: Pseudo dice [0.9712, 0.87, 0.904, 0.8874, 0.8894, 0.638] 
+2023-10-16 18:42:00.188511: Epoch time: 346.93 s 
+2023-10-16 18:42:01.446100:  
+2023-10-16 18:42:01.446201: Epoch 720 
+2023-10-16 18:42:01.446325: Current learning rate: 0.00318 
+2023-10-16 18:47:48.306199: train_loss -0.7908 
+2023-10-16 18:47:48.306342: val_loss -0.715 
+2023-10-16 18:47:48.306471: Pseudo dice [0.9722, 0.8798, 0.9101, 0.8619, 0.8633, 0.6799] 
+2023-10-16 18:47:48.306579: Epoch time: 346.86 s 
+2023-10-16 18:47:49.570007:  
+2023-10-16 18:47:49.570175: Epoch 721 
+2023-10-16 18:47:49.570375: Current learning rate: 0.00317 
+2023-10-16 18:53:36.487244: train_loss -0.7834 
+2023-10-16 18:53:36.487411: val_loss -0.7024 
+2023-10-16 18:53:36.487536: Pseudo dice [0.9739, 0.8812, 0.9163, 0.8823, 0.9016, 0.6657] 
+2023-10-16 18:53:36.487633: Epoch time: 346.92 s 
+2023-10-16 18:53:37.910301:  
+2023-10-16 18:53:37.910411: Epoch 722 
+2023-10-16 18:53:37.910543: Current learning rate: 0.00316 
+2023-10-16 18:59:24.792783: train_loss -0.7423 
+2023-10-16 18:59:24.792937: val_loss -0.5908 
+2023-10-16 18:59:24.793062: Pseudo dice [0.9718, 0.8649, 0.9003, 0.8871, 0.9164, 0.6585] 
+2023-10-16 18:59:24.793158: Epoch time: 346.88 s 
+2023-10-16 18:59:26.048174:  
+2023-10-16 18:59:26.048283: Epoch 723 
+2023-10-16 18:59:26.048398: Current learning rate: 0.00315 
+2023-10-16 19:05:12.939356: train_loss -0.7433 
+2023-10-16 19:05:12.939518: val_loss -0.6831 
+2023-10-16 19:05:12.939631: Pseudo dice [0.9692, 0.8628, 0.9104, 0.8846, 0.901, 0.6258] 
+2023-10-16 19:05:12.939718: Epoch time: 346.89 s 
+2023-10-16 19:05:14.202779:  
+2023-10-16 19:05:14.202989: Epoch 724 
+2023-10-16 19:05:14.203168: Current learning rate: 0.00314 
+2023-10-16 19:11:01.255048: train_loss -0.7776 
+2023-10-16 19:11:01.255198: val_loss -0.6905 
+2023-10-16 19:11:01.255312: Pseudo dice [0.9727, 0.8823, 0.9072, 0.9037, 0.8895, 0.6804] 
+2023-10-16 19:11:01.255404: Epoch time: 347.05 s 
+2023-10-16 19:11:02.504785:  
+2023-10-16 19:11:02.504904: Epoch 725 
+2023-10-16 19:11:02.505020: Current learning rate: 0.00313 
+2023-10-16 19:16:49.462324: train_loss -0.7808 
+2023-10-16 19:16:49.462468: val_loss -0.6713 
+2023-10-16 19:16:49.462594: Pseudo dice [0.971, 0.8769, 0.9221, 0.8643, 0.8723, 0.6858] 
+2023-10-16 19:16:49.462681: Epoch time: 346.96 s 
+2023-10-16 19:16:50.711224:  
+2023-10-16 19:16:50.711414: Epoch 726 
+2023-10-16 19:16:50.711580: Current learning rate: 0.00312 
+2023-10-16 19:22:37.729152: train_loss -0.7868 
+2023-10-16 19:22:37.729310: val_loss -0.6498 
+2023-10-16 19:22:37.729414: Pseudo dice [0.9713, 0.8733, 0.8978, 0.8517, 0.8767, 0.6395] 
+2023-10-16 19:22:37.729499: Epoch time: 347.02 s 
+2023-10-16 19:22:38.988612:  
+2023-10-16 19:22:38.988800: Epoch 727 
+2023-10-16 19:22:38.988944: Current learning rate: 0.00311 
+2023-10-16 19:28:25.938247: train_loss -0.7688 
+2023-10-16 19:28:25.938400: val_loss -0.6864 
+2023-10-16 19:28:25.938513: Pseudo dice [0.967, 0.8752, 0.8958, 0.8807, 0.8955, 0.6826] 
+2023-10-16 19:28:25.938601: Epoch time: 346.95 s 
+2023-10-16 19:28:27.357462:  
+2023-10-16 19:28:27.357578: Epoch 728 
+2023-10-16 19:28:27.357693: Current learning rate: 0.0031 
+2023-10-16 19:34:14.377090: train_loss -0.7517 
+2023-10-16 19:34:14.377239: val_loss -0.6426 
+2023-10-16 19:34:14.377358: Pseudo dice [0.9716, 0.8628, 0.8938, 0.875, 0.8832, 0.6524] 
+2023-10-16 19:34:14.377446: Epoch time: 347.02 s 
+2023-10-16 19:34:15.624686:  
+2023-10-16 19:34:15.624795: Epoch 729 
+2023-10-16 19:34:15.624912: Current learning rate: 0.00309 
+2023-10-16 19:40:02.607368: train_loss -0.7796 
+2023-10-16 19:40:02.607537: val_loss -0.6619 
+2023-10-16 19:40:02.607661: Pseudo dice [0.9724, 0.8814, 0.8943, 0.8721, 0.8429, 0.678] 
+2023-10-16 19:40:02.607759: Epoch time: 346.98 s 
+2023-10-16 19:40:03.855425:  
+2023-10-16 19:40:03.855531: Epoch 730 
+2023-10-16 19:40:03.855642: Current learning rate: 0.00308 
+2023-10-16 19:45:50.807545: train_loss -0.782 
+2023-10-16 19:45:50.807684: val_loss -0.6797 
+2023-10-16 19:45:50.807798: Pseudo dice [0.9722, 0.8782, 0.8965, 0.8921, 0.8947, 0.6896] 
+2023-10-16 19:45:50.807889: Epoch time: 346.95 s 
+2023-10-16 19:45:52.052246:  
+2023-10-16 19:45:52.052373: Epoch 731 
+2023-10-16 19:45:52.052481: Current learning rate: 0.00307 
+2023-10-16 19:51:39.053820: train_loss -0.7904 
+2023-10-16 19:51:39.053964: val_loss -0.6774 
+2023-10-16 19:51:39.054094: Pseudo dice [0.9718, 0.8854, 0.9172, 0.8836, 0.8633, 0.6705] 
+2023-10-16 19:51:39.054178: Epoch time: 347.0 s 
+2023-10-16 19:51:40.560286:  
+2023-10-16 19:51:40.560477: Epoch 732 
+2023-10-16 19:51:40.560627: Current learning rate: 0.00306 
+2023-10-16 19:57:27.542472: train_loss -0.7769 
+2023-10-16 19:57:27.542644: val_loss -0.7191 
+2023-10-16 19:57:27.542748: Pseudo dice [0.9723, 0.8758, 0.9074, 0.8964, 0.8692, 0.6413] 
+2023-10-16 19:57:27.542835: Epoch time: 346.98 s 
+2023-10-16 19:57:28.820989:  
+2023-10-16 19:57:28.821165: Epoch 733 
+2023-10-16 19:57:28.821347: Current learning rate: 0.00305 
+2023-10-16 20:03:15.776599: train_loss -0.7644 
+2023-10-16 20:03:15.776749: val_loss -0.6748 
+2023-10-16 20:03:15.776865: Pseudo dice [0.9678, 0.8599, 0.9078, 0.8708, 0.9094, 0.6484] 
+2023-10-16 20:03:15.776958: Epoch time: 346.96 s 
+2023-10-16 20:03:17.026584:  
+2023-10-16 20:03:17.026691: Epoch 734 
+2023-10-16 20:03:17.026812: Current learning rate: 0.00304 
+2023-10-16 20:09:03.869975: train_loss -0.7608 
+2023-10-16 20:09:03.870114: val_loss -0.6832 
+2023-10-16 20:09:03.870232: Pseudo dice [0.9709, 0.8705, 0.9131, 0.9043, 0.9058, 0.6642] 
+2023-10-16 20:09:03.870320: Epoch time: 346.84 s 
+2023-10-16 20:09:05.310963:  
+2023-10-16 20:09:05.311097: Epoch 735 
+2023-10-16 20:09:05.311204: Current learning rate: 0.00303 
+2023-10-16 20:14:52.073126: train_loss -0.7843 
+2023-10-16 20:14:52.073279: val_loss -0.6834 
+2023-10-16 20:14:52.073405: Pseudo dice [0.971, 0.8622, 0.9047, 0.905, 0.8859, 0.6482] 
+2023-10-16 20:14:52.073491: Epoch time: 346.76 s 
+2023-10-16 20:14:53.319446:  
+2023-10-16 20:14:53.319551: Epoch 736 
+2023-10-16 20:14:53.319667: Current learning rate: 0.00302 
+2023-10-16 20:20:40.196349: train_loss -0.7921 
+2023-10-16 20:20:40.196598: val_loss -0.6307 
+2023-10-16 20:20:40.196746: Pseudo dice [0.971, 0.8672, 0.9097, 0.8688, 0.9309, 0.6475] 
+2023-10-16 20:20:40.196844: Epoch time: 346.88 s 
+2023-10-16 20:20:41.443801:  
+2023-10-16 20:20:41.443908: Epoch 737 
+2023-10-16 20:20:41.444032: Current learning rate: 0.00301 
+2023-10-16 20:26:28.367692: train_loss -0.7729 
+2023-10-16 20:26:28.367858: val_loss -0.6829 
+2023-10-16 20:26:28.367961: Pseudo dice [0.9629, 0.881, 0.9185, 0.8668, 0.8715, 0.6689] 
+2023-10-16 20:26:28.368046: Epoch time: 346.92 s 
+2023-10-16 20:26:29.612815:  
+2023-10-16 20:26:29.612922: Epoch 738 
+2023-10-16 20:26:29.613036: Current learning rate: 0.003 
+2023-10-16 20:32:16.500695: train_loss -0.8012 
+2023-10-16 20:32:16.500844: val_loss -0.6784 
+2023-10-16 20:32:16.500956: Pseudo dice [0.9708, 0.8696, 0.9136, 0.8691, 0.8652, 0.6666] 
+2023-10-16 20:32:16.501048: Epoch time: 346.89 s 
+2023-10-16 20:32:17.760465:  
+2023-10-16 20:32:17.760568: Epoch 739 
+2023-10-16 20:32:17.760695: Current learning rate: 0.00299 
+2023-10-16 20:38:04.592379: train_loss -0.7674 
+2023-10-16 20:38:04.592530: val_loss -0.6908 
+2023-10-16 20:38:04.592652: Pseudo dice [0.9724, 0.8714, 0.902, 0.8697, 0.8764, 0.6592] 
+2023-10-16 20:38:04.592737: Epoch time: 346.83 s 
+2023-10-16 20:38:05.837263:  
+2023-10-16 20:38:05.837362: Epoch 740 
+2023-10-16 20:38:05.837475: Current learning rate: 0.00297 
+2023-10-16 20:43:52.660144: train_loss -0.7516 
+2023-10-16 20:43:52.660285: val_loss -0.6461 
+2023-10-16 20:43:52.660398: Pseudo dice [0.9706, 0.87, 0.8958, 0.8801, 0.8641, 0.7011] 
+2023-10-16 20:43:52.660489: Epoch time: 346.82 s 
+2023-10-16 20:43:53.907556:  
+2023-10-16 20:43:53.907661: Epoch 741 
+2023-10-16 20:43:53.907781: Current learning rate: 0.00296 
+2023-10-16 20:49:40.771184: train_loss -0.7764 
+2023-10-16 20:49:40.771330: val_loss -0.6637 
+2023-10-16 20:49:40.771452: Pseudo dice [0.9724, 0.8785, 0.9024, 0.8671, 0.8626, 0.6238] 
+2023-10-16 20:49:40.771538: Epoch time: 346.86 s 
+2023-10-16 20:49:42.199558:  
+2023-10-16 20:49:42.199669: Epoch 742 
+2023-10-16 20:49:42.199785: Current learning rate: 0.00295 
+2023-10-16 20:55:29.083561: train_loss -0.7812 
+2023-10-16 20:55:29.083701: val_loss -0.6515 
+2023-10-16 20:55:29.083814: Pseudo dice [0.9702, 0.8539, 0.9124, 0.8706, 0.8343, 0.6397] 
+2023-10-16 20:55:29.083905: Epoch time: 346.88 s 
+2023-10-16 20:55:30.326373:  
+2023-10-16 20:55:30.326590: Epoch 743 
+2023-10-16 20:55:30.326766: Current learning rate: 0.00294 
+2023-10-16 21:01:17.272231: train_loss -0.7812 
+2023-10-16 21:01:17.272433: val_loss -0.6915 
+2023-10-16 21:01:17.272554: Pseudo dice [0.9715, 0.8707, 0.8996, 0.8269, 0.8827, 0.6523] 
+2023-10-16 21:01:17.272651: Epoch time: 346.95 s 
+2023-10-16 21:01:18.527537:  
+2023-10-16 21:01:18.527642: Epoch 744 
+2023-10-16 21:01:18.527767: Current learning rate: 0.00293 
+2023-10-16 21:07:05.379056: train_loss -0.7603 
+2023-10-16 21:07:05.379226: val_loss -0.6876 
+2023-10-16 21:07:05.379349: Pseudo dice [0.9726, 0.8639, 0.9083, 0.8809, 0.8898, 0.6921] 
+2023-10-16 21:07:05.379447: Epoch time: 346.85 s 
+2023-10-16 21:07:06.626625:  
+2023-10-16 21:07:06.626747: Epoch 745 
+2023-10-16 21:07:06.626876: Current learning rate: 0.00292 
+2023-10-16 21:12:53.451590: train_loss -0.7823 
+2023-10-16 21:12:53.451732: val_loss -0.6655 
+2023-10-16 21:12:53.451844: Pseudo dice [0.9683, 0.8804, 0.899, 0.8178, 0.8839, 0.6644] 
+2023-10-16 21:12:53.451934: Epoch time: 346.83 s 
+2023-10-16 21:12:54.696416:  
+2023-10-16 21:12:54.696591: Epoch 746 
+2023-10-16 21:12:54.696789: Current learning rate: 0.00291 
+2023-10-16 21:18:41.674343: train_loss -0.7672 
+2023-10-16 21:18:41.674484: val_loss -0.678 
+2023-10-16 21:18:41.674613: Pseudo dice [0.9738, 0.8722, 0.909, 0.9068, 0.9027, 0.6562] 
+2023-10-16 21:18:41.674699: Epoch time: 346.98 s 
+2023-10-16 21:18:42.962177:  
+2023-10-16 21:18:42.962278: Epoch 747 
+2023-10-16 21:18:42.962391: Current learning rate: 0.0029 
+2023-10-16 21:24:29.754190: train_loss -0.7823 
+2023-10-16 21:24:29.754351: val_loss -0.6516 
+2023-10-16 21:24:29.754453: Pseudo dice [0.9742, 0.8777, 0.9024, 0.8644, 0.8886, 0.684] 
+2023-10-16 21:24:29.754547: Epoch time: 346.79 s 
+2023-10-16 21:24:31.000975:  
+2023-10-16 21:24:31.001076: Epoch 748 
+2023-10-16 21:24:31.001190: Current learning rate: 0.00289 
+2023-10-16 21:30:18.081588: train_loss -0.7763 
+2023-10-16 21:30:18.081762: val_loss -0.6687 
+2023-10-16 21:30:18.081884: Pseudo dice [0.9736, 0.8657, 0.9113, 0.8781, 0.8645, 0.6676] 
+2023-10-16 21:30:18.081988: Epoch time: 347.08 s 
+2023-10-16 21:30:19.327024:  
+2023-10-16 21:30:19.327136: Epoch 749 
+2023-10-16 21:30:19.327245: Current learning rate: 0.00288 
+2023-10-16 21:36:06.313214: train_loss -0.769 
+2023-10-16 21:36:06.313378: val_loss -0.6887 
+2023-10-16 21:36:06.313504: Pseudo dice [0.9696, 0.8651, 0.9186, 0.8477, 0.8635, 0.6643] 
+2023-10-16 21:36:06.313602: Epoch time: 346.99 s 
+2023-10-16 21:36:09.322969:  
+2023-10-16 21:36:09.323163: Epoch 750 
+2023-10-16 21:36:09.323311: Current learning rate: 0.00287 
+2023-10-16 21:41:56.237420: train_loss -0.7798 
+2023-10-16 21:41:56.237570: val_loss -0.6466 
+2023-10-16 21:41:56.237676: Pseudo dice [0.9738, 0.8774, 0.8983, 0.8788, 0.8519, 0.6585] 
+2023-10-16 21:41:56.237762: Epoch time: 346.92 s 
+2023-10-16 21:41:57.492184:  
+2023-10-16 21:41:57.492290: Epoch 751 
+2023-10-16 21:41:57.492398: Current learning rate: 0.00286 
+2023-10-16 21:47:44.454530: train_loss -0.7814 
+2023-10-16 21:47:44.454681: val_loss -0.6659 
+2023-10-16 21:47:44.454795: Pseudo dice [0.9697, 0.8644, 0.9237, 0.9063, 0.8612, 0.6375] 
+2023-10-16 21:47:44.454881: Epoch time: 346.96 s 
+2023-10-16 21:47:45.699300:  
+2023-10-16 21:47:45.699405: Epoch 752 
+2023-10-16 21:47:45.699520: Current learning rate: 0.00285 
+2023-10-16 21:53:32.651409: train_loss -0.805 
+2023-10-16 21:53:32.651662: val_loss -0.6821 
+2023-10-16 21:53:32.651787: Pseudo dice [0.9717, 0.8596, 0.9134, 0.8739, 0.8912, 0.6436] 
+2023-10-16 21:53:32.651883: Epoch time: 346.95 s 
+2023-10-16 21:53:33.894313:  
+2023-10-16 21:53:33.894414: Epoch 753 
+2023-10-16 21:53:33.894536: Current learning rate: 0.00284 
+2023-10-16 21:59:20.734253: train_loss -0.7614 
+2023-10-16 21:59:20.734413: val_loss -0.7219 
+2023-10-16 21:59:20.734549: Pseudo dice [0.9706, 0.8697, 0.9083, 0.8744, 0.8853, 0.6663] 
+2023-10-16 21:59:20.734730: Epoch time: 346.84 s 
+2023-10-16 21:59:21.983121:  
+2023-10-16 21:59:21.983289: Epoch 754 
+2023-10-16 21:59:21.983458: Current learning rate: 0.00283 
+2023-10-16 22:05:08.729062: train_loss -0.7599 
+2023-10-16 22:05:08.729207: val_loss -0.6608 
+2023-10-16 22:05:08.729323: Pseudo dice [0.9738, 0.8761, 0.9012, 0.9026, 0.9033, 0.6759] 
+2023-10-16 22:05:08.729420: Epoch time: 346.75 s 
+2023-10-16 22:05:10.152111:  
+2023-10-16 22:05:10.152219: Epoch 755 
+2023-10-16 22:05:10.152333: Current learning rate: 0.00282 
+2023-10-16 22:10:56.905413: train_loss -0.7884 
+2023-10-16 22:10:56.905557: val_loss -0.7104 
+2023-10-16 22:10:56.905695: Pseudo dice [0.9683, 0.8676, 0.9096, 0.8374, 0.8668, 0.6558] 
+2023-10-16 22:10:56.905791: Epoch time: 346.75 s 
+2023-10-16 22:10:58.149326:  
+2023-10-16 22:10:58.149428: Epoch 756 
+2023-10-16 22:10:58.149547: Current learning rate: 0.00281 
+2023-10-16 22:16:44.896919: train_loss -0.7781 
+2023-10-16 22:16:44.897083: val_loss -0.7192 
+2023-10-16 22:16:44.897204: Pseudo dice [0.9706, 0.8774, 0.9037, 0.8773, 0.9042, 0.677] 
+2023-10-16 22:16:44.897301: Epoch time: 346.75 s 
+2023-10-16 22:16:46.143886:  
+2023-10-16 22:16:46.144010: Epoch 757 
+2023-10-16 22:16:46.144125: Current learning rate: 0.0028 
+2023-10-16 22:22:33.012759: train_loss -0.7888 
+2023-10-16 22:22:33.012903: val_loss -0.6814 
+2023-10-16 22:22:33.013015: Pseudo dice [0.9729, 0.8683, 0.9104, 0.8744, 0.8768, 0.7198] 
+2023-10-16 22:22:33.013106: Epoch time: 346.87 s 
+2023-10-16 22:22:34.259762:  
+2023-10-16 22:22:34.259865: Epoch 758 
+2023-10-16 22:22:34.259990: Current learning rate: 0.00279 
+2023-10-16 22:28:21.064841: train_loss -0.7361 
+2023-10-16 22:28:21.064984: val_loss -0.6864 
+2023-10-16 22:28:21.065100: Pseudo dice [0.9648, 0.8821, 0.9176, 0.8619, 0.8589, 0.6633] 
+2023-10-16 22:28:21.065188: Epoch time: 346.81 s 
+2023-10-16 22:28:22.319604:  
+2023-10-16 22:28:22.319710: Epoch 759 
+2023-10-16 22:28:22.319818: Current learning rate: 0.00278 
+2023-10-16 22:34:09.133929: train_loss -0.7883 
+2023-10-16 22:34:09.134073: val_loss -0.6856 
+2023-10-16 22:34:09.134192: Pseudo dice [0.9726, 0.8688, 0.9055, 0.8427, 0.8812, 0.6554] 
+2023-10-16 22:34:09.134277: Epoch time: 346.82 s 
+2023-10-16 22:34:10.381863:  
+2023-10-16 22:34:10.382043: Epoch 760 
+2023-10-16 22:34:10.382210: Current learning rate: 0.00277 
+2023-10-16 22:39:57.259468: train_loss -0.7825 
+2023-10-16 22:39:57.259617: val_loss -0.6793 
+2023-10-16 22:39:57.259732: Pseudo dice [0.9727, 0.8735, 0.9076, 0.8929, 0.8783, 0.6945] 
+2023-10-16 22:39:57.259821: Epoch time: 346.88 s 
+2023-10-16 22:39:58.531965:  
+2023-10-16 22:39:58.532070: Epoch 761 
+2023-10-16 22:39:58.532183: Current learning rate: 0.00276 
+2023-10-16 22:45:45.485026: train_loss -0.7712 
+2023-10-16 22:45:45.485170: val_loss -0.6874 
+2023-10-16 22:45:45.485283: Pseudo dice [0.9723, 0.8793, 0.9192, 0.8844, 0.9144, 0.6516] 
+2023-10-16 22:45:45.485373: Epoch time: 346.95 s 
+2023-10-16 22:45:46.926359:  
+2023-10-16 22:45:46.926492: Epoch 762 
+2023-10-16 22:45:46.926620: Current learning rate: 0.00275 
+2023-10-16 22:51:33.977682: train_loss -0.7857 
+2023-10-16 22:51:33.977825: val_loss -0.6443 
+2023-10-16 22:51:33.977939: Pseudo dice [0.9727, 0.8767, 0.8949, 0.8151, 0.8862, 0.6678] 
+2023-10-16 22:51:33.978030: Epoch time: 347.05 s 
+2023-10-16 22:51:35.253114:  
+2023-10-16 22:51:35.253222: Epoch 763 
+2023-10-16 22:51:35.253348: Current learning rate: 0.00274 
+2023-10-16 22:57:22.227417: train_loss -0.7944 
+2023-10-16 22:57:22.227562: val_loss -0.6669 
+2023-10-16 22:57:22.227674: Pseudo dice [0.9687, 0.8757, 0.9031, 0.8526, 0.863, 0.6839] 
+2023-10-16 22:57:22.227766: Epoch time: 346.98 s 
+2023-10-16 22:57:23.495120:  
+2023-10-16 22:57:23.495229: Epoch 764 
+2023-10-16 22:57:23.495349: Current learning rate: 0.00273 
+2023-10-16 23:03:10.324488: train_loss -0.7739 
+2023-10-16 23:03:10.324644: val_loss -0.6643 
+2023-10-16 23:03:10.324748: Pseudo dice [0.9693, 0.8623, 0.9022, 0.8465, 0.868, 0.6316] 
+2023-10-16 23:03:10.324842: Epoch time: 346.83 s 
+2023-10-16 23:03:11.590431:  
+2023-10-16 23:03:11.590558: Epoch 765 
+2023-10-16 23:03:11.590662: Current learning rate: 0.00272 
+2023-10-16 23:08:58.534127: train_loss -0.7627 
+2023-10-16 23:08:58.534281: val_loss -0.6885 
+2023-10-16 23:08:58.534384: Pseudo dice [0.9727, 0.8742, 0.8991, 0.8921, 0.8761, 0.721] 
+2023-10-16 23:08:58.534471: Epoch time: 346.94 s 
+2023-10-16 23:08:59.799573:  
+2023-10-16 23:08:59.799681: Epoch 766 
+2023-10-16 23:08:59.799800: Current learning rate: 0.00271 
+2023-10-16 23:14:46.783532: train_loss -0.7838 
+2023-10-16 23:14:46.783687: val_loss -0.6636 
+2023-10-16 23:14:46.783791: Pseudo dice [0.9722, 0.8678, 0.9032, 0.8832, 0.8708, 0.6741] 
+2023-10-16 23:14:46.783879: Epoch time: 346.98 s 
+2023-10-16 23:14:48.045738:  
+2023-10-16 23:14:48.045854: Epoch 767 
+2023-10-16 23:14:48.045959: Current learning rate: 0.0027 
+2023-10-16 23:20:34.960293: train_loss -0.7738 
+2023-10-16 23:20:34.960457: val_loss -0.6693 
+2023-10-16 23:20:34.960583: Pseudo dice [0.9721, 0.8671, 0.9128, 0.8691, 0.88, 0.6513] 
+2023-10-16 23:20:34.960680: Epoch time: 346.92 s 
+2023-10-16 23:20:36.431780:  
+2023-10-16 23:20:36.431914: Epoch 768 
+2023-10-16 23:20:36.432017: Current learning rate: 0.00268 
+2023-10-16 23:26:23.355504: train_loss -0.7621 
+2023-10-16 23:26:23.355654: val_loss -0.6347 
+2023-10-16 23:26:23.355771: Pseudo dice [0.9736, 0.8705, 0.8865, 0.8463, 0.8879, 0.6394] 
+2023-10-16 23:26:23.355858: Epoch time: 346.92 s 
+2023-10-16 23:26:24.643907:  
+2023-10-16 23:26:24.644099: Epoch 769 
+2023-10-16 23:26:24.644284: Current learning rate: 0.00267 
+2023-10-16 23:32:11.696220: train_loss -0.772 
+2023-10-16 23:32:11.696359: val_loss -0.7176 
+2023-10-16 23:32:11.696473: Pseudo dice [0.9737, 0.8809, 0.9153, 0.8991, 0.8772, 0.673] 
+2023-10-16 23:32:11.696564: Epoch time: 347.05 s 
+2023-10-16 23:32:12.978057:  
+2023-10-16 23:32:12.978164: Epoch 770 
+2023-10-16 23:32:12.978276: Current learning rate: 0.00266 
+2023-10-16 23:37:59.855270: train_loss -0.7751 
+2023-10-16 23:37:59.855424: val_loss -0.6703 
+2023-10-16 23:37:59.855527: Pseudo dice [0.9715, 0.8624, 0.904, 0.8745, 0.8474, 0.6303] 
+2023-10-16 23:37:59.855613: Epoch time: 346.88 s 
+2023-10-16 23:38:01.119642:  
+2023-10-16 23:38:01.119751: Epoch 771 
+2023-10-16 23:38:01.119865: Current learning rate: 0.00265 
+2023-10-16 23:43:48.086070: train_loss -0.7857 
+2023-10-16 23:43:48.086206: val_loss -0.7146 
+2023-10-16 23:43:48.086318: Pseudo dice [0.973, 0.8639, 0.899, 0.8673, 0.8598, 0.6267] 
+2023-10-16 23:43:48.086406: Epoch time: 346.97 s 
+2023-10-16 23:43:49.371821:  
+2023-10-16 23:43:49.372040: Epoch 772 
+2023-10-16 23:43:49.372244: Current learning rate: 0.00264 
+2023-10-16 23:49:36.241262: train_loss -0.7812 
+2023-10-16 23:49:36.241413: val_loss -0.6925 
+2023-10-16 23:49:36.241530: Pseudo dice [0.9729, 0.8709, 0.9125, 0.8367, 0.8972, 0.6604] 
+2023-10-16 23:49:36.241616: Epoch time: 346.87 s 
+2023-10-16 23:49:37.501805:  
+2023-10-16 23:49:37.501961: Epoch 773 
+2023-10-16 23:49:37.502124: Current learning rate: 0.00263 

Dataset720_TSPrime/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json

@@ -0,0 +1,454 @@
+{
+    "dataset_name": "Dataset720_TSPrime",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        2.5,
+        1.269531011581421,
+        1.269531011581421
+    ],
+    "original_median_shape_after_transp": [
+        241,
+        512,
+        512
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 12,
+            "patch_size": [
+                512,
+                512
+            ],
+            "median_image_size_in_voxels": [
+                512.0,
+                512.0
+            ],
+            "spacing": [
+                1.269531011581421,
+                1.269531011581421
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                7,
+                7
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                80,
+                192,
+                160
+            ],
+            "median_image_size_in_voxels": [
+                130,
+                275,
+                275
+            ],
+            "spacing": [
+                4.650736429273743,
+                2.361701649461784,
+                2.361701649461784
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                4,
+                5,
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+    "dataloader_train.transform": "Compose ( [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 = [80, 192, 160], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), 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 ), 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 = None ), 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], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f1bfba5f110>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f1bfb5c31d0>",
+    "dataloader_val.num_processes": "6",
+    "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], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'Ctvp': 1}, 'numTraining': 60, 'file_ending': '.nii.gz', 'numTest': 0}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA GeForce GTX 1080 Ti",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f1bfb5c2f10>",
+    "hostname": "vipadmin-Z10PE-D16-WS",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f1bfb5c2ed0>",
+    "local_rank": "0",
+    "log_file": "./data/nnUNet_results/Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres/fold_0/training_log_2023_11_5_22_05_41.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f1bfbb7d950>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f1bfba5f950>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset721_TSPrimeCTVP', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [7, 7], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [130, 275, 275], 'spacing': [4.650736429273743, 2.361701649461784, 2.361701649461784], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 882.0, 'mean': 45.35713577270508, 'median': 48.0, 'min': -118.0, 'percentile_00_5': -48.0, 'percentile_99_5': 103.0, 'std': 26.203161239624023}}}, 'configuration': '3d_lowres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'Ctvp': 1}, 'numTraining': 60, 'file_ending': '.nii.gz', 'numTest': 0}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "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)",
+    "output_folder": "./data/nnUNet_results/Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres/fold_0",
+    "output_folder_base": "./data/nnUNet_results/Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset721_TSPrimeCTVP', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 12, 'patch_size': [512, 512], 'median_image_size_in_voxels': [512.0, 512.0], 'spacing': [1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [7, 7], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [130, 275, 275], 'spacing': [4.650736429273743, 2.361701649461784, 2.361701649461784], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [241.0, 512.0, 512.0], 'spacing': [2.5, 1.269531011581421, 1.269531011581421], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 882.0, 'mean': 45.35713577270508, 'median': 48.0, 'min': -118.0, 'percentile_00_5': -48.0, 'percentile_99_5': 103.0, 'std': 26.203161239624023}}}",
+    "preprocessed_dataset_folder": "./data/nnUNet_preprocessed/Dataset721_TSPrimeCTVP/nnUNetPlans_3d_lowres",
+    "preprocessed_dataset_folder_base": "./data/nnUNet_preprocessed/Dataset721_TSPrimeCTVP",
+    "save_every": "50",
+    "torch_version": "2.0.1+cu117",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres/fold_0/network_architecture

@@ -0,0 +1,171 @@
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+	"/outputs/110" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/111" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/112" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/113" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/114" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/115" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/116" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/117" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/118" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/119" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/120" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/121" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/122" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/123" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/124" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/125" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/126" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/127" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/128" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/129" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/130" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/131" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/132" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/133" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/134" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/135" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/136" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/137" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/138" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/139" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 2, 2]</td></tr></table>>]
+	"/outputs/140" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/141" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/142" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/143" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/144" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/145" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [1, 2, 2], stride: [1, 2, 2]</td></tr></table>>]
+	"/outputs/146" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/147" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/148" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/149" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/150" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/151" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/152" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/153" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/154" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/155" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/156" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/157" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/158" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/159" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/160" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/161" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/162" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/163" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/164" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/165" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/166" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/167" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/168" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/169" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/170" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/171" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/172" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/173" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/174" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/175" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/176" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/177" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/178" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/179" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/180" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/181" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>ConvTranspose, kernel_size: [2, 2, 2], stride: [2, 2, 2]</td></tr></table>>]
+	"/outputs/182" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Concat</td></tr></table>>]
+	"/outputs/183" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/184" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/185" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/186" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [3, 3, 3], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/187" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>InstanceNormalization</td></tr></table>>]
+	"/outputs/188" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>LeakyRelu</td></tr></table>>]
+	"/outputs/189" [label=<<table border='0' cellborder='0' cellpadding='0'><tr><td cellpadding='6'>Conv, kernel_size: [1, 1, 1], stride: [1, 1, 1]</td></tr></table>>]
+	"/outputs/109" -> "/outputs/110" [label="1x32x80x192x160"]
+	"/outputs/110" -> "/outputs/111" [label="1x32x80x192x160"]
+	"/outputs/111" -> "/outputs/112" [label="1x32x80x192x160"]
+	"/outputs/112" -> "/outputs/113" [label="1x32x80x192x160"]
+	"/outputs/113" -> "/outputs/114" [label="1x32x80x192x160"]
+	"/outputs/114" -> "/outputs/115" [label="1x32x80x192x160"]
+	"/outputs/114" -> "/outputs/182" [label="1x32x80x192x160"]
+	"/outputs/115" -> "/outputs/116" [label="1x64x40x96x80"]
+	"/outputs/116" -> "/outputs/117" [label="1x64x40x96x80"]
+	"/outputs/117" -> "/outputs/118" [label="1x64x40x96x80"]
+	"/outputs/118" -> "/outputs/119" [label="1x64x40x96x80"]
+	"/outputs/119" -> "/outputs/120" [label="1x64x40x96x80"]
+	"/outputs/120" -> "/outputs/121" [label="1x64x40x96x80"]
+	"/outputs/120" -> "/outputs/173" [label="1x64x40x96x80"]
+	"/outputs/121" -> "/outputs/122" [label="1x128x20x48x40"]
+	"/outputs/122" -> "/outputs/123" [label="1x128x20x48x40"]
+	"/outputs/123" -> "/outputs/124" [label="1x128x20x48x40"]
+	"/outputs/124" -> "/outputs/125" [label="1x128x20x48x40"]
+	"/outputs/125" -> "/outputs/126" [label="1x128x20x48x40"]
+	"/outputs/126" -> "/outputs/127" [label="1x128x20x48x40"]
+	"/outputs/126" -> "/outputs/164" [label="1x128x20x48x40"]
+	"/outputs/127" -> "/outputs/128" [label="1x256x10x24x20"]
+	"/outputs/128" -> "/outputs/129" [label="1x256x10x24x20"]
+	"/outputs/129" -> "/outputs/130" [label="1x256x10x24x20"]
+	"/outputs/130" -> "/outputs/131" [label="1x256x10x24x20"]
+	"/outputs/131" -> "/outputs/132" [label="1x256x10x24x20"]
+	"/outputs/132" -> "/outputs/133" [label="1x256x10x24x20"]
+	"/outputs/132" -> "/outputs/155" [label="1x256x10x24x20"]
+	"/outputs/133" -> "/outputs/134" [label="1x320x5x12x10"]
+	"/outputs/134" -> "/outputs/135" [label="1x320x5x12x10"]
+	"/outputs/135" -> "/outputs/136" [label="1x320x5x12x10"]
+	"/outputs/136" -> "/outputs/137" [label="1x320x5x12x10"]
+	"/outputs/137" -> "/outputs/138" [label="1x320x5x12x10"]
+	"/outputs/138" -> "/outputs/139" [label="1x320x5x12x10"]
+	"/outputs/138" -> "/outputs/146" [label="1x320x5x12x10"]
+	"/outputs/139" -> "/outputs/140" [label="1x320x5x6x5"]
+	"/outputs/140" -> "/outputs/141" [label="1x320x5x6x5"]
+	"/outputs/141" -> "/outputs/142" [label="1x320x5x6x5"]
+	"/outputs/142" -> "/outputs/143" [label="1x320x5x6x5"]
+	"/outputs/143" -> "/outputs/144" [label="1x320x5x6x5"]
+	"/outputs/144" -> "/outputs/145" [label="1x320x5x6x5"]
+	"/outputs/145" -> "/outputs/146" [label="1x320x5x12x10"]
+	"/outputs/146" -> "/outputs/147" [label="1x640x5x12x10"]
+	"/outputs/147" -> "/outputs/148" [label="1x320x5x12x10"]
+	"/outputs/148" -> "/outputs/149" [label="1x320x5x12x10"]
+	"/outputs/149" -> "/outputs/150" [label="1x320x5x12x10"]
+	"/outputs/150" -> "/outputs/151" [label="1x320x5x12x10"]
+	"/outputs/151" -> "/outputs/152" [label="1x320x5x12x10"]
+	"/outputs/152" -> "/outputs/153" [label="1x320x5x12x10"]
+	"/outputs/152" -> "/outputs/154" [label="1x320x5x12x10"]
+	"/outputs/154" -> "/outputs/155" [label="1x256x10x24x20"]
+	"/outputs/155" -> "/outputs/156" [label="1x512x10x24x20"]
+	"/outputs/156" -> "/outputs/157" [label="1x256x10x24x20"]
+	"/outputs/157" -> "/outputs/158" [label="1x256x10x24x20"]
+	"/outputs/158" -> "/outputs/159" [label="1x256x10x24x20"]
+	"/outputs/159" -> "/outputs/160" [label="1x256x10x24x20"]
+	"/outputs/160" -> "/outputs/161" [label="1x256x10x24x20"]
+	"/outputs/161" -> "/outputs/162" [label="1x256x10x24x20"]
+	"/outputs/161" -> "/outputs/163" [label="1x256x10x24x20"]
+	"/outputs/163" -> "/outputs/164" [label="1x128x20x48x40"]
+	"/outputs/164" -> "/outputs/165" [label="1x256x20x48x40"]
+	"/outputs/165" -> "/outputs/166" [label="1x128x20x48x40"]
+	"/outputs/166" -> "/outputs/167" [label="1x128x20x48x40"]
+	"/outputs/167" -> "/outputs/168" [label="1x128x20x48x40"]
+	"/outputs/168" -> "/outputs/169" [label="1x128x20x48x40"]
+	"/outputs/169" -> "/outputs/170" [label="1x128x20x48x40"]
+	"/outputs/170" -> "/outputs/171" [label="1x128x20x48x40"]
+	"/outputs/170" -> "/outputs/172" [label="1x128x20x48x40"]
+	"/outputs/172" -> "/outputs/173" [label="1x64x40x96x80"]
+	"/outputs/173" -> "/outputs/174" [label="1x128x40x96x80"]
+	"/outputs/174" -> "/outputs/175" [label="1x64x40x96x80"]
+	"/outputs/175" -> "/outputs/176" [label="1x64x40x96x80"]
+	"/outputs/176" -> "/outputs/177" [label="1x64x40x96x80"]
+	"/outputs/177" -> "/outputs/178" [label="1x64x40x96x80"]
+	"/outputs/178" -> "/outputs/179" [label="1x64x40x96x80"]
+	"/outputs/179" -> "/outputs/180" [label="1x64x40x96x80"]
+	"/outputs/179" -> "/outputs/181" [label="1x64x40x96x80"]
+	"/outputs/181" -> "/outputs/182" [label="1x32x80x192x160"]
+	"/outputs/182" -> "/outputs/183" [label="1x64x80x192x160"]
+	"/outputs/183" -> "/outputs/184" [label="1x32x80x192x160"]
+	"/outputs/184" -> "/outputs/185" [label="1x32x80x192x160"]
+	"/outputs/185" -> "/outputs/186" [label="1x32x80x192x160"]
+	"/outputs/186" -> "/outputs/187" [label="1x32x80x192x160"]
+	"/outputs/187" -> "/outputs/188" [label="1x32x80x192x160"]
+	"/outputs/188" -> "/outputs/189" [label="1x32x80x192x160"]
+}

Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres/fold_0/training_log_2023_11_5_22_05_41.txt

@@ -0,0 +1,660 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+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.
+#######################################################################
+ 
+
+This is the configuration used by this training:
+Configuration name: 3d_lowres
+ {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [80, 192, 160], 'median_image_size_in_voxels': [130, 275, 275], 'spacing': [4.650736429273743, 2.361701649461784, 2.361701649461784], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset721_TSPrimeCTVP', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [2.5, 1.269531011581421, 1.269531011581421], 'original_median_shape_after_transp': [241, 512, 512], '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': 882.0, 'mean': 45.35713577270508, 'median': 48.0, 'min': -118.0, 'percentile_00_5': -48.0, 'percentile_99_5': 103.0, 'std': 26.203161239624023}}} 
+ 
+2023-11-05 22:05:43.766089: unpacking dataset... 
+2023-11-05 22:05:52.983822: unpacking done... 
+2023-11-05 22:05:53.001199: do_dummy_2d_data_aug: False 
+2023-11-05 22:05:53.001824: Using splits from existing split file: ./data/nnUNet_preprocessed/Dataset721_TSPrimeCTVP/splits_final.json 
+2023-11-05 22:05:53.022583: The split file contains 5 splits. 
+2023-11-05 22:05:53.022662: Desired fold for training: 0 
+2023-11-05 22:05:53.022732: This split has 48 training and 12 validation cases. 
+2023-11-05 22:06:32.735970: Unable to plot network architecture: 
+2023-11-05 22:06:32.736120: failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH 
+2023-11-05 22:06:32.837145:  
+2023-11-05 22:06:32.837218: Epoch 0 
+2023-11-05 22:06:32.837328: Current learning rate: 0.01 
+2023-11-05 22:20:54.774893: train_loss 0.0022 
+2023-11-05 22:20:54.775081: val_loss -0.1688 
+2023-11-05 22:20:54.775169: Pseudo dice [0.0] 
+2023-11-05 22:20:54.775265: Epoch time: 861.94 s 
+2023-11-05 22:20:54.775342: Yayy! New best EMA pseudo Dice: 0.0 
+2023-11-05 22:20:56.371249:  
+2023-11-05 22:20:56.371469: Epoch 1 
+2023-11-05 22:20:56.371657: Current learning rate: 0.00999 
+2023-11-05 22:31:50.078054: train_loss -0.4984 
+2023-11-05 22:31:50.078262: val_loss -0.6578 
+2023-11-05 22:31:50.078343: Pseudo dice [0.7269] 
+2023-11-05 22:31:50.078430: Epoch time: 653.71 s 
+2023-11-05 22:31:50.078506: Yayy! New best EMA pseudo Dice: 0.0727 
+2023-11-05 22:31:53.171840:  
+2023-11-05 22:31:53.172024: Epoch 2 
+2023-11-05 22:31:53.172153: Current learning rate: 0.00998 
+2023-11-05 22:42:48.347788: train_loss -0.6775 
+2023-11-05 22:42:48.347950: val_loss -0.7266 
+2023-11-05 22:42:48.348027: Pseudo dice [0.7937] 
+2023-11-05 22:42:48.348120: Epoch time: 655.18 s 
+2023-11-05 22:42:48.348189: Yayy! New best EMA pseudo Dice: 0.1448 
+2023-11-05 22:42:51.579526:  
+2023-11-05 22:42:51.579650: Epoch 3 
+2023-11-05 22:42:51.579753: Current learning rate: 0.00997 
+2023-11-05 22:53:46.176134: train_loss -0.7185 
+2023-11-05 22:53:46.176269: val_loss -0.7461 
+2023-11-05 22:53:46.176361: Pseudo dice [0.8043] 
+2023-11-05 22:53:46.176449: Epoch time: 654.6 s 
+2023-11-05 22:53:46.176519: Yayy! New best EMA pseudo Dice: 0.2107 
+2023-11-05 22:53:49.545828:  
+2023-11-05 22:53:49.545945: Epoch 4 
+2023-11-05 22:53:49.546050: Current learning rate: 0.00996 
+2023-11-05 23:04:43.303561: train_loss -0.7317 
+2023-11-05 23:04:43.303715: val_loss -0.7646 
+2023-11-05 23:04:43.303797: Pseudo dice [0.8278] 
+2023-11-05 23:04:43.303883: Epoch time: 653.76 s 
+2023-11-05 23:04:43.303957: Yayy! New best EMA pseudo Dice: 0.2725 
+2023-11-05 23:04:46.916903:  
+2023-11-05 23:04:46.917109: Epoch 5 
+2023-11-05 23:04:46.917217: Current learning rate: 0.00995 
+2023-11-05 23:15:41.269564: train_loss -0.7454 
+2023-11-05 23:15:41.269719: val_loss -0.776 
+2023-11-05 23:15:41.269796: Pseudo dice [0.8309] 
+2023-11-05 23:15:41.269877: Epoch time: 654.35 s 
+2023-11-05 23:15:41.269946: Yayy! New best EMA pseudo Dice: 0.3283 
+2023-11-05 23:15:44.430620:  
+2023-11-05 23:15:44.430745: Epoch 6 
+2023-11-05 23:15:44.430851: Current learning rate: 0.00995 
+2023-11-05 23:26:37.331294: train_loss -0.7739 
+2023-11-05 23:26:37.331445: val_loss -0.7867 
+2023-11-05 23:26:37.331522: Pseudo dice [0.8319] 
+2023-11-05 23:26:37.331612: Epoch time: 652.9 s 
+2023-11-05 23:26:37.331681: Yayy! New best EMA pseudo Dice: 0.3787 
+2023-11-05 23:26:40.667429:  
+2023-11-05 23:26:40.667527: Epoch 7 
+2023-11-05 23:26:40.667621: Current learning rate: 0.00994 
+2023-11-05 23:37:36.057433: train_loss -0.7721 
+2023-11-05 23:37:36.057588: val_loss -0.7854 
+2023-11-05 23:37:36.057666: Pseudo dice [0.8414] 
+2023-11-05 23:37:36.057748: Epoch time: 655.39 s 
+2023-11-05 23:37:36.057818: Yayy! New best EMA pseudo Dice: 0.4249 
+2023-11-05 23:37:39.249048:  
+2023-11-05 23:37:39.249249: Epoch 8 
+2023-11-05 23:37:39.249468: Current learning rate: 0.00993 
+2023-11-05 23:48:34.520747: train_loss -0.7639 
+2023-11-05 23:48:34.520913: val_loss -0.7863 
+2023-11-05 23:48:34.520990: Pseudo dice [0.8342] 
+2023-11-05 23:48:34.521071: Epoch time: 655.27 s 
+2023-11-05 23:48:34.521140: Yayy! New best EMA pseudo Dice: 0.4659 
+2023-11-05 23:48:37.934005:  
+2023-11-05 23:48:37.934186: Epoch 9 
+2023-11-05 23:48:37.934292: Current learning rate: 0.00992 
+2023-11-05 23:59:31.744097: train_loss -0.7754 
+2023-11-05 23:59:31.744260: val_loss -0.7949 
+2023-11-05 23:59:31.744335: Pseudo dice [0.83] 
+2023-11-05 23:59:31.744416: Epoch time: 653.81 s 
+2023-11-05 23:59:31.744484: Yayy! New best EMA pseudo Dice: 0.5023 
+2023-11-05 23:59:35.221768:  
+2023-11-05 23:59:35.221899: Epoch 10 
+2023-11-05 23:59:35.222059: Current learning rate: 0.00991 
+2023-11-06 00:10:30.542706: train_loss -0.7745 
+2023-11-06 00:10:30.542849: val_loss -0.7849 
+2023-11-06 00:10:30.542925: Pseudo dice [0.8183] 
+2023-11-06 00:10:30.543005: Epoch time: 655.32 s 
+2023-11-06 00:10:30.543073: Yayy! New best EMA pseudo Dice: 0.5339 
+2023-11-06 00:10:33.828026:  
+2023-11-06 00:10:33.828140: Epoch 11 
+2023-11-06 00:10:33.828271: Current learning rate: 0.0099 
+2023-11-06 00:21:29.170071: train_loss -0.7769 
+2023-11-06 00:21:29.170231: val_loss -0.7886 
+2023-11-06 00:21:29.170306: Pseudo dice [0.8436] 
+2023-11-06 00:21:29.170388: Epoch time: 655.34 s 
+2023-11-06 00:21:29.170456: Yayy! New best EMA pseudo Dice: 0.5648 
+2023-11-06 00:21:32.330414:  
+2023-11-06 00:21:32.330636: Epoch 12 
+2023-11-06 00:21:32.330763: Current learning rate: 0.00989 
+2023-11-06 00:32:31.568352: train_loss -0.7846 
+2023-11-06 00:32:31.568504: val_loss -0.7826 
+2023-11-06 00:32:31.568580: Pseudo dice [0.8258] 
+2023-11-06 00:32:31.568660: Epoch time: 659.24 s 
+2023-11-06 00:32:31.568729: Yayy! New best EMA pseudo Dice: 0.5909 
+2023-11-06 00:32:34.977341:  
+2023-11-06 00:32:34.977602: Epoch 13 
+2023-11-06 00:32:34.977876: Current learning rate: 0.00988 
+2023-11-06 00:43:36.160741: train_loss -0.7869 
+2023-11-06 00:43:36.160885: val_loss -0.7834 
+2023-11-06 00:43:36.160981: Pseudo dice [0.8317] 
+2023-11-06 00:43:36.161071: Epoch time: 661.18 s 
+2023-11-06 00:43:36.161148: Yayy! New best EMA pseudo Dice: 0.615 
+2023-11-06 00:43:42.027902:  
+2023-11-06 00:43:42.028009: Epoch 14 
+2023-11-06 00:43:42.028111: Current learning rate: 0.00987 
+2023-11-06 00:54:42.362827: train_loss -0.785 
+2023-11-06 00:54:42.362983: val_loss -0.7905 
+2023-11-06 00:54:42.363072: Pseudo dice [0.8346] 
+2023-11-06 00:54:42.363162: Epoch time: 660.34 s 
+2023-11-06 00:54:42.363242: Yayy! New best EMA pseudo Dice: 0.637 
+2023-11-06 00:54:45.816739:  
+2023-11-06 00:54:45.816881: Epoch 15 
+2023-11-06 00:54:45.816998: Current learning rate: 0.00986 
+2023-11-06 01:05:39.600759: train_loss -0.7845 
+2023-11-06 01:05:39.600898: val_loss -0.7838 
+2023-11-06 01:05:39.600987: Pseudo dice [0.8295] 
+2023-11-06 01:05:39.601068: Epoch time: 653.78 s 
+2023-11-06 01:05:39.601136: Yayy! New best EMA pseudo Dice: 0.6562 
+2023-11-06 01:05:43.176803:  
+2023-11-06 01:05:43.176908: Epoch 16 
+2023-11-06 01:05:43.177009: Current learning rate: 0.00986 
+2023-11-06 01:16:50.574224: train_loss -0.7986 
+2023-11-06 01:16:50.574384: val_loss -0.8016 
+2023-11-06 01:16:50.574479: Pseudo dice [0.845] 
+2023-11-06 01:16:50.574569: Epoch time: 667.4 s 
+2023-11-06 01:16:50.574645: Yayy! New best EMA pseudo Dice: 0.6751 
+2023-11-06 01:16:53.886550:  
+2023-11-06 01:16:53.886660: Epoch 17 
+2023-11-06 01:16:53.886781: Current learning rate: 0.00985 
+2023-11-06 01:27:54.560711: train_loss -0.7931 
+2023-11-06 01:27:54.560856: val_loss -0.81 
+2023-11-06 01:27:54.560950: Pseudo dice [0.8518] 
+2023-11-06 01:27:54.561041: Epoch time: 660.67 s 
+2023-11-06 01:27:54.561118: Yayy! New best EMA pseudo Dice: 0.6928 
+2023-11-06 01:27:57.874895:  
+2023-11-06 01:27:57.875184: Epoch 18 
+2023-11-06 01:27:57.875363: Current learning rate: 0.00984 
+2023-11-06 01:38:55.045314: train_loss -0.7949 
+2023-11-06 01:38:55.045470: val_loss -0.7958 
+2023-11-06 01:38:55.045547: Pseudo dice [0.828] 
+2023-11-06 01:38:55.045628: Epoch time: 657.17 s 
+2023-11-06 01:38:55.045697: Yayy! New best EMA pseudo Dice: 0.7063 
+2023-11-06 01:38:58.180024:  
+2023-11-06 01:38:58.180334: Epoch 19 
+2023-11-06 01:38:58.180515: Current learning rate: 0.00983 
+2023-11-06 01:47:52.886719: train_loss -0.7978 
+2023-11-06 01:47:52.886871: val_loss -0.8033 
+2023-11-06 01:47:52.886948: Pseudo dice [0.8408] 
+2023-11-06 01:47:52.887028: Epoch time: 534.71 s 
+2023-11-06 01:47:52.887096: Yayy! New best EMA pseudo Dice: 0.7197 
+2023-11-06 01:47:56.292678:  
+2023-11-06 01:47:56.292835: Epoch 20 
+2023-11-06 01:47:56.292980: Current learning rate: 0.00982 
+2023-11-06 01:55:44.553798: train_loss -0.8072 
+2023-11-06 01:55:44.553949: val_loss -0.8016 
+2023-11-06 01:55:44.554031: Pseudo dice [0.8365] 
+2023-11-06 01:55:44.554116: Epoch time: 468.26 s 
+2023-11-06 01:55:44.554189: Yayy! New best EMA pseudo Dice: 0.7314 
+2023-11-06 01:55:47.659638:  
+2023-11-06 01:55:47.659833: Epoch 21 
+2023-11-06 01:55:47.660119: Current learning rate: 0.00981 
+2023-11-06 02:03:35.627137: train_loss -0.8073 
+2023-11-06 02:03:35.627289: val_loss -0.809 
+2023-11-06 02:03:35.627386: Pseudo dice [0.8394] 
+2023-11-06 02:03:35.627477: Epoch time: 467.97 s 
+2023-11-06 02:03:35.627555: Yayy! New best EMA pseudo Dice: 0.7422 
+2023-11-06 02:03:38.898279:  
+2023-11-06 02:03:38.898384: Epoch 22 
+2023-11-06 02:03:38.898483: Current learning rate: 0.0098 
+2023-11-06 02:13:19.098450: train_loss -0.8012 
+2023-11-06 02:13:19.098597: val_loss -0.794 
+2023-11-06 02:13:19.098699: Pseudo dice [0.8268] 
+2023-11-06 02:13:19.098793: Epoch time: 580.2 s 
+2023-11-06 02:13:19.098870: Yayy! New best EMA pseudo Dice: 0.7507 
+2023-11-06 02:13:22.567191:  
+2023-11-06 02:13:22.567326: Epoch 23 
+2023-11-06 02:13:22.567432: Current learning rate: 0.00979 
+2023-11-06 02:24:37.768249: train_loss -0.8039 
+2023-11-06 02:24:37.768389: val_loss -0.8143 
+2023-11-06 02:24:37.768477: Pseudo dice [0.8576] 
+2023-11-06 02:24:37.768565: Epoch time: 675.2 s 
+2023-11-06 02:24:37.768634: Yayy! New best EMA pseudo Dice: 0.7614 
+2023-11-06 02:24:41.063595:  
+2023-11-06 02:24:41.063719: Epoch 24 
+2023-11-06 02:24:41.063822: Current learning rate: 0.00978 
+2023-11-06 02:35:56.216298: train_loss -0.8141 
+2023-11-06 02:35:56.216446: val_loss -0.8112 
+2023-11-06 02:35:56.216523: Pseudo dice [0.8314] 
+2023-11-06 02:35:56.216605: Epoch time: 675.15 s 
+2023-11-06 02:35:56.216681: Yayy! New best EMA pseudo Dice: 0.7684 
+2023-11-06 02:35:59.473060:  
+2023-11-06 02:35:59.473267: Epoch 25 
+2023-11-06 02:35:59.473394: Current learning rate: 0.00977 
+2023-11-06 02:47:14.841282: train_loss -0.8238 
+2023-11-06 02:47:14.841446: val_loss -0.8127 
+2023-11-06 02:47:14.841526: Pseudo dice [0.8517] 
+2023-11-06 02:47:14.841607: Epoch time: 675.37 s 
+2023-11-06 02:47:14.841675: Yayy! New best EMA pseudo Dice: 0.7767 
+2023-11-06 02:47:18.199337:  
+2023-11-06 02:47:18.199439: Epoch 26 
+2023-11-06 02:47:18.199549: Current learning rate: 0.00977 
+2023-11-06 02:58:33.463852: train_loss -0.8094 
+2023-11-06 02:58:33.464010: val_loss -0.8178 
+2023-11-06 02:58:33.464087: Pseudo dice [0.8539] 
+2023-11-06 02:58:33.464169: Epoch time: 675.27 s 
+2023-11-06 02:58:33.464237: Yayy! New best EMA pseudo Dice: 0.7844 
+2023-11-06 02:58:36.681627:  
+2023-11-06 02:58:36.681730: Epoch 27 
+2023-11-06 02:58:36.681832: Current learning rate: 0.00976 
+2023-11-06 03:09:51.961360: train_loss -0.8114 
+2023-11-06 03:09:51.961498: val_loss -0.7815 
+2023-11-06 03:09:51.961591: Pseudo dice [0.8219] 
+2023-11-06 03:09:51.961673: Epoch time: 675.28 s 
+2023-11-06 03:09:51.961741: Yayy! New best EMA pseudo Dice: 0.7882 
+2023-11-06 03:09:55.427147:  
+2023-11-06 03:09:55.427268: Epoch 28 
+2023-11-06 03:09:55.427380: Current learning rate: 0.00975 
+2023-11-06 03:21:10.383088: train_loss -0.7997 
+2023-11-06 03:21:10.383252: val_loss -0.7981 
+2023-11-06 03:21:10.383328: Pseudo dice [0.8349] 
+2023-11-06 03:21:10.383410: Epoch time: 674.96 s 
+2023-11-06 03:21:10.383477: Yayy! New best EMA pseudo Dice: 0.7928 
+2023-11-06 03:21:13.641899:  
+2023-11-06 03:21:13.641999: Epoch 29 
+2023-11-06 03:21:13.642121: Current learning rate: 0.00974 
+2023-11-06 03:32:28.908478: train_loss -0.806 
+2023-11-06 03:32:28.908639: val_loss -0.8071 
+2023-11-06 03:32:28.908733: Pseudo dice [0.8445] 
+2023-11-06 03:32:28.908826: Epoch time: 675.27 s 
+2023-11-06 03:32:28.908903: Yayy! New best EMA pseudo Dice: 0.798 
+2023-11-06 03:32:32.713846:  
+2023-11-06 03:32:32.714119: Epoch 30 
+2023-11-06 03:32:32.714329: Current learning rate: 0.00973 
+2023-11-06 03:43:47.583081: train_loss -0.8078 
+2023-11-06 03:43:47.583235: val_loss -0.7946 
+2023-11-06 03:43:47.583393: Pseudo dice [0.8358] 
+2023-11-06 03:43:47.583498: Epoch time: 674.87 s 
+2023-11-06 03:43:47.583567: Yayy! New best EMA pseudo Dice: 0.8018 
+2023-11-06 03:43:50.914172:  
+2023-11-06 03:43:50.914282: Epoch 31 
+2023-11-06 03:43:50.914385: Current learning rate: 0.00972 
+2023-11-06 03:55:05.869875: train_loss -0.8134 
+2023-11-06 03:55:05.870045: val_loss -0.8119 
+2023-11-06 03:55:05.870121: Pseudo dice [0.8536] 
+2023-11-06 03:55:05.870202: Epoch time: 674.96 s 
+2023-11-06 03:55:05.870270: Yayy! New best EMA pseudo Dice: 0.807 
+2023-11-06 03:55:09.261483:  
+2023-11-06 03:55:09.261622: Epoch 32 
+2023-11-06 03:55:09.261738: Current learning rate: 0.00971 
+2023-11-06 04:06:24.365536: train_loss -0.814 
+2023-11-06 04:06:24.365684: val_loss -0.799 
+2023-11-06 04:06:24.365768: Pseudo dice [0.8439] 
+2023-11-06 04:06:24.365851: Epoch time: 675.1 s 
+2023-11-06 04:06:24.365922: Yayy! New best EMA pseudo Dice: 0.8107 
+2023-11-06 04:06:27.632730:  
+2023-11-06 04:06:27.632976: Epoch 33 
+2023-11-06 04:06:27.633147: Current learning rate: 0.0097 
+2023-11-06 04:17:42.891809: train_loss -0.8178 
+2023-11-06 04:17:42.891958: val_loss -0.8109 
+2023-11-06 04:17:42.892051: Pseudo dice [0.8469] 
+2023-11-06 04:17:42.892142: Epoch time: 675.26 s 
+2023-11-06 04:17:42.892221: Yayy! New best EMA pseudo Dice: 0.8143 
+2023-11-06 04:17:46.135388:  
+2023-11-06 04:17:46.135488: Epoch 34 
+2023-11-06 04:17:46.135598: Current learning rate: 0.00969 
+2023-11-06 04:29:01.376284: train_loss -0.817 
+2023-11-06 04:29:01.376450: val_loss -0.8104 
+2023-11-06 04:29:01.376533: Pseudo dice [0.855] 
+2023-11-06 04:29:01.376614: Epoch time: 675.24 s 
+2023-11-06 04:29:01.376683: Yayy! New best EMA pseudo Dice: 0.8184 
+2023-11-06 04:29:04.748322:  
+2023-11-06 04:29:04.748513: Epoch 35 
+2023-11-06 04:29:04.748706: Current learning rate: 0.00968 
+2023-11-06 04:40:19.958354: train_loss -0.8086 
+2023-11-06 04:40:19.958521: val_loss -0.8051 
+2023-11-06 04:40:19.958597: Pseudo dice [0.8335] 
+2023-11-06 04:40:19.958687: Epoch time: 675.21 s 
+2023-11-06 04:40:19.958761: Yayy! New best EMA pseudo Dice: 0.8199 
+2023-11-06 04:40:23.530188:  
+2023-11-06 04:40:23.530292: Epoch 36 
+2023-11-06 04:40:23.530401: Current learning rate: 0.00968 
+2023-11-06 04:51:38.573648: train_loss -0.8159 
+2023-11-06 04:51:38.573810: val_loss -0.8143 
+2023-11-06 04:51:38.573904: Pseudo dice [0.8454] 
+2023-11-06 04:51:38.573995: Epoch time: 675.04 s 
+2023-11-06 04:51:38.574072: Yayy! New best EMA pseudo Dice: 0.8224 
+2023-11-06 04:51:42.123522:  
+2023-11-06 04:51:42.123701: Epoch 37 
+2023-11-06 04:51:42.123864: Current learning rate: 0.00967 
+2023-11-06 05:02:57.064787: train_loss -0.8144 
+2023-11-06 05:02:57.064952: val_loss -0.81 
+2023-11-06 05:02:57.065049: Pseudo dice [0.8435] 
+2023-11-06 05:02:57.065142: Epoch time: 674.94 s 
+2023-11-06 05:02:57.065221: Yayy! New best EMA pseudo Dice: 0.8245 
+2023-11-06 05:03:00.421975:  
+2023-11-06 05:03:00.422125: Epoch 38 
+2023-11-06 05:03:00.422287: Current learning rate: 0.00966 
+2023-11-06 05:14:15.585457: train_loss -0.8254 
+2023-11-06 05:14:15.585616: val_loss -0.7961 
+2023-11-06 05:14:15.585695: Pseudo dice [0.83] 
+2023-11-06 05:14:15.585777: Epoch time: 675.16 s 
+2023-11-06 05:14:15.585849: Yayy! New best EMA pseudo Dice: 0.8251 
+2023-11-06 05:14:19.062620:  
+2023-11-06 05:14:19.062906: Epoch 39 
+2023-11-06 05:14:19.063134: Current learning rate: 0.00965 
+2023-11-06 05:25:34.054948: train_loss -0.8194 
+2023-11-06 05:25:34.055121: val_loss -0.8239 
+2023-11-06 05:25:34.055197: Pseudo dice [0.8627] 
+2023-11-06 05:25:34.055279: Epoch time: 674.99 s 
+2023-11-06 05:25:34.055348: Yayy! New best EMA pseudo Dice: 0.8288 
+2023-11-06 05:25:37.355520:  
+2023-11-06 05:25:37.355628: Epoch 40 
+2023-11-06 05:25:37.355733: Current learning rate: 0.00964 
+2023-11-06 05:36:52.323830: train_loss -0.8123 
+2023-11-06 05:36:52.323998: val_loss -0.8085 
+2023-11-06 05:36:52.324074: Pseudo dice [0.8389] 
+2023-11-06 05:36:52.324154: Epoch time: 674.97 s 
+2023-11-06 05:36:52.324223: Yayy! New best EMA pseudo Dice: 0.8298 
+2023-11-06 05:36:55.576262:  
+2023-11-06 05:36:55.576443: Epoch 41 
+2023-11-06 05:36:55.576548: Current learning rate: 0.00963 
+2023-11-06 05:48:11.067998: train_loss -0.8271 
+2023-11-06 05:48:11.068160: val_loss -0.8201 
+2023-11-06 05:48:11.068256: Pseudo dice [0.857] 
+2023-11-06 05:48:11.068349: Epoch time: 675.49 s 
+2023-11-06 05:48:11.068427: Yayy! New best EMA pseudo Dice: 0.8326 
+2023-11-06 05:48:14.317959:  
+2023-11-06 05:48:14.318098: Epoch 42 
+2023-11-06 05:48:14.318215: Current learning rate: 0.00962 
+2023-11-06 05:59:29.383212: train_loss -0.8256 
+2023-11-06 05:59:29.383366: val_loss -0.8221 
+2023-11-06 05:59:29.383451: Pseudo dice [0.8575] 
+2023-11-06 05:59:29.383550: Epoch time: 675.07 s 
+2023-11-06 05:59:29.383627: Yayy! New best EMA pseudo Dice: 0.835 
+2023-11-06 05:59:32.576050:  
+2023-11-06 05:59:32.576236: Epoch 43 
+2023-11-06 05:59:32.576383: Current learning rate: 0.00961 
+2023-11-06 06:10:47.765534: train_loss -0.8276 
+2023-11-06 06:10:47.765698: val_loss -0.8066 
+2023-11-06 06:10:47.765776: Pseudo dice [0.8416] 
+2023-11-06 06:10:47.765857: Epoch time: 675.19 s 
+2023-11-06 06:10:47.765927: Yayy! New best EMA pseudo Dice: 0.8357 
+2023-11-06 06:10:51.414655:  
+2023-11-06 06:10:51.414837: Epoch 44 
+2023-11-06 06:10:51.415005: Current learning rate: 0.0096 
+2023-11-06 06:22:06.945473: train_loss -0.8151 
+2023-11-06 06:22:06.945662: val_loss -0.8149 
+2023-11-06 06:22:06.945739: Pseudo dice [0.842] 
+2023-11-06 06:22:06.945817: Epoch time: 675.53 s 
+2023-11-06 06:22:06.945896: Yayy! New best EMA pseudo Dice: 0.8363 
+2023-11-06 06:22:10.103697:  
+2023-11-06 06:22:10.103803: Epoch 45 
+2023-11-06 06:22:10.103918: Current learning rate: 0.00959 
+2023-11-06 06:33:25.378408: train_loss -0.8281 
+2023-11-06 06:33:25.378564: val_loss -0.8194 
+2023-11-06 06:33:25.378639: Pseudo dice [0.8511] 
+2023-11-06 06:33:25.378727: Epoch time: 675.28 s 
+2023-11-06 06:33:25.378796: Yayy! New best EMA pseudo Dice: 0.8378 
+2023-11-06 06:33:28.743157:  
+2023-11-06 06:33:28.743263: Epoch 46 
+2023-11-06 06:33:28.743382: Current learning rate: 0.00959 
+2023-11-06 06:44:43.864779: train_loss -0.8172 
+2023-11-06 06:44:43.864925: val_loss -0.8076 
+2023-11-06 06:44:43.865001: Pseudo dice [0.8374] 
+2023-11-06 06:44:43.865081: Epoch time: 675.12 s 
+2023-11-06 06:44:45.082992:  
+2023-11-06 06:44:45.083101: Epoch 47 
+2023-11-06 06:44:45.083211: Current learning rate: 0.00958 
+2023-11-06 06:55:59.903310: train_loss -0.8225 
+2023-11-06 06:55:59.903490: val_loss -0.8182 
+2023-11-06 06:55:59.903566: Pseudo dice [0.8473] 
+2023-11-06 06:55:59.903647: Epoch time: 674.82 s 
+2023-11-06 06:55:59.903717: Yayy! New best EMA pseudo Dice: 0.8387 
+2023-11-06 06:56:03.120151:  
+2023-11-06 06:56:03.120266: Epoch 48 
+2023-11-06 06:56:03.120366: Current learning rate: 0.00957 
+2023-11-06 07:07:18.367775: train_loss -0.8177 
+2023-11-06 07:07:18.367926: val_loss -0.809 
+2023-11-06 07:07:18.368003: Pseudo dice [0.834] 
+2023-11-06 07:07:18.368084: Epoch time: 675.25 s 
+2023-11-06 07:07:19.602919:  
+2023-11-06 07:07:19.603036: Epoch 49 
+2023-11-06 07:07:19.603153: Current learning rate: 0.00956 
+2023-11-06 07:18:34.630416: train_loss -0.8252 
+2023-11-06 07:18:34.630569: val_loss -0.8214 
+2023-11-06 07:18:34.630645: Pseudo dice [0.8596] 
+2023-11-06 07:18:34.630736: Epoch time: 675.03 s 
+2023-11-06 07:18:34.913779: Yayy! New best EMA pseudo Dice: 0.8404 
+2023-11-06 07:18:38.290022:  
+2023-11-06 07:18:38.290306: Epoch 50 
+2023-11-06 07:18:38.290472: Current learning rate: 0.00955 
+2023-11-06 07:29:52.160251: train_loss -0.8324 
+2023-11-06 07:29:52.160417: val_loss -0.8225 
+2023-11-06 07:29:52.160571: Pseudo dice [0.8528] 
+2023-11-06 07:29:52.160689: Epoch time: 673.87 s 
+2023-11-06 07:29:52.160758: Yayy! New best EMA pseudo Dice: 0.8416 
+2023-11-06 07:29:55.579350:  
+2023-11-06 07:29:55.579564: Epoch 51 
+2023-11-06 07:29:55.579664: Current learning rate: 0.00954 
+2023-11-06 07:41:10.809812: train_loss -0.8262 
+2023-11-06 07:41:10.809976: val_loss -0.8195 
+2023-11-06 07:41:10.810052: Pseudo dice [0.8512] 
+2023-11-06 07:41:10.810133: Epoch time: 675.23 s 
+2023-11-06 07:41:10.810200: Yayy! New best EMA pseudo Dice: 0.8426 
+2023-11-06 07:41:14.117213:  
+2023-11-06 07:41:14.117320: Epoch 52 
+2023-11-06 07:41:14.117436: Current learning rate: 0.00953 
+2023-11-06 07:52:29.440799: train_loss -0.8204 
+2023-11-06 07:52:29.440960: val_loss -0.8119 
+2023-11-06 07:52:29.441036: Pseudo dice [0.8433] 
+2023-11-06 07:52:29.441117: Epoch time: 675.32 s 
+2023-11-06 07:52:29.441186: Yayy! New best EMA pseudo Dice: 0.8427 
+2023-11-06 07:52:32.941524:  
+2023-11-06 07:52:32.941636: Epoch 53 
+2023-11-06 07:52:32.941733: Current learning rate: 0.00952 
+2023-11-06 08:03:48.102913: train_loss -0.8276 
+2023-11-06 08:03:48.103097: val_loss -0.8167 
+2023-11-06 08:03:48.103188: Pseudo dice [0.861] 
+2023-11-06 08:03:48.103277: Epoch time: 675.16 s 
+2023-11-06 08:03:48.103354: Yayy! New best EMA pseudo Dice: 0.8445 
+2023-11-06 08:03:51.334111:  
+2023-11-06 08:03:51.334226: Epoch 54 
+2023-11-06 08:03:51.334339: Current learning rate: 0.00951 
+2023-11-06 08:15:06.513220: train_loss -0.8313 
+2023-11-06 08:15:06.513460: val_loss -0.8093 
+2023-11-06 08:15:06.513551: Pseudo dice [0.8479] 
+2023-11-06 08:15:06.513641: Epoch time: 675.18 s 
+2023-11-06 08:15:06.513718: Yayy! New best EMA pseudo Dice: 0.8448 
+2023-11-06 08:15:09.747582:  
+2023-11-06 08:15:09.747695: Epoch 55 
+2023-11-06 08:15:09.747810: Current learning rate: 0.0095 
+2023-11-06 08:26:25.128954: train_loss -0.8234 
+2023-11-06 08:26:25.129091: val_loss -0.8087 
+2023-11-06 08:26:25.129179: Pseudo dice [0.8479] 
+2023-11-06 08:26:25.129266: Epoch time: 675.38 s 
+2023-11-06 08:26:25.129333: Yayy! New best EMA pseudo Dice: 0.8451 
+2023-11-06 08:26:28.348276:  
+2023-11-06 08:26:28.348374: Epoch 56 
+2023-11-06 08:26:28.348496: Current learning rate: 0.00949 
+2023-11-06 08:37:43.825908: train_loss -0.8124 
+2023-11-06 08:37:43.826058: val_loss -0.8055 
+2023-11-06 08:37:43.826153: Pseudo dice [0.8376] 
+2023-11-06 08:37:43.826247: Epoch time: 675.48 s 
+2023-11-06 08:37:45.064820:  
+2023-11-06 08:37:45.064925: Epoch 57 
+2023-11-06 08:37:45.065036: Current learning rate: 0.00949 
+2023-11-06 08:49:00.095835: train_loss -0.8276 
+2023-11-06 08:49:00.096008: val_loss -0.8191 
+2023-11-06 08:49:00.096088: Pseudo dice [0.8536] 
+2023-11-06 08:49:00.096174: Epoch time: 675.03 s 
+2023-11-06 08:49:00.096246: Yayy! New best EMA pseudo Dice: 0.8453 
+2023-11-06 08:49:03.525604:  
+2023-11-06 08:49:03.525789: Epoch 58 
+2023-11-06 08:49:03.525964: Current learning rate: 0.00948 
+2023-11-06 09:00:18.875708: train_loss -0.8285 
+2023-11-06 09:00:18.875870: val_loss -0.8237 
+2023-11-06 09:00:18.875946: Pseudo dice [0.8581] 
+2023-11-06 09:00:18.876027: Epoch time: 675.35 s 
+2023-11-06 09:00:18.876095: Yayy! New best EMA pseudo Dice: 0.8466 
+2023-11-06 09:00:22.098460:  
+2023-11-06 09:00:22.098590: Epoch 59 
+2023-11-06 09:00:22.098714: Current learning rate: 0.00947 
+2023-11-06 09:11:37.533084: train_loss -0.8272 
+2023-11-06 09:11:37.533242: val_loss -0.791 
+2023-11-06 09:11:37.533318: Pseudo dice [0.8158] 
+2023-11-06 09:11:37.533400: Epoch time: 675.44 s 
+2023-11-06 09:11:38.800722:  
+2023-11-06 09:11:38.800860: Epoch 60 
+2023-11-06 09:11:38.800962: Current learning rate: 0.00946 
+2023-11-06 09:22:53.990348: train_loss -0.8271 
+2023-11-06 09:22:53.990512: val_loss -0.8294 
+2023-11-06 09:22:53.990592: Pseudo dice [0.8748] 
+2023-11-06 09:22:53.990684: Epoch time: 675.19 s 
+2023-11-06 09:22:53.990766: Yayy! New best EMA pseudo Dice: 0.8466 
+2023-11-06 09:22:57.349074:  
+2023-11-06 09:22:57.349289: Epoch 61 
+2023-11-06 09:22:57.349463: Current learning rate: 0.00945 
+2023-11-06 09:34:12.470989: train_loss -0.8302 
+2023-11-06 09:34:12.471159: val_loss -0.8152 
+2023-11-06 09:34:12.471240: Pseudo dice [0.8533] 
+2023-11-06 09:34:12.471326: Epoch time: 675.12 s 
+2023-11-06 09:34:12.471398: Yayy! New best EMA pseudo Dice: 0.8473 
+2023-11-06 09:34:15.608224:  
+2023-11-06 09:34:15.608330: Epoch 62 
+2023-11-06 09:34:15.608428: Current learning rate: 0.00944 
+2023-11-06 09:45:30.830228: train_loss -0.8108 
+2023-11-06 09:45:30.830392: val_loss -0.8124 
+2023-11-06 09:45:30.830467: Pseudo dice [0.8537] 
+2023-11-06 09:45:30.830546: Epoch time: 675.22 s 
+2023-11-06 09:45:30.830615: Yayy! New best EMA pseudo Dice: 0.8479 
+2023-11-06 09:45:33.973119:  
+2023-11-06 09:45:33.973341: Epoch 63 
+2023-11-06 09:45:33.973521: Current learning rate: 0.00943 
+2023-11-06 09:56:48.860524: train_loss -0.8232 
+2023-11-06 09:56:48.860660: val_loss -0.7923 
+2023-11-06 09:56:48.860746: Pseudo dice [0.8356] 
+2023-11-06 09:56:48.860831: Epoch time: 674.89 s 
+2023-11-06 09:56:50.298269:  
+2023-11-06 09:56:50.298393: Epoch 64 
+2023-11-06 09:56:50.298507: Current learning rate: 0.00942 
+2023-11-06 10:08:05.669342: train_loss -0.8194 
+2023-11-06 10:08:05.669511: val_loss -0.8066 
+2023-11-06 10:08:05.669608: Pseudo dice [0.8408] 
+2023-11-06 10:08:05.669699: Epoch time: 675.37 s 
+2023-11-06 10:08:06.933493:  
+2023-11-06 10:08:06.933608: Epoch 65 
+2023-11-06 10:08:06.933718: Current learning rate: 0.00941 
+2023-11-06 10:19:22.076491: train_loss -0.8295 
+2023-11-06 10:19:22.076648: val_loss -0.8209 
+2023-11-06 10:19:22.076744: Pseudo dice [0.8535] 
+2023-11-06 10:19:22.076838: Epoch time: 675.14 s 
+2023-11-06 10:19:23.367105:  
+2023-11-06 10:19:23.367233: Epoch 66 
+2023-11-06 10:19:23.367350: Current learning rate: 0.0094 
+2023-11-06 10:30:38.725112: train_loss -0.825 
+2023-11-06 10:30:38.725279: val_loss -0.8273 
+2023-11-06 10:30:38.725372: Pseudo dice [0.8589] 
+2023-11-06 10:30:38.725462: Epoch time: 675.36 s 
+2023-11-06 10:30:38.725539: Yayy! New best EMA pseudo Dice: 0.8481 
+2023-11-06 10:30:42.055256:  
+2023-11-06 10:30:42.055468: Epoch 67 
+2023-11-06 10:30:42.055651: Current learning rate: 0.00939 
+2023-11-06 10:41:57.480700: train_loss -0.8361 
+2023-11-06 10:41:57.480846: val_loss -0.8078 
+2023-11-06 10:41:57.480921: Pseudo dice [0.8426] 
+2023-11-06 10:41:57.481000: Epoch time: 675.43 s 
+2023-11-06 10:41:58.767543:  
+2023-11-06 10:41:58.767738: Epoch 68 
+2023-11-06 10:41:58.767924: Current learning rate: 0.00939 
+2023-11-06 10:53:14.281489: train_loss -0.835 
+2023-11-06 10:53:14.281637: val_loss -0.8196 
+2023-11-06 10:53:14.281713: Pseudo dice [0.8561] 
+2023-11-06 10:53:14.281793: Epoch time: 675.51 s 
+2023-11-06 10:53:14.281860: Yayy! New best EMA pseudo Dice: 0.8484 
+2023-11-06 10:53:17.731063:  
+2023-11-06 10:53:17.731177: Epoch 69 
+2023-11-06 10:53:17.731296: Current learning rate: 0.00938 
+2023-11-06 11:04:32.854343: train_loss -0.8306 
+2023-11-06 11:04:32.854506: val_loss -0.8266 
+2023-11-06 11:04:32.854598: Pseudo dice [0.8572] 
+2023-11-06 11:04:32.854696: Epoch time: 675.12 s 
+2023-11-06 11:04:32.854775: Yayy! New best EMA pseudo Dice: 0.8493 
+2023-11-06 11:04:36.198146:  
+2023-11-06 11:04:36.198331: Epoch 70 
+2023-11-06 11:04:36.198503: Current learning rate: 0.00937 
+2023-11-06 11:15:51.137854: train_loss -0.8385 
+2023-11-06 11:15:51.138000: val_loss -0.8133 
+2023-11-06 11:15:51.138093: Pseudo dice [0.8431] 
+2023-11-06 11:15:51.138183: Epoch time: 674.94 s 
+2023-11-06 11:15:52.630492:  
+2023-11-06 11:15:52.630608: Epoch 71 
+2023-11-06 11:15:52.630735: Current learning rate: 0.00936 
+2023-11-06 11:27:07.819234: train_loss -0.8339 
+2023-11-06 11:27:07.819484: val_loss -0.8214 
+2023-11-06 11:27:07.819611: Pseudo dice [0.8487] 
+2023-11-06 11:27:07.819704: Epoch time: 675.19 s 
+2023-11-06 11:27:09.109598:  
+2023-11-06 11:27:09.109764: Epoch 72 
+2023-11-06 11:27:09.109937: Current learning rate: 0.00935 
+2023-11-06 11:38:24.488279: train_loss -0.84 
+2023-11-06 11:38:24.488451: val_loss -0.8316 
+2023-11-06 11:38:24.488527: Pseudo dice [0.8615] 
+2023-11-06 11:38:24.488606: Epoch time: 675.38 s 
+2023-11-06 11:38:24.488675: Yayy! New best EMA pseudo Dice: 0.8499 
+2023-11-06 11:38:27.703335:  
+2023-11-06 11:38:27.703459: Epoch 73 
+2023-11-06 11:38:27.703562: Current learning rate: 0.00934 
+2023-11-06 11:49:43.036126: train_loss -0.8416 
+2023-11-06 11:49:43.036298: val_loss -0.8257 
+2023-11-06 11:49:43.036396: Pseudo dice [0.8603] 
+2023-11-06 11:49:43.036489: Epoch time: 675.33 s 
+2023-11-06 11:49:43.036568: Yayy! New best EMA pseudo Dice: 0.851 
+2023-11-06 11:49:46.209569:  
+2023-11-06 11:49:46.209720: Epoch 74 
+2023-11-06 11:49:46.209849: Current learning rate: 0.00933 
+2023-11-06 12:01:01.503134: train_loss -0.8418 
+2023-11-06 12:01:01.503297: val_loss -0.8301 
+2023-11-06 12:01:01.503382: Pseudo dice [0.8594] 
+2023-11-06 12:01:01.503466: Epoch time: 675.29 s 
+2023-11-06 12:01:01.503535: Yayy! New best EMA pseudo Dice: 0.8518 
+2023-11-06 12:01:04.783809:  
+2023-11-06 12:01:04.783910: Epoch 75 
+2023-11-06 12:01:04.784027: Current learning rate: 0.00932 
+2023-11-06 12:12:19.949506: train_loss -0.8463 
+2023-11-06 12:12:19.949670: val_loss -0.8123 
+2023-11-06 12:12:19.949746: Pseudo dice [0.8351] 
+2023-11-06 12:12:19.949828: Epoch time: 675.17 s 
+2023-11-06 12:12:21.242239:  
+2023-11-06 12:12:21.242361: Epoch 76 
+2023-11-06 12:12:21.242465: Current learning rate: 0.00931 
+2023-11-06 12:23:36.276375: train_loss -0.8364 
+2023-11-06 12:23:36.276535: val_loss -0.8107 
+2023-11-06 12:23:36.276609: Pseudo dice [0.8454] 
+2023-11-06 12:23:36.276692: Epoch time: 675.03 s 
+2023-11-06 12:23:37.558540:  
+2023-11-06 12:23:37.558640: Epoch 77 
+2023-11-06 12:23:37.558762: Current learning rate: 0.0093 
+2023-11-06 12:34:52.820284: train_loss -0.8417 
+2023-11-06 12:34:52.820433: val_loss -0.8144 
+2023-11-06 12:34:52.820513: Pseudo dice [0.8607] 
+2023-11-06 12:34:52.820607: Epoch time: 675.26 s 
+2023-11-06 12:34:54.330649:  
+2023-11-06 12:34:54.330824: Epoch 78 
+2023-11-06 12:34:54.330933: Current learning rate: 0.0093 
+2023-11-06 12:46:09.541902: train_loss -0.8413 
+2023-11-06 12:46:09.542046: val_loss -0.8263 
+2023-11-06 12:46:09.542131: Pseudo dice [0.8589] 
+2023-11-06 12:46:09.542212: Epoch time: 675.21 s 
+2023-11-06 12:46:10.856498:  
+2023-11-06 12:46:10.856613: Epoch 79 
+2023-11-06 12:46:10.856715: Current learning rate: 0.00929 
+2023-11-06 12:57:25.954721: train_loss -0.8449 
+2023-11-06 12:57:25.954871: val_loss -0.8188 
+2023-11-06 12:57:25.954963: Pseudo dice [0.8579] 
+2023-11-06 12:57:25.955053: Epoch time: 675.1 s 
+2023-11-06 12:57:25.955130: Yayy! New best EMA pseudo Dice: 0.8522 
+2023-11-06 12:57:29.184287:  
+2023-11-06 12:57:29.184399: Epoch 80 
+2023-11-06 12:57:29.184514: Current learning rate: 0.00928 
+2023-11-06 13:08:44.293826: train_loss -0.831 
+2023-11-06 13:08:44.293987: val_loss -0.825 
+2023-11-06 13:08:44.294063: Pseudo dice [0.8536] 
+2023-11-06 13:08:44.294145: Epoch time: 675.11 s 
+2023-11-06 13:08:44.294213: Yayy! New best EMA pseudo Dice: 0.8523 
+2023-11-06 13:08:47.574882:  
+2023-11-06 13:08:47.574989: Epoch 81 
+2023-11-06 13:08:47.575088: Current learning rate: 0.00927 

Dataset721_TSPrimeCTVP/nnUNetTrainer__nnUNetPlans__3d_lowres/plans.json

@@ -0,0 +1,454 @@
+{
+    "dataset_name": "Dataset721_TSPrimeCTVP",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        2.5,
+        1.269531011581421,
+        1.269531011581421
+    ],
+    "original_median_shape_after_transp": [
+        241,
+        512,
+        512
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 12,
+            "patch_size": [
+                512,
+                512
+            ],
+            "median_image_size_in_voxels": [
+                512.0,
+                512.0
+            ],
+            "spacing": [
+                1.269531011581421,
+                1.269531011581421
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                7,
+                7
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
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