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checkpoints/atlas_pooled10_v2_rareboost/debug.json

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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [1536, 896], 'median_image_size_in_voxels': [1636.0, 972.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 9, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
+    "configuration_name": "2d",
+    "cudnn_version": 91002,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7d5b78dfd6a0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7d5b78dff350>",
+    "dataloader_train.generator.transforms": "ComposeTransforms( transforms = [SpatialTransform( patch_size = [1536, 896], patch_center_dist_from_border = [0, 0], random_crop = False, p_elastic_deform = 0, elastic_deform_scale = (0, 0.2), elastic_deform_magnitude = (0, 0.2), p_rotation = 0.2, rotation = (-0.2617993877991494, 0.2617993877991494), p_rot_per_axis = 1, p_scaling = 0.2, scaling = (0.7, 1.4), p_synchronize_scaling_across_axes = 1, p_synchronize_def_scale_across_axes = 0, bg_style_seg_sampling = False, mode_seg = 'bilinear', border_mode_seg = 'constant', center_deformation = True, mode_image = 'bilinear', padding_mode_image = 'zeros', padding_value_seg = -1, padding_value_image = 0, align_corners = False, _grid_cache = {} ), RandomTransform(p=0.1, transform=GaussianNoiseTransform( noise_variance = (0, 0.1), p_per_channel = 1, synchronize_channels = True )), RandomTransform(p=0.2, transform=GaussianBlurTransform( blur_sigma = (0.5, 1.0), benchmark = True, synchronize_channels = False, synchronize_axes = False, p_per_channel = 0.5, benchmark_use_fft = {}, benchmark_num_runs = 9 )), RandomTransform(p=0.15, transform=MultiplicativeBrightnessTransform( multiplier_range = BGContrast(contrast_range=(0.75, 1.25)), synchronize_channels = False, p_per_channel = 1 )), RandomTransform(p=0.15, transform=ContrastTransform( contrast_range = BGContrast(contrast_range=(0.75, 1.25)), preserve_range = True, synchronize_channels = False, p_per_channel = 1.0 )), RandomTransform(p=0.25, transform=SimulateLowResolutionTransform( scale = (0.5, 1), synchronize_channels = False, synchronize_axes = True, ignore_axes = None, allowed_channels = None, p_per_channel = 0.5, upmodes = {1: 'linear', 2: 'bilinear', 3: 'trilinear'} )), RandomTransform(p=0.1, transform=GammaTransform( gamma = BGContrast(contrast_range=(0.7, 1.5)), p_invert_image = 1.0, synchronize_channels = False, p_per_channel = 1.0, p_retain_stats = 1.0 )), RandomTransform(p=0.3, transform=GammaTransform( gamma = BGContrast(contrast_range=(0.7, 1.5)), p_invert_image = 0.0, synchronize_channels = False, p_per_channel = 1.0, p_retain_stats = 1.0 )), MirrorTransform( allowed_axes = (0, 1) ), RemoveLabelTransform( segmentation_channels = None, label_value = -1, set_to = 0 ), DownsampleSegForDSTransform( ds_scales = [[np.float64(1.0), np.float64(1.0)], [np.float64(0.5), np.float64(0.5)], [np.float64(0.25), np.float64(0.25)], [np.float64(0.125), np.float64(0.125)], [np.float64(0.0625), np.float64(0.0625)], [np.float64(0.03125), np.float64(0.03125)], [np.float64(0.015625), np.float64(0.015625)], [np.float64(0.0078125), np.float64(0.0078125)]] )] )",
+    "dataloader_train.num_processes": "4",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7d5b78dfe720>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7d5b78dfdb50>",
+    "dataloader_val.generator.transforms": "ComposeTransforms( transforms = [RemoveLabelTransform( segmentation_channels = None, label_value = -1, set_to = 0 ), DownsampleSegForDSTransform( ds_scales = [[np.float64(1.0), np.float64(1.0)], [np.float64(0.5), np.float64(0.5)], [np.float64(0.25), np.float64(0.25)], [np.float64(0.125), np.float64(0.125)], [np.float64(0.0625), np.float64(0.0625)], [np.float64(0.03125), np.float64(0.03125)], [np.float64(0.015625), np.float64(0.015625)], [np.float64(0.0078125), np.float64(0.0078125)]] )] )",
+    "dataloader_val.num_processes": "2",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'grayscale'}, 'labels': {'background': 0, 'Liver': 1, 'Allantois': 2, 'Peritoneal_cavity': 3, 'Pericardial_cavity': 4, 'Neural_tube': 5, 'Mesonephros': 6, 'Mesonephric_duct': 7, 'Respiratory_system': 8, 'Trigeminal_ganglion_V': 9, 'Facial_vestibulocochlear_ganglia': 10}, 'numTraining': 3455, 'file_ending': '.png'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "Tesla T4",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x7d5b8b79be00>",
+    "hostname": "0a0fb57360f0",
+    "inference_allowed_mirroring_axes": "(0, 1)",
+    "initial_lr": "0.001",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7d5b8c6b3da0>",
+    "local_rank": "0",
+    "log_file": "/kaggle/temp/nnunet_atlas_pooled10/nnunet/results/Dataset002_AtlasPooled10/nnUNetTrainer_RareBoost__nnUNetPlans__2d/fold_0/training_log_2026_9_6_15_00_35.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.MetaLogger object at 0x7d5b8b79bec0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7d5b8be98710>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset002_AtlasPooled10', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1636, 972], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [1536, 896], 'median_image_size_in_voxels': [1636.0, 972.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 9, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 121.69818719773791, 'median': 115.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 255.0, 'std': 71.09662689955772}}}, 'configuration': '2d', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'grayscale'}, 'labels': {'background': 0, 'Liver': 1, 'Allantois': 2, 'Peritoneal_cavity': 3, 'Pericardial_cavity': 4, 'Neural_tube': 5, 'Mesonephros': 6, 'Mesonephric_duct': 7, 'Respiratory_system': 8, 'Trigeminal_ganglion_V': 9, 'Facial_vestibulocochlear_ganglia': 10}, 'numTraining': 3455, 'file_ending': '.png'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "150",
+    "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    fused: None\n    initial_lr: 0.001\n    lr: 0.001\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/kaggle/temp/nnunet_atlas_pooled10/nnunet/results/Dataset002_AtlasPooled10/nnUNetTrainer_RareBoost__nnUNetPlans__2d/fold_0",
+    "output_folder_base": "/kaggle/temp/nnunet_atlas_pooled10/nnunet/results/Dataset002_AtlasPooled10/nnUNetTrainer_RareBoost__nnUNetPlans__2d",
+    "oversample_foreground_percent": "0.5",
+    "plans_manager": "{'dataset_name': 'Dataset002_AtlasPooled10', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1636, 972], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [1536, 896], 'median_image_size_in_voxels': [1636.0, 972.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], '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}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 9, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 121.69818719773791, 'median': 115.0, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 255.0, 'std': 71.09662689955772}}}",
+    "preprocessed_dataset_folder": "/kaggle/temp/nnunet_atlas_pooled10/nnunet/preprocessed/Dataset002_AtlasPooled10/nnUNetPlans_2d",
+    "preprocessed_dataset_folder_base": "/kaggle/temp/nnunet_atlas_pooled10/nnunet/preprocessed/Dataset002_AtlasPooled10",
+    "probabilistic_oversampling": "False",
+    "save_every": "50",
+    "torch_version": "2.10.0+cu128",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}