Delete nnunetv2/Dataset504_Tissue

nnunetv2/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json

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-{
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nnunetv2/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json

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nnunetv2/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/checkpoint_final.pth

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nnunetv2/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/debug.json

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-{
-    "_best_ema": "None",
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-    "configuration_name": "3d_fullres",
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-    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd8bf252c40>",
-    "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 = [112, 160, 128], 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) ), MaskTransform( apply_to_channels = [0], seg_key = 'seg', data_key = 'data', set_outside_to = 0, mask_idx_in_seg = 0 ), 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 0x7fd8bf275a00>",
-    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd8bf275c40>",
-    "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": "{'description': '', 'labels': {'background': 0, 'CSF': 1, 'Cortical Gray Matter': 2, 'White Matter': 3, 'Gray Matter': 4, 'Brain Stem': 5, 'Cerebellum': 6}, 'licence': 'hands off!', 'name': 'pcnsl_tissue', 'numTraining': 67, 'reference': '', 'release': '0.0', 'channel_names': {'0': 'T1'}, 'file_ending': '.nii.gz'}",
-    "device": "cuda:0",
-    "disable_checkpointing": "False",
-    "enable_deep_supervision": "True",
-    "fold": "3",
-    "folder_with_segs_from_previous_stage": "None",
-    "gpu_name": "NVIDIA A100-SXM4-40GB",
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-    "hostname": "anahita",
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-    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fd8bf638340>",
-    "local_rank": "0",
-    "log_file": "/working/i2_phi3/CNS_lymphoma/repos/nnunet-2/nnUNet/nnunetv2/results/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3/training_log_2024_1_18_01_59_19.txt",
-    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fd8bf6381c0>",
-    "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 0x7fd8bf275bb0>",
-    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset504_Tissue', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.2000000476837158, 0.859375, 0.859375], 'original_median_shape_after_transp': [120, 196, 165], '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': 104, 'patch_size': [192, 160], 'median_image_size_in_voxels': [192.0, 160.0], 'spacing': [0.859375, 0.859375], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [True], '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': [5, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [117.0, 192.0, 160.0], 'spacing': [1.2000000476837158, 0.859375, 0.859375], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [True], '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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 93203.234375, 'mean': 1838.2391357421875, 'median': 359.3999938964844, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 45620.28125, 'std': 6418.310546875}}}, 'configuration': '3d_fullres', 'fold': 3, 'dataset_json': {'description': '', 'labels': {'background': 0, 'CSF': 1, 'Cortical Gray Matter': 2, 'White Matter': 3, 'Gray Matter': 4, 'Brain Stem': 5, 'Cerebellum': 6}, 'licence': 'hands off!', 'name': 'pcnsl_tissue', 'numTraining': 67, 'reference': '', 'release': '0.0', 'channel_names': {'0': 'T1'}, 'file_ending': '.nii.gz'}, '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": "/working/i2_phi3/CNS_lymphoma/repos/nnunet-2/nnUNet/nnunetv2/results/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_3",
-    "output_folder_base": "/working/i2_phi3/CNS_lymphoma/repos/nnunet-2/nnUNet/nnunetv2/results/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres",
-    "oversample_foreground_percent": "0.33",
-    "plans_manager": "{'dataset_name': 'Dataset504_Tissue', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.2000000476837158, 0.859375, 0.859375], 'original_median_shape_after_transp': [120, 196, 165], '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': 104, 'patch_size': [192, 160], 'median_image_size_in_voxels': [192.0, 160.0], 'spacing': [0.859375, 0.859375], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [True], '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': [5, 5], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [117.0, 192.0, 160.0], 'spacing': [1.2000000476837158, 0.859375, 0.859375], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [True], '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}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 93203.234375, 'mean': 1838.2391357421875, 'median': 359.3999938964844, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 45620.28125, 'std': 6418.310546875}}}",
-    "preprocessed_dataset_folder": "/working/i2_phi3/CNS_lymphoma/repos/nnunet-2/nnUNet/nnunetv2/preprocessed/Dataset504_Tissue/nnUNetPlans_3d_fullres",
-    "preprocessed_dataset_folder_base": "/working/i2_phi3/CNS_lymphoma/repos/nnunet-2/nnUNet/nnunetv2/preprocessed/Dataset504_Tissue",
-    "save_every": "50",
-    "torch_version": "2.1.2+cu121",
-    "unpack_dataset": "True",
-    "was_initialized": "True",
-    "weight_decay": "3e-05"
-}

nnunetv2/Dataset504_Tissue/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json

@@ -1,294 +0,0 @@
-{
-    "dataset_name": "Dataset504_Tissue",
-    "plans_name": "nnUNetPlans",
-    "original_median_spacing_after_transp": [
-        1.2000000476837158,
-        0.859375,
-        0.859375
-    ],
-    "original_median_shape_after_transp": [
-        120,
-        196,
-        165
-    ],
-    "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": 104,
-            "patch_size": [
-                192,
-                160
-            ],
-            "median_image_size_in_voxels": [
-                192.0,
-                160.0
-            ],
-            "spacing": [
-                0.859375,
-                0.859375
-            ],
-            "normalization_schemes": [
-                "ZScoreNormalization"
-            ],
-            "use_mask_for_norm": [
-                true
-            ],
-            "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": [
-                5,
-                5
-            ],
-            "pool_op_kernel_sizes": [
-                [
-                    1,
-                    1
-                ],
-                [
-                    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
-                ]
-            ],
-            "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_fullres": {
-            "data_identifier": "nnUNetPlans_3d_fullres",
-            "preprocessor_name": "DefaultPreprocessor",
-            "batch_size": 2,
-            "patch_size": [
-                112,
-                160,
-                128
-            ],
-            "median_image_size_in_voxels": [
-                117.0,
-                192.0,
-                160.0
-            ],
-            "spacing": [
-                1.2000000476837158,
-                0.859375,
-                0.859375
-            ],
-            "normalization_schemes": [
-                "ZScoreNormalization"
-            ],
-            "use_mask_for_norm": [
-                true
-            ],
-            "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": 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": false
-        }
-    },
-    "experiment_planner_used": "ExperimentPlanner",
-    "label_manager": "LabelManager",
-    "foreground_intensity_properties_per_channel": {
-        "0": {
-            "max": 93203.234375,
-            "mean": 1838.2391357421875,
-            "median": 359.3999938964844,
-            "min": 0.0,
-            "percentile_00_5": 0.0,
-            "percentile_99_5": 45620.28125,
-            "std": 6418.310546875
-        }
-    }
-}