diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..043f963c18794b48611a8f16a4267a9c7795767b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,40 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/progress.png filter=lfs diff=lfs merge=lfs -text +nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/progress.png filter=lfs diff=lfs merge=lfs -text +nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/progress.png filter=lfs diff=lfs merge=lfs -text +nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/progress.png filter=lfs diff=lfs merge=lfs -text +nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/progress.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..3d6b053b061beba49b69badcdf46e2245641318b --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +# The parent research repository ignores generated images globally. These +# original nnU-Net plots are intentional public provenance artifacts. +!**/logs/progress.png diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..64e3f2f3c40e198b18d91733e48820d1fa7fc5ef --- /dev/null +++ b/LICENSE @@ -0,0 +1,41 @@ +BodyCompositionCT-ResEncL license notice + +Licensed Material +----------------- + +To the extent that copyright or similar rights subsist, the original +BodyCompositionCT-ResEncL trained model weights, the included training and +evaluation records, and original documentation and metadata distributed in +this repository are available under the Creative Commons Attribution 4.0 +International Public License (CC BY 4.0). + +Canonical license URL: +https://creativecommons.org/licenses/by/4.0/ + +Full legal code: +https://creativecommons.org/licenses/by/4.0/legalcode.en + +Attribution +----------- + +When sharing the Licensed Material, including a modified version, provide +appropriate credit, link to CC BY 4.0, and indicate whether changes were made. +A suitable attribution is: + + BodyCompositionCT-ResEncL by Felix O. Hofmann, + https://huggingface.co/fhofmann/BodyCompositionCT-ResEncL, + licensed under CC BY 4.0; changes indicated where applicable. + +Scope exclusions +---------------- + +This notice does not apply to, and does not relicense, source CT images, +third-party source annotations, upstream model weights, or third-party +software. Those materials are not distributed in this repository. See +THIRD_PARTY_NOTICES.md for provenance and upstream terms. + +No executable training or inference code is covered by this notice. + +The full CC BY 4.0 legal code controls if this notice and the license differ. +The Licensed Material is provided without warranties, to the extent permitted +by law. diff --git a/MODEL_ARTIFACTS.sha256 b/MODEL_ARTIFACTS.sha256 new file mode 100644 index 0000000000000000000000000000000000000000..4f64de0bb2643d50bee0220a5f4c52fbaf37f537 --- /dev/null +++ b/MODEL_ARTIFACTS.sha256 @@ -0,0 +1,8 @@ +8de2ff6b2b4229312517acf9bfd3dd4d23cc7ebedc48c0ad8e36bd3ba10e7f1d nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/dataset.json +1790c1825529e29de536c73f66f5fd8ee12b9812271e93434e76a000c1e7cae0 nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json +03db52a2a175b6c46f0f8ba75f3d969a3d33e8f567ced072cfa4bc1e8715a82b nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/postprocessing.pkl +d4eb90614fef10c2eb9aad574d4cb3517511d224f2f2da897fe4e4618513cd77 nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth +f520d3293d667986dd89fae115e3602af9f40c98413a08808d1cbb0ce08e074c nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/checkpoint_final.pth +eeca2b9d95426147c6d533ee82ea43c215e33a785faaeb2443e5def4328bf42c nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth +8a2d7fc6a32dccad3a43cd7fcad392b38df490303baf3842e8ee57587bcfbf4a nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth +9d4c69c505961643bc97490e7a162258ac0bd3d2d17a6f58b8900d2143946231 nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18be3ad3ede54c43b6431c3d2b6a8a8e35230d17 --- /dev/null +++ b/README.md @@ -0,0 +1,208 @@ +--- +pipeline_tag: image-segmentation +license: cc-by-4.0 +tags: +- medical +- ct +- body-composition +- 3d-segmentation +- nnunet +- residual-encoder +--- + +# BodyCompositionCT-ResEncL + +BodyCompositionCT-ResEncL is a 3D nnU-Net v2 model for segmenting native +body-composition compartments and supporting anatomical structures in CT. This +is a five-fold ResEnc L ensemble. For single-checkpoint inference, see +[BodyCompositionCT-ResEncM](https://huggingface.co/fhofmann/BodyCompositionCT-ResEncM). + +## Model overview + +| Property | Value | +| --- | --- | +| Task | Mutually exclusive 3D semantic segmentation | +| Input | One CT volume | +| Architecture | nnU-Net v2, `3d_fullres`, ResEnc L | +| Checkpoints | Five cross-validation folds, used as an ensemble | +| Training set | 1,656 CT scans | +| Native output | Background plus seven foreground labels | +| License | CC BY 4.0 | + +## Native output labels + +| ID | Label | Description | +| ---: | --- | --- | +| 0 | Background | | +| 1 | Muscle | Muscle compartment | +| 2 | Bone | Bone | +| 3 | Subcutaneous | Subcutaneous compartment | +| 4 | Abdominal | Abdominal compartment, with organs and larger vessels excluded | +| 5 | Thoracic | Thoracic compartment, with organs and larger vessels excluded | +| 6 | Heart | Heart | +| 7 | Lungs | Lungs | + +Labels 1, 3, 4, and 5 denote anatomical compartments rather than final +attenuation-defined tissue masks. The muscle compartment can contain inter- and +intramuscular connective tissue, smaller vessels, and larger fat-attenuation +regions. IMAT is not a separate model class; CT-visible IMAT can be derived +downstream within label 1 using HU-thresholding. All downstream tissue +measurements should state their HU range, cleanup rules, and source compartment. + +The machine-readable mapping is stored in +[dataset.json](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/dataset.json). + +## Training data and target construction + +The training set contains 750 CT scans from +[SAROS](https://doi.org/10.1038/s41597-024-03337-6) and 906 from the +[TotalSegmentator v2 dataset](https://doi.org/10.5281/zenodo.10047292). +Another 332 cases (150 SAROS and 182 TotalSegmentator) were reserved and were +not used for training. + +Targets were constructed from dense +[BOA](https://doi.org/10.1097/RLI.0000000000001040) Task542 body-region +predictions, sparse expert-reviewed SAROS labels, and TotalSegmentator-derived +anatomical masks. Reviewed SAROS labels replaced predictions on annotated +slices. TotalSegmentator muscle, bone, lung, tracheal, body-trunk, organ, +vessel, and neural masks supplied corrections and exclusions. HU-guided +candidate selection, morphological cleanup, model-assisted completion, and +manual review produced the final seven foreground labels. + +Only the open TotalSegmentator `total` and `body` tasks were used for target +construction; the separately licensed `tissue_types` and `vertebrae_body` +tasks were not used. + +Training logs, plans, configuration snapshots, progress plots, and internal +cross-validation summaries are included with the model. + +## Inference + +The model uses the standard nnU-Net v2 results layout. The commands below were +tested with nnU-Net v2 2.5.2. Input files must be unwindowed CT NIfTI volumes +containing Hounsfield-unit values and named `_0000.nii.gz`. Do not +normalize or resample them manually; nnU-Net applies the preprocessing stored +in the plans. + +For integration with preprocessing and downstream tissue derivation, see the +[BodyComposition pipeline](https://github.com/fohofmann/BodyComposition). + +Set the nnU-Net results directory and download the model: + +```bash +export nnUNet_results="/path/to/nnUNet_results" + +hf download fhofmann/BodyCompositionCT-ResEncL \ + --local-dir "${nnUNet_results}/Dataset611_Tissue" +``` + +Optionally verify the model artifacts: + +```bash +cd "${nnUNet_results}/Dataset611_Tissue" +sha256sum -c MODEL_ARTIFACTS.sha256 +``` + +Run five-fold ensemble inference: + +```bash +nnUNetv2_predict \ + -d Dataset611_Tissue \ + -i INPUT_FOLDER \ + -o OUTPUT_FOLDER \ + -f 0 1 2 3 4 \ + -tr nnUNetTrainer \ + -c 3d_fullres \ + -p nnUNetResEncUNetLPlans +``` + +Cross-validation selected largest-component postprocessing for label 6 +(heart): + +```bash +MODEL_FOLDER="${nnUNet_results}/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres" + +nnUNetv2_apply_postprocessing \ + -i OUTPUT_FOLDER \ + -o OUTPUT_FOLDER_POSTPROCESSED \ + -pp_pkl_file "${MODEL_FOLDER}/crossval_results_folds_0_1_2_3_4/postprocessing.pkl" \ + -plans_json "${MODEL_FOLDER}/plans.json" \ + -dataset_json "${MODEL_FOLDER}/dataset.json" +``` + +With NumPy 2.4 or newer, use `acvl-utils>=0.2.6,<0.3` for this +postprocessing step. + +The checkpoints and postprocessing file are serialized PyTorch/Python +artifacts. Load them only from a trusted copy of this repository. + +## Evaluation status + +The included +[cross-validation summary](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/summary.json) +reports internal five-fold cross-validation across the 1,656 training cases; +each case was evaluated by a fold that did not train on it. + +## Intended use and limitations + +The model is intended to provide compartment masks for research pipelines, +including downstream body-composition measurements derived with CT attenuation thresholds. + +- It is not intended for diagnosis, treatment decisions, or stand-alone + clinical measurement. +- Training targets combine automated segmentations, deterministic corrections, + and manual review; residual source-model and labeling errors can remain. +- Performance may vary across populations, scanners, acquisition protocols, + contrast phases, artifacts, implants, and uncommon anatomy. +- The targets focus on the body trunk; extremity use is out of scope. +- External validation is required for each intended population and + measurement definition. + +## License and attribution + +The trained weights, model documentation, metadata, and included +training/evaluation records are released under +[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Reuse requires +appropriate credit, a link to the license, and an indication of changes. See +[LICENSE](LICENSE) for the attribution format. + +Training source data, source annotations, upstream checkpoints, and +third-party software are not redistributed and retain their original terms: + +| Upstream material | Role | Terms | +| --- | --- | --- | +| SAROS labels and split metadata | Sparse reviewed labels | CC BY 4.0 | +| SAROS source CT scans | Training images | Collection-specific TCIA terms, including restricted, CC BY, and CC BY-NC collections | +| BOA Task542 weights | Dense initial body-region predictions | MIT | +| TotalSegmentator v2 dataset | Training CT scans and anatomical labels | CC BY 4.0 | +| TotalSegmentator `total` and `body` tasks | Corrections and exclusions | Apache-2.0 | +| nnU-Net v2 | Training framework and architecture | Apache-2.0 | + +See [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) for source links and the +complete provenance and license summary. + +## References + +- Hofmann FO et al. *Validation of body composition parameters extracted via + deep learning-based segmentation from routine computed tomographies.* + Scientific Reports (2025). + https://doi.org/10.1038/s41598-025-96238-6 +- Haubold J et al. *SAROS: A dataset for whole-body region and organ + segmentation in CT imaging.* Scientific Data (2024). + https://doi.org/10.1038/s41597-024-03337-6 +- Haubold J et al. *BOA: A CT-Based Body and Organ Analysis for Radiologists at + the Point of Care.* Investigative Radiology (2023). + https://doi.org/10.1097/RLI.0000000000001040 +- Wasserthal J et al. *TotalSegmentator: Robust Segmentation of 104 Anatomic + Structures in CT Images.* Radiology: Artificial Intelligence (2023). + https://doi.org/10.1148/ryai.230024 +- Isensee F et al. *nnU-Net: a self-configuring method for deep + learning-based biomedical image segmentation.* Nature Methods (2021). + https://doi.org/10.1038/s41592-020-01008-z +- Isensee F et al. *nnU-Net Revisited: A Call for Rigorous Validation in 3D + Medical Image Segmentation.* arXiv (2024). + https://doi.org/10.48550/arXiv.2404.09556 + +Questions and feedback are welcome in the repository's +[Community](https://huggingface.co/fhofmann/BodyCompositionCT-ResEncL/discussions) +section. diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md new file mode 100644 index 0000000000000000000000000000000000000000..8819b50fcd3f1298f351bb82b53896a370582463 --- /dev/null +++ b/THIRD_PARTY_NOTICES.md @@ -0,0 +1,24 @@ +# Third-party provenance and licenses + +This repository distributes BodyCompositionCT-ResEncL trained weights, +documentation, metadata, and nnU-Net training and evaluation records. Source +CT images, source annotations, upstream checkpoints, and third-party code are +not redistributed. + +| Material or software | Use | Upstream terms | +| --- | --- | --- | +| SAROS segmentations and split metadata | Sparse reviewed body-region labels used during target construction | CC BY 4.0; [dataset](https://doi.org/10.25737/SZ96-ZG60), [paper](https://doi.org/10.1038/s41597-024-03337-6) | +| SAROS source CT images | Training images | Collection-specific TCIA terms, including TCIA Restricted, CC BY 3.0, CC BY-NC 3.0, and CC BY 4.0; not redistributed. [SAROS/TCIA page](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=157287899) | +| BOA Task542 body-region weights | Dense body-region predictions used during target construction | MIT; [weights](https://doi.org/10.5281/zenodo.7918824), [paper](https://doi.org/10.1097/RLI.0000000000001040) | +| TotalSegmentator v2 CT dataset | Training CT images and anatomical labels | CC BY 4.0; [dataset](https://doi.org/10.5281/zenodo.10047292) | +| TotalSegmentator `total` and `body` tasks | Anatomical corrections and exclusions | Apache-2.0; [repository](https://github.com/wasserth/TotalSegmentator), [paper](https://doi.org/10.1148/ryai.230024) | +| nnU-Net v2 and residual-encoder configuration | Training framework and model architecture | Apache-2.0; [repository](https://github.com/MIC-DKFZ/nnUNet), [nnU-Net paper](https://doi.org/10.1038/s41592-020-01008-z), [residual-encoder paper](https://doi.org/10.48550/arXiv.2404.09556) | + +CC BY 4.0 applies to the BodyCompositionCT trained weights, documentation, +metadata, and included training/evaluation records. Source data, source labels, +upstream checkpoints, and third-party code are not included and remain under +their original terms. The listed upstream terms do not impose a +non-commercial or share-alike condition on this model package. + +The target construction used the open TotalSegmentator `total` and `body` +tasks, not the separately licensed `tissue_types` or `vertebrae_body` tasks. diff --git a/TRAINING_ARTIFACTS.sha256 b/TRAINING_ARTIFACTS.sha256 new file mode 100644 index 0000000000000000000000000000000000000000..fe04c5f1bf1e181813b1885752bd67de47ffdbd9 --- /dev/null +++ b/TRAINING_ARTIFACTS.sha256 @@ -0,0 +1,97 @@ +a50a652f4400e8aee0bb1a3b716fd55c5ff42aa51af2f4eb404058b42cbf9b56 nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/postprocessed/summary.json +dce71996932bb34e4cbbafd44e03ace1f82ab4258062c42f1377b1cc6166150e nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/crossval_results_folds_0_1_2_3_4/postprocessing.json +7852a00bb34b56a66877eb413b32c38a7e168e04e89afc5494e9749085377b67 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Training CTs originated from the SAROS and TotalSegmentator datasets and are not included.'}", + "device": "cuda:0", + "disable_checkpointing": "False", + "enable_deep_supervision": "True", + "fold": "0", + "folder_with_segs_from_previous_stage": "None", + "gpu_name": "NVIDIA A100-SXM4-40GB", + "grad_scaler": "", + "hostname": "", + "inference_allowed_mirroring_axes": "(0, 1, 2)", + "initial_lr": "0.01", + "is_cascaded": "False", + "is_ddp": "False", + "label_manager": "", + "local_rank": "0", + "log_file": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2024_9_11_14_42_12.txt", + "logger": "", + "loss": "DeepSupervisionWrapper(\n (loss): DC_and_CE_loss(\n (ce): RobustCrossEntropyLoss()\n (dc): MemoryEfficientSoftDiceLoss()\n )\n)", + "lr_scheduler": "", + "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], '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': 89, 'patch_size': [320, 320], 'median_image_size_in_voxels': [257.0, 260.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 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]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'muscle': 1, 'bone': 2, 'subcutaneous': 3, 'abdominal': 4, 'thoracic': 5, 'heart': 6, 'lungs': 7}, 'numTraining': 1656, 'file_ending': '.nii.gz', 'dataset_name': 'BodyCompositionCT Compartment Labels v1', 'reference': 'https://doi.org/10.1038/s41598-025-96238-6; https://doi.org/10.1038/s41597-024-03337-6', 'release': '2024-08-29', 'license': 'CC BY 4.0', 'description': 'BodyCompositionCT segmentation labels. Labels 1, 3, 4, and 5 denote anatomical compartments; labels 2, 6, and 7 denote bone, heart, and lungs. The Abdominal and Thoracic compartments exclude organs and larger vessels. IMAT is not a separate output label. Training CTs originated from the SAROS and TotalSegmentator datasets and are not included.'}, 'unpack_dataset': False, 'device': device(type='cuda')}", + "network": "OptimizedModule", + "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 fused: None\n initial_lr: 0.01\n lr: 1.995262314968881e-05\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)", + "output_folder": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0", + "output_folder_base": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres", + "oversample_foreground_percent": "0.33", + "plans_manager": "{'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], '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': 89, 'patch_size': [320, 320], 'median_image_size_in_voxels': [257.0, 260.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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': 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6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}", + "preprocessed_dataset_folder": "/data2/nnUNet_preprocessed/Dataset611_Tissue/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data2/nnUNet_preprocessed/Dataset611_Tissue", + "save_every": "50", + "torch_version": "2.3.0+cu121", + "unpack_dataset": "False", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/progress.png b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..648cc46673cc9a1c4305a0c7c8c4bb0484a38a06 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0f7d2a390a984f01327a8cf6854bb19502fd392ad02e49d25ba58cc6a2bc6b1 +size 636436 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_06_01_54.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_06_01_54.txt new file mode 100644 index 0000000000000000000000000000000000000000..a9e3fb18f131f1916a2c6899aafbd821a1ddb9c4 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_06_01_54.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 06:01:57.850573: Using torch.compile... +2024-09-10 06:02:04.490486: do_dummy_2d_data_aug: False +2024-09-10 06:02:04.496485: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 06:02:04.498019: The split file contains 5 splits. +2024-09-10 06:02:04.498554: Desired fold for training: 0 +2024-09-10 06:02:04.499049: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 06:02:20.093287: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 06:02:22.551358: +2024-09-10 06:02:22.572980: Epoch 700 +2024-09-10 06:02:22.574784: Current learning rate: 0.00338 +2024-09-10 06:12:33.532906: train_loss -0.7484 +2024-09-10 06:12:33.535982: val_loss -0.7562 +2024-09-10 06:12:33.536785: Pseudo dice [0.9656, 0.9596, 0.9797, 0.9479, 0.8752, 0.9722, 0.9912] +2024-09-10 06:12:33.537721: Epoch time: 610.99 s +2024-09-10 06:12:35.774765: +2024-09-10 06:12:35.775748: Epoch 701 +2024-09-10 06:12:35.776415: Current learning rate: 0.00337 +2024-09-10 06:18:03.709767: train_loss -0.7485 +2024-09-10 06:18:03.725631: val_loss -0.7711 +2024-09-10 06:18:03.726658: Pseudo dice [0.9649, 0.958, 0.9809, 0.9499, 0.8832, 0.974, 0.9914] +2024-09-10 06:18:03.728029: Epoch time: 327.94 s +2024-09-10 06:18:09.599058: +2024-09-10 06:18:09.612869: Epoch 702 +2024-09-10 06:18:09.614774: Current learning rate: 0.00336 +2024-09-10 06:23:58.681911: train_loss -0.7579 +2024-09-10 06:23:58.697533: val_loss -0.7459 +2024-09-10 06:23:58.698497: Pseudo dice [0.9634, 0.9575, 0.9772, 0.952, 0.8776, 0.9721, 0.9891] +2024-09-10 06:23:58.699421: Epoch time: 349.09 s +2024-09-10 06:24:03.894700: +2024-09-10 06:24:03.908867: Epoch 703 +2024-09-10 06:24:03.909787: Current learning rate: 0.00335 +2024-09-10 06:29:45.790906: train_loss -0.7545 +2024-09-10 06:29:45.808509: val_loss -0.762 +2024-09-10 06:29:45.811442: Pseudo dice [0.9682, 0.9607, 0.9823, 0.9474, 0.8731, 0.9717, 0.9916] +2024-09-10 06:29:45.814188: Epoch time: 341.9 s +2024-09-10 06:29:50.702482: +2024-09-10 06:29:50.704561: Epoch 704 +2024-09-10 06:29:50.705794: Current learning rate: 0.00334 +2024-09-10 06:35:24.447131: train_loss -0.7444 +2024-09-10 06:35:24.450030: val_loss -0.7661 +2024-09-10 06:35:24.450981: Pseudo dice [0.9661, 0.9596, 0.9792, 0.9515, 0.8796, 0.9718, 0.9914] +2024-09-10 06:35:24.451738: Epoch time: 333.76 s +2024-09-10 06:35:28.289249: +2024-09-10 06:35:28.290351: Epoch 705 +2024-09-10 06:35:28.291122: Current learning rate: 0.00333 +2024-09-10 06:40:52.697942: train_loss -0.752 +2024-09-10 06:40:52.700243: val_loss -0.739 +2024-09-10 06:40:52.701034: Pseudo dice [0.9643, 0.9576, 0.9799, 0.9438, 0.8814, 0.9707, 0.9909] +2024-09-10 06:40:52.701737: Epoch time: 324.41 s +2024-09-10 06:40:56.353730: +2024-09-10 06:40:56.368740: Epoch 706 +2024-09-10 06:40:56.369806: Current learning rate: 0.00332 +2024-09-10 06:46:58.570993: train_loss -0.749 +2024-09-10 06:46:58.591032: val_loss -0.7939 +2024-09-10 06:46:58.592181: Pseudo dice [0.967, 0.961, 0.9824, 0.9569, 0.8822, 0.9701, 0.9905] +2024-09-10 06:46:58.592841: Epoch time: 362.22 s +2024-09-10 06:47:03.359958: +2024-09-10 06:47:03.361192: Epoch 707 +2024-09-10 06:47:03.362050: Current learning rate: 0.00331 +2024-09-10 06:52:42.934469: train_loss -0.7294 +2024-09-10 06:52:42.936774: val_loss -0.738 +2024-09-10 06:52:42.937844: Pseudo dice [0.9648, 0.9556, 0.9792, 0.9464, 0.8805, 0.9704, 0.9904] +2024-09-10 06:52:42.938608: Epoch time: 339.58 s +2024-09-10 06:52:48.532223: +2024-09-10 06:52:48.549480: Epoch 708 +2024-09-10 06:52:48.550958: Current learning rate: 0.0033 +2024-09-10 06:58:22.803580: train_loss -0.7491 +2024-09-10 06:58:22.816117: val_loss -0.7796 +2024-09-10 06:58:22.817590: Pseudo dice [0.967, 0.9606, 0.9798, 0.9535, 0.8809, 0.9741, 0.9914] +2024-09-10 06:58:22.818572: Epoch time: 334.28 s +2024-09-10 06:58:25.372842: +2024-09-10 06:58:25.388440: Epoch 709 +2024-09-10 06:58:25.389801: Current learning rate: 0.00329 +2024-09-10 07:04:09.460056: train_loss -0.7273 +2024-09-10 07:04:09.479719: val_loss -0.7658 +2024-09-10 07:04:09.481483: Pseudo dice [0.9601, 0.9546, 0.9752, 0.9499, 0.8746, 0.9726, 0.9899] +2024-09-10 07:04:09.483304: Epoch time: 344.09 s +2024-09-10 07:04:14.614452: +2024-09-10 07:04:14.615935: Epoch 710 +2024-09-10 07:04:14.616790: Current learning rate: 0.00328 +2024-09-10 07:10:19.316053: train_loss -0.7427 +2024-09-10 07:10:19.326909: val_loss -0.7297 +2024-09-10 07:10:19.328372: Pseudo dice [0.9636, 0.9605, 0.9772, 0.9499, 0.8697, 0.9647, 0.9888] +2024-09-10 07:10:19.329490: Epoch time: 364.71 s +2024-09-10 07:10:27.098127: +2024-09-10 07:10:27.100003: Epoch 711 +2024-09-10 07:10:27.101314: Current learning rate: 0.00327 +2024-09-10 07:15:53.554344: train_loss -0.732 +2024-09-10 07:15:53.569824: val_loss -0.7547 +2024-09-10 07:15:53.570755: Pseudo dice [0.9587, 0.9563, 0.9739, 0.9493, 0.8707, 0.971, 0.9886] +2024-09-10 07:15:53.571505: Epoch time: 326.46 s +2024-09-10 07:15:58.310495: +2024-09-10 07:15:58.323893: Epoch 712 +2024-09-10 07:15:58.325227: Current learning rate: 0.00326 +2024-09-10 07:21:28.172969: train_loss -0.7479 +2024-09-10 07:21:28.180860: val_loss -0.7564 +2024-09-10 07:21:28.182811: Pseudo dice [0.9645, 0.9576, 0.9782, 0.9466, 0.8876, 0.9729, 0.9923] +2024-09-10 07:21:28.195731: Epoch time: 329.88 s +2024-09-10 07:21:32.233674: +2024-09-10 07:21:32.234771: Epoch 713 +2024-09-10 07:21:32.235443: Current learning rate: 0.00325 +2024-09-10 07:26:55.375213: train_loss -0.7341 +2024-09-10 07:26:55.387078: val_loss -0.763 +2024-09-10 07:26:55.388745: Pseudo dice [0.9685, 0.9594, 0.982, 0.9513, 0.881, 0.9723, 0.9916] +2024-09-10 07:26:55.390136: Epoch time: 323.15 s +2024-09-10 07:26:59.677455: +2024-09-10 07:26:59.678836: Epoch 714 +2024-09-10 07:26:59.679956: Current learning rate: 0.00324 +2024-09-10 07:32:04.013855: train_loss -0.7418 +2024-09-10 07:32:04.030016: val_loss -0.7405 +2024-09-10 07:32:04.031161: Pseudo dice [0.9665, 0.9599, 0.9812, 0.9506, 0.8826, 0.9729, 0.992] +2024-09-10 07:32:04.032031: Epoch time: 304.34 s +2024-09-10 07:32:07.416206: +2024-09-10 07:32:07.424847: Epoch 715 +2024-09-10 07:32:07.431886: Current learning rate: 0.00323 +2024-09-10 07:37:14.992293: train_loss -0.7523 +2024-09-10 07:37:15.007690: val_loss -0.7572 +2024-09-10 07:37:15.008777: Pseudo dice [0.9661, 0.9589, 0.9811, 0.9514, 0.8876, 0.9737, 0.9924] +2024-09-10 07:37:15.009937: Epoch time: 307.58 s +2024-09-10 07:37:18.638755: +2024-09-10 07:37:18.639966: Epoch 716 +2024-09-10 07:37:18.640823: Current learning rate: 0.00322 +2024-09-10 07:42:37.825489: train_loss -0.7722 +2024-09-10 07:42:37.837811: val_loss -0.7525 +2024-09-10 07:42:37.838633: Pseudo dice [0.96, 0.9569, 0.9779, 0.9473, 0.8825, 0.9681, 0.9901] +2024-09-10 07:42:37.839351: Epoch time: 319.19 s +2024-09-10 07:42:41.588879: +2024-09-10 07:42:41.590043: Epoch 717 +2024-09-10 07:42:41.591161: Current learning rate: 0.00321 +2024-09-10 07:47:59.035605: train_loss -0.7572 +2024-09-10 07:47:59.037019: val_loss -0.7439 +2024-09-10 07:47:59.038152: Pseudo dice [0.9642, 0.96, 0.9751, 0.9526, 0.8782, 0.9715, 0.9904] +2024-09-10 07:47:59.038947: Epoch time: 317.45 s +2024-09-10 07:48:02.149020: +2024-09-10 07:48:02.162264: Epoch 718 +2024-09-10 07:48:02.163433: Current learning rate: 0.0032 +2024-09-10 07:53:14.334403: train_loss -0.7426 +2024-09-10 07:53:14.349552: val_loss -0.7562 +2024-09-10 07:53:14.350237: Pseudo dice [0.967, 0.9589, 0.978, 0.9514, 0.8761, 0.971, 0.991] +2024-09-10 07:53:14.350884: Epoch time: 312.19 s +2024-09-10 07:53:16.818849: +2024-09-10 07:53:16.820216: Epoch 719 +2024-09-10 07:53:16.821135: Current learning rate: 0.00319 +2024-09-10 07:58:35.380176: train_loss -0.7331 +2024-09-10 07:58:35.382705: val_loss -0.7707 +2024-09-10 07:58:35.383569: Pseudo dice [0.9668, 0.9594, 0.9811, 0.9508, 0.889, 0.9728, 0.9917] +2024-09-10 07:58:35.384311: Epoch time: 318.57 s +2024-09-10 07:58:39.596968: +2024-09-10 07:58:39.597984: Epoch 720 +2024-09-10 07:58:39.598633: Current learning rate: 0.00318 +2024-09-10 08:04:25.452646: train_loss -0.7681 +2024-09-10 08:04:25.476781: val_loss -0.7379 +2024-09-10 08:04:25.477633: Pseudo dice [0.965, 0.9593, 0.9783, 0.9457, 0.8902, 0.9734, 0.9918] +2024-09-10 08:04:25.478295: Epoch time: 345.86 s +2024-09-10 08:04:29.159787: +2024-09-10 08:04:29.160789: Epoch 721 +2024-09-10 08:04:29.161513: Current learning rate: 0.00317 +2024-09-10 08:09:54.951123: train_loss -0.7452 +2024-09-10 08:09:54.965280: val_loss -0.7165 +2024-09-10 08:09:54.966173: Pseudo dice [0.9663, 0.9598, 0.9787, 0.9524, 0.8033, 0.9686, 0.9696] +2024-09-10 08:09:54.967025: Epoch time: 325.8 s +2024-09-10 08:09:59.020332: +2024-09-10 08:09:59.030329: Epoch 722 +2024-09-10 08:09:59.031564: Current learning rate: 0.00316 +2024-09-10 08:15:17.747986: train_loss -0.7489 +2024-09-10 08:15:17.762274: val_loss -0.7454 +2024-09-10 08:15:17.763060: Pseudo dice [0.9635, 0.9575, 0.9814, 0.9432, 0.8788, 0.9692, 0.9899] +2024-09-10 08:15:17.764153: Epoch time: 318.74 s +2024-09-10 08:15:21.646237: +2024-09-10 08:15:21.660679: Epoch 723 +2024-09-10 08:15:21.662282: Current learning rate: 0.00315 +2024-09-10 08:20:41.083967: train_loss -0.7433 +2024-09-10 08:20:41.100617: val_loss -0.7172 +2024-09-10 08:20:41.101437: Pseudo dice [0.9628, 0.9569, 0.9786, 0.9505, 0.8767, 0.9692, 0.9921] +2024-09-10 08:20:41.102371: Epoch time: 319.44 s +2024-09-10 08:20:46.548774: +2024-09-10 08:20:46.549780: Epoch 724 +2024-09-10 08:20:46.550503: Current learning rate: 0.00314 +2024-09-10 08:26:05.036397: train_loss -0.7319 +2024-09-10 08:26:05.084071: val_loss -0.7578 +2024-09-10 08:26:05.085435: Pseudo dice [0.9634, 0.9564, 0.9777, 0.946, 0.8752, 0.9675, 0.9905] +2024-09-10 08:26:05.086814: Epoch time: 318.49 s +2024-09-10 08:26:08.890269: +2024-09-10 08:26:08.904677: Epoch 725 +2024-09-10 08:26:08.906828: Current learning rate: 0.00313 +2024-09-10 08:31:04.922113: train_loss -0.7381 +2024-09-10 08:31:04.945651: val_loss -0.7628 +2024-09-10 08:31:04.947149: Pseudo dice [0.9654, 0.9579, 0.9812, 0.9517, 0.887, 0.9721, 0.9919] +2024-09-10 08:31:04.948715: Epoch time: 296.04 s +2024-09-10 08:31:08.459878: +2024-09-10 08:31:08.472639: Epoch 726 +2024-09-10 08:31:08.473903: Current learning rate: 0.00312 +2024-09-10 08:36:00.379124: train_loss -0.7466 +2024-09-10 08:36:00.381635: val_loss -0.7574 +2024-09-10 08:36:00.382702: Pseudo dice [0.9613, 0.9565, 0.9759, 0.949, 0.8737, 0.9685, 0.9908] +2024-09-10 08:36:00.383642: Epoch time: 291.92 s +2024-09-10 08:36:03.692892: +2024-09-10 08:36:03.693839: Epoch 727 +2024-09-10 08:36:03.694498: Current learning rate: 0.00311 +2024-09-10 08:40:51.811333: train_loss -0.7511 +2024-09-10 08:40:51.818222: val_loss -0.747 +2024-09-10 08:40:51.832871: Pseudo dice [0.9641, 0.9595, 0.9788, 0.9495, 0.8694, 0.9713, 0.982] +2024-09-10 08:40:51.839284: Epoch time: 288.12 s +2024-09-10 08:40:55.281301: +2024-09-10 08:40:55.282167: Epoch 728 +2024-09-10 08:40:55.282895: Current learning rate: 0.0031 +2024-09-10 08:46:00.566040: train_loss -0.7397 +2024-09-10 08:46:00.567489: val_loss -0.7667 +2024-09-10 08:46:00.568313: Pseudo dice [0.9695, 0.9614, 0.9829, 0.9547, 0.8818, 0.9655, 0.9908] +2024-09-10 08:46:00.569346: Epoch time: 305.29 s +2024-09-10 08:46:03.354722: +2024-09-10 08:46:03.355724: Epoch 729 +2024-09-10 08:46:03.356523: Current learning rate: 0.00309 +2024-09-10 08:51:01.292442: train_loss -0.7402 +2024-09-10 08:51:01.308835: val_loss -0.7658 +2024-09-10 08:51:01.309609: Pseudo dice [0.9638, 0.9571, 0.9782, 0.9493, 0.886, 0.9728, 0.991] +2024-09-10 08:51:01.310486: Epoch time: 297.94 s +2024-09-10 08:51:04.567293: +2024-09-10 08:51:04.568478: Epoch 730 +2024-09-10 08:51:04.569283: Current learning rate: 0.00308 +2024-09-10 08:55:43.892802: train_loss -0.7424 +2024-09-10 08:55:43.909172: val_loss -0.7567 +2024-09-10 08:55:43.910249: Pseudo dice [0.9651, 0.9586, 0.9767, 0.9476, 0.8795, 0.9727, 0.9915] +2024-09-10 08:55:43.911278: Epoch time: 279.33 s +2024-09-10 08:55:47.696615: +2024-09-10 08:55:47.697651: Epoch 731 +2024-09-10 08:55:47.698334: Current learning rate: 0.00307 +2024-09-10 09:00:53.539560: train_loss -0.7437 +2024-09-10 09:00:53.541253: val_loss -0.7561 +2024-09-10 09:00:53.542266: Pseudo dice [0.9662, 0.9587, 0.9788, 0.9509, 0.8868, 0.9713, 0.9911] +2024-09-10 09:00:53.543218: Epoch time: 305.85 s +2024-09-10 09:00:57.372066: +2024-09-10 09:00:57.389518: Epoch 732 +2024-09-10 09:00:57.390610: Current learning rate: 0.00306 +2024-09-10 09:06:02.895200: train_loss -0.719 +2024-09-10 09:06:02.908918: val_loss -0.7585 +2024-09-10 09:06:02.909700: Pseudo dice [0.9664, 0.9591, 0.9805, 0.9559, 0.8857, 0.9706, 0.9917] +2024-09-10 09:06:02.910350: Epoch time: 305.54 s +2024-09-10 09:06:07.837104: +2024-09-10 09:06:07.838431: Epoch 733 +2024-09-10 09:06:07.839212: Current learning rate: 0.00305 +2024-09-10 09:11:34.247800: train_loss -0.7351 +2024-09-10 09:11:34.272656: val_loss -0.738 +2024-09-10 09:11:34.273517: Pseudo dice [0.9676, 0.9616, 0.9832, 0.9532, 0.8714, 0.9714, 0.9889] +2024-09-10 09:11:34.274329: Epoch time: 326.42 s +2024-09-10 09:11:37.012565: +2024-09-10 09:11:37.022403: Epoch 734 +2024-09-10 09:11:37.023259: Current learning rate: 0.00304 +2024-09-10 09:16:46.848454: train_loss -0.7429 +2024-09-10 09:16:46.865039: val_loss -0.7571 +2024-09-10 09:16:46.867296: Pseudo dice [0.9661, 0.9568, 0.9807, 0.9543, 0.8851, 0.9721, 0.9902] +2024-09-10 09:16:46.869032: Epoch time: 309.84 s +2024-09-10 09:16:50.432327: +2024-09-10 09:16:50.433357: Epoch 735 +2024-09-10 09:16:50.434065: Current learning rate: 0.00303 +2024-09-10 09:22:15.423707: train_loss -0.7588 +2024-09-10 09:22:15.428261: val_loss -0.7234 +2024-09-10 09:22:15.429213: Pseudo dice [0.9677, 0.9611, 0.9821, 0.9532, 0.8726, 0.9706, 0.9891] +2024-09-10 09:22:15.430222: Epoch time: 325.0 s +2024-09-10 09:22:19.165472: +2024-09-10 09:22:19.166869: Epoch 736 +2024-09-10 09:22:19.167741: Current learning rate: 0.00302 +2024-09-10 09:27:46.251540: train_loss -0.7481 +2024-09-10 09:27:46.270417: val_loss -0.7722 +2024-09-10 09:27:46.271682: Pseudo dice [0.9653, 0.9608, 0.9809, 0.9574, 0.8943, 0.9743, 0.992] +2024-09-10 09:27:46.272621: Epoch time: 327.09 s +2024-09-10 09:27:50.206029: +2024-09-10 09:27:50.220505: Epoch 737 +2024-09-10 09:27:50.221392: Current learning rate: 0.00301 +2024-09-10 09:33:15.823102: train_loss -0.7539 +2024-09-10 09:33:15.834583: val_loss -0.7305 +2024-09-10 09:33:15.836233: Pseudo dice [0.9667, 0.9608, 0.977, 0.9566, 0.8834, 0.9714, 0.9894] +2024-09-10 09:33:15.837576: Epoch time: 325.62 s +2024-09-10 09:33:20.984639: +2024-09-10 09:33:20.986485: Epoch 738 +2024-09-10 09:33:21.004009: Current learning rate: 0.003 +2024-09-10 09:38:25.179481: train_loss -0.7527 +2024-09-10 09:38:25.201435: val_loss -0.7671 +2024-09-10 09:38:25.202555: Pseudo dice [0.9634, 0.9569, 0.9777, 0.9412, 0.8806, 0.9737, 0.9918] +2024-09-10 09:38:25.203305: Epoch time: 304.21 s +2024-09-10 09:38:28.911854: +2024-09-10 09:38:28.924540: Epoch 739 +2024-09-10 09:38:28.925508: Current learning rate: 0.00299 +2024-09-10 09:43:58.082158: train_loss -0.7545 +2024-09-10 09:43:58.088454: val_loss -0.7454 +2024-09-10 09:43:58.089738: Pseudo dice [0.9662, 0.962, 0.9798, 0.9509, 0.8754, 0.9728, 0.9907] +2024-09-10 09:43:58.090609: Epoch time: 329.18 s +2024-09-10 09:44:02.371006: +2024-09-10 09:44:02.373269: Epoch 740 +2024-09-10 09:44:02.375094: Current learning rate: 0.00297 +2024-09-10 09:49:29.716605: train_loss -0.7537 +2024-09-10 09:49:29.718259: val_loss -0.7115 +2024-09-10 09:49:29.719058: Pseudo dice [0.9659, 0.9605, 0.978, 0.951, 0.883, 0.9705, 0.9913] +2024-09-10 09:49:29.719823: Epoch time: 327.36 s +2024-09-10 09:49:32.486867: +2024-09-10 09:49:32.490034: Epoch 741 +2024-09-10 09:49:32.496768: Current learning rate: 0.00296 +2024-09-10 09:54:51.388134: train_loss -0.7562 +2024-09-10 09:54:51.406152: val_loss -0.7523 +2024-09-10 09:54:51.406888: Pseudo dice [0.9677, 0.9589, 0.9792, 0.9502, 0.8872, 0.9745, 0.9922] +2024-09-10 09:54:51.407562: Epoch time: 318.91 s +2024-09-10 09:54:55.793814: +2024-09-10 09:54:55.808591: Epoch 742 +2024-09-10 09:54:55.809764: Current learning rate: 0.00295 +2024-09-10 10:00:25.497274: train_loss -0.7472 +2024-09-10 10:00:25.517614: val_loss -0.737 +2024-09-10 10:00:25.518855: Pseudo dice [0.9652, 0.9594, 0.9794, 0.9523, 0.8834, 0.9746, 0.9911] +2024-09-10 10:00:25.520098: Epoch time: 329.71 s +2024-09-10 10:00:29.597307: +2024-09-10 10:00:29.608852: Epoch 743 +2024-09-10 10:00:29.610221: Current learning rate: 0.00294 +2024-09-10 10:05:50.179054: train_loss -0.7561 +2024-09-10 10:05:50.181725: val_loss -0.7695 +2024-09-10 10:05:50.184157: Pseudo dice [0.9676, 0.96, 0.9824, 0.9556, 0.882, 0.973, 0.9928] +2024-09-10 10:05:50.186323: Epoch time: 320.59 s +2024-09-10 10:05:55.154057: +2024-09-10 10:05:55.175898: Epoch 744 +2024-09-10 10:05:55.177965: Current learning rate: 0.00293 +2024-09-10 10:11:19.303221: train_loss -0.7486 +2024-09-10 10:11:19.304680: val_loss -0.7663 +2024-09-10 10:11:19.305425: Pseudo dice [0.9659, 0.9583, 0.9791, 0.9531, 0.8897, 0.9741, 0.9924] +2024-09-10 10:11:19.306101: Epoch time: 324.16 s +2024-09-10 10:11:23.056201: +2024-09-10 10:11:23.057628: Epoch 745 +2024-09-10 10:11:23.059000: Current learning rate: 0.00292 +2024-09-10 10:16:41.512507: train_loss -0.7543 +2024-09-10 10:16:41.532231: val_loss -0.7629 +2024-09-10 10:16:41.533408: Pseudo dice [0.9688, 0.9633, 0.9806, 0.9523, 0.8888, 0.9738, 0.9922] +2024-09-10 10:16:41.534184: Epoch time: 318.46 s +2024-09-10 10:16:45.727365: +2024-09-10 10:16:45.728715: Epoch 746 +2024-09-10 10:16:45.729676: Current learning rate: 0.00291 +2024-09-10 10:22:14.202525: train_loss -0.7457 +2024-09-10 10:22:14.205240: val_loss -0.7796 +2024-09-10 10:22:14.206892: Pseudo dice [0.9647, 0.9595, 0.98, 0.9471, 0.8847, 0.9759, 0.9917] +2024-09-10 10:22:14.208655: Epoch time: 328.48 s +2024-09-10 10:22:19.135295: +2024-09-10 10:22:19.163850: Epoch 747 +2024-09-10 10:22:19.165286: Current learning rate: 0.0029 +2024-09-10 10:27:38.663837: train_loss -0.7449 +2024-09-10 10:27:38.677553: val_loss -0.7474 +2024-09-10 10:27:38.678643: Pseudo dice [0.9659, 0.9593, 0.9826, 0.9592, 0.881, 0.9732, 0.9905] +2024-09-10 10:27:38.679459: Epoch time: 319.54 s +2024-09-10 10:27:42.680107: +2024-09-10 10:27:42.681638: Epoch 748 +2024-09-10 10:27:42.682947: Current learning rate: 0.00289 +2024-09-10 10:32:45.454682: train_loss -0.7523 +2024-09-10 10:32:45.468880: val_loss -0.7331 +2024-09-10 10:32:45.469691: Pseudo dice [0.9614, 0.9594, 0.9754, 0.9497, 0.8806, 0.9716, 0.9917] +2024-09-10 10:32:45.470315: Epoch time: 302.82 s +2024-09-10 10:32:49.081366: +2024-09-10 10:32:49.097005: Epoch 749 +2024-09-10 10:32:49.097912: Current learning rate: 0.00288 +2024-09-10 10:38:17.797346: train_loss -0.7382 +2024-09-10 10:38:17.821672: val_loss -0.7485 +2024-09-10 10:38:17.822992: Pseudo dice [0.9654, 0.9553, 0.981, 0.9525, 0.8844, 0.9755, 0.9907] +2024-09-10 10:38:17.824611: Epoch time: 328.72 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_14_01_21.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_14_01_21.txt new file mode 100644 index 0000000000000000000000000000000000000000..5bfed53a46c4601d261d6a8b1df63112e6071193 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_14_01_21.txt @@ -0,0 +1,381 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 14:01:25.334631: Using torch.compile... +2024-09-10 14:01:31.559520: do_dummy_2d_data_aug: False +2024-09-10 14:01:31.565278: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 14:01:31.566705: The split file contains 5 splits. +2024-09-10 14:01:31.567277: Desired fold for training: 0 +2024-09-10 14:01:31.568035: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 14:01:46.896044: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 14:01:49.842394: +2024-09-10 14:01:49.856340: Epoch 750 +2024-09-10 14:01:49.857303: Current learning rate: 0.00287 +2024-09-10 14:12:22.665801: train_loss -0.7379 +2024-09-10 14:12:22.668086: val_loss -0.7587 +2024-09-10 14:12:22.669441: Pseudo dice [0.9642, 0.9569, 0.9787, 0.9491, 0.8768, 0.9699, 0.9907] +2024-09-10 14:12:22.670309: Epoch time: 632.83 s +2024-09-10 14:12:24.869202: +2024-09-10 14:12:24.870508: Epoch 751 +2024-09-10 14:12:24.871398: Current learning rate: 0.00286 +2024-09-10 14:18:03.701409: train_loss -0.7428 +2024-09-10 14:18:03.717026: val_loss -0.7344 +2024-09-10 14:18:03.717813: Pseudo dice [0.9661, 0.9596, 0.9802, 0.9483, 0.8836, 0.9738, 0.9924] +2024-09-10 14:18:03.718457: Epoch time: 338.84 s +2024-09-10 14:18:07.530936: +2024-09-10 14:18:07.546041: Epoch 752 +2024-09-10 14:18:07.547215: Current learning rate: 0.00285 +2024-09-10 14:23:15.292379: train_loss -0.7558 +2024-09-10 14:23:15.317312: val_loss -0.7503 +2024-09-10 14:23:15.318569: Pseudo dice [0.9636, 0.9587, 0.9782, 0.9465, 0.8858, 0.9721, 0.9901] +2024-09-10 14:23:15.319365: Epoch time: 307.77 s +2024-09-10 14:23:20.339790: +2024-09-10 14:23:20.355727: Epoch 753 +2024-09-10 14:23:20.356681: Current learning rate: 0.00284 +2024-09-10 14:28:46.548770: train_loss -0.7498 +2024-09-10 14:28:46.550328: val_loss -0.7571 +2024-09-10 14:28:46.551145: Pseudo dice [0.9681, 0.962, 0.9835, 0.952, 0.8789, 0.9719, 0.9915] +2024-09-10 14:28:46.552081: Epoch time: 326.21 s +2024-09-10 14:28:49.122567: +2024-09-10 14:28:49.123538: Epoch 754 +2024-09-10 14:28:49.124275: Current learning rate: 0.00283 +2024-09-10 14:34:19.810613: train_loss -0.7658 +2024-09-10 14:34:19.812106: val_loss -0.735 +2024-09-10 14:34:19.812976: Pseudo dice [0.9624, 0.9614, 0.9771, 0.9487, 0.8725, 0.9724, 0.989] +2024-09-10 14:34:19.813709: Epoch time: 330.69 s +2024-09-10 14:34:23.329513: +2024-09-10 14:34:23.333582: Epoch 755 +2024-09-10 14:34:23.334498: Current learning rate: 0.00282 +2024-09-10 14:40:17.447798: train_loss -0.7575 +2024-09-10 14:40:17.456855: val_loss -0.7581 +2024-09-10 14:40:17.457907: Pseudo dice [0.9646, 0.9609, 0.9779, 0.9507, 0.8819, 0.9738, 0.9921] +2024-09-10 14:40:17.458663: Epoch time: 354.12 s +2024-09-10 14:40:21.229109: +2024-09-10 14:40:21.230293: Epoch 756 +2024-09-10 14:40:21.230962: Current learning rate: 0.00281 +2024-09-10 14:45:46.521513: train_loss -0.7403 +2024-09-10 14:45:46.536389: val_loss -0.7472 +2024-09-10 14:45:46.537924: Pseudo dice [0.9669, 0.9606, 0.9805, 0.9564, 0.8911, 0.9713, 0.9921] +2024-09-10 14:45:46.539620: Epoch time: 325.3 s +2024-09-10 14:45:49.950255: +2024-09-10 14:45:49.951690: Epoch 757 +2024-09-10 14:45:49.952878: Current learning rate: 0.0028 +2024-09-10 14:51:16.818525: train_loss -0.7511 +2024-09-10 14:51:16.834203: val_loss -0.7641 +2024-09-10 14:51:16.835614: Pseudo dice [0.9667, 0.9592, 0.982, 0.9516, 0.8839, 0.9749, 0.9912] +2024-09-10 14:51:16.836935: Epoch time: 326.87 s +2024-09-10 14:51:20.735552: +2024-09-10 14:51:20.752663: Epoch 758 +2024-09-10 14:51:20.754129: Current learning rate: 0.00279 +2024-09-10 14:56:24.395454: train_loss -0.7564 +2024-09-10 14:56:24.416099: val_loss -0.7224 +2024-09-10 14:56:24.417122: Pseudo dice [0.9679, 0.9595, 0.9794, 0.9508, 0.8783, 0.9643, 0.9899] +2024-09-10 14:56:24.418055: Epoch time: 303.67 s +2024-09-10 14:56:28.662873: +2024-09-10 14:56:28.684591: Epoch 759 +2024-09-10 14:56:28.685510: Current learning rate: 0.00278 +2024-09-10 15:01:52.406280: train_loss -0.7484 +2024-09-10 15:01:52.409988: val_loss -0.7525 +2024-09-10 15:01:52.411182: Pseudo dice [0.9654, 0.9603, 0.9795, 0.9563, 0.8917, 0.9746, 0.9916] +2024-09-10 15:01:52.412645: Epoch time: 323.75 s +2024-09-10 15:01:56.365463: +2024-09-10 15:01:56.366586: Epoch 760 +2024-09-10 15:01:56.367866: Current learning rate: 0.00277 +2024-09-10 15:07:18.073082: train_loss -0.7636 +2024-09-10 15:07:18.081700: val_loss -0.7816 +2024-09-10 15:07:18.082945: Pseudo dice [0.9691, 0.9635, 0.9834, 0.9566, 0.8838, 0.9748, 0.992] +2024-09-10 15:07:18.084403: Epoch time: 321.71 s +2024-09-10 15:07:21.983414: +2024-09-10 15:07:21.984714: Epoch 761 +2024-09-10 15:07:21.985811: Current learning rate: 0.00276 +2024-09-10 15:12:57.292038: train_loss -0.7433 +2024-09-10 15:12:57.298297: val_loss -0.7455 +2024-09-10 15:12:57.299958: Pseudo dice [0.9671, 0.9601, 0.9801, 0.9547, 0.8881, 0.9726, 0.9912] +2024-09-10 15:12:57.301045: Epoch time: 335.31 s +2024-09-10 15:12:57.302062: Yayy! New best EMA pseudo Dice: 0.958 +2024-09-10 15:13:02.529963: +2024-09-10 15:13:02.531058: Epoch 762 +2024-09-10 15:13:02.531790: Current learning rate: 0.00275 +2024-09-10 15:18:05.854571: train_loss -0.7561 +2024-09-10 15:18:05.857706: val_loss -0.7443 +2024-09-10 15:18:05.858592: Pseudo dice [0.9684, 0.9626, 0.9812, 0.9534, 0.8809, 0.9724, 0.9905] +2024-09-10 15:18:05.859487: Epoch time: 303.33 s +2024-09-10 15:18:05.860255: Yayy! New best EMA pseudo Dice: 0.9581 +2024-09-10 15:18:11.699374: +2024-09-10 15:18:11.700419: Epoch 763 +2024-09-10 15:18:11.701123: Current learning rate: 0.00274 +2024-09-10 15:23:19.471516: train_loss -0.7555 +2024-09-10 15:23:19.484194: val_loss -0.7173 +2024-09-10 15:23:19.485199: Pseudo dice [0.9632, 0.9587, 0.9726, 0.9493, 0.8791, 0.9679, 0.9894] +2024-09-10 15:23:19.486158: Epoch time: 307.78 s +2024-09-10 15:23:26.965884: +2024-09-10 15:23:26.966797: Epoch 764 +2024-09-10 15:23:26.967412: Current learning rate: 0.00273 +2024-09-10 15:28:27.349823: train_loss -0.7574 +2024-09-10 15:28:27.358124: val_loss -0.769 +2024-09-10 15:28:27.358959: Pseudo dice [0.9661, 0.9609, 0.9818, 0.9472, 0.8757, 0.9709, 0.99] +2024-09-10 15:28:27.359738: Epoch time: 300.39 s +2024-09-10 15:28:30.805175: +2024-09-10 15:28:30.816907: Epoch 765 +2024-09-10 15:28:30.818031: Current learning rate: 0.00272 +2024-09-10 15:33:49.443150: train_loss -0.7559 +2024-09-10 15:33:49.462296: val_loss -0.7388 +2024-09-10 15:33:49.463255: Pseudo dice [0.9661, 0.9576, 0.9734, 0.9544, 0.8843, 0.974, 0.9923] +2024-09-10 15:33:49.464047: Epoch time: 318.65 s +2024-09-10 15:33:53.000027: +2024-09-10 15:33:53.017675: Epoch 766 +2024-09-10 15:33:53.019699: Current learning rate: 0.00271 +2024-09-10 15:39:22.040284: train_loss -0.746 +2024-09-10 15:39:22.057331: val_loss -0.721 +2024-09-10 15:39:22.058513: Pseudo dice [0.9651, 0.9579, 0.9782, 0.9498, 0.874, 0.9712, 0.9918] +2024-09-10 15:39:22.059448: Epoch time: 329.07 s +2024-09-10 15:39:26.165140: +2024-09-10 15:39:26.166705: Epoch 767 +2024-09-10 15:39:26.167719: Current learning rate: 0.0027 +2024-09-10 15:44:44.581660: train_loss -0.7656 +2024-09-10 15:44:44.584445: val_loss -0.7351 +2024-09-10 15:44:44.586183: Pseudo dice [0.9665, 0.9591, 0.9809, 0.95, 0.8766, 0.9702, 0.9903] +2024-09-10 15:44:44.587681: Epoch time: 318.42 s +2024-09-10 15:44:49.682264: +2024-09-10 15:44:49.693428: Epoch 768 +2024-09-10 15:44:49.700503: Current learning rate: 0.00268 +2024-09-10 15:50:16.199108: train_loss -0.7538 +2024-09-10 15:50:16.214719: val_loss -0.7194 +2024-09-10 15:50:16.215862: Pseudo dice [0.9673, 0.9631, 0.98, 0.9532, 0.8877, 0.9693, 0.9914] +2024-09-10 15:50:16.216904: Epoch time: 326.53 s +2024-09-10 15:50:20.401755: +2024-09-10 15:50:20.403061: Epoch 769 +2024-09-10 15:50:20.403856: Current learning rate: 0.00267 +2024-09-10 15:55:31.000018: train_loss -0.7478 +2024-09-10 15:55:31.025123: val_loss -0.7771 +2024-09-10 15:55:31.025965: Pseudo dice [0.9659, 0.9603, 0.9778, 0.9536, 0.8852, 0.974, 0.9932] +2024-09-10 15:55:31.026724: Epoch time: 310.62 s +2024-09-10 15:55:35.089316: +2024-09-10 15:55:35.100553: Epoch 770 +2024-09-10 15:55:35.101502: Current learning rate: 0.00266 +2024-09-10 16:00:59.098639: train_loss -0.7632 +2024-09-10 16:00:59.117647: val_loss -0.7368 +2024-09-10 16:00:59.120845: Pseudo dice [0.9661, 0.9599, 0.9795, 0.9558, 0.8858, 0.9754, 0.9915] +2024-09-10 16:00:59.121758: Epoch time: 324.02 s +2024-09-10 16:01:02.128864: +2024-09-10 16:01:02.131096: Epoch 771 +2024-09-10 16:01:02.133176: Current learning rate: 0.00265 +2024-09-10 16:07:15.680054: train_loss -0.7497 +2024-09-10 16:07:15.696036: val_loss -0.7688 +2024-09-10 16:07:15.697366: Pseudo dice [0.9667, 0.9607, 0.9802, 0.9527, 0.8866, 0.973, 0.9919] +2024-09-10 16:07:15.698737: Epoch time: 373.57 s +2024-09-10 16:07:21.786573: +2024-09-10 16:07:21.800867: Epoch 772 +2024-09-10 16:07:21.802254: Current learning rate: 0.00264 +2024-09-10 16:13:12.975405: train_loss -0.7518 +2024-09-10 16:13:12.979225: val_loss -0.7257 +2024-09-10 16:13:12.980200: Pseudo dice [0.967, 0.9605, 0.9789, 0.9552, 0.8754, 0.9729, 0.9904] +2024-09-10 16:13:12.981109: Epoch time: 351.2 s +2024-09-10 16:13:16.684483: +2024-09-10 16:13:16.685725: Epoch 773 +2024-09-10 16:13:16.686542: Current learning rate: 0.00263 +2024-09-10 16:19:34.404686: train_loss -0.7638 +2024-09-10 16:19:34.423745: val_loss -0.7528 +2024-09-10 16:19:34.425227: Pseudo dice [0.9675, 0.9599, 0.9809, 0.946, 0.8869, 0.9757, 0.9926] +2024-09-10 16:19:34.426459: Epoch time: 377.73 s +2024-09-10 16:19:39.533136: +2024-09-10 16:19:39.550072: Epoch 774 +2024-09-10 16:19:39.553699: Current learning rate: 0.00262 +2024-09-10 16:25:27.145916: train_loss -0.7525 +2024-09-10 16:25:27.147061: val_loss -0.7358 +2024-09-10 16:25:27.147926: Pseudo dice [0.9658, 0.9596, 0.9803, 0.9542, 0.8943, 0.9751, 0.9917] +2024-09-10 16:25:27.148573: Epoch time: 347.68 s +2024-09-10 16:25:32.311577: +2024-09-10 16:25:32.324703: Epoch 775 +2024-09-10 16:25:32.325664: Current learning rate: 0.00261 +2024-09-10 16:31:47.686040: train_loss -0.749 +2024-09-10 16:31:47.714433: val_loss -0.7339 +2024-09-10 16:31:47.715873: Pseudo dice [0.9649, 0.9607, 0.9774, 0.9526, 0.8801, 0.9715, 0.9914] +2024-09-10 16:31:47.717080: Epoch time: 375.38 s +2024-09-10 16:31:51.068707: +2024-09-10 16:31:51.070547: Epoch 776 +2024-09-10 16:31:51.073613: Current learning rate: 0.0026 +2024-09-10 16:37:36.753850: train_loss -0.7733 +2024-09-10 16:37:36.769849: val_loss -0.7423 +2024-09-10 16:37:36.770799: Pseudo dice [0.9656, 0.9601, 0.9771, 0.9509, 0.8816, 0.9707, 0.991] +2024-09-10 16:37:36.771560: Epoch time: 345.72 s +2024-09-10 16:37:39.762406: +2024-09-10 16:37:39.763832: Epoch 777 +2024-09-10 16:37:39.765234: Current learning rate: 0.00259 +2024-09-10 16:43:26.462552: train_loss -0.747 +2024-09-10 16:43:26.481086: val_loss -0.7805 +2024-09-10 16:43:26.482179: Pseudo dice [0.9657, 0.9609, 0.9792, 0.9472, 0.8825, 0.9734, 0.9919] +2024-09-10 16:43:26.483065: Epoch time: 346.71 s +2024-09-10 16:43:34.113965: +2024-09-10 16:43:34.115405: Epoch 778 +2024-09-10 16:43:34.116257: Current learning rate: 0.00258 +2024-09-10 16:49:37.445837: train_loss -0.7484 +2024-09-10 16:49:37.464026: val_loss -0.768 +2024-09-10 16:49:37.465849: Pseudo dice [0.9654, 0.9579, 0.9801, 0.9524, 0.8771, 0.9723, 0.9908] +2024-09-10 16:49:37.467586: Epoch time: 363.34 s +2024-09-10 16:49:41.264227: +2024-09-10 16:49:41.281134: Epoch 779 +2024-09-10 16:49:41.282037: Current learning rate: 0.00257 +2024-09-10 16:55:11.935289: train_loss -0.7504 +2024-09-10 16:55:11.949516: val_loss -0.7432 +2024-09-10 16:55:11.950313: Pseudo dice [0.9652, 0.9593, 0.9792, 0.9477, 0.8691, 0.9646, 0.9891] +2024-09-10 16:55:11.951024: Epoch time: 330.68 s +2024-09-10 16:55:15.440273: +2024-09-10 16:55:15.452744: Epoch 780 +2024-09-10 16:55:15.454142: Current learning rate: 0.00256 +2024-09-10 17:00:43.984660: train_loss -0.7425 +2024-09-10 17:00:43.987951: val_loss -0.7257 +2024-09-10 17:00:43.988902: Pseudo dice [0.9642, 0.9562, 0.9792, 0.9488, 0.8757, 0.9665, 0.9898] +2024-09-10 17:00:43.989901: Epoch time: 328.55 s +2024-09-10 17:00:48.141197: +2024-09-10 17:00:48.155814: Epoch 781 +2024-09-10 17:00:48.157258: Current learning rate: 0.00255 +2024-09-10 17:06:33.287584: train_loss -0.7595 +2024-09-10 17:06:33.290191: val_loss -0.765 +2024-09-10 17:06:33.291116: Pseudo dice [0.9654, 0.9603, 0.9791, 0.9535, 0.8815, 0.9729, 0.9898] +2024-09-10 17:06:33.292076: Epoch time: 345.15 s +2024-09-10 17:06:37.961965: +2024-09-10 17:06:37.963621: Epoch 782 +2024-09-10 17:06:37.965865: Current learning rate: 0.00254 +2024-09-10 17:12:34.668179: train_loss -0.7454 +2024-09-10 17:12:34.670557: val_loss -0.7373 +2024-09-10 17:12:34.671575: Pseudo dice [0.9675, 0.9608, 0.9807, 0.9488, 0.8822, 0.9725, 0.9898] +2024-09-10 17:12:34.672390: Epoch time: 356.71 s +2024-09-10 17:12:37.200679: +2024-09-10 17:12:37.201802: Epoch 783 +2024-09-10 17:12:37.202647: Current learning rate: 0.00253 +2024-09-10 17:18:13.629493: train_loss -0.7491 +2024-09-10 17:18:13.631454: val_loss -0.7508 +2024-09-10 17:18:13.632145: Pseudo dice [0.9684, 0.9606, 0.9809, 0.9483, 0.8873, 0.9721, 0.993] +2024-09-10 17:18:13.632806: Epoch time: 336.43 s +2024-09-10 17:18:16.668499: +2024-09-10 17:18:16.682928: Epoch 784 +2024-09-10 17:18:16.685323: Current learning rate: 0.00252 +2024-09-10 17:24:01.678583: train_loss -0.7477 +2024-09-10 17:24:01.714487: val_loss -0.7559 +2024-09-10 17:24:01.715734: Pseudo dice [0.9655, 0.9606, 0.9793, 0.956, 0.889, 0.9746, 0.9913] +2024-09-10 17:24:01.716669: Epoch time: 345.02 s +2024-09-10 17:24:05.578907: +2024-09-10 17:24:05.595757: Epoch 785 +2024-09-10 17:24:05.596767: Current learning rate: 0.00251 +2024-09-10 17:30:13.550675: train_loss -0.7442 +2024-09-10 17:30:13.566168: val_loss -0.7344 +2024-09-10 17:30:13.567394: Pseudo dice [0.9686, 0.9606, 0.9837, 0.9516, 0.8909, 0.9738, 0.9921] +2024-09-10 17:30:13.568320: Epoch time: 367.98 s +2024-09-10 17:30:16.495269: +2024-09-10 17:30:16.496487: Epoch 786 +2024-09-10 17:30:16.497365: Current learning rate: 0.0025 +2024-09-10 17:36:27.017260: train_loss -0.7464 +2024-09-10 17:36:27.033589: val_loss -0.7507 +2024-09-10 17:36:27.034370: Pseudo dice [0.9682, 0.9597, 0.9824, 0.9464, 0.8901, 0.9719, 0.9915] +2024-09-10 17:36:27.035021: Epoch time: 370.55 s +2024-09-10 17:36:32.425173: +2024-09-10 17:36:32.426262: Epoch 787 +2024-09-10 17:36:32.426908: Current learning rate: 0.00249 +2024-09-10 17:42:57.237439: train_loss -0.7412 +2024-09-10 17:42:57.247410: val_loss -0.7216 +2024-09-10 17:42:57.248407: Pseudo dice [0.9681, 0.9609, 0.9805, 0.9536, 0.8814, 0.9726, 0.9919] +2024-09-10 17:42:57.249103: Epoch time: 384.82 s +2024-09-10 17:43:01.741999: +2024-09-10 17:43:01.759870: Epoch 788 +2024-09-10 17:43:01.761583: Current learning rate: 0.00248 +2024-09-10 17:48:45.775778: train_loss -0.7669 +2024-09-10 17:48:45.792547: val_loss -0.7731 +2024-09-10 17:48:45.794382: Pseudo dice [0.9654, 0.9613, 0.9764, 0.9555, 0.8889, 0.9734, 0.9927] +2024-09-10 17:48:45.795994: Epoch time: 344.04 s +2024-09-10 17:48:50.527246: +2024-09-10 17:48:50.529085: Epoch 789 +2024-09-10 17:48:50.529991: Current learning rate: 0.00247 +2024-09-10 17:54:57.031866: train_loss -0.7536 +2024-09-10 17:54:57.043720: val_loss -0.7333 +2024-09-10 17:54:57.045006: Pseudo dice [0.9661, 0.9596, 0.9811, 0.9564, 0.886, 0.9752, 0.9916] +2024-09-10 17:54:57.046297: Epoch time: 366.51 s +2024-09-10 17:54:57.047377: Yayy! New best EMA pseudo Dice: 0.9581 +2024-09-10 17:55:05.024088: +2024-09-10 17:55:05.026838: Epoch 790 +2024-09-10 17:55:05.027666: Current learning rate: 0.00245 +2024-09-10 18:01:08.045951: train_loss -0.741 +2024-09-10 18:01:08.066731: val_loss -0.7362 +2024-09-10 18:01:08.067589: Pseudo dice [0.9636, 0.9613, 0.9762, 0.9504, 0.8898, 0.9711, 0.9912] +2024-09-10 18:01:08.068248: Epoch time: 363.03 s +2024-09-10 18:01:13.553292: +2024-09-10 18:01:13.554456: Epoch 791 +2024-09-10 18:01:13.555285: Current learning rate: 0.00244 +2024-09-10 18:06:54.397203: train_loss -0.756 +2024-09-10 18:06:54.412700: val_loss -0.7716 +2024-09-10 18:06:54.413638: Pseudo dice [0.9653, 0.9609, 0.9778, 0.9513, 0.891, 0.9738, 0.9929] +2024-09-10 18:06:54.414823: Epoch time: 340.85 s +2024-09-10 18:06:54.415619: Yayy! New best EMA pseudo Dice: 0.9582 +2024-09-10 18:07:02.630214: +2024-09-10 18:07:02.631558: Epoch 792 +2024-09-10 18:07:02.632483: Current learning rate: 0.00243 +2024-09-10 18:12:44.424041: train_loss -0.7529 +2024-09-10 18:12:44.437725: val_loss -0.7191 +2024-09-10 18:12:44.438604: Pseudo dice [0.9654, 0.958, 0.9807, 0.9525, 0.8888, 0.9736, 0.9914] +2024-09-10 18:12:44.439799: Epoch time: 341.8 s +2024-09-10 18:12:44.440570: Yayy! New best EMA pseudo Dice: 0.9582 +2024-09-10 18:12:50.610886: +2024-09-10 18:12:50.612005: Epoch 793 +2024-09-10 18:12:50.612802: Current learning rate: 0.00242 +2024-09-10 18:18:38.073766: train_loss -0.7653 +2024-09-10 18:18:38.075972: val_loss -0.7689 +2024-09-10 18:18:38.076772: Pseudo dice [0.9641, 0.9616, 0.9779, 0.9546, 0.893, 0.9729, 0.9911] +2024-09-10 18:18:38.077584: Epoch time: 347.47 s +2024-09-10 18:18:38.078228: Yayy! New best EMA pseudo Dice: 0.9583 +2024-09-10 18:18:45.529763: +2024-09-10 18:18:45.531196: Epoch 794 +2024-09-10 18:18:45.532077: Current learning rate: 0.00241 +2024-09-10 18:24:46.789898: train_loss -0.7541 +2024-09-10 18:24:46.796895: val_loss -0.775 +2024-09-10 18:24:46.798059: Pseudo dice [0.968, 0.961, 0.9819, 0.956, 0.8918, 0.9734, 0.9918] +2024-09-10 18:24:46.799569: Epoch time: 361.27 s +2024-09-10 18:24:46.800295: Yayy! New best EMA pseudo Dice: 0.9585 +2024-09-10 18:24:55.149126: +2024-09-10 18:24:55.150212: Epoch 795 +2024-09-10 18:24:55.151265: Current learning rate: 0.0024 +2024-09-10 18:30:43.256553: train_loss -0.7525 +2024-09-10 18:30:43.258993: val_loss -0.7736 +2024-09-10 18:30:43.259969: Pseudo dice [0.9692, 0.9606, 0.9811, 0.9558, 0.899, 0.9762, 0.9929] +2024-09-10 18:30:43.261051: Epoch time: 348.11 s +2024-09-10 18:30:43.261737: Yayy! New best EMA pseudo Dice: 0.9589 +2024-09-10 18:30:48.945561: +2024-09-10 18:30:48.946953: Epoch 796 +2024-09-10 18:30:48.947547: Current learning rate: 0.00239 +2024-09-10 18:36:46.325408: train_loss -0.7625 +2024-09-10 18:36:46.331793: val_loss -0.7743 +2024-09-10 18:36:46.333856: Pseudo dice [0.9692, 0.9613, 0.9827, 0.952, 0.8863, 0.9747, 0.992] +2024-09-10 18:36:46.335740: Epoch time: 357.38 s +2024-09-10 18:36:46.337752: Yayy! New best EMA pseudo Dice: 0.959 +2024-09-10 18:36:55.598679: +2024-09-10 18:36:55.599812: Epoch 797 +2024-09-10 18:36:55.601157: Current learning rate: 0.00238 +2024-09-10 18:42:35.597479: train_loss -0.754 +2024-09-10 18:42:35.612140: val_loss -0.7508 +2024-09-10 18:42:35.613616: Pseudo dice [0.9696, 0.9638, 0.9817, 0.9588, 0.8827, 0.9711, 0.9934] +2024-09-10 18:42:35.614598: Epoch time: 340.0 s +2024-09-10 18:42:35.615371: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-10 18:42:42.208056: +2024-09-10 18:42:42.209141: Epoch 798 +2024-09-10 18:42:42.209815: Current learning rate: 0.00237 +2024-09-10 18:48:32.330175: train_loss -0.7501 +2024-09-10 18:48:32.341763: val_loss -0.7334 +2024-09-10 18:48:32.342779: Pseudo dice [0.9654, 0.9614, 0.9765, 0.9503, 0.8835, 0.9741, 0.99] +2024-09-10 18:48:32.343461: Epoch time: 350.13 s +2024-09-10 18:48:35.693275: +2024-09-10 18:48:35.708390: Epoch 799 +2024-09-10 18:48:35.709264: Current learning rate: 0.00236 +2024-09-10 18:54:19.490940: train_loss -0.7615 +2024-09-10 18:54:19.516934: val_loss -0.7423 +2024-09-10 18:54:19.520457: Pseudo dice [0.9616, 0.9571, 0.9757, 0.948, 0.8826, 0.972, 0.9922] +2024-09-10 18:54:19.521461: Epoch time: 343.8 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_22_43_26.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_22_43_26.txt new file mode 100644 index 0000000000000000000000000000000000000000..03cd900253a30babb39eea41420657cbe778d949 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_10_22_43_26.txt @@ -0,0 +1,735 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 22:43:37.507574: Using torch.compile... +2024-09-10 22:43:49.296293: do_dummy_2d_data_aug: False +2024-09-10 22:43:49.302640: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 22:43:49.304840: The split file contains 5 splits. +2024-09-10 22:43:49.305415: Desired fold for training: 0 +2024-09-10 22:43:49.305972: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 22:44:01.158609: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 22:44:05.169764: +2024-09-10 22:44:05.171315: Epoch 800 +2024-09-10 22:44:05.172596: Current learning rate: 0.00235 +2024-09-10 22:57:36.856871: train_loss -0.7647 +2024-09-10 22:57:36.858979: val_loss -0.7372 +2024-09-10 22:57:36.859646: Pseudo dice [0.9677, 0.9605, 0.9831, 0.9511, 0.8915, 0.9743, 0.9921] +2024-09-10 22:57:36.860265: Epoch time: 811.69 s +2024-09-10 22:57:38.347289: +2024-09-10 22:57:38.348130: Epoch 801 +2024-09-10 22:57:38.348826: Current learning rate: 0.00234 +2024-09-10 23:02:18.484663: train_loss -0.7571 +2024-09-10 23:02:18.497958: val_loss -0.7453 +2024-09-10 23:02:18.498791: Pseudo dice [0.9689, 0.9612, 0.9823, 0.9571, 0.8917, 0.9745, 0.9905] +2024-09-10 23:02:18.499503: Epoch time: 280.14 s +2024-09-10 23:02:21.026017: +2024-09-10 23:02:21.026958: Epoch 802 +2024-09-10 23:02:21.027874: Current learning rate: 0.00233 +2024-09-10 23:06:52.324094: train_loss -0.7584 +2024-09-10 23:06:52.345623: val_loss -0.786 +2024-09-10 23:06:52.346612: Pseudo dice [0.9675, 0.9619, 0.9807, 0.9522, 0.884, 0.9715, 0.9885] +2024-09-10 23:06:52.347515: Epoch time: 271.3 s +2024-09-10 23:06:54.858696: +2024-09-10 23:06:54.860654: Epoch 803 +2024-09-10 23:06:54.861854: Current learning rate: 0.00232 +2024-09-10 23:11:17.185200: train_loss -0.7499 +2024-09-10 23:11:17.198082: val_loss -0.7381 +2024-09-10 23:11:17.198870: Pseudo dice [0.9643, 0.9602, 0.9768, 0.9556, 0.877, 0.9711, 0.9895] +2024-09-10 23:11:17.199747: Epoch time: 262.33 s +2024-09-10 23:11:20.789693: +2024-09-10 23:11:20.803706: Epoch 804 +2024-09-10 23:11:20.804586: Current learning rate: 0.00231 +2024-09-10 23:15:48.485649: train_loss -0.7512 +2024-09-10 23:15:48.496685: val_loss -0.7636 +2024-09-10 23:15:48.497454: Pseudo dice [0.9684, 0.9593, 0.9821, 0.9554, 0.8923, 0.9741, 0.9917] +2024-09-10 23:15:48.498261: Epoch time: 267.7 s +2024-09-10 23:15:51.749179: +2024-09-10 23:15:51.764097: Epoch 805 +2024-09-10 23:15:51.765172: Current learning rate: 0.0023 +2024-09-10 23:20:16.105614: train_loss -0.7525 +2024-09-10 23:20:16.107822: val_loss -0.7547 +2024-09-10 23:20:16.108618: Pseudo dice [0.9652, 0.9621, 0.9789, 0.9473, 0.8768, 0.9716, 0.9897] +2024-09-10 23:20:16.109310: Epoch time: 264.36 s +2024-09-10 23:20:19.159151: +2024-09-10 23:20:19.160056: Epoch 806 +2024-09-10 23:20:19.160824: Current learning rate: 0.00229 +2024-09-10 23:24:51.429153: train_loss -0.7748 +2024-09-10 23:24:51.446084: val_loss -0.7709 +2024-09-10 23:24:51.446886: Pseudo dice [0.9663, 0.9624, 0.9797, 0.9568, 0.8849, 0.9761, 0.9907] +2024-09-10 23:24:51.447602: Epoch time: 272.27 s +2024-09-10 23:24:55.168958: +2024-09-10 23:24:55.184758: Epoch 807 +2024-09-10 23:24:55.185578: Current learning rate: 0.00228 +2024-09-10 23:29:23.051042: train_loss -0.7623 +2024-09-10 23:29:23.065700: val_loss -0.7338 +2024-09-10 23:29:23.066477: Pseudo dice [0.9692, 0.9631, 0.9826, 0.9511, 0.8928, 0.9753, 0.9928] +2024-09-10 23:29:23.067184: Epoch time: 267.89 s +2024-09-10 23:29:25.706478: +2024-09-10 23:29:25.707392: Epoch 808 +2024-09-10 23:29:25.708046: Current learning rate: 0.00226 +2024-09-10 23:33:50.048418: train_loss -0.7487 +2024-09-10 23:33:50.066668: val_loss -0.7713 +2024-09-10 23:33:50.067729: Pseudo dice [0.9665, 0.9593, 0.9807, 0.9558, 0.8907, 0.9754, 0.9917] +2024-09-10 23:33:50.068772: Epoch time: 264.35 s +2024-09-10 23:33:54.328150: +2024-09-10 23:33:54.329012: Epoch 809 +2024-09-10 23:33:54.329691: Current learning rate: 0.00225 +2024-09-10 23:38:23.070516: train_loss -0.7411 +2024-09-10 23:38:23.084304: val_loss -0.7845 +2024-09-10 23:38:23.085688: Pseudo dice [0.9686, 0.9614, 0.9823, 0.9547, 0.896, 0.9756, 0.9928] +2024-09-10 23:38:23.086539: Epoch time: 268.75 s +2024-09-10 23:38:23.087473: Yayy! New best EMA pseudo Dice: 0.9592 +2024-09-10 23:38:29.791807: +2024-09-10 23:38:29.792677: Epoch 810 +2024-09-10 23:38:29.793334: Current learning rate: 0.00224 +2024-09-10 23:42:48.615979: train_loss -0.7364 +2024-09-10 23:42:48.634731: val_loss -0.7641 +2024-09-10 23:42:48.635605: Pseudo dice [0.9648, 0.9585, 0.9805, 0.9541, 0.887, 0.973, 0.9905] +2024-09-10 23:42:48.636373: Epoch time: 258.83 s +2024-09-10 23:42:51.569154: +2024-09-10 23:42:51.570195: Epoch 811 +2024-09-10 23:42:51.570883: Current learning rate: 0.00223 +2024-09-10 23:47:23.033719: train_loss -0.7397 +2024-09-10 23:47:23.049904: val_loss -0.7751 +2024-09-10 23:47:23.050691: Pseudo dice [0.966, 0.9601, 0.977, 0.9522, 0.893, 0.9724, 0.9917] +2024-09-10 23:47:23.051404: Epoch time: 271.47 s +2024-09-10 23:47:25.972187: +2024-09-10 23:47:25.973094: Epoch 812 +2024-09-10 23:47:25.973817: Current learning rate: 0.00222 +2024-09-10 23:51:29.205055: train_loss -0.7377 +2024-09-10 23:51:29.222021: val_loss -0.793 +2024-09-10 23:51:29.222969: Pseudo dice [0.9685, 0.9625, 0.9825, 0.9507, 0.8886, 0.9738, 0.9915] +2024-09-10 23:51:29.223695: Epoch time: 243.24 s +2024-09-10 23:51:34.440753: +2024-09-10 23:51:34.441726: Epoch 813 +2024-09-10 23:51:34.442343: Current learning rate: 0.00221 +2024-09-10 23:56:01.402431: train_loss -0.7517 +2024-09-10 23:56:01.414263: val_loss -0.7667 +2024-09-10 23:56:01.415147: Pseudo dice [0.9641, 0.9602, 0.9785, 0.9539, 0.8827, 0.9689, 0.9896] +2024-09-10 23:56:01.415926: Epoch time: 266.97 s +2024-09-10 23:56:04.589951: +2024-09-10 23:56:04.590808: Epoch 814 +2024-09-10 23:56:04.591452: Current learning rate: 0.0022 +2024-09-11 00:00:24.989139: train_loss -0.7404 +2024-09-11 00:00:24.991754: val_loss -0.7559 +2024-09-11 00:00:25.009501: Pseudo dice [0.9664, 0.9595, 0.9807, 0.955, 0.8926, 0.9691, 0.9899] +2024-09-11 00:00:25.011135: Epoch time: 260.4 s +2024-09-11 00:00:28.500921: +2024-09-11 00:00:28.502079: Epoch 815 +2024-09-11 00:00:28.502777: Current learning rate: 0.00219 +2024-09-11 00:05:00.014333: train_loss -0.7489 +2024-09-11 00:05:00.026152: val_loss -0.7143 +2024-09-11 00:05:00.027280: Pseudo dice [0.9671, 0.9593, 0.9809, 0.9533, 0.8906, 0.9702, 0.9889] +2024-09-11 00:05:00.028013: Epoch time: 271.52 s +2024-09-11 00:05:03.694023: +2024-09-11 00:05:03.695045: Epoch 816 +2024-09-11 00:05:03.695799: Current learning rate: 0.00218 +2024-09-11 00:09:19.007727: train_loss -0.7662 +2024-09-11 00:09:19.028161: val_loss -0.7792 +2024-09-11 00:09:19.029060: Pseudo dice [0.9686, 0.9622, 0.9824, 0.9588, 0.878, 0.9747, 0.9908] +2024-09-11 00:09:19.029722: Epoch time: 255.32 s +2024-09-11 00:09:21.884318: +2024-09-11 00:09:21.885164: Epoch 817 +2024-09-11 00:09:21.885860: Current learning rate: 0.00217 +2024-09-11 00:13:34.059092: train_loss -0.7588 +2024-09-11 00:13:34.061319: val_loss -0.7421 +2024-09-11 00:13:34.062016: Pseudo dice [0.9686, 0.9611, 0.9808, 0.9494, 0.8919, 0.9749, 0.9926] +2024-09-11 00:13:34.062727: Epoch time: 252.18 s +2024-09-11 00:13:36.340027: +2024-09-11 00:13:36.340977: Epoch 818 +2024-09-11 00:13:36.341695: Current learning rate: 0.00216 +2024-09-11 00:18:09.100689: train_loss -0.7569 +2024-09-11 00:18:09.102839: val_loss -0.7736 +2024-09-11 00:18:09.103541: Pseudo dice [0.967, 0.9611, 0.9794, 0.954, 0.8875, 0.974, 0.9893] +2024-09-11 00:18:09.116219: Epoch time: 272.77 s +2024-09-11 00:18:12.361102: +2024-09-11 00:18:12.372544: Epoch 819 +2024-09-11 00:18:12.373238: Current learning rate: 0.00215 +2024-09-11 00:22:39.563144: train_loss -0.7571 +2024-09-11 00:22:39.577379: val_loss -0.7185 +2024-09-11 00:22:39.578172: Pseudo dice [0.9673, 0.9606, 0.981, 0.945, 0.8868, 0.9724, 0.9913] +2024-09-11 00:22:39.578762: Epoch time: 267.21 s +2024-09-11 00:22:43.401114: +2024-09-11 00:22:43.402091: Epoch 820 +2024-09-11 00:22:43.402835: Current learning rate: 0.00214 +2024-09-11 00:27:08.992467: train_loss -0.7612 +2024-09-11 00:27:08.994691: val_loss -0.7641 +2024-09-11 00:27:08.995373: Pseudo dice [0.9691, 0.9614, 0.9811, 0.9554, 0.8916, 0.976, 0.9923] +2024-09-11 00:27:08.996118: Epoch time: 265.6 s +2024-09-11 00:27:12.413179: +2024-09-11 00:27:12.432688: Epoch 821 +2024-09-11 00:27:12.433432: Current learning rate: 0.00213 +2024-09-11 00:31:20.770205: train_loss -0.7537 +2024-09-11 00:31:20.788205: val_loss -0.7491 +2024-09-11 00:31:20.789159: Pseudo dice [0.964, 0.9569, 0.9785, 0.9499, 0.8805, 0.9755, 0.9926] +2024-09-11 00:31:20.789876: Epoch time: 248.36 s +2024-09-11 00:31:23.909444: +2024-09-11 00:31:23.910511: Epoch 822 +2024-09-11 00:31:23.911250: Current learning rate: 0.00212 +2024-09-11 00:35:52.163964: train_loss -0.7548 +2024-09-11 00:35:52.182156: val_loss -0.74 +2024-09-11 00:35:52.183042: Pseudo dice [0.9688, 0.9628, 0.9817, 0.9579, 0.8826, 0.9741, 0.9912] +2024-09-11 00:35:52.183705: Epoch time: 268.26 s +2024-09-11 00:35:54.965486: +2024-09-11 00:35:54.966423: Epoch 823 +2024-09-11 00:35:54.967094: Current learning rate: 0.0021 +2024-09-11 00:40:22.059033: train_loss -0.7633 +2024-09-11 00:40:22.062001: val_loss -0.7467 +2024-09-11 00:40:22.063321: Pseudo dice [0.9657, 0.9592, 0.9806, 0.9537, 0.8909, 0.9754, 0.9917] +2024-09-11 00:40:22.064241: Epoch time: 267.1 s +2024-09-11 00:40:25.591956: +2024-09-11 00:40:25.608759: Epoch 824 +2024-09-11 00:40:25.609652: Current learning rate: 0.00209 +2024-09-11 00:44:56.670736: train_loss -0.7703 +2024-09-11 00:44:56.686031: val_loss -0.7715 +2024-09-11 00:44:56.686783: Pseudo dice [0.9667, 0.9606, 0.9805, 0.9516, 0.8898, 0.9763, 0.9917] +2024-09-11 00:44:56.687420: Epoch time: 271.09 s +2024-09-11 00:45:00.105107: +2024-09-11 00:45:00.106144: Epoch 825 +2024-09-11 00:45:00.106802: Current learning rate: 0.00208 +2024-09-11 00:49:22.684099: train_loss -0.7659 +2024-09-11 00:49:22.694318: val_loss -0.7498 +2024-09-11 00:49:22.695263: Pseudo dice [0.9673, 0.9611, 0.982, 0.9578, 0.8925, 0.9747, 0.9914] +2024-09-11 00:49:22.695985: Epoch time: 262.58 s +2024-09-11 00:49:22.696851: Yayy! New best EMA pseudo Dice: 0.9593 +2024-09-11 00:49:31.722444: +2024-09-11 00:49:31.723362: Epoch 826 +2024-09-11 00:49:31.724037: Current learning rate: 0.00207 +2024-09-11 00:54:01.507206: train_loss -0.7479 +2024-09-11 00:54:01.521574: val_loss -0.7805 +2024-09-11 00:54:01.522352: Pseudo dice [0.965, 0.9595, 0.9791, 0.9499, 0.8922, 0.9744, 0.9916] +2024-09-11 00:54:01.523014: Epoch time: 269.79 s +2024-09-11 00:54:04.135038: +2024-09-11 00:54:04.148698: Epoch 827 +2024-09-11 00:54:04.149524: Current learning rate: 0.00206 +2024-09-11 00:58:37.098170: train_loss -0.7504 +2024-09-11 00:58:37.112507: val_loss -0.725 +2024-09-11 00:58:37.113240: Pseudo dice [0.9681, 0.9603, 0.9806, 0.9578, 0.8902, 0.9749, 0.9924] +2024-09-11 00:58:37.113916: Epoch time: 272.97 s +2024-09-11 00:58:37.114523: Yayy! New best EMA pseudo Dice: 0.9594 +2024-09-11 00:58:44.746222: +2024-09-11 00:58:44.747029: Epoch 828 +2024-09-11 00:58:44.747711: Current learning rate: 0.00205 +2024-09-11 01:03:19.491414: train_loss -0.7601 +2024-09-11 01:03:19.494245: val_loss -0.7483 +2024-09-11 01:03:19.495023: Pseudo dice [0.9676, 0.9609, 0.9802, 0.952, 0.8863, 0.9736, 0.9921] +2024-09-11 01:03:19.495642: Epoch time: 274.75 s +2024-09-11 01:03:22.536151: +2024-09-11 01:03:22.545611: Epoch 829 +2024-09-11 01:03:22.546332: Current learning rate: 0.00204 +2024-09-11 01:07:49.393862: train_loss -0.7655 +2024-09-11 01:07:49.403459: val_loss -0.7827 +2024-09-11 01:07:49.404456: Pseudo dice [0.9675, 0.958, 0.9804, 0.9555, 0.8981, 0.9773, 0.9927] +2024-09-11 01:07:49.405195: Epoch time: 266.86 s +2024-09-11 01:07:49.405799: Yayy! New best EMA pseudo Dice: 0.9595 +2024-09-11 01:07:55.091319: +2024-09-11 01:07:55.092148: Epoch 830 +2024-09-11 01:07:55.092742: Current learning rate: 0.00203 +2024-09-11 01:12:21.152513: train_loss -0.7547 +2024-09-11 01:12:21.154998: val_loss -0.7678 +2024-09-11 01:12:21.156138: Pseudo dice [0.9654, 0.959, 0.9795, 0.9517, 0.8933, 0.9752, 0.9918] +2024-09-11 01:12:21.157058: Epoch time: 266.06 s +2024-09-11 01:12:23.545516: +2024-09-11 01:12:23.546422: Epoch 831 +2024-09-11 01:12:23.547116: Current learning rate: 0.00202 +2024-09-11 01:16:50.709289: train_loss -0.757 +2024-09-11 01:16:50.711642: val_loss -0.7569 +2024-09-11 01:16:50.712494: Pseudo dice [0.9684, 0.9592, 0.9828, 0.9533, 0.8953, 0.9756, 0.9921] +2024-09-11 01:16:50.713378: Epoch time: 267.17 s +2024-09-11 01:16:50.714199: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-11 01:16:56.348132: +2024-09-11 01:16:56.348956: Epoch 832 +2024-09-11 01:16:56.349635: Current learning rate: 0.00201 +2024-09-11 01:21:35.984761: train_loss -0.7425 +2024-09-11 01:21:36.002298: val_loss -0.7533 +2024-09-11 01:21:36.003208: Pseudo dice [0.9678, 0.9608, 0.9825, 0.9543, 0.882, 0.9749, 0.9903] +2024-09-11 01:21:36.003866: Epoch time: 279.64 s +2024-09-11 01:21:39.113856: +2024-09-11 01:21:39.120610: Epoch 833 +2024-09-11 01:21:39.121630: Current learning rate: 0.002 +2024-09-11 01:26:09.698287: train_loss -0.7477 +2024-09-11 01:26:09.710795: val_loss -0.7405 +2024-09-11 01:26:09.711775: Pseudo dice [0.9678, 0.96, 0.9811, 0.9553, 0.8835, 0.9725, 0.9898] +2024-09-11 01:26:09.712612: Epoch time: 270.59 s +2024-09-11 01:26:11.886251: +2024-09-11 01:26:11.887302: Epoch 834 +2024-09-11 01:26:11.888103: Current learning rate: 0.00199 +2024-09-11 01:30:39.440245: train_loss -0.7615 +2024-09-11 01:30:39.460171: val_loss -0.763 +2024-09-11 01:30:39.461125: Pseudo dice [0.9663, 0.9605, 0.9796, 0.9517, 0.8899, 0.9743, 0.9918] +2024-09-11 01:30:39.461860: Epoch time: 267.56 s +2024-09-11 01:30:42.830239: +2024-09-11 01:30:42.831411: Epoch 835 +2024-09-11 01:30:42.832217: Current learning rate: 0.00198 +2024-09-11 01:35:03.724127: train_loss -0.7677 +2024-09-11 01:35:03.742862: val_loss -0.7546 +2024-09-11 01:35:03.743965: Pseudo dice [0.9655, 0.96, 0.9777, 0.9581, 0.8906, 0.9755, 0.9926] +2024-09-11 01:35:03.745112: Epoch time: 260.9 s +2024-09-11 01:35:07.141934: +2024-09-11 01:35:07.142874: Epoch 836 +2024-09-11 01:35:07.143518: Current learning rate: 0.00196 +2024-09-11 01:39:37.593487: train_loss -0.7638 +2024-09-11 01:39:37.595499: val_loss -0.7827 +2024-09-11 01:39:37.596048: Pseudo dice [0.9675, 0.9599, 0.9836, 0.9533, 0.894, 0.9735, 0.9908] +2024-09-11 01:39:37.596686: Epoch time: 270.46 s +2024-09-11 01:39:41.124541: +2024-09-11 01:39:41.125857: Epoch 837 +2024-09-11 01:39:41.126502: Current learning rate: 0.00195 +2024-09-11 01:44:06.644764: train_loss -0.762 +2024-09-11 01:44:06.672940: val_loss -0.6976 +2024-09-11 01:44:06.674176: Pseudo dice [0.9667, 0.9599, 0.9806, 0.9507, 0.8867, 0.9764, 0.9919] +2024-09-11 01:44:06.675440: Epoch time: 265.52 s +2024-09-11 01:44:08.985332: +2024-09-11 01:44:08.986197: Epoch 838 +2024-09-11 01:44:08.986930: Current learning rate: 0.00194 +2024-09-11 01:48:33.977267: train_loss -0.7584 +2024-09-11 01:48:33.990197: val_loss -0.7567 +2024-09-11 01:48:33.991079: Pseudo dice [0.9686, 0.9618, 0.9798, 0.9565, 0.895, 0.9752, 0.992] +2024-09-11 01:48:33.991814: Epoch time: 265.0 s +2024-09-11 01:48:33.992537: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-11 01:48:39.463746: +2024-09-11 01:48:39.464823: Epoch 839 +2024-09-11 01:48:39.465606: Current learning rate: 0.00193 +2024-09-11 01:53:07.624941: train_loss -0.7705 +2024-09-11 01:53:07.640293: val_loss -0.7624 +2024-09-11 01:53:07.641051: Pseudo dice [0.9666, 0.9621, 0.9802, 0.9561, 0.8925, 0.9744, 0.9911] +2024-09-11 01:53:07.641663: Epoch time: 268.17 s +2024-09-11 01:53:07.642328: Yayy! New best EMA pseudo Dice: 0.9598 +2024-09-11 01:53:13.341983: +2024-09-11 01:53:13.342878: Epoch 840 +2024-09-11 01:53:13.343595: Current learning rate: 0.00192 +2024-09-11 01:57:46.078647: train_loss -0.759 +2024-09-11 01:57:46.080700: val_loss -0.8041 +2024-09-11 01:57:46.081443: Pseudo dice [0.964, 0.9601, 0.9792, 0.9504, 0.893, 0.9747, 0.9913] +2024-09-11 01:57:46.082179: Epoch time: 272.74 s +2024-09-11 01:57:48.999862: +2024-09-11 01:57:49.000765: Epoch 841 +2024-09-11 01:57:49.001435: Current learning rate: 0.00191 +2024-09-11 02:02:15.709567: train_loss -0.7553 +2024-09-11 02:02:15.712052: val_loss -0.7322 +2024-09-11 02:02:15.713012: Pseudo dice [0.9647, 0.9593, 0.9776, 0.9602, 0.8859, 0.9713, 0.9904] +2024-09-11 02:02:15.713847: Epoch time: 266.71 s +2024-09-11 02:02:18.653764: +2024-09-11 02:02:18.654958: Epoch 842 +2024-09-11 02:02:18.655755: Current learning rate: 0.0019 +2024-09-11 02:06:34.146202: train_loss -0.7576 +2024-09-11 02:06:34.148634: val_loss -0.7457 +2024-09-11 02:06:34.149560: Pseudo dice [0.9664, 0.9597, 0.9791, 0.9568, 0.8879, 0.9733, 0.9909] +2024-09-11 02:06:34.150321: Epoch time: 255.5 s +2024-09-11 02:06:37.183732: +2024-09-11 02:06:37.184541: Epoch 843 +2024-09-11 02:06:37.185289: Current learning rate: 0.00189 +2024-09-11 02:10:54.876759: train_loss -0.7694 +2024-09-11 02:10:54.890020: val_loss -0.7267 +2024-09-11 02:10:54.890858: Pseudo dice [0.9665, 0.9608, 0.9802, 0.9543, 0.8682, 0.9729, 0.9887] +2024-09-11 02:10:54.891617: Epoch time: 257.7 s +2024-09-11 02:10:57.827165: +2024-09-11 02:10:57.828238: Epoch 844 +2024-09-11 02:10:57.829010: Current learning rate: 0.00188 +2024-09-11 02:15:18.272626: train_loss -0.7643 +2024-09-11 02:15:18.290583: val_loss -0.7674 +2024-09-11 02:15:18.291526: Pseudo dice [0.9668, 0.9606, 0.9814, 0.9589, 0.896, 0.9751, 0.9924] +2024-09-11 02:15:18.292289: Epoch time: 260.45 s +2024-09-11 02:15:21.945880: +2024-09-11 02:15:21.946803: Epoch 845 +2024-09-11 02:15:21.947613: Current learning rate: 0.00187 +2024-09-11 02:19:36.388237: train_loss -0.7456 +2024-09-11 02:19:36.402101: val_loss -0.7392 +2024-09-11 02:19:36.402983: Pseudo dice [0.9655, 0.9594, 0.9793, 0.9561, 0.8798, 0.9764, 0.9921] +2024-09-11 02:19:36.403761: Epoch time: 254.46 s +2024-09-11 02:19:39.253796: +2024-09-11 02:19:39.268707: Epoch 846 +2024-09-11 02:19:39.269433: Current learning rate: 0.00186 +2024-09-11 02:24:17.678725: train_loss -0.7625 +2024-09-11 02:24:17.688502: val_loss -0.7284 +2024-09-11 02:24:17.689395: Pseudo dice [0.965, 0.9618, 0.9772, 0.9411, 0.8855, 0.9698, 0.9916] +2024-09-11 02:24:17.690179: Epoch time: 278.43 s +2024-09-11 02:24:20.402789: +2024-09-11 02:24:20.407670: Epoch 847 +2024-09-11 02:24:20.408511: Current learning rate: 0.00185 +2024-09-11 02:28:52.005735: train_loss -0.7636 +2024-09-11 02:28:52.007968: val_loss -0.7493 +2024-09-11 02:28:52.008761: Pseudo dice [0.9678, 0.9611, 0.9802, 0.958, 0.8914, 0.9752, 0.9922] +2024-09-11 02:28:52.009514: Epoch time: 271.61 s +2024-09-11 02:28:54.186046: +2024-09-11 02:28:54.196174: Epoch 848 +2024-09-11 02:28:54.197049: Current learning rate: 0.00184 +2024-09-11 02:33:05.325280: train_loss -0.7527 +2024-09-11 02:33:05.338658: val_loss -0.7935 +2024-09-11 02:33:05.339464: Pseudo dice [0.9665, 0.9599, 0.9813, 0.9559, 0.8958, 0.9775, 0.9934] +2024-09-11 02:33:05.340177: Epoch time: 251.14 s +2024-09-11 02:33:08.971378: +2024-09-11 02:33:08.972382: Epoch 849 +2024-09-11 02:33:08.973099: Current learning rate: 0.00182 +2024-09-11 02:37:30.393058: train_loss -0.7724 +2024-09-11 02:37:30.395226: val_loss -0.7681 +2024-09-11 02:37:30.395941: Pseudo dice [0.9679, 0.9612, 0.9807, 0.9568, 0.8939, 0.9762, 0.9927] +2024-09-11 02:37:30.396704: Epoch time: 261.43 s +2024-09-11 02:37:37.791873: +2024-09-11 02:37:37.794857: Epoch 850 +2024-09-11 02:37:37.795637: Current learning rate: 0.00181 +2024-09-11 02:42:08.446002: train_loss -0.7647 +2024-09-11 02:42:08.453862: val_loss -0.7788 +2024-09-11 02:42:08.454680: Pseudo dice [0.9681, 0.9592, 0.9818, 0.9536, 0.8893, 0.9715, 0.9923] +2024-09-11 02:42:08.455285: Epoch time: 270.66 s +2024-09-11 02:42:12.224164: +2024-09-11 02:42:12.240108: Epoch 851 +2024-09-11 02:42:12.240900: Current learning rate: 0.0018 +2024-09-11 02:46:37.253752: train_loss -0.7726 +2024-09-11 02:46:37.270374: val_loss -0.7973 +2024-09-11 02:46:37.271279: Pseudo dice [0.9694, 0.9611, 0.9816, 0.9495, 0.8895, 0.9747, 0.9921] +2024-09-11 02:46:37.272125: Epoch time: 265.03 s +2024-09-11 02:46:40.742684: +2024-09-11 02:46:40.743560: Epoch 852 +2024-09-11 02:46:40.744322: Current learning rate: 0.00179 +2024-09-11 02:51:05.109256: train_loss -0.7521 +2024-09-11 02:51:05.111487: val_loss -0.7101 +2024-09-11 02:51:05.112178: Pseudo dice [0.9616, 0.9582, 0.977, 0.9514, 0.8771, 0.9721, 0.9899] +2024-09-11 02:51:05.112866: Epoch time: 264.37 s +2024-09-11 02:51:07.321008: +2024-09-11 02:51:07.321949: Epoch 853 +2024-09-11 02:51:07.322582: Current learning rate: 0.00178 +2024-09-11 02:55:36.726450: train_loss -0.7694 +2024-09-11 02:55:36.742529: val_loss -0.7372 +2024-09-11 02:55:36.743530: Pseudo dice [0.9623, 0.9607, 0.9742, 0.9516, 0.8881, 0.9746, 0.9907] +2024-09-11 02:55:36.744367: Epoch time: 269.41 s +2024-09-11 02:55:39.492322: +2024-09-11 02:55:39.508791: Epoch 854 +2024-09-11 02:55:39.509676: Current learning rate: 0.00177 +2024-09-11 03:00:09.226566: train_loss -0.7486 +2024-09-11 03:00:09.228781: val_loss -0.7387 +2024-09-11 03:00:09.229525: Pseudo dice [0.9684, 0.9622, 0.9823, 0.9606, 0.8968, 0.9745, 0.9915] +2024-09-11 03:00:09.230401: Epoch time: 269.74 s +2024-09-11 03:00:11.653191: +2024-09-11 03:00:11.654074: Epoch 855 +2024-09-11 03:00:11.654674: Current learning rate: 0.00176 +2024-09-11 03:04:27.206041: train_loss -0.7486 +2024-09-11 03:04:27.208305: val_loss -0.7813 +2024-09-11 03:04:27.209079: Pseudo dice [0.9675, 0.9599, 0.9775, 0.954, 0.8885, 0.976, 0.9921] +2024-09-11 03:04:27.209796: Epoch time: 255.56 s +2024-09-11 03:04:30.226479: +2024-09-11 03:04:30.240646: Epoch 856 +2024-09-11 03:04:30.241415: Current learning rate: 0.00175 +2024-09-11 03:08:58.989056: train_loss -0.7635 +2024-09-11 03:08:58.991685: val_loss -0.7473 +2024-09-11 03:08:58.992738: Pseudo dice [0.9659, 0.9618, 0.9768, 0.9549, 0.8863, 0.9728, 0.9916] +2024-09-11 03:08:58.993707: Epoch time: 268.77 s +2024-09-11 03:09:02.079131: +2024-09-11 03:09:02.080602: Epoch 857 +2024-09-11 03:09:02.081459: Current learning rate: 0.00174 +2024-09-11 03:13:41.571193: train_loss -0.7762 +2024-09-11 03:13:41.589794: val_loss -0.7866 +2024-09-11 03:13:41.590537: Pseudo dice [0.97, 0.9613, 0.9839, 0.9506, 0.8979, 0.9738, 0.9917] +2024-09-11 03:13:41.591188: Epoch time: 279.5 s +2024-09-11 03:13:45.527460: +2024-09-11 03:13:45.528339: Epoch 858 +2024-09-11 03:13:45.529031: Current learning rate: 0.00173 +2024-09-11 03:18:11.013948: train_loss -0.7628 +2024-09-11 03:18:11.030043: val_loss -0.7376 +2024-09-11 03:18:11.030862: Pseudo dice [0.9638, 0.9577, 0.9783, 0.9485, 0.886, 0.9724, 0.9927] +2024-09-11 03:18:11.031590: Epoch time: 265.49 s +2024-09-11 03:18:13.978278: +2024-09-11 03:18:13.992662: Epoch 859 +2024-09-11 03:18:13.993527: Current learning rate: 0.00172 +2024-09-11 03:22:42.005041: train_loss -0.744 +2024-09-11 03:22:42.008622: val_loss -0.7922 +2024-09-11 03:22:42.015064: Pseudo dice [0.963, 0.9608, 0.9772, 0.9548, 0.8897, 0.9751, 0.9918] +2024-09-11 03:22:42.016419: Epoch time: 268.03 s +2024-09-11 03:22:46.108822: +2024-09-11 03:22:46.109671: Epoch 860 +2024-09-11 03:22:46.110353: Current learning rate: 0.0017 +2024-09-11 03:26:58.065907: train_loss -0.7609 +2024-09-11 03:26:58.079335: val_loss -0.77 +2024-09-11 03:26:58.080037: Pseudo dice [0.9656, 0.9596, 0.9802, 0.95, 0.8917, 0.9758, 0.993] +2024-09-11 03:26:58.080784: Epoch time: 251.96 s +2024-09-11 03:27:00.519470: +2024-09-11 03:27:00.524029: Epoch 861 +2024-09-11 03:27:00.537033: Current learning rate: 0.00169 +2024-09-11 03:31:35.347943: train_loss -0.759 +2024-09-11 03:31:35.366052: val_loss -0.7955 +2024-09-11 03:31:35.366789: Pseudo dice [0.9702, 0.9621, 0.9836, 0.9618, 0.8998, 0.9772, 0.9932] +2024-09-11 03:31:35.367437: Epoch time: 274.83 s +2024-09-11 03:31:37.920418: +2024-09-11 03:31:37.921955: Epoch 862 +2024-09-11 03:31:37.923147: Current learning rate: 0.00168 +2024-09-11 03:35:49.910747: train_loss -0.765 +2024-09-11 03:35:49.937979: val_loss -0.7631 +2024-09-11 03:35:49.938720: Pseudo dice [0.9666, 0.9608, 0.9805, 0.9571, 0.8925, 0.9776, 0.9922] +2024-09-11 03:35:49.939400: Epoch time: 252.0 s +2024-09-11 03:35:49.939950: Yayy! New best EMA pseudo Dice: 0.9598 +2024-09-11 03:35:55.395312: +2024-09-11 03:35:55.396288: Epoch 863 +2024-09-11 03:35:55.397057: Current learning rate: 0.00167 +2024-09-11 03:40:26.724904: train_loss -0.7702 +2024-09-11 03:40:26.727446: val_loss -0.7405 +2024-09-11 03:40:26.728662: Pseudo dice [0.9657, 0.9606, 0.9816, 0.9491, 0.8918, 0.9744, 0.9904] +2024-09-11 03:40:26.729887: Epoch time: 271.33 s +2024-09-11 03:40:29.693026: +2024-09-11 03:40:29.694131: Epoch 864 +2024-09-11 03:40:29.694977: Current learning rate: 0.00166 +2024-09-11 03:44:37.001080: train_loss -0.7685 +2024-09-11 03:44:37.003372: val_loss -0.7129 +2024-09-11 03:44:37.004104: Pseudo dice [0.9652, 0.9623, 0.9786, 0.9561, 0.8933, 0.9763, 0.9921] +2024-09-11 03:44:37.004807: Epoch time: 247.31 s +2024-09-11 03:44:37.005462: Yayy! New best EMA pseudo Dice: 0.9598 +2024-09-11 03:44:42.142283: +2024-09-11 03:44:42.143064: Epoch 865 +2024-09-11 03:44:42.143675: Current learning rate: 0.00165 +2024-09-11 03:49:12.523652: train_loss -0.76 +2024-09-11 03:49:12.525864: val_loss -0.7562 +2024-09-11 03:49:12.526850: Pseudo dice [0.9683, 0.9626, 0.9816, 0.9537, 0.8957, 0.9764, 0.9931] +2024-09-11 03:49:12.527623: Epoch time: 270.38 s +2024-09-11 03:49:12.528246: Yayy! New best EMA pseudo Dice: 0.96 +2024-09-11 03:49:18.032412: +2024-09-11 03:49:18.033370: Epoch 866 +2024-09-11 03:49:18.034116: Current learning rate: 0.00164 +2024-09-11 03:53:38.886426: train_loss -0.7678 +2024-09-11 03:53:38.918139: val_loss -0.7416 +2024-09-11 03:53:38.919213: Pseudo dice [0.9696, 0.9619, 0.9828, 0.958, 0.8955, 0.978, 0.9923] +2024-09-11 03:53:38.920664: Epoch time: 260.86 s +2024-09-11 03:53:38.921835: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-11 03:53:44.866935: +2024-09-11 03:53:44.871603: Epoch 867 +2024-09-11 03:53:44.872360: Current learning rate: 0.00163 +2024-09-11 03:58:14.548129: train_loss -0.7427 +2024-09-11 03:58:14.550487: val_loss -0.7713 +2024-09-11 03:58:14.551311: Pseudo dice [0.9686, 0.9644, 0.9813, 0.956, 0.8948, 0.9758, 0.9935] +2024-09-11 03:58:14.551912: Epoch time: 269.69 s +2024-09-11 03:58:14.552524: Yayy! New best EMA pseudo Dice: 0.9604 +2024-09-11 03:58:20.484246: +2024-09-11 03:58:20.485253: Epoch 868 +2024-09-11 03:58:20.486063: Current learning rate: 0.00162 +2024-09-11 04:02:56.362588: train_loss -0.7588 +2024-09-11 04:02:56.394077: val_loss -0.7824 +2024-09-11 04:02:56.394936: Pseudo dice [0.9675, 0.9588, 0.982, 0.9568, 0.8882, 0.972, 0.9912] +2024-09-11 04:02:56.395673: Epoch time: 275.88 s +2024-09-11 04:02:59.287622: +2024-09-11 04:02:59.288436: Epoch 869 +2024-09-11 04:02:59.289008: Current learning rate: 0.00161 +2024-09-11 04:07:17.303384: train_loss -0.7519 +2024-09-11 04:07:17.313398: val_loss -0.7283 +2024-09-11 04:07:17.314484: Pseudo dice [0.9667, 0.9594, 0.9791, 0.9511, 0.8862, 0.9763, 0.9925] +2024-09-11 04:07:17.315239: Epoch time: 258.02 s +2024-09-11 04:07:21.348095: +2024-09-11 04:07:21.349435: Epoch 870 +2024-09-11 04:07:21.350636: Current learning rate: 0.00159 +2024-09-11 04:11:52.687038: train_loss -0.7302 +2024-09-11 04:11:52.708845: val_loss -0.7473 +2024-09-11 04:11:52.709603: Pseudo dice [0.968, 0.9625, 0.981, 0.9585, 0.9006, 0.9753, 0.9919] +2024-09-11 04:11:52.710265: Epoch time: 271.36 s +2024-09-11 04:11:55.340812: +2024-09-11 04:11:55.341740: Epoch 871 +2024-09-11 04:11:55.342424: Current learning rate: 0.00158 +2024-09-11 04:16:02.635607: train_loss -0.7545 +2024-09-11 04:16:02.646049: val_loss -0.7744 +2024-09-11 04:16:02.646826: Pseudo dice [0.9679, 0.9593, 0.9809, 0.9574, 0.8908, 0.9765, 0.9921] +2024-09-11 04:16:02.647488: Epoch time: 247.3 s +2024-09-11 04:16:02.648350: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-11 04:16:07.917110: +2024-09-11 04:16:07.918044: Epoch 872 +2024-09-11 04:16:07.918763: Current learning rate: 0.00157 +2024-09-11 04:20:17.071277: train_loss -0.7405 +2024-09-11 04:20:17.085047: val_loss -0.7502 +2024-09-11 04:20:17.085758: Pseudo dice [0.9685, 0.9623, 0.9808, 0.9505, 0.8909, 0.9739, 0.9922] +2024-09-11 04:20:17.086428: Epoch time: 249.16 s +2024-09-11 04:20:21.573069: +2024-09-11 04:20:21.574282: Epoch 873 +2024-09-11 04:20:21.575067: Current learning rate: 0.00156 +2024-09-11 04:24:20.389166: train_loss -0.7606 +2024-09-11 04:24:20.390934: val_loss -0.7528 +2024-09-11 04:24:20.392318: Pseudo dice [0.9662, 0.9606, 0.9783, 0.9519, 0.887, 0.9744, 0.9911] +2024-09-11 04:24:20.393430: Epoch time: 238.82 s +2024-09-11 04:24:23.916157: +2024-09-11 04:24:23.917610: Epoch 874 +2024-09-11 04:24:23.918945: Current learning rate: 0.00155 +2024-09-11 04:28:45.324992: train_loss -0.7576 +2024-09-11 04:28:45.326374: val_loss -0.7606 +2024-09-11 04:28:45.327129: Pseudo dice [0.9689, 0.9595, 0.9822, 0.9534, 0.8809, 0.9755, 0.9921] +2024-09-11 04:28:45.327755: Epoch time: 261.43 s +2024-09-11 04:28:48.292748: +2024-09-11 04:28:48.293686: Epoch 875 +2024-09-11 04:28:48.294235: Current learning rate: 0.00154 +2024-09-11 04:33:01.380162: train_loss -0.7671 +2024-09-11 04:33:01.397216: val_loss -0.756 +2024-09-11 04:33:01.398138: Pseudo dice [0.9691, 0.9634, 0.9821, 0.954, 0.8997, 0.9778, 0.9925] +2024-09-11 04:33:01.398975: Epoch time: 253.09 s +2024-09-11 04:33:04.120488: +2024-09-11 04:33:04.121372: Epoch 876 +2024-09-11 04:33:04.122044: Current learning rate: 0.00153 +2024-09-11 04:37:15.234803: train_loss -0.7742 +2024-09-11 04:37:15.235819: val_loss -0.7649 +2024-09-11 04:37:15.248218: Pseudo dice [0.9684, 0.96, 0.9806, 0.9565, 0.8871, 0.9763, 0.9912] +2024-09-11 04:37:15.249216: Epoch time: 251.12 s +2024-09-11 04:37:18.081259: +2024-09-11 04:37:18.082445: Epoch 877 +2024-09-11 04:37:18.083130: Current learning rate: 0.00152 +2024-09-11 04:41:22.346179: train_loss -0.7701 +2024-09-11 04:41:22.347446: val_loss -0.7437 +2024-09-11 04:41:22.350510: Pseudo dice [0.9685, 0.9625, 0.9815, 0.9544, 0.898, 0.9774, 0.9917] +2024-09-11 04:41:22.364756: Epoch time: 244.27 s +2024-09-11 04:41:22.365486: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-11 04:41:28.729218: +2024-09-11 04:41:28.730129: Epoch 878 +2024-09-11 04:41:28.730807: Current learning rate: 0.00151 +2024-09-11 04:45:34.012250: train_loss -0.7565 +2024-09-11 04:45:34.013546: val_loss -0.7427 +2024-09-11 04:45:34.014422: Pseudo dice [0.9675, 0.961, 0.981, 0.9561, 0.8844, 0.9738, 0.9903] +2024-09-11 04:45:34.015286: Epoch time: 245.29 s +2024-09-11 04:45:36.484837: +2024-09-11 04:45:36.486164: Epoch 879 +2024-09-11 04:45:36.487020: Current learning rate: 0.00149 +2024-09-11 04:49:49.751562: train_loss -0.7458 +2024-09-11 04:49:49.765218: val_loss -0.7581 +2024-09-11 04:49:49.766078: Pseudo dice [0.9655, 0.9597, 0.98, 0.9468, 0.8937, 0.9742, 0.9915] +2024-09-11 04:49:49.766789: Epoch time: 253.27 s +2024-09-11 04:49:53.218366: +2024-09-11 04:49:53.219226: Epoch 880 +2024-09-11 04:49:53.219814: Current learning rate: 0.00148 +2024-09-11 04:54:20.895023: train_loss -0.758 +2024-09-11 04:54:20.897418: val_loss -0.7746 +2024-09-11 04:54:20.898278: Pseudo dice [0.9685, 0.9601, 0.9832, 0.9569, 0.8968, 0.9761, 0.9925] +2024-09-11 04:54:20.899077: Epoch time: 267.68 s +2024-09-11 04:54:23.556073: +2024-09-11 04:54:23.580591: Epoch 881 +2024-09-11 04:54:23.581526: Current learning rate: 0.00147 +2024-09-11 04:58:40.450597: train_loss -0.7614 +2024-09-11 04:58:40.453625: val_loss -0.7756 +2024-09-11 04:58:40.454965: Pseudo dice [0.9681, 0.9602, 0.9809, 0.956, 0.8944, 0.9772, 0.9922] +2024-09-11 04:58:40.456208: Epoch time: 256.9 s +2024-09-11 04:58:43.325846: +2024-09-11 04:58:43.336594: Epoch 882 +2024-09-11 04:58:43.337345: Current learning rate: 0.00146 +2024-09-11 05:02:58.219018: train_loss -0.7589 +2024-09-11 05:02:58.242656: val_loss -0.7543 +2024-09-11 05:02:58.243536: Pseudo dice [0.9686, 0.9612, 0.9827, 0.9497, 0.8912, 0.9746, 0.9922] +2024-09-11 05:02:58.244544: Epoch time: 254.9 s +2024-09-11 05:03:00.392754: +2024-09-11 05:03:00.402297: Epoch 883 +2024-09-11 05:03:00.403168: Current learning rate: 0.00145 +2024-09-11 05:07:19.963916: train_loss -0.7479 +2024-09-11 05:07:19.966211: val_loss -0.7509 +2024-09-11 05:07:19.967130: Pseudo dice [0.9681, 0.9587, 0.9801, 0.9543, 0.8828, 0.9747, 0.9927] +2024-09-11 05:07:19.967874: Epoch time: 259.58 s +2024-09-11 05:07:22.132529: +2024-09-11 05:07:22.133702: Epoch 884 +2024-09-11 05:07:22.134374: Current learning rate: 0.00144 +2024-09-11 05:11:27.335818: train_loss -0.7619 +2024-09-11 05:11:27.354020: val_loss -0.8241 +2024-09-11 05:11:27.354808: Pseudo dice [0.9689, 0.9597, 0.9815, 0.9525, 0.8969, 0.9766, 0.9936] +2024-09-11 05:11:27.355473: Epoch time: 245.21 s +2024-09-11 05:11:29.625394: +2024-09-11 05:11:29.626307: Epoch 885 +2024-09-11 05:11:29.627098: Current learning rate: 0.00143 +2024-09-11 05:15:36.151596: train_loss -0.7473 +2024-09-11 05:15:36.173859: val_loss -0.7394 +2024-09-11 05:15:36.174771: Pseudo dice [0.9685, 0.9626, 0.9815, 0.9567, 0.8855, 0.9766, 0.99] +2024-09-11 05:15:36.175458: Epoch time: 246.53 s +2024-09-11 05:15:39.299651: +2024-09-11 05:15:39.312074: Epoch 886 +2024-09-11 05:15:39.313025: Current learning rate: 0.00142 +2024-09-11 05:20:01.084349: train_loss -0.7554 +2024-09-11 05:20:01.101389: val_loss -0.7431 +2024-09-11 05:20:01.102286: Pseudo dice [0.9683, 0.9614, 0.9813, 0.9529, 0.892, 0.9761, 0.9922] +2024-09-11 05:20:01.103225: Epoch time: 261.79 s +2024-09-11 05:20:03.792285: +2024-09-11 05:20:03.793142: Epoch 887 +2024-09-11 05:20:03.793779: Current learning rate: 0.00141 +2024-09-11 05:24:24.810841: train_loss -0.7684 +2024-09-11 05:24:24.831241: val_loss -0.7639 +2024-09-11 05:24:24.832231: Pseudo dice [0.9664, 0.9605, 0.98, 0.9516, 0.8821, 0.9712, 0.9911] +2024-09-11 05:24:24.833243: Epoch time: 261.02 s +2024-09-11 05:24:27.792157: +2024-09-11 05:24:27.793046: Epoch 888 +2024-09-11 05:24:27.793765: Current learning rate: 0.00139 +2024-09-11 05:28:29.793407: train_loss -0.7608 +2024-09-11 05:28:29.806039: val_loss -0.7496 +2024-09-11 05:28:29.806935: Pseudo dice [0.9679, 0.9623, 0.9814, 0.9541, 0.893, 0.9747, 0.9918] +2024-09-11 05:28:29.807881: Epoch time: 242.01 s +2024-09-11 05:28:32.475602: +2024-09-11 05:28:32.476922: Epoch 889 +2024-09-11 05:28:32.478189: Current learning rate: 0.00138 +2024-09-11 05:33:03.988381: train_loss -0.7645 +2024-09-11 05:33:03.990689: val_loss -0.7824 +2024-09-11 05:33:03.991505: Pseudo dice [0.9684, 0.9613, 0.9833, 0.9554, 0.8963, 0.9759, 0.9914] +2024-09-11 05:33:03.992284: Epoch time: 271.52 s +2024-09-11 05:33:07.073079: +2024-09-11 05:33:07.073940: Epoch 890 +2024-09-11 05:33:07.074616: Current learning rate: 0.00137 +2024-09-11 05:37:28.390971: train_loss -0.7613 +2024-09-11 05:37:28.403391: val_loss -0.7328 +2024-09-11 05:37:28.404361: Pseudo dice [0.9687, 0.9621, 0.9815, 0.9549, 0.8729, 0.974, 0.991] +2024-09-11 05:37:28.405186: Epoch time: 261.32 s +2024-09-11 05:37:31.857012: +2024-09-11 05:37:31.857840: Epoch 891 +2024-09-11 05:37:31.858470: Current learning rate: 0.00136 +2024-09-11 05:41:46.655240: train_loss -0.7545 +2024-09-11 05:41:46.673676: val_loss -0.7423 +2024-09-11 05:41:46.674573: Pseudo dice [0.9614, 0.9564, 0.9755, 0.9429, 0.8811, 0.9716, 0.9902] +2024-09-11 05:41:46.675406: Epoch time: 254.8 s +2024-09-11 05:41:48.967540: +2024-09-11 05:41:48.968874: Epoch 892 +2024-09-11 05:41:48.969960: Current learning rate: 0.00135 +2024-09-11 05:46:11.654995: train_loss -0.7626 +2024-09-11 05:46:11.685592: val_loss -0.751 +2024-09-11 05:46:11.686354: Pseudo dice [0.9653, 0.9596, 0.979, 0.9532, 0.8942, 0.9761, 0.9928] +2024-09-11 05:46:11.687067: Epoch time: 262.69 s +2024-09-11 05:46:14.816617: +2024-09-11 05:46:14.817649: Epoch 893 +2024-09-11 05:46:14.818458: Current learning rate: 0.00134 +2024-09-11 05:50:13.755594: train_loss -0.7554 +2024-09-11 05:50:13.782176: val_loss -0.7691 +2024-09-11 05:50:13.782865: Pseudo dice [0.9698, 0.9637, 0.9827, 0.9543, 0.8927, 0.9728, 0.9913] +2024-09-11 05:50:13.783561: Epoch time: 238.94 s +2024-09-11 05:50:16.813588: +2024-09-11 05:50:16.824711: Epoch 894 +2024-09-11 05:50:16.825495: Current learning rate: 0.00133 +2024-09-11 05:54:38.600609: train_loss -0.7548 +2024-09-11 05:54:38.603007: val_loss -0.7707 +2024-09-11 05:54:38.603765: Pseudo dice [0.9682, 0.9617, 0.9804, 0.9551, 0.8909, 0.9766, 0.992] +2024-09-11 05:54:38.604448: Epoch time: 261.79 s +2024-09-11 05:54:41.329116: +2024-09-11 05:54:41.344957: Epoch 895 +2024-09-11 05:54:41.345931: Current learning rate: 0.00132 +2024-09-11 05:58:52.269751: train_loss -0.7552 +2024-09-11 05:58:52.286211: val_loss -0.7841 +2024-09-11 05:58:52.287031: Pseudo dice [0.9667, 0.9618, 0.982, 0.9578, 0.898, 0.9776, 0.9927] +2024-09-11 05:58:52.287676: Epoch time: 250.94 s +2024-09-11 05:58:55.846067: +2024-09-11 05:58:55.847193: Epoch 896 +2024-09-11 05:58:55.848145: Current learning rate: 0.0013 +2024-09-11 06:03:10.694668: train_loss -0.7518 +2024-09-11 06:03:10.710068: val_loss -0.7629 +2024-09-11 06:03:10.710861: Pseudo dice [0.9666, 0.9607, 0.9805, 0.9528, 0.896, 0.9754, 0.9918] +2024-09-11 06:03:10.711624: Epoch time: 254.85 s +2024-09-11 06:03:13.161616: +2024-09-11 06:03:13.176764: Epoch 897 +2024-09-11 06:03:13.177775: Current learning rate: 0.00129 +2024-09-11 06:07:48.310026: train_loss -0.7573 +2024-09-11 06:07:48.312304: val_loss -0.7849 +2024-09-11 06:07:48.312991: Pseudo dice [0.9672, 0.9598, 0.9824, 0.9549, 0.8908, 0.9758, 0.9919] +2024-09-11 06:07:48.313711: Epoch time: 275.15 s +2024-09-11 06:07:52.497692: +2024-09-11 06:07:52.498563: Epoch 898 +2024-09-11 06:07:52.499333: Current learning rate: 0.00128 +2024-09-11 06:12:13.669310: train_loss -0.7544 +2024-09-11 06:12:13.671950: val_loss -0.753 +2024-09-11 06:12:13.672985: Pseudo dice [0.9683, 0.9626, 0.9814, 0.9592, 0.8961, 0.9761, 0.9908] +2024-09-11 06:12:13.673842: Epoch time: 261.18 s +2024-09-11 06:12:15.848286: +2024-09-11 06:12:15.857540: Epoch 899 +2024-09-11 06:12:15.858582: Current learning rate: 0.00127 +2024-09-11 06:16:34.712342: train_loss -0.7635 +2024-09-11 06:16:34.714981: val_loss -0.7749 +2024-09-11 06:16:34.715840: Pseudo dice [0.9702, 0.9642, 0.9829, 0.958, 0.8902, 0.9753, 0.9917] +2024-09-11 06:16:34.716564: Epoch time: 258.85 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_06_42_34.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_06_42_34.txt new file mode 100644 index 0000000000000000000000000000000000000000..69836610f3b46ac0f9324d97eca98f3b4b3d9297 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_06_42_34.txt @@ -0,0 +1,745 @@ + +####################################################################### +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. +####################################################################### + +2024-09-11 06:42:43.955588: Using torch.compile... +2024-09-11 06:42:58.104252: do_dummy_2d_data_aug: False +2024-09-11 06:42:58.110365: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 06:42:58.112206: The split file contains 5 splits. +2024-09-11 06:42:58.112992: Desired fold for training: 0 +2024-09-11 06:42:58.113758: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-11 06:43:09.345969: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-11 06:43:20.669152: +2024-09-11 06:43:20.680806: Epoch 900 +2024-09-11 06:43:20.681933: Current learning rate: 0.00126 +2024-09-11 06:56:41.016367: train_loss -0.7805 +2024-09-11 06:56:41.018920: val_loss -0.7607 +2024-09-11 06:56:41.020010: Pseudo dice [0.9677, 0.9624, 0.9797, 0.9553, 0.8819, 0.9763, 0.9913] +2024-09-11 06:56:41.020890: Epoch time: 800.35 s +2024-09-11 06:56:42.531756: +2024-09-11 06:56:42.532915: Epoch 901 +2024-09-11 06:56:42.533906: Current learning rate: 0.00125 +2024-09-11 07:00:42.477643: train_loss -0.761 +2024-09-11 07:00:42.497912: val_loss -0.78 +2024-09-11 07:00:42.498853: Pseudo dice [0.9678, 0.9592, 0.9817, 0.9567, 0.8943, 0.9771, 0.9916] +2024-09-11 07:00:42.499707: Epoch time: 239.95 s +2024-09-11 07:00:45.211778: +2024-09-11 07:00:45.212790: Epoch 902 +2024-09-11 07:00:45.213735: Current learning rate: 0.00124 +2024-09-11 07:04:53.324517: train_loss -0.752 +2024-09-11 07:04:53.345953: val_loss -0.7729 +2024-09-11 07:04:53.347249: Pseudo dice [0.9701, 0.9633, 0.9809, 0.9551, 0.8891, 0.9754, 0.9927] +2024-09-11 07:04:53.348390: Epoch time: 248.12 s +2024-09-11 07:04:53.349449: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-11 07:05:01.124498: +2024-09-11 07:05:01.125554: Epoch 903 +2024-09-11 07:05:01.126439: Current learning rate: 0.00122 +2024-09-11 07:09:12.109399: train_loss -0.7659 +2024-09-11 07:09:12.111792: val_loss -0.7752 +2024-09-11 07:09:12.112767: Pseudo dice [0.9677, 0.9582, 0.9818, 0.9536, 0.8932, 0.9782, 0.9925] +2024-09-11 07:09:12.113844: Epoch time: 250.99 s +2024-09-11 07:09:12.114812: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-11 07:09:17.170037: +2024-09-11 07:09:17.171251: Epoch 904 +2024-09-11 07:09:17.172101: Current learning rate: 0.00121 +2024-09-11 07:13:30.619256: train_loss -0.7726 +2024-09-11 07:13:30.633017: val_loss -0.7422 +2024-09-11 07:13:30.634171: Pseudo dice [0.9686, 0.9615, 0.9821, 0.9559, 0.8939, 0.9764, 0.9919] +2024-09-11 07:13:30.635428: Epoch time: 253.45 s +2024-09-11 07:13:30.636450: Yayy! New best EMA pseudo Dice: 0.9606 +2024-09-11 07:13:35.943038: +2024-09-11 07:13:35.944103: Epoch 905 +2024-09-11 07:13:35.944946: Current learning rate: 0.0012 +2024-09-11 07:17:34.317254: train_loss -0.7668 +2024-09-11 07:17:34.323313: val_loss -0.7795 +2024-09-11 07:17:34.324394: Pseudo dice [0.9684, 0.962, 0.9825, 0.9561, 0.898, 0.9756, 0.9919] +2024-09-11 07:17:34.325468: Epoch time: 238.38 s +2024-09-11 07:17:34.326455: Yayy! New best EMA pseudo Dice: 0.9607 +2024-09-11 07:17:38.343637: +2024-09-11 07:17:38.344640: Epoch 906 +2024-09-11 07:17:38.345498: Current learning rate: 0.00119 +2024-09-11 07:21:43.696677: train_loss -0.7521 +2024-09-11 07:21:43.718045: val_loss -0.7603 +2024-09-11 07:21:43.719106: Pseudo dice [0.9705, 0.9622, 0.9824, 0.9564, 0.894, 0.978, 0.9934] +2024-09-11 07:21:43.720222: Epoch time: 245.36 s +2024-09-11 07:21:43.721218: Yayy! New best EMA pseudo Dice: 0.9609 +2024-09-11 07:21:49.551921: +2024-09-11 07:21:49.553099: Epoch 907 +2024-09-11 07:21:49.553987: Current learning rate: 0.00118 +2024-09-11 07:26:07.068816: train_loss -0.7597 +2024-09-11 07:26:07.086238: val_loss -0.7502 +2024-09-11 07:26:07.087224: Pseudo dice [0.9668, 0.9618, 0.9789, 0.9555, 0.8882, 0.9767, 0.9911] +2024-09-11 07:26:07.088150: Epoch time: 257.52 s +2024-09-11 07:26:10.465950: +2024-09-11 07:26:10.480926: Epoch 908 +2024-09-11 07:26:10.482188: Current learning rate: 0.00117 +2024-09-11 07:30:30.942825: train_loss -0.7598 +2024-09-11 07:30:30.945637: val_loss -0.7903 +2024-09-11 07:30:30.946789: Pseudo dice [0.9692, 0.9616, 0.9828, 0.9552, 0.8889, 0.9751, 0.9919] +2024-09-11 07:30:30.948109: Epoch time: 260.48 s +2024-09-11 07:30:33.169305: +2024-09-11 07:30:33.170758: Epoch 909 +2024-09-11 07:30:33.172656: Current learning rate: 0.00116 +2024-09-11 07:34:47.436793: train_loss -0.7589 +2024-09-11 07:34:47.454203: val_loss -0.7652 +2024-09-11 07:34:47.455263: Pseudo dice [0.97, 0.962, 0.9834, 0.9543, 0.8847, 0.9772, 0.9919] +2024-09-11 07:34:47.456154: Epoch time: 254.28 s +2024-09-11 07:34:50.084702: +2024-09-11 07:34:50.100896: Epoch 910 +2024-09-11 07:34:50.101998: Current learning rate: 0.00115 +2024-09-11 07:39:06.096661: train_loss -0.7615 +2024-09-11 07:39:06.118515: val_loss -0.7507 +2024-09-11 07:39:06.119525: Pseudo dice [0.9695, 0.9606, 0.9825, 0.9512, 0.8902, 0.9751, 0.9903] +2024-09-11 07:39:06.120591: Epoch time: 256.02 s +2024-09-11 07:39:09.414688: +2024-09-11 07:39:09.429099: Epoch 911 +2024-09-11 07:39:09.430174: Current learning rate: 0.00113 +2024-09-11 07:43:35.656014: train_loss -0.7641 +2024-09-11 07:43:35.670226: val_loss -0.7829 +2024-09-11 07:43:35.671210: Pseudo dice [0.971, 0.9616, 0.9839, 0.9587, 0.9023, 0.9772, 0.9927] +2024-09-11 07:43:35.672120: Epoch time: 266.25 s +2024-09-11 07:43:35.672956: Yayy! New best EMA pseudo Dice: 0.961 +2024-09-11 07:43:41.194665: +2024-09-11 07:43:41.195712: Epoch 912 +2024-09-11 07:43:41.196752: Current learning rate: 0.00112 +2024-09-11 07:47:52.456986: train_loss -0.7523 +2024-09-11 07:47:52.459291: val_loss -0.7337 +2024-09-11 07:47:52.462662: Pseudo dice [0.9654, 0.9614, 0.9776, 0.9534, 0.8971, 0.9761, 0.9914] +2024-09-11 07:47:52.464623: Epoch time: 251.27 s +2024-09-11 07:47:55.451865: +2024-09-11 07:47:55.464856: Epoch 913 +2024-09-11 07:47:55.465919: Current learning rate: 0.00111 +2024-09-11 07:52:10.987466: train_loss -0.7295 +2024-09-11 07:52:11.022217: val_loss -0.7964 +2024-09-11 07:52:11.023154: Pseudo dice [0.9679, 0.9615, 0.9813, 0.9571, 0.9005, 0.9769, 0.9922] +2024-09-11 07:52:11.023968: Epoch time: 255.54 s +2024-09-11 07:52:11.024862: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-11 07:52:19.665240: +2024-09-11 07:52:19.666380: Epoch 914 +2024-09-11 07:52:19.667264: Current learning rate: 0.0011 +2024-09-11 07:56:28.464936: train_loss -0.7566 +2024-09-11 07:56:28.467633: val_loss -0.7697 +2024-09-11 07:56:28.469032: Pseudo dice [0.9675, 0.9596, 0.9814, 0.9606, 0.8913, 0.9746, 0.9921] +2024-09-11 07:56:28.469991: Epoch time: 248.8 s +2024-09-11 07:56:30.963891: +2024-09-11 07:56:30.965012: Epoch 915 +2024-09-11 07:56:30.965822: Current learning rate: 0.00109 +2024-09-11 08:00:52.992411: train_loss -0.7508 +2024-09-11 08:00:52.994988: val_loss -0.7453 +2024-09-11 08:00:52.996210: Pseudo dice [0.9696, 0.9615, 0.9829, 0.956, 0.9003, 0.9784, 0.9938] +2024-09-11 08:00:52.997212: Epoch time: 262.03 s +2024-09-11 08:00:52.998293: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-11 08:00:59.282679: +2024-09-11 08:00:59.283898: Epoch 916 +2024-09-11 08:00:59.284843: Current learning rate: 0.00108 +2024-09-11 08:05:23.211220: train_loss -0.7533 +2024-09-11 08:05:23.226760: val_loss -0.761 +2024-09-11 08:05:23.228145: Pseudo dice [0.9661, 0.9612, 0.9809, 0.9495, 0.888, 0.9744, 0.9899] +2024-09-11 08:05:23.229370: Epoch time: 263.93 s +2024-09-11 08:05:26.383017: +2024-09-11 08:05:26.384238: Epoch 917 +2024-09-11 08:05:26.385168: Current learning rate: 0.00106 +2024-09-11 08:09:53.407262: train_loss -0.7573 +2024-09-11 08:09:53.422227: val_loss -0.7769 +2024-09-11 08:09:53.423178: Pseudo dice [0.9676, 0.9619, 0.9795, 0.9589, 0.8977, 0.9767, 0.992] +2024-09-11 08:09:53.424092: Epoch time: 267.03 s +2024-09-11 08:09:55.838941: +2024-09-11 08:09:55.852922: Epoch 918 +2024-09-11 08:09:55.854269: Current learning rate: 0.00105 +2024-09-11 08:14:27.264977: train_loss -0.7671 +2024-09-11 08:14:27.280342: val_loss -0.7428 +2024-09-11 08:14:27.281346: Pseudo dice [0.9696, 0.961, 0.9822, 0.9534, 0.9013, 0.9788, 0.9934] +2024-09-11 08:14:27.282194: Epoch time: 271.43 s +2024-09-11 08:14:27.282973: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-11 08:14:32.622827: +2024-09-11 08:14:32.623771: Epoch 919 +2024-09-11 08:14:32.624683: Current learning rate: 0.00104 +2024-09-11 08:18:52.694377: train_loss -0.7656 +2024-09-11 08:18:52.696897: val_loss -0.7876 +2024-09-11 08:18:52.697920: Pseudo dice [0.9677, 0.9599, 0.9828, 0.9553, 0.8968, 0.9746, 0.9921] +2024-09-11 08:18:52.698739: Epoch time: 260.07 s +2024-09-11 08:18:52.699510: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-11 08:18:58.868754: +2024-09-11 08:18:58.869789: Epoch 920 +2024-09-11 08:18:58.870702: Current learning rate: 0.00103 +2024-09-11 08:23:06.593619: train_loss -0.7454 +2024-09-11 08:23:06.612321: val_loss -0.7629 +2024-09-11 08:23:06.613984: Pseudo dice [0.9667, 0.9609, 0.9799, 0.9476, 0.8772, 0.9738, 0.9906] +2024-09-11 08:23:06.615607: Epoch time: 247.73 s +2024-09-11 08:23:09.148295: +2024-09-11 08:23:09.149673: Epoch 921 +2024-09-11 08:23:09.150871: Current learning rate: 0.00102 +2024-09-11 08:27:15.171075: train_loss -0.7559 +2024-09-11 08:27:15.186013: val_loss -0.7543 +2024-09-11 08:27:15.187144: Pseudo dice [0.9683, 0.9626, 0.9793, 0.9519, 0.886, 0.9763, 0.9922] +2024-09-11 08:27:15.188299: Epoch time: 246.03 s +2024-09-11 08:27:18.770245: +2024-09-11 08:27:18.771507: Epoch 922 +2024-09-11 08:27:18.772423: Current learning rate: 0.00101 +2024-09-11 08:31:42.832949: train_loss -0.7473 +2024-09-11 08:31:42.848210: val_loss -0.7905 +2024-09-11 08:31:42.849806: Pseudo dice [0.9647, 0.9607, 0.9798, 0.9579, 0.8872, 0.9726, 0.9897] +2024-09-11 08:31:42.851027: Epoch time: 264.07 s +2024-09-11 08:31:45.811810: +2024-09-11 08:31:45.812869: Epoch 923 +2024-09-11 08:31:45.813728: Current learning rate: 0.001 +2024-09-11 08:35:43.546498: train_loss -0.7543 +2024-09-11 08:35:43.574221: val_loss -0.741 +2024-09-11 08:35:43.575313: Pseudo dice [0.9679, 0.9605, 0.9805, 0.9541, 0.9013, 0.9792, 0.9932] +2024-09-11 08:35:43.576406: Epoch time: 237.74 s +2024-09-11 08:35:46.958209: +2024-09-11 08:35:46.959536: Epoch 924 +2024-09-11 08:35:46.960535: Current learning rate: 0.00098 +2024-09-11 08:40:18.003703: train_loss -0.7576 +2024-09-11 08:40:18.018072: val_loss -0.7457 +2024-09-11 08:40:18.018886: Pseudo dice [0.9679, 0.9588, 0.98, 0.9515, 0.8924, 0.9736, 0.9917] +2024-09-11 08:40:18.019685: Epoch time: 271.05 s +2024-09-11 08:40:20.383985: +2024-09-11 08:40:20.392510: Epoch 925 +2024-09-11 08:40:20.393448: Current learning rate: 0.00097 +2024-09-11 08:44:50.291665: train_loss -0.7606 +2024-09-11 08:44:50.294539: val_loss -0.7427 +2024-09-11 08:44:50.295760: Pseudo dice [0.9705, 0.9645, 0.9814, 0.9576, 0.9026, 0.9785, 0.9914] +2024-09-11 08:44:50.297002: Epoch time: 269.91 s +2024-09-11 08:44:52.704976: +2024-09-11 08:44:52.706521: Epoch 926 +2024-09-11 08:44:52.707743: Current learning rate: 0.00096 +2024-09-11 08:49:02.557230: train_loss -0.7623 +2024-09-11 08:49:02.559686: val_loss -0.7516 +2024-09-11 08:49:02.560687: Pseudo dice [0.9679, 0.9634, 0.9807, 0.9569, 0.8941, 0.9766, 0.9924] +2024-09-11 08:49:02.561591: Epoch time: 249.86 s +2024-09-11 08:49:05.071858: +2024-09-11 08:49:05.073080: Epoch 927 +2024-09-11 08:49:05.074116: Current learning rate: 0.00095 +2024-09-11 08:53:31.511159: train_loss -0.7777 +2024-09-11 08:53:31.513991: val_loss -0.7461 +2024-09-11 08:53:31.515135: Pseudo dice [0.9683, 0.9608, 0.9827, 0.9541, 0.8953, 0.9759, 0.9909] +2024-09-11 08:53:31.516167: Epoch time: 266.44 s +2024-09-11 08:53:35.237016: +2024-09-11 08:53:35.238127: Epoch 928 +2024-09-11 08:53:35.239002: Current learning rate: 0.00094 +2024-09-11 08:57:46.749549: train_loss -0.7525 +2024-09-11 08:57:46.763717: val_loss -0.7588 +2024-09-11 08:57:46.765603: Pseudo dice [0.968, 0.9604, 0.9817, 0.9538, 0.9003, 0.9778, 0.9918] +2024-09-11 08:57:46.767316: Epoch time: 251.52 s +2024-09-11 08:57:49.904560: +2024-09-11 08:57:49.905654: Epoch 929 +2024-09-11 08:57:49.906587: Current learning rate: 0.00092 +2024-09-11 09:02:04.392052: train_loss -0.7711 +2024-09-11 09:02:04.408714: val_loss -0.7308 +2024-09-11 09:02:04.410290: Pseudo dice [0.9696, 0.9603, 0.9827, 0.9599, 0.8997, 0.9784, 0.9931] +2024-09-11 09:02:04.411726: Epoch time: 254.49 s +2024-09-11 09:02:04.413197: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-11 09:02:10.218758: +2024-09-11 09:02:10.219818: Epoch 930 +2024-09-11 09:02:10.220773: Current learning rate: 0.00091 +2024-09-11 09:06:41.615867: train_loss -0.7664 +2024-09-11 09:06:41.634505: val_loss -0.7954 +2024-09-11 09:06:41.635555: Pseudo dice [0.9683, 0.9599, 0.9815, 0.9555, 0.8859, 0.9771, 0.9907] +2024-09-11 09:06:41.636966: Epoch time: 271.4 s +2024-09-11 09:06:44.576936: +2024-09-11 09:06:44.578174: Epoch 931 +2024-09-11 09:06:44.579063: Current learning rate: 0.0009 +2024-09-11 09:10:53.011118: train_loss -0.7403 +2024-09-11 09:10:53.022735: val_loss -0.788 +2024-09-11 09:10:53.024102: Pseudo dice [0.9706, 0.9611, 0.9838, 0.9565, 0.8959, 0.9761, 0.9934] +2024-09-11 09:10:53.025874: Epoch time: 248.44 s +2024-09-11 09:10:55.258239: +2024-09-11 09:10:55.259472: Epoch 932 +2024-09-11 09:10:55.260378: Current learning rate: 0.00089 +2024-09-11 09:15:02.170901: train_loss -0.7595 +2024-09-11 09:15:02.186184: val_loss -0.7308 +2024-09-11 09:15:02.187125: Pseudo dice [0.9677, 0.9627, 0.9816, 0.9578, 0.8919, 0.9774, 0.9929] +2024-09-11 09:15:02.188067: Epoch time: 246.92 s +2024-09-11 09:15:02.188971: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-11 09:15:08.598662: +2024-09-11 09:15:08.599732: Epoch 933 +2024-09-11 09:15:08.600814: Current learning rate: 0.00088 +2024-09-11 09:19:13.780659: train_loss -0.7608 +2024-09-11 09:19:13.806079: val_loss -0.7253 +2024-09-11 09:19:13.807025: Pseudo dice [0.9697, 0.9624, 0.9812, 0.9522, 0.8937, 0.974, 0.9926] +2024-09-11 09:19:13.807930: Epoch time: 245.19 s +2024-09-11 09:19:16.918927: +2024-09-11 09:19:16.932929: Epoch 934 +2024-09-11 09:19:16.934030: Current learning rate: 0.00087 +2024-09-11 09:23:40.655202: train_loss -0.7616 +2024-09-11 09:23:40.669581: val_loss -0.7899 +2024-09-11 09:23:40.670483: Pseudo dice [0.9671, 0.9603, 0.9797, 0.9508, 0.8859, 0.9738, 0.9905] +2024-09-11 09:23:40.671397: Epoch time: 263.74 s +2024-09-11 09:23:43.209140: +2024-09-11 09:23:43.210144: Epoch 935 +2024-09-11 09:23:43.210898: Current learning rate: 0.00085 +2024-09-11 09:27:57.010201: train_loss -0.745 +2024-09-11 09:27:57.038164: val_loss -0.7714 +2024-09-11 09:27:57.039228: Pseudo dice [0.965, 0.9579, 0.9778, 0.9473, 0.8913, 0.9718, 0.9918] +2024-09-11 09:27:57.040359: Epoch time: 253.81 s +2024-09-11 09:27:59.809429: +2024-09-11 09:27:59.810646: Epoch 936 +2024-09-11 09:27:59.812175: Current learning rate: 0.00084 +2024-09-11 09:32:22.859803: train_loss -0.7568 +2024-09-11 09:32:22.873101: val_loss -0.7543 +2024-09-11 09:32:22.874133: Pseudo dice [0.9696, 0.9621, 0.9834, 0.956, 0.8931, 0.9764, 0.992] +2024-09-11 09:32:22.875098: Epoch time: 263.05 s +2024-09-11 09:32:25.457957: +2024-09-11 09:32:25.459091: Epoch 937 +2024-09-11 09:32:25.459965: Current learning rate: 0.00083 +2024-09-11 09:36:56.823257: train_loss -0.761 +2024-09-11 09:36:56.826056: val_loss -0.7703 +2024-09-11 09:36:56.827053: Pseudo dice [0.9682, 0.9614, 0.9803, 0.9592, 0.8873, 0.9758, 0.9927] +2024-09-11 09:36:56.827936: Epoch time: 271.37 s +2024-09-11 09:36:59.457748: +2024-09-11 09:36:59.458987: Epoch 938 +2024-09-11 09:36:59.459864: Current learning rate: 0.00082 +2024-09-11 09:41:05.878016: train_loss -0.7716 +2024-09-11 09:41:05.896201: val_loss -0.7612 +2024-09-11 09:41:05.897270: Pseudo dice [0.9683, 0.9605, 0.9805, 0.957, 0.8959, 0.9771, 0.9925] +2024-09-11 09:41:05.898156: Epoch time: 246.43 s +2024-09-11 09:41:10.204725: +2024-09-11 09:41:10.205964: Epoch 939 +2024-09-11 09:41:10.206994: Current learning rate: 0.00081 +2024-09-11 09:45:15.820887: train_loss -0.7734 +2024-09-11 09:45:15.823267: val_loss -0.752 +2024-09-11 09:45:15.824153: Pseudo dice [0.9674, 0.9622, 0.979, 0.9588, 0.9004, 0.977, 0.9922] +2024-09-11 09:45:15.825151: Epoch time: 245.62 s +2024-09-11 09:45:18.541648: +2024-09-11 09:45:18.543062: Epoch 940 +2024-09-11 09:45:18.544200: Current learning rate: 0.00079 +2024-09-11 09:49:37.468518: train_loss -0.7482 +2024-09-11 09:49:37.471064: val_loss -0.7515 +2024-09-11 09:49:37.472030: Pseudo dice [0.9692, 0.9609, 0.9832, 0.9521, 0.8983, 0.9755, 0.9922] +2024-09-11 09:49:37.473090: Epoch time: 258.93 s +2024-09-11 09:49:39.948511: +2024-09-11 09:49:39.949609: Epoch 941 +2024-09-11 09:49:39.950590: Current learning rate: 0.00078 +2024-09-11 09:54:02.748780: train_loss -0.763 +2024-09-11 09:54:02.762614: val_loss -0.7756 +2024-09-11 09:54:02.763978: Pseudo dice [0.9685, 0.9597, 0.9812, 0.95, 0.8944, 0.9752, 0.9924] +2024-09-11 09:54:02.765341: Epoch time: 262.8 s +2024-09-11 09:54:05.568476: +2024-09-11 09:54:05.569722: Epoch 942 +2024-09-11 09:54:05.570613: Current learning rate: 0.00077 +2024-09-11 09:58:29.661246: train_loss -0.7661 +2024-09-11 09:58:29.663887: val_loss -0.7626 +2024-09-11 09:58:29.664824: Pseudo dice [0.9689, 0.9619, 0.9836, 0.9612, 0.8866, 0.9733, 0.9906] +2024-09-11 09:58:29.665709: Epoch time: 264.1 s +2024-09-11 09:58:31.762126: +2024-09-11 09:58:31.763774: Epoch 943 +2024-09-11 09:58:31.764993: Current learning rate: 0.00076 +2024-09-11 10:02:42.237260: train_loss -0.7659 +2024-09-11 10:02:42.250286: val_loss -0.791 +2024-09-11 10:02:42.251252: Pseudo dice [0.9671, 0.9589, 0.9831, 0.9568, 0.8943, 0.977, 0.9923] +2024-09-11 10:02:42.252145: Epoch time: 250.48 s +2024-09-11 10:02:45.448495: +2024-09-11 10:02:45.449687: Epoch 944 +2024-09-11 10:02:45.450596: Current learning rate: 0.00075 +2024-09-11 10:07:04.149459: train_loss -0.767 +2024-09-11 10:07:04.151973: val_loss -0.7633 +2024-09-11 10:07:04.153176: Pseudo dice [0.9683, 0.9621, 0.9809, 0.9585, 0.9022, 0.9784, 0.9929] +2024-09-11 10:07:04.154303: Epoch time: 258.71 s +2024-09-11 10:07:06.156991: +2024-09-11 10:07:06.158021: Epoch 945 +2024-09-11 10:07:06.158975: Current learning rate: 0.00074 +2024-09-11 10:11:03.632760: train_loss -0.7566 +2024-09-11 10:11:03.650357: val_loss -0.7792 +2024-09-11 10:11:03.651270: Pseudo dice [0.9697, 0.9634, 0.9829, 0.9549, 0.9027, 0.9758, 0.9929] +2024-09-11 10:11:03.652110: Epoch time: 237.48 s +2024-09-11 10:11:03.653034: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-11 10:11:10.639747: +2024-09-11 10:11:10.640836: Epoch 946 +2024-09-11 10:11:10.641740: Current learning rate: 0.00072 +2024-09-11 10:15:28.520560: train_loss -0.7756 +2024-09-11 10:15:28.523004: val_loss -0.7699 +2024-09-11 10:15:28.523918: Pseudo dice [0.9686, 0.9617, 0.9823, 0.9558, 0.8983, 0.9776, 0.9935] +2024-09-11 10:15:28.524806: Epoch time: 257.88 s +2024-09-11 10:15:28.525658: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-11 10:15:33.938874: +2024-09-11 10:15:33.939874: Epoch 947 +2024-09-11 10:15:33.940738: Current learning rate: 0.00071 +2024-09-11 10:19:44.571703: train_loss -0.7781 +2024-09-11 10:19:44.598437: val_loss -0.7604 +2024-09-11 10:19:44.600027: Pseudo dice [0.9653, 0.9626, 0.9793, 0.9575, 0.905, 0.9742, 0.9932] +2024-09-11 10:19:44.601341: Epoch time: 250.64 s +2024-09-11 10:19:44.602401: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-11 10:19:49.703750: +2024-09-11 10:19:49.704767: Epoch 948 +2024-09-11 10:19:49.705577: Current learning rate: 0.0007 +2024-09-11 10:23:38.538011: train_loss -0.768 +2024-09-11 10:23:38.550249: val_loss -0.7296 +2024-09-11 10:23:38.551395: Pseudo dice [0.9672, 0.963, 0.9804, 0.9597, 0.8961, 0.9708, 0.9915] +2024-09-11 10:23:38.552420: Epoch time: 228.84 s +2024-09-11 10:23:42.699131: +2024-09-11 10:23:42.708749: Epoch 949 +2024-09-11 10:23:42.709824: Current learning rate: 0.00069 +2024-09-11 10:28:06.733817: train_loss -0.7712 +2024-09-11 10:28:06.736451: val_loss -0.7403 +2024-09-11 10:28:06.737439: Pseudo dice [0.9641, 0.9615, 0.9762, 0.9514, 0.898, 0.971, 0.992] +2024-09-11 10:28:06.738263: Epoch time: 264.04 s +2024-09-11 10:28:13.168733: +2024-09-11 10:28:13.169751: Epoch 950 +2024-09-11 10:28:13.170622: Current learning rate: 0.00067 +2024-09-11 10:32:24.011608: train_loss -0.7677 +2024-09-11 10:32:24.014278: val_loss -0.7573 +2024-09-11 10:32:24.015496: Pseudo dice [0.9677, 0.9615, 0.9814, 0.9594, 0.9, 0.9752, 0.9924] +2024-09-11 10:32:24.016535: Epoch time: 250.85 s +2024-09-11 10:32:27.223788: +2024-09-11 10:32:27.224876: Epoch 951 +2024-09-11 10:32:27.225770: Current learning rate: 0.00066 +2024-09-11 10:37:08.296685: train_loss -0.7505 +2024-09-11 10:37:08.299106: val_loss -0.7845 +2024-09-11 10:37:08.300176: Pseudo dice [0.9704, 0.9619, 0.984, 0.9609, 0.8984, 0.9773, 0.9924] +2024-09-11 10:37:08.301032: Epoch time: 281.08 s +2024-09-11 10:37:08.301878: Yayy! New best EMA pseudo Dice: 0.9617 +2024-09-11 10:37:13.676119: +2024-09-11 10:37:13.677203: Epoch 952 +2024-09-11 10:37:13.678171: Current learning rate: 0.00065 +2024-09-11 10:41:30.900789: train_loss -0.7687 +2024-09-11 10:41:30.904145: val_loss -0.7536 +2024-09-11 10:41:30.905716: Pseudo dice [0.9692, 0.9647, 0.9826, 0.9613, 0.9021, 0.9754, 0.9925] +2024-09-11 10:41:30.907202: Epoch time: 257.23 s +2024-09-11 10:41:30.909037: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-11 10:41:35.637291: +2024-09-11 10:41:35.638612: Epoch 953 +2024-09-11 10:41:35.639542: Current learning rate: 0.00064 +2024-09-11 10:45:45.168978: train_loss -0.7444 +2024-09-11 10:45:45.171564: val_loss -0.7688 +2024-09-11 10:45:45.172622: Pseudo dice [0.9703, 0.9625, 0.9819, 0.9546, 0.8953, 0.9782, 0.9923] +2024-09-11 10:45:45.173801: Epoch time: 249.53 s +2024-09-11 10:45:45.174731: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-11 10:45:50.813589: +2024-09-11 10:45:50.814742: Epoch 954 +2024-09-11 10:45:50.815639: Current learning rate: 0.00063 +2024-09-11 10:50:24.180819: train_loss -0.7785 +2024-09-11 10:50:24.194451: val_loss -0.774 +2024-09-11 10:50:24.195322: Pseudo dice [0.9688, 0.9627, 0.9807, 0.959, 0.8993, 0.9773, 0.9929] +2024-09-11 10:50:24.196207: Epoch time: 273.37 s +2024-09-11 10:50:24.197034: Yayy! New best EMA pseudo Dice: 0.962 +2024-09-11 10:50:29.907140: +2024-09-11 10:50:29.908403: Epoch 955 +2024-09-11 10:50:29.909360: Current learning rate: 0.00061 +2024-09-11 10:54:36.875162: train_loss -0.7453 +2024-09-11 10:54:36.890401: val_loss -0.7905 +2024-09-11 10:54:36.891334: Pseudo dice [0.9711, 0.9623, 0.9831, 0.9551, 0.9009, 0.9793, 0.9926] +2024-09-11 10:54:36.892220: Epoch time: 246.97 s +2024-09-11 10:54:36.893054: Yayy! New best EMA pseudo Dice: 0.9622 +2024-09-11 10:54:43.173856: +2024-09-11 10:54:43.174947: Epoch 956 +2024-09-11 10:54:43.175773: Current learning rate: 0.0006 +2024-09-11 10:59:14.641101: train_loss -0.7717 +2024-09-11 10:59:14.643530: val_loss -0.7897 +2024-09-11 10:59:14.660195: Pseudo dice [0.9716, 0.9641, 0.9851, 0.9584, 0.898, 0.9764, 0.9919] +2024-09-11 10:59:14.661227: Epoch time: 271.47 s +2024-09-11 10:59:14.662130: Yayy! New best EMA pseudo Dice: 0.9623 +2024-09-11 10:59:19.848502: +2024-09-11 10:59:19.849865: Epoch 957 +2024-09-11 10:59:19.850779: Current learning rate: 0.00059 +2024-09-11 11:03:30.148680: train_loss -0.7567 +2024-09-11 11:03:30.166423: val_loss -0.7883 +2024-09-11 11:03:30.167554: Pseudo dice [0.9701, 0.9645, 0.9809, 0.9568, 0.9057, 0.9787, 0.9941] +2024-09-11 11:03:30.168635: Epoch time: 250.3 s +2024-09-11 11:03:30.169647: Yayy! New best EMA pseudo Dice: 0.9625 +2024-09-11 11:03:36.541507: +2024-09-11 11:03:36.542454: Epoch 958 +2024-09-11 11:03:36.543231: Current learning rate: 0.00058 +2024-09-11 11:07:59.373691: train_loss -0.7728 +2024-09-11 11:07:59.389529: val_loss -0.7739 +2024-09-11 11:07:59.390650: Pseudo dice [0.9658, 0.9606, 0.9786, 0.9522, 0.8853, 0.975, 0.9906] +2024-09-11 11:07:59.391612: Epoch time: 262.84 s +2024-09-11 11:08:01.437805: +2024-09-11 11:08:01.439093: Epoch 959 +2024-09-11 11:08:01.440165: Current learning rate: 0.00056 +2024-09-11 11:12:21.098939: train_loss -0.7501 +2024-09-11 11:12:21.115607: val_loss -0.8076 +2024-09-11 11:12:21.116545: Pseudo dice [0.9694, 0.9615, 0.9826, 0.9575, 0.9026, 0.978, 0.9922] +2024-09-11 11:12:21.117344: Epoch time: 259.67 s +2024-09-11 11:12:23.858516: +2024-09-11 11:12:23.859805: Epoch 960 +2024-09-11 11:12:23.860932: Current learning rate: 0.00055 +2024-09-11 11:16:56.551478: train_loss -0.758 +2024-09-11 11:16:56.566367: val_loss -0.7572 +2024-09-11 11:16:56.567739: Pseudo dice [0.9701, 0.9627, 0.9839, 0.9501, 0.8937, 0.9758, 0.9919] +2024-09-11 11:16:56.569148: Epoch time: 272.7 s +2024-09-11 11:17:00.123444: +2024-09-11 11:17:00.124679: Epoch 961 +2024-09-11 11:17:00.125571: Current learning rate: 0.00054 +2024-09-11 11:21:19.729711: train_loss -0.757 +2024-09-11 11:21:19.731974: val_loss -0.7884 +2024-09-11 11:21:19.732912: Pseudo dice [0.9718, 0.9641, 0.9841, 0.9567, 0.9002, 0.9796, 0.9932] +2024-09-11 11:21:19.733728: Epoch time: 259.61 s +2024-09-11 11:21:22.890033: +2024-09-11 11:21:22.891289: Epoch 962 +2024-09-11 11:21:22.892284: Current learning rate: 0.00053 +2024-09-11 11:25:43.793201: train_loss -0.775 +2024-09-11 11:25:43.795660: val_loss -0.7716 +2024-09-11 11:25:43.796688: Pseudo dice [0.9688, 0.9599, 0.9842, 0.9554, 0.9067, 0.979, 0.9938] +2024-09-11 11:25:43.797681: Epoch time: 260.91 s +2024-09-11 11:25:46.184753: +2024-09-11 11:25:46.186539: Epoch 963 +2024-09-11 11:25:46.187673: Current learning rate: 0.00051 +2024-09-11 11:30:00.879099: train_loss -0.7647 +2024-09-11 11:30:00.881492: val_loss -0.7884 +2024-09-11 11:30:00.882409: Pseudo dice [0.9701, 0.9625, 0.983, 0.9577, 0.8965, 0.975, 0.9922] +2024-09-11 11:30:00.883263: Epoch time: 254.7 s +2024-09-11 11:30:03.768404: +2024-09-11 11:30:03.769610: Epoch 964 +2024-09-11 11:30:03.770519: Current learning rate: 0.0005 +2024-09-11 11:34:19.642773: train_loss -0.7772 +2024-09-11 11:34:19.645524: val_loss -0.7773 +2024-09-11 11:34:19.647798: Pseudo dice [0.969, 0.9616, 0.9803, 0.9558, 0.9027, 0.9772, 0.9926] +2024-09-11 11:34:19.648657: Epoch time: 255.88 s +2024-09-11 11:34:22.522580: +2024-09-11 11:34:22.536093: Epoch 965 +2024-09-11 11:34:22.537188: Current learning rate: 0.00049 +2024-09-11 11:38:34.908293: train_loss -0.7597 +2024-09-11 11:38:34.910764: val_loss -0.7398 +2024-09-11 11:38:34.911610: Pseudo dice [0.9665, 0.9621, 0.9811, 0.9496, 0.8938, 0.9722, 0.9902] +2024-09-11 11:38:34.917102: Epoch time: 252.39 s +2024-09-11 11:38:37.672382: +2024-09-11 11:38:37.673411: Epoch 966 +2024-09-11 11:38:37.674285: Current learning rate: 0.00048 +2024-09-11 11:42:54.640455: train_loss -0.746 +2024-09-11 11:42:54.642926: val_loss -0.7642 +2024-09-11 11:42:54.662355: Pseudo dice [0.9696, 0.9616, 0.9803, 0.9597, 0.8945, 0.9779, 0.9911] +2024-09-11 11:42:54.672341: Epoch time: 256.97 s +2024-09-11 11:42:57.128911: +2024-09-11 11:42:57.130191: Epoch 967 +2024-09-11 11:42:57.131051: Current learning rate: 0.00046 +2024-09-11 11:47:04.002091: train_loss -0.7738 +2024-09-11 11:47:04.018485: val_loss -0.752 +2024-09-11 11:47:04.019714: Pseudo dice [0.97, 0.9605, 0.9839, 0.957, 0.8951, 0.978, 0.9922] +2024-09-11 11:47:04.020711: Epoch time: 246.88 s +2024-09-11 11:47:06.577738: +2024-09-11 11:47:06.578834: Epoch 968 +2024-09-11 11:47:06.579642: Current learning rate: 0.00045 +2024-09-11 11:51:25.322892: train_loss -0.7827 +2024-09-11 11:51:25.325197: val_loss -0.792 +2024-09-11 11:51:25.326036: Pseudo dice [0.9694, 0.9608, 0.983, 0.9571, 0.8954, 0.9756, 0.9926] +2024-09-11 11:51:25.326827: Epoch time: 258.75 s +2024-09-11 11:51:27.947942: +2024-09-11 11:51:27.949189: Epoch 969 +2024-09-11 11:51:27.950036: Current learning rate: 0.00044 +2024-09-11 11:55:42.773873: train_loss -0.7582 +2024-09-11 11:55:42.792164: val_loss -0.7374 +2024-09-11 11:55:42.793238: Pseudo dice [0.9643, 0.9618, 0.9761, 0.9524, 0.8985, 0.9753, 0.9911] +2024-09-11 11:55:42.794133: Epoch time: 254.83 s +2024-09-11 11:55:44.854235: +2024-09-11 11:55:44.865260: Epoch 970 +2024-09-11 11:55:44.866244: Current learning rate: 0.00043 +2024-09-11 11:59:57.450652: train_loss -0.7726 +2024-09-11 11:59:57.454373: val_loss -0.7456 +2024-09-11 11:59:57.456535: Pseudo dice [0.9679, 0.9601, 0.9791, 0.9535, 0.8981, 0.9776, 0.993] +2024-09-11 11:59:57.458340: Epoch time: 252.6 s +2024-09-11 12:00:00.760573: +2024-09-11 12:00:00.761593: Epoch 971 +2024-09-11 12:00:00.762515: Current learning rate: 0.00041 +2024-09-11 12:04:20.464401: train_loss -0.7714 +2024-09-11 12:04:20.482563: val_loss -0.7624 +2024-09-11 12:04:20.483717: Pseudo dice [0.9714, 0.962, 0.9847, 0.9582, 0.9001, 0.9791, 0.9933] +2024-09-11 12:04:20.485023: Epoch time: 259.71 s +2024-09-11 12:04:23.909567: +2024-09-11 12:04:23.927719: Epoch 972 +2024-09-11 12:04:23.936457: Current learning rate: 0.0004 +2024-09-11 12:08:34.829508: train_loss -0.755 +2024-09-11 12:08:34.846513: val_loss -0.7837 +2024-09-11 12:08:34.847462: Pseudo dice [0.9681, 0.9621, 0.9805, 0.9581, 0.9041, 0.9788, 0.9922] +2024-09-11 12:08:34.848390: Epoch time: 250.93 s +2024-09-11 12:08:37.602744: +2024-09-11 12:08:37.603952: Epoch 973 +2024-09-11 12:08:37.605196: Current learning rate: 0.00039 +2024-09-11 12:12:44.745166: train_loss -0.7658 +2024-09-11 12:12:44.759796: val_loss -0.7664 +2024-09-11 12:12:44.760762: Pseudo dice [0.9701, 0.9622, 0.9826, 0.9546, 0.9015, 0.9778, 0.993] +2024-09-11 12:12:44.761606: Epoch time: 247.15 s +2024-09-11 12:12:48.509961: +2024-09-11 12:12:48.511137: Epoch 974 +2024-09-11 12:12:48.511969: Current learning rate: 0.00037 +2024-09-11 12:17:00.820264: train_loss -0.7561 +2024-09-11 12:17:00.838116: val_loss -0.7661 +2024-09-11 12:17:00.839053: Pseudo dice [0.9679, 0.962, 0.9811, 0.9491, 0.8979, 0.9767, 0.9929] +2024-09-11 12:17:00.839855: Epoch time: 252.31 s +2024-09-11 12:17:03.800106: +2024-09-11 12:17:03.801229: Epoch 975 +2024-09-11 12:17:03.802115: Current learning rate: 0.00036 +2024-09-11 12:21:18.362956: train_loss -0.7663 +2024-09-11 12:21:18.365547: val_loss -0.739 +2024-09-11 12:21:18.366482: Pseudo dice [0.9701, 0.9634, 0.9826, 0.9567, 0.8929, 0.9775, 0.9914] +2024-09-11 12:21:18.368720: Epoch time: 254.57 s +2024-09-11 12:21:20.657519: +2024-09-11 12:21:20.658730: Epoch 976 +2024-09-11 12:21:20.659669: Current learning rate: 0.00035 +2024-09-11 12:25:36.345321: train_loss -0.7738 +2024-09-11 12:25:36.358181: val_loss -0.7832 +2024-09-11 12:25:36.359024: Pseudo dice [0.9667, 0.9587, 0.9807, 0.9481, 0.8798, 0.9733, 0.9907] +2024-09-11 12:25:36.359831: Epoch time: 255.69 s +2024-09-11 12:25:40.197331: +2024-09-11 12:25:40.206364: Epoch 977 +2024-09-11 12:25:40.207462: Current learning rate: 0.00034 +2024-09-11 12:29:53.495682: train_loss -0.7581 +2024-09-11 12:29:53.510183: val_loss -0.7497 +2024-09-11 12:29:53.511133: Pseudo dice [0.971, 0.9636, 0.9832, 0.9563, 0.8935, 0.9776, 0.9922] +2024-09-11 12:29:53.511915: Epoch time: 253.3 s +2024-09-11 12:29:57.375715: +2024-09-11 12:29:57.377044: Epoch 978 +2024-09-11 12:29:57.377893: Current learning rate: 0.00032 +2024-09-11 12:34:01.702424: train_loss -0.7654 +2024-09-11 12:34:01.727551: val_loss -0.7919 +2024-09-11 12:34:01.729389: Pseudo dice [0.9694, 0.9638, 0.9828, 0.9571, 0.9052, 0.9777, 0.9931] +2024-09-11 12:34:01.731446: Epoch time: 244.33 s +2024-09-11 12:34:04.956527: +2024-09-11 12:34:04.971535: Epoch 979 +2024-09-11 12:34:04.972583: Current learning rate: 0.00031 +2024-09-11 12:38:37.783706: train_loss -0.7619 +2024-09-11 12:38:37.796341: val_loss -0.732 +2024-09-11 12:38:37.797319: Pseudo dice [0.9715, 0.9635, 0.9853, 0.9608, 0.9045, 0.9771, 0.9941] +2024-09-11 12:38:37.798322: Epoch time: 272.83 s +2024-09-11 12:38:41.011697: +2024-09-11 12:38:41.012821: Epoch 980 +2024-09-11 12:38:41.013712: Current learning rate: 0.0003 +2024-09-11 12:42:58.534469: train_loss -0.7779 +2024-09-11 12:42:58.536972: val_loss -0.7875 +2024-09-11 12:42:58.537909: Pseudo dice [0.9704, 0.9621, 0.9836, 0.9594, 0.8945, 0.9768, 0.9924] +2024-09-11 12:42:58.540106: Epoch time: 257.53 s +2024-09-11 12:43:02.142568: +2024-09-11 12:43:02.156768: Epoch 981 +2024-09-11 12:43:02.157645: Current learning rate: 0.00028 +2024-09-11 12:47:35.863388: train_loss -0.7696 +2024-09-11 12:47:35.886685: val_loss -0.75 +2024-09-11 12:47:35.888756: Pseudo dice [0.9696, 0.9609, 0.9835, 0.9568, 0.894, 0.9767, 0.9891] +2024-09-11 12:47:35.890779: Epoch time: 273.73 s +2024-09-11 12:47:38.951373: +2024-09-11 12:47:38.964771: Epoch 982 +2024-09-11 12:47:38.965686: Current learning rate: 0.00027 +2024-09-11 12:52:09.932828: train_loss -0.7728 +2024-09-11 12:52:09.950235: val_loss -0.7546 +2024-09-11 12:52:09.951257: Pseudo dice [0.968, 0.962, 0.9826, 0.9546, 0.8989, 0.9774, 0.9918] +2024-09-11 12:52:09.952234: Epoch time: 270.99 s +2024-09-11 12:52:12.691525: +2024-09-11 12:52:12.692919: Epoch 983 +2024-09-11 12:52:12.693814: Current learning rate: 0.00026 +2024-09-11 12:56:31.792660: train_loss -0.761 +2024-09-11 12:56:31.812747: val_loss -0.7779 +2024-09-11 12:56:31.814341: Pseudo dice [0.9699, 0.961, 0.9838, 0.9548, 0.8876, 0.9769, 0.9908] +2024-09-11 12:56:31.815807: Epoch time: 259.11 s +2024-09-11 12:56:34.990519: +2024-09-11 12:56:35.004860: Epoch 984 +2024-09-11 12:56:35.005823: Current learning rate: 0.00024 +2024-09-11 13:00:49.403516: train_loss -0.7587 +2024-09-11 13:00:49.426301: val_loss -0.7601 +2024-09-11 13:00:49.427152: Pseudo dice [0.9708, 0.9611, 0.9822, 0.955, 0.901, 0.9774, 0.9934] +2024-09-11 13:00:49.427973: Epoch time: 254.42 s +2024-09-11 13:00:51.882875: +2024-09-11 13:00:51.884182: Epoch 985 +2024-09-11 13:00:51.885120: Current learning rate: 0.00023 +2024-09-11 13:05:00.649125: train_loss -0.7706 +2024-09-11 13:05:00.674364: val_loss -0.7683 +2024-09-11 13:05:00.675431: Pseudo dice [0.9676, 0.9615, 0.9797, 0.9555, 0.8948, 0.9734, 0.992] +2024-09-11 13:05:00.676332: Epoch time: 248.77 s +2024-09-11 13:05:05.292860: +2024-09-11 13:05:05.294160: Epoch 986 +2024-09-11 13:05:05.295311: Current learning rate: 0.00021 +2024-09-11 13:09:35.803567: train_loss -0.7709 +2024-09-11 13:09:35.811006: val_loss -0.7671 +2024-09-11 13:09:35.811932: Pseudo dice [0.97, 0.9639, 0.9815, 0.9574, 0.8891, 0.9763, 0.9924] +2024-09-11 13:09:35.814192: Epoch time: 270.52 s +2024-09-11 13:09:37.981429: +2024-09-11 13:09:37.982648: Epoch 987 +2024-09-11 13:09:37.983451: Current learning rate: 0.0002 +2024-09-11 13:13:50.922006: train_loss -0.7707 +2024-09-11 13:13:50.924616: val_loss -0.7724 +2024-09-11 13:13:50.925687: Pseudo dice [0.9681, 0.9634, 0.9795, 0.9578, 0.8913, 0.9781, 0.9922] +2024-09-11 13:13:50.926837: Epoch time: 252.95 s +2024-09-11 13:13:56.565234: +2024-09-11 13:13:56.566260: Epoch 988 +2024-09-11 13:13:56.567060: Current learning rate: 0.00019 +2024-09-11 13:17:59.180007: train_loss -0.78 +2024-09-11 13:17:59.194244: val_loss -0.7725 +2024-09-11 13:17:59.195503: Pseudo dice [0.9687, 0.9621, 0.9806, 0.9589, 0.9049, 0.9783, 0.9932] +2024-09-11 13:17:59.196585: Epoch time: 242.62 s +2024-09-11 13:18:02.097016: +2024-09-11 13:18:02.108839: Epoch 989 +2024-09-11 13:18:02.109847: Current learning rate: 0.00017 +2024-09-11 13:22:25.657456: train_loss -0.7566 +2024-09-11 13:22:25.673336: val_loss -0.7746 +2024-09-11 13:22:25.674519: Pseudo dice [0.9679, 0.9606, 0.9803, 0.9556, 0.8973, 0.9742, 0.9904] +2024-09-11 13:22:25.675690: Epoch time: 263.56 s +2024-09-11 13:22:29.810372: +2024-09-11 13:22:29.811426: Epoch 990 +2024-09-11 13:22:29.812312: Current learning rate: 0.00016 +2024-09-11 13:26:49.777784: train_loss -0.7779 +2024-09-11 13:26:49.780258: val_loss -0.7268 +2024-09-11 13:26:49.781380: Pseudo dice [0.9713, 0.964, 0.9824, 0.9603, 0.8981, 0.9789, 0.9924] +2024-09-11 13:26:49.782323: Epoch time: 259.97 s +2024-09-11 13:26:52.965548: +2024-09-11 13:26:52.972050: Epoch 991 +2024-09-11 13:26:52.973118: Current learning rate: 0.00014 +2024-09-11 13:31:26.584204: train_loss -0.7749 +2024-09-11 13:31:26.586752: val_loss -0.7458 +2024-09-11 13:31:26.587821: Pseudo dice [0.9687, 0.9635, 0.9823, 0.9568, 0.8919, 0.9771, 0.992] +2024-09-11 13:31:26.588687: Epoch time: 273.62 s +2024-09-11 13:31:29.276920: +2024-09-11 13:31:29.278246: Epoch 992 +2024-09-11 13:31:29.279256: Current learning rate: 0.00013 +2024-09-11 13:36:11.480074: train_loss -0.7656 +2024-09-11 13:36:11.502034: val_loss -0.7624 +2024-09-11 13:36:11.503003: Pseudo dice [0.966, 0.9606, 0.9797, 0.9545, 0.8984, 0.9764, 0.9927] +2024-09-11 13:36:11.503813: Epoch time: 282.21 s +2024-09-11 13:36:15.128443: +2024-09-11 13:36:15.129680: Epoch 993 +2024-09-11 13:36:15.130535: Current learning rate: 0.00011 +2024-09-11 13:40:28.346234: train_loss -0.7641 +2024-09-11 13:40:28.348825: val_loss -0.7733 +2024-09-11 13:40:28.349958: Pseudo dice [0.9717, 0.9625, 0.9852, 0.963, 0.9027, 0.9807, 0.993] +2024-09-11 13:40:28.350958: Epoch time: 253.22 s +2024-09-11 13:40:31.697006: +2024-09-11 13:40:31.712900: Epoch 994 +2024-09-11 13:40:31.714020: Current learning rate: 0.0001 +2024-09-11 13:45:00.623549: train_loss -0.7641 +2024-09-11 13:45:00.638420: val_loss -0.7924 +2024-09-11 13:45:00.639381: Pseudo dice [0.9691, 0.965, 0.9832, 0.9594, 0.8998, 0.9779, 0.9914] +2024-09-11 13:45:00.640342: Epoch time: 268.93 s +2024-09-11 13:45:03.684332: +2024-09-11 13:45:03.685490: Epoch 995 +2024-09-11 13:45:03.686527: Current learning rate: 8e-05 +2024-09-11 13:49:33.707000: train_loss -0.7634 +2024-09-11 13:49:33.709461: val_loss -0.7704 +2024-09-11 13:49:33.710348: Pseudo dice [0.9696, 0.9627, 0.9828, 0.9535, 0.894, 0.9764, 0.9915] +2024-09-11 13:49:33.711191: Epoch time: 270.03 s +2024-09-11 13:49:36.740651: +2024-09-11 13:49:36.741695: Epoch 996 +2024-09-11 13:49:36.742584: Current learning rate: 7e-05 +2024-09-11 13:53:35.389385: train_loss -0.776 +2024-09-11 13:53:35.408181: val_loss -0.7619 +2024-09-11 13:53:35.409313: Pseudo dice [0.9703, 0.9635, 0.9824, 0.9599, 0.9043, 0.9798, 0.9936] +2024-09-11 13:53:35.410180: Epoch time: 238.65 s +2024-09-11 13:53:35.411024: Yayy! New best EMA pseudo Dice: 0.9627 +2024-09-11 13:53:41.007111: +2024-09-11 13:53:41.008262: Epoch 997 +2024-09-11 13:53:41.009265: Current learning rate: 5e-05 +2024-09-11 13:57:55.062960: train_loss -0.762 +2024-09-11 13:57:55.065319: val_loss -0.769 +2024-09-11 13:57:55.066213: Pseudo dice [0.9666, 0.9613, 0.9799, 0.9593, 0.8982, 0.9764, 0.992] +2024-09-11 13:57:55.068638: Epoch time: 254.06 s +2024-09-11 13:57:58.181869: +2024-09-11 13:57:58.183187: Epoch 998 +2024-09-11 13:57:58.184292: Current learning rate: 4e-05 +2024-09-11 14:02:22.507598: train_loss -0.7548 +2024-09-11 14:02:22.510154: val_loss -0.7943 +2024-09-11 14:02:22.511076: Pseudo dice [0.9689, 0.9635, 0.9822, 0.9576, 0.8938, 0.9766, 0.9905] +2024-09-11 14:02:22.511816: Epoch time: 264.33 s +2024-09-11 14:02:25.065914: +2024-09-11 14:02:25.067001: Epoch 999 +2024-09-11 14:02:25.067907: Current learning rate: 2e-05 +2024-09-11 14:06:40.736854: train_loss -0.7754 +2024-09-11 14:06:40.765770: val_loss -0.7543 +2024-09-11 14:06:40.766656: Pseudo dice [0.9669, 0.9605, 0.98, 0.9568, 0.9003, 0.9761, 0.993] +2024-09-11 14:06:40.767414: Epoch time: 255.68 s +2024-09-11 14:06:47.314023: Training done. \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_14_42_12.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_14_42_12.txt new file mode 100644 index 0000000000000000000000000000000000000000..7cd672b63ac4fa715e175f603ec808fceed52ab5 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_11_14_42_12.txt @@ -0,0 +1,692 @@ + +####################################################################### +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. +####################################################################### + +2024-09-11 14:42:20.747971: Using torch.compile... +2024-09-11 14:42:33.838852: do_dummy_2d_data_aug: False +2024-09-11 14:42:33.844605: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 14:42:33.846171: The split file contains 5 splits. +2024-09-11 14:42:33.846608: Desired fold for training: 0 +2024-09-11 14:42:33.846963: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-11 14:42:47.569272: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-11 14:42:58.177481: Training done. +2024-09-11 14:42:58.400854: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 14:42:58.403152: The split file contains 5 splits. +2024-09-11 14:42:58.403682: Desired fold for training: 0 +2024-09-11 14:42:58.404181: This split has 1324 training and 332 validation cases. +2024-09-11 14:42:58.411775: predicting saros-023 +2024-09-11 14:42:58.711264: saros-023, shape torch.Size([1, 263, 234, 234]), rank 0 +2024-09-11 14:46:07.585252: predicting saros-030 +2024-09-11 14:46:08.107234: saros-030, shape torch.Size([1, 323, 260, 260]), rank 0 +2024-09-11 14:46:32.492860: predicting saros-031 +2024-09-11 14:46:32.656633: saros-031, shape torch.Size([1, 123, 253, 253]), rank 0 +2024-09-11 14:46:51.526160: predicting saros-033 +2024-09-11 14:46:51.962795: saros-033, shape torch.Size([1, 290, 253, 253]), rank 0 +2024-09-11 14:47:11.013598: predicting saros-039 +2024-09-11 14:47:11.522647: saros-039, shape torch.Size([1, 327, 240, 240]), rank 0 +2024-09-11 14:47:44.277232: predicting saros-044 +2024-09-11 14:47:44.611222: saros-044, shape torch.Size([1, 193, 251, 251]), rank 0 +2024-09-11 14:48:10.728248: predicting saros-049 +2024-09-11 14:48:11.052203: saros-049, shape torch.Size([1, 227, 252, 252]), rank 0 +2024-09-11 14:48:38.607302: predicting saros-060 +2024-09-11 14:48:38.908901: saros-060, shape torch.Size([1, 170, 253, 253]), rank 0 +2024-09-11 14:48:42.038875: predicting saros-063 +2024-09-11 14:48:42.305028: saros-063, shape torch.Size([1, 207, 240, 240]), rank 0 +2024-09-11 14:49:13.900374: predicting saros-065 +2024-09-11 14:49:14.241746: saros-065, shape torch.Size([1, 213, 269, 269]), rank 0 +2024-09-11 14:49:46.228956: predicting saros-066 +2024-09-11 14:49:46.401727: saros-066, shape torch.Size([1, 100, 259, 259]), rank 0 +2024-09-11 14:50:16.250091: predicting saros-067 +2024-09-11 14:50:16.505183: saros-067, shape torch.Size([1, 203, 240, 240]), rank 0 +2024-09-11 14:50:32.179102: predicting saros-070 +2024-09-11 14:50:32.577975: saros-070, shape torch.Size([1, 207, 293, 293]), rank 0 +2024-09-11 14:51:08.727600: predicting saros-072 +2024-09-11 14:51:08.992149: saros-072, shape torch.Size([1, 137, 307, 307]), rank 0 +2024-09-11 14:51:35.876575: predicting saros-076 +2024-09-11 14:51:36.155357: saros-076, shape torch.Size([1, 140, 307, 307]), rank 0 +2024-09-11 14:51:41.046729: predicting saros-078 +2024-09-11 14:51:41.484664: saros-078, shape torch.Size([1, 310, 253, 253]), rank 0 +2024-09-11 14:52:00.709760: predicting saros-086 +2024-09-11 14:52:01.185341: saros-086, shape torch.Size([1, 313, 266, 266]), rank 0 +2024-09-11 14:54:20.988514: predicting saros-096 +2024-09-11 14:54:21.255213: saros-096, shape torch.Size([1, 207, 253, 253]), rank 0 +2024-09-11 14:54:26.290879: predicting saros-098 +2024-09-11 14:54:26.545714: saros-098, shape torch.Size([1, 207, 240, 240]), rank 0 +2024-09-11 14:54:31.770304: predicting saros-110 +2024-09-11 14:54:32.415299: saros-110, shape torch.Size([1, 320, 307, 307]), rank 0 +2024-09-11 14:54:46.848687: predicting saros-120 +2024-09-11 14:54:47.471577: saros-120, shape torch.Size([1, 290, 289, 289]), rank 0 +2024-09-11 14:55:15.518659: predicting saros-136 +2024-09-11 14:55:15.749262: saros-136, shape torch.Size([1, 137, 267, 267]), rank 0 +2024-09-11 14:55:34.816509: predicting saros-137 +2024-09-11 14:55:35.173473: saros-137, shape torch.Size([1, 287, 240, 240]), rank 0 +2024-09-11 14:55:39.918804: predicting saros-139 +2024-09-11 14:55:40.038218: saros-139, shape torch.Size([1, 127, 200, 200]), rank 0 +2024-09-11 14:55:59.061361: predicting saros-141 +2024-09-11 14:55:59.494850: saros-141, shape torch.Size([1, 183, 333, 333]), rank 0 +2024-09-11 14:56:23.314246: predicting saros-168 +2024-09-11 14:56:23.751375: saros-168, shape torch.Size([1, 233, 283, 283]), rank 0 +2024-09-11 14:56:47.192683: predicting saros-174 +2024-09-11 14:56:47.532131: saros-174, shape torch.Size([1, 153, 333, 333]), rank 0 +2024-09-11 14:56:52.469497: predicting saros-192 +2024-09-11 14:56:53.140015: saros-192, shape torch.Size([1, 303, 320, 320]), rank 0 +2024-09-11 14:57:24.058341: predicting saros-196 +2024-09-11 14:57:24.398132: saros-196, shape torch.Size([1, 137, 333, 333]), rank 0 +2024-09-11 14:57:30.045830: predicting saros-198 +2024-09-11 14:57:30.899012: saros-198, shape torch.Size([1, 367, 333, 333]), rank 0 +2024-09-11 14:57:45.093846: predicting saros-204 +2024-09-11 14:57:45.690043: saros-204, shape torch.Size([1, 343, 273, 273]), rank 0 +2024-09-11 14:58:01.461616: predicting saros-208 +2024-09-11 14:58:01.796349: saros-208, shape torch.Size([1, 143, 320, 320]), rank 0 +2024-09-11 14:58:06.388645: predicting saros-209 +2024-09-11 14:58:06.727299: saros-209, shape torch.Size([1, 153, 320, 320]), rank 0 +2024-09-11 14:58:11.707617: predicting saros-210 +2024-09-11 14:58:12.348337: saros-210, shape torch.Size([1, 337, 287, 287]), rank 0 +2024-09-11 14:58:19.737812: predicting saros-211 +2024-09-11 14:58:19.927122: saros-211, shape torch.Size([1, 143, 239, 239]), rank 0 +2024-09-11 14:58:23.014731: predicting saros-213 +2024-09-11 14:58:23.662251: saros-213, shape torch.Size([1, 340, 275, 275]), rank 0 +2024-09-11 14:58:30.979269: predicting saros-215 +2024-09-11 14:58:31.474462: saros-215, shape torch.Size([1, 290, 277, 277]), rank 0 +2024-09-11 14:58:38.669736: predicting saros-218 +2024-09-11 14:58:39.353624: saros-218, shape torch.Size([1, 303, 260, 260]), rank 0 +2024-09-11 14:58:46.127944: predicting saros-227 +2024-09-11 14:58:46.384503: saros-227, shape torch.Size([1, 160, 251, 251]), rank 0 +2024-09-11 14:58:49.169574: predicting saros-231 +2024-09-11 14:58:49.992952: saros-231, shape torch.Size([1, 343, 333, 333]), rank 0 +2024-09-11 14:59:05.375624: predicting saros-235 +2024-09-11 14:59:06.046223: saros-235, shape torch.Size([1, 323, 298, 298]), rank 0 +2024-09-11 14:59:21.203490: predicting saros-239 +2024-09-11 14:59:21.371104: saros-239, shape torch.Size([1, 110, 253, 253]), rank 0 +2024-09-11 14:59:24.129834: predicting saros-247 +2024-09-11 14:59:24.328514: saros-247, shape torch.Size([1, 137, 267, 267]), rank 0 +2024-09-11 14:59:27.839057: predicting saros-260 +2024-09-11 14:59:28.460262: saros-260, shape torch.Size([1, 337, 300, 300]), rank 0 +2024-09-11 14:59:44.042921: predicting saros-265 +2024-09-11 14:59:44.635044: saros-265, shape torch.Size([1, 297, 321, 321]), rank 0 +2024-09-11 14:59:59.500978: predicting saros-280 +2024-09-11 14:59:59.985846: saros-280, shape torch.Size([1, 300, 264, 264]), rank 0 +2024-09-11 15:00:08.531799: predicting saros-291 +2024-09-11 15:00:08.920744: saros-291, shape torch.Size([1, 300, 233, 233]), rank 0 +2024-09-11 15:00:16.062977: predicting saros-294 +2024-09-11 15:00:16.428468: saros-294, shape torch.Size([1, 147, 315, 315]), rank 0 +2024-09-11 15:00:21.959772: predicting saros-298 +2024-09-11 15:00:22.613900: saros-298, shape torch.Size([1, 307, 313, 313]), rank 0 +2024-09-11 15:00:38.443233: predicting saros-299 +2024-09-11 15:00:39.037169: saros-299, shape torch.Size([1, 300, 296, 296]), rank 0 +2024-09-11 15:00:54.487052: predicting saros-302 +2024-09-11 15:00:54.673365: saros-302, shape torch.Size([1, 160, 240, 240]), rank 0 +2024-09-11 15:00:58.248307: predicting saros-314 +2024-09-11 15:00:58.492154: saros-314, shape torch.Size([1, 220, 223, 223]), rank 0 +2024-09-11 15:01:03.635339: predicting saros-326 +2024-09-11 15:01:04.141603: saros-326, shape torch.Size([1, 223, 333, 333]), rank 0 +2024-09-11 15:01:15.182584: predicting saros-328 +2024-09-11 15:01:15.504896: saros-328, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-11 15:01:20.379667: predicting saros-333 +2024-09-11 15:01:20.807786: saros-333, shape torch.Size([1, 213, 333, 333]), rank 0 +2024-09-11 15:01:31.865210: predicting saros-338 +2024-09-11 15:01:32.147314: saros-338, shape torch.Size([1, 220, 227, 227]), rank 0 +2024-09-11 15:01:37.638852: predicting saros-344 +2024-09-11 15:01:37.910794: saros-344, shape torch.Size([1, 213, 226, 226]), rank 0 +2024-09-11 15:01:43.978094: predicting saros-346 +2024-09-11 15:01:44.230205: saros-346, shape torch.Size([1, 180, 222, 222]), rank 0 +2024-09-11 15:01:47.762891: predicting saros-352 +2024-09-11 15:01:48.078210: saros-352, shape torch.Size([1, 200, 254, 254]), rank 0 +2024-09-11 15:01:53.666324: predicting saros-361 +2024-09-11 15:01:54.325386: saros-361, shape torch.Size([1, 273, 333, 333]), rank 0 +2024-09-11 15:02:05.712353: predicting saros-363 +2024-09-11 15:02:05.918192: saros-363, shape torch.Size([1, 193, 227, 227]), rank 0 +2024-09-11 15:02:10.725890: predicting saros-376 +2024-09-11 15:02:11.212141: saros-376, shape torch.Size([1, 257, 313, 313]), rank 0 +2024-09-11 15:02:22.002859: predicting saros-381 +2024-09-11 15:02:22.187593: saros-381, shape torch.Size([1, 150, 240, 240]), rank 0 +2024-09-11 15:02:25.770661: predicting saros-390 +2024-09-11 15:02:26.162654: saros-390, shape torch.Size([1, 187, 333, 333]), rank 0 +2024-09-11 15:02:31.920219: predicting saros-396 +2024-09-11 15:02:32.132850: saros-396, shape torch.Size([1, 187, 195, 195]), rank 0 +2024-09-11 15:02:35.278546: predicting saros-398 +2024-09-11 15:02:35.718767: saros-398, shape torch.Size([1, 183, 333, 333]), rank 0 +2024-09-11 15:02:42.637803: predicting saros-405 +2024-09-11 15:02:42.916741: saros-405, shape torch.Size([1, 200, 240, 240]), rank 0 +2024-09-11 15:02:48.115961: predicting saros-412 +2024-09-11 15:02:48.504338: saros-412, shape torch.Size([1, 203, 281, 281]), rank 0 +2024-09-11 15:02:54.272602: predicting saros-422 +2024-09-11 15:02:54.569873: saros-422, shape torch.Size([1, 200, 248, 248]), rank 0 +2024-09-11 15:02:59.356724: predicting saros-428 +2024-09-11 15:02:59.704739: saros-428, shape torch.Size([1, 220, 267, 267]), rank 0 +2024-09-11 15:03:04.310900: predicting saros-431 +2024-09-11 15:03:04.488351: saros-431, shape torch.Size([1, 207, 181, 181]), rank 0 +2024-09-11 15:03:05.973466: predicting saros-435 +2024-09-11 15:03:06.275788: saros-435, shape torch.Size([1, 210, 266, 266]), rank 0 +2024-09-11 15:03:11.526795: predicting saros-447 +2024-09-11 15:03:11.772806: saros-447, shape torch.Size([1, 207, 210, 210]), rank 0 +2024-09-11 15:03:16.665877: predicting saros-448 +2024-09-11 15:03:17.153212: saros-448, shape torch.Size([1, 227, 316, 316]), rank 0 +2024-09-11 15:03:27.096136: predicting saros-449 +2024-09-11 15:03:27.452766: saros-449, shape torch.Size([1, 220, 227, 227]), rank 0 +2024-09-11 15:03:32.883471: predicting saros-453 +2024-09-11 15:03:33.111802: saros-453, shape torch.Size([1, 210, 204, 204]), rank 0 +2024-09-11 15:03:38.511441: predicting saros-465 +2024-09-11 15:03:39.000205: saros-465, shape torch.Size([1, 253, 287, 287]), rank 0 +2024-09-11 15:03:43.852454: predicting saros-481 +2024-09-11 15:03:44.351125: saros-481, shape torch.Size([1, 253, 287, 287]), rank 0 +2024-09-11 15:03:49.380824: predicting saros-485 +2024-09-11 15:03:49.857776: saros-485, shape torch.Size([1, 260, 269, 269]), rank 0 +2024-09-11 15:03:56.261278: predicting saros-493 +2024-09-11 15:03:56.909898: saros-493, shape torch.Size([1, 263, 333, 333]), rank 0 +2024-09-11 15:04:07.173815: predicting saros-494 +2024-09-11 15:04:07.374961: saros-494, shape torch.Size([1, 253, 183, 183]), rank 0 +2024-09-11 15:04:08.827655: predicting saros-512 +2024-09-11 15:04:09.225861: saros-512, shape torch.Size([1, 220, 257, 257]), rank 0 +2024-09-11 15:04:13.765269: predicting saros-516 +2024-09-11 15:04:14.030460: saros-516, shape torch.Size([1, 187, 240, 240]), rank 0 +2024-09-11 15:04:16.630085: predicting saros-519 +2024-09-11 15:04:17.313325: saros-519, shape torch.Size([1, 283, 333, 333]), rank 0 +2024-09-11 15:04:27.947069: predicting saros-522 +2024-09-11 15:04:28.277662: saros-522, shape torch.Size([1, 233, 237, 237]), rank 0 +2024-09-11 15:04:33.416668: predicting saros-525 +2024-09-11 15:04:33.825752: saros-525, shape torch.Size([1, 240, 267, 267]), rank 0 +2024-09-11 15:04:38.792591: predicting saros-533 +2024-09-11 15:04:39.120663: saros-533, shape torch.Size([1, 233, 244, 244]), rank 0 +2024-09-11 15:04:44.244573: predicting saros-536 +2024-09-11 15:04:44.638949: saros-536, shape torch.Size([1, 260, 277, 277]), rank 0 +2024-09-11 15:04:50.390510: predicting saros-542 +2024-09-11 15:04:50.764847: saros-542, shape torch.Size([1, 220, 267, 267]), rank 0 +2024-09-11 15:04:56.592160: predicting saros-543 +2024-09-11 15:04:56.822675: saros-543, shape torch.Size([1, 147, 253, 253]), rank 0 +2024-09-11 15:05:00.157324: predicting saros-545 +2024-09-11 15:05:00.458949: saros-545, shape torch.Size([1, 233, 232, 232]), rank 0 +2024-09-11 15:05:06.334726: predicting saros-549 +2024-09-11 15:05:06.616570: saros-549, shape torch.Size([1, 243, 222, 222]), rank 0 +2024-09-11 15:05:11.859503: predicting saros-568 +2024-09-11 15:05:12.251145: saros-568, shape torch.Size([1, 260, 275, 275]), rank 0 +2024-09-11 15:05:17.894793: predicting saros-570 +2024-09-11 15:05:18.687936: saros-570, shape torch.Size([1, 353, 333, 333]), rank 0 +2024-09-11 15:05:34.583586: predicting saros-571 +2024-09-11 15:05:35.094753: saros-571, shape torch.Size([1, 240, 307, 307]), rank 0 +2024-09-11 15:05:46.181615: predicting saros-575 +2024-09-11 15:05:46.569641: saros-575, shape torch.Size([1, 253, 250, 250]), rank 0 +2024-09-11 15:05:52.140265: predicting saros-582 +2024-09-11 15:05:52.393627: saros-582, shape torch.Size([1, 160, 260, 260]), rank 0 +2024-09-11 15:05:55.865710: predicting saros-583 +2024-09-11 15:05:56.413641: saros-583, shape torch.Size([1, 273, 333, 333]), rank 0 +2024-09-11 15:06:07.978706: predicting saros-588 +2024-09-11 15:06:08.493527: saros-588, shape torch.Size([1, 240, 307, 307]), rank 0 +2024-09-11 15:06:19.387559: predicting saros-589 +2024-09-11 15:06:20.103736: saros-589, shape torch.Size([1, 353, 333, 333]), rank 0 +2024-09-11 15:06:35.872642: predicting saros-603 +2024-09-11 15:06:38.222896: saros-603, shape torch.Size([1, 737, 400, 400]), rank 0 +2024-09-11 15:07:39.184692: predicting saros-605 +2024-09-11 15:07:40.433363: saros-605, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:08:06.194476: predicting saros-615 +2024-09-11 15:08:07.259978: saros-615, shape torch.Size([1, 497, 333, 333]), rank 0 +2024-09-11 15:08:32.826353: predicting saros-625 +2024-09-11 15:08:34.082288: saros-625, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:08:59.261874: predicting saros-631 +2024-09-11 15:09:00.923403: saros-631, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 15:09:30.886213: predicting saros-649 +2024-09-11 15:09:32.110008: saros-649, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-11 15:09:56.956487: predicting saros-652 +2024-09-11 15:09:58.246038: saros-652, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:10:23.248206: predicting saros-657 +2024-09-11 15:10:24.282265: saros-657, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:10:45.049803: predicting saros-658 +2024-09-11 15:10:46.135105: saros-658, shape torch.Size([1, 497, 333, 333]), rank 0 +2024-09-11 15:11:11.313942: predicting saros-664 +2024-09-11 15:11:13.063085: saros-664, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:11:47.779721: predicting saros-668 +2024-09-11 15:11:48.994719: saros-668, shape torch.Size([1, 483, 333, 333]), rank 0 +2024-09-11 15:12:14.558729: predicting saros-669 +2024-09-11 15:12:15.789567: saros-669, shape torch.Size([1, 567, 333, 333]), rank 0 +2024-09-11 15:12:40.766508: predicting saros-673 +2024-09-11 15:12:42.240496: saros-673, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 15:13:12.722158: predicting saros-675 +2024-09-11 15:13:14.029688: saros-675, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-11 15:13:39.377151: predicting saros-688 +2024-09-11 15:13:40.641755: saros-688, shape torch.Size([1, 487, 333, 333]), rank 0 +2024-09-11 15:14:06.101424: predicting saros-693 +2024-09-11 15:14:07.382483: saros-693, shape torch.Size([1, 567, 333, 333]), rank 0 +2024-09-11 15:14:32.650226: predicting saros-709 +2024-09-11 15:14:34.356812: saros-709, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-11 15:15:04.165902: predicting saros-711 +2024-09-11 15:15:05.553644: saros-711, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:15:30.386236: predicting saros-731 +2024-09-11 15:15:31.995224: saros-731, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 15:16:01.599917: predicting saros-734 +2024-09-11 15:16:02.951835: saros-734, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 15:16:33.233624: predicting saros-739 +2024-09-11 15:16:34.202432: saros-739, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:16:54.770679: predicting saros-741 +2024-09-11 15:16:56.220111: saros-741, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:17:22.227511: predicting saros-743 +2024-09-11 15:17:23.684939: saros-743, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 15:17:53.209739: predicting saros-744 +2024-09-11 15:17:54.373634: saros-744, shape torch.Size([1, 493, 333, 333]), rank 0 +2024-09-11 15:18:19.723549: predicting saros-745 +2024-09-11 15:18:21.149374: saros-745, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:18:46.417352: predicting saros-751 +2024-09-11 15:18:47.903865: saros-751, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:19:22.707343: predicting saros-756 +2024-09-11 15:19:24.276844: saros-756, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:19:59.658061: predicting saros-774 +2024-09-11 15:20:00.908715: saros-774, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:20:26.821769: predicting saros-777 +2024-09-11 15:20:27.804826: saros-777, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:20:48.010694: predicting saros-778 +2024-09-11 15:20:49.561494: saros-778, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:21:23.672243: predicting saros-783 +2024-09-11 15:21:26.080562: saros-783, shape torch.Size([1, 1267, 333, 333]), rank 0 +2024-09-11 15:22:29.986797: predicting saros-789 +2024-09-11 15:22:31.631210: saros-789, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:23:06.613932: predicting saros-794 +2024-09-11 15:23:07.916623: saros-794, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 15:23:37.703503: predicting saros-800 +2024-09-11 15:23:38.703450: saros-800, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:23:59.160469: predicting saros-801 +2024-09-11 15:24:00.503760: saros-801, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 15:24:30.194169: predicting saros-807 +2024-09-11 15:24:31.796144: saros-807, shape torch.Size([1, 677, 333, 333]), rank 0 +2024-09-11 15:25:06.048196: predicting saros-809 +2024-09-11 15:25:07.444818: saros-809, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:25:42.114877: predicting saros-820 +2024-09-11 15:25:44.836538: saros-820, shape torch.Size([1, 730, 467, 467]), rank 0 +2024-09-11 15:26:46.532171: predicting saros-826 +2024-09-11 15:26:48.053841: saros-826, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 15:27:17.560173: predicting saros-827 +2024-09-11 15:27:18.873240: saros-827, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 15:27:49.600133: predicting saros-829 +2024-09-11 15:27:50.600619: saros-829, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:28:11.825669: predicting saros-832 +2024-09-11 15:28:14.032345: saros-832, shape torch.Size([1, 647, 467, 467]), rank 0 +2024-09-11 15:29:06.420720: predicting saros-834 +2024-09-11 15:29:07.953499: saros-834, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 15:29:38.306506: predicting saros-837 +2024-09-11 15:29:39.373389: saros-837, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 15:29:59.440566: predicting saros-843 +2024-09-11 15:30:00.869207: saros-843, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 15:30:34.977491: predicting saros-857 +2024-09-11 15:30:36.437589: saros-857, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-11 15:31:06.544766: predicting saros-874 +2024-09-11 15:31:07.803932: saros-874, shape torch.Size([1, 603, 333, 333]), rank 0 +2024-09-11 15:31:38.290126: predicting saros-885 +2024-09-11 15:31:39.678406: saros-885, shape torch.Size([1, 650, 333, 333]), rank 0 +2024-09-11 15:32:09.774080: predicting saros-891 +2024-09-11 15:32:11.095427: saros-891, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 15:32:36.545115: predicting saros-895 +2024-09-11 15:32:37.866714: saros-895, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 15:33:08.869126: predicting tseg-s0009 +2024-09-11 15:33:08.932684: tseg-s0009, shape torch.Size([1, 185, 119, 119]), rank 0 +2024-09-11 15:33:10.911857: predicting tseg-s0010 +2024-09-11 15:33:11.232141: tseg-s0010, shape torch.Size([1, 283, 259, 259]), rank 0 +2024-09-11 15:33:17.200051: predicting tseg-s0022 +2024-09-11 15:33:17.632990: tseg-s0022, shape torch.Size([1, 250, 295, 295]), rank 0 +2024-09-11 15:33:28.188624: predicting tseg-s0024 +2024-09-11 15:33:28.933658: tseg-s0024, shape torch.Size([1, 486, 287, 287]), rank 0 +2024-09-11 15:33:41.219859: predicting tseg-s0030 +2024-09-11 15:33:41.740979: tseg-s0030, shape torch.Size([1, 317, 309, 309]), rank 0 +2024-09-11 15:33:57.521790: predicting tseg-s0034 +2024-09-11 15:33:57.974566: tseg-s0034, shape torch.Size([1, 283, 308, 308]), rank 0 +2024-09-11 15:34:08.736745: predicting tseg-s0035 +2024-09-11 15:34:08.980199: tseg-s0035, shape torch.Size([1, 145, 320, 320]), rank 0 +2024-09-11 15:34:14.839770: predicting tseg-s0036 +2024-09-11 15:34:14.887512: tseg-s0036, shape torch.Size([1, 137, 120, 120]), rank 0 +2024-09-11 15:34:15.916331: predicting tseg-s0049 +2024-09-11 15:34:16.576978: tseg-s0049, shape torch.Size([1, 455, 221, 221]), rank 0 +2024-09-11 15:34:25.696866: predicting tseg-s0050 +2024-09-11 15:34:26.104031: tseg-s0050, shape torch.Size([1, 399, 182, 261]), rank 0 +2024-09-11 15:34:31.666040: predicting tseg-s0052 +2024-09-11 15:34:32.021051: tseg-s0052, shape torch.Size([1, 294, 249, 249]), rank 0 +2024-09-11 15:34:39.045621: predicting tseg-s0059 +2024-09-11 15:34:39.478890: tseg-s0059, shape torch.Size([1, 449, 233, 233]), rank 0 +2024-09-11 15:34:48.498040: predicting tseg-s0072 +2024-09-11 15:34:48.869492: tseg-s0072, shape torch.Size([1, 269, 286, 286]), rank 0 +2024-09-11 15:34:54.404231: predicting tseg-s0073 +2024-09-11 15:34:54.715248: tseg-s0073, shape torch.Size([1, 265, 244, 247]), rank 0 +2024-09-11 15:34:59.783901: predicting tseg-s0076 +2024-09-11 15:35:00.556227: tseg-s0076, shape torch.Size([1, 441, 319, 319]), rank 0 +2024-09-11 15:35:21.171546: predicting tseg-s0088 +2024-09-11 15:35:21.316465: tseg-s0088, shape torch.Size([1, 90, 311, 311]), rank 0 +2024-09-11 15:35:27.636009: predicting tseg-s0089 +2024-09-11 15:35:27.879429: tseg-s0089, shape torch.Size([1, 263, 221, 221]), rank 0 +2024-09-11 15:35:33.770001: predicting tseg-s0100 +2024-09-11 15:35:33.799414: tseg-s0100, shape torch.Size([1, 121, 91, 91]), rank 0 +2024-09-11 15:35:34.952139: predicting tseg-s0101 +2024-09-11 15:35:34.993347: tseg-s0101, shape torch.Size([1, 74, 119, 119]), rank 0 +2024-09-11 15:35:36.863073: predicting tseg-s0129 +2024-09-11 15:35:36.883281: tseg-s0129, shape torch.Size([1, 148, 111, 50]), rank 0 +2024-09-11 15:35:38.670236: predicting tseg-s0136 +2024-09-11 15:35:38.712862: tseg-s0136, shape torch.Size([1, 148, 111, 81]), rank 0 +2024-09-11 15:35:40.221082: predicting tseg-s0151 +2024-09-11 15:35:40.485752: tseg-s0151, shape torch.Size([1, 292, 184, 292]), rank 0 +2024-09-11 15:35:46.419679: predicting tseg-s0152 +2024-09-11 15:35:46.621781: tseg-s0152, shape torch.Size([1, 224, 233, 233]), rank 0 +2024-09-11 15:35:51.888277: predicting tseg-s0157 +2024-09-11 15:35:52.397153: tseg-s0157, shape torch.Size([1, 261, 314, 314]), rank 0 +2024-09-11 15:36:03.640614: predicting tseg-s0165 +2024-09-11 15:36:03.794891: tseg-s0165, shape torch.Size([1, 91, 304, 304]), rank 0 +2024-09-11 15:36:09.224602: predicting tseg-s0181 +2024-09-11 15:36:09.287484: tseg-s0181, shape torch.Size([1, 52, 244, 244]), rank 0 +2024-09-11 15:36:12.758029: predicting tseg-s0187 +2024-09-11 15:36:12.827737: tseg-s0187, shape torch.Size([1, 125, 180, 180]), rank 0 +2024-09-11 15:36:14.324458: predicting tseg-s0191 +2024-09-11 15:36:14.400964: tseg-s0191, shape torch.Size([1, 338, 111, 98]), rank 0 +2024-09-11 15:36:17.678600: predicting tseg-s0192 +2024-09-11 15:36:17.886015: tseg-s0192, shape torch.Size([1, 296, 136, 239]), rank 0 +2024-09-11 15:36:23.194876: predicting tseg-s0194 +2024-09-11 15:36:23.445512: tseg-s0194, shape torch.Size([1, 279, 227, 227]), rank 0 +2024-09-11 15:36:29.424992: predicting tseg-s0210 +2024-09-11 15:36:30.011349: tseg-s0210, shape torch.Size([1, 317, 333, 333]), rank 0 +2024-09-11 15:36:44.877178: predicting tseg-s0213 +2024-09-11 15:36:45.046093: tseg-s0213, shape torch.Size([1, 267, 166, 267]), rank 0 +2024-09-11 15:36:47.860050: predicting tseg-s0223 +2024-09-11 15:36:48.070052: tseg-s0223, shape torch.Size([1, 191, 251, 251]), rank 0 +2024-09-11 15:36:50.988376: predicting tseg-s0240 +2024-09-11 15:36:51.344191: tseg-s0240, shape torch.Size([1, 204, 309, 309]), rank 0 +2024-09-11 15:37:01.203655: predicting tseg-s0245 +2024-09-11 15:37:01.342366: tseg-s0245, shape torch.Size([1, 95, 277, 277]), rank 0 +2024-09-11 15:37:05.082696: predicting tseg-s0246 +2024-09-11 15:37:05.163658: tseg-s0246, shape torch.Size([1, 77, 258, 258]), rank 0 +2024-09-11 15:37:07.731127: predicting tseg-s0255 +2024-09-11 15:37:08.096921: tseg-s0255, shape torch.Size([1, 306, 261, 261]), rank 0 +2024-09-11 15:37:15.055928: predicting tseg-s0258 +2024-09-11 15:37:15.182320: tseg-s0258, shape torch.Size([1, 127, 242, 242]), rank 0 +2024-09-11 15:37:18.067078: predicting tseg-s0261 +2024-09-11 15:37:18.215603: tseg-s0261, shape torch.Size([1, 191, 223, 223]), rank 0 +2024-09-11 15:37:21.788229: predicting tseg-s0265 +2024-09-11 15:37:21.847379: tseg-s0265, shape torch.Size([1, 44, 261, 261]), rank 0 +2024-09-11 15:37:24.376139: predicting tseg-s0270 +2024-09-11 15:37:24.529775: tseg-s0270, shape torch.Size([1, 91, 333, 333]), rank 0 +2024-09-11 15:37:29.785848: predicting tseg-s0292 +2024-09-11 15:37:29.870551: tseg-s0292, shape torch.Size([1, 233, 148, 148]), rank 0 +2024-09-11 15:37:31.831834: predicting tseg-s0322 +2024-09-11 15:37:32.187626: tseg-s0322, shape torch.Size([1, 209, 299, 299]), rank 0 +2024-09-11 15:37:42.524143: predicting tseg-s0339 +2024-09-11 15:37:42.830003: tseg-s0339, shape torch.Size([1, 281, 240, 240]), rank 0 +2024-09-11 15:37:47.905285: predicting tseg-s0345 +2024-09-11 15:37:48.573336: tseg-s0345, shape torch.Size([1, 476, 261, 238]), rank 0 +2024-09-11 15:37:58.249831: predicting tseg-s0346 +2024-09-11 15:37:58.440039: tseg-s0346, shape torch.Size([1, 241, 215, 215]), rank 0 +2024-09-11 15:38:03.765691: predicting tseg-s0358 +2024-09-11 15:38:04.328240: tseg-s0358, shape torch.Size([1, 523, 255, 255]), rank 0 +2024-09-11 15:38:16.353117: predicting tseg-s0362 +2024-09-11 15:38:17.020844: tseg-s0362, shape torch.Size([1, 417, 285, 285]), rank 0 +2024-09-11 15:38:27.001556: predicting tseg-s0365 +2024-09-11 15:38:27.278321: tseg-s0365, shape torch.Size([1, 222, 257, 257]), rank 0 +2024-09-11 15:38:32.930881: predicting tseg-s0367 +2024-09-11 15:38:33.140398: tseg-s0367, shape torch.Size([1, 205, 212, 212]), rank 0 +2024-09-11 15:38:38.874372: predicting tseg-s0370 +2024-09-11 15:38:39.518934: tseg-s0370, shape torch.Size([1, 437, 202, 437]), rank 0 +2024-09-11 15:38:57.234018: predicting tseg-s0385 +2024-09-11 15:38:57.424066: tseg-s0385, shape torch.Size([1, 182, 242, 242]), rank 0 +2024-09-11 15:39:01.586006: predicting tseg-s0395 +2024-09-11 15:39:01.656881: tseg-s0395, shape torch.Size([1, 38, 333, 333]), rank 0 +2024-09-11 15:39:06.999526: predicting tseg-s0413 +2024-09-11 15:39:07.291722: tseg-s0413, shape torch.Size([1, 204, 261, 261]), rank 0 +2024-09-11 15:39:13.121565: predicting tseg-s0421 +2024-09-11 15:39:13.311814: tseg-s0421, shape torch.Size([1, 171, 244, 244]), rank 0 +2024-09-11 15:39:17.476803: predicting tseg-s0452 +2024-09-11 15:39:17.658292: tseg-s0452, shape torch.Size([1, 182, 211, 211]), rank 0 +2024-09-11 15:39:20.566069: predicting tseg-s0454 +2024-09-11 15:39:20.940674: tseg-s0454, shape torch.Size([1, 221, 311, 311]), rank 0 +2024-09-11 15:39:31.508420: predicting tseg-s0455 +2024-09-11 15:39:31.726700: tseg-s0455, shape torch.Size([1, 207, 219, 219]), rank 0 +2024-09-11 15:39:36.443608: predicting tseg-s0456 +2024-09-11 15:39:36.882854: tseg-s0456, shape torch.Size([1, 390, 265, 265]), rank 0 +2024-09-11 15:39:46.355769: predicting tseg-s0460 +2024-09-11 15:39:46.398904: tseg-s0460, shape torch.Size([1, 102, 139, 114]), rank 0 +2024-09-11 15:39:48.424260: predicting tseg-s0475 +2024-09-11 15:39:48.672071: tseg-s0475, shape torch.Size([1, 225, 277, 277]), rank 0 +2024-09-11 15:39:53.224854: predicting tseg-s0476 +2024-09-11 15:39:53.883873: tseg-s0476, shape torch.Size([1, 463, 283, 283]), rank 0 +2024-09-11 15:40:03.805820: predicting tseg-s0480 +2024-09-11 15:40:04.242305: tseg-s0480, shape torch.Size([1, 328, 233, 233]), rank 0 +2024-09-11 15:40:12.395459: predicting tseg-s0490 +2024-09-11 15:40:12.606292: tseg-s0490, shape torch.Size([1, 193, 237, 237]), rank 0 +2024-09-11 15:40:18.835870: predicting tseg-s0498 +2024-09-11 15:40:19.107632: tseg-s0498, shape torch.Size([1, 225, 237, 237]), rank 0 +2024-09-11 15:40:24.278078: predicting tseg-s0501 +2024-09-11 15:40:24.307077: tseg-s0501, shape torch.Size([1, 143, 125, 77]), rank 0 +2024-09-11 15:40:25.267347: predicting tseg-s0508 +2024-09-11 15:40:25.475317: tseg-s0508, shape torch.Size([1, 219, 221, 221]), rank 0 +2024-09-11 15:40:30.617367: predicting tseg-s0509 +2024-09-11 15:40:30.930094: tseg-s0509, shape torch.Size([1, 315, 227, 227]), rank 0 +2024-09-11 15:40:38.556401: predicting tseg-s0516 +2024-09-11 15:40:38.999018: tseg-s0516, shape torch.Size([1, 413, 253, 253]), rank 0 +2024-09-11 15:40:48.885719: predicting tseg-s0521 +2024-09-11 15:40:48.906779: tseg-s0521, shape torch.Size([1, 133, 120, 53]), rank 0 +2024-09-11 15:40:50.808405: predicting tseg-s0536 +2024-09-11 15:40:51.351341: tseg-s0536, shape torch.Size([1, 426, 269, 269]), rank 0 +2024-09-11 15:41:00.857618: predicting tseg-s0548 +2024-09-11 15:41:01.200970: tseg-s0548, shape torch.Size([1, 337, 171, 337]), rank 0 +2024-09-11 15:41:07.484910: predicting tseg-s0573 +2024-09-11 15:41:07.708768: tseg-s0573, shape torch.Size([1, 247, 223, 223]), rank 0 +2024-09-11 15:41:12.667338: predicting tseg-s0618 +2024-09-11 15:41:12.831267: tseg-s0618, shape torch.Size([1, 189, 196, 196]), rank 0 +2024-09-11 15:41:16.215146: predicting tseg-s0635 +2024-09-11 15:41:16.722118: tseg-s0635, shape torch.Size([1, 381, 291, 291]), rank 0 +2024-09-11 15:41:32.065883: predicting tseg-s0636 +2024-09-11 15:41:32.687350: tseg-s0636, shape torch.Size([1, 477, 277, 277]), rank 0 +2024-09-11 15:41:43.704839: predicting tseg-s0637 +2024-09-11 15:41:44.181577: tseg-s0637, shape torch.Size([1, 437, 244, 244]), rank 0 +2024-09-11 15:41:54.269129: predicting tseg-s0640 +2024-09-11 15:41:54.359046: tseg-s0640, shape torch.Size([1, 72, 267, 267]), rank 0 +2024-09-11 15:41:57.401608: predicting tseg-s0642 +2024-09-11 15:41:57.529014: tseg-s0642, shape torch.Size([1, 99, 143, 143]), rank 0 +2024-09-11 15:41:58.546741: predicting tseg-s0646 +2024-09-11 15:41:58.797281: tseg-s0646, shape torch.Size([1, 215, 275, 275]), rank 0 +2024-09-11 15:42:04.556622: predicting tseg-s0649 +2024-09-11 15:42:05.019861: tseg-s0649, shape torch.Size([1, 388, 265, 265]), rank 0 +2024-09-11 15:42:14.644178: predicting tseg-s0650 +2024-09-11 15:42:15.481563: tseg-s0650, shape torch.Size([1, 462, 333, 333]), rank 0 +2024-09-11 15:42:35.597201: predicting tseg-s0657 +2024-09-11 15:42:36.246187: tseg-s0657, shape torch.Size([1, 488, 297, 297]), rank 0 +2024-09-11 15:43:00.733211: predicting tseg-s0658 +2024-09-11 15:43:00.963557: tseg-s0658, shape torch.Size([1, 215, 247, 247]), rank 0 +2024-09-11 15:43:06.221485: predicting tseg-s0660 +2024-09-11 15:43:06.425958: tseg-s0660, shape torch.Size([1, 252, 202, 202]), rank 0 +2024-09-11 15:43:11.374661: predicting tseg-s0664 +2024-09-11 15:43:12.068846: tseg-s0664, shape torch.Size([1, 405, 332, 332]), rank 0 +2024-09-11 15:43:31.869711: predicting tseg-s0671 +2024-09-11 15:43:31.946428: tseg-s0671, shape torch.Size([1, 87, 205, 205]), rank 0 +2024-09-11 15:43:35.494915: predicting tseg-s0672 +2024-09-11 15:43:35.651243: tseg-s0672, shape torch.Size([1, 200, 200, 200]), rank 0 +2024-09-11 15:43:41.057059: predicting tseg-s0676 +2024-09-11 15:43:41.115931: tseg-s0676, shape torch.Size([1, 38, 333, 333]), rank 0 +2024-09-11 15:43:47.021440: predicting tseg-s0677 +2024-09-11 15:43:47.252157: tseg-s0677, shape torch.Size([1, 145, 276, 276]), rank 0 +2024-09-11 15:43:50.228077: predicting tseg-s0681 +2024-09-11 15:43:50.381910: tseg-s0681, shape torch.Size([1, 167, 203, 203]), rank 0 +2024-09-11 15:43:53.791317: predicting tseg-s0682 +2024-09-11 15:43:54.184090: tseg-s0682, shape torch.Size([1, 317, 317, 206]), rank 0 +2024-09-11 15:44:05.180116: predicting tseg-s0683 +2024-09-11 15:44:05.675859: tseg-s0683, shape torch.Size([1, 288, 309, 309]), rank 0 +2024-09-11 15:44:16.999524: predicting tseg-s0690 +2024-09-11 15:44:17.471372: tseg-s0690, shape torch.Size([1, 538, 162, 281]), rank 0 +2024-09-11 15:44:25.107952: predicting tseg-s0695 +2024-09-11 15:44:25.266692: tseg-s0695, shape torch.Size([1, 167, 219, 219]), rank 0 +2024-09-11 15:44:29.912467: predicting tseg-s0698 +2024-09-11 15:44:30.148532: tseg-s0698, shape torch.Size([1, 185, 239, 239]), rank 0 +2024-09-11 15:44:33.251875: predicting tseg-s0699 +2024-09-11 15:44:33.549918: tseg-s0699, shape torch.Size([1, 258, 248, 248]), rank 0 +2024-09-11 15:44:38.527636: predicting tseg-s0705 +2024-09-11 15:44:38.926532: tseg-s0705, shape torch.Size([1, 301, 267, 267]), rank 0 +2024-09-11 15:44:45.830054: predicting tseg-s0709 +2024-09-11 15:44:46.081931: tseg-s0709, shape torch.Size([1, 274, 215, 215]), rank 0 +2024-09-11 15:44:51.454467: predicting tseg-s0715 +2024-09-11 15:44:51.498870: tseg-s0715, shape torch.Size([1, 47, 225, 225]), rank 0 +2024-09-11 15:44:54.631855: predicting tseg-s0729 +2024-09-11 15:44:54.780576: tseg-s0729, shape torch.Size([1, 206, 155, 197]), rank 0 +2024-09-11 15:44:57.734489: predicting tseg-s0743 +2024-09-11 15:44:57.955478: tseg-s0743, shape torch.Size([1, 207, 231, 231]), rank 0 +2024-09-11 15:45:03.205392: predicting tseg-s0756 +2024-09-11 15:45:03.267617: tseg-s0756, shape torch.Size([1, 182, 129, 129]), rank 0 +2024-09-11 15:45:04.556480: predicting tseg-s0765 +2024-09-11 15:45:04.902002: tseg-s0765, shape torch.Size([1, 396, 228, 228]), rank 0 +2024-09-11 15:45:14.179216: predicting tseg-s0773 +2024-09-11 15:45:14.347950: tseg-s0773, shape torch.Size([1, 190, 221, 221]), rank 0 +2024-09-11 15:45:17.364441: predicting tseg-s0782 +2024-09-11 15:45:17.432534: tseg-s0782, shape torch.Size([1, 79, 177, 255]), rank 0 +2024-09-11 15:45:20.773692: predicting tseg-s0787 +2024-09-11 15:45:20.912895: tseg-s0787, shape torch.Size([1, 155, 221, 221]), rank 0 +2024-09-11 15:45:23.946516: predicting tseg-s0820 +2024-09-11 15:45:24.134807: tseg-s0820, shape torch.Size([1, 252, 193, 193]), rank 0 +2024-09-11 15:45:30.053610: predicting tseg-s0839 +2024-09-11 15:45:30.077007: tseg-s0839, shape torch.Size([1, 83, 96, 96]), rank 0 +2024-09-11 15:45:31.889846: predicting tseg-s0842 +2024-09-11 15:45:32.316487: tseg-s0842, shape torch.Size([1, 450, 244, 244]), rank 0 +2024-09-11 15:45:41.883389: predicting tseg-s0850 +2024-09-11 15:45:41.975946: tseg-s0850, shape torch.Size([1, 275, 133, 133]), rank 0 +2024-09-11 15:45:44.613921: predicting tseg-s0852 +2024-09-11 15:45:44.674370: tseg-s0852, shape torch.Size([1, 37, 219, 219]), rank 0 +2024-09-11 15:45:47.802595: predicting tseg-s0859 +2024-09-11 15:45:48.208175: tseg-s0859, shape torch.Size([1, 450, 240, 240]), rank 0 +2024-09-11 15:45:58.265607: predicting tseg-s0878 +2024-09-11 15:45:58.771012: tseg-s0878, shape torch.Size([1, 427, 274, 274]), rank 0 +2024-09-11 15:46:08.581841: predicting tseg-s0898 +2024-09-11 15:46:08.767381: tseg-s0898, shape torch.Size([1, 185, 233, 233]), rank 0 +2024-09-11 15:46:12.051209: predicting tseg-s0919 +2024-09-11 15:46:12.330306: tseg-s0919, shape torch.Size([1, 415, 168, 221]), rank 0 +2024-09-11 15:46:18.935464: predicting tseg-s0921 +2024-09-11 15:46:19.040508: tseg-s0921, shape torch.Size([1, 94, 259, 259]), rank 0 +2024-09-11 15:46:21.925604: predicting tseg-s0922 +2024-09-11 15:46:21.973984: tseg-s0922, shape torch.Size([1, 120, 143, 120]), rank 0 +2024-09-11 15:46:24.026375: predicting tseg-s0934 +2024-09-11 15:46:24.443188: tseg-s0934, shape torch.Size([1, 293, 293, 293]), rank 0 +2024-09-11 15:46:39.997542: predicting tseg-s0938 +2024-09-11 15:46:40.067672: tseg-s0938, shape torch.Size([1, 70, 238, 238]), rank 0 +2024-09-11 15:46:43.724188: predicting tseg-s0947 +2024-09-11 15:46:43.904665: tseg-s0947, shape torch.Size([1, 167, 207, 207]), rank 0 +2024-09-11 15:46:47.043308: predicting tseg-s0952 +2024-09-11 15:46:47.143323: tseg-s0952, shape torch.Size([1, 269, 118, 118]), rank 0 +2024-09-11 15:46:49.835108: predicting tseg-s0958 +2024-09-11 15:46:49.913436: tseg-s0958, shape torch.Size([1, 131, 151, 151]), rank 0 +2024-09-11 15:46:51.595983: predicting tseg-s0959 +2024-09-11 15:46:51.979954: tseg-s0959, shape torch.Size([1, 413, 233, 233]), rank 0 +2024-09-11 15:47:02.038716: predicting tseg-s0961 +2024-09-11 15:47:02.492978: tseg-s0961, shape torch.Size([1, 413, 253, 253]), rank 0 +2024-09-11 15:47:12.315862: predicting tseg-s0971 +2024-09-11 15:47:12.550562: tseg-s0971, shape torch.Size([1, 183, 285, 285]), rank 0 +2024-09-11 15:47:16.689226: predicting tseg-s0972 +2024-09-11 15:47:16.741230: tseg-s0972, shape torch.Size([1, 128, 142, 142]), rank 0 +2024-09-11 15:47:18.857023: predicting tseg-s0973 +2024-09-11 15:47:18.995258: tseg-s0973, shape torch.Size([1, 200, 143, 205]), rank 0 +2024-09-11 15:47:22.063305: predicting tseg-s0987 +2024-09-11 15:47:22.103767: tseg-s0987, shape torch.Size([1, 134, 116, 116]), rank 0 +2024-09-11 15:47:23.247091: predicting tseg-s0991 +2024-09-11 15:47:23.566370: tseg-s0991, shape torch.Size([1, 411, 208, 208]), rank 0 +2024-09-11 15:47:32.910937: predicting tseg-s0996 +2024-09-11 15:47:32.977835: tseg-s0996, shape torch.Size([1, 64, 242, 242]), rank 0 +2024-09-11 15:47:36.201883: predicting tseg-s1001 +2024-09-11 15:47:36.323148: tseg-s1001, shape torch.Size([1, 159, 173, 173]), rank 0 +2024-09-11 15:47:38.609835: predicting tseg-s1006 +2024-09-11 15:47:39.116134: tseg-s1006, shape torch.Size([1, 415, 264, 264]), rank 0 +2024-09-11 15:47:48.795296: predicting tseg-s1016 +2024-09-11 15:47:49.651943: tseg-s1016, shape torch.Size([1, 471, 333, 333]), rank 0 +2024-09-11 15:48:09.846838: predicting tseg-s1017 +2024-09-11 15:48:09.960303: tseg-s1017, shape torch.Size([1, 277, 144, 144]), rank 0 +2024-09-11 15:48:12.048384: predicting tseg-s1024 +2024-09-11 15:48:12.841198: tseg-s1024, shape torch.Size([1, 415, 333, 333]), rank 0 +2024-09-11 15:48:33.487054: predicting tseg-s1025 +2024-09-11 15:48:33.528234: tseg-s1025, shape torch.Size([1, 123, 118, 118]), rank 0 +2024-09-11 15:48:35.630943: predicting tseg-s1026 +2024-09-11 15:48:35.715674: tseg-s1026, shape torch.Size([1, 147, 145, 145]), rank 0 +2024-09-11 15:48:37.211678: predicting tseg-s1031 +2024-09-11 15:48:37.781932: tseg-s1031, shape torch.Size([1, 419, 287, 287]), rank 0 +2024-09-11 15:48:47.114262: predicting tseg-s1044 +2024-09-11 15:48:47.652913: tseg-s1044, shape torch.Size([1, 459, 257, 257]), rank 0 +2024-09-11 15:48:56.827708: predicting tseg-s1048 +2024-09-11 15:48:56.865227: tseg-s1048, shape torch.Size([1, 33, 235, 235]), rank 0 +2024-09-11 15:48:59.508470: predicting tseg-s1049 +2024-09-11 15:48:59.686770: tseg-s1049, shape torch.Size([1, 133, 221, 221]), rank 0 +2024-09-11 15:49:02.409835: predicting tseg-s1052 +2024-09-11 15:49:02.634907: tseg-s1052, shape torch.Size([1, 221, 236, 236]), rank 0 +2024-09-11 15:49:07.366782: predicting tseg-s1066 +2024-09-11 15:49:07.579480: tseg-s1066, shape torch.Size([1, 203, 235, 235]), rank 0 +2024-09-11 15:49:12.702661: predicting tseg-s1072 +2024-09-11 15:49:12.755720: tseg-s1072, shape torch.Size([1, 109, 167, 167]), rank 0 +2024-09-11 15:49:13.844671: predicting tseg-s1093 +2024-09-11 15:49:13.991130: tseg-s1093, shape torch.Size([1, 239, 199, 199]), rank 0 +2024-09-11 15:49:18.776337: predicting tseg-s1097 +2024-09-11 15:49:18.850184: tseg-s1097, shape torch.Size([1, 127, 108, 108]), rank 0 +2024-09-11 15:49:20.332702: predicting tseg-s1105 +2024-09-11 15:49:20.685963: tseg-s1105, shape torch.Size([1, 407, 221, 221]), rank 0 +2024-09-11 15:49:30.600305: predicting tseg-s1120 +2024-09-11 15:49:31.056652: tseg-s1120, shape torch.Size([1, 407, 247, 247]), rank 0 +2024-09-11 15:49:41.473990: predicting tseg-s1140 +2024-09-11 15:49:41.836769: tseg-s1140, shape torch.Size([1, 233, 253, 253]), rank 0 +2024-09-11 15:49:46.522774: predicting tseg-s1144 +2024-09-11 15:49:46.754643: tseg-s1144, shape torch.Size([1, 225, 228, 228]), rank 0 +2024-09-11 15:49:51.769601: predicting tseg-s1149 +2024-09-11 15:49:52.004099: tseg-s1149, shape torch.Size([1, 329, 183, 215]), rank 0 +2024-09-11 15:49:56.288889: predicting tseg-s1150 +2024-09-11 15:49:56.334206: tseg-s1150, shape torch.Size([1, 101, 167, 167]), rank 0 +2024-09-11 15:49:57.825839: predicting tseg-s1179 +2024-09-11 15:49:57.883091: tseg-s1179, shape torch.Size([1, 136, 117, 117]), rank 0 +2024-09-11 15:49:58.901908: predicting tseg-s1185 +2024-09-11 15:49:58.950892: tseg-s1185, shape torch.Size([1, 172, 106, 133]), rank 0 +2024-09-11 15:49:59.956174: predicting tseg-s1196 +2024-09-11 15:50:00.035324: tseg-s1196, shape torch.Size([1, 73, 244, 244]), rank 0 +2024-09-11 15:50:03.014206: predicting tseg-s1222 +2024-09-11 15:50:03.091376: tseg-s1222, shape torch.Size([1, 61, 264, 264]), rank 0 +2024-09-11 15:50:05.835126: predicting tseg-s1224 +2024-09-11 15:50:06.371402: tseg-s1224, shape torch.Size([1, 452, 272, 272]), rank 0 +2024-09-11 15:50:15.491425: predicting tseg-s1241 +2024-09-11 15:50:15.806377: tseg-s1241, shape torch.Size([1, 163, 317, 317]), rank 0 +2024-09-11 15:50:22.034570: predicting tseg-s1245 +2024-09-11 15:50:22.217099: tseg-s1245, shape torch.Size([1, 181, 228, 228]), rank 0 +2024-09-11 15:50:25.602744: predicting tseg-s1252 +2024-09-11 15:50:25.926575: tseg-s1252, shape torch.Size([1, 506, 120, 276]), rank 0 +2024-09-11 15:50:32.545990: predicting tseg-s1269 +2024-09-11 15:50:32.724561: tseg-s1269, shape torch.Size([1, 191, 221, 221]), rank 0 +2024-09-11 15:50:36.258348: predicting tseg-s1278 +2024-09-11 15:50:36.665212: tseg-s1278, shape torch.Size([1, 239, 304, 304]), rank 0 +2024-09-11 15:50:47.281194: predicting tseg-s1280 +2024-09-11 15:50:47.408512: tseg-s1280, shape torch.Size([1, 144, 221, 221]), rank 0 +2024-09-11 15:50:51.389398: predicting tseg-s1281 +2024-09-11 15:50:51.629098: tseg-s1281, shape torch.Size([1, 217, 211, 211]), rank 0 +2024-09-11 15:50:56.928339: predicting tseg-s1287 +2024-09-11 15:50:57.327010: tseg-s1287, shape torch.Size([1, 440, 239, 239]), rank 0 +2024-09-11 15:51:06.657785: predicting tseg-s1288 +2024-09-11 15:51:06.824239: tseg-s1288, shape torch.Size([1, 174, 249, 249]), rank 0 +2024-09-11 15:51:10.579380: predicting tseg-s1291 +2024-09-11 15:51:10.887608: tseg-s1291, shape torch.Size([1, 314, 244, 244]), rank 0 +2024-09-11 15:51:18.716779: predicting tseg-s1303 +2024-09-11 15:51:18.824845: tseg-s1303, shape torch.Size([1, 387, 113, 113]), rank 0 +2024-09-11 15:51:23.220937: predicting tseg-s1314 +2024-09-11 15:51:24.129713: tseg-s1314, shape torch.Size([1, 546, 333, 333]), rank 0 +2024-09-11 15:51:50.177865: predicting tseg-s1321 +2024-09-11 15:51:50.826770: tseg-s1321, shape torch.Size([1, 540, 275, 275]), rank 0 +2024-09-11 15:52:03.099888: predicting tseg-s1325 +2024-09-11 15:52:03.123472: tseg-s1325, shape torch.Size([1, 77, 117, 124]), rank 0 +2024-09-11 15:52:04.483666: predicting tseg-s1330 +2024-09-11 15:52:04.725744: tseg-s1330, shape torch.Size([1, 231, 221, 221]), rank 0 +2024-09-11 15:52:10.342980: predicting tseg-s1335 +2024-09-11 15:52:10.384854: tseg-s1335, shape torch.Size([1, 123, 124, 124]), rank 0 +2024-09-11 15:52:12.419085: predicting tseg-s1337 +2024-09-11 15:52:12.734698: tseg-s1337, shape torch.Size([1, 231, 280, 280]), rank 0 +2024-09-11 15:52:18.676626: predicting tseg-s1348 +2024-09-11 15:52:19.158483: tseg-s1348, shape torch.Size([1, 437, 239, 239]), rank 0 +2024-09-11 15:52:28.368381: predicting tseg-s1363 +2024-09-11 15:52:28.941036: tseg-s1363, shape torch.Size([1, 389, 296, 296]), rank 0 +2024-09-11 15:52:48.597420: predicting tseg-s1365 +2024-09-11 15:52:49.145975: tseg-s1365, shape torch.Size([1, 438, 277, 277]), rank 0 +2024-09-11 15:52:59.514684: predicting tseg-s1373 +2024-09-11 15:52:59.815665: tseg-s1373, shape torch.Size([1, 299, 237, 237]), rank 0 +2024-09-11 15:53:07.391535: predicting tseg-s1380 +2024-09-11 15:53:08.169013: tseg-s1380, shape torch.Size([1, 543, 320, 320]), rank 0 +2024-09-11 15:53:33.103341: predicting tseg-s1388 +2024-09-11 15:53:34.031970: tseg-s1388, shape torch.Size([1, 525, 316, 316]), rank 0 +2024-09-11 15:53:58.867141: predicting tseg-s1391 +2024-09-11 15:53:59.300273: tseg-s1391, shape torch.Size([1, 335, 270, 270]), rank 0 +2024-09-11 15:55:30.107743: Validation complete +2024-09-11 15:55:30.108363: Mean Validation Dice: 0.95026322089997 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_4_16_32_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_4_16_32_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..c626b4a52d5a3af11940e1e6957fead2ebdf48cf --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_4_16_32_30.txt @@ -0,0 +1,421 @@ + +####################################################################### +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. +####################################################################### + +2024-09-04 16:32:30.218034: do_dummy_2d_data_aug: False +2024-09-04 16:32:30.311831: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-04 16:32:30.335496: The split file contains 5 splits. +2024-09-04 16:32:30.336184: Desired fold for training: 0 +2024-09-04 16:32:30.336764: This split has 1324 training and 332 validation cases. +2024-09-04 16:32:47.851581: Using torch.compile... + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-04 16:32:51.586586: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-04 16:32:52.534976: +2024-09-04 16:32:52.546859: Epoch 0 +2024-09-04 16:32:52.550118: Current learning rate: 0.01 +2024-09-04 16:51:24.612396: train_loss 0.7547 +2024-09-04 16:51:24.643302: val_loss 0.4523 +2024-09-04 16:51:24.752502: Pseudo dice [0.5423, 0.6452, 0.54, 0.0, 0.0, 0.0002, 0.7808] +2024-09-04 16:51:24.786732: Epoch time: 1112.08 s +2024-09-04 16:51:24.865516: Yayy! New best EMA pseudo Dice: 0.3584 +2024-09-04 16:51:32.985522: +2024-09-04 16:51:32.986421: Epoch 1 +2024-09-04 16:51:32.987107: Current learning rate: 0.00999 +2024-09-04 16:56:24.137516: train_loss 0.2939 +2024-09-04 16:56:24.151670: val_loss 0.1497 +2024-09-04 16:56:24.163294: Pseudo dice [0.7175, 0.7252, 0.7676, 0.4352, 0.0, 0.0, 0.8717] +2024-09-04 16:56:24.165364: Epoch time: 291.16 s +2024-09-04 16:56:24.168368: Yayy! New best EMA pseudo Dice: 0.3728 +2024-09-04 16:56:36.555887: +2024-09-04 16:56:36.559146: Epoch 2 +2024-09-04 16:56:36.559916: Current learning rate: 0.00998 +2024-09-04 17:01:19.423206: train_loss 0.063 +2024-09-04 17:01:19.442916: val_loss -0.0934 +2024-09-04 17:01:19.444020: Pseudo dice [0.7779, 0.7838, 0.8483, 0.68, 0.0, 0.3531, 0.9309] +2024-09-04 17:01:19.445312: Epoch time: 282.87 s +2024-09-04 17:01:19.448059: Yayy! New best EMA pseudo Dice: 0.398 +2024-09-04 17:01:27.934186: +2024-09-04 17:01:27.935896: Epoch 3 +2024-09-04 17:01:27.937940: Current learning rate: 0.00997 +2024-09-04 17:06:03.374408: train_loss -0.1004 +2024-09-04 17:06:03.381803: val_loss -0.2345 +2024-09-04 17:06:03.382577: Pseudo dice [0.8125, 0.7954, 0.8747, 0.7598, 0.0003, 0.5964, 0.9404] +2024-09-04 17:06:03.383222: Epoch time: 275.44 s +2024-09-04 17:06:03.384866: Yayy! New best EMA pseudo Dice: 0.4265 +2024-09-04 17:06:15.923822: +2024-09-04 17:06:15.924788: Epoch 4 +2024-09-04 17:06:15.925978: Current learning rate: 0.00996 +2024-09-04 17:10:45.592276: train_loss -0.2565 +2024-09-04 17:10:45.616945: val_loss -0.3592 +2024-09-04 17:10:45.617808: Pseudo dice [0.8331, 0.8255, 0.9009, 0.8142, 0.5395, 0.7342, 0.9608] +2024-09-04 17:10:45.618937: Epoch time: 269.67 s +2024-09-04 17:10:45.619994: Yayy! New best EMA pseudo Dice: 0.4639 +2024-09-04 17:10:53.790197: +2024-09-04 17:10:53.791399: Epoch 5 +2024-09-04 17:10:53.792274: Current learning rate: 0.00995 +2024-09-04 17:15:14.032312: train_loss -0.3292 +2024-09-04 17:15:14.043661: val_loss -0.3818 +2024-09-04 17:15:14.044614: Pseudo dice [0.8606, 0.8479, 0.9059, 0.8215, 0.5992, 0.7641, 0.9523] +2024-09-04 17:15:14.045571: Epoch time: 260.25 s +2024-09-04 17:15:14.046739: Yayy! New best EMA pseudo Dice: 0.4997 +2024-09-04 17:15:22.602915: +2024-09-04 17:15:22.603893: Epoch 6 +2024-09-04 17:15:22.604948: Current learning rate: 0.00995 +2024-09-04 17:19:45.656605: train_loss -0.4039 +2024-09-04 17:19:45.673092: val_loss -0.4176 +2024-09-04 17:19:45.674106: Pseudo dice [0.8648, 0.8614, 0.916, 0.8345, 0.654, 0.7925, 0.9538] +2024-09-04 17:19:45.675675: Epoch time: 263.06 s +2024-09-04 17:19:45.676730: Yayy! New best EMA pseudo Dice: 0.5337 +2024-09-04 17:19:54.990601: +2024-09-04 17:19:54.991739: Epoch 7 +2024-09-04 17:19:54.993209: Current learning rate: 0.00994 +2024-09-04 17:24:32.373308: train_loss -0.4445 +2024-09-04 17:24:32.408194: val_loss -0.4712 +2024-09-04 17:24:32.409995: Pseudo dice [0.8795, 0.8752, 0.9191, 0.8513, 0.6788, 0.8166, 0.9689] +2024-09-04 17:24:32.411205: Epoch time: 277.39 s +2024-09-04 17:24:32.412353: Yayy! New best EMA pseudo Dice: 0.5659 +2024-09-04 17:24:41.460957: +2024-09-04 17:24:41.462443: Epoch 8 +2024-09-04 17:24:41.463712: Current learning rate: 0.00993 +2024-09-04 17:29:26.742865: train_loss -0.4432 +2024-09-04 17:29:26.754167: val_loss -0.5133 +2024-09-04 17:29:26.755791: Pseudo dice [0.8897, 0.8842, 0.9284, 0.8782, 0.7271, 0.8676, 0.9682] +2024-09-04 17:29:26.757086: Epoch time: 285.29 s +2024-09-04 17:29:26.758361: Yayy! New best EMA pseudo Dice: 0.5971 +2024-09-04 17:29:35.991218: +2024-09-04 17:29:35.992455: Epoch 9 +2024-09-04 17:29:35.993681: Current learning rate: 0.00992 +2024-09-04 17:34:29.842972: train_loss -0.4569 +2024-09-04 17:34:29.862843: val_loss -0.4729 +2024-09-04 17:34:29.865907: Pseudo dice [0.8754, 0.8861, 0.9128, 0.8687, 0.7067, 0.8688, 0.9596] +2024-09-04 17:34:29.869218: Epoch time: 293.86 s +2024-09-04 17:34:29.872642: Yayy! New best EMA pseudo Dice: 0.6242 +2024-09-04 17:34:41.889093: +2024-09-04 17:34:41.891325: Epoch 10 +2024-09-04 17:34:41.892658: Current learning rate: 0.00991 +2024-09-04 17:39:24.977340: train_loss -0.5082 +2024-09-04 17:39:25.006922: val_loss -0.5397 +2024-09-04 17:39:25.008078: Pseudo dice [0.9059, 0.8942, 0.9358, 0.8808, 0.7215, 0.8668, 0.9758] +2024-09-04 17:39:25.009228: Epoch time: 283.09 s +2024-09-04 17:39:25.009968: Yayy! New best EMA pseudo Dice: 0.6501 +2024-09-04 17:39:33.939900: +2024-09-04 17:39:33.940859: Epoch 11 +2024-09-04 17:39:33.941608: Current learning rate: 0.0099 +2024-09-04 17:44:18.562391: train_loss -0.5233 +2024-09-04 17:44:18.577966: val_loss -0.5443 +2024-09-04 17:44:18.579406: Pseudo dice [0.9078, 0.9046, 0.9439, 0.8875, 0.7472, 0.8605, 0.9742] +2024-09-04 17:44:18.581187: Epoch time: 284.63 s +2024-09-04 17:44:18.582305: Yayy! New best EMA pseudo Dice: 0.674 +2024-09-04 17:44:29.195353: +2024-09-04 17:44:29.196336: Epoch 12 +2024-09-04 17:44:29.197275: Current learning rate: 0.00989 +2024-09-04 17:49:18.455374: train_loss -0.5204 +2024-09-04 17:49:18.457193: val_loss -0.5423 +2024-09-04 17:49:18.458745: Pseudo dice [0.9071, 0.9057, 0.9384, 0.8706, 0.7333, 0.8945, 0.9734] +2024-09-04 17:49:18.461026: Epoch time: 289.26 s +2024-09-04 17:49:18.461983: Yayy! New best EMA pseudo Dice: 0.6955 +2024-09-04 17:49:28.664373: +2024-09-04 17:49:28.665843: Epoch 13 +2024-09-04 17:49:28.667287: Current learning rate: 0.00988 +2024-09-04 17:54:18.317021: train_loss -0.523 +2024-09-04 17:54:18.331530: val_loss -0.5482 +2024-09-04 17:54:18.333296: Pseudo dice [0.8981, 0.9085, 0.9331, 0.8746, 0.7353, 0.8892, 0.9675] +2024-09-04 17:54:18.335130: Epoch time: 289.66 s +2024-09-04 17:54:18.336870: Yayy! New best EMA pseudo Dice: 0.7146 +2024-09-04 17:54:26.259611: +2024-09-04 17:54:26.260521: Epoch 14 +2024-09-04 17:54:26.261147: Current learning rate: 0.00987 +2024-09-04 17:58:59.624392: train_loss -0.5438 +2024-09-04 17:58:59.652597: val_loss -0.5228 +2024-09-04 17:58:59.653479: Pseudo dice [0.9045, 0.8953, 0.9289, 0.8765, 0.7409, 0.8705, 0.9777] +2024-09-04 17:58:59.654440: Epoch time: 273.37 s +2024-09-04 17:58:59.655292: Yayy! New best EMA pseudo Dice: 0.7316 +2024-09-04 17:59:08.628466: +2024-09-04 17:59:08.629604: Epoch 15 +2024-09-04 17:59:08.631393: Current learning rate: 0.00986 +2024-09-04 18:03:43.788582: train_loss -0.5556 +2024-09-04 18:03:43.790476: val_loss -0.5822 +2024-09-04 18:03:43.791738: Pseudo dice [0.9193, 0.915, 0.951, 0.8882, 0.7388, 0.8935, 0.9792] +2024-09-04 18:03:43.792913: Epoch time: 275.17 s +2024-09-04 18:03:43.794135: Yayy! New best EMA pseudo Dice: 0.7483 +2024-09-04 18:03:53.143680: +2024-09-04 18:03:53.144819: Epoch 16 +2024-09-04 18:03:53.145871: Current learning rate: 0.00986 +2024-09-04 18:08:36.780902: train_loss -0.5631 +2024-09-04 18:08:36.783308: val_loss -0.5927 +2024-09-04 18:08:36.794814: Pseudo dice [0.9193, 0.9048, 0.9518, 0.8734, 0.7429, 0.9019, 0.9648] +2024-09-04 18:08:36.796035: Epoch time: 283.64 s +2024-09-04 18:08:36.797330: Yayy! New best EMA pseudo Dice: 0.7628 +2024-09-04 18:08:46.369114: +2024-09-04 18:08:46.370061: Epoch 17 +2024-09-04 18:08:46.370837: Current learning rate: 0.00985 +2024-09-04 18:13:39.265606: train_loss -0.5752 +2024-09-04 18:13:39.267560: val_loss -0.5819 +2024-09-04 18:13:39.268833: Pseudo dice [0.9198, 0.912, 0.9451, 0.8834, 0.7479, 0.9102, 0.9766] +2024-09-04 18:13:39.269947: Epoch time: 292.9 s +2024-09-04 18:13:39.271144: Yayy! New best EMA pseudo Dice: 0.7765 +2024-09-04 18:13:49.091163: +2024-09-04 18:13:49.092105: Epoch 18 +2024-09-04 18:13:49.092958: Current learning rate: 0.00984 +2024-09-04 18:18:24.432969: train_loss -0.57 +2024-09-04 18:18:24.447181: val_loss -0.6306 +2024-09-04 18:18:24.448127: Pseudo dice [0.9243, 0.9143, 0.9547, 0.9028, 0.778, 0.9133, 0.9795] +2024-09-04 18:18:24.449202: Epoch time: 275.34 s +2024-09-04 18:18:24.450162: Yayy! New best EMA pseudo Dice: 0.7898 +2024-09-04 18:18:36.707651: +2024-09-04 18:18:36.708612: Epoch 19 +2024-09-04 18:18:36.709405: Current learning rate: 0.00983 +2024-09-04 18:23:21.543234: train_loss -0.5984 +2024-09-04 18:23:21.550392: val_loss -0.6191 +2024-09-04 18:23:21.551593: Pseudo dice [0.9317, 0.9244, 0.96, 0.9122, 0.7736, 0.9162, 0.9795] +2024-09-04 18:23:21.552891: Epoch time: 284.84 s +2024-09-04 18:23:21.553576: Yayy! New best EMA pseudo Dice: 0.8022 +2024-09-04 18:23:30.312485: +2024-09-04 18:23:30.313557: Epoch 20 +2024-09-04 18:23:30.314290: Current learning rate: 0.00982 +2024-09-04 18:28:14.552891: train_loss -0.5788 +2024-09-04 18:28:14.569499: val_loss -0.6177 +2024-09-04 18:28:14.570488: Pseudo dice [0.9256, 0.9221, 0.95, 0.8957, 0.7734, 0.9128, 0.98] +2024-09-04 18:28:14.572204: Epoch time: 284.24 s +2024-09-04 18:28:14.572948: Yayy! New best EMA pseudo Dice: 0.8128 +2024-09-04 18:28:25.412246: +2024-09-04 18:28:25.413156: Epoch 21 +2024-09-04 18:28:25.413829: Current learning rate: 0.00981 +2024-09-04 18:33:13.218303: train_loss -0.5958 +2024-09-04 18:33:13.234316: val_loss -0.6365 +2024-09-04 18:33:13.235354: Pseudo dice [0.9269, 0.9219, 0.9533, 0.8952, 0.7518, 0.9096, 0.9824] +2024-09-04 18:33:13.236520: Epoch time: 287.81 s +2024-09-04 18:33:13.237374: Yayy! New best EMA pseudo Dice: 0.8221 +2024-09-04 18:33:23.928073: +2024-09-04 18:33:23.929306: Epoch 22 +2024-09-04 18:33:23.930051: Current learning rate: 0.0098 +2024-09-04 18:38:15.768845: train_loss -0.5779 +2024-09-04 18:38:15.778701: val_loss -0.6482 +2024-09-04 18:38:15.780410: Pseudo dice [0.9302, 0.924, 0.9597, 0.9098, 0.7807, 0.9052, 0.9821] +2024-09-04 18:38:15.781348: Epoch time: 291.85 s +2024-09-04 18:38:15.782116: Yayy! New best EMA pseudo Dice: 0.8312 +2024-09-04 18:38:24.616342: +2024-09-04 18:38:24.617306: Epoch 23 +2024-09-04 18:38:24.618434: Current learning rate: 0.00979 +2024-09-04 18:43:10.864866: train_loss -0.6123 +2024-09-04 18:43:10.892257: val_loss -0.636 +2024-09-04 18:43:10.893089: Pseudo dice [0.9292, 0.9198, 0.9525, 0.9027, 0.7913, 0.9237, 0.9847] +2024-09-04 18:43:10.893968: Epoch time: 286.25 s +2024-09-04 18:43:10.894890: Yayy! New best EMA pseudo Dice: 0.8396 +2024-09-04 18:43:19.525567: +2024-09-04 18:43:19.526874: Epoch 24 +2024-09-04 18:43:19.527814: Current learning rate: 0.00978 +2024-09-04 18:48:07.164886: train_loss -0.612 +2024-09-04 18:48:07.179700: val_loss -0.6436 +2024-09-04 18:48:07.180490: Pseudo dice [0.9327, 0.9288, 0.9614, 0.9116, 0.7967, 0.9263, 0.9839] +2024-09-04 18:48:07.181215: Epoch time: 287.64 s +2024-09-04 18:48:07.182050: Yayy! New best EMA pseudo Dice: 0.8477 +2024-09-04 18:48:16.660843: +2024-09-04 18:48:16.661720: Epoch 25 +2024-09-04 18:48:16.662668: Current learning rate: 0.00977 +2024-09-04 18:52:46.166545: train_loss -0.6198 +2024-09-04 18:52:46.172196: val_loss -0.6601 +2024-09-04 18:52:46.173663: Pseudo dice [0.9393, 0.9284, 0.9643, 0.9117, 0.8057, 0.9302, 0.9807] +2024-09-04 18:52:46.174593: Epoch time: 269.51 s +2024-09-04 18:52:46.175371: Yayy! New best EMA pseudo Dice: 0.8552 +2024-09-04 18:52:55.552907: +2024-09-04 18:52:55.650879: Epoch 26 +2024-09-04 18:52:55.662783: Current learning rate: 0.00977 +2024-09-04 18:57:28.899668: train_loss -0.6334 +2024-09-04 18:57:28.912791: val_loss -0.6534 +2024-09-04 18:57:28.915751: Pseudo dice [0.9325, 0.9201, 0.9579, 0.9071, 0.7616, 0.925, 0.9821] +2024-09-04 18:57:28.917275: Epoch time: 273.35 s +2024-09-04 18:57:28.918379: Yayy! New best EMA pseudo Dice: 0.8609 +2024-09-04 18:57:39.056529: +2024-09-04 18:57:39.057535: Epoch 27 +2024-09-04 18:57:39.058389: Current learning rate: 0.00976 +2024-09-04 19:02:33.488252: train_loss -0.6186 +2024-09-04 19:02:33.492045: val_loss -0.6613 +2024-09-04 19:02:33.493300: Pseudo dice [0.9354, 0.9303, 0.9602, 0.9113, 0.7742, 0.9271, 0.9818] +2024-09-04 19:02:33.494189: Epoch time: 294.44 s +2024-09-04 19:02:33.495009: Yayy! New best EMA pseudo Dice: 0.8665 +2024-09-04 19:02:42.617097: +2024-09-04 19:02:42.618124: Epoch 28 +2024-09-04 19:02:42.618894: Current learning rate: 0.00975 +2024-09-04 19:07:22.019218: train_loss -0.6283 +2024-09-04 19:07:22.030541: val_loss -0.6497 +2024-09-04 19:07:22.031543: Pseudo dice [0.9372, 0.9328, 0.9637, 0.9098, 0.7992, 0.9263, 0.9851] +2024-09-04 19:07:22.032308: Epoch time: 279.41 s +2024-09-04 19:07:22.032980: Yayy! New best EMA pseudo Dice: 0.8721 +2024-09-04 19:07:31.192338: +2024-09-04 19:07:31.193504: Epoch 29 +2024-09-04 19:07:31.194317: Current learning rate: 0.00974 +2024-09-04 19:12:13.368532: train_loss -0.6591 +2024-09-04 19:12:13.371889: val_loss -0.6697 +2024-09-04 19:12:13.372864: Pseudo dice [0.9435, 0.9299, 0.9687, 0.9203, 0.7864, 0.9375, 0.9822] +2024-09-04 19:12:13.373648: Epoch time: 282.18 s +2024-09-04 19:12:13.374867: Yayy! New best EMA pseudo Dice: 0.8773 +2024-09-04 19:12:22.672293: +2024-09-04 19:12:22.673205: Epoch 30 +2024-09-04 19:12:22.673933: Current learning rate: 0.00973 +2024-09-04 19:17:12.998903: train_loss -0.6534 +2024-09-04 19:17:13.016715: val_loss -0.672 +2024-09-04 19:17:13.018040: Pseudo dice [0.9426, 0.9329, 0.9679, 0.9129, 0.8091, 0.9269, 0.9873] +2024-09-04 19:17:13.019034: Epoch time: 290.33 s +2024-09-04 19:17:13.020182: Yayy! New best EMA pseudo Dice: 0.8821 +2024-09-04 19:17:23.694266: +2024-09-04 19:17:23.695148: Epoch 31 +2024-09-04 19:17:23.696340: Current learning rate: 0.00972 +2024-09-04 19:21:59.641176: train_loss -0.6496 +2024-09-04 19:21:59.642713: val_loss -0.6605 +2024-09-04 19:21:59.643871: Pseudo dice [0.9394, 0.927, 0.9645, 0.9158, 0.8142, 0.9352, 0.9852] +2024-09-04 19:21:59.645078: Epoch time: 275.95 s +2024-09-04 19:21:59.646324: Yayy! New best EMA pseudo Dice: 0.8865 +2024-09-04 19:22:10.994848: +2024-09-04 19:22:10.995768: Epoch 32 +2024-09-04 19:22:10.996507: Current learning rate: 0.00971 +2024-09-04 19:27:00.267269: train_loss -0.6246 +2024-09-04 19:27:00.287695: val_loss -0.6521 +2024-09-04 19:27:00.289492: Pseudo dice [0.937, 0.9344, 0.9589, 0.9148, 0.8121, 0.9225, 0.9864] +2024-09-04 19:27:00.291428: Epoch time: 289.28 s +2024-09-04 19:27:00.293444: Yayy! New best EMA pseudo Dice: 0.8902 +2024-09-04 19:27:09.814674: +2024-09-04 19:27:09.815859: Epoch 33 +2024-09-04 19:27:09.817088: Current learning rate: 0.0097 +2024-09-04 19:32:01.618886: train_loss -0.645 +2024-09-04 19:32:01.639531: val_loss -0.6831 +2024-09-04 19:32:01.641026: Pseudo dice [0.9404, 0.9326, 0.9624, 0.9183, 0.8226, 0.9287, 0.9843] +2024-09-04 19:32:01.642243: Epoch time: 291.81 s +2024-09-04 19:32:01.643702: Yayy! New best EMA pseudo Dice: 0.8939 +2024-09-04 19:32:11.534695: +2024-09-04 19:32:11.546265: Epoch 34 +2024-09-04 19:32:11.612686: Current learning rate: 0.00969 +2024-09-04 19:36:52.251600: train_loss -0.6687 +2024-09-04 19:36:52.260559: val_loss -0.6855 +2024-09-04 19:36:52.262238: Pseudo dice [0.9431, 0.9322, 0.9622, 0.9128, 0.8006, 0.9408, 0.9857] +2024-09-04 19:36:52.264317: Epoch time: 280.72 s +2024-09-04 19:36:52.265575: Yayy! New best EMA pseudo Dice: 0.897 +2024-09-04 19:37:02.565159: +2024-09-04 19:37:02.577355: Epoch 35 +2024-09-04 19:37:02.592764: Current learning rate: 0.00968 +2024-09-04 19:41:43.568752: train_loss -0.6469 +2024-09-04 19:41:43.591609: val_loss -0.6886 +2024-09-04 19:41:43.593227: Pseudo dice [0.9465, 0.9363, 0.9668, 0.9242, 0.7978, 0.9325, 0.9869] +2024-09-04 19:41:43.594650: Epoch time: 281.01 s +2024-09-04 19:41:43.595665: Yayy! New best EMA pseudo Dice: 0.9001 +2024-09-04 19:41:57.437129: +2024-09-04 19:41:57.438313: Epoch 36 +2024-09-04 19:41:57.439175: Current learning rate: 0.00968 +2024-09-04 19:46:30.953845: train_loss -0.658 +2024-09-04 19:46:31.132960: val_loss -0.6681 +2024-09-04 19:46:31.150600: Pseudo dice [0.9438, 0.9374, 0.9696, 0.9213, 0.8086, 0.9378, 0.9855] +2024-09-04 19:46:31.166784: Epoch time: 273.52 s +2024-09-04 19:46:31.178261: Yayy! New best EMA pseudo Dice: 0.903 +2024-09-04 19:46:40.969545: +2024-09-04 19:46:40.970600: Epoch 37 +2024-09-04 19:46:40.971322: Current learning rate: 0.00967 +2024-09-04 19:51:10.313670: train_loss -0.6567 +2024-09-04 19:51:10.326305: val_loss -0.6718 +2024-09-04 19:51:10.327846: Pseudo dice [0.9435, 0.9346, 0.9603, 0.9194, 0.8131, 0.9425, 0.987] +2024-09-04 19:51:10.329134: Epoch time: 269.35 s +2024-09-04 19:51:10.330189: Yayy! New best EMA pseudo Dice: 0.9055 +2024-09-04 19:51:19.291771: +2024-09-04 19:51:19.292917: Epoch 38 +2024-09-04 19:51:19.293958: Current learning rate: 0.00966 +2024-09-04 19:55:53.196341: train_loss -0.6692 +2024-09-04 19:55:53.213023: val_loss -0.6345 +2024-09-04 19:55:53.214654: Pseudo dice [0.9421, 0.9401, 0.9615, 0.9147, 0.8107, 0.94, 0.9825] +2024-09-04 19:55:53.215887: Epoch time: 273.91 s +2024-09-04 19:55:53.217148: Yayy! New best EMA pseudo Dice: 0.9077 +2024-09-04 19:56:03.630352: +2024-09-04 19:56:03.631492: Epoch 39 +2024-09-04 19:56:03.632380: Current learning rate: 0.00965 +2024-09-04 20:00:57.705683: train_loss -0.6787 +2024-09-04 20:00:57.720947: val_loss -0.7142 +2024-09-04 20:00:57.722802: Pseudo dice [0.9431, 0.9326, 0.965, 0.9181, 0.813, 0.9403, 0.9856] +2024-09-04 20:00:57.724499: Epoch time: 294.08 s +2024-09-04 20:00:57.725927: Yayy! New best EMA pseudo Dice: 0.9098 +2024-09-04 20:01:06.429250: +2024-09-04 20:01:06.430412: Epoch 40 +2024-09-04 20:01:06.431465: Current learning rate: 0.00964 +2024-09-04 20:05:41.807588: train_loss -0.6756 +2024-09-04 20:05:41.824279: val_loss -0.6753 +2024-09-04 20:05:41.825323: Pseudo dice [0.9473, 0.9414, 0.9647, 0.9235, 0.8217, 0.9312, 0.9859] +2024-09-04 20:05:41.826500: Epoch time: 275.38 s +2024-09-04 20:05:41.827209: Yayy! New best EMA pseudo Dice: 0.9119 +2024-09-04 20:05:50.679820: +2024-09-04 20:05:50.681713: Epoch 41 +2024-09-04 20:05:50.682781: Current learning rate: 0.00963 +2024-09-04 20:10:30.328226: train_loss -0.6558 +2024-09-04 20:10:30.347022: val_loss -0.6969 +2024-09-04 20:10:30.348571: Pseudo dice [0.9474, 0.9403, 0.9687, 0.9339, 0.8349, 0.9438, 0.9851] +2024-09-04 20:10:30.349951: Epoch time: 279.65 s +2024-09-04 20:10:30.351240: Yayy! New best EMA pseudo Dice: 0.9143 +2024-09-04 20:10:42.991168: +2024-09-04 20:10:42.992094: Epoch 42 +2024-09-04 20:10:42.992786: Current learning rate: 0.00962 +2024-09-04 20:15:20.561590: train_loss -0.6464 +2024-09-04 20:15:20.578927: val_loss -0.6551 +2024-09-04 20:15:20.580329: Pseudo dice [0.9368, 0.9298, 0.9601, 0.9165, 0.8036, 0.9412, 0.9844] +2024-09-04 20:15:20.581067: Epoch time: 277.57 s +2024-09-04 20:15:20.581661: Yayy! New best EMA pseudo Dice: 0.9154 +2024-09-04 20:15:30.264488: +2024-09-04 20:15:30.265436: Epoch 43 +2024-09-04 20:15:30.266375: Current learning rate: 0.00961 +2024-09-04 20:19:59.850217: train_loss -0.6108 +2024-09-04 20:19:59.867406: val_loss -0.644 +2024-09-04 20:19:59.869079: Pseudo dice [0.9345, 0.9238, 0.9612, 0.9041, 0.7874, 0.9288, 0.979] +2024-09-04 20:19:59.870160: Epoch time: 269.59 s +2024-09-04 20:19:59.871398: Yayy! New best EMA pseudo Dice: 0.9155 +2024-09-04 20:20:09.463664: +2024-09-04 20:20:09.464597: Epoch 44 +2024-09-04 20:20:09.465289: Current learning rate: 0.0096 +2024-09-04 20:24:47.967054: train_loss -0.6369 +2024-09-04 20:24:47.994912: val_loss -0.6531 +2024-09-04 20:24:47.995966: Pseudo dice [0.9438, 0.9357, 0.9636, 0.9217, 0.7969, 0.9355, 0.985] +2024-09-04 20:24:47.996834: Epoch time: 278.51 s +2024-09-04 20:24:47.997881: Yayy! New best EMA pseudo Dice: 0.9166 +2024-09-04 20:24:57.418353: +2024-09-04 20:24:57.419597: Epoch 45 +2024-09-04 20:24:57.420449: Current learning rate: 0.00959 +2024-09-04 20:29:42.877529: train_loss -0.6429 +2024-09-04 20:29:42.880110: val_loss -0.6677 +2024-09-04 20:29:42.881012: Pseudo dice [0.9402, 0.9271, 0.9629, 0.919, 0.7882, 0.9225, 0.9754] +2024-09-04 20:29:42.881983: Epoch time: 285.46 s +2024-09-04 20:29:42.882914: Yayy! New best EMA pseudo Dice: 0.9168 +2024-09-04 20:29:50.832762: +2024-09-04 20:29:50.833780: Epoch 46 +2024-09-04 20:29:50.834540: Current learning rate: 0.00959 +2024-09-04 20:34:31.621396: train_loss -0.6433 +2024-09-04 20:34:31.624602: val_loss -0.6411 +2024-09-04 20:34:31.625569: Pseudo dice [0.938, 0.9324, 0.9626, 0.9087, 0.7978, 0.9268, 0.9802] +2024-09-04 20:34:31.626339: Epoch time: 280.79 s +2024-09-04 20:34:31.627103: Yayy! New best EMA pseudo Dice: 0.9173 +2024-09-04 20:34:40.600052: +2024-09-04 20:34:40.601156: Epoch 47 +2024-09-04 20:34:40.601946: Current learning rate: 0.00958 +2024-09-04 20:39:24.346736: train_loss -0.653 +2024-09-04 20:39:24.364691: val_loss -0.68 +2024-09-04 20:39:24.365805: Pseudo dice [0.9456, 0.9405, 0.9653, 0.9092, 0.8135, 0.9398, 0.9864] +2024-09-04 20:39:24.366634: Epoch time: 283.75 s +2024-09-04 20:39:24.367288: Yayy! New best EMA pseudo Dice: 0.9184 +2024-09-04 20:39:35.388669: +2024-09-04 20:39:35.480217: Epoch 48 +2024-09-04 20:39:35.488962: Current learning rate: 0.00957 +2024-09-04 20:43:59.941428: train_loss -0.6675 +2024-09-04 20:43:59.955547: val_loss -0.6975 +2024-09-04 20:43:59.956751: Pseudo dice [0.9439, 0.9378, 0.9656, 0.9249, 0.8014, 0.9388, 0.9834] +2024-09-04 20:43:59.957748: Epoch time: 264.56 s +2024-09-04 20:43:59.959002: Yayy! New best EMA pseudo Dice: 0.9193 +2024-09-04 20:44:08.939043: +2024-09-04 20:44:08.940154: Epoch 49 +2024-09-04 20:44:08.941007: Current learning rate: 0.00956 +2024-09-04 20:48:50.915356: train_loss -0.69 +2024-09-04 20:48:50.919242: val_loss -0.6971 +2024-09-04 20:48:50.920112: Pseudo dice [0.941, 0.935, 0.9677, 0.9179, 0.8085, 0.9399, 0.9826] +2024-09-04 20:48:50.920878: Epoch time: 281.98 s +2024-09-04 20:48:57.553629: Yayy! New best EMA pseudo Dice: 0.9202 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_00_32_01.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_00_32_01.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c89b8d41390afbc9cfd5d92e2a82658b6888a6a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_00_32_01.txt @@ -0,0 +1,406 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 00:32:04.857353: Using torch.compile... +2024-09-05 00:32:14.546425: do_dummy_2d_data_aug: False +2024-09-05 00:32:14.552568: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 00:32:14.553875: The split file contains 5 splits. +2024-09-05 00:32:14.554505: Desired fold for training: 0 +2024-09-05 00:32:14.555068: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 00:32:26.637626: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 00:32:29.606219: +2024-09-05 00:32:29.607365: Epoch 50 +2024-09-05 00:32:29.610240: Current learning rate: 0.00955 +2024-09-05 00:50:44.713544: train_loss -0.666 +2024-09-05 00:50:44.714832: val_loss -0.6859 +2024-09-05 00:50:44.715503: Pseudo dice [0.9385, 0.9392, 0.9556, 0.9278, 0.813, 0.9389, 0.9853] +2024-09-05 00:50:44.716128: Epoch time: 1095.11 s +2024-09-05 00:50:44.716744: Yayy! New best EMA pseudo Dice: 0.921 +2024-09-05 00:50:58.636887: +2024-09-05 00:50:58.705183: Epoch 51 +2024-09-05 00:50:58.742226: Current learning rate: 0.00954 +2024-09-05 00:55:35.898707: train_loss -0.6779 +2024-09-05 00:55:35.900918: val_loss -0.6715 +2024-09-05 00:55:35.915755: Pseudo dice [0.9464, 0.9374, 0.9657, 0.9222, 0.8219, 0.9491, 0.9875] +2024-09-05 00:55:35.917289: Epoch time: 277.26 s +2024-09-05 00:55:35.919353: Yayy! New best EMA pseudo Dice: 0.9222 +2024-09-05 00:55:45.843538: +2024-09-05 00:55:45.844574: Epoch 52 +2024-09-05 00:55:45.845566: Current learning rate: 0.00953 +2024-09-05 01:00:39.537770: train_loss -0.6716 +2024-09-05 01:00:39.557428: val_loss -0.702 +2024-09-05 01:00:39.560374: Pseudo dice [0.9469, 0.9395, 0.9656, 0.9317, 0.8235, 0.9465, 0.9877] +2024-09-05 01:00:39.562542: Epoch time: 293.7 s +2024-09-05 01:00:39.564588: Yayy! New best EMA pseudo Dice: 0.9234 +2024-09-05 01:00:50.028852: +2024-09-05 01:00:50.171027: Epoch 53 +2024-09-05 01:00:50.196065: Current learning rate: 0.00952 +2024-09-05 01:05:50.617430: train_loss -0.6707 +2024-09-05 01:05:50.618985: val_loss -0.6854 +2024-09-05 01:05:50.620179: Pseudo dice [0.9456, 0.9432, 0.9637, 0.9184, 0.8167, 0.936, 0.9871] +2024-09-05 01:05:50.620906: Epoch time: 300.59 s +2024-09-05 01:05:50.621560: Yayy! New best EMA pseudo Dice: 0.9241 +2024-09-05 01:05:58.238500: +2024-09-05 01:05:58.239326: Epoch 54 +2024-09-05 01:05:58.240119: Current learning rate: 0.00951 +2024-09-05 01:10:31.552188: train_loss -0.6681 +2024-09-05 01:10:31.561504: val_loss -0.6754 +2024-09-05 01:10:31.562711: Pseudo dice [0.9507, 0.9463, 0.9672, 0.9251, 0.8066, 0.9417, 0.9864] +2024-09-05 01:10:31.563936: Epoch time: 273.32 s +2024-09-05 01:10:31.565495: Yayy! New best EMA pseudo Dice: 0.9249 +2024-09-05 01:10:40.767559: +2024-09-05 01:10:40.768440: Epoch 55 +2024-09-05 01:10:40.769206: Current learning rate: 0.0095 +2024-09-05 01:15:14.068273: train_loss -0.6682 +2024-09-05 01:15:14.083787: val_loss -0.6603 +2024-09-05 01:15:14.086058: Pseudo dice [0.9437, 0.94, 0.9684, 0.9275, 0.8264, 0.9426, 0.9853] +2024-09-05 01:15:14.098573: Epoch time: 273.3 s +2024-09-05 01:15:14.099905: Yayy! New best EMA pseudo Dice: 0.9257 +2024-09-05 01:15:24.243653: +2024-09-05 01:15:24.244639: Epoch 56 +2024-09-05 01:15:24.245347: Current learning rate: 0.00949 +2024-09-05 01:20:00.112716: train_loss -0.6691 +2024-09-05 01:20:00.114193: val_loss -0.7002 +2024-09-05 01:20:00.115168: Pseudo dice [0.9497, 0.9417, 0.9675, 0.9282, 0.8216, 0.9417, 0.9886] +2024-09-05 01:20:00.115972: Epoch time: 275.87 s +2024-09-05 01:20:00.116610: Yayy! New best EMA pseudo Dice: 0.9266 +2024-09-05 01:20:09.531249: +2024-09-05 01:20:09.532138: Epoch 57 +2024-09-05 01:20:09.532786: Current learning rate: 0.00949 +2024-09-05 01:24:54.307930: train_loss -0.6989 +2024-09-05 01:24:54.323140: val_loss -0.6433 +2024-09-05 01:24:54.324343: Pseudo dice [0.9454, 0.9427, 0.9647, 0.9341, 0.8303, 0.9425, 0.9893] +2024-09-05 01:24:54.325421: Epoch time: 284.78 s +2024-09-05 01:24:54.327017: Yayy! New best EMA pseudo Dice: 0.9275 +2024-09-05 01:25:03.499497: +2024-09-05 01:25:03.500589: Epoch 58 +2024-09-05 01:25:03.501464: Current learning rate: 0.00948 +2024-09-05 01:29:39.341948: train_loss -0.6755 +2024-09-05 01:29:39.343663: val_loss -0.7074 +2024-09-05 01:29:39.344664: Pseudo dice [0.9518, 0.9437, 0.9694, 0.9311, 0.8293, 0.9507, 0.9875] +2024-09-05 01:29:39.345448: Epoch time: 275.85 s +2024-09-05 01:29:39.346465: Yayy! New best EMA pseudo Dice: 0.9285 +2024-09-05 01:29:48.165974: +2024-09-05 01:29:48.166937: Epoch 59 +2024-09-05 01:29:48.167663: Current learning rate: 0.00947 +2024-09-05 01:34:30.861431: train_loss -0.7004 +2024-09-05 01:34:30.869909: val_loss -0.6664 +2024-09-05 01:34:30.870995: Pseudo dice [0.9469, 0.9441, 0.9665, 0.9228, 0.8237, 0.9468, 0.9864] +2024-09-05 01:34:30.872004: Epoch time: 282.7 s +2024-09-05 01:34:30.873252: Yayy! New best EMA pseudo Dice: 0.929 +2024-09-05 01:34:40.340709: +2024-09-05 01:34:40.342631: Epoch 60 +2024-09-05 01:34:40.343294: Current learning rate: 0.00946 +2024-09-05 01:39:38.137101: train_loss -0.6829 +2024-09-05 01:39:38.154015: val_loss -0.703 +2024-09-05 01:39:38.155312: Pseudo dice [0.9495, 0.9445, 0.9664, 0.9305, 0.8127, 0.9475, 0.9876] +2024-09-05 01:39:38.156441: Epoch time: 297.8 s +2024-09-05 01:39:38.159112: Yayy! New best EMA pseudo Dice: 0.9295 +2024-09-05 01:39:48.073462: +2024-09-05 01:39:48.074967: Epoch 61 +2024-09-05 01:39:48.075746: Current learning rate: 0.00945 +2024-09-05 01:44:48.194967: train_loss -0.6812 +2024-09-05 01:44:48.212122: val_loss -0.6581 +2024-09-05 01:44:48.213044: Pseudo dice [0.9519, 0.9464, 0.9693, 0.9229, 0.8236, 0.9459, 0.9859] +2024-09-05 01:44:48.213841: Epoch time: 300.13 s +2024-09-05 01:44:48.215828: Yayy! New best EMA pseudo Dice: 0.9301 +2024-09-05 01:44:57.340414: +2024-09-05 01:44:57.342596: Epoch 62 +2024-09-05 01:44:57.343770: Current learning rate: 0.00944 +2024-09-05 01:49:51.226254: train_loss -0.6688 +2024-09-05 01:49:51.257847: val_loss -0.7136 +2024-09-05 01:49:51.259135: Pseudo dice [0.9528, 0.945, 0.9705, 0.936, 0.8306, 0.9494, 0.9877] +2024-09-05 01:49:51.260118: Epoch time: 293.89 s +2024-09-05 01:49:51.261089: Yayy! New best EMA pseudo Dice: 0.931 +2024-09-05 01:50:01.983498: +2024-09-05 01:50:01.984474: Epoch 63 +2024-09-05 01:50:01.985412: Current learning rate: 0.00943 +2024-09-05 01:54:46.456017: train_loss -0.6828 +2024-09-05 01:54:46.472460: val_loss -0.7092 +2024-09-05 01:54:46.474447: Pseudo dice [0.9526, 0.9452, 0.9704, 0.9296, 0.8211, 0.9512, 0.9846] +2024-09-05 01:54:46.476675: Epoch time: 284.48 s +2024-09-05 01:54:46.478270: Yayy! New best EMA pseudo Dice: 0.9315 +2024-09-05 01:54:55.803722: +2024-09-05 01:54:55.804576: Epoch 64 +2024-09-05 01:54:55.805278: Current learning rate: 0.00942 +2024-09-05 01:59:30.370342: train_loss -0.694 +2024-09-05 01:59:30.372179: val_loss -0.6903 +2024-09-05 01:59:30.373056: Pseudo dice [0.9519, 0.9447, 0.9717, 0.935, 0.8396, 0.954, 0.9891] +2024-09-05 01:59:30.374134: Epoch time: 274.57 s +2024-09-05 01:59:30.374859: Yayy! New best EMA pseudo Dice: 0.9324 +2024-09-05 01:59:39.732435: +2024-09-05 01:59:39.733352: Epoch 65 +2024-09-05 01:59:39.734106: Current learning rate: 0.00941 +2024-09-05 02:04:14.223453: train_loss -0.6871 +2024-09-05 02:04:14.225106: val_loss -0.7212 +2024-09-05 02:04:14.226405: Pseudo dice [0.9537, 0.9428, 0.9696, 0.9326, 0.8276, 0.953, 0.9879] +2024-09-05 02:04:14.227726: Epoch time: 274.49 s +2024-09-05 02:04:14.228932: Yayy! New best EMA pseudo Dice: 0.933 +2024-09-05 02:04:23.957844: +2024-09-05 02:04:23.958938: Epoch 66 +2024-09-05 02:04:23.960122: Current learning rate: 0.0094 +2024-09-05 02:09:00.069996: train_loss -0.6895 +2024-09-05 02:09:00.071325: val_loss -0.6728 +2024-09-05 02:09:00.072145: Pseudo dice [0.9561, 0.9498, 0.9719, 0.9356, 0.8332, 0.9567, 0.987] +2024-09-05 02:09:00.072868: Epoch time: 276.12 s +2024-09-05 02:09:00.073477: Yayy! New best EMA pseudo Dice: 0.9339 +2024-09-05 02:09:09.863219: +2024-09-05 02:09:09.921320: Epoch 67 +2024-09-05 02:09:09.970711: Current learning rate: 0.00939 +2024-09-05 02:14:06.097743: train_loss -0.6976 +2024-09-05 02:14:06.099289: val_loss -0.6758 +2024-09-05 02:14:06.100039: Pseudo dice [0.953, 0.9458, 0.9709, 0.9282, 0.8168, 0.95, 0.9866] +2024-09-05 02:14:06.100765: Epoch time: 296.24 s +2024-09-05 02:14:06.101425: Yayy! New best EMA pseudo Dice: 0.9341 +2024-09-05 02:14:14.664114: +2024-09-05 02:14:14.664978: Epoch 68 +2024-09-05 02:14:14.665607: Current learning rate: 0.00939 +2024-09-05 02:18:59.727147: train_loss -0.6824 +2024-09-05 02:18:59.739378: val_loss -0.7232 +2024-09-05 02:18:59.740648: Pseudo dice [0.9518, 0.9446, 0.9724, 0.9341, 0.8304, 0.9534, 0.9877] +2024-09-05 02:18:59.742047: Epoch time: 285.07 s +2024-09-05 02:18:59.743136: Yayy! New best EMA pseudo Dice: 0.9346 +2024-09-05 02:19:09.975715: +2024-09-05 02:19:09.976790: Epoch 69 +2024-09-05 02:19:09.977535: Current learning rate: 0.00938 +2024-09-05 02:23:50.739699: train_loss -0.6774 +2024-09-05 02:23:50.757589: val_loss -0.6727 +2024-09-05 02:23:50.758693: Pseudo dice [0.9519, 0.9441, 0.971, 0.9258, 0.8339, 0.939, 0.9875] +2024-09-05 02:23:50.759561: Epoch time: 280.77 s +2024-09-05 02:23:50.760396: Yayy! New best EMA pseudo Dice: 0.9347 +2024-09-05 02:24:01.619776: +2024-09-05 02:24:01.620881: Epoch 70 +2024-09-05 02:24:01.621812: Current learning rate: 0.00937 +2024-09-05 02:28:15.868261: train_loss -0.6846 +2024-09-05 02:28:15.870803: val_loss -0.684 +2024-09-05 02:28:15.871849: Pseudo dice [0.9514, 0.9438, 0.9699, 0.9339, 0.8217, 0.9515, 0.9876] +2024-09-05 02:28:15.873020: Epoch time: 254.25 s +2024-09-05 02:28:15.873666: Yayy! New best EMA pseudo Dice: 0.935 +2024-09-05 02:28:25.436288: +2024-09-05 02:28:25.437185: Epoch 71 +2024-09-05 02:28:25.437797: Current learning rate: 0.00936 +2024-09-05 02:33:27.600688: train_loss -0.6957 +2024-09-05 02:33:27.602425: val_loss -0.7211 +2024-09-05 02:33:27.603283: Pseudo dice [0.9529, 0.949, 0.9737, 0.9339, 0.8345, 0.9555, 0.9868] +2024-09-05 02:33:27.604011: Epoch time: 302.17 s +2024-09-05 02:33:27.604931: Yayy! New best EMA pseudo Dice: 0.9356 +2024-09-05 02:33:35.860476: +2024-09-05 02:33:35.861429: Epoch 72 +2024-09-05 02:33:35.862263: Current learning rate: 0.00935 +2024-09-05 02:38:11.094638: train_loss -0.6943 +2024-09-05 02:38:11.107465: val_loss -0.7076 +2024-09-05 02:38:11.108584: Pseudo dice [0.9555, 0.9484, 0.9717, 0.9375, 0.8381, 0.9556, 0.9876] +2024-09-05 02:38:11.109715: Epoch time: 275.24 s +2024-09-05 02:38:11.110811: Yayy! New best EMA pseudo Dice: 0.9362 +2024-09-05 02:38:20.609249: +2024-09-05 02:38:20.610256: Epoch 73 +2024-09-05 02:38:20.610952: Current learning rate: 0.00934 +2024-09-05 02:43:01.260146: train_loss -0.692 +2024-09-05 02:43:01.275070: val_loss -0.6878 +2024-09-05 02:43:01.276119: Pseudo dice [0.9538, 0.9479, 0.9715, 0.9363, 0.8377, 0.9479, 0.988] +2024-09-05 02:43:01.277088: Epoch time: 280.66 s +2024-09-05 02:43:01.278121: Yayy! New best EMA pseudo Dice: 0.9366 +2024-09-05 02:43:11.010301: +2024-09-05 02:43:11.011320: Epoch 74 +2024-09-05 02:43:11.011992: Current learning rate: 0.00933 +2024-09-05 02:48:00.586725: train_loss -0.6863 +2024-09-05 02:48:00.589230: val_loss -0.6816 +2024-09-05 02:48:00.590130: Pseudo dice [0.9526, 0.9427, 0.9741, 0.9319, 0.8156, 0.9511, 0.9853] +2024-09-05 02:48:00.591296: Epoch time: 289.58 s +2024-09-05 02:48:03.360751: +2024-09-05 02:48:03.375100: Epoch 75 +2024-09-05 02:48:03.376758: Current learning rate: 0.00932 +2024-09-05 02:52:57.283037: train_loss -0.6783 +2024-09-05 02:52:57.286605: val_loss -0.6897 +2024-09-05 02:52:57.288078: Pseudo dice [0.9449, 0.9382, 0.9613, 0.9221, 0.8236, 0.9312, 0.9864] +2024-09-05 02:52:57.289421: Epoch time: 293.93 s +2024-09-05 02:53:00.818273: +2024-09-05 02:53:00.819642: Epoch 76 +2024-09-05 02:53:00.820717: Current learning rate: 0.00931 +2024-09-05 02:57:42.713946: train_loss -0.6664 +2024-09-05 02:57:42.715797: val_loss -0.711 +2024-09-05 02:57:42.716834: Pseudo dice [0.9458, 0.9424, 0.9676, 0.9321, 0.8128, 0.9519, 0.9857] +2024-09-05 02:57:42.717802: Epoch time: 281.9 s +2024-09-05 02:57:47.059335: +2024-09-05 02:57:47.071101: Epoch 77 +2024-09-05 02:57:47.072948: Current learning rate: 0.0093 +2024-09-05 03:02:47.575464: train_loss -0.6748 +2024-09-05 03:02:47.593459: val_loss -0.6337 +2024-09-05 03:02:47.594798: Pseudo dice [0.9327, 0.929, 0.9505, 0.9097, 0.7938, 0.9444, 0.9827] +2024-09-05 03:02:47.595932: Epoch time: 300.52 s +2024-09-05 03:02:51.195390: +2024-09-05 03:02:51.196410: Epoch 78 +2024-09-05 03:02:51.197325: Current learning rate: 0.0093 +2024-09-05 03:07:53.565698: train_loss -0.6467 +2024-09-05 03:07:53.579485: val_loss -0.7055 +2024-09-05 03:07:53.582584: Pseudo dice [0.9445, 0.9388, 0.9632, 0.9249, 0.7979, 0.9405, 0.9876] +2024-09-05 03:07:53.584043: Epoch time: 302.38 s +2024-09-05 03:07:57.714933: +2024-09-05 03:07:57.715857: Epoch 79 +2024-09-05 03:07:57.716509: Current learning rate: 0.00929 +2024-09-05 03:12:29.396415: train_loss -0.6753 +2024-09-05 03:12:29.411074: val_loss -0.6847 +2024-09-05 03:12:29.412162: Pseudo dice [0.9473, 0.9422, 0.9667, 0.9299, 0.8183, 0.948, 0.9852] +2024-09-05 03:12:29.413049: Epoch time: 271.69 s +2024-09-05 03:12:32.142717: +2024-09-05 03:12:32.150262: Epoch 80 +2024-09-05 03:12:32.151416: Current learning rate: 0.00928 +2024-09-05 03:17:18.118812: train_loss -0.6766 +2024-09-05 03:17:18.137106: val_loss -0.6944 +2024-09-05 03:17:18.138120: Pseudo dice [0.9539, 0.9452, 0.9727, 0.9342, 0.8324, 0.9457, 0.9843] +2024-09-05 03:17:18.139318: Epoch time: 285.98 s +2024-09-05 03:17:21.106868: +2024-09-05 03:17:21.107991: Epoch 81 +2024-09-05 03:17:21.109013: Current learning rate: 0.00927 +2024-09-05 03:21:58.229145: train_loss -0.6729 +2024-09-05 03:21:58.240179: val_loss -0.7244 +2024-09-05 03:21:58.241088: Pseudo dice [0.9501, 0.9431, 0.9706, 0.9251, 0.8312, 0.9473, 0.9871] +2024-09-05 03:21:58.241991: Epoch time: 277.13 s +2024-09-05 03:22:00.654082: +2024-09-05 03:22:00.655357: Epoch 82 +2024-09-05 03:22:00.656168: Current learning rate: 0.00926 +2024-09-05 03:26:20.289703: train_loss -0.683 +2024-09-05 03:26:20.314905: val_loss -0.685 +2024-09-05 03:26:20.316253: Pseudo dice [0.9528, 0.9429, 0.9706, 0.9309, 0.8343, 0.9483, 0.9866] +2024-09-05 03:26:20.317143: Epoch time: 259.64 s +2024-09-05 03:26:22.948915: +2024-09-05 03:26:22.950395: Epoch 83 +2024-09-05 03:26:22.951883: Current learning rate: 0.00925 +2024-09-05 03:31:00.384805: train_loss -0.6803 +2024-09-05 03:31:00.400467: val_loss -0.7041 +2024-09-05 03:31:00.401435: Pseudo dice [0.9481, 0.9444, 0.9667, 0.9239, 0.8219, 0.9503, 0.9848] +2024-09-05 03:31:00.402587: Epoch time: 277.44 s +2024-09-05 03:31:03.769664: +2024-09-05 03:31:03.771199: Epoch 84 +2024-09-05 03:31:03.771974: Current learning rate: 0.00924 +2024-09-05 03:36:05.690095: train_loss -0.6821 +2024-09-05 03:36:05.709452: val_loss -0.6769 +2024-09-05 03:36:05.710881: Pseudo dice [0.9518, 0.947, 0.9709, 0.9288, 0.8448, 0.954, 0.9892] +2024-09-05 03:36:05.712079: Epoch time: 301.93 s +2024-09-05 03:36:08.842082: +2024-09-05 03:36:08.843840: Epoch 85 +2024-09-05 03:36:08.845258: Current learning rate: 0.00923 +2024-09-05 03:40:46.907560: train_loss -0.6716 +2024-09-05 03:40:46.924983: val_loss -0.7143 +2024-09-05 03:40:46.926442: Pseudo dice [0.954, 0.9478, 0.9717, 0.9369, 0.826, 0.9393, 0.9869] +2024-09-05 03:40:46.927313: Epoch time: 278.07 s +2024-09-05 03:40:49.967817: +2024-09-05 03:40:49.968958: Epoch 86 +2024-09-05 03:40:49.969985: Current learning rate: 0.00922 +2024-09-05 03:45:53.603011: train_loss -0.6847 +2024-09-05 03:45:53.710146: val_loss -0.698 +2024-09-05 03:45:53.723418: Pseudo dice [0.9588, 0.9509, 0.9768, 0.9354, 0.8347, 0.9554, 0.9879] +2024-09-05 03:45:53.784474: Epoch time: 303.64 s +2024-09-05 03:45:56.527849: +2024-09-05 03:45:56.628083: Epoch 87 +2024-09-05 03:45:56.660907: Current learning rate: 0.00921 +2024-09-05 03:50:38.697884: train_loss -0.6941 +2024-09-05 03:50:38.715154: val_loss -0.7327 +2024-09-05 03:50:38.716730: Pseudo dice [0.9542, 0.9465, 0.9726, 0.9395, 0.8426, 0.9551, 0.9886] +2024-09-05 03:50:38.717924: Epoch time: 282.17 s +2024-09-05 03:50:38.718673: Yayy! New best EMA pseudo Dice: 0.9369 +2024-09-05 03:50:47.588348: +2024-09-05 03:50:47.589488: Epoch 88 +2024-09-05 03:50:47.590379: Current learning rate: 0.0092 +2024-09-05 03:55:52.910986: train_loss -0.7017 +2024-09-05 03:55:52.914770: val_loss -0.7089 +2024-09-05 03:55:52.915883: Pseudo dice [0.9547, 0.9481, 0.9737, 0.9343, 0.8283, 0.9566, 0.9871] +2024-09-05 03:55:52.916693: Epoch time: 305.33 s +2024-09-05 03:55:52.917501: Yayy! New best EMA pseudo Dice: 0.9372 +2024-09-05 03:56:03.132331: +2024-09-05 03:56:03.133401: Epoch 89 +2024-09-05 03:56:03.134263: Current learning rate: 0.0092 +2024-09-05 04:00:27.409684: train_loss -0.7069 +2024-09-05 04:00:27.425274: val_loss -0.6943 +2024-09-05 04:00:27.426237: Pseudo dice [0.9526, 0.9441, 0.9726, 0.9273, 0.8316, 0.9525, 0.9871] +2024-09-05 04:00:27.427098: Epoch time: 264.28 s +2024-09-05 04:00:27.429356: Yayy! New best EMA pseudo Dice: 0.9373 +2024-09-05 04:00:35.589062: +2024-09-05 04:00:35.590019: Epoch 90 +2024-09-05 04:00:35.591064: Current learning rate: 0.00919 +2024-09-05 04:05:12.368295: train_loss -0.676 +2024-09-05 04:05:12.371636: val_loss -0.7418 +2024-09-05 04:05:12.372845: Pseudo dice [0.9557, 0.9411, 0.974, 0.9364, 0.8349, 0.9528, 0.9882] +2024-09-05 04:05:12.373776: Epoch time: 276.78 s +2024-09-05 04:05:12.374369: Yayy! New best EMA pseudo Dice: 0.9377 +2024-09-05 04:05:20.930224: +2024-09-05 04:05:20.931069: Epoch 91 +2024-09-05 04:05:20.931903: Current learning rate: 0.00918 +2024-09-05 04:10:05.032981: train_loss -0.7013 +2024-09-05 04:10:05.035286: val_loss -0.686 +2024-09-05 04:10:05.036085: Pseudo dice [0.9518, 0.9439, 0.9732, 0.9287, 0.8142, 0.9481, 0.9849] +2024-09-05 04:10:05.037136: Epoch time: 284.11 s +2024-09-05 04:10:07.741518: +2024-09-05 04:10:07.744024: Epoch 92 +2024-09-05 04:10:07.744800: Current learning rate: 0.00917 +2024-09-05 04:14:41.820854: train_loss -0.7047 +2024-09-05 04:14:41.845477: val_loss -0.7377 +2024-09-05 04:14:41.847953: Pseudo dice [0.9573, 0.9499, 0.9709, 0.9413, 0.8474, 0.9565, 0.989] +2024-09-05 04:14:41.848968: Epoch time: 274.09 s +2024-09-05 04:14:41.849868: Yayy! New best EMA pseudo Dice: 0.9381 +2024-09-05 04:14:50.620000: +2024-09-05 04:14:50.620878: Epoch 93 +2024-09-05 04:14:50.621497: Current learning rate: 0.00916 +2024-09-05 04:19:34.318589: train_loss -0.7058 +2024-09-05 04:19:34.322588: val_loss -0.685 +2024-09-05 04:19:34.324006: Pseudo dice [0.9537, 0.9484, 0.9711, 0.933, 0.8434, 0.9488, 0.9861] +2024-09-05 04:19:34.325122: Epoch time: 283.7 s +2024-09-05 04:19:34.326102: Yayy! New best EMA pseudo Dice: 0.9384 +2024-09-05 04:19:44.123462: +2024-09-05 04:19:44.124618: Epoch 94 +2024-09-05 04:19:44.125493: Current learning rate: 0.00915 +2024-09-05 04:24:20.165589: train_loss -0.7079 +2024-09-05 04:24:20.167353: val_loss -0.6983 +2024-09-05 04:24:20.168311: Pseudo dice [0.9521, 0.9451, 0.9699, 0.9301, 0.8236, 0.9553, 0.9861] +2024-09-05 04:24:20.169137: Epoch time: 276.05 s +2024-09-05 04:24:22.585601: +2024-09-05 04:24:22.586749: Epoch 95 +2024-09-05 04:24:22.587588: Current learning rate: 0.00914 +2024-09-05 04:28:51.029822: train_loss -0.696 +2024-09-05 04:28:51.052405: val_loss -0.7098 +2024-09-05 04:28:51.053502: Pseudo dice [0.9555, 0.9484, 0.9709, 0.936, 0.8447, 0.9571, 0.9875] +2024-09-05 04:28:51.054587: Epoch time: 268.45 s +2024-09-05 04:28:51.055656: Yayy! New best EMA pseudo Dice: 0.9387 +2024-09-05 04:29:04.078850: +2024-09-05 04:29:04.079699: Epoch 96 +2024-09-05 04:29:04.080369: Current learning rate: 0.00913 +2024-09-05 04:33:59.188837: train_loss -0.7071 +2024-09-05 04:33:59.200398: val_loss -0.6945 +2024-09-05 04:33:59.201288: Pseudo dice [0.9547, 0.9446, 0.9766, 0.9375, 0.8315, 0.9528, 0.9877] +2024-09-05 04:33:59.202234: Epoch time: 295.11 s +2024-09-05 04:33:59.202987: Yayy! New best EMA pseudo Dice: 0.9389 +2024-09-05 04:34:09.635742: +2024-09-05 04:34:09.636971: Epoch 97 +2024-09-05 04:34:09.637798: Current learning rate: 0.00912 +2024-09-05 04:38:53.084657: train_loss -0.7068 +2024-09-05 04:38:53.116046: val_loss -0.7039 +2024-09-05 04:38:53.117336: Pseudo dice [0.9538, 0.9516, 0.97, 0.9331, 0.8391, 0.9536, 0.9879] +2024-09-05 04:38:53.118927: Epoch time: 283.45 s +2024-09-05 04:38:53.120070: Yayy! New best EMA pseudo Dice: 0.9392 +2024-09-05 04:39:02.452936: +2024-09-05 04:39:02.454643: Epoch 98 +2024-09-05 04:39:02.457238: Current learning rate: 0.00911 +2024-09-05 04:43:36.117567: train_loss -0.7181 +2024-09-05 04:43:36.119013: val_loss -0.7041 +2024-09-05 04:43:36.119990: Pseudo dice [0.9554, 0.9458, 0.9733, 0.9304, 0.839, 0.9542, 0.9873] +2024-09-05 04:43:36.120871: Epoch time: 273.67 s +2024-09-05 04:43:36.121834: Yayy! New best EMA pseudo Dice: 0.9393 +2024-09-05 04:43:45.331787: +2024-09-05 04:43:45.332751: Epoch 99 +2024-09-05 04:43:45.333519: Current learning rate: 0.0091 +2024-09-05 04:48:36.631470: train_loss -0.6965 +2024-09-05 04:48:36.647426: val_loss -0.7389 +2024-09-05 04:48:36.648618: Pseudo dice [0.9594, 0.9513, 0.9743, 0.9423, 0.8439, 0.9575, 0.9883] +2024-09-05 04:48:36.649562: Epoch time: 291.3 s +2024-09-05 04:48:45.144687: Yayy! New best EMA pseudo Dice: 0.9399 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_08_31_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_08_31_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..8265fcc16c48025f2f85a39286a26f9623125628 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_08_31_30.txt @@ -0,0 +1,394 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 08:31:33.791886: Using torch.compile... +2024-09-05 08:31:42.736804: do_dummy_2d_data_aug: False +2024-09-05 08:31:42.742971: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 08:31:42.744489: The split file contains 5 splits. +2024-09-05 08:31:42.745110: Desired fold for training: 0 +2024-09-05 08:31:42.745684: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 08:31:54.278436: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 08:31:55.938057: +2024-09-05 08:31:55.950758: Epoch 100 +2024-09-05 08:31:55.952600: Current learning rate: 0.0091 +2024-09-05 08:50:27.089407: train_loss -0.705 +2024-09-05 08:50:27.090862: val_loss -0.6685 +2024-09-05 08:50:27.091622: Pseudo dice [0.9526, 0.9471, 0.9695, 0.937, 0.8358, 0.9562, 0.9845] +2024-09-05 08:50:27.092400: Epoch time: 1111.16 s +2024-09-05 08:50:27.093047: Yayy! New best EMA pseudo Dice: 0.94 +2024-09-05 08:50:34.912471: +2024-09-05 08:50:34.913294: Epoch 101 +2024-09-05 08:50:34.913781: Current learning rate: 0.00909 +2024-09-05 08:54:58.876535: train_loss -0.6965 +2024-09-05 08:54:58.887389: val_loss -0.6913 +2024-09-05 08:54:58.888862: Pseudo dice [0.9546, 0.9451, 0.9696, 0.9373, 0.8299, 0.9558, 0.9876] +2024-09-05 08:54:58.890111: Epoch time: 263.97 s +2024-09-05 08:54:58.892781: Yayy! New best EMA pseudo Dice: 0.94 +2024-09-05 08:55:07.111125: +2024-09-05 08:55:07.111877: Epoch 102 +2024-09-05 08:55:07.112417: Current learning rate: 0.00908 +2024-09-05 08:59:41.146031: train_loss -0.707 +2024-09-05 08:59:41.147572: val_loss -0.6448 +2024-09-05 08:59:41.148183: Pseudo dice [0.9502, 0.9462, 0.968, 0.9327, 0.8261, 0.9068, 0.9875] +2024-09-05 08:59:41.148826: Epoch time: 274.04 s +2024-09-05 08:59:43.658873: +2024-09-05 08:59:43.674195: Epoch 103 +2024-09-05 08:59:43.675440: Current learning rate: 0.00907 +2024-09-05 09:04:27.663525: train_loss -0.6848 +2024-09-05 09:04:27.664976: val_loss -0.7081 +2024-09-05 09:04:27.665750: Pseudo dice [0.9501, 0.9456, 0.9724, 0.9315, 0.8382, 0.9504, 0.9879] +2024-09-05 09:04:27.666523: Epoch time: 284.01 s +2024-09-05 09:04:31.611662: +2024-09-05 09:04:31.612638: Epoch 104 +2024-09-05 09:04:31.613331: Current learning rate: 0.00906 +2024-09-05 09:09:06.190820: train_loss -0.6919 +2024-09-05 09:09:06.206773: val_loss -0.6759 +2024-09-05 09:09:06.207633: Pseudo dice [0.9516, 0.9503, 0.9704, 0.932, 0.8528, 0.9518, 0.9869] +2024-09-05 09:09:06.208310: Epoch time: 274.58 s +2024-09-05 09:09:08.835996: +2024-09-05 09:09:08.837058: Epoch 105 +2024-09-05 09:09:08.837647: Current learning rate: 0.00905 +2024-09-05 09:13:48.451684: train_loss -0.7018 +2024-09-05 09:13:48.459122: val_loss -0.6839 +2024-09-05 09:13:48.460006: Pseudo dice [0.9555, 0.9489, 0.9711, 0.9401, 0.8518, 0.9555, 0.9876] +2024-09-05 09:13:48.460622: Epoch time: 279.62 s +2024-09-05 09:13:50.949326: +2024-09-05 09:13:50.950331: Epoch 106 +2024-09-05 09:13:50.951338: Current learning rate: 0.00904 +2024-09-05 09:18:31.255238: train_loss -0.7085 +2024-09-05 09:18:31.270198: val_loss -0.7061 +2024-09-05 09:18:31.271889: Pseudo dice [0.9561, 0.9487, 0.9742, 0.9407, 0.8417, 0.9567, 0.9879] +2024-09-05 09:18:31.274106: Epoch time: 280.31 s +2024-09-05 09:18:31.276635: Yayy! New best EMA pseudo Dice: 0.9403 +2024-09-05 09:18:39.857884: +2024-09-05 09:18:39.858677: Epoch 107 +2024-09-05 09:18:39.859376: Current learning rate: 0.00903 +2024-09-05 09:23:12.842974: train_loss -0.6829 +2024-09-05 09:23:12.870975: val_loss -0.6751 +2024-09-05 09:23:12.872079: Pseudo dice [0.9551, 0.952, 0.9719, 0.9363, 0.8463, 0.9507, 0.9872] +2024-09-05 09:23:12.873170: Epoch time: 272.99 s +2024-09-05 09:23:12.873983: Yayy! New best EMA pseudo Dice: 0.9406 +2024-09-05 09:23:21.194571: +2024-09-05 09:23:21.195681: Epoch 108 +2024-09-05 09:23:21.196999: Current learning rate: 0.00902 +2024-09-05 09:28:04.830091: train_loss -0.7028 +2024-09-05 09:28:04.848047: val_loss -0.746 +2024-09-05 09:28:04.848866: Pseudo dice [0.9557, 0.9463, 0.9727, 0.9319, 0.845, 0.9517, 0.9869] +2024-09-05 09:28:04.849694: Epoch time: 283.64 s +2024-09-05 09:28:04.850562: Yayy! New best EMA pseudo Dice: 0.9406 +2024-09-05 09:28:14.028874: +2024-09-05 09:28:14.029940: Epoch 109 +2024-09-05 09:28:14.030728: Current learning rate: 0.00901 +2024-09-05 09:32:33.565977: train_loss -0.6763 +2024-09-05 09:32:33.570082: val_loss -0.7217 +2024-09-05 09:32:33.570839: Pseudo dice [0.9548, 0.9511, 0.9744, 0.9406, 0.847, 0.9567, 0.9878] +2024-09-05 09:32:33.571423: Epoch time: 259.54 s +2024-09-05 09:32:33.572038: Yayy! New best EMA pseudo Dice: 0.941 +2024-09-05 09:32:42.279923: +2024-09-05 09:32:42.280665: Epoch 110 +2024-09-05 09:32:42.281219: Current learning rate: 0.009 +2024-09-05 09:37:12.487998: train_loss -0.7075 +2024-09-05 09:37:12.551328: val_loss -0.7321 +2024-09-05 09:37:12.552289: Pseudo dice [0.9566, 0.9488, 0.9745, 0.9397, 0.8472, 0.9602, 0.9892] +2024-09-05 09:37:12.553035: Epoch time: 270.21 s +2024-09-05 09:37:12.553579: Yayy! New best EMA pseudo Dice: 0.9415 +2024-09-05 09:37:20.268404: +2024-09-05 09:37:20.269186: Epoch 111 +2024-09-05 09:37:20.269744: Current learning rate: 0.009 +2024-09-05 09:42:00.148455: train_loss -0.682 +2024-09-05 09:42:00.167209: val_loss -0.6718 +2024-09-05 09:42:00.167922: Pseudo dice [0.9515, 0.9479, 0.9678, 0.9332, 0.7979, 0.9513, 0.9853] +2024-09-05 09:42:00.168656: Epoch time: 279.88 s +2024-09-05 09:42:02.712103: +2024-09-05 09:42:02.713485: Epoch 112 +2024-09-05 09:42:02.714421: Current learning rate: 0.00899 +2024-09-05 09:46:36.981990: train_loss -0.6893 +2024-09-05 09:46:36.984857: val_loss -0.7267 +2024-09-05 09:46:36.985767: Pseudo dice [0.9564, 0.9483, 0.9737, 0.936, 0.8547, 0.9593, 0.9893] +2024-09-05 09:46:36.986682: Epoch time: 274.27 s +2024-09-05 09:46:39.408468: +2024-09-05 09:46:39.409366: Epoch 113 +2024-09-05 09:46:39.410108: Current learning rate: 0.00898 +2024-09-05 09:51:34.192128: train_loss -0.6917 +2024-09-05 09:51:34.216773: val_loss -0.7442 +2024-09-05 09:51:34.217605: Pseudo dice [0.9562, 0.9428, 0.9709, 0.9422, 0.8439, 0.9527, 0.9866] +2024-09-05 09:51:34.218253: Epoch time: 294.79 s +2024-09-05 09:51:36.644227: +2024-09-05 09:51:36.645267: Epoch 114 +2024-09-05 09:51:36.645923: Current learning rate: 0.00897 +2024-09-05 09:55:58.862603: train_loss -0.7018 +2024-09-05 09:55:58.864016: val_loss -0.7099 +2024-09-05 09:55:58.865019: Pseudo dice [0.9546, 0.9492, 0.9731, 0.9353, 0.8349, 0.9581, 0.9875] +2024-09-05 09:55:58.865691: Epoch time: 262.22 s +2024-09-05 09:56:02.911829: +2024-09-05 09:56:02.926658: Epoch 115 +2024-09-05 09:56:02.927508: Current learning rate: 0.00896 +2024-09-05 10:00:43.608797: train_loss -0.6916 +2024-09-05 10:00:43.616673: val_loss -0.7027 +2024-09-05 10:00:43.618003: Pseudo dice [0.9583, 0.9525, 0.9754, 0.9403, 0.843, 0.9602, 0.9892] +2024-09-05 10:00:43.619066: Epoch time: 280.7 s +2024-09-05 10:00:43.619881: Yayy! New best EMA pseudo Dice: 0.9417 +2024-09-05 10:00:52.367775: +2024-09-05 10:00:52.368668: Epoch 116 +2024-09-05 10:00:52.369438: Current learning rate: 0.00895 +2024-09-05 10:05:33.767412: train_loss -0.7022 +2024-09-05 10:05:33.769071: val_loss -0.744 +2024-09-05 10:05:33.786280: Pseudo dice [0.9566, 0.9484, 0.9746, 0.9405, 0.8532, 0.9384, 0.9892] +2024-09-05 10:05:33.794727: Epoch time: 281.4 s +2024-09-05 10:05:33.795464: Yayy! New best EMA pseudo Dice: 0.9419 +2024-09-05 10:05:42.262891: +2024-09-05 10:05:42.263763: Epoch 117 +2024-09-05 10:05:42.264347: Current learning rate: 0.00894 +2024-09-05 10:10:09.350181: train_loss -0.7033 +2024-09-05 10:10:09.352794: val_loss -0.7001 +2024-09-05 10:10:09.353674: Pseudo dice [0.9576, 0.9509, 0.9752, 0.9408, 0.8573, 0.9526, 0.9889] +2024-09-05 10:10:09.369806: Epoch time: 267.09 s +2024-09-05 10:10:09.386639: Yayy! New best EMA pseudo Dice: 0.9423 +2024-09-05 10:10:19.512285: +2024-09-05 10:10:19.513151: Epoch 118 +2024-09-05 10:10:19.513940: Current learning rate: 0.00893 +2024-09-05 10:14:57.567624: train_loss -0.7132 +2024-09-05 10:14:57.569076: val_loss -0.7213 +2024-09-05 10:14:57.569889: Pseudo dice [0.9609, 0.9533, 0.9768, 0.941, 0.8507, 0.9622, 0.9857] +2024-09-05 10:14:57.570544: Epoch time: 278.06 s +2024-09-05 10:14:57.571515: Yayy! New best EMA pseudo Dice: 0.9428 +2024-09-05 10:15:06.580484: +2024-09-05 10:15:06.581460: Epoch 119 +2024-09-05 10:15:06.582438: Current learning rate: 0.00892 +2024-09-05 10:19:50.665343: train_loss -0.718 +2024-09-05 10:19:50.668527: val_loss -0.7147 +2024-09-05 10:19:50.669376: Pseudo dice [0.9535, 0.9497, 0.9709, 0.925, 0.8323, 0.9577, 0.9896] +2024-09-05 10:19:50.672900: Epoch time: 284.09 s +2024-09-05 10:19:53.484023: +2024-09-05 10:19:53.484874: Epoch 120 +2024-09-05 10:19:53.485564: Current learning rate: 0.00891 +2024-09-05 10:24:37.565263: train_loss -0.7075 +2024-09-05 10:24:37.579084: val_loss -0.7525 +2024-09-05 10:24:37.580091: Pseudo dice [0.9579, 0.9521, 0.9737, 0.9399, 0.8477, 0.958, 0.9876] +2024-09-05 10:24:37.580788: Epoch time: 284.09 s +2024-09-05 10:24:39.793303: +2024-09-05 10:24:39.795532: Epoch 121 +2024-09-05 10:24:39.796145: Current learning rate: 0.0089 +2024-09-05 10:29:18.214857: train_loss -0.6912 +2024-09-05 10:29:18.239968: val_loss -0.7215 +2024-09-05 10:29:18.241069: Pseudo dice [0.9553, 0.949, 0.9725, 0.9308, 0.8399, 0.9581, 0.9884] +2024-09-05 10:29:18.241995: Epoch time: 278.43 s +2024-09-05 10:29:20.725494: +2024-09-05 10:29:20.726389: Epoch 122 +2024-09-05 10:29:20.727086: Current learning rate: 0.00889 +2024-09-05 10:33:55.156586: train_loss -0.7059 +2024-09-05 10:33:55.166357: val_loss -0.7166 +2024-09-05 10:33:55.167267: Pseudo dice [0.9537, 0.9465, 0.9705, 0.9387, 0.8309, 0.9501, 0.9857] +2024-09-05 10:33:55.168248: Epoch time: 274.44 s +2024-09-05 10:33:57.671613: +2024-09-05 10:33:57.689941: Epoch 123 +2024-09-05 10:33:57.690939: Current learning rate: 0.00889 +2024-09-05 10:38:21.012477: train_loss -0.7044 +2024-09-05 10:38:21.025206: val_loss -0.703 +2024-09-05 10:38:21.025988: Pseudo dice [0.9576, 0.95, 0.9739, 0.9344, 0.8346, 0.9559, 0.9867] +2024-09-05 10:38:21.026950: Epoch time: 263.36 s +2024-09-05 10:38:24.426492: +2024-09-05 10:38:24.427408: Epoch 124 +2024-09-05 10:38:24.428055: Current learning rate: 0.00888 +2024-09-05 10:43:18.964696: train_loss -0.7021 +2024-09-05 10:43:18.969211: val_loss -0.7356 +2024-09-05 10:43:18.970610: Pseudo dice [0.9578, 0.9528, 0.9746, 0.937, 0.8332, 0.9494, 0.9876] +2024-09-05 10:43:18.971966: Epoch time: 294.54 s +2024-09-05 10:43:21.257270: +2024-09-05 10:43:21.258263: Epoch 125 +2024-09-05 10:43:21.259050: Current learning rate: 0.00887 +2024-09-05 10:47:45.710503: train_loss -0.7211 +2024-09-05 10:47:45.711966: val_loss -0.7172 +2024-09-05 10:47:45.712581: Pseudo dice [0.9585, 0.9514, 0.9746, 0.934, 0.8453, 0.955, 0.988] +2024-09-05 10:47:45.713318: Epoch time: 264.46 s +2024-09-05 10:47:48.264259: +2024-09-05 10:47:48.265147: Epoch 126 +2024-09-05 10:47:48.265873: Current learning rate: 0.00886 +2024-09-05 10:52:34.509533: train_loss -0.7119 +2024-09-05 10:52:34.511611: val_loss -0.686 +2024-09-05 10:52:34.513368: Pseudo dice [0.9601, 0.9511, 0.9762, 0.9387, 0.83, 0.954, 0.9882] +2024-09-05 10:52:34.514530: Epoch time: 286.25 s +2024-09-05 10:52:38.065735: +2024-09-05 10:52:38.066900: Epoch 127 +2024-09-05 10:52:38.067740: Current learning rate: 0.00885 +2024-09-05 10:57:21.281061: train_loss -0.7086 +2024-09-05 10:57:21.292991: val_loss -0.6917 +2024-09-05 10:57:21.294309: Pseudo dice [0.9524, 0.9476, 0.9689, 0.9371, 0.8341, 0.9529, 0.9855] +2024-09-05 10:57:21.295094: Epoch time: 283.22 s +2024-09-05 10:57:24.093887: +2024-09-05 10:57:24.094779: Epoch 128 +2024-09-05 10:57:24.095411: Current learning rate: 0.00884 +2024-09-05 11:02:19.833550: train_loss -0.6924 +2024-09-05 11:02:19.851095: val_loss -0.7199 +2024-09-05 11:02:19.858641: Pseudo dice [0.952, 0.9486, 0.9675, 0.9366, 0.8449, 0.9583, 0.9845] +2024-09-05 11:02:19.862434: Epoch time: 295.74 s +2024-09-05 11:02:22.249124: +2024-09-05 11:02:22.251416: Epoch 129 +2024-09-05 11:02:22.253581: Current learning rate: 0.00883 +2024-09-05 11:07:10.335835: train_loss -0.7206 +2024-09-05 11:07:10.337374: val_loss -0.7266 +2024-09-05 11:07:10.338109: Pseudo dice [0.9594, 0.9548, 0.9772, 0.943, 0.8568, 0.9617, 0.9858] +2024-09-05 11:07:10.339780: Epoch time: 288.09 s +2024-09-05 11:07:12.672329: +2024-09-05 11:07:12.673326: Epoch 130 +2024-09-05 11:07:12.673888: Current learning rate: 0.00882 +2024-09-05 11:11:55.963713: train_loss -0.7191 +2024-09-05 11:11:55.990924: val_loss -0.7081 +2024-09-05 11:11:55.991768: Pseudo dice [0.9526, 0.9481, 0.9676, 0.9464, 0.8561, 0.9543, 0.9876] +2024-09-05 11:11:55.992546: Epoch time: 283.3 s +2024-09-05 11:11:55.993223: Yayy! New best EMA pseudo Dice: 0.943 +2024-09-05 11:12:04.119151: +2024-09-05 11:12:04.121111: Epoch 131 +2024-09-05 11:12:04.123664: Current learning rate: 0.00881 +2024-09-05 11:16:24.946059: train_loss -0.7158 +2024-09-05 11:16:24.963580: val_loss -0.7443 +2024-09-05 11:16:24.964913: Pseudo dice [0.9584, 0.9509, 0.9739, 0.9336, 0.8453, 0.9511, 0.9877] +2024-09-05 11:16:24.966341: Epoch time: 260.83 s +2024-09-05 11:16:24.967621: Yayy! New best EMA pseudo Dice: 0.943 +2024-09-05 11:16:33.134722: +2024-09-05 11:16:33.135606: Epoch 132 +2024-09-05 11:16:33.136261: Current learning rate: 0.0088 +2024-09-05 11:21:00.147625: train_loss -0.6902 +2024-09-05 11:21:00.158601: val_loss -0.7335 +2024-09-05 11:21:00.159343: Pseudo dice [0.9599, 0.9512, 0.977, 0.9377, 0.8551, 0.9629, 0.9887] +2024-09-05 11:21:00.160099: Epoch time: 267.02 s +2024-09-05 11:21:00.160653: Yayy! New best EMA pseudo Dice: 0.9434 +2024-09-05 11:21:09.873146: +2024-09-05 11:21:09.873924: Epoch 133 +2024-09-05 11:21:09.874502: Current learning rate: 0.00879 +2024-09-05 11:25:28.391029: train_loss -0.7111 +2024-09-05 11:25:28.424236: val_loss -0.6916 +2024-09-05 11:25:28.425438: Pseudo dice [0.9563, 0.9519, 0.9716, 0.9393, 0.844, 0.9568, 0.9889] +2024-09-05 11:25:28.426058: Epoch time: 258.52 s +2024-09-05 11:25:28.426905: Yayy! New best EMA pseudo Dice: 0.9435 +2024-09-05 11:25:37.920934: +2024-09-05 11:25:37.921854: Epoch 134 +2024-09-05 11:25:37.922633: Current learning rate: 0.00879 +2024-09-05 11:30:09.721164: train_loss -0.7161 +2024-09-05 11:30:09.723393: val_loss -0.7005 +2024-09-05 11:30:09.724198: Pseudo dice [0.957, 0.9514, 0.9742, 0.9313, 0.8338, 0.9546, 0.9881] +2024-09-05 11:30:09.724806: Epoch time: 271.81 s +2024-09-05 11:30:11.882450: +2024-09-05 11:30:11.883672: Epoch 135 +2024-09-05 11:30:11.884293: Current learning rate: 0.00878 +2024-09-05 11:34:43.952901: train_loss -0.7221 +2024-09-05 11:34:43.956115: val_loss -0.7002 +2024-09-05 11:34:43.957419: Pseudo dice [0.9598, 0.9518, 0.9733, 0.9376, 0.8461, 0.9612, 0.9889] +2024-09-05 11:34:43.958086: Epoch time: 272.08 s +2024-09-05 11:34:43.959118: Yayy! New best EMA pseudo Dice: 0.9435 +2024-09-05 11:34:52.033961: +2024-09-05 11:34:52.034847: Epoch 136 +2024-09-05 11:34:52.035497: Current learning rate: 0.00877 +2024-09-05 11:39:39.139255: train_loss -0.7103 +2024-09-05 11:39:39.141555: val_loss -0.7127 +2024-09-05 11:39:39.142130: Pseudo dice [0.9582, 0.9502, 0.9762, 0.9387, 0.8391, 0.959, 0.9878] +2024-09-05 11:39:39.142875: Epoch time: 287.11 s +2024-09-05 11:39:39.143398: Yayy! New best EMA pseudo Dice: 0.9436 +2024-09-05 11:39:47.289981: +2024-09-05 11:39:47.290783: Epoch 137 +2024-09-05 11:39:47.291403: Current learning rate: 0.00876 +2024-09-05 11:44:18.743298: train_loss -0.7179 +2024-09-05 11:44:18.746574: val_loss -0.7201 +2024-09-05 11:44:18.747404: Pseudo dice [0.9565, 0.9517, 0.973, 0.9384, 0.8491, 0.9573, 0.9897] +2024-09-05 11:44:18.748511: Epoch time: 271.46 s +2024-09-05 11:44:18.749377: Yayy! New best EMA pseudo Dice: 0.9437 +2024-09-05 11:44:28.894542: +2024-09-05 11:44:28.895589: Epoch 138 +2024-09-05 11:44:28.896357: Current learning rate: 0.00875 +2024-09-05 11:49:05.269133: train_loss -0.7117 +2024-09-05 11:49:05.272372: val_loss -0.7382 +2024-09-05 11:49:05.273381: Pseudo dice [0.9584, 0.9506, 0.9748, 0.94, 0.8536, 0.9585, 0.9887] +2024-09-05 11:49:05.274408: Epoch time: 276.38 s +2024-09-05 11:49:05.275112: Yayy! New best EMA pseudo Dice: 0.944 +2024-09-05 11:49:12.800072: +2024-09-05 11:49:12.801045: Epoch 139 +2024-09-05 11:49:12.801717: Current learning rate: 0.00874 +2024-09-05 11:53:37.600915: train_loss -0.7212 +2024-09-05 11:53:37.603848: val_loss -0.7139 +2024-09-05 11:53:37.604887: Pseudo dice [0.9586, 0.953, 0.9737, 0.9468, 0.8557, 0.9573, 0.9901] +2024-09-05 11:53:37.606223: Epoch time: 264.8 s +2024-09-05 11:53:37.607081: Yayy! New best EMA pseudo Dice: 0.9444 +2024-09-05 11:53:45.968114: +2024-09-05 11:53:45.969033: Epoch 140 +2024-09-05 11:53:45.969635: Current learning rate: 0.00873 +2024-09-05 11:58:19.584594: train_loss -0.7263 +2024-09-05 11:58:19.587180: val_loss -0.7109 +2024-09-05 11:58:19.587955: Pseudo dice [0.9579, 0.9503, 0.9692, 0.9404, 0.867, 0.956, 0.9901] +2024-09-05 11:58:19.588639: Epoch time: 273.62 s +2024-09-05 11:58:19.589290: Yayy! New best EMA pseudo Dice: 0.9447 +2024-09-05 11:58:27.613800: +2024-09-05 11:58:27.614672: Epoch 141 +2024-09-05 11:58:27.615329: Current learning rate: 0.00872 +2024-09-05 12:03:11.264485: train_loss -0.7151 +2024-09-05 12:03:11.266899: val_loss -0.7137 +2024-09-05 12:03:11.267687: Pseudo dice [0.9497, 0.9454, 0.9711, 0.9354, 0.8487, 0.9547, 0.9902] +2024-09-05 12:03:11.268525: Epoch time: 283.65 s +2024-09-05 12:03:15.750800: +2024-09-05 12:03:15.752442: Epoch 142 +2024-09-05 12:03:15.753248: Current learning rate: 0.00871 +2024-09-05 12:07:49.836756: train_loss -0.6953 +2024-09-05 12:07:49.839632: val_loss -0.7252 +2024-09-05 12:07:49.840572: Pseudo dice [0.9553, 0.9488, 0.9746, 0.9261, 0.8307, 0.9518, 0.989] +2024-09-05 12:07:49.841883: Epoch time: 274.09 s +2024-09-05 12:07:53.073098: +2024-09-05 12:07:53.073984: Epoch 143 +2024-09-05 12:07:53.074636: Current learning rate: 0.0087 +2024-09-05 12:12:35.461232: train_loss -0.7052 +2024-09-05 12:12:35.469450: val_loss -0.7183 +2024-09-05 12:12:35.470131: Pseudo dice [0.9568, 0.9484, 0.9739, 0.9466, 0.8531, 0.9577, 0.9897] +2024-09-05 12:12:35.470809: Epoch time: 282.39 s +2024-09-05 12:12:38.272817: +2024-09-05 12:12:38.273803: Epoch 144 +2024-09-05 12:12:38.274493: Current learning rate: 0.00869 +2024-09-05 12:17:15.281117: train_loss -0.7138 +2024-09-05 12:17:15.282886: val_loss -0.7262 +2024-09-05 12:17:15.283513: Pseudo dice [0.9584, 0.9538, 0.9741, 0.9492, 0.8555, 0.9603, 0.9904] +2024-09-05 12:17:15.284355: Epoch time: 277.01 s +2024-09-05 12:17:17.615475: +2024-09-05 12:17:17.616538: Epoch 145 +2024-09-05 12:17:17.617204: Current learning rate: 0.00868 +2024-09-05 12:21:58.237548: train_loss -0.7172 +2024-09-05 12:21:58.256968: val_loss -0.7144 +2024-09-05 12:21:58.257753: Pseudo dice [0.9572, 0.9536, 0.9742, 0.9384, 0.8556, 0.9595, 0.9899] +2024-09-05 12:21:58.258553: Epoch time: 280.63 s +2024-09-05 12:21:58.259136: Yayy! New best EMA pseudo Dice: 0.9449 +2024-09-05 12:22:06.673335: +2024-09-05 12:22:06.674195: Epoch 146 +2024-09-05 12:22:06.674786: Current learning rate: 0.00868 +2024-09-05 12:26:59.060425: train_loss -0.6983 +2024-09-05 12:26:59.062918: val_loss -0.723 +2024-09-05 12:26:59.064022: Pseudo dice [0.9578, 0.9483, 0.9764, 0.9417, 0.8327, 0.9576, 0.987] +2024-09-05 12:26:59.065417: Epoch time: 292.39 s +2024-09-05 12:27:02.050327: +2024-09-05 12:27:02.061985: Epoch 147 +2024-09-05 12:27:02.063771: Current learning rate: 0.00867 +2024-09-05 12:31:29.781280: train_loss -0.7134 +2024-09-05 12:31:29.795758: val_loss -0.72 +2024-09-05 12:31:29.796530: Pseudo dice [0.9566, 0.9496, 0.9748, 0.9414, 0.8483, 0.9607, 0.9886] +2024-09-05 12:31:29.797140: Epoch time: 267.79 s +2024-09-05 12:31:32.550988: +2024-09-05 12:31:32.552139: Epoch 148 +2024-09-05 12:31:32.553009: Current learning rate: 0.00866 +2024-09-05 12:36:25.971860: train_loss -0.7088 +2024-09-05 12:36:25.981588: val_loss -0.7358 +2024-09-05 12:36:25.982729: Pseudo dice [0.9581, 0.9479, 0.9719, 0.9401, 0.865, 0.9607, 0.9898] +2024-09-05 12:36:25.984284: Epoch time: 293.43 s +2024-09-05 12:36:25.985542: Yayy! New best EMA pseudo Dice: 0.9451 +2024-09-05 12:36:34.056867: +2024-09-05 12:36:34.057711: Epoch 149 +2024-09-05 12:36:34.058443: Current learning rate: 0.00865 +2024-09-05 12:41:04.382096: train_loss -0.7064 +2024-09-05 12:41:04.384546: val_loss -0.6911 +2024-09-05 12:41:04.386032: Pseudo dice [0.9553, 0.9509, 0.974, 0.9424, 0.841, 0.9614, 0.9861] +2024-09-05 12:41:04.386887: Epoch time: 270.33 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_16_30_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_16_30_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc886c403c61b4637e971ed11e491459b4569817 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_5_16_30_45.txt @@ -0,0 +1,381 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 16:30:48.114985: Using torch.compile... +2024-09-05 16:30:57.262532: do_dummy_2d_data_aug: False +2024-09-05 16:30:57.268567: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 16:30:57.269850: The split file contains 5 splits. +2024-09-05 16:30:57.270423: Desired fold for training: 0 +2024-09-05 16:30:57.271030: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 16:31:08.556564: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 16:31:10.073337: +2024-09-05 16:31:10.075511: Epoch 150 +2024-09-05 16:31:10.078360: Current learning rate: 0.00864 +2024-09-05 16:49:44.442162: train_loss -0.7122 +2024-09-05 16:49:44.443352: val_loss -0.7159 +2024-09-05 16:49:44.444150: Pseudo dice [0.9567, 0.9515, 0.9696, 0.9372, 0.845, 0.9574, 0.9871] +2024-09-05 16:49:44.444802: Epoch time: 1114.37 s +2024-09-05 16:49:46.112066: +2024-09-05 16:49:46.223099: Epoch 151 +2024-09-05 16:49:46.331435: Current learning rate: 0.00863 +2024-09-05 16:54:29.417373: train_loss -0.7305 +2024-09-05 16:54:29.429139: val_loss -0.7137 +2024-09-05 16:54:29.429954: Pseudo dice [0.9586, 0.9538, 0.9743, 0.9428, 0.8347, 0.9527, 0.9863] +2024-09-05 16:54:29.430523: Epoch time: 283.31 s +2024-09-05 16:54:31.797340: +2024-09-05 16:54:31.798274: Epoch 152 +2024-09-05 16:54:31.799184: Current learning rate: 0.00862 +2024-09-05 16:59:20.116929: train_loss -0.7374 +2024-09-05 16:59:20.118404: val_loss -0.7409 +2024-09-05 16:59:20.119238: Pseudo dice [0.9616, 0.9512, 0.977, 0.9382, 0.8517, 0.9619, 0.9908] +2024-09-05 16:59:20.120194: Epoch time: 288.32 s +2024-09-05 16:59:23.341870: +2024-09-05 16:59:23.342713: Epoch 153 +2024-09-05 16:59:23.343449: Current learning rate: 0.00861 +2024-09-05 17:03:53.803570: train_loss -0.713 +2024-09-05 17:03:53.814083: val_loss -0.7026 +2024-09-05 17:03:53.814965: Pseudo dice [0.9585, 0.9509, 0.9753, 0.9376, 0.8424, 0.9513, 0.9868] +2024-09-05 17:03:53.815760: Epoch time: 270.47 s +2024-09-05 17:03:56.688178: +2024-09-05 17:03:56.696356: Epoch 154 +2024-09-05 17:03:56.697041: Current learning rate: 0.0086 +2024-09-05 17:08:27.807127: train_loss -0.7249 +2024-09-05 17:08:27.809022: val_loss -0.7449 +2024-09-05 17:08:27.810742: Pseudo dice [0.9592, 0.9495, 0.9762, 0.9331, 0.8658, 0.962, 0.9913] +2024-09-05 17:08:27.812331: Epoch time: 271.12 s +2024-09-05 17:08:27.813962: Yayy! New best EMA pseudo Dice: 0.9451 +2024-09-05 17:08:42.729360: +2024-09-05 17:08:42.781580: Epoch 155 +2024-09-05 17:08:42.843530: Current learning rate: 0.00859 +2024-09-05 17:13:36.178315: train_loss -0.715 +2024-09-05 17:13:36.180984: val_loss -0.7043 +2024-09-05 17:13:36.181750: Pseudo dice [0.959, 0.9545, 0.9727, 0.9451, 0.8557, 0.9621, 0.9875] +2024-09-05 17:13:36.182761: Epoch time: 293.45 s +2024-09-05 17:13:36.183593: Yayy! New best EMA pseudo Dice: 0.9454 +2024-09-05 17:13:45.053745: +2024-09-05 17:13:45.054882: Epoch 156 +2024-09-05 17:13:45.055664: Current learning rate: 0.00858 +2024-09-05 17:18:32.228349: train_loss -0.7139 +2024-09-05 17:18:32.240942: val_loss -0.6976 +2024-09-05 17:18:32.241735: Pseudo dice [0.9554, 0.9479, 0.9751, 0.9431, 0.8535, 0.9629, 0.9897] +2024-09-05 17:18:32.242606: Epoch time: 287.18 s +2024-09-05 17:18:32.243301: Yayy! New best EMA pseudo Dice: 0.9456 +2024-09-05 17:18:43.507134: +2024-09-05 17:18:43.508090: Epoch 157 +2024-09-05 17:18:43.509127: Current learning rate: 0.00858 +2024-09-05 17:23:44.457680: train_loss -0.7084 +2024-09-05 17:23:44.459941: val_loss -0.6921 +2024-09-05 17:23:44.460915: Pseudo dice [0.9519, 0.9496, 0.9691, 0.9391, 0.8332, 0.9602, 0.9877] +2024-09-05 17:23:44.461725: Epoch time: 300.96 s +2024-09-05 17:23:47.816007: +2024-09-05 17:23:47.821062: Epoch 158 +2024-09-05 17:23:47.830581: Current learning rate: 0.00857 +2024-09-05 17:29:01.012879: train_loss -0.709 +2024-09-05 17:29:01.028203: val_loss -0.7347 +2024-09-05 17:29:01.029303: Pseudo dice [0.9581, 0.9499, 0.9661, 0.9379, 0.8532, 0.9601, 0.9898] +2024-09-05 17:29:01.030416: Epoch time: 313.2 s +2024-09-05 17:29:03.948863: +2024-09-05 17:29:03.950119: Epoch 159 +2024-09-05 17:29:03.950909: Current learning rate: 0.00856 +2024-09-05 17:34:14.904611: train_loss -0.7164 +2024-09-05 17:34:14.907138: val_loss -0.7323 +2024-09-05 17:34:14.908109: Pseudo dice [0.9592, 0.9497, 0.9764, 0.9404, 0.8513, 0.9643, 0.9877] +2024-09-05 17:34:14.908777: Epoch time: 310.96 s +2024-09-05 17:34:17.721457: +2024-09-05 17:34:17.722587: Epoch 160 +2024-09-05 17:34:17.723581: Current learning rate: 0.00855 +2024-09-05 17:39:27.472079: train_loss -0.7276 +2024-09-05 17:39:27.478722: val_loss -0.7148 +2024-09-05 17:39:27.480130: Pseudo dice [0.9555, 0.9517, 0.9719, 0.9384, 0.8549, 0.9587, 0.9902] +2024-09-05 17:39:27.481592: Epoch time: 309.76 s +2024-09-05 17:39:31.826578: +2024-09-05 17:39:31.828084: Epoch 161 +2024-09-05 17:39:31.829499: Current learning rate: 0.00854 +2024-09-05 17:44:47.105471: train_loss -0.7189 +2024-09-05 17:44:47.107126: val_loss -0.7345 +2024-09-05 17:44:47.107942: Pseudo dice [0.9569, 0.9489, 0.9736, 0.9398, 0.8561, 0.9602, 0.9878] +2024-09-05 17:44:47.108828: Epoch time: 315.28 s +2024-09-05 17:44:49.884308: +2024-09-05 17:44:49.885559: Epoch 162 +2024-09-05 17:44:49.886342: Current learning rate: 0.00853 +2024-09-05 17:50:11.903840: train_loss -0.7064 +2024-09-05 17:50:11.916186: val_loss -0.7159 +2024-09-05 17:50:11.917502: Pseudo dice [0.9598, 0.9551, 0.9742, 0.9329, 0.8521, 0.9587, 0.9883] +2024-09-05 17:50:11.921231: Epoch time: 322.03 s +2024-09-05 17:50:14.889348: +2024-09-05 17:50:14.891330: Epoch 163 +2024-09-05 17:50:14.893092: Current learning rate: 0.00852 +2024-09-05 17:55:38.135309: train_loss -0.7161 +2024-09-05 17:55:38.137069: val_loss -0.714 +2024-09-05 17:55:38.138048: Pseudo dice [0.9516, 0.9516, 0.9729, 0.9345, 0.8592, 0.9545, 0.9891] +2024-09-05 17:55:38.138751: Epoch time: 323.25 s +2024-09-05 17:55:40.861348: +2024-09-05 17:55:40.862534: Epoch 164 +2024-09-05 17:55:40.863469: Current learning rate: 0.00851 +2024-09-05 18:00:59.919786: train_loss -0.7106 +2024-09-05 18:00:59.936834: val_loss -0.7279 +2024-09-05 18:00:59.938690: Pseudo dice [0.9587, 0.9536, 0.9742, 0.941, 0.849, 0.9599, 0.9893] +2024-09-05 18:00:59.940453: Epoch time: 319.06 s +2024-09-05 18:01:02.883392: +2024-09-05 18:01:02.885017: Epoch 165 +2024-09-05 18:01:02.895176: Current learning rate: 0.0085 +2024-09-05 18:06:03.292455: train_loss -0.7075 +2024-09-05 18:06:03.302773: val_loss -0.7156 +2024-09-05 18:06:03.303597: Pseudo dice [0.9583, 0.9526, 0.9728, 0.9438, 0.8318, 0.9563, 0.9882] +2024-09-05 18:06:03.304214: Epoch time: 300.41 s +2024-09-05 18:06:06.247953: +2024-09-05 18:06:06.248957: Epoch 166 +2024-09-05 18:06:06.249748: Current learning rate: 0.00849 +2024-09-05 18:11:29.421145: train_loss -0.7283 +2024-09-05 18:11:29.431584: val_loss -0.7297 +2024-09-05 18:11:29.432477: Pseudo dice [0.9597, 0.9537, 0.9774, 0.9425, 0.8623, 0.9658, 0.9904] +2024-09-05 18:11:29.433407: Epoch time: 323.18 s +2024-09-05 18:11:29.434325: Yayy! New best EMA pseudo Dice: 0.9458 +2024-09-05 18:11:40.926808: +2024-09-05 18:11:40.927939: Epoch 167 +2024-09-05 18:11:40.928674: Current learning rate: 0.00848 +2024-09-05 18:16:32.755958: train_loss -0.7146 +2024-09-05 18:16:32.763396: val_loss -0.7215 +2024-09-05 18:16:32.764482: Pseudo dice [0.9588, 0.9505, 0.9745, 0.9386, 0.845, 0.9615, 0.9894] +2024-09-05 18:16:32.765835: Epoch time: 291.83 s +2024-09-05 18:16:35.800260: +2024-09-05 18:16:35.802013: Epoch 168 +2024-09-05 18:16:35.803008: Current learning rate: 0.00847 +2024-09-05 18:22:18.212695: train_loss -0.7033 +2024-09-05 18:22:18.223934: val_loss -0.7092 +2024-09-05 18:22:18.328689: Pseudo dice [0.953, 0.9448, 0.9697, 0.916, 0.8421, 0.9488, 0.9896] +2024-09-05 18:22:18.514692: Epoch time: 342.42 s +2024-09-05 18:22:21.986162: +2024-09-05 18:22:22.158234: Epoch 169 +2024-09-05 18:22:22.172883: Current learning rate: 0.00847 +2024-09-05 18:27:37.210751: train_loss -0.6948 +2024-09-05 18:27:37.213777: val_loss -0.6227 +2024-09-05 18:27:37.214720: Pseudo dice [0.9392, 0.9363, 0.9576, 0.927, 0.8126, 0.9334, 0.9531] +2024-09-05 18:27:37.215740: Epoch time: 315.23 s +2024-09-05 18:27:41.455231: +2024-09-05 18:27:41.456412: Epoch 170 +2024-09-05 18:27:41.457230: Current learning rate: 0.00846 +2024-09-05 18:32:21.131667: train_loss -0.658 +2024-09-05 18:32:21.136093: val_loss -0.687 +2024-09-05 18:32:21.138000: Pseudo dice [0.9492, 0.9417, 0.967, 0.9386, 0.8178, 0.9453, 0.9863] +2024-09-05 18:32:21.141623: Epoch time: 279.68 s +2024-09-05 18:32:24.184405: +2024-09-05 18:32:24.201986: Epoch 171 +2024-09-05 18:32:24.203416: Current learning rate: 0.00845 +2024-09-05 18:37:18.088207: train_loss -0.6665 +2024-09-05 18:37:18.090526: val_loss -0.6717 +2024-09-05 18:37:18.091947: Pseudo dice [0.9487, 0.9459, 0.968, 0.9259, 0.7972, 0.9382, 0.9872] +2024-09-05 18:37:18.093262: Epoch time: 293.91 s +2024-09-05 18:37:20.779493: +2024-09-05 18:37:20.780373: Epoch 172 +2024-09-05 18:37:20.781380: Current learning rate: 0.00844 +2024-09-05 18:42:35.502051: train_loss -0.6705 +2024-09-05 18:42:35.519377: val_loss -0.7124 +2024-09-05 18:42:35.520105: Pseudo dice [0.957, 0.9476, 0.9749, 0.9235, 0.8256, 0.889, 0.987] +2024-09-05 18:42:35.520958: Epoch time: 314.73 s +2024-09-05 18:42:38.459105: +2024-09-05 18:42:38.460451: Epoch 173 +2024-09-05 18:42:38.466115: Current learning rate: 0.00843 +2024-09-05 18:47:54.194916: train_loss -0.7001 +2024-09-05 18:47:54.204261: val_loss -0.72 +2024-09-05 18:47:54.207060: Pseudo dice [0.951, 0.9451, 0.9698, 0.9266, 0.8198, 0.9478, 0.9786] +2024-09-05 18:47:54.207880: Epoch time: 315.74 s +2024-09-05 18:47:56.942095: +2024-09-05 18:47:56.942849: Epoch 174 +2024-09-05 18:47:56.943376: Current learning rate: 0.00842 +2024-09-05 18:53:07.834568: train_loss -0.7054 +2024-09-05 18:53:07.837194: val_loss -0.7195 +2024-09-05 18:53:07.837943: Pseudo dice [0.9518, 0.9466, 0.9666, 0.9225, 0.8416, 0.9548, 0.9882] +2024-09-05 18:53:07.838770: Epoch time: 310.9 s +2024-09-05 18:53:10.705376: +2024-09-05 18:53:10.718719: Epoch 175 +2024-09-05 18:53:10.719728: Current learning rate: 0.00841 +2024-09-05 18:58:09.840608: train_loss -0.7052 +2024-09-05 18:58:09.843425: val_loss -0.6919 +2024-09-05 18:58:09.844614: Pseudo dice [0.9593, 0.9537, 0.9738, 0.9411, 0.8434, 0.9571, 0.9902] +2024-09-05 18:58:09.846097: Epoch time: 299.14 s +2024-09-05 18:58:12.611383: +2024-09-05 18:58:12.612728: Epoch 176 +2024-09-05 18:58:12.614429: Current learning rate: 0.0084 +2024-09-05 19:03:05.981251: train_loss -0.7013 +2024-09-05 19:03:05.983712: val_loss -0.7603 +2024-09-05 19:03:05.984639: Pseudo dice [0.9577, 0.9482, 0.977, 0.9399, 0.8422, 0.9588, 0.989] +2024-09-05 19:03:05.985269: Epoch time: 293.38 s +2024-09-05 19:03:08.788787: +2024-09-05 19:03:08.790200: Epoch 177 +2024-09-05 19:03:08.791935: Current learning rate: 0.00839 +2024-09-05 19:08:04.795175: train_loss -0.7198 +2024-09-05 19:08:04.798770: val_loss -0.6857 +2024-09-05 19:08:04.799721: Pseudo dice [0.9595, 0.954, 0.9746, 0.9363, 0.8578, 0.9612, 0.9866] +2024-09-05 19:08:04.801000: Epoch time: 296.01 s +2024-09-05 19:08:07.896269: +2024-09-05 19:08:07.898165: Epoch 178 +2024-09-05 19:08:07.899421: Current learning rate: 0.00838 +2024-09-05 19:13:16.986581: train_loss -0.7166 +2024-09-05 19:13:16.994135: val_loss -0.7252 +2024-09-05 19:13:16.995075: Pseudo dice [0.9579, 0.9507, 0.9751, 0.9389, 0.8474, 0.955, 0.9892] +2024-09-05 19:13:16.995995: Epoch time: 309.1 s +2024-09-05 19:13:19.860654: +2024-09-05 19:13:19.863173: Epoch 179 +2024-09-05 19:13:19.863966: Current learning rate: 0.00837 +2024-09-05 19:18:18.341134: train_loss -0.7059 +2024-09-05 19:18:18.343864: val_loss -0.7053 +2024-09-05 19:18:18.344598: Pseudo dice [0.9556, 0.9513, 0.9766, 0.9349, 0.8407, 0.948, 0.9888] +2024-09-05 19:18:18.345456: Epoch time: 298.49 s +2024-09-05 19:18:21.275726: +2024-09-05 19:18:21.276885: Epoch 180 +2024-09-05 19:18:21.277759: Current learning rate: 0.00836 +2024-09-05 19:23:11.084792: train_loss -0.7107 +2024-09-05 19:23:11.086635: val_loss -0.7389 +2024-09-05 19:23:11.087657: Pseudo dice [0.9609, 0.9537, 0.9775, 0.9455, 0.8594, 0.9622, 0.9904] +2024-09-05 19:23:11.088973: Epoch time: 289.82 s +2024-09-05 19:23:13.797527: +2024-09-05 19:23:13.798673: Epoch 181 +2024-09-05 19:23:13.799311: Current learning rate: 0.00836 +2024-09-05 19:28:03.306115: train_loss -0.7166 +2024-09-05 19:28:03.310154: val_loss -0.7041 +2024-09-05 19:28:03.311154: Pseudo dice [0.9536, 0.9468, 0.9753, 0.9434, 0.8499, 0.9587, 0.9852] +2024-09-05 19:28:03.311821: Epoch time: 289.51 s +2024-09-05 19:28:05.094583: +2024-09-05 19:28:05.106780: Epoch 182 +2024-09-05 19:28:05.129761: Current learning rate: 0.00835 +2024-09-05 19:33:06.767954: train_loss -0.7196 +2024-09-05 19:33:06.770571: val_loss -0.7105 +2024-09-05 19:33:06.771456: Pseudo dice [0.9562, 0.9524, 0.9737, 0.9429, 0.8422, 0.9608, 0.9875] +2024-09-05 19:33:06.772255: Epoch time: 301.68 s +2024-09-05 19:33:09.328145: +2024-09-05 19:33:09.329277: Epoch 183 +2024-09-05 19:33:09.330016: Current learning rate: 0.00834 +2024-09-05 19:38:13.818111: train_loss -0.7161 +2024-09-05 19:38:13.820596: val_loss -0.7249 +2024-09-05 19:38:13.821902: Pseudo dice [0.9563, 0.9546, 0.9719, 0.9293, 0.843, 0.9395, 0.9894] +2024-09-05 19:38:13.822969: Epoch time: 304.5 s +2024-09-05 19:38:16.304637: +2024-09-05 19:38:16.306035: Epoch 184 +2024-09-05 19:38:16.306870: Current learning rate: 0.00833 +2024-09-05 19:43:08.959489: train_loss -0.716 +2024-09-05 19:43:08.966759: val_loss -0.7393 +2024-09-05 19:43:08.968566: Pseudo dice [0.9605, 0.9519, 0.9775, 0.9432, 0.8544, 0.9625, 0.9896] +2024-09-05 19:43:08.971541: Epoch time: 292.66 s +2024-09-05 19:43:11.787981: +2024-09-05 19:43:11.802578: Epoch 185 +2024-09-05 19:43:11.803469: Current learning rate: 0.00832 +2024-09-05 19:48:04.631490: train_loss -0.7162 +2024-09-05 19:48:04.648006: val_loss -0.7484 +2024-09-05 19:48:04.649102: Pseudo dice [0.9618, 0.953, 0.9796, 0.9453, 0.866, 0.9612, 0.9899] +2024-09-05 19:48:04.650046: Epoch time: 292.85 s +2024-09-05 19:48:08.548029: +2024-09-05 19:48:08.549146: Epoch 186 +2024-09-05 19:48:08.549885: Current learning rate: 0.00831 +2024-09-05 19:53:19.028008: train_loss -0.7317 +2024-09-05 19:53:19.031244: val_loss -0.6991 +2024-09-05 19:53:19.032429: Pseudo dice [0.9619, 0.951, 0.9784, 0.9402, 0.8599, 0.9659, 0.9902] +2024-09-05 19:53:19.033651: Epoch time: 310.49 s +2024-09-05 19:53:21.686572: +2024-09-05 19:53:21.687691: Epoch 187 +2024-09-05 19:53:21.688583: Current learning rate: 0.0083 +2024-09-05 19:58:19.506046: train_loss -0.7072 +2024-09-05 19:58:19.512932: val_loss -0.7106 +2024-09-05 19:58:19.515827: Pseudo dice [0.9618, 0.9524, 0.9776, 0.9472, 0.863, 0.9644, 0.9881] +2024-09-05 19:58:19.518199: Epoch time: 297.83 s +2024-09-05 19:58:22.317222: +2024-09-05 19:58:22.331334: Epoch 188 +2024-09-05 19:58:22.333735: Current learning rate: 0.00829 +2024-09-05 20:02:56.163078: train_loss -0.7244 +2024-09-05 20:02:56.184625: val_loss -0.7526 +2024-09-05 20:02:56.186074: Pseudo dice [0.9585, 0.9475, 0.9768, 0.9457, 0.8575, 0.9638, 0.9908] +2024-09-05 20:02:56.188075: Epoch time: 273.85 s +2024-09-05 20:02:59.312097: +2024-09-05 20:02:59.315305: Epoch 189 +2024-09-05 20:02:59.317619: Current learning rate: 0.00828 +2024-09-05 20:08:16.031520: train_loss -0.7224 +2024-09-05 20:08:16.033214: val_loss -0.7202 +2024-09-05 20:08:16.034182: Pseudo dice [0.9561, 0.9533, 0.973, 0.9367, 0.8472, 0.9504, 0.9867] +2024-09-05 20:08:16.034968: Epoch time: 316.73 s +2024-09-05 20:08:18.658028: +2024-09-05 20:08:18.659136: Epoch 190 +2024-09-05 20:08:18.659982: Current learning rate: 0.00827 +2024-09-05 20:13:14.787448: train_loss -0.7066 +2024-09-05 20:13:14.788872: val_loss -0.7309 +2024-09-05 20:13:14.789662: Pseudo dice [0.9581, 0.9484, 0.9743, 0.9346, 0.8528, 0.9609, 0.9901] +2024-09-05 20:13:14.790435: Epoch time: 296.14 s +2024-09-05 20:13:20.406848: +2024-09-05 20:13:20.408874: Epoch 191 +2024-09-05 20:13:20.409744: Current learning rate: 0.00826 +2024-09-05 20:18:11.034729: train_loss -0.7162 +2024-09-05 20:18:11.037455: val_loss -0.6978 +2024-09-05 20:18:11.038411: Pseudo dice [0.9601, 0.9533, 0.97, 0.9422, 0.8616, 0.9608, 0.9886] +2024-09-05 20:18:11.039357: Epoch time: 290.63 s +2024-09-05 20:18:13.698372: +2024-09-05 20:18:13.699559: Epoch 192 +2024-09-05 20:18:13.700414: Current learning rate: 0.00825 +2024-09-05 20:22:41.568854: train_loss -0.7175 +2024-09-05 20:22:41.571423: val_loss -0.7349 +2024-09-05 20:22:41.572232: Pseudo dice [0.9602, 0.9515, 0.9794, 0.9494, 0.8658, 0.9617, 0.9883] +2024-09-05 20:22:41.572966: Epoch time: 267.88 s +2024-09-05 20:22:41.573581: Yayy! New best EMA pseudo Dice: 0.9461 +2024-09-05 20:22:49.669066: +2024-09-05 20:22:49.670355: Epoch 193 +2024-09-05 20:22:49.671318: Current learning rate: 0.00824 +2024-09-05 20:27:45.617928: train_loss -0.7177 +2024-09-05 20:27:45.643369: val_loss -0.7194 +2024-09-05 20:27:45.644147: Pseudo dice [0.9565, 0.953, 0.9694, 0.9398, 0.8533, 0.9572, 0.9886] +2024-09-05 20:27:45.644924: Epoch time: 295.95 s +2024-09-05 20:27:48.703001: +2024-09-05 20:27:48.704185: Epoch 194 +2024-09-05 20:27:48.705006: Current learning rate: 0.00824 +2024-09-05 20:32:44.072810: train_loss -0.7184 +2024-09-05 20:32:44.095464: val_loss -0.7262 +2024-09-05 20:32:44.096206: Pseudo dice [0.9593, 0.9566, 0.9752, 0.9453, 0.8603, 0.9586, 0.9899] +2024-09-05 20:32:44.096908: Epoch time: 295.38 s +2024-09-05 20:32:44.097409: Yayy! New best EMA pseudo Dice: 0.9464 +2024-09-05 20:32:52.997644: +2024-09-05 20:32:52.998940: Epoch 195 +2024-09-05 20:32:53.000013: Current learning rate: 0.00823 +2024-09-05 20:37:34.965105: train_loss -0.707 +2024-09-05 20:37:34.980283: val_loss -0.7139 +2024-09-05 20:37:34.981395: Pseudo dice [0.9552, 0.9496, 0.974, 0.9416, 0.8555, 0.9626, 0.9872] +2024-09-05 20:37:34.982595: Epoch time: 281.97 s +2024-09-05 20:37:34.983679: Yayy! New best EMA pseudo Dice: 0.9464 +2024-09-05 20:37:44.225758: +2024-09-05 20:37:44.226894: Epoch 196 +2024-09-05 20:37:44.227674: Current learning rate: 0.00822 +2024-09-05 20:42:42.959352: train_loss -0.7099 +2024-09-05 20:42:42.976209: val_loss -0.7244 +2024-09-05 20:42:42.976979: Pseudo dice [0.9592, 0.9501, 0.976, 0.9417, 0.8597, 0.9638, 0.9898] +2024-09-05 20:42:42.978330: Epoch time: 298.74 s +2024-09-05 20:42:42.978946: Yayy! New best EMA pseudo Dice: 0.9466 +2024-09-05 20:42:52.891050: +2024-09-05 20:42:52.892356: Epoch 197 +2024-09-05 20:42:52.893057: Current learning rate: 0.00821 +2024-09-05 20:47:47.942312: train_loss -0.7054 +2024-09-05 20:47:47.959987: val_loss -0.6963 +2024-09-05 20:47:47.960710: Pseudo dice [0.9577, 0.9499, 0.9721, 0.9405, 0.8555, 0.9655, 0.9897] +2024-09-05 20:47:47.961491: Epoch time: 295.06 s +2024-09-05 20:47:47.962097: Yayy! New best EMA pseudo Dice: 0.9467 +2024-09-05 20:47:55.651182: +2024-09-05 20:47:55.652272: Epoch 198 +2024-09-05 20:47:55.653009: Current learning rate: 0.0082 +2024-09-05 20:52:35.850996: train_loss -0.7227 +2024-09-05 20:52:35.853804: val_loss -0.7324 +2024-09-05 20:52:35.854800: Pseudo dice [0.9585, 0.9539, 0.977, 0.9459, 0.8569, 0.9601, 0.9898] +2024-09-05 20:52:35.855587: Epoch time: 280.2 s +2024-09-05 20:52:35.856343: Yayy! New best EMA pseudo Dice: 0.9469 +2024-09-05 20:52:45.939383: +2024-09-05 20:52:45.940439: Epoch 199 +2024-09-05 20:52:45.941283: Current learning rate: 0.00819 +2024-09-05 20:57:37.682140: train_loss -0.7131 +2024-09-05 20:57:37.700161: val_loss -0.7161 +2024-09-05 20:57:37.700987: Pseudo dice [0.9578, 0.9516, 0.9733, 0.9411, 0.8464, 0.953, 0.9839] +2024-09-05 20:57:37.701706: Epoch time: 291.75 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_00_30_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_00_30_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7deb512bac65f0af43559e786a4d046af42e64c --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_00_30_15.txt @@ -0,0 +1,377 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 00:30:18.748406: Using torch.compile... +2024-09-06 00:30:29.672037: do_dummy_2d_data_aug: False +2024-09-06 00:30:29.679499: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 00:30:29.681705: The split file contains 5 splits. +2024-09-06 00:30:29.682391: Desired fold for training: 0 +2024-09-06 00:30:29.682986: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 00:30:43.502952: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 00:30:45.356498: +2024-09-06 00:30:45.357683: Epoch 200 +2024-09-06 00:30:45.359113: Current learning rate: 0.00818 +2024-09-06 00:49:19.709007: train_loss -0.7197 +2024-09-06 00:49:19.710448: val_loss -0.733 +2024-09-06 00:49:19.711691: Pseudo dice [0.9567, 0.9472, 0.9738, 0.932, 0.8681, 0.9632, 0.9901] +2024-09-06 00:49:19.712334: Epoch time: 1114.36 s +2024-09-06 00:49:21.535512: +2024-09-06 00:49:21.536278: Epoch 201 +2024-09-06 00:49:21.536889: Current learning rate: 0.00817 +2024-09-06 00:54:12.670696: train_loss -0.7038 +2024-09-06 00:54:12.673242: val_loss -0.7393 +2024-09-06 00:54:12.677469: Pseudo dice [0.9578, 0.9503, 0.9777, 0.9384, 0.8552, 0.9643, 0.9903] +2024-09-06 00:54:12.679554: Epoch time: 291.14 s +2024-09-06 00:54:16.312773: +2024-09-06 00:54:16.314003: Epoch 202 +2024-09-06 00:54:16.315553: Current learning rate: 0.00816 +2024-09-06 00:59:16.935077: train_loss -0.7186 +2024-09-06 00:59:16.951245: val_loss -0.7484 +2024-09-06 00:59:16.952184: Pseudo dice [0.9626, 0.9543, 0.9783, 0.9441, 0.861, 0.9609, 0.9901] +2024-09-06 00:59:16.953223: Epoch time: 300.63 s +2024-09-06 00:59:16.954887: Yayy! New best EMA pseudo Dice: 0.9471 +2024-09-06 00:59:25.665431: +2024-09-06 00:59:25.666492: Epoch 203 +2024-09-06 00:59:25.667216: Current learning rate: 0.00815 +2024-09-06 01:04:22.590609: train_loss -0.7265 +2024-09-06 01:04:22.611345: val_loss -0.7707 +2024-09-06 01:04:22.612344: Pseudo dice [0.9597, 0.9549, 0.9781, 0.9449, 0.8552, 0.9652, 0.9892] +2024-09-06 01:04:22.613173: Epoch time: 296.93 s +2024-09-06 01:04:22.613755: Yayy! New best EMA pseudo Dice: 0.9474 +2024-09-06 01:04:31.702869: +2024-09-06 01:04:31.704043: Epoch 204 +2024-09-06 01:04:31.704952: Current learning rate: 0.00814 +2024-09-06 01:09:34.823595: train_loss -0.7182 +2024-09-06 01:09:34.850940: val_loss -0.726 +2024-09-06 01:09:34.851900: Pseudo dice [0.9605, 0.9535, 0.9756, 0.9417, 0.8644, 0.9618, 0.9899] +2024-09-06 01:09:34.853168: Epoch time: 303.13 s +2024-09-06 01:09:34.854176: Yayy! New best EMA pseudo Dice: 0.9476 +2024-09-06 01:09:45.984502: +2024-09-06 01:09:45.985740: Epoch 205 +2024-09-06 01:09:45.986832: Current learning rate: 0.00813 +2024-09-06 01:14:25.190864: train_loss -0.7277 +2024-09-06 01:14:25.196058: val_loss -0.721 +2024-09-06 01:14:25.197195: Pseudo dice [0.9602, 0.9557, 0.9772, 0.9456, 0.8707, 0.9666, 0.9909] +2024-09-06 01:14:25.198205: Epoch time: 279.21 s +2024-09-06 01:14:25.199691: Yayy! New best EMA pseudo Dice: 0.9481 +2024-09-06 01:14:33.905180: +2024-09-06 01:14:33.906341: Epoch 206 +2024-09-06 01:14:33.908055: Current learning rate: 0.00813 +2024-09-06 01:19:08.363782: train_loss -0.7338 +2024-09-06 01:19:08.380578: val_loss -0.7317 +2024-09-06 01:19:08.382201: Pseudo dice [0.961, 0.9544, 0.9781, 0.9375, 0.8539, 0.9616, 0.9868] +2024-09-06 01:19:08.383540: Epoch time: 274.46 s +2024-09-06 01:19:11.438634: +2024-09-06 01:19:11.439728: Epoch 207 +2024-09-06 01:19:11.440758: Current learning rate: 0.00812 +2024-09-06 01:23:46.682500: train_loss -0.7149 +2024-09-06 01:23:46.695547: val_loss -0.761 +2024-09-06 01:23:46.696697: Pseudo dice [0.9635, 0.9551, 0.9775, 0.9416, 0.8663, 0.9649, 0.9907] +2024-09-06 01:23:46.697685: Epoch time: 275.25 s +2024-09-06 01:23:46.698786: Yayy! New best EMA pseudo Dice: 0.9484 +2024-09-06 01:23:56.647382: +2024-09-06 01:23:56.648497: Epoch 208 +2024-09-06 01:23:56.650118: Current learning rate: 0.00811 +2024-09-06 01:28:42.299994: train_loss -0.7252 +2024-09-06 01:28:42.327152: val_loss -0.717 +2024-09-06 01:28:42.328504: Pseudo dice [0.9606, 0.9531, 0.9734, 0.9372, 0.8487, 0.9592, 0.9917] +2024-09-06 01:28:42.330049: Epoch time: 285.66 s +2024-09-06 01:28:46.592069: +2024-09-06 01:28:46.593695: Epoch 209 +2024-09-06 01:28:46.595210: Current learning rate: 0.0081 +2024-09-06 01:33:34.728768: train_loss -0.7193 +2024-09-06 01:33:34.743100: val_loss -0.745 +2024-09-06 01:33:34.744865: Pseudo dice [0.9571, 0.9528, 0.9724, 0.935, 0.844, 0.9449, 0.9897] +2024-09-06 01:33:34.746463: Epoch time: 288.14 s +2024-09-06 01:33:40.208870: +2024-09-06 01:33:40.210091: Epoch 210 +2024-09-06 01:33:40.210822: Current learning rate: 0.00809 +2024-09-06 01:38:43.422899: train_loss -0.7174 +2024-09-06 01:38:43.476975: val_loss -0.7184 +2024-09-06 01:38:43.479088: Pseudo dice [0.9598, 0.951, 0.9764, 0.9406, 0.8564, 0.9613, 0.991] +2024-09-06 01:38:43.480343: Epoch time: 303.22 s +2024-09-06 01:38:46.928847: +2024-09-06 01:38:46.930384: Epoch 211 +2024-09-06 01:38:46.931407: Current learning rate: 0.00808 +2024-09-06 01:43:29.223972: train_loss -0.722 +2024-09-06 01:43:29.225368: val_loss -0.6892 +2024-09-06 01:43:29.227592: Pseudo dice [0.9569, 0.9529, 0.9702, 0.9421, 0.8625, 0.962, 0.9861] +2024-09-06 01:43:29.229423: Epoch time: 282.3 s +2024-09-06 01:43:32.972460: +2024-09-06 01:43:32.973629: Epoch 212 +2024-09-06 01:43:32.975618: Current learning rate: 0.00807 +2024-09-06 01:48:31.792915: train_loss -0.7225 +2024-09-06 01:48:31.807120: val_loss -0.6823 +2024-09-06 01:48:31.808671: Pseudo dice [0.9581, 0.9538, 0.9748, 0.9417, 0.855, 0.9582, 0.9885] +2024-09-06 01:48:31.810430: Epoch time: 298.83 s +2024-09-06 01:48:36.098278: +2024-09-06 01:48:36.255568: Epoch 213 +2024-09-06 01:48:36.352691: Current learning rate: 0.00806 +2024-09-06 01:53:45.125396: train_loss -0.7309 +2024-09-06 01:53:45.139587: val_loss -0.7716 +2024-09-06 01:53:45.141289: Pseudo dice [0.9612, 0.9522, 0.9781, 0.9438, 0.87, 0.9655, 0.9915] +2024-09-06 01:53:45.142573: Epoch time: 309.03 s +2024-09-06 01:53:47.747496: +2024-09-06 01:53:47.748860: Epoch 214 +2024-09-06 01:53:47.749963: Current learning rate: 0.00805 +2024-09-06 01:58:41.096746: train_loss -0.7315 +2024-09-06 01:58:41.111232: val_loss -0.7046 +2024-09-06 01:58:41.112204: Pseudo dice [0.9603, 0.9544, 0.974, 0.9436, 0.8421, 0.9626, 0.9884] +2024-09-06 01:58:41.113263: Epoch time: 293.36 s +2024-09-06 01:58:44.247914: +2024-09-06 01:58:44.249413: Epoch 215 +2024-09-06 01:58:44.250449: Current learning rate: 0.00804 +2024-09-06 02:03:40.158468: train_loss -0.7405 +2024-09-06 02:03:40.175660: val_loss -0.752 +2024-09-06 02:03:40.176786: Pseudo dice [0.9614, 0.9549, 0.9783, 0.9431, 0.8612, 0.9656, 0.9886] +2024-09-06 02:03:40.178319: Epoch time: 295.92 s +2024-09-06 02:03:43.976397: +2024-09-06 02:03:43.977557: Epoch 216 +2024-09-06 02:03:43.978547: Current learning rate: 0.00803 +2024-09-06 02:08:27.727669: train_loss -0.747 +2024-09-06 02:08:27.729919: val_loss -0.7183 +2024-09-06 02:08:27.731541: Pseudo dice [0.9617, 0.9572, 0.976, 0.9423, 0.862, 0.9638, 0.991] +2024-09-06 02:08:27.732474: Epoch time: 283.76 s +2024-09-06 02:08:30.704503: +2024-09-06 02:08:30.705905: Epoch 217 +2024-09-06 02:08:30.706978: Current learning rate: 0.00802 +2024-09-06 02:13:27.771409: train_loss -0.7343 +2024-09-06 02:13:27.782614: val_loss -0.7073 +2024-09-06 02:13:27.783693: Pseudo dice [0.9591, 0.9521, 0.9771, 0.9416, 0.8716, 0.9659, 0.9896] +2024-09-06 02:13:27.784934: Epoch time: 297.07 s +2024-09-06 02:13:27.786247: Yayy! New best EMA pseudo Dice: 0.9486 +2024-09-06 02:13:36.575148: +2024-09-06 02:13:36.576195: Epoch 218 +2024-09-06 02:13:36.576971: Current learning rate: 0.00801 +2024-09-06 02:18:04.448159: train_loss -0.7215 +2024-09-06 02:18:04.482294: val_loss -0.7129 +2024-09-06 02:18:04.483432: Pseudo dice [0.9524, 0.9461, 0.9647, 0.943, 0.8433, 0.9586, 0.9836] +2024-09-06 02:18:04.484507: Epoch time: 267.88 s +2024-09-06 02:18:08.683927: +2024-09-06 02:18:08.684886: Epoch 219 +2024-09-06 02:18:08.686263: Current learning rate: 0.00801 +2024-09-06 02:23:01.208061: train_loss -0.7232 +2024-09-06 02:23:01.213077: val_loss -0.7058 +2024-09-06 02:23:01.214100: Pseudo dice [0.9584, 0.9512, 0.9756, 0.9407, 0.8347, 0.9534, 0.9866] +2024-09-06 02:23:01.215068: Epoch time: 292.53 s +2024-09-06 02:23:04.427767: +2024-09-06 02:23:04.442731: Epoch 220 +2024-09-06 02:23:04.443924: Current learning rate: 0.008 +2024-09-06 02:28:04.658054: train_loss -0.7306 +2024-09-06 02:28:04.659988: val_loss -0.767 +2024-09-06 02:28:04.661466: Pseudo dice [0.9531, 0.9494, 0.9735, 0.9287, 0.8436, 0.9579, 0.9868] +2024-09-06 02:28:04.663083: Epoch time: 300.24 s +2024-09-06 02:28:07.331052: +2024-09-06 02:28:07.332044: Epoch 221 +2024-09-06 02:28:07.333454: Current learning rate: 0.00799 +2024-09-06 02:33:06.074684: train_loss -0.7285 +2024-09-06 02:33:06.095173: val_loss -0.7035 +2024-09-06 02:33:06.096123: Pseudo dice [0.96, 0.9538, 0.9767, 0.9435, 0.8704, 0.9652, 0.992] +2024-09-06 02:33:06.097165: Epoch time: 298.75 s +2024-09-06 02:33:08.784328: +2024-09-06 02:33:08.785387: Epoch 222 +2024-09-06 02:33:08.786337: Current learning rate: 0.00798 +2024-09-06 02:37:48.663088: train_loss -0.7141 +2024-09-06 02:37:48.682818: val_loss -0.7088 +2024-09-06 02:37:48.684209: Pseudo dice [0.9564, 0.9524, 0.9715, 0.93, 0.8552, 0.9594, 0.9866] +2024-09-06 02:37:48.690876: Epoch time: 279.88 s +2024-09-06 02:37:54.499005: +2024-09-06 02:37:54.516167: Epoch 223 +2024-09-06 02:37:54.517303: Current learning rate: 0.00797 +2024-09-06 02:42:48.628111: train_loss -0.7296 +2024-09-06 02:42:48.634796: val_loss -0.7522 +2024-09-06 02:42:48.637048: Pseudo dice [0.9592, 0.9524, 0.9758, 0.941, 0.8634, 0.9584, 0.9897] +2024-09-06 02:42:48.639147: Epoch time: 294.13 s +2024-09-06 02:42:51.450813: +2024-09-06 02:42:51.453963: Epoch 224 +2024-09-06 02:42:51.456500: Current learning rate: 0.00796 +2024-09-06 02:47:28.958263: train_loss -0.7189 +2024-09-06 02:47:28.962323: val_loss -0.7292 +2024-09-06 02:47:28.963392: Pseudo dice [0.962, 0.9535, 0.98, 0.9442, 0.8649, 0.966, 0.9899] +2024-09-06 02:47:28.964371: Epoch time: 277.52 s +2024-09-06 02:47:31.409544: +2024-09-06 02:47:31.417783: Epoch 225 +2024-09-06 02:47:31.419326: Current learning rate: 0.00795 +2024-09-06 02:52:15.611368: train_loss -0.701 +2024-09-06 02:52:15.630230: val_loss -0.6924 +2024-09-06 02:52:15.632282: Pseudo dice [0.9542, 0.9508, 0.9653, 0.9333, 0.8425, 0.954, 0.9887] +2024-09-06 02:52:15.633504: Epoch time: 284.21 s +2024-09-06 02:52:18.359162: +2024-09-06 02:52:18.360061: Epoch 226 +2024-09-06 02:52:18.361582: Current learning rate: 0.00794 +2024-09-06 02:57:34.823433: train_loss -0.7236 +2024-09-06 02:57:34.827872: val_loss -0.735 +2024-09-06 02:57:34.830027: Pseudo dice [0.9557, 0.95, 0.9723, 0.942, 0.8728, 0.9621, 0.9892] +2024-09-06 02:57:34.852192: Epoch time: 316.47 s +2024-09-06 02:57:37.726950: +2024-09-06 02:57:37.728012: Epoch 227 +2024-09-06 02:57:37.728767: Current learning rate: 0.00793 +2024-09-06 03:02:49.217053: train_loss -0.7136 +2024-09-06 03:02:49.223451: val_loss -0.7356 +2024-09-06 03:02:49.224415: Pseudo dice [0.9621, 0.9528, 0.9747, 0.9394, 0.8642, 0.9637, 0.9902] +2024-09-06 03:02:49.225380: Epoch time: 311.5 s +2024-09-06 03:02:52.175227: +2024-09-06 03:02:52.176430: Epoch 228 +2024-09-06 03:02:52.177474: Current learning rate: 0.00792 +2024-09-06 03:07:36.578289: train_loss -0.7106 +2024-09-06 03:07:36.591661: val_loss -0.7004 +2024-09-06 03:07:36.592958: Pseudo dice [0.9532, 0.9501, 0.9726, 0.9355, 0.8482, 0.9599, 0.9857] +2024-09-06 03:07:36.594723: Epoch time: 284.41 s +2024-09-06 03:07:40.619189: +2024-09-06 03:07:40.624582: Epoch 229 +2024-09-06 03:07:40.630245: Current learning rate: 0.00791 +2024-09-06 03:12:59.078507: train_loss -0.7228 +2024-09-06 03:12:59.102506: val_loss -0.7147 +2024-09-06 03:12:59.104485: Pseudo dice [0.9609, 0.9523, 0.9737, 0.9393, 0.8482, 0.9636, 0.9879] +2024-09-06 03:12:59.106255: Epoch time: 318.46 s +2024-09-06 03:13:01.829627: +2024-09-06 03:13:01.831453: Epoch 230 +2024-09-06 03:13:01.833091: Current learning rate: 0.0079 +2024-09-06 03:18:08.206165: train_loss -0.7167 +2024-09-06 03:18:08.221507: val_loss -0.7578 +2024-09-06 03:18:08.222798: Pseudo dice [0.9592, 0.9512, 0.9774, 0.9445, 0.8571, 0.963, 0.9868] +2024-09-06 03:18:08.224926: Epoch time: 306.38 s +2024-09-06 03:18:11.530344: +2024-09-06 03:18:11.531760: Epoch 231 +2024-09-06 03:18:11.533835: Current learning rate: 0.00789 +2024-09-06 03:23:18.466065: train_loss -0.7209 +2024-09-06 03:23:18.482060: val_loss -0.7237 +2024-09-06 03:23:18.483339: Pseudo dice [0.9565, 0.9531, 0.9707, 0.9408, 0.8483, 0.9613, 0.9877] +2024-09-06 03:23:18.485325: Epoch time: 306.94 s +2024-09-06 03:23:22.611795: +2024-09-06 03:23:22.612975: Epoch 232 +2024-09-06 03:23:22.613734: Current learning rate: 0.00789 +2024-09-06 03:28:31.924875: train_loss -0.7077 +2024-09-06 03:28:31.939265: val_loss -0.747 +2024-09-06 03:28:31.940355: Pseudo dice [0.9597, 0.9531, 0.9758, 0.9456, 0.859, 0.964, 0.9912] +2024-09-06 03:28:31.941232: Epoch time: 309.32 s +2024-09-06 03:28:34.487183: +2024-09-06 03:28:34.488476: Epoch 233 +2024-09-06 03:28:34.490498: Current learning rate: 0.00788 +2024-09-06 03:33:29.443756: train_loss -0.723 +2024-09-06 03:33:29.459399: val_loss -0.7141 +2024-09-06 03:33:29.460609: Pseudo dice [0.9608, 0.9545, 0.9747, 0.9371, 0.8335, 0.9557, 0.9842] +2024-09-06 03:33:29.461631: Epoch time: 294.96 s +2024-09-06 03:33:31.879740: +2024-09-06 03:33:31.882222: Epoch 234 +2024-09-06 03:33:31.884731: Current learning rate: 0.00787 +2024-09-06 03:38:07.175757: train_loss -0.7192 +2024-09-06 03:38:07.191338: val_loss -0.7099 +2024-09-06 03:38:07.194007: Pseudo dice [0.9595, 0.9535, 0.9732, 0.9395, 0.8703, 0.9694, 0.9914] +2024-09-06 03:38:07.195677: Epoch time: 275.3 s +2024-09-06 03:38:10.188084: +2024-09-06 03:38:10.189098: Epoch 235 +2024-09-06 03:38:10.191091: Current learning rate: 0.00786 +2024-09-06 03:43:12.383831: train_loss -0.7122 +2024-09-06 03:43:12.385934: val_loss -0.734 +2024-09-06 03:43:12.387510: Pseudo dice [0.9613, 0.9547, 0.9762, 0.9403, 0.8598, 0.968, 0.9903] +2024-09-06 03:43:12.389002: Epoch time: 302.2 s +2024-09-06 03:43:16.614503: +2024-09-06 03:43:16.617566: Epoch 236 +2024-09-06 03:43:16.619176: Current learning rate: 0.00785 +2024-09-06 03:48:17.312998: train_loss -0.7405 +2024-09-06 03:48:17.319325: val_loss -0.7292 +2024-09-06 03:48:17.320494: Pseudo dice [0.9602, 0.9511, 0.9755, 0.9454, 0.8614, 0.9646, 0.9909] +2024-09-06 03:48:17.321475: Epoch time: 300.71 s +2024-09-06 03:48:20.016560: +2024-09-06 03:48:20.017827: Epoch 237 +2024-09-06 03:48:20.018931: Current learning rate: 0.00784 +2024-09-06 03:53:21.942369: train_loss -0.7195 +2024-09-06 03:53:21.959852: val_loss -0.6972 +2024-09-06 03:53:21.961850: Pseudo dice [0.9615, 0.9556, 0.9771, 0.9444, 0.87, 0.9622, 0.9903] +2024-09-06 03:53:21.963533: Epoch time: 301.93 s +2024-09-06 03:53:26.634168: +2024-09-06 03:53:26.635419: Epoch 238 +2024-09-06 03:53:26.636423: Current learning rate: 0.00783 +2024-09-06 03:57:58.709191: train_loss -0.7193 +2024-09-06 03:57:58.726895: val_loss -0.7081 +2024-09-06 03:57:58.728381: Pseudo dice [0.9549, 0.9471, 0.97, 0.9341, 0.8602, 0.962, 0.9871] +2024-09-06 03:57:58.729815: Epoch time: 272.08 s +2024-09-06 03:58:01.693040: +2024-09-06 03:58:01.698633: Epoch 239 +2024-09-06 03:58:01.700191: Current learning rate: 0.00782 +2024-09-06 04:03:01.324342: train_loss -0.6985 +2024-09-06 04:03:01.339963: val_loss -0.6968 +2024-09-06 04:03:01.341563: Pseudo dice [0.9527, 0.9425, 0.9702, 0.9371, 0.8479, 0.9547, 0.989] +2024-09-06 04:03:01.342995: Epoch time: 299.64 s +2024-09-06 04:03:05.464072: +2024-09-06 04:03:05.465962: Epoch 240 +2024-09-06 04:03:05.468151: Current learning rate: 0.00781 +2024-09-06 04:08:20.821044: train_loss -0.6949 +2024-09-06 04:08:20.836121: val_loss -0.7003 +2024-09-06 04:08:20.837790: Pseudo dice [0.9547, 0.9469, 0.9675, 0.9291, 0.8406, 0.9584, 0.989] +2024-09-06 04:08:20.839037: Epoch time: 315.36 s +2024-09-06 04:08:25.238890: +2024-09-06 04:08:25.240288: Epoch 241 +2024-09-06 04:08:25.241644: Current learning rate: 0.0078 +2024-09-06 04:13:14.862656: train_loss -0.7019 +2024-09-06 04:13:14.878060: val_loss -0.7327 +2024-09-06 04:13:14.879256: Pseudo dice [0.9559, 0.9491, 0.9695, 0.9288, 0.8411, 0.9583, 0.9861] +2024-09-06 04:13:14.880452: Epoch time: 289.63 s +2024-09-06 04:13:18.917195: +2024-09-06 04:13:18.918253: Epoch 242 +2024-09-06 04:13:18.919509: Current learning rate: 0.00779 +2024-09-06 04:18:15.342429: train_loss -0.7147 +2024-09-06 04:18:15.366108: val_loss -0.7507 +2024-09-06 04:18:15.367650: Pseudo dice [0.9593, 0.9511, 0.9751, 0.9418, 0.8555, 0.962, 0.9866] +2024-09-06 04:18:15.369013: Epoch time: 296.43 s +2024-09-06 04:18:18.731974: +2024-09-06 04:18:18.733001: Epoch 243 +2024-09-06 04:18:18.733729: Current learning rate: 0.00778 +2024-09-06 04:23:33.314143: train_loss -0.7194 +2024-09-06 04:23:33.324331: val_loss -0.7062 +2024-09-06 04:23:33.325703: Pseudo dice [0.9618, 0.9554, 0.978, 0.9435, 0.8561, 0.962, 0.9889] +2024-09-06 04:23:33.327491: Epoch time: 314.59 s +2024-09-06 04:23:36.664651: +2024-09-06 04:23:36.665737: Epoch 244 +2024-09-06 04:23:36.666528: Current learning rate: 0.00777 +2024-09-06 04:28:27.404965: train_loss -0.7092 +2024-09-06 04:28:27.419681: val_loss -0.6862 +2024-09-06 04:28:27.421154: Pseudo dice [0.9565, 0.9525, 0.9753, 0.9361, 0.83, 0.9621, 0.9877] +2024-09-06 04:28:27.422619: Epoch time: 290.75 s +2024-09-06 04:28:29.854615: +2024-09-06 04:28:29.867332: Epoch 245 +2024-09-06 04:28:29.868367: Current learning rate: 0.00777 +2024-09-06 04:33:44.739736: train_loss -0.7166 +2024-09-06 04:33:44.759472: val_loss -0.7389 +2024-09-06 04:33:44.760595: Pseudo dice [0.9549, 0.9514, 0.9709, 0.9351, 0.8561, 0.9599, 0.9859] +2024-09-06 04:33:44.761728: Epoch time: 314.89 s +2024-09-06 04:33:48.027031: +2024-09-06 04:33:48.028280: Epoch 246 +2024-09-06 04:33:48.029373: Current learning rate: 0.00776 +2024-09-06 04:38:26.994797: train_loss -0.7002 +2024-09-06 04:38:27.011744: val_loss -0.776 +2024-09-06 04:38:27.013300: Pseudo dice [0.9609, 0.9506, 0.9724, 0.9429, 0.8544, 0.9617, 0.987] +2024-09-06 04:38:27.014641: Epoch time: 278.97 s +2024-09-06 04:38:31.344168: +2024-09-06 04:38:31.354794: Epoch 247 +2024-09-06 04:38:31.355917: Current learning rate: 0.00775 +2024-09-06 04:43:52.012709: train_loss -0.7046 +2024-09-06 04:43:52.039072: val_loss -0.7327 +2024-09-06 04:43:52.040153: Pseudo dice [0.9559, 0.953, 0.9729, 0.9393, 0.8486, 0.9578, 0.9877] +2024-09-06 04:43:52.041215: Epoch time: 320.67 s +2024-09-06 04:43:54.668405: +2024-09-06 04:43:54.669653: Epoch 248 +2024-09-06 04:43:54.670659: Current learning rate: 0.00774 +2024-09-06 04:48:48.780704: train_loss -0.7347 +2024-09-06 04:48:48.801455: val_loss -0.7414 +2024-09-06 04:48:48.802574: Pseudo dice [0.9625, 0.9568, 0.9773, 0.9508, 0.871, 0.9668, 0.9903] +2024-09-06 04:48:48.803625: Epoch time: 294.12 s +2024-09-06 04:48:51.592553: +2024-09-06 04:48:51.593833: Epoch 249 +2024-09-06 04:48:51.595046: Current learning rate: 0.00773 +2024-09-06 04:53:47.614362: train_loss -0.7278 +2024-09-06 04:53:47.631975: val_loss -0.7574 +2024-09-06 04:53:47.633262: Pseudo dice [0.9613, 0.9554, 0.9767, 0.9456, 0.8764, 0.9662, 0.9911] +2024-09-06 04:53:47.634322: Epoch time: 296.03 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_08_29_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_08_29_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..44cbea3e7a769335a5c52236e4aa8f93b6b672b7 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_6_08_29_45.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 08:29:48.994122: Using torch.compile... +2024-09-06 08:29:57.625815: do_dummy_2d_data_aug: False +2024-09-06 08:29:57.632146: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 08:29:57.633682: The split file contains 5 splits. +2024-09-06 08:29:57.634734: Desired fold for training: 0 +2024-09-06 08:29:57.635884: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 08:30:09.015288: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 08:30:10.407605: +2024-09-06 08:30:10.408697: Epoch 250 +2024-09-06 08:30:10.409446: Current learning rate: 0.00772 +2024-09-06 08:48:45.077104: train_loss -0.7309 +2024-09-06 08:48:45.078999: val_loss -0.7392 +2024-09-06 08:48:45.079787: Pseudo dice [0.9574, 0.9516, 0.974, 0.945, 0.8824, 0.9672, 0.9908] +2024-09-06 08:48:45.080860: Epoch time: 1114.68 s +2024-09-06 08:48:46.551185: +2024-09-06 08:48:46.552291: Epoch 251 +2024-09-06 08:48:46.552903: Current learning rate: 0.00771 +2024-09-06 08:53:25.064588: train_loss -0.7123 +2024-09-06 08:53:25.080446: val_loss -0.7484 +2024-09-06 08:53:25.084228: Pseudo dice [0.96, 0.9531, 0.9771, 0.9453, 0.8528, 0.96, 0.9876] +2024-09-06 08:53:25.086016: Epoch time: 278.52 s +2024-09-06 08:53:27.628021: +2024-09-06 08:53:27.629596: Epoch 252 +2024-09-06 08:53:27.630670: Current learning rate: 0.0077 +2024-09-06 08:58:12.139567: train_loss -0.7282 +2024-09-06 08:58:12.142772: val_loss -0.7422 +2024-09-06 08:58:12.143601: Pseudo dice [0.9624, 0.9525, 0.9785, 0.9478, 0.8654, 0.9603, 0.9901] +2024-09-06 08:58:12.144854: Epoch time: 284.52 s +2024-09-06 08:58:14.357054: +2024-09-06 08:58:14.359048: Epoch 253 +2024-09-06 08:58:14.361125: Current learning rate: 0.00769 +2024-09-06 09:02:43.428458: train_loss -0.7206 +2024-09-06 09:02:43.438855: val_loss -0.7234 +2024-09-06 09:02:43.439797: Pseudo dice [0.9577, 0.9511, 0.975, 0.9419, 0.8521, 0.9546, 0.9791] +2024-09-06 09:02:43.440676: Epoch time: 269.08 s +2024-09-06 09:02:45.944849: +2024-09-06 09:02:45.966828: Epoch 254 +2024-09-06 09:02:45.968289: Current learning rate: 0.00768 +2024-09-06 09:07:42.874733: train_loss -0.6864 +2024-09-06 09:07:42.876143: val_loss -0.7068 +2024-09-06 09:07:42.876965: Pseudo dice [0.9587, 0.9508, 0.9745, 0.9405, 0.8433, 0.9564, 0.9859] +2024-09-06 09:07:42.877908: Epoch time: 296.94 s +2024-09-06 09:07:45.470359: +2024-09-06 09:07:45.471290: Epoch 255 +2024-09-06 09:07:45.472020: Current learning rate: 0.00767 +2024-09-06 09:12:30.792439: train_loss -0.6823 +2024-09-06 09:12:30.803426: val_loss -0.6865 +2024-09-06 09:12:30.804416: Pseudo dice [0.9533, 0.949, 0.9676, 0.9323, 0.8391, 0.9475, 0.988] +2024-09-06 09:12:30.805439: Epoch time: 285.33 s +2024-09-06 09:12:33.567458: +2024-09-06 09:12:33.568624: Epoch 256 +2024-09-06 09:12:33.569525: Current learning rate: 0.00766 +2024-09-06 09:17:11.068534: train_loss -0.71 +2024-09-06 09:17:11.103504: val_loss -0.6808 +2024-09-06 09:17:11.104675: Pseudo dice [0.9565, 0.9493, 0.9713, 0.9366, 0.8451, 0.958, 0.99] +2024-09-06 09:17:11.105765: Epoch time: 277.51 s +2024-09-06 09:17:13.822534: +2024-09-06 09:17:13.824080: Epoch 257 +2024-09-06 09:17:13.825274: Current learning rate: 0.00765 +2024-09-06 09:22:06.112202: train_loss -0.7203 +2024-09-06 09:22:06.118097: val_loss -0.7205 +2024-09-06 09:22:06.121712: Pseudo dice [0.9526, 0.9502, 0.967, 0.9344, 0.8394, 0.9418, 0.9887] +2024-09-06 09:22:06.123230: Epoch time: 292.29 s +2024-09-06 09:22:08.493690: +2024-09-06 09:22:08.494943: Epoch 258 +2024-09-06 09:22:08.496368: Current learning rate: 0.00764 +2024-09-06 09:26:49.622883: train_loss -0.6925 +2024-09-06 09:26:49.625492: val_loss -0.7233 +2024-09-06 09:26:49.626437: Pseudo dice [0.9585, 0.9532, 0.9727, 0.9424, 0.8695, 0.963, 0.9902] +2024-09-06 09:26:49.627050: Epoch time: 281.13 s +2024-09-06 09:26:52.523840: +2024-09-06 09:26:52.525406: Epoch 259 +2024-09-06 09:26:52.526544: Current learning rate: 0.00764 +2024-09-06 09:31:27.917837: train_loss -0.7254 +2024-09-06 09:31:27.919382: val_loss -0.7384 +2024-09-06 09:31:27.920216: Pseudo dice [0.9623, 0.9528, 0.9784, 0.9411, 0.8693, 0.9657, 0.9883] +2024-09-06 09:31:27.921021: Epoch time: 275.4 s +2024-09-06 09:31:30.417217: +2024-09-06 09:31:30.418830: Epoch 260 +2024-09-06 09:31:30.420324: Current learning rate: 0.00763 +2024-09-06 09:36:19.646469: train_loss -0.7288 +2024-09-06 09:36:19.665890: val_loss -0.7183 +2024-09-06 09:36:19.667123: Pseudo dice [0.9617, 0.9533, 0.9765, 0.9455, 0.8574, 0.9627, 0.9834] +2024-09-06 09:36:19.668110: Epoch time: 289.23 s +2024-09-06 09:36:21.952087: +2024-09-06 09:36:21.953436: Epoch 261 +2024-09-06 09:36:21.954593: Current learning rate: 0.00762 +2024-09-06 09:41:09.344661: train_loss -0.729 +2024-09-06 09:41:09.356866: val_loss -0.7292 +2024-09-06 09:41:09.358010: Pseudo dice [0.961, 0.9563, 0.9752, 0.9405, 0.8537, 0.9667, 0.9891] +2024-09-06 09:41:09.359145: Epoch time: 287.4 s +2024-09-06 09:41:11.808854: +2024-09-06 09:41:11.810191: Epoch 262 +2024-09-06 09:41:11.811725: Current learning rate: 0.00761 +2024-09-06 09:45:56.696283: train_loss -0.7238 +2024-09-06 09:45:56.711065: val_loss -0.7195 +2024-09-06 09:45:56.712116: Pseudo dice [0.962, 0.9554, 0.9763, 0.9461, 0.8584, 0.966, 0.9893] +2024-09-06 09:45:56.713089: Epoch time: 284.89 s +2024-09-06 09:46:01.962770: +2024-09-06 09:46:01.964093: Epoch 263 +2024-09-06 09:46:01.965685: Current learning rate: 0.0076 +2024-09-06 09:50:29.661900: train_loss -0.7247 +2024-09-06 09:50:29.663431: val_loss -0.7313 +2024-09-06 09:50:29.665888: Pseudo dice [0.9585, 0.9544, 0.9755, 0.9443, 0.853, 0.9664, 0.9894] +2024-09-06 09:50:29.667940: Epoch time: 267.7 s +2024-09-06 09:50:32.151922: +2024-09-06 09:50:32.153653: Epoch 264 +2024-09-06 09:50:32.154581: Current learning rate: 0.00759 +2024-09-06 09:55:28.095882: train_loss -0.7238 +2024-09-06 09:55:28.172501: val_loss -0.7575 +2024-09-06 09:55:28.279125: Pseudo dice [0.9623, 0.9529, 0.9802, 0.9491, 0.8448, 0.9605, 0.9881] +2024-09-06 09:55:28.359420: Epoch time: 295.95 s +2024-09-06 09:55:31.207213: +2024-09-06 09:55:31.252320: Epoch 265 +2024-09-06 09:55:31.264490: Current learning rate: 0.00758 +2024-09-06 10:00:01.883580: train_loss -0.7382 +2024-09-06 10:00:01.895177: val_loss -0.7409 +2024-09-06 10:00:01.896446: Pseudo dice [0.9636, 0.9561, 0.9797, 0.9478, 0.8716, 0.9666, 0.9911] +2024-09-06 10:00:01.898204: Epoch time: 270.68 s +2024-09-06 10:00:04.337948: +2024-09-06 10:00:04.339113: Epoch 266 +2024-09-06 10:00:04.339895: Current learning rate: 0.00757 +2024-09-06 10:04:44.704264: train_loss -0.7209 +2024-09-06 10:04:44.714233: val_loss -0.7193 +2024-09-06 10:04:44.714933: Pseudo dice [0.9585, 0.956, 0.9745, 0.9423, 0.857, 0.9667, 0.9907] +2024-09-06 10:04:44.715626: Epoch time: 280.37 s +2024-09-06 10:04:47.378757: +2024-09-06 10:04:47.380338: Epoch 267 +2024-09-06 10:04:47.381978: Current learning rate: 0.00756 +2024-09-06 10:09:30.516746: train_loss -0.7259 +2024-09-06 10:09:30.518122: val_loss -0.7544 +2024-09-06 10:09:30.519482: Pseudo dice [0.9635, 0.9569, 0.9784, 0.9473, 0.8774, 0.9667, 0.9904] +2024-09-06 10:09:30.520857: Epoch time: 283.14 s +2024-09-06 10:09:30.522125: Yayy! New best EMA pseudo Dice: 0.9489 +2024-09-06 10:09:38.690483: +2024-09-06 10:09:38.692281: Epoch 268 +2024-09-06 10:09:38.693356: Current learning rate: 0.00755 +2024-09-06 10:14:10.462499: train_loss -0.7177 +2024-09-06 10:14:10.478870: val_loss -0.7362 +2024-09-06 10:14:10.482990: Pseudo dice [0.9611, 0.9539, 0.9758, 0.949, 0.8612, 0.9608, 0.9899] +2024-09-06 10:14:10.484320: Epoch time: 271.78 s +2024-09-06 10:14:10.485623: Yayy! New best EMA pseudo Dice: 0.9491 +2024-09-06 10:14:20.810888: +2024-09-06 10:14:20.812407: Epoch 269 +2024-09-06 10:14:20.814531: Current learning rate: 0.00754 +2024-09-06 10:18:58.003771: train_loss -0.7258 +2024-09-06 10:18:58.018603: val_loss -0.7392 +2024-09-06 10:18:58.019859: Pseudo dice [0.9589, 0.9503, 0.9735, 0.9351, 0.8322, 0.964, 0.9889] +2024-09-06 10:18:58.021008: Epoch time: 277.2 s +2024-09-06 10:19:00.640474: +2024-09-06 10:19:00.654775: Epoch 270 +2024-09-06 10:19:00.656074: Current learning rate: 0.00753 +2024-09-06 10:23:40.658885: train_loss -0.7145 +2024-09-06 10:23:40.660455: val_loss -0.7132 +2024-09-06 10:23:40.661654: Pseudo dice [0.9613, 0.9562, 0.9756, 0.9364, 0.8587, 0.9628, 0.9895] +2024-09-06 10:23:40.662546: Epoch time: 280.02 s +2024-09-06 10:23:42.941468: +2024-09-06 10:23:42.942725: Epoch 271 +2024-09-06 10:23:42.943799: Current learning rate: 0.00752 +2024-09-06 10:28:34.030941: train_loss -0.7282 +2024-09-06 10:28:34.032391: val_loss -0.7018 +2024-09-06 10:28:34.033112: Pseudo dice [0.9614, 0.9577, 0.9748, 0.9448, 0.862, 0.9644, 0.9893] +2024-09-06 10:28:34.033695: Epoch time: 291.1 s +2024-09-06 10:28:36.660552: +2024-09-06 10:28:36.673987: Epoch 272 +2024-09-06 10:28:36.674964: Current learning rate: 0.00751 +2024-09-06 10:33:23.135737: train_loss -0.732 +2024-09-06 10:33:23.145850: val_loss -0.7284 +2024-09-06 10:33:23.147203: Pseudo dice [0.9617, 0.9556, 0.9767, 0.9427, 0.8552, 0.9489, 0.9882] +2024-09-06 10:33:23.148084: Epoch time: 286.48 s +2024-09-06 10:33:26.206274: +2024-09-06 10:33:26.207210: Epoch 273 +2024-09-06 10:33:26.207968: Current learning rate: 0.00751 +2024-09-06 10:38:15.726804: train_loss -0.72 +2024-09-06 10:38:15.730450: val_loss -0.7231 +2024-09-06 10:38:15.731577: Pseudo dice [0.9575, 0.9535, 0.9725, 0.9379, 0.8527, 0.9639, 0.9871] +2024-09-06 10:38:15.732403: Epoch time: 289.53 s +2024-09-06 10:38:17.984197: +2024-09-06 10:38:17.985347: Epoch 274 +2024-09-06 10:38:17.986183: Current learning rate: 0.0075 +2024-09-06 10:43:14.269817: train_loss -0.7223 +2024-09-06 10:43:14.273100: val_loss -0.7169 +2024-09-06 10:43:14.274970: Pseudo dice [0.9613, 0.9557, 0.9748, 0.9418, 0.8533, 0.9643, 0.989] +2024-09-06 10:43:14.275923: Epoch time: 296.29 s +2024-09-06 10:43:16.975421: +2024-09-06 10:43:16.976673: Epoch 275 +2024-09-06 10:43:16.977518: Current learning rate: 0.00749 +2024-09-06 10:48:05.444328: train_loss -0.7301 +2024-09-06 10:48:05.446144: val_loss -0.7694 +2024-09-06 10:48:05.447009: Pseudo dice [0.9611, 0.9556, 0.9755, 0.9491, 0.8463, 0.9621, 0.9889] +2024-09-06 10:48:05.447808: Epoch time: 288.48 s +2024-09-06 10:48:08.003027: +2024-09-06 10:48:08.017258: Epoch 276 +2024-09-06 10:48:08.088838: Current learning rate: 0.00748 +2024-09-06 10:52:57.179498: train_loss -0.7348 +2024-09-06 10:52:57.180974: val_loss -0.7223 +2024-09-06 10:52:57.181875: Pseudo dice [0.9568, 0.952, 0.9731, 0.9323, 0.8387, 0.9538, 0.9881] +2024-09-06 10:52:57.182550: Epoch time: 289.18 s +2024-09-06 10:52:59.555490: +2024-09-06 10:52:59.556467: Epoch 277 +2024-09-06 10:52:59.557156: Current learning rate: 0.00747 +2024-09-06 10:57:47.860164: train_loss -0.7338 +2024-09-06 10:57:47.861593: val_loss -0.6563 +2024-09-06 10:57:47.862337: Pseudo dice [0.9561, 0.952, 0.9683, 0.9466, 0.8727, 0.9647, 0.9893] +2024-09-06 10:57:47.862916: Epoch time: 288.31 s +2024-09-06 10:57:50.167647: +2024-09-06 10:57:50.168710: Epoch 278 +2024-09-06 10:57:50.169474: Current learning rate: 0.00746 +2024-09-06 11:02:52.184298: train_loss -0.6972 +2024-09-06 11:02:52.199683: val_loss -0.7041 +2024-09-06 11:02:52.200416: Pseudo dice [0.9537, 0.9495, 0.9712, 0.9326, 0.8433, 0.9521, 0.9887] +2024-09-06 11:02:52.201254: Epoch time: 302.02 s +2024-09-06 11:02:54.695942: +2024-09-06 11:02:54.706518: Epoch 279 +2024-09-06 11:02:54.707545: Current learning rate: 0.00745 +2024-09-06 11:07:30.945929: train_loss -0.7034 +2024-09-06 11:07:30.959983: val_loss -0.7326 +2024-09-06 11:07:30.960780: Pseudo dice [0.9586, 0.9479, 0.9738, 0.9349, 0.8532, 0.9544, 0.9842] +2024-09-06 11:07:30.961565: Epoch time: 276.26 s +2024-09-06 11:07:33.292559: +2024-09-06 11:07:33.294294: Epoch 280 +2024-09-06 11:07:33.295611: Current learning rate: 0.00744 +2024-09-06 11:12:14.119891: train_loss -0.706 +2024-09-06 11:12:14.121186: val_loss -0.7439 +2024-09-06 11:12:14.121779: Pseudo dice [0.9609, 0.9539, 0.9761, 0.9417, 0.846, 0.9574, 0.991] +2024-09-06 11:12:14.122602: Epoch time: 280.83 s +2024-09-06 11:12:16.575278: +2024-09-06 11:12:16.576413: Epoch 281 +2024-09-06 11:12:16.577347: Current learning rate: 0.00743 +2024-09-06 11:17:06.594928: train_loss -0.7297 +2024-09-06 11:17:06.612010: val_loss -0.6999 +2024-09-06 11:17:06.612751: Pseudo dice [0.963, 0.9576, 0.9771, 0.9451, 0.8683, 0.9633, 0.9905] +2024-09-06 11:17:06.614867: Epoch time: 290.03 s +2024-09-06 11:17:09.073092: +2024-09-06 11:17:09.073972: Epoch 282 +2024-09-06 11:17:09.074598: Current learning rate: 0.00742 +2024-09-06 11:21:39.051363: train_loss -0.7178 +2024-09-06 11:21:39.064565: val_loss -0.7213 +2024-09-06 11:21:39.065686: Pseudo dice [0.9589, 0.9524, 0.976, 0.9402, 0.8582, 0.962, 0.9889] +2024-09-06 11:21:39.066565: Epoch time: 269.98 s +2024-09-06 11:21:41.760868: +2024-09-06 11:21:41.762682: Epoch 283 +2024-09-06 11:21:41.764105: Current learning rate: 0.00741 +2024-09-06 11:26:18.210586: train_loss -0.7121 +2024-09-06 11:26:18.212690: val_loss -0.7334 +2024-09-06 11:26:18.214006: Pseudo dice [0.9594, 0.9501, 0.9745, 0.9431, 0.8498, 0.9612, 0.9904] +2024-09-06 11:26:18.215194: Epoch time: 276.46 s +2024-09-06 11:26:20.715587: +2024-09-06 11:26:20.716769: Epoch 284 +2024-09-06 11:26:20.717489: Current learning rate: 0.0074 +2024-09-06 11:30:58.850335: train_loss -0.7244 +2024-09-06 11:30:58.863975: val_loss -0.741 +2024-09-06 11:30:58.865368: Pseudo dice [0.9614, 0.9542, 0.9773, 0.9419, 0.8588, 0.964, 0.99] +2024-09-06 11:30:58.866576: Epoch time: 278.14 s +2024-09-06 11:31:01.238369: +2024-09-06 11:31:01.239411: Epoch 285 +2024-09-06 11:31:01.240242: Current learning rate: 0.00739 +2024-09-06 11:35:49.187400: train_loss -0.7263 +2024-09-06 11:35:49.189435: val_loss -0.7403 +2024-09-06 11:35:49.194974: Pseudo dice [0.9622, 0.9533, 0.9774, 0.9411, 0.8652, 0.968, 0.9899] +2024-09-06 11:35:49.196062: Epoch time: 287.95 s +2024-09-06 11:35:51.864773: +2024-09-06 11:35:51.867490: Epoch 286 +2024-09-06 11:35:51.868260: Current learning rate: 0.00738 +2024-09-06 11:40:51.074928: train_loss -0.7529 +2024-09-06 11:40:51.085083: val_loss -0.7293 +2024-09-06 11:40:51.085893: Pseudo dice [0.9576, 0.9524, 0.9758, 0.9364, 0.8396, 0.9622, 0.9883] +2024-09-06 11:40:51.086719: Epoch time: 299.22 s +2024-09-06 11:40:54.051197: +2024-09-06 11:40:54.061141: Epoch 287 +2024-09-06 11:40:54.062051: Current learning rate: 0.00738 +2024-09-06 11:45:37.192760: train_loss -0.7371 +2024-09-06 11:45:37.195271: val_loss -0.7312 +2024-09-06 11:45:37.196125: Pseudo dice [0.96, 0.9567, 0.9745, 0.9454, 0.856, 0.9678, 0.9915] +2024-09-06 11:45:37.196910: Epoch time: 283.15 s +2024-09-06 11:45:40.023453: +2024-09-06 11:45:40.034708: Epoch 288 +2024-09-06 11:45:40.035694: Current learning rate: 0.00737 +2024-09-06 11:50:18.340444: train_loss -0.7251 +2024-09-06 11:50:18.343143: val_loss -0.7336 +2024-09-06 11:50:18.343830: Pseudo dice [0.9602, 0.9534, 0.9756, 0.9441, 0.8521, 0.9615, 0.9888] +2024-09-06 11:50:18.344478: Epoch time: 278.32 s +2024-09-06 11:50:19.834654: +2024-09-06 11:50:19.835576: Epoch 289 +2024-09-06 11:50:19.836403: Current learning rate: 0.00736 +2024-09-06 11:55:07.388682: train_loss -0.7038 +2024-09-06 11:55:07.392196: val_loss -0.7423 +2024-09-06 11:55:07.393341: Pseudo dice [0.9588, 0.9499, 0.9746, 0.9458, 0.8558, 0.9568, 0.9895] +2024-09-06 11:55:07.397889: Epoch time: 287.56 s +2024-09-06 11:55:10.287790: +2024-09-06 11:55:10.288855: Epoch 290 +2024-09-06 11:55:10.289639: Current learning rate: 0.00735 +2024-09-06 11:59:51.952800: train_loss -0.7338 +2024-09-06 11:59:51.955139: val_loss -0.713 +2024-09-06 11:59:51.955858: Pseudo dice [0.9596, 0.9547, 0.9766, 0.9397, 0.8701, 0.964, 0.99] +2024-09-06 11:59:51.960043: Epoch time: 281.67 s +2024-09-06 11:59:54.681270: +2024-09-06 11:59:54.682394: Epoch 291 +2024-09-06 11:59:54.683363: Current learning rate: 0.00734 +2024-09-06 12:04:42.300007: train_loss -0.739 +2024-09-06 12:04:42.313241: val_loss -0.7485 +2024-09-06 12:04:42.314497: Pseudo dice [0.9622, 0.9549, 0.9795, 0.9422, 0.8758, 0.9664, 0.9906] +2024-09-06 12:04:42.315140: Epoch time: 287.62 s +2024-09-06 12:04:44.985881: +2024-09-06 12:04:44.987147: Epoch 292 +2024-09-06 12:04:44.988369: Current learning rate: 0.00733 +2024-09-06 12:09:22.330969: train_loss -0.7263 +2024-09-06 12:09:22.347856: val_loss -0.7132 +2024-09-06 12:09:22.348575: Pseudo dice [0.9611, 0.9553, 0.9801, 0.9462, 0.8638, 0.9653, 0.9899] +2024-09-06 12:09:22.349232: Epoch time: 277.35 s +2024-09-06 12:09:25.551800: +2024-09-06 12:09:25.552817: Epoch 293 +2024-09-06 12:09:25.553547: Current learning rate: 0.00732 +2024-09-06 12:14:20.282400: train_loss -0.7232 +2024-09-06 12:14:20.285016: val_loss -0.7504 +2024-09-06 12:14:20.285948: Pseudo dice [0.9601, 0.9541, 0.9782, 0.9402, 0.8613, 0.9625, 0.99] +2024-09-06 12:14:20.286967: Epoch time: 294.74 s +2024-09-06 12:14:22.377725: +2024-09-06 12:14:22.378789: Epoch 294 +2024-09-06 12:14:22.379566: Current learning rate: 0.00731 +2024-09-06 12:19:14.386244: train_loss -0.7287 +2024-09-06 12:19:14.388952: val_loss -0.739 +2024-09-06 12:19:14.389970: Pseudo dice [0.9609, 0.953, 0.9757, 0.9385, 0.864, 0.9681, 0.9903] +2024-09-06 12:19:14.390919: Epoch time: 292.01 s +2024-09-06 12:19:14.391644: Yayy! New best EMA pseudo Dice: 0.9491 +2024-09-06 12:19:23.794246: +2024-09-06 12:19:23.795141: Epoch 295 +2024-09-06 12:19:23.795801: Current learning rate: 0.0073 +2024-09-06 12:24:14.551243: train_loss -0.7258 +2024-09-06 12:24:14.555010: val_loss -0.7242 +2024-09-06 12:24:14.556180: Pseudo dice [0.9592, 0.9547, 0.9715, 0.9418, 0.851, 0.9644, 0.9885] +2024-09-06 12:24:14.557477: Epoch time: 290.76 s +2024-09-06 12:24:17.775421: +2024-09-06 12:24:17.776398: Epoch 296 +2024-09-06 12:24:17.777110: Current learning rate: 0.00729 +2024-09-06 12:28:56.693326: train_loss -0.715 +2024-09-06 12:28:56.695658: val_loss -0.7527 +2024-09-06 12:28:56.696609: Pseudo dice [0.9608, 0.9561, 0.9757, 0.939, 0.8606, 0.9668, 0.9882] +2024-09-06 12:28:56.697395: Epoch time: 278.92 s +2024-09-06 12:28:59.296468: +2024-09-06 12:28:59.297447: Epoch 297 +2024-09-06 12:28:59.298124: Current learning rate: 0.00728 +2024-09-06 12:33:24.848214: train_loss -0.7275 +2024-09-06 12:33:24.849643: val_loss -0.6978 +2024-09-06 12:33:24.850316: Pseudo dice [0.9622, 0.961, 0.9778, 0.9423, 0.8795, 0.9653, 0.9917] +2024-09-06 12:33:24.851068: Epoch time: 265.56 s +2024-09-06 12:33:24.851774: Yayy! New best EMA pseudo Dice: 0.9495 +2024-09-06 12:33:33.109207: +2024-09-06 12:33:33.110083: Epoch 298 +2024-09-06 12:33:33.110802: Current learning rate: 0.00727 +2024-09-06 12:38:09.914125: train_loss -0.7291 +2024-09-06 12:38:09.930555: val_loss -0.753 +2024-09-06 12:38:09.931412: Pseudo dice [0.9616, 0.955, 0.9773, 0.9451, 0.8623, 0.9675, 0.9901] +2024-09-06 12:38:09.932142: Epoch time: 276.81 s +2024-09-06 12:38:09.932884: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 12:38:18.487256: +2024-09-06 12:38:18.488249: Epoch 299 +2024-09-06 12:38:18.489026: Current learning rate: 0.00726 +2024-09-06 12:42:51.125676: train_loss -0.7318 +2024-09-06 12:42:51.139068: val_loss -0.7435 +2024-09-06 12:42:51.139867: Pseudo dice [0.9634, 0.9552, 0.9802, 0.9419, 0.8501, 0.9666, 0.9862] +2024-09-06 12:42:51.140481: Epoch time: 272.64 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_14_05_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_14_05_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..325575f596f13eb44a372e4f3251d159a6a252b8 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_14_05_16.txt @@ -0,0 +1,381 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 14:05:20.211550: Using torch.compile... +2024-09-07 14:05:26.355340: do_dummy_2d_data_aug: False +2024-09-07 14:05:26.360875: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 14:05:26.361969: The split file contains 5 splits. +2024-09-07 14:05:26.362309: Desired fold for training: 0 +2024-09-07 14:05:26.362671: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 14:05:37.234429: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 14:05:38.169745: +2024-09-07 14:05:38.180290: Epoch 300 +2024-09-07 14:05:38.181097: Current learning rate: 0.00725 +2024-09-07 14:21:06.131748: train_loss -0.7185 +2024-09-07 14:21:06.133882: val_loss -0.7748 +2024-09-07 14:21:06.134394: Pseudo dice [0.9629, 0.9543, 0.9769, 0.9452, 0.8606, 0.966, 0.9908] +2024-09-07 14:21:06.134853: Epoch time: 927.97 s +2024-09-07 14:21:06.135258: Yayy! New best EMA pseudo Dice: 0.9498 +2024-09-07 14:21:09.365612: +2024-09-07 14:21:09.366222: Epoch 301 +2024-09-07 14:21:09.366655: Current learning rate: 0.00724 +2024-09-07 14:25:56.813189: train_loss -0.7423 +2024-09-07 14:25:56.831361: val_loss -0.7112 +2024-09-07 14:25:56.832139: Pseudo dice [0.964, 0.9545, 0.9758, 0.9432, 0.8693, 0.9665, 0.9889] +2024-09-07 14:25:56.832676: Epoch time: 287.45 s +2024-09-07 14:25:56.835197: Yayy! New best EMA pseudo Dice: 0.95 +2024-09-07 14:26:03.225982: +2024-09-07 14:26:03.226821: Epoch 302 +2024-09-07 14:26:03.227569: Current learning rate: 0.00724 +2024-09-07 14:31:13.508010: train_loss -0.7356 +2024-09-07 14:31:13.515500: val_loss -0.739 +2024-09-07 14:31:13.517344: Pseudo dice [0.9638, 0.9554, 0.9793, 0.9409, 0.8487, 0.9683, 0.9883] +2024-09-07 14:31:13.518197: Epoch time: 310.27 s +2024-09-07 14:31:16.800194: +2024-09-07 14:31:16.810489: Epoch 303 +2024-09-07 14:31:16.812279: Current learning rate: 0.00723 +2024-09-07 14:36:15.654032: train_loss -0.7307 +2024-09-07 14:36:15.666058: val_loss -0.7301 +2024-09-07 14:36:15.666713: Pseudo dice [0.9586, 0.9527, 0.9764, 0.9402, 0.8682, 0.9661, 0.9896] +2024-09-07 14:36:15.667223: Epoch time: 298.86 s +2024-09-07 14:36:19.585510: +2024-09-07 14:36:19.586416: Epoch 304 +2024-09-07 14:36:19.586941: Current learning rate: 0.00722 +2024-09-07 14:41:11.860701: train_loss -0.7326 +2024-09-07 14:41:11.877699: val_loss -0.7474 +2024-09-07 14:41:11.878488: Pseudo dice [0.9604, 0.9509, 0.9749, 0.9456, 0.8667, 0.9676, 0.989] +2024-09-07 14:41:11.879152: Epoch time: 292.28 s +2024-09-07 14:41:11.880046: Yayy! New best EMA pseudo Dice: 0.95 +2024-09-07 14:41:18.735421: +2024-09-07 14:41:18.736454: Epoch 305 +2024-09-07 14:41:18.737009: Current learning rate: 0.00721 +2024-09-07 14:47:00.103184: train_loss -0.7253 +2024-09-07 14:47:00.119946: val_loss -0.7255 +2024-09-07 14:47:00.120981: Pseudo dice [0.9618, 0.957, 0.9729, 0.9469, 0.87, 0.964, 0.9917] +2024-09-07 14:47:00.121698: Epoch time: 341.37 s +2024-09-07 14:47:00.122293: Yayy! New best EMA pseudo Dice: 0.9502 +2024-09-07 14:47:06.525476: +2024-09-07 14:47:06.526784: Epoch 306 +2024-09-07 14:47:06.527516: Current learning rate: 0.0072 +2024-09-07 14:53:10.953646: train_loss -0.7372 +2024-09-07 14:53:10.957335: val_loss -0.7583 +2024-09-07 14:53:10.957885: Pseudo dice [0.9613, 0.9545, 0.9767, 0.9428, 0.8657, 0.966, 0.9886] +2024-09-07 14:53:10.958390: Epoch time: 364.44 s +2024-09-07 14:53:10.958806: Yayy! New best EMA pseudo Dice: 0.9503 +2024-09-07 14:53:17.428514: +2024-09-07 14:53:17.429941: Epoch 307 +2024-09-07 14:53:17.430575: Current learning rate: 0.00719 +2024-09-07 14:58:24.560125: train_loss -0.7394 +2024-09-07 14:58:24.568820: val_loss -0.7651 +2024-09-07 14:58:24.569927: Pseudo dice [0.9604, 0.9527, 0.979, 0.9428, 0.8671, 0.9634, 0.9892] +2024-09-07 14:58:24.570935: Epoch time: 307.14 s +2024-09-07 14:58:24.571971: Yayy! New best EMA pseudo Dice: 0.9503 +2024-09-07 14:58:33.835978: +2024-09-07 14:58:33.837277: Epoch 308 +2024-09-07 14:58:33.838047: Current learning rate: 0.00718 +2024-09-07 15:04:05.191993: train_loss -0.7097 +2024-09-07 15:04:05.207944: val_loss -0.7448 +2024-09-07 15:04:05.208855: Pseudo dice [0.9604, 0.9541, 0.9769, 0.9447, 0.8665, 0.9666, 0.9899] +2024-09-07 15:04:05.209736: Epoch time: 331.36 s +2024-09-07 15:04:05.210654: Yayy! New best EMA pseudo Dice: 0.9504 +2024-09-07 15:04:11.876983: +2024-09-07 15:04:11.888408: Epoch 309 +2024-09-07 15:04:11.890238: Current learning rate: 0.00717 +2024-09-07 15:09:15.504750: train_loss -0.709 +2024-09-07 15:09:15.522550: val_loss -0.7462 +2024-09-07 15:09:15.523728: Pseudo dice [0.9613, 0.9561, 0.9765, 0.9382, 0.8543, 0.9638, 0.9887] +2024-09-07 15:09:15.524667: Epoch time: 303.63 s +2024-09-07 15:09:18.866999: +2024-09-07 15:09:18.888518: Epoch 310 +2024-09-07 15:09:18.889418: Current learning rate: 0.00716 +2024-09-07 15:14:53.530306: train_loss -0.7318 +2024-09-07 15:14:53.539969: val_loss -0.7597 +2024-09-07 15:14:53.540622: Pseudo dice [0.9634, 0.9554, 0.9787, 0.9423, 0.8697, 0.9667, 0.9919] +2024-09-07 15:14:53.541312: Epoch time: 334.67 s +2024-09-07 15:14:53.541790: Yayy! New best EMA pseudo Dice: 0.9504 +2024-09-07 15:14:58.520967: +2024-09-07 15:14:58.521922: Epoch 311 +2024-09-07 15:14:58.522592: Current learning rate: 0.00715 +2024-09-07 15:19:45.103633: train_loss -0.7157 +2024-09-07 15:19:45.111042: val_loss -0.7308 +2024-09-07 15:19:45.111658: Pseudo dice [0.952, 0.952, 0.9705, 0.9351, 0.8561, 0.9626, 0.9884] +2024-09-07 15:19:45.112583: Epoch time: 286.59 s +2024-09-07 15:19:49.409302: +2024-09-07 15:19:49.410480: Epoch 312 +2024-09-07 15:19:49.411468: Current learning rate: 0.00714 +2024-09-07 15:24:46.767203: train_loss -0.7184 +2024-09-07 15:24:46.769648: val_loss -0.7047 +2024-09-07 15:24:46.770441: Pseudo dice [0.9589, 0.9538, 0.976, 0.9346, 0.8551, 0.9636, 0.9905] +2024-09-07 15:24:46.770977: Epoch time: 297.36 s +2024-09-07 15:24:50.928598: +2024-09-07 15:24:50.940536: Epoch 313 +2024-09-07 15:24:50.941383: Current learning rate: 0.00713 +2024-09-07 15:29:47.518383: train_loss -0.7084 +2024-09-07 15:29:47.539891: val_loss -0.6927 +2024-09-07 15:29:47.540832: Pseudo dice [0.9517, 0.9466, 0.9677, 0.9215, 0.8298, 0.9514, 0.988] +2024-09-07 15:29:47.541476: Epoch time: 296.59 s +2024-09-07 15:29:50.333994: +2024-09-07 15:29:50.335002: Epoch 314 +2024-09-07 15:29:50.335717: Current learning rate: 0.00712 +2024-09-07 15:34:48.038609: train_loss -0.7024 +2024-09-07 15:34:48.070246: val_loss -0.6764 +2024-09-07 15:34:48.071455: Pseudo dice [0.9547, 0.9519, 0.9647, 0.9335, 0.8499, 0.9604, 0.9882] +2024-09-07 15:34:48.072145: Epoch time: 297.71 s +2024-09-07 15:34:51.793449: +2024-09-07 15:34:51.794598: Epoch 315 +2024-09-07 15:34:51.795295: Current learning rate: 0.00711 +2024-09-07 15:39:58.278038: train_loss -0.7298 +2024-09-07 15:39:58.280163: val_loss -0.6892 +2024-09-07 15:39:58.280854: Pseudo dice [0.9552, 0.9512, 0.9689, 0.9381, 0.8609, 0.9586, 0.989] +2024-09-07 15:39:58.281337: Epoch time: 306.49 s +2024-09-07 15:40:01.840103: +2024-09-07 15:40:01.855578: Epoch 316 +2024-09-07 15:40:01.856363: Current learning rate: 0.0071 +2024-09-07 15:44:57.414978: train_loss -0.7298 +2024-09-07 15:44:57.433329: val_loss -0.7565 +2024-09-07 15:44:57.433932: Pseudo dice [0.9586, 0.9505, 0.9757, 0.9394, 0.8631, 0.9652, 0.9898] +2024-09-07 15:44:57.434442: Epoch time: 295.58 s +2024-09-07 15:45:01.346862: +2024-09-07 15:45:01.363747: Epoch 317 +2024-09-07 15:45:01.364573: Current learning rate: 0.0071 +2024-09-07 15:50:07.575720: train_loss -0.7245 +2024-09-07 15:50:07.589530: val_loss -0.7397 +2024-09-07 15:50:07.590140: Pseudo dice [0.9577, 0.9517, 0.9765, 0.9373, 0.8528, 0.9586, 0.9885] +2024-09-07 15:50:07.590635: Epoch time: 306.23 s +2024-09-07 15:50:11.655424: +2024-09-07 15:50:11.668409: Epoch 318 +2024-09-07 15:50:11.669251: Current learning rate: 0.00709 +2024-09-07 15:55:10.637693: train_loss -0.7127 +2024-09-07 15:55:10.660609: val_loss -0.7116 +2024-09-07 15:55:10.664783: Pseudo dice [0.9595, 0.9526, 0.9778, 0.9425, 0.8539, 0.9575, 0.9875] +2024-09-07 15:55:10.669531: Epoch time: 298.99 s +2024-09-07 15:55:13.806468: +2024-09-07 15:55:13.820531: Epoch 319 +2024-09-07 15:55:13.822236: Current learning rate: 0.00708 +2024-09-07 16:00:14.988022: train_loss -0.7268 +2024-09-07 16:00:14.990448: val_loss -0.7517 +2024-09-07 16:00:14.991291: Pseudo dice [0.9607, 0.9543, 0.9761, 0.9394, 0.8602, 0.9625, 0.9905] +2024-09-07 16:00:14.991992: Epoch time: 301.19 s +2024-09-07 16:00:19.202133: +2024-09-07 16:00:19.218832: Epoch 320 +2024-09-07 16:00:19.221745: Current learning rate: 0.00707 +2024-09-07 16:04:59.690048: train_loss -0.7351 +2024-09-07 16:04:59.701367: val_loss -0.7663 +2024-09-07 16:04:59.701941: Pseudo dice [0.9621, 0.9546, 0.9788, 0.9507, 0.872, 0.962, 0.9917] +2024-09-07 16:04:59.702421: Epoch time: 280.49 s +2024-09-07 16:05:05.281230: +2024-09-07 16:05:05.296343: Epoch 321 +2024-09-07 16:05:05.297038: Current learning rate: 0.00706 +2024-09-07 16:10:02.005168: train_loss -0.7232 +2024-09-07 16:10:02.020911: val_loss -0.7235 +2024-09-07 16:10:02.022459: Pseudo dice [0.9608, 0.958, 0.9758, 0.9435, 0.8678, 0.9652, 0.9908] +2024-09-07 16:10:02.024105: Epoch time: 296.73 s +2024-09-07 16:10:05.514442: +2024-09-07 16:10:05.515237: Epoch 322 +2024-09-07 16:10:05.515829: Current learning rate: 0.00705 +2024-09-07 16:14:57.809315: train_loss -0.7353 +2024-09-07 16:14:57.816815: val_loss -0.74 +2024-09-07 16:14:57.817441: Pseudo dice [0.96, 0.9528, 0.976, 0.9428, 0.8681, 0.9666, 0.989] +2024-09-07 16:14:57.818413: Epoch time: 292.3 s +2024-09-07 16:15:01.155203: +2024-09-07 16:15:01.156909: Epoch 323 +2024-09-07 16:15:01.158196: Current learning rate: 0.00704 +2024-09-07 16:19:51.425664: train_loss -0.7467 +2024-09-07 16:19:51.434794: val_loss -0.7223 +2024-09-07 16:19:51.435388: Pseudo dice [0.9626, 0.9552, 0.9786, 0.9503, 0.8778, 0.9685, 0.9904] +2024-09-07 16:19:51.436045: Epoch time: 290.29 s +2024-09-07 16:19:54.683493: +2024-09-07 16:19:54.684294: Epoch 324 +2024-09-07 16:19:54.684914: Current learning rate: 0.00703 +2024-09-07 16:24:47.870667: train_loss -0.7139 +2024-09-07 16:24:47.878586: val_loss -0.7515 +2024-09-07 16:24:47.879161: Pseudo dice [0.9601, 0.954, 0.9756, 0.9491, 0.8497, 0.9606, 0.9861] +2024-09-07 16:24:47.880057: Epoch time: 293.19 s +2024-09-07 16:24:51.351400: +2024-09-07 16:24:51.364268: Epoch 325 +2024-09-07 16:24:51.365142: Current learning rate: 0.00702 +2024-09-07 16:29:36.766147: train_loss -0.7208 +2024-09-07 16:29:36.787106: val_loss -0.7181 +2024-09-07 16:29:36.787991: Pseudo dice [0.9611, 0.9575, 0.9791, 0.9411, 0.8585, 0.9628, 0.9885] +2024-09-07 16:29:36.788597: Epoch time: 285.42 s +2024-09-07 16:29:41.038020: +2024-09-07 16:29:41.038833: Epoch 326 +2024-09-07 16:29:41.039712: Current learning rate: 0.00701 +2024-09-07 16:34:36.655526: train_loss -0.7318 +2024-09-07 16:34:36.668836: val_loss -0.6933 +2024-09-07 16:34:36.669505: Pseudo dice [0.9598, 0.9535, 0.973, 0.9431, 0.8518, 0.9482, 0.9875] +2024-09-07 16:34:36.670385: Epoch time: 295.62 s +2024-09-07 16:34:40.657646: +2024-09-07 16:34:40.658860: Epoch 327 +2024-09-07 16:34:40.659608: Current learning rate: 0.007 +2024-09-07 16:39:14.438781: train_loss -0.7279 +2024-09-07 16:39:14.453470: val_loss -0.7467 +2024-09-07 16:39:14.454147: Pseudo dice [0.9605, 0.9539, 0.9777, 0.9483, 0.8675, 0.9664, 0.9896] +2024-09-07 16:39:14.454621: Epoch time: 273.79 s +2024-09-07 16:39:19.501521: +2024-09-07 16:39:19.502612: Epoch 328 +2024-09-07 16:39:19.503579: Current learning rate: 0.00699 +2024-09-07 16:44:08.699061: train_loss -0.7294 +2024-09-07 16:44:08.714282: val_loss -0.7179 +2024-09-07 16:44:08.715529: Pseudo dice [0.9613, 0.9532, 0.9761, 0.944, 0.8569, 0.9655, 0.9902] +2024-09-07 16:44:08.716230: Epoch time: 289.23 s +2024-09-07 16:44:13.349552: +2024-09-07 16:44:13.364552: Epoch 329 +2024-09-07 16:44:13.365245: Current learning rate: 0.00698 +2024-09-07 16:48:46.150386: train_loss -0.7221 +2024-09-07 16:48:46.151761: val_loss -0.7572 +2024-09-07 16:48:46.152644: Pseudo dice [0.9616, 0.9562, 0.9767, 0.9493, 0.8718, 0.9661, 0.9908] +2024-09-07 16:48:46.153157: Epoch time: 272.81 s +2024-09-07 16:48:48.524622: +2024-09-07 16:48:48.526134: Epoch 330 +2024-09-07 16:48:48.527404: Current learning rate: 0.00697 +2024-09-07 16:53:26.500555: train_loss -0.723 +2024-09-07 16:53:26.503242: val_loss -0.6895 +2024-09-07 16:53:26.504159: Pseudo dice [0.9631, 0.9543, 0.9774, 0.9508, 0.8717, 0.9662, 0.9922] +2024-09-07 16:53:26.504861: Epoch time: 277.98 s +2024-09-07 16:53:29.138929: +2024-09-07 16:53:29.139846: Epoch 331 +2024-09-07 16:53:29.140493: Current learning rate: 0.00696 +2024-09-07 16:58:22.131825: train_loss -0.7348 +2024-09-07 16:58:22.134392: val_loss -0.7324 +2024-09-07 16:58:22.134932: Pseudo dice [0.9614, 0.9537, 0.9788, 0.9466, 0.8643, 0.9702, 0.991] +2024-09-07 16:58:22.135427: Epoch time: 293.0 s +2024-09-07 16:58:23.906757: +2024-09-07 16:58:23.907948: Epoch 332 +2024-09-07 16:58:23.908987: Current learning rate: 0.00696 +2024-09-07 17:03:17.475259: train_loss -0.7392 +2024-09-07 17:03:17.488908: val_loss -0.715 +2024-09-07 17:03:17.489629: Pseudo dice [0.9636, 0.9555, 0.9758, 0.9436, 0.856, 0.9669, 0.9892] +2024-09-07 17:03:17.490151: Epoch time: 293.57 s +2024-09-07 17:03:19.004263: +2024-09-07 17:03:19.004913: Epoch 333 +2024-09-07 17:03:19.005412: Current learning rate: 0.00695 +2024-09-07 17:08:14.388670: train_loss -0.7391 +2024-09-07 17:08:14.390670: val_loss -0.7567 +2024-09-07 17:08:14.391225: Pseudo dice [0.9566, 0.9546, 0.9718, 0.9448, 0.8634, 0.9648, 0.9896] +2024-09-07 17:08:14.391709: Epoch time: 295.39 s +2024-09-07 17:08:16.728722: +2024-09-07 17:08:16.729742: Epoch 334 +2024-09-07 17:08:16.730396: Current learning rate: 0.00694 +2024-09-07 17:13:20.321036: train_loss -0.739 +2024-09-07 17:13:20.329638: val_loss -0.7128 +2024-09-07 17:13:20.331043: Pseudo dice [0.9608, 0.9564, 0.9788, 0.951, 0.8543, 0.9577, 0.9894] +2024-09-07 17:13:20.332093: Epoch time: 303.6 s +2024-09-07 17:13:23.444603: +2024-09-07 17:13:23.452359: Epoch 335 +2024-09-07 17:13:23.453120: Current learning rate: 0.00693 +2024-09-07 17:18:29.195244: train_loss -0.7447 +2024-09-07 17:18:29.197700: val_loss -0.737 +2024-09-07 17:18:29.198383: Pseudo dice [0.9587, 0.9542, 0.9733, 0.9444, 0.8704, 0.9629, 0.9905] +2024-09-07 17:18:29.198867: Epoch time: 305.75 s +2024-09-07 17:18:31.143310: +2024-09-07 17:18:31.144173: Epoch 336 +2024-09-07 17:18:31.145186: Current learning rate: 0.00692 +2024-09-07 17:23:26.146067: train_loss -0.7197 +2024-09-07 17:23:26.148017: val_loss -0.7349 +2024-09-07 17:23:26.148636: Pseudo dice [0.9557, 0.9505, 0.9709, 0.934, 0.856, 0.9637, 0.9873] +2024-09-07 17:23:26.150118: Epoch time: 295.01 s +2024-09-07 17:23:29.088973: +2024-09-07 17:23:29.104570: Epoch 337 +2024-09-07 17:23:29.105495: Current learning rate: 0.00691 +2024-09-07 17:28:14.209732: train_loss -0.7049 +2024-09-07 17:28:14.216383: val_loss -0.6777 +2024-09-07 17:28:14.218108: Pseudo dice [0.9549, 0.9516, 0.9609, 0.9386, 0.8509, 0.9612, 0.9892] +2024-09-07 17:28:14.219580: Epoch time: 285.13 s +2024-09-07 17:28:16.710897: +2024-09-07 17:28:16.712247: Epoch 338 +2024-09-07 17:28:16.716460: Current learning rate: 0.0069 +2024-09-07 17:33:00.319731: train_loss -0.7025 +2024-09-07 17:33:00.321445: val_loss -0.7386 +2024-09-07 17:33:00.322223: Pseudo dice [0.9577, 0.9531, 0.9728, 0.9382, 0.8395, 0.9537, 0.986] +2024-09-07 17:33:00.322852: Epoch time: 283.61 s +2024-09-07 17:33:02.646727: +2024-09-07 17:33:02.647617: Epoch 339 +2024-09-07 17:33:02.648157: Current learning rate: 0.00689 +2024-09-07 17:37:38.335892: train_loss -0.7324 +2024-09-07 17:37:38.338752: val_loss -0.7298 +2024-09-07 17:37:38.339359: Pseudo dice [0.9609, 0.9553, 0.9752, 0.9478, 0.8569, 0.9684, 0.9888] +2024-09-07 17:37:38.339828: Epoch time: 275.7 s +2024-09-07 17:37:40.745775: +2024-09-07 17:37:40.748784: Epoch 340 +2024-09-07 17:37:40.749523: Current learning rate: 0.00688 +2024-09-07 17:42:43.349739: train_loss -0.7186 +2024-09-07 17:42:43.351878: val_loss -0.7419 +2024-09-07 17:42:43.352903: Pseudo dice [0.9576, 0.9535, 0.9725, 0.9427, 0.8667, 0.9662, 0.9913] +2024-09-07 17:42:43.354307: Epoch time: 302.61 s +2024-09-07 17:42:45.683198: +2024-09-07 17:42:45.684571: Epoch 341 +2024-09-07 17:42:45.686590: Current learning rate: 0.00687 +2024-09-07 17:47:50.813298: train_loss -0.7146 +2024-09-07 17:47:50.815315: val_loss -0.7488 +2024-09-07 17:47:50.816007: Pseudo dice [0.9615, 0.9556, 0.9761, 0.9425, 0.857, 0.9658, 0.9879] +2024-09-07 17:47:50.816512: Epoch time: 305.14 s +2024-09-07 17:47:53.849271: +2024-09-07 17:47:53.850272: Epoch 342 +2024-09-07 17:47:53.850903: Current learning rate: 0.00686 +2024-09-07 17:52:38.542030: train_loss -0.7323 +2024-09-07 17:52:38.544999: val_loss -0.7418 +2024-09-07 17:52:38.545694: Pseudo dice [0.9616, 0.9543, 0.9764, 0.9402, 0.87, 0.969, 0.9921] +2024-09-07 17:52:38.546269: Epoch time: 284.7 s +2024-09-07 17:52:40.576671: +2024-09-07 17:52:40.578038: Epoch 343 +2024-09-07 17:52:40.578753: Current learning rate: 0.00685 +2024-09-07 17:57:28.543870: train_loss -0.7246 +2024-09-07 17:57:28.546666: val_loss -0.726 +2024-09-07 17:57:28.547240: Pseudo dice [0.9634, 0.9562, 0.977, 0.941, 0.8583, 0.9666, 0.9866] +2024-09-07 17:57:28.547739: Epoch time: 287.97 s +2024-09-07 17:57:30.929904: +2024-09-07 17:57:30.932262: Epoch 344 +2024-09-07 17:57:30.932799: Current learning rate: 0.00684 +2024-09-07 18:02:27.066391: train_loss -0.7369 +2024-09-07 18:02:27.069017: val_loss -0.7629 +2024-09-07 18:02:27.069769: Pseudo dice [0.9614, 0.954, 0.9762, 0.9418, 0.8554, 0.9619, 0.9892] +2024-09-07 18:02:27.070428: Epoch time: 296.14 s +2024-09-07 18:02:29.028707: +2024-09-07 18:02:29.029940: Epoch 345 +2024-09-07 18:02:29.030596: Current learning rate: 0.00683 +2024-09-07 18:07:26.449173: train_loss -0.7175 +2024-09-07 18:07:26.451295: val_loss -0.7544 +2024-09-07 18:07:26.452050: Pseudo dice [0.9613, 0.9536, 0.9744, 0.9414, 0.8735, 0.9677, 0.99] +2024-09-07 18:07:26.452772: Epoch time: 297.43 s +2024-09-07 18:07:28.887923: +2024-09-07 18:07:28.888702: Epoch 346 +2024-09-07 18:07:28.889186: Current learning rate: 0.00682 +2024-09-07 18:12:29.947708: train_loss -0.7224 +2024-09-07 18:12:29.949685: val_loss -0.7766 +2024-09-07 18:12:29.951088: Pseudo dice [0.9637, 0.9557, 0.9764, 0.9458, 0.8738, 0.9697, 0.9901] +2024-09-07 18:12:29.953254: Epoch time: 301.06 s +2024-09-07 18:12:32.358244: +2024-09-07 18:12:32.361089: Epoch 347 +2024-09-07 18:12:32.361938: Current learning rate: 0.00681 +2024-09-07 18:17:19.423126: train_loss -0.742 +2024-09-07 18:17:19.425624: val_loss -0.7493 +2024-09-07 18:17:19.426346: Pseudo dice [0.9639, 0.9549, 0.9793, 0.9494, 0.8767, 0.9683, 0.9924] +2024-09-07 18:17:19.427009: Epoch time: 287.07 s +2024-09-07 18:17:21.716196: +2024-09-07 18:17:21.718820: Epoch 348 +2024-09-07 18:17:21.724336: Current learning rate: 0.0068 +2024-09-07 18:22:06.737370: train_loss -0.7314 +2024-09-07 18:22:06.739894: val_loss -0.7368 +2024-09-07 18:22:06.740448: Pseudo dice [0.9635, 0.9552, 0.9789, 0.9502, 0.8843, 0.9687, 0.991] +2024-09-07 18:22:06.741175: Epoch time: 285.03 s +2024-09-07 18:22:06.741831: Yayy! New best EMA pseudo Dice: 0.9509 +2024-09-07 18:22:13.061412: +2024-09-07 18:22:13.062170: Epoch 349 +2024-09-07 18:22:13.062658: Current learning rate: 0.0068 +2024-09-07 18:26:58.115860: train_loss -0.7425 +2024-09-07 18:26:58.117644: val_loss -0.7282 +2024-09-07 18:26:58.118496: Pseudo dice [0.9615, 0.9565, 0.9747, 0.9453, 0.8679, 0.9665, 0.9911] +2024-09-07 18:26:58.119119: Epoch time: 285.06 s +2024-09-07 18:26:59.997838: Yayy! New best EMA pseudo Dice: 0.951 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_22_04_47.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_22_04_47.txt new file mode 100644 index 0000000000000000000000000000000000000000..6df04fb724df6a4eced7eedb296af19d83d318cb --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_7_22_04_47.txt @@ -0,0 +1,379 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 22:04:50.436455: Using torch.compile... +2024-09-07 22:04:57.912631: do_dummy_2d_data_aug: False +2024-09-07 22:04:57.924833: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 22:04:57.925860: The split file contains 5 splits. +2024-09-07 22:04:57.926275: Desired fold for training: 0 +2024-09-07 22:04:57.926643: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 22:05:14.131174: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 22:05:15.006320: +2024-09-07 22:05:15.023768: Epoch 350 +2024-09-07 22:05:15.025942: Current learning rate: 0.00679 +2024-09-07 22:20:03.486652: train_loss -0.7348 +2024-09-07 22:20:03.489151: val_loss -0.7481 +2024-09-07 22:20:03.489682: Pseudo dice [0.9619, 0.9576, 0.976, 0.9498, 0.8646, 0.9674, 0.9882] +2024-09-07 22:20:03.490121: Epoch time: 888.49 s +2024-09-07 22:20:03.490541: Yayy! New best EMA pseudo Dice: 0.9512 +2024-09-07 22:20:07.474930: +2024-09-07 22:20:07.475547: Epoch 351 +2024-09-07 22:20:07.476246: Current learning rate: 0.00678 +2024-09-07 22:24:52.430929: train_loss -0.7264 +2024-09-07 22:24:52.447058: val_loss -0.7304 +2024-09-07 22:24:52.447796: Pseudo dice [0.9615, 0.9567, 0.9744, 0.9448, 0.8601, 0.9675, 0.9886] +2024-09-07 22:24:52.448460: Epoch time: 284.96 s +2024-09-07 22:24:56.069815: +2024-09-07 22:24:56.070733: Epoch 352 +2024-09-07 22:24:56.071400: Current learning rate: 0.00677 +2024-09-07 22:29:27.276925: train_loss -0.7275 +2024-09-07 22:29:27.279526: val_loss -0.7501 +2024-09-07 22:29:27.280204: Pseudo dice [0.9652, 0.9598, 0.979, 0.9458, 0.8709, 0.9619, 0.9891] +2024-09-07 22:29:27.280817: Epoch time: 271.21 s +2024-09-07 22:29:27.281518: Yayy! New best EMA pseudo Dice: 0.9513 +2024-09-07 22:29:35.233615: +2024-09-07 22:29:35.240188: Epoch 353 +2024-09-07 22:29:35.240692: Current learning rate: 0.00676 +2024-09-07 22:34:01.228054: train_loss -0.7313 +2024-09-07 22:34:01.240554: val_loss -0.743 +2024-09-07 22:34:01.241293: Pseudo dice [0.9657, 0.9575, 0.9798, 0.9481, 0.8789, 0.9708, 0.9919] +2024-09-07 22:34:01.241909: Epoch time: 266.0 s +2024-09-07 22:34:01.242370: Yayy! New best EMA pseudo Dice: 0.9518 +2024-09-07 22:34:07.361151: +2024-09-07 22:34:07.362123: Epoch 354 +2024-09-07 22:34:07.362683: Current learning rate: 0.00675 +2024-09-07 22:38:32.082459: train_loss -0.7235 +2024-09-07 22:38:32.107754: val_loss -0.7235 +2024-09-07 22:38:32.108586: Pseudo dice [0.9649, 0.9579, 0.9799, 0.9517, 0.8419, 0.9627, 0.9867] +2024-09-07 22:38:32.109203: Epoch time: 264.73 s +2024-09-07 22:38:36.039857: +2024-09-07 22:38:36.052402: Epoch 355 +2024-09-07 22:38:36.064449: Current learning rate: 0.00674 +2024-09-07 22:43:24.784304: train_loss -0.7339 +2024-09-07 22:43:24.803568: val_loss -0.7074 +2024-09-07 22:43:24.804627: Pseudo dice [0.9579, 0.9523, 0.9724, 0.9408, 0.8685, 0.9661, 0.9878] +2024-09-07 22:43:24.806409: Epoch time: 288.75 s +2024-09-07 22:43:27.290543: +2024-09-07 22:43:27.304433: Epoch 356 +2024-09-07 22:43:27.306978: Current learning rate: 0.00673 +2024-09-07 22:48:19.434531: train_loss -0.7334 +2024-09-07 22:48:19.449667: val_loss -0.7314 +2024-09-07 22:48:19.450305: Pseudo dice [0.9626, 0.9577, 0.9761, 0.9516, 0.8708, 0.9616, 0.9906] +2024-09-07 22:48:19.450804: Epoch time: 292.15 s +2024-09-07 22:48:22.621067: +2024-09-07 22:48:22.622461: Epoch 357 +2024-09-07 22:48:22.623893: Current learning rate: 0.00672 +2024-09-07 22:53:26.883884: train_loss -0.7379 +2024-09-07 22:53:26.886493: val_loss -0.7662 +2024-09-07 22:53:26.887113: Pseudo dice [0.9647, 0.959, 0.9808, 0.9482, 0.8637, 0.9649, 0.9892] +2024-09-07 22:53:26.887936: Epoch time: 304.27 s +2024-09-07 22:53:28.638150: +2024-09-07 22:53:28.639353: Epoch 358 +2024-09-07 22:53:28.641742: Current learning rate: 0.00671 +2024-09-07 22:58:22.097706: train_loss -0.7445 +2024-09-07 22:58:22.104040: val_loss -0.7524 +2024-09-07 22:58:22.104566: Pseudo dice [0.9631, 0.9584, 0.9776, 0.9386, 0.8725, 0.9673, 0.99] +2024-09-07 22:58:22.106256: Epoch time: 293.46 s +2024-09-07 22:58:23.731670: +2024-09-07 22:58:23.732604: Epoch 359 +2024-09-07 22:58:23.734023: Current learning rate: 0.0067 +2024-09-07 23:02:55.951777: train_loss -0.7466 +2024-09-07 23:02:55.953769: val_loss -0.7159 +2024-09-07 23:02:55.954276: Pseudo dice [0.9631, 0.9528, 0.9781, 0.9445, 0.8693, 0.9669, 0.992] +2024-09-07 23:02:55.954754: Epoch time: 272.22 s +2024-09-07 23:02:55.955176: Yayy! New best EMA pseudo Dice: 0.9518 +2024-09-07 23:02:59.629727: +2024-09-07 23:02:59.630902: Epoch 360 +2024-09-07 23:02:59.632901: Current learning rate: 0.00669 +2024-09-07 23:07:44.682072: train_loss -0.7207 +2024-09-07 23:07:44.683780: val_loss -0.734 +2024-09-07 23:07:44.685427: Pseudo dice [0.9662, 0.9599, 0.981, 0.9546, 0.8727, 0.9664, 0.9872] +2024-09-07 23:07:44.686458: Epoch time: 285.06 s +2024-09-07 23:07:44.688083: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-07 23:07:48.196903: +2024-09-07 23:07:48.202654: Epoch 361 +2024-09-07 23:07:48.203948: Current learning rate: 0.00668 +2024-09-07 23:12:17.617336: train_loss -0.7371 +2024-09-07 23:12:17.633031: val_loss -0.7174 +2024-09-07 23:12:17.634238: Pseudo dice [0.9632, 0.9564, 0.9793, 0.9444, 0.8828, 0.9685, 0.9914] +2024-09-07 23:12:17.635134: Epoch time: 269.43 s +2024-09-07 23:12:17.636246: Yayy! New best EMA pseudo Dice: 0.9525 +2024-09-07 23:12:21.628394: +2024-09-07 23:12:21.629321: Epoch 362 +2024-09-07 23:12:21.630131: Current learning rate: 0.00667 +2024-09-07 23:16:49.394240: train_loss -0.752 +2024-09-07 23:16:49.396390: val_loss -0.7295 +2024-09-07 23:16:49.396987: Pseudo dice [0.9599, 0.9577, 0.9756, 0.9497, 0.8582, 0.9653, 0.9873] +2024-09-07 23:16:49.397500: Epoch time: 267.77 s +2024-09-07 23:16:51.538909: +2024-09-07 23:16:51.539838: Epoch 363 +2024-09-07 23:16:51.540418: Current learning rate: 0.00666 +2024-09-07 23:21:29.090170: train_loss -0.7278 +2024-09-07 23:21:29.092506: val_loss -0.7248 +2024-09-07 23:21:29.093424: Pseudo dice [0.9621, 0.9545, 0.9756, 0.9475, 0.865, 0.9647, 0.9907] +2024-09-07 23:21:29.094384: Epoch time: 277.56 s +2024-09-07 23:21:31.086623: +2024-09-07 23:21:31.087512: Epoch 364 +2024-09-07 23:21:31.088445: Current learning rate: 0.00665 +2024-09-07 23:26:15.183840: train_loss -0.7379 +2024-09-07 23:26:15.186132: val_loss -0.7314 +2024-09-07 23:26:15.186718: Pseudo dice [0.9623, 0.9551, 0.9768, 0.9476, 0.8739, 0.9688, 0.9914] +2024-09-07 23:26:15.187627: Epoch time: 284.1 s +2024-09-07 23:26:20.044234: +2024-09-07 23:26:20.046443: Epoch 365 +2024-09-07 23:26:20.047663: Current learning rate: 0.00665 +2024-09-07 23:31:06.510001: train_loss -0.7204 +2024-09-07 23:31:06.512149: val_loss -0.7488 +2024-09-07 23:31:06.512859: Pseudo dice [0.9583, 0.9522, 0.9684, 0.9422, 0.8601, 0.9574, 0.9893] +2024-09-07 23:31:06.513555: Epoch time: 286.47 s +2024-09-07 23:31:08.417214: +2024-09-07 23:31:08.417883: Epoch 366 +2024-09-07 23:31:08.418654: Current learning rate: 0.00664 +2024-09-07 23:36:03.652073: train_loss -0.7356 +2024-09-07 23:36:03.658689: val_loss -0.7349 +2024-09-07 23:36:03.663128: Pseudo dice [0.9601, 0.9572, 0.9769, 0.9443, 0.8748, 0.9678, 0.988] +2024-09-07 23:36:03.665215: Epoch time: 295.24 s +2024-09-07 23:36:05.906566: +2024-09-07 23:36:05.908732: Epoch 367 +2024-09-07 23:36:05.910182: Current learning rate: 0.00663 +2024-09-07 23:40:55.081939: train_loss -0.7548 +2024-09-07 23:40:55.083933: val_loss -0.7278 +2024-09-07 23:40:55.084474: Pseudo dice [0.9629, 0.9615, 0.9754, 0.9506, 0.8683, 0.9654, 0.9893] +2024-09-07 23:40:55.085031: Epoch time: 289.18 s +2024-09-07 23:40:57.238287: +2024-09-07 23:40:57.239110: Epoch 368 +2024-09-07 23:40:57.239876: Current learning rate: 0.00662 +2024-09-07 23:46:09.441271: train_loss -0.7418 +2024-09-07 23:46:09.454659: val_loss -0.7254 +2024-09-07 23:46:09.456121: Pseudo dice [0.9631, 0.9559, 0.979, 0.9471, 0.8706, 0.9652, 0.9906] +2024-09-07 23:46:09.457182: Epoch time: 312.21 s +2024-09-07 23:46:11.970695: +2024-09-07 23:46:11.971725: Epoch 369 +2024-09-07 23:46:11.972404: Current learning rate: 0.00661 +2024-09-07 23:52:16.300404: train_loss -0.74 +2024-09-07 23:52:16.303117: val_loss -0.7248 +2024-09-07 23:52:16.304048: Pseudo dice [0.9601, 0.9541, 0.9756, 0.9463, 0.8715, 0.9664, 0.9911] +2024-09-07 23:52:16.304669: Epoch time: 364.34 s +2024-09-07 23:52:18.643195: +2024-09-07 23:52:18.643970: Epoch 370 +2024-09-07 23:52:18.644596: Current learning rate: 0.0066 +2024-09-07 23:58:01.773705: train_loss -0.7218 +2024-09-07 23:58:01.776166: val_loss -0.7479 +2024-09-07 23:58:01.777072: Pseudo dice [0.9636, 0.9577, 0.9788, 0.948, 0.8776, 0.9719, 0.9932] +2024-09-07 23:58:01.777585: Epoch time: 343.14 s +2024-09-07 23:58:01.778045: Yayy! New best EMA pseudo Dice: 0.9525 +2024-09-07 23:58:07.947380: +2024-09-07 23:58:07.948505: Epoch 371 +2024-09-07 23:58:07.949520: Current learning rate: 0.00659 +2024-09-08 00:03:44.083137: train_loss -0.7363 +2024-09-08 00:03:44.085596: val_loss -0.7215 +2024-09-08 00:03:44.086218: Pseudo dice [0.9636, 0.9559, 0.9788, 0.946, 0.8614, 0.9585, 0.9884] +2024-09-08 00:03:44.086729: Epoch time: 336.14 s +2024-09-08 00:03:46.818362: +2024-09-08 00:03:46.819499: Epoch 372 +2024-09-08 00:03:46.820522: Current learning rate: 0.00658 +2024-09-08 00:09:32.929209: train_loss -0.7332 +2024-09-08 00:09:32.932279: val_loss -0.745 +2024-09-08 00:09:32.932979: Pseudo dice [0.9642, 0.9584, 0.9767, 0.952, 0.8837, 0.9693, 0.992] +2024-09-08 00:09:32.933601: Epoch time: 346.12 s +2024-09-08 00:09:32.934094: Yayy! New best EMA pseudo Dice: 0.9527 +2024-09-08 00:09:37.829674: +2024-09-08 00:09:37.830686: Epoch 373 +2024-09-08 00:09:37.831239: Current learning rate: 0.00657 +2024-09-08 00:14:49.411239: train_loss -0.7291 +2024-09-08 00:14:49.413806: val_loss -0.7112 +2024-09-08 00:14:49.414559: Pseudo dice [0.9575, 0.9553, 0.978, 0.9379, 0.8685, 0.9532, 0.99] +2024-09-08 00:14:49.415106: Epoch time: 311.59 s +2024-09-08 00:14:51.810179: +2024-09-08 00:14:51.812260: Epoch 374 +2024-09-08 00:14:51.813382: Current learning rate: 0.00656 +2024-09-08 00:19:44.990911: train_loss -0.7211 +2024-09-08 00:19:45.006667: val_loss -0.7234 +2024-09-08 00:19:45.007273: Pseudo dice [0.9609, 0.9545, 0.9755, 0.9469, 0.8735, 0.9656, 0.9912] +2024-09-08 00:19:45.007875: Epoch time: 293.2 s +2024-09-08 00:19:47.548718: +2024-09-08 00:19:47.549706: Epoch 375 +2024-09-08 00:19:47.551049: Current learning rate: 0.00655 +2024-09-08 00:24:52.217963: train_loss -0.7212 +2024-09-08 00:24:52.220148: val_loss -0.7087 +2024-09-08 00:24:52.220783: Pseudo dice [0.961, 0.9549, 0.9672, 0.9458, 0.8527, 0.9654, 0.9896] +2024-09-08 00:24:52.221347: Epoch time: 304.68 s +2024-09-08 00:24:54.139486: +2024-09-08 00:24:54.140689: Epoch 376 +2024-09-08 00:24:54.141441: Current learning rate: 0.00654 +2024-09-08 00:29:34.073451: train_loss -0.7236 +2024-09-08 00:29:34.077057: val_loss -0.7045 +2024-09-08 00:29:34.077683: Pseudo dice [0.9596, 0.9566, 0.9734, 0.9353, 0.8675, 0.9682, 0.9903] +2024-09-08 00:29:34.078281: Epoch time: 279.94 s +2024-09-08 00:29:36.399098: +2024-09-08 00:29:36.400293: Epoch 377 +2024-09-08 00:29:36.401027: Current learning rate: 0.00653 +2024-09-08 00:34:31.736445: train_loss -0.727 +2024-09-08 00:34:31.738098: val_loss -0.7567 +2024-09-08 00:34:31.739261: Pseudo dice [0.9622, 0.9562, 0.9781, 0.9447, 0.8681, 0.9651, 0.9897] +2024-09-08 00:34:31.740235: Epoch time: 295.34 s +2024-09-08 00:34:35.439516: +2024-09-08 00:34:35.443596: Epoch 378 +2024-09-08 00:34:35.444572: Current learning rate: 0.00652 +2024-09-08 00:39:31.576055: train_loss -0.7202 +2024-09-08 00:39:31.577361: val_loss -0.7386 +2024-09-08 00:39:31.578000: Pseudo dice [0.964, 0.9571, 0.9798, 0.9435, 0.8666, 0.9643, 0.9902] +2024-09-08 00:39:31.578533: Epoch time: 296.14 s +2024-09-08 00:39:33.191946: +2024-09-08 00:39:33.193068: Epoch 379 +2024-09-08 00:39:33.193743: Current learning rate: 0.00651 +2024-09-08 00:44:09.351942: train_loss -0.7283 +2024-09-08 00:44:09.353483: val_loss -0.7226 +2024-09-08 00:44:09.354486: Pseudo dice [0.9634, 0.9533, 0.98, 0.9418, 0.8436, 0.9641, 0.9889] +2024-09-08 00:44:09.355370: Epoch time: 276.16 s +2024-09-08 00:44:11.640047: +2024-09-08 00:44:11.641275: Epoch 380 +2024-09-08 00:44:11.642010: Current learning rate: 0.0065 +2024-09-08 00:49:13.149114: train_loss -0.7346 +2024-09-08 00:49:13.152743: val_loss -0.7316 +2024-09-08 00:49:13.153794: Pseudo dice [0.96, 0.9541, 0.9719, 0.9432, 0.8672, 0.966, 0.9899] +2024-09-08 00:49:13.156345: Epoch time: 301.52 s +2024-09-08 00:49:15.439008: +2024-09-08 00:49:15.439958: Epoch 381 +2024-09-08 00:49:15.440609: Current learning rate: 0.00649 +2024-09-08 00:54:00.546713: train_loss -0.7272 +2024-09-08 00:54:00.547955: val_loss -0.7798 +2024-09-08 00:54:00.548475: Pseudo dice [0.9616, 0.9561, 0.9783, 0.9464, 0.8654, 0.9661, 0.9895] +2024-09-08 00:54:00.549080: Epoch time: 285.11 s +2024-09-08 00:54:02.546525: +2024-09-08 00:54:02.547561: Epoch 382 +2024-09-08 00:54:02.548214: Current learning rate: 0.00648 +2024-09-08 00:58:58.576061: train_loss -0.719 +2024-09-08 00:58:58.578232: val_loss -0.7448 +2024-09-08 00:58:58.578677: Pseudo dice [0.9617, 0.9563, 0.9791, 0.9484, 0.8731, 0.9678, 0.9912] +2024-09-08 00:58:58.579104: Epoch time: 296.04 s +2024-09-08 00:59:00.221547: +2024-09-08 00:59:00.222251: Epoch 383 +2024-09-08 00:59:00.223145: Current learning rate: 0.00648 +2024-09-08 01:04:03.407910: train_loss -0.7293 +2024-09-08 01:04:03.410652: val_loss -0.7395 +2024-09-08 01:04:03.411332: Pseudo dice [0.9609, 0.9561, 0.9775, 0.9481, 0.8727, 0.9671, 0.9909] +2024-09-08 01:04:03.411814: Epoch time: 303.19 s +2024-09-08 01:04:05.816084: +2024-09-08 01:04:05.817439: Epoch 384 +2024-09-08 01:04:05.818743: Current learning rate: 0.00647 +2024-09-08 01:09:06.522553: train_loss -0.7213 +2024-09-08 01:09:06.525675: val_loss -0.7314 +2024-09-08 01:09:06.526363: Pseudo dice [0.9623, 0.9583, 0.9767, 0.9484, 0.8666, 0.9699, 0.9905] +2024-09-08 01:09:06.527015: Epoch time: 300.71 s +2024-09-08 01:09:08.057703: +2024-09-08 01:09:08.058463: Epoch 385 +2024-09-08 01:09:08.058985: Current learning rate: 0.00646 +2024-09-08 01:13:49.323172: train_loss -0.7264 +2024-09-08 01:13:49.337723: val_loss -0.7348 +2024-09-08 01:13:49.338467: Pseudo dice [0.9607, 0.9528, 0.9767, 0.944, 0.8583, 0.9673, 0.9879] +2024-09-08 01:13:49.339068: Epoch time: 281.27 s +2024-09-08 01:13:51.685326: +2024-09-08 01:13:51.686450: Epoch 386 +2024-09-08 01:13:51.687132: Current learning rate: 0.00645 +2024-09-08 01:18:40.290848: train_loss -0.7381 +2024-09-08 01:18:40.297343: val_loss -0.7599 +2024-09-08 01:18:40.298711: Pseudo dice [0.9613, 0.9562, 0.9776, 0.9493, 0.8708, 0.97, 0.9901] +2024-09-08 01:18:40.300279: Epoch time: 288.61 s +2024-09-08 01:18:42.402846: +2024-09-08 01:18:42.403584: Epoch 387 +2024-09-08 01:18:42.404164: Current learning rate: 0.00644 +2024-09-08 01:23:24.243913: train_loss -0.7385 +2024-09-08 01:23:24.246147: val_loss -0.7283 +2024-09-08 01:23:24.246714: Pseudo dice [0.9626, 0.96, 0.9767, 0.9487, 0.872, 0.9663, 0.9917] +2024-09-08 01:23:24.247227: Epoch time: 281.84 s +2024-09-08 01:23:26.284719: +2024-09-08 01:23:26.285917: Epoch 388 +2024-09-08 01:23:26.286703: Current learning rate: 0.00643 +2024-09-08 01:27:52.871003: train_loss -0.7216 +2024-09-08 01:27:52.874235: val_loss -0.699 +2024-09-08 01:27:52.875526: Pseudo dice [0.9616, 0.9568, 0.9751, 0.9458, 0.8651, 0.9666, 0.9895] +2024-09-08 01:27:52.876401: Epoch time: 266.59 s +2024-09-08 01:27:56.437101: +2024-09-08 01:27:56.438122: Epoch 389 +2024-09-08 01:27:56.438797: Current learning rate: 0.00642 +2024-09-08 01:32:40.023240: train_loss -0.7338 +2024-09-08 01:32:40.025555: val_loss -0.7345 +2024-09-08 01:32:40.026439: Pseudo dice [0.9629, 0.9555, 0.9802, 0.9434, 0.8781, 0.9665, 0.9913] +2024-09-08 01:32:40.027032: Epoch time: 283.59 s +2024-09-08 01:32:42.271252: +2024-09-08 01:32:42.272675: Epoch 390 +2024-09-08 01:32:42.273480: Current learning rate: 0.00641 +2024-09-08 01:37:09.879209: train_loss -0.7424 +2024-09-08 01:37:09.881777: val_loss -0.7194 +2024-09-08 01:37:09.882622: Pseudo dice [0.9638, 0.9586, 0.9756, 0.9451, 0.863, 0.9655, 0.9897] +2024-09-08 01:37:09.883272: Epoch time: 267.61 s +2024-09-08 01:37:14.029768: +2024-09-08 01:37:14.030790: Epoch 391 +2024-09-08 01:37:14.031358: Current learning rate: 0.0064 +2024-09-08 01:41:56.367385: train_loss -0.7513 +2024-09-08 01:41:56.369679: val_loss -0.7239 +2024-09-08 01:41:56.370354: Pseudo dice [0.9617, 0.9536, 0.9795, 0.9457, 0.8671, 0.9686, 0.9916] +2024-09-08 01:41:56.370860: Epoch time: 282.34 s +2024-09-08 01:41:58.451717: +2024-09-08 01:41:58.452411: Epoch 392 +2024-09-08 01:41:58.452900: Current learning rate: 0.00639 +2024-09-08 01:46:50.131638: train_loss -0.7099 +2024-09-08 01:46:50.134486: val_loss -0.718 +2024-09-08 01:46:50.135189: Pseudo dice [0.9585, 0.9512, 0.9781, 0.9316, 0.8623, 0.9625, 0.9893] +2024-09-08 01:46:50.135905: Epoch time: 291.68 s +2024-09-08 01:46:52.147480: +2024-09-08 01:46:52.148285: Epoch 393 +2024-09-08 01:46:52.148881: Current learning rate: 0.00638 +2024-09-08 01:51:44.209609: train_loss -0.7224 +2024-09-08 01:51:44.221107: val_loss -0.7612 +2024-09-08 01:51:44.222059: Pseudo dice [0.9631, 0.9578, 0.9786, 0.9489, 0.8631, 0.967, 0.9897] +2024-09-08 01:51:44.222746: Epoch time: 292.07 s +2024-09-08 01:51:47.007719: +2024-09-08 01:51:47.008863: Epoch 394 +2024-09-08 01:51:47.009501: Current learning rate: 0.00637 +2024-09-08 01:56:42.218983: train_loss -0.7286 +2024-09-08 01:56:42.221816: val_loss -0.7328 +2024-09-08 01:56:42.222394: Pseudo dice [0.9637, 0.9594, 0.9785, 0.9509, 0.8781, 0.968, 0.9903] +2024-09-08 01:56:42.222859: Epoch time: 295.22 s +2024-09-08 01:56:44.163985: +2024-09-08 01:56:44.165304: Epoch 395 +2024-09-08 01:56:44.166382: Current learning rate: 0.00636 +2024-09-08 02:01:33.682235: train_loss -0.7097 +2024-09-08 02:01:33.684346: val_loss -0.7484 +2024-09-08 02:01:33.685336: Pseudo dice [0.957, 0.9533, 0.9741, 0.9382, 0.8616, 0.9626, 0.9898] +2024-09-08 02:01:33.686122: Epoch time: 289.52 s +2024-09-08 02:01:36.712077: +2024-09-08 02:01:36.713010: Epoch 396 +2024-09-08 02:01:36.713566: Current learning rate: 0.00635 +2024-09-08 02:06:13.505131: train_loss -0.7329 +2024-09-08 02:06:13.507925: val_loss -0.7732 +2024-09-08 02:06:13.508677: Pseudo dice [0.9628, 0.9541, 0.979, 0.9408, 0.8714, 0.9708, 0.9908] +2024-09-08 02:06:13.509211: Epoch time: 276.8 s +2024-09-08 02:06:15.560633: +2024-09-08 02:06:15.561455: Epoch 397 +2024-09-08 02:06:15.562352: Current learning rate: 0.00634 +2024-09-08 02:11:07.614176: train_loss -0.7104 +2024-09-08 02:11:07.616350: val_loss -0.7455 +2024-09-08 02:11:07.617117: Pseudo dice [0.9618, 0.9567, 0.9754, 0.9468, 0.8654, 0.9656, 0.9903] +2024-09-08 02:11:07.617598: Epoch time: 292.06 s +2024-09-08 02:11:10.406317: +2024-09-08 02:11:10.407481: Epoch 398 +2024-09-08 02:11:10.408231: Current learning rate: 0.00633 +2024-09-08 02:16:30.152310: train_loss -0.7291 +2024-09-08 02:16:30.157570: val_loss -0.7392 +2024-09-08 02:16:30.158858: Pseudo dice [0.9614, 0.9553, 0.976, 0.942, 0.8669, 0.9647, 0.9916] +2024-09-08 02:16:30.159933: Epoch time: 319.75 s +2024-09-08 02:16:32.820824: +2024-09-08 02:16:32.822403: Epoch 399 +2024-09-08 02:16:32.824252: Current learning rate: 0.00632 +2024-09-08 02:21:28.454255: train_loss -0.7168 +2024-09-08 02:21:28.457417: val_loss -0.7374 +2024-09-08 02:21:28.458918: Pseudo dice [0.9624, 0.9553, 0.9753, 0.9445, 0.8561, 0.9632, 0.988] +2024-09-08 02:21:28.459815: Epoch time: 295.64 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_06_04_18.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_06_04_18.txt new file mode 100644 index 0000000000000000000000000000000000000000..144caf754e4b9522e63499b5afbb015fbf50ac0a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_06_04_18.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 06:04:22.193567: Using torch.compile... +2024-09-08 06:04:28.562759: do_dummy_2d_data_aug: False +2024-09-08 06:04:28.577198: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 06:04:28.579905: The split file contains 5 splits. +2024-09-08 06:04:28.581280: Desired fold for training: 0 +2024-09-08 06:04:28.582640: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 06:04:40.483038: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 06:04:41.596569: +2024-09-08 06:04:41.602085: Epoch 400 +2024-09-08 06:04:41.604947: Current learning rate: 0.00631 +2024-09-08 06:19:31.794949: train_loss -0.7402 +2024-09-08 06:19:31.808858: val_loss -0.704 +2024-09-08 06:19:31.809546: Pseudo dice [0.9621, 0.951, 0.9777, 0.9489, 0.8541, 0.9672, 0.9894] +2024-09-08 06:19:31.810093: Epoch time: 890.21 s +2024-09-08 06:19:34.378989: +2024-09-08 06:19:34.379738: Epoch 401 +2024-09-08 06:19:34.380462: Current learning rate: 0.0063 +2024-09-08 06:23:48.994299: train_loss -0.7224 +2024-09-08 06:23:49.005950: val_loss -0.7473 +2024-09-08 06:23:49.007166: Pseudo dice [0.9613, 0.9568, 0.9762, 0.9414, 0.8494, 0.9644, 0.989] +2024-09-08 06:23:49.008452: Epoch time: 254.62 s +2024-09-08 06:23:51.630479: +2024-09-08 06:23:51.632395: Epoch 402 +2024-09-08 06:23:51.633239: Current learning rate: 0.0063 +2024-09-08 06:28:26.933783: train_loss -0.7253 +2024-09-08 06:28:26.948979: val_loss -0.7277 +2024-09-08 06:28:26.952197: Pseudo dice [0.9639, 0.9589, 0.9757, 0.9493, 0.8742, 0.966, 0.9912] +2024-09-08 06:28:26.952993: Epoch time: 275.31 s +2024-09-08 06:28:31.224618: +2024-09-08 06:28:31.226063: Epoch 403 +2024-09-08 06:28:31.227442: Current learning rate: 0.00629 +2024-09-08 06:33:11.356989: train_loss -0.7381 +2024-09-08 06:33:11.359057: val_loss -0.7065 +2024-09-08 06:33:11.359636: Pseudo dice [0.9564, 0.9539, 0.968, 0.9471, 0.8674, 0.9641, 0.9884] +2024-09-08 06:33:11.360134: Epoch time: 280.14 s +2024-09-08 06:33:18.352405: +2024-09-08 06:33:18.353296: Epoch 404 +2024-09-08 06:33:18.354028: Current learning rate: 0.00628 +2024-09-08 06:38:10.204727: train_loss -0.7163 +2024-09-08 06:38:10.220631: val_loss -0.7647 +2024-09-08 06:38:10.221112: Pseudo dice [0.9636, 0.9571, 0.9778, 0.9434, 0.869, 0.9712, 0.991] +2024-09-08 06:38:10.221553: Epoch time: 291.86 s +2024-09-08 06:38:13.816221: +2024-09-08 06:38:13.831028: Epoch 405 +2024-09-08 06:38:13.832027: Current learning rate: 0.00627 +2024-09-08 06:43:01.659381: train_loss -0.7474 +2024-09-08 06:43:01.672629: val_loss -0.7452 +2024-09-08 06:43:01.673951: Pseudo dice [0.9617, 0.9573, 0.9762, 0.947, 0.8761, 0.9706, 0.9915] +2024-09-08 06:43:01.675949: Epoch time: 287.85 s +2024-09-08 06:43:05.849131: +2024-09-08 06:43:05.850045: Epoch 406 +2024-09-08 06:43:05.851887: Current learning rate: 0.00626 +2024-09-08 06:47:29.177991: train_loss -0.753 +2024-09-08 06:47:29.195497: val_loss -0.7231 +2024-09-08 06:47:29.196134: Pseudo dice [0.9585, 0.9567, 0.968, 0.936, 0.8723, 0.9598, 0.9911] +2024-09-08 06:47:29.196585: Epoch time: 263.33 s +2024-09-08 06:47:33.212547: +2024-09-08 06:47:33.222683: Epoch 407 +2024-09-08 06:47:33.224624: Current learning rate: 0.00625 +2024-09-08 06:52:01.350911: train_loss -0.7395 +2024-09-08 06:52:01.361780: val_loss -0.725 +2024-09-08 06:52:01.362455: Pseudo dice [0.9594, 0.9541, 0.9766, 0.9466, 0.8586, 0.9639, 0.9896] +2024-09-08 06:52:01.362957: Epoch time: 268.15 s +2024-09-08 06:52:03.939089: +2024-09-08 06:52:03.940401: Epoch 408 +2024-09-08 06:52:03.941464: Current learning rate: 0.00624 +2024-09-08 06:56:51.567301: train_loss -0.7249 +2024-09-08 06:56:51.571867: val_loss -0.7539 +2024-09-08 06:56:51.573717: Pseudo dice [0.9631, 0.9546, 0.9798, 0.9502, 0.8742, 0.9685, 0.9898] +2024-09-08 06:56:51.576737: Epoch time: 287.63 s +2024-09-08 06:56:54.278836: +2024-09-08 06:56:54.290875: Epoch 409 +2024-09-08 06:56:54.294496: Current learning rate: 0.00623 +2024-09-08 07:01:32.565291: train_loss -0.7403 +2024-09-08 07:01:32.604045: val_loss -0.7368 +2024-09-08 07:01:32.604958: Pseudo dice [0.9608, 0.9581, 0.9751, 0.9468, 0.8797, 0.9673, 0.989] +2024-09-08 07:01:32.605963: Epoch time: 278.29 s +2024-09-08 07:01:36.423521: +2024-09-08 07:01:36.431705: Epoch 410 +2024-09-08 07:01:36.432385: Current learning rate: 0.00622 +2024-09-08 07:06:22.141404: train_loss -0.7393 +2024-09-08 07:06:22.167896: val_loss -0.7535 +2024-09-08 07:06:22.169122: Pseudo dice [0.9582, 0.9554, 0.9753, 0.939, 0.8727, 0.9695, 0.9901] +2024-09-08 07:06:22.170677: Epoch time: 285.72 s +2024-09-08 07:06:24.825240: +2024-09-08 07:06:24.826205: Epoch 411 +2024-09-08 07:06:24.827078: Current learning rate: 0.00621 +2024-09-08 07:11:05.147049: train_loss -0.7366 +2024-09-08 07:11:05.148562: val_loss -0.7363 +2024-09-08 07:11:05.149152: Pseudo dice [0.9657, 0.958, 0.9811, 0.9476, 0.8707, 0.9692, 0.9876] +2024-09-08 07:11:05.149610: Epoch time: 280.33 s +2024-09-08 07:11:08.449164: +2024-09-08 07:11:08.459367: Epoch 412 +2024-09-08 07:11:08.460382: Current learning rate: 0.0062 +2024-09-08 07:15:23.899632: train_loss -0.733 +2024-09-08 07:15:23.902883: val_loss -0.7198 +2024-09-08 07:15:23.903626: Pseudo dice [0.9575, 0.9559, 0.9757, 0.9438, 0.8687, 0.9689, 0.9912] +2024-09-08 07:15:23.922610: Epoch time: 255.46 s +2024-09-08 07:15:27.517142: +2024-09-08 07:15:27.517950: Epoch 413 +2024-09-08 07:15:27.518616: Current learning rate: 0.00619 +2024-09-08 07:20:07.430195: train_loss -0.7366 +2024-09-08 07:20:07.446144: val_loss -0.7335 +2024-09-08 07:20:07.446884: Pseudo dice [0.9608, 0.9567, 0.9772, 0.9445, 0.8803, 0.9672, 0.9915] +2024-09-08 07:20:07.447557: Epoch time: 279.92 s +2024-09-08 07:20:09.685631: +2024-09-08 07:20:09.686524: Epoch 414 +2024-09-08 07:20:09.687622: Current learning rate: 0.00618 +2024-09-08 07:24:35.586179: train_loss -0.7147 +2024-09-08 07:24:35.606990: val_loss -0.7408 +2024-09-08 07:24:35.608306: Pseudo dice [0.9602, 0.9549, 0.975, 0.939, 0.8653, 0.9666, 0.9892] +2024-09-08 07:24:35.608960: Epoch time: 265.91 s +2024-09-08 07:24:38.224800: +2024-09-08 07:24:38.226091: Epoch 415 +2024-09-08 07:24:38.227743: Current learning rate: 0.00617 +2024-09-08 07:29:05.084931: train_loss -0.7358 +2024-09-08 07:29:05.100874: val_loss -0.7579 +2024-09-08 07:29:05.101579: Pseudo dice [0.9622, 0.9542, 0.9771, 0.9484, 0.8647, 0.9661, 0.9905] +2024-09-08 07:29:05.102459: Epoch time: 266.86 s +2024-09-08 07:29:09.444978: +2024-09-08 07:29:09.445989: Epoch 416 +2024-09-08 07:29:09.446972: Current learning rate: 0.00616 +2024-09-08 07:34:07.115925: train_loss -0.7358 +2024-09-08 07:34:07.117124: val_loss -0.7638 +2024-09-08 07:34:07.117755: Pseudo dice [0.9611, 0.9558, 0.9802, 0.9521, 0.8789, 0.9684, 0.991] +2024-09-08 07:34:07.118278: Epoch time: 297.68 s +2024-09-08 07:34:10.820659: +2024-09-08 07:34:10.821537: Epoch 417 +2024-09-08 07:34:10.822822: Current learning rate: 0.00615 +2024-09-08 07:38:54.197576: train_loss -0.7425 +2024-09-08 07:38:54.209773: val_loss -0.7382 +2024-09-08 07:38:54.210826: Pseudo dice [0.9611, 0.9587, 0.9756, 0.9525, 0.8745, 0.9669, 0.9901] +2024-09-08 07:38:54.212089: Epoch time: 283.4 s +2024-09-08 07:38:58.517443: +2024-09-08 07:38:58.518976: Epoch 418 +2024-09-08 07:38:58.519728: Current learning rate: 0.00614 +2024-09-08 07:43:21.601232: train_loss -0.7365 +2024-09-08 07:43:21.603275: val_loss -0.7603 +2024-09-08 07:43:21.603871: Pseudo dice [0.9626, 0.9576, 0.9783, 0.9507, 0.8571, 0.969, 0.9896] +2024-09-08 07:43:21.604456: Epoch time: 263.09 s +2024-09-08 07:43:25.037050: +2024-09-08 07:43:25.037924: Epoch 419 +2024-09-08 07:43:25.038460: Current learning rate: 0.00613 +2024-09-08 07:48:08.599259: train_loss -0.733 +2024-09-08 07:48:08.601630: val_loss -0.754 +2024-09-08 07:48:08.602112: Pseudo dice [0.9615, 0.9557, 0.9776, 0.9443, 0.877, 0.9642, 0.9879] +2024-09-08 07:48:08.602822: Epoch time: 283.57 s +2024-09-08 07:48:12.081840: +2024-09-08 07:48:12.082721: Epoch 420 +2024-09-08 07:48:12.083684: Current learning rate: 0.00612 +2024-09-08 07:52:52.581078: train_loss -0.727 +2024-09-08 07:52:52.596547: val_loss -0.7388 +2024-09-08 07:52:52.597222: Pseudo dice [0.9608, 0.957, 0.9741, 0.9498, 0.8686, 0.9651, 0.9901] +2024-09-08 07:52:52.597740: Epoch time: 280.5 s +2024-09-08 07:52:55.526819: +2024-09-08 07:52:55.528126: Epoch 421 +2024-09-08 07:52:55.529222: Current learning rate: 0.00612 +2024-09-08 07:57:35.148510: train_loss -0.747 +2024-09-08 07:57:35.165871: val_loss -0.735 +2024-09-08 07:57:35.166712: Pseudo dice [0.9587, 0.9569, 0.9704, 0.9467, 0.8607, 0.9654, 0.9893] +2024-09-08 07:57:35.167300: Epoch time: 279.63 s +2024-09-08 07:57:38.701868: +2024-09-08 07:57:38.702712: Epoch 422 +2024-09-08 07:57:38.703423: Current learning rate: 0.00611 +2024-09-08 08:02:15.810580: train_loss -0.7333 +2024-09-08 08:02:15.812270: val_loss -0.7429 +2024-09-08 08:02:15.812923: Pseudo dice [0.9573, 0.9522, 0.9738, 0.946, 0.8715, 0.9644, 0.9904] +2024-09-08 08:02:15.813479: Epoch time: 277.11 s +2024-09-08 08:02:19.090212: +2024-09-08 08:02:19.104286: Epoch 423 +2024-09-08 08:02:19.105099: Current learning rate: 0.0061 +2024-09-08 08:07:05.076993: train_loss -0.7238 +2024-09-08 08:07:05.090237: val_loss -0.6932 +2024-09-08 08:07:05.091145: Pseudo dice [0.9644, 0.9588, 0.9787, 0.9403, 0.8533, 0.9653, 0.9858] +2024-09-08 08:07:05.091892: Epoch time: 285.99 s +2024-09-08 08:07:08.137350: +2024-09-08 08:07:08.152411: Epoch 424 +2024-09-08 08:07:08.153125: Current learning rate: 0.00609 +2024-09-08 08:11:57.378681: train_loss -0.7197 +2024-09-08 08:11:57.393942: val_loss -0.7422 +2024-09-08 08:11:57.394540: Pseudo dice [0.9613, 0.957, 0.9783, 0.95, 0.8502, 0.9603, 0.9895] +2024-09-08 08:11:57.395114: Epoch time: 289.25 s +2024-09-08 08:12:01.649635: +2024-09-08 08:12:01.650611: Epoch 425 +2024-09-08 08:12:01.651283: Current learning rate: 0.00608 +2024-09-08 08:16:36.565814: train_loss -0.7391 +2024-09-08 08:16:36.581043: val_loss -0.7615 +2024-09-08 08:16:36.581843: Pseudo dice [0.9648, 0.9583, 0.9795, 0.9515, 0.8586, 0.9695, 0.9908] +2024-09-08 08:16:36.582673: Epoch time: 274.92 s +2024-09-08 08:16:38.534082: +2024-09-08 08:16:38.534970: Epoch 426 +2024-09-08 08:16:38.535664: Current learning rate: 0.00607 +2024-09-08 08:21:13.612743: train_loss -0.7404 +2024-09-08 08:21:13.629608: val_loss -0.7458 +2024-09-08 08:21:13.630304: Pseudo dice [0.9661, 0.9589, 0.9812, 0.9477, 0.8808, 0.9662, 0.992] +2024-09-08 08:21:13.630837: Epoch time: 275.08 s +2024-09-08 08:21:16.666022: +2024-09-08 08:21:16.667156: Epoch 427 +2024-09-08 08:21:16.668455: Current learning rate: 0.00606 +2024-09-08 08:26:02.271393: train_loss -0.7445 +2024-09-08 08:26:02.284708: val_loss -0.7544 +2024-09-08 08:26:02.285416: Pseudo dice [0.9643, 0.9582, 0.9799, 0.9478, 0.8789, 0.9716, 0.9903] +2024-09-08 08:26:02.285905: Epoch time: 285.63 s +2024-09-08 08:26:05.482395: +2024-09-08 08:26:05.496532: Epoch 428 +2024-09-08 08:26:05.497360: Current learning rate: 0.00605 +2024-09-08 08:30:38.453242: train_loss -0.7371 +2024-09-08 08:30:38.468756: val_loss -0.7429 +2024-09-08 08:30:38.469408: Pseudo dice [0.9603, 0.9551, 0.9761, 0.9441, 0.8615, 0.9672, 0.9888] +2024-09-08 08:30:38.470107: Epoch time: 272.98 s +2024-09-08 08:30:41.704401: +2024-09-08 08:30:41.716837: Epoch 429 +2024-09-08 08:30:41.717662: Current learning rate: 0.00604 +2024-09-08 08:35:09.136237: train_loss -0.741 +2024-09-08 08:35:09.138193: val_loss -0.766 +2024-09-08 08:35:09.138626: Pseudo dice [0.9638, 0.9573, 0.9792, 0.9481, 0.8754, 0.9714, 0.9915] +2024-09-08 08:35:09.139398: Epoch time: 267.44 s +2024-09-08 08:35:11.167464: +2024-09-08 08:35:11.169136: Epoch 430 +2024-09-08 08:35:11.169874: Current learning rate: 0.00603 +2024-09-08 08:39:47.998859: train_loss -0.7386 +2024-09-08 08:39:48.025909: val_loss -0.7423 +2024-09-08 08:39:48.026582: Pseudo dice [0.966, 0.9604, 0.9803, 0.9516, 0.88, 0.9691, 0.9913] +2024-09-08 08:39:48.027131: Epoch time: 276.84 s +2024-09-08 08:39:48.027980: Yayy! New best EMA pseudo Dice: 0.9531 +2024-09-08 08:39:52.897735: +2024-09-08 08:39:52.899119: Epoch 431 +2024-09-08 08:39:52.899678: Current learning rate: 0.00602 +2024-09-08 08:44:19.176020: train_loss -0.7433 +2024-09-08 08:44:19.192432: val_loss -0.7594 +2024-09-08 08:44:19.193481: Pseudo dice [0.964, 0.9574, 0.9798, 0.9485, 0.8761, 0.9635, 0.9915] +2024-09-08 08:44:19.194652: Epoch time: 266.28 s +2024-09-08 08:44:19.195271: Yayy! New best EMA pseudo Dice: 0.9532 +2024-09-08 08:44:24.287724: +2024-09-08 08:44:24.288585: Epoch 432 +2024-09-08 08:44:24.289621: Current learning rate: 0.00601 +2024-09-08 08:48:56.047546: train_loss -0.7462 +2024-09-08 08:48:56.075791: val_loss -0.7301 +2024-09-08 08:48:56.077080: Pseudo dice [0.9635, 0.9587, 0.9753, 0.9534, 0.8663, 0.9688, 0.9915] +2024-09-08 08:48:56.077764: Epoch time: 271.76 s +2024-09-08 08:48:56.078608: Yayy! New best EMA pseudo Dice: 0.9533 +2024-09-08 08:49:01.617318: +2024-09-08 08:49:01.618092: Epoch 433 +2024-09-08 08:49:01.618860: Current learning rate: 0.006 +2024-09-08 08:53:34.412666: train_loss -0.7258 +2024-09-08 08:53:34.427341: val_loss -0.7855 +2024-09-08 08:53:34.428288: Pseudo dice [0.9652, 0.9588, 0.9798, 0.951, 0.8735, 0.9698, 0.9911] +2024-09-08 08:53:34.428846: Epoch time: 272.8 s +2024-09-08 08:53:34.429378: Yayy! New best EMA pseudo Dice: 0.9535 +2024-09-08 08:53:38.877488: +2024-09-08 08:53:38.896415: Epoch 434 +2024-09-08 08:53:38.897361: Current learning rate: 0.00599 +2024-09-08 08:58:15.673039: train_loss -0.7443 +2024-09-08 08:58:15.684889: val_loss -0.7288 +2024-09-08 08:58:15.685495: Pseudo dice [0.962, 0.9553, 0.9777, 0.9477, 0.8595, 0.9634, 0.9868] +2024-09-08 08:58:15.686707: Epoch time: 276.8 s +2024-09-08 08:58:19.993075: +2024-09-08 08:58:19.994365: Epoch 435 +2024-09-08 08:58:19.995552: Current learning rate: 0.00598 +2024-09-08 09:03:05.120696: train_loss -0.7456 +2024-09-08 09:03:05.135335: val_loss -0.7553 +2024-09-08 09:03:05.135975: Pseudo dice [0.9633, 0.9576, 0.9773, 0.948, 0.874, 0.9672, 0.9893] +2024-09-08 09:03:05.136507: Epoch time: 285.13 s +2024-09-08 09:03:09.448594: +2024-09-08 09:03:09.458641: Epoch 436 +2024-09-08 09:03:09.459513: Current learning rate: 0.00597 +2024-09-08 09:08:13.405124: train_loss -0.7269 +2024-09-08 09:08:13.425016: val_loss -0.7768 +2024-09-08 09:08:13.425620: Pseudo dice [0.9643, 0.9579, 0.9775, 0.9469, 0.8685, 0.9682, 0.9883] +2024-09-08 09:08:13.426120: Epoch time: 303.96 s +2024-09-08 09:08:17.274575: +2024-09-08 09:08:17.282861: Epoch 437 +2024-09-08 09:08:17.283921: Current learning rate: 0.00596 +2024-09-08 09:13:11.738591: train_loss -0.7414 +2024-09-08 09:13:11.741165: val_loss -0.7061 +2024-09-08 09:13:11.742334: Pseudo dice [0.9607, 0.9577, 0.9745, 0.9446, 0.8593, 0.9644, 0.9889] +2024-09-08 09:13:11.742894: Epoch time: 294.47 s +2024-09-08 09:13:14.023639: +2024-09-08 09:13:14.025067: Epoch 438 +2024-09-08 09:13:14.025946: Current learning rate: 0.00595 +2024-09-08 09:18:11.808816: train_loss -0.7453 +2024-09-08 09:18:11.828659: val_loss -0.7649 +2024-09-08 09:18:11.829284: Pseudo dice [0.9633, 0.9576, 0.9778, 0.9468, 0.8723, 0.9713, 0.9901] +2024-09-08 09:18:11.829819: Epoch time: 297.79 s +2024-09-08 09:18:15.878048: +2024-09-08 09:18:15.892728: Epoch 439 +2024-09-08 09:18:15.893859: Current learning rate: 0.00594 +2024-09-08 09:22:47.540752: train_loss -0.7321 +2024-09-08 09:22:47.557376: val_loss -0.7171 +2024-09-08 09:22:47.557983: Pseudo dice [0.9636, 0.9585, 0.9796, 0.9427, 0.8693, 0.969, 0.988] +2024-09-08 09:22:47.558476: Epoch time: 271.67 s +2024-09-08 09:22:50.931998: +2024-09-08 09:22:50.945727: Epoch 440 +2024-09-08 09:22:50.946419: Current learning rate: 0.00593 +2024-09-08 09:27:31.305115: train_loss -0.7259 +2024-09-08 09:27:31.306319: val_loss -0.7298 +2024-09-08 09:27:31.306819: Pseudo dice [0.9638, 0.9573, 0.9799, 0.9475, 0.8742, 0.9669, 0.9896] +2024-09-08 09:27:31.307284: Epoch time: 280.38 s +2024-09-08 09:27:33.360057: +2024-09-08 09:27:33.368863: Epoch 441 +2024-09-08 09:27:33.370065: Current learning rate: 0.00592 +2024-09-08 09:31:50.235046: train_loss -0.7418 +2024-09-08 09:31:50.248972: val_loss -0.742 +2024-09-08 09:31:50.249537: Pseudo dice [0.961, 0.9544, 0.9766, 0.9531, 0.8699, 0.9703, 0.9913] +2024-09-08 09:31:50.250056: Epoch time: 256.88 s +2024-09-08 09:31:53.677500: +2024-09-08 09:31:53.692350: Epoch 442 +2024-09-08 09:31:53.693120: Current learning rate: 0.00592 +2024-09-08 09:36:36.349734: train_loss -0.7091 +2024-09-08 09:36:36.353318: val_loss -0.747 +2024-09-08 09:36:36.354146: Pseudo dice [0.961, 0.9561, 0.9741, 0.9469, 0.8683, 0.9686, 0.9885] +2024-09-08 09:36:36.354878: Epoch time: 282.68 s +2024-09-08 09:36:38.898738: +2024-09-08 09:36:38.912314: Epoch 443 +2024-09-08 09:36:38.912998: Current learning rate: 0.00591 +2024-09-08 09:41:30.879353: train_loss -0.7463 +2024-09-08 09:41:30.881398: val_loss -0.7807 +2024-09-08 09:41:30.884803: Pseudo dice [0.963, 0.957, 0.9774, 0.9414, 0.8721, 0.9704, 0.9912] +2024-09-08 09:41:30.885758: Epoch time: 291.99 s +2024-09-08 09:41:35.480605: +2024-09-08 09:41:35.497084: Epoch 444 +2024-09-08 09:41:35.498196: Current learning rate: 0.0059 +2024-09-08 09:46:28.111218: train_loss -0.7373 +2024-09-08 09:46:28.133878: val_loss -0.7297 +2024-09-08 09:46:28.134509: Pseudo dice [0.9633, 0.956, 0.9794, 0.9428, 0.8661, 0.9707, 0.9906] +2024-09-08 09:46:28.135001: Epoch time: 292.63 s +2024-09-08 09:46:32.483340: +2024-09-08 09:46:32.507755: Epoch 445 +2024-09-08 09:46:32.516580: Current learning rate: 0.00589 +2024-09-08 09:51:18.458857: train_loss -0.7477 +2024-09-08 09:51:18.474284: val_loss -0.7547 +2024-09-08 09:51:18.475072: Pseudo dice [0.962, 0.9563, 0.975, 0.9437, 0.8746, 0.9616, 0.9904] +2024-09-08 09:51:18.476141: Epoch time: 285.98 s +2024-09-08 09:51:22.542915: +2024-09-08 09:51:22.543994: Epoch 446 +2024-09-08 09:51:22.544759: Current learning rate: 0.00588 +2024-09-08 09:57:10.944582: train_loss -0.7182 +2024-09-08 09:57:10.967875: val_loss -0.7763 +2024-09-08 09:57:10.968704: Pseudo dice [0.9662, 0.9581, 0.9813, 0.9536, 0.8686, 0.9666, 0.9906] +2024-09-08 09:57:10.969384: Epoch time: 348.41 s +2024-09-08 09:57:14.840603: +2024-09-08 09:57:14.852614: Epoch 447 +2024-09-08 09:57:14.853506: Current learning rate: 0.00587 +2024-09-08 10:02:25.189314: train_loss -0.7364 +2024-09-08 10:02:25.206264: val_loss -0.7327 +2024-09-08 10:02:25.206967: Pseudo dice [0.9627, 0.9573, 0.9789, 0.9468, 0.8839, 0.9728, 0.9923] +2024-09-08 10:02:25.207716: Epoch time: 310.35 s +2024-09-08 10:02:29.165924: +2024-09-08 10:02:29.166973: Epoch 448 +2024-09-08 10:02:29.167690: Current learning rate: 0.00586 +2024-09-08 10:07:23.298169: train_loss -0.7478 +2024-09-08 10:07:23.308639: val_loss -0.7471 +2024-09-08 10:07:23.309225: Pseudo dice [0.9637, 0.9588, 0.9754, 0.9473, 0.8813, 0.9708, 0.9912] +2024-09-08 10:07:23.309756: Epoch time: 294.14 s +2024-09-08 10:07:23.310180: Yayy! New best EMA pseudo Dice: 0.9537 +2024-09-08 10:07:30.399394: +2024-09-08 10:07:30.405236: Epoch 449 +2024-09-08 10:07:30.405912: Current learning rate: 0.00585 +2024-09-08 10:12:22.842064: train_loss -0.7315 +2024-09-08 10:12:22.857921: val_loss -0.7415 +2024-09-08 10:12:22.858720: Pseudo dice [0.9629, 0.957, 0.9785, 0.9466, 0.869, 0.9613, 0.9904] +2024-09-08 10:12:22.859258: Epoch time: 292.45 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_14_03_50.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_14_03_50.txt new file mode 100644 index 0000000000000000000000000000000000000000..b02d1e3ac4208a80f751d6372389d54fe4d6ef98 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_14_03_50.txt @@ -0,0 +1,377 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 14:03:54.273034: Using torch.compile... +2024-09-08 14:04:00.812350: do_dummy_2d_data_aug: False +2024-09-08 14:04:00.817891: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 14:04:00.819102: The split file contains 5 splits. +2024-09-08 14:04:00.819449: Desired fold for training: 0 +2024-09-08 14:04:00.819814: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 14:04:14.637826: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 14:04:15.432646: +2024-09-08 14:04:15.448308: Epoch 450 +2024-09-08 14:04:15.449104: Current learning rate: 0.00584 +2024-09-08 14:19:21.055386: train_loss -0.729 +2024-09-08 14:19:21.056510: val_loss -0.7575 +2024-09-08 14:19:21.057150: Pseudo dice [0.9644, 0.9568, 0.9797, 0.9501, 0.8764, 0.9656, 0.9895] +2024-09-08 14:19:21.057648: Epoch time: 905.63 s +2024-09-08 14:19:22.966642: +2024-09-08 14:19:22.967255: Epoch 451 +2024-09-08 14:19:22.967729: Current learning rate: 0.00583 +2024-09-08 14:23:56.220978: train_loss -0.7364 +2024-09-08 14:23:56.232841: val_loss -0.7659 +2024-09-08 14:23:56.234119: Pseudo dice [0.9637, 0.9574, 0.9795, 0.9529, 0.8816, 0.967, 0.9906] +2024-09-08 14:23:56.235387: Epoch time: 273.26 s +2024-09-08 14:23:56.236839: Yayy! New best EMA pseudo Dice: 0.9539 +2024-09-08 14:24:02.555536: +2024-09-08 14:24:02.556917: Epoch 452 +2024-09-08 14:24:02.557823: Current learning rate: 0.00582 +2024-09-08 14:28:43.547737: train_loss -0.7445 +2024-09-08 14:28:43.564761: val_loss -0.7633 +2024-09-08 14:28:43.566080: Pseudo dice [0.963, 0.9583, 0.9779, 0.9511, 0.8653, 0.9657, 0.9884] +2024-09-08 14:28:43.567370: Epoch time: 281.0 s +2024-09-08 14:28:46.936069: +2024-09-08 14:28:46.937122: Epoch 453 +2024-09-08 14:28:46.938704: Current learning rate: 0.00581 +2024-09-08 14:33:15.625608: train_loss -0.7446 +2024-09-08 14:33:15.642755: val_loss -0.7644 +2024-09-08 14:33:15.644174: Pseudo dice [0.9627, 0.9581, 0.9783, 0.9542, 0.8911, 0.9631, 0.9922] +2024-09-08 14:33:15.646398: Epoch time: 268.69 s +2024-09-08 14:33:15.648586: Yayy! New best EMA pseudo Dice: 0.9541 +2024-09-08 14:33:21.765078: +2024-09-08 14:33:21.766016: Epoch 454 +2024-09-08 14:33:21.767278: Current learning rate: 0.0058 +2024-09-08 14:38:06.244682: train_loss -0.734 +2024-09-08 14:38:06.247196: val_loss -0.776 +2024-09-08 14:38:06.248614: Pseudo dice [0.964, 0.9565, 0.9789, 0.9427, 0.8638, 0.9643, 0.9915] +2024-09-08 14:38:06.249601: Epoch time: 284.48 s +2024-09-08 14:38:09.352744: +2024-09-08 14:38:09.360338: Epoch 455 +2024-09-08 14:38:09.362347: Current learning rate: 0.00579 +2024-09-08 14:42:46.768827: train_loss -0.7482 +2024-09-08 14:42:46.775206: val_loss -0.734 +2024-09-08 14:42:46.775881: Pseudo dice [0.9622, 0.9589, 0.9758, 0.949, 0.8878, 0.9666, 0.9912] +2024-09-08 14:42:46.776459: Epoch time: 277.42 s +2024-09-08 14:42:49.901395: +2024-09-08 14:42:49.902648: Epoch 456 +2024-09-08 14:42:49.904244: Current learning rate: 0.00578 +2024-09-08 14:47:20.604915: train_loss -0.7488 +2024-09-08 14:47:20.608722: val_loss -0.7434 +2024-09-08 14:47:20.610051: Pseudo dice [0.964, 0.9582, 0.9783, 0.9504, 0.8749, 0.9695, 0.9902] +2024-09-08 14:47:20.611022: Epoch time: 270.71 s +2024-09-08 14:47:20.611476: Yayy! New best EMA pseudo Dice: 0.9542 +2024-09-08 14:47:27.046451: +2024-09-08 14:47:27.047326: Epoch 457 +2024-09-08 14:47:27.047998: Current learning rate: 0.00577 +2024-09-08 14:51:58.114892: train_loss -0.7331 +2024-09-08 14:51:58.134455: val_loss -0.7469 +2024-09-08 14:51:58.135078: Pseudo dice [0.9628, 0.958, 0.9773, 0.9549, 0.8823, 0.9688, 0.9902] +2024-09-08 14:51:58.135691: Epoch time: 271.08 s +2024-09-08 14:51:58.136132: Yayy! New best EMA pseudo Dice: 0.9544 +2024-09-08 14:52:03.530777: +2024-09-08 14:52:03.536152: Epoch 458 +2024-09-08 14:52:03.536934: Current learning rate: 0.00576 +2024-09-08 14:56:39.930537: train_loss -0.7468 +2024-09-08 14:56:39.944676: val_loss -0.7407 +2024-09-08 14:56:39.945642: Pseudo dice [0.9655, 0.9588, 0.98, 0.9503, 0.8802, 0.9707, 0.991] +2024-09-08 14:56:39.947066: Epoch time: 276.4 s +2024-09-08 14:56:39.948561: Yayy! New best EMA pseudo Dice: 0.9546 +2024-09-08 14:56:45.622419: +2024-09-08 14:56:45.625689: Epoch 459 +2024-09-08 14:56:45.631925: Current learning rate: 0.00575 +2024-09-08 15:01:14.803575: train_loss -0.7575 +2024-09-08 15:01:14.805884: val_loss -0.7663 +2024-09-08 15:01:14.806454: Pseudo dice [0.9664, 0.9581, 0.9804, 0.9582, 0.8767, 0.9718, 0.9911] +2024-09-08 15:01:14.806988: Epoch time: 269.19 s +2024-09-08 15:01:14.807405: Yayy! New best EMA pseudo Dice: 0.9549 +2024-09-08 15:01:20.098241: +2024-09-08 15:01:20.112271: Epoch 460 +2024-09-08 15:01:20.113254: Current learning rate: 0.00574 +2024-09-08 15:05:53.326271: train_loss -0.7451 +2024-09-08 15:05:53.333956: val_loss -0.749 +2024-09-08 15:05:53.338461: Pseudo dice [0.9639, 0.9552, 0.9783, 0.9365, 0.8679, 0.9691, 0.9877] +2024-09-08 15:05:53.339280: Epoch time: 273.24 s +2024-09-08 15:05:56.845785: +2024-09-08 15:05:56.847011: Epoch 461 +2024-09-08 15:05:56.847887: Current learning rate: 0.00573 +2024-09-08 15:10:43.252251: train_loss -0.7356 +2024-09-08 15:10:43.269571: val_loss -0.75 +2024-09-08 15:10:43.270249: Pseudo dice [0.9604, 0.9554, 0.9768, 0.9473, 0.8806, 0.9637, 0.9907] +2024-09-08 15:10:43.270705: Epoch time: 286.41 s +2024-09-08 15:10:46.929237: +2024-09-08 15:10:46.930241: Epoch 462 +2024-09-08 15:10:46.931373: Current learning rate: 0.00572 +2024-09-08 15:15:25.175457: train_loss -0.743 +2024-09-08 15:15:25.205906: val_loss -0.7194 +2024-09-08 15:15:25.207107: Pseudo dice [0.9623, 0.9578, 0.9766, 0.9507, 0.8564, 0.9618, 0.9886] +2024-09-08 15:15:25.207915: Epoch time: 278.28 s +2024-09-08 15:15:29.217550: +2024-09-08 15:15:29.218421: Epoch 463 +2024-09-08 15:15:29.218971: Current learning rate: 0.00571 +2024-09-08 15:19:58.829310: train_loss -0.7397 +2024-09-08 15:19:58.839213: val_loss -0.7296 +2024-09-08 15:19:58.839933: Pseudo dice [0.9644, 0.958, 0.9798, 0.9523, 0.8692, 0.9669, 0.9894] +2024-09-08 15:19:58.840410: Epoch time: 269.62 s +2024-09-08 15:20:02.190151: +2024-09-08 15:20:02.204475: Epoch 464 +2024-09-08 15:20:02.205246: Current learning rate: 0.0057 +2024-09-08 15:24:55.980764: train_loss -0.7414 +2024-09-08 15:24:56.001651: val_loss -0.746 +2024-09-08 15:24:56.002485: Pseudo dice [0.9653, 0.9595, 0.981, 0.949, 0.8794, 0.9706, 0.9904] +2024-09-08 15:24:56.003367: Epoch time: 293.8 s +2024-09-08 15:25:00.005211: +2024-09-08 15:25:00.014069: Epoch 465 +2024-09-08 15:25:00.014949: Current learning rate: 0.0057 +2024-09-08 15:29:32.115931: train_loss -0.7477 +2024-09-08 15:29:32.141378: val_loss -0.7238 +2024-09-08 15:29:32.142029: Pseudo dice [0.9641, 0.9563, 0.9779, 0.9536, 0.87, 0.9691, 0.9908] +2024-09-08 15:29:32.142523: Epoch time: 272.12 s +2024-09-08 15:29:35.843097: +2024-09-08 15:29:35.844414: Epoch 466 +2024-09-08 15:29:35.845122: Current learning rate: 0.00569 +2024-09-08 15:34:31.367184: train_loss -0.747 +2024-09-08 15:34:31.389351: val_loss -0.7874 +2024-09-08 15:34:31.390003: Pseudo dice [0.9639, 0.9581, 0.9799, 0.9528, 0.881, 0.9711, 0.9905] +2024-09-08 15:34:31.390546: Epoch time: 295.53 s +2024-09-08 15:34:36.070287: +2024-09-08 15:34:36.071303: Epoch 467 +2024-09-08 15:34:36.071917: Current learning rate: 0.00568 +2024-09-08 15:39:21.476404: train_loss -0.7671 +2024-09-08 15:39:21.478941: val_loss -0.7385 +2024-09-08 15:39:21.481158: Pseudo dice [0.9632, 0.9573, 0.9789, 0.9501, 0.8693, 0.9678, 0.9906] +2024-09-08 15:39:21.483124: Epoch time: 285.41 s +2024-09-08 15:39:25.697406: +2024-09-08 15:39:25.698460: Epoch 468 +2024-09-08 15:39:25.699121: Current learning rate: 0.00567 +2024-09-08 15:44:00.497136: train_loss -0.7403 +2024-09-08 15:44:00.499443: val_loss -0.751 +2024-09-08 15:44:00.500064: Pseudo dice [0.9636, 0.9585, 0.9779, 0.9462, 0.8686, 0.9685, 0.9897] +2024-09-08 15:44:00.500610: Epoch time: 274.8 s +2024-09-08 15:44:03.263258: +2024-09-08 15:44:03.264322: Epoch 469 +2024-09-08 15:44:03.264895: Current learning rate: 0.00566 +2024-09-08 15:48:35.522401: train_loss -0.7432 +2024-09-08 15:48:35.536925: val_loss -0.754 +2024-09-08 15:48:35.537537: Pseudo dice [0.9641, 0.9567, 0.9793, 0.9517, 0.8734, 0.9692, 0.9915] +2024-09-08 15:48:35.538066: Epoch time: 272.26 s +2024-09-08 15:48:39.221956: +2024-09-08 15:48:39.223129: Epoch 470 +2024-09-08 15:48:39.223932: Current learning rate: 0.00565 +2024-09-08 15:53:12.586804: train_loss -0.7437 +2024-09-08 15:53:12.588314: val_loss -0.7458 +2024-09-08 15:53:12.589282: Pseudo dice [0.9647, 0.9561, 0.977, 0.9516, 0.8748, 0.9613, 0.9912] +2024-09-08 15:53:12.590236: Epoch time: 273.37 s +2024-09-08 15:53:15.889401: +2024-09-08 15:53:15.890385: Epoch 471 +2024-09-08 15:53:15.891900: Current learning rate: 0.00564 +2024-09-08 15:57:55.593725: train_loss -0.7554 +2024-09-08 15:57:55.596084: val_loss -0.7409 +2024-09-08 15:57:55.597616: Pseudo dice [0.9635, 0.9578, 0.9799, 0.9455, 0.8808, 0.9694, 0.9902] +2024-09-08 15:57:55.598948: Epoch time: 279.71 s +2024-09-08 15:57:57.922307: +2024-09-08 15:57:57.948412: Epoch 472 +2024-09-08 15:57:57.949350: Current learning rate: 0.00563 +2024-09-08 16:02:40.056642: train_loss -0.7385 +2024-09-08 16:02:40.059980: val_loss -0.7415 +2024-09-08 16:02:40.060819: Pseudo dice [0.9635, 0.9569, 0.9748, 0.9475, 0.8771, 0.9643, 0.9902] +2024-09-08 16:02:40.061575: Epoch time: 282.14 s +2024-09-08 16:02:43.785878: +2024-09-08 16:02:43.787081: Epoch 473 +2024-09-08 16:02:43.787759: Current learning rate: 0.00562 +2024-09-08 16:07:38.200438: train_loss -0.7291 +2024-09-08 16:07:38.219869: val_loss -0.7305 +2024-09-08 16:07:38.221150: Pseudo dice [0.9629, 0.9578, 0.9753, 0.9433, 0.879, 0.9677, 0.9914] +2024-09-08 16:07:38.222874: Epoch time: 294.42 s +2024-09-08 16:07:42.857609: +2024-09-08 16:07:42.872407: Epoch 474 +2024-09-08 16:07:42.873230: Current learning rate: 0.00561 +2024-09-08 16:12:32.885076: train_loss -0.7518 +2024-09-08 16:12:32.910330: val_loss -0.7658 +2024-09-08 16:12:32.911003: Pseudo dice [0.9637, 0.9572, 0.9779, 0.9499, 0.8671, 0.9675, 0.9879] +2024-09-08 16:12:32.911712: Epoch time: 290.03 s +2024-09-08 16:12:37.048969: +2024-09-08 16:12:37.060367: Epoch 475 +2024-09-08 16:12:37.061133: Current learning rate: 0.0056 +2024-09-08 16:17:18.160405: train_loss -0.745 +2024-09-08 16:17:18.168730: val_loss -0.7366 +2024-09-08 16:17:18.169273: Pseudo dice [0.9652, 0.9579, 0.9787, 0.9477, 0.8763, 0.9685, 0.9895] +2024-09-08 16:17:18.169785: Epoch time: 281.12 s +2024-09-08 16:17:20.886443: +2024-09-08 16:17:20.887330: Epoch 476 +2024-09-08 16:17:20.888005: Current learning rate: 0.00559 +2024-09-08 16:21:49.571983: train_loss -0.7389 +2024-09-08 16:21:49.584606: val_loss -0.7072 +2024-09-08 16:21:49.585277: Pseudo dice [0.9605, 0.9571, 0.9748, 0.9514, 0.8721, 0.9707, 0.9897] +2024-09-08 16:21:49.585841: Epoch time: 268.69 s +2024-09-08 16:21:54.276763: +2024-09-08 16:21:54.288524: Epoch 477 +2024-09-08 16:21:54.289860: Current learning rate: 0.00558 +2024-09-08 16:26:26.635769: train_loss -0.7234 +2024-09-08 16:26:26.643193: val_loss -0.7284 +2024-09-08 16:26:26.643939: Pseudo dice [0.9604, 0.955, 0.976, 0.9408, 0.8644, 0.9604, 0.9903] +2024-09-08 16:26:26.644573: Epoch time: 272.36 s +2024-09-08 16:26:30.024885: +2024-09-08 16:26:30.025684: Epoch 478 +2024-09-08 16:26:30.026194: Current learning rate: 0.00557 +2024-09-08 16:31:25.987343: train_loss -0.7155 +2024-09-08 16:31:26.001643: val_loss -0.7432 +2024-09-08 16:31:26.002565: Pseudo dice [0.9617, 0.9553, 0.9753, 0.9442, 0.8661, 0.9674, 0.9889] +2024-09-08 16:31:26.003067: Epoch time: 295.97 s +2024-09-08 16:31:29.404931: +2024-09-08 16:31:29.410775: Epoch 479 +2024-09-08 16:31:29.411551: Current learning rate: 0.00556 +2024-09-08 16:36:14.983012: train_loss -0.7494 +2024-09-08 16:36:14.984317: val_loss -0.773 +2024-09-08 16:36:14.984837: Pseudo dice [0.966, 0.9591, 0.9794, 0.9505, 0.877, 0.9698, 0.9909] +2024-09-08 16:36:14.985323: Epoch time: 285.58 s +2024-09-08 16:36:18.768875: +2024-09-08 16:36:18.769656: Epoch 480 +2024-09-08 16:36:18.770175: Current learning rate: 0.00555 +2024-09-08 16:40:53.388192: train_loss -0.7246 +2024-09-08 16:40:53.395907: val_loss -0.721 +2024-09-08 16:40:53.396740: Pseudo dice [0.9623, 0.956, 0.9726, 0.9471, 0.8743, 0.9671, 0.987] +2024-09-08 16:40:53.397686: Epoch time: 274.62 s +2024-09-08 16:41:00.589523: +2024-09-08 16:41:00.604748: Epoch 481 +2024-09-08 16:41:00.605890: Current learning rate: 0.00554 +2024-09-08 16:45:48.452660: train_loss -0.7505 +2024-09-08 16:45:48.472543: val_loss -0.7647 +2024-09-08 16:45:48.473340: Pseudo dice [0.9639, 0.9572, 0.9793, 0.951, 0.8753, 0.9669, 0.9898] +2024-09-08 16:45:48.474020: Epoch time: 287.87 s +2024-09-08 16:45:50.852355: +2024-09-08 16:45:50.865699: Epoch 482 +2024-09-08 16:45:50.866596: Current learning rate: 0.00553 +2024-09-08 16:50:31.511200: train_loss -0.7407 +2024-09-08 16:50:31.524805: val_loss -0.733 +2024-09-08 16:50:31.525574: Pseudo dice [0.9642, 0.9607, 0.9784, 0.9513, 0.8851, 0.9671, 0.9899] +2024-09-08 16:50:31.526270: Epoch time: 280.66 s +2024-09-08 16:50:34.909134: +2024-09-08 16:50:34.916449: Epoch 483 +2024-09-08 16:50:34.917330: Current learning rate: 0.00552 +2024-09-08 16:55:19.692008: train_loss -0.7429 +2024-09-08 16:55:19.697055: val_loss -0.7558 +2024-09-08 16:55:19.697802: Pseudo dice [0.9648, 0.9577, 0.9806, 0.9475, 0.8803, 0.9718, 0.9918] +2024-09-08 16:55:19.698457: Epoch time: 284.8 s +2024-09-08 16:55:24.267371: +2024-09-08 16:55:24.268308: Epoch 484 +2024-09-08 16:55:24.269118: Current learning rate: 0.00551 +2024-09-08 17:00:19.173885: train_loss -0.7353 +2024-09-08 17:00:19.191831: val_loss -0.7314 +2024-09-08 17:00:19.192655: Pseudo dice [0.9651, 0.9563, 0.9798, 0.9481, 0.8754, 0.969, 0.9923] +2024-09-08 17:00:19.193178: Epoch time: 294.91 s +2024-09-08 17:00:22.829947: +2024-09-08 17:00:22.830877: Epoch 485 +2024-09-08 17:00:22.831615: Current learning rate: 0.0055 +2024-09-08 17:04:52.509879: train_loss -0.7482 +2024-09-08 17:04:52.521424: val_loss -0.7194 +2024-09-08 17:04:52.522354: Pseudo dice [0.9626, 0.9606, 0.9761, 0.9512, 0.8726, 0.9678, 0.9907] +2024-09-08 17:04:52.522909: Epoch time: 269.69 s +2024-09-08 17:04:56.827775: +2024-09-08 17:04:56.852458: Epoch 486 +2024-09-08 17:04:56.853212: Current learning rate: 0.00549 +2024-09-08 17:09:43.398751: train_loss -0.7411 +2024-09-08 17:09:43.412667: val_loss -0.7377 +2024-09-08 17:09:43.413243: Pseudo dice [0.9592, 0.9545, 0.9762, 0.9477, 0.8708, 0.9681, 0.9892] +2024-09-08 17:09:43.413745: Epoch time: 286.58 s +2024-09-08 17:09:47.497977: +2024-09-08 17:09:47.512257: Epoch 487 +2024-09-08 17:09:47.513063: Current learning rate: 0.00548 +2024-09-08 17:14:37.665298: train_loss -0.7496 +2024-09-08 17:14:37.685325: val_loss -0.7761 +2024-09-08 17:14:37.685966: Pseudo dice [0.9643, 0.9576, 0.9777, 0.9475, 0.8694, 0.9718, 0.9892] +2024-09-08 17:14:37.686655: Epoch time: 290.17 s +2024-09-08 17:14:41.808928: +2024-09-08 17:14:41.820244: Epoch 488 +2024-09-08 17:14:41.820826: Current learning rate: 0.00547 +2024-09-08 17:19:33.705006: train_loss -0.7168 +2024-09-08 17:19:33.707323: val_loss -0.6909 +2024-09-08 17:19:33.707994: Pseudo dice [0.9602, 0.9537, 0.9736, 0.9381, 0.859, 0.9597, 0.9837] +2024-09-08 17:19:33.709045: Epoch time: 291.9 s +2024-09-08 17:19:38.073471: +2024-09-08 17:19:38.084363: Epoch 489 +2024-09-08 17:19:38.086061: Current learning rate: 0.00546 +2024-09-08 17:24:19.430495: train_loss -0.7143 +2024-09-08 17:24:19.432654: val_loss -0.7146 +2024-09-08 17:24:19.433167: Pseudo dice [0.9591, 0.9546, 0.9706, 0.9413, 0.859, 0.9665, 0.9911] +2024-09-08 17:24:19.433730: Epoch time: 281.36 s +2024-09-08 17:24:23.686472: +2024-09-08 17:24:23.700433: Epoch 490 +2024-09-08 17:24:23.701468: Current learning rate: 0.00546 +2024-09-08 17:29:12.087641: train_loss -0.7267 +2024-09-08 17:29:12.102175: val_loss -0.7364 +2024-09-08 17:29:12.102848: Pseudo dice [0.9636, 0.9572, 0.9763, 0.9481, 0.8701, 0.9659, 0.9867] +2024-09-08 17:29:12.103354: Epoch time: 288.41 s +2024-09-08 17:29:16.392080: +2024-09-08 17:29:16.408401: Epoch 491 +2024-09-08 17:29:16.409109: Current learning rate: 0.00545 +2024-09-08 17:34:05.145178: train_loss -0.7127 +2024-09-08 17:34:05.156855: val_loss -0.746 +2024-09-08 17:34:05.158473: Pseudo dice [0.9568, 0.9508, 0.9748, 0.9336, 0.8547, 0.9572, 0.989] +2024-09-08 17:34:05.159881: Epoch time: 288.76 s +2024-09-08 17:34:09.269094: +2024-09-08 17:34:09.270730: Epoch 492 +2024-09-08 17:34:09.272146: Current learning rate: 0.00544 +2024-09-08 17:38:44.515565: train_loss -0.7265 +2024-09-08 17:38:44.533928: val_loss -0.7307 +2024-09-08 17:38:44.534496: Pseudo dice [0.9606, 0.9536, 0.9769, 0.9381, 0.8623, 0.9613, 0.9899] +2024-09-08 17:38:44.534989: Epoch time: 275.25 s +2024-09-08 17:38:48.620709: +2024-09-08 17:38:48.621719: Epoch 493 +2024-09-08 17:38:48.622262: Current learning rate: 0.00543 +2024-09-08 17:43:21.038129: train_loss -0.7207 +2024-09-08 17:43:21.040355: val_loss -0.7683 +2024-09-08 17:43:21.041092: Pseudo dice [0.9626, 0.955, 0.9759, 0.9484, 0.8672, 0.9693, 0.9916] +2024-09-08 17:43:21.041804: Epoch time: 272.42 s +2024-09-08 17:43:24.900459: +2024-09-08 17:43:24.901376: Epoch 494 +2024-09-08 17:43:24.902142: Current learning rate: 0.00542 +2024-09-08 17:48:20.685215: train_loss -0.7517 +2024-09-08 17:48:20.715850: val_loss -0.748 +2024-09-08 17:48:20.716600: Pseudo dice [0.9633, 0.9581, 0.9784, 0.9481, 0.8911, 0.9702, 0.9916] +2024-09-08 17:48:20.717116: Epoch time: 295.79 s +2024-09-08 17:48:25.174015: +2024-09-08 17:48:25.175609: Epoch 495 +2024-09-08 17:48:25.177071: Current learning rate: 0.00541 +2024-09-08 17:52:59.490682: train_loss -0.7397 +2024-09-08 17:52:59.511573: val_loss -0.7427 +2024-09-08 17:52:59.512279: Pseudo dice [0.9634, 0.9563, 0.9786, 0.9489, 0.8753, 0.9672, 0.9905] +2024-09-08 17:52:59.513068: Epoch time: 274.32 s +2024-09-08 17:53:03.957477: +2024-09-08 17:53:03.961877: Epoch 496 +2024-09-08 17:53:03.962720: Current learning rate: 0.0054 +2024-09-08 17:57:40.447337: train_loss -0.7366 +2024-09-08 17:57:40.449561: val_loss -0.7436 +2024-09-08 17:57:40.450066: Pseudo dice [0.9651, 0.9591, 0.9779, 0.9477, 0.877, 0.9725, 0.9909] +2024-09-08 17:57:40.450645: Epoch time: 276.49 s +2024-09-08 17:57:44.504834: +2024-09-08 17:57:44.516523: Epoch 497 +2024-09-08 17:57:44.517441: Current learning rate: 0.00539 +2024-09-08 18:02:30.814933: train_loss -0.7359 +2024-09-08 18:02:30.827422: val_loss -0.7497 +2024-09-08 18:02:30.828358: Pseudo dice [0.9635, 0.956, 0.9799, 0.9495, 0.8774, 0.9645, 0.9915] +2024-09-08 18:02:30.829067: Epoch time: 286.32 s +2024-09-08 18:02:34.890568: +2024-09-08 18:02:34.891672: Epoch 498 +2024-09-08 18:02:34.893206: Current learning rate: 0.00538 +2024-09-08 18:06:51.609505: train_loss -0.7106 +2024-09-08 18:06:51.632809: val_loss -0.6811 +2024-09-08 18:06:51.633418: Pseudo dice [0.9583, 0.9556, 0.9717, 0.9371, 0.8567, 0.9608, 0.9867] +2024-09-08 18:06:51.633897: Epoch time: 256.73 s +2024-09-08 18:06:55.066619: +2024-09-08 18:06:55.076257: Epoch 499 +2024-09-08 18:06:55.076997: Current learning rate: 0.00537 +2024-09-08 18:11:54.942662: train_loss -0.71 +2024-09-08 18:11:54.956793: val_loss -0.7029 +2024-09-08 18:11:54.957465: Pseudo dice [0.9556, 0.9479, 0.9719, 0.9325, 0.8461, 0.9529, 0.988] +2024-09-08 18:11:54.958055: Epoch time: 299.88 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_22_03_23.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_22_03_23.txt new file mode 100644 index 0000000000000000000000000000000000000000..7ba572be9675623c2362349b55876a799b8c4482 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_8_22_03_23.txt @@ -0,0 +1,372 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 22:03:26.611473: Using torch.compile... +2024-09-08 22:03:33.033154: do_dummy_2d_data_aug: False +2024-09-08 22:03:33.045603: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 22:03:33.046647: The split file contains 5 splits. +2024-09-08 22:03:33.047061: Desired fold for training: 0 +2024-09-08 22:03:33.047448: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 22:03:56.420992: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 22:03:57.952710: +2024-09-08 22:03:57.964610: Epoch 500 +2024-09-08 22:03:57.965943: Current learning rate: 0.00536 +2024-09-08 22:19:27.422375: train_loss -0.7028 +2024-09-08 22:19:27.424257: val_loss -0.7499 +2024-09-08 22:19:27.424704: Pseudo dice [0.9565, 0.9497, 0.9719, 0.94, 0.8496, 0.9619, 0.9893] +2024-09-08 22:19:27.425285: Epoch time: 929.48 s +2024-09-08 22:19:29.612756: +2024-09-08 22:19:29.614308: Epoch 501 +2024-09-08 22:19:29.615089: Current learning rate: 0.00535 +2024-09-08 22:26:26.622248: train_loss -0.7332 +2024-09-08 22:26:26.634089: val_loss -0.7213 +2024-09-08 22:26:26.636500: Pseudo dice [0.9607, 0.953, 0.9773, 0.9425, 0.8495, 0.9603, 0.9902] +2024-09-08 22:26:26.638394: Epoch time: 417.04 s +2024-09-08 22:26:30.912707: +2024-09-08 22:26:30.939817: Epoch 502 +2024-09-08 22:26:30.941099: Current learning rate: 0.00534 +2024-09-08 22:33:41.010464: train_loss -0.7423 +2024-09-08 22:33:41.057413: val_loss -0.7489 +2024-09-08 22:33:41.058676: Pseudo dice [0.9621, 0.9582, 0.9746, 0.9483, 0.8715, 0.9684, 0.9886] +2024-09-08 22:33:41.059583: Epoch time: 430.1 s +2024-09-08 22:33:45.717312: +2024-09-08 22:33:45.740955: Epoch 503 +2024-09-08 22:33:45.742644: Current learning rate: 0.00533 +2024-09-08 22:40:55.652017: train_loss -0.7326 +2024-09-08 22:40:55.669107: val_loss -0.7486 +2024-09-08 22:40:55.669807: Pseudo dice [0.9603, 0.9546, 0.9748, 0.9484, 0.8694, 0.9687, 0.9885] +2024-09-08 22:40:55.670464: Epoch time: 429.95 s +2024-09-08 22:41:01.601744: +2024-09-08 22:41:01.603212: Epoch 504 +2024-09-08 22:41:01.604028: Current learning rate: 0.00532 +2024-09-08 22:48:04.672549: train_loss -0.7333 +2024-09-08 22:48:04.675196: val_loss -0.7604 +2024-09-08 22:48:04.675842: Pseudo dice [0.9643, 0.9561, 0.9785, 0.9446, 0.8565, 0.9662, 0.9883] +2024-09-08 22:48:04.676446: Epoch time: 423.08 s +2024-09-08 22:48:08.910458: +2024-09-08 22:48:08.911749: Epoch 505 +2024-09-08 22:48:08.913869: Current learning rate: 0.00531 +2024-09-08 22:55:06.415637: train_loss -0.7346 +2024-09-08 22:55:06.429293: val_loss -0.7515 +2024-09-08 22:55:06.430404: Pseudo dice [0.963, 0.9564, 0.9782, 0.9478, 0.8616, 0.9632, 0.9888] +2024-09-08 22:55:06.431367: Epoch time: 417.51 s +2024-09-08 22:55:11.279435: +2024-09-08 22:55:11.280894: Epoch 506 +2024-09-08 22:55:11.293131: Current learning rate: 0.0053 +2024-09-08 23:01:53.388918: train_loss -0.7486 +2024-09-08 23:01:53.393240: val_loss -0.7576 +2024-09-08 23:01:53.393996: Pseudo dice [0.9629, 0.9581, 0.9798, 0.9386, 0.8818, 0.9675, 0.9914] +2024-09-08 23:01:53.395457: Epoch time: 402.15 s +2024-09-08 23:01:58.895018: +2024-09-08 23:01:58.909301: Epoch 507 +2024-09-08 23:01:58.910817: Current learning rate: 0.00529 +2024-09-08 23:09:07.472039: train_loss -0.7394 +2024-09-08 23:09:07.482513: val_loss -0.7296 +2024-09-08 23:09:07.483279: Pseudo dice [0.9599, 0.9541, 0.9771, 0.9414, 0.8366, 0.964, 0.9859] +2024-09-08 23:09:07.483835: Epoch time: 428.62 s +2024-09-08 23:09:11.802317: +2024-09-08 23:09:11.824444: Epoch 508 +2024-09-08 23:09:11.825875: Current learning rate: 0.00528 +2024-09-08 23:16:11.654880: train_loss -0.7199 +2024-09-08 23:16:11.666627: val_loss -0.7716 +2024-09-08 23:16:11.667734: Pseudo dice [0.9601, 0.954, 0.9719, 0.9447, 0.8728, 0.9685, 0.9918] +2024-09-08 23:16:11.668591: Epoch time: 419.86 s +2024-09-08 23:16:16.208230: +2024-09-08 23:16:16.225000: Epoch 509 +2024-09-08 23:16:16.226320: Current learning rate: 0.00527 +2024-09-08 23:23:11.642759: train_loss -0.7252 +2024-09-08 23:23:11.644531: val_loss -0.7344 +2024-09-08 23:23:11.645201: Pseudo dice [0.9618, 0.9555, 0.9762, 0.9474, 0.8724, 0.9704, 0.9905] +2024-09-08 23:23:11.645857: Epoch time: 415.46 s +2024-09-08 23:23:16.833740: +2024-09-08 23:23:16.862376: Epoch 510 +2024-09-08 23:23:16.863755: Current learning rate: 0.00526 +2024-09-08 23:30:30.379908: train_loss -0.7237 +2024-09-08 23:30:30.384095: val_loss -0.7065 +2024-09-08 23:30:30.386073: Pseudo dice [0.9605, 0.9574, 0.9753, 0.9469, 0.8718, 0.9676, 0.9909] +2024-09-08 23:30:30.386997: Epoch time: 433.56 s +2024-09-08 23:30:35.647508: +2024-09-08 23:30:35.650318: Epoch 511 +2024-09-08 23:30:35.653536: Current learning rate: 0.00525 +2024-09-08 23:37:47.536136: train_loss -0.7231 +2024-09-08 23:37:47.546269: val_loss -0.7245 +2024-09-08 23:37:47.547252: Pseudo dice [0.9607, 0.9539, 0.975, 0.9384, 0.8453, 0.964, 0.9864] +2024-09-08 23:37:47.548748: Epoch time: 431.9 s +2024-09-08 23:37:51.277704: +2024-09-08 23:37:51.279005: Epoch 512 +2024-09-08 23:37:51.279802: Current learning rate: 0.00524 +2024-09-08 23:44:41.059899: train_loss -0.7425 +2024-09-08 23:44:41.091231: val_loss -0.7454 +2024-09-08 23:44:41.092193: Pseudo dice [0.9633, 0.9563, 0.9793, 0.9545, 0.8712, 0.9698, 0.9822] +2024-09-08 23:44:41.093141: Epoch time: 409.79 s +2024-09-08 23:44:46.925447: +2024-09-08 23:44:46.947827: Epoch 513 +2024-09-08 23:44:46.948973: Current learning rate: 0.00523 +2024-09-08 23:52:07.988736: train_loss -0.7325 +2024-09-08 23:52:07.990494: val_loss -0.7151 +2024-09-08 23:52:07.992243: Pseudo dice [0.9628, 0.9556, 0.9791, 0.9478, 0.8706, 0.9665, 0.9905] +2024-09-08 23:52:07.993863: Epoch time: 441.07 s +2024-09-08 23:52:13.001881: +2024-09-08 23:52:13.003160: Epoch 514 +2024-09-08 23:52:13.004694: Current learning rate: 0.00522 +2024-09-08 23:59:15.486805: train_loss -0.7415 +2024-09-08 23:59:15.498006: val_loss -0.7484 +2024-09-08 23:59:15.498791: Pseudo dice [0.9627, 0.9578, 0.9798, 0.9495, 0.8665, 0.9615, 0.9909] +2024-09-08 23:59:15.499434: Epoch time: 422.49 s +2024-09-08 23:59:22.867746: +2024-09-08 23:59:22.869993: Epoch 515 +2024-09-08 23:59:22.871035: Current learning rate: 0.00521 +2024-09-09 00:06:36.349681: train_loss -0.7198 +2024-09-09 00:06:36.377197: val_loss -0.759 +2024-09-09 00:06:36.378409: Pseudo dice [0.9591, 0.9548, 0.9714, 0.9442, 0.8573, 0.9643, 0.9897] +2024-09-09 00:06:36.379909: Epoch time: 433.49 s +2024-09-09 00:06:40.498843: +2024-09-09 00:06:40.500320: Epoch 516 +2024-09-09 00:06:40.501693: Current learning rate: 0.0052 +2024-09-09 00:13:48.139335: train_loss -0.7372 +2024-09-09 00:13:48.148175: val_loss -0.7396 +2024-09-09 00:13:48.149760: Pseudo dice [0.963, 0.9571, 0.9762, 0.9429, 0.8771, 0.9696, 0.9901] +2024-09-09 00:13:48.150892: Epoch time: 427.65 s +2024-09-09 00:13:52.609016: +2024-09-09 00:13:52.615847: Epoch 517 +2024-09-09 00:13:52.617053: Current learning rate: 0.00519 +2024-09-09 00:21:04.958247: train_loss -0.7399 +2024-09-09 00:21:04.961122: val_loss -0.7303 +2024-09-09 00:21:04.963357: Pseudo dice [0.9663, 0.9602, 0.9818, 0.9515, 0.8749, 0.9644, 0.9919] +2024-09-09 00:21:04.965775: Epoch time: 432.36 s +2024-09-09 00:21:09.383941: +2024-09-09 00:21:09.396617: Epoch 518 +2024-09-09 00:21:09.398214: Current learning rate: 0.00518 +2024-09-09 00:28:24.910494: train_loss -0.7435 +2024-09-09 00:28:24.934504: val_loss -0.7518 +2024-09-09 00:28:24.935480: Pseudo dice [0.962, 0.955, 0.9762, 0.9506, 0.8663, 0.9693, 0.9897] +2024-09-09 00:28:24.936373: Epoch time: 435.53 s +2024-09-09 00:28:28.414535: +2024-09-09 00:28:28.415797: Epoch 519 +2024-09-09 00:28:28.416475: Current learning rate: 0.00518 +2024-09-09 00:35:09.133358: train_loss -0.737 +2024-09-09 00:35:09.155640: val_loss -0.7346 +2024-09-09 00:35:09.157016: Pseudo dice [0.9601, 0.9575, 0.9765, 0.9527, 0.8622, 0.9694, 0.9898] +2024-09-09 00:35:09.158292: Epoch time: 400.72 s +2024-09-09 00:35:14.151243: +2024-09-09 00:35:14.152608: Epoch 520 +2024-09-09 00:35:14.154913: Current learning rate: 0.00517 +2024-09-09 00:42:37.157781: train_loss -0.7446 +2024-09-09 00:42:37.179786: val_loss -0.7528 +2024-09-09 00:42:37.182991: Pseudo dice [0.9619, 0.9556, 0.9752, 0.9445, 0.8681, 0.9693, 0.9912] +2024-09-09 00:42:37.185348: Epoch time: 443.01 s +2024-09-09 00:42:41.419550: +2024-09-09 00:42:41.436176: Epoch 521 +2024-09-09 00:42:41.437085: Current learning rate: 0.00516 +2024-09-09 00:49:46.179238: train_loss -0.753 +2024-09-09 00:49:46.187469: val_loss -0.7412 +2024-09-09 00:49:46.188428: Pseudo dice [0.9642, 0.9565, 0.9788, 0.9492, 0.8797, 0.9681, 0.9917] +2024-09-09 00:49:46.189920: Epoch time: 424.77 s +2024-09-09 00:49:51.086575: +2024-09-09 00:49:51.087970: Epoch 522 +2024-09-09 00:49:51.089629: Current learning rate: 0.00515 +2024-09-09 00:56:44.551215: train_loss -0.7494 +2024-09-09 00:56:44.574269: val_loss -0.7298 +2024-09-09 00:56:44.576070: Pseudo dice [0.9641, 0.9566, 0.9773, 0.952, 0.8813, 0.9695, 0.9901] +2024-09-09 00:56:44.578560: Epoch time: 413.46 s +2024-09-09 00:56:49.379161: +2024-09-09 00:56:49.380402: Epoch 523 +2024-09-09 00:56:49.381860: Current learning rate: 0.00514 +2024-09-09 01:04:00.100420: train_loss -0.752 +2024-09-09 01:04:00.105503: val_loss -0.7653 +2024-09-09 01:04:00.107009: Pseudo dice [0.9628, 0.9563, 0.9779, 0.9465, 0.8793, 0.9703, 0.9907] +2024-09-09 01:04:00.108047: Epoch time: 430.73 s +2024-09-09 01:04:05.637643: +2024-09-09 01:04:05.667927: Epoch 524 +2024-09-09 01:04:05.675185: Current learning rate: 0.00513 +2024-09-09 01:10:57.912194: train_loss -0.7427 +2024-09-09 01:10:57.926602: val_loss -0.7518 +2024-09-09 01:10:57.927423: Pseudo dice [0.9631, 0.9575, 0.9775, 0.9488, 0.8826, 0.9683, 0.991] +2024-09-09 01:10:57.928049: Epoch time: 412.28 s +2024-09-09 01:11:02.332352: +2024-09-09 01:11:02.348680: Epoch 525 +2024-09-09 01:11:02.349631: Current learning rate: 0.00512 +2024-09-09 01:18:18.272375: train_loss -0.7462 +2024-09-09 01:18:18.294726: val_loss -0.75 +2024-09-09 01:18:18.296029: Pseudo dice [0.9627, 0.9564, 0.9777, 0.9441, 0.8732, 0.9693, 0.9911] +2024-09-09 01:18:18.297350: Epoch time: 435.96 s +2024-09-09 01:18:23.093782: +2024-09-09 01:18:23.094761: Epoch 526 +2024-09-09 01:18:23.095388: Current learning rate: 0.00511 +2024-09-09 01:25:48.080901: train_loss -0.7411 +2024-09-09 01:25:48.100637: val_loss -0.7477 +2024-09-09 01:25:48.102691: Pseudo dice [0.9607, 0.9572, 0.9783, 0.9473, 0.8608, 0.9637, 0.9909] +2024-09-09 01:25:48.104345: Epoch time: 444.99 s +2024-09-09 01:25:52.252002: +2024-09-09 01:25:52.254082: Epoch 527 +2024-09-09 01:25:52.256802: Current learning rate: 0.0051 +2024-09-09 01:32:32.252851: train_loss -0.7356 +2024-09-09 01:32:32.270261: val_loss -0.7719 +2024-09-09 01:32:32.271268: Pseudo dice [0.9626, 0.9573, 0.9773, 0.9489, 0.8751, 0.9691, 0.9908] +2024-09-09 01:32:32.273330: Epoch time: 400.06 s +2024-09-09 01:32:36.966910: +2024-09-09 01:32:36.968307: Epoch 528 +2024-09-09 01:32:36.969315: Current learning rate: 0.00509 +2024-09-09 01:39:23.048784: train_loss -0.7274 +2024-09-09 01:39:23.050275: val_loss -0.7668 +2024-09-09 01:39:23.051062: Pseudo dice [0.9635, 0.9557, 0.9786, 0.9465, 0.8759, 0.9681, 0.9919] +2024-09-09 01:39:23.051639: Epoch time: 406.12 s +2024-09-09 01:39:31.634104: +2024-09-09 01:39:31.649540: Epoch 529 +2024-09-09 01:39:31.650859: Current learning rate: 0.00508 +2024-09-09 01:46:42.835015: train_loss -0.7415 +2024-09-09 01:46:42.840488: val_loss -0.7056 +2024-09-09 01:46:42.841342: Pseudo dice [0.9633, 0.9588, 0.9771, 0.9508, 0.8792, 0.9632, 0.9917] +2024-09-09 01:46:42.842641: Epoch time: 431.21 s +2024-09-09 01:46:46.792964: +2024-09-09 01:46:46.800486: Epoch 530 +2024-09-09 01:46:46.801373: Current learning rate: 0.00507 +2024-09-09 01:53:31.901354: train_loss -0.7337 +2024-09-09 01:53:31.908507: val_loss -0.7498 +2024-09-09 01:53:31.917368: Pseudo dice [0.9645, 0.9583, 0.979, 0.9502, 0.8714, 0.9656, 0.9907] +2024-09-09 01:53:31.918660: Epoch time: 405.11 s +2024-09-09 01:53:36.427330: +2024-09-09 01:53:36.428402: Epoch 531 +2024-09-09 01:53:36.429186: Current learning rate: 0.00506 +2024-09-09 02:00:36.452327: train_loss -0.7545 +2024-09-09 02:00:36.466014: val_loss -0.7585 +2024-09-09 02:00:36.466751: Pseudo dice [0.9637, 0.957, 0.9789, 0.949, 0.8861, 0.9681, 0.9898] +2024-09-09 02:00:36.467340: Epoch time: 420.03 s +2024-09-09 02:00:40.566417: +2024-09-09 02:00:40.595812: Epoch 532 +2024-09-09 02:00:40.597094: Current learning rate: 0.00505 +2024-09-09 02:07:42.646651: train_loss -0.7379 +2024-09-09 02:07:42.658346: val_loss -0.6982 +2024-09-09 02:07:42.659220: Pseudo dice [0.9651, 0.9617, 0.9755, 0.9513, 0.8796, 0.9652, 0.9921] +2024-09-09 02:07:42.660768: Epoch time: 422.12 s +2024-09-09 02:07:47.824237: +2024-09-09 02:07:47.832447: Epoch 533 +2024-09-09 02:07:47.833329: Current learning rate: 0.00504 +2024-09-09 02:15:26.030177: train_loss -0.7375 +2024-09-09 02:15:26.049806: val_loss -0.7341 +2024-09-09 02:15:26.052723: Pseudo dice [0.966, 0.957, 0.9788, 0.9501, 0.8792, 0.9731, 0.9923] +2024-09-09 02:15:26.056521: Epoch time: 458.21 s +2024-09-09 02:15:31.081652: +2024-09-09 02:15:31.093081: Epoch 534 +2024-09-09 02:15:31.094097: Current learning rate: 0.00503 +2024-09-09 02:22:16.154021: train_loss -0.742 +2024-09-09 02:22:16.156181: val_loss -0.7342 +2024-09-09 02:22:16.157982: Pseudo dice [0.9633, 0.9585, 0.9799, 0.9526, 0.8804, 0.9669, 0.9909] +2024-09-09 02:22:16.159049: Epoch time: 405.08 s +2024-09-09 02:22:20.971762: +2024-09-09 02:22:20.987857: Epoch 535 +2024-09-09 02:22:20.989058: Current learning rate: 0.00502 +2024-09-09 02:29:34.142971: train_loss -0.7405 +2024-09-09 02:29:34.161362: val_loss -0.7314 +2024-09-09 02:29:34.162304: Pseudo dice [0.9629, 0.9559, 0.9809, 0.9439, 0.8813, 0.9594, 0.9905] +2024-09-09 02:29:34.163161: Epoch time: 433.18 s +2024-09-09 02:29:38.012434: +2024-09-09 02:29:38.020862: Epoch 536 +2024-09-09 02:29:38.033003: Current learning rate: 0.00501 +2024-09-09 02:36:34.930632: train_loss -0.7403 +2024-09-09 02:36:34.945403: val_loss -0.7599 +2024-09-09 02:36:34.946938: Pseudo dice [0.9635, 0.9537, 0.9792, 0.9556, 0.8822, 0.9697, 0.9914] +2024-09-09 02:36:34.948141: Epoch time: 416.92 s +2024-09-09 02:36:40.128442: +2024-09-09 02:36:40.129683: Epoch 537 +2024-09-09 02:36:40.130308: Current learning rate: 0.005 +2024-09-09 02:43:43.711485: train_loss -0.7482 +2024-09-09 02:43:43.731683: val_loss -0.7758 +2024-09-09 02:43:43.732773: Pseudo dice [0.9628, 0.959, 0.9785, 0.9478, 0.8744, 0.9729, 0.9908] +2024-09-09 02:43:43.733778: Epoch time: 423.59 s +2024-09-09 02:43:47.941031: +2024-09-09 02:43:47.942713: Epoch 538 +2024-09-09 02:43:47.943864: Current learning rate: 0.00499 +2024-09-09 02:50:58.619169: train_loss -0.7519 +2024-09-09 02:50:58.624243: val_loss -0.7546 +2024-09-09 02:50:58.625406: Pseudo dice [0.9618, 0.9571, 0.9779, 0.9496, 0.8742, 0.9668, 0.9905] +2024-09-09 02:50:58.626124: Epoch time: 430.71 s +2024-09-09 02:51:02.172618: +2024-09-09 02:51:02.188685: Epoch 539 +2024-09-09 02:51:02.189926: Current learning rate: 0.00498 +2024-09-09 02:58:04.613096: train_loss -0.7457 +2024-09-09 02:58:04.630820: val_loss -0.7363 +2024-09-09 02:58:04.631771: Pseudo dice [0.9646, 0.9588, 0.9791, 0.9519, 0.8627, 0.9706, 0.9893] +2024-09-09 02:58:04.632935: Epoch time: 422.45 s +2024-09-09 02:58:08.603350: +2024-09-09 02:58:08.604928: Epoch 540 +2024-09-09 02:58:08.605855: Current learning rate: 0.00497 +2024-09-09 03:04:55.462516: train_loss -0.7337 +2024-09-09 03:04:55.464782: val_loss -0.7251 +2024-09-09 03:04:55.466579: Pseudo dice [0.9637, 0.959, 0.9769, 0.9515, 0.8862, 0.9727, 0.9922] +2024-09-09 03:04:55.468508: Epoch time: 406.9 s +2024-09-09 03:04:58.663852: +2024-09-09 03:04:58.665101: Epoch 541 +2024-09-09 03:04:58.667375: Current learning rate: 0.00496 +2024-09-09 03:11:54.142319: train_loss -0.7507 +2024-09-09 03:11:54.162728: val_loss -0.7477 +2024-09-09 03:11:54.163573: Pseudo dice [0.9634, 0.9574, 0.9775, 0.9491, 0.888, 0.9692, 0.9923] +2024-09-09 03:11:54.164125: Epoch time: 415.49 s +2024-09-09 03:11:54.164925: Yayy! New best EMA pseudo Dice: 0.955 +2024-09-09 03:12:01.946622: +2024-09-09 03:12:01.947476: Epoch 542 +2024-09-09 03:12:01.948216: Current learning rate: 0.00495 +2024-09-09 03:18:54.112858: train_loss -0.7527 +2024-09-09 03:18:54.125053: val_loss -0.7262 +2024-09-09 03:18:54.125736: Pseudo dice [0.965, 0.96, 0.979, 0.9519, 0.8562, 0.9675, 0.9907] +2024-09-09 03:18:54.126278: Epoch time: 412.17 s +2024-09-09 03:18:59.753225: +2024-09-09 03:18:59.758618: Epoch 543 +2024-09-09 03:18:59.759809: Current learning rate: 0.00494 +2024-09-09 03:26:25.024886: train_loss -0.755 +2024-09-09 03:26:25.041915: val_loss -0.7689 +2024-09-09 03:26:25.042770: Pseudo dice [0.9669, 0.9586, 0.9806, 0.9465, 0.875, 0.9704, 0.9911] +2024-09-09 03:26:25.043872: Epoch time: 445.29 s +2024-09-09 03:26:29.238159: +2024-09-09 03:26:29.260485: Epoch 544 +2024-09-09 03:26:29.261836: Current learning rate: 0.00493 +2024-09-09 03:33:30.646930: train_loss -0.7501 +2024-09-09 03:33:30.662839: val_loss -0.756 +2024-09-09 03:33:30.664230: Pseudo dice [0.9634, 0.9576, 0.9785, 0.9547, 0.8706, 0.9682, 0.9902] +2024-09-09 03:33:30.665212: Epoch time: 421.45 s +2024-09-09 03:33:34.497860: +2024-09-09 03:33:34.499135: Epoch 545 +2024-09-09 03:33:34.499970: Current learning rate: 0.00492 +2024-09-09 03:40:44.608960: train_loss -0.7551 +2024-09-09 03:40:44.627418: val_loss -0.7151 +2024-09-09 03:40:44.629699: Pseudo dice [0.9634, 0.9572, 0.9772, 0.9511, 0.8639, 0.9691, 0.9907] +2024-09-09 03:40:44.631454: Epoch time: 430.12 s +2024-09-09 03:40:49.792561: +2024-09-09 03:40:49.808835: Epoch 546 +2024-09-09 03:40:49.809810: Current learning rate: 0.00491 +2024-09-09 03:48:07.343748: train_loss -0.7464 +2024-09-09 03:48:07.352260: val_loss -0.7268 +2024-09-09 03:48:07.358860: Pseudo dice [0.9634, 0.9585, 0.9781, 0.9454, 0.8661, 0.9691, 0.9884] +2024-09-09 03:48:07.360052: Epoch time: 437.53 s +2024-09-09 03:48:10.925629: +2024-09-09 03:48:10.941538: Epoch 547 +2024-09-09 03:48:10.942811: Current learning rate: 0.0049 +2024-09-09 03:55:23.184469: train_loss -0.7552 +2024-09-09 03:55:23.193044: val_loss -0.7353 +2024-09-09 03:55:23.194158: Pseudo dice [0.9622, 0.9575, 0.9764, 0.9444, 0.8695, 0.9741, 0.9896] +2024-09-09 03:55:23.195702: Epoch time: 432.26 s +2024-09-09 03:55:27.070409: +2024-09-09 03:55:27.084537: Epoch 548 +2024-09-09 03:55:27.085498: Current learning rate: 0.00489 +2024-09-09 04:02:45.789802: train_loss -0.7539 +2024-09-09 04:02:45.795880: val_loss -0.726 +2024-09-09 04:02:45.796606: Pseudo dice [0.9652, 0.9581, 0.9815, 0.9559, 0.8685, 0.9694, 0.9898] +2024-09-09 04:02:45.797302: Epoch time: 438.73 s +2024-09-09 04:02:48.858298: +2024-09-09 04:02:48.872604: Epoch 549 +2024-09-09 04:02:48.874770: Current learning rate: 0.00488 +2024-09-09 04:09:57.124061: train_loss -0.754 +2024-09-09 04:09:57.145263: val_loss -0.7483 +2024-09-09 04:09:57.146107: Pseudo dice [0.9621, 0.9595, 0.9754, 0.9485, 0.8588, 0.9685, 0.9893] +2024-09-09 04:09:57.147251: Epoch time: 428.27 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_06_02_53.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_06_02_53.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae14cc544ba1fe008af334daa77fa4b7dbb6160b --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_06_02_53.txt @@ -0,0 +1,375 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 06:02:56.631961: Using torch.compile... +2024-09-09 06:03:03.025392: do_dummy_2d_data_aug: False +2024-09-09 06:03:03.039730: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 06:03:03.044098: The split file contains 5 splits. +2024-09-09 06:03:03.044453: Desired fold for training: 0 +2024-09-09 06:03:03.044780: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 06:03:25.906877: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 06:03:27.689810: +2024-09-09 06:03:27.694401: Epoch 550 +2024-09-09 06:03:27.696295: Current learning rate: 0.00487 +2024-09-09 06:20:17.913351: train_loss -0.7603 +2024-09-09 06:20:17.914644: val_loss -0.7451 +2024-09-09 06:20:17.915235: Pseudo dice [0.9663, 0.9605, 0.9814, 0.9543, 0.8816, 0.9704, 0.9906] +2024-09-09 06:20:17.915749: Epoch time: 1010.23 s +2024-09-09 06:20:19.751630: +2024-09-09 06:20:19.753316: Epoch 551 +2024-09-09 06:20:19.754013: Current learning rate: 0.00486 +2024-09-09 06:27:24.723177: train_loss -0.7402 +2024-09-09 06:27:24.741417: val_loss -0.7546 +2024-09-09 06:27:24.742229: Pseudo dice [0.9643, 0.9605, 0.9761, 0.9498, 0.8719, 0.9713, 0.9912] +2024-09-09 06:27:24.742959: Epoch time: 424.98 s +2024-09-09 06:27:28.735312: +2024-09-09 06:27:28.749013: Epoch 552 +2024-09-09 06:27:28.751254: Current learning rate: 0.00485 +2024-09-09 06:34:38.765999: train_loss -0.7352 +2024-09-09 06:34:38.775469: val_loss -0.7566 +2024-09-09 06:34:38.778468: Pseudo dice [0.9672, 0.9583, 0.9821, 0.95, 0.892, 0.974, 0.992] +2024-09-09 06:34:38.780082: Epoch time: 430.04 s +2024-09-09 06:34:38.781903: Yayy! New best EMA pseudo Dice: 0.9551 +2024-09-09 06:34:48.572045: +2024-09-09 06:34:48.573036: Epoch 553 +2024-09-09 06:34:48.573621: Current learning rate: 0.00484 +2024-09-09 06:41:51.449909: train_loss -0.7506 +2024-09-09 06:41:51.465674: val_loss -0.7387 +2024-09-09 06:41:51.468258: Pseudo dice [0.9656, 0.9598, 0.9796, 0.9511, 0.8794, 0.9696, 0.99] +2024-09-09 06:41:51.473429: Epoch time: 422.88 s +2024-09-09 06:41:51.474679: Yayy! New best EMA pseudo Dice: 0.9552 +2024-09-09 06:42:02.146034: +2024-09-09 06:42:02.146793: Epoch 554 +2024-09-09 06:42:02.147368: Current learning rate: 0.00484 +2024-09-09 06:49:16.500631: train_loss -0.7419 +2024-09-09 06:49:16.505303: val_loss -0.7441 +2024-09-09 06:49:16.509568: Pseudo dice [0.9632, 0.9578, 0.978, 0.9503, 0.8638, 0.9692, 0.9882] +2024-09-09 06:49:16.513167: Epoch time: 434.36 s +2024-09-09 06:49:20.232500: +2024-09-09 06:49:20.233488: Epoch 555 +2024-09-09 06:49:20.234432: Current learning rate: 0.00483 +2024-09-09 06:56:19.590824: train_loss -0.7409 +2024-09-09 06:56:19.605192: val_loss -0.7666 +2024-09-09 06:56:19.605913: Pseudo dice [0.9652, 0.9586, 0.9781, 0.9475, 0.8761, 0.9702, 0.9904] +2024-09-09 06:56:19.606752: Epoch time: 419.36 s +2024-09-09 06:56:23.776696: +2024-09-09 06:56:23.792892: Epoch 556 +2024-09-09 06:56:23.795945: Current learning rate: 0.00482 +2024-09-09 07:03:51.696842: train_loss -0.752 +2024-09-09 07:03:51.713595: val_loss -0.7419 +2024-09-09 07:03:51.716576: Pseudo dice [0.9622, 0.9549, 0.9791, 0.9524, 0.8759, 0.9702, 0.9895] +2024-09-09 07:03:51.718041: Epoch time: 447.96 s +2024-09-09 07:03:56.490799: +2024-09-09 07:03:56.492417: Epoch 557 +2024-09-09 07:03:56.493801: Current learning rate: 0.00481 +2024-09-09 07:11:19.434313: train_loss -0.7424 +2024-09-09 07:11:19.437880: val_loss -0.7761 +2024-09-09 07:11:19.439711: Pseudo dice [0.9667, 0.9586, 0.9825, 0.9497, 0.8835, 0.9716, 0.9912] +2024-09-09 07:11:19.441727: Epoch time: 442.95 s +2024-09-09 07:11:19.442419: Yayy! New best EMA pseudo Dice: 0.9553 +2024-09-09 07:11:31.041974: +2024-09-09 07:11:31.044365: Epoch 558 +2024-09-09 07:11:31.045725: Current learning rate: 0.0048 +2024-09-09 07:18:20.227630: train_loss -0.7389 +2024-09-09 07:18:20.250443: val_loss -0.7581 +2024-09-09 07:18:20.251400: Pseudo dice [0.9611, 0.9563, 0.9759, 0.9471, 0.8744, 0.9691, 0.9906] +2024-09-09 07:18:20.252699: Epoch time: 409.19 s +2024-09-09 07:18:25.107147: +2024-09-09 07:18:25.109285: Epoch 559 +2024-09-09 07:18:25.110723: Current learning rate: 0.00479 +2024-09-09 07:25:18.813308: train_loss -0.7432 +2024-09-09 07:25:18.842348: val_loss -0.7322 +2024-09-09 07:25:18.844029: Pseudo dice [0.966, 0.9591, 0.9826, 0.9466, 0.8724, 0.9683, 0.9897] +2024-09-09 07:25:18.844870: Epoch time: 413.71 s +2024-09-09 07:25:23.513362: +2024-09-09 07:25:23.519943: Epoch 560 +2024-09-09 07:25:23.529958: Current learning rate: 0.00478 +2024-09-09 07:32:46.066507: train_loss -0.7347 +2024-09-09 07:32:46.081511: val_loss -0.7397 +2024-09-09 07:32:46.082279: Pseudo dice [0.9593, 0.9582, 0.9742, 0.9515, 0.8827, 0.9658, 0.9922] +2024-09-09 07:32:46.082835: Epoch time: 442.57 s +2024-09-09 07:32:49.756862: +2024-09-09 07:32:49.768819: Epoch 561 +2024-09-09 07:32:49.769796: Current learning rate: 0.00477 +2024-09-09 07:40:00.743493: train_loss -0.7223 +2024-09-09 07:40:00.765564: val_loss -0.6993 +2024-09-09 07:40:00.768086: Pseudo dice [0.9581, 0.9527, 0.9724, 0.9409, 0.8642, 0.9585, 0.9894] +2024-09-09 07:40:00.770141: Epoch time: 431.0 s +2024-09-09 07:40:03.840492: +2024-09-09 07:40:03.841902: Epoch 562 +2024-09-09 07:40:03.842684: Current learning rate: 0.00476 +2024-09-09 07:47:09.079212: train_loss -0.7206 +2024-09-09 07:47:09.090026: val_loss -0.752 +2024-09-09 07:47:09.090764: Pseudo dice [0.96, 0.9534, 0.9746, 0.9447, 0.8735, 0.9668, 0.9926] +2024-09-09 07:47:09.094072: Epoch time: 425.24 s +2024-09-09 07:47:13.835169: +2024-09-09 07:47:13.837013: Epoch 563 +2024-09-09 07:47:13.838615: Current learning rate: 0.00475 +2024-09-09 07:54:25.573789: train_loss -0.7255 +2024-09-09 07:54:25.575709: val_loss -0.7457 +2024-09-09 07:54:25.576385: Pseudo dice [0.9618, 0.9558, 0.9766, 0.9446, 0.8649, 0.9616, 0.9899] +2024-09-09 07:54:25.577358: Epoch time: 431.75 s +2024-09-09 07:54:29.343794: +2024-09-09 07:54:29.360898: Epoch 564 +2024-09-09 07:54:29.361995: Current learning rate: 0.00474 +2024-09-09 08:01:48.338226: train_loss -0.7342 +2024-09-09 08:01:48.341051: val_loss -0.7061 +2024-09-09 08:01:48.342759: Pseudo dice [0.9648, 0.9561, 0.9802, 0.9486, 0.8752, 0.9561, 0.9915] +2024-09-09 08:01:48.344805: Epoch time: 439.05 s +2024-09-09 08:01:53.479789: +2024-09-09 08:01:53.496809: Epoch 565 +2024-09-09 08:01:53.498163: Current learning rate: 0.00473 +2024-09-09 08:09:05.839331: train_loss -0.7511 +2024-09-09 08:09:05.857362: val_loss -0.699 +2024-09-09 08:09:05.869035: Pseudo dice [0.964, 0.9597, 0.977, 0.9514, 0.8756, 0.9708, 0.9915] +2024-09-09 08:09:05.869846: Epoch time: 432.37 s +2024-09-09 08:09:15.274359: +2024-09-09 08:09:15.276090: Epoch 566 +2024-09-09 08:09:15.276901: Current learning rate: 0.00472 +2024-09-09 08:16:09.583379: train_loss -0.7315 +2024-09-09 08:16:09.609302: val_loss -0.7208 +2024-09-09 08:16:09.611184: Pseudo dice [0.9615, 0.9541, 0.9741, 0.9443, 0.8665, 0.9691, 0.9907] +2024-09-09 08:16:09.612337: Epoch time: 414.31 s +2024-09-09 08:16:14.462742: +2024-09-09 08:16:14.477462: Epoch 567 +2024-09-09 08:16:14.479534: Current learning rate: 0.00471 +2024-09-09 08:23:16.848541: train_loss -0.7439 +2024-09-09 08:23:16.865277: val_loss -0.7794 +2024-09-09 08:23:16.868563: Pseudo dice [0.962, 0.9578, 0.9753, 0.9481, 0.8651, 0.9599, 0.9897] +2024-09-09 08:23:16.871205: Epoch time: 422.39 s +2024-09-09 08:23:21.493345: +2024-09-09 08:23:21.494296: Epoch 568 +2024-09-09 08:23:21.494945: Current learning rate: 0.0047 +2024-09-09 08:30:40.391170: train_loss -0.7447 +2024-09-09 08:30:40.410253: val_loss -0.7391 +2024-09-09 08:30:40.412991: Pseudo dice [0.964, 0.9587, 0.9803, 0.9488, 0.8684, 0.9552, 0.9918] +2024-09-09 08:30:40.418377: Epoch time: 438.9 s +2024-09-09 08:30:43.999080: +2024-09-09 08:30:44.012684: Epoch 569 +2024-09-09 08:30:44.013535: Current learning rate: 0.00469 +2024-09-09 08:37:37.768157: train_loss -0.7203 +2024-09-09 08:37:37.769874: val_loss -0.7374 +2024-09-09 08:37:37.770739: Pseudo dice [0.9654, 0.9581, 0.9807, 0.9553, 0.8806, 0.9714, 0.9898] +2024-09-09 08:37:37.771320: Epoch time: 413.78 s +2024-09-09 08:37:40.483245: +2024-09-09 08:37:40.484608: Epoch 570 +2024-09-09 08:37:40.485559: Current learning rate: 0.00468 +2024-09-09 08:44:37.175408: train_loss -0.7268 +2024-09-09 08:44:37.193683: val_loss -0.724 +2024-09-09 08:44:37.194942: Pseudo dice [0.9616, 0.9545, 0.9779, 0.9466, 0.8654, 0.9686, 0.9886] +2024-09-09 08:44:37.196084: Epoch time: 416.7 s +2024-09-09 08:44:41.940787: +2024-09-09 08:44:41.957372: Epoch 571 +2024-09-09 08:44:41.958112: Current learning rate: 0.00467 +2024-09-09 08:51:22.110503: train_loss -0.7372 +2024-09-09 08:51:22.121001: val_loss -0.7325 +2024-09-09 08:51:22.121934: Pseudo dice [0.9625, 0.9567, 0.9792, 0.9413, 0.8767, 0.9676, 0.9907] +2024-09-09 08:51:22.123304: Epoch time: 400.17 s +2024-09-09 08:51:25.947127: +2024-09-09 08:51:25.971808: Epoch 572 +2024-09-09 08:51:25.973206: Current learning rate: 0.00466 +2024-09-09 08:58:48.936965: train_loss -0.7385 +2024-09-09 08:58:48.955417: val_loss -0.7664 +2024-09-09 08:58:48.956340: Pseudo dice [0.9633, 0.9565, 0.9798, 0.9491, 0.8695, 0.9697, 0.9886] +2024-09-09 08:58:48.957205: Epoch time: 442.99 s +2024-09-09 08:58:52.549312: +2024-09-09 08:58:52.560695: Epoch 573 +2024-09-09 08:58:52.561872: Current learning rate: 0.00465 +2024-09-09 09:05:59.966615: train_loss -0.7402 +2024-09-09 09:05:59.969907: val_loss -0.7162 +2024-09-09 09:05:59.971672: Pseudo dice [0.9638, 0.9579, 0.9793, 0.9487, 0.8753, 0.9697, 0.9908] +2024-09-09 09:05:59.973351: Epoch time: 427.42 s +2024-09-09 09:06:04.258865: +2024-09-09 09:06:04.272478: Epoch 574 +2024-09-09 09:06:04.273300: Current learning rate: 0.00464 +2024-09-09 09:13:07.119146: train_loss -0.7349 +2024-09-09 09:13:07.125919: val_loss -0.7689 +2024-09-09 09:13:07.128433: Pseudo dice [0.962, 0.956, 0.9778, 0.9532, 0.8782, 0.9673, 0.9906] +2024-09-09 09:13:07.130149: Epoch time: 422.87 s +2024-09-09 09:13:11.680481: +2024-09-09 09:13:11.692973: Epoch 575 +2024-09-09 09:13:11.694064: Current learning rate: 0.00463 +2024-09-09 09:19:59.884737: train_loss -0.7394 +2024-09-09 09:19:59.913286: val_loss -0.7055 +2024-09-09 09:19:59.914445: Pseudo dice [0.9591, 0.9582, 0.9731, 0.9487, 0.8721, 0.9675, 0.9891] +2024-09-09 09:19:59.915174: Epoch time: 408.21 s +2024-09-09 09:20:05.039048: +2024-09-09 09:20:05.043177: Epoch 576 +2024-09-09 09:20:05.044502: Current learning rate: 0.00462 +2024-09-09 09:27:29.440382: train_loss -0.7141 +2024-09-09 09:27:29.445983: val_loss -0.7719 +2024-09-09 09:27:29.447628: Pseudo dice [0.9643, 0.9592, 0.9761, 0.9478, 0.8556, 0.9678, 0.9882] +2024-09-09 09:27:29.448671: Epoch time: 444.48 s +2024-09-09 09:27:34.413881: +2024-09-09 09:27:34.415181: Epoch 577 +2024-09-09 09:27:34.416935: Current learning rate: 0.00461 +2024-09-09 09:34:43.934671: train_loss -0.7463 +2024-09-09 09:34:43.940225: val_loss -0.7685 +2024-09-09 09:34:43.941898: Pseudo dice [0.9637, 0.9531, 0.9798, 0.9484, 0.8725, 0.9681, 0.9918] +2024-09-09 09:34:43.942795: Epoch time: 429.53 s +2024-09-09 09:34:49.086019: +2024-09-09 09:34:49.096748: Epoch 578 +2024-09-09 09:34:49.097638: Current learning rate: 0.0046 +2024-09-09 09:41:45.468446: train_loss -0.7412 +2024-09-09 09:41:45.489094: val_loss -0.7146 +2024-09-09 09:41:45.490330: Pseudo dice [0.9638, 0.9595, 0.9745, 0.9518, 0.862, 0.9659, 0.9897] +2024-09-09 09:41:45.493163: Epoch time: 416.39 s +2024-09-09 09:41:49.429968: +2024-09-09 09:41:49.431081: Epoch 579 +2024-09-09 09:41:49.431961: Current learning rate: 0.00459 +2024-09-09 09:48:39.335858: train_loss -0.7389 +2024-09-09 09:48:39.353025: val_loss -0.7392 +2024-09-09 09:48:39.355876: Pseudo dice [0.9621, 0.9564, 0.9766, 0.9471, 0.8639, 0.9683, 0.9906] +2024-09-09 09:48:39.358107: Epoch time: 409.91 s +2024-09-09 09:48:46.567490: +2024-09-09 09:48:46.568933: Epoch 580 +2024-09-09 09:48:46.569597: Current learning rate: 0.00458 +2024-09-09 09:55:58.945194: train_loss -0.724 +2024-09-09 09:55:58.961113: val_loss -0.745 +2024-09-09 09:55:58.962118: Pseudo dice [0.9631, 0.9581, 0.9779, 0.9544, 0.8763, 0.968, 0.9905] +2024-09-09 09:55:58.963211: Epoch time: 432.38 s +2024-09-09 09:56:04.345268: +2024-09-09 09:56:04.361156: Epoch 581 +2024-09-09 09:56:04.361786: Current learning rate: 0.00457 +2024-09-09 10:03:14.467088: train_loss -0.7464 +2024-09-09 10:03:14.481934: val_loss -0.7254 +2024-09-09 10:03:14.482704: Pseudo dice [0.9662, 0.9577, 0.9769, 0.9492, 0.8768, 0.9721, 0.9911] +2024-09-09 10:03:14.483270: Epoch time: 430.13 s +2024-09-09 10:03:19.818254: +2024-09-09 10:03:19.821606: Epoch 582 +2024-09-09 10:03:19.822964: Current learning rate: 0.00456 +2024-09-09 10:11:33.882074: train_loss -0.7563 +2024-09-09 10:11:33.888083: val_loss -0.742 +2024-09-09 10:11:33.889486: Pseudo dice [0.9648, 0.9589, 0.979, 0.95, 0.8739, 0.9697, 0.9881] +2024-09-09 10:11:33.890876: Epoch time: 494.07 s +2024-09-09 10:11:40.523521: +2024-09-09 10:11:40.541254: Epoch 583 +2024-09-09 10:11:40.542951: Current learning rate: 0.00455 +2024-09-09 10:20:03.945407: train_loss -0.7403 +2024-09-09 10:20:03.963030: val_loss -0.7278 +2024-09-09 10:20:03.964042: Pseudo dice [0.9642, 0.9588, 0.9784, 0.9444, 0.8742, 0.968, 0.9915] +2024-09-09 10:20:03.964858: Epoch time: 503.43 s +2024-09-09 10:20:08.677163: +2024-09-09 10:20:08.679908: Epoch 584 +2024-09-09 10:20:08.682932: Current learning rate: 0.00454 +2024-09-09 10:28:11.210809: train_loss -0.7444 +2024-09-09 10:28:11.219982: val_loss -0.7712 +2024-09-09 10:28:11.221347: Pseudo dice [0.9652, 0.956, 0.9782, 0.9492, 0.8879, 0.9731, 0.9919] +2024-09-09 10:28:11.223473: Epoch time: 482.54 s +2024-09-09 10:28:16.790022: +2024-09-09 10:28:16.806266: Epoch 585 +2024-09-09 10:28:16.807581: Current learning rate: 0.00453 +2024-09-09 10:36:15.782964: train_loss -0.7488 +2024-09-09 10:36:15.798307: val_loss -0.7148 +2024-09-09 10:36:15.799568: Pseudo dice [0.9661, 0.9605, 0.982, 0.9529, 0.8761, 0.9728, 0.9905] +2024-09-09 10:36:15.801071: Epoch time: 479.0 s +2024-09-09 10:36:19.288614: +2024-09-09 10:36:19.290022: Epoch 586 +2024-09-09 10:36:19.290700: Current learning rate: 0.00452 +2024-09-09 10:43:53.465867: train_loss -0.7423 +2024-09-09 10:43:53.477727: val_loss -0.7528 +2024-09-09 10:43:53.479944: Pseudo dice [0.9628, 0.9549, 0.9777, 0.9431, 0.8751, 0.972, 0.9919] +2024-09-09 10:43:53.480514: Epoch time: 454.19 s +2024-09-09 10:43:58.706894: +2024-09-09 10:43:58.721505: Epoch 587 +2024-09-09 10:43:58.723094: Current learning rate: 0.00451 +2024-09-09 10:51:28.002802: train_loss -0.7496 +2024-09-09 10:51:28.004390: val_loss -0.7496 +2024-09-09 10:51:28.005829: Pseudo dice [0.9637, 0.9582, 0.979, 0.9519, 0.8757, 0.9701, 0.9899] +2024-09-09 10:51:28.007830: Epoch time: 449.31 s +2024-09-09 10:51:33.282417: +2024-09-09 10:51:33.284086: Epoch 588 +2024-09-09 10:51:33.285663: Current learning rate: 0.0045 +2024-09-09 10:59:20.195071: train_loss -0.745 +2024-09-09 10:59:20.196671: val_loss -0.7597 +2024-09-09 10:59:20.197358: Pseudo dice [0.9642, 0.9579, 0.9791, 0.9545, 0.8799, 0.9686, 0.9903] +2024-09-09 10:59:20.197896: Epoch time: 466.92 s +2024-09-09 10:59:26.500173: +2024-09-09 10:59:26.531780: Epoch 589 +2024-09-09 10:59:26.532927: Current learning rate: 0.00449 +2024-09-09 11:07:25.035481: train_loss -0.7518 +2024-09-09 11:07:25.061036: val_loss -0.7698 +2024-09-09 11:07:25.063335: Pseudo dice [0.9615, 0.9559, 0.9779, 0.9451, 0.8671, 0.967, 0.9912] +2024-09-09 11:07:25.065454: Epoch time: 478.56 s +2024-09-09 11:07:31.057538: +2024-09-09 11:07:31.061849: Epoch 590 +2024-09-09 11:07:31.064251: Current learning rate: 0.00448 +2024-09-09 11:15:48.503275: train_loss -0.728 +2024-09-09 11:15:48.525023: val_loss -0.7601 +2024-09-09 11:15:48.526494: Pseudo dice [0.966, 0.9589, 0.9802, 0.9534, 0.8615, 0.9694, 0.9916] +2024-09-09 11:15:48.528924: Epoch time: 497.45 s +2024-09-09 11:15:54.384403: +2024-09-09 11:15:54.385720: Epoch 591 +2024-09-09 11:15:54.387324: Current learning rate: 0.00447 +2024-09-09 11:24:45.620971: train_loss -0.7458 +2024-09-09 11:24:45.648223: val_loss -0.7691 +2024-09-09 11:24:45.650250: Pseudo dice [0.9627, 0.9587, 0.9793, 0.948, 0.8801, 0.9687, 0.9904] +2024-09-09 11:24:45.651764: Epoch time: 531.24 s +2024-09-09 11:24:51.709139: +2024-09-09 11:24:51.711725: Epoch 592 +2024-09-09 11:24:51.713498: Current learning rate: 0.00446 +2024-09-09 11:34:07.159482: train_loss -0.7466 +2024-09-09 11:34:07.177322: val_loss -0.75 +2024-09-09 11:34:07.183793: Pseudo dice [0.9651, 0.9585, 0.9784, 0.9492, 0.8789, 0.9704, 0.9924] +2024-09-09 11:34:07.186668: Epoch time: 555.49 s +2024-09-09 11:34:14.259421: +2024-09-09 11:34:14.261096: Epoch 593 +2024-09-09 11:34:14.262521: Current learning rate: 0.00445 +2024-09-09 11:43:46.516201: train_loss -0.7277 +2024-09-09 11:43:46.520841: val_loss -0.7676 +2024-09-09 11:43:46.522246: Pseudo dice [0.9628, 0.9553, 0.979, 0.9457, 0.8839, 0.9718, 0.9915] +2024-09-09 11:43:46.524162: Epoch time: 572.26 s +2024-09-09 11:43:55.221014: +2024-09-09 11:43:55.222780: Epoch 594 +2024-09-09 11:43:55.223958: Current learning rate: 0.00444 +2024-09-09 11:53:15.024175: train_loss -0.7491 +2024-09-09 11:53:15.028970: val_loss -0.7463 +2024-09-09 11:53:15.031193: Pseudo dice [0.9663, 0.9584, 0.9782, 0.9553, 0.8763, 0.9651, 0.9911] +2024-09-09 11:53:15.034906: Epoch time: 559.81 s +2024-09-09 11:53:20.195601: +2024-09-09 11:53:20.198505: Epoch 595 +2024-09-09 11:53:20.199568: Current learning rate: 0.00443 +2024-09-09 12:02:29.224554: train_loss -0.7449 +2024-09-09 12:02:29.408757: val_loss -0.7367 +2024-09-09 12:02:29.416837: Pseudo dice [0.9621, 0.9577, 0.9758, 0.9492, 0.8813, 0.9667, 0.9902] +2024-09-09 12:02:29.448704: Epoch time: 549.05 s +2024-09-09 12:02:37.644966: +2024-09-09 12:02:37.646846: Epoch 596 +2024-09-09 12:02:37.650980: Current learning rate: 0.00442 +2024-09-09 12:12:15.481374: train_loss -0.7303 +2024-09-09 12:12:15.494081: val_loss -0.7459 +2024-09-09 12:12:15.494882: Pseudo dice [0.9693, 0.9624, 0.9831, 0.9555, 0.8758, 0.9664, 0.9906] +2024-09-09 12:12:15.495456: Epoch time: 577.92 s +2024-09-09 12:12:19.206968: +2024-09-09 12:12:19.220928: Epoch 597 +2024-09-09 12:12:19.221877: Current learning rate: 0.00441 +2024-09-09 12:21:44.688620: train_loss -0.7411 +2024-09-09 12:21:44.721741: val_loss -0.7606 +2024-09-09 12:21:44.724080: Pseudo dice [0.9638, 0.958, 0.9796, 0.9491, 0.8696, 0.9707, 0.9899] +2024-09-09 12:21:44.726192: Epoch time: 565.49 s +2024-09-09 12:21:51.303209: +2024-09-09 12:21:51.317165: Epoch 598 +2024-09-09 12:21:51.318915: Current learning rate: 0.0044 +2024-09-09 12:31:25.272320: train_loss -0.7325 +2024-09-09 12:31:25.274565: val_loss -0.767 +2024-09-09 12:31:25.276626: Pseudo dice [0.9618, 0.9538, 0.977, 0.9497, 0.8864, 0.9706, 0.9921] +2024-09-09 12:31:25.316452: Epoch time: 574.01 s +2024-09-09 12:31:30.110684: +2024-09-09 12:31:30.128879: Epoch 599 +2024-09-09 12:31:30.132127: Current learning rate: 0.00439 +2024-09-09 12:40:50.062476: train_loss -0.7468 +2024-09-09 12:40:50.064575: val_loss -0.739 +2024-09-09 12:40:50.066683: Pseudo dice [0.9629, 0.9572, 0.9784, 0.9533, 0.8841, 0.9732, 0.9924] +2024-09-09 12:40:50.068943: Epoch time: 560.02 s +2024-09-09 12:40:56.341649: Yayy! New best EMA pseudo Dice: 0.9554 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_14_03_19.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_14_03_19.txt new file mode 100644 index 0000000000000000000000000000000000000000..65876518028d6903da00cdd376dfd83f6ebe4088 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_14_03_19.txt @@ -0,0 +1,379 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 14:03:22.076601: Using torch.compile... +2024-09-09 14:03:28.212011: do_dummy_2d_data_aug: False +2024-09-09 14:03:28.217779: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 14:03:28.219218: The split file contains 5 splits. +2024-09-09 14:03:28.219802: Desired fold for training: 0 +2024-09-09 14:03:28.220347: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 14:03:38.612104: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 14:03:39.715178: +2024-09-09 14:03:39.727012: Epoch 600 +2024-09-09 14:03:39.728021: Current learning rate: 0.00438 +2024-09-09 14:11:59.416926: train_loss -0.7422 +2024-09-09 14:11:59.419363: val_loss -0.7325 +2024-09-09 14:11:59.420147: Pseudo dice [0.9657, 0.9593, 0.9795, 0.9518, 0.8836, 0.9712, 0.9904] +2024-09-09 14:11:59.420814: Epoch time: 499.71 s +2024-09-09 14:11:59.421353: Yayy! New best EMA pseudo Dice: 0.9556 +2024-09-09 14:12:02.452893: +2024-09-09 14:12:02.453717: Epoch 601 +2024-09-09 14:12:02.454346: Current learning rate: 0.00437 +2024-09-09 14:16:35.120170: train_loss -0.7364 +2024-09-09 14:16:35.134027: val_loss -0.7579 +2024-09-09 14:16:35.134976: Pseudo dice [0.9672, 0.9607, 0.9823, 0.9545, 0.8774, 0.9684, 0.9889] +2024-09-09 14:16:35.135902: Epoch time: 272.67 s +2024-09-09 14:16:35.137845: Yayy! New best EMA pseudo Dice: 0.9557 +2024-09-09 14:16:40.408855: +2024-09-09 14:16:40.424344: Epoch 602 +2024-09-09 14:16:40.425403: Current learning rate: 0.00436 +2024-09-09 14:21:45.694649: train_loss -0.748 +2024-09-09 14:21:45.696903: val_loss -0.7883 +2024-09-09 14:21:45.697672: Pseudo dice [0.9649, 0.9589, 0.9786, 0.9435, 0.8691, 0.9687, 0.9905] +2024-09-09 14:21:45.698351: Epoch time: 305.29 s +2024-09-09 14:21:48.685863: +2024-09-09 14:21:48.687200: Epoch 603 +2024-09-09 14:21:48.688124: Current learning rate: 0.00435 +2024-09-09 14:26:52.402236: train_loss -0.7314 +2024-09-09 14:26:52.409422: val_loss -0.7335 +2024-09-09 14:26:52.410270: Pseudo dice [0.965, 0.9585, 0.9798, 0.9526, 0.8799, 0.97, 0.9912] +2024-09-09 14:26:52.410928: Epoch time: 303.72 s +2024-09-09 14:26:55.218347: +2024-09-09 14:26:55.219481: Epoch 604 +2024-09-09 14:26:55.220280: Current learning rate: 0.00434 +2024-09-09 14:31:48.754349: train_loss -0.7133 +2024-09-09 14:31:48.756225: val_loss -0.7526 +2024-09-09 14:31:48.756888: Pseudo dice [0.9592, 0.9506, 0.9745, 0.9426, 0.848, 0.9635, 0.9862] +2024-09-09 14:31:48.757500: Epoch time: 293.54 s +2024-09-09 14:31:54.272987: +2024-09-09 14:31:54.274343: Epoch 605 +2024-09-09 14:31:54.275200: Current learning rate: 0.00433 +2024-09-09 14:36:48.683113: train_loss -0.7182 +2024-09-09 14:36:48.685116: val_loss -0.7704 +2024-09-09 14:36:48.685783: Pseudo dice [0.9578, 0.9519, 0.9732, 0.9411, 0.8512, 0.9573, 0.9881] +2024-09-09 14:36:48.686482: Epoch time: 294.42 s +2024-09-09 14:36:51.799385: +2024-09-09 14:36:51.833958: Epoch 606 +2024-09-09 14:36:51.835211: Current learning rate: 0.00432 +2024-09-09 14:41:30.004753: train_loss -0.7218 +2024-09-09 14:41:30.029559: val_loss -0.7377 +2024-09-09 14:41:30.030435: Pseudo dice [0.9591, 0.9539, 0.9741, 0.9439, 0.8711, 0.9659, 0.99] +2024-09-09 14:41:30.031131: Epoch time: 278.21 s +2024-09-09 14:41:33.277447: +2024-09-09 14:41:33.295761: Epoch 607 +2024-09-09 14:41:33.296906: Current learning rate: 0.00431 +2024-09-09 14:46:29.152950: train_loss -0.734 +2024-09-09 14:46:29.167913: val_loss -0.7718 +2024-09-09 14:46:29.169107: Pseudo dice [0.9621, 0.9563, 0.9758, 0.9456, 0.8737, 0.9702, 0.9899] +2024-09-09 14:46:29.169804: Epoch time: 295.88 s +2024-09-09 14:46:32.245308: +2024-09-09 14:46:32.246365: Epoch 608 +2024-09-09 14:46:32.247142: Current learning rate: 0.0043 +2024-09-09 14:51:39.533293: train_loss -0.7411 +2024-09-09 14:51:39.535317: val_loss -0.7571 +2024-09-09 14:51:39.536027: Pseudo dice [0.9651, 0.9572, 0.9794, 0.9465, 0.8695, 0.9696, 0.9902] +2024-09-09 14:51:39.536741: Epoch time: 307.29 s +2024-09-09 14:51:42.682621: +2024-09-09 14:51:42.684038: Epoch 609 +2024-09-09 14:51:42.684955: Current learning rate: 0.00429 +2024-09-09 14:56:31.931863: train_loss -0.7451 +2024-09-09 14:56:31.934075: val_loss -0.7675 +2024-09-09 14:56:31.935049: Pseudo dice [0.9628, 0.9583, 0.9752, 0.9461, 0.8762, 0.9727, 0.9918] +2024-09-09 14:56:31.935938: Epoch time: 289.25 s +2024-09-09 14:56:35.297440: +2024-09-09 14:56:35.298479: Epoch 610 +2024-09-09 14:56:35.299164: Current learning rate: 0.00429 +2024-09-09 15:01:21.293403: train_loss -0.7548 +2024-09-09 15:01:21.309429: val_loss -0.7716 +2024-09-09 15:01:21.310223: Pseudo dice [0.9661, 0.9597, 0.9798, 0.9474, 0.8825, 0.9701, 0.9895] +2024-09-09 15:01:21.310869: Epoch time: 286.0 s +2024-09-09 15:01:25.081353: +2024-09-09 15:01:25.091021: Epoch 611 +2024-09-09 15:01:25.092171: Current learning rate: 0.00428 +2024-09-09 15:05:58.622586: train_loss -0.7429 +2024-09-09 15:05:58.645321: val_loss -0.7182 +2024-09-09 15:05:58.646045: Pseudo dice [0.9635, 0.9589, 0.975, 0.9479, 0.8663, 0.9682, 0.9903] +2024-09-09 15:05:58.646835: Epoch time: 273.55 s +2024-09-09 15:06:02.454710: +2024-09-09 15:06:02.455966: Epoch 612 +2024-09-09 15:06:02.456789: Current learning rate: 0.00427 +2024-09-09 15:10:37.636665: train_loss -0.7381 +2024-09-09 15:10:37.646020: val_loss -0.7652 +2024-09-09 15:10:37.646905: Pseudo dice [0.9647, 0.9583, 0.9796, 0.9522, 0.8871, 0.9715, 0.9911] +2024-09-09 15:10:37.648996: Epoch time: 275.19 s +2024-09-09 15:10:41.478444: +2024-09-09 15:10:41.492481: Epoch 613 +2024-09-09 15:10:41.493296: Current learning rate: 0.00426 +2024-09-09 15:14:58.072337: train_loss -0.7366 +2024-09-09 15:14:58.085584: val_loss -0.7364 +2024-09-09 15:14:58.086416: Pseudo dice [0.9623, 0.9585, 0.9767, 0.9464, 0.8753, 0.9703, 0.991] +2024-09-09 15:14:58.087026: Epoch time: 256.6 s +2024-09-09 15:15:00.576244: +2024-09-09 15:15:00.577509: Epoch 614 +2024-09-09 15:15:00.578336: Current learning rate: 0.00425 +2024-09-09 15:19:23.149049: train_loss -0.7539 +2024-09-09 15:19:23.150834: val_loss -0.7586 +2024-09-09 15:19:23.151572: Pseudo dice [0.9637, 0.9574, 0.9783, 0.9462, 0.8747, 0.9726, 0.9916] +2024-09-09 15:19:23.152222: Epoch time: 262.58 s +2024-09-09 15:19:25.674244: +2024-09-09 15:19:25.675217: Epoch 615 +2024-09-09 15:19:25.675809: Current learning rate: 0.00424 +2024-09-09 15:23:55.370193: train_loss -0.7586 +2024-09-09 15:23:55.381642: val_loss -0.7567 +2024-09-09 15:23:55.392031: Pseudo dice [0.9628, 0.9584, 0.9738, 0.9504, 0.8854, 0.9735, 0.9902] +2024-09-09 15:23:55.393099: Epoch time: 269.7 s +2024-09-09 15:23:58.334517: +2024-09-09 15:23:58.355759: Epoch 616 +2024-09-09 15:23:58.356756: Current learning rate: 0.00423 +2024-09-09 15:28:55.204910: train_loss -0.747 +2024-09-09 15:28:55.207375: val_loss -0.7437 +2024-09-09 15:28:55.208094: Pseudo dice [0.964, 0.96, 0.9793, 0.9513, 0.8802, 0.968, 0.9897] +2024-09-09 15:28:55.208695: Epoch time: 296.88 s +2024-09-09 15:28:58.940400: +2024-09-09 15:28:58.941791: Epoch 617 +2024-09-09 15:28:58.942800: Current learning rate: 0.00422 +2024-09-09 15:34:06.003500: train_loss -0.7403 +2024-09-09 15:34:06.005576: val_loss -0.7604 +2024-09-09 15:34:06.006370: Pseudo dice [0.9605, 0.9584, 0.9721, 0.9439, 0.8702, 0.9632, 0.9902] +2024-09-09 15:34:06.007088: Epoch time: 307.08 s +2024-09-09 15:34:08.603883: +2024-09-09 15:34:08.605194: Epoch 618 +2024-09-09 15:34:08.606259: Current learning rate: 0.00421 +2024-09-09 15:39:05.335115: train_loss -0.7271 +2024-09-09 15:39:05.337793: val_loss -0.7294 +2024-09-09 15:39:05.339176: Pseudo dice [0.9592, 0.9571, 0.9747, 0.945, 0.8709, 0.9699, 0.9907] +2024-09-09 15:39:05.340258: Epoch time: 296.74 s +2024-09-09 15:39:09.705041: +2024-09-09 15:39:09.706099: Epoch 619 +2024-09-09 15:39:09.706744: Current learning rate: 0.0042 +2024-09-09 15:44:23.080055: train_loss -0.7331 +2024-09-09 15:44:23.097711: val_loss -0.7584 +2024-09-09 15:44:23.098778: Pseudo dice [0.9623, 0.9562, 0.9753, 0.9457, 0.8652, 0.9651, 0.9895] +2024-09-09 15:44:23.100104: Epoch time: 313.38 s +2024-09-09 15:44:26.440796: +2024-09-09 15:44:26.452566: Epoch 620 +2024-09-09 15:44:26.453738: Current learning rate: 0.00419 +2024-09-09 15:49:10.806569: train_loss -0.7382 +2024-09-09 15:49:10.809245: val_loss -0.7542 +2024-09-09 15:49:10.810108: Pseudo dice [0.9638, 0.9553, 0.9793, 0.9503, 0.8776, 0.9726, 0.9914] +2024-09-09 15:49:10.810765: Epoch time: 284.37 s +2024-09-09 15:49:14.124080: +2024-09-09 15:49:14.125385: Epoch 621 +2024-09-09 15:49:14.126131: Current learning rate: 0.00418 +2024-09-09 15:54:29.241578: train_loss -0.7476 +2024-09-09 15:54:29.257683: val_loss -0.7506 +2024-09-09 15:54:29.258484: Pseudo dice [0.965, 0.957, 0.9787, 0.9516, 0.8769, 0.9706, 0.9916] +2024-09-09 15:54:29.259104: Epoch time: 315.13 s +2024-09-09 15:54:32.508845: +2024-09-09 15:54:32.510001: Epoch 622 +2024-09-09 15:54:32.510721: Current learning rate: 0.00417 +2024-09-09 15:59:31.662357: train_loss -0.7481 +2024-09-09 15:59:31.694438: val_loss -0.7549 +2024-09-09 15:59:31.695517: Pseudo dice [0.9668, 0.9607, 0.9803, 0.9529, 0.8761, 0.9698, 0.9902] +2024-09-09 15:59:31.696429: Epoch time: 299.16 s +2024-09-09 15:59:35.837550: +2024-09-09 15:59:35.838664: Epoch 623 +2024-09-09 15:59:35.839518: Current learning rate: 0.00416 +2024-09-09 16:04:20.507539: train_loss -0.7357 +2024-09-09 16:04:20.524469: val_loss -0.7438 +2024-09-09 16:04:20.525537: Pseudo dice [0.9608, 0.9555, 0.9748, 0.9406, 0.8637, 0.9672, 0.9864] +2024-09-09 16:04:20.526342: Epoch time: 284.68 s +2024-09-09 16:04:23.241707: +2024-09-09 16:04:23.242855: Epoch 624 +2024-09-09 16:04:23.243686: Current learning rate: 0.00415 +2024-09-09 16:09:36.133538: train_loss -0.7336 +2024-09-09 16:09:36.149508: val_loss -0.6819 +2024-09-09 16:09:36.150252: Pseudo dice [0.9638, 0.9582, 0.9782, 0.9484, 0.8817, 0.9725, 0.992] +2024-09-09 16:09:36.150864: Epoch time: 312.9 s +2024-09-09 16:09:39.595434: +2024-09-09 16:09:39.620378: Epoch 625 +2024-09-09 16:09:39.637002: Current learning rate: 0.00414 +2024-09-09 16:14:24.890019: train_loss -0.7329 +2024-09-09 16:14:24.902401: val_loss -0.7844 +2024-09-09 16:14:24.903314: Pseudo dice [0.9637, 0.957, 0.9782, 0.9508, 0.8879, 0.9708, 0.9926] +2024-09-09 16:14:24.904173: Epoch time: 285.3 s +2024-09-09 16:14:29.681528: +2024-09-09 16:14:29.682505: Epoch 626 +2024-09-09 16:14:29.683182: Current learning rate: 0.00413 +2024-09-09 16:19:38.026428: train_loss -0.7441 +2024-09-09 16:19:38.058457: val_loss -0.7094 +2024-09-09 16:19:38.059409: Pseudo dice [0.9633, 0.9582, 0.9766, 0.9501, 0.8785, 0.9713, 0.9914] +2024-09-09 16:19:38.060169: Epoch time: 308.35 s +2024-09-09 16:19:41.150708: +2024-09-09 16:19:41.159856: Epoch 627 +2024-09-09 16:19:41.160829: Current learning rate: 0.00412 +2024-09-09 16:24:43.345859: train_loss -0.7552 +2024-09-09 16:24:43.347795: val_loss -0.7791 +2024-09-09 16:24:43.348482: Pseudo dice [0.9687, 0.9616, 0.9827, 0.95, 0.8834, 0.9703, 0.9908] +2024-09-09 16:24:43.349091: Epoch time: 302.2 s +2024-09-09 16:24:46.359220: +2024-09-09 16:24:46.360339: Epoch 628 +2024-09-09 16:24:46.361092: Current learning rate: 0.00411 +2024-09-09 16:29:55.546157: train_loss -0.7562 +2024-09-09 16:29:55.549491: val_loss -0.6949 +2024-09-09 16:29:55.550644: Pseudo dice [0.9651, 0.9578, 0.9789, 0.9463, 0.8586, 0.972, 0.9884] +2024-09-09 16:29:55.551581: Epoch time: 309.2 s +2024-09-09 16:29:58.661152: +2024-09-09 16:29:58.662293: Epoch 629 +2024-09-09 16:29:58.663255: Current learning rate: 0.0041 +2024-09-09 16:35:50.020266: train_loss -0.7384 +2024-09-09 16:35:50.055580: val_loss -0.7043 +2024-09-09 16:35:50.056670: Pseudo dice [0.9649, 0.9592, 0.9796, 0.9493, 0.8761, 0.9715, 0.9896] +2024-09-09 16:35:50.057805: Epoch time: 351.36 s +2024-09-09 16:35:53.288487: +2024-09-09 16:35:53.299706: Epoch 630 +2024-09-09 16:35:53.300564: Current learning rate: 0.00409 +2024-09-09 16:42:47.649271: train_loss -0.7594 +2024-09-09 16:42:47.653062: val_loss -0.7455 +2024-09-09 16:42:47.653816: Pseudo dice [0.9654, 0.9582, 0.9799, 0.9498, 0.8737, 0.9708, 0.9907] +2024-09-09 16:42:47.654556: Epoch time: 414.37 s +2024-09-09 16:42:53.078510: +2024-09-09 16:42:53.081245: Epoch 631 +2024-09-09 16:42:53.082629: Current learning rate: 0.00408 +2024-09-09 16:50:26.585078: train_loss -0.7426 +2024-09-09 16:50:26.587542: val_loss -0.7652 +2024-09-09 16:50:26.588605: Pseudo dice [0.9653, 0.9591, 0.9795, 0.9573, 0.8799, 0.9707, 0.9916] +2024-09-09 16:50:26.589410: Epoch time: 453.53 s +2024-09-09 16:50:38.345997: +2024-09-09 16:50:38.348694: Epoch 632 +2024-09-09 16:50:38.349601: Current learning rate: 0.00407 +2024-09-09 16:58:54.092952: train_loss -0.7271 +2024-09-09 16:58:54.097241: val_loss -0.7649 +2024-09-09 16:58:54.098630: Pseudo dice [0.9639, 0.9589, 0.9759, 0.9506, 0.8875, 0.9736, 0.992] +2024-09-09 16:58:54.099923: Epoch time: 495.76 s +2024-09-09 16:59:00.257211: +2024-09-09 16:59:00.259009: Epoch 633 +2024-09-09 16:59:00.260136: Current learning rate: 0.00406 +2024-09-09 17:07:27.570461: train_loss -0.7437 +2024-09-09 17:07:27.578601: val_loss -0.7651 +2024-09-09 17:07:27.582284: Pseudo dice [0.965, 0.9566, 0.9782, 0.9481, 0.8689, 0.9691, 0.9897] +2024-09-09 17:07:27.586010: Epoch time: 507.32 s +2024-09-09 17:07:33.934267: +2024-09-09 17:07:33.935801: Epoch 634 +2024-09-09 17:07:33.936939: Current learning rate: 0.00405 +2024-09-09 17:15:35.128105: train_loss -0.7422 +2024-09-09 17:15:35.132767: val_loss -0.7507 +2024-09-09 17:15:35.134817: Pseudo dice [0.963, 0.957, 0.9783, 0.9575, 0.8766, 0.9725, 0.9911] +2024-09-09 17:15:35.135737: Epoch time: 481.2 s +2024-09-09 17:15:40.973423: +2024-09-09 17:15:40.974965: Epoch 635 +2024-09-09 17:15:40.977189: Current learning rate: 0.00404 +2024-09-09 17:24:06.149046: train_loss -0.7481 +2024-09-09 17:24:06.151471: val_loss -0.7689 +2024-09-09 17:24:06.153125: Pseudo dice [0.9648, 0.9573, 0.9804, 0.9515, 0.8827, 0.9705, 0.9908] +2024-09-09 17:24:06.154147: Epoch time: 505.18 s +2024-09-09 17:24:12.615417: +2024-09-09 17:24:12.617611: Epoch 636 +2024-09-09 17:24:12.618599: Current learning rate: 0.00403 +2024-09-09 17:32:36.255636: train_loss -0.7364 +2024-09-09 17:32:36.260107: val_loss -0.7631 +2024-09-09 17:32:36.261040: Pseudo dice [0.9641, 0.9593, 0.9796, 0.9527, 0.8781, 0.9697, 0.9915] +2024-09-09 17:32:36.261916: Epoch time: 503.66 s +2024-09-09 17:32:39.180224: +2024-09-09 17:32:39.181864: Epoch 637 +2024-09-09 17:32:39.183678: Current learning rate: 0.00402 +2024-09-09 17:38:06.837975: train_loss -0.7451 +2024-09-09 17:38:06.842221: val_loss -0.7501 +2024-09-09 17:38:06.843815: Pseudo dice [0.9621, 0.9582, 0.9759, 0.9511, 0.8863, 0.9732, 0.9917] +2024-09-09 17:38:06.845093: Epoch time: 327.67 s +2024-09-09 17:38:06.847601: Yayy! New best EMA pseudo Dice: 0.9558 +2024-09-09 17:38:13.609845: +2024-09-09 17:38:13.610870: Epoch 638 +2024-09-09 17:38:13.612443: Current learning rate: 0.00401 +2024-09-09 17:43:32.459765: train_loss -0.7533 +2024-09-09 17:43:32.462000: val_loss -0.7757 +2024-09-09 17:43:32.462749: Pseudo dice [0.9656, 0.958, 0.9805, 0.9562, 0.8802, 0.9734, 0.989] +2024-09-09 17:43:32.463422: Epoch time: 318.86 s +2024-09-09 17:43:32.464028: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-09 17:43:39.542725: +2024-09-09 17:43:39.544365: Epoch 639 +2024-09-09 17:43:39.545213: Current learning rate: 0.004 +2024-09-09 17:48:51.193573: train_loss -0.7541 +2024-09-09 17:48:51.213397: val_loss -0.7693 +2024-09-09 17:48:51.215893: Pseudo dice [0.964, 0.9591, 0.978, 0.9498, 0.8759, 0.9703, 0.9907] +2024-09-09 17:48:51.217686: Epoch time: 311.66 s +2024-09-09 17:48:55.016494: +2024-09-09 17:48:55.018605: Epoch 640 +2024-09-09 17:48:55.020026: Current learning rate: 0.00399 +2024-09-09 17:54:08.133231: train_loss -0.7377 +2024-09-09 17:54:08.135722: val_loss -0.7561 +2024-09-09 17:54:08.136395: Pseudo dice [0.9652, 0.959, 0.9794, 0.9503, 0.8755, 0.9703, 0.9907] +2024-09-09 17:54:08.137085: Epoch time: 313.16 s +2024-09-09 17:54:11.002209: +2024-09-09 17:54:11.003322: Epoch 641 +2024-09-09 17:54:11.004114: Current learning rate: 0.00398 +2024-09-09 17:59:33.455332: train_loss -0.7475 +2024-09-09 17:59:33.458094: val_loss -0.7531 +2024-09-09 17:59:33.458885: Pseudo dice [0.965, 0.9572, 0.9791, 0.9523, 0.8883, 0.9676, 0.9928] +2024-09-09 17:59:33.459574: Epoch time: 322.46 s +2024-09-09 17:59:33.460237: Yayy! New best EMA pseudo Dice: 0.956 +2024-09-09 17:59:39.763316: +2024-09-09 17:59:39.764744: Epoch 642 +2024-09-09 17:59:39.765556: Current learning rate: 0.00397 +2024-09-09 18:04:57.942556: train_loss -0.7359 +2024-09-09 18:04:57.945580: val_loss -0.7542 +2024-09-09 18:04:57.946388: Pseudo dice [0.9663, 0.9591, 0.9792, 0.9513, 0.8897, 0.9712, 0.9918] +2024-09-09 18:04:57.947139: Epoch time: 318.18 s +2024-09-09 18:04:57.947829: Yayy! New best EMA pseudo Dice: 0.9563 +2024-09-09 18:05:04.315767: +2024-09-09 18:05:04.317210: Epoch 643 +2024-09-09 18:05:04.318023: Current learning rate: 0.00396 +2024-09-09 18:10:20.711021: train_loss -0.7477 +2024-09-09 18:10:20.712394: val_loss -0.7503 +2024-09-09 18:10:20.713129: Pseudo dice [0.9659, 0.959, 0.9795, 0.9447, 0.8793, 0.9734, 0.9917] +2024-09-09 18:10:20.713948: Epoch time: 316.4 s +2024-09-09 18:10:22.817966: +2024-09-09 18:10:22.819468: Epoch 644 +2024-09-09 18:10:22.820858: Current learning rate: 0.00395 +2024-09-09 18:15:39.370100: train_loss -0.746 +2024-09-09 18:15:39.373694: val_loss -0.7478 +2024-09-09 18:15:39.375593: Pseudo dice [0.9655, 0.9583, 0.9805, 0.9392, 0.8769, 0.9612, 0.9895] +2024-09-09 18:15:39.377716: Epoch time: 316.56 s +2024-09-09 18:15:44.098774: +2024-09-09 18:15:44.101436: Epoch 645 +2024-09-09 18:15:44.102561: Current learning rate: 0.00394 +2024-09-09 18:21:02.724511: train_loss -0.736 +2024-09-09 18:21:02.727781: val_loss -0.7321 +2024-09-09 18:21:02.728988: Pseudo dice [0.9646, 0.9577, 0.9787, 0.9504, 0.8722, 0.9698, 0.99] +2024-09-09 18:21:02.729839: Epoch time: 318.64 s +2024-09-09 18:21:05.474806: +2024-09-09 18:21:05.476289: Epoch 646 +2024-09-09 18:21:05.477223: Current learning rate: 0.00393 +2024-09-09 18:26:31.738006: train_loss -0.7574 +2024-09-09 18:26:31.739866: val_loss -0.7487 +2024-09-09 18:26:31.740853: Pseudo dice [0.9675, 0.9592, 0.9803, 0.9554, 0.8775, 0.9739, 0.9896] +2024-09-09 18:26:31.741754: Epoch time: 326.27 s +2024-09-09 18:26:34.422112: +2024-09-09 18:26:34.423337: Epoch 647 +2024-09-09 18:26:34.424088: Current learning rate: 0.00392 +2024-09-09 18:31:52.812871: train_loss -0.7459 +2024-09-09 18:31:52.815935: val_loss -0.7325 +2024-09-09 18:31:52.816846: Pseudo dice [0.9609, 0.9578, 0.9759, 0.9438, 0.8974, 0.9726, 0.9927] +2024-09-09 18:31:52.817971: Epoch time: 318.4 s +2024-09-09 18:31:55.960718: +2024-09-09 18:31:55.962753: Epoch 648 +2024-09-09 18:31:55.963823: Current learning rate: 0.00391 +2024-09-09 18:37:07.309243: train_loss -0.7547 +2024-09-09 18:37:07.315973: val_loss -0.7708 +2024-09-09 18:37:07.317032: Pseudo dice [0.9659, 0.9591, 0.9795, 0.956, 0.8844, 0.9751, 0.9917] +2024-09-09 18:37:07.318601: Epoch time: 311.36 s +2024-09-09 18:37:07.319734: Yayy! New best EMA pseudo Dice: 0.9564 +2024-09-09 18:37:13.013046: +2024-09-09 18:37:13.015067: Epoch 649 +2024-09-09 18:37:13.016473: Current learning rate: 0.0039 +2024-09-09 18:42:27.664673: train_loss -0.76 +2024-09-09 18:42:27.667174: val_loss -0.7477 +2024-09-09 18:42:27.668156: Pseudo dice [0.9687, 0.961, 0.982, 0.9582, 0.8891, 0.9761, 0.9928] +2024-09-09 18:42:27.669147: Epoch time: 314.67 s +2024-09-09 18:42:31.091205: Yayy! New best EMA pseudo Dice: 0.9569 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_22_02_26.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_22_02_26.txt new file mode 100644 index 0000000000000000000000000000000000000000..46c2ff50821da2306c1fb294f8fc7f49d2844414 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs/training_log_2024_9_9_22_02_26.txt @@ -0,0 +1,378 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 22:02:30.504366: Using torch.compile... +2024-09-09 22:02:38.536471: do_dummy_2d_data_aug: False +2024-09-09 22:02:38.553031: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 22:02:38.563509: The split file contains 5 splits. +2024-09-09 22:02:38.564045: Desired fold for training: 0 +2024-09-09 22:02:38.564492: This split has 1324 training and 332 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 22:02:53.915228: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 22:02:54.779288: +2024-09-09 22:02:54.780607: Epoch 650 +2024-09-09 22:02:54.781664: Current learning rate: 0.00389 +2024-09-09 22:14:22.540025: train_loss -0.7455 +2024-09-09 22:14:22.542140: val_loss -0.7692 +2024-09-09 22:14:22.542728: Pseudo dice [0.9651, 0.9592, 0.9801, 0.941, 0.8846, 0.975, 0.9926] +2024-09-09 22:14:22.543295: Epoch time: 687.79 s +2024-09-09 22:14:22.543883: Yayy! New best EMA pseudo Dice: 0.9569 +2024-09-09 22:14:27.730831: +2024-09-09 22:14:27.731484: Epoch 651 +2024-09-09 22:14:27.731899: Current learning rate: 0.00388 +2024-09-09 22:19:36.442305: train_loss -0.7479 +2024-09-09 22:19:36.444684: val_loss -0.751 +2024-09-09 22:19:36.446591: Pseudo dice [0.963, 0.9586, 0.9775, 0.9543, 0.8758, 0.969, 0.989] +2024-09-09 22:19:36.447936: Epoch time: 308.71 s +2024-09-09 22:19:39.256821: +2024-09-09 22:19:39.258682: Epoch 652 +2024-09-09 22:19:39.259560: Current learning rate: 0.00387 +2024-09-09 22:25:08.892207: train_loss -0.7526 +2024-09-09 22:25:08.895167: val_loss -0.7471 +2024-09-09 22:25:08.896349: Pseudo dice [0.9636, 0.9592, 0.977, 0.9521, 0.8792, 0.9637, 0.9877] +2024-09-09 22:25:08.897243: Epoch time: 329.65 s +2024-09-09 22:25:11.231498: +2024-09-09 22:25:11.232445: Epoch 653 +2024-09-09 22:25:11.233183: Current learning rate: 0.00386 +2024-09-09 22:30:18.995815: train_loss -0.7504 +2024-09-09 22:30:18.999334: val_loss -0.7329 +2024-09-09 22:30:19.000483: Pseudo dice [0.9649, 0.9602, 0.9779, 0.953, 0.8871, 0.9724, 0.9921] +2024-09-09 22:30:19.001879: Epoch time: 307.77 s +2024-09-09 22:30:21.590392: +2024-09-09 22:30:21.591694: Epoch 654 +2024-09-09 22:30:21.593402: Current learning rate: 0.00385 +2024-09-09 22:35:27.492182: train_loss -0.7507 +2024-09-09 22:35:27.494580: val_loss -0.7604 +2024-09-09 22:35:27.495290: Pseudo dice [0.9668, 0.9611, 0.9795, 0.9518, 0.8893, 0.9747, 0.9923] +2024-09-09 22:35:27.497130: Epoch time: 305.91 s +2024-09-09 22:35:27.497811: Yayy! New best EMA pseudo Dice: 0.9569 +2024-09-09 22:35:32.491446: +2024-09-09 22:35:32.492679: Epoch 655 +2024-09-09 22:35:32.493257: Current learning rate: 0.00384 +2024-09-09 22:40:47.639734: train_loss -0.7509 +2024-09-09 22:40:47.642272: val_loss -0.7269 +2024-09-09 22:40:47.652800: Pseudo dice [0.9645, 0.9573, 0.9799, 0.9506, 0.8732, 0.969, 0.9902] +2024-09-09 22:40:47.653910: Epoch time: 315.15 s +2024-09-09 22:40:49.670734: +2024-09-09 22:40:49.671553: Epoch 656 +2024-09-09 22:40:49.672069: Current learning rate: 0.00383 +2024-09-09 22:46:01.609303: train_loss -0.7526 +2024-09-09 22:46:01.611939: val_loss -0.7683 +2024-09-09 22:46:01.613046: Pseudo dice [0.964, 0.9572, 0.9792, 0.9509, 0.8848, 0.9678, 0.9926] +2024-09-09 22:46:01.614046: Epoch time: 311.94 s +2024-09-09 22:46:04.051718: +2024-09-09 22:46:04.053025: Epoch 657 +2024-09-09 22:46:04.054495: Current learning rate: 0.00382 +2024-09-09 22:51:23.494652: train_loss -0.7458 +2024-09-09 22:51:23.496440: val_loss -0.743 +2024-09-09 22:51:23.496903: Pseudo dice [0.9657, 0.9591, 0.9796, 0.9474, 0.876, 0.9659, 0.9861] +2024-09-09 22:51:23.497379: Epoch time: 319.45 s +2024-09-09 22:51:25.933519: +2024-09-09 22:51:25.934756: Epoch 658 +2024-09-09 22:51:25.935389: Current learning rate: 0.00381 +2024-09-09 22:56:18.098736: train_loss -0.7574 +2024-09-09 22:56:18.117017: val_loss -0.7375 +2024-09-09 22:56:18.118707: Pseudo dice [0.9683, 0.9617, 0.9814, 0.9527, 0.8852, 0.9718, 0.9923] +2024-09-09 22:56:18.119505: Epoch time: 292.17 s +2024-09-09 22:56:22.491721: +2024-09-09 22:56:22.492935: Epoch 659 +2024-09-09 22:56:22.493730: Current learning rate: 0.0038 +2024-09-09 23:01:30.419495: train_loss -0.74 +2024-09-09 23:01:30.421592: val_loss -0.7434 +2024-09-09 23:01:30.422400: Pseudo dice [0.9667, 0.9591, 0.9805, 0.9499, 0.8883, 0.9726, 0.9924] +2024-09-09 23:01:30.423352: Epoch time: 307.93 s +2024-09-09 23:01:33.426079: +2024-09-09 23:01:33.427255: Epoch 660 +2024-09-09 23:01:33.427850: Current learning rate: 0.00379 +2024-09-09 23:06:26.770225: train_loss -0.7508 +2024-09-09 23:06:26.773126: val_loss -0.7397 +2024-09-09 23:06:26.774027: Pseudo dice [0.9657, 0.9601, 0.9791, 0.9445, 0.8712, 0.971, 0.9918] +2024-09-09 23:06:26.775289: Epoch time: 293.35 s +2024-09-09 23:06:29.821057: +2024-09-09 23:06:29.822011: Epoch 661 +2024-09-09 23:06:29.822683: Current learning rate: 0.00378 +2024-09-09 23:11:39.608925: train_loss -0.7396 +2024-09-09 23:11:39.612111: val_loss -0.7642 +2024-09-09 23:11:39.613157: Pseudo dice [0.963, 0.9594, 0.9753, 0.9548, 0.8864, 0.9718, 0.9917] +2024-09-09 23:11:39.614154: Epoch time: 309.79 s +2024-09-09 23:11:44.600822: +2024-09-09 23:11:44.602450: Epoch 662 +2024-09-09 23:11:44.603137: Current learning rate: 0.00377 +2024-09-09 23:16:44.431016: train_loss -0.7558 +2024-09-09 23:16:44.432510: val_loss -0.7106 +2024-09-09 23:16:44.433169: Pseudo dice [0.9633, 0.9582, 0.9765, 0.9478, 0.874, 0.9634, 0.9894] +2024-09-09 23:16:44.434615: Epoch time: 299.84 s +2024-09-09 23:16:46.009891: +2024-09-09 23:16:46.010668: Epoch 663 +2024-09-09 23:16:46.011672: Current learning rate: 0.00376 +2024-09-09 23:22:08.423873: train_loss -0.7433 +2024-09-09 23:22:08.425832: val_loss -0.7791 +2024-09-09 23:22:08.427141: Pseudo dice [0.9685, 0.961, 0.9823, 0.9482, 0.8704, 0.9715, 0.993] +2024-09-09 23:22:08.427829: Epoch time: 322.42 s +2024-09-09 23:22:10.939173: +2024-09-09 23:22:10.940185: Epoch 664 +2024-09-09 23:22:10.940918: Current learning rate: 0.00375 +2024-09-09 23:27:18.711446: train_loss -0.7496 +2024-09-09 23:27:18.713885: val_loss -0.7651 +2024-09-09 23:27:18.714545: Pseudo dice [0.9683, 0.9606, 0.982, 0.955, 0.8886, 0.9731, 0.9931] +2024-09-09 23:27:18.715077: Epoch time: 307.78 s +2024-09-09 23:27:21.015511: +2024-09-09 23:27:21.016696: Epoch 665 +2024-09-09 23:27:21.017322: Current learning rate: 0.00374 +2024-09-09 23:32:20.055047: train_loss -0.7358 +2024-09-09 23:32:20.060367: val_loss -0.7681 +2024-09-09 23:32:20.061120: Pseudo dice [0.9665, 0.9588, 0.9801, 0.9523, 0.8793, 0.9703, 0.9917] +2024-09-09 23:32:20.062119: Epoch time: 299.05 s +2024-09-09 23:32:21.752975: +2024-09-09 23:32:21.753976: Epoch 666 +2024-09-09 23:32:21.754600: Current learning rate: 0.00373 +2024-09-09 23:37:33.970813: train_loss -0.7439 +2024-09-09 23:37:33.972721: val_loss -0.7782 +2024-09-09 23:37:33.973320: Pseudo dice [0.9663, 0.9603, 0.9791, 0.9519, 0.8749, 0.9735, 0.9909] +2024-09-09 23:37:33.973810: Epoch time: 312.22 s +2024-09-09 23:37:35.568632: +2024-09-09 23:37:35.569721: Epoch 667 +2024-09-09 23:37:35.570318: Current learning rate: 0.00372 +2024-09-09 23:42:39.844890: train_loss -0.7502 +2024-09-09 23:42:39.845814: val_loss -0.7296 +2024-09-09 23:42:39.846316: Pseudo dice [0.9657, 0.9588, 0.9777, 0.9528, 0.8503, 0.9705, 0.9869] +2024-09-09 23:42:39.846986: Epoch time: 304.28 s +2024-09-09 23:42:41.645841: +2024-09-09 23:42:41.646742: Epoch 668 +2024-09-09 23:42:41.647248: Current learning rate: 0.00371 +2024-09-09 23:47:33.076339: train_loss -0.7532 +2024-09-09 23:47:33.079078: val_loss -0.7407 +2024-09-09 23:47:33.079861: Pseudo dice [0.9686, 0.9575, 0.9828, 0.9543, 0.8818, 0.9734, 0.991] +2024-09-09 23:47:33.080653: Epoch time: 291.43 s +2024-09-09 23:47:35.267970: +2024-09-09 23:47:35.269120: Epoch 669 +2024-09-09 23:47:35.269863: Current learning rate: 0.0037 +2024-09-09 23:52:57.021216: train_loss -0.742 +2024-09-09 23:52:57.022505: val_loss -0.7496 +2024-09-09 23:52:57.023003: Pseudo dice [0.9658, 0.9588, 0.9814, 0.9558, 0.8782, 0.9711, 0.9913] +2024-09-09 23:52:57.023520: Epoch time: 321.76 s +2024-09-09 23:52:59.497163: +2024-09-09 23:52:59.499933: Epoch 670 +2024-09-09 23:52:59.501264: Current learning rate: 0.00369 +2024-09-09 23:58:05.950350: train_loss -0.746 +2024-09-09 23:58:05.952013: val_loss -0.7614 +2024-09-09 23:58:05.952921: Pseudo dice [0.9671, 0.9583, 0.9817, 0.9501, 0.8872, 0.973, 0.9911] +2024-09-09 23:58:05.954615: Epoch time: 306.5 s +2024-09-09 23:58:09.064539: +2024-09-09 23:58:09.066676: Epoch 671 +2024-09-09 23:58:09.067545: Current learning rate: 0.00368 +2024-09-10 00:03:11.638621: train_loss -0.7451 +2024-09-10 00:03:11.655130: val_loss -0.7315 +2024-09-10 00:03:11.668555: Pseudo dice [0.964, 0.9572, 0.9804, 0.954, 0.8674, 0.9693, 0.9883] +2024-09-10 00:03:11.669424: Epoch time: 302.58 s +2024-09-10 00:03:14.126904: +2024-09-10 00:03:14.128334: Epoch 672 +2024-09-10 00:03:14.129396: Current learning rate: 0.00367 +2024-09-10 00:08:35.397063: train_loss -0.7412 +2024-09-10 00:08:35.399824: val_loss -0.7492 +2024-09-10 00:08:35.400788: Pseudo dice [0.9651, 0.9585, 0.9783, 0.9534, 0.8806, 0.9728, 0.9903] +2024-09-10 00:08:35.401423: Epoch time: 321.28 s +2024-09-10 00:08:37.740830: +2024-09-10 00:08:37.741859: Epoch 673 +2024-09-10 00:08:37.742382: Current learning rate: 0.00366 +2024-09-10 00:13:30.040856: train_loss -0.7602 +2024-09-10 00:13:30.045089: val_loss -0.7511 +2024-09-10 00:13:30.046138: Pseudo dice [0.9651, 0.9598, 0.977, 0.9497, 0.8852, 0.9677, 0.9924] +2024-09-10 00:13:30.046991: Epoch time: 292.3 s +2024-09-10 00:13:31.948530: +2024-09-10 00:13:31.950986: Epoch 674 +2024-09-10 00:13:31.951591: Current learning rate: 0.00365 +2024-09-10 00:18:49.059054: train_loss -0.7298 +2024-09-10 00:18:49.061367: val_loss -0.7731 +2024-09-10 00:18:49.061970: Pseudo dice [0.9661, 0.9601, 0.9795, 0.9485, 0.8678, 0.9699, 0.9904] +2024-09-10 00:18:49.062526: Epoch time: 317.12 s +2024-09-10 00:18:50.665370: +2024-09-10 00:18:50.667296: Epoch 675 +2024-09-10 00:18:50.667847: Current learning rate: 0.00364 +2024-09-10 00:24:01.334194: train_loss -0.7406 +2024-09-10 00:24:01.336218: val_loss -0.7519 +2024-09-10 00:24:01.336763: Pseudo dice [0.9631, 0.9605, 0.9764, 0.9561, 0.8834, 0.9696, 0.9895] +2024-09-10 00:24:01.337320: Epoch time: 310.67 s +2024-09-10 00:24:06.574935: +2024-09-10 00:24:06.576070: Epoch 676 +2024-09-10 00:24:06.576634: Current learning rate: 0.00363 +2024-09-10 00:29:46.384372: train_loss -0.7608 +2024-09-10 00:29:46.401513: val_loss -0.7518 +2024-09-10 00:29:46.402094: Pseudo dice [0.9662, 0.9617, 0.9815, 0.9531, 0.8881, 0.9734, 0.9918] +2024-09-10 00:29:46.402793: Epoch time: 339.81 s +2024-09-10 00:29:48.955803: +2024-09-10 00:29:48.957118: Epoch 677 +2024-09-10 00:29:48.957787: Current learning rate: 0.00362 +2024-09-10 00:34:12.094804: train_loss -0.7426 +2024-09-10 00:34:12.102292: val_loss -0.7242 +2024-09-10 00:34:12.102987: Pseudo dice [0.9636, 0.9585, 0.9785, 0.945, 0.8886, 0.9721, 0.991] +2024-09-10 00:34:12.103492: Epoch time: 263.14 s +2024-09-10 00:34:14.495895: +2024-09-10 00:34:14.496659: Epoch 678 +2024-09-10 00:34:14.497171: Current learning rate: 0.00361 +2024-09-10 00:38:56.906136: train_loss -0.7512 +2024-09-10 00:38:56.908399: val_loss -0.7459 +2024-09-10 00:38:56.909414: Pseudo dice [0.9636, 0.9623, 0.979, 0.9512, 0.8763, 0.9709, 0.9904] +2024-09-10 00:38:56.909931: Epoch time: 282.42 s +2024-09-10 00:39:01.139002: +2024-09-10 00:39:01.140134: Epoch 679 +2024-09-10 00:39:01.140792: Current learning rate: 0.0036 +2024-09-10 00:44:05.565263: train_loss -0.7479 +2024-09-10 00:44:05.584229: val_loss -0.7328 +2024-09-10 00:44:05.585788: Pseudo dice [0.9651, 0.9574, 0.9806, 0.9461, 0.8809, 0.9694, 0.9913] +2024-09-10 00:44:05.587556: Epoch time: 304.43 s +2024-09-10 00:44:09.684393: +2024-09-10 00:44:09.685212: Epoch 680 +2024-09-10 00:44:09.685719: Current learning rate: 0.00359 +2024-09-10 00:48:57.732038: train_loss -0.7558 +2024-09-10 00:48:57.734887: val_loss -0.7846 +2024-09-10 00:48:57.735481: Pseudo dice [0.9623, 0.9589, 0.9769, 0.9547, 0.8811, 0.9709, 0.9909] +2024-09-10 00:48:57.736181: Epoch time: 288.07 s +2024-09-10 00:49:00.236826: +2024-09-10 00:49:00.254577: Epoch 681 +2024-09-10 00:49:00.255731: Current learning rate: 0.00358 +2024-09-10 00:54:07.638384: train_loss -0.7554 +2024-09-10 00:54:07.639819: val_loss -0.7848 +2024-09-10 00:54:07.640355: Pseudo dice [0.9647, 0.96, 0.9801, 0.9538, 0.8888, 0.973, 0.9917] +2024-09-10 00:54:07.641067: Epoch time: 307.43 s +2024-09-10 00:54:10.672413: +2024-09-10 00:54:10.673163: Epoch 682 +2024-09-10 00:54:10.673633: Current learning rate: 0.00357 +2024-09-10 00:59:05.362923: train_loss -0.7534 +2024-09-10 00:59:05.365310: val_loss -0.7625 +2024-09-10 00:59:05.366199: Pseudo dice [0.9662, 0.9607, 0.9803, 0.951, 0.8763, 0.9694, 0.9901] +2024-09-10 00:59:05.366746: Epoch time: 294.69 s +2024-09-10 00:59:08.501666: +2024-09-10 00:59:08.503011: Epoch 683 +2024-09-10 00:59:08.503871: Current learning rate: 0.00356 +2024-09-10 01:04:12.570729: train_loss -0.7579 +2024-09-10 01:04:12.572309: val_loss -0.7805 +2024-09-10 01:04:12.572880: Pseudo dice [0.9652, 0.959, 0.9801, 0.9566, 0.8813, 0.9698, 0.9904] +2024-09-10 01:04:12.573430: Epoch time: 304.08 s +2024-09-10 01:04:15.292505: +2024-09-10 01:04:15.293412: Epoch 684 +2024-09-10 01:04:15.293960: Current learning rate: 0.00355 +2024-09-10 01:09:15.219102: train_loss -0.7617 +2024-09-10 01:09:15.233573: val_loss -0.7476 +2024-09-10 01:09:15.234190: Pseudo dice [0.9661, 0.9591, 0.9802, 0.9494, 0.8917, 0.9748, 0.9924] +2024-09-10 01:09:15.234674: Epoch time: 299.93 s +2024-09-10 01:09:15.235125: Yayy! New best EMA pseudo Dice: 0.9571 +2024-09-10 01:09:21.513952: +2024-09-10 01:09:21.514624: Epoch 685 +2024-09-10 01:09:21.515165: Current learning rate: 0.00354 +2024-09-10 01:15:01.913410: train_loss -0.7464 +2024-09-10 01:15:01.915752: val_loss -0.7654 +2024-09-10 01:15:01.916366: Pseudo dice [0.9632, 0.9576, 0.978, 0.9433, 0.8806, 0.9746, 0.9919] +2024-09-10 01:15:01.916890: Epoch time: 340.4 s +2024-09-10 01:15:05.483780: +2024-09-10 01:15:05.484935: Epoch 686 +2024-09-10 01:15:05.485422: Current learning rate: 0.00353 +2024-09-10 01:20:13.200473: train_loss -0.7545 +2024-09-10 01:20:13.218056: val_loss -0.7448 +2024-09-10 01:20:13.219244: Pseudo dice [0.9667, 0.9588, 0.9805, 0.9468, 0.8796, 0.9732, 0.9911] +2024-09-10 01:20:13.220396: Epoch time: 307.72 s +2024-09-10 01:20:17.085012: +2024-09-10 01:20:17.086096: Epoch 687 +2024-09-10 01:20:17.086658: Current learning rate: 0.00352 +2024-09-10 01:25:15.340430: train_loss -0.7473 +2024-09-10 01:25:15.357077: val_loss -0.75 +2024-09-10 01:25:15.358498: Pseudo dice [0.9662, 0.9582, 0.9817, 0.9536, 0.8835, 0.9732, 0.992] +2024-09-10 01:25:15.360530: Epoch time: 298.26 s +2024-09-10 01:25:19.059854: +2024-09-10 01:25:19.067837: Epoch 688 +2024-09-10 01:25:19.068745: Current learning rate: 0.00351 +2024-09-10 01:30:32.740366: train_loss -0.7517 +2024-09-10 01:30:32.757750: val_loss -0.7643 +2024-09-10 01:30:32.758472: Pseudo dice [0.9668, 0.96, 0.9811, 0.9561, 0.8991, 0.9717, 0.9931] +2024-09-10 01:30:32.759062: Epoch time: 313.69 s +2024-09-10 01:30:32.759704: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-10 01:30:40.976373: +2024-09-10 01:30:40.977125: Epoch 689 +2024-09-10 01:30:40.977599: Current learning rate: 0.0035 +2024-09-10 01:35:41.269190: train_loss -0.7497 +2024-09-10 01:35:41.272079: val_loss -0.7371 +2024-09-10 01:35:41.272789: Pseudo dice [0.9674, 0.9601, 0.9804, 0.955, 0.8774, 0.9721, 0.9902] +2024-09-10 01:35:41.273569: Epoch time: 300.3 s +2024-09-10 01:35:41.274174: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-10 01:35:47.232876: +2024-09-10 01:35:47.235120: Epoch 690 +2024-09-10 01:35:47.236014: Current learning rate: 0.00349 +2024-09-10 01:40:58.707801: train_loss -0.7525 +2024-09-10 01:40:58.726016: val_loss -0.7515 +2024-09-10 01:40:58.727302: Pseudo dice [0.9673, 0.9587, 0.9804, 0.9538, 0.8883, 0.9751, 0.9924] +2024-09-10 01:40:58.728222: Epoch time: 311.48 s +2024-09-10 01:40:58.729037: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-10 01:41:03.957660: +2024-09-10 01:41:03.959685: Epoch 691 +2024-09-10 01:41:03.960304: Current learning rate: 0.00348 +2024-09-10 01:46:05.423006: train_loss -0.7566 +2024-09-10 01:46:05.437431: val_loss -0.7424 +2024-09-10 01:46:05.438417: Pseudo dice [0.9676, 0.9605, 0.9802, 0.9545, 0.8931, 0.9752, 0.9933] +2024-09-10 01:46:05.439303: Epoch time: 301.47 s +2024-09-10 01:46:05.440109: Yayy! New best EMA pseudo Dice: 0.958 +2024-09-10 01:46:11.323905: +2024-09-10 01:46:11.324744: Epoch 692 +2024-09-10 01:46:11.325266: Current learning rate: 0.00346 +2024-09-10 01:51:08.980366: train_loss -0.7515 +2024-09-10 01:51:08.991356: val_loss -0.725 +2024-09-10 01:51:08.992049: Pseudo dice [0.9657, 0.961, 0.9758, 0.9455, 0.8725, 0.9711, 0.9882] +2024-09-10 01:51:08.992569: Epoch time: 297.66 s +2024-09-10 01:51:12.742285: +2024-09-10 01:51:12.746354: Epoch 693 +2024-09-10 01:51:12.747406: Current learning rate: 0.00345 +2024-09-10 01:56:11.470374: train_loss -0.7586 +2024-09-10 01:56:11.493537: val_loss -0.7432 +2024-09-10 01:56:11.494525: Pseudo dice [0.9636, 0.9567, 0.9788, 0.9523, 0.8907, 0.9721, 0.9902] +2024-09-10 01:56:11.495476: Epoch time: 298.76 s +2024-09-10 01:56:15.671787: +2024-09-10 01:56:15.673621: Epoch 694 +2024-09-10 01:56:15.674832: Current learning rate: 0.00344 +2024-09-10 02:01:19.900070: train_loss -0.7599 +2024-09-10 02:01:19.903444: val_loss -0.7573 +2024-09-10 02:01:19.904361: Pseudo dice [0.9661, 0.9568, 0.9784, 0.9496, 0.8784, 0.9725, 0.9915] +2024-09-10 02:01:19.905275: Epoch time: 304.24 s +2024-09-10 02:01:22.855867: +2024-09-10 02:01:22.861775: Epoch 695 +2024-09-10 02:01:22.863057: Current learning rate: 0.00343 +2024-09-10 02:06:19.978373: train_loss -0.7367 +2024-09-10 02:06:19.981886: val_loss -0.7482 +2024-09-10 02:06:19.982986: Pseudo dice [0.9635, 0.9583, 0.9762, 0.947, 0.872, 0.9674, 0.9906] +2024-09-10 02:06:19.983984: Epoch time: 297.13 s +2024-09-10 02:06:22.773354: +2024-09-10 02:06:22.775465: Epoch 696 +2024-09-10 02:06:22.776300: Current learning rate: 0.00342 +2024-09-10 02:11:26.968598: train_loss -0.7536 +2024-09-10 02:11:26.982416: val_loss -0.7637 +2024-09-10 02:11:26.983337: Pseudo dice [0.9662, 0.9583, 0.98, 0.9563, 0.8705, 0.9673, 0.9909] +2024-09-10 02:11:26.983988: Epoch time: 304.2 s +2024-09-10 02:11:30.186913: +2024-09-10 02:11:30.200740: Epoch 697 +2024-09-10 02:11:30.201854: Current learning rate: 0.00341 +2024-09-10 02:16:36.315631: train_loss -0.7561 +2024-09-10 02:16:36.333670: val_loss -0.7667 +2024-09-10 02:16:36.334540: Pseudo dice [0.9652, 0.9595, 0.9789, 0.9512, 0.8935, 0.9732, 0.993] +2024-09-10 02:16:36.335100: Epoch time: 306.14 s +2024-09-10 02:16:39.486709: +2024-09-10 02:16:39.500395: Epoch 698 +2024-09-10 02:16:39.501105: Current learning rate: 0.0034 +2024-09-10 02:22:05.721783: train_loss -0.7423 +2024-09-10 02:22:05.724572: val_loss -0.7927 +2024-09-10 02:22:05.725654: Pseudo dice [0.965, 0.9587, 0.9773, 0.9519, 0.8758, 0.9692, 0.9907] +2024-09-10 02:22:05.726506: Epoch time: 326.24 s +2024-09-10 02:22:09.718465: +2024-09-10 02:22:09.719639: Epoch 699 +2024-09-10 02:22:09.720284: Current learning rate: 0.00339 +2024-09-10 02:27:21.526678: train_loss -0.7363 +2024-09-10 02:27:21.553207: val_loss -0.7426 +2024-09-10 02:27:21.554153: Pseudo dice [0.9653, 0.9587, 0.9782, 0.9492, 0.8927, 0.9708, 0.9912] +2024-09-10 02:27:21.555145: Epoch time: 311.81 s \ No 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'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 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]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}", + "preprocessed_dataset_folder": "/data2/nnUNet_preprocessed/Dataset611_Tissue/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data2/nnUNet_preprocessed/Dataset611_Tissue", + "save_every": "50", + "torch_version": "2.3.0+cu121", + "unpack_dataset": "False", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/README.md b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fc6048748e53c52bb48601dc12dfdc98929dfcde --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/README.md @@ -0,0 +1,6 @@ +# Training log note + +The fold's `debug.json` names +`training_log_2024_9_10_13_25_58.txt` as the active nnU-Net log. That file is +not included; the available timestamped log segments are provided in this +directory. diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/progress.png b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..d79bbba51a51e512cab9528c70e7e17c2d5f7414 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8da68332e056494c7d97d305d52eda20b11f0475aec7eeed46d992799cacbcee +size 644050 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_00_29_55.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_00_29_55.txt new file mode 100644 index 0000000000000000000000000000000000000000..d627575ff2f225e7913eda3461a6c2e227765cc8 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_00_29_55.txt @@ -0,0 +1,731 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 00:30:07.902106: Using torch.compile... +2024-09-10 00:30:22.458913: do_dummy_2d_data_aug: False +2024-09-10 00:30:22.466082: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 00:30:22.468709: The split file contains 5 splits. +2024-09-10 00:30:22.469503: Desired fold for training: 1 +2024-09-10 00:30:22.470166: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 00:30:32.779784: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 00:30:33.676261: +2024-09-10 00:30:33.677311: Epoch 850 +2024-09-10 00:30:33.678330: Current learning rate: 0.00181 +2024-09-10 00:43:45.141574: train_loss -0.7504 +2024-09-10 00:43:45.142846: val_loss -0.7925 +2024-09-10 00:43:45.143560: Pseudo dice [0.9667, 0.9584, 0.9803, 0.9573, 0.8797, 0.976, 0.9897] +2024-09-10 00:43:45.144227: Epoch time: 791.47 s +2024-09-10 00:43:46.672191: +2024-09-10 00:43:46.673070: Epoch 851 +2024-09-10 00:43:46.673869: Current learning rate: 0.0018 +2024-09-10 00:48:11.613314: train_loss -0.7599 +2024-09-10 00:48:11.625389: val_loss -0.7798 +2024-09-10 00:48:11.626238: Pseudo dice [0.9679, 0.9589, 0.9822, 0.9569, 0.8773, 0.9751, 0.9914] +2024-09-10 00:48:11.627036: Epoch time: 264.95 s +2024-09-10 00:48:14.180066: +2024-09-10 00:48:14.181808: Epoch 852 +2024-09-10 00:48:14.182657: Current learning rate: 0.00179 +2024-09-10 00:52:55.435977: train_loss -0.7611 +2024-09-10 00:52:55.450034: val_loss -0.7356 +2024-09-10 00:52:55.450975: Pseudo dice [0.9671, 0.9593, 0.9786, 0.9519, 0.8491, 0.959, 0.9859] +2024-09-10 00:52:55.451765: Epoch time: 281.26 s +2024-09-10 00:52:59.818980: +2024-09-10 00:52:59.820045: Epoch 853 +2024-09-10 00:52:59.821000: Current learning rate: 0.00178 +2024-09-10 00:57:10.270520: train_loss -0.75 +2024-09-10 00:57:10.280888: val_loss -0.7477 +2024-09-10 00:57:10.285181: Pseudo dice [0.9698, 0.9612, 0.9845, 0.9535, 0.8803, 0.9771, 0.9901] +2024-09-10 00:57:10.289349: Epoch time: 250.46 s +2024-09-10 00:57:13.272086: +2024-09-10 00:57:13.284780: Epoch 854 +2024-09-10 00:57:13.285762: Current learning rate: 0.00177 +2024-09-10 01:01:18.508296: train_loss -0.7657 +2024-09-10 01:01:18.510279: val_loss -0.7824 +2024-09-10 01:01:18.511038: Pseudo dice [0.9695, 0.9602, 0.9822, 0.9558, 0.8816, 0.9761, 0.9905] +2024-09-10 01:01:18.511941: Epoch time: 245.24 s +2024-09-10 01:01:20.666391: +2024-09-10 01:01:20.667335: Epoch 855 +2024-09-10 01:01:20.668314: Current learning rate: 0.00176 +2024-09-10 01:05:39.924460: train_loss -0.7514 +2024-09-10 01:05:39.935322: val_loss -0.7818 +2024-09-10 01:05:39.936352: Pseudo dice [0.9668, 0.9599, 0.9795, 0.9471, 0.8847, 0.961, 0.9887] +2024-09-10 01:05:39.937233: Epoch time: 259.26 s +2024-09-10 01:05:42.372734: +2024-09-10 01:05:42.373627: Epoch 856 +2024-09-10 01:05:42.374468: Current learning rate: 0.00175 +2024-09-10 01:10:06.157843: train_loss -0.7609 +2024-09-10 01:10:06.160284: val_loss -0.7497 +2024-09-10 01:10:06.161222: Pseudo dice [0.969, 0.9602, 0.9817, 0.9539, 0.884, 0.96, 0.9895] +2024-09-10 01:10:06.162094: Epoch time: 263.79 s +2024-09-10 01:10:08.273700: +2024-09-10 01:10:08.274942: Epoch 857 +2024-09-10 01:10:08.276023: Current learning rate: 0.00174 +2024-09-10 01:14:24.041171: train_loss -0.7595 +2024-09-10 01:14:24.043435: val_loss -0.6921 +2024-09-10 01:14:24.044297: Pseudo dice [0.9691, 0.9616, 0.9813, 0.9521, 0.8656, 0.9749, 0.9885] +2024-09-10 01:14:24.045099: Epoch time: 255.78 s +2024-09-10 01:14:26.328173: +2024-09-10 01:14:26.331344: Epoch 858 +2024-09-10 01:14:26.338712: Current learning rate: 0.00173 +2024-09-10 01:18:51.933078: train_loss -0.754 +2024-09-10 01:18:51.953599: val_loss -0.7198 +2024-09-10 01:18:51.954451: Pseudo dice [0.9685, 0.9608, 0.9801, 0.9563, 0.8862, 0.9707, 0.9897] +2024-09-10 01:18:51.955248: Epoch time: 265.62 s +2024-09-10 01:18:54.112124: +2024-09-10 01:18:54.121661: Epoch 859 +2024-09-10 01:18:54.122658: Current learning rate: 0.00172 +2024-09-10 01:22:58.549546: train_loss -0.7631 +2024-09-10 01:22:58.566025: val_loss -0.7491 +2024-09-10 01:22:58.567056: Pseudo dice [0.9688, 0.9632, 0.9811, 0.9549, 0.8905, 0.9757, 0.9919] +2024-09-10 01:22:58.567885: Epoch time: 244.44 s +2024-09-10 01:23:00.633244: +2024-09-10 01:23:00.634444: Epoch 860 +2024-09-10 01:23:00.635361: Current learning rate: 0.0017 +2024-09-10 01:27:21.785263: train_loss -0.759 +2024-09-10 01:27:21.802197: val_loss -0.7657 +2024-09-10 01:27:21.803106: Pseudo dice [0.9673, 0.9611, 0.9816, 0.9559, 0.881, 0.9761, 0.9899] +2024-09-10 01:27:21.803934: Epoch time: 261.16 s +2024-09-10 01:27:24.187389: +2024-09-10 01:27:24.189377: Epoch 861 +2024-09-10 01:27:24.190260: Current learning rate: 0.00169 +2024-09-10 01:31:31.529446: train_loss -0.7671 +2024-09-10 01:31:31.557709: val_loss -0.7178 +2024-09-10 01:31:31.558642: Pseudo dice [0.9683, 0.9627, 0.98, 0.9522, 0.8602, 0.9584, 0.9852] +2024-09-10 01:31:31.559443: Epoch time: 247.35 s +2024-09-10 01:31:33.676797: +2024-09-10 01:31:33.678420: Epoch 862 +2024-09-10 01:31:33.679239: Current learning rate: 0.00168 +2024-09-10 01:36:00.097215: train_loss -0.7656 +2024-09-10 01:36:00.099515: val_loss -0.7345 +2024-09-10 01:36:00.100303: Pseudo dice [0.9665, 0.9599, 0.9795, 0.9524, 0.8749, 0.9706, 0.9896] +2024-09-10 01:36:00.101201: Epoch time: 266.43 s +2024-09-10 01:36:02.433436: +2024-09-10 01:36:02.436891: Epoch 863 +2024-09-10 01:36:02.448588: Current learning rate: 0.00167 +2024-09-10 01:40:21.986691: train_loss -0.7667 +2024-09-10 01:40:21.989348: val_loss -0.7622 +2024-09-10 01:40:21.990220: Pseudo dice [0.97, 0.9629, 0.9814, 0.9526, 0.8871, 0.9756, 0.9906] +2024-09-10 01:40:21.991002: Epoch time: 259.56 s +2024-09-10 01:40:24.174196: +2024-09-10 01:40:24.175406: Epoch 864 +2024-09-10 01:40:24.176223: Current learning rate: 0.00166 +2024-09-10 01:44:41.988790: train_loss -0.7622 +2024-09-10 01:44:41.990226: val_loss -0.7461 +2024-09-10 01:44:41.991010: Pseudo dice [0.9692, 0.9633, 0.9823, 0.9529, 0.8729, 0.9765, 0.9907] +2024-09-10 01:44:41.991743: Epoch time: 257.82 s +2024-09-10 01:44:44.214972: +2024-09-10 01:44:44.216231: Epoch 865 +2024-09-10 01:44:44.217420: Current learning rate: 0.00165 +2024-09-10 01:49:07.746939: train_loss -0.7617 +2024-09-10 01:49:07.761455: val_loss -0.7303 +2024-09-10 01:49:07.762344: Pseudo dice [0.9661, 0.958, 0.9779, 0.9533, 0.8554, 0.948, 0.9813] +2024-09-10 01:49:07.763420: Epoch time: 263.54 s +2024-09-10 01:49:10.073784: +2024-09-10 01:49:10.074992: Epoch 866 +2024-09-10 01:49:10.075902: Current learning rate: 0.00164 +2024-09-10 01:53:49.011041: train_loss -0.754 +2024-09-10 01:53:49.025340: val_loss -0.717 +2024-09-10 01:53:49.026263: Pseudo dice [0.9684, 0.9594, 0.9806, 0.9527, 0.8656, 0.9624, 0.9878] +2024-09-10 01:53:49.027184: Epoch time: 278.94 s +2024-09-10 01:53:51.118798: +2024-09-10 01:53:51.119724: Epoch 867 +2024-09-10 01:53:51.123022: Current learning rate: 0.00163 +2024-09-10 01:58:22.343446: train_loss -0.7594 +2024-09-10 01:58:22.358111: val_loss -0.7753 +2024-09-10 01:58:22.359041: Pseudo dice [0.962, 0.961, 0.9749, 0.9522, 0.8569, 0.9738, 0.9838] +2024-09-10 01:58:22.359849: Epoch time: 271.23 s +2024-09-10 01:58:24.564665: +2024-09-10 01:58:24.566299: Epoch 868 +2024-09-10 01:58:24.568919: Current learning rate: 0.00162 +2024-09-10 02:02:50.486759: train_loss -0.7596 +2024-09-10 02:02:50.501134: val_loss -0.7543 +2024-09-10 02:02:50.502156: Pseudo dice [0.9664, 0.9594, 0.9797, 0.9529, 0.8897, 0.9749, 0.9908] +2024-09-10 02:02:50.502981: Epoch time: 265.93 s +2024-09-10 02:02:54.336710: +2024-09-10 02:02:54.337740: Epoch 869 +2024-09-10 02:02:54.338579: Current learning rate: 0.00161 +2024-09-10 02:07:11.461520: train_loss -0.7523 +2024-09-10 02:07:11.477249: val_loss -0.7296 +2024-09-10 02:07:11.478138: Pseudo dice [0.9666, 0.9583, 0.9788, 0.9483, 0.8786, 0.9773, 0.9906] +2024-09-10 02:07:11.478918: Epoch time: 257.13 s +2024-09-10 02:07:14.621212: +2024-09-10 02:07:14.637511: Epoch 870 +2024-09-10 02:07:14.638369: Current learning rate: 0.00159 +2024-09-10 02:11:47.473406: train_loss -0.7619 +2024-09-10 02:11:47.474868: val_loss -0.7818 +2024-09-10 02:11:47.475775: Pseudo dice [0.9696, 0.9606, 0.9813, 0.9538, 0.8932, 0.9671, 0.9901] +2024-09-10 02:11:47.476611: Epoch time: 272.87 s +2024-09-10 02:11:49.700612: +2024-09-10 02:11:49.701663: Epoch 871 +2024-09-10 02:11:49.702579: Current learning rate: 0.00158 +2024-09-10 02:15:56.930458: train_loss -0.7486 +2024-09-10 02:15:56.932179: val_loss -0.7661 +2024-09-10 02:15:56.933134: Pseudo dice [0.9688, 0.9612, 0.9839, 0.9547, 0.8806, 0.9748, 0.9897] +2024-09-10 02:15:56.933980: Epoch time: 247.24 s +2024-09-10 02:15:58.909010: +2024-09-10 02:15:58.910240: Epoch 872 +2024-09-10 02:15:58.911097: Current learning rate: 0.00157 +2024-09-10 02:20:18.618672: train_loss -0.748 +2024-09-10 02:20:18.619946: val_loss -0.7517 +2024-09-10 02:20:18.620711: Pseudo dice [0.9669, 0.9614, 0.9782, 0.9549, 0.8826, 0.9757, 0.9915] +2024-09-10 02:20:18.621583: Epoch time: 259.72 s +2024-09-10 02:20:20.953693: +2024-09-10 02:20:20.954798: Epoch 873 +2024-09-10 02:20:20.955734: Current learning rate: 0.00156 +2024-09-10 02:25:09.596462: train_loss -0.7567 +2024-09-10 02:25:09.612190: val_loss -0.7505 +2024-09-10 02:25:09.613248: Pseudo dice [0.9656, 0.9592, 0.9787, 0.9531, 0.8777, 0.9624, 0.9868] +2024-09-10 02:25:09.614091: Epoch time: 288.65 s +2024-09-10 02:25:11.676674: +2024-09-10 02:25:11.688684: Epoch 874 +2024-09-10 02:25:11.689497: Current learning rate: 0.00155 +2024-09-10 02:29:43.582289: train_loss -0.7548 +2024-09-10 02:29:43.602155: val_loss -0.7529 +2024-09-10 02:29:43.603242: Pseudo dice [0.9664, 0.9606, 0.9798, 0.9578, 0.8919, 0.9747, 0.9914] +2024-09-10 02:29:43.604176: Epoch time: 271.92 s +2024-09-10 02:29:47.989177: +2024-09-10 02:29:48.000740: Epoch 875 +2024-09-10 02:29:48.001482: Current learning rate: 0.00154 +2024-09-10 02:34:13.660772: train_loss -0.7597 +2024-09-10 02:34:13.663220: val_loss -0.8149 +2024-09-10 02:34:13.664198: Pseudo dice [0.9685, 0.9598, 0.9818, 0.9567, 0.8863, 0.9741, 0.9912] +2024-09-10 02:34:13.665115: Epoch time: 265.68 s +2024-09-10 02:34:15.950113: +2024-09-10 02:34:15.951347: Epoch 876 +2024-09-10 02:34:15.952237: Current learning rate: 0.00153 +2024-09-10 02:38:51.010022: train_loss -0.7407 +2024-09-10 02:38:51.012499: val_loss -0.7973 +2024-09-10 02:38:51.013367: Pseudo dice [0.9664, 0.9575, 0.9808, 0.946, 0.8761, 0.9755, 0.9925] +2024-09-10 02:38:51.014305: Epoch time: 275.06 s +2024-09-10 02:38:53.300185: +2024-09-10 02:38:53.301195: Epoch 877 +2024-09-10 02:38:53.302009: Current learning rate: 0.00152 +2024-09-10 02:43:17.090586: train_loss -0.7568 +2024-09-10 02:43:17.109500: val_loss -0.7566 +2024-09-10 02:43:17.110895: Pseudo dice [0.9687, 0.9622, 0.9802, 0.9574, 0.8942, 0.9629, 0.9886] +2024-09-10 02:43:17.112324: Epoch time: 263.8 s +2024-09-10 02:43:19.248592: +2024-09-10 02:43:19.249783: Epoch 878 +2024-09-10 02:43:19.250557: Current learning rate: 0.00151 +2024-09-10 02:47:34.828390: train_loss -0.7563 +2024-09-10 02:47:34.830472: val_loss -0.7238 +2024-09-10 02:47:34.831289: Pseudo dice [0.9693, 0.9623, 0.9815, 0.9561, 0.8596, 0.9612, 0.9851] +2024-09-10 02:47:34.832108: Epoch time: 255.58 s +2024-09-10 02:47:37.685658: +2024-09-10 02:47:37.700762: Epoch 879 +2024-09-10 02:47:37.701792: Current learning rate: 0.00149 +2024-09-10 02:51:53.467058: train_loss -0.7569 +2024-09-10 02:51:53.469382: val_loss -0.7865 +2024-09-10 02:51:53.470249: Pseudo dice [0.9668, 0.9602, 0.9798, 0.9494, 0.8778, 0.9732, 0.9885] +2024-09-10 02:51:53.471129: Epoch time: 255.79 s +2024-09-10 02:51:57.234113: +2024-09-10 02:51:57.235025: Epoch 880 +2024-09-10 02:51:57.235795: Current learning rate: 0.00148 +2024-09-10 02:56:15.767797: train_loss -0.7594 +2024-09-10 02:56:15.774634: val_loss -0.7546 +2024-09-10 02:56:15.776584: Pseudo dice [0.9667, 0.9617, 0.9772, 0.9602, 0.8861, 0.9708, 0.9912] +2024-09-10 02:56:15.778902: Epoch time: 258.54 s +2024-09-10 02:56:18.134292: +2024-09-10 02:56:18.135299: Epoch 881 +2024-09-10 02:56:18.136040: Current learning rate: 0.00147 +2024-09-10 03:00:36.575429: train_loss -0.7618 +2024-09-10 03:00:36.586568: val_loss -0.7809 +2024-09-10 03:00:36.587639: Pseudo dice [0.9695, 0.9613, 0.9822, 0.9552, 0.8968, 0.9731, 0.9923] +2024-09-10 03:00:36.591253: Epoch time: 258.45 s +2024-09-10 03:00:40.521236: +2024-09-10 03:00:40.524994: Epoch 882 +2024-09-10 03:00:40.525887: Current learning rate: 0.00146 +2024-09-10 03:04:57.311944: train_loss -0.7539 +2024-09-10 03:04:57.326109: val_loss -0.7159 +2024-09-10 03:04:57.326982: Pseudo dice [0.9662, 0.9576, 0.9781, 0.9542, 0.8598, 0.9515, 0.982] +2024-09-10 03:04:57.327767: Epoch time: 256.8 s +2024-09-10 03:05:00.497779: +2024-09-10 03:05:00.516117: Epoch 883 +2024-09-10 03:05:00.517289: Current learning rate: 0.00145 +2024-09-10 03:09:17.043113: train_loss -0.7579 +2024-09-10 03:09:17.061356: val_loss -0.7527 +2024-09-10 03:09:17.062274: Pseudo dice [0.9675, 0.9613, 0.9778, 0.9562, 0.8616, 0.9665, 0.9869] +2024-09-10 03:09:17.063104: Epoch time: 256.55 s +2024-09-10 03:09:19.200767: +2024-09-10 03:09:19.202165: Epoch 884 +2024-09-10 03:09:19.203156: Current learning rate: 0.00144 +2024-09-10 03:13:41.885376: train_loss -0.7527 +2024-09-10 03:13:41.887939: val_loss -0.722 +2024-09-10 03:13:41.888900: Pseudo dice [0.9661, 0.9606, 0.9784, 0.9549, 0.8852, 0.9575, 0.9882] +2024-09-10 03:13:41.889828: Epoch time: 262.69 s +2024-09-10 03:13:44.061514: +2024-09-10 03:13:44.062622: Epoch 885 +2024-09-10 03:13:44.063424: Current learning rate: 0.00143 +2024-09-10 03:18:10.171670: train_loss -0.7533 +2024-09-10 03:18:10.182597: val_loss -0.7552 +2024-09-10 03:18:10.183532: Pseudo dice [0.9695, 0.9627, 0.9821, 0.9579, 0.9028, 0.975, 0.993] +2024-09-10 03:18:10.184234: Epoch time: 266.11 s +2024-09-10 03:18:13.228441: +2024-09-10 03:18:13.229393: Epoch 886 +2024-09-10 03:18:13.230224: Current learning rate: 0.00142 +2024-09-10 03:22:31.323687: train_loss -0.7629 +2024-09-10 03:22:31.326230: val_loss -0.7498 +2024-09-10 03:22:31.327191: Pseudo dice [0.9674, 0.9614, 0.9803, 0.9537, 0.8775, 0.9738, 0.9897] +2024-09-10 03:22:31.328079: Epoch time: 258.1 s +2024-09-10 03:22:33.883821: +2024-09-10 03:22:33.884704: Epoch 887 +2024-09-10 03:22:33.885330: Current learning rate: 0.00141 +2024-09-10 03:26:58.595236: train_loss -0.7712 +2024-09-10 03:26:58.620865: val_loss -0.7656 +2024-09-10 03:26:58.621986: Pseudo dice [0.9701, 0.9611, 0.9831, 0.9588, 0.8864, 0.9733, 0.9915] +2024-09-10 03:26:58.622836: Epoch time: 264.72 s +2024-09-10 03:27:00.781526: +2024-09-10 03:27:00.782727: Epoch 888 +2024-09-10 03:27:00.783791: Current learning rate: 0.00139 +2024-09-10 03:31:24.825476: train_loss -0.7582 +2024-09-10 03:31:24.827739: val_loss -0.7553 +2024-09-10 03:31:24.828800: Pseudo dice [0.9686, 0.9598, 0.9805, 0.9558, 0.8877, 0.964, 0.9903] +2024-09-10 03:31:24.829726: Epoch time: 264.05 s +2024-09-10 03:31:26.908785: +2024-09-10 03:31:26.910093: Epoch 889 +2024-09-10 03:31:26.910991: Current learning rate: 0.00138 +2024-09-10 03:35:42.705982: train_loss -0.7493 +2024-09-10 03:35:42.707609: val_loss -0.7676 +2024-09-10 03:35:42.708676: Pseudo dice [0.9673, 0.9607, 0.9797, 0.9553, 0.9011, 0.9757, 0.9925] +2024-09-10 03:35:42.709754: Epoch time: 255.81 s +2024-09-10 03:35:42.710759: Yayy! New best EMA pseudo Dice: 0.958 +2024-09-10 03:35:48.973598: +2024-09-10 03:35:48.976108: Epoch 890 +2024-09-10 03:35:48.980395: Current learning rate: 0.00137 +2024-09-10 03:40:09.276987: train_loss -0.7548 +2024-09-10 03:40:09.278677: val_loss -0.7886 +2024-09-10 03:40:09.279812: Pseudo dice [0.9691, 0.9606, 0.9835, 0.9529, 0.8403, 0.9757, 0.9866] +2024-09-10 03:40:09.292748: Epoch time: 260.31 s +2024-09-10 03:40:12.176678: +2024-09-10 03:40:12.178038: Epoch 891 +2024-09-10 03:40:12.179152: Current learning rate: 0.00136 +2024-09-10 03:44:38.864587: train_loss -0.7509 +2024-09-10 03:44:38.865926: val_loss -0.7811 +2024-09-10 03:44:38.866769: Pseudo dice [0.9677, 0.9613, 0.9791, 0.9519, 0.8897, 0.9773, 0.9928] +2024-09-10 03:44:38.867617: Epoch time: 266.69 s +2024-09-10 03:44:41.203419: +2024-09-10 03:44:41.204440: Epoch 892 +2024-09-10 03:44:41.205282: Current learning rate: 0.00135 +2024-09-10 03:48:58.814748: train_loss -0.7455 +2024-09-10 03:48:58.817049: val_loss -0.7432 +2024-09-10 03:48:58.817895: Pseudo dice [0.9687, 0.9615, 0.9807, 0.9569, 0.8879, 0.9634, 0.9891] +2024-09-10 03:48:58.818647: Epoch time: 257.62 s +2024-09-10 03:49:02.294158: +2024-09-10 03:49:02.295355: Epoch 893 +2024-09-10 03:49:02.296181: Current learning rate: 0.00134 +2024-09-10 03:53:27.790557: train_loss -0.749 +2024-09-10 03:53:27.809851: val_loss -0.7731 +2024-09-10 03:53:27.810784: Pseudo dice [0.9679, 0.96, 0.9792, 0.9528, 0.8787, 0.9734, 0.9902] +2024-09-10 03:53:27.811588: Epoch time: 265.5 s +2024-09-10 03:53:30.996159: +2024-09-10 03:53:30.997283: Epoch 894 +2024-09-10 03:53:30.998427: Current learning rate: 0.00133 +2024-09-10 03:57:53.317465: train_loss -0.7659 +2024-09-10 03:57:53.333199: val_loss -0.7261 +2024-09-10 03:57:53.334030: Pseudo dice [0.9685, 0.9603, 0.9802, 0.9564, 0.8605, 0.9497, 0.9834] +2024-09-10 03:57:53.334851: Epoch time: 262.33 s +2024-09-10 03:57:55.495175: +2024-09-10 03:57:55.496176: Epoch 895 +2024-09-10 03:57:55.497066: Current learning rate: 0.00132 +2024-09-10 04:01:58.829972: train_loss -0.7647 +2024-09-10 04:01:58.832467: val_loss -0.7826 +2024-09-10 04:01:58.833530: Pseudo dice [0.968, 0.9608, 0.9822, 0.9534, 0.8699, 0.9759, 0.9875] +2024-09-10 04:01:58.834542: Epoch time: 243.34 s +2024-09-10 04:02:02.189770: +2024-09-10 04:02:02.190847: Epoch 896 +2024-09-10 04:02:02.191798: Current learning rate: 0.0013 +2024-09-10 04:06:30.920287: train_loss -0.7784 +2024-09-10 04:06:30.922795: val_loss -0.77 +2024-09-10 04:06:30.923685: Pseudo dice [0.9689, 0.9595, 0.9832, 0.9556, 0.9056, 0.966, 0.9899] +2024-09-10 04:06:30.924634: Epoch time: 268.74 s +2024-09-10 04:06:33.169254: +2024-09-10 04:06:33.170517: Epoch 897 +2024-09-10 04:06:33.171382: Current learning rate: 0.00129 +2024-09-10 04:10:38.168092: train_loss -0.7611 +2024-09-10 04:10:38.170607: val_loss -0.7746 +2024-09-10 04:10:38.171612: Pseudo dice [0.9689, 0.9634, 0.9819, 0.9562, 0.8889, 0.975, 0.9907] +2024-09-10 04:10:38.172555: Epoch time: 245.0 s +2024-09-10 04:10:40.221392: +2024-09-10 04:10:40.222494: Epoch 898 +2024-09-10 04:10:40.223433: Current learning rate: 0.00128 +2024-09-10 04:15:07.650064: train_loss -0.7552 +2024-09-10 04:15:07.652351: val_loss -0.7626 +2024-09-10 04:15:07.653211: Pseudo dice [0.9686, 0.9603, 0.981, 0.9537, 0.8941, 0.9759, 0.9931] +2024-09-10 04:15:07.654010: Epoch time: 267.43 s +2024-09-10 04:15:07.654846: Yayy! New best EMA pseudo Dice: 0.9581 +2024-09-10 04:15:13.247589: +2024-09-10 04:15:13.248498: Epoch 899 +2024-09-10 04:15:13.249266: Current learning rate: 0.00127 +2024-09-10 04:19:31.158303: train_loss -0.7617 +2024-09-10 04:19:31.160720: val_loss -0.7182 +2024-09-10 04:19:31.161535: Pseudo dice [0.9667, 0.958, 0.9778, 0.9519, 0.8747, 0.9635, 0.9866] +2024-09-10 04:19:31.162355: Epoch time: 257.91 s +2024-09-10 04:19:38.321789: +2024-09-10 04:19:38.322526: Epoch 900 +2024-09-10 04:19:38.324923: Current learning rate: 0.00126 +2024-09-10 04:24:04.173394: train_loss -0.7589 +2024-09-10 04:24:04.190137: val_loss -0.778 +2024-09-10 04:24:04.190985: Pseudo dice [0.969, 0.9605, 0.9818, 0.9549, 0.8915, 0.9763, 0.9913] +2024-09-10 04:24:04.191741: Epoch time: 265.85 s +2024-09-10 04:24:06.451766: +2024-09-10 04:24:06.452797: Epoch 901 +2024-09-10 04:24:06.453756: Current learning rate: 0.00125 +2024-09-10 04:28:41.069669: train_loss -0.7382 +2024-09-10 04:28:41.071850: val_loss -0.7451 +2024-09-10 04:28:41.072718: Pseudo dice [0.9666, 0.9632, 0.9778, 0.9558, 0.8998, 0.9572, 0.9895] +2024-09-10 04:28:41.073478: Epoch time: 274.62 s +2024-09-10 04:28:43.188509: +2024-09-10 04:28:43.189820: Epoch 902 +2024-09-10 04:28:43.190733: Current learning rate: 0.00124 +2024-09-10 04:33:06.425782: train_loss -0.7597 +2024-09-10 04:33:06.428336: val_loss -0.76 +2024-09-10 04:33:06.429282: Pseudo dice [0.9698, 0.9613, 0.9827, 0.9542, 0.8772, 0.9751, 0.9877] +2024-09-10 04:33:06.430074: Epoch time: 263.24 s +2024-09-10 04:33:08.655818: +2024-09-10 04:33:08.657154: Epoch 903 +2024-09-10 04:33:08.657959: Current learning rate: 0.00122 +2024-09-10 04:37:31.026425: train_loss -0.7599 +2024-09-10 04:37:31.028894: val_loss -0.7551 +2024-09-10 04:37:31.030082: Pseudo dice [0.969, 0.9611, 0.9803, 0.9512, 0.8759, 0.9744, 0.9903] +2024-09-10 04:37:31.031134: Epoch time: 262.38 s +2024-09-10 04:37:33.043823: +2024-09-10 04:37:33.044871: Epoch 904 +2024-09-10 04:37:33.045707: Current learning rate: 0.00121 +2024-09-10 04:41:55.533458: train_loss -0.7496 +2024-09-10 04:41:55.556180: val_loss -0.7535 +2024-09-10 04:41:55.557327: Pseudo dice [0.9666, 0.9605, 0.9793, 0.9541, 0.8883, 0.9514, 0.987] +2024-09-10 04:41:55.558352: Epoch time: 262.49 s +2024-09-10 04:41:57.946866: +2024-09-10 04:41:57.948556: Epoch 905 +2024-09-10 04:41:57.949742: Current learning rate: 0.0012 +2024-09-10 04:46:26.018152: train_loss -0.7587 +2024-09-10 04:46:26.030154: val_loss -0.7808 +2024-09-10 04:46:26.030950: Pseudo dice [0.9661, 0.9606, 0.9789, 0.9538, 0.891, 0.9751, 0.9913] +2024-09-10 04:46:26.031688: Epoch time: 268.08 s +2024-09-10 04:46:30.450604: +2024-09-10 04:46:30.451826: Epoch 906 +2024-09-10 04:46:30.452865: Current learning rate: 0.00119 +2024-09-10 04:50:33.813455: train_loss -0.7569 +2024-09-10 04:50:33.815964: val_loss -0.7275 +2024-09-10 04:50:33.822151: Pseudo dice [0.9695, 0.961, 0.9822, 0.9543, 0.8668, 0.9617, 0.9881] +2024-09-10 04:50:33.823282: Epoch time: 243.37 s +2024-09-10 04:50:35.884213: +2024-09-10 04:50:35.896740: Epoch 907 +2024-09-10 04:50:35.897987: Current learning rate: 0.00118 +2024-09-10 04:54:56.645038: train_loss -0.7635 +2024-09-10 04:54:56.647342: val_loss -0.7842 +2024-09-10 04:54:56.652108: Pseudo dice [0.9686, 0.961, 0.9817, 0.9551, 0.8844, 0.9759, 0.9898] +2024-09-10 04:54:56.664918: Epoch time: 260.77 s +2024-09-10 04:54:58.833936: +2024-09-10 04:54:58.848763: Epoch 908 +2024-09-10 04:54:58.849743: Current learning rate: 0.00117 +2024-09-10 04:59:33.980863: train_loss -0.7505 +2024-09-10 04:59:33.983251: val_loss -0.7604 +2024-09-10 04:59:33.984030: Pseudo dice [0.9688, 0.9619, 0.9825, 0.9534, 0.8673, 0.9751, 0.9899] +2024-09-10 04:59:33.984976: Epoch time: 275.15 s +2024-09-10 04:59:36.268055: +2024-09-10 04:59:36.269098: Epoch 909 +2024-09-10 04:59:36.269996: Current learning rate: 0.00116 +2024-09-10 05:03:45.116561: train_loss -0.7582 +2024-09-10 05:03:45.118853: val_loss -0.7909 +2024-09-10 05:03:45.119697: Pseudo dice [0.9655, 0.9611, 0.9765, 0.9512, 0.9003, 0.9767, 0.9929] +2024-09-10 05:03:45.120602: Epoch time: 248.85 s +2024-09-10 05:03:47.161979: +2024-09-10 05:03:47.163217: Epoch 910 +2024-09-10 05:03:47.163918: Current learning rate: 0.00115 +2024-09-10 05:08:03.477975: train_loss -0.7561 +2024-09-10 05:08:03.479637: val_loss -0.7386 +2024-09-10 05:08:03.481359: Pseudo dice [0.9666, 0.9601, 0.9805, 0.9487, 0.8791, 0.9748, 0.9904] +2024-09-10 05:08:03.482060: Epoch time: 256.32 s +2024-09-10 05:08:05.351092: +2024-09-10 05:08:05.352289: Epoch 911 +2024-09-10 05:08:05.353424: Current learning rate: 0.00113 +2024-09-10 05:12:26.923873: train_loss -0.7592 +2024-09-10 05:12:26.925558: val_loss -0.7658 +2024-09-10 05:12:26.926535: Pseudo dice [0.9665, 0.9605, 0.9793, 0.9565, 0.8794, 0.9632, 0.9877] +2024-09-10 05:12:26.927413: Epoch time: 261.59 s +2024-09-10 05:12:29.032792: +2024-09-10 05:12:29.044599: Epoch 912 +2024-09-10 05:12:29.056832: Current learning rate: 0.00112 +2024-09-10 05:16:50.071507: train_loss -0.7634 +2024-09-10 05:16:50.073029: val_loss -0.7864 +2024-09-10 05:16:50.074008: Pseudo dice [0.9697, 0.9618, 0.984, 0.956, 0.904, 0.9764, 0.9931] +2024-09-10 05:16:50.075016: Epoch time: 261.04 s +2024-09-10 05:16:50.075892: Yayy! New best EMA pseudo Dice: 0.9583 +2024-09-10 05:16:57.202446: +2024-09-10 05:16:57.203416: Epoch 913 +2024-09-10 05:16:57.204243: Current learning rate: 0.00111 +2024-09-10 05:21:12.825587: train_loss -0.7696 +2024-09-10 05:21:12.827066: val_loss -0.756 +2024-09-10 05:21:12.828078: Pseudo dice [0.9678, 0.9599, 0.9816, 0.9526, 0.8906, 0.9716, 0.991] +2024-09-10 05:21:12.828930: Epoch time: 255.63 s +2024-09-10 05:21:12.829733: Yayy! New best EMA pseudo Dice: 0.9584 +2024-09-10 05:21:19.460657: +2024-09-10 05:21:19.461687: Epoch 914 +2024-09-10 05:21:19.462525: Current learning rate: 0.0011 +2024-09-10 05:25:58.018361: train_loss -0.7583 +2024-09-10 05:25:58.027924: val_loss -0.7594 +2024-09-10 05:25:58.044861: Pseudo dice [0.9698, 0.9627, 0.9817, 0.9597, 0.8954, 0.9621, 0.9888] +2024-09-10 05:25:58.045880: Epoch time: 278.56 s +2024-09-10 05:25:58.046677: Yayy! New best EMA pseudo Dice: 0.9586 +2024-09-10 05:26:04.459800: +2024-09-10 05:26:04.460850: Epoch 915 +2024-09-10 05:26:04.461690: Current learning rate: 0.00109 +2024-09-10 05:30:27.661016: train_loss -0.7553 +2024-09-10 05:30:27.668194: val_loss -0.7605 +2024-09-10 05:30:27.670716: Pseudo dice [0.9681, 0.9608, 0.981, 0.9574, 0.8474, 0.9764, 0.9859] +2024-09-10 05:30:27.672872: Epoch time: 263.21 s +2024-09-10 05:30:30.039252: +2024-09-10 05:30:30.040310: Epoch 916 +2024-09-10 05:30:30.041274: Current learning rate: 0.00108 +2024-09-10 05:35:02.460812: train_loss -0.7666 +2024-09-10 05:35:02.462050: val_loss -0.7824 +2024-09-10 05:35:02.462851: Pseudo dice [0.9659, 0.9578, 0.9801, 0.953, 0.8876, 0.9638, 0.989] +2024-09-10 05:35:02.463612: Epoch time: 272.43 s +2024-09-10 05:35:05.461330: +2024-09-10 05:35:05.462343: Epoch 917 +2024-09-10 05:35:05.463233: Current learning rate: 0.00106 +2024-09-10 05:39:19.596706: train_loss -0.7557 +2024-09-10 05:39:19.598334: val_loss -0.7518 +2024-09-10 05:39:19.599213: Pseudo dice [0.968, 0.9636, 0.9797, 0.9556, 0.8983, 0.9553, 0.9882] +2024-09-10 05:39:19.616211: Epoch time: 254.14 s +2024-09-10 05:39:22.796942: +2024-09-10 05:39:22.798033: Epoch 918 +2024-09-10 05:39:22.798856: Current learning rate: 0.00105 +2024-09-10 05:43:48.286592: train_loss -0.7457 +2024-09-10 05:43:48.288489: val_loss -0.7574 +2024-09-10 05:43:48.289824: Pseudo dice [0.9665, 0.9593, 0.9796, 0.9551, 0.8772, 0.9764, 0.9892] +2024-09-10 05:43:48.291094: Epoch time: 265.49 s +2024-09-10 05:43:51.770049: +2024-09-10 05:43:51.796050: Epoch 919 +2024-09-10 05:43:51.800709: Current learning rate: 0.00104 +2024-09-10 05:48:04.092755: train_loss -0.758 +2024-09-10 05:48:04.095180: val_loss -0.7825 +2024-09-10 05:48:04.095948: Pseudo dice [0.9685, 0.9595, 0.9818, 0.9574, 0.8929, 0.9763, 0.9923] +2024-09-10 05:48:04.096796: Epoch time: 252.33 s +2024-09-10 05:48:06.173418: +2024-09-10 05:48:06.174655: Epoch 920 +2024-09-10 05:48:06.175384: Current learning rate: 0.00103 +2024-09-10 05:52:32.620292: train_loss -0.7636 +2024-09-10 05:52:32.622651: val_loss -0.741 +2024-09-10 05:52:32.623495: Pseudo dice [0.9652, 0.9574, 0.9769, 0.9452, 0.8795, 0.9639, 0.9876] +2024-09-10 05:52:32.624316: Epoch time: 266.45 s +2024-09-10 05:52:34.792922: +2024-09-10 05:52:34.793904: Epoch 921 +2024-09-10 05:52:34.794754: Current learning rate: 0.00102 +2024-09-10 05:56:49.243024: train_loss -0.7526 +2024-09-10 05:56:49.246147: val_loss -0.7647 +2024-09-10 05:56:49.247461: Pseudo dice [0.968, 0.962, 0.9813, 0.9586, 0.8867, 0.9752, 0.9912] +2024-09-10 05:56:49.249207: Epoch time: 254.46 s +2024-09-10 05:56:51.988348: +2024-09-10 05:56:51.989612: Epoch 922 +2024-09-10 05:56:51.990462: Current learning rate: 0.00101 +2024-09-10 06:01:09.324709: train_loss -0.7573 +2024-09-10 06:01:09.336730: val_loss -0.7664 +2024-09-10 06:01:09.337657: Pseudo dice [0.9691, 0.9632, 0.9843, 0.9582, 0.8899, 0.975, 0.9924] +2024-09-10 06:01:09.338460: Epoch time: 257.34 s +2024-09-10 06:01:11.419113: +2024-09-10 06:01:11.420202: Epoch 923 +2024-09-10 06:01:11.421014: Current learning rate: 0.001 +2024-09-10 06:05:23.400592: train_loss -0.7717 +2024-09-10 06:05:23.402701: val_loss -0.7715 +2024-09-10 06:05:23.403664: Pseudo dice [0.97, 0.9613, 0.9819, 0.9594, 0.898, 0.9768, 0.9926] +2024-09-10 06:05:23.404582: Epoch time: 251.99 s +2024-09-10 06:05:23.405368: Yayy! New best EMA pseudo Dice: 0.9589 +2024-09-10 06:05:29.043216: +2024-09-10 06:05:29.044240: Epoch 924 +2024-09-10 06:05:29.045117: Current learning rate: 0.00098 +2024-09-10 06:09:42.372036: train_loss -0.7689 +2024-09-10 06:09:42.374274: val_loss -0.7219 +2024-09-10 06:09:42.375167: Pseudo dice [0.9691, 0.9605, 0.981, 0.9562, 0.8903, 0.9766, 0.9911] +2024-09-10 06:09:42.376091: Epoch time: 253.33 s +2024-09-10 06:09:42.376918: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-10 06:09:47.828635: +2024-09-10 06:09:47.829526: Epoch 925 +2024-09-10 06:09:47.830235: Current learning rate: 0.00097 +2024-09-10 06:14:04.575035: train_loss -0.7651 +2024-09-10 06:14:04.590035: val_loss -0.7415 +2024-09-10 06:14:04.590837: Pseudo dice [0.9698, 0.9612, 0.9818, 0.9588, 0.8872, 0.9591, 0.9895] +2024-09-10 06:14:04.591595: Epoch time: 256.75 s +2024-09-10 06:14:07.597708: +2024-09-10 06:14:07.598728: Epoch 926 +2024-09-10 06:14:07.599530: Current learning rate: 0.00096 +2024-09-10 06:18:15.686060: train_loss -0.7659 +2024-09-10 06:18:15.699839: val_loss -0.7494 +2024-09-10 06:18:15.700713: Pseudo dice [0.9689, 0.9606, 0.9807, 0.9468, 0.8666, 0.9762, 0.9904] +2024-09-10 06:18:15.701474: Epoch time: 248.09 s +2024-09-10 06:18:17.925530: +2024-09-10 06:18:17.948065: Epoch 927 +2024-09-10 06:18:17.956848: Current learning rate: 0.00095 +2024-09-10 06:22:24.804133: train_loss -0.7678 +2024-09-10 06:22:24.806863: val_loss -0.753 +2024-09-10 06:22:24.807850: Pseudo dice [0.9688, 0.9609, 0.981, 0.9559, 0.8955, 0.9752, 0.9922] +2024-09-10 06:22:24.822645: Epoch time: 246.88 s +2024-09-10 06:22:27.048846: +2024-09-10 06:22:27.050052: Epoch 928 +2024-09-10 06:22:27.050946: Current learning rate: 0.00094 +2024-09-10 06:26:48.164773: train_loss -0.7473 +2024-09-10 06:26:48.168431: val_loss -0.7578 +2024-09-10 06:26:48.169276: Pseudo dice [0.9648, 0.9609, 0.9788, 0.9551, 0.8759, 0.9663, 0.9871] +2024-09-10 06:26:48.170074: Epoch time: 261.12 s +2024-09-10 06:26:50.286616: +2024-09-10 06:26:50.296858: Epoch 929 +2024-09-10 06:26:50.297937: Current learning rate: 0.00092 +2024-09-10 06:31:01.881661: train_loss -0.7652 +2024-09-10 06:31:01.884202: val_loss -0.7412 +2024-09-10 06:31:01.885150: Pseudo dice [0.9662, 0.9593, 0.9783, 0.9557, 0.8962, 0.9775, 0.9933] +2024-09-10 06:31:01.886064: Epoch time: 251.6 s +2024-09-10 06:31:04.006404: +2024-09-10 06:31:04.007437: Epoch 930 +2024-09-10 06:31:04.008301: Current learning rate: 0.00091 +2024-09-10 06:35:26.414431: train_loss -0.7559 +2024-09-10 06:35:26.415974: val_loss -0.7606 +2024-09-10 06:35:26.417326: Pseudo dice [0.9667, 0.9637, 0.979, 0.9578, 0.8852, 0.9749, 0.99] +2024-09-10 06:35:26.418282: Epoch time: 262.41 s +2024-09-10 06:35:30.239909: +2024-09-10 06:35:30.241150: Epoch 931 +2024-09-10 06:35:30.242082: Current learning rate: 0.0009 +2024-09-10 06:39:53.822426: train_loss -0.7518 +2024-09-10 06:39:53.828318: val_loss -0.7826 +2024-09-10 06:39:53.829212: Pseudo dice [0.9674, 0.9581, 0.9808, 0.9544, 0.8937, 0.9768, 0.9926] +2024-09-10 06:39:53.829923: Epoch time: 263.59 s +2024-09-10 06:39:56.325358: +2024-09-10 06:39:56.326773: Epoch 932 +2024-09-10 06:39:56.327600: Current learning rate: 0.00089 +2024-09-10 06:44:09.073115: train_loss -0.7784 +2024-09-10 06:44:09.083470: val_loss -0.7548 +2024-09-10 06:44:09.084514: Pseudo dice [0.9659, 0.9611, 0.977, 0.9493, 0.8915, 0.9559, 0.9894] +2024-09-10 06:44:09.085604: Epoch time: 252.75 s +2024-09-10 06:44:11.545546: +2024-09-10 06:44:11.561062: Epoch 933 +2024-09-10 06:44:11.562012: Current learning rate: 0.00088 +2024-09-10 06:48:20.242607: train_loss -0.7798 +2024-09-10 06:48:20.244939: val_loss -0.7651 +2024-09-10 06:48:20.245767: Pseudo dice [0.9698, 0.9627, 0.9817, 0.9593, 0.8961, 0.9773, 0.9919] +2024-09-10 06:48:20.246538: Epoch time: 248.7 s +2024-09-10 06:48:20.247271: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-10 06:48:26.834677: +2024-09-10 06:48:26.835728: Epoch 934 +2024-09-10 06:48:26.836558: Current learning rate: 0.00087 +2024-09-10 06:52:43.097926: train_loss -0.7688 +2024-09-10 06:52:43.122013: val_loss -0.7461 +2024-09-10 06:52:43.122771: Pseudo dice [0.9701, 0.9623, 0.9824, 0.9543, 0.8744, 0.9768, 0.9905] +2024-09-10 06:52:43.123448: Epoch time: 256.27 s +2024-09-10 06:52:44.963970: +2024-09-10 06:52:44.964965: Epoch 935 +2024-09-10 06:52:44.965757: Current learning rate: 0.00085 +2024-09-10 06:57:00.275165: train_loss -0.7499 +2024-09-10 06:57:00.277745: val_loss -0.7745 +2024-09-10 06:57:00.279010: Pseudo dice [0.9683, 0.9595, 0.9794, 0.9505, 0.9022, 0.9773, 0.9935] +2024-09-10 06:57:00.280115: Epoch time: 255.31 s +2024-09-10 06:57:00.281114: Yayy! New best EMA pseudo Dice: 0.9593 +2024-09-10 06:57:06.003128: +2024-09-10 06:57:06.004707: Epoch 936 +2024-09-10 06:57:06.006065: Current learning rate: 0.00084 +2024-09-10 07:01:05.555847: train_loss -0.7516 +2024-09-10 07:01:05.575397: val_loss -0.7568 +2024-09-10 07:01:05.579184: Pseudo dice [0.9689, 0.9616, 0.9807, 0.9549, 0.8845, 0.9719, 0.9911] +2024-09-10 07:01:05.585726: Epoch time: 239.56 s +2024-09-10 07:01:07.908835: +2024-09-10 07:01:07.909919: Epoch 937 +2024-09-10 07:01:07.910908: Current learning rate: 0.00083 +2024-09-10 07:05:27.045790: train_loss -0.7705 +2024-09-10 07:05:27.058914: val_loss -0.7513 +2024-09-10 07:05:27.059985: Pseudo dice [0.9701, 0.9629, 0.9829, 0.9525, 0.8787, 0.9661, 0.9884] +2024-09-10 07:05:27.060909: Epoch time: 259.14 s +2024-09-10 07:05:29.499325: +2024-09-10 07:05:29.500722: Epoch 938 +2024-09-10 07:05:29.501672: Current learning rate: 0.00082 +2024-09-10 07:09:47.254112: train_loss -0.7547 +2024-09-10 07:09:47.256373: val_loss -0.7513 +2024-09-10 07:09:47.257279: Pseudo dice [0.9696, 0.9628, 0.9815, 0.9549, 0.865, 0.9773, 0.9887] +2024-09-10 07:09:47.258163: Epoch time: 257.76 s +2024-09-10 07:09:49.519105: +2024-09-10 07:09:49.532917: Epoch 939 +2024-09-10 07:09:49.534052: Current learning rate: 0.00081 +2024-09-10 07:14:11.524248: train_loss -0.7669 +2024-09-10 07:14:11.531827: val_loss -0.772 +2024-09-10 07:14:11.532851: Pseudo dice [0.9676, 0.9575, 0.9807, 0.9489, 0.8648, 0.9742, 0.9894] +2024-09-10 07:14:11.533763: Epoch time: 262.01 s +2024-09-10 07:14:13.805543: +2024-09-10 07:14:13.806805: Epoch 940 +2024-09-10 07:14:13.807889: Current learning rate: 0.00079 +2024-09-10 07:18:28.167822: train_loss -0.7657 +2024-09-10 07:18:28.186063: val_loss -0.7881 +2024-09-10 07:18:28.186927: Pseudo dice [0.97, 0.9639, 0.985, 0.9557, 0.8961, 0.9757, 0.9924] +2024-09-10 07:18:28.187697: Epoch time: 254.37 s +2024-09-10 07:18:30.361286: +2024-09-10 07:18:30.362671: Epoch 941 +2024-09-10 07:18:30.363530: Current learning rate: 0.00078 +2024-09-10 07:22:53.372204: train_loss -0.7586 +2024-09-10 07:22:53.374609: val_loss -0.7301 +2024-09-10 07:22:53.375444: Pseudo dice [0.9676, 0.9608, 0.9773, 0.956, 0.8679, 0.9735, 0.9877] +2024-09-10 07:22:53.376293: Epoch time: 263.02 s +2024-09-10 07:22:55.545067: +2024-09-10 07:22:55.546203: Epoch 942 +2024-09-10 07:22:55.547205: Current learning rate: 0.00077 +2024-09-10 07:27:19.859588: train_loss -0.7679 +2024-09-10 07:27:19.881313: val_loss -0.77 +2024-09-10 07:27:19.882209: Pseudo dice [0.9693, 0.9609, 0.9798, 0.9527, 0.8922, 0.9661, 0.9922] +2024-09-10 07:27:19.883103: Epoch time: 264.32 s +2024-09-10 07:27:22.062572: +2024-09-10 07:27:22.063637: Epoch 943 +2024-09-10 07:27:22.064444: Current learning rate: 0.00076 +2024-09-10 07:31:35.353094: train_loss -0.7494 +2024-09-10 07:31:35.363405: val_loss -0.7465 +2024-09-10 07:31:35.364335: Pseudo dice [0.9675, 0.9606, 0.9816, 0.9564, 0.8933, 0.9783, 0.9938] +2024-09-10 07:31:35.365239: Epoch time: 253.3 s +2024-09-10 07:31:39.000305: +2024-09-10 07:31:39.001380: Epoch 944 +2024-09-10 07:31:39.002299: Current learning rate: 0.00075 +2024-09-10 07:35:49.533276: train_loss -0.7413 +2024-09-10 07:35:49.535599: val_loss -0.7577 +2024-09-10 07:35:49.536375: Pseudo dice [0.9718, 0.9618, 0.9829, 0.9563, 0.9115, 0.9778, 0.9936] +2024-09-10 07:35:49.537128: Epoch time: 250.54 s +2024-09-10 07:35:49.537812: Yayy! New best EMA pseudo Dice: 0.9596 +2024-09-10 07:35:55.534897: +2024-09-10 07:35:55.536010: Epoch 945 +2024-09-10 07:35:55.536962: Current learning rate: 0.00074 +2024-09-10 07:40:22.635794: train_loss -0.7623 +2024-09-10 07:40:22.651957: val_loss -0.7582 +2024-09-10 07:40:22.652942: Pseudo dice [0.9643, 0.963, 0.9744, 0.9541, 0.8736, 0.9766, 0.9883] +2024-09-10 07:40:22.653785: Epoch time: 267.11 s +2024-09-10 07:40:25.089509: +2024-09-10 07:40:25.090676: Epoch 946 +2024-09-10 07:40:25.091514: Current learning rate: 0.00072 +2024-09-10 07:44:43.533230: train_loss -0.7482 +2024-09-10 07:44:43.535627: val_loss -0.7654 +2024-09-10 07:44:43.536483: Pseudo dice [0.9692, 0.963, 0.9805, 0.9531, 0.8938, 0.9773, 0.9915] +2024-09-10 07:44:43.537310: Epoch time: 258.45 s +2024-09-10 07:44:45.456373: +2024-09-10 07:44:45.457400: Epoch 947 +2024-09-10 07:44:45.458195: Current learning rate: 0.00071 +2024-09-10 07:49:16.590781: train_loss -0.7474 +2024-09-10 07:49:16.613976: val_loss -0.7623 +2024-09-10 07:49:16.614937: Pseudo dice [0.9677, 0.9624, 0.9788, 0.9546, 0.8833, 0.9749, 0.9904] +2024-09-10 07:49:16.615774: Epoch time: 271.14 s +2024-09-10 07:49:19.099542: +2024-09-10 07:49:19.100855: Epoch 948 +2024-09-10 07:49:19.101755: Current learning rate: 0.0007 +2024-09-10 07:53:31.166726: train_loss -0.75 +2024-09-10 07:53:31.172567: val_loss -0.7465 +2024-09-10 07:53:31.173455: Pseudo dice [0.9703, 0.9605, 0.9824, 0.958, 0.8433, 0.9598, 0.985] +2024-09-10 07:53:31.174263: Epoch time: 252.07 s +2024-09-10 07:53:33.730836: +2024-09-10 07:53:33.732014: Epoch 949 +2024-09-10 07:53:33.732848: Current learning rate: 0.00069 +2024-09-10 07:57:57.530195: train_loss -0.7663 +2024-09-10 07:57:57.549515: val_loss -0.7694 +2024-09-10 07:57:57.554563: Pseudo dice [0.9684, 0.9612, 0.9806, 0.956, 0.8978, 0.9771, 0.9925] +2024-09-10 07:57:57.558441: Epoch time: 263.8 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_08_29_27.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_08_29_27.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e14b6f51aead1e9de98b2de137ba2b00b862526 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_10_08_29_27.txt @@ -0,0 +1,1054 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 08:29:37.225371: Using torch.compile... +2024-09-10 08:29:48.483300: do_dummy_2d_data_aug: False +2024-09-10 08:29:48.489361: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 08:29:48.491213: The split file contains 5 splits. +2024-09-10 08:29:48.491637: Desired fold for training: 1 +2024-09-10 08:29:48.492125: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 08:30:01.741758: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 08:30:06.133267: +2024-09-10 08:30:06.137024: Epoch 950 +2024-09-10 08:30:06.137905: Current learning rate: 0.00067 +2024-09-10 08:46:48.554314: train_loss -0.7582 +2024-09-10 08:46:48.555476: val_loss -0.7187 +2024-09-10 08:46:48.556108: Pseudo dice [0.9692, 0.9629, 0.9795, 0.9599, 0.8714, 0.9772, 0.9887] +2024-09-10 08:46:48.556695: Epoch time: 1002.43 s +2024-09-10 08:46:50.106956: +2024-09-10 08:46:50.107785: Epoch 951 +2024-09-10 08:46:50.108374: Current learning rate: 0.00066 +2024-09-10 08:51:26.838150: train_loss -0.7619 +2024-09-10 08:51:26.856495: val_loss -0.7577 +2024-09-10 08:51:26.857786: Pseudo dice [0.9677, 0.9615, 0.9796, 0.9502, 0.8786, 0.9762, 0.991] +2024-09-10 08:51:26.859135: Epoch time: 276.73 s +2024-09-10 08:51:29.367889: +2024-09-10 08:51:29.380855: Epoch 952 +2024-09-10 08:51:29.381869: Current learning rate: 0.00065 +2024-09-10 08:55:56.799245: train_loss -0.7591 +2024-09-10 08:55:56.826287: val_loss -0.7486 +2024-09-10 08:55:56.827275: Pseudo dice [0.9688, 0.9611, 0.9822, 0.9552, 0.8716, 0.9604, 0.9868] +2024-09-10 08:55:56.828213: Epoch time: 267.44 s +2024-09-10 08:55:59.566823: +2024-09-10 08:55:59.567679: Epoch 953 +2024-09-10 08:55:59.568444: Current learning rate: 0.00064 +2024-09-10 09:00:26.893699: train_loss -0.7581 +2024-09-10 09:00:26.895701: val_loss -0.7805 +2024-09-10 09:00:26.907245: Pseudo dice [0.9675, 0.961, 0.9787, 0.9557, 0.8927, 0.9759, 0.9915] +2024-09-10 09:00:26.920708: Epoch time: 267.33 s +2024-09-10 09:00:30.431035: +2024-09-10 09:00:30.431942: Epoch 954 +2024-09-10 09:00:30.432680: Current learning rate: 0.00063 +2024-09-10 09:04:49.909173: train_loss -0.7694 +2024-09-10 09:04:49.929714: val_loss -0.7576 +2024-09-10 09:04:49.930436: Pseudo dice [0.9687, 0.9614, 0.9821, 0.9529, 0.8835, 0.9771, 0.9924] +2024-09-10 09:04:49.931133: Epoch time: 259.48 s +2024-09-10 09:04:53.355830: +2024-09-10 09:04:53.368724: Epoch 955 +2024-09-10 09:04:53.369674: Current learning rate: 0.00061 +2024-09-10 09:09:30.206495: train_loss -0.7567 +2024-09-10 09:09:30.234628: val_loss -0.7701 +2024-09-10 09:09:30.236053: Pseudo dice [0.967, 0.9609, 0.9787, 0.9512, 0.8697, 0.9772, 0.9889] +2024-09-10 09:09:30.237138: Epoch time: 276.86 s +2024-09-10 09:09:33.451286: +2024-09-10 09:09:33.463702: Epoch 956 +2024-09-10 09:09:33.464659: Current learning rate: 0.0006 +2024-09-10 09:14:27.922222: train_loss -0.7609 +2024-09-10 09:14:27.937140: val_loss -0.7529 +2024-09-10 09:14:27.937896: Pseudo dice [0.97, 0.9616, 0.9822, 0.9553, 0.8968, 0.9664, 0.9913] +2024-09-10 09:14:27.938678: Epoch time: 294.48 s +2024-09-10 09:14:31.208779: +2024-09-10 09:14:31.220621: Epoch 957 +2024-09-10 09:14:31.221415: Current learning rate: 0.00059 +2024-09-10 09:19:03.239066: train_loss -0.7488 +2024-09-10 09:19:03.254275: val_loss -0.7532 +2024-09-10 09:19:03.255178: Pseudo dice [0.9697, 0.9608, 0.9829, 0.9585, 0.8962, 0.9511, 0.9873] +2024-09-10 09:19:03.256019: Epoch time: 272.04 s +2024-09-10 09:19:05.895707: +2024-09-10 09:19:05.912103: Epoch 958 +2024-09-10 09:19:05.913097: Current learning rate: 0.00058 +2024-09-10 09:23:49.792643: train_loss -0.7706 +2024-09-10 09:23:49.793969: val_loss -0.7614 +2024-09-10 09:23:49.794815: Pseudo dice [0.9679, 0.9612, 0.9787, 0.954, 0.886, 0.9758, 0.9916] +2024-09-10 09:23:49.795621: Epoch time: 283.9 s +2024-09-10 09:23:53.243169: +2024-09-10 09:23:53.256748: Epoch 959 +2024-09-10 09:23:53.257547: Current learning rate: 0.00056 +2024-09-10 09:28:33.890625: train_loss -0.7648 +2024-09-10 09:28:33.905273: val_loss -0.7623 +2024-09-10 09:28:33.906444: Pseudo dice [0.9675, 0.9592, 0.9801, 0.949, 0.8513, 0.9766, 0.9884] +2024-09-10 09:28:33.907589: Epoch time: 280.65 s +2024-09-10 09:28:36.284932: +2024-09-10 09:28:36.297088: Epoch 960 +2024-09-10 09:28:36.298202: Current learning rate: 0.00055 +2024-09-10 09:33:16.327193: train_loss -0.7613 +2024-09-10 09:33:16.331599: val_loss -0.7809 +2024-09-10 09:33:16.336712: Pseudo dice [0.9696, 0.9601, 0.9818, 0.956, 0.9057, 0.9546, 0.9883] +2024-09-10 09:33:16.337470: Epoch time: 280.05 s +2024-09-10 09:33:18.957974: +2024-09-10 09:33:18.958792: Epoch 961 +2024-09-10 09:33:18.959445: Current learning rate: 0.00054 +2024-09-10 09:38:10.198527: train_loss -0.7683 +2024-09-10 09:38:10.213464: val_loss -0.7777 +2024-09-10 09:38:10.214437: Pseudo dice [0.9681, 0.9581, 0.983, 0.9561, 0.8896, 0.9637, 0.9891] +2024-09-10 09:38:10.215422: Epoch time: 291.24 s +2024-09-10 09:38:12.847270: +2024-09-10 09:38:12.864103: Epoch 962 +2024-09-10 09:38:12.865067: Current learning rate: 0.00053 +2024-09-10 09:42:48.379891: train_loss -0.7731 +2024-09-10 09:42:48.382085: val_loss -0.7822 +2024-09-10 09:42:48.382762: Pseudo dice [0.9708, 0.9625, 0.9845, 0.9579, 0.9003, 0.966, 0.9906] +2024-09-10 09:42:48.383580: Epoch time: 275.54 s +2024-09-10 09:42:52.009767: +2024-09-10 09:42:52.024778: Epoch 963 +2024-09-10 09:42:52.025777: Current learning rate: 0.00051 +2024-09-10 09:47:32.489001: train_loss -0.7714 +2024-09-10 09:47:32.505862: val_loss -0.7731 +2024-09-10 09:47:32.506644: Pseudo dice [0.9704, 0.9628, 0.9832, 0.9583, 0.884, 0.9654, 0.9883] +2024-09-10 09:47:32.507293: Epoch time: 280.48 s +2024-09-10 09:47:36.159363: +2024-09-10 09:47:36.172861: Epoch 964 +2024-09-10 09:47:36.173662: Current learning rate: 0.0005 +2024-09-10 09:51:48.444608: train_loss -0.773 +2024-09-10 09:51:48.451307: val_loss -0.7757 +2024-09-10 09:51:48.452085: Pseudo dice [0.9676, 0.9612, 0.9784, 0.9569, 0.8811, 0.9773, 0.99] +2024-09-10 09:51:48.452927: Epoch time: 252.29 s +2024-09-10 09:51:51.128967: +2024-09-10 09:51:51.130070: Epoch 965 +2024-09-10 09:51:51.130783: Current learning rate: 0.00049 +2024-09-10 09:56:02.366265: train_loss -0.7577 +2024-09-10 09:56:02.381255: val_loss -0.7757 +2024-09-10 09:56:02.382028: Pseudo dice [0.9699, 0.9626, 0.9825, 0.9571, 0.8887, 0.9774, 0.9916] +2024-09-10 09:56:02.382985: Epoch time: 251.24 s +2024-09-10 09:56:05.958422: +2024-09-10 09:56:05.959858: Epoch 966 +2024-09-10 09:56:05.961766: Current learning rate: 0.00048 +2024-09-10 10:00:29.981308: train_loss -0.7582 +2024-09-10 10:00:29.989092: val_loss -0.7718 +2024-09-10 10:00:29.990085: Pseudo dice [0.9678, 0.9624, 0.9781, 0.9585, 0.8806, 0.9769, 0.9895] +2024-09-10 10:00:29.991668: Epoch time: 264.03 s +2024-09-10 10:00:33.916290: +2024-09-10 10:00:33.928711: Epoch 967 +2024-09-10 10:00:33.929532: Current learning rate: 0.00046 +2024-09-10 10:05:13.090747: train_loss -0.7661 +2024-09-10 10:05:13.108236: val_loss -0.7717 +2024-09-10 10:05:13.109250: Pseudo dice [0.969, 0.9606, 0.9796, 0.9566, 0.8961, 0.9772, 0.9926] +2024-09-10 10:05:13.109971: Epoch time: 279.18 s +2024-09-10 10:05:15.813483: +2024-09-10 10:05:15.814380: Epoch 968 +2024-09-10 10:05:15.814975: Current learning rate: 0.00045 +2024-09-10 10:09:38.244115: train_loss -0.7761 +2024-09-10 10:09:38.245542: val_loss -0.7516 +2024-09-10 10:09:38.246348: Pseudo dice [0.9673, 0.9613, 0.9795, 0.9527, 0.8785, 0.9633, 0.9862] +2024-09-10 10:09:38.247142: Epoch time: 262.44 s +2024-09-10 10:09:41.255548: +2024-09-10 10:09:41.268698: Epoch 969 +2024-09-10 10:09:41.269500: Current learning rate: 0.00044 +2024-09-10 10:14:09.710062: train_loss -0.7756 +2024-09-10 10:14:09.712465: val_loss -0.7452 +2024-09-10 10:14:09.713173: Pseudo dice [0.9683, 0.9604, 0.9799, 0.9556, 0.8861, 0.9758, 0.9909] +2024-09-10 10:14:09.713756: Epoch time: 268.46 s +2024-09-10 10:14:13.009937: +2024-09-10 10:14:13.010835: Epoch 970 +2024-09-10 10:14:13.011470: Current learning rate: 0.00043 +2024-09-10 10:18:33.108193: train_loss -0.7591 +2024-09-10 10:18:33.123045: val_loss -0.7443 +2024-09-10 10:18:33.124199: Pseudo dice [0.9675, 0.9614, 0.9774, 0.9508, 0.877, 0.976, 0.9893] +2024-09-10 10:18:33.125107: Epoch time: 260.1 s +2024-09-10 10:18:35.943322: +2024-09-10 10:18:35.944194: Epoch 971 +2024-09-10 10:18:35.944920: Current learning rate: 0.00041 +2024-09-10 10:22:58.904608: train_loss -0.7649 +2024-09-10 10:22:58.906815: val_loss -0.778 +2024-09-10 10:22:58.907553: Pseudo dice [0.9658, 0.9601, 0.9781, 0.9515, 0.8683, 0.9742, 0.9884] +2024-09-10 10:22:58.908133: Epoch time: 262.97 s +2024-09-10 10:23:02.729292: +2024-09-10 10:23:02.730444: Epoch 972 +2024-09-10 10:23:02.731252: Current learning rate: 0.0004 +2024-09-10 10:27:41.171043: train_loss -0.7645 +2024-09-10 10:27:41.173527: val_loss -0.7401 +2024-09-10 10:27:41.174312: Pseudo dice [0.9695, 0.9615, 0.9814, 0.9555, 0.8758, 0.9645, 0.9882] +2024-09-10 10:27:41.174980: Epoch time: 278.45 s +2024-09-10 10:27:44.005050: +2024-09-10 10:27:44.005888: Epoch 973 +2024-09-10 10:27:44.006670: Current learning rate: 0.00039 +2024-09-10 10:32:12.216469: train_loss -0.7757 +2024-09-10 10:32:12.231104: val_loss -0.7573 +2024-09-10 10:32:12.231826: Pseudo dice [0.9681, 0.9592, 0.9807, 0.9544, 0.8845, 0.9736, 0.9913] +2024-09-10 10:32:12.233620: Epoch time: 268.22 s +2024-09-10 10:32:15.022367: +2024-09-10 10:32:15.032091: Epoch 974 +2024-09-10 10:32:15.033014: Current learning rate: 0.00037 +2024-09-10 10:36:46.559494: train_loss -0.7649 +2024-09-10 10:36:46.573973: val_loss -0.8081 +2024-09-10 10:36:46.574748: Pseudo dice [0.9695, 0.959, 0.9814, 0.9571, 0.8996, 0.977, 0.9931] +2024-09-10 10:36:46.575504: Epoch time: 271.54 s +2024-09-10 10:36:49.973804: +2024-09-10 10:36:49.984785: Epoch 975 +2024-09-10 10:36:49.985707: Current learning rate: 0.00036 +2024-09-10 10:41:20.572410: train_loss -0.7442 +2024-09-10 10:41:20.588845: val_loss -0.7394 +2024-09-10 10:41:20.589780: Pseudo dice [0.9701, 0.9618, 0.9821, 0.9571, 0.8697, 0.9632, 0.9857] +2024-09-10 10:41:20.590553: Epoch time: 270.6 s +2024-09-10 10:41:23.969845: +2024-09-10 10:41:23.970845: Epoch 976 +2024-09-10 10:41:23.971596: Current learning rate: 0.00035 +2024-09-10 10:46:17.799031: train_loss -0.7681 +2024-09-10 10:46:17.801483: val_loss -0.8065 +2024-09-10 10:46:17.802342: Pseudo dice [0.971, 0.9614, 0.9825, 0.9567, 0.8902, 0.9759, 0.991] +2024-09-10 10:46:17.803077: Epoch time: 293.83 s +2024-09-10 10:46:20.922901: +2024-09-10 10:46:20.923695: Epoch 977 +2024-09-10 10:46:20.940289: Current learning rate: 0.00034 +2024-09-10 10:50:57.227098: train_loss -0.783 +2024-09-10 10:50:57.231740: val_loss -0.7918 +2024-09-10 10:50:57.234128: Pseudo dice [0.9705, 0.9631, 0.9828, 0.9614, 0.9113, 0.9783, 0.9931] +2024-09-10 10:50:57.241935: Epoch time: 276.31 s +2024-09-10 10:51:00.891597: +2024-09-10 10:51:00.904808: Epoch 978 +2024-09-10 10:51:00.905723: Current learning rate: 0.00032 +2024-09-10 10:55:37.234911: train_loss -0.7787 +2024-09-10 10:55:37.243323: val_loss -0.7591 +2024-09-10 10:55:37.244189: Pseudo dice [0.9679, 0.9615, 0.979, 0.9566, 0.9043, 0.978, 0.9917] +2024-09-10 10:55:37.245078: Epoch time: 276.35 s +2024-09-10 10:55:37.245728: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-10 10:55:43.240841: +2024-09-10 10:55:43.241717: Epoch 979 +2024-09-10 10:55:43.242448: Current learning rate: 0.00031 +2024-09-10 11:00:24.032337: train_loss -0.7673 +2024-09-10 11:00:24.052553: val_loss -0.7539 +2024-09-10 11:00:24.053331: Pseudo dice [0.9693, 0.9634, 0.9819, 0.9597, 0.8914, 0.9776, 0.9912] +2024-09-10 11:00:24.054076: Epoch time: 280.8 s +2024-09-10 11:00:24.054729: Yayy! New best EMA pseudo Dice: 0.96 +2024-09-10 11:00:29.866855: +2024-09-10 11:00:29.867837: Epoch 980 +2024-09-10 11:00:29.868663: Current learning rate: 0.0003 +2024-09-10 11:05:08.868864: train_loss -0.7773 +2024-09-10 11:05:08.885309: val_loss -0.7752 +2024-09-10 11:05:08.886329: Pseudo dice [0.9686, 0.9606, 0.9807, 0.9574, 0.8975, 0.9771, 0.9931] +2024-09-10 11:05:08.887120: Epoch time: 279.01 s +2024-09-10 11:05:08.887903: Yayy! New best EMA pseudo Dice: 0.9602 +2024-09-10 11:05:15.459151: +2024-09-10 11:05:15.460121: Epoch 981 +2024-09-10 11:05:15.460822: Current learning rate: 0.00028 +2024-09-10 11:09:50.410739: train_loss -0.7734 +2024-09-10 11:09:50.437191: val_loss -0.7799 +2024-09-10 11:09:50.438245: Pseudo dice [0.9676, 0.9601, 0.9792, 0.9563, 0.8946, 0.9735, 0.9906] +2024-09-10 11:09:50.439071: Epoch time: 274.96 s +2024-09-10 11:09:50.439735: Yayy! New best EMA pseudo Dice: 0.9602 +2024-09-10 11:09:56.282006: +2024-09-10 11:09:56.282942: Epoch 982 +2024-09-10 11:09:56.283646: Current learning rate: 0.00027 +2024-09-10 11:14:26.459307: train_loss -0.7618 +2024-09-10 11:14:26.480921: val_loss -0.7747 +2024-09-10 11:14:26.481755: Pseudo dice [0.9671, 0.9573, 0.9808, 0.9556, 0.9006, 0.9661, 0.9908] +2024-09-10 11:14:26.482472: Epoch time: 270.18 s +2024-09-10 11:14:30.617548: +2024-09-10 11:14:30.618746: Epoch 983 +2024-09-10 11:14:30.619467: Current learning rate: 0.00026 +2024-09-10 11:19:00.162123: train_loss -0.7653 +2024-09-10 11:19:00.175327: val_loss -0.7662 +2024-09-10 11:19:00.176429: Pseudo dice [0.97, 0.9646, 0.9832, 0.9548, 0.8669, 0.9764, 0.9881] +2024-09-10 11:19:00.177421: Epoch time: 269.55 s +2024-09-10 11:19:03.405085: +2024-09-10 11:19:03.405988: Epoch 984 +2024-09-10 11:19:03.406575: Current learning rate: 0.00024 +2024-09-10 11:23:34.307810: train_loss -0.7699 +2024-09-10 11:23:34.317140: val_loss -0.7769 +2024-09-10 11:23:34.318020: Pseudo dice [0.9699, 0.9632, 0.9816, 0.9556, 0.8954, 0.9618, 0.9895] +2024-09-10 11:23:34.319224: Epoch time: 270.91 s +2024-09-10 11:23:37.461198: +2024-09-10 11:23:37.462309: Epoch 985 +2024-09-10 11:23:37.463021: Current learning rate: 0.00023 +2024-09-10 11:27:58.907676: train_loss -0.7662 +2024-09-10 11:27:58.922392: val_loss -0.7631 +2024-09-10 11:27:58.923414: Pseudo dice [0.9721, 0.9647, 0.984, 0.9616, 0.9014, 0.9646, 0.9906] +2024-09-10 11:27:58.924388: Epoch time: 261.45 s +2024-09-10 11:28:02.344844: +2024-09-10 11:28:02.345956: Epoch 986 +2024-09-10 11:28:02.346606: Current learning rate: 0.00021 +2024-09-10 11:32:29.299577: train_loss -0.7641 +2024-09-10 11:32:29.313340: val_loss -0.7897 +2024-09-10 11:32:29.314418: Pseudo dice [0.9711, 0.9622, 0.9839, 0.9591, 0.8976, 0.9776, 0.9928] +2024-09-10 11:32:29.315289: Epoch time: 266.96 s +2024-09-10 11:32:29.315901: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-10 11:32:36.205130: +2024-09-10 11:32:36.206204: Epoch 987 +2024-09-10 11:32:36.206988: Current learning rate: 0.0002 +2024-09-10 11:37:02.719902: train_loss -0.7641 +2024-09-10 11:37:02.721562: val_loss -0.7487 +2024-09-10 11:37:02.722271: Pseudo dice [0.9694, 0.9624, 0.9806, 0.9627, 0.8954, 0.9759, 0.9903] +2024-09-10 11:37:02.722961: Epoch time: 266.52 s +2024-09-10 11:37:02.723520: Yayy! New best EMA pseudo Dice: 0.9607 +2024-09-10 11:37:09.465965: +2024-09-10 11:37:09.466956: Epoch 988 +2024-09-10 11:37:09.467606: Current learning rate: 0.00019 +2024-09-10 11:41:46.652526: train_loss -0.7908 +2024-09-10 11:41:46.658344: val_loss -0.7825 +2024-09-10 11:41:46.659273: Pseudo dice [0.9699, 0.9626, 0.9821, 0.9588, 0.906, 0.9624, 0.991] +2024-09-10 11:41:46.663346: Epoch time: 277.19 s +2024-09-10 11:41:46.664181: Yayy! New best EMA pseudo Dice: 0.9608 +2024-09-10 11:41:53.104045: +2024-09-10 11:41:53.104858: Epoch 989 +2024-09-10 11:41:53.105572: Current learning rate: 0.00017 +2024-09-10 11:46:13.609880: train_loss -0.7607 +2024-09-10 11:46:13.632907: val_loss -0.8003 +2024-09-10 11:46:13.633680: Pseudo dice [0.97, 0.9616, 0.9833, 0.9511, 0.8972, 0.9769, 0.9931] +2024-09-10 11:46:13.634456: Epoch time: 260.51 s +2024-09-10 11:46:13.635124: Yayy! New best EMA pseudo Dice: 0.9609 +2024-09-10 11:46:19.664327: +2024-09-10 11:46:19.665242: Epoch 990 +2024-09-10 11:46:19.665925: Current learning rate: 0.00016 +2024-09-10 11:50:30.125223: train_loss -0.787 +2024-09-10 11:50:30.127539: val_loss -0.8119 +2024-09-10 11:50:30.128190: Pseudo dice [0.9711, 0.9631, 0.9836, 0.958, 0.8901, 0.9772, 0.9911] +2024-09-10 11:50:30.129002: Epoch time: 250.46 s +2024-09-10 11:50:30.129649: Yayy! New best EMA pseudo Dice: 0.961 +2024-09-10 11:50:36.326822: +2024-09-10 11:50:36.327598: Epoch 991 +2024-09-10 11:50:36.328182: Current learning rate: 0.00014 +2024-09-10 11:55:07.458744: train_loss -0.7678 +2024-09-10 11:55:07.460057: val_loss -0.7593 +2024-09-10 11:55:07.460861: Pseudo dice [0.9689, 0.9623, 0.981, 0.96, 0.8949, 0.9776, 0.9919] +2024-09-10 11:55:07.461720: Epoch time: 271.14 s +2024-09-10 11:55:07.462679: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-10 11:55:13.424548: +2024-09-10 11:55:13.425507: Epoch 992 +2024-09-10 11:55:13.426335: Current learning rate: 0.00013 +2024-09-10 11:59:39.943476: train_loss -0.755 +2024-09-10 11:59:39.958046: val_loss -0.7773 +2024-09-10 11:59:39.958901: Pseudo dice [0.9645, 0.9588, 0.9785, 0.9566, 0.8898, 0.9762, 0.992] +2024-09-10 11:59:39.959548: Epoch time: 266.52 s +2024-09-10 11:59:42.556968: +2024-09-10 11:59:42.557827: Epoch 993 +2024-09-10 11:59:42.558452: Current learning rate: 0.00011 +2024-09-10 12:03:57.020513: train_loss -0.7582 +2024-09-10 12:03:57.022946: val_loss -0.7654 +2024-09-10 12:03:57.023671: Pseudo dice [0.9684, 0.9609, 0.9814, 0.9603, 0.9003, 0.9772, 0.9924] +2024-09-10 12:03:57.024410: Epoch time: 254.47 s +2024-09-10 12:03:57.025147: Yayy! New best EMA pseudo Dice: 0.9612 +2024-09-10 12:04:02.861754: +2024-09-10 12:04:02.862796: Epoch 994 +2024-09-10 12:04:02.863601: Current learning rate: 0.0001 +2024-09-10 12:08:31.567364: train_loss -0.7725 +2024-09-10 12:08:31.571005: val_loss -0.7757 +2024-09-10 12:08:31.571710: Pseudo dice [0.9691, 0.9609, 0.9815, 0.9542, 0.8984, 0.9662, 0.9909] +2024-09-10 12:08:31.572357: Epoch time: 268.71 s +2024-09-10 12:08:33.602363: +2024-09-10 12:08:33.616684: Epoch 995 +2024-09-10 12:08:33.617510: Current learning rate: 8e-05 +2024-09-10 12:13:07.659117: train_loss -0.7742 +2024-09-10 12:13:07.681635: val_loss -0.7667 +2024-09-10 12:13:07.682946: Pseudo dice [0.9726, 0.9646, 0.9859, 0.9611, 0.9004, 0.9763, 0.9912] +2024-09-10 12:13:07.684081: Epoch time: 274.06 s +2024-09-10 12:13:07.685158: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-10 12:13:13.644514: +2024-09-10 12:13:13.645438: Epoch 996 +2024-09-10 12:13:13.646118: Current learning rate: 7e-05 +2024-09-10 12:17:38.868143: train_loss -0.7602 +2024-09-10 12:17:38.885116: val_loss -0.7634 +2024-09-10 12:17:38.885849: Pseudo dice [0.9697, 0.9636, 0.9822, 0.9593, 0.9072, 0.9768, 0.993] +2024-09-10 12:17:38.886549: Epoch time: 265.23 s +2024-09-10 12:17:38.887154: Yayy! New best EMA pseudo Dice: 0.9617 +2024-09-10 12:17:44.499961: +2024-09-10 12:17:44.500835: Epoch 997 +2024-09-10 12:17:44.501553: Current learning rate: 5e-05 +2024-09-10 12:22:22.249170: train_loss -0.7723 +2024-09-10 12:22:22.265131: val_loss -0.7964 +2024-09-10 12:22:22.266015: Pseudo dice [0.9709, 0.962, 0.9813, 0.9509, 0.8992, 0.9718, 0.9911] +2024-09-10 12:22:22.266733: Epoch time: 277.75 s +2024-09-10 12:22:26.682065: +2024-09-10 12:22:26.683177: Epoch 998 +2024-09-10 12:22:26.683873: Current learning rate: 4e-05 +2024-09-10 12:26:49.741565: train_loss -0.7812 +2024-09-10 12:26:49.757162: val_loss -0.8126 +2024-09-10 12:26:49.758154: Pseudo dice [0.969, 0.959, 0.9837, 0.9585, 0.9024, 0.9782, 0.9935] +2024-09-10 12:26:49.758948: Epoch time: 263.06 s +2024-09-10 12:26:49.759586: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-10 12:26:56.457734: +2024-09-10 12:26:56.464732: Epoch 999 +2024-09-10 12:26:56.465559: Current learning rate: 2e-05 +2024-09-10 12:31:29.140393: train_loss -0.7407 +2024-09-10 12:31:29.148701: val_loss -0.7112 +2024-09-10 12:31:29.149606: Pseudo dice [0.9708, 0.9623, 0.9841, 0.9562, 0.8481, 0.9364, 0.9781] +2024-09-10 12:31:29.150458: Epoch time: 272.69 s +2024-09-10 12:31:36.262199: Training done. +2024-09-10 12:31:36.296587: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 12:31:36.299175: The split file contains 5 splits. +2024-09-10 12:31:36.299687: Desired fold for training: 1 +2024-09-10 12:31:36.300214: This split has 1325 training and 331 validation cases. +2024-09-10 12:31:36.309337: predicting saros-002 +2024-09-10 12:31:36.858610: saros-002, shape torch.Size([1, 323, 281, 281]), rank 0 +2024-09-10 12:33:53.586158: predicting saros-005 +2024-09-10 12:33:53.991145: saros-005, shape torch.Size([1, 253, 240, 240]), rank 0 +2024-09-10 12:33:57.678060: predicting saros-007 +2024-09-10 12:33:58.129935: saros-007, shape torch.Size([1, 323, 227, 227]), rank 0 +2024-09-10 12:34:03.605711: predicting saros-010 +2024-09-10 12:34:03.937168: saros-010, shape torch.Size([1, 123, 323, 323]), rank 0 +2024-09-10 12:34:08.110714: predicting saros-025 +2024-09-10 12:34:08.640528: saros-025, shape torch.Size([1, 320, 289, 289]), rank 0 +2024-09-10 12:34:20.941646: predicting saros-028 +2024-09-10 12:34:21.669165: saros-028, shape torch.Size([1, 297, 309, 309]), rank 0 +2024-09-10 12:34:33.983100: predicting saros-029 +2024-09-10 12:34:34.425101: saros-029, shape torch.Size([1, 293, 230, 230]), rank 0 +2024-09-10 12:34:39.946807: predicting saros-054 +2024-09-10 12:34:40.269359: saros-054, shape torch.Size([1, 140, 321, 321]), rank 0 +2024-09-10 12:34:44.475962: predicting saros-055 +2024-09-10 12:34:45.020467: saros-055, shape torch.Size([1, 300, 267, 267]), rank 0 +2024-09-10 12:34:50.566189: predicting saros-069 +2024-09-10 12:34:50.935525: saros-069, shape torch.Size([1, 307, 236, 236]), rank 0 +2024-09-10 12:34:56.451090: predicting saros-077 +2024-09-10 12:34:56.748878: saros-077, shape torch.Size([1, 167, 267, 267]), rank 0 +2024-09-10 12:34:58.690203: predicting saros-079 +2024-09-10 12:34:59.344941: saros-079, shape torch.Size([1, 313, 300, 300]), rank 0 +2024-09-10 12:35:11.644070: predicting saros-081 +2024-09-10 12:35:12.157771: saros-081, shape torch.Size([1, 227, 307, 307]), rank 0 +2024-09-10 12:35:20.410964: predicting saros-082 +2024-09-10 12:35:20.709146: saros-082, shape torch.Size([1, 180, 253, 253]), rank 0 +2024-09-10 12:35:22.632583: predicting saros-084 +2024-09-10 12:35:23.362446: saros-084, shape torch.Size([1, 343, 298, 298]), rank 0 +2024-09-10 12:35:35.669823: predicting saros-090 +2024-09-10 12:35:36.361255: saros-090, shape torch.Size([1, 353, 301, 301]), rank 0 +2024-09-10 12:35:48.707488: predicting saros-097 +2024-09-10 12:35:49.095124: saros-097, shape torch.Size([1, 143, 333, 333]), rank 0 +2024-09-10 12:35:53.317142: predicting saros-101 +2024-09-10 12:35:53.847534: saros-101, shape torch.Size([1, 313, 253, 253]), rank 0 +2024-09-10 12:35:59.392470: predicting saros-106 +2024-09-10 12:36:00.057401: saros-106, shape torch.Size([1, 307, 305, 305]), rank 0 +2024-09-10 12:36:12.373046: predicting saros-109 +2024-09-10 12:36:12.894006: saros-109, shape torch.Size([1, 200, 331, 331]), rank 0 +2024-09-10 12:36:21.151391: predicting saros-118 +2024-09-10 12:36:21.377269: saros-118, shape torch.Size([1, 207, 240, 240]), rank 0 +2024-09-10 12:36:25.120202: predicting saros-131 +2024-09-10 12:36:25.534675: saros-131, shape torch.Size([1, 200, 293, 293]), rank 0 +2024-09-10 12:36:33.753484: predicting saros-133 +2024-09-10 12:36:33.955783: saros-133, shape torch.Size([1, 153, 240, 240]), rank 0 +2024-09-10 12:36:35.846687: predicting saros-135 +2024-09-10 12:36:36.578053: saros-135, shape torch.Size([1, 330, 333, 333]), rank 0 +2024-09-10 12:36:48.989578: predicting saros-148 +2024-09-10 12:36:49.224592: saros-148, shape torch.Size([1, 140, 267, 267]), rank 0 +2024-09-10 12:36:51.158711: predicting saros-155 +2024-09-10 12:36:51.763847: saros-155, shape torch.Size([1, 313, 287, 287]), rank 0 +2024-09-10 12:36:57.369071: predicting saros-158 +2024-09-10 12:36:57.796457: saros-158, shape torch.Size([1, 273, 253, 253]), rank 0 +2024-09-10 12:37:01.527429: predicting saros-164 +2024-09-10 12:37:01.905950: saros-164, shape torch.Size([1, 173, 313, 313]), rank 0 +2024-09-10 12:37:06.121175: predicting saros-165 +2024-09-10 12:37:06.387272: saros-165, shape torch.Size([1, 167, 240, 240]), rank 0 +2024-09-10 12:37:08.332984: predicting saros-172 +2024-09-10 12:37:08.536488: saros-172, shape torch.Size([1, 147, 237, 237]), rank 0 +2024-09-10 12:37:10.424962: predicting saros-173 +2024-09-10 12:37:10.683406: saros-173, shape torch.Size([1, 133, 320, 320]), rank 0 +2024-09-10 12:37:14.879227: predicting saros-181 +2024-09-10 12:37:15.431044: saros-181, shape torch.Size([1, 297, 302, 302]), rank 0 +2024-09-10 12:37:27.753890: predicting saros-185 +2024-09-10 12:37:28.114309: saros-185, shape torch.Size([1, 143, 333, 333]), rank 0 +2024-09-10 12:37:32.366340: predicting saros-193 +2024-09-10 12:37:32.758316: saros-193, shape torch.Size([1, 147, 307, 307]), rank 0 +2024-09-10 12:37:36.974803: predicting saros-199 +2024-09-10 12:37:37.341293: saros-199, shape torch.Size([1, 257, 243, 243]), rank 0 +2024-09-10 12:37:41.068396: predicting saros-212 +2024-09-10 12:37:41.762642: saros-212, shape torch.Size([1, 327, 329, 329]), rank 0 +2024-09-10 12:37:54.094620: predicting saros-220 +2024-09-10 12:37:54.304840: saros-220, shape torch.Size([1, 180, 240, 240]), rank 0 +2024-09-10 12:37:56.243925: predicting saros-234 +2024-09-10 12:37:56.584596: saros-234, shape torch.Size([1, 177, 293, 293]), rank 0 +2024-09-10 12:38:00.757508: predicting saros-244 +2024-09-10 12:38:01.115180: saros-244, shape torch.Size([1, 150, 320, 320]), rank 0 +2024-09-10 12:38:05.326512: predicting saros-250 +2024-09-10 12:38:05.679086: saros-250, shape torch.Size([1, 157, 333, 333]), rank 0 +2024-09-10 12:38:09.872819: predicting saros-254 +2024-09-10 12:38:10.350500: saros-254, shape torch.Size([1, 330, 253, 253]), rank 0 +2024-09-10 12:38:15.909896: predicting saros-259 +2024-09-10 12:38:16.190290: saros-259, shape torch.Size([1, 130, 307, 307]), rank 0 +2024-09-10 12:38:20.390612: predicting saros-264 +2024-09-10 12:38:21.082508: saros-264, shape torch.Size([1, 323, 297, 297]), rank 0 +2024-09-10 12:38:33.426380: predicting saros-266 +2024-09-10 12:38:33.656855: saros-266, shape torch.Size([1, 163, 253, 253]), rank 0 +2024-09-10 12:38:35.596403: predicting saros-275 +2024-09-10 12:38:36.320090: saros-275, shape torch.Size([1, 343, 325, 325]), rank 0 +2024-09-10 12:38:48.638834: predicting saros-281 +2024-09-10 12:38:49.436601: saros-281, shape torch.Size([1, 393, 300, 300]), rank 0 +2024-09-10 12:39:05.833340: predicting saros-286 +2024-09-10 12:39:06.040999: saros-286, shape torch.Size([1, 137, 267, 267]), rank 0 +2024-09-10 12:39:07.983869: predicting saros-290 +2024-09-10 12:39:08.304111: saros-290, shape torch.Size([1, 183, 276, 276]), rank 0 +2024-09-10 12:39:10.227714: predicting saros-292 +2024-09-10 12:39:10.492966: saros-292, shape torch.Size([1, 180, 253, 253]), rank 0 +2024-09-10 12:39:12.439424: predicting saros-296 +2024-09-10 12:39:12.872450: saros-296, shape torch.Size([1, 323, 253, 253]), rank 0 +2024-09-10 12:39:18.399213: predicting saros-300 +2024-09-10 12:39:18.660909: saros-300, shape torch.Size([1, 203, 217, 217]), rank 0 +2024-09-10 12:39:22.325221: predicting saros-306 +2024-09-10 12:39:22.557366: saros-306, shape torch.Size([1, 180, 184, 184]), rank 0 +2024-09-10 12:39:23.124089: predicting saros-312 +2024-09-10 12:39:23.319345: saros-312, shape torch.Size([1, 200, 180, 180]), rank 0 +2024-09-10 12:40:47.406436: predicting saros-316 +2024-09-10 12:40:47.618143: saros-316, shape torch.Size([1, 220, 221, 221]), rank 0 +2024-09-10 12:40:51.270763: predicting saros-319 +2024-09-10 12:40:51.515487: saros-319, shape torch.Size([1, 193, 221, 221]), rank 0 +2024-09-10 12:40:55.161184: predicting saros-321 +2024-09-10 12:40:55.406496: saros-321, shape torch.Size([1, 223, 225, 225]), rank 0 +2024-09-10 12:40:59.110018: predicting saros-323 +2024-09-10 12:40:59.330532: saros-323, shape torch.Size([1, 187, 195, 195]), rank 0 +2024-09-10 12:41:01.220541: predicting saros-334 +2024-09-10 12:41:01.641894: saros-334, shape torch.Size([1, 247, 261, 261]), rank 0 +2024-09-10 12:41:05.362823: predicting saros-351 +2024-09-10 12:41:05.591122: saros-351, shape torch.Size([1, 203, 200, 200]), rank 0 +2024-09-10 12:41:09.253998: predicting saros-357 +2024-09-10 12:41:09.453462: saros-357, shape torch.Size([1, 177, 214, 214]), rank 0 +2024-09-10 12:41:11.360849: predicting saros-360 +2024-09-10 12:41:11.601134: saros-360, shape torch.Size([1, 187, 215, 215]), rank 0 +2024-09-10 12:41:13.505020: predicting saros-365 +2024-09-10 12:41:13.802171: saros-365, shape torch.Size([1, 240, 227, 227]), rank 0 +2024-09-10 12:41:17.504948: predicting saros-368 +2024-09-10 12:41:18.158620: saros-368, shape torch.Size([1, 273, 333, 333]), rank 0 +2024-09-10 12:41:26.483354: predicting saros-370 +2024-09-10 12:41:26.780535: saros-370, shape torch.Size([1, 233, 240, 240]), rank 0 +2024-09-10 12:41:30.494467: predicting saros-375 +2024-09-10 12:41:30.779261: saros-375, shape torch.Size([1, 160, 240, 240]), rank 0 +2024-09-10 12:41:32.708078: predicting saros-380 +2024-09-10 12:41:33.072980: saros-380, shape torch.Size([1, 227, 220, 220]), rank 0 +2024-09-10 12:41:36.808225: predicting saros-394 +2024-09-10 12:41:37.332286: saros-394, shape torch.Size([1, 247, 267, 267]), rank 0 +2024-09-10 12:41:41.125561: predicting saros-397 +2024-09-10 12:41:41.443775: saros-397, shape torch.Size([1, 190, 270, 270]), rank 0 +2024-09-10 12:41:43.400352: predicting saros-404 +2024-09-10 12:41:43.626534: saros-404, shape torch.Size([1, 200, 233, 233]), rank 0 +2024-09-10 12:41:47.334725: predicting saros-409 +2024-09-10 12:41:47.584544: saros-409, shape torch.Size([1, 193, 209, 209]), rank 0 +2024-09-10 12:41:51.292331: predicting saros-416 +2024-09-10 12:41:51.777346: saros-416, shape torch.Size([1, 190, 333, 333]), rank 0 +2024-09-10 12:41:56.082215: predicting saros-417 +2024-09-10 12:41:56.288080: saros-417, shape torch.Size([1, 187, 214, 214]), rank 0 +2024-09-10 12:41:58.268253: predicting saros-429 +2024-09-10 12:41:58.809011: saros-429, shape torch.Size([1, 243, 333, 333]), rank 0 +2024-09-10 12:42:07.116747: predicting saros-430 +2024-09-10 12:42:07.352995: saros-430, shape torch.Size([1, 213, 207, 207]), rank 0 +2024-09-10 12:42:11.059656: predicting saros-440 +2024-09-10 12:42:11.362317: saros-440, shape torch.Size([1, 210, 226, 226]), rank 0 +2024-09-10 12:42:15.088258: predicting saros-451 +2024-09-10 12:42:15.551666: saros-451, shape torch.Size([1, 223, 250, 250]), rank 0 +2024-09-10 12:42:19.268684: predicting saros-462 +2024-09-10 12:42:19.813826: saros-462, shape torch.Size([1, 267, 333, 333]), rank 0 +2024-09-10 12:42:28.098276: predicting saros-464 +2024-09-10 12:42:28.356064: saros-464, shape torch.Size([1, 237, 207, 207]), rank 0 +2024-09-10 12:42:32.051141: predicting saros-473 +2024-09-10 12:42:32.547144: saros-473, shape torch.Size([1, 247, 293, 293]), rank 0 +2024-09-10 12:42:40.793848: predicting saros-482 +2024-09-10 12:42:41.236373: saros-482, shape torch.Size([1, 247, 293, 293]), rank 0 +2024-09-10 12:42:49.492620: predicting saros-483 +2024-09-10 12:42:49.950558: saros-483, shape torch.Size([1, 217, 333, 333]), rank 0 +2024-09-10 12:42:58.292600: predicting saros-486 +2024-09-10 12:42:58.832105: saros-486, shape torch.Size([1, 210, 283, 283]), rank 0 +2024-09-10 12:43:02.587624: predicting saros-487 +2024-09-10 12:43:03.135922: saros-487, shape torch.Size([1, 267, 333, 333]), rank 0 +2024-09-10 12:43:11.428249: predicting saros-499 +2024-09-10 12:43:11.933360: saros-499, shape torch.Size([1, 243, 333, 333]), rank 0 +2024-09-10 12:43:20.232151: predicting saros-500 +2024-09-10 12:43:20.468812: saros-500, shape torch.Size([1, 207, 233, 233]), rank 0 +2024-09-10 12:43:24.229628: predicting saros-507 +2024-09-10 12:43:24.604272: saros-507, shape torch.Size([1, 203, 333, 333]), rank 0 +2024-09-10 12:43:32.840491: predicting saros-513 +2024-09-10 12:43:33.240469: saros-513, shape torch.Size([1, 297, 245, 245]), rank 0 +2024-09-10 12:43:38.805643: predicting saros-518 +2024-09-10 12:43:39.238706: saros-518, shape torch.Size([1, 230, 269, 269]), rank 0 +2024-09-10 12:43:42.980361: predicting saros-521 +2024-09-10 12:43:43.344361: saros-521, shape torch.Size([1, 217, 253, 253]), rank 0 +2024-09-10 12:43:47.088903: predicting saros-526 +2024-09-10 12:43:47.666392: saros-526, shape torch.Size([1, 253, 293, 293]), rank 0 +2024-09-10 12:43:55.936860: predicting saros-528 +2024-09-10 12:43:56.250699: saros-528, shape torch.Size([1, 213, 253, 253]), rank 0 +2024-09-10 12:43:59.970832: predicting saros-539 +2024-09-10 12:44:00.340275: saros-539, shape torch.Size([1, 230, 267, 267]), rank 0 +2024-09-10 12:44:04.066918: predicting saros-567 +2024-09-10 12:44:04.537412: saros-567, shape torch.Size([1, 220, 333, 333]), rank 0 +2024-09-10 12:44:12.787854: predicting saros-569 +2024-09-10 12:44:13.070490: saros-569, shape torch.Size([1, 190, 256, 256]), rank 0 +2024-09-10 12:44:15.006258: predicting saros-576 +2024-09-10 12:44:15.386593: saros-576, shape torch.Size([1, 247, 267, 267]), rank 0 +2024-09-10 12:44:19.142874: predicting saros-578 +2024-09-10 12:44:19.517707: saros-578, shape torch.Size([1, 253, 245, 245]), rank 0 +2024-09-10 12:44:23.272259: predicting saros-585 +2024-09-10 12:44:23.772959: saros-585, shape torch.Size([1, 163, 333, 333]), rank 0 +2024-09-10 12:44:28.104301: predicting saros-591 +2024-09-10 12:44:28.543718: saros-591, shape torch.Size([1, 180, 333, 333]), rank 0 +2024-09-10 12:44:32.806332: predicting saros-594 +2024-09-10 12:44:33.192243: saros-594, shape torch.Size([1, 233, 239, 239]), rank 0 +2024-09-10 12:44:36.914104: predicting saros-599 +2024-09-10 12:44:37.300796: saros-599, shape torch.Size([1, 233, 267, 267]), rank 0 +2024-09-10 12:44:41.047199: predicting saros-602 +2024-09-10 12:44:42.596228: saros-602, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 12:45:07.299498: predicting saros-613 +2024-09-10 12:45:08.838869: saros-613, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 12:45:37.868965: predicting saros-617 +2024-09-10 12:45:39.271357: saros-617, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 12:46:03.959875: predicting saros-618 +2024-09-10 12:46:05.244302: saros-618, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 12:46:29.915151: predicting saros-621 +2024-09-10 12:46:31.768408: saros-621, shape torch.Size([1, 547, 467, 467]), rank 0 +2024-09-10 12:47:08.626675: predicting saros-629 +2024-09-10 12:47:10.691188: saros-629, shape torch.Size([1, 660, 400, 400]), rank 0 +2024-09-10 12:47:55.830988: predicting saros-633 +2024-09-10 12:47:57.143032: saros-633, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 12:48:18.053372: predicting saros-638 +2024-09-10 12:48:19.203965: saros-638, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 12:48:39.758882: predicting saros-640 +2024-09-10 12:48:41.262595: saros-640, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 12:49:10.319536: predicting saros-644 +2024-09-10 12:49:11.795057: saros-644, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 12:49:36.692882: predicting saros-653 +2024-09-10 12:49:37.774747: saros-653, shape torch.Size([1, 497, 333, 333]), rank 0 +2024-09-10 12:49:58.300188: predicting saros-655 +2024-09-10 12:49:59.664117: saros-655, shape torch.Size([1, 677, 333, 333]), rank 0 +2024-09-10 12:50:28.535355: predicting saros-659 +2024-09-10 12:50:29.816750: saros-659, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 12:50:50.531069: predicting saros-665 +2024-09-10 12:50:53.103903: saros-665, shape torch.Size([1, 1117, 333, 333]), rank 0 +2024-09-10 12:51:38.574339: predicting saros-678 +2024-09-10 12:51:40.110138: saros-678, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 12:52:04.938130: predicting saros-687 +2024-09-10 12:52:06.272820: saros-687, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-10 12:52:30.980633: predicting saros-692 +2024-09-10 12:52:32.220215: saros-692, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-10 12:52:48.706521: predicting saros-697 +2024-09-10 12:52:50.115915: saros-697, shape torch.Size([1, 650, 333, 333]), rank 0 +2024-09-10 12:53:14.972332: predicting saros-705 +2024-09-10 12:53:16.692097: saros-705, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 12:53:41.924753: predicting saros-714 +2024-09-10 12:53:44.954979: saros-714, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 12:54:09.688677: predicting saros-720 +2024-09-10 12:54:11.093140: saros-720, shape torch.Size([1, 627, 333, 333]), rank 0 +2024-09-10 12:54:35.811893: predicting saros-721 +2024-09-10 12:54:37.197316: saros-721, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 12:55:01.962361: predicting saros-723 +2024-09-10 12:55:03.133108: saros-723, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 12:55:23.763428: predicting saros-730 +2024-09-10 12:55:25.013896: saros-730, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 12:55:49.690985: predicting saros-742 +2024-09-10 12:55:51.041313: saros-742, shape torch.Size([1, 527, 333, 333]), rank 0 +2024-09-10 12:56:11.763128: predicting saros-752 +2024-09-10 12:56:13.348484: saros-752, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 12:56:42.481115: predicting saros-758 +2024-09-10 12:56:43.839500: saros-758, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 12:57:13.007977: predicting saros-768 +2024-09-10 12:57:14.377662: saros-768, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 12:57:43.688136: predicting saros-779 +2024-09-10 12:57:45.830691: saros-779, shape torch.Size([1, 1180, 333, 333]), rank 0 +2024-09-10 12:58:35.438074: predicting saros-787 +2024-09-10 12:58:36.819927: saros-787, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 12:58:57.562515: predicting saros-802 +2024-09-10 12:58:59.036436: saros-802, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-10 12:59:23.861387: predicting saros-803 +2024-09-10 12:59:25.127130: saros-803, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 12:59:45.764300: predicting saros-808 +2024-09-10 12:59:47.282605: saros-808, shape torch.Size([1, 727, 333, 333]), rank 0 +2024-09-10 13:00:16.390196: predicting saros-815 +2024-09-10 13:00:17.750358: saros-815, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 13:00:42.432550: predicting saros-823 +2024-09-10 13:00:43.555562: saros-823, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-10 13:01:00.069578: predicting saros-828 +2024-09-10 13:01:01.540369: saros-828, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 13:01:30.361769: predicting saros-836 +2024-09-10 13:01:31.917630: saros-836, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 13:01:56.681763: predicting saros-841 +2024-09-10 13:01:58.116331: saros-841, shape torch.Size([1, 650, 333, 333]), rank 0 +2024-09-10 13:02:22.897768: predicting saros-842 +2024-09-10 13:02:24.342128: saros-842, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 13:02:49.153936: predicting saros-844 +2024-09-10 13:02:50.233400: saros-844, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-10 13:03:06.784205: predicting saros-845 +2024-09-10 13:03:08.212893: saros-845, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 13:03:33.003594: predicting saros-850 +2024-09-10 13:03:34.546664: saros-850, shape torch.Size([1, 727, 333, 333]), rank 0 +2024-09-10 13:04:03.635909: predicting saros-861 +2024-09-10 13:04:05.150535: saros-861, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 13:04:29.957352: predicting saros-862 +2024-09-10 13:04:31.435339: saros-862, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 13:05:00.309417: predicting saros-863 +2024-09-10 13:05:01.861233: saros-863, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 13:05:30.881630: predicting saros-878 +2024-09-10 13:05:32.408070: saros-878, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 13:06:01.343868: predicting saros-886 +2024-09-10 13:06:02.833857: saros-886, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 13:06:31.906523: predicting saros-893 +2024-09-10 13:06:32.913841: saros-893, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-10 13:06:49.387379: predicting saros-894 +2024-09-10 13:06:50.663203: saros-894, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 13:07:15.440437: predicting saros-897 +2024-09-10 13:07:17.616784: saros-897, shape torch.Size([1, 650, 440, 440]), rank 0 +2024-09-10 13:08:01.763572: predicting tseg-s0028 +2024-09-10 13:08:02.265465: tseg-s0028, shape torch.Size([1, 394, 299, 299]), rank 0 +2024-09-10 13:08:18.818898: predicting tseg-s0045 +2024-09-10 13:08:19.102122: tseg-s0045, shape torch.Size([1, 306, 231, 231]), rank 0 +2024-09-10 13:08:24.741470: predicting tseg-s0054 +2024-09-10 13:08:25.096044: tseg-s0054, shape torch.Size([1, 273, 261, 261]), rank 0 +2024-09-10 13:08:28.886979: predicting tseg-s0063 +2024-09-10 13:08:28.992097: tseg-s0063, shape torch.Size([1, 209, 156, 209]), rank 0 +2024-09-10 13:08:30.897987: predicting tseg-s0065 +2024-09-10 13:08:31.306428: tseg-s0065, shape torch.Size([1, 409, 242, 242]), rank 0 +2024-09-10 13:08:38.678081: predicting tseg-s0074 +2024-09-10 13:08:38.822227: tseg-s0074, shape torch.Size([1, 180, 192, 251]), rank 0 +2024-09-10 13:08:39.820775: predicting tseg-s0077 +2024-09-10 13:08:40.212812: tseg-s0077, shape torch.Size([1, 272, 255, 255]), rank 0 +2024-09-10 13:08:43.982980: predicting tseg-s0086 +2024-09-10 13:08:44.672961: tseg-s0086, shape torch.Size([1, 430, 430, 273]), rank 0 +2024-09-10 13:08:59.365632: predicting tseg-s0102 +2024-09-10 13:08:59.641777: tseg-s0102, shape torch.Size([1, 369, 152, 369]), rank 0 +2024-09-10 13:09:03.939281: predicting tseg-s0104 +2024-09-10 13:09:04.240887: tseg-s0104, shape torch.Size([1, 202, 295, 295]), rank 0 +2024-09-10 13:09:12.513465: predicting tseg-s0108 +2024-09-10 13:09:13.004520: tseg-s0108, shape torch.Size([1, 293, 288, 288]), rank 0 +2024-09-10 13:09:18.674138: predicting tseg-s0120 +2024-09-10 13:09:19.090747: tseg-s0120, shape torch.Size([1, 355, 254, 261]), rank 0 +2024-09-10 13:09:24.718786: predicting tseg-s0128 +2024-09-10 13:09:24.784056: tseg-s0128, shape torch.Size([1, 52, 261, 261]), rank 0 +2024-09-10 13:09:26.612399: predicting tseg-s0138 +2024-09-10 13:09:26.820332: tseg-s0138, shape torch.Size([1, 215, 221, 221]), rank 0 +2024-09-10 13:09:30.506569: predicting tseg-s0140 +2024-09-10 13:09:30.584835: tseg-s0140, shape torch.Size([1, 125, 196, 196]), rank 0 +2024-09-10 13:09:32.456442: predicting tseg-s0141 +2024-09-10 13:09:32.889339: tseg-s0141, shape torch.Size([1, 406, 168, 406]), rank 0 +2024-09-10 13:09:40.342833: predicting tseg-s0143 +2024-09-10 13:09:40.548068: tseg-s0143, shape torch.Size([1, 267, 166, 279]), rank 0 +2024-09-10 13:09:42.502724: predicting tseg-s0150 +2024-09-10 13:09:42.895074: tseg-s0150, shape torch.Size([1, 296, 247, 247]), rank 0 +2024-09-10 13:09:48.460680: predicting tseg-s0160 +2024-09-10 13:09:48.609437: tseg-s0160, shape torch.Size([1, 95, 321, 321]), rank 0 +2024-09-10 13:09:52.855851: predicting tseg-s0171 +2024-09-10 13:09:53.208772: tseg-s0171, shape torch.Size([1, 409, 222, 222]), rank 0 +2024-09-10 13:10:00.555918: predicting tseg-s0174 +2024-09-10 13:10:00.672637: tseg-s0174, shape torch.Size([1, 253, 133, 194]), rank 0 +2024-09-10 13:10:02.556416: predicting tseg-s0211 +2024-09-10 13:10:02.659815: tseg-s0211, shape torch.Size([1, 59, 261, 261]), rank 0 +2024-09-10 13:10:04.551345: predicting tseg-s0212 +2024-09-10 13:10:04.840075: tseg-s0212, shape torch.Size([1, 327, 184, 262]), rank 0 +2024-09-10 13:10:07.710683: predicting tseg-s0215 +2024-09-10 13:10:07.788781: tseg-s0215, shape torch.Size([1, 57, 249, 249]), rank 0 +2024-09-10 13:10:09.681077: predicting tseg-s0216 +2024-09-10 13:10:09.917161: tseg-s0216, shape torch.Size([1, 179, 288, 288]), rank 0 +2024-09-10 13:10:11.892837: predicting tseg-s0220 +2024-09-10 13:10:12.131537: tseg-s0220, shape torch.Size([1, 181, 253, 253]), rank 0 +2024-09-10 13:10:14.097182: predicting tseg-s0228 +2024-09-10 13:10:14.398773: tseg-s0228, shape torch.Size([1, 267, 267, 254]), rank 0 +2024-09-10 13:10:18.152603: predicting tseg-s0231 +2024-09-10 13:10:18.332161: tseg-s0231, shape torch.Size([1, 237, 158, 248]), rank 0 +2024-09-10 13:10:20.319211: predicting tseg-s0233 +2024-09-10 13:10:20.347425: tseg-s0233, shape torch.Size([1, 153, 91, 91]), rank 0 +2024-09-10 13:10:20.827332: predicting tseg-s0243 +2024-09-10 13:10:21.141345: tseg-s0243, shape torch.Size([1, 285, 253, 253]), rank 0 +2024-09-10 13:10:24.875197: predicting tseg-s0250 +2024-09-10 13:10:25.421129: tseg-s0250, shape torch.Size([1, 301, 307, 307]), rank 0 +2024-09-10 13:10:37.796527: predicting tseg-s0256 +2024-09-10 13:10:37.935781: tseg-s0256, shape torch.Size([1, 151, 231, 231]), rank 0 +2024-09-10 13:10:39.832496: predicting tseg-s0266 +2024-09-10 13:10:39.998380: tseg-s0266, shape torch.Size([1, 97, 302, 302]), rank 0 +2024-09-10 13:10:44.155386: predicting tseg-s0277 +2024-09-10 13:10:44.291839: tseg-s0277, shape torch.Size([1, 223, 162, 223]), rank 0 +2024-09-10 13:10:46.208797: predicting tseg-s0283 +2024-09-10 13:10:46.303630: tseg-s0283, shape torch.Size([1, 96, 232, 232]), rank 0 +2024-09-10 13:10:48.200531: predicting tseg-s0290 +2024-09-10 13:10:48.362191: tseg-s0290, shape torch.Size([1, 115, 285, 285]), rank 0 +2024-09-10 13:10:50.275250: predicting tseg-s0294 +2024-09-10 13:10:50.391282: tseg-s0294, shape torch.Size([1, 71, 301, 301]), rank 0 +2024-09-10 13:10:54.548351: predicting tseg-s0296 +2024-09-10 13:10:54.559698: tseg-s0296, shape torch.Size([1, 60, 80, 80]), rank 0 +2024-09-10 13:10:55.035226: predicting tseg-s0324 +2024-09-10 13:10:55.235258: tseg-s0324, shape torch.Size([1, 261, 200, 200]), rank 0 +2024-09-10 13:10:58.904962: predicting tseg-s0333 +2024-09-10 13:10:59.075521: tseg-s0333, shape torch.Size([1, 217, 219, 219]), rank 0 +2024-09-10 13:11:02.780535: predicting tseg-s0334 +2024-09-10 13:11:03.393595: tseg-s0334, shape torch.Size([1, 469, 291, 291]), rank 0 +2024-09-10 13:11:19.818583: predicting tseg-s0341 +2024-09-10 13:11:20.084182: tseg-s0341, shape torch.Size([1, 275, 247, 247]), rank 0 +2024-09-10 13:11:23.902013: predicting tseg-s0344 +2024-09-10 13:11:24.418565: tseg-s0344, shape torch.Size([1, 384, 283, 283]), rank 0 +2024-09-10 13:11:30.074203: predicting tseg-s0356 +2024-09-10 13:11:30.100876: tseg-s0356, shape torch.Size([1, 89, 107, 107]), rank 0 +2024-09-10 13:11:30.590909: predicting tseg-s0357 +2024-09-10 13:11:30.787707: tseg-s0357, shape torch.Size([1, 215, 223, 223]), rank 0 +2024-09-10 13:11:34.467418: predicting tseg-s0360 +2024-09-10 13:11:34.524705: tseg-s0360, shape torch.Size([1, 53, 253, 253]), rank 0 +2024-09-10 13:11:36.385646: predicting tseg-s0363 +2024-09-10 13:11:36.588720: tseg-s0363, shape torch.Size([1, 208, 228, 228]), rank 0 +2024-09-10 13:11:40.284648: predicting tseg-s0372 +2024-09-10 13:11:40.634818: tseg-s0372, shape torch.Size([1, 295, 256, 256]), rank 0 +2024-09-10 13:11:46.203086: predicting tseg-s0392 +2024-09-10 13:11:46.535606: tseg-s0392, shape torch.Size([1, 270, 267, 267]), rank 0 +2024-09-10 13:11:50.334939: predicting tseg-s0403 +2024-09-10 13:11:50.835933: tseg-s0403, shape torch.Size([1, 391, 267, 267]), rank 0 +2024-09-10 13:11:58.278367: predicting tseg-s0405 +2024-09-10 13:11:58.408896: tseg-s0405, shape torch.Size([1, 182, 196, 196]), rank 0 +2024-09-10 13:12:00.294005: predicting tseg-s0407 +2024-09-10 13:12:00.526701: tseg-s0407, shape torch.Size([1, 228, 215, 215]), rank 0 +2024-09-10 13:12:04.229506: predicting tseg-s0414 +2024-09-10 13:12:04.369434: tseg-s0414, shape torch.Size([1, 171, 199, 199]), rank 0 +2024-09-10 13:12:06.269614: predicting tseg-s0425 +2024-09-10 13:12:06.541636: tseg-s0425, shape torch.Size([1, 196, 260, 260]), rank 0 +2024-09-10 13:12:10.260618: predicting tseg-s0428 +2024-09-10 13:12:10.730385: tseg-s0428, shape torch.Size([1, 317, 307, 307]), rank 0 +2024-09-10 13:12:23.079827: predicting tseg-s0429 +2024-09-10 13:12:23.533924: tseg-s0429, shape torch.Size([1, 433, 254, 254]), rank 0 +2024-09-10 13:12:31.008829: predicting tseg-s0439 +2024-09-10 13:12:31.418005: tseg-s0439, shape torch.Size([1, 334, 221, 307]), rank 0 +2024-09-10 13:12:39.752917: predicting tseg-s0442 +2024-09-10 13:12:39.981934: tseg-s0442, shape torch.Size([1, 178, 264, 264]), rank 0 +2024-09-10 13:12:41.935952: predicting tseg-s0461 +2024-09-10 13:12:42.498417: tseg-s0461, shape torch.Size([1, 447, 269, 269]), rank 0 +2024-09-10 13:12:49.934301: predicting tseg-s0466 +2024-09-10 13:12:50.005198: tseg-s0466, shape torch.Size([1, 143, 173, 173]), rank 0 +2024-09-10 13:12:50.528234: predicting tseg-s0471 +2024-09-10 13:12:50.672365: tseg-s0471, shape torch.Size([1, 142, 227, 227]), rank 0 +2024-09-10 13:12:52.612878: predicting tseg-s0473 +2024-09-10 13:12:52.898112: tseg-s0473, shape torch.Size([1, 289, 242, 242]), rank 0 +2024-09-10 13:12:58.413717: predicting tseg-s0481 +2024-09-10 13:12:58.587977: tseg-s0481, shape torch.Size([1, 185, 207, 207]), rank 0 +2024-09-10 13:13:00.528398: predicting tseg-s0495 +2024-09-10 13:13:00.894602: tseg-s0495, shape torch.Size([1, 332, 247, 247]), rank 0 +2024-09-10 13:13:06.462233: predicting tseg-s0500 +2024-09-10 13:13:07.019562: tseg-s0500, shape torch.Size([1, 354, 307, 307]), rank 0 +2024-09-10 13:13:19.404243: predicting tseg-s0502 +2024-09-10 13:13:19.757299: tseg-s0502, shape torch.Size([1, 402, 215, 215]), rank 0 +2024-09-10 13:13:27.163871: predicting tseg-s0515 +2024-09-10 13:13:27.304413: tseg-s0515, shape torch.Size([1, 176, 205, 205]), rank 0 +2024-09-10 13:13:29.249837: predicting tseg-s0518 +2024-09-10 13:13:29.353481: tseg-s0518, shape torch.Size([1, 236, 145, 145]), rank 0 +2024-09-10 13:13:30.334086: predicting tseg-s0519 +2024-09-10 13:13:30.906770: tseg-s0519, shape torch.Size([1, 433, 283, 283]), rank 0 +2024-09-10 13:13:38.356550: predicting tseg-s0522 +2024-09-10 13:13:38.558709: tseg-s0522, shape torch.Size([1, 199, 249, 249]), rank 0 +2024-09-10 13:13:42.347908: predicting tseg-s0525 +2024-09-10 13:13:42.418925: tseg-s0525, shape torch.Size([1, 56, 297, 297]), rank 0 +2024-09-10 13:13:46.559601: predicting tseg-s0529 +2024-09-10 13:13:46.903960: tseg-s0529, shape torch.Size([1, 304, 247, 247]), rank 0 +2024-09-10 13:13:52.444678: predicting tseg-s0545 +2024-09-10 13:13:52.741713: tseg-s0545, shape torch.Size([1, 240, 257, 257]), rank 0 +2024-09-10 13:13:56.514254: predicting tseg-s0553 +2024-09-10 13:13:56.887577: tseg-s0553, shape torch.Size([1, 414, 233, 233]), rank 0 +2024-09-10 13:14:04.267659: predicting tseg-s0556 +2024-09-10 13:14:05.054357: tseg-s0556, shape torch.Size([1, 484, 333, 333]), rank 0 +2024-09-10 13:14:25.638755: predicting tseg-s0565 +2024-09-10 13:14:25.841482: tseg-s0565, shape torch.Size([1, 204, 245, 245]), rank 0 +2024-09-10 13:14:29.580484: predicting tseg-s0566 +2024-09-10 13:14:29.901909: tseg-s0566, shape torch.Size([1, 319, 161, 319]), rank 0 +2024-09-10 13:14:34.166824: predicting tseg-s0567 +2024-09-10 13:14:34.215038: tseg-s0567, shape torch.Size([1, 112, 161, 161]), rank 0 +2024-09-10 13:14:34.722493: predicting tseg-s0572 +2024-09-10 13:14:34.805659: tseg-s0572, shape torch.Size([1, 133, 185, 185]), rank 0 +2024-09-10 13:14:35.323714: predicting tseg-s0574 +2024-09-10 13:14:35.840222: tseg-s0574, shape torch.Size([1, 441, 251, 251]), rank 0 +2024-09-10 13:14:43.217476: predicting tseg-s0575 +2024-09-10 13:14:43.255105: tseg-s0575, shape torch.Size([1, 103, 143, 143]), rank 0 +2024-09-10 13:14:43.744535: predicting tseg-s0578 +2024-09-10 13:14:44.130836: tseg-s0578, shape torch.Size([1, 395, 223, 223]), rank 0 +2024-09-10 13:14:51.472107: predicting tseg-s0579 +2024-09-10 13:14:51.545609: tseg-s0579, shape torch.Size([1, 213, 128, 128]), rank 0 +2024-09-10 13:14:52.505121: predicting tseg-s0580 +2024-09-10 13:14:52.810101: tseg-s0580, shape torch.Size([1, 288, 181, 288]), rank 0 +2024-09-10 13:14:54.768904: predicting tseg-s0581 +2024-09-10 13:14:54.988677: tseg-s0581, shape torch.Size([1, 198, 233, 233]), rank 0 +2024-09-10 13:14:58.714020: predicting tseg-s0587 +2024-09-10 13:14:59.276187: tseg-s0587, shape torch.Size([1, 410, 300, 300]), rank 0 +2024-09-10 13:15:15.674031: predicting tseg-s0589 +2024-09-10 13:15:16.212518: tseg-s0589, shape torch.Size([1, 423, 265, 265]), rank 0 +2024-09-10 13:15:23.692127: predicting tseg-s0590 +2024-09-10 13:15:23.874649: tseg-s0590, shape torch.Size([1, 260, 192, 260]), rank 0 +2024-09-10 13:15:25.804942: predicting tseg-s0594 +2024-09-10 13:15:25.943519: tseg-s0594, shape torch.Size([1, 121, 216, 216]), rank 0 +2024-09-10 13:15:27.834215: predicting tseg-s0601 +2024-09-10 13:15:27.972707: tseg-s0601, shape torch.Size([1, 150, 218, 218]), rank 0 +2024-09-10 13:15:29.861240: predicting tseg-s0604 +2024-09-10 13:15:30.039398: tseg-s0604, shape torch.Size([1, 208, 199, 199]), rank 0 +2024-09-10 13:15:33.717805: predicting tseg-s0610 +2024-09-10 13:15:33.929014: tseg-s0610, shape torch.Size([1, 206, 240, 240]), rank 0 +2024-09-10 13:15:37.627915: predicting tseg-s0612 +2024-09-10 13:15:38.176778: tseg-s0612, shape torch.Size([1, 411, 272, 272]), rank 0 +2024-09-10 13:15:45.607896: predicting tseg-s0613 +2024-09-10 13:15:46.186995: tseg-s0613, shape torch.Size([1, 417, 272, 272]), rank 0 +2024-09-10 13:15:53.676399: predicting tseg-s0620 +2024-09-10 13:15:54.060115: tseg-s0620, shape torch.Size([1, 300, 244, 244]), rank 0 +2024-09-10 13:15:59.664978: predicting tseg-s0627 +2024-09-10 13:15:59.856832: tseg-s0627, shape torch.Size([1, 157, 264, 264]), rank 0 +2024-09-10 13:16:01.815063: predicting tseg-s0643 +2024-09-10 13:16:01.987832: tseg-s0643, shape torch.Size([1, 263, 178, 178]), rank 0 +2024-09-10 13:16:03.003422: predicting tseg-s0644 +2024-09-10 13:16:03.480129: tseg-s0644, shape torch.Size([1, 389, 272, 272]), rank 0 +2024-09-10 13:16:10.877111: predicting tseg-s0654 +2024-09-10 13:16:11.079732: tseg-s0654, shape torch.Size([1, 205, 222, 222]), rank 0 +2024-09-10 13:16:14.842746: predicting tseg-s0655 +2024-09-10 13:16:14.862959: tseg-s0655, shape torch.Size([1, 92, 95, 95]), rank 0 +2024-09-10 13:16:15.340579: predicting tseg-s0661 +2024-09-10 13:16:15.774084: tseg-s0661, shape torch.Size([1, 390, 252, 252]), rank 0 +2024-09-10 13:16:23.124087: predicting tseg-s0675 +2024-09-10 13:16:23.482658: tseg-s0675, shape torch.Size([1, 209, 309, 309]), rank 0 +2024-09-10 13:16:31.782362: predicting tseg-s0686 +2024-09-10 13:16:32.290437: tseg-s0686, shape torch.Size([1, 407, 265, 265]), rank 0 +2024-09-10 13:16:39.735724: predicting tseg-s0714 +2024-09-10 13:16:39.960831: tseg-s0714, shape torch.Size([1, 183, 262, 262]), rank 0 +2024-09-10 13:16:41.911601: predicting tseg-s0716 +2024-09-10 13:16:42.153174: tseg-s0716, shape torch.Size([1, 228, 238, 238]), rank 0 +2024-09-10 13:16:45.879828: predicting tseg-s0721 +2024-09-10 13:16:46.135558: tseg-s0721, shape torch.Size([1, 265, 227, 227]), rank 0 +2024-09-10 13:16:49.865994: predicting tseg-s0738 +2024-09-10 13:16:49.915123: tseg-s0738, shape torch.Size([1, 141, 119, 119]), rank 0 +2024-09-10 13:16:50.401254: predicting tseg-s0764 +2024-09-10 13:16:50.998100: tseg-s0764, shape torch.Size([1, 405, 291, 291]), rank 0 +2024-09-10 13:17:07.361961: predicting tseg-s0771 +2024-09-10 13:17:07.764560: tseg-s0771, shape torch.Size([1, 247, 310, 310]), rank 0 +2024-09-10 13:17:16.103611: predicting tseg-s0778 +2024-09-10 13:17:16.634349: tseg-s0778, shape torch.Size([1, 431, 262, 262]), rank 0 +2024-09-10 13:17:24.099394: predicting tseg-s0785 +2024-09-10 13:17:24.238564: tseg-s0785, shape torch.Size([1, 211, 179, 209]), rank 0 +2024-09-10 13:17:26.134026: predicting tseg-s0786 +2024-09-10 13:17:26.236464: tseg-s0786, shape torch.Size([1, 155, 174, 174]), rank 0 +2024-09-10 13:17:26.766490: predicting tseg-s0788 +2024-09-10 13:17:27.033212: tseg-s0788, shape torch.Size([1, 267, 225, 225]), rank 0 +2024-09-10 13:17:30.747708: predicting tseg-s0789 +2024-09-10 13:17:31.077266: tseg-s0789, shape torch.Size([1, 242, 279, 279]), rank 0 +2024-09-10 13:17:34.855817: predicting tseg-s0810 +2024-09-10 13:17:35.278612: tseg-s0810, shape torch.Size([1, 289, 289, 289]), rank 0 +2024-09-10 13:17:47.629306: predicting tseg-s0812 +2024-09-10 13:17:48.688504: tseg-s0812, shape torch.Size([1, 549, 327, 327]), rank 0 +2024-09-10 13:18:09.337291: predicting tseg-s0815 +2024-09-10 13:18:09.587710: tseg-s0815, shape torch.Size([1, 267, 253, 253]), rank 0 +2024-09-10 13:18:13.349431: predicting tseg-s0824 +2024-09-10 13:18:13.561021: tseg-s0824, shape torch.Size([1, 200, 183, 265]), rank 0 +2024-09-10 13:18:15.511178: predicting tseg-s0831 +2024-09-10 13:18:16.026786: tseg-s0831, shape torch.Size([1, 524, 253, 253]), rank 0 +2024-09-10 13:18:25.246438: predicting tseg-s0832 +2024-09-10 13:18:25.365604: tseg-s0832, shape torch.Size([1, 104, 257, 257]), rank 0 +2024-09-10 13:18:27.249902: predicting tseg-s0863 +2024-09-10 13:18:27.768467: tseg-s0863, shape torch.Size([1, 435, 263, 263]), rank 0 +2024-09-10 13:18:35.191302: predicting tseg-s0869 +2024-09-10 13:18:35.733908: tseg-s0869, shape torch.Size([1, 459, 261, 261]), rank 0 +2024-09-10 13:18:43.231477: predicting tseg-s0876 +2024-09-10 13:18:43.260825: tseg-s0876, shape torch.Size([1, 95, 119, 119]), rank 0 +2024-09-10 13:18:43.759232: predicting tseg-s0880 +2024-09-10 13:18:44.353836: tseg-s0880, shape torch.Size([1, 463, 291, 291]), rank 0 +2024-09-10 13:19:00.756094: predicting tseg-s0884 +2024-09-10 13:19:01.384492: tseg-s0884, shape torch.Size([1, 455, 272, 272]), rank 0 +2024-09-10 13:19:08.930197: predicting tseg-s0889 +2024-09-10 13:19:09.157362: tseg-s0889, shape torch.Size([1, 203, 251, 251]), rank 0 +2024-09-10 13:19:12.941825: predicting tseg-s0891 +2024-09-10 13:19:12.981162: tseg-s0891, shape torch.Size([1, 131, 117, 117]), rank 0 +2024-09-10 13:19:13.462410: predicting tseg-s0908 +2024-09-10 13:19:13.698482: tseg-s0908, shape torch.Size([1, 180, 261, 261]), rank 0 +2024-09-10 13:19:15.644192: predicting tseg-s0913 +2024-09-10 13:19:16.289337: tseg-s0913, shape torch.Size([1, 465, 289, 289]), rank 0 +2024-09-10 13:19:32.717375: predicting tseg-s0943 +2024-09-10 13:19:33.011103: tseg-s0943, shape torch.Size([1, 252, 289, 289]), rank 0 +2024-09-10 13:19:41.379472: predicting tseg-s0945 +2024-09-10 13:19:41.933556: tseg-s0945, shape torch.Size([1, 399, 285, 285]), rank 0 +2024-09-10 13:19:49.384325: predicting tseg-s0946 +2024-09-10 13:19:49.583113: tseg-s0946, shape torch.Size([1, 212, 236, 236]), rank 0 +2024-09-10 13:19:53.348772: predicting tseg-s0957 +2024-09-10 13:19:53.818739: tseg-s0957, shape torch.Size([1, 437, 248, 248]), rank 0 +2024-09-10 13:20:01.233418: predicting tseg-s0970 +2024-09-10 13:20:02.050682: tseg-s0970, shape torch.Size([1, 511, 333, 333]), rank 0 +2024-09-10 13:20:22.683846: predicting tseg-s0979 +2024-09-10 13:20:23.088574: tseg-s0979, shape torch.Size([1, 349, 193, 432]), rank 0 +2024-09-10 13:20:34.218049: predicting tseg-s0981 +2024-09-10 13:20:34.373030: tseg-s0981, shape torch.Size([1, 174, 221, 221]), rank 0 +2024-09-10 13:20:36.259832: predicting tseg-s0997 +2024-09-10 13:20:36.296472: tseg-s0997, shape torch.Size([1, 96, 137, 137]), rank 0 +2024-09-10 13:20:36.780007: predicting tseg-s1010 +2024-09-10 13:20:36.909846: tseg-s1010, shape torch.Size([1, 169, 208, 208]), rank 0 +2024-09-10 13:20:38.816072: predicting tseg-s1014 +2024-09-10 13:20:38.848826: tseg-s1014, shape torch.Size([1, 116, 133, 111]), rank 0 +2024-09-10 13:20:39.340490: predicting tseg-s1021 +2024-09-10 13:20:39.455910: tseg-s1021, shape torch.Size([1, 136, 136, 136]), rank 0 +2024-09-10 13:20:39.949530: predicting tseg-s1022 +2024-09-10 13:20:40.400983: tseg-s1022, shape torch.Size([1, 440, 236, 236]), rank 0 +2024-09-10 13:20:47.764235: predicting tseg-s1028 +2024-09-10 13:20:48.054185: tseg-s1028, shape torch.Size([1, 282, 232, 232]), rank 0 +2024-09-10 13:20:51.855031: predicting tseg-s1037 +2024-09-10 13:20:52.159924: tseg-s1037, shape torch.Size([1, 285, 216, 250]), rank 0 +2024-09-10 13:20:55.919836: predicting tseg-s1043 +2024-09-10 13:20:55.990260: tseg-s1043, shape torch.Size([1, 157, 164, 164]), rank 0 +2024-09-10 13:20:56.504504: predicting tseg-s1055 +2024-09-10 13:20:56.541001: tseg-s1055, shape torch.Size([1, 70, 171, 171]), rank 0 +2024-09-10 13:20:57.040486: predicting tseg-s1068 +2024-09-10 13:20:57.134644: tseg-s1068, shape torch.Size([1, 80, 223, 223]), rank 0 +2024-09-10 13:20:59.006423: predicting tseg-s1089 +2024-09-10 13:20:59.683665: tseg-s1089, shape torch.Size([1, 550, 265, 265]), rank 0 +2024-09-10 13:21:08.911531: predicting tseg-s1100 +2024-09-10 13:21:09.183090: tseg-s1100, shape torch.Size([1, 286, 231, 231]), rank 0 +2024-09-10 13:21:12.990459: predicting tseg-s1106 +2024-09-10 13:21:13.016589: tseg-s1106, shape torch.Size([1, 92, 110, 110]), rank 0 +2024-09-10 13:21:13.499974: predicting tseg-s1113 +2024-09-10 13:21:13.683656: tseg-s1113, shape torch.Size([1, 219, 206, 206]), rank 0 +2024-09-10 13:21:17.359551: predicting tseg-s1122 +2024-09-10 13:21:17.401614: tseg-s1122, shape torch.Size([1, 156, 114, 114]), rank 0 +2024-09-10 13:21:17.880599: predicting tseg-s1123 +2024-09-10 13:21:18.615965: tseg-s1123, shape torch.Size([1, 545, 291, 291]), rank 0 +2024-09-10 13:21:39.088234: predicting tseg-s1155 +2024-09-10 13:21:39.337158: tseg-s1155, shape torch.Size([1, 201, 271, 271]), rank 0 +2024-09-10 13:21:43.087080: predicting tseg-s1157 +2024-09-10 13:21:43.331696: tseg-s1157, shape torch.Size([1, 189, 253, 253]), rank 0 +2024-09-10 13:21:45.308692: predicting tseg-s1169 +2024-09-10 13:21:45.361249: tseg-s1169, shape torch.Size([1, 106, 151, 151]), rank 0 +2024-09-10 13:21:45.843914: predicting tseg-s1183 +2024-09-10 13:21:46.204071: tseg-s1183, shape torch.Size([1, 335, 216, 254]), rank 0 +2024-09-10 13:21:51.754401: predicting tseg-s1203 +2024-09-10 13:21:51.846997: tseg-s1203, shape torch.Size([1, 133, 183, 183]), rank 0 +2024-09-10 13:21:52.370925: predicting tseg-s1206 +2024-09-10 13:21:53.153483: tseg-s1206, shape torch.Size([1, 538, 279, 279]), rank 0 +2024-09-10 13:22:02.419373: predicting tseg-s1220 +2024-09-10 13:22:02.515095: tseg-s1220, shape torch.Size([1, 163, 198, 198]), rank 0 +2024-09-10 13:22:04.393296: predicting tseg-s1227 +2024-09-10 13:22:04.435395: tseg-s1227, shape torch.Size([1, 98, 165, 165]), rank 0 +2024-09-10 13:22:04.940524: predicting tseg-s1228 +2024-09-10 13:22:05.734110: tseg-s1228, shape torch.Size([1, 402, 321, 321]), rank 0 +2024-09-10 13:22:22.167756: predicting tseg-s1231 +2024-09-10 13:22:22.281830: tseg-s1231, shape torch.Size([1, 229, 177, 177]), rank 0 +2024-09-10 13:22:23.308969: predicting tseg-s1236 +2024-09-10 13:22:23.582358: tseg-s1236, shape torch.Size([1, 136, 325, 325]), rank 0 +2024-09-10 13:22:27.874378: predicting tseg-s1246 +2024-09-10 13:22:27.969404: tseg-s1246, shape torch.Size([1, 64, 297, 297]), rank 0 +2024-09-10 13:22:32.117252: predicting tseg-s1256 +2024-09-10 13:22:32.457185: tseg-s1256, shape torch.Size([1, 228, 292, 292]), rank 0 +2024-09-10 13:22:40.697243: predicting tseg-s1258 +2024-09-10 13:22:40.809024: tseg-s1258, shape torch.Size([1, 122, 221, 221]), rank 0 +2024-09-10 13:22:42.683892: predicting tseg-s1262 +2024-09-10 13:22:42.977808: tseg-s1262, shape torch.Size([1, 274, 221, 221]), rank 0 +2024-09-10 13:22:46.695786: predicting tseg-s1270 +2024-09-10 13:22:46.926581: tseg-s1270, shape torch.Size([1, 209, 233, 233]), rank 0 +2024-09-10 13:22:50.635095: predicting tseg-s1277 +2024-09-10 13:22:50.817372: tseg-s1277, shape torch.Size([1, 164, 251, 251]), rank 0 +2024-09-10 13:22:52.746039: predicting tseg-s1290 +2024-09-10 13:22:52.770205: tseg-s1290, shape torch.Size([1, 32, 176, 176]), rank 0 +2024-09-10 13:22:53.245815: predicting tseg-s1294 +2024-09-10 13:22:53.517237: tseg-s1294, shape torch.Size([1, 278, 238, 238]), rank 0 +2024-09-10 13:22:57.223475: predicting tseg-s1298 +2024-09-10 13:22:57.608674: tseg-s1298, shape torch.Size([1, 237, 297, 297]), rank 0 +2024-09-10 13:23:05.883107: predicting tseg-s1311 +2024-09-10 13:23:06.341203: tseg-s1311, shape torch.Size([1, 289, 288, 288]), rank 0 +2024-09-10 13:23:11.957228: predicting tseg-s1336 +2024-09-10 13:23:12.577431: tseg-s1336, shape torch.Size([1, 416, 285, 285]), rank 0 +2024-09-10 13:23:20.083518: predicting tseg-s1350 +2024-09-10 13:23:21.043629: tseg-s1350, shape torch.Size([1, 539, 333, 333]), rank 0 +2024-09-10 13:23:41.675411: predicting tseg-s1353 +2024-09-10 13:23:41.937196: tseg-s1353, shape torch.Size([1, 212, 249, 249]), rank 0 +2024-09-10 13:23:45.694959: predicting tseg-s1359 +2024-09-10 13:23:45.777134: tseg-s1359, shape torch.Size([1, 196, 140, 140]), rank 0 +2024-09-10 13:23:46.760756: predicting tseg-s1361 +2024-09-10 13:23:47.382580: tseg-s1361, shape torch.Size([1, 475, 283, 283]), rank 0 +2024-09-10 13:23:54.832520: predicting tseg-s1387 +2024-09-10 13:23:55.460428: tseg-s1387, shape torch.Size([1, 453, 281, 281]), rank 0 +2024-09-10 13:24:03.010964: predicting tseg-s1390 +2024-09-10 13:24:03.533559: tseg-s1390, shape torch.Size([1, 335, 285, 285]), rank 0 +2024-09-10 13:24:09.234885: predicting tseg-s1394 +2024-09-10 13:24:09.644821: tseg-s1394, shape torch.Size([1, 306, 261, 261]), rank 0 +2024-09-10 13:25:42.150737: Validation complete +2024-09-10 13:25:42.151508: Mean Validation Dice: 0.945008093016126 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_4_16_32_31.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_4_16_32_31.txt new file mode 100644 index 0000000000000000000000000000000000000000..3a07a2bd3f361c26195869fa4b1b60ab0ebea2ee --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_4_16_32_31.txt @@ -0,0 +1,803 @@ + +####################################################################### +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. +####################################################################### + +2024-09-04 16:32:31.301408: do_dummy_2d_data_aug: False +2024-09-04 16:32:31.307724: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-04 16:32:31.309105: The split file contains 5 splits. +2024-09-04 16:32:31.309533: Desired fold for training: 1 +2024-09-04 16:32:31.309917: This split has 1325 training and 331 validation cases. +2024-09-04 16:32:48.052401: Using torch.compile... + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-04 16:32:51.086858: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-04 16:32:52.927613: +2024-09-04 16:32:52.932694: Epoch 0 +2024-09-04 16:32:52.942069: Current learning rate: 0.01 +2024-09-04 16:51:22.141526: train_loss 0.8145 +2024-09-04 16:51:22.147962: val_loss 0.4547 +2024-09-04 16:51:22.148629: Pseudo dice [0.552, 0.6481, 0.468, 0.0, 0.0, 0.0, 0.7819] +2024-09-04 16:51:22.149359: Epoch time: 1109.22 s +2024-09-04 16:51:22.150030: Yayy! New best EMA pseudo Dice: 0.35 +2024-09-04 16:51:33.198290: +2024-09-04 16:51:33.199123: Epoch 1 +2024-09-04 16:51:33.199790: Current learning rate: 0.00999 +2024-09-04 16:55:47.720632: train_loss 0.3023 +2024-09-04 16:55:47.726296: val_loss 0.0981 +2024-09-04 16:55:47.727041: Pseudo dice [0.7345, 0.6946, 0.7738, 0.6432, 0.0, 0.0, 0.9011] +2024-09-04 16:55:47.727960: Epoch time: 254.53 s +2024-09-04 16:55:47.728801: Yayy! New best EMA pseudo Dice: 0.3685 +2024-09-04 16:55:55.881844: +2024-09-04 16:55:55.883279: Epoch 2 +2024-09-04 16:55:55.884563: Current learning rate: 0.00998 +2024-09-04 16:59:55.501038: train_loss 0.02 +2024-09-04 16:59:55.503590: val_loss -0.0426 +2024-09-04 16:59:55.504542: Pseudo dice [0.7627, 0.7588, 0.8304, 0.7126, 0.0028, 0.2477, 0.9027] +2024-09-04 16:59:55.505362: Epoch time: 239.62 s +2024-09-04 16:59:55.506061: Yayy! New best EMA pseudo Dice: 0.3919 +2024-09-04 17:00:03.753970: +2024-09-04 17:00:03.754830: Epoch 3 +2024-09-04 17:00:03.755399: Current learning rate: 0.00997 +2024-09-04 17:04:11.310206: train_loss -0.1274 +2024-09-04 17:04:11.314772: val_loss -0.2806 +2024-09-04 17:04:11.316435: Pseudo dice [0.8281, 0.8112, 0.8881, 0.7738, 0.1976, 0.6395, 0.9427] +2024-09-04 17:04:11.317780: Epoch time: 247.56 s +2024-09-04 17:04:11.318952: Yayy! New best EMA pseudo Dice: 0.4253 +2024-09-04 17:04:19.048672: +2024-09-04 17:04:19.049675: Epoch 4 +2024-09-04 17:04:19.050588: Current learning rate: 0.00996 +2024-09-04 17:08:28.612441: train_loss -0.2639 +2024-09-04 17:08:28.627337: val_loss -0.3494 +2024-09-04 17:08:28.628248: Pseudo dice [0.8375, 0.8252, 0.8945, 0.8002, 0.5543, 0.7422, 0.9557] +2024-09-04 17:08:28.629013: Epoch time: 249.57 s +2024-09-04 17:08:28.629661: Yayy! New best EMA pseudo Dice: 0.4629 +2024-09-04 17:08:37.269554: +2024-09-04 17:08:37.270661: Epoch 5 +2024-09-04 17:08:37.271667: Current learning rate: 0.00995 +2024-09-04 17:12:48.640265: train_loss -0.3649 +2024-09-04 17:12:48.643614: val_loss -0.3828 +2024-09-04 17:12:48.644808: Pseudo dice [0.8551, 0.8498, 0.8965, 0.8283, 0.6273, 0.7781, 0.9459] +2024-09-04 17:12:48.645692: Epoch time: 251.37 s +2024-09-04 17:12:48.646511: Yayy! New best EMA pseudo Dice: 0.4992 +2024-09-04 17:12:55.624857: +2024-09-04 17:12:55.625726: Epoch 6 +2024-09-04 17:12:55.626498: Current learning rate: 0.00995 +2024-09-04 17:17:07.804024: train_loss -0.387 +2024-09-04 17:17:07.823037: val_loss -0.4663 +2024-09-04 17:17:07.823791: Pseudo dice [0.879, 0.8676, 0.9189, 0.8534, 0.6841, 0.7891, 0.9692] +2024-09-04 17:17:07.824573: Epoch time: 252.18 s +2024-09-04 17:17:07.825217: Yayy! New best EMA pseudo Dice: 0.5345 +2024-09-04 17:17:16.133538: +2024-09-04 17:17:16.135384: Epoch 7 +2024-09-04 17:17:16.137033: Current learning rate: 0.00994 +2024-09-04 17:21:38.763865: train_loss -0.424 +2024-09-04 17:21:38.779323: val_loss -0.4048 +2024-09-04 17:21:38.780288: Pseudo dice [0.8613, 0.8703, 0.901, 0.8493, 0.6871, 0.8112, 0.9641] +2024-09-04 17:21:38.781196: Epoch time: 262.64 s +2024-09-04 17:21:38.782130: Yayy! New best EMA pseudo Dice: 0.5659 +2024-09-04 17:21:46.929882: +2024-09-04 17:21:46.931554: Epoch 8 +2024-09-04 17:21:46.932648: Current learning rate: 0.00993 +2024-09-04 17:26:08.777971: train_loss -0.455 +2024-09-04 17:26:08.791963: val_loss -0.462 +2024-09-04 17:26:08.792798: Pseudo dice [0.8794, 0.8727, 0.9145, 0.858, 0.6664, 0.8372, 0.9578] +2024-09-04 17:26:08.793536: Epoch time: 261.85 s +2024-09-04 17:26:08.794872: Yayy! New best EMA pseudo Dice: 0.5949 +2024-09-04 17:26:16.464414: +2024-09-04 17:26:16.465279: Epoch 9 +2024-09-04 17:26:16.465895: Current learning rate: 0.00992 +2024-09-04 17:30:27.501481: train_loss -0.4978 +2024-09-04 17:30:27.504099: val_loss -0.5295 +2024-09-04 17:30:27.504816: Pseudo dice [0.8946, 0.8987, 0.9272, 0.8686, 0.7083, 0.8831, 0.975] +2024-09-04 17:30:27.505904: Epoch time: 251.04 s +2024-09-04 17:30:27.506672: Yayy! New best EMA pseudo Dice: 0.6233 +2024-09-04 17:30:35.890127: +2024-09-04 17:30:35.891496: Epoch 10 +2024-09-04 17:30:35.892809: Current learning rate: 0.00991 +2024-09-04 17:34:43.641668: train_loss -0.5048 +2024-09-04 17:34:43.643223: val_loss -0.4738 +2024-09-04 17:34:43.644495: Pseudo dice [0.8834, 0.8851, 0.9131, 0.8502, 0.7267, 0.8533, 0.9673] +2024-09-04 17:34:43.646155: Epoch time: 247.76 s +2024-09-04 17:34:43.647396: Yayy! New best EMA pseudo Dice: 0.6478 +2024-09-04 17:34:53.053837: +2024-09-04 17:34:53.054836: Epoch 11 +2024-09-04 17:34:53.055565: Current learning rate: 0.0099 +2024-09-04 17:38:53.170877: train_loss -0.5179 +2024-09-04 17:38:53.174992: val_loss -0.5631 +2024-09-04 17:38:53.176498: Pseudo dice [0.9072, 0.902, 0.9387, 0.8807, 0.7307, 0.8889, 0.9748] +2024-09-04 17:38:53.177814: Epoch time: 240.12 s +2024-09-04 17:38:53.178964: Yayy! New best EMA pseudo Dice: 0.6719 +2024-09-04 17:39:00.840950: +2024-09-04 17:39:00.842179: Epoch 12 +2024-09-04 17:39:00.843600: Current learning rate: 0.00989 +2024-09-04 17:43:15.768178: train_loss -0.5207 +2024-09-04 17:43:15.784092: val_loss -0.5283 +2024-09-04 17:43:15.785001: Pseudo dice [0.9075, 0.9094, 0.9402, 0.8897, 0.7255, 0.8841, 0.9426] +2024-09-04 17:43:15.786054: Epoch time: 254.93 s +2024-09-04 17:43:15.786829: Yayy! New best EMA pseudo Dice: 0.6933 +2024-09-04 17:43:24.372845: +2024-09-04 17:43:24.374087: Epoch 13 +2024-09-04 17:43:24.374833: Current learning rate: 0.00988 +2024-09-04 17:47:40.641185: train_loss -0.5151 +2024-09-04 17:47:40.656319: val_loss -0.5488 +2024-09-04 17:47:40.657529: Pseudo dice [0.9124, 0.91, 0.9445, 0.8814, 0.7379, 0.8781, 0.9745] +2024-09-04 17:47:40.658350: Epoch time: 256.27 s +2024-09-04 17:47:40.659020: Yayy! New best EMA pseudo Dice: 0.7131 +2024-09-04 17:47:48.938149: +2024-09-04 17:47:48.939095: Epoch 14 +2024-09-04 17:47:48.939863: Current learning rate: 0.00987 +2024-09-04 17:52:03.865386: train_loss -0.5524 +2024-09-04 17:52:03.867910: val_loss -0.5943 +2024-09-04 17:52:03.868679: Pseudo dice [0.9128, 0.9075, 0.947, 0.8765, 0.7372, 0.899, 0.9716] +2024-09-04 17:52:03.869416: Epoch time: 254.93 s +2024-09-04 17:52:03.870070: Yayy! New best EMA pseudo Dice: 0.7311 +2024-09-04 17:52:11.898160: +2024-09-04 17:52:11.899292: Epoch 15 +2024-09-04 17:52:11.900279: Current learning rate: 0.00986 +2024-09-04 17:56:13.048997: train_loss -0.5494 +2024-09-04 17:56:13.066928: val_loss -0.5767 +2024-09-04 17:56:13.067949: Pseudo dice [0.9203, 0.9143, 0.9463, 0.8775, 0.7495, 0.9001, 0.9695] +2024-09-04 17:56:13.068696: Epoch time: 241.15 s +2024-09-04 17:56:13.069655: Yayy! New best EMA pseudo Dice: 0.7477 +2024-09-04 17:56:21.884744: +2024-09-04 17:56:21.885789: Epoch 16 +2024-09-04 17:56:21.888192: Current learning rate: 0.00986 +2024-09-04 18:00:43.287884: train_loss -0.5903 +2024-09-04 18:00:43.289466: val_loss -0.612 +2024-09-04 18:00:43.290725: Pseudo dice [0.9248, 0.912, 0.9556, 0.903, 0.7764, 0.9024, 0.9766] +2024-09-04 18:00:43.291772: Epoch time: 261.41 s +2024-09-04 18:00:43.292720: Yayy! New best EMA pseudo Dice: 0.7636 +2024-09-04 18:00:52.063607: +2024-09-04 18:00:52.064583: Epoch 17 +2024-09-04 18:00:52.065426: Current learning rate: 0.00985 +2024-09-04 18:05:02.062862: train_loss -0.5814 +2024-09-04 18:05:02.064780: val_loss -0.6096 +2024-09-04 18:05:02.065883: Pseudo dice [0.9262, 0.9166, 0.9576, 0.9046, 0.7678, 0.9005, 0.9763] +2024-09-04 18:05:02.066792: Epoch time: 250.0 s +2024-09-04 18:05:02.067623: Yayy! New best EMA pseudo Dice: 0.778 +2024-09-04 18:05:16.336205: +2024-09-04 18:05:16.337061: Epoch 18 +2024-09-04 18:05:16.337709: Current learning rate: 0.00984 +2024-09-04 18:09:27.747186: train_loss -0.5923 +2024-09-04 18:09:27.754764: val_loss -0.5887 +2024-09-04 18:09:27.755681: Pseudo dice [0.9249, 0.9157, 0.9558, 0.9107, 0.7609, 0.9035, 0.98] +2024-09-04 18:09:27.756390: Epoch time: 251.41 s +2024-09-04 18:09:27.757388: Yayy! New best EMA pseudo Dice: 0.7909 +2024-09-04 18:09:37.600469: +2024-09-04 18:09:37.601458: Epoch 19 +2024-09-04 18:09:37.602204: Current learning rate: 0.00983 +2024-09-04 18:14:14.771101: train_loss -0.6097 +2024-09-04 18:14:14.789595: val_loss -0.5657 +2024-09-04 18:14:14.790488: Pseudo dice [0.9304, 0.9279, 0.9508, 0.8996, 0.7258, 0.8993, 0.9725] +2024-09-04 18:14:14.791251: Epoch time: 277.18 s +2024-09-04 18:14:14.791991: Yayy! New best EMA pseudo Dice: 0.8019 +2024-09-04 18:14:24.660793: +2024-09-04 18:14:24.663285: Epoch 20 +2024-09-04 18:14:24.666022: Current learning rate: 0.00982 +2024-09-04 18:18:23.079908: train_loss -0.605 +2024-09-04 18:18:23.081358: val_loss -0.6122 +2024-09-04 18:18:23.082165: Pseudo dice [0.929, 0.9171, 0.9574, 0.8867, 0.7678, 0.913, 0.969] +2024-09-04 18:18:23.083967: Epoch time: 238.42 s +2024-09-04 18:18:23.084647: Yayy! New best EMA pseudo Dice: 0.8123 +2024-09-04 18:18:33.888383: +2024-09-04 18:18:33.900058: Epoch 21 +2024-09-04 18:18:34.041418: Current learning rate: 0.00981 +2024-09-04 18:22:44.389045: train_loss -0.6224 +2024-09-04 18:22:44.392040: val_loss -0.6319 +2024-09-04 18:22:44.393360: Pseudo dice [0.9329, 0.9187, 0.9606, 0.9174, 0.7406, 0.9217, 0.9735] +2024-09-04 18:22:44.394754: Epoch time: 250.5 s +2024-09-04 18:22:44.396650: Yayy! New best EMA pseudo Dice: 0.822 +2024-09-04 18:22:53.993726: +2024-09-04 18:22:53.994720: Epoch 22 +2024-09-04 18:22:53.995767: Current learning rate: 0.0098 +2024-09-04 18:27:07.357257: train_loss -0.6214 +2024-09-04 18:27:07.360274: val_loss -0.6526 +2024-09-04 18:27:07.361139: Pseudo dice [0.9362, 0.9251, 0.9619, 0.9074, 0.7703, 0.9198, 0.9832] +2024-09-04 18:27:07.361850: Epoch time: 253.37 s +2024-09-04 18:27:07.362568: Yayy! New best EMA pseudo Dice: 0.8313 +2024-09-04 18:27:16.544787: +2024-09-04 18:27:16.590405: Epoch 23 +2024-09-04 18:27:16.601921: Current learning rate: 0.00979 +2024-09-04 18:31:37.326186: train_loss -0.6277 +2024-09-04 18:31:37.328880: val_loss -0.6639 +2024-09-04 18:31:37.329648: Pseudo dice [0.9402, 0.9297, 0.9625, 0.9126, 0.7726, 0.9335, 0.9769] +2024-09-04 18:31:37.330342: Epoch time: 260.79 s +2024-09-04 18:31:37.331239: Yayy! New best EMA pseudo Dice: 0.84 +2024-09-04 18:31:47.273249: +2024-09-04 18:31:47.275213: Epoch 24 +2024-09-04 18:31:47.278204: Current learning rate: 0.00978 +2024-09-04 18:35:55.462771: train_loss -0.6357 +2024-09-04 18:35:55.465489: val_loss -0.6442 +2024-09-04 18:35:55.466382: Pseudo dice [0.9351, 0.9286, 0.9604, 0.9066, 0.7714, 0.928, 0.9809] +2024-09-04 18:35:55.467285: Epoch time: 248.2 s +2024-09-04 18:35:55.468005: Yayy! New best EMA pseudo Dice: 0.8476 +2024-09-04 18:36:04.541625: +2024-09-04 18:36:04.542670: Epoch 25 +2024-09-04 18:36:04.543471: Current learning rate: 0.00977 +2024-09-04 18:39:59.501812: train_loss -0.6243 +2024-09-04 18:39:59.504179: val_loss -0.6813 +2024-09-04 18:39:59.504903: Pseudo dice [0.9375, 0.9254, 0.9611, 0.916, 0.757, 0.9219, 0.9827] +2024-09-04 18:39:59.506082: Epoch time: 234.96 s +2024-09-04 18:39:59.506964: Yayy! New best EMA pseudo Dice: 0.8543 +2024-09-04 18:40:07.676234: +2024-09-04 18:40:07.677971: Epoch 26 +2024-09-04 18:40:07.679004: Current learning rate: 0.00977 +2024-09-04 18:44:04.448398: train_loss -0.6292 +2024-09-04 18:44:04.451147: val_loss -0.6039 +2024-09-04 18:44:04.452828: Pseudo dice [0.9322, 0.9258, 0.9569, 0.9112, 0.7978, 0.9239, 0.9796] +2024-09-04 18:44:04.454314: Epoch time: 236.78 s +2024-09-04 18:44:04.455589: Yayy! New best EMA pseudo Dice: 0.8607 +2024-09-04 18:44:14.453599: +2024-09-04 18:44:14.455084: Epoch 27 +2024-09-04 18:44:14.455971: Current learning rate: 0.00976 +2024-09-04 18:48:32.422119: train_loss -0.6293 +2024-09-04 18:48:32.424381: val_loss -0.6287 +2024-09-04 18:48:32.426039: Pseudo dice [0.9317, 0.926, 0.9537, 0.9179, 0.8014, 0.9215, 0.9815] +2024-09-04 18:48:32.427134: Epoch time: 257.97 s +2024-09-04 18:48:32.427767: Yayy! New best EMA pseudo Dice: 0.8665 +2024-09-04 18:48:40.754309: +2024-09-04 18:48:40.855555: Epoch 28 +2024-09-04 18:48:40.866445: Current learning rate: 0.00975 +2024-09-04 18:52:53.476957: train_loss -0.6485 +2024-09-04 18:52:53.551562: val_loss -0.6657 +2024-09-04 18:52:53.558395: Pseudo dice [0.9419, 0.9349, 0.9623, 0.92, 0.8086, 0.9374, 0.9808] +2024-09-04 18:52:53.606143: Epoch time: 252.73 s +2024-09-04 18:52:53.618906: Yayy! New best EMA pseudo Dice: 0.8725 +2024-09-04 18:53:02.249013: +2024-09-04 18:53:02.250028: Epoch 29 +2024-09-04 18:53:02.250717: Current learning rate: 0.00974 +2024-09-04 18:57:03.227215: train_loss -0.6171 +2024-09-04 18:57:03.230040: val_loss -0.623 +2024-09-04 18:57:03.231046: Pseudo dice [0.9373, 0.932, 0.9599, 0.9136, 0.762, 0.9042, 0.9749] +2024-09-04 18:57:03.232068: Epoch time: 240.98 s +2024-09-04 18:57:03.232776: Yayy! New best EMA pseudo Dice: 0.8765 +2024-09-04 18:57:12.956476: +2024-09-04 18:57:12.958612: Epoch 30 +2024-09-04 18:57:12.961517: Current learning rate: 0.00973 +2024-09-04 19:01:37.464990: train_loss -0.6181 +2024-09-04 19:01:37.486073: val_loss -0.6492 +2024-09-04 19:01:37.487121: Pseudo dice [0.9405, 0.9321, 0.9599, 0.9168, 0.7682, 0.9328, 0.9836] +2024-09-04 19:01:37.487888: Epoch time: 264.51 s +2024-09-04 19:01:37.488527: Yayy! New best EMA pseudo Dice: 0.8807 +2024-09-04 19:01:46.793260: +2024-09-04 19:01:46.794797: Epoch 31 +2024-09-04 19:01:46.795594: Current learning rate: 0.00972 +2024-09-04 19:06:09.794203: train_loss -0.6302 +2024-09-04 19:06:09.805069: val_loss -0.6893 +2024-09-04 19:06:09.806204: Pseudo dice [0.9385, 0.9326, 0.9588, 0.92, 0.799, 0.9344, 0.9806] +2024-09-04 19:06:09.807181: Epoch time: 263.01 s +2024-09-04 19:06:09.808114: Yayy! New best EMA pseudo Dice: 0.885 +2024-09-04 19:06:18.786137: +2024-09-04 19:06:18.787087: Epoch 32 +2024-09-04 19:06:18.787892: Current learning rate: 0.00971 +2024-09-04 19:10:41.697400: train_loss -0.6342 +2024-09-04 19:10:41.710980: val_loss -0.6297 +2024-09-04 19:10:41.711752: Pseudo dice [0.9336, 0.933, 0.961, 0.9163, 0.7794, 0.9142, 0.98] +2024-09-04 19:10:41.712793: Epoch time: 262.91 s +2024-09-04 19:10:41.713409: Yayy! New best EMA pseudo Dice: 0.8882 +2024-09-04 19:10:51.096732: +2024-09-04 19:10:51.097894: Epoch 33 +2024-09-04 19:10:51.098690: Current learning rate: 0.0097 +2024-09-04 19:15:06.753737: train_loss -0.6423 +2024-09-04 19:15:06.756399: val_loss -0.6683 +2024-09-04 19:15:06.758917: Pseudo dice [0.9412, 0.9318, 0.9633, 0.9211, 0.7961, 0.9236, 0.9848] +2024-09-04 19:15:06.760385: Epoch time: 255.66 s +2024-09-04 19:15:06.761760: Yayy! New best EMA pseudo Dice: 0.8917 +2024-09-04 19:15:16.480782: +2024-09-04 19:15:16.481841: Epoch 34 +2024-09-04 19:15:16.482950: Current learning rate: 0.00969 +2024-09-04 19:19:37.819028: train_loss -0.656 +2024-09-04 19:19:37.821667: val_loss -0.6215 +2024-09-04 19:19:37.822444: Pseudo dice [0.9442, 0.9352, 0.9671, 0.9173, 0.7796, 0.9114, 0.9748] +2024-09-04 19:19:37.823218: Epoch time: 261.34 s +2024-09-04 19:19:37.823923: Yayy! New best EMA pseudo Dice: 0.8943 +2024-09-04 19:19:46.774284: +2024-09-04 19:19:46.775656: Epoch 35 +2024-09-04 19:19:46.777948: Current learning rate: 0.00968 +2024-09-04 19:24:07.790254: train_loss -0.6385 +2024-09-04 19:24:07.792915: val_loss -0.6503 +2024-09-04 19:24:07.794134: Pseudo dice [0.9387, 0.9311, 0.9616, 0.911, 0.792, 0.9185, 0.9816] +2024-09-04 19:24:07.795018: Epoch time: 261.02 s +2024-09-04 19:24:07.795667: Yayy! New best EMA pseudo Dice: 0.8968 +2024-09-04 19:24:16.398162: +2024-09-04 19:24:16.399016: Epoch 36 +2024-09-04 19:24:16.399854: Current learning rate: 0.00968 +2024-09-04 19:28:34.515062: train_loss -0.6501 +2024-09-04 19:28:34.518035: val_loss -0.6802 +2024-09-04 19:28:34.518802: Pseudo dice [0.9435, 0.937, 0.9652, 0.9199, 0.8066, 0.9414, 0.9824] +2024-09-04 19:28:34.519521: Epoch time: 258.12 s +2024-09-04 19:28:34.520204: Yayy! New best EMA pseudo Dice: 0.9 +2024-09-04 19:28:43.718831: +2024-09-04 19:28:43.719978: Epoch 37 +2024-09-04 19:28:43.720772: Current learning rate: 0.00967 +2024-09-04 19:33:01.618123: train_loss -0.6512 +2024-09-04 19:33:01.658939: val_loss -0.6479 +2024-09-04 19:33:01.659880: Pseudo dice [0.9451, 0.9364, 0.967, 0.9219, 0.7934, 0.939, 0.9797] +2024-09-04 19:33:01.660649: Epoch time: 257.9 s +2024-09-04 19:33:01.661304: Yayy! New best EMA pseudo Dice: 0.9026 +2024-09-04 19:33:10.507086: +2024-09-04 19:33:10.507887: Epoch 38 +2024-09-04 19:33:10.508556: Current learning rate: 0.00966 +2024-09-04 19:37:26.946207: train_loss -0.6458 +2024-09-04 19:37:26.947889: val_loss -0.6635 +2024-09-04 19:37:26.948846: Pseudo dice [0.9427, 0.9369, 0.961, 0.9199, 0.8102, 0.944, 0.9864] +2024-09-04 19:37:26.949585: Epoch time: 256.44 s +2024-09-04 19:37:26.950158: Yayy! New best EMA pseudo Dice: 0.9052 +2024-09-04 19:37:35.682974: +2024-09-04 19:37:35.683971: Epoch 39 +2024-09-04 19:37:35.684794: Current learning rate: 0.00965 +2024-09-04 19:41:43.338387: train_loss -0.6577 +2024-09-04 19:41:43.341192: val_loss -0.6666 +2024-09-04 19:41:43.342746: Pseudo dice [0.9439, 0.94, 0.9649, 0.9121, 0.7771, 0.9379, 0.9841] +2024-09-04 19:41:43.344664: Epoch time: 247.66 s +2024-09-04 19:41:43.345946: Yayy! New best EMA pseudo Dice: 0.9069 +2024-09-04 19:41:55.238460: +2024-09-04 19:41:55.239320: Epoch 40 +2024-09-04 19:41:55.239933: Current learning rate: 0.00964 +2024-09-04 19:46:06.800844: train_loss -0.6865 +2024-09-04 19:46:06.818527: val_loss -0.7004 +2024-09-04 19:46:06.819841: Pseudo dice [0.9531, 0.9419, 0.9752, 0.9327, 0.8144, 0.9323, 0.9839] +2024-09-04 19:46:06.821660: Epoch time: 251.57 s +2024-09-04 19:46:06.822718: Yayy! New best EMA pseudo Dice: 0.9096 +2024-09-04 19:46:16.044251: +2024-09-04 19:46:16.045279: Epoch 41 +2024-09-04 19:46:16.047212: Current learning rate: 0.00963 +2024-09-04 19:50:21.706962: train_loss -0.6767 +2024-09-04 19:50:21.710218: val_loss -0.668 +2024-09-04 19:50:21.711163: Pseudo dice [0.95, 0.9408, 0.9671, 0.9244, 0.7771, 0.934, 0.9842] +2024-09-04 19:50:21.712412: Epoch time: 245.67 s +2024-09-04 19:50:21.713526: Yayy! New best EMA pseudo Dice: 0.9112 +2024-09-04 19:50:31.172663: +2024-09-04 19:50:31.174095: Epoch 42 +2024-09-04 19:50:31.175037: Current learning rate: 0.00962 +2024-09-04 19:54:46.644233: train_loss -0.6689 +2024-09-04 19:54:46.660393: val_loss -0.6752 +2024-09-04 19:54:46.661417: Pseudo dice [0.9433, 0.9385, 0.9564, 0.919, 0.7994, 0.9451, 0.9845] +2024-09-04 19:54:46.662567: Epoch time: 255.48 s +2024-09-04 19:54:46.663424: Yayy! New best EMA pseudo Dice: 0.9127 +2024-09-04 19:54:54.718816: +2024-09-04 19:54:54.719855: Epoch 43 +2024-09-04 19:54:54.720541: Current learning rate: 0.00961 +2024-09-04 19:58:57.522810: train_loss -0.6472 +2024-09-04 19:58:57.524452: val_loss -0.6965 +2024-09-04 19:58:57.525276: Pseudo dice [0.9457, 0.9381, 0.9648, 0.9187, 0.8189, 0.9458, 0.9853] +2024-09-04 19:58:57.526165: Epoch time: 242.81 s +2024-09-04 19:58:57.526880: Yayy! New best EMA pseudo Dice: 0.9145 +2024-09-04 19:59:07.725524: +2024-09-04 19:59:07.726561: Epoch 44 +2024-09-04 19:59:07.727263: Current learning rate: 0.0096 +2024-09-04 20:03:25.229734: train_loss -0.6725 +2024-09-04 20:03:25.239110: val_loss -0.677 +2024-09-04 20:03:25.241648: Pseudo dice [0.942, 0.9351, 0.9655, 0.9248, 0.8198, 0.9365, 0.9877] +2024-09-04 20:03:25.244228: Epoch time: 257.51 s +2024-09-04 20:03:25.246914: Yayy! New best EMA pseudo Dice: 0.9161 +2024-09-04 20:03:37.311316: +2024-09-04 20:03:37.466535: Epoch 45 +2024-09-04 20:03:37.574805: Current learning rate: 0.00959 +2024-09-04 20:07:53.688877: train_loss -0.6473 +2024-09-04 20:07:53.691976: val_loss -0.6972 +2024-09-04 20:07:53.692842: Pseudo dice [0.944, 0.936, 0.9656, 0.9253, 0.7937, 0.941, 0.9836] +2024-09-04 20:07:53.693656: Epoch time: 256.38 s +2024-09-04 20:07:53.694282: Yayy! New best EMA pseudo Dice: 0.9172 +2024-09-04 20:08:02.211903: +2024-09-04 20:08:02.213436: Epoch 46 +2024-09-04 20:08:02.214867: Current learning rate: 0.00959 +2024-09-04 20:12:33.740609: train_loss -0.6586 +2024-09-04 20:12:33.742086: val_loss -0.6832 +2024-09-04 20:12:33.742806: Pseudo dice [0.9449, 0.936, 0.9675, 0.9312, 0.8349, 0.9448, 0.9879] +2024-09-04 20:12:33.743528: Epoch time: 271.53 s +2024-09-04 20:12:33.744155: Yayy! New best EMA pseudo Dice: 0.919 +2024-09-04 20:12:42.120206: +2024-09-04 20:12:42.121243: Epoch 47 +2024-09-04 20:12:42.121995: Current learning rate: 0.00958 +2024-09-04 20:16:52.592433: train_loss -0.6655 +2024-09-04 20:16:52.594077: val_loss -0.6736 +2024-09-04 20:16:52.595061: Pseudo dice [0.9461, 0.9421, 0.9635, 0.9269, 0.8014, 0.9413, 0.9856] +2024-09-04 20:16:52.595945: Epoch time: 250.48 s +2024-09-04 20:16:52.596655: Yayy! New best EMA pseudo Dice: 0.9201 +2024-09-04 20:17:01.632829: +2024-09-04 20:17:01.633547: Epoch 48 +2024-09-04 20:17:01.634141: Current learning rate: 0.00957 +2024-09-04 20:21:32.822239: train_loss -0.6675 +2024-09-04 20:21:32.839175: val_loss -0.6917 +2024-09-04 20:21:32.839936: Pseudo dice [0.9503, 0.9397, 0.9715, 0.9283, 0.8012, 0.9275, 0.9839] +2024-09-04 20:21:32.840659: Epoch time: 271.19 s +2024-09-04 20:21:32.841664: Yayy! New best EMA pseudo Dice: 0.9209 +2024-09-04 20:21:42.505443: +2024-09-04 20:21:42.507249: Epoch 49 +2024-09-04 20:21:42.508735: Current learning rate: 0.00956 +2024-09-04 20:25:58.593129: train_loss -0.6793 +2024-09-04 20:25:58.612925: val_loss -0.6955 +2024-09-04 20:25:58.614158: Pseudo dice [0.9489, 0.9418, 0.9675, 0.927, 0.8294, 0.9401, 0.9831] +2024-09-04 20:25:58.615065: Epoch time: 256.09 s +2024-09-04 20:26:06.949714: Yayy! New best EMA pseudo Dice: 0.9222 +2024-09-04 20:26:17.964293: +2024-09-04 20:26:17.965151: Epoch 50 +2024-09-04 20:26:17.965706: Current learning rate: 0.00955 +2024-09-04 20:30:34.236341: train_loss -0.6794 +2024-09-04 20:30:34.251460: val_loss -0.6901 +2024-09-04 20:30:34.252841: Pseudo dice [0.9526, 0.942, 0.9705, 0.9304, 0.8188, 0.9513, 0.9894] +2024-09-04 20:30:34.254277: Epoch time: 256.28 s +2024-09-04 20:30:34.255047: Yayy! New best EMA pseudo Dice: 0.9237 +2024-09-04 20:30:42.215657: +2024-09-04 20:30:42.216680: Epoch 51 +2024-09-04 20:30:42.217365: Current learning rate: 0.00954 +2024-09-04 20:35:19.022812: train_loss -0.669 +2024-09-04 20:35:19.024242: val_loss -0.6698 +2024-09-04 20:35:19.024906: Pseudo dice [0.9471, 0.9418, 0.9672, 0.9283, 0.7997, 0.9412, 0.9866] +2024-09-04 20:35:19.025945: Epoch time: 276.81 s +2024-09-04 20:35:19.041352: Yayy! New best EMA pseudo Dice: 0.9243 +2024-09-04 20:35:26.897513: +2024-09-04 20:35:26.898768: Epoch 52 +2024-09-04 20:35:26.899576: Current learning rate: 0.00953 +2024-09-04 20:39:27.631464: train_loss -0.6907 +2024-09-04 20:39:27.733220: val_loss -0.6662 +2024-09-04 20:39:27.741695: Pseudo dice [0.9473, 0.9427, 0.9648, 0.9325, 0.8137, 0.9333, 0.9823] +2024-09-04 20:39:27.749815: Epoch time: 240.74 s +2024-09-04 20:39:27.810889: Yayy! New best EMA pseudo Dice: 0.925 +2024-09-04 20:39:37.984676: +2024-09-04 20:39:37.985948: Epoch 53 +2024-09-04 20:39:37.986760: Current learning rate: 0.00952 +2024-09-04 20:43:32.459882: train_loss -0.6777 +2024-09-04 20:43:32.471121: val_loss -0.7016 +2024-09-04 20:43:32.471938: Pseudo dice [0.9508, 0.9443, 0.9691, 0.9369, 0.8222, 0.9503, 0.9874] +2024-09-04 20:43:32.472722: Epoch time: 234.48 s +2024-09-04 20:43:32.473357: Yayy! New best EMA pseudo Dice: 0.9262 +2024-09-04 20:43:42.440517: +2024-09-04 20:43:42.441837: Epoch 54 +2024-09-04 20:43:42.442633: Current learning rate: 0.00951 +2024-09-04 20:48:11.342720: train_loss -0.6703 +2024-09-04 20:48:11.344958: val_loss -0.6657 +2024-09-04 20:48:11.345891: Pseudo dice [0.9458, 0.9454, 0.9642, 0.9217, 0.819, 0.9518, 0.9886] +2024-09-04 20:48:11.346607: Epoch time: 268.91 s +2024-09-04 20:48:11.347269: Yayy! New best EMA pseudo Dice: 0.927 +2024-09-04 20:48:19.140746: +2024-09-04 20:48:19.141625: Epoch 55 +2024-09-04 20:48:19.142266: Current learning rate: 0.0095 +2024-09-04 20:52:46.077113: train_loss -0.7044 +2024-09-04 20:52:46.091200: val_loss -0.7088 +2024-09-04 20:52:46.092031: Pseudo dice [0.9485, 0.9402, 0.966, 0.9203, 0.82, 0.9513, 0.9895] +2024-09-04 20:52:46.092742: Epoch time: 266.94 s +2024-09-04 20:52:46.093393: Yayy! New best EMA pseudo Dice: 0.9276 +2024-09-04 20:52:55.901249: +2024-09-04 20:52:55.902239: Epoch 56 +2024-09-04 20:52:55.902954: Current learning rate: 0.00949 +2024-09-04 20:57:12.831497: train_loss -0.704 +2024-09-04 20:57:12.833002: val_loss -0.689 +2024-09-04 20:57:12.833839: Pseudo dice [0.9492, 0.9466, 0.9699, 0.9324, 0.8027, 0.9509, 0.9843] +2024-09-04 20:57:12.834584: Epoch time: 256.93 s +2024-09-04 20:57:12.835215: Yayy! New best EMA pseudo Dice: 0.9283 +2024-09-04 20:57:26.388783: +2024-09-04 20:57:26.389708: Epoch 57 +2024-09-04 20:57:26.390344: Current learning rate: 0.00949 +2024-09-04 21:01:41.190010: train_loss -0.6955 +2024-09-04 21:01:41.191450: val_loss -0.6531 +2024-09-04 21:01:41.192397: Pseudo dice [0.9484, 0.9422, 0.9686, 0.9217, 0.7912, 0.9088, 0.9786] +2024-09-04 21:01:41.193324: Epoch time: 254.8 s +2024-09-04 21:01:43.339845: +2024-09-04 21:01:43.340929: Epoch 58 +2024-09-04 21:01:43.341763: Current learning rate: 0.00948 +2024-09-04 21:06:08.824463: train_loss -0.675 +2024-09-04 21:06:08.836200: val_loss -0.7156 +2024-09-04 21:06:08.837057: Pseudo dice [0.948, 0.936, 0.9727, 0.9277, 0.8174, 0.9298, 0.9853] +2024-09-04 21:06:08.837882: Epoch time: 265.49 s +2024-09-04 21:06:11.035717: +2024-09-04 21:06:11.036651: Epoch 59 +2024-09-04 21:06:11.037422: Current learning rate: 0.00947 +2024-09-04 21:10:37.520109: train_loss -0.688 +2024-09-04 21:10:37.521582: val_loss -0.6907 +2024-09-04 21:10:37.522302: Pseudo dice [0.9481, 0.9432, 0.9652, 0.928, 0.8228, 0.9486, 0.9884] +2024-09-04 21:10:37.523071: Epoch time: 266.49 s +2024-09-04 21:10:37.523660: Yayy! New best EMA pseudo Dice: 0.9287 +2024-09-04 21:10:47.010734: +2024-09-04 21:10:47.012194: Epoch 60 +2024-09-04 21:10:47.014413: Current learning rate: 0.00946 +2024-09-04 21:14:53.736247: train_loss -0.6864 +2024-09-04 21:14:53.743622: val_loss -0.6458 +2024-09-04 21:14:53.744584: Pseudo dice [0.954, 0.9478, 0.9734, 0.9284, 0.7667, 0.9321, 0.9809] +2024-09-04 21:14:53.745387: Epoch time: 246.73 s +2024-09-04 21:14:56.609351: +2024-09-04 21:14:56.610416: Epoch 61 +2024-09-04 21:14:56.611130: Current learning rate: 0.00945 +2024-09-04 21:19:02.646597: train_loss -0.6722 +2024-09-04 21:19:02.658417: val_loss -0.7036 +2024-09-04 21:19:02.659390: Pseudo dice [0.9543, 0.9474, 0.9716, 0.9339, 0.8396, 0.9494, 0.991] +2024-09-04 21:19:02.660182: Epoch time: 246.04 s +2024-09-04 21:19:02.660850: Yayy! New best EMA pseudo Dice: 0.9297 +2024-09-04 21:19:13.775784: +2024-09-04 21:19:13.776693: Epoch 62 +2024-09-04 21:19:13.777271: Current learning rate: 0.00944 +2024-09-04 21:23:28.925298: train_loss -0.6911 +2024-09-04 21:23:28.932149: val_loss -0.7186 +2024-09-04 21:23:28.932989: Pseudo dice [0.9544, 0.9471, 0.9718, 0.9289, 0.8372, 0.9507, 0.99] +2024-09-04 21:23:28.934500: Epoch time: 255.15 s +2024-09-04 21:23:28.935168: Yayy! New best EMA pseudo Dice: 0.9308 +2024-09-04 21:23:37.673012: +2024-09-04 21:23:37.674298: Epoch 63 +2024-09-04 21:23:37.675288: Current learning rate: 0.00943 +2024-09-04 21:27:46.258311: train_loss -0.6864 +2024-09-04 21:27:46.259780: val_loss -0.7165 +2024-09-04 21:27:46.260565: Pseudo dice [0.9471, 0.9414, 0.9678, 0.9294, 0.8148, 0.9524, 0.9862] +2024-09-04 21:27:46.261404: Epoch time: 248.59 s +2024-09-04 21:27:46.262372: Yayy! New best EMA pseudo Dice: 0.9311 +2024-09-04 21:27:53.473321: +2024-09-04 21:27:53.474534: Epoch 64 +2024-09-04 21:27:53.475393: Current learning rate: 0.00942 +2024-09-04 21:32:12.151477: train_loss -0.706 +2024-09-04 21:32:12.152819: val_loss -0.6865 +2024-09-04 21:32:12.153624: Pseudo dice [0.9501, 0.9412, 0.9665, 0.9367, 0.826, 0.9389, 0.9829] +2024-09-04 21:32:12.154407: Epoch time: 258.68 s +2024-09-04 21:32:12.155207: Yayy! New best EMA pseudo Dice: 0.9314 +2024-09-04 21:32:21.244930: +2024-09-04 21:32:21.246037: Epoch 65 +2024-09-04 21:32:21.246931: Current learning rate: 0.00941 +2024-09-04 21:36:36.963862: train_loss -0.6838 +2024-09-04 21:36:36.965444: val_loss -0.6795 +2024-09-04 21:36:36.966430: Pseudo dice [0.9538, 0.9455, 0.9704, 0.9322, 0.8117, 0.9284, 0.9803] +2024-09-04 21:36:36.967271: Epoch time: 255.72 s +2024-09-04 21:36:36.967930: Yayy! New best EMA pseudo Dice: 0.9315 +2024-09-04 21:36:50.383324: +2024-09-04 21:36:50.384266: Epoch 66 +2024-09-04 21:36:50.385087: Current learning rate: 0.0094 +2024-09-04 21:40:59.889405: train_loss -0.6959 +2024-09-04 21:40:59.902677: val_loss -0.6915 +2024-09-04 21:40:59.903573: Pseudo dice [0.9548, 0.9443, 0.9726, 0.9332, 0.829, 0.9484, 0.9873] +2024-09-04 21:40:59.904420: Epoch time: 249.51 s +2024-09-04 21:40:59.905333: Yayy! New best EMA pseudo Dice: 0.9322 +2024-09-04 21:41:09.037478: +2024-09-04 21:41:09.038809: Epoch 67 +2024-09-04 21:41:09.040017: Current learning rate: 0.00939 +2024-09-04 21:45:23.267352: train_loss -0.701 +2024-09-04 21:45:23.268747: val_loss -0.7187 +2024-09-04 21:45:23.269557: Pseudo dice [0.953, 0.947, 0.9703, 0.9323, 0.8322, 0.9552, 0.9861] +2024-09-04 21:45:23.270270: Epoch time: 254.23 s +2024-09-04 21:45:23.270936: Yayy! New best EMA pseudo Dice: 0.9329 +2024-09-04 21:45:33.000012: +2024-09-04 21:45:33.001405: Epoch 68 +2024-09-04 21:45:33.002290: Current learning rate: 0.00939 +2024-09-04 21:50:11.729523: train_loss -0.6774 +2024-09-04 21:50:11.739674: val_loss -0.6971 +2024-09-04 21:50:11.740541: Pseudo dice [0.9492, 0.9414, 0.9656, 0.9325, 0.8177, 0.9511, 0.9845] +2024-09-04 21:50:11.741287: Epoch time: 278.73 s +2024-09-04 21:50:11.741939: Yayy! New best EMA pseudo Dice: 0.9331 +2024-09-04 21:50:21.674184: +2024-09-04 21:50:21.675714: Epoch 69 +2024-09-04 21:50:21.676581: Current learning rate: 0.00938 +2024-09-04 21:54:36.164093: train_loss -0.6864 +2024-09-04 21:54:36.166407: val_loss -0.6702 +2024-09-04 21:54:36.167382: Pseudo dice [0.9506, 0.9447, 0.9638, 0.9266, 0.8358, 0.9527, 0.9871] +2024-09-04 21:54:36.168271: Epoch time: 254.49 s +2024-09-04 21:54:36.169044: Yayy! New best EMA pseudo Dice: 0.9335 +2024-09-04 21:54:45.090587: +2024-09-04 21:54:45.094315: Epoch 70 +2024-09-04 21:54:45.095773: Current learning rate: 0.00937 +2024-09-04 21:59:05.596321: train_loss -0.6843 +2024-09-04 21:59:05.599839: val_loss -0.6776 +2024-09-04 21:59:05.601965: Pseudo dice [0.9502, 0.9455, 0.9697, 0.9297, 0.789, 0.9357, 0.9832] +2024-09-04 21:59:05.603722: Epoch time: 260.51 s +2024-09-04 21:59:08.222161: +2024-09-04 21:59:08.223454: Epoch 71 +2024-09-04 21:59:08.225039: Current learning rate: 0.00936 +2024-09-04 22:03:25.444475: train_loss -0.6773 +2024-09-04 22:03:25.461291: val_loss -0.7034 +2024-09-04 22:03:25.463233: Pseudo dice [0.9506, 0.9461, 0.9728, 0.9264, 0.7816, 0.9436, 0.9829] +2024-09-04 22:03:25.464550: Epoch time: 257.23 s +2024-09-04 22:03:28.286146: +2024-09-04 22:03:28.287042: Epoch 72 +2024-09-04 22:03:28.288044: Current learning rate: 0.00935 +2024-09-04 22:07:56.589908: train_loss -0.676 +2024-09-04 22:07:56.592748: val_loss -0.7106 +2024-09-04 22:07:56.594192: Pseudo dice [0.9498, 0.9443, 0.9679, 0.9352, 0.8363, 0.949, 0.9881] +2024-09-04 22:07:56.594989: Epoch time: 268.31 s +2024-09-04 22:07:59.016798: +2024-09-04 22:07:59.018361: Epoch 73 +2024-09-04 22:07:59.019387: Current learning rate: 0.00934 +2024-09-04 22:12:10.962900: train_loss -0.6944 +2024-09-04 22:12:10.988453: val_loss -0.7345 +2024-09-04 22:12:10.989335: Pseudo dice [0.953, 0.9464, 0.9688, 0.9312, 0.8376, 0.9547, 0.9861] +2024-09-04 22:12:10.990126: Epoch time: 251.95 s +2024-09-04 22:12:10.990777: Yayy! New best EMA pseudo Dice: 0.9339 +2024-09-04 22:12:20.340696: +2024-09-04 22:12:20.342320: Epoch 74 +2024-09-04 22:12:20.343804: Current learning rate: 0.00933 +2024-09-04 22:16:37.127758: train_loss -0.7028 +2024-09-04 22:16:37.139318: val_loss -0.6601 +2024-09-04 22:16:37.140315: Pseudo dice [0.9559, 0.9491, 0.972, 0.9351, 0.7929, 0.9582, 0.9858] +2024-09-04 22:16:37.141051: Epoch time: 256.79 s +2024-09-04 22:16:37.141732: Yayy! New best EMA pseudo Dice: 0.9341 +2024-09-04 22:16:45.366501: +2024-09-04 22:16:45.367412: Epoch 75 +2024-09-04 22:16:45.368114: Current learning rate: 0.00932 +2024-09-04 22:20:53.724226: train_loss -0.6911 +2024-09-04 22:20:53.743236: val_loss -0.7022 +2024-09-04 22:20:53.744314: Pseudo dice [0.9521, 0.9447, 0.9685, 0.9276, 0.8, 0.9551, 0.9854] +2024-09-04 22:20:53.745087: Epoch time: 248.36 s +2024-09-04 22:20:56.392700: +2024-09-04 22:20:56.393583: Epoch 76 +2024-09-04 22:20:56.394276: Current learning rate: 0.00931 +2024-09-04 22:25:09.910873: train_loss -0.6568 +2024-09-04 22:25:09.912542: val_loss -0.6821 +2024-09-04 22:25:09.913388: Pseudo dice [0.9506, 0.9425, 0.9661, 0.9287, 0.805, 0.9522, 0.9867] +2024-09-04 22:25:09.914093: Epoch time: 253.52 s +2024-09-04 22:25:14.846961: +2024-09-04 22:25:14.848009: Epoch 77 +2024-09-04 22:25:14.848976: Current learning rate: 0.0093 +2024-09-04 22:29:20.263861: train_loss -0.6912 +2024-09-04 22:29:20.267976: val_loss -0.6904 +2024-09-04 22:29:20.268896: Pseudo dice [0.9474, 0.9447, 0.9629, 0.9321, 0.8318, 0.9363, 0.9861] +2024-09-04 22:29:20.269670: Epoch time: 245.42 s +2024-09-04 22:29:22.740394: +2024-09-04 22:29:22.741494: Epoch 78 +2024-09-04 22:29:22.742417: Current learning rate: 0.0093 +2024-09-04 22:33:35.598497: train_loss -0.6921 +2024-09-04 22:33:35.600885: val_loss -0.69 +2024-09-04 22:33:35.601596: Pseudo dice [0.9543, 0.9479, 0.9727, 0.9365, 0.8347, 0.9516, 0.9782] +2024-09-04 22:33:35.609249: Epoch time: 252.86 s +2024-09-04 22:33:35.609932: Yayy! New best EMA pseudo Dice: 0.9345 +2024-09-04 22:33:45.005116: +2024-09-04 22:33:45.006168: Epoch 79 +2024-09-04 22:33:45.006907: Current learning rate: 0.00929 +2024-09-04 22:38:10.332013: train_loss -0.6818 +2024-09-04 22:38:10.335527: val_loss -0.6838 +2024-09-04 22:38:10.338245: Pseudo dice [0.951, 0.9462, 0.9722, 0.934, 0.7909, 0.9509, 0.9818] +2024-09-04 22:38:10.340152: Epoch time: 265.33 s +2024-09-04 22:38:13.075952: +2024-09-04 22:38:13.076941: Epoch 80 +2024-09-04 22:38:13.077773: Current learning rate: 0.00928 +2024-09-04 22:42:35.059345: train_loss -0.6848 +2024-09-04 22:42:35.076002: val_loss -0.7061 +2024-09-04 22:42:35.076787: Pseudo dice [0.9544, 0.949, 0.9742, 0.9303, 0.8185, 0.9512, 0.9854] +2024-09-04 22:42:35.077506: Epoch time: 261.99 s +2024-09-04 22:42:35.078262: Yayy! New best EMA pseudo Dice: 0.9346 +2024-09-04 22:42:44.420386: +2024-09-04 22:42:44.421973: Epoch 81 +2024-09-04 22:42:44.423437: Current learning rate: 0.00927 +2024-09-04 22:47:01.176721: train_loss -0.7011 +2024-09-04 22:47:01.179547: val_loss -0.6666 +2024-09-04 22:47:01.180347: Pseudo dice [0.9513, 0.9453, 0.9705, 0.9244, 0.8011, 0.9425, 0.9823] +2024-09-04 22:47:01.181156: Epoch time: 256.76 s +2024-09-04 22:47:03.683347: +2024-09-04 22:47:03.684167: Epoch 82 +2024-09-04 22:47:03.684864: Current learning rate: 0.00926 +2024-09-04 22:51:15.681484: train_loss -0.6872 +2024-09-04 22:51:15.703704: val_loss -0.7227 +2024-09-04 22:51:15.704460: Pseudo dice [0.9531, 0.9463, 0.9678, 0.9338, 0.8287, 0.9367, 0.9872] +2024-09-04 22:51:15.705162: Epoch time: 252.0 s +2024-09-04 22:51:18.255170: +2024-09-04 22:51:18.256083: Epoch 83 +2024-09-04 22:51:18.257018: Current learning rate: 0.00925 +2024-09-04 22:55:37.510646: train_loss -0.6902 +2024-09-04 22:55:37.514484: val_loss -0.721 +2024-09-04 22:55:37.515754: Pseudo dice [0.9533, 0.9462, 0.9725, 0.9318, 0.8281, 0.9327, 0.9839] +2024-09-04 22:55:37.517020: Epoch time: 259.26 s +2024-09-04 22:55:39.732846: +2024-09-04 22:55:39.733781: Epoch 84 +2024-09-04 22:55:39.734411: Current learning rate: 0.00924 +2024-09-04 23:00:02.192448: train_loss -0.689 +2024-09-04 23:00:02.194084: val_loss -0.7155 +2024-09-04 23:00:02.194777: Pseudo dice [0.9503, 0.9384, 0.9702, 0.9234, 0.838, 0.9549, 0.9893] +2024-09-04 23:00:02.195663: Epoch time: 262.46 s +2024-09-04 23:00:02.196831: Yayy! New best EMA pseudo Dice: 0.9349 +2024-09-04 23:00:11.753716: +2024-09-04 23:00:11.754668: Epoch 85 +2024-09-04 23:00:11.755314: Current learning rate: 0.00923 +2024-09-04 23:04:33.416870: train_loss -0.7044 +2024-09-04 23:04:33.418496: val_loss -0.6906 +2024-09-04 23:04:33.419470: Pseudo dice [0.9498, 0.9424, 0.9666, 0.9324, 0.8282, 0.9535, 0.9872] +2024-09-04 23:04:33.420400: Epoch time: 261.67 s +2024-09-04 23:04:33.421311: Yayy! New best EMA pseudo Dice: 0.9351 +2024-09-04 23:04:42.750010: +2024-09-04 23:04:42.750917: Epoch 86 +2024-09-04 23:04:42.751633: Current learning rate: 0.00922 +2024-09-04 23:08:49.558491: train_loss -0.696 +2024-09-04 23:08:49.567885: val_loss -0.7148 +2024-09-04 23:08:49.569456: Pseudo dice [0.9527, 0.9464, 0.9709, 0.9341, 0.8336, 0.9546, 0.9887] +2024-09-04 23:08:49.570698: Epoch time: 246.81 s +2024-09-04 23:08:49.571380: Yayy! New best EMA pseudo Dice: 0.9356 +2024-09-04 23:08:58.792642: +2024-09-04 23:08:58.793629: Epoch 87 +2024-09-04 23:08:58.794290: Current learning rate: 0.00921 +2024-09-04 23:13:17.238262: train_loss -0.707 +2024-09-04 23:13:17.241642: val_loss -0.7256 +2024-09-04 23:13:17.242425: Pseudo dice [0.9553, 0.9489, 0.9714, 0.9323, 0.8442, 0.9482, 0.9871] +2024-09-04 23:13:17.243218: Epoch time: 258.45 s +2024-09-04 23:13:17.244066: Yayy! New best EMA pseudo Dice: 0.9362 +2024-09-04 23:13:26.514562: +2024-09-04 23:13:26.515552: Epoch 88 +2024-09-04 23:13:26.516300: Current learning rate: 0.0092 +2024-09-04 23:17:48.322264: train_loss -0.689 +2024-09-04 23:17:48.336895: val_loss -0.6899 +2024-09-04 23:17:48.337962: Pseudo dice [0.9529, 0.9452, 0.9683, 0.9362, 0.8259, 0.9522, 0.986] +2024-09-04 23:17:48.338811: Epoch time: 261.81 s +2024-09-04 23:17:48.339536: Yayy! New best EMA pseudo Dice: 0.9364 +2024-09-04 23:17:57.263329: +2024-09-04 23:17:57.264475: Epoch 89 +2024-09-04 23:17:57.265422: Current learning rate: 0.0092 +2024-09-04 23:22:14.083997: train_loss -0.6797 +2024-09-04 23:22:14.086270: val_loss -0.6987 +2024-09-04 23:22:14.087468: Pseudo dice [0.9543, 0.948, 0.9716, 0.9389, 0.8432, 0.9404, 0.9844] +2024-09-04 23:22:14.088573: Epoch time: 256.83 s +2024-09-04 23:22:14.089653: Yayy! New best EMA pseudo Dice: 0.9367 +2024-09-04 23:22:26.514561: +2024-09-04 23:22:26.515484: Epoch 90 +2024-09-04 23:22:26.516211: Current learning rate: 0.00919 +2024-09-04 23:26:25.550919: train_loss -0.693 +2024-09-04 23:26:25.563319: val_loss -0.7071 +2024-09-04 23:26:25.564263: Pseudo dice [0.9545, 0.9489, 0.9701, 0.9291, 0.7965, 0.957, 0.985] +2024-09-04 23:26:25.565158: Epoch time: 239.04 s +2024-09-04 23:26:27.772421: +2024-09-04 23:26:27.773525: Epoch 91 +2024-09-04 23:26:27.774289: Current learning rate: 0.00918 +2024-09-04 23:30:50.780244: train_loss -0.7042 +2024-09-04 23:30:50.795738: val_loss -0.699 +2024-09-04 23:30:50.796919: Pseudo dice [0.9564, 0.9462, 0.9746, 0.9258, 0.8287, 0.9313, 0.9829] +2024-09-04 23:30:50.798089: Epoch time: 263.01 s +2024-09-04 23:30:53.175341: +2024-09-04 23:30:53.176527: Epoch 92 +2024-09-04 23:30:53.177350: Current learning rate: 0.00917 +2024-09-04 23:35:11.173343: train_loss -0.7094 +2024-09-04 23:35:11.184299: val_loss -0.6859 +2024-09-04 23:35:11.185154: Pseudo dice [0.9554, 0.9475, 0.9701, 0.9249, 0.8158, 0.9495, 0.9847] +2024-09-04 23:35:11.186014: Epoch time: 258.0 s +2024-09-04 23:35:13.362675: +2024-09-04 23:35:13.363671: Epoch 93 +2024-09-04 23:35:13.364440: Current learning rate: 0.00916 +2024-09-04 23:39:31.612030: train_loss -0.7117 +2024-09-04 23:39:31.628175: val_loss -0.6929 +2024-09-04 23:39:31.629158: Pseudo dice [0.9538, 0.948, 0.9732, 0.9325, 0.8355, 0.9559, 0.985] +2024-09-04 23:39:31.630069: Epoch time: 258.25 s +2024-09-04 23:39:34.078006: +2024-09-04 23:39:34.079369: Epoch 94 +2024-09-04 23:39:34.080454: Current learning rate: 0.00915 +2024-09-04 23:44:14.852712: train_loss -0.6931 +2024-09-04 23:44:14.883322: val_loss -0.7102 +2024-09-04 23:44:14.884426: Pseudo dice [0.9539, 0.9478, 0.9674, 0.9337, 0.8489, 0.9553, 0.9878] +2024-09-04 23:44:14.885260: Epoch time: 280.78 s +2024-09-04 23:44:14.886099: Yayy! New best EMA pseudo Dice: 0.9372 +2024-09-04 23:44:24.853061: +2024-09-04 23:44:24.854070: Epoch 95 +2024-09-04 23:44:24.854775: Current learning rate: 0.00914 +2024-09-04 23:48:36.961980: train_loss -0.6912 +2024-09-04 23:48:36.964269: val_loss -0.6552 +2024-09-04 23:48:36.965169: Pseudo dice [0.9518, 0.9427, 0.9692, 0.9308, 0.8219, 0.9331, 0.9852] +2024-09-04 23:48:36.965952: Epoch time: 252.11 s +2024-09-04 23:48:39.103974: +2024-09-04 23:48:39.104932: Epoch 96 +2024-09-04 23:48:39.105648: Current learning rate: 0.00913 +2024-09-04 23:53:00.925603: train_loss -0.7007 +2024-09-04 23:53:00.927022: val_loss -0.7328 +2024-09-04 23:53:00.927879: Pseudo dice [0.9527, 0.9447, 0.9727, 0.9376, 0.8356, 0.9574, 0.9896] +2024-09-04 23:53:00.928501: Epoch time: 261.83 s +2024-09-04 23:53:00.929124: Yayy! New best EMA pseudo Dice: 0.9373 +2024-09-04 23:53:09.734805: +2024-09-04 23:53:09.735590: Epoch 97 +2024-09-04 23:53:09.736139: Current learning rate: 0.00912 +2024-09-04 23:57:16.504366: train_loss -0.6877 +2024-09-04 23:57:16.515740: val_loss -0.7018 +2024-09-04 23:57:16.516662: Pseudo dice [0.9546, 0.9484, 0.9698, 0.9306, 0.8384, 0.9427, 0.9882] +2024-09-04 23:57:16.517486: Epoch time: 246.77 s +2024-09-04 23:57:16.518301: Yayy! New best EMA pseudo Dice: 0.9375 +2024-09-04 23:57:24.624479: +2024-09-04 23:57:24.625303: Epoch 98 +2024-09-04 23:57:24.625942: Current learning rate: 0.00911 +2024-09-05 00:01:28.335772: train_loss -0.6988 +2024-09-05 00:01:28.365686: val_loss -0.7266 +2024-09-05 00:01:28.366526: Pseudo dice [0.9551, 0.947, 0.972, 0.9361, 0.8126, 0.9553, 0.9849] +2024-09-05 00:01:28.367469: Epoch time: 243.71 s +2024-09-05 00:01:28.368316: Yayy! New best EMA pseudo Dice: 0.9375 +2024-09-05 00:01:38.501884: +2024-09-05 00:01:38.550622: Epoch 99 +2024-09-05 00:01:38.613036: Current learning rate: 0.0091 +2024-09-05 00:05:48.422024: train_loss -0.6953 +2024-09-05 00:05:48.423505: val_loss -0.685 +2024-09-05 00:05:48.424504: Pseudo dice [0.9539, 0.9485, 0.9736, 0.9335, 0.8344, 0.9557, 0.9854] +2024-09-05 00:05:48.426402: Epoch time: 249.92 s +2024-09-05 00:05:55.095816: Yayy! New best EMA pseudo Dice: 0.9378 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_00_32_01.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_00_32_01.txt new file mode 100644 index 0000000000000000000000000000000000000000..9fb3df40dd53b05e31fced81606b4759cc98753a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_00_32_01.txt @@ -0,0 +1,751 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 00:32:04.933750: Using torch.compile... +2024-09-05 00:32:14.345654: do_dummy_2d_data_aug: False +2024-09-05 00:32:14.352063: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 00:32:14.354359: The split file contains 5 splits. +2024-09-05 00:32:14.354964: Desired fold for training: 1 +2024-09-05 00:32:14.355574: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 00:32:24.401877: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 00:32:28.210971: +2024-09-05 00:32:28.223851: Epoch 100 +2024-09-05 00:32:28.226165: Current learning rate: 0.0091 +2024-09-05 00:50:39.088884: train_loss -0.6994 +2024-09-05 00:50:39.113271: val_loss -0.6814 +2024-09-05 00:50:39.113806: Pseudo dice [0.9572, 0.9491, 0.9729, 0.9364, 0.7831, 0.9509, 0.9849] +2024-09-05 00:50:39.114477: Epoch time: 1090.88 s +2024-09-05 00:50:41.232970: +2024-09-05 00:50:41.234310: Epoch 101 +2024-09-05 00:50:41.235425: Current learning rate: 0.00909 +2024-09-05 00:54:53.940321: train_loss -0.6976 +2024-09-05 00:54:53.956495: val_loss -0.6821 +2024-09-05 00:54:53.957441: Pseudo dice [0.9523, 0.9485, 0.9679, 0.9337, 0.8274, 0.9419, 0.9863] +2024-09-05 00:54:53.959132: Epoch time: 252.71 s +2024-09-05 00:54:56.859149: +2024-09-05 00:54:56.860163: Epoch 102 +2024-09-05 00:54:56.860947: Current learning rate: 0.00908 +2024-09-05 00:59:02.167294: train_loss -0.7047 +2024-09-05 00:59:02.175528: val_loss -0.7078 +2024-09-05 00:59:02.176631: Pseudo dice [0.9557, 0.9493, 0.9711, 0.9319, 0.8392, 0.9594, 0.9866] +2024-09-05 00:59:02.177516: Epoch time: 245.31 s +2024-09-05 00:59:02.178564: Yayy! New best EMA pseudo Dice: 0.9378 +2024-09-05 00:59:10.470510: +2024-09-05 00:59:10.471866: Epoch 103 +2024-09-05 00:59:10.473238: Current learning rate: 0.00907 +2024-09-05 01:03:09.841889: train_loss -0.7014 +2024-09-05 01:03:09.857424: val_loss -0.7023 +2024-09-05 01:03:09.858269: Pseudo dice [0.9533, 0.9434, 0.97, 0.9287, 0.8375, 0.9504, 0.9871] +2024-09-05 01:03:09.859127: Epoch time: 239.38 s +2024-09-05 01:03:09.859794: Yayy! New best EMA pseudo Dice: 0.9379 +2024-09-05 01:03:19.886041: +2024-09-05 01:03:19.887090: Epoch 104 +2024-09-05 01:03:19.887753: Current learning rate: 0.00906 +2024-09-05 01:07:43.770973: train_loss -0.6861 +2024-09-05 01:07:43.864116: val_loss -0.7114 +2024-09-05 01:07:43.993143: Pseudo dice [0.954, 0.9464, 0.9701, 0.9333, 0.8221, 0.9478, 0.9821] +2024-09-05 01:07:44.049083: Epoch time: 263.89 s +2024-09-05 01:07:46.810663: +2024-09-05 01:07:46.826787: Epoch 105 +2024-09-05 01:07:46.875192: Current learning rate: 0.00905 +2024-09-05 01:12:25.618450: train_loss -0.7111 +2024-09-05 01:12:25.634814: val_loss -0.6876 +2024-09-05 01:12:25.635790: Pseudo dice [0.9528, 0.95, 0.9753, 0.9302, 0.7908, 0.9536, 0.9879] +2024-09-05 01:12:25.636536: Epoch time: 278.81 s +2024-09-05 01:12:28.245269: +2024-09-05 01:12:28.246374: Epoch 106 +2024-09-05 01:12:28.247085: Current learning rate: 0.00904 +2024-09-05 01:16:55.594903: train_loss -0.7133 +2024-09-05 01:16:55.596123: val_loss -0.723 +2024-09-05 01:16:55.596798: Pseudo dice [0.9569, 0.9469, 0.976, 0.9301, 0.8533, 0.9576, 0.9904] +2024-09-05 01:16:55.597370: Epoch time: 267.36 s +2024-09-05 01:16:55.597887: Yayy! New best EMA pseudo Dice: 0.9381 +2024-09-05 01:17:05.232583: +2024-09-05 01:17:05.233516: Epoch 107 +2024-09-05 01:17:05.234137: Current learning rate: 0.00903 +2024-09-05 01:21:24.819471: train_loss -0.7046 +2024-09-05 01:21:24.835108: val_loss -0.6741 +2024-09-05 01:21:24.836040: Pseudo dice [0.9474, 0.9448, 0.9639, 0.9285, 0.8065, 0.9559, 0.9849] +2024-09-05 01:21:24.836756: Epoch time: 259.59 s +2024-09-05 01:21:27.297509: +2024-09-05 01:21:27.298625: Epoch 108 +2024-09-05 01:21:27.299443: Current learning rate: 0.00902 +2024-09-05 01:25:38.995745: train_loss -0.6691 +2024-09-05 01:25:38.998658: val_loss -0.6918 +2024-09-05 01:25:39.000405: Pseudo dice [0.9513, 0.9468, 0.9677, 0.9318, 0.8578, 0.9443, 0.9834] +2024-09-05 01:25:39.003280: Epoch time: 251.7 s +2024-09-05 01:25:41.329351: +2024-09-05 01:25:41.330672: Epoch 109 +2024-09-05 01:25:41.331681: Current learning rate: 0.00901 +2024-09-05 01:30:02.676511: train_loss -0.6952 +2024-09-05 01:30:02.679241: val_loss -0.6843 +2024-09-05 01:30:02.680009: Pseudo dice [0.9457, 0.9454, 0.97, 0.9307, 0.8256, 0.9511, 0.9874] +2024-09-05 01:30:02.680646: Epoch time: 261.35 s +2024-09-05 01:30:04.813842: +2024-09-05 01:30:04.815108: Epoch 110 +2024-09-05 01:30:04.815978: Current learning rate: 0.009 +2024-09-05 01:34:17.722495: train_loss -0.688 +2024-09-05 01:34:17.726942: val_loss -0.7412 +2024-09-05 01:34:17.727903: Pseudo dice [0.954, 0.9461, 0.9704, 0.9367, 0.845, 0.9553, 0.985] +2024-09-05 01:34:17.728837: Epoch time: 252.91 s +2024-09-05 01:34:17.729506: Yayy! New best EMA pseudo Dice: 0.9382 +2024-09-05 01:34:27.096425: +2024-09-05 01:34:27.098069: Epoch 111 +2024-09-05 01:34:27.099911: Current learning rate: 0.009 +2024-09-05 01:38:40.992336: train_loss -0.6849 +2024-09-05 01:38:41.036831: val_loss -0.7131 +2024-09-05 01:38:41.038125: Pseudo dice [0.9513, 0.9456, 0.967, 0.928, 0.8275, 0.9399, 0.982] +2024-09-05 01:38:41.039319: Epoch time: 253.9 s +2024-09-05 01:38:43.559494: +2024-09-05 01:38:43.560437: Epoch 112 +2024-09-05 01:38:43.561154: Current learning rate: 0.00899 +2024-09-05 01:42:59.056451: train_loss -0.6805 +2024-09-05 01:42:59.087975: val_loss -0.7228 +2024-09-05 01:42:59.089586: Pseudo dice [0.9543, 0.9461, 0.9733, 0.9388, 0.8484, 0.9327, 0.9834] +2024-09-05 01:42:59.092656: Epoch time: 255.5 s +2024-09-05 01:43:01.699583: +2024-09-05 01:43:01.700642: Epoch 113 +2024-09-05 01:43:01.701372: Current learning rate: 0.00898 +2024-09-05 01:47:20.057437: train_loss -0.6944 +2024-09-05 01:47:20.074954: val_loss -0.7355 +2024-09-05 01:47:20.075950: Pseudo dice [0.9577, 0.9496, 0.9739, 0.9312, 0.8366, 0.9525, 0.9853] +2024-09-05 01:47:20.076590: Epoch time: 258.36 s +2024-09-05 01:47:20.077032: Yayy! New best EMA pseudo Dice: 0.9383 +2024-09-05 01:47:28.431579: +2024-09-05 01:47:28.432450: Epoch 114 +2024-09-05 01:47:28.433090: Current learning rate: 0.00897 +2024-09-05 01:51:54.387755: train_loss -0.7042 +2024-09-05 01:51:54.389522: val_loss -0.7154 +2024-09-05 01:51:54.390819: Pseudo dice [0.9558, 0.9496, 0.9742, 0.9354, 0.836, 0.9539, 0.9877] +2024-09-05 01:51:54.391881: Epoch time: 265.96 s +2024-09-05 01:51:54.393192: Yayy! New best EMA pseudo Dice: 0.9386 +2024-09-05 01:52:01.975235: +2024-09-05 01:52:01.976077: Epoch 115 +2024-09-05 01:52:01.976865: Current learning rate: 0.00896 +2024-09-05 01:55:59.958044: train_loss -0.6958 +2024-09-05 01:55:59.972429: val_loss -0.7037 +2024-09-05 01:55:59.973250: Pseudo dice [0.9544, 0.9474, 0.9722, 0.9368, 0.8571, 0.9473, 0.9871] +2024-09-05 01:55:59.973851: Epoch time: 237.99 s +2024-09-05 01:55:59.974458: Yayy! New best EMA pseudo Dice: 0.9391 +2024-09-05 01:56:08.007931: +2024-09-05 01:56:08.008895: Epoch 116 +2024-09-05 01:56:08.009548: Current learning rate: 0.00895 +2024-09-05 02:00:27.077677: train_loss -0.7018 +2024-09-05 02:00:27.089087: val_loss -0.6956 +2024-09-05 02:00:27.090033: Pseudo dice [0.9557, 0.9486, 0.9696, 0.938, 0.8541, 0.9346, 0.9843] +2024-09-05 02:00:27.090947: Epoch time: 259.08 s +2024-09-05 02:00:27.091893: Yayy! New best EMA pseudo Dice: 0.9392 +2024-09-05 02:00:35.791486: +2024-09-05 02:00:35.792504: Epoch 117 +2024-09-05 02:00:35.793500: Current learning rate: 0.00894 +2024-09-05 02:04:29.311742: train_loss -0.716 +2024-09-05 02:04:29.313657: val_loss -0.7215 +2024-09-05 02:04:29.314636: Pseudo dice [0.9586, 0.9499, 0.9748, 0.9392, 0.8371, 0.9469, 0.9875] +2024-09-05 02:04:29.315603: Epoch time: 233.52 s +2024-09-05 02:04:29.316379: Yayy! New best EMA pseudo Dice: 0.9395 +2024-09-05 02:04:37.957067: +2024-09-05 02:04:37.958205: Epoch 118 +2024-09-05 02:04:37.959017: Current learning rate: 0.00893 +2024-09-05 02:09:03.036039: train_loss -0.7245 +2024-09-05 02:09:03.108124: val_loss -0.7171 +2024-09-05 02:09:03.226568: Pseudo dice [0.958, 0.9507, 0.9722, 0.9409, 0.8409, 0.9498, 0.9873] +2024-09-05 02:09:03.257064: Epoch time: 265.08 s +2024-09-05 02:09:03.314618: Yayy! New best EMA pseudo Dice: 0.9398 +2024-09-05 02:09:13.049713: +2024-09-05 02:09:13.050801: Epoch 119 +2024-09-05 02:09:13.051647: Current learning rate: 0.00892 +2024-09-05 02:13:30.232911: train_loss -0.6972 +2024-09-05 02:13:30.235705: val_loss -0.7396 +2024-09-05 02:13:30.237212: Pseudo dice [0.9535, 0.9434, 0.9652, 0.9389, 0.8518, 0.9475, 0.9886] +2024-09-05 02:13:30.239057: Epoch time: 257.19 s +2024-09-05 02:13:30.240508: Yayy! New best EMA pseudo Dice: 0.94 +2024-09-05 02:13:39.567597: +2024-09-05 02:13:39.568432: Epoch 120 +2024-09-05 02:13:39.569061: Current learning rate: 0.00891 +2024-09-05 02:18:10.721829: train_loss -0.7041 +2024-09-05 02:18:10.726842: val_loss -0.7264 +2024-09-05 02:18:10.727715: Pseudo dice [0.9553, 0.9479, 0.9732, 0.9346, 0.8444, 0.9603, 0.9902] +2024-09-05 02:18:10.728447: Epoch time: 271.16 s +2024-09-05 02:18:10.729846: Yayy! New best EMA pseudo Dice: 0.9404 +2024-09-05 02:18:20.931734: +2024-09-05 02:18:21.000622: Epoch 121 +2024-09-05 02:18:21.029672: Current learning rate: 0.0089 +2024-09-05 02:22:32.204762: train_loss -0.7168 +2024-09-05 02:22:32.206239: val_loss -0.7351 +2024-09-05 02:22:32.207146: Pseudo dice [0.9601, 0.9514, 0.9766, 0.9442, 0.8333, 0.9484, 0.9863] +2024-09-05 02:22:32.207964: Epoch time: 251.28 s +2024-09-05 02:22:32.208723: Yayy! New best EMA pseudo Dice: 0.9406 +2024-09-05 02:22:41.606737: +2024-09-05 02:22:41.607771: Epoch 122 +2024-09-05 02:22:41.608648: Current learning rate: 0.00889 +2024-09-05 02:27:00.104816: train_loss -0.722 +2024-09-05 02:27:00.107837: val_loss -0.6979 +2024-09-05 02:27:00.108615: Pseudo dice [0.9573, 0.9515, 0.9735, 0.9335, 0.8274, 0.9419, 0.9828] +2024-09-05 02:27:00.109341: Epoch time: 258.5 s +2024-09-05 02:27:02.398405: +2024-09-05 02:27:02.401157: Epoch 123 +2024-09-05 02:27:02.403251: Current learning rate: 0.00889 +2024-09-05 02:31:41.027454: train_loss -0.7247 +2024-09-05 02:31:41.112145: val_loss -0.6807 +2024-09-05 02:31:41.117227: Pseudo dice [0.9583, 0.9469, 0.9738, 0.9306, 0.8367, 0.9545, 0.9858] +2024-09-05 02:31:41.117840: Epoch time: 278.63 s +2024-09-05 02:31:43.199028: +2024-09-05 02:31:43.199929: Epoch 124 +2024-09-05 02:31:43.200824: Current learning rate: 0.00888 +2024-09-05 02:35:55.484602: train_loss -0.7027 +2024-09-05 02:35:55.486887: val_loss -0.7155 +2024-09-05 02:35:55.487596: Pseudo dice [0.959, 0.952, 0.9771, 0.941, 0.8602, 0.9533, 0.9899] +2024-09-05 02:35:55.488257: Epoch time: 252.29 s +2024-09-05 02:35:55.488863: Yayy! New best EMA pseudo Dice: 0.9411 +2024-09-05 02:36:03.899374: +2024-09-05 02:36:03.900406: Epoch 125 +2024-09-05 02:36:03.901078: Current learning rate: 0.00887 +2024-09-05 02:40:15.822854: train_loss -0.6949 +2024-09-05 02:40:15.825258: val_loss -0.6865 +2024-09-05 02:40:15.825916: Pseudo dice [0.9568, 0.9481, 0.9732, 0.9358, 0.8416, 0.9439, 0.9851] +2024-09-05 02:40:15.826617: Epoch time: 251.93 s +2024-09-05 02:40:18.050791: +2024-09-05 02:40:18.051872: Epoch 126 +2024-09-05 02:40:18.052616: Current learning rate: 0.00886 +2024-09-05 02:44:51.136711: train_loss -0.7203 +2024-09-05 02:44:51.139336: val_loss -0.7472 +2024-09-05 02:44:51.140313: Pseudo dice [0.956, 0.9445, 0.9719, 0.9301, 0.8353, 0.9326, 0.988] +2024-09-05 02:44:51.141148: Epoch time: 273.09 s +2024-09-05 02:44:53.649613: +2024-09-05 02:44:53.651678: Epoch 127 +2024-09-05 02:44:53.662577: Current learning rate: 0.00885 +2024-09-05 02:48:58.411976: train_loss -0.7169 +2024-09-05 02:48:58.414576: val_loss -0.6972 +2024-09-05 02:48:58.415481: Pseudo dice [0.9587, 0.9497, 0.9765, 0.9424, 0.8141, 0.9459, 0.9839] +2024-09-05 02:48:58.416419: Epoch time: 244.77 s +2024-09-05 02:49:00.891543: +2024-09-05 02:49:00.892745: Epoch 128 +2024-09-05 02:49:00.893573: Current learning rate: 0.00884 +2024-09-05 02:53:24.714107: train_loss -0.7094 +2024-09-05 02:53:24.715424: val_loss -0.7298 +2024-09-05 02:53:24.716175: Pseudo dice [0.9557, 0.9485, 0.9675, 0.9357, 0.8479, 0.9473, 0.9885] +2024-09-05 02:53:24.716907: Epoch time: 263.83 s +2024-09-05 02:53:26.983090: +2024-09-05 02:53:26.984671: Epoch 129 +2024-09-05 02:53:26.986069: Current learning rate: 0.00883 +2024-09-05 02:57:34.001836: train_loss -0.6911 +2024-09-05 02:57:34.004962: val_loss -0.7401 +2024-09-05 02:57:34.006409: Pseudo dice [0.9582, 0.9494, 0.9746, 0.9356, 0.8514, 0.9556, 0.9882] +2024-09-05 02:57:34.007790: Epoch time: 247.02 s +2024-09-05 02:57:36.561189: +2024-09-05 02:57:36.574717: Epoch 130 +2024-09-05 02:57:36.575514: Current learning rate: 0.00882 +2024-09-05 03:02:00.002078: train_loss -0.6985 +2024-09-05 03:02:00.003559: val_loss -0.7291 +2024-09-05 03:02:00.004360: Pseudo dice [0.955, 0.9502, 0.9688, 0.9395, 0.8508, 0.9589, 0.9879] +2024-09-05 03:02:00.005077: Epoch time: 263.45 s +2024-09-05 03:02:00.005728: Yayy! New best EMA pseudo Dice: 0.9413 +2024-09-05 03:02:08.444097: +2024-09-05 03:02:08.445105: Epoch 131 +2024-09-05 03:02:08.445818: Current learning rate: 0.00881 +2024-09-05 03:06:28.411340: train_loss -0.6953 +2024-09-05 03:06:28.412686: val_loss -0.6922 +2024-09-05 03:06:28.413476: Pseudo dice [0.9556, 0.95, 0.9712, 0.9279, 0.8526, 0.946, 0.9847] +2024-09-05 03:06:28.414314: Epoch time: 259.97 s +2024-09-05 03:06:30.692343: +2024-09-05 03:06:30.693364: Epoch 132 +2024-09-05 03:06:30.694971: Current learning rate: 0.0088 +2024-09-05 03:10:45.009533: train_loss -0.7017 +2024-09-05 03:10:45.015788: val_loss -0.6868 +2024-09-05 03:10:45.017494: Pseudo dice [0.9557, 0.952, 0.9715, 0.9368, 0.8187, 0.948, 0.9853] +2024-09-05 03:10:45.020862: Epoch time: 254.32 s +2024-09-05 03:10:47.387719: +2024-09-05 03:10:47.388550: Epoch 133 +2024-09-05 03:10:47.389199: Current learning rate: 0.00879 +2024-09-05 03:14:55.290151: train_loss -0.7178 +2024-09-05 03:14:55.295564: val_loss -0.7298 +2024-09-05 03:14:55.296592: Pseudo dice [0.9576, 0.9481, 0.974, 0.9368, 0.8359, 0.9596, 0.9844] +2024-09-05 03:14:55.299008: Epoch time: 247.91 s +2024-09-05 03:14:57.451571: +2024-09-05 03:14:57.453404: Epoch 134 +2024-09-05 03:14:57.454780: Current learning rate: 0.00879 +2024-09-05 03:19:14.816658: train_loss -0.706 +2024-09-05 03:19:14.833705: val_loss -0.7174 +2024-09-05 03:19:14.834623: Pseudo dice [0.96, 0.9512, 0.9735, 0.9299, 0.8533, 0.9336, 0.9845] +2024-09-05 03:19:14.835804: Epoch time: 257.37 s +2024-09-05 03:19:18.265651: +2024-09-05 03:19:18.266546: Epoch 135 +2024-09-05 03:19:18.267307: Current learning rate: 0.00878 +2024-09-05 03:23:24.036807: train_loss -0.7158 +2024-09-05 03:23:24.049974: val_loss -0.7058 +2024-09-05 03:23:24.051357: Pseudo dice [0.9582, 0.9509, 0.9745, 0.937, 0.8274, 0.9526, 0.9851] +2024-09-05 03:23:24.052333: Epoch time: 245.78 s +2024-09-05 03:23:26.407350: +2024-09-05 03:23:26.408226: Epoch 136 +2024-09-05 03:23:26.408931: Current learning rate: 0.00877 +2024-09-05 03:27:25.095440: train_loss -0.7021 +2024-09-05 03:27:25.108187: val_loss -0.6913 +2024-09-05 03:27:25.109202: Pseudo dice [0.9543, 0.9503, 0.9698, 0.9369, 0.8428, 0.9574, 0.9901] +2024-09-05 03:27:25.109944: Epoch time: 238.69 s +2024-09-05 03:27:27.760906: +2024-09-05 03:27:27.761806: Epoch 137 +2024-09-05 03:27:27.762467: Current learning rate: 0.00876 +2024-09-05 03:32:10.320718: train_loss -0.7064 +2024-09-05 03:32:10.336483: val_loss -0.7063 +2024-09-05 03:32:10.337295: Pseudo dice [0.96, 0.9543, 0.9757, 0.9394, 0.8387, 0.9589, 0.987] +2024-09-05 03:32:10.338065: Epoch time: 282.56 s +2024-09-05 03:32:10.338785: Yayy! New best EMA pseudo Dice: 0.9417 +2024-09-05 03:32:18.678455: +2024-09-05 03:32:18.679561: Epoch 138 +2024-09-05 03:32:18.680495: Current learning rate: 0.00875 +2024-09-05 03:36:32.611626: train_loss -0.7278 +2024-09-05 03:36:32.627017: val_loss -0.7545 +2024-09-05 03:36:32.627944: Pseudo dice [0.9613, 0.9522, 0.9769, 0.9405, 0.8612, 0.9638, 0.9906] +2024-09-05 03:36:32.628702: Epoch time: 253.94 s +2024-09-05 03:36:32.629360: Yayy! New best EMA pseudo Dice: 0.9424 +2024-09-05 03:36:41.104943: +2024-09-05 03:36:41.106069: Epoch 139 +2024-09-05 03:36:41.106797: Current learning rate: 0.00874 +2024-09-05 03:41:07.151157: train_loss -0.7325 +2024-09-05 03:41:07.155334: val_loss -0.782 +2024-09-05 03:41:07.156312: Pseudo dice [0.9621, 0.9534, 0.9792, 0.9381, 0.8639, 0.9542, 0.9894] +2024-09-05 03:41:07.157152: Epoch time: 266.05 s +2024-09-05 03:41:07.157667: Yayy! New best EMA pseudo Dice: 0.9431 +2024-09-05 03:41:15.896826: +2024-09-05 03:41:15.897581: Epoch 140 +2024-09-05 03:41:15.898150: Current learning rate: 0.00873 +2024-09-05 03:45:27.747180: train_loss -0.7305 +2024-09-05 03:45:27.749628: val_loss -0.7013 +2024-09-05 03:45:27.750582: Pseudo dice [0.9604, 0.9527, 0.9758, 0.9415, 0.8422, 0.9533, 0.9881] +2024-09-05 03:45:27.751281: Epoch time: 251.85 s +2024-09-05 03:45:27.751946: Yayy! New best EMA pseudo Dice: 0.9432 +2024-09-05 03:45:37.103988: +2024-09-05 03:45:37.105145: Epoch 141 +2024-09-05 03:45:37.106460: Current learning rate: 0.00872 +2024-09-05 03:49:39.158767: train_loss -0.725 +2024-09-05 03:49:39.174775: val_loss -0.7194 +2024-09-05 03:49:39.175910: Pseudo dice [0.9608, 0.9502, 0.9772, 0.9404, 0.8572, 0.9485, 0.9877] +2024-09-05 03:49:39.176831: Epoch time: 242.06 s +2024-09-05 03:49:39.177632: Yayy! New best EMA pseudo Dice: 0.9435 +2024-09-05 03:49:47.427225: +2024-09-05 03:49:47.428182: Epoch 142 +2024-09-05 03:49:47.428828: Current learning rate: 0.00871 +2024-09-05 03:54:05.059782: train_loss -0.6999 +2024-09-05 03:54:05.061288: val_loss -0.6901 +2024-09-05 03:54:05.062765: Pseudo dice [0.9579, 0.9468, 0.9711, 0.9374, 0.8121, 0.9602, 0.9864] +2024-09-05 03:54:05.063699: Epoch time: 257.64 s +2024-09-05 03:54:07.432936: +2024-09-05 03:54:07.447243: Epoch 143 +2024-09-05 03:54:07.449144: Current learning rate: 0.0087 +2024-09-05 03:58:02.718345: train_loss -0.7167 +2024-09-05 03:58:02.732136: val_loss -0.7259 +2024-09-05 03:58:02.733265: Pseudo dice [0.9613, 0.9501, 0.9757, 0.9383, 0.8494, 0.9545, 0.9882] +2024-09-05 03:58:02.734348: Epoch time: 235.29 s +2024-09-05 03:58:05.184630: +2024-09-05 03:58:05.185528: Epoch 144 +2024-09-05 03:58:05.186122: Current learning rate: 0.00869 +2024-09-05 04:02:24.104208: train_loss -0.7207 +2024-09-05 04:02:24.106761: val_loss -0.6826 +2024-09-05 04:02:24.107471: Pseudo dice [0.9573, 0.9526, 0.9718, 0.9365, 0.8578, 0.9492, 0.9864] +2024-09-05 04:02:24.108085: Epoch time: 258.92 s +2024-09-05 04:02:26.440330: +2024-09-05 04:02:26.441585: Epoch 145 +2024-09-05 04:02:26.442538: Current learning rate: 0.00868 +2024-09-05 04:06:54.380394: train_loss -0.7281 +2024-09-05 04:06:54.384151: val_loss -0.7149 +2024-09-05 04:06:54.385452: Pseudo dice [0.9574, 0.9525, 0.9724, 0.9359, 0.8248, 0.9598, 0.9857] +2024-09-05 04:06:54.386642: Epoch time: 267.95 s +2024-09-05 04:06:57.227664: +2024-09-05 04:06:57.228609: Epoch 146 +2024-09-05 04:06:57.229265: Current learning rate: 0.00868 +2024-09-05 04:11:14.062163: train_loss -0.7334 +2024-09-05 04:11:14.080070: val_loss -0.7348 +2024-09-05 04:11:14.080923: Pseudo dice [0.9583, 0.9545, 0.9747, 0.9432, 0.8573, 0.9622, 0.9897] +2024-09-05 04:11:14.081839: Epoch time: 256.84 s +2024-09-05 04:11:14.082604: Yayy! New best EMA pseudo Dice: 0.9437 +2024-09-05 04:11:23.240465: +2024-09-05 04:11:23.241542: Epoch 147 +2024-09-05 04:11:23.242357: Current learning rate: 0.00867 +2024-09-05 04:15:41.032583: train_loss -0.7134 +2024-09-05 04:15:41.047054: val_loss -0.7268 +2024-09-05 04:15:41.047879: Pseudo dice [0.9576, 0.9508, 0.9739, 0.9428, 0.8532, 0.9605, 0.9889] +2024-09-05 04:15:41.048610: Epoch time: 257.8 s +2024-09-05 04:15:41.049212: Yayy! New best EMA pseudo Dice: 0.944 +2024-09-05 04:15:50.222366: +2024-09-05 04:15:50.224131: Epoch 148 +2024-09-05 04:15:50.224787: Current learning rate: 0.00866 +2024-09-05 04:20:14.731270: train_loss -0.7278 +2024-09-05 04:20:14.748465: val_loss -0.7199 +2024-09-05 04:20:14.749603: Pseudo dice [0.9587, 0.9534, 0.9767, 0.9427, 0.839, 0.9622, 0.9869] +2024-09-05 04:20:14.750749: Epoch time: 264.51 s +2024-09-05 04:20:14.751683: Yayy! New best EMA pseudo Dice: 0.9442 +2024-09-05 04:20:24.436531: +2024-09-05 04:20:24.437541: Epoch 149 +2024-09-05 04:20:24.438293: Current learning rate: 0.00865 +2024-09-05 04:24:50.775753: train_loss -0.7076 +2024-09-05 04:24:50.799974: val_loss -0.7113 +2024-09-05 04:24:50.800963: Pseudo dice [0.9567, 0.9522, 0.9699, 0.9424, 0.8591, 0.9579, 0.9879] +2024-09-05 04:24:50.802020: Epoch time: 266.34 s +2024-09-05 04:24:57.550898: Yayy! New best EMA pseudo Dice: 0.9444 +2024-09-05 04:25:05.215537: +2024-09-05 04:25:05.216326: Epoch 150 +2024-09-05 04:25:05.216887: Current learning rate: 0.00864 +2024-09-05 04:28:50.565195: train_loss -0.7242 +2024-09-05 04:28:50.578622: val_loss -0.7318 +2024-09-05 04:28:50.580344: Pseudo dice [0.9567, 0.9522, 0.9723, 0.943, 0.8438, 0.9596, 0.9879] +2024-09-05 04:28:50.581493: Epoch time: 225.35 s +2024-09-05 04:28:50.582593: Yayy! New best EMA pseudo Dice: 0.9445 +2024-09-05 04:29:03.088023: +2024-09-05 04:29:03.089172: Epoch 151 +2024-09-05 04:29:03.090060: Current learning rate: 0.00863 +2024-09-05 04:33:18.036907: train_loss -0.7106 +2024-09-05 04:33:18.043236: val_loss -0.7512 +2024-09-05 04:33:18.044117: Pseudo dice [0.9551, 0.9497, 0.9695, 0.9362, 0.8424, 0.9545, 0.989] +2024-09-05 04:33:18.044883: Epoch time: 254.95 s +2024-09-05 04:33:20.322969: +2024-09-05 04:33:20.324186: Epoch 152 +2024-09-05 04:33:20.324903: Current learning rate: 0.00862 +2024-09-05 04:37:46.187275: train_loss -0.7133 +2024-09-05 04:37:46.199042: val_loss -0.7333 +2024-09-05 04:37:46.200229: Pseudo dice [0.9578, 0.9511, 0.9725, 0.9389, 0.8581, 0.9533, 0.9874] +2024-09-05 04:37:46.201067: Epoch time: 265.87 s +2024-09-05 04:37:48.561749: +2024-09-05 04:37:48.563017: Epoch 153 +2024-09-05 04:37:48.564327: Current learning rate: 0.00861 +2024-09-05 04:42:21.507703: train_loss -0.7246 +2024-09-05 04:42:21.520274: val_loss -0.7495 +2024-09-05 04:42:21.522043: Pseudo dice [0.9592, 0.951, 0.9748, 0.9396, 0.8553, 0.955, 0.9883] +2024-09-05 04:42:21.523290: Epoch time: 272.95 s +2024-09-05 04:42:21.524337: Yayy! New best EMA pseudo Dice: 0.9446 +2024-09-05 04:42:30.876923: +2024-09-05 04:42:30.878459: Epoch 154 +2024-09-05 04:42:30.880438: Current learning rate: 0.0086 +2024-09-05 04:46:39.556049: train_loss -0.7117 +2024-09-05 04:46:39.570054: val_loss -0.7161 +2024-09-05 04:46:39.571044: Pseudo dice [0.9543, 0.9491, 0.9703, 0.9363, 0.8253, 0.9573, 0.9858] +2024-09-05 04:46:39.571815: Epoch time: 248.68 s +2024-09-05 04:46:42.042052: +2024-09-05 04:46:42.043226: Epoch 155 +2024-09-05 04:46:42.044189: Current learning rate: 0.00859 +2024-09-05 04:50:56.870792: train_loss -0.71 +2024-09-05 04:50:56.872856: val_loss -0.7211 +2024-09-05 04:50:56.873434: Pseudo dice [0.958, 0.9501, 0.9715, 0.9404, 0.8436, 0.9512, 0.9869] +2024-09-05 04:50:56.874071: Epoch time: 254.83 s +2024-09-05 04:50:59.021909: +2024-09-05 04:50:59.034598: Epoch 156 +2024-09-05 04:50:59.035733: Current learning rate: 0.00858 +2024-09-05 04:55:05.235263: train_loss -0.6928 +2024-09-05 04:55:05.240200: val_loss -0.7559 +2024-09-05 04:55:05.241217: Pseudo dice [0.9576, 0.9524, 0.9706, 0.9386, 0.8313, 0.9596, 0.985] +2024-09-05 04:55:05.242187: Epoch time: 246.22 s +2024-09-05 04:55:08.854886: +2024-09-05 04:55:08.856678: Epoch 157 +2024-09-05 04:55:08.858013: Current learning rate: 0.00858 +2024-09-05 04:59:15.720236: train_loss -0.7087 +2024-09-05 04:59:15.734694: val_loss -0.6603 +2024-09-05 04:59:15.735381: Pseudo dice [0.9588, 0.9493, 0.9733, 0.9428, 0.8411, 0.9564, 0.9873] +2024-09-05 04:59:15.736203: Epoch time: 246.87 s +2024-09-05 04:59:18.049399: +2024-09-05 04:59:18.051012: Epoch 158 +2024-09-05 04:59:18.051631: Current learning rate: 0.00857 +2024-09-05 05:03:47.406560: train_loss -0.6992 +2024-09-05 05:03:47.408013: val_loss -0.6565 +2024-09-05 05:03:47.408938: Pseudo dice [0.958, 0.9518, 0.9705, 0.9331, 0.82, 0.9133, 0.9791] +2024-09-05 05:03:47.410219: Epoch time: 269.36 s +2024-09-05 05:03:49.664395: +2024-09-05 05:03:49.665412: Epoch 159 +2024-09-05 05:03:49.665994: Current learning rate: 0.00856 +2024-09-05 05:08:16.944342: train_loss -0.7017 +2024-09-05 05:08:16.958479: val_loss -0.6929 +2024-09-05 05:08:16.959359: Pseudo dice [0.9537, 0.9481, 0.9728, 0.9354, 0.8233, 0.9422, 0.9822] +2024-09-05 05:08:16.960319: Epoch time: 267.28 s +2024-09-05 05:08:19.527735: +2024-09-05 05:08:19.528698: Epoch 160 +2024-09-05 05:08:19.529347: Current learning rate: 0.00855 +2024-09-05 05:12:54.067979: train_loss -0.688 +2024-09-05 05:12:54.084380: val_loss -0.705 +2024-09-05 05:12:54.085477: Pseudo dice [0.9529, 0.9464, 0.9696, 0.9327, 0.8239, 0.9392, 0.9843] +2024-09-05 05:12:54.086504: Epoch time: 274.56 s +2024-09-05 05:12:56.794091: +2024-09-05 05:12:56.795063: Epoch 161 +2024-09-05 05:12:56.795822: Current learning rate: 0.00854 +2024-09-05 05:17:15.172951: train_loss -0.7192 +2024-09-05 05:17:15.202838: val_loss -0.6932 +2024-09-05 05:17:15.203670: Pseudo dice [0.9607, 0.9515, 0.9746, 0.9347, 0.8514, 0.9527, 0.9867] +2024-09-05 05:17:15.204590: Epoch time: 258.38 s +2024-09-05 05:17:17.425633: +2024-09-05 05:17:17.426882: Epoch 162 +2024-09-05 05:17:17.427634: Current learning rate: 0.00853 +2024-09-05 05:21:37.629490: train_loss -0.7198 +2024-09-05 05:21:37.650313: val_loss -0.7347 +2024-09-05 05:21:37.658698: Pseudo dice [0.9609, 0.9535, 0.9778, 0.944, 0.8613, 0.963, 0.9891] +2024-09-05 05:21:37.659543: Epoch time: 260.21 s +2024-09-05 05:21:40.151760: +2024-09-05 05:21:40.160730: Epoch 163 +2024-09-05 05:21:40.161724: Current learning rate: 0.00852 +2024-09-05 05:25:55.160012: train_loss -0.7178 +2024-09-05 05:25:55.163786: val_loss -0.7235 +2024-09-05 05:25:55.165128: Pseudo dice [0.9592, 0.9525, 0.9761, 0.9404, 0.8597, 0.9537, 0.9865] +2024-09-05 05:25:55.167833: Epoch time: 255.01 s +2024-09-05 05:25:57.971824: +2024-09-05 05:25:57.973070: Epoch 164 +2024-09-05 05:25:57.973912: Current learning rate: 0.00851 +2024-09-05 05:30:34.463081: train_loss -0.7101 +2024-09-05 05:30:34.468208: val_loss -0.716 +2024-09-05 05:30:34.469114: Pseudo dice [0.9568, 0.9524, 0.9724, 0.9396, 0.8231, 0.9595, 0.9868] +2024-09-05 05:30:34.469968: Epoch time: 276.5 s +2024-09-05 05:30:37.340467: +2024-09-05 05:30:37.341633: Epoch 165 +2024-09-05 05:30:37.342873: Current learning rate: 0.0085 +2024-09-05 05:34:46.620669: train_loss -0.7265 +2024-09-05 05:34:46.640160: val_loss -0.7185 +2024-09-05 05:34:46.641001: Pseudo dice [0.961, 0.9558, 0.9773, 0.9431, 0.8589, 0.9641, 0.9908] +2024-09-05 05:34:46.641837: Epoch time: 249.28 s +2024-09-05 05:34:49.054760: +2024-09-05 05:34:49.055801: Epoch 166 +2024-09-05 05:34:49.056596: Current learning rate: 0.00849 +2024-09-05 05:39:01.984835: train_loss -0.7343 +2024-09-05 05:39:02.000361: val_loss -0.7577 +2024-09-05 05:39:02.001442: Pseudo dice [0.9598, 0.9519, 0.9761, 0.9425, 0.8454, 0.9613, 0.9884] +2024-09-05 05:39:02.002495: Epoch time: 252.94 s +2024-09-05 05:39:04.326895: +2024-09-05 05:39:04.328124: Epoch 167 +2024-09-05 05:39:04.328988: Current learning rate: 0.00848 +2024-09-05 05:43:33.559618: train_loss -0.732 +2024-09-05 05:43:33.575932: val_loss -0.746 +2024-09-05 05:43:33.576680: Pseudo dice [0.96, 0.9535, 0.9758, 0.9438, 0.84, 0.9639, 0.9894] +2024-09-05 05:43:33.577701: Epoch time: 269.24 s +2024-09-05 05:43:37.154890: +2024-09-05 05:43:37.155849: Epoch 168 +2024-09-05 05:43:37.156608: Current learning rate: 0.00847 +2024-09-05 05:47:46.750987: train_loss -0.7257 +2024-09-05 05:47:46.753395: val_loss -0.7323 +2024-09-05 05:47:46.754058: Pseudo dice [0.9618, 0.9523, 0.9772, 0.9436, 0.8468, 0.9633, 0.9895] +2024-09-05 05:47:46.754926: Epoch time: 249.6 s +2024-09-05 05:47:49.220923: +2024-09-05 05:47:49.222101: Epoch 169 +2024-09-05 05:47:49.222984: Current learning rate: 0.00847 +2024-09-05 05:52:17.530054: train_loss -0.7102 +2024-09-05 05:52:17.532665: val_loss -0.7087 +2024-09-05 05:52:17.533477: Pseudo dice [0.9604, 0.9541, 0.975, 0.9412, 0.8548, 0.9635, 0.9886] +2024-09-05 05:52:17.534205: Epoch time: 268.31 s +2024-09-05 05:52:17.534851: Yayy! New best EMA pseudo Dice: 0.9449 +2024-09-05 05:52:26.731272: +2024-09-05 05:52:26.732196: Epoch 170 +2024-09-05 05:52:26.732852: Current learning rate: 0.00846 +2024-09-05 05:56:35.230042: train_loss -0.7292 +2024-09-05 05:56:35.235672: val_loss -0.6697 +2024-09-05 05:56:35.236619: Pseudo dice [0.9577, 0.954, 0.972, 0.9382, 0.8127, 0.9604, 0.9867] +2024-09-05 05:56:35.237467: Epoch time: 248.5 s +2024-09-05 05:56:37.869203: +2024-09-05 05:56:37.870240: Epoch 171 +2024-09-05 05:56:37.871169: Current learning rate: 0.00845 +2024-09-05 06:01:00.291316: train_loss -0.7159 +2024-09-05 06:01:00.293823: val_loss -0.7144 +2024-09-05 06:01:00.294826: Pseudo dice [0.9619, 0.9555, 0.9778, 0.9442, 0.8396, 0.9528, 0.9862] +2024-09-05 06:01:00.295568: Epoch time: 262.43 s +2024-09-05 06:01:02.502797: +2024-09-05 06:01:02.509922: Epoch 172 +2024-09-05 06:01:02.510809: Current learning rate: 0.00844 +2024-09-05 06:05:35.743873: train_loss -0.7307 +2024-09-05 06:05:35.758671: val_loss -0.7541 +2024-09-05 06:05:35.759543: Pseudo dice [0.9599, 0.9525, 0.9748, 0.9404, 0.8199, 0.9517, 0.9845] +2024-09-05 06:05:35.760252: Epoch time: 273.25 s +2024-09-05 06:05:38.258042: +2024-09-05 06:05:38.259026: Epoch 173 +2024-09-05 06:05:38.259851: Current learning rate: 0.00843 +2024-09-05 06:09:47.529916: train_loss -0.73 +2024-09-05 06:09:47.552312: val_loss -0.7436 +2024-09-05 06:09:47.553282: Pseudo dice [0.9629, 0.955, 0.9773, 0.9442, 0.863, 0.9655, 0.9901] +2024-09-05 06:09:47.554279: Epoch time: 249.28 s +2024-09-05 06:09:49.666810: +2024-09-05 06:09:49.667950: Epoch 174 +2024-09-05 06:09:49.679840: Current learning rate: 0.00842 +2024-09-05 06:13:55.562871: train_loss -0.7139 +2024-09-05 06:13:55.564468: val_loss -0.6909 +2024-09-05 06:13:55.565547: Pseudo dice [0.959, 0.9501, 0.9757, 0.9392, 0.8492, 0.9539, 0.9889] +2024-09-05 06:13:55.566752: Epoch time: 245.9 s +2024-09-05 06:13:57.775856: +2024-09-05 06:13:57.776915: Epoch 175 +2024-09-05 06:13:57.777527: Current learning rate: 0.00841 +2024-09-05 06:18:09.458808: train_loss -0.7212 +2024-09-05 06:18:09.472120: val_loss -0.7079 +2024-09-05 06:18:09.472957: Pseudo dice [0.9578, 0.9504, 0.9714, 0.9338, 0.8233, 0.9602, 0.9865] +2024-09-05 06:18:09.473735: Epoch time: 251.69 s +2024-09-05 06:18:11.960035: +2024-09-05 06:18:11.961174: Epoch 176 +2024-09-05 06:18:11.962186: Current learning rate: 0.0084 +2024-09-05 06:22:35.338854: train_loss -0.7312 +2024-09-05 06:22:35.341667: val_loss -0.7036 +2024-09-05 06:22:35.342402: Pseudo dice [0.9564, 0.9537, 0.9701, 0.9459, 0.8621, 0.9519, 0.9894] +2024-09-05 06:22:35.343147: Epoch time: 263.38 s +2024-09-05 06:22:37.811287: +2024-09-05 06:22:37.816554: Epoch 177 +2024-09-05 06:22:37.817784: Current learning rate: 0.00839 +2024-09-05 06:27:04.597347: train_loss -0.7089 +2024-09-05 06:27:04.599768: val_loss -0.7157 +2024-09-05 06:27:04.600525: Pseudo dice [0.9611, 0.9558, 0.9767, 0.9435, 0.8509, 0.9591, 0.9898] +2024-09-05 06:27:04.601187: Epoch time: 266.79 s +2024-09-05 06:27:04.601801: Yayy! New best EMA pseudo Dice: 0.945 +2024-09-05 06:27:12.945230: +2024-09-05 06:27:12.946094: Epoch 178 +2024-09-05 06:27:12.946937: Current learning rate: 0.00838 +2024-09-05 06:31:31.763850: train_loss -0.7007 +2024-09-05 06:31:31.766374: val_loss -0.7155 +2024-09-05 06:31:31.767488: Pseudo dice [0.9574, 0.9457, 0.9724, 0.9319, 0.8609, 0.9588, 0.9892] +2024-09-05 06:31:31.768506: Epoch time: 258.82 s +2024-09-05 06:31:31.769296: Yayy! New best EMA pseudo Dice: 0.945 +2024-09-05 06:31:42.700526: +2024-09-05 06:31:42.701968: Epoch 179 +2024-09-05 06:31:42.702995: Current learning rate: 0.00837 +2024-09-05 06:35:51.507354: train_loss -0.7234 +2024-09-05 06:35:51.510065: val_loss -0.6943 +2024-09-05 06:35:51.511158: Pseudo dice [0.9587, 0.9534, 0.9732, 0.9358, 0.824, 0.9535, 0.9849] +2024-09-05 06:35:51.512237: Epoch time: 248.81 s +2024-09-05 06:35:53.883847: +2024-09-05 06:35:53.884940: Epoch 180 +2024-09-05 06:35:53.885740: Current learning rate: 0.00836 +2024-09-05 06:40:10.903544: train_loss -0.7256 +2024-09-05 06:40:10.915359: val_loss -0.7944 +2024-09-05 06:40:10.916442: Pseudo dice [0.9599, 0.9501, 0.9765, 0.938, 0.8677, 0.9622, 0.9905] +2024-09-05 06:40:10.917725: Epoch time: 257.02 s +2024-09-05 06:40:10.918648: Yayy! New best EMA pseudo Dice: 0.9451 +2024-09-05 06:40:22.060162: +2024-09-05 06:40:22.061146: Epoch 181 +2024-09-05 06:40:22.061737: Current learning rate: 0.00836 +2024-09-05 06:44:32.292855: train_loss -0.7023 +2024-09-05 06:44:32.315485: val_loss -0.727 +2024-09-05 06:44:32.316855: Pseudo dice [0.9521, 0.9448, 0.9652, 0.9261, 0.8256, 0.9561, 0.9838] +2024-09-05 06:44:32.318197: Epoch time: 250.24 s +2024-09-05 06:44:35.109599: +2024-09-05 06:44:35.110771: Epoch 182 +2024-09-05 06:44:35.111665: Current learning rate: 0.00835 +2024-09-05 06:49:00.583512: train_loss -0.6874 +2024-09-05 06:49:00.601601: val_loss -0.703 +2024-09-05 06:49:00.602538: Pseudo dice [0.9531, 0.9449, 0.9703, 0.9265, 0.8147, 0.9586, 0.9889] +2024-09-05 06:49:00.603536: Epoch time: 265.48 s +2024-09-05 06:49:03.230735: +2024-09-05 06:49:03.246572: Epoch 183 +2024-09-05 06:49:03.247226: Current learning rate: 0.00834 +2024-09-05 06:53:25.849182: train_loss -0.6798 +2024-09-05 06:53:25.850672: val_loss -0.6667 +2024-09-05 06:53:25.851490: Pseudo dice [0.9482, 0.945, 0.9641, 0.9284, 0.7931, 0.952, 0.983] +2024-09-05 06:53:25.852210: Epoch time: 262.62 s +2024-09-05 06:53:28.316292: +2024-09-05 06:53:28.317664: Epoch 184 +2024-09-05 06:53:28.319738: Current learning rate: 0.00833 +2024-09-05 06:57:52.102338: train_loss -0.6557 +2024-09-05 06:57:52.109751: val_loss -0.677 +2024-09-05 06:57:52.110547: Pseudo dice [0.9478, 0.9424, 0.9665, 0.9269, 0.8063, 0.9428, 0.9808] +2024-09-05 06:57:52.111295: Epoch time: 263.79 s +2024-09-05 06:57:54.618802: +2024-09-05 06:57:54.620246: Epoch 185 +2024-09-05 06:57:54.621486: Current learning rate: 0.00832 +2024-09-05 07:02:10.544934: train_loss -0.6718 +2024-09-05 07:02:10.548110: val_loss -0.6232 +2024-09-05 07:02:10.549015: Pseudo dice [0.9395, 0.9431, 0.9255, 0.921, 0.8186, 0.9293, 0.9799] +2024-09-05 07:02:10.550016: Epoch time: 255.93 s +2024-09-05 07:02:12.604955: +2024-09-05 07:02:12.605994: Epoch 186 +2024-09-05 07:02:12.606839: Current learning rate: 0.00831 +2024-09-05 07:06:33.148760: train_loss -0.6656 +2024-09-05 07:06:33.151691: val_loss -0.674 +2024-09-05 07:06:33.166096: Pseudo dice [0.9517, 0.9472, 0.9704, 0.9306, 0.816, 0.9533, 0.9842] +2024-09-05 07:06:33.167036: Epoch time: 260.55 s +2024-09-05 07:06:35.890461: +2024-09-05 07:06:35.891324: Epoch 187 +2024-09-05 07:06:35.891913: Current learning rate: 0.0083 +2024-09-05 07:10:53.487675: train_loss -0.694 +2024-09-05 07:10:53.490724: val_loss -0.6956 +2024-09-05 07:10:53.491322: Pseudo dice [0.9533, 0.9469, 0.9667, 0.9289, 0.8508, 0.9578, 0.9884] +2024-09-05 07:10:53.492017: Epoch time: 257.6 s +2024-09-05 07:10:55.597616: +2024-09-05 07:10:55.598430: Epoch 188 +2024-09-05 07:10:55.599140: Current learning rate: 0.00829 +2024-09-05 07:15:18.040254: train_loss -0.7078 +2024-09-05 07:15:18.043059: val_loss -0.715 +2024-09-05 07:15:18.043901: Pseudo dice [0.9561, 0.9506, 0.9709, 0.9409, 0.8089, 0.9577, 0.9862] +2024-09-05 07:15:18.044618: Epoch time: 262.45 s +2024-09-05 07:15:20.380877: +2024-09-05 07:15:20.382144: Epoch 189 +2024-09-05 07:15:20.383062: Current learning rate: 0.00828 +2024-09-05 07:19:46.486942: train_loss -0.7034 +2024-09-05 07:19:46.503037: val_loss -0.7224 +2024-09-05 07:19:46.504374: Pseudo dice [0.9564, 0.9498, 0.9752, 0.9328, 0.8354, 0.9466, 0.9841] +2024-09-05 07:19:46.505669: Epoch time: 266.11 s +2024-09-05 07:19:48.606486: +2024-09-05 07:19:48.607700: Epoch 190 +2024-09-05 07:19:48.608498: Current learning rate: 0.00827 +2024-09-05 07:24:02.785319: train_loss -0.711 +2024-09-05 07:24:02.810062: val_loss -0.7339 +2024-09-05 07:24:02.811123: Pseudo dice [0.9551, 0.9489, 0.9731, 0.9347, 0.8569, 0.958, 0.988] +2024-09-05 07:24:02.811852: Epoch time: 254.18 s +2024-09-05 07:24:05.535685: +2024-09-05 07:24:05.536786: Epoch 191 +2024-09-05 07:24:05.537583: Current learning rate: 0.00826 +2024-09-05 07:28:37.952503: train_loss -0.7186 +2024-09-05 07:28:37.962081: val_loss -0.7248 +2024-09-05 07:28:37.962940: Pseudo dice [0.9581, 0.9517, 0.9759, 0.9384, 0.8539, 0.9594, 0.987] +2024-09-05 07:28:37.964132: Epoch time: 272.42 s +2024-09-05 07:28:40.163987: +2024-09-05 07:28:40.165572: Epoch 192 +2024-09-05 07:28:40.166826: Current learning rate: 0.00825 +2024-09-05 07:32:47.566516: train_loss -0.7107 +2024-09-05 07:32:47.580122: val_loss -0.6774 +2024-09-05 07:32:47.581930: Pseudo dice [0.9587, 0.9526, 0.9756, 0.9386, 0.8439, 0.9434, 0.9846] +2024-09-05 07:32:47.584150: Epoch time: 247.41 s +2024-09-05 07:32:50.178530: +2024-09-05 07:32:50.179701: Epoch 193 +2024-09-05 07:32:50.180884: Current learning rate: 0.00824 +2024-09-05 07:37:22.827636: train_loss -0.7087 +2024-09-05 07:37:22.836481: val_loss -0.7028 +2024-09-05 07:37:22.837754: Pseudo dice [0.9577, 0.9505, 0.9728, 0.9398, 0.8369, 0.9565, 0.9869] +2024-09-05 07:37:22.838750: Epoch time: 272.66 s +2024-09-05 07:37:26.415017: +2024-09-05 07:37:26.415964: Epoch 194 +2024-09-05 07:37:26.416676: Current learning rate: 0.00824 +2024-09-05 07:41:32.479230: train_loss -0.7162 +2024-09-05 07:41:32.481534: val_loss -0.7109 +2024-09-05 07:41:32.482499: Pseudo dice [0.9489, 0.9467, 0.9601, 0.9303, 0.8355, 0.954, 0.9893] +2024-09-05 07:41:32.483776: Epoch time: 246.07 s +2024-09-05 07:41:35.048364: +2024-09-05 07:41:35.049563: Epoch 195 +2024-09-05 07:41:35.050519: Current learning rate: 0.00823 +2024-09-05 07:46:07.120910: train_loss -0.6985 +2024-09-05 07:46:07.137904: val_loss -0.7575 +2024-09-05 07:46:07.139334: Pseudo dice [0.9553, 0.9479, 0.9711, 0.9373, 0.8394, 0.9616, 0.9875] +2024-09-05 07:46:07.140856: Epoch time: 272.08 s +2024-09-05 07:46:09.998978: +2024-09-05 07:46:09.999873: Epoch 196 +2024-09-05 07:46:10.000980: Current learning rate: 0.00822 +2024-09-05 07:50:19.214230: train_loss -0.7172 +2024-09-05 07:50:19.217213: val_loss -0.6835 +2024-09-05 07:50:19.218486: Pseudo dice [0.9585, 0.953, 0.9731, 0.9432, 0.8291, 0.9579, 0.9848] +2024-09-05 07:50:19.219594: Epoch time: 249.22 s +2024-09-05 07:50:21.413366: +2024-09-05 07:50:21.414409: Epoch 197 +2024-09-05 07:50:21.415295: Current learning rate: 0.00821 +2024-09-05 07:54:51.335896: train_loss -0.7354 +2024-09-05 07:54:51.338834: val_loss -0.7341 +2024-09-05 07:54:51.364189: Pseudo dice [0.958, 0.951, 0.9692, 0.9414, 0.8659, 0.9628, 0.9901] +2024-09-05 07:54:51.365081: Epoch time: 269.93 s +2024-09-05 07:54:55.546695: +2024-09-05 07:54:55.547556: Epoch 198 +2024-09-05 07:54:55.548226: Current learning rate: 0.0082 +2024-09-05 07:59:00.407340: train_loss -0.7136 +2024-09-05 07:59:00.409824: val_loss -0.7228 +2024-09-05 07:59:00.410566: Pseudo dice [0.9584, 0.9517, 0.971, 0.9403, 0.8096, 0.9589, 0.983] +2024-09-05 07:59:00.411374: Epoch time: 244.87 s +2024-09-05 07:59:02.929405: +2024-09-05 07:59:02.930773: Epoch 199 +2024-09-05 07:59:02.933249: Current learning rate: 0.00819 +2024-09-05 08:03:41.000070: train_loss -0.6987 +2024-09-05 08:03:41.002698: val_loss -0.7264 +2024-09-05 08:03:41.003581: Pseudo dice [0.9583, 0.9512, 0.9729, 0.9364, 0.838, 0.9571, 0.989] +2024-09-05 08:03:41.004517: Epoch time: 278.08 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_08_31_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_08_31_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..fbacc009a3131e71cede7bba838840fc36a370ab --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_08_31_30.txt @@ -0,0 +1,380 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 08:31:33.825712: Using torch.compile... +2024-09-05 08:31:42.566489: do_dummy_2d_data_aug: False +2024-09-05 08:31:42.573230: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 08:31:42.574775: The split file contains 5 splits. +2024-09-05 08:31:42.575571: Desired fold for training: 1 +2024-09-05 08:31:42.576392: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 08:31:56.690818: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 08:31:58.406949: +2024-09-05 08:31:58.417595: Epoch 200 +2024-09-05 08:31:58.420135: Current learning rate: 0.00818 +2024-09-05 08:50:27.953803: train_loss -0.7164 +2024-09-05 08:50:28.004357: val_loss -0.7345 +2024-09-05 08:50:28.005304: Pseudo dice [0.9581, 0.9499, 0.9724, 0.9405, 0.8638, 0.9623, 0.9891] +2024-09-05 08:50:28.005985: Epoch time: 1109.55 s +2024-09-05 08:50:30.463046: +2024-09-05 08:50:30.514359: Epoch 201 +2024-09-05 08:50:30.538584: Current learning rate: 0.00817 +2024-09-05 08:55:14.354450: train_loss -0.7072 +2024-09-05 08:55:14.371284: val_loss -0.7171 +2024-09-05 08:55:14.372291: Pseudo dice [0.9578, 0.9539, 0.9712, 0.9399, 0.8286, 0.9574, 0.9869] +2024-09-05 08:55:14.373119: Epoch time: 283.9 s +2024-09-05 08:55:17.527075: +2024-09-05 08:55:17.528149: Epoch 202 +2024-09-05 08:55:17.529286: Current learning rate: 0.00816 +2024-09-05 09:00:04.581638: train_loss -0.7213 +2024-09-05 09:00:04.593039: val_loss -0.7355 +2024-09-05 09:00:04.594044: Pseudo dice [0.961, 0.9524, 0.9778, 0.9438, 0.8517, 0.9392, 0.9845] +2024-09-05 09:00:04.594943: Epoch time: 287.06 s +2024-09-05 09:00:08.144962: +2024-09-05 09:00:08.145939: Epoch 203 +2024-09-05 09:00:08.146784: Current learning rate: 0.00815 +2024-09-05 09:04:57.866594: train_loss -0.7287 +2024-09-05 09:04:57.875469: val_loss -0.7133 +2024-09-05 09:04:57.876445: Pseudo dice [0.9634, 0.9507, 0.9787, 0.95, 0.8597, 0.9632, 0.9882] +2024-09-05 09:04:57.877269: Epoch time: 289.73 s +2024-09-05 09:05:00.704402: +2024-09-05 09:05:00.705713: Epoch 204 +2024-09-05 09:05:00.706994: Current learning rate: 0.00814 +2024-09-05 09:09:56.957373: train_loss -0.7389 +2024-09-05 09:09:56.979114: val_loss -0.748 +2024-09-05 09:09:56.980083: Pseudo dice [0.9591, 0.9559, 0.9712, 0.9427, 0.8482, 0.9544, 0.9863] +2024-09-05 09:09:56.980975: Epoch time: 296.26 s +2024-09-05 09:10:01.116535: +2024-09-05 09:10:01.117676: Epoch 205 +2024-09-05 09:10:01.118602: Current learning rate: 0.00813 +2024-09-05 09:14:57.258225: train_loss -0.7166 +2024-09-05 09:14:57.274171: val_loss -0.7242 +2024-09-05 09:14:57.275348: Pseudo dice [0.963, 0.9535, 0.9793, 0.9421, 0.8467, 0.959, 0.9862] +2024-09-05 09:14:57.276417: Epoch time: 296.15 s +2024-09-05 09:15:01.109316: +2024-09-05 09:15:01.110532: Epoch 206 +2024-09-05 09:15:01.111389: Current learning rate: 0.00813 +2024-09-05 09:20:15.330712: train_loss -0.7206 +2024-09-05 09:20:15.332543: val_loss -0.7505 +2024-09-05 09:20:15.333744: Pseudo dice [0.9621, 0.9544, 0.9781, 0.9434, 0.864, 0.9662, 0.9904] +2024-09-05 09:20:15.335139: Epoch time: 314.23 s +2024-09-05 09:20:18.195364: +2024-09-05 09:20:18.196980: Epoch 207 +2024-09-05 09:20:18.197921: Current learning rate: 0.00812 +2024-09-05 09:25:13.518951: train_loss -0.7245 +2024-09-05 09:25:13.520522: val_loss -0.7351 +2024-09-05 09:25:13.521447: Pseudo dice [0.958, 0.9541, 0.9743, 0.9444, 0.8653, 0.963, 0.9898] +2024-09-05 09:25:13.522278: Epoch time: 295.33 s +2024-09-05 09:25:13.523060: Yayy! New best EMA pseudo Dice: 0.9451 +2024-09-05 09:25:22.060504: +2024-09-05 09:25:22.061477: Epoch 208 +2024-09-05 09:25:22.062378: Current learning rate: 0.00811 +2024-09-05 09:30:09.777131: train_loss -0.728 +2024-09-05 09:30:09.793167: val_loss -0.6826 +2024-09-05 09:30:09.794161: Pseudo dice [0.96, 0.9536, 0.9728, 0.9368, 0.843, 0.9425, 0.9858] +2024-09-05 09:30:09.794984: Epoch time: 287.72 s +2024-09-05 09:30:12.740765: +2024-09-05 09:30:12.741687: Epoch 209 +2024-09-05 09:30:12.742844: Current learning rate: 0.0081 +2024-09-05 09:35:08.615083: train_loss -0.6987 +2024-09-05 09:35:08.631693: val_loss -0.7109 +2024-09-05 09:35:08.632884: Pseudo dice [0.9562, 0.9455, 0.9717, 0.9387, 0.8281, 0.9609, 0.9871] +2024-09-05 09:35:08.634052: Epoch time: 295.88 s +2024-09-05 09:35:11.420941: +2024-09-05 09:35:11.422269: Epoch 210 +2024-09-05 09:35:11.423224: Current learning rate: 0.00809 +2024-09-05 09:40:12.576192: train_loss -0.7119 +2024-09-05 09:40:12.592174: val_loss -0.7157 +2024-09-05 09:40:12.593331: Pseudo dice [0.958, 0.9502, 0.9749, 0.9411, 0.848, 0.9513, 0.9863] +2024-09-05 09:40:12.594541: Epoch time: 301.16 s +2024-09-05 09:40:15.218506: +2024-09-05 09:40:15.219687: Epoch 211 +2024-09-05 09:40:15.220639: Current learning rate: 0.00808 +2024-09-05 09:45:06.164464: train_loss -0.7177 +2024-09-05 09:45:06.166766: val_loss -0.7141 +2024-09-05 09:45:06.167652: Pseudo dice [0.9597, 0.9532, 0.9747, 0.9387, 0.8349, 0.954, 0.9875] +2024-09-05 09:45:06.168652: Epoch time: 290.95 s +2024-09-05 09:45:09.430495: +2024-09-05 09:45:09.431916: Epoch 212 +2024-09-05 09:45:09.432953: Current learning rate: 0.00807 +2024-09-05 09:50:09.434871: train_loss -0.7075 +2024-09-05 09:50:09.436374: val_loss -0.6647 +2024-09-05 09:50:09.437260: Pseudo dice [0.9511, 0.9475, 0.9665, 0.9274, 0.7931, 0.9293, 0.9858] +2024-09-05 09:50:09.438166: Epoch time: 300.01 s +2024-09-05 09:50:13.328559: +2024-09-05 09:50:13.329783: Epoch 213 +2024-09-05 09:50:13.330655: Current learning rate: 0.00806 +2024-09-05 09:55:00.343926: train_loss -0.6967 +2024-09-05 09:55:00.363862: val_loss -0.6872 +2024-09-05 09:55:00.365073: Pseudo dice [0.9563, 0.9499, 0.9713, 0.935, 0.8064, 0.9567, 0.9837] +2024-09-05 09:55:00.366116: Epoch time: 287.02 s +2024-09-05 09:55:03.414621: +2024-09-05 09:55:03.415776: Epoch 214 +2024-09-05 09:55:03.416641: Current learning rate: 0.00805 +2024-09-05 09:59:55.536554: train_loss -0.7018 +2024-09-05 09:59:55.538761: val_loss -0.7362 +2024-09-05 09:59:55.540103: Pseudo dice [0.9579, 0.9486, 0.9757, 0.9434, 0.8158, 0.9628, 0.9856] +2024-09-05 09:59:55.541357: Epoch time: 292.13 s +2024-09-05 09:59:58.289466: +2024-09-05 09:59:58.290562: Epoch 215 +2024-09-05 09:59:58.291508: Current learning rate: 0.00804 +2024-09-05 10:05:07.680708: train_loss -0.7209 +2024-09-05 10:05:07.691942: val_loss -0.7138 +2024-09-05 10:05:07.693122: Pseudo dice [0.9617, 0.9564, 0.9743, 0.9481, 0.8473, 0.9534, 0.9741] +2024-09-05 10:05:07.694228: Epoch time: 309.4 s +2024-09-05 10:05:11.861206: +2024-09-05 10:05:11.862307: Epoch 216 +2024-09-05 10:05:11.863110: Current learning rate: 0.00803 +2024-09-05 10:10:24.528059: train_loss -0.724 +2024-09-05 10:10:24.543235: val_loss -0.6782 +2024-09-05 10:10:24.544257: Pseudo dice [0.9511, 0.9524, 0.9688, 0.9241, 0.8409, 0.9629, 0.9878] +2024-09-05 10:10:24.545362: Epoch time: 312.67 s +2024-09-05 10:10:27.665190: +2024-09-05 10:10:27.671084: Epoch 217 +2024-09-05 10:10:27.672056: Current learning rate: 0.00802 +2024-09-05 10:15:08.764438: train_loss -0.6897 +2024-09-05 10:15:08.766568: val_loss -0.6968 +2024-09-05 10:15:08.767968: Pseudo dice [0.9503, 0.9502, 0.9612, 0.9392, 0.8442, 0.9439, 0.9852] +2024-09-05 10:15:08.769272: Epoch time: 281.1 s +2024-09-05 10:15:11.945740: +2024-09-05 10:15:11.947160: Epoch 218 +2024-09-05 10:15:11.948276: Current learning rate: 0.00801 +2024-09-05 10:19:30.835873: train_loss -0.721 +2024-09-05 10:19:30.837654: val_loss -0.7207 +2024-09-05 10:19:30.838643: Pseudo dice [0.9558, 0.9503, 0.975, 0.943, 0.8545, 0.9611, 0.9872] +2024-09-05 10:19:30.839653: Epoch time: 258.9 s +2024-09-05 10:19:33.576518: +2024-09-05 10:19:33.577636: Epoch 219 +2024-09-05 10:19:33.578622: Current learning rate: 0.00801 +2024-09-05 10:24:09.833591: train_loss -0.7053 +2024-09-05 10:24:09.847283: val_loss -0.7311 +2024-09-05 10:24:09.848394: Pseudo dice [0.9601, 0.9536, 0.975, 0.9398, 0.8495, 0.9637, 0.9905] +2024-09-05 10:24:09.849282: Epoch time: 276.27 s +2024-09-05 10:24:12.400368: +2024-09-05 10:24:12.401893: Epoch 220 +2024-09-05 10:24:12.402894: Current learning rate: 0.008 +2024-09-05 10:29:09.739615: train_loss -0.709 +2024-09-05 10:29:09.755261: val_loss -0.7334 +2024-09-05 10:29:09.756185: Pseudo dice [0.9555, 0.9511, 0.9722, 0.9433, 0.8595, 0.9502, 0.9892] +2024-09-05 10:29:09.757094: Epoch time: 297.34 s +2024-09-05 10:29:13.947438: +2024-09-05 10:29:13.948641: Epoch 221 +2024-09-05 10:29:13.949693: Current learning rate: 0.00799 +2024-09-05 10:33:55.368097: train_loss -0.7209 +2024-09-05 10:33:55.369594: val_loss -0.7178 +2024-09-05 10:33:55.370583: Pseudo dice [0.9598, 0.9536, 0.9748, 0.9424, 0.8693, 0.9669, 0.9906] +2024-09-05 10:33:55.371371: Epoch time: 281.43 s +2024-09-05 10:33:59.214845: +2024-09-05 10:33:59.216704: Epoch 222 +2024-09-05 10:33:59.217535: Current learning rate: 0.00798 +2024-09-05 10:38:52.676672: train_loss -0.7192 +2024-09-05 10:38:52.688397: val_loss -0.7525 +2024-09-05 10:38:52.689610: Pseudo dice [0.959, 0.9527, 0.9735, 0.9483, 0.8612, 0.9541, 0.9865] +2024-09-05 10:38:52.690793: Epoch time: 293.47 s +2024-09-05 10:38:55.355811: +2024-09-05 10:38:55.357041: Epoch 223 +2024-09-05 10:38:55.358004: Current learning rate: 0.00797 +2024-09-05 10:43:44.708907: train_loss -0.709 +2024-09-05 10:43:44.723196: val_loss -0.739 +2024-09-05 10:43:44.724251: Pseudo dice [0.956, 0.9518, 0.9743, 0.9358, 0.8476, 0.9628, 0.9888] +2024-09-05 10:43:44.725163: Epoch time: 289.36 s +2024-09-05 10:43:47.402081: +2024-09-05 10:43:47.403444: Epoch 224 +2024-09-05 10:43:47.404515: Current learning rate: 0.00796 +2024-09-05 10:48:27.112138: train_loss -0.7203 +2024-09-05 10:48:27.114848: val_loss -0.7401 +2024-09-05 10:48:27.115839: Pseudo dice [0.9609, 0.9556, 0.9752, 0.9407, 0.8712, 0.9573, 0.9879] +2024-09-05 10:48:27.116845: Epoch time: 279.71 s +2024-09-05 10:48:31.087731: +2024-09-05 10:48:31.089050: Epoch 225 +2024-09-05 10:48:31.089839: Current learning rate: 0.00795 +2024-09-05 10:53:16.014835: train_loss -0.7283 +2024-09-05 10:53:16.027961: val_loss -0.7513 +2024-09-05 10:53:16.028972: Pseudo dice [0.9574, 0.9507, 0.9713, 0.9408, 0.8582, 0.96, 0.9874] +2024-09-05 10:53:16.029847: Epoch time: 284.93 s +2024-09-05 10:53:16.030667: Yayy! New best EMA pseudo Dice: 0.9452 +2024-09-05 10:53:24.353070: +2024-09-05 10:53:24.353915: Epoch 226 +2024-09-05 10:53:24.354615: Current learning rate: 0.00794 +2024-09-05 10:58:03.340238: train_loss -0.724 +2024-09-05 10:58:03.346715: val_loss -0.746 +2024-09-05 10:58:03.347703: Pseudo dice [0.9631, 0.9533, 0.9783, 0.9459, 0.8489, 0.9483, 0.9865] +2024-09-05 10:58:03.348495: Epoch time: 278.99 s +2024-09-05 10:58:03.349249: Yayy! New best EMA pseudo Dice: 0.9453 +2024-09-05 10:58:11.651080: +2024-09-05 10:58:11.652010: Epoch 227 +2024-09-05 10:58:11.652757: Current learning rate: 0.00793 +2024-09-05 11:02:54.298186: train_loss -0.7333 +2024-09-05 11:02:54.302948: val_loss -0.7444 +2024-09-05 11:02:54.303987: Pseudo dice [0.9582, 0.9516, 0.9726, 0.9453, 0.8707, 0.9637, 0.99] +2024-09-05 11:02:54.304856: Epoch time: 282.65 s +2024-09-05 11:02:54.305669: Yayy! New best EMA pseudo Dice: 0.9458 +2024-09-05 11:03:03.319197: +2024-09-05 11:03:03.320199: Epoch 228 +2024-09-05 11:03:03.321137: Current learning rate: 0.00792 +2024-09-05 11:07:48.871033: train_loss -0.7214 +2024-09-05 11:07:48.888949: val_loss -0.7474 +2024-09-05 11:07:48.890823: Pseudo dice [0.9593, 0.9536, 0.9728, 0.9411, 0.8455, 0.9597, 0.9903] +2024-09-05 11:07:48.892435: Epoch time: 285.56 s +2024-09-05 11:07:48.893731: Yayy! New best EMA pseudo Dice: 0.9458 +2024-09-05 11:07:57.201512: +2024-09-05 11:07:57.202603: Epoch 229 +2024-09-05 11:07:57.203527: Current learning rate: 0.00791 +2024-09-05 11:12:27.938564: train_loss -0.7241 +2024-09-05 11:12:27.940283: val_loss -0.7186 +2024-09-05 11:12:27.941524: Pseudo dice [0.955, 0.9554, 0.9701, 0.9424, 0.8556, 0.9637, 0.9895] +2024-09-05 11:12:27.942621: Epoch time: 270.74 s +2024-09-05 11:12:27.943653: Yayy! New best EMA pseudo Dice: 0.946 +2024-09-05 11:12:35.739894: +2024-09-05 11:12:35.740795: Epoch 230 +2024-09-05 11:12:35.741506: Current learning rate: 0.0079 +2024-09-05 11:17:19.120358: train_loss -0.7103 +2024-09-05 11:17:19.142138: val_loss -0.7166 +2024-09-05 11:17:19.143813: Pseudo dice [0.9557, 0.9513, 0.9714, 0.9347, 0.835, 0.9582, 0.9871] +2024-09-05 11:17:19.146966: Epoch time: 283.38 s +2024-09-05 11:17:21.924044: +2024-09-05 11:17:21.934694: Epoch 231 +2024-09-05 11:17:21.935803: Current learning rate: 0.00789 +2024-09-05 11:22:11.012977: train_loss -0.7074 +2024-09-05 11:22:11.036512: val_loss -0.6848 +2024-09-05 11:22:11.037540: Pseudo dice [0.9555, 0.9518, 0.969, 0.9338, 0.8271, 0.9453, 0.9851] +2024-09-05 11:22:11.038494: Epoch time: 289.09 s +2024-09-05 11:22:14.176419: +2024-09-05 11:22:14.177469: Epoch 232 +2024-09-05 11:22:14.178451: Current learning rate: 0.00789 +2024-09-05 11:27:17.644546: train_loss -0.6933 +2024-09-05 11:27:17.666308: val_loss -0.7388 +2024-09-05 11:27:17.667715: Pseudo dice [0.9602, 0.9529, 0.9714, 0.9382, 0.8511, 0.9633, 0.9885] +2024-09-05 11:27:17.668873: Epoch time: 303.47 s +2024-09-05 11:27:20.690073: +2024-09-05 11:27:20.700537: Epoch 233 +2024-09-05 11:27:20.704432: Current learning rate: 0.00788 +2024-09-05 11:31:50.336660: train_loss -0.7322 +2024-09-05 11:31:50.351124: val_loss -0.7215 +2024-09-05 11:31:50.352525: Pseudo dice [0.9595, 0.9538, 0.9764, 0.941, 0.8357, 0.9572, 0.9857] +2024-09-05 11:31:50.353645: Epoch time: 269.65 s +2024-09-05 11:31:53.610057: +2024-09-05 11:31:53.611147: Epoch 234 +2024-09-05 11:31:53.611993: Current learning rate: 0.00787 +2024-09-05 11:36:46.577296: train_loss -0.7208 +2024-09-05 11:36:46.587635: val_loss -0.7134 +2024-09-05 11:36:46.594843: Pseudo dice [0.9618, 0.9549, 0.9788, 0.9438, 0.8451, 0.9643, 0.9889] +2024-09-05 11:36:46.595840: Epoch time: 292.97 s +2024-09-05 11:36:50.256189: +2024-09-05 11:36:50.269975: Epoch 235 +2024-09-05 11:36:50.271227: Current learning rate: 0.00786 +2024-09-05 11:41:28.734398: train_loss -0.7285 +2024-09-05 11:41:28.736209: val_loss -0.7189 +2024-09-05 11:41:28.737324: Pseudo dice [0.9617, 0.9551, 0.9741, 0.9407, 0.8692, 0.967, 0.9909] +2024-09-05 11:41:28.738201: Epoch time: 278.48 s +2024-09-05 11:41:31.875889: +2024-09-05 11:41:31.877490: Epoch 236 +2024-09-05 11:41:31.878747: Current learning rate: 0.00785 +2024-09-05 11:46:15.839751: train_loss -0.7252 +2024-09-05 11:46:15.842577: val_loss -0.7202 +2024-09-05 11:46:15.844606: Pseudo dice [0.9585, 0.9523, 0.9712, 0.9415, 0.8282, 0.9633, 0.9853] +2024-09-05 11:46:15.846509: Epoch time: 283.97 s +2024-09-05 11:46:19.423476: +2024-09-05 11:46:19.425615: Epoch 237 +2024-09-05 11:46:19.427062: Current learning rate: 0.00784 +2024-09-05 11:51:05.669129: train_loss -0.7216 +2024-09-05 11:51:05.681461: val_loss -0.729 +2024-09-05 11:51:05.682633: Pseudo dice [0.9622, 0.9509, 0.9777, 0.9423, 0.8305, 0.952, 0.986] +2024-09-05 11:51:05.684323: Epoch time: 286.25 s +2024-09-05 11:51:08.749205: +2024-09-05 11:51:08.754188: Epoch 238 +2024-09-05 11:51:08.766090: Current learning rate: 0.00783 +2024-09-05 11:56:05.634062: train_loss -0.7129 +2024-09-05 11:56:05.637005: val_loss -0.7607 +2024-09-05 11:56:05.638187: Pseudo dice [0.9609, 0.9498, 0.9756, 0.9433, 0.8358, 0.9618, 0.9852] +2024-09-05 11:56:05.639409: Epoch time: 296.89 s +2024-09-05 11:56:08.748591: +2024-09-05 11:56:08.749625: Epoch 239 +2024-09-05 11:56:08.750435: Current learning rate: 0.00782 +2024-09-05 12:01:28.112942: train_loss -0.7168 +2024-09-05 12:01:28.114818: val_loss -0.7081 +2024-09-05 12:01:28.116095: Pseudo dice [0.9588, 0.9497, 0.9713, 0.9379, 0.8114, 0.9596, 0.9873] +2024-09-05 12:01:28.117162: Epoch time: 319.37 s +2024-09-05 12:01:32.934388: +2024-09-05 12:01:32.935488: Epoch 240 +2024-09-05 12:01:32.936501: Current learning rate: 0.00781 +2024-09-05 12:06:05.727517: train_loss -0.7035 +2024-09-05 12:06:05.743367: val_loss -0.7201 +2024-09-05 12:06:05.751552: Pseudo dice [0.96, 0.9539, 0.9724, 0.9456, 0.8187, 0.9627, 0.9862] +2024-09-05 12:06:05.752811: Epoch time: 272.8 s +2024-09-05 12:06:09.080197: +2024-09-05 12:06:09.092918: Epoch 241 +2024-09-05 12:06:09.094181: Current learning rate: 0.0078 +2024-09-05 12:10:58.365844: train_loss -0.7268 +2024-09-05 12:10:58.383943: val_loss -0.7447 +2024-09-05 12:10:58.385165: Pseudo dice [0.9573, 0.9509, 0.9731, 0.9461, 0.8603, 0.9641, 0.9877] +2024-09-05 12:10:58.386081: Epoch time: 289.29 s +2024-09-05 12:11:01.979908: +2024-09-05 12:11:01.982184: Epoch 242 +2024-09-05 12:11:01.984314: Current learning rate: 0.00779 +2024-09-05 12:15:42.470558: train_loss -0.7391 +2024-09-05 12:15:42.483200: val_loss -0.6967 +2024-09-05 12:15:42.484180: Pseudo dice [0.9646, 0.9562, 0.9791, 0.9438, 0.8568, 0.9569, 0.9875] +2024-09-05 12:15:42.485017: Epoch time: 280.5 s +2024-09-05 12:15:46.163462: +2024-09-05 12:15:46.174773: Epoch 243 +2024-09-05 12:15:46.175774: Current learning rate: 0.00778 +2024-09-05 12:20:43.734342: train_loss -0.7357 +2024-09-05 12:20:43.747333: val_loss -0.7758 +2024-09-05 12:20:43.748291: Pseudo dice [0.9613, 0.9538, 0.9757, 0.9469, 0.8707, 0.9506, 0.9894] +2024-09-05 12:20:43.749254: Epoch time: 297.58 s +2024-09-05 12:20:47.785276: +2024-09-05 12:20:47.786606: Epoch 244 +2024-09-05 12:20:47.787700: Current learning rate: 0.00777 +2024-09-05 12:25:32.126989: train_loss -0.7246 +2024-09-05 12:25:32.131009: val_loss -0.7073 +2024-09-05 12:25:32.132184: Pseudo dice [0.9598, 0.9534, 0.9736, 0.941, 0.8534, 0.9614, 0.9893] +2024-09-05 12:25:32.133136: Epoch time: 284.35 s +2024-09-05 12:25:35.787421: +2024-09-05 12:25:35.788619: Epoch 245 +2024-09-05 12:25:35.789809: Current learning rate: 0.00777 +2024-09-05 12:30:17.631586: train_loss -0.712 +2024-09-05 12:30:17.656488: val_loss -0.7436 +2024-09-05 12:30:17.657912: Pseudo dice [0.9589, 0.9537, 0.9731, 0.9448, 0.8589, 0.9628, 0.9893] +2024-09-05 12:30:17.659005: Epoch time: 281.85 s +2024-09-05 12:30:17.660338: Yayy! New best EMA pseudo Dice: 0.9462 +2024-09-05 12:30:26.152414: +2024-09-05 12:30:26.153373: Epoch 246 +2024-09-05 12:30:26.154335: Current learning rate: 0.00776 +2024-09-05 12:35:19.366426: train_loss -0.7166 +2024-09-05 12:35:19.384280: val_loss -0.7171 +2024-09-05 12:35:19.385658: Pseudo dice [0.9618, 0.954, 0.9758, 0.9402, 0.849, 0.9546, 0.9871] +2024-09-05 12:35:19.386699: Epoch time: 293.22 s +2024-09-05 12:35:22.901428: +2024-09-05 12:35:22.902989: Epoch 247 +2024-09-05 12:35:22.904460: Current learning rate: 0.00775 +2024-09-05 12:40:25.823773: train_loss -0.7275 +2024-09-05 12:40:25.839047: val_loss -0.7171 +2024-09-05 12:40:25.840401: Pseudo dice [0.9591, 0.9553, 0.9731, 0.9339, 0.849, 0.9529, 0.985] +2024-09-05 12:40:25.841591: Epoch time: 302.93 s +2024-09-05 12:40:28.438842: +2024-09-05 12:40:28.440168: Epoch 248 +2024-09-05 12:40:28.441172: Current learning rate: 0.00774 +2024-09-05 12:45:21.532862: train_loss -0.7334 +2024-09-05 12:45:21.546228: val_loss -0.7515 +2024-09-05 12:45:21.547747: Pseudo dice [0.9617, 0.9513, 0.9756, 0.9408, 0.8632, 0.9674, 0.9898] +2024-09-05 12:45:21.549126: Epoch time: 293.1 s +2024-09-05 12:45:21.550408: Yayy! New best EMA pseudo Dice: 0.9464 +2024-09-05 12:45:31.162979: +2024-09-05 12:45:31.163944: Epoch 249 +2024-09-05 12:45:31.164754: Current learning rate: 0.00773 +2024-09-05 12:50:00.194288: train_loss -0.7168 +2024-09-05 12:50:00.268017: val_loss -0.7339 +2024-09-05 12:50:00.477587: Pseudo dice [0.961, 0.9532, 0.9768, 0.943, 0.861, 0.9663, 0.9902] +2024-09-05 12:50:00.558198: Epoch time: 269.03 s +2024-09-05 12:50:08.139919: Yayy! New best EMA pseudo Dice: 0.9468 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_16_30_44.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_16_30_44.txt new file mode 100644 index 0000000000000000000000000000000000000000..1285440ca6e09a0bd8fc4d9400da1e4f890c4992 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_5_16_30_44.txt @@ -0,0 +1,380 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 16:30:47.501100: Using torch.compile... +2024-09-05 16:30:56.102847: do_dummy_2d_data_aug: False +2024-09-05 16:30:56.109239: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 16:30:56.111737: The split file contains 5 splits. +2024-09-05 16:30:56.112572: Desired fold for training: 1 +2024-09-05 16:30:56.113266: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 16:31:10.166732: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 16:31:11.830219: +2024-09-05 16:31:11.846095: Epoch 250 +2024-09-05 16:31:11.848173: Current learning rate: 0.00772 +2024-09-05 16:49:44.473948: train_loss -0.7112 +2024-09-05 16:49:44.475238: val_loss -0.7554 +2024-09-05 16:49:44.475936: Pseudo dice [0.9601, 0.9537, 0.971, 0.9456, 0.8493, 0.9628, 0.989] +2024-09-05 16:49:44.476560: Epoch time: 1112.65 s +2024-09-05 16:49:44.477160: Yayy! New best EMA pseudo Dice: 0.9468 +2024-09-05 16:49:57.546696: +2024-09-05 16:49:57.547638: Epoch 251 +2024-09-05 16:49:57.548614: Current learning rate: 0.00771 +2024-09-05 16:54:19.688939: train_loss -0.7386 +2024-09-05 16:54:19.690474: val_loss -0.726 +2024-09-05 16:54:19.692465: Pseudo dice [0.9612, 0.957, 0.9734, 0.9468, 0.8433, 0.9598, 0.983] +2024-09-05 16:54:19.694126: Epoch time: 262.15 s +2024-09-05 16:54:23.141976: +2024-09-05 16:54:23.143144: Epoch 252 +2024-09-05 16:54:23.144448: Current learning rate: 0.0077 +2024-09-05 16:58:55.218054: train_loss -0.7265 +2024-09-05 16:58:55.235441: val_loss -0.7442 +2024-09-05 16:58:55.236510: Pseudo dice [0.963, 0.9543, 0.9756, 0.9409, 0.8502, 0.9613, 0.9879] +2024-09-05 16:58:55.238135: Epoch time: 272.08 s +2024-09-05 16:58:55.239020: Yayy! New best EMA pseudo Dice: 0.9469 +2024-09-05 16:59:05.480499: +2024-09-05 16:59:05.481353: Epoch 253 +2024-09-05 16:59:05.482084: Current learning rate: 0.00769 +2024-09-05 17:03:29.589779: train_loss -0.7292 +2024-09-05 17:03:29.607002: val_loss -0.7749 +2024-09-05 17:03:29.609052: Pseudo dice [0.9616, 0.9561, 0.976, 0.9459, 0.8622, 0.9646, 0.9911] +2024-09-05 17:03:29.610291: Epoch time: 264.11 s +2024-09-05 17:03:29.611227: Yayy! New best EMA pseudo Dice: 0.9473 +2024-09-05 17:03:39.101381: +2024-09-05 17:03:39.102527: Epoch 254 +2024-09-05 17:03:39.103298: Current learning rate: 0.00768 +2024-09-05 17:08:26.841527: train_loss -0.7226 +2024-09-05 17:08:26.855083: val_loss -0.7506 +2024-09-05 17:08:26.856436: Pseudo dice [0.9629, 0.9541, 0.9798, 0.9463, 0.8747, 0.9685, 0.9907] +2024-09-05 17:08:26.857765: Epoch time: 287.74 s +2024-09-05 17:08:26.858772: Yayy! New best EMA pseudo Dice: 0.9479 +2024-09-05 17:08:40.821829: +2024-09-05 17:08:40.832804: Epoch 255 +2024-09-05 17:08:40.856264: Current learning rate: 0.00767 +2024-09-05 17:13:21.567890: train_loss -0.7222 +2024-09-05 17:13:21.569758: val_loss -0.7284 +2024-09-05 17:13:21.570447: Pseudo dice [0.96, 0.9498, 0.975, 0.9427, 0.8459, 0.9644, 0.9912] +2024-09-05 17:13:21.571601: Epoch time: 280.75 s +2024-09-05 17:13:25.625668: +2024-09-05 17:13:25.626712: Epoch 256 +2024-09-05 17:13:25.627776: Current learning rate: 0.00766 +2024-09-05 17:18:02.350731: train_loss -0.7321 +2024-09-05 17:18:02.369256: val_loss -0.7082 +2024-09-05 17:18:02.370311: Pseudo dice [0.9591, 0.9533, 0.9707, 0.9467, 0.8429, 0.9573, 0.9877] +2024-09-05 17:18:02.371385: Epoch time: 276.73 s +2024-09-05 17:18:06.295470: +2024-09-05 17:18:06.302672: Epoch 257 +2024-09-05 17:18:06.303519: Current learning rate: 0.00765 +2024-09-05 17:22:52.130106: train_loss -0.7274 +2024-09-05 17:22:52.134175: val_loss -0.7527 +2024-09-05 17:22:52.135477: Pseudo dice [0.9586, 0.9489, 0.9722, 0.9462, 0.8726, 0.9649, 0.9903] +2024-09-05 17:22:52.136623: Epoch time: 285.84 s +2024-09-05 17:22:54.617947: +2024-09-05 17:22:54.618942: Epoch 258 +2024-09-05 17:22:54.619626: Current learning rate: 0.00764 +2024-09-05 17:27:31.115554: train_loss -0.7248 +2024-09-05 17:27:31.120062: val_loss -0.7272 +2024-09-05 17:27:31.121069: Pseudo dice [0.9606, 0.9564, 0.9769, 0.9439, 0.86, 0.9636, 0.9902] +2024-09-05 17:27:31.122094: Epoch time: 276.5 s +2024-09-05 17:27:31.123038: Yayy! New best EMA pseudo Dice: 0.9481 +2024-09-05 17:27:44.076805: +2024-09-05 17:27:44.077666: Epoch 259 +2024-09-05 17:27:44.078305: Current learning rate: 0.00764 +2024-09-05 17:32:08.144152: train_loss -0.7374 +2024-09-05 17:32:08.172084: val_loss -0.7064 +2024-09-05 17:32:08.172804: Pseudo dice [0.9625, 0.9564, 0.9763, 0.9396, 0.8533, 0.9559, 0.9861] +2024-09-05 17:32:08.174030: Epoch time: 264.07 s +2024-09-05 17:32:11.010748: +2024-09-05 17:32:11.011945: Epoch 260 +2024-09-05 17:32:11.012888: Current learning rate: 0.00763 +2024-09-05 17:36:34.796634: train_loss -0.7255 +2024-09-05 17:36:34.799467: val_loss -0.7362 +2024-09-05 17:36:34.800291: Pseudo dice [0.9618, 0.9543, 0.9765, 0.9479, 0.8404, 0.9614, 0.9875] +2024-09-05 17:36:34.800995: Epoch time: 263.79 s +2024-09-05 17:36:36.801501: +2024-09-05 17:36:36.802474: Epoch 261 +2024-09-05 17:36:36.803183: Current learning rate: 0.00762 +2024-09-05 17:41:22.080729: train_loss -0.7404 +2024-09-05 17:41:22.098784: val_loss -0.7523 +2024-09-05 17:41:22.100819: Pseudo dice [0.9633, 0.9551, 0.9775, 0.9482, 0.8285, 0.9603, 0.9864] +2024-09-05 17:41:22.101972: Epoch time: 285.28 s +2024-09-05 17:41:25.774073: +2024-09-05 17:41:25.786953: Epoch 262 +2024-09-05 17:41:25.788243: Current learning rate: 0.00761 +2024-09-05 17:45:56.128303: train_loss -0.7382 +2024-09-05 17:45:56.137326: val_loss -0.7209 +2024-09-05 17:45:56.138983: Pseudo dice [0.9597, 0.953, 0.974, 0.9482, 0.8701, 0.9605, 0.9907] +2024-09-05 17:45:56.140470: Epoch time: 270.36 s +2024-09-05 17:45:59.344178: +2024-09-05 17:45:59.345308: Epoch 263 +2024-09-05 17:45:59.346009: Current learning rate: 0.0076 +2024-09-05 17:50:30.565744: train_loss -0.7225 +2024-09-05 17:50:30.587035: val_loss -0.7078 +2024-09-05 17:50:30.588252: Pseudo dice [0.9624, 0.9543, 0.9775, 0.9458, 0.8223, 0.9521, 0.9859] +2024-09-05 17:50:30.589157: Epoch time: 271.23 s +2024-09-05 17:50:35.626016: +2024-09-05 17:50:35.626931: Epoch 264 +2024-09-05 17:50:35.627636: Current learning rate: 0.00759 +2024-09-05 17:55:14.808623: train_loss -0.7318 +2024-09-05 17:55:14.817393: val_loss -0.7257 +2024-09-05 17:55:14.839207: Pseudo dice [0.9616, 0.9536, 0.9755, 0.9458, 0.8618, 0.9528, 0.9877] +2024-09-05 17:55:14.840773: Epoch time: 279.19 s +2024-09-05 17:55:17.852928: +2024-09-05 17:55:17.854064: Epoch 265 +2024-09-05 17:55:17.854833: Current learning rate: 0.00758 +2024-09-05 17:59:59.812349: train_loss -0.7267 +2024-09-05 17:59:59.829601: val_loss -0.7215 +2024-09-05 17:59:59.830899: Pseudo dice [0.9628, 0.9556, 0.9789, 0.9483, 0.8617, 0.9655, 0.9901] +2024-09-05 17:59:59.832322: Epoch time: 281.96 s +2024-09-05 18:00:02.616292: +2024-09-05 18:00:02.617643: Epoch 266 +2024-09-05 18:00:02.629491: Current learning rate: 0.00757 +2024-09-05 18:04:51.761682: train_loss -0.7264 +2024-09-05 18:04:51.777611: val_loss -0.7471 +2024-09-05 18:04:51.778873: Pseudo dice [0.9641, 0.9563, 0.9791, 0.9432, 0.8607, 0.9651, 0.9897] +2024-09-05 18:04:51.779981: Epoch time: 289.15 s +2024-09-05 18:04:51.780680: Yayy! New best EMA pseudo Dice: 0.9483 +2024-09-05 18:05:03.184642: +2024-09-05 18:05:03.185876: Epoch 267 +2024-09-05 18:05:03.186595: Current learning rate: 0.00756 +2024-09-05 18:09:41.688554: train_loss -0.7437 +2024-09-05 18:09:41.708076: val_loss -0.6994 +2024-09-05 18:09:41.708829: Pseudo dice [0.9548, 0.9523, 0.9708, 0.9406, 0.8304, 0.9619, 0.9866] +2024-09-05 18:09:41.709556: Epoch time: 278.51 s +2024-09-05 18:09:44.854420: +2024-09-05 18:09:44.855512: Epoch 268 +2024-09-05 18:09:44.856240: Current learning rate: 0.00755 +2024-09-05 18:14:07.379044: train_loss -0.7244 +2024-09-05 18:14:07.395183: val_loss -0.721 +2024-09-05 18:14:07.396436: Pseudo dice [0.9615, 0.9549, 0.9751, 0.947, 0.868, 0.9534, 0.9888] +2024-09-05 18:14:07.397142: Epoch time: 262.53 s +2024-09-05 18:14:10.448498: +2024-09-05 18:14:10.449551: Epoch 269 +2024-09-05 18:14:10.450831: Current learning rate: 0.00754 +2024-09-05 18:18:57.212291: train_loss -0.7051 +2024-09-05 18:18:57.227618: val_loss -0.7533 +2024-09-05 18:18:57.229482: Pseudo dice [0.9612, 0.9543, 0.9774, 0.9463, 0.8815, 0.9662, 0.9914] +2024-09-05 18:18:57.231198: Epoch time: 286.77 s +2024-09-05 18:18:57.233373: Yayy! New best EMA pseudo Dice: 0.9486 +2024-09-05 18:19:07.910305: +2024-09-05 18:19:07.911399: Epoch 270 +2024-09-05 18:19:07.912152: Current learning rate: 0.00753 +2024-09-05 18:23:37.065549: train_loss -0.7302 +2024-09-05 18:23:37.078218: val_loss -0.7177 +2024-09-05 18:23:37.087042: Pseudo dice [0.9644, 0.9594, 0.9787, 0.9461, 0.8816, 0.9678, 0.9909] +2024-09-05 18:23:37.088424: Epoch time: 269.16 s +2024-09-05 18:23:37.089788: Yayy! New best EMA pseudo Dice: 0.9493 +2024-09-05 18:23:47.974558: +2024-09-05 18:23:47.975425: Epoch 271 +2024-09-05 18:23:47.976109: Current learning rate: 0.00752 +2024-09-05 18:28:14.096804: train_loss -0.7231 +2024-09-05 18:28:14.111461: val_loss -0.7349 +2024-09-05 18:28:14.112725: Pseudo dice [0.9628, 0.9545, 0.9786, 0.943, 0.8617, 0.9532, 0.9881] +2024-09-05 18:28:14.114328: Epoch time: 266.13 s +2024-09-05 18:28:17.021837: +2024-09-05 18:28:17.023290: Epoch 272 +2024-09-05 18:28:17.024195: Current learning rate: 0.00751 +2024-09-05 18:32:36.620067: train_loss -0.7286 +2024-09-05 18:32:36.640674: val_loss -0.7517 +2024-09-05 18:32:36.642277: Pseudo dice [0.9618, 0.954, 0.9789, 0.9461, 0.8725, 0.9602, 0.9881] +2024-09-05 18:32:36.645272: Epoch time: 259.6 s +2024-09-05 18:32:36.646952: Yayy! New best EMA pseudo Dice: 0.9495 +2024-09-05 18:32:45.988365: +2024-09-05 18:32:45.990104: Epoch 273 +2024-09-05 18:32:45.990716: Current learning rate: 0.00751 +2024-09-05 18:37:27.539583: train_loss -0.7322 +2024-09-05 18:37:27.558803: val_loss -0.7135 +2024-09-05 18:37:27.559719: Pseudo dice [0.9586, 0.9498, 0.9735, 0.9379, 0.8067, 0.953, 0.9866] +2024-09-05 18:37:27.560664: Epoch time: 281.55 s +2024-09-05 18:37:30.737687: +2024-09-05 18:37:30.738789: Epoch 274 +2024-09-05 18:37:30.739926: Current learning rate: 0.0075 +2024-09-05 18:42:08.061331: train_loss -0.7078 +2024-09-05 18:42:08.079944: val_loss -0.7124 +2024-09-05 18:42:08.081192: Pseudo dice [0.9555, 0.9517, 0.9731, 0.9378, 0.8382, 0.9607, 0.9883] +2024-09-05 18:42:08.082259: Epoch time: 277.33 s +2024-09-05 18:42:12.987913: +2024-09-05 18:42:12.989039: Epoch 275 +2024-09-05 18:42:12.989887: Current learning rate: 0.00749 +2024-09-05 18:46:39.643689: train_loss -0.7134 +2024-09-05 18:46:39.670806: val_loss -0.7215 +2024-09-05 18:46:39.671808: Pseudo dice [0.9588, 0.9517, 0.9764, 0.9452, 0.8432, 0.9516, 0.9836] +2024-09-05 18:46:39.672585: Epoch time: 266.66 s +2024-09-05 18:46:42.021214: +2024-09-05 18:46:42.022161: Epoch 276 +2024-09-05 18:46:42.023163: Current learning rate: 0.00748 +2024-09-05 18:51:21.135320: train_loss -0.7185 +2024-09-05 18:51:21.160240: val_loss -0.7382 +2024-09-05 18:51:21.161637: Pseudo dice [0.9558, 0.9521, 0.9733, 0.935, 0.8437, 0.96, 0.9891] +2024-09-05 18:51:21.163069: Epoch time: 279.12 s +2024-09-05 18:51:25.009561: +2024-09-05 18:51:25.010633: Epoch 277 +2024-09-05 18:51:25.011495: Current learning rate: 0.00747 +2024-09-05 18:56:04.389940: train_loss -0.7234 +2024-09-05 18:56:04.392800: val_loss -0.6943 +2024-09-05 18:56:04.393951: Pseudo dice [0.958, 0.9509, 0.97, 0.9367, 0.8084, 0.9601, 0.9869] +2024-09-05 18:56:04.394796: Epoch time: 279.39 s +2024-09-05 18:56:07.152891: +2024-09-05 18:56:07.154004: Epoch 278 +2024-09-05 18:56:07.154806: Current learning rate: 0.00746 +2024-09-05 19:00:54.239625: train_loss -0.7263 +2024-09-05 19:00:54.260895: val_loss -0.7674 +2024-09-05 19:00:54.261768: Pseudo dice [0.9614, 0.9545, 0.9782, 0.942, 0.8601, 0.9622, 0.9877] +2024-09-05 19:00:54.262880: Epoch time: 287.09 s +2024-09-05 19:00:56.970668: +2024-09-05 19:00:56.972254: Epoch 279 +2024-09-05 19:00:56.973041: Current learning rate: 0.00745 +2024-09-05 19:05:46.858689: train_loss -0.7136 +2024-09-05 19:05:46.876893: val_loss -0.6872 +2024-09-05 19:05:46.879907: Pseudo dice [0.9629, 0.9554, 0.977, 0.949, 0.8702, 0.9283, 0.9829] +2024-09-05 19:05:46.880790: Epoch time: 289.89 s +2024-09-05 19:05:49.543659: +2024-09-05 19:05:49.545276: Epoch 280 +2024-09-05 19:05:49.546560: Current learning rate: 0.00744 +2024-09-05 19:10:37.571570: train_loss -0.7377 +2024-09-05 19:10:37.588516: val_loss -0.7558 +2024-09-05 19:10:37.589533: Pseudo dice [0.9582, 0.9501, 0.9707, 0.9355, 0.8525, 0.968, 0.9896] +2024-09-05 19:10:37.590504: Epoch time: 288.03 s +2024-09-05 19:10:40.573905: +2024-09-05 19:10:40.574962: Epoch 281 +2024-09-05 19:10:40.575655: Current learning rate: 0.00743 +2024-09-05 19:15:35.838412: train_loss -0.7293 +2024-09-05 19:15:35.850971: val_loss -0.7319 +2024-09-05 19:15:35.852045: Pseudo dice [0.9581, 0.9531, 0.9725, 0.941, 0.8552, 0.9478, 0.9865] +2024-09-05 19:15:35.853343: Epoch time: 295.27 s +2024-09-05 19:15:39.810629: +2024-09-05 19:15:39.811783: Epoch 282 +2024-09-05 19:15:39.813122: Current learning rate: 0.00742 +2024-09-05 19:20:21.387222: train_loss -0.7311 +2024-09-05 19:20:21.402876: val_loss -0.7325 +2024-09-05 19:20:21.403725: Pseudo dice [0.9628, 0.9546, 0.979, 0.939, 0.8567, 0.9538, 0.9869] +2024-09-05 19:20:21.404453: Epoch time: 281.58 s +2024-09-05 19:20:23.622490: +2024-09-05 19:20:23.628328: Epoch 283 +2024-09-05 19:20:23.629449: Current learning rate: 0.00741 +2024-09-05 19:25:04.671936: train_loss -0.7251 +2024-09-05 19:25:04.684555: val_loss -0.7469 +2024-09-05 19:25:04.686148: Pseudo dice [0.9618, 0.9568, 0.9763, 0.9442, 0.861, 0.9604, 0.9904] +2024-09-05 19:25:04.689284: Epoch time: 281.06 s +2024-09-05 19:25:08.104754: +2024-09-05 19:25:08.105775: Epoch 284 +2024-09-05 19:25:08.106741: Current learning rate: 0.0074 +2024-09-05 19:30:02.973333: train_loss -0.7327 +2024-09-05 19:30:03.000656: val_loss -0.718 +2024-09-05 19:30:03.002429: Pseudo dice [0.9615, 0.9557, 0.9781, 0.9452, 0.8416, 0.9628, 0.9858] +2024-09-05 19:30:03.003530: Epoch time: 294.87 s +2024-09-05 19:30:05.325366: +2024-09-05 19:30:05.326522: Epoch 285 +2024-09-05 19:30:05.327256: Current learning rate: 0.00739 +2024-09-05 19:34:54.711669: train_loss -0.7235 +2024-09-05 19:34:54.722985: val_loss -0.7501 +2024-09-05 19:34:54.724192: Pseudo dice [0.9655, 0.9589, 0.9799, 0.9496, 0.8764, 0.9669, 0.9905] +2024-09-05 19:34:54.725398: Epoch time: 289.39 s +2024-09-05 19:34:57.428100: +2024-09-05 19:34:57.429184: Epoch 286 +2024-09-05 19:34:57.430264: Current learning rate: 0.00738 +2024-09-05 19:39:38.999117: train_loss -0.7272 +2024-09-05 19:39:39.027936: val_loss -0.7382 +2024-09-05 19:39:39.030070: Pseudo dice [0.9636, 0.9549, 0.9772, 0.951, 0.8769, 0.9651, 0.9911] +2024-09-05 19:39:39.031223: Epoch time: 281.58 s +2024-09-05 19:39:42.165237: +2024-09-05 19:39:42.166808: Epoch 287 +2024-09-05 19:39:42.168855: Current learning rate: 0.00738 +2024-09-05 19:44:26.218091: train_loss -0.7253 +2024-09-05 19:44:26.235983: val_loss -0.7152 +2024-09-05 19:44:26.237038: Pseudo dice [0.9626, 0.9551, 0.9758, 0.9502, 0.8744, 0.962, 0.9914] +2024-09-05 19:44:26.237982: Epoch time: 284.06 s +2024-09-05 19:44:29.004699: +2024-09-05 19:44:29.006158: Epoch 288 +2024-09-05 19:44:29.007044: Current learning rate: 0.00737 +2024-09-05 19:48:58.347052: train_loss -0.7342 +2024-09-05 19:48:58.356830: val_loss -0.701 +2024-09-05 19:48:58.357989: Pseudo dice [0.9604, 0.9564, 0.9746, 0.9447, 0.8319, 0.9561, 0.9845] +2024-09-05 19:48:58.358652: Epoch time: 269.35 s +2024-09-05 19:49:00.674439: +2024-09-05 19:49:00.675686: Epoch 289 +2024-09-05 19:49:00.676697: Current learning rate: 0.00736 +2024-09-05 19:53:59.328114: train_loss -0.7377 +2024-09-05 19:53:59.329747: val_loss -0.7537 +2024-09-05 19:53:59.331101: Pseudo dice [0.9622, 0.9529, 0.9768, 0.9422, 0.8573, 0.9442, 0.9863] +2024-09-05 19:53:59.332084: Epoch time: 298.66 s +2024-09-05 19:54:03.457360: +2024-09-05 19:54:03.458494: Epoch 290 +2024-09-05 19:54:03.459824: Current learning rate: 0.00735 +2024-09-05 19:58:30.289511: train_loss -0.7369 +2024-09-05 19:58:30.291406: val_loss -0.6841 +2024-09-05 19:58:30.292700: Pseudo dice [0.9624, 0.9529, 0.9749, 0.9391, 0.781, 0.9644, 0.9805] +2024-09-05 19:58:30.294552: Epoch time: 266.84 s +2024-09-05 19:58:32.918502: +2024-09-05 19:58:32.920567: Epoch 291 +2024-09-05 19:58:32.921588: Current learning rate: 0.00734 +2024-09-05 20:03:10.129813: train_loss -0.7232 +2024-09-05 20:03:10.131941: val_loss -0.7481 +2024-09-05 20:03:10.133272: Pseudo dice [0.9598, 0.955, 0.9739, 0.9409, 0.8605, 0.9622, 0.9904] +2024-09-05 20:03:10.134356: Epoch time: 277.22 s +2024-09-05 20:03:14.355944: +2024-09-05 20:03:14.357083: Epoch 292 +2024-09-05 20:03:14.358161: Current learning rate: 0.00733 +2024-09-05 20:08:01.414275: train_loss -0.7275 +2024-09-05 20:08:01.427104: val_loss -0.725 +2024-09-05 20:08:01.428145: Pseudo dice [0.9565, 0.9527, 0.9718, 0.9413, 0.8352, 0.9644, 0.9875] +2024-09-05 20:08:01.429024: Epoch time: 287.06 s +2024-09-05 20:08:05.073892: +2024-09-05 20:08:05.076706: Epoch 293 +2024-09-05 20:08:05.091072: Current learning rate: 0.00732 +2024-09-05 20:12:47.522749: train_loss -0.7255 +2024-09-05 20:12:47.535323: val_loss -0.7704 +2024-09-05 20:12:47.536798: Pseudo dice [0.9649, 0.956, 0.9796, 0.9503, 0.8787, 0.9668, 0.9914] +2024-09-05 20:12:47.539284: Epoch time: 282.45 s +2024-09-05 20:12:51.391332: +2024-09-05 20:12:51.392383: Epoch 294 +2024-09-05 20:12:51.393634: Current learning rate: 0.00731 +2024-09-05 20:17:31.207071: train_loss -0.7323 +2024-09-05 20:17:31.209042: val_loss -0.7135 +2024-09-05 20:17:31.210431: Pseudo dice [0.9651, 0.9584, 0.9804, 0.9496, 0.8797, 0.9603, 0.9903] +2024-09-05 20:17:31.217326: Epoch time: 279.82 s +2024-09-05 20:17:33.534726: +2024-09-05 20:17:33.535796: Epoch 295 +2024-09-05 20:17:33.536586: Current learning rate: 0.0073 +2024-09-05 20:22:17.373584: train_loss -0.7387 +2024-09-05 20:22:17.385717: val_loss -0.6884 +2024-09-05 20:22:17.387286: Pseudo dice [0.9631, 0.9545, 0.9764, 0.9444, 0.8404, 0.9675, 0.9868] +2024-09-05 20:22:17.399090: Epoch time: 283.84 s +2024-09-05 20:22:20.146827: +2024-09-05 20:22:20.147748: Epoch 296 +2024-09-05 20:22:20.149408: Current learning rate: 0.00729 +2024-09-05 20:27:09.761381: train_loss -0.7135 +2024-09-05 20:27:09.782037: val_loss -0.7058 +2024-09-05 20:27:09.783222: Pseudo dice [0.9607, 0.9555, 0.9755, 0.9439, 0.8691, 0.9583, 0.9883] +2024-09-05 20:27:09.784065: Epoch time: 289.62 s +2024-09-05 20:27:12.641581: +2024-09-05 20:27:12.642658: Epoch 297 +2024-09-05 20:27:12.643450: Current learning rate: 0.00728 +2024-09-05 20:31:41.244422: train_loss -0.7142 +2024-09-05 20:31:41.246903: val_loss -0.7156 +2024-09-05 20:31:41.247593: Pseudo dice [0.9611, 0.9526, 0.9738, 0.9411, 0.8488, 0.9512, 0.9861] +2024-09-05 20:31:41.248227: Epoch time: 268.61 s +2024-09-05 20:31:43.656495: +2024-09-05 20:31:43.657434: Epoch 298 +2024-09-05 20:31:43.658146: Current learning rate: 0.00727 +2024-09-05 20:36:09.031399: train_loss -0.7141 +2024-09-05 20:36:09.051348: val_loss -0.7385 +2024-09-05 20:36:09.053831: Pseudo dice [0.9609, 0.9525, 0.9771, 0.9409, 0.8635, 0.9488, 0.986] +2024-09-05 20:36:09.054931: Epoch time: 265.38 s +2024-09-05 20:36:11.947258: +2024-09-05 20:36:11.952237: Epoch 299 +2024-09-05 20:36:11.953971: Current learning rate: 0.00726 +2024-09-05 20:41:05.691143: train_loss -0.7428 +2024-09-05 20:41:05.706790: val_loss -0.6937 +2024-09-05 20:41:05.708368: Pseudo dice [0.9621, 0.9537, 0.9748, 0.9514, 0.8718, 0.9642, 0.9898] +2024-09-05 20:41:05.710289: Epoch time: 293.75 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_00_30_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_00_30_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..1c3c11035f63c54b1914ba226ebd7ac9be502b2a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_00_30_15.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 00:30:18.941883: Using torch.compile... +2024-09-06 00:30:30.120279: do_dummy_2d_data_aug: False +2024-09-06 00:30:30.126404: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 00:30:30.128010: The split file contains 5 splits. +2024-09-06 00:30:30.128867: Desired fold for training: 1 +2024-09-06 00:30:30.129645: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 00:30:43.950471: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 00:30:45.884275: +2024-09-06 00:30:45.885711: Epoch 300 +2024-09-06 00:30:45.887359: Current learning rate: 0.00725 +2024-09-06 00:49:20.522009: train_loss -0.7209 +2024-09-06 00:49:20.523568: val_loss -0.7538 +2024-09-06 00:49:20.524604: Pseudo dice [0.961, 0.9552, 0.9749, 0.9459, 0.8518, 0.9636, 0.9865] +2024-09-06 00:49:20.525582: Epoch time: 1114.64 s +2024-09-06 00:49:22.606277: +2024-09-06 00:49:22.607806: Epoch 301 +2024-09-06 00:49:22.609293: Current learning rate: 0.00724 +2024-09-06 00:54:05.755775: train_loss -0.7198 +2024-09-06 00:54:05.770677: val_loss -0.7464 +2024-09-06 00:54:05.772932: Pseudo dice [0.9608, 0.9542, 0.9766, 0.9458, 0.8658, 0.9616, 0.9861] +2024-09-06 00:54:05.774800: Epoch time: 283.15 s +2024-09-06 00:54:09.614479: +2024-09-06 00:54:09.625670: Epoch 302 +2024-09-06 00:54:09.629101: Current learning rate: 0.00724 +2024-09-06 00:58:55.118804: train_loss -0.7291 +2024-09-06 00:58:55.133913: val_loss -0.7251 +2024-09-06 00:58:55.135499: Pseudo dice [0.9625, 0.9569, 0.9781, 0.9388, 0.8562, 0.9649, 0.9881] +2024-09-06 00:58:55.138267: Epoch time: 285.51 s +2024-09-06 00:58:59.245308: +2024-09-06 00:58:59.247244: Epoch 303 +2024-09-06 00:58:59.249388: Current learning rate: 0.00723 +2024-09-06 01:03:54.929202: train_loss -0.7355 +2024-09-06 01:03:54.955788: val_loss -0.7615 +2024-09-06 01:03:54.957922: Pseudo dice [0.9624, 0.9567, 0.974, 0.9544, 0.8552, 0.9477, 0.9847] +2024-09-06 01:03:54.959323: Epoch time: 295.69 s +2024-09-06 01:03:59.715424: +2024-09-06 01:03:59.882242: Epoch 304 +2024-09-06 01:03:59.897356: Current learning rate: 0.00722 +2024-09-06 01:09:10.675407: train_loss -0.7348 +2024-09-06 01:09:10.678034: val_loss -0.7439 +2024-09-06 01:09:10.680684: Pseudo dice [0.9635, 0.9578, 0.9783, 0.9445, 0.841, 0.9539, 0.9828] +2024-09-06 01:09:10.683003: Epoch time: 310.97 s +2024-09-06 01:09:13.921696: +2024-09-06 01:09:13.923074: Epoch 305 +2024-09-06 01:09:13.924228: Current learning rate: 0.00721 +2024-09-06 01:13:59.470675: train_loss -0.7255 +2024-09-06 01:13:59.481188: val_loss -0.7501 +2024-09-06 01:13:59.482258: Pseudo dice [0.9635, 0.9564, 0.9799, 0.943, 0.86, 0.9481, 0.9866] +2024-09-06 01:13:59.483495: Epoch time: 285.56 s +2024-09-06 01:14:02.419447: +2024-09-06 01:14:02.431532: Epoch 306 +2024-09-06 01:14:02.433697: Current learning rate: 0.0072 +2024-09-06 01:19:02.194757: train_loss -0.7267 +2024-09-06 01:19:02.212128: val_loss -0.7434 +2024-09-06 01:19:02.214402: Pseudo dice [0.9625, 0.9572, 0.9783, 0.9492, 0.8699, 0.951, 0.9888] +2024-09-06 01:19:02.215967: Epoch time: 299.78 s +2024-09-06 01:19:05.876177: +2024-09-06 01:19:05.887079: Epoch 307 +2024-09-06 01:19:05.888583: Current learning rate: 0.00719 +2024-09-06 01:24:14.700285: train_loss -0.7302 +2024-09-06 01:24:14.715981: val_loss -0.7607 +2024-09-06 01:24:14.717503: Pseudo dice [0.9596, 0.9556, 0.9738, 0.9475, 0.8686, 0.9543, 0.9883] +2024-09-06 01:24:14.718855: Epoch time: 308.83 s +2024-09-06 01:24:18.018312: +2024-09-06 01:24:18.019645: Epoch 308 +2024-09-06 01:24:18.020775: Current learning rate: 0.00718 +2024-09-06 01:29:00.847761: train_loss -0.7309 +2024-09-06 01:29:00.863078: val_loss -0.7154 +2024-09-06 01:29:00.864280: Pseudo dice [0.9621, 0.9577, 0.9769, 0.9491, 0.8522, 0.9506, 0.9844] +2024-09-06 01:29:00.865772: Epoch time: 282.84 s +2024-09-06 01:29:04.877289: +2024-09-06 01:29:04.878890: Epoch 309 +2024-09-06 01:29:04.880373: Current learning rate: 0.00717 +2024-09-06 01:33:45.955226: train_loss -0.7352 +2024-09-06 01:33:45.958667: val_loss -0.7284 +2024-09-06 01:33:45.960208: Pseudo dice [0.96, 0.9552, 0.9772, 0.9439, 0.8482, 0.9457, 0.9826] +2024-09-06 01:33:45.961718: Epoch time: 281.08 s +2024-09-06 01:33:49.296502: +2024-09-06 01:33:49.297988: Epoch 310 +2024-09-06 01:33:49.299231: Current learning rate: 0.00716 +2024-09-06 01:38:40.151799: train_loss -0.7295 +2024-09-06 01:38:40.175146: val_loss -0.7448 +2024-09-06 01:38:40.176567: Pseudo dice [0.9619, 0.9538, 0.9786, 0.9466, 0.8751, 0.9679, 0.9907] +2024-09-06 01:38:40.177484: Epoch time: 290.86 s +2024-09-06 01:38:43.484946: +2024-09-06 01:38:43.493314: Epoch 311 +2024-09-06 01:38:43.495567: Current learning rate: 0.00715 +2024-09-06 01:43:44.562276: train_loss -0.7306 +2024-09-06 01:43:44.575350: val_loss -0.7159 +2024-09-06 01:43:44.576475: Pseudo dice [0.9593, 0.9537, 0.9755, 0.9431, 0.8616, 0.9657, 0.9883] +2024-09-06 01:43:44.577454: Epoch time: 301.08 s +2024-09-06 01:43:48.043246: +2024-09-06 01:43:48.083405: Epoch 312 +2024-09-06 01:43:48.324823: Current learning rate: 0.00714 +2024-09-06 01:48:40.206774: train_loss -0.7287 +2024-09-06 01:48:40.223713: val_loss -0.7378 +2024-09-06 01:48:40.225476: Pseudo dice [0.9623, 0.9559, 0.9772, 0.9429, 0.8511, 0.9574, 0.9865] +2024-09-06 01:48:40.227518: Epoch time: 292.17 s +2024-09-06 01:48:44.020147: +2024-09-06 01:48:44.021523: Epoch 313 +2024-09-06 01:48:44.022612: Current learning rate: 0.00713 +2024-09-06 01:53:32.129734: train_loss -0.7262 +2024-09-06 01:53:32.139764: val_loss -0.711 +2024-09-06 01:53:32.143195: Pseudo dice [0.9642, 0.9555, 0.9786, 0.9465, 0.8534, 0.9635, 0.9888] +2024-09-06 01:53:32.144758: Epoch time: 288.12 s +2024-09-06 01:53:35.759312: +2024-09-06 01:53:35.770837: Epoch 314 +2024-09-06 01:53:35.772622: Current learning rate: 0.00712 +2024-09-06 01:58:28.155164: train_loss -0.7256 +2024-09-06 01:58:28.159862: val_loss -0.7376 +2024-09-06 01:58:28.161339: Pseudo dice [0.9617, 0.9553, 0.9764, 0.9451, 0.823, 0.9585, 0.9858] +2024-09-06 01:58:28.162625: Epoch time: 292.4 s +2024-09-06 01:58:31.031976: +2024-09-06 01:58:31.033308: Epoch 315 +2024-09-06 01:58:31.034563: Current learning rate: 0.00711 +2024-09-06 02:03:13.959616: train_loss -0.7355 +2024-09-06 02:03:13.961967: val_loss -0.7496 +2024-09-06 02:03:13.963183: Pseudo dice [0.9608, 0.9525, 0.976, 0.9435, 0.8522, 0.958, 0.9871] +2024-09-06 02:03:13.965300: Epoch time: 282.93 s +2024-09-06 02:03:17.582888: +2024-09-06 02:03:17.594898: Epoch 316 +2024-09-06 02:03:17.596598: Current learning rate: 0.0071 +2024-09-06 02:08:27.760520: train_loss -0.7312 +2024-09-06 02:08:27.763051: val_loss -0.7608 +2024-09-06 02:08:27.764408: Pseudo dice [0.961, 0.9546, 0.9753, 0.9441, 0.8734, 0.9672, 0.9896] +2024-09-06 02:08:27.765624: Epoch time: 310.18 s +2024-09-06 02:08:30.835538: +2024-09-06 02:08:30.836675: Epoch 317 +2024-09-06 02:08:30.837537: Current learning rate: 0.0071 +2024-09-06 02:13:26.292201: train_loss -0.734 +2024-09-06 02:13:26.296823: val_loss -0.7329 +2024-09-06 02:13:26.299602: Pseudo dice [0.9589, 0.9571, 0.9722, 0.9459, 0.8767, 0.9683, 0.9918] +2024-09-06 02:13:26.301488: Epoch time: 295.46 s +2024-09-06 02:13:30.440337: +2024-09-06 02:13:30.441720: Epoch 318 +2024-09-06 02:13:30.443355: Current learning rate: 0.00709 +2024-09-06 02:18:42.645640: train_loss -0.7259 +2024-09-06 02:18:42.657907: val_loss -0.7149 +2024-09-06 02:18:42.659800: Pseudo dice [0.962, 0.9566, 0.9736, 0.9481, 0.8667, 0.9516, 0.9876] +2024-09-06 02:18:42.661431: Epoch time: 312.21 s +2024-09-06 02:18:45.795168: +2024-09-06 02:18:45.796621: Epoch 319 +2024-09-06 02:18:45.797852: Current learning rate: 0.00708 +2024-09-06 02:23:36.208740: train_loss -0.7331 +2024-09-06 02:23:36.219032: val_loss -0.7585 +2024-09-06 02:23:36.220232: Pseudo dice [0.9613, 0.9535, 0.9766, 0.9422, 0.8582, 0.9653, 0.9894] +2024-09-06 02:23:36.221378: Epoch time: 290.42 s +2024-09-06 02:23:39.385369: +2024-09-06 02:23:39.386981: Epoch 320 +2024-09-06 02:23:39.388596: Current learning rate: 0.00707 +2024-09-06 02:28:42.456412: train_loss -0.7181 +2024-09-06 02:28:42.467588: val_loss -0.7394 +2024-09-06 02:28:42.469019: Pseudo dice [0.9633, 0.9526, 0.9786, 0.9472, 0.8364, 0.9491, 0.9832] +2024-09-06 02:28:42.470305: Epoch time: 303.08 s +2024-09-06 02:28:45.823186: +2024-09-06 02:28:45.824720: Epoch 321 +2024-09-06 02:28:45.826298: Current learning rate: 0.00706 +2024-09-06 02:33:42.964424: train_loss -0.7117 +2024-09-06 02:33:42.979256: val_loss -0.7093 +2024-09-06 02:33:42.980611: Pseudo dice [0.9575, 0.9491, 0.9743, 0.9415, 0.8632, 0.9489, 0.9843] +2024-09-06 02:33:42.981710: Epoch time: 297.15 s +2024-09-06 02:33:45.848534: +2024-09-06 02:33:45.862921: Epoch 322 +2024-09-06 02:33:45.864307: Current learning rate: 0.00705 +2024-09-06 02:38:37.645555: train_loss -0.7164 +2024-09-06 02:38:37.647649: val_loss -0.7572 +2024-09-06 02:38:37.649065: Pseudo dice [0.9632, 0.9538, 0.9784, 0.9438, 0.8584, 0.9625, 0.989] +2024-09-06 02:38:37.650226: Epoch time: 291.81 s +2024-09-06 02:38:41.336379: +2024-09-06 02:38:41.346853: Epoch 323 +2024-09-06 02:38:41.348101: Current learning rate: 0.00704 +2024-09-06 02:43:34.326158: train_loss -0.7169 +2024-09-06 02:43:34.335663: val_loss -0.7192 +2024-09-06 02:43:34.337236: Pseudo dice [0.9609, 0.9533, 0.9762, 0.9435, 0.8486, 0.9392, 0.9828] +2024-09-06 02:43:34.338809: Epoch time: 293.0 s +2024-09-06 02:43:37.135121: +2024-09-06 02:43:37.137222: Epoch 324 +2024-09-06 02:43:37.139503: Current learning rate: 0.00703 +2024-09-06 02:48:33.247695: train_loss -0.7347 +2024-09-06 02:48:33.259495: val_loss -0.7239 +2024-09-06 02:48:33.261237: Pseudo dice [0.9604, 0.9553, 0.974, 0.9434, 0.8362, 0.9664, 0.9856] +2024-09-06 02:48:33.263816: Epoch time: 296.12 s +2024-09-06 02:48:37.899799: +2024-09-06 02:48:37.901567: Epoch 325 +2024-09-06 02:48:37.902846: Current learning rate: 0.00702 +2024-09-06 02:53:27.367983: train_loss -0.7352 +2024-09-06 02:53:27.378951: val_loss -0.7146 +2024-09-06 02:53:27.380508: Pseudo dice [0.961, 0.956, 0.9763, 0.9416, 0.8532, 0.9641, 0.9868] +2024-09-06 02:53:27.382380: Epoch time: 289.47 s +2024-09-06 02:53:31.938049: +2024-09-06 02:53:31.939560: Epoch 326 +2024-09-06 02:53:31.940664: Current learning rate: 0.00701 +2024-09-06 02:58:30.268272: train_loss -0.7253 +2024-09-06 02:58:30.283571: val_loss -0.7306 +2024-09-06 02:58:30.284677: Pseudo dice [0.9606, 0.9541, 0.9756, 0.9484, 0.8339, 0.96, 0.9843] +2024-09-06 02:58:30.299117: Epoch time: 298.33 s +2024-09-06 02:58:34.117348: +2024-09-06 02:58:34.119769: Epoch 327 +2024-09-06 02:58:34.121121: Current learning rate: 0.007 +2024-09-06 03:03:36.627390: train_loss -0.731 +2024-09-06 03:03:36.643997: val_loss -0.6724 +2024-09-06 03:03:36.645772: Pseudo dice [0.9639, 0.9571, 0.9777, 0.9469, 0.8321, 0.9632, 0.9863] +2024-09-06 03:03:36.647254: Epoch time: 302.52 s +2024-09-06 03:03:39.475609: +2024-09-06 03:03:39.476804: Epoch 328 +2024-09-06 03:03:39.478113: Current learning rate: 0.00699 +2024-09-06 03:08:30.172147: train_loss -0.7345 +2024-09-06 03:08:30.187993: val_loss -0.743 +2024-09-06 03:08:30.199418: Pseudo dice [0.964, 0.9556, 0.9783, 0.9473, 0.8816, 0.9404, 0.9854] +2024-09-06 03:08:30.201170: Epoch time: 290.7 s +2024-09-06 03:08:32.819110: +2024-09-06 03:08:32.822574: Epoch 329 +2024-09-06 03:08:32.823839: Current learning rate: 0.00698 +2024-09-06 03:13:22.553358: train_loss -0.7342 +2024-09-06 03:13:22.572113: val_loss -0.7027 +2024-09-06 03:13:22.573826: Pseudo dice [0.9643, 0.9583, 0.9795, 0.9462, 0.8074, 0.9459, 0.9812] +2024-09-06 03:13:22.575031: Epoch time: 289.74 s +2024-09-06 03:13:25.444438: +2024-09-06 03:13:25.445884: Epoch 330 +2024-09-06 03:13:25.446909: Current learning rate: 0.00697 +2024-09-06 03:18:19.391366: train_loss -0.7402 +2024-09-06 03:18:19.407364: val_loss -0.7448 +2024-09-06 03:18:19.408545: Pseudo dice [0.9625, 0.9597, 0.9788, 0.9412, 0.8365, 0.953, 0.9811] +2024-09-06 03:18:19.409479: Epoch time: 293.97 s +2024-09-06 03:18:22.576971: +2024-09-06 03:18:22.578332: Epoch 331 +2024-09-06 03:18:22.579573: Current learning rate: 0.00696 +2024-09-06 03:23:22.677944: train_loss -0.7245 +2024-09-06 03:23:22.680842: val_loss -0.7525 +2024-09-06 03:23:22.682757: Pseudo dice [0.9619, 0.9562, 0.9777, 0.9422, 0.8789, 0.9685, 0.9893] +2024-09-06 03:23:22.684632: Epoch time: 300.11 s +2024-09-06 03:23:25.570875: +2024-09-06 03:23:25.575344: Epoch 332 +2024-09-06 03:23:25.582378: Current learning rate: 0.00696 +2024-09-06 03:28:11.489895: train_loss -0.7338 +2024-09-06 03:28:11.515478: val_loss -0.7213 +2024-09-06 03:28:11.523664: Pseudo dice [0.9637, 0.9558, 0.9784, 0.944, 0.8391, 0.9644, 0.9855] +2024-09-06 03:28:11.526047: Epoch time: 285.92 s +2024-09-06 03:28:14.789893: +2024-09-06 03:28:14.791456: Epoch 333 +2024-09-06 03:28:14.794143: Current learning rate: 0.00695 +2024-09-06 03:33:06.500146: train_loss -0.7101 +2024-09-06 03:33:06.507489: val_loss -0.6934 +2024-09-06 03:33:06.509506: Pseudo dice [0.9541, 0.9525, 0.9649, 0.923, 0.8561, 0.9663, 0.99] +2024-09-06 03:33:06.510882: Epoch time: 291.72 s +2024-09-06 03:33:09.713635: +2024-09-06 03:33:09.727430: Epoch 334 +2024-09-06 03:33:09.729836: Current learning rate: 0.00694 +2024-09-06 03:38:05.270776: train_loss -0.7223 +2024-09-06 03:38:05.275131: val_loss -0.7414 +2024-09-06 03:38:05.290349: Pseudo dice [0.9622, 0.9557, 0.9751, 0.9439, 0.848, 0.9607, 0.9863] +2024-09-06 03:38:05.291923: Epoch time: 295.56 s +2024-09-06 03:38:08.032239: +2024-09-06 03:38:08.033529: Epoch 335 +2024-09-06 03:38:08.034876: Current learning rate: 0.00693 +2024-09-06 03:43:00.801107: train_loss -0.7199 +2024-09-06 03:43:00.828011: val_loss -0.7256 +2024-09-06 03:43:00.829677: Pseudo dice [0.9581, 0.9519, 0.9699, 0.9418, 0.8639, 0.9543, 0.9877] +2024-09-06 03:43:00.831417: Epoch time: 292.77 s +2024-09-06 03:43:04.311705: +2024-09-06 03:43:04.326969: Epoch 336 +2024-09-06 03:43:04.329255: Current learning rate: 0.00692 +2024-09-06 03:48:16.295691: train_loss -0.6967 +2024-09-06 03:48:16.298478: val_loss -0.6745 +2024-09-06 03:48:16.300527: Pseudo dice [0.9502, 0.9424, 0.966, 0.9336, 0.8112, 0.9566, 0.9826] +2024-09-06 03:48:16.303621: Epoch time: 311.99 s +2024-09-06 03:48:19.990195: +2024-09-06 03:48:19.991621: Epoch 337 +2024-09-06 03:48:19.992983: Current learning rate: 0.00691 +2024-09-06 03:53:06.991296: train_loss -0.6973 +2024-09-06 03:53:06.996852: val_loss -0.7046 +2024-09-06 03:53:06.999031: Pseudo dice [0.9567, 0.9488, 0.9671, 0.9353, 0.8518, 0.9583, 0.9887] +2024-09-06 03:53:07.001413: Epoch time: 287.01 s +2024-09-06 03:53:10.920349: +2024-09-06 03:53:10.921526: Epoch 338 +2024-09-06 03:53:10.922908: Current learning rate: 0.0069 +2024-09-06 03:58:10.669721: train_loss -0.694 +2024-09-06 03:58:10.672786: val_loss -0.7069 +2024-09-06 03:58:10.673967: Pseudo dice [0.9511, 0.9495, 0.9676, 0.9453, 0.8607, 0.9513, 0.9855] +2024-09-06 03:58:10.674926: Epoch time: 299.75 s +2024-09-06 03:58:14.172410: +2024-09-06 03:58:14.173630: Epoch 339 +2024-09-06 03:58:14.174754: Current learning rate: 0.00689 +2024-09-06 04:03:02.360500: train_loss -0.7051 +2024-09-06 04:03:02.362987: val_loss -0.6979 +2024-09-06 04:03:02.364269: Pseudo dice [0.9565, 0.9524, 0.97, 0.9446, 0.8133, 0.9624, 0.9848] +2024-09-06 04:03:02.366428: Epoch time: 288.19 s +2024-09-06 04:03:05.042458: +2024-09-06 04:03:05.044165: Epoch 340 +2024-09-06 04:03:05.045372: Current learning rate: 0.00688 +2024-09-06 04:07:50.748454: train_loss -0.7233 +2024-09-06 04:07:50.767525: val_loss -0.7693 +2024-09-06 04:07:50.768807: Pseudo dice [0.9595, 0.9515, 0.9752, 0.9396, 0.866, 0.9653, 0.9914] +2024-09-06 04:07:50.769924: Epoch time: 285.71 s +2024-09-06 04:07:54.152903: +2024-09-06 04:07:54.166990: Epoch 341 +2024-09-06 04:07:54.168497: Current learning rate: 0.00687 +2024-09-06 04:12:42.126843: train_loss -0.7168 +2024-09-06 04:12:42.128729: val_loss -0.7246 +2024-09-06 04:12:42.130022: Pseudo dice [0.9619, 0.9545, 0.9742, 0.9444, 0.8607, 0.9536, 0.9875] +2024-09-06 04:12:42.131737: Epoch time: 287.98 s +2024-09-06 04:12:45.162790: +2024-09-06 04:12:45.164021: Epoch 342 +2024-09-06 04:12:45.165160: Current learning rate: 0.00686 +2024-09-06 04:17:30.481417: train_loss -0.7211 +2024-09-06 04:17:30.510863: val_loss -0.7413 +2024-09-06 04:17:30.512592: Pseudo dice [0.965, 0.9553, 0.9805, 0.9493, 0.8601, 0.9655, 0.9874] +2024-09-06 04:17:30.514143: Epoch time: 285.32 s +2024-09-06 04:17:34.478215: +2024-09-06 04:17:34.479672: Epoch 343 +2024-09-06 04:17:34.481792: Current learning rate: 0.00685 +2024-09-06 04:22:11.497254: train_loss -0.7224 +2024-09-06 04:22:11.520312: val_loss -0.7352 +2024-09-06 04:22:11.522054: Pseudo dice [0.9604, 0.9531, 0.9746, 0.9396, 0.835, 0.9656, 0.9894] +2024-09-06 04:22:11.523405: Epoch time: 277.03 s +2024-09-06 04:22:15.899872: +2024-09-06 04:22:15.901562: Epoch 344 +2024-09-06 04:22:15.902907: Current learning rate: 0.00684 +2024-09-06 04:27:03.509989: train_loss -0.7278 +2024-09-06 04:27:03.527405: val_loss -0.7274 +2024-09-06 04:27:03.528441: Pseudo dice [0.9586, 0.953, 0.9706, 0.9463, 0.8519, 0.9461, 0.9863] +2024-09-06 04:27:03.529411: Epoch time: 287.62 s +2024-09-06 04:27:06.129685: +2024-09-06 04:27:06.131283: Epoch 345 +2024-09-06 04:27:06.132353: Current learning rate: 0.00683 +2024-09-06 04:32:02.808479: train_loss -0.7106 +2024-09-06 04:32:02.823579: val_loss -0.746 +2024-09-06 04:32:02.824592: Pseudo dice [0.9614, 0.9534, 0.9755, 0.9417, 0.8692, 0.9668, 0.9916] +2024-09-06 04:32:02.826001: Epoch time: 296.68 s +2024-09-06 04:32:05.603723: +2024-09-06 04:32:05.605526: Epoch 346 +2024-09-06 04:32:05.607323: Current learning rate: 0.00682 +2024-09-06 04:37:01.023230: train_loss -0.721 +2024-09-06 04:37:01.043474: val_loss -0.7483 +2024-09-06 04:37:01.044737: Pseudo dice [0.9631, 0.9573, 0.9794, 0.9461, 0.8255, 0.965, 0.9857] +2024-09-06 04:37:01.045914: Epoch time: 295.42 s +2024-09-06 04:37:04.735427: +2024-09-06 04:37:04.736970: Epoch 347 +2024-09-06 04:37:04.737926: Current learning rate: 0.00681 +2024-09-06 04:41:54.846451: train_loss -0.7226 +2024-09-06 04:41:54.852011: val_loss -0.7494 +2024-09-06 04:41:54.853085: Pseudo dice [0.9608, 0.954, 0.9778, 0.9499, 0.862, 0.9687, 0.9877] +2024-09-06 04:41:54.854256: Epoch time: 290.12 s +2024-09-06 04:41:58.672594: +2024-09-06 04:41:58.674183: Epoch 348 +2024-09-06 04:41:58.675673: Current learning rate: 0.0068 +2024-09-06 04:47:02.840694: train_loss -0.7256 +2024-09-06 04:47:02.850189: val_loss -0.7428 +2024-09-06 04:47:02.853023: Pseudo dice [0.9599, 0.9546, 0.9739, 0.9461, 0.8694, 0.9655, 0.9889] +2024-09-06 04:47:02.855072: Epoch time: 304.17 s +2024-09-06 04:47:06.757977: +2024-09-06 04:47:06.759236: Epoch 349 +2024-09-06 04:47:06.760936: Current learning rate: 0.0068 +2024-09-06 04:51:51.725698: train_loss -0.7313 +2024-09-06 04:51:51.735869: val_loss -0.7465 +2024-09-06 04:51:51.737767: Pseudo dice [0.9621, 0.955, 0.9763, 0.9425, 0.8597, 0.967, 0.989] +2024-09-06 04:51:51.739540: Epoch time: 284.97 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_08_29_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_08_29_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..0571b2308528130f9c943dc8762aad687b31a20d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_08_29_45.txt @@ -0,0 +1,382 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 08:29:48.103674: Using torch.compile... +2024-09-06 08:29:56.209432: do_dummy_2d_data_aug: False +2024-09-06 08:29:56.216229: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 08:29:56.218817: The split file contains 5 splits. +2024-09-06 08:29:56.219441: Desired fold for training: 1 +2024-09-06 08:29:56.220096: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 08:30:10.130210: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 08:30:11.906002: +2024-09-06 08:30:11.906985: Epoch 350 +2024-09-06 08:30:11.908757: Current learning rate: 0.00679 +2024-09-06 08:48:47.074912: train_loss -0.7379 +2024-09-06 08:48:47.076344: val_loss -0.716 +2024-09-06 08:48:47.077098: Pseudo dice [0.9604, 0.9543, 0.9756, 0.9428, 0.8523, 0.943, 0.9841] +2024-09-06 08:48:47.077811: Epoch time: 1115.18 s +2024-09-06 08:48:48.883025: +2024-09-06 08:48:48.884074: Epoch 351 +2024-09-06 08:48:48.884713: Current learning rate: 0.00678 +2024-09-06 08:53:19.406682: train_loss -0.7392 +2024-09-06 08:53:19.422330: val_loss -0.734 +2024-09-06 08:53:19.423275: Pseudo dice [0.9625, 0.9577, 0.9775, 0.9506, 0.8501, 0.9661, 0.9873] +2024-09-06 08:53:19.426159: Epoch time: 270.53 s +2024-09-06 08:53:21.957958: +2024-09-06 08:53:21.959732: Epoch 352 +2024-09-06 08:53:21.961427: Current learning rate: 0.00677 +2024-09-06 08:57:56.972915: train_loss -0.7119 +2024-09-06 08:57:56.986759: val_loss -0.7055 +2024-09-06 08:57:56.988198: Pseudo dice [0.9632, 0.9545, 0.9753, 0.9419, 0.8362, 0.9524, 0.9875] +2024-09-06 08:57:56.989229: Epoch time: 275.02 s +2024-09-06 08:58:01.078187: +2024-09-06 08:58:01.079324: Epoch 353 +2024-09-06 08:58:01.080467: Current learning rate: 0.00676 +2024-09-06 09:02:36.613803: train_loss -0.7431 +2024-09-06 09:02:36.630949: val_loss -0.7598 +2024-09-06 09:02:36.631930: Pseudo dice [0.9648, 0.9564, 0.9783, 0.9476, 0.871, 0.9656, 0.9901] +2024-09-06 09:02:36.632768: Epoch time: 275.54 s +2024-09-06 09:02:40.735322: +2024-09-06 09:02:40.736558: Epoch 354 +2024-09-06 09:02:40.737519: Current learning rate: 0.00675 +2024-09-06 09:07:31.956928: train_loss -0.7307 +2024-09-06 09:07:31.958641: val_loss -0.7376 +2024-09-06 09:07:31.959455: Pseudo dice [0.9594, 0.956, 0.9713, 0.9404, 0.8528, 0.9639, 0.9879] +2024-09-06 09:07:31.960214: Epoch time: 291.23 s +2024-09-06 09:07:34.387265: +2024-09-06 09:07:34.389180: Epoch 355 +2024-09-06 09:07:34.398815: Current learning rate: 0.00674 +2024-09-06 09:12:22.053252: train_loss -0.7331 +2024-09-06 09:12:22.059973: val_loss -0.7455 +2024-09-06 09:12:22.060854: Pseudo dice [0.9634, 0.9567, 0.9785, 0.9465, 0.8377, 0.9633, 0.9861] +2024-09-06 09:12:22.062194: Epoch time: 287.67 s +2024-09-06 09:12:25.375845: +2024-09-06 09:12:25.377037: Epoch 356 +2024-09-06 09:12:25.380638: Current learning rate: 0.00673 +2024-09-06 09:17:19.470980: train_loss -0.739 +2024-09-06 09:17:19.481537: val_loss -0.7267 +2024-09-06 09:17:19.482968: Pseudo dice [0.9636, 0.9557, 0.9794, 0.9461, 0.853, 0.969, 0.9896] +2024-09-06 09:17:19.484096: Epoch time: 294.1 s +2024-09-06 09:17:22.841408: +2024-09-06 09:17:22.855376: Epoch 357 +2024-09-06 09:17:22.856488: Current learning rate: 0.00672 +2024-09-06 09:22:11.593046: train_loss -0.738 +2024-09-06 09:22:11.606914: val_loss -0.739 +2024-09-06 09:22:11.607924: Pseudo dice [0.9615, 0.9539, 0.9769, 0.9494, 0.8679, 0.9685, 0.9897] +2024-09-06 09:22:11.608946: Epoch time: 288.76 s +2024-09-06 09:22:14.959300: +2024-09-06 09:22:14.960329: Epoch 358 +2024-09-06 09:22:14.961164: Current learning rate: 0.00671 +2024-09-06 09:27:00.870107: train_loss -0.7419 +2024-09-06 09:27:00.883203: val_loss -0.7446 +2024-09-06 09:27:00.884263: Pseudo dice [0.9623, 0.959, 0.9763, 0.9517, 0.8634, 0.9604, 0.9861] +2024-09-06 09:27:00.884963: Epoch time: 285.92 s +2024-09-06 09:27:03.919564: +2024-09-06 09:27:03.920681: Epoch 359 +2024-09-06 09:27:03.921508: Current learning rate: 0.0067 +2024-09-06 09:31:55.940557: train_loss -0.7418 +2024-09-06 09:31:55.955081: val_loss -0.746 +2024-09-06 09:31:55.955925: Pseudo dice [0.9623, 0.9541, 0.9773, 0.9467, 0.8575, 0.9697, 0.9892] +2024-09-06 09:31:55.956668: Epoch time: 292.03 s +2024-09-06 09:31:58.822868: +2024-09-06 09:31:58.824053: Epoch 360 +2024-09-06 09:31:58.825361: Current learning rate: 0.00669 +2024-09-06 09:36:37.652502: train_loss -0.7415 +2024-09-06 09:36:37.660807: val_loss -0.7462 +2024-09-06 09:36:37.661793: Pseudo dice [0.9619, 0.9533, 0.9761, 0.9397, 0.8666, 0.9677, 0.9904] +2024-09-06 09:36:37.662727: Epoch time: 278.83 s +2024-09-06 09:36:40.898276: +2024-09-06 09:36:40.899295: Epoch 361 +2024-09-06 09:36:40.900289: Current learning rate: 0.00668 +2024-09-06 09:41:24.631229: train_loss -0.7274 +2024-09-06 09:41:24.645689: val_loss -0.7404 +2024-09-06 09:41:24.647165: Pseudo dice [0.9629, 0.9575, 0.976, 0.9483, 0.8473, 0.9655, 0.986] +2024-09-06 09:41:24.649223: Epoch time: 283.74 s +2024-09-06 09:41:28.307224: +2024-09-06 09:41:28.308457: Epoch 362 +2024-09-06 09:41:28.309120: Current learning rate: 0.00667 +2024-09-06 09:46:00.644626: train_loss -0.7368 +2024-09-06 09:46:00.655749: val_loss -0.7522 +2024-09-06 09:46:00.656810: Pseudo dice [0.9596, 0.9558, 0.9747, 0.9457, 0.8596, 0.9639, 0.9888] +2024-09-06 09:46:00.658134: Epoch time: 272.34 s +2024-09-06 09:46:04.750879: +2024-09-06 09:46:04.752047: Epoch 363 +2024-09-06 09:46:04.753166: Current learning rate: 0.00666 +2024-09-06 09:50:50.980735: train_loss -0.7417 +2024-09-06 09:50:51.023040: val_loss -0.7725 +2024-09-06 09:50:51.024075: Pseudo dice [0.9635, 0.9541, 0.9764, 0.9472, 0.8576, 0.965, 0.9887] +2024-09-06 09:50:51.024995: Epoch time: 286.24 s +2024-09-06 09:50:54.811031: +2024-09-06 09:50:54.814706: Epoch 364 +2024-09-06 09:50:54.815690: Current learning rate: 0.00665 +2024-09-06 09:55:24.233462: train_loss -0.7346 +2024-09-06 09:55:24.236019: val_loss -0.752 +2024-09-06 09:55:24.237427: Pseudo dice [0.964, 0.9551, 0.9786, 0.9472, 0.8549, 0.9698, 0.9884] +2024-09-06 09:55:24.241967: Epoch time: 269.43 s +2024-09-06 09:55:24.254973: Yayy! New best EMA pseudo Dice: 0.9496 +2024-09-06 09:55:34.285617: +2024-09-06 09:55:34.286776: Epoch 365 +2024-09-06 09:55:34.287688: Current learning rate: 0.00665 +2024-09-06 10:00:13.660185: train_loss -0.7332 +2024-09-06 10:00:13.678910: val_loss -0.7421 +2024-09-06 10:00:13.680530: Pseudo dice [0.9629, 0.9554, 0.9761, 0.9443, 0.8508, 0.9684, 0.9897] +2024-09-06 10:00:13.681679: Epoch time: 279.38 s +2024-09-06 10:00:13.683065: Yayy! New best EMA pseudo Dice: 0.9496 +2024-09-06 10:00:24.062034: +2024-09-06 10:00:24.063039: Epoch 366 +2024-09-06 10:00:24.063760: Current learning rate: 0.00664 +2024-09-06 10:04:59.181149: train_loss -0.7498 +2024-09-06 10:04:59.191600: val_loss -0.7713 +2024-09-06 10:04:59.193262: Pseudo dice [0.9615, 0.9565, 0.9751, 0.9447, 0.867, 0.963, 0.9886] +2024-09-06 10:04:59.194625: Epoch time: 275.12 s +2024-09-06 10:04:59.195580: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 10:05:08.143336: +2024-09-06 10:05:08.144245: Epoch 367 +2024-09-06 10:05:08.145068: Current learning rate: 0.00663 +2024-09-06 10:09:50.338075: train_loss -0.7453 +2024-09-06 10:09:50.351639: val_loss -0.7351 +2024-09-06 10:09:50.352785: Pseudo dice [0.9594, 0.957, 0.9709, 0.9471, 0.8662, 0.9582, 0.988] +2024-09-06 10:09:50.353772: Epoch time: 282.2 s +2024-09-06 10:09:53.452492: +2024-09-06 10:09:53.453703: Epoch 368 +2024-09-06 10:09:53.454741: Current learning rate: 0.00662 +2024-09-06 10:14:19.158549: train_loss -0.7435 +2024-09-06 10:14:19.171047: val_loss -0.7499 +2024-09-06 10:14:19.172161: Pseudo dice [0.9633, 0.9559, 0.9776, 0.9479, 0.851, 0.9673, 0.9874] +2024-09-06 10:14:19.173136: Epoch time: 265.71 s +2024-09-06 10:14:19.174173: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 10:14:29.117687: +2024-09-06 10:14:29.118726: Epoch 369 +2024-09-06 10:14:29.119481: Current learning rate: 0.00661 +2024-09-06 10:19:09.782426: train_loss -0.7204 +2024-09-06 10:19:09.791437: val_loss -0.7445 +2024-09-06 10:19:09.792581: Pseudo dice [0.9642, 0.9563, 0.9781, 0.9485, 0.8633, 0.97, 0.988] +2024-09-06 10:19:09.793625: Epoch time: 280.67 s +2024-09-06 10:19:09.794932: Yayy! New best EMA pseudo Dice: 0.95 +2024-09-06 10:19:17.483839: +2024-09-06 10:19:17.484969: Epoch 370 +2024-09-06 10:19:17.485551: Current learning rate: 0.0066 +2024-09-06 10:24:12.003322: train_loss -0.7372 +2024-09-06 10:24:12.005392: val_loss -0.7169 +2024-09-06 10:24:12.006414: Pseudo dice [0.9633, 0.9557, 0.978, 0.9448, 0.8536, 0.9683, 0.9901] +2024-09-06 10:24:12.007324: Epoch time: 294.52 s +2024-09-06 10:24:12.008378: Yayy! New best EMA pseudo Dice: 0.9501 +2024-09-06 10:24:21.180121: +2024-09-06 10:24:21.181088: Epoch 371 +2024-09-06 10:24:21.181656: Current learning rate: 0.00659 +2024-09-06 10:29:15.942341: train_loss -0.7297 +2024-09-06 10:29:15.944020: val_loss -0.7512 +2024-09-06 10:29:15.944737: Pseudo dice [0.9644, 0.956, 0.9786, 0.9495, 0.8424, 0.9609, 0.9855] +2024-09-06 10:29:15.945843: Epoch time: 294.77 s +2024-09-06 10:29:19.476502: +2024-09-06 10:29:19.477952: Epoch 372 +2024-09-06 10:29:19.480187: Current learning rate: 0.00658 +2024-09-06 10:34:02.127073: train_loss -0.7344 +2024-09-06 10:34:02.143734: val_loss -0.7402 +2024-09-06 10:34:02.145015: Pseudo dice [0.9612, 0.9533, 0.9771, 0.9439, 0.857, 0.9563, 0.9874] +2024-09-06 10:34:02.148026: Epoch time: 282.66 s +2024-09-06 10:34:05.165494: +2024-09-06 10:34:05.168141: Epoch 373 +2024-09-06 10:34:05.171957: Current learning rate: 0.00657 +2024-09-06 10:38:48.806200: train_loss -0.7333 +2024-09-06 10:38:48.831187: val_loss -0.7707 +2024-09-06 10:38:48.832561: Pseudo dice [0.9643, 0.9556, 0.9794, 0.9495, 0.866, 0.9663, 0.9881] +2024-09-06 10:38:48.833624: Epoch time: 283.65 s +2024-09-06 10:38:53.291646: +2024-09-06 10:38:53.292791: Epoch 374 +2024-09-06 10:38:53.293633: Current learning rate: 0.00656 +2024-09-06 10:44:04.449541: train_loss -0.7294 +2024-09-06 10:44:04.462859: val_loss -0.7216 +2024-09-06 10:44:04.463723: Pseudo dice [0.959, 0.9545, 0.9742, 0.942, 0.8667, 0.9513, 0.9867] +2024-09-06 10:44:04.464424: Epoch time: 311.16 s +2024-09-06 10:44:07.158694: +2024-09-06 10:44:07.160118: Epoch 375 +2024-09-06 10:44:07.161054: Current learning rate: 0.00655 +2024-09-06 10:48:58.950650: train_loss -0.7252 +2024-09-06 10:48:58.963125: val_loss -0.7701 +2024-09-06 10:48:58.964037: Pseudo dice [0.9621, 0.9545, 0.9786, 0.9447, 0.8812, 0.9699, 0.9921] +2024-09-06 10:48:58.964983: Epoch time: 291.8 s +2024-09-06 10:48:58.965937: Yayy! New best EMA pseudo Dice: 0.9503 +2024-09-06 10:49:11.742391: +2024-09-06 10:49:11.744159: Epoch 376 +2024-09-06 10:49:11.744949: Current learning rate: 0.00654 +2024-09-06 10:53:51.862494: train_loss -0.7132 +2024-09-06 10:53:51.867667: val_loss -0.7025 +2024-09-06 10:53:51.869374: Pseudo dice [0.9526, 0.9493, 0.9693, 0.936, 0.852, 0.9502, 0.9839] +2024-09-06 10:53:51.870995: Epoch time: 280.12 s +2024-09-06 10:53:54.260594: +2024-09-06 10:53:54.261732: Epoch 377 +2024-09-06 10:53:54.262452: Current learning rate: 0.00653 +2024-09-06 10:58:48.220788: train_loss -0.7101 +2024-09-06 10:58:48.236174: val_loss -0.7342 +2024-09-06 10:58:48.236964: Pseudo dice [0.9602, 0.955, 0.9735, 0.9437, 0.852, 0.9651, 0.9885] +2024-09-06 10:58:48.238006: Epoch time: 293.97 s +2024-09-06 10:58:51.143979: +2024-09-06 10:58:51.145549: Epoch 378 +2024-09-06 10:58:51.146994: Current learning rate: 0.00652 +2024-09-06 11:03:32.105277: train_loss -0.7247 +2024-09-06 11:03:32.119680: val_loss -0.7178 +2024-09-06 11:03:32.120666: Pseudo dice [0.959, 0.9572, 0.9737, 0.9507, 0.8642, 0.9664, 0.9899] +2024-09-06 11:03:32.121378: Epoch time: 280.97 s +2024-09-06 11:03:35.217400: +2024-09-06 11:03:35.218814: Epoch 379 +2024-09-06 11:03:35.219897: Current learning rate: 0.00651 +2024-09-06 11:08:21.170814: train_loss -0.723 +2024-09-06 11:08:21.191164: val_loss -0.7807 +2024-09-06 11:08:21.192144: Pseudo dice [0.9606, 0.9542, 0.9726, 0.9486, 0.8799, 0.9687, 0.9918] +2024-09-06 11:08:21.192922: Epoch time: 285.96 s +2024-09-06 11:08:24.342794: +2024-09-06 11:08:24.346228: Epoch 380 +2024-09-06 11:08:24.347435: Current learning rate: 0.0065 +2024-09-06 11:13:07.850982: train_loss -0.7297 +2024-09-06 11:13:07.869084: val_loss -0.7396 +2024-09-06 11:13:07.870213: Pseudo dice [0.9593, 0.9546, 0.9756, 0.9401, 0.8681, 0.9628, 0.9899] +2024-09-06 11:13:07.871475: Epoch time: 283.51 s +2024-09-06 11:13:11.186252: +2024-09-06 11:13:11.187594: Epoch 381 +2024-09-06 11:13:11.188755: Current learning rate: 0.00649 +2024-09-06 11:18:06.545371: train_loss -0.7283 +2024-09-06 11:18:06.559165: val_loss -0.7415 +2024-09-06 11:18:06.559986: Pseudo dice [0.9608, 0.9537, 0.9734, 0.9439, 0.8507, 0.9651, 0.9884] +2024-09-06 11:18:06.561041: Epoch time: 295.36 s +2024-09-06 11:18:10.077229: +2024-09-06 11:18:10.090817: Epoch 382 +2024-09-06 11:18:10.091919: Current learning rate: 0.00648 +2024-09-06 11:23:18.356697: train_loss -0.725 +2024-09-06 11:23:18.374021: val_loss -0.774 +2024-09-06 11:23:18.375465: Pseudo dice [0.9653, 0.9554, 0.9785, 0.9473, 0.8662, 0.9651, 0.9897] +2024-09-06 11:23:18.376621: Epoch time: 308.29 s +2024-09-06 11:23:21.852401: +2024-09-06 11:23:21.853691: Epoch 383 +2024-09-06 11:23:21.854772: Current learning rate: 0.00648 +2024-09-06 11:28:04.409747: train_loss -0.7367 +2024-09-06 11:28:04.416933: val_loss -0.7299 +2024-09-06 11:28:04.417846: Pseudo dice [0.9659, 0.9602, 0.978, 0.9506, 0.8835, 0.9682, 0.9906] +2024-09-06 11:28:04.419455: Epoch time: 282.56 s +2024-09-06 11:28:04.420533: Yayy! New best EMA pseudo Dice: 0.9507 +2024-09-06 11:28:14.216035: +2024-09-06 11:28:14.216952: Epoch 384 +2024-09-06 11:28:14.217642: Current learning rate: 0.00647 +2024-09-06 11:33:14.095424: train_loss -0.7198 +2024-09-06 11:33:14.108788: val_loss -0.7093 +2024-09-06 11:33:14.110525: Pseudo dice [0.9663, 0.9564, 0.9803, 0.9506, 0.8506, 0.9655, 0.9882] +2024-09-06 11:33:14.111628: Epoch time: 299.88 s +2024-09-06 11:33:14.112557: Yayy! New best EMA pseudo Dice: 0.9508 +2024-09-06 11:33:24.086188: +2024-09-06 11:33:24.087140: Epoch 385 +2024-09-06 11:33:24.087868: Current learning rate: 0.00646 +2024-09-06 11:38:35.226732: train_loss -0.733 +2024-09-06 11:38:35.248819: val_loss -0.7551 +2024-09-06 11:38:35.250482: Pseudo dice [0.9618, 0.9542, 0.9777, 0.9443, 0.8681, 0.9661, 0.9902] +2024-09-06 11:38:35.252128: Epoch time: 311.14 s +2024-09-06 11:38:35.253244: Yayy! New best EMA pseudo Dice: 0.9509 +2024-09-06 11:38:45.194410: +2024-09-06 11:38:45.195509: Epoch 386 +2024-09-06 11:38:45.196394: Current learning rate: 0.00645 +2024-09-06 11:43:35.927369: train_loss -0.7376 +2024-09-06 11:43:35.940130: val_loss -0.7457 +2024-09-06 11:43:35.941713: Pseudo dice [0.9632, 0.9572, 0.9795, 0.9487, 0.8468, 0.9681, 0.9863] +2024-09-06 11:43:35.942920: Epoch time: 290.74 s +2024-09-06 11:43:39.415270: +2024-09-06 11:43:39.416399: Epoch 387 +2024-09-06 11:43:39.417109: Current learning rate: 0.00644 +2024-09-06 11:48:04.193703: train_loss -0.7531 +2024-09-06 11:48:04.208260: val_loss -0.7227 +2024-09-06 11:48:04.209031: Pseudo dice [0.9636, 0.9563, 0.9779, 0.9539, 0.8885, 0.9683, 0.992] +2024-09-06 11:48:04.209912: Epoch time: 264.78 s +2024-09-06 11:48:04.210876: Yayy! New best EMA pseudo Dice: 0.9514 +2024-09-06 11:48:14.027666: +2024-09-06 11:48:14.028934: Epoch 388 +2024-09-06 11:48:14.029974: Current learning rate: 0.00643 +2024-09-06 11:52:51.734327: train_loss -0.7315 +2024-09-06 11:52:51.753295: val_loss -0.7245 +2024-09-06 11:52:51.756803: Pseudo dice [0.9582, 0.9515, 0.9741, 0.9362, 0.8592, 0.9585, 0.99] +2024-09-06 11:52:51.758563: Epoch time: 277.71 s +2024-09-06 11:52:55.168216: +2024-09-06 11:52:55.169655: Epoch 389 +2024-09-06 11:52:55.170723: Current learning rate: 0.00642 +2024-09-06 11:57:32.425533: train_loss -0.7429 +2024-09-06 11:57:32.444756: val_loss -0.7661 +2024-09-06 11:57:32.445616: Pseudo dice [0.9597, 0.9556, 0.9743, 0.9456, 0.8568, 0.9687, 0.9888] +2024-09-06 11:57:32.447735: Epoch time: 277.26 s +2024-09-06 11:57:35.644283: +2024-09-06 11:57:35.645361: Epoch 390 +2024-09-06 11:57:35.646531: Current learning rate: 0.00641 +2024-09-06 12:02:15.990442: train_loss -0.7137 +2024-09-06 12:02:16.009212: val_loss -0.7661 +2024-09-06 12:02:16.011115: Pseudo dice [0.9602, 0.9552, 0.9759, 0.9465, 0.8634, 0.9656, 0.9907] +2024-09-06 12:02:16.012635: Epoch time: 280.35 s +2024-09-06 12:02:19.277959: +2024-09-06 12:02:19.279305: Epoch 391 +2024-09-06 12:02:19.280135: Current learning rate: 0.0064 +2024-09-06 12:07:14.088805: train_loss -0.7303 +2024-09-06 12:07:14.109235: val_loss -0.7103 +2024-09-06 12:07:14.110594: Pseudo dice [0.9627, 0.9562, 0.9785, 0.9483, 0.8616, 0.9514, 0.9857] +2024-09-06 12:07:14.111759: Epoch time: 294.82 s +2024-09-06 12:07:17.992329: +2024-09-06 12:07:17.993301: Epoch 392 +2024-09-06 12:07:17.994167: Current learning rate: 0.00639 +2024-09-06 12:12:14.088682: train_loss -0.7463 +2024-09-06 12:12:14.104335: val_loss -0.7457 +2024-09-06 12:12:14.105422: Pseudo dice [0.9637, 0.9565, 0.9786, 0.9443, 0.8662, 0.9654, 0.9889] +2024-09-06 12:12:14.106763: Epoch time: 296.1 s +2024-09-06 12:12:17.567632: +2024-09-06 12:12:17.568741: Epoch 393 +2024-09-06 12:12:17.569573: Current learning rate: 0.00638 +2024-09-06 12:16:57.636936: train_loss -0.7205 +2024-09-06 12:16:57.660451: val_loss -0.7303 +2024-09-06 12:16:57.662096: Pseudo dice [0.9595, 0.954, 0.9748, 0.9421, 0.8603, 0.9474, 0.9859] +2024-09-06 12:16:57.663033: Epoch time: 280.08 s +2024-09-06 12:17:00.483908: +2024-09-06 12:17:00.485980: Epoch 394 +2024-09-06 12:17:00.487252: Current learning rate: 0.00637 +2024-09-06 12:21:37.891836: train_loss -0.7286 +2024-09-06 12:21:37.908675: val_loss -0.728 +2024-09-06 12:21:37.910094: Pseudo dice [0.9608, 0.9531, 0.9766, 0.9314, 0.8393, 0.9584, 0.989] +2024-09-06 12:21:37.910865: Epoch time: 277.41 s +2024-09-06 12:21:42.807360: +2024-09-06 12:21:42.814832: Epoch 395 +2024-09-06 12:21:42.817587: Current learning rate: 0.00636 +2024-09-06 12:26:14.613429: train_loss -0.7324 +2024-09-06 12:26:14.631275: val_loss -0.7591 +2024-09-06 12:26:14.632809: Pseudo dice [0.9635, 0.9579, 0.9773, 0.9469, 0.8404, 0.9649, 0.986] +2024-09-06 12:26:14.634526: Epoch time: 271.81 s +2024-09-06 12:26:17.652535: +2024-09-06 12:26:17.653744: Epoch 396 +2024-09-06 12:26:17.654428: Current learning rate: 0.00635 +2024-09-06 12:31:00.962514: train_loss -0.724 +2024-09-06 12:31:00.964587: val_loss -0.7135 +2024-09-06 12:31:00.966193: Pseudo dice [0.9636, 0.9545, 0.9761, 0.947, 0.8305, 0.9686, 0.9851] +2024-09-06 12:31:00.967592: Epoch time: 283.32 s +2024-09-06 12:31:03.950299: +2024-09-06 12:31:03.951891: Epoch 397 +2024-09-06 12:31:03.953142: Current learning rate: 0.00634 +2024-09-06 12:35:53.142778: train_loss -0.7321 +2024-09-06 12:35:53.159268: val_loss -0.732 +2024-09-06 12:35:53.160575: Pseudo dice [0.9571, 0.9539, 0.9683, 0.936, 0.856, 0.9636, 0.9887] +2024-09-06 12:35:53.161469: Epoch time: 289.2 s +2024-09-06 12:35:57.006634: +2024-09-06 12:35:57.007956: Epoch 398 +2024-09-06 12:35:57.008940: Current learning rate: 0.00633 +2024-09-06 12:40:37.573433: train_loss -0.7366 +2024-09-06 12:40:37.595457: val_loss -0.7479 +2024-09-06 12:40:37.603186: Pseudo dice [0.9581, 0.9494, 0.9689, 0.9429, 0.8484, 0.9673, 0.9892] +2024-09-06 12:40:37.604399: Epoch time: 280.57 s +2024-09-06 12:40:41.700734: +2024-09-06 12:40:41.702115: Epoch 399 +2024-09-06 12:40:41.702921: Current learning rate: 0.00632 +2024-09-06 12:45:24.561687: train_loss -0.7137 +2024-09-06 12:45:24.563180: val_loss -0.7292 +2024-09-06 12:45:24.564435: Pseudo dice [0.9529, 0.9484, 0.9708, 0.9321, 0.8428, 0.9581, 0.9888] +2024-09-06 12:45:24.565773: Epoch time: 282.87 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_17_12_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_17_12_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a18cf0e05e8e14589b83d94d59b1cf3cb88e24c --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_6_17_12_15.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 17:12:18.640642: Using torch.compile... +2024-09-06 17:12:24.492839: do_dummy_2d_data_aug: False +2024-09-06 17:12:24.504089: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 17:12:24.518806: The split file contains 5 splits. +2024-09-06 17:12:24.519246: Desired fold for training: 1 +2024-09-06 17:12:24.519669: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 17:12:40.725603: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 17:12:41.784923: +2024-09-06 17:12:41.785813: Epoch 400 +2024-09-06 17:12:41.786529: Current learning rate: 0.00631 +2024-09-06 17:27:25.474089: train_loss -0.7024 +2024-09-06 17:27:25.476377: val_loss -0.7687 +2024-09-06 17:27:25.476799: Pseudo dice [0.9605, 0.9515, 0.9713, 0.9424, 0.8582, 0.9646, 0.9891] +2024-09-06 17:27:25.477174: Epoch time: 883.69 s +2024-09-06 17:27:27.056375: +2024-09-06 17:27:27.057071: Epoch 401 +2024-09-06 17:27:27.057580: Current learning rate: 0.0063 +2024-09-06 17:35:13.643147: train_loss -0.7312 +2024-09-06 17:35:13.656501: val_loss -0.7299 +2024-09-06 17:35:13.657221: Pseudo dice [0.9645, 0.9545, 0.9779, 0.9477, 0.8615, 0.9534, 0.9885] +2024-09-06 17:35:13.657808: Epoch time: 466.59 s +2024-09-06 17:35:17.032650: +2024-09-06 17:35:17.033617: Epoch 402 +2024-09-06 17:35:17.035744: Current learning rate: 0.0063 +2024-09-06 17:42:50.418836: train_loss -0.7386 +2024-09-06 17:42:50.422960: val_loss -0.7397 +2024-09-06 17:42:50.423609: Pseudo dice [0.9627, 0.9561, 0.977, 0.9482, 0.8698, 0.9684, 0.9904] +2024-09-06 17:42:50.424043: Epoch time: 453.39 s +2024-09-06 17:42:54.697127: +2024-09-06 17:42:54.697871: Epoch 403 +2024-09-06 17:42:54.698562: Current learning rate: 0.00629 +2024-09-06 17:49:18.405139: train_loss -0.7361 +2024-09-06 17:49:18.415253: val_loss -0.7656 +2024-09-06 17:49:18.415852: Pseudo dice [0.9651, 0.9574, 0.9802, 0.9528, 0.8432, 0.9673, 0.9877] +2024-09-06 17:49:18.416373: Epoch time: 383.71 s +2024-09-06 17:49:21.674520: +2024-09-06 17:49:21.685134: Epoch 404 +2024-09-06 17:49:21.696264: Current learning rate: 0.00628 +2024-09-06 17:56:04.035905: train_loss -0.751 +2024-09-06 17:56:04.039096: val_loss -0.7359 +2024-09-06 17:56:04.039982: Pseudo dice [0.9612, 0.953, 0.9738, 0.9446, 0.8628, 0.9661, 0.9895] +2024-09-06 17:56:04.040512: Epoch time: 402.37 s +2024-09-06 17:56:07.729840: +2024-09-06 17:56:07.730535: Epoch 405 +2024-09-06 17:56:07.731085: Current learning rate: 0.00627 +2024-09-06 18:03:01.028334: train_loss -0.7417 +2024-09-06 18:03:01.031118: val_loss -0.7533 +2024-09-06 18:03:01.031927: Pseudo dice [0.9636, 0.9566, 0.9772, 0.9486, 0.8652, 0.9479, 0.9856] +2024-09-06 18:03:01.032688: Epoch time: 413.3 s +2024-09-06 18:03:05.363978: +2024-09-06 18:03:05.365708: Epoch 406 +2024-09-06 18:03:05.366650: Current learning rate: 0.00626 +2024-09-06 18:09:42.696064: train_loss -0.7388 +2024-09-06 18:09:42.741408: val_loss -0.732 +2024-09-06 18:09:42.742010: Pseudo dice [0.9639, 0.9559, 0.9775, 0.947, 0.8878, 0.9682, 0.992] +2024-09-06 18:09:42.742564: Epoch time: 397.36 s +2024-09-06 18:09:45.385661: +2024-09-06 18:09:45.386774: Epoch 407 +2024-09-06 18:09:45.387564: Current learning rate: 0.00625 +2024-09-06 18:16:10.537705: train_loss -0.739 +2024-09-06 18:16:10.550797: val_loss -0.738 +2024-09-06 18:16:10.551314: Pseudo dice [0.9642, 0.9584, 0.9783, 0.9521, 0.862, 0.9526, 0.9845] +2024-09-06 18:16:10.551830: Epoch time: 385.16 s +2024-09-06 18:16:13.070060: +2024-09-06 18:16:13.082893: Epoch 408 +2024-09-06 18:16:13.086282: Current learning rate: 0.00624 +2024-09-06 18:22:11.094954: train_loss -0.7162 +2024-09-06 18:22:11.097720: val_loss -0.743 +2024-09-06 18:22:11.098245: Pseudo dice [0.9577, 0.9508, 0.971, 0.9495, 0.8699, 0.9551, 0.9886] +2024-09-06 18:22:11.098652: Epoch time: 358.05 s +2024-09-06 18:22:14.183839: +2024-09-06 18:22:14.184865: Epoch 409 +2024-09-06 18:22:14.186183: Current learning rate: 0.00623 +2024-09-06 18:28:36.419572: train_loss -0.7364 +2024-09-06 18:28:36.429886: val_loss -0.7762 +2024-09-06 18:28:36.430471: Pseudo dice [0.9611, 0.954, 0.9774, 0.9432, 0.8732, 0.9701, 0.9911] +2024-09-06 18:28:36.431188: Epoch time: 382.24 s +2024-09-06 18:28:38.490380: +2024-09-06 18:28:38.491103: Epoch 410 +2024-09-06 18:28:38.491668: Current learning rate: 0.00622 +2024-09-06 18:35:31.522377: train_loss -0.7388 +2024-09-06 18:35:31.525028: val_loss -0.7342 +2024-09-06 18:35:31.525496: Pseudo dice [0.9557, 0.9541, 0.9692, 0.9459, 0.8826, 0.9694, 0.9911] +2024-09-06 18:35:31.525925: Epoch time: 413.04 s +2024-09-06 18:35:33.790506: +2024-09-06 18:35:33.791207: Epoch 411 +2024-09-06 18:35:33.791734: Current learning rate: 0.00621 +2024-09-06 18:41:41.850688: train_loss -0.7451 +2024-09-06 18:41:41.874255: val_loss -0.737 +2024-09-06 18:41:41.874846: Pseudo dice [0.9626, 0.9576, 0.9754, 0.9529, 0.8666, 0.9687, 0.9898] +2024-09-06 18:41:41.875315: Epoch time: 368.06 s +2024-09-06 18:41:46.353563: +2024-09-06 18:41:46.355417: Epoch 412 +2024-09-06 18:41:46.356445: Current learning rate: 0.0062 +2024-09-06 18:47:32.269139: train_loss -0.7286 +2024-09-06 18:47:32.281071: val_loss -0.7345 +2024-09-06 18:47:32.281629: Pseudo dice [0.9629, 0.9527, 0.9774, 0.9376, 0.8618, 0.9428, 0.988] +2024-09-06 18:47:32.282122: Epoch time: 345.93 s +2024-09-06 18:47:35.604229: +2024-09-06 18:47:35.605670: Epoch 413 +2024-09-06 18:47:35.606375: Current learning rate: 0.00619 +2024-09-06 18:53:18.179193: train_loss -0.7204 +2024-09-06 18:53:18.190120: val_loss -0.7209 +2024-09-06 18:53:18.190689: Pseudo dice [0.966, 0.9572, 0.9783, 0.947, 0.86, 0.9566, 0.9868] +2024-09-06 18:53:18.191306: Epoch time: 342.58 s +2024-09-06 18:53:21.377557: +2024-09-06 18:53:21.393129: Epoch 414 +2024-09-06 18:53:21.394852: Current learning rate: 0.00618 +2024-09-06 18:59:07.656974: train_loss -0.7309 +2024-09-06 18:59:07.662637: val_loss -0.7345 +2024-09-06 18:59:07.663747: Pseudo dice [0.9626, 0.9557, 0.9777, 0.9489, 0.872, 0.9686, 0.9894] +2024-09-06 18:59:07.664695: Epoch time: 346.29 s +2024-09-06 18:59:10.125341: +2024-09-06 18:59:10.126042: Epoch 415 +2024-09-06 18:59:10.127111: Current learning rate: 0.00617 +2024-09-06 19:05:56.768124: train_loss -0.7397 +2024-09-06 19:05:56.772161: val_loss -0.7365 +2024-09-06 19:05:56.773006: Pseudo dice [0.9637, 0.9561, 0.9788, 0.9435, 0.8269, 0.9555, 0.9807] +2024-09-06 19:05:56.774385: Epoch time: 406.65 s +2024-09-06 19:06:02.864478: +2024-09-06 19:06:02.865355: Epoch 416 +2024-09-06 19:06:02.866163: Current learning rate: 0.00616 +2024-09-06 19:12:48.628213: train_loss -0.7281 +2024-09-06 19:12:48.645880: val_loss -0.7495 +2024-09-06 19:12:48.646564: Pseudo dice [0.9611, 0.9531, 0.9761, 0.9418, 0.8325, 0.9608, 0.9872] +2024-09-06 19:12:48.647083: Epoch time: 405.86 s +2024-09-06 19:12:51.026134: +2024-09-06 19:12:51.027147: Epoch 417 +2024-09-06 19:12:51.027911: Current learning rate: 0.00615 +2024-09-06 19:18:44.657540: train_loss -0.7391 +2024-09-06 19:18:44.671350: val_loss -0.7399 +2024-09-06 19:18:44.672131: Pseudo dice [0.9613, 0.9565, 0.9773, 0.9507, 0.8829, 0.9672, 0.9904] +2024-09-06 19:18:44.672861: Epoch time: 353.64 s +2024-09-06 19:18:48.559953: +2024-09-06 19:18:48.560610: Epoch 418 +2024-09-06 19:18:48.561154: Current learning rate: 0.00614 +2024-09-06 19:24:21.152240: train_loss -0.7202 +2024-09-06 19:24:21.154925: val_loss -0.7158 +2024-09-06 19:24:21.155585: Pseudo dice [0.9595, 0.9534, 0.9734, 0.944, 0.8572, 0.9657, 0.9892] +2024-09-06 19:24:21.156106: Epoch time: 332.6 s +2024-09-06 19:24:24.336041: +2024-09-06 19:24:24.336736: Epoch 419 +2024-09-06 19:24:24.337244: Current learning rate: 0.00613 +2024-09-06 19:30:26.067106: train_loss -0.7264 +2024-09-06 19:30:26.081312: val_loss -0.6977 +2024-09-06 19:30:26.082131: Pseudo dice [0.9632, 0.9547, 0.9784, 0.948, 0.8727, 0.9676, 0.9916] +2024-09-06 19:30:26.082918: Epoch time: 361.73 s +2024-09-06 19:30:29.185604: +2024-09-06 19:30:29.186596: Epoch 420 +2024-09-06 19:30:29.187252: Current learning rate: 0.00612 +2024-09-06 19:35:55.590300: train_loss -0.7241 +2024-09-06 19:35:55.604800: val_loss -0.6999 +2024-09-06 19:35:55.605349: Pseudo dice [0.9625, 0.9544, 0.9764, 0.9472, 0.8456, 0.9651, 0.9877] +2024-09-06 19:35:55.605827: Epoch time: 326.41 s +2024-09-06 19:35:58.152256: +2024-09-06 19:35:58.153470: Epoch 421 +2024-09-06 19:35:58.154444: Current learning rate: 0.00612 +2024-09-06 19:41:31.942719: train_loss -0.709 +2024-09-06 19:41:31.966460: val_loss -0.7129 +2024-09-06 19:41:31.967280: Pseudo dice [0.957, 0.9508, 0.9753, 0.941, 0.8602, 0.9662, 0.9886] +2024-09-06 19:41:31.967947: Epoch time: 333.8 s +2024-09-06 19:41:37.166809: +2024-09-06 19:41:37.180453: Epoch 422 +2024-09-06 19:41:37.181297: Current learning rate: 0.00611 +2024-09-06 19:47:37.667884: train_loss -0.7151 +2024-09-06 19:47:37.669918: val_loss -0.7269 +2024-09-06 19:47:37.670572: Pseudo dice [0.9588, 0.9553, 0.973, 0.9413, 0.8482, 0.9649, 0.9883] +2024-09-06 19:47:37.671103: Epoch time: 360.51 s +2024-09-06 19:47:39.653139: +2024-09-06 19:47:39.653755: Epoch 423 +2024-09-06 19:47:39.654217: Current learning rate: 0.0061 +2024-09-06 19:52:32.861964: train_loss -0.7231 +2024-09-06 19:52:32.878186: val_loss -0.7224 +2024-09-06 19:52:32.879109: Pseudo dice [0.9611, 0.9565, 0.9773, 0.9434, 0.8753, 0.9683, 0.9907] +2024-09-06 19:52:32.879884: Epoch time: 293.21 s +2024-09-06 19:52:35.909427: +2024-09-06 19:52:35.910486: Epoch 424 +2024-09-06 19:52:35.912039: Current learning rate: 0.00609 +2024-09-06 19:57:21.784041: train_loss -0.7257 +2024-09-06 19:57:21.801421: val_loss -0.7617 +2024-09-06 19:57:21.801980: Pseudo dice [0.9629, 0.9555, 0.9785, 0.9483, 0.8783, 0.9633, 0.9906] +2024-09-06 19:57:21.802502: Epoch time: 285.88 s +2024-09-06 19:57:24.191899: +2024-09-06 19:57:24.208611: Epoch 425 +2024-09-06 19:57:24.209673: Current learning rate: 0.00608 +2024-09-06 20:02:29.516250: train_loss -0.7186 +2024-09-06 20:02:29.529777: val_loss -0.7231 +2024-09-06 20:02:29.530351: Pseudo dice [0.9552, 0.9543, 0.9667, 0.9435, 0.8716, 0.9668, 0.9899] +2024-09-06 20:02:29.530788: Epoch time: 305.33 s +2024-09-06 20:02:32.633964: +2024-09-06 20:02:32.635334: Epoch 426 +2024-09-06 20:02:32.636091: Current learning rate: 0.00607 +2024-09-06 20:07:33.497641: train_loss -0.7391 +2024-09-06 20:07:33.513557: val_loss -0.7144 +2024-09-06 20:07:33.514077: Pseudo dice [0.9618, 0.9536, 0.9741, 0.9494, 0.8562, 0.9647, 0.9893] +2024-09-06 20:07:33.514690: Epoch time: 300.87 s +2024-09-06 20:07:36.466695: +2024-09-06 20:07:36.479996: Epoch 427 +2024-09-06 20:07:36.480618: Current learning rate: 0.00606 +2024-09-06 20:12:12.475966: train_loss -0.7305 +2024-09-06 20:12:12.493407: val_loss -0.7203 +2024-09-06 20:12:12.494081: Pseudo dice [0.9592, 0.9557, 0.9723, 0.947, 0.877, 0.9574, 0.9872] +2024-09-06 20:12:12.494557: Epoch time: 276.01 s +2024-09-06 20:12:15.952065: +2024-09-06 20:12:15.967752: Epoch 428 +2024-09-06 20:12:15.968550: Current learning rate: 0.00605 +2024-09-06 20:16:47.871441: train_loss -0.7397 +2024-09-06 20:16:47.893573: val_loss -0.7362 +2024-09-06 20:16:47.894141: Pseudo dice [0.9622, 0.9552, 0.9791, 0.9481, 0.8316, 0.9714, 0.9861] +2024-09-06 20:16:47.894645: Epoch time: 271.92 s +2024-09-06 20:16:50.800658: +2024-09-06 20:16:50.806758: Epoch 429 +2024-09-06 20:16:50.808310: Current learning rate: 0.00604 +2024-09-06 20:21:21.571056: train_loss -0.7312 +2024-09-06 20:21:21.573888: val_loss -0.7488 +2024-09-06 20:21:21.574526: Pseudo dice [0.963, 0.9576, 0.9788, 0.9448, 0.876, 0.97, 0.9909] +2024-09-06 20:21:21.575206: Epoch time: 270.77 s +2024-09-06 20:21:24.073954: +2024-09-06 20:21:24.074999: Epoch 430 +2024-09-06 20:21:24.075841: Current learning rate: 0.00603 +2024-09-06 20:25:49.865250: train_loss -0.7209 +2024-09-06 20:25:49.867080: val_loss -0.7246 +2024-09-06 20:25:49.867563: Pseudo dice [0.9652, 0.9575, 0.9801, 0.9485, 0.8673, 0.969, 0.9902] +2024-09-06 20:25:49.868127: Epoch time: 265.8 s +2024-09-06 20:25:52.644730: +2024-09-06 20:25:52.645442: Epoch 431 +2024-09-06 20:25:52.645979: Current learning rate: 0.00602 +2024-09-06 20:29:59.282262: train_loss -0.7405 +2024-09-06 20:29:59.298391: val_loss -0.7605 +2024-09-06 20:29:59.299408: Pseudo dice [0.9633, 0.9553, 0.978, 0.9482, 0.84, 0.9518, 0.9806] +2024-09-06 20:29:59.300223: Epoch time: 246.64 s +2024-09-06 20:30:01.232596: +2024-09-06 20:30:01.243752: Epoch 432 +2024-09-06 20:30:01.244549: Current learning rate: 0.00601 +2024-09-06 20:34:31.200018: train_loss -0.7177 +2024-09-06 20:34:31.202907: val_loss -0.7446 +2024-09-06 20:34:31.203540: Pseudo dice [0.9648, 0.9582, 0.9799, 0.95, 0.8648, 0.9686, 0.99] +2024-09-06 20:34:31.204087: Epoch time: 269.97 s +2024-09-06 20:34:35.054965: +2024-09-06 20:34:35.055710: Epoch 433 +2024-09-06 20:34:35.056215: Current learning rate: 0.006 +2024-09-06 20:39:00.752800: train_loss -0.7241 +2024-09-06 20:39:00.763984: val_loss -0.7242 +2024-09-06 20:39:00.764506: Pseudo dice [0.9603, 0.9591, 0.9737, 0.9474, 0.8641, 0.9572, 0.9853] +2024-09-06 20:39:00.765291: Epoch time: 265.7 s +2024-09-06 20:39:02.983848: +2024-09-06 20:39:02.985054: Epoch 434 +2024-09-06 20:39:02.986631: Current learning rate: 0.00599 +2024-09-06 20:43:15.911738: train_loss -0.7236 +2024-09-06 20:43:15.913710: val_loss -0.7325 +2024-09-06 20:43:15.914171: Pseudo dice [0.9616, 0.953, 0.9769, 0.9378, 0.8495, 0.9505, 0.9863] +2024-09-06 20:43:15.914611: Epoch time: 252.93 s +2024-09-06 20:43:18.225527: +2024-09-06 20:43:18.226233: Epoch 435 +2024-09-06 20:43:18.226729: Current learning rate: 0.00598 +2024-09-06 20:47:38.560026: train_loss -0.7212 +2024-09-06 20:47:38.562544: val_loss -0.7314 +2024-09-06 20:47:38.563092: Pseudo dice [0.9634, 0.957, 0.9774, 0.9427, 0.8546, 0.9603, 0.9864] +2024-09-06 20:47:38.563604: Epoch time: 260.34 s +2024-09-06 20:47:41.508625: +2024-09-06 20:47:41.509687: Epoch 436 +2024-09-06 20:47:41.511312: Current learning rate: 0.00597 +2024-09-06 20:52:04.036726: train_loss -0.745 +2024-09-06 20:52:04.053630: val_loss -0.7211 +2024-09-06 20:52:04.054122: Pseudo dice [0.9601, 0.9567, 0.9741, 0.9459, 0.845, 0.9546, 0.9809] +2024-09-06 20:52:04.054652: Epoch time: 262.53 s +2024-09-06 20:52:06.713593: +2024-09-06 20:52:06.721968: Epoch 437 +2024-09-06 20:52:06.722636: Current learning rate: 0.00596 +2024-09-06 20:56:28.283972: train_loss -0.7264 +2024-09-06 20:56:28.309575: val_loss -0.7373 +2024-09-06 20:56:28.310080: Pseudo dice [0.9629, 0.9561, 0.9769, 0.9479, 0.8796, 0.9703, 0.9906] +2024-09-06 20:56:28.310509: Epoch time: 261.57 s +2024-09-06 20:56:31.848563: +2024-09-06 20:56:31.866300: Epoch 438 +2024-09-06 20:56:31.867204: Current learning rate: 0.00595 +2024-09-06 21:00:57.411681: train_loss -0.733 +2024-09-06 21:00:57.425348: val_loss -0.7297 +2024-09-06 21:00:57.425982: Pseudo dice [0.9651, 0.9553, 0.9779, 0.9441, 0.8566, 0.9597, 0.9871] +2024-09-06 21:00:57.426462: Epoch time: 265.59 s +2024-09-06 21:01:00.469901: +2024-09-06 21:01:00.470716: Epoch 439 +2024-09-06 21:01:00.471376: Current learning rate: 0.00594 +2024-09-06 21:05:38.748994: train_loss -0.7275 +2024-09-06 21:05:38.765310: val_loss -0.7755 +2024-09-06 21:05:38.765841: Pseudo dice [0.9634, 0.9571, 0.9797, 0.9475, 0.8625, 0.9647, 0.9901] +2024-09-06 21:05:38.766284: Epoch time: 278.28 s +2024-09-06 21:05:41.837699: +2024-09-06 21:05:41.838524: Epoch 440 +2024-09-06 21:05:41.839131: Current learning rate: 0.00593 +2024-09-06 21:10:02.078124: train_loss -0.7335 +2024-09-06 21:10:02.080365: val_loss -0.7334 +2024-09-06 21:10:02.080945: Pseudo dice [0.9627, 0.9566, 0.9774, 0.9452, 0.867, 0.9659, 0.9873] +2024-09-06 21:10:02.081464: Epoch time: 260.25 s +2024-09-06 21:10:04.875108: +2024-09-06 21:10:04.888159: Epoch 441 +2024-09-06 21:10:04.888772: Current learning rate: 0.00592 +2024-09-06 21:14:18.617335: train_loss -0.7388 +2024-09-06 21:14:18.644519: val_loss -0.7316 +2024-09-06 21:14:18.645116: Pseudo dice [0.9657, 0.9584, 0.9806, 0.9508, 0.8771, 0.9688, 0.9913] +2024-09-06 21:14:18.645566: Epoch time: 253.75 s +2024-09-06 21:14:20.944518: +2024-09-06 21:14:20.960045: Epoch 442 +2024-09-06 21:14:20.960897: Current learning rate: 0.00592 +2024-09-06 21:19:05.521219: train_loss -0.7484 +2024-09-06 21:19:05.536276: val_loss -0.7439 +2024-09-06 21:19:05.536901: Pseudo dice [0.965, 0.9573, 0.978, 0.9464, 0.8563, 0.9664, 0.9906] +2024-09-06 21:19:05.537727: Epoch time: 284.58 s +2024-09-06 21:19:09.137648: +2024-09-06 21:19:09.156127: Epoch 443 +2024-09-06 21:19:09.157147: Current learning rate: 0.00591 +2024-09-06 21:23:17.328776: train_loss -0.7343 +2024-09-06 21:23:17.336023: val_loss -0.7625 +2024-09-06 21:23:17.336533: Pseudo dice [0.9627, 0.9587, 0.9765, 0.9497, 0.8717, 0.9696, 0.9908] +2024-09-06 21:23:17.336994: Epoch time: 248.2 s +2024-09-06 21:23:20.503945: +2024-09-06 21:23:20.504543: Epoch 444 +2024-09-06 21:23:20.505008: Current learning rate: 0.0059 +2024-09-06 21:27:50.002724: train_loss -0.7415 +2024-09-06 21:27:50.016312: val_loss -0.7206 +2024-09-06 21:27:50.016997: Pseudo dice [0.9641, 0.9582, 0.977, 0.9457, 0.8504, 0.9438, 0.9835] +2024-09-06 21:27:50.017582: Epoch time: 269.5 s +2024-09-06 21:27:52.916221: +2024-09-06 21:27:52.924209: Epoch 445 +2024-09-06 21:27:52.925005: Current learning rate: 0.00589 +2024-09-06 21:32:34.263280: train_loss -0.7358 +2024-09-06 21:32:34.289190: val_loss -0.7583 +2024-09-06 21:32:34.289742: Pseudo dice [0.9632, 0.9565, 0.9763, 0.9469, 0.8778, 0.969, 0.991] +2024-09-06 21:32:34.290203: Epoch time: 281.35 s +2024-09-06 21:32:37.176885: +2024-09-06 21:32:37.177518: Epoch 446 +2024-09-06 21:32:37.178272: Current learning rate: 0.00588 +2024-09-06 21:37:35.828228: train_loss -0.7391 +2024-09-06 21:37:35.840137: val_loss -0.7045 +2024-09-06 21:37:35.840896: Pseudo dice [0.9605, 0.9513, 0.9759, 0.9492, 0.857, 0.9528, 0.9872] +2024-09-06 21:37:35.841449: Epoch time: 298.66 s +2024-09-06 21:37:40.377759: +2024-09-06 21:37:40.378397: Epoch 447 +2024-09-06 21:37:40.378823: Current learning rate: 0.00587 +2024-09-06 21:42:29.529096: train_loss -0.7425 +2024-09-06 21:42:29.541913: val_loss -0.7334 +2024-09-06 21:42:29.544212: Pseudo dice [0.9611, 0.9555, 0.9729, 0.9387, 0.8654, 0.965, 0.9898] +2024-09-06 21:42:29.544709: Epoch time: 289.16 s +2024-09-06 21:42:34.402220: +2024-09-06 21:42:34.416421: Epoch 448 +2024-09-06 21:42:34.417199: Current learning rate: 0.00586 +2024-09-06 21:47:18.146460: train_loss -0.7278 +2024-09-06 21:47:18.159294: val_loss -0.7279 +2024-09-06 21:47:18.160074: Pseudo dice [0.9647, 0.958, 0.9812, 0.9496, 0.8616, 0.9666, 0.9898] +2024-09-06 21:47:18.161088: Epoch time: 283.75 s +2024-09-06 21:47:21.403991: +2024-09-06 21:47:21.413511: Epoch 449 +2024-09-06 21:47:21.414217: Current learning rate: 0.00585 +2024-09-06 21:51:59.711279: train_loss -0.7179 +2024-09-06 21:51:59.725250: val_loss -0.7098 +2024-09-06 21:51:59.725861: Pseudo dice [0.9642, 0.9556, 0.9776, 0.9442, 0.8428, 0.9689, 0.9849] +2024-09-06 21:51:59.726341: Epoch time: 278.31 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_14_05_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_14_05_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..f75528e64ebe72a2e67bc2c6ac4179aaa5d3a473 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_14_05_16.txt @@ -0,0 +1,373 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 14:05:19.560474: Using torch.compile... +2024-09-07 14:05:25.981516: do_dummy_2d_data_aug: False +2024-09-07 14:05:25.987476: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 14:05:25.989626: The split file contains 5 splits. +2024-09-07 14:05:25.990552: Desired fold for training: 1 +2024-09-07 14:05:25.991089: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 14:05:43.841383: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 14:05:45.582211: +2024-09-07 14:05:45.600064: Epoch 450 +2024-09-07 14:05:45.604346: Current learning rate: 0.00584 +2024-09-07 14:24:14.053076: train_loss -0.7444 +2024-09-07 14:24:14.055376: val_loss -0.7604 +2024-09-07 14:24:14.056417: Pseudo dice [0.9649, 0.9565, 0.9781, 0.95, 0.874, 0.956, 0.9896] +2024-09-07 14:24:14.057210: Epoch time: 1108.48 s +2024-09-07 14:24:16.646980: +2024-09-07 14:24:16.648176: Epoch 451 +2024-09-07 14:24:16.648967: Current learning rate: 0.00583 +2024-09-07 14:31:50.110551: train_loss -0.74 +2024-09-07 14:31:50.129311: val_loss -0.7347 +2024-09-07 14:31:50.130499: Pseudo dice [0.9633, 0.958, 0.9788, 0.9483, 0.8462, 0.9688, 0.9887] +2024-09-07 14:31:50.131449: Epoch time: 453.47 s +2024-09-07 14:31:53.875885: +2024-09-07 14:31:53.877291: Epoch 452 +2024-09-07 14:31:53.878617: Current learning rate: 0.00582 +2024-09-07 14:39:26.670203: train_loss -0.7454 +2024-09-07 14:39:26.673620: val_loss -0.7458 +2024-09-07 14:39:26.675415: Pseudo dice [0.9651, 0.9542, 0.9803, 0.949, 0.8564, 0.9583, 0.9855] +2024-09-07 14:39:26.676216: Epoch time: 452.8 s +2024-09-07 14:39:30.711248: +2024-09-07 14:39:30.727787: Epoch 453 +2024-09-07 14:39:30.729001: Current learning rate: 0.00581 +2024-09-07 14:47:29.440723: train_loss -0.7145 +2024-09-07 14:47:29.448656: val_loss -0.7343 +2024-09-07 14:47:29.450079: Pseudo dice [0.961, 0.9557, 0.9737, 0.9446, 0.8591, 0.9618, 0.988] +2024-09-07 14:47:29.451703: Epoch time: 478.74 s +2024-09-07 14:47:33.321514: +2024-09-07 14:47:33.322760: Epoch 454 +2024-09-07 14:47:33.323783: Current learning rate: 0.0058 +2024-09-07 14:55:49.645158: train_loss -0.7346 +2024-09-07 14:55:49.664622: val_loss -0.7009 +2024-09-07 14:55:49.665489: Pseudo dice [0.9614, 0.9552, 0.9742, 0.9446, 0.8649, 0.9628, 0.9897] +2024-09-07 14:55:49.666220: Epoch time: 496.34 s +2024-09-07 14:55:56.431030: +2024-09-07 14:55:56.432599: Epoch 455 +2024-09-07 14:55:56.433487: Current learning rate: 0.00579 +2024-09-07 15:04:10.354673: train_loss -0.7255 +2024-09-07 15:04:10.376522: val_loss -0.7568 +2024-09-07 15:04:10.387614: Pseudo dice [0.9632, 0.9565, 0.9782, 0.9451, 0.8717, 0.9569, 0.9885] +2024-09-07 15:04:10.392314: Epoch time: 493.93 s +2024-09-07 15:04:14.683731: +2024-09-07 15:04:14.699590: Epoch 456 +2024-09-07 15:04:14.703272: Current learning rate: 0.00578 +2024-09-07 15:11:15.463906: train_loss -0.7526 +2024-09-07 15:11:15.474091: val_loss -0.6902 +2024-09-07 15:11:15.475332: Pseudo dice [0.9634, 0.957, 0.978, 0.9486, 0.8469, 0.9551, 0.9846] +2024-09-07 15:11:15.476855: Epoch time: 420.79 s +2024-09-07 15:11:20.276490: +2024-09-07 15:11:20.292882: Epoch 457 +2024-09-07 15:11:20.293828: Current learning rate: 0.00577 +2024-09-07 15:18:29.490092: train_loss -0.7342 +2024-09-07 15:18:29.519701: val_loss -0.7775 +2024-09-07 15:18:29.521712: Pseudo dice [0.9657, 0.9567, 0.9811, 0.9555, 0.8791, 0.9694, 0.993] +2024-09-07 15:18:29.523133: Epoch time: 429.22 s +2024-09-07 15:18:36.295188: +2024-09-07 15:18:36.298867: Epoch 458 +2024-09-07 15:18:36.302421: Current learning rate: 0.00576 +2024-09-07 15:25:52.996993: train_loss -0.7424 +2024-09-07 15:25:53.018279: val_loss -0.7462 +2024-09-07 15:25:53.021342: Pseudo dice [0.9623, 0.9584, 0.9743, 0.9443, 0.8634, 0.9677, 0.9877] +2024-09-07 15:25:53.022325: Epoch time: 436.73 s +2024-09-07 15:25:57.567185: +2024-09-07 15:25:57.580750: Epoch 459 +2024-09-07 15:25:57.582550: Current learning rate: 0.00575 +2024-09-07 15:33:22.499581: train_loss -0.7472 +2024-09-07 15:33:22.513659: val_loss -0.718 +2024-09-07 15:33:22.514517: Pseudo dice [0.9604, 0.9547, 0.9757, 0.9419, 0.8635, 0.9625, 0.9903] +2024-09-07 15:33:22.515227: Epoch time: 444.94 s +2024-09-07 15:33:26.403259: +2024-09-07 15:33:26.404909: Epoch 460 +2024-09-07 15:33:26.405802: Current learning rate: 0.00574 +2024-09-07 15:41:03.716638: train_loss -0.7302 +2024-09-07 15:41:03.719032: val_loss -0.747 +2024-09-07 15:41:03.719777: Pseudo dice [0.9629, 0.9555, 0.9783, 0.9471, 0.855, 0.967, 0.9894] +2024-09-07 15:41:03.720469: Epoch time: 457.32 s +2024-09-07 15:41:07.473203: +2024-09-07 15:41:07.474758: Epoch 461 +2024-09-07 15:41:07.476378: Current learning rate: 0.00573 +2024-09-07 15:48:16.466712: train_loss -0.7195 +2024-09-07 15:48:16.476370: val_loss -0.7323 +2024-09-07 15:48:16.478272: Pseudo dice [0.9631, 0.9577, 0.9786, 0.9441, 0.868, 0.9616, 0.9876] +2024-09-07 15:48:16.480057: Epoch time: 429.0 s +2024-09-07 15:48:20.258442: +2024-09-07 15:48:20.271150: Epoch 462 +2024-09-07 15:48:20.272856: Current learning rate: 0.00572 +2024-09-07 15:55:18.583315: train_loss -0.7187 +2024-09-07 15:55:18.587054: val_loss -0.7234 +2024-09-07 15:55:18.589132: Pseudo dice [0.9654, 0.9569, 0.9798, 0.9458, 0.8645, 0.9473, 0.9848] +2024-09-07 15:55:18.591212: Epoch time: 418.36 s +2024-09-07 15:55:23.100567: +2024-09-07 15:55:23.101794: Epoch 463 +2024-09-07 15:55:23.104567: Current learning rate: 0.00571 +2024-09-07 16:02:22.950456: train_loss -0.732 +2024-09-07 16:02:22.975701: val_loss -0.7223 +2024-09-07 16:02:22.976630: Pseudo dice [0.9627, 0.9546, 0.9757, 0.948, 0.8616, 0.9591, 0.9888] +2024-09-07 16:02:22.977304: Epoch time: 419.87 s +2024-09-07 16:02:27.184300: +2024-09-07 16:02:27.186159: Epoch 464 +2024-09-07 16:02:27.187334: Current learning rate: 0.0057 +2024-09-07 16:09:33.288958: train_loss -0.7485 +2024-09-07 16:09:33.305279: val_loss -0.7474 +2024-09-07 16:09:33.306281: Pseudo dice [0.9663, 0.9572, 0.9801, 0.9442, 0.8738, 0.9659, 0.9916] +2024-09-07 16:09:33.307178: Epoch time: 426.14 s +2024-09-07 16:09:36.952105: +2024-09-07 16:09:36.967840: Epoch 465 +2024-09-07 16:09:36.974753: Current learning rate: 0.0057 +2024-09-07 16:16:29.701430: train_loss -0.7329 +2024-09-07 16:16:29.706266: val_loss -0.7623 +2024-09-07 16:16:29.707710: Pseudo dice [0.959, 0.9535, 0.9723, 0.9497, 0.8766, 0.956, 0.9896] +2024-09-07 16:16:29.709680: Epoch time: 412.76 s +2024-09-07 16:16:41.358479: +2024-09-07 16:16:41.370647: Epoch 466 +2024-09-07 16:16:41.372231: Current learning rate: 0.00569 +2024-09-07 16:24:04.283282: train_loss -0.7175 +2024-09-07 16:24:04.300947: val_loss -0.7336 +2024-09-07 16:24:04.302794: Pseudo dice [0.9611, 0.9553, 0.9766, 0.9409, 0.8572, 0.9503, 0.9866] +2024-09-07 16:24:04.304275: Epoch time: 442.93 s +2024-09-07 16:24:07.649179: +2024-09-07 16:24:07.651943: Epoch 467 +2024-09-07 16:24:07.652869: Current learning rate: 0.00568 +2024-09-07 16:31:01.005773: train_loss -0.7355 +2024-09-07 16:31:01.029804: val_loss -0.7287 +2024-09-07 16:31:01.031934: Pseudo dice [0.9613, 0.9545, 0.9757, 0.9446, 0.866, 0.9515, 0.9881] +2024-09-07 16:31:01.033741: Epoch time: 413.36 s +2024-09-07 16:31:05.633139: +2024-09-07 16:31:05.650280: Epoch 468 +2024-09-07 16:31:05.651988: Current learning rate: 0.00567 +2024-09-07 16:38:10.382010: train_loss -0.7213 +2024-09-07 16:38:10.397908: val_loss -0.6952 +2024-09-07 16:38:10.399674: Pseudo dice [0.9588, 0.9519, 0.9715, 0.9329, 0.8339, 0.9495, 0.9834] +2024-09-07 16:38:10.402426: Epoch time: 424.75 s +2024-09-07 16:38:13.957599: +2024-09-07 16:38:13.973252: Epoch 469 +2024-09-07 16:38:13.974936: Current learning rate: 0.00566 +2024-09-07 16:45:32.028459: train_loss -0.6965 +2024-09-07 16:45:32.037712: val_loss -0.7355 +2024-09-07 16:45:32.038717: Pseudo dice [0.9592, 0.9487, 0.9752, 0.9356, 0.8591, 0.9665, 0.9908] +2024-09-07 16:45:32.039531: Epoch time: 438.08 s +2024-09-07 16:45:37.020987: +2024-09-07 16:45:37.051894: Epoch 470 +2024-09-07 16:45:37.055070: Current learning rate: 0.00565 +2024-09-07 16:53:00.403705: train_loss -0.7024 +2024-09-07 16:53:00.407124: val_loss -0.7068 +2024-09-07 16:53:00.408577: Pseudo dice [0.9603, 0.9501, 0.9724, 0.9432, 0.8277, 0.9656, 0.9877] +2024-09-07 16:53:00.410167: Epoch time: 443.39 s +2024-09-07 16:53:06.428147: +2024-09-07 16:53:06.443900: Epoch 471 +2024-09-07 16:53:06.446186: Current learning rate: 0.00564 +2024-09-07 16:59:57.550220: train_loss -0.7272 +2024-09-07 16:59:57.569263: val_loss -0.7309 +2024-09-07 16:59:57.570448: Pseudo dice [0.96, 0.9557, 0.9724, 0.948, 0.8675, 0.9674, 0.9913] +2024-09-07 16:59:57.571888: Epoch time: 411.13 s +2024-09-07 17:00:01.683234: +2024-09-07 17:00:01.697786: Epoch 472 +2024-09-07 17:00:01.699291: Current learning rate: 0.00563 +2024-09-07 17:07:17.128064: train_loss -0.7339 +2024-09-07 17:07:17.131284: val_loss -0.7364 +2024-09-07 17:07:17.133998: Pseudo dice [0.9623, 0.9564, 0.9773, 0.9471, 0.8755, 0.9559, 0.9883] +2024-09-07 17:07:17.136229: Epoch time: 435.46 s +2024-09-07 17:07:21.719164: +2024-09-07 17:07:21.728770: Epoch 473 +2024-09-07 17:07:21.730186: Current learning rate: 0.00562 +2024-09-07 17:14:33.611333: train_loss -0.7337 +2024-09-07 17:14:33.615066: val_loss -0.7386 +2024-09-07 17:14:33.617495: Pseudo dice [0.9571, 0.9549, 0.9704, 0.9373, 0.8656, 0.9672, 0.9906] +2024-09-07 17:14:33.619527: Epoch time: 431.9 s +2024-09-07 17:14:37.831811: +2024-09-07 17:14:37.845005: Epoch 474 +2024-09-07 17:14:37.846279: Current learning rate: 0.00561 +2024-09-07 17:21:42.755908: train_loss -0.7369 +2024-09-07 17:21:42.765899: val_loss -0.7355 +2024-09-07 17:21:42.767559: Pseudo dice [0.9629, 0.9567, 0.9794, 0.9517, 0.8772, 0.958, 0.9874] +2024-09-07 17:21:42.768526: Epoch time: 424.93 s +2024-09-07 17:21:45.991551: +2024-09-07 17:21:45.996121: Epoch 475 +2024-09-07 17:21:45.998345: Current learning rate: 0.0056 +2024-09-07 17:28:33.843607: train_loss -0.7226 +2024-09-07 17:28:33.848171: val_loss -0.7342 +2024-09-07 17:28:33.850610: Pseudo dice [0.9541, 0.9516, 0.9685, 0.9369, 0.8513, 0.9662, 0.986] +2024-09-07 17:28:33.852985: Epoch time: 407.88 s +2024-09-07 17:28:39.615159: +2024-09-07 17:28:39.633088: Epoch 476 +2024-09-07 17:28:39.636145: Current learning rate: 0.00559 +2024-09-07 17:35:20.825737: train_loss -0.7364 +2024-09-07 17:35:20.828707: val_loss -0.7476 +2024-09-07 17:35:20.830656: Pseudo dice [0.9596, 0.9583, 0.9745, 0.9554, 0.876, 0.9699, 0.9898] +2024-09-07 17:35:20.831704: Epoch time: 401.24 s +2024-09-07 17:35:25.009374: +2024-09-07 17:35:25.016883: Epoch 477 +2024-09-07 17:35:25.017999: Current learning rate: 0.00558 +2024-09-07 17:42:21.825239: train_loss -0.7352 +2024-09-07 17:42:21.850258: val_loss -0.7191 +2024-09-07 17:42:21.851419: Pseudo dice [0.9632, 0.9577, 0.9776, 0.94, 0.8332, 0.9649, 0.9871] +2024-09-07 17:42:21.852825: Epoch time: 416.82 s +2024-09-07 17:42:27.363130: +2024-09-07 17:42:27.372869: Epoch 478 +2024-09-07 17:42:27.375530: Current learning rate: 0.00557 +2024-09-07 17:49:34.520597: train_loss -0.735 +2024-09-07 17:49:34.547544: val_loss -0.7564 +2024-09-07 17:49:34.548667: Pseudo dice [0.9669, 0.9581, 0.9793, 0.9478, 0.8763, 0.9657, 0.9915] +2024-09-07 17:49:34.550246: Epoch time: 427.19 s +2024-09-07 17:49:39.117971: +2024-09-07 17:49:39.124814: Epoch 479 +2024-09-07 17:49:39.125789: Current learning rate: 0.00556 +2024-09-07 17:56:30.838347: train_loss -0.7466 +2024-09-07 17:56:30.842175: val_loss -0.7273 +2024-09-07 17:56:30.843230: Pseudo dice [0.9614, 0.9525, 0.9703, 0.9402, 0.8398, 0.9674, 0.9865] +2024-09-07 17:56:30.844037: Epoch time: 411.73 s +2024-09-07 17:56:36.078005: +2024-09-07 17:56:36.084642: Epoch 480 +2024-09-07 17:56:36.086105: Current learning rate: 0.00555 +2024-09-07 18:03:35.773607: train_loss -0.7354 +2024-09-07 18:03:35.794289: val_loss -0.7646 +2024-09-07 18:03:35.796064: Pseudo dice [0.9625, 0.9575, 0.9788, 0.9454, 0.8769, 0.968, 0.9925] +2024-09-07 18:03:35.798887: Epoch time: 419.71 s +2024-09-07 18:03:45.129850: +2024-09-07 18:03:45.144995: Epoch 481 +2024-09-07 18:03:45.146458: Current learning rate: 0.00554 +2024-09-07 18:10:34.869570: train_loss -0.7283 +2024-09-07 18:10:34.885940: val_loss -0.7382 +2024-09-07 18:10:34.887567: Pseudo dice [0.9641, 0.9583, 0.9777, 0.9452, 0.8573, 0.9663, 0.9855] +2024-09-07 18:10:34.888602: Epoch time: 409.75 s +2024-09-07 18:10:38.943146: +2024-09-07 18:10:38.956925: Epoch 482 +2024-09-07 18:10:38.957881: Current learning rate: 0.00553 +2024-09-07 18:18:14.417495: train_loss -0.7375 +2024-09-07 18:18:14.433619: val_loss -0.7311 +2024-09-07 18:18:14.434977: Pseudo dice [0.9607, 0.9566, 0.9752, 0.9454, 0.851, 0.9572, 0.9853] +2024-09-07 18:18:14.436162: Epoch time: 455.48 s +2024-09-07 18:18:18.340506: +2024-09-07 18:18:18.353173: Epoch 483 +2024-09-07 18:18:18.354883: Current learning rate: 0.00552 +2024-09-07 18:25:12.150919: train_loss -0.7347 +2024-09-07 18:25:12.173210: val_loss -0.7365 +2024-09-07 18:25:12.175915: Pseudo dice [0.9647, 0.9574, 0.9794, 0.9501, 0.8628, 0.9652, 0.9882] +2024-09-07 18:25:12.179572: Epoch time: 413.86 s +2024-09-07 18:25:16.567327: +2024-09-07 18:25:16.587813: Epoch 484 +2024-09-07 18:25:16.589131: Current learning rate: 0.00551 +2024-09-07 18:32:04.114299: train_loss -0.754 +2024-09-07 18:32:04.137744: val_loss -0.7647 +2024-09-07 18:32:04.139642: Pseudo dice [0.9642, 0.9561, 0.9779, 0.9453, 0.8773, 0.9696, 0.9911] +2024-09-07 18:32:04.141192: Epoch time: 407.55 s +2024-09-07 18:32:08.704132: +2024-09-07 18:32:08.716792: Epoch 485 +2024-09-07 18:32:08.717748: Current learning rate: 0.0055 +2024-09-07 18:38:55.549684: train_loss -0.7379 +2024-09-07 18:38:55.567608: val_loss -0.7348 +2024-09-07 18:38:55.568691: Pseudo dice [0.9671, 0.9589, 0.981, 0.9562, 0.8703, 0.9558, 0.987] +2024-09-07 18:38:55.569515: Epoch time: 406.85 s +2024-09-07 18:38:59.181295: +2024-09-07 18:38:59.199934: Epoch 486 +2024-09-07 18:38:59.202152: Current learning rate: 0.00549 +2024-09-07 18:46:12.272828: train_loss -0.741 +2024-09-07 18:46:12.290392: val_loss -0.7673 +2024-09-07 18:46:12.291991: Pseudo dice [0.9644, 0.9572, 0.9772, 0.9465, 0.8826, 0.9593, 0.9898] +2024-09-07 18:46:12.293669: Epoch time: 433.13 s +2024-09-07 18:46:16.329551: +2024-09-07 18:46:16.331976: Epoch 487 +2024-09-07 18:46:16.333098: Current learning rate: 0.00548 +2024-09-07 18:53:10.152207: train_loss -0.7414 +2024-09-07 18:53:10.160948: val_loss -0.7576 +2024-09-07 18:53:10.162853: Pseudo dice [0.9662, 0.9588, 0.9817, 0.9482, 0.8553, 0.9654, 0.9881] +2024-09-07 18:53:10.164055: Epoch time: 413.83 s +2024-09-07 18:53:14.405246: +2024-09-07 18:53:14.408451: Epoch 488 +2024-09-07 18:53:14.409894: Current learning rate: 0.00547 +2024-09-07 19:00:17.808727: train_loss -0.7296 +2024-09-07 19:00:17.831557: val_loss -0.7496 +2024-09-07 19:00:17.834088: Pseudo dice [0.9648, 0.9578, 0.9794, 0.9502, 0.885, 0.969, 0.9915] +2024-09-07 19:00:17.845219: Epoch time: 423.46 s +2024-09-07 19:00:17.847060: Yayy! New best EMA pseudo Dice: 0.9518 +2024-09-07 19:00:28.180236: +2024-09-07 19:00:28.181479: Epoch 489 +2024-09-07 19:00:28.182291: Current learning rate: 0.00546 +2024-09-07 19:07:23.832050: train_loss -0.7505 +2024-09-07 19:07:23.850753: val_loss -0.7442 +2024-09-07 19:07:23.852781: Pseudo dice [0.9619, 0.9558, 0.9759, 0.9463, 0.8717, 0.9608, 0.9865] +2024-09-07 19:07:23.854842: Epoch time: 415.66 s +2024-09-07 19:07:28.939769: +2024-09-07 19:07:28.948745: Epoch 490 +2024-09-07 19:07:28.950205: Current learning rate: 0.00546 +2024-09-07 19:14:32.753010: train_loss -0.7417 +2024-09-07 19:14:32.760028: val_loss -0.7518 +2024-09-07 19:14:32.760997: Pseudo dice [0.9646, 0.958, 0.9768, 0.9521, 0.8804, 0.9698, 0.9902] +2024-09-07 19:14:32.762203: Epoch time: 423.82 s +2024-09-07 19:14:32.763785: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-07 19:14:42.272476: +2024-09-07 19:14:42.273767: Epoch 491 +2024-09-07 19:14:42.274560: Current learning rate: 0.00545 +2024-09-07 19:22:01.476944: train_loss -0.7443 +2024-09-07 19:22:01.494510: val_loss -0.7622 +2024-09-07 19:22:01.496187: Pseudo dice [0.962, 0.9536, 0.9768, 0.9409, 0.8449, 0.9614, 0.9893] +2024-09-07 19:22:01.498293: Epoch time: 439.21 s +2024-09-07 19:22:06.043861: +2024-09-07 19:22:06.045473: Epoch 492 +2024-09-07 19:22:06.048016: Current learning rate: 0.00544 +2024-09-07 19:29:18.350727: train_loss -0.716 +2024-09-07 19:29:18.357026: val_loss -0.7358 +2024-09-07 19:29:18.359725: Pseudo dice [0.9634, 0.9576, 0.9747, 0.9496, 0.8573, 0.9646, 0.9865] +2024-09-07 19:29:18.360833: Epoch time: 432.31 s +2024-09-07 19:29:22.649858: +2024-09-07 19:29:22.664762: Epoch 493 +2024-09-07 19:29:22.665743: Current learning rate: 0.00543 +2024-09-07 19:36:19.153601: train_loss -0.7489 +2024-09-07 19:36:19.163612: val_loss -0.7968 +2024-09-07 19:36:19.165341: Pseudo dice [0.9634, 0.9541, 0.9773, 0.95, 0.8702, 0.9692, 0.992] +2024-09-07 19:36:19.166468: Epoch time: 416.51 s +2024-09-07 19:36:25.285217: +2024-09-07 19:36:25.286734: Epoch 494 +2024-09-07 19:36:25.287726: Current learning rate: 0.00542 +2024-09-07 19:43:39.693748: train_loss -0.7323 +2024-09-07 19:43:39.701944: val_loss -0.713 +2024-09-07 19:43:39.703077: Pseudo dice [0.9611, 0.9565, 0.9754, 0.9488, 0.8841, 0.9586, 0.9912] +2024-09-07 19:43:39.705238: Epoch time: 434.43 s +2024-09-07 19:43:44.911214: +2024-09-07 19:43:44.920810: Epoch 495 +2024-09-07 19:43:44.921809: Current learning rate: 0.00541 +2024-09-07 19:50:40.551356: train_loss -0.7162 +2024-09-07 19:50:40.564754: val_loss -0.6874 +2024-09-07 19:50:40.566417: Pseudo dice [0.9604, 0.9548, 0.9649, 0.94, 0.8352, 0.9517, 0.984] +2024-09-07 19:50:40.568866: Epoch time: 415.7 s +2024-09-07 19:50:44.831706: +2024-09-07 19:50:44.848766: Epoch 496 +2024-09-07 19:50:44.851297: Current learning rate: 0.0054 +2024-09-07 19:58:18.256136: train_loss -0.7225 +2024-09-07 19:58:18.259226: val_loss -0.6992 +2024-09-07 19:58:18.261594: Pseudo dice [0.9627, 0.9537, 0.9766, 0.948, 0.8737, 0.9335, 0.984] +2024-09-07 19:58:18.263122: Epoch time: 453.41 s +2024-09-07 19:58:23.402942: +2024-09-07 19:58:23.418317: Epoch 497 +2024-09-07 19:58:23.420514: Current learning rate: 0.00539 +2024-09-07 20:05:38.436595: train_loss -0.7293 +2024-09-07 20:05:38.452387: val_loss -0.7487 +2024-09-07 20:05:38.454169: Pseudo dice [0.9608, 0.9532, 0.977, 0.9459, 0.8606, 0.963, 0.9904] +2024-09-07 20:05:38.455134: Epoch time: 435.04 s +2024-09-07 20:05:41.628126: +2024-09-07 20:05:41.634161: Epoch 498 +2024-09-07 20:05:41.635503: Current learning rate: 0.00538 +2024-09-07 20:12:35.202531: train_loss -0.7269 +2024-09-07 20:12:35.204808: val_loss -0.7416 +2024-09-07 20:12:35.206339: Pseudo dice [0.9631, 0.9561, 0.978, 0.95, 0.8678, 0.9666, 0.9891] +2024-09-07 20:12:35.207552: Epoch time: 413.58 s +2024-09-07 20:12:40.688826: +2024-09-07 20:12:40.695918: Epoch 499 +2024-09-07 20:12:40.699225: Current learning rate: 0.00537 +2024-09-07 20:19:48.504694: train_loss -0.7553 +2024-09-07 20:19:48.521519: val_loss -0.7095 +2024-09-07 20:19:48.523088: Pseudo dice [0.9614, 0.9562, 0.9748, 0.9441, 0.8605, 0.9598, 0.9852] +2024-09-07 20:19:48.524008: Epoch time: 427.83 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_22_04_47.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_22_04_47.txt new file mode 100644 index 0000000000000000000000000000000000000000..c10bab5798d7cc27780c1abe487b140e0f8d6f2e --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_7_22_04_47.txt @@ -0,0 +1,385 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 22:04:50.674146: Using torch.compile... +2024-09-07 22:04:57.750741: do_dummy_2d_data_aug: False +2024-09-07 22:04:57.788201: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 22:04:57.819455: The split file contains 5 splits. +2024-09-07 22:04:57.820192: Desired fold for training: 1 +2024-09-07 22:04:57.820838: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 22:05:11.427322: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 22:05:12.598591: +2024-09-07 22:05:12.608421: Epoch 500 +2024-09-07 22:05:12.610506: Current learning rate: 0.00536 +2024-09-07 22:21:04.260250: train_loss -0.7398 +2024-09-07 22:21:04.263559: val_loss -0.7209 +2024-09-07 22:21:04.264463: Pseudo dice [0.9621, 0.9562, 0.9767, 0.9483, 0.8616, 0.9663, 0.9913] +2024-09-07 22:21:04.265229: Epoch time: 951.67 s +2024-09-07 22:21:06.024849: +2024-09-07 22:21:06.025752: Epoch 501 +2024-09-07 22:21:06.026429: Current learning rate: 0.00535 +2024-09-07 22:27:01.595051: train_loss -0.7322 +2024-09-07 22:27:01.610305: val_loss -0.7543 +2024-09-07 22:27:01.611291: Pseudo dice [0.9664, 0.9573, 0.9804, 0.9528, 0.8841, 0.9482, 0.9869] +2024-09-07 22:27:01.612160: Epoch time: 355.57 s +2024-09-07 22:27:05.025893: +2024-09-07 22:27:05.036460: Epoch 502 +2024-09-07 22:27:05.037537: Current learning rate: 0.00534 +2024-09-07 22:32:54.964900: train_loss -0.7431 +2024-09-07 22:32:54.976601: val_loss -0.7513 +2024-09-07 22:32:54.977866: Pseudo dice [0.9629, 0.9557, 0.9766, 0.9519, 0.8571, 0.9663, 0.9919] +2024-09-07 22:32:54.979050: Epoch time: 349.94 s +2024-09-07 22:32:59.705626: +2024-09-07 22:32:59.708675: Epoch 503 +2024-09-07 22:32:59.709657: Current learning rate: 0.00533 +2024-09-07 22:38:27.674155: train_loss -0.7444 +2024-09-07 22:38:27.675317: val_loss -0.7549 +2024-09-07 22:38:27.676009: Pseudo dice [0.9654, 0.957, 0.9789, 0.95, 0.8875, 0.9723, 0.9918] +2024-09-07 22:38:27.676690: Epoch time: 327.97 s +2024-09-07 22:38:33.919375: +2024-09-07 22:38:33.920747: Epoch 504 +2024-09-07 22:38:33.921581: Current learning rate: 0.00532 +2024-09-07 22:44:03.917117: train_loss -0.7518 +2024-09-07 22:44:03.929006: val_loss -0.7331 +2024-09-07 22:44:03.929857: Pseudo dice [0.9653, 0.9578, 0.9778, 0.9478, 0.8685, 0.9683, 0.9885] +2024-09-07 22:44:03.930684: Epoch time: 330.0 s +2024-09-07 22:44:06.844565: +2024-09-07 22:44:06.845719: Epoch 505 +2024-09-07 22:44:06.846606: Current learning rate: 0.00531 +2024-09-07 22:49:36.800798: train_loss -0.7424 +2024-09-07 22:49:36.816910: val_loss -0.7577 +2024-09-07 22:49:36.818784: Pseudo dice [0.9646, 0.9562, 0.9783, 0.9502, 0.8739, 0.9717, 0.9907] +2024-09-07 22:49:36.820037: Epoch time: 329.96 s +2024-09-07 22:49:36.821064: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-07 22:49:44.642648: +2024-09-07 22:49:44.644330: Epoch 506 +2024-09-07 22:49:44.646209: Current learning rate: 0.0053 +2024-09-07 22:55:08.754494: train_loss -0.7401 +2024-09-07 22:55:08.771585: val_loss -0.7548 +2024-09-07 22:55:08.772772: Pseudo dice [0.9659, 0.959, 0.9802, 0.9524, 0.8752, 0.9707, 0.9911] +2024-09-07 22:55:08.773713: Epoch time: 324.12 s +2024-09-07 22:55:08.774830: Yayy! New best EMA pseudo Dice: 0.9526 +2024-09-07 22:55:15.407771: +2024-09-07 22:55:15.408896: Epoch 507 +2024-09-07 22:55:15.409642: Current learning rate: 0.00529 +2024-09-07 23:00:39.652704: train_loss -0.754 +2024-09-07 23:00:39.655074: val_loss -0.7342 +2024-09-07 23:00:39.656199: Pseudo dice [0.963, 0.9564, 0.9763, 0.953, 0.8771, 0.9629, 0.9893] +2024-09-07 23:00:39.657193: Epoch time: 324.25 s +2024-09-07 23:00:39.658129: Yayy! New best EMA pseudo Dice: 0.9528 +2024-09-07 23:00:46.556384: +2024-09-07 23:00:46.557528: Epoch 508 +2024-09-07 23:00:46.558377: Current learning rate: 0.00528 +2024-09-07 23:06:12.891781: train_loss -0.7333 +2024-09-07 23:06:12.906415: val_loss -0.7385 +2024-09-07 23:06:12.907601: Pseudo dice [0.9623, 0.955, 0.9775, 0.9495, 0.8686, 0.9657, 0.9893] +2024-09-07 23:06:12.908571: Epoch time: 326.34 s +2024-09-07 23:06:15.618807: +2024-09-07 23:06:15.620229: Epoch 509 +2024-09-07 23:06:15.621056: Current learning rate: 0.00527 +2024-09-07 23:11:33.792571: train_loss -0.7277 +2024-09-07 23:11:33.810715: val_loss -0.7356 +2024-09-07 23:11:33.812034: Pseudo dice [0.9616, 0.9562, 0.9722, 0.9455, 0.8789, 0.9714, 0.9914] +2024-09-07 23:11:33.813629: Epoch time: 318.18 s +2024-09-07 23:11:33.814553: Yayy! New best EMA pseudo Dice: 0.9529 +2024-09-07 23:11:39.533535: +2024-09-07 23:11:39.534552: Epoch 510 +2024-09-07 23:11:39.535235: Current learning rate: 0.00526 +2024-09-07 23:16:55.929312: train_loss -0.742 +2024-09-07 23:16:55.945905: val_loss -0.7288 +2024-09-07 23:16:55.946886: Pseudo dice [0.9621, 0.9565, 0.9755, 0.9465, 0.8699, 0.9684, 0.9893] +2024-09-07 23:16:55.947719: Epoch time: 316.4 s +2024-09-07 23:16:59.511015: +2024-09-07 23:16:59.512851: Epoch 511 +2024-09-07 23:16:59.514108: Current learning rate: 0.00525 +2024-09-07 23:22:36.673199: train_loss -0.7521 +2024-09-07 23:22:36.676011: val_loss -0.7665 +2024-09-07 23:22:36.677162: Pseudo dice [0.9642, 0.9569, 0.9784, 0.9499, 0.8657, 0.969, 0.9902] +2024-09-07 23:22:36.678409: Epoch time: 337.17 s +2024-09-07 23:22:36.679560: Yayy! New best EMA pseudo Dice: 0.9529 +2024-09-07 23:22:41.167937: +2024-09-07 23:22:41.170083: Epoch 512 +2024-09-07 23:22:41.170802: Current learning rate: 0.00524 +2024-09-07 23:28:03.595278: train_loss -0.7398 +2024-09-07 23:28:03.609780: val_loss -0.7419 +2024-09-07 23:28:03.610916: Pseudo dice [0.9646, 0.9554, 0.9795, 0.9525, 0.8626, 0.9684, 0.9889] +2024-09-07 23:28:03.611777: Epoch time: 322.43 s +2024-09-07 23:28:03.613013: Yayy! New best EMA pseudo Dice: 0.9529 +2024-09-07 23:28:08.306090: +2024-09-07 23:28:08.307370: Epoch 513 +2024-09-07 23:28:08.308791: Current learning rate: 0.00523 +2024-09-07 23:33:36.025498: train_loss -0.7332 +2024-09-07 23:33:36.042332: val_loss -0.737 +2024-09-07 23:33:36.043935: Pseudo dice [0.9654, 0.9579, 0.9806, 0.9505, 0.8779, 0.964, 0.9888] +2024-09-07 23:33:36.048609: Epoch time: 327.72 s +2024-09-07 23:33:36.050602: Yayy! New best EMA pseudo Dice: 0.9531 +2024-09-07 23:33:41.603685: +2024-09-07 23:33:41.605055: Epoch 514 +2024-09-07 23:33:41.606210: Current learning rate: 0.00522 +2024-09-07 23:39:32.964052: train_loss -0.7403 +2024-09-07 23:39:32.967588: val_loss -0.7349 +2024-09-07 23:39:32.969013: Pseudo dice [0.9628, 0.9573, 0.9766, 0.9495, 0.8624, 0.9722, 0.9916] +2024-09-07 23:39:32.969997: Epoch time: 351.36 s +2024-09-07 23:39:32.971043: Yayy! New best EMA pseudo Dice: 0.9531 +2024-09-07 23:39:39.154284: +2024-09-07 23:39:39.155391: Epoch 515 +2024-09-07 23:39:39.156266: Current learning rate: 0.00521 +2024-09-07 23:45:12.937903: train_loss -0.7412 +2024-09-07 23:45:12.947747: val_loss -0.7155 +2024-09-07 23:45:12.948617: Pseudo dice [0.9678, 0.9578, 0.9813, 0.9505, 0.8112, 0.9664, 0.9823] +2024-09-07 23:45:12.949275: Epoch time: 333.79 s +2024-09-07 23:45:16.321935: +2024-09-07 23:45:16.337783: Epoch 516 +2024-09-07 23:45:16.339195: Current learning rate: 0.0052 +2024-09-07 23:52:10.594391: train_loss -0.7383 +2024-09-07 23:52:10.608208: val_loss -0.7503 +2024-09-07 23:52:10.609088: Pseudo dice [0.9628, 0.9564, 0.9791, 0.9502, 0.8518, 0.9668, 0.9889] +2024-09-07 23:52:10.609827: Epoch time: 414.28 s +2024-09-07 23:52:14.357829: +2024-09-07 23:52:14.373017: Epoch 517 +2024-09-07 23:52:14.374698: Current learning rate: 0.00519 +2024-09-07 23:58:58.745918: train_loss -0.755 +2024-09-07 23:58:58.756342: val_loss -0.7609 +2024-09-07 23:58:58.757741: Pseudo dice [0.9654, 0.9558, 0.9776, 0.9489, 0.8694, 0.9565, 0.9898] +2024-09-07 23:58:58.758785: Epoch time: 404.4 s +2024-09-07 23:59:02.348619: +2024-09-07 23:59:02.361170: Epoch 518 +2024-09-07 23:59:02.362031: Current learning rate: 0.00518 +2024-09-08 00:05:36.059265: train_loss -0.7263 +2024-09-08 00:05:36.073974: val_loss -0.7102 +2024-09-08 00:05:36.075844: Pseudo dice [0.9663, 0.96, 0.9798, 0.9495, 0.8797, 0.9728, 0.9917] +2024-09-08 00:05:36.077649: Epoch time: 393.72 s +2024-09-08 00:05:38.927080: +2024-09-08 00:05:38.936911: Epoch 519 +2024-09-08 00:05:38.937690: Current learning rate: 0.00518 +2024-09-08 00:11:09.908194: train_loss -0.7547 +2024-09-08 00:11:09.916192: val_loss -0.7732 +2024-09-08 00:11:09.918207: Pseudo dice [0.9647, 0.9575, 0.9774, 0.9479, 0.8344, 0.9717, 0.9894] +2024-09-08 00:11:09.920044: Epoch time: 330.99 s +2024-09-08 00:11:13.167482: +2024-09-08 00:11:13.169036: Epoch 520 +2024-09-08 00:11:13.170200: Current learning rate: 0.00517 +2024-09-08 00:17:04.781944: train_loss -0.7514 +2024-09-08 00:17:04.785143: val_loss -0.7299 +2024-09-08 00:17:04.786303: Pseudo dice [0.9668, 0.9598, 0.9814, 0.9529, 0.8306, 0.9691, 0.9853] +2024-09-08 00:17:04.787292: Epoch time: 351.62 s +2024-09-08 00:17:08.020002: +2024-09-08 00:17:08.021100: Epoch 521 +2024-09-08 00:17:08.021905: Current learning rate: 0.00516 +2024-09-08 00:22:44.999535: train_loss -0.7378 +2024-09-08 00:22:45.005912: val_loss -0.7616 +2024-09-08 00:22:45.007336: Pseudo dice [0.9669, 0.9595, 0.9812, 0.9537, 0.8836, 0.9623, 0.9897] +2024-09-08 00:22:45.008552: Epoch time: 336.99 s +2024-09-08 00:22:48.521873: +2024-09-08 00:22:48.536937: Epoch 522 +2024-09-08 00:22:48.537869: Current learning rate: 0.00515 +2024-09-08 00:28:27.182241: train_loss -0.7467 +2024-09-08 00:28:27.197551: val_loss -0.7577 +2024-09-08 00:28:27.198842: Pseudo dice [0.9645, 0.9595, 0.9763, 0.953, 0.8651, 0.9704, 0.9893] +2024-09-08 00:28:27.199883: Epoch time: 338.67 s +2024-09-08 00:28:31.277493: +2024-09-08 00:28:31.278579: Epoch 523 +2024-09-08 00:28:31.279397: Current learning rate: 0.00514 +2024-09-08 00:34:11.171606: train_loss -0.7492 +2024-09-08 00:34:11.194882: val_loss -0.7649 +2024-09-08 00:34:11.196158: Pseudo dice [0.9634, 0.9588, 0.9747, 0.9474, 0.8871, 0.9706, 0.9911] +2024-09-08 00:34:11.196872: Epoch time: 339.9 s +2024-09-08 00:34:14.613741: +2024-09-08 00:34:14.627441: Epoch 524 +2024-09-08 00:34:14.629097: Current learning rate: 0.00513 +2024-09-08 00:40:02.588592: train_loss -0.7472 +2024-09-08 00:40:02.590621: val_loss -0.7029 +2024-09-08 00:40:02.592004: Pseudo dice [0.9623, 0.9569, 0.9759, 0.9507, 0.8606, 0.9545, 0.9818] +2024-09-08 00:40:02.592931: Epoch time: 347.98 s +2024-09-08 00:40:05.773774: +2024-09-08 00:40:05.774814: Epoch 525 +2024-09-08 00:40:05.775472: Current learning rate: 0.00512 +2024-09-08 00:45:27.993200: train_loss -0.7445 +2024-09-08 00:45:28.012009: val_loss -0.7702 +2024-09-08 00:45:28.013687: Pseudo dice [0.9661, 0.9589, 0.9807, 0.9503, 0.8932, 0.9509, 0.9879] +2024-09-08 00:45:28.014740: Epoch time: 322.22 s +2024-09-08 00:45:32.190089: +2024-09-08 00:45:32.191692: Epoch 526 +2024-09-08 00:45:32.193161: Current learning rate: 0.00511 +2024-09-08 00:50:56.829152: train_loss -0.7548 +2024-09-08 00:50:56.847797: val_loss -0.7534 +2024-09-08 00:50:56.849786: Pseudo dice [0.9662, 0.9575, 0.9801, 0.9499, 0.886, 0.9725, 0.9908] +2024-09-08 00:50:56.851396: Epoch time: 324.64 s +2024-09-08 00:50:56.852328: Yayy! New best EMA pseudo Dice: 0.9533 +2024-09-08 00:51:05.249813: +2024-09-08 00:51:05.250811: Epoch 527 +2024-09-08 00:51:05.251546: Current learning rate: 0.0051 +2024-09-08 00:56:31.967358: train_loss -0.7288 +2024-09-08 00:56:31.970717: val_loss -0.7245 +2024-09-08 00:56:31.971931: Pseudo dice [0.9646, 0.9574, 0.9802, 0.9523, 0.8378, 0.9684, 0.9848] +2024-09-08 00:56:31.972950: Epoch time: 326.72 s +2024-09-08 00:56:36.443444: +2024-09-08 00:56:36.444331: Epoch 528 +2024-09-08 00:56:36.444964: Current learning rate: 0.00509 +2024-09-08 01:02:30.080415: train_loss -0.7467 +2024-09-08 01:02:30.082865: val_loss -0.7411 +2024-09-08 01:02:30.084260: Pseudo dice [0.9612, 0.9535, 0.9728, 0.9488, 0.8687, 0.971, 0.9914] +2024-09-08 01:02:30.085149: Epoch time: 353.64 s +2024-09-08 01:02:33.965918: +2024-09-08 01:02:33.969100: Epoch 529 +2024-09-08 01:02:33.970279: Current learning rate: 0.00508 +2024-09-08 01:08:03.797636: train_loss -0.7532 +2024-09-08 01:08:03.814926: val_loss -0.7286 +2024-09-08 01:08:03.816105: Pseudo dice [0.9653, 0.9599, 0.9787, 0.953, 0.8716, 0.9703, 0.9913] +2024-09-08 01:08:03.816940: Epoch time: 329.84 s +2024-09-08 01:08:07.616443: +2024-09-08 01:08:07.628764: Epoch 530 +2024-09-08 01:08:07.629886: Current learning rate: 0.00507 +2024-09-08 01:13:59.198542: train_loss -0.7349 +2024-09-08 01:13:59.209613: val_loss -0.7768 +2024-09-08 01:13:59.210567: Pseudo dice [0.9666, 0.955, 0.9803, 0.9488, 0.881, 0.9705, 0.9921] +2024-09-08 01:13:59.211408: Epoch time: 351.59 s +2024-09-08 01:13:59.212460: Yayy! New best EMA pseudo Dice: 0.9535 +2024-09-08 01:14:06.092178: +2024-09-08 01:14:06.093483: Epoch 531 +2024-09-08 01:14:06.094298: Current learning rate: 0.00506 +2024-09-08 01:19:41.145960: train_loss -0.7287 +2024-09-08 01:19:41.149267: val_loss -0.737 +2024-09-08 01:19:41.150827: Pseudo dice [0.9643, 0.9587, 0.9748, 0.9525, 0.8691, 0.9661, 0.9885] +2024-09-08 01:19:41.152224: Epoch time: 335.06 s +2024-09-08 01:19:44.278082: +2024-09-08 01:19:44.279293: Epoch 532 +2024-09-08 01:19:44.280030: Current learning rate: 0.00505 +2024-09-08 01:25:20.521732: train_loss -0.7188 +2024-09-08 01:25:20.537614: val_loss -0.7661 +2024-09-08 01:25:20.538392: Pseudo dice [0.9637, 0.9568, 0.9773, 0.953, 0.8634, 0.9576, 0.9862] +2024-09-08 01:25:20.539106: Epoch time: 336.26 s +2024-09-08 01:25:24.300575: +2024-09-08 01:25:24.302175: Epoch 533 +2024-09-08 01:25:24.303513: Current learning rate: 0.00504 +2024-09-08 01:31:42.191687: train_loss -0.7531 +2024-09-08 01:31:42.211203: val_loss -0.7741 +2024-09-08 01:31:42.212934: Pseudo dice [0.962, 0.9565, 0.9734, 0.9511, 0.8854, 0.9722, 0.9917] +2024-09-08 01:31:42.214158: Epoch time: 377.92 s +2024-09-08 01:31:42.215092: Yayy! New best EMA pseudo Dice: 0.9535 +2024-09-08 01:31:48.820838: +2024-09-08 01:31:48.821676: Epoch 534 +2024-09-08 01:31:48.822562: Current learning rate: 0.00503 +2024-09-08 01:37:31.884438: train_loss -0.7385 +2024-09-08 01:37:31.886100: val_loss -0.7746 +2024-09-08 01:37:31.887375: Pseudo dice [0.9626, 0.9575, 0.9741, 0.9507, 0.8849, 0.9705, 0.9909] +2024-09-08 01:37:31.888183: Epoch time: 343.07 s +2024-09-08 01:37:31.888944: Yayy! New best EMA pseudo Dice: 0.9538 +2024-09-08 01:37:37.881222: +2024-09-08 01:37:37.882759: Epoch 535 +2024-09-08 01:37:37.883676: Current learning rate: 0.00502 +2024-09-08 01:43:47.670296: train_loss -0.7477 +2024-09-08 01:43:47.678148: val_loss -0.7407 +2024-09-08 01:43:47.680726: Pseudo dice [0.9656, 0.9593, 0.9777, 0.9554, 0.8878, 0.9555, 0.9891] +2024-09-08 01:43:47.694768: Epoch time: 369.78 s +2024-09-08 01:43:47.696836: Yayy! New best EMA pseudo Dice: 0.954 +2024-09-08 01:43:53.887015: +2024-09-08 01:43:53.888157: Epoch 536 +2024-09-08 01:43:53.888961: Current learning rate: 0.00501 +2024-09-08 01:49:35.421767: train_loss -0.7402 +2024-09-08 01:49:35.427513: val_loss -0.7446 +2024-09-08 01:49:35.428667: Pseudo dice [0.9652, 0.958, 0.9811, 0.9497, 0.8645, 0.9718, 0.9884] +2024-09-08 01:49:35.429588: Epoch time: 341.54 s +2024-09-08 01:49:35.430348: Yayy! New best EMA pseudo Dice: 0.954 +2024-09-08 01:49:43.140928: +2024-09-08 01:49:43.152977: Epoch 537 +2024-09-08 01:49:43.155103: Current learning rate: 0.005 +2024-09-08 01:55:16.378089: train_loss -0.7392 +2024-09-08 01:55:16.381190: val_loss -0.7069 +2024-09-08 01:55:16.383257: Pseudo dice [0.9621, 0.958, 0.975, 0.9532, 0.8808, 0.9359, 0.9828] +2024-09-08 01:55:16.384174: Epoch time: 333.24 s +2024-09-08 01:55:19.113398: +2024-09-08 01:55:19.114724: Epoch 538 +2024-09-08 01:55:19.115589: Current learning rate: 0.00499 +2024-09-08 02:00:51.115720: train_loss -0.7352 +2024-09-08 02:00:51.118815: val_loss -0.7647 +2024-09-08 02:00:51.119727: Pseudo dice [0.9628, 0.9556, 0.976, 0.9493, 0.8605, 0.9595, 0.9869] +2024-09-08 02:00:51.120524: Epoch time: 332.01 s +2024-09-08 02:00:55.032301: +2024-09-08 02:00:55.033691: Epoch 539 +2024-09-08 02:00:55.034566: Current learning rate: 0.00498 +2024-09-08 02:06:57.293925: train_loss -0.7316 +2024-09-08 02:06:57.309182: val_loss -0.7042 +2024-09-08 02:06:57.309959: Pseudo dice [0.9615, 0.9546, 0.9737, 0.9498, 0.8474, 0.9693, 0.9888] +2024-09-08 02:06:57.310859: Epoch time: 362.27 s +2024-09-08 02:07:02.363613: +2024-09-08 02:07:02.365546: Epoch 540 +2024-09-08 02:07:02.366598: Current learning rate: 0.00497 +2024-09-08 02:12:33.163837: train_loss -0.7304 +2024-09-08 02:12:33.176619: val_loss -0.7435 +2024-09-08 02:12:33.177656: Pseudo dice [0.9618, 0.9575, 0.9756, 0.9477, 0.8719, 0.9708, 0.9888] +2024-09-08 02:12:33.178734: Epoch time: 330.81 s +2024-09-08 02:12:36.840210: +2024-09-08 02:12:36.842304: Epoch 541 +2024-09-08 02:12:36.843409: Current learning rate: 0.00496 +2024-09-08 02:18:29.393152: train_loss -0.7537 +2024-09-08 02:18:29.407128: val_loss -0.7473 +2024-09-08 02:18:29.409021: Pseudo dice [0.9603, 0.9567, 0.9724, 0.9456, 0.841, 0.9701, 0.9873] +2024-09-08 02:18:29.410938: Epoch time: 352.57 s +2024-09-08 02:18:33.340688: +2024-09-08 02:18:33.341793: Epoch 542 +2024-09-08 02:18:33.342881: Current learning rate: 0.00495 +2024-09-08 02:24:15.424063: train_loss -0.74 +2024-09-08 02:24:15.452756: val_loss -0.7629 +2024-09-08 02:24:15.453601: Pseudo dice [0.9639, 0.9561, 0.9772, 0.9489, 0.8715, 0.9712, 0.991] +2024-09-08 02:24:15.454668: Epoch time: 342.09 s +2024-09-08 02:24:19.456515: +2024-09-08 02:24:19.457755: Epoch 543 +2024-09-08 02:24:19.459323: Current learning rate: 0.00494 +2024-09-08 02:30:12.622942: train_loss -0.7177 +2024-09-08 02:30:12.638146: val_loss -0.7834 +2024-09-08 02:30:12.639125: Pseudo dice [0.9662, 0.9569, 0.9804, 0.9523, 0.8934, 0.9736, 0.9928] +2024-09-08 02:30:12.641610: Epoch time: 353.17 s +2024-09-08 02:30:15.984111: +2024-09-08 02:30:15.996773: Epoch 544 +2024-09-08 02:30:15.998461: Current learning rate: 0.00493 +2024-09-08 02:36:00.920480: train_loss -0.7251 +2024-09-08 02:36:00.938487: val_loss -0.7268 +2024-09-08 02:36:00.939322: Pseudo dice [0.9634, 0.9596, 0.9786, 0.954, 0.8527, 0.9688, 0.9908] +2024-09-08 02:36:00.939994: Epoch time: 344.94 s +2024-09-08 02:36:04.220381: +2024-09-08 02:36:04.224324: Epoch 545 +2024-09-08 02:36:04.225306: Current learning rate: 0.00492 +2024-09-08 02:41:59.952780: train_loss -0.7314 +2024-09-08 02:41:59.969000: val_loss -0.7253 +2024-09-08 02:41:59.969958: Pseudo dice [0.9657, 0.959, 0.9775, 0.9467, 0.8669, 0.9649, 0.991] +2024-09-08 02:41:59.970735: Epoch time: 355.74 s +2024-09-08 02:42:02.733647: +2024-09-08 02:42:02.734620: Epoch 546 +2024-09-08 02:42:02.735401: Current learning rate: 0.00491 +2024-09-08 02:48:38.178225: train_loss -0.7495 +2024-09-08 02:48:38.180454: val_loss -0.7448 +2024-09-08 02:48:38.181200: Pseudo dice [0.9635, 0.9568, 0.9765, 0.9487, 0.8596, 0.9724, 0.9897] +2024-09-08 02:48:38.181846: Epoch time: 395.45 s +2024-09-08 02:48:43.200014: +2024-09-08 02:48:43.207662: Epoch 547 +2024-09-08 02:48:43.208690: Current learning rate: 0.0049 +2024-09-08 02:54:54.700977: train_loss -0.751 +2024-09-08 02:54:54.718369: val_loss -0.7561 +2024-09-08 02:54:54.719600: Pseudo dice [0.9634, 0.955, 0.9781, 0.9453, 0.8582, 0.9654, 0.9899] +2024-09-08 02:54:54.720823: Epoch time: 371.51 s +2024-09-08 02:54:59.948493: +2024-09-08 02:54:59.949560: Epoch 548 +2024-09-08 02:54:59.950268: Current learning rate: 0.00489 +2024-09-08 03:01:42.142303: train_loss -0.7332 +2024-09-08 03:01:42.145561: val_loss -0.7559 +2024-09-08 03:01:42.146505: Pseudo dice [0.9617, 0.9571, 0.9767, 0.9435, 0.8759, 0.9689, 0.9899] +2024-09-08 03:01:42.147262: Epoch time: 402.17 s +2024-09-08 03:01:46.628851: +2024-09-08 03:01:46.629889: Epoch 549 +2024-09-08 03:01:46.630774: Current learning rate: 0.00488 +2024-09-08 03:08:21.165940: train_loss -0.7236 +2024-09-08 03:08:21.181121: val_loss -0.7404 +2024-09-08 03:08:21.182082: Pseudo dice [0.9604, 0.9555, 0.9765, 0.947, 0.8472, 0.9569, 0.9868] +2024-09-08 03:08:21.182888: Epoch time: 394.54 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_06_04_19.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_06_04_19.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2ab76d61eb7996f1b198e2dd3cdcc76c7be88f5 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_06_04_19.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 06:04:22.079294: Using torch.compile... +2024-09-08 06:04:28.515802: do_dummy_2d_data_aug: False +2024-09-08 06:04:28.526347: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 06:04:28.527666: The split file contains 5 splits. +2024-09-08 06:04:28.528140: Desired fold for training: 1 +2024-09-08 06:04:28.529213: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 06:04:49.572695: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 06:04:51.616194: +2024-09-08 06:04:51.617338: Epoch 550 +2024-09-08 06:04:51.619163: Current learning rate: 0.00487 +2024-09-08 06:21:22.566859: train_loss -0.7357 +2024-09-08 06:21:22.567931: val_loss -0.7533 +2024-09-08 06:21:22.568467: Pseudo dice [0.9639, 0.9565, 0.9801, 0.9479, 0.8816, 0.9671, 0.9892] +2024-09-08 06:21:22.568964: Epoch time: 990.98 s +2024-09-08 06:21:24.625087: +2024-09-08 06:21:24.626044: Epoch 551 +2024-09-08 06:21:24.626592: Current learning rate: 0.00486 +2024-09-08 06:28:22.179258: train_loss -0.7364 +2024-09-08 06:28:22.185380: val_loss -0.7517 +2024-09-08 06:28:22.187280: Pseudo dice [0.962, 0.9575, 0.9719, 0.9526, 0.8724, 0.9693, 0.9905] +2024-09-08 06:28:22.188462: Epoch time: 417.56 s +2024-09-08 06:28:25.284415: +2024-09-08 06:28:25.285740: Epoch 552 +2024-09-08 06:28:25.290121: Current learning rate: 0.00485 +2024-09-08 06:35:27.351705: train_loss -0.7203 +2024-09-08 06:35:27.364842: val_loss -0.7608 +2024-09-08 06:35:27.367098: Pseudo dice [0.965, 0.957, 0.9816, 0.9556, 0.8754, 0.9689, 0.9912] +2024-09-08 06:35:27.368690: Epoch time: 422.07 s +2024-09-08 06:35:35.491405: +2024-09-08 06:35:35.492534: Epoch 553 +2024-09-08 06:35:35.493144: Current learning rate: 0.00484 +2024-09-08 06:42:02.728533: train_loss -0.7307 +2024-09-08 06:42:02.756212: val_loss -0.7661 +2024-09-08 06:42:02.757463: Pseudo dice [0.9657, 0.9564, 0.9806, 0.9486, 0.8846, 0.9715, 0.9918] +2024-09-08 06:42:02.767209: Epoch time: 387.24 s +2024-09-08 06:42:06.966345: +2024-09-08 06:42:06.967665: Epoch 554 +2024-09-08 06:42:06.968623: Current learning rate: 0.00484 +2024-09-08 06:49:10.569196: train_loss -0.753 +2024-09-08 06:49:10.585589: val_loss -0.7516 +2024-09-08 06:49:10.586466: Pseudo dice [0.9667, 0.9594, 0.9789, 0.9506, 0.8688, 0.9658, 0.989] +2024-09-08 06:49:10.587391: Epoch time: 423.61 s +2024-09-08 06:49:14.497692: +2024-09-08 06:49:14.507124: Epoch 555 +2024-09-08 06:49:14.508720: Current learning rate: 0.00483 +2024-09-08 06:56:09.328785: train_loss -0.7361 +2024-09-08 06:56:09.341942: val_loss -0.7466 +2024-09-08 06:56:09.343103: Pseudo dice [0.9665, 0.9562, 0.9815, 0.9477, 0.8729, 0.97, 0.99] +2024-09-08 06:56:09.345228: Epoch time: 414.86 s +2024-09-08 06:56:15.479536: +2024-09-08 06:56:15.480926: Epoch 556 +2024-09-08 06:56:15.481653: Current learning rate: 0.00482 +2024-09-08 07:03:28.573389: train_loss -0.7267 +2024-09-08 07:03:28.583635: val_loss -0.7477 +2024-09-08 07:03:28.593943: Pseudo dice [0.9638, 0.9569, 0.9779, 0.9488, 0.8421, 0.9686, 0.9859] +2024-09-08 07:03:28.595046: Epoch time: 433.1 s +2024-09-08 07:03:32.503529: +2024-09-08 07:03:32.505416: Epoch 557 +2024-09-08 07:03:32.506197: Current learning rate: 0.00481 +2024-09-08 07:10:32.693113: train_loss -0.7341 +2024-09-08 07:10:32.708273: val_loss -0.7401 +2024-09-08 07:10:32.709764: Pseudo dice [0.964, 0.9556, 0.9769, 0.947, 0.8652, 0.9652, 0.9888] +2024-09-08 07:10:32.711343: Epoch time: 420.2 s +2024-09-08 07:10:37.638936: +2024-09-08 07:10:37.649868: Epoch 558 +2024-09-08 07:10:37.652884: Current learning rate: 0.0048 +2024-09-08 07:17:44.132939: train_loss -0.7469 +2024-09-08 07:17:44.149875: val_loss -0.7927 +2024-09-08 07:17:44.151684: Pseudo dice [0.9626, 0.9564, 0.977, 0.9461, 0.8659, 0.9726, 0.9902] +2024-09-08 07:17:44.152790: Epoch time: 426.5 s +2024-09-08 07:17:50.583582: +2024-09-08 07:17:50.585327: Epoch 559 +2024-09-08 07:17:50.587239: Current learning rate: 0.00479 +2024-09-08 07:25:05.823083: train_loss -0.7371 +2024-09-08 07:25:05.846468: val_loss -0.7522 +2024-09-08 07:25:05.848701: Pseudo dice [0.9661, 0.9594, 0.9806, 0.9485, 0.8439, 0.9706, 0.987] +2024-09-08 07:25:05.850524: Epoch time: 435.25 s +2024-09-08 07:25:10.439954: +2024-09-08 07:25:10.452865: Epoch 560 +2024-09-08 07:25:10.453949: Current learning rate: 0.00478 +2024-09-08 07:32:16.429193: train_loss -0.7456 +2024-09-08 07:32:16.446017: val_loss -0.7734 +2024-09-08 07:32:16.448599: Pseudo dice [0.9658, 0.9542, 0.9795, 0.9477, 0.8663, 0.9673, 0.9907] +2024-09-08 07:32:16.449904: Epoch time: 426.0 s +2024-09-08 07:32:21.743321: +2024-09-08 07:32:21.757258: Epoch 561 +2024-09-08 07:32:21.759320: Current learning rate: 0.00477 +2024-09-08 07:39:31.907516: train_loss -0.727 +2024-09-08 07:39:31.918428: val_loss -0.7667 +2024-09-08 07:39:31.919836: Pseudo dice [0.963, 0.9562, 0.9763, 0.9475, 0.8741, 0.967, 0.9896] +2024-09-08 07:39:31.920885: Epoch time: 430.17 s +2024-09-08 07:39:36.770010: +2024-09-08 07:39:36.791947: Epoch 562 +2024-09-08 07:39:36.794681: Current learning rate: 0.00476 +2024-09-08 07:46:49.531029: train_loss -0.7501 +2024-09-08 07:46:49.552875: val_loss -0.7163 +2024-09-08 07:46:49.554066: Pseudo dice [0.9645, 0.9577, 0.9759, 0.954, 0.8708, 0.9677, 0.9884] +2024-09-08 07:46:49.554850: Epoch time: 432.77 s +2024-09-08 07:46:52.816509: +2024-09-08 07:46:52.817464: Epoch 563 +2024-09-08 07:46:52.818107: Current learning rate: 0.00475 +2024-09-08 07:53:29.248298: train_loss -0.7444 +2024-09-08 07:53:29.262259: val_loss -0.7471 +2024-09-08 07:53:29.263617: Pseudo dice [0.9661, 0.9575, 0.9772, 0.95, 0.8889, 0.9716, 0.9924] +2024-09-08 07:53:29.264791: Epoch time: 396.44 s +2024-09-08 07:53:35.519367: +2024-09-08 07:53:35.520673: Epoch 564 +2024-09-08 07:53:35.521738: Current learning rate: 0.00474 +2024-09-08 08:00:24.850162: train_loss -0.7402 +2024-09-08 08:00:24.866335: val_loss -0.7286 +2024-09-08 08:00:24.868208: Pseudo dice [0.9637, 0.9559, 0.9779, 0.9502, 0.8669, 0.9693, 0.9901] +2024-09-08 08:00:24.869345: Epoch time: 409.35 s +2024-09-08 08:00:29.836670: +2024-09-08 08:00:29.838030: Epoch 565 +2024-09-08 08:00:29.838978: Current learning rate: 0.00473 +2024-09-08 08:07:25.612590: train_loss -0.7449 +2024-09-08 08:07:25.629421: val_loss -0.7346 +2024-09-08 08:07:25.630336: Pseudo dice [0.9635, 0.9585, 0.9775, 0.9521, 0.8759, 0.969, 0.9897] +2024-09-08 08:07:25.631364: Epoch time: 415.79 s +2024-09-08 08:07:30.506193: +2024-09-08 08:07:30.507936: Epoch 566 +2024-09-08 08:07:30.508976: Current learning rate: 0.00472 +2024-09-08 08:14:52.105720: train_loss -0.7392 +2024-09-08 08:14:52.114990: val_loss -0.753 +2024-09-08 08:14:52.115730: Pseudo dice [0.9669, 0.9578, 0.9816, 0.9526, 0.8615, 0.9619, 0.9871] +2024-09-08 08:14:52.116407: Epoch time: 441.61 s +2024-09-08 08:14:56.147709: +2024-09-08 08:14:56.149414: Epoch 567 +2024-09-08 08:14:56.150523: Current learning rate: 0.00471 +2024-09-08 08:21:55.222508: train_loss -0.7594 +2024-09-08 08:21:55.225084: val_loss -0.7261 +2024-09-08 08:21:55.225899: Pseudo dice [0.9639, 0.9578, 0.9773, 0.9529, 0.8635, 0.9643, 0.9869] +2024-09-08 08:21:55.226936: Epoch time: 419.08 s +2024-09-08 08:22:01.644041: +2024-09-08 08:22:01.645670: Epoch 568 +2024-09-08 08:22:01.647079: Current learning rate: 0.0047 +2024-09-08 08:29:08.395699: train_loss -0.7469 +2024-09-08 08:29:08.413495: val_loss -0.7721 +2024-09-08 08:29:08.414476: Pseudo dice [0.9641, 0.9577, 0.9771, 0.9504, 0.8828, 0.9718, 0.9922] +2024-09-08 08:29:08.415085: Epoch time: 426.76 s +2024-09-08 08:29:13.437941: +2024-09-08 08:29:13.449809: Epoch 569 +2024-09-08 08:29:13.451413: Current learning rate: 0.00469 +2024-09-08 08:36:21.215547: train_loss -0.7497 +2024-09-08 08:36:21.261510: val_loss -0.7296 +2024-09-08 08:36:21.262854: Pseudo dice [0.9652, 0.9578, 0.9795, 0.9534, 0.8554, 0.971, 0.9858] +2024-09-08 08:36:21.264467: Epoch time: 427.78 s +2024-09-08 08:36:26.008781: +2024-09-08 08:36:26.021121: Epoch 570 +2024-09-08 08:36:26.022480: Current learning rate: 0.00468 +2024-09-08 08:43:25.376986: train_loss -0.7358 +2024-09-08 08:43:25.393101: val_loss -0.7454 +2024-09-08 08:43:25.394502: Pseudo dice [0.9653, 0.9616, 0.9787, 0.9516, 0.8387, 0.9691, 0.9834] +2024-09-08 08:43:25.395729: Epoch time: 419.39 s +2024-09-08 08:43:30.052409: +2024-09-08 08:43:30.053997: Epoch 571 +2024-09-08 08:43:30.056172: Current learning rate: 0.00467 +2024-09-08 08:50:49.494513: train_loss -0.754 +2024-09-08 08:50:49.497321: val_loss -0.7591 +2024-09-08 08:50:49.498055: Pseudo dice [0.9682, 0.96, 0.9812, 0.9544, 0.8794, 0.9703, 0.9893] +2024-09-08 08:50:49.498626: Epoch time: 439.46 s +2024-09-08 08:50:53.399482: +2024-09-08 08:50:53.411857: Epoch 572 +2024-09-08 08:50:53.422729: Current learning rate: 0.00466 +2024-09-08 08:58:01.286947: train_loss -0.7512 +2024-09-08 08:58:01.317718: val_loss -0.7274 +2024-09-08 08:58:01.318638: Pseudo dice [0.9642, 0.9578, 0.9782, 0.9466, 0.8356, 0.9349, 0.985] +2024-09-08 08:58:01.324455: Epoch time: 427.9 s +2024-09-08 08:58:06.543476: +2024-09-08 08:58:06.544402: Epoch 573 +2024-09-08 08:58:06.545037: Current learning rate: 0.00465 +2024-09-08 09:05:01.446519: train_loss -0.7322 +2024-09-08 09:05:01.453146: val_loss -0.7587 +2024-09-08 09:05:01.453891: Pseudo dice [0.9648, 0.9562, 0.9731, 0.9514, 0.8538, 0.9683, 0.9882] +2024-09-08 09:05:01.454548: Epoch time: 414.91 s +2024-09-08 09:05:05.301014: +2024-09-08 09:05:05.308641: Epoch 574 +2024-09-08 09:05:05.309408: Current learning rate: 0.00464 +2024-09-08 09:12:25.268308: train_loss -0.7639 +2024-09-08 09:12:25.285356: val_loss -0.7256 +2024-09-08 09:12:25.286169: Pseudo dice [0.9659, 0.957, 0.9796, 0.9481, 0.8445, 0.9716, 0.9865] +2024-09-08 09:12:25.286901: Epoch time: 439.97 s +2024-09-08 09:12:29.951123: +2024-09-08 09:12:29.965187: Epoch 575 +2024-09-08 09:12:29.966426: Current learning rate: 0.00463 +2024-09-08 09:19:29.070986: train_loss -0.7455 +2024-09-08 09:19:29.087665: val_loss -0.7624 +2024-09-08 09:19:29.088694: Pseudo dice [0.966, 0.9592, 0.9788, 0.9488, 0.8815, 0.9736, 0.9916] +2024-09-08 09:19:29.089570: Epoch time: 419.13 s +2024-09-08 09:19:33.420486: +2024-09-08 09:19:33.428694: Epoch 576 +2024-09-08 09:19:33.429616: Current learning rate: 0.00462 +2024-09-08 09:26:41.001717: train_loss -0.7519 +2024-09-08 09:26:41.023399: val_loss -0.7431 +2024-09-08 09:26:41.025956: Pseudo dice [0.9656, 0.9575, 0.9795, 0.951, 0.8697, 0.9597, 0.9874] +2024-09-08 09:26:41.027428: Epoch time: 427.59 s +2024-09-08 09:26:46.635887: +2024-09-08 09:26:46.637200: Epoch 577 +2024-09-08 09:26:46.638012: Current learning rate: 0.00461 +2024-09-08 09:34:05.673298: train_loss -0.7352 +2024-09-08 09:34:05.690128: val_loss -0.7468 +2024-09-08 09:34:05.690888: Pseudo dice [0.9652, 0.9597, 0.9768, 0.9524, 0.8914, 0.9702, 0.9923] +2024-09-08 09:34:05.691406: Epoch time: 439.04 s +2024-09-08 09:34:10.430973: +2024-09-08 09:34:10.447790: Epoch 578 +2024-09-08 09:34:10.448928: Current learning rate: 0.0046 +2024-09-08 09:41:08.782332: train_loss -0.7339 +2024-09-08 09:41:08.794093: val_loss -0.7533 +2024-09-08 09:41:08.795003: Pseudo dice [0.9666, 0.9618, 0.9829, 0.955, 0.8607, 0.9738, 0.9839] +2024-09-08 09:41:08.795818: Epoch time: 418.36 s +2024-09-08 09:41:12.869904: +2024-09-08 09:41:12.874829: Epoch 579 +2024-09-08 09:41:12.877595: Current learning rate: 0.00459 +2024-09-08 09:48:10.940846: train_loss -0.7466 +2024-09-08 09:48:10.943521: val_loss -0.7053 +2024-09-08 09:48:10.944422: Pseudo dice [0.9656, 0.9591, 0.9799, 0.9473, 0.8537, 0.9563, 0.9854] +2024-09-08 09:48:10.945073: Epoch time: 418.08 s +2024-09-08 09:48:14.727269: +2024-09-08 09:48:14.728740: Epoch 580 +2024-09-08 09:48:14.730203: Current learning rate: 0.00458 +2024-09-08 09:55:48.275274: train_loss -0.7531 +2024-09-08 09:55:48.292317: val_loss -0.7462 +2024-09-08 09:55:48.295104: Pseudo dice [0.9638, 0.9582, 0.9771, 0.9485, 0.8556, 0.9706, 0.9896] +2024-09-08 09:55:48.296798: Epoch time: 453.55 s +2024-09-08 09:55:52.498986: +2024-09-08 09:55:52.513125: Epoch 581 +2024-09-08 09:55:52.514836: Current learning rate: 0.00457 +2024-09-08 10:02:53.815802: train_loss -0.7346 +2024-09-08 10:02:53.817806: val_loss -0.7887 +2024-09-08 10:02:53.818419: Pseudo dice [0.9679, 0.9603, 0.9815, 0.9503, 0.888, 0.9729, 0.9919] +2024-09-08 10:02:53.819066: Epoch time: 421.32 s +2024-09-08 10:03:00.323438: +2024-09-08 10:03:00.324892: Epoch 582 +2024-09-08 10:03:00.325826: Current learning rate: 0.00456 +2024-09-08 10:09:55.317453: train_loss -0.7423 +2024-09-08 10:09:55.333493: val_loss -0.7285 +2024-09-08 10:09:55.334862: Pseudo dice [0.9631, 0.9596, 0.98, 0.9489, 0.8622, 0.9689, 0.9872] +2024-09-08 10:09:55.335598: Epoch time: 415.0 s +2024-09-08 10:09:59.975956: +2024-09-08 10:09:59.996402: Epoch 583 +2024-09-08 10:09:59.997514: Current learning rate: 0.00455 +2024-09-08 10:17:05.819732: train_loss -0.7508 +2024-09-08 10:17:05.838582: val_loss -0.69 +2024-09-08 10:17:05.849712: Pseudo dice [0.9659, 0.958, 0.9779, 0.9462, 0.8772, 0.9459, 0.9869] +2024-09-08 10:17:05.850524: Epoch time: 425.85 s +2024-09-08 10:17:09.760718: +2024-09-08 10:17:09.762504: Epoch 584 +2024-09-08 10:17:09.763799: Current learning rate: 0.00454 +2024-09-08 10:24:11.706522: train_loss -0.7423 +2024-09-08 10:24:11.727130: val_loss -0.7873 +2024-09-08 10:24:11.729897: Pseudo dice [0.9644, 0.9585, 0.9778, 0.9491, 0.8821, 0.9721, 0.9896] +2024-09-08 10:24:11.731173: Epoch time: 421.97 s +2024-09-08 10:24:16.520425: +2024-09-08 10:24:16.535541: Epoch 585 +2024-09-08 10:24:16.536501: Current learning rate: 0.00453 +2024-09-08 10:31:40.562498: train_loss -0.738 +2024-09-08 10:31:40.581931: val_loss -0.7877 +2024-09-08 10:31:40.584046: Pseudo dice [0.963, 0.9592, 0.9795, 0.9505, 0.883, 0.9711, 0.9921] +2024-09-08 10:31:40.584936: Epoch time: 444.05 s +2024-09-08 10:31:44.333756: +2024-09-08 10:31:44.335323: Epoch 586 +2024-09-08 10:31:44.336054: Current learning rate: 0.00452 +2024-09-08 10:39:01.826580: train_loss -0.7449 +2024-09-08 10:39:01.860188: val_loss -0.7621 +2024-09-08 10:39:01.861084: Pseudo dice [0.9592, 0.955, 0.9727, 0.9496, 0.8644, 0.9699, 0.9862] +2024-09-08 10:39:01.862589: Epoch time: 437.5 s +2024-09-08 10:39:07.406064: +2024-09-08 10:39:07.408306: Epoch 587 +2024-09-08 10:39:07.409297: Current learning rate: 0.00451 +2024-09-08 10:46:45.331769: train_loss -0.7312 +2024-09-08 10:46:45.353139: val_loss -0.7419 +2024-09-08 10:46:45.354021: Pseudo dice [0.9606, 0.9527, 0.97, 0.9491, 0.8688, 0.9506, 0.9878] +2024-09-08 10:46:45.354613: Epoch time: 457.94 s +2024-09-08 10:46:51.018821: +2024-09-08 10:46:51.020606: Epoch 588 +2024-09-08 10:46:51.021612: Current learning rate: 0.0045 +2024-09-08 10:54:12.405961: train_loss -0.7353 +2024-09-08 10:54:12.414100: val_loss -0.7557 +2024-09-08 10:54:12.415712: Pseudo dice [0.9616, 0.9532, 0.9783, 0.9446, 0.808, 0.966, 0.9846] +2024-09-08 10:54:12.416661: Epoch time: 441.39 s +2024-09-08 10:54:17.136434: +2024-09-08 10:54:17.153266: Epoch 589 +2024-09-08 10:54:17.154963: Current learning rate: 0.00449 +2024-09-08 11:01:26.753543: train_loss -0.7222 +2024-09-08 11:01:26.756577: val_loss -0.7434 +2024-09-08 11:01:26.758053: Pseudo dice [0.962, 0.9539, 0.9759, 0.9447, 0.8548, 0.9682, 0.9872] +2024-09-08 11:01:26.760476: Epoch time: 429.63 s +2024-09-08 11:01:32.759284: +2024-09-08 11:01:32.772820: Epoch 590 +2024-09-08 11:01:32.773770: Current learning rate: 0.00448 +2024-09-08 11:08:33.645902: train_loss -0.7323 +2024-09-08 11:08:33.662971: val_loss -0.7512 +2024-09-08 11:08:33.664754: Pseudo dice [0.9626, 0.9581, 0.9774, 0.9479, 0.8677, 0.9743, 0.9917] +2024-09-08 11:08:33.665590: Epoch time: 420.9 s +2024-09-08 11:08:39.158598: +2024-09-08 11:08:39.160126: Epoch 591 +2024-09-08 11:08:39.161013: Current learning rate: 0.00447 +2024-09-08 11:15:56.451518: train_loss -0.7389 +2024-09-08 11:15:56.461730: val_loss -0.7358 +2024-09-08 11:15:56.462892: Pseudo dice [0.9628, 0.9566, 0.9782, 0.9472, 0.8827, 0.9712, 0.9914] +2024-09-08 11:15:56.465631: Epoch time: 437.32 s +2024-09-08 11:15:59.153925: +2024-09-08 11:15:59.178362: Epoch 592 +2024-09-08 11:15:59.180211: Current learning rate: 0.00446 +2024-09-08 11:23:01.367608: train_loss -0.7254 +2024-09-08 11:23:01.369587: val_loss -0.7549 +2024-09-08 11:23:01.370137: Pseudo dice [0.9627, 0.9558, 0.9752, 0.9474, 0.8615, 0.9717, 0.9886] +2024-09-08 11:23:01.370719: Epoch time: 422.23 s +2024-09-08 11:23:05.905914: +2024-09-08 11:23:05.913651: Epoch 593 +2024-09-08 11:23:05.915266: Current learning rate: 0.00445 +2024-09-08 11:30:08.825274: train_loss -0.7281 +2024-09-08 11:30:08.841324: val_loss -0.7529 +2024-09-08 11:30:08.842422: Pseudo dice [0.9632, 0.9585, 0.9758, 0.9464, 0.873, 0.9722, 0.9901] +2024-09-08 11:30:08.843596: Epoch time: 422.92 s +2024-09-08 11:30:13.869040: +2024-09-08 11:30:13.870739: Epoch 594 +2024-09-08 11:30:13.871861: Current learning rate: 0.00444 +2024-09-08 11:37:03.851319: train_loss -0.7441 +2024-09-08 11:37:03.857438: val_loss -0.751 +2024-09-08 11:37:03.859250: Pseudo dice [0.9662, 0.9594, 0.9812, 0.9486, 0.828, 0.9571, 0.9815] +2024-09-08 11:37:03.861022: Epoch time: 410.03 s +2024-09-08 11:37:09.192788: +2024-09-08 11:37:09.211874: Epoch 595 +2024-09-08 11:37:09.224855: Current learning rate: 0.00443 +2024-09-08 11:44:29.180949: train_loss -0.7367 +2024-09-08 11:44:29.191557: val_loss -0.7414 +2024-09-08 11:44:29.197737: Pseudo dice [0.9651, 0.9595, 0.9789, 0.9512, 0.8798, 0.9694, 0.9907] +2024-09-08 11:44:29.199425: Epoch time: 440.0 s +2024-09-08 11:44:35.866668: +2024-09-08 11:44:35.868125: Epoch 596 +2024-09-08 11:44:35.868805: Current learning rate: 0.00442 +2024-09-08 11:51:47.536812: train_loss -0.7454 +2024-09-08 11:51:47.550010: val_loss -0.7406 +2024-09-08 11:51:47.552645: Pseudo dice [0.9673, 0.9601, 0.9814, 0.9468, 0.8687, 0.9522, 0.985] +2024-09-08 11:51:47.553348: Epoch time: 431.68 s +2024-09-08 11:51:53.153302: +2024-09-08 11:51:53.168972: Epoch 597 +2024-09-08 11:51:53.171214: Current learning rate: 0.00441 +2024-09-08 11:59:17.636064: train_loss -0.7387 +2024-09-08 11:59:17.662217: val_loss -0.7658 +2024-09-08 11:59:17.663377: Pseudo dice [0.9617, 0.9567, 0.9721, 0.947, 0.8512, 0.9716, 0.987] +2024-09-08 11:59:17.665171: Epoch time: 444.49 s +2024-09-08 11:59:21.969736: +2024-09-08 11:59:21.977046: Epoch 598 +2024-09-08 11:59:21.978669: Current learning rate: 0.0044 +2024-09-08 12:06:20.877653: train_loss -0.7312 +2024-09-08 12:06:20.901286: val_loss -0.7516 +2024-09-08 12:06:20.902623: Pseudo dice [0.9636, 0.9564, 0.9781, 0.9469, 0.8627, 0.9725, 0.9894] +2024-09-08 12:06:20.903337: Epoch time: 418.91 s +2024-09-08 12:06:25.034833: +2024-09-08 12:06:25.037385: Epoch 599 +2024-09-08 12:06:25.040460: Current learning rate: 0.00439 +2024-09-08 12:13:16.099741: train_loss -0.7375 +2024-09-08 12:13:16.117563: val_loss -0.7337 +2024-09-08 12:13:16.118913: Pseudo dice [0.9618, 0.9586, 0.9748, 0.9514, 0.8811, 0.972, 0.9913] +2024-09-08 12:13:16.120046: Epoch time: 411.13 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_14_03_51.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_14_03_51.txt new file mode 100644 index 0000000000000000000000000000000000000000..4906e5b06ff60e53cc625e2a6e8a966d3e2346e7 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_14_03_51.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 14:03:54.251299: Using torch.compile... +2024-09-08 14:04:00.981562: do_dummy_2d_data_aug: False +2024-09-08 14:04:00.987532: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 14:04:00.989883: The split file contains 5 splits. +2024-09-08 14:04:00.990716: Desired fold for training: 1 +2024-09-08 14:04:00.991443: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 14:04:22.910527: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 14:04:24.176077: +2024-09-08 14:04:24.200229: Epoch 600 +2024-09-08 14:04:24.203039: Current learning rate: 0.00438 +2024-09-08 14:21:33.796278: train_loss -0.7544 +2024-09-08 14:21:33.797802: val_loss -0.7469 +2024-09-08 14:21:33.798381: Pseudo dice [0.9637, 0.9611, 0.9766, 0.9528, 0.8815, 0.9713, 0.9919] +2024-09-08 14:21:33.799009: Epoch time: 1029.63 s +2024-09-08 14:21:35.899408: +2024-09-08 14:21:35.900672: Epoch 601 +2024-09-08 14:21:35.901641: Current learning rate: 0.00437 +2024-09-08 14:28:16.764735: train_loss -0.741 +2024-09-08 14:28:16.801218: val_loss -0.7095 +2024-09-08 14:28:16.802483: Pseudo dice [0.9641, 0.9552, 0.9792, 0.9469, 0.8665, 0.9554, 0.9887] +2024-09-08 14:28:16.804797: Epoch time: 400.87 s +2024-09-08 14:28:22.085637: +2024-09-08 14:28:22.095277: Epoch 602 +2024-09-08 14:28:22.105911: Current learning rate: 0.00436 +2024-09-08 14:35:25.300925: train_loss -0.7338 +2024-09-08 14:35:25.324296: val_loss -0.7122 +2024-09-08 14:35:25.326435: Pseudo dice [0.9629, 0.9586, 0.9736, 0.9465, 0.868, 0.9672, 0.9883] +2024-09-08 14:35:25.328180: Epoch time: 423.23 s +2024-09-08 14:35:29.098388: +2024-09-08 14:35:29.120772: Epoch 603 +2024-09-08 14:35:29.122287: Current learning rate: 0.00435 +2024-09-08 14:42:04.188989: train_loss -0.7404 +2024-09-08 14:42:04.203224: val_loss -0.74 +2024-09-08 14:42:04.204904: Pseudo dice [0.9671, 0.9568, 0.9794, 0.9513, 0.8879, 0.9613, 0.9915] +2024-09-08 14:42:04.206502: Epoch time: 395.1 s +2024-09-08 14:42:09.046004: +2024-09-08 14:42:09.051630: Epoch 604 +2024-09-08 14:42:09.060079: Current learning rate: 0.00434 +2024-09-08 14:49:23.611322: train_loss -0.7446 +2024-09-08 14:49:23.613981: val_loss -0.74 +2024-09-08 14:49:23.615406: Pseudo dice [0.9643, 0.9623, 0.9781, 0.9483, 0.8552, 0.9701, 0.9879] +2024-09-08 14:49:23.616549: Epoch time: 434.57 s +2024-09-08 14:49:28.407104: +2024-09-08 14:49:28.409295: Epoch 605 +2024-09-08 14:49:28.410341: Current learning rate: 0.00433 +2024-09-08 14:56:32.627908: train_loss -0.7558 +2024-09-08 14:56:32.631783: val_loss -0.75 +2024-09-08 14:56:32.632501: Pseudo dice [0.9637, 0.9573, 0.9774, 0.953, 0.8776, 0.9694, 0.9895] +2024-09-08 14:56:32.633243: Epoch time: 424.23 s +2024-09-08 14:56:36.735565: +2024-09-08 14:56:36.736749: Epoch 606 +2024-09-08 14:56:36.737570: Current learning rate: 0.00432 +2024-09-08 15:03:37.373952: train_loss -0.7446 +2024-09-08 15:03:37.393177: val_loss -0.7415 +2024-09-08 15:03:37.395617: Pseudo dice [0.9645, 0.9588, 0.9793, 0.9532, 0.8634, 0.9739, 0.988] +2024-09-08 15:03:37.396428: Epoch time: 420.64 s +2024-09-08 15:03:41.300539: +2024-09-08 15:03:41.316761: Epoch 607 +2024-09-08 15:03:41.318400: Current learning rate: 0.00431 +2024-09-08 15:10:52.576064: train_loss -0.7415 +2024-09-08 15:10:52.589192: val_loss -0.7336 +2024-09-08 15:10:52.590557: Pseudo dice [0.966, 0.9582, 0.98, 0.9512, 0.8774, 0.9695, 0.9893] +2024-09-08 15:10:52.592065: Epoch time: 431.28 s +2024-09-08 15:10:57.097565: +2024-09-08 15:10:57.113201: Epoch 608 +2024-09-08 15:10:57.114035: Current learning rate: 0.0043 +2024-09-08 15:17:59.386730: train_loss -0.7375 +2024-09-08 15:17:59.405384: val_loss -0.7172 +2024-09-08 15:17:59.408384: Pseudo dice [0.9644, 0.958, 0.9775, 0.9484, 0.8638, 0.9327, 0.9855] +2024-09-08 15:17:59.409588: Epoch time: 422.29 s +2024-09-08 15:18:03.902062: +2024-09-08 15:18:03.903867: Epoch 609 +2024-09-08 15:18:03.905323: Current learning rate: 0.00429 +2024-09-08 15:25:27.975230: train_loss -0.7412 +2024-09-08 15:25:27.980525: val_loss -0.764 +2024-09-08 15:25:27.981719: Pseudo dice [0.9629, 0.9558, 0.9755, 0.9483, 0.8685, 0.9697, 0.989] +2024-09-08 15:25:27.984560: Epoch time: 444.11 s +2024-09-08 15:25:32.118298: +2024-09-08 15:25:32.124472: Epoch 610 +2024-09-08 15:25:32.136578: Current learning rate: 0.00429 +2024-09-08 15:32:30.337079: train_loss -0.7309 +2024-09-08 15:32:30.356268: val_loss -0.7944 +2024-09-08 15:32:30.357334: Pseudo dice [0.9674, 0.959, 0.9798, 0.9534, 0.89, 0.9698, 0.9921] +2024-09-08 15:32:30.359037: Epoch time: 418.22 s +2024-09-08 15:32:33.805831: +2024-09-08 15:32:33.808111: Epoch 611 +2024-09-08 15:32:33.809066: Current learning rate: 0.00428 +2024-09-08 15:39:36.596174: train_loss -0.739 +2024-09-08 15:39:36.599437: val_loss -0.7347 +2024-09-08 15:39:36.601437: Pseudo dice [0.9652, 0.9567, 0.9776, 0.9492, 0.8649, 0.9694, 0.9899] +2024-09-08 15:39:36.602911: Epoch time: 422.8 s +2024-09-08 15:39:38.765071: +2024-09-08 15:39:38.766791: Epoch 612 +2024-09-08 15:39:38.767616: Current learning rate: 0.00427 +2024-09-08 15:46:26.158061: train_loss -0.7361 +2024-09-08 15:46:26.168836: val_loss -0.7367 +2024-09-08 15:46:26.170033: Pseudo dice [0.9617, 0.9568, 0.975, 0.9508, 0.8489, 0.9708, 0.987] +2024-09-08 15:46:26.170895: Epoch time: 407.4 s +2024-09-08 15:46:29.336887: +2024-09-08 15:46:29.343817: Epoch 613 +2024-09-08 15:46:29.344709: Current learning rate: 0.00426 +2024-09-08 15:53:08.777802: train_loss -0.7464 +2024-09-08 15:53:08.806005: val_loss -0.7745 +2024-09-08 15:53:08.807237: Pseudo dice [0.963, 0.9599, 0.9785, 0.9527, 0.8522, 0.9704, 0.9868] +2024-09-08 15:53:08.808053: Epoch time: 399.45 s +2024-09-08 15:53:13.937424: +2024-09-08 15:53:13.939078: Epoch 614 +2024-09-08 15:53:13.940144: Current learning rate: 0.00425 +2024-09-08 16:00:25.059518: train_loss -0.7238 +2024-09-08 16:00:25.073870: val_loss -0.759 +2024-09-08 16:00:25.074840: Pseudo dice [0.9649, 0.9586, 0.98, 0.9489, 0.8727, 0.9676, 0.9915] +2024-09-08 16:00:25.075418: Epoch time: 431.15 s +2024-09-08 16:00:32.693254: +2024-09-08 16:00:32.695813: Epoch 615 +2024-09-08 16:00:32.697466: Current learning rate: 0.00424 +2024-09-08 16:07:32.963498: train_loss -0.7514 +2024-09-08 16:07:32.966487: val_loss -0.7258 +2024-09-08 16:07:32.967937: Pseudo dice [0.9638, 0.9577, 0.9742, 0.9523, 0.8466, 0.9689, 0.9856] +2024-09-08 16:07:32.970134: Epoch time: 420.31 s +2024-09-08 16:07:37.599557: +2024-09-08 16:07:37.601418: Epoch 616 +2024-09-08 16:07:37.602761: Current learning rate: 0.00423 +2024-09-08 16:14:44.461045: train_loss -0.7295 +2024-09-08 16:14:44.479874: val_loss -0.7551 +2024-09-08 16:14:44.481041: Pseudo dice [0.9653, 0.959, 0.9799, 0.9506, 0.8844, 0.9693, 0.992] +2024-09-08 16:14:44.483039: Epoch time: 426.87 s +2024-09-08 16:14:49.225684: +2024-09-08 16:14:49.226913: Epoch 617 +2024-09-08 16:14:49.227782: Current learning rate: 0.00422 +2024-09-08 16:21:27.830706: train_loss -0.7478 +2024-09-08 16:21:27.845919: val_loss -0.7531 +2024-09-08 16:21:27.846632: Pseudo dice [0.9665, 0.9572, 0.98, 0.952, 0.8472, 0.9686, 0.9878] +2024-09-08 16:21:27.847247: Epoch time: 398.61 s +2024-09-08 16:21:31.553434: +2024-09-08 16:21:31.554519: Epoch 618 +2024-09-08 16:21:31.555140: Current learning rate: 0.00421 +2024-09-08 16:28:33.982637: train_loss -0.7393 +2024-09-08 16:28:33.986867: val_loss -0.7409 +2024-09-08 16:28:33.988461: Pseudo dice [0.9667, 0.9582, 0.9797, 0.9518, 0.8313, 0.9699, 0.9855] +2024-09-08 16:28:33.989164: Epoch time: 422.43 s +2024-09-08 16:28:37.663685: +2024-09-08 16:28:37.676693: Epoch 619 +2024-09-08 16:28:37.677639: Current learning rate: 0.0042 +2024-09-08 16:35:38.896989: train_loss -0.7339 +2024-09-08 16:35:38.902470: val_loss -0.7722 +2024-09-08 16:35:38.903217: Pseudo dice [0.9644, 0.9567, 0.9791, 0.9465, 0.8614, 0.9706, 0.9875] +2024-09-08 16:35:38.904376: Epoch time: 421.24 s +2024-09-08 16:35:43.398204: +2024-09-08 16:35:43.399808: Epoch 620 +2024-09-08 16:35:43.400986: Current learning rate: 0.00419 +2024-09-08 16:42:38.354218: train_loss -0.7383 +2024-09-08 16:42:38.367860: val_loss -0.762 +2024-09-08 16:42:38.369791: Pseudo dice [0.9674, 0.9586, 0.9794, 0.9479, 0.8782, 0.9591, 0.9891] +2024-09-08 16:42:38.371176: Epoch time: 414.98 s +2024-09-08 16:42:42.536770: +2024-09-08 16:42:42.538537: Epoch 621 +2024-09-08 16:42:42.540082: Current learning rate: 0.00418 +2024-09-08 16:49:37.602371: train_loss -0.7468 +2024-09-08 16:49:37.619234: val_loss -0.7553 +2024-09-08 16:49:37.620775: Pseudo dice [0.9642, 0.9564, 0.9792, 0.9511, 0.8897, 0.9585, 0.9879] +2024-09-08 16:49:37.623478: Epoch time: 415.09 s +2024-09-08 16:49:41.736701: +2024-09-08 16:49:41.737960: Epoch 622 +2024-09-08 16:49:41.738982: Current learning rate: 0.00417 +2024-09-08 16:57:00.225505: train_loss -0.7555 +2024-09-08 16:57:00.241268: val_loss -0.7521 +2024-09-08 16:57:00.242028: Pseudo dice [0.9633, 0.9594, 0.9738, 0.9556, 0.8882, 0.9698, 0.9918] +2024-09-08 16:57:00.242612: Epoch time: 438.49 s +2024-09-08 16:57:05.003558: +2024-09-08 16:57:05.016965: Epoch 623 +2024-09-08 16:57:05.018569: Current learning rate: 0.00416 +2024-09-08 17:04:18.585550: train_loss -0.758 +2024-09-08 17:04:18.601700: val_loss -0.77 +2024-09-08 17:04:18.603072: Pseudo dice [0.9637, 0.9587, 0.9764, 0.9524, 0.8817, 0.9558, 0.9897] +2024-09-08 17:04:18.604381: Epoch time: 433.59 s +2024-09-08 17:04:22.361584: +2024-09-08 17:04:22.377590: Epoch 624 +2024-09-08 17:04:22.378974: Current learning rate: 0.00415 +2024-09-08 17:12:02.641216: train_loss -0.7474 +2024-09-08 17:12:02.643926: val_loss -0.7762 +2024-09-08 17:12:02.644722: Pseudo dice [0.9644, 0.9563, 0.9776, 0.9518, 0.8766, 0.9689, 0.9924] +2024-09-08 17:12:02.645761: Epoch time: 460.29 s +2024-09-08 17:12:06.670298: +2024-09-08 17:12:06.671858: Epoch 625 +2024-09-08 17:12:06.672533: Current learning rate: 0.00414 +2024-09-08 17:19:12.656729: train_loss -0.7529 +2024-09-08 17:19:12.675665: val_loss -0.7573 +2024-09-08 17:19:12.677282: Pseudo dice [0.9666, 0.9585, 0.9797, 0.9531, 0.8843, 0.967, 0.991] +2024-09-08 17:19:12.677974: Epoch time: 425.99 s +2024-09-08 17:19:12.679165: Yayy! New best EMA pseudo Dice: 0.9541 +2024-09-08 17:19:22.405693: +2024-09-08 17:19:22.421293: Epoch 626 +2024-09-08 17:19:22.422796: Current learning rate: 0.00413 +2024-09-08 17:26:28.408559: train_loss -0.7329 +2024-09-08 17:26:28.419936: val_loss -0.7095 +2024-09-08 17:26:28.421022: Pseudo dice [0.965, 0.9601, 0.9768, 0.9525, 0.8842, 0.9719, 0.9921] +2024-09-08 17:26:28.423514: Epoch time: 426.09 s +2024-09-08 17:26:28.424205: Yayy! New best EMA pseudo Dice: 0.9544 +2024-09-08 17:26:41.002501: +2024-09-08 17:26:41.016872: Epoch 627 +2024-09-08 17:26:41.018455: Current learning rate: 0.00412 +2024-09-08 17:33:35.039047: train_loss -0.7575 +2024-09-08 17:33:35.055825: val_loss -0.7363 +2024-09-08 17:33:35.057375: Pseudo dice [0.9652, 0.9575, 0.9789, 0.9505, 0.8778, 0.9571, 0.9899] +2024-09-08 17:33:35.059776: Epoch time: 414.04 s +2024-09-08 17:33:43.712499: +2024-09-08 17:33:43.728810: Epoch 628 +2024-09-08 17:33:43.729678: Current learning rate: 0.00411 +2024-09-08 17:40:35.629311: train_loss -0.753 +2024-09-08 17:40:35.645424: val_loss -0.7374 +2024-09-08 17:40:35.646527: Pseudo dice [0.9662, 0.9575, 0.981, 0.9538, 0.8876, 0.9716, 0.9907] +2024-09-08 17:40:35.647553: Epoch time: 411.93 s +2024-09-08 17:40:35.648542: Yayy! New best EMA pseudo Dice: 0.9548 +2024-09-08 17:40:45.802482: +2024-09-08 17:40:45.820989: Epoch 629 +2024-09-08 17:40:45.821888: Current learning rate: 0.0041 +2024-09-08 17:47:35.407494: train_loss -0.737 +2024-09-08 17:47:35.417527: val_loss -0.7542 +2024-09-08 17:47:35.421254: Pseudo dice [0.9636, 0.959, 0.977, 0.9476, 0.8556, 0.9379, 0.9892] +2024-09-08 17:47:35.422904: Epoch time: 409.61 s +2024-09-08 17:47:39.598314: +2024-09-08 17:47:39.612797: Epoch 630 +2024-09-08 17:47:39.613718: Current learning rate: 0.00409 +2024-09-08 17:54:56.762951: train_loss -0.7559 +2024-09-08 17:54:56.777996: val_loss -0.7498 +2024-09-08 17:54:56.779117: Pseudo dice [0.9632, 0.9588, 0.9759, 0.952, 0.8759, 0.9707, 0.991] +2024-09-08 17:54:56.779876: Epoch time: 437.17 s +2024-09-08 17:55:00.936268: +2024-09-08 17:55:00.947367: Epoch 631 +2024-09-08 17:55:00.948789: Current learning rate: 0.00408 +2024-09-08 18:02:15.679113: train_loss -0.7374 +2024-09-08 18:02:15.715739: val_loss -0.7531 +2024-09-08 18:02:15.717021: Pseudo dice [0.9639, 0.9559, 0.978, 0.953, 0.8803, 0.9697, 0.9914] +2024-09-08 18:02:15.717883: Epoch time: 434.75 s +2024-09-08 18:02:20.665698: +2024-09-08 18:02:20.680643: Epoch 632 +2024-09-08 18:02:20.681463: Current learning rate: 0.00407 +2024-09-08 18:09:48.652988: train_loss -0.7463 +2024-09-08 18:09:48.665820: val_loss -0.7484 +2024-09-08 18:09:48.667955: Pseudo dice [0.9674, 0.9581, 0.9799, 0.9412, 0.8683, 0.9714, 0.9908] +2024-09-08 18:09:48.671964: Epoch time: 447.99 s +2024-09-08 18:09:53.102523: +2024-09-08 18:09:53.110953: Epoch 633 +2024-09-08 18:09:53.114148: Current learning rate: 0.00406 +2024-09-08 18:16:51.337292: train_loss -0.7354 +2024-09-08 18:16:51.349604: val_loss -0.7433 +2024-09-08 18:16:51.350487: Pseudo dice [0.966, 0.957, 0.9782, 0.9524, 0.8759, 0.9709, 0.9904] +2024-09-08 18:16:51.351215: Epoch time: 418.26 s +2024-09-08 18:16:56.405714: +2024-09-08 18:16:56.421130: Epoch 634 +2024-09-08 18:16:56.423877: Current learning rate: 0.00405 +2024-09-08 18:24:04.901159: train_loss -0.7421 +2024-09-08 18:24:04.905961: val_loss -0.7387 +2024-09-08 18:24:04.907211: Pseudo dice [0.9677, 0.9629, 0.9808, 0.9496, 0.8787, 0.9586, 0.9888] +2024-09-08 18:24:04.908574: Epoch time: 428.52 s +2024-09-08 18:24:09.432075: +2024-09-08 18:24:09.448542: Epoch 635 +2024-09-08 18:24:09.449600: Current learning rate: 0.00404 +2024-09-08 18:31:00.200061: train_loss -0.7538 +2024-09-08 18:31:00.215730: val_loss -0.7736 +2024-09-08 18:31:00.217130: Pseudo dice [0.9659, 0.9581, 0.9808, 0.9542, 0.867, 0.9695, 0.9887] +2024-09-08 18:31:00.217830: Epoch time: 410.79 s +2024-09-08 18:31:05.432815: +2024-09-08 18:31:05.451076: Epoch 636 +2024-09-08 18:31:05.452306: Current learning rate: 0.00403 +2024-09-08 18:38:22.928368: train_loss -0.7437 +2024-09-08 18:38:22.945431: val_loss -0.7494 +2024-09-08 18:38:22.946990: Pseudo dice [0.9656, 0.9575, 0.9765, 0.9422, 0.8543, 0.9406, 0.9836] +2024-09-08 18:38:22.948848: Epoch time: 437.5 s +2024-09-08 18:38:26.619596: +2024-09-08 18:38:26.634409: Epoch 637 +2024-09-08 18:38:26.636070: Current learning rate: 0.00402 +2024-09-08 18:45:40.718263: train_loss -0.7438 +2024-09-08 18:45:40.733471: val_loss -0.7187 +2024-09-08 18:45:40.735303: Pseudo dice [0.9667, 0.9584, 0.9789, 0.9513, 0.869, 0.971, 0.9883] +2024-09-08 18:45:40.737529: Epoch time: 434.1 s +2024-09-08 18:45:44.715289: +2024-09-08 18:45:44.717168: Epoch 638 +2024-09-08 18:45:44.718650: Current learning rate: 0.00401 +2024-09-08 18:52:49.183169: train_loss -0.7362 +2024-09-08 18:52:49.204779: val_loss -0.7695 +2024-09-08 18:52:49.205826: Pseudo dice [0.9652, 0.9593, 0.9767, 0.9477, 0.8855, 0.9677, 0.9905] +2024-09-08 18:52:49.206996: Epoch time: 424.51 s +2024-09-08 18:52:53.156201: +2024-09-08 18:52:53.170483: Epoch 639 +2024-09-08 18:52:53.172157: Current learning rate: 0.004 +2024-09-08 18:59:36.056054: train_loss -0.7495 +2024-09-08 18:59:36.081675: val_loss -0.7628 +2024-09-08 18:59:36.082538: Pseudo dice [0.9696, 0.9599, 0.9821, 0.9537, 0.8597, 0.9738, 0.9893] +2024-09-08 18:59:36.085241: Epoch time: 402.91 s +2024-09-08 18:59:43.285990: +2024-09-08 18:59:43.298052: Epoch 640 +2024-09-08 18:59:43.300259: Current learning rate: 0.00399 +2024-09-08 19:06:38.761262: train_loss -0.7514 +2024-09-08 19:06:38.773870: val_loss -0.7517 +2024-09-08 19:06:38.775257: Pseudo dice [0.9645, 0.9583, 0.9784, 0.953, 0.8651, 0.9726, 0.9897] +2024-09-08 19:06:38.776478: Epoch time: 415.51 s +2024-09-08 19:06:45.617541: +2024-09-08 19:06:45.628955: Epoch 641 +2024-09-08 19:06:45.630374: Current learning rate: 0.00398 +2024-09-08 19:13:36.472608: train_loss -0.7386 +2024-09-08 19:13:36.485448: val_loss -0.738 +2024-09-08 19:13:36.486796: Pseudo dice [0.9653, 0.9607, 0.9787, 0.9534, 0.8809, 0.9567, 0.9877] +2024-09-08 19:13:36.488444: Epoch time: 410.86 s +2024-09-08 19:13:40.776444: +2024-09-08 19:13:40.781588: Epoch 642 +2024-09-08 19:13:40.784534: Current learning rate: 0.00397 +2024-09-08 19:20:47.368433: train_loss -0.7397 +2024-09-08 19:20:47.372537: val_loss -0.7382 +2024-09-08 19:20:47.375964: Pseudo dice [0.964, 0.9587, 0.9781, 0.947, 0.874, 0.9648, 0.9881] +2024-09-08 19:20:47.377300: Epoch time: 426.62 s +2024-09-08 19:20:52.422918: +2024-09-08 19:20:52.424357: Epoch 643 +2024-09-08 19:20:52.425587: Current learning rate: 0.00396 +2024-09-08 19:27:59.930465: train_loss -0.7439 +2024-09-08 19:27:59.946794: val_loss -0.748 +2024-09-08 19:27:59.947803: Pseudo dice [0.9668, 0.9581, 0.9796, 0.9503, 0.8924, 0.9728, 0.9928] +2024-09-08 19:27:59.948629: Epoch time: 427.51 s +2024-09-08 19:28:06.540114: +2024-09-08 19:28:06.542134: Epoch 644 +2024-09-08 19:28:06.543426: Current learning rate: 0.00395 +2024-09-08 19:35:23.368322: train_loss -0.7271 +2024-09-08 19:35:23.371042: val_loss -0.7361 +2024-09-08 19:35:23.372412: Pseudo dice [0.9644, 0.9547, 0.9794, 0.9457, 0.8798, 0.9681, 0.9902] +2024-09-08 19:35:23.373653: Epoch time: 436.89 s +2024-09-08 19:35:26.578341: +2024-09-08 19:35:26.592649: Epoch 645 +2024-09-08 19:35:26.596160: Current learning rate: 0.00394 +2024-09-08 19:42:27.950354: train_loss -0.7474 +2024-09-08 19:42:27.965058: val_loss -0.7378 +2024-09-08 19:42:27.965766: Pseudo dice [0.966, 0.9598, 0.9798, 0.9548, 0.894, 0.9606, 0.9877] +2024-09-08 19:42:27.966438: Epoch time: 421.45 s +2024-09-08 19:42:27.967183: Yayy! New best EMA pseudo Dice: 0.9549 +2024-09-08 19:42:36.443670: +2024-09-08 19:42:36.445398: Epoch 646 +2024-09-08 19:42:36.446128: Current learning rate: 0.00393 +2024-09-08 19:49:21.137051: train_loss -0.761 +2024-09-08 19:49:21.165726: val_loss -0.8002 +2024-09-08 19:49:21.169265: Pseudo dice [0.9669, 0.959, 0.9805, 0.9497, 0.8927, 0.9738, 0.9915] +2024-09-08 19:49:21.171049: Epoch time: 404.7 s +2024-09-08 19:49:21.172378: Yayy! New best EMA pseudo Dice: 0.9554 +2024-09-08 19:49:29.443165: +2024-09-08 19:49:29.444253: Epoch 647 +2024-09-08 19:49:29.445397: Current learning rate: 0.00392 +2024-09-08 19:56:16.783422: train_loss -0.7479 +2024-09-08 19:56:16.815699: val_loss -0.741 +2024-09-08 19:56:16.818576: Pseudo dice [0.9654, 0.9584, 0.9786, 0.9482, 0.864, 0.9677, 0.9907] +2024-09-08 19:56:16.820315: Epoch time: 407.34 s +2024-09-08 19:56:22.751594: +2024-09-08 19:56:22.767842: Epoch 648 +2024-09-08 19:56:22.769144: Current learning rate: 0.00391 +2024-09-08 20:03:07.210587: train_loss -0.7574 +2024-09-08 20:03:07.231877: val_loss -0.7487 +2024-09-08 20:03:07.234244: Pseudo dice [0.9616, 0.9576, 0.9736, 0.9517, 0.8746, 0.9704, 0.9898] +2024-09-08 20:03:07.235945: Epoch time: 404.47 s +2024-09-08 20:03:11.953296: +2024-09-08 20:03:11.954780: Epoch 649 +2024-09-08 20:03:11.955613: Current learning rate: 0.0039 +2024-09-08 20:10:21.531179: train_loss -0.7106 +2024-09-08 20:10:21.545262: val_loss -0.7651 +2024-09-08 20:10:21.546018: Pseudo dice [0.956, 0.9545, 0.9673, 0.9446, 0.8859, 0.9631, 0.9911] +2024-09-08 20:10:21.546574: Epoch time: 429.58 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_22_03_23.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_22_03_23.txt new file mode 100644 index 0000000000000000000000000000000000000000..13934d3417720a47f1127ac821487ab6025efb13 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_8_22_03_23.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 22:03:26.647955: Using torch.compile... +2024-09-08 22:03:33.018701: do_dummy_2d_data_aug: False +2024-09-08 22:03:33.031236: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 22:03:33.032465: The split file contains 5 splits. +2024-09-08 22:03:33.033023: Desired fold for training: 1 +2024-09-08 22:03:33.033585: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 22:03:46.909412: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 22:03:47.892667: +2024-09-08 22:03:47.904276: Epoch 650 +2024-09-08 22:03:47.905181: Current learning rate: 0.00389 +2024-09-08 22:17:01.015258: train_loss -0.7258 +2024-09-08 22:17:01.016371: val_loss -0.7442 +2024-09-08 22:17:01.016969: Pseudo dice [0.9659, 0.9584, 0.9797, 0.953, 0.875, 0.9546, 0.9867] +2024-09-08 22:17:01.017635: Epoch time: 793.13 s +2024-09-08 22:17:02.498582: +2024-09-08 22:17:02.499794: Epoch 651 +2024-09-08 22:17:02.500400: Current learning rate: 0.00388 +2024-09-08 22:21:41.899116: train_loss -0.7298 +2024-09-08 22:21:41.916492: val_loss -0.7459 +2024-09-08 22:21:41.917301: Pseudo dice [0.9636, 0.9581, 0.9766, 0.9485, 0.8527, 0.9561, 0.9839] +2024-09-08 22:21:41.917971: Epoch time: 279.4 s +2024-09-08 22:21:45.422944: +2024-09-08 22:21:45.424385: Epoch 652 +2024-09-08 22:21:45.425881: Current learning rate: 0.00387 +2024-09-08 22:26:20.016092: train_loss -0.7562 +2024-09-08 22:26:20.033054: val_loss -0.7322 +2024-09-08 22:26:20.034536: Pseudo dice [0.9635, 0.9559, 0.9758, 0.951, 0.8709, 0.9716, 0.9888] +2024-09-08 22:26:20.035857: Epoch time: 274.6 s +2024-09-08 22:26:23.080957: +2024-09-08 22:26:23.082566: Epoch 653 +2024-09-08 22:26:23.084538: Current learning rate: 0.00386 +2024-09-08 22:30:45.012451: train_loss -0.7499 +2024-09-08 22:30:45.015019: val_loss -0.7696 +2024-09-08 22:30:45.015875: Pseudo dice [0.9678, 0.9589, 0.9798, 0.9533, 0.8925, 0.9693, 0.9909] +2024-09-08 22:30:45.016547: Epoch time: 261.94 s +2024-09-08 22:30:48.688381: +2024-09-08 22:30:48.689354: Epoch 654 +2024-09-08 22:30:48.691038: Current learning rate: 0.00385 +2024-09-08 22:35:32.169955: train_loss -0.7382 +2024-09-08 22:35:32.175091: val_loss -0.7398 +2024-09-08 22:35:32.176172: Pseudo dice [0.9649, 0.9585, 0.9787, 0.9547, 0.8821, 0.9586, 0.988] +2024-09-08 22:35:32.176881: Epoch time: 283.49 s +2024-09-08 22:35:36.154009: +2024-09-08 22:35:36.168302: Epoch 655 +2024-09-08 22:35:36.169366: Current learning rate: 0.00384 +2024-09-08 22:40:36.674786: train_loss -0.7459 +2024-09-08 22:40:36.701701: val_loss -0.7612 +2024-09-08 22:40:36.702673: Pseudo dice [0.9657, 0.958, 0.9781, 0.9503, 0.8786, 0.9727, 0.9911] +2024-09-08 22:40:36.703442: Epoch time: 300.53 s +2024-09-08 22:40:40.738300: +2024-09-08 22:40:40.752553: Epoch 656 +2024-09-08 22:40:40.753650: Current learning rate: 0.00383 +2024-09-08 22:45:11.307414: train_loss -0.7444 +2024-09-08 22:45:11.332801: val_loss -0.7359 +2024-09-08 22:45:11.334638: Pseudo dice [0.9659, 0.9582, 0.9796, 0.9515, 0.8854, 0.9622, 0.9889] +2024-09-08 22:45:11.336349: Epoch time: 270.57 s +2024-09-08 22:45:14.724794: +2024-09-08 22:45:14.726316: Epoch 657 +2024-09-08 22:45:14.728122: Current learning rate: 0.00382 +2024-09-08 22:49:59.571167: train_loss -0.7543 +2024-09-08 22:49:59.602681: val_loss -0.7611 +2024-09-08 22:49:59.603692: Pseudo dice [0.9671, 0.9605, 0.9819, 0.9539, 0.8822, 0.9631, 0.9886] +2024-09-08 22:49:59.604723: Epoch time: 284.87 s +2024-09-08 22:50:03.264385: +2024-09-08 22:50:03.265570: Epoch 658 +2024-09-08 22:50:03.266650: Current learning rate: 0.00381 +2024-09-08 22:54:37.360385: train_loss -0.7438 +2024-09-08 22:54:37.375083: val_loss -0.7566 +2024-09-08 22:54:37.375949: Pseudo dice [0.965, 0.9598, 0.9758, 0.95, 0.8763, 0.9674, 0.9905] +2024-09-08 22:54:37.376699: Epoch time: 274.1 s +2024-09-08 22:54:40.721081: +2024-09-08 22:54:40.732594: Epoch 659 +2024-09-08 22:54:40.733552: Current learning rate: 0.0038 +2024-09-08 22:59:32.552389: train_loss -0.7367 +2024-09-08 22:59:32.571303: val_loss -0.7516 +2024-09-08 22:59:32.572888: Pseudo dice [0.9625, 0.9594, 0.9756, 0.9462, 0.8556, 0.9707, 0.9887] +2024-09-08 22:59:32.574378: Epoch time: 291.84 s +2024-09-08 22:59:37.580657: +2024-09-08 22:59:37.583051: Epoch 660 +2024-09-08 22:59:37.585510: Current learning rate: 0.00379 +2024-09-08 23:04:28.295589: train_loss -0.7321 +2024-09-08 23:04:28.308950: val_loss -0.732 +2024-09-08 23:04:28.309715: Pseudo dice [0.9614, 0.9552, 0.9723, 0.9469, 0.8599, 0.9471, 0.9861] +2024-09-08 23:04:28.310398: Epoch time: 290.72 s +2024-09-08 23:04:31.937831: +2024-09-08 23:04:31.939027: Epoch 661 +2024-09-08 23:04:31.939938: Current learning rate: 0.00378 +2024-09-08 23:09:15.073074: train_loss -0.7516 +2024-09-08 23:09:15.088913: val_loss -0.7339 +2024-09-08 23:09:15.090520: Pseudo dice [0.9643, 0.9564, 0.9792, 0.9498, 0.8737, 0.957, 0.989] +2024-09-08 23:09:15.091238: Epoch time: 283.14 s +2024-09-08 23:09:18.097881: +2024-09-08 23:09:18.115854: Epoch 662 +2024-09-08 23:09:18.117302: Current learning rate: 0.00377 +2024-09-08 23:14:09.808216: train_loss -0.7605 +2024-09-08 23:14:09.824984: val_loss -0.7643 +2024-09-08 23:14:09.825898: Pseudo dice [0.9683, 0.9593, 0.9801, 0.9541, 0.8759, 0.974, 0.9915] +2024-09-08 23:14:09.826667: Epoch time: 291.72 s +2024-09-08 23:14:13.015856: +2024-09-08 23:14:13.031928: Epoch 663 +2024-09-08 23:14:13.033684: Current learning rate: 0.00376 +2024-09-08 23:18:45.775266: train_loss -0.771 +2024-09-08 23:18:45.776718: val_loss -0.7524 +2024-09-08 23:18:45.777356: Pseudo dice [0.9682, 0.9597, 0.9801, 0.9506, 0.898, 0.9652, 0.9906] +2024-09-08 23:18:45.778127: Epoch time: 272.77 s +2024-09-08 23:18:48.307826: +2024-09-08 23:18:48.308878: Epoch 664 +2024-09-08 23:18:48.309592: Current learning rate: 0.00375 +2024-09-08 23:23:21.945589: train_loss -0.7529 +2024-09-08 23:23:21.964149: val_loss -0.7346 +2024-09-08 23:23:21.964983: Pseudo dice [0.9608, 0.9564, 0.976, 0.9442, 0.8645, 0.9522, 0.9846] +2024-09-08 23:23:21.965709: Epoch time: 273.64 s +2024-09-08 23:23:25.994037: +2024-09-08 23:23:25.995254: Epoch 665 +2024-09-08 23:23:25.996437: Current learning rate: 0.00374 +2024-09-08 23:28:06.086543: train_loss -0.7358 +2024-09-08 23:28:06.102089: val_loss -0.7785 +2024-09-08 23:28:06.103596: Pseudo dice [0.9622, 0.9569, 0.9771, 0.9543, 0.8724, 0.9689, 0.9913] +2024-09-08 23:28:06.105399: Epoch time: 280.1 s +2024-09-08 23:28:09.092639: +2024-09-08 23:28:09.093599: Epoch 666 +2024-09-08 23:28:09.094327: Current learning rate: 0.00373 +2024-09-08 23:33:07.368144: train_loss -0.7553 +2024-09-08 23:33:07.369481: val_loss -0.7615 +2024-09-08 23:33:07.370174: Pseudo dice [0.9631, 0.9572, 0.9764, 0.9538, 0.8879, 0.9731, 0.9911] +2024-09-08 23:33:07.370897: Epoch time: 298.28 s +2024-09-08 23:33:09.864625: +2024-09-08 23:33:09.865831: Epoch 667 +2024-09-08 23:33:09.866518: Current learning rate: 0.00372 +2024-09-08 23:38:03.789526: train_loss -0.7501 +2024-09-08 23:38:03.791782: val_loss -0.7748 +2024-09-08 23:38:03.793116: Pseudo dice [0.9662, 0.9587, 0.9796, 0.9496, 0.8839, 0.9721, 0.9921] +2024-09-08 23:38:03.794034: Epoch time: 293.93 s +2024-09-08 23:38:07.232606: +2024-09-08 23:38:07.234055: Epoch 668 +2024-09-08 23:38:07.235349: Current learning rate: 0.00371 +2024-09-08 23:42:50.121075: train_loss -0.7371 +2024-09-08 23:42:50.133460: val_loss -0.7464 +2024-09-08 23:42:50.134202: Pseudo dice [0.9662, 0.9589, 0.9805, 0.9513, 0.8576, 0.9589, 0.9859] +2024-09-08 23:42:50.134804: Epoch time: 282.89 s +2024-09-08 23:42:54.126316: +2024-09-08 23:42:54.127350: Epoch 669 +2024-09-08 23:42:54.128151: Current learning rate: 0.0037 +2024-09-08 23:47:38.403533: train_loss -0.7541 +2024-09-08 23:47:38.412795: val_loss -0.76 +2024-09-08 23:47:38.413539: Pseudo dice [0.9644, 0.9578, 0.9779, 0.9547, 0.8653, 0.9712, 0.9916] +2024-09-08 23:47:38.414195: Epoch time: 284.28 s +2024-09-08 23:47:42.654619: +2024-09-08 23:47:42.675797: Epoch 670 +2024-09-08 23:47:42.684453: Current learning rate: 0.00369 +2024-09-08 23:52:34.941202: train_loss -0.7613 +2024-09-08 23:52:34.951444: val_loss -0.7456 +2024-09-08 23:52:34.952491: Pseudo dice [0.967, 0.9614, 0.9801, 0.9475, 0.8636, 0.9704, 0.9889] +2024-09-08 23:52:34.954855: Epoch time: 292.29 s +2024-09-08 23:52:38.109916: +2024-09-08 23:52:38.111781: Epoch 671 +2024-09-08 23:52:38.113446: Current learning rate: 0.00368 +2024-09-08 23:57:11.302598: train_loss -0.7581 +2024-09-08 23:57:11.327334: val_loss -0.7832 +2024-09-08 23:57:11.328791: Pseudo dice [0.9679, 0.9593, 0.9825, 0.9578, 0.8874, 0.9746, 0.9904] +2024-09-08 23:57:11.330201: Epoch time: 273.2 s +2024-09-08 23:57:15.892033: +2024-09-08 23:57:15.892933: Epoch 672 +2024-09-08 23:57:15.893565: Current learning rate: 0.00367 +2024-09-09 00:01:59.932347: train_loss -0.7581 +2024-09-09 00:01:59.941146: val_loss -0.7544 +2024-09-09 00:01:59.942424: Pseudo dice [0.9655, 0.957, 0.9785, 0.9504, 0.879, 0.9716, 0.9915] +2024-09-09 00:01:59.943330: Epoch time: 284.05 s +2024-09-09 00:02:03.281240: +2024-09-09 00:02:03.296022: Epoch 673 +2024-09-09 00:02:03.297552: Current learning rate: 0.00366 +2024-09-09 00:06:28.779673: train_loss -0.7501 +2024-09-09 00:06:28.796022: val_loss -0.7552 +2024-09-09 00:06:28.798597: Pseudo dice [0.9676, 0.9616, 0.9809, 0.9518, 0.8937, 0.9706, 0.9927] +2024-09-09 00:06:28.800919: Epoch time: 265.5 s +2024-09-09 00:06:28.803471: Yayy! New best EMA pseudo Dice: 0.9556 +2024-09-09 00:06:36.722381: +2024-09-09 00:06:36.723326: Epoch 674 +2024-09-09 00:06:36.724325: Current learning rate: 0.00365 +2024-09-09 00:11:08.975053: train_loss -0.7437 +2024-09-09 00:11:08.987721: val_loss -0.7424 +2024-09-09 00:11:08.988569: Pseudo dice [0.9646, 0.9581, 0.9809, 0.9495, 0.8807, 0.9729, 0.9923] +2024-09-09 00:11:08.989378: Epoch time: 272.26 s +2024-09-09 00:11:08.990230: Yayy! New best EMA pseudo Dice: 0.9557 +2024-09-09 00:11:14.514865: +2024-09-09 00:11:14.515875: Epoch 675 +2024-09-09 00:11:14.516717: Current learning rate: 0.00364 +2024-09-09 00:16:04.001026: train_loss -0.7394 +2024-09-09 00:16:04.017724: val_loss -0.7337 +2024-09-09 00:16:04.018619: Pseudo dice [0.9672, 0.9609, 0.9805, 0.9519, 0.8808, 0.9716, 0.9927] +2024-09-09 00:16:04.019449: Epoch time: 289.49 s +2024-09-09 00:16:04.020291: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-09 00:16:09.644160: +2024-09-09 00:16:09.645123: Epoch 676 +2024-09-09 00:16:09.645840: Current learning rate: 0.00363 +2024-09-09 00:20:53.974637: train_loss -0.75 +2024-09-09 00:20:53.994398: val_loss -0.7598 +2024-09-09 00:20:53.996114: Pseudo dice [0.9693, 0.9603, 0.9817, 0.9524, 0.8732, 0.9698, 0.991] +2024-09-09 00:20:53.997299: Epoch time: 284.34 s +2024-09-09 00:20:53.998687: Yayy! New best EMA pseudo Dice: 0.956 +2024-09-09 00:20:58.922591: +2024-09-09 00:20:58.923745: Epoch 677 +2024-09-09 00:20:58.924914: Current learning rate: 0.00362 +2024-09-09 00:25:42.977435: train_loss -0.76 +2024-09-09 00:25:42.979934: val_loss -0.7832 +2024-09-09 00:25:42.980632: Pseudo dice [0.9657, 0.9567, 0.9793, 0.954, 0.8856, 0.9716, 0.9908] +2024-09-09 00:25:42.981259: Epoch time: 284.06 s +2024-09-09 00:25:42.981976: Yayy! New best EMA pseudo Dice: 0.9562 +2024-09-09 00:25:48.486228: +2024-09-09 00:25:48.487247: Epoch 678 +2024-09-09 00:25:48.487907: Current learning rate: 0.00361 +2024-09-09 00:30:28.729239: train_loss -0.7511 +2024-09-09 00:30:28.753389: val_loss -0.7173 +2024-09-09 00:30:28.754145: Pseudo dice [0.9674, 0.9578, 0.9792, 0.942, 0.8248, 0.9576, 0.9851] +2024-09-09 00:30:28.754726: Epoch time: 280.25 s +2024-09-09 00:30:31.708711: +2024-09-09 00:30:31.709663: Epoch 679 +2024-09-09 00:30:31.710459: Current learning rate: 0.0036 +2024-09-09 00:35:28.428416: train_loss -0.7461 +2024-09-09 00:35:28.453371: val_loss -0.73 +2024-09-09 00:35:28.454944: Pseudo dice [0.9658, 0.9581, 0.9792, 0.9475, 0.8806, 0.9693, 0.9903] +2024-09-09 00:35:28.456537: Epoch time: 296.72 s +2024-09-09 00:35:31.583044: +2024-09-09 00:35:31.584686: Epoch 680 +2024-09-09 00:35:31.585878: Current learning rate: 0.00359 +2024-09-09 00:40:08.777732: train_loss -0.7534 +2024-09-09 00:40:08.794940: val_loss -0.7303 +2024-09-09 00:40:08.796031: Pseudo dice [0.9661, 0.9584, 0.9803, 0.9479, 0.8807, 0.9704, 0.9923] +2024-09-09 00:40:08.797324: Epoch time: 277.2 s +2024-09-09 00:40:12.176245: +2024-09-09 00:40:12.192364: Epoch 681 +2024-09-09 00:40:12.193266: Current learning rate: 0.00358 +2024-09-09 00:44:55.948980: train_loss -0.7484 +2024-09-09 00:44:55.953813: val_loss -0.7255 +2024-09-09 00:44:55.954796: Pseudo dice [0.9656, 0.9592, 0.9789, 0.9513, 0.8582, 0.9483, 0.9843] +2024-09-09 00:44:55.955635: Epoch time: 283.78 s +2024-09-09 00:44:59.861327: +2024-09-09 00:44:59.876528: Epoch 682 +2024-09-09 00:44:59.877607: Current learning rate: 0.00357 +2024-09-09 00:49:47.948339: train_loss -0.7661 +2024-09-09 00:49:47.962560: val_loss -0.7102 +2024-09-09 00:49:47.964368: Pseudo dice [0.968, 0.9604, 0.9809, 0.9521, 0.8669, 0.9724, 0.9893] +2024-09-09 00:49:47.965762: Epoch time: 288.09 s +2024-09-09 00:49:50.520417: +2024-09-09 00:49:50.521338: Epoch 683 +2024-09-09 00:49:50.522736: Current learning rate: 0.00356 +2024-09-09 00:54:38.322671: train_loss -0.7517 +2024-09-09 00:54:38.337814: val_loss -0.7511 +2024-09-09 00:54:38.338803: Pseudo dice [0.9668, 0.9592, 0.9795, 0.9505, 0.8285, 0.969, 0.9832] +2024-09-09 00:54:38.339706: Epoch time: 287.81 s +2024-09-09 00:54:41.688069: +2024-09-09 00:54:41.702578: Epoch 684 +2024-09-09 00:54:41.712276: Current learning rate: 0.00355 +2024-09-09 00:59:28.790060: train_loss -0.7326 +2024-09-09 00:59:28.810938: val_loss -0.7733 +2024-09-09 00:59:28.811847: Pseudo dice [0.9638, 0.9605, 0.9772, 0.9537, 0.8906, 0.9722, 0.991] +2024-09-09 00:59:28.812509: Epoch time: 287.11 s +2024-09-09 00:59:35.692592: +2024-09-09 00:59:35.693488: Epoch 685 +2024-09-09 00:59:35.694127: Current learning rate: 0.00354 +2024-09-09 01:04:20.830775: train_loss -0.7252 +2024-09-09 01:04:20.839989: val_loss -0.782 +2024-09-09 01:04:20.840817: Pseudo dice [0.9625, 0.9573, 0.9774, 0.9501, 0.8787, 0.9726, 0.9916] +2024-09-09 01:04:20.841614: Epoch time: 285.14 s +2024-09-09 01:04:24.313102: +2024-09-09 01:04:24.324443: Epoch 686 +2024-09-09 01:04:24.326315: Current learning rate: 0.00353 +2024-09-09 01:08:55.621853: train_loss -0.7419 +2024-09-09 01:08:55.634838: val_loss -0.7833 +2024-09-09 01:08:55.635559: Pseudo dice [0.9633, 0.9571, 0.9757, 0.955, 0.8741, 0.9711, 0.9897] +2024-09-09 01:08:55.636297: Epoch time: 271.32 s +2024-09-09 01:08:58.976185: +2024-09-09 01:08:58.977109: Epoch 687 +2024-09-09 01:08:58.977821: Current learning rate: 0.00352 +2024-09-09 01:13:45.839841: train_loss -0.7611 +2024-09-09 01:13:45.854351: val_loss -0.749 +2024-09-09 01:13:45.855188: Pseudo dice [0.9637, 0.9586, 0.9774, 0.953, 0.8732, 0.9737, 0.989] +2024-09-09 01:13:45.855834: Epoch time: 286.87 s +2024-09-09 01:13:48.672552: +2024-09-09 01:13:48.673787: Epoch 688 +2024-09-09 01:13:48.674592: Current learning rate: 0.00351 +2024-09-09 01:18:21.463458: train_loss -0.7424 +2024-09-09 01:18:21.483862: val_loss -0.716 +2024-09-09 01:18:21.486331: Pseudo dice [0.9646, 0.9573, 0.9788, 0.9529, 0.8617, 0.9687, 0.9896] +2024-09-09 01:18:21.488428: Epoch time: 272.8 s +2024-09-09 01:18:25.336762: +2024-09-09 01:18:25.338324: Epoch 689 +2024-09-09 01:18:25.339169: Current learning rate: 0.0035 +2024-09-09 01:22:58.829246: train_loss -0.76 +2024-09-09 01:22:58.837680: val_loss -0.7551 +2024-09-09 01:22:58.838534: Pseudo dice [0.964, 0.9567, 0.9767, 0.955, 0.8641, 0.9745, 0.9903] +2024-09-09 01:22:58.839442: Epoch time: 273.5 s +2024-09-09 01:23:02.605338: +2024-09-09 01:23:02.606308: Epoch 690 +2024-09-09 01:23:02.606950: Current learning rate: 0.00349 +2024-09-09 01:27:44.842148: train_loss -0.7486 +2024-09-09 01:27:44.858119: val_loss -0.7638 +2024-09-09 01:27:44.859723: Pseudo dice [0.9655, 0.9583, 0.9802, 0.9511, 0.8777, 0.9724, 0.9921] +2024-09-09 01:27:44.860928: Epoch time: 282.24 s +2024-09-09 01:27:48.032431: +2024-09-09 01:27:48.044308: Epoch 691 +2024-09-09 01:27:48.045010: Current learning rate: 0.00348 +2024-09-09 01:32:40.238310: train_loss -0.7357 +2024-09-09 01:32:40.261228: val_loss -0.7381 +2024-09-09 01:32:40.262133: Pseudo dice [0.964, 0.9562, 0.978, 0.9475, 0.8653, 0.9594, 0.9863] +2024-09-09 01:32:40.263004: Epoch time: 292.21 s +2024-09-09 01:32:43.636026: +2024-09-09 01:32:43.637312: Epoch 692 +2024-09-09 01:32:43.638240: Current learning rate: 0.00346 +2024-09-09 01:37:29.320757: train_loss -0.7453 +2024-09-09 01:37:29.323310: val_loss -0.7375 +2024-09-09 01:37:29.324029: Pseudo dice [0.9586, 0.9571, 0.9734, 0.9474, 0.886, 0.9683, 0.9918] +2024-09-09 01:37:29.324742: Epoch time: 285.69 s +2024-09-09 01:37:31.926863: +2024-09-09 01:37:31.927851: Epoch 693 +2024-09-09 01:37:31.928484: Current learning rate: 0.00345 +2024-09-09 01:42:17.753509: train_loss -0.748 +2024-09-09 01:42:17.778188: val_loss -0.7764 +2024-09-09 01:42:17.779312: Pseudo dice [0.9683, 0.9595, 0.9818, 0.9542, 0.8924, 0.973, 0.9921] +2024-09-09 01:42:17.780190: Epoch time: 285.83 s +2024-09-09 01:42:21.688361: +2024-09-09 01:42:21.689396: Epoch 694 +2024-09-09 01:42:21.690058: Current learning rate: 0.00344 +2024-09-09 01:46:47.713999: train_loss -0.7476 +2024-09-09 01:46:47.723335: val_loss -0.7253 +2024-09-09 01:46:47.724779: Pseudo dice [0.9682, 0.9587, 0.9817, 0.9515, 0.8646, 0.9199, 0.9785] +2024-09-09 01:46:47.726220: Epoch time: 266.03 s +2024-09-09 01:46:51.054677: +2024-09-09 01:46:51.055830: Epoch 695 +2024-09-09 01:46:51.056645: Current learning rate: 0.00343 +2024-09-09 01:51:44.871192: train_loss -0.7579 +2024-09-09 01:51:44.893317: val_loss -0.7785 +2024-09-09 01:51:44.895853: Pseudo dice [0.9664, 0.9592, 0.9807, 0.9539, 0.8534, 0.9706, 0.9871] +2024-09-09 01:51:44.896638: Epoch time: 293.82 s +2024-09-09 01:51:47.313574: +2024-09-09 01:51:47.314790: Epoch 696 +2024-09-09 01:51:47.315489: Current learning rate: 0.00342 +2024-09-09 01:56:28.328609: train_loss -0.7264 +2024-09-09 01:56:28.340220: val_loss -0.748 +2024-09-09 01:56:28.341009: Pseudo dice [0.9679, 0.9618, 0.9778, 0.9525, 0.8407, 0.9573, 0.9856] +2024-09-09 01:56:28.341597: Epoch time: 281.02 s +2024-09-09 01:56:32.219259: +2024-09-09 01:56:32.232276: Epoch 697 +2024-09-09 01:56:32.233034: Current learning rate: 0.00341 +2024-09-09 02:01:09.385631: train_loss -0.7397 +2024-09-09 02:01:09.396597: val_loss -0.7648 +2024-09-09 02:01:09.397527: Pseudo dice [0.9635, 0.9565, 0.9765, 0.9499, 0.8598, 0.9716, 0.9883] +2024-09-09 02:01:09.398308: Epoch time: 277.17 s +2024-09-09 02:01:12.106103: +2024-09-09 02:01:12.107185: Epoch 698 +2024-09-09 02:01:12.107958: Current learning rate: 0.0034 +2024-09-09 02:06:09.034435: train_loss -0.7409 +2024-09-09 02:06:09.046691: val_loss -0.7133 +2024-09-09 02:06:09.047867: Pseudo dice [0.9634, 0.9595, 0.9758, 0.954, 0.8835, 0.9551, 0.9841] +2024-09-09 02:06:09.048720: Epoch time: 296.93 s +2024-09-09 02:06:12.858654: +2024-09-09 02:06:12.859541: Epoch 699 +2024-09-09 02:06:12.860284: Current learning rate: 0.00339 +2024-09-09 02:10:49.390341: train_loss -0.7227 +2024-09-09 02:10:49.393140: val_loss -0.7414 +2024-09-09 02:10:49.394068: Pseudo dice [0.9645, 0.9583, 0.9797, 0.9544, 0.8872, 0.9642, 0.9893] +2024-09-09 02:10:49.394830: Epoch time: 276.54 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_06_02_53.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_06_02_53.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc004433dc27b4a0e419a69c7200012b72d310ea --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_06_02_53.txt @@ -0,0 +1,372 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 06:02:56.778202: Using torch.compile... +2024-09-09 06:03:03.092299: do_dummy_2d_data_aug: False +2024-09-09 06:03:03.103279: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 06:03:03.104521: The split file contains 5 splits. +2024-09-09 06:03:03.104924: Desired fold for training: 1 +2024-09-09 06:03:03.105288: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 06:03:16.358802: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 06:03:17.390148: +2024-09-09 06:03:17.391786: Epoch 700 +2024-09-09 06:03:17.394311: Current learning rate: 0.00338 +2024-09-09 06:18:43.602243: train_loss -0.7476 +2024-09-09 06:18:43.604459: val_loss -0.7345 +2024-09-09 06:18:43.605507: Pseudo dice [0.9637, 0.9593, 0.9788, 0.9532, 0.8734, 0.9627, 0.9868] +2024-09-09 06:18:43.606491: Epoch time: 926.22 s +2024-09-09 06:18:45.440882: +2024-09-09 06:18:45.441756: Epoch 701 +2024-09-09 06:18:45.442852: Current learning rate: 0.00337 +2024-09-09 06:24:01.192089: train_loss -0.753 +2024-09-09 06:24:01.210246: val_loss -0.7759 +2024-09-09 06:24:01.211147: Pseudo dice [0.9646, 0.9581, 0.9783, 0.9508, 0.8846, 0.9738, 0.9903] +2024-09-09 06:24:01.211859: Epoch time: 315.75 s +2024-09-09 06:24:04.719154: +2024-09-09 06:24:04.720481: Epoch 702 +2024-09-09 06:24:04.721740: Current learning rate: 0.00336 +2024-09-09 06:29:53.836524: train_loss -0.7372 +2024-09-09 06:29:53.861530: val_loss -0.759 +2024-09-09 06:29:53.862194: Pseudo dice [0.966, 0.9572, 0.9771, 0.9475, 0.8458, 0.9712, 0.9865] +2024-09-09 06:29:53.862723: Epoch time: 349.12 s +2024-09-09 06:29:57.974987: +2024-09-09 06:29:57.976067: Epoch 703 +2024-09-09 06:29:57.976776: Current learning rate: 0.00335 +2024-09-09 06:35:28.295175: train_loss -0.7659 +2024-09-09 06:35:28.306043: val_loss -0.7644 +2024-09-09 06:35:28.306870: Pseudo dice [0.9658, 0.9622, 0.9799, 0.9566, 0.8852, 0.9743, 0.9925] +2024-09-09 06:35:28.307902: Epoch time: 330.33 s +2024-09-09 06:35:34.077867: +2024-09-09 06:35:34.079472: Epoch 704 +2024-09-09 06:35:34.080357: Current learning rate: 0.00334 +2024-09-09 06:41:08.471130: train_loss -0.7574 +2024-09-09 06:41:08.484205: val_loss -0.7551 +2024-09-09 06:41:08.486439: Pseudo dice [0.964, 0.9591, 0.9768, 0.9517, 0.8697, 0.9662, 0.9887] +2024-09-09 06:41:08.487795: Epoch time: 334.4 s +2024-09-09 06:41:12.932688: +2024-09-09 06:41:12.933559: Epoch 705 +2024-09-09 06:41:12.934412: Current learning rate: 0.00333 +2024-09-09 06:46:43.483049: train_loss -0.753 +2024-09-09 06:46:43.508766: val_loss -0.7741 +2024-09-09 06:46:43.510021: Pseudo dice [0.9626, 0.9595, 0.9766, 0.9482, 0.8607, 0.9715, 0.9874] +2024-09-09 06:46:43.511263: Epoch time: 330.56 s +2024-09-09 06:46:47.109652: +2024-09-09 06:46:47.110538: Epoch 706 +2024-09-09 06:46:47.111093: Current learning rate: 0.00332 +2024-09-09 06:52:25.878007: train_loss -0.7415 +2024-09-09 06:52:25.879794: val_loss -0.7504 +2024-09-09 06:52:25.880516: Pseudo dice [0.967, 0.9593, 0.9794, 0.9563, 0.8838, 0.973, 0.9922] +2024-09-09 06:52:25.881128: Epoch time: 338.77 s +2024-09-09 06:52:29.098577: +2024-09-09 06:52:29.099848: Epoch 707 +2024-09-09 06:52:29.100521: Current learning rate: 0.00331 +2024-09-09 06:58:09.136641: train_loss -0.7442 +2024-09-09 06:58:09.141532: val_loss -0.743 +2024-09-09 06:58:09.142412: Pseudo dice [0.9655, 0.9595, 0.9783, 0.9545, 0.887, 0.9718, 0.9891] +2024-09-09 06:58:09.143280: Epoch time: 340.04 s +2024-09-09 06:58:12.652112: +2024-09-09 06:58:12.679001: Epoch 708 +2024-09-09 06:58:12.680172: Current learning rate: 0.0033 +2024-09-09 07:03:26.272421: train_loss -0.7452 +2024-09-09 07:03:26.287632: val_loss -0.766 +2024-09-09 07:03:26.288692: Pseudo dice [0.9669, 0.9602, 0.9778, 0.9457, 0.8902, 0.9721, 0.9912] +2024-09-09 07:03:26.290283: Epoch time: 313.63 s +2024-09-09 07:03:30.378541: +2024-09-09 07:03:30.388876: Epoch 709 +2024-09-09 07:03:30.389795: Current learning rate: 0.00329 +2024-09-09 07:08:57.961622: train_loss -0.762 +2024-09-09 07:08:57.978632: val_loss -0.7422 +2024-09-09 07:08:57.981272: Pseudo dice [0.9664, 0.9594, 0.9802, 0.9482, 0.8529, 0.9726, 0.9867] +2024-09-09 07:08:57.983870: Epoch time: 327.59 s +2024-09-09 07:09:02.234838: +2024-09-09 07:09:02.248436: Epoch 710 +2024-09-09 07:09:02.249241: Current learning rate: 0.00328 +2024-09-09 07:14:31.316636: train_loss -0.7469 +2024-09-09 07:14:31.319173: val_loss -0.7237 +2024-09-09 07:14:31.319836: Pseudo dice [0.9675, 0.961, 0.981, 0.9539, 0.8755, 0.9719, 0.9906] +2024-09-09 07:14:31.320389: Epoch time: 329.09 s +2024-09-09 07:14:35.684810: +2024-09-09 07:14:35.700361: Epoch 711 +2024-09-09 07:14:35.701144: Current learning rate: 0.00327 +2024-09-09 07:20:08.037688: train_loss -0.7463 +2024-09-09 07:20:08.069312: val_loss -0.7877 +2024-09-09 07:20:08.070474: Pseudo dice [0.965, 0.9597, 0.9781, 0.9471, 0.8598, 0.971, 0.9883] +2024-09-09 07:20:08.071311: Epoch time: 332.36 s +2024-09-09 07:20:12.142129: +2024-09-09 07:20:12.143776: Epoch 712 +2024-09-09 07:20:12.145006: Current learning rate: 0.00326 +2024-09-09 07:25:25.741321: train_loss -0.7436 +2024-09-09 07:25:25.743331: val_loss -0.7751 +2024-09-09 07:25:25.744063: Pseudo dice [0.9655, 0.9577, 0.98, 0.9484, 0.8629, 0.9721, 0.9878] +2024-09-09 07:25:25.744771: Epoch time: 313.61 s +2024-09-09 07:25:31.783180: +2024-09-09 07:25:31.796654: Epoch 713 +2024-09-09 07:25:31.797451: Current learning rate: 0.00325 +2024-09-09 07:30:52.993678: train_loss -0.7525 +2024-09-09 07:30:53.008893: val_loss -0.7539 +2024-09-09 07:30:53.009925: Pseudo dice [0.9669, 0.9584, 0.9782, 0.9548, 0.8672, 0.9711, 0.9891] +2024-09-09 07:30:53.010598: Epoch time: 321.22 s +2024-09-09 07:30:56.374607: +2024-09-09 07:30:56.375451: Epoch 714 +2024-09-09 07:30:56.376092: Current learning rate: 0.00324 +2024-09-09 07:36:35.140410: train_loss -0.7381 +2024-09-09 07:36:35.143301: val_loss -0.772 +2024-09-09 07:36:35.144284: Pseudo dice [0.9671, 0.9593, 0.9783, 0.9492, 0.873, 0.9736, 0.9897] +2024-09-09 07:36:35.144973: Epoch time: 338.77 s +2024-09-09 07:36:40.337693: +2024-09-09 07:36:40.346523: Epoch 715 +2024-09-09 07:36:40.347997: Current learning rate: 0.00323 +2024-09-09 07:42:26.951836: train_loss -0.7386 +2024-09-09 07:42:26.956562: val_loss -0.7915 +2024-09-09 07:42:26.957484: Pseudo dice [0.9659, 0.9598, 0.9799, 0.954, 0.885, 0.9657, 0.9867] +2024-09-09 07:42:26.958677: Epoch time: 346.62 s +2024-09-09 07:42:30.785387: +2024-09-09 07:42:30.800512: Epoch 716 +2024-09-09 07:42:30.801304: Current learning rate: 0.00322 +2024-09-09 07:47:52.189302: train_loss -0.75 +2024-09-09 07:47:52.204641: val_loss -0.7741 +2024-09-09 07:47:52.205276: Pseudo dice [0.9655, 0.9558, 0.9803, 0.9465, 0.841, 0.9728, 0.9857] +2024-09-09 07:47:52.205834: Epoch time: 321.41 s +2024-09-09 07:47:56.880606: +2024-09-09 07:47:56.881504: Epoch 717 +2024-09-09 07:47:56.882075: Current learning rate: 0.00321 +2024-09-09 07:53:11.239039: train_loss -0.7484 +2024-09-09 07:53:11.249323: val_loss -0.8075 +2024-09-09 07:53:11.249925: Pseudo dice [0.9673, 0.9603, 0.9812, 0.946, 0.8771, 0.9732, 0.9909] +2024-09-09 07:53:11.250440: Epoch time: 314.36 s +2024-09-09 07:53:16.222738: +2024-09-09 07:53:16.224182: Epoch 718 +2024-09-09 07:53:16.226389: Current learning rate: 0.0032 +2024-09-09 07:58:24.961779: train_loss -0.7539 +2024-09-09 07:58:24.964768: val_loss -0.7482 +2024-09-09 07:58:24.965539: Pseudo dice [0.9671, 0.9606, 0.9775, 0.9514, 0.8804, 0.9595, 0.989] +2024-09-09 07:58:24.966168: Epoch time: 308.74 s +2024-09-09 07:58:30.018130: +2024-09-09 07:58:30.028700: Epoch 719 +2024-09-09 07:58:30.029634: Current learning rate: 0.00319 +2024-09-09 08:04:11.449731: train_loss -0.7645 +2024-09-09 08:04:11.465246: val_loss -0.7628 +2024-09-09 08:04:11.466406: Pseudo dice [0.9668, 0.9588, 0.9807, 0.955, 0.8833, 0.9646, 0.9904] +2024-09-09 08:04:11.467100: Epoch time: 341.44 s +2024-09-09 08:04:15.440177: +2024-09-09 08:04:15.442181: Epoch 720 +2024-09-09 08:04:15.443831: Current learning rate: 0.00318 +2024-09-09 08:09:42.673975: train_loss -0.7338 +2024-09-09 08:09:42.692791: val_loss -0.7489 +2024-09-09 08:09:42.693507: Pseudo dice [0.9644, 0.9575, 0.9791, 0.9477, 0.8582, 0.9715, 0.9881] +2024-09-09 08:09:42.694005: Epoch time: 327.24 s +2024-09-09 08:09:47.719586: +2024-09-09 08:09:47.731800: Epoch 721 +2024-09-09 08:09:47.732928: Current learning rate: 0.00317 +2024-09-09 08:15:27.330887: train_loss -0.7311 +2024-09-09 08:15:27.345948: val_loss -0.748 +2024-09-09 08:15:27.347906: Pseudo dice [0.9631, 0.9567, 0.9767, 0.9501, 0.8633, 0.9664, 0.9888] +2024-09-09 08:15:27.349761: Epoch time: 339.62 s +2024-09-09 08:15:31.926171: +2024-09-09 08:15:31.927521: Epoch 722 +2024-09-09 08:15:31.928978: Current learning rate: 0.00316 +2024-09-09 08:20:58.146586: train_loss -0.755 +2024-09-09 08:20:58.163732: val_loss -0.7275 +2024-09-09 08:20:58.164982: Pseudo dice [0.9619, 0.9577, 0.9762, 0.9489, 0.8519, 0.9707, 0.9858] +2024-09-09 08:20:58.165980: Epoch time: 326.23 s +2024-09-09 08:21:02.212332: +2024-09-09 08:21:02.231844: Epoch 723 +2024-09-09 08:21:02.233629: Current learning rate: 0.00315 +2024-09-09 08:26:33.870771: train_loss -0.7312 +2024-09-09 08:26:33.890748: val_loss -0.7369 +2024-09-09 08:26:33.892495: Pseudo dice [0.9634, 0.9556, 0.9775, 0.9498, 0.841, 0.9717, 0.9866] +2024-09-09 08:26:33.893850: Epoch time: 331.66 s +2024-09-09 08:26:37.011587: +2024-09-09 08:26:37.012636: Epoch 724 +2024-09-09 08:26:37.013276: Current learning rate: 0.00314 +2024-09-09 08:31:57.235441: train_loss -0.738 +2024-09-09 08:31:57.239813: val_loss -0.7992 +2024-09-09 08:31:57.240413: Pseudo dice [0.9656, 0.9571, 0.9812, 0.9494, 0.8851, 0.9711, 0.9932] +2024-09-09 08:31:57.240983: Epoch time: 320.23 s +2024-09-09 08:32:01.622152: +2024-09-09 08:32:01.636481: Epoch 725 +2024-09-09 08:32:01.637249: Current learning rate: 0.00313 +2024-09-09 08:37:34.386156: train_loss -0.7458 +2024-09-09 08:37:34.420096: val_loss -0.7652 +2024-09-09 08:37:34.421095: Pseudo dice [0.9669, 0.9583, 0.9808, 0.9527, 0.8787, 0.9744, 0.9906] +2024-09-09 08:37:34.421744: Epoch time: 332.77 s +2024-09-09 08:37:38.328328: +2024-09-09 08:37:38.341232: Epoch 726 +2024-09-09 08:37:38.342329: Current learning rate: 0.00312 +2024-09-09 08:43:07.848350: train_loss -0.7228 +2024-09-09 08:43:07.861565: val_loss -0.7439 +2024-09-09 08:43:07.863726: Pseudo dice [0.9646, 0.9588, 0.9795, 0.9537, 0.8764, 0.9644, 0.9873] +2024-09-09 08:43:07.865544: Epoch time: 329.53 s +2024-09-09 08:43:12.589735: +2024-09-09 08:43:12.591079: Epoch 727 +2024-09-09 08:43:12.593780: Current learning rate: 0.00311 +2024-09-09 08:48:13.055454: train_loss -0.7637 +2024-09-09 08:48:13.072898: val_loss -0.7226 +2024-09-09 08:48:13.073795: Pseudo dice [0.9667, 0.9633, 0.9803, 0.9561, 0.8498, 0.9669, 0.987] +2024-09-09 08:48:13.074793: Epoch time: 300.47 s +2024-09-09 08:48:17.362163: +2024-09-09 08:48:17.363314: Epoch 728 +2024-09-09 08:48:17.364051: Current learning rate: 0.0031 +2024-09-09 08:53:53.151495: train_loss -0.7497 +2024-09-09 08:53:53.155201: val_loss -0.7631 +2024-09-09 08:53:53.155820: Pseudo dice [0.9655, 0.9585, 0.9805, 0.9532, 0.8824, 0.9609, 0.9873] +2024-09-09 08:53:53.156382: Epoch time: 335.79 s +2024-09-09 08:53:56.962564: +2024-09-09 08:53:56.976441: Epoch 729 +2024-09-09 08:53:56.977386: Current learning rate: 0.00309 +2024-09-09 08:59:22.213373: train_loss -0.758 +2024-09-09 08:59:22.229458: val_loss -0.7443 +2024-09-09 08:59:22.230595: Pseudo dice [0.9658, 0.9563, 0.9791, 0.9508, 0.8883, 0.9612, 0.9903] +2024-09-09 08:59:22.231480: Epoch time: 325.29 s +2024-09-09 08:59:26.710751: +2024-09-09 08:59:26.712127: Epoch 730 +2024-09-09 08:59:26.712951: Current learning rate: 0.00308 +2024-09-09 09:04:51.408711: train_loss -0.7653 +2024-09-09 09:04:51.410872: val_loss -0.7448 +2024-09-09 09:04:51.412162: Pseudo dice [0.9677, 0.9605, 0.9773, 0.9571, 0.8933, 0.9466, 0.9874] +2024-09-09 09:04:51.413230: Epoch time: 324.7 s +2024-09-09 09:04:54.135665: +2024-09-09 09:04:54.136445: Epoch 731 +2024-09-09 09:04:54.137012: Current learning rate: 0.00307 +2024-09-09 09:09:44.969518: train_loss -0.7347 +2024-09-09 09:09:44.981471: val_loss -0.7583 +2024-09-09 09:09:44.982399: Pseudo dice [0.9656, 0.9606, 0.9769, 0.9527, 0.8821, 0.9699, 0.9893] +2024-09-09 09:09:44.983477: Epoch time: 290.84 s +2024-09-09 09:09:49.702322: +2024-09-09 09:09:49.703587: Epoch 732 +2024-09-09 09:09:49.704316: Current learning rate: 0.00306 +2024-09-09 09:15:20.879826: train_loss -0.7455 +2024-09-09 09:15:20.881443: val_loss -0.7609 +2024-09-09 09:15:20.882153: Pseudo dice [0.9665, 0.9578, 0.9811, 0.957, 0.8689, 0.961, 0.9889] +2024-09-09 09:15:20.882823: Epoch time: 331.18 s +2024-09-09 09:15:24.472342: +2024-09-09 09:15:24.473226: Epoch 733 +2024-09-09 09:15:24.473786: Current learning rate: 0.00305 +2024-09-09 09:21:00.298153: train_loss -0.7582 +2024-09-09 09:21:00.305768: val_loss -0.7197 +2024-09-09 09:21:00.306588: Pseudo dice [0.9637, 0.9601, 0.977, 0.9495, 0.8754, 0.971, 0.9911] +2024-09-09 09:21:00.307226: Epoch time: 335.83 s +2024-09-09 09:21:03.981755: +2024-09-09 09:21:03.995771: Epoch 734 +2024-09-09 09:21:03.996803: Current learning rate: 0.00304 +2024-09-09 09:26:15.952042: train_loss -0.7481 +2024-09-09 09:26:15.969745: val_loss -0.7408 +2024-09-09 09:26:15.970510: Pseudo dice [0.9673, 0.96, 0.9805, 0.9539, 0.8858, 0.9653, 0.9914] +2024-09-09 09:26:15.971209: Epoch time: 311.98 s +2024-09-09 09:26:19.729238: +2024-09-09 09:26:19.730189: Epoch 735 +2024-09-09 09:26:19.731279: Current learning rate: 0.00303 +2024-09-09 09:31:18.503775: train_loss -0.7278 +2024-09-09 09:31:18.507917: val_loss -0.7503 +2024-09-09 09:31:18.508511: Pseudo dice [0.9625, 0.9575, 0.9738, 0.9516, 0.8202, 0.9729, 0.9826] +2024-09-09 09:31:18.508950: Epoch time: 298.78 s +2024-09-09 09:31:21.990587: +2024-09-09 09:31:21.999902: Epoch 736 +2024-09-09 09:31:22.000796: Current learning rate: 0.00302 +2024-09-09 09:36:49.813327: train_loss -0.7483 +2024-09-09 09:36:49.831720: val_loss -0.7755 +2024-09-09 09:36:49.832427: Pseudo dice [0.9643, 0.9569, 0.9778, 0.9524, 0.8763, 0.9696, 0.9894] +2024-09-09 09:36:49.833484: Epoch time: 327.83 s +2024-09-09 09:36:52.941921: +2024-09-09 09:36:52.942993: Epoch 737 +2024-09-09 09:36:52.943544: Current learning rate: 0.00301 +2024-09-09 09:42:20.501266: train_loss -0.7521 +2024-09-09 09:42:20.525249: val_loss -0.752 +2024-09-09 09:42:20.527411: Pseudo dice [0.9678, 0.9606, 0.981, 0.9566, 0.8786, 0.9615, 0.988] +2024-09-09 09:42:20.528279: Epoch time: 327.56 s +2024-09-09 09:42:24.169730: +2024-09-09 09:42:24.179075: Epoch 738 +2024-09-09 09:42:24.180861: Current learning rate: 0.003 +2024-09-09 09:47:52.688048: train_loss -0.7565 +2024-09-09 09:47:52.690296: val_loss -0.7342 +2024-09-09 09:47:52.691679: Pseudo dice [0.9637, 0.9574, 0.9761, 0.9535, 0.8791, 0.9592, 0.9868] +2024-09-09 09:47:52.692627: Epoch time: 328.52 s +2024-09-09 09:47:56.411593: +2024-09-09 09:47:56.413876: Epoch 739 +2024-09-09 09:47:56.415421: Current learning rate: 0.00299 +2024-09-09 09:53:35.153805: train_loss -0.7591 +2024-09-09 09:53:35.186471: val_loss -0.7618 +2024-09-09 09:53:35.188499: Pseudo dice [0.9655, 0.9585, 0.9795, 0.9506, 0.8943, 0.976, 0.9922] +2024-09-09 09:53:35.190290: Epoch time: 338.75 s +2024-09-09 09:53:39.257003: +2024-09-09 09:53:39.267826: Epoch 740 +2024-09-09 09:53:39.268996: Current learning rate: 0.00297 +2024-09-09 09:58:54.143202: train_loss -0.7502 +2024-09-09 09:58:54.146996: val_loss -0.733 +2024-09-09 09:58:54.147793: Pseudo dice [0.9669, 0.9594, 0.9788, 0.9552, 0.8769, 0.9607, 0.9892] +2024-09-09 09:58:54.148341: Epoch time: 314.89 s +2024-09-09 09:58:59.184417: +2024-09-09 09:58:59.200657: Epoch 741 +2024-09-09 09:58:59.201605: Current learning rate: 0.00296 +2024-09-09 10:04:26.052108: train_loss -0.7538 +2024-09-09 10:04:26.066481: val_loss -0.7384 +2024-09-09 10:04:26.067595: Pseudo dice [0.9638, 0.9596, 0.9748, 0.9509, 0.879, 0.9732, 0.9901] +2024-09-09 10:04:26.068431: Epoch time: 326.88 s +2024-09-09 10:04:31.175553: +2024-09-09 10:04:31.199953: Epoch 742 +2024-09-09 10:04:31.201620: Current learning rate: 0.00295 +2024-09-09 10:09:59.045551: train_loss -0.7492 +2024-09-09 10:09:59.055098: val_loss -0.7728 +2024-09-09 10:09:59.056944: Pseudo dice [0.9637, 0.9608, 0.9775, 0.9506, 0.8666, 0.9693, 0.988] +2024-09-09 10:09:59.058129: Epoch time: 327.88 s +2024-09-09 10:10:03.118141: +2024-09-09 10:10:03.125480: Epoch 743 +2024-09-09 10:10:03.127003: Current learning rate: 0.00294 +2024-09-09 10:15:46.714214: train_loss -0.7243 +2024-09-09 10:15:46.718639: val_loss -0.7718 +2024-09-09 10:15:46.720015: Pseudo dice [0.9672, 0.959, 0.9788, 0.9539, 0.8854, 0.9721, 0.9913] +2024-09-09 10:15:46.720927: Epoch time: 343.6 s +2024-09-09 10:15:50.003808: +2024-09-09 10:15:50.008302: Epoch 744 +2024-09-09 10:15:50.020927: Current learning rate: 0.00293 +2024-09-09 10:21:25.439595: train_loss -0.7589 +2024-09-09 10:21:25.457389: val_loss -0.7556 +2024-09-09 10:21:25.458077: Pseudo dice [0.9654, 0.958, 0.9784, 0.9484, 0.8557, 0.9622, 0.9853] +2024-09-09 10:21:25.458605: Epoch time: 335.44 s +2024-09-09 10:21:29.223449: +2024-09-09 10:21:29.237110: Epoch 745 +2024-09-09 10:21:29.238356: Current learning rate: 0.00292 +2024-09-09 10:27:05.906119: train_loss -0.7575 +2024-09-09 10:27:05.921939: val_loss -0.7839 +2024-09-09 10:27:05.923173: Pseudo dice [0.9679, 0.9595, 0.9805, 0.9556, 0.8927, 0.9739, 0.9926] +2024-09-09 10:27:05.923878: Epoch time: 336.69 s +2024-09-09 10:27:10.960427: +2024-09-09 10:27:10.962018: Epoch 746 +2024-09-09 10:27:10.962734: Current learning rate: 0.00291 +2024-09-09 10:32:31.807064: train_loss -0.7414 +2024-09-09 10:32:31.828371: val_loss -0.7823 +2024-09-09 10:32:31.829404: Pseudo dice [0.9674, 0.962, 0.9809, 0.9555, 0.8683, 0.9715, 0.9883] +2024-09-09 10:32:31.830101: Epoch time: 320.85 s +2024-09-09 10:32:36.221107: +2024-09-09 10:32:36.222060: Epoch 747 +2024-09-09 10:32:36.222646: Current learning rate: 0.0029 +2024-09-09 10:37:58.683180: train_loss -0.7651 +2024-09-09 10:37:58.686925: val_loss -0.7429 +2024-09-09 10:37:58.687852: Pseudo dice [0.9669, 0.9566, 0.9801, 0.9471, 0.8793, 0.9733, 0.9904] +2024-09-09 10:37:58.688435: Epoch time: 322.47 s +2024-09-09 10:38:03.233938: +2024-09-09 10:38:03.234712: Epoch 748 +2024-09-09 10:38:03.235355: Current learning rate: 0.00289 +2024-09-09 10:43:28.369273: train_loss -0.7508 +2024-09-09 10:43:28.371969: val_loss -0.7632 +2024-09-09 10:43:28.372746: Pseudo dice [0.9681, 0.9612, 0.9813, 0.9574, 0.896, 0.9736, 0.9922] +2024-09-09 10:43:28.374294: Epoch time: 325.14 s +2024-09-09 10:43:31.584833: +2024-09-09 10:43:31.586613: Epoch 749 +2024-09-09 10:43:31.589254: Current learning rate: 0.00288 +2024-09-09 10:48:59.865265: train_loss -0.7654 +2024-09-09 10:48:59.881898: val_loss -0.7379 +2024-09-09 10:48:59.882719: Pseudo dice [0.9657, 0.9579, 0.979, 0.9469, 0.8919, 0.9739, 0.9931] +2024-09-09 10:48:59.883309: Epoch time: 328.29 s +2024-09-09 10:49:03.483332: Yayy! New best EMA pseudo Dice: 0.9563 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_16_30_49.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_16_30_49.txt new file mode 100644 index 0000000000000000000000000000000000000000..0529fe7058bbdaf1458ffa8ff43ccecb05625643 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/logs/training_log_2024_9_9_16_30_49.txt @@ -0,0 +1,730 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 16:30:52.510519: Using torch.compile... +2024-09-09 16:30:58.546954: do_dummy_2d_data_aug: False +2024-09-09 16:30:58.552907: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 16:30:58.554199: The split file contains 5 splits. +2024-09-09 16:30:58.554538: Desired fold for training: 1 +2024-09-09 16:30:58.554850: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 16:31:10.559519: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 16:31:11.530770: +2024-09-09 16:31:11.543731: Epoch 750 +2024-09-09 16:31:11.552402: Current learning rate: 0.00287 +2024-09-09 16:39:11.405309: train_loss -0.7373 +2024-09-09 16:39:11.407076: val_loss -0.7712 +2024-09-09 16:39:11.407791: Pseudo dice [0.9676, 0.9613, 0.9813, 0.9566, 0.8765, 0.9739, 0.9905] +2024-09-09 16:39:11.408317: Epoch time: 479.88 s +2024-09-09 16:39:11.408853: Yayy! New best EMA pseudo Dice: 0.9565 +2024-09-09 16:39:15.021374: +2024-09-09 16:39:15.021891: Epoch 751 +2024-09-09 16:39:15.022269: Current learning rate: 0.00286 +2024-09-09 16:43:24.170385: train_loss -0.7567 +2024-09-09 16:43:24.184901: val_loss -0.7444 +2024-09-09 16:43:24.185716: Pseudo dice [0.9682, 0.9622, 0.9827, 0.9552, 0.8887, 0.9606, 0.9894] +2024-09-09 16:43:24.186227: Epoch time: 249.15 s +2024-09-09 16:43:24.187109: Yayy! New best EMA pseudo Dice: 0.9567 +2024-09-09 16:43:28.666354: +2024-09-09 16:43:28.667197: Epoch 752 +2024-09-09 16:43:28.667748: Current learning rate: 0.00285 +2024-09-09 16:47:37.777891: train_loss -0.7498 +2024-09-09 16:47:37.797119: val_loss -0.7808 +2024-09-09 16:47:37.797606: Pseudo dice [0.97, 0.9619, 0.9819, 0.9558, 0.8873, 0.9726, 0.9918] +2024-09-09 16:47:37.798090: Epoch time: 249.12 s +2024-09-09 16:47:37.798542: Yayy! New best EMA pseudo Dice: 0.957 +2024-09-09 16:47:43.294490: +2024-09-09 16:47:43.295617: Epoch 753 +2024-09-09 16:47:43.296395: Current learning rate: 0.00284 +2024-09-09 16:51:55.724055: train_loss -0.7559 +2024-09-09 16:51:55.741074: val_loss -0.7423 +2024-09-09 16:51:55.741591: Pseudo dice [0.9681, 0.9615, 0.9806, 0.9579, 0.8903, 0.966, 0.9902] +2024-09-09 16:51:55.742033: Epoch time: 252.43 s +2024-09-09 16:51:55.742446: Yayy! New best EMA pseudo Dice: 0.9573 +2024-09-09 16:52:02.690163: +2024-09-09 16:52:02.690989: Epoch 754 +2024-09-09 16:52:02.692082: Current learning rate: 0.00283 +2024-09-09 16:56:26.542817: train_loss -0.7608 +2024-09-09 16:56:26.569473: val_loss -0.773 +2024-09-09 16:56:26.570216: Pseudo dice [0.9666, 0.9584, 0.9785, 0.9509, 0.888, 0.9744, 0.9928] +2024-09-09 16:56:26.571021: Epoch time: 263.86 s +2024-09-09 16:56:26.571588: Yayy! New best EMA pseudo Dice: 0.9574 +2024-09-09 16:56:31.378812: +2024-09-09 16:56:31.379873: Epoch 755 +2024-09-09 16:56:31.380816: Current learning rate: 0.00282 +2024-09-09 17:00:38.921808: train_loss -0.7544 +2024-09-09 17:00:38.936697: val_loss -0.7449 +2024-09-09 17:00:38.937344: Pseudo dice [0.968, 0.9591, 0.9791, 0.95, 0.8737, 0.974, 0.9901] +2024-09-09 17:00:38.938025: Epoch time: 247.55 s +2024-09-09 17:00:42.219878: +2024-09-09 17:00:42.224912: Epoch 756 +2024-09-09 17:00:42.225720: Current learning rate: 0.00281 +2024-09-09 17:05:08.698587: train_loss -0.7544 +2024-09-09 17:05:08.709287: val_loss -0.7448 +2024-09-09 17:05:08.709810: Pseudo dice [0.9663, 0.9598, 0.9819, 0.9534, 0.8649, 0.9489, 0.9836] +2024-09-09 17:05:08.710351: Epoch time: 266.48 s +2024-09-09 17:05:12.006016: +2024-09-09 17:05:12.020276: Epoch 757 +2024-09-09 17:05:12.020821: Current learning rate: 0.0028 +2024-09-09 17:09:19.436254: train_loss -0.7514 +2024-09-09 17:09:19.454224: val_loss -0.7629 +2024-09-09 17:09:19.455094: Pseudo dice [0.9669, 0.9594, 0.978, 0.954, 0.847, 0.9737, 0.9894] +2024-09-09 17:09:19.455928: Epoch time: 247.44 s +2024-09-09 17:09:21.975960: +2024-09-09 17:09:21.992207: Epoch 758 +2024-09-09 17:09:21.992779: Current learning rate: 0.00279 +2024-09-09 17:13:42.589162: train_loss -0.7404 +2024-09-09 17:13:42.605931: val_loss -0.728 +2024-09-09 17:13:42.606528: Pseudo dice [0.9675, 0.9611, 0.9796, 0.9537, 0.8912, 0.9751, 0.9935] +2024-09-09 17:13:42.607077: Epoch time: 260.62 s +2024-09-09 17:13:45.816802: +2024-09-09 17:13:45.817548: Epoch 759 +2024-09-09 17:13:45.818049: Current learning rate: 0.00278 +2024-09-09 17:18:01.274576: train_loss -0.7479 +2024-09-09 17:18:01.289041: val_loss -0.726 +2024-09-09 17:18:01.289802: Pseudo dice [0.9673, 0.9602, 0.9779, 0.95, 0.8797, 0.9739, 0.9915] +2024-09-09 17:18:01.290450: Epoch time: 255.46 s +2024-09-09 17:18:05.061462: +2024-09-09 17:18:05.062494: Epoch 760 +2024-09-09 17:18:05.063534: Current learning rate: 0.00277 +2024-09-09 17:22:10.413040: train_loss -0.7604 +2024-09-09 17:22:10.429721: val_loss -0.7354 +2024-09-09 17:22:10.430312: Pseudo dice [0.967, 0.9597, 0.979, 0.9552, 0.878, 0.9706, 0.9906] +2024-09-09 17:22:10.430969: Epoch time: 245.36 s +2024-09-09 17:22:13.119400: +2024-09-09 17:22:13.132974: Epoch 761 +2024-09-09 17:22:13.140477: Current learning rate: 0.00276 +2024-09-09 17:26:25.089423: train_loss -0.7565 +2024-09-09 17:26:25.103328: val_loss -0.7053 +2024-09-09 17:26:25.103921: Pseudo dice [0.969, 0.9609, 0.981, 0.9542, 0.8404, 0.9597, 0.9844] +2024-09-09 17:26:25.104438: Epoch time: 251.97 s +2024-09-09 17:26:28.346808: +2024-09-09 17:26:28.347752: Epoch 762 +2024-09-09 17:26:28.348340: Current learning rate: 0.00275 +2024-09-09 17:30:51.316437: train_loss -0.7577 +2024-09-09 17:30:51.337545: val_loss -0.8022 +2024-09-09 17:30:51.338119: Pseudo dice [0.9689, 0.9597, 0.9823, 0.9529, 0.8961, 0.9736, 0.9933] +2024-09-09 17:30:51.338625: Epoch time: 262.97 s +2024-09-09 17:30:54.301238: +2024-09-09 17:30:54.302087: Epoch 763 +2024-09-09 17:30:54.302980: Current learning rate: 0.00274 +2024-09-09 17:34:57.363498: train_loss -0.7472 +2024-09-09 17:34:57.377245: val_loss -0.7612 +2024-09-09 17:34:57.377897: Pseudo dice [0.9669, 0.9586, 0.9789, 0.9504, 0.88, 0.9719, 0.9911] +2024-09-09 17:34:57.378335: Epoch time: 243.07 s +2024-09-09 17:35:00.346730: +2024-09-09 17:35:00.360596: Epoch 764 +2024-09-09 17:35:00.361393: Current learning rate: 0.00273 +2024-09-09 17:39:18.488778: train_loss -0.7453 +2024-09-09 17:39:18.504778: val_loss -0.7179 +2024-09-09 17:39:18.505311: Pseudo dice [0.9656, 0.9612, 0.9767, 0.9493, 0.8404, 0.9727, 0.9839] +2024-09-09 17:39:18.505898: Epoch time: 258.15 s +2024-09-09 17:39:21.514248: +2024-09-09 17:39:21.515077: Epoch 765 +2024-09-09 17:39:21.515799: Current learning rate: 0.00272 +2024-09-09 17:43:35.309303: train_loss -0.7545 +2024-09-09 17:43:35.328811: val_loss -0.7801 +2024-09-09 17:43:35.329398: Pseudo dice [0.9666, 0.9611, 0.9816, 0.9574, 0.891, 0.974, 0.9923] +2024-09-09 17:43:35.329847: Epoch time: 253.8 s +2024-09-09 17:43:38.514514: +2024-09-09 17:43:38.528195: Epoch 766 +2024-09-09 17:43:38.528771: Current learning rate: 0.00271 +2024-09-09 17:48:01.318332: train_loss -0.7622 +2024-09-09 17:48:01.339812: val_loss -0.7535 +2024-09-09 17:48:01.340532: Pseudo dice [0.9635, 0.9556, 0.9745, 0.9494, 0.8482, 0.9522, 0.9811] +2024-09-09 17:48:01.341045: Epoch time: 262.81 s +2024-09-09 17:48:04.292809: +2024-09-09 17:48:04.293555: Epoch 767 +2024-09-09 17:48:04.294054: Current learning rate: 0.0027 +2024-09-09 17:52:25.028972: train_loss -0.743 +2024-09-09 17:52:25.045400: val_loss -0.7882 +2024-09-09 17:52:25.045933: Pseudo dice [0.9633, 0.9587, 0.9756, 0.9497, 0.8778, 0.9702, 0.9905] +2024-09-09 17:52:25.046554: Epoch time: 260.74 s +2024-09-09 17:52:28.044577: +2024-09-09 17:52:28.052361: Epoch 768 +2024-09-09 17:52:28.053085: Current learning rate: 0.00268 +2024-09-09 17:56:51.567713: train_loss -0.7672 +2024-09-09 17:56:51.569153: val_loss -0.749 +2024-09-09 17:56:51.569880: Pseudo dice [0.9662, 0.9593, 0.9801, 0.9512, 0.8538, 0.968, 0.9874] +2024-09-09 17:56:51.570714: Epoch time: 263.53 s +2024-09-09 17:56:54.748283: +2024-09-09 17:56:54.749183: Epoch 769 +2024-09-09 17:56:54.749725: Current learning rate: 0.00267 +2024-09-09 18:00:57.508587: train_loss -0.752 +2024-09-09 18:00:57.525210: val_loss -0.736 +2024-09-09 18:00:57.525794: Pseudo dice [0.9663, 0.9589, 0.977, 0.9503, 0.8368, 0.972, 0.985] +2024-09-09 18:00:57.526316: Epoch time: 242.76 s +2024-09-09 18:01:00.470499: +2024-09-09 18:01:00.484239: Epoch 770 +2024-09-09 18:01:00.484860: Current learning rate: 0.00266 +2024-09-09 18:05:18.897233: train_loss -0.7495 +2024-09-09 18:05:18.908611: val_loss -0.7285 +2024-09-09 18:05:18.909551: Pseudo dice [0.9675, 0.9596, 0.9811, 0.954, 0.8442, 0.9722, 0.9855] +2024-09-09 18:05:18.910357: Epoch time: 258.43 s +2024-09-09 18:05:23.095398: +2024-09-09 18:05:23.108540: Epoch 771 +2024-09-09 18:05:23.109504: Current learning rate: 0.00265 +2024-09-09 18:09:26.142378: train_loss -0.7619 +2024-09-09 18:09:26.165144: val_loss -0.7683 +2024-09-09 18:09:26.165682: Pseudo dice [0.9655, 0.9573, 0.9778, 0.9526, 0.8862, 0.9688, 0.9906] +2024-09-09 18:09:26.166161: Epoch time: 243.05 s +2024-09-09 18:09:29.516280: +2024-09-09 18:09:29.517187: Epoch 772 +2024-09-09 18:09:29.517791: Current learning rate: 0.00264 +2024-09-09 18:13:43.393243: train_loss -0.7542 +2024-09-09 18:13:43.413832: val_loss -0.7474 +2024-09-09 18:13:43.414763: Pseudo dice [0.9677, 0.9588, 0.9826, 0.9532, 0.8731, 0.9727, 0.9893] +2024-09-09 18:13:43.415676: Epoch time: 253.88 s +2024-09-09 18:13:45.653063: +2024-09-09 18:13:45.653772: Epoch 773 +2024-09-09 18:13:45.654372: Current learning rate: 0.00263 +2024-09-09 18:17:45.687769: train_loss -0.7487 +2024-09-09 18:17:45.691869: val_loss -0.7444 +2024-09-09 18:17:45.692537: Pseudo dice [0.9676, 0.9611, 0.9812, 0.9498, 0.872, 0.9608, 0.9877] +2024-09-09 18:17:45.693272: Epoch time: 240.04 s +2024-09-09 18:17:49.195506: +2024-09-09 18:17:49.196271: Epoch 774 +2024-09-09 18:17:49.197028: Current learning rate: 0.00262 +2024-09-09 18:22:05.420043: train_loss -0.7493 +2024-09-09 18:22:05.422922: val_loss -0.7517 +2024-09-09 18:22:05.423716: Pseudo dice [0.9657, 0.9572, 0.9794, 0.9517, 0.8784, 0.9589, 0.9898] +2024-09-09 18:22:05.424595: Epoch time: 256.24 s +2024-09-09 18:22:08.797506: +2024-09-09 18:22:08.798191: Epoch 775 +2024-09-09 18:22:08.798691: Current learning rate: 0.00261 +2024-09-09 18:26:31.070599: train_loss -0.7459 +2024-09-09 18:26:31.086859: val_loss -0.7523 +2024-09-09 18:26:31.087850: Pseudo dice [0.9683, 0.9626, 0.9783, 0.9524, 0.8693, 0.9704, 0.9883] +2024-09-09 18:26:31.088503: Epoch time: 262.28 s +2024-09-09 18:26:34.662922: +2024-09-09 18:26:34.677302: Epoch 776 +2024-09-09 18:26:34.678616: Current learning rate: 0.0026 +2024-09-09 18:30:43.445999: train_loss -0.7621 +2024-09-09 18:30:43.447784: val_loss -0.7428 +2024-09-09 18:30:43.448377: Pseudo dice [0.9669, 0.9613, 0.977, 0.9516, 0.8717, 0.9726, 0.9889] +2024-09-09 18:30:43.448829: Epoch time: 248.82 s +2024-09-09 18:30:46.723165: +2024-09-09 18:30:46.736609: Epoch 777 +2024-09-09 18:30:46.737522: Current learning rate: 0.00259 +2024-09-09 18:34:46.616869: train_loss -0.7554 +2024-09-09 18:34:46.633959: val_loss -0.7525 +2024-09-09 18:34:46.634562: Pseudo dice [0.9694, 0.9611, 0.9826, 0.9555, 0.8661, 0.9729, 0.9883] +2024-09-09 18:34:46.635340: Epoch time: 239.9 s +2024-09-09 18:34:49.162506: +2024-09-09 18:34:49.163388: Epoch 778 +2024-09-09 18:34:49.163962: Current learning rate: 0.00258 +2024-09-09 18:38:58.906423: train_loss -0.7592 +2024-09-09 18:38:58.920322: val_loss -0.7663 +2024-09-09 18:38:58.921391: Pseudo dice [0.965, 0.9566, 0.9786, 0.9525, 0.8624, 0.974, 0.9891] +2024-09-09 18:38:58.922378: Epoch time: 249.76 s +2024-09-09 18:39:02.319690: +2024-09-09 18:39:02.320692: Epoch 779 +2024-09-09 18:39:02.321353: Current learning rate: 0.00257 +2024-09-09 18:42:58.721421: train_loss -0.7661 +2024-09-09 18:42:58.722957: val_loss -0.7229 +2024-09-09 18:42:58.723629: Pseudo dice [0.9693, 0.961, 0.9805, 0.9503, 0.8858, 0.9726, 0.9926] +2024-09-09 18:42:58.724423: Epoch time: 236.42 s +2024-09-09 18:43:01.765491: +2024-09-09 18:43:01.766351: Epoch 780 +2024-09-09 18:43:01.766924: Current learning rate: 0.00256 +2024-09-09 18:47:20.671956: train_loss -0.7472 +2024-09-09 18:47:20.689315: val_loss -0.738 +2024-09-09 18:47:20.689879: Pseudo dice [0.9656, 0.958, 0.9769, 0.9489, 0.8765, 0.9565, 0.9882] +2024-09-09 18:47:20.690411: Epoch time: 258.91 s +2024-09-09 18:47:23.577270: +2024-09-09 18:47:23.591158: Epoch 781 +2024-09-09 18:47:23.592204: Current learning rate: 0.00255 +2024-09-09 18:51:38.457541: train_loss -0.7272 +2024-09-09 18:51:38.471884: val_loss -0.7401 +2024-09-09 18:51:38.472742: Pseudo dice [0.9662, 0.9603, 0.9806, 0.9559, 0.8866, 0.9731, 0.9908] +2024-09-09 18:51:38.473526: Epoch time: 254.88 s +2024-09-09 18:51:40.863990: +2024-09-09 18:51:40.865203: Epoch 782 +2024-09-09 18:51:40.865991: Current learning rate: 0.00254 +2024-09-09 18:55:56.692674: train_loss -0.7482 +2024-09-09 18:55:56.708405: val_loss -0.733 +2024-09-09 18:55:56.708913: Pseudo dice [0.9631, 0.9587, 0.9734, 0.9491, 0.8902, 0.9668, 0.9893] +2024-09-09 18:55:56.709375: Epoch time: 255.83 s +2024-09-09 18:55:59.739894: +2024-09-09 18:55:59.740804: Epoch 783 +2024-09-09 18:55:59.741698: Current learning rate: 0.00253 +2024-09-09 19:00:10.343252: train_loss -0.7572 +2024-09-09 19:00:10.350147: val_loss -0.7295 +2024-09-09 19:00:10.350778: Pseudo dice [0.9662, 0.9604, 0.979, 0.955, 0.8578, 0.9593, 0.9863] +2024-09-09 19:00:10.351629: Epoch time: 250.61 s +2024-09-09 19:00:13.391974: +2024-09-09 19:00:13.393564: Epoch 784 +2024-09-09 19:00:13.394767: Current learning rate: 0.00252 +2024-09-09 19:04:12.581590: train_loss -0.7581 +2024-09-09 19:04:12.596982: val_loss -0.7307 +2024-09-09 19:04:12.598663: Pseudo dice [0.9663, 0.956, 0.9784, 0.9458, 0.8752, 0.9716, 0.9903] +2024-09-09 19:04:12.599507: Epoch time: 239.19 s +2024-09-09 19:04:14.997227: +2024-09-09 19:04:15.008321: Epoch 785 +2024-09-09 19:04:15.009193: Current learning rate: 0.00251 +2024-09-09 19:08:12.385538: train_loss -0.744 +2024-09-09 19:08:12.401155: val_loss -0.7845 +2024-09-09 19:08:12.401802: Pseudo dice [0.9677, 0.9609, 0.9805, 0.9543, 0.8734, 0.9747, 0.9903] +2024-09-09 19:08:12.402352: Epoch time: 237.39 s +2024-09-09 19:08:15.357349: +2024-09-09 19:08:15.358149: Epoch 786 +2024-09-09 19:08:15.358707: Current learning rate: 0.0025 +2024-09-09 19:12:06.010977: train_loss -0.7498 +2024-09-09 19:12:06.025481: val_loss -0.772 +2024-09-09 19:12:06.025990: Pseudo dice [0.9689, 0.9598, 0.9827, 0.9554, 0.8563, 0.9724, 0.9865] +2024-09-09 19:12:06.027091: Epoch time: 230.66 s +2024-09-09 19:12:08.477132: +2024-09-09 19:12:08.478399: Epoch 787 +2024-09-09 19:12:08.479944: Current learning rate: 0.00249 +2024-09-09 19:16:24.064523: train_loss -0.7546 +2024-09-09 19:16:24.081422: val_loss -0.7583 +2024-09-09 19:16:24.082017: Pseudo dice [0.9686, 0.9596, 0.9806, 0.956, 0.8762, 0.9731, 0.9899] +2024-09-09 19:16:24.082511: Epoch time: 255.59 s +2024-09-09 19:16:27.006451: +2024-09-09 19:16:27.007761: Epoch 788 +2024-09-09 19:16:27.008694: Current learning rate: 0.00248 +2024-09-09 19:20:17.407040: train_loss -0.7618 +2024-09-09 19:20:17.424587: val_loss -0.7501 +2024-09-09 19:20:17.425281: Pseudo dice [0.9674, 0.9609, 0.9802, 0.9534, 0.896, 0.9702, 0.9914] +2024-09-09 19:20:17.425805: Epoch time: 230.41 s +2024-09-09 19:20:20.932715: +2024-09-09 19:20:20.949610: Epoch 789 +2024-09-09 19:20:20.951592: Current learning rate: 0.00247 +2024-09-09 19:24:29.203432: train_loss -0.751 +2024-09-09 19:24:29.205543: val_loss -0.7226 +2024-09-09 19:24:29.206656: Pseudo dice [0.9674, 0.9609, 0.9795, 0.9511, 0.8729, 0.9721, 0.9894] +2024-09-09 19:24:29.208091: Epoch time: 248.28 s +2024-09-09 19:24:32.333484: +2024-09-09 19:24:32.346505: Epoch 790 +2024-09-09 19:24:32.347431: Current learning rate: 0.00245 +2024-09-09 19:28:36.067480: train_loss -0.7509 +2024-09-09 19:28:36.084801: val_loss -0.7934 +2024-09-09 19:28:36.085453: Pseudo dice [0.9682, 0.9606, 0.9801, 0.9529, 0.892, 0.974, 0.9931] +2024-09-09 19:28:36.086101: Epoch time: 243.74 s +2024-09-09 19:28:39.352225: +2024-09-09 19:28:39.368599: Epoch 791 +2024-09-09 19:28:39.369559: Current learning rate: 0.00244 +2024-09-09 19:32:42.317749: train_loss -0.7474 +2024-09-09 19:32:42.335137: val_loss -0.7202 +2024-09-09 19:32:42.335692: Pseudo dice [0.964, 0.9583, 0.9763, 0.9514, 0.8673, 0.9591, 0.9858] +2024-09-09 19:32:42.336164: Epoch time: 242.97 s +2024-09-09 19:32:45.330972: +2024-09-09 19:32:45.332256: Epoch 792 +2024-09-09 19:32:45.333010: Current learning rate: 0.00243 +2024-09-09 19:36:55.886056: train_loss -0.7386 +2024-09-09 19:36:55.909695: val_loss -0.748 +2024-09-09 19:36:55.910571: Pseudo dice [0.9686, 0.9606, 0.9821, 0.9513, 0.8862, 0.9611, 0.9899] +2024-09-09 19:36:55.911286: Epoch time: 250.56 s +2024-09-09 19:36:58.246497: +2024-09-09 19:36:58.247403: Epoch 793 +2024-09-09 19:36:58.248332: Current learning rate: 0.00242 +2024-09-09 19:41:18.407863: train_loss -0.7537 +2024-09-09 19:41:18.423832: val_loss -0.7281 +2024-09-09 19:41:18.424591: Pseudo dice [0.9669, 0.9602, 0.9807, 0.9501, 0.8769, 0.9736, 0.9903] +2024-09-09 19:41:18.425069: Epoch time: 260.17 s +2024-09-09 19:41:21.002435: +2024-09-09 19:41:21.023739: Epoch 794 +2024-09-09 19:41:21.026017: Current learning rate: 0.00241 +2024-09-09 19:45:43.792956: train_loss -0.7523 +2024-09-09 19:45:43.794454: val_loss -0.7521 +2024-09-09 19:45:43.795060: Pseudo dice [0.9682, 0.9597, 0.9827, 0.9566, 0.8731, 0.9614, 0.9877] +2024-09-09 19:45:43.795737: Epoch time: 262.79 s +2024-09-09 19:45:46.005447: +2024-09-09 19:45:46.006751: Epoch 795 +2024-09-09 19:45:46.007784: Current learning rate: 0.0024 +2024-09-09 19:49:58.881851: train_loss -0.7581 +2024-09-09 19:49:58.905361: val_loss -0.7505 +2024-09-09 19:49:58.908252: Pseudo dice [0.9675, 0.9615, 0.9783, 0.9545, 0.8917, 0.9743, 0.9922] +2024-09-09 19:49:58.908872: Epoch time: 252.88 s +2024-09-09 19:50:01.864535: +2024-09-09 19:50:01.865508: Epoch 796 +2024-09-09 19:50:01.866066: Current learning rate: 0.00239 +2024-09-09 19:53:51.605522: train_loss -0.7596 +2024-09-09 19:53:51.619257: val_loss -0.7551 +2024-09-09 19:53:51.620160: Pseudo dice [0.9663, 0.9568, 0.9791, 0.9517, 0.888, 0.9747, 0.992] +2024-09-09 19:53:51.621058: Epoch time: 229.75 s +2024-09-09 19:53:54.157300: +2024-09-09 19:53:54.171787: Epoch 797 +2024-09-09 19:53:54.172648: Current learning rate: 0.00238 +2024-09-09 19:58:02.228445: train_loss -0.7687 +2024-09-09 19:58:02.241276: val_loss -0.7518 +2024-09-09 19:58:02.242002: Pseudo dice [0.9673, 0.9592, 0.9807, 0.951, 0.8267, 0.9738, 0.9854] +2024-09-09 19:58:02.242838: Epoch time: 248.08 s +2024-09-09 19:58:05.240740: +2024-09-09 19:58:05.256291: Epoch 798 +2024-09-09 19:58:05.257033: Current learning rate: 0.00237 +2024-09-09 20:02:07.609728: train_loss -0.7511 +2024-09-09 20:02:07.625061: val_loss -0.7525 +2024-09-09 20:02:07.625602: Pseudo dice [0.9668, 0.9601, 0.9782, 0.9528, 0.8657, 0.9728, 0.9889] +2024-09-09 20:02:07.626011: Epoch time: 242.37 s +2024-09-09 20:02:10.574770: +2024-09-09 20:02:10.582101: Epoch 799 +2024-09-09 20:02:10.583233: Current learning rate: 0.00236 +2024-09-09 20:06:21.505441: train_loss -0.7401 +2024-09-09 20:06:21.519722: val_loss -0.7283 +2024-09-09 20:06:21.520267: Pseudo dice [0.9674, 0.9616, 0.9814, 0.9528, 0.887, 0.9744, 0.9903] +2024-09-09 20:06:21.520700: Epoch time: 250.94 s +2024-09-09 20:06:27.078702: +2024-09-09 20:06:27.079494: Epoch 800 +2024-09-09 20:06:27.080093: Current learning rate: 0.00235 +2024-09-09 20:10:34.929596: train_loss -0.7527 +2024-09-09 20:10:34.956398: val_loss -0.804 +2024-09-09 20:10:34.956911: Pseudo dice [0.9634, 0.9578, 0.9788, 0.9442, 0.8734, 0.9734, 0.9898] +2024-09-09 20:10:34.957356: Epoch time: 247.86 s +2024-09-09 20:10:38.459723: +2024-09-09 20:10:38.460591: Epoch 801 +2024-09-09 20:10:38.461279: Current learning rate: 0.00234 +2024-09-09 20:14:26.506424: train_loss -0.7507 +2024-09-09 20:14:26.508198: val_loss -0.7707 +2024-09-09 20:14:26.508664: Pseudo dice [0.9675, 0.9599, 0.9801, 0.9552, 0.8814, 0.965, 0.9906] +2024-09-09 20:14:26.509164: Epoch time: 228.05 s +2024-09-09 20:14:29.776695: +2024-09-09 20:14:29.786187: Epoch 802 +2024-09-09 20:14:29.786870: Current learning rate: 0.00233 +2024-09-09 20:18:30.071307: train_loss -0.7593 +2024-09-09 20:18:30.081852: val_loss -0.7459 +2024-09-09 20:18:30.082820: Pseudo dice [0.9679, 0.9609, 0.9801, 0.9551, 0.8693, 0.9645, 0.9877] +2024-09-09 20:18:30.083710: Epoch time: 240.3 s +2024-09-09 20:18:32.614596: +2024-09-09 20:18:32.628281: Epoch 803 +2024-09-09 20:18:32.628899: Current learning rate: 0.00232 +2024-09-09 20:22:32.444439: train_loss -0.7504 +2024-09-09 20:22:32.457282: val_loss -0.7636 +2024-09-09 20:22:32.458080: Pseudo dice [0.9676, 0.9614, 0.981, 0.9561, 0.8983, 0.9744, 0.9914] +2024-09-09 20:22:32.458619: Epoch time: 239.83 s +2024-09-09 20:22:36.209215: +2024-09-09 20:22:36.227748: Epoch 804 +2024-09-09 20:22:36.228463: Current learning rate: 0.00231 +2024-09-09 20:26:45.117093: train_loss -0.7743 +2024-09-09 20:26:45.120446: val_loss -0.7578 +2024-09-09 20:26:45.121168: Pseudo dice [0.969, 0.9617, 0.981, 0.9555, 0.8917, 0.9719, 0.992] +2024-09-09 20:26:45.122130: Epoch time: 248.91 s +2024-09-09 20:26:47.674909: +2024-09-09 20:26:47.675870: Epoch 805 +2024-09-09 20:26:47.676466: Current learning rate: 0.0023 +2024-09-09 20:30:43.349884: train_loss -0.7376 +2024-09-09 20:30:43.364605: val_loss -0.702 +2024-09-09 20:30:43.365085: Pseudo dice [0.9672, 0.9598, 0.9811, 0.9515, 0.871, 0.9745, 0.989] +2024-09-09 20:30:43.365519: Epoch time: 235.68 s +2024-09-09 20:30:45.901289: +2024-09-09 20:30:45.915780: Epoch 806 +2024-09-09 20:30:45.916752: Current learning rate: 0.00229 +2024-09-09 20:35:00.314544: train_loss -0.7538 +2024-09-09 20:35:00.332401: val_loss -0.7836 +2024-09-09 20:35:00.332935: Pseudo dice [0.9684, 0.9613, 0.9824, 0.954, 0.8866, 0.9729, 0.9919] +2024-09-09 20:35:00.335335: Epoch time: 254.42 s +2024-09-09 20:35:03.597580: +2024-09-09 20:35:03.598556: Epoch 807 +2024-09-09 20:35:03.599602: Current learning rate: 0.00228 +2024-09-09 20:39:06.796805: train_loss -0.7482 +2024-09-09 20:39:06.799874: val_loss -0.7285 +2024-09-09 20:39:06.800746: Pseudo dice [0.9661, 0.9612, 0.977, 0.9528, 0.8792, 0.9741, 0.9891] +2024-09-09 20:39:06.801485: Epoch time: 243.2 s +2024-09-09 20:39:09.956480: +2024-09-09 20:39:09.957267: Epoch 808 +2024-09-09 20:39:09.957870: Current learning rate: 0.00226 +2024-09-09 20:43:31.177871: train_loss -0.7462 +2024-09-09 20:43:31.179670: val_loss -0.7786 +2024-09-09 20:43:31.180161: Pseudo dice [0.969, 0.9608, 0.9826, 0.9558, 0.8929, 0.9718, 0.9908] +2024-09-09 20:43:31.180622: Epoch time: 261.23 s +2024-09-09 20:43:31.181082: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-09 20:43:34.954025: +2024-09-09 20:43:34.955296: Epoch 809 +2024-09-09 20:43:34.955956: Current learning rate: 0.00225 +2024-09-09 20:47:31.911440: train_loss -0.7478 +2024-09-09 20:47:31.913149: val_loss -0.7592 +2024-09-09 20:47:31.913714: Pseudo dice [0.9669, 0.9586, 0.9795, 0.9518, 0.8677, 0.9635, 0.9891] +2024-09-09 20:47:31.914881: Epoch time: 236.96 s +2024-09-09 20:47:36.054434: +2024-09-09 20:47:36.055607: Epoch 810 +2024-09-09 20:47:36.057186: Current learning rate: 0.00224 +2024-09-09 20:51:54.926097: train_loss -0.7489 +2024-09-09 20:51:54.927340: val_loss -0.7381 +2024-09-09 20:51:54.927825: Pseudo dice [0.9666, 0.9597, 0.9792, 0.9531, 0.8906, 0.9756, 0.9918] +2024-09-09 20:51:54.928263: Epoch time: 258.88 s +2024-09-09 20:51:58.357238: +2024-09-09 20:51:58.358160: Epoch 811 +2024-09-09 20:51:58.359131: Current learning rate: 0.00223 +2024-09-09 20:55:55.795301: train_loss -0.7468 +2024-09-09 20:55:55.802619: val_loss -0.7331 +2024-09-09 20:55:55.803229: Pseudo dice [0.9655, 0.9589, 0.9782, 0.9531, 0.8791, 0.9662, 0.9875] +2024-09-09 20:55:55.803713: Epoch time: 237.44 s +2024-09-09 20:55:58.853121: +2024-09-09 20:55:58.853883: Epoch 812 +2024-09-09 20:55:58.854790: Current learning rate: 0.00222 +2024-09-09 21:00:10.171192: train_loss -0.7443 +2024-09-09 21:00:10.176447: val_loss -0.7754 +2024-09-09 21:00:10.176945: Pseudo dice [0.9667, 0.9599, 0.9796, 0.9524, 0.8887, 0.9745, 0.9913] +2024-09-09 21:00:10.177379: Epoch time: 251.32 s +2024-09-09 21:00:12.799534: +2024-09-09 21:00:12.800392: Epoch 813 +2024-09-09 21:00:12.800846: Current learning rate: 0.00221 +2024-09-09 21:04:17.505166: train_loss -0.7633 +2024-09-09 21:04:17.519491: val_loss -0.7762 +2024-09-09 21:04:17.520042: Pseudo dice [0.9624, 0.9578, 0.9747, 0.9521, 0.8818, 0.9751, 0.9918] +2024-09-09 21:04:17.520588: Epoch time: 244.71 s +2024-09-09 21:04:21.651085: +2024-09-09 21:04:21.652601: Epoch 814 +2024-09-09 21:04:21.653150: Current learning rate: 0.0022 +2024-09-09 21:08:36.690898: train_loss -0.7633 +2024-09-09 21:08:36.705045: val_loss -0.7625 +2024-09-09 21:08:36.705524: Pseudo dice [0.9671, 0.9604, 0.9795, 0.9571, 0.8782, 0.9611, 0.9883] +2024-09-09 21:08:36.706024: Epoch time: 255.04 s +2024-09-09 21:08:39.315002: +2024-09-09 21:08:39.315756: Epoch 815 +2024-09-09 21:08:39.316445: Current learning rate: 0.00219 +2024-09-09 21:12:34.698769: train_loss -0.7604 +2024-09-09 21:12:34.713136: val_loss -0.7307 +2024-09-09 21:12:34.713704: Pseudo dice [0.9655, 0.9595, 0.9772, 0.9571, 0.8954, 0.9563, 0.9873] +2024-09-09 21:12:34.714207: Epoch time: 235.39 s +2024-09-09 21:12:37.167930: +2024-09-09 21:12:37.168884: Epoch 816 +2024-09-09 21:12:37.183928: Current learning rate: 0.00218 +2024-09-09 21:16:44.582211: train_loss -0.7431 +2024-09-09 21:16:44.583666: val_loss -0.8003 +2024-09-09 21:16:44.584558: Pseudo dice [0.9673, 0.9589, 0.9819, 0.9554, 0.8923, 0.9747, 0.993] +2024-09-09 21:16:44.585556: Epoch time: 247.42 s +2024-09-09 21:16:47.589342: +2024-09-09 21:16:47.590235: Epoch 817 +2024-09-09 21:16:47.590819: Current learning rate: 0.00217 +2024-09-09 21:20:49.832618: train_loss -0.7538 +2024-09-09 21:20:49.849743: val_loss -0.7865 +2024-09-09 21:20:49.850746: Pseudo dice [0.9682, 0.962, 0.98, 0.9482, 0.8654, 0.9749, 0.9902] +2024-09-09 21:20:49.851293: Epoch time: 242.25 s +2024-09-09 21:20:53.464986: +2024-09-09 21:20:53.465826: Epoch 818 +2024-09-09 21:20:53.466453: Current learning rate: 0.00216 +2024-09-09 21:24:58.076133: train_loss -0.7571 +2024-09-09 21:24:58.078163: val_loss -0.7464 +2024-09-09 21:24:58.078805: Pseudo dice [0.9688, 0.961, 0.9815, 0.9539, 0.8501, 0.9708, 0.987] +2024-09-09 21:24:58.079332: Epoch time: 244.62 s +2024-09-09 21:25:01.505206: +2024-09-09 21:25:01.505993: Epoch 819 +2024-09-09 21:25:01.506475: Current learning rate: 0.00215 +2024-09-09 21:29:03.057120: train_loss -0.7524 +2024-09-09 21:29:03.073447: val_loss -0.7753 +2024-09-09 21:29:03.074048: Pseudo dice [0.9692, 0.9609, 0.9812, 0.9564, 0.8934, 0.9765, 0.991] +2024-09-09 21:29:03.074561: Epoch time: 241.56 s +2024-09-09 21:29:05.518821: +2024-09-09 21:29:05.524323: Epoch 820 +2024-09-09 21:29:05.525018: Current learning rate: 0.00214 +2024-09-09 21:33:18.047282: train_loss -0.7464 +2024-09-09 21:33:18.069351: val_loss -0.7491 +2024-09-09 21:33:18.069939: Pseudo dice [0.9647, 0.9596, 0.9775, 0.9532, 0.8937, 0.9606, 0.9897] +2024-09-09 21:33:18.070601: Epoch time: 252.53 s +2024-09-09 21:33:20.894536: +2024-09-09 21:33:20.895278: Epoch 821 +2024-09-09 21:33:20.895966: Current learning rate: 0.00213 +2024-09-09 21:37:35.295116: train_loss -0.7575 +2024-09-09 21:37:35.308979: val_loss -0.785 +2024-09-09 21:37:35.309881: Pseudo dice [0.9661, 0.9604, 0.9788, 0.9585, 0.8953, 0.9767, 0.9934] +2024-09-09 21:37:35.310628: Epoch time: 254.41 s +2024-09-09 21:37:35.311228: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 21:37:39.343986: +2024-09-09 21:37:39.344852: Epoch 822 +2024-09-09 21:37:39.345318: Current learning rate: 0.00212 +2024-09-09 21:41:40.603885: train_loss -0.7691 +2024-09-09 21:41:40.620735: val_loss -0.7566 +2024-09-09 21:41:40.621298: Pseudo dice [0.9689, 0.9607, 0.9815, 0.954, 0.8776, 0.9619, 0.9871] +2024-09-09 21:41:40.621733: Epoch time: 241.26 s +2024-09-09 21:41:43.704446: +2024-09-09 21:41:43.719837: Epoch 823 +2024-09-09 21:41:43.725707: Current learning rate: 0.0021 +2024-09-09 21:45:48.026804: train_loss -0.7543 +2024-09-09 21:45:48.041373: val_loss -0.7476 +2024-09-09 21:45:48.041898: Pseudo dice [0.9696, 0.9599, 0.982, 0.9558, 0.888, 0.9704, 0.991] +2024-09-09 21:45:48.042356: Epoch time: 244.34 s +2024-09-09 21:45:48.042906: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 21:45:53.874871: +2024-09-09 21:45:53.875728: Epoch 824 +2024-09-09 21:45:53.876269: Current learning rate: 0.00209 +2024-09-09 21:50:20.486294: train_loss -0.7478 +2024-09-09 21:50:20.494118: val_loss -0.7646 +2024-09-09 21:50:20.494641: Pseudo dice [0.9647, 0.9602, 0.977, 0.9559, 0.8676, 0.9727, 0.9889] +2024-09-09 21:50:20.495153: Epoch time: 266.62 s +2024-09-09 21:50:22.767112: +2024-09-09 21:50:22.768057: Epoch 825 +2024-09-09 21:50:22.768665: Current learning rate: 0.00208 +2024-09-09 21:54:14.365018: train_loss -0.7599 +2024-09-09 21:54:14.366033: val_loss -0.75 +2024-09-09 21:54:14.366516: Pseudo dice [0.9673, 0.9611, 0.9805, 0.9532, 0.883, 0.9745, 0.992] +2024-09-09 21:54:14.366991: Epoch time: 231.6 s +2024-09-09 21:54:17.244527: +2024-09-09 21:54:17.245378: Epoch 826 +2024-09-09 21:54:17.246094: Current learning rate: 0.00207 +2024-09-09 21:58:29.756421: train_loss -0.7672 +2024-09-09 21:58:29.758175: val_loss -0.7329 +2024-09-09 21:58:29.758645: Pseudo dice [0.9658, 0.9577, 0.9788, 0.9466, 0.8359, 0.9607, 0.9827] +2024-09-09 21:58:29.759091: Epoch time: 252.53 s +2024-09-09 21:58:31.701492: +2024-09-09 21:58:31.702238: Epoch 827 +2024-09-09 21:58:31.702818: Current learning rate: 0.00206 +2024-09-09 22:02:29.391602: train_loss -0.7535 +2024-09-09 22:02:29.392730: val_loss -0.7719 +2024-09-09 22:02:29.393216: Pseudo dice [0.9673, 0.9619, 0.98, 0.9542, 0.89, 0.9754, 0.9911] +2024-09-09 22:02:29.393633: Epoch time: 237.69 s +2024-09-09 22:02:33.654377: +2024-09-09 22:02:33.655767: Epoch 828 +2024-09-09 22:02:33.656378: Current learning rate: 0.00205 +2024-09-09 22:06:43.850390: train_loss -0.7461 +2024-09-09 22:06:43.880894: val_loss -0.7518 +2024-09-09 22:06:43.881398: Pseudo dice [0.9684, 0.9599, 0.9826, 0.9521, 0.8924, 0.9588, 0.9904] +2024-09-09 22:06:43.881926: Epoch time: 250.2 s +2024-09-09 22:06:47.046770: +2024-09-09 22:06:47.047625: Epoch 829 +2024-09-09 22:06:47.048290: Current learning rate: 0.00204 +2024-09-09 22:10:59.277584: train_loss -0.7517 +2024-09-09 22:10:59.303848: val_loss -0.7383 +2024-09-09 22:10:59.304608: Pseudo dice [0.9645, 0.9606, 0.9768, 0.9557, 0.8848, 0.9742, 0.9913] +2024-09-09 22:10:59.305633: Epoch time: 252.24 s +2024-09-09 22:11:02.442048: +2024-09-09 22:11:02.456257: Epoch 830 +2024-09-09 22:11:02.456844: Current learning rate: 0.00203 +2024-09-09 22:15:06.190603: train_loss -0.7511 +2024-09-09 22:15:06.202251: val_loss -0.7138 +2024-09-09 22:15:06.202789: Pseudo dice [0.9669, 0.9596, 0.9796, 0.9546, 0.8451, 0.9725, 0.9839] +2024-09-09 22:15:06.203237: Epoch time: 243.75 s +2024-09-09 22:15:09.284657: +2024-09-09 22:15:09.285455: Epoch 831 +2024-09-09 22:15:09.285951: Current learning rate: 0.00202 +2024-09-09 22:19:12.811234: train_loss -0.7632 +2024-09-09 22:19:12.812916: val_loss -0.7859 +2024-09-09 22:19:12.813359: Pseudo dice [0.9678, 0.9595, 0.9812, 0.9535, 0.8804, 0.9643, 0.9887] +2024-09-09 22:19:12.813777: Epoch time: 243.53 s +2024-09-09 22:19:16.803535: +2024-09-09 22:19:16.816390: Epoch 832 +2024-09-09 22:19:16.817708: Current learning rate: 0.00201 +2024-09-09 22:23:31.259449: train_loss -0.7601 +2024-09-09 22:23:31.273949: val_loss -0.7591 +2024-09-09 22:23:31.274657: Pseudo dice [0.9677, 0.9613, 0.9806, 0.9562, 0.8835, 0.9663, 0.9897] +2024-09-09 22:23:31.275299: Epoch time: 254.46 s +2024-09-09 22:23:34.228680: +2024-09-09 22:23:34.240303: Epoch 833 +2024-09-09 22:23:34.240885: Current learning rate: 0.002 +2024-09-09 22:27:39.869954: train_loss -0.7628 +2024-09-09 22:27:39.884678: val_loss -0.7507 +2024-09-09 22:27:39.885267: Pseudo dice [0.9654, 0.9606, 0.9791, 0.957, 0.8903, 0.9639, 0.9898] +2024-09-09 22:27:39.885721: Epoch time: 245.65 s +2024-09-09 22:27:42.140521: +2024-09-09 22:27:42.141372: Epoch 834 +2024-09-09 22:27:42.142183: Current learning rate: 0.00199 +2024-09-09 22:31:25.974130: train_loss -0.7648 +2024-09-09 22:31:26.003726: val_loss -0.7518 +2024-09-09 22:31:26.006379: Pseudo dice [0.9677, 0.9606, 0.9788, 0.9547, 0.8966, 0.9738, 0.9921] +2024-09-09 22:31:26.007215: Epoch time: 223.84 s +2024-09-09 22:31:29.529112: +2024-09-09 22:31:29.536238: Epoch 835 +2024-09-09 22:31:29.536827: Current learning rate: 0.00198 +2024-09-09 22:35:41.297070: train_loss -0.7447 +2024-09-09 22:35:41.298099: val_loss -0.7618 +2024-09-09 22:35:41.298583: Pseudo dice [0.9659, 0.9589, 0.9791, 0.9508, 0.8683, 0.9742, 0.9911] +2024-09-09 22:35:41.299040: Epoch time: 251.77 s +2024-09-09 22:35:44.337715: +2024-09-09 22:35:44.352198: Epoch 836 +2024-09-09 22:35:44.352739: Current learning rate: 0.00196 +2024-09-09 22:40:06.916570: train_loss -0.7709 +2024-09-09 22:40:06.928693: val_loss -0.7774 +2024-09-09 22:40:06.929324: Pseudo dice [0.9672, 0.9595, 0.98, 0.9509, 0.8819, 0.9609, 0.9895] +2024-09-09 22:40:06.929765: Epoch time: 262.58 s +2024-09-09 22:40:09.260764: +2024-09-09 22:40:09.261527: Epoch 837 +2024-09-09 22:40:09.262037: Current learning rate: 0.00195 +2024-09-09 22:44:16.865767: train_loss -0.7605 +2024-09-09 22:44:16.893621: val_loss -0.7659 +2024-09-09 22:44:16.894189: Pseudo dice [0.969, 0.9635, 0.9811, 0.955, 0.8955, 0.9724, 0.9887] +2024-09-09 22:44:16.894788: Epoch time: 247.61 s +2024-09-09 22:44:19.832628: +2024-09-09 22:44:19.833328: Epoch 838 +2024-09-09 22:44:19.834000: Current learning rate: 0.00194 +2024-09-09 22:48:24.660527: train_loss -0.7725 +2024-09-09 22:48:24.676242: val_loss -0.7425 +2024-09-09 22:48:24.676734: Pseudo dice [0.9687, 0.9604, 0.9812, 0.9555, 0.8682, 0.9731, 0.9896] +2024-09-09 22:48:24.677165: Epoch time: 244.83 s +2024-09-09 22:48:27.745847: +2024-09-09 22:48:27.760323: Epoch 839 +2024-09-09 22:48:27.761006: Current learning rate: 0.00193 +2024-09-09 22:52:28.428409: train_loss -0.7532 +2024-09-09 22:52:28.430657: val_loss -0.7808 +2024-09-09 22:52:28.431263: Pseudo dice [0.9695, 0.9617, 0.9843, 0.9568, 0.8945, 0.9689, 0.9905] +2024-09-09 22:52:28.432033: Epoch time: 240.69 s +2024-09-09 22:52:30.410405: +2024-09-09 22:52:30.411379: Epoch 840 +2024-09-09 22:52:30.411981: Current learning rate: 0.00192 +2024-09-09 22:56:28.303178: train_loss -0.7677 +2024-09-09 22:56:28.316663: val_loss -0.7584 +2024-09-09 22:56:28.317297: Pseudo dice [0.9657, 0.9581, 0.9772, 0.9543, 0.8607, 0.9721, 0.9865] +2024-09-09 22:56:28.318365: Epoch time: 237.9 s +2024-09-09 22:56:30.636797: +2024-09-09 22:56:30.640200: Epoch 841 +2024-09-09 22:56:30.640827: Current learning rate: 0.00191 +2024-09-09 23:00:35.566610: train_loss -0.7591 +2024-09-09 23:00:35.579078: val_loss -0.7615 +2024-09-09 23:00:35.580314: Pseudo dice [0.9701, 0.9616, 0.9832, 0.9533, 0.8904, 0.9665, 0.9903] +2024-09-09 23:00:35.581547: Epoch time: 244.93 s +2024-09-09 23:00:39.690374: +2024-09-09 23:00:39.695955: Epoch 842 +2024-09-09 23:00:39.696801: Current learning rate: 0.0019 +2024-09-09 23:04:48.644529: train_loss -0.7593 +2024-09-09 23:04:48.660634: val_loss -0.813 +2024-09-09 23:04:48.661706: Pseudo dice [0.9682, 0.9605, 0.9806, 0.9572, 0.8801, 0.9753, 0.992] +2024-09-09 23:04:48.662455: Epoch time: 248.96 s +2024-09-09 23:04:51.906074: +2024-09-09 23:04:51.907178: Epoch 843 +2024-09-09 23:04:51.907955: Current learning rate: 0.00189 +2024-09-09 23:08:55.215506: train_loss -0.7516 +2024-09-09 23:08:55.233688: val_loss -0.7113 +2024-09-09 23:08:55.234691: Pseudo dice [0.9655, 0.9589, 0.9767, 0.9513, 0.8854, 0.9738, 0.9909] +2024-09-09 23:08:55.235740: Epoch time: 243.31 s +2024-09-09 23:08:58.472759: +2024-09-09 23:08:58.473427: Epoch 844 +2024-09-09 23:08:58.473961: Current learning rate: 0.00188 +2024-09-09 23:13:00.724804: train_loss -0.7535 +2024-09-09 23:13:00.746387: val_loss -0.7522 +2024-09-09 23:13:00.746940: Pseudo dice [0.9658, 0.962, 0.9774, 0.9584, 0.8999, 0.9598, 0.9889] +2024-09-09 23:13:00.747502: Epoch time: 242.26 s +2024-09-09 23:13:03.419449: +2024-09-09 23:13:03.426689: Epoch 845 +2024-09-09 23:13:03.427273: Current learning rate: 0.00187 +2024-09-09 23:17:05.481520: train_loss -0.7491 +2024-09-09 23:17:05.493771: val_loss -0.7326 +2024-09-09 23:17:05.494438: Pseudo dice [0.9705, 0.9634, 0.9828, 0.9594, 0.8587, 0.9755, 0.989] +2024-09-09 23:17:05.495000: Epoch time: 242.07 s +2024-09-09 23:17:07.743975: +2024-09-09 23:17:07.744909: Epoch 846 +2024-09-09 23:17:07.745959: Current learning rate: 0.00186 +2024-09-09 23:21:03.291197: train_loss -0.7588 +2024-09-09 23:21:03.293040: val_loss -0.7343 +2024-09-09 23:21:03.293609: Pseudo dice [0.9678, 0.9627, 0.9793, 0.9572, 0.8768, 0.9728, 0.9884] +2024-09-09 23:21:03.294060: Epoch time: 235.55 s +2024-09-09 23:21:06.245127: +2024-09-09 23:21:06.260243: Epoch 847 +2024-09-09 23:21:06.260895: Current learning rate: 0.00185 +2024-09-09 23:25:22.993521: train_loss -0.7506 +2024-09-09 23:25:23.009859: val_loss -0.7582 +2024-09-09 23:25:23.010560: Pseudo dice [0.9705, 0.9623, 0.9836, 0.9571, 0.8943, 0.9622, 0.99] +2024-09-09 23:25:23.011197: Epoch time: 256.75 s +2024-09-09 23:25:23.011963: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 23:25:27.027851: +2024-09-09 23:25:27.028530: Epoch 848 +2024-09-09 23:25:27.029066: Current learning rate: 0.00184 +2024-09-09 23:29:38.733233: train_loss -0.7536 +2024-09-09 23:29:38.747122: val_loss -0.7624 +2024-09-09 23:29:38.747622: Pseudo dice [0.9654, 0.9608, 0.978, 0.9518, 0.8639, 0.9744, 0.9891] +2024-09-09 23:29:38.748111: Epoch time: 251.71 s +2024-09-09 23:29:41.578804: +2024-09-09 23:29:41.579511: Epoch 849 +2024-09-09 23:29:41.580049: Current learning rate: 0.00182 +2024-09-09 23:33:43.073680: train_loss -0.7596 +2024-09-09 23:33:43.087071: val_loss -0.7344 +2024-09-09 23:33:43.087590: Pseudo dice [0.9663, 0.9626, 0.9784, 0.9589, 0.829, 0.9582, 0.9766] +2024-09-09 23:33:43.088045: Epoch time: 241.5 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/checkpoint_final.pth new file mode 100644 index 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Training CTs originated from the SAROS and TotalSegmentator datasets and are not included.'}, 'unpack_dataset': False, 'device': device(type='cuda')}", + "network": "OptimizedModule", + "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 fused: None\n initial_lr: 0.01\n lr: 0.0006867729140492686\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)", + "output_folder": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2", + "output_folder_base": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres", + "oversample_foreground_percent": "0.33", + "plans_manager": "{'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], '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': 89, 'patch_size': [320, 320], 'median_image_size_in_voxels': [257.0, 260.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 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]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}", + "preprocessed_dataset_folder": "/data2/nnUNet_preprocessed/Dataset611_Tissue/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data2/nnUNet_preprocessed/Dataset611_Tissue", + "save_every": "50", + "torch_version": "2.3.0+cu121", + "unpack_dataset": "False", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/progress.png b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..8bd633884278babd1c1def68ca6b9829ac8e74d6 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:369223df1451bfe1ffce6a0c79dbab18e5cad51a9c080ca87203a663f52e2568 +size 643917 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_00_29_57.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_00_29_57.txt new file mode 100644 index 0000000000000000000000000000000000000000..95acf03a02805ee1167c206e6202cf551bf5ba01 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_00_29_57.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 00:30:05.553659: Using torch.compile... +2024-09-10 00:30:18.774989: do_dummy_2d_data_aug: False +2024-09-10 00:30:18.792236: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 00:30:18.804609: The split file contains 5 splits. +2024-09-10 00:30:18.805763: Desired fold for training: 2 +2024-09-10 00:30:18.806896: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 00:30:31.865588: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 00:30:35.037721: +2024-09-10 00:30:35.045820: Epoch 700 +2024-09-10 00:30:35.056651: Current learning rate: 0.00338 +2024-09-10 00:41:49.254835: train_loss -0.7425 +2024-09-10 00:41:49.256800: val_loss -0.7065 +2024-09-10 00:41:49.257253: Pseudo dice [0.9654, 0.9585, 0.9781, 0.9493, 0.8883, 0.9733, 0.9923] +2024-09-10 00:41:49.257693: Epoch time: 674.27 s +2024-09-10 00:41:51.058055: +2024-09-10 00:41:51.058804: Epoch 701 +2024-09-10 00:41:51.059319: Current learning rate: 0.00337 +2024-09-10 00:46:57.890199: train_loss -0.7555 +2024-09-10 00:46:57.892796: val_loss -0.759 +2024-09-10 00:46:57.893484: Pseudo dice [0.9681, 0.9617, 0.981, 0.9566, 0.8928, 0.9733, 0.9922] +2024-09-10 00:46:57.894000: Epoch time: 306.84 s +2024-09-10 00:47:00.328539: +2024-09-10 00:47:00.329987: Epoch 702 +2024-09-10 00:47:00.330833: Current learning rate: 0.00336 +2024-09-10 00:52:28.156373: train_loss -0.7497 +2024-09-10 00:52:28.159986: val_loss -0.7415 +2024-09-10 00:52:28.161554: Pseudo dice [0.9663, 0.9612, 0.9807, 0.9547, 0.8927, 0.9719, 0.9921] +2024-09-10 00:52:28.162270: Epoch time: 327.83 s +2024-09-10 00:52:31.612039: +2024-09-10 00:52:31.612949: Epoch 703 +2024-09-10 00:52:31.613675: Current learning rate: 0.00335 +2024-09-10 00:57:43.427147: train_loss -0.7474 +2024-09-10 00:57:43.438224: val_loss -0.7635 +2024-09-10 00:57:43.439120: Pseudo dice [0.9664, 0.9596, 0.9795, 0.9574, 0.8737, 0.9731, 0.9912] +2024-09-10 00:57:43.448658: Epoch time: 311.82 s +2024-09-10 00:57:46.334994: +2024-09-10 00:57:46.336068: Epoch 704 +2024-09-10 00:57:46.336750: Current learning rate: 0.00334 +2024-09-10 01:03:01.065778: train_loss -0.7531 +2024-09-10 01:03:01.068880: val_loss -0.7454 +2024-09-10 01:03:01.069441: Pseudo dice [0.9662, 0.9609, 0.9777, 0.9521, 0.8734, 0.9711, 0.9913] +2024-09-10 01:03:01.069906: Epoch time: 314.74 s +2024-09-10 01:03:04.249473: +2024-09-10 01:03:04.250413: Epoch 705 +2024-09-10 01:03:04.251016: Current learning rate: 0.00333 +2024-09-10 01:08:00.681740: train_loss -0.7382 +2024-09-10 01:08:00.692990: val_loss -0.7668 +2024-09-10 01:08:00.693557: Pseudo dice [0.9651, 0.9616, 0.9772, 0.9556, 0.8715, 0.9734, 0.9904] +2024-09-10 01:08:00.694087: Epoch time: 296.44 s +2024-09-10 01:08:03.717845: +2024-09-10 01:08:03.719328: Epoch 706 +2024-09-10 01:08:03.720093: Current learning rate: 0.00332 +2024-09-10 01:13:15.266425: train_loss -0.745 +2024-09-10 01:13:15.267902: val_loss -0.7524 +2024-09-10 01:13:15.268707: Pseudo dice [0.9626, 0.9584, 0.9774, 0.9459, 0.8879, 0.9714, 0.993] +2024-09-10 01:13:15.269495: Epoch time: 311.56 s +2024-09-10 01:13:17.792880: +2024-09-10 01:13:17.793977: Epoch 707 +2024-09-10 01:13:17.794581: Current learning rate: 0.00331 +2024-09-10 01:18:13.924510: train_loss -0.756 +2024-09-10 01:18:13.931885: val_loss -0.7318 +2024-09-10 01:18:13.932785: Pseudo dice [0.9677, 0.9604, 0.981, 0.9553, 0.8931, 0.9744, 0.9929] +2024-09-10 01:18:13.933460: Epoch time: 296.16 s +2024-09-10 01:18:16.507324: +2024-09-10 01:18:16.508458: Epoch 708 +2024-09-10 01:18:16.509417: Current learning rate: 0.0033 +2024-09-10 01:23:45.858035: train_loss -0.7496 +2024-09-10 01:23:45.867940: val_loss -0.7624 +2024-09-10 01:23:45.869017: Pseudo dice [0.9693, 0.961, 0.9826, 0.9515, 0.8872, 0.9745, 0.9932] +2024-09-10 01:23:45.870146: Epoch time: 329.36 s +2024-09-10 01:23:48.722795: +2024-09-10 01:23:48.724125: Epoch 709 +2024-09-10 01:23:48.724935: Current learning rate: 0.00329 +2024-09-10 01:29:03.802654: train_loss -0.7539 +2024-09-10 01:29:03.805491: val_loss -0.7335 +2024-09-10 01:29:03.806217: Pseudo dice [0.9648, 0.9622, 0.977, 0.9554, 0.8965, 0.9755, 0.9921] +2024-09-10 01:29:03.806782: Epoch time: 315.09 s +2024-09-10 01:29:09.332553: +2024-09-10 01:29:09.333731: Epoch 710 +2024-09-10 01:29:09.334455: Current learning rate: 0.00328 +2024-09-10 01:33:51.904809: train_loss -0.7538 +2024-09-10 01:33:51.921211: val_loss -0.7491 +2024-09-10 01:33:51.922600: Pseudo dice [0.9682, 0.9607, 0.9792, 0.9531, 0.8979, 0.9755, 0.9932] +2024-09-10 01:33:51.923515: Epoch time: 282.58 s +2024-09-10 01:33:54.753306: +2024-09-10 01:33:54.754346: Epoch 711 +2024-09-10 01:33:54.755318: Current learning rate: 0.00327 +2024-09-10 01:39:03.216293: train_loss -0.7582 +2024-09-10 01:39:03.217915: val_loss -0.7765 +2024-09-10 01:39:03.218623: Pseudo dice [0.9641, 0.9608, 0.9773, 0.9529, 0.8861, 0.9745, 0.9922] +2024-09-10 01:39:03.219317: Epoch time: 308.47 s +2024-09-10 01:39:05.714388: +2024-09-10 01:39:05.715568: Epoch 712 +2024-09-10 01:39:05.716169: Current learning rate: 0.00326 +2024-09-10 01:44:01.365521: train_loss -0.7427 +2024-09-10 01:44:01.367993: val_loss -0.7784 +2024-09-10 01:44:01.368671: Pseudo dice [0.9618, 0.9598, 0.9776, 0.9552, 0.8902, 0.9741, 0.9915] +2024-09-10 01:44:01.369233: Epoch time: 295.66 s +2024-09-10 01:44:03.780005: +2024-09-10 01:44:03.792911: Epoch 713 +2024-09-10 01:44:03.794072: Current learning rate: 0.00325 +2024-09-10 01:49:10.693730: train_loss -0.7499 +2024-09-10 01:49:10.695682: val_loss -0.7684 +2024-09-10 01:49:10.696384: Pseudo dice [0.9681, 0.9622, 0.98, 0.9519, 0.892, 0.9722, 0.993] +2024-09-10 01:49:10.696862: Epoch time: 306.92 s +2024-09-10 01:49:13.865120: +2024-09-10 01:49:13.880470: Epoch 714 +2024-09-10 01:49:13.881339: Current learning rate: 0.00324 +2024-09-10 01:54:11.670738: train_loss -0.7546 +2024-09-10 01:54:11.673543: val_loss -0.7504 +2024-09-10 01:54:11.674154: Pseudo dice [0.9683, 0.9617, 0.9823, 0.954, 0.8938, 0.9736, 0.9925] +2024-09-10 01:54:11.674984: Epoch time: 297.81 s +2024-09-10 01:54:11.675406: Yayy! New best EMA pseudo Dice: 0.9589 +2024-09-10 01:54:17.751474: +2024-09-10 01:54:17.753110: Epoch 715 +2024-09-10 01:54:17.754132: Current learning rate: 0.00323 +2024-09-10 01:59:22.442038: train_loss -0.7315 +2024-09-10 01:59:22.449637: val_loss -0.7782 +2024-09-10 01:59:22.450568: Pseudo dice [0.9659, 0.9619, 0.9774, 0.9549, 0.891, 0.9734, 0.9918] +2024-09-10 01:59:22.451072: Epoch time: 304.7 s +2024-09-10 01:59:22.451510: Yayy! New best EMA pseudo Dice: 0.959 +2024-09-10 01:59:29.209251: +2024-09-10 01:59:29.211148: Epoch 716 +2024-09-10 01:59:29.212311: Current learning rate: 0.00322 +2024-09-10 02:04:47.549202: train_loss -0.7434 +2024-09-10 02:04:47.553251: val_loss -0.7832 +2024-09-10 02:04:47.554206: Pseudo dice [0.9671, 0.9623, 0.9817, 0.9531, 0.8935, 0.9728, 0.9922] +2024-09-10 02:04:47.555336: Epoch time: 318.35 s +2024-09-10 02:04:47.557695: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-10 02:04:54.506464: +2024-09-10 02:04:54.507734: Epoch 717 +2024-09-10 02:04:54.508618: Current learning rate: 0.00321 +2024-09-10 02:10:19.099116: train_loss -0.7492 +2024-09-10 02:10:19.101957: val_loss -0.7706 +2024-09-10 02:10:19.103169: Pseudo dice [0.9685, 0.9607, 0.983, 0.9538, 0.8888, 0.9702, 0.9916] +2024-09-10 02:10:19.104061: Epoch time: 324.6 s +2024-09-10 02:10:19.104656: Yayy! New best EMA pseudo Dice: 0.9592 +2024-09-10 02:10:24.346456: +2024-09-10 02:10:24.348260: Epoch 718 +2024-09-10 02:10:24.348874: Current learning rate: 0.0032 +2024-09-10 02:15:17.503938: train_loss -0.7415 +2024-09-10 02:15:17.506630: val_loss -0.7895 +2024-09-10 02:15:17.507623: Pseudo dice [0.9613, 0.9589, 0.9753, 0.9539, 0.898, 0.9742, 0.9921] +2024-09-10 02:15:17.508291: Epoch time: 293.16 s +2024-09-10 02:15:19.879780: +2024-09-10 02:15:19.885259: Epoch 719 +2024-09-10 02:15:19.887171: Current learning rate: 0.00319 +2024-09-10 02:20:04.694322: train_loss -0.7589 +2024-09-10 02:20:04.709307: val_loss -0.7407 +2024-09-10 02:20:04.709930: Pseudo dice [0.9599, 0.9576, 0.9738, 0.9506, 0.89, 0.9751, 0.9932] +2024-09-10 02:20:04.710410: Epoch time: 284.83 s +2024-09-10 02:20:07.880648: +2024-09-10 02:20:07.881496: Epoch 720 +2024-09-10 02:20:07.882485: Current learning rate: 0.00318 +2024-09-10 02:25:18.622819: train_loss -0.7547 +2024-09-10 02:25:18.643999: val_loss -0.7335 +2024-09-10 02:25:18.645229: Pseudo dice [0.9621, 0.961, 0.9755, 0.9567, 0.8858, 0.9676, 0.991] +2024-09-10 02:25:18.646368: Epoch time: 310.77 s +2024-09-10 02:25:21.317951: +2024-09-10 02:25:21.318986: Epoch 721 +2024-09-10 02:25:21.319660: Current learning rate: 0.00317 +2024-09-10 02:30:32.247597: train_loss -0.7553 +2024-09-10 02:30:32.264414: val_loss -0.7527 +2024-09-10 02:30:32.264999: Pseudo dice [0.967, 0.9616, 0.9798, 0.9536, 0.8909, 0.9728, 0.9928] +2024-09-10 02:30:32.265510: Epoch time: 310.94 s +2024-09-10 02:30:36.874588: +2024-09-10 02:30:36.875688: Epoch 722 +2024-09-10 02:30:36.876508: Current learning rate: 0.00316 +2024-09-10 02:35:40.897054: train_loss -0.7474 +2024-09-10 02:35:40.899443: val_loss -0.7627 +2024-09-10 02:35:40.900054: Pseudo dice [0.9638, 0.9597, 0.9764, 0.955, 0.8896, 0.9741, 0.9927] +2024-09-10 02:35:40.900599: Epoch time: 304.03 s +2024-09-10 02:35:43.287688: +2024-09-10 02:35:43.288867: Epoch 723 +2024-09-10 02:35:43.289504: Current learning rate: 0.00315 +2024-09-10 02:40:57.781469: train_loss -0.7543 +2024-09-10 02:40:57.797985: val_loss -0.7343 +2024-09-10 02:40:57.800960: Pseudo dice [0.965, 0.9582, 0.9791, 0.9591, 0.8985, 0.9697, 0.9918] +2024-09-10 02:40:57.802177: Epoch time: 314.5 s +2024-09-10 02:41:00.511640: +2024-09-10 02:41:00.512664: Epoch 724 +2024-09-10 02:41:00.513603: Current learning rate: 0.00314 +2024-09-10 02:46:14.045953: train_loss -0.7471 +2024-09-10 02:46:14.063947: val_loss -0.7701 +2024-09-10 02:46:14.064956: Pseudo dice [0.9651, 0.9605, 0.9767, 0.9543, 0.8916, 0.9735, 0.9917] +2024-09-10 02:46:14.065542: Epoch time: 313.54 s +2024-09-10 02:46:16.312519: +2024-09-10 02:46:16.313794: Epoch 725 +2024-09-10 02:46:16.314573: Current learning rate: 0.00313 +2024-09-10 02:51:37.582512: train_loss -0.7619 +2024-09-10 02:51:37.598040: val_loss -0.7575 +2024-09-10 02:51:37.598582: Pseudo dice [0.9615, 0.9595, 0.9761, 0.9549, 0.8984, 0.9739, 0.9927] +2024-09-10 02:51:37.599033: Epoch time: 321.27 s +2024-09-10 02:51:40.266831: +2024-09-10 02:51:40.267905: Epoch 726 +2024-09-10 02:51:40.268487: Current learning rate: 0.00312 +2024-09-10 02:56:56.515310: train_loss -0.7466 +2024-09-10 02:56:56.525248: val_loss -0.7739 +2024-09-10 02:56:56.525804: Pseudo dice [0.964, 0.9594, 0.9761, 0.9465, 0.8891, 0.973, 0.9925] +2024-09-10 02:56:56.526353: Epoch time: 316.25 s +2024-09-10 02:57:00.113357: +2024-09-10 02:57:00.114309: Epoch 727 +2024-09-10 02:57:00.115157: Current learning rate: 0.00311 +2024-09-10 03:02:11.270983: train_loss -0.7484 +2024-09-10 03:02:11.272962: val_loss -0.7585 +2024-09-10 03:02:11.280432: Pseudo dice [0.9682, 0.9609, 0.9781, 0.954, 0.8972, 0.9734, 0.9939] +2024-09-10 03:02:11.292325: Epoch time: 311.16 s +2024-09-10 03:02:14.255774: +2024-09-10 03:02:14.256657: Epoch 728 +2024-09-10 03:02:14.257272: Current learning rate: 0.0031 +2024-09-10 03:07:22.332476: train_loss -0.7422 +2024-09-10 03:07:22.334509: val_loss -0.7623 +2024-09-10 03:07:22.335548: Pseudo dice [0.9662, 0.9621, 0.9785, 0.9555, 0.8949, 0.9735, 0.9925] +2024-09-10 03:07:22.356023: Epoch time: 308.09 s +2024-09-10 03:07:22.364383: Yayy! New best EMA pseudo Dice: 0.9592 +2024-09-10 03:07:29.350569: +2024-09-10 03:07:29.351718: Epoch 729 +2024-09-10 03:07:29.352431: Current learning rate: 0.00309 +2024-09-10 03:12:48.057896: train_loss -0.7549 +2024-09-10 03:12:48.059864: val_loss -0.7722 +2024-09-10 03:12:48.060443: Pseudo dice [0.9654, 0.9592, 0.9739, 0.9495, 0.8895, 0.9741, 0.9924] +2024-09-10 03:12:48.060943: Epoch time: 318.71 s +2024-09-10 03:12:50.385196: +2024-09-10 03:12:50.386290: Epoch 730 +2024-09-10 03:12:50.386832: Current learning rate: 0.00308 +2024-09-10 03:17:43.602722: train_loss -0.7268 +2024-09-10 03:17:43.606262: val_loss -0.7391 +2024-09-10 03:17:43.607083: Pseudo dice [0.9666, 0.9592, 0.9799, 0.947, 0.8745, 0.969, 0.9901] +2024-09-10 03:17:43.623964: Epoch time: 293.22 s +2024-09-10 03:17:47.077633: +2024-09-10 03:17:47.099739: Epoch 731 +2024-09-10 03:17:47.110117: Current learning rate: 0.00307 +2024-09-10 03:23:12.649604: train_loss -0.7307 +2024-09-10 03:23:12.673683: val_loss -0.7619 +2024-09-10 03:23:12.674879: Pseudo dice [0.966, 0.9599, 0.9799, 0.9535, 0.8875, 0.972, 0.993] +2024-09-10 03:23:12.675560: Epoch time: 325.59 s +2024-09-10 03:23:15.439865: +2024-09-10 03:23:15.440750: Epoch 732 +2024-09-10 03:23:15.441240: Current learning rate: 0.00306 +2024-09-10 03:28:32.016671: train_loss -0.7564 +2024-09-10 03:28:32.017840: val_loss -0.7739 +2024-09-10 03:28:32.018395: Pseudo dice [0.9658, 0.9604, 0.979, 0.9512, 0.8866, 0.9743, 0.9926] +2024-09-10 03:28:32.018846: Epoch time: 316.58 s +2024-09-10 03:28:34.461571: +2024-09-10 03:28:34.462871: Epoch 733 +2024-09-10 03:28:34.463630: Current learning rate: 0.00305 +2024-09-10 03:33:23.548891: train_loss -0.7391 +2024-09-10 03:33:23.565467: val_loss -0.7553 +2024-09-10 03:33:23.566053: Pseudo dice [0.9643, 0.96, 0.9775, 0.9555, 0.8938, 0.9739, 0.9916] +2024-09-10 03:33:23.566582: Epoch time: 289.09 s +2024-09-10 03:33:26.359386: +2024-09-10 03:33:26.360507: Epoch 734 +2024-09-10 03:33:26.361123: Current learning rate: 0.00304 +2024-09-10 03:38:41.355788: train_loss -0.7505 +2024-09-10 03:38:41.360668: val_loss -0.6976 +2024-09-10 03:38:41.361441: Pseudo dice [0.9639, 0.9609, 0.9774, 0.9561, 0.8909, 0.9746, 0.9922] +2024-09-10 03:38:41.361969: Epoch time: 315.0 s +2024-09-10 03:38:45.399796: +2024-09-10 03:38:45.400953: Epoch 735 +2024-09-10 03:38:45.401651: Current learning rate: 0.00303 +2024-09-10 03:43:52.964303: train_loss -0.7392 +2024-09-10 03:43:52.966444: val_loss -0.768 +2024-09-10 03:43:52.967058: Pseudo dice [0.9681, 0.9608, 0.98, 0.9529, 0.8891, 0.9719, 0.9923] +2024-09-10 03:43:52.967656: Epoch time: 307.57 s +2024-09-10 03:43:55.857495: +2024-09-10 03:43:55.859016: Epoch 736 +2024-09-10 03:43:55.859686: Current learning rate: 0.00302 +2024-09-10 03:48:53.437560: train_loss -0.7479 +2024-09-10 03:48:53.464018: val_loss -0.799 +2024-09-10 03:48:53.472411: Pseudo dice [0.9668, 0.9601, 0.9796, 0.9537, 0.8924, 0.9746, 0.9932] +2024-09-10 03:48:53.472954: Epoch time: 297.59 s +2024-09-10 03:48:55.962886: +2024-09-10 03:48:55.964288: Epoch 737 +2024-09-10 03:48:55.965012: Current learning rate: 0.00301 +2024-09-10 03:53:54.737186: train_loss -0.7527 +2024-09-10 03:53:54.754855: val_loss -0.7812 +2024-09-10 03:53:54.755576: Pseudo dice [0.9639, 0.9586, 0.9766, 0.9524, 0.8954, 0.975, 0.9927] +2024-09-10 03:53:54.756268: Epoch time: 298.78 s +2024-09-10 03:53:57.921476: +2024-09-10 03:53:57.923507: Epoch 738 +2024-09-10 03:53:57.924914: Current learning rate: 0.003 +2024-09-10 03:59:07.661856: train_loss -0.7637 +2024-09-10 03:59:07.664038: val_loss -0.7769 +2024-09-10 03:59:07.664588: Pseudo dice [0.9671, 0.9599, 0.9784, 0.953, 0.8896, 0.9741, 0.9921] +2024-09-10 03:59:07.665118: Epoch time: 309.75 s +2024-09-10 03:59:10.414262: +2024-09-10 03:59:10.415208: Epoch 739 +2024-09-10 03:59:10.415907: Current learning rate: 0.00299 +2024-09-10 04:04:12.678293: train_loss -0.7575 +2024-09-10 04:04:12.680833: val_loss -0.7355 +2024-09-10 04:04:12.681533: Pseudo dice [0.9598, 0.9574, 0.973, 0.9513, 0.8911, 0.9739, 0.9923] +2024-09-10 04:04:12.682080: Epoch time: 302.27 s +2024-09-10 04:04:15.721096: +2024-09-10 04:04:15.722218: Epoch 740 +2024-09-10 04:04:15.723157: Current learning rate: 0.00297 +2024-09-10 04:09:08.373874: train_loss -0.7485 +2024-09-10 04:09:08.398164: val_loss -0.7439 +2024-09-10 04:09:08.399455: Pseudo dice [0.9643, 0.959, 0.9798, 0.9513, 0.8834, 0.9733, 0.9903] +2024-09-10 04:09:08.400512: Epoch time: 292.66 s +2024-09-10 04:09:10.715180: +2024-09-10 04:09:10.716045: Epoch 741 +2024-09-10 04:09:10.716757: Current learning rate: 0.00296 +2024-09-10 04:14:16.065660: train_loss -0.7536 +2024-09-10 04:14:16.068315: val_loss -0.7583 +2024-09-10 04:14:16.068990: Pseudo dice [0.9657, 0.9588, 0.9795, 0.9569, 0.8795, 0.9739, 0.992] +2024-09-10 04:14:16.069584: Epoch time: 305.36 s +2024-09-10 04:14:19.156261: +2024-09-10 04:14:19.157120: Epoch 742 +2024-09-10 04:14:19.157712: Current learning rate: 0.00295 +2024-09-10 04:19:19.511607: train_loss -0.7499 +2024-09-10 04:19:19.524547: val_loss -0.7614 +2024-09-10 04:19:19.526119: Pseudo dice [0.9686, 0.9623, 0.9811, 0.9477, 0.8869, 0.9694, 0.9917] +2024-09-10 04:19:19.527604: Epoch time: 300.36 s +2024-09-10 04:19:23.699151: +2024-09-10 04:19:23.700228: Epoch 743 +2024-09-10 04:19:23.700886: Current learning rate: 0.00294 +2024-09-10 04:24:28.707081: train_loss -0.7642 +2024-09-10 04:24:28.710151: val_loss -0.7115 +2024-09-10 04:24:28.710685: Pseudo dice [0.9654, 0.9582, 0.9786, 0.953, 0.8734, 0.9729, 0.9909] +2024-09-10 04:24:28.711349: Epoch time: 305.01 s +2024-09-10 04:24:32.258352: +2024-09-10 04:24:32.259827: Epoch 744 +2024-09-10 04:24:32.260984: Current learning rate: 0.00293 +2024-09-10 04:29:29.500701: train_loss -0.7479 +2024-09-10 04:29:29.509649: val_loss -0.7508 +2024-09-10 04:29:29.510203: Pseudo dice [0.9668, 0.9597, 0.9778, 0.9515, 0.8904, 0.9664, 0.9917] +2024-09-10 04:29:29.510777: Epoch time: 297.25 s +2024-09-10 04:29:32.694831: +2024-09-10 04:29:32.704279: Epoch 745 +2024-09-10 04:29:32.704863: Current learning rate: 0.00292 +2024-09-10 04:34:54.588208: train_loss -0.7475 +2024-09-10 04:34:54.609317: val_loss -0.7354 +2024-09-10 04:34:54.610293: Pseudo dice [0.9635, 0.9592, 0.9733, 0.9498, 0.8652, 0.9722, 0.991] +2024-09-10 04:34:54.610811: Epoch time: 321.9 s +2024-09-10 04:34:58.631691: +2024-09-10 04:34:58.644594: Epoch 746 +2024-09-10 04:34:58.645329: Current learning rate: 0.00291 +2024-09-10 04:40:07.263944: train_loss -0.7507 +2024-09-10 04:40:07.277352: val_loss -0.7762 +2024-09-10 04:40:07.278309: Pseudo dice [0.9652, 0.9611, 0.9787, 0.9528, 0.8863, 0.9696, 0.991] +2024-09-10 04:40:07.279202: Epoch time: 308.64 s +2024-09-10 04:40:10.548939: +2024-09-10 04:40:10.549905: Epoch 747 +2024-09-10 04:40:10.550567: Current learning rate: 0.0029 +2024-09-10 04:45:31.751631: train_loss -0.7502 +2024-09-10 04:45:31.761490: val_loss -0.7495 +2024-09-10 04:45:31.762142: Pseudo dice [0.9617, 0.9589, 0.973, 0.9584, 0.8773, 0.9703, 0.9924] +2024-09-10 04:45:31.762839: Epoch time: 321.21 s +2024-09-10 04:45:34.993938: +2024-09-10 04:45:34.995179: Epoch 748 +2024-09-10 04:45:34.995885: Current learning rate: 0.00289 +2024-09-10 04:50:41.517162: train_loss -0.7421 +2024-09-10 04:50:41.521320: val_loss -0.7906 +2024-09-10 04:50:41.522589: Pseudo dice [0.9694, 0.9615, 0.9822, 0.9546, 0.8926, 0.969, 0.994] +2024-09-10 04:50:41.523407: Epoch time: 306.53 s +2024-09-10 04:50:48.218272: +2024-09-10 04:50:48.219395: Epoch 749 +2024-09-10 04:50:48.220438: Current learning rate: 0.00288 +2024-09-10 04:56:11.047584: train_loss -0.7521 +2024-09-10 04:56:11.059853: val_loss -0.7671 +2024-09-10 04:56:11.060457: Pseudo dice [0.963, 0.9589, 0.9745, 0.9489, 0.8832, 0.9736, 0.9912] +2024-09-10 04:56:11.061210: Epoch time: 322.83 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_08_29_27.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_08_29_27.txt new file mode 100644 index 0000000000000000000000000000000000000000..91db84101a2eae4fcd6a597cf7d58a3721ff4e82 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_08_29_27.txt @@ -0,0 +1,734 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 08:29:37.533916: Using torch.compile... +2024-09-10 08:29:49.999061: do_dummy_2d_data_aug: False +2024-09-10 08:29:50.004689: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 08:29:50.005967: The split file contains 5 splits. +2024-09-10 08:29:50.006511: Desired fold for training: 2 +2024-09-10 08:29:50.007002: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 08:30:04.679004: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 08:30:08.766126: +2024-09-10 08:30:08.767088: Epoch 750 +2024-09-10 08:30:08.767719: Current learning rate: 0.00287 +2024-09-10 08:46:43.769426: train_loss -0.7506 +2024-09-10 08:46:43.771466: val_loss -0.7579 +2024-09-10 08:46:43.772154: Pseudo dice [0.9674, 0.9584, 0.9803, 0.9526, 0.8855, 0.9717, 0.9921] +2024-09-10 08:46:43.772774: Epoch time: 995.01 s +2024-09-10 08:46:45.470989: +2024-09-10 08:46:45.471813: Epoch 751 +2024-09-10 08:46:45.472383: Current learning rate: 0.00286 +2024-09-10 08:51:20.021878: train_loss -0.7337 +2024-09-10 08:51:20.038030: val_loss -0.7543 +2024-09-10 08:51:20.038779: Pseudo dice [0.9647, 0.9591, 0.9775, 0.947, 0.8879, 0.9738, 0.9921] +2024-09-10 08:51:20.039443: Epoch time: 274.55 s +2024-09-10 08:51:23.190942: +2024-09-10 08:51:23.192009: Epoch 752 +2024-09-10 08:51:23.192881: Current learning rate: 0.00285 +2024-09-10 08:56:08.096821: train_loss -0.7386 +2024-09-10 08:56:08.098192: val_loss -0.7499 +2024-09-10 08:56:08.098997: Pseudo dice [0.9652, 0.9595, 0.9755, 0.9518, 0.8958, 0.9756, 0.9934] +2024-09-10 08:56:08.099638: Epoch time: 284.91 s +2024-09-10 08:56:12.843880: +2024-09-10 08:56:12.845015: Epoch 753 +2024-09-10 08:56:12.845893: Current learning rate: 0.00284 +2024-09-10 09:00:57.979101: train_loss -0.7563 +2024-09-10 09:00:57.993392: val_loss -0.7397 +2024-09-10 09:00:57.994401: Pseudo dice [0.9659, 0.9604, 0.9762, 0.9584, 0.9022, 0.974, 0.993] +2024-09-10 09:00:57.995535: Epoch time: 285.14 s +2024-09-10 09:01:00.157688: +2024-09-10 09:01:00.158619: Epoch 754 +2024-09-10 09:01:00.159293: Current learning rate: 0.00283 +2024-09-10 09:05:15.258817: train_loss -0.7527 +2024-09-10 09:05:15.270063: val_loss -0.7508 +2024-09-10 09:05:15.270903: Pseudo dice [0.9674, 0.96, 0.9796, 0.9507, 0.8903, 0.9734, 0.9919] +2024-09-10 09:05:15.271605: Epoch time: 255.11 s +2024-09-10 09:05:20.054914: +2024-09-10 09:05:20.080099: Epoch 755 +2024-09-10 09:05:20.081048: Current learning rate: 0.00282 +2024-09-10 09:09:47.745826: train_loss -0.7506 +2024-09-10 09:09:47.751241: val_loss -0.7534 +2024-09-10 09:09:47.751932: Pseudo dice [0.9663, 0.9618, 0.9789, 0.9558, 0.8938, 0.9719, 0.9934] +2024-09-10 09:09:47.752685: Epoch time: 267.7 s +2024-09-10 09:09:50.793220: +2024-09-10 09:09:50.802412: Epoch 756 +2024-09-10 09:09:50.803166: Current learning rate: 0.00281 +2024-09-10 09:14:21.499976: train_loss -0.7647 +2024-09-10 09:14:21.526414: val_loss -0.7525 +2024-09-10 09:14:21.527222: Pseudo dice [0.9655, 0.9587, 0.9761, 0.9564, 0.8879, 0.9735, 0.9921] +2024-09-10 09:14:21.527964: Epoch time: 270.71 s +2024-09-10 09:14:24.759808: +2024-09-10 09:14:24.768687: Epoch 757 +2024-09-10 09:14:24.769544: Current learning rate: 0.0028 +2024-09-10 09:19:07.335049: train_loss -0.7532 +2024-09-10 09:19:07.361589: val_loss -0.7868 +2024-09-10 09:19:07.363004: Pseudo dice [0.9639, 0.9604, 0.9759, 0.9501, 0.8882, 0.971, 0.9924] +2024-09-10 09:19:07.364091: Epoch time: 282.58 s +2024-09-10 09:19:10.123456: +2024-09-10 09:19:10.137060: Epoch 758 +2024-09-10 09:19:10.137874: Current learning rate: 0.00279 +2024-09-10 09:23:46.017158: train_loss -0.7445 +2024-09-10 09:23:46.038185: val_loss -0.7686 +2024-09-10 09:23:46.039153: Pseudo dice [0.9667, 0.9615, 0.9799, 0.9554, 0.8645, 0.9715, 0.9908] +2024-09-10 09:23:46.039918: Epoch time: 275.9 s +2024-09-10 09:23:49.713908: +2024-09-10 09:23:49.728679: Epoch 759 +2024-09-10 09:23:49.729467: Current learning rate: 0.00278 +2024-09-10 09:28:23.889709: train_loss -0.729 +2024-09-10 09:28:23.918021: val_loss -0.7649 +2024-09-10 09:28:23.918806: Pseudo dice [0.9656, 0.9593, 0.9782, 0.9565, 0.8867, 0.9735, 0.9918] +2024-09-10 09:28:23.919564: Epoch time: 274.18 s +2024-09-10 09:28:27.198659: +2024-09-10 09:28:27.199819: Epoch 760 +2024-09-10 09:28:27.200854: Current learning rate: 0.00277 +2024-09-10 09:32:47.428159: train_loss -0.7479 +2024-09-10 09:32:47.446044: val_loss -0.7862 +2024-09-10 09:32:47.446729: Pseudo dice [0.9638, 0.96, 0.9772, 0.9524, 0.8701, 0.9718, 0.9904] +2024-09-10 09:32:47.447450: Epoch time: 260.24 s +2024-09-10 09:32:51.931977: +2024-09-10 09:32:51.933241: Epoch 761 +2024-09-10 09:32:51.934034: Current learning rate: 0.00276 +2024-09-10 09:37:22.912038: train_loss -0.7617 +2024-09-10 09:37:22.933659: val_loss -0.7576 +2024-09-10 09:37:22.934475: Pseudo dice [0.9656, 0.9614, 0.9792, 0.9529, 0.8839, 0.9754, 0.9913] +2024-09-10 09:37:22.935304: Epoch time: 270.99 s +2024-09-10 09:37:26.092892: +2024-09-10 09:37:26.093791: Epoch 762 +2024-09-10 09:37:26.094435: Current learning rate: 0.00275 +2024-09-10 09:41:47.321125: train_loss -0.7498 +2024-09-10 09:41:47.333484: val_loss -0.7146 +2024-09-10 09:41:47.334680: Pseudo dice [0.9568, 0.9595, 0.9691, 0.9555, 0.8904, 0.9717, 0.9913] +2024-09-10 09:41:47.335423: Epoch time: 261.23 s +2024-09-10 09:41:50.553252: +2024-09-10 09:41:50.568718: Epoch 763 +2024-09-10 09:41:50.569501: Current learning rate: 0.00274 +2024-09-10 09:46:31.389815: train_loss -0.7346 +2024-09-10 09:46:31.405692: val_loss -0.763 +2024-09-10 09:46:31.406619: Pseudo dice [0.9667, 0.9593, 0.9817, 0.9518, 0.8918, 0.9727, 0.9919] +2024-09-10 09:46:31.407262: Epoch time: 280.84 s +2024-09-10 09:46:34.124424: +2024-09-10 09:46:34.132689: Epoch 764 +2024-09-10 09:46:34.133490: Current learning rate: 0.00273 +2024-09-10 09:51:03.203876: train_loss -0.7393 +2024-09-10 09:51:03.225180: val_loss -0.7784 +2024-09-10 09:51:03.225989: Pseudo dice [0.965, 0.9616, 0.9765, 0.9583, 0.8942, 0.976, 0.9926] +2024-09-10 09:51:03.226639: Epoch time: 269.08 s +2024-09-10 09:51:06.120143: +2024-09-10 09:51:06.132885: Epoch 765 +2024-09-10 09:51:06.133761: Current learning rate: 0.00272 +2024-09-10 09:55:35.365497: train_loss -0.7454 +2024-09-10 09:55:35.367825: val_loss -0.768 +2024-09-10 09:55:35.368595: Pseudo dice [0.9662, 0.9599, 0.9821, 0.9526, 0.8701, 0.9713, 0.9902] +2024-09-10 09:55:35.369610: Epoch time: 269.25 s +2024-09-10 09:55:38.812304: +2024-09-10 09:55:38.820614: Epoch 766 +2024-09-10 09:55:38.821377: Current learning rate: 0.00271 +2024-09-10 10:00:26.637930: train_loss -0.7544 +2024-09-10 10:00:26.652208: val_loss -0.7315 +2024-09-10 10:00:26.665113: Pseudo dice [0.9667, 0.9625, 0.9774, 0.9459, 0.8881, 0.9736, 0.9923] +2024-09-10 10:00:26.666179: Epoch time: 287.83 s +2024-09-10 10:00:31.443436: +2024-09-10 10:00:31.456692: Epoch 767 +2024-09-10 10:00:31.457585: Current learning rate: 0.0027 +2024-09-10 10:05:04.108856: train_loss -0.7511 +2024-09-10 10:05:04.111141: val_loss -0.7668 +2024-09-10 10:05:04.111813: Pseudo dice [0.9662, 0.9582, 0.978, 0.9525, 0.8855, 0.9735, 0.9904] +2024-09-10 10:05:04.112511: Epoch time: 272.67 s +2024-09-10 10:05:06.680558: +2024-09-10 10:05:06.681480: Epoch 768 +2024-09-10 10:05:06.682318: Current learning rate: 0.00268 +2024-09-10 10:09:33.231072: train_loss -0.7491 +2024-09-10 10:09:33.261770: val_loss -0.7814 +2024-09-10 10:09:33.262483: Pseudo dice [0.9633, 0.9589, 0.9783, 0.9481, 0.884, 0.973, 0.992] +2024-09-10 10:09:33.263128: Epoch time: 266.55 s +2024-09-10 10:09:37.589595: +2024-09-10 10:09:37.591612: Epoch 769 +2024-09-10 10:09:37.593995: Current learning rate: 0.00267 +2024-09-10 10:14:16.733912: train_loss -0.737 +2024-09-10 10:14:16.735373: val_loss -0.7293 +2024-09-10 10:14:16.736026: Pseudo dice [0.9648, 0.9597, 0.9781, 0.9552, 0.8791, 0.9732, 0.9912] +2024-09-10 10:14:16.736663: Epoch time: 279.16 s +2024-09-10 10:14:19.725501: +2024-09-10 10:14:19.731769: Epoch 770 +2024-09-10 10:14:19.732817: Current learning rate: 0.00266 +2024-09-10 10:18:51.348286: train_loss -0.7532 +2024-09-10 10:18:51.365193: val_loss -0.7788 +2024-09-10 10:18:51.365956: Pseudo dice [0.9655, 0.959, 0.9783, 0.9562, 0.894, 0.9726, 0.9923] +2024-09-10 10:18:51.366598: Epoch time: 271.63 s +2024-09-10 10:18:53.996279: +2024-09-10 10:18:53.997187: Epoch 771 +2024-09-10 10:18:53.997847: Current learning rate: 0.00265 +2024-09-10 10:23:38.695143: train_loss -0.74 +2024-09-10 10:23:38.720885: val_loss -0.7545 +2024-09-10 10:23:38.721824: Pseudo dice [0.9647, 0.959, 0.9747, 0.9527, 0.8882, 0.9733, 0.9935] +2024-09-10 10:23:38.722610: Epoch time: 284.7 s +2024-09-10 10:23:42.942196: +2024-09-10 10:23:42.943326: Epoch 772 +2024-09-10 10:23:42.944252: Current learning rate: 0.00264 +2024-09-10 10:28:27.923942: train_loss -0.7411 +2024-09-10 10:28:27.929074: val_loss -0.7626 +2024-09-10 10:28:27.929868: Pseudo dice [0.966, 0.9612, 0.9759, 0.9534, 0.8981, 0.976, 0.9931] +2024-09-10 10:28:27.930484: Epoch time: 284.99 s +2024-09-10 10:28:32.065212: +2024-09-10 10:28:32.066511: Epoch 773 +2024-09-10 10:28:32.067899: Current learning rate: 0.00263 +2024-09-10 10:32:42.515910: train_loss -0.7516 +2024-09-10 10:32:42.529145: val_loss -0.7538 +2024-09-10 10:32:42.530013: Pseudo dice [0.9647, 0.9595, 0.9753, 0.9556, 0.8851, 0.9744, 0.9923] +2024-09-10 10:32:42.530667: Epoch time: 250.47 s +2024-09-10 10:32:47.003989: +2024-09-10 10:32:47.004910: Epoch 774 +2024-09-10 10:32:47.005627: Current learning rate: 0.00262 +2024-09-10 10:37:32.858699: train_loss -0.7483 +2024-09-10 10:37:32.865889: val_loss -0.7834 +2024-09-10 10:37:32.866681: Pseudo dice [0.9659, 0.9616, 0.9795, 0.9542, 0.8845, 0.9718, 0.9921] +2024-09-10 10:37:32.867381: Epoch time: 285.86 s +2024-09-10 10:37:35.218268: +2024-09-10 10:37:35.219304: Epoch 775 +2024-09-10 10:37:35.219955: Current learning rate: 0.00261 +2024-09-10 10:41:58.503946: train_loss -0.7478 +2024-09-10 10:41:58.521163: val_loss -0.782 +2024-09-10 10:41:58.521954: Pseudo dice [0.9672, 0.9601, 0.9817, 0.9485, 0.8961, 0.9716, 0.9935] +2024-09-10 10:41:58.522770: Epoch time: 263.29 s +2024-09-10 10:42:00.984329: +2024-09-10 10:42:00.985309: Epoch 776 +2024-09-10 10:42:00.986096: Current learning rate: 0.0026 +2024-09-10 10:46:38.037031: train_loss -0.7502 +2024-09-10 10:46:38.052569: val_loss -0.7277 +2024-09-10 10:46:38.053330: Pseudo dice [0.9643, 0.9587, 0.976, 0.9548, 0.8944, 0.9735, 0.9919] +2024-09-10 10:46:38.054091: Epoch time: 277.06 s +2024-09-10 10:46:41.022788: +2024-09-10 10:46:41.023678: Epoch 777 +2024-09-10 10:46:41.024327: Current learning rate: 0.00259 +2024-09-10 10:51:00.896979: train_loss -0.7532 +2024-09-10 10:51:00.913208: val_loss -0.7731 +2024-09-10 10:51:00.913995: Pseudo dice [0.9695, 0.9626, 0.9842, 0.9578, 0.8972, 0.974, 0.9928] +2024-09-10 10:51:00.914809: Epoch time: 259.88 s +2024-09-10 10:51:04.986209: +2024-09-10 10:51:04.987231: Epoch 778 +2024-09-10 10:51:04.988397: Current learning rate: 0.00258 +2024-09-10 10:55:41.708566: train_loss -0.7409 +2024-09-10 10:55:41.717151: val_loss -0.7625 +2024-09-10 10:55:41.717931: Pseudo dice [0.9693, 0.9599, 0.98, 0.9525, 0.8806, 0.9749, 0.991] +2024-09-10 10:55:41.718581: Epoch time: 276.73 s +2024-09-10 10:55:45.025611: +2024-09-10 10:55:45.028231: Epoch 779 +2024-09-10 10:55:45.034108: Current learning rate: 0.00257 +2024-09-10 11:00:38.632519: train_loss -0.7614 +2024-09-10 11:00:38.649257: val_loss -0.7654 +2024-09-10 11:00:38.650239: Pseudo dice [0.9646, 0.9601, 0.9794, 0.9545, 0.8838, 0.9744, 0.9926] +2024-09-10 11:00:38.651054: Epoch time: 293.61 s +2024-09-10 11:00:42.751601: +2024-09-10 11:00:42.764627: Epoch 780 +2024-09-10 11:00:42.765349: Current learning rate: 0.00256 +2024-09-10 11:05:04.859772: train_loss -0.7439 +2024-09-10 11:05:04.882443: val_loss -0.73 +2024-09-10 11:05:04.883183: Pseudo dice [0.9657, 0.9616, 0.9783, 0.9514, 0.8887, 0.9738, 0.992] +2024-09-10 11:05:04.883878: Epoch time: 262.11 s +2024-09-10 11:05:08.368895: +2024-09-10 11:05:08.384966: Epoch 781 +2024-09-10 11:05:08.386016: Current learning rate: 0.00255 +2024-09-10 11:09:33.174947: train_loss -0.7369 +2024-09-10 11:09:33.177855: val_loss -0.7418 +2024-09-10 11:09:33.178903: Pseudo dice [0.9646, 0.96, 0.9766, 0.9495, 0.8969, 0.9755, 0.9917] +2024-09-10 11:09:33.179621: Epoch time: 264.81 s +2024-09-10 11:09:36.812059: +2024-09-10 11:09:36.817339: Epoch 782 +2024-09-10 11:09:36.818130: Current learning rate: 0.00254 +2024-09-10 11:13:58.896446: train_loss -0.761 +2024-09-10 11:13:58.925739: val_loss -0.79 +2024-09-10 11:13:58.926452: Pseudo dice [0.9674, 0.9629, 0.9804, 0.9602, 0.8958, 0.9757, 0.9926] +2024-09-10 11:13:58.927076: Epoch time: 262.09 s +2024-09-10 11:14:02.428624: +2024-09-10 11:14:02.429664: Epoch 783 +2024-09-10 11:14:02.430367: Current learning rate: 0.00253 +2024-09-10 11:18:46.176857: train_loss -0.759 +2024-09-10 11:18:46.179015: val_loss -0.7567 +2024-09-10 11:18:46.179701: Pseudo dice [0.9676, 0.9617, 0.9817, 0.9539, 0.8986, 0.9751, 0.9926] +2024-09-10 11:18:46.180408: Epoch time: 283.75 s +2024-09-10 11:18:46.181051: Yayy! New best EMA pseudo Dice: 0.9594 +2024-09-10 11:18:51.626732: +2024-09-10 11:18:51.627573: Epoch 784 +2024-09-10 11:18:51.628257: Current learning rate: 0.00252 +2024-09-10 11:23:13.626920: train_loss -0.7412 +2024-09-10 11:23:13.629616: val_loss -0.7726 +2024-09-10 11:23:13.630358: Pseudo dice [0.9648, 0.9592, 0.9797, 0.9531, 0.8915, 0.9721, 0.9924] +2024-09-10 11:23:13.631025: Epoch time: 262.0 s +2024-09-10 11:23:17.518887: +2024-09-10 11:23:17.519918: Epoch 785 +2024-09-10 11:23:17.520760: Current learning rate: 0.00251 +2024-09-10 11:28:00.373871: train_loss -0.7473 +2024-09-10 11:28:00.375315: val_loss -0.7627 +2024-09-10 11:28:00.376602: Pseudo dice [0.9653, 0.9613, 0.9791, 0.9441, 0.8782, 0.9714, 0.9913] +2024-09-10 11:28:00.377355: Epoch time: 282.86 s +2024-09-10 11:28:03.366188: +2024-09-10 11:28:03.380782: Epoch 786 +2024-09-10 11:28:03.381538: Current learning rate: 0.0025 +2024-09-10 11:32:19.687177: train_loss -0.7456 +2024-09-10 11:32:19.690336: val_loss -0.7973 +2024-09-10 11:32:19.691557: Pseudo dice [0.9684, 0.9594, 0.9818, 0.9551, 0.8876, 0.9721, 0.9919] +2024-09-10 11:32:19.692772: Epoch time: 256.33 s +2024-09-10 11:32:23.475413: +2024-09-10 11:32:23.476287: Epoch 787 +2024-09-10 11:32:23.477024: Current learning rate: 0.00249 +2024-09-10 11:36:55.039962: train_loss -0.7389 +2024-09-10 11:36:55.055740: val_loss -0.8075 +2024-09-10 11:36:55.056499: Pseudo dice [0.9654, 0.9613, 0.9782, 0.9525, 0.8891, 0.9755, 0.9914] +2024-09-10 11:36:55.057285: Epoch time: 271.57 s +2024-09-10 11:36:58.306669: +2024-09-10 11:36:58.309920: Epoch 788 +2024-09-10 11:36:58.312501: Current learning rate: 0.00248 +2024-09-10 11:41:26.493209: train_loss -0.758 +2024-09-10 11:41:26.506750: val_loss -0.7787 +2024-09-10 11:41:26.507615: Pseudo dice [0.9687, 0.9619, 0.9809, 0.9525, 0.8953, 0.973, 0.9912] +2024-09-10 11:41:26.508475: Epoch time: 268.19 s +2024-09-10 11:41:29.937370: +2024-09-10 11:41:29.938737: Epoch 789 +2024-09-10 11:41:29.939743: Current learning rate: 0.00247 +2024-09-10 11:46:01.337509: train_loss -0.7521 +2024-09-10 11:46:01.340455: val_loss -0.7656 +2024-09-10 11:46:01.341172: Pseudo dice [0.9649, 0.9602, 0.9777, 0.9512, 0.8837, 0.9732, 0.9923] +2024-09-10 11:46:01.341874: Epoch time: 271.41 s +2024-09-10 11:46:03.809068: +2024-09-10 11:46:03.809949: Epoch 790 +2024-09-10 11:46:03.810909: Current learning rate: 0.00245 +2024-09-10 11:50:33.264998: train_loss -0.7617 +2024-09-10 11:50:33.299623: val_loss -0.7688 +2024-09-10 11:50:33.311521: Pseudo dice [0.9627, 0.956, 0.9761, 0.9599, 0.885, 0.975, 0.9916] +2024-09-10 11:50:33.334716: Epoch time: 269.46 s +2024-09-10 11:50:36.382442: +2024-09-10 11:50:36.383272: Epoch 791 +2024-09-10 11:50:36.383870: Current learning rate: 0.00244 +2024-09-10 11:55:05.246642: train_loss -0.7558 +2024-09-10 11:55:05.249059: val_loss -0.7728 +2024-09-10 11:55:05.254995: Pseudo dice [0.9675, 0.9593, 0.9803, 0.9535, 0.8817, 0.9729, 0.9914] +2024-09-10 11:55:05.255929: Epoch time: 268.87 s +2024-09-10 11:55:08.542973: +2024-09-10 11:55:08.556674: Epoch 792 +2024-09-10 11:55:08.557711: Current learning rate: 0.00243 +2024-09-10 11:59:41.383858: train_loss -0.7571 +2024-09-10 11:59:41.401540: val_loss -0.7009 +2024-09-10 11:59:41.402588: Pseudo dice [0.9682, 0.963, 0.9809, 0.956, 0.8955, 0.9746, 0.9925] +2024-09-10 11:59:41.403545: Epoch time: 272.85 s +2024-09-10 11:59:44.315031: +2024-09-10 11:59:44.316364: Epoch 793 +2024-09-10 11:59:44.317360: Current learning rate: 0.00242 +2024-09-10 12:04:25.562105: train_loss -0.7534 +2024-09-10 12:04:25.577135: val_loss -0.7762 +2024-09-10 12:04:25.578217: Pseudo dice [0.9666, 0.9626, 0.9793, 0.9573, 0.8887, 0.9712, 0.9914] +2024-09-10 12:04:25.579083: Epoch time: 281.25 s +2024-09-10 12:04:28.677011: +2024-09-10 12:04:28.678107: Epoch 794 +2024-09-10 12:04:28.678882: Current learning rate: 0.00241 +2024-09-10 12:08:45.943423: train_loss -0.7525 +2024-09-10 12:08:45.952825: val_loss -0.7734 +2024-09-10 12:08:45.953832: Pseudo dice [0.9657, 0.9619, 0.9792, 0.9565, 0.899, 0.9768, 0.9925] +2024-09-10 12:08:45.954717: Epoch time: 257.27 s +2024-09-10 12:08:45.955767: Yayy! New best EMA pseudo Dice: 0.9594 +2024-09-10 12:08:51.786339: +2024-09-10 12:08:51.787166: Epoch 795 +2024-09-10 12:08:51.787774: Current learning rate: 0.0024 +2024-09-10 12:13:01.269488: train_loss -0.7353 +2024-09-10 12:13:01.286602: val_loss -0.782 +2024-09-10 12:13:01.287559: Pseudo dice [0.9641, 0.9576, 0.9791, 0.9534, 0.8876, 0.9738, 0.9922] +2024-09-10 12:13:01.288354: Epoch time: 249.49 s +2024-09-10 12:13:04.104627: +2024-09-10 12:13:04.116879: Epoch 796 +2024-09-10 12:13:04.118062: Current learning rate: 0.00239 +2024-09-10 12:17:09.507604: train_loss -0.75 +2024-09-10 12:17:09.521744: val_loss -0.7628 +2024-09-10 12:17:09.522917: Pseudo dice [0.9639, 0.9619, 0.9734, 0.9437, 0.894, 0.974, 0.9929] +2024-09-10 12:17:09.523695: Epoch time: 245.41 s +2024-09-10 12:17:13.067760: +2024-09-10 12:17:13.068675: Epoch 797 +2024-09-10 12:17:13.069330: Current learning rate: 0.00238 +2024-09-10 12:21:33.046328: train_loss -0.7612 +2024-09-10 12:21:33.048314: val_loss -0.7502 +2024-09-10 12:21:33.048939: Pseudo dice [0.9611, 0.9605, 0.9744, 0.9512, 0.8955, 0.9757, 0.9924] +2024-09-10 12:21:33.049511: Epoch time: 259.98 s +2024-09-10 12:21:36.542698: +2024-09-10 12:21:36.543828: Epoch 798 +2024-09-10 12:21:36.544730: Current learning rate: 0.00237 +2024-09-10 12:25:57.048794: train_loss -0.7472 +2024-09-10 12:25:57.051198: val_loss -0.753 +2024-09-10 12:25:57.051840: Pseudo dice [0.9649, 0.961, 0.9799, 0.9563, 0.893, 0.9742, 0.9922] +2024-09-10 12:25:57.068815: Epoch time: 260.51 s +2024-09-10 12:25:59.818593: +2024-09-10 12:25:59.819678: Epoch 799 +2024-09-10 12:25:59.820433: Current learning rate: 0.00236 +2024-09-10 12:30:25.920108: train_loss -0.7506 +2024-09-10 12:30:25.938066: val_loss -0.7658 +2024-09-10 12:30:25.939027: Pseudo dice [0.9662, 0.9567, 0.9799, 0.9516, 0.882, 0.9741, 0.9904] +2024-09-10 12:30:25.939930: Epoch time: 266.11 s +2024-09-10 12:30:31.460611: +2024-09-10 12:30:31.461456: Epoch 800 +2024-09-10 12:30:31.462122: Current learning rate: 0.00235 +2024-09-10 12:34:37.763186: train_loss -0.7743 +2024-09-10 12:34:37.785085: val_loss -0.7706 +2024-09-10 12:34:37.785992: Pseudo dice [0.9644, 0.9614, 0.9774, 0.9549, 0.8761, 0.9746, 0.9911] +2024-09-10 12:34:37.786748: Epoch time: 246.31 s +2024-09-10 12:34:41.217731: +2024-09-10 12:34:41.232953: Epoch 801 +2024-09-10 12:34:41.234439: Current learning rate: 0.00234 +2024-09-10 12:39:09.986698: train_loss -0.7515 +2024-09-10 12:39:10.001194: val_loss -0.7894 +2024-09-10 12:39:10.002336: Pseudo dice [0.9659, 0.9583, 0.9784, 0.9566, 0.8979, 0.9756, 0.9935] +2024-09-10 12:39:10.003530: Epoch time: 268.77 s +2024-09-10 12:39:13.159288: +2024-09-10 12:39:13.176107: Epoch 802 +2024-09-10 12:39:13.177072: Current learning rate: 0.00233 +2024-09-10 12:43:40.440964: train_loss -0.7379 +2024-09-10 12:43:40.453672: val_loss -0.787 +2024-09-10 12:43:40.455058: Pseudo dice [0.9674, 0.9592, 0.9793, 0.9566, 0.899, 0.975, 0.9926] +2024-09-10 12:43:40.456075: Epoch time: 267.29 s +2024-09-10 12:43:43.841154: +2024-09-10 12:43:43.841984: Epoch 803 +2024-09-10 12:43:43.842512: Current learning rate: 0.00232 +2024-09-10 12:47:50.450423: train_loss -0.7608 +2024-09-10 12:47:50.451893: val_loss -0.7882 +2024-09-10 12:47:50.452613: Pseudo dice [0.9696, 0.9611, 0.9818, 0.9521, 0.9021, 0.9768, 0.9931] +2024-09-10 12:47:50.453305: Epoch time: 246.61 s +2024-09-10 12:47:50.453949: Yayy! New best EMA pseudo Dice: 0.9596 +2024-09-10 12:47:55.508371: +2024-09-10 12:47:55.509255: Epoch 804 +2024-09-10 12:47:55.509916: Current learning rate: 0.00231 +2024-09-10 12:52:20.224671: train_loss -0.7451 +2024-09-10 12:52:20.225940: val_loss -0.7682 +2024-09-10 12:52:20.226677: Pseudo dice [0.9661, 0.9624, 0.978, 0.9546, 0.8998, 0.9737, 0.9931] +2024-09-10 12:52:20.227304: Epoch time: 264.72 s +2024-09-10 12:52:20.227843: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-10 12:52:26.463170: +2024-09-10 12:52:26.464264: Epoch 805 +2024-09-10 12:52:26.464991: Current learning rate: 0.0023 +2024-09-10 12:56:47.400025: train_loss -0.7441 +2024-09-10 12:56:47.409260: val_loss -0.786 +2024-09-10 12:56:47.410394: Pseudo dice [0.9644, 0.9594, 0.9745, 0.9549, 0.892, 0.973, 0.9921] +2024-09-10 12:56:47.411193: Epoch time: 260.94 s +2024-09-10 12:56:51.681348: +2024-09-10 12:56:51.682188: Epoch 806 +2024-09-10 12:56:51.682814: Current learning rate: 0.00229 +2024-09-10 13:01:28.010404: train_loss -0.7564 +2024-09-10 13:01:28.011883: val_loss -0.7725 +2024-09-10 13:01:28.012684: Pseudo dice [0.9676, 0.9628, 0.9774, 0.9554, 0.8831, 0.9744, 0.9923] +2024-09-10 13:01:28.013902: Epoch time: 276.33 s +2024-09-10 13:01:32.326285: +2024-09-10 13:01:32.327482: Epoch 807 +2024-09-10 13:01:32.328262: Current learning rate: 0.00228 +2024-09-10 13:06:01.962257: train_loss -0.7574 +2024-09-10 13:06:01.963556: val_loss -0.7599 +2024-09-10 13:06:01.980212: Pseudo dice [0.9683, 0.9635, 0.9811, 0.9532, 0.8962, 0.9748, 0.9917] +2024-09-10 13:06:01.981076: Epoch time: 269.64 s +2024-09-10 13:06:06.482152: +2024-09-10 13:06:06.483193: Epoch 808 +2024-09-10 13:06:06.483888: Current learning rate: 0.00226 +2024-09-10 13:10:35.968713: train_loss -0.7463 +2024-09-10 13:10:35.969954: val_loss -0.7904 +2024-09-10 13:10:35.970677: Pseudo dice [0.9666, 0.9601, 0.9797, 0.9522, 0.8864, 0.9745, 0.9929] +2024-09-10 13:10:35.971427: Epoch time: 269.49 s +2024-09-10 13:10:39.757042: +2024-09-10 13:10:39.776782: Epoch 809 +2024-09-10 13:10:39.777725: Current learning rate: 0.00225 +2024-09-10 13:14:58.064539: train_loss -0.7586 +2024-09-10 13:14:58.081088: val_loss -0.7706 +2024-09-10 13:14:58.081853: Pseudo dice [0.9656, 0.9615, 0.9776, 0.9517, 0.8933, 0.9746, 0.9921] +2024-09-10 13:14:58.082483: Epoch time: 258.31 s +2024-09-10 13:15:01.453689: +2024-09-10 13:15:01.456841: Epoch 810 +2024-09-10 13:15:01.457966: Current learning rate: 0.00224 +2024-09-10 13:19:20.748818: train_loss -0.7608 +2024-09-10 13:19:20.759094: val_loss -0.7374 +2024-09-10 13:19:20.762766: Pseudo dice [0.9657, 0.9615, 0.9787, 0.9552, 0.8964, 0.9747, 0.9932] +2024-09-10 13:19:20.766352: Epoch time: 259.3 s +2024-09-10 13:19:20.769150: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-10 13:19:26.664395: +2024-09-10 13:19:26.665428: Epoch 811 +2024-09-10 13:19:26.666143: Current learning rate: 0.00223 +2024-09-10 13:23:38.193120: train_loss -0.7456 +2024-09-10 13:23:38.205553: val_loss -0.7587 +2024-09-10 13:23:38.207197: Pseudo dice [0.9667, 0.9612, 0.9789, 0.9553, 0.8935, 0.9731, 0.9924] +2024-09-10 13:23:38.209072: Epoch time: 251.53 s +2024-09-10 13:23:38.210831: Yayy! New best EMA pseudo Dice: 0.9598 +2024-09-10 13:23:45.592927: +2024-09-10 13:23:45.594059: Epoch 812 +2024-09-10 13:23:45.594941: Current learning rate: 0.00222 +2024-09-10 13:28:14.971419: train_loss -0.7566 +2024-09-10 13:28:14.990053: val_loss -0.7714 +2024-09-10 13:28:14.990911: Pseudo dice [0.9675, 0.9608, 0.9797, 0.9562, 0.9033, 0.9768, 0.9937] +2024-09-10 13:28:14.991605: Epoch time: 269.38 s +2024-09-10 13:28:14.992368: Yayy! New best EMA pseudo Dice: 0.9601 +2024-09-10 13:28:20.905430: +2024-09-10 13:28:20.906395: Epoch 813 +2024-09-10 13:28:20.907092: Current learning rate: 0.00221 +2024-09-10 13:32:18.675692: train_loss -0.7606 +2024-09-10 13:32:18.677576: val_loss -0.7422 +2024-09-10 13:32:18.678085: Pseudo dice [0.9676, 0.9592, 0.9815, 0.9595, 0.9012, 0.9766, 0.9928] +2024-09-10 13:32:18.678622: Epoch time: 237.77 s +2024-09-10 13:32:18.679141: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-10 13:32:25.094104: +2024-09-10 13:32:25.094914: Epoch 814 +2024-09-10 13:32:25.095595: Current learning rate: 0.0022 +2024-09-10 13:36:31.549059: train_loss -0.7588 +2024-09-10 13:36:31.551370: val_loss -0.756 +2024-09-10 13:36:31.556338: Pseudo dice [0.9668, 0.9629, 0.9801, 0.9577, 0.8901, 0.9753, 0.991] +2024-09-10 13:36:31.557100: Epoch time: 246.46 s +2024-09-10 13:36:31.557813: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-10 13:36:36.490734: +2024-09-10 13:36:36.495789: Epoch 815 +2024-09-10 13:36:36.499167: Current learning rate: 0.00219 +2024-09-10 13:40:41.949729: train_loss -0.7594 +2024-09-10 13:40:41.956130: val_loss -0.7987 +2024-09-10 13:40:41.957124: Pseudo dice [0.9649, 0.9593, 0.9789, 0.952, 0.8955, 0.9756, 0.9925] +2024-09-10 13:40:41.957953: Epoch time: 245.46 s +2024-09-10 13:40:46.272420: +2024-09-10 13:40:46.273369: Epoch 816 +2024-09-10 13:40:46.274091: Current learning rate: 0.00218 +2024-09-10 13:44:55.469277: train_loss -0.7443 +2024-09-10 13:44:55.486390: val_loss -0.7585 +2024-09-10 13:44:55.487331: Pseudo dice [0.9669, 0.9599, 0.9785, 0.9553, 0.8882, 0.9727, 0.9923] +2024-09-10 13:44:55.488195: Epoch time: 249.2 s +2024-09-10 13:44:58.366167: +2024-09-10 13:44:58.367750: Epoch 817 +2024-09-10 13:44:58.369761: Current learning rate: 0.00217 +2024-09-10 13:49:13.737199: train_loss -0.7412 +2024-09-10 13:49:13.740081: val_loss -0.7524 +2024-09-10 13:49:13.741000: Pseudo dice [0.9676, 0.9586, 0.9803, 0.9595, 0.8819, 0.9759, 0.991] +2024-09-10 13:49:13.741736: Epoch time: 255.38 s +2024-09-10 13:49:16.578977: +2024-09-10 13:49:16.580043: Epoch 818 +2024-09-10 13:49:16.580791: Current learning rate: 0.00216 +2024-09-10 13:53:37.222984: train_loss -0.7445 +2024-09-10 13:53:37.225618: val_loss -0.7552 +2024-09-10 13:53:37.226455: Pseudo dice [0.9646, 0.9599, 0.9773, 0.9535, 0.8976, 0.9733, 0.9939] +2024-09-10 13:53:37.227141: Epoch time: 260.65 s +2024-09-10 13:53:40.143013: +2024-09-10 13:53:40.157051: Epoch 819 +2024-09-10 13:53:40.158377: Current learning rate: 0.00215 +2024-09-10 13:57:57.942463: train_loss -0.7653 +2024-09-10 13:57:57.955204: val_loss -0.7752 +2024-09-10 13:57:57.957662: Pseudo dice [0.9658, 0.9615, 0.9783, 0.9553, 0.89, 0.9761, 0.9927] +2024-09-10 13:57:57.960106: Epoch time: 257.8 s +2024-09-10 13:58:01.260898: +2024-09-10 13:58:01.261674: Epoch 820 +2024-09-10 13:58:01.262478: Current learning rate: 0.00214 +2024-09-10 14:02:27.911285: train_loss -0.7406 +2024-09-10 14:02:27.933463: val_loss -0.7667 +2024-09-10 14:02:27.935368: Pseudo dice [0.9646, 0.9587, 0.975, 0.9522, 0.878, 0.9697, 0.9891] +2024-09-10 14:02:27.937262: Epoch time: 266.66 s +2024-09-10 14:02:31.527881: +2024-09-10 14:02:31.540738: Epoch 821 +2024-09-10 14:02:31.541540: Current learning rate: 0.00213 +2024-09-10 14:06:43.141792: train_loss -0.7428 +2024-09-10 14:06:43.143959: val_loss -0.769 +2024-09-10 14:06:43.144826: Pseudo dice [0.9635, 0.9614, 0.9768, 0.9539, 0.8932, 0.9698, 0.9922] +2024-09-10 14:06:43.145553: Epoch time: 251.62 s +2024-09-10 14:06:46.936074: +2024-09-10 14:06:46.936911: Epoch 822 +2024-09-10 14:06:46.937520: Current learning rate: 0.00212 +2024-09-10 14:11:08.576352: train_loss -0.7457 +2024-09-10 14:11:08.587956: val_loss -0.7611 +2024-09-10 14:11:08.589103: Pseudo dice [0.9677, 0.9611, 0.9808, 0.9572, 0.8963, 0.9761, 0.9924] +2024-09-10 14:11:08.589952: Epoch time: 261.64 s +2024-09-10 14:11:11.947437: +2024-09-10 14:11:11.948400: Epoch 823 +2024-09-10 14:11:11.949055: Current learning rate: 0.0021 +2024-09-10 14:15:32.877398: train_loss -0.7428 +2024-09-10 14:15:32.900477: val_loss -0.7327 +2024-09-10 14:15:32.901241: Pseudo dice [0.9677, 0.9632, 0.9808, 0.9591, 0.9043, 0.9731, 0.993] +2024-09-10 14:15:32.902108: Epoch time: 260.94 s +2024-09-10 14:15:36.456340: +2024-09-10 14:15:36.457190: Epoch 824 +2024-09-10 14:15:36.457794: Current learning rate: 0.00209 +2024-09-10 14:19:51.542387: train_loss -0.7592 +2024-09-10 14:19:51.544873: val_loss -0.7784 +2024-09-10 14:19:51.545766: Pseudo dice [0.9664, 0.958, 0.9791, 0.9535, 0.8873, 0.9742, 0.9923] +2024-09-10 14:19:51.546852: Epoch time: 255.09 s +2024-09-10 14:19:55.261130: +2024-09-10 14:19:55.276812: Epoch 825 +2024-09-10 14:19:55.277887: Current learning rate: 0.00208 +2024-09-10 14:24:07.202195: train_loss -0.7398 +2024-09-10 14:24:07.225740: val_loss -0.7942 +2024-09-10 14:24:07.226897: Pseudo dice [0.9685, 0.9595, 0.9825, 0.9552, 0.8912, 0.9747, 0.9916] +2024-09-10 14:24:07.227908: Epoch time: 251.95 s +2024-09-10 14:24:09.383764: +2024-09-10 14:24:09.384817: Epoch 826 +2024-09-10 14:24:09.385389: Current learning rate: 0.00207 +2024-09-10 14:28:39.741878: train_loss -0.7667 +2024-09-10 14:28:39.761189: val_loss -0.7194 +2024-09-10 14:28:39.762919: Pseudo dice [0.9632, 0.9594, 0.9772, 0.9554, 0.8925, 0.9745, 0.9925] +2024-09-10 14:28:39.764608: Epoch time: 270.36 s +2024-09-10 14:28:42.479532: +2024-09-10 14:28:42.492077: Epoch 827 +2024-09-10 14:28:42.492928: Current learning rate: 0.00206 +2024-09-10 14:32:53.601400: train_loss -0.7758 +2024-09-10 14:32:53.618186: val_loss -0.7529 +2024-09-10 14:32:53.618849: Pseudo dice [0.969, 0.9631, 0.981, 0.954, 0.8955, 0.9766, 0.9933] +2024-09-10 14:32:53.619479: Epoch time: 251.13 s +2024-09-10 14:32:56.919050: +2024-09-10 14:32:56.933135: Epoch 828 +2024-09-10 14:32:56.934833: Current learning rate: 0.00205 +2024-09-10 14:37:24.506824: train_loss -0.751 +2024-09-10 14:37:24.509168: val_loss -0.7268 +2024-09-10 14:37:24.510023: Pseudo dice [0.9654, 0.9605, 0.9785, 0.9549, 0.881, 0.9752, 0.9926] +2024-09-10 14:37:24.510712: Epoch time: 267.59 s +2024-09-10 14:37:27.903501: +2024-09-10 14:37:27.904487: Epoch 829 +2024-09-10 14:37:27.905275: Current learning rate: 0.00204 +2024-09-10 14:41:46.159423: train_loss -0.7475 +2024-09-10 14:41:46.185652: val_loss -0.7455 +2024-09-10 14:41:46.186281: Pseudo dice [0.9607, 0.962, 0.973, 0.9581, 0.8939, 0.9767, 0.992] +2024-09-10 14:41:46.186882: Epoch time: 258.26 s +2024-09-10 14:41:50.227988: +2024-09-10 14:41:50.229044: Epoch 830 +2024-09-10 14:41:50.229995: Current learning rate: 0.00203 +2024-09-10 14:46:05.557125: train_loss -0.7652 +2024-09-10 14:46:05.559300: val_loss -0.7751 +2024-09-10 14:46:05.560082: Pseudo dice [0.9684, 0.9599, 0.9797, 0.953, 0.8946, 0.9765, 0.9934] +2024-09-10 14:46:05.560751: Epoch time: 255.34 s +2024-09-10 14:46:08.358084: +2024-09-10 14:46:08.358738: Epoch 831 +2024-09-10 14:46:08.359310: Current learning rate: 0.00202 +2024-09-10 14:50:38.034523: train_loss -0.7479 +2024-09-10 14:50:38.050160: val_loss -0.7964 +2024-09-10 14:50:38.050917: Pseudo dice [0.964, 0.9591, 0.9766, 0.9557, 0.8837, 0.974, 0.9917] +2024-09-10 14:50:38.051961: Epoch time: 269.68 s +2024-09-10 14:50:41.117444: +2024-09-10 14:50:41.128643: Epoch 832 +2024-09-10 14:50:41.129503: Current learning rate: 0.00201 +2024-09-10 14:55:13.487174: train_loss -0.7655 +2024-09-10 14:55:13.502158: val_loss -0.7334 +2024-09-10 14:55:13.503001: Pseudo dice [0.9602, 0.9585, 0.9771, 0.9585, 0.8933, 0.9753, 0.9919] +2024-09-10 14:55:13.503721: Epoch time: 272.37 s +2024-09-10 14:55:16.885527: +2024-09-10 14:55:16.886490: Epoch 833 +2024-09-10 14:55:16.887305: Current learning rate: 0.002 +2024-09-10 14:59:45.132639: train_loss -0.7547 +2024-09-10 14:59:45.150266: val_loss -0.7626 +2024-09-10 14:59:45.150979: Pseudo dice [0.9665, 0.9594, 0.9808, 0.9589, 0.8905, 0.9742, 0.9915] +2024-09-10 14:59:45.151694: Epoch time: 268.25 s +2024-09-10 14:59:47.556030: +2024-09-10 14:59:47.556928: Epoch 834 +2024-09-10 14:59:47.557728: Current learning rate: 0.00199 +2024-09-10 15:04:27.556920: train_loss -0.7626 +2024-09-10 15:04:27.582355: val_loss -0.782 +2024-09-10 15:04:27.583222: Pseudo dice [0.9686, 0.9621, 0.9817, 0.9577, 0.8986, 0.973, 0.9931] +2024-09-10 15:04:27.583963: Epoch time: 280.01 s +2024-09-10 15:04:31.384951: +2024-09-10 15:04:31.385962: Epoch 835 +2024-09-10 15:04:31.386626: Current learning rate: 0.00198 +2024-09-10 15:08:50.277501: train_loss -0.7441 +2024-09-10 15:08:50.281082: val_loss -0.726 +2024-09-10 15:08:50.281887: Pseudo dice [0.9664, 0.9593, 0.9799, 0.9558, 0.8903, 0.9744, 0.9928] +2024-09-10 15:08:50.282527: Epoch time: 258.9 s +2024-09-10 15:08:53.383605: +2024-09-10 15:08:53.384834: Epoch 836 +2024-09-10 15:08:53.385865: Current learning rate: 0.00196 +2024-09-10 15:13:13.133818: train_loss -0.7695 +2024-09-10 15:13:13.150229: val_loss -0.7625 +2024-09-10 15:13:13.150945: Pseudo dice [0.9657, 0.9606, 0.9793, 0.9541, 0.8945, 0.9753, 0.9924] +2024-09-10 15:13:13.151646: Epoch time: 259.76 s +2024-09-10 15:13:16.196116: +2024-09-10 15:13:16.203656: Epoch 837 +2024-09-10 15:13:16.204462: Current learning rate: 0.00195 +2024-09-10 15:17:36.370270: train_loss -0.758 +2024-09-10 15:17:36.385735: val_loss -0.7691 +2024-09-10 15:17:36.386474: Pseudo dice [0.9676, 0.9635, 0.982, 0.9615, 0.8864, 0.9723, 0.9912] +2024-09-10 15:17:36.387098: Epoch time: 260.18 s +2024-09-10 15:17:38.735930: +2024-09-10 15:17:38.736908: Epoch 838 +2024-09-10 15:17:38.737817: Current learning rate: 0.00194 +2024-09-10 15:22:03.143827: train_loss -0.7507 +2024-09-10 15:22:03.158038: val_loss -0.7546 +2024-09-10 15:22:03.158745: Pseudo dice [0.9663, 0.962, 0.9794, 0.9529, 0.9005, 0.9751, 0.9936] +2024-09-10 15:22:03.159439: Epoch time: 264.41 s +2024-09-10 15:22:06.456726: +2024-09-10 15:22:06.468964: Epoch 839 +2024-09-10 15:22:06.470356: Current learning rate: 0.00193 +2024-09-10 15:26:38.711827: train_loss -0.7593 +2024-09-10 15:26:38.726131: val_loss -0.7444 +2024-09-10 15:26:38.726901: Pseudo dice [0.9697, 0.9619, 0.9819, 0.9588, 0.8908, 0.9754, 0.9917] +2024-09-10 15:26:38.727517: Epoch time: 272.26 s +2024-09-10 15:26:41.584190: +2024-09-10 15:26:41.584958: Epoch 840 +2024-09-10 15:26:41.585653: Current learning rate: 0.00192 +2024-09-10 15:30:57.081916: train_loss -0.7511 +2024-09-10 15:30:57.103121: val_loss -0.7556 +2024-09-10 15:30:57.105935: Pseudo dice [0.9681, 0.9611, 0.9812, 0.9548, 0.8945, 0.9719, 0.992] +2024-09-10 15:30:57.107355: Epoch time: 255.5 s +2024-09-10 15:30:57.109253: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-10 15:31:04.290759: +2024-09-10 15:31:04.301278: Epoch 841 +2024-09-10 15:31:04.302316: Current learning rate: 0.00191 +2024-09-10 15:35:19.696826: train_loss -0.7454 +2024-09-10 15:35:19.710087: val_loss -0.7852 +2024-09-10 15:35:19.710998: Pseudo dice [0.9686, 0.9627, 0.9827, 0.9558, 0.9004, 0.9775, 0.9933] +2024-09-10 15:35:19.711752: Epoch time: 255.41 s +2024-09-10 15:35:19.712544: Yayy! New best EMA pseudo Dice: 0.9606 +2024-09-10 15:35:26.007027: +2024-09-10 15:35:26.008043: Epoch 842 +2024-09-10 15:35:26.008940: Current learning rate: 0.0019 +2024-09-10 15:39:39.880861: train_loss -0.751 +2024-09-10 15:39:39.891022: val_loss -0.7939 +2024-09-10 15:39:39.891757: Pseudo dice [0.968, 0.9606, 0.9796, 0.9559, 0.9022, 0.9747, 0.9928] +2024-09-10 15:39:39.892368: Epoch time: 253.88 s +2024-09-10 15:39:39.892992: Yayy! New best EMA pseudo Dice: 0.9607 +2024-09-10 15:39:45.851805: +2024-09-10 15:39:45.852653: Epoch 843 +2024-09-10 15:39:45.853267: Current learning rate: 0.00189 +2024-09-10 15:43:59.879504: train_loss -0.7433 +2024-09-10 15:43:59.881996: val_loss -0.7191 +2024-09-10 15:43:59.882814: Pseudo dice [0.9716, 0.9636, 0.9835, 0.9546, 0.8891, 0.9726, 0.9913] +2024-09-10 15:43:59.883413: Epoch time: 254.03 s +2024-09-10 15:43:59.883908: Yayy! New best EMA pseudo Dice: 0.9608 +2024-09-10 15:44:06.523413: +2024-09-10 15:44:06.524353: Epoch 844 +2024-09-10 15:44:06.524951: Current learning rate: 0.00188 +2024-09-10 15:48:16.504782: train_loss -0.7557 +2024-09-10 15:48:16.519065: val_loss -0.7559 +2024-09-10 15:48:16.519936: Pseudo dice [0.9652, 0.9622, 0.9766, 0.9506, 0.875, 0.974, 0.9911] +2024-09-10 15:48:16.520803: Epoch time: 249.99 s +2024-09-10 15:48:18.977286: +2024-09-10 15:48:18.978206: Epoch 845 +2024-09-10 15:48:18.978894: Current learning rate: 0.00187 +2024-09-10 15:52:29.596085: train_loss -0.7377 +2024-09-10 15:52:29.598480: val_loss -0.7829 +2024-09-10 15:52:29.599272: Pseudo dice [0.968, 0.9641, 0.9821, 0.9546, 0.8922, 0.9754, 0.992] +2024-09-10 15:52:29.600090: Epoch time: 250.62 s +2024-09-10 15:52:32.414563: +2024-09-10 15:52:32.420774: Epoch 846 +2024-09-10 15:52:32.421748: Current learning rate: 0.00186 +2024-09-10 15:56:48.637609: train_loss -0.7615 +2024-09-10 15:56:48.639911: val_loss -0.7691 +2024-09-10 15:56:48.640745: Pseudo dice [0.9679, 0.9595, 0.9799, 0.9581, 0.893, 0.976, 0.9929] +2024-09-10 15:56:48.641526: Epoch time: 256.23 s +2024-09-10 15:56:51.265895: +2024-09-10 15:56:51.280856: Epoch 847 +2024-09-10 15:56:51.281576: Current learning rate: 0.00185 +2024-09-10 16:01:01.718811: train_loss -0.7518 +2024-09-10 16:01:01.734456: val_loss -0.7608 +2024-09-10 16:01:01.735454: Pseudo dice [0.967, 0.9604, 0.9787, 0.9558, 0.9004, 0.9755, 0.993] +2024-09-10 16:01:01.736357: Epoch time: 250.46 s +2024-09-10 16:01:04.788388: +2024-09-10 16:01:04.800795: Epoch 848 +2024-09-10 16:01:04.801664: Current learning rate: 0.00184 +2024-09-10 16:05:24.303276: train_loss -0.7536 +2024-09-10 16:05:24.314076: val_loss -0.7282 +2024-09-10 16:05:24.314713: Pseudo dice [0.9677, 0.9624, 0.9782, 0.9544, 0.8952, 0.9751, 0.9929] +2024-09-10 16:05:24.315266: Epoch time: 259.52 s +2024-09-10 16:05:27.778489: +2024-09-10 16:05:27.779723: Epoch 849 +2024-09-10 16:05:27.780810: Current learning rate: 0.00182 +2024-09-10 16:09:41.892989: train_loss -0.7579 +2024-09-10 16:09:41.910685: val_loss -0.7568 +2024-09-10 16:09:41.911399: Pseudo dice [0.9681, 0.9624, 0.9804, 0.9566, 0.8935, 0.9742, 0.9915] +2024-09-10 16:09:41.912139: Epoch time: 254.12 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_16_28_32.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_16_28_32.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a790489cc3916fa09df27610d9ac3477dfdc71e --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_10_16_28_32.txt @@ -0,0 +1,737 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 16:28:35.641560: Using torch.compile... +2024-09-10 16:28:47.575983: do_dummy_2d_data_aug: False +2024-09-10 16:28:47.581827: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 16:28:47.583394: The split file contains 5 splits. +2024-09-10 16:28:47.583918: Desired fold for training: 2 +2024-09-10 16:28:47.584505: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 16:29:01.076648: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 16:29:07.426054: +2024-09-10 16:29:07.436640: Epoch 850 +2024-09-10 16:29:07.437457: Current learning rate: 0.00181 +2024-09-10 16:40:59.692322: train_loss -0.76 +2024-09-10 16:40:59.694232: val_loss -0.7675 +2024-09-10 16:40:59.694808: Pseudo dice [0.9667, 0.9592, 0.9789, 0.9573, 0.8999, 0.9742, 0.9932] +2024-09-10 16:40:59.695291: Epoch time: 712.27 s +2024-09-10 16:41:01.143468: +2024-09-10 16:41:01.144386: Epoch 851 +2024-09-10 16:41:01.145155: Current learning rate: 0.0018 +2024-09-10 16:45:12.020877: train_loss -0.745 +2024-09-10 16:45:12.023064: val_loss -0.7559 +2024-09-10 16:45:12.023720: Pseudo dice [0.9652, 0.9593, 0.9759, 0.9592, 0.9019, 0.9752, 0.9936] +2024-09-10 16:45:12.024350: Epoch time: 250.88 s +2024-09-10 16:45:12.024999: Yayy! New best EMA pseudo Dice: 0.9608 +2024-09-10 16:45:17.077893: +2024-09-10 16:45:17.078772: Epoch 852 +2024-09-10 16:45:17.079445: Current learning rate: 0.00179 +2024-09-10 16:49:27.998992: train_loss -0.7483 +2024-09-10 16:49:28.009646: val_loss -0.7776 +2024-09-10 16:49:28.010437: Pseudo dice [0.9676, 0.9627, 0.9802, 0.9553, 0.8884, 0.9747, 0.9916] +2024-09-10 16:49:28.011155: Epoch time: 250.92 s +2024-09-10 16:49:32.232183: +2024-09-10 16:49:32.233146: Epoch 853 +2024-09-10 16:49:32.233899: Current learning rate: 0.00178 +2024-09-10 16:53:41.200870: train_loss -0.7686 +2024-09-10 16:53:41.211850: val_loss -0.7779 +2024-09-10 16:53:41.212517: Pseudo dice [0.9714, 0.9633, 0.9835, 0.9536, 0.8997, 0.9762, 0.9937] +2024-09-10 16:53:41.213303: Epoch time: 248.98 s +2024-09-10 16:53:41.214021: Yayy! New best EMA pseudo Dice: 0.961 +2024-09-10 16:53:48.032663: +2024-09-10 16:53:48.033515: Epoch 854 +2024-09-10 16:53:48.034174: Current learning rate: 0.00177 +2024-09-10 16:58:01.322259: train_loss -0.7536 +2024-09-10 16:58:01.324283: val_loss -0.7611 +2024-09-10 16:58:01.325053: Pseudo dice [0.9671, 0.9614, 0.9795, 0.9557, 0.8841, 0.9758, 0.9916] +2024-09-10 16:58:01.325696: Epoch time: 253.29 s +2024-09-10 16:58:03.978772: +2024-09-10 16:58:03.992579: Epoch 855 +2024-09-10 16:58:03.993308: Current learning rate: 0.00176 +2024-09-10 17:02:21.913320: train_loss -0.7508 +2024-09-10 17:02:21.917607: val_loss -0.7517 +2024-09-10 17:02:21.918507: Pseudo dice [0.9643, 0.9604, 0.976, 0.9544, 0.8909, 0.9755, 0.9932] +2024-09-10 17:02:21.919255: Epoch time: 257.94 s +2024-09-10 17:02:24.574945: +2024-09-10 17:02:24.575787: Epoch 856 +2024-09-10 17:02:24.576536: Current learning rate: 0.00175 +2024-09-10 17:06:49.279822: train_loss -0.7428 +2024-09-10 17:06:49.295668: val_loss -0.7523 +2024-09-10 17:06:49.296506: Pseudo dice [0.9691, 0.9612, 0.9803, 0.9571, 0.9012, 0.9771, 0.9937] +2024-09-10 17:06:49.297350: Epoch time: 264.71 s +2024-09-10 17:06:53.284354: +2024-09-10 17:06:53.296756: Epoch 857 +2024-09-10 17:06:53.297680: Current learning rate: 0.00174 +2024-09-10 17:11:12.088585: train_loss -0.7426 +2024-09-10 17:11:12.090798: val_loss -0.7928 +2024-09-10 17:11:12.091400: Pseudo dice [0.9674, 0.9599, 0.9786, 0.9536, 0.8953, 0.9759, 0.993] +2024-09-10 17:11:12.092134: Epoch time: 258.81 s +2024-09-10 17:11:15.390368: +2024-09-10 17:11:15.391244: Epoch 858 +2024-09-10 17:11:15.391869: Current learning rate: 0.00173 +2024-09-10 17:15:33.608928: train_loss -0.7556 +2024-09-10 17:15:33.634436: val_loss -0.773 +2024-09-10 17:15:33.635636: Pseudo dice [0.9693, 0.9618, 0.9796, 0.9552, 0.8931, 0.9768, 0.9925] +2024-09-10 17:15:33.636511: Epoch time: 258.22 s +2024-09-10 17:15:36.873562: +2024-09-10 17:15:36.874715: Epoch 859 +2024-09-10 17:15:36.875578: Current learning rate: 0.00172 +2024-09-10 17:19:56.917630: train_loss -0.749 +2024-09-10 17:19:56.934277: val_loss -0.7733 +2024-09-10 17:19:56.935606: Pseudo dice [0.969, 0.9613, 0.981, 0.9566, 0.8972, 0.9752, 0.9915] +2024-09-10 17:19:56.936778: Epoch time: 260.05 s +2024-09-10 17:20:01.020190: +2024-09-10 17:20:01.036962: Epoch 860 +2024-09-10 17:20:01.038327: Current learning rate: 0.0017 +2024-09-10 17:24:18.732595: train_loss -0.7621 +2024-09-10 17:24:18.750179: val_loss -0.7672 +2024-09-10 17:24:18.750943: Pseudo dice [0.9681, 0.9636, 0.9802, 0.9572, 0.9002, 0.9778, 0.9933] +2024-09-10 17:24:18.751606: Epoch time: 257.72 s +2024-09-10 17:24:18.752279: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-10 17:24:25.092411: +2024-09-10 17:24:25.093537: Epoch 861 +2024-09-10 17:24:25.094262: Current learning rate: 0.00169 +2024-09-10 17:28:55.772197: train_loss -0.7601 +2024-09-10 17:28:55.790575: val_loss -0.7448 +2024-09-10 17:28:55.791550: Pseudo dice [0.9693, 0.9628, 0.9798, 0.9519, 0.8972, 0.9758, 0.9923] +2024-09-10 17:28:55.792450: Epoch time: 270.68 s +2024-09-10 17:28:55.793184: Yayy! New best EMA pseudo Dice: 0.9612 +2024-09-10 17:29:01.717164: +2024-09-10 17:29:01.717955: Epoch 862 +2024-09-10 17:29:01.718592: Current learning rate: 0.00168 +2024-09-10 17:33:20.033476: train_loss -0.7712 +2024-09-10 17:33:20.035834: val_loss -0.7335 +2024-09-10 17:33:20.038663: Pseudo dice [0.9682, 0.9605, 0.9832, 0.9548, 0.9003, 0.9756, 0.9928] +2024-09-10 17:33:20.039284: Epoch time: 258.32 s +2024-09-10 17:33:20.039756: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-10 17:33:26.819235: +2024-09-10 17:33:26.820146: Epoch 863 +2024-09-10 17:33:26.820888: Current learning rate: 0.00167 +2024-09-10 17:37:33.123044: train_loss -0.7625 +2024-09-10 17:37:33.133856: val_loss -0.7397 +2024-09-10 17:37:33.134643: Pseudo dice [0.9685, 0.9639, 0.9804, 0.9563, 0.8913, 0.9747, 0.9926] +2024-09-10 17:37:33.136142: Epoch time: 246.31 s +2024-09-10 17:37:36.463496: +2024-09-10 17:37:36.476648: Epoch 864 +2024-09-10 17:37:36.477387: Current learning rate: 0.00166 +2024-09-10 17:42:20.469316: train_loss -0.7525 +2024-09-10 17:42:20.471626: val_loss -0.7816 +2024-09-10 17:42:20.472410: Pseudo dice [0.9668, 0.9614, 0.9785, 0.9565, 0.8958, 0.9767, 0.9927] +2024-09-10 17:42:20.473299: Epoch time: 284.01 s +2024-09-10 17:42:24.745255: +2024-09-10 17:42:24.746179: Epoch 865 +2024-09-10 17:42:24.746917: Current learning rate: 0.00165 +2024-09-10 17:46:46.765687: train_loss -0.7602 +2024-09-10 17:46:46.794688: val_loss -0.7351 +2024-09-10 17:46:46.795767: Pseudo dice [0.9654, 0.9605, 0.9778, 0.9585, 0.8973, 0.977, 0.993] +2024-09-10 17:46:46.796703: Epoch time: 262.03 s +2024-09-10 17:46:49.695201: +2024-09-10 17:46:49.696088: Epoch 866 +2024-09-10 17:46:49.696823: Current learning rate: 0.00164 +2024-09-10 17:51:10.035183: train_loss -0.7651 +2024-09-10 17:51:10.047150: val_loss -0.7472 +2024-09-10 17:51:10.047833: Pseudo dice [0.9692, 0.9636, 0.9821, 0.9575, 0.8914, 0.9772, 0.9917] +2024-09-10 17:51:10.048468: Epoch time: 260.34 s +2024-09-10 17:51:10.048974: Yayy! New best EMA pseudo Dice: 0.9613 +2024-09-10 17:51:16.523457: +2024-09-10 17:51:16.524139: Epoch 867 +2024-09-10 17:51:16.524715: Current learning rate: 0.00163 +2024-09-10 17:55:36.014643: train_loss -0.7476 +2024-09-10 17:55:36.029891: val_loss -0.8091 +2024-09-10 17:55:36.030594: Pseudo dice [0.9665, 0.9594, 0.977, 0.9543, 0.8939, 0.9756, 0.9933] +2024-09-10 17:55:36.031151: Epoch time: 259.49 s +2024-09-10 17:55:38.473218: +2024-09-10 17:55:38.488611: Epoch 868 +2024-09-10 17:55:38.489399: Current learning rate: 0.00162 +2024-09-10 18:00:06.188367: train_loss -0.759 +2024-09-10 18:00:06.196146: val_loss -0.7435 +2024-09-10 18:00:06.196906: Pseudo dice [0.9669, 0.9614, 0.9805, 0.9536, 0.8918, 0.9744, 0.9931] +2024-09-10 18:00:06.197699: Epoch time: 267.72 s +2024-09-10 18:00:09.280309: +2024-09-10 18:00:09.296594: Epoch 869 +2024-09-10 18:00:09.297376: Current learning rate: 0.00161 +2024-09-10 18:04:35.943814: train_loss -0.7687 +2024-09-10 18:04:35.946168: val_loss -0.7873 +2024-09-10 18:04:35.946906: Pseudo dice [0.9663, 0.9633, 0.9772, 0.9568, 0.899, 0.9739, 0.9928] +2024-09-10 18:04:35.947701: Epoch time: 266.67 s +2024-09-10 18:04:39.696794: +2024-09-10 18:04:39.702561: Epoch 870 +2024-09-10 18:04:39.703394: Current learning rate: 0.00159 +2024-09-10 18:08:40.596121: train_loss -0.7608 +2024-09-10 18:08:40.598217: val_loss -0.7789 +2024-09-10 18:08:40.598866: Pseudo dice [0.9637, 0.9598, 0.9753, 0.9493, 0.88, 0.976, 0.9913] +2024-09-10 18:08:40.599470: Epoch time: 240.9 s +2024-09-10 18:08:42.714267: +2024-09-10 18:08:42.715039: Epoch 871 +2024-09-10 18:08:42.715702: Current learning rate: 0.00158 +2024-09-10 18:12:49.625040: train_loss -0.7571 +2024-09-10 18:12:49.639581: val_loss -0.7715 +2024-09-10 18:12:49.640821: Pseudo dice [0.9681, 0.9606, 0.9812, 0.9571, 0.8985, 0.9782, 0.9926] +2024-09-10 18:12:49.641421: Epoch time: 246.92 s +2024-09-10 18:12:53.145499: +2024-09-10 18:12:53.146408: Epoch 872 +2024-09-10 18:12:53.147199: Current learning rate: 0.00157 +2024-09-10 18:17:08.105865: train_loss -0.7522 +2024-09-10 18:17:08.125634: val_loss -0.7577 +2024-09-10 18:17:08.126421: Pseudo dice [0.9686, 0.9635, 0.982, 0.9568, 0.898, 0.975, 0.9926] +2024-09-10 18:17:08.127050: Epoch time: 254.97 s +2024-09-10 18:17:12.155972: +2024-09-10 18:17:12.157019: Epoch 873 +2024-09-10 18:17:12.157786: Current learning rate: 0.00156 +2024-09-10 18:21:49.942301: train_loss -0.7648 +2024-09-10 18:21:49.958137: val_loss -0.7791 +2024-09-10 18:21:49.959092: Pseudo dice [0.9651, 0.9629, 0.9789, 0.9569, 0.8904, 0.9735, 0.9927] +2024-09-10 18:21:49.959778: Epoch time: 277.79 s +2024-09-10 18:21:52.805009: +2024-09-10 18:21:52.806139: Epoch 874 +2024-09-10 18:21:52.806840: Current learning rate: 0.00155 +2024-09-10 18:26:19.765196: train_loss -0.7329 +2024-09-10 18:26:19.781799: val_loss -0.7948 +2024-09-10 18:26:19.782504: Pseudo dice [0.9663, 0.96, 0.98, 0.9555, 0.8996, 0.9744, 0.9923] +2024-09-10 18:26:19.783049: Epoch time: 266.96 s +2024-09-10 18:26:22.683157: +2024-09-10 18:26:22.692668: Epoch 875 +2024-09-10 18:26:22.693378: Current learning rate: 0.00154 +2024-09-10 18:30:56.505926: train_loss -0.7487 +2024-09-10 18:30:56.508453: val_loss -0.7701 +2024-09-10 18:30:56.509325: Pseudo dice [0.9638, 0.96, 0.9773, 0.9572, 0.8935, 0.976, 0.9928] +2024-09-10 18:30:56.510048: Epoch time: 273.84 s +2024-09-10 18:30:59.606491: +2024-09-10 18:30:59.624100: Epoch 876 +2024-09-10 18:30:59.625005: Current learning rate: 0.00153 +2024-09-10 18:35:21.068844: train_loss -0.7608 +2024-09-10 18:35:21.071489: val_loss -0.7426 +2024-09-10 18:35:21.088390: Pseudo dice [0.964, 0.9592, 0.9779, 0.9521, 0.8762, 0.9753, 0.9903] +2024-09-10 18:35:21.089381: Epoch time: 261.47 s +2024-09-10 18:35:24.166354: +2024-09-10 18:35:24.167355: Epoch 877 +2024-09-10 18:35:24.168094: Current learning rate: 0.00152 +2024-09-10 18:39:24.679461: train_loss -0.7512 +2024-09-10 18:39:24.695637: val_loss -0.7711 +2024-09-10 18:39:24.697285: Pseudo dice [0.9623, 0.9598, 0.9737, 0.9566, 0.895, 0.9764, 0.9923] +2024-09-10 18:39:24.698429: Epoch time: 240.52 s +2024-09-10 18:39:29.916447: +2024-09-10 18:39:29.917471: Epoch 878 +2024-09-10 18:39:29.918163: Current learning rate: 0.00151 +2024-09-10 18:43:45.184690: train_loss -0.7389 +2024-09-10 18:43:45.192353: val_loss -0.7832 +2024-09-10 18:43:45.193123: Pseudo dice [0.9659, 0.9628, 0.9782, 0.9548, 0.8978, 0.973, 0.9927] +2024-09-10 18:43:45.193860: Epoch time: 255.27 s +2024-09-10 18:43:47.945143: +2024-09-10 18:43:47.946149: Epoch 879 +2024-09-10 18:43:47.946996: Current learning rate: 0.00149 +2024-09-10 18:48:07.585633: train_loss -0.7595 +2024-09-10 18:48:07.597954: val_loss -0.7301 +2024-09-10 18:48:07.598688: Pseudo dice [0.9668, 0.9634, 0.9782, 0.9538, 0.8831, 0.9761, 0.9924] +2024-09-10 18:48:07.599394: Epoch time: 259.65 s +2024-09-10 18:48:10.963328: +2024-09-10 18:48:10.972792: Epoch 880 +2024-09-10 18:48:10.973488: Current learning rate: 0.00148 +2024-09-10 18:52:19.953373: train_loss -0.763 +2024-09-10 18:52:19.968381: val_loss -0.7594 +2024-09-10 18:52:19.969384: Pseudo dice [0.9651, 0.9617, 0.9776, 0.9579, 0.8969, 0.9766, 0.9929] +2024-09-10 18:52:19.970224: Epoch time: 249.0 s +2024-09-10 18:52:23.312233: +2024-09-10 18:52:23.324600: Epoch 881 +2024-09-10 18:52:23.325248: Current learning rate: 0.00147 +2024-09-10 18:56:52.764324: train_loss -0.7611 +2024-09-10 18:56:52.781556: val_loss -0.7918 +2024-09-10 18:56:52.782262: Pseudo dice [0.9646, 0.9623, 0.9793, 0.959, 0.9083, 0.9731, 0.9921] +2024-09-10 18:56:52.783137: Epoch time: 269.46 s +2024-09-10 18:56:55.148156: +2024-09-10 18:56:55.149112: Epoch 882 +2024-09-10 18:56:55.149862: Current learning rate: 0.00146 +2024-09-10 19:01:08.044705: train_loss -0.7774 +2024-09-10 19:01:08.047203: val_loss -0.7292 +2024-09-10 19:01:08.047893: Pseudo dice [0.9661, 0.9612, 0.9791, 0.9585, 0.888, 0.9751, 0.9921] +2024-09-10 19:01:08.048467: Epoch time: 252.9 s +2024-09-10 19:01:10.793614: +2024-09-10 19:01:10.804750: Epoch 883 +2024-09-10 19:01:10.805677: Current learning rate: 0.00145 +2024-09-10 19:05:39.969986: train_loss -0.7644 +2024-09-10 19:05:39.980270: val_loss -0.7723 +2024-09-10 19:05:39.981289: Pseudo dice [0.9674, 0.9602, 0.9796, 0.9608, 0.8998, 0.9762, 0.9936] +2024-09-10 19:05:39.982136: Epoch time: 269.18 s +2024-09-10 19:05:42.567679: +2024-09-10 19:05:42.568591: Epoch 884 +2024-09-10 19:05:42.569236: Current learning rate: 0.00144 +2024-09-10 19:09:56.600703: train_loss -0.7406 +2024-09-10 19:09:56.621746: val_loss -0.7587 +2024-09-10 19:09:56.622410: Pseudo dice [0.9645, 0.9611, 0.9784, 0.9519, 0.8921, 0.9765, 0.9924] +2024-09-10 19:09:56.623099: Epoch time: 254.04 s +2024-09-10 19:09:59.731584: +2024-09-10 19:09:59.744925: Epoch 885 +2024-09-10 19:09:59.746220: Current learning rate: 0.00143 +2024-09-10 19:14:10.930443: train_loss -0.7563 +2024-09-10 19:14:10.944169: val_loss -0.7515 +2024-09-10 19:14:10.945058: Pseudo dice [0.9688, 0.9614, 0.9795, 0.9591, 0.8825, 0.9758, 0.9913] +2024-09-10 19:14:10.945653: Epoch time: 251.2 s +2024-09-10 19:14:13.897269: +2024-09-10 19:14:13.898102: Epoch 886 +2024-09-10 19:14:13.898748: Current learning rate: 0.00142 +2024-09-10 19:18:39.112848: train_loss -0.7638 +2024-09-10 19:18:39.125918: val_loss -0.7828 +2024-09-10 19:18:39.126570: Pseudo dice [0.9658, 0.9608, 0.9788, 0.9577, 0.9089, 0.978, 0.9934] +2024-09-10 19:18:39.127151: Epoch time: 265.22 s +2024-09-10 19:18:42.665878: +2024-09-10 19:18:42.680792: Epoch 887 +2024-09-10 19:18:42.681978: Current learning rate: 0.00141 +2024-09-10 19:23:04.585772: train_loss -0.7519 +2024-09-10 19:23:04.588093: val_loss -0.7291 +2024-09-10 19:23:04.604310: Pseudo dice [0.9662, 0.9618, 0.9794, 0.9566, 0.8917, 0.9754, 0.9921] +2024-09-10 19:23:04.605325: Epoch time: 261.93 s +2024-09-10 19:23:08.109000: +2024-09-10 19:23:08.124626: Epoch 888 +2024-09-10 19:23:08.125368: Current learning rate: 0.00139 +2024-09-10 19:27:12.911398: train_loss -0.7519 +2024-09-10 19:27:12.913779: val_loss -0.7644 +2024-09-10 19:27:12.914473: Pseudo dice [0.9685, 0.9639, 0.9799, 0.9586, 0.9018, 0.9766, 0.9928] +2024-09-10 19:27:12.915208: Epoch time: 244.81 s +2024-09-10 19:27:15.592602: +2024-09-10 19:27:15.604654: Epoch 889 +2024-09-10 19:27:15.605430: Current learning rate: 0.00138 +2024-09-10 19:31:43.859117: train_loss -0.7623 +2024-09-10 19:31:43.869996: val_loss -0.8011 +2024-09-10 19:31:43.870736: Pseudo dice [0.9687, 0.9621, 0.9813, 0.958, 0.8878, 0.9746, 0.991] +2024-09-10 19:31:43.871398: Epoch time: 268.27 s +2024-09-10 19:31:46.668017: +2024-09-10 19:31:46.668920: Epoch 890 +2024-09-10 19:31:46.669575: Current learning rate: 0.00137 +2024-09-10 19:36:04.555149: train_loss -0.761 +2024-09-10 19:36:04.557263: val_loss -0.7502 +2024-09-10 19:36:04.558009: Pseudo dice [0.9632, 0.9595, 0.976, 0.9471, 0.8857, 0.9754, 0.991] +2024-09-10 19:36:04.558593: Epoch time: 257.89 s +2024-09-10 19:36:07.091330: +2024-09-10 19:36:07.104652: Epoch 891 +2024-09-10 19:36:07.105284: Current learning rate: 0.00136 +2024-09-10 19:40:32.584301: train_loss -0.7662 +2024-09-10 19:40:32.600173: val_loss -0.7619 +2024-09-10 19:40:32.600889: Pseudo dice [0.9666, 0.9628, 0.9812, 0.9564, 0.9008, 0.9752, 0.9931] +2024-09-10 19:40:32.601583: Epoch time: 265.5 s +2024-09-10 19:40:35.409692: +2024-09-10 19:40:35.415977: Epoch 892 +2024-09-10 19:40:35.416782: Current learning rate: 0.00135 +2024-09-10 19:44:59.112183: train_loss -0.7394 +2024-09-10 19:44:59.126235: val_loss -0.7669 +2024-09-10 19:44:59.127055: Pseudo dice [0.9664, 0.961, 0.9801, 0.9538, 0.8922, 0.9757, 0.9923] +2024-09-10 19:44:59.127731: Epoch time: 263.71 s +2024-09-10 19:45:01.912642: +2024-09-10 19:45:01.913524: Epoch 893 +2024-09-10 19:45:01.914324: Current learning rate: 0.00134 +2024-09-10 19:49:23.053449: train_loss -0.7492 +2024-09-10 19:49:23.075159: val_loss -0.7762 +2024-09-10 19:49:23.075947: Pseudo dice [0.9659, 0.9601, 0.9772, 0.9548, 0.8941, 0.9761, 0.9921] +2024-09-10 19:49:23.076555: Epoch time: 261.15 s +2024-09-10 19:49:25.863801: +2024-09-10 19:49:25.865175: Epoch 894 +2024-09-10 19:49:25.866005: Current learning rate: 0.00133 +2024-09-10 19:53:52.079259: train_loss -0.7521 +2024-09-10 19:53:52.097263: val_loss -0.7928 +2024-09-10 19:53:52.097859: Pseudo dice [0.9681, 0.9622, 0.98, 0.9549, 0.9046, 0.9769, 0.9932] +2024-09-10 19:53:52.098377: Epoch time: 266.22 s +2024-09-10 19:53:54.478745: +2024-09-10 19:53:54.479470: Epoch 895 +2024-09-10 19:53:54.479949: Current learning rate: 0.00132 +2024-09-10 19:58:04.423027: train_loss -0.7443 +2024-09-10 19:58:04.439062: val_loss -0.7396 +2024-09-10 19:58:04.439811: Pseudo dice [0.9684, 0.9618, 0.9804, 0.9596, 0.9066, 0.9784, 0.9936] +2024-09-10 19:58:04.440448: Epoch time: 249.95 s +2024-09-10 19:58:07.102135: +2024-09-10 19:58:07.116646: Epoch 896 +2024-09-10 19:58:07.117345: Current learning rate: 0.0013 +2024-09-10 20:02:39.847909: train_loss -0.7765 +2024-09-10 20:02:39.849824: val_loss -0.7854 +2024-09-10 20:02:39.850503: Pseudo dice [0.9686, 0.9626, 0.9799, 0.9581, 0.9043, 0.977, 0.9925] +2024-09-10 20:02:39.851136: Epoch time: 272.75 s +2024-09-10 20:02:39.851661: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-10 20:02:46.064209: +2024-09-10 20:02:46.065015: Epoch 897 +2024-09-10 20:02:46.065727: Current learning rate: 0.00129 +2024-09-10 20:07:13.352546: train_loss -0.7535 +2024-09-10 20:07:13.366220: val_loss -0.7421 +2024-09-10 20:07:13.366996: Pseudo dice [0.9686, 0.9646, 0.9793, 0.9549, 0.892, 0.9767, 0.9921] +2024-09-10 20:07:13.367788: Epoch time: 267.29 s +2024-09-10 20:07:16.431967: +2024-09-10 20:07:16.433448: Epoch 898 +2024-09-10 20:07:16.434449: Current learning rate: 0.00128 +2024-09-10 20:11:52.749918: train_loss -0.7648 +2024-09-10 20:11:52.765899: val_loss -0.7504 +2024-09-10 20:11:52.766652: Pseudo dice [0.9672, 0.9607, 0.9794, 0.9582, 0.8888, 0.9754, 0.993] +2024-09-10 20:11:52.767275: Epoch time: 276.32 s +2024-09-10 20:11:55.525965: +2024-09-10 20:11:55.526792: Epoch 899 +2024-09-10 20:11:55.540745: Current learning rate: 0.00127 +2024-09-10 20:16:03.924402: train_loss -0.765 +2024-09-10 20:16:03.928481: val_loss -0.7515 +2024-09-10 20:16:03.929432: Pseudo dice [0.9705, 0.9606, 0.9815, 0.9572, 0.9084, 0.9765, 0.9938] +2024-09-10 20:16:03.930124: Epoch time: 248.4 s +2024-09-10 20:16:08.172396: Yayy! New best EMA pseudo Dice: 0.9615 +2024-09-10 20:16:14.208593: +2024-09-10 20:16:14.209421: Epoch 900 +2024-09-10 20:16:14.210152: Current learning rate: 0.00126 +2024-09-10 20:20:30.665165: train_loss -0.7724 +2024-09-10 20:20:30.675846: val_loss -0.7794 +2024-09-10 20:20:30.676644: Pseudo dice [0.968, 0.9623, 0.9803, 0.9559, 0.8996, 0.9747, 0.9928] +2024-09-10 20:20:30.677380: Epoch time: 256.46 s +2024-09-10 20:20:30.678006: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-10 20:20:37.158554: +2024-09-10 20:20:37.160926: Epoch 901 +2024-09-10 20:20:37.163109: Current learning rate: 0.00125 +2024-09-10 20:25:01.787322: train_loss -0.7616 +2024-09-10 20:25:01.797798: val_loss -0.7799 +2024-09-10 20:25:01.798483: Pseudo dice [0.9683, 0.9626, 0.9794, 0.9627, 0.9109, 0.978, 0.994] +2024-09-10 20:25:01.799070: Epoch time: 264.63 s +2024-09-10 20:25:01.799876: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-10 20:25:09.285170: +2024-09-10 20:25:09.286027: Epoch 902 +2024-09-10 20:25:09.286702: Current learning rate: 0.00124 +2024-09-10 20:29:24.914346: train_loss -0.7443 +2024-09-10 20:29:24.917019: val_loss -0.7464 +2024-09-10 20:29:24.918030: Pseudo dice [0.9688, 0.9601, 0.9813, 0.9479, 0.8835, 0.9721, 0.991] +2024-09-10 20:29:24.918893: Epoch time: 255.63 s +2024-09-10 20:29:28.258511: +2024-09-10 20:29:28.259531: Epoch 903 +2024-09-10 20:29:28.260151: Current learning rate: 0.00122 +2024-09-10 20:33:41.902115: train_loss -0.7597 +2024-09-10 20:33:41.929684: val_loss -0.7647 +2024-09-10 20:33:41.930527: Pseudo dice [0.9688, 0.9635, 0.9814, 0.9547, 0.8983, 0.9752, 0.9924] +2024-09-10 20:33:41.931215: Epoch time: 253.65 s +2024-09-10 20:33:45.381030: +2024-09-10 20:33:45.381866: Epoch 904 +2024-09-10 20:33:45.382588: Current learning rate: 0.00121 +2024-09-10 20:38:02.286986: train_loss -0.7647 +2024-09-10 20:38:02.294217: val_loss -0.7532 +2024-09-10 20:38:02.295086: Pseudo dice [0.9663, 0.9616, 0.9808, 0.9574, 0.8907, 0.9777, 0.9921] +2024-09-10 20:38:02.295931: Epoch time: 256.91 s +2024-09-10 20:38:05.137449: +2024-09-10 20:38:05.152870: Epoch 905 +2024-09-10 20:38:05.153598: Current learning rate: 0.0012 +2024-09-10 20:42:10.940973: train_loss -0.7599 +2024-09-10 20:42:10.961898: val_loss -0.7536 +2024-09-10 20:42:10.962789: Pseudo dice [0.9663, 0.9629, 0.9786, 0.9574, 0.9058, 0.9767, 0.9939] +2024-09-10 20:42:10.964084: Epoch time: 245.81 s +2024-09-10 20:42:14.044824: +2024-09-10 20:42:14.045877: Epoch 906 +2024-09-10 20:42:14.046704: Current learning rate: 0.00119 +2024-09-10 20:46:21.564811: train_loss -0.7624 +2024-09-10 20:46:21.581911: val_loss -0.7389 +2024-09-10 20:46:21.582613: Pseudo dice [0.9703, 0.9618, 0.9806, 0.9578, 0.8941, 0.9763, 0.9927] +2024-09-10 20:46:21.583300: Epoch time: 247.52 s +2024-09-10 20:46:24.229632: +2024-09-10 20:46:24.249425: Epoch 907 +2024-09-10 20:46:24.251853: Current learning rate: 0.00118 +2024-09-10 20:50:45.625696: train_loss -0.7666 +2024-09-10 20:50:45.637799: val_loss -0.7872 +2024-09-10 20:50:45.638503: Pseudo dice [0.9702, 0.9627, 0.9811, 0.9572, 0.9044, 0.9787, 0.9936] +2024-09-10 20:50:45.639187: Epoch time: 261.4 s +2024-09-10 20:50:48.322867: +2024-09-10 20:50:48.323671: Epoch 908 +2024-09-10 20:50:48.324278: Current learning rate: 0.00117 +2024-09-10 20:54:59.445077: train_loss -0.7403 +2024-09-10 20:54:59.459593: val_loss -0.7472 +2024-09-10 20:54:59.460250: Pseudo dice [0.9656, 0.9628, 0.977, 0.9592, 0.9021, 0.9763, 0.9929] +2024-09-10 20:54:59.460814: Epoch time: 251.13 s +2024-09-10 20:54:59.461637: Yayy! New best EMA pseudo Dice: 0.962 +2024-09-10 20:55:05.314844: +2024-09-10 20:55:05.315779: Epoch 909 +2024-09-10 20:55:05.316561: Current learning rate: 0.00116 +2024-09-10 20:59:19.782007: train_loss -0.7556 +2024-09-10 20:59:19.798017: val_loss -0.7479 +2024-09-10 20:59:19.798771: Pseudo dice [0.9689, 0.9627, 0.9801, 0.9592, 0.8965, 0.9776, 0.9927] +2024-09-10 20:59:19.799548: Epoch time: 254.47 s +2024-09-10 20:59:19.800156: Yayy! New best EMA pseudo Dice: 0.962 +2024-09-10 20:59:26.173574: +2024-09-10 20:59:26.174339: Epoch 910 +2024-09-10 20:59:26.174968: Current learning rate: 0.00115 +2024-09-10 21:03:47.300911: train_loss -0.767 +2024-09-10 21:03:47.317952: val_loss -0.7732 +2024-09-10 21:03:47.318641: Pseudo dice [0.9661, 0.9607, 0.9761, 0.9536, 0.902, 0.9766, 0.9931] +2024-09-10 21:03:47.319424: Epoch time: 261.13 s +2024-09-10 21:03:49.816672: +2024-09-10 21:03:49.817462: Epoch 911 +2024-09-10 21:03:49.818218: Current learning rate: 0.00113 +2024-09-10 21:08:23.818495: train_loss -0.7608 +2024-09-10 21:08:23.821117: val_loss -0.744 +2024-09-10 21:08:23.821980: Pseudo dice [0.968, 0.9617, 0.9775, 0.9576, 0.8947, 0.976, 0.9926] +2024-09-10 21:08:23.822743: Epoch time: 274.01 s +2024-09-10 21:08:26.305050: +2024-09-10 21:08:26.305895: Epoch 912 +2024-09-10 21:08:26.306496: Current learning rate: 0.00112 +2024-09-10 21:12:40.866892: train_loss -0.7606 +2024-09-10 21:12:40.881305: val_loss -0.7613 +2024-09-10 21:12:40.882075: Pseudo dice [0.9672, 0.9618, 0.9785, 0.9572, 0.9055, 0.9768, 0.9936] +2024-09-10 21:12:40.882667: Epoch time: 254.57 s +2024-09-10 21:12:42.868050: +2024-09-10 21:12:42.869120: Epoch 913 +2024-09-10 21:12:42.869845: Current learning rate: 0.00111 +2024-09-10 21:16:51.519407: train_loss -0.7646 +2024-09-10 21:16:51.521832: val_loss -0.7636 +2024-09-10 21:16:51.522646: Pseudo dice [0.9694, 0.9633, 0.9827, 0.9604, 0.8911, 0.9766, 0.9931] +2024-09-10 21:16:51.523395: Epoch time: 248.66 s +2024-09-10 21:16:54.242734: +2024-09-10 21:16:54.243808: Epoch 914 +2024-09-10 21:16:54.244511: Current learning rate: 0.0011 +2024-09-10 21:21:17.613968: train_loss -0.7651 +2024-09-10 21:21:17.629918: val_loss -0.7533 +2024-09-10 21:21:17.630763: Pseudo dice [0.9673, 0.9641, 0.9784, 0.9563, 0.9091, 0.9771, 0.9931] +2024-09-10 21:21:17.631462: Epoch time: 263.38 s +2024-09-10 21:21:17.631988: Yayy! New best EMA pseudo Dice: 0.9622 +2024-09-10 21:21:23.208135: +2024-09-10 21:21:23.209001: Epoch 915 +2024-09-10 21:21:23.209677: Current learning rate: 0.00109 +2024-09-10 21:25:41.407643: train_loss -0.7541 +2024-09-10 21:25:41.410897: val_loss -0.7433 +2024-09-10 21:25:41.412199: Pseudo dice [0.9661, 0.9634, 0.9765, 0.9607, 0.8891, 0.9761, 0.9922] +2024-09-10 21:25:41.420723: Epoch time: 258.2 s +2024-09-10 21:25:44.267397: +2024-09-10 21:25:44.277795: Epoch 916 +2024-09-10 21:25:44.278559: Current learning rate: 0.00108 +2024-09-10 21:30:01.300499: train_loss -0.7662 +2024-09-10 21:30:01.310750: val_loss -0.7661 +2024-09-10 21:30:01.311394: Pseudo dice [0.9698, 0.9618, 0.9826, 0.9558, 0.9047, 0.9766, 0.9931] +2024-09-10 21:30:01.311924: Epoch time: 257.04 s +2024-09-10 21:30:04.219612: +2024-09-10 21:30:04.220601: Epoch 917 +2024-09-10 21:30:04.221246: Current learning rate: 0.00106 +2024-09-10 21:34:10.514112: train_loss -0.7612 +2024-09-10 21:34:10.525962: val_loss -0.7609 +2024-09-10 21:34:10.526814: Pseudo dice [0.9662, 0.9616, 0.9792, 0.9548, 0.8981, 0.9746, 0.9927] +2024-09-10 21:34:10.527714: Epoch time: 246.3 s +2024-09-10 21:34:13.972255: +2024-09-10 21:34:13.973121: Epoch 918 +2024-09-10 21:34:13.973887: Current learning rate: 0.00105 +2024-09-10 21:38:31.402092: train_loss -0.7657 +2024-09-10 21:38:31.405205: val_loss -0.7533 +2024-09-10 21:38:31.413060: Pseudo dice [0.966, 0.9607, 0.9784, 0.9565, 0.8976, 0.9749, 0.9934] +2024-09-10 21:38:31.413835: Epoch time: 257.43 s +2024-09-10 21:38:34.823490: +2024-09-10 21:38:34.835048: Epoch 919 +2024-09-10 21:38:34.835896: Current learning rate: 0.00104 +2024-09-10 21:43:09.903755: train_loss -0.7576 +2024-09-10 21:43:09.917909: val_loss -0.7774 +2024-09-10 21:43:09.918669: Pseudo dice [0.9658, 0.9611, 0.9781, 0.9584, 0.8898, 0.9747, 0.991] +2024-09-10 21:43:09.919377: Epoch time: 275.09 s +2024-09-10 21:43:12.560797: +2024-09-10 21:43:12.561656: Epoch 920 +2024-09-10 21:43:12.562366: Current learning rate: 0.00103 +2024-09-10 21:47:27.841523: train_loss -0.7586 +2024-09-10 21:47:27.857962: val_loss -0.7715 +2024-09-10 21:47:27.858703: Pseudo dice [0.9696, 0.962, 0.9801, 0.9543, 0.8927, 0.9766, 0.9933] +2024-09-10 21:47:27.859396: Epoch time: 255.29 s +2024-09-10 21:47:32.208331: +2024-09-10 21:47:32.209072: Epoch 921 +2024-09-10 21:47:32.209637: Current learning rate: 0.00102 +2024-09-10 21:51:44.936736: train_loss -0.7547 +2024-09-10 21:51:44.950316: val_loss -0.7827 +2024-09-10 21:51:44.951178: Pseudo dice [0.9677, 0.9604, 0.9773, 0.9578, 0.8908, 0.9782, 0.9922] +2024-09-10 21:51:44.951957: Epoch time: 252.73 s +2024-09-10 21:51:47.976910: +2024-09-10 21:51:47.977802: Epoch 922 +2024-09-10 21:51:47.978559: Current learning rate: 0.00101 +2024-09-10 21:56:21.623584: train_loss -0.7605 +2024-09-10 21:56:21.626427: val_loss -0.7479 +2024-09-10 21:56:21.627477: Pseudo dice [0.9625, 0.9607, 0.9741, 0.9528, 0.8989, 0.975, 0.993] +2024-09-10 21:56:21.628281: Epoch time: 273.65 s +2024-09-10 21:56:23.921721: +2024-09-10 21:56:23.922671: Epoch 923 +2024-09-10 21:56:23.923244: Current learning rate: 0.001 +2024-09-10 22:00:45.858468: train_loss -0.7587 +2024-09-10 22:00:45.869544: val_loss -0.753 +2024-09-10 22:00:45.870362: Pseudo dice [0.9656, 0.9616, 0.9756, 0.9539, 0.8902, 0.9767, 0.9929] +2024-09-10 22:00:45.871019: Epoch time: 261.94 s +2024-09-10 22:00:49.013321: +2024-09-10 22:00:49.014711: Epoch 924 +2024-09-10 22:00:49.015516: Current learning rate: 0.00098 +2024-09-10 22:04:56.480654: train_loss -0.7744 +2024-09-10 22:04:56.494089: val_loss -0.7829 +2024-09-10 22:04:56.494788: Pseudo dice [0.9687, 0.963, 0.9797, 0.9536, 0.8986, 0.9778, 0.9926] +2024-09-10 22:04:56.495404: Epoch time: 247.47 s +2024-09-10 22:04:59.331033: +2024-09-10 22:04:59.332167: Epoch 925 +2024-09-10 22:04:59.337523: Current learning rate: 0.00097 +2024-09-10 22:09:20.286048: train_loss -0.762 +2024-09-10 22:09:20.302028: val_loss -0.7839 +2024-09-10 22:09:20.302649: Pseudo dice [0.9673, 0.9609, 0.9803, 0.9596, 0.8983, 0.9787, 0.9929] +2024-09-10 22:09:20.303180: Epoch time: 260.96 s +2024-09-10 22:09:23.062464: +2024-09-10 22:09:23.063314: Epoch 926 +2024-09-10 22:09:23.063917: Current learning rate: 0.00096 +2024-09-10 22:13:29.579432: train_loss -0.7554 +2024-09-10 22:13:29.593901: val_loss -0.758 +2024-09-10 22:13:29.594773: Pseudo dice [0.969, 0.9625, 0.9812, 0.9537, 0.9063, 0.9758, 0.9932] +2024-09-10 22:13:29.595573: Epoch time: 246.52 s +2024-09-10 22:13:33.328983: +2024-09-10 22:13:33.344745: Epoch 927 +2024-09-10 22:13:33.345658: Current learning rate: 0.00095 +2024-09-10 22:18:04.002902: train_loss -0.7536 +2024-09-10 22:18:04.017180: val_loss -0.7662 +2024-09-10 22:18:04.017881: Pseudo dice [0.9677, 0.9631, 0.9782, 0.9602, 0.9013, 0.9767, 0.993] +2024-09-10 22:18:04.018560: Epoch time: 270.68 s +2024-09-10 22:18:06.757584: +2024-09-10 22:18:06.758400: Epoch 928 +2024-09-10 22:18:06.759023: Current learning rate: 0.00094 +2024-09-10 22:22:27.584542: train_loss -0.767 +2024-09-10 22:22:27.586825: val_loss -0.7646 +2024-09-10 22:22:27.587493: Pseudo dice [0.971, 0.9644, 0.9818, 0.9588, 0.8958, 0.978, 0.9926] +2024-09-10 22:22:27.588052: Epoch time: 260.83 s +2024-09-10 22:22:31.649317: +2024-09-10 22:22:31.650731: Epoch 929 +2024-09-10 22:22:31.651739: Current learning rate: 0.00092 +2024-09-10 22:26:47.543607: train_loss -0.7628 +2024-09-10 22:26:47.561967: val_loss -0.7143 +2024-09-10 22:26:47.562629: Pseudo dice [0.9677, 0.9617, 0.9792, 0.9557, 0.9015, 0.9779, 0.9936] +2024-09-10 22:26:47.563224: Epoch time: 255.9 s +2024-09-10 22:26:51.706256: +2024-09-10 22:26:51.720767: Epoch 930 +2024-09-10 22:26:51.721667: Current learning rate: 0.00091 +2024-09-10 22:31:31.510183: train_loss -0.7567 +2024-09-10 22:31:31.512714: val_loss -0.7865 +2024-09-10 22:31:31.513728: Pseudo dice [0.9675, 0.9612, 0.9812, 0.9563, 0.8813, 0.9767, 0.9905] +2024-09-10 22:31:31.514778: Epoch time: 279.81 s +2024-09-10 22:31:34.131629: +2024-09-10 22:31:34.148856: Epoch 931 +2024-09-10 22:31:34.149760: Current learning rate: 0.0009 +2024-09-10 22:36:14.757252: train_loss -0.7525 +2024-09-10 22:36:14.759346: val_loss -0.7268 +2024-09-10 22:36:14.759984: Pseudo dice [0.9654, 0.9618, 0.9796, 0.9577, 0.9004, 0.978, 0.9929] +2024-09-10 22:36:14.760751: Epoch time: 280.63 s +2024-09-10 22:36:18.124685: +2024-09-10 22:36:18.125560: Epoch 932 +2024-09-10 22:36:18.126251: Current learning rate: 0.00089 +2024-09-10 22:40:31.227380: train_loss -0.752 +2024-09-10 22:40:31.250049: val_loss -0.7984 +2024-09-10 22:40:31.250979: Pseudo dice [0.9676, 0.9608, 0.9791, 0.9551, 0.9006, 0.9752, 0.993] +2024-09-10 22:40:31.251736: Epoch time: 253.11 s +2024-09-10 22:40:34.335414: +2024-09-10 22:40:34.348714: Epoch 933 +2024-09-10 22:40:34.349503: Current learning rate: 0.00088 +2024-09-10 22:44:47.989753: train_loss -0.7572 +2024-09-10 22:44:48.017691: val_loss -0.7768 +2024-09-10 22:44:48.018520: Pseudo dice [0.9687, 0.9625, 0.9801, 0.9574, 0.8957, 0.9777, 0.9929] +2024-09-10 22:44:48.019478: Epoch time: 253.66 s +2024-09-10 22:44:51.241508: +2024-09-10 22:44:51.256729: Epoch 934 +2024-09-10 22:44:51.257541: Current learning rate: 0.00087 +2024-09-10 22:49:25.561622: train_loss -0.7509 +2024-09-10 22:49:25.589487: val_loss -0.8013 +2024-09-10 22:49:25.590216: Pseudo dice [0.9699, 0.9637, 0.9829, 0.9564, 0.9018, 0.9783, 0.9935] +2024-09-10 22:49:25.590851: Epoch time: 274.33 s +2024-09-10 22:49:27.828491: +2024-09-10 22:49:27.829494: Epoch 935 +2024-09-10 22:49:27.830249: Current learning rate: 0.00085 +2024-09-10 22:53:47.434623: train_loss -0.7578 +2024-09-10 22:53:47.436913: val_loss -0.7619 +2024-09-10 22:53:47.437633: Pseudo dice [0.9649, 0.9616, 0.9772, 0.9487, 0.8913, 0.9775, 0.9915] +2024-09-10 22:53:47.438352: Epoch time: 259.61 s +2024-09-10 22:53:49.603015: +2024-09-10 22:53:49.604727: Epoch 936 +2024-09-10 22:53:49.606030: Current learning rate: 0.00084 +2024-09-10 22:58:12.693512: train_loss -0.7612 +2024-09-10 22:58:12.694951: val_loss -0.7813 +2024-09-10 22:58:12.695817: Pseudo dice [0.9682, 0.9624, 0.9786, 0.9561, 0.8938, 0.9779, 0.9924] +2024-09-10 22:58:12.696606: Epoch time: 263.1 s +2024-09-10 22:58:15.307031: +2024-09-10 22:58:15.309172: Epoch 937 +2024-09-10 22:58:15.309980: Current learning rate: 0.00083 +2024-09-10 23:02:53.460449: train_loss -0.7625 +2024-09-10 23:02:53.461710: val_loss -0.7514 +2024-09-10 23:02:53.462417: Pseudo dice [0.9689, 0.9636, 0.9793, 0.956, 0.9065, 0.9752, 0.9938] +2024-09-10 23:02:53.463060: Epoch time: 278.16 s +2024-09-10 23:02:55.752932: +2024-09-10 23:02:55.753836: Epoch 938 +2024-09-10 23:02:55.754536: Current learning rate: 0.00082 +2024-09-10 23:07:33.643286: train_loss -0.7695 +2024-09-10 23:07:33.660164: val_loss -0.7591 +2024-09-10 23:07:33.661004: Pseudo dice [0.9653, 0.9608, 0.9771, 0.9574, 0.9017, 0.9789, 0.9933] +2024-09-10 23:07:33.661695: Epoch time: 277.89 s +2024-09-10 23:07:36.782332: +2024-09-10 23:07:36.784168: Epoch 939 +2024-09-10 23:07:36.785350: Current learning rate: 0.00081 +2024-09-10 23:12:08.622517: train_loss -0.7615 +2024-09-10 23:12:08.623853: val_loss -0.777 +2024-09-10 23:12:08.624750: Pseudo dice [0.9707, 0.9626, 0.9821, 0.9552, 0.8941, 0.9766, 0.9922] +2024-09-10 23:12:08.625535: Epoch time: 271.85 s +2024-09-10 23:12:11.881812: +2024-09-10 23:12:11.882932: Epoch 940 +2024-09-10 23:12:11.883604: Current learning rate: 0.00079 +2024-09-10 23:16:46.898906: train_loss -0.7739 +2024-09-10 23:16:46.908351: val_loss -0.7561 +2024-09-10 23:16:46.909142: Pseudo dice [0.9713, 0.9632, 0.9818, 0.9524, 0.8973, 0.9775, 0.9934] +2024-09-10 23:16:46.909834: Epoch time: 275.02 s +2024-09-10 23:16:49.665280: +2024-09-10 23:16:49.667192: Epoch 941 +2024-09-10 23:16:49.667964: Current learning rate: 0.00078 +2024-09-10 23:21:11.288465: train_loss -0.7487 +2024-09-10 23:21:11.299672: val_loss -0.7821 +2024-09-10 23:21:11.300373: Pseudo dice [0.9681, 0.9606, 0.9792, 0.954, 0.9021, 0.9766, 0.993] +2024-09-10 23:21:11.301117: Epoch time: 261.63 s +2024-09-10 23:21:15.554881: +2024-09-10 23:21:15.556980: Epoch 942 +2024-09-10 23:21:15.558590: Current learning rate: 0.00077 +2024-09-10 23:26:03.904100: train_loss -0.7604 +2024-09-10 23:26:03.922791: val_loss -0.7443 +2024-09-10 23:26:03.923627: Pseudo dice [0.9699, 0.9626, 0.9807, 0.9564, 0.9026, 0.9766, 0.9929] +2024-09-10 23:26:03.924301: Epoch time: 288.35 s +2024-09-10 23:26:06.032273: +2024-09-10 23:26:06.033240: Epoch 943 +2024-09-10 23:26:06.034033: Current learning rate: 0.00076 +2024-09-10 23:30:28.533010: train_loss -0.7583 +2024-09-10 23:30:28.536717: val_loss -0.7675 +2024-09-10 23:30:28.537777: Pseudo dice [0.9654, 0.9606, 0.9796, 0.9561, 0.903, 0.9787, 0.9924] +2024-09-10 23:30:28.538784: Epoch time: 262.51 s +2024-09-10 23:30:31.205334: +2024-09-10 23:30:31.206318: Epoch 944 +2024-09-10 23:30:31.206973: Current learning rate: 0.00075 +2024-09-10 23:35:09.139782: train_loss -0.7609 +2024-09-10 23:35:09.148714: val_loss -0.7846 +2024-09-10 23:35:09.149575: Pseudo dice [0.9657, 0.9637, 0.9777, 0.9597, 0.9026, 0.9765, 0.9938] +2024-09-10 23:35:09.150306: Epoch time: 277.94 s +2024-09-10 23:35:12.975060: +2024-09-10 23:35:12.976092: Epoch 945 +2024-09-10 23:35:12.976774: Current learning rate: 0.00074 +2024-09-10 23:39:49.393277: train_loss -0.7599 +2024-09-10 23:39:49.396130: val_loss -0.7761 +2024-09-10 23:39:49.397227: Pseudo dice [0.9672, 0.9626, 0.9775, 0.9575, 0.8993, 0.9747, 0.9913] +2024-09-10 23:39:49.398583: Epoch time: 276.42 s +2024-09-10 23:39:52.369695: +2024-09-10 23:39:52.370981: Epoch 946 +2024-09-10 23:39:52.372128: Current learning rate: 0.00072 +2024-09-10 23:43:57.685063: train_loss -0.775 +2024-09-10 23:43:57.698155: val_loss -0.7533 +2024-09-10 23:43:57.698974: Pseudo dice [0.9696, 0.964, 0.9798, 0.962, 0.9096, 0.9787, 0.9936] +2024-09-10 23:43:57.699654: Epoch time: 245.32 s +2024-09-10 23:43:57.700311: Yayy! New best EMA pseudo Dice: 0.9624 +2024-09-10 23:44:04.944252: +2024-09-10 23:44:04.945123: Epoch 947 +2024-09-10 23:44:04.945855: Current learning rate: 0.00071 +2024-09-10 23:48:33.593060: train_loss -0.7465 +2024-09-10 23:48:33.595585: val_loss -0.7794 +2024-09-10 23:48:33.596688: Pseudo dice [0.9708, 0.9623, 0.983, 0.9572, 0.9039, 0.9805, 0.9935] +2024-09-10 23:48:33.597682: Epoch time: 268.65 s +2024-09-10 23:48:33.598617: Yayy! New best EMA pseudo Dice: 0.9626 +2024-09-10 23:48:40.365282: +2024-09-10 23:48:40.366422: Epoch 948 +2024-09-10 23:48:40.367123: Current learning rate: 0.0007 +2024-09-10 23:53:20.258377: train_loss -0.762 +2024-09-10 23:53:20.266964: val_loss -0.7657 +2024-09-10 23:53:20.267703: Pseudo dice [0.9711, 0.9652, 0.984, 0.9587, 0.9052, 0.9779, 0.9934] +2024-09-10 23:53:20.268256: Epoch time: 279.9 s +2024-09-10 23:53:20.268822: Yayy! New best EMA pseudo Dice: 0.9628 +2024-09-10 23:53:25.650163: +2024-09-10 23:53:25.650873: Epoch 949 +2024-09-10 23:53:25.651437: Current learning rate: 0.00069 +2024-09-10 23:57:37.680993: train_loss -0.7637 +2024-09-10 23:57:37.698114: val_loss -0.7941 +2024-09-10 23:57:37.698863: Pseudo dice [0.9687, 0.9633, 0.9822, 0.9595, 0.8952, 0.9746, 0.9912] +2024-09-10 23:57:37.699507: Epoch time: 252.03 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_11_00_05_58.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_11_00_05_58.txt new file mode 100644 index 0000000000000000000000000000000000000000..754996362c9d3f73526c54ea6c4c04e63d82617d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_11_00_05_58.txt @@ -0,0 +1,1043 @@ + +####################################################################### +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. +####################################################################### + +2024-09-11 00:06:07.449599: Using torch.compile... +2024-09-11 00:06:20.353707: do_dummy_2d_data_aug: False +2024-09-11 00:06:20.359878: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 00:06:20.361828: The split file contains 5 splits. +2024-09-11 00:06:20.362428: Desired fold for training: 2 +2024-09-11 00:06:20.363013: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-11 00:06:34.081596: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-11 00:06:43.448459: +2024-09-11 00:06:43.449455: Epoch 950 +2024-09-11 00:06:43.450307: Current learning rate: 0.00067 +2024-09-11 00:20:11.908074: train_loss -0.7765 +2024-09-11 00:20:11.910681: val_loss -0.8074 +2024-09-11 00:20:11.911529: Pseudo dice [0.97, 0.9618, 0.9831, 0.9536, 0.9044, 0.9786, 0.9936] +2024-09-11 00:20:11.912193: Epoch time: 808.47 s +2024-09-11 00:20:11.912933: Yayy! New best EMA pseudo Dice: 0.9628 +2024-09-11 00:20:16.362882: +2024-09-11 00:20:16.363615: Epoch 951 +2024-09-11 00:20:16.364269: Current learning rate: 0.00066 +2024-09-11 00:24:39.378418: train_loss -0.7582 +2024-09-11 00:24:39.392758: val_loss -0.7793 +2024-09-11 00:24:39.393599: Pseudo dice [0.9668, 0.9595, 0.9792, 0.9581, 0.8897, 0.977, 0.9924] +2024-09-11 00:24:39.394614: Epoch time: 263.02 s +2024-09-11 00:24:43.107158: +2024-09-11 00:24:43.121178: Epoch 952 +2024-09-11 00:24:43.122521: Current learning rate: 0.00065 +2024-09-11 00:29:08.423877: train_loss -0.752 +2024-09-11 00:29:08.439451: val_loss -0.7894 +2024-09-11 00:29:08.441570: Pseudo dice [0.9679, 0.961, 0.9816, 0.9598, 0.9048, 0.9782, 0.9935] +2024-09-11 00:29:08.443685: Epoch time: 265.32 s +2024-09-11 00:29:11.313701: +2024-09-11 00:29:11.328673: Epoch 953 +2024-09-11 00:29:11.329446: Current learning rate: 0.00064 +2024-09-11 00:33:46.995578: train_loss -0.7682 +2024-09-11 00:33:47.010179: val_loss -0.7617 +2024-09-11 00:33:47.010933: Pseudo dice [0.9683, 0.9629, 0.9796, 0.9555, 0.9047, 0.9771, 0.9938] +2024-09-11 00:33:47.011617: Epoch time: 275.69 s +2024-09-11 00:33:49.701004: +2024-09-11 00:33:49.724021: Epoch 954 +2024-09-11 00:33:49.725160: Current learning rate: 0.00063 +2024-09-11 00:38:04.909805: train_loss -0.7685 +2024-09-11 00:38:04.911919: val_loss -0.7819 +2024-09-11 00:38:04.924772: Pseudo dice [0.9699, 0.9638, 0.9817, 0.9563, 0.8944, 0.975, 0.9921] +2024-09-11 00:38:04.925538: Epoch time: 255.21 s +2024-09-11 00:38:08.642944: +2024-09-11 00:38:08.656711: Epoch 955 +2024-09-11 00:38:08.657576: Current learning rate: 0.00061 +2024-09-11 00:42:48.332280: train_loss -0.7591 +2024-09-11 00:42:48.334629: val_loss -0.7893 +2024-09-11 00:42:48.335353: Pseudo dice [0.9693, 0.9606, 0.9808, 0.9544, 0.8883, 0.9761, 0.9922] +2024-09-11 00:42:48.335964: Epoch time: 279.69 s +2024-09-11 00:42:51.429036: +2024-09-11 00:42:51.444717: Epoch 956 +2024-09-11 00:42:51.445556: Current learning rate: 0.0006 +2024-09-11 00:47:23.242100: train_loss -0.7559 +2024-09-11 00:47:23.261935: val_loss -0.7768 +2024-09-11 00:47:23.262686: Pseudo dice [0.9682, 0.9618, 0.9813, 0.9564, 0.8945, 0.975, 0.9919] +2024-09-11 00:47:23.263240: Epoch time: 271.82 s +2024-09-11 00:47:26.248952: +2024-09-11 00:47:26.249830: Epoch 957 +2024-09-11 00:47:26.250490: Current learning rate: 0.00059 +2024-09-11 00:52:03.367489: train_loss -0.7664 +2024-09-11 00:52:03.390233: val_loss -0.7845 +2024-09-11 00:52:03.391339: Pseudo dice [0.9657, 0.9602, 0.9779, 0.9605, 0.8984, 0.9792, 0.994] +2024-09-11 00:52:03.392541: Epoch time: 277.12 s +2024-09-11 00:52:06.732546: +2024-09-11 00:52:06.733321: Epoch 958 +2024-09-11 00:52:06.733890: Current learning rate: 0.00058 +2024-09-11 00:56:43.459624: train_loss -0.7688 +2024-09-11 00:56:43.477991: val_loss -0.7946 +2024-09-11 00:56:43.479096: Pseudo dice [0.9691, 0.9641, 0.9792, 0.9624, 0.9123, 0.9773, 0.9933] +2024-09-11 00:56:43.480055: Epoch time: 276.73 s +2024-09-11 00:56:46.759679: +2024-09-11 00:56:46.763817: Epoch 959 +2024-09-11 00:56:46.764702: Current learning rate: 0.00056 +2024-09-11 01:01:24.234974: train_loss -0.7686 +2024-09-11 01:01:24.237373: val_loss -0.8029 +2024-09-11 01:01:24.238132: Pseudo dice [0.9662, 0.9606, 0.9781, 0.9569, 0.9036, 0.9773, 0.9936] +2024-09-11 01:01:24.238836: Epoch time: 277.49 s +2024-09-11 01:01:27.461522: +2024-09-11 01:01:27.476684: Epoch 960 +2024-09-11 01:01:27.477432: Current learning rate: 0.00055 +2024-09-11 01:05:46.094276: train_loss -0.7467 +2024-09-11 01:05:46.096672: val_loss -0.7675 +2024-09-11 01:05:46.097481: Pseudo dice [0.9673, 0.9624, 0.979, 0.9599, 0.9034, 0.9771, 0.9928] +2024-09-11 01:05:46.098184: Epoch time: 258.64 s +2024-09-11 01:05:49.111280: +2024-09-11 01:05:49.119159: Epoch 961 +2024-09-11 01:05:49.120403: Current learning rate: 0.00054 +2024-09-11 01:10:21.390321: train_loss -0.7673 +2024-09-11 01:10:21.406147: val_loss -0.7824 +2024-09-11 01:10:21.407035: Pseudo dice [0.9687, 0.9628, 0.9813, 0.9553, 0.9017, 0.9754, 0.9926] +2024-09-11 01:10:21.407790: Epoch time: 272.28 s +2024-09-11 01:10:24.161201: +2024-09-11 01:10:24.172613: Epoch 962 +2024-09-11 01:10:24.173487: Current learning rate: 0.00053 +2024-09-11 01:14:44.326254: train_loss -0.7635 +2024-09-11 01:14:44.341937: val_loss -0.7767 +2024-09-11 01:14:44.342718: Pseudo dice [0.969, 0.9605, 0.9798, 0.9547, 0.8826, 0.9769, 0.9921] +2024-09-11 01:14:44.343348: Epoch time: 260.17 s +2024-09-11 01:14:47.532814: +2024-09-11 01:14:47.548874: Epoch 963 +2024-09-11 01:14:47.549718: Current learning rate: 0.00051 +2024-09-11 01:19:03.044357: train_loss -0.7553 +2024-09-11 01:19:03.057070: val_loss -0.7738 +2024-09-11 01:19:03.058190: Pseudo dice [0.9704, 0.9609, 0.9818, 0.9582, 0.9005, 0.9765, 0.9923] +2024-09-11 01:19:03.059211: Epoch time: 255.52 s +2024-09-11 01:19:06.925936: +2024-09-11 01:19:06.926706: Epoch 964 +2024-09-11 01:19:06.927285: Current learning rate: 0.0005 +2024-09-11 01:23:42.858200: train_loss -0.781 +2024-09-11 01:23:42.875013: val_loss -0.7834 +2024-09-11 01:23:42.876030: Pseudo dice [0.9706, 0.9642, 0.9822, 0.9578, 0.8997, 0.9772, 0.9927] +2024-09-11 01:23:42.876830: Epoch time: 275.94 s +2024-09-11 01:23:47.002792: +2024-09-11 01:23:47.012880: Epoch 965 +2024-09-11 01:23:47.013980: Current learning rate: 0.00049 +2024-09-11 01:28:22.911545: train_loss -0.766 +2024-09-11 01:28:22.942444: val_loss -0.7762 +2024-09-11 01:28:22.944684: Pseudo dice [0.9697, 0.9658, 0.9805, 0.9589, 0.9067, 0.9756, 0.9929] +2024-09-11 01:28:22.952451: Epoch time: 275.91 s +2024-09-11 01:28:25.622056: +2024-09-11 01:28:25.636802: Epoch 966 +2024-09-11 01:28:25.637723: Current learning rate: 0.00048 +2024-09-11 01:32:46.699336: train_loss -0.75 +2024-09-11 01:32:46.713433: val_loss -0.761 +2024-09-11 01:32:46.714187: Pseudo dice [0.9679, 0.9621, 0.9788, 0.9558, 0.9024, 0.9776, 0.9934] +2024-09-11 01:32:46.714907: Epoch time: 261.08 s +2024-09-11 01:32:49.834565: +2024-09-11 01:32:49.847922: Epoch 967 +2024-09-11 01:32:49.849150: Current learning rate: 0.00046 +2024-09-11 01:37:23.361484: train_loss -0.7618 +2024-09-11 01:37:23.363689: val_loss -0.785 +2024-09-11 01:37:23.364378: Pseudo dice [0.9697, 0.9625, 0.9828, 0.9574, 0.8988, 0.9761, 0.9928] +2024-09-11 01:37:23.365053: Epoch time: 273.53 s +2024-09-11 01:37:27.482631: +2024-09-11 01:37:27.483527: Epoch 968 +2024-09-11 01:37:27.484229: Current learning rate: 0.00045 +2024-09-11 01:41:57.994494: train_loss -0.7643 +2024-09-11 01:41:57.996782: val_loss -0.719 +2024-09-11 01:41:57.997554: Pseudo dice [0.968, 0.9622, 0.9784, 0.9538, 0.8945, 0.9775, 0.9924] +2024-09-11 01:41:57.998173: Epoch time: 270.52 s +2024-09-11 01:42:01.414790: +2024-09-11 01:42:01.415635: Epoch 969 +2024-09-11 01:42:01.416390: Current learning rate: 0.00044 +2024-09-11 01:46:25.280135: train_loss -0.7638 +2024-09-11 01:46:25.298216: val_loss -0.7876 +2024-09-11 01:46:25.298923: Pseudo dice [0.9674, 0.961, 0.9781, 0.9578, 0.9052, 0.9782, 0.9936] +2024-09-11 01:46:25.299644: Epoch time: 263.87 s +2024-09-11 01:46:28.059649: +2024-09-11 01:46:28.060536: Epoch 970 +2024-09-11 01:46:28.061235: Current learning rate: 0.00043 +2024-09-11 01:51:02.222607: train_loss -0.7716 +2024-09-11 01:51:02.238019: val_loss -0.7958 +2024-09-11 01:51:02.238824: Pseudo dice [0.9706, 0.9632, 0.9798, 0.9541, 0.9055, 0.9792, 0.994] +2024-09-11 01:51:02.239539: Epoch time: 274.17 s +2024-09-11 01:51:04.933121: +2024-09-11 01:51:04.934086: Epoch 971 +2024-09-11 01:51:04.934844: Current learning rate: 0.00041 +2024-09-11 01:55:24.035799: train_loss -0.7611 +2024-09-11 01:55:24.051198: val_loss -0.7674 +2024-09-11 01:55:24.051921: Pseudo dice [0.9664, 0.9626, 0.9782, 0.9571, 0.9009, 0.9781, 0.9935] +2024-09-11 01:55:24.052657: Epoch time: 259.11 s +2024-09-11 01:55:27.182762: +2024-09-11 01:55:27.192950: Epoch 972 +2024-09-11 01:55:27.194024: Current learning rate: 0.0004 +2024-09-11 01:59:56.869200: train_loss -0.7697 +2024-09-11 01:59:56.878122: val_loss -0.7862 +2024-09-11 01:59:56.878875: Pseudo dice [0.968, 0.9638, 0.9785, 0.9547, 0.9013, 0.9754, 0.9928] +2024-09-11 01:59:56.879553: Epoch time: 269.69 s +2024-09-11 01:59:59.676582: +2024-09-11 01:59:59.677413: Epoch 973 +2024-09-11 01:59:59.678228: Current learning rate: 0.00039 +2024-09-11 02:04:36.626498: train_loss -0.7647 +2024-09-11 02:04:36.629069: val_loss -0.7924 +2024-09-11 02:04:36.629853: Pseudo dice [0.9701, 0.9618, 0.9807, 0.9568, 0.889, 0.9756, 0.992] +2024-09-11 02:04:36.630531: Epoch time: 276.96 s +2024-09-11 02:04:38.838834: +2024-09-11 02:04:38.839658: Epoch 974 +2024-09-11 02:04:38.840294: Current learning rate: 0.00037 +2024-09-11 02:09:01.287522: train_loss -0.7617 +2024-09-11 02:09:01.301972: val_loss -0.7658 +2024-09-11 02:09:01.302673: Pseudo dice [0.965, 0.9609, 0.977, 0.9591, 0.9082, 0.9798, 0.9936] +2024-09-11 02:09:01.303300: Epoch time: 262.45 s +2024-09-11 02:09:04.061926: +2024-09-11 02:09:04.062892: Epoch 975 +2024-09-11 02:09:04.063621: Current learning rate: 0.00036 +2024-09-11 02:13:29.578968: train_loss -0.7481 +2024-09-11 02:13:29.594430: val_loss -0.7364 +2024-09-11 02:13:29.595658: Pseudo dice [0.9705, 0.9621, 0.9823, 0.9591, 0.9065, 0.9769, 0.9934] +2024-09-11 02:13:29.596671: Epoch time: 265.52 s +2024-09-11 02:13:33.049114: +2024-09-11 02:13:33.064657: Epoch 976 +2024-09-11 02:13:33.065430: Current learning rate: 0.00035 +2024-09-11 02:17:49.512938: train_loss -0.7746 +2024-09-11 02:17:49.526134: val_loss -0.792 +2024-09-11 02:17:49.527112: Pseudo dice [0.9685, 0.9617, 0.9797, 0.9585, 0.904, 0.9798, 0.9938] +2024-09-11 02:17:49.527944: Epoch time: 256.47 s +2024-09-11 02:17:52.618775: +2024-09-11 02:17:52.633375: Epoch 977 +2024-09-11 02:17:52.634335: Current learning rate: 0.00034 +2024-09-11 02:22:10.623706: train_loss -0.7715 +2024-09-11 02:22:10.637921: val_loss -0.7802 +2024-09-11 02:22:10.638707: Pseudo dice [0.9707, 0.9653, 0.9806, 0.9556, 0.8986, 0.9758, 0.9924] +2024-09-11 02:22:10.639459: Epoch time: 258.01 s +2024-09-11 02:22:13.893632: +2024-09-11 02:22:13.894511: Epoch 978 +2024-09-11 02:22:13.895165: Current learning rate: 0.00032 +2024-09-11 02:26:41.166926: train_loss -0.7633 +2024-09-11 02:26:41.171191: val_loss -0.7693 +2024-09-11 02:26:41.171908: Pseudo dice [0.9667, 0.9628, 0.9763, 0.9536, 0.8971, 0.9783, 0.9935] +2024-09-11 02:26:41.172533: Epoch time: 267.28 s +2024-09-11 02:26:43.731723: +2024-09-11 02:26:43.745106: Epoch 979 +2024-09-11 02:26:43.746175: Current learning rate: 0.00031 +2024-09-11 02:31:08.603479: train_loss -0.7575 +2024-09-11 02:31:08.615587: val_loss -0.7863 +2024-09-11 02:31:08.616409: Pseudo dice [0.9672, 0.9628, 0.9791, 0.9599, 0.9012, 0.9788, 0.9939] +2024-09-11 02:31:08.617256: Epoch time: 264.88 s +2024-09-11 02:31:11.644588: +2024-09-11 02:31:11.645516: Epoch 980 +2024-09-11 02:31:11.646324: Current learning rate: 0.0003 +2024-09-11 02:35:42.365314: train_loss -0.7483 +2024-09-11 02:35:42.366928: val_loss -0.7733 +2024-09-11 02:35:42.367451: Pseudo dice [0.9667, 0.9622, 0.9797, 0.9614, 0.8956, 0.9761, 0.9929] +2024-09-11 02:35:42.367976: Epoch time: 270.72 s +2024-09-11 02:35:45.675147: +2024-09-11 02:35:45.688867: Epoch 981 +2024-09-11 02:35:45.689926: Current learning rate: 0.00028 +2024-09-11 02:40:23.264141: train_loss -0.7601 +2024-09-11 02:40:23.266517: val_loss -0.7562 +2024-09-11 02:40:23.267285: Pseudo dice [0.9668, 0.9609, 0.9778, 0.9563, 0.8953, 0.9749, 0.9914] +2024-09-11 02:40:23.267976: Epoch time: 277.58 s +2024-09-11 02:40:26.138208: +2024-09-11 02:40:26.156746: Epoch 982 +2024-09-11 02:40:26.157624: Current learning rate: 0.00027 +2024-09-11 02:44:34.023134: train_loss -0.7632 +2024-09-11 02:44:34.028508: val_loss -0.7739 +2024-09-11 02:44:34.029678: Pseudo dice [0.963, 0.9617, 0.973, 0.9575, 0.903, 0.9767, 0.9933] +2024-09-11 02:44:34.030754: Epoch time: 247.89 s +2024-09-11 02:44:37.201889: +2024-09-11 02:44:37.203024: Epoch 983 +2024-09-11 02:44:37.204191: Current learning rate: 0.00026 +2024-09-11 02:49:10.165844: train_loss -0.7599 +2024-09-11 02:49:10.180212: val_loss -0.7861 +2024-09-11 02:49:10.181249: Pseudo dice [0.9685, 0.9619, 0.9806, 0.9602, 0.9073, 0.9781, 0.9931] +2024-09-11 02:49:10.181983: Epoch time: 272.97 s +2024-09-11 02:49:12.576075: +2024-09-11 02:49:12.588619: Epoch 984 +2024-09-11 02:49:12.589399: Current learning rate: 0.00024 +2024-09-11 02:53:27.711977: train_loss -0.7735 +2024-09-11 02:53:27.725397: val_loss -0.7567 +2024-09-11 02:53:27.726295: Pseudo dice [0.9686, 0.9611, 0.9796, 0.9532, 0.8867, 0.9776, 0.9918] +2024-09-11 02:53:27.727035: Epoch time: 255.14 s +2024-09-11 02:53:30.422948: +2024-09-11 02:53:30.424029: Epoch 985 +2024-09-11 02:53:30.424886: Current learning rate: 0.00023 +2024-09-11 02:57:45.537331: train_loss -0.7668 +2024-09-11 02:57:45.539565: val_loss -0.7563 +2024-09-11 02:57:45.540308: Pseudo dice [0.9688, 0.9612, 0.9798, 0.9522, 0.9, 0.9784, 0.9939] +2024-09-11 02:57:45.541054: Epoch time: 255.12 s +2024-09-11 02:57:48.134237: +2024-09-11 02:57:48.135006: Epoch 986 +2024-09-11 02:57:48.135689: Current learning rate: 0.00021 +2024-09-11 03:02:16.994847: train_loss -0.7676 +2024-09-11 03:02:17.017347: val_loss -0.8066 +2024-09-11 03:02:17.018166: Pseudo dice [0.9688, 0.9627, 0.9828, 0.9573, 0.9009, 0.9781, 0.9936] +2024-09-11 03:02:17.018928: Epoch time: 268.87 s +2024-09-11 03:02:20.139694: +2024-09-11 03:02:20.140585: Epoch 987 +2024-09-11 03:02:20.141265: Current learning rate: 0.0002 +2024-09-11 03:06:38.900741: train_loss -0.766 +2024-09-11 03:06:38.903014: val_loss -0.7801 +2024-09-11 03:06:38.903608: Pseudo dice [0.9701, 0.9641, 0.9806, 0.9569, 0.908, 0.9781, 0.9938] +2024-09-11 03:06:38.904726: Epoch time: 258.77 s +2024-09-11 03:06:41.921025: +2024-09-11 03:06:41.921976: Epoch 988 +2024-09-11 03:06:41.923028: Current learning rate: 0.00019 +2024-09-11 03:11:05.136857: train_loss -0.7515 +2024-09-11 03:11:05.154127: val_loss -0.7677 +2024-09-11 03:11:05.154976: Pseudo dice [0.9692, 0.9628, 0.9794, 0.957, 0.8999, 0.9797, 0.993] +2024-09-11 03:11:05.155726: Epoch time: 263.22 s +2024-09-11 03:11:08.575290: +2024-09-11 03:11:08.576891: Epoch 989 +2024-09-11 03:11:08.578076: Current learning rate: 0.00017 +2024-09-11 03:15:34.898404: train_loss -0.7774 +2024-09-11 03:15:34.929818: val_loss -0.7762 +2024-09-11 03:15:34.930639: Pseudo dice [0.9708, 0.9619, 0.9843, 0.9537, 0.9053, 0.9784, 0.9939] +2024-09-11 03:15:34.931286: Epoch time: 266.33 s +2024-09-11 03:15:39.524149: +2024-09-11 03:15:39.525057: Epoch 990 +2024-09-11 03:15:39.525755: Current learning rate: 0.00016 +2024-09-11 03:20:05.111566: train_loss -0.7694 +2024-09-11 03:20:05.133845: val_loss -0.7774 +2024-09-11 03:20:05.134602: Pseudo dice [0.9679, 0.9611, 0.9821, 0.9601, 0.9063, 0.9767, 0.9933] +2024-09-11 03:20:05.135200: Epoch time: 265.59 s +2024-09-11 03:20:05.135795: Yayy! New best EMA pseudo Dice: 0.9629 +2024-09-11 03:20:11.470440: +2024-09-11 03:20:11.471322: Epoch 991 +2024-09-11 03:20:11.472036: Current learning rate: 0.00014 +2024-09-11 03:24:33.681620: train_loss -0.77 +2024-09-11 03:24:33.687004: val_loss -0.7363 +2024-09-11 03:24:33.688135: Pseudo dice [0.9691, 0.9633, 0.9805, 0.9518, 0.8865, 0.9754, 0.9906] +2024-09-11 03:24:33.689111: Epoch time: 262.22 s +2024-09-11 03:24:36.990812: +2024-09-11 03:24:37.004691: Epoch 992 +2024-09-11 03:24:37.005533: Current learning rate: 0.00013 +2024-09-11 03:28:59.074887: train_loss -0.7561 +2024-09-11 03:28:59.093217: val_loss -0.7339 +2024-09-11 03:28:59.094080: Pseudo dice [0.9679, 0.9611, 0.981, 0.9575, 0.9085, 0.9793, 0.9929] +2024-09-11 03:28:59.094764: Epoch time: 262.09 s +2024-09-11 03:29:01.841920: +2024-09-11 03:29:01.842872: Epoch 993 +2024-09-11 03:29:01.843614: Current learning rate: 0.00011 +2024-09-11 03:33:41.890721: train_loss -0.7596 +2024-09-11 03:33:41.892873: val_loss -0.8074 +2024-09-11 03:33:41.893641: Pseudo dice [0.9694, 0.9631, 0.9808, 0.9568, 0.9057, 0.9764, 0.9921] +2024-09-11 03:33:41.894314: Epoch time: 280.05 s +2024-09-11 03:33:44.880802: +2024-09-11 03:33:44.881663: Epoch 994 +2024-09-11 03:33:44.882302: Current learning rate: 0.0001 +2024-09-11 03:38:25.452988: train_loss -0.764 +2024-09-11 03:38:25.466071: val_loss -0.7891 +2024-09-11 03:38:25.466856: Pseudo dice [0.9708, 0.9637, 0.9833, 0.9546, 0.9013, 0.9755, 0.9924] +2024-09-11 03:38:25.467664: Epoch time: 280.58 s +2024-09-11 03:38:27.886873: +2024-09-11 03:38:27.887703: Epoch 995 +2024-09-11 03:38:27.888413: Current learning rate: 8e-05 +2024-09-11 03:43:02.029632: train_loss -0.7545 +2024-09-11 03:43:02.031881: val_loss -0.757 +2024-09-11 03:43:02.032727: Pseudo dice [0.9679, 0.9621, 0.9819, 0.9599, 0.8959, 0.978, 0.9932] +2024-09-11 03:43:02.033450: Epoch time: 274.15 s +2024-09-11 03:43:05.284827: +2024-09-11 03:43:05.296124: Epoch 996 +2024-09-11 03:43:05.297222: Current learning rate: 7e-05 +2024-09-11 03:47:32.980831: train_loss -0.7551 +2024-09-11 03:47:32.994021: val_loss -0.7741 +2024-09-11 03:47:32.994952: Pseudo dice [0.9686, 0.9627, 0.9799, 0.9537, 0.9073, 0.9788, 0.9931] +2024-09-11 03:47:32.995652: Epoch time: 267.7 s +2024-09-11 03:47:35.941681: +2024-09-11 03:47:35.956686: Epoch 997 +2024-09-11 03:47:35.957623: Current learning rate: 5e-05 +2024-09-11 03:52:10.040598: train_loss -0.7594 +2024-09-11 03:52:10.050173: val_loss -0.7682 +2024-09-11 03:52:10.051002: Pseudo dice [0.9663, 0.9621, 0.9807, 0.9582, 0.9045, 0.9777, 0.9933] +2024-09-11 03:52:10.051756: Epoch time: 274.1 s +2024-09-11 03:52:10.052505: Yayy! New best EMA pseudo Dice: 0.9629 +2024-09-11 03:52:16.197416: +2024-09-11 03:52:16.198378: Epoch 998 +2024-09-11 03:52:16.199047: Current learning rate: 4e-05 +2024-09-11 03:56:26.093355: train_loss -0.7602 +2024-09-11 03:56:26.096134: val_loss -0.7499 +2024-09-11 03:56:26.097278: Pseudo dice [0.9706, 0.9623, 0.9815, 0.9558, 0.8849, 0.9778, 0.9912] +2024-09-11 03:56:26.098108: Epoch time: 249.9 s +2024-09-11 03:56:29.217293: +2024-09-11 03:56:29.218039: Epoch 999 +2024-09-11 03:56:29.218690: Current learning rate: 2e-05 +2024-09-11 04:00:51.045233: train_loss -0.7613 +2024-09-11 04:00:51.061759: val_loss -0.7792 +2024-09-11 04:00:51.062510: Pseudo dice [0.9655, 0.963, 0.9779, 0.9593, 0.9102, 0.9773, 0.993] +2024-09-11 04:00:51.063182: Epoch time: 261.83 s +2024-09-11 04:00:57.589480: Training done. +2024-09-11 04:00:57.621778: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 04:00:57.623969: The split file contains 5 splits. +2024-09-11 04:00:57.624606: Desired fold for training: 2 +2024-09-11 04:00:57.625130: This split has 1325 training and 331 validation cases. +2024-09-11 04:00:57.633060: predicting saros-006 +2024-09-11 04:00:58.078339: saros-006, shape torch.Size([1, 300, 240, 240]), rank 0 +2024-09-11 04:03:21.630297: predicting saros-011 +2024-09-11 04:03:21.993916: saros-011, shape torch.Size([1, 293, 227, 227]), rank 0 +2024-09-11 04:03:27.448330: predicting saros-012 +2024-09-11 04:03:27.856846: saros-012, shape torch.Size([1, 217, 267, 267]), rank 0 +2024-09-11 04:03:31.533611: predicting saros-018 +2024-09-11 04:03:32.036412: saros-018, shape torch.Size([1, 270, 253, 253]), rank 0 +2024-09-11 04:03:35.739412: predicting saros-024 +2024-09-11 04:03:36.069408: saros-024, shape torch.Size([1, 180, 273, 273]), rank 0 +2024-09-11 04:03:38.002027: predicting saros-041 +2024-09-11 04:03:38.299198: saros-041, shape torch.Size([1, 183, 233, 233]), rank 0 +2024-09-11 04:03:40.180431: predicting saros-042 +2024-09-11 04:03:40.565469: saros-042, shape torch.Size([1, 283, 227, 227]), rank 0 +2024-09-11 04:03:44.236305: predicting saros-043 +2024-09-11 04:03:44.777447: saros-043, shape torch.Size([1, 217, 320, 320]), rank 0 +2024-09-11 04:03:53.012169: predicting saros-051 +2024-09-11 04:03:53.673612: saros-051, shape torch.Size([1, 273, 281, 281]), rank 0 +2024-09-11 04:03:57.436926: predicting saros-056 +2024-09-11 04:03:57.830421: saros-056, shape torch.Size([1, 280, 245, 245]), rank 0 +2024-09-11 04:04:01.556867: predicting saros-061 +2024-09-11 04:04:01.677382: saros-061, shape torch.Size([1, 150, 173, 173]), rank 0 +2024-09-11 04:04:02.188141: predicting saros-073 +2024-09-11 04:04:02.607372: saros-073, shape torch.Size([1, 310, 239, 239]), rank 0 +2024-09-11 04:04:08.132460: predicting saros-074 +2024-09-11 04:04:08.707901: saros-074, shape torch.Size([1, 303, 286, 286]), rank 0 +2024-09-11 04:04:14.281750: predicting saros-083 +2024-09-11 04:04:14.571443: saros-083, shape torch.Size([1, 157, 283, 283]), rank 0 +2024-09-11 04:04:16.526555: predicting saros-089 +2024-09-11 04:04:16.822405: saros-089, shape torch.Size([1, 293, 200, 200]), rank 0 +2024-09-11 04:04:22.270672: predicting saros-092 +2024-09-11 04:04:22.648563: saros-092, shape torch.Size([1, 183, 281, 281]), rank 0 +2024-09-11 04:04:24.636282: predicting saros-094 +2024-09-11 04:04:25.462111: saros-094, shape torch.Size([1, 287, 333, 333]), rank 0 +2024-09-11 04:04:33.836793: predicting saros-104 +2024-09-11 04:04:34.404156: saros-104, shape torch.Size([1, 313, 261, 261]), rank 0 +2024-09-11 04:04:39.986284: predicting saros-108 +2024-09-11 04:04:40.382502: saros-108, shape torch.Size([1, 293, 236, 236]), rank 0 +2024-09-11 04:04:45.898736: predicting saros-113 +2024-09-11 04:04:46.302883: saros-113, shape torch.Size([1, 307, 240, 240]), rank 0 +2024-09-11 04:04:51.802990: predicting saros-114 +2024-09-11 04:04:52.398581: saros-114, shape torch.Size([1, 293, 311, 311]), rank 0 +2024-09-11 04:05:04.670104: predicting saros-123 +2024-09-11 04:05:04.822461: saros-123, shape torch.Size([1, 130, 233, 233]), rank 0 +2024-09-11 04:05:06.676138: predicting saros-132 +2024-09-11 04:05:07.119988: saros-132, shape torch.Size([1, 293, 241, 241]), rank 0 +2024-09-11 04:05:12.614753: predicting saros-140 +2024-09-11 04:05:13.037141: saros-140, shape torch.Size([1, 283, 253, 253]), rank 0 +2024-09-11 04:05:16.762160: predicting saros-145 +2024-09-11 04:05:17.126142: saros-145, shape torch.Size([1, 273, 227, 227]), rank 0 +2024-09-11 04:05:20.806493: predicting saros-153 +2024-09-11 04:05:21.023422: saros-153, shape torch.Size([1, 133, 280, 280]), rank 0 +2024-09-11 04:05:22.916529: predicting saros-163 +2024-09-11 04:05:23.798330: saros-163, shape torch.Size([1, 323, 320, 320]), rank 0 +2024-09-11 04:05:36.130346: predicting saros-167 +2024-09-11 04:05:36.410427: saros-167, shape torch.Size([1, 177, 247, 247]), rank 0 +2024-09-11 04:05:38.439532: predicting saros-176 +2024-09-11 04:05:38.692095: saros-176, shape torch.Size([1, 150, 280, 280]), rank 0 +2024-09-11 04:05:40.618890: predicting saros-177 +2024-09-11 04:05:40.971248: saros-177, shape torch.Size([1, 157, 320, 320]), rank 0 +2024-09-11 04:05:45.235447: predicting saros-178 +2024-09-11 04:05:46.014770: saros-178, shape torch.Size([1, 327, 305, 305]), rank 0 +2024-09-11 04:05:58.400821: predicting saros-182 +2024-09-11 04:05:58.607493: saros-182, shape torch.Size([1, 140, 227, 227]), rank 0 +2024-09-11 04:06:00.455039: predicting saros-184 +2024-09-11 04:06:00.873090: saros-184, shape torch.Size([1, 203, 276, 276]), rank 0 +2024-09-11 04:06:04.613264: predicting saros-188 +2024-09-11 04:06:04.849045: saros-188, shape torch.Size([1, 157, 227, 227]), rank 0 +2024-09-11 04:06:06.739866: predicting saros-203 +2024-09-11 04:06:07.182952: saros-203, shape torch.Size([1, 290, 269, 269]), rank 0 +2024-09-11 04:06:12.868219: predicting saros-221 +2024-09-11 04:06:13.672205: saros-221, shape torch.Size([1, 370, 295, 295]), rank 0 +2024-09-11 04:06:26.046239: predicting saros-222 +2024-09-11 04:06:26.217275: saros-222, shape torch.Size([1, 140, 240, 240]), rank 0 +2024-09-11 04:06:28.116322: predicting saros-223 +2024-09-11 04:06:28.402175: saros-223, shape torch.Size([1, 190, 250, 250]), rank 0 +2024-09-11 04:06:30.344291: predicting saros-236 +2024-09-11 04:06:30.775269: saros-236, shape torch.Size([1, 207, 293, 293]), rank 0 +2024-09-11 04:06:38.943305: predicting saros-237 +2024-09-11 04:06:39.478542: saros-237, shape torch.Size([1, 240, 310, 310]), rank 0 +2024-09-11 04:06:47.698328: predicting saros-248 +2024-09-11 04:06:48.063312: saros-248, shape torch.Size([1, 307, 227, 227]), rank 0 +2024-09-11 04:06:53.559644: predicting saros-249 +2024-09-11 04:06:53.851729: saros-249, shape torch.Size([1, 133, 320, 320]), rank 0 +2024-09-11 04:06:57.993364: predicting saros-256 +2024-09-11 04:06:58.574566: saros-256, shape torch.Size([1, 357, 279, 279]), rank 0 +2024-09-11 04:07:04.142183: predicting saros-257 +2024-09-11 04:07:04.458684: saros-257, shape torch.Size([1, 193, 241, 241]), rank 0 +2024-09-11 04:07:08.208414: predicting saros-271 +2024-09-11 04:07:08.621254: saros-271, shape torch.Size([1, 157, 293, 293]), rank 0 +2024-09-11 04:07:12.811561: predicting saros-272 +2024-09-11 04:07:13.638169: saros-272, shape torch.Size([1, 343, 333, 333]), rank 0 +2024-09-11 04:07:25.939853: predicting saros-277 +2024-09-11 04:07:26.093434: saros-277, shape torch.Size([1, 123, 240, 240]), rank 0 +2024-09-11 04:07:27.972102: predicting saros-284 +2024-09-11 04:07:28.168772: saros-284, shape torch.Size([1, 133, 267, 267]), rank 0 +2024-09-11 04:07:30.081427: predicting saros-285 +2024-09-11 04:07:30.595412: saros-285, shape torch.Size([1, 307, 253, 253]), rank 0 +2024-09-11 04:07:36.122589: predicting saros-289 +2024-09-11 04:07:36.554844: saros-289, shape torch.Size([1, 260, 267, 267]), rank 0 +2024-09-11 04:07:40.298165: predicting saros-303 +2024-09-11 04:07:40.620603: saros-303, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-11 04:07:44.320499: predicting saros-305 +2024-09-11 04:07:44.536499: saros-305, shape torch.Size([1, 223, 196, 196]), rank 0 +2024-09-11 04:07:48.223358: predicting saros-311 +2024-09-11 04:07:48.528877: saros-311, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-11 04:07:52.216967: predicting saros-327 +2024-09-11 04:07:52.636866: saros-327, shape torch.Size([1, 187, 333, 333]), rank 0 +2024-09-11 04:07:56.956110: predicting saros-329 +2024-09-11 04:07:57.234873: saros-329, shape torch.Size([1, 193, 253, 253]), rank 0 +2024-09-11 04:08:01.009843: predicting saros-331 +2024-09-11 04:08:01.257951: saros-331, shape torch.Size([1, 223, 196, 196]), rank 0 +2024-09-11 04:08:04.928115: predicting saros-336 +2024-09-11 04:08:05.230545: saros-336, shape torch.Size([1, 203, 227, 227]), rank 0 +2024-09-11 04:08:08.916218: predicting saros-342 +2024-09-11 04:08:09.190520: saros-342, shape torch.Size([1, 180, 229, 229]), rank 0 +2024-09-11 04:08:11.171332: predicting saros-350 +2024-09-11 04:08:11.539790: saros-350, shape torch.Size([1, 193, 253, 253]), rank 0 +2024-09-11 04:08:15.255720: predicting saros-355 +2024-09-11 04:08:15.583172: saros-355, shape torch.Size([1, 227, 245, 245]), rank 0 +2024-09-11 04:08:19.320261: predicting saros-359 +2024-09-11 04:08:19.659317: saros-359, shape torch.Size([1, 187, 231, 231]), rank 0 +2024-09-11 04:08:21.588882: predicting saros-362 +2024-09-11 04:08:21.898452: saros-362, shape torch.Size([1, 190, 233, 233]), rank 0 +2024-09-11 04:08:23.842557: predicting saros-377 +2024-09-11 04:08:24.242318: saros-377, shape torch.Size([1, 187, 333, 333]), rank 0 +2024-09-11 04:08:28.489402: predicting saros-382 +2024-09-11 04:08:28.880115: saros-382, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-11 04:08:32.584512: predicting saros-383 +2024-09-11 04:08:32.946704: saros-383, shape torch.Size([1, 230, 267, 267]), rank 0 +2024-09-11 04:08:37.179438: predicting saros-408 +2024-09-11 04:08:37.522552: saros-408, shape torch.Size([1, 213, 240, 240]), rank 0 +2024-09-11 04:08:41.240548: predicting saros-411 +2024-09-11 04:08:41.702409: saros-411, shape torch.Size([1, 227, 299, 299]), rank 0 +2024-09-11 04:08:49.952581: predicting saros-420 +2024-09-11 04:08:50.322175: saros-420, shape torch.Size([1, 237, 218, 218]), rank 0 +2024-09-11 04:08:54.032191: predicting saros-425 +2024-09-11 04:08:54.345565: saros-425, shape torch.Size([1, 200, 215, 215]), rank 0 +2024-09-11 04:08:58.016615: predicting saros-432 +2024-09-11 04:08:58.304533: saros-432, shape torch.Size([1, 213, 240, 240]), rank 0 +2024-09-11 04:09:02.049072: predicting saros-433 +2024-09-11 04:09:02.333725: saros-433, shape torch.Size([1, 187, 240, 240]), rank 0 +2024-09-11 04:09:04.264215: predicting saros-436 +2024-09-11 04:09:04.516216: saros-436, shape torch.Size([1, 173, 199, 199]), rank 0 +2024-09-11 04:09:06.451110: predicting saros-442 +2024-09-11 04:09:06.834048: saros-442, shape torch.Size([1, 220, 273, 273]), rank 0 +2024-09-11 04:09:10.563609: predicting saros-444 +2024-09-11 04:09:10.842056: saros-444, shape torch.Size([1, 193, 240, 240]), rank 0 +2024-09-11 04:09:14.523235: predicting saros-446 +2024-09-11 04:09:14.797714: saros-446, shape torch.Size([1, 187, 240, 240]), rank 0 +2024-09-11 04:09:16.763204: predicting saros-450 +2024-09-11 04:09:17.029927: saros-450, shape torch.Size([1, 197, 238, 238]), rank 0 +2024-09-11 04:09:21.506980: predicting saros-467 +2024-09-11 04:09:21.976661: saros-467, shape torch.Size([1, 173, 310, 310]), rank 0 +2024-09-11 04:09:26.180675: predicting saros-468 +2024-09-11 04:09:26.532710: saros-468, shape torch.Size([1, 193, 280, 280]), rank 0 +2024-09-11 04:09:30.360268: predicting saros-477 +2024-09-11 04:09:31.013297: saros-477, shape torch.Size([1, 220, 293, 293]), rank 0 +2024-09-11 04:09:39.296238: predicting saros-478 +2024-09-11 04:09:39.669661: saros-478, shape torch.Size([1, 173, 267, 267]), rank 0 +2024-09-11 04:09:41.620871: predicting saros-495 +2024-09-11 04:09:42.263301: saros-495, shape torch.Size([1, 290, 290, 290]), rank 0 +2024-09-11 04:09:54.494894: predicting saros-497 +2024-09-11 04:09:54.912805: saros-497, shape torch.Size([1, 233, 280, 280]), rank 0 +2024-09-11 04:09:58.657557: predicting saros-501 +2024-09-11 04:09:59.039495: saros-501, shape torch.Size([1, 240, 237, 237]), rank 0 +2024-09-11 04:10:02.773203: predicting saros-506 +2024-09-11 04:10:03.253651: saros-506, shape torch.Size([1, 217, 333, 333]), rank 0 +2024-09-11 04:10:11.479827: predicting saros-514 +2024-09-11 04:10:11.703663: saros-514, shape torch.Size([1, 187, 240, 240]), rank 0 +2024-09-11 04:10:13.651497: predicting saros-527 +2024-09-11 04:10:13.928503: saros-527, shape torch.Size([1, 247, 221, 221]), rank 0 +2024-09-11 04:10:17.598299: predicting saros-530 +2024-09-11 04:10:17.977999: saros-530, shape torch.Size([1, 220, 280, 280]), rank 0 +2024-09-11 04:10:21.717410: predicting saros-531 +2024-09-11 04:10:22.233066: saros-531, shape torch.Size([1, 273, 308, 308]), rank 0 +2024-09-11 04:10:30.564497: predicting saros-537 +2024-09-11 04:10:31.005410: saros-537, shape torch.Size([1, 177, 333, 333]), rank 0 +2024-09-11 04:10:35.255564: predicting saros-541 +2024-09-11 04:10:35.616511: saros-541, shape torch.Size([1, 207, 269, 269]), rank 0 +2024-09-11 04:10:39.315718: predicting saros-547 +2024-09-11 04:10:39.945618: saros-547, shape torch.Size([1, 273, 308, 308]), rank 0 +2024-09-11 04:10:48.217492: predicting saros-548 +2024-09-11 04:10:48.582628: saros-548, shape torch.Size([1, 207, 253, 253]), rank 0 +2024-09-11 04:10:52.313614: predicting saros-551 +2024-09-11 04:10:52.608058: saros-551, shape torch.Size([1, 190, 255, 255]), rank 0 +2024-09-11 04:10:54.525800: predicting saros-552 +2024-09-11 04:10:54.941689: saros-552, shape torch.Size([1, 243, 267, 267]), rank 0 +2024-09-11 04:10:58.759288: predicting saros-558 +2024-09-11 04:10:59.243632: saros-558, shape torch.Size([1, 227, 293, 293]), rank 0 +2024-09-11 04:11:07.436596: predicting saros-581 +2024-09-11 04:11:07.776381: saros-581, shape torch.Size([1, 247, 228, 228]), rank 0 +2024-09-11 04:11:11.466533: predicting saros-584 +2024-09-11 04:11:11.875445: saros-584, shape torch.Size([1, 213, 293, 293]), rank 0 +2024-09-11 04:11:20.052881: predicting saros-587 +2024-09-11 04:11:20.339903: saros-587, shape torch.Size([1, 193, 233, 233]), rank 0 +2024-09-11 04:11:24.018021: predicting saros-597 +2024-09-11 04:11:24.368313: saros-597, shape torch.Size([1, 210, 268, 268]), rank 0 +2024-09-11 04:11:28.093954: predicting saros-598 +2024-09-11 04:11:28.550364: saros-598, shape torch.Size([1, 190, 333, 333]), rank 0 +2024-09-11 04:11:32.796840: predicting saros-601 +2024-09-11 04:11:34.075189: saros-601, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:11:58.610601: predicting saros-604 +2024-09-11 04:12:00.075253: saros-604, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:12:28.667705: predicting saros-611 +2024-09-11 04:12:30.012753: saros-611, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:12:54.610745: predicting saros-626 +2024-09-11 04:12:56.801923: saros-626, shape torch.Size([1, 677, 467, 467]), rank 0 +2024-09-11 04:13:48.013348: predicting saros-628 +2024-09-11 04:13:48.932892: saros-628, shape torch.Size([1, 487, 333, 333]), rank 0 +2024-09-11 04:14:09.630763: predicting saros-630 +2024-09-11 04:14:10.872212: saros-630, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:14:35.408339: predicting saros-636 +2024-09-11 04:14:36.931294: saros-636, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:15:01.673194: predicting saros-660 +2024-09-11 04:15:03.067554: saros-660, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-11 04:15:23.588188: predicting saros-662 +2024-09-11 04:15:24.602734: saros-662, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:15:41.008833: predicting saros-667 +2024-09-11 04:15:42.222895: saros-667, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:16:06.690814: predicting saros-682 +2024-09-11 04:16:07.955862: saros-682, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:16:32.491640: predicting saros-691 +2024-09-11 04:16:33.705575: saros-691, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:16:58.164539: predicting saros-695 +2024-09-11 04:16:59.350597: saros-695, shape torch.Size([1, 513, 333, 333]), rank 0 +2024-09-11 04:17:19.842547: predicting saros-696 +2024-09-11 04:17:21.109201: saros-696, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:17:41.684152: predicting saros-708 +2024-09-11 04:17:42.880990: saros-708, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:18:03.395093: predicting saros-712 +2024-09-11 04:18:05.128623: saros-712, shape torch.Size([1, 773, 333, 333]), rank 0 +2024-09-11 04:18:37.953875: predicting saros-713 +2024-09-11 04:18:40.169943: saros-713, shape torch.Size([1, 590, 467, 467]), rank 0 +2024-09-11 04:19:24.197154: predicting saros-715 +2024-09-11 04:19:25.509387: saros-715, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:19:50.315727: predicting saros-716 +2024-09-11 04:19:51.570573: saros-716, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:20:16.100957: predicting saros-717 +2024-09-11 04:20:17.335039: saros-717, shape torch.Size([1, 587, 333, 333]), rank 0 +2024-09-11 04:20:41.901851: predicting saros-722 +2024-09-11 04:20:42.883151: saros-722, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:20:59.275651: predicting saros-735 +2024-09-11 04:21:00.393011: saros-735, shape torch.Size([1, 487, 333, 333]), rank 0 +2024-09-11 04:21:20.908561: predicting saros-737 +2024-09-11 04:21:21.632030: saros-737, shape torch.Size([1, 450, 267, 267]), rank 0 +2024-09-11 04:21:29.023803: predicting saros-740 +2024-09-11 04:21:30.267292: saros-740, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:21:54.724750: predicting saros-749 +2024-09-11 04:21:55.488477: saros-749, shape torch.Size([1, 483, 267, 267]), rank 0 +2024-09-11 04:22:04.703617: predicting saros-750 +2024-09-11 04:22:06.133602: saros-750, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-11 04:22:30.667507: predicting saros-772 +2024-09-11 04:22:31.691916: saros-772, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:22:48.090343: predicting saros-773 +2024-09-11 04:22:49.511515: saros-773, shape torch.Size([1, 663, 333, 333]), rank 0 +2024-09-11 04:23:14.059312: predicting saros-781 +2024-09-11 04:23:15.098651: saros-781, shape torch.Size([1, 487, 333, 333]), rank 0 +2024-09-11 04:23:35.549921: predicting saros-784 +2024-09-11 04:23:36.580273: saros-784, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:23:52.972651: predicting saros-785 +2024-09-11 04:23:54.311561: saros-785, shape torch.Size([1, 540, 333, 333]), rank 0 +2024-09-11 04:24:14.766987: predicting saros-790 +2024-09-11 04:24:16.050359: saros-790, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:24:40.536721: predicting saros-792 +2024-09-11 04:24:42.044549: saros-792, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:25:10.722085: predicting saros-793 +2024-09-11 04:25:12.386807: saros-793, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:25:41.067570: predicting saros-797 +2024-09-11 04:25:42.806707: saros-797, shape torch.Size([1, 677, 333, 333]), rank 0 +2024-09-11 04:26:11.967337: predicting saros-799 +2024-09-11 04:26:13.268109: saros-799, shape torch.Size([1, 567, 333, 333]), rank 0 +2024-09-11 04:26:33.785055: predicting saros-813 +2024-09-11 04:26:35.008088: saros-813, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:26:55.549560: predicting saros-816 +2024-09-11 04:26:57.119943: saros-816, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:27:25.716923: predicting saros-824 +2024-09-11 04:27:27.230977: saros-824, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:27:55.872887: predicting saros-835 +2024-09-11 04:27:57.267934: saros-835, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:28:21.808023: predicting saros-848 +2024-09-11 04:28:22.995062: saros-848, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:28:47.491691: predicting saros-855 +2024-09-11 04:28:49.299438: saros-855, shape torch.Size([1, 737, 333, 333]), rank 0 +2024-09-11 04:29:18.078727: predicting saros-875 +2024-09-11 04:29:19.507260: saros-875, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:29:48.047852: predicting saros-879 +2024-09-11 04:29:49.452724: saros-879, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-11 04:30:14.013889: predicting saros-883 +2024-09-11 04:30:15.449361: saros-883, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 04:30:39.984462: predicting saros-888 +2024-09-11 04:30:41.261501: saros-888, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:31:01.760762: predicting saros-889 +2024-09-11 04:31:03.076260: saros-889, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:31:27.576609: predicting saros-890 +2024-09-11 04:31:29.006854: saros-890, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:31:57.697501: predicting saros-898 +2024-09-11 04:31:59.071442: saros-898, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:32:23.642080: predicting saros-899 +2024-09-11 04:32:25.030635: saros-899, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 04:32:49.600025: predicting tseg-s0031 +2024-09-11 04:32:49.972197: tseg-s0031, shape torch.Size([1, 280, 288, 288]), rank 0 +2024-09-11 04:32:53.781735: predicting tseg-s0032 +2024-09-11 04:32:53.977073: tseg-s0032, shape torch.Size([1, 216, 221, 221]), rank 0 +2024-09-11 04:32:57.715863: predicting tseg-s0037 +2024-09-11 04:32:57.977108: tseg-s0037, shape torch.Size([1, 217, 253, 253]), rank 0 +2024-09-11 04:33:01.688080: predicting tseg-s0057 +2024-09-11 04:33:01.977527: tseg-s0057, shape torch.Size([1, 184, 293, 293]), rank 0 +2024-09-11 04:33:06.252954: predicting tseg-s0069 +2024-09-11 04:33:06.421356: tseg-s0069, shape torch.Size([1, 116, 305, 305]), rank 0 +2024-09-11 04:33:10.646561: predicting tseg-s0075 +2024-09-11 04:33:11.042162: tseg-s0075, shape torch.Size([1, 317, 363, 220]), rank 0 +2024-09-11 04:33:19.273034: predicting tseg-s0080 +2024-09-11 04:33:19.648271: tseg-s0080, shape torch.Size([1, 233, 306, 306]), rank 0 +2024-09-11 04:33:27.924734: predicting tseg-s0091 +2024-09-11 04:33:28.579446: tseg-s0091, shape torch.Size([1, 437, 312, 312]), rank 0 +2024-09-11 04:33:44.947252: predicting tseg-s0092 +2024-09-11 04:33:45.091239: tseg-s0092, shape torch.Size([1, 203, 167, 243]), rank 0 +2024-09-11 04:33:46.974079: predicting tseg-s0109 +2024-09-11 04:33:47.386031: tseg-s0109, shape torch.Size([1, 303, 252, 303]), rank 0 +2024-09-11 04:33:55.586826: predicting tseg-s0123 +2024-09-11 04:33:55.814185: tseg-s0123, shape torch.Size([1, 213, 194, 267]), rank 0 +2024-09-11 04:33:59.548654: predicting tseg-s0124 +2024-09-11 04:34:00.023488: tseg-s0124, shape torch.Size([1, 431, 159, 431]), rank 0 +2024-09-11 04:34:07.474480: predicting tseg-s0133 +2024-09-11 04:34:07.995235: tseg-s0133, shape torch.Size([1, 301, 307, 307]), rank 0 +2024-09-11 04:34:20.292231: predicting tseg-s0153 +2024-09-11 04:34:20.538973: tseg-s0153, shape torch.Size([1, 299, 195, 248]), rank 0 +2024-09-11 04:34:26.123704: predicting tseg-s0158 +2024-09-11 04:34:26.461185: tseg-s0158, shape torch.Size([1, 271, 253, 253]), rank 0 +2024-09-11 04:34:30.212559: predicting tseg-s0161 +2024-09-11 04:34:30.463575: tseg-s0161, shape torch.Size([1, 300, 181, 246]), rank 0 +2024-09-11 04:34:33.311455: predicting tseg-s0163 +2024-09-11 04:34:33.932363: tseg-s0163, shape torch.Size([1, 440, 291, 291]), rank 0 +2024-09-11 04:34:50.274842: predicting tseg-s0166 +2024-09-11 04:34:50.573328: tseg-s0166, shape torch.Size([1, 321, 321, 200]), rank 0 +2024-09-11 04:34:58.875630: predicting tseg-s0168 +2024-09-11 04:34:59.136271: tseg-s0168, shape torch.Size([1, 280, 228, 228]), rank 0 +2024-09-11 04:35:02.884522: predicting tseg-s0178 +2024-09-11 04:35:03.240150: tseg-s0178, shape torch.Size([1, 310, 261, 261]), rank 0 +2024-09-11 04:35:08.774411: predicting tseg-s0182 +2024-09-11 04:35:09.139203: tseg-s0182, shape torch.Size([1, 303, 239, 239]), rank 0 +2024-09-11 04:35:14.692142: predicting tseg-s0197 +2024-09-11 04:35:14.901696: tseg-s0197, shape torch.Size([1, 261, 162, 261]), rank 0 +2024-09-11 04:35:16.841641: predicting tseg-s0204 +2024-09-11 04:35:17.197301: tseg-s0204, shape torch.Size([1, 321, 236, 290]), rank 0 +2024-09-11 04:35:25.413212: predicting tseg-s0227 +2024-09-11 04:35:25.712137: tseg-s0227, shape torch.Size([1, 247, 264, 264]), rank 0 +2024-09-11 04:35:29.471592: predicting tseg-s0237 +2024-09-11 04:35:29.516712: tseg-s0237, shape torch.Size([1, 39, 249, 249]), rank 0 +2024-09-11 04:35:31.363334: predicting tseg-s0239 +2024-09-11 04:35:31.851749: tseg-s0239, shape torch.Size([1, 412, 283, 283]), rank 0 +2024-09-11 04:35:39.211644: predicting tseg-s0249 +2024-09-11 04:35:39.308798: tseg-s0249, shape torch.Size([1, 69, 285, 285]), rank 0 +2024-09-11 04:35:41.147411: predicting tseg-s0257 +2024-09-11 04:35:41.432512: tseg-s0257, shape torch.Size([1, 263, 233, 233]), rank 0 +2024-09-11 04:35:45.132152: predicting tseg-s0262 +2024-09-11 04:35:45.277608: tseg-s0262, shape torch.Size([1, 83, 307, 307]), rank 0 +2024-09-11 04:35:49.406392: predicting tseg-s0287 +2024-09-11 04:35:50.505752: tseg-s0287, shape torch.Size([1, 835, 317, 317]), rank 0 +2024-09-11 04:36:23.122370: predicting tseg-s0298 +2024-09-11 04:36:23.456978: tseg-s0298, shape torch.Size([1, 348, 206, 348]), rank 0 +2024-09-11 04:36:31.877893: predicting tseg-s0304 +2024-09-11 04:36:32.081850: tseg-s0304, shape torch.Size([1, 231, 231, 234]), rank 0 +2024-09-11 04:36:35.850726: predicting tseg-s0307 +2024-09-11 04:36:36.150489: tseg-s0307, shape torch.Size([1, 287, 240, 240]), rank 0 +2024-09-11 04:36:39.871403: predicting tseg-s0312 +2024-09-11 04:36:39.899164: tseg-s0312, shape torch.Size([1, 131, 95, 95]), rank 0 +2024-09-11 04:36:40.384969: predicting tseg-s0316 +2024-09-11 04:36:40.448656: tseg-s0316, shape torch.Size([1, 58, 225, 225]), rank 0 +2024-09-11 04:36:42.260189: predicting tseg-s0328 +2024-09-11 04:36:42.574767: tseg-s0328, shape torch.Size([1, 269, 265, 265]), rank 0 +2024-09-11 04:36:46.282408: predicting tseg-s0329 +2024-09-11 04:36:46.802246: tseg-s0329, shape torch.Size([1, 329, 329, 304]), rank 0 +2024-09-11 04:36:59.107716: predicting tseg-s0331 +2024-09-11 04:36:59.324704: tseg-s0331, shape torch.Size([1, 209, 253, 253]), rank 0 +2024-09-11 04:37:03.083894: predicting tseg-s0349 +2024-09-11 04:37:03.392563: tseg-s0349, shape torch.Size([1, 231, 268, 268]), rank 0 +2024-09-11 04:37:07.128570: predicting tseg-s0350 +2024-09-11 04:37:07.611833: tseg-s0350, shape torch.Size([1, 415, 266, 266]), rank 0 +2024-09-11 04:37:15.006447: predicting tseg-s0368 +2024-09-11 04:37:15.027976: tseg-s0368, shape torch.Size([1, 75, 107, 107]), rank 0 +2024-09-11 04:37:15.520931: predicting tseg-s0375 +2024-09-11 04:37:16.042308: tseg-s0375, shape torch.Size([1, 440, 259, 259]), rank 0 +2024-09-11 04:37:23.377498: predicting tseg-s0377 +2024-09-11 04:37:23.687989: tseg-s0377, shape torch.Size([1, 259, 248, 248]), rank 0 +2024-09-11 04:37:27.462406: predicting tseg-s0383 +2024-09-11 04:37:27.746076: tseg-s0383, shape torch.Size([1, 385, 145, 247]), rank 0 +2024-09-11 04:37:31.483889: predicting tseg-s0391 +2024-09-11 04:37:31.564630: tseg-s0391, shape torch.Size([1, 112, 193, 193]), rank 0 +2024-09-11 04:37:33.408489: predicting tseg-s0393 +2024-09-11 04:37:33.605093: tseg-s0393, shape torch.Size([1, 206, 221, 221]), rank 0 +2024-09-11 04:37:37.277748: predicting tseg-s0399 +2024-09-11 04:37:37.438684: tseg-s0399, shape torch.Size([1, 157, 227, 227]), rank 0 +2024-09-11 04:37:39.338959: predicting tseg-s0400 +2024-09-11 04:37:39.468510: tseg-s0400, shape torch.Size([1, 203, 160, 225]), rank 0 +2024-09-11 04:37:41.341410: predicting tseg-s0401 +2024-09-11 04:37:41.613521: tseg-s0401, shape torch.Size([1, 294, 233, 233]), rank 0 +2024-09-11 04:37:47.106381: predicting tseg-s0402 +2024-09-11 04:37:47.626225: tseg-s0402, shape torch.Size([1, 429, 264, 264]), rank 0 +2024-09-11 04:37:55.025235: predicting tseg-s0418 +2024-09-11 04:37:55.085484: tseg-s0418, shape torch.Size([1, 62, 235, 235]), rank 0 +2024-09-11 04:37:56.913808: predicting tseg-s0424 +2024-09-11 04:37:56.973724: tseg-s0424, shape torch.Size([1, 153, 130, 130]), rank 0 +2024-09-11 04:37:57.470625: predicting tseg-s0426 +2024-09-11 04:37:57.629195: tseg-s0426, shape torch.Size([1, 156, 239, 239]), rank 0 +2024-09-11 04:37:59.506682: predicting tseg-s0430 +2024-09-11 04:37:59.747041: tseg-s0430, shape torch.Size([1, 217, 249, 249]), rank 0 +2024-09-11 04:38:03.433912: predicting tseg-s0444 +2024-09-11 04:38:03.581249: tseg-s0444, shape torch.Size([1, 178, 194, 194]), rank 0 +2024-09-11 04:38:05.481209: predicting tseg-s0445 +2024-09-11 04:38:05.624185: tseg-s0445, shape torch.Size([1, 134, 239, 239]), rank 0 +2024-09-11 04:38:07.456685: predicting tseg-s0449 +2024-09-11 04:38:07.493579: tseg-s0449, shape torch.Size([1, 107, 140, 140]), rank 0 +2024-09-11 04:38:07.970491: predicting tseg-s0478 +2024-09-11 04:38:08.095081: tseg-s0478, shape torch.Size([1, 237, 181, 181]), rank 0 +2024-09-11 04:39:10.763194: predicting tseg-s0491 +2024-09-11 04:39:10.856738: tseg-s0491, shape torch.Size([1, 173, 170, 173]), rank 0 +2024-09-11 04:39:11.350743: predicting tseg-s0497 +2024-09-11 04:39:11.513840: tseg-s0497, shape torch.Size([1, 244, 188, 188]), rank 0 +2024-09-11 04:39:12.507378: predicting tseg-s0503 +2024-09-11 04:39:12.643835: tseg-s0503, shape torch.Size([1, 233, 174, 174]), rank 0 +2024-09-11 04:39:13.601945: predicting tseg-s0511 +2024-09-11 04:39:14.227942: tseg-s0511, shape torch.Size([1, 465, 284, 284]), rank 0 +2024-09-11 04:39:21.623061: predicting tseg-s0513 +2024-09-11 04:39:22.192743: tseg-s0513, shape torch.Size([1, 342, 307, 307]), rank 0 +2024-09-11 04:39:34.554241: predicting tseg-s0537 +2024-09-11 04:39:34.660243: tseg-s0537, shape torch.Size([1, 166, 198, 198]), rank 0 +2024-09-11 04:39:36.495515: predicting tseg-s0546 +2024-09-11 04:39:37.227702: tseg-s0546, shape torch.Size([1, 458, 304, 304]), rank 0 +2024-09-11 04:39:53.551452: predicting tseg-s0549 +2024-09-11 04:39:54.306872: tseg-s0549, shape torch.Size([1, 461, 305, 305]), rank 0 +2024-09-11 04:40:10.746040: predicting tseg-s0550 +2024-09-11 04:40:11.474690: tseg-s0550, shape torch.Size([1, 462, 294, 294]), rank 0 +2024-09-11 04:40:27.934100: predicting tseg-s0571 +2024-09-11 04:40:28.365386: tseg-s0571, shape torch.Size([1, 409, 242, 242]), rank 0 +2024-09-11 04:40:35.798499: predicting tseg-s0584 +2024-09-11 04:40:36.075640: tseg-s0584, shape torch.Size([1, 228, 256, 256]), rank 0 +2024-09-11 04:40:39.836136: predicting tseg-s0586 +2024-09-11 04:40:40.136415: tseg-s0586, shape torch.Size([1, 257, 247, 247]), rank 0 +2024-09-11 04:40:43.868837: predicting tseg-s0591 +2024-09-11 04:40:44.554806: tseg-s0591, shape torch.Size([1, 530, 275, 275]), rank 0 +2024-09-11 04:40:53.812024: predicting tseg-s0606 +2024-09-11 04:40:54.118014: tseg-s0606, shape torch.Size([1, 226, 297, 297]), rank 0 +2024-09-11 04:41:02.450206: predicting tseg-s0607 +2024-09-11 04:41:02.879850: tseg-s0607, shape torch.Size([1, 338, 338, 235]), rank 0 +2024-09-11 04:41:11.160641: predicting tseg-s0615 +2024-09-11 04:41:11.299901: tseg-s0615, shape torch.Size([1, 118, 273, 273]), rank 0 +2024-09-11 04:41:13.194377: predicting tseg-s0617 +2024-09-11 04:41:13.668633: tseg-s0617, shape torch.Size([1, 431, 251, 251]), rank 0 +2024-09-11 04:41:21.031925: predicting tseg-s0619 +2024-09-11 04:41:21.309998: tseg-s0619, shape torch.Size([1, 395, 155, 225]), rank 0 +2024-09-11 04:41:25.130664: predicting tseg-s0626 +2024-09-11 04:41:25.540922: tseg-s0626, shape torch.Size([1, 396, 227, 227]), rank 0 +2024-09-11 04:41:32.885274: predicting tseg-s0629 +2024-09-11 04:41:33.308202: tseg-s0629, shape torch.Size([1, 440, 244, 244]), rank 0 +2024-09-11 04:41:40.695447: predicting tseg-s0632 +2024-09-11 04:41:41.007698: tseg-s0632, shape torch.Size([1, 220, 281, 281]), rank 0 +2024-09-11 04:41:44.803248: predicting tseg-s0638 +2024-09-11 04:41:44.838517: tseg-s0638, shape torch.Size([1, 92, 129, 129]), rank 0 +2024-09-11 04:41:45.324188: predicting tseg-s0651 +2024-09-11 04:41:45.600945: tseg-s0651, shape torch.Size([1, 277, 218, 277]), rank 0 +2024-09-11 04:41:49.310047: predicting tseg-s0652 +2024-09-11 04:41:49.348244: tseg-s0652, shape torch.Size([1, 136, 117, 117]), rank 0 +2024-09-11 04:41:49.832077: predicting tseg-s0663 +2024-09-11 04:41:50.252177: tseg-s0663, shape torch.Size([1, 418, 255, 255]), rank 0 +2024-09-11 04:41:57.589730: predicting tseg-s0665 +2024-09-11 04:41:57.737379: tseg-s0665, shape torch.Size([1, 166, 227, 227]), rank 0 +2024-09-11 04:41:59.619273: predicting tseg-s0674 +2024-09-11 04:41:59.846164: tseg-s0674, shape torch.Size([1, 223, 221, 221]), rank 0 +2024-09-11 04:42:03.532207: predicting tseg-s0706 +2024-09-11 04:42:03.569886: tseg-s0706, shape torch.Size([1, 68, 194, 194]), rank 0 +2024-09-11 04:42:05.402608: predicting tseg-s0707 +2024-09-11 04:42:05.746407: tseg-s0707, shape torch.Size([1, 298, 267, 267]), rank 0 +2024-09-11 04:42:11.272504: predicting tseg-s0713 +2024-09-11 04:42:11.307168: tseg-s0713, shape torch.Size([1, 76, 161, 161]), rank 0 +2024-09-11 04:42:11.793314: predicting tseg-s0717 +2024-09-11 04:42:11.965348: tseg-s0717, shape torch.Size([1, 205, 199, 199]), rank 0 +2024-09-11 04:42:15.652054: predicting tseg-s0733 +2024-09-11 04:42:16.184460: tseg-s0733, shape torch.Size([1, 434, 283, 283]), rank 0 +2024-09-11 04:42:23.594237: predicting tseg-s0734 +2024-09-11 04:42:23.716240: tseg-s0734, shape torch.Size([1, 145, 224, 223]), rank 0 +2024-09-11 04:42:25.599002: predicting tseg-s0736 +2024-09-11 04:42:25.669372: tseg-s0736, shape torch.Size([1, 149, 155, 155]), rank 0 +2024-09-11 04:42:26.163912: predicting tseg-s0749 +2024-09-11 04:42:26.563170: tseg-s0749, shape torch.Size([1, 313, 260, 260]), rank 0 +2024-09-11 04:42:32.098173: predicting tseg-s0767 +2024-09-11 04:42:32.171579: tseg-s0767, shape torch.Size([1, 69, 245, 245]), rank 0 +2024-09-11 04:42:33.994716: predicting tseg-s0790 +2024-09-11 04:42:34.653272: tseg-s0790, shape torch.Size([1, 538, 291, 291]), rank 0 +2024-09-11 04:42:55.078484: predicting tseg-s0797 +2024-09-11 04:42:55.667467: tseg-s0797, shape torch.Size([1, 433, 264, 264]), rank 0 +2024-09-11 04:43:03.174892: predicting tseg-s0807 +2024-09-11 04:43:03.670222: tseg-s0807, shape torch.Size([1, 454, 249, 249]), rank 0 +2024-09-11 04:43:11.108132: predicting tseg-s0811 +2024-09-11 04:43:11.320934: tseg-s0811, shape torch.Size([1, 223, 233, 233]), rank 0 +2024-09-11 04:43:15.104158: predicting tseg-s0814 +2024-09-11 04:43:15.210132: tseg-s0814, shape torch.Size([1, 176, 174, 174]), rank 0 +2024-09-11 04:43:15.736400: predicting tseg-s0818 +2024-09-11 04:43:15.905506: tseg-s0818, shape torch.Size([1, 165, 216, 216]), rank 0 +2024-09-11 04:43:17.786123: predicting tseg-s0836 +2024-09-11 04:43:18.198783: tseg-s0836, shape torch.Size([1, 439, 231, 231]), rank 0 +2024-09-11 04:43:25.541162: predicting tseg-s0838 +2024-09-11 04:43:25.583912: tseg-s0838, shape torch.Size([1, 115, 131, 131]), rank 0 +2024-09-11 04:43:26.082039: predicting tseg-s0847 +2024-09-11 04:43:26.408188: tseg-s0847, shape torch.Size([1, 224, 275, 275]), rank 0 +2024-09-11 04:43:30.125217: predicting tseg-s0855 +2024-09-11 04:43:30.183136: tseg-s0855, shape torch.Size([1, 114, 167, 167]), rank 0 +2024-09-11 04:43:30.671061: predicting tseg-s0857 +2024-09-11 04:43:30.805053: tseg-s0857, shape torch.Size([1, 227, 179, 179]), rank 0 +2024-09-11 04:43:31.785920: predicting tseg-s0858 +2024-09-11 04:43:31.842409: tseg-s0858, shape torch.Size([1, 124, 145, 145]), rank 0 +2024-09-11 04:43:32.326486: predicting tseg-s0875 +2024-09-11 04:43:32.427460: tseg-s0875, shape torch.Size([1, 170, 190, 190]), rank 0 +2024-09-11 04:43:32.920467: predicting tseg-s0883 +2024-09-11 04:43:32.973017: tseg-s0883, shape torch.Size([1, 119, 142, 142]), rank 0 +2024-09-11 04:43:33.451598: predicting tseg-s0890 +2024-09-11 04:43:33.647395: tseg-s0890, shape torch.Size([1, 191, 233, 233]), rank 0 +2024-09-11 04:43:35.564965: predicting tseg-s0899 +2024-09-11 04:43:36.201926: tseg-s0899, shape torch.Size([1, 456, 286, 286]), rank 0 +2024-09-11 04:43:43.647558: predicting tseg-s0901 +2024-09-11 04:43:43.692813: tseg-s0901, shape torch.Size([1, 152, 122, 122]), rank 0 +2024-09-11 04:43:44.201144: predicting tseg-s0904 +2024-09-11 04:43:44.666140: tseg-s0904, shape torch.Size([1, 433, 215, 275]), rank 0 +2024-09-11 04:43:52.003392: predicting tseg-s0912 +2024-09-11 04:43:52.540909: tseg-s0912, shape torch.Size([1, 451, 272, 272]), rank 0 +2024-09-11 04:43:59.994276: predicting tseg-s0925 +2024-09-11 04:44:00.050010: tseg-s0925, shape torch.Size([1, 143, 145, 145]), rank 0 +2024-09-11 04:44:00.554817: predicting tseg-s0928 +2024-09-11 04:44:01.134552: tseg-s0928, shape torch.Size([1, 417, 282, 282]), rank 0 +2024-09-11 04:44:08.528363: predicting tseg-s0937 +2024-09-11 04:44:09.152722: tseg-s0937, shape torch.Size([1, 548, 253, 253]), rank 0 +2024-09-11 04:44:18.418026: predicting tseg-s0939 +2024-09-11 04:44:18.890054: tseg-s0939, shape torch.Size([1, 440, 233, 233]), rank 0 +2024-09-11 04:44:26.307977: predicting tseg-s0940 +2024-09-11 04:44:26.751850: tseg-s0940, shape torch.Size([1, 439, 231, 231]), rank 0 +2024-09-11 04:44:34.130038: predicting tseg-s0941 +2024-09-11 04:44:34.378235: tseg-s0941, shape torch.Size([1, 230, 240, 240]), rank 0 +2024-09-11 04:44:38.134432: predicting tseg-s0953 +2024-09-11 04:44:38.271578: tseg-s0953, shape torch.Size([1, 135, 239, 239]), rank 0 +2024-09-11 04:44:40.177347: predicting tseg-s0954 +2024-09-11 04:44:40.403437: tseg-s0954, shape torch.Size([1, 204, 233, 233]), rank 0 +2024-09-11 04:44:44.089986: predicting tseg-s0956 +2024-09-11 04:44:44.202084: tseg-s0956, shape torch.Size([1, 84, 260, 260]), rank 0 +2024-09-11 04:44:46.060218: predicting tseg-s0962 +2024-09-11 04:44:46.203391: tseg-s0962, shape torch.Size([1, 98, 308, 308]), rank 0 +2024-09-11 04:44:50.324094: predicting tseg-s0983 +2024-09-11 04:44:50.740183: tseg-s0983, shape torch.Size([1, 433, 240, 240]), rank 0 +2024-09-11 04:44:58.077132: predicting tseg-s0992 +2024-09-11 04:44:58.520303: tseg-s0992, shape torch.Size([1, 413, 249, 249]), rank 0 +2024-09-11 04:45:05.909157: predicting tseg-s0995 +2024-09-11 04:45:06.014771: tseg-s0995, shape torch.Size([1, 76, 287, 287]), rank 0 +2024-09-11 04:45:07.841604: predicting tseg-s1002 +2024-09-11 04:45:07.894419: tseg-s1002, shape torch.Size([1, 146, 132, 132]), rank 0 +2024-09-11 04:45:08.385809: predicting tseg-s1029 +2024-09-11 04:45:09.199862: tseg-s1029, shape torch.Size([1, 545, 321, 321]), rank 0 +2024-09-11 04:45:29.661021: predicting tseg-s1032 +2024-09-11 04:45:29.706135: tseg-s1032, shape torch.Size([1, 167, 155, 81]), rank 0 +2024-09-11 04:45:30.222036: predicting tseg-s1033 +2024-09-11 04:45:30.433381: tseg-s1033, shape torch.Size([1, 157, 216, 216]), rank 0 +2024-09-11 04:45:32.309085: predicting tseg-s1045 +2024-09-11 04:45:32.963663: tseg-s1045, shape torch.Size([1, 645, 205, 299]), rank 0 +2024-09-11 04:45:49.315731: predicting tseg-s1059 +2024-09-11 04:45:49.409069: tseg-s1059, shape torch.Size([1, 147, 150, 245]), rank 0 +2024-09-11 04:45:50.357890: predicting tseg-s1060 +2024-09-11 04:45:50.571284: tseg-s1060, shape torch.Size([1, 233, 227, 227]), rank 0 +2024-09-11 04:45:54.264051: predicting tseg-s1061 +2024-09-11 04:45:54.781524: tseg-s1061, shape torch.Size([1, 424, 285, 285]), rank 0 +2024-09-11 04:46:02.199720: predicting tseg-s1073 +2024-09-11 04:46:02.381765: tseg-s1073, shape torch.Size([1, 190, 238, 238]), rank 0 +2024-09-11 04:46:04.303931: predicting tseg-s1077 +2024-09-11 04:46:04.574891: tseg-s1077, shape torch.Size([1, 278, 221, 221]), rank 0 +2024-09-11 04:46:08.286607: predicting tseg-s1079 +2024-09-11 04:46:08.487233: tseg-s1079, shape torch.Size([1, 190, 233, 233]), rank 0 +2024-09-11 04:46:10.412117: predicting tseg-s1084 +2024-09-11 04:46:10.471247: tseg-s1084, shape torch.Size([1, 81, 169, 237]), rank 0 +2024-09-11 04:46:11.395641: predicting tseg-s1104 +2024-09-11 04:46:11.831875: tseg-s1104, shape torch.Size([1, 726, 138, 298]), rank 0 +2024-09-11 04:46:21.490929: predicting tseg-s1109 +2024-09-11 04:46:21.560443: tseg-s1109, shape torch.Size([1, 60, 267, 267]), rank 0 +2024-09-11 04:46:23.386776: predicting tseg-s1125 +2024-09-11 04:46:23.635846: tseg-s1125, shape torch.Size([1, 245, 247, 247]), rank 0 +2024-09-11 04:46:27.346234: predicting tseg-s1131 +2024-09-11 04:46:27.672747: tseg-s1131, shape torch.Size([1, 299, 179, 365]), rank 0 +2024-09-11 04:46:31.921244: predicting tseg-s1145 +2024-09-11 04:46:32.309738: tseg-s1145, shape torch.Size([1, 415, 219, 219]), rank 0 +2024-09-11 04:46:39.641676: predicting tseg-s1151 +2024-09-11 04:46:40.013515: tseg-s1151, shape torch.Size([1, 379, 240, 240]), rank 0 +2024-09-11 04:46:45.581881: predicting tseg-s1156 +2024-09-11 04:46:45.681335: tseg-s1156, shape torch.Size([1, 235, 163, 163]), rank 0 +2024-09-11 04:46:46.632469: predicting tseg-s1162 +2024-09-11 04:46:46.847075: tseg-s1162, shape torch.Size([1, 203, 222, 222]), rank 0 +2024-09-11 04:46:50.554631: predicting tseg-s1165 +2024-09-11 04:46:50.634176: tseg-s1165, shape torch.Size([1, 279, 113, 113]), rank 0 +2024-09-11 04:46:51.609169: predicting tseg-s1167 +2024-09-11 04:46:51.804850: tseg-s1167, shape torch.Size([1, 211, 219, 219]), rank 0 +2024-09-11 04:46:55.473259: predicting tseg-s1168 +2024-09-11 04:46:55.540756: tseg-s1168, shape torch.Size([1, 128, 177, 177]), rank 0 +2024-09-11 04:46:56.023732: predicting tseg-s1173 +2024-09-11 04:46:56.351733: tseg-s1173, shape torch.Size([1, 229, 255, 255]), rank 0 +2024-09-11 04:47:00.070605: predicting tseg-s1194 +2024-09-11 04:47:00.135242: tseg-s1194, shape torch.Size([1, 147, 162, 162]), rank 0 +2024-09-11 04:47:00.621179: predicting tseg-s1201 +2024-09-11 04:47:00.863381: tseg-s1201, shape torch.Size([1, 184, 223, 223]), rank 0 +2024-09-11 04:47:02.798522: predicting tseg-s1226 +2024-09-11 04:47:02.987276: tseg-s1226, shape torch.Size([1, 144, 260, 260]), rank 0 +2024-09-11 04:47:04.885468: predicting tseg-s1230 +2024-09-11 04:47:05.350052: tseg-s1230, shape torch.Size([1, 486, 236, 236]), rank 0 +2024-09-11 04:47:14.494202: predicting tseg-s1244 +2024-09-11 04:47:14.855461: tseg-s1244, shape torch.Size([1, 319, 244, 244]), rank 0 +2024-09-11 04:47:20.442852: predicting tseg-s1247 +2024-09-11 04:47:20.963691: tseg-s1247, shape torch.Size([1, 431, 259, 259]), rank 0 +2024-09-11 04:47:28.376037: predicting tseg-s1251 +2024-09-11 04:47:29.022606: tseg-s1251, shape torch.Size([1, 472, 280, 280]), rank 0 +2024-09-11 04:47:36.493141: predicting tseg-s1255 +2024-09-11 04:47:36.728490: tseg-s1255, shape torch.Size([1, 193, 277, 277]), rank 0 +2024-09-11 04:47:40.526857: predicting tseg-s1259 +2024-09-11 04:47:40.592516: tseg-s1259, shape torch.Size([1, 120, 165, 165]), rank 0 +2024-09-11 04:47:41.085112: predicting tseg-s1261 +2024-09-11 04:47:41.233273: tseg-s1261, shape torch.Size([1, 138, 240, 240]), rank 0 +2024-09-11 04:47:43.107414: predicting tseg-s1268 +2024-09-11 04:47:43.257367: tseg-s1268, shape torch.Size([1, 134, 244, 244]), rank 0 +2024-09-11 04:47:45.148757: predicting tseg-s1271 +2024-09-11 04:47:45.228784: tseg-s1271, shape torch.Size([1, 90, 228, 228]), rank 0 +2024-09-11 04:47:47.076267: predicting tseg-s1273 +2024-09-11 04:47:47.635004: tseg-s1273, shape torch.Size([1, 465, 263, 263]), rank 0 +2024-09-11 04:47:55.030005: predicting tseg-s1300 +2024-09-11 04:47:55.078335: tseg-s1300, shape torch.Size([1, 101, 167, 167]), rank 0 +2024-09-11 04:47:55.575463: predicting tseg-s1308 +2024-09-11 04:47:55.847493: tseg-s1308, shape torch.Size([1, 197, 259, 259]), rank 0 +2024-09-11 04:47:59.538569: predicting tseg-s1309 +2024-09-11 04:47:59.901835: tseg-s1309, shape torch.Size([1, 311, 275, 275]), rank 0 +2024-09-11 04:48:05.459250: predicting tseg-s1327 +2024-09-11 04:48:05.624943: tseg-s1327, shape torch.Size([1, 198, 221, 221]), rank 0 +2024-09-11 04:48:09.331652: predicting tseg-s1328 +2024-09-11 04:48:09.379188: tseg-s1328, shape torch.Size([1, 47, 247, 247]), rank 0 +2024-09-11 04:48:11.212251: predicting tseg-s1331 +2024-09-11 04:48:11.463878: tseg-s1331, shape torch.Size([1, 238, 240, 240]), rank 0 +2024-09-11 04:48:15.147850: predicting tseg-s1341 +2024-09-11 04:48:15.240484: tseg-s1341, shape torch.Size([1, 101, 225, 225]), rank 0 +2024-09-11 04:48:17.090250: predicting tseg-s1349 +2024-09-11 04:48:17.681463: tseg-s1349, shape torch.Size([1, 517, 255, 255]), rank 0 +2024-09-11 04:48:26.889305: predicting tseg-s1352 +2024-09-11 04:48:26.948644: tseg-s1352, shape torch.Size([1, 201, 145, 93]), rank 0 +2024-09-11 04:48:27.892974: predicting tseg-s1354 +2024-09-11 04:48:28.335854: tseg-s1354, shape torch.Size([1, 289, 293, 293]), rank 0 +2024-09-11 04:48:40.555381: predicting tseg-s1364 +2024-09-11 04:48:40.987044: tseg-s1364, shape torch.Size([1, 413, 247, 247]), rank 0 +2024-09-11 04:48:48.380824: predicting tseg-s1368 +2024-09-11 04:48:48.706254: tseg-s1368, shape torch.Size([1, 280, 247, 247]), rank 0 +2024-09-11 04:48:52.513511: predicting tseg-s1374 +2024-09-11 04:48:52.825908: tseg-s1374, shape torch.Size([1, 267, 256, 256]), rank 0 +2024-09-11 04:48:56.582675: predicting tseg-s1375 +2024-09-11 04:48:56.826621: tseg-s1375, shape torch.Size([1, 165, 276, 276]), rank 0 +2024-09-11 04:48:58.793168: predicting tseg-s1382 +2024-09-11 04:48:59.356278: tseg-s1382, shape torch.Size([1, 465, 279, 279]), rank 0 +2024-09-11 04:49:06.789251: predicting tseg-s1383 +2024-09-11 04:49:07.242898: tseg-s1383, shape torch.Size([1, 320, 274, 274]), rank 0 +2024-09-11 04:49:12.908083: predicting tseg-s1384 +2024-09-11 04:49:13.708123: tseg-s1384, shape torch.Size([1, 523, 329, 329]), rank 0 +2024-09-11 04:49:34.217153: predicting tseg-s1395 +2024-09-11 04:49:34.805428: tseg-s1395, shape torch.Size([1, 409, 281, 281]), rank 0 +2024-09-11 04:51:09.868841: Validation complete +2024-09-11 04:51:09.869468: Mean Validation Dice: 0.9510368456528298 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_4_16_32_33.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_4_16_32_33.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a0e618806eafcaa6343d5ccceaaec4528dc543a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_4_16_32_33.txt @@ -0,0 +1,421 @@ + +####################################################################### +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. +####################################################################### + +2024-09-04 16:32:33.481144: do_dummy_2d_data_aug: False +2024-09-04 16:32:33.486778: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-04 16:32:33.488173: The split file contains 5 splits. +2024-09-04 16:32:33.488753: Desired fold for training: 2 +2024-09-04 16:32:33.489231: This split has 1325 training and 331 validation cases. +2024-09-04 16:32:53.762476: Using torch.compile... + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-04 16:32:57.853750: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-04 16:32:59.122113: +2024-09-04 16:32:59.123878: Epoch 0 +2024-09-04 16:32:59.125568: Current learning rate: 0.01 +2024-09-04 16:51:23.543190: train_loss 0.8017 +2024-09-04 16:51:23.833229: val_loss 0.4404 +2024-09-04 16:51:23.935910: Pseudo dice [0.4821, 0.6077, 0.3297, 0.0, 0.0, 0.0, 0.8139] +2024-09-04 16:51:23.944956: Epoch time: 1104.43 s +2024-09-04 16:51:24.018358: Yayy! New best EMA pseudo Dice: 0.3191 +2024-09-04 16:51:32.941130: +2024-09-04 16:51:32.942049: Epoch 1 +2024-09-04 16:51:32.942755: Current learning rate: 0.00999 +2024-09-04 16:56:24.505270: train_loss 0.3295 +2024-09-04 16:56:24.516310: val_loss 0.1346 +2024-09-04 16:56:24.517368: Pseudo dice [0.7023, 0.6581, 0.7475, 0.5927, 0.0, 0.0, 0.8385] +2024-09-04 16:56:24.518430: Epoch time: 291.57 s +2024-09-04 16:56:24.519464: Yayy! New best EMA pseudo Dice: 0.3377 +2024-09-04 16:56:37.151900: +2024-09-04 16:56:37.154576: Epoch 2 +2024-09-04 16:56:37.155327: Current learning rate: 0.00998 +2024-09-04 17:01:26.668821: train_loss 0.0916 +2024-09-04 17:01:26.683557: val_loss -0.0782 +2024-09-04 17:01:26.684738: Pseudo dice [0.7893, 0.7561, 0.8507, 0.7079, 0.0, 0.1837, 0.9012] +2024-09-04 17:01:26.687893: Epoch time: 289.52 s +2024-09-04 17:01:26.689939: Yayy! New best EMA pseudo Dice: 0.3638 +2024-09-04 17:01:35.833271: +2024-09-04 17:01:35.834953: Epoch 3 +2024-09-04 17:01:35.837165: Current learning rate: 0.00997 +2024-09-04 17:06:17.853048: train_loss -0.0725 +2024-09-04 17:06:17.871094: val_loss -0.1978 +2024-09-04 17:06:17.872330: Pseudo dice [0.8099, 0.808, 0.8666, 0.7611, 0.0112, 0.6088, 0.9394] +2024-09-04 17:06:17.873262: Epoch time: 282.02 s +2024-09-04 17:06:17.874430: Yayy! New best EMA pseudo Dice: 0.396 +2024-09-04 17:06:26.347355: +2024-09-04 17:06:26.348204: Epoch 4 +2024-09-04 17:06:26.348872: Current learning rate: 0.00996 +2024-09-04 17:11:09.622067: train_loss -0.2146 +2024-09-04 17:11:09.639885: val_loss -0.3046 +2024-09-04 17:11:09.641154: Pseudo dice [0.8259, 0.8239, 0.8722, 0.7832, 0.5321, 0.6868, 0.9517] +2024-09-04 17:11:09.642453: Epoch time: 283.28 s +2024-09-04 17:11:09.643794: Yayy! New best EMA pseudo Dice: 0.4347 +2024-09-04 17:11:19.499171: +2024-09-04 17:11:19.500315: Epoch 5 +2024-09-04 17:11:19.501288: Current learning rate: 0.00995 +2024-09-04 17:16:09.125291: train_loss -0.3142 +2024-09-04 17:16:09.137062: val_loss -0.4006 +2024-09-04 17:16:09.138345: Pseudo dice [0.8558, 0.8523, 0.9017, 0.8287, 0.6344, 0.7905, 0.9646] +2024-09-04 17:16:09.141316: Epoch time: 289.63 s +2024-09-04 17:16:09.142785: Yayy! New best EMA pseudo Dice: 0.4745 +2024-09-04 17:16:17.296560: +2024-09-04 17:16:17.297484: Epoch 6 +2024-09-04 17:16:17.298326: Current learning rate: 0.00995 +2024-09-04 17:21:00.664604: train_loss -0.3904 +2024-09-04 17:21:00.680651: val_loss -0.4162 +2024-09-04 17:21:00.682028: Pseudo dice [0.8713, 0.8672, 0.909, 0.8541, 0.6394, 0.7926, 0.9727] +2024-09-04 17:21:00.683936: Epoch time: 283.37 s +2024-09-04 17:21:00.685614: Yayy! New best EMA pseudo Dice: 0.5114 +2024-09-04 17:21:10.726966: +2024-09-04 17:21:10.728158: Epoch 7 +2024-09-04 17:21:10.729717: Current learning rate: 0.00994 +2024-09-04 17:26:15.039712: train_loss -0.4148 +2024-09-04 17:26:15.042217: val_loss -0.4771 +2024-09-04 17:26:15.044007: Pseudo dice [0.8767, 0.8499, 0.9158, 0.833, 0.6925, 0.8495, 0.9678] +2024-09-04 17:26:15.046029: Epoch time: 304.32 s +2024-09-04 17:26:15.047397: Yayy! New best EMA pseudo Dice: 0.5458 +2024-09-04 17:26:25.598643: +2024-09-04 17:26:25.599766: Epoch 8 +2024-09-04 17:26:25.600749: Current learning rate: 0.00993 +2024-09-04 17:31:17.220076: train_loss -0.4312 +2024-09-04 17:31:17.235202: val_loss -0.5395 +2024-09-04 17:31:17.236317: Pseudo dice [0.8911, 0.8854, 0.9377, 0.8517, 0.7358, 0.8612, 0.9774] +2024-09-04 17:31:17.237430: Epoch time: 291.63 s +2024-09-04 17:31:17.238579: Yayy! New best EMA pseudo Dice: 0.5789 +2024-09-04 17:31:27.596679: +2024-09-04 17:31:27.622146: Epoch 9 +2024-09-04 17:31:27.715370: Current learning rate: 0.00992 +2024-09-04 17:36:11.656539: train_loss -0.4495 +2024-09-04 17:36:11.669567: val_loss -0.4982 +2024-09-04 17:36:11.670562: Pseudo dice [0.8866, 0.8817, 0.9226, 0.8571, 0.6924, 0.8351, 0.9642] +2024-09-04 17:36:11.671625: Epoch time: 284.06 s +2024-09-04 17:36:11.672871: Yayy! New best EMA pseudo Dice: 0.6073 +2024-09-04 17:36:19.391863: +2024-09-04 17:36:19.393211: Epoch 10 +2024-09-04 17:36:19.394135: Current learning rate: 0.00991 +2024-09-04 17:40:46.829260: train_loss -0.4972 +2024-09-04 17:40:46.830924: val_loss -0.5679 +2024-09-04 17:40:46.832226: Pseudo dice [0.9083, 0.8943, 0.9334, 0.8611, 0.7478, 0.8757, 0.9786] +2024-09-04 17:40:46.833531: Epoch time: 267.44 s +2024-09-04 17:40:46.834771: Yayy! New best EMA pseudo Dice: 0.6351 +2024-09-04 17:40:56.283842: +2024-09-04 17:40:56.327705: Epoch 11 +2024-09-04 17:40:56.329060: Current learning rate: 0.0099 +2024-09-04 17:46:05.721474: train_loss -0.5047 +2024-09-04 17:46:05.729925: val_loss -0.555 +2024-09-04 17:46:05.731243: Pseudo dice [0.9127, 0.9051, 0.9414, 0.8897, 0.734, 0.8795, 0.9816] +2024-09-04 17:46:05.733185: Epoch time: 309.44 s +2024-09-04 17:46:05.734490: Yayy! New best EMA pseudo Dice: 0.6608 +2024-09-04 17:46:16.592273: +2024-09-04 17:46:16.593638: Epoch 12 +2024-09-04 17:46:16.597340: Current learning rate: 0.00989 +2024-09-04 17:50:39.374661: train_loss -0.4931 +2024-09-04 17:50:39.394552: val_loss -0.5518 +2024-09-04 17:50:39.395590: Pseudo dice [0.9068, 0.9013, 0.9376, 0.8811, 0.7435, 0.8795, 0.9698] +2024-09-04 17:50:39.396826: Epoch time: 262.79 s +2024-09-04 17:50:39.398396: Yayy! New best EMA pseudo Dice: 0.6836 +2024-09-04 17:50:48.468871: +2024-09-04 17:50:48.470596: Epoch 13 +2024-09-04 17:50:48.478187: Current learning rate: 0.00988 +2024-09-04 17:55:30.886973: train_loss -0.5026 +2024-09-04 17:55:30.892002: val_loss -0.5485 +2024-09-04 17:55:30.893427: Pseudo dice [0.9016, 0.9028, 0.9353, 0.8715, 0.7359, 0.8757, 0.9625] +2024-09-04 17:55:30.895835: Epoch time: 282.42 s +2024-09-04 17:55:30.896815: Yayy! New best EMA pseudo Dice: 0.7036 +2024-09-04 17:55:40.931771: +2024-09-04 17:55:40.933577: Epoch 14 +2024-09-04 17:55:40.935263: Current learning rate: 0.00987 +2024-09-04 18:00:16.367888: train_loss -0.5323 +2024-09-04 18:00:16.371431: val_loss -0.6072 +2024-09-04 18:00:16.373179: Pseudo dice [0.9234, 0.9129, 0.9485, 0.9014, 0.7543, 0.896, 0.9801] +2024-09-04 18:00:16.374855: Epoch time: 275.44 s +2024-09-04 18:00:16.376354: Yayy! New best EMA pseudo Dice: 0.7235 +2024-09-04 18:00:24.664220: +2024-09-04 18:00:24.665648: Epoch 15 +2024-09-04 18:00:24.666744: Current learning rate: 0.00986 +2024-09-04 18:05:00.813212: train_loss -0.5714 +2024-09-04 18:05:00.846790: val_loss -0.6271 +2024-09-04 18:05:00.848310: Pseudo dice [0.9254, 0.9165, 0.9509, 0.8957, 0.7591, 0.8988, 0.9797] +2024-09-04 18:05:00.849354: Epoch time: 276.15 s +2024-09-04 18:05:00.850468: Yayy! New best EMA pseudo Dice: 0.7415 +2024-09-04 18:05:16.388489: +2024-09-04 18:05:16.389440: Epoch 16 +2024-09-04 18:05:16.390370: Current learning rate: 0.00986 +2024-09-04 18:09:54.791043: train_loss -0.5816 +2024-09-04 18:09:54.818076: val_loss -0.6171 +2024-09-04 18:09:54.819180: Pseudo dice [0.9269, 0.9169, 0.9503, 0.8936, 0.7557, 0.8994, 0.9818] +2024-09-04 18:09:54.820415: Epoch time: 278.41 s +2024-09-04 18:09:54.821543: Yayy! New best EMA pseudo Dice: 0.7577 +2024-09-04 18:10:04.798912: +2024-09-04 18:10:04.800234: Epoch 17 +2024-09-04 18:10:04.801503: Current learning rate: 0.00985 +2024-09-04 18:14:45.615305: train_loss -0.5977 +2024-09-04 18:14:45.631194: val_loss -0.62 +2024-09-04 18:14:45.632051: Pseudo dice [0.9218, 0.9142, 0.9521, 0.8894, 0.7603, 0.9136, 0.9793] +2024-09-04 18:14:45.633046: Epoch time: 280.82 s +2024-09-04 18:14:45.634023: Yayy! New best EMA pseudo Dice: 0.7724 +2024-09-04 18:14:55.820575: +2024-09-04 18:14:55.821477: Epoch 18 +2024-09-04 18:14:55.822126: Current learning rate: 0.00984 +2024-09-04 18:19:22.193633: train_loss -0.5945 +2024-09-04 18:19:22.209629: val_loss -0.5804 +2024-09-04 18:19:22.210950: Pseudo dice [0.9207, 0.9171, 0.951, 0.8921, 0.74, 0.8394, 0.9813] +2024-09-04 18:19:22.212359: Epoch time: 266.38 s +2024-09-04 18:19:22.213566: Yayy! New best EMA pseudo Dice: 0.7843 +2024-09-04 18:19:32.619020: +2024-09-04 18:19:32.620033: Epoch 19 +2024-09-04 18:19:32.620863: Current learning rate: 0.00983 +2024-09-04 18:24:19.591614: train_loss -0.5789 +2024-09-04 18:24:19.593585: val_loss -0.6409 +2024-09-04 18:24:19.594913: Pseudo dice [0.9259, 0.9137, 0.9525, 0.9031, 0.7674, 0.9157, 0.9828] +2024-09-04 18:24:19.596239: Epoch time: 286.98 s +2024-09-04 18:24:19.597398: Yayy! New best EMA pseudo Dice: 0.7967 +2024-09-04 18:24:32.171621: +2024-09-04 18:24:32.172709: Epoch 20 +2024-09-04 18:24:32.173585: Current learning rate: 0.00982 +2024-09-04 18:28:54.404936: train_loss -0.6129 +2024-09-04 18:28:54.419820: val_loss -0.6031 +2024-09-04 18:28:54.420936: Pseudo dice [0.9301, 0.9203, 0.9533, 0.8964, 0.7652, 0.9132, 0.9812] +2024-09-04 18:28:54.422320: Epoch time: 262.24 s +2024-09-04 18:28:54.423294: Yayy! New best EMA pseudo Dice: 0.8079 +2024-09-04 18:29:07.148586: +2024-09-04 18:29:07.281220: Epoch 21 +2024-09-04 18:29:07.335747: Current learning rate: 0.00981 +2024-09-04 18:33:52.490430: train_loss -0.6191 +2024-09-04 18:33:52.592860: val_loss -0.6237 +2024-09-04 18:33:52.665633: Pseudo dice [0.9252, 0.9137, 0.9576, 0.8971, 0.7746, 0.9184, 0.9829] +2024-09-04 18:33:52.679150: Epoch time: 285.35 s +2024-09-04 18:33:52.733486: Yayy! New best EMA pseudo Dice: 0.8181 +2024-09-04 18:34:02.594633: +2024-09-04 18:34:02.595646: Epoch 22 +2024-09-04 18:34:02.596288: Current learning rate: 0.0098 +2024-09-04 18:38:45.008197: train_loss -0.635 +2024-09-04 18:38:45.009978: val_loss -0.669 +2024-09-04 18:38:45.010938: Pseudo dice [0.935, 0.9252, 0.9601, 0.9109, 0.7948, 0.9262, 0.9863] +2024-09-04 18:38:45.011957: Epoch time: 282.42 s +2024-09-04 18:38:45.013069: Yayy! New best EMA pseudo Dice: 0.8283 +2024-09-04 18:38:53.860810: +2024-09-04 18:38:53.862071: Epoch 23 +2024-09-04 18:38:53.862770: Current learning rate: 0.00979 +2024-09-04 18:43:43.300156: train_loss -0.621 +2024-09-04 18:43:43.327455: val_loss -0.6286 +2024-09-04 18:43:43.328827: Pseudo dice [0.932, 0.9284, 0.9595, 0.9037, 0.7845, 0.9198, 0.9808] +2024-09-04 18:43:43.330312: Epoch time: 289.44 s +2024-09-04 18:43:43.331672: Yayy! New best EMA pseudo Dice: 0.837 +2024-09-04 18:43:53.708198: +2024-09-04 18:43:53.709534: Epoch 24 +2024-09-04 18:43:53.710383: Current learning rate: 0.00978 +2024-09-04 18:48:37.075724: train_loss -0.6215 +2024-09-04 18:48:37.159403: val_loss -0.6721 +2024-09-04 18:48:37.399478: Pseudo dice [0.936, 0.9289, 0.9614, 0.9146, 0.8142, 0.9337, 0.9852] +2024-09-04 18:48:37.463401: Epoch time: 283.37 s +2024-09-04 18:48:37.473986: Yayy! New best EMA pseudo Dice: 0.8458 +2024-09-04 18:48:48.262928: +2024-09-04 18:48:48.263870: Epoch 25 +2024-09-04 18:48:48.264560: Current learning rate: 0.00977 +2024-09-04 18:53:42.090880: train_loss -0.6105 +2024-09-04 18:53:42.108207: val_loss -0.6316 +2024-09-04 18:53:42.109519: Pseudo dice [0.9332, 0.9286, 0.9526, 0.9077, 0.7865, 0.9176, 0.986] +2024-09-04 18:53:42.110512: Epoch time: 293.83 s +2024-09-04 18:53:42.111955: Yayy! New best EMA pseudo Dice: 0.8528 +2024-09-04 18:53:52.048932: +2024-09-04 18:53:52.050073: Epoch 26 +2024-09-04 18:53:52.051033: Current learning rate: 0.00977 +2024-09-04 18:58:40.250173: train_loss -0.6233 +2024-09-04 18:58:40.268072: val_loss -0.6239 +2024-09-04 18:58:40.269549: Pseudo dice [0.9355, 0.9231, 0.9584, 0.9126, 0.7761, 0.9161, 0.9811] +2024-09-04 18:58:40.270605: Epoch time: 288.2 s +2024-09-04 18:58:40.271812: Yayy! New best EMA pseudo Dice: 0.859 +2024-09-04 18:58:50.041296: +2024-09-04 18:58:50.042608: Epoch 27 +2024-09-04 18:58:50.043391: Current learning rate: 0.00976 +2024-09-04 19:03:19.987481: train_loss -0.6258 +2024-09-04 19:03:19.989156: val_loss -0.6437 +2024-09-04 19:03:19.990277: Pseudo dice [0.94, 0.9312, 0.9603, 0.9062, 0.8005, 0.9367, 0.9847] +2024-09-04 19:03:19.991471: Epoch time: 269.95 s +2024-09-04 19:03:19.992607: Yayy! New best EMA pseudo Dice: 0.8654 +2024-09-04 19:03:28.975486: +2024-09-04 19:03:28.976620: Epoch 28 +2024-09-04 19:03:28.977644: Current learning rate: 0.00975 +2024-09-04 19:08:01.498538: train_loss -0.6189 +2024-09-04 19:08:01.515202: val_loss -0.6822 +2024-09-04 19:08:01.516030: Pseudo dice [0.942, 0.9361, 0.9604, 0.9188, 0.7896, 0.917, 0.9831] +2024-09-04 19:08:01.517009: Epoch time: 272.53 s +2024-09-04 19:08:01.517713: Yayy! New best EMA pseudo Dice: 0.8709 +2024-09-04 19:08:09.766451: +2024-09-04 19:08:09.767295: Epoch 29 +2024-09-04 19:08:09.768047: Current learning rate: 0.00974 +2024-09-04 19:12:58.107760: train_loss -0.6411 +2024-09-04 19:12:58.119153: val_loss -0.6503 +2024-09-04 19:12:58.120156: Pseudo dice [0.9401, 0.9324, 0.9637, 0.9195, 0.8042, 0.938, 0.9862] +2024-09-04 19:12:58.121132: Epoch time: 288.34 s +2024-09-04 19:12:58.122384: Yayy! New best EMA pseudo Dice: 0.8765 +2024-09-04 19:13:08.175500: +2024-09-04 19:13:08.176648: Epoch 30 +2024-09-04 19:13:08.177350: Current learning rate: 0.00973 +2024-09-04 19:17:48.728104: train_loss -0.607 +2024-09-04 19:17:48.729907: val_loss -0.6203 +2024-09-04 19:17:48.730757: Pseudo dice [0.9186, 0.9243, 0.9563, 0.9052, 0.7949, 0.9247, 0.9806] +2024-09-04 19:17:48.731706: Epoch time: 280.56 s +2024-09-04 19:17:48.732388: Yayy! New best EMA pseudo Dice: 0.8803 +2024-09-04 19:17:58.269079: +2024-09-04 19:17:58.270120: Epoch 31 +2024-09-04 19:17:58.270824: Current learning rate: 0.00972 +2024-09-04 19:22:31.982799: train_loss -0.6189 +2024-09-04 19:22:31.984334: val_loss -0.6448 +2024-09-04 19:22:31.985384: Pseudo dice [0.9318, 0.9298, 0.9538, 0.9065, 0.7861, 0.9283, 0.9845] +2024-09-04 19:22:31.986566: Epoch time: 273.72 s +2024-09-04 19:22:31.987568: Yayy! New best EMA pseudo Dice: 0.884 +2024-09-04 19:22:41.134586: +2024-09-04 19:22:41.135599: Epoch 32 +2024-09-04 19:22:41.136291: Current learning rate: 0.00971 +2024-09-04 19:27:11.109629: train_loss -0.6429 +2024-09-04 19:27:11.121054: val_loss -0.6742 +2024-09-04 19:27:11.122039: Pseudo dice [0.9413, 0.932, 0.9629, 0.9168, 0.794, 0.9403, 0.9835] +2024-09-04 19:27:11.123294: Epoch time: 269.98 s +2024-09-04 19:27:11.124017: Yayy! New best EMA pseudo Dice: 0.8881 +2024-09-04 19:27:22.671712: +2024-09-04 19:27:22.672566: Epoch 33 +2024-09-04 19:27:22.673481: Current learning rate: 0.0097 +2024-09-04 19:32:08.192984: train_loss -0.6252 +2024-09-04 19:32:08.232165: val_loss -0.6687 +2024-09-04 19:32:08.282923: Pseudo dice [0.9436, 0.934, 0.9631, 0.9187, 0.8069, 0.9314, 0.9869] +2024-09-04 19:32:08.321152: Epoch time: 285.52 s +2024-09-04 19:32:08.385013: Yayy! New best EMA pseudo Dice: 0.8919 +2024-09-04 19:32:18.893954: +2024-09-04 19:32:18.895539: Epoch 34 +2024-09-04 19:32:18.896699: Current learning rate: 0.00969 +2024-09-04 19:36:58.568750: train_loss -0.6234 +2024-09-04 19:36:58.614475: val_loss -0.6416 +2024-09-04 19:36:58.677061: Pseudo dice [0.9382, 0.9259, 0.963, 0.9147, 0.7895, 0.9289, 0.9858] +2024-09-04 19:36:58.830536: Epoch time: 279.68 s +2024-09-04 19:36:58.969069: Yayy! New best EMA pseudo Dice: 0.8948 +2024-09-04 19:37:07.336457: +2024-09-04 19:37:07.338037: Epoch 35 +2024-09-04 19:37:07.338856: Current learning rate: 0.00968 +2024-09-04 19:41:34.168603: train_loss -0.6522 +2024-09-04 19:41:34.190999: val_loss -0.6848 +2024-09-04 19:41:34.192375: Pseudo dice [0.9406, 0.9391, 0.9656, 0.9194, 0.7935, 0.935, 0.9829] +2024-09-04 19:41:34.193919: Epoch time: 266.84 s +2024-09-04 19:41:34.194913: Yayy! New best EMA pseudo Dice: 0.8978 +2024-09-04 19:41:44.277542: +2024-09-04 19:41:44.278875: Epoch 36 +2024-09-04 19:41:44.280801: Current learning rate: 0.00968 +2024-09-04 19:46:26.659600: train_loss -0.6524 +2024-09-04 19:46:26.666007: val_loss -0.6776 +2024-09-04 19:46:26.678719: Pseudo dice [0.9457, 0.9343, 0.9664, 0.9265, 0.8231, 0.9418, 0.9855] +2024-09-04 19:46:26.680092: Epoch time: 282.39 s +2024-09-04 19:46:26.681438: Yayy! New best EMA pseudo Dice: 0.9012 +2024-09-04 19:46:36.715444: +2024-09-04 19:46:36.732182: Epoch 37 +2024-09-04 19:46:36.816409: Current learning rate: 0.00967 +2024-09-04 19:51:34.067033: train_loss -0.6553 +2024-09-04 19:51:34.077009: val_loss -0.6683 +2024-09-04 19:51:34.077921: Pseudo dice [0.9472, 0.9391, 0.9665, 0.9212, 0.8268, 0.9443, 0.9866] +2024-09-04 19:51:34.094870: Epoch time: 297.36 s +2024-09-04 19:51:34.096273: Yayy! New best EMA pseudo Dice: 0.9044 +2024-09-04 19:51:43.265177: +2024-09-04 19:51:43.266167: Epoch 38 +2024-09-04 19:51:43.267050: Current learning rate: 0.00966 +2024-09-04 19:56:25.977915: train_loss -0.6466 +2024-09-04 19:56:25.989035: val_loss -0.6416 +2024-09-04 19:56:25.990099: Pseudo dice [0.9435, 0.9394, 0.9587, 0.9231, 0.8066, 0.925, 0.9842] +2024-09-04 19:56:25.991152: Epoch time: 282.72 s +2024-09-04 19:56:25.992269: Yayy! New best EMA pseudo Dice: 0.9066 +2024-09-04 19:56:35.372636: +2024-09-04 19:56:35.373578: Epoch 39 +2024-09-04 19:56:35.374354: Current learning rate: 0.00965 +2024-09-04 20:01:29.955279: train_loss -0.6621 +2024-09-04 20:01:29.966045: val_loss -0.6734 +2024-09-04 20:01:29.967654: Pseudo dice [0.945, 0.9413, 0.9687, 0.9214, 0.8193, 0.941, 0.989] +2024-09-04 20:01:29.968822: Epoch time: 294.59 s +2024-09-04 20:01:29.970402: Yayy! New best EMA pseudo Dice: 0.9091 +2024-09-04 20:01:40.983062: +2024-09-04 20:01:40.984115: Epoch 40 +2024-09-04 20:01:40.984870: Current learning rate: 0.00964 +2024-09-04 20:06:24.737178: train_loss -0.6384 +2024-09-04 20:06:24.750871: val_loss -0.7071 +2024-09-04 20:06:24.751904: Pseudo dice [0.9437, 0.9359, 0.9654, 0.9187, 0.8093, 0.9365, 0.9881] +2024-09-04 20:06:24.752876: Epoch time: 283.76 s +2024-09-04 20:06:24.753888: Yayy! New best EMA pseudo Dice: 0.911 +2024-09-04 20:06:34.707842: +2024-09-04 20:06:34.709143: Epoch 41 +2024-09-04 20:06:34.710207: Current learning rate: 0.00963 +2024-09-04 20:11:29.511852: train_loss -0.666 +2024-09-04 20:11:29.527181: val_loss -0.646 +2024-09-04 20:11:29.528046: Pseudo dice [0.9409, 0.9345, 0.9603, 0.9213, 0.7961, 0.9377, 0.986] +2024-09-04 20:11:29.529036: Epoch time: 294.81 s +2024-09-04 20:11:29.529777: Yayy! New best EMA pseudo Dice: 0.9125 +2024-09-04 20:11:37.460645: +2024-09-04 20:11:37.461610: Epoch 42 +2024-09-04 20:11:37.462322: Current learning rate: 0.00962 +2024-09-04 20:16:14.058472: train_loss -0.6581 +2024-09-04 20:16:14.078171: val_loss -0.6838 +2024-09-04 20:16:14.079321: Pseudo dice [0.9469, 0.9372, 0.9695, 0.9241, 0.817, 0.9394, 0.9886] +2024-09-04 20:16:14.080807: Epoch time: 276.6 s +2024-09-04 20:16:14.081944: Yayy! New best EMA pseudo Dice: 0.9144 +2024-09-04 20:16:24.539013: +2024-09-04 20:16:24.540258: Epoch 43 +2024-09-04 20:16:24.541223: Current learning rate: 0.00961 +2024-09-04 20:21:00.557090: train_loss -0.666 +2024-09-04 20:21:00.572887: val_loss -0.6791 +2024-09-04 20:21:00.573647: Pseudo dice [0.9453, 0.9378, 0.9681, 0.9273, 0.8234, 0.9455, 0.9867] +2024-09-04 20:21:00.574562: Epoch time: 276.02 s +2024-09-04 20:21:00.575660: Yayy! New best EMA pseudo Dice: 0.9163 +2024-09-04 20:21:10.806114: +2024-09-04 20:21:10.806978: Epoch 44 +2024-09-04 20:21:10.807988: Current learning rate: 0.0096 +2024-09-04 20:26:01.132728: train_loss -0.6458 +2024-09-04 20:26:01.240944: val_loss -0.6779 +2024-09-04 20:26:01.247684: Pseudo dice [0.9427, 0.9333, 0.9633, 0.9203, 0.8129, 0.9415, 0.9877] +2024-09-04 20:26:01.259192: Epoch time: 290.33 s +2024-09-04 20:26:01.433124: Yayy! New best EMA pseudo Dice: 0.9175 +2024-09-04 20:26:16.020827: +2024-09-04 20:26:16.075540: Epoch 45 +2024-09-04 20:26:16.128499: Current learning rate: 0.00959 +2024-09-04 20:30:58.026842: train_loss -0.6693 +2024-09-04 20:30:58.048928: val_loss -0.7 +2024-09-04 20:30:58.049963: Pseudo dice [0.9434, 0.9393, 0.9637, 0.9248, 0.8251, 0.9433, 0.9886] +2024-09-04 20:30:58.050796: Epoch time: 282.01 s +2024-09-04 20:30:58.051875: Yayy! New best EMA pseudo Dice: 0.919 +2024-09-04 20:31:07.614941: +2024-09-04 20:31:07.615960: Epoch 46 +2024-09-04 20:31:07.616795: Current learning rate: 0.00959 +2024-09-04 20:36:05.705186: train_loss -0.6597 +2024-09-04 20:36:05.724614: val_loss -0.6719 +2024-09-04 20:36:05.725476: Pseudo dice [0.9489, 0.9409, 0.9673, 0.9319, 0.8191, 0.9449, 0.9875] +2024-09-04 20:36:05.727390: Epoch time: 298.1 s +2024-09-04 20:36:05.728399: Yayy! New best EMA pseudo Dice: 0.9206 +2024-09-04 20:36:14.981581: +2024-09-04 20:36:14.982708: Epoch 47 +2024-09-04 20:36:14.983404: Current learning rate: 0.00958 +2024-09-04 20:41:02.070366: train_loss -0.6779 +2024-09-04 20:41:02.093466: val_loss -0.6908 +2024-09-04 20:41:02.094516: Pseudo dice [0.9475, 0.9424, 0.9602, 0.933, 0.8187, 0.9436, 0.9876] +2024-09-04 20:41:02.095324: Epoch time: 287.09 s +2024-09-04 20:41:02.096049: Yayy! New best EMA pseudo Dice: 0.9219 +2024-09-04 20:41:11.406332: +2024-09-04 20:41:11.407361: Epoch 48 +2024-09-04 20:41:11.408115: Current learning rate: 0.00957 +2024-09-04 20:45:59.771436: train_loss -0.6804 +2024-09-04 20:45:59.773892: val_loss -0.7265 +2024-09-04 20:45:59.775067: Pseudo dice [0.9519, 0.9406, 0.971, 0.9273, 0.827, 0.9454, 0.9895] +2024-09-04 20:45:59.776424: Epoch time: 288.37 s +2024-09-04 20:45:59.777729: Yayy! New best EMA pseudo Dice: 0.9233 +2024-09-04 20:46:09.112426: +2024-09-04 20:46:09.113330: Epoch 49 +2024-09-04 20:46:09.115138: Current learning rate: 0.00956 +2024-09-04 20:50:44.894711: train_loss -0.6795 +2024-09-04 20:50:44.911211: val_loss -0.6872 +2024-09-04 20:50:44.912072: Pseudo dice [0.9446, 0.936, 0.9639, 0.9189, 0.8164, 0.947, 0.988] +2024-09-04 20:50:44.913139: Epoch time: 275.79 s +2024-09-04 20:50:52.531818: Yayy! New best EMA pseudo Dice: 0.924 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_00_32_01.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_00_32_01.txt new file mode 100644 index 0000000000000000000000000000000000000000..9d3e012bca48a4c16622ac73a89d74c94b416c38 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_00_32_01.txt @@ -0,0 +1,402 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 00:32:04.890923: Using torch.compile... +2024-09-05 00:32:14.355003: do_dummy_2d_data_aug: False +2024-09-05 00:32:14.360830: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 00:32:14.362117: The split file contains 5 splits. +2024-09-05 00:32:14.362728: Desired fold for training: 2 +2024-09-05 00:32:14.363368: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 00:32:27.173826: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 00:32:29.842358: +2024-09-05 00:32:29.858909: Epoch 50 +2024-09-05 00:32:29.860887: Current learning rate: 0.00955 +2024-09-05 00:50:47.525405: train_loss -0.6636 +2024-09-05 00:50:47.603783: val_loss -0.6562 +2024-09-05 00:50:47.648743: Pseudo dice [0.9437, 0.9405, 0.9651, 0.9288, 0.7998, 0.945, 0.9856] +2024-09-05 00:50:47.730656: Epoch time: 1097.69 s +2024-09-05 00:50:47.794362: Yayy! New best EMA pseudo Dice: 0.9246 +2024-09-05 00:51:03.857314: +2024-09-05 00:51:03.858336: Epoch 51 +2024-09-05 00:51:03.859114: Current learning rate: 0.00954 +2024-09-05 00:55:51.249945: train_loss -0.6664 +2024-09-05 00:55:51.383422: val_loss -0.6955 +2024-09-05 00:55:51.441087: Pseudo dice [0.9517, 0.9428, 0.9711, 0.9272, 0.7978, 0.9413, 0.9844] +2024-09-05 00:55:51.467739: Epoch time: 287.4 s +2024-09-05 00:55:51.568408: Yayy! New best EMA pseudo Dice: 0.9252 +2024-09-05 00:56:03.229114: +2024-09-05 00:56:03.230116: Epoch 52 +2024-09-05 00:56:03.230763: Current learning rate: 0.00953 +2024-09-05 01:00:58.164604: train_loss -0.6348 +2024-09-05 01:00:58.175513: val_loss -0.7221 +2024-09-05 01:00:58.177177: Pseudo dice [0.9512, 0.9395, 0.9715, 0.9312, 0.8345, 0.9471, 0.9887] +2024-09-05 01:00:58.178768: Epoch time: 294.94 s +2024-09-05 01:00:58.179937: Yayy! New best EMA pseudo Dice: 0.9265 +2024-09-05 01:01:07.654813: +2024-09-05 01:01:07.655798: Epoch 53 +2024-09-05 01:01:07.656466: Current learning rate: 0.00952 +2024-09-05 01:05:40.390346: train_loss -0.6639 +2024-09-05 01:05:40.392762: val_loss -0.6809 +2024-09-05 01:05:40.394475: Pseudo dice [0.9446, 0.9374, 0.9655, 0.9264, 0.8009, 0.9335, 0.9877] +2024-09-05 01:05:40.395432: Epoch time: 272.74 s +2024-09-05 01:05:40.396807: Yayy! New best EMA pseudo Dice: 0.9266 +2024-09-05 01:05:49.847719: +2024-09-05 01:05:49.848724: Epoch 54 +2024-09-05 01:05:49.849544: Current learning rate: 0.00951 +2024-09-05 01:10:16.236480: train_loss -0.6443 +2024-09-05 01:10:16.239166: val_loss -0.6701 +2024-09-05 01:10:16.240731: Pseudo dice [0.9419, 0.9369, 0.9598, 0.9189, 0.8128, 0.9438, 0.9882] +2024-09-05 01:10:16.242315: Epoch time: 266.39 s +2024-09-05 01:10:16.244138: Yayy! New best EMA pseudo Dice: 0.9268 +2024-09-05 01:10:26.080207: +2024-09-05 01:10:26.081387: Epoch 55 +2024-09-05 01:10:26.082493: Current learning rate: 0.0095 +2024-09-05 01:14:58.752154: train_loss -0.6403 +2024-09-05 01:14:58.760374: val_loss -0.6718 +2024-09-05 01:14:58.761549: Pseudo dice [0.9383, 0.9358, 0.9538, 0.9281, 0.7975, 0.9437, 0.9866] +2024-09-05 01:14:58.763138: Epoch time: 272.68 s +2024-09-05 01:15:01.570777: +2024-09-05 01:15:01.650792: Epoch 56 +2024-09-05 01:15:01.710367: Current learning rate: 0.00949 +2024-09-05 01:19:33.259955: train_loss -0.6663 +2024-09-05 01:19:33.277574: val_loss -0.6914 +2024-09-05 01:19:33.278775: Pseudo dice [0.9494, 0.9398, 0.9714, 0.925, 0.8145, 0.9331, 0.9876] +2024-09-05 01:19:33.279925: Epoch time: 271.69 s +2024-09-05 01:19:33.280965: Yayy! New best EMA pseudo Dice: 0.9273 +2024-09-05 01:19:43.946041: +2024-09-05 01:19:43.947389: Epoch 57 +2024-09-05 01:19:43.948817: Current learning rate: 0.00949 +2024-09-05 01:24:21.832624: train_loss -0.685 +2024-09-05 01:24:21.838803: val_loss -0.6973 +2024-09-05 01:24:21.840109: Pseudo dice [0.9489, 0.9427, 0.9714, 0.9221, 0.8269, 0.9469, 0.9872] +2024-09-05 01:24:21.840942: Epoch time: 277.89 s +2024-09-05 01:24:21.841641: Yayy! New best EMA pseudo Dice: 0.9281 +2024-09-05 01:24:31.598478: +2024-09-05 01:24:31.599431: Epoch 58 +2024-09-05 01:24:31.600985: Current learning rate: 0.00948 +2024-09-05 01:28:50.876248: train_loss -0.6844 +2024-09-05 01:28:50.900272: val_loss -0.7036 +2024-09-05 01:28:50.901276: Pseudo dice [0.9492, 0.9428, 0.9677, 0.9346, 0.8255, 0.9513, 0.9873] +2024-09-05 01:28:50.902056: Epoch time: 259.28 s +2024-09-05 01:28:50.902827: Yayy! New best EMA pseudo Dice: 0.9289 +2024-09-05 01:29:00.690245: +2024-09-05 01:29:00.691234: Epoch 59 +2024-09-05 01:29:00.692351: Current learning rate: 0.00947 +2024-09-05 01:33:44.161541: train_loss -0.6781 +2024-09-05 01:33:44.171251: val_loss -0.7264 +2024-09-05 01:33:44.173089: Pseudo dice [0.954, 0.9437, 0.9693, 0.9333, 0.8277, 0.9499, 0.9879] +2024-09-05 01:33:44.174948: Epoch time: 283.48 s +2024-09-05 01:33:44.176806: Yayy! New best EMA pseudo Dice: 0.9298 +2024-09-05 01:33:53.870541: +2024-09-05 01:33:53.871389: Epoch 60 +2024-09-05 01:33:53.872161: Current learning rate: 0.00946 +2024-09-05 01:38:31.399559: train_loss -0.6739 +2024-09-05 01:38:31.401030: val_loss -0.7022 +2024-09-05 01:38:31.402950: Pseudo dice [0.9481, 0.9419, 0.9676, 0.9308, 0.822, 0.9557, 0.9899] +2024-09-05 01:38:31.404767: Epoch time: 277.53 s +2024-09-05 01:38:31.406342: Yayy! New best EMA pseudo Dice: 0.9305 +2024-09-05 01:38:41.040672: +2024-09-05 01:38:41.041801: Epoch 61 +2024-09-05 01:38:41.043163: Current learning rate: 0.00945 +2024-09-05 01:43:25.323277: train_loss -0.677 +2024-09-05 01:43:25.340511: val_loss -0.6787 +2024-09-05 01:43:25.342046: Pseudo dice [0.9475, 0.941, 0.9671, 0.9161, 0.8047, 0.9478, 0.983] +2024-09-05 01:43:25.347563: Epoch time: 284.29 s +2024-09-05 01:43:30.861596: +2024-09-05 01:43:30.862877: Epoch 62 +2024-09-05 01:43:30.864268: Current learning rate: 0.00944 +2024-09-05 01:48:01.819467: train_loss -0.678 +2024-09-05 01:48:01.840970: val_loss -0.7241 +2024-09-05 01:48:01.842066: Pseudo dice [0.9545, 0.9452, 0.9728, 0.9322, 0.8306, 0.9522, 0.9883] +2024-09-05 01:48:01.842805: Epoch time: 270.96 s +2024-09-05 01:48:01.843452: Yayy! New best EMA pseudo Dice: 0.9313 +2024-09-05 01:48:11.523285: +2024-09-05 01:48:11.524199: Epoch 63 +2024-09-05 01:48:11.524796: Current learning rate: 0.00943 +2024-09-05 01:52:47.120192: train_loss -0.6919 +2024-09-05 01:52:47.135625: val_loss -0.6814 +2024-09-05 01:52:47.136865: Pseudo dice [0.9483, 0.9416, 0.9613, 0.9253, 0.8427, 0.9504, 0.9882] +2024-09-05 01:52:47.138126: Epoch time: 275.6 s +2024-09-05 01:52:47.139046: Yayy! New best EMA pseudo Dice: 0.9319 +2024-09-05 01:52:55.947641: +2024-09-05 01:52:55.948590: Epoch 64 +2024-09-05 01:52:55.949274: Current learning rate: 0.00942 +2024-09-05 01:57:35.198941: train_loss -0.6881 +2024-09-05 01:57:35.213745: val_loss -0.7324 +2024-09-05 01:57:35.214602: Pseudo dice [0.9543, 0.9452, 0.9704, 0.9357, 0.8402, 0.9454, 0.9891] +2024-09-05 01:57:35.215674: Epoch time: 279.26 s +2024-09-05 01:57:35.216628: Yayy! New best EMA pseudo Dice: 0.9327 +2024-09-05 01:57:45.953309: +2024-09-05 01:57:45.954314: Epoch 65 +2024-09-05 01:57:45.955129: Current learning rate: 0.00941 +2024-09-05 02:02:15.516203: train_loss -0.6677 +2024-09-05 02:02:15.533234: val_loss -0.6891 +2024-09-05 02:02:15.534260: Pseudo dice [0.9525, 0.9457, 0.9683, 0.9362, 0.8221, 0.9562, 0.9889] +2024-09-05 02:02:15.547659: Epoch time: 269.57 s +2024-09-05 02:02:15.548853: Yayy! New best EMA pseudo Dice: 0.9333 +2024-09-05 02:02:24.238121: +2024-09-05 02:02:24.239082: Epoch 66 +2024-09-05 02:02:24.239758: Current learning rate: 0.0094 +2024-09-05 02:07:05.051993: train_loss -0.6896 +2024-09-05 02:07:05.081664: val_loss -0.6884 +2024-09-05 02:07:05.083136: Pseudo dice [0.9525, 0.9483, 0.9702, 0.9241, 0.8283, 0.9499, 0.9871] +2024-09-05 02:07:05.084157: Epoch time: 280.82 s +2024-09-05 02:07:05.085118: Yayy! New best EMA pseudo Dice: 0.9337 +2024-09-05 02:07:14.846512: +2024-09-05 02:07:14.847541: Epoch 67 +2024-09-05 02:07:14.848327: Current learning rate: 0.00939 +2024-09-05 02:12:12.792194: train_loss -0.6971 +2024-09-05 02:12:12.817054: val_loss -0.7169 +2024-09-05 02:12:12.817806: Pseudo dice [0.9498, 0.9412, 0.9693, 0.9289, 0.8314, 0.9503, 0.9887] +2024-09-05 02:12:12.818948: Epoch time: 297.95 s +2024-09-05 02:12:12.819939: Yayy! New best EMA pseudo Dice: 0.934 +2024-09-05 02:12:22.548370: +2024-09-05 02:12:22.549266: Epoch 68 +2024-09-05 02:12:22.550075: Current learning rate: 0.00939 +2024-09-05 02:17:08.750086: train_loss -0.6889 +2024-09-05 02:17:08.757481: val_loss -0.7039 +2024-09-05 02:17:08.758272: Pseudo dice [0.9516, 0.9468, 0.9683, 0.9366, 0.8286, 0.9487, 0.9879] +2024-09-05 02:17:08.759239: Epoch time: 286.21 s +2024-09-05 02:17:08.760167: Yayy! New best EMA pseudo Dice: 0.9344 +2024-09-05 02:17:18.196463: +2024-09-05 02:17:18.197574: Epoch 69 +2024-09-05 02:17:18.198460: Current learning rate: 0.00938 +2024-09-05 02:22:10.931821: train_loss -0.6856 +2024-09-05 02:22:10.934770: val_loss -0.7134 +2024-09-05 02:22:10.935874: Pseudo dice [0.9537, 0.9436, 0.9696, 0.9353, 0.8288, 0.9521, 0.9887] +2024-09-05 02:22:10.936967: Epoch time: 292.74 s +2024-09-05 02:22:10.937762: Yayy! New best EMA pseudo Dice: 0.9349 +2024-09-05 02:22:21.269639: +2024-09-05 02:22:21.270869: Epoch 70 +2024-09-05 02:22:21.271752: Current learning rate: 0.00937 +2024-09-05 02:27:07.883052: train_loss -0.6842 +2024-09-05 02:27:07.884686: val_loss -0.7008 +2024-09-05 02:27:07.885798: Pseudo dice [0.9498, 0.941, 0.9712, 0.9327, 0.8321, 0.9462, 0.9865] +2024-09-05 02:27:07.886696: Epoch time: 286.62 s +2024-09-05 02:27:07.887392: Yayy! New best EMA pseudo Dice: 0.9351 +2024-09-05 02:27:16.394389: +2024-09-05 02:27:16.395527: Epoch 71 +2024-09-05 02:27:16.396635: Current learning rate: 0.00936 +2024-09-05 02:32:10.810695: train_loss -0.6896 +2024-09-05 02:32:10.826386: val_loss -0.7039 +2024-09-05 02:32:10.829002: Pseudo dice [0.9543, 0.9485, 0.9728, 0.9373, 0.84, 0.9559, 0.9889] +2024-09-05 02:32:10.830130: Epoch time: 294.42 s +2024-09-05 02:32:10.831584: Yayy! New best EMA pseudo Dice: 0.9358 +2024-09-05 02:32:20.982337: +2024-09-05 02:32:20.983642: Epoch 72 +2024-09-05 02:32:20.985360: Current learning rate: 0.00935 +2024-09-05 02:37:21.965314: train_loss -0.6867 +2024-09-05 02:37:21.979190: val_loss -0.685 +2024-09-05 02:37:21.980107: Pseudo dice [0.9485, 0.9428, 0.9645, 0.9311, 0.8243, 0.9436, 0.985] +2024-09-05 02:37:21.980958: Epoch time: 300.99 s +2024-09-05 02:37:25.901544: +2024-09-05 02:37:25.903209: Epoch 73 +2024-09-05 02:37:25.905272: Current learning rate: 0.00934 +2024-09-05 02:42:19.145204: train_loss -0.6901 +2024-09-05 02:42:19.244840: val_loss -0.7248 +2024-09-05 02:42:19.342700: Pseudo dice [0.956, 0.946, 0.9735, 0.9306, 0.8479, 0.9541, 0.9889] +2024-09-05 02:42:19.409032: Epoch time: 293.25 s +2024-09-05 02:42:19.483132: Yayy! New best EMA pseudo Dice: 0.9364 +2024-09-05 02:42:28.919306: +2024-09-05 02:42:28.920082: Epoch 74 +2024-09-05 02:42:28.920745: Current learning rate: 0.00933 +2024-09-05 02:47:19.417561: train_loss -0.6887 +2024-09-05 02:47:19.419227: val_loss -0.7219 +2024-09-05 02:47:19.420184: Pseudo dice [0.9499, 0.9456, 0.9692, 0.9332, 0.826, 0.9556, 0.9873] +2024-09-05 02:47:19.421091: Epoch time: 290.5 s +2024-09-05 02:47:19.421878: Yayy! New best EMA pseudo Dice: 0.9365 +2024-09-05 02:47:29.112874: +2024-09-05 02:47:29.113837: Epoch 75 +2024-09-05 02:47:29.114525: Current learning rate: 0.00932 +2024-09-05 02:52:02.835599: train_loss -0.706 +2024-09-05 02:52:02.854927: val_loss -0.7344 +2024-09-05 02:52:02.856115: Pseudo dice [0.9505, 0.9449, 0.9721, 0.9385, 0.8359, 0.955, 0.9873] +2024-09-05 02:52:02.857145: Epoch time: 273.73 s +2024-09-05 02:52:02.857880: Yayy! New best EMA pseudo Dice: 0.9369 +2024-09-05 02:52:13.960665: +2024-09-05 02:52:13.961635: Epoch 76 +2024-09-05 02:52:13.962443: Current learning rate: 0.00931 +2024-09-05 02:56:42.982696: train_loss -0.6907 +2024-09-05 02:56:43.002193: val_loss -0.7071 +2024-09-05 02:56:43.003830: Pseudo dice [0.9567, 0.9479, 0.9746, 0.9345, 0.8303, 0.956, 0.9872] +2024-09-05 02:56:43.005285: Epoch time: 269.03 s +2024-09-05 02:56:43.006603: Yayy! New best EMA pseudo Dice: 0.9374 +2024-09-05 02:56:52.790109: +2024-09-05 02:56:52.790821: Epoch 77 +2024-09-05 02:56:52.791476: Current learning rate: 0.0093 +2024-09-05 03:01:36.397490: train_loss -0.6716 +2024-09-05 03:01:36.412251: val_loss -0.6454 +2024-09-05 03:01:36.413616: Pseudo dice [0.9413, 0.9431, 0.9621, 0.9093, 0.8146, 0.9396, 0.9736] +2024-09-05 03:01:36.414538: Epoch time: 283.61 s +2024-09-05 03:01:40.318229: +2024-09-05 03:01:40.319128: Epoch 78 +2024-09-05 03:01:40.320361: Current learning rate: 0.0093 +2024-09-05 03:06:19.083628: train_loss -0.6418 +2024-09-05 03:06:19.163898: val_loss -0.6707 +2024-09-05 03:06:19.186532: Pseudo dice [0.9473, 0.9399, 0.9669, 0.9233, 0.8151, 0.9364, 0.9741] +2024-09-05 03:06:19.263129: Epoch time: 278.77 s +2024-09-05 03:06:22.659202: +2024-09-05 03:06:22.660451: Epoch 79 +2024-09-05 03:06:22.661438: Current learning rate: 0.00929 +2024-09-05 03:11:02.207145: train_loss -0.6515 +2024-09-05 03:11:02.208803: val_loss -0.6814 +2024-09-05 03:11:02.210163: Pseudo dice [0.9405, 0.9405, 0.9609, 0.9177, 0.8242, 0.9358, 0.9882] +2024-09-05 03:11:02.211318: Epoch time: 279.55 s +2024-09-05 03:11:05.555873: +2024-09-05 03:11:05.556861: Epoch 80 +2024-09-05 03:11:05.557569: Current learning rate: 0.00928 +2024-09-05 03:15:20.659727: train_loss -0.6936 +2024-09-05 03:15:20.666901: val_loss -0.7049 +2024-09-05 03:15:20.668679: Pseudo dice [0.9481, 0.9441, 0.9644, 0.9265, 0.8255, 0.9506, 0.9863] +2024-09-05 03:15:20.670835: Epoch time: 255.11 s +2024-09-05 03:15:23.316550: +2024-09-05 03:15:23.317944: Epoch 81 +2024-09-05 03:15:23.318795: Current learning rate: 0.00927 +2024-09-05 03:20:19.427214: train_loss -0.6835 +2024-09-05 03:20:19.444029: val_loss -0.7077 +2024-09-05 03:20:19.445184: Pseudo dice [0.9535, 0.9424, 0.9712, 0.9362, 0.828, 0.9485, 0.9879] +2024-09-05 03:20:19.445951: Epoch time: 296.12 s +2024-09-05 03:20:23.741604: +2024-09-05 03:20:23.742822: Epoch 82 +2024-09-05 03:20:23.744199: Current learning rate: 0.00926 +2024-09-05 03:25:06.615177: train_loss -0.6919 +2024-09-05 03:25:06.617655: val_loss -0.6558 +2024-09-05 03:25:06.618451: Pseudo dice [0.9499, 0.9432, 0.9685, 0.9383, 0.8284, 0.9473, 0.9874] +2024-09-05 03:25:06.619204: Epoch time: 282.88 s +2024-09-05 03:25:10.132584: +2024-09-05 03:25:10.133663: Epoch 83 +2024-09-05 03:25:10.134416: Current learning rate: 0.00925 +2024-09-05 03:29:52.735844: train_loss -0.6713 +2024-09-05 03:29:52.753011: val_loss -0.6929 +2024-09-05 03:29:52.754093: Pseudo dice [0.949, 0.9399, 0.9648, 0.9272, 0.8223, 0.9501, 0.9865] +2024-09-05 03:29:52.755173: Epoch time: 282.61 s +2024-09-05 03:29:57.013913: +2024-09-05 03:29:57.015020: Epoch 84 +2024-09-05 03:29:57.016279: Current learning rate: 0.00924 +2024-09-05 03:34:44.471053: train_loss -0.6899 +2024-09-05 03:34:44.481492: val_loss -0.7143 +2024-09-05 03:34:44.482977: Pseudo dice [0.9499, 0.9485, 0.9651, 0.9301, 0.8437, 0.9576, 0.9889] +2024-09-05 03:34:44.484426: Epoch time: 287.46 s +2024-09-05 03:34:50.793659: +2024-09-05 03:34:50.794773: Epoch 85 +2024-09-05 03:34:50.795908: Current learning rate: 0.00923 +2024-09-05 03:39:25.836866: train_loss -0.701 +2024-09-05 03:39:25.854070: val_loss -0.7283 +2024-09-05 03:39:25.854934: Pseudo dice [0.954, 0.9477, 0.9724, 0.9387, 0.8494, 0.9558, 0.9902] +2024-09-05 03:39:25.855868: Epoch time: 275.05 s +2024-09-05 03:39:28.521207: +2024-09-05 03:39:28.522217: Epoch 86 +2024-09-05 03:39:28.522941: Current learning rate: 0.00922 +2024-09-05 03:44:18.247715: train_loss -0.6876 +2024-09-05 03:44:18.253023: val_loss -0.6914 +2024-09-05 03:44:18.254271: Pseudo dice [0.9519, 0.9474, 0.9667, 0.9316, 0.845, 0.9586, 0.9897] +2024-09-05 03:44:18.255590: Epoch time: 289.73 s +2024-09-05 03:44:22.004535: +2024-09-05 03:44:22.005619: Epoch 87 +2024-09-05 03:44:22.006757: Current learning rate: 0.00921 +2024-09-05 03:49:02.538012: train_loss -0.6808 +2024-09-05 03:49:02.549853: val_loss -0.7019 +2024-09-05 03:49:02.550742: Pseudo dice [0.9472, 0.9425, 0.9621, 0.9397, 0.8308, 0.9513, 0.9874] +2024-09-05 03:49:02.551515: Epoch time: 280.54 s +2024-09-05 03:49:04.931179: +2024-09-05 03:49:04.946051: Epoch 88 +2024-09-05 03:49:04.947421: Current learning rate: 0.0092 +2024-09-05 03:54:01.866760: train_loss -0.6542 +2024-09-05 03:54:01.885623: val_loss -0.7112 +2024-09-05 03:54:01.886843: Pseudo dice [0.9506, 0.9446, 0.9694, 0.933, 0.8317, 0.9484, 0.9863] +2024-09-05 03:54:01.887880: Epoch time: 296.94 s +2024-09-05 03:54:05.266170: +2024-09-05 03:54:05.278659: Epoch 89 +2024-09-05 03:54:05.279559: Current learning rate: 0.0092 +2024-09-05 03:59:02.674352: train_loss -0.6941 +2024-09-05 03:59:02.678139: val_loss -0.7102 +2024-09-05 03:59:02.679720: Pseudo dice [0.955, 0.9472, 0.9692, 0.927, 0.8427, 0.9591, 0.9899] +2024-09-05 03:59:02.680861: Epoch time: 297.41 s +2024-09-05 03:59:02.682338: Yayy! New best EMA pseudo Dice: 0.9377 +2024-09-05 03:59:13.474953: +2024-09-05 03:59:13.475985: Epoch 90 +2024-09-05 03:59:13.476956: Current learning rate: 0.00919 +2024-09-05 04:04:00.065892: train_loss -0.6836 +2024-09-05 04:04:00.082766: val_loss -0.7173 +2024-09-05 04:04:00.084086: Pseudo dice [0.9544, 0.9454, 0.9724, 0.9413, 0.85, 0.953, 0.9895] +2024-09-05 04:04:00.086061: Epoch time: 286.6 s +2024-09-05 04:04:00.087675: Yayy! New best EMA pseudo Dice: 0.9383 +2024-09-05 04:04:10.296064: +2024-09-05 04:04:10.297473: Epoch 91 +2024-09-05 04:04:10.298582: Current learning rate: 0.00918 +2024-09-05 04:08:44.537674: train_loss -0.7115 +2024-09-05 04:08:44.557411: val_loss -0.7138 +2024-09-05 04:08:44.559329: Pseudo dice [0.9556, 0.9471, 0.9709, 0.9326, 0.8353, 0.9587, 0.9892] +2024-09-05 04:08:44.561490: Epoch time: 274.25 s +2024-09-05 04:08:44.562660: Yayy! New best EMA pseudo Dice: 0.9386 +2024-09-05 04:08:53.815612: +2024-09-05 04:08:53.816485: Epoch 92 +2024-09-05 04:08:53.817541: Current learning rate: 0.00917 +2024-09-05 04:13:43.396006: train_loss -0.687 +2024-09-05 04:13:43.411023: val_loss -0.6839 +2024-09-05 04:13:43.412962: Pseudo dice [0.9489, 0.9437, 0.9648, 0.9355, 0.845, 0.954, 0.9887] +2024-09-05 04:13:43.414629: Epoch time: 289.58 s +2024-09-05 04:13:43.416258: Yayy! New best EMA pseudo Dice: 0.9387 +2024-09-05 04:13:53.875838: +2024-09-05 04:13:53.876976: Epoch 93 +2024-09-05 04:13:53.877835: Current learning rate: 0.00916 +2024-09-05 04:18:49.845667: train_loss -0.678 +2024-09-05 04:18:49.866757: val_loss -0.72 +2024-09-05 04:18:49.867944: Pseudo dice [0.9551, 0.9485, 0.9732, 0.9387, 0.8483, 0.953, 0.9894] +2024-09-05 04:18:49.868999: Epoch time: 295.98 s +2024-09-05 04:18:49.869768: Yayy! New best EMA pseudo Dice: 0.9392 +2024-09-05 04:18:59.556309: +2024-09-05 04:18:59.557480: Epoch 94 +2024-09-05 04:18:59.558577: Current learning rate: 0.00915 +2024-09-05 04:23:40.594823: train_loss -0.6914 +2024-09-05 04:23:40.597274: val_loss -0.7206 +2024-09-05 04:23:40.599015: Pseudo dice [0.9579, 0.9488, 0.9718, 0.9318, 0.842, 0.9555, 0.9894] +2024-09-05 04:23:40.600318: Epoch time: 281.04 s +2024-09-05 04:23:40.601653: Yayy! New best EMA pseudo Dice: 0.9396 +2024-09-05 04:23:49.339533: +2024-09-05 04:23:49.340533: Epoch 95 +2024-09-05 04:23:49.341201: Current learning rate: 0.00914 +2024-09-05 04:28:25.886081: train_loss -0.6831 +2024-09-05 04:28:25.888135: val_loss -0.7099 +2024-09-05 04:28:25.889310: Pseudo dice [0.9545, 0.9483, 0.9702, 0.9405, 0.8371, 0.9563, 0.9871] +2024-09-05 04:28:25.890459: Epoch time: 276.55 s +2024-09-05 04:28:25.891658: Yayy! New best EMA pseudo Dice: 0.9398 +2024-09-05 04:28:33.935638: +2024-09-05 04:28:33.936885: Epoch 96 +2024-09-05 04:28:33.938314: Current learning rate: 0.00913 +2024-09-05 04:33:14.432384: train_loss -0.6862 +2024-09-05 04:33:14.442940: val_loss -0.6662 +2024-09-05 04:33:14.444045: Pseudo dice [0.9498, 0.9477, 0.9639, 0.9305, 0.8265, 0.9439, 0.989] +2024-09-05 04:33:14.445180: Epoch time: 280.5 s +2024-09-05 04:33:19.410407: +2024-09-05 04:33:19.412934: Epoch 97 +2024-09-05 04:33:19.414941: Current learning rate: 0.00912 +2024-09-05 04:38:13.686227: train_loss -0.6795 +2024-09-05 04:38:13.699150: val_loss -0.7136 +2024-09-05 04:38:13.700036: Pseudo dice [0.9518, 0.9431, 0.9686, 0.9277, 0.8409, 0.9467, 0.9889] +2024-09-05 04:38:13.700818: Epoch time: 294.28 s +2024-09-05 04:38:16.923001: +2024-09-05 04:38:16.934648: Epoch 98 +2024-09-05 04:38:16.935749: Current learning rate: 0.00911 +2024-09-05 04:42:48.919916: train_loss -0.6581 +2024-09-05 04:42:48.936929: val_loss -0.6899 +2024-09-05 04:42:48.947308: Pseudo dice [0.9362, 0.9357, 0.9566, 0.9284, 0.8376, 0.9457, 0.9881] +2024-09-05 04:42:48.949161: Epoch time: 272.0 s +2024-09-05 04:42:51.456429: +2024-09-05 04:42:51.457435: Epoch 99 +2024-09-05 04:42:51.458469: Current learning rate: 0.0091 +2024-09-05 04:47:51.029214: train_loss -0.678 +2024-09-05 04:47:51.050763: val_loss -0.684 +2024-09-05 04:47:51.051702: Pseudo dice [0.9471, 0.9398, 0.9622, 0.929, 0.8276, 0.9466, 0.9879] +2024-09-05 04:47:51.052501: Epoch time: 299.58 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_08_31_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_08_31_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..2631c9f9fd965683aa07705410f7679e17696e45 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_08_31_30.txt @@ -0,0 +1,389 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 08:31:33.890226: Using torch.compile... +2024-09-05 08:31:42.616506: do_dummy_2d_data_aug: False +2024-09-05 08:31:42.622297: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 08:31:42.623637: The split file contains 5 splits. +2024-09-05 08:31:42.624220: Desired fold for training: 2 +2024-09-05 08:31:42.624830: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 08:31:56.021452: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 08:31:57.468896: +2024-09-05 08:31:57.490311: Epoch 100 +2024-09-05 08:31:57.494833: Current learning rate: 0.0091 +2024-09-05 08:50:27.150676: train_loss -0.6789 +2024-09-05 08:50:27.152241: val_loss -0.7158 +2024-09-05 08:50:27.153048: Pseudo dice [0.9543, 0.9473, 0.9698, 0.9336, 0.8431, 0.9524, 0.9892] +2024-09-05 08:50:27.153956: Epoch time: 1109.69 s +2024-09-05 08:50:29.406087: +2024-09-05 08:50:29.413666: Epoch 101 +2024-09-05 08:50:29.420372: Current learning rate: 0.00909 +2024-09-05 08:55:12.983169: train_loss -0.6983 +2024-09-05 08:55:12.985440: val_loss -0.6961 +2024-09-05 08:55:12.987102: Pseudo dice [0.9568, 0.9514, 0.9733, 0.9329, 0.8214, 0.9498, 0.9872] +2024-09-05 08:55:12.988069: Epoch time: 283.58 s +2024-09-05 08:55:15.487688: +2024-09-05 08:55:15.489476: Epoch 102 +2024-09-05 08:55:15.490647: Current learning rate: 0.00908 +2024-09-05 08:59:52.428374: train_loss -0.7068 +2024-09-05 08:59:52.440597: val_loss -0.7076 +2024-09-05 08:59:52.442084: Pseudo dice [0.956, 0.9517, 0.9759, 0.9405, 0.848, 0.9581, 0.99] +2024-09-05 08:59:52.444244: Epoch time: 276.95 s +2024-09-05 08:59:54.857635: +2024-09-05 08:59:54.858821: Epoch 103 +2024-09-05 08:59:54.859525: Current learning rate: 0.00907 +2024-09-05 09:04:27.979195: train_loss -0.6951 +2024-09-05 09:04:27.985928: val_loss -0.7163 +2024-09-05 09:04:27.998725: Pseudo dice [0.9554, 0.9471, 0.9676, 0.9357, 0.829, 0.9537, 0.9881] +2024-09-05 09:04:27.999700: Epoch time: 273.13 s +2024-09-05 09:04:32.735649: +2024-09-05 09:04:32.737575: Epoch 104 +2024-09-05 09:04:32.738472: Current learning rate: 0.00906 +2024-09-05 09:09:34.576485: train_loss -0.6926 +2024-09-05 09:09:34.578249: val_loss -0.7177 +2024-09-05 09:09:34.579282: Pseudo dice [0.9574, 0.9472, 0.9743, 0.9414, 0.8499, 0.9572, 0.9889] +2024-09-05 09:09:34.580087: Epoch time: 301.85 s +2024-09-05 09:09:34.580865: Yayy! New best EMA pseudo Dice: 0.9399 +2024-09-05 09:09:43.494358: +2024-09-05 09:09:43.496251: Epoch 105 +2024-09-05 09:09:43.497511: Current learning rate: 0.00905 +2024-09-05 09:14:30.791483: train_loss -0.7101 +2024-09-05 09:14:30.806858: val_loss -0.7234 +2024-09-05 09:14:30.807728: Pseudo dice [0.9596, 0.9506, 0.9757, 0.9369, 0.858, 0.9593, 0.9906] +2024-09-05 09:14:30.808469: Epoch time: 287.3 s +2024-09-05 09:14:30.809113: Yayy! New best EMA pseudo Dice: 0.9406 +2024-09-05 09:14:39.611110: +2024-09-05 09:14:39.612029: Epoch 106 +2024-09-05 09:14:39.612684: Current learning rate: 0.00904 +2024-09-05 09:19:43.477052: train_loss -0.7158 +2024-09-05 09:19:43.492677: val_loss -0.7213 +2024-09-05 09:19:43.493402: Pseudo dice [0.9558, 0.949, 0.9696, 0.9332, 0.8291, 0.9591, 0.9892] +2024-09-05 09:19:43.494342: Epoch time: 303.87 s +2024-09-05 09:19:43.495062: Yayy! New best EMA pseudo Dice: 0.9406 +2024-09-05 09:19:52.559175: +2024-09-05 09:19:52.560035: Epoch 107 +2024-09-05 09:19:52.560748: Current learning rate: 0.00903 +2024-09-05 09:24:42.346086: train_loss -0.7155 +2024-09-05 09:24:42.348241: val_loss -0.742 +2024-09-05 09:24:42.348878: Pseudo dice [0.9581, 0.9498, 0.9776, 0.942, 0.8557, 0.9589, 0.9886] +2024-09-05 09:24:42.349485: Epoch time: 289.79 s +2024-09-05 09:24:42.350067: Yayy! New best EMA pseudo Dice: 0.9413 +2024-09-05 09:24:50.474056: +2024-09-05 09:24:50.474976: Epoch 108 +2024-09-05 09:24:50.475649: Current learning rate: 0.00902 +2024-09-05 09:29:33.411300: train_loss -0.701 +2024-09-05 09:29:33.420033: val_loss -0.6934 +2024-09-05 09:29:33.421094: Pseudo dice [0.9547, 0.9508, 0.97, 0.9351, 0.8336, 0.9499, 0.9866] +2024-09-05 09:29:33.422237: Epoch time: 282.94 s +2024-09-05 09:29:36.890106: +2024-09-05 09:29:36.891146: Epoch 109 +2024-09-05 09:29:36.892089: Current learning rate: 0.00901 +2024-09-05 09:34:32.371690: train_loss -0.6956 +2024-09-05 09:34:32.387073: val_loss -0.7057 +2024-09-05 09:34:32.387940: Pseudo dice [0.9544, 0.9484, 0.9711, 0.9307, 0.8119, 0.9489, 0.9862] +2024-09-05 09:34:32.389060: Epoch time: 295.49 s +2024-09-05 09:34:35.315827: +2024-09-05 09:34:35.317055: Epoch 110 +2024-09-05 09:34:35.317922: Current learning rate: 0.009 +2024-09-05 09:39:14.460548: train_loss -0.7055 +2024-09-05 09:39:14.475284: val_loss -0.7291 +2024-09-05 09:39:14.476199: Pseudo dice [0.9505, 0.9454, 0.9688, 0.9387, 0.8429, 0.9525, 0.9859] +2024-09-05 09:39:14.477054: Epoch time: 279.15 s +2024-09-05 09:39:17.120399: +2024-09-05 09:39:17.121258: Epoch 111 +2024-09-05 09:39:17.121815: Current learning rate: 0.009 +2024-09-05 09:44:09.295621: train_loss -0.6864 +2024-09-05 09:44:09.311412: val_loss -0.7382 +2024-09-05 09:44:09.313317: Pseudo dice [0.9542, 0.9492, 0.9693, 0.9358, 0.8558, 0.9493, 0.9875] +2024-09-05 09:44:09.314764: Epoch time: 292.18 s +2024-09-05 09:44:13.024149: +2024-09-05 09:44:13.030493: Epoch 112 +2024-09-05 09:44:13.031360: Current learning rate: 0.00899 +2024-09-05 09:49:16.150294: train_loss -0.6884 +2024-09-05 09:49:16.157754: val_loss -0.6725 +2024-09-05 09:49:16.158904: Pseudo dice [0.9587, 0.9492, 0.9727, 0.9403, 0.8529, 0.957, 0.9888] +2024-09-05 09:49:16.159697: Epoch time: 303.13 s +2024-09-05 09:49:16.160395: Yayy! New best EMA pseudo Dice: 0.9414 +2024-09-05 09:49:25.544308: +2024-09-05 09:49:25.546872: Epoch 113 +2024-09-05 09:49:25.549977: Current learning rate: 0.00898 +2024-09-05 09:54:03.115854: train_loss -0.7043 +2024-09-05 09:54:03.130110: val_loss -0.6995 +2024-09-05 09:54:03.131168: Pseudo dice [0.9566, 0.9466, 0.9715, 0.9423, 0.8665, 0.9605, 0.991] +2024-09-05 09:54:03.132223: Epoch time: 277.58 s +2024-09-05 09:54:03.133152: Yayy! New best EMA pseudo Dice: 0.942 +2024-09-05 09:54:13.396513: +2024-09-05 09:54:13.397914: Epoch 114 +2024-09-05 09:54:13.399247: Current learning rate: 0.00897 +2024-09-05 09:59:18.201051: train_loss -0.702 +2024-09-05 09:59:18.204779: val_loss -0.7591 +2024-09-05 09:59:18.205789: Pseudo dice [0.9585, 0.9504, 0.9758, 0.9439, 0.8516, 0.9592, 0.9904] +2024-09-05 09:59:18.206791: Epoch time: 304.81 s +2024-09-05 09:59:18.207567: Yayy! New best EMA pseudo Dice: 0.9425 +2024-09-05 09:59:29.534469: +2024-09-05 09:59:29.535420: Epoch 115 +2024-09-05 09:59:29.536069: Current learning rate: 0.00896 +2024-09-05 10:04:03.647612: train_loss -0.6897 +2024-09-05 10:04:03.651517: val_loss -0.7289 +2024-09-05 10:04:03.652914: Pseudo dice [0.9561, 0.9497, 0.9702, 0.9378, 0.863, 0.9608, 0.9895] +2024-09-05 10:04:03.654035: Epoch time: 274.12 s +2024-09-05 10:04:03.655277: Yayy! New best EMA pseudo Dice: 0.9429 +2024-09-05 10:04:13.054131: +2024-09-05 10:04:13.055290: Epoch 116 +2024-09-05 10:04:13.056018: Current learning rate: 0.00895 +2024-09-05 10:08:57.085945: train_loss -0.6984 +2024-09-05 10:08:57.106089: val_loss -0.6838 +2024-09-05 10:08:57.107403: Pseudo dice [0.9524, 0.9466, 0.9695, 0.9406, 0.8386, 0.9582, 0.9881] +2024-09-05 10:08:57.108474: Epoch time: 284.04 s +2024-09-05 10:09:00.952421: +2024-09-05 10:09:00.959295: Epoch 117 +2024-09-05 10:09:00.960618: Current learning rate: 0.00894 +2024-09-05 10:14:04.522696: train_loss -0.7117 +2024-09-05 10:14:04.526986: val_loss -0.7394 +2024-09-05 10:14:04.528067: Pseudo dice [0.9573, 0.9503, 0.9755, 0.936, 0.8444, 0.954, 0.9901] +2024-09-05 10:14:04.529134: Epoch time: 303.58 s +2024-09-05 10:14:04.530043: Yayy! New best EMA pseudo Dice: 0.943 +2024-09-05 10:14:13.860141: +2024-09-05 10:14:13.861088: Epoch 118 +2024-09-05 10:14:13.861796: Current learning rate: 0.00893 +2024-09-05 10:18:47.959147: train_loss -0.7202 +2024-09-05 10:18:47.972386: val_loss -0.7159 +2024-09-05 10:18:47.973182: Pseudo dice [0.9603, 0.9498, 0.9769, 0.9424, 0.8462, 0.9466, 0.9891] +2024-09-05 10:18:47.974150: Epoch time: 274.1 s +2024-09-05 10:18:47.975351: Yayy! New best EMA pseudo Dice: 0.9431 +2024-09-05 10:18:58.669593: +2024-09-05 10:18:58.670679: Epoch 119 +2024-09-05 10:18:58.671460: Current learning rate: 0.00892 +2024-09-05 10:23:38.860769: train_loss -0.6949 +2024-09-05 10:23:38.881379: val_loss -0.6827 +2024-09-05 10:23:38.882293: Pseudo dice [0.9583, 0.9537, 0.9708, 0.9416, 0.8479, 0.9582, 0.9897] +2024-09-05 10:23:38.883139: Epoch time: 280.2 s +2024-09-05 10:23:38.883904: Yayy! New best EMA pseudo Dice: 0.9434 +2024-09-05 10:23:47.844706: +2024-09-05 10:23:47.848128: Epoch 120 +2024-09-05 10:23:47.848760: Current learning rate: 0.00891 +2024-09-05 10:28:14.150811: train_loss -0.7189 +2024-09-05 10:28:14.154714: val_loss -0.6817 +2024-09-05 10:28:14.156120: Pseudo dice [0.958, 0.95, 0.9745, 0.9429, 0.8535, 0.9606, 0.9905] +2024-09-05 10:28:14.157620: Epoch time: 266.31 s +2024-09-05 10:28:14.158600: Yayy! New best EMA pseudo Dice: 0.9437 +2024-09-05 10:28:23.386719: +2024-09-05 10:28:23.387720: Epoch 121 +2024-09-05 10:28:23.388397: Current learning rate: 0.0089 +2024-09-05 10:32:58.652434: train_loss -0.7011 +2024-09-05 10:32:58.655286: val_loss -0.712 +2024-09-05 10:32:58.656117: Pseudo dice [0.9592, 0.9526, 0.9743, 0.9401, 0.8411, 0.9612, 0.9886] +2024-09-05 10:32:58.657039: Epoch time: 275.27 s +2024-09-05 10:32:58.657835: Yayy! New best EMA pseudo Dice: 0.9439 +2024-09-05 10:33:07.617249: +2024-09-05 10:33:07.618208: Epoch 122 +2024-09-05 10:33:07.618843: Current learning rate: 0.00889 +2024-09-05 10:37:50.500982: train_loss -0.7222 +2024-09-05 10:37:50.515226: val_loss -0.7275 +2024-09-05 10:37:50.516115: Pseudo dice [0.9593, 0.9531, 0.9755, 0.9418, 0.8623, 0.96, 0.991] +2024-09-05 10:37:50.516879: Epoch time: 282.89 s +2024-09-05 10:37:50.517572: Yayy! New best EMA pseudo Dice: 0.9444 +2024-09-05 10:38:00.427937: +2024-09-05 10:38:00.428732: Epoch 123 +2024-09-05 10:38:00.429377: Current learning rate: 0.00889 +2024-09-05 10:42:27.628074: train_loss -0.7101 +2024-09-05 10:42:27.643191: val_loss -0.7223 +2024-09-05 10:42:27.644187: Pseudo dice [0.9589, 0.9488, 0.9704, 0.9431, 0.8489, 0.9575, 0.9889] +2024-09-05 10:42:27.645110: Epoch time: 267.2 s +2024-09-05 10:42:27.645996: Yayy! New best EMA pseudo Dice: 0.9445 +2024-09-05 10:42:38.295930: +2024-09-05 10:42:38.296953: Epoch 124 +2024-09-05 10:42:38.297560: Current learning rate: 0.00888 +2024-09-05 10:47:17.101206: train_loss -0.7063 +2024-09-05 10:47:17.127426: val_loss -0.7101 +2024-09-05 10:47:17.128573: Pseudo dice [0.9533, 0.9469, 0.9693, 0.9304, 0.8562, 0.9601, 0.9901] +2024-09-05 10:47:17.129396: Epoch time: 278.81 s +2024-09-05 10:47:21.442836: +2024-09-05 10:47:21.443975: Epoch 125 +2024-09-05 10:47:21.444719: Current learning rate: 0.00887 +2024-09-05 10:52:26.323023: train_loss -0.7157 +2024-09-05 10:52:26.331625: val_loss -0.7477 +2024-09-05 10:52:26.333202: Pseudo dice [0.9547, 0.9509, 0.9701, 0.9367, 0.8519, 0.9521, 0.9889] +2024-09-05 10:52:26.334737: Epoch time: 304.89 s +2024-09-05 10:52:30.505057: +2024-09-05 10:52:30.506393: Epoch 126 +2024-09-05 10:52:30.507473: Current learning rate: 0.00886 +2024-09-05 10:57:31.962282: train_loss -0.7136 +2024-09-05 10:57:31.967910: val_loss -0.7358 +2024-09-05 10:57:31.970238: Pseudo dice [0.9587, 0.9444, 0.9737, 0.9306, 0.8324, 0.9589, 0.9876] +2024-09-05 10:57:31.972318: Epoch time: 301.46 s +2024-09-05 10:57:34.872636: +2024-09-05 10:57:34.873895: Epoch 127 +2024-09-05 10:57:34.874701: Current learning rate: 0.00885 +2024-09-05 11:02:28.869092: train_loss -0.7123 +2024-09-05 11:02:28.883112: val_loss -0.7013 +2024-09-05 11:02:28.884188: Pseudo dice [0.954, 0.9483, 0.9692, 0.9293, 0.8396, 0.9559, 0.9882] +2024-09-05 11:02:28.884882: Epoch time: 294.0 s +2024-09-05 11:02:31.911005: +2024-09-05 11:02:31.912222: Epoch 128 +2024-09-05 11:02:31.913164: Current learning rate: 0.00884 +2024-09-05 11:07:17.323852: train_loss -0.7116 +2024-09-05 11:07:17.331320: val_loss -0.7155 +2024-09-05 11:07:17.332238: Pseudo dice [0.9598, 0.9535, 0.9747, 0.943, 0.8378, 0.9483, 0.989] +2024-09-05 11:07:17.333131: Epoch time: 285.42 s +2024-09-05 11:07:20.961567: +2024-09-05 11:07:20.967130: Epoch 129 +2024-09-05 11:07:20.967919: Current learning rate: 0.00883 +2024-09-05 11:12:11.072668: train_loss -0.7086 +2024-09-05 11:12:11.074899: val_loss -0.741 +2024-09-05 11:12:11.076110: Pseudo dice [0.9576, 0.9512, 0.9709, 0.9365, 0.8509, 0.9605, 0.9892] +2024-09-05 11:12:11.077727: Epoch time: 290.12 s +2024-09-05 11:12:15.737394: +2024-09-05 11:12:15.738408: Epoch 130 +2024-09-05 11:12:15.739264: Current learning rate: 0.00882 +2024-09-05 11:17:08.055500: train_loss -0.6975 +2024-09-05 11:17:08.056947: val_loss -0.7416 +2024-09-05 11:17:08.058423: Pseudo dice [0.9557, 0.9486, 0.9715, 0.9347, 0.8404, 0.9581, 0.9893] +2024-09-05 11:17:08.060520: Epoch time: 292.32 s +2024-09-05 11:17:11.406041: +2024-09-05 11:17:11.407194: Epoch 131 +2024-09-05 11:17:11.410193: Current learning rate: 0.00881 +2024-09-05 11:21:50.643028: train_loss -0.7147 +2024-09-05 11:21:50.656354: val_loss -0.7205 +2024-09-05 11:21:50.657384: Pseudo dice [0.9611, 0.952, 0.9743, 0.9419, 0.8614, 0.9613, 0.9899] +2024-09-05 11:21:50.658643: Epoch time: 279.24 s +2024-09-05 11:21:54.243457: +2024-09-05 11:21:54.244590: Epoch 132 +2024-09-05 11:21:54.245477: Current learning rate: 0.0088 +2024-09-05 11:26:39.087564: train_loss -0.7126 +2024-09-05 11:26:39.134553: val_loss -0.7355 +2024-09-05 11:26:39.173440: Pseudo dice [0.958, 0.9513, 0.9757, 0.9427, 0.8562, 0.9625, 0.9911] +2024-09-05 11:26:39.264614: Epoch time: 284.85 s +2024-09-05 11:26:39.334712: Yayy! New best EMA pseudo Dice: 0.9446 +2024-09-05 11:26:49.674364: +2024-09-05 11:26:49.675547: Epoch 133 +2024-09-05 11:26:49.676513: Current learning rate: 0.00879 +2024-09-05 11:31:40.734974: train_loss -0.6939 +2024-09-05 11:31:40.772712: val_loss -0.74 +2024-09-05 11:31:40.856496: Pseudo dice [0.955, 0.9487, 0.9723, 0.9404, 0.8489, 0.9601, 0.9878] +2024-09-05 11:31:40.868147: Epoch time: 291.06 s +2024-09-05 11:31:40.962538: Yayy! New best EMA pseudo Dice: 0.9446 +2024-09-05 11:31:51.090791: +2024-09-05 11:31:51.091774: Epoch 134 +2024-09-05 11:31:51.092490: Current learning rate: 0.00879 +2024-09-05 11:36:30.444001: train_loss -0.7161 +2024-09-05 11:36:30.463531: val_loss -0.7102 +2024-09-05 11:36:30.465314: Pseudo dice [0.9544, 0.9533, 0.9681, 0.9434, 0.8668, 0.9564, 0.9908] +2024-09-05 11:36:30.467008: Epoch time: 279.36 s +2024-09-05 11:36:30.468377: Yayy! New best EMA pseudo Dice: 0.9449 +2024-09-05 11:36:41.635192: +2024-09-05 11:36:41.638593: Epoch 135 +2024-09-05 11:36:41.641769: Current learning rate: 0.00878 +2024-09-05 11:41:21.576635: train_loss -0.7068 +2024-09-05 11:41:21.579649: val_loss -0.6796 +2024-09-05 11:41:21.580689: Pseudo dice [0.9453, 0.9403, 0.9614, 0.9357, 0.823, 0.952, 0.9872] +2024-09-05 11:41:21.581533: Epoch time: 279.95 s +2024-09-05 11:41:24.290114: +2024-09-05 11:41:24.291646: Epoch 136 +2024-09-05 11:41:24.292529: Current learning rate: 0.00877 +2024-09-05 11:46:10.635416: train_loss -0.7026 +2024-09-05 11:46:10.649434: val_loss -0.7039 +2024-09-05 11:46:10.650709: Pseudo dice [0.9559, 0.9476, 0.9713, 0.9361, 0.8548, 0.9586, 0.9916] +2024-09-05 11:46:10.652389: Epoch time: 286.35 s +2024-09-05 11:46:16.916127: +2024-09-05 11:46:16.917289: Epoch 137 +2024-09-05 11:46:16.918804: Current learning rate: 0.00876 +2024-09-05 11:51:32.254948: train_loss -0.6912 +2024-09-05 11:51:32.275118: val_loss -0.7095 +2024-09-05 11:51:32.277537: Pseudo dice [0.9513, 0.9466, 0.9535, 0.9216, 0.8276, 0.959, 0.987] +2024-09-05 11:51:32.279622: Epoch time: 315.34 s +2024-09-05 11:51:36.007800: +2024-09-05 11:51:36.018630: Epoch 138 +2024-09-05 11:51:36.020161: Current learning rate: 0.00875 +2024-09-05 11:56:34.620150: train_loss -0.7028 +2024-09-05 11:56:34.622232: val_loss -0.6895 +2024-09-05 11:56:34.623238: Pseudo dice [0.9559, 0.9491, 0.973, 0.9428, 0.858, 0.9584, 0.9909] +2024-09-05 11:56:34.623980: Epoch time: 298.62 s +2024-09-05 11:56:36.601699: +2024-09-05 11:56:36.602751: Epoch 139 +2024-09-05 11:56:36.603630: Current learning rate: 0.00874 +2024-09-05 12:01:14.515540: train_loss -0.6752 +2024-09-05 12:01:14.536821: val_loss -0.7141 +2024-09-05 12:01:14.538396: Pseudo dice [0.9571, 0.9516, 0.974, 0.9401, 0.8583, 0.9579, 0.9895] +2024-09-05 12:01:14.539566: Epoch time: 277.92 s +2024-09-05 12:01:18.290273: +2024-09-05 12:01:18.291587: Epoch 140 +2024-09-05 12:01:18.293168: Current learning rate: 0.00873 +2024-09-05 12:06:03.675959: train_loss -0.7106 +2024-09-05 12:06:03.692055: val_loss -0.7324 +2024-09-05 12:06:03.692843: Pseudo dice [0.9537, 0.9533, 0.9701, 0.9418, 0.8431, 0.9559, 0.989] +2024-09-05 12:06:03.693784: Epoch time: 285.39 s +2024-09-05 12:06:06.435030: +2024-09-05 12:06:06.436045: Epoch 141 +2024-09-05 12:06:06.436679: Current learning rate: 0.00872 +2024-09-05 12:10:37.459531: train_loss -0.7106 +2024-09-05 12:10:37.476537: val_loss -0.7068 +2024-09-05 12:10:37.477417: Pseudo dice [0.9596, 0.9536, 0.9725, 0.9364, 0.8555, 0.9636, 0.9903] +2024-09-05 12:10:37.478132: Epoch time: 271.03 s +2024-09-05 12:10:40.050116: +2024-09-05 12:10:40.052150: Epoch 142 +2024-09-05 12:10:40.053635: Current learning rate: 0.00871 +2024-09-05 12:15:24.773639: train_loss -0.7217 +2024-09-05 12:15:24.788876: val_loss -0.7319 +2024-09-05 12:15:24.789792: Pseudo dice [0.9609, 0.9509, 0.9774, 0.944, 0.8574, 0.9647, 0.9911] +2024-09-05 12:15:24.790774: Epoch time: 284.73 s +2024-09-05 12:15:28.633798: +2024-09-05 12:15:28.635731: Epoch 143 +2024-09-05 12:15:28.637060: Current learning rate: 0.0087 +2024-09-05 12:20:25.496122: train_loss -0.6994 +2024-09-05 12:20:25.512040: val_loss -0.6674 +2024-09-05 12:20:25.513109: Pseudo dice [0.9502, 0.9443, 0.9673, 0.9344, 0.8423, 0.9547, 0.9874] +2024-09-05 12:20:25.514086: Epoch time: 296.87 s +2024-09-05 12:20:29.581849: +2024-09-05 12:20:29.583011: Epoch 144 +2024-09-05 12:20:29.584183: Current learning rate: 0.00869 +2024-09-05 12:25:32.375061: train_loss -0.6855 +2024-09-05 12:25:32.391118: val_loss -0.7419 +2024-09-05 12:25:32.392063: Pseudo dice [0.9552, 0.9495, 0.9717, 0.9266, 0.846, 0.9575, 0.9879] +2024-09-05 12:25:32.392791: Epoch time: 302.8 s +2024-09-05 12:25:35.634912: +2024-09-05 12:25:35.635979: Epoch 145 +2024-09-05 12:25:35.636881: Current learning rate: 0.00868 +2024-09-05 12:30:24.485754: train_loss -0.7088 +2024-09-05 12:30:24.533065: val_loss -0.7569 +2024-09-05 12:30:24.574202: Pseudo dice [0.9558, 0.9509, 0.9759, 0.9425, 0.8474, 0.9611, 0.9884] +2024-09-05 12:30:24.575534: Epoch time: 288.86 s +2024-09-05 12:30:28.182469: +2024-09-05 12:30:28.183765: Epoch 146 +2024-09-05 12:30:28.184483: Current learning rate: 0.00868 +2024-09-05 12:35:27.154261: train_loss -0.6944 +2024-09-05 12:35:27.156453: val_loss -0.7146 +2024-09-05 12:35:27.157556: Pseudo dice [0.9545, 0.947, 0.9727, 0.9382, 0.843, 0.9621, 0.9889] +2024-09-05 12:35:27.159063: Epoch time: 298.98 s +2024-09-05 12:35:30.200208: +2024-09-05 12:35:30.201502: Epoch 147 +2024-09-05 12:35:30.202583: Current learning rate: 0.00867 +2024-09-05 12:40:21.437604: train_loss -0.7032 +2024-09-05 12:40:21.440427: val_loss -0.7232 +2024-09-05 12:40:21.441418: Pseudo dice [0.9526, 0.9481, 0.966, 0.9417, 0.8519, 0.9548, 0.9883] +2024-09-05 12:40:21.442277: Epoch time: 291.24 s +2024-09-05 12:40:24.179362: +2024-09-05 12:40:24.180487: Epoch 148 +2024-09-05 12:40:24.181406: Current learning rate: 0.00866 +2024-09-05 12:45:19.781258: train_loss -0.6948 +2024-09-05 12:45:19.782722: val_loss -0.7342 +2024-09-05 12:45:19.784302: Pseudo dice [0.9541, 0.9477, 0.9706, 0.9419, 0.857, 0.9577, 0.99] +2024-09-05 12:45:19.785678: Epoch time: 295.61 s +2024-09-05 12:45:23.054274: +2024-09-05 12:45:23.055356: Epoch 149 +2024-09-05 12:45:23.056134: Current learning rate: 0.00865 +2024-09-05 12:49:55.876616: train_loss -0.7069 +2024-09-05 12:49:55.892051: val_loss -0.6914 +2024-09-05 12:49:55.892861: Pseudo dice [0.9605, 0.953, 0.9752, 0.9462, 0.8642, 0.9608, 0.9904] +2024-09-05 12:49:55.893571: Epoch time: 272.83 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_16_30_44.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_16_30_44.txt new file mode 100644 index 0000000000000000000000000000000000000000..45c6f0c65f4ef3869f3679c83547cd98b30bbff0 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_5_16_30_44.txt @@ -0,0 +1,382 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 16:30:47.992054: Using torch.compile... +2024-09-05 16:30:57.098668: do_dummy_2d_data_aug: False +2024-09-05 16:30:57.104738: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 16:30:57.106078: The split file contains 5 splits. +2024-09-05 16:30:57.106730: Desired fold for training: 2 +2024-09-05 16:30:57.107294: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 16:31:10.180930: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 16:31:12.105652: +2024-09-05 16:31:12.106740: Epoch 150 +2024-09-05 16:31:12.108612: Current learning rate: 0.00864 +2024-09-05 16:49:44.715182: train_loss -0.7103 +2024-09-05 16:49:44.716408: val_loss -0.6971 +2024-09-05 16:49:44.717002: Pseudo dice [0.9586, 0.9526, 0.9747, 0.9438, 0.8318, 0.9571, 0.9872] +2024-09-05 16:49:44.717963: Epoch time: 1112.62 s +2024-09-05 16:49:46.502503: +2024-09-05 16:49:46.694118: Epoch 151 +2024-09-05 16:49:46.778574: Current learning rate: 0.00863 +2024-09-05 16:54:22.272807: train_loss -0.7122 +2024-09-05 16:54:22.284225: val_loss -0.7492 +2024-09-05 16:54:22.285687: Pseudo dice [0.9606, 0.9506, 0.9742, 0.9391, 0.8685, 0.9644, 0.9923] +2024-09-05 16:54:22.287499: Epoch time: 275.78 s +2024-09-05 16:54:22.291196: Yayy! New best EMA pseudo Dice: 0.9453 +2024-09-05 16:54:31.380467: +2024-09-05 16:54:31.381375: Epoch 152 +2024-09-05 16:54:31.381984: Current learning rate: 0.00862 +2024-09-05 16:59:11.951632: train_loss -0.707 +2024-09-05 16:59:11.967146: val_loss -0.7247 +2024-09-05 16:59:11.968092: Pseudo dice [0.9579, 0.9515, 0.9757, 0.9366, 0.8461, 0.9555, 0.9897] +2024-09-05 16:59:11.968874: Epoch time: 280.58 s +2024-09-05 16:59:15.108133: +2024-09-05 16:59:15.109130: Epoch 153 +2024-09-05 16:59:15.110016: Current learning rate: 0.00861 +2024-09-05 17:04:08.835192: train_loss -0.7092 +2024-09-05 17:04:08.836738: val_loss -0.7177 +2024-09-05 17:04:08.837611: Pseudo dice [0.9558, 0.9501, 0.9732, 0.9366, 0.849, 0.9568, 0.9875] +2024-09-05 17:04:08.838469: Epoch time: 293.73 s +2024-09-05 17:04:12.027088: +2024-09-05 17:04:12.088633: Epoch 154 +2024-09-05 17:04:12.098178: Current learning rate: 0.0086 +2024-09-05 17:09:06.757413: train_loss -0.6775 +2024-09-05 17:09:06.768800: val_loss -0.702 +2024-09-05 17:09:06.769692: Pseudo dice [0.9484, 0.9428, 0.9642, 0.9331, 0.8461, 0.9528, 0.9889] +2024-09-05 17:09:06.770729: Epoch time: 294.74 s +2024-09-05 17:09:10.429377: +2024-09-05 17:09:10.430437: Epoch 155 +2024-09-05 17:09:10.431515: Current learning rate: 0.00859 +2024-09-05 17:13:55.247776: train_loss -0.6896 +2024-09-05 17:13:55.266769: val_loss -0.6999 +2024-09-05 17:13:55.267733: Pseudo dice [0.9546, 0.9484, 0.9711, 0.9398, 0.8285, 0.9487, 0.9877] +2024-09-05 17:13:55.268466: Epoch time: 284.82 s +2024-09-05 17:13:59.194045: +2024-09-05 17:13:59.195103: Epoch 156 +2024-09-05 17:13:59.195810: Current learning rate: 0.00858 +2024-09-05 17:18:48.911319: train_loss -0.6801 +2024-09-05 17:18:48.917081: val_loss -0.7001 +2024-09-05 17:18:48.918035: Pseudo dice [0.9517, 0.9498, 0.9697, 0.939, 0.8422, 0.9564, 0.9897] +2024-09-05 17:18:48.918833: Epoch time: 289.72 s +2024-09-05 17:18:52.678918: +2024-09-05 17:18:52.688624: Epoch 157 +2024-09-05 17:18:52.690226: Current learning rate: 0.00858 +2024-09-05 17:24:24.027610: train_loss -0.6787 +2024-09-05 17:24:24.029812: val_loss -0.7146 +2024-09-05 17:24:24.030553: Pseudo dice [0.9538, 0.9438, 0.9745, 0.9369, 0.8295, 0.9569, 0.9881] +2024-09-05 17:24:24.031364: Epoch time: 331.35 s +2024-09-05 17:24:27.543999: +2024-09-05 17:24:27.545169: Epoch 158 +2024-09-05 17:24:27.546227: Current learning rate: 0.00857 +2024-09-05 17:29:36.712891: train_loss -0.6935 +2024-09-05 17:29:36.727352: val_loss -0.7237 +2024-09-05 17:29:36.728298: Pseudo dice [0.9503, 0.9434, 0.9653, 0.9334, 0.8532, 0.955, 0.9899] +2024-09-05 17:29:36.729451: Epoch time: 309.17 s +2024-09-05 17:29:39.836159: +2024-09-05 17:29:39.837122: Epoch 159 +2024-09-05 17:29:39.837757: Current learning rate: 0.00856 +2024-09-05 17:34:46.983477: train_loss -0.6997 +2024-09-05 17:34:46.984858: val_loss -0.7323 +2024-09-05 17:34:46.985551: Pseudo dice [0.9584, 0.9509, 0.9768, 0.9412, 0.8362, 0.9598, 0.99] +2024-09-05 17:34:46.986177: Epoch time: 307.15 s +2024-09-05 17:34:49.867831: +2024-09-05 17:34:49.869358: Epoch 160 +2024-09-05 17:34:49.870042: Current learning rate: 0.00855 +2024-09-05 17:40:13.926644: train_loss -0.6989 +2024-09-05 17:40:13.928548: val_loss -0.7133 +2024-09-05 17:40:13.929517: Pseudo dice [0.9583, 0.9522, 0.9751, 0.9397, 0.8567, 0.9626, 0.9901] +2024-09-05 17:40:13.930479: Epoch time: 324.06 s +2024-09-05 17:40:18.263801: +2024-09-05 17:40:18.264787: Epoch 161 +2024-09-05 17:40:18.265593: Current learning rate: 0.00854 +2024-09-05 17:45:47.854331: train_loss -0.7089 +2024-09-05 17:45:47.855917: val_loss -0.7205 +2024-09-05 17:45:47.857154: Pseudo dice [0.955, 0.9516, 0.9691, 0.9369, 0.8352, 0.9591, 0.9889] +2024-09-05 17:45:47.858443: Epoch time: 329.6 s +2024-09-05 17:45:55.132316: +2024-09-05 17:45:55.143429: Epoch 162 +2024-09-05 17:45:55.144923: Current learning rate: 0.00853 +2024-09-05 17:51:16.925603: train_loss -0.7082 +2024-09-05 17:51:16.928975: val_loss -0.7776 +2024-09-05 17:51:16.934278: Pseudo dice [0.9567, 0.9542, 0.9763, 0.9428, 0.8618, 0.9625, 0.9916] +2024-09-05 17:51:16.935072: Epoch time: 321.8 s +2024-09-05 17:51:20.456674: +2024-09-05 17:51:20.457825: Epoch 163 +2024-09-05 17:51:20.458683: Current learning rate: 0.00852 +2024-09-05 17:57:31.324530: train_loss -0.7088 +2024-09-05 17:57:31.327869: val_loss -0.7543 +2024-09-05 17:57:31.329299: Pseudo dice [0.9607, 0.9511, 0.9774, 0.9466, 0.8555, 0.9647, 0.9904] +2024-09-05 17:57:31.330138: Epoch time: 370.88 s +2024-09-05 17:57:34.565119: +2024-09-05 17:57:34.566313: Epoch 164 +2024-09-05 17:57:34.567213: Current learning rate: 0.00851 +2024-09-05 18:02:44.615853: train_loss -0.7028 +2024-09-05 18:02:44.631611: val_loss -0.7302 +2024-09-05 18:02:44.632932: Pseudo dice [0.958, 0.9525, 0.9769, 0.9433, 0.8536, 0.9604, 0.9898] +2024-09-05 18:02:44.633895: Epoch time: 310.06 s +2024-09-05 18:02:48.064645: +2024-09-05 18:02:48.065762: Epoch 165 +2024-09-05 18:02:48.066884: Current learning rate: 0.0085 +2024-09-05 18:08:07.036413: train_loss -0.7157 +2024-09-05 18:08:07.048172: val_loss -0.7456 +2024-09-05 18:08:07.049752: Pseudo dice [0.9595, 0.9514, 0.9738, 0.9456, 0.8556, 0.9601, 0.9878] +2024-09-05 18:08:07.051242: Epoch time: 318.98 s +2024-09-05 18:08:07.052530: Yayy! New best EMA pseudo Dice: 0.9454 +2024-09-05 18:08:17.492229: +2024-09-05 18:08:17.492963: Epoch 166 +2024-09-05 18:08:17.493562: Current learning rate: 0.00849 +2024-09-05 18:13:28.171995: train_loss -0.6959 +2024-09-05 18:13:28.183822: val_loss -0.6971 +2024-09-05 18:13:28.184717: Pseudo dice [0.9506, 0.9479, 0.957, 0.9335, 0.831, 0.9571, 0.9871] +2024-09-05 18:13:28.185927: Epoch time: 310.68 s +2024-09-05 18:13:32.967544: +2024-09-05 18:13:32.968732: Epoch 167 +2024-09-05 18:13:32.969493: Current learning rate: 0.00848 +2024-09-05 18:18:56.238761: train_loss -0.7059 +2024-09-05 18:18:56.251575: val_loss -0.733 +2024-09-05 18:18:56.252594: Pseudo dice [0.96, 0.9508, 0.9742, 0.9394, 0.8651, 0.9612, 0.9904] +2024-09-05 18:18:56.253337: Epoch time: 323.28 s +2024-09-05 18:19:00.364244: +2024-09-05 18:19:00.558849: Epoch 168 +2024-09-05 18:19:00.571512: Current learning rate: 0.00847 +2024-09-05 18:24:48.105599: train_loss -0.7186 +2024-09-05 18:24:48.121452: val_loss -0.7315 +2024-09-05 18:24:48.122206: Pseudo dice [0.9584, 0.9519, 0.9715, 0.9416, 0.854, 0.9611, 0.9908] +2024-09-05 18:24:48.122871: Epoch time: 347.75 s +2024-09-05 18:24:52.160018: +2024-09-05 18:24:52.161059: Epoch 169 +2024-09-05 18:24:52.161993: Current learning rate: 0.00847 +2024-09-05 18:29:49.961274: train_loss -0.7077 +2024-09-05 18:29:49.976456: val_loss -0.7307 +2024-09-05 18:29:49.977684: Pseudo dice [0.9598, 0.9532, 0.9757, 0.9425, 0.8711, 0.9636, 0.9923] +2024-09-05 18:29:49.978559: Epoch time: 297.81 s +2024-09-05 18:29:49.979259: Yayy! New best EMA pseudo Dice: 0.9458 +2024-09-05 18:29:59.827918: +2024-09-05 18:29:59.828928: Epoch 170 +2024-09-05 18:29:59.829658: Current learning rate: 0.00846 +2024-09-05 18:35:06.174910: train_loss -0.7038 +2024-09-05 18:35:06.183769: val_loss -0.7186 +2024-09-05 18:35:06.184545: Pseudo dice [0.9579, 0.9527, 0.9705, 0.9339, 0.8515, 0.9601, 0.9892] +2024-09-05 18:35:06.185238: Epoch time: 306.35 s +2024-09-05 18:35:10.793894: +2024-09-05 18:35:10.806697: Epoch 171 +2024-09-05 18:35:10.807582: Current learning rate: 0.00845 +2024-09-05 18:40:34.173550: train_loss -0.7113 +2024-09-05 18:40:34.186951: val_loss -0.7306 +2024-09-05 18:40:34.188105: Pseudo dice [0.9576, 0.9528, 0.9723, 0.9376, 0.8551, 0.9604, 0.9903] +2024-09-05 18:40:34.189187: Epoch time: 323.39 s +2024-09-05 18:40:34.190138: Yayy! New best EMA pseudo Dice: 0.9458 +2024-09-05 18:40:42.753090: +2024-09-05 18:40:42.753985: Epoch 172 +2024-09-05 18:40:42.754580: Current learning rate: 0.00844 +2024-09-05 18:45:48.439413: train_loss -0.7292 +2024-09-05 18:45:48.446988: val_loss -0.726 +2024-09-05 18:45:48.448290: Pseudo dice [0.9604, 0.956, 0.9752, 0.9463, 0.8596, 0.9614, 0.9879] +2024-09-05 18:45:48.449266: Epoch time: 305.69 s +2024-09-05 18:45:48.450126: Yayy! New best EMA pseudo Dice: 0.9462 +2024-09-05 18:45:58.210958: +2024-09-05 18:45:58.211780: Epoch 173 +2024-09-05 18:45:58.212548: Current learning rate: 0.00843 +2024-09-05 18:51:05.608776: train_loss -0.709 +2024-09-05 18:51:05.614089: val_loss -0.7632 +2024-09-05 18:51:05.616320: Pseudo dice [0.9576, 0.953, 0.9734, 0.9419, 0.8448, 0.9551, 0.9882] +2024-09-05 18:51:05.617658: Epoch time: 307.4 s +2024-09-05 18:51:08.695650: +2024-09-05 18:51:08.696914: Epoch 174 +2024-09-05 18:51:08.697678: Current learning rate: 0.00842 +2024-09-05 18:56:05.202338: train_loss -0.7283 +2024-09-05 18:56:05.203989: val_loss -0.6936 +2024-09-05 18:56:05.204870: Pseudo dice [0.9571, 0.9506, 0.9723, 0.9345, 0.8441, 0.9583, 0.9896] +2024-09-05 18:56:05.206306: Epoch time: 296.51 s +2024-09-05 18:56:09.400629: +2024-09-05 18:56:09.401460: Epoch 175 +2024-09-05 18:56:09.402127: Current learning rate: 0.00841 +2024-09-05 19:01:15.287297: train_loss -0.7284 +2024-09-05 19:01:15.303357: val_loss -0.7431 +2024-09-05 19:01:15.304428: Pseudo dice [0.9598, 0.9543, 0.9754, 0.9493, 0.8787, 0.9662, 0.992] +2024-09-05 19:01:15.305235: Epoch time: 305.89 s +2024-09-05 19:01:15.306083: Yayy! New best EMA pseudo Dice: 0.9466 +2024-09-05 19:01:26.092925: +2024-09-05 19:01:26.093930: Epoch 176 +2024-09-05 19:01:26.094718: Current learning rate: 0.0084 +2024-09-05 19:06:50.207673: train_loss -0.7198 +2024-09-05 19:06:50.220864: val_loss -0.7024 +2024-09-05 19:06:50.221972: Pseudo dice [0.9594, 0.9551, 0.9723, 0.9407, 0.8628, 0.9631, 0.9907] +2024-09-05 19:06:50.222898: Epoch time: 324.12 s +2024-09-05 19:06:50.223874: Yayy! New best EMA pseudo Dice: 0.9469 +2024-09-05 19:06:59.722802: +2024-09-05 19:06:59.723778: Epoch 177 +2024-09-05 19:06:59.724567: Current learning rate: 0.00839 +2024-09-05 19:12:13.772892: train_loss -0.7023 +2024-09-05 19:12:13.778273: val_loss -0.7559 +2024-09-05 19:12:13.779763: Pseudo dice [0.9632, 0.9547, 0.9772, 0.9464, 0.8542, 0.9617, 0.9902] +2024-09-05 19:12:13.781556: Epoch time: 314.05 s +2024-09-05 19:12:13.783845: Yayy! New best EMA pseudo Dice: 0.9472 +2024-09-05 19:12:24.704301: +2024-09-05 19:12:24.705188: Epoch 178 +2024-09-05 19:12:24.705727: Current learning rate: 0.00838 +2024-09-05 19:17:32.400839: train_loss -0.7247 +2024-09-05 19:17:32.402406: val_loss -0.7145 +2024-09-05 19:17:32.404469: Pseudo dice [0.9599, 0.9559, 0.9774, 0.9426, 0.8736, 0.9683, 0.991] +2024-09-05 19:17:32.405711: Epoch time: 307.7 s +2024-09-05 19:17:32.406517: Yayy! New best EMA pseudo Dice: 0.9477 +2024-09-05 19:17:45.135684: +2024-09-05 19:17:45.136724: Epoch 179 +2024-09-05 19:17:45.137370: Current learning rate: 0.00837 +2024-09-05 19:22:51.094009: train_loss -0.7037 +2024-09-05 19:22:51.123610: val_loss -0.7567 +2024-09-05 19:22:51.124418: Pseudo dice [0.96, 0.9538, 0.9777, 0.9413, 0.8583, 0.9636, 0.9903] +2024-09-05 19:22:51.125088: Epoch time: 305.96 s +2024-09-05 19:22:51.125849: Yayy! New best EMA pseudo Dice: 0.9479 +2024-09-05 19:23:02.189787: +2024-09-05 19:23:02.190811: Epoch 180 +2024-09-05 19:23:02.191649: Current learning rate: 0.00836 +2024-09-05 19:28:03.134912: train_loss -0.7087 +2024-09-05 19:28:03.137320: val_loss -0.7358 +2024-09-05 19:28:03.138032: Pseudo dice [0.9593, 0.9507, 0.9745, 0.9443, 0.8615, 0.9626, 0.9901] +2024-09-05 19:28:03.138767: Epoch time: 300.95 s +2024-09-05 19:28:03.139367: Yayy! New best EMA pseudo Dice: 0.948 +2024-09-05 19:28:11.456303: +2024-09-05 19:28:11.457334: Epoch 181 +2024-09-05 19:28:11.458081: Current learning rate: 0.00836 +2024-09-05 19:33:16.255846: train_loss -0.7073 +2024-09-05 19:33:16.257337: val_loss -0.7269 +2024-09-05 19:33:16.258191: Pseudo dice [0.9568, 0.9497, 0.9759, 0.9409, 0.8544, 0.9568, 0.9908] +2024-09-05 19:33:16.259156: Epoch time: 304.8 s +2024-09-05 19:33:20.604849: +2024-09-05 19:33:20.606869: Epoch 182 +2024-09-05 19:33:20.608059: Current learning rate: 0.00835 +2024-09-05 19:38:17.448013: train_loss -0.6665 +2024-09-05 19:38:17.463200: val_loss -0.6981 +2024-09-05 19:38:17.464690: Pseudo dice [0.9537, 0.9494, 0.9709, 0.9361, 0.8372, 0.9559, 0.9866] +2024-09-05 19:38:17.465744: Epoch time: 296.85 s +2024-09-05 19:38:21.068403: +2024-09-05 19:38:21.069372: Epoch 183 +2024-09-05 19:38:21.070262: Current learning rate: 0.00834 +2024-09-05 19:43:15.537477: train_loss -0.6848 +2024-09-05 19:43:15.551045: val_loss -0.7073 +2024-09-05 19:43:15.551899: Pseudo dice [0.9561, 0.9466, 0.9724, 0.9405, 0.8579, 0.9569, 0.9907] +2024-09-05 19:43:15.552574: Epoch time: 294.47 s +2024-09-05 19:43:18.712749: +2024-09-05 19:43:18.713632: Epoch 184 +2024-09-05 19:43:18.714949: Current learning rate: 0.00833 +2024-09-05 19:48:32.031931: train_loss -0.6894 +2024-09-05 19:48:32.033833: val_loss -0.6918 +2024-09-05 19:48:32.034858: Pseudo dice [0.9573, 0.9492, 0.972, 0.9295, 0.8467, 0.9598, 0.9903] +2024-09-05 19:48:32.035894: Epoch time: 313.33 s +2024-09-05 19:48:34.827449: +2024-09-05 19:48:34.830005: Epoch 185 +2024-09-05 19:48:34.832931: Current learning rate: 0.00832 +2024-09-05 19:53:41.270882: train_loss -0.7105 +2024-09-05 19:53:41.282017: val_loss -0.7292 +2024-09-05 19:53:41.284255: Pseudo dice [0.9573, 0.9531, 0.9707, 0.9416, 0.8544, 0.9608, 0.9897] +2024-09-05 19:53:41.285909: Epoch time: 306.45 s +2024-09-05 19:53:44.812502: +2024-09-05 19:53:44.814000: Epoch 186 +2024-09-05 19:53:44.816043: Current learning rate: 0.00831 +2024-09-05 19:58:37.067815: train_loss -0.7103 +2024-09-05 19:58:37.075273: val_loss -0.7081 +2024-09-05 19:58:37.076186: Pseudo dice [0.9531, 0.9486, 0.9701, 0.9424, 0.8399, 0.9573, 0.9909] +2024-09-05 19:58:37.076985: Epoch time: 292.26 s +2024-09-05 19:58:40.006788: +2024-09-05 19:58:40.016113: Epoch 187 +2024-09-05 19:58:40.139337: Current learning rate: 0.0083 +2024-09-05 20:03:44.156166: train_loss -0.7153 +2024-09-05 20:03:44.171195: val_loss -0.7448 +2024-09-05 20:03:44.172033: Pseudo dice [0.9522, 0.9469, 0.9679, 0.9364, 0.8603, 0.9621, 0.9889] +2024-09-05 20:03:44.172766: Epoch time: 304.16 s +2024-09-05 20:03:47.253398: +2024-09-05 20:03:47.254371: Epoch 188 +2024-09-05 20:03:47.255013: Current learning rate: 0.00829 +2024-09-05 20:08:35.907809: train_loss -0.6985 +2024-09-05 20:08:35.932274: val_loss -0.7357 +2024-09-05 20:08:35.933311: Pseudo dice [0.9571, 0.951, 0.9732, 0.9416, 0.8558, 0.9565, 0.989] +2024-09-05 20:08:35.934328: Epoch time: 288.66 s +2024-09-05 20:08:39.598050: +2024-09-05 20:08:39.599412: Epoch 189 +2024-09-05 20:08:39.600577: Current learning rate: 0.00828 +2024-09-05 20:13:53.576323: train_loss -0.7152 +2024-09-05 20:13:53.598972: val_loss -0.7239 +2024-09-05 20:13:53.600156: Pseudo dice [0.9563, 0.9497, 0.9708, 0.9343, 0.8477, 0.9558, 0.9865] +2024-09-05 20:13:53.601728: Epoch time: 313.98 s +2024-09-05 20:13:57.270549: +2024-09-05 20:13:57.286799: Epoch 190 +2024-09-05 20:13:57.288022: Current learning rate: 0.00827 +2024-09-05 20:18:34.030729: train_loss -0.6825 +2024-09-05 20:18:34.042274: val_loss -0.6874 +2024-09-05 20:18:34.043239: Pseudo dice [0.9421, 0.939, 0.9561, 0.9188, 0.8273, 0.9532, 0.984] +2024-09-05 20:18:34.044058: Epoch time: 276.77 s +2024-09-05 20:18:37.701944: +2024-09-05 20:18:37.718584: Epoch 191 +2024-09-05 20:18:37.719552: Current learning rate: 0.00826 +2024-09-05 20:23:33.009939: train_loss -0.6516 +2024-09-05 20:23:33.020636: val_loss -0.7076 +2024-09-05 20:23:33.021529: Pseudo dice [0.9505, 0.9366, 0.9601, 0.932, 0.842, 0.9542, 0.9834] +2024-09-05 20:23:33.022698: Epoch time: 295.31 s +2024-09-05 20:23:36.279020: +2024-09-05 20:23:36.280012: Epoch 192 +2024-09-05 20:23:36.280623: Current learning rate: 0.00825 +2024-09-05 20:29:00.059054: train_loss -0.6874 +2024-09-05 20:29:00.061835: val_loss -0.7174 +2024-09-05 20:29:00.062618: Pseudo dice [0.9542, 0.9472, 0.971, 0.9333, 0.8377, 0.9567, 0.9876] +2024-09-05 20:29:00.063480: Epoch time: 323.79 s +2024-09-05 20:29:03.650164: +2024-09-05 20:29:03.651205: Epoch 193 +2024-09-05 20:29:03.651872: Current learning rate: 0.00824 +2024-09-05 20:34:17.200711: train_loss -0.6967 +2024-09-05 20:34:17.215916: val_loss -0.7192 +2024-09-05 20:34:17.216658: Pseudo dice [0.9573, 0.951, 0.9741, 0.9446, 0.8488, 0.9596, 0.9893] +2024-09-05 20:34:17.217348: Epoch time: 313.56 s +2024-09-05 20:34:20.687465: +2024-09-05 20:34:20.688636: Epoch 194 +2024-09-05 20:34:20.689365: Current learning rate: 0.00824 +2024-09-05 20:39:20.562323: train_loss -0.7113 +2024-09-05 20:39:20.574044: val_loss -0.7274 +2024-09-05 20:39:20.586249: Pseudo dice [0.9572, 0.9481, 0.9719, 0.9465, 0.8538, 0.9617, 0.99] +2024-09-05 20:39:20.587330: Epoch time: 299.88 s +2024-09-05 20:39:23.805798: +2024-09-05 20:39:23.807171: Epoch 195 +2024-09-05 20:39:23.808234: Current learning rate: 0.00823 +2024-09-05 20:44:05.946690: train_loss -0.7059 +2024-09-05 20:44:05.950999: val_loss -0.7028 +2024-09-05 20:44:05.952045: Pseudo dice [0.9579, 0.9517, 0.9728, 0.9424, 0.8641, 0.9638, 0.9901] +2024-09-05 20:44:05.952856: Epoch time: 282.15 s +2024-09-05 20:44:08.952352: +2024-09-05 20:44:08.959110: Epoch 196 +2024-09-05 20:44:08.967305: Current learning rate: 0.00822 +2024-09-05 20:48:49.589093: train_loss -0.7167 +2024-09-05 20:48:49.603792: val_loss -0.717 +2024-09-05 20:48:49.604670: Pseudo dice [0.9577, 0.9523, 0.9717, 0.9428, 0.8586, 0.9649, 0.9875] +2024-09-05 20:48:49.605362: Epoch time: 280.64 s +2024-09-05 20:48:52.806493: +2024-09-05 20:48:52.807827: Epoch 197 +2024-09-05 20:48:52.809156: Current learning rate: 0.00821 +2024-09-05 20:53:58.638560: train_loss -0.7238 +2024-09-05 20:53:58.655427: val_loss -0.7467 +2024-09-05 20:53:58.656811: Pseudo dice [0.9617, 0.9523, 0.9766, 0.9452, 0.8638, 0.9627, 0.991] +2024-09-05 20:53:58.658260: Epoch time: 305.84 s +2024-09-05 20:54:02.164465: +2024-09-05 20:54:02.165653: Epoch 198 +2024-09-05 20:54:02.166786: Current learning rate: 0.0082 +2024-09-05 20:59:20.778226: train_loss -0.7224 +2024-09-05 20:59:20.791203: val_loss -0.7202 +2024-09-05 20:59:20.792132: Pseudo dice [0.9545, 0.9538, 0.9669, 0.947, 0.8598, 0.9585, 0.9898] +2024-09-05 20:59:20.793099: Epoch time: 318.62 s +2024-09-05 20:59:25.160993: +2024-09-05 20:59:25.162099: Epoch 199 +2024-09-05 20:59:25.162815: Current learning rate: 0.00819 +2024-09-05 21:04:16.888479: train_loss -0.7256 +2024-09-05 21:04:16.896280: val_loss -0.7598 +2024-09-05 21:04:16.897124: Pseudo dice [0.9629, 0.9532, 0.9768, 0.9449, 0.8756, 0.9623, 0.9905] +2024-09-05 21:04:16.897933: Epoch time: 291.73 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_00_30_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_00_30_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..2974a0971361d4d5d08a5e150808dab099446211 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_00_30_15.txt @@ -0,0 +1,750 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 00:30:18.911804: Using torch.compile... +2024-09-06 00:30:29.925232: do_dummy_2d_data_aug: False +2024-09-06 00:30:29.931367: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 00:30:29.932863: The split file contains 5 splits. +2024-09-06 00:30:29.933619: Desired fold for training: 2 +2024-09-06 00:30:29.934441: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 00:30:40.623703: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 00:30:41.890836: +2024-09-06 00:30:41.893034: Epoch 200 +2024-09-06 00:30:41.896332: Current learning rate: 0.00818 +2024-09-06 00:49:15.260688: train_loss -0.7004 +2024-09-06 00:49:15.262997: val_loss -0.7082 +2024-09-06 00:49:15.264067: Pseudo dice [0.9544, 0.9486, 0.9701, 0.9412, 0.8607, 0.9605, 0.9898] +2024-09-06 00:49:15.266118: Epoch time: 1113.38 s +2024-09-06 00:49:16.786773: +2024-09-06 00:49:16.788070: Epoch 201 +2024-09-06 00:49:16.789037: Current learning rate: 0.00817 +2024-09-06 00:53:54.616950: train_loss -0.7031 +2024-09-06 00:53:54.627244: val_loss -0.7005 +2024-09-06 00:53:54.628116: Pseudo dice [0.9576, 0.9523, 0.9719, 0.942, 0.8628, 0.9633, 0.9907] +2024-09-06 00:53:54.630690: Epoch time: 277.83 s +2024-09-06 00:53:57.364749: +2024-09-06 00:53:57.366749: Epoch 202 +2024-09-06 00:53:57.369272: Current learning rate: 0.00816 +2024-09-06 00:58:29.330386: train_loss -0.7236 +2024-09-06 00:58:29.344530: val_loss -0.684 +2024-09-06 00:58:29.345481: Pseudo dice [0.9561, 0.9524, 0.9679, 0.9402, 0.8687, 0.9635, 0.9909] +2024-09-06 00:58:29.346470: Epoch time: 271.97 s +2024-09-06 00:58:31.749158: +2024-09-06 00:58:31.751460: Epoch 203 +2024-09-06 00:58:31.753494: Current learning rate: 0.00815 +2024-09-06 01:03:08.775553: train_loss -0.711 +2024-09-06 01:03:08.792425: val_loss -0.725 +2024-09-06 01:03:08.793520: Pseudo dice [0.9556, 0.9513, 0.9706, 0.9423, 0.8419, 0.9613, 0.9899] +2024-09-06 01:03:08.794514: Epoch time: 277.03 s +2024-09-06 01:03:13.764555: +2024-09-06 01:03:13.765939: Epoch 204 +2024-09-06 01:03:13.767468: Current learning rate: 0.00814 +2024-09-06 01:07:48.279070: train_loss -0.7118 +2024-09-06 01:07:48.281774: val_loss -0.7481 +2024-09-06 01:07:48.282817: Pseudo dice [0.9541, 0.9505, 0.9704, 0.9382, 0.8566, 0.9633, 0.9908] +2024-09-06 01:07:48.283903: Epoch time: 274.52 s +2024-09-06 01:07:53.278781: +2024-09-06 01:07:53.294651: Epoch 205 +2024-09-06 01:07:53.296360: Current learning rate: 0.00813 +2024-09-06 01:12:35.160307: train_loss -0.7043 +2024-09-06 01:12:35.163486: val_loss -0.7407 +2024-09-06 01:12:35.164649: Pseudo dice [0.9568, 0.9492, 0.9726, 0.9473, 0.8627, 0.9612, 0.991] +2024-09-06 01:12:35.165649: Epoch time: 281.89 s +2024-09-06 01:12:37.512254: +2024-09-06 01:12:37.513366: Epoch 206 +2024-09-06 01:12:37.514178: Current learning rate: 0.00813 +2024-09-06 01:16:55.506859: train_loss -0.7119 +2024-09-06 01:16:55.509437: val_loss -0.6987 +2024-09-06 01:16:55.510399: Pseudo dice [0.9574, 0.9536, 0.9733, 0.9435, 0.8451, 0.955, 0.99] +2024-09-06 01:16:55.511228: Epoch time: 258.0 s +2024-09-06 01:16:57.595555: +2024-09-06 01:16:57.596849: Epoch 207 +2024-09-06 01:16:57.597935: Current learning rate: 0.00812 +2024-09-06 01:21:12.929763: train_loss -0.7182 +2024-09-06 01:21:12.936318: val_loss -0.7144 +2024-09-06 01:21:12.937464: Pseudo dice [0.9581, 0.9537, 0.9735, 0.9482, 0.858, 0.9595, 0.9907] +2024-09-06 01:21:12.939492: Epoch time: 255.34 s +2024-09-06 01:21:15.680679: +2024-09-06 01:21:15.681966: Epoch 208 +2024-09-06 01:21:15.683028: Current learning rate: 0.00811 +2024-09-06 01:25:54.714886: train_loss -0.7113 +2024-09-06 01:25:54.730213: val_loss -0.7575 +2024-09-06 01:25:54.732894: Pseudo dice [0.9627, 0.9546, 0.9763, 0.9478, 0.873, 0.9638, 0.9902] +2024-09-06 01:25:54.734154: Epoch time: 279.04 s +2024-09-06 01:25:57.103889: +2024-09-06 01:25:57.105175: Epoch 209 +2024-09-06 01:25:57.106141: Current learning rate: 0.0081 +2024-09-06 01:30:23.129282: train_loss -0.7125 +2024-09-06 01:30:23.139700: val_loss -0.7258 +2024-09-06 01:30:23.140832: Pseudo dice [0.9587, 0.9513, 0.9742, 0.9419, 0.8586, 0.9632, 0.9913] +2024-09-06 01:30:23.141726: Epoch time: 266.03 s +2024-09-06 01:30:25.848161: +2024-09-06 01:30:25.849551: Epoch 210 +2024-09-06 01:30:25.850617: Current learning rate: 0.00809 +2024-09-06 01:34:48.791561: train_loss -0.7301 +2024-09-06 01:34:48.799111: val_loss -0.7511 +2024-09-06 01:34:48.800410: Pseudo dice [0.963, 0.9543, 0.9793, 0.9439, 0.8691, 0.9661, 0.9905] +2024-09-06 01:34:48.801781: Epoch time: 262.95 s +2024-09-06 01:34:48.803087: Yayy! New best EMA pseudo Dice: 0.948 +2024-09-06 01:34:56.966444: +2024-09-06 01:34:56.967722: Epoch 211 +2024-09-06 01:34:56.968609: Current learning rate: 0.00808 +2024-09-06 01:39:28.553637: train_loss -0.7115 +2024-09-06 01:39:28.556332: val_loss -0.7542 +2024-09-06 01:39:28.557378: Pseudo dice [0.9623, 0.9497, 0.9764, 0.9477, 0.8719, 0.9618, 0.9916] +2024-09-06 01:39:28.558420: Epoch time: 271.59 s +2024-09-06 01:39:28.559400: Yayy! New best EMA pseudo Dice: 0.9484 +2024-09-06 01:39:37.918196: +2024-09-06 01:39:37.919498: Epoch 212 +2024-09-06 01:39:37.920444: Current learning rate: 0.00807 +2024-09-06 01:43:44.952672: train_loss -0.7188 +2024-09-06 01:43:44.956917: val_loss -0.728 +2024-09-06 01:43:44.962367: Pseudo dice [0.9586, 0.9521, 0.9765, 0.9393, 0.8609, 0.9655, 0.9904] +2024-09-06 01:43:44.964525: Epoch time: 247.04 s +2024-09-06 01:43:44.965990: Yayy! New best EMA pseudo Dice: 0.9484 +2024-09-06 01:43:53.972938: +2024-09-06 01:43:53.974298: Epoch 213 +2024-09-06 01:43:53.975249: Current learning rate: 0.00806 +2024-09-06 01:48:33.300392: train_loss -0.7312 +2024-09-06 01:48:33.306334: val_loss -0.731 +2024-09-06 01:48:33.317574: Pseudo dice [0.9589, 0.9523, 0.9768, 0.9459, 0.8738, 0.9667, 0.992] +2024-09-06 01:48:33.318833: Epoch time: 279.33 s +2024-09-06 01:48:33.319768: Yayy! New best EMA pseudo Dice: 0.9488 +2024-09-06 01:48:41.917298: +2024-09-06 01:48:41.920037: Epoch 214 +2024-09-06 01:48:41.923834: Current learning rate: 0.00805 +2024-09-06 01:53:18.373123: train_loss -0.7331 +2024-09-06 01:53:18.426891: val_loss -0.7236 +2024-09-06 01:53:18.428159: Pseudo dice [0.9592, 0.9548, 0.9732, 0.951, 0.8727, 0.9641, 0.9905] +2024-09-06 01:53:18.429479: Epoch time: 276.46 s +2024-09-06 01:53:18.430948: Yayy! New best EMA pseudo Dice: 0.9492 +2024-09-06 01:53:27.149944: +2024-09-06 01:53:27.152020: Epoch 215 +2024-09-06 01:53:27.154377: Current learning rate: 0.00804 +2024-09-06 01:57:32.832757: train_loss -0.7091 +2024-09-06 01:57:32.835216: val_loss -0.7386 +2024-09-06 01:57:32.836257: Pseudo dice [0.9606, 0.9518, 0.9755, 0.943, 0.8657, 0.964, 0.9911] +2024-09-06 01:57:32.837213: Epoch time: 245.69 s +2024-09-06 01:57:32.854038: Yayy! New best EMA pseudo Dice: 0.9493 +2024-09-06 01:57:40.906841: +2024-09-06 01:57:40.908155: Epoch 216 +2024-09-06 01:57:40.909172: Current learning rate: 0.00803 +2024-09-06 02:01:53.651247: train_loss -0.7057 +2024-09-06 02:01:53.660405: val_loss -0.7177 +2024-09-06 02:01:53.661578: Pseudo dice [0.9615, 0.9526, 0.9778, 0.9457, 0.8647, 0.9631, 0.991] +2024-09-06 02:01:53.662808: Epoch time: 252.75 s +2024-09-06 02:01:53.663913: Yayy! New best EMA pseudo Dice: 0.9494 +2024-09-06 02:02:02.081765: +2024-09-06 02:02:02.083109: Epoch 217 +2024-09-06 02:02:02.084146: Current learning rate: 0.00802 +2024-09-06 02:06:15.881928: train_loss -0.7069 +2024-09-06 02:06:15.900373: val_loss -0.7477 +2024-09-06 02:06:15.901926: Pseudo dice [0.9573, 0.953, 0.9715, 0.9445, 0.8628, 0.9642, 0.9916] +2024-09-06 02:06:15.903907: Epoch time: 253.81 s +2024-09-06 02:06:18.455472: +2024-09-06 02:06:18.456616: Epoch 218 +2024-09-06 02:06:18.457507: Current learning rate: 0.00801 +2024-09-06 02:10:53.142478: train_loss -0.729 +2024-09-06 02:10:53.145253: val_loss -0.7632 +2024-09-06 02:10:53.146484: Pseudo dice [0.9616, 0.9553, 0.9751, 0.9502, 0.862, 0.9675, 0.9915] +2024-09-06 02:10:53.147684: Epoch time: 274.69 s +2024-09-06 02:10:53.148715: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 02:11:00.468660: +2024-09-06 02:11:00.469837: Epoch 219 +2024-09-06 02:11:00.470764: Current learning rate: 0.00801 +2024-09-06 02:15:10.496113: train_loss -0.7343 +2024-09-06 02:15:10.510086: val_loss -0.7506 +2024-09-06 02:15:10.511981: Pseudo dice [0.9624, 0.9541, 0.9784, 0.9412, 0.8631, 0.9617, 0.9899] +2024-09-06 02:15:10.513177: Epoch time: 250.03 s +2024-09-06 02:15:10.530050: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 02:15:19.309290: +2024-09-06 02:15:19.310500: Epoch 220 +2024-09-06 02:15:19.311333: Current learning rate: 0.008 +2024-09-06 02:19:29.595625: train_loss -0.7219 +2024-09-06 02:19:29.599036: val_loss -0.7309 +2024-09-06 02:19:29.600077: Pseudo dice [0.965, 0.9578, 0.9764, 0.9466, 0.8725, 0.967, 0.9918] +2024-09-06 02:19:29.600873: Epoch time: 250.29 s +2024-09-06 02:19:29.601772: Yayy! New best EMA pseudo Dice: 0.9501 +2024-09-06 02:19:38.893281: +2024-09-06 02:19:38.894479: Epoch 221 +2024-09-06 02:19:38.895423: Current learning rate: 0.00799 +2024-09-06 02:23:47.821634: train_loss -0.7326 +2024-09-06 02:23:47.831409: val_loss -0.7473 +2024-09-06 02:23:47.832642: Pseudo dice [0.9622, 0.9561, 0.9765, 0.9382, 0.8663, 0.9632, 0.9913] +2024-09-06 02:23:47.833812: Epoch time: 248.93 s +2024-09-06 02:23:47.834733: Yayy! New best EMA pseudo Dice: 0.9502 +2024-09-06 02:23:57.576761: +2024-09-06 02:23:57.578127: Epoch 222 +2024-09-06 02:23:57.579214: Current learning rate: 0.00798 +2024-09-06 02:28:02.294093: train_loss -0.711 +2024-09-06 02:28:02.305013: val_loss -0.7465 +2024-09-06 02:28:02.306286: Pseudo dice [0.9599, 0.9539, 0.9734, 0.941, 0.8608, 0.9671, 0.9915] +2024-09-06 02:28:02.322918: Epoch time: 244.72 s +2024-09-06 02:28:04.805943: +2024-09-06 02:28:04.807558: Epoch 223 +2024-09-06 02:28:04.809288: Current learning rate: 0.00797 +2024-09-06 02:32:14.906762: train_loss -0.7373 +2024-09-06 02:32:14.909239: val_loss -0.707 +2024-09-06 02:32:14.910143: Pseudo dice [0.963, 0.9576, 0.9768, 0.9477, 0.8711, 0.9668, 0.9918] +2024-09-06 02:32:14.911045: Epoch time: 250.11 s +2024-09-06 02:32:14.911868: Yayy! New best EMA pseudo Dice: 0.9505 +2024-09-06 02:32:23.785329: +2024-09-06 02:32:23.786812: Epoch 224 +2024-09-06 02:32:23.787815: Current learning rate: 0.00796 +2024-09-06 02:36:46.645931: train_loss -0.726 +2024-09-06 02:36:46.649591: val_loss -0.7589 +2024-09-06 02:36:46.651110: Pseudo dice [0.9599, 0.9552, 0.9757, 0.9438, 0.8719, 0.9666, 0.991] +2024-09-06 02:36:46.652510: Epoch time: 262.86 s +2024-09-06 02:36:46.653851: Yayy! New best EMA pseudo Dice: 0.9506 +2024-09-06 02:36:55.239429: +2024-09-06 02:36:55.240829: Epoch 225 +2024-09-06 02:36:55.242020: Current learning rate: 0.00795 +2024-09-06 02:41:18.210189: train_loss -0.7242 +2024-09-06 02:41:18.228216: val_loss -0.7229 +2024-09-06 02:41:18.229465: Pseudo dice [0.9611, 0.9556, 0.9748, 0.9372, 0.8757, 0.9655, 0.9918] +2024-09-06 02:41:18.230630: Epoch time: 262.98 s +2024-09-06 02:41:18.231704: Yayy! New best EMA pseudo Dice: 0.9507 +2024-09-06 02:41:26.874436: +2024-09-06 02:41:26.875858: Epoch 226 +2024-09-06 02:41:26.876924: Current learning rate: 0.00794 +2024-09-06 02:45:46.596531: train_loss -0.7227 +2024-09-06 02:45:46.600592: val_loss -0.7485 +2024-09-06 02:45:46.602571: Pseudo dice [0.9585, 0.954, 0.9723, 0.9478, 0.8707, 0.9634, 0.9908] +2024-09-06 02:45:46.604491: Epoch time: 259.73 s +2024-09-06 02:45:46.605799: Yayy! New best EMA pseudo Dice: 0.9508 +2024-09-06 02:45:54.581311: +2024-09-06 02:45:54.582525: Epoch 227 +2024-09-06 02:45:54.583529: Current learning rate: 0.00793 +2024-09-06 02:50:06.951265: train_loss -0.7294 +2024-09-06 02:50:06.957680: val_loss -0.7 +2024-09-06 02:50:06.960560: Pseudo dice [0.9599, 0.9534, 0.9724, 0.9459, 0.8418, 0.9631, 0.9883] +2024-09-06 02:50:06.962175: Epoch time: 252.37 s +2024-09-06 02:50:10.553483: +2024-09-06 02:50:10.567444: Epoch 228 +2024-09-06 02:50:10.568515: Current learning rate: 0.00792 +2024-09-06 02:54:29.471487: train_loss -0.726 +2024-09-06 02:54:29.474366: val_loss -0.7853 +2024-09-06 02:54:29.475525: Pseudo dice [0.9605, 0.9536, 0.9746, 0.9402, 0.8512, 0.9636, 0.9902] +2024-09-06 02:54:29.476878: Epoch time: 258.92 s +2024-09-06 02:54:31.709087: +2024-09-06 02:54:31.723077: Epoch 229 +2024-09-06 02:54:31.724389: Current learning rate: 0.00791 +2024-09-06 02:58:52.223475: train_loss -0.7234 +2024-09-06 02:58:52.225487: val_loss -0.7546 +2024-09-06 02:58:52.226759: Pseudo dice [0.9653, 0.9577, 0.9809, 0.948, 0.8625, 0.9673, 0.9903] +2024-09-06 02:58:52.228126: Epoch time: 260.52 s +2024-09-06 02:58:55.158304: +2024-09-06 02:58:55.159747: Epoch 230 +2024-09-06 02:58:55.160858: Current learning rate: 0.0079 +2024-09-06 03:03:06.889586: train_loss -0.7258 +2024-09-06 03:03:06.897693: val_loss -0.7423 +2024-09-06 03:03:06.898787: Pseudo dice [0.9641, 0.9555, 0.9788, 0.9484, 0.8685, 0.9642, 0.9917] +2024-09-06 03:03:06.900234: Epoch time: 251.74 s +2024-09-06 03:03:09.348719: +2024-09-06 03:03:09.350344: Epoch 231 +2024-09-06 03:03:09.351515: Current learning rate: 0.00789 +2024-09-06 03:07:32.292263: train_loss -0.7244 +2024-09-06 03:07:32.300424: val_loss -0.7428 +2024-09-06 03:07:32.302704: Pseudo dice [0.9544, 0.9536, 0.9687, 0.9412, 0.8591, 0.9663, 0.99] +2024-09-06 03:07:32.304114: Epoch time: 262.95 s +2024-09-06 03:07:34.795516: +2024-09-06 03:07:34.796963: Epoch 232 +2024-09-06 03:07:34.798600: Current learning rate: 0.00789 +2024-09-06 03:11:54.488963: train_loss -0.7336 +2024-09-06 03:11:54.492887: val_loss -0.7368 +2024-09-06 03:11:54.494485: Pseudo dice [0.9619, 0.9519, 0.9756, 0.9459, 0.8658, 0.9655, 0.9902] +2024-09-06 03:11:54.498260: Epoch time: 259.7 s +2024-09-06 03:11:56.807971: +2024-09-06 03:11:56.809182: Epoch 233 +2024-09-06 03:11:56.810156: Current learning rate: 0.00788 +2024-09-06 03:16:28.572280: train_loss -0.7269 +2024-09-06 03:16:28.575093: val_loss -0.7238 +2024-09-06 03:16:28.576177: Pseudo dice [0.9576, 0.9566, 0.9719, 0.9444, 0.8642, 0.9653, 0.9895] +2024-09-06 03:16:28.577096: Epoch time: 271.77 s +2024-09-06 03:16:32.064945: +2024-09-06 03:16:32.066296: Epoch 234 +2024-09-06 03:16:32.067335: Current learning rate: 0.00787 +2024-09-06 03:20:49.557056: train_loss -0.7145 +2024-09-06 03:20:49.560323: val_loss -0.7051 +2024-09-06 03:20:49.561497: Pseudo dice [0.9553, 0.9498, 0.9719, 0.9324, 0.8601, 0.9591, 0.9868] +2024-09-06 03:20:49.562626: Epoch time: 257.5 s +2024-09-06 03:20:51.959301: +2024-09-06 03:20:51.960934: Epoch 235 +2024-09-06 03:20:51.962123: Current learning rate: 0.00786 +2024-09-06 03:25:18.273813: train_loss -0.7034 +2024-09-06 03:25:18.276548: val_loss -0.7156 +2024-09-06 03:25:18.278011: Pseudo dice [0.9557, 0.954, 0.9721, 0.9422, 0.8591, 0.9577, 0.9875] +2024-09-06 03:25:18.279098: Epoch time: 266.32 s +2024-09-06 03:25:20.559520: +2024-09-06 03:25:20.560601: Epoch 236 +2024-09-06 03:25:20.561427: Current learning rate: 0.00785 +2024-09-06 03:29:33.753947: train_loss -0.7131 +2024-09-06 03:29:33.772729: val_loss -0.6984 +2024-09-06 03:29:33.773696: Pseudo dice [0.951, 0.9467, 0.9662, 0.9348, 0.8361, 0.9398, 0.9883] +2024-09-06 03:29:33.774627: Epoch time: 253.2 s +2024-09-06 03:29:36.239151: +2024-09-06 03:29:36.270782: Epoch 237 +2024-09-06 03:29:36.271887: Current learning rate: 0.00784 +2024-09-06 03:33:47.222121: train_loss -0.678 +2024-09-06 03:33:47.224609: val_loss -0.7228 +2024-09-06 03:33:47.234872: Pseudo dice [0.9527, 0.9429, 0.9711, 0.9363, 0.8389, 0.9482, 0.985] +2024-09-06 03:33:47.236057: Epoch time: 250.99 s +2024-09-06 03:33:49.757131: +2024-09-06 03:33:49.758496: Epoch 238 +2024-09-06 03:33:49.759412: Current learning rate: 0.00783 +2024-09-06 03:38:14.589176: train_loss -0.6773 +2024-09-06 03:38:14.603361: val_loss -0.7046 +2024-09-06 03:38:14.605332: Pseudo dice [0.9507, 0.945, 0.9635, 0.9224, 0.8364, 0.9556, 0.9868] +2024-09-06 03:38:14.607492: Epoch time: 264.84 s +2024-09-06 03:38:16.918893: +2024-09-06 03:38:16.920245: Epoch 239 +2024-09-06 03:38:16.921588: Current learning rate: 0.00782 +2024-09-06 03:42:32.723456: train_loss -0.6502 +2024-09-06 03:42:32.726532: val_loss -0.6611 +2024-09-06 03:42:32.727736: Pseudo dice [0.9473, 0.9414, 0.9659, 0.922, 0.8373, 0.9552, 0.9867] +2024-09-06 03:42:32.728718: Epoch time: 255.81 s +2024-09-06 03:42:35.334985: +2024-09-06 03:42:35.336123: Epoch 240 +2024-09-06 03:42:35.336900: Current learning rate: 0.00781 +2024-09-06 03:47:18.142313: train_loss -0.6943 +2024-09-06 03:47:18.144890: val_loss -0.7332 +2024-09-06 03:47:18.145930: Pseudo dice [0.9563, 0.95, 0.9721, 0.9332, 0.8347, 0.9584, 0.9897] +2024-09-06 03:47:18.146864: Epoch time: 282.81 s +2024-09-06 03:47:20.538336: +2024-09-06 03:47:20.539493: Epoch 241 +2024-09-06 03:47:20.540370: Current learning rate: 0.0078 +2024-09-06 03:51:58.031823: train_loss -0.7185 +2024-09-06 03:51:58.044247: val_loss -0.7047 +2024-09-06 03:51:58.045495: Pseudo dice [0.9612, 0.9548, 0.9728, 0.941, 0.839, 0.9605, 0.9874] +2024-09-06 03:51:58.049719: Epoch time: 277.5 s +2024-09-06 03:52:02.553458: +2024-09-06 03:52:02.554929: Epoch 242 +2024-09-06 03:52:02.556035: Current learning rate: 0.00779 +2024-09-06 03:56:10.301945: train_loss -0.7063 +2024-09-06 03:56:10.315463: val_loss -0.7332 +2024-09-06 03:56:10.316486: Pseudo dice [0.9603, 0.9522, 0.9743, 0.9448, 0.8652, 0.9666, 0.9905] +2024-09-06 03:56:10.317487: Epoch time: 247.75 s +2024-09-06 03:56:12.710566: +2024-09-06 03:56:12.712568: Epoch 243 +2024-09-06 03:56:12.714211: Current learning rate: 0.00778 +2024-09-06 04:00:29.077794: train_loss -0.7154 +2024-09-06 04:00:29.088331: val_loss -0.7294 +2024-09-06 04:00:29.089278: Pseudo dice [0.9563, 0.9532, 0.9738, 0.9474, 0.8687, 0.9629, 0.9911] +2024-09-06 04:00:29.092122: Epoch time: 256.37 s +2024-09-06 04:00:31.433540: +2024-09-06 04:00:31.434737: Epoch 244 +2024-09-06 04:00:31.435621: Current learning rate: 0.00777 +2024-09-06 04:05:06.633599: train_loss -0.7182 +2024-09-06 04:05:06.636294: val_loss -0.7409 +2024-09-06 04:05:06.637234: Pseudo dice [0.964, 0.9535, 0.9774, 0.9483, 0.8614, 0.9662, 0.9908] +2024-09-06 04:05:06.638151: Epoch time: 275.2 s +2024-09-06 04:05:09.084460: +2024-09-06 04:05:09.094973: Epoch 245 +2024-09-06 04:05:09.096263: Current learning rate: 0.00777 +2024-09-06 04:09:03.165546: train_loss -0.7189 +2024-09-06 04:09:03.192613: val_loss -0.7439 +2024-09-06 04:09:03.193674: Pseudo dice [0.9598, 0.9533, 0.9735, 0.9519, 0.8609, 0.9645, 0.9891] +2024-09-06 04:09:03.194604: Epoch time: 234.09 s +2024-09-06 04:09:05.654450: +2024-09-06 04:09:05.655508: Epoch 246 +2024-09-06 04:09:05.670675: Current learning rate: 0.00776 +2024-09-06 04:13:30.478283: train_loss -0.7187 +2024-09-06 04:13:30.479973: val_loss -0.7474 +2024-09-06 04:13:30.481109: Pseudo dice [0.9586, 0.9522, 0.9736, 0.9411, 0.8688, 0.9632, 0.9912] +2024-09-06 04:13:30.482104: Epoch time: 264.83 s +2024-09-06 04:13:34.497100: +2024-09-06 04:13:34.498250: Epoch 247 +2024-09-06 04:13:34.499374: Current learning rate: 0.00775 +2024-09-06 04:17:51.180479: train_loss -0.7202 +2024-09-06 04:17:51.182184: val_loss -0.7523 +2024-09-06 04:17:51.183394: Pseudo dice [0.9563, 0.9531, 0.9706, 0.9419, 0.8577, 0.9625, 0.9903] +2024-09-06 04:17:51.184602: Epoch time: 256.69 s +2024-09-06 04:17:53.530740: +2024-09-06 04:17:53.547065: Epoch 248 +2024-09-06 04:17:53.548653: Current learning rate: 0.00774 +2024-09-06 04:22:09.232560: train_loss -0.7218 +2024-09-06 04:22:09.246488: val_loss -0.7432 +2024-09-06 04:22:09.259796: Pseudo dice [0.9591, 0.9534, 0.9745, 0.9433, 0.8716, 0.9643, 0.9903] +2024-09-06 04:22:09.261014: Epoch time: 255.71 s +2024-09-06 04:22:11.379678: +2024-09-06 04:22:11.387406: Epoch 249 +2024-09-06 04:22:11.388882: Current learning rate: 0.00773 +2024-09-06 04:26:34.709086: train_loss -0.7119 +2024-09-06 04:26:34.727976: val_loss -0.7359 +2024-09-06 04:26:34.729398: Pseudo dice [0.9579, 0.9529, 0.975, 0.9488, 0.8782, 0.9666, 0.9913] +2024-09-06 04:26:34.730779: Epoch time: 263.33 s +2024-09-06 04:26:43.718916: +2024-09-06 04:26:43.720221: Epoch 250 +2024-09-06 04:26:43.721404: Current learning rate: 0.00772 +2024-09-06 04:30:59.516854: train_loss -0.7127 +2024-09-06 04:30:59.519773: val_loss -0.7549 +2024-09-06 04:30:59.520942: Pseudo dice [0.9614, 0.9523, 0.9744, 0.9452, 0.873, 0.9653, 0.9911] +2024-09-06 04:30:59.521978: Epoch time: 255.8 s +2024-09-06 04:31:01.881050: +2024-09-06 04:31:01.883782: Epoch 251 +2024-09-06 04:31:01.885417: Current learning rate: 0.00771 +2024-09-06 04:35:14.503944: train_loss -0.7221 +2024-09-06 04:35:14.520449: val_loss -0.7175 +2024-09-06 04:35:14.521752: Pseudo dice [0.9606, 0.9535, 0.9745, 0.9431, 0.8435, 0.9505, 0.9842] +2024-09-06 04:35:14.522893: Epoch time: 252.63 s +2024-09-06 04:35:16.949032: +2024-09-06 04:35:16.956657: Epoch 252 +2024-09-06 04:35:16.957705: Current learning rate: 0.0077 +2024-09-06 04:40:02.156956: train_loss -0.7147 +2024-09-06 04:40:02.172684: val_loss -0.712 +2024-09-06 04:40:02.173723: Pseudo dice [0.9574, 0.9513, 0.9701, 0.9404, 0.8598, 0.964, 0.9895] +2024-09-06 04:40:02.175041: Epoch time: 285.21 s +2024-09-06 04:40:04.573956: +2024-09-06 04:40:04.575249: Epoch 253 +2024-09-06 04:40:04.576341: Current learning rate: 0.00769 +2024-09-06 04:44:47.473227: train_loss -0.7384 +2024-09-06 04:44:47.489195: val_loss -0.6911 +2024-09-06 04:44:47.490948: Pseudo dice [0.9607, 0.9561, 0.9735, 0.9431, 0.8632, 0.9642, 0.9895] +2024-09-06 04:44:47.492400: Epoch time: 282.9 s +2024-09-06 04:44:49.803518: +2024-09-06 04:44:49.804710: Epoch 254 +2024-09-06 04:44:49.805573: Current learning rate: 0.00768 +2024-09-06 04:49:16.174444: train_loss -0.7092 +2024-09-06 04:49:16.176431: val_loss -0.7403 +2024-09-06 04:49:16.179485: Pseudo dice [0.9574, 0.9492, 0.9736, 0.9374, 0.8563, 0.9619, 0.9887] +2024-09-06 04:49:16.182558: Epoch time: 266.38 s +2024-09-06 04:49:18.384063: +2024-09-06 04:49:18.398748: Epoch 255 +2024-09-06 04:49:18.399829: Current learning rate: 0.00767 +2024-09-06 04:53:37.220702: train_loss -0.7154 +2024-09-06 04:53:37.237638: val_loss -0.71 +2024-09-06 04:53:37.239221: Pseudo dice [0.9616, 0.9553, 0.977, 0.9409, 0.8455, 0.9579, 0.9877] +2024-09-06 04:53:37.240729: Epoch time: 258.84 s +2024-09-06 04:53:41.235816: +2024-09-06 04:53:41.237000: Epoch 256 +2024-09-06 04:53:41.237744: Current learning rate: 0.00766 +2024-09-06 04:57:40.358615: train_loss -0.6945 +2024-09-06 04:57:40.367734: val_loss -0.7112 +2024-09-06 04:57:40.369021: Pseudo dice [0.9594, 0.9529, 0.97, 0.9402, 0.8534, 0.96, 0.9904] +2024-09-06 04:57:40.371047: Epoch time: 239.13 s +2024-09-06 04:57:42.988058: +2024-09-06 04:57:42.989660: Epoch 257 +2024-09-06 04:57:42.991012: Current learning rate: 0.00765 +2024-09-06 05:02:18.736407: train_loss -0.7099 +2024-09-06 05:02:18.738800: val_loss -0.7365 +2024-09-06 05:02:18.740180: Pseudo dice [0.9603, 0.9503, 0.9769, 0.9447, 0.8675, 0.9642, 0.9916] +2024-09-06 05:02:18.741575: Epoch time: 275.75 s +2024-09-06 05:02:21.136792: +2024-09-06 05:02:21.154816: Epoch 258 +2024-09-06 05:02:21.156308: Current learning rate: 0.00764 +2024-09-06 05:06:24.228111: train_loss -0.7185 +2024-09-06 05:06:24.235198: val_loss -0.7387 +2024-09-06 05:06:24.236415: Pseudo dice [0.9575, 0.9519, 0.9721, 0.9446, 0.867, 0.9649, 0.9918] +2024-09-06 05:06:24.237848: Epoch time: 243.1 s +2024-09-06 05:06:26.484827: +2024-09-06 05:06:26.486002: Epoch 259 +2024-09-06 05:06:26.487648: Current learning rate: 0.00764 +2024-09-06 05:10:44.737940: train_loss -0.7111 +2024-09-06 05:10:44.741193: val_loss -0.7137 +2024-09-06 05:10:44.742305: Pseudo dice [0.9548, 0.9515, 0.9664, 0.941, 0.8696, 0.9643, 0.9807] +2024-09-06 05:10:44.743259: Epoch time: 258.26 s +2024-09-06 05:10:48.518559: +2024-09-06 05:10:48.520590: Epoch 260 +2024-09-06 05:10:48.522444: Current learning rate: 0.00763 +2024-09-06 05:15:20.407042: train_loss -0.7272 +2024-09-06 05:15:20.409879: val_loss -0.7648 +2024-09-06 05:15:20.411373: Pseudo dice [0.9568, 0.9514, 0.9729, 0.9426, 0.859, 0.9635, 0.9911] +2024-09-06 05:15:20.412609: Epoch time: 271.89 s +2024-09-06 05:15:22.746179: +2024-09-06 05:15:22.747319: Epoch 261 +2024-09-06 05:15:22.748241: Current learning rate: 0.00762 +2024-09-06 05:19:46.495537: train_loss -0.7118 +2024-09-06 05:19:46.512436: val_loss -0.7415 +2024-09-06 05:19:46.513558: Pseudo dice [0.9608, 0.955, 0.9771, 0.943, 0.8652, 0.9667, 0.9918] +2024-09-06 05:19:46.514624: Epoch time: 263.75 s +2024-09-06 05:19:48.689342: +2024-09-06 05:19:48.690627: Epoch 262 +2024-09-06 05:19:48.691698: Current learning rate: 0.00761 +2024-09-06 05:24:11.418724: train_loss -0.7168 +2024-09-06 05:24:11.422262: val_loss -0.7587 +2024-09-06 05:24:11.423510: Pseudo dice [0.9588, 0.9546, 0.9741, 0.9391, 0.8768, 0.9659, 0.9914] +2024-09-06 05:24:11.424851: Epoch time: 262.73 s +2024-09-06 05:24:13.665353: +2024-09-06 05:24:13.666783: Epoch 263 +2024-09-06 05:24:13.667855: Current learning rate: 0.0076 +2024-09-06 05:28:27.011810: train_loss -0.7161 +2024-09-06 05:28:27.026559: val_loss -0.7814 +2024-09-06 05:28:27.028043: Pseudo dice [0.9594, 0.9546, 0.971, 0.9479, 0.8788, 0.9681, 0.9899] +2024-09-06 05:28:27.029650: Epoch time: 253.35 s +2024-09-06 05:28:29.590451: +2024-09-06 05:28:29.603241: Epoch 264 +2024-09-06 05:28:29.604536: Current learning rate: 0.00759 +2024-09-06 05:32:53.162772: train_loss -0.7283 +2024-09-06 05:32:53.179417: val_loss -0.7584 +2024-09-06 05:32:53.180429: Pseudo dice [0.9565, 0.9543, 0.9722, 0.9453, 0.8787, 0.9692, 0.9921] +2024-09-06 05:32:53.181238: Epoch time: 263.58 s +2024-09-06 05:32:55.391199: +2024-09-06 05:32:55.392723: Epoch 265 +2024-09-06 05:32:55.393734: Current learning rate: 0.00758 +2024-09-06 05:37:20.270270: train_loss -0.7327 +2024-09-06 05:37:20.283399: val_loss -0.7509 +2024-09-06 05:37:20.284738: Pseudo dice [0.9626, 0.9525, 0.9764, 0.9515, 0.8543, 0.9669, 0.9898] +2024-09-06 05:37:20.296412: Epoch time: 264.88 s +2024-09-06 05:37:22.949710: +2024-09-06 05:37:22.951045: Epoch 266 +2024-09-06 05:37:22.952838: Current learning rate: 0.00757 +2024-09-06 05:41:47.555667: train_loss -0.7314 +2024-09-06 05:41:47.557599: val_loss -0.7486 +2024-09-06 05:41:47.558712: Pseudo dice [0.9608, 0.9541, 0.972, 0.9487, 0.8726, 0.965, 0.9909] +2024-09-06 05:41:47.559614: Epoch time: 264.61 s +2024-09-06 05:41:49.730768: +2024-09-06 05:41:49.731957: Epoch 267 +2024-09-06 05:41:49.732893: Current learning rate: 0.00756 +2024-09-06 05:46:10.533841: train_loss -0.74 +2024-09-06 05:46:10.545558: val_loss -0.712 +2024-09-06 05:46:10.547056: Pseudo dice [0.9635, 0.9553, 0.9768, 0.944, 0.8728, 0.9672, 0.9916] +2024-09-06 05:46:10.548290: Epoch time: 260.81 s +2024-09-06 05:46:12.892914: +2024-09-06 05:46:12.895120: Epoch 268 +2024-09-06 05:46:12.896400: Current learning rate: 0.00755 +2024-09-06 05:50:45.334110: train_loss -0.7287 +2024-09-06 05:50:45.335709: val_loss -0.7244 +2024-09-06 05:50:45.336603: Pseudo dice [0.9632, 0.9572, 0.9762, 0.9438, 0.8658, 0.9678, 0.9906] +2024-09-06 05:50:45.337514: Epoch time: 272.45 s +2024-09-06 05:50:47.428822: +2024-09-06 05:50:47.430022: Epoch 269 +2024-09-06 05:50:47.431105: Current learning rate: 0.00754 +2024-09-06 05:55:17.498157: train_loss -0.7243 +2024-09-06 05:55:17.499994: val_loss -0.7379 +2024-09-06 05:55:17.501254: Pseudo dice [0.9636, 0.9559, 0.9782, 0.947, 0.8705, 0.9633, 0.9909] +2024-09-06 05:55:17.502510: Epoch time: 270.07 s +2024-09-06 05:55:19.657234: +2024-09-06 05:55:19.658306: Epoch 270 +2024-09-06 05:55:19.659186: Current learning rate: 0.00753 +2024-09-06 05:59:38.831972: train_loss -0.7218 +2024-09-06 05:59:38.834169: val_loss -0.7447 +2024-09-06 05:59:38.835155: Pseudo dice [0.9594, 0.9559, 0.974, 0.9439, 0.8633, 0.9616, 0.9893] +2024-09-06 05:59:38.836227: Epoch time: 259.18 s +2024-09-06 05:59:41.375442: +2024-09-06 05:59:41.376774: Epoch 271 +2024-09-06 05:59:41.377556: Current learning rate: 0.00752 +2024-09-06 06:03:59.470125: train_loss -0.7172 +2024-09-06 06:03:59.472088: val_loss -0.7395 +2024-09-06 06:03:59.473348: Pseudo dice [0.9601, 0.9537, 0.9735, 0.9507, 0.8748, 0.9664, 0.9913] +2024-09-06 06:03:59.474387: Epoch time: 258.1 s +2024-09-06 06:03:59.475625: Yayy! New best EMA pseudo Dice: 0.9508 +2024-09-06 06:04:05.941359: +2024-09-06 06:04:05.942454: Epoch 272 +2024-09-06 06:04:05.943513: Current learning rate: 0.00751 +2024-09-06 06:08:23.390060: train_loss -0.7321 +2024-09-06 06:08:23.392417: val_loss -0.7087 +2024-09-06 06:08:23.394078: Pseudo dice [0.9595, 0.9548, 0.9735, 0.9449, 0.8667, 0.9663, 0.9902] +2024-09-06 06:08:23.395473: Epoch time: 257.45 s +2024-09-06 06:08:23.396891: Yayy! New best EMA pseudo Dice: 0.9508 +2024-09-06 06:08:31.916547: +2024-09-06 06:08:31.917673: Epoch 273 +2024-09-06 06:08:31.918658: Current learning rate: 0.00751 +2024-09-06 06:12:52.438344: train_loss -0.7289 +2024-09-06 06:12:52.456518: val_loss -0.7444 +2024-09-06 06:12:52.457535: Pseudo dice [0.9601, 0.9545, 0.9759, 0.945, 0.872, 0.968, 0.9904] +2024-09-06 06:12:52.458637: Epoch time: 260.53 s +2024-09-06 06:12:52.459554: Yayy! New best EMA pseudo Dice: 0.9509 +2024-09-06 06:13:01.051022: +2024-09-06 06:13:01.052303: Epoch 274 +2024-09-06 06:13:01.053209: Current learning rate: 0.0075 +2024-09-06 06:17:40.126159: train_loss -0.7239 +2024-09-06 06:17:40.143363: val_loss -0.7496 +2024-09-06 06:17:40.144566: Pseudo dice [0.9641, 0.9565, 0.9801, 0.948, 0.8703, 0.9646, 0.992] +2024-09-06 06:17:40.145766: Epoch time: 279.08 s +2024-09-06 06:17:40.146984: Yayy! New best EMA pseudo Dice: 0.9512 +2024-09-06 06:17:47.844030: +2024-09-06 06:17:47.845130: Epoch 275 +2024-09-06 06:17:47.846018: Current learning rate: 0.00749 +2024-09-06 06:22:04.769355: train_loss -0.7378 +2024-09-06 06:22:04.786999: val_loss -0.7447 +2024-09-06 06:22:04.788154: Pseudo dice [0.9581, 0.9531, 0.9751, 0.9458, 0.8785, 0.968, 0.9923] +2024-09-06 06:22:04.789148: Epoch time: 256.93 s +2024-09-06 06:22:04.789968: Yayy! New best EMA pseudo Dice: 0.9514 +2024-09-06 06:22:12.583282: +2024-09-06 06:22:12.584653: Epoch 276 +2024-09-06 06:22:12.585598: Current learning rate: 0.00748 +2024-09-06 06:26:34.907692: train_loss -0.7154 +2024-09-06 06:26:34.925660: val_loss -0.7606 +2024-09-06 06:26:34.926649: Pseudo dice [0.962, 0.9572, 0.977, 0.9486, 0.8709, 0.9633, 0.9905] +2024-09-06 06:26:34.927553: Epoch time: 262.33 s +2024-09-06 06:26:34.928389: Yayy! New best EMA pseudo Dice: 0.9515 +2024-09-06 06:26:44.199399: +2024-09-06 06:26:44.200813: Epoch 277 +2024-09-06 06:26:44.201663: Current learning rate: 0.00747 +2024-09-06 06:31:11.976500: train_loss -0.724 +2024-09-06 06:31:11.979534: val_loss -0.7199 +2024-09-06 06:31:11.982324: Pseudo dice [0.9579, 0.955, 0.9721, 0.9381, 0.8582, 0.9663, 0.9913] +2024-09-06 06:31:11.984917: Epoch time: 267.78 s +2024-09-06 06:31:14.326903: +2024-09-06 06:31:14.330231: Epoch 278 +2024-09-06 06:31:14.333289: Current learning rate: 0.00746 +2024-09-06 06:35:42.219498: train_loss -0.7261 +2024-09-06 06:35:42.230283: val_loss -0.7649 +2024-09-06 06:35:42.231781: Pseudo dice [0.9612, 0.9532, 0.9769, 0.9466, 0.8813, 0.966, 0.9909] +2024-09-06 06:35:42.232583: Epoch time: 267.9 s +2024-09-06 06:35:44.438998: +2024-09-06 06:35:44.440482: Epoch 279 +2024-09-06 06:35:44.441544: Current learning rate: 0.00745 +2024-09-06 06:40:08.362663: train_loss -0.723 +2024-09-06 06:40:08.364331: val_loss -0.7328 +2024-09-06 06:40:08.365539: Pseudo dice [0.9594, 0.9564, 0.9728, 0.9467, 0.8774, 0.9662, 0.9917] +2024-09-06 06:40:08.366458: Epoch time: 263.93 s +2024-09-06 06:40:08.367308: Yayy! New best EMA pseudo Dice: 0.9516 +2024-09-06 06:40:15.996944: +2024-09-06 06:40:15.998060: Epoch 280 +2024-09-06 06:40:15.999014: Current learning rate: 0.00744 +2024-09-06 06:44:19.017231: train_loss -0.7203 +2024-09-06 06:44:19.020405: val_loss -0.7369 +2024-09-06 06:44:19.021531: Pseudo dice [0.9613, 0.9572, 0.9724, 0.9462, 0.8751, 0.9651, 0.9908] +2024-09-06 06:44:19.022563: Epoch time: 243.02 s +2024-09-06 06:44:19.023517: Yayy! New best EMA pseudo Dice: 0.9517 +2024-09-06 06:44:27.390971: +2024-09-06 06:44:27.392289: Epoch 281 +2024-09-06 06:44:27.393333: Current learning rate: 0.00743 +2024-09-06 06:48:32.168464: train_loss -0.7268 +2024-09-06 06:48:32.174758: val_loss -0.7214 +2024-09-06 06:48:32.177272: Pseudo dice [0.9595, 0.9576, 0.9726, 0.9427, 0.8618, 0.9654, 0.9884] +2024-09-06 06:48:32.181238: Epoch time: 244.78 s +2024-09-06 06:48:37.814876: +2024-09-06 06:48:37.841909: Epoch 282 +2024-09-06 06:48:37.847982: Current learning rate: 0.00742 +2024-09-06 06:53:06.889199: train_loss -0.7341 +2024-09-06 06:53:06.890999: val_loss -0.7258 +2024-09-06 06:53:06.892353: Pseudo dice [0.9557, 0.9534, 0.9705, 0.9493, 0.875, 0.9668, 0.9913] +2024-09-06 06:53:06.893193: Epoch time: 269.08 s +2024-09-06 06:53:10.451678: +2024-09-06 06:53:10.453096: Epoch 283 +2024-09-06 06:53:10.454153: Current learning rate: 0.00741 +2024-09-06 06:57:26.067564: train_loss -0.7273 +2024-09-06 06:57:26.098006: val_loss -0.723 +2024-09-06 06:57:26.100276: Pseudo dice [0.9559, 0.9562, 0.9689, 0.9476, 0.8656, 0.9652, 0.9903] +2024-09-06 06:57:26.101758: Epoch time: 255.62 s +2024-09-06 06:57:28.468300: +2024-09-06 06:57:28.469350: Epoch 284 +2024-09-06 06:57:28.470246: Current learning rate: 0.0074 +2024-09-06 07:02:06.908400: train_loss -0.7284 +2024-09-06 07:02:06.923873: val_loss -0.7108 +2024-09-06 07:02:06.924808: Pseudo dice [0.9657, 0.9578, 0.9775, 0.9478, 0.8501, 0.9662, 0.9904] +2024-09-06 07:02:06.925796: Epoch time: 278.45 s +2024-09-06 07:02:09.085324: +2024-09-06 07:02:09.086617: Epoch 285 +2024-09-06 07:02:09.090739: Current learning rate: 0.00739 +2024-09-06 07:06:31.664315: train_loss -0.7306 +2024-09-06 07:06:31.673235: val_loss -0.7361 +2024-09-06 07:06:31.674199: Pseudo dice [0.9615, 0.954, 0.9774, 0.9448, 0.8624, 0.9636, 0.9895] +2024-09-06 07:06:31.675101: Epoch time: 262.58 s +2024-09-06 07:06:34.832867: +2024-09-06 07:06:34.834402: Epoch 286 +2024-09-06 07:06:34.835653: Current learning rate: 0.00738 +2024-09-06 07:11:15.669103: train_loss -0.7212 +2024-09-06 07:11:15.677553: val_loss -0.7369 +2024-09-06 07:11:15.679534: Pseudo dice [0.964, 0.9558, 0.9792, 0.9492, 0.863, 0.9646, 0.9888] +2024-09-06 07:11:15.681154: Epoch time: 280.84 s +2024-09-06 07:11:18.329906: +2024-09-06 07:11:18.331083: Epoch 287 +2024-09-06 07:11:18.332316: Current learning rate: 0.00738 +2024-09-06 07:15:46.266092: train_loss -0.7311 +2024-09-06 07:15:46.284299: val_loss -0.7605 +2024-09-06 07:15:46.285476: Pseudo dice [0.9637, 0.956, 0.9791, 0.9456, 0.8791, 0.9685, 0.9921] +2024-09-06 07:15:46.286525: Epoch time: 267.94 s +2024-09-06 07:15:48.889738: +2024-09-06 07:15:48.891214: Epoch 288 +2024-09-06 07:15:48.892627: Current learning rate: 0.00737 +2024-09-06 07:20:27.399868: train_loss -0.7291 +2024-09-06 07:20:27.402761: val_loss -0.7677 +2024-09-06 07:20:27.403762: Pseudo dice [0.9629, 0.9561, 0.9771, 0.9473, 0.865, 0.9666, 0.9903] +2024-09-06 07:20:27.404684: Epoch time: 278.52 s +2024-09-06 07:20:27.405600: Yayy! New best EMA pseudo Dice: 0.9517 +2024-09-06 07:20:35.810281: +2024-09-06 07:20:35.811871: Epoch 289 +2024-09-06 07:20:35.813385: Current learning rate: 0.00736 +2024-09-06 07:24:57.347820: train_loss -0.7328 +2024-09-06 07:24:57.350593: val_loss -0.7666 +2024-09-06 07:24:57.351499: Pseudo dice [0.9612, 0.9558, 0.977, 0.9467, 0.8816, 0.9668, 0.9909] +2024-09-06 07:24:57.352802: Epoch time: 261.54 s +2024-09-06 07:24:57.353806: Yayy! New best EMA pseudo Dice: 0.952 +2024-09-06 07:25:05.807881: +2024-09-06 07:25:05.809066: Epoch 290 +2024-09-06 07:25:05.810047: Current learning rate: 0.00735 +2024-09-06 07:29:11.334292: train_loss -0.7177 +2024-09-06 07:29:11.338558: val_loss -0.7279 +2024-09-06 07:29:11.339621: Pseudo dice [0.9582, 0.9572, 0.9714, 0.9486, 0.8793, 0.9676, 0.9919] +2024-09-06 07:29:11.341191: Epoch time: 245.53 s +2024-09-06 07:29:11.342201: Yayy! New best EMA pseudo Dice: 0.9521 +2024-09-06 07:29:19.667971: +2024-09-06 07:29:19.669306: Epoch 291 +2024-09-06 07:29:19.670711: Current learning rate: 0.00734 +2024-09-06 07:33:30.328662: train_loss -0.7335 +2024-09-06 07:33:30.338735: val_loss -0.6979 +2024-09-06 07:33:30.357144: Pseudo dice [0.9612, 0.9585, 0.9749, 0.9452, 0.8776, 0.958, 0.9916] +2024-09-06 07:33:30.359481: Epoch time: 250.66 s +2024-09-06 07:33:30.360760: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-06 07:33:38.417525: +2024-09-06 07:33:38.419431: Epoch 292 +2024-09-06 07:33:38.421274: Current learning rate: 0.00733 +2024-09-06 07:37:58.443351: train_loss -0.7182 +2024-09-06 07:37:58.447284: val_loss -0.7505 +2024-09-06 07:37:58.448857: Pseudo dice [0.9585, 0.9518, 0.9715, 0.9446, 0.8659, 0.9626, 0.9906] +2024-09-06 07:37:58.449962: Epoch time: 260.03 s +2024-09-06 07:38:01.226636: +2024-09-06 07:38:01.227625: Epoch 293 +2024-09-06 07:38:01.228434: Current learning rate: 0.00732 +2024-09-06 07:42:35.134843: train_loss -0.7046 +2024-09-06 07:42:35.145699: val_loss -0.7061 +2024-09-06 07:42:35.146662: Pseudo dice [0.9622, 0.9543, 0.9785, 0.9468, 0.8722, 0.9639, 0.9921] +2024-09-06 07:42:35.147538: Epoch time: 273.9 s +2024-09-06 07:42:38.380619: +2024-09-06 07:42:38.381903: Epoch 294 +2024-09-06 07:42:38.382882: Current learning rate: 0.00731 +2024-09-06 07:47:16.548210: train_loss -0.7281 +2024-09-06 07:47:16.552943: val_loss -0.7357 +2024-09-06 07:47:16.554149: Pseudo dice [0.9609, 0.9559, 0.9752, 0.9452, 0.8781, 0.9666, 0.9917] +2024-09-06 07:47:16.555351: Epoch time: 278.17 s +2024-09-06 07:47:20.062165: +2024-09-06 07:47:20.063717: Epoch 295 +2024-09-06 07:47:20.064772: Current learning rate: 0.0073 +2024-09-06 07:52:02.758674: train_loss -0.7238 +2024-09-06 07:52:02.760530: val_loss -0.7588 +2024-09-06 07:52:02.761677: Pseudo dice [0.9648, 0.9582, 0.979, 0.9436, 0.8656, 0.9663, 0.9915] +2024-09-06 07:52:02.763087: Epoch time: 282.7 s +2024-09-06 07:52:02.764116: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-06 07:52:10.425993: +2024-09-06 07:52:10.427198: Epoch 296 +2024-09-06 07:52:10.428115: Current learning rate: 0.00729 +2024-09-06 07:56:40.331636: train_loss -0.7286 +2024-09-06 07:56:40.334882: val_loss -0.7381 +2024-09-06 07:56:40.336028: Pseudo dice [0.9605, 0.9534, 0.9761, 0.9509, 0.8748, 0.9625, 0.9909] +2024-09-06 07:56:40.339219: Epoch time: 269.91 s +2024-09-06 07:56:40.341093: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-06 07:56:48.144809: +2024-09-06 07:56:48.146034: Epoch 297 +2024-09-06 07:56:48.146983: Current learning rate: 0.00728 +2024-09-06 08:01:16.628211: train_loss -0.7195 +2024-09-06 08:01:16.643081: val_loss -0.7551 +2024-09-06 08:01:16.644322: Pseudo dice [0.9615, 0.9563, 0.9725, 0.942, 0.8575, 0.9597, 0.9873] +2024-09-06 08:01:16.645383: Epoch time: 268.49 s +2024-09-06 08:01:19.529256: +2024-09-06 08:01:19.530762: Epoch 298 +2024-09-06 08:01:19.532165: Current learning rate: 0.00727 +2024-09-06 08:05:46.742179: train_loss -0.7207 +2024-09-06 08:05:46.744393: val_loss -0.7263 +2024-09-06 08:05:46.745823: Pseudo dice [0.9564, 0.9545, 0.9716, 0.9353, 0.866, 0.9625, 0.991] +2024-09-06 08:05:46.747123: Epoch time: 267.22 s +2024-09-06 08:05:49.089253: +2024-09-06 08:05:49.090603: Epoch 299 +2024-09-06 08:05:49.091659: Current learning rate: 0.00726 +2024-09-06 08:10:07.580243: train_loss -0.7213 +2024-09-06 08:10:07.582655: val_loss -0.7746 +2024-09-06 08:10:07.583823: Pseudo dice [0.9599, 0.954, 0.9755, 0.9453, 0.8539, 0.9629, 0.9891] +2024-09-06 08:10:07.585423: Epoch time: 258.5 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_08_29_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_08_29_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..2bbd8f1aa61b0edcd84b36bff1ef272beafe9db3 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_6_08_29_45.txt @@ -0,0 +1,381 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 08:29:48.869936: Using torch.compile... +2024-09-06 08:29:57.380813: do_dummy_2d_data_aug: False +2024-09-06 08:29:57.386525: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 08:29:57.387854: The split file contains 5 splits. +2024-09-06 08:29:57.388441: Desired fold for training: 2 +2024-09-06 08:29:57.389107: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 08:30:10.925045: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 08:30:12.220559: +2024-09-06 08:30:12.221829: Epoch 300 +2024-09-06 08:30:12.222985: Current learning rate: 0.00725 +2024-09-06 08:48:47.464920: train_loss -0.7225 +2024-09-06 08:48:47.466155: val_loss -0.7504 +2024-09-06 08:48:47.466885: Pseudo dice [0.9598, 0.9542, 0.9753, 0.9412, 0.8731, 0.9647, 0.991] +2024-09-06 08:48:47.467673: Epoch time: 1115.25 s +2024-09-06 08:48:49.377599: +2024-09-06 08:48:49.378669: Epoch 301 +2024-09-06 08:48:49.379370: Current learning rate: 0.00724 +2024-09-06 08:53:15.787658: train_loss -0.73 +2024-09-06 08:53:15.789593: val_loss -0.7347 +2024-09-06 08:53:15.799809: Pseudo dice [0.9603, 0.9564, 0.9749, 0.9446, 0.8628, 0.9649, 0.9913] +2024-09-06 08:53:15.800565: Epoch time: 266.42 s +2024-09-06 08:53:20.038549: +2024-09-06 08:53:20.039381: Epoch 302 +2024-09-06 08:53:20.040094: Current learning rate: 0.00724 +2024-09-06 08:58:05.853227: train_loss -0.7139 +2024-09-06 08:58:05.879004: val_loss -0.7856 +2024-09-06 08:58:05.880758: Pseudo dice [0.9594, 0.9562, 0.9751, 0.9477, 0.8712, 0.9667, 0.9921] +2024-09-06 08:58:05.881871: Epoch time: 285.82 s +2024-09-06 08:58:08.640496: +2024-09-06 08:58:08.651914: Epoch 303 +2024-09-06 08:58:08.653075: Current learning rate: 0.00723 +2024-09-06 09:03:13.118797: train_loss -0.7267 +2024-09-06 09:03:13.135308: val_loss -0.7407 +2024-09-06 09:03:13.136735: Pseudo dice [0.9595, 0.9536, 0.9722, 0.9445, 0.8477, 0.9667, 0.9894] +2024-09-06 09:03:13.138069: Epoch time: 304.48 s +2024-09-06 09:03:16.279332: +2024-09-06 09:03:16.280672: Epoch 304 +2024-09-06 09:03:16.282119: Current learning rate: 0.00722 +2024-09-06 09:08:09.393321: train_loss -0.7218 +2024-09-06 09:08:09.407244: val_loss -0.7611 +2024-09-06 09:08:09.408508: Pseudo dice [0.9647, 0.9558, 0.9802, 0.9478, 0.8645, 0.9678, 0.9905] +2024-09-06 09:08:09.410414: Epoch time: 293.12 s +2024-09-06 09:08:11.923417: +2024-09-06 09:08:11.924826: Epoch 305 +2024-09-06 09:08:11.926134: Current learning rate: 0.00721 +2024-09-06 09:13:25.084624: train_loss -0.7205 +2024-09-06 09:13:25.112971: val_loss -0.7505 +2024-09-06 09:13:25.114182: Pseudo dice [0.9642, 0.9569, 0.9771, 0.9437, 0.8643, 0.9657, 0.9905] +2024-09-06 09:13:25.114928: Epoch time: 313.17 s +2024-09-06 09:13:28.039337: +2024-09-06 09:13:28.040540: Epoch 306 +2024-09-06 09:13:28.041635: Current learning rate: 0.0072 +2024-09-06 09:18:22.930827: train_loss -0.7296 +2024-09-06 09:18:22.935228: val_loss -0.7336 +2024-09-06 09:18:22.936916: Pseudo dice [0.9626, 0.9566, 0.9774, 0.9455, 0.872, 0.9648, 0.9918] +2024-09-06 09:18:22.938608: Epoch time: 294.9 s +2024-09-06 09:18:26.516951: +2024-09-06 09:18:26.517941: Epoch 307 +2024-09-06 09:18:26.519412: Current learning rate: 0.00719 +2024-09-06 09:23:11.550629: train_loss -0.7307 +2024-09-06 09:23:11.552861: val_loss -0.7496 +2024-09-06 09:23:11.553495: Pseudo dice [0.9599, 0.9543, 0.9754, 0.9479, 0.8689, 0.9618, 0.9905] +2024-09-06 09:23:11.554177: Epoch time: 285.04 s +2024-09-06 09:23:14.150618: +2024-09-06 09:23:14.151687: Epoch 308 +2024-09-06 09:23:14.152781: Current learning rate: 0.00718 +2024-09-06 09:28:10.888770: train_loss -0.7178 +2024-09-06 09:28:10.904567: val_loss -0.7528 +2024-09-06 09:28:10.905923: Pseudo dice [0.9611, 0.956, 0.9796, 0.9452, 0.881, 0.9657, 0.9919] +2024-09-06 09:28:10.907237: Epoch time: 296.74 s +2024-09-06 09:28:15.023853: +2024-09-06 09:28:15.039011: Epoch 309 +2024-09-06 09:28:15.041020: Current learning rate: 0.00717 +2024-09-06 09:33:02.274086: train_loss -0.7229 +2024-09-06 09:33:02.276341: val_loss -0.7178 +2024-09-06 09:33:02.277811: Pseudo dice [0.9601, 0.9555, 0.9743, 0.9519, 0.8517, 0.963, 0.9894] +2024-09-06 09:33:02.279162: Epoch time: 287.26 s +2024-09-06 09:33:04.794570: +2024-09-06 09:33:04.795956: Epoch 310 +2024-09-06 09:33:04.797302: Current learning rate: 0.00716 +2024-09-06 09:37:41.086664: train_loss -0.7254 +2024-09-06 09:37:41.108047: val_loss -0.7392 +2024-09-06 09:37:41.108813: Pseudo dice [0.9633, 0.9571, 0.9749, 0.9495, 0.8716, 0.963, 0.9916] +2024-09-06 09:37:41.109803: Epoch time: 276.3 s +2024-09-06 09:37:45.210384: +2024-09-06 09:37:45.211310: Epoch 311 +2024-09-06 09:37:45.211922: Current learning rate: 0.00715 +2024-09-06 09:42:40.446036: train_loss -0.7242 +2024-09-06 09:42:40.467680: val_loss -0.7533 +2024-09-06 09:42:40.469243: Pseudo dice [0.9631, 0.957, 0.9781, 0.9485, 0.8596, 0.9669, 0.99] +2024-09-06 09:42:40.470391: Epoch time: 295.24 s +2024-09-06 09:42:43.618451: +2024-09-06 09:42:43.619807: Epoch 312 +2024-09-06 09:42:43.621013: Current learning rate: 0.00714 +2024-09-06 09:47:35.952643: train_loss -0.7321 +2024-09-06 09:47:35.966805: val_loss -0.7383 +2024-09-06 09:47:35.967815: Pseudo dice [0.9603, 0.9545, 0.9763, 0.932, 0.875, 0.9695, 0.9916] +2024-09-06 09:47:35.968636: Epoch time: 292.34 s +2024-09-06 09:47:39.597468: +2024-09-06 09:47:39.598559: Epoch 313 +2024-09-06 09:47:39.599255: Current learning rate: 0.00713 +2024-09-06 09:52:43.762645: train_loss -0.7314 +2024-09-06 09:52:43.779305: val_loss -0.7816 +2024-09-06 09:52:43.780489: Pseudo dice [0.9619, 0.957, 0.9772, 0.9449, 0.8712, 0.9658, 0.9907] +2024-09-06 09:52:43.782461: Epoch time: 304.17 s +2024-09-06 09:52:46.728832: +2024-09-06 09:52:46.730186: Epoch 314 +2024-09-06 09:52:46.731152: Current learning rate: 0.00712 +2024-09-06 09:57:14.245381: train_loss -0.7335 +2024-09-06 09:57:14.266931: val_loss -0.7672 +2024-09-06 09:57:14.268178: Pseudo dice [0.9621, 0.9547, 0.9775, 0.9477, 0.8693, 0.9677, 0.9896] +2024-09-06 09:57:14.269117: Epoch time: 267.52 s +2024-09-06 09:57:18.208384: +2024-09-06 09:57:18.209198: Epoch 315 +2024-09-06 09:57:18.210116: Current learning rate: 0.00711 +2024-09-06 10:01:58.611508: train_loss -0.7265 +2024-09-06 10:01:58.650038: val_loss -0.6993 +2024-09-06 10:01:58.651173: Pseudo dice [0.9638, 0.9583, 0.9773, 0.95, 0.8791, 0.9685, 0.9913] +2024-09-06 10:01:58.652322: Epoch time: 280.41 s +2024-09-06 10:02:01.711185: +2024-09-06 10:02:01.712375: Epoch 316 +2024-09-06 10:02:01.713478: Current learning rate: 0.0071 +2024-09-06 10:06:41.387180: train_loss -0.7413 +2024-09-06 10:06:41.400852: val_loss -0.7804 +2024-09-06 10:06:41.402313: Pseudo dice [0.9626, 0.9533, 0.9772, 0.9479, 0.8781, 0.9686, 0.9901] +2024-09-06 10:06:41.403499: Epoch time: 279.68 s +2024-09-06 10:06:41.404780: Yayy! New best EMA pseudo Dice: 0.9523 +2024-09-06 10:06:49.956809: +2024-09-06 10:06:49.957590: Epoch 317 +2024-09-06 10:06:49.958216: Current learning rate: 0.0071 +2024-09-06 10:11:31.004437: train_loss -0.7321 +2024-09-06 10:11:31.020972: val_loss -0.765 +2024-09-06 10:11:31.022639: Pseudo dice [0.9655, 0.9589, 0.9807, 0.9497, 0.8713, 0.9675, 0.992] +2024-09-06 10:11:31.023700: Epoch time: 281.05 s +2024-09-06 10:11:31.025011: Yayy! New best EMA pseudo Dice: 0.9526 +2024-09-06 10:11:39.201719: +2024-09-06 10:11:39.202780: Epoch 318 +2024-09-06 10:11:39.203968: Current learning rate: 0.00709 +2024-09-06 10:16:25.937043: train_loss -0.731 +2024-09-06 10:16:25.950131: val_loss -0.7162 +2024-09-06 10:16:25.956160: Pseudo dice [0.9627, 0.9564, 0.978, 0.9504, 0.8842, 0.9666, 0.9917] +2024-09-06 10:16:25.962236: Epoch time: 286.74 s +2024-09-06 10:16:25.967913: Yayy! New best EMA pseudo Dice: 0.9529 +2024-09-06 10:16:35.500201: +2024-09-06 10:16:35.501238: Epoch 319 +2024-09-06 10:16:35.502364: Current learning rate: 0.00708 +2024-09-06 10:21:24.219221: train_loss -0.738 +2024-09-06 10:21:24.234607: val_loss -0.7532 +2024-09-06 10:21:24.236645: Pseudo dice [0.9638, 0.9569, 0.978, 0.9472, 0.8833, 0.9673, 0.9913] +2024-09-06 10:21:24.238109: Epoch time: 288.72 s +2024-09-06 10:21:24.239583: Yayy! New best EMA pseudo Dice: 0.9532 +2024-09-06 10:21:33.468845: +2024-09-06 10:21:33.470044: Epoch 320 +2024-09-06 10:21:33.470868: Current learning rate: 0.00707 +2024-09-06 10:26:23.560895: train_loss -0.7156 +2024-09-06 10:26:23.575969: val_loss -0.733 +2024-09-06 10:26:23.576738: Pseudo dice [0.9628, 0.954, 0.9761, 0.9493, 0.8599, 0.9645, 0.9909] +2024-09-06 10:26:23.577394: Epoch time: 290.1 s +2024-09-06 10:26:26.701140: +2024-09-06 10:26:26.702212: Epoch 321 +2024-09-06 10:26:26.702841: Current learning rate: 0.00706 +2024-09-06 10:31:37.370245: train_loss -0.7291 +2024-09-06 10:31:37.385027: val_loss -0.7518 +2024-09-06 10:31:37.386074: Pseudo dice [0.9644, 0.9572, 0.9793, 0.9506, 0.8912, 0.9692, 0.9919] +2024-09-06 10:31:37.387458: Epoch time: 310.67 s +2024-09-06 10:31:37.388823: Yayy! New best EMA pseudo Dice: 0.9534 +2024-09-06 10:31:46.327756: +2024-09-06 10:31:46.328795: Epoch 322 +2024-09-06 10:31:46.329886: Current learning rate: 0.00705 +2024-09-06 10:36:42.680033: train_loss -0.7319 +2024-09-06 10:36:42.696485: val_loss -0.7274 +2024-09-06 10:36:42.697913: Pseudo dice [0.9623, 0.9564, 0.9745, 0.9459, 0.8787, 0.9702, 0.9916] +2024-09-06 10:36:42.699269: Epoch time: 296.36 s +2024-09-06 10:36:42.700741: Yayy! New best EMA pseudo Dice: 0.9535 +2024-09-06 10:36:51.004863: +2024-09-06 10:36:51.005992: Epoch 323 +2024-09-06 10:36:51.007867: Current learning rate: 0.00704 +2024-09-06 10:41:44.275481: train_loss -0.738 +2024-09-06 10:41:44.276875: val_loss -0.7708 +2024-09-06 10:41:44.280361: Pseudo dice [0.9621, 0.9573, 0.9771, 0.9524, 0.8726, 0.9673, 0.9914] +2024-09-06 10:41:44.281139: Epoch time: 293.27 s +2024-09-06 10:41:44.281972: Yayy! New best EMA pseudo Dice: 0.9536 +2024-09-06 10:41:53.486376: +2024-09-06 10:41:53.487242: Epoch 324 +2024-09-06 10:41:53.488245: Current learning rate: 0.00703 +2024-09-06 10:46:37.453569: train_loss -0.7201 +2024-09-06 10:46:37.468152: val_loss -0.7326 +2024-09-06 10:46:37.469081: Pseudo dice [0.9633, 0.9573, 0.9755, 0.9512, 0.8813, 0.9691, 0.9906] +2024-09-06 10:46:37.469901: Epoch time: 283.97 s +2024-09-06 10:46:37.471246: Yayy! New best EMA pseudo Dice: 0.9538 +2024-09-06 10:46:47.194887: +2024-09-06 10:46:47.195685: Epoch 325 +2024-09-06 10:46:47.196608: Current learning rate: 0.00702 +2024-09-06 10:51:26.760237: train_loss -0.7343 +2024-09-06 10:51:26.788900: val_loss -0.7318 +2024-09-06 10:51:26.790070: Pseudo dice [0.9644, 0.9591, 0.9773, 0.952, 0.8771, 0.9694, 0.9913] +2024-09-06 10:51:26.791453: Epoch time: 279.57 s +2024-09-06 10:51:26.792750: Yayy! New best EMA pseudo Dice: 0.954 +2024-09-06 10:51:35.480156: +2024-09-06 10:51:35.481093: Epoch 326 +2024-09-06 10:51:35.482083: Current learning rate: 0.00701 +2024-09-06 10:56:24.530824: train_loss -0.7505 +2024-09-06 10:56:24.535568: val_loss -0.7784 +2024-09-06 10:56:24.536825: Pseudo dice [0.9639, 0.9554, 0.9792, 0.9491, 0.8785, 0.972, 0.9919] +2024-09-06 10:56:24.537939: Epoch time: 289.06 s +2024-09-06 10:56:24.538760: Yayy! New best EMA pseudo Dice: 0.9542 +2024-09-06 10:56:33.913791: +2024-09-06 10:56:33.914447: Epoch 327 +2024-09-06 10:56:33.915712: Current learning rate: 0.007 +2024-09-06 11:01:34.557011: train_loss -0.75 +2024-09-06 11:01:34.572113: val_loss -0.7343 +2024-09-06 11:01:34.572946: Pseudo dice [0.9615, 0.9555, 0.9757, 0.9451, 0.8817, 0.9619, 0.9907] +2024-09-06 11:01:34.573584: Epoch time: 300.65 s +2024-09-06 11:01:37.191585: +2024-09-06 11:01:37.192915: Epoch 328 +2024-09-06 11:01:37.194235: Current learning rate: 0.00699 +2024-09-06 11:06:24.125121: train_loss -0.735 +2024-09-06 11:06:24.128028: val_loss -0.7331 +2024-09-06 11:06:24.129316: Pseudo dice [0.9616, 0.9553, 0.9757, 0.9464, 0.8492, 0.966, 0.9895] +2024-09-06 11:06:24.130990: Epoch time: 286.94 s +2024-09-06 11:06:27.430705: +2024-09-06 11:06:27.431972: Epoch 329 +2024-09-06 11:06:27.435908: Current learning rate: 0.00698 +2024-09-06 11:11:34.742045: train_loss -0.7345 +2024-09-06 11:11:34.751152: val_loss -0.7399 +2024-09-06 11:11:34.752032: Pseudo dice [0.9615, 0.9551, 0.9748, 0.9495, 0.8653, 0.9627, 0.9913] +2024-09-06 11:11:34.752836: Epoch time: 307.32 s +2024-09-06 11:11:38.174309: +2024-09-06 11:11:38.175249: Epoch 330 +2024-09-06 11:11:38.175967: Current learning rate: 0.00697 +2024-09-06 11:16:25.070851: train_loss -0.7151 +2024-09-06 11:16:25.087413: val_loss -0.7432 +2024-09-06 11:16:25.088737: Pseudo dice [0.9597, 0.9527, 0.9751, 0.9414, 0.8591, 0.9676, 0.9908] +2024-09-06 11:16:25.089795: Epoch time: 286.9 s +2024-09-06 11:16:27.751252: +2024-09-06 11:16:27.752612: Epoch 331 +2024-09-06 11:16:27.753680: Current learning rate: 0.00696 +2024-09-06 11:21:18.361364: train_loss -0.7322 +2024-09-06 11:21:18.362675: val_loss -0.737 +2024-09-06 11:21:18.363473: Pseudo dice [0.9611, 0.9544, 0.9762, 0.9449, 0.8784, 0.9658, 0.9911] +2024-09-06 11:21:18.364174: Epoch time: 290.61 s +2024-09-06 11:21:22.103207: +2024-09-06 11:21:22.104365: Epoch 332 +2024-09-06 11:21:22.106213: Current learning rate: 0.00696 +2024-09-06 11:26:16.473320: train_loss -0.7428 +2024-09-06 11:26:16.476883: val_loss -0.7302 +2024-09-06 11:26:16.477917: Pseudo dice [0.9582, 0.952, 0.9746, 0.9433, 0.8535, 0.955, 0.9872] +2024-09-06 11:26:16.479000: Epoch time: 294.38 s +2024-09-06 11:26:21.123195: +2024-09-06 11:26:21.137501: Epoch 333 +2024-09-06 11:26:21.140302: Current learning rate: 0.00695 +2024-09-06 11:31:20.049664: train_loss -0.7241 +2024-09-06 11:31:20.055914: val_loss -0.7652 +2024-09-06 11:31:20.057361: Pseudo dice [0.9631, 0.9579, 0.977, 0.945, 0.8709, 0.9683, 0.9912] +2024-09-06 11:31:20.058093: Epoch time: 298.93 s +2024-09-06 11:31:22.689975: +2024-09-06 11:31:22.691211: Epoch 334 +2024-09-06 11:31:22.692043: Current learning rate: 0.00694 +2024-09-06 11:36:16.622607: train_loss -0.742 +2024-09-06 11:36:16.655009: val_loss -0.7326 +2024-09-06 11:36:16.655918: Pseudo dice [0.9618, 0.9557, 0.9747, 0.9433, 0.8735, 0.9686, 0.9911] +2024-09-06 11:36:16.656643: Epoch time: 293.94 s +2024-09-06 11:36:20.654957: +2024-09-06 11:36:20.655833: Epoch 335 +2024-09-06 11:36:20.656528: Current learning rate: 0.00693 +2024-09-06 11:41:02.521757: train_loss -0.7307 +2024-09-06 11:41:02.526773: val_loss -0.7495 +2024-09-06 11:41:02.527489: Pseudo dice [0.9605, 0.9587, 0.9712, 0.9486, 0.8872, 0.9666, 0.9917] +2024-09-06 11:41:02.528210: Epoch time: 281.87 s +2024-09-06 11:41:06.471339: +2024-09-06 11:41:06.486700: Epoch 336 +2024-09-06 11:41:06.487578: Current learning rate: 0.00692 +2024-09-06 11:45:39.608369: train_loss -0.7489 +2024-09-06 11:45:39.610720: val_loss -0.7445 +2024-09-06 11:45:39.611823: Pseudo dice [0.9579, 0.9578, 0.9726, 0.9478, 0.877, 0.965, 0.9895] +2024-09-06 11:45:39.612681: Epoch time: 273.14 s +2024-09-06 11:45:43.949094: +2024-09-06 11:45:43.950021: Epoch 337 +2024-09-06 11:45:43.950762: Current learning rate: 0.00691 +2024-09-06 11:50:32.763213: train_loss -0.7393 +2024-09-06 11:50:32.779022: val_loss -0.7426 +2024-09-06 11:50:32.779856: Pseudo dice [0.9611, 0.9555, 0.9759, 0.9454, 0.8712, 0.9702, 0.9916] +2024-09-06 11:50:32.780543: Epoch time: 288.82 s +2024-09-06 11:50:36.066017: +2024-09-06 11:50:36.067178: Epoch 338 +2024-09-06 11:50:36.067852: Current learning rate: 0.0069 +2024-09-06 11:55:43.173263: train_loss -0.7273 +2024-09-06 11:55:43.175067: val_loss -0.7835 +2024-09-06 11:55:43.176103: Pseudo dice [0.9602, 0.9536, 0.973, 0.9436, 0.8657, 0.9649, 0.9903] +2024-09-06 11:55:43.177549: Epoch time: 307.11 s +2024-09-06 11:55:45.481941: +2024-09-06 11:55:45.483695: Epoch 339 +2024-09-06 11:55:45.485550: Current learning rate: 0.00689 +2024-09-06 12:00:31.522596: train_loss -0.7297 +2024-09-06 12:00:31.536944: val_loss -0.7475 +2024-09-06 12:00:31.537970: Pseudo dice [0.9621, 0.9545, 0.9775, 0.946, 0.8809, 0.969, 0.9916] +2024-09-06 12:00:31.538818: Epoch time: 286.05 s +2024-09-06 12:00:34.412687: +2024-09-06 12:00:34.414170: Epoch 340 +2024-09-06 12:00:34.415129: Current learning rate: 0.00688 +2024-09-06 12:05:41.674214: train_loss -0.7385 +2024-09-06 12:05:41.686029: val_loss -0.7273 +2024-09-06 12:05:41.686752: Pseudo dice [0.9638, 0.9567, 0.9786, 0.951, 0.8783, 0.9684, 0.9899] +2024-09-06 12:05:41.687349: Epoch time: 307.27 s +2024-09-06 12:05:46.346793: +2024-09-06 12:05:46.348020: Epoch 341 +2024-09-06 12:05:46.348941: Current learning rate: 0.00687 +2024-09-06 12:10:33.035985: train_loss -0.7525 +2024-09-06 12:10:33.045265: val_loss -0.7847 +2024-09-06 12:10:33.046079: Pseudo dice [0.9616, 0.9571, 0.9771, 0.9488, 0.8763, 0.9683, 0.9913] +2024-09-06 12:10:33.046736: Epoch time: 286.69 s +2024-09-06 12:10:36.232784: +2024-09-06 12:10:36.233724: Epoch 342 +2024-09-06 12:10:36.234460: Current learning rate: 0.00686 +2024-09-06 12:15:41.016481: train_loss -0.73 +2024-09-06 12:15:41.019687: val_loss -0.7705 +2024-09-06 12:15:41.021471: Pseudo dice [0.9653, 0.9588, 0.9791, 0.9516, 0.875, 0.9685, 0.99] +2024-09-06 12:15:41.022748: Epoch time: 304.79 s +2024-09-06 12:15:43.499012: +2024-09-06 12:15:43.499898: Epoch 343 +2024-09-06 12:15:43.500619: Current learning rate: 0.00685 +2024-09-06 12:20:36.746315: train_loss -0.7467 +2024-09-06 12:20:36.760098: val_loss -0.7663 +2024-09-06 12:20:36.761097: Pseudo dice [0.962, 0.9575, 0.9772, 0.9495, 0.8711, 0.9651, 0.9903] +2024-09-06 12:20:36.762030: Epoch time: 293.25 s +2024-09-06 12:20:41.050576: +2024-09-06 12:20:41.051501: Epoch 344 +2024-09-06 12:20:41.052207: Current learning rate: 0.00684 +2024-09-06 12:25:19.311338: train_loss -0.7214 +2024-09-06 12:25:19.325980: val_loss -0.7324 +2024-09-06 12:25:19.326812: Pseudo dice [0.9625, 0.9553, 0.9792, 0.9468, 0.8852, 0.9675, 0.9922] +2024-09-06 12:25:19.328150: Epoch time: 278.27 s +2024-09-06 12:25:22.928343: +2024-09-06 12:25:22.929146: Epoch 345 +2024-09-06 12:25:22.929926: Current learning rate: 0.00683 +2024-09-06 12:30:20.209343: train_loss -0.7254 +2024-09-06 12:30:20.223325: val_loss -0.7197 +2024-09-06 12:30:20.224151: Pseudo dice [0.9616, 0.9582, 0.9744, 0.9454, 0.8824, 0.9694, 0.992] +2024-09-06 12:30:20.224913: Epoch time: 297.29 s +2024-09-06 12:30:23.743028: +2024-09-06 12:30:23.744183: Epoch 346 +2024-09-06 12:30:23.745183: Current learning rate: 0.00682 +2024-09-06 12:35:14.159295: train_loss -0.7245 +2024-09-06 12:35:14.162442: val_loss -0.743 +2024-09-06 12:35:14.163427: Pseudo dice [0.9612, 0.9554, 0.979, 0.9501, 0.8828, 0.9685, 0.9904] +2024-09-06 12:35:14.164274: Epoch time: 290.42 s +2024-09-06 12:35:17.956861: +2024-09-06 12:35:17.957762: Epoch 347 +2024-09-06 12:35:17.958579: Current learning rate: 0.00681 +2024-09-06 12:40:02.568656: train_loss -0.727 +2024-09-06 12:40:02.592170: val_loss -0.7301 +2024-09-06 12:40:02.592973: Pseudo dice [0.9569, 0.9521, 0.9734, 0.948, 0.8732, 0.9694, 0.9914] +2024-09-06 12:40:02.593714: Epoch time: 284.62 s +2024-09-06 12:40:05.881681: +2024-09-06 12:40:05.882824: Epoch 348 +2024-09-06 12:40:05.883561: Current learning rate: 0.0068 +2024-09-06 12:44:48.057667: train_loss -0.7204 +2024-09-06 12:44:48.067475: val_loss -0.7626 +2024-09-06 12:44:48.074267: Pseudo dice [0.9565, 0.9539, 0.972, 0.9413, 0.8562, 0.9611, 0.9893] +2024-09-06 12:44:48.075518: Epoch time: 282.18 s +2024-09-06 12:44:51.322128: +2024-09-06 12:44:51.323005: Epoch 349 +2024-09-06 12:44:51.323665: Current learning rate: 0.0068 +2024-09-06 12:49:56.054432: train_loss -0.6966 +2024-09-06 12:49:56.069792: val_loss -0.7489 +2024-09-06 12:49:56.070937: Pseudo dice [0.9583, 0.9531, 0.9748, 0.94, 0.8565, 0.9418, 0.9893] +2024-09-06 12:49:56.072017: Epoch time: 304.74 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_14_05_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_14_05_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd5f29fa6168848a526eca4a6818db3451442f64 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_14_05_16.txt @@ -0,0 +1,372 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 14:05:19.149785: Using torch.compile... +2024-09-07 14:05:24.858248: do_dummy_2d_data_aug: False +2024-09-07 14:05:24.878513: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 14:05:24.895469: The split file contains 5 splits. +2024-09-07 14:05:24.896024: Desired fold for training: 2 +2024-09-07 14:05:24.896502: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 14:05:41.127778: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 14:05:42.240709: +2024-09-07 14:05:42.256445: Epoch 350 +2024-09-07 14:05:42.257405: Current learning rate: 0.00679 +2024-09-07 14:22:06.233508: train_loss -0.6995 +2024-09-07 14:22:06.235826: val_loss -0.7393 +2024-09-07 14:22:06.236700: Pseudo dice [0.9585, 0.9522, 0.972, 0.9381, 0.8572, 0.9602, 0.9896] +2024-09-07 14:22:06.237344: Epoch time: 984.0 s +2024-09-07 14:22:08.077699: +2024-09-07 14:22:08.078754: Epoch 351 +2024-09-07 14:22:08.079330: Current learning rate: 0.00678 +2024-09-07 14:29:13.701948: train_loss -0.7234 +2024-09-07 14:29:13.705407: val_loss -0.7058 +2024-09-07 14:29:13.706481: Pseudo dice [0.9584, 0.9558, 0.9729, 0.9432, 0.8709, 0.9675, 0.9912] +2024-09-07 14:29:13.713593: Epoch time: 425.63 s +2024-09-07 14:29:17.200239: +2024-09-07 14:29:17.202130: Epoch 352 +2024-09-07 14:29:17.203861: Current learning rate: 0.00677 +2024-09-07 14:36:09.170829: train_loss -0.7235 +2024-09-07 14:36:09.185367: val_loss -0.7543 +2024-09-07 14:36:09.186457: Pseudo dice [0.9592, 0.9566, 0.9717, 0.9467, 0.8669, 0.9644, 0.9903] +2024-09-07 14:36:09.187250: Epoch time: 411.99 s +2024-09-07 14:36:12.873859: +2024-09-07 14:36:12.888546: Epoch 353 +2024-09-07 14:36:12.889365: Current learning rate: 0.00676 +2024-09-07 14:42:44.807558: train_loss -0.7083 +2024-09-07 14:42:44.811204: val_loss -0.6873 +2024-09-07 14:42:44.812211: Pseudo dice [0.9597, 0.9525, 0.9752, 0.9442, 0.8713, 0.9665, 0.9895] +2024-09-07 14:42:44.813143: Epoch time: 391.94 s +2024-09-07 14:42:48.928410: +2024-09-07 14:42:48.936399: Epoch 354 +2024-09-07 14:42:48.937156: Current learning rate: 0.00675 +2024-09-07 14:50:20.266319: train_loss -0.7098 +2024-09-07 14:50:20.287760: val_loss -0.7675 +2024-09-07 14:50:20.288808: Pseudo dice [0.9586, 0.9569, 0.974, 0.9475, 0.8826, 0.9671, 0.9913] +2024-09-07 14:50:20.289834: Epoch time: 451.35 s +2024-09-07 14:50:24.069774: +2024-09-07 14:50:24.070892: Epoch 355 +2024-09-07 14:50:24.071704: Current learning rate: 0.00674 +2024-09-07 14:57:21.164743: train_loss -0.736 +2024-09-07 14:57:21.166552: val_loss -0.7424 +2024-09-07 14:57:21.167585: Pseudo dice [0.9628, 0.9553, 0.9784, 0.9453, 0.8836, 0.9671, 0.9909] +2024-09-07 14:57:21.168324: Epoch time: 417.1 s +2024-09-07 14:57:25.703676: +2024-09-07 14:57:25.704860: Epoch 356 +2024-09-07 14:57:25.705521: Current learning rate: 0.00673 +2024-09-07 15:03:26.216569: train_loss -0.7441 +2024-09-07 15:03:26.228024: val_loss -0.7517 +2024-09-07 15:03:26.229251: Pseudo dice [0.9625, 0.9595, 0.9787, 0.9483, 0.8862, 0.9699, 0.9927] +2024-09-07 15:03:26.230520: Epoch time: 360.52 s +2024-09-07 15:03:29.094595: +2024-09-07 15:03:29.097381: Epoch 357 +2024-09-07 15:03:29.099352: Current learning rate: 0.00672 +2024-09-07 15:09:50.577056: train_loss -0.7278 +2024-09-07 15:09:50.613576: val_loss -0.7496 +2024-09-07 15:09:50.614353: Pseudo dice [0.9636, 0.9569, 0.978, 0.9422, 0.864, 0.9655, 0.9898] +2024-09-07 15:09:50.615056: Epoch time: 381.49 s +2024-09-07 15:09:55.414501: +2024-09-07 15:09:55.415975: Epoch 358 +2024-09-07 15:09:55.416858: Current learning rate: 0.00671 +2024-09-07 15:16:17.524096: train_loss -0.711 +2024-09-07 15:16:17.526253: val_loss -0.6991 +2024-09-07 15:16:17.527081: Pseudo dice [0.9599, 0.9557, 0.9752, 0.9471, 0.8629, 0.9689, 0.9897] +2024-09-07 15:16:17.527733: Epoch time: 382.12 s +2024-09-07 15:16:20.236357: +2024-09-07 15:16:20.237864: Epoch 359 +2024-09-07 15:16:20.238915: Current learning rate: 0.0067 +2024-09-07 15:22:42.715981: train_loss -0.7268 +2024-09-07 15:22:42.734145: val_loss -0.7792 +2024-09-07 15:22:42.735040: Pseudo dice [0.9633, 0.9569, 0.977, 0.949, 0.8765, 0.9679, 0.9908] +2024-09-07 15:22:42.735922: Epoch time: 382.49 s +2024-09-07 15:22:47.210201: +2024-09-07 15:22:47.221471: Epoch 360 +2024-09-07 15:22:47.223792: Current learning rate: 0.00669 +2024-09-07 15:28:51.217828: train_loss -0.742 +2024-09-07 15:28:51.235261: val_loss -0.7094 +2024-09-07 15:28:51.236722: Pseudo dice [0.9601, 0.9526, 0.9759, 0.9489, 0.86, 0.9654, 0.9897] +2024-09-07 15:28:51.238175: Epoch time: 364.02 s +2024-09-07 15:28:55.796744: +2024-09-07 15:28:55.797817: Epoch 361 +2024-09-07 15:28:55.798451: Current learning rate: 0.00668 +2024-09-07 15:35:28.731390: train_loss -0.7386 +2024-09-07 15:35:28.753196: val_loss -0.7485 +2024-09-07 15:35:28.754154: Pseudo dice [0.9631, 0.9569, 0.9779, 0.9551, 0.883, 0.9675, 0.9893] +2024-09-07 15:35:28.755232: Epoch time: 392.94 s +2024-09-07 15:35:34.017932: +2024-09-07 15:35:34.019905: Epoch 362 +2024-09-07 15:35:34.021204: Current learning rate: 0.00667 +2024-09-07 15:42:14.765382: train_loss -0.7418 +2024-09-07 15:42:14.768740: val_loss -0.7192 +2024-09-07 15:42:14.769497: Pseudo dice [0.9605, 0.9563, 0.974, 0.9387, 0.8723, 0.9695, 0.9916] +2024-09-07 15:42:14.770241: Epoch time: 400.76 s +2024-09-07 15:42:18.860107: +2024-09-07 15:42:18.861650: Epoch 363 +2024-09-07 15:42:18.862843: Current learning rate: 0.00666 +2024-09-07 15:48:22.236535: train_loss -0.7374 +2024-09-07 15:48:22.252981: val_loss -0.699 +2024-09-07 15:48:22.254174: Pseudo dice [0.9582, 0.9551, 0.9727, 0.9518, 0.8526, 0.9636, 0.9886] +2024-09-07 15:48:22.255532: Epoch time: 363.4 s +2024-09-07 15:48:31.041668: +2024-09-07 15:48:31.043687: Epoch 364 +2024-09-07 15:48:31.044733: Current learning rate: 0.00665 +2024-09-07 15:54:37.452888: train_loss -0.7374 +2024-09-07 15:54:37.455355: val_loss -0.7628 +2024-09-07 15:54:37.456227: Pseudo dice [0.9652, 0.9591, 0.9769, 0.9524, 0.8816, 0.9712, 0.9912] +2024-09-07 15:54:37.457052: Epoch time: 366.42 s +2024-09-07 15:54:42.986387: +2024-09-07 15:54:42.987557: Epoch 365 +2024-09-07 15:54:42.988393: Current learning rate: 0.00665 +2024-09-07 16:00:47.035296: train_loss -0.7127 +2024-09-07 16:00:47.037140: val_loss -0.7848 +2024-09-07 16:00:47.038382: Pseudo dice [0.9637, 0.9573, 0.9781, 0.9455, 0.8832, 0.971, 0.9928] +2024-09-07 16:00:47.039755: Epoch time: 364.05 s +2024-09-07 16:00:50.938027: +2024-09-07 16:00:50.956667: Epoch 366 +2024-09-07 16:00:50.957846: Current learning rate: 0.00664 +2024-09-07 16:07:08.911806: train_loss -0.7262 +2024-09-07 16:07:08.929701: val_loss -0.7666 +2024-09-07 16:07:08.930424: Pseudo dice [0.9625, 0.9554, 0.9766, 0.9446, 0.8806, 0.9674, 0.9924] +2024-09-07 16:07:08.931187: Epoch time: 377.98 s +2024-09-07 16:07:13.112524: +2024-09-07 16:07:13.113729: Epoch 367 +2024-09-07 16:07:13.114676: Current learning rate: 0.00663 +2024-09-07 16:13:37.856963: train_loss -0.7246 +2024-09-07 16:13:37.885478: val_loss -0.7639 +2024-09-07 16:13:37.886184: Pseudo dice [0.9556, 0.9536, 0.9682, 0.946, 0.8805, 0.9664, 0.9911] +2024-09-07 16:13:37.886755: Epoch time: 384.76 s +2024-09-07 16:13:41.795561: +2024-09-07 16:13:41.819807: Epoch 368 +2024-09-07 16:13:41.821643: Current learning rate: 0.00662 +2024-09-07 16:20:06.403971: train_loss -0.7265 +2024-09-07 16:20:06.405465: val_loss -0.7456 +2024-09-07 16:20:06.406155: Pseudo dice [0.961, 0.9559, 0.9743, 0.9459, 0.8709, 0.9668, 0.9907] +2024-09-07 16:20:06.406919: Epoch time: 384.62 s +2024-09-07 16:20:09.775934: +2024-09-07 16:20:09.777280: Epoch 369 +2024-09-07 16:20:09.778101: Current learning rate: 0.00661 +2024-09-07 16:26:00.460726: train_loss -0.7241 +2024-09-07 16:26:00.462948: val_loss -0.7713 +2024-09-07 16:26:00.463695: Pseudo dice [0.9554, 0.9552, 0.9729, 0.9477, 0.8794, 0.9697, 0.9905] +2024-09-07 16:26:00.464407: Epoch time: 350.69 s +2024-09-07 16:26:03.833820: +2024-09-07 16:26:03.835170: Epoch 370 +2024-09-07 16:26:03.836011: Current learning rate: 0.0066 +2024-09-07 16:32:34.466703: train_loss -0.7474 +2024-09-07 16:32:34.469709: val_loss -0.712 +2024-09-07 16:32:34.471186: Pseudo dice [0.9603, 0.9555, 0.974, 0.9445, 0.8696, 0.9685, 0.9902] +2024-09-07 16:32:34.472115: Epoch time: 390.64 s +2024-09-07 16:32:38.934526: +2024-09-07 16:32:38.948920: Epoch 371 +2024-09-07 16:32:38.950315: Current learning rate: 0.00659 +2024-09-07 16:38:33.734338: train_loss -0.7202 +2024-09-07 16:38:33.735967: val_loss -0.7827 +2024-09-07 16:38:33.736694: Pseudo dice [0.9641, 0.9567, 0.9766, 0.9497, 0.8802, 0.9648, 0.9931] +2024-09-07 16:38:33.737345: Epoch time: 354.81 s +2024-09-07 16:38:37.375364: +2024-09-07 16:38:37.389486: Epoch 372 +2024-09-07 16:38:37.391694: Current learning rate: 0.00658 +2024-09-07 16:44:38.249243: train_loss -0.7464 +2024-09-07 16:44:38.251476: val_loss -0.724 +2024-09-07 16:44:38.252191: Pseudo dice [0.9603, 0.9557, 0.9756, 0.9466, 0.8911, 0.9699, 0.9926] +2024-09-07 16:44:38.252851: Epoch time: 360.88 s +2024-09-07 16:44:41.678713: +2024-09-07 16:44:41.680164: Epoch 373 +2024-09-07 16:44:41.681162: Current learning rate: 0.00657 +2024-09-07 16:50:48.281005: train_loss -0.7217 +2024-09-07 16:50:48.304415: val_loss -0.782 +2024-09-07 16:50:48.307247: Pseudo dice [0.9643, 0.9585, 0.9762, 0.9499, 0.8772, 0.9705, 0.9916] +2024-09-07 16:50:48.309326: Epoch time: 366.61 s +2024-09-07 16:50:53.258778: +2024-09-07 16:50:53.272490: Epoch 374 +2024-09-07 16:50:53.273476: Current learning rate: 0.00656 +2024-09-07 16:57:03.013880: train_loss -0.7363 +2024-09-07 16:57:03.037992: val_loss -0.7696 +2024-09-07 16:57:03.039104: Pseudo dice [0.966, 0.9584, 0.9813, 0.9508, 0.8803, 0.9674, 0.9923] +2024-09-07 16:57:03.039915: Epoch time: 369.76 s +2024-09-07 16:57:07.106153: +2024-09-07 16:57:07.120883: Epoch 375 +2024-09-07 16:57:07.122034: Current learning rate: 0.00655 +2024-09-07 17:03:06.595727: train_loss -0.7138 +2024-09-07 17:03:06.612730: val_loss -0.7251 +2024-09-07 17:03:06.613626: Pseudo dice [0.9603, 0.9554, 0.974, 0.9447, 0.8647, 0.9538, 0.9868] +2024-09-07 17:03:06.614383: Epoch time: 359.5 s +2024-09-07 17:03:09.828543: +2024-09-07 17:03:09.844764: Epoch 376 +2024-09-07 17:03:09.845857: Current learning rate: 0.00654 +2024-09-07 17:09:21.685957: train_loss -0.7095 +2024-09-07 17:09:21.697076: val_loss -0.7463 +2024-09-07 17:09:21.698269: Pseudo dice [0.9578, 0.9498, 0.9739, 0.9387, 0.8637, 0.9514, 0.9907] +2024-09-07 17:09:21.699337: Epoch time: 371.87 s +2024-09-07 17:09:28.755318: +2024-09-07 17:09:28.763572: Epoch 377 +2024-09-07 17:09:28.765914: Current learning rate: 0.00653 +2024-09-07 17:15:23.128603: train_loss -0.6965 +2024-09-07 17:15:23.141532: val_loss -0.7379 +2024-09-07 17:15:23.142535: Pseudo dice [0.96, 0.9557, 0.9734, 0.9436, 0.858, 0.9632, 0.9894] +2024-09-07 17:15:23.143399: Epoch time: 354.39 s +2024-09-07 17:15:26.814636: +2024-09-07 17:15:26.816468: Epoch 378 +2024-09-07 17:15:26.817672: Current learning rate: 0.00652 +2024-09-07 17:21:00.603637: train_loss -0.7358 +2024-09-07 17:21:00.618400: val_loss -0.7661 +2024-09-07 17:21:00.620001: Pseudo dice [0.9617, 0.9568, 0.976, 0.9458, 0.8802, 0.968, 0.9924] +2024-09-07 17:21:00.621291: Epoch time: 333.81 s +2024-09-07 17:21:05.739119: +2024-09-07 17:21:05.753275: Epoch 379 +2024-09-07 17:21:05.754945: Current learning rate: 0.00651 +2024-09-07 17:26:47.866594: train_loss -0.7208 +2024-09-07 17:26:47.868256: val_loss -0.6958 +2024-09-07 17:26:47.869220: Pseudo dice [0.9563, 0.9506, 0.9711, 0.9414, 0.8768, 0.9677, 0.9929] +2024-09-07 17:26:47.870163: Epoch time: 342.13 s +2024-09-07 17:26:52.186803: +2024-09-07 17:26:52.188253: Epoch 380 +2024-09-07 17:26:52.189067: Current learning rate: 0.0065 +2024-09-07 17:32:54.237954: train_loss -0.7209 +2024-09-07 17:32:54.240780: val_loss -0.7187 +2024-09-07 17:32:54.241822: Pseudo dice [0.9575, 0.9575, 0.9703, 0.9379, 0.8833, 0.9698, 0.9913] +2024-09-07 17:32:54.242536: Epoch time: 362.06 s +2024-09-07 17:32:57.301235: +2024-09-07 17:32:57.313162: Epoch 381 +2024-09-07 17:32:57.314337: Current learning rate: 0.00649 +2024-09-07 17:38:54.800484: train_loss -0.7316 +2024-09-07 17:38:54.802937: val_loss -0.7146 +2024-09-07 17:38:54.803775: Pseudo dice [0.9581, 0.9565, 0.9745, 0.9465, 0.8841, 0.9695, 0.9913] +2024-09-07 17:38:54.804619: Epoch time: 357.51 s +2024-09-07 17:39:01.360720: +2024-09-07 17:39:01.372661: Epoch 382 +2024-09-07 17:39:01.373596: Current learning rate: 0.00648 +2024-09-07 17:45:10.570426: train_loss -0.7186 +2024-09-07 17:45:10.598684: val_loss -0.7714 +2024-09-07 17:45:10.600688: Pseudo dice [0.9604, 0.9572, 0.9733, 0.9468, 0.8785, 0.9695, 0.9914] +2024-09-07 17:45:10.602062: Epoch time: 369.22 s +2024-09-07 17:45:14.596942: +2024-09-07 17:45:14.598711: Epoch 383 +2024-09-07 17:45:14.599612: Current learning rate: 0.00648 +2024-09-07 17:50:58.108256: train_loss -0.7351 +2024-09-07 17:50:58.121922: val_loss -0.7218 +2024-09-07 17:50:58.122790: Pseudo dice [0.9655, 0.962, 0.9803, 0.9478, 0.8808, 0.9699, 0.9916] +2024-09-07 17:50:58.123758: Epoch time: 343.51 s +2024-09-07 17:51:03.414173: +2024-09-07 17:51:03.428769: Epoch 384 +2024-09-07 17:51:03.430079: Current learning rate: 0.00647 +2024-09-07 17:56:38.321592: train_loss -0.743 +2024-09-07 17:56:38.333629: val_loss -0.764 +2024-09-07 17:56:38.334754: Pseudo dice [0.9614, 0.9572, 0.9738, 0.9453, 0.8669, 0.9688, 0.9902] +2024-09-07 17:56:38.335684: Epoch time: 334.92 s +2024-09-07 17:56:42.469210: +2024-09-07 17:56:42.470279: Epoch 385 +2024-09-07 17:56:42.471135: Current learning rate: 0.00646 +2024-09-07 18:02:39.171815: train_loss -0.7475 +2024-09-07 18:02:39.183856: val_loss -0.732 +2024-09-07 18:02:39.184662: Pseudo dice [0.962, 0.9573, 0.974, 0.942, 0.8658, 0.9652, 0.9915] +2024-09-07 18:02:39.185316: Epoch time: 356.71 s +2024-09-07 18:02:42.181456: +2024-09-07 18:02:42.183105: Epoch 386 +2024-09-07 18:02:42.184219: Current learning rate: 0.00645 +2024-09-07 18:08:22.164477: train_loss -0.7316 +2024-09-07 18:08:22.180141: val_loss -0.7376 +2024-09-07 18:08:22.181265: Pseudo dice [0.9634, 0.9578, 0.9765, 0.945, 0.8569, 0.966, 0.99] +2024-09-07 18:08:22.182060: Epoch time: 339.99 s +2024-09-07 18:08:26.936370: +2024-09-07 18:08:26.938206: Epoch 387 +2024-09-07 18:08:26.938967: Current learning rate: 0.00644 +2024-09-07 18:13:59.301663: train_loss -0.7302 +2024-09-07 18:13:59.314895: val_loss -0.7247 +2024-09-07 18:13:59.316018: Pseudo dice [0.9521, 0.9481, 0.9683, 0.9465, 0.882, 0.9697, 0.9922] +2024-09-07 18:13:59.316950: Epoch time: 332.37 s +2024-09-07 18:14:04.176557: +2024-09-07 18:14:04.177993: Epoch 388 +2024-09-07 18:14:04.178822: Current learning rate: 0.00643 +2024-09-07 18:20:07.467085: train_loss -0.704 +2024-09-07 18:20:07.477383: val_loss -0.7387 +2024-09-07 18:20:07.478417: Pseudo dice [0.9586, 0.9509, 0.975, 0.9428, 0.8639, 0.9639, 0.99] +2024-09-07 18:20:07.479570: Epoch time: 363.3 s +2024-09-07 18:20:11.691825: +2024-09-07 18:20:11.693323: Epoch 389 +2024-09-07 18:20:11.694185: Current learning rate: 0.00642 +2024-09-07 18:25:57.241549: train_loss -0.7371 +2024-09-07 18:25:57.257379: val_loss -0.76 +2024-09-07 18:25:57.258304: Pseudo dice [0.9612, 0.9567, 0.9759, 0.9468, 0.8853, 0.9706, 0.9924] +2024-09-07 18:25:57.259233: Epoch time: 345.56 s +2024-09-07 18:26:01.368834: +2024-09-07 18:26:01.375901: Epoch 390 +2024-09-07 18:26:01.377650: Current learning rate: 0.00641 +2024-09-07 18:31:44.660181: train_loss -0.7356 +2024-09-07 18:31:44.689703: val_loss -0.7142 +2024-09-07 18:31:44.691008: Pseudo dice [0.9615, 0.9559, 0.9742, 0.954, 0.8884, 0.968, 0.9921] +2024-09-07 18:31:44.692053: Epoch time: 343.32 s +2024-09-07 18:31:50.865684: +2024-09-07 18:31:50.867185: Epoch 391 +2024-09-07 18:31:50.868184: Current learning rate: 0.0064 +2024-09-07 18:37:37.528020: train_loss -0.7388 +2024-09-07 18:37:37.537297: val_loss -0.7365 +2024-09-07 18:37:37.538287: Pseudo dice [0.9634, 0.9597, 0.9785, 0.9546, 0.8805, 0.9707, 0.9923] +2024-09-07 18:37:37.538993: Epoch time: 346.67 s +2024-09-07 18:37:41.057113: +2024-09-07 18:37:41.058788: Epoch 392 +2024-09-07 18:37:41.059702: Current learning rate: 0.00639 +2024-09-07 18:43:47.416978: train_loss -0.7271 +2024-09-07 18:43:47.418849: val_loss -0.7121 +2024-09-07 18:43:47.420182: Pseudo dice [0.9635, 0.9578, 0.9766, 0.9452, 0.8615, 0.9664, 0.9924] +2024-09-07 18:43:47.421363: Epoch time: 366.37 s +2024-09-07 18:43:50.552354: +2024-09-07 18:43:50.561642: Epoch 393 +2024-09-07 18:43:50.562792: Current learning rate: 0.00638 +2024-09-07 18:49:47.859038: train_loss -0.731 +2024-09-07 18:49:47.874102: val_loss -0.7444 +2024-09-07 18:49:47.874966: Pseudo dice [0.9639, 0.9584, 0.976, 0.9487, 0.8768, 0.9715, 0.993] +2024-09-07 18:49:47.876301: Epoch time: 357.32 s +2024-09-07 18:49:53.786053: +2024-09-07 18:49:53.787227: Epoch 394 +2024-09-07 18:49:53.787908: Current learning rate: 0.00637 +2024-09-07 18:55:57.057173: train_loss -0.742 +2024-09-07 18:55:57.060683: val_loss -0.73 +2024-09-07 18:55:57.062378: Pseudo dice [0.9663, 0.9608, 0.9792, 0.9522, 0.8914, 0.9694, 0.9914] +2024-09-07 18:55:57.064209: Epoch time: 363.28 s +2024-09-07 18:56:01.930735: +2024-09-07 18:56:01.931969: Epoch 395 +2024-09-07 18:56:01.932904: Current learning rate: 0.00636 +2024-09-07 19:02:04.195531: train_loss -0.7477 +2024-09-07 19:02:04.213159: val_loss -0.7524 +2024-09-07 19:02:04.214558: Pseudo dice [0.9622, 0.9567, 0.9737, 0.9489, 0.8793, 0.967, 0.991] +2024-09-07 19:02:04.215570: Epoch time: 362.27 s +2024-09-07 19:02:08.907161: +2024-09-07 19:02:08.920840: Epoch 396 +2024-09-07 19:02:08.921943: Current learning rate: 0.00635 +2024-09-07 19:08:06.225405: train_loss -0.7369 +2024-09-07 19:08:06.240240: val_loss -0.7471 +2024-09-07 19:08:06.243308: Pseudo dice [0.964, 0.957, 0.9761, 0.9476, 0.8898, 0.9708, 0.9933] +2024-09-07 19:08:06.245092: Epoch time: 357.33 s +2024-09-07 19:08:06.246054: Yayy! New best EMA pseudo Dice: 0.9542 +2024-09-07 19:08:12.166340: +2024-09-07 19:08:12.167390: Epoch 397 +2024-09-07 19:08:12.168456: Current learning rate: 0.00634 +2024-09-07 19:14:10.798037: train_loss -0.7134 +2024-09-07 19:14:10.802383: val_loss -0.7571 +2024-09-07 19:14:10.804083: Pseudo dice [0.9613, 0.9565, 0.9741, 0.9437, 0.8711, 0.9696, 0.9928] +2024-09-07 19:14:10.805133: Epoch time: 358.63 s +2024-09-07 19:14:14.900904: +2024-09-07 19:14:14.902226: Epoch 398 +2024-09-07 19:14:14.903974: Current learning rate: 0.00633 +2024-09-07 19:20:35.672045: train_loss -0.7346 +2024-09-07 19:20:35.682619: val_loss -0.7615 +2024-09-07 19:20:35.685392: Pseudo dice [0.9667, 0.9595, 0.9819, 0.9462, 0.8661, 0.9658, 0.9912] +2024-09-07 19:20:35.686340: Epoch time: 380.78 s +2024-09-07 19:20:38.989200: +2024-09-07 19:20:39.004688: Epoch 399 +2024-09-07 19:20:39.005520: Current learning rate: 0.00632 +2024-09-07 19:26:34.108985: train_loss -0.7279 +2024-09-07 19:26:34.121181: val_loss -0.722 +2024-09-07 19:26:34.121973: Pseudo dice [0.9623, 0.9587, 0.9758, 0.9438, 0.8695, 0.9695, 0.9917] +2024-09-07 19:26:34.122703: Epoch time: 355.12 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_22_04_47.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_22_04_47.txt new file mode 100644 index 0000000000000000000000000000000000000000..e6d4a135ff92aeb5c0560c56053f3d625ea65f77 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_7_22_04_47.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 22:04:50.613146: Using torch.compile... +2024-09-07 22:04:57.898926: do_dummy_2d_data_aug: False +2024-09-07 22:04:57.910286: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 22:04:57.911557: The split file contains 5 splits. +2024-09-07 22:04:57.912122: Desired fold for training: 2 +2024-09-07 22:04:57.912618: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 22:05:17.513006: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 22:05:18.671475: +2024-09-07 22:05:18.673267: Epoch 400 +2024-09-07 22:05:18.674860: Current learning rate: 0.00631 +2024-09-07 22:24:04.644839: train_loss -0.7355 +2024-09-07 22:24:04.646458: val_loss -0.689 +2024-09-07 22:24:04.647783: Pseudo dice [0.9626, 0.9574, 0.9764, 0.9467, 0.8902, 0.9643, 0.9919] +2024-09-07 22:24:04.648673: Epoch time: 1126.0 s +2024-09-07 22:24:06.564733: +2024-09-07 22:24:06.566022: Epoch 401 +2024-09-07 22:24:06.566866: Current learning rate: 0.0063 +2024-09-07 22:30:52.767708: train_loss -0.7308 +2024-09-07 22:30:52.781330: val_loss -0.7361 +2024-09-07 22:30:52.782331: Pseudo dice [0.9613, 0.9569, 0.9746, 0.9486, 0.8799, 0.9691, 0.9918] +2024-09-07 22:30:52.783166: Epoch time: 406.21 s +2024-09-07 22:30:58.602252: +2024-09-07 22:30:58.616845: Epoch 402 +2024-09-07 22:30:58.618444: Current learning rate: 0.0063 +2024-09-07 22:38:16.289616: train_loss -0.6913 +2024-09-07 22:38:16.310383: val_loss -0.709 +2024-09-07 22:38:16.313217: Pseudo dice [0.9499, 0.9473, 0.9647, 0.9296, 0.8567, 0.9539, 0.9857] +2024-09-07 22:38:16.314710: Epoch time: 437.71 s +2024-09-07 22:38:21.029996: +2024-09-07 22:38:21.031456: Epoch 403 +2024-09-07 22:38:21.032369: Current learning rate: 0.00629 +2024-09-07 22:45:51.577133: train_loss -0.6742 +2024-09-07 22:45:51.596514: val_loss -0.6683 +2024-09-07 22:45:51.601071: Pseudo dice [0.948, 0.9423, 0.9677, 0.931, 0.8012, 0.9483, 0.9869] +2024-09-07 22:45:51.607205: Epoch time: 450.55 s +2024-09-07 22:45:55.858894: +2024-09-07 22:45:55.860373: Epoch 404 +2024-09-07 22:45:55.861785: Current learning rate: 0.00628 +2024-09-07 22:53:10.506864: train_loss -0.6226 +2024-09-07 22:53:10.518101: val_loss -0.6929 +2024-09-07 22:53:10.519434: Pseudo dice [0.9433, 0.9436, 0.9558, 0.9326, 0.8411, 0.9521, 0.9872] +2024-09-07 22:53:10.521041: Epoch time: 434.66 s +2024-09-07 22:53:15.663167: +2024-09-07 22:53:15.664967: Epoch 405 +2024-09-07 22:53:15.665890: Current learning rate: 0.00627 +2024-09-07 23:00:12.040249: train_loss -0.67 +2024-09-07 23:00:12.047231: val_loss -0.6929 +2024-09-07 23:00:12.048891: Pseudo dice [0.9502, 0.9423, 0.9636, 0.9261, 0.8359, 0.9491, 0.9874] +2024-09-07 23:00:12.050793: Epoch time: 416.38 s +2024-09-07 23:00:15.780181: +2024-09-07 23:00:15.781918: Epoch 406 +2024-09-07 23:00:15.783282: Current learning rate: 0.00626 +2024-09-07 23:07:09.928324: train_loss -0.6348 +2024-09-07 23:07:09.930940: val_loss -0.5914 +2024-09-07 23:07:09.931844: Pseudo dice [0.9321, 0.9263, 0.9419, 0.9068, 0.7841, 0.9203, 0.9782] +2024-09-07 23:07:09.932639: Epoch time: 414.16 s +2024-09-07 23:07:14.358502: +2024-09-07 23:07:14.361014: Epoch 407 +2024-09-07 23:07:14.361960: Current learning rate: 0.00625 +2024-09-07 23:14:25.091264: train_loss -0.6364 +2024-09-07 23:14:25.110354: val_loss -0.6848 +2024-09-07 23:14:25.111480: Pseudo dice [0.9513, 0.9455, 0.9711, 0.932, 0.8041, 0.9496, 0.9833] +2024-09-07 23:14:25.112280: Epoch time: 430.75 s +2024-09-07 23:14:30.149481: +2024-09-07 23:14:30.167872: Epoch 408 +2024-09-07 23:14:30.169425: Current learning rate: 0.00624 +2024-09-07 23:21:51.021472: train_loss -0.6907 +2024-09-07 23:21:51.023445: val_loss -0.706 +2024-09-07 23:21:51.024359: Pseudo dice [0.9556, 0.9511, 0.9724, 0.9367, 0.8352, 0.9503, 0.9889] +2024-09-07 23:21:51.025307: Epoch time: 440.88 s +2024-09-07 23:21:54.889757: +2024-09-07 23:21:54.891181: Epoch 409 +2024-09-07 23:21:54.891978: Current learning rate: 0.00623 +2024-09-07 23:29:04.743860: train_loss -0.6764 +2024-09-07 23:29:04.759595: val_loss -0.6944 +2024-09-07 23:29:04.760818: Pseudo dice [0.9534, 0.9546, 0.9671, 0.9384, 0.8544, 0.9605, 0.9897] +2024-09-07 23:29:04.761702: Epoch time: 429.86 s +2024-09-07 23:29:10.042520: +2024-09-07 23:29:10.048751: Epoch 410 +2024-09-07 23:29:10.050530: Current learning rate: 0.00622 +2024-09-07 23:36:22.007868: train_loss -0.701 +2024-09-07 23:36:22.013276: val_loss -0.7473 +2024-09-07 23:36:22.014791: Pseudo dice [0.9607, 0.9527, 0.9784, 0.9378, 0.8444, 0.9604, 0.9897] +2024-09-07 23:36:22.019351: Epoch time: 431.98 s +2024-09-07 23:36:27.590998: +2024-09-07 23:36:27.604813: Epoch 411 +2024-09-07 23:36:27.606548: Current learning rate: 0.00621 +2024-09-07 23:43:55.856835: train_loss -0.7077 +2024-09-07 23:43:55.859946: val_loss -0.7257 +2024-09-07 23:43:55.861244: Pseudo dice [0.9606, 0.9571, 0.9762, 0.9421, 0.8681, 0.9647, 0.991] +2024-09-07 23:43:55.862268: Epoch time: 448.27 s +2024-09-07 23:43:59.944904: +2024-09-07 23:43:59.961205: Epoch 412 +2024-09-07 23:43:59.962771: Current learning rate: 0.0062 +2024-09-07 23:51:44.326855: train_loss -0.7053 +2024-09-07 23:51:44.333077: val_loss -0.7286 +2024-09-07 23:51:44.343188: Pseudo dice [0.9573, 0.9526, 0.9717, 0.9345, 0.859, 0.9643, 0.9915] +2024-09-07 23:51:44.344607: Epoch time: 464.41 s +2024-09-07 23:51:47.990533: +2024-09-07 23:51:47.992688: Epoch 413 +2024-09-07 23:51:47.994167: Current learning rate: 0.00619 +2024-09-07 23:59:04.984024: train_loss -0.702 +2024-09-07 23:59:04.987913: val_loss -0.7001 +2024-09-07 23:59:04.991120: Pseudo dice [0.9463, 0.948, 0.9622, 0.9322, 0.8278, 0.9548, 0.9894] +2024-09-07 23:59:05.001499: Epoch time: 437.03 s +2024-09-07 23:59:09.714735: +2024-09-07 23:59:09.726843: Epoch 414 +2024-09-07 23:59:09.728559: Current learning rate: 0.00618 +2024-09-08 00:06:31.724615: train_loss -0.7007 +2024-09-08 00:06:31.745016: val_loss -0.7216 +2024-09-08 00:06:31.748109: Pseudo dice [0.9609, 0.9517, 0.9757, 0.9433, 0.8635, 0.9617, 0.9907] +2024-09-08 00:06:31.751135: Epoch time: 442.02 s +2024-09-08 00:06:40.481618: +2024-09-08 00:06:40.483102: Epoch 415 +2024-09-08 00:06:40.484126: Current learning rate: 0.00617 +2024-09-08 00:13:28.971621: train_loss -0.703 +2024-09-08 00:13:28.997303: val_loss -0.722 +2024-09-08 00:13:28.998740: Pseudo dice [0.9544, 0.9514, 0.9614, 0.9352, 0.8625, 0.9656, 0.9912] +2024-09-08 00:13:29.000836: Epoch time: 408.5 s +2024-09-08 00:13:33.678010: +2024-09-08 00:13:33.679552: Epoch 416 +2024-09-08 00:13:33.680413: Current learning rate: 0.00616 +2024-09-08 00:21:03.516823: train_loss -0.7207 +2024-09-08 00:21:03.538271: val_loss -0.7135 +2024-09-08 00:21:03.540881: Pseudo dice [0.9588, 0.9523, 0.9746, 0.9433, 0.8791, 0.9603, 0.9913] +2024-09-08 00:21:03.542361: Epoch time: 449.84 s +2024-09-08 00:21:10.291039: +2024-09-08 00:21:10.305147: Epoch 417 +2024-09-08 00:21:10.306759: Current learning rate: 0.00615 +2024-09-08 00:28:20.365127: train_loss -0.7223 +2024-09-08 00:28:20.381818: val_loss -0.745 +2024-09-08 00:28:20.384155: Pseudo dice [0.9604, 0.9516, 0.9742, 0.9479, 0.8696, 0.9665, 0.9919] +2024-09-08 00:28:20.386425: Epoch time: 430.08 s +2024-09-08 00:28:24.891429: +2024-09-08 00:28:24.892997: Epoch 418 +2024-09-08 00:28:24.896571: Current learning rate: 0.00614 +2024-09-08 00:35:45.231616: train_loss -0.719 +2024-09-08 00:35:45.253294: val_loss -0.7074 +2024-09-08 00:35:45.254210: Pseudo dice [0.9602, 0.9543, 0.9741, 0.9477, 0.8641, 0.9648, 0.9892] +2024-09-08 00:35:45.255213: Epoch time: 440.35 s +2024-09-08 00:35:48.420244: +2024-09-08 00:35:48.434023: Epoch 419 +2024-09-08 00:35:48.436762: Current learning rate: 0.00613 +2024-09-08 00:43:10.950538: train_loss -0.7174 +2024-09-08 00:43:10.953216: val_loss -0.7396 +2024-09-08 00:43:10.954876: Pseudo dice [0.9548, 0.9514, 0.9637, 0.938, 0.8551, 0.9609, 0.9906] +2024-09-08 00:43:10.964799: Epoch time: 442.55 s +2024-09-08 00:43:16.676426: +2024-09-08 00:43:16.678012: Epoch 420 +2024-09-08 00:43:16.680097: Current learning rate: 0.00612 +2024-09-08 00:50:23.159021: train_loss -0.7278 +2024-09-08 00:50:23.178740: val_loss -0.7458 +2024-09-08 00:50:23.181496: Pseudo dice [0.9594, 0.9545, 0.9748, 0.9438, 0.8636, 0.9681, 0.9908] +2024-09-08 00:50:23.184517: Epoch time: 426.51 s +2024-09-08 00:50:27.395964: +2024-09-08 00:50:27.408782: Epoch 421 +2024-09-08 00:50:27.409795: Current learning rate: 0.00612 +2024-09-08 00:57:34.692312: train_loss -0.7212 +2024-09-08 00:57:34.697830: val_loss -0.7356 +2024-09-08 00:57:34.708913: Pseudo dice [0.961, 0.954, 0.9767, 0.9506, 0.8763, 0.9677, 0.9917] +2024-09-08 00:57:34.710346: Epoch time: 427.3 s +2024-09-08 00:57:37.956671: +2024-09-08 00:57:37.958150: Epoch 422 +2024-09-08 00:57:37.959494: Current learning rate: 0.00611 +2024-09-08 01:04:54.900127: train_loss -0.7143 +2024-09-08 01:04:54.917591: val_loss -0.726 +2024-09-08 01:04:54.920507: Pseudo dice [0.9572, 0.9531, 0.9705, 0.9432, 0.8529, 0.9639, 0.9897] +2024-09-08 01:04:54.922313: Epoch time: 436.97 s +2024-09-08 01:04:59.742095: +2024-09-08 01:04:59.760103: Epoch 423 +2024-09-08 01:04:59.761950: Current learning rate: 0.0061 +2024-09-08 01:12:15.118010: train_loss -0.7257 +2024-09-08 01:12:15.149446: val_loss -0.725 +2024-09-08 01:12:15.151218: Pseudo dice [0.9639, 0.956, 0.9781, 0.9476, 0.864, 0.9644, 0.9904] +2024-09-08 01:12:15.152242: Epoch time: 435.42 s +2024-09-08 01:12:20.590399: +2024-09-08 01:12:20.592045: Epoch 424 +2024-09-08 01:12:20.594255: Current learning rate: 0.00609 +2024-09-08 01:19:20.748371: train_loss -0.7268 +2024-09-08 01:19:20.767121: val_loss -0.7165 +2024-09-08 01:19:20.769299: Pseudo dice [0.965, 0.9554, 0.9791, 0.9465, 0.8851, 0.9685, 0.9924] +2024-09-08 01:19:20.770875: Epoch time: 420.16 s +2024-09-08 01:19:26.884089: +2024-09-08 01:19:26.886410: Epoch 425 +2024-09-08 01:19:26.887562: Current learning rate: 0.00608 +2024-09-08 01:26:45.991780: train_loss -0.727 +2024-09-08 01:26:46.014191: val_loss -0.7128 +2024-09-08 01:26:46.017164: Pseudo dice [0.9595, 0.9551, 0.9734, 0.9444, 0.8806, 0.9708, 0.991] +2024-09-08 01:26:46.019670: Epoch time: 439.16 s +2024-09-08 01:26:52.190756: +2024-09-08 01:26:52.204979: Epoch 426 +2024-09-08 01:26:52.206133: Current learning rate: 0.00607 +2024-09-08 01:33:31.856261: train_loss -0.7213 +2024-09-08 01:33:31.859479: val_loss -0.6988 +2024-09-08 01:33:31.860500: Pseudo dice [0.9618, 0.9561, 0.9753, 0.9469, 0.8532, 0.9666, 0.9879] +2024-09-08 01:33:31.862763: Epoch time: 399.67 s +2024-09-08 01:33:36.313172: +2024-09-08 01:33:36.324893: Epoch 427 +2024-09-08 01:33:36.325829: Current learning rate: 0.00606 +2024-09-08 01:41:01.591046: train_loss -0.7328 +2024-09-08 01:41:01.605812: val_loss -0.7248 +2024-09-08 01:41:01.607583: Pseudo dice [0.9623, 0.9567, 0.9756, 0.9474, 0.8779, 0.9697, 0.9918] +2024-09-08 01:41:01.608898: Epoch time: 445.28 s +2024-09-08 01:41:06.872849: +2024-09-08 01:41:06.888971: Epoch 428 +2024-09-08 01:41:06.891960: Current learning rate: 0.00605 +2024-09-08 01:48:43.601216: train_loss -0.7288 +2024-09-08 01:48:43.604722: val_loss -0.7349 +2024-09-08 01:48:43.606644: Pseudo dice [0.9581, 0.9563, 0.9727, 0.9434, 0.8614, 0.9684, 0.9887] +2024-09-08 01:48:43.607964: Epoch time: 456.74 s +2024-09-08 01:48:49.866589: +2024-09-08 01:48:49.867765: Epoch 429 +2024-09-08 01:48:49.868461: Current learning rate: 0.00604 +2024-09-08 01:55:31.169281: train_loss -0.7278 +2024-09-08 01:55:31.177589: val_loss -0.7532 +2024-09-08 01:55:31.178730: Pseudo dice [0.9628, 0.9576, 0.9748, 0.9446, 0.8703, 0.9674, 0.9883] +2024-09-08 01:55:31.179509: Epoch time: 401.31 s +2024-09-08 01:55:35.310643: +2024-09-08 01:55:35.312153: Epoch 430 +2024-09-08 01:55:35.314589: Current learning rate: 0.00603 +2024-09-08 02:02:30.002611: train_loss -0.7217 +2024-09-08 02:02:30.017285: val_loss -0.723 +2024-09-08 02:02:30.018524: Pseudo dice [0.9625, 0.954, 0.979, 0.9505, 0.879, 0.9699, 0.9905] +2024-09-08 02:02:30.020809: Epoch time: 414.71 s +2024-09-08 02:02:34.994312: +2024-09-08 02:02:35.011890: Epoch 431 +2024-09-08 02:02:35.015334: Current learning rate: 0.00602 +2024-09-08 02:09:35.273103: train_loss -0.7301 +2024-09-08 02:09:35.283340: val_loss -0.7392 +2024-09-08 02:09:35.285437: Pseudo dice [0.9624, 0.9572, 0.9794, 0.9478, 0.8864, 0.9693, 0.9922] +2024-09-08 02:09:35.287578: Epoch time: 420.29 s +2024-09-08 02:09:39.380934: +2024-09-08 02:09:39.395860: Epoch 432 +2024-09-08 02:09:39.398197: Current learning rate: 0.00601 +2024-09-08 02:16:45.098027: train_loss -0.7307 +2024-09-08 02:16:45.113738: val_loss -0.7257 +2024-09-08 02:16:45.115172: Pseudo dice [0.961, 0.9551, 0.9755, 0.9454, 0.8783, 0.969, 0.9919] +2024-09-08 02:16:45.116787: Epoch time: 425.73 s +2024-09-08 02:16:51.065436: +2024-09-08 02:16:51.080084: Epoch 433 +2024-09-08 02:16:51.081417: Current learning rate: 0.006 +2024-09-08 02:23:38.230959: train_loss -0.7329 +2024-09-08 02:23:38.247869: val_loss -0.7461 +2024-09-08 02:23:38.250481: Pseudo dice [0.9594, 0.9554, 0.9753, 0.9428, 0.886, 0.9699, 0.9921] +2024-09-08 02:23:38.251975: Epoch time: 407.17 s +2024-09-08 02:23:42.279479: +2024-09-08 02:23:42.282204: Epoch 434 +2024-09-08 02:23:42.283847: Current learning rate: 0.00599 +2024-09-08 02:30:54.928842: train_loss -0.7375 +2024-09-08 02:30:54.932025: val_loss -0.7215 +2024-09-08 02:30:54.933811: Pseudo dice [0.9643, 0.957, 0.978, 0.9542, 0.8834, 0.9708, 0.9918] +2024-09-08 02:30:54.934646: Epoch time: 432.69 s +2024-09-08 02:30:59.845587: +2024-09-08 02:30:59.846915: Epoch 435 +2024-09-08 02:30:59.847906: Current learning rate: 0.00598 +2024-09-08 02:37:59.073396: train_loss -0.7442 +2024-09-08 02:37:59.098769: val_loss -0.7725 +2024-09-08 02:37:59.099868: Pseudo dice [0.9645, 0.9572, 0.9798, 0.9548, 0.8911, 0.9678, 0.992] +2024-09-08 02:37:59.100673: Epoch time: 419.23 s +2024-09-08 02:38:02.963237: +2024-09-08 02:38:02.965883: Epoch 436 +2024-09-08 02:38:02.967372: Current learning rate: 0.00597 +2024-09-08 02:45:24.259632: train_loss -0.7275 +2024-09-08 02:45:24.271870: val_loss -0.6922 +2024-09-08 02:45:24.274157: Pseudo dice [0.9627, 0.9576, 0.9777, 0.9445, 0.8663, 0.9681, 0.9912] +2024-09-08 02:45:24.276010: Epoch time: 441.32 s +2024-09-08 02:45:28.474081: +2024-09-08 02:45:28.475733: Epoch 437 +2024-09-08 02:45:28.477066: Current learning rate: 0.00596 +2024-09-08 02:52:18.694433: train_loss -0.7329 +2024-09-08 02:52:18.709483: val_loss -0.7346 +2024-09-08 02:52:18.712190: Pseudo dice [0.9627, 0.9546, 0.9745, 0.9472, 0.8903, 0.9701, 0.9918] +2024-09-08 02:52:18.714117: Epoch time: 410.23 s +2024-09-08 02:52:22.420091: +2024-09-08 02:52:22.421639: Epoch 438 +2024-09-08 02:52:22.423551: Current learning rate: 0.00595 +2024-09-08 02:59:31.258687: train_loss -0.738 +2024-09-08 02:59:31.264228: val_loss -0.7781 +2024-09-08 02:59:31.265310: Pseudo dice [0.9622, 0.9559, 0.9758, 0.9487, 0.8674, 0.9677, 0.9923] +2024-09-08 02:59:31.266155: Epoch time: 428.85 s +2024-09-08 02:59:35.359359: +2024-09-08 02:59:35.377670: Epoch 439 +2024-09-08 02:59:35.379088: Current learning rate: 0.00594 +2024-09-08 03:06:20.699319: train_loss -0.7363 +2024-09-08 03:06:20.717251: val_loss -0.7454 +2024-09-08 03:06:20.719460: Pseudo dice [0.965, 0.9574, 0.9796, 0.9507, 0.876, 0.9703, 0.9928] +2024-09-08 03:06:20.720191: Epoch time: 405.36 s +2024-09-08 03:06:24.914585: +2024-09-08 03:06:24.917183: Epoch 440 +2024-09-08 03:06:24.918221: Current learning rate: 0.00593 +2024-09-08 03:13:32.438821: train_loss -0.7317 +2024-09-08 03:13:32.441325: val_loss -0.7405 +2024-09-08 03:13:32.442904: Pseudo dice [0.9644, 0.9577, 0.9778, 0.9457, 0.8569, 0.9693, 0.9898] +2024-09-08 03:13:32.444079: Epoch time: 427.55 s +2024-09-08 03:13:36.458227: +2024-09-08 03:13:36.459405: Epoch 441 +2024-09-08 03:13:36.460151: Current learning rate: 0.00592 +2024-09-08 03:20:49.105194: train_loss -0.7347 +2024-09-08 03:20:49.123440: val_loss -0.7769 +2024-09-08 03:20:49.127022: Pseudo dice [0.9642, 0.9574, 0.9792, 0.9525, 0.879, 0.9648, 0.9918] +2024-09-08 03:20:49.128844: Epoch time: 432.65 s +2024-09-08 03:20:54.184314: +2024-09-08 03:20:54.197064: Epoch 442 +2024-09-08 03:20:54.198788: Current learning rate: 0.00592 +2024-09-08 03:28:07.524569: train_loss -0.7274 +2024-09-08 03:28:07.529130: val_loss -0.7248 +2024-09-08 03:28:07.530043: Pseudo dice [0.9615, 0.9549, 0.974, 0.9436, 0.8508, 0.9618, 0.9912] +2024-09-08 03:28:07.531162: Epoch time: 433.35 s +2024-09-08 03:28:10.577936: +2024-09-08 03:28:10.579824: Epoch 443 +2024-09-08 03:28:10.580905: Current learning rate: 0.00591 +2024-09-08 03:34:57.091557: train_loss -0.7167 +2024-09-08 03:34:57.097830: val_loss -0.7192 +2024-09-08 03:34:57.099482: Pseudo dice [0.9659, 0.9582, 0.9773, 0.9465, 0.8659, 0.9674, 0.9911] +2024-09-08 03:34:57.100520: Epoch time: 406.56 s +2024-09-08 03:35:05.020742: +2024-09-08 03:35:05.040275: Epoch 444 +2024-09-08 03:35:05.045240: Current learning rate: 0.0059 +2024-09-08 03:42:20.229692: train_loss -0.7184 +2024-09-08 03:42:20.231717: val_loss -0.7639 +2024-09-08 03:42:20.232698: Pseudo dice [0.9648, 0.9585, 0.9777, 0.9497, 0.8919, 0.9713, 0.9928] +2024-09-08 03:42:20.233555: Epoch time: 435.21 s +2024-09-08 03:42:24.141643: +2024-09-08 03:42:24.154449: Epoch 445 +2024-09-08 03:42:24.155665: Current learning rate: 0.00589 +2024-09-08 03:49:20.460916: train_loss -0.7198 +2024-09-08 03:49:20.470405: val_loss -0.7237 +2024-09-08 03:49:20.471619: Pseudo dice [0.9647, 0.9587, 0.981, 0.9488, 0.8695, 0.9698, 0.9913] +2024-09-08 03:49:20.472644: Epoch time: 416.33 s +2024-09-08 03:49:24.370445: +2024-09-08 03:49:24.385097: Epoch 446 +2024-09-08 03:49:24.386338: Current learning rate: 0.00588 +2024-09-08 03:56:22.641287: train_loss -0.7306 +2024-09-08 03:56:22.657967: val_loss -0.7456 +2024-09-08 03:56:22.660012: Pseudo dice [0.9648, 0.9607, 0.9776, 0.951, 0.8794, 0.9688, 0.991] +2024-09-08 03:56:22.661322: Epoch time: 418.28 s +2024-09-08 03:56:26.866338: +2024-09-08 03:56:26.868581: Epoch 447 +2024-09-08 03:56:26.871201: Current learning rate: 0.00587 +2024-09-08 04:03:22.535979: train_loss -0.7405 +2024-09-08 04:03:22.538515: val_loss -0.745 +2024-09-08 04:03:22.539479: Pseudo dice [0.9639, 0.9578, 0.977, 0.9475, 0.8686, 0.9676, 0.9912] +2024-09-08 04:03:22.540359: Epoch time: 415.68 s +2024-09-08 04:03:27.003890: +2024-09-08 04:03:27.023902: Epoch 448 +2024-09-08 04:03:27.025780: Current learning rate: 0.00586 +2024-09-08 04:10:31.032831: train_loss -0.7494 +2024-09-08 04:10:31.035297: val_loss -0.7588 +2024-09-08 04:10:31.036457: Pseudo dice [0.9617, 0.9586, 0.9758, 0.9497, 0.8834, 0.9708, 0.9921] +2024-09-08 04:10:31.038691: Epoch time: 424.05 s +2024-09-08 04:10:35.837030: +2024-09-08 04:10:35.838648: Epoch 449 +2024-09-08 04:10:35.839740: Current learning rate: 0.00585 +2024-09-08 04:17:48.621678: train_loss -0.7339 +2024-09-08 04:17:48.633947: val_loss -0.7723 +2024-09-08 04:17:48.634977: Pseudo dice [0.9613, 0.9578, 0.9757, 0.9469, 0.8823, 0.9704, 0.9906] +2024-09-08 04:17:48.635906: Epoch time: 432.87 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_06_04_18.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_06_04_18.txt new file mode 100644 index 0000000000000000000000000000000000000000..b8a3c8e348b164ffcd247ed773ff5988e604fd01 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_06_04_18.txt @@ -0,0 +1,382 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 06:04:21.445888: Using torch.compile... +2024-09-08 06:04:27.604375: do_dummy_2d_data_aug: False +2024-09-08 06:04:27.614832: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 06:04:27.615845: The split file contains 5 splits. +2024-09-08 06:04:27.616191: Desired fold for training: 2 +2024-09-08 06:04:27.616609: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 06:04:44.577245: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 06:04:45.834479: +2024-09-08 06:04:45.851788: Epoch 450 +2024-09-08 06:04:45.853355: Current learning rate: 0.00584 +2024-09-08 06:20:28.839939: train_loss -0.7352 +2024-09-08 06:20:28.841408: val_loss -0.7602 +2024-09-08 06:20:28.841917: Pseudo dice [0.962, 0.9555, 0.977, 0.9499, 0.8752, 0.9685, 0.9891] +2024-09-08 06:20:28.842394: Epoch time: 943.02 s +2024-09-08 06:20:30.770461: +2024-09-08 06:20:30.772849: Epoch 451 +2024-09-08 06:20:30.773559: Current learning rate: 0.00583 +2024-09-08 06:25:54.622712: train_loss -0.7361 +2024-09-08 06:25:54.636162: val_loss -0.6922 +2024-09-08 06:25:54.636875: Pseudo dice [0.962, 0.9574, 0.978, 0.9401, 0.886, 0.969, 0.9922] +2024-09-08 06:25:54.637424: Epoch time: 323.86 s +2024-09-08 06:25:58.198818: +2024-09-08 06:25:58.227887: Epoch 452 +2024-09-08 06:25:58.229321: Current learning rate: 0.00582 +2024-09-08 06:31:41.047506: train_loss -0.7295 +2024-09-08 06:31:41.061720: val_loss -0.7569 +2024-09-08 06:31:41.062521: Pseudo dice [0.9649, 0.9588, 0.9762, 0.9529, 0.8829, 0.9708, 0.9927] +2024-09-08 06:31:41.063109: Epoch time: 342.86 s +2024-09-08 06:31:41.063684: Yayy! New best EMA pseudo Dice: 0.9545 +2024-09-08 06:31:50.773429: +2024-09-08 06:31:50.774190: Epoch 453 +2024-09-08 06:31:50.774764: Current learning rate: 0.00581 +2024-09-08 06:37:21.962877: train_loss -0.7194 +2024-09-08 06:37:21.974334: val_loss -0.7672 +2024-09-08 06:37:21.975138: Pseudo dice [0.963, 0.957, 0.977, 0.9539, 0.8804, 0.9707, 0.9909] +2024-09-08 06:37:21.975766: Epoch time: 331.19 s +2024-09-08 06:37:21.976244: Yayy! New best EMA pseudo Dice: 0.9546 +2024-09-08 06:37:28.039221: +2024-09-08 06:37:28.040445: Epoch 454 +2024-09-08 06:37:28.041208: Current learning rate: 0.0058 +2024-09-08 06:43:09.983799: train_loss -0.7137 +2024-09-08 06:43:10.001301: val_loss -0.7342 +2024-09-08 06:43:10.003350: Pseudo dice [0.9644, 0.9572, 0.9784, 0.9429, 0.8707, 0.9678, 0.9911] +2024-09-08 06:43:10.004386: Epoch time: 341.95 s +2024-09-08 06:43:13.286760: +2024-09-08 06:43:13.300399: Epoch 455 +2024-09-08 06:43:13.301293: Current learning rate: 0.00579 +2024-09-08 06:48:48.282416: train_loss -0.7191 +2024-09-08 06:48:48.303392: val_loss -0.7301 +2024-09-08 06:48:48.305460: Pseudo dice [0.9601, 0.9565, 0.972, 0.946, 0.8714, 0.9682, 0.9918] +2024-09-08 06:48:48.306994: Epoch time: 335.0 s +2024-09-08 06:48:51.257004: +2024-09-08 06:48:51.258475: Epoch 456 +2024-09-08 06:48:51.260516: Current learning rate: 0.00578 +2024-09-08 06:54:25.858945: train_loss -0.7238 +2024-09-08 06:54:25.874715: val_loss -0.7447 +2024-09-08 06:54:25.875916: Pseudo dice [0.9587, 0.9558, 0.9731, 0.952, 0.8649, 0.9649, 0.9895] +2024-09-08 06:54:25.876656: Epoch time: 334.64 s +2024-09-08 06:54:28.595428: +2024-09-08 06:54:28.609708: Epoch 457 +2024-09-08 06:54:28.610535: Current learning rate: 0.00577 +2024-09-08 07:00:03.364489: train_loss -0.7268 +2024-09-08 07:00:03.383897: val_loss -0.7042 +2024-09-08 07:00:03.385259: Pseudo dice [0.963, 0.956, 0.9767, 0.9526, 0.8771, 0.9711, 0.9908] +2024-09-08 07:00:03.386025: Epoch time: 334.78 s +2024-09-08 07:00:07.831588: +2024-09-08 07:00:07.844383: Epoch 458 +2024-09-08 07:00:07.845154: Current learning rate: 0.00576 +2024-09-08 07:05:22.321828: train_loss -0.7343 +2024-09-08 07:05:22.336879: val_loss -0.7426 +2024-09-08 07:05:22.337949: Pseudo dice [0.9612, 0.9561, 0.9735, 0.9448, 0.8679, 0.9704, 0.9892] +2024-09-08 07:05:22.338912: Epoch time: 314.5 s +2024-09-08 07:05:26.782616: +2024-09-08 07:05:26.795949: Epoch 459 +2024-09-08 07:05:26.797240: Current learning rate: 0.00575 +2024-09-08 07:11:17.998951: train_loss -0.7341 +2024-09-08 07:11:18.021257: val_loss -0.769 +2024-09-08 07:11:18.023557: Pseudo dice [0.9662, 0.9605, 0.9782, 0.9525, 0.8888, 0.9698, 0.9911] +2024-09-08 07:11:18.025182: Epoch time: 351.22 s +2024-09-08 07:11:21.142910: +2024-09-08 07:11:21.156852: Epoch 460 +2024-09-08 07:11:21.158038: Current learning rate: 0.00574 +2024-09-08 07:17:01.139106: train_loss -0.7308 +2024-09-08 07:17:01.147535: val_loss -0.7545 +2024-09-08 07:17:01.148333: Pseudo dice [0.9671, 0.9588, 0.9806, 0.9493, 0.8884, 0.9682, 0.9909] +2024-09-08 07:17:01.149069: Epoch time: 340.01 s +2024-09-08 07:17:01.149577: Yayy! New best EMA pseudo Dice: 0.9546 +2024-09-08 07:17:08.181770: +2024-09-08 07:17:08.182572: Epoch 461 +2024-09-08 07:17:08.183909: Current learning rate: 0.00573 +2024-09-08 07:22:34.329117: train_loss -0.7244 +2024-09-08 07:22:34.345518: val_loss -0.7796 +2024-09-08 07:22:34.346478: Pseudo dice [0.964, 0.9583, 0.9785, 0.9466, 0.875, 0.9695, 0.9894] +2024-09-08 07:22:34.347219: Epoch time: 326.15 s +2024-09-08 07:22:39.250499: +2024-09-08 07:22:39.251693: Epoch 462 +2024-09-08 07:22:39.252675: Current learning rate: 0.00572 +2024-09-08 07:28:27.445197: train_loss -0.7408 +2024-09-08 07:28:27.449198: val_loss -0.7631 +2024-09-08 07:28:27.450083: Pseudo dice [0.9634, 0.9585, 0.9785, 0.9553, 0.8949, 0.9734, 0.9932] +2024-09-08 07:28:27.452821: Epoch time: 348.2 s +2024-09-08 07:28:27.453647: Yayy! New best EMA pseudo Dice: 0.9551 +2024-09-08 07:28:33.800821: +2024-09-08 07:28:33.802145: Epoch 463 +2024-09-08 07:28:33.802940: Current learning rate: 0.00571 +2024-09-08 07:34:03.915546: train_loss -0.7383 +2024-09-08 07:34:03.917640: val_loss -0.7206 +2024-09-08 07:34:03.918181: Pseudo dice [0.9618, 0.957, 0.9756, 0.9512, 0.8815, 0.9692, 0.9909] +2024-09-08 07:34:03.918641: Epoch time: 330.12 s +2024-09-08 07:34:03.919074: Yayy! New best EMA pseudo Dice: 0.9551 +2024-09-08 07:34:11.388115: +2024-09-08 07:34:11.388916: Epoch 464 +2024-09-08 07:34:11.389466: Current learning rate: 0.0057 +2024-09-08 07:40:11.385090: train_loss -0.7261 +2024-09-08 07:40:11.406549: val_loss -0.7692 +2024-09-08 07:40:11.409247: Pseudo dice [0.9633, 0.9578, 0.9775, 0.9561, 0.8792, 0.9653, 0.9899] +2024-09-08 07:40:11.411995: Epoch time: 360.0 s +2024-09-08 07:40:11.415207: Yayy! New best EMA pseudo Dice: 0.9552 +2024-09-08 07:40:18.689336: +2024-09-08 07:40:18.690425: Epoch 465 +2024-09-08 07:40:18.691036: Current learning rate: 0.0057 +2024-09-08 07:45:41.502301: train_loss -0.743 +2024-09-08 07:45:41.524842: val_loss -0.783 +2024-09-08 07:45:41.525608: Pseudo dice [0.9647, 0.9605, 0.9778, 0.9509, 0.8862, 0.9711, 0.992] +2024-09-08 07:45:41.526306: Epoch time: 322.82 s +2024-09-08 07:45:41.526834: Yayy! New best EMA pseudo Dice: 0.9554 +2024-09-08 07:45:49.225518: +2024-09-08 07:45:49.227461: Epoch 466 +2024-09-08 07:45:49.228533: Current learning rate: 0.00569 +2024-09-08 07:51:35.354820: train_loss -0.7289 +2024-09-08 07:51:35.377606: val_loss -0.7272 +2024-09-08 07:51:35.378175: Pseudo dice [0.9641, 0.9565, 0.9779, 0.9508, 0.8498, 0.9695, 0.9889] +2024-09-08 07:51:35.378654: Epoch time: 346.13 s +2024-09-08 07:51:39.714607: +2024-09-08 07:51:39.728848: Epoch 467 +2024-09-08 07:51:39.729831: Current learning rate: 0.00568 +2024-09-08 07:57:41.520140: train_loss -0.7477 +2024-09-08 07:57:41.533175: val_loss -0.7553 +2024-09-08 07:57:41.533974: Pseudo dice [0.9613, 0.9587, 0.9754, 0.9553, 0.8941, 0.9722, 0.9923] +2024-09-08 07:57:41.534611: Epoch time: 361.81 s +2024-09-08 07:57:45.686723: +2024-09-08 07:57:45.688470: Epoch 468 +2024-09-08 07:57:45.689658: Current learning rate: 0.00567 +2024-09-08 08:03:30.208203: train_loss -0.73 +2024-09-08 08:03:30.212054: val_loss -0.7398 +2024-09-08 08:03:30.213648: Pseudo dice [0.9622, 0.956, 0.9754, 0.9487, 0.8652, 0.9676, 0.9902] +2024-09-08 08:03:30.214928: Epoch time: 344.59 s +2024-09-08 08:03:33.894789: +2024-09-08 08:03:33.896093: Epoch 469 +2024-09-08 08:03:33.897221: Current learning rate: 0.00566 +2024-09-08 08:09:04.673104: train_loss -0.7406 +2024-09-08 08:09:04.689775: val_loss -0.7619 +2024-09-08 08:09:04.692037: Pseudo dice [0.9626, 0.9598, 0.977, 0.9535, 0.8955, 0.9732, 0.9932] +2024-09-08 08:09:04.693270: Epoch time: 330.79 s +2024-09-08 08:09:04.694785: Yayy! New best EMA pseudo Dice: 0.9554 +2024-09-08 08:09:13.206285: +2024-09-08 08:09:13.211515: Epoch 470 +2024-09-08 08:09:13.212667: Current learning rate: 0.00565 +2024-09-08 08:14:45.694203: train_loss -0.7248 +2024-09-08 08:14:45.704319: val_loss -0.7315 +2024-09-08 08:14:45.706551: Pseudo dice [0.9667, 0.9589, 0.9801, 0.9524, 0.8768, 0.9705, 0.9896] +2024-09-08 08:14:45.707662: Epoch time: 332.49 s +2024-09-08 08:14:45.708631: Yayy! New best EMA pseudo Dice: 0.9555 +2024-09-08 08:14:53.705727: +2024-09-08 08:14:53.706771: Epoch 471 +2024-09-08 08:14:53.708645: Current learning rate: 0.00564 +2024-09-08 08:20:13.298673: train_loss -0.733 +2024-09-08 08:20:13.314806: val_loss -0.7486 +2024-09-08 08:20:13.315419: Pseudo dice [0.962, 0.9616, 0.9762, 0.9565, 0.8908, 0.9699, 0.9915] +2024-09-08 08:20:13.315918: Epoch time: 319.6 s +2024-09-08 08:20:13.316623: Yayy! New best EMA pseudo Dice: 0.9558 +2024-09-08 08:20:18.423779: +2024-09-08 08:20:18.427854: Epoch 472 +2024-09-08 08:20:18.429702: Current learning rate: 0.00563 +2024-09-08 08:25:42.572605: train_loss -0.7458 +2024-09-08 08:25:42.575520: val_loss -0.7481 +2024-09-08 08:25:42.578446: Pseudo dice [0.9617, 0.9579, 0.9773, 0.9483, 0.8798, 0.97, 0.9913] +2024-09-08 08:25:42.579352: Epoch time: 324.15 s +2024-09-08 08:25:46.181784: +2024-09-08 08:25:46.182691: Epoch 473 +2024-09-08 08:25:46.183281: Current learning rate: 0.00562 +2024-09-08 08:31:15.324257: train_loss -0.7605 +2024-09-08 08:31:15.340715: val_loss -0.7464 +2024-09-08 08:31:15.341659: Pseudo dice [0.9636, 0.9573, 0.9787, 0.9486, 0.8813, 0.9693, 0.9914] +2024-09-08 08:31:15.342680: Epoch time: 329.15 s +2024-09-08 08:31:18.744635: +2024-09-08 08:31:18.749870: Epoch 474 +2024-09-08 08:31:18.751034: Current learning rate: 0.00561 +2024-09-08 08:36:55.796865: train_loss -0.7442 +2024-09-08 08:36:55.813182: val_loss -0.7573 +2024-09-08 08:36:55.824567: Pseudo dice [0.9633, 0.9576, 0.9779, 0.9493, 0.8871, 0.9715, 0.9925] +2024-09-08 08:36:55.825140: Epoch time: 337.07 s +2024-09-08 08:36:55.825818: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-08 08:37:01.808353: +2024-09-08 08:37:01.810185: Epoch 475 +2024-09-08 08:37:01.812250: Current learning rate: 0.0056 +2024-09-08 08:43:03.297091: train_loss -0.7354 +2024-09-08 08:43:03.312991: val_loss -0.7277 +2024-09-08 08:43:03.313779: Pseudo dice [0.9659, 0.9587, 0.9793, 0.9488, 0.851, 0.9686, 0.9888] +2024-09-08 08:43:03.315104: Epoch time: 361.49 s +2024-09-08 08:43:06.890479: +2024-09-08 08:43:06.891397: Epoch 476 +2024-09-08 08:43:06.892987: Current learning rate: 0.00559 +2024-09-08 08:48:47.209372: train_loss -0.7264 +2024-09-08 08:48:47.232304: val_loss -0.7276 +2024-09-08 08:48:47.233389: Pseudo dice [0.9625, 0.9585, 0.9758, 0.9507, 0.8905, 0.9719, 0.9926] +2024-09-08 08:48:47.234064: Epoch time: 340.33 s +2024-09-08 08:48:53.902145: +2024-09-08 08:48:53.915756: Epoch 477 +2024-09-08 08:48:53.916629: Current learning rate: 0.00558 +2024-09-08 08:54:19.362205: train_loss -0.7371 +2024-09-08 08:54:19.377221: val_loss -0.7315 +2024-09-08 08:54:19.378197: Pseudo dice [0.9596, 0.9581, 0.9728, 0.9499, 0.8565, 0.9688, 0.9895] +2024-09-08 08:54:19.379161: Epoch time: 325.47 s +2024-09-08 08:54:22.641574: +2024-09-08 08:54:22.642708: Epoch 478 +2024-09-08 08:54:22.643331: Current learning rate: 0.00557 +2024-09-08 09:00:28.907352: train_loss -0.7256 +2024-09-08 09:00:28.933424: val_loss -0.7529 +2024-09-08 09:00:28.934192: Pseudo dice [0.965, 0.9579, 0.9787, 0.9532, 0.8826, 0.9644, 0.9925] +2024-09-08 09:00:28.934730: Epoch time: 366.27 s +2024-09-08 09:00:31.899186: +2024-09-08 09:00:31.911365: Epoch 479 +2024-09-08 09:00:31.913889: Current learning rate: 0.00556 +2024-09-08 09:05:55.272484: train_loss -0.732 +2024-09-08 09:05:55.274059: val_loss -0.7299 +2024-09-08 09:05:55.274722: Pseudo dice [0.9645, 0.9593, 0.9774, 0.9532, 0.8907, 0.9705, 0.9922] +2024-09-08 09:05:55.275337: Epoch time: 323.39 s +2024-09-08 09:05:58.782006: +2024-09-08 09:05:58.782995: Epoch 480 +2024-09-08 09:05:58.783736: Current learning rate: 0.00555 +2024-09-08 09:11:47.332003: train_loss -0.7377 +2024-09-08 09:11:47.342125: val_loss -0.741 +2024-09-08 09:11:47.342671: Pseudo dice [0.9617, 0.959, 0.9764, 0.9508, 0.8834, 0.9707, 0.9921] +2024-09-08 09:11:47.343192: Epoch time: 348.56 s +2024-09-08 09:11:50.451034: +2024-09-08 09:11:50.464482: Epoch 481 +2024-09-08 09:11:50.465279: Current learning rate: 0.00554 +2024-09-08 09:17:29.186557: train_loss -0.7275 +2024-09-08 09:17:29.200976: val_loss -0.7604 +2024-09-08 09:17:29.202697: Pseudo dice [0.9635, 0.9574, 0.978, 0.9517, 0.868, 0.971, 0.9883] +2024-09-08 09:17:29.203468: Epoch time: 338.76 s +2024-09-08 09:17:33.023233: +2024-09-08 09:17:33.036459: Epoch 482 +2024-09-08 09:17:33.037881: Current learning rate: 0.00553 +2024-09-08 09:23:15.029234: train_loss -0.737 +2024-09-08 09:23:15.044970: val_loss -0.7488 +2024-09-08 09:23:15.045709: Pseudo dice [0.9606, 0.9605, 0.9736, 0.947, 0.8891, 0.9708, 0.9923] +2024-09-08 09:23:15.046288: Epoch time: 342.01 s +2024-09-08 09:23:18.296789: +2024-09-08 09:23:18.298175: Epoch 483 +2024-09-08 09:23:18.299067: Current learning rate: 0.00552 +2024-09-08 09:28:50.307879: train_loss -0.7188 +2024-09-08 09:28:50.321516: val_loss -0.743 +2024-09-08 09:28:50.322648: Pseudo dice [0.9548, 0.9499, 0.9689, 0.9381, 0.8568, 0.9655, 0.991] +2024-09-08 09:28:50.323213: Epoch time: 332.02 s +2024-09-08 09:28:54.721900: +2024-09-08 09:28:54.723307: Epoch 484 +2024-09-08 09:28:54.724317: Current learning rate: 0.00551 +2024-09-08 09:34:12.790795: train_loss -0.6852 +2024-09-08 09:34:12.792572: val_loss -0.692 +2024-09-08 09:34:12.793362: Pseudo dice [0.9524, 0.9488, 0.9691, 0.9326, 0.8546, 0.9529, 0.9892] +2024-09-08 09:34:12.793995: Epoch time: 318.07 s +2024-09-08 09:34:17.656466: +2024-09-08 09:34:17.657937: Epoch 485 +2024-09-08 09:34:17.658870: Current learning rate: 0.0055 +2024-09-08 09:39:54.483061: train_loss -0.6975 +2024-09-08 09:39:54.496631: val_loss -0.7271 +2024-09-08 09:39:54.497181: Pseudo dice [0.9613, 0.9556, 0.9743, 0.942, 0.862, 0.9645, 0.9909] +2024-09-08 09:39:54.497670: Epoch time: 336.85 s +2024-09-08 09:39:57.449984: +2024-09-08 09:39:57.464453: Epoch 486 +2024-09-08 09:39:57.465554: Current learning rate: 0.00549 +2024-09-08 09:45:22.349535: train_loss -0.7082 +2024-09-08 09:45:22.365685: val_loss -0.7456 +2024-09-08 09:45:22.366320: Pseudo dice [0.9592, 0.9513, 0.9734, 0.9423, 0.8734, 0.9667, 0.9898] +2024-09-08 09:45:22.366867: Epoch time: 324.91 s +2024-09-08 09:45:25.882922: +2024-09-08 09:45:25.884301: Epoch 487 +2024-09-08 09:45:25.885005: Current learning rate: 0.00548 +2024-09-08 09:51:03.913253: train_loss -0.7306 +2024-09-08 09:51:03.932125: val_loss -0.7256 +2024-09-08 09:51:03.933428: Pseudo dice [0.9589, 0.9528, 0.9748, 0.9449, 0.838, 0.9628, 0.9907] +2024-09-08 09:51:03.934123: Epoch time: 338.04 s +2024-09-08 09:51:09.264702: +2024-09-08 09:51:09.266162: Epoch 488 +2024-09-08 09:51:09.267387: Current learning rate: 0.00547 +2024-09-08 09:57:37.378980: train_loss -0.7182 +2024-09-08 09:57:37.396290: val_loss -0.7679 +2024-09-08 09:57:37.398203: Pseudo dice [0.9641, 0.9604, 0.9794, 0.9518, 0.8769, 0.9652, 0.9906] +2024-09-08 09:57:37.399158: Epoch time: 388.16 s +2024-09-08 09:57:42.969887: +2024-09-08 09:57:42.985188: Epoch 489 +2024-09-08 09:57:42.986263: Current learning rate: 0.00546 +2024-09-08 10:03:34.455204: train_loss -0.7485 +2024-09-08 10:03:34.466725: val_loss -0.732 +2024-09-08 10:03:34.467391: Pseudo dice [0.9569, 0.9553, 0.9729, 0.9454, 0.8788, 0.9687, 0.992] +2024-09-08 10:03:34.468008: Epoch time: 351.5 s +2024-09-08 10:03:38.121424: +2024-09-08 10:03:38.122608: Epoch 490 +2024-09-08 10:03:38.123304: Current learning rate: 0.00546 +2024-09-08 10:09:36.581527: train_loss -0.7384 +2024-09-08 10:09:36.599878: val_loss -0.7542 +2024-09-08 10:09:36.600508: Pseudo dice [0.9612, 0.9576, 0.976, 0.9497, 0.8716, 0.9694, 0.991] +2024-09-08 10:09:36.600996: Epoch time: 358.47 s +2024-09-08 10:09:39.301076: +2024-09-08 10:09:39.316465: Epoch 491 +2024-09-08 10:09:39.317247: Current learning rate: 0.00545 +2024-09-08 10:15:28.858230: train_loss -0.7358 +2024-09-08 10:15:28.873030: val_loss -0.7431 +2024-09-08 10:15:28.873819: Pseudo dice [0.9652, 0.9583, 0.9784, 0.948, 0.874, 0.9693, 0.9911] +2024-09-08 10:15:28.874606: Epoch time: 349.56 s +2024-09-08 10:15:35.598311: +2024-09-08 10:15:35.599625: Epoch 492 +2024-09-08 10:15:35.600347: Current learning rate: 0.00544 +2024-09-08 10:21:17.799985: train_loss -0.7111 +2024-09-08 10:21:17.818818: val_loss -0.7584 +2024-09-08 10:21:17.819813: Pseudo dice [0.9636, 0.9566, 0.9778, 0.9449, 0.885, 0.9702, 0.9927] +2024-09-08 10:21:17.820504: Epoch time: 342.21 s +2024-09-08 10:21:21.005293: +2024-09-08 10:21:21.006744: Epoch 493 +2024-09-08 10:21:21.007461: Current learning rate: 0.00543 +2024-09-08 10:27:06.220569: train_loss -0.7054 +2024-09-08 10:27:06.242123: val_loss -0.7006 +2024-09-08 10:27:06.244179: Pseudo dice [0.9578, 0.9498, 0.9707, 0.9342, 0.8583, 0.9623, 0.9871] +2024-09-08 10:27:06.245293: Epoch time: 345.22 s +2024-09-08 10:27:10.590525: +2024-09-08 10:27:10.592031: Epoch 494 +2024-09-08 10:27:10.592834: Current learning rate: 0.00542 +2024-09-08 10:33:05.486214: train_loss -0.722 +2024-09-08 10:33:05.494755: val_loss -0.7291 +2024-09-08 10:33:05.495431: Pseudo dice [0.9613, 0.9564, 0.9763, 0.9473, 0.8915, 0.9709, 0.9924] +2024-09-08 10:33:05.495988: Epoch time: 354.9 s +2024-09-08 10:33:10.605804: +2024-09-08 10:33:10.606991: Epoch 495 +2024-09-08 10:33:10.607672: Current learning rate: 0.00541 +2024-09-08 10:38:44.616294: train_loss -0.7341 +2024-09-08 10:38:44.627664: val_loss -0.7231 +2024-09-08 10:38:44.640733: Pseudo dice [0.9632, 0.9583, 0.9786, 0.95, 0.8704, 0.9688, 0.991] +2024-09-08 10:38:44.641854: Epoch time: 334.02 s +2024-09-08 10:38:47.219329: +2024-09-08 10:38:47.221308: Epoch 496 +2024-09-08 10:38:47.224169: Current learning rate: 0.0054 +2024-09-08 10:44:58.375725: train_loss -0.7247 +2024-09-08 10:44:58.405785: val_loss -0.7326 +2024-09-08 10:44:58.406344: Pseudo dice [0.9607, 0.9572, 0.9732, 0.9481, 0.8614, 0.9692, 0.9902] +2024-09-08 10:44:58.406983: Epoch time: 371.19 s +2024-09-08 10:45:03.107674: +2024-09-08 10:45:03.108955: Epoch 497 +2024-09-08 10:45:03.109600: Current learning rate: 0.00539 +2024-09-08 10:50:51.929364: train_loss -0.7345 +2024-09-08 10:50:51.931909: val_loss -0.7419 +2024-09-08 10:50:51.932718: Pseudo dice [0.9577, 0.9562, 0.97, 0.9455, 0.8568, 0.9671, 0.9925] +2024-09-08 10:50:51.933310: Epoch time: 348.83 s +2024-09-08 10:50:56.559520: +2024-09-08 10:50:56.572801: Epoch 498 +2024-09-08 10:50:56.573976: Current learning rate: 0.00538 +2024-09-08 10:56:43.721079: train_loss -0.7093 +2024-09-08 10:56:43.724479: val_loss -0.7144 +2024-09-08 10:56:43.725434: Pseudo dice [0.9611, 0.9577, 0.9742, 0.9437, 0.8826, 0.9643, 0.9919] +2024-09-08 10:56:43.726005: Epoch time: 347.17 s +2024-09-08 10:56:46.896168: +2024-09-08 10:56:46.912623: Epoch 499 +2024-09-08 10:56:46.913642: Current learning rate: 0.00537 +2024-09-08 11:02:19.803954: train_loss -0.7418 +2024-09-08 11:02:19.813653: val_loss -0.7166 +2024-09-08 11:02:19.814430: Pseudo dice [0.9619, 0.9566, 0.9763, 0.9455, 0.8858, 0.9696, 0.9906] +2024-09-08 11:02:19.815293: Epoch time: 332.92 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_14_03_50.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_14_03_50.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcbb5d80afc0f041949e5af59ac94f09768d3ea5 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_14_03_50.txt @@ -0,0 +1,379 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 14:03:53.626837: Using torch.compile... +2024-09-08 14:03:59.704240: do_dummy_2d_data_aug: False +2024-09-08 14:03:59.710045: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 14:03:59.711377: The split file contains 5 splits. +2024-09-08 14:03:59.711806: Desired fold for training: 2 +2024-09-08 14:03:59.712132: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 14:04:14.541567: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 14:04:15.812156: +2024-09-08 14:04:15.814110: Epoch 500 +2024-09-08 14:04:15.816204: Current learning rate: 0.00536 +2024-09-08 14:19:51.780434: train_loss -0.7293 +2024-09-08 14:19:51.781952: val_loss -0.7306 +2024-09-08 14:19:51.782486: Pseudo dice [0.9625, 0.9576, 0.974, 0.9488, 0.8695, 0.9668, 0.9913] +2024-09-08 14:19:51.782965: Epoch time: 935.98 s +2024-09-08 14:19:53.799782: +2024-09-08 14:19:53.800659: Epoch 501 +2024-09-08 14:19:53.801159: Current learning rate: 0.00535 +2024-09-08 14:25:28.218333: train_loss -0.7182 +2024-09-08 14:25:28.222345: val_loss -0.7404 +2024-09-08 14:25:28.233583: Pseudo dice [0.9616, 0.9564, 0.9736, 0.9512, 0.8818, 0.9679, 0.9903] +2024-09-08 14:25:28.235009: Epoch time: 334.42 s +2024-09-08 14:25:32.334402: +2024-09-08 14:25:32.348858: Epoch 502 +2024-09-08 14:25:32.350152: Current learning rate: 0.00534 +2024-09-08 14:31:03.424551: train_loss -0.7428 +2024-09-08 14:31:03.427112: val_loss -0.7156 +2024-09-08 14:31:03.427843: Pseudo dice [0.9614, 0.9565, 0.9749, 0.9494, 0.8753, 0.9695, 0.9911] +2024-09-08 14:31:03.428475: Epoch time: 331.1 s +2024-09-08 14:31:07.327725: +2024-09-08 14:31:07.340974: Epoch 503 +2024-09-08 14:31:07.342048: Current learning rate: 0.00533 +2024-09-08 14:36:51.896770: train_loss -0.7318 +2024-09-08 14:36:51.909827: val_loss -0.744 +2024-09-08 14:36:51.910717: Pseudo dice [0.9613, 0.9578, 0.9762, 0.9478, 0.8809, 0.9729, 0.9917] +2024-09-08 14:36:51.911274: Epoch time: 344.57 s +2024-09-08 14:36:55.852185: +2024-09-08 14:36:55.859272: Epoch 504 +2024-09-08 14:36:55.860165: Current learning rate: 0.00532 +2024-09-08 14:42:52.647814: train_loss -0.744 +2024-09-08 14:42:52.660792: val_loss -0.7453 +2024-09-08 14:42:52.663568: Pseudo dice [0.965, 0.9585, 0.9787, 0.9526, 0.8867, 0.9664, 0.9924] +2024-09-08 14:42:52.666790: Epoch time: 356.8 s +2024-09-08 14:42:55.323346: +2024-09-08 14:42:55.328423: Epoch 505 +2024-09-08 14:42:55.334459: Current learning rate: 0.00531 +2024-09-08 14:48:41.688158: train_loss -0.7365 +2024-09-08 14:48:41.697541: val_loss -0.7356 +2024-09-08 14:48:41.698082: Pseudo dice [0.9639, 0.9593, 0.9777, 0.9581, 0.8842, 0.9673, 0.992] +2024-09-08 14:48:41.698567: Epoch time: 346.38 s +2024-09-08 14:48:46.048942: +2024-09-08 14:48:46.050022: Epoch 506 +2024-09-08 14:48:46.051144: Current learning rate: 0.0053 +2024-09-08 14:54:21.001148: train_loss -0.7226 +2024-09-08 14:54:21.003620: val_loss -0.7754 +2024-09-08 14:54:21.004195: Pseudo dice [0.9658, 0.9601, 0.9809, 0.9532, 0.8867, 0.9719, 0.9924] +2024-09-08 14:54:21.004669: Epoch time: 334.96 s +2024-09-08 14:54:24.359184: +2024-09-08 14:54:24.372280: Epoch 507 +2024-09-08 14:54:24.373054: Current learning rate: 0.00529 +2024-09-08 14:59:47.679450: train_loss -0.7501 +2024-09-08 14:59:47.695874: val_loss -0.7502 +2024-09-08 14:59:47.697377: Pseudo dice [0.9637, 0.9565, 0.9785, 0.9511, 0.8725, 0.9665, 0.9909] +2024-09-08 14:59:47.699296: Epoch time: 323.32 s +2024-09-08 14:59:51.947526: +2024-09-08 14:59:51.964854: Epoch 508 +2024-09-08 14:59:51.966642: Current learning rate: 0.00528 +2024-09-08 15:05:26.249554: train_loss -0.727 +2024-09-08 15:05:26.253815: val_loss -0.7437 +2024-09-08 15:05:26.254612: Pseudo dice [0.9621, 0.9575, 0.9769, 0.9502, 0.8737, 0.9688, 0.9912] +2024-09-08 15:05:26.255269: Epoch time: 334.31 s +2024-09-08 15:05:29.374089: +2024-09-08 15:05:29.375067: Epoch 509 +2024-09-08 15:05:29.376175: Current learning rate: 0.00527 +2024-09-08 15:10:55.670916: train_loss -0.7402 +2024-09-08 15:10:55.687881: val_loss -0.7451 +2024-09-08 15:10:55.688676: Pseudo dice [0.962, 0.9591, 0.9754, 0.9501, 0.877, 0.97, 0.992] +2024-09-08 15:10:55.689106: Epoch time: 326.3 s +2024-09-08 15:10:58.817906: +2024-09-08 15:10:58.819020: Epoch 510 +2024-09-08 15:10:58.819573: Current learning rate: 0.00526 +2024-09-08 15:16:25.317211: train_loss -0.7455 +2024-09-08 15:16:25.334510: val_loss -0.7354 +2024-09-08 15:16:25.335707: Pseudo dice [0.9629, 0.9569, 0.9745, 0.9529, 0.8921, 0.9689, 0.992] +2024-09-08 15:16:25.336629: Epoch time: 326.51 s +2024-09-08 15:16:28.588727: +2024-09-08 15:16:28.589832: Epoch 511 +2024-09-08 15:16:28.590580: Current learning rate: 0.00525 +2024-09-08 15:22:04.428754: train_loss -0.751 +2024-09-08 15:22:04.431395: val_loss -0.6951 +2024-09-08 15:22:04.432263: Pseudo dice [0.958, 0.956, 0.9723, 0.9478, 0.8804, 0.9709, 0.9911] +2024-09-08 15:22:04.432939: Epoch time: 335.85 s +2024-09-08 15:22:08.064802: +2024-09-08 15:22:08.076595: Epoch 512 +2024-09-08 15:22:08.077713: Current learning rate: 0.00524 +2024-09-08 15:27:09.237080: train_loss -0.7307 +2024-09-08 15:27:09.253583: val_loss -0.7626 +2024-09-08 15:27:09.254763: Pseudo dice [0.9618, 0.9569, 0.9768, 0.9437, 0.8595, 0.9701, 0.9909] +2024-09-08 15:27:09.255466: Epoch time: 301.18 s +2024-09-08 15:27:13.352174: +2024-09-08 15:27:13.353531: Epoch 513 +2024-09-08 15:27:13.354066: Current learning rate: 0.00523 +2024-09-08 15:33:06.982797: train_loss -0.7348 +2024-09-08 15:33:07.000436: val_loss -0.7955 +2024-09-08 15:33:07.001635: Pseudo dice [0.9613, 0.955, 0.9757, 0.9454, 0.8715, 0.9673, 0.9916] +2024-09-08 15:33:07.004582: Epoch time: 353.65 s +2024-09-08 15:33:10.739749: +2024-09-08 15:33:10.759803: Epoch 514 +2024-09-08 15:33:10.761594: Current learning rate: 0.00522 +2024-09-08 15:38:55.324773: train_loss -0.7463 +2024-09-08 15:38:55.326892: val_loss -0.7543 +2024-09-08 15:38:55.327371: Pseudo dice [0.9595, 0.958, 0.9741, 0.9506, 0.8878, 0.9715, 0.9916] +2024-09-08 15:38:55.327949: Epoch time: 344.59 s +2024-09-08 15:38:59.087032: +2024-09-08 15:38:59.099778: Epoch 515 +2024-09-08 15:38:59.101269: Current learning rate: 0.00521 +2024-09-08 15:44:49.948982: train_loss -0.7249 +2024-09-08 15:44:49.964998: val_loss -0.7502 +2024-09-08 15:44:49.966095: Pseudo dice [0.9653, 0.9604, 0.977, 0.9526, 0.8909, 0.9727, 0.9924] +2024-09-08 15:44:49.967400: Epoch time: 350.87 s +2024-09-08 15:44:53.182116: +2024-09-08 15:44:53.183060: Epoch 516 +2024-09-08 15:44:53.183591: Current learning rate: 0.0052 +2024-09-08 15:50:34.211706: train_loss -0.738 +2024-09-08 15:50:34.240240: val_loss -0.7559 +2024-09-08 15:50:34.241795: Pseudo dice [0.9632, 0.9578, 0.9749, 0.9479, 0.8734, 0.9709, 0.9905] +2024-09-08 15:50:34.243148: Epoch time: 341.04 s +2024-09-08 15:50:38.242162: +2024-09-08 15:50:38.243134: Epoch 517 +2024-09-08 15:50:38.243723: Current learning rate: 0.00519 +2024-09-08 15:56:04.375918: train_loss -0.7458 +2024-09-08 15:56:04.378500: val_loss -0.7519 +2024-09-08 15:56:04.379080: Pseudo dice [0.9623, 0.9586, 0.9753, 0.9537, 0.8825, 0.9712, 0.9922] +2024-09-08 15:56:04.379640: Epoch time: 326.14 s +2024-09-08 15:56:07.630089: +2024-09-08 15:56:07.638873: Epoch 518 +2024-09-08 15:56:07.639443: Current learning rate: 0.00518 +2024-09-08 16:02:01.085078: train_loss -0.7336 +2024-09-08 16:02:01.102238: val_loss -0.7544 +2024-09-08 16:02:01.102939: Pseudo dice [0.9669, 0.9608, 0.9777, 0.9491, 0.8913, 0.9722, 0.9928] +2024-09-08 16:02:01.103502: Epoch time: 353.47 s +2024-09-08 16:02:05.070662: +2024-09-08 16:02:05.085160: Epoch 519 +2024-09-08 16:02:05.089518: Current learning rate: 0.00518 +2024-09-08 16:07:23.011869: train_loss -0.7517 +2024-09-08 16:07:23.016797: val_loss -0.7461 +2024-09-08 16:07:23.017851: Pseudo dice [0.9625, 0.9554, 0.9755, 0.9455, 0.8773, 0.9692, 0.9926] +2024-09-08 16:07:23.019052: Epoch time: 317.95 s +2024-09-08 16:07:26.117949: +2024-09-08 16:07:26.119201: Epoch 520 +2024-09-08 16:07:26.120391: Current learning rate: 0.00517 +2024-09-08 16:13:19.488742: train_loss -0.7317 +2024-09-08 16:13:19.505959: val_loss -0.7221 +2024-09-08 16:13:19.507731: Pseudo dice [0.9667, 0.9594, 0.9794, 0.956, 0.8737, 0.967, 0.9901] +2024-09-08 16:13:19.509137: Epoch time: 353.38 s +2024-09-08 16:13:23.485298: +2024-09-08 16:13:23.500705: Epoch 521 +2024-09-08 16:13:23.502145: Current learning rate: 0.00516 +2024-09-08 16:19:04.662852: train_loss -0.7356 +2024-09-08 16:19:04.669081: val_loss -0.7808 +2024-09-08 16:19:04.680724: Pseudo dice [0.9638, 0.9579, 0.9803, 0.9492, 0.8805, 0.9696, 0.9911] +2024-09-08 16:19:04.681836: Epoch time: 341.19 s +2024-09-08 16:19:07.953756: +2024-09-08 16:19:07.954770: Epoch 522 +2024-09-08 16:19:07.955431: Current learning rate: 0.00515 +2024-09-08 16:24:40.614571: train_loss -0.7325 +2024-09-08 16:24:40.617038: val_loss -0.7313 +2024-09-08 16:24:40.617991: Pseudo dice [0.9653, 0.9596, 0.9771, 0.9517, 0.876, 0.9709, 0.9913] +2024-09-08 16:24:40.618605: Epoch time: 332.67 s +2024-09-08 16:24:44.005748: +2024-09-08 16:24:44.006912: Epoch 523 +2024-09-08 16:24:44.007568: Current learning rate: 0.00514 +2024-09-08 16:30:12.770655: train_loss -0.7376 +2024-09-08 16:30:12.785103: val_loss -0.7441 +2024-09-08 16:30:12.786971: Pseudo dice [0.96, 0.959, 0.9735, 0.9512, 0.8811, 0.9673, 0.9915] +2024-09-08 16:30:12.788144: Epoch time: 328.77 s +2024-09-08 16:30:15.335501: +2024-09-08 16:30:15.336755: Epoch 524 +2024-09-08 16:30:15.337409: Current learning rate: 0.00513 +2024-09-08 16:35:39.961088: train_loss -0.74 +2024-09-08 16:35:39.963430: val_loss -0.7531 +2024-09-08 16:35:39.965385: Pseudo dice [0.9678, 0.9628, 0.9807, 0.9559, 0.8851, 0.9741, 0.9926] +2024-09-08 16:35:39.966570: Epoch time: 324.63 s +2024-09-08 16:35:44.175802: +2024-09-08 16:35:44.192941: Epoch 525 +2024-09-08 16:35:44.194890: Current learning rate: 0.00512 +2024-09-08 16:41:20.160816: train_loss -0.7554 +2024-09-08 16:41:20.173758: val_loss -0.7741 +2024-09-08 16:41:20.175035: Pseudo dice [0.9678, 0.9616, 0.9812, 0.9531, 0.8932, 0.9733, 0.9925] +2024-09-08 16:41:20.175881: Epoch time: 335.99 s +2024-09-08 16:41:20.176543: Yayy! New best EMA pseudo Dice: 0.9563 +2024-09-08 16:41:26.859151: +2024-09-08 16:41:26.861053: Epoch 526 +2024-09-08 16:41:26.864437: Current learning rate: 0.00511 +2024-09-08 16:46:43.331099: train_loss -0.7394 +2024-09-08 16:46:43.341764: val_loss -0.7747 +2024-09-08 16:46:43.342710: Pseudo dice [0.962, 0.9579, 0.9761, 0.9522, 0.8932, 0.9748, 0.9935] +2024-09-08 16:46:43.343365: Epoch time: 316.48 s +2024-09-08 16:46:43.343949: Yayy! New best EMA pseudo Dice: 0.9565 +2024-09-08 16:46:53.980277: +2024-09-08 16:46:53.983210: Epoch 527 +2024-09-08 16:46:53.984583: Current learning rate: 0.0051 +2024-09-08 16:52:39.329140: train_loss -0.7377 +2024-09-08 16:52:39.341334: val_loss -0.7837 +2024-09-08 16:52:39.342090: Pseudo dice [0.9634, 0.9549, 0.9773, 0.9529, 0.8871, 0.9713, 0.9915] +2024-09-08 16:52:39.342680: Epoch time: 345.35 s +2024-09-08 16:52:39.343329: Yayy! New best EMA pseudo Dice: 0.9565 +2024-09-08 16:52:44.663096: +2024-09-08 16:52:44.663974: Epoch 528 +2024-09-08 16:52:44.664556: Current learning rate: 0.00509 +2024-09-08 16:58:09.264192: train_loss -0.7249 +2024-09-08 16:58:09.282305: val_loss -0.7747 +2024-09-08 16:58:09.282864: Pseudo dice [0.9674, 0.9604, 0.9814, 0.953, 0.8846, 0.9719, 0.9927] +2024-09-08 16:58:09.283350: Epoch time: 324.61 s +2024-09-08 16:58:09.283835: Yayy! New best EMA pseudo Dice: 0.9568 +2024-09-08 16:58:16.089635: +2024-09-08 16:58:16.104987: Epoch 529 +2024-09-08 16:58:16.107382: Current learning rate: 0.00508 +2024-09-08 17:03:48.028727: train_loss -0.7444 +2024-09-08 17:03:48.031104: val_loss -0.7579 +2024-09-08 17:03:48.031596: Pseudo dice [0.9633, 0.9585, 0.9775, 0.9512, 0.884, 0.9707, 0.9923] +2024-09-08 17:03:48.032027: Epoch time: 331.95 s +2024-09-08 17:03:48.032435: Yayy! New best EMA pseudo Dice: 0.9568 +2024-09-08 17:03:54.157651: +2024-09-08 17:03:54.158689: Epoch 530 +2024-09-08 17:03:54.160105: Current learning rate: 0.00507 +2024-09-08 17:09:27.709650: train_loss -0.7483 +2024-09-08 17:09:27.726573: val_loss -0.7612 +2024-09-08 17:09:27.727520: Pseudo dice [0.9605, 0.9579, 0.9768, 0.9466, 0.8766, 0.9709, 0.9913] +2024-09-08 17:09:27.728180: Epoch time: 333.56 s +2024-09-08 17:09:31.052204: +2024-09-08 17:09:31.070531: Epoch 531 +2024-09-08 17:09:31.071845: Current learning rate: 0.00506 +2024-09-08 17:14:55.325603: train_loss -0.747 +2024-09-08 17:14:55.352538: val_loss -0.7785 +2024-09-08 17:14:55.353153: Pseudo dice [0.9642, 0.9592, 0.9762, 0.9543, 0.8942, 0.9736, 0.9926] +2024-09-08 17:14:55.353668: Epoch time: 324.28 s +2024-09-08 17:14:55.354052: Yayy! New best EMA pseudo Dice: 0.9568 +2024-09-08 17:15:00.822427: +2024-09-08 17:15:00.823501: Epoch 532 +2024-09-08 17:15:00.824141: Current learning rate: 0.00505 +2024-09-08 17:20:50.496532: train_loss -0.7331 +2024-09-08 17:20:50.498706: val_loss -0.7408 +2024-09-08 17:20:50.500010: Pseudo dice [0.9637, 0.9577, 0.9731, 0.9496, 0.8686, 0.9699, 0.9902] +2024-09-08 17:20:50.501294: Epoch time: 349.68 s +2024-09-08 17:20:52.986315: +2024-09-08 17:20:52.997683: Epoch 533 +2024-09-08 17:20:52.999148: Current learning rate: 0.00504 +2024-09-08 17:26:30.163779: train_loss -0.7447 +2024-09-08 17:26:30.165724: val_loss -0.7735 +2024-09-08 17:26:30.167187: Pseudo dice [0.9637, 0.9596, 0.981, 0.9564, 0.8922, 0.973, 0.991] +2024-09-08 17:26:30.168725: Epoch time: 337.18 s +2024-09-08 17:26:34.040769: +2024-09-08 17:26:34.042999: Epoch 534 +2024-09-08 17:26:34.045610: Current learning rate: 0.00503 +2024-09-08 17:31:54.274446: train_loss -0.7209 +2024-09-08 17:31:54.290000: val_loss -0.7331 +2024-09-08 17:31:54.290723: Pseudo dice [0.966, 0.9597, 0.9812, 0.9542, 0.8667, 0.9699, 0.9907] +2024-09-08 17:31:54.291291: Epoch time: 320.25 s +2024-09-08 17:31:56.784628: +2024-09-08 17:31:56.785986: Epoch 535 +2024-09-08 17:31:56.786755: Current learning rate: 0.00502 +2024-09-08 17:37:42.310533: train_loss -0.7283 +2024-09-08 17:37:42.325515: val_loss -0.7045 +2024-09-08 17:37:42.326195: Pseudo dice [0.9637, 0.9592, 0.9753, 0.9503, 0.8867, 0.9727, 0.9924] +2024-09-08 17:37:42.326704: Epoch time: 345.53 s +2024-09-08 17:37:47.345874: +2024-09-08 17:37:47.347287: Epoch 536 +2024-09-08 17:37:47.348249: Current learning rate: 0.00501 +2024-09-08 17:43:25.400972: train_loss -0.7287 +2024-09-08 17:43:25.424999: val_loss -0.7576 +2024-09-08 17:43:25.425742: Pseudo dice [0.962, 0.9576, 0.978, 0.9421, 0.8824, 0.9698, 0.9916] +2024-09-08 17:43:25.426360: Epoch time: 338.07 s +2024-09-08 17:43:28.488205: +2024-09-08 17:43:28.488970: Epoch 537 +2024-09-08 17:43:28.489754: Current learning rate: 0.005 +2024-09-08 17:49:17.344877: train_loss -0.7363 +2024-09-08 17:49:17.347669: val_loss -0.7269 +2024-09-08 17:49:17.348389: Pseudo dice [0.9608, 0.9584, 0.9765, 0.9559, 0.8865, 0.9693, 0.9917] +2024-09-08 17:49:17.348868: Epoch time: 348.86 s +2024-09-08 17:49:21.114911: +2024-09-08 17:49:21.128643: Epoch 538 +2024-09-08 17:49:21.129753: Current learning rate: 0.00499 +2024-09-08 17:55:08.398720: train_loss -0.7389 +2024-09-08 17:55:08.413600: val_loss -0.7964 +2024-09-08 17:55:08.415151: Pseudo dice [0.9665, 0.9586, 0.9818, 0.9517, 0.8851, 0.974, 0.9918] +2024-09-08 17:55:08.415994: Epoch time: 347.3 s +2024-09-08 17:55:12.526148: +2024-09-08 17:55:12.536661: Epoch 539 +2024-09-08 17:55:12.537911: Current learning rate: 0.00498 +2024-09-08 18:00:46.689943: train_loss -0.7525 +2024-09-08 18:00:46.710370: val_loss -0.7235 +2024-09-08 18:00:46.717596: Pseudo dice [0.9644, 0.9569, 0.9795, 0.9526, 0.8911, 0.9726, 0.9936] +2024-09-08 18:00:46.718835: Epoch time: 334.18 s +2024-09-08 18:00:46.719751: Yayy! New best EMA pseudo Dice: 0.9569 +2024-09-08 18:00:54.556642: +2024-09-08 18:00:54.558719: Epoch 540 +2024-09-08 18:00:54.559480: Current learning rate: 0.00497 +2024-09-08 18:06:27.505960: train_loss -0.7513 +2024-09-08 18:06:27.509557: val_loss -0.7484 +2024-09-08 18:06:27.510172: Pseudo dice [0.966, 0.9602, 0.9782, 0.9527, 0.8895, 0.9723, 0.9919] +2024-09-08 18:06:27.510733: Epoch time: 332.96 s +2024-09-08 18:06:27.511188: Yayy! New best EMA pseudo Dice: 0.9571 +2024-09-08 18:06:35.081168: +2024-09-08 18:06:35.083638: Epoch 541 +2024-09-08 18:06:35.084631: Current learning rate: 0.00496 +2024-09-08 18:12:10.909577: train_loss -0.7391 +2024-09-08 18:12:10.936864: val_loss -0.7389 +2024-09-08 18:12:10.937809: Pseudo dice [0.9588, 0.9583, 0.9699, 0.9511, 0.846, 0.9669, 0.988] +2024-09-08 18:12:10.938444: Epoch time: 335.84 s +2024-09-08 18:12:14.690974: +2024-09-08 18:12:14.700467: Epoch 542 +2024-09-08 18:12:14.701062: Current learning rate: 0.00495 +2024-09-08 18:17:37.906576: train_loss -0.7346 +2024-09-08 18:17:37.914406: val_loss -0.7868 +2024-09-08 18:17:37.916723: Pseudo dice [0.966, 0.9542, 0.9812, 0.9493, 0.8945, 0.9737, 0.994] +2024-09-08 18:17:37.920028: Epoch time: 323.22 s +2024-09-08 18:17:41.778443: +2024-09-08 18:17:41.792583: Epoch 543 +2024-09-08 18:17:41.793267: Current learning rate: 0.00494 +2024-09-08 18:23:17.608946: train_loss -0.7458 +2024-09-08 18:23:17.624032: val_loss -0.7455 +2024-09-08 18:23:17.624930: Pseudo dice [0.963, 0.956, 0.9763, 0.9513, 0.8718, 0.9716, 0.9908] +2024-09-08 18:23:17.625612: Epoch time: 335.84 s +2024-09-08 18:23:20.582796: +2024-09-08 18:23:20.597259: Epoch 544 +2024-09-08 18:23:20.599592: Current learning rate: 0.00493 +2024-09-08 18:28:41.067217: train_loss -0.74 +2024-09-08 18:28:41.068582: val_loss -0.7415 +2024-09-08 18:28:41.069151: Pseudo dice [0.9665, 0.9571, 0.9794, 0.9537, 0.878, 0.9716, 0.9911] +2024-09-08 18:28:41.069710: Epoch time: 320.49 s +2024-09-08 18:28:44.177186: +2024-09-08 18:28:44.178200: Epoch 545 +2024-09-08 18:28:44.178694: Current learning rate: 0.00492 +2024-09-08 18:33:59.300417: train_loss -0.7486 +2024-09-08 18:33:59.305270: val_loss -0.7907 +2024-09-08 18:33:59.306175: Pseudo dice [0.9664, 0.9602, 0.9796, 0.9514, 0.8959, 0.9689, 0.9927] +2024-09-08 18:33:59.306890: Epoch time: 315.13 s +2024-09-08 18:34:02.151721: +2024-09-08 18:34:02.153868: Epoch 546 +2024-09-08 18:34:02.155170: Current learning rate: 0.00491 +2024-09-08 18:39:30.724059: train_loss -0.7458 +2024-09-08 18:39:30.739219: val_loss -0.7433 +2024-09-08 18:39:30.740005: Pseudo dice [0.9626, 0.9579, 0.9798, 0.9515, 0.8795, 0.9707, 0.9893] +2024-09-08 18:39:30.740567: Epoch time: 328.58 s +2024-09-08 18:39:33.589497: +2024-09-08 18:39:33.590430: Epoch 547 +2024-09-08 18:39:33.591455: Current learning rate: 0.0049 +2024-09-08 18:45:03.278445: train_loss -0.7344 +2024-09-08 18:45:03.304803: val_loss -0.7454 +2024-09-08 18:45:03.305584: Pseudo dice [0.961, 0.9554, 0.9763, 0.9538, 0.8891, 0.9713, 0.9919] +2024-09-08 18:45:03.306142: Epoch time: 329.69 s +2024-09-08 18:45:06.890357: +2024-09-08 18:45:06.891930: Epoch 548 +2024-09-08 18:45:06.893522: Current learning rate: 0.00489 +2024-09-08 18:50:39.601282: train_loss -0.7336 +2024-09-08 18:50:39.618047: val_loss -0.7054 +2024-09-08 18:50:39.619290: Pseudo dice [0.9636, 0.9595, 0.9758, 0.9524, 0.8896, 0.9734, 0.992] +2024-09-08 18:50:39.619995: Epoch time: 332.72 s +2024-09-08 18:50:42.607399: +2024-09-08 18:50:42.608524: Epoch 549 +2024-09-08 18:50:42.609457: Current learning rate: 0.00488 +2024-09-08 18:56:12.412195: train_loss -0.7413 +2024-09-08 18:56:12.438465: val_loss -0.7679 +2024-09-08 18:56:12.439800: Pseudo dice [0.9662, 0.9602, 0.9803, 0.9516, 0.8906, 0.9721, 0.9921] +2024-09-08 18:56:12.440812: Epoch time: 329.81 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_22_03_23.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_22_03_23.txt new file mode 100644 index 0000000000000000000000000000000000000000..356046eecd5495004eb57c9b68354ca8abb86111 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_8_22_03_23.txt @@ -0,0 +1,375 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 22:03:26.538706: Using torch.compile... +2024-09-08 22:03:32.862088: do_dummy_2d_data_aug: False +2024-09-08 22:03:32.867847: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 22:03:32.869069: The split file contains 5 splits. +2024-09-08 22:03:32.869425: Desired fold for training: 2 +2024-09-08 22:03:32.869838: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 22:03:54.278547: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 22:03:55.471968: +2024-09-08 22:03:55.487739: Epoch 550 +2024-09-08 22:03:55.491058: Current learning rate: 0.00487 +2024-09-08 22:19:28.331493: train_loss -0.7326 +2024-09-08 22:19:28.333068: val_loss -0.7686 +2024-09-08 22:19:28.333630: Pseudo dice [0.9647, 0.957, 0.9769, 0.9472, 0.8753, 0.9699, 0.9914] +2024-09-08 22:19:28.334186: Epoch time: 932.93 s +2024-09-08 22:19:30.685737: +2024-09-08 22:19:30.686766: Epoch 551 +2024-09-08 22:19:30.687500: Current learning rate: 0.00486 +2024-09-08 22:26:36.699268: train_loss -0.7482 +2024-09-08 22:26:36.700821: val_loss -0.7157 +2024-09-08 22:26:36.701493: Pseudo dice [0.963, 0.9587, 0.9749, 0.9498, 0.893, 0.9717, 0.9919] +2024-09-08 22:26:36.702090: Epoch time: 426.02 s +2024-09-08 22:26:42.174206: +2024-09-08 22:26:42.196912: Epoch 552 +2024-09-08 22:26:42.198293: Current learning rate: 0.00485 +2024-09-08 22:33:45.267772: train_loss -0.7388 +2024-09-08 22:33:45.288909: val_loss -0.7529 +2024-09-08 22:33:45.289805: Pseudo dice [0.9652, 0.9586, 0.9777, 0.9498, 0.8889, 0.9687, 0.9918] +2024-09-08 22:33:45.290511: Epoch time: 423.12 s +2024-09-08 22:33:50.191873: +2024-09-08 22:33:50.212791: Epoch 553 +2024-09-08 22:33:50.213895: Current learning rate: 0.00484 +2024-09-08 22:41:10.496675: train_loss -0.7434 +2024-09-08 22:41:10.509114: val_loss -0.7568 +2024-09-08 22:41:10.510094: Pseudo dice [0.9662, 0.9617, 0.9785, 0.956, 0.8974, 0.9722, 0.993] +2024-09-08 22:41:10.511068: Epoch time: 440.32 s +2024-09-08 22:41:10.511893: Yayy! New best EMA pseudo Dice: 0.9573 +2024-09-08 22:41:18.556431: +2024-09-08 22:41:18.557469: Epoch 554 +2024-09-08 22:41:18.558244: Current learning rate: 0.00484 +2024-09-08 22:48:34.510399: train_loss -0.7441 +2024-09-08 22:48:34.533200: val_loss -0.7389 +2024-09-08 22:48:34.534147: Pseudo dice [0.9649, 0.961, 0.9778, 0.9538, 0.8829, 0.969, 0.9892] +2024-09-08 22:48:34.534744: Epoch time: 435.96 s +2024-09-08 22:48:39.673669: +2024-09-08 22:48:39.688597: Epoch 555 +2024-09-08 22:48:39.689647: Current learning rate: 0.00483 +2024-09-08 22:55:45.299595: train_loss -0.7372 +2024-09-08 22:55:45.323391: val_loss -0.7551 +2024-09-08 22:55:45.324622: Pseudo dice [0.9627, 0.958, 0.9754, 0.9582, 0.8848, 0.971, 0.9904] +2024-09-08 22:55:45.325541: Epoch time: 425.65 s +2024-09-08 22:55:49.870077: +2024-09-08 22:55:49.871295: Epoch 556 +2024-09-08 22:55:49.871980: Current learning rate: 0.00482 +2024-09-08 23:02:56.993884: train_loss -0.7349 +2024-09-08 23:02:57.005195: val_loss -0.7285 +2024-09-08 23:02:57.006243: Pseudo dice [0.9645, 0.9597, 0.9794, 0.9492, 0.8763, 0.9724, 0.9902] +2024-09-08 23:02:57.007208: Epoch time: 427.13 s +2024-09-08 23:03:00.726203: +2024-09-08 23:03:00.740784: Epoch 557 +2024-09-08 23:03:00.741783: Current learning rate: 0.00481 +2024-09-08 23:10:15.722263: train_loss -0.7285 +2024-09-08 23:10:15.726526: val_loss -0.7512 +2024-09-08 23:10:15.727354: Pseudo dice [0.9607, 0.9572, 0.973, 0.9405, 0.8677, 0.9698, 0.9907] +2024-09-08 23:10:15.727985: Epoch time: 435.01 s +2024-09-08 23:10:19.543460: +2024-09-08 23:10:19.555824: Epoch 558 +2024-09-08 23:10:19.557465: Current learning rate: 0.0048 +2024-09-08 23:17:24.121658: train_loss -0.712 +2024-09-08 23:17:24.140037: val_loss -0.7023 +2024-09-08 23:17:24.141157: Pseudo dice [0.9604, 0.9583, 0.9734, 0.9469, 0.8801, 0.9702, 0.9918] +2024-09-08 23:17:24.142205: Epoch time: 424.61 s +2024-09-08 23:17:29.728482: +2024-09-08 23:17:29.742521: Epoch 559 +2024-09-08 23:17:29.743570: Current learning rate: 0.00479 +2024-09-08 23:24:35.286962: train_loss -0.7505 +2024-09-08 23:24:35.290501: val_loss -0.7449 +2024-09-08 23:24:35.292709: Pseudo dice [0.9627, 0.959, 0.9757, 0.9549, 0.8874, 0.9702, 0.9918] +2024-09-08 23:24:35.294560: Epoch time: 425.64 s +2024-09-08 23:24:39.000451: +2024-09-08 23:24:39.002198: Epoch 560 +2024-09-08 23:24:39.003342: Current learning rate: 0.00478 +2024-09-08 23:31:23.686781: train_loss -0.7329 +2024-09-08 23:31:23.701339: val_loss -0.7275 +2024-09-08 23:31:23.702567: Pseudo dice [0.96, 0.9586, 0.9739, 0.9501, 0.8784, 0.9707, 0.9907] +2024-09-08 23:31:23.703608: Epoch time: 404.73 s +2024-09-08 23:31:26.940699: +2024-09-08 23:31:26.943865: Epoch 561 +2024-09-08 23:31:26.945267: Current learning rate: 0.00477 +2024-09-08 23:38:21.058471: train_loss -0.7531 +2024-09-08 23:38:21.069635: val_loss -0.7693 +2024-09-08 23:38:21.070920: Pseudo dice [0.9637, 0.9598, 0.9787, 0.9487, 0.8808, 0.9684, 0.992] +2024-09-08 23:38:21.072473: Epoch time: 414.13 s +2024-09-08 23:38:26.434268: +2024-09-08 23:38:26.435356: Epoch 562 +2024-09-08 23:38:26.436042: Current learning rate: 0.00476 +2024-09-08 23:45:52.331048: train_loss -0.7234 +2024-09-08 23:45:52.335859: val_loss -0.7588 +2024-09-08 23:45:52.336971: Pseudo dice [0.964, 0.9548, 0.9779, 0.9532, 0.8844, 0.9706, 0.9928] +2024-09-08 23:45:52.337608: Epoch time: 445.9 s +2024-09-08 23:45:58.910530: +2024-09-08 23:45:58.911471: Epoch 563 +2024-09-08 23:45:58.912025: Current learning rate: 0.00475 +2024-09-08 23:52:56.749421: train_loss -0.7493 +2024-09-08 23:52:56.751223: val_loss -0.7701 +2024-09-08 23:52:56.751940: Pseudo dice [0.964, 0.958, 0.9766, 0.9493, 0.8663, 0.9691, 0.9903] +2024-09-08 23:52:56.753717: Epoch time: 417.84 s +2024-09-08 23:52:59.435032: +2024-09-08 23:52:59.436633: Epoch 564 +2024-09-08 23:52:59.437568: Current learning rate: 0.00474 +2024-09-09 00:00:10.231252: train_loss -0.7589 +2024-09-09 00:00:10.245122: val_loss -0.769 +2024-09-09 00:00:10.246096: Pseudo dice [0.9601, 0.957, 0.9718, 0.9487, 0.8774, 0.9736, 0.9915] +2024-09-09 00:00:10.246920: Epoch time: 430.8 s +2024-09-09 00:00:15.375943: +2024-09-09 00:00:15.391873: Epoch 565 +2024-09-09 00:00:15.393401: Current learning rate: 0.00473 +2024-09-09 00:07:37.195942: train_loss -0.7386 +2024-09-09 00:07:37.223367: val_loss -0.7537 +2024-09-09 00:07:37.224731: Pseudo dice [0.9649, 0.9583, 0.9788, 0.9539, 0.8832, 0.9713, 0.9907] +2024-09-09 00:07:37.226629: Epoch time: 441.84 s +2024-09-09 00:07:41.404677: +2024-09-09 00:07:41.406082: Epoch 566 +2024-09-09 00:07:41.406746: Current learning rate: 0.00472 +2024-09-09 00:14:41.534027: train_loss -0.7392 +2024-09-09 00:14:41.549247: val_loss -0.7367 +2024-09-09 00:14:41.550121: Pseudo dice [0.9603, 0.9597, 0.9715, 0.9496, 0.8685, 0.9657, 0.9905] +2024-09-09 00:14:41.550745: Epoch time: 420.14 s +2024-09-09 00:14:45.870085: +2024-09-09 00:14:45.877287: Epoch 567 +2024-09-09 00:14:45.878553: Current learning rate: 0.00471 +2024-09-09 00:21:43.246880: train_loss -0.7081 +2024-09-09 00:21:43.264049: val_loss -0.733 +2024-09-09 00:21:43.266315: Pseudo dice [0.9642, 0.9573, 0.9775, 0.9389, 0.8793, 0.9695, 0.9915] +2024-09-09 00:21:43.267291: Epoch time: 417.39 s +2024-09-09 00:21:46.773546: +2024-09-09 00:21:46.774421: Epoch 568 +2024-09-09 00:21:46.775190: Current learning rate: 0.0047 +2024-09-09 00:28:40.129228: train_loss -0.7257 +2024-09-09 00:28:40.131511: val_loss -0.7199 +2024-09-09 00:28:40.132740: Pseudo dice [0.9575, 0.9568, 0.9707, 0.95, 0.8856, 0.9712, 0.9928] +2024-09-09 00:28:40.133879: Epoch time: 413.36 s +2024-09-09 00:28:43.585414: +2024-09-09 00:28:43.600689: Epoch 569 +2024-09-09 00:28:43.601728: Current learning rate: 0.00469 +2024-09-09 00:35:55.586061: train_loss -0.7472 +2024-09-09 00:35:55.601307: val_loss -0.7164 +2024-09-09 00:35:55.612655: Pseudo dice [0.9634, 0.9552, 0.9808, 0.9503, 0.8874, 0.9691, 0.9924] +2024-09-09 00:35:55.613541: Epoch time: 432.01 s +2024-09-09 00:35:59.881225: +2024-09-09 00:35:59.892831: Epoch 570 +2024-09-09 00:35:59.894104: Current learning rate: 0.00468 +2024-09-09 00:43:16.503143: train_loss -0.731 +2024-09-09 00:43:16.517003: val_loss -0.7464 +2024-09-09 00:43:16.517985: Pseudo dice [0.9648, 0.9601, 0.9761, 0.9485, 0.8932, 0.9719, 0.9903] +2024-09-09 00:43:16.518804: Epoch time: 436.67 s +2024-09-09 00:43:21.643003: +2024-09-09 00:43:21.655535: Epoch 571 +2024-09-09 00:43:21.656666: Current learning rate: 0.00467 +2024-09-09 00:50:43.615848: train_loss -0.7472 +2024-09-09 00:50:43.641596: val_loss -0.7848 +2024-09-09 00:50:43.642950: Pseudo dice [0.9645, 0.9572, 0.9798, 0.953, 0.8923, 0.9722, 0.9918] +2024-09-09 00:50:43.643641: Epoch time: 441.98 s +2024-09-09 00:50:48.833308: +2024-09-09 00:50:48.844656: Epoch 572 +2024-09-09 00:50:48.845451: Current learning rate: 0.00466 +2024-09-09 00:57:52.159317: train_loss -0.7303 +2024-09-09 00:57:52.175435: val_loss -0.7525 +2024-09-09 00:57:52.177922: Pseudo dice [0.9612, 0.9576, 0.9748, 0.9516, 0.8908, 0.9743, 0.9925] +2024-09-09 00:57:52.181556: Epoch time: 423.33 s +2024-09-09 00:57:55.922011: +2024-09-09 00:57:55.923431: Epoch 573 +2024-09-09 00:57:55.924397: Current learning rate: 0.00465 +2024-09-09 01:04:57.680023: train_loss -0.7432 +2024-09-09 01:04:57.697443: val_loss -0.7864 +2024-09-09 01:04:57.698920: Pseudo dice [0.9623, 0.9576, 0.9761, 0.9515, 0.8899, 0.9743, 0.9926] +2024-09-09 01:04:57.701002: Epoch time: 421.83 s +2024-09-09 01:05:02.841346: +2024-09-09 01:05:02.843701: Epoch 574 +2024-09-09 01:05:02.845501: Current learning rate: 0.00464 +2024-09-09 01:11:48.587267: train_loss -0.7432 +2024-09-09 01:11:48.590376: val_loss -0.7669 +2024-09-09 01:11:48.591822: Pseudo dice [0.9651, 0.9587, 0.9788, 0.9501, 0.8863, 0.9723, 0.9928] +2024-09-09 01:11:48.593715: Epoch time: 405.77 s +2024-09-09 01:11:52.796099: +2024-09-09 01:11:52.812754: Epoch 575 +2024-09-09 01:11:52.813758: Current learning rate: 0.00463 +2024-09-09 01:18:58.121827: train_loss -0.7505 +2024-09-09 01:18:58.133427: val_loss -0.7266 +2024-09-09 01:18:58.134873: Pseudo dice [0.9657, 0.9608, 0.9806, 0.9539, 0.8933, 0.9703, 0.9918] +2024-09-09 01:18:58.135920: Epoch time: 425.34 s +2024-09-09 01:19:03.082981: +2024-09-09 01:19:03.096593: Epoch 576 +2024-09-09 01:19:03.097536: Current learning rate: 0.00462 +2024-09-09 01:26:17.233803: train_loss -0.7395 +2024-09-09 01:26:17.261439: val_loss -0.7635 +2024-09-09 01:26:17.262424: Pseudo dice [0.9621, 0.9596, 0.9741, 0.9502, 0.886, 0.9719, 0.9926] +2024-09-09 01:26:17.263252: Epoch time: 434.16 s +2024-09-09 01:26:24.542467: +2024-09-09 01:26:24.543866: Epoch 577 +2024-09-09 01:26:24.544632: Current learning rate: 0.00461 +2024-09-09 01:33:41.045725: train_loss -0.7368 +2024-09-09 01:33:41.059593: val_loss -0.7986 +2024-09-09 01:33:41.061496: Pseudo dice [0.966, 0.9592, 0.98, 0.9545, 0.8841, 0.9714, 0.9924] +2024-09-09 01:33:41.062464: Epoch time: 436.51 s +2024-09-09 01:33:45.165621: +2024-09-09 01:33:45.167037: Epoch 578 +2024-09-09 01:33:45.168280: Current learning rate: 0.0046 +2024-09-09 01:40:31.344788: train_loss -0.7382 +2024-09-09 01:40:31.349139: val_loss -0.7242 +2024-09-09 01:40:31.349968: Pseudo dice [0.9679, 0.9595, 0.9799, 0.9541, 0.8739, 0.9699, 0.9866] +2024-09-09 01:40:31.350809: Epoch time: 406.2 s +2024-09-09 01:40:36.426802: +2024-09-09 01:40:36.428052: Epoch 579 +2024-09-09 01:40:36.428707: Current learning rate: 0.00459 +2024-09-09 01:47:34.623781: train_loss -0.7461 +2024-09-09 01:47:34.625420: val_loss -0.7393 +2024-09-09 01:47:34.626065: Pseudo dice [0.9651, 0.9569, 0.9791, 0.9487, 0.8521, 0.9702, 0.9887] +2024-09-09 01:47:34.626774: Epoch time: 418.2 s +2024-09-09 01:47:38.946234: +2024-09-09 01:47:38.960607: Epoch 580 +2024-09-09 01:47:38.961865: Current learning rate: 0.00458 +2024-09-09 01:54:54.366256: train_loss -0.7464 +2024-09-09 01:54:54.384014: val_loss -0.7442 +2024-09-09 01:54:54.384684: Pseudo dice [0.9657, 0.9591, 0.9823, 0.9528, 0.8839, 0.9693, 0.9916] +2024-09-09 01:54:54.385248: Epoch time: 435.43 s +2024-09-09 01:54:58.438811: +2024-09-09 01:54:58.439813: Epoch 581 +2024-09-09 01:54:58.440881: Current learning rate: 0.00457 +2024-09-09 02:02:26.917483: train_loss -0.7508 +2024-09-09 02:02:26.926157: val_loss -0.7518 +2024-09-09 02:02:26.927494: Pseudo dice [0.9624, 0.9572, 0.9744, 0.9518, 0.8859, 0.9715, 0.9909] +2024-09-09 02:02:26.928167: Epoch time: 448.49 s +2024-09-09 02:02:31.281358: +2024-09-09 02:02:31.304563: Epoch 582 +2024-09-09 02:02:31.306410: Current learning rate: 0.00456 +2024-09-09 02:09:39.307789: train_loss -0.7322 +2024-09-09 02:09:39.340350: val_loss -0.786 +2024-09-09 02:09:39.350283: Pseudo dice [0.9641, 0.9566, 0.9779, 0.9533, 0.8829, 0.9713, 0.9907] +2024-09-09 02:09:39.352175: Epoch time: 428.06 s +2024-09-09 02:09:45.190077: +2024-09-09 02:09:45.191517: Epoch 583 +2024-09-09 02:09:45.192436: Current learning rate: 0.00455 +2024-09-09 02:17:03.154964: train_loss -0.7352 +2024-09-09 02:17:03.181281: val_loss -0.764 +2024-09-09 02:17:03.182584: Pseudo dice [0.9656, 0.9589, 0.9753, 0.9483, 0.8641, 0.9719, 0.9914] +2024-09-09 02:17:03.183495: Epoch time: 438.0 s +2024-09-09 02:17:07.887032: +2024-09-09 02:17:07.888230: Epoch 584 +2024-09-09 02:17:07.888883: Current learning rate: 0.00454 +2024-09-09 02:24:19.073812: train_loss -0.7375 +2024-09-09 02:24:19.097414: val_loss -0.7629 +2024-09-09 02:24:19.098049: Pseudo dice [0.964, 0.9596, 0.9765, 0.9543, 0.8963, 0.9725, 0.9926] +2024-09-09 02:24:19.098537: Epoch time: 431.19 s +2024-09-09 02:24:22.070136: +2024-09-09 02:24:22.084885: Epoch 585 +2024-09-09 02:24:22.086186: Current learning rate: 0.00453 +2024-09-09 02:31:34.590007: train_loss -0.7389 +2024-09-09 02:31:34.594502: val_loss -0.7542 +2024-09-09 02:31:34.595502: Pseudo dice [0.965, 0.959, 0.9786, 0.9588, 0.8924, 0.9712, 0.9918] +2024-09-09 02:31:34.596760: Epoch time: 432.53 s +2024-09-09 02:31:39.469460: +2024-09-09 02:31:39.484800: Epoch 586 +2024-09-09 02:31:39.485941: Current learning rate: 0.00452 +2024-09-09 02:38:44.281853: train_loss -0.7477 +2024-09-09 02:38:44.298998: val_loss -0.7266 +2024-09-09 02:38:44.324214: Pseudo dice [0.9631, 0.9577, 0.9779, 0.9469, 0.8809, 0.9721, 0.9923] +2024-09-09 02:38:44.332766: Epoch time: 424.82 s +2024-09-09 02:38:48.357355: +2024-09-09 02:38:48.368999: Epoch 587 +2024-09-09 02:38:48.369973: Current learning rate: 0.00451 +2024-09-09 02:45:41.946652: train_loss -0.7394 +2024-09-09 02:45:41.961670: val_loss -0.7507 +2024-09-09 02:45:41.963463: Pseudo dice [0.966, 0.9597, 0.9806, 0.95, 0.8886, 0.9721, 0.9934] +2024-09-09 02:45:41.965076: Epoch time: 413.6 s +2024-09-09 02:45:46.326486: +2024-09-09 02:45:46.340915: Epoch 588 +2024-09-09 02:45:46.342961: Current learning rate: 0.0045 +2024-09-09 02:52:57.124990: train_loss -0.7348 +2024-09-09 02:52:57.148993: val_loss -0.7387 +2024-09-09 02:52:57.149953: Pseudo dice [0.9637, 0.9586, 0.9769, 0.956, 0.8876, 0.9715, 0.9926] +2024-09-09 02:52:57.150611: Epoch time: 430.81 s +2024-09-09 02:53:02.163726: +2024-09-09 02:53:02.171856: Epoch 589 +2024-09-09 02:53:02.172788: Current learning rate: 0.00449 +2024-09-09 03:00:17.016028: train_loss -0.7424 +2024-09-09 03:00:17.033114: val_loss -0.7524 +2024-09-09 03:00:17.034209: Pseudo dice [0.9651, 0.9586, 0.9777, 0.9541, 0.8796, 0.9731, 0.9911] +2024-09-09 03:00:17.035145: Epoch time: 434.86 s +2024-09-09 03:00:21.864042: +2024-09-09 03:00:21.877211: Epoch 590 +2024-09-09 03:00:21.878128: Current learning rate: 0.00448 +2024-09-09 03:07:24.699613: train_loss -0.7371 +2024-09-09 03:07:24.722958: val_loss -0.7135 +2024-09-09 03:07:24.732102: Pseudo dice [0.9611, 0.9625, 0.9736, 0.9514, 0.8753, 0.9709, 0.9907] +2024-09-09 03:07:24.733350: Epoch time: 422.85 s +2024-09-09 03:07:31.472426: +2024-09-09 03:07:31.473867: Epoch 591 +2024-09-09 03:07:31.474491: Current learning rate: 0.00447 +2024-09-09 03:14:32.284122: train_loss -0.7301 +2024-09-09 03:14:32.298884: val_loss -0.7753 +2024-09-09 03:14:32.299845: Pseudo dice [0.9635, 0.9581, 0.9768, 0.9569, 0.8847, 0.9716, 0.9903] +2024-09-09 03:14:32.301188: Epoch time: 420.82 s +2024-09-09 03:14:37.354524: +2024-09-09 03:14:37.355601: Epoch 592 +2024-09-09 03:14:37.356228: Current learning rate: 0.00446 +2024-09-09 03:21:41.185391: train_loss -0.7416 +2024-09-09 03:21:41.203599: val_loss -0.7252 +2024-09-09 03:21:41.204877: Pseudo dice [0.9643, 0.96, 0.9784, 0.9581, 0.8927, 0.9723, 0.9931] +2024-09-09 03:21:41.206357: Epoch time: 423.84 s +2024-09-09 03:21:45.385911: +2024-09-09 03:21:45.387277: Epoch 593 +2024-09-09 03:21:45.388653: Current learning rate: 0.00445 +2024-09-09 03:28:59.773166: train_loss -0.7465 +2024-09-09 03:28:59.781444: val_loss -0.7862 +2024-09-09 03:28:59.782340: Pseudo dice [0.9656, 0.9597, 0.981, 0.9549, 0.8966, 0.9739, 0.9929] +2024-09-09 03:28:59.785707: Epoch time: 434.39 s +2024-09-09 03:28:59.786463: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-09 03:29:08.141977: +2024-09-09 03:29:08.142942: Epoch 594 +2024-09-09 03:29:08.143481: Current learning rate: 0.00444 +2024-09-09 03:35:46.740777: train_loss -0.743 +2024-09-09 03:35:46.746039: val_loss -0.7455 +2024-09-09 03:35:46.746874: Pseudo dice [0.9651, 0.9598, 0.9784, 0.9549, 0.8881, 0.9711, 0.9912] +2024-09-09 03:35:46.747754: Epoch time: 398.6 s +2024-09-09 03:35:46.748716: Yayy! New best EMA pseudo Dice: 0.9576 +2024-09-09 03:35:55.816449: +2024-09-09 03:35:55.817988: Epoch 595 +2024-09-09 03:35:55.818723: Current learning rate: 0.00443 +2024-09-09 03:42:48.756051: train_loss -0.7556 +2024-09-09 03:42:48.768985: val_loss -0.7461 +2024-09-09 03:42:48.769688: Pseudo dice [0.9634, 0.96, 0.9776, 0.9495, 0.8946, 0.9721, 0.9912] +2024-09-09 03:42:48.770316: Epoch time: 412.95 s +2024-09-09 03:42:48.772610: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 03:42:56.864432: +2024-09-09 03:42:56.865494: Epoch 596 +2024-09-09 03:42:56.866056: Current learning rate: 0.00442 +2024-09-09 03:49:53.715641: train_loss -0.7414 +2024-09-09 03:49:53.731272: val_loss -0.7509 +2024-09-09 03:49:53.732067: Pseudo dice [0.9628, 0.9593, 0.9768, 0.9486, 0.8819, 0.972, 0.9917] +2024-09-09 03:49:53.733135: Epoch time: 416.86 s +2024-09-09 03:49:58.615562: +2024-09-09 03:49:58.628905: Epoch 597 +2024-09-09 03:49:58.630105: Current learning rate: 0.00441 +2024-09-09 03:56:53.146106: train_loss -0.7591 +2024-09-09 03:56:53.161399: val_loss -0.7629 +2024-09-09 03:56:53.162322: Pseudo dice [0.9608, 0.9575, 0.9748, 0.9528, 0.8885, 0.9712, 0.993] +2024-09-09 03:56:53.163556: Epoch time: 414.55 s +2024-09-09 03:56:58.361011: +2024-09-09 03:56:58.376273: Epoch 598 +2024-09-09 03:56:58.379746: Current learning rate: 0.0044 +2024-09-09 04:03:54.380206: train_loss -0.7245 +2024-09-09 04:03:54.394218: val_loss -0.7509 +2024-09-09 04:03:54.395077: Pseudo dice [0.9645, 0.9594, 0.9804, 0.948, 0.8743, 0.9708, 0.9917] +2024-09-09 04:03:54.396734: Epoch time: 416.03 s +2024-09-09 04:03:58.974823: +2024-09-09 04:03:58.976295: Epoch 599 +2024-09-09 04:03:58.977226: Current learning rate: 0.00439 +2024-09-09 04:11:22.469828: train_loss -0.7432 +2024-09-09 04:11:22.499367: val_loss -0.7164 +2024-09-09 04:11:22.502374: Pseudo dice [0.9586, 0.9589, 0.9699, 0.9443, 0.8674, 0.9722, 0.9889] +2024-09-09 04:11:22.503911: Epoch time: 443.5 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_06_02_54.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_06_02_54.txt new file mode 100644 index 0000000000000000000000000000000000000000..9191d4cb49ac6788b5e22cfe21cc7ac3d12d690c --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_06_02_54.txt @@ -0,0 +1,373 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 06:02:56.950222: Using torch.compile... +2024-09-09 06:03:03.029840: do_dummy_2d_data_aug: False +2024-09-09 06:03:03.043079: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 06:03:03.049602: The split file contains 5 splits. +2024-09-09 06:03:03.049963: Desired fold for training: 2 +2024-09-09 06:03:03.050306: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 06:03:25.247781: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 06:03:27.411189: +2024-09-09 06:03:27.423788: Epoch 600 +2024-09-09 06:03:27.429134: Current learning rate: 0.00438 +2024-09-09 06:20:15.242896: train_loss -0.7617 +2024-09-09 06:20:15.245301: val_loss -0.7638 +2024-09-09 06:20:15.245857: Pseudo dice [0.9676, 0.959, 0.9802, 0.95, 0.8842, 0.9759, 0.9914] +2024-09-09 06:20:15.246463: Epoch time: 1007.84 s +2024-09-09 06:20:17.478743: +2024-09-09 06:20:17.479760: Epoch 601 +2024-09-09 06:20:17.480365: Current learning rate: 0.00437 +2024-09-09 06:26:55.626777: train_loss -0.754 +2024-09-09 06:26:55.642218: val_loss -0.7655 +2024-09-09 06:26:55.643193: Pseudo dice [0.963, 0.9571, 0.9754, 0.9482, 0.8867, 0.9732, 0.9921] +2024-09-09 06:26:55.643811: Epoch time: 398.15 s +2024-09-09 06:27:00.473166: +2024-09-09 06:27:00.481426: Epoch 602 +2024-09-09 06:27:00.484834: Current learning rate: 0.00436 +2024-09-09 06:34:23.553585: train_loss -0.7479 +2024-09-09 06:34:23.581663: val_loss -0.7935 +2024-09-09 06:34:23.585756: Pseudo dice [0.9675, 0.9599, 0.9794, 0.9539, 0.8953, 0.9759, 0.9926] +2024-09-09 06:34:23.589119: Epoch time: 443.12 s +2024-09-09 06:34:26.951948: +2024-09-09 06:34:26.965618: Epoch 603 +2024-09-09 06:34:26.966702: Current learning rate: 0.00435 +2024-09-09 06:41:36.037462: train_loss -0.7535 +2024-09-09 06:41:36.055438: val_loss -0.734 +2024-09-09 06:41:36.061840: Pseudo dice [0.9595, 0.9612, 0.9738, 0.9506, 0.8694, 0.971, 0.9903] +2024-09-09 06:41:36.067371: Epoch time: 429.09 s +2024-09-09 06:41:44.562901: +2024-09-09 06:41:44.563782: Epoch 604 +2024-09-09 06:41:44.564385: Current learning rate: 0.00434 +2024-09-09 06:48:48.705008: train_loss -0.7409 +2024-09-09 06:48:48.726112: val_loss -0.7516 +2024-09-09 06:48:48.728140: Pseudo dice [0.9661, 0.9609, 0.9781, 0.9565, 0.887, 0.9736, 0.9919] +2024-09-09 06:48:48.731722: Epoch time: 424.15 s +2024-09-09 06:48:53.715508: +2024-09-09 06:48:53.717325: Epoch 605 +2024-09-09 06:48:53.719216: Current learning rate: 0.00433 +2024-09-09 06:56:07.898799: train_loss -0.7372 +2024-09-09 06:56:07.900715: val_loss -0.7541 +2024-09-09 06:56:07.901495: Pseudo dice [0.962, 0.9622, 0.9747, 0.9511, 0.8926, 0.9729, 0.9927] +2024-09-09 06:56:07.902332: Epoch time: 434.19 s +2024-09-09 06:56:12.826663: +2024-09-09 06:56:12.849938: Epoch 606 +2024-09-09 06:56:12.851698: Current learning rate: 0.00432 +2024-09-09 07:03:18.146184: train_loss -0.7549 +2024-09-09 07:03:18.148910: val_loss -0.7607 +2024-09-09 07:03:18.149639: Pseudo dice [0.9657, 0.9583, 0.9805, 0.9563, 0.8856, 0.9725, 0.9929] +2024-09-09 07:03:18.150264: Epoch time: 425.33 s +2024-09-09 07:03:20.892528: +2024-09-09 07:03:20.894319: Epoch 607 +2024-09-09 07:03:20.895409: Current learning rate: 0.00431 +2024-09-09 07:10:30.748242: train_loss -0.7388 +2024-09-09 07:10:30.772339: val_loss -0.7454 +2024-09-09 07:10:30.774498: Pseudo dice [0.9661, 0.9589, 0.9773, 0.9556, 0.8811, 0.9725, 0.9927] +2024-09-09 07:10:30.775732: Epoch time: 429.9 s +2024-09-09 07:10:35.050074: +2024-09-09 07:10:35.064677: Epoch 608 +2024-09-09 07:10:35.065629: Current learning rate: 0.0043 +2024-09-09 07:17:47.094805: train_loss -0.7466 +2024-09-09 07:17:47.110157: val_loss -0.744 +2024-09-09 07:17:47.111510: Pseudo dice [0.9628, 0.96, 0.9753, 0.9492, 0.8844, 0.9724, 0.9923] +2024-09-09 07:17:47.113859: Epoch time: 432.05 s +2024-09-09 07:17:52.208656: +2024-09-09 07:17:52.213530: Epoch 609 +2024-09-09 07:17:52.224963: Current learning rate: 0.00429 +2024-09-09 07:24:48.432679: train_loss -0.7574 +2024-09-09 07:24:48.451123: val_loss -0.7073 +2024-09-09 07:24:48.453218: Pseudo dice [0.9683, 0.9608, 0.9824, 0.9546, 0.8858, 0.9752, 0.9936] +2024-09-09 07:24:48.457077: Epoch time: 416.23 s +2024-09-09 07:24:53.140310: +2024-09-09 07:24:53.142112: Epoch 610 +2024-09-09 07:24:53.143293: Current learning rate: 0.00429 +2024-09-09 07:32:21.783065: train_loss -0.7394 +2024-09-09 07:32:21.806448: val_loss -0.746 +2024-09-09 07:32:21.808039: Pseudo dice [0.9623, 0.9591, 0.9794, 0.9509, 0.897, 0.9701, 0.9918] +2024-09-09 07:32:21.808693: Epoch time: 448.68 s +2024-09-09 07:32:21.810457: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 07:32:29.841188: +2024-09-09 07:32:29.842270: Epoch 611 +2024-09-09 07:32:29.842873: Current learning rate: 0.00428 +2024-09-09 07:39:16.040486: train_loss -0.7577 +2024-09-09 07:39:16.045499: val_loss -0.7339 +2024-09-09 07:39:16.046707: Pseudo dice [0.9616, 0.9599, 0.9738, 0.9518, 0.8779, 0.9665, 0.9916] +2024-09-09 07:39:16.047867: Epoch time: 406.2 s +2024-09-09 07:39:20.095410: +2024-09-09 07:39:20.098491: Epoch 612 +2024-09-09 07:39:20.099724: Current learning rate: 0.00427 +2024-09-09 07:46:37.690500: train_loss -0.7299 +2024-09-09 07:46:37.708822: val_loss -0.7691 +2024-09-09 07:46:37.710100: Pseudo dice [0.9677, 0.961, 0.9784, 0.9536, 0.8956, 0.9736, 0.992] +2024-09-09 07:46:37.710850: Epoch time: 437.6 s +2024-09-09 07:46:42.261224: +2024-09-09 07:46:42.264815: Epoch 613 +2024-09-09 07:46:42.266291: Current learning rate: 0.00426 +2024-09-09 07:53:46.247376: train_loss -0.729 +2024-09-09 07:53:46.269562: val_loss -0.7921 +2024-09-09 07:53:46.272051: Pseudo dice [0.9655, 0.9584, 0.9805, 0.9533, 0.8878, 0.9732, 0.9922] +2024-09-09 07:53:46.274468: Epoch time: 424.03 s +2024-09-09 07:53:46.276923: Yayy! New best EMA pseudo Dice: 0.9578 +2024-09-09 07:53:55.933673: +2024-09-09 07:53:55.935603: Epoch 614 +2024-09-09 07:53:55.937055: Current learning rate: 0.00425 +2024-09-09 08:00:49.736499: train_loss -0.7371 +2024-09-09 08:00:49.754962: val_loss -0.7523 +2024-09-09 08:00:49.755862: Pseudo dice [0.963, 0.9598, 0.9765, 0.9512, 0.8829, 0.9735, 0.9917] +2024-09-09 08:00:49.756961: Epoch time: 413.89 s +2024-09-09 08:00:54.224440: +2024-09-09 08:00:54.240928: Epoch 615 +2024-09-09 08:00:54.242097: Current learning rate: 0.00424 +2024-09-09 08:07:47.688052: train_loss -0.7542 +2024-09-09 08:07:47.706808: val_loss -0.7345 +2024-09-09 08:07:47.707842: Pseudo dice [0.9675, 0.96, 0.9807, 0.9516, 0.8739, 0.9723, 0.9894] +2024-09-09 08:07:47.708561: Epoch time: 413.48 s +2024-09-09 08:07:51.711214: +2024-09-09 08:07:51.718552: Epoch 616 +2024-09-09 08:07:51.719402: Current learning rate: 0.00423 +2024-09-09 08:14:33.210331: train_loss -0.7414 +2024-09-09 08:14:33.227769: val_loss -0.7473 +2024-09-09 08:14:33.229084: Pseudo dice [0.966, 0.9596, 0.9782, 0.9555, 0.8746, 0.9724, 0.9909] +2024-09-09 08:14:33.230439: Epoch time: 401.5 s +2024-09-09 08:14:42.422315: +2024-09-09 08:14:42.423188: Epoch 617 +2024-09-09 08:14:42.423703: Current learning rate: 0.00422 +2024-09-09 08:21:26.408775: train_loss -0.746 +2024-09-09 08:21:26.425382: val_loss -0.7631 +2024-09-09 08:21:26.426276: Pseudo dice [0.9679, 0.9595, 0.98, 0.9502, 0.8773, 0.9734, 0.9914] +2024-09-09 08:21:26.426853: Epoch time: 403.99 s +2024-09-09 08:21:30.954631: +2024-09-09 08:21:30.956182: Epoch 618 +2024-09-09 08:21:30.957790: Current learning rate: 0.00421 +2024-09-09 08:28:58.196321: train_loss -0.7377 +2024-09-09 08:28:58.200120: val_loss -0.7222 +2024-09-09 08:28:58.202068: Pseudo dice [0.9606, 0.9551, 0.9684, 0.9499, 0.8879, 0.9729, 0.9924] +2024-09-09 08:28:58.203886: Epoch time: 447.26 s +2024-09-09 08:29:02.867001: +2024-09-09 08:29:02.872729: Epoch 619 +2024-09-09 08:29:02.874115: Current learning rate: 0.0042 +2024-09-09 08:36:29.200829: train_loss -0.7352 +2024-09-09 08:36:29.215063: val_loss -0.7708 +2024-09-09 08:36:29.217307: Pseudo dice [0.9649, 0.9607, 0.976, 0.9532, 0.8957, 0.9734, 0.9923] +2024-09-09 08:36:29.225587: Epoch time: 446.36 s +2024-09-09 08:36:33.882768: +2024-09-09 08:36:33.884516: Epoch 620 +2024-09-09 08:36:33.885503: Current learning rate: 0.00419 +2024-09-09 08:43:38.293753: train_loss -0.7497 +2024-09-09 08:43:38.295831: val_loss -0.8003 +2024-09-09 08:43:38.296552: Pseudo dice [0.965, 0.9589, 0.9788, 0.9519, 0.8907, 0.9726, 0.9926] +2024-09-09 08:43:38.298666: Epoch time: 424.45 s +2024-09-09 08:43:42.286297: +2024-09-09 08:43:42.288301: Epoch 621 +2024-09-09 08:43:42.289648: Current learning rate: 0.00418 +2024-09-09 08:50:29.955088: train_loss -0.7438 +2024-09-09 08:50:29.981251: val_loss -0.7824 +2024-09-09 08:50:29.983319: Pseudo dice [0.9611, 0.9571, 0.9756, 0.9518, 0.8858, 0.9738, 0.9924] +2024-09-09 08:50:29.985041: Epoch time: 407.69 s +2024-09-09 08:50:34.348743: +2024-09-09 08:50:34.361267: Epoch 622 +2024-09-09 08:50:34.362613: Current learning rate: 0.00417 +2024-09-09 08:57:56.409571: train_loss -0.7411 +2024-09-09 08:57:56.425631: val_loss -0.7349 +2024-09-09 08:57:56.426818: Pseudo dice [0.9627, 0.9572, 0.9749, 0.9445, 0.8677, 0.9649, 0.9904] +2024-09-09 08:57:56.427680: Epoch time: 442.07 s +2024-09-09 08:58:01.223858: +2024-09-09 08:58:01.251796: Epoch 623 +2024-09-09 08:58:01.253178: Current learning rate: 0.00416 +2024-09-09 09:05:00.847128: train_loss -0.7245 +2024-09-09 09:05:00.856115: val_loss -0.7561 +2024-09-09 09:05:00.857110: Pseudo dice [0.9671, 0.9601, 0.9799, 0.9517, 0.8884, 0.9729, 0.9921] +2024-09-09 09:05:00.857919: Epoch time: 419.68 s +2024-09-09 09:05:04.988205: +2024-09-09 09:05:04.999786: Epoch 624 +2024-09-09 09:05:05.002122: Current learning rate: 0.00415 +2024-09-09 09:11:58.202910: train_loss -0.744 +2024-09-09 09:11:58.223713: val_loss -0.7514 +2024-09-09 09:11:58.225969: Pseudo dice [0.9646, 0.9587, 0.9735, 0.9528, 0.8624, 0.9662, 0.9904] +2024-09-09 09:11:58.227827: Epoch time: 413.22 s +2024-09-09 09:12:01.780781: +2024-09-09 09:12:01.799117: Epoch 625 +2024-09-09 09:12:01.800498: Current learning rate: 0.00414 +2024-09-09 09:19:04.828291: train_loss -0.7428 +2024-09-09 09:19:04.832661: val_loss -0.7522 +2024-09-09 09:19:04.834037: Pseudo dice [0.964, 0.9607, 0.977, 0.9492, 0.88, 0.9723, 0.9916] +2024-09-09 09:19:04.835063: Epoch time: 423.06 s +2024-09-09 09:19:09.119566: +2024-09-09 09:19:09.121256: Epoch 626 +2024-09-09 09:19:09.122091: Current learning rate: 0.00413 +2024-09-09 09:26:18.593300: train_loss -0.7511 +2024-09-09 09:26:18.609405: val_loss -0.7623 +2024-09-09 09:26:18.610175: Pseudo dice [0.9658, 0.9591, 0.9802, 0.9495, 0.8769, 0.9703, 0.9916] +2024-09-09 09:26:18.610958: Epoch time: 429.49 s +2024-09-09 09:26:23.710937: +2024-09-09 09:26:23.711976: Epoch 627 +2024-09-09 09:26:23.712700: Current learning rate: 0.00412 +2024-09-09 09:33:34.901164: train_loss -0.7396 +2024-09-09 09:33:34.903077: val_loss -0.7416 +2024-09-09 09:33:34.903954: Pseudo dice [0.9594, 0.9596, 0.9707, 0.9474, 0.8833, 0.9698, 0.9917] +2024-09-09 09:33:34.904610: Epoch time: 431.2 s +2024-09-09 09:33:39.054486: +2024-09-09 09:33:39.055847: Epoch 628 +2024-09-09 09:33:39.056559: Current learning rate: 0.00411 +2024-09-09 09:40:37.723316: train_loss -0.724 +2024-09-09 09:40:37.729505: val_loss -0.7387 +2024-09-09 09:40:37.731041: Pseudo dice [0.9663, 0.9606, 0.98, 0.9522, 0.8582, 0.965, 0.9881] +2024-09-09 09:40:37.732588: Epoch time: 418.67 s +2024-09-09 09:40:42.430305: +2024-09-09 09:40:42.431540: Epoch 629 +2024-09-09 09:40:42.432872: Current learning rate: 0.0041 +2024-09-09 09:47:51.012752: train_loss -0.7443 +2024-09-09 09:47:51.029310: val_loss -0.7498 +2024-09-09 09:47:51.030267: Pseudo dice [0.963, 0.9602, 0.9781, 0.9544, 0.8898, 0.9713, 0.9915] +2024-09-09 09:47:51.031240: Epoch time: 428.59 s +2024-09-09 09:47:55.855219: +2024-09-09 09:47:55.869025: Epoch 630 +2024-09-09 09:47:55.869966: Current learning rate: 0.00409 +2024-09-09 09:55:02.393730: train_loss -0.7398 +2024-09-09 09:55:02.397300: val_loss -0.7348 +2024-09-09 09:55:02.398042: Pseudo dice [0.9664, 0.96, 0.9798, 0.9525, 0.8902, 0.97, 0.9919] +2024-09-09 09:55:02.398953: Epoch time: 426.54 s +2024-09-09 09:55:09.540772: +2024-09-09 09:55:09.542563: Epoch 631 +2024-09-09 09:55:09.543435: Current learning rate: 0.00408 +2024-09-09 10:02:09.590416: train_loss -0.7183 +2024-09-09 10:02:09.605081: val_loss -0.7441 +2024-09-09 10:02:09.608065: Pseudo dice [0.9648, 0.9604, 0.9778, 0.9552, 0.8937, 0.9722, 0.994] +2024-09-09 10:02:09.609314: Epoch time: 420.06 s +2024-09-09 10:02:14.453404: +2024-09-09 10:02:14.455256: Epoch 632 +2024-09-09 10:02:14.456007: Current learning rate: 0.00407 +2024-09-09 10:10:07.873984: train_loss -0.7238 +2024-09-09 10:10:07.888975: val_loss -0.742 +2024-09-09 10:10:07.890064: Pseudo dice [0.9626, 0.957, 0.9754, 0.9518, 0.8823, 0.975, 0.9916] +2024-09-09 10:10:07.891068: Epoch time: 473.43 s +2024-09-09 10:10:14.032512: +2024-09-09 10:10:14.056001: Epoch 633 +2024-09-09 10:10:14.057982: Current learning rate: 0.00406 +2024-09-09 10:18:12.836342: train_loss -0.739 +2024-09-09 10:18:12.900295: val_loss -0.7221 +2024-09-09 10:18:12.903672: Pseudo dice [0.9646, 0.9599, 0.9766, 0.9543, 0.894, 0.9708, 0.9928] +2024-09-09 10:18:12.905485: Epoch time: 478.85 s +2024-09-09 10:18:19.034088: +2024-09-09 10:18:19.057245: Epoch 634 +2024-09-09 10:18:19.058946: Current learning rate: 0.00405 +2024-09-09 10:26:41.448530: train_loss -0.7475 +2024-09-09 10:26:41.453337: val_loss -0.7294 +2024-09-09 10:26:41.454360: Pseudo dice [0.9633, 0.9591, 0.9774, 0.954, 0.8888, 0.9736, 0.9926] +2024-09-09 10:26:41.455440: Epoch time: 502.44 s +2024-09-09 10:26:46.683177: +2024-09-09 10:26:46.684922: Epoch 635 +2024-09-09 10:26:46.685965: Current learning rate: 0.00404 +2024-09-09 10:35:17.013668: train_loss -0.7238 +2024-09-09 10:35:17.015539: val_loss -0.741 +2024-09-09 10:35:17.016323: Pseudo dice [0.9619, 0.9524, 0.9741, 0.9405, 0.8655, 0.9646, 0.9896] +2024-09-09 10:35:17.017328: Epoch time: 510.34 s +2024-09-09 10:35:19.851906: +2024-09-09 10:35:19.852941: Epoch 636 +2024-09-09 10:35:19.853956: Current learning rate: 0.00403 +2024-09-09 10:43:10.583712: train_loss -0.7211 +2024-09-09 10:43:10.591055: val_loss -0.6987 +2024-09-09 10:43:10.592400: Pseudo dice [0.9604, 0.957, 0.9676, 0.9449, 0.8744, 0.9677, 0.9872] +2024-09-09 10:43:10.594941: Epoch time: 470.74 s +2024-09-09 10:43:15.441630: +2024-09-09 10:43:15.443216: Epoch 637 +2024-09-09 10:43:15.444117: Current learning rate: 0.00402 +2024-09-09 10:50:49.610774: train_loss -0.7306 +2024-09-09 10:50:49.635194: val_loss -0.7538 +2024-09-09 10:50:49.638576: Pseudo dice [0.9619, 0.9551, 0.9747, 0.9475, 0.875, 0.9686, 0.9903] +2024-09-09 10:50:49.642174: Epoch time: 454.18 s +2024-09-09 10:50:54.430691: +2024-09-09 10:50:54.431987: Epoch 638 +2024-09-09 10:50:54.432611: Current learning rate: 0.00401 +2024-09-09 10:58:41.980604: train_loss -0.7338 +2024-09-09 10:58:41.986314: val_loss -0.7292 +2024-09-09 10:58:41.990077: Pseudo dice [0.9642, 0.9585, 0.9778, 0.9503, 0.8726, 0.9685, 0.9908] +2024-09-09 10:58:41.990882: Epoch time: 467.53 s +2024-09-09 10:58:49.370806: +2024-09-09 10:58:49.373962: Epoch 639 +2024-09-09 10:58:49.379360: Current learning rate: 0.004 +2024-09-09 11:07:27.343467: train_loss -0.7418 +2024-09-09 11:07:27.347893: val_loss -0.7309 +2024-09-09 11:07:27.349541: Pseudo dice [0.9642, 0.9584, 0.9765, 0.9535, 0.8901, 0.9736, 0.992] +2024-09-09 11:07:27.363223: Epoch time: 517.99 s +2024-09-09 11:07:32.479819: +2024-09-09 11:07:32.494317: Epoch 640 +2024-09-09 11:07:32.497502: Current learning rate: 0.00399 +2024-09-09 11:16:20.338953: train_loss -0.7534 +2024-09-09 11:16:20.414150: val_loss -0.7769 +2024-09-09 11:16:20.415013: Pseudo dice [0.9654, 0.9613, 0.9784, 0.9464, 0.8825, 0.9594, 0.9922] +2024-09-09 11:16:20.416170: Epoch time: 527.86 s +2024-09-09 11:16:24.746129: +2024-09-09 11:16:24.747495: Epoch 641 +2024-09-09 11:16:24.748802: Current learning rate: 0.00398 +2024-09-09 11:24:46.263028: train_loss -0.7264 +2024-09-09 11:24:46.266123: val_loss -0.768 +2024-09-09 11:24:46.269106: Pseudo dice [0.9648, 0.9585, 0.9758, 0.9559, 0.8799, 0.9722, 0.991] +2024-09-09 11:24:46.270807: Epoch time: 501.52 s +2024-09-09 11:24:51.482048: +2024-09-09 11:24:51.491761: Epoch 642 +2024-09-09 11:24:51.494155: Current learning rate: 0.00397 +2024-09-09 11:34:18.003467: train_loss -0.722 +2024-09-09 11:34:18.021617: val_loss -0.7739 +2024-09-09 11:34:18.023382: Pseudo dice [0.9655, 0.9588, 0.9772, 0.9481, 0.8811, 0.9705, 0.9905] +2024-09-09 11:34:18.025005: Epoch time: 566.53 s +2024-09-09 11:34:25.374955: +2024-09-09 11:34:25.419384: Epoch 643 +2024-09-09 11:34:25.426514: Current learning rate: 0.00396 +2024-09-09 11:43:54.915174: train_loss -0.728 +2024-09-09 11:43:54.917320: val_loss -0.7421 +2024-09-09 11:43:54.917907: Pseudo dice [0.9667, 0.9606, 0.9821, 0.9486, 0.8858, 0.9724, 0.9932] +2024-09-09 11:43:54.918500: Epoch time: 569.55 s +2024-09-09 11:43:58.825246: +2024-09-09 11:43:58.827094: Epoch 644 +2024-09-09 11:43:58.829639: Current learning rate: 0.00395 +2024-09-09 11:53:11.795431: train_loss -0.7505 +2024-09-09 11:53:11.799767: val_loss -0.7889 +2024-09-09 11:53:11.802503: Pseudo dice [0.9609, 0.9575, 0.9744, 0.9523, 0.8792, 0.9697, 0.9913] +2024-09-09 11:53:11.803231: Epoch time: 552.98 s +2024-09-09 11:53:22.833051: +2024-09-09 11:53:22.844313: Epoch 645 +2024-09-09 11:53:22.846514: Current learning rate: 0.00394 +2024-09-09 12:03:07.637069: train_loss -0.7472 +2024-09-09 12:03:07.700983: val_loss -0.7804 +2024-09-09 12:03:07.702684: Pseudo dice [0.9602, 0.959, 0.9721, 0.9573, 0.8901, 0.973, 0.9921] +2024-09-09 12:03:07.704860: Epoch time: 584.81 s +2024-09-09 12:03:12.359087: +2024-09-09 12:03:12.372752: Epoch 646 +2024-09-09 12:03:12.375185: Current learning rate: 0.00393 +2024-09-09 12:12:35.945066: train_loss -0.7465 +2024-09-09 12:12:35.947848: val_loss -0.7352 +2024-09-09 12:12:35.948915: Pseudo dice [0.9601, 0.9556, 0.9753, 0.9516, 0.8895, 0.9724, 0.9926] +2024-09-09 12:12:35.949704: Epoch time: 563.59 s +2024-09-09 12:12:38.952704: +2024-09-09 12:12:38.953890: Epoch 647 +2024-09-09 12:12:38.954580: Current learning rate: 0.00392 +2024-09-09 12:22:11.133737: train_loss -0.7297 +2024-09-09 12:22:11.140090: val_loss -0.7495 +2024-09-09 12:22:11.141453: Pseudo dice [0.9651, 0.9581, 0.9798, 0.9514, 0.8728, 0.9714, 0.9909] +2024-09-09 12:22:11.142190: Epoch time: 572.19 s +2024-09-09 12:22:19.803244: +2024-09-09 12:22:19.805317: Epoch 648 +2024-09-09 12:22:19.806731: Current learning rate: 0.00391 +2024-09-09 12:31:59.473539: train_loss -0.747 +2024-09-09 12:31:59.493518: val_loss -0.7404 +2024-09-09 12:31:59.494286: Pseudo dice [0.9675, 0.9609, 0.9793, 0.9502, 0.8884, 0.9736, 0.9929] +2024-09-09 12:31:59.494951: Epoch time: 579.71 s +2024-09-09 12:32:05.298029: +2024-09-09 12:32:05.311862: Epoch 649 +2024-09-09 12:32:05.314428: Current learning rate: 0.0039 +2024-09-09 12:41:27.943052: train_loss -0.7588 +2024-09-09 12:41:27.957600: val_loss -0.7587 +2024-09-09 12:41:27.959338: Pseudo dice [0.9669, 0.9586, 0.9787, 0.9504, 0.8834, 0.973, 0.9916] +2024-09-09 12:41:27.960195: Epoch time: 562.65 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_16_30_50.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_16_30_50.txt new file mode 100644 index 0000000000000000000000000000000000000000..c11f8ad65b60c3bc5a752d8a4645e4e9774aa75d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_2/logs/training_log_2024_9_9_16_30_50.txt @@ -0,0 +1,378 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 16:30:52.963049: Using torch.compile... +2024-09-09 16:30:59.294428: do_dummy_2d_data_aug: False +2024-09-09 16:30:59.309105: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 16:30:59.310211: The split file contains 5 splits. +2024-09-09 16:30:59.310577: Desired fold for training: 2 +2024-09-09 16:30:59.310912: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 16:31:17.758262: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 16:31:19.091119: +2024-09-09 16:31:19.122685: Epoch 650 +2024-09-09 16:31:19.124124: Current learning rate: 0.00389 +2024-09-09 16:48:17.386376: train_loss -0.7371 +2024-09-09 16:48:17.388006: val_loss -0.7772 +2024-09-09 16:48:17.388556: Pseudo dice [0.965, 0.9605, 0.9793, 0.9521, 0.8779, 0.968, 0.9899] +2024-09-09 16:48:17.389050: Epoch time: 1018.29 s +2024-09-09 16:48:24.501410: +2024-09-09 16:48:24.503203: Epoch 651 +2024-09-09 16:48:24.503850: Current learning rate: 0.00388 +2024-09-09 16:59:36.949450: train_loss -0.7431 +2024-09-09 16:59:36.951955: val_loss -0.7491 +2024-09-09 16:59:36.953657: Pseudo dice [0.9651, 0.9593, 0.9793, 0.9515, 0.8861, 0.9699, 0.9908] +2024-09-09 16:59:36.954776: Epoch time: 672.46 s +2024-09-09 16:59:56.428207: +2024-09-09 16:59:56.429877: Epoch 652 +2024-09-09 16:59:56.430433: Current learning rate: 0.00387 +2024-09-09 17:11:17.685143: train_loss -0.7454 +2024-09-09 17:11:17.725891: val_loss -0.7682 +2024-09-09 17:11:17.726755: Pseudo dice [0.9629, 0.9594, 0.9749, 0.9547, 0.8875, 0.9728, 0.9922] +2024-09-09 17:11:17.727991: Epoch time: 681.26 s +2024-09-09 17:11:26.404321: +2024-09-09 17:11:26.435204: Epoch 653 +2024-09-09 17:11:26.436845: Current learning rate: 0.00386 +2024-09-09 17:23:20.493045: train_loss -0.7635 +2024-09-09 17:23:20.499265: val_loss -0.7675 +2024-09-09 17:23:20.504116: Pseudo dice [0.9654, 0.9588, 0.9749, 0.953, 0.8769, 0.9704, 0.9915] +2024-09-09 17:23:20.507166: Epoch time: 714.12 s +2024-09-09 17:23:28.021085: +2024-09-09 17:23:28.022030: Epoch 654 +2024-09-09 17:23:28.022793: Current learning rate: 0.00385 +2024-09-09 17:32:53.217566: train_loss -0.7505 +2024-09-09 17:32:53.219482: val_loss -0.7607 +2024-09-09 17:32:53.220818: Pseudo dice [0.9626, 0.9555, 0.9762, 0.9501, 0.8788, 0.9747, 0.992] +2024-09-09 17:32:53.222341: Epoch time: 565.2 s +2024-09-09 17:32:57.129413: +2024-09-09 17:32:57.151891: Epoch 655 +2024-09-09 17:32:57.153136: Current learning rate: 0.00384 +2024-09-09 17:38:41.935263: train_loss -0.7309 +2024-09-09 17:38:41.946533: val_loss -0.7497 +2024-09-09 17:38:41.947373: Pseudo dice [0.9626, 0.9583, 0.9777, 0.9522, 0.8674, 0.9707, 0.988] +2024-09-09 17:38:41.948164: Epoch time: 344.83 s +2024-09-09 17:38:45.702811: +2024-09-09 17:38:45.720815: Epoch 656 +2024-09-09 17:38:45.721597: Current learning rate: 0.00383 +2024-09-09 17:44:25.410192: train_loss -0.7411 +2024-09-09 17:44:25.424891: val_loss -0.7737 +2024-09-09 17:44:25.425779: Pseudo dice [0.965, 0.9569, 0.9784, 0.9488, 0.8814, 0.9716, 0.9925] +2024-09-09 17:44:25.426574: Epoch time: 339.72 s +2024-09-09 17:44:28.844044: +2024-09-09 17:44:28.845335: Epoch 657 +2024-09-09 17:44:28.846183: Current learning rate: 0.00382 +2024-09-09 17:50:14.051716: train_loss -0.7377 +2024-09-09 17:50:14.056718: val_loss -0.7302 +2024-09-09 17:50:14.057733: Pseudo dice [0.965, 0.9546, 0.9762, 0.9458, 0.865, 0.9719, 0.9903] +2024-09-09 17:50:14.058745: Epoch time: 345.21 s +2024-09-09 17:50:20.065772: +2024-09-09 17:50:20.080479: Epoch 658 +2024-09-09 17:50:20.081412: Current learning rate: 0.00381 +2024-09-09 17:56:03.516688: train_loss -0.7354 +2024-09-09 17:56:03.529433: val_loss -0.7552 +2024-09-09 17:56:03.530052: Pseudo dice [0.9638, 0.9597, 0.9773, 0.954, 0.8735, 0.9729, 0.9902] +2024-09-09 17:56:03.530546: Epoch time: 343.46 s +2024-09-09 17:56:06.245483: +2024-09-09 17:56:06.246822: Epoch 659 +2024-09-09 17:56:06.247597: Current learning rate: 0.0038 +2024-09-09 18:01:45.621076: train_loss -0.741 +2024-09-09 18:01:45.641840: val_loss -0.7071 +2024-09-09 18:01:45.643056: Pseudo dice [0.9627, 0.9584, 0.9747, 0.9519, 0.8865, 0.9698, 0.9925] +2024-09-09 18:01:45.643890: Epoch time: 339.38 s +2024-09-09 18:01:49.769626: +2024-09-09 18:01:49.782571: Epoch 660 +2024-09-09 18:01:49.783722: Current learning rate: 0.00379 +2024-09-09 18:07:24.987390: train_loss -0.7433 +2024-09-09 18:07:24.990282: val_loss -0.7317 +2024-09-09 18:07:24.990956: Pseudo dice [0.9646, 0.9565, 0.9765, 0.9518, 0.8852, 0.9741, 0.9906] +2024-09-09 18:07:24.991553: Epoch time: 335.23 s +2024-09-09 18:07:27.848819: +2024-09-09 18:07:27.864212: Epoch 661 +2024-09-09 18:07:27.865586: Current learning rate: 0.00378 +2024-09-09 18:13:29.857932: train_loss -0.749 +2024-09-09 18:13:29.874465: val_loss -0.7523 +2024-09-09 18:13:29.875213: Pseudo dice [0.9671, 0.9604, 0.9799, 0.9556, 0.8915, 0.9725, 0.9922] +2024-09-09 18:13:29.876093: Epoch time: 362.01 s +2024-09-09 18:13:33.450750: +2024-09-09 18:13:33.451825: Epoch 662 +2024-09-09 18:13:33.452698: Current learning rate: 0.00377 +2024-09-09 18:19:15.821540: train_loss -0.7547 +2024-09-09 18:19:15.825150: val_loss -0.7671 +2024-09-09 18:19:15.825863: Pseudo dice [0.9667, 0.9629, 0.9791, 0.9538, 0.8929, 0.9735, 0.9923] +2024-09-09 18:19:15.826522: Epoch time: 342.38 s +2024-09-09 18:19:20.207589: +2024-09-09 18:19:20.208894: Epoch 663 +2024-09-09 18:19:20.223047: Current learning rate: 0.00376 +2024-09-09 18:25:26.634897: train_loss -0.7448 +2024-09-09 18:25:26.646116: val_loss -0.7216 +2024-09-09 18:25:26.646973: Pseudo dice [0.9627, 0.9604, 0.9778, 0.9535, 0.8936, 0.974, 0.991] +2024-09-09 18:25:26.647733: Epoch time: 366.43 s +2024-09-09 18:25:31.260697: +2024-09-09 18:25:31.270118: Epoch 664 +2024-09-09 18:25:31.270742: Current learning rate: 0.00375 +2024-09-09 18:31:01.175185: train_loss -0.7606 +2024-09-09 18:31:01.189037: val_loss -0.7377 +2024-09-09 18:31:01.189719: Pseudo dice [0.9591, 0.9572, 0.9732, 0.9495, 0.8811, 0.965, 0.991] +2024-09-09 18:31:01.190300: Epoch time: 329.92 s +2024-09-09 18:31:07.304041: +2024-09-09 18:31:07.316918: Epoch 665 +2024-09-09 18:31:07.317792: Current learning rate: 0.00374 +2024-09-09 18:37:07.334124: train_loss -0.7456 +2024-09-09 18:37:07.343499: val_loss -0.7767 +2024-09-09 18:37:07.344891: Pseudo dice [0.9618, 0.956, 0.977, 0.9476, 0.8792, 0.9691, 0.9908] +2024-09-09 18:37:07.346926: Epoch time: 360.04 s +2024-09-09 18:37:13.395123: +2024-09-09 18:37:13.411758: Epoch 666 +2024-09-09 18:37:13.413262: Current learning rate: 0.00373 +2024-09-09 18:42:59.068370: train_loss -0.7445 +2024-09-09 18:42:59.100015: val_loss -0.7487 +2024-09-09 18:42:59.101158: Pseudo dice [0.9573, 0.9523, 0.9696, 0.947, 0.8715, 0.9675, 0.9913] +2024-09-09 18:42:59.101712: Epoch time: 345.69 s +2024-09-09 18:43:03.178955: +2024-09-09 18:43:03.180223: Epoch 667 +2024-09-09 18:43:03.181264: Current learning rate: 0.00372 +2024-09-09 18:48:58.207755: train_loss -0.7219 +2024-09-09 18:48:58.230087: val_loss -0.7362 +2024-09-09 18:48:58.230938: Pseudo dice [0.9608, 0.9611, 0.9743, 0.946, 0.8879, 0.9724, 0.9922] +2024-09-09 18:48:58.231863: Epoch time: 355.04 s +2024-09-09 18:49:01.660008: +2024-09-09 18:49:01.672754: Epoch 668 +2024-09-09 18:49:01.673456: Current learning rate: 0.00371 +2024-09-09 18:54:41.964309: train_loss -0.7536 +2024-09-09 18:54:41.981647: val_loss -0.7836 +2024-09-09 18:54:41.982256: Pseudo dice [0.9635, 0.9573, 0.9769, 0.9495, 0.8926, 0.9718, 0.9919] +2024-09-09 18:54:41.982854: Epoch time: 340.31 s +2024-09-09 18:54:46.136917: +2024-09-09 18:54:46.152457: Epoch 669 +2024-09-09 18:54:46.153168: Current learning rate: 0.0037 +2024-09-09 19:00:43.473645: train_loss -0.7438 +2024-09-09 19:00:43.480805: val_loss -0.7616 +2024-09-09 19:00:43.481471: Pseudo dice [0.9661, 0.9612, 0.9773, 0.9508, 0.8935, 0.975, 0.9928] +2024-09-09 19:00:43.482284: Epoch time: 357.34 s +2024-09-09 19:00:48.418711: +2024-09-09 19:00:48.419893: Epoch 670 +2024-09-09 19:00:48.421069: Current learning rate: 0.00369 +2024-09-09 19:06:28.838692: train_loss -0.7381 +2024-09-09 19:06:28.857809: val_loss -0.7587 +2024-09-09 19:06:28.858553: Pseudo dice [0.9638, 0.9592, 0.9764, 0.9534, 0.8822, 0.9713, 0.9922] +2024-09-09 19:06:28.859173: Epoch time: 340.43 s +2024-09-09 19:06:32.468116: +2024-09-09 19:06:32.469208: Epoch 671 +2024-09-09 19:06:32.469808: Current learning rate: 0.00368 +2024-09-09 19:12:11.266068: train_loss -0.7421 +2024-09-09 19:12:11.267927: val_loss -0.7713 +2024-09-09 19:12:11.268627: Pseudo dice [0.9661, 0.9563, 0.9807, 0.9443, 0.8881, 0.9734, 0.9929] +2024-09-09 19:12:11.269219: Epoch time: 338.8 s +2024-09-09 19:12:16.098721: +2024-09-09 19:12:16.100092: Epoch 672 +2024-09-09 19:12:16.101212: Current learning rate: 0.00367 +2024-09-09 19:17:52.909824: train_loss -0.7595 +2024-09-09 19:17:52.926332: val_loss -0.7261 +2024-09-09 19:17:52.926962: Pseudo dice [0.9653, 0.962, 0.9776, 0.9548, 0.8891, 0.9752, 0.9931] +2024-09-09 19:17:52.927557: Epoch time: 336.82 s +2024-09-09 19:17:55.415302: +2024-09-09 19:17:55.428615: Epoch 673 +2024-09-09 19:17:55.429378: Current learning rate: 0.00366 +2024-09-09 19:24:00.267982: train_loss -0.7537 +2024-09-09 19:24:00.286647: val_loss -0.764 +2024-09-09 19:24:00.287442: Pseudo dice [0.9656, 0.96, 0.9807, 0.9557, 0.886, 0.9743, 0.9921] +2024-09-09 19:24:00.288069: Epoch time: 364.86 s +2024-09-09 19:24:04.653526: +2024-09-09 19:24:04.671848: Epoch 674 +2024-09-09 19:24:04.673015: Current learning rate: 0.00365 +2024-09-09 19:29:59.448065: train_loss -0.7473 +2024-09-09 19:29:59.455761: val_loss -0.7434 +2024-09-09 19:29:59.456363: Pseudo dice [0.9671, 0.9605, 0.9775, 0.9533, 0.8861, 0.9727, 0.9911] +2024-09-09 19:29:59.456866: Epoch time: 354.8 s +2024-09-09 19:30:02.867216: +2024-09-09 19:30:02.873280: Epoch 675 +2024-09-09 19:30:02.874403: Current learning rate: 0.00364 +2024-09-09 19:35:46.338322: train_loss -0.75 +2024-09-09 19:35:46.339998: val_loss -0.7664 +2024-09-09 19:35:46.340715: Pseudo dice [0.9681, 0.9615, 0.9805, 0.9528, 0.882, 0.9726, 0.9924] +2024-09-09 19:35:46.341251: Epoch time: 343.48 s +2024-09-09 19:35:49.521863: +2024-09-09 19:35:49.523090: Epoch 676 +2024-09-09 19:35:49.523738: Current learning rate: 0.00363 +2024-09-09 19:41:41.263804: train_loss -0.7526 +2024-09-09 19:41:41.282581: val_loss -0.7354 +2024-09-09 19:41:41.283897: Pseudo dice [0.9669, 0.9636, 0.9767, 0.9533, 0.8915, 0.9721, 0.9916] +2024-09-09 19:41:41.284498: Epoch time: 351.75 s +2024-09-09 19:41:44.757336: +2024-09-09 19:41:44.772727: Epoch 677 +2024-09-09 19:41:44.773798: Current learning rate: 0.00362 +2024-09-09 19:47:43.139353: train_loss -0.7286 +2024-09-09 19:47:43.142071: val_loss -0.7247 +2024-09-09 19:47:43.142703: Pseudo dice [0.9616, 0.9577, 0.9757, 0.9533, 0.8903, 0.9701, 0.9904] +2024-09-09 19:47:43.143270: Epoch time: 358.39 s +2024-09-09 19:47:46.822422: +2024-09-09 19:47:46.832554: Epoch 678 +2024-09-09 19:47:46.833223: Current learning rate: 0.00361 +2024-09-09 19:53:50.208643: train_loss -0.7451 +2024-09-09 19:53:50.226177: val_loss -0.7921 +2024-09-09 19:53:50.227127: Pseudo dice [0.9674, 0.9595, 0.9816, 0.9507, 0.8831, 0.9726, 0.9911] +2024-09-09 19:53:50.228256: Epoch time: 363.39 s +2024-09-09 19:53:53.566915: +2024-09-09 19:53:53.578691: Epoch 679 +2024-09-09 19:53:53.579483: Current learning rate: 0.0036 +2024-09-09 19:59:47.687113: train_loss -0.7461 +2024-09-09 19:59:47.694197: val_loss -0.7067 +2024-09-09 19:59:47.694818: Pseudo dice [0.9649, 0.9614, 0.9799, 0.9581, 0.8906, 0.972, 0.9907] +2024-09-09 19:59:47.695367: Epoch time: 354.13 s +2024-09-09 19:59:54.980181: +2024-09-09 19:59:54.981526: Epoch 680 +2024-09-09 19:59:54.982176: Current learning rate: 0.00359 +2024-09-09 20:05:46.446990: train_loss -0.7331 +2024-09-09 20:05:46.461006: val_loss -0.7647 +2024-09-09 20:05:46.461605: Pseudo dice [0.9669, 0.9573, 0.9795, 0.9562, 0.8893, 0.9732, 0.993] +2024-09-09 20:05:46.462152: Epoch time: 351.47 s +2024-09-09 20:05:46.462677: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 20:05:55.537689: +2024-09-09 20:05:55.544663: Epoch 681 +2024-09-09 20:05:55.556031: Current learning rate: 0.00358 +2024-09-09 20:12:06.027157: train_loss -0.7605 +2024-09-09 20:12:06.030797: val_loss -0.7858 +2024-09-09 20:12:06.031672: Pseudo dice [0.9683, 0.9624, 0.9826, 0.9579, 0.8949, 0.9721, 0.9923] +2024-09-09 20:12:06.032483: Epoch time: 370.49 s +2024-09-09 20:12:06.033139: Yayy! New best EMA pseudo Dice: 0.9583 +2024-09-09 20:12:12.322567: +2024-09-09 20:12:12.323226: Epoch 682 +2024-09-09 20:12:12.323684: Current learning rate: 0.00357 +2024-09-09 20:18:13.920949: train_loss -0.7358 +2024-09-09 20:18:13.949361: val_loss -0.7616 +2024-09-09 20:18:13.950068: Pseudo dice [0.9642, 0.9604, 0.9785, 0.9519, 0.8921, 0.973, 0.9931] +2024-09-09 20:18:13.950606: Epoch time: 361.6 s +2024-09-09 20:18:13.951161: Yayy! New best EMA pseudo Dice: 0.9584 +2024-09-09 20:18:21.224609: +2024-09-09 20:18:21.225543: Epoch 683 +2024-09-09 20:18:21.226052: Current learning rate: 0.00356 +2024-09-09 20:24:03.201126: train_loss -0.742 +2024-09-09 20:24:03.214256: val_loss -0.756 +2024-09-09 20:24:03.224725: Pseudo dice [0.9657, 0.9605, 0.9773, 0.9521, 0.8946, 0.9744, 0.9926] +2024-09-09 20:24:03.225472: Epoch time: 341.98 s +2024-09-09 20:24:03.225976: Yayy! New best EMA pseudo Dice: 0.9585 +2024-09-09 20:24:11.513920: +2024-09-09 20:24:11.515156: Epoch 684 +2024-09-09 20:24:11.515819: Current learning rate: 0.00355 +2024-09-09 20:29:57.777757: train_loss -0.7511 +2024-09-09 20:29:57.780371: val_loss -0.755 +2024-09-09 20:29:57.781063: Pseudo dice [0.9665, 0.9595, 0.9796, 0.9537, 0.8889, 0.9731, 0.9926] +2024-09-09 20:29:57.783675: Epoch time: 346.27 s +2024-09-09 20:29:57.784320: Yayy! New best EMA pseudo Dice: 0.9586 +2024-09-09 20:30:07.298602: +2024-09-09 20:30:07.312523: Epoch 685 +2024-09-09 20:30:07.313378: Current learning rate: 0.00354 +2024-09-09 20:36:14.542954: train_loss -0.7413 +2024-09-09 20:36:14.552982: val_loss -0.77 +2024-09-09 20:36:14.553745: Pseudo dice [0.9644, 0.9603, 0.978, 0.9524, 0.8875, 0.9723, 0.9922] +2024-09-09 20:36:14.554661: Epoch time: 367.25 s +2024-09-09 20:36:17.826632: +2024-09-09 20:36:17.840394: Epoch 686 +2024-09-09 20:36:17.841226: Current learning rate: 0.00353 +2024-09-09 20:42:04.327952: train_loss -0.7489 +2024-09-09 20:42:04.331085: val_loss -0.7476 +2024-09-09 20:42:04.331794: Pseudo dice [0.9628, 0.961, 0.9764, 0.9543, 0.8937, 0.9739, 0.9923] +2024-09-09 20:42:04.332419: Epoch time: 346.51 s +2024-09-09 20:42:04.333137: Yayy! New best EMA pseudo Dice: 0.9586 +2024-09-09 20:42:11.616359: +2024-09-09 20:42:11.617621: Epoch 687 +2024-09-09 20:42:11.618295: Current learning rate: 0.00352 +2024-09-09 20:47:56.467208: train_loss -0.7476 +2024-09-09 20:47:56.481617: val_loss -0.7372 +2024-09-09 20:47:56.482811: Pseudo dice [0.9634, 0.9603, 0.9745, 0.9469, 0.8836, 0.9694, 0.9926] +2024-09-09 20:47:56.483676: Epoch time: 344.88 s +2024-09-09 20:47:59.634024: +2024-09-09 20:47:59.635035: Epoch 688 +2024-09-09 20:47:59.636459: Current learning rate: 0.00351 +2024-09-09 20:53:54.224932: train_loss -0.7583 +2024-09-09 20:53:54.257875: val_loss -0.7606 +2024-09-09 20:53:54.258644: Pseudo dice [0.9664, 0.9601, 0.9809, 0.9566, 0.8938, 0.9758, 0.9928] +2024-09-09 20:53:54.259234: Epoch time: 354.6 s +2024-09-09 20:53:58.555238: +2024-09-09 20:53:58.560173: Epoch 689 +2024-09-09 20:53:58.560784: Current learning rate: 0.0035 +2024-09-09 20:59:59.887260: train_loss -0.7514 +2024-09-09 20:59:59.897608: val_loss -0.757 +2024-09-09 20:59:59.899858: Pseudo dice [0.9677, 0.9632, 0.9811, 0.9586, 0.8865, 0.974, 0.9903] +2024-09-09 20:59:59.913208: Epoch time: 361.34 s +2024-09-09 20:59:59.915072: Yayy! New best EMA pseudo Dice: 0.9587 +2024-09-09 21:00:09.051578: +2024-09-09 21:00:09.052376: Epoch 690 +2024-09-09 21:00:09.052943: Current learning rate: 0.00349 +2024-09-09 21:05:59.264801: train_loss -0.7613 +2024-09-09 21:05:59.290784: val_loss -0.7553 +2024-09-09 21:05:59.291821: Pseudo dice [0.9623, 0.9579, 0.9746, 0.9486, 0.881, 0.9634, 0.9915] +2024-09-09 21:05:59.293402: Epoch time: 350.22 s +2024-09-09 21:06:03.416286: +2024-09-09 21:06:03.433001: Epoch 691 +2024-09-09 21:06:03.434166: Current learning rate: 0.00348 +2024-09-09 21:12:07.705767: train_loss -0.7425 +2024-09-09 21:12:07.713042: val_loss -0.7578 +2024-09-09 21:12:07.713720: Pseudo dice [0.9644, 0.9589, 0.9764, 0.9559, 0.8835, 0.972, 0.9908] +2024-09-09 21:12:07.714338: Epoch time: 364.3 s +2024-09-09 21:12:12.365777: +2024-09-09 21:12:12.366909: Epoch 692 +2024-09-09 21:12:12.367981: Current learning rate: 0.00346 +2024-09-09 21:17:51.607407: train_loss -0.7526 +2024-09-09 21:17:51.609970: val_loss -0.7831 +2024-09-09 21:17:51.611728: Pseudo dice [0.9586, 0.9569, 0.9666, 0.9511, 0.8836, 0.9735, 0.9927] +2024-09-09 21:17:51.612876: Epoch time: 339.27 s +2024-09-09 21:17:55.605693: +2024-09-09 21:17:55.606997: Epoch 693 +2024-09-09 21:17:55.608057: Current learning rate: 0.00345 +2024-09-09 21:23:40.543120: train_loss -0.7514 +2024-09-09 21:23:40.558878: val_loss -0.7423 +2024-09-09 21:23:40.559844: Pseudo dice [0.9645, 0.9581, 0.9778, 0.9553, 0.8757, 0.9711, 0.9896] +2024-09-09 21:23:40.560707: Epoch time: 344.94 s +2024-09-09 21:23:43.436632: +2024-09-09 21:23:43.448309: Epoch 694 +2024-09-09 21:23:43.449051: Current learning rate: 0.00344 +2024-09-09 21:29:46.536842: train_loss -0.735 +2024-09-09 21:29:46.538681: val_loss -0.786 +2024-09-09 21:29:46.539414: Pseudo dice [0.9689, 0.9628, 0.9831, 0.9551, 0.8764, 0.9728, 0.9911] +2024-09-09 21:29:46.540230: Epoch time: 363.11 s +2024-09-09 21:29:50.095056: +2024-09-09 21:29:50.112063: Epoch 695 +2024-09-09 21:29:50.120867: Current learning rate: 0.00343 +2024-09-09 21:35:38.962221: train_loss -0.754 +2024-09-09 21:35:38.994017: val_loss -0.7286 +2024-09-09 21:35:38.994897: Pseudo dice [0.9656, 0.961, 0.9812, 0.9566, 0.8837, 0.974, 0.9921] +2024-09-09 21:35:38.995417: Epoch time: 348.89 s +2024-09-09 21:35:42.217329: +2024-09-09 21:35:42.232641: Epoch 696 +2024-09-09 21:35:42.233572: Current learning rate: 0.00342 +2024-09-09 21:41:39.728181: train_loss -0.7476 +2024-09-09 21:41:39.743519: val_loss -0.7545 +2024-09-09 21:41:39.744279: Pseudo dice [0.9621, 0.9585, 0.9736, 0.9513, 0.8808, 0.9732, 0.9915] +2024-09-09 21:41:39.745013: Epoch time: 357.52 s +2024-09-09 21:41:43.677198: +2024-09-09 21:41:43.678838: Epoch 697 +2024-09-09 21:41:43.679904: Current learning rate: 0.00341 +2024-09-09 21:47:32.090299: train_loss -0.7465 +2024-09-09 21:47:32.113413: val_loss -0.7479 +2024-09-09 21:47:32.114084: Pseudo dice [0.9695, 0.9615, 0.9815, 0.9551, 0.8788, 0.9743, 0.9922] +2024-09-09 21:47:32.114641: Epoch time: 348.42 s +2024-09-09 21:47:36.539750: +2024-09-09 21:47:36.540874: Epoch 698 +2024-09-09 21:47:36.541540: Current learning rate: 0.0034 +2024-09-09 21:53:39.300516: train_loss -0.7423 +2024-09-09 21:53:39.302608: val_loss -0.7601 +2024-09-09 21:53:39.303199: Pseudo dice [0.9648, 0.9583, 0.9775, 0.9507, 0.8843, 0.9691, 0.9918] +2024-09-09 21:53:39.303760: Epoch time: 362.77 s +2024-09-09 21:53:42.013760: +2024-09-09 21:53:42.014538: Epoch 699 +2024-09-09 21:53:42.015230: Current learning rate: 0.00339 +2024-09-09 21:59:41.992964: train_loss -0.736 +2024-09-09 21:59:42.004886: val_loss -0.7821 +2024-09-09 21:59:42.005521: Pseudo dice [0.9681, 0.9594, 0.9802, 0.9492, 0.8711, 0.9713, 0.9916] +2024-09-09 21:59:42.006123: Epoch time: 359.99 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/checkpoint_final.pth new file mode 100644 index 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Training CTs originated from the SAROS and TotalSegmentator datasets and are not included.'}, 'unpack_dataset': False, 'device': device(type='cuda')}", + "network": "OptimizedModule", + "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 fused: None\n initial_lr: 0.01\n lr: 1.995262314968881e-05\n maximize: False\n momentum: 0.99\n nesterov: True\n weight_decay: 3e-05\n)", + "output_folder": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3", + "output_folder_base": "/data/nnUNet_results/Dataset611_Tissue/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres", + "oversample_foreground_percent": "0.33", + "plans_manager": "{'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], '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': 89, 'patch_size': [320, 320], 'median_image_size_in_voxels': [257.0, 260.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 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]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}", + "preprocessed_dataset_folder": "/data2/nnUNet_preprocessed/Dataset611_Tissue/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data2/nnUNet_preprocessed/Dataset611_Tissue", + "save_every": "50", + "torch_version": "2.3.0+cu121", + "unpack_dataset": "False", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/README.md b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bbeff95c790bb2bfecf6601e818142117565d5f7 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/README.md @@ -0,0 +1,6 @@ +# Training log note + +The fold's `debug.json` names +`training_log_2024_9_10_09_53_29.txt` as the active nnU-Net log. That file is +not included; the available timestamped log segments are provided in this +directory. diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/progress.png b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..9d0718b8af85f854abee93649299518000853b85 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d4b4482fdeb6679f999f8adb43ebce2973eaabb53f654d4493c97cd5b9f3708 +size 637820 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_10_00_53_20.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_10_00_53_20.txt new file mode 100644 index 0000000000000000000000000000000000000000..3aff784e7b4143b9c5bdf4bc1e3b66499df13db2 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_10_00_53_20.txt @@ -0,0 +1,1412 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 00:53:27.808444: Using torch.compile... +2024-09-10 00:53:36.475785: do_dummy_2d_data_aug: False +2024-09-10 00:53:36.491126: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 00:53:36.503252: The split file contains 5 splits. +2024-09-10 00:53:36.503987: Desired fold for training: 3 +2024-09-10 00:53:36.504570: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 00:53:48.325083: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 00:53:51.259078: +2024-09-10 00:53:51.259954: Epoch 900 +2024-09-10 00:53:51.260709: Current learning rate: 0.00126 +2024-09-10 01:04:09.080775: train_loss -0.7481 +2024-09-10 01:04:09.082815: val_loss -0.736 +2024-09-10 01:04:09.083606: Pseudo dice [0.9666, 0.9616, 0.9782, 0.9567, 0.8981, 0.9752, 0.9931] +2024-09-10 01:04:09.084251: Epoch time: 617.83 s +2024-09-10 01:04:10.753026: +2024-09-10 01:04:10.753904: Epoch 901 +2024-09-10 01:04:10.754522: Current learning rate: 0.00125 +2024-09-10 01:08:11.983733: train_loss -0.7533 +2024-09-10 01:08:11.995164: val_loss -0.762 +2024-09-10 01:08:11.995940: Pseudo dice [0.9666, 0.9633, 0.9776, 0.9567, 0.8998, 0.974, 0.993] +2024-09-10 01:08:11.996656: Epoch time: 241.23 s +2024-09-10 01:08:15.432137: +2024-09-10 01:08:15.433277: Epoch 902 +2024-09-10 01:08:15.434557: Current learning rate: 0.00124 +2024-09-10 01:12:23.265150: train_loss -0.7584 +2024-09-10 01:12:23.277192: val_loss -0.7667 +2024-09-10 01:12:23.278188: Pseudo dice [0.9647, 0.9617, 0.9767, 0.9554, 0.8622, 0.9769, 0.9898] +2024-09-10 01:12:23.279072: Epoch time: 247.84 s +2024-09-10 01:12:27.530073: +2024-09-10 01:12:27.531136: Epoch 903 +2024-09-10 01:12:27.531775: Current learning rate: 0.00122 +2024-09-10 01:16:33.010407: train_loss -0.7475 +2024-09-10 01:16:33.025526: val_loss -0.7472 +2024-09-10 01:16:33.026198: Pseudo dice [0.9681, 0.9634, 0.9811, 0.9559, 0.8919, 0.9727, 0.9904] +2024-09-10 01:16:33.026857: Epoch time: 245.48 s +2024-09-10 01:16:35.989118: +2024-09-10 01:16:35.990091: Epoch 904 +2024-09-10 01:16:35.990753: Current learning rate: 0.00121 +2024-09-10 01:20:34.272057: train_loss -0.7548 +2024-09-10 01:20:34.284806: val_loss -0.767 +2024-09-10 01:20:34.285724: Pseudo dice [0.9656, 0.9614, 0.9777, 0.9537, 0.8839, 0.976, 0.9909] +2024-09-10 01:20:34.286489: Epoch time: 238.29 s +2024-09-10 01:20:37.538760: +2024-09-10 01:20:37.539971: Epoch 905 +2024-09-10 01:20:37.540913: Current learning rate: 0.0012 +2024-09-10 01:25:01.786091: train_loss -0.7559 +2024-09-10 01:25:01.789556: val_loss -0.7913 +2024-09-10 01:25:01.790888: Pseudo dice [0.9664, 0.9627, 0.9788, 0.9524, 0.9048, 0.9745, 0.9919] +2024-09-10 01:25:01.791945: Epoch time: 264.25 s +2024-09-10 01:25:04.946994: +2024-09-10 01:25:04.960932: Epoch 906 +2024-09-10 01:25:04.962271: Current learning rate: 0.00119 +2024-09-10 01:29:23.943171: train_loss -0.7531 +2024-09-10 01:29:23.976793: val_loss -0.8156 +2024-09-10 01:29:23.977590: Pseudo dice [0.9702, 0.9629, 0.9819, 0.9555, 0.9002, 0.9762, 0.9933] +2024-09-10 01:29:23.978225: Epoch time: 259.0 s +2024-09-10 01:29:26.967449: +2024-09-10 01:29:26.968360: Epoch 907 +2024-09-10 01:29:26.968986: Current learning rate: 0.00118 +2024-09-10 01:33:49.574296: train_loss -0.7492 +2024-09-10 01:33:49.577461: val_loss -0.7374 +2024-09-10 01:33:49.578420: Pseudo dice [0.9709, 0.9632, 0.9823, 0.9635, 0.8879, 0.9744, 0.9917] +2024-09-10 01:33:49.579660: Epoch time: 262.61 s +2024-09-10 01:33:52.968521: +2024-09-10 01:33:52.977816: Epoch 908 +2024-09-10 01:33:52.978597: Current learning rate: 0.00117 +2024-09-10 01:38:15.086553: train_loss -0.7553 +2024-09-10 01:38:15.113806: val_loss -0.7885 +2024-09-10 01:38:15.114486: Pseudo dice [0.9655, 0.9623, 0.9793, 0.9529, 0.8964, 0.9783, 0.9938] +2024-09-10 01:38:15.115167: Epoch time: 262.13 s +2024-09-10 01:38:18.381403: +2024-09-10 01:38:18.382510: Epoch 909 +2024-09-10 01:38:18.383432: Current learning rate: 0.00116 +2024-09-10 01:42:33.009696: train_loss -0.7431 +2024-09-10 01:42:33.020087: val_loss -0.7167 +2024-09-10 01:42:33.020864: Pseudo dice [0.9662, 0.9609, 0.9782, 0.9441, 0.8986, 0.9773, 0.9925] +2024-09-10 01:42:33.021628: Epoch time: 254.63 s +2024-09-10 01:42:35.877430: +2024-09-10 01:42:35.878545: Epoch 910 +2024-09-10 01:42:35.879197: Current learning rate: 0.00115 +2024-09-10 01:46:46.108685: train_loss -0.7637 +2024-09-10 01:46:46.132861: val_loss -0.7599 +2024-09-10 01:46:46.133647: Pseudo dice [0.963, 0.9616, 0.972, 0.9484, 0.896, 0.9767, 0.9933] +2024-09-10 01:46:46.134229: Epoch time: 250.24 s +2024-09-10 01:46:48.944645: +2024-09-10 01:46:48.945707: Epoch 911 +2024-09-10 01:46:48.946505: Current learning rate: 0.00113 +2024-09-10 01:51:03.498112: train_loss -0.7546 +2024-09-10 01:51:03.515095: val_loss -0.8056 +2024-09-10 01:51:03.516192: Pseudo dice [0.9661, 0.9604, 0.98, 0.949, 0.8882, 0.9727, 0.9918] +2024-09-10 01:51:03.517116: Epoch time: 254.56 s +2024-09-10 01:51:06.604111: +2024-09-10 01:51:06.619748: Epoch 912 +2024-09-10 01:51:06.620522: Current learning rate: 0.00112 +2024-09-10 01:55:20.912184: train_loss -0.7534 +2024-09-10 01:55:20.919288: val_loss -0.7544 +2024-09-10 01:55:20.920262: Pseudo dice [0.9665, 0.9607, 0.9796, 0.9566, 0.8958, 0.9759, 0.9914] +2024-09-10 01:55:20.920955: Epoch time: 254.31 s +2024-09-10 01:55:23.698062: +2024-09-10 01:55:23.707943: Epoch 913 +2024-09-10 01:55:23.709256: Current learning rate: 0.00111 +2024-09-10 01:59:28.050475: train_loss -0.7627 +2024-09-10 01:59:28.076724: val_loss -0.7994 +2024-09-10 01:59:28.077748: Pseudo dice [0.9667, 0.9613, 0.982, 0.9564, 0.8993, 0.9768, 0.9927] +2024-09-10 01:59:28.078673: Epoch time: 244.36 s +2024-09-10 01:59:30.753419: +2024-09-10 01:59:30.754662: Epoch 914 +2024-09-10 01:59:30.755414: Current learning rate: 0.0011 +2024-09-10 02:03:46.196332: train_loss -0.7679 +2024-09-10 02:03:46.204184: val_loss -0.7798 +2024-09-10 02:03:46.205168: Pseudo dice [0.9688, 0.9627, 0.9819, 0.9591, 0.8876, 0.9762, 0.9915] +2024-09-10 02:03:46.205818: Epoch time: 255.45 s +2024-09-10 02:03:48.974685: +2024-09-10 02:03:48.975694: Epoch 915 +2024-09-10 02:03:48.976417: Current learning rate: 0.00109 +2024-09-10 02:07:49.963254: train_loss -0.761 +2024-09-10 02:07:49.977608: val_loss -0.7123 +2024-09-10 02:07:49.978392: Pseudo dice [0.9665, 0.9624, 0.9768, 0.9542, 0.872, 0.9769, 0.9905] +2024-09-10 02:07:49.979331: Epoch time: 240.99 s +2024-09-10 02:07:52.621984: +2024-09-10 02:07:52.635513: Epoch 916 +2024-09-10 02:07:52.640137: Current learning rate: 0.00108 +2024-09-10 02:11:59.585760: train_loss -0.7494 +2024-09-10 02:11:59.586997: val_loss -0.7404 +2024-09-10 02:11:59.587718: Pseudo dice [0.9668, 0.9645, 0.9795, 0.9513, 0.9065, 0.975, 0.9936] +2024-09-10 02:11:59.588383: Epoch time: 246.97 s +2024-09-10 02:12:02.572352: +2024-09-10 02:12:02.573656: Epoch 917 +2024-09-10 02:12:02.574688: Current learning rate: 0.00106 +2024-09-10 02:16:07.783855: train_loss -0.752 +2024-09-10 02:16:07.785372: val_loss -0.7711 +2024-09-10 02:16:07.786347: Pseudo dice [0.9676, 0.9621, 0.9808, 0.951, 0.901, 0.978, 0.9941] +2024-09-10 02:16:07.787199: Epoch time: 245.23 s +2024-09-10 02:16:10.972898: +2024-09-10 02:16:10.973880: Epoch 918 +2024-09-10 02:16:10.974627: Current learning rate: 0.00105 +2024-09-10 02:20:35.237389: train_loss -0.764 +2024-09-10 02:20:35.252726: val_loss -0.7694 +2024-09-10 02:20:35.253433: Pseudo dice [0.9682, 0.962, 0.9821, 0.9535, 0.8924, 0.9727, 0.9934] +2024-09-10 02:20:35.254141: Epoch time: 264.27 s +2024-09-10 02:20:38.893507: +2024-09-10 02:20:38.894732: Epoch 919 +2024-09-10 02:20:38.895447: Current learning rate: 0.00104 +2024-09-10 02:24:41.921802: train_loss -0.7648 +2024-09-10 02:24:41.937413: val_loss -0.7627 +2024-09-10 02:24:41.938264: Pseudo dice [0.969, 0.9638, 0.982, 0.9542, 0.9004, 0.9745, 0.9923] +2024-09-10 02:24:41.938872: Epoch time: 243.03 s +2024-09-10 02:24:41.939504: Yayy! New best EMA pseudo Dice: 0.9606 +2024-09-10 02:24:45.820726: +2024-09-10 02:24:45.821561: Epoch 920 +2024-09-10 02:24:45.822229: Current learning rate: 0.00103 +2024-09-10 02:28:44.244234: train_loss -0.7818 +2024-09-10 02:28:44.261364: val_loss -0.7578 +2024-09-10 02:28:44.262065: Pseudo dice [0.9714, 0.9639, 0.9821, 0.9567, 0.9056, 0.9772, 0.9925] +2024-09-10 02:28:44.262663: Epoch time: 238.43 s +2024-09-10 02:28:44.263337: Yayy! New best EMA pseudo Dice: 0.961 +2024-09-10 02:28:49.497777: +2024-09-10 02:28:49.499227: Epoch 921 +2024-09-10 02:28:49.499976: Current learning rate: 0.00102 +2024-09-10 02:32:53.852264: train_loss -0.7602 +2024-09-10 02:32:53.864854: val_loss -0.753 +2024-09-10 02:32:53.865632: Pseudo dice [0.9659, 0.9605, 0.9754, 0.9451, 0.886, 0.9747, 0.9927] +2024-09-10 02:32:53.866302: Epoch time: 244.36 s +2024-09-10 02:32:56.945992: +2024-09-10 02:32:56.960512: Epoch 922 +2024-09-10 02:32:56.961287: Current learning rate: 0.00101 +2024-09-10 02:37:03.919713: train_loss -0.7524 +2024-09-10 02:37:03.937455: val_loss -0.7637 +2024-09-10 02:37:03.938183: Pseudo dice [0.9644, 0.9615, 0.9796, 0.9565, 0.8913, 0.9774, 0.9919] +2024-09-10 02:37:03.938799: Epoch time: 246.98 s +2024-09-10 02:37:06.947427: +2024-09-10 02:37:06.948366: Epoch 923 +2024-09-10 02:37:06.949095: Current learning rate: 0.001 +2024-09-10 02:41:30.129271: train_loss -0.7669 +2024-09-10 02:41:30.140647: val_loss -0.8012 +2024-09-10 02:41:30.141366: Pseudo dice [0.9703, 0.9623, 0.9821, 0.9534, 0.9066, 0.9764, 0.9929] +2024-09-10 02:41:30.141995: Epoch time: 263.19 s +2024-09-10 02:41:32.372012: +2024-09-10 02:41:32.382390: Epoch 924 +2024-09-10 02:41:32.383118: Current learning rate: 0.00098 +2024-09-10 02:45:41.703062: train_loss -0.7588 +2024-09-10 02:45:41.717518: val_loss -0.7764 +2024-09-10 02:45:41.718279: Pseudo dice [0.9647, 0.9622, 0.9798, 0.9527, 0.9015, 0.9772, 0.9934] +2024-09-10 02:45:41.718946: Epoch time: 249.34 s +2024-09-10 02:45:44.403325: +2024-09-10 02:45:44.416618: Epoch 925 +2024-09-10 02:45:44.417655: Current learning rate: 0.00097 +2024-09-10 02:49:55.410930: train_loss -0.7658 +2024-09-10 02:49:55.425385: val_loss -0.8032 +2024-09-10 02:49:55.426065: Pseudo dice [0.9685, 0.9647, 0.9797, 0.9561, 0.899, 0.9753, 0.9926] +2024-09-10 02:49:55.426790: Epoch time: 251.01 s +2024-09-10 02:49:55.427390: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-10 02:49:59.990437: +2024-09-10 02:49:59.991563: Epoch 926 +2024-09-10 02:49:59.992353: Current learning rate: 0.00096 +2024-09-10 02:54:07.421088: train_loss -0.7613 +2024-09-10 02:54:07.423219: val_loss -0.7821 +2024-09-10 02:54:07.423907: Pseudo dice [0.9664, 0.9613, 0.9796, 0.9526, 0.8972, 0.9781, 0.9929] +2024-09-10 02:54:07.424524: Epoch time: 247.41 s +2024-09-10 02:54:07.425074: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-10 02:54:12.831998: +2024-09-10 02:54:12.832950: Epoch 927 +2024-09-10 02:54:12.833659: Current learning rate: 0.00095 +2024-09-10 02:58:09.916976: train_loss -0.7583 +2024-09-10 02:58:09.933536: val_loss -0.7594 +2024-09-10 02:58:09.934450: Pseudo dice [0.9664, 0.9606, 0.9778, 0.9507, 0.9036, 0.9722, 0.9919] +2024-09-10 02:58:09.935151: Epoch time: 237.09 s +2024-09-10 02:58:12.150628: +2024-09-10 02:58:12.155252: Epoch 928 +2024-09-10 02:58:12.157012: Current learning rate: 0.00094 +2024-09-10 03:02:23.575600: train_loss -0.7629 +2024-09-10 03:02:23.600843: val_loss -0.8056 +2024-09-10 03:02:23.601578: Pseudo dice [0.9695, 0.9613, 0.9826, 0.9573, 0.8983, 0.9735, 0.9928] +2024-09-10 03:02:23.602200: Epoch time: 251.43 s +2024-09-10 03:02:23.602937: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-10 03:02:28.303025: +2024-09-10 03:02:28.304084: Epoch 929 +2024-09-10 03:02:28.304787: Current learning rate: 0.00092 +2024-09-10 03:06:47.022305: train_loss -0.7672 +2024-09-10 03:06:47.029492: val_loss -0.7635 +2024-09-10 03:06:47.030231: Pseudo dice [0.9674, 0.9608, 0.9795, 0.9558, 0.9019, 0.977, 0.9919] +2024-09-10 03:06:47.030869: Epoch time: 258.72 s +2024-09-10 03:06:47.031547: Yayy! New best EMA pseudo Dice: 0.9612 +2024-09-10 03:06:52.712629: +2024-09-10 03:06:52.713990: Epoch 930 +2024-09-10 03:06:52.714963: Current learning rate: 0.00091 +2024-09-10 03:10:48.264992: train_loss -0.7569 +2024-09-10 03:10:48.267697: val_loss -0.7608 +2024-09-10 03:10:48.268493: Pseudo dice [0.9661, 0.9611, 0.9777, 0.9555, 0.896, 0.9772, 0.9934] +2024-09-10 03:10:48.269175: Epoch time: 235.56 s +2024-09-10 03:10:51.025111: +2024-09-10 03:10:51.026039: Epoch 931 +2024-09-10 03:10:51.026788: Current learning rate: 0.0009 +2024-09-10 03:15:07.106764: train_loss -0.7599 +2024-09-10 03:15:07.108980: val_loss -0.7444 +2024-09-10 03:15:07.109828: Pseudo dice [0.9694, 0.9644, 0.9816, 0.9536, 0.8965, 0.9753, 0.9897] +2024-09-10 03:15:07.110549: Epoch time: 256.09 s +2024-09-10 03:15:07.111172: Yayy! New best EMA pseudo Dice: 0.9612 +2024-09-10 03:15:12.435611: +2024-09-10 03:15:12.436598: Epoch 932 +2024-09-10 03:15:12.437350: Current learning rate: 0.00089 +2024-09-10 03:19:08.516670: train_loss -0.7533 +2024-09-10 03:19:08.517960: val_loss -0.7708 +2024-09-10 03:19:08.518680: Pseudo dice [0.9677, 0.9619, 0.9808, 0.9542, 0.9085, 0.9724, 0.9912] +2024-09-10 03:19:08.519306: Epoch time: 236.09 s +2024-09-10 03:19:08.519903: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-10 03:19:13.270964: +2024-09-10 03:19:13.272708: Epoch 933 +2024-09-10 03:19:13.273430: Current learning rate: 0.00088 +2024-09-10 03:23:24.997048: train_loss -0.7534 +2024-09-10 03:23:24.998839: val_loss -0.7826 +2024-09-10 03:23:24.999630: Pseudo dice [0.9662, 0.9623, 0.9803, 0.9553, 0.8955, 0.9778, 0.9934] +2024-09-10 03:23:25.000373: Epoch time: 251.73 s +2024-09-10 03:23:25.001219: Yayy! New best EMA pseudo Dice: 0.9614 +2024-09-10 03:23:30.377066: +2024-09-10 03:23:30.379289: Epoch 934 +2024-09-10 03:23:30.380223: Current learning rate: 0.00087 +2024-09-10 03:27:40.908649: train_loss -0.7555 +2024-09-10 03:27:40.923717: val_loss -0.7437 +2024-09-10 03:27:40.924380: Pseudo dice [0.9647, 0.964, 0.9792, 0.9516, 0.8999, 0.9739, 0.9916] +2024-09-10 03:27:40.924984: Epoch time: 250.54 s +2024-09-10 03:27:44.624381: +2024-09-10 03:27:44.625450: Epoch 935 +2024-09-10 03:27:44.626148: Current learning rate: 0.00085 +2024-09-10 03:32:07.086171: train_loss -0.7674 +2024-09-10 03:32:07.117337: val_loss -0.7758 +2024-09-10 03:32:07.118068: Pseudo dice [0.9666, 0.9618, 0.9784, 0.9509, 0.9036, 0.9765, 0.9926] +2024-09-10 03:32:07.118752: Epoch time: 262.47 s +2024-09-10 03:32:09.678120: +2024-09-10 03:32:09.686260: Epoch 936 +2024-09-10 03:32:09.687077: Current learning rate: 0.00084 +2024-09-10 03:36:23.634690: train_loss -0.7487 +2024-09-10 03:36:23.643036: val_loss -0.7814 +2024-09-10 03:36:23.643885: Pseudo dice [0.9685, 0.9632, 0.9811, 0.9555, 0.9097, 0.9758, 0.9927] +2024-09-10 03:36:23.645389: Epoch time: 253.96 s +2024-09-10 03:36:23.646223: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-10 03:36:28.209383: +2024-09-10 03:36:28.210555: Epoch 937 +2024-09-10 03:36:28.211379: Current learning rate: 0.00083 +2024-09-10 03:40:40.035186: train_loss -0.76 +2024-09-10 03:40:40.048874: val_loss -0.7504 +2024-09-10 03:40:40.049792: Pseudo dice [0.962, 0.9616, 0.976, 0.952, 0.8972, 0.9721, 0.9913] +2024-09-10 03:40:40.050644: Epoch time: 251.83 s +2024-09-10 03:40:43.169750: +2024-09-10 03:40:43.180429: Epoch 938 +2024-09-10 03:40:43.181359: Current learning rate: 0.00082 +2024-09-10 03:44:38.111238: train_loss -0.7623 +2024-09-10 03:44:38.112532: val_loss -0.7563 +2024-09-10 03:44:38.113229: Pseudo dice [0.9677, 0.9623, 0.9811, 0.9537, 0.8929, 0.9792, 0.9934] +2024-09-10 03:44:38.113900: Epoch time: 234.95 s +2024-09-10 03:44:41.027281: +2024-09-10 03:44:41.028371: Epoch 939 +2024-09-10 03:44:41.029193: Current learning rate: 0.00081 +2024-09-10 03:48:52.475606: train_loss -0.7478 +2024-09-10 03:48:52.491016: val_loss -0.7837 +2024-09-10 03:48:52.492311: Pseudo dice [0.9659, 0.9614, 0.9773, 0.9544, 0.9042, 0.9778, 0.9931] +2024-09-10 03:48:52.493515: Epoch time: 251.45 s +2024-09-10 03:48:55.512573: +2024-09-10 03:48:55.528358: Epoch 940 +2024-09-10 03:48:55.529033: Current learning rate: 0.00079 +2024-09-10 03:52:53.896981: train_loss -0.766 +2024-09-10 03:52:53.899165: val_loss -0.766 +2024-09-10 03:52:53.900003: Pseudo dice [0.9691, 0.9625, 0.9809, 0.956, 0.9036, 0.9786, 0.9929] +2024-09-10 03:52:53.900705: Epoch time: 238.39 s +2024-09-10 03:52:53.901339: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-10 03:52:58.949409: +2024-09-10 03:52:58.950277: Epoch 941 +2024-09-10 03:52:58.950871: Current learning rate: 0.00078 +2024-09-10 03:57:18.484039: train_loss -0.7524 +2024-09-10 03:57:18.485449: val_loss -0.7692 +2024-09-10 03:57:18.486223: Pseudo dice [0.9654, 0.9597, 0.9778, 0.9525, 0.894, 0.9771, 0.9923] +2024-09-10 03:57:18.486836: Epoch time: 259.54 s +2024-09-10 03:57:21.453711: +2024-09-10 03:57:21.454864: Epoch 942 +2024-09-10 03:57:21.455632: Current learning rate: 0.00077 +2024-09-10 04:01:42.607595: train_loss -0.748 +2024-09-10 04:01:42.614365: val_loss -0.7866 +2024-09-10 04:01:42.615287: Pseudo dice [0.9668, 0.9616, 0.9807, 0.9579, 0.906, 0.9789, 0.9929] +2024-09-10 04:01:42.615982: Epoch time: 261.16 s +2024-09-10 04:01:42.617105: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-10 04:01:46.807715: +2024-09-10 04:01:46.809103: Epoch 943 +2024-09-10 04:01:46.810305: Current learning rate: 0.00076 +2024-09-10 04:05:53.938924: train_loss -0.7625 +2024-09-10 04:05:53.953852: val_loss -0.7586 +2024-09-10 04:05:53.954545: Pseudo dice [0.9671, 0.9605, 0.9805, 0.9539, 0.8691, 0.9763, 0.9907] +2024-09-10 04:05:53.955224: Epoch time: 247.14 s +2024-09-10 04:05:56.688719: +2024-09-10 04:05:56.689782: Epoch 944 +2024-09-10 04:05:56.690463: Current learning rate: 0.00075 +2024-09-10 04:10:13.639890: train_loss -0.7736 +2024-09-10 04:10:13.650369: val_loss -0.8253 +2024-09-10 04:10:13.651109: Pseudo dice [0.9665, 0.9594, 0.9806, 0.9516, 0.9056, 0.9782, 0.9924] +2024-09-10 04:10:13.651792: Epoch time: 256.96 s +2024-09-10 04:10:16.581172: +2024-09-10 04:10:16.609248: Epoch 945 +2024-09-10 04:10:16.610193: Current learning rate: 0.00074 +2024-09-10 04:14:16.953215: train_loss -0.7722 +2024-09-10 04:14:16.968675: val_loss -0.7688 +2024-09-10 04:14:16.969543: Pseudo dice [0.9647, 0.9617, 0.976, 0.954, 0.8986, 0.9775, 0.9932] +2024-09-10 04:14:16.970301: Epoch time: 240.38 s +2024-09-10 04:14:20.306654: +2024-09-10 04:14:20.320347: Epoch 946 +2024-09-10 04:14:20.321166: Current learning rate: 0.00072 +2024-09-10 04:18:24.015418: train_loss -0.7547 +2024-09-10 04:18:24.026353: val_loss -0.7395 +2024-09-10 04:18:24.027137: Pseudo dice [0.968, 0.9627, 0.9796, 0.959, 0.8976, 0.9748, 0.9932] +2024-09-10 04:18:24.027869: Epoch time: 243.71 s +2024-09-10 04:18:27.399407: +2024-09-10 04:18:27.412416: Epoch 947 +2024-09-10 04:18:27.413477: Current learning rate: 0.00071 +2024-09-10 04:22:20.393003: train_loss -0.7607 +2024-09-10 04:22:20.406904: val_loss -0.7262 +2024-09-10 04:22:20.407766: Pseudo dice [0.967, 0.9625, 0.9779, 0.956, 0.8705, 0.976, 0.9905] +2024-09-10 04:22:20.408457: Epoch time: 233.0 s +2024-09-10 04:22:23.669003: +2024-09-10 04:22:23.670051: Epoch 948 +2024-09-10 04:22:23.670773: Current learning rate: 0.0007 +2024-09-10 04:26:30.101238: train_loss -0.7656 +2024-09-10 04:26:30.112603: val_loss -0.7458 +2024-09-10 04:26:30.113374: Pseudo dice [0.9659, 0.9606, 0.9814, 0.9616, 0.888, 0.9761, 0.9913] +2024-09-10 04:26:30.114075: Epoch time: 246.44 s +2024-09-10 04:26:32.578457: +2024-09-10 04:26:32.589034: Epoch 949 +2024-09-10 04:26:32.600314: Current learning rate: 0.00069 +2024-09-10 04:30:33.424194: train_loss -0.7535 +2024-09-10 04:30:33.426406: val_loss -0.757 +2024-09-10 04:30:33.427157: Pseudo dice [0.9659, 0.9592, 0.9781, 0.9542, 0.8893, 0.9779, 0.9925] +2024-09-10 04:30:33.428004: Epoch time: 240.85 s +2024-09-10 04:30:39.750170: +2024-09-10 04:30:39.751455: Epoch 950 +2024-09-10 04:30:39.752706: Current learning rate: 0.00067 +2024-09-10 04:35:03.431716: train_loss -0.757 +2024-09-10 04:35:03.448803: val_loss -0.7724 +2024-09-10 04:35:03.449614: Pseudo dice [0.9665, 0.961, 0.9798, 0.9568, 0.9007, 0.9733, 0.9925] +2024-09-10 04:35:03.450517: Epoch time: 263.7 s +2024-09-10 04:35:06.184785: +2024-09-10 04:35:06.196631: Epoch 951 +2024-09-10 04:35:06.197703: Current learning rate: 0.00066 +2024-09-10 04:39:20.642716: train_loss -0.756 +2024-09-10 04:39:20.658087: val_loss -0.7793 +2024-09-10 04:39:20.659080: Pseudo dice [0.9689, 0.9612, 0.9826, 0.9514, 0.9084, 0.9732, 0.9931] +2024-09-10 04:39:20.660293: Epoch time: 254.47 s +2024-09-10 04:39:24.062959: +2024-09-10 04:39:24.076467: Epoch 952 +2024-09-10 04:39:24.077231: Current learning rate: 0.00065 +2024-09-10 04:43:23.922181: train_loss -0.765 +2024-09-10 04:43:23.944725: val_loss -0.7564 +2024-09-10 04:43:23.945623: Pseudo dice [0.9684, 0.9639, 0.9807, 0.958, 0.8996, 0.975, 0.9934] +2024-09-10 04:43:23.946445: Epoch time: 239.86 s +2024-09-10 04:43:26.452933: +2024-09-10 04:43:26.464262: Epoch 953 +2024-09-10 04:43:26.465069: Current learning rate: 0.00064 +2024-09-10 04:47:49.756819: train_loss -0.7512 +2024-09-10 04:47:49.759098: val_loss -0.7758 +2024-09-10 04:47:49.799204: Pseudo dice [0.9685, 0.9608, 0.981, 0.9544, 0.8936, 0.9743, 0.9924] +2024-09-10 04:47:49.815995: Epoch time: 263.31 s +2024-09-10 04:47:52.080432: +2024-09-10 04:47:52.082113: Epoch 954 +2024-09-10 04:47:52.082817: Current learning rate: 0.00063 +2024-09-10 04:52:06.857968: train_loss -0.7771 +2024-09-10 04:52:06.871205: val_loss -0.7584 +2024-09-10 04:52:06.872174: Pseudo dice [0.9654, 0.9615, 0.9779, 0.9536, 0.9001, 0.9748, 0.9925] +2024-09-10 04:52:06.873064: Epoch time: 254.78 s +2024-09-10 04:52:09.862800: +2024-09-10 04:52:09.863795: Epoch 955 +2024-09-10 04:52:09.864654: Current learning rate: 0.00061 +2024-09-10 04:56:23.360027: train_loss -0.7637 +2024-09-10 04:56:23.384731: val_loss -0.7926 +2024-09-10 04:56:23.385628: Pseudo dice [0.9691, 0.9615, 0.9784, 0.9551, 0.9072, 0.9779, 0.9934] +2024-09-10 04:56:23.386394: Epoch time: 253.5 s +2024-09-10 04:56:25.398514: +2024-09-10 04:56:25.399600: Epoch 956 +2024-09-10 04:56:25.400317: Current learning rate: 0.0006 +2024-09-10 05:00:50.117946: train_loss -0.7596 +2024-09-10 05:00:50.141278: val_loss -0.7519 +2024-09-10 05:00:50.142062: Pseudo dice [0.9688, 0.9618, 0.9806, 0.9511, 0.8975, 0.9791, 0.9926] +2024-09-10 05:00:50.142751: Epoch time: 264.72 s +2024-09-10 05:00:53.678810: +2024-09-10 05:00:53.680177: Epoch 957 +2024-09-10 05:00:53.681082: Current learning rate: 0.00059 +2024-09-10 05:05:07.589389: train_loss -0.7556 +2024-09-10 05:05:07.591442: val_loss -0.7687 +2024-09-10 05:05:07.592128: Pseudo dice [0.9709, 0.9643, 0.9824, 0.9569, 0.9059, 0.9781, 0.9931] +2024-09-10 05:05:07.592754: Epoch time: 253.92 s +2024-09-10 05:05:07.593274: Yayy! New best EMA pseudo Dice: 0.9617 +2024-09-10 05:05:10.716228: +2024-09-10 05:05:10.717141: Epoch 958 +2024-09-10 05:05:10.717914: Current learning rate: 0.00058 +2024-09-10 05:09:01.088115: train_loss -0.7577 +2024-09-10 05:09:01.089753: val_loss -0.762 +2024-09-10 05:09:01.090419: Pseudo dice [0.9668, 0.9637, 0.9798, 0.956, 0.9053, 0.979, 0.9936] +2024-09-10 05:09:01.091011: Epoch time: 230.38 s +2024-09-10 05:09:01.091864: Yayy! New best EMA pseudo Dice: 0.9618 +2024-09-10 05:09:05.964858: +2024-09-10 05:09:05.965748: Epoch 959 +2024-09-10 05:09:05.966391: Current learning rate: 0.00056 +2024-09-10 05:13:09.818472: train_loss -0.7674 +2024-09-10 05:13:09.829815: val_loss -0.7552 +2024-09-10 05:13:09.830857: Pseudo dice [0.9667, 0.9612, 0.9784, 0.9542, 0.8906, 0.9732, 0.9899] +2024-09-10 05:13:09.831757: Epoch time: 243.86 s +2024-09-10 05:13:12.672808: +2024-09-10 05:13:12.673776: Epoch 960 +2024-09-10 05:13:12.674652: Current learning rate: 0.00055 +2024-09-10 05:17:11.915200: train_loss -0.7719 +2024-09-10 05:17:11.916792: val_loss -0.7738 +2024-09-10 05:17:11.917535: Pseudo dice [0.97, 0.9624, 0.9827, 0.95, 0.9065, 0.9745, 0.9935] +2024-09-10 05:17:11.918228: Epoch time: 239.25 s +2024-09-10 05:17:14.913689: +2024-09-10 05:17:14.915902: Epoch 961 +2024-09-10 05:17:14.917781: Current learning rate: 0.00054 +2024-09-10 05:21:22.518194: train_loss -0.7816 +2024-09-10 05:21:22.520222: val_loss -0.7599 +2024-09-10 05:21:22.520969: Pseudo dice [0.9662, 0.9618, 0.9788, 0.9496, 0.8953, 0.9775, 0.9921] +2024-09-10 05:21:22.521705: Epoch time: 247.61 s +2024-09-10 05:21:25.755567: +2024-09-10 05:21:25.768867: Epoch 962 +2024-09-10 05:21:25.770027: Current learning rate: 0.00053 +2024-09-10 05:25:43.080186: train_loss -0.7405 +2024-09-10 05:25:43.089921: val_loss -0.7545 +2024-09-10 05:25:43.090600: Pseudo dice [0.9681, 0.96, 0.9781, 0.9504, 0.8931, 0.9759, 0.9923] +2024-09-10 05:25:43.091321: Epoch time: 257.33 s +2024-09-10 05:25:46.546170: +2024-09-10 05:25:46.547430: Epoch 963 +2024-09-10 05:25:46.548177: Current learning rate: 0.00051 +2024-09-10 05:29:59.800584: train_loss -0.7744 +2024-09-10 05:29:59.812755: val_loss -0.7866 +2024-09-10 05:29:59.813511: Pseudo dice [0.9666, 0.9616, 0.979, 0.9554, 0.9073, 0.9797, 0.994] +2024-09-10 05:29:59.814268: Epoch time: 253.26 s +2024-09-10 05:30:02.706550: +2024-09-10 05:30:02.707573: Epoch 964 +2024-09-10 05:30:02.708418: Current learning rate: 0.0005 +2024-09-10 05:33:54.921509: train_loss -0.7443 +2024-09-10 05:33:54.924057: val_loss -0.7718 +2024-09-10 05:33:54.925085: Pseudo dice [0.9686, 0.961, 0.9814, 0.9578, 0.9056, 0.9783, 0.993] +2024-09-10 05:33:54.926023: Epoch time: 232.22 s +2024-09-10 05:33:58.003937: +2024-09-10 05:33:58.005182: Epoch 965 +2024-09-10 05:33:58.006221: Current learning rate: 0.00049 +2024-09-10 05:38:00.937197: train_loss -0.7675 +2024-09-10 05:38:00.939255: val_loss -0.7455 +2024-09-10 05:38:00.940699: Pseudo dice [0.9673, 0.9607, 0.9794, 0.9542, 0.9047, 0.9798, 0.994] +2024-09-10 05:38:00.942090: Epoch time: 242.94 s +2024-09-10 05:38:00.943423: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-10 05:38:05.509315: +2024-09-10 05:38:05.510417: Epoch 966 +2024-09-10 05:38:05.511119: Current learning rate: 0.00048 +2024-09-10 05:42:16.259908: train_loss -0.7569 +2024-09-10 05:42:16.277624: val_loss -0.7955 +2024-09-10 05:42:16.278367: Pseudo dice [0.9674, 0.9634, 0.981, 0.9575, 0.9057, 0.9795, 0.9934] +2024-09-10 05:42:16.279118: Epoch time: 250.76 s +2024-09-10 05:42:16.279813: Yayy! New best EMA pseudo Dice: 0.9621 +2024-09-10 05:42:21.369746: +2024-09-10 05:42:21.382248: Epoch 967 +2024-09-10 05:42:21.383818: Current learning rate: 0.00046 +2024-09-10 05:46:24.536307: train_loss -0.7758 +2024-09-10 05:46:24.551240: val_loss -0.7836 +2024-09-10 05:46:24.552012: Pseudo dice [0.9684, 0.9624, 0.9824, 0.9546, 0.8994, 0.9748, 0.9927] +2024-09-10 05:46:24.552648: Epoch time: 243.17 s +2024-09-10 05:46:24.553275: Yayy! New best EMA pseudo Dice: 0.9621 +2024-09-10 05:46:31.030617: +2024-09-10 05:46:31.031811: Epoch 968 +2024-09-10 05:46:31.032660: Current learning rate: 0.00045 +2024-09-10 05:50:31.108779: train_loss -0.7519 +2024-09-10 05:50:31.113068: val_loss -0.7511 +2024-09-10 05:50:31.113919: Pseudo dice [0.9681, 0.9646, 0.9781, 0.9561, 0.9074, 0.9804, 0.9944] +2024-09-10 05:50:31.114550: Epoch time: 240.08 s +2024-09-10 05:50:31.115177: Yayy! New best EMA pseudo Dice: 0.9623 +2024-09-10 05:50:36.756145: +2024-09-10 05:50:36.757497: Epoch 969 +2024-09-10 05:50:36.758548: Current learning rate: 0.00044 +2024-09-10 05:54:43.417327: train_loss -0.7481 +2024-09-10 05:54:43.419904: val_loss -0.7486 +2024-09-10 05:54:43.420975: Pseudo dice [0.9645, 0.9604, 0.9738, 0.95, 0.9053, 0.9772, 0.9932] +2024-09-10 05:54:43.422116: Epoch time: 246.67 s +2024-09-10 05:54:47.622485: +2024-09-10 05:54:47.623663: Epoch 970 +2024-09-10 05:54:47.624505: Current learning rate: 0.00043 +2024-09-10 05:58:50.195310: train_loss -0.7609 +2024-09-10 05:58:50.213114: val_loss -0.7597 +2024-09-10 05:58:50.213936: Pseudo dice [0.9659, 0.9608, 0.9788, 0.9549, 0.9004, 0.979, 0.9931] +2024-09-10 05:58:50.214700: Epoch time: 242.58 s +2024-09-10 05:58:53.131889: +2024-09-10 05:58:53.132909: Epoch 971 +2024-09-10 05:58:53.133626: Current learning rate: 0.00041 +2024-09-10 06:02:57.917782: train_loss -0.76 +2024-09-10 06:02:57.930055: val_loss -0.776 +2024-09-10 06:02:57.930833: Pseudo dice [0.9671, 0.9604, 0.9791, 0.9532, 0.9031, 0.9783, 0.993] +2024-09-10 06:02:57.931503: Epoch time: 244.79 s +2024-09-10 06:03:01.070232: +2024-09-10 06:03:01.071350: Epoch 972 +2024-09-10 06:03:01.072518: Current learning rate: 0.0004 +2024-09-10 06:07:15.007509: train_loss -0.7729 +2024-09-10 06:07:15.025789: val_loss -0.7618 +2024-09-10 06:07:15.026733: Pseudo dice [0.9684, 0.9615, 0.9806, 0.9557, 0.8828, 0.9782, 0.9914] +2024-09-10 06:07:15.027488: Epoch time: 253.94 s +2024-09-10 06:07:18.064688: +2024-09-10 06:07:18.065583: Epoch 973 +2024-09-10 06:07:18.066338: Current learning rate: 0.00039 +2024-09-10 06:11:19.810108: train_loss -0.7731 +2024-09-10 06:11:19.825518: val_loss -0.7632 +2024-09-10 06:11:19.826253: Pseudo dice [0.9667, 0.961, 0.9816, 0.9551, 0.9103, 0.9795, 0.9938] +2024-09-10 06:11:19.827091: Epoch time: 241.75 s +2024-09-10 06:11:22.975140: +2024-09-10 06:11:22.976461: Epoch 974 +2024-09-10 06:11:22.977260: Current learning rate: 0.00037 +2024-09-10 06:15:29.411737: train_loss -0.7697 +2024-09-10 06:15:29.425402: val_loss -0.7769 +2024-09-10 06:15:29.426142: Pseudo dice [0.9673, 0.9608, 0.9784, 0.957, 0.9015, 0.9768, 0.9921] +2024-09-10 06:15:29.426780: Epoch time: 246.44 s +2024-09-10 06:15:32.073380: +2024-09-10 06:15:32.087825: Epoch 975 +2024-09-10 06:15:32.096214: Current learning rate: 0.00036 +2024-09-10 06:19:31.000129: train_loss -0.7869 +2024-09-10 06:19:31.002791: val_loss -0.763 +2024-09-10 06:19:31.003802: Pseudo dice [0.9691, 0.9623, 0.9808, 0.9566, 0.911, 0.9761, 0.9932] +2024-09-10 06:19:31.004714: Epoch time: 238.93 s +2024-09-10 06:19:33.069070: +2024-09-10 06:19:33.070234: Epoch 976 +2024-09-10 06:19:33.071219: Current learning rate: 0.00035 +2024-09-10 06:23:44.981484: train_loss -0.7575 +2024-09-10 06:23:44.985975: val_loss -0.7444 +2024-09-10 06:23:44.986913: Pseudo dice [0.9651, 0.9618, 0.9782, 0.9514, 0.9023, 0.9771, 0.9918] +2024-09-10 06:23:44.987649: Epoch time: 251.92 s +2024-09-10 06:23:47.771575: +2024-09-10 06:23:47.773088: Epoch 977 +2024-09-10 06:23:47.774904: Current learning rate: 0.00034 +2024-09-10 06:28:11.533474: train_loss -0.7724 +2024-09-10 06:28:11.546757: val_loss -0.7566 +2024-09-10 06:28:11.547527: Pseudo dice [0.9661, 0.9636, 0.9783, 0.9555, 0.8952, 0.9779, 0.9931] +2024-09-10 06:28:11.548161: Epoch time: 263.77 s +2024-09-10 06:28:14.995295: +2024-09-10 06:28:14.996763: Epoch 978 +2024-09-10 06:28:14.997895: Current learning rate: 0.00032 +2024-09-10 06:32:07.318462: train_loss -0.7643 +2024-09-10 06:32:07.323933: val_loss -0.7309 +2024-09-10 06:32:07.324697: Pseudo dice [0.9653, 0.9631, 0.9792, 0.9567, 0.9061, 0.979, 0.9929] +2024-09-10 06:32:07.325338: Epoch time: 232.33 s +2024-09-10 06:32:10.260990: +2024-09-10 06:32:10.262101: Epoch 979 +2024-09-10 06:32:10.262928: Current learning rate: 0.00031 +2024-09-10 06:36:24.179243: train_loss -0.7577 +2024-09-10 06:36:24.192823: val_loss -0.7783 +2024-09-10 06:36:24.193759: Pseudo dice [0.9711, 0.9635, 0.9827, 0.9551, 0.906, 0.979, 0.9927] +2024-09-10 06:36:24.194562: Epoch time: 253.92 s +2024-09-10 06:36:24.195474: Yayy! New best EMA pseudo Dice: 0.9624 +2024-09-10 06:36:28.644590: +2024-09-10 06:36:28.645570: Epoch 980 +2024-09-10 06:36:28.646316: Current learning rate: 0.0003 +2024-09-10 06:40:54.169900: train_loss -0.7697 +2024-09-10 06:40:54.184798: val_loss -0.7953 +2024-09-10 06:40:54.185592: Pseudo dice [0.9687, 0.9606, 0.9826, 0.9559, 0.8895, 0.9774, 0.9927] +2024-09-10 06:40:54.186306: Epoch time: 265.53 s +2024-09-10 06:40:58.754472: +2024-09-10 06:40:58.755880: Epoch 981 +2024-09-10 06:40:58.756830: Current learning rate: 0.00028 +2024-09-10 06:45:07.765872: train_loss -0.7744 +2024-09-10 06:45:07.781649: val_loss -0.7733 +2024-09-10 06:45:07.782414: Pseudo dice [0.9709, 0.9645, 0.9829, 0.957, 0.9093, 0.9786, 0.9939] +2024-09-10 06:45:07.783124: Epoch time: 249.02 s +2024-09-10 06:45:07.783791: Yayy! New best EMA pseudo Dice: 0.9626 +2024-09-10 06:45:12.627920: +2024-09-10 06:45:12.628881: Epoch 982 +2024-09-10 06:45:12.629501: Current learning rate: 0.00027 +2024-09-10 06:49:02.943246: train_loss -0.7692 +2024-09-10 06:49:02.957866: val_loss -0.7537 +2024-09-10 06:49:02.958841: Pseudo dice [0.9681, 0.9619, 0.9814, 0.9561, 0.9044, 0.9759, 0.993] +2024-09-10 06:49:02.959720: Epoch time: 230.32 s +2024-09-10 06:49:02.960425: Yayy! New best EMA pseudo Dice: 0.9626 +2024-09-10 06:49:09.009371: +2024-09-10 06:49:09.010725: Epoch 983 +2024-09-10 06:49:09.011524: Current learning rate: 0.00026 +2024-09-10 06:53:19.933727: train_loss -0.7774 +2024-09-10 06:53:19.959817: val_loss -0.7959 +2024-09-10 06:53:19.960740: Pseudo dice [0.9705, 0.9633, 0.9807, 0.9554, 0.9005, 0.9772, 0.993] +2024-09-10 06:53:19.961487: Epoch time: 250.93 s +2024-09-10 06:53:19.962193: Yayy! New best EMA pseudo Dice: 0.9626 +2024-09-10 06:53:24.516557: +2024-09-10 06:53:24.517501: Epoch 984 +2024-09-10 06:53:24.518164: Current learning rate: 0.00024 +2024-09-10 06:57:36.611029: train_loss -0.767 +2024-09-10 06:57:36.640628: val_loss -0.7586 +2024-09-10 06:57:36.641426: Pseudo dice [0.9673, 0.962, 0.9803, 0.9457, 0.893, 0.9747, 0.9912] +2024-09-10 06:57:36.642189: Epoch time: 252.1 s +2024-09-10 06:57:39.639097: +2024-09-10 06:57:39.652508: Epoch 985 +2024-09-10 06:57:39.653312: Current learning rate: 0.00023 +2024-09-10 07:01:36.607698: train_loss -0.7659 +2024-09-10 07:01:36.625503: val_loss -0.7498 +2024-09-10 07:01:36.626372: Pseudo dice [0.968, 0.9626, 0.9816, 0.9538, 0.885, 0.9798, 0.9916] +2024-09-10 07:01:36.627091: Epoch time: 236.97 s +2024-09-10 07:01:39.965463: +2024-09-10 07:01:39.966442: Epoch 986 +2024-09-10 07:01:39.967583: Current learning rate: 0.00021 +2024-09-10 07:05:37.114399: train_loss -0.7577 +2024-09-10 07:05:37.128428: val_loss -0.7798 +2024-09-10 07:05:37.129143: Pseudo dice [0.9685, 0.9621, 0.9822, 0.9552, 0.9121, 0.9735, 0.9936] +2024-09-10 07:05:37.129945: Epoch time: 237.15 s +2024-09-10 07:05:39.767411: +2024-09-10 07:05:39.768593: Epoch 987 +2024-09-10 07:05:39.780277: Current learning rate: 0.0002 +2024-09-10 07:09:43.783215: train_loss -0.7631 +2024-09-10 07:09:43.789723: val_loss -0.7606 +2024-09-10 07:09:43.790850: Pseudo dice [0.9642, 0.9602, 0.9762, 0.9541, 0.8987, 0.9794, 0.9937] +2024-09-10 07:09:43.791834: Epoch time: 244.02 s +2024-09-10 07:09:47.165646: +2024-09-10 07:09:47.166689: Epoch 988 +2024-09-10 07:09:47.167358: Current learning rate: 0.00019 +2024-09-10 07:14:04.494103: train_loss -0.7622 +2024-09-10 07:14:04.509503: val_loss -0.7683 +2024-09-10 07:14:04.510305: Pseudo dice [0.9682, 0.9619, 0.9789, 0.9543, 0.9023, 0.9731, 0.9918] +2024-09-10 07:14:04.510985: Epoch time: 257.33 s +2024-09-10 07:14:07.286877: +2024-09-10 07:14:07.287988: Epoch 989 +2024-09-10 07:14:07.288751: Current learning rate: 0.00017 +2024-09-10 07:18:01.083572: train_loss -0.7626 +2024-09-10 07:18:01.100961: val_loss -0.8003 +2024-09-10 07:18:01.101650: Pseudo dice [0.9686, 0.9602, 0.9814, 0.9559, 0.9056, 0.9755, 0.9932] +2024-09-10 07:18:01.102283: Epoch time: 233.8 s +2024-09-10 07:18:04.136259: +2024-09-10 07:18:04.137247: Epoch 990 +2024-09-10 07:18:04.137846: Current learning rate: 0.00016 +2024-09-10 07:22:18.061632: train_loss -0.7758 +2024-09-10 07:22:18.076735: val_loss -0.7145 +2024-09-10 07:22:18.077483: Pseudo dice [0.9646, 0.9607, 0.9755, 0.9535, 0.8829, 0.9756, 0.9916] +2024-09-10 07:22:18.078130: Epoch time: 253.93 s +2024-09-10 07:22:21.100209: +2024-09-10 07:22:21.101387: Epoch 991 +2024-09-10 07:22:21.102561: Current learning rate: 0.00014 +2024-09-10 07:26:38.669617: train_loss -0.7673 +2024-09-10 07:26:38.686779: val_loss -0.7279 +2024-09-10 07:26:38.687877: Pseudo dice [0.9639, 0.962, 0.9773, 0.9544, 0.8956, 0.9718, 0.9923] +2024-09-10 07:26:38.688938: Epoch time: 257.59 s +2024-09-10 07:26:41.600142: +2024-09-10 07:26:41.601315: Epoch 992 +2024-09-10 07:26:41.602176: Current learning rate: 0.00013 +2024-09-10 07:30:38.800835: train_loss -0.7646 +2024-09-10 07:30:38.810749: val_loss -0.8085 +2024-09-10 07:30:38.811592: Pseudo dice [0.9695, 0.9622, 0.982, 0.9582, 0.8989, 0.9786, 0.9933] +2024-09-10 07:30:38.812356: Epoch time: 237.21 s +2024-09-10 07:30:42.385174: +2024-09-10 07:30:42.386227: Epoch 993 +2024-09-10 07:30:42.387050: Current learning rate: 0.00011 +2024-09-10 07:35:01.945995: train_loss -0.7618 +2024-09-10 07:35:01.948404: val_loss -0.7777 +2024-09-10 07:35:01.949174: Pseudo dice [0.9705, 0.9652, 0.9793, 0.957, 0.9027, 0.9789, 0.9928] +2024-09-10 07:35:01.949823: Epoch time: 259.57 s +2024-09-10 07:35:04.935429: +2024-09-10 07:35:04.948272: Epoch 994 +2024-09-10 07:35:04.949052: Current learning rate: 0.0001 +2024-09-10 07:39:07.758006: train_loss -0.758 +2024-09-10 07:39:07.773570: val_loss -0.7482 +2024-09-10 07:39:07.774361: Pseudo dice [0.9662, 0.961, 0.9789, 0.959, 0.9036, 0.9737, 0.9926] +2024-09-10 07:39:07.775092: Epoch time: 242.83 s +2024-09-10 07:39:12.983854: +2024-09-10 07:39:12.985357: Epoch 995 +2024-09-10 07:39:12.986056: Current learning rate: 8e-05 +2024-09-10 07:43:12.685982: train_loss -0.7556 +2024-09-10 07:43:12.700564: val_loss -0.766 +2024-09-10 07:43:12.701309: Pseudo dice [0.9685, 0.9632, 0.9792, 0.9581, 0.9057, 0.9784, 0.9937] +2024-09-10 07:43:12.702126: Epoch time: 239.71 s +2024-09-10 07:43:15.293288: +2024-09-10 07:43:15.295290: Epoch 996 +2024-09-10 07:43:15.296237: Current learning rate: 7e-05 +2024-09-10 07:47:26.506623: train_loss -0.7661 +2024-09-10 07:47:26.508030: val_loss -0.7898 +2024-09-10 07:47:26.508775: Pseudo dice [0.9669, 0.9613, 0.9795, 0.9585, 0.9089, 0.9784, 0.993] +2024-09-10 07:47:26.509408: Epoch time: 251.22 s +2024-09-10 07:47:29.585193: +2024-09-10 07:47:29.586304: Epoch 997 +2024-09-10 07:47:29.587080: Current learning rate: 5e-05 +2024-09-10 07:51:46.038151: train_loss -0.7634 +2024-09-10 07:51:46.068927: val_loss -0.7582 +2024-09-10 07:51:46.069839: Pseudo dice [0.9661, 0.9599, 0.9768, 0.9574, 0.8908, 0.9785, 0.9913] +2024-09-10 07:51:46.072267: Epoch time: 256.46 s +2024-09-10 07:51:49.124090: +2024-09-10 07:51:49.136529: Epoch 998 +2024-09-10 07:51:49.137359: Current learning rate: 4e-05 +2024-09-10 07:55:36.358130: train_loss -0.7829 +2024-09-10 07:55:36.374484: val_loss -0.7537 +2024-09-10 07:55:36.375418: Pseudo dice [0.9673, 0.9617, 0.9801, 0.9581, 0.8978, 0.9756, 0.9921] +2024-09-10 07:55:36.376319: Epoch time: 227.24 s +2024-09-10 07:55:39.241031: +2024-09-10 07:55:39.242124: Epoch 999 +2024-09-10 07:55:39.243122: Current learning rate: 2e-05 +2024-09-10 07:59:53.925188: train_loss -0.7583 +2024-09-10 07:59:53.927202: val_loss -0.7812 +2024-09-10 07:59:53.927935: Pseudo dice [0.9649, 0.9624, 0.9769, 0.9553, 0.8982, 0.9742, 0.992] +2024-09-10 07:59:53.928541: Epoch time: 254.69 s +2024-09-10 07:59:59.971446: Training done. +2024-09-10 08:00:00.039878: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 08:00:00.055044: The split file contains 5 splits. +2024-09-10 08:00:00.056093: Desired fold for training: 3 +2024-09-10 08:00:00.056840: This split has 1325 training and 331 validation cases. +2024-09-10 08:00:00.065937: predicting saros-000 +2024-09-10 08:00:00.628109: saros-000, shape torch.Size([1, 313, 267, 267]), rank 0 +2024-09-10 08:01:16.769078: predicting saros-003 +2024-09-10 08:01:17.049610: saros-003, shape torch.Size([1, 167, 247, 247]), rank 0 +2024-09-10 08:01:18.944894: predicting saros-009 +2024-09-10 08:01:19.364233: saros-009, shape torch.Size([1, 180, 292, 292]), rank 0 +2024-09-10 08:01:23.546021: predicting saros-015 +2024-09-10 08:01:23.915606: saros-015, shape torch.Size([1, 290, 229, 229]), rank 0 +2024-09-10 08:01:29.385307: predicting saros-019 +2024-09-10 08:01:29.864320: saros-019, shape torch.Size([1, 277, 278, 278]), rank 0 +2024-09-10 08:01:33.602597: predicting saros-022 +2024-09-10 08:01:34.144173: saros-022, shape torch.Size([1, 310, 267, 267]), rank 0 +2024-09-10 08:01:39.682147: predicting saros-046 +2024-09-10 08:01:39.942628: saros-046, shape torch.Size([1, 157, 239, 239]), rank 0 +2024-09-10 08:01:41.894198: predicting saros-050 +2024-09-10 08:01:42.333372: saros-050, shape torch.Size([1, 147, 333, 333]), rank 0 +2024-09-10 08:01:46.537039: predicting saros-057 +2024-09-10 08:01:46.859457: saros-057, shape torch.Size([1, 187, 260, 260]), rank 0 +2024-09-10 08:01:48.819784: predicting saros-059 +2024-09-10 08:01:49.055322: saros-059, shape torch.Size([1, 170, 213, 213]), rank 0 +2024-09-10 08:01:50.946079: predicting saros-068 +2024-09-10 08:01:51.256581: saros-068, shape torch.Size([1, 173, 245, 245]), rank 0 +2024-09-10 08:01:53.164564: predicting saros-075 +2024-09-10 08:01:53.795311: saros-075, shape torch.Size([1, 297, 222, 222]), rank 0 +2024-09-10 08:01:59.331897: predicting saros-088 +2024-09-10 08:01:59.817075: saros-088, shape torch.Size([1, 210, 287, 287]), rank 0 +2024-09-10 08:02:03.624046: predicting saros-093 +2024-09-10 08:02:03.897898: saros-093, shape torch.Size([1, 103, 231, 231]), rank 0 +2024-09-10 08:02:05.819916: predicting saros-100 +2024-09-10 08:02:06.303910: saros-100, shape torch.Size([1, 207, 253, 253]), rank 0 +2024-09-10 08:02:10.011560: predicting saros-107 +2024-09-10 08:02:10.271554: saros-107, shape torch.Size([1, 187, 227, 227]), rank 0 +2024-09-10 08:02:12.195919: predicting saros-116 +2024-09-10 08:02:12.770187: saros-116, shape torch.Size([1, 273, 213, 213]), rank 0 +2024-09-10 08:02:16.627928: predicting saros-117 +2024-09-10 08:02:17.070970: saros-117, shape torch.Size([1, 143, 307, 307]), rank 0 +2024-09-10 08:02:21.299930: predicting saros-124 +2024-09-10 08:02:22.046976: saros-124, shape torch.Size([1, 310, 320, 320]), rank 0 +2024-09-10 08:02:34.443904: predicting saros-125 +2024-09-10 08:02:34.815834: saros-125, shape torch.Size([1, 140, 307, 307]), rank 0 +2024-09-10 08:02:39.041180: predicting saros-126 +2024-09-10 08:02:39.348713: saros-126, shape torch.Size([1, 243, 232, 232]), rank 0 +2024-09-10 08:02:43.052170: predicting saros-128 +2024-09-10 08:02:43.328986: saros-128, shape torch.Size([1, 207, 240, 240]), rank 0 +2024-09-10 08:02:47.018608: predicting saros-142 +2024-09-10 08:02:47.243286: saros-142, shape torch.Size([1, 153, 253, 253]), rank 0 +2024-09-10 08:02:49.144265: predicting saros-144 +2024-09-10 08:02:49.312802: saros-144, shape torch.Size([1, 140, 227, 227]), rank 0 +2024-09-10 08:02:51.198865: predicting saros-149 +2024-09-10 08:02:51.571671: saros-149, shape torch.Size([1, 220, 281, 281]), rank 0 +2024-09-10 08:02:55.324600: predicting saros-152 +2024-09-10 08:02:55.848089: saros-152, shape torch.Size([1, 337, 258, 258]), rank 0 +2024-09-10 08:03:01.451589: predicting saros-154 +2024-09-10 08:03:02.114953: saros-154, shape torch.Size([1, 437, 259, 259]), rank 0 +2024-09-10 08:03:09.599948: predicting saros-157 +2024-09-10 08:03:09.953107: saros-157, shape torch.Size([1, 147, 293, 293]), rank 0 +2024-09-10 08:03:14.131272: predicting saros-162 +2024-09-10 08:03:14.769211: saros-162, shape torch.Size([1, 343, 299, 299]), rank 0 +2024-09-10 08:03:27.094786: predicting saros-169 +2024-09-10 08:03:27.396162: saros-169, shape torch.Size([1, 140, 293, 293]), rank 0 +2024-09-10 08:03:31.566078: predicting saros-171 +2024-09-10 08:03:31.817422: saros-171, shape torch.Size([1, 160, 247, 247]), rank 0 +2024-09-10 08:03:33.753148: predicting saros-175 +2024-09-10 08:03:34.203223: saros-175, shape torch.Size([1, 170, 333, 333]), rank 0 +2024-09-10 08:03:38.430779: predicting saros-179 +2024-09-10 08:03:38.714104: saros-179, shape torch.Size([1, 160, 273, 273]), rank 0 +2024-09-10 08:03:40.648583: predicting saros-180 +2024-09-10 08:03:40.989540: saros-180, shape torch.Size([1, 187, 281, 281]), rank 0 +2024-09-10 08:03:42.938790: predicting saros-190 +2024-09-10 08:03:43.421357: saros-190, shape torch.Size([1, 283, 287, 287]), rank 0 +2024-09-10 08:03:47.185357: predicting saros-194 +2024-09-10 08:03:47.487400: saros-194, shape torch.Size([1, 207, 267, 267]), rank 0 +2024-09-10 08:03:51.209276: predicting saros-195 +2024-09-10 08:03:51.450377: saros-195, shape torch.Size([1, 107, 294, 294]), rank 0 +2024-09-10 08:03:55.607326: predicting saros-225 +2024-09-10 08:03:56.132229: saros-225, shape torch.Size([1, 337, 233, 233]), rank 0 +2024-09-10 08:04:01.740613: predicting saros-228 +2024-09-10 08:04:02.061316: saros-228, shape torch.Size([1, 190, 247, 247]), rank 0 +2024-09-10 08:04:04.008839: predicting saros-229 +2024-09-10 08:04:04.247394: saros-229, shape torch.Size([1, 137, 280, 280]), rank 0 +2024-09-10 08:04:06.227523: predicting saros-238 +2024-09-10 08:04:06.723739: saros-238, shape torch.Size([1, 350, 241, 241]), rank 0 +2024-09-10 08:04:12.276639: predicting saros-245 +2024-09-10 08:04:12.697707: saros-245, shape torch.Size([1, 293, 253, 253]), rank 0 +2024-09-10 08:04:18.224516: predicting saros-255 +2024-09-10 08:04:18.532945: saros-255, shape torch.Size([1, 140, 295, 295]), rank 0 +2024-09-10 08:04:22.686419: predicting saros-261 +2024-09-10 08:04:23.290816: saros-261, shape torch.Size([1, 300, 320, 320]), rank 0 +2024-09-10 08:04:35.663186: predicting saros-263 +2024-09-10 08:04:36.305706: saros-263, shape torch.Size([1, 323, 294, 294]), rank 0 +2024-09-10 08:04:48.670938: predicting saros-268 +2024-09-10 08:04:49.421541: saros-268, shape torch.Size([1, 317, 333, 333]), rank 0 +2024-09-10 08:05:01.793387: predicting saros-274 +2024-09-10 08:05:02.038300: saros-274, shape torch.Size([1, 137, 320, 320]), rank 0 +2024-09-10 08:05:06.307989: predicting saros-278 +2024-09-10 08:05:06.569741: saros-278, shape torch.Size([1, 133, 267, 267]), rank 0 +2024-09-10 08:05:08.466695: predicting saros-283 +2024-09-10 08:05:08.751893: saros-283, shape torch.Size([1, 160, 267, 267]), rank 0 +2024-09-10 08:05:10.680846: predicting saros-287 +2024-09-10 08:05:11.062299: saros-287, shape torch.Size([1, 317, 228, 228]), rank 0 +2024-09-10 08:05:16.582531: predicting saros-304 +2024-09-10 08:05:16.880779: saros-304, shape torch.Size([1, 197, 247, 247]), rank 0 +2024-09-10 08:05:20.590394: predicting saros-307 +2024-09-10 08:05:21.063245: saros-307, shape torch.Size([1, 233, 240, 240]), rank 0 +2024-09-10 08:05:24.812045: predicting saros-310 +2024-09-10 08:05:25.135498: saros-310, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-10 08:05:28.828514: predicting saros-318 +2024-09-10 08:05:29.129666: saros-318, shape torch.Size([1, 217, 233, 233]), rank 0 +2024-09-10 08:05:32.821970: predicting saros-320 +2024-09-10 08:05:33.169674: saros-320, shape torch.Size([1, 220, 250, 250]), rank 0 +2024-09-10 08:05:36.913558: predicting saros-322 +2024-09-10 08:05:37.406034: saros-322, shape torch.Size([1, 227, 300, 300]), rank 0 +2024-09-10 08:05:45.676062: predicting saros-324 +2024-09-10 08:05:46.015097: saros-324, shape torch.Size([1, 227, 256, 256]), rank 0 +2024-09-10 08:05:49.754621: predicting saros-340 +2024-09-10 08:05:50.280277: saros-340, shape torch.Size([1, 203, 333, 333]), rank 0 +2024-09-10 08:05:58.518187: predicting saros-341 +2024-09-10 08:05:58.669663: saros-341, shape torch.Size([1, 227, 173, 173]), rank 0 +2024-09-10 08:06:41.440930: predicting saros-349 +2024-09-10 08:06:41.787513: saros-349, shape torch.Size([1, 200, 267, 267]), rank 0 +2024-09-10 08:06:45.462226: predicting saros-353 +2024-09-10 08:06:45.695245: saros-353, shape torch.Size([1, 233, 193, 193]), rank 0 +2024-09-10 08:06:49.342834: predicting saros-354 +2024-09-10 08:06:49.577384: saros-354, shape torch.Size([1, 200, 217, 217]), rank 0 +2024-09-10 08:06:53.233778: predicting saros-356 +2024-09-10 08:06:53.503392: saros-356, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-10 08:06:57.192651: predicting saros-369 +2024-09-10 08:06:57.569823: saros-369, shape torch.Size([1, 220, 267, 267]), rank 0 +2024-09-10 08:07:01.284552: predicting saros-371 +2024-09-10 08:07:01.584140: saros-371, shape torch.Size([1, 217, 229, 229]), rank 0 +2024-09-10 08:07:05.285657: predicting saros-386 +2024-09-10 08:07:05.488274: saros-386, shape torch.Size([1, 203, 200, 200]), rank 0 +2024-09-10 08:07:09.156703: predicting saros-393 +2024-09-10 08:07:09.381035: saros-393, shape torch.Size([1, 193, 220, 220]), rank 0 +2024-09-10 08:07:13.115999: predicting saros-395 +2024-09-10 08:07:13.664929: saros-395, shape torch.Size([1, 220, 280, 280]), rank 0 +2024-09-10 08:07:17.481968: predicting saros-399 +2024-09-10 08:07:18.025659: saros-399, shape torch.Size([1, 233, 333, 333]), rank 0 +2024-09-10 08:07:26.302570: predicting saros-407 +2024-09-10 08:07:26.563268: saros-407, shape torch.Size([1, 193, 240, 240]), rank 0 +2024-09-10 08:07:30.285978: predicting saros-423 +2024-09-10 08:07:30.631941: saros-423, shape torch.Size([1, 220, 253, 253]), rank 0 +2024-09-10 08:07:34.372694: predicting saros-424 +2024-09-10 08:07:34.770078: saros-424, shape torch.Size([1, 247, 253, 253]), rank 0 +2024-09-10 08:07:38.497329: predicting saros-434 +2024-09-10 08:07:38.804843: saros-434, shape torch.Size([1, 240, 215, 215]), rank 0 +2024-09-10 08:07:42.503413: predicting saros-443 +2024-09-10 08:07:42.792840: saros-443, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-10 08:07:46.545331: predicting saros-445 +2024-09-10 08:07:46.942381: saros-445, shape torch.Size([1, 220, 267, 267]), rank 0 +2024-09-10 08:07:50.732169: predicting saros-457 +2024-09-10 08:07:51.040581: saros-457, shape torch.Size([1, 200, 231, 231]), rank 0 +2024-09-10 08:07:54.737594: predicting saros-469 +2024-09-10 08:07:54.970301: saros-469, shape torch.Size([1, 207, 217, 217]), rank 0 +2024-09-10 08:07:58.672137: predicting saros-470 +2024-09-10 08:07:58.982248: saros-470, shape torch.Size([1, 203, 240, 240]), rank 0 +2024-09-10 08:08:02.718387: predicting saros-479 +2024-09-10 08:08:03.120085: saros-479, shape torch.Size([1, 217, 267, 267]), rank 0 +2024-09-10 08:08:06.847760: predicting saros-480 +2024-09-10 08:08:07.258141: saros-480, shape torch.Size([1, 253, 247, 247]), rank 0 +2024-09-10 08:08:10.991209: predicting saros-490 +2024-09-10 08:08:11.286927: saros-490, shape torch.Size([1, 213, 239, 239]), rank 0 +2024-09-10 08:08:15.023885: predicting saros-511 +2024-09-10 08:08:15.363762: saros-511, shape torch.Size([1, 223, 231, 231]), rank 0 +2024-09-10 08:08:19.073272: predicting saros-515 +2024-09-10 08:08:19.564452: saros-515, shape torch.Size([1, 227, 333, 333]), rank 0 +2024-09-10 08:08:27.873607: predicting saros-517 +2024-09-10 08:08:28.256207: saros-517, shape torch.Size([1, 197, 307, 307]), rank 0 +2024-09-10 08:08:36.503206: predicting saros-523 +2024-09-10 08:08:36.876694: saros-523, shape torch.Size([1, 190, 253, 253]), rank 0 +2024-09-10 08:08:38.817793: predicting saros-529 +2024-09-10 08:08:39.149068: saros-529, shape torch.Size([1, 167, 285, 285]), rank 0 +2024-09-10 08:08:41.101110: predicting saros-532 +2024-09-10 08:08:41.480830: saros-532, shape torch.Size([1, 247, 253, 253]), rank 0 +2024-09-10 08:08:45.250600: predicting saros-534 +2024-09-10 08:08:45.500397: saros-534, shape torch.Size([1, 253, 199, 199]), rank 0 +2024-09-10 08:08:49.184661: predicting saros-535 +2024-09-10 08:08:49.554780: saros-535, shape torch.Size([1, 227, 267, 267]), rank 0 +2024-09-10 08:08:53.283180: predicting saros-538 +2024-09-10 08:08:53.752759: saros-538, shape torch.Size([1, 187, 333, 333]), rank 0 +2024-09-10 08:08:58.044676: predicting saros-544 +2024-09-10 08:08:58.333309: saros-544, shape torch.Size([1, 197, 218, 218]), rank 0 +2024-09-10 08:09:02.102943: predicting saros-554 +2024-09-10 08:09:02.344212: saros-554, shape torch.Size([1, 223, 213, 213]), rank 0 +2024-09-10 08:09:06.026003: predicting saros-557 +2024-09-10 08:09:06.355489: saros-557, shape torch.Size([1, 210, 253, 253]), rank 0 +2024-09-10 08:09:10.069170: predicting saros-559 +2024-09-10 08:09:10.558608: saros-559, shape torch.Size([1, 227, 333, 333]), rank 0 +2024-09-10 08:09:18.824316: predicting saros-572 +2024-09-10 08:09:19.458587: saros-572, shape torch.Size([1, 243, 333, 333]), rank 0 +2024-09-10 08:09:27.761030: predicting saros-580 +2024-09-10 08:09:28.215268: saros-580, shape torch.Size([1, 177, 333, 333]), rank 0 +2024-09-10 08:09:32.473583: predicting saros-590 +2024-09-10 08:09:32.756254: saros-590, shape torch.Size([1, 187, 236, 236]), rank 0 +2024-09-10 08:09:34.676232: predicting saros-593 +2024-09-10 08:09:35.032787: saros-593, shape torch.Size([1, 210, 280, 280]), rank 0 +2024-09-10 08:09:38.754364: predicting saros-595 +2024-09-10 08:09:39.205256: saros-595, shape torch.Size([1, 210, 333, 333]), rank 0 +2024-09-10 08:09:47.476081: predicting saros-596 +2024-09-10 08:09:47.985999: saros-596, shape torch.Size([1, 227, 333, 333]), rank 0 +2024-09-10 08:09:56.264984: predicting saros-606 +2024-09-10 08:09:57.302435: saros-606, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 08:10:17.980620: predicting saros-608 +2024-09-10 08:10:19.595396: saros-608, shape torch.Size([1, 747, 333, 333]), rank 0 +2024-09-10 08:10:48.498005: predicting saros-610 +2024-09-10 08:10:49.914317: saros-610, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:11:18.720380: predicting saros-616 +2024-09-10 08:11:20.129913: saros-616, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-10 08:11:44.900253: predicting saros-620 +2024-09-10 08:11:46.154196: saros-620, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 08:12:06.823242: predicting saros-622 +2024-09-10 08:12:08.016591: saros-622, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 08:12:32.694506: predicting saros-623 +2024-09-10 08:12:34.147383: saros-623, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:13:02.948764: predicting saros-634 +2024-09-10 08:13:04.240469: saros-634, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:13:28.956064: predicting saros-635 +2024-09-10 08:13:30.548389: saros-635, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 08:13:55.348595: predicting saros-643 +2024-09-10 08:13:56.755668: saros-643, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:14:21.538976: predicting saros-650 +2024-09-10 08:14:22.779221: saros-650, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:14:47.523512: predicting saros-651 +2024-09-10 08:14:48.801706: saros-651, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 08:15:09.484915: predicting saros-656 +2024-09-10 08:15:10.611863: saros-656, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 08:15:35.317179: predicting saros-666 +2024-09-10 08:15:36.527994: saros-666, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:16:01.233437: predicting saros-670 +2024-09-10 08:16:02.529539: saros-670, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:16:27.255857: predicting saros-672 +2024-09-10 08:16:28.309935: saros-672, shape torch.Size([1, 507, 333, 333]), rank 0 +2024-09-10 08:16:48.969422: predicting saros-679 +2024-09-10 08:16:49.954738: saros-679, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-10 08:17:06.574883: predicting saros-680 +2024-09-10 08:17:07.822859: saros-680, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:17:32.571703: predicting saros-684 +2024-09-10 08:17:34.023292: saros-684, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:17:58.797225: predicting saros-689 +2024-09-10 08:17:59.898129: saros-689, shape torch.Size([1, 497, 333, 333]), rank 0 +2024-09-10 08:18:20.550901: predicting saros-703 +2024-09-10 08:18:21.808547: saros-703, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 08:18:46.650457: predicting saros-710 +2024-09-10 08:18:47.818280: saros-710, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 08:19:12.573939: predicting saros-728 +2024-09-10 08:19:13.697319: saros-728, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 08:19:34.367764: predicting saros-732 +2024-09-10 08:19:35.846904: saros-732, shape torch.Size([1, 807, 333, 333]), rank 0 +2024-09-10 08:20:08.849358: predicting saros-746 +2024-09-10 08:20:10.197400: saros-746, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-10 08:20:34.969970: predicting saros-755 +2024-09-10 08:20:36.414835: saros-755, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:21:05.370600: predicting saros-764 +2024-09-10 08:21:06.936971: saros-764, shape torch.Size([1, 730, 333, 333]), rank 0 +2024-09-10 08:21:35.799608: predicting saros-766 +2024-09-10 08:21:37.284009: saros-766, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:22:06.172964: predicting saros-770 +2024-09-10 08:22:07.628729: saros-770, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:22:36.482950: predicting saros-798 +2024-09-10 08:22:37.707254: saros-798, shape torch.Size([1, 603, 333, 333]), rank 0 +2024-09-10 08:23:02.424651: predicting saros-806 +2024-09-10 08:23:03.961292: saros-806, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:23:32.786011: predicting saros-811 +2024-09-10 08:23:34.282171: saros-811, shape torch.Size([1, 730, 333, 333]), rank 0 +2024-09-10 08:24:03.279445: predicting saros-812 +2024-09-10 08:24:04.576939: saros-812, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 08:24:25.258577: predicting saros-814 +2024-09-10 08:24:26.471182: saros-814, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-10 08:24:47.128882: predicting saros-821 +2024-09-10 08:24:48.127432: saros-821, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-10 08:25:04.673059: predicting saros-825 +2024-09-10 08:25:06.100905: saros-825, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:25:34.891406: predicting saros-830 +2024-09-10 08:25:35.942988: saros-830, shape torch.Size([1, 487, 333, 333]), rank 0 +2024-09-10 08:25:56.545196: predicting saros-838 +2024-09-10 08:25:57.651587: saros-838, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 08:26:18.269045: predicting saros-847 +2024-09-10 08:26:19.567737: saros-847, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-10 08:26:44.309316: predicting saros-851 +2024-09-10 08:26:45.519926: saros-851, shape torch.Size([1, 567, 333, 333]), rank 0 +2024-09-10 08:27:06.190348: predicting saros-852 +2024-09-10 08:27:07.609902: saros-852, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-10 08:27:32.334811: predicting saros-864 +2024-09-10 08:27:33.214749: saros-864, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-10 08:27:49.780503: predicting saros-865 +2024-09-10 08:27:50.996006: saros-865, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-10 08:28:11.608218: predicting saros-866 +2024-09-10 08:28:12.916675: saros-866, shape torch.Size([1, 660, 333, 333]), rank 0 +2024-09-10 08:28:37.676909: predicting saros-867 +2024-09-10 08:28:38.717877: saros-867, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-10 08:28:55.378757: predicting saros-868 +2024-09-10 08:28:56.834354: saros-868, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:29:25.664226: predicting saros-869 +2024-09-10 08:29:26.900806: saros-869, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-10 08:29:51.654646: predicting saros-870 +2024-09-10 08:29:53.074331: saros-870, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-10 08:30:21.904847: predicting saros-882 +2024-09-10 08:30:23.118658: saros-882, shape torch.Size([1, 543, 333, 333]), rank 0 +2024-09-10 08:30:43.792478: predicting saros-884 +2024-09-10 08:30:45.034244: saros-884, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-10 08:31:09.851473: predicting tseg-s0012 +2024-09-10 08:31:10.049514: tseg-s0012, shape torch.Size([1, 315, 145, 238]), rank 0 +2024-09-10 08:31:12.937631: predicting tseg-s0014 +2024-09-10 08:31:13.374526: tseg-s0014, shape torch.Size([1, 308, 286, 286]), rank 0 +2024-09-10 08:31:18.967308: predicting tseg-s0015 +2024-09-10 08:31:19.453285: tseg-s0015, shape torch.Size([1, 344, 293, 293]), rank 0 +2024-09-10 08:31:31.885961: predicting tseg-s0016 +2024-09-10 08:31:32.255351: tseg-s0016, shape torch.Size([1, 294, 259, 259]), rank 0 +2024-09-10 08:31:37.856002: predicting tseg-s0019 +2024-09-10 08:31:38.222046: tseg-s0019, shape torch.Size([1, 407, 221, 221]), rank 0 +2024-09-10 08:31:45.586673: predicting tseg-s0039 +2024-09-10 08:31:45.702032: tseg-s0039, shape torch.Size([1, 92, 296, 296]), rank 0 +2024-09-10 08:31:49.873944: predicting tseg-s0046 +2024-09-10 08:31:50.161355: tseg-s0046, shape torch.Size([1, 213, 247, 247]), rank 0 +2024-09-10 08:31:53.877864: predicting tseg-s0053 +2024-09-10 08:31:54.443535: tseg-s0053, shape torch.Size([1, 346, 333, 333]), rank 0 +2024-09-10 08:32:06.851663: predicting tseg-s0058 +2024-09-10 08:32:07.405473: tseg-s0058, shape torch.Size([1, 325, 298, 298]), rank 0 +2024-09-10 08:32:19.833025: predicting tseg-s0071 +2024-09-10 08:32:20.239007: tseg-s0071, shape torch.Size([1, 278, 308, 308]), rank 0 +2024-09-10 08:32:28.591070: predicting tseg-s0083 +2024-09-10 08:32:28.657608: tseg-s0083, shape torch.Size([1, 124, 156, 156]), rank 0 +2024-09-10 08:32:29.156060: predicting tseg-s0096 +2024-09-10 08:32:29.200512: tseg-s0096, shape torch.Size([1, 14, 259, 259]), rank 0 +2024-09-10 08:32:31.031778: predicting tseg-s0098 +2024-09-10 08:32:31.212849: tseg-s0098, shape torch.Size([1, 231, 170, 231]), rank 0 +2024-09-10 08:32:33.101520: predicting tseg-s0110 +2024-09-10 08:32:33.230319: tseg-s0110, shape torch.Size([1, 165, 154, 165]), rank 0 +2024-09-10 08:32:33.775975: predicting tseg-s0111 +2024-09-10 08:32:34.034956: tseg-s0111, shape torch.Size([1, 247, 221, 221]), rank 0 +2024-09-10 08:32:37.726635: predicting tseg-s0139 +2024-09-10 08:32:38.065522: tseg-s0139, shape torch.Size([1, 285, 280, 280]), rank 0 +2024-09-10 08:32:41.837452: predicting tseg-s0145 +2024-09-10 08:32:42.224349: tseg-s0145, shape torch.Size([1, 447, 219, 222]), rank 0 +2024-09-10 08:32:49.608406: predicting tseg-s0147 +2024-09-10 08:32:49.967671: tseg-s0147, shape torch.Size([1, 310, 310, 220]), rank 0 +2024-09-10 08:32:58.236679: predicting tseg-s0156 +2024-09-10 08:32:58.451235: tseg-s0156, shape torch.Size([1, 101, 333, 333]), rank 0 +2024-09-10 08:33:02.690764: predicting tseg-s0159 +2024-09-10 08:33:02.747865: tseg-s0159, shape torch.Size([1, 74, 173, 173]), rank 0 +2024-09-10 08:33:03.240925: predicting tseg-s0167 +2024-09-10 08:33:03.356950: tseg-s0167, shape torch.Size([1, 149, 177, 177]), rank 0 +2024-09-10 08:33:03.870454: predicting tseg-s0172 +2024-09-10 08:33:03.896862: tseg-s0172, shape torch.Size([1, 159, 86, 86]), rank 0 +2024-09-10 08:33:04.385658: predicting tseg-s0175 +2024-09-10 08:33:04.624160: tseg-s0175, shape torch.Size([1, 159, 253, 253]), rank 0 +2024-09-10 08:33:06.529443: predicting tseg-s0179 +2024-09-10 08:33:06.823464: tseg-s0179, shape torch.Size([1, 247, 248, 248]), rank 0 +2024-09-10 08:33:10.566602: predicting tseg-s0183 +2024-09-10 08:33:10.927995: tseg-s0183, shape torch.Size([1, 330, 201, 330]), rank 0 +2024-09-10 08:33:19.200919: predicting tseg-s0184 +2024-09-10 08:33:19.353497: tseg-s0184, shape torch.Size([1, 189, 188, 223]), rank 0 +2024-09-10 08:33:20.369641: predicting tseg-s0185 +2024-09-10 08:33:20.413567: tseg-s0185, shape torch.Size([1, 93, 98, 98]), rank 0 +2024-09-10 08:33:20.910911: predicting tseg-s0190 +2024-09-10 08:33:21.296806: tseg-s0190, shape torch.Size([1, 237, 257, 257]), rank 0 +2024-09-10 08:33:25.021214: predicting tseg-s0201 +2024-09-10 08:33:25.343011: tseg-s0201, shape torch.Size([1, 219, 293, 293]), rank 0 +2024-09-10 08:33:33.598860: predicting tseg-s0206 +2024-09-10 08:33:33.953368: tseg-s0206, shape torch.Size([1, 288, 240, 240]), rank 0 +2024-09-10 08:33:37.715725: predicting tseg-s0217 +2024-09-10 08:33:37.743992: tseg-s0217, shape torch.Size([1, 73, 133, 133]), rank 0 +2024-09-10 08:33:38.238550: predicting tseg-s0219 +2024-09-10 08:33:38.276526: tseg-s0219, shape torch.Size([1, 60, 173, 173]), rank 0 +2024-09-10 08:33:38.755046: predicting tseg-s0222 +2024-09-10 08:33:38.937492: tseg-s0222, shape torch.Size([1, 137, 280, 280]), rank 0 +2024-09-10 08:33:40.847979: predicting tseg-s0229 +2024-09-10 08:33:41.021234: tseg-s0229, shape torch.Size([1, 108, 269, 269]), rank 0 +2024-09-10 08:33:42.951195: predicting tseg-s0248 +2024-09-10 08:33:43.358384: tseg-s0248, shape torch.Size([1, 280, 299, 299]), rank 0 +2024-09-10 08:33:51.634133: predicting tseg-s0252 +2024-09-10 08:33:51.889670: tseg-s0252, shape torch.Size([1, 267, 227, 227]), rank 0 +2024-09-10 08:33:55.656542: predicting tseg-s0259 +2024-09-10 08:33:55.700270: tseg-s0259, shape torch.Size([1, 165, 113, 165]), rank 0 +2024-09-10 08:33:56.188860: predicting tseg-s0263 +2024-09-10 08:33:56.301635: tseg-s0263, shape torch.Size([1, 161, 171, 171]), rank 0 +2024-09-10 08:33:56.848824: predicting tseg-s0271 +2024-09-10 08:33:57.113447: tseg-s0271, shape torch.Size([1, 255, 221, 221]), rank 0 +2024-09-10 08:34:00.821832: predicting tseg-s0275 +2024-09-10 08:34:01.117273: tseg-s0275, shape torch.Size([1, 245, 283, 283]), rank 0 +2024-09-10 08:34:04.877881: predicting tseg-s0288 +2024-09-10 08:34:04.916201: tseg-s0288, shape torch.Size([1, 152, 111, 111]), rank 0 +2024-09-10 08:34:05.405442: predicting tseg-s0300 +2024-09-10 08:34:05.851714: tseg-s0300, shape torch.Size([1, 278, 325, 325]), rank 0 +2024-09-10 08:34:14.147358: predicting tseg-s0303 +2024-09-10 08:34:14.394972: tseg-s0303, shape torch.Size([1, 231, 231, 252]), rank 0 +2024-09-10 08:34:18.183196: predicting tseg-s0305 +2024-09-10 08:34:18.357958: tseg-s0305, shape torch.Size([1, 98, 319, 319]), rank 0 +2024-09-10 08:34:22.567557: predicting tseg-s0306 +2024-09-10 08:34:22.790859: tseg-s0306, shape torch.Size([1, 225, 202, 245]), rank 0 +2024-09-10 08:34:26.501598: predicting tseg-s0310 +2024-09-10 08:34:26.709190: tseg-s0310, shape torch.Size([1, 184, 235, 235]), rank 0 +2024-09-10 08:34:28.631274: predicting tseg-s0313 +2024-09-10 08:34:28.674336: tseg-s0313, shape torch.Size([1, 170, 108, 108]), rank 0 +2024-09-10 08:34:29.176012: predicting tseg-s0315 +2024-09-10 08:34:29.671037: tseg-s0315, shape torch.Size([1, 305, 297, 297]), rank 0 +2024-09-10 08:34:41.973747: predicting tseg-s0319 +2024-09-10 08:34:42.347109: tseg-s0319, shape torch.Size([1, 338, 213, 270]), rank 0 +2024-09-10 08:34:47.994949: predicting tseg-s0321 +2024-09-10 08:34:48.410800: tseg-s0321, shape torch.Size([1, 303, 272, 272]), rank 0 +2024-09-10 08:34:53.997609: predicting tseg-s0335 +2024-09-10 08:34:54.045501: tseg-s0335, shape torch.Size([1, 89, 89, 89]), rank 0 +2024-09-10 08:34:54.536309: predicting tseg-s0340 +2024-09-10 08:34:54.585790: tseg-s0340, shape torch.Size([1, 100, 163, 163]), rank 0 +2024-09-10 08:34:55.067083: predicting tseg-s0369 +2024-09-10 08:34:55.498729: tseg-s0369, shape torch.Size([1, 439, 248, 248]), rank 0 +2024-09-10 08:35:02.875608: predicting tseg-s0371 +2024-09-10 08:35:03.146332: tseg-s0371, shape torch.Size([1, 283, 233, 233]), rank 0 +2024-09-10 08:35:06.914123: predicting tseg-s0378 +2024-09-10 08:35:07.051925: tseg-s0378, shape torch.Size([1, 179, 183, 183]), rank 0 +2024-09-10 08:35:07.587392: predicting tseg-s0379 +2024-09-10 08:35:07.643935: tseg-s0379, shape torch.Size([1, 126, 95, 135]), rank 0 +2024-09-10 08:35:08.144262: predicting tseg-s0381 +2024-09-10 08:35:08.315705: tseg-s0381, shape torch.Size([1, 192, 219, 219]), rank 0 +2024-09-10 08:35:10.191593: predicting tseg-s0390 +2024-09-10 08:35:10.523017: tseg-s0390, shape torch.Size([1, 299, 247, 247]), rank 0 +2024-09-10 08:35:16.061320: predicting tseg-s0394 +2024-09-10 08:35:16.323571: tseg-s0394, shape torch.Size([1, 275, 225, 225]), rank 0 +2024-09-10 08:35:20.066013: predicting tseg-s0398 +2024-09-10 08:35:20.281372: tseg-s0398, shape torch.Size([1, 201, 221, 221]), rank 0 +2024-09-10 08:35:23.999056: predicting tseg-s0408 +2024-09-10 08:35:24.477983: tseg-s0408, shape torch.Size([1, 391, 279, 279]), rank 0 +2024-09-10 08:35:31.891848: predicting tseg-s0412 +2024-09-10 08:35:32.505205: tseg-s0412, shape torch.Size([1, 431, 313, 313]), rank 0 +2024-09-10 08:35:49.043569: predicting tseg-s0422 +2024-09-10 08:35:49.269002: tseg-s0422, shape torch.Size([1, 226, 247, 247]), rank 0 +2024-09-10 08:35:53.066981: predicting tseg-s0431 +2024-09-10 08:35:53.353848: tseg-s0431, shape torch.Size([1, 235, 249, 249]), rank 0 +2024-09-10 08:35:57.104338: predicting tseg-s0433 +2024-09-10 08:35:57.289695: tseg-s0433, shape torch.Size([1, 175, 233, 233]), rank 0 +2024-09-10 08:35:59.239851: predicting tseg-s0437 +2024-09-10 08:35:59.491421: tseg-s0437, shape torch.Size([1, 294, 180, 294]), rank 0 +2024-09-10 08:36:03.699549: predicting tseg-s0438 +2024-09-10 08:36:03.926485: tseg-s0438, shape torch.Size([1, 202, 255, 255]), rank 0 +2024-09-10 08:36:07.648632: predicting tseg-s0447 +2024-09-10 08:36:08.165566: tseg-s0447, shape torch.Size([1, 427, 285, 285]), rank 0 +2024-09-10 08:36:15.605210: predicting tseg-s0465 +2024-09-10 08:36:15.644173: tseg-s0465, shape torch.Size([1, 113, 122, 122]), rank 0 +2024-09-10 08:36:16.144715: predicting tseg-s0469 +2024-09-10 08:36:16.352902: tseg-s0469, shape torch.Size([1, 236, 209, 209]), rank 0 +2024-09-10 08:36:20.041954: predicting tseg-s0474 +2024-09-10 08:36:20.069969: tseg-s0474, shape torch.Size([1, 73, 137, 137]), rank 0 +2024-09-10 08:36:20.552419: predicting tseg-s0477 +2024-09-10 08:36:20.953817: tseg-s0477, shape torch.Size([1, 404, 242, 242]), rank 0 +2024-09-10 08:36:28.312840: predicting tseg-s0483 +2024-09-10 08:36:28.803823: tseg-s0483, shape torch.Size([1, 444, 266, 266]), rank 0 +2024-09-10 08:36:36.259143: predicting tseg-s0486 +2024-09-10 08:36:36.402122: tseg-s0486, shape torch.Size([1, 130, 249, 249]), rank 0 +2024-09-10 08:36:38.303756: predicting tseg-s0487 +2024-09-10 08:36:38.517312: tseg-s0487, shape torch.Size([1, 203, 213, 213]), rank 0 +2024-09-10 08:36:42.212748: predicting tseg-s0488 +2024-09-10 08:36:42.418646: tseg-s0488, shape torch.Size([1, 215, 221, 221]), rank 0 +2024-09-10 08:36:46.127508: predicting tseg-s0492 +2024-09-10 08:36:46.249427: tseg-s0492, shape torch.Size([1, 76, 333, 333]), rank 0 +2024-09-10 08:36:50.417522: predicting tseg-s0506 +2024-09-10 08:36:50.571924: tseg-s0506, shape torch.Size([1, 177, 231, 231]), rank 0 +2024-09-10 08:36:52.471151: predicting tseg-s0507 +2024-09-10 08:36:52.990264: tseg-s0507, shape torch.Size([1, 441, 252, 252]), rank 0 +2024-09-10 08:37:00.396886: predicting tseg-s0517 +2024-09-10 08:37:00.688023: tseg-s0517, shape torch.Size([1, 213, 270, 270]), rank 0 +2024-09-10 08:37:04.459737: predicting tseg-s0528 +2024-09-10 08:37:04.556826: tseg-s0528, shape torch.Size([1, 147, 183, 183]), rank 0 +2024-09-10 08:37:05.089132: predicting tseg-s0530 +2024-09-10 08:37:05.238251: tseg-s0530, shape torch.Size([1, 165, 135, 236]), rank 0 +2024-09-10 08:37:06.220461: predicting tseg-s0541 +2024-09-10 08:37:06.685822: tseg-s0541, shape torch.Size([1, 320, 319, 319]), rank 0 +2024-09-10 08:37:19.037794: predicting tseg-s0583 +2024-09-10 08:37:19.491843: tseg-s0583, shape torch.Size([1, 424, 232, 232]), rank 0 +2024-09-10 08:37:26.937917: predicting tseg-s0588 +2024-09-10 08:37:27.008141: tseg-s0588, shape torch.Size([1, 117, 184, 183]), rank 0 +2024-09-10 08:37:27.523739: predicting tseg-s0592 +2024-09-10 08:37:28.261830: tseg-s0592, shape torch.Size([1, 403, 319, 319]), rank 0 +2024-09-10 08:37:44.751160: predicting tseg-s0600 +2024-09-10 08:37:45.027593: tseg-s0600, shape torch.Size([1, 298, 227, 227]), rank 0 +2024-09-10 08:37:50.667164: predicting tseg-s0602 +2024-09-10 08:37:50.986572: tseg-s0602, shape torch.Size([1, 419, 165, 248]), rank 0 +2024-09-10 08:37:54.775293: predicting tseg-s0621 +2024-09-10 08:37:55.342106: tseg-s0621, shape torch.Size([1, 453, 310, 270]), rank 0 +2024-09-10 08:38:06.404102: predicting tseg-s0623 +2024-09-10 08:38:06.927412: tseg-s0623, shape torch.Size([1, 442, 242, 242]), rank 0 +2024-09-10 08:38:14.386369: predicting tseg-s0639 +2024-09-10 08:38:14.852394: tseg-s0639, shape torch.Size([1, 425, 208, 313]), rank 0 +2024-09-10 08:38:25.876480: predicting tseg-s0645 +2024-09-10 08:38:26.061620: tseg-s0645, shape torch.Size([1, 173, 219, 219]), rank 0 +2024-09-10 08:38:28.007609: predicting tseg-s0666 +2024-09-10 08:38:28.068253: tseg-s0666, shape torch.Size([1, 56, 259, 259]), rank 0 +2024-09-10 08:38:29.951360: predicting tseg-s0669 +2024-09-10 08:38:30.531042: tseg-s0669, shape torch.Size([1, 443, 299, 299]), rank 0 +2024-09-10 08:38:46.976723: predicting tseg-s0679 +2024-09-10 08:38:47.019485: tseg-s0679, shape torch.Size([1, 297, 107, 63]), rank 0 +2024-09-10 08:38:48.434648: predicting tseg-s0685 +2024-09-10 08:38:49.086163: tseg-s0685, shape torch.Size([1, 520, 263, 263]), rank 0 +2024-09-10 08:38:58.333942: predicting tseg-s0688 +2024-09-10 08:38:58.581056: tseg-s0688, shape torch.Size([1, 234, 240, 240]), rank 0 +2024-09-10 08:39:02.389391: predicting tseg-s0693 +2024-09-10 08:39:02.450544: tseg-s0693, shape torch.Size([1, 65, 232, 232]), rank 0 +2024-09-10 08:39:04.339590: predicting tseg-s0696 +2024-09-10 08:39:04.508662: tseg-s0696, shape torch.Size([1, 207, 206, 206]), rank 0 +2024-09-10 08:39:08.182719: predicting tseg-s0697 +2024-09-10 08:39:08.411341: tseg-s0697, shape torch.Size([1, 264, 221, 221]), rank 0 +2024-09-10 08:39:12.120405: predicting tseg-s0720 +2024-09-10 08:39:12.766194: tseg-s0720, shape torch.Size([1, 445, 307, 307]), rank 0 +2024-09-10 08:39:29.282520: predicting tseg-s0723 +2024-09-10 08:39:29.578460: tseg-s0723, shape torch.Size([1, 415, 187, 222]), rank 0 +2024-09-10 08:39:33.488523: predicting tseg-s0724 +2024-09-10 08:39:33.832762: tseg-s0724, shape torch.Size([1, 299, 259, 259]), rank 0 +2024-09-10 08:39:39.417307: predicting tseg-s0728 +2024-09-10 08:39:39.670850: tseg-s0728, shape torch.Size([1, 270, 195, 195]), rank 0 +2024-09-10 08:39:43.412615: predicting tseg-s0741 +2024-09-10 08:39:43.454648: tseg-s0741, shape torch.Size([1, 40, 242, 242]), rank 0 +2024-09-10 08:39:45.322187: predicting tseg-s0747 +2024-09-10 08:39:45.591839: tseg-s0747, shape torch.Size([1, 211, 266, 266]), rank 0 +2024-09-10 08:39:49.305003: predicting tseg-s0766 +2024-09-10 08:39:49.354405: tseg-s0766, shape torch.Size([1, 100, 138, 118]), rank 0 +2024-09-10 08:39:49.840307: predicting tseg-s0769 +2024-09-10 08:39:50.061896: tseg-s0769, shape torch.Size([1, 191, 245, 244]), rank 0 +2024-09-10 08:39:51.970397: predicting tseg-s0819 +2024-09-10 08:39:52.112869: tseg-s0819, shape torch.Size([1, 157, 212, 212]), rank 0 +2024-09-10 08:39:54.011705: predicting tseg-s0821 +2024-09-10 08:39:54.220216: tseg-s0821, shape torch.Size([1, 244, 213, 213]), rank 0 +2024-09-10 08:39:57.914861: predicting tseg-s0830 +2024-09-10 08:39:58.396284: tseg-s0830, shape torch.Size([1, 440, 248, 248]), rank 0 +2024-09-10 08:40:05.792422: predicting tseg-s0840 +2024-09-10 08:40:05.851134: tseg-s0840, shape torch.Size([1, 137, 144, 144]), rank 0 +2024-09-10 08:40:06.364587: predicting tseg-s0846 +2024-09-10 08:40:06.407990: tseg-s0846, shape torch.Size([1, 75, 102, 102]), rank 0 +2024-09-10 08:40:06.901699: predicting tseg-s0853 +2024-09-10 08:40:07.059811: tseg-s0853, shape torch.Size([1, 162, 175, 175]), rank 0 +2024-09-10 08:40:07.639912: predicting tseg-s0862 +2024-09-10 08:40:07.910862: tseg-s0862, shape torch.Size([1, 183, 272, 272]), rank 0 +2024-09-10 08:40:09.862182: predicting tseg-s0864 +2024-09-10 08:40:10.372376: tseg-s0864, shape torch.Size([1, 391, 286, 286]), rank 0 +2024-09-10 08:40:17.803326: predicting tseg-s0879 +2024-09-10 08:40:17.858425: tseg-s0879, shape torch.Size([1, 118, 133, 133]), rank 0 +2024-09-10 08:40:18.367260: predicting tseg-s0881 +2024-09-10 08:40:18.408465: tseg-s0881, shape torch.Size([1, 135, 112, 112]), rank 0 +2024-09-10 08:40:18.895276: predicting tseg-s0895 +2024-09-10 08:40:19.105490: tseg-s0895, shape torch.Size([1, 218, 231, 231]), rank 0 +2024-09-10 08:40:22.793566: predicting tseg-s0897 +2024-09-10 08:40:23.013008: tseg-s0897, shape torch.Size([1, 263, 220, 219]), rank 0 +2024-09-10 08:40:26.723197: predicting tseg-s0918 +2024-09-10 08:40:27.269480: tseg-s0918, shape torch.Size([1, 459, 252, 252]), rank 0 +2024-09-10 08:40:34.685120: predicting tseg-s0931 +2024-09-10 08:40:35.166838: tseg-s0931, shape torch.Size([1, 479, 187, 244]), rank 0 +2024-09-10 08:40:39.016265: predicting tseg-s0935 +2024-09-10 08:40:39.069062: tseg-s0935, shape torch.Size([1, 151, 131, 131]), rank 0 +2024-09-10 08:40:39.567127: predicting tseg-s0963 +2024-09-10 08:40:39.877450: tseg-s0963, shape torch.Size([1, 402, 208, 208]), rank 0 +2024-09-10 08:40:47.194406: predicting tseg-s0965 +2024-09-10 08:40:47.495027: tseg-s0965, shape torch.Size([1, 309, 215, 215]), rank 0 +2024-09-10 08:40:53.034256: predicting tseg-s0968 +2024-09-10 08:40:53.080126: tseg-s0968, shape torch.Size([1, 59, 193, 193]), rank 0 +2024-09-10 08:40:54.928509: predicting tseg-s0980 +2024-09-10 08:40:54.994400: tseg-s0980, shape torch.Size([1, 94, 120, 120]), rank 0 +2024-09-10 08:40:55.474227: predicting tseg-s0988 +2024-09-10 08:40:55.922324: tseg-s0988, shape torch.Size([1, 273, 333, 333]), rank 0 +2024-09-10 08:41:04.224964: predicting tseg-s0989 +2024-09-10 08:41:04.314620: tseg-s0989, shape torch.Size([1, 71, 181, 230]), rank 0 +2024-09-10 08:41:05.263215: predicting tseg-s0999 +2024-09-10 08:41:05.501749: tseg-s0999, shape torch.Size([1, 175, 251, 251]), rank 0 +2024-09-10 08:41:07.453174: predicting tseg-s1012 +2024-09-10 08:41:08.017616: tseg-s1012, shape torch.Size([1, 453, 285, 285]), rank 0 +2024-09-10 08:41:15.475055: predicting tseg-s1013 +2024-09-10 08:41:15.696792: tseg-s1013, shape torch.Size([1, 268, 221, 221]), rank 0 +2024-09-10 08:41:19.473410: predicting tseg-s1036 +2024-09-10 08:41:19.541538: tseg-s1036, shape torch.Size([1, 147, 163, 163]), rank 0 +2024-09-10 08:41:20.051351: predicting tseg-s1070 +2024-09-10 08:41:20.548971: tseg-s1070, shape torch.Size([1, 428, 247, 247]), rank 0 +2024-09-10 08:41:27.928765: predicting tseg-s1082 +2024-09-10 08:41:28.838313: tseg-s1082, shape torch.Size([1, 542, 333, 333]), rank 0 +2024-09-10 08:41:49.524446: predicting tseg-s1085 +2024-09-10 08:41:49.922492: tseg-s1085, shape torch.Size([1, 443, 236, 236]), rank 0 +2024-09-10 08:41:57.406477: predicting tseg-s1086 +2024-09-10 08:41:58.044497: tseg-s1086, shape torch.Size([1, 443, 271, 271]), rank 0 +2024-09-10 08:42:05.508228: predicting tseg-s1101 +2024-09-10 08:42:05.785107: tseg-s1101, shape torch.Size([1, 205, 269, 269]), rank 0 +2024-09-10 08:42:09.567692: predicting tseg-s1102 +2024-09-10 08:42:09.937346: tseg-s1102, shape torch.Size([1, 311, 268, 268]), rank 0 +2024-09-10 08:42:15.512191: predicting tseg-s1107 +2024-09-10 08:42:15.774737: tseg-s1107, shape torch.Size([1, 253, 229, 228]), rank 0 +2024-09-10 08:42:19.532611: predicting tseg-s1111 +2024-09-10 08:42:19.912067: tseg-s1111, shape torch.Size([1, 419, 231, 231]), rank 0 +2024-09-10 08:42:27.277797: predicting tseg-s1127 +2024-09-10 08:42:27.831805: tseg-s1127, shape torch.Size([1, 409, 275, 275]), rank 0 +2024-09-10 08:42:35.262839: predicting tseg-s1128 +2024-09-10 08:42:35.404839: tseg-s1128, shape torch.Size([1, 159, 225, 225]), rank 0 +2024-09-10 08:42:37.300590: predicting tseg-s1129 +2024-09-10 08:42:37.468364: tseg-s1129, shape torch.Size([1, 251, 174, 174]), rank 0 +2024-09-10 08:42:38.455640: predicting tseg-s1132 +2024-09-10 08:42:38.707375: tseg-s1132, shape torch.Size([1, 214, 244, 244]), rank 0 +2024-09-10 08:42:42.423636: predicting tseg-s1138 +2024-09-10 08:42:42.604889: tseg-s1138, shape torch.Size([1, 260, 182, 182]), rank 0 +2024-09-10 08:42:43.598239: predicting tseg-s1153 +2024-09-10 08:42:43.737906: tseg-s1153, shape torch.Size([1, 145, 242, 242]), rank 0 +2024-09-10 08:42:45.643423: predicting tseg-s1158 +2024-09-10 08:42:45.834456: tseg-s1158, shape torch.Size([1, 166, 231, 231]), rank 0 +2024-09-10 08:42:47.735354: predicting tseg-s1161 +2024-09-10 08:42:48.550688: tseg-s1161, shape torch.Size([1, 544, 313, 313]), rank 0 +2024-09-10 08:43:09.160552: predicting tseg-s1171 +2024-09-10 08:43:10.114905: tseg-s1171, shape torch.Size([1, 544, 333, 333]), rank 0 +2024-09-10 08:43:30.918570: predicting tseg-s1175 +2024-09-10 08:43:31.016062: tseg-s1175, shape torch.Size([1, 88, 267, 267]), rank 0 +2024-09-10 08:43:32.931741: predicting tseg-s1208 +2024-09-10 08:43:33.378187: tseg-s1208, shape torch.Size([1, 448, 221, 221]), rank 0 +2024-09-10 08:43:40.798677: predicting tseg-s1209 +2024-09-10 08:43:41.559361: tseg-s1209, shape torch.Size([1, 446, 303, 303]), rank 0 +2024-09-10 08:43:58.070584: predicting tseg-s1215 +2024-09-10 08:43:58.176413: tseg-s1215, shape torch.Size([1, 92, 303, 303]), rank 0 +2024-09-10 08:44:02.396603: predicting tseg-s1218 +2024-09-10 08:44:02.446149: tseg-s1218, shape torch.Size([1, 45, 249, 249]), rank 0 +2024-09-10 08:44:04.328784: predicting tseg-s1243 +2024-09-10 08:44:04.420570: tseg-s1243, shape torch.Size([1, 150, 139, 139]), rank 0 +2024-09-10 08:44:04.909345: predicting tseg-s1250 +2024-09-10 08:44:04.965392: tseg-s1250, shape torch.Size([1, 59, 219, 219]), rank 0 +2024-09-10 08:44:06.789503: predicting tseg-s1260 +2024-09-10 08:44:06.858440: tseg-s1260, shape torch.Size([1, 113, 135, 135]), rank 0 +2024-09-10 08:44:07.338843: predicting tseg-s1264 +2024-09-10 08:44:07.704409: tseg-s1264, shape torch.Size([1, 273, 285, 285]), rank 0 +2024-09-10 08:44:11.457844: predicting tseg-s1267 +2024-09-10 08:44:11.903178: tseg-s1267, shape torch.Size([1, 457, 244, 244]), rank 0 +2024-09-10 08:44:19.320241: predicting tseg-s1289 +2024-09-10 08:44:19.674957: tseg-s1289, shape torch.Size([1, 275, 263, 263]), rank 0 +2024-09-10 08:44:23.469332: predicting tseg-s1299 +2024-09-10 08:44:23.552231: tseg-s1299, shape torch.Size([1, 143, 158, 106]), rank 0 +2024-09-10 08:44:24.045240: predicting tseg-s1304 +2024-09-10 08:44:24.946869: tseg-s1304, shape torch.Size([1, 546, 333, 333]), rank 0 +2024-09-10 08:44:45.551493: predicting tseg-s1312 +2024-09-10 08:44:45.693971: tseg-s1312, shape torch.Size([1, 91, 319, 319]), rank 0 +2024-09-10 08:44:49.881857: predicting tseg-s1318 +2024-09-10 08:44:49.990388: tseg-s1318, shape torch.Size([1, 100, 236, 236]), rank 0 +2024-09-10 08:44:51.881339: predicting tseg-s1319 +2024-09-10 08:44:52.307070: tseg-s1319, shape torch.Size([1, 427, 247, 247]), rank 0 +2024-09-10 08:44:59.680362: predicting tseg-s1329 +2024-09-10 08:44:59.933311: tseg-s1329, shape torch.Size([1, 287, 175, 287]), rank 0 +2024-09-10 08:45:01.886447: predicting tseg-s1333 +2024-09-10 08:45:01.928143: tseg-s1333, shape torch.Size([1, 99, 141, 141]), rank 0 +2024-09-10 08:45:02.414293: predicting tseg-s1334 +2024-09-10 08:45:02.886251: tseg-s1334, shape torch.Size([1, 510, 221, 221]), rank 0 +2024-09-10 08:45:12.044799: predicting tseg-s1338 +2024-09-10 08:45:12.144025: tseg-s1338, shape torch.Size([1, 249, 135, 135]), rank 0 +2024-09-10 08:45:13.114296: predicting tseg-s1342 +2024-09-10 08:45:13.258532: tseg-s1342, shape torch.Size([1, 82, 279, 279]), rank 0 +2024-09-10 08:45:15.216655: predicting tseg-s1362 +2024-09-10 08:45:15.717600: tseg-s1362, shape torch.Size([1, 429, 275, 275]), rank 0 +2024-09-10 08:45:23.144444: predicting tseg-s1369 +2024-09-10 08:45:23.889833: tseg-s1369, shape torch.Size([1, 549, 290, 290]), rank 0 +2024-09-10 08:45:44.473646: predicting tseg-s1370 +2024-09-10 08:45:44.715587: tseg-s1370, shape torch.Size([1, 271, 221, 221]), rank 0 +2024-09-10 08:45:48.448776: predicting tseg-s1371 +2024-09-10 08:45:49.355107: tseg-s1371, shape torch.Size([1, 525, 333, 333]), rank 0 +2024-09-10 08:46:09.995675: predicting tseg-s1372 +2024-09-10 08:46:10.450617: tseg-s1372, shape torch.Size([1, 428, 236, 236]), rank 0 +2024-09-10 08:46:17.998982: predicting tseg-s1378 +2024-09-10 08:46:18.119953: tseg-s1378, shape torch.Size([1, 115, 267, 267]), rank 0 +2024-09-10 08:46:20.063145: predicting tseg-s1385 +2024-09-10 08:46:20.266781: tseg-s1385, shape torch.Size([1, 169, 249, 249]), rank 0 +2024-09-10 08:46:22.194610: predicting tseg-s1397 +2024-09-10 08:46:23.026960: tseg-s1397, shape torch.Size([1, 547, 320, 320]), rank 0 +2024-09-10 08:46:43.606476: predicting tseg-s1399 +2024-09-10 08:46:43.853296: tseg-s1399, shape torch.Size([1, 239, 244, 244]), rank 0 +2024-09-10 08:46:47.592756: predicting tseg-s1404 +2024-09-10 08:46:48.076023: tseg-s1404, shape torch.Size([1, 421, 236, 236]), rank 0 +2024-09-10 08:47:53.885258: Validation complete +2024-09-10 08:47:53.885930: Mean Validation Dice: 0.9459480052167205 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_4_16_32_36.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_4_16_32_36.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5abdaa8583806767e3fca3b66841f763ae3639c --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_4_16_32_36.txt @@ -0,0 +1,804 @@ + +####################################################################### +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. +####################################################################### + +2024-09-04 16:32:36.692157: do_dummy_2d_data_aug: False +2024-09-04 16:32:36.699791: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-04 16:32:36.701352: The split file contains 5 splits. +2024-09-04 16:32:36.701926: Desired fold for training: 3 +2024-09-04 16:32:36.703193: This split has 1325 training and 331 validation cases. +2024-09-04 16:32:59.205092: Using torch.compile... + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-04 16:33:02.158521: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-04 16:33:03.264519: +2024-09-04 16:33:03.265689: Epoch 0 +2024-09-04 16:33:03.266922: Current learning rate: 0.01 +2024-09-04 16:51:22.375038: train_loss 0.7733 +2024-09-04 16:51:22.376314: val_loss 0.4148 +2024-09-04 16:51:22.378596: Pseudo dice [0.5199, 0.6201, 0.6501, 0.1528, 0.0, 0.0, 0.8277] +2024-09-04 16:51:22.379282: Epoch time: 1099.11 s +2024-09-04 16:51:22.379913: Yayy! New best EMA pseudo Dice: 0.3958 +2024-09-04 16:51:32.509790: +2024-09-04 16:51:32.510573: Epoch 1 +2024-09-04 16:51:32.511248: Current learning rate: 0.00999 +2024-09-04 16:55:54.058508: train_loss 0.3129 +2024-09-04 16:55:54.150877: val_loss 0.1259 +2024-09-04 16:55:54.158798: Pseudo dice [0.6878, 0.6607, 0.7596, 0.5171, 0.0, 0.0196, 0.8854] +2024-09-04 16:55:54.171331: Epoch time: 261.55 s +2024-09-04 16:55:54.217077: Yayy! New best EMA pseudo Dice: 0.4066 +2024-09-04 16:56:02.607162: +2024-09-04 16:56:02.650367: Epoch 2 +2024-09-04 16:56:02.660098: Current learning rate: 0.00998 +2024-09-04 17:00:10.694277: train_loss 0.0645 +2024-09-04 17:00:10.711418: val_loss -0.0588 +2024-09-04 17:00:10.712497: Pseudo dice [0.7769, 0.7659, 0.8387, 0.6987, 0.0, 0.3498, 0.8884] +2024-09-04 17:00:10.713507: Epoch time: 248.09 s +2024-09-04 17:00:10.714290: Yayy! New best EMA pseudo Dice: 0.4277 +2024-09-04 17:00:20.439020: +2024-09-04 17:00:20.439900: Epoch 3 +2024-09-04 17:00:20.440578: Current learning rate: 0.00997 +2024-09-04 17:04:44.246516: train_loss -0.108 +2024-09-04 17:04:44.259758: val_loss -0.2478 +2024-09-04 17:04:44.261054: Pseudo dice [0.8182, 0.805, 0.9024, 0.7454, 0.0, 0.5411, 0.9527] +2024-09-04 17:04:44.262012: Epoch time: 263.81 s +2024-09-04 17:04:44.262771: Yayy! New best EMA pseudo Dice: 0.453 +2024-09-04 17:04:52.932901: +2024-09-04 17:04:52.933930: Epoch 4 +2024-09-04 17:04:52.934711: Current learning rate: 0.00996 +2024-09-04 17:09:03.840491: train_loss -0.2418 +2024-09-04 17:09:03.853062: val_loss -0.329 +2024-09-04 17:09:03.853885: Pseudo dice [0.825, 0.8137, 0.8745, 0.7619, 0.4412, 0.6577, 0.9497] +2024-09-04 17:09:03.854724: Epoch time: 250.91 s +2024-09-04 17:09:03.855639: Yayy! New best EMA pseudo Dice: 0.4837 +2024-09-04 17:09:12.533778: +2024-09-04 17:09:12.535007: Epoch 5 +2024-09-04 17:09:12.536250: Current learning rate: 0.00995 +2024-09-04 17:13:43.232836: train_loss -0.3133 +2024-09-04 17:13:43.237992: val_loss -0.3704 +2024-09-04 17:13:43.238952: Pseudo dice [0.8541, 0.8429, 0.8966, 0.8275, 0.5775, 0.7353, 0.9629] +2024-09-04 17:13:43.239976: Epoch time: 270.7 s +2024-09-04 17:13:43.240702: Yayy! New best EMA pseudo Dice: 0.5167 +2024-09-04 17:13:51.795762: +2024-09-04 17:13:51.798659: Epoch 6 +2024-09-04 17:13:51.800683: Current learning rate: 0.00995 +2024-09-04 17:18:07.738100: train_loss -0.3698 +2024-09-04 17:18:07.748589: val_loss -0.45 +2024-09-04 17:18:07.749611: Pseudo dice [0.864, 0.8536, 0.9156, 0.8378, 0.6545, 0.8056, 0.9663] +2024-09-04 17:18:07.754991: Epoch time: 255.95 s +2024-09-04 17:18:07.757266: Yayy! New best EMA pseudo Dice: 0.5493 +2024-09-04 17:18:15.858757: +2024-09-04 17:18:15.859543: Epoch 7 +2024-09-04 17:18:15.860183: Current learning rate: 0.00994 +2024-09-04 17:22:30.672283: train_loss -0.4168 +2024-09-04 17:22:30.675484: val_loss -0.4856 +2024-09-04 17:22:30.676815: Pseudo dice [0.8823, 0.8689, 0.9261, 0.8562, 0.6488, 0.8212, 0.967] +2024-09-04 17:22:30.677910: Epoch time: 254.82 s +2024-09-04 17:22:30.678938: Yayy! New best EMA pseudo Dice: 0.5797 +2024-09-04 17:22:38.817039: +2024-09-04 17:22:38.817925: Epoch 8 +2024-09-04 17:22:38.818635: Current learning rate: 0.00993 +2024-09-04 17:27:04.213509: train_loss -0.4925 +2024-09-04 17:27:04.227471: val_loss -0.4781 +2024-09-04 17:27:04.228438: Pseudo dice [0.8828, 0.8899, 0.9146, 0.8347, 0.6942, 0.8615, 0.9638] +2024-09-04 17:27:04.229348: Epoch time: 265.4 s +2024-09-04 17:27:04.230257: Yayy! New best EMA pseudo Dice: 0.608 +2024-09-04 17:27:12.298127: +2024-09-04 17:27:12.299492: Epoch 9 +2024-09-04 17:27:12.300477: Current learning rate: 0.00992 +2024-09-04 17:31:18.945658: train_loss -0.518 +2024-09-04 17:31:18.962740: val_loss -0.4875 +2024-09-04 17:31:18.968786: Pseudo dice [0.8847, 0.8839, 0.9224, 0.8588, 0.7073, 0.8743, 0.973] +2024-09-04 17:31:18.969620: Epoch time: 246.65 s +2024-09-04 17:31:18.971388: Yayy! New best EMA pseudo Dice: 0.6344 +2024-09-04 17:31:29.816399: +2024-09-04 17:31:29.817461: Epoch 10 +2024-09-04 17:31:29.818442: Current learning rate: 0.00991 +2024-09-04 17:35:47.680595: train_loss -0.4789 +2024-09-04 17:35:47.700220: val_loss -0.5357 +2024-09-04 17:35:47.701969: Pseudo dice [0.8932, 0.8962, 0.9228, 0.8688, 0.7322, 0.8588, 0.9757] +2024-09-04 17:35:47.702856: Epoch time: 257.87 s +2024-09-04 17:35:47.703645: Yayy! New best EMA pseudo Dice: 0.6588 +2024-09-04 17:35:55.525333: +2024-09-04 17:35:55.526628: Epoch 11 +2024-09-04 17:35:55.527956: Current learning rate: 0.0099 +2024-09-04 17:40:20.347385: train_loss -0.5024 +2024-09-04 17:40:20.356231: val_loss -0.5388 +2024-09-04 17:40:20.357086: Pseudo dice [0.8986, 0.8929, 0.9389, 0.8756, 0.728, 0.835, 0.9759] +2024-09-04 17:40:20.357885: Epoch time: 264.83 s +2024-09-04 17:40:20.358522: Yayy! New best EMA pseudo Dice: 0.6807 +2024-09-04 17:40:29.393413: +2024-09-04 17:40:29.395993: Epoch 12 +2024-09-04 17:40:29.397284: Current learning rate: 0.00989 +2024-09-04 17:44:56.742430: train_loss -0.5414 +2024-09-04 17:44:56.745091: val_loss -0.5321 +2024-09-04 17:44:56.745951: Pseudo dice [0.9006, 0.8989, 0.9326, 0.8728, 0.6952, 0.8644, 0.9772] +2024-09-04 17:44:56.746784: Epoch time: 267.35 s +2024-09-04 17:44:56.747548: Yayy! New best EMA pseudo Dice: 0.7004 +2024-09-04 17:45:05.434935: +2024-09-04 17:45:05.435858: Epoch 13 +2024-09-04 17:45:05.436516: Current learning rate: 0.00988 +2024-09-04 17:49:08.051256: train_loss -0.5418 +2024-09-04 17:49:08.054128: val_loss -0.5388 +2024-09-04 17:49:08.055907: Pseudo dice [0.9061, 0.9013, 0.9197, 0.835, 0.7427, 0.8696, 0.9765] +2024-09-04 17:49:08.056922: Epoch time: 242.62 s +2024-09-04 17:49:08.057684: Yayy! New best EMA pseudo Dice: 0.7182 +2024-09-04 17:49:15.775957: +2024-09-04 17:49:15.779851: Epoch 14 +2024-09-04 17:49:15.781458: Current learning rate: 0.00987 +2024-09-04 17:53:33.497604: train_loss -0.5577 +2024-09-04 17:53:33.516568: val_loss -0.5787 +2024-09-04 17:53:33.517551: Pseudo dice [0.9091, 0.9054, 0.9447, 0.8891, 0.768, 0.8981, 0.982] +2024-09-04 17:53:33.518911: Epoch time: 257.72 s +2024-09-04 17:53:33.520055: Yayy! New best EMA pseudo Dice: 0.7363 +2024-09-04 17:53:44.191138: +2024-09-04 17:53:44.192288: Epoch 15 +2024-09-04 17:53:44.192973: Current learning rate: 0.00986 +2024-09-04 17:57:57.451145: train_loss -0.5729 +2024-09-04 17:57:57.468485: val_loss -0.5778 +2024-09-04 17:57:57.469353: Pseudo dice [0.9161, 0.9189, 0.9541, 0.9001, 0.7653, 0.8976, 0.9718] +2024-09-04 17:57:57.470522: Epoch time: 253.26 s +2024-09-04 17:57:57.471615: Yayy! New best EMA pseudo Dice: 0.753 +2024-09-04 17:58:06.226479: +2024-09-04 17:58:06.227700: Epoch 16 +2024-09-04 17:58:06.228812: Current learning rate: 0.00986 +2024-09-04 18:02:19.058819: train_loss -0.5745 +2024-09-04 18:02:19.073070: val_loss -0.5905 +2024-09-04 18:02:19.075255: Pseudo dice [0.9182, 0.9104, 0.9515, 0.8907, 0.7483, 0.9047, 0.9625] +2024-09-04 18:02:19.076367: Epoch time: 252.84 s +2024-09-04 18:02:19.077287: Yayy! New best EMA pseudo Dice: 0.7675 +2024-09-04 18:02:28.166973: +2024-09-04 18:02:28.167749: Epoch 17 +2024-09-04 18:02:28.168293: Current learning rate: 0.00985 +2024-09-04 18:06:50.236839: train_loss -0.6002 +2024-09-04 18:06:50.252110: val_loss -0.5993 +2024-09-04 18:06:50.252990: Pseudo dice [0.9194, 0.912, 0.9461, 0.8848, 0.7518, 0.907, 0.9813] +2024-09-04 18:06:50.253884: Epoch time: 262.07 s +2024-09-04 18:06:50.254685: Yayy! New best EMA pseudo Dice: 0.7808 +2024-09-04 18:07:00.004765: +2024-09-04 18:07:00.005742: Epoch 18 +2024-09-04 18:07:00.006609: Current learning rate: 0.00984 +2024-09-04 18:11:12.912400: train_loss -0.5879 +2024-09-04 18:11:12.923814: val_loss -0.5687 +2024-09-04 18:11:12.924903: Pseudo dice [0.9105, 0.9055, 0.9362, 0.8565, 0.7638, 0.8977, 0.9736] +2024-09-04 18:11:12.926031: Epoch time: 252.91 s +2024-09-04 18:11:12.926931: Yayy! New best EMA pseudo Dice: 0.7919 +2024-09-04 18:11:22.154959: +2024-09-04 18:11:22.155990: Epoch 19 +2024-09-04 18:11:22.156656: Current learning rate: 0.00983 +2024-09-04 18:15:35.342929: train_loss -0.5306 +2024-09-04 18:15:35.352190: val_loss -0.6033 +2024-09-04 18:15:35.352925: Pseudo dice [0.9163, 0.9122, 0.9504, 0.8939, 0.7685, 0.9082, 0.9815] +2024-09-04 18:15:35.353723: Epoch time: 253.19 s +2024-09-04 18:15:35.354402: Yayy! New best EMA pseudo Dice: 0.8032 +2024-09-04 18:15:45.050139: +2024-09-04 18:15:45.051570: Epoch 20 +2024-09-04 18:15:45.052304: Current learning rate: 0.00982 +2024-09-04 18:19:57.236105: train_loss -0.584 +2024-09-04 18:19:57.237466: val_loss -0.6385 +2024-09-04 18:19:57.238072: Pseudo dice [0.917, 0.9152, 0.9539, 0.9063, 0.7873, 0.9088, 0.9855] +2024-09-04 18:19:57.240237: Epoch time: 252.19 s +2024-09-04 18:19:57.240798: Yayy! New best EMA pseudo Dice: 0.8139 +2024-09-04 18:20:05.442266: +2024-09-04 18:20:05.443227: Epoch 21 +2024-09-04 18:20:05.443999: Current learning rate: 0.00981 +2024-09-04 18:24:18.497389: train_loss -0.6035 +2024-09-04 18:24:18.512355: val_loss -0.6384 +2024-09-04 18:24:18.514094: Pseudo dice [0.9314, 0.9196, 0.9548, 0.9019, 0.7591, 0.9008, 0.9794] +2024-09-04 18:24:18.515195: Epoch time: 253.06 s +2024-09-04 18:24:18.516206: Yayy! New best EMA pseudo Dice: 0.8232 +2024-09-04 18:24:31.301354: +2024-09-04 18:24:31.302209: Epoch 22 +2024-09-04 18:24:31.302881: Current learning rate: 0.0098 +2024-09-04 18:28:57.794601: train_loss -0.6147 +2024-09-04 18:28:57.810993: val_loss -0.5979 +2024-09-04 18:28:57.811835: Pseudo dice [0.9303, 0.9245, 0.9551, 0.8943, 0.7615, 0.9215, 0.9824] +2024-09-04 18:28:57.812525: Epoch time: 266.5 s +2024-09-04 18:28:57.813215: Yayy! New best EMA pseudo Dice: 0.8319 +2024-09-04 18:29:09.824240: +2024-09-04 18:29:09.825068: Epoch 23 +2024-09-04 18:29:09.825768: Current learning rate: 0.00979 +2024-09-04 18:33:47.473181: train_loss -0.6073 +2024-09-04 18:33:47.474990: val_loss -0.6094 +2024-09-04 18:33:47.475790: Pseudo dice [0.9243, 0.9172, 0.949, 0.8747, 0.7946, 0.9195, 0.9863] +2024-09-04 18:33:47.476769: Epoch time: 277.65 s +2024-09-04 18:33:47.477527: Yayy! New best EMA pseudo Dice: 0.8396 +2024-09-04 18:33:56.377220: +2024-09-04 18:33:56.573314: Epoch 24 +2024-09-04 18:33:56.585917: Current learning rate: 0.00978 +2024-09-04 18:38:05.238424: train_loss -0.6059 +2024-09-04 18:38:05.256670: val_loss -0.6187 +2024-09-04 18:38:05.258017: Pseudo dice [0.9285, 0.9253, 0.9475, 0.9041, 0.7654, 0.9236, 0.9829] +2024-09-04 18:38:05.259371: Epoch time: 248.87 s +2024-09-04 18:38:05.261044: Yayy! New best EMA pseudo Dice: 0.8468 +2024-09-04 18:38:14.913888: +2024-09-04 18:38:14.915126: Epoch 25 +2024-09-04 18:38:14.916064: Current learning rate: 0.00977 +2024-09-04 18:42:28.427054: train_loss -0.6162 +2024-09-04 18:42:28.428968: val_loss -0.5876 +2024-09-04 18:42:28.430293: Pseudo dice [0.9212, 0.9072, 0.9473, 0.8967, 0.7751, 0.9245, 0.9829] +2024-09-04 18:42:28.431800: Epoch time: 253.52 s +2024-09-04 18:42:28.433457: Yayy! New best EMA pseudo Dice: 0.8529 +2024-09-04 18:42:36.855131: +2024-09-04 18:42:36.856054: Epoch 26 +2024-09-04 18:42:36.856733: Current learning rate: 0.00977 +2024-09-04 18:46:42.659026: train_loss -0.5989 +2024-09-04 18:46:42.661580: val_loss -0.5976 +2024-09-04 18:46:42.663410: Pseudo dice [0.9252, 0.9182, 0.9506, 0.8807, 0.7873, 0.918, 0.9824] +2024-09-04 18:46:42.665506: Epoch time: 245.81 s +2024-09-04 18:46:42.666718: Yayy! New best EMA pseudo Dice: 0.8585 +2024-09-04 18:46:52.383798: +2024-09-04 18:46:52.385065: Epoch 27 +2024-09-04 18:46:52.385880: Current learning rate: 0.00976 +2024-09-04 18:51:11.926248: train_loss -0.6251 +2024-09-04 18:51:11.940709: val_loss -0.6664 +2024-09-04 18:51:11.941700: Pseudo dice [0.9366, 0.9229, 0.957, 0.903, 0.7669, 0.9231, 0.9854] +2024-09-04 18:51:11.942743: Epoch time: 259.55 s +2024-09-04 18:51:11.943852: Yayy! New best EMA pseudo Dice: 0.864 +2024-09-04 18:51:21.189015: +2024-09-04 18:51:21.190168: Epoch 28 +2024-09-04 18:51:21.191137: Current learning rate: 0.00975 +2024-09-04 18:55:28.403998: train_loss -0.6399 +2024-09-04 18:55:28.427420: val_loss -0.637 +2024-09-04 18:55:28.428212: Pseudo dice [0.9305, 0.9274, 0.947, 0.8959, 0.768, 0.9268, 0.9826] +2024-09-04 18:55:28.428909: Epoch time: 247.22 s +2024-09-04 18:55:28.429444: Yayy! New best EMA pseudo Dice: 0.8687 +2024-09-04 18:55:38.587188: +2024-09-04 18:55:38.588035: Epoch 29 +2024-09-04 18:55:38.588682: Current learning rate: 0.00974 +2024-09-04 18:59:53.911343: train_loss -0.6381 +2024-09-04 18:59:53.930028: val_loss -0.6339 +2024-09-04 18:59:53.931084: Pseudo dice [0.9355, 0.9307, 0.9613, 0.9122, 0.8033, 0.9266, 0.9828] +2024-09-04 18:59:53.931850: Epoch time: 255.33 s +2024-09-04 18:59:53.932475: Yayy! New best EMA pseudo Dice: 0.874 +2024-09-04 19:00:02.905599: +2024-09-04 19:00:02.906716: Epoch 30 +2024-09-04 19:00:02.907511: Current learning rate: 0.00973 +2024-09-04 19:04:15.748186: train_loss -0.6457 +2024-09-04 19:04:15.758428: val_loss -0.6562 +2024-09-04 19:04:15.759329: Pseudo dice [0.937, 0.9331, 0.9616, 0.9122, 0.805, 0.9312, 0.9864] +2024-09-04 19:04:15.760148: Epoch time: 252.85 s +2024-09-04 19:04:15.760767: Yayy! New best EMA pseudo Dice: 0.879 +2024-09-04 19:04:25.194895: +2024-09-04 19:04:25.195763: Epoch 31 +2024-09-04 19:04:25.196491: Current learning rate: 0.00972 +2024-09-04 19:08:25.443619: train_loss -0.6291 +2024-09-04 19:08:25.468018: val_loss -0.6315 +2024-09-04 19:08:25.469058: Pseudo dice [0.9351, 0.9299, 0.9595, 0.9076, 0.781, 0.9321, 0.985] +2024-09-04 19:08:25.470231: Epoch time: 240.25 s +2024-09-04 19:08:25.471022: Yayy! New best EMA pseudo Dice: 0.883 +2024-09-04 19:08:34.560937: +2024-09-04 19:08:34.561930: Epoch 32 +2024-09-04 19:08:34.562862: Current learning rate: 0.00971 +2024-09-04 19:12:49.130454: train_loss -0.6543 +2024-09-04 19:12:49.145892: val_loss -0.6326 +2024-09-04 19:12:49.146951: Pseudo dice [0.934, 0.9332, 0.9582, 0.9178, 0.7837, 0.9109, 0.981] +2024-09-04 19:12:49.148479: Epoch time: 254.57 s +2024-09-04 19:12:49.149507: Yayy! New best EMA pseudo Dice: 0.8864 +2024-09-04 19:12:57.557544: +2024-09-04 19:12:57.560055: Epoch 33 +2024-09-04 19:12:57.562174: Current learning rate: 0.0097 +2024-09-04 19:17:25.002371: train_loss -0.6292 +2024-09-04 19:17:25.018878: val_loss -0.6428 +2024-09-04 19:17:25.020334: Pseudo dice [0.9328, 0.9275, 0.9585, 0.9034, 0.7897, 0.9353, 0.9836] +2024-09-04 19:17:25.021802: Epoch time: 267.45 s +2024-09-04 19:17:25.022702: Yayy! New best EMA pseudo Dice: 0.8896 +2024-09-04 19:17:35.138474: +2024-09-04 19:17:35.140403: Epoch 34 +2024-09-04 19:17:35.142887: Current learning rate: 0.00969 +2024-09-04 19:21:42.952987: train_loss -0.6339 +2024-09-04 19:21:42.970668: val_loss -0.6671 +2024-09-04 19:21:42.972126: Pseudo dice [0.939, 0.9267, 0.9593, 0.9171, 0.8121, 0.9151, 0.9836] +2024-09-04 19:21:42.973332: Epoch time: 247.82 s +2024-09-04 19:21:42.974259: Yayy! New best EMA pseudo Dice: 0.8928 +2024-09-04 19:21:52.648125: +2024-09-04 19:21:52.649457: Epoch 35 +2024-09-04 19:21:52.651357: Current learning rate: 0.00968 +2024-09-04 19:26:05.014851: train_loss -0.627 +2024-09-04 19:26:05.030622: val_loss -0.6434 +2024-09-04 19:26:05.031365: Pseudo dice [0.9326, 0.9262, 0.956, 0.9093, 0.7889, 0.9272, 0.9852] +2024-09-04 19:26:05.032079: Epoch time: 252.37 s +2024-09-04 19:26:05.032767: Yayy! New best EMA pseudo Dice: 0.8953 +2024-09-04 19:26:14.169231: +2024-09-04 19:26:14.170032: Epoch 36 +2024-09-04 19:26:14.170880: Current learning rate: 0.00968 +2024-09-04 19:30:26.822694: train_loss -0.6445 +2024-09-04 19:30:26.840337: val_loss -0.6941 +2024-09-04 19:30:26.841391: Pseudo dice [0.94, 0.936, 0.9666, 0.9229, 0.8242, 0.9382, 0.9853] +2024-09-04 19:30:26.842355: Epoch time: 252.66 s +2024-09-04 19:30:26.843385: Yayy! New best EMA pseudo Dice: 0.8988 +2024-09-04 19:30:35.984596: +2024-09-04 19:30:35.986376: Epoch 37 +2024-09-04 19:30:35.987046: Current learning rate: 0.00967 +2024-09-04 19:35:08.420726: train_loss -0.663 +2024-09-04 19:35:08.435192: val_loss -0.6634 +2024-09-04 19:35:08.436060: Pseudo dice [0.9433, 0.9352, 0.9664, 0.9261, 0.8002, 0.9266, 0.9873] +2024-09-04 19:35:08.436746: Epoch time: 272.44 s +2024-09-04 19:35:08.437730: Yayy! New best EMA pseudo Dice: 0.9016 +2024-09-04 19:35:17.289011: +2024-09-04 19:35:17.289849: Epoch 38 +2024-09-04 19:35:17.290584: Current learning rate: 0.00966 +2024-09-04 19:39:25.957671: train_loss -0.6764 +2024-09-04 19:39:25.987208: val_loss -0.6704 +2024-09-04 19:39:25.988051: Pseudo dice [0.9417, 0.9395, 0.9634, 0.9125, 0.8012, 0.9255, 0.9871] +2024-09-04 19:39:25.989238: Epoch time: 248.67 s +2024-09-04 19:39:25.989883: Yayy! New best EMA pseudo Dice: 0.9039 +2024-09-04 19:39:34.505060: +2024-09-04 19:39:34.506047: Epoch 39 +2024-09-04 19:39:34.506915: Current learning rate: 0.00965 +2024-09-04 19:43:42.411848: train_loss -0.6611 +2024-09-04 19:43:42.428197: val_loss -0.6671 +2024-09-04 19:43:42.431312: Pseudo dice [0.9419, 0.9354, 0.9634, 0.9141, 0.8047, 0.9319, 0.9848] +2024-09-04 19:43:42.432979: Epoch time: 247.91 s +2024-09-04 19:43:42.435082: Yayy! New best EMA pseudo Dice: 0.906 +2024-09-04 19:43:51.408295: +2024-09-04 19:43:51.410373: Epoch 40 +2024-09-04 19:43:51.412117: Current learning rate: 0.00964 +2024-09-04 19:48:03.903912: train_loss -0.6684 +2024-09-04 19:48:03.911767: val_loss -0.6804 +2024-09-04 19:48:03.912963: Pseudo dice [0.942, 0.9348, 0.9658, 0.9178, 0.8097, 0.9349, 0.9857] +2024-09-04 19:48:03.913939: Epoch time: 252.5 s +2024-09-04 19:48:03.914694: Yayy! New best EMA pseudo Dice: 0.9081 +2024-09-04 19:48:12.484123: +2024-09-04 19:48:12.484986: Epoch 41 +2024-09-04 19:48:12.485717: Current learning rate: 0.00963 +2024-09-04 19:52:31.528462: train_loss -0.6556 +2024-09-04 19:52:31.543088: val_loss -0.6427 +2024-09-04 19:52:31.543933: Pseudo dice [0.9453, 0.9392, 0.9622, 0.9101, 0.8251, 0.9298, 0.9879] +2024-09-04 19:52:31.544659: Epoch time: 259.05 s +2024-09-04 19:52:31.545301: Yayy! New best EMA pseudo Dice: 0.9102 +2024-09-04 19:52:40.094021: +2024-09-04 19:52:40.095224: Epoch 42 +2024-09-04 19:52:40.095962: Current learning rate: 0.00962 +2024-09-04 19:57:04.266539: train_loss -0.6598 +2024-09-04 19:57:04.284517: val_loss -0.6928 +2024-09-04 19:57:04.285374: Pseudo dice [0.9348, 0.9345, 0.9615, 0.9147, 0.828, 0.9431, 0.9873] +2024-09-04 19:57:04.286738: Epoch time: 264.18 s +2024-09-04 19:57:04.287602: Yayy! New best EMA pseudo Dice: 0.9121 +2024-09-04 19:57:12.183361: +2024-09-04 19:57:12.184302: Epoch 43 +2024-09-04 19:57:12.185198: Current learning rate: 0.00961 +2024-09-04 20:01:49.113060: train_loss -0.6741 +2024-09-04 20:01:49.118233: val_loss -0.6771 +2024-09-04 20:01:49.119319: Pseudo dice [0.9444, 0.9389, 0.9653, 0.9214, 0.7933, 0.9478, 0.9865] +2024-09-04 20:01:49.120612: Epoch time: 276.93 s +2024-09-04 20:01:49.121651: Yayy! New best EMA pseudo Dice: 0.9137 +2024-09-04 20:01:58.540175: +2024-09-04 20:01:58.542188: Epoch 44 +2024-09-04 20:01:58.543087: Current learning rate: 0.0096 +2024-09-04 20:06:09.261756: train_loss -0.6663 +2024-09-04 20:06:09.272600: val_loss -0.6707 +2024-09-04 20:06:09.273564: Pseudo dice [0.9465, 0.943, 0.9653, 0.9271, 0.8265, 0.9388, 0.9871] +2024-09-04 20:06:09.274801: Epoch time: 250.73 s +2024-09-04 20:06:09.276512: Yayy! New best EMA pseudo Dice: 0.9157 +2024-09-04 20:06:17.794057: +2024-09-04 20:06:17.795475: Epoch 45 +2024-09-04 20:06:17.796994: Current learning rate: 0.00959 +2024-09-04 20:10:31.962626: train_loss -0.701 +2024-09-04 20:10:31.977333: val_loss -0.7046 +2024-09-04 20:10:31.978264: Pseudo dice [0.9465, 0.9422, 0.9646, 0.9153, 0.8344, 0.9444, 0.9894] +2024-09-04 20:10:31.979068: Epoch time: 254.17 s +2024-09-04 20:10:31.979758: Yayy! New best EMA pseudo Dice: 0.9175 +2024-09-04 20:10:45.880744: +2024-09-04 20:10:45.881568: Epoch 46 +2024-09-04 20:10:45.882273: Current learning rate: 0.00959 +2024-09-04 20:14:52.820115: train_loss -0.6602 +2024-09-04 20:14:52.836618: val_loss -0.6498 +2024-09-04 20:14:52.837611: Pseudo dice [0.9394, 0.936, 0.9621, 0.9139, 0.7891, 0.8985, 0.9815] +2024-09-04 20:14:52.838461: Epoch time: 246.94 s +2024-09-04 20:14:56.262111: +2024-09-04 20:14:56.263525: Epoch 47 +2024-09-04 20:14:56.265193: Current learning rate: 0.00958 +2024-09-04 20:19:34.169354: train_loss -0.6551 +2024-09-04 20:19:34.172152: val_loss -0.6827 +2024-09-04 20:19:34.172963: Pseudo dice [0.9463, 0.9376, 0.966, 0.9216, 0.8047, 0.9364, 0.987] +2024-09-04 20:19:34.173791: Epoch time: 277.91 s +2024-09-04 20:19:34.174498: Yayy! New best EMA pseudo Dice: 0.9186 +2024-09-04 20:19:43.371690: +2024-09-04 20:19:43.373852: Epoch 48 +2024-09-04 20:19:43.375346: Current learning rate: 0.00957 +2024-09-04 20:24:03.811183: train_loss -0.6484 +2024-09-04 20:24:03.823951: val_loss -0.683 +2024-09-04 20:24:03.824684: Pseudo dice [0.9452, 0.9389, 0.9607, 0.9214, 0.8349, 0.9383, 0.9885] +2024-09-04 20:24:03.825392: Epoch time: 260.44 s +2024-09-04 20:24:03.826114: Yayy! New best EMA pseudo Dice: 0.92 +2024-09-04 20:24:13.691252: +2024-09-04 20:24:13.692137: Epoch 49 +2024-09-04 20:24:13.692722: Current learning rate: 0.00956 +2024-09-04 20:28:30.892582: train_loss -0.6614 +2024-09-04 20:28:30.907053: val_loss -0.6834 +2024-09-04 20:28:30.907857: Pseudo dice [0.9456, 0.9386, 0.966, 0.9263, 0.7968, 0.9403, 0.9844] +2024-09-04 20:28:30.908508: Epoch time: 257.21 s +2024-09-04 20:28:38.201769: Yayy! New best EMA pseudo Dice: 0.9208 +2024-09-04 20:28:45.385088: +2024-09-04 20:28:45.385812: Epoch 50 +2024-09-04 20:28:45.386413: Current learning rate: 0.00955 +2024-09-04 20:32:52.077715: train_loss -0.676 +2024-09-04 20:32:52.091567: val_loss -0.6969 +2024-09-04 20:32:52.092728: Pseudo dice [0.9458, 0.9393, 0.9652, 0.9154, 0.8238, 0.9476, 0.9884] +2024-09-04 20:32:52.093786: Epoch time: 246.7 s +2024-09-04 20:32:52.096406: Yayy! New best EMA pseudo Dice: 0.9219 +2024-09-04 20:33:04.446666: +2024-09-04 20:33:04.447461: Epoch 51 +2024-09-04 20:33:04.448020: Current learning rate: 0.00954 +2024-09-04 20:37:15.165276: train_loss -0.6623 +2024-09-04 20:37:15.180492: val_loss -0.6997 +2024-09-04 20:37:15.181492: Pseudo dice [0.9459, 0.9392, 0.9656, 0.9198, 0.8114, 0.9468, 0.9859] +2024-09-04 20:37:15.182245: Epoch time: 250.72 s +2024-09-04 20:37:15.182971: Yayy! New best EMA pseudo Dice: 0.9228 +2024-09-04 20:37:23.090150: +2024-09-04 20:37:23.091052: Epoch 52 +2024-09-04 20:37:23.091740: Current learning rate: 0.00953 +2024-09-04 20:41:35.840289: train_loss -0.6735 +2024-09-04 20:41:35.855142: val_loss -0.695 +2024-09-04 20:41:35.855950: Pseudo dice [0.9462, 0.9372, 0.9651, 0.9142, 0.828, 0.9401, 0.9887] +2024-09-04 20:41:35.856620: Epoch time: 252.75 s +2024-09-04 20:41:35.857185: Yayy! New best EMA pseudo Dice: 0.9237 +2024-09-04 20:41:44.104710: +2024-09-04 20:41:44.105552: Epoch 53 +2024-09-04 20:41:44.106272: Current learning rate: 0.00952 +2024-09-04 20:45:50.678186: train_loss -0.6853 +2024-09-04 20:45:50.697203: val_loss -0.6575 +2024-09-04 20:45:50.698895: Pseudo dice [0.9453, 0.941, 0.9636, 0.9201, 0.8327, 0.9491, 0.9901] +2024-09-04 20:45:50.700217: Epoch time: 246.58 s +2024-09-04 20:45:50.701420: Yayy! New best EMA pseudo Dice: 0.9248 +2024-09-04 20:46:00.546641: +2024-09-04 20:46:00.547837: Epoch 54 +2024-09-04 20:46:00.549167: Current learning rate: 0.00951 +2024-09-04 20:50:17.297934: train_loss -0.6637 +2024-09-04 20:50:17.300914: val_loss -0.7041 +2024-09-04 20:50:17.302008: Pseudo dice [0.946, 0.9387, 0.9633, 0.925, 0.8358, 0.9491, 0.9873] +2024-09-04 20:50:17.303457: Epoch time: 256.75 s +2024-09-04 20:50:17.304655: Yayy! New best EMA pseudo Dice: 0.9258 +2024-09-04 20:50:26.390718: +2024-09-04 20:50:26.393224: Epoch 55 +2024-09-04 20:50:26.395492: Current learning rate: 0.0095 +2024-09-04 20:54:41.545034: train_loss -0.6782 +2024-09-04 20:54:41.563993: val_loss -0.6935 +2024-09-04 20:54:41.564843: Pseudo dice [0.9491, 0.9389, 0.9686, 0.9172, 0.8081, 0.9368, 0.987] +2024-09-04 20:54:41.565696: Epoch time: 255.16 s +2024-09-04 20:54:41.566405: Yayy! New best EMA pseudo Dice: 0.9261 +2024-09-04 20:54:51.598608: +2024-09-04 20:54:51.599442: Epoch 56 +2024-09-04 20:54:51.600079: Current learning rate: 0.00949 +2024-09-04 20:59:16.113570: train_loss -0.6708 +2024-09-04 20:59:16.128901: val_loss -0.6879 +2024-09-04 20:59:16.130619: Pseudo dice [0.9452, 0.9417, 0.9672, 0.9219, 0.8151, 0.9443, 0.9886] +2024-09-04 20:59:16.132259: Epoch time: 264.52 s +2024-09-04 20:59:16.133727: Yayy! New best EMA pseudo Dice: 0.9267 +2024-09-04 20:59:25.298027: +2024-09-04 20:59:25.298821: Epoch 57 +2024-09-04 20:59:25.299483: Current learning rate: 0.00949 +2024-09-04 21:03:41.542904: train_loss -0.6728 +2024-09-04 21:03:41.559610: val_loss -0.7148 +2024-09-04 21:03:41.560911: Pseudo dice [0.9474, 0.9413, 0.9673, 0.9234, 0.8356, 0.9497, 0.988] +2024-09-04 21:03:41.562238: Epoch time: 256.25 s +2024-09-04 21:03:41.563494: Yayy! New best EMA pseudo Dice: 0.9277 +2024-09-04 21:03:51.448211: +2024-09-04 21:03:51.449256: Epoch 58 +2024-09-04 21:03:51.449897: Current learning rate: 0.00948 +2024-09-04 21:08:05.043382: train_loss -0.6734 +2024-09-04 21:08:05.045078: val_loss -0.717 +2024-09-04 21:08:05.045948: Pseudo dice [0.9445, 0.9411, 0.9646, 0.9224, 0.8304, 0.9474, 0.9879] +2024-09-04 21:08:05.046766: Epoch time: 253.6 s +2024-09-04 21:08:05.047793: Yayy! New best EMA pseudo Dice: 0.9283 +2024-09-04 21:08:13.334900: +2024-09-04 21:08:13.335731: Epoch 59 +2024-09-04 21:08:13.336478: Current learning rate: 0.00947 +2024-09-04 21:12:31.984033: train_loss -0.6784 +2024-09-04 21:12:32.001805: val_loss -0.7138 +2024-09-04 21:12:32.003049: Pseudo dice [0.9516, 0.9446, 0.9669, 0.933, 0.835, 0.9506, 0.9898] +2024-09-04 21:12:32.004420: Epoch time: 258.65 s +2024-09-04 21:12:32.005680: Yayy! New best EMA pseudo Dice: 0.9293 +2024-09-04 21:12:41.094384: +2024-09-04 21:12:41.113667: Epoch 60 +2024-09-04 21:12:41.155472: Current learning rate: 0.00946 +2024-09-04 21:16:50.824275: train_loss -0.7006 +2024-09-04 21:16:50.831416: val_loss -0.7126 +2024-09-04 21:16:50.832250: Pseudo dice [0.949, 0.9443, 0.9694, 0.9276, 0.8417, 0.9552, 0.9888] +2024-09-04 21:16:50.833029: Epoch time: 249.73 s +2024-09-04 21:16:50.833630: Yayy! New best EMA pseudo Dice: 0.9304 +2024-09-04 21:17:00.087374: +2024-09-04 21:17:00.088103: Epoch 61 +2024-09-04 21:17:00.088717: Current learning rate: 0.00945 +2024-09-04 21:21:22.926079: train_loss -0.6854 +2024-09-04 21:21:22.927837: val_loss -0.6971 +2024-09-04 21:21:22.928889: Pseudo dice [0.9475, 0.9427, 0.9689, 0.9274, 0.8212, 0.9488, 0.9852] +2024-09-04 21:21:22.929628: Epoch time: 262.84 s +2024-09-04 21:21:22.930521: Yayy! New best EMA pseudo Dice: 0.9308 +2024-09-04 21:21:31.650942: +2024-09-04 21:21:31.652010: Epoch 62 +2024-09-04 21:21:31.652833: Current learning rate: 0.00944 +2024-09-04 21:25:33.443722: train_loss -0.6813 +2024-09-04 21:25:33.446704: val_loss -0.6861 +2024-09-04 21:25:33.448545: Pseudo dice [0.9463, 0.9449, 0.965, 0.9193, 0.832, 0.9456, 0.989] +2024-09-04 21:25:33.450338: Epoch time: 241.8 s +2024-09-04 21:25:33.451274: Yayy! New best EMA pseudo Dice: 0.9312 +2024-09-04 21:25:42.799066: +2024-09-04 21:25:42.800133: Epoch 63 +2024-09-04 21:25:42.801252: Current learning rate: 0.00943 +2024-09-04 21:29:47.112876: train_loss -0.6836 +2024-09-04 21:29:47.137996: val_loss -0.6854 +2024-09-04 21:29:47.142195: Pseudo dice [0.9497, 0.9467, 0.9698, 0.922, 0.842, 0.9535, 0.9893] +2024-09-04 21:29:47.146800: Epoch time: 244.32 s +2024-09-04 21:29:47.152590: Yayy! New best EMA pseudo Dice: 0.9319 +2024-09-04 21:29:54.324238: +2024-09-04 21:29:54.332860: Epoch 64 +2024-09-04 21:29:54.334070: Current learning rate: 0.00942 +2024-09-04 21:34:09.666244: train_loss -0.6846 +2024-09-04 21:34:09.668391: val_loss -0.7225 +2024-09-04 21:34:09.669582: Pseudo dice [0.9499, 0.9417, 0.9671, 0.924, 0.8475, 0.9457, 0.9885] +2024-09-04 21:34:09.670868: Epoch time: 255.35 s +2024-09-04 21:34:09.671957: Yayy! New best EMA pseudo Dice: 0.9325 +2024-09-04 21:34:17.459870: +2024-09-04 21:34:17.460799: Epoch 65 +2024-09-04 21:34:17.461527: Current learning rate: 0.00941 +2024-09-04 21:38:36.829120: train_loss -0.6907 +2024-09-04 21:38:36.844134: val_loss -0.7109 +2024-09-04 21:38:36.844860: Pseudo dice [0.9523, 0.9489, 0.968, 0.9286, 0.8398, 0.9516, 0.9899] +2024-09-04 21:38:36.845590: Epoch time: 259.37 s +2024-09-04 21:38:36.846278: Yayy! New best EMA pseudo Dice: 0.9333 +2024-09-04 21:38:46.048431: +2024-09-04 21:38:46.050414: Epoch 66 +2024-09-04 21:38:46.051801: Current learning rate: 0.0094 +2024-09-04 21:42:38.687264: train_loss -0.7123 +2024-09-04 21:42:38.704995: val_loss -0.7336 +2024-09-04 21:42:38.706546: Pseudo dice [0.954, 0.9442, 0.9716, 0.9344, 0.8459, 0.95, 0.9898] +2024-09-04 21:42:38.708280: Epoch time: 232.64 s +2024-09-04 21:42:38.709805: Yayy! New best EMA pseudo Dice: 0.9341 +2024-09-04 21:42:48.946638: +2024-09-04 21:42:48.947342: Epoch 67 +2024-09-04 21:42:48.947973: Current learning rate: 0.00939 +2024-09-04 21:47:10.532708: train_loss -0.7159 +2024-09-04 21:47:10.546985: val_loss -0.6867 +2024-09-04 21:47:10.548013: Pseudo dice [0.9508, 0.9462, 0.9712, 0.923, 0.8019, 0.9513, 0.9862] +2024-09-04 21:47:10.550329: Epoch time: 261.59 s +2024-09-04 21:47:14.858369: +2024-09-04 21:47:14.866696: Epoch 68 +2024-09-04 21:47:14.867640: Current learning rate: 0.00939 +2024-09-04 21:51:32.518320: train_loss -0.6818 +2024-09-04 21:51:32.531612: val_loss -0.7099 +2024-09-04 21:51:32.533105: Pseudo dice [0.9521, 0.9442, 0.9689, 0.9323, 0.84, 0.9469, 0.9882] +2024-09-04 21:51:32.534262: Epoch time: 257.67 s +2024-09-04 21:51:32.535361: Yayy! New best EMA pseudo Dice: 0.9345 +2024-09-04 21:51:42.350462: +2024-09-04 21:51:42.351015: Epoch 69 +2024-09-04 21:51:42.351539: Current learning rate: 0.00938 +2024-09-04 21:55:55.668920: train_loss -0.6888 +2024-09-04 21:55:55.683570: val_loss -0.7327 +2024-09-04 21:55:55.685338: Pseudo dice [0.954, 0.9454, 0.9711, 0.9352, 0.8402, 0.9556, 0.9898] +2024-09-04 21:55:55.687348: Epoch time: 253.32 s +2024-09-04 21:55:55.689110: Yayy! New best EMA pseudo Dice: 0.9352 +2024-09-04 21:56:03.009197: +2024-09-04 21:56:03.010077: Epoch 70 +2024-09-04 21:56:03.011456: Current learning rate: 0.00937 +2024-09-04 22:00:12.827780: train_loss -0.6939 +2024-09-04 22:00:12.829700: val_loss -0.719 +2024-09-04 22:00:12.830916: Pseudo dice [0.9516, 0.9493, 0.9682, 0.931, 0.8442, 0.9517, 0.9892] +2024-09-04 22:00:12.833420: Epoch time: 249.82 s +2024-09-04 22:00:12.834619: Yayy! New best EMA pseudo Dice: 0.9357 +2024-09-04 22:00:21.872662: +2024-09-04 22:00:21.873528: Epoch 71 +2024-09-04 22:00:21.874470: Current learning rate: 0.00936 +2024-09-04 22:04:55.511764: train_loss -0.7024 +2024-09-04 22:04:55.519146: val_loss -0.6954 +2024-09-04 22:04:55.520112: Pseudo dice [0.9526, 0.9476, 0.9708, 0.9366, 0.838, 0.945, 0.989] +2024-09-04 22:04:55.520779: Epoch time: 273.64 s +2024-09-04 22:04:55.521301: Yayy! New best EMA pseudo Dice: 0.9362 +2024-09-04 22:05:04.075384: +2024-09-04 22:05:04.076377: Epoch 72 +2024-09-04 22:05:04.077066: Current learning rate: 0.00935 +2024-09-04 22:09:47.531192: train_loss -0.6697 +2024-09-04 22:09:47.542979: val_loss -0.6862 +2024-09-04 22:09:47.543769: Pseudo dice [0.9449, 0.9393, 0.9664, 0.9343, 0.8353, 0.9464, 0.9872] +2024-09-04 22:09:47.544496: Epoch time: 283.46 s +2024-09-04 22:09:47.545169: Yayy! New best EMA pseudo Dice: 0.9362 +2024-09-04 22:09:57.083575: +2024-09-04 22:09:57.084401: Epoch 73 +2024-09-04 22:09:57.085086: Current learning rate: 0.00934 +2024-09-04 22:14:19.406416: train_loss -0.6909 +2024-09-04 22:14:19.407854: val_loss -0.6951 +2024-09-04 22:14:19.408648: Pseudo dice [0.949, 0.9457, 0.9686, 0.9273, 0.8429, 0.9481, 0.9872] +2024-09-04 22:14:19.409296: Epoch time: 262.33 s +2024-09-04 22:14:19.409922: Yayy! New best EMA pseudo Dice: 0.9364 +2024-09-04 22:14:27.499996: +2024-09-04 22:14:27.501129: Epoch 74 +2024-09-04 22:14:27.501941: Current learning rate: 0.00933 +2024-09-04 22:18:28.840488: train_loss -0.693 +2024-09-04 22:18:28.843045: val_loss -0.7042 +2024-09-04 22:18:28.843795: Pseudo dice [0.9516, 0.9478, 0.9669, 0.924, 0.8227, 0.9525, 0.9849] +2024-09-04 22:18:28.844572: Epoch time: 241.34 s +2024-09-04 22:18:31.136288: +2024-09-04 22:18:31.137281: Epoch 75 +2024-09-04 22:18:31.138189: Current learning rate: 0.00932 +2024-09-04 22:22:39.908574: train_loss -0.6889 +2024-09-04 22:22:39.911474: val_loss -0.6946 +2024-09-04 22:22:39.912622: Pseudo dice [0.9521, 0.9469, 0.9702, 0.9287, 0.838, 0.95, 0.9887] +2024-09-04 22:22:39.913446: Epoch time: 248.78 s +2024-09-04 22:22:39.914145: Yayy! New best EMA pseudo Dice: 0.9366 +2024-09-04 22:22:47.975878: +2024-09-04 22:22:47.976681: Epoch 76 +2024-09-04 22:22:47.977282: Current learning rate: 0.00931 +2024-09-04 22:26:47.847577: train_loss -0.6898 +2024-09-04 22:26:47.864066: val_loss -0.7124 +2024-09-04 22:26:47.864958: Pseudo dice [0.9484, 0.9432, 0.9589, 0.9282, 0.8329, 0.9501, 0.9895] +2024-09-04 22:26:47.865689: Epoch time: 239.88 s +2024-09-04 22:26:50.174945: +2024-09-04 22:26:50.175982: Epoch 77 +2024-09-04 22:26:50.176682: Current learning rate: 0.0093 +2024-09-04 22:31:09.523880: train_loss -0.6828 +2024-09-04 22:31:09.536642: val_loss -0.6519 +2024-09-04 22:31:09.538152: Pseudo dice [0.9513, 0.9479, 0.9668, 0.9249, 0.8171, 0.9533, 0.9854] +2024-09-04 22:31:09.541155: Epoch time: 259.35 s +2024-09-04 22:31:12.396502: +2024-09-04 22:31:12.397225: Epoch 78 +2024-09-04 22:31:12.397753: Current learning rate: 0.0093 +2024-09-04 22:35:29.966779: train_loss -0.6801 +2024-09-04 22:35:29.968650: val_loss -0.7167 +2024-09-04 22:35:29.970069: Pseudo dice [0.9482, 0.942, 0.9636, 0.9216, 0.8306, 0.9548, 0.9858] +2024-09-04 22:35:29.971712: Epoch time: 257.58 s +2024-09-04 22:35:34.656581: +2024-09-04 22:35:34.657658: Epoch 79 +2024-09-04 22:35:34.658478: Current learning rate: 0.00929 +2024-09-04 22:39:57.109525: train_loss -0.6954 +2024-09-04 22:39:57.121617: val_loss -0.7091 +2024-09-04 22:39:57.122561: Pseudo dice [0.9517, 0.9446, 0.9679, 0.9371, 0.8324, 0.9521, 0.9887] +2024-09-04 22:39:57.123218: Epoch time: 262.46 s +2024-09-04 22:40:00.044216: +2024-09-04 22:40:00.045382: Epoch 80 +2024-09-04 22:40:00.046394: Current learning rate: 0.00928 +2024-09-04 22:44:14.023921: train_loss -0.6972 +2024-09-04 22:44:14.026736: val_loss -0.6821 +2024-09-04 22:44:14.028127: Pseudo dice [0.9526, 0.9464, 0.9691, 0.9264, 0.8366, 0.9417, 0.99] +2024-09-04 22:44:14.029264: Epoch time: 253.98 s +2024-09-04 22:44:14.030195: Yayy! New best EMA pseudo Dice: 0.9367 +2024-09-04 22:44:23.707848: +2024-09-04 22:44:23.718032: Epoch 81 +2024-09-04 22:44:23.729991: Current learning rate: 0.00927 +2024-09-04 22:48:29.182129: train_loss -0.6875 +2024-09-04 22:48:29.200653: val_loss -0.6828 +2024-09-04 22:48:29.201500: Pseudo dice [0.9501, 0.9482, 0.9666, 0.9371, 0.8301, 0.9528, 0.9884] +2024-09-04 22:48:29.202268: Epoch time: 245.48 s +2024-09-04 22:48:29.202973: Yayy! New best EMA pseudo Dice: 0.9369 +2024-09-04 22:48:37.127232: +2024-09-04 22:48:37.128145: Epoch 82 +2024-09-04 22:48:37.128758: Current learning rate: 0.00926 +2024-09-04 22:52:50.004459: train_loss -0.6945 +2024-09-04 22:52:50.006100: val_loss -0.718 +2024-09-04 22:52:50.006956: Pseudo dice [0.9514, 0.9449, 0.9712, 0.9282, 0.8455, 0.9581, 0.989] +2024-09-04 22:52:50.008106: Epoch time: 252.88 s +2024-09-04 22:52:50.009110: Yayy! New best EMA pseudo Dice: 0.9373 +2024-09-04 22:52:59.972758: +2024-09-04 22:53:00.046525: Epoch 83 +2024-09-04 22:53:00.054195: Current learning rate: 0.00925 +2024-09-04 22:57:15.776726: train_loss -0.7069 +2024-09-04 22:57:15.791271: val_loss -0.7351 +2024-09-04 22:57:15.792174: Pseudo dice [0.9503, 0.9439, 0.9707, 0.935, 0.8364, 0.9552, 0.9894] +2024-09-04 22:57:15.793025: Epoch time: 255.81 s +2024-09-04 22:57:15.793682: Yayy! New best EMA pseudo Dice: 0.9376 +2024-09-04 22:57:25.649785: +2024-09-04 22:57:25.651209: Epoch 84 +2024-09-04 22:57:25.653884: Current learning rate: 0.00924 +2024-09-04 23:01:40.298116: train_loss -0.6929 +2024-09-04 23:01:40.301648: val_loss -0.7187 +2024-09-04 23:01:40.304480: Pseudo dice [0.9544, 0.947, 0.973, 0.9346, 0.8121, 0.952, 0.9869] +2024-09-04 23:01:40.306956: Epoch time: 254.65 s +2024-09-04 23:01:43.097192: +2024-09-04 23:01:43.098095: Epoch 85 +2024-09-04 23:01:43.098809: Current learning rate: 0.00923 +2024-09-04 23:06:08.427094: train_loss -0.7052 +2024-09-04 23:06:08.444163: val_loss -0.6896 +2024-09-04 23:06:08.444925: Pseudo dice [0.9526, 0.9446, 0.9734, 0.9366, 0.8531, 0.9494, 0.989] +2024-09-04 23:06:08.445632: Epoch time: 265.33 s +2024-09-04 23:06:08.446461: Yayy! New best EMA pseudo Dice: 0.9381 +2024-09-04 23:06:17.796608: +2024-09-04 23:06:17.797472: Epoch 86 +2024-09-04 23:06:17.798699: Current learning rate: 0.00922 +2024-09-04 23:10:41.472565: train_loss -0.6878 +2024-09-04 23:10:41.488036: val_loss -0.7033 +2024-09-04 23:10:41.488767: Pseudo dice [0.952, 0.946, 0.9682, 0.9274, 0.8217, 0.941, 0.9857] +2024-09-04 23:10:41.489495: Epoch time: 263.68 s +2024-09-04 23:10:43.818200: +2024-09-04 23:10:43.827900: Epoch 87 +2024-09-04 23:10:43.828821: Current learning rate: 0.00921 +2024-09-04 23:14:54.028089: train_loss -0.6819 +2024-09-04 23:14:54.031744: val_loss -0.7149 +2024-09-04 23:14:54.032773: Pseudo dice [0.9453, 0.9402, 0.9623, 0.9253, 0.8499, 0.9512, 0.9909] +2024-09-04 23:14:54.033654: Epoch time: 250.21 s +2024-09-04 23:14:56.322498: +2024-09-04 23:14:56.323922: Epoch 88 +2024-09-04 23:14:56.325300: Current learning rate: 0.0092 +2024-09-04 23:19:13.433396: train_loss -0.6897 +2024-09-04 23:19:13.456160: val_loss -0.7167 +2024-09-04 23:19:13.457811: Pseudo dice [0.9541, 0.9463, 0.9746, 0.9344, 0.8457, 0.9457, 0.9879] +2024-09-04 23:19:13.458982: Epoch time: 257.12 s +2024-09-04 23:19:13.459943: Yayy! New best EMA pseudo Dice: 0.9381 +2024-09-04 23:19:22.750943: +2024-09-04 23:19:22.752031: Epoch 89 +2024-09-04 23:19:22.752670: Current learning rate: 0.0092 +2024-09-04 23:23:50.957062: train_loss -0.6989 +2024-09-04 23:23:50.971088: val_loss -0.7188 +2024-09-04 23:23:50.971911: Pseudo dice [0.9529, 0.9445, 0.9695, 0.9353, 0.8483, 0.9472, 0.9897] +2024-09-04 23:23:50.972682: Epoch time: 268.21 s +2024-09-04 23:23:50.973306: Yayy! New best EMA pseudo Dice: 0.9384 +2024-09-04 23:24:00.141209: +2024-09-04 23:24:00.142244: Epoch 90 +2024-09-04 23:24:00.143089: Current learning rate: 0.00919 +2024-09-04 23:28:27.507037: train_loss -0.6982 +2024-09-04 23:28:27.509148: val_loss -0.6985 +2024-09-04 23:28:27.509828: Pseudo dice [0.9503, 0.945, 0.9669, 0.9233, 0.8461, 0.9441, 0.9888] +2024-09-04 23:28:27.510515: Epoch time: 267.37 s +2024-09-04 23:28:29.847244: +2024-09-04 23:28:29.848187: Epoch 91 +2024-09-04 23:28:29.848879: Current learning rate: 0.00918 +2024-09-04 23:32:46.300975: train_loss -0.6691 +2024-09-04 23:32:46.331685: val_loss -0.6932 +2024-09-04 23:32:46.333248: Pseudo dice [0.9479, 0.9418, 0.9621, 0.9202, 0.8228, 0.9502, 0.988] +2024-09-04 23:32:46.335177: Epoch time: 256.46 s +2024-09-04 23:32:48.703559: +2024-09-04 23:32:48.722039: Epoch 92 +2024-09-04 23:32:48.723919: Current learning rate: 0.00917 +2024-09-04 23:37:17.209241: train_loss -0.6872 +2024-09-04 23:37:17.216820: val_loss -0.7223 +2024-09-04 23:37:17.218481: Pseudo dice [0.953, 0.9454, 0.9714, 0.9267, 0.807, 0.9535, 0.9866] +2024-09-04 23:37:17.219991: Epoch time: 268.51 s +2024-09-04 23:37:20.689582: +2024-09-04 23:37:20.690583: Epoch 93 +2024-09-04 23:37:20.691426: Current learning rate: 0.00916 +2024-09-04 23:42:03.504081: train_loss -0.6926 +2024-09-04 23:42:03.520296: val_loss -0.6847 +2024-09-04 23:42:03.521368: Pseudo dice [0.9547, 0.9463, 0.9741, 0.9348, 0.799, 0.9472, 0.9857] +2024-09-04 23:42:03.522537: Epoch time: 282.82 s +2024-09-04 23:42:06.039717: +2024-09-04 23:42:06.041162: Epoch 94 +2024-09-04 23:42:06.042576: Current learning rate: 0.00915 +2024-09-04 23:46:33.436576: train_loss -0.7002 +2024-09-04 23:46:33.459337: val_loss -0.706 +2024-09-04 23:46:33.460208: Pseudo dice [0.9552, 0.9486, 0.9723, 0.9304, 0.8402, 0.9556, 0.9893] +2024-09-04 23:46:33.461219: Epoch time: 267.4 s +2024-09-04 23:46:37.356631: +2024-09-04 23:46:37.358516: Epoch 95 +2024-09-04 23:46:37.370972: Current learning rate: 0.00914 +2024-09-04 23:50:55.259990: train_loss -0.7101 +2024-09-04 23:50:55.265409: val_loss -0.6864 +2024-09-04 23:50:55.268912: Pseudo dice [0.9553, 0.9518, 0.9718, 0.9362, 0.8462, 0.9515, 0.9888] +2024-09-04 23:50:55.269899: Epoch time: 257.91 s +2024-09-04 23:50:57.930534: +2024-09-04 23:50:57.931463: Epoch 96 +2024-09-04 23:50:57.932249: Current learning rate: 0.00913 +2024-09-04 23:55:30.311732: train_loss -0.7124 +2024-09-04 23:55:30.335750: val_loss -0.7031 +2024-09-04 23:55:30.336497: Pseudo dice [0.9528, 0.9468, 0.9688, 0.9389, 0.8367, 0.9551, 0.9898] +2024-09-04 23:55:30.337159: Epoch time: 272.39 s +2024-09-04 23:55:30.337728: Yayy! New best EMA pseudo Dice: 0.9385 +2024-09-04 23:55:38.341117: +2024-09-04 23:55:38.341917: Epoch 97 +2024-09-04 23:55:38.342511: Current learning rate: 0.00912 +2024-09-04 23:59:55.115672: train_loss -0.6986 +2024-09-04 23:59:55.132287: val_loss -0.7199 +2024-09-04 23:59:55.133202: Pseudo dice [0.9547, 0.9472, 0.9689, 0.9413, 0.8539, 0.9518, 0.9912] +2024-09-04 23:59:55.133970: Epoch time: 256.78 s +2024-09-04 23:59:55.134654: Yayy! New best EMA pseudo Dice: 0.9391 +2024-09-05 00:00:03.198381: +2024-09-05 00:00:03.199619: Epoch 98 +2024-09-05 00:00:03.200385: Current learning rate: 0.00911 +2024-09-05 00:04:25.537587: train_loss -0.7184 +2024-09-05 00:04:25.554356: val_loss -0.7325 +2024-09-05 00:04:25.555315: Pseudo dice [0.9538, 0.9485, 0.9715, 0.9381, 0.842, 0.9414, 0.9893] +2024-09-05 00:04:25.556394: Epoch time: 262.34 s +2024-09-05 00:04:25.557052: Yayy! New best EMA pseudo Dice: 0.9392 +2024-09-05 00:04:34.388286: +2024-09-05 00:04:34.389084: Epoch 99 +2024-09-05 00:04:34.389697: Current learning rate: 0.0091 +2024-09-05 00:08:29.603572: train_loss -0.6878 +2024-09-05 00:08:29.606678: val_loss -0.6965 +2024-09-05 00:08:29.614797: Pseudo dice [0.944, 0.9454, 0.9638, 0.9266, 0.8405, 0.9492, 0.9871] +2024-09-05 00:08:29.615748: Epoch time: 235.22 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_00_32_01.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_00_32_01.txt new file mode 100644 index 0000000000000000000000000000000000000000..20b7564092297729ac1f40141592ee0cdcd49c26 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_00_32_01.txt @@ -0,0 +1,752 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 00:32:04.737633: Using torch.compile... +2024-09-05 00:32:14.524666: do_dummy_2d_data_aug: False +2024-09-05 00:32:14.530713: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 00:32:14.532344: The split file contains 5 splits. +2024-09-05 00:32:14.533180: Desired fold for training: 3 +2024-09-05 00:32:14.534012: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 00:32:25.234962: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 00:32:26.061375: +2024-09-05 00:32:26.063843: Epoch 100 +2024-09-05 00:32:26.066390: Current learning rate: 0.0091 +2024-09-05 00:50:42.964129: train_loss -0.6894 +2024-09-05 00:50:42.965650: val_loss -0.7197 +2024-09-05 00:50:42.966489: Pseudo dice [0.9503, 0.9464, 0.97, 0.9281, 0.8446, 0.9497, 0.9874] +2024-09-05 00:50:42.967426: Epoch time: 1096.91 s +2024-09-05 00:50:44.909880: +2024-09-05 00:50:44.910898: Epoch 101 +2024-09-05 00:50:44.911732: Current learning rate: 0.00909 +2024-09-05 00:54:55.290215: train_loss -0.7014 +2024-09-05 00:54:55.307807: val_loss -0.7039 +2024-09-05 00:54:55.309961: Pseudo dice [0.9515, 0.9449, 0.969, 0.938, 0.8345, 0.9505, 0.9902] +2024-09-05 00:54:55.311463: Epoch time: 250.38 s +2024-09-05 00:54:57.691016: +2024-09-05 00:54:57.692549: Epoch 102 +2024-09-05 00:54:57.693680: Current learning rate: 0.00908 +2024-09-05 00:58:50.419183: train_loss -0.6926 +2024-09-05 00:58:50.431120: val_loss -0.7339 +2024-09-05 00:58:50.432163: Pseudo dice [0.9529, 0.9482, 0.9657, 0.9313, 0.8437, 0.953, 0.9887] +2024-09-05 00:58:50.433100: Epoch time: 232.73 s +2024-09-05 00:58:50.433961: Yayy! New best EMA pseudo Dice: 0.9392 +2024-09-05 00:59:01.402260: +2024-09-05 00:59:01.403457: Epoch 103 +2024-09-05 00:59:01.404500: Current learning rate: 0.00907 +2024-09-05 01:03:18.362316: train_loss -0.7051 +2024-09-05 01:03:18.364069: val_loss -0.7143 +2024-09-05 01:03:18.365002: Pseudo dice [0.9499, 0.946, 0.9688, 0.9303, 0.8467, 0.9528, 0.9874] +2024-09-05 01:03:18.365881: Epoch time: 256.96 s +2024-09-05 01:03:18.366764: Yayy! New best EMA pseudo Dice: 0.9393 +2024-09-05 01:03:26.503870: +2024-09-05 01:03:26.504935: Epoch 104 +2024-09-05 01:03:26.505780: Current learning rate: 0.00906 +2024-09-05 01:07:40.479578: train_loss -0.701 +2024-09-05 01:07:40.505505: val_loss -0.6926 +2024-09-05 01:07:40.506530: Pseudo dice [0.9515, 0.9471, 0.9665, 0.9375, 0.8533, 0.9545, 0.9892] +2024-09-05 01:07:40.507482: Epoch time: 253.98 s +2024-09-05 01:07:40.508354: Yayy! New best EMA pseudo Dice: 0.9397 +2024-09-05 01:07:49.349661: +2024-09-05 01:07:49.350870: Epoch 105 +2024-09-05 01:07:49.351780: Current learning rate: 0.00905 +2024-09-05 01:11:48.354604: train_loss -0.7048 +2024-09-05 01:11:48.372663: val_loss -0.7463 +2024-09-05 01:11:48.374629: Pseudo dice [0.9559, 0.9484, 0.9732, 0.9255, 0.8463, 0.9585, 0.9899] +2024-09-05 01:11:48.376117: Epoch time: 239.01 s +2024-09-05 01:11:48.377101: Yayy! New best EMA pseudo Dice: 0.94 +2024-09-05 01:11:56.893735: +2024-09-05 01:11:56.895180: Epoch 106 +2024-09-05 01:11:56.896122: Current learning rate: 0.00904 +2024-09-05 01:16:12.511055: train_loss -0.688 +2024-09-05 01:16:12.520856: val_loss -0.6989 +2024-09-05 01:16:12.521714: Pseudo dice [0.9556, 0.9463, 0.9711, 0.9365, 0.8449, 0.9516, 0.987] +2024-09-05 01:16:12.522596: Epoch time: 255.62 s +2024-09-05 01:16:12.523356: Yayy! New best EMA pseudo Dice: 0.9402 +2024-09-05 01:16:21.722802: +2024-09-05 01:16:21.724107: Epoch 107 +2024-09-05 01:16:21.724973: Current learning rate: 0.00903 +2024-09-05 01:20:42.666491: train_loss -0.6941 +2024-09-05 01:20:42.670015: val_loss -0.7126 +2024-09-05 01:20:42.670845: Pseudo dice [0.9557, 0.9472, 0.975, 0.9412, 0.8484, 0.9373, 0.9892] +2024-09-05 01:20:42.671762: Epoch time: 260.95 s +2024-09-05 01:20:42.673120: Yayy! New best EMA pseudo Dice: 0.9403 +2024-09-05 01:20:51.760528: +2024-09-05 01:20:51.761765: Epoch 108 +2024-09-05 01:20:51.762640: Current learning rate: 0.00902 +2024-09-05 01:25:13.899114: train_loss -0.6955 +2024-09-05 01:25:13.900631: val_loss -0.7056 +2024-09-05 01:25:13.901534: Pseudo dice [0.953, 0.9476, 0.9686, 0.9302, 0.8473, 0.959, 0.9906] +2024-09-05 01:25:13.902351: Epoch time: 262.14 s +2024-09-05 01:25:13.903182: Yayy! New best EMA pseudo Dice: 0.9405 +2024-09-05 01:25:23.331016: +2024-09-05 01:25:23.332094: Epoch 109 +2024-09-05 01:25:23.332968: Current learning rate: 0.00901 +2024-09-05 01:29:22.060731: train_loss -0.6669 +2024-09-05 01:29:22.074932: val_loss -0.7045 +2024-09-05 01:29:22.075980: Pseudo dice [0.9485, 0.9429, 0.9673, 0.9238, 0.8439, 0.9552, 0.9892] +2024-09-05 01:29:22.076962: Epoch time: 238.73 s +2024-09-05 01:29:25.080310: +2024-09-05 01:29:25.082191: Epoch 110 +2024-09-05 01:29:25.084127: Current learning rate: 0.009 +2024-09-05 01:33:39.448640: train_loss -0.6888 +2024-09-05 01:33:39.464256: val_loss -0.6972 +2024-09-05 01:33:39.465262: Pseudo dice [0.9549, 0.9461, 0.9712, 0.927, 0.8248, 0.9438, 0.9859] +2024-09-05 01:33:39.466259: Epoch time: 254.38 s +2024-09-05 01:33:42.110479: +2024-09-05 01:33:42.112579: Epoch 111 +2024-09-05 01:33:42.114728: Current learning rate: 0.009 +2024-09-05 01:37:57.410805: train_loss -0.6941 +2024-09-05 01:37:57.418841: val_loss -0.6857 +2024-09-05 01:37:57.419891: Pseudo dice [0.9506, 0.9478, 0.9697, 0.9274, 0.8271, 0.957, 0.9868] +2024-09-05 01:37:57.421396: Epoch time: 255.3 s +2024-09-05 01:37:59.593336: +2024-09-05 01:37:59.594685: Epoch 112 +2024-09-05 01:37:59.595567: Current learning rate: 0.00899 +2024-09-05 01:42:31.277701: train_loss -0.7185 +2024-09-05 01:42:31.280386: val_loss -0.7439 +2024-09-05 01:42:31.281376: Pseudo dice [0.9584, 0.951, 0.9749, 0.9367, 0.8547, 0.9575, 0.9905] +2024-09-05 01:42:31.282326: Epoch time: 271.69 s +2024-09-05 01:42:34.264177: +2024-09-05 01:42:34.265558: Epoch 113 +2024-09-05 01:42:34.266661: Current learning rate: 0.00898 +2024-09-05 01:46:51.772310: train_loss -0.6961 +2024-09-05 01:46:51.774926: val_loss -0.7482 +2024-09-05 01:46:51.775910: Pseudo dice [0.944, 0.9306, 0.9687, 0.9294, 0.8462, 0.9462, 0.9885] +2024-09-05 01:46:51.776880: Epoch time: 257.51 s +2024-09-05 01:46:54.813930: +2024-09-05 01:46:54.815081: Epoch 114 +2024-09-05 01:46:54.815991: Current learning rate: 0.00897 +2024-09-05 01:51:32.883656: train_loss -0.6912 +2024-09-05 01:51:32.896310: val_loss -0.7377 +2024-09-05 01:51:32.897424: Pseudo dice [0.9523, 0.9431, 0.9704, 0.9376, 0.8488, 0.9509, 0.9898] +2024-09-05 01:51:32.898256: Epoch time: 278.07 s +2024-09-05 01:51:35.950407: +2024-09-05 01:51:35.951640: Epoch 115 +2024-09-05 01:51:35.952515: Current learning rate: 0.00896 +2024-09-05 01:55:57.277817: train_loss -0.6705 +2024-09-05 01:55:57.280737: val_loss -0.739 +2024-09-05 01:55:57.281644: Pseudo dice [0.9534, 0.945, 0.9685, 0.9357, 0.8506, 0.948, 0.9891] +2024-09-05 01:55:57.282561: Epoch time: 261.33 s +2024-09-05 01:56:00.311963: +2024-09-05 01:56:00.325589: Epoch 116 +2024-09-05 01:56:00.329534: Current learning rate: 0.00895 +2024-09-05 02:00:29.686266: train_loss -0.6848 +2024-09-05 02:00:29.756794: val_loss -0.6912 +2024-09-05 02:00:29.809706: Pseudo dice [0.9513, 0.9456, 0.968, 0.9293, 0.8303, 0.9528, 0.9853] +2024-09-05 02:00:29.822289: Epoch time: 269.38 s +2024-09-05 02:00:32.476383: +2024-09-05 02:00:32.527091: Epoch 117 +2024-09-05 02:00:32.580082: Current learning rate: 0.00894 +2024-09-05 02:04:59.657811: train_loss -0.7098 +2024-09-05 02:04:59.680290: val_loss -0.7408 +2024-09-05 02:04:59.681255: Pseudo dice [0.9522, 0.949, 0.9674, 0.9306, 0.8278, 0.9589, 0.9878] +2024-09-05 02:04:59.682291: Epoch time: 267.19 s +2024-09-05 02:05:02.378620: +2024-09-05 02:05:02.379803: Epoch 118 +2024-09-05 02:05:02.380771: Current learning rate: 0.00893 +2024-09-05 02:09:40.655009: train_loss -0.7204 +2024-09-05 02:09:40.671284: val_loss -0.731 +2024-09-05 02:09:40.672277: Pseudo dice [0.9546, 0.9494, 0.9719, 0.9377, 0.8574, 0.9577, 0.9891] +2024-09-05 02:09:40.673218: Epoch time: 278.28 s +2024-09-05 02:09:43.105549: +2024-09-05 02:09:43.114479: Epoch 119 +2024-09-05 02:09:43.115505: Current learning rate: 0.00892 +2024-09-05 02:13:57.715369: train_loss -0.7193 +2024-09-05 02:13:57.732707: val_loss -0.6986 +2024-09-05 02:13:57.734994: Pseudo dice [0.953, 0.9528, 0.9697, 0.9339, 0.8558, 0.9602, 0.9908] +2024-09-05 02:13:57.737444: Epoch time: 254.61 s +2024-09-05 02:13:57.739531: Yayy! New best EMA pseudo Dice: 0.9409 +2024-09-05 02:14:06.140575: +2024-09-05 02:14:06.141669: Epoch 120 +2024-09-05 02:14:06.142521: Current learning rate: 0.00891 +2024-09-05 02:18:11.692790: train_loss -0.6943 +2024-09-05 02:18:11.707628: val_loss -0.6952 +2024-09-05 02:18:11.708881: Pseudo dice [0.9503, 0.942, 0.9651, 0.9166, 0.8298, 0.9505, 0.9878] +2024-09-05 02:18:11.710115: Epoch time: 245.56 s +2024-09-05 02:18:14.799227: +2024-09-05 02:18:14.881977: Epoch 121 +2024-09-05 02:18:14.891094: Current learning rate: 0.0089 +2024-09-05 02:22:42.338830: train_loss -0.6858 +2024-09-05 02:22:42.351327: val_loss -0.7114 +2024-09-05 02:22:42.352734: Pseudo dice [0.9565, 0.951, 0.9747, 0.9378, 0.852, 0.9568, 0.9893] +2024-09-05 02:22:42.354179: Epoch time: 267.54 s +2024-09-05 02:22:44.850996: +2024-09-05 02:22:44.852422: Epoch 122 +2024-09-05 02:22:44.854017: Current learning rate: 0.00889 +2024-09-05 02:27:13.823228: train_loss -0.7093 +2024-09-05 02:27:13.919679: val_loss -0.7117 +2024-09-05 02:27:13.932000: Pseudo dice [0.9541, 0.9504, 0.9706, 0.9329, 0.8617, 0.9585, 0.9909] +2024-09-05 02:27:13.978411: Epoch time: 268.98 s +2024-09-05 02:27:14.091281: Yayy! New best EMA pseudo Dice: 0.9413 +2024-09-05 02:27:22.908637: +2024-09-05 02:27:22.910384: Epoch 123 +2024-09-05 02:27:22.911902: Current learning rate: 0.00889 +2024-09-05 02:31:34.497513: train_loss -0.7157 +2024-09-05 02:31:34.507157: val_loss -0.7158 +2024-09-05 02:31:34.508211: Pseudo dice [0.954, 0.9479, 0.9702, 0.929, 0.8547, 0.9642, 0.9894] +2024-09-05 02:31:34.509311: Epoch time: 251.59 s +2024-09-05 02:31:34.510197: Yayy! New best EMA pseudo Dice: 0.9416 +2024-09-05 02:31:42.401794: +2024-09-05 02:31:42.402802: Epoch 124 +2024-09-05 02:31:42.403664: Current learning rate: 0.00888 +2024-09-05 02:36:06.363893: train_loss -0.7114 +2024-09-05 02:36:06.374972: val_loss -0.7235 +2024-09-05 02:36:06.376150: Pseudo dice [0.9568, 0.9484, 0.9713, 0.9377, 0.8623, 0.9556, 0.9908] +2024-09-05 02:36:06.377146: Epoch time: 263.97 s +2024-09-05 02:36:06.378007: Yayy! New best EMA pseudo Dice: 0.942 +2024-09-05 02:36:15.757503: +2024-09-05 02:36:15.758631: Epoch 125 +2024-09-05 02:36:15.759510: Current learning rate: 0.00887 +2024-09-05 02:40:36.519581: train_loss -0.7159 +2024-09-05 02:40:36.529682: val_loss -0.7597 +2024-09-05 02:40:36.530639: Pseudo dice [0.9557, 0.9491, 0.9732, 0.9255, 0.8365, 0.9539, 0.9881] +2024-09-05 02:40:36.531555: Epoch time: 260.77 s +2024-09-05 02:40:39.521918: +2024-09-05 02:40:39.523154: Epoch 126 +2024-09-05 02:40:39.524095: Current learning rate: 0.00886 +2024-09-05 02:45:11.508166: train_loss -0.7236 +2024-09-05 02:45:11.510277: val_loss -0.7056 +2024-09-05 02:45:11.511652: Pseudo dice [0.9565, 0.9509, 0.9681, 0.9375, 0.8507, 0.9501, 0.9899] +2024-09-05 02:45:11.512792: Epoch time: 271.99 s +2024-09-05 02:45:13.819630: +2024-09-05 02:45:13.820816: Epoch 127 +2024-09-05 02:45:13.821773: Current learning rate: 0.00885 +2024-09-05 02:49:30.799123: train_loss -0.69 +2024-09-05 02:49:30.811210: val_loss -0.6915 +2024-09-05 02:49:30.812393: Pseudo dice [0.9464, 0.9435, 0.9649, 0.9219, 0.8396, 0.9565, 0.9907] +2024-09-05 02:49:30.813319: Epoch time: 256.98 s +2024-09-05 02:49:34.347783: +2024-09-05 02:49:34.353774: Epoch 128 +2024-09-05 02:49:34.354813: Current learning rate: 0.00884 +2024-09-05 02:53:44.126115: train_loss -0.701 +2024-09-05 02:53:44.143465: val_loss -0.7057 +2024-09-05 02:53:44.144597: Pseudo dice [0.9584, 0.9479, 0.9744, 0.9363, 0.8429, 0.9534, 0.9896] +2024-09-05 02:53:44.145539: Epoch time: 249.78 s +2024-09-05 02:53:47.281876: +2024-09-05 02:53:47.283100: Epoch 129 +2024-09-05 02:53:47.284071: Current learning rate: 0.00883 +2024-09-05 02:58:02.794899: train_loss -0.6904 +2024-09-05 02:58:02.796929: val_loss -0.6982 +2024-09-05 02:58:02.797891: Pseudo dice [0.9509, 0.9487, 0.9668, 0.9355, 0.8517, 0.955, 0.9906] +2024-09-05 02:58:02.798827: Epoch time: 255.52 s +2024-09-05 02:58:05.287039: +2024-09-05 02:58:05.302618: Epoch 130 +2024-09-05 02:58:05.303630: Current learning rate: 0.00882 +2024-09-05 03:02:20.963479: train_loss -0.706 +2024-09-05 03:02:20.979269: val_loss -0.7267 +2024-09-05 03:02:20.980178: Pseudo dice [0.9513, 0.9458, 0.9692, 0.934, 0.8374, 0.9563, 0.9873] +2024-09-05 03:02:20.981078: Epoch time: 255.68 s +2024-09-05 03:02:23.836992: +2024-09-05 03:02:23.838124: Epoch 131 +2024-09-05 03:02:23.839061: Current learning rate: 0.00881 +2024-09-05 03:06:29.655962: train_loss -0.7091 +2024-09-05 03:06:29.674975: val_loss -0.7164 +2024-09-05 03:06:29.676214: Pseudo dice [0.9572, 0.9516, 0.9726, 0.9415, 0.8461, 0.9589, 0.9917] +2024-09-05 03:06:29.677418: Epoch time: 245.82 s +2024-09-05 03:06:29.678537: Yayy! New best EMA pseudo Dice: 0.9421 +2024-09-05 03:06:38.042753: +2024-09-05 03:06:38.043886: Epoch 132 +2024-09-05 03:06:38.044671: Current learning rate: 0.0088 +2024-09-05 03:10:57.144172: train_loss -0.6979 +2024-09-05 03:10:57.146039: val_loss -0.6854 +2024-09-05 03:10:57.147590: Pseudo dice [0.9485, 0.9422, 0.9501, 0.9155, 0.8139, 0.9491, 0.9849] +2024-09-05 03:10:57.149827: Epoch time: 259.1 s +2024-09-05 03:10:59.319288: +2024-09-05 03:10:59.322205: Epoch 133 +2024-09-05 03:10:59.324216: Current learning rate: 0.00879 +2024-09-05 03:14:56.846823: train_loss -0.6879 +2024-09-05 03:14:56.848563: val_loss -0.7397 +2024-09-05 03:14:56.849488: Pseudo dice [0.9561, 0.9507, 0.9711, 0.9342, 0.8525, 0.9583, 0.99] +2024-09-05 03:14:56.850969: Epoch time: 237.53 s +2024-09-05 03:14:59.627749: +2024-09-05 03:14:59.630339: Epoch 134 +2024-09-05 03:14:59.631724: Current learning rate: 0.00879 +2024-09-05 03:19:30.509516: train_loss -0.7253 +2024-09-05 03:19:30.511147: val_loss -0.7396 +2024-09-05 03:19:30.512141: Pseudo dice [0.958, 0.9501, 0.9763, 0.9346, 0.8496, 0.9582, 0.9874] +2024-09-05 03:19:30.513063: Epoch time: 270.89 s +2024-09-05 03:19:33.009837: +2024-09-05 03:19:33.010917: Epoch 135 +2024-09-05 03:19:33.011839: Current learning rate: 0.00878 +2024-09-05 03:23:50.092019: train_loss -0.7227 +2024-09-05 03:23:50.107468: val_loss -0.6998 +2024-09-05 03:23:50.108687: Pseudo dice [0.9527, 0.9499, 0.9717, 0.9339, 0.8556, 0.9558, 0.9902] +2024-09-05 03:23:50.109766: Epoch time: 257.09 s +2024-09-05 03:23:53.203652: +2024-09-05 03:23:53.204793: Epoch 136 +2024-09-05 03:23:53.205958: Current learning rate: 0.00877 +2024-09-05 03:28:15.063390: train_loss -0.6992 +2024-09-05 03:28:15.075374: val_loss -0.7372 +2024-09-05 03:28:15.077598: Pseudo dice [0.9525, 0.9479, 0.9662, 0.9412, 0.8625, 0.9542, 0.9849] +2024-09-05 03:28:15.078864: Epoch time: 261.86 s +2024-09-05 03:28:18.708111: +2024-09-05 03:28:18.709409: Epoch 137 +2024-09-05 03:28:18.710353: Current learning rate: 0.00876 +2024-09-05 03:32:25.558543: train_loss -0.699 +2024-09-05 03:32:25.577600: val_loss -0.7076 +2024-09-05 03:32:25.578730: Pseudo dice [0.9571, 0.9502, 0.975, 0.9403, 0.8655, 0.954, 0.9892] +2024-09-05 03:32:25.579823: Epoch time: 246.86 s +2024-09-05 03:32:25.580884: Yayy! New best EMA pseudo Dice: 0.9426 +2024-09-05 03:32:35.210725: +2024-09-05 03:32:35.211921: Epoch 138 +2024-09-05 03:32:35.212793: Current learning rate: 0.00875 +2024-09-05 03:37:02.834341: train_loss -0.7012 +2024-09-05 03:37:02.837406: val_loss -0.7338 +2024-09-05 03:37:02.838377: Pseudo dice [0.9539, 0.9465, 0.9705, 0.9383, 0.8492, 0.9602, 0.9903] +2024-09-05 03:37:02.839352: Epoch time: 267.63 s +2024-09-05 03:37:02.840161: Yayy! New best EMA pseudo Dice: 0.9427 +2024-09-05 03:37:11.420882: +2024-09-05 03:37:11.422185: Epoch 139 +2024-09-05 03:37:11.423187: Current learning rate: 0.00874 +2024-09-05 03:41:28.176380: train_loss -0.6978 +2024-09-05 03:41:28.191470: val_loss -0.7264 +2024-09-05 03:41:28.192834: Pseudo dice [0.9555, 0.9502, 0.9712, 0.9324, 0.8505, 0.9613, 0.9906] +2024-09-05 03:41:28.195139: Epoch time: 256.76 s +2024-09-05 03:41:28.196377: Yayy! New best EMA pseudo Dice: 0.9429 +2024-09-05 03:41:36.263769: +2024-09-05 03:41:36.264986: Epoch 140 +2024-09-05 03:41:36.265888: Current learning rate: 0.00873 +2024-09-05 03:45:50.859130: train_loss -0.7005 +2024-09-05 03:45:50.875364: val_loss -0.7037 +2024-09-05 03:45:50.876449: Pseudo dice [0.951, 0.951, 0.9684, 0.9364, 0.8471, 0.9602, 0.989] +2024-09-05 03:45:50.877537: Epoch time: 254.6 s +2024-09-05 03:45:50.878993: Yayy! New best EMA pseudo Dice: 0.943 +2024-09-05 03:45:59.037252: +2024-09-05 03:45:59.038428: Epoch 141 +2024-09-05 03:45:59.039343: Current learning rate: 0.00872 +2024-09-05 03:50:12.661125: train_loss -0.719 +2024-09-05 03:50:12.663833: val_loss -0.7277 +2024-09-05 03:50:12.664834: Pseudo dice [0.9573, 0.9521, 0.9741, 0.9361, 0.863, 0.9591, 0.9915] +2024-09-05 03:50:12.665646: Epoch time: 253.63 s +2024-09-05 03:50:12.666553: Yayy! New best EMA pseudo Dice: 0.9434 +2024-09-05 03:50:20.523494: +2024-09-05 03:50:20.524882: Epoch 142 +2024-09-05 03:50:20.525703: Current learning rate: 0.00871 +2024-09-05 03:54:43.598372: train_loss -0.7002 +2024-09-05 03:54:43.600509: val_loss -0.7421 +2024-09-05 03:54:43.601752: Pseudo dice [0.9542, 0.9496, 0.9734, 0.9323, 0.8661, 0.9586, 0.9894] +2024-09-05 03:54:43.602710: Epoch time: 263.08 s +2024-09-05 03:54:43.603716: Yayy! New best EMA pseudo Dice: 0.9437 +2024-09-05 03:54:53.211250: +2024-09-05 03:54:53.226723: Epoch 143 +2024-09-05 03:54:53.227951: Current learning rate: 0.0087 +2024-09-05 03:59:14.537504: train_loss -0.7059 +2024-09-05 03:59:14.551819: val_loss -0.7092 +2024-09-05 03:59:14.553957: Pseudo dice [0.9533, 0.9481, 0.9705, 0.9239, 0.8449, 0.9458, 0.9884] +2024-09-05 03:59:14.555687: Epoch time: 261.33 s +2024-09-05 03:59:18.143659: +2024-09-05 03:59:18.145186: Epoch 144 +2024-09-05 03:59:18.147053: Current learning rate: 0.00869 +2024-09-05 04:03:38.936794: train_loss -0.7008 +2024-09-05 04:03:38.960295: val_loss -0.7349 +2024-09-05 04:03:38.961246: Pseudo dice [0.9538, 0.95, 0.9701, 0.9357, 0.8534, 0.9531, 0.9909] +2024-09-05 04:03:38.962103: Epoch time: 260.8 s +2024-09-05 04:03:42.373221: +2024-09-05 04:03:42.374652: Epoch 145 +2024-09-05 04:03:42.376071: Current learning rate: 0.00868 +2024-09-05 04:07:59.470274: train_loss -0.7083 +2024-09-05 04:07:59.487924: val_loss -0.7081 +2024-09-05 04:07:59.488895: Pseudo dice [0.9565, 0.9494, 0.971, 0.9381, 0.8425, 0.9618, 0.987] +2024-09-05 04:07:59.489922: Epoch time: 257.1 s +2024-09-05 04:08:01.972825: +2024-09-05 04:08:01.973879: Epoch 146 +2024-09-05 04:08:01.974710: Current learning rate: 0.00868 +2024-09-05 04:12:27.588450: train_loss -0.6846 +2024-09-05 04:12:27.616250: val_loss -0.7097 +2024-09-05 04:12:27.617211: Pseudo dice [0.9463, 0.9437, 0.9602, 0.9283, 0.8466, 0.9548, 0.9884] +2024-09-05 04:12:27.618138: Epoch time: 265.62 s +2024-09-05 04:12:30.052229: +2024-09-05 04:12:30.053351: Epoch 147 +2024-09-05 04:12:30.054276: Current learning rate: 0.00867 +2024-09-05 04:16:46.404119: train_loss -0.7033 +2024-09-05 04:16:46.427309: val_loss -0.6824 +2024-09-05 04:16:46.428228: Pseudo dice [0.9488, 0.9469, 0.9638, 0.9298, 0.8436, 0.9557, 0.9883] +2024-09-05 04:16:46.429149: Epoch time: 256.37 s +2024-09-05 04:16:48.689217: +2024-09-05 04:16:48.695122: Epoch 148 +2024-09-05 04:16:48.696035: Current learning rate: 0.00866 +2024-09-05 04:21:12.583464: train_loss -0.6927 +2024-09-05 04:21:12.585612: val_loss -0.7623 +2024-09-05 04:21:12.586683: Pseudo dice [0.956, 0.95, 0.9753, 0.9395, 0.8536, 0.9584, 0.9894] +2024-09-05 04:21:12.587654: Epoch time: 263.9 s +2024-09-05 04:21:14.805094: +2024-09-05 04:21:14.822196: Epoch 149 +2024-09-05 04:21:14.823079: Current learning rate: 0.00865 +2024-09-05 04:25:31.789766: train_loss -0.7116 +2024-09-05 04:25:31.795498: val_loss -0.7173 +2024-09-05 04:25:31.796552: Pseudo dice [0.9504, 0.9466, 0.9688, 0.9369, 0.8347, 0.9557, 0.9862] +2024-09-05 04:25:31.797399: Epoch time: 256.99 s +2024-09-05 04:25:41.040540: +2024-09-05 04:25:41.042338: Epoch 150 +2024-09-05 04:25:41.043359: Current learning rate: 0.00864 +2024-09-05 04:30:06.658885: train_loss -0.7229 +2024-09-05 04:30:06.669951: val_loss -0.7183 +2024-09-05 04:30:06.671153: Pseudo dice [0.9537, 0.9508, 0.9714, 0.9377, 0.8614, 0.9579, 0.9894] +2024-09-05 04:30:06.672189: Epoch time: 265.62 s +2024-09-05 04:30:08.974431: +2024-09-05 04:30:08.975631: Epoch 151 +2024-09-05 04:30:08.976858: Current learning rate: 0.00863 +2024-09-05 04:34:46.973268: train_loss -0.7014 +2024-09-05 04:34:46.986053: val_loss -0.7635 +2024-09-05 04:34:46.987316: Pseudo dice [0.9551, 0.9469, 0.9715, 0.9331, 0.863, 0.9624, 0.9896] +2024-09-05 04:34:46.988611: Epoch time: 278.0 s +2024-09-05 04:34:50.085971: +2024-09-05 04:34:50.087071: Epoch 152 +2024-09-05 04:34:50.088084: Current learning rate: 0.00862 +2024-09-05 04:39:02.317935: train_loss -0.7161 +2024-09-05 04:39:02.330433: val_loss -0.7067 +2024-09-05 04:39:02.346134: Pseudo dice [0.956, 0.9505, 0.9701, 0.9341, 0.8557, 0.9595, 0.9859] +2024-09-05 04:39:02.347264: Epoch time: 252.24 s +2024-09-05 04:39:05.907188: +2024-09-05 04:39:05.908405: Epoch 153 +2024-09-05 04:39:05.909261: Current learning rate: 0.00861 +2024-09-05 04:43:33.052056: train_loss -0.7109 +2024-09-05 04:43:33.078204: val_loss -0.7299 +2024-09-05 04:43:33.079211: Pseudo dice [0.9563, 0.9517, 0.9728, 0.9402, 0.852, 0.9588, 0.9915] +2024-09-05 04:43:33.080151: Epoch time: 267.15 s +2024-09-05 04:43:35.672013: +2024-09-05 04:43:35.673752: Epoch 154 +2024-09-05 04:43:35.675156: Current learning rate: 0.0086 +2024-09-05 04:47:42.490052: train_loss -0.7063 +2024-09-05 04:47:42.493114: val_loss -0.7293 +2024-09-05 04:47:42.494372: Pseudo dice [0.9545, 0.9498, 0.9724, 0.9331, 0.8592, 0.9557, 0.9898] +2024-09-05 04:47:42.495412: Epoch time: 246.82 s +2024-09-05 04:47:42.496274: Yayy! New best EMA pseudo Dice: 0.9438 +2024-09-05 04:47:50.816730: +2024-09-05 04:47:50.817848: Epoch 155 +2024-09-05 04:47:50.818951: Current learning rate: 0.00859 +2024-09-05 04:52:01.672931: train_loss -0.7117 +2024-09-05 04:52:01.690099: val_loss -0.759 +2024-09-05 04:52:01.691287: Pseudo dice [0.9517, 0.9496, 0.9711, 0.9377, 0.8396, 0.9615, 0.9878] +2024-09-05 04:52:01.692393: Epoch time: 250.86 s +2024-09-05 04:52:05.080970: +2024-09-05 04:52:05.081934: Epoch 156 +2024-09-05 04:52:05.082742: Current learning rate: 0.00858 +2024-09-05 04:56:34.623919: train_loss -0.7353 +2024-09-05 04:56:34.626271: val_loss -0.7377 +2024-09-05 04:56:34.627153: Pseudo dice [0.9591, 0.9541, 0.9725, 0.9422, 0.8703, 0.9605, 0.9904] +2024-09-05 04:56:34.628017: Epoch time: 269.55 s +2024-09-05 04:56:34.628708: Yayy! New best EMA pseudo Dice: 0.9443 +2024-09-05 04:56:42.634789: +2024-09-05 04:56:42.635877: Epoch 157 +2024-09-05 04:56:42.636748: Current learning rate: 0.00858 +2024-09-05 05:01:01.042431: train_loss -0.7166 +2024-09-05 05:01:01.060829: val_loss -0.7041 +2024-09-05 05:01:01.061934: Pseudo dice [0.9559, 0.9526, 0.9724, 0.9333, 0.8404, 0.9656, 0.9891] +2024-09-05 05:01:01.063012: Epoch time: 258.41 s +2024-09-05 05:01:04.598361: +2024-09-05 05:01:04.599565: Epoch 158 +2024-09-05 05:01:04.600432: Current learning rate: 0.00857 +2024-09-05 05:05:28.627299: train_loss -0.726 +2024-09-05 05:05:28.629899: val_loss -0.7169 +2024-09-05 05:05:28.631229: Pseudo dice [0.9556, 0.9516, 0.9705, 0.9405, 0.8703, 0.9568, 0.9906] +2024-09-05 05:05:28.632504: Epoch time: 264.03 s +2024-09-05 05:05:28.634526: Yayy! New best EMA pseudo Dice: 0.9446 +2024-09-05 05:05:37.824832: +2024-09-05 05:05:37.826200: Epoch 159 +2024-09-05 05:05:37.827228: Current learning rate: 0.00856 +2024-09-05 05:09:59.621818: train_loss -0.713 +2024-09-05 05:09:59.624783: val_loss -0.7034 +2024-09-05 05:09:59.626001: Pseudo dice [0.9539, 0.9493, 0.9693, 0.9308, 0.8424, 0.956, 0.989] +2024-09-05 05:09:59.627151: Epoch time: 261.8 s +2024-09-05 05:10:02.226166: +2024-09-05 05:10:02.227368: Epoch 160 +2024-09-05 05:10:02.228325: Current learning rate: 0.00855 +2024-09-05 05:14:20.938295: train_loss -0.7214 +2024-09-05 05:14:20.942397: val_loss -0.7561 +2024-09-05 05:14:20.943935: Pseudo dice [0.9553, 0.9518, 0.9736, 0.9417, 0.8674, 0.9618, 0.991] +2024-09-05 05:14:20.945387: Epoch time: 258.72 s +2024-09-05 05:14:20.946586: Yayy! New best EMA pseudo Dice: 0.9448 +2024-09-05 05:14:29.645753: +2024-09-05 05:14:29.646905: Epoch 161 +2024-09-05 05:14:29.647727: Current learning rate: 0.00854 +2024-09-05 05:19:04.599942: train_loss -0.7221 +2024-09-05 05:19:04.603802: val_loss -0.7162 +2024-09-05 05:19:04.606049: Pseudo dice [0.9592, 0.953, 0.9737, 0.9436, 0.8527, 0.9523, 0.9899] +2024-09-05 05:19:04.607561: Epoch time: 274.96 s +2024-09-05 05:19:04.608902: Yayy! New best EMA pseudo Dice: 0.9449 +2024-09-05 05:19:11.805359: +2024-09-05 05:19:11.806424: Epoch 162 +2024-09-05 05:19:11.807312: Current learning rate: 0.00853 +2024-09-05 05:23:38.476896: train_loss -0.7216 +2024-09-05 05:23:38.492273: val_loss -0.7213 +2024-09-05 05:23:38.493320: Pseudo dice [0.9545, 0.9475, 0.9677, 0.9374, 0.8577, 0.9599, 0.9904] +2024-09-05 05:23:38.494277: Epoch time: 266.67 s +2024-09-05 05:23:38.495095: Yayy! New best EMA pseudo Dice: 0.945 +2024-09-05 05:23:46.754145: +2024-09-05 05:23:46.755627: Epoch 163 +2024-09-05 05:23:46.757015: Current learning rate: 0.00852 +2024-09-05 05:28:11.008788: train_loss -0.7072 +2024-09-05 05:28:11.024415: val_loss -0.7162 +2024-09-05 05:28:11.025545: Pseudo dice [0.9546, 0.9514, 0.9734, 0.9376, 0.8693, 0.9562, 0.9911] +2024-09-05 05:28:11.026850: Epoch time: 264.26 s +2024-09-05 05:28:11.027912: Yayy! New best EMA pseudo Dice: 0.9452 +2024-09-05 05:28:20.075975: +2024-09-05 05:28:20.077149: Epoch 164 +2024-09-05 05:28:20.077882: Current learning rate: 0.00851 +2024-09-05 05:32:33.012704: train_loss -0.7099 +2024-09-05 05:32:33.027576: val_loss -0.7225 +2024-09-05 05:32:33.028743: Pseudo dice [0.9497, 0.9504, 0.9675, 0.9368, 0.8461, 0.9585, 0.9898] +2024-09-05 05:32:33.030013: Epoch time: 252.94 s +2024-09-05 05:32:36.125699: +2024-09-05 05:32:36.127209: Epoch 165 +2024-09-05 05:32:36.128588: Current learning rate: 0.0085 +2024-09-05 05:37:13.899160: train_loss -0.7213 +2024-09-05 05:37:13.900954: val_loss -0.7315 +2024-09-05 05:37:13.901960: Pseudo dice [0.959, 0.9514, 0.9743, 0.9378, 0.8653, 0.9615, 0.9906] +2024-09-05 05:37:13.902854: Epoch time: 277.78 s +2024-09-05 05:37:13.903720: Yayy! New best EMA pseudo Dice: 0.9453 +2024-09-05 05:37:22.739503: +2024-09-05 05:37:22.740608: Epoch 166 +2024-09-05 05:37:22.741463: Current learning rate: 0.00849 +2024-09-05 05:41:44.448090: train_loss -0.7135 +2024-09-05 05:41:44.449909: val_loss -0.7601 +2024-09-05 05:41:44.450902: Pseudo dice [0.9598, 0.9527, 0.9762, 0.9412, 0.8451, 0.9651, 0.9883] +2024-09-05 05:41:44.451864: Epoch time: 261.71 s +2024-09-05 05:41:44.452852: Yayy! New best EMA pseudo Dice: 0.9455 +2024-09-05 05:41:54.075177: +2024-09-05 05:41:54.076552: Epoch 167 +2024-09-05 05:41:54.077548: Current learning rate: 0.00848 +2024-09-05 05:46:21.442508: train_loss -0.7183 +2024-09-05 05:46:21.444411: val_loss -0.7148 +2024-09-05 05:46:21.445466: Pseudo dice [0.9589, 0.9532, 0.975, 0.943, 0.8734, 0.961, 0.9905] +2024-09-05 05:46:21.455161: Epoch time: 267.37 s +2024-09-05 05:46:21.456959: Yayy! New best EMA pseudo Dice: 0.946 +2024-09-05 05:46:30.638743: +2024-09-05 05:46:30.641569: Epoch 168 +2024-09-05 05:46:30.644422: Current learning rate: 0.00847 +2024-09-05 05:50:35.286353: train_loss -0.7224 +2024-09-05 05:50:35.306207: val_loss -0.7084 +2024-09-05 05:50:35.308328: Pseudo dice [0.9597, 0.9528, 0.9752, 0.94, 0.8418, 0.9616, 0.9875] +2024-09-05 05:50:35.310007: Epoch time: 244.65 s +2024-09-05 05:50:38.110266: +2024-09-05 05:50:38.123136: Epoch 169 +2024-09-05 05:50:38.125003: Current learning rate: 0.00847 +2024-09-05 05:54:59.685416: train_loss -0.7114 +2024-09-05 05:54:59.703091: val_loss -0.7497 +2024-09-05 05:54:59.704175: Pseudo dice [0.9553, 0.9478, 0.9726, 0.9306, 0.854, 0.957, 0.9898] +2024-09-05 05:54:59.705198: Epoch time: 261.58 s +2024-09-05 05:55:03.316710: +2024-09-05 05:55:03.317940: Epoch 170 +2024-09-05 05:55:03.318860: Current learning rate: 0.00846 +2024-09-05 05:59:31.662762: train_loss -0.6882 +2024-09-05 05:59:31.664442: val_loss -0.7325 +2024-09-05 05:59:31.665397: Pseudo dice [0.9498, 0.9425, 0.9731, 0.9324, 0.8432, 0.958, 0.9914] +2024-09-05 05:59:31.666235: Epoch time: 268.35 s +2024-09-05 05:59:35.453443: +2024-09-05 05:59:35.454922: Epoch 171 +2024-09-05 05:59:35.455866: Current learning rate: 0.00845 +2024-09-05 06:03:51.192128: train_loss -0.706 +2024-09-05 06:03:51.210606: val_loss -0.714 +2024-09-05 06:03:51.212627: Pseudo dice [0.9546, 0.9534, 0.9683, 0.9347, 0.8545, 0.9627, 0.9904] +2024-09-05 06:03:51.214301: Epoch time: 255.74 s +2024-09-05 06:03:54.902364: +2024-09-05 06:03:54.904038: Epoch 172 +2024-09-05 06:03:54.905445: Current learning rate: 0.00844 +2024-09-05 06:08:03.610315: train_loss -0.7025 +2024-09-05 06:08:03.622474: val_loss -0.7201 +2024-09-05 06:08:03.623719: Pseudo dice [0.9553, 0.9474, 0.9755, 0.9335, 0.8322, 0.9609, 0.9872] +2024-09-05 06:08:03.624851: Epoch time: 248.71 s +2024-09-05 06:08:07.050744: +2024-09-05 06:08:07.062621: Epoch 173 +2024-09-05 06:08:07.063614: Current learning rate: 0.00843 +2024-09-05 06:12:20.812555: train_loss -0.7048 +2024-09-05 06:12:20.828326: val_loss -0.7337 +2024-09-05 06:12:20.829324: Pseudo dice [0.9569, 0.9533, 0.9732, 0.9349, 0.8459, 0.9629, 0.9892] +2024-09-05 06:12:20.830291: Epoch time: 253.77 s +2024-09-05 06:12:24.459067: +2024-09-05 06:12:24.470673: Epoch 174 +2024-09-05 06:12:24.471706: Current learning rate: 0.00842 +2024-09-05 06:16:40.852600: train_loss -0.716 +2024-09-05 06:16:40.867667: val_loss -0.7163 +2024-09-05 06:16:40.869250: Pseudo dice [0.9557, 0.9525, 0.9711, 0.9416, 0.8604, 0.9597, 0.9909] +2024-09-05 06:16:40.870682: Epoch time: 256.4 s +2024-09-05 06:16:43.361366: +2024-09-05 06:16:43.362901: Epoch 175 +2024-09-05 06:16:43.363907: Current learning rate: 0.00841 +2024-09-05 06:21:08.196949: train_loss -0.7049 +2024-09-05 06:21:08.199690: val_loss -0.7677 +2024-09-05 06:21:08.201015: Pseudo dice [0.9588, 0.9515, 0.9751, 0.9392, 0.8436, 0.9615, 0.9886] +2024-09-05 06:21:08.202682: Epoch time: 264.84 s +2024-09-05 06:21:10.668951: +2024-09-05 06:21:10.670624: Epoch 176 +2024-09-05 06:21:10.672210: Current learning rate: 0.0084 +2024-09-05 06:25:36.295459: train_loss -0.7019 +2024-09-05 06:25:36.298716: val_loss -0.7304 +2024-09-05 06:25:36.301466: Pseudo dice [0.9562, 0.9487, 0.975, 0.9246, 0.8301, 0.9573, 0.9877] +2024-09-05 06:25:36.303202: Epoch time: 265.63 s +2024-09-05 06:25:38.653702: +2024-09-05 06:25:38.655044: Epoch 177 +2024-09-05 06:25:38.658823: Current learning rate: 0.00839 +2024-09-05 06:30:01.358975: train_loss -0.7257 +2024-09-05 06:30:01.376111: val_loss -0.7235 +2024-09-05 06:30:01.377525: Pseudo dice [0.957, 0.9503, 0.9738, 0.9349, 0.8353, 0.9585, 0.9881] +2024-09-05 06:30:01.378992: Epoch time: 262.71 s +2024-09-05 06:30:04.082263: +2024-09-05 06:30:04.083513: Epoch 178 +2024-09-05 06:30:04.084473: Current learning rate: 0.00838 +2024-09-05 06:34:19.055333: train_loss -0.7243 +2024-09-05 06:34:19.095923: val_loss -0.7218 +2024-09-05 06:34:19.097609: Pseudo dice [0.9604, 0.954, 0.976, 0.9491, 0.857, 0.9592, 0.9905] +2024-09-05 06:34:19.098867: Epoch time: 254.98 s +2024-09-05 06:34:21.976804: +2024-09-05 06:34:21.979531: Epoch 179 +2024-09-05 06:34:21.980774: Current learning rate: 0.00837 +2024-09-05 06:38:46.434364: train_loss -0.7161 +2024-09-05 06:38:46.436371: val_loss -0.7427 +2024-09-05 06:38:46.437623: Pseudo dice [0.9544, 0.9524, 0.9724, 0.9447, 0.864, 0.9554, 0.9895] +2024-09-05 06:38:46.438748: Epoch time: 264.46 s +2024-09-05 06:38:48.744256: +2024-09-05 06:38:48.745418: Epoch 180 +2024-09-05 06:38:48.746357: Current learning rate: 0.00836 +2024-09-05 06:43:04.275208: train_loss -0.7176 +2024-09-05 06:43:04.282236: val_loss -0.7447 +2024-09-05 06:43:04.295800: Pseudo dice [0.9567, 0.9527, 0.9737, 0.9437, 0.8718, 0.9628, 0.9914] +2024-09-05 06:43:04.298043: Epoch time: 255.54 s +2024-09-05 06:43:06.803191: +2024-09-05 06:43:06.804430: Epoch 181 +2024-09-05 06:43:06.805676: Current learning rate: 0.00836 +2024-09-05 06:47:30.702392: train_loss -0.7336 +2024-09-05 06:47:30.719763: val_loss -0.685 +2024-09-05 06:47:30.721020: Pseudo dice [0.96, 0.9551, 0.9743, 0.9429, 0.8568, 0.9633, 0.9895] +2024-09-05 06:47:30.722343: Epoch time: 263.9 s +2024-09-05 06:47:30.723531: Yayy! New best EMA pseudo Dice: 0.9461 +2024-09-05 06:47:39.067943: +2024-09-05 06:47:39.069495: Epoch 182 +2024-09-05 06:47:39.070947: Current learning rate: 0.00835 +2024-09-05 06:51:56.457452: train_loss -0.7241 +2024-09-05 06:51:56.472884: val_loss -0.7369 +2024-09-05 06:51:56.473999: Pseudo dice [0.9543, 0.9498, 0.9676, 0.9366, 0.8692, 0.9573, 0.9895] +2024-09-05 06:51:56.474974: Epoch time: 257.39 s +2024-09-05 06:51:56.475876: Yayy! New best EMA pseudo Dice: 0.9461 +2024-09-05 06:52:05.439713: +2024-09-05 06:52:05.440849: Epoch 183 +2024-09-05 06:52:05.441727: Current learning rate: 0.00834 +2024-09-05 06:56:09.574893: train_loss -0.7079 +2024-09-05 06:56:09.588269: val_loss -0.7268 +2024-09-05 06:56:09.589179: Pseudo dice [0.9567, 0.9528, 0.9736, 0.9422, 0.8571, 0.9518, 0.9904] +2024-09-05 06:56:09.590106: Epoch time: 244.14 s +2024-09-05 06:56:09.591136: Yayy! New best EMA pseudo Dice: 0.9461 +2024-09-05 06:56:17.517278: +2024-09-05 06:56:17.518705: Epoch 184 +2024-09-05 06:56:17.519710: Current learning rate: 0.00833 +2024-09-05 07:00:38.154068: train_loss -0.7002 +2024-09-05 07:00:38.168444: val_loss -0.7086 +2024-09-05 07:00:38.169452: Pseudo dice [0.9471, 0.9443, 0.9658, 0.9275, 0.8241, 0.9355, 0.9874] +2024-09-05 07:00:38.170342: Epoch time: 260.64 s +2024-09-05 07:00:42.052974: +2024-09-05 07:00:42.054394: Epoch 185 +2024-09-05 07:00:42.055583: Current learning rate: 0.00832 +2024-09-05 07:04:56.052721: train_loss -0.6956 +2024-09-05 07:04:56.076499: val_loss -0.7353 +2024-09-05 07:04:56.077826: Pseudo dice [0.9564, 0.9502, 0.973, 0.9445, 0.8808, 0.9603, 0.9906] +2024-09-05 07:04:56.078989: Epoch time: 254.0 s +2024-09-05 07:04:59.059781: +2024-09-05 07:04:59.061570: Epoch 186 +2024-09-05 07:04:59.063172: Current learning rate: 0.00831 +2024-09-05 07:09:15.852257: train_loss -0.7238 +2024-09-05 07:09:15.880219: val_loss -0.767 +2024-09-05 07:09:15.881196: Pseudo dice [0.9625, 0.9552, 0.975, 0.9383, 0.8678, 0.9587, 0.9914] +2024-09-05 07:09:15.882242: Epoch time: 256.8 s +2024-09-05 07:09:19.283468: +2024-09-05 07:09:19.284974: Epoch 187 +2024-09-05 07:09:19.285942: Current learning rate: 0.0083 +2024-09-05 07:13:32.316882: train_loss -0.706 +2024-09-05 07:13:32.324371: val_loss -0.7345 +2024-09-05 07:13:32.326358: Pseudo dice [0.9508, 0.9508, 0.963, 0.942, 0.8663, 0.9608, 0.9888] +2024-09-05 07:13:32.328984: Epoch time: 253.04 s +2024-09-05 07:13:35.518601: +2024-09-05 07:13:35.520093: Epoch 188 +2024-09-05 07:13:35.521666: Current learning rate: 0.00829 +2024-09-05 07:17:47.834278: train_loss -0.7044 +2024-09-05 07:17:47.872364: val_loss -0.7128 +2024-09-05 07:17:47.928544: Pseudo dice [0.9518, 0.9505, 0.9692, 0.9254, 0.8359, 0.9431, 0.988] +2024-09-05 07:17:48.014252: Epoch time: 252.32 s +2024-09-05 07:17:51.226544: +2024-09-05 07:17:51.239263: Epoch 189 +2024-09-05 07:17:51.240940: Current learning rate: 0.00828 +2024-09-05 07:22:08.495923: train_loss -0.6887 +2024-09-05 07:22:08.514254: val_loss -0.7108 +2024-09-05 07:22:08.515476: Pseudo dice [0.9516, 0.9502, 0.9684, 0.9336, 0.8162, 0.9545, 0.9828] +2024-09-05 07:22:08.516442: Epoch time: 257.28 s +2024-09-05 07:22:11.305302: +2024-09-05 07:22:11.306995: Epoch 190 +2024-09-05 07:22:11.307810: Current learning rate: 0.00827 +2024-09-05 07:26:36.250857: train_loss -0.7178 +2024-09-05 07:26:36.254715: val_loss -0.7417 +2024-09-05 07:26:36.256052: Pseudo dice [0.9558, 0.9504, 0.9699, 0.9399, 0.8523, 0.9552, 0.9883] +2024-09-05 07:26:36.257102: Epoch time: 264.95 s +2024-09-05 07:26:38.504616: +2024-09-05 07:26:38.513432: Epoch 191 +2024-09-05 07:26:38.514468: Current learning rate: 0.00826 +2024-09-05 07:30:57.461282: train_loss -0.7186 +2024-09-05 07:30:57.476718: val_loss -0.7293 +2024-09-05 07:30:57.478184: Pseudo dice [0.9536, 0.9513, 0.9697, 0.9394, 0.8501, 0.9646, 0.9886] +2024-09-05 07:30:57.480352: Epoch time: 258.96 s +2024-09-05 07:31:00.863581: +2024-09-05 07:31:00.868074: Epoch 192 +2024-09-05 07:31:00.869653: Current learning rate: 0.00825 +2024-09-05 07:35:13.828422: train_loss -0.7301 +2024-09-05 07:35:13.844035: val_loss -0.7244 +2024-09-05 07:35:13.845107: Pseudo dice [0.959, 0.9497, 0.9744, 0.941, 0.8699, 0.9633, 0.9914] +2024-09-05 07:35:13.846036: Epoch time: 252.97 s +2024-09-05 07:35:17.568007: +2024-09-05 07:35:17.569181: Epoch 193 +2024-09-05 07:35:17.569994: Current learning rate: 0.00824 +2024-09-05 07:39:32.756363: train_loss -0.7313 +2024-09-05 07:39:32.771744: val_loss -0.7229 +2024-09-05 07:39:32.772828: Pseudo dice [0.9602, 0.9546, 0.9773, 0.9437, 0.8376, 0.9618, 0.9868] +2024-09-05 07:39:32.773915: Epoch time: 255.19 s +2024-09-05 07:39:36.637019: +2024-09-05 07:39:36.638081: Epoch 194 +2024-09-05 07:39:36.639010: Current learning rate: 0.00824 +2024-09-05 07:44:02.875981: train_loss -0.7024 +2024-09-05 07:44:02.879038: val_loss -0.7224 +2024-09-05 07:44:02.879853: Pseudo dice [0.9545, 0.9488, 0.9672, 0.9335, 0.8542, 0.9556, 0.9871] +2024-09-05 07:44:02.880689: Epoch time: 266.24 s +2024-09-05 07:44:05.585261: +2024-09-05 07:44:05.586602: Epoch 195 +2024-09-05 07:44:05.587697: Current learning rate: 0.00823 +2024-09-05 07:48:03.542958: train_loss -0.6831 +2024-09-05 07:48:03.559904: val_loss -0.6948 +2024-09-05 07:48:03.561156: Pseudo dice [0.9506, 0.9424, 0.9679, 0.9399, 0.8333, 0.9468, 0.987] +2024-09-05 07:48:03.562587: Epoch time: 237.96 s +2024-09-05 07:48:06.817080: +2024-09-05 07:48:06.818300: Epoch 196 +2024-09-05 07:48:06.819407: Current learning rate: 0.00822 +2024-09-05 07:52:24.381046: train_loss -0.6845 +2024-09-05 07:52:24.395155: val_loss -0.7323 +2024-09-05 07:52:24.396610: Pseudo dice [0.9553, 0.9462, 0.9715, 0.931, 0.8338, 0.9587, 0.987] +2024-09-05 07:52:24.398195: Epoch time: 257.57 s +2024-09-05 07:52:27.888247: +2024-09-05 07:52:27.902858: Epoch 197 +2024-09-05 07:52:27.903911: Current learning rate: 0.00821 +2024-09-05 07:57:01.922595: train_loss -0.7124 +2024-09-05 07:57:01.924404: val_loss -0.7431 +2024-09-05 07:57:01.926202: Pseudo dice [0.9585, 0.9529, 0.9712, 0.9413, 0.8639, 0.9634, 0.9915] +2024-09-05 07:57:01.927608: Epoch time: 274.04 s +2024-09-05 07:57:05.865564: +2024-09-05 07:57:05.866696: Epoch 198 +2024-09-05 07:57:05.867574: Current learning rate: 0.0082 +2024-09-05 08:01:22.618183: train_loss -0.7133 +2024-09-05 08:01:22.627592: val_loss -0.7581 +2024-09-05 08:01:22.628852: Pseudo dice [0.9606, 0.9517, 0.9776, 0.9442, 0.8584, 0.9607, 0.9869] +2024-09-05 08:01:22.630265: Epoch time: 256.76 s +2024-09-05 08:01:26.009475: +2024-09-05 08:01:26.010677: Epoch 199 +2024-09-05 08:01:26.011626: Current learning rate: 0.00819 +2024-09-05 08:06:15.930770: train_loss -0.7284 +2024-09-05 08:06:15.942067: val_loss -0.7387 +2024-09-05 08:06:15.943394: Pseudo dice [0.9612, 0.9532, 0.9762, 0.9379, 0.8398, 0.9568, 0.9872] +2024-09-05 08:06:15.944617: Epoch time: 289.93 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_08_31_29.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_08_31_29.txt new file mode 100644 index 0000000000000000000000000000000000000000..182313c636e5d132dd80e107cb675e039ebb47e2 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_08_31_29.txt @@ -0,0 +1,382 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 08:31:33.050565: Using torch.compile... +2024-09-05 08:31:41.919443: do_dummy_2d_data_aug: False +2024-09-05 08:31:41.925871: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 08:31:41.928209: The split file contains 5 splits. +2024-09-05 08:31:41.928868: Desired fold for training: 3 +2024-09-05 08:31:41.929450: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 08:31:54.109248: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 08:31:55.791973: +2024-09-05 08:31:55.792847: Epoch 200 +2024-09-05 08:31:55.793650: Current learning rate: 0.00818 +2024-09-05 08:50:02.859738: train_loss -0.7005 +2024-09-05 08:50:02.862356: val_loss -0.7223 +2024-09-05 08:50:02.863538: Pseudo dice [0.9531, 0.9489, 0.9714, 0.9338, 0.8583, 0.9584, 0.989] +2024-09-05 08:50:02.864553: Epoch time: 1087.07 s +2024-09-05 08:50:04.988299: +2024-09-05 08:50:04.989009: Epoch 201 +2024-09-05 08:50:04.989559: Current learning rate: 0.00817 +2024-09-05 08:54:32.998193: train_loss -0.7028 +2024-09-05 08:54:33.002046: val_loss -0.7495 +2024-09-05 08:54:33.003572: Pseudo dice [0.9573, 0.9518, 0.9737, 0.9391, 0.8577, 0.9548, 0.9882] +2024-09-05 08:54:33.005040: Epoch time: 268.01 s +2024-09-05 08:54:36.646616: +2024-09-05 08:54:36.647714: Epoch 202 +2024-09-05 08:54:36.648526: Current learning rate: 0.00816 +2024-09-05 08:59:22.693639: train_loss -0.6935 +2024-09-05 08:59:22.709545: val_loss -0.7339 +2024-09-05 08:59:22.711013: Pseudo dice [0.9515, 0.9514, 0.9678, 0.9394, 0.8543, 0.9544, 0.9898] +2024-09-05 08:59:22.712416: Epoch time: 286.05 s +2024-09-05 08:59:25.267635: +2024-09-05 08:59:25.270139: Epoch 203 +2024-09-05 08:59:25.270930: Current learning rate: 0.00815 +2024-09-05 09:04:07.709721: train_loss -0.7045 +2024-09-05 09:04:07.713737: val_loss -0.6766 +2024-09-05 09:04:07.714971: Pseudo dice [0.9469, 0.9411, 0.9647, 0.9222, 0.8056, 0.9571, 0.9844] +2024-09-05 09:04:07.716984: Epoch time: 282.45 s +2024-09-05 09:04:10.529472: +2024-09-05 09:04:10.542517: Epoch 204 +2024-09-05 09:04:10.543250: Current learning rate: 0.00814 +2024-09-05 09:08:40.070315: train_loss -0.705 +2024-09-05 09:08:40.096477: val_loss -0.6916 +2024-09-05 09:08:40.097733: Pseudo dice [0.9564, 0.9533, 0.9699, 0.9312, 0.8582, 0.9539, 0.9878] +2024-09-05 09:08:40.098726: Epoch time: 269.55 s +2024-09-05 09:08:43.329243: +2024-09-05 09:08:43.330844: Epoch 205 +2024-09-05 09:08:43.331553: Current learning rate: 0.00813 +2024-09-05 09:13:36.171293: train_loss -0.7082 +2024-09-05 09:13:36.283535: val_loss -0.7219 +2024-09-05 09:13:36.353765: Pseudo dice [0.955, 0.9527, 0.9693, 0.9392, 0.8642, 0.962, 0.986] +2024-09-05 09:13:36.560042: Epoch time: 292.85 s +2024-09-05 09:13:39.490623: +2024-09-05 09:13:39.529287: Epoch 206 +2024-09-05 09:13:39.593844: Current learning rate: 0.00813 +2024-09-05 09:18:14.805707: train_loss -0.7023 +2024-09-05 09:18:14.826463: val_loss -0.7368 +2024-09-05 09:18:14.827741: Pseudo dice [0.9551, 0.9525, 0.9693, 0.9343, 0.862, 0.9609, 0.9894] +2024-09-05 09:18:14.829378: Epoch time: 275.32 s +2024-09-05 09:18:17.555936: +2024-09-05 09:18:17.557415: Epoch 207 +2024-09-05 09:18:17.558425: Current learning rate: 0.00812 +2024-09-05 09:23:16.408867: train_loss -0.7122 +2024-09-05 09:23:16.550646: val_loss -0.7385 +2024-09-05 09:23:16.606373: Pseudo dice [0.9537, 0.9474, 0.9701, 0.9438, 0.8571, 0.957, 0.9895] +2024-09-05 09:23:16.619764: Epoch time: 298.86 s +2024-09-05 09:23:20.124988: +2024-09-05 09:23:20.138978: Epoch 208 +2024-09-05 09:23:20.140645: Current learning rate: 0.00811 +2024-09-05 09:28:15.716929: train_loss -0.7123 +2024-09-05 09:28:15.726643: val_loss -0.7178 +2024-09-05 09:28:15.727451: Pseudo dice [0.9577, 0.9538, 0.9741, 0.9403, 0.8659, 0.9631, 0.9919] +2024-09-05 09:28:15.728143: Epoch time: 295.6 s +2024-09-05 09:28:18.580544: +2024-09-05 09:28:18.582241: Epoch 209 +2024-09-05 09:28:18.583291: Current learning rate: 0.0081 +2024-09-05 09:33:00.775615: train_loss -0.7203 +2024-09-05 09:33:00.786820: val_loss -0.7748 +2024-09-05 09:33:00.787649: Pseudo dice [0.9593, 0.9522, 0.978, 0.9455, 0.8744, 0.9622, 0.9891] +2024-09-05 09:33:00.788292: Epoch time: 282.2 s +2024-09-05 09:33:03.315508: +2024-09-05 09:33:03.317510: Epoch 210 +2024-09-05 09:33:03.318563: Current learning rate: 0.00809 +2024-09-05 09:37:46.619607: train_loss -0.72 +2024-09-05 09:37:46.634132: val_loss -0.7216 +2024-09-05 09:37:46.635031: Pseudo dice [0.956, 0.9515, 0.9676, 0.9425, 0.8658, 0.9581, 0.9889] +2024-09-05 09:37:46.635821: Epoch time: 283.31 s +2024-09-05 09:37:49.298481: +2024-09-05 09:37:49.299485: Epoch 211 +2024-09-05 09:37:49.300150: Current learning rate: 0.00808 +2024-09-05 09:42:32.733170: train_loss -0.7375 +2024-09-05 09:42:32.743786: val_loss -0.7359 +2024-09-05 09:42:32.744662: Pseudo dice [0.9556, 0.9491, 0.9714, 0.9436, 0.864, 0.9644, 0.9905] +2024-09-05 09:42:32.745271: Epoch time: 283.44 s +2024-09-05 09:42:35.894230: +2024-09-05 09:42:35.895121: Epoch 212 +2024-09-05 09:42:35.895880: Current learning rate: 0.00807 +2024-09-05 09:47:31.592209: train_loss -0.7237 +2024-09-05 09:47:31.593613: val_loss -0.7169 +2024-09-05 09:47:31.594412: Pseudo dice [0.957, 0.9492, 0.9694, 0.9467, 0.8705, 0.9601, 0.99] +2024-09-05 09:47:31.595120: Epoch time: 295.7 s +2024-09-05 09:47:31.596097: Yayy! New best EMA pseudo Dice: 0.9462 +2024-09-05 09:47:39.891339: +2024-09-05 09:47:39.892319: Epoch 213 +2024-09-05 09:47:39.892970: Current learning rate: 0.00806 +2024-09-05 09:52:19.008193: train_loss -0.7322 +2024-09-05 09:52:19.014351: val_loss -0.7501 +2024-09-05 09:52:19.015588: Pseudo dice [0.9609, 0.9527, 0.9744, 0.9419, 0.846, 0.9631, 0.9886] +2024-09-05 09:52:19.017045: Epoch time: 279.12 s +2024-09-05 09:52:19.017934: Yayy! New best EMA pseudo Dice: 0.9463 +2024-09-05 09:52:27.824175: +2024-09-05 09:52:27.825046: Epoch 214 +2024-09-05 09:52:27.825700: Current learning rate: 0.00805 +2024-09-05 09:57:14.167028: train_loss -0.7254 +2024-09-05 09:57:14.170408: val_loss -0.7486 +2024-09-05 09:57:14.174447: Pseudo dice [0.9567, 0.9548, 0.971, 0.9439, 0.8725, 0.9625, 0.9911] +2024-09-05 09:57:14.175518: Epoch time: 286.35 s +2024-09-05 09:57:14.176359: Yayy! New best EMA pseudo Dice: 0.9467 +2024-09-05 09:57:23.362543: +2024-09-05 09:57:23.363442: Epoch 215 +2024-09-05 09:57:23.364123: Current learning rate: 0.00804 +2024-09-05 10:02:15.724280: train_loss -0.7236 +2024-09-05 10:02:15.728658: val_loss -0.7448 +2024-09-05 10:02:15.729817: Pseudo dice [0.9557, 0.9511, 0.9712, 0.9418, 0.8687, 0.9648, 0.991] +2024-09-05 10:02:15.731521: Epoch time: 292.37 s +2024-09-05 10:02:15.750877: Yayy! New best EMA pseudo Dice: 0.9469 +2024-09-05 10:02:24.685631: +2024-09-05 10:02:24.686699: Epoch 216 +2024-09-05 10:02:24.687653: Current learning rate: 0.00803 +2024-09-05 10:06:49.491371: train_loss -0.7326 +2024-09-05 10:06:49.494965: val_loss -0.727 +2024-09-05 10:06:49.497086: Pseudo dice [0.961, 0.9542, 0.9764, 0.9346, 0.8379, 0.9651, 0.9887] +2024-09-05 10:06:49.498091: Epoch time: 264.81 s +2024-09-05 10:06:53.028337: +2024-09-05 10:06:53.029243: Epoch 217 +2024-09-05 10:06:53.029922: Current learning rate: 0.00802 +2024-09-05 10:11:38.063609: train_loss -0.7259 +2024-09-05 10:11:38.066206: val_loss -0.7327 +2024-09-05 10:11:38.066819: Pseudo dice [0.9535, 0.9562, 0.9681, 0.9442, 0.8698, 0.964, 0.9909] +2024-09-05 10:11:38.067959: Epoch time: 285.04 s +2024-09-05 10:11:38.068713: Yayy! New best EMA pseudo Dice: 0.9471 +2024-09-05 10:11:47.535518: +2024-09-05 10:11:47.536700: Epoch 218 +2024-09-05 10:11:47.537598: Current learning rate: 0.00801 +2024-09-05 10:16:47.741672: train_loss -0.746 +2024-09-05 10:16:47.756556: val_loss -0.7338 +2024-09-05 10:16:47.757964: Pseudo dice [0.956, 0.9522, 0.9703, 0.9395, 0.8707, 0.9606, 0.9895] +2024-09-05 10:16:47.759413: Epoch time: 300.21 s +2024-09-05 10:16:47.760977: Yayy! New best EMA pseudo Dice: 0.9472 +2024-09-05 10:16:56.008915: +2024-09-05 10:16:56.009707: Epoch 219 +2024-09-05 10:16:56.010247: Current learning rate: 0.00801 +2024-09-05 10:21:44.288929: train_loss -0.7129 +2024-09-05 10:21:44.304079: val_loss -0.7206 +2024-09-05 10:21:44.304865: Pseudo dice [0.9522, 0.9501, 0.9666, 0.9332, 0.8666, 0.9662, 0.9907] +2024-09-05 10:21:44.305652: Epoch time: 288.28 s +2024-09-05 10:21:47.218164: +2024-09-05 10:21:47.219101: Epoch 220 +2024-09-05 10:21:47.219759: Current learning rate: 0.008 +2024-09-05 10:26:24.489451: train_loss -0.735 +2024-09-05 10:26:24.508471: val_loss -0.7191 +2024-09-05 10:26:24.509944: Pseudo dice [0.9569, 0.9516, 0.9718, 0.9368, 0.8457, 0.9543, 0.9864] +2024-09-05 10:26:24.514828: Epoch time: 277.28 s +2024-09-05 10:26:27.071079: +2024-09-05 10:26:27.072121: Epoch 221 +2024-09-05 10:26:27.072917: Current learning rate: 0.00799 +2024-09-05 10:31:04.710291: train_loss -0.719 +2024-09-05 10:31:04.712807: val_loss -0.7295 +2024-09-05 10:31:04.713518: Pseudo dice [0.9605, 0.9562, 0.9736, 0.9498, 0.8748, 0.9637, 0.9916] +2024-09-05 10:31:04.714259: Epoch time: 277.64 s +2024-09-05 10:31:04.714935: Yayy! New best EMA pseudo Dice: 0.9474 +2024-09-05 10:31:12.066721: +2024-09-05 10:31:12.067634: Epoch 222 +2024-09-05 10:31:12.068328: Current learning rate: 0.00798 +2024-09-05 10:35:56.577312: train_loss -0.7272 +2024-09-05 10:35:56.599855: val_loss -0.7114 +2024-09-05 10:35:56.600768: Pseudo dice [0.9573, 0.9524, 0.9695, 0.9376, 0.8287, 0.9625, 0.9849] +2024-09-05 10:35:56.601450: Epoch time: 284.51 s +2024-09-05 10:35:59.924165: +2024-09-05 10:35:59.925087: Epoch 223 +2024-09-05 10:35:59.925615: Current learning rate: 0.00797 +2024-09-05 10:40:36.763180: train_loss -0.7194 +2024-09-05 10:40:36.784459: val_loss -0.7545 +2024-09-05 10:40:36.785225: Pseudo dice [0.9577, 0.9501, 0.9747, 0.9331, 0.8649, 0.9604, 0.9882] +2024-09-05 10:40:36.786198: Epoch time: 276.84 s +2024-09-05 10:40:39.244411: +2024-09-05 10:40:39.245896: Epoch 224 +2024-09-05 10:40:39.246687: Current learning rate: 0.00796 +2024-09-05 10:45:21.328503: train_loss -0.7206 +2024-09-05 10:45:21.330977: val_loss -0.706 +2024-09-05 10:45:21.331629: Pseudo dice [0.9512, 0.9503, 0.9682, 0.9247, 0.8653, 0.9653, 0.9901] +2024-09-05 10:45:21.332276: Epoch time: 282.09 s +2024-09-05 10:45:25.118423: +2024-09-05 10:45:25.119884: Epoch 225 +2024-09-05 10:45:25.120878: Current learning rate: 0.00795 +2024-09-05 10:49:57.485047: train_loss -0.7133 +2024-09-05 10:49:57.489050: val_loss -0.7268 +2024-09-05 10:49:57.490721: Pseudo dice [0.956, 0.9572, 0.969, 0.944, 0.8666, 0.965, 0.9906] +2024-09-05 10:49:57.492262: Epoch time: 272.37 s +2024-09-05 10:50:00.289780: +2024-09-05 10:50:00.290711: Epoch 226 +2024-09-05 10:50:00.291346: Current learning rate: 0.00794 +2024-09-05 10:54:26.872663: train_loss -0.7316 +2024-09-05 10:54:26.889061: val_loss -0.7425 +2024-09-05 10:54:26.890618: Pseudo dice [0.9604, 0.9554, 0.9747, 0.9447, 0.8775, 0.9673, 0.9924] +2024-09-05 10:54:26.891871: Epoch time: 266.59 s +2024-09-05 10:54:26.893059: Yayy! New best EMA pseudo Dice: 0.9476 +2024-09-05 10:54:36.728437: +2024-09-05 10:54:36.729322: Epoch 227 +2024-09-05 10:54:36.729986: Current learning rate: 0.00793 +2024-09-05 10:59:01.020832: train_loss -0.7367 +2024-09-05 10:59:01.022328: val_loss -0.7591 +2024-09-05 10:59:01.023125: Pseudo dice [0.9603, 0.9555, 0.9754, 0.9455, 0.8638, 0.9634, 0.9909] +2024-09-05 10:59:01.023832: Epoch time: 264.3 s +2024-09-05 10:59:01.024540: Yayy! New best EMA pseudo Dice: 0.9479 +2024-09-05 10:59:09.689207: +2024-09-05 10:59:09.690283: Epoch 228 +2024-09-05 10:59:09.690854: Current learning rate: 0.00792 +2024-09-05 11:03:43.120434: train_loss -0.7141 +2024-09-05 11:03:43.147245: val_loss -0.7628 +2024-09-05 11:03:43.148910: Pseudo dice [0.9631, 0.9562, 0.9778, 0.9512, 0.8823, 0.9632, 0.9912] +2024-09-05 11:03:43.150025: Epoch time: 273.44 s +2024-09-05 11:03:43.150769: Yayy! New best EMA pseudo Dice: 0.9486 +2024-09-05 11:03:53.632864: +2024-09-05 11:03:53.633921: Epoch 229 +2024-09-05 11:03:53.634855: Current learning rate: 0.00791 +2024-09-05 11:08:40.357388: train_loss -0.7247 +2024-09-05 11:08:40.370950: val_loss -0.713 +2024-09-05 11:08:40.371755: Pseudo dice [0.9615, 0.9534, 0.9765, 0.9474, 0.8687, 0.959, 0.9908] +2024-09-05 11:08:40.372307: Epoch time: 286.73 s +2024-09-05 11:08:40.372929: Yayy! New best EMA pseudo Dice: 0.9489 +2024-09-05 11:08:49.733922: +2024-09-05 11:08:49.735794: Epoch 230 +2024-09-05 11:08:49.736474: Current learning rate: 0.0079 +2024-09-05 11:13:46.629263: train_loss -0.7102 +2024-09-05 11:13:46.631704: val_loss -0.7425 +2024-09-05 11:13:46.632580: Pseudo dice [0.9534, 0.9504, 0.9723, 0.9303, 0.8697, 0.9592, 0.9911] +2024-09-05 11:13:46.633459: Epoch time: 296.9 s +2024-09-05 11:13:49.134631: +2024-09-05 11:13:49.135610: Epoch 231 +2024-09-05 11:13:49.136372: Current learning rate: 0.00789 +2024-09-05 11:18:44.817639: train_loss -0.73 +2024-09-05 11:18:44.833258: val_loss -0.7614 +2024-09-05 11:18:44.834112: Pseudo dice [0.9592, 0.9544, 0.9754, 0.9425, 0.8719, 0.9564, 0.989] +2024-09-05 11:18:44.835052: Epoch time: 295.71 s +2024-09-05 11:18:47.333448: +2024-09-05 11:18:47.345994: Epoch 232 +2024-09-05 11:18:47.354616: Current learning rate: 0.00789 +2024-09-05 11:23:41.163962: train_loss -0.7333 +2024-09-05 11:23:41.168101: val_loss -0.6887 +2024-09-05 11:23:41.169211: Pseudo dice [0.958, 0.9526, 0.9759, 0.9411, 0.8334, 0.963, 0.9866] +2024-09-05 11:23:41.170502: Epoch time: 293.84 s +2024-09-05 11:23:43.529431: +2024-09-05 11:23:43.530716: Epoch 233 +2024-09-05 11:23:43.531786: Current learning rate: 0.00788 +2024-09-05 11:28:29.489172: train_loss -0.7325 +2024-09-05 11:28:29.499574: val_loss -0.7507 +2024-09-05 11:28:29.500361: Pseudo dice [0.9511, 0.9436, 0.9679, 0.9382, 0.8708, 0.9585, 0.9914] +2024-09-05 11:28:29.501102: Epoch time: 285.97 s +2024-09-05 11:28:33.764925: +2024-09-05 11:28:33.765798: Epoch 234 +2024-09-05 11:28:33.766506: Current learning rate: 0.00787 +2024-09-05 11:33:06.839161: train_loss -0.7286 +2024-09-05 11:33:06.853258: val_loss -0.7125 +2024-09-05 11:33:06.854388: Pseudo dice [0.9609, 0.9545, 0.9758, 0.9465, 0.8528, 0.9566, 0.9884] +2024-09-05 11:33:06.855349: Epoch time: 273.08 s +2024-09-05 11:33:10.002422: +2024-09-05 11:33:10.003436: Epoch 235 +2024-09-05 11:33:10.004109: Current learning rate: 0.00786 +2024-09-05 11:38:04.084668: train_loss -0.7192 +2024-09-05 11:38:04.094954: val_loss -0.7416 +2024-09-05 11:38:04.096027: Pseudo dice [0.9612, 0.956, 0.9775, 0.9389, 0.861, 0.9582, 0.9882] +2024-09-05 11:38:04.096952: Epoch time: 294.09 s +2024-09-05 11:38:07.151323: +2024-09-05 11:38:07.152378: Epoch 236 +2024-09-05 11:38:07.153002: Current learning rate: 0.00785 +2024-09-05 11:42:44.523681: train_loss -0.7092 +2024-09-05 11:42:44.526566: val_loss -0.7547 +2024-09-05 11:42:44.527393: Pseudo dice [0.9589, 0.9497, 0.9759, 0.9389, 0.8548, 0.9547, 0.9899] +2024-09-05 11:42:44.528245: Epoch time: 277.38 s +2024-09-05 11:42:47.086644: +2024-09-05 11:42:47.087556: Epoch 237 +2024-09-05 11:42:47.088310: Current learning rate: 0.00784 +2024-09-05 11:47:35.575598: train_loss -0.7134 +2024-09-05 11:47:35.578244: val_loss -0.7363 +2024-09-05 11:47:35.579036: Pseudo dice [0.9569, 0.9488, 0.9742, 0.9374, 0.8547, 0.958, 0.9888] +2024-09-05 11:47:35.579739: Epoch time: 288.49 s +2024-09-05 11:47:38.449200: +2024-09-05 11:47:38.461360: Epoch 238 +2024-09-05 11:47:38.462179: Current learning rate: 0.00783 +2024-09-05 11:52:15.278320: train_loss -0.7016 +2024-09-05 11:52:15.282788: val_loss -0.7385 +2024-09-05 11:52:15.286638: Pseudo dice [0.9595, 0.9529, 0.9737, 0.9402, 0.8808, 0.9611, 0.9922] +2024-09-05 11:52:15.288742: Epoch time: 276.83 s +2024-09-05 11:52:17.825654: +2024-09-05 11:52:17.830656: Epoch 239 +2024-09-05 11:52:17.831580: Current learning rate: 0.00782 +2024-09-05 11:57:10.054307: train_loss -0.7062 +2024-09-05 11:57:10.058409: val_loss -0.7456 +2024-09-05 11:57:10.059148: Pseudo dice [0.959, 0.9486, 0.9725, 0.9358, 0.8615, 0.9549, 0.9908] +2024-09-05 11:57:10.059851: Epoch time: 292.24 s +2024-09-05 11:57:12.550815: +2024-09-05 11:57:12.554585: Epoch 240 +2024-09-05 11:57:12.555434: Current learning rate: 0.00781 +2024-09-05 12:01:54.865225: train_loss -0.7089 +2024-09-05 12:01:54.886055: val_loss -0.7184 +2024-09-05 12:01:54.894733: Pseudo dice [0.958, 0.9519, 0.9752, 0.9298, 0.8493, 0.9654, 0.9878] +2024-09-05 12:01:54.895535: Epoch time: 282.32 s +2024-09-05 12:01:57.975338: +2024-09-05 12:01:57.976708: Epoch 241 +2024-09-05 12:01:57.978109: Current learning rate: 0.0078 +2024-09-05 12:06:44.416769: train_loss -0.7345 +2024-09-05 12:06:44.419324: val_loss -0.74 +2024-09-05 12:06:44.420953: Pseudo dice [0.9589, 0.9528, 0.9719, 0.9423, 0.8756, 0.9672, 0.99] +2024-09-05 12:06:44.422567: Epoch time: 286.45 s +2024-09-05 12:06:46.819739: +2024-09-05 12:06:46.820855: Epoch 242 +2024-09-05 12:06:46.821744: Current learning rate: 0.00779 +2024-09-05 12:11:23.377918: train_loss -0.7112 +2024-09-05 12:11:23.395034: val_loss -0.724 +2024-09-05 12:11:23.395832: Pseudo dice [0.9517, 0.9441, 0.9691, 0.9349, 0.8617, 0.9434, 0.9892] +2024-09-05 12:11:23.396470: Epoch time: 276.56 s +2024-09-05 12:11:26.891068: +2024-09-05 12:11:26.892473: Epoch 243 +2024-09-05 12:11:26.893519: Current learning rate: 0.00778 +2024-09-05 12:16:17.132916: train_loss -0.723 +2024-09-05 12:16:17.144027: val_loss -0.7356 +2024-09-05 12:16:17.155231: Pseudo dice [0.953, 0.9479, 0.9668, 0.9333, 0.8563, 0.9559, 0.9882] +2024-09-05 12:16:17.156316: Epoch time: 290.25 s +2024-09-05 12:16:20.250666: +2024-09-05 12:16:20.251887: Epoch 244 +2024-09-05 12:16:20.253318: Current learning rate: 0.00777 +2024-09-05 12:21:03.374183: train_loss -0.7154 +2024-09-05 12:21:03.376541: val_loss -0.7336 +2024-09-05 12:21:03.377426: Pseudo dice [0.9551, 0.9449, 0.9703, 0.9307, 0.8701, 0.963, 0.9911] +2024-09-05 12:21:03.378099: Epoch time: 283.13 s +2024-09-05 12:21:06.356573: +2024-09-05 12:21:06.357528: Epoch 245 +2024-09-05 12:21:06.358288: Current learning rate: 0.00777 +2024-09-05 12:26:11.471192: train_loss -0.6885 +2024-09-05 12:26:11.485532: val_loss -0.745 +2024-09-05 12:26:11.486426: Pseudo dice [0.9549, 0.9511, 0.9707, 0.9291, 0.858, 0.9628, 0.991] +2024-09-05 12:26:11.487421: Epoch time: 305.12 s +2024-09-05 12:26:15.446972: +2024-09-05 12:26:15.448637: Epoch 246 +2024-09-05 12:26:15.450320: Current learning rate: 0.00776 +2024-09-05 12:30:46.475398: train_loss -0.7172 +2024-09-05 12:30:46.477338: val_loss -0.7314 +2024-09-05 12:30:46.478444: Pseudo dice [0.9595, 0.9542, 0.9754, 0.9421, 0.8696, 0.9644, 0.9897] +2024-09-05 12:30:46.479464: Epoch time: 271.03 s +2024-09-05 12:30:49.429542: +2024-09-05 12:30:49.442714: Epoch 247 +2024-09-05 12:30:49.443699: Current learning rate: 0.00775 +2024-09-05 12:35:42.920858: train_loss -0.7137 +2024-09-05 12:35:42.942961: val_loss -0.7371 +2024-09-05 12:35:42.943743: Pseudo dice [0.958, 0.9523, 0.9737, 0.9363, 0.8223, 0.961, 0.9854] +2024-09-05 12:35:42.944490: Epoch time: 293.5 s +2024-09-05 12:35:45.753800: +2024-09-05 12:35:45.766690: Epoch 248 +2024-09-05 12:35:45.767598: Current learning rate: 0.00774 +2024-09-05 12:40:27.098730: train_loss -0.7117 +2024-09-05 12:40:27.115114: val_loss -0.7139 +2024-09-05 12:40:27.116083: Pseudo dice [0.9578, 0.9542, 0.9753, 0.9379, 0.8668, 0.965, 0.9918] +2024-09-05 12:40:27.116943: Epoch time: 281.35 s +2024-09-05 12:40:30.423361: +2024-09-05 12:40:30.424467: Epoch 249 +2024-09-05 12:40:30.425216: Current learning rate: 0.00773 +2024-09-05 12:45:09.447023: train_loss -0.7237 +2024-09-05 12:45:09.461878: val_loss -0.7711 +2024-09-05 12:45:09.463356: Pseudo dice [0.9594, 0.9524, 0.9738, 0.9292, 0.8663, 0.9634, 0.9892] +2024-09-05 12:45:09.464358: Epoch time: 279.03 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_16_30_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_16_30_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..3518f0923cea0579b7dbc5a4c945a9924585175f --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_5_16_30_45.txt @@ -0,0 +1,732 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 16:30:48.219455: Using torch.compile... +2024-09-05 16:30:57.505712: do_dummy_2d_data_aug: False +2024-09-05 16:30:57.511866: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 16:30:57.513570: The split file contains 5 splits. +2024-09-05 16:30:57.514391: Desired fold for training: 3 +2024-09-05 16:30:57.515293: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 16:31:11.579376: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 16:31:13.220489: +2024-09-05 16:31:13.221772: Epoch 250 +2024-09-05 16:31:13.223976: Current learning rate: 0.00772 +2024-09-05 16:49:44.101008: train_loss -0.7193 +2024-09-05 16:49:44.104185: val_loss -0.7609 +2024-09-05 16:49:44.105171: Pseudo dice [0.9589, 0.9527, 0.9746, 0.9414, 0.8603, 0.9613, 0.991] +2024-09-05 16:49:44.105939: Epoch time: 1110.89 s +2024-09-05 16:49:46.312718: +2024-09-05 16:49:46.375577: Epoch 251 +2024-09-05 16:49:46.497487: Current learning rate: 0.00771 +2024-09-05 16:54:15.242975: train_loss -0.7165 +2024-09-05 16:54:15.257024: val_loss -0.7493 +2024-09-05 16:54:15.259403: Pseudo dice [0.96, 0.9527, 0.9761, 0.9453, 0.8543, 0.9641, 0.9899] +2024-09-05 16:54:15.261205: Epoch time: 268.93 s +2024-09-05 16:54:21.269652: +2024-09-05 16:54:21.270862: Epoch 252 +2024-09-05 16:54:21.273081: Current learning rate: 0.0077 +2024-09-05 16:58:50.859572: train_loss -0.7386 +2024-09-05 16:58:50.904411: val_loss -0.7338 +2024-09-05 16:58:50.906512: Pseudo dice [0.962, 0.9533, 0.9754, 0.941, 0.8641, 0.9658, 0.9902] +2024-09-05 16:58:50.908463: Epoch time: 269.59 s +2024-09-05 16:58:55.115354: +2024-09-05 16:58:55.116669: Epoch 253 +2024-09-05 16:58:55.118183: Current learning rate: 0.00769 +2024-09-05 17:03:18.522617: train_loss -0.7307 +2024-09-05 17:03:18.528337: val_loss -0.762 +2024-09-05 17:03:18.529531: Pseudo dice [0.9638, 0.9566, 0.9763, 0.9474, 0.8633, 0.9663, 0.9876] +2024-09-05 17:03:18.531147: Epoch time: 263.41 s +2024-09-05 17:03:21.909412: +2024-09-05 17:03:21.911130: Epoch 254 +2024-09-05 17:03:21.913097: Current learning rate: 0.00768 +2024-09-05 17:07:50.963459: train_loss -0.7247 +2024-09-05 17:07:50.966143: val_loss -0.7461 +2024-09-05 17:07:50.967076: Pseudo dice [0.9618, 0.9565, 0.9779, 0.9437, 0.8801, 0.9586, 0.9903] +2024-09-05 17:07:50.968264: Epoch time: 269.06 s +2024-09-05 17:07:53.695409: +2024-09-05 17:07:53.696862: Epoch 255 +2024-09-05 17:07:53.697882: Current learning rate: 0.00767 +2024-09-05 17:12:30.354528: train_loss -0.7262 +2024-09-05 17:12:30.356763: val_loss -0.7454 +2024-09-05 17:12:30.357844: Pseudo dice [0.9585, 0.9556, 0.9741, 0.9472, 0.8661, 0.9667, 0.9911] +2024-09-05 17:12:30.358641: Epoch time: 276.66 s +2024-09-05 17:12:33.808297: +2024-09-05 17:12:33.809383: Epoch 256 +2024-09-05 17:12:33.810809: Current learning rate: 0.00766 +2024-09-05 17:16:50.374416: train_loss -0.7316 +2024-09-05 17:16:50.390640: val_loss -0.7408 +2024-09-05 17:16:50.392468: Pseudo dice [0.9592, 0.9517, 0.9739, 0.9375, 0.8674, 0.9598, 0.9912] +2024-09-05 17:16:50.394016: Epoch time: 256.57 s +2024-09-05 17:16:53.491154: +2024-09-05 17:16:53.492497: Epoch 257 +2024-09-05 17:16:53.493387: Current learning rate: 0.00765 +2024-09-05 17:21:26.870454: train_loss -0.7283 +2024-09-05 17:21:26.889461: val_loss -0.751 +2024-09-05 17:21:26.890601: Pseudo dice [0.9574, 0.9552, 0.9711, 0.9404, 0.8769, 0.9615, 0.9907] +2024-09-05 17:21:26.891567: Epoch time: 273.38 s +2024-09-05 17:21:26.892517: Yayy! New best EMA pseudo Dice: 0.9489 +2024-09-05 17:21:38.097823: +2024-09-05 17:21:38.098887: Epoch 258 +2024-09-05 17:21:38.099797: Current learning rate: 0.00764 +2024-09-05 17:26:12.865749: train_loss -0.719 +2024-09-05 17:26:12.870070: val_loss -0.7374 +2024-09-05 17:26:12.871590: Pseudo dice [0.9579, 0.9514, 0.9732, 0.9334, 0.8657, 0.9647, 0.9903] +2024-09-05 17:26:12.873237: Epoch time: 274.77 s +2024-09-05 17:26:16.422570: +2024-09-05 17:26:16.423829: Epoch 259 +2024-09-05 17:26:16.424754: Current learning rate: 0.00764 +2024-09-05 17:30:39.576988: train_loss -0.7312 +2024-09-05 17:30:39.579940: val_loss -0.7005 +2024-09-05 17:30:39.580989: Pseudo dice [0.959, 0.9557, 0.9723, 0.9358, 0.8726, 0.9657, 0.9897] +2024-09-05 17:30:39.582130: Epoch time: 263.16 s +2024-09-05 17:30:39.583171: Yayy! New best EMA pseudo Dice: 0.949 +2024-09-05 17:30:50.503162: +2024-09-05 17:30:50.504406: Epoch 260 +2024-09-05 17:30:50.505248: Current learning rate: 0.00763 +2024-09-05 17:35:11.953069: train_loss -0.7483 +2024-09-05 17:35:11.967514: val_loss -0.7348 +2024-09-05 17:35:11.968658: Pseudo dice [0.9601, 0.9555, 0.9737, 0.9426, 0.8777, 0.9661, 0.9911] +2024-09-05 17:35:11.970012: Epoch time: 261.45 s +2024-09-05 17:35:11.970789: Yayy! New best EMA pseudo Dice: 0.9493 +2024-09-05 17:35:21.447540: +2024-09-05 17:35:21.448902: Epoch 261 +2024-09-05 17:35:21.449961: Current learning rate: 0.00762 +2024-09-05 17:39:41.831476: train_loss -0.7247 +2024-09-05 17:39:41.845971: val_loss -0.7323 +2024-09-05 17:39:41.848233: Pseudo dice [0.9587, 0.9542, 0.9753, 0.9422, 0.8592, 0.9593, 0.9892] +2024-09-05 17:39:41.850271: Epoch time: 260.39 s +2024-09-05 17:39:45.148562: +2024-09-05 17:39:45.151731: Epoch 262 +2024-09-05 17:39:45.152888: Current learning rate: 0.00761 +2024-09-05 17:44:14.094654: train_loss -0.667 +2024-09-05 17:44:14.108498: val_loss -0.6926 +2024-09-05 17:44:14.110364: Pseudo dice [0.9432, 0.9439, 0.9574, 0.9209, 0.843, 0.9575, 0.9896] +2024-09-05 17:44:14.113376: Epoch time: 268.95 s +2024-09-05 17:44:17.387880: +2024-09-05 17:44:17.389272: Epoch 263 +2024-09-05 17:44:17.390294: Current learning rate: 0.0076 +2024-09-05 17:48:54.365558: train_loss -0.6551 +2024-09-05 17:48:54.385167: val_loss -0.6956 +2024-09-05 17:48:54.386233: Pseudo dice [0.9477, 0.9402, 0.9627, 0.9223, 0.8306, 0.9541, 0.9896] +2024-09-05 17:48:54.388322: Epoch time: 276.98 s +2024-09-05 17:48:59.126129: +2024-09-05 17:48:59.127900: Epoch 264 +2024-09-05 17:48:59.129558: Current learning rate: 0.00759 +2024-09-05 17:53:34.353998: train_loss -0.6989 +2024-09-05 17:53:34.372880: val_loss -0.6916 +2024-09-05 17:53:34.373917: Pseudo dice [0.9511, 0.9478, 0.9672, 0.9373, 0.8274, 0.9559, 0.9813] +2024-09-05 17:53:34.375713: Epoch time: 275.23 s +2024-09-05 17:53:37.348179: +2024-09-05 17:53:37.349444: Epoch 265 +2024-09-05 17:53:37.351116: Current learning rate: 0.00758 +2024-09-05 17:58:17.855953: train_loss -0.6966 +2024-09-05 17:58:17.872483: val_loss -0.7231 +2024-09-05 17:58:17.874217: Pseudo dice [0.9503, 0.946, 0.9674, 0.9263, 0.8281, 0.9569, 0.986] +2024-09-05 17:58:17.878023: Epoch time: 280.51 s +2024-09-05 17:58:20.954577: +2024-09-05 17:58:20.956046: Epoch 266 +2024-09-05 17:58:20.957016: Current learning rate: 0.00757 +2024-09-05 18:02:32.479018: train_loss -0.71 +2024-09-05 18:02:32.497505: val_loss -0.7326 +2024-09-05 18:02:32.499744: Pseudo dice [0.9592, 0.9481, 0.9704, 0.9389, 0.8541, 0.956, 0.9907] +2024-09-05 18:02:32.501044: Epoch time: 251.53 s +2024-09-05 18:02:35.103799: +2024-09-05 18:02:35.105329: Epoch 267 +2024-09-05 18:02:35.106607: Current learning rate: 0.00756 +2024-09-05 18:07:13.031350: train_loss -0.7297 +2024-09-05 18:07:13.038041: val_loss -0.7529 +2024-09-05 18:07:13.039515: Pseudo dice [0.9606, 0.9517, 0.9767, 0.942, 0.868, 0.9579, 0.99] +2024-09-05 18:07:13.041215: Epoch time: 277.93 s +2024-09-05 18:07:16.495220: +2024-09-05 18:07:16.496560: Epoch 268 +2024-09-05 18:07:16.497532: Current learning rate: 0.00755 +2024-09-05 18:12:15.769276: train_loss -0.7169 +2024-09-05 18:12:15.779838: val_loss -0.7313 +2024-09-05 18:12:15.781379: Pseudo dice [0.9565, 0.9554, 0.9701, 0.9416, 0.8679, 0.9621, 0.9882] +2024-09-05 18:12:15.782614: Epoch time: 299.28 s +2024-09-05 18:12:19.707418: +2024-09-05 18:12:19.708686: Epoch 269 +2024-09-05 18:12:19.710326: Current learning rate: 0.00754 +2024-09-05 18:16:55.791113: train_loss -0.7116 +2024-09-05 18:16:55.793122: val_loss -0.7382 +2024-09-05 18:16:55.795046: Pseudo dice [0.9576, 0.9556, 0.97, 0.9435, 0.865, 0.9652, 0.991] +2024-09-05 18:16:55.796785: Epoch time: 276.09 s +2024-09-05 18:16:58.373543: +2024-09-05 18:16:58.374916: Epoch 270 +2024-09-05 18:16:58.376519: Current learning rate: 0.00753 +2024-09-05 18:21:41.536004: train_loss -0.7452 +2024-09-05 18:21:41.554782: val_loss -0.7346 +2024-09-05 18:21:41.556245: Pseudo dice [0.9627, 0.9562, 0.9787, 0.9467, 0.8729, 0.965, 0.9909] +2024-09-05 18:21:41.558639: Epoch time: 283.17 s +2024-09-05 18:21:44.720558: +2024-09-05 18:21:44.722250: Epoch 271 +2024-09-05 18:21:44.723618: Current learning rate: 0.00752 +2024-09-05 18:26:12.582909: train_loss -0.7176 +2024-09-05 18:26:12.599956: val_loss -0.7289 +2024-09-05 18:26:12.601216: Pseudo dice [0.9537, 0.9475, 0.9666, 0.9332, 0.8344, 0.9608, 0.9877] +2024-09-05 18:26:12.602246: Epoch time: 267.87 s +2024-09-05 18:26:16.037353: +2024-09-05 18:26:16.038720: Epoch 272 +2024-09-05 18:26:16.040154: Current learning rate: 0.00751 +2024-09-05 18:30:41.879472: train_loss -0.7227 +2024-09-05 18:30:41.888990: val_loss -0.7382 +2024-09-05 18:30:41.890223: Pseudo dice [0.958, 0.9511, 0.9741, 0.9331, 0.8635, 0.9633, 0.9909] +2024-09-05 18:30:41.891305: Epoch time: 265.85 s +2024-09-05 18:30:45.309227: +2024-09-05 18:30:45.310542: Epoch 273 +2024-09-05 18:30:45.311441: Current learning rate: 0.00751 +2024-09-05 18:35:36.388880: train_loss -0.7147 +2024-09-05 18:35:36.398103: val_loss -0.7373 +2024-09-05 18:35:36.400047: Pseudo dice [0.9559, 0.9471, 0.974, 0.9393, 0.838, 0.965, 0.988] +2024-09-05 18:35:36.402538: Epoch time: 291.08 s +2024-09-05 18:35:38.810604: +2024-09-05 18:35:38.812050: Epoch 274 +2024-09-05 18:35:38.813380: Current learning rate: 0.0075 +2024-09-05 18:40:14.553644: train_loss -0.7244 +2024-09-05 18:40:14.589628: val_loss -0.7545 +2024-09-05 18:40:14.591204: Pseudo dice [0.9579, 0.9501, 0.9728, 0.9359, 0.8754, 0.966, 0.9911] +2024-09-05 18:40:14.592353: Epoch time: 275.75 s +2024-09-05 18:40:18.502547: +2024-09-05 18:40:18.503833: Epoch 275 +2024-09-05 18:40:18.504862: Current learning rate: 0.00749 +2024-09-05 18:44:51.009902: train_loss -0.7316 +2024-09-05 18:44:51.027492: val_loss -0.7418 +2024-09-05 18:44:51.028508: Pseudo dice [0.9554, 0.952, 0.9692, 0.9395, 0.8774, 0.967, 0.9922] +2024-09-05 18:44:51.029533: Epoch time: 272.51 s +2024-09-05 18:44:54.708688: +2024-09-05 18:44:54.710036: Epoch 276 +2024-09-05 18:44:54.711196: Current learning rate: 0.00748 +2024-09-05 18:49:24.961037: train_loss -0.7147 +2024-09-05 18:49:24.976307: val_loss -0.7293 +2024-09-05 18:49:24.978968: Pseudo dice [0.9568, 0.9541, 0.9718, 0.9455, 0.8787, 0.9685, 0.992] +2024-09-05 18:49:24.980290: Epoch time: 270.26 s +2024-09-05 18:49:29.336465: +2024-09-05 18:49:29.337770: Epoch 277 +2024-09-05 18:49:29.338738: Current learning rate: 0.00747 +2024-09-05 18:53:59.100487: train_loss -0.7314 +2024-09-05 18:53:59.119457: val_loss -0.7236 +2024-09-05 18:53:59.121340: Pseudo dice [0.9576, 0.955, 0.9712, 0.9416, 0.8768, 0.9651, 0.9918] +2024-09-05 18:53:59.123151: Epoch time: 269.77 s +2024-09-05 18:54:02.187928: +2024-09-05 18:54:02.194258: Epoch 278 +2024-09-05 18:54:02.195562: Current learning rate: 0.00746 +2024-09-05 18:58:28.863354: train_loss -0.7328 +2024-09-05 18:58:28.875461: val_loss -0.7354 +2024-09-05 18:58:28.876814: Pseudo dice [0.9563, 0.9535, 0.9762, 0.9443, 0.8671, 0.9635, 0.9889] +2024-09-05 18:58:28.878017: Epoch time: 266.68 s +2024-09-05 18:58:32.115522: +2024-09-05 18:58:32.116979: Epoch 279 +2024-09-05 18:58:32.118173: Current learning rate: 0.00745 +2024-09-05 19:03:15.937602: train_loss -0.7339 +2024-09-05 19:03:15.939709: val_loss -0.7273 +2024-09-05 19:03:15.941576: Pseudo dice [0.9599, 0.955, 0.9699, 0.9449, 0.8689, 0.9672, 0.9906] +2024-09-05 19:03:15.942566: Epoch time: 283.83 s +2024-09-05 19:03:18.708963: +2024-09-05 19:03:18.710867: Epoch 280 +2024-09-05 19:03:18.711954: Current learning rate: 0.00744 +2024-09-05 19:07:33.312977: train_loss -0.7343 +2024-09-05 19:07:33.327418: val_loss -0.7594 +2024-09-05 19:07:33.328799: Pseudo dice [0.9649, 0.9523, 0.9791, 0.9387, 0.848, 0.9637, 0.9897] +2024-09-05 19:07:33.330152: Epoch time: 254.61 s +2024-09-05 19:07:36.618073: +2024-09-05 19:07:36.619778: Epoch 281 +2024-09-05 19:07:36.620971: Current learning rate: 0.00743 +2024-09-05 19:12:18.016513: train_loss -0.7332 +2024-09-05 19:12:18.113696: val_loss -0.7124 +2024-09-05 19:12:18.121641: Pseudo dice [0.9614, 0.9531, 0.9735, 0.9464, 0.8787, 0.9683, 0.9927] +2024-09-05 19:12:18.134961: Epoch time: 281.4 s +2024-09-05 19:12:21.813164: +2024-09-05 19:12:21.823513: Epoch 282 +2024-09-05 19:12:21.896406: Current learning rate: 0.00742 +2024-09-05 19:16:51.443347: train_loss -0.73 +2024-09-05 19:16:51.462504: val_loss -0.7305 +2024-09-05 19:16:51.464216: Pseudo dice [0.9626, 0.9564, 0.9752, 0.9479, 0.8448, 0.9677, 0.9905] +2024-09-05 19:16:51.466104: Epoch time: 269.63 s +2024-09-05 19:16:55.783410: +2024-09-05 19:16:55.785026: Epoch 283 +2024-09-05 19:16:55.786270: Current learning rate: 0.00741 +2024-09-05 19:21:40.948424: train_loss -0.7122 +2024-09-05 19:21:40.993582: val_loss -0.7198 +2024-09-05 19:21:40.994843: Pseudo dice [0.9631, 0.955, 0.9769, 0.9489, 0.8706, 0.9648, 0.9895] +2024-09-05 19:21:40.996021: Epoch time: 285.17 s +2024-09-05 19:21:44.527067: +2024-09-05 19:21:44.528311: Epoch 284 +2024-09-05 19:21:44.529747: Current learning rate: 0.0074 +2024-09-05 19:26:18.553802: train_loss -0.7267 +2024-09-05 19:26:18.579329: val_loss -0.7633 +2024-09-05 19:26:18.580504: Pseudo dice [0.9614, 0.9553, 0.9776, 0.9412, 0.8659, 0.966, 0.9897] +2024-09-05 19:26:18.581622: Epoch time: 274.03 s +2024-09-05 19:26:18.582506: Yayy! New best EMA pseudo Dice: 0.9494 +2024-09-05 19:26:27.009441: +2024-09-05 19:26:27.010793: Epoch 285 +2024-09-05 19:26:27.012461: Current learning rate: 0.00739 +2024-09-05 19:30:57.868953: train_loss -0.7253 +2024-09-05 19:30:57.871766: val_loss -0.7662 +2024-09-05 19:30:57.873464: Pseudo dice [0.9616, 0.9545, 0.9763, 0.9492, 0.8755, 0.9641, 0.9921] +2024-09-05 19:30:57.874562: Epoch time: 270.86 s +2024-09-05 19:30:57.875473: Yayy! New best EMA pseudo Dice: 0.9498 +2024-09-05 19:31:07.504920: +2024-09-05 19:31:07.506226: Epoch 286 +2024-09-05 19:31:07.507173: Current learning rate: 0.00738 +2024-09-05 19:35:23.962965: train_loss -0.7316 +2024-09-05 19:35:23.964706: val_loss -0.7359 +2024-09-05 19:35:23.965841: Pseudo dice [0.9622, 0.9568, 0.9752, 0.9457, 0.8765, 0.9672, 0.9917] +2024-09-05 19:35:23.966721: Epoch time: 256.46 s +2024-09-05 19:35:23.967950: Yayy! New best EMA pseudo Dice: 0.9502 +2024-09-05 19:35:31.440835: +2024-09-05 19:35:31.441952: Epoch 287 +2024-09-05 19:35:31.442804: Current learning rate: 0.00738 +2024-09-05 19:40:05.307612: train_loss -0.7182 +2024-09-05 19:40:05.309443: val_loss -0.754 +2024-09-05 19:40:05.310443: Pseudo dice [0.961, 0.9534, 0.9768, 0.9456, 0.8191, 0.9694, 0.987] +2024-09-05 19:40:05.311436: Epoch time: 273.87 s +2024-09-05 19:40:08.218154: +2024-09-05 19:40:08.219359: Epoch 288 +2024-09-05 19:40:08.220280: Current learning rate: 0.00737 +2024-09-05 19:44:53.407161: train_loss -0.7367 +2024-09-05 19:44:53.410100: val_loss -0.7373 +2024-09-05 19:44:53.411831: Pseudo dice [0.9614, 0.9543, 0.9764, 0.9418, 0.877, 0.966, 0.9912] +2024-09-05 19:44:53.413720: Epoch time: 285.19 s +2024-09-05 19:44:56.785960: +2024-09-05 19:44:56.788475: Epoch 289 +2024-09-05 19:44:56.789679: Current learning rate: 0.00736 +2024-09-05 19:49:31.249352: train_loss -0.729 +2024-09-05 19:49:31.255061: val_loss -0.7382 +2024-09-05 19:49:31.256886: Pseudo dice [0.9599, 0.9522, 0.9746, 0.9401, 0.8718, 0.963, 0.9905] +2024-09-05 19:49:31.258408: Epoch time: 274.47 s +2024-09-05 19:49:34.824474: +2024-09-05 19:49:34.838890: Epoch 290 +2024-09-05 19:49:34.840230: Current learning rate: 0.00735 +2024-09-05 19:54:03.111449: train_loss -0.7331 +2024-09-05 19:54:03.127400: val_loss -0.7667 +2024-09-05 19:54:03.128935: Pseudo dice [0.9625, 0.9548, 0.9716, 0.9402, 0.8751, 0.9665, 0.9914] +2024-09-05 19:54:03.130333: Epoch time: 268.29 s +2024-09-05 19:54:05.607253: +2024-09-05 19:54:05.608629: Epoch 291 +2024-09-05 19:54:05.609678: Current learning rate: 0.00734 +2024-09-05 19:58:36.218223: train_loss -0.726 +2024-09-05 19:58:36.221202: val_loss -0.7555 +2024-09-05 19:58:36.222263: Pseudo dice [0.9614, 0.9543, 0.977, 0.9449, 0.8772, 0.9653, 0.9917] +2024-09-05 19:58:36.223160: Epoch time: 270.62 s +2024-09-05 19:58:36.223959: Yayy! New best EMA pseudo Dice: 0.9504 +2024-09-05 19:58:43.580302: +2024-09-05 19:58:43.581997: Epoch 292 +2024-09-05 19:58:43.588326: Current learning rate: 0.00733 +2024-09-05 20:03:17.059344: train_loss -0.7404 +2024-09-05 20:03:17.061256: val_loss -0.7189 +2024-09-05 20:03:17.062736: Pseudo dice [0.9599, 0.9572, 0.9739, 0.9496, 0.8826, 0.967, 0.9918] +2024-09-05 20:03:17.063751: Epoch time: 273.48 s +2024-09-05 20:03:17.065516: Yayy! New best EMA pseudo Dice: 0.9509 +2024-09-05 20:03:26.494471: +2024-09-05 20:03:26.495641: Epoch 293 +2024-09-05 20:03:26.496624: Current learning rate: 0.00732 +2024-09-05 20:07:34.979069: train_loss -0.7288 +2024-09-05 20:07:34.983106: val_loss -0.7462 +2024-09-05 20:07:34.984738: Pseudo dice [0.9604, 0.957, 0.9744, 0.9436, 0.8608, 0.9679, 0.9899] +2024-09-05 20:07:34.986309: Epoch time: 248.49 s +2024-09-05 20:07:38.177582: +2024-09-05 20:07:38.179309: Epoch 294 +2024-09-05 20:07:38.180570: Current learning rate: 0.00731 +2024-09-05 20:12:19.857232: train_loss -0.7314 +2024-09-05 20:12:19.868136: val_loss -0.7806 +2024-09-05 20:12:19.870119: Pseudo dice [0.9574, 0.9525, 0.9704, 0.9425, 0.8537, 0.9669, 0.989] +2024-09-05 20:12:19.872947: Epoch time: 281.68 s +2024-09-05 20:12:22.533396: +2024-09-05 20:12:22.534833: Epoch 295 +2024-09-05 20:12:22.535632: Current learning rate: 0.0073 +2024-09-05 20:16:56.267886: train_loss -0.7346 +2024-09-05 20:16:56.300361: val_loss -0.7254 +2024-09-05 20:16:56.302384: Pseudo dice [0.9628, 0.954, 0.9799, 0.9445, 0.8584, 0.968, 0.9878] +2024-09-05 20:16:56.304140: Epoch time: 273.74 s +2024-09-05 20:16:58.672719: +2024-09-05 20:16:58.674212: Epoch 296 +2024-09-05 20:16:58.675281: Current learning rate: 0.00729 +2024-09-05 20:21:30.710516: train_loss -0.7253 +2024-09-05 20:21:30.726945: val_loss -0.7771 +2024-09-05 20:21:30.728390: Pseudo dice [0.9609, 0.9525, 0.9753, 0.9404, 0.8784, 0.9681, 0.9908] +2024-09-05 20:21:30.729780: Epoch time: 272.04 s +2024-09-05 20:21:33.948747: +2024-09-05 20:21:33.950032: Epoch 297 +2024-09-05 20:21:33.951125: Current learning rate: 0.00728 +2024-09-05 20:26:20.050756: train_loss -0.7233 +2024-09-05 20:26:20.061482: val_loss -0.6885 +2024-09-05 20:26:20.063802: Pseudo dice [0.9626, 0.9578, 0.9709, 0.9423, 0.8293, 0.9573, 0.9872] +2024-09-05 20:26:20.065002: Epoch time: 286.11 s +2024-09-05 20:26:24.583466: +2024-09-05 20:26:24.584794: Epoch 298 +2024-09-05 20:26:24.585756: Current learning rate: 0.00727 +2024-09-05 20:31:00.043938: train_loss -0.7334 +2024-09-05 20:31:00.060028: val_loss -0.7387 +2024-09-05 20:31:00.061771: Pseudo dice [0.9623, 0.955, 0.9751, 0.9422, 0.8766, 0.9685, 0.9923] +2024-09-05 20:31:00.063103: Epoch time: 275.47 s +2024-09-05 20:31:02.700535: +2024-09-05 20:31:02.702024: Epoch 299 +2024-09-05 20:31:02.703069: Current learning rate: 0.00726 +2024-09-05 20:35:43.847983: train_loss -0.7258 +2024-09-05 20:35:43.852853: val_loss -0.7609 +2024-09-05 20:35:43.855958: Pseudo dice [0.9588, 0.9553, 0.9728, 0.946, 0.8677, 0.9675, 0.9896] +2024-09-05 20:35:43.857311: Epoch time: 281.15 s +2024-09-05 20:35:52.169970: +2024-09-05 20:35:52.171193: Epoch 300 +2024-09-05 20:35:52.176616: Current learning rate: 0.00725 +2024-09-05 20:40:14.736849: train_loss -0.7267 +2024-09-05 20:40:14.746499: val_loss -0.7331 +2024-09-05 20:40:14.748089: Pseudo dice [0.9544, 0.9537, 0.967, 0.9396, 0.8492, 0.9688, 0.9891] +2024-09-05 20:40:14.762242: Epoch time: 262.57 s +2024-09-05 20:40:18.646151: +2024-09-05 20:40:18.647528: Epoch 301 +2024-09-05 20:40:18.648572: Current learning rate: 0.00724 +2024-09-05 20:44:35.167990: train_loss -0.7508 +2024-09-05 20:44:35.171148: val_loss -0.7641 +2024-09-05 20:44:35.173513: Pseudo dice [0.9596, 0.9525, 0.9776, 0.9424, 0.8638, 0.9611, 0.9891] +2024-09-05 20:44:35.175643: Epoch time: 256.53 s +2024-09-05 20:44:38.212730: +2024-09-05 20:44:38.226694: Epoch 302 +2024-09-05 20:44:38.227882: Current learning rate: 0.00724 +2024-09-05 20:49:13.843178: train_loss -0.7401 +2024-09-05 20:49:13.847889: val_loss -0.7502 +2024-09-05 20:49:13.849328: Pseudo dice [0.9639, 0.9574, 0.9798, 0.9465, 0.8885, 0.9645, 0.9907] +2024-09-05 20:49:13.850950: Epoch time: 275.64 s +2024-09-05 20:49:17.304260: +2024-09-05 20:49:17.318927: Epoch 303 +2024-09-05 20:49:17.320203: Current learning rate: 0.00723 +2024-09-05 20:53:52.922897: train_loss -0.7243 +2024-09-05 20:53:52.943318: val_loss -0.7428 +2024-09-05 20:53:52.944804: Pseudo dice [0.9565, 0.9503, 0.9739, 0.9436, 0.8636, 0.9534, 0.9914] +2024-09-05 20:53:52.946017: Epoch time: 275.62 s +2024-09-05 20:53:57.690725: +2024-09-05 20:53:57.691846: Epoch 304 +2024-09-05 20:53:57.693287: Current learning rate: 0.00722 +2024-09-05 20:58:40.994570: train_loss -0.7225 +2024-09-05 20:58:41.014215: val_loss -0.7126 +2024-09-05 20:58:41.015627: Pseudo dice [0.9613, 0.9558, 0.9757, 0.9381, 0.8515, 0.9648, 0.9893] +2024-09-05 20:58:41.016871: Epoch time: 283.31 s +2024-09-05 20:58:44.707454: +2024-09-05 20:58:44.709165: Epoch 305 +2024-09-05 20:58:44.710798: Current learning rate: 0.00721 +2024-09-05 21:03:14.229692: train_loss -0.7217 +2024-09-05 21:03:14.243371: val_loss -0.7353 +2024-09-05 21:03:14.244505: Pseudo dice [0.9545, 0.9526, 0.9721, 0.9406, 0.8357, 0.9654, 0.9882] +2024-09-05 21:03:14.245485: Epoch time: 269.53 s +2024-09-05 21:03:17.964854: +2024-09-05 21:03:17.977505: Epoch 306 +2024-09-05 21:03:17.978846: Current learning rate: 0.0072 +2024-09-05 21:07:49.178490: train_loss -0.7239 +2024-09-05 21:07:49.199591: val_loss -0.7666 +2024-09-05 21:07:49.201083: Pseudo dice [0.96, 0.9541, 0.9755, 0.9421, 0.8746, 0.9663, 0.9897] +2024-09-05 21:07:49.202800: Epoch time: 271.22 s +2024-09-05 21:07:52.262474: +2024-09-05 21:07:52.263724: Epoch 307 +2024-09-05 21:07:52.264911: Current learning rate: 0.00719 +2024-09-05 21:12:33.897527: train_loss -0.7337 +2024-09-05 21:12:33.899505: val_loss -0.7618 +2024-09-05 21:12:33.900585: Pseudo dice [0.9637, 0.9598, 0.9782, 0.9439, 0.8796, 0.9635, 0.9912] +2024-09-05 21:12:33.901471: Epoch time: 281.64 s +2024-09-05 21:12:36.223814: +2024-09-05 21:12:36.225074: Epoch 308 +2024-09-05 21:12:36.226072: Current learning rate: 0.00718 +2024-09-05 21:17:07.349830: train_loss -0.7391 +2024-09-05 21:17:07.351929: val_loss -0.706 +2024-09-05 21:17:07.353335: Pseudo dice [0.9604, 0.9558, 0.976, 0.9437, 0.8716, 0.9676, 0.9906] +2024-09-05 21:17:07.354590: Epoch time: 271.13 s +2024-09-05 21:17:10.282764: +2024-09-05 21:17:10.284265: Epoch 309 +2024-09-05 21:17:10.285371: Current learning rate: 0.00717 +2024-09-05 21:21:37.864995: train_loss -0.7366 +2024-09-05 21:21:37.887446: val_loss -0.777 +2024-09-05 21:21:37.888683: Pseudo dice [0.9624, 0.9556, 0.9728, 0.9337, 0.8895, 0.9677, 0.9923] +2024-09-05 21:21:37.889577: Epoch time: 267.59 s +2024-09-05 21:21:41.231487: +2024-09-05 21:21:41.233748: Epoch 310 +2024-09-05 21:21:41.235001: Current learning rate: 0.00716 +2024-09-05 21:26:22.048040: train_loss -0.7277 +2024-09-05 21:26:22.063479: val_loss -0.7288 +2024-09-05 21:26:22.064988: Pseudo dice [0.955, 0.9537, 0.969, 0.9351, 0.8664, 0.9649, 0.9907] +2024-09-05 21:26:22.066612: Epoch time: 280.82 s +2024-09-05 21:26:26.406214: +2024-09-05 21:26:26.407424: Epoch 311 +2024-09-05 21:26:26.408531: Current learning rate: 0.00715 +2024-09-05 21:31:09.157696: train_loss -0.7289 +2024-09-05 21:31:09.170048: val_loss -0.7595 +2024-09-05 21:31:09.171793: Pseudo dice [0.9607, 0.9525, 0.974, 0.946, 0.8732, 0.9661, 0.9905] +2024-09-05 21:31:09.173543: Epoch time: 282.76 s +2024-09-05 21:31:11.815812: +2024-09-05 21:31:11.818499: Epoch 312 +2024-09-05 21:31:11.819509: Current learning rate: 0.00714 +2024-09-05 21:35:40.482459: train_loss -0.7087 +2024-09-05 21:35:40.500410: val_loss -0.6929 +2024-09-05 21:35:40.505604: Pseudo dice [0.9563, 0.9548, 0.9689, 0.9406, 0.8294, 0.9601, 0.9835] +2024-09-05 21:35:40.507108: Epoch time: 268.67 s +2024-09-05 21:35:43.653040: +2024-09-05 21:35:43.654350: Epoch 313 +2024-09-05 21:35:43.655191: Current learning rate: 0.00713 +2024-09-05 21:40:06.986480: train_loss -0.7232 +2024-09-05 21:40:07.006454: val_loss -0.7437 +2024-09-05 21:40:07.009116: Pseudo dice [0.9542, 0.9523, 0.9717, 0.9418, 0.8785, 0.9619, 0.9902] +2024-09-05 21:40:07.010823: Epoch time: 263.34 s +2024-09-05 21:40:12.422087: +2024-09-05 21:40:12.423386: Epoch 314 +2024-09-05 21:40:12.424364: Current learning rate: 0.00712 +2024-09-05 21:44:54.643849: train_loss -0.7031 +2024-09-05 21:44:54.664824: val_loss -0.706 +2024-09-05 21:44:54.665799: Pseudo dice [0.959, 0.9548, 0.9725, 0.9417, 0.8629, 0.9662, 0.9896] +2024-09-05 21:44:54.666841: Epoch time: 282.23 s +2024-09-05 21:44:57.800467: +2024-09-05 21:44:57.801904: Epoch 315 +2024-09-05 21:44:57.803003: Current learning rate: 0.00711 +2024-09-05 21:49:36.661124: train_loss -0.7402 +2024-09-05 21:49:36.703202: val_loss -0.7437 +2024-09-05 21:49:36.715784: Pseudo dice [0.9591, 0.9493, 0.9772, 0.9384, 0.8742, 0.9705, 0.9918] +2024-09-05 21:49:36.717381: Epoch time: 278.87 s +2024-09-05 21:49:40.264870: +2024-09-05 21:49:40.274848: Epoch 316 +2024-09-05 21:49:40.275960: Current learning rate: 0.0071 +2024-09-05 21:54:13.715783: train_loss -0.734 +2024-09-05 21:54:13.732461: val_loss -0.7326 +2024-09-05 21:54:13.733478: Pseudo dice [0.9584, 0.954, 0.9753, 0.9437, 0.8775, 0.9644, 0.9896] +2024-09-05 21:54:13.734473: Epoch time: 273.46 s +2024-09-05 21:54:16.266581: +2024-09-05 21:54:16.267924: Epoch 317 +2024-09-05 21:54:16.269266: Current learning rate: 0.0071 +2024-09-05 21:59:03.910797: train_loss -0.7313 +2024-09-05 21:59:03.923259: val_loss -0.7242 +2024-09-05 21:59:03.924482: Pseudo dice [0.9588, 0.9551, 0.9733, 0.944, 0.859, 0.966, 0.9877] +2024-09-05 21:59:03.925376: Epoch time: 287.65 s +2024-09-05 21:59:07.576958: +2024-09-05 21:59:07.578228: Epoch 318 +2024-09-05 21:59:07.579139: Current learning rate: 0.00709 +2024-09-05 22:03:50.685317: train_loss -0.7238 +2024-09-05 22:03:50.689041: val_loss -0.7228 +2024-09-05 22:03:50.689898: Pseudo dice [0.9602, 0.954, 0.975, 0.94, 0.8459, 0.9599, 0.9873] +2024-09-05 22:03:50.690778: Epoch time: 283.11 s +2024-09-05 22:03:52.899333: +2024-09-05 22:03:52.900754: Epoch 319 +2024-09-05 22:03:52.901988: Current learning rate: 0.00708 +2024-09-05 22:08:21.903008: train_loss -0.7212 +2024-09-05 22:08:21.915565: val_loss -0.7377 +2024-09-05 22:08:21.916837: Pseudo dice [0.9579, 0.9521, 0.9747, 0.9379, 0.877, 0.9557, 0.9894] +2024-09-05 22:08:21.917961: Epoch time: 269.01 s +2024-09-05 22:08:25.071898: +2024-09-05 22:08:25.073298: Epoch 320 +2024-09-05 22:08:25.074359: Current learning rate: 0.00707 +2024-09-05 22:12:40.784490: train_loss -0.723 +2024-09-05 22:12:40.789277: val_loss -0.7398 +2024-09-05 22:12:40.790658: Pseudo dice [0.9578, 0.95, 0.9728, 0.9384, 0.8651, 0.9671, 0.9894] +2024-09-05 22:12:40.792212: Epoch time: 255.72 s +2024-09-05 22:12:43.484683: +2024-09-05 22:12:43.486183: Epoch 321 +2024-09-05 22:12:43.487902: Current learning rate: 0.00706 +2024-09-05 22:17:25.928075: train_loss -0.733 +2024-09-05 22:17:25.951005: val_loss -0.746 +2024-09-05 22:17:25.952400: Pseudo dice [0.9558, 0.9519, 0.9668, 0.9409, 0.8708, 0.969, 0.9923] +2024-09-05 22:17:25.953711: Epoch time: 282.45 s +2024-09-05 22:17:29.195224: +2024-09-05 22:17:29.196616: Epoch 322 +2024-09-05 22:17:29.197598: Current learning rate: 0.00705 +2024-09-05 22:21:58.472160: train_loss -0.7151 +2024-09-05 22:21:58.475292: val_loss -0.6999 +2024-09-05 22:21:58.476897: Pseudo dice [0.9576, 0.953, 0.9749, 0.9404, 0.8695, 0.9649, 0.991] +2024-09-05 22:21:58.478498: Epoch time: 269.28 s +2024-09-05 22:22:00.672226: +2024-09-05 22:22:00.673575: Epoch 323 +2024-09-05 22:22:00.674614: Current learning rate: 0.00704 +2024-09-05 22:26:37.657211: train_loss -0.7295 +2024-09-05 22:26:37.671426: val_loss -0.7404 +2024-09-05 22:26:37.672646: Pseudo dice [0.9606, 0.952, 0.9774, 0.9407, 0.85, 0.9669, 0.9904] +2024-09-05 22:26:37.673995: Epoch time: 276.97 s +2024-09-05 22:26:41.879353: +2024-09-05 22:26:41.880589: Epoch 324 +2024-09-05 22:26:41.881527: Current learning rate: 0.00703 +2024-09-05 22:31:16.308285: train_loss -0.7339 +2024-09-05 22:31:16.311214: val_loss -0.7428 +2024-09-05 22:31:16.313909: Pseudo dice [0.9624, 0.9565, 0.9775, 0.9331, 0.8682, 0.9672, 0.9913] +2024-09-05 22:31:16.315649: Epoch time: 274.43 s +2024-09-05 22:31:19.513926: +2024-09-05 22:31:19.530003: Epoch 325 +2024-09-05 22:31:19.531494: Current learning rate: 0.00702 +2024-09-05 22:36:14.629592: train_loss -0.7054 +2024-09-05 22:36:14.639670: val_loss -0.741 +2024-09-05 22:36:14.641555: Pseudo dice [0.9532, 0.9483, 0.9708, 0.935, 0.8325, 0.9564, 0.989] +2024-09-05 22:36:14.643263: Epoch time: 295.12 s +2024-09-05 22:36:18.055165: +2024-09-05 22:36:18.066003: Epoch 326 +2024-09-05 22:36:18.067853: Current learning rate: 0.00701 +2024-09-05 22:41:03.534624: train_loss -0.7076 +2024-09-05 22:41:03.546597: val_loss -0.7293 +2024-09-05 22:41:03.547881: Pseudo dice [0.9548, 0.9499, 0.9733, 0.9352, 0.8441, 0.9586, 0.9825] +2024-09-05 22:41:03.549168: Epoch time: 285.48 s +2024-09-05 22:41:07.512274: +2024-09-05 22:41:07.513757: Epoch 327 +2024-09-05 22:41:07.516119: Current learning rate: 0.007 +2024-09-05 22:45:44.832167: train_loss -0.7067 +2024-09-05 22:45:44.846861: val_loss -0.7516 +2024-09-05 22:45:44.848314: Pseudo dice [0.9596, 0.9529, 0.9753, 0.9385, 0.8705, 0.9654, 0.9912] +2024-09-05 22:45:44.849953: Epoch time: 277.33 s +2024-09-05 22:45:47.308836: +2024-09-05 22:45:47.311386: Epoch 328 +2024-09-05 22:45:47.312743: Current learning rate: 0.00699 +2024-09-05 22:50:15.299489: train_loss -0.7311 +2024-09-05 22:50:15.303201: val_loss -0.7294 +2024-09-05 22:50:15.304456: Pseudo dice [0.9594, 0.9536, 0.9744, 0.9415, 0.8823, 0.9665, 0.9921] +2024-09-05 22:50:15.305349: Epoch time: 268.0 s +2024-09-05 22:50:17.813329: +2024-09-05 22:50:17.826005: Epoch 329 +2024-09-05 22:50:17.828167: Current learning rate: 0.00698 +2024-09-05 22:54:48.100015: train_loss -0.7333 +2024-09-05 22:54:48.115229: val_loss -0.7711 +2024-09-05 22:54:48.117005: Pseudo dice [0.9608, 0.9542, 0.9761, 0.9462, 0.8884, 0.9681, 0.9918] +2024-09-05 22:54:48.119250: Epoch time: 270.29 s +2024-09-05 22:54:51.732718: +2024-09-05 22:54:51.734084: Epoch 330 +2024-09-05 22:54:51.735261: Current learning rate: 0.00697 +2024-09-05 22:59:23.622987: train_loss -0.718 +2024-09-05 22:59:23.647724: val_loss -0.7422 +2024-09-05 22:59:23.649037: Pseudo dice [0.9617, 0.9557, 0.9759, 0.9447, 0.8594, 0.9673, 0.9896] +2024-09-05 22:59:23.650192: Epoch time: 271.9 s +2024-09-05 22:59:27.518990: +2024-09-05 22:59:27.520495: Epoch 331 +2024-09-05 22:59:27.521427: Current learning rate: 0.00696 +2024-09-05 23:04:00.211958: train_loss -0.7328 +2024-09-05 23:04:00.220838: val_loss -0.7311 +2024-09-05 23:04:00.221835: Pseudo dice [0.9608, 0.9551, 0.9775, 0.9375, 0.8353, 0.9649, 0.9875] +2024-09-05 23:04:00.222819: Epoch time: 272.7 s +2024-09-05 23:04:02.686081: +2024-09-05 23:04:02.687406: Epoch 332 +2024-09-05 23:04:02.688284: Current learning rate: 0.00696 +2024-09-05 23:08:27.076674: train_loss -0.736 +2024-09-05 23:08:27.091191: val_loss -0.7448 +2024-09-05 23:08:27.092198: Pseudo dice [0.9636, 0.9544, 0.9792, 0.9423, 0.8618, 0.9628, 0.9901] +2024-09-05 23:08:27.093139: Epoch time: 264.4 s +2024-09-05 23:08:29.492756: +2024-09-05 23:08:29.502816: Epoch 333 +2024-09-05 23:08:29.503993: Current learning rate: 0.00695 +2024-09-05 23:13:12.304028: train_loss -0.746 +2024-09-05 23:13:12.339356: val_loss -0.7578 +2024-09-05 23:13:12.341053: Pseudo dice [0.963, 0.9583, 0.9777, 0.9474, 0.8816, 0.9676, 0.9929] +2024-09-05 23:13:12.343200: Epoch time: 282.82 s +2024-09-05 23:13:15.617669: +2024-09-05 23:13:15.634840: Epoch 334 +2024-09-05 23:13:15.636206: Current learning rate: 0.00694 +2024-09-05 23:17:54.637521: train_loss -0.7279 +2024-09-05 23:17:54.653998: val_loss -0.7231 +2024-09-05 23:17:54.655204: Pseudo dice [0.9576, 0.9561, 0.9709, 0.9366, 0.8734, 0.936, 0.9904] +2024-09-05 23:17:54.656329: Epoch time: 279.02 s +2024-09-05 23:17:57.907143: +2024-09-05 23:17:57.908325: Epoch 335 +2024-09-05 23:17:57.909240: Current learning rate: 0.00693 +2024-09-05 23:22:21.426184: train_loss -0.7307 +2024-09-05 23:22:21.451507: val_loss -0.7508 +2024-09-05 23:22:21.452970: Pseudo dice [0.9592, 0.9522, 0.9759, 0.941, 0.8755, 0.9621, 0.9916] +2024-09-05 23:22:21.454228: Epoch time: 263.52 s +2024-09-05 23:22:24.981190: +2024-09-05 23:22:24.987867: Epoch 336 +2024-09-05 23:22:24.989602: Current learning rate: 0.00692 +2024-09-05 23:27:10.223459: train_loss -0.7285 +2024-09-05 23:27:10.235379: val_loss -0.7423 +2024-09-05 23:27:10.237371: Pseudo dice [0.9571, 0.9493, 0.971, 0.9379, 0.8646, 0.9599, 0.9907] +2024-09-05 23:27:10.238938: Epoch time: 285.25 s +2024-09-05 23:27:12.651618: +2024-09-05 23:27:12.653175: Epoch 337 +2024-09-05 23:27:12.654493: Current learning rate: 0.00691 +2024-09-05 23:31:39.698559: train_loss -0.7243 +2024-09-05 23:31:39.703060: val_loss -0.7262 +2024-09-05 23:31:39.704038: Pseudo dice [0.9608, 0.9562, 0.9769, 0.9335, 0.8314, 0.9666, 0.9862] +2024-09-05 23:31:39.705075: Epoch time: 267.05 s +2024-09-05 23:31:43.926380: +2024-09-05 23:31:43.927841: Epoch 338 +2024-09-05 23:31:43.928892: Current learning rate: 0.0069 +2024-09-05 23:36:35.795580: train_loss -0.7356 +2024-09-05 23:36:35.799098: val_loss -0.7418 +2024-09-05 23:36:35.801806: Pseudo dice [0.9615, 0.9558, 0.9751, 0.9376, 0.8729, 0.9614, 0.9881] +2024-09-05 23:36:35.802697: Epoch time: 291.87 s +2024-09-05 23:36:38.230570: +2024-09-05 23:36:38.231720: Epoch 339 +2024-09-05 23:36:38.232567: Current learning rate: 0.00689 +2024-09-05 23:40:58.779525: train_loss -0.7402 +2024-09-05 23:40:58.815879: val_loss -0.7472 +2024-09-05 23:40:58.817671: Pseudo dice [0.9612, 0.9546, 0.9752, 0.9335, 0.884, 0.9614, 0.9905] +2024-09-05 23:40:58.818823: Epoch time: 260.55 s +2024-09-05 23:41:01.869265: +2024-09-05 23:41:01.870785: Epoch 340 +2024-09-05 23:41:01.872148: Current learning rate: 0.00688 +2024-09-05 23:45:43.973099: train_loss -0.7273 +2024-09-05 23:45:43.976532: val_loss -0.7035 +2024-09-05 23:45:43.978008: Pseudo dice [0.9588, 0.9543, 0.976, 0.9487, 0.8758, 0.9661, 0.9922] +2024-09-05 23:45:43.979286: Epoch time: 282.11 s +2024-09-05 23:45:47.664528: +2024-09-05 23:45:47.665957: Epoch 341 +2024-09-05 23:45:47.666986: Current learning rate: 0.00687 +2024-09-05 23:50:17.408347: train_loss -0.7267 +2024-09-05 23:50:17.420598: val_loss -0.7101 +2024-09-05 23:50:17.421799: Pseudo dice [0.9602, 0.9546, 0.9782, 0.9497, 0.8738, 0.9646, 0.991] +2024-09-05 23:50:17.423562: Epoch time: 269.75 s +2024-09-05 23:50:21.510331: +2024-09-05 23:50:21.511425: Epoch 342 +2024-09-05 23:50:21.512401: Current learning rate: 0.00686 +2024-09-05 23:55:00.262767: train_loss -0.7326 +2024-09-05 23:55:00.264522: val_loss -0.716 +2024-09-05 23:55:00.265588: Pseudo dice [0.9597, 0.9544, 0.9754, 0.9406, 0.8689, 0.9656, 0.9907] +2024-09-05 23:55:00.278952: Epoch time: 278.76 s +2024-09-05 23:55:03.443345: +2024-09-05 23:55:03.444742: Epoch 343 +2024-09-05 23:55:03.446797: Current learning rate: 0.00685 +2024-09-05 23:59:51.293909: train_loss -0.7294 +2024-09-05 23:59:51.312338: val_loss -0.7619 +2024-09-05 23:59:51.313690: Pseudo dice [0.9628, 0.9561, 0.9771, 0.9483, 0.8908, 0.9665, 0.9911] +2024-09-05 23:59:51.315225: Epoch time: 287.86 s +2024-09-05 23:59:54.102734: +2024-09-05 23:59:54.103962: Epoch 344 +2024-09-05 23:59:54.104904: Current learning rate: 0.00684 +2024-09-06 00:04:39.356379: train_loss -0.7311 +2024-09-06 00:04:39.358819: val_loss -0.7751 +2024-09-06 00:04:39.360674: Pseudo dice [0.9596, 0.9562, 0.9699, 0.943, 0.8882, 0.9701, 0.9933] +2024-09-06 00:04:39.362431: Epoch time: 285.26 s +2024-09-06 00:04:39.363894: Yayy! New best EMA pseudo Dice: 0.9511 +2024-09-06 00:04:48.589489: +2024-09-06 00:04:48.591607: Epoch 345 +2024-09-06 00:04:48.592528: Current learning rate: 0.00683 +2024-09-06 00:09:22.885288: train_loss -0.7388 +2024-09-06 00:09:22.900712: val_loss -0.7779 +2024-09-06 00:09:22.902648: Pseudo dice [0.96, 0.9582, 0.9734, 0.9419, 0.872, 0.9654, 0.9889] +2024-09-06 00:09:22.904726: Epoch time: 274.3 s +2024-09-06 00:09:22.906465: Yayy! New best EMA pseudo Dice: 0.9511 +2024-09-06 00:09:31.687281: +2024-09-06 00:09:31.688751: Epoch 346 +2024-09-06 00:09:31.689653: Current learning rate: 0.00682 +2024-09-06 00:13:47.505751: train_loss -0.7343 +2024-09-06 00:13:47.519697: val_loss -0.7387 +2024-09-06 00:13:47.520833: Pseudo dice [0.9602, 0.9547, 0.976, 0.9442, 0.8747, 0.9675, 0.9922] +2024-09-06 00:13:47.521916: Epoch time: 255.82 s +2024-09-06 00:13:47.523437: Yayy! New best EMA pseudo Dice: 0.9513 +2024-09-06 00:13:56.253757: +2024-09-06 00:13:56.255070: Epoch 347 +2024-09-06 00:13:56.255969: Current learning rate: 0.00681 +2024-09-06 00:18:24.117980: train_loss -0.7244 +2024-09-06 00:18:24.139345: val_loss -0.7402 +2024-09-06 00:18:24.140413: Pseudo dice [0.9595, 0.9538, 0.9741, 0.9439, 0.8612, 0.9609, 0.9895] +2024-09-06 00:18:24.141292: Epoch time: 267.87 s +2024-09-06 00:18:26.790852: +2024-09-06 00:18:26.797728: Epoch 348 +2024-09-06 00:18:26.799514: Current learning rate: 0.0068 +2024-09-06 00:22:58.566623: train_loss -0.7267 +2024-09-06 00:22:58.569450: val_loss -0.7613 +2024-09-06 00:22:58.570246: Pseudo dice [0.9595, 0.9542, 0.9728, 0.948, 0.8727, 0.9662, 0.9916] +2024-09-06 00:22:58.571062: Epoch time: 271.78 s +2024-09-06 00:23:02.397263: +2024-09-06 00:23:02.398700: Epoch 349 +2024-09-06 00:23:02.399895: Current learning rate: 0.0068 +2024-09-06 00:27:49.308518: train_loss -0.7185 +2024-09-06 00:27:49.329291: val_loss -0.6978 +2024-09-06 00:27:49.330832: Pseudo dice [0.9595, 0.9559, 0.9751, 0.9444, 0.8123, 0.9612, 0.9828] +2024-09-06 00:27:49.332305: Epoch time: 286.92 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_00_30_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_00_30_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..54d0721401d0081bfb770e072658a8a3bb9c79ad --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_00_30_15.txt @@ -0,0 +1,378 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 00:30:19.025128: Using torch.compile... +2024-09-06 00:30:30.158703: do_dummy_2d_data_aug: False +2024-09-06 00:30:30.164604: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 00:30:30.166191: The split file contains 5 splits. +2024-09-06 00:30:30.167237: Desired fold for training: 3 +2024-09-06 00:30:30.167976: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 00:30:42.968832: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 00:30:44.219528: +2024-09-06 00:30:44.235687: Epoch 350 +2024-09-06 00:30:44.238041: Current learning rate: 0.00679 +2024-09-06 00:49:19.136670: train_loss -0.7208 +2024-09-06 00:49:19.139463: val_loss -0.7447 +2024-09-06 00:49:19.140620: Pseudo dice [0.963, 0.9586, 0.9774, 0.9438, 0.8423, 0.9697, 0.9874] +2024-09-06 00:49:19.142630: Epoch time: 1114.93 s +2024-09-06 00:49:20.821031: +2024-09-06 00:49:20.822027: Epoch 351 +2024-09-06 00:49:20.822827: Current learning rate: 0.00678 +2024-09-06 00:54:08.077754: train_loss -0.7392 +2024-09-06 00:54:08.081187: val_loss -0.7362 +2024-09-06 00:54:08.083094: Pseudo dice [0.9635, 0.9578, 0.9787, 0.9467, 0.8843, 0.9657, 0.9916] +2024-09-06 00:54:08.084478: Epoch time: 287.26 s +2024-09-06 00:54:11.153883: +2024-09-06 00:54:11.155146: Epoch 352 +2024-09-06 00:54:11.156980: Current learning rate: 0.00677 +2024-09-06 00:59:01.903059: train_loss -0.7336 +2024-09-06 00:59:01.916124: val_loss -0.7558 +2024-09-06 00:59:01.918354: Pseudo dice [0.9606, 0.9559, 0.9765, 0.9487, 0.8874, 0.9671, 0.9902] +2024-09-06 00:59:01.926140: Epoch time: 290.75 s +2024-09-06 00:59:04.742223: +2024-09-06 00:59:04.751731: Epoch 353 +2024-09-06 00:59:04.753641: Current learning rate: 0.00676 +2024-09-06 01:03:56.557023: train_loss -0.7221 +2024-09-06 01:03:56.571373: val_loss -0.7514 +2024-09-06 01:03:56.573926: Pseudo dice [0.963, 0.9583, 0.9763, 0.9478, 0.8707, 0.9645, 0.9912] +2024-09-06 01:03:56.575022: Epoch time: 291.82 s +2024-09-06 01:03:56.576699: Yayy! New best EMA pseudo Dice: 0.9513 +2024-09-06 01:04:07.376640: +2024-09-06 01:04:07.378074: Epoch 354 +2024-09-06 01:04:07.379363: Current learning rate: 0.00675 +2024-09-06 01:08:54.671856: train_loss -0.7408 +2024-09-06 01:08:54.679314: val_loss -0.7555 +2024-09-06 01:08:54.680452: Pseudo dice [0.9621, 0.9566, 0.9758, 0.9421, 0.8862, 0.9705, 0.9922] +2024-09-06 01:08:54.682346: Epoch time: 287.3 s +2024-09-06 01:08:54.684420: Yayy! New best EMA pseudo Dice: 0.9516 +2024-09-06 01:09:04.295541: +2024-09-06 01:09:04.296714: Epoch 355 +2024-09-06 01:09:04.297820: Current learning rate: 0.00674 +2024-09-06 01:13:48.910236: train_loss -0.7278 +2024-09-06 01:13:48.915976: val_loss -0.7566 +2024-09-06 01:13:48.917398: Pseudo dice [0.9621, 0.9572, 0.9745, 0.9506, 0.8773, 0.965, 0.9921] +2024-09-06 01:13:48.918644: Epoch time: 284.62 s +2024-09-06 01:13:48.920004: Yayy! New best EMA pseudo Dice: 0.9519 +2024-09-06 01:13:56.454925: +2024-09-06 01:13:56.456283: Epoch 356 +2024-09-06 01:13:56.457334: Current learning rate: 0.00673 +2024-09-06 01:18:30.515396: train_loss -0.7258 +2024-09-06 01:18:30.542404: val_loss -0.7456 +2024-09-06 01:18:30.545675: Pseudo dice [0.9592, 0.9575, 0.9731, 0.951, 0.889, 0.966, 0.9914] +2024-09-06 01:18:30.549237: Epoch time: 274.06 s +2024-09-06 01:18:30.552144: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-06 01:18:41.528905: +2024-09-06 01:18:41.530315: Epoch 357 +2024-09-06 01:18:41.531595: Current learning rate: 0.00672 +2024-09-06 01:23:35.096003: train_loss -0.7297 +2024-09-06 01:23:35.113452: val_loss -0.7 +2024-09-06 01:23:35.117203: Pseudo dice [0.9578, 0.9577, 0.9713, 0.9414, 0.8881, 0.9695, 0.9916] +2024-09-06 01:23:35.118720: Epoch time: 293.57 s +2024-09-06 01:23:35.120094: Yayy! New best EMA pseudo Dice: 0.9524 +2024-09-06 01:23:44.354671: +2024-09-06 01:23:44.356105: Epoch 358 +2024-09-06 01:23:44.357564: Current learning rate: 0.00671 +2024-09-06 01:28:15.182123: train_loss -0.7366 +2024-09-06 01:28:15.208135: val_loss -0.7574 +2024-09-06 01:28:15.209278: Pseudo dice [0.9619, 0.9573, 0.9747, 0.9472, 0.8844, 0.964, 0.9924] +2024-09-06 01:28:15.210501: Epoch time: 270.83 s +2024-09-06 01:28:15.211689: Yayy! New best EMA pseudo Dice: 0.9526 +2024-09-06 01:28:24.367217: +2024-09-06 01:28:24.369011: Epoch 359 +2024-09-06 01:28:24.370032: Current learning rate: 0.0067 +2024-09-06 01:33:16.632250: train_loss -0.7207 +2024-09-06 01:33:16.676493: val_loss -0.6845 +2024-09-06 01:33:16.678287: Pseudo dice [0.9607, 0.9551, 0.9736, 0.9398, 0.8488, 0.9626, 0.9882] +2024-09-06 01:33:16.680015: Epoch time: 292.27 s +2024-09-06 01:33:21.034537: +2024-09-06 01:33:21.035789: Epoch 360 +2024-09-06 01:33:21.037076: Current learning rate: 0.00669 +2024-09-06 01:38:19.043660: train_loss -0.7416 +2024-09-06 01:38:19.047354: val_loss -0.7475 +2024-09-06 01:38:19.049517: Pseudo dice [0.957, 0.9548, 0.972, 0.9442, 0.8809, 0.9633, 0.9911] +2024-09-06 01:38:19.050573: Epoch time: 298.01 s +2024-09-06 01:38:22.908569: +2024-09-06 01:38:22.926721: Epoch 361 +2024-09-06 01:38:22.928680: Current learning rate: 0.00668 +2024-09-06 01:43:07.618500: train_loss -0.7485 +2024-09-06 01:43:07.639573: val_loss -0.7296 +2024-09-06 01:43:07.641123: Pseudo dice [0.9614, 0.9566, 0.9744, 0.9472, 0.8824, 0.9691, 0.9921] +2024-09-06 01:43:07.642658: Epoch time: 284.72 s +2024-09-06 01:43:10.794489: +2024-09-06 01:43:10.796288: Epoch 362 +2024-09-06 01:43:10.797825: Current learning rate: 0.00667 +2024-09-06 01:48:14.139359: train_loss -0.758 +2024-09-06 01:48:14.142407: val_loss -0.7648 +2024-09-06 01:48:14.143531: Pseudo dice [0.9626, 0.957, 0.9773, 0.9436, 0.8751, 0.9698, 0.9929] +2024-09-06 01:48:14.144538: Epoch time: 303.35 s +2024-09-06 01:48:16.886267: +2024-09-06 01:48:16.888610: Epoch 363 +2024-09-06 01:48:16.890287: Current learning rate: 0.00666 +2024-09-06 01:53:09.436005: train_loss -0.7293 +2024-09-06 01:53:09.449347: val_loss -0.722 +2024-09-06 01:53:09.450819: Pseudo dice [0.9638, 0.9594, 0.9781, 0.952, 0.8875, 0.9703, 0.9922] +2024-09-06 01:53:09.452727: Epoch time: 292.56 s +2024-09-06 01:53:09.453571: Yayy! New best EMA pseudo Dice: 0.953 +2024-09-06 01:53:21.400921: +2024-09-06 01:53:21.516701: Epoch 364 +2024-09-06 01:53:21.579576: Current learning rate: 0.00665 +2024-09-06 01:58:22.989880: train_loss -0.7293 +2024-09-06 01:58:22.992970: val_loss -0.7257 +2024-09-06 01:58:22.994894: Pseudo dice [0.962, 0.9554, 0.9759, 0.9447, 0.8536, 0.9685, 0.9898] +2024-09-06 01:58:22.996192: Epoch time: 301.59 s +2024-09-06 01:58:25.427354: +2024-09-06 01:58:25.429567: Epoch 365 +2024-09-06 01:58:25.431279: Current learning rate: 0.00665 +2024-09-06 02:03:03.524529: train_loss -0.728 +2024-09-06 02:03:03.527239: val_loss -0.7419 +2024-09-06 02:03:03.529393: Pseudo dice [0.9607, 0.9566, 0.977, 0.941, 0.862, 0.9614, 0.9895] +2024-09-06 02:03:03.530752: Epoch time: 278.1 s +2024-09-06 02:03:06.786918: +2024-09-06 02:03:06.788365: Epoch 366 +2024-09-06 02:03:06.790079: Current learning rate: 0.00664 +2024-09-06 02:08:01.998868: train_loss -0.7286 +2024-09-06 02:08:02.016339: val_loss -0.7519 +2024-09-06 02:08:02.018135: Pseudo dice [0.963, 0.9568, 0.9763, 0.9393, 0.8564, 0.9668, 0.9884] +2024-09-06 02:08:02.019548: Epoch time: 295.22 s +2024-09-06 02:08:06.072746: +2024-09-06 02:08:06.074074: Epoch 367 +2024-09-06 02:08:06.076448: Current learning rate: 0.00663 +2024-09-06 02:13:18.233422: train_loss -0.7335 +2024-09-06 02:13:18.275796: val_loss -0.7358 +2024-09-06 02:13:18.277688: Pseudo dice [0.9598, 0.9567, 0.9753, 0.9458, 0.8745, 0.9577, 0.9896] +2024-09-06 02:13:18.279571: Epoch time: 312.17 s +2024-09-06 02:13:22.027887: +2024-09-06 02:13:22.029462: Epoch 368 +2024-09-06 02:13:22.030872: Current learning rate: 0.00662 +2024-09-06 02:18:31.842985: train_loss -0.7376 +2024-09-06 02:18:31.859588: val_loss -0.7653 +2024-09-06 02:18:31.861356: Pseudo dice [0.9598, 0.9575, 0.9758, 0.9457, 0.8766, 0.9722, 0.9913] +2024-09-06 02:18:31.875723: Epoch time: 309.82 s +2024-09-06 02:18:35.495038: +2024-09-06 02:18:35.504947: Epoch 369 +2024-09-06 02:18:35.506430: Current learning rate: 0.00661 +2024-09-06 02:23:31.316798: train_loss -0.7373 +2024-09-06 02:23:31.331795: val_loss -0.7858 +2024-09-06 02:23:31.333088: Pseudo dice [0.9554, 0.9533, 0.9699, 0.9465, 0.885, 0.9676, 0.9924] +2024-09-06 02:23:31.334656: Epoch time: 295.83 s +2024-09-06 02:23:34.270051: +2024-09-06 02:23:34.271831: Epoch 370 +2024-09-06 02:23:34.273063: Current learning rate: 0.0066 +2024-09-06 02:28:28.356844: train_loss -0.742 +2024-09-06 02:28:28.379259: val_loss -0.715 +2024-09-06 02:28:28.381567: Pseudo dice [0.9613, 0.9574, 0.9766, 0.9483, 0.8844, 0.9665, 0.9912] +2024-09-06 02:28:28.383321: Epoch time: 294.09 s +2024-09-06 02:28:32.364031: +2024-09-06 02:28:32.365502: Epoch 371 +2024-09-06 02:28:32.367656: Current learning rate: 0.00659 +2024-09-06 02:33:33.202755: train_loss -0.7358 +2024-09-06 02:33:33.217060: val_loss -0.7441 +2024-09-06 02:33:33.218357: Pseudo dice [0.9609, 0.956, 0.9755, 0.9383, 0.8792, 0.9615, 0.9907] +2024-09-06 02:33:33.219826: Epoch time: 300.84 s +2024-09-06 02:33:36.908523: +2024-09-06 02:33:36.909845: Epoch 372 +2024-09-06 02:33:36.911127: Current learning rate: 0.00658 +2024-09-06 02:38:25.927481: train_loss -0.7461 +2024-09-06 02:38:25.930080: val_loss -0.7166 +2024-09-06 02:38:25.931904: Pseudo dice [0.9595, 0.9487, 0.9712, 0.9406, 0.8397, 0.9602, 0.9882] +2024-09-06 02:38:25.933643: Epoch time: 289.02 s +2024-09-06 02:38:29.405674: +2024-09-06 02:38:29.407667: Epoch 373 +2024-09-06 02:38:29.409495: Current learning rate: 0.00657 +2024-09-06 02:43:30.670896: train_loss -0.731 +2024-09-06 02:43:30.673673: val_loss -0.7448 +2024-09-06 02:43:30.675694: Pseudo dice [0.9641, 0.9573, 0.9801, 0.9507, 0.8657, 0.9637, 0.984] +2024-09-06 02:43:30.677108: Epoch time: 301.27 s +2024-09-06 02:43:34.754511: +2024-09-06 02:43:34.756163: Epoch 374 +2024-09-06 02:43:34.757849: Current learning rate: 0.00656 +2024-09-06 02:48:40.069681: train_loss -0.7293 +2024-09-06 02:48:40.072117: val_loss -0.7459 +2024-09-06 02:48:40.073668: Pseudo dice [0.959, 0.9547, 0.9748, 0.9395, 0.8714, 0.9656, 0.9908] +2024-09-06 02:48:40.074884: Epoch time: 305.32 s +2024-09-06 02:48:43.701798: +2024-09-06 02:48:43.718183: Epoch 375 +2024-09-06 02:48:43.719752: Current learning rate: 0.00655 +2024-09-06 02:53:35.975435: train_loss -0.7353 +2024-09-06 02:53:35.977831: val_loss -0.7331 +2024-09-06 02:53:35.979533: Pseudo dice [0.9614, 0.9552, 0.9755, 0.9396, 0.8516, 0.9626, 0.9887] +2024-09-06 02:53:35.983714: Epoch time: 292.28 s +2024-09-06 02:53:38.852412: +2024-09-06 02:53:38.853749: Epoch 376 +2024-09-06 02:53:38.854694: Current learning rate: 0.00654 +2024-09-06 02:58:20.426152: train_loss -0.7306 +2024-09-06 02:58:20.442695: val_loss -0.7482 +2024-09-06 02:58:20.444244: Pseudo dice [0.9628, 0.9573, 0.9782, 0.9467, 0.8903, 0.9701, 0.9922] +2024-09-06 02:58:20.446027: Epoch time: 281.58 s +2024-09-06 02:58:24.025774: +2024-09-06 02:58:24.034841: Epoch 377 +2024-09-06 02:58:24.036428: Current learning rate: 0.00653 +2024-09-06 03:03:23.202924: train_loss -0.7394 +2024-09-06 03:03:23.216366: val_loss -0.7173 +2024-09-06 03:03:23.218295: Pseudo dice [0.9587, 0.9546, 0.9716, 0.9403, 0.8863, 0.9694, 0.9924] +2024-09-06 03:03:23.221207: Epoch time: 299.18 s +2024-09-06 03:03:26.926356: +2024-09-06 03:03:26.927788: Epoch 378 +2024-09-06 03:03:26.929670: Current learning rate: 0.00652 +2024-09-06 03:08:24.978012: train_loss -0.7429 +2024-09-06 03:08:24.992287: val_loss -0.767 +2024-09-06 03:08:24.994178: Pseudo dice [0.962, 0.9543, 0.9773, 0.9445, 0.8762, 0.9707, 0.9915] +2024-09-06 03:08:24.996043: Epoch time: 298.06 s +2024-09-06 03:08:28.220679: +2024-09-06 03:08:28.230713: Epoch 379 +2024-09-06 03:08:28.232369: Current learning rate: 0.00651 +2024-09-06 03:13:14.530707: train_loss -0.7461 +2024-09-06 03:13:14.533401: val_loss -0.7344 +2024-09-06 03:13:14.535125: Pseudo dice [0.9631, 0.9563, 0.9774, 0.9414, 0.8905, 0.9684, 0.9925] +2024-09-06 03:13:14.536828: Epoch time: 286.32 s +2024-09-06 03:13:17.183256: +2024-09-06 03:13:17.185011: Epoch 380 +2024-09-06 03:13:17.186648: Current learning rate: 0.0065 +2024-09-06 03:18:10.183800: train_loss -0.74 +2024-09-06 03:18:10.199618: val_loss -0.7275 +2024-09-06 03:18:10.201239: Pseudo dice [0.9559, 0.957, 0.9704, 0.9411, 0.8604, 0.9674, 0.9887] +2024-09-06 03:18:10.202627: Epoch time: 293.01 s +2024-09-06 03:18:13.561422: +2024-09-06 03:18:13.562952: Epoch 381 +2024-09-06 03:18:13.564531: Current learning rate: 0.00649 +2024-09-06 03:23:10.358963: train_loss -0.746 +2024-09-06 03:23:10.382184: val_loss -0.731 +2024-09-06 03:23:10.384437: Pseudo dice [0.963, 0.9582, 0.9773, 0.9434, 0.8799, 0.9684, 0.9924] +2024-09-06 03:23:10.386736: Epoch time: 296.8 s +2024-09-06 03:23:14.743086: +2024-09-06 03:23:14.744519: Epoch 382 +2024-09-06 03:23:14.746436: Current learning rate: 0.00648 +2024-09-06 03:28:09.988845: train_loss -0.731 +2024-09-06 03:28:10.009973: val_loss -0.7306 +2024-09-06 03:28:10.012447: Pseudo dice [0.9586, 0.9555, 0.9732, 0.9428, 0.8821, 0.9707, 0.9911] +2024-09-06 03:28:10.013827: Epoch time: 295.25 s +2024-09-06 03:28:12.624229: +2024-09-06 03:28:12.626098: Epoch 383 +2024-09-06 03:28:12.627993: Current learning rate: 0.00648 +2024-09-06 03:33:10.138829: train_loss -0.7236 +2024-09-06 03:33:10.141032: val_loss -0.7331 +2024-09-06 03:33:10.142994: Pseudo dice [0.9532, 0.9489, 0.9698, 0.9368, 0.875, 0.9568, 0.9912] +2024-09-06 03:33:10.145881: Epoch time: 297.52 s +2024-09-06 03:33:13.524894: +2024-09-06 03:33:13.526757: Epoch 384 +2024-09-06 03:33:13.529173: Current learning rate: 0.00647 +2024-09-06 03:38:30.605616: train_loss -0.7284 +2024-09-06 03:38:30.609302: val_loss -0.7637 +2024-09-06 03:38:30.610760: Pseudo dice [0.9606, 0.9547, 0.9756, 0.9488, 0.882, 0.9702, 0.9928] +2024-09-06 03:38:30.611941: Epoch time: 317.09 s +2024-09-06 03:38:33.401674: +2024-09-06 03:38:33.403115: Epoch 385 +2024-09-06 03:38:33.404157: Current learning rate: 0.00646 +2024-09-06 03:43:21.052878: train_loss -0.7361 +2024-09-06 03:43:21.055344: val_loss -0.7499 +2024-09-06 03:43:21.056500: Pseudo dice [0.9599, 0.9545, 0.973, 0.943, 0.8846, 0.9702, 0.9916] +2024-09-06 03:43:21.057485: Epoch time: 287.66 s +2024-09-06 03:43:23.730694: +2024-09-06 03:43:23.732346: Epoch 386 +2024-09-06 03:43:23.733275: Current learning rate: 0.00645 +2024-09-06 03:48:10.561608: train_loss -0.719 +2024-09-06 03:48:10.579146: val_loss -0.7299 +2024-09-06 03:48:10.580688: Pseudo dice [0.9601, 0.9559, 0.9753, 0.9334, 0.8604, 0.9515, 0.989] +2024-09-06 03:48:10.582624: Epoch time: 286.84 s +2024-09-06 03:48:13.933356: +2024-09-06 03:48:13.935265: Epoch 387 +2024-09-06 03:48:13.936410: Current learning rate: 0.00644 +2024-09-06 03:53:06.811089: train_loss -0.6796 +2024-09-06 03:53:06.841623: val_loss -0.6913 +2024-09-06 03:53:06.844813: Pseudo dice [0.9487, 0.9477, 0.9622, 0.9241, 0.8397, 0.9346, 0.9808] +2024-09-06 03:53:06.846066: Epoch time: 292.88 s +2024-09-06 03:53:10.126526: +2024-09-06 03:53:10.146012: Epoch 388 +2024-09-06 03:53:10.155072: Current learning rate: 0.00643 +2024-09-06 03:57:49.303110: train_loss -0.6556 +2024-09-06 03:57:49.332777: val_loss -0.6859 +2024-09-06 03:57:49.335279: Pseudo dice [0.939, 0.9362, 0.9565, 0.9178, 0.829, 0.9378, 0.9848] +2024-09-06 03:57:49.337499: Epoch time: 279.18 s +2024-09-06 03:57:54.423101: +2024-09-06 03:57:54.424698: Epoch 389 +2024-09-06 03:57:54.425885: Current learning rate: 0.00642 +2024-09-06 04:02:52.788303: train_loss -0.6881 +2024-09-06 04:02:52.817196: val_loss -0.663 +2024-09-06 04:02:52.819046: Pseudo dice [0.9455, 0.9456, 0.9661, 0.9238, 0.8306, 0.9401, 0.9763] +2024-09-06 04:02:52.821055: Epoch time: 298.37 s +2024-09-06 04:02:55.918566: +2024-09-06 04:02:55.920089: Epoch 390 +2024-09-06 04:02:55.922247: Current learning rate: 0.00641 +2024-09-06 04:07:34.671868: train_loss -0.6662 +2024-09-06 04:07:34.698828: val_loss -0.7031 +2024-09-06 04:07:34.700783: Pseudo dice [0.953, 0.9488, 0.962, 0.931, 0.8465, 0.9589, 0.9898] +2024-09-06 04:07:34.703035: Epoch time: 278.76 s +2024-09-06 04:07:38.107835: +2024-09-06 04:07:38.135569: Epoch 391 +2024-09-06 04:07:38.137628: Current learning rate: 0.0064 +2024-09-06 04:12:24.090191: train_loss -0.6983 +2024-09-06 04:12:24.105708: val_loss -0.751 +2024-09-06 04:12:24.107512: Pseudo dice [0.9497, 0.9474, 0.9652, 0.9363, 0.8604, 0.9606, 0.9897] +2024-09-06 04:12:24.108754: Epoch time: 285.99 s +2024-09-06 04:12:26.977497: +2024-09-06 04:12:26.978875: Epoch 392 +2024-09-06 04:12:26.980279: Current learning rate: 0.00639 +2024-09-06 04:17:43.490790: train_loss -0.7095 +2024-09-06 04:17:43.499764: val_loss -0.7746 +2024-09-06 04:17:43.506172: Pseudo dice [0.9566, 0.9515, 0.9739, 0.9427, 0.8768, 0.9656, 0.9923] +2024-09-06 04:17:43.509255: Epoch time: 316.52 s +2024-09-06 04:17:46.688647: +2024-09-06 04:17:46.690547: Epoch 393 +2024-09-06 04:17:46.692462: Current learning rate: 0.00638 +2024-09-06 04:22:44.042249: train_loss -0.7284 +2024-09-06 04:22:44.045975: val_loss -0.7522 +2024-09-06 04:22:44.047554: Pseudo dice [0.9618, 0.9575, 0.9753, 0.9396, 0.8791, 0.9673, 0.9915] +2024-09-06 04:22:44.067723: Epoch time: 297.36 s +2024-09-06 04:22:47.305255: +2024-09-06 04:22:47.306945: Epoch 394 +2024-09-06 04:22:47.308234: Current learning rate: 0.00637 +2024-09-06 04:27:41.144197: train_loss -0.7289 +2024-09-06 04:27:41.161784: val_loss -0.7802 +2024-09-06 04:27:41.163310: Pseudo dice [0.9621, 0.9538, 0.978, 0.9447, 0.8862, 0.9667, 0.9922] +2024-09-06 04:27:41.164811: Epoch time: 293.84 s +2024-09-06 04:27:45.129642: +2024-09-06 04:27:45.133715: Epoch 395 +2024-09-06 04:27:45.136128: Current learning rate: 0.00636 +2024-09-06 04:32:27.396663: train_loss -0.6939 +2024-09-06 04:32:27.399022: val_loss -0.7125 +2024-09-06 04:32:27.400233: Pseudo dice [0.9508, 0.9479, 0.9674, 0.9361, 0.8574, 0.9639, 0.9908] +2024-09-06 04:32:27.401438: Epoch time: 282.27 s +2024-09-06 04:32:30.610497: +2024-09-06 04:32:30.611979: Epoch 396 +2024-09-06 04:32:30.613096: Current learning rate: 0.00635 +2024-09-06 04:37:31.133586: train_loss -0.6948 +2024-09-06 04:37:31.152277: val_loss -0.7348 +2024-09-06 04:37:31.153713: Pseudo dice [0.9484, 0.948, 0.9643, 0.9247, 0.848, 0.961, 0.9879] +2024-09-06 04:37:31.155746: Epoch time: 300.53 s +2024-09-06 04:37:35.139889: +2024-09-06 04:37:35.141517: Epoch 397 +2024-09-06 04:37:35.142868: Current learning rate: 0.00634 +2024-09-06 04:42:23.473955: train_loss -0.7136 +2024-09-06 04:42:23.476218: val_loss -0.6823 +2024-09-06 04:42:23.477303: Pseudo dice [0.9538, 0.9514, 0.9718, 0.9357, 0.8603, 0.9649, 0.9893] +2024-09-06 04:42:23.478463: Epoch time: 288.34 s +2024-09-06 04:42:26.623288: +2024-09-06 04:42:26.638959: Epoch 398 +2024-09-06 04:42:26.640185: Current learning rate: 0.00633 +2024-09-06 04:47:18.188651: train_loss -0.7228 +2024-09-06 04:47:18.201845: val_loss -0.7056 +2024-09-06 04:47:18.204151: Pseudo dice [0.959, 0.9557, 0.9755, 0.9429, 0.8792, 0.9656, 0.9907] +2024-09-06 04:47:18.206115: Epoch time: 291.57 s +2024-09-06 04:47:22.384467: +2024-09-06 04:47:22.385945: Epoch 399 +2024-09-06 04:47:22.386942: Current learning rate: 0.00632 +2024-09-06 04:52:21.897329: train_loss -0.7236 +2024-09-06 04:52:21.920828: val_loss -0.7432 +2024-09-06 04:52:21.922166: Pseudo dice [0.9594, 0.9532, 0.9765, 0.9386, 0.8587, 0.9592, 0.9901] +2024-09-06 04:52:21.923485: Epoch time: 299.52 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_08_29_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_08_29_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..dfa1f9be662ad4a25c4f3288f5a7229bcb44a3dc --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_08_29_45.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 08:29:48.940022: Using torch.compile... +2024-09-06 08:29:57.762306: do_dummy_2d_data_aug: False +2024-09-06 08:29:57.768437: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 08:29:57.770016: The split file contains 5 splits. +2024-09-06 08:29:57.770862: Desired fold for training: 3 +2024-09-06 08:29:57.771660: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 08:30:11.819804: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 08:30:12.777659: +2024-09-06 08:30:12.786926: Epoch 400 +2024-09-06 08:30:12.789306: Current learning rate: 0.00631 +2024-09-06 08:48:47.621989: train_loss -0.7177 +2024-09-06 08:48:47.625460: val_loss -0.7456 +2024-09-06 08:48:47.628152: Pseudo dice [0.956, 0.9524, 0.9737, 0.9386, 0.8777, 0.967, 0.9917] +2024-09-06 08:48:47.629549: Epoch time: 1114.85 s +2024-09-06 08:48:49.332432: +2024-09-06 08:48:49.333683: Epoch 401 +2024-09-06 08:48:49.334612: Current learning rate: 0.0063 +2024-09-06 08:53:38.176974: train_loss -0.7295 +2024-09-06 08:53:38.178981: val_loss -0.7134 +2024-09-06 08:53:38.181097: Pseudo dice [0.9552, 0.9538, 0.9667, 0.9334, 0.8733, 0.9677, 0.9922] +2024-09-06 08:53:38.182209: Epoch time: 288.85 s +2024-09-06 08:53:40.653030: +2024-09-06 08:53:40.654423: Epoch 402 +2024-09-06 08:53:40.655592: Current learning rate: 0.0063 +2024-09-06 08:58:28.267841: train_loss -0.7287 +2024-09-06 08:58:28.283476: val_loss -0.7567 +2024-09-06 08:58:28.284735: Pseudo dice [0.9592, 0.9518, 0.9769, 0.9397, 0.8719, 0.9653, 0.9912] +2024-09-06 08:58:28.285570: Epoch time: 287.62 s +2024-09-06 08:58:32.966697: +2024-09-06 08:58:32.968088: Epoch 403 +2024-09-06 08:58:32.969227: Current learning rate: 0.00629 +2024-09-06 09:03:21.150972: train_loss -0.7195 +2024-09-06 09:03:21.168125: val_loss -0.7374 +2024-09-06 09:03:21.169398: Pseudo dice [0.9601, 0.9539, 0.9728, 0.9348, 0.8745, 0.9626, 0.9909] +2024-09-06 09:03:21.170606: Epoch time: 288.19 s +2024-09-06 09:03:26.841069: +2024-09-06 09:03:26.842621: Epoch 404 +2024-09-06 09:03:26.843474: Current learning rate: 0.00628 +2024-09-06 09:08:30.451069: train_loss -0.7216 +2024-09-06 09:08:30.465261: val_loss -0.7254 +2024-09-06 09:08:30.466264: Pseudo dice [0.9568, 0.952, 0.9718, 0.9431, 0.8616, 0.9648, 0.9906] +2024-09-06 09:08:30.467326: Epoch time: 303.61 s +2024-09-06 09:08:34.252907: +2024-09-06 09:08:34.254427: Epoch 405 +2024-09-06 09:08:34.256550: Current learning rate: 0.00627 +2024-09-06 09:13:35.175177: train_loss -0.7305 +2024-09-06 09:13:35.177036: val_loss -0.724 +2024-09-06 09:13:35.178123: Pseudo dice [0.9582, 0.9527, 0.9756, 0.9456, 0.8711, 0.9612, 0.9892] +2024-09-06 09:13:35.179089: Epoch time: 300.93 s +2024-09-06 09:13:38.443838: +2024-09-06 09:13:38.445517: Epoch 406 +2024-09-06 09:13:38.448210: Current learning rate: 0.00626 +2024-09-06 09:18:45.082491: train_loss -0.7315 +2024-09-06 09:18:45.090929: val_loss -0.7539 +2024-09-06 09:18:45.092077: Pseudo dice [0.9598, 0.9553, 0.9764, 0.9358, 0.8563, 0.9588, 0.9897] +2024-09-06 09:18:45.093198: Epoch time: 306.65 s +2024-09-06 09:18:49.339124: +2024-09-06 09:18:49.355316: Epoch 407 +2024-09-06 09:18:49.356318: Current learning rate: 0.00625 +2024-09-06 09:23:48.868080: train_loss -0.7275 +2024-09-06 09:23:48.870253: val_loss -0.7301 +2024-09-06 09:23:48.871438: Pseudo dice [0.9569, 0.9549, 0.9726, 0.9368, 0.8676, 0.9671, 0.9892] +2024-09-06 09:23:48.872524: Epoch time: 299.53 s +2024-09-06 09:23:53.215462: +2024-09-06 09:23:53.216775: Epoch 408 +2024-09-06 09:23:53.217776: Current learning rate: 0.00624 +2024-09-06 09:28:15.991122: train_loss -0.726 +2024-09-06 09:28:16.007285: val_loss -0.7269 +2024-09-06 09:28:16.008572: Pseudo dice [0.9603, 0.9548, 0.9743, 0.9402, 0.8506, 0.9645, 0.9898] +2024-09-06 09:28:16.009900: Epoch time: 262.78 s +2024-09-06 09:28:19.407316: +2024-09-06 09:28:19.408928: Epoch 409 +2024-09-06 09:28:19.410194: Current learning rate: 0.00623 +2024-09-06 09:33:01.320379: train_loss -0.7322 +2024-09-06 09:33:01.337360: val_loss -0.7785 +2024-09-06 09:33:01.338490: Pseudo dice [0.9632, 0.9548, 0.9794, 0.9479, 0.8731, 0.9692, 0.9915] +2024-09-06 09:33:01.339565: Epoch time: 281.92 s +2024-09-06 09:33:04.343886: +2024-09-06 09:33:04.345306: Epoch 410 +2024-09-06 09:33:04.346394: Current learning rate: 0.00622 +2024-09-06 09:38:13.127557: train_loss -0.7271 +2024-09-06 09:38:13.146183: val_loss -0.7245 +2024-09-06 09:38:13.150163: Pseudo dice [0.9558, 0.9512, 0.9708, 0.9449, 0.8671, 0.9655, 0.9907] +2024-09-06 09:38:13.152185: Epoch time: 308.79 s +2024-09-06 09:38:16.593076: +2024-09-06 09:38:16.594559: Epoch 411 +2024-09-06 09:38:16.595655: Current learning rate: 0.00621 +2024-09-06 09:42:47.133131: train_loss -0.7182 +2024-09-06 09:42:47.155003: val_loss -0.72 +2024-09-06 09:42:47.156072: Pseudo dice [0.9585, 0.9535, 0.976, 0.9485, 0.8652, 0.9616, 0.9901] +2024-09-06 09:42:47.157117: Epoch time: 270.55 s +2024-09-06 09:42:50.963307: +2024-09-06 09:42:50.964515: Epoch 412 +2024-09-06 09:42:50.965384: Current learning rate: 0.0062 +2024-09-06 09:47:20.950696: train_loss -0.709 +2024-09-06 09:47:20.954846: val_loss -0.7202 +2024-09-06 09:47:20.955893: Pseudo dice [0.956, 0.9499, 0.9704, 0.9383, 0.8739, 0.9661, 0.9895] +2024-09-06 09:47:20.956948: Epoch time: 269.99 s +2024-09-06 09:47:24.377654: +2024-09-06 09:47:24.379042: Epoch 413 +2024-09-06 09:47:24.380198: Current learning rate: 0.00619 +2024-09-06 09:52:11.980837: train_loss -0.7332 +2024-09-06 09:52:11.995240: val_loss -0.7254 +2024-09-06 09:52:11.997139: Pseudo dice [0.9608, 0.9545, 0.9762, 0.9443, 0.8879, 0.969, 0.9918] +2024-09-06 09:52:11.998202: Epoch time: 287.61 s +2024-09-06 09:52:15.118015: +2024-09-06 09:52:15.119319: Epoch 414 +2024-09-06 09:52:15.120937: Current learning rate: 0.00618 +2024-09-06 09:56:59.420314: train_loss -0.7248 +2024-09-06 09:56:59.436203: val_loss -0.7575 +2024-09-06 09:56:59.437306: Pseudo dice [0.9626, 0.9577, 0.9769, 0.9431, 0.8775, 0.968, 0.991] +2024-09-06 09:56:59.438208: Epoch time: 284.31 s +2024-09-06 09:57:03.038611: +2024-09-06 09:57:03.040011: Epoch 415 +2024-09-06 09:57:03.041018: Current learning rate: 0.00617 +2024-09-06 10:01:48.477143: train_loss -0.7206 +2024-09-06 10:01:48.484396: val_loss -0.7485 +2024-09-06 10:01:48.486617: Pseudo dice [0.9624, 0.9575, 0.9765, 0.9427, 0.8774, 0.9686, 0.9906] +2024-09-06 10:01:48.488256: Epoch time: 285.45 s +2024-09-06 10:01:50.939887: +2024-09-06 10:01:50.942447: Epoch 416 +2024-09-06 10:01:50.944536: Current learning rate: 0.00616 +2024-09-06 10:06:46.103631: train_loss -0.7262 +2024-09-06 10:06:46.204273: val_loss -0.7483 +2024-09-06 10:06:46.217611: Pseudo dice [0.9591, 0.9556, 0.9759, 0.9461, 0.8747, 0.9662, 0.9911] +2024-09-06 10:06:46.225182: Epoch time: 295.17 s +2024-09-06 10:06:48.726973: +2024-09-06 10:06:48.728328: Epoch 417 +2024-09-06 10:06:48.729244: Current learning rate: 0.00615 +2024-09-06 10:11:55.239561: train_loss -0.7375 +2024-09-06 10:11:55.241466: val_loss -0.7466 +2024-09-06 10:11:55.242382: Pseudo dice [0.9647, 0.9567, 0.9762, 0.9457, 0.8827, 0.9626, 0.9904] +2024-09-06 10:11:55.243216: Epoch time: 306.52 s +2024-09-06 10:11:59.425363: +2024-09-06 10:11:59.426959: Epoch 418 +2024-09-06 10:11:59.428248: Current learning rate: 0.00614 +2024-09-06 10:16:43.111582: train_loss -0.7318 +2024-09-06 10:16:43.119348: val_loss -0.7195 +2024-09-06 10:16:43.120412: Pseudo dice [0.9575, 0.9527, 0.975, 0.941, 0.8434, 0.9657, 0.9884] +2024-09-06 10:16:43.121445: Epoch time: 283.69 s +2024-09-06 10:16:45.940602: +2024-09-06 10:16:45.942788: Epoch 419 +2024-09-06 10:16:45.944156: Current learning rate: 0.00613 +2024-09-06 10:21:39.502341: train_loss -0.7404 +2024-09-06 10:21:39.508787: val_loss -0.7555 +2024-09-06 10:21:39.509775: Pseudo dice [0.9622, 0.9578, 0.9751, 0.9426, 0.8712, 0.9661, 0.9914] +2024-09-06 10:21:39.510852: Epoch time: 293.57 s +2024-09-06 10:21:42.260087: +2024-09-06 10:21:42.261343: Epoch 420 +2024-09-06 10:21:42.262225: Current learning rate: 0.00612 +2024-09-06 10:26:51.976438: train_loss -0.7248 +2024-09-06 10:26:52.003154: val_loss -0.7669 +2024-09-06 10:26:52.004533: Pseudo dice [0.9601, 0.9541, 0.9752, 0.9434, 0.8869, 0.9689, 0.9915] +2024-09-06 10:26:52.006715: Epoch time: 309.72 s +2024-09-06 10:26:55.691739: +2024-09-06 10:26:55.707087: Epoch 421 +2024-09-06 10:26:55.708658: Current learning rate: 0.00612 +2024-09-06 10:31:46.734519: train_loss -0.746 +2024-09-06 10:31:46.750818: val_loss -0.75 +2024-09-06 10:31:46.751982: Pseudo dice [0.96, 0.9554, 0.9729, 0.9483, 0.8853, 0.9679, 0.9918] +2024-09-06 10:31:46.752912: Epoch time: 291.05 s +2024-09-06 10:31:50.378718: +2024-09-06 10:31:50.380212: Epoch 422 +2024-09-06 10:31:50.381349: Current learning rate: 0.00611 +2024-09-06 10:37:03.747489: train_loss -0.7389 +2024-09-06 10:37:03.768514: val_loss -0.7534 +2024-09-06 10:37:03.769847: Pseudo dice [0.9563, 0.9535, 0.9684, 0.939, 0.8783, 0.9702, 0.9923] +2024-09-06 10:37:03.770700: Epoch time: 313.37 s +2024-09-06 10:37:06.479947: +2024-09-06 10:37:06.484150: Epoch 423 +2024-09-06 10:37:06.485646: Current learning rate: 0.0061 +2024-09-06 10:41:36.781320: train_loss -0.735 +2024-09-06 10:41:36.798445: val_loss -0.7575 +2024-09-06 10:41:36.799973: Pseudo dice [0.9596, 0.9564, 0.9749, 0.9476, 0.8871, 0.9693, 0.9929] +2024-09-06 10:41:36.802161: Epoch time: 270.31 s +2024-09-06 10:41:39.857563: +2024-09-06 10:41:39.862352: Epoch 424 +2024-09-06 10:41:39.864390: Current learning rate: 0.00609 +2024-09-06 10:46:37.753469: train_loss -0.7128 +2024-09-06 10:46:37.767284: val_loss -0.7428 +2024-09-06 10:46:37.768775: Pseudo dice [0.9611, 0.9575, 0.9777, 0.9461, 0.8826, 0.9723, 0.9927] +2024-09-06 10:46:37.770164: Epoch time: 297.9 s +2024-09-06 10:46:41.170410: +2024-09-06 10:46:41.183902: Epoch 425 +2024-09-06 10:46:41.289978: Current learning rate: 0.00608 +2024-09-06 10:51:35.217774: train_loss -0.7362 +2024-09-06 10:51:35.231803: val_loss -0.7162 +2024-09-06 10:51:35.233466: Pseudo dice [0.9579, 0.9531, 0.9717, 0.9409, 0.8854, 0.9665, 0.9915] +2024-09-06 10:51:35.234967: Epoch time: 294.05 s +2024-09-06 10:51:38.606472: +2024-09-06 10:51:38.611083: Epoch 426 +2024-09-06 10:51:38.612273: Current learning rate: 0.00607 +2024-09-06 10:56:38.634776: train_loss -0.7038 +2024-09-06 10:56:38.650401: val_loss -0.7724 +2024-09-06 10:56:38.652234: Pseudo dice [0.9642, 0.955, 0.9782, 0.9457, 0.8767, 0.9702, 0.9908] +2024-09-06 10:56:38.654447: Epoch time: 300.04 s +2024-09-06 10:56:41.589646: +2024-09-06 10:56:41.602643: Epoch 427 +2024-09-06 10:56:41.604287: Current learning rate: 0.00606 +2024-09-06 11:01:34.841987: train_loss -0.733 +2024-09-06 11:01:34.853748: val_loss -0.7576 +2024-09-06 11:01:34.855066: Pseudo dice [0.9565, 0.9556, 0.9703, 0.9435, 0.8453, 0.9662, 0.9872] +2024-09-06 11:01:34.856159: Epoch time: 293.26 s +2024-09-06 11:01:39.852944: +2024-09-06 11:01:39.854097: Epoch 428 +2024-09-06 11:01:39.855026: Current learning rate: 0.00605 +2024-09-06 11:06:28.171009: train_loss -0.719 +2024-09-06 11:06:28.193846: val_loss -0.7224 +2024-09-06 11:06:28.194910: Pseudo dice [0.9596, 0.9567, 0.9753, 0.9324, 0.8556, 0.9646, 0.9894] +2024-09-06 11:06:28.196106: Epoch time: 288.32 s +2024-09-06 11:06:31.244555: +2024-09-06 11:06:31.246706: Epoch 429 +2024-09-06 11:06:31.248358: Current learning rate: 0.00604 +2024-09-06 11:11:20.794919: train_loss -0.741 +2024-09-06 11:11:20.810036: val_loss -0.7996 +2024-09-06 11:11:20.811196: Pseudo dice [0.963, 0.9556, 0.9774, 0.9474, 0.8862, 0.9683, 0.9921] +2024-09-06 11:11:20.812168: Epoch time: 289.56 s +2024-09-06 11:11:24.153259: +2024-09-06 11:11:24.166963: Epoch 430 +2024-09-06 11:11:24.168429: Current learning rate: 0.00603 +2024-09-06 11:15:56.446453: train_loss -0.7364 +2024-09-06 11:15:56.449819: val_loss -0.7257 +2024-09-06 11:15:56.451334: Pseudo dice [0.9622, 0.9557, 0.9743, 0.9482, 0.887, 0.9634, 0.9911] +2024-09-06 11:15:56.452718: Epoch time: 272.3 s +2024-09-06 11:15:58.574276: +2024-09-06 11:15:58.575872: Epoch 431 +2024-09-06 11:15:58.577137: Current learning rate: 0.00602 +2024-09-06 11:20:28.577146: train_loss -0.7476 +2024-09-06 11:20:28.587705: val_loss -0.7761 +2024-09-06 11:20:28.588738: Pseudo dice [0.9655, 0.9589, 0.9784, 0.946, 0.8585, 0.9717, 0.9907] +2024-09-06 11:20:28.589792: Epoch time: 270.01 s +2024-09-06 11:20:32.203032: +2024-09-06 11:20:32.204490: Epoch 432 +2024-09-06 11:20:32.205467: Current learning rate: 0.00601 +2024-09-06 11:25:11.999780: train_loss -0.7563 +2024-09-06 11:25:12.016631: val_loss -0.7744 +2024-09-06 11:25:12.017901: Pseudo dice [0.9623, 0.9571, 0.9767, 0.9481, 0.8834, 0.9677, 0.9922] +2024-09-06 11:25:12.019054: Epoch time: 279.8 s +2024-09-06 11:25:15.198282: +2024-09-06 11:25:15.211071: Epoch 433 +2024-09-06 11:25:15.212759: Current learning rate: 0.006 +2024-09-06 11:29:54.351831: train_loss -0.7351 +2024-09-06 11:29:54.372489: val_loss -0.7807 +2024-09-06 11:29:54.373588: Pseudo dice [0.9641, 0.958, 0.9766, 0.9482, 0.8771, 0.9658, 0.9896] +2024-09-06 11:29:54.374635: Epoch time: 279.16 s +2024-09-06 11:29:58.075942: +2024-09-06 11:29:58.077259: Epoch 434 +2024-09-06 11:29:58.078428: Current learning rate: 0.00599 +2024-09-06 11:34:51.449272: train_loss -0.7304 +2024-09-06 11:34:51.462763: val_loss -0.7746 +2024-09-06 11:34:51.464121: Pseudo dice [0.962, 0.9564, 0.9762, 0.9433, 0.8897, 0.9689, 0.9923] +2024-09-06 11:34:51.465236: Epoch time: 293.38 s +2024-09-06 11:34:55.284877: +2024-09-06 11:34:55.289425: Epoch 435 +2024-09-06 11:34:55.290663: Current learning rate: 0.00598 +2024-09-06 11:39:48.192723: train_loss -0.7428 +2024-09-06 11:39:48.209769: val_loss -0.773 +2024-09-06 11:39:48.210833: Pseudo dice [0.9593, 0.9528, 0.9773, 0.9438, 0.8583, 0.9661, 0.9893] +2024-09-06 11:39:48.211840: Epoch time: 292.91 s +2024-09-06 11:39:50.913143: +2024-09-06 11:39:50.914609: Epoch 436 +2024-09-06 11:39:50.915995: Current learning rate: 0.00597 +2024-09-06 11:44:17.696461: train_loss -0.7452 +2024-09-06 11:44:17.719280: val_loss -0.754 +2024-09-06 11:44:17.720289: Pseudo dice [0.9599, 0.9555, 0.9765, 0.9421, 0.8874, 0.9674, 0.9913] +2024-09-06 11:44:17.721222: Epoch time: 266.79 s +2024-09-06 11:44:20.828329: +2024-09-06 11:44:20.829615: Epoch 437 +2024-09-06 11:44:20.830469: Current learning rate: 0.00596 +2024-09-06 11:49:10.441976: train_loss -0.7404 +2024-09-06 11:49:10.459028: val_loss -0.7019 +2024-09-06 11:49:10.460478: Pseudo dice [0.9603, 0.9535, 0.9772, 0.9526, 0.8712, 0.971, 0.9901] +2024-09-06 11:49:10.462184: Epoch time: 289.62 s +2024-09-06 11:49:13.668152: +2024-09-06 11:49:13.669546: Epoch 438 +2024-09-06 11:49:13.671206: Current learning rate: 0.00595 +2024-09-06 11:54:05.813015: train_loss -0.7408 +2024-09-06 11:54:05.833516: val_loss -0.7102 +2024-09-06 11:54:05.835426: Pseudo dice [0.9557, 0.9546, 0.9698, 0.9376, 0.8803, 0.9579, 0.9898] +2024-09-06 11:54:05.837257: Epoch time: 292.15 s +2024-09-06 11:54:09.454450: +2024-09-06 11:54:09.455839: Epoch 439 +2024-09-06 11:54:09.457124: Current learning rate: 0.00594 +2024-09-06 11:58:51.310229: train_loss -0.7358 +2024-09-06 11:58:51.324791: val_loss -0.7272 +2024-09-06 11:58:51.326577: Pseudo dice [0.9609, 0.9567, 0.9751, 0.9362, 0.8562, 0.9641, 0.9895] +2024-09-06 11:58:51.328632: Epoch time: 281.86 s +2024-09-06 11:58:55.480258: +2024-09-06 11:58:55.482206: Epoch 440 +2024-09-06 11:58:55.483278: Current learning rate: 0.00593 +2024-09-06 12:03:49.872303: train_loss -0.7151 +2024-09-06 12:03:49.890121: val_loss -0.7211 +2024-09-06 12:03:49.891580: Pseudo dice [0.956, 0.9538, 0.9705, 0.9392, 0.869, 0.9674, 0.9898] +2024-09-06 12:03:49.892536: Epoch time: 294.4 s +2024-09-06 12:03:52.521092: +2024-09-06 12:03:52.522444: Epoch 441 +2024-09-06 12:03:52.523550: Current learning rate: 0.00592 +2024-09-06 12:08:34.963096: train_loss -0.7317 +2024-09-06 12:08:34.979274: val_loss -0.7702 +2024-09-06 12:08:34.980441: Pseudo dice [0.9601, 0.954, 0.9761, 0.944, 0.8616, 0.9664, 0.9888] +2024-09-06 12:08:34.981413: Epoch time: 282.45 s +2024-09-06 12:08:39.827436: +2024-09-06 12:08:39.828853: Epoch 442 +2024-09-06 12:08:39.830026: Current learning rate: 0.00592 +2024-09-06 12:13:12.809469: train_loss -0.7311 +2024-09-06 12:13:12.823602: val_loss -0.7687 +2024-09-06 12:13:12.824759: Pseudo dice [0.9564, 0.9581, 0.9708, 0.9379, 0.884, 0.9697, 0.9916] +2024-09-06 12:13:12.825892: Epoch time: 272.99 s +2024-09-06 12:13:16.787298: +2024-09-06 12:13:16.788799: Epoch 443 +2024-09-06 12:13:16.789851: Current learning rate: 0.00591 +2024-09-06 12:18:20.314667: train_loss -0.7298 +2024-09-06 12:18:20.331329: val_loss -0.7553 +2024-09-06 12:18:20.332353: Pseudo dice [0.9634, 0.9563, 0.9766, 0.9433, 0.8822, 0.9705, 0.9918] +2024-09-06 12:18:20.333267: Epoch time: 303.55 s +2024-09-06 12:18:23.583003: +2024-09-06 12:18:23.584188: Epoch 444 +2024-09-06 12:18:23.585080: Current learning rate: 0.0059 +2024-09-06 12:23:09.170151: train_loss -0.7351 +2024-09-06 12:23:09.173315: val_loss -0.7302 +2024-09-06 12:23:09.174410: Pseudo dice [0.9595, 0.9573, 0.9716, 0.9446, 0.8792, 0.9614, 0.9912] +2024-09-06 12:23:09.175393: Epoch time: 285.59 s +2024-09-06 12:23:11.688999: +2024-09-06 12:23:11.691036: Epoch 445 +2024-09-06 12:23:11.692125: Current learning rate: 0.00589 +2024-09-06 12:28:11.033268: train_loss -0.7326 +2024-09-06 12:28:11.050003: val_loss -0.7333 +2024-09-06 12:28:11.051616: Pseudo dice [0.9634, 0.9539, 0.9757, 0.9378, 0.8537, 0.9686, 0.9897] +2024-09-06 12:28:11.052578: Epoch time: 299.35 s +2024-09-06 12:28:14.294276: +2024-09-06 12:28:14.309267: Epoch 446 +2024-09-06 12:28:14.310337: Current learning rate: 0.00588 +2024-09-06 12:33:07.669431: train_loss -0.7374 +2024-09-06 12:33:07.683441: val_loss -0.752 +2024-09-06 12:33:07.684871: Pseudo dice [0.9638, 0.9585, 0.9766, 0.9469, 0.882, 0.9687, 0.9908] +2024-09-06 12:33:07.686507: Epoch time: 293.38 s +2024-09-06 12:33:10.864408: +2024-09-06 12:33:10.866093: Epoch 447 +2024-09-06 12:33:10.867457: Current learning rate: 0.00587 +2024-09-06 12:37:36.645514: train_loss -0.7322 +2024-09-06 12:37:36.649906: val_loss -0.7081 +2024-09-06 12:37:36.652092: Pseudo dice [0.9569, 0.9554, 0.9686, 0.9362, 0.8637, 0.9689, 0.9899] +2024-09-06 12:37:36.653527: Epoch time: 265.79 s +2024-09-06 12:37:39.754317: +2024-09-06 12:37:39.755749: Epoch 448 +2024-09-06 12:37:39.756764: Current learning rate: 0.00586 +2024-09-06 12:42:18.715916: train_loss -0.7189 +2024-09-06 12:42:18.744670: val_loss -0.7014 +2024-09-06 12:42:18.745828: Pseudo dice [0.9588, 0.9525, 0.9739, 0.9393, 0.8706, 0.9644, 0.9912] +2024-09-06 12:42:18.746943: Epoch time: 278.97 s +2024-09-06 12:42:22.858832: +2024-09-06 12:42:22.860318: Epoch 449 +2024-09-06 12:42:22.861495: Current learning rate: 0.00585 +2024-09-06 12:47:22.055632: train_loss -0.7321 +2024-09-06 12:47:22.072716: val_loss -0.7015 +2024-09-06 12:47:22.074021: Pseudo dice [0.9613, 0.9538, 0.9764, 0.9473, 0.8544, 0.9644, 0.9863] +2024-09-06 12:47:22.075013: Epoch time: 299.2 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_17_12_21.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_17_12_21.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea69ceaf8a5c8073dc2a3fb545601d684924ee2d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_6_17_12_21.txt @@ -0,0 +1,388 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 17:12:24.647751: Using torch.compile... +2024-09-06 17:12:29.699456: do_dummy_2d_data_aug: False +2024-09-06 17:12:29.706372: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 17:12:29.708420: The split file contains 5 splits. +2024-09-06 17:12:29.709196: Desired fold for training: 3 +2024-09-06 17:12:29.710147: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 17:12:40.432063: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 17:12:41.317642: +2024-09-06 17:12:41.333964: Epoch 450 +2024-09-06 17:12:41.336089: Current learning rate: 0.00584 +2024-09-06 17:23:57.674015: train_loss -0.7396 +2024-09-06 17:23:57.676437: val_loss -0.7609 +2024-09-06 17:23:57.677329: Pseudo dice [0.9617, 0.9591, 0.9746, 0.9433, 0.8825, 0.9669, 0.9904] +2024-09-06 17:23:57.678147: Epoch time: 676.36 s +2024-09-06 17:23:59.682415: +2024-09-06 17:23:59.683464: Epoch 451 +2024-09-06 17:23:59.684250: Current learning rate: 0.00583 +2024-09-06 17:28:27.887913: train_loss -0.7363 +2024-09-06 17:28:27.900617: val_loss -0.7514 +2024-09-06 17:28:27.901996: Pseudo dice [0.9618, 0.9583, 0.9768, 0.9463, 0.8851, 0.9722, 0.9917] +2024-09-06 17:28:27.903123: Epoch time: 268.21 s +2024-09-06 17:28:30.761398: +2024-09-06 17:28:30.762711: Epoch 452 +2024-09-06 17:28:30.764364: Current learning rate: 0.00582 +2024-09-06 17:33:26.202627: train_loss -0.7267 +2024-09-06 17:33:26.210826: val_loss -0.7632 +2024-09-06 17:33:26.211884: Pseudo dice [0.963, 0.9597, 0.9766, 0.9481, 0.8903, 0.9682, 0.9913] +2024-09-06 17:33:26.213074: Epoch time: 295.45 s +2024-09-06 17:33:28.718139: +2024-09-06 17:33:28.719619: Epoch 453 +2024-09-06 17:33:28.721159: Current learning rate: 0.00581 +2024-09-06 17:38:21.621357: train_loss -0.7335 +2024-09-06 17:38:21.626416: val_loss -0.7545 +2024-09-06 17:38:21.627689: Pseudo dice [0.9643, 0.9572, 0.9789, 0.9433, 0.8889, 0.9713, 0.9925] +2024-09-06 17:38:21.629498: Epoch time: 292.91 s +2024-09-06 17:38:27.479789: +2024-09-06 17:38:27.480979: Epoch 454 +2024-09-06 17:38:27.481961: Current learning rate: 0.0058 +2024-09-06 17:43:10.662355: train_loss -0.7295 +2024-09-06 17:43:10.687046: val_loss -0.7311 +2024-09-06 17:43:10.688231: Pseudo dice [0.9548, 0.9557, 0.9682, 0.9441, 0.8424, 0.972, 0.9887] +2024-09-06 17:43:10.689385: Epoch time: 283.19 s +2024-09-06 17:43:13.723761: +2024-09-06 17:43:13.725408: Epoch 455 +2024-09-06 17:43:13.727073: Current learning rate: 0.00579 +2024-09-06 17:48:08.829672: train_loss -0.7417 +2024-09-06 17:48:08.848241: val_loss -0.7396 +2024-09-06 17:48:08.850486: Pseudo dice [0.9629, 0.956, 0.9791, 0.9437, 0.8869, 0.9672, 0.9912] +2024-09-06 17:48:08.854278: Epoch time: 295.11 s +2024-09-06 17:48:12.332365: +2024-09-06 17:48:12.333645: Epoch 456 +2024-09-06 17:48:12.334910: Current learning rate: 0.00578 +2024-09-06 17:53:16.069841: train_loss -0.7357 +2024-09-06 17:53:16.083518: val_loss -0.7146 +2024-09-06 17:53:16.084789: Pseudo dice [0.9577, 0.9553, 0.9709, 0.9415, 0.8744, 0.9594, 0.99] +2024-09-06 17:53:16.086540: Epoch time: 303.74 s +2024-09-06 17:53:20.779502: +2024-09-06 17:53:20.780831: Epoch 457 +2024-09-06 17:53:20.782015: Current learning rate: 0.00577 +2024-09-06 17:58:52.204267: train_loss -0.7384 +2024-09-06 17:58:52.210099: val_loss -0.7718 +2024-09-06 17:58:52.211516: Pseudo dice [0.9574, 0.9558, 0.9769, 0.9477, 0.8819, 0.9692, 0.9919] +2024-09-06 17:58:52.213537: Epoch time: 331.43 s +2024-09-06 17:58:55.474537: +2024-09-06 17:58:55.475799: Epoch 458 +2024-09-06 17:58:55.477118: Current learning rate: 0.00576 +2024-09-06 18:04:17.545513: train_loss -0.7255 +2024-09-06 18:04:17.567028: val_loss -0.7575 +2024-09-06 18:04:17.568177: Pseudo dice [0.9598, 0.9562, 0.9742, 0.9483, 0.8791, 0.9676, 0.9892] +2024-09-06 18:04:17.569118: Epoch time: 322.08 s +2024-09-06 18:04:20.291184: +2024-09-06 18:04:20.292439: Epoch 459 +2024-09-06 18:04:20.293409: Current learning rate: 0.00575 +2024-09-06 18:09:31.966452: train_loss -0.7273 +2024-09-06 18:09:31.969800: val_loss -0.7483 +2024-09-06 18:09:31.974712: Pseudo dice [0.9619, 0.9556, 0.9757, 0.9487, 0.8829, 0.9667, 0.9924] +2024-09-06 18:09:31.976067: Epoch time: 311.68 s +2024-09-06 18:09:35.571265: +2024-09-06 18:09:35.572524: Epoch 460 +2024-09-06 18:09:35.573924: Current learning rate: 0.00574 +2024-09-06 18:14:53.354409: train_loss -0.7447 +2024-09-06 18:14:53.357524: val_loss -0.7307 +2024-09-06 18:14:53.358664: Pseudo dice [0.9638, 0.9577, 0.9762, 0.9444, 0.8771, 0.9718, 0.9906] +2024-09-06 18:14:53.359854: Epoch time: 317.79 s +2024-09-06 18:14:53.360926: Yayy! New best EMA pseudo Dice: 0.953 +2024-09-06 18:15:00.243408: +2024-09-06 18:15:00.244553: Epoch 461 +2024-09-06 18:15:00.245758: Current learning rate: 0.00573 +2024-09-06 18:20:43.706679: train_loss -0.7406 +2024-09-06 18:20:43.715172: val_loss -0.7569 +2024-09-06 18:20:43.716459: Pseudo dice [0.963, 0.9581, 0.9783, 0.9416, 0.8815, 0.9701, 0.99] +2024-09-06 18:20:43.717922: Epoch time: 343.47 s +2024-09-06 18:20:43.718934: Yayy! New best EMA pseudo Dice: 0.9532 +2024-09-06 18:20:51.183259: +2024-09-06 18:20:51.184551: Epoch 462 +2024-09-06 18:20:51.185978: Current learning rate: 0.00572 +2024-09-06 18:26:23.137323: train_loss -0.7266 +2024-09-06 18:26:23.145491: val_loss -0.726 +2024-09-06 18:26:23.154780: Pseudo dice [0.9587, 0.9545, 0.9722, 0.9425, 0.8711, 0.9675, 0.9905] +2024-09-06 18:26:23.164013: Epoch time: 331.96 s +2024-09-06 18:26:27.076957: +2024-09-06 18:26:27.078159: Epoch 463 +2024-09-06 18:26:27.079237: Current learning rate: 0.00571 +2024-09-06 18:31:55.973698: train_loss -0.7292 +2024-09-06 18:31:55.977017: val_loss -0.7161 +2024-09-06 18:31:55.977964: Pseudo dice [0.9597, 0.9559, 0.9739, 0.9461, 0.8822, 0.9662, 0.9927] +2024-09-06 18:31:55.978859: Epoch time: 328.9 s +2024-09-06 18:31:58.394338: +2024-09-06 18:31:58.395561: Epoch 464 +2024-09-06 18:31:58.396450: Current learning rate: 0.0057 +2024-09-06 18:37:52.604905: train_loss -0.7334 +2024-09-06 18:37:52.614136: val_loss -0.756 +2024-09-06 18:37:52.616305: Pseudo dice [0.9623, 0.9551, 0.9773, 0.9516, 0.8807, 0.9712, 0.9918] +2024-09-06 18:37:52.618419: Epoch time: 354.22 s +2024-09-06 18:37:52.620065: Yayy! New best EMA pseudo Dice: 0.9533 +2024-09-06 18:38:01.137604: +2024-09-06 18:38:01.139202: Epoch 465 +2024-09-06 18:38:01.140415: Current learning rate: 0.0057 +2024-09-06 18:43:56.823548: train_loss -0.7291 +2024-09-06 18:43:56.855303: val_loss -0.7523 +2024-09-06 18:43:56.856270: Pseudo dice [0.9645, 0.9576, 0.9798, 0.9484, 0.8798, 0.9715, 0.9918] +2024-09-06 18:43:56.857265: Epoch time: 355.69 s +2024-09-06 18:43:56.858238: Yayy! New best EMA pseudo Dice: 0.9536 +2024-09-06 18:44:05.637304: +2024-09-06 18:44:05.638533: Epoch 466 +2024-09-06 18:44:05.643255: Current learning rate: 0.00569 +2024-09-06 18:50:04.421007: train_loss -0.7216 +2024-09-06 18:50:04.450900: val_loss -0.7618 +2024-09-06 18:50:04.452969: Pseudo dice [0.9599, 0.958, 0.9726, 0.9513, 0.8785, 0.9718, 0.9922] +2024-09-06 18:50:04.454529: Epoch time: 358.79 s +2024-09-06 18:50:04.456464: Yayy! New best EMA pseudo Dice: 0.9537 +2024-09-06 18:50:12.891268: +2024-09-06 18:50:12.892814: Epoch 467 +2024-09-06 18:50:12.893780: Current learning rate: 0.00568 +2024-09-06 18:55:58.225604: train_loss -0.7316 +2024-09-06 18:55:58.236373: val_loss -0.7448 +2024-09-06 18:55:58.237474: Pseudo dice [0.9602, 0.958, 0.9745, 0.9294, 0.8517, 0.9577, 0.9903] +2024-09-06 18:55:58.238729: Epoch time: 345.34 s +2024-09-06 18:56:01.902015: +2024-09-06 18:56:01.903265: Epoch 468 +2024-09-06 18:56:01.904233: Current learning rate: 0.00567 +2024-09-06 19:01:55.392982: train_loss -0.7204 +2024-09-06 19:01:55.412101: val_loss -0.7212 +2024-09-06 19:01:55.414173: Pseudo dice [0.9581, 0.9564, 0.975, 0.9403, 0.8788, 0.9619, 0.9903] +2024-09-06 19:01:55.415989: Epoch time: 353.5 s +2024-09-06 19:01:58.317055: +2024-09-06 19:01:58.318576: Epoch 469 +2024-09-06 19:01:58.319558: Current learning rate: 0.00566 +2024-09-06 19:07:54.847344: train_loss -0.7427 +2024-09-06 19:07:54.850844: val_loss -0.7947 +2024-09-06 19:07:54.852330: Pseudo dice [0.9641, 0.9581, 0.9786, 0.9494, 0.8917, 0.9674, 0.9929] +2024-09-06 19:07:54.853272: Epoch time: 356.54 s +2024-09-06 19:07:58.126275: +2024-09-06 19:07:58.130485: Epoch 470 +2024-09-06 19:07:58.131452: Current learning rate: 0.00565 +2024-09-06 19:13:51.355981: train_loss -0.7458 +2024-09-06 19:13:51.359524: val_loss -0.7559 +2024-09-06 19:13:51.360564: Pseudo dice [0.9606, 0.9562, 0.974, 0.9467, 0.8871, 0.9733, 0.9928] +2024-09-06 19:13:51.361752: Epoch time: 353.23 s +2024-09-06 19:13:55.003581: +2024-09-06 19:13:55.005194: Epoch 471 +2024-09-06 19:13:55.006277: Current learning rate: 0.00564 +2024-09-06 19:20:09.089327: train_loss -0.7193 +2024-09-06 19:20:09.127424: val_loss -0.7163 +2024-09-06 19:20:09.128645: Pseudo dice [0.9592, 0.9548, 0.9755, 0.9448, 0.8853, 0.9712, 0.9926] +2024-09-06 19:20:09.129765: Epoch time: 374.09 s +2024-09-06 19:20:12.322280: +2024-09-06 19:20:12.323532: Epoch 472 +2024-09-06 19:20:12.324522: Current learning rate: 0.00563 +2024-09-06 19:26:26.499779: train_loss -0.7355 +2024-09-06 19:26:26.509169: val_loss -0.7417 +2024-09-06 19:26:26.510915: Pseudo dice [0.9622, 0.9572, 0.9762, 0.948, 0.8763, 0.9716, 0.992] +2024-09-06 19:26:26.512178: Epoch time: 374.18 s +2024-09-06 19:26:26.513094: Yayy! New best EMA pseudo Dice: 0.9538 +2024-09-06 19:26:33.203396: +2024-09-06 19:26:33.204756: Epoch 473 +2024-09-06 19:26:33.205808: Current learning rate: 0.00562 +2024-09-06 19:32:38.859562: train_loss -0.7504 +2024-09-06 19:32:38.863193: val_loss -0.7616 +2024-09-06 19:32:38.864882: Pseudo dice [0.9618, 0.9589, 0.9708, 0.9495, 0.8898, 0.9713, 0.9925] +2024-09-06 19:32:38.866442: Epoch time: 365.66 s +2024-09-06 19:32:38.867901: Yayy! New best EMA pseudo Dice: 0.954 +2024-09-06 19:32:45.373241: +2024-09-06 19:32:45.374548: Epoch 474 +2024-09-06 19:32:45.375557: Current learning rate: 0.00561 +2024-09-06 19:38:53.805047: train_loss -0.747 +2024-09-06 19:38:53.808543: val_loss -0.7306 +2024-09-06 19:38:53.809633: Pseudo dice [0.9597, 0.9577, 0.9764, 0.9438, 0.8894, 0.9692, 0.9915] +2024-09-06 19:38:53.811212: Epoch time: 368.44 s +2024-09-06 19:38:53.812442: Yayy! New best EMA pseudo Dice: 0.9542 +2024-09-06 19:39:02.106629: +2024-09-06 19:39:02.107875: Epoch 475 +2024-09-06 19:39:02.108860: Current learning rate: 0.0056 +2024-09-06 19:45:58.792201: train_loss -0.746 +2024-09-06 19:45:58.807301: val_loss -0.7825 +2024-09-06 19:45:58.809076: Pseudo dice [0.9602, 0.9582, 0.9775, 0.9497, 0.8797, 0.9693, 0.9914] +2024-09-06 19:45:58.810489: Epoch time: 416.69 s +2024-09-06 19:45:58.811786: Yayy! New best EMA pseudo Dice: 0.9543 +2024-09-06 19:46:06.589722: +2024-09-06 19:46:06.590943: Epoch 476 +2024-09-06 19:46:06.591905: Current learning rate: 0.00559 +2024-09-06 19:52:18.094370: train_loss -0.7261 +2024-09-06 19:52:18.110414: val_loss -0.7926 +2024-09-06 19:52:18.111699: Pseudo dice [0.9631, 0.9554, 0.9773, 0.949, 0.8901, 0.9696, 0.9931] +2024-09-06 19:52:18.113333: Epoch time: 371.51 s +2024-09-06 19:52:18.114545: Yayy! New best EMA pseudo Dice: 0.9545 +2024-09-06 19:52:27.582857: +2024-09-06 19:52:27.584327: Epoch 477 +2024-09-06 19:52:27.585378: Current learning rate: 0.00558 +2024-09-06 19:59:09.426209: train_loss -0.7481 +2024-09-06 19:59:09.439531: val_loss -0.753 +2024-09-06 19:59:09.440992: Pseudo dice [0.9648, 0.9576, 0.9777, 0.9514, 0.8911, 0.9715, 0.9917] +2024-09-06 19:59:09.442365: Epoch time: 401.85 s +2024-09-06 19:59:09.443489: Yayy! New best EMA pseudo Dice: 0.9549 +2024-09-06 19:59:17.522061: +2024-09-06 19:59:17.523490: Epoch 478 +2024-09-06 19:59:17.524888: Current learning rate: 0.00557 +2024-09-06 20:05:48.365298: train_loss -0.7554 +2024-09-06 20:05:48.389027: val_loss -0.7632 +2024-09-06 20:05:48.390468: Pseudo dice [0.964, 0.9591, 0.9772, 0.9467, 0.884, 0.97, 0.9907] +2024-09-06 20:05:48.391657: Epoch time: 390.85 s +2024-09-06 20:05:48.392882: Yayy! New best EMA pseudo Dice: 0.955 +2024-09-06 20:05:56.195392: +2024-09-06 20:05:56.196905: Epoch 479 +2024-09-06 20:05:56.197945: Current learning rate: 0.00556 +2024-09-06 20:12:48.787327: train_loss -0.7388 +2024-09-06 20:12:48.806246: val_loss -0.7583 +2024-09-06 20:12:48.807299: Pseudo dice [0.9662, 0.9583, 0.9816, 0.9514, 0.8634, 0.968, 0.9904] +2024-09-06 20:12:48.808511: Epoch time: 412.6 s +2024-09-06 20:12:53.009202: +2024-09-06 20:12:53.022831: Epoch 480 +2024-09-06 20:12:53.024134: Current learning rate: 0.00555 +2024-09-06 20:19:20.970756: train_loss -0.747 +2024-09-06 20:19:20.978859: val_loss -0.7501 +2024-09-06 20:19:20.980790: Pseudo dice [0.9624, 0.9547, 0.9775, 0.9443, 0.8887, 0.9707, 0.9927] +2024-09-06 20:19:20.982236: Epoch time: 387.97 s +2024-09-06 20:19:20.983337: Yayy! New best EMA pseudo Dice: 0.955 +2024-09-06 20:19:29.280448: +2024-09-06 20:19:29.288720: Epoch 481 +2024-09-06 20:19:29.289702: Current learning rate: 0.00554 +2024-09-06 20:26:23.519092: train_loss -0.7358 +2024-09-06 20:26:23.539009: val_loss -0.7444 +2024-09-06 20:26:23.540365: Pseudo dice [0.9658, 0.9576, 0.9807, 0.9513, 0.8773, 0.9724, 0.9918] +2024-09-06 20:26:23.542229: Epoch time: 414.24 s +2024-09-06 20:26:23.543877: Yayy! New best EMA pseudo Dice: 0.9552 +2024-09-06 20:26:31.979540: +2024-09-06 20:26:31.981084: Epoch 482 +2024-09-06 20:26:31.982226: Current learning rate: 0.00553 +2024-09-06 20:33:12.468188: train_loss -0.7326 +2024-09-06 20:33:12.489302: val_loss -0.7509 +2024-09-06 20:33:12.491047: Pseudo dice [0.963, 0.9606, 0.9791, 0.9506, 0.887, 0.9717, 0.9927] +2024-09-06 20:33:12.492476: Epoch time: 400.49 s +2024-09-06 20:33:12.493460: Yayy! New best EMA pseudo Dice: 0.9554 +2024-09-06 20:33:21.425071: +2024-09-06 20:33:21.426371: Epoch 483 +2024-09-06 20:33:21.427439: Current learning rate: 0.00552 +2024-09-06 20:40:18.123328: train_loss -0.7402 +2024-09-06 20:40:18.136329: val_loss -0.7326 +2024-09-06 20:40:18.137614: Pseudo dice [0.9623, 0.9576, 0.9751, 0.9488, 0.8873, 0.9705, 0.9928] +2024-09-06 20:40:18.138697: Epoch time: 416.7 s +2024-09-06 20:40:18.139518: Yayy! New best EMA pseudo Dice: 0.9555 +2024-09-06 20:40:26.215214: +2024-09-06 20:40:26.216696: Epoch 484 +2024-09-06 20:40:26.217577: Current learning rate: 0.00551 +2024-09-06 20:46:56.810751: train_loss -0.7295 +2024-09-06 20:46:56.821391: val_loss -0.7691 +2024-09-06 20:46:56.822656: Pseudo dice [0.9628, 0.9587, 0.9794, 0.9515, 0.8713, 0.9634, 0.9912] +2024-09-06 20:46:56.823776: Epoch time: 390.6 s +2024-09-06 20:47:00.862911: +2024-09-06 20:47:00.874875: Epoch 485 +2024-09-06 20:47:00.877363: Current learning rate: 0.0055 +2024-09-06 20:54:07.530298: train_loss -0.7392 +2024-09-06 20:54:07.550900: val_loss -0.7505 +2024-09-06 20:54:07.553042: Pseudo dice [0.9609, 0.9551, 0.976, 0.9484, 0.8783, 0.9699, 0.9914] +2024-09-06 20:54:07.555371: Epoch time: 426.67 s +2024-09-06 20:54:11.850348: +2024-09-06 20:54:11.851426: Epoch 486 +2024-09-06 20:54:11.852509: Current learning rate: 0.00549 +2024-09-06 21:01:01.335875: train_loss -0.7346 +2024-09-06 21:01:01.352594: val_loss -0.7659 +2024-09-06 21:01:01.354759: Pseudo dice [0.9619, 0.9586, 0.9758, 0.9495, 0.8976, 0.9706, 0.9925] +2024-09-06 21:01:01.357187: Epoch time: 409.49 s +2024-09-06 21:01:01.358154: Yayy! New best EMA pseudo Dice: 0.9555 +2024-09-06 21:01:09.340584: +2024-09-06 21:01:09.341983: Epoch 487 +2024-09-06 21:01:09.343205: Current learning rate: 0.00548 +2024-09-06 21:07:39.122457: train_loss -0.7336 +2024-09-06 21:07:39.172089: val_loss -0.7279 +2024-09-06 21:07:39.173628: Pseudo dice [0.9644, 0.9592, 0.9784, 0.9457, 0.8642, 0.9731, 0.9899] +2024-09-06 21:07:39.174817: Epoch time: 389.79 s +2024-09-06 21:07:42.749190: +2024-09-06 21:07:42.750838: Epoch 488 +2024-09-06 21:07:42.752032: Current learning rate: 0.00547 +2024-09-06 21:14:16.366995: train_loss -0.7328 +2024-09-06 21:14:16.377787: val_loss -0.7345 +2024-09-06 21:14:16.379108: Pseudo dice [0.9616, 0.958, 0.9779, 0.9439, 0.8577, 0.9594, 0.9884] +2024-09-06 21:14:16.380469: Epoch time: 393.62 s +2024-09-06 21:14:19.941948: +2024-09-06 21:14:19.943327: Epoch 489 +2024-09-06 21:14:19.944342: Current learning rate: 0.00546 +2024-09-06 21:21:26.482082: train_loss -0.7516 +2024-09-06 21:21:26.485808: val_loss -0.6937 +2024-09-06 21:21:26.487719: Pseudo dice [0.961, 0.9573, 0.9731, 0.9489, 0.8327, 0.9707, 0.9867] +2024-09-06 21:21:26.489100: Epoch time: 426.55 s +2024-09-06 21:21:31.012874: +2024-09-06 21:21:31.027887: Epoch 490 +2024-09-06 21:21:31.029152: Current learning rate: 0.00546 +2024-09-06 21:28:25.786915: train_loss -0.7482 +2024-09-06 21:28:25.812153: val_loss -0.7725 +2024-09-06 21:28:25.813197: Pseudo dice [0.9661, 0.9586, 0.9813, 0.9506, 0.8627, 0.965, 0.9896] +2024-09-06 21:28:25.814142: Epoch time: 414.78 s +2024-09-06 21:28:29.573065: +2024-09-06 21:28:29.574449: Epoch 491 +2024-09-06 21:28:29.575554: Current learning rate: 0.00545 +2024-09-06 21:35:04.596865: train_loss -0.7385 +2024-09-06 21:35:04.638070: val_loss -0.7584 +2024-09-06 21:35:04.639535: Pseudo dice [0.9645, 0.956, 0.9788, 0.9426, 0.883, 0.9695, 0.9917] +2024-09-06 21:35:04.640490: Epoch time: 395.03 s +2024-09-06 21:35:08.295338: +2024-09-06 21:35:08.296954: Epoch 492 +2024-09-06 21:35:08.298024: Current learning rate: 0.00544 +2024-09-06 21:41:31.505142: train_loss -0.7489 +2024-09-06 21:41:31.516294: val_loss -0.7778 +2024-09-06 21:41:31.519265: Pseudo dice [0.9633, 0.9573, 0.9769, 0.9549, 0.8973, 0.9686, 0.9918] +2024-09-06 21:41:31.525827: Epoch time: 383.21 s +2024-09-06 21:41:33.857156: +2024-09-06 21:41:33.858629: Epoch 493 +2024-09-06 21:41:33.859461: Current learning rate: 0.00543 +2024-09-06 21:48:35.934159: train_loss -0.7348 +2024-09-06 21:48:35.940447: val_loss -0.7206 +2024-09-06 21:48:35.941576: Pseudo dice [0.9465, 0.958, 0.9629, 0.9477, 0.8789, 0.969, 0.9911] +2024-09-06 21:48:35.942539: Epoch time: 422.08 s +2024-09-06 21:48:39.532481: +2024-09-06 21:48:39.533820: Epoch 494 +2024-09-06 21:48:39.534842: Current learning rate: 0.00542 +2024-09-06 21:55:32.857647: train_loss -0.7429 +2024-09-06 21:55:32.890591: val_loss -0.7642 +2024-09-06 21:55:32.891968: Pseudo dice [0.9655, 0.9586, 0.978, 0.9459, 0.8828, 0.9692, 0.9897] +2024-09-06 21:55:32.893052: Epoch time: 413.33 s +2024-09-06 21:55:36.336068: +2024-09-06 21:55:36.337842: Epoch 495 +2024-09-06 21:55:36.339349: Current learning rate: 0.00541 +2024-09-06 22:02:15.479561: train_loss -0.767 +2024-09-06 22:02:15.484436: val_loss -0.7649 +2024-09-06 22:02:15.486107: Pseudo dice [0.9602, 0.9597, 0.9697, 0.949, 0.8806, 0.9678, 0.9906] +2024-09-06 22:02:15.490337: Epoch time: 399.15 s +2024-09-06 22:02:19.246498: +2024-09-06 22:02:19.247961: Epoch 496 +2024-09-06 22:02:19.248996: Current learning rate: 0.0054 +2024-09-06 22:09:16.118621: train_loss -0.7405 +2024-09-06 22:09:16.137475: val_loss -0.7278 +2024-09-06 22:09:16.138969: Pseudo dice [0.9599, 0.9565, 0.9736, 0.9425, 0.8819, 0.9695, 0.9903] +2024-09-06 22:09:16.140179: Epoch time: 416.88 s +2024-09-06 22:09:19.755180: +2024-09-06 22:09:19.756828: Epoch 497 +2024-09-06 22:09:19.757809: Current learning rate: 0.00539 +2024-09-06 22:16:03.508446: train_loss -0.7584 +2024-09-06 22:16:03.534255: val_loss -0.7276 +2024-09-06 22:16:03.536673: Pseudo dice [0.9622, 0.9595, 0.9772, 0.9402, 0.9004, 0.9735, 0.9927] +2024-09-06 22:16:03.539718: Epoch time: 403.76 s +2024-09-06 22:16:05.945970: +2024-09-06 22:16:05.947545: Epoch 498 +2024-09-06 22:16:05.948593: Current learning rate: 0.00538 +2024-09-06 22:23:26.187196: train_loss -0.7261 +2024-09-06 22:23:26.224526: val_loss -0.7388 +2024-09-06 22:23:26.225976: Pseudo dice [0.9649, 0.957, 0.9778, 0.949, 0.882, 0.9703, 0.9922] +2024-09-06 22:23:26.227389: Epoch time: 440.25 s +2024-09-06 22:23:30.130676: +2024-09-06 22:23:30.132030: Epoch 499 +2024-09-06 22:23:30.133070: Current learning rate: 0.00537 +2024-09-06 22:30:22.040267: train_loss -0.7411 +2024-09-06 22:30:22.048841: val_loss -0.7462 +2024-09-06 22:30:22.049909: Pseudo dice [0.9652, 0.9581, 0.9779, 0.9555, 0.8823, 0.9713, 0.9913] +2024-09-06 22:30:22.050863: Epoch time: 411.91 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_14_05_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_14_05_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..0fc7bf4e41c5894e132e80b8f8d257fbd905bebe --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_14_05_16.txt @@ -0,0 +1,374 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 14:05:20.169238: Using torch.compile... +2024-09-07 14:05:26.820023: do_dummy_2d_data_aug: False +2024-09-07 14:05:26.825522: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 14:05:26.826838: The split file contains 5 splits. +2024-09-07 14:05:26.827379: Desired fold for training: 3 +2024-09-07 14:05:26.827986: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 14:05:48.267628: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 14:05:50.713548: +2024-09-07 14:05:50.728533: Epoch 500 +2024-09-07 14:05:50.741967: Current learning rate: 0.00536 +2024-09-07 14:24:12.753956: train_loss -0.725 +2024-09-07 14:24:12.757126: val_loss -0.7215 +2024-09-07 14:24:12.757888: Pseudo dice [0.9624, 0.9593, 0.977, 0.949, 0.8658, 0.9686, 0.9899] +2024-09-07 14:24:12.758533: Epoch time: 1102.06 s +2024-09-07 14:24:15.021757: +2024-09-07 14:24:15.022944: Epoch 501 +2024-09-07 14:24:15.023738: Current learning rate: 0.00535 +2024-09-07 14:31:52.202535: train_loss -0.7336 +2024-09-07 14:31:52.206017: val_loss -0.7311 +2024-09-07 14:31:52.207226: Pseudo dice [0.965, 0.9602, 0.9799, 0.9486, 0.8755, 0.9712, 0.9906] +2024-09-07 14:31:52.208389: Epoch time: 457.19 s +2024-09-07 14:31:56.810960: +2024-09-07 14:31:56.813598: Epoch 502 +2024-09-07 14:31:56.815275: Current learning rate: 0.00534 +2024-09-07 14:39:49.773288: train_loss -0.7363 +2024-09-07 14:39:49.805415: val_loss -0.725 +2024-09-07 14:39:49.806454: Pseudo dice [0.9671, 0.9603, 0.9792, 0.943, 0.8784, 0.9708, 0.9921] +2024-09-07 14:39:49.807209: Epoch time: 472.98 s +2024-09-07 14:39:54.494435: +2024-09-07 14:39:54.565110: Epoch 503 +2024-09-07 14:39:54.567794: Current learning rate: 0.00533 +2024-09-07 14:47:47.587079: train_loss -0.7498 +2024-09-07 14:47:47.602082: val_loss -0.7812 +2024-09-07 14:47:47.603195: Pseudo dice [0.966, 0.9597, 0.9801, 0.9478, 0.8888, 0.9707, 0.9912] +2024-09-07 14:47:47.604150: Epoch time: 473.1 s +2024-09-07 14:47:52.023128: +2024-09-07 14:47:52.031930: Epoch 504 +2024-09-07 14:47:52.033549: Current learning rate: 0.00532 +2024-09-07 14:56:47.912117: train_loss -0.7301 +2024-09-07 14:56:47.915246: val_loss -0.7782 +2024-09-07 14:56:47.916949: Pseudo dice [0.9657, 0.9612, 0.9808, 0.9447, 0.8921, 0.9713, 0.9915] +2024-09-07 14:56:47.918175: Epoch time: 535.92 s +2024-09-07 14:56:47.919365: Yayy! New best EMA pseudo Dice: 0.9556 +2024-09-07 14:56:58.612188: +2024-09-07 14:56:58.615840: Epoch 505 +2024-09-07 14:56:58.617100: Current learning rate: 0.00531 +2024-09-07 15:04:50.284974: train_loss -0.7345 +2024-09-07 15:04:50.312895: val_loss -0.7294 +2024-09-07 15:04:50.336794: Pseudo dice [0.9661, 0.9578, 0.9813, 0.9503, 0.8873, 0.97, 0.9914] +2024-09-07 15:04:50.340250: Epoch time: 471.68 s +2024-09-07 15:04:50.341432: Yayy! New best EMA pseudo Dice: 0.9558 +2024-09-07 15:04:59.760430: +2024-09-07 15:04:59.764040: Epoch 506 +2024-09-07 15:04:59.765588: Current learning rate: 0.0053 +2024-09-07 15:12:02.054559: train_loss -0.7498 +2024-09-07 15:12:02.070319: val_loss -0.7774 +2024-09-07 15:12:02.071955: Pseudo dice [0.9628, 0.9564, 0.9778, 0.945, 0.8922, 0.973, 0.9928] +2024-09-07 15:12:02.074167: Epoch time: 422.3 s +2024-09-07 15:12:02.076864: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-07 15:12:12.637052: +2024-09-07 15:12:12.638431: Epoch 507 +2024-09-07 15:12:12.639454: Current learning rate: 0.00529 +2024-09-07 15:19:16.031357: train_loss -0.7435 +2024-09-07 15:19:16.049813: val_loss -0.7277 +2024-09-07 15:19:16.051884: Pseudo dice [0.9616, 0.9577, 0.9769, 0.9398, 0.8804, 0.9683, 0.9917] +2024-09-07 15:19:16.053265: Epoch time: 423.4 s +2024-09-07 15:19:19.602586: +2024-09-07 15:19:19.616901: Epoch 508 +2024-09-07 15:19:19.618037: Current learning rate: 0.00528 +2024-09-07 15:26:16.270105: train_loss -0.7454 +2024-09-07 15:26:16.300368: val_loss -0.7651 +2024-09-07 15:26:16.302886: Pseudo dice [0.9603, 0.9545, 0.9757, 0.9468, 0.8974, 0.9626, 0.9929] +2024-09-07 15:26:16.304473: Epoch time: 416.67 s +2024-09-07 15:26:21.880784: +2024-09-07 15:26:21.894231: Epoch 509 +2024-09-07 15:26:21.896651: Current learning rate: 0.00527 +2024-09-07 15:33:47.839257: train_loss -0.7329 +2024-09-07 15:33:47.868366: val_loss -0.7496 +2024-09-07 15:33:47.871296: Pseudo dice [0.9608, 0.9557, 0.9771, 0.9389, 0.8601, 0.9673, 0.9905] +2024-09-07 15:33:47.872696: Epoch time: 445.97 s +2024-09-07 15:33:53.563734: +2024-09-07 15:33:53.583882: Epoch 510 +2024-09-07 15:33:53.585679: Current learning rate: 0.00526 +2024-09-07 15:41:15.403139: train_loss -0.7405 +2024-09-07 15:41:15.454477: val_loss -0.7523 +2024-09-07 15:41:15.456783: Pseudo dice [0.9629, 0.9593, 0.9786, 0.955, 0.8528, 0.971, 0.9877] +2024-09-07 15:41:15.458342: Epoch time: 441.85 s +2024-09-07 15:41:21.241759: +2024-09-07 15:41:21.254704: Epoch 511 +2024-09-07 15:41:21.255825: Current learning rate: 0.00525 +2024-09-07 15:48:25.997534: train_loss -0.7496 +2024-09-07 15:48:26.013707: val_loss -0.7533 +2024-09-07 15:48:26.014788: Pseudo dice [0.9627, 0.9588, 0.9761, 0.9408, 0.8627, 0.9718, 0.9889] +2024-09-07 15:48:26.015669: Epoch time: 424.77 s +2024-09-07 15:48:29.741352: +2024-09-07 15:48:29.743148: Epoch 512 +2024-09-07 15:48:29.744110: Current learning rate: 0.00524 +2024-09-07 15:55:37.338553: train_loss -0.7269 +2024-09-07 15:55:37.354164: val_loss -0.7639 +2024-09-07 15:55:37.355962: Pseudo dice [0.9619, 0.9574, 0.9781, 0.9468, 0.875, 0.9681, 0.99] +2024-09-07 15:55:37.357508: Epoch time: 427.6 s +2024-09-07 15:55:47.903734: +2024-09-07 15:55:47.918032: Epoch 513 +2024-09-07 15:55:47.919698: Current learning rate: 0.00523 +2024-09-07 16:02:47.378649: train_loss -0.7343 +2024-09-07 16:02:47.382148: val_loss -0.7495 +2024-09-07 16:02:47.386070: Pseudo dice [0.9639, 0.9577, 0.9794, 0.9446, 0.8569, 0.9688, 0.9914] +2024-09-07 16:02:47.390293: Epoch time: 419.5 s +2024-09-07 16:02:52.423217: +2024-09-07 16:02:52.424987: Epoch 514 +2024-09-07 16:02:52.427604: Current learning rate: 0.00522 +2024-09-07 16:09:37.687263: train_loss -0.7354 +2024-09-07 16:09:37.701296: val_loss -0.7749 +2024-09-07 16:09:37.702351: Pseudo dice [0.9647, 0.9562, 0.98, 0.9475, 0.8886, 0.9713, 0.9911] +2024-09-07 16:09:37.703347: Epoch time: 405.27 s +2024-09-07 16:09:40.852684: +2024-09-07 16:09:40.854256: Epoch 515 +2024-09-07 16:09:40.855064: Current learning rate: 0.00521 +2024-09-07 16:16:52.160011: train_loss -0.7436 +2024-09-07 16:16:52.175546: val_loss -0.757 +2024-09-07 16:16:52.176801: Pseudo dice [0.9655, 0.9587, 0.9794, 0.953, 0.876, 0.9701, 0.9911] +2024-09-07 16:16:52.177724: Epoch time: 431.31 s +2024-09-07 16:16:58.245937: +2024-09-07 16:16:58.247336: Epoch 516 +2024-09-07 16:16:58.248620: Current learning rate: 0.0052 +2024-09-07 16:23:43.425518: train_loss -0.736 +2024-09-07 16:23:43.444478: val_loss -0.7718 +2024-09-07 16:23:43.445957: Pseudo dice [0.9602, 0.9543, 0.9744, 0.9451, 0.889, 0.9737, 0.9924] +2024-09-07 16:23:43.447403: Epoch time: 405.19 s +2024-09-07 16:23:47.281647: +2024-09-07 16:23:47.283211: Epoch 517 +2024-09-07 16:23:47.284802: Current learning rate: 0.00519 +2024-09-07 16:30:51.868927: train_loss -0.7437 +2024-09-07 16:30:51.884761: val_loss -0.759 +2024-09-07 16:30:51.888194: Pseudo dice [0.9626, 0.9574, 0.9788, 0.9423, 0.8824, 0.9663, 0.9902] +2024-09-07 16:30:51.890030: Epoch time: 424.59 s +2024-09-07 16:30:55.372302: +2024-09-07 16:30:55.380066: Epoch 518 +2024-09-07 16:30:55.391392: Current learning rate: 0.00518 +2024-09-07 16:38:05.827711: train_loss -0.7452 +2024-09-07 16:38:05.842511: val_loss -0.7832 +2024-09-07 16:38:05.843832: Pseudo dice [0.9666, 0.9601, 0.9793, 0.9527, 0.8849, 0.9702, 0.9902] +2024-09-07 16:38:05.844838: Epoch time: 430.46 s +2024-09-07 16:38:10.693969: +2024-09-07 16:38:10.709041: Epoch 519 +2024-09-07 16:38:10.710648: Current learning rate: 0.00518 +2024-09-07 16:45:36.016897: train_loss -0.7341 +2024-09-07 16:45:36.033558: val_loss -0.7214 +2024-09-07 16:45:36.034977: Pseudo dice [0.9605, 0.9581, 0.9758, 0.9432, 0.8752, 0.9598, 0.9899] +2024-09-07 16:45:36.037097: Epoch time: 445.33 s +2024-09-07 16:45:41.347868: +2024-09-07 16:45:41.365508: Epoch 520 +2024-09-07 16:45:41.367399: Current learning rate: 0.00517 +2024-09-07 16:52:39.827600: train_loss -0.7296 +2024-09-07 16:52:39.836743: val_loss -0.7426 +2024-09-07 16:52:39.839725: Pseudo dice [0.9614, 0.9566, 0.9759, 0.9453, 0.8672, 0.967, 0.9891] +2024-09-07 16:52:39.843599: Epoch time: 418.5 s +2024-09-07 16:52:44.611476: +2024-09-07 16:52:44.613830: Epoch 521 +2024-09-07 16:52:44.619003: Current learning rate: 0.00516 +2024-09-07 16:59:45.393327: train_loss -0.7278 +2024-09-07 16:59:45.395975: val_loss -0.7491 +2024-09-07 16:59:45.397079: Pseudo dice [0.9641, 0.9574, 0.9776, 0.951, 0.8912, 0.9712, 0.9926] +2024-09-07 16:59:45.397966: Epoch time: 420.79 s +2024-09-07 16:59:50.421198: +2024-09-07 16:59:50.445167: Epoch 522 +2024-09-07 16:59:50.447979: Current learning rate: 0.00515 +2024-09-07 17:06:54.767404: train_loss -0.7341 +2024-09-07 17:06:54.777785: val_loss -0.787 +2024-09-07 17:06:54.780311: Pseudo dice [0.9609, 0.9579, 0.9778, 0.9527, 0.8734, 0.9673, 0.9899] +2024-09-07 17:06:54.783270: Epoch time: 424.37 s +2024-09-07 17:06:58.214533: +2024-09-07 17:06:58.230979: Epoch 523 +2024-09-07 17:06:58.233256: Current learning rate: 0.00514 +2024-09-07 17:14:27.098756: train_loss -0.7393 +2024-09-07 17:14:27.114243: val_loss -0.7227 +2024-09-07 17:14:27.117754: Pseudo dice [0.9621, 0.9548, 0.976, 0.9417, 0.8675, 0.9694, 0.9912] +2024-09-07 17:14:27.121851: Epoch time: 448.89 s +2024-09-07 17:14:31.495731: +2024-09-07 17:14:31.517626: Epoch 524 +2024-09-07 17:14:31.519728: Current learning rate: 0.00513 +2024-09-07 17:21:25.377885: train_loss -0.733 +2024-09-07 17:21:25.380863: val_loss -0.7502 +2024-09-07 17:21:25.381751: Pseudo dice [0.9599, 0.9557, 0.9751, 0.94, 0.8727, 0.9667, 0.9907] +2024-09-07 17:21:25.382510: Epoch time: 413.89 s +2024-09-07 17:21:30.233524: +2024-09-07 17:21:30.248917: Epoch 525 +2024-09-07 17:21:30.250276: Current learning rate: 0.00512 +2024-09-07 17:28:49.733695: train_loss -0.7276 +2024-09-07 17:28:49.748932: val_loss -0.7379 +2024-09-07 17:28:49.749969: Pseudo dice [0.9529, 0.9506, 0.9663, 0.9452, 0.8713, 0.9589, 0.9911] +2024-09-07 17:28:49.750807: Epoch time: 439.51 s +2024-09-07 17:28:52.804248: +2024-09-07 17:28:52.805547: Epoch 526 +2024-09-07 17:28:52.806311: Current learning rate: 0.00511 +2024-09-07 17:35:58.096662: train_loss -0.7233 +2024-09-07 17:35:58.113787: val_loss -0.6941 +2024-09-07 17:35:58.115973: Pseudo dice [0.9576, 0.9568, 0.9683, 0.9374, 0.8743, 0.9622, 0.9903] +2024-09-07 17:35:58.117112: Epoch time: 425.32 s +2024-09-07 17:36:02.035502: +2024-09-07 17:36:02.037350: Epoch 527 +2024-09-07 17:36:02.039756: Current learning rate: 0.0051 +2024-09-07 17:43:16.943140: train_loss -0.7262 +2024-09-07 17:43:16.949610: val_loss -0.78 +2024-09-07 17:43:16.954048: Pseudo dice [0.9606, 0.9547, 0.9774, 0.9429, 0.884, 0.9665, 0.9926] +2024-09-07 17:43:16.955158: Epoch time: 434.91 s +2024-09-07 17:43:22.427669: +2024-09-07 17:43:22.429675: Epoch 528 +2024-09-07 17:43:22.431169: Current learning rate: 0.00509 +2024-09-07 17:50:26.778609: train_loss -0.7266 +2024-09-07 17:50:26.803540: val_loss -0.7434 +2024-09-07 17:50:26.804797: Pseudo dice [0.9604, 0.9559, 0.975, 0.954, 0.8883, 0.9704, 0.9923] +2024-09-07 17:50:26.806473: Epoch time: 424.36 s +2024-09-07 17:50:32.673573: +2024-09-07 17:50:32.675409: Epoch 529 +2024-09-07 17:50:32.677209: Current learning rate: 0.00508 +2024-09-07 17:57:20.583223: train_loss -0.7591 +2024-09-07 17:57:20.598432: val_loss -0.7493 +2024-09-07 17:57:20.600534: Pseudo dice [0.9623, 0.9572, 0.9765, 0.9425, 0.8849, 0.9687, 0.9918] +2024-09-07 17:57:20.601458: Epoch time: 407.93 s +2024-09-07 17:57:26.030312: +2024-09-07 17:57:26.045683: Epoch 530 +2024-09-07 17:57:26.047005: Current learning rate: 0.00507 +2024-09-07 18:04:38.478974: train_loss -0.7284 +2024-09-07 18:04:38.505838: val_loss -0.7516 +2024-09-07 18:04:38.506667: Pseudo dice [0.9593, 0.9582, 0.9704, 0.9375, 0.8813, 0.9715, 0.9925] +2024-09-07 18:04:38.507360: Epoch time: 432.46 s +2024-09-07 18:04:42.393825: +2024-09-07 18:04:42.405253: Epoch 531 +2024-09-07 18:04:42.406365: Current learning rate: 0.00506 +2024-09-07 18:11:30.528258: train_loss -0.7258 +2024-09-07 18:11:30.532685: val_loss -0.7453 +2024-09-07 18:11:30.534537: Pseudo dice [0.9629, 0.9582, 0.9765, 0.949, 0.8782, 0.9675, 0.9912] +2024-09-07 18:11:30.536250: Epoch time: 408.14 s +2024-09-07 18:11:34.585082: +2024-09-07 18:11:34.586404: Epoch 532 +2024-09-07 18:11:34.587270: Current learning rate: 0.00505 +2024-09-07 18:19:07.487611: train_loss -0.7288 +2024-09-07 18:19:07.507454: val_loss -0.7374 +2024-09-07 18:19:07.508474: Pseudo dice [0.9646, 0.9589, 0.9768, 0.9438, 0.8768, 0.9687, 0.9918] +2024-09-07 18:19:07.509821: Epoch time: 452.91 s +2024-09-07 18:19:11.346383: +2024-09-07 18:19:11.348257: Epoch 533 +2024-09-07 18:19:11.349105: Current learning rate: 0.00504 +2024-09-07 18:26:15.599146: train_loss -0.7461 +2024-09-07 18:26:15.604742: val_loss -0.7431 +2024-09-07 18:26:15.605615: Pseudo dice [0.9627, 0.96, 0.9762, 0.9487, 0.8835, 0.9685, 0.9897] +2024-09-07 18:26:15.607252: Epoch time: 424.26 s +2024-09-07 18:26:20.142913: +2024-09-07 18:26:20.149078: Epoch 534 +2024-09-07 18:26:20.150232: Current learning rate: 0.00503 +2024-09-07 18:33:18.148831: train_loss -0.739 +2024-09-07 18:33:18.153260: val_loss -0.7541 +2024-09-07 18:33:18.154998: Pseudo dice [0.9577, 0.9574, 0.9735, 0.9457, 0.8807, 0.969, 0.9908] +2024-09-07 18:33:18.156453: Epoch time: 418.01 s +2024-09-07 18:33:23.148990: +2024-09-07 18:33:23.150441: Epoch 535 +2024-09-07 18:33:23.151321: Current learning rate: 0.00502 +2024-09-07 18:40:11.176400: train_loss -0.7465 +2024-09-07 18:40:11.190607: val_loss -0.7495 +2024-09-07 18:40:11.193247: Pseudo dice [0.9603, 0.9554, 0.9765, 0.9467, 0.8939, 0.9714, 0.9931] +2024-09-07 18:40:11.194775: Epoch time: 408.03 s +2024-09-07 18:40:14.981023: +2024-09-07 18:40:14.982863: Epoch 536 +2024-09-07 18:40:14.984170: Current learning rate: 0.00501 +2024-09-07 18:47:29.222294: train_loss -0.7341 +2024-09-07 18:47:29.226222: val_loss -0.7404 +2024-09-07 18:47:29.227919: Pseudo dice [0.9599, 0.9561, 0.9744, 0.9435, 0.8786, 0.9669, 0.99] +2024-09-07 18:47:29.237509: Epoch time: 434.26 s +2024-09-07 18:47:32.930365: +2024-09-07 18:47:32.931903: Epoch 537 +2024-09-07 18:47:32.932840: Current learning rate: 0.005 +2024-09-07 18:54:30.618606: train_loss -0.7376 +2024-09-07 18:54:30.638778: val_loss -0.74 +2024-09-07 18:54:30.639634: Pseudo dice [0.9605, 0.9576, 0.9725, 0.9475, 0.8874, 0.9743, 0.9923] +2024-09-07 18:54:30.640892: Epoch time: 417.69 s +2024-09-07 18:54:34.677898: +2024-09-07 18:54:34.700806: Epoch 538 +2024-09-07 18:54:34.702114: Current learning rate: 0.00499 +2024-09-07 19:01:20.179702: train_loss -0.741 +2024-09-07 19:01:20.186361: val_loss -0.7345 +2024-09-07 19:01:20.187852: Pseudo dice [0.9638, 0.9588, 0.973, 0.9473, 0.8729, 0.9722, 0.9907] +2024-09-07 19:01:20.188672: Epoch time: 405.51 s +2024-09-07 19:01:24.161364: +2024-09-07 19:01:24.170978: Epoch 539 +2024-09-07 19:01:24.172250: Current learning rate: 0.00498 +2024-09-07 19:08:13.260126: train_loss -0.7427 +2024-09-07 19:08:13.267672: val_loss -0.7481 +2024-09-07 19:08:13.269023: Pseudo dice [0.9604, 0.9583, 0.9734, 0.9489, 0.8775, 0.9675, 0.9901] +2024-09-07 19:08:13.271060: Epoch time: 409.1 s +2024-09-07 19:08:18.142843: +2024-09-07 19:08:18.156688: Epoch 540 +2024-09-07 19:08:18.157778: Current learning rate: 0.00497 +2024-09-07 19:15:19.892779: train_loss -0.7211 +2024-09-07 19:15:19.895797: val_loss -0.7459 +2024-09-07 19:15:19.896781: Pseudo dice [0.9584, 0.9592, 0.9739, 0.9508, 0.8847, 0.9699, 0.9921] +2024-09-07 19:15:19.897601: Epoch time: 421.76 s +2024-09-07 19:15:24.176581: +2024-09-07 19:15:24.189058: Epoch 541 +2024-09-07 19:15:24.191280: Current learning rate: 0.00496 +2024-09-07 19:22:28.363886: train_loss -0.7522 +2024-09-07 19:22:28.366088: val_loss -0.7499 +2024-09-07 19:22:28.367175: Pseudo dice [0.9625, 0.9613, 0.9742, 0.9519, 0.8847, 0.9691, 0.9924] +2024-09-07 19:22:28.368191: Epoch time: 424.19 s +2024-09-07 19:22:35.412967: +2024-09-07 19:22:35.414765: Epoch 542 +2024-09-07 19:22:35.416077: Current learning rate: 0.00495 +2024-09-07 19:29:19.662325: train_loss -0.7299 +2024-09-07 19:29:19.677681: val_loss -0.7608 +2024-09-07 19:29:19.679168: Pseudo dice [0.9632, 0.9582, 0.978, 0.9541, 0.879, 0.9695, 0.9918] +2024-09-07 19:29:19.681159: Epoch time: 404.26 s +2024-09-07 19:29:24.702359: +2024-09-07 19:29:24.703997: Epoch 543 +2024-09-07 19:29:24.705336: Current learning rate: 0.00494 +2024-09-07 19:36:24.555916: train_loss -0.7506 +2024-09-07 19:36:24.558043: val_loss -0.7272 +2024-09-07 19:36:24.559012: Pseudo dice [0.9609, 0.9581, 0.9749, 0.9448, 0.8826, 0.9725, 0.9904] +2024-09-07 19:36:24.567163: Epoch time: 419.89 s +2024-09-07 19:36:28.531021: +2024-09-07 19:36:28.543821: Epoch 544 +2024-09-07 19:36:28.545561: Current learning rate: 0.00493 +2024-09-07 19:43:13.708854: train_loss -0.7441 +2024-09-07 19:43:13.737393: val_loss -0.7373 +2024-09-07 19:43:13.738417: Pseudo dice [0.9635, 0.9581, 0.9747, 0.9444, 0.8819, 0.9701, 0.99] +2024-09-07 19:43:13.739228: Epoch time: 405.18 s +2024-09-07 19:43:17.426098: +2024-09-07 19:43:17.445983: Epoch 545 +2024-09-07 19:43:17.447326: Current learning rate: 0.00492 +2024-09-07 19:50:34.592448: train_loss -0.7501 +2024-09-07 19:50:34.609503: val_loss -0.7261 +2024-09-07 19:50:34.611956: Pseudo dice [0.9611, 0.9587, 0.9776, 0.9487, 0.8693, 0.9716, 0.9913] +2024-09-07 19:50:34.613070: Epoch time: 437.17 s +2024-09-07 19:50:38.414151: +2024-09-07 19:50:38.435056: Epoch 546 +2024-09-07 19:50:38.438599: Current learning rate: 0.00491 +2024-09-07 19:57:55.168274: train_loss -0.7294 +2024-09-07 19:57:55.194197: val_loss -0.7249 +2024-09-07 19:57:55.197780: Pseudo dice [0.9582, 0.9556, 0.9734, 0.952, 0.886, 0.9687, 0.9924] +2024-09-07 19:57:55.199605: Epoch time: 436.76 s +2024-09-07 19:58:00.301425: +2024-09-07 19:58:00.310796: Epoch 547 +2024-09-07 19:58:00.312437: Current learning rate: 0.0049 +2024-09-07 20:05:03.667768: train_loss -0.7373 +2024-09-07 20:05:03.672049: val_loss -0.7363 +2024-09-07 20:05:03.673457: Pseudo dice [0.9665, 0.96, 0.9784, 0.9468, 0.867, 0.9627, 0.9895] +2024-09-07 20:05:03.674389: Epoch time: 423.37 s +2024-09-07 20:05:07.596187: +2024-09-07 20:05:07.597801: Epoch 548 +2024-09-07 20:05:07.599380: Current learning rate: 0.00489 +2024-09-07 20:12:12.719948: train_loss -0.741 +2024-09-07 20:12:12.737073: val_loss -0.7443 +2024-09-07 20:12:12.738027: Pseudo dice [0.9666, 0.9589, 0.9811, 0.9494, 0.8484, 0.9721, 0.9893] +2024-09-07 20:12:12.738819: Epoch time: 425.13 s +2024-09-07 20:12:16.804790: +2024-09-07 20:12:16.815528: Epoch 549 +2024-09-07 20:12:16.818198: Current learning rate: 0.00488 +2024-09-07 20:19:15.456585: train_loss -0.7377 +2024-09-07 20:19:15.462671: val_loss -0.73 +2024-09-07 20:19:15.464787: Pseudo dice [0.9621, 0.9583, 0.9765, 0.9461, 0.8802, 0.9728, 0.9912] +2024-09-07 20:19:15.465784: Epoch time: 418.66 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_22_04_47.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_22_04_47.txt new file mode 100644 index 0000000000000000000000000000000000000000..22fc55c3e3655d04ef466035fc4f6474542d2808 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_7_22_04_47.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 22:04:50.817107: Using torch.compile... +2024-09-07 22:04:58.050185: do_dummy_2d_data_aug: False +2024-09-07 22:04:58.062447: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 22:04:58.063668: The split file contains 5 splits. +2024-09-07 22:04:58.064269: Desired fold for training: 3 +2024-09-07 22:04:58.064823: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 22:05:18.082624: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 22:05:19.462555: +2024-09-07 22:05:19.476667: Epoch 550 +2024-09-07 22:05:19.478658: Current learning rate: 0.00487 +2024-09-07 22:24:06.065556: train_loss -0.7425 +2024-09-07 22:24:06.067351: val_loss -0.7214 +2024-09-07 22:24:06.068220: Pseudo dice [0.9607, 0.9553, 0.9747, 0.9484, 0.8864, 0.9673, 0.9915] +2024-09-07 22:24:06.068918: Epoch time: 1126.61 s +2024-09-07 22:24:08.594705: +2024-09-07 22:24:08.596877: Epoch 551 +2024-09-07 22:24:08.598170: Current learning rate: 0.00486 +2024-09-07 22:31:05.513267: train_loss -0.7383 +2024-09-07 22:31:05.530404: val_loss -0.7758 +2024-09-07 22:31:05.532186: Pseudo dice [0.9606, 0.9544, 0.9764, 0.9422, 0.8875, 0.967, 0.9931] +2024-09-07 22:31:05.533473: Epoch time: 416.93 s +2024-09-07 22:31:09.332566: +2024-09-07 22:31:09.334034: Epoch 552 +2024-09-07 22:31:09.335035: Current learning rate: 0.00485 +2024-09-07 22:38:19.282516: train_loss -0.7566 +2024-09-07 22:38:19.297601: val_loss -0.764 +2024-09-07 22:38:19.298559: Pseudo dice [0.9592, 0.9586, 0.976, 0.9457, 0.8868, 0.9699, 0.993] +2024-09-07 22:38:19.299406: Epoch time: 429.96 s +2024-09-07 22:38:27.665397: +2024-09-07 22:38:27.666683: Epoch 553 +2024-09-07 22:38:27.667471: Current learning rate: 0.00484 +2024-09-07 22:45:43.994078: train_loss -0.7449 +2024-09-07 22:45:44.000459: val_loss -0.7607 +2024-09-07 22:45:44.001875: Pseudo dice [0.964, 0.958, 0.9787, 0.9407, 0.8766, 0.9695, 0.9913] +2024-09-07 22:45:44.002757: Epoch time: 436.33 s +2024-09-07 22:45:48.926081: +2024-09-07 22:45:48.941107: Epoch 554 +2024-09-07 22:45:48.942321: Current learning rate: 0.00484 +2024-09-07 22:52:53.121890: train_loss -0.7353 +2024-09-07 22:52:53.128196: val_loss -0.7243 +2024-09-07 22:52:53.130244: Pseudo dice [0.9645, 0.9595, 0.9776, 0.9492, 0.8577, 0.9685, 0.9897] +2024-09-07 22:52:53.131745: Epoch time: 424.2 s +2024-09-07 22:52:58.365755: +2024-09-07 22:52:58.381194: Epoch 555 +2024-09-07 22:52:58.384131: Current learning rate: 0.00483 +2024-09-07 23:00:16.750302: train_loss -0.7264 +2024-09-07 23:00:16.771188: val_loss -0.7435 +2024-09-07 23:00:16.774990: Pseudo dice [0.9609, 0.9554, 0.976, 0.9476, 0.876, 0.9692, 0.992] +2024-09-07 23:00:16.777428: Epoch time: 438.41 s +2024-09-07 23:00:22.376039: +2024-09-07 23:00:22.399801: Epoch 556 +2024-09-07 23:00:22.401585: Current learning rate: 0.00482 +2024-09-07 23:07:38.549541: train_loss -0.7406 +2024-09-07 23:07:38.551945: val_loss -0.7339 +2024-09-07 23:07:38.553886: Pseudo dice [0.9579, 0.9565, 0.973, 0.9455, 0.884, 0.9714, 0.991] +2024-09-07 23:07:38.555473: Epoch time: 436.18 s +2024-09-07 23:07:41.699726: +2024-09-07 23:07:41.701183: Epoch 557 +2024-09-07 23:07:41.702203: Current learning rate: 0.00481 +2024-09-07 23:14:49.936431: train_loss -0.7494 +2024-09-07 23:14:49.954266: val_loss -0.7482 +2024-09-07 23:14:49.957313: Pseudo dice [0.9602, 0.9575, 0.9708, 0.9479, 0.8745, 0.9707, 0.9909] +2024-09-07 23:14:49.959160: Epoch time: 428.26 s +2024-09-07 23:14:54.618042: +2024-09-07 23:14:54.619627: Epoch 558 +2024-09-07 23:14:54.620491: Current learning rate: 0.0048 +2024-09-07 23:22:05.900359: train_loss -0.7394 +2024-09-07 23:22:05.917616: val_loss -0.7255 +2024-09-07 23:22:05.918632: Pseudo dice [0.9639, 0.96, 0.9778, 0.9524, 0.8871, 0.9729, 0.9906] +2024-09-07 23:22:05.919475: Epoch time: 431.29 s +2024-09-07 23:22:09.873015: +2024-09-07 23:22:09.879541: Epoch 559 +2024-09-07 23:22:09.880586: Current learning rate: 0.00479 +2024-09-07 23:29:17.545947: train_loss -0.7358 +2024-09-07 23:29:17.562142: val_loss -0.7772 +2024-09-07 23:29:17.564591: Pseudo dice [0.9616, 0.956, 0.9757, 0.949, 0.8753, 0.973, 0.9904] +2024-09-07 23:29:17.566842: Epoch time: 427.68 s +2024-09-07 23:29:23.636086: +2024-09-07 23:29:23.637571: Epoch 560 +2024-09-07 23:29:23.638739: Current learning rate: 0.00478 +2024-09-07 23:36:23.229985: train_loss -0.7524 +2024-09-07 23:36:23.246658: val_loss -0.7841 +2024-09-07 23:36:23.249841: Pseudo dice [0.9645, 0.9563, 0.9798, 0.946, 0.8789, 0.9681, 0.9916] +2024-09-07 23:36:23.252330: Epoch time: 419.6 s +2024-09-07 23:36:28.789717: +2024-09-07 23:36:28.791840: Epoch 561 +2024-09-07 23:36:28.794387: Current learning rate: 0.00477 +2024-09-07 23:44:04.414377: train_loss -0.7246 +2024-09-07 23:44:04.432451: val_loss -0.7516 +2024-09-07 23:44:04.434297: Pseudo dice [0.9611, 0.9564, 0.9755, 0.9448, 0.8761, 0.9704, 0.9915] +2024-09-07 23:44:04.435334: Epoch time: 455.66 s +2024-09-07 23:44:07.165633: +2024-09-07 23:44:07.167959: Epoch 562 +2024-09-07 23:44:07.169831: Current learning rate: 0.00476 +2024-09-07 23:51:11.711551: train_loss -0.7339 +2024-09-07 23:51:11.734685: val_loss -0.7482 +2024-09-07 23:51:11.735731: Pseudo dice [0.9571, 0.9556, 0.9734, 0.9442, 0.872, 0.9681, 0.9909] +2024-09-07 23:51:11.736422: Epoch time: 424.57 s +2024-09-07 23:51:15.539364: +2024-09-07 23:51:15.551289: Epoch 563 +2024-09-07 23:51:15.553991: Current learning rate: 0.00475 +2024-09-07 23:58:29.629112: train_loss -0.7378 +2024-09-07 23:58:29.642095: val_loss -0.7536 +2024-09-07 23:58:29.644109: Pseudo dice [0.9609, 0.9554, 0.9777, 0.9398, 0.8833, 0.9696, 0.9905] +2024-09-07 23:58:29.645357: Epoch time: 434.16 s +2024-09-07 23:58:34.434790: +2024-09-07 23:58:34.449003: Epoch 564 +2024-09-07 23:58:34.450637: Current learning rate: 0.00474 +2024-09-08 00:05:36.146102: train_loss -0.7383 +2024-09-08 00:05:36.157081: val_loss -0.7242 +2024-09-08 00:05:36.158911: Pseudo dice [0.9601, 0.9542, 0.97, 0.9427, 0.8456, 0.9632, 0.9887] +2024-09-08 00:05:36.160249: Epoch time: 421.72 s +2024-09-08 00:05:41.317054: +2024-09-08 00:05:41.323685: Epoch 565 +2024-09-08 00:05:41.326753: Current learning rate: 0.00473 +2024-09-08 00:13:09.146415: train_loss -0.6997 +2024-09-08 00:13:09.178021: val_loss -0.7265 +2024-09-08 00:13:09.179920: Pseudo dice [0.9544, 0.9499, 0.9747, 0.9342, 0.8562, 0.9556, 0.9894] +2024-09-08 00:13:09.181618: Epoch time: 447.89 s +2024-09-08 00:13:13.217217: +2024-09-08 00:13:13.219025: Epoch 566 +2024-09-08 00:13:13.220891: Current learning rate: 0.00472 +2024-09-08 00:20:19.867341: train_loss -0.711 +2024-09-08 00:20:19.878758: val_loss -0.7531 +2024-09-08 00:20:19.882109: Pseudo dice [0.9567, 0.9514, 0.9701, 0.9384, 0.8582, 0.9639, 0.9878] +2024-09-08 00:20:19.884786: Epoch time: 426.75 s +2024-09-08 00:20:28.186625: +2024-09-08 00:20:28.188208: Epoch 567 +2024-09-08 00:20:28.189489: Current learning rate: 0.00471 +2024-09-08 00:27:48.557565: train_loss -0.7092 +2024-09-08 00:27:48.572354: val_loss -0.7342 +2024-09-08 00:27:48.576031: Pseudo dice [0.9547, 0.9506, 0.9714, 0.9416, 0.8784, 0.9622, 0.9917] +2024-09-08 00:27:48.578285: Epoch time: 440.38 s +2024-09-08 00:27:53.394925: +2024-09-08 00:27:53.401391: Epoch 568 +2024-09-08 00:27:53.403360: Current learning rate: 0.0047 +2024-09-08 00:35:18.667325: train_loss -0.7172 +2024-09-08 00:35:18.671554: val_loss -0.7462 +2024-09-08 00:35:18.672629: Pseudo dice [0.9613, 0.9564, 0.9771, 0.9395, 0.8682, 0.9673, 0.9908] +2024-09-08 00:35:18.673494: Epoch time: 445.3 s +2024-09-08 00:35:23.910830: +2024-09-08 00:35:23.912548: Epoch 569 +2024-09-08 00:35:23.914185: Current learning rate: 0.00469 +2024-09-08 00:42:27.255586: train_loss -0.7374 +2024-09-08 00:42:27.300857: val_loss -0.7216 +2024-09-08 00:42:27.303209: Pseudo dice [0.9597, 0.9552, 0.9736, 0.948, 0.8641, 0.9688, 0.99] +2024-09-08 00:42:27.306302: Epoch time: 423.35 s +2024-09-08 00:42:31.642652: +2024-09-08 00:42:31.644456: Epoch 570 +2024-09-08 00:42:31.645427: Current learning rate: 0.00468 +2024-09-08 00:49:49.849834: train_loss -0.7387 +2024-09-08 00:49:49.859334: val_loss -0.7369 +2024-09-08 00:49:49.860416: Pseudo dice [0.9602, 0.9559, 0.9763, 0.9463, 0.8803, 0.967, 0.9904] +2024-09-08 00:49:49.862924: Epoch time: 438.21 s +2024-09-08 00:49:54.075485: +2024-09-08 00:49:54.077365: Epoch 571 +2024-09-08 00:49:54.078918: Current learning rate: 0.00467 +2024-09-08 00:57:02.269070: train_loss -0.7503 +2024-09-08 00:57:02.293424: val_loss -0.7301 +2024-09-08 00:57:02.294871: Pseudo dice [0.963, 0.9573, 0.9759, 0.9499, 0.8716, 0.9707, 0.9886] +2024-09-08 00:57:02.296417: Epoch time: 428.2 s +2024-09-08 00:57:07.091933: +2024-09-08 00:57:07.108807: Epoch 572 +2024-09-08 00:57:07.110614: Current learning rate: 0.00466 +2024-09-08 01:04:32.785010: train_loss -0.7164 +2024-09-08 01:04:32.799086: val_loss -0.7348 +2024-09-08 01:04:32.801395: Pseudo dice [0.9622, 0.9589, 0.9767, 0.9444, 0.8692, 0.9691, 0.991] +2024-09-08 01:04:32.803231: Epoch time: 445.7 s +2024-09-08 01:04:37.232641: +2024-09-08 01:04:37.249266: Epoch 573 +2024-09-08 01:04:37.250388: Current learning rate: 0.00465 +2024-09-08 01:11:49.225897: train_loss -0.7278 +2024-09-08 01:11:49.244286: val_loss -0.7119 +2024-09-08 01:11:49.245800: Pseudo dice [0.96, 0.9576, 0.9712, 0.9486, 0.8367, 0.9732, 0.9874] +2024-09-08 01:11:49.246883: Epoch time: 432.0 s +2024-09-08 01:11:53.307710: +2024-09-08 01:11:53.316133: Epoch 574 +2024-09-08 01:11:53.317309: Current learning rate: 0.00464 +2024-09-08 01:19:05.735097: train_loss -0.7447 +2024-09-08 01:19:05.755065: val_loss -0.7549 +2024-09-08 01:19:05.758809: Pseudo dice [0.964, 0.958, 0.9779, 0.9451, 0.894, 0.9719, 0.9929] +2024-09-08 01:19:05.760777: Epoch time: 432.44 s +2024-09-08 01:19:10.310256: +2024-09-08 01:19:10.312119: Epoch 575 +2024-09-08 01:19:10.313250: Current learning rate: 0.00463 +2024-09-08 01:26:33.907186: train_loss -0.716 +2024-09-08 01:26:33.916276: val_loss -0.7119 +2024-09-08 01:26:33.918116: Pseudo dice [0.9513, 0.952, 0.9607, 0.9251, 0.8655, 0.9574, 0.9843] +2024-09-08 01:26:33.919633: Epoch time: 443.63 s +2024-09-08 01:26:37.088867: +2024-09-08 01:26:37.090274: Epoch 576 +2024-09-08 01:26:37.091687: Current learning rate: 0.00462 +2024-09-08 01:33:43.610064: train_loss -0.7155 +2024-09-08 01:33:43.626012: val_loss -0.7195 +2024-09-08 01:33:43.626949: Pseudo dice [0.9567, 0.9551, 0.9697, 0.9423, 0.8654, 0.9682, 0.99] +2024-09-08 01:33:43.627719: Epoch time: 426.53 s +2024-09-08 01:33:47.141530: +2024-09-08 01:33:47.143074: Epoch 577 +2024-09-08 01:33:47.143873: Current learning rate: 0.00461 +2024-09-08 01:40:56.926993: train_loss -0.7312 +2024-09-08 01:40:56.948630: val_loss -0.7558 +2024-09-08 01:40:56.950222: Pseudo dice [0.954, 0.9544, 0.9698, 0.9422, 0.8878, 0.967, 0.9922] +2024-09-08 01:40:56.951036: Epoch time: 429.79 s +2024-09-08 01:41:00.869362: +2024-09-08 01:41:00.871597: Epoch 578 +2024-09-08 01:41:00.881375: Current learning rate: 0.0046 +2024-09-08 01:48:01.618616: train_loss -0.7461 +2024-09-08 01:48:01.623462: val_loss -0.7585 +2024-09-08 01:48:01.624481: Pseudo dice [0.9635, 0.9564, 0.977, 0.9504, 0.8816, 0.9702, 0.9926] +2024-09-08 01:48:01.625358: Epoch time: 420.77 s +2024-09-08 01:48:05.676542: +2024-09-08 01:48:05.677845: Epoch 579 +2024-09-08 01:48:05.678547: Current learning rate: 0.00459 +2024-09-08 01:55:04.231479: train_loss -0.739 +2024-09-08 01:55:04.234787: val_loss -0.7692 +2024-09-08 01:55:04.236726: Pseudo dice [0.9627, 0.958, 0.9772, 0.9479, 0.8876, 0.9679, 0.9917] +2024-09-08 01:55:04.238246: Epoch time: 418.56 s +2024-09-08 01:55:08.910839: +2024-09-08 01:55:08.912792: Epoch 580 +2024-09-08 01:55:08.915540: Current learning rate: 0.00458 +2024-09-08 02:02:24.134758: train_loss -0.7245 +2024-09-08 02:02:24.165188: val_loss -0.732 +2024-09-08 02:02:24.167639: Pseudo dice [0.9601, 0.9563, 0.9738, 0.9414, 0.8809, 0.9662, 0.9899] +2024-09-08 02:02:24.170185: Epoch time: 435.23 s +2024-09-08 02:02:28.843261: +2024-09-08 02:02:28.845160: Epoch 581 +2024-09-08 02:02:28.847551: Current learning rate: 0.00457 +2024-09-08 02:09:42.766901: train_loss -0.7319 +2024-09-08 02:09:42.781922: val_loss -0.7569 +2024-09-08 02:09:42.783225: Pseudo dice [0.959, 0.9554, 0.9707, 0.9461, 0.8848, 0.9704, 0.9911] +2024-09-08 02:09:42.784654: Epoch time: 433.99 s +2024-09-08 02:09:51.358151: +2024-09-08 02:09:51.359638: Epoch 582 +2024-09-08 02:09:51.360738: Current learning rate: 0.00456 +2024-09-08 02:17:08.461470: train_loss -0.721 +2024-09-08 02:17:08.465546: val_loss -0.7831 +2024-09-08 02:17:08.468047: Pseudo dice [0.9589, 0.9545, 0.9755, 0.9449, 0.8924, 0.97, 0.9911] +2024-09-08 02:17:08.481435: Epoch time: 437.11 s +2024-09-08 02:17:13.596766: +2024-09-08 02:17:13.608977: Epoch 583 +2024-09-08 02:17:13.610145: Current learning rate: 0.00455 +2024-09-08 02:24:30.662234: train_loss -0.7339 +2024-09-08 02:24:30.678093: val_loss -0.7732 +2024-09-08 02:24:30.679280: Pseudo dice [0.9609, 0.9566, 0.975, 0.947, 0.8879, 0.9708, 0.9913] +2024-09-08 02:24:30.680880: Epoch time: 437.07 s +2024-09-08 02:24:35.462315: +2024-09-08 02:24:35.464370: Epoch 584 +2024-09-08 02:24:35.466901: Current learning rate: 0.00454 +2024-09-08 02:31:44.146448: train_loss -0.749 +2024-09-08 02:31:44.148823: val_loss -0.7316 +2024-09-08 02:31:44.149760: Pseudo dice [0.9632, 0.9578, 0.9776, 0.9497, 0.8657, 0.9634, 0.989] +2024-09-08 02:31:44.150768: Epoch time: 428.7 s +2024-09-08 02:31:48.964864: +2024-09-08 02:31:48.980768: Epoch 585 +2024-09-08 02:31:48.983273: Current learning rate: 0.00453 +2024-09-08 02:39:07.026730: train_loss -0.7471 +2024-09-08 02:39:07.033247: val_loss -0.752 +2024-09-08 02:39:07.035466: Pseudo dice [0.9655, 0.9587, 0.9772, 0.9534, 0.8934, 0.972, 0.9921] +2024-09-08 02:39:07.036718: Epoch time: 438.07 s +2024-09-08 02:39:12.495295: +2024-09-08 02:39:12.511287: Epoch 586 +2024-09-08 02:39:12.513845: Current learning rate: 0.00452 +2024-09-08 02:46:06.895420: train_loss -0.7387 +2024-09-08 02:46:06.897553: val_loss -0.7157 +2024-09-08 02:46:06.899131: Pseudo dice [0.953, 0.9556, 0.9617, 0.9428, 0.8733, 0.969, 0.9905] +2024-09-08 02:46:06.900645: Epoch time: 414.41 s +2024-09-08 02:46:11.779257: +2024-09-08 02:46:11.792881: Epoch 587 +2024-09-08 02:46:11.794494: Current learning rate: 0.00451 +2024-09-08 02:53:26.627763: train_loss -0.7331 +2024-09-08 02:53:26.633427: val_loss -0.7474 +2024-09-08 02:53:26.635113: Pseudo dice [0.9649, 0.957, 0.9765, 0.9474, 0.8865, 0.9652, 0.9926] +2024-09-08 02:53:26.636590: Epoch time: 434.86 s +2024-09-08 02:53:30.812708: +2024-09-08 02:53:30.824862: Epoch 588 +2024-09-08 02:53:30.825994: Current learning rate: 0.0045 +2024-09-08 03:00:30.790358: train_loss -0.7539 +2024-09-08 03:00:30.806522: val_loss -0.7176 +2024-09-08 03:00:30.811257: Pseudo dice [0.959, 0.9591, 0.9746, 0.9487, 0.8857, 0.9658, 0.9924] +2024-09-08 03:00:30.813421: Epoch time: 419.99 s +2024-09-08 03:00:35.994864: +2024-09-08 03:00:35.996491: Epoch 589 +2024-09-08 03:00:35.997494: Current learning rate: 0.00449 +2024-09-08 03:07:30.963970: train_loss -0.7393 +2024-09-08 03:07:30.990174: val_loss -0.7022 +2024-09-08 03:07:30.991118: Pseudo dice [0.955, 0.9468, 0.9707, 0.9298, 0.8562, 0.9713, 0.99] +2024-09-08 03:07:30.991858: Epoch time: 414.98 s +2024-09-08 03:07:34.985441: +2024-09-08 03:07:34.997962: Epoch 590 +2024-09-08 03:07:34.999439: Current learning rate: 0.00448 +2024-09-08 03:14:45.930084: train_loss -0.7356 +2024-09-08 03:14:45.953260: val_loss -0.7492 +2024-09-08 03:14:45.956305: Pseudo dice [0.9614, 0.9563, 0.9758, 0.9471, 0.8657, 0.969, 0.9896] +2024-09-08 03:14:45.959119: Epoch time: 430.95 s +2024-09-08 03:14:50.755603: +2024-09-08 03:14:50.757353: Epoch 591 +2024-09-08 03:14:50.758249: Current learning rate: 0.00447 +2024-09-08 03:21:59.766810: train_loss -0.7362 +2024-09-08 03:21:59.784633: val_loss -0.7463 +2024-09-08 03:21:59.787093: Pseudo dice [0.9663, 0.9593, 0.9803, 0.9479, 0.8729, 0.9733, 0.9912] +2024-09-08 03:21:59.788885: Epoch time: 429.02 s +2024-09-08 03:22:04.609234: +2024-09-08 03:22:04.611164: Epoch 592 +2024-09-08 03:22:04.612470: Current learning rate: 0.00446 +2024-09-08 03:29:18.426752: train_loss -0.7351 +2024-09-08 03:29:18.442680: val_loss -0.722 +2024-09-08 03:29:18.444951: Pseudo dice [0.9633, 0.961, 0.9765, 0.9487, 0.8785, 0.9684, 0.9916] +2024-09-08 03:29:18.446648: Epoch time: 433.85 s +2024-09-08 03:29:22.454737: +2024-09-08 03:29:22.468753: Epoch 593 +2024-09-08 03:29:22.470227: Current learning rate: 0.00445 +2024-09-08 03:36:34.961982: train_loss -0.7398 +2024-09-08 03:36:34.968606: val_loss -0.7575 +2024-09-08 03:36:34.969432: Pseudo dice [0.9661, 0.9581, 0.9762, 0.9504, 0.871, 0.9711, 0.9911] +2024-09-08 03:36:34.970300: Epoch time: 432.52 s +2024-09-08 03:36:39.198104: +2024-09-08 03:36:39.212976: Epoch 594 +2024-09-08 03:36:39.214333: Current learning rate: 0.00444 +2024-09-08 03:44:07.175011: train_loss -0.7419 +2024-09-08 03:44:07.181887: val_loss -0.743 +2024-09-08 03:44:07.185067: Pseudo dice [0.963, 0.9563, 0.977, 0.9502, 0.8675, 0.967, 0.9904] +2024-09-08 03:44:07.187304: Epoch time: 447.98 s +2024-09-08 03:44:15.082469: +2024-09-08 03:44:15.099037: Epoch 595 +2024-09-08 03:44:15.100746: Current learning rate: 0.00443 +2024-09-08 03:51:33.451711: train_loss -0.726 +2024-09-08 03:51:33.473666: val_loss -0.7565 +2024-09-08 03:51:33.475940: Pseudo dice [0.9635, 0.9591, 0.9778, 0.9482, 0.8891, 0.9717, 0.9934] +2024-09-08 03:51:33.478560: Epoch time: 438.37 s +2024-09-08 03:51:36.544583: +2024-09-08 03:51:36.546077: Epoch 596 +2024-09-08 03:51:36.547052: Current learning rate: 0.00442 +2024-09-08 03:58:58.501360: train_loss -0.7405 +2024-09-08 03:58:58.521464: val_loss -0.7284 +2024-09-08 03:58:58.523869: Pseudo dice [0.9618, 0.9574, 0.9744, 0.9478, 0.848, 0.9728, 0.9891] +2024-09-08 03:58:58.525092: Epoch time: 441.96 s +2024-09-08 03:59:03.019514: +2024-09-08 03:59:03.021061: Epoch 597 +2024-09-08 03:59:03.022723: Current learning rate: 0.00441 +2024-09-08 04:06:25.347500: train_loss -0.7285 +2024-09-08 04:06:25.373973: val_loss -0.756 +2024-09-08 04:06:25.375298: Pseudo dice [0.9566, 0.9536, 0.9719, 0.9307, 0.8698, 0.9671, 0.9908] +2024-09-08 04:06:25.376576: Epoch time: 442.33 s +2024-09-08 04:06:31.888466: +2024-09-08 04:06:31.904537: Epoch 598 +2024-09-08 04:06:31.908044: Current learning rate: 0.0044 +2024-09-08 04:13:25.628222: train_loss -0.7264 +2024-09-08 04:13:25.646682: val_loss -0.7517 +2024-09-08 04:13:25.647885: Pseudo dice [0.9644, 0.9587, 0.9775, 0.9425, 0.873, 0.9703, 0.9903] +2024-09-08 04:13:25.649445: Epoch time: 413.76 s +2024-09-08 04:13:30.773342: +2024-09-08 04:13:30.775151: Epoch 599 +2024-09-08 04:13:30.776205: Current learning rate: 0.00439 +2024-09-08 04:20:23.311819: train_loss -0.7534 +2024-09-08 04:20:23.333626: val_loss -0.7626 +2024-09-08 04:20:23.336172: Pseudo dice [0.9628, 0.9576, 0.9776, 0.9533, 0.8865, 0.9673, 0.9921] +2024-09-08 04:20:23.337809: Epoch time: 412.56 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_06_04_18.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_06_04_18.txt new file mode 100644 index 0000000000000000000000000000000000000000..0607944dbe6eedad496119f165e742666159c054 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_06_04_18.txt @@ -0,0 +1,383 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 06:04:21.961512: Using torch.compile... +2024-09-08 06:04:28.343561: do_dummy_2d_data_aug: False +2024-09-08 06:04:28.352824: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 06:04:28.354116: The split file contains 5 splits. +2024-09-08 06:04:28.354712: Desired fold for training: 3 +2024-09-08 06:04:28.355259: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 06:04:49.567997: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 06:04:51.753498: +2024-09-08 06:04:51.761584: Epoch 600 +2024-09-08 06:04:51.763750: Current learning rate: 0.00438 +2024-09-08 06:21:45.971075: train_loss -0.7385 +2024-09-08 06:21:45.973566: val_loss -0.7601 +2024-09-08 06:21:45.974292: Pseudo dice [0.9656, 0.9615, 0.9797, 0.9459, 0.8871, 0.9701, 0.9909] +2024-09-08 06:21:45.974952: Epoch time: 1014.23 s +2024-09-08 06:21:47.685049: +2024-09-08 06:21:47.686240: Epoch 601 +2024-09-08 06:21:47.687089: Current learning rate: 0.00437 +2024-09-08 06:28:26.399815: train_loss -0.7225 +2024-09-08 06:28:26.404351: val_loss -0.7606 +2024-09-08 06:28:26.408242: Pseudo dice [0.9611, 0.9581, 0.9742, 0.9468, 0.8924, 0.972, 0.9927] +2024-09-08 06:28:26.412186: Epoch time: 398.72 s +2024-09-08 06:28:30.162005: +2024-09-08 06:28:30.163822: Epoch 602 +2024-09-08 06:28:30.166287: Current learning rate: 0.00436 +2024-09-08 06:35:21.012493: train_loss -0.7355 +2024-09-08 06:35:21.032469: val_loss -0.752 +2024-09-08 06:35:21.034524: Pseudo dice [0.9595, 0.9578, 0.9774, 0.9378, 0.8723, 0.9683, 0.9918] +2024-09-08 06:35:21.037416: Epoch time: 410.87 s +2024-09-08 06:35:26.717760: +2024-09-08 06:35:26.719265: Epoch 603 +2024-09-08 06:35:26.720189: Current learning rate: 0.00435 +2024-09-08 06:42:43.810407: train_loss -0.7386 +2024-09-08 06:42:43.826713: val_loss -0.7554 +2024-09-08 06:42:43.829670: Pseudo dice [0.9655, 0.9587, 0.9782, 0.9452, 0.8868, 0.9759, 0.9928] +2024-09-08 06:42:43.831453: Epoch time: 437.1 s +2024-09-08 06:42:48.386455: +2024-09-08 06:42:48.388232: Epoch 604 +2024-09-08 06:42:48.390281: Current learning rate: 0.00434 +2024-09-08 06:49:33.235806: train_loss -0.7518 +2024-09-08 06:49:33.238177: val_loss -0.7473 +2024-09-08 06:49:33.239527: Pseudo dice [0.9625, 0.9587, 0.9773, 0.9489, 0.8838, 0.9728, 0.9906] +2024-09-08 06:49:33.240564: Epoch time: 404.86 s +2024-09-08 06:49:38.084593: +2024-09-08 06:49:38.105761: Epoch 605 +2024-09-08 06:49:38.107463: Current learning rate: 0.00433 +2024-09-08 06:56:55.518161: train_loss -0.7407 +2024-09-08 06:56:55.541528: val_loss -0.7372 +2024-09-08 06:56:55.542970: Pseudo dice [0.9642, 0.9599, 0.9793, 0.9527, 0.8635, 0.9696, 0.9893] +2024-09-08 06:56:55.544328: Epoch time: 437.48 s +2024-09-08 06:57:00.956927: +2024-09-08 06:57:00.958726: Epoch 606 +2024-09-08 06:57:00.959838: Current learning rate: 0.00432 +2024-09-08 07:04:26.364440: train_loss -0.7392 +2024-09-08 07:04:26.378623: val_loss -0.7551 +2024-09-08 07:04:26.380129: Pseudo dice [0.9621, 0.9613, 0.9727, 0.9483, 0.8843, 0.9634, 0.9898] +2024-09-08 07:04:26.382101: Epoch time: 445.43 s +2024-09-08 07:04:31.251823: +2024-09-08 07:04:31.252934: Epoch 607 +2024-09-08 07:04:31.254181: Current learning rate: 0.00431 +2024-09-08 07:11:27.150670: train_loss -0.7329 +2024-09-08 07:11:27.166135: val_loss -0.739 +2024-09-08 07:11:27.176740: Pseudo dice [0.9587, 0.9586, 0.9713, 0.946, 0.8587, 0.9715, 0.989] +2024-09-08 07:11:27.177734: Epoch time: 415.9 s +2024-09-08 07:11:31.484996: +2024-09-08 07:11:31.492523: Epoch 608 +2024-09-08 07:11:31.495864: Current learning rate: 0.0043 +2024-09-08 07:18:48.602143: train_loss -0.7461 +2024-09-08 07:18:48.617533: val_loss -0.7753 +2024-09-08 07:18:48.620430: Pseudo dice [0.9639, 0.9572, 0.9783, 0.9474, 0.8838, 0.9683, 0.9914] +2024-09-08 07:18:48.621974: Epoch time: 437.12 s +2024-09-08 07:18:53.880503: +2024-09-08 07:18:53.893294: Epoch 609 +2024-09-08 07:18:53.895408: Current learning rate: 0.00429 +2024-09-08 07:26:01.799367: train_loss -0.7475 +2024-09-08 07:26:01.814879: val_loss -0.7347 +2024-09-08 07:26:01.815992: Pseudo dice [0.9636, 0.9584, 0.9772, 0.9434, 0.8789, 0.9679, 0.9927] +2024-09-08 07:26:01.818306: Epoch time: 427.93 s +2024-09-08 07:26:05.582111: +2024-09-08 07:26:05.585299: Epoch 610 +2024-09-08 07:26:05.586858: Current learning rate: 0.00429 +2024-09-08 07:33:01.018099: train_loss -0.7418 +2024-09-08 07:33:01.020689: val_loss -0.756 +2024-09-08 07:33:01.022114: Pseudo dice [0.9602, 0.9592, 0.9742, 0.9476, 0.8789, 0.9722, 0.9908] +2024-09-08 07:33:01.022851: Epoch time: 415.49 s +2024-09-08 07:33:05.744494: +2024-09-08 07:33:05.746186: Epoch 611 +2024-09-08 07:33:05.747298: Current learning rate: 0.00428 +2024-09-08 07:40:13.285256: train_loss -0.7372 +2024-09-08 07:40:13.288846: val_loss -0.7487 +2024-09-08 07:40:13.290727: Pseudo dice [0.9603, 0.9581, 0.9753, 0.9502, 0.8725, 0.9727, 0.9909] +2024-09-08 07:40:13.291621: Epoch time: 427.58 s +2024-09-08 07:40:17.197616: +2024-09-08 07:40:17.204814: Epoch 612 +2024-09-08 07:40:17.207572: Current learning rate: 0.00427 +2024-09-08 07:47:34.481216: train_loss -0.7283 +2024-09-08 07:47:34.497459: val_loss -0.7495 +2024-09-08 07:47:34.499493: Pseudo dice [0.9621, 0.9568, 0.9767, 0.9407, 0.8753, 0.9688, 0.992] +2024-09-08 07:47:34.500639: Epoch time: 437.29 s +2024-09-08 07:47:40.414858: +2024-09-08 07:47:40.436804: Epoch 613 +2024-09-08 07:47:40.438317: Current learning rate: 0.00426 +2024-09-08 07:54:34.570610: train_loss -0.7464 +2024-09-08 07:54:34.573430: val_loss -0.7207 +2024-09-08 07:54:34.574445: Pseudo dice [0.962, 0.9559, 0.9715, 0.9427, 0.8851, 0.9656, 0.9913] +2024-09-08 07:54:34.575197: Epoch time: 414.16 s +2024-09-08 07:54:39.046275: +2024-09-08 07:54:39.061657: Epoch 614 +2024-09-08 07:54:39.063080: Current learning rate: 0.00425 +2024-09-08 08:01:51.025098: train_loss -0.7432 +2024-09-08 08:01:51.044668: val_loss -0.755 +2024-09-08 08:01:51.045618: Pseudo dice [0.9641, 0.957, 0.978, 0.944, 0.886, 0.9662, 0.9906] +2024-09-08 08:01:51.046517: Epoch time: 432.0 s +2024-09-08 08:01:55.526172: +2024-09-08 08:01:55.540733: Epoch 615 +2024-09-08 08:01:55.541672: Current learning rate: 0.00424 +2024-09-08 08:08:53.650092: train_loss -0.7316 +2024-09-08 08:08:53.675200: val_loss -0.7652 +2024-09-08 08:08:53.676338: Pseudo dice [0.9629, 0.9603, 0.9758, 0.949, 0.8872, 0.9711, 0.9918] +2024-09-08 08:08:53.678089: Epoch time: 418.13 s +2024-09-08 08:08:57.694029: +2024-09-08 08:08:57.708985: Epoch 616 +2024-09-08 08:08:57.710045: Current learning rate: 0.00423 +2024-09-08 08:15:57.463243: train_loss -0.7391 +2024-09-08 08:15:57.477686: val_loss -0.7508 +2024-09-08 08:15:57.478936: Pseudo dice [0.9616, 0.9593, 0.9771, 0.9511, 0.8819, 0.9691, 0.9898] +2024-09-08 08:15:57.480057: Epoch time: 419.78 s +2024-09-08 08:16:02.220204: +2024-09-08 08:16:02.222044: Epoch 617 +2024-09-08 08:16:02.223243: Current learning rate: 0.00422 +2024-09-08 08:23:09.205029: train_loss -0.7442 +2024-09-08 08:23:09.225289: val_loss -0.7605 +2024-09-08 08:23:09.226604: Pseudo dice [0.965, 0.9587, 0.9797, 0.9539, 0.8857, 0.9706, 0.9913] +2024-09-08 08:23:09.227527: Epoch time: 427.01 s +2024-09-08 08:23:15.384019: +2024-09-08 08:23:15.385497: Epoch 618 +2024-09-08 08:23:15.386362: Current learning rate: 0.00421 +2024-09-08 08:30:19.845522: train_loss -0.743 +2024-09-08 08:30:19.862088: val_loss -0.7598 +2024-09-08 08:30:19.863370: Pseudo dice [0.9584, 0.9561, 0.9749, 0.9455, 0.8904, 0.9744, 0.9923] +2024-09-08 08:30:19.864665: Epoch time: 424.47 s +2024-09-08 08:30:22.470252: +2024-09-08 08:30:22.493207: Epoch 619 +2024-09-08 08:30:22.494982: Current learning rate: 0.0042 +2024-09-08 08:37:31.847808: train_loss -0.7568 +2024-09-08 08:37:31.868315: val_loss -0.7782 +2024-09-08 08:37:31.869853: Pseudo dice [0.9603, 0.9599, 0.9743, 0.9479, 0.8915, 0.9689, 0.9905] +2024-09-08 08:37:31.872034: Epoch time: 429.38 s +2024-09-08 08:37:37.375167: +2024-09-08 08:37:37.378584: Epoch 620 +2024-09-08 08:37:37.383527: Current learning rate: 0.00419 +2024-09-08 08:44:53.733624: train_loss -0.7553 +2024-09-08 08:44:53.736230: val_loss -0.7672 +2024-09-08 08:44:53.752174: Pseudo dice [0.9643, 0.959, 0.9768, 0.9511, 0.8967, 0.9737, 0.9929] +2024-09-08 08:44:53.753360: Epoch time: 436.37 s +2024-09-08 08:44:57.785955: +2024-09-08 08:44:57.787707: Epoch 621 +2024-09-08 08:44:57.789163: Current learning rate: 0.00418 +2024-09-08 08:52:02.910480: train_loss -0.7342 +2024-09-08 08:52:02.917603: val_loss -0.743 +2024-09-08 08:52:02.919276: Pseudo dice [0.9667, 0.9601, 0.9774, 0.949, 0.8794, 0.968, 0.9916] +2024-09-08 08:52:02.920698: Epoch time: 425.13 s +2024-09-08 08:52:06.767470: +2024-09-08 08:52:06.769179: Epoch 622 +2024-09-08 08:52:06.770455: Current learning rate: 0.00417 +2024-09-08 08:59:23.764778: train_loss -0.7357 +2024-09-08 08:59:23.780598: val_loss -0.7657 +2024-09-08 08:59:23.781780: Pseudo dice [0.9621, 0.9578, 0.9729, 0.9443, 0.8823, 0.9654, 0.9901] +2024-09-08 08:59:23.782686: Epoch time: 437.02 s +2024-09-08 08:59:27.634427: +2024-09-08 08:59:27.639556: Epoch 623 +2024-09-08 08:59:27.648302: Current learning rate: 0.00416 +2024-09-08 09:06:45.982670: train_loss -0.7359 +2024-09-08 09:06:45.996647: val_loss -0.7904 +2024-09-08 09:06:45.997709: Pseudo dice [0.9637, 0.9591, 0.9774, 0.9431, 0.8835, 0.9694, 0.9916] +2024-09-08 09:06:45.998577: Epoch time: 438.35 s +2024-09-08 09:06:50.851900: +2024-09-08 09:06:50.860693: Epoch 624 +2024-09-08 09:06:50.863755: Current learning rate: 0.00415 +2024-09-08 09:13:48.931100: train_loss -0.7336 +2024-09-08 09:13:48.945745: val_loss -0.736 +2024-09-08 09:13:48.947065: Pseudo dice [0.9645, 0.9591, 0.9779, 0.9504, 0.8922, 0.9738, 0.9913] +2024-09-08 09:13:48.948133: Epoch time: 418.1 s +2024-09-08 09:13:53.135897: +2024-09-08 09:13:53.137530: Epoch 625 +2024-09-08 09:13:53.138836: Current learning rate: 0.00414 +2024-09-08 09:21:17.180993: train_loss -0.7442 +2024-09-08 09:21:17.199284: val_loss -0.7632 +2024-09-08 09:21:17.201254: Pseudo dice [0.9657, 0.9572, 0.9799, 0.9457, 0.8826, 0.9664, 0.9912] +2024-09-08 09:21:17.202842: Epoch time: 444.05 s +2024-09-08 09:21:22.569906: +2024-09-08 09:21:22.571213: Epoch 626 +2024-09-08 09:21:22.572751: Current learning rate: 0.00413 +2024-09-08 09:28:19.564755: train_loss -0.7333 +2024-09-08 09:28:19.572300: val_loss -0.7296 +2024-09-08 09:28:19.573279: Pseudo dice [0.9636, 0.9597, 0.9784, 0.9531, 0.8845, 0.9745, 0.9924] +2024-09-08 09:28:19.574041: Epoch time: 417.0 s +2024-09-08 09:28:19.574968: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-08 09:28:29.682537: +2024-09-08 09:28:29.696862: Epoch 627 +2024-09-08 09:28:29.698019: Current learning rate: 0.00412 +2024-09-08 09:35:21.297553: train_loss -0.7285 +2024-09-08 09:35:21.301930: val_loss -0.7829 +2024-09-08 09:35:21.303149: Pseudo dice [0.9626, 0.9585, 0.9771, 0.9425, 0.8939, 0.9708, 0.9923] +2024-09-08 09:35:21.304264: Epoch time: 411.62 s +2024-09-08 09:35:21.305095: Yayy! New best EMA pseudo Dice: 0.956 +2024-09-08 09:35:32.637869: +2024-09-08 09:35:32.654678: Epoch 628 +2024-09-08 09:35:32.656653: Current learning rate: 0.00411 +2024-09-08 09:42:35.127304: train_loss -0.7476 +2024-09-08 09:42:35.132994: val_loss -0.769 +2024-09-08 09:42:35.134987: Pseudo dice [0.9633, 0.958, 0.9767, 0.9522, 0.8898, 0.9726, 0.9918] +2024-09-08 09:42:35.136434: Epoch time: 422.5 s +2024-09-08 09:42:35.137563: Yayy! New best EMA pseudo Dice: 0.9562 +2024-09-08 09:42:44.205844: +2024-09-08 09:42:44.207783: Epoch 629 +2024-09-08 09:42:44.209164: Current learning rate: 0.0041 +2024-09-08 09:49:34.368508: train_loss -0.732 +2024-09-08 09:49:34.379878: val_loss -0.7283 +2024-09-08 09:49:34.385263: Pseudo dice [0.9661, 0.962, 0.98, 0.9535, 0.8979, 0.9764, 0.9931] +2024-09-08 09:49:34.387605: Epoch time: 410.18 s +2024-09-08 09:49:34.389333: Yayy! New best EMA pseudo Dice: 0.9567 +2024-09-08 09:49:44.816512: +2024-09-08 09:49:44.817924: Epoch 630 +2024-09-08 09:49:44.818882: Current learning rate: 0.00409 +2024-09-08 09:56:45.337324: train_loss -0.7279 +2024-09-08 09:56:45.349769: val_loss -0.7473 +2024-09-08 09:56:45.351203: Pseudo dice [0.9656, 0.9589, 0.9791, 0.9526, 0.8551, 0.9676, 0.9899] +2024-09-08 09:56:45.352867: Epoch time: 420.52 s +2024-09-08 09:56:50.525066: +2024-09-08 09:56:50.526428: Epoch 631 +2024-09-08 09:56:50.527380: Current learning rate: 0.00408 +2024-09-08 10:04:05.302470: train_loss -0.7363 +2024-09-08 10:04:05.320404: val_loss -0.7542 +2024-09-08 10:04:05.321502: Pseudo dice [0.9626, 0.961, 0.9743, 0.9499, 0.887, 0.9713, 0.9909] +2024-09-08 10:04:05.328820: Epoch time: 434.78 s +2024-09-08 10:04:08.901991: +2024-09-08 10:04:08.905563: Epoch 632 +2024-09-08 10:04:08.909031: Current learning rate: 0.00407 +2024-09-08 10:11:15.076726: train_loss -0.7393 +2024-09-08 10:11:15.079677: val_loss -0.7585 +2024-09-08 10:11:15.080619: Pseudo dice [0.9655, 0.9594, 0.9772, 0.9573, 0.9027, 0.9691, 0.9932] +2024-09-08 10:11:15.081392: Epoch time: 426.2 s +2024-09-08 10:11:15.082309: Yayy! New best EMA pseudo Dice: 0.9568 +2024-09-08 10:11:24.032219: +2024-09-08 10:11:24.033827: Epoch 633 +2024-09-08 10:11:24.034695: Current learning rate: 0.00406 +2024-09-08 10:18:08.328407: train_loss -0.7461 +2024-09-08 10:18:08.330174: val_loss -0.7586 +2024-09-08 10:18:08.331223: Pseudo dice [0.9662, 0.9581, 0.9805, 0.9459, 0.8901, 0.9634, 0.9893] +2024-09-08 10:18:08.332049: Epoch time: 404.3 s +2024-09-08 10:18:13.288371: +2024-09-08 10:18:13.290275: Epoch 634 +2024-09-08 10:18:13.291744: Current learning rate: 0.00405 +2024-09-08 10:25:08.165237: train_loss -0.7501 +2024-09-08 10:25:08.169047: val_loss -0.7428 +2024-09-08 10:25:08.170310: Pseudo dice [0.9634, 0.9622, 0.9757, 0.9562, 0.898, 0.975, 0.9926] +2024-09-08 10:25:08.171244: Epoch time: 414.92 s +2024-09-08 10:25:08.171953: Yayy! New best EMA pseudo Dice: 0.9571 +2024-09-08 10:25:16.535192: +2024-09-08 10:25:16.536231: Epoch 635 +2024-09-08 10:25:16.536913: Current learning rate: 0.00404 +2024-09-08 10:31:53.942866: train_loss -0.7516 +2024-09-08 10:31:53.961816: val_loss -0.7526 +2024-09-08 10:31:53.963002: Pseudo dice [0.9658, 0.9596, 0.9801, 0.9506, 0.891, 0.97, 0.9927] +2024-09-08 10:31:53.964070: Epoch time: 397.41 s +2024-09-08 10:31:53.964838: Yayy! New best EMA pseudo Dice: 0.9572 +2024-09-08 10:32:03.830021: +2024-09-08 10:32:03.840911: Epoch 636 +2024-09-08 10:32:03.842297: Current learning rate: 0.00403 +2024-09-08 10:39:09.768502: train_loss -0.7518 +2024-09-08 10:39:09.771213: val_loss -0.7753 +2024-09-08 10:39:09.772768: Pseudo dice [0.9631, 0.9581, 0.9784, 0.9482, 0.8889, 0.9725, 0.9917] +2024-09-08 10:39:09.774210: Epoch time: 425.96 s +2024-09-08 10:39:09.775062: Yayy! New best EMA pseudo Dice: 0.9572 +2024-09-08 10:39:18.862897: +2024-09-08 10:39:18.875667: Epoch 637 +2024-09-08 10:39:18.877210: Current learning rate: 0.00402 +2024-09-08 10:46:33.291274: train_loss -0.7347 +2024-09-08 10:46:33.297199: val_loss -0.7522 +2024-09-08 10:46:33.298661: Pseudo dice [0.9668, 0.9603, 0.9814, 0.9468, 0.8887, 0.9725, 0.9924] +2024-09-08 10:46:33.299895: Epoch time: 434.44 s +2024-09-08 10:46:33.300607: Yayy! New best EMA pseudo Dice: 0.9574 +2024-09-08 10:46:40.872510: +2024-09-08 10:46:40.874540: Epoch 638 +2024-09-08 10:46:40.875432: Current learning rate: 0.00401 +2024-09-08 10:53:34.259668: train_loss -0.7515 +2024-09-08 10:53:34.275126: val_loss -0.78 +2024-09-08 10:53:34.276207: Pseudo dice [0.9675, 0.9598, 0.9803, 0.9493, 0.8837, 0.9718, 0.9928] +2024-09-08 10:53:34.277285: Epoch time: 413.39 s +2024-09-08 10:53:34.278238: Yayy! New best EMA pseudo Dice: 0.9574 +2024-09-08 10:53:41.905228: +2024-09-08 10:53:41.906632: Epoch 639 +2024-09-08 10:53:41.907501: Current learning rate: 0.004 +2024-09-08 11:00:51.507880: train_loss -0.7424 +2024-09-08 11:00:51.514976: val_loss -0.7354 +2024-09-08 11:00:51.516706: Pseudo dice [0.9647, 0.9594, 0.9802, 0.9525, 0.8881, 0.9731, 0.9912] +2024-09-08 11:00:51.517960: Epoch time: 429.61 s +2024-09-08 11:00:51.518950: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-08 11:00:59.552880: +2024-09-08 11:00:59.554033: Epoch 640 +2024-09-08 11:00:59.554839: Current learning rate: 0.00399 +2024-09-08 11:07:57.026921: train_loss -0.748 +2024-09-08 11:07:57.041540: val_loss -0.7321 +2024-09-08 11:07:57.042955: Pseudo dice [0.9662, 0.9588, 0.9778, 0.9466, 0.8908, 0.9746, 0.9927] +2024-09-08 11:07:57.043897: Epoch time: 417.48 s +2024-09-08 11:07:57.044696: Yayy! New best EMA pseudo Dice: 0.9576 +2024-09-08 11:08:06.736204: +2024-09-08 11:08:06.737395: Epoch 641 +2024-09-08 11:08:06.738165: Current learning rate: 0.00398 +2024-09-08 11:15:12.943122: train_loss -0.7413 +2024-09-08 11:15:12.957948: val_loss -0.7625 +2024-09-08 11:15:12.959433: Pseudo dice [0.9652, 0.9589, 0.9781, 0.947, 0.882, 0.971, 0.9907] +2024-09-08 11:15:12.961121: Epoch time: 426.21 s +2024-09-08 11:15:16.144183: +2024-09-08 11:15:16.157033: Epoch 642 +2024-09-08 11:15:16.158149: Current learning rate: 0.00397 +2024-09-08 11:22:36.347505: train_loss -0.7488 +2024-09-08 11:22:36.350661: val_loss -0.762 +2024-09-08 11:22:36.353028: Pseudo dice [0.9661, 0.9604, 0.9787, 0.9469, 0.8875, 0.9716, 0.9917] +2024-09-08 11:22:36.353983: Epoch time: 440.21 s +2024-09-08 11:22:40.289456: +2024-09-08 11:22:40.296950: Epoch 643 +2024-09-08 11:22:40.298590: Current learning rate: 0.00396 +2024-09-08 11:29:32.709879: train_loss -0.7324 +2024-09-08 11:29:32.726753: val_loss -0.7477 +2024-09-08 11:29:32.728108: Pseudo dice [0.9593, 0.9575, 0.9717, 0.9499, 0.8909, 0.9701, 0.9934] +2024-09-08 11:29:32.728965: Epoch time: 412.49 s +2024-09-08 11:29:38.786664: +2024-09-08 11:29:38.806063: Epoch 644 +2024-09-08 11:29:38.808509: Current learning rate: 0.00395 +2024-09-08 11:36:56.938067: train_loss -0.7455 +2024-09-08 11:36:56.942870: val_loss -0.7414 +2024-09-08 11:36:56.943681: Pseudo dice [0.9612, 0.9579, 0.978, 0.9532, 0.8886, 0.971, 0.9921] +2024-09-08 11:36:56.944389: Epoch time: 438.16 s +2024-09-08 11:37:02.194779: +2024-09-08 11:37:02.196362: Epoch 645 +2024-09-08 11:37:02.199926: Current learning rate: 0.00394 +2024-09-08 11:44:14.170804: train_loss -0.7455 +2024-09-08 11:44:14.184489: val_loss -0.7363 +2024-09-08 11:44:14.185917: Pseudo dice [0.9616, 0.959, 0.9741, 0.9477, 0.8849, 0.9721, 0.9924] +2024-09-08 11:44:14.189765: Epoch time: 431.98 s +2024-09-08 11:44:18.981398: +2024-09-08 11:44:18.983062: Epoch 646 +2024-09-08 11:44:18.984049: Current learning rate: 0.00393 +2024-09-08 11:50:58.414949: train_loss -0.742 +2024-09-08 11:50:58.425644: val_loss -0.7443 +2024-09-08 11:50:58.426991: Pseudo dice [0.9657, 0.961, 0.9799, 0.9495, 0.8879, 0.9725, 0.9912] +2024-09-08 11:50:58.428101: Epoch time: 399.45 s +2024-09-08 11:51:02.542685: +2024-09-08 11:51:02.544312: Epoch 647 +2024-09-08 11:51:02.545225: Current learning rate: 0.00392 +2024-09-08 11:58:08.434335: train_loss -0.745 +2024-09-08 11:58:08.443303: val_loss -0.7488 +2024-09-08 11:58:08.446231: Pseudo dice [0.9638, 0.957, 0.9761, 0.9463, 0.8883, 0.9731, 0.9924] +2024-09-08 11:58:08.448978: Epoch time: 425.9 s +2024-09-08 11:58:14.564022: +2024-09-08 11:58:14.582421: Epoch 648 +2024-09-08 11:58:14.583981: Current learning rate: 0.00391 +2024-09-08 12:05:22.453721: train_loss -0.7569 +2024-09-08 12:05:22.463402: val_loss -0.7503 +2024-09-08 12:05:22.472549: Pseudo dice [0.9629, 0.9582, 0.978, 0.953, 0.8914, 0.9741, 0.9928] +2024-09-08 12:05:22.473613: Epoch time: 427.9 s +2024-09-08 12:05:27.247185: +2024-09-08 12:05:27.249012: Epoch 649 +2024-09-08 12:05:27.249979: Current learning rate: 0.0039 +2024-09-08 12:12:53.528045: train_loss -0.7535 +2024-09-08 12:12:53.534064: val_loss -0.7608 +2024-09-08 12:12:53.535146: Pseudo dice [0.9619, 0.9583, 0.9737, 0.946, 0.8577, 0.9688, 0.9895] +2024-09-08 12:12:53.535966: Epoch time: 446.29 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_14_03_51.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_14_03_51.txt new file mode 100644 index 0000000000000000000000000000000000000000..14ce9c150980b50d79291d9915bb5a3bab8e0d33 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_14_03_51.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 14:03:54.292108: Using torch.compile... +2024-09-08 14:04:01.200494: do_dummy_2d_data_aug: False +2024-09-08 14:04:01.206420: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 14:04:01.207715: The split file contains 5 splits. +2024-09-08 14:04:01.210318: Desired fold for training: 3 +2024-09-08 14:04:01.214924: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 14:04:21.853440: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 14:04:23.219275: +2024-09-08 14:04:23.221359: Epoch 650 +2024-09-08 14:04:23.224379: Current learning rate: 0.00389 +2024-09-08 14:21:32.874098: train_loss -0.7502 +2024-09-08 14:21:32.877012: val_loss -0.7334 +2024-09-08 14:21:32.878416: Pseudo dice [0.9668, 0.9604, 0.978, 0.9457, 0.8533, 0.9744, 0.9891] +2024-09-08 14:21:32.879802: Epoch time: 1029.7 s +2024-09-08 14:21:35.127328: +2024-09-08 14:21:35.128338: Epoch 651 +2024-09-08 14:21:35.129947: Current learning rate: 0.00388 +2024-09-08 14:28:32.504937: train_loss -0.7395 +2024-09-08 14:28:32.522312: val_loss -0.7523 +2024-09-08 14:28:32.524168: Pseudo dice [0.9619, 0.9602, 0.9755, 0.947, 0.8857, 0.9721, 0.9919] +2024-09-08 14:28:32.526763: Epoch time: 417.38 s +2024-09-08 14:28:39.856623: +2024-09-08 14:28:39.872762: Epoch 652 +2024-09-08 14:28:39.875364: Current learning rate: 0.00387 +2024-09-08 14:35:49.931933: train_loss -0.7575 +2024-09-08 14:35:49.939081: val_loss -0.7929 +2024-09-08 14:35:49.940446: Pseudo dice [0.965, 0.9601, 0.9759, 0.9503, 0.8998, 0.974, 0.9928] +2024-09-08 14:35:49.941339: Epoch time: 430.09 s +2024-09-08 14:35:53.545060: +2024-09-08 14:35:53.546546: Epoch 653 +2024-09-08 14:35:53.547659: Current learning rate: 0.00386 +2024-09-08 14:43:03.074729: train_loss -0.7637 +2024-09-08 14:43:03.089885: val_loss -0.7769 +2024-09-08 14:43:03.091433: Pseudo dice [0.9644, 0.9589, 0.9774, 0.9532, 0.8821, 0.974, 0.9921] +2024-09-08 14:43:03.093246: Epoch time: 429.55 s +2024-09-08 14:43:07.135361: +2024-09-08 14:43:07.137181: Epoch 654 +2024-09-08 14:43:07.138287: Current learning rate: 0.00385 +2024-09-08 14:50:17.350246: train_loss -0.7349 +2024-09-08 14:50:17.354650: val_loss -0.7792 +2024-09-08 14:50:17.356546: Pseudo dice [0.9647, 0.9547, 0.977, 0.9468, 0.895, 0.9688, 0.9932] +2024-09-08 14:50:17.358922: Epoch time: 430.28 s +2024-09-08 14:50:20.994219: +2024-09-08 14:50:21.004854: Epoch 655 +2024-09-08 14:50:21.006267: Current learning rate: 0.00384 +2024-09-08 14:57:35.524599: train_loss -0.7452 +2024-09-08 14:57:35.531126: val_loss -0.7941 +2024-09-08 14:57:35.532770: Pseudo dice [0.9664, 0.9597, 0.9784, 0.9556, 0.8888, 0.9734, 0.9917] +2024-09-08 14:57:35.534350: Epoch time: 434.54 s +2024-09-08 14:57:40.077589: +2024-09-08 14:57:40.080406: Epoch 656 +2024-09-08 14:57:40.081875: Current learning rate: 0.00383 +2024-09-08 15:04:34.271571: train_loss -0.7257 +2024-09-08 15:04:34.274559: val_loss -0.7859 +2024-09-08 15:04:34.275892: Pseudo dice [0.9647, 0.9583, 0.9798, 0.9524, 0.8919, 0.9707, 0.9907] +2024-09-08 15:04:34.277244: Epoch time: 414.22 s +2024-09-08 15:04:38.366394: +2024-09-08 15:04:38.383174: Epoch 657 +2024-09-08 15:04:38.385054: Current learning rate: 0.00382 +2024-09-08 15:11:58.228468: train_loss -0.755 +2024-09-08 15:11:58.247543: val_loss -0.7523 +2024-09-08 15:11:58.248883: Pseudo dice [0.9668, 0.9605, 0.9799, 0.9504, 0.8805, 0.9708, 0.9917] +2024-09-08 15:11:58.250198: Epoch time: 439.87 s +2024-09-08 15:12:02.213989: +2024-09-08 15:12:02.215906: Epoch 658 +2024-09-08 15:12:02.216784: Current learning rate: 0.00381 +2024-09-08 15:19:09.600518: train_loss -0.7352 +2024-09-08 15:19:09.604297: val_loss -0.7536 +2024-09-08 15:19:09.605437: Pseudo dice [0.9617, 0.9581, 0.9783, 0.9517, 0.8942, 0.9713, 0.9916] +2024-09-08 15:19:09.606657: Epoch time: 427.39 s +2024-09-08 15:19:12.784387: +2024-09-08 15:19:12.786268: Epoch 659 +2024-09-08 15:19:12.787262: Current learning rate: 0.0038 +2024-09-08 15:26:13.409047: train_loss -0.7456 +2024-09-08 15:26:13.428015: val_loss -0.7257 +2024-09-08 15:26:13.429302: Pseudo dice [0.9631, 0.9584, 0.9764, 0.9496, 0.8846, 0.9614, 0.9921] +2024-09-08 15:26:13.430473: Epoch time: 420.64 s +2024-09-08 15:26:17.598687: +2024-09-08 15:26:17.612114: Epoch 660 +2024-09-08 15:26:17.613169: Current learning rate: 0.00379 +2024-09-08 15:33:23.688318: train_loss -0.7442 +2024-09-08 15:33:23.710618: val_loss -0.7468 +2024-09-08 15:33:23.711629: Pseudo dice [0.9628, 0.9586, 0.9803, 0.9492, 0.8699, 0.9699, 0.9887] +2024-09-08 15:33:23.712820: Epoch time: 426.1 s +2024-09-08 15:33:28.115564: +2024-09-08 15:33:28.129163: Epoch 661 +2024-09-08 15:33:28.131054: Current learning rate: 0.00378 +2024-09-08 15:40:40.583702: train_loss -0.743 +2024-09-08 15:40:40.604200: val_loss -0.7518 +2024-09-08 15:40:40.607230: Pseudo dice [0.9628, 0.9593, 0.9757, 0.9497, 0.8866, 0.9692, 0.9928] +2024-09-08 15:40:40.609547: Epoch time: 432.5 s +2024-09-08 15:40:43.590142: +2024-09-08 15:40:43.591995: Epoch 662 +2024-09-08 15:40:43.593137: Current learning rate: 0.00377 +2024-09-08 15:47:48.322477: train_loss -0.7451 +2024-09-08 15:47:48.335061: val_loss -0.7528 +2024-09-08 15:47:48.336864: Pseudo dice [0.963, 0.959, 0.974, 0.9477, 0.8529, 0.9627, 0.987] +2024-09-08 15:47:48.338291: Epoch time: 424.75 s +2024-09-08 15:47:56.461661: +2024-09-08 15:47:56.463360: Epoch 663 +2024-09-08 15:47:56.464281: Current learning rate: 0.00376 +2024-09-08 15:55:24.249830: train_loss -0.7538 +2024-09-08 15:55:24.251525: val_loss -0.7844 +2024-09-08 15:55:24.252396: Pseudo dice [0.9621, 0.9572, 0.9751, 0.9489, 0.8955, 0.9739, 0.9928] +2024-09-08 15:55:24.253142: Epoch time: 447.8 s +2024-09-08 15:55:28.946925: +2024-09-08 15:55:28.948736: Epoch 664 +2024-09-08 15:55:28.951021: Current learning rate: 0.00375 +2024-09-08 16:02:36.203105: train_loss -0.7448 +2024-09-08 16:02:36.206554: val_loss -0.7698 +2024-09-08 16:02:36.209225: Pseudo dice [0.9646, 0.961, 0.9789, 0.9473, 0.8687, 0.9726, 0.9905] +2024-09-08 16:02:36.219103: Epoch time: 427.26 s +2024-09-08 16:02:40.844027: +2024-09-08 16:02:40.845539: Epoch 665 +2024-09-08 16:02:40.846385: Current learning rate: 0.00374 +2024-09-08 16:09:44.812669: train_loss -0.7444 +2024-09-08 16:09:44.817361: val_loss -0.7802 +2024-09-08 16:09:44.819503: Pseudo dice [0.9636, 0.9574, 0.9763, 0.9513, 0.8963, 0.9754, 0.9928] +2024-09-08 16:09:44.822164: Epoch time: 423.97 s +2024-09-08 16:09:48.403240: +2024-09-08 16:09:48.404469: Epoch 666 +2024-09-08 16:09:48.405277: Current learning rate: 0.00373 +2024-09-08 16:16:53.226725: train_loss -0.7439 +2024-09-08 16:16:53.229810: val_loss -0.7708 +2024-09-08 16:16:53.230725: Pseudo dice [0.9616, 0.9579, 0.9751, 0.9508, 0.8951, 0.9736, 0.9933] +2024-09-08 16:16:53.231698: Epoch time: 424.83 s +2024-09-08 16:16:57.148185: +2024-09-08 16:16:57.161372: Epoch 667 +2024-09-08 16:16:57.162710: Current learning rate: 0.00372 +2024-09-08 16:24:00.408597: train_loss -0.7508 +2024-09-08 16:24:00.411937: val_loss -0.7694 +2024-09-08 16:24:00.413836: Pseudo dice [0.9651, 0.958, 0.979, 0.946, 0.8938, 0.9739, 0.9921] +2024-09-08 16:24:00.425041: Epoch time: 423.27 s +2024-09-08 16:24:04.731855: +2024-09-08 16:24:04.748750: Epoch 668 +2024-09-08 16:24:04.749727: Current learning rate: 0.00371 +2024-09-08 16:31:05.072541: train_loss -0.7428 +2024-09-08 16:31:05.096492: val_loss -0.7361 +2024-09-08 16:31:05.106400: Pseudo dice [0.9585, 0.9572, 0.9684, 0.9488, 0.859, 0.9749, 0.9895] +2024-09-08 16:31:05.108931: Epoch time: 420.35 s +2024-09-08 16:31:08.536246: +2024-09-08 16:31:08.537831: Epoch 669 +2024-09-08 16:31:08.539070: Current learning rate: 0.0037 +2024-09-08 16:38:02.689967: train_loss -0.7456 +2024-09-08 16:38:02.692532: val_loss -0.7482 +2024-09-08 16:38:02.693948: Pseudo dice [0.9622, 0.9582, 0.9778, 0.9479, 0.8911, 0.9723, 0.9921] +2024-09-08 16:38:02.695024: Epoch time: 414.16 s +2024-09-08 16:38:07.697855: +2024-09-08 16:38:07.700649: Epoch 670 +2024-09-08 16:38:07.702216: Current learning rate: 0.00369 +2024-09-08 16:45:15.666511: train_loss -0.7305 +2024-09-08 16:45:15.672655: val_loss -0.749 +2024-09-08 16:45:15.674321: Pseudo dice [0.9612, 0.9584, 0.9757, 0.9387, 0.888, 0.9741, 0.992] +2024-09-08 16:45:15.676553: Epoch time: 427.97 s +2024-09-08 16:45:19.888038: +2024-09-08 16:45:19.893170: Epoch 671 +2024-09-08 16:45:19.909079: Current learning rate: 0.00368 +2024-09-08 16:52:16.608920: train_loss -0.7455 +2024-09-08 16:52:16.627034: val_loss -0.7695 +2024-09-08 16:52:16.630683: Pseudo dice [0.9623, 0.962, 0.9775, 0.9529, 0.8987, 0.9714, 0.9932] +2024-09-08 16:52:16.632830: Epoch time: 416.76 s +2024-09-08 16:52:22.479874: +2024-09-08 16:52:22.497023: Epoch 672 +2024-09-08 16:52:22.498432: Current learning rate: 0.00367 +2024-09-08 16:59:31.000864: train_loss -0.7535 +2024-09-08 16:59:31.018684: val_loss -0.7433 +2024-09-08 16:59:31.019496: Pseudo dice [0.9603, 0.9581, 0.9753, 0.9463, 0.8888, 0.974, 0.9914] +2024-09-08 16:59:31.020298: Epoch time: 428.53 s +2024-09-08 16:59:35.153696: +2024-09-08 16:59:35.164158: Epoch 673 +2024-09-08 16:59:35.165100: Current learning rate: 0.00366 +2024-09-08 17:06:30.752665: train_loss -0.7424 +2024-09-08 17:06:30.769526: val_loss -0.745 +2024-09-08 17:06:30.770519: Pseudo dice [0.9609, 0.9566, 0.9759, 0.9479, 0.8952, 0.9741, 0.9933] +2024-09-08 17:06:30.771406: Epoch time: 415.61 s +2024-09-08 17:06:34.981667: +2024-09-08 17:06:34.985646: Epoch 674 +2024-09-08 17:06:34.995742: Current learning rate: 0.00365 +2024-09-08 17:13:14.813694: train_loss -0.7334 +2024-09-08 17:13:14.831233: val_loss -0.7419 +2024-09-08 17:13:14.832908: Pseudo dice [0.9558, 0.9553, 0.9687, 0.947, 0.8579, 0.9711, 0.9903] +2024-09-08 17:13:14.835278: Epoch time: 399.85 s +2024-09-08 17:13:20.271229: +2024-09-08 17:13:20.273311: Epoch 675 +2024-09-08 17:13:20.274726: Current learning rate: 0.00364 +2024-09-08 17:20:11.496218: train_loss -0.7382 +2024-09-08 17:20:11.498368: val_loss -0.7563 +2024-09-08 17:20:11.499187: Pseudo dice [0.959, 0.9582, 0.9702, 0.9495, 0.8906, 0.9736, 0.9925] +2024-09-08 17:20:11.500011: Epoch time: 411.25 s +2024-09-08 17:20:16.054317: +2024-09-08 17:20:16.055912: Epoch 676 +2024-09-08 17:20:16.057060: Current learning rate: 0.00363 +2024-09-08 17:27:18.525242: train_loss -0.7399 +2024-09-08 17:27:18.549212: val_loss -0.7267 +2024-09-08 17:27:18.551353: Pseudo dice [0.9636, 0.9598, 0.9772, 0.9554, 0.8854, 0.9689, 0.9912] +2024-09-08 17:27:18.554001: Epoch time: 422.48 s +2024-09-08 17:27:25.929700: +2024-09-08 17:27:25.931275: Epoch 677 +2024-09-08 17:27:25.932162: Current learning rate: 0.00362 +2024-09-08 17:34:10.521131: train_loss -0.754 +2024-09-08 17:34:10.539340: val_loss -0.7377 +2024-09-08 17:34:10.540628: Pseudo dice [0.9652, 0.9603, 0.9757, 0.9478, 0.8935, 0.9712, 0.992] +2024-09-08 17:34:10.544075: Epoch time: 404.6 s +2024-09-08 17:34:15.814889: +2024-09-08 17:34:15.829115: Epoch 678 +2024-09-08 17:34:15.830341: Current learning rate: 0.00361 +2024-09-08 17:41:20.083735: train_loss -0.7501 +2024-09-08 17:41:20.086457: val_loss -0.7629 +2024-09-08 17:41:20.087626: Pseudo dice [0.9661, 0.9596, 0.979, 0.9454, 0.8725, 0.9731, 0.9896] +2024-09-08 17:41:20.088660: Epoch time: 424.28 s +2024-09-08 17:41:24.586579: +2024-09-08 17:41:24.605788: Epoch 679 +2024-09-08 17:41:24.607754: Current learning rate: 0.0036 +2024-09-08 17:48:23.243587: train_loss -0.7568 +2024-09-08 17:48:23.245832: val_loss -0.7503 +2024-09-08 17:48:23.247349: Pseudo dice [0.9617, 0.9597, 0.9735, 0.9488, 0.8929, 0.9705, 0.9923] +2024-09-08 17:48:23.248540: Epoch time: 418.66 s +2024-09-08 17:48:27.528726: +2024-09-08 17:48:27.540539: Epoch 680 +2024-09-08 17:48:27.541902: Current learning rate: 0.00359 +2024-09-08 17:55:37.062762: train_loss -0.7533 +2024-09-08 17:55:37.067429: val_loss -0.7786 +2024-09-08 17:55:37.068712: Pseudo dice [0.9652, 0.9611, 0.98, 0.9498, 0.8895, 0.9737, 0.9923] +2024-09-08 17:55:37.069550: Epoch time: 429.54 s +2024-09-08 17:55:40.591329: +2024-09-08 17:55:40.602666: Epoch 681 +2024-09-08 17:55:40.604317: Current learning rate: 0.00358 +2024-09-08 18:02:29.264738: train_loss -0.7459 +2024-09-08 18:02:29.284917: val_loss -0.7437 +2024-09-08 18:02:29.287183: Pseudo dice [0.9644, 0.9573, 0.9758, 0.9491, 0.8626, 0.9751, 0.9904] +2024-09-08 18:02:29.289066: Epoch time: 408.68 s +2024-09-08 18:02:35.194697: +2024-09-08 18:02:35.196321: Epoch 682 +2024-09-08 18:02:35.197544: Current learning rate: 0.00357 +2024-09-08 18:09:55.084481: train_loss -0.747 +2024-09-08 18:09:55.086627: val_loss -0.7337 +2024-09-08 18:09:55.088229: Pseudo dice [0.9643, 0.9619, 0.9743, 0.9553, 0.8725, 0.974, 0.9909] +2024-09-08 18:09:55.089760: Epoch time: 439.9 s +2024-09-08 18:09:58.251549: +2024-09-08 18:09:58.264903: Epoch 683 +2024-09-08 18:09:58.266004: Current learning rate: 0.00356 +2024-09-08 18:17:04.898016: train_loss -0.7437 +2024-09-08 18:17:04.902836: val_loss -0.7349 +2024-09-08 18:17:04.905397: Pseudo dice [0.9634, 0.9599, 0.9785, 0.9455, 0.8885, 0.9701, 0.9923] +2024-09-08 18:17:04.908274: Epoch time: 426.66 s +2024-09-08 18:17:09.420868: +2024-09-08 18:17:09.433938: Epoch 684 +2024-09-08 18:17:09.435790: Current learning rate: 0.00355 +2024-09-08 18:24:11.160534: train_loss -0.7332 +2024-09-08 18:24:11.163372: val_loss -0.7536 +2024-09-08 18:24:11.168939: Pseudo dice [0.9607, 0.9591, 0.9748, 0.9517, 0.8796, 0.9651, 0.9901] +2024-09-08 18:24:11.171059: Epoch time: 421.8 s +2024-09-08 18:24:14.707475: +2024-09-08 18:24:14.709315: Epoch 685 +2024-09-08 18:24:14.710330: Current learning rate: 0.00354 +2024-09-08 18:31:17.362669: train_loss -0.7486 +2024-09-08 18:31:17.370848: val_loss -0.7525 +2024-09-08 18:31:17.372660: Pseudo dice [0.9648, 0.9595, 0.9773, 0.9487, 0.8817, 0.9702, 0.9912] +2024-09-08 18:31:17.374371: Epoch time: 422.67 s +2024-09-08 18:31:22.660176: +2024-09-08 18:31:22.665141: Epoch 686 +2024-09-08 18:31:22.668613: Current learning rate: 0.00353 +2024-09-08 18:38:35.589524: train_loss -0.7471 +2024-09-08 18:38:35.593538: val_loss -0.7616 +2024-09-08 18:38:35.594846: Pseudo dice [0.9667, 0.962, 0.9797, 0.9515, 0.8897, 0.9726, 0.9925] +2024-09-08 18:38:35.596613: Epoch time: 432.95 s +2024-09-08 18:38:40.752597: +2024-09-08 18:38:40.764858: Epoch 687 +2024-09-08 18:38:40.766070: Current learning rate: 0.00352 +2024-09-08 18:45:50.460675: train_loss -0.756 +2024-09-08 18:45:50.477705: val_loss -0.7497 +2024-09-08 18:45:50.479254: Pseudo dice [0.9635, 0.9579, 0.9782, 0.9437, 0.9007, 0.9757, 0.9932] +2024-09-08 18:45:50.481726: Epoch time: 429.71 s +2024-09-08 18:45:55.001337: +2024-09-08 18:45:55.002866: Epoch 688 +2024-09-08 18:45:55.003781: Current learning rate: 0.00351 +2024-09-08 18:52:34.729371: train_loss -0.7348 +2024-09-08 18:52:34.733384: val_loss -0.7599 +2024-09-08 18:52:34.737025: Pseudo dice [0.966, 0.9592, 0.9808, 0.9529, 0.8797, 0.9736, 0.9891] +2024-09-08 18:52:34.737953: Epoch time: 399.74 s +2024-09-08 18:52:39.130401: +2024-09-08 18:52:39.145250: Epoch 689 +2024-09-08 18:52:39.147034: Current learning rate: 0.0035 +2024-09-08 18:59:44.956762: train_loss -0.7527 +2024-09-08 18:59:44.960295: val_loss -0.7433 +2024-09-08 18:59:44.962607: Pseudo dice [0.9641, 0.961, 0.9768, 0.9507, 0.8889, 0.9675, 0.9923] +2024-09-08 18:59:44.965118: Epoch time: 425.83 s +2024-09-08 18:59:51.326402: +2024-09-08 18:59:51.328618: Epoch 690 +2024-09-08 18:59:51.344740: Current learning rate: 0.00349 +2024-09-08 19:07:02.508356: train_loss -0.7293 +2024-09-08 19:07:02.525791: val_loss -0.7817 +2024-09-08 19:07:02.527559: Pseudo dice [0.9618, 0.9602, 0.9764, 0.948, 0.8886, 0.9728, 0.993] +2024-09-08 19:07:02.528826: Epoch time: 431.23 s +2024-09-08 19:07:09.810937: +2024-09-08 19:07:09.815120: Epoch 691 +2024-09-08 19:07:09.816296: Current learning rate: 0.00348 +2024-09-08 19:14:05.339115: train_loss -0.7301 +2024-09-08 19:14:05.355725: val_loss -0.7516 +2024-09-08 19:14:05.357593: Pseudo dice [0.9602, 0.954, 0.9742, 0.9442, 0.8939, 0.9693, 0.991] +2024-09-08 19:14:05.359205: Epoch time: 415.53 s +2024-09-08 19:14:09.706973: +2024-09-08 19:14:09.720918: Epoch 692 +2024-09-08 19:14:09.723561: Current learning rate: 0.00346 +2024-09-08 19:21:12.601683: train_loss -0.7208 +2024-09-08 19:21:12.617746: val_loss -0.7658 +2024-09-08 19:21:12.619794: Pseudo dice [0.9615, 0.957, 0.9736, 0.9499, 0.8767, 0.9714, 0.9903] +2024-09-08 19:21:12.621293: Epoch time: 422.9 s +2024-09-08 19:21:17.697745: +2024-09-08 19:21:17.699748: Epoch 693 +2024-09-08 19:21:17.701719: Current learning rate: 0.00345 +2024-09-08 19:28:11.858507: train_loss -0.7574 +2024-09-08 19:28:11.876631: val_loss -0.7745 +2024-09-08 19:28:11.878895: Pseudo dice [0.9583, 0.9557, 0.9757, 0.9441, 0.8875, 0.9707, 0.9924] +2024-09-08 19:28:11.880530: Epoch time: 414.17 s +2024-09-08 19:28:16.806655: +2024-09-08 19:28:16.808986: Epoch 694 +2024-09-08 19:28:16.811613: Current learning rate: 0.00344 +2024-09-08 19:35:27.071487: train_loss -0.7434 +2024-09-08 19:35:27.075832: val_loss -0.7637 +2024-09-08 19:35:27.078513: Pseudo dice [0.9646, 0.9576, 0.9774, 0.9487, 0.8785, 0.9676, 0.9913] +2024-09-08 19:35:27.080721: Epoch time: 430.27 s +2024-09-08 19:35:33.519850: +2024-09-08 19:35:33.538453: Epoch 695 +2024-09-08 19:35:33.540829: Current learning rate: 0.00343 +2024-09-08 19:42:44.800019: train_loss -0.7363 +2024-09-08 19:42:44.803454: val_loss -0.7802 +2024-09-08 19:42:44.805130: Pseudo dice [0.9626, 0.9554, 0.9767, 0.9428, 0.8888, 0.9686, 0.9909] +2024-09-08 19:42:44.806919: Epoch time: 431.31 s +2024-09-08 19:42:48.665199: +2024-09-08 19:42:48.680752: Epoch 696 +2024-09-08 19:42:48.682147: Current learning rate: 0.00342 +2024-09-08 19:49:58.487555: train_loss -0.7345 +2024-09-08 19:49:58.523314: val_loss -0.7205 +2024-09-08 19:49:58.524558: Pseudo dice [0.959, 0.9484, 0.9737, 0.9438, 0.8814, 0.964, 0.992] +2024-09-08 19:49:58.525604: Epoch time: 429.83 s +2024-09-08 19:50:02.563187: +2024-09-08 19:50:02.565358: Epoch 697 +2024-09-08 19:50:02.567218: Current learning rate: 0.00341 +2024-09-08 19:57:15.509276: train_loss -0.7346 +2024-09-08 19:57:15.539004: val_loss -0.75 +2024-09-08 19:57:15.541348: Pseudo dice [0.9628, 0.956, 0.9781, 0.9449, 0.8516, 0.9684, 0.987] +2024-09-08 19:57:15.542233: Epoch time: 432.95 s +2024-09-08 19:57:19.711620: +2024-09-08 19:57:19.733482: Epoch 698 +2024-09-08 19:57:19.735509: Current learning rate: 0.0034 +2024-09-08 20:04:22.178226: train_loss -0.7438 +2024-09-08 20:04:22.182011: val_loss -0.7749 +2024-09-08 20:04:22.184741: Pseudo dice [0.9621, 0.9555, 0.9764, 0.9439, 0.8943, 0.967, 0.9917] +2024-09-08 20:04:22.187790: Epoch time: 422.47 s +2024-09-08 20:04:27.658855: +2024-09-08 20:04:27.660662: Epoch 699 +2024-09-08 20:04:27.662037: Current learning rate: 0.00339 +2024-09-08 20:11:20.238197: train_loss -0.7406 +2024-09-08 20:11:20.257055: val_loss -0.7501 +2024-09-08 20:11:20.268940: Pseudo dice [0.9596, 0.9571, 0.9742, 0.9466, 0.8879, 0.9692, 0.9909] +2024-09-08 20:11:20.270704: Epoch time: 412.6 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_22_03_23.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_22_03_23.txt new file mode 100644 index 0000000000000000000000000000000000000000..3da0720f6193915cbfca18d026ac203bd859733d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_8_22_03_23.txt @@ -0,0 +1,378 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 22:03:26.685549: Using torch.compile... +2024-09-08 22:03:33.160327: do_dummy_2d_data_aug: False +2024-09-08 22:03:33.172919: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 22:03:33.174067: The split file contains 5 splits. +2024-09-08 22:03:33.174581: Desired fold for training: 3 +2024-09-08 22:03:33.175069: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 22:03:47.501203: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 22:03:48.537825: +2024-09-08 22:03:48.552519: Epoch 700 +2024-09-08 22:03:48.554134: Current learning rate: 0.00338 +2024-09-08 22:17:00.902602: train_loss -0.7389 +2024-09-08 22:17:00.905364: val_loss -0.7304 +2024-09-08 22:17:00.906183: Pseudo dice [0.9636, 0.9574, 0.9751, 0.9438, 0.8739, 0.9707, 0.9914] +2024-09-08 22:17:00.906836: Epoch time: 792.4 s +2024-09-08 22:17:02.438112: +2024-09-08 22:17:02.439855: Epoch 701 +2024-09-08 22:17:02.440648: Current learning rate: 0.00337 +2024-09-08 22:21:34.352952: train_loss -0.7381 +2024-09-08 22:21:34.371548: val_loss -0.765 +2024-09-08 22:21:34.373223: Pseudo dice [0.9649, 0.9593, 0.9751, 0.9541, 0.8779, 0.9721, 0.9898] +2024-09-08 22:21:34.374651: Epoch time: 271.92 s +2024-09-08 22:21:37.284055: +2024-09-08 22:21:37.285322: Epoch 702 +2024-09-08 22:21:37.286317: Current learning rate: 0.00336 +2024-09-08 22:26:19.574224: train_loss -0.7358 +2024-09-08 22:26:19.581276: val_loss -0.6983 +2024-09-08 22:26:19.584226: Pseudo dice [0.9647, 0.9558, 0.9798, 0.9514, 0.8719, 0.9656, 0.9901] +2024-09-08 22:26:19.586272: Epoch time: 282.29 s +2024-09-08 22:26:22.945137: +2024-09-08 22:26:22.946616: Epoch 703 +2024-09-08 22:26:22.948749: Current learning rate: 0.00335 +2024-09-08 22:31:02.970955: train_loss -0.7348 +2024-09-08 22:31:02.992914: val_loss -0.729 +2024-09-08 22:31:02.993888: Pseudo dice [0.9628, 0.958, 0.9778, 0.9435, 0.8649, 0.9725, 0.9893] +2024-09-08 22:31:02.994640: Epoch time: 280.03 s +2024-09-08 22:31:05.654945: +2024-09-08 22:31:05.668377: Epoch 704 +2024-09-08 22:31:05.669388: Current learning rate: 0.00334 +2024-09-08 22:36:05.221235: train_loss -0.7254 +2024-09-08 22:36:05.233263: val_loss -0.7482 +2024-09-08 22:36:05.234150: Pseudo dice [0.963, 0.9599, 0.9764, 0.9481, 0.8856, 0.9673, 0.9925] +2024-09-08 22:36:05.234937: Epoch time: 299.57 s +2024-09-08 22:36:08.706877: +2024-09-08 22:36:08.707946: Epoch 705 +2024-09-08 22:36:08.708998: Current learning rate: 0.00333 +2024-09-08 22:40:43.132524: train_loss -0.75 +2024-09-08 22:40:43.135766: val_loss -0.7383 +2024-09-08 22:40:43.136574: Pseudo dice [0.9653, 0.9609, 0.9789, 0.9554, 0.8579, 0.9731, 0.9878] +2024-09-08 22:40:43.137211: Epoch time: 274.43 s +2024-09-08 22:40:46.773030: +2024-09-08 22:40:46.774809: Epoch 706 +2024-09-08 22:40:46.776482: Current learning rate: 0.00332 +2024-09-08 22:45:31.112935: train_loss -0.7325 +2024-09-08 22:45:31.128928: val_loss -0.7368 +2024-09-08 22:45:31.129755: Pseudo dice [0.9641, 0.9592, 0.9758, 0.9468, 0.888, 0.9656, 0.9913] +2024-09-08 22:45:31.130404: Epoch time: 284.34 s +2024-09-08 22:45:35.238032: +2024-09-08 22:45:35.252557: Epoch 707 +2024-09-08 22:45:35.253437: Current learning rate: 0.00331 +2024-09-08 22:50:15.705409: train_loss -0.7571 +2024-09-08 22:50:15.721008: val_loss -0.769 +2024-09-08 22:50:15.722011: Pseudo dice [0.9631, 0.9593, 0.9791, 0.9491, 0.8902, 0.9684, 0.9922] +2024-09-08 22:50:15.723195: Epoch time: 280.47 s +2024-09-08 22:50:19.371614: +2024-09-08 22:50:19.372954: Epoch 708 +2024-09-08 22:50:19.373974: Current learning rate: 0.0033 +2024-09-08 22:54:56.826539: train_loss -0.7412 +2024-09-08 22:54:56.841226: val_loss -0.7588 +2024-09-08 22:54:56.842544: Pseudo dice [0.963, 0.9593, 0.9763, 0.9497, 0.874, 0.9712, 0.9906] +2024-09-08 22:54:56.843606: Epoch time: 277.46 s +2024-09-08 22:54:59.933477: +2024-09-08 22:54:59.934943: Epoch 709 +2024-09-08 22:54:59.935987: Current learning rate: 0.00329 +2024-09-08 22:59:33.821954: train_loss -0.7382 +2024-09-08 22:59:33.823547: val_loss -0.764 +2024-09-08 22:59:33.824617: Pseudo dice [0.9651, 0.9592, 0.98, 0.9456, 0.886, 0.974, 0.9928] +2024-09-08 22:59:33.825491: Epoch time: 273.89 s +2024-09-08 22:59:37.847923: +2024-09-08 22:59:37.865165: Epoch 710 +2024-09-08 22:59:37.866847: Current learning rate: 0.00328 +2024-09-08 23:04:18.564828: train_loss -0.7542 +2024-09-08 23:04:18.578706: val_loss -0.7589 +2024-09-08 23:04:18.579817: Pseudo dice [0.964, 0.9605, 0.9768, 0.9503, 0.8743, 0.9747, 0.9904] +2024-09-08 23:04:18.581230: Epoch time: 280.72 s +2024-09-08 23:04:21.323333: +2024-09-08 23:04:21.324430: Epoch 711 +2024-09-08 23:04:21.325206: Current learning rate: 0.00327 +2024-09-08 23:09:08.579373: train_loss -0.7624 +2024-09-08 23:09:08.593096: val_loss -0.7359 +2024-09-08 23:09:08.594559: Pseudo dice [0.9612, 0.9589, 0.975, 0.9489, 0.884, 0.9738, 0.9919] +2024-09-08 23:09:08.595878: Epoch time: 287.26 s +2024-09-08 23:09:11.414616: +2024-09-08 23:09:11.416106: Epoch 712 +2024-09-08 23:09:11.417377: Current learning rate: 0.00326 +2024-09-08 23:14:00.148915: train_loss -0.7536 +2024-09-08 23:14:00.151675: val_loss -0.7427 +2024-09-08 23:14:00.152512: Pseudo dice [0.9612, 0.9586, 0.9753, 0.9454, 0.891, 0.9714, 0.9922] +2024-09-08 23:14:00.153235: Epoch time: 288.74 s +2024-09-08 23:14:03.369779: +2024-09-08 23:14:03.370947: Epoch 713 +2024-09-08 23:14:03.371937: Current learning rate: 0.00325 +2024-09-08 23:18:34.278274: train_loss -0.7403 +2024-09-08 23:18:34.285782: val_loss -0.7268 +2024-09-08 23:18:34.286561: Pseudo dice [0.9605, 0.9572, 0.9724, 0.9506, 0.8999, 0.9751, 0.9923] +2024-09-08 23:18:34.287437: Epoch time: 270.91 s +2024-09-08 23:18:40.408308: +2024-09-08 23:18:40.409467: Epoch 714 +2024-09-08 23:18:40.410174: Current learning rate: 0.00324 +2024-09-08 23:23:10.384127: train_loss -0.7451 +2024-09-08 23:23:10.391718: val_loss -0.7508 +2024-09-08 23:23:10.393520: Pseudo dice [0.9651, 0.9618, 0.9768, 0.949, 0.889, 0.9701, 0.9906] +2024-09-08 23:23:10.394456: Epoch time: 269.98 s +2024-09-08 23:23:14.001236: +2024-09-08 23:23:14.012438: Epoch 715 +2024-09-08 23:23:14.013490: Current learning rate: 0.00323 +2024-09-08 23:28:11.083197: train_loss -0.7471 +2024-09-08 23:28:11.085043: val_loss -0.7672 +2024-09-08 23:28:11.086414: Pseudo dice [0.9643, 0.9606, 0.9766, 0.9436, 0.8917, 0.9721, 0.9921] +2024-09-08 23:28:11.087815: Epoch time: 297.09 s +2024-09-08 23:28:14.392163: +2024-09-08 23:28:14.393220: Epoch 716 +2024-09-08 23:28:14.394203: Current learning rate: 0.00322 +2024-09-08 23:33:00.516962: train_loss -0.7586 +2024-09-08 23:33:00.521881: val_loss -0.7489 +2024-09-08 23:33:00.523119: Pseudo dice [0.963, 0.9596, 0.9738, 0.9528, 0.9031, 0.9721, 0.9926] +2024-09-08 23:33:00.524187: Epoch time: 286.13 s +2024-09-08 23:33:05.310948: +2024-09-08 23:33:05.324880: Epoch 717 +2024-09-08 23:33:05.325923: Current learning rate: 0.00321 +2024-09-08 23:37:55.853389: train_loss -0.7356 +2024-09-08 23:37:55.868996: val_loss -0.7469 +2024-09-08 23:37:55.869770: Pseudo dice [0.9635, 0.9602, 0.9734, 0.9458, 0.8936, 0.9704, 0.9924] +2024-09-08 23:37:55.870470: Epoch time: 290.55 s +2024-09-08 23:37:59.615322: +2024-09-08 23:37:59.632510: Epoch 718 +2024-09-08 23:37:59.633367: Current learning rate: 0.0032 +2024-09-08 23:42:53.164964: train_loss -0.7582 +2024-09-08 23:42:53.167410: val_loss -0.741 +2024-09-08 23:42:53.168493: Pseudo dice [0.9578, 0.9588, 0.971, 0.9482, 0.8868, 0.9693, 0.992] +2024-09-08 23:42:53.169525: Epoch time: 293.56 s +2024-09-08 23:42:56.728082: +2024-09-08 23:42:56.730630: Epoch 719 +2024-09-08 23:42:56.732537: Current learning rate: 0.00319 +2024-09-08 23:47:28.194958: train_loss -0.7612 +2024-09-08 23:47:28.209700: val_loss -0.7278 +2024-09-08 23:47:28.210430: Pseudo dice [0.9673, 0.9621, 0.9768, 0.9538, 0.855, 0.973, 0.9898] +2024-09-08 23:47:28.211111: Epoch time: 271.47 s +2024-09-08 23:47:32.229613: +2024-09-08 23:47:32.230687: Epoch 720 +2024-09-08 23:47:32.231375: Current learning rate: 0.00318 +2024-09-08 23:52:03.067245: train_loss -0.7506 +2024-09-08 23:52:03.069462: val_loss -0.7515 +2024-09-08 23:52:03.070195: Pseudo dice [0.967, 0.9622, 0.9788, 0.9513, 0.9015, 0.9741, 0.9937] +2024-09-08 23:52:03.070947: Epoch time: 270.84 s +2024-09-08 23:52:06.072460: +2024-09-08 23:52:06.073912: Epoch 721 +2024-09-08 23:52:06.075693: Current learning rate: 0.00317 +2024-09-08 23:57:12.964942: train_loss -0.7512 +2024-09-08 23:57:12.988109: val_loss -0.7186 +2024-09-08 23:57:12.989540: Pseudo dice [0.9651, 0.9572, 0.9785, 0.9439, 0.8502, 0.9642, 0.989] +2024-09-08 23:57:12.991295: Epoch time: 306.9 s +2024-09-08 23:57:17.304341: +2024-09-08 23:57:17.316381: Epoch 722 +2024-09-08 23:57:17.318059: Current learning rate: 0.00316 +2024-09-09 00:02:07.696252: train_loss -0.7316 +2024-09-09 00:02:07.702782: val_loss -0.7723 +2024-09-09 00:02:07.704124: Pseudo dice [0.9654, 0.958, 0.9793, 0.9496, 0.8731, 0.9704, 0.9903] +2024-09-09 00:02:07.705391: Epoch time: 290.4 s +2024-09-09 00:02:10.976616: +2024-09-09 00:02:10.977931: Epoch 723 +2024-09-09 00:02:10.979483: Current learning rate: 0.00315 +2024-09-09 00:06:47.076892: train_loss -0.7231 +2024-09-09 00:06:47.078743: val_loss -0.7868 +2024-09-09 00:06:47.080037: Pseudo dice [0.9608, 0.9571, 0.9768, 0.9556, 0.8962, 0.9737, 0.9934] +2024-09-09 00:06:47.080779: Epoch time: 276.1 s +2024-09-09 00:06:50.106447: +2024-09-09 00:06:50.107953: Epoch 724 +2024-09-09 00:06:50.109636: Current learning rate: 0.00314 +2024-09-09 00:11:54.635316: train_loss -0.7467 +2024-09-09 00:11:54.652704: val_loss -0.7753 +2024-09-09 00:11:54.653647: Pseudo dice [0.9633, 0.9565, 0.9765, 0.9427, 0.8937, 0.9711, 0.9921] +2024-09-09 00:11:54.654670: Epoch time: 304.53 s +2024-09-09 00:11:58.120209: +2024-09-09 00:11:58.132379: Epoch 725 +2024-09-09 00:11:58.133240: Current learning rate: 0.00313 +2024-09-09 00:16:40.881113: train_loss -0.7313 +2024-09-09 00:16:40.901288: val_loss -0.743 +2024-09-09 00:16:40.902046: Pseudo dice [0.9628, 0.9571, 0.9746, 0.948, 0.887, 0.9726, 0.9928] +2024-09-09 00:16:40.902703: Epoch time: 282.77 s +2024-09-09 00:16:44.073595: +2024-09-09 00:16:44.074820: Epoch 726 +2024-09-09 00:16:44.075808: Current learning rate: 0.00312 +2024-09-09 00:21:33.762010: train_loss -0.7381 +2024-09-09 00:21:33.776890: val_loss -0.7423 +2024-09-09 00:21:33.778158: Pseudo dice [0.9634, 0.9571, 0.9785, 0.9533, 0.8988, 0.9737, 0.9917] +2024-09-09 00:21:33.778973: Epoch time: 289.69 s +2024-09-09 00:21:37.801562: +2024-09-09 00:21:37.802600: Epoch 727 +2024-09-09 00:21:37.803665: Current learning rate: 0.00311 +2024-09-09 00:26:21.995178: train_loss -0.7451 +2024-09-09 00:26:22.029207: val_loss -0.7243 +2024-09-09 00:26:22.030196: Pseudo dice [0.9626, 0.9583, 0.9742, 0.9475, 0.885, 0.9739, 0.9911] +2024-09-09 00:26:22.030900: Epoch time: 284.2 s +2024-09-09 00:26:26.849648: +2024-09-09 00:26:26.850819: Epoch 728 +2024-09-09 00:26:26.851497: Current learning rate: 0.0031 +2024-09-09 00:31:06.936682: train_loss -0.7565 +2024-09-09 00:31:06.938183: val_loss -0.7256 +2024-09-09 00:31:06.938957: Pseudo dice [0.9666, 0.9603, 0.9782, 0.9515, 0.8643, 0.9702, 0.9898] +2024-09-09 00:31:06.939575: Epoch time: 280.09 s +2024-09-09 00:31:09.362755: +2024-09-09 00:31:09.364102: Epoch 729 +2024-09-09 00:31:09.365019: Current learning rate: 0.00309 +2024-09-09 00:35:43.223754: train_loss -0.7479 +2024-09-09 00:35:43.226124: val_loss -0.758 +2024-09-09 00:35:43.227806: Pseudo dice [0.963, 0.9581, 0.9764, 0.947, 0.8925, 0.9744, 0.9918] +2024-09-09 00:35:43.228738: Epoch time: 273.87 s +2024-09-09 00:35:45.878016: +2024-09-09 00:35:45.879399: Epoch 730 +2024-09-09 00:35:45.880520: Current learning rate: 0.00308 +2024-09-09 00:40:32.307810: train_loss -0.7497 +2024-09-09 00:40:32.309638: val_loss -0.7452 +2024-09-09 00:40:32.310518: Pseudo dice [0.9641, 0.9546, 0.9782, 0.9486, 0.8901, 0.9742, 0.9925] +2024-09-09 00:40:32.311384: Epoch time: 286.44 s +2024-09-09 00:40:35.675280: +2024-09-09 00:40:35.676397: Epoch 731 +2024-09-09 00:40:35.677035: Current learning rate: 0.00307 +2024-09-09 00:45:24.438314: train_loss -0.7466 +2024-09-09 00:45:24.449843: val_loss -0.7316 +2024-09-09 00:45:24.450631: Pseudo dice [0.9664, 0.9609, 0.9785, 0.9543, 0.888, 0.9766, 0.9929] +2024-09-09 00:45:24.451445: Epoch time: 288.77 s +2024-09-09 00:45:27.358435: +2024-09-09 00:45:27.359703: Epoch 732 +2024-09-09 00:45:27.360409: Current learning rate: 0.00306 +2024-09-09 00:50:15.813161: train_loss -0.74 +2024-09-09 00:50:15.815220: val_loss -0.7458 +2024-09-09 00:50:15.815989: Pseudo dice [0.9605, 0.9574, 0.9754, 0.9472, 0.8997, 0.9727, 0.9934] +2024-09-09 00:50:15.816926: Epoch time: 288.46 s +2024-09-09 00:50:20.579734: +2024-09-09 00:50:20.596959: Epoch 733 +2024-09-09 00:50:20.598653: Current learning rate: 0.00305 +2024-09-09 00:55:19.739578: train_loss -0.7477 +2024-09-09 00:55:19.764736: val_loss -0.7289 +2024-09-09 00:55:19.765784: Pseudo dice [0.962, 0.9597, 0.975, 0.9507, 0.8837, 0.9709, 0.992] +2024-09-09 00:55:19.766707: Epoch time: 299.18 s +2024-09-09 00:55:23.503307: +2024-09-09 00:55:23.519917: Epoch 734 +2024-09-09 00:55:23.521925: Current learning rate: 0.00304 +2024-09-09 01:00:19.966101: train_loss -0.7502 +2024-09-09 01:00:19.980907: val_loss -0.7737 +2024-09-09 01:00:19.982064: Pseudo dice [0.9664, 0.959, 0.9805, 0.9518, 0.8943, 0.973, 0.9926] +2024-09-09 01:00:19.983291: Epoch time: 296.47 s +2024-09-09 01:00:24.285746: +2024-09-09 01:00:24.300545: Epoch 735 +2024-09-09 01:00:24.301456: Current learning rate: 0.00303 +2024-09-09 01:05:00.859953: train_loss -0.756 +2024-09-09 01:05:00.879498: val_loss -0.7816 +2024-09-09 01:05:00.881030: Pseudo dice [0.9666, 0.9581, 0.9811, 0.9489, 0.901, 0.9711, 0.993] +2024-09-09 01:05:00.882416: Epoch time: 276.58 s +2024-09-09 01:05:04.227958: +2024-09-09 01:05:04.248462: Epoch 736 +2024-09-09 01:05:04.249396: Current learning rate: 0.00302 +2024-09-09 01:09:56.279594: train_loss -0.7621 +2024-09-09 01:09:56.294805: val_loss -0.759 +2024-09-09 01:09:56.296481: Pseudo dice [0.9675, 0.9636, 0.9802, 0.95, 0.8969, 0.9741, 0.9921] +2024-09-09 01:09:56.297447: Epoch time: 292.06 s +2024-09-09 01:09:56.298636: Yayy! New best EMA pseudo Dice: 0.9578 +2024-09-09 01:10:01.824547: +2024-09-09 01:10:01.830630: Epoch 737 +2024-09-09 01:10:01.831747: Current learning rate: 0.00301 +2024-09-09 01:14:39.576037: train_loss -0.7513 +2024-09-09 01:14:39.593295: val_loss -0.7826 +2024-09-09 01:14:39.604491: Pseudo dice [0.9657, 0.96, 0.9782, 0.9492, 0.8913, 0.9746, 0.9918] +2024-09-09 01:14:39.605417: Epoch time: 277.76 s +2024-09-09 01:14:39.606402: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 01:14:45.211577: +2024-09-09 01:14:45.212603: Epoch 738 +2024-09-09 01:14:45.213349: Current learning rate: 0.003 +2024-09-09 01:19:34.890491: train_loss -0.7476 +2024-09-09 01:19:34.905942: val_loss -0.764 +2024-09-09 01:19:34.907345: Pseudo dice [0.9682, 0.9602, 0.9827, 0.9539, 0.8778, 0.9706, 0.9925] +2024-09-09 01:19:34.908582: Epoch time: 289.68 s +2024-09-09 01:19:34.909240: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 01:19:41.589906: +2024-09-09 01:19:41.591004: Epoch 739 +2024-09-09 01:19:41.591845: Current learning rate: 0.00299 +2024-09-09 01:24:26.777118: train_loss -0.7405 +2024-09-09 01:24:26.790997: val_loss -0.7536 +2024-09-09 01:24:26.792518: Pseudo dice [0.967, 0.9605, 0.978, 0.9505, 0.9043, 0.974, 0.9936] +2024-09-09 01:24:26.793534: Epoch time: 285.19 s +2024-09-09 01:24:26.794583: Yayy! New best EMA pseudo Dice: 0.9582 +2024-09-09 01:24:34.652421: +2024-09-09 01:24:34.653566: Epoch 740 +2024-09-09 01:24:34.654342: Current learning rate: 0.00297 +2024-09-09 01:29:13.017379: train_loss -0.7532 +2024-09-09 01:29:13.034875: val_loss -0.7841 +2024-09-09 01:29:13.035945: Pseudo dice [0.9645, 0.9595, 0.9796, 0.9565, 0.9, 0.973, 0.9929] +2024-09-09 01:29:13.036783: Epoch time: 278.37 s +2024-09-09 01:29:13.037480: Yayy! New best EMA pseudo Dice: 0.9585 +2024-09-09 01:29:18.806064: +2024-09-09 01:29:18.809116: Epoch 741 +2024-09-09 01:29:18.809876: Current learning rate: 0.00296 +2024-09-09 01:34:07.754013: train_loss -0.7541 +2024-09-09 01:34:07.773389: val_loss -0.7419 +2024-09-09 01:34:07.774528: Pseudo dice [0.9675, 0.9612, 0.9786, 0.9542, 0.8907, 0.9747, 0.9923] +2024-09-09 01:34:07.775666: Epoch time: 288.95 s +2024-09-09 01:34:07.776644: Yayy! New best EMA pseudo Dice: 0.9586 +2024-09-09 01:34:12.533408: +2024-09-09 01:34:12.534468: Epoch 742 +2024-09-09 01:34:12.535154: Current learning rate: 0.00295 +2024-09-09 01:38:57.816922: train_loss -0.7436 +2024-09-09 01:38:57.823076: val_loss -0.761 +2024-09-09 01:38:57.824874: Pseudo dice [0.9661, 0.9623, 0.9806, 0.9522, 0.8918, 0.9742, 0.992] +2024-09-09 01:38:57.827119: Epoch time: 285.29 s +2024-09-09 01:38:57.828870: Yayy! New best EMA pseudo Dice: 0.9587 +2024-09-09 01:39:03.600935: +2024-09-09 01:39:03.602395: Epoch 743 +2024-09-09 01:39:03.603273: Current learning rate: 0.00294 +2024-09-09 01:43:54.604628: train_loss -0.7544 +2024-09-09 01:43:54.629920: val_loss -0.7503 +2024-09-09 01:43:54.630781: Pseudo dice [0.9634, 0.9597, 0.9771, 0.9496, 0.874, 0.9718, 0.9892] +2024-09-09 01:43:54.631619: Epoch time: 291.01 s +2024-09-09 01:43:57.930758: +2024-09-09 01:43:57.931993: Epoch 744 +2024-09-09 01:43:57.932683: Current learning rate: 0.00293 +2024-09-09 01:48:29.698068: train_loss -0.7482 +2024-09-09 01:48:29.714574: val_loss -0.772 +2024-09-09 01:48:29.715425: Pseudo dice [0.959, 0.958, 0.9747, 0.9526, 0.8875, 0.9744, 0.9927] +2024-09-09 01:48:29.716217: Epoch time: 271.77 s +2024-09-09 01:48:32.539767: +2024-09-09 01:48:32.552517: Epoch 745 +2024-09-09 01:48:32.553536: Current learning rate: 0.00292 +2024-09-09 01:53:23.382344: train_loss -0.7532 +2024-09-09 01:53:23.385621: val_loss -0.7741 +2024-09-09 01:53:23.386285: Pseudo dice [0.9641, 0.9585, 0.9782, 0.9495, 0.8556, 0.9738, 0.9905] +2024-09-09 01:53:23.386968: Epoch time: 290.85 s +2024-09-09 01:53:25.806742: +2024-09-09 01:53:25.807931: Epoch 746 +2024-09-09 01:53:25.808812: Current learning rate: 0.00291 +2024-09-09 01:58:01.393771: train_loss -0.7491 +2024-09-09 01:58:01.401832: val_loss -0.763 +2024-09-09 01:58:01.402939: Pseudo dice [0.9648, 0.9609, 0.9788, 0.9516, 0.8928, 0.9693, 0.992] +2024-09-09 01:58:01.404243: Epoch time: 275.59 s +2024-09-09 01:58:05.237653: +2024-09-09 01:58:05.239410: Epoch 747 +2024-09-09 01:58:05.240535: Current learning rate: 0.0029 +2024-09-09 02:03:04.026614: train_loss -0.7599 +2024-09-09 02:03:04.028774: val_loss -0.7653 +2024-09-09 02:03:04.030331: Pseudo dice [0.9662, 0.9615, 0.9796, 0.9488, 0.8961, 0.9713, 0.9928] +2024-09-09 02:03:04.031505: Epoch time: 298.8 s +2024-09-09 02:03:07.368462: +2024-09-09 02:03:07.376473: Epoch 748 +2024-09-09 02:03:07.377744: Current learning rate: 0.00289 +2024-09-09 02:07:36.252780: train_loss -0.7512 +2024-09-09 02:07:36.270627: val_loss -0.7488 +2024-09-09 02:07:36.272049: Pseudo dice [0.9656, 0.9582, 0.9768, 0.9479, 0.8928, 0.9684, 0.9929] +2024-09-09 02:07:36.272843: Epoch time: 268.9 s +2024-09-09 02:07:40.924937: +2024-09-09 02:07:40.926652: Epoch 749 +2024-09-09 02:07:40.927671: Current learning rate: 0.00288 +2024-09-09 02:12:13.640864: train_loss -0.7526 +2024-09-09 02:12:13.652874: val_loss -0.7848 +2024-09-09 02:12:13.654010: Pseudo dice [0.9674, 0.9615, 0.9793, 0.9521, 0.8997, 0.9729, 0.9935] +2024-09-09 02:12:13.655030: Epoch time: 272.72 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_06_02_53.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_06_02_53.txt new file mode 100644 index 0000000000000000000000000000000000000000..1887c12a7ad450dc49b746425579e991726d199c --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_06_02_53.txt @@ -0,0 +1,375 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 06:02:56.459930: Using torch.compile... +2024-09-09 06:03:03.061851: do_dummy_2d_data_aug: False +2024-09-09 06:03:03.072328: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 06:03:03.073527: The split file contains 5 splits. +2024-09-09 06:03:03.074080: Desired fold for training: 3 +2024-09-09 06:03:03.074680: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 06:03:17.905357: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 06:03:18.959010: +2024-09-09 06:03:18.979725: Epoch 750 +2024-09-09 06:03:18.981792: Current learning rate: 0.00287 +2024-09-09 06:18:11.166847: train_loss -0.7557 +2024-09-09 06:18:11.168365: val_loss -0.7744 +2024-09-09 06:18:11.169128: Pseudo dice [0.9667, 0.9632, 0.9775, 0.957, 0.8939, 0.9718, 0.9919] +2024-09-09 06:18:11.169773: Epoch time: 892.21 s +2024-09-09 06:18:12.784515: +2024-09-09 06:18:12.785487: Epoch 751 +2024-09-09 06:18:12.786196: Current learning rate: 0.00286 +2024-09-09 06:22:51.642984: train_loss -0.7571 +2024-09-09 06:22:51.655814: val_loss -0.7635 +2024-09-09 06:22:51.656592: Pseudo dice [0.9657, 0.9618, 0.9765, 0.9521, 0.8972, 0.9754, 0.9923] +2024-09-09 06:22:51.657261: Epoch time: 278.86 s +2024-09-09 06:22:54.077664: +2024-09-09 06:22:54.095765: Epoch 752 +2024-09-09 06:22:54.097206: Current learning rate: 0.00285 +2024-09-09 06:28:00.102415: train_loss -0.7515 +2024-09-09 06:28:00.113528: val_loss -0.7613 +2024-09-09 06:28:00.115026: Pseudo dice [0.9656, 0.9603, 0.9774, 0.9538, 0.8506, 0.9744, 0.989] +2024-09-09 06:28:00.116398: Epoch time: 306.03 s +2024-09-09 06:28:02.932916: +2024-09-09 06:28:02.948363: Epoch 753 +2024-09-09 06:28:02.949583: Current learning rate: 0.00284 +2024-09-09 06:32:32.235755: train_loss -0.7511 +2024-09-09 06:32:32.253111: val_loss -0.7703 +2024-09-09 06:32:32.254962: Pseudo dice [0.9651, 0.9591, 0.9784, 0.9511, 0.8934, 0.9737, 0.9926] +2024-09-09 06:32:32.257329: Epoch time: 269.31 s +2024-09-09 06:32:36.110368: +2024-09-09 06:32:36.124458: Epoch 754 +2024-09-09 06:32:36.125314: Current learning rate: 0.00283 +2024-09-09 06:37:12.387992: train_loss -0.7376 +2024-09-09 06:37:12.393772: val_loss -0.7972 +2024-09-09 06:37:12.395928: Pseudo dice [0.9641, 0.9607, 0.9765, 0.9514, 0.9029, 0.973, 0.9928] +2024-09-09 06:37:12.397105: Epoch time: 276.28 s +2024-09-09 06:37:16.068971: +2024-09-09 06:37:16.070207: Epoch 755 +2024-09-09 06:37:16.071034: Current learning rate: 0.00282 +2024-09-09 06:42:15.308669: train_loss -0.7527 +2024-09-09 06:42:15.312199: val_loss -0.7529 +2024-09-09 06:42:15.313324: Pseudo dice [0.9646, 0.9586, 0.9773, 0.9536, 0.8998, 0.9753, 0.9933] +2024-09-09 06:42:15.314215: Epoch time: 299.24 s +2024-09-09 06:42:17.887443: +2024-09-09 06:42:17.916473: Epoch 756 +2024-09-09 06:42:17.917385: Current learning rate: 0.00281 +2024-09-09 06:46:57.708095: train_loss -0.7428 +2024-09-09 06:46:57.724695: val_loss -0.7791 +2024-09-09 06:46:57.726156: Pseudo dice [0.9646, 0.9603, 0.9773, 0.9502, 0.8764, 0.9728, 0.9915] +2024-09-09 06:46:57.727827: Epoch time: 279.83 s +2024-09-09 06:47:02.013232: +2024-09-09 06:47:02.014401: Epoch 757 +2024-09-09 06:47:02.015366: Current learning rate: 0.0028 +2024-09-09 06:51:55.562672: train_loss -0.7352 +2024-09-09 06:51:55.587383: val_loss -0.7472 +2024-09-09 06:51:55.588650: Pseudo dice [0.963, 0.9629, 0.9742, 0.953, 0.8838, 0.9734, 0.9909] +2024-09-09 06:51:55.589452: Epoch time: 293.55 s +2024-09-09 06:51:58.042783: +2024-09-09 06:51:58.044176: Epoch 758 +2024-09-09 06:51:58.045079: Current learning rate: 0.00279 +2024-09-09 06:56:33.441900: train_loss -0.7559 +2024-09-09 06:56:33.461606: val_loss -0.7882 +2024-09-09 06:56:33.464110: Pseudo dice [0.9661, 0.9594, 0.978, 0.9532, 0.9036, 0.9736, 0.9938] +2024-09-09 06:56:33.465973: Epoch time: 275.4 s +2024-09-09 06:56:36.732289: +2024-09-09 06:56:36.733515: Epoch 759 +2024-09-09 06:56:36.734430: Current learning rate: 0.00278 +2024-09-09 07:01:23.825932: train_loss -0.7378 +2024-09-09 07:01:23.841480: val_loss -0.7754 +2024-09-09 07:01:23.842811: Pseudo dice [0.9636, 0.9605, 0.9764, 0.951, 0.9054, 0.9719, 0.9926] +2024-09-09 07:01:23.845039: Epoch time: 287.1 s +2024-09-09 07:01:27.913089: +2024-09-09 07:01:27.926312: Epoch 760 +2024-09-09 07:01:27.927588: Current learning rate: 0.00277 +2024-09-09 07:06:19.337158: train_loss -0.7739 +2024-09-09 07:06:19.361811: val_loss -0.751 +2024-09-09 07:06:19.362605: Pseudo dice [0.9627, 0.9581, 0.9765, 0.9487, 0.8952, 0.9694, 0.9927] +2024-09-09 07:06:19.363573: Epoch time: 291.43 s +2024-09-09 07:06:22.064683: +2024-09-09 07:06:22.065895: Epoch 761 +2024-09-09 07:06:22.067045: Current learning rate: 0.00276 +2024-09-09 07:10:53.898182: train_loss -0.7502 +2024-09-09 07:10:53.900313: val_loss -0.7419 +2024-09-09 07:10:53.901991: Pseudo dice [0.9658, 0.9592, 0.9778, 0.9429, 0.8925, 0.9745, 0.9932] +2024-09-09 07:10:53.904310: Epoch time: 271.84 s +2024-09-09 07:10:57.711065: +2024-09-09 07:10:57.724921: Epoch 762 +2024-09-09 07:10:57.728090: Current learning rate: 0.00275 +2024-09-09 07:15:46.292026: train_loss -0.7531 +2024-09-09 07:15:46.311172: val_loss -0.7521 +2024-09-09 07:15:46.311942: Pseudo dice [0.9662, 0.9609, 0.978, 0.952, 0.8965, 0.977, 0.9935] +2024-09-09 07:15:46.312596: Epoch time: 288.59 s +2024-09-09 07:15:49.538285: +2024-09-09 07:15:49.540272: Epoch 763 +2024-09-09 07:15:49.541526: Current learning rate: 0.00274 +2024-09-09 07:20:40.567359: train_loss -0.7625 +2024-09-09 07:20:40.587481: val_loss -0.752 +2024-09-09 07:20:40.589766: Pseudo dice [0.9621, 0.9583, 0.9761, 0.956, 0.9022, 0.9725, 0.9934] +2024-09-09 07:20:40.591897: Epoch time: 291.04 s +2024-09-09 07:20:40.593723: Yayy! New best EMA pseudo Dice: 0.9588 +2024-09-09 07:20:49.295580: +2024-09-09 07:20:49.297133: Epoch 764 +2024-09-09 07:20:49.298364: Current learning rate: 0.00273 +2024-09-09 07:25:35.890112: train_loss -0.7443 +2024-09-09 07:25:35.912794: val_loss -0.7829 +2024-09-09 07:25:35.914289: Pseudo dice [0.9648, 0.9584, 0.9774, 0.9437, 0.9055, 0.9768, 0.9932] +2024-09-09 07:25:35.916134: Epoch time: 286.6 s +2024-09-09 07:25:35.917681: Yayy! New best EMA pseudo Dice: 0.959 +2024-09-09 07:25:41.457879: +2024-09-09 07:25:41.459056: Epoch 765 +2024-09-09 07:25:41.459859: Current learning rate: 0.00272 +2024-09-09 07:30:17.825787: train_loss -0.7647 +2024-09-09 07:30:17.830354: val_loss -0.7947 +2024-09-09 07:30:17.832654: Pseudo dice [0.9679, 0.9605, 0.982, 0.9518, 0.8957, 0.9741, 0.9918] +2024-09-09 07:30:17.834162: Epoch time: 276.37 s +2024-09-09 07:30:17.835589: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-09 07:30:22.785583: +2024-09-09 07:30:22.786616: Epoch 766 +2024-09-09 07:30:22.787294: Current learning rate: 0.00271 +2024-09-09 07:35:05.861736: train_loss -0.745 +2024-09-09 07:35:05.880330: val_loss -0.7454 +2024-09-09 07:35:05.881558: Pseudo dice [0.9647, 0.9599, 0.9773, 0.9533, 0.8971, 0.9735, 0.9917] +2024-09-09 07:35:05.882501: Epoch time: 283.08 s +2024-09-09 07:35:05.883276: Yayy! New best EMA pseudo Dice: 0.9592 +2024-09-09 07:35:11.476143: +2024-09-09 07:35:11.477897: Epoch 767 +2024-09-09 07:35:11.479067: Current learning rate: 0.0027 +2024-09-09 07:39:59.598350: train_loss -0.7467 +2024-09-09 07:39:59.599967: val_loss -0.7855 +2024-09-09 07:39:59.600720: Pseudo dice [0.9646, 0.9614, 0.9781, 0.9429, 0.8987, 0.9732, 0.9915] +2024-09-09 07:39:59.601384: Epoch time: 288.13 s +2024-09-09 07:40:02.293175: +2024-09-09 07:40:02.304605: Epoch 768 +2024-09-09 07:40:02.305728: Current learning rate: 0.00268 +2024-09-09 07:44:43.934586: train_loss -0.7446 +2024-09-09 07:44:43.937156: val_loss -0.7596 +2024-09-09 07:44:43.937917: Pseudo dice [0.9641, 0.958, 0.9762, 0.9465, 0.8428, 0.9701, 0.9873] +2024-09-09 07:44:43.938565: Epoch time: 281.65 s +2024-09-09 07:44:47.304714: +2024-09-09 07:44:47.320422: Epoch 769 +2024-09-09 07:44:47.321590: Current learning rate: 0.00267 +2024-09-09 07:49:21.365831: train_loss -0.7544 +2024-09-09 07:49:21.381843: val_loss -0.7675 +2024-09-09 07:49:21.382799: Pseudo dice [0.9645, 0.9623, 0.9811, 0.9546, 0.9, 0.9703, 0.9924] +2024-09-09 07:49:21.383886: Epoch time: 274.07 s +2024-09-09 07:49:24.916134: +2024-09-09 07:49:24.918665: Epoch 770 +2024-09-09 07:49:24.920341: Current learning rate: 0.00266 +2024-09-09 07:54:13.612911: train_loss -0.7398 +2024-09-09 07:54:13.632802: val_loss -0.7254 +2024-09-09 07:54:13.634284: Pseudo dice [0.9654, 0.9588, 0.9778, 0.9492, 0.8923, 0.9622, 0.9905] +2024-09-09 07:54:13.636029: Epoch time: 288.7 s +2024-09-09 07:54:17.180425: +2024-09-09 07:54:17.182135: Epoch 771 +2024-09-09 07:54:17.183854: Current learning rate: 0.00265 +2024-09-09 07:59:08.660811: train_loss -0.7563 +2024-09-09 07:59:08.663152: val_loss -0.766 +2024-09-09 07:59:08.664703: Pseudo dice [0.963, 0.9606, 0.9758, 0.9536, 0.8974, 0.9731, 0.993] +2024-09-09 07:59:08.666000: Epoch time: 291.48 s +2024-09-09 07:59:12.293655: +2024-09-09 07:59:12.304577: Epoch 772 +2024-09-09 07:59:12.305779: Current learning rate: 0.00264 +2024-09-09 08:04:08.691185: train_loss -0.7452 +2024-09-09 08:04:08.706523: val_loss -0.7284 +2024-09-09 08:04:08.707670: Pseudo dice [0.963, 0.9578, 0.9747, 0.9538, 0.8909, 0.9753, 0.9931] +2024-09-09 08:04:08.708540: Epoch time: 296.4 s +2024-09-09 08:04:12.057190: +2024-09-09 08:04:12.058449: Epoch 773 +2024-09-09 08:04:12.059156: Current learning rate: 0.00263 +2024-09-09 08:08:49.929590: train_loss -0.7566 +2024-09-09 08:08:49.947374: val_loss -0.7576 +2024-09-09 08:08:49.950060: Pseudo dice [0.9626, 0.9575, 0.9783, 0.9494, 0.9015, 0.9714, 0.9939] +2024-09-09 08:08:49.950881: Epoch time: 277.88 s +2024-09-09 08:08:53.361963: +2024-09-09 08:08:53.376686: Epoch 774 +2024-09-09 08:08:53.377610: Current learning rate: 0.00262 +2024-09-09 08:13:28.988608: train_loss -0.7348 +2024-09-09 08:13:29.005793: val_loss -0.7639 +2024-09-09 08:13:29.007992: Pseudo dice [0.9665, 0.9592, 0.9788, 0.9559, 0.8942, 0.9743, 0.9928] +2024-09-09 08:13:29.008829: Epoch time: 275.63 s +2024-09-09 08:13:32.660866: +2024-09-09 08:13:32.662099: Epoch 775 +2024-09-09 08:13:32.663484: Current learning rate: 0.00261 +2024-09-09 08:18:11.910786: train_loss -0.7601 +2024-09-09 08:18:11.914954: val_loss -0.7555 +2024-09-09 08:18:11.916088: Pseudo dice [0.9674, 0.9614, 0.9817, 0.9463, 0.8644, 0.9744, 0.9884] +2024-09-09 08:18:11.916950: Epoch time: 279.25 s +2024-09-09 08:18:15.231674: +2024-09-09 08:18:15.233132: Epoch 776 +2024-09-09 08:18:15.234090: Current learning rate: 0.0026 +2024-09-09 08:23:14.962531: train_loss -0.7604 +2024-09-09 08:23:14.980486: val_loss -0.7661 +2024-09-09 08:23:14.981522: Pseudo dice [0.968, 0.9603, 0.9811, 0.9533, 0.89, 0.9758, 0.9918] +2024-09-09 08:23:14.982308: Epoch time: 299.73 s +2024-09-09 08:23:18.325559: +2024-09-09 08:23:18.336544: Epoch 777 +2024-09-09 08:23:18.337390: Current learning rate: 0.00259 +2024-09-09 08:28:10.460208: train_loss -0.7471 +2024-09-09 08:28:10.478224: val_loss -0.7612 +2024-09-09 08:28:10.479454: Pseudo dice [0.9644, 0.9584, 0.9795, 0.9492, 0.8991, 0.9739, 0.9924] +2024-09-09 08:28:10.480654: Epoch time: 292.14 s +2024-09-09 08:28:12.976244: +2024-09-09 08:28:12.977535: Epoch 778 +2024-09-09 08:28:12.978914: Current learning rate: 0.00258 +2024-09-09 08:32:51.000098: train_loss -0.7458 +2024-09-09 08:32:51.017545: val_loss -0.7563 +2024-09-09 08:32:51.018973: Pseudo dice [0.9647, 0.96, 0.9791, 0.948, 0.893, 0.9758, 0.9923] +2024-09-09 08:32:51.020565: Epoch time: 278.04 s +2024-09-09 08:32:55.234055: +2024-09-09 08:32:55.235618: Epoch 779 +2024-09-09 08:32:55.236436: Current learning rate: 0.00257 +2024-09-09 08:37:23.787430: train_loss -0.7548 +2024-09-09 08:37:23.806763: val_loss -0.767 +2024-09-09 08:37:23.809359: Pseudo dice [0.9656, 0.9607, 0.9777, 0.952, 0.8843, 0.9759, 0.9916] +2024-09-09 08:37:23.812098: Epoch time: 268.56 s +2024-09-09 08:37:27.849843: +2024-09-09 08:37:27.873013: Epoch 780 +2024-09-09 08:37:27.874180: Current learning rate: 0.00256 +2024-09-09 08:42:39.716198: train_loss -0.7534 +2024-09-09 08:42:39.718621: val_loss -0.7537 +2024-09-09 08:42:39.719470: Pseudo dice [0.9671, 0.9624, 0.9802, 0.9494, 0.8912, 0.9737, 0.9926] +2024-09-09 08:42:39.720169: Epoch time: 311.88 s +2024-09-09 08:42:43.190027: +2024-09-09 08:42:43.191399: Epoch 781 +2024-09-09 08:42:43.192463: Current learning rate: 0.00255 +2024-09-09 08:47:26.972614: train_loss -0.7217 +2024-09-09 08:47:26.981971: val_loss -0.767 +2024-09-09 08:47:26.982864: Pseudo dice [0.966, 0.9596, 0.9809, 0.9507, 0.8959, 0.9738, 0.992] +2024-09-09 08:47:26.983587: Epoch time: 283.79 s +2024-09-09 08:47:29.961433: +2024-09-09 08:47:29.962516: Epoch 782 +2024-09-09 08:47:29.963795: Current learning rate: 0.00254 +2024-09-09 08:52:27.986082: train_loss -0.7582 +2024-09-09 08:52:28.003044: val_loss -0.7639 +2024-09-09 08:52:28.004298: Pseudo dice [0.967, 0.9635, 0.9785, 0.9485, 0.8575, 0.9716, 0.9897] +2024-09-09 08:52:28.005623: Epoch time: 298.03 s +2024-09-09 08:52:30.714856: +2024-09-09 08:52:30.731792: Epoch 783 +2024-09-09 08:52:30.733175: Current learning rate: 0.00253 +2024-09-09 08:57:17.739037: train_loss -0.7684 +2024-09-09 08:57:17.763169: val_loss -0.7118 +2024-09-09 08:57:17.768439: Pseudo dice [0.9606, 0.9567, 0.9774, 0.9541, 0.9032, 0.9675, 0.9925] +2024-09-09 08:57:17.771569: Epoch time: 287.03 s +2024-09-09 08:57:22.239570: +2024-09-09 08:57:22.240709: Epoch 784 +2024-09-09 08:57:22.241614: Current learning rate: 0.00252 +2024-09-09 09:02:08.478883: train_loss -0.759 +2024-09-09 09:02:08.506413: val_loss -0.7658 +2024-09-09 09:02:08.507557: Pseudo dice [0.9632, 0.956, 0.9779, 0.9495, 0.8657, 0.9738, 0.9905] +2024-09-09 09:02:08.510067: Epoch time: 286.24 s +2024-09-09 09:02:12.222878: +2024-09-09 09:02:12.236383: Epoch 785 +2024-09-09 09:02:12.237133: Current learning rate: 0.00251 +2024-09-09 09:07:13.550452: train_loss -0.7438 +2024-09-09 09:07:13.552557: val_loss -0.7838 +2024-09-09 09:07:13.553333: Pseudo dice [0.9634, 0.959, 0.9787, 0.9477, 0.8819, 0.9706, 0.9903] +2024-09-09 09:07:13.554055: Epoch time: 301.33 s +2024-09-09 09:07:16.270549: +2024-09-09 09:07:16.271792: Epoch 786 +2024-09-09 09:07:16.272768: Current learning rate: 0.0025 +2024-09-09 09:12:00.638685: train_loss -0.7452 +2024-09-09 09:12:00.664577: val_loss -0.8019 +2024-09-09 09:12:00.666198: Pseudo dice [0.9628, 0.9579, 0.9764, 0.9559, 0.8982, 0.9721, 0.9928] +2024-09-09 09:12:00.667260: Epoch time: 284.39 s +2024-09-09 09:12:03.080875: +2024-09-09 09:12:03.084577: Epoch 787 +2024-09-09 09:12:03.086974: Current learning rate: 0.00249 +2024-09-09 09:16:52.498772: train_loss -0.7425 +2024-09-09 09:16:52.505044: val_loss -0.7349 +2024-09-09 09:16:52.507433: Pseudo dice [0.9637, 0.9566, 0.9777, 0.9487, 0.885, 0.9708, 0.9917] +2024-09-09 09:16:52.508823: Epoch time: 289.42 s +2024-09-09 09:16:56.986218: +2024-09-09 09:16:57.000720: Epoch 788 +2024-09-09 09:16:57.002003: Current learning rate: 0.00248 +2024-09-09 09:22:01.326554: train_loss -0.7596 +2024-09-09 09:22:01.341821: val_loss -0.7679 +2024-09-09 09:22:01.342615: Pseudo dice [0.9667, 0.9578, 0.9791, 0.958, 0.8889, 0.975, 0.9918] +2024-09-09 09:22:01.343411: Epoch time: 304.35 s +2024-09-09 09:22:05.782360: +2024-09-09 09:22:05.783533: Epoch 789 +2024-09-09 09:22:05.784244: Current learning rate: 0.00247 +2024-09-09 09:26:53.747159: train_loss -0.7441 +2024-09-09 09:26:53.750135: val_loss -0.7838 +2024-09-09 09:26:53.752548: Pseudo dice [0.9673, 0.9585, 0.9787, 0.9488, 0.8865, 0.9739, 0.9911] +2024-09-09 09:26:53.754114: Epoch time: 287.97 s +2024-09-09 09:26:56.543736: +2024-09-09 09:26:56.560521: Epoch 790 +2024-09-09 09:26:56.561401: Current learning rate: 0.00245 +2024-09-09 09:31:47.085660: train_loss -0.7493 +2024-09-09 09:31:47.097481: val_loss -0.7451 +2024-09-09 09:31:47.099219: Pseudo dice [0.9574, 0.9582, 0.9723, 0.9541, 0.8908, 0.9746, 0.9923] +2024-09-09 09:31:47.100256: Epoch time: 290.55 s +2024-09-09 09:31:51.059936: +2024-09-09 09:31:51.061689: Epoch 791 +2024-09-09 09:31:51.063640: Current learning rate: 0.00244 +2024-09-09 09:37:06.829076: train_loss -0.7497 +2024-09-09 09:37:06.831362: val_loss -0.77 +2024-09-09 09:37:06.832665: Pseudo dice [0.9654, 0.9607, 0.9763, 0.9485, 0.9019, 0.9736, 0.9936] +2024-09-09 09:37:06.833926: Epoch time: 315.8 s +2024-09-09 09:37:11.054600: +2024-09-09 09:37:11.070817: Epoch 792 +2024-09-09 09:37:11.074012: Current learning rate: 0.00243 +2024-09-09 09:42:42.298927: train_loss -0.7291 +2024-09-09 09:42:42.311589: val_loss -0.7058 +2024-09-09 09:42:42.313650: Pseudo dice [0.9622, 0.96, 0.9754, 0.9518, 0.8727, 0.9693, 0.9916] +2024-09-09 09:42:42.315551: Epoch time: 331.25 s +2024-09-09 09:42:47.228620: +2024-09-09 09:42:47.233709: Epoch 793 +2024-09-09 09:42:47.235419: Current learning rate: 0.00242 +2024-09-09 09:48:07.182428: train_loss -0.7622 +2024-09-09 09:48:07.199847: val_loss -0.8018 +2024-09-09 09:48:07.201096: Pseudo dice [0.9668, 0.9596, 0.9784, 0.9523, 0.894, 0.9748, 0.9932] +2024-09-09 09:48:07.202122: Epoch time: 319.97 s +2024-09-09 09:48:11.876050: +2024-09-09 09:48:11.877694: Epoch 794 +2024-09-09 09:48:11.880586: Current learning rate: 0.00241 +2024-09-09 09:53:52.927524: train_loss -0.7554 +2024-09-09 09:53:52.938720: val_loss -0.7293 +2024-09-09 09:53:52.939830: Pseudo dice [0.9653, 0.962, 0.9789, 0.9554, 0.8877, 0.9774, 0.9916] +2024-09-09 09:53:52.940742: Epoch time: 341.07 s +2024-09-09 09:53:56.185323: +2024-09-09 09:53:56.200660: Epoch 795 +2024-09-09 09:53:56.201983: Current learning rate: 0.0024 +2024-09-09 09:59:33.534462: train_loss -0.7545 +2024-09-09 09:59:33.561414: val_loss -0.7794 +2024-09-09 09:59:33.562824: Pseudo dice [0.9632, 0.9595, 0.9738, 0.9539, 0.8948, 0.9748, 0.9927] +2024-09-09 09:59:33.564032: Epoch time: 337.35 s +2024-09-09 09:59:37.960014: +2024-09-09 09:59:37.976426: Epoch 796 +2024-09-09 09:59:37.977655: Current learning rate: 0.00239 +2024-09-09 10:06:15.141392: train_loss -0.7398 +2024-09-09 10:06:15.182918: val_loss -0.7057 +2024-09-09 10:06:15.184141: Pseudo dice [0.9636, 0.9589, 0.9776, 0.9459, 0.8944, 0.9744, 0.9924] +2024-09-09 10:06:15.185219: Epoch time: 397.19 s +2024-09-09 10:06:18.422522: +2024-09-09 10:06:18.424584: Epoch 797 +2024-09-09 10:06:18.426145: Current learning rate: 0.00238 +2024-09-09 10:13:33.083542: train_loss -0.7499 +2024-09-09 10:13:33.105431: val_loss -0.7621 +2024-09-09 10:13:33.110131: Pseudo dice [0.9608, 0.9588, 0.9751, 0.9496, 0.8933, 0.9704, 0.9923] +2024-09-09 10:13:33.111178: Epoch time: 434.67 s +2024-09-09 10:13:37.066763: +2024-09-09 10:13:37.119076: Epoch 798 +2024-09-09 10:13:37.120628: Current learning rate: 0.00237 +2024-09-09 10:20:51.928750: train_loss -0.7592 +2024-09-09 10:20:51.945335: val_loss -0.8093 +2024-09-09 10:20:51.946548: Pseudo dice [0.9649, 0.961, 0.9769, 0.9539, 0.8939, 0.9767, 0.9921] +2024-09-09 10:20:51.947624: Epoch time: 434.87 s +2024-09-09 10:20:55.590673: +2024-09-09 10:20:55.604628: Epoch 799 +2024-09-09 10:20:55.605752: Current learning rate: 0.00236 +2024-09-09 10:27:59.239953: train_loss -0.748 +2024-09-09 10:27:59.245244: val_loss -0.7869 +2024-09-09 10:27:59.246759: Pseudo dice [0.9649, 0.9608, 0.9786, 0.9432, 0.8505, 0.9728, 0.9882] +2024-09-09 10:27:59.248241: Epoch time: 423.65 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_16_54_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_16_54_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca0cb762d8458451c9978334e803fafe39e4b0b2 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_3/logs/training_log_2024_9_9_16_54_15.txt @@ -0,0 +1,730 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 16:54:18.721103: Using torch.compile... +2024-09-09 16:54:25.521704: do_dummy_2d_data_aug: False +2024-09-09 16:54:25.540599: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 16:54:25.542086: The split file contains 5 splits. +2024-09-09 16:54:25.543045: Desired fold for training: 3 +2024-09-09 16:54:25.543730: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 16:54:39.488051: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 16:54:40.272406: +2024-09-09 16:54:40.273271: Epoch 800 +2024-09-09 16:54:40.274094: Current learning rate: 0.00235 +2024-09-09 17:10:21.643767: train_loss -0.7501 +2024-09-09 17:10:21.646049: val_loss -0.7348 +2024-09-09 17:10:21.646859: Pseudo dice [0.9632, 0.9611, 0.976, 0.949, 0.8825, 0.9683, 0.9896] +2024-09-09 17:10:21.647500: Epoch time: 941.38 s +2024-09-09 17:10:23.194299: +2024-09-09 17:10:23.195200: Epoch 801 +2024-09-09 17:10:23.195963: Current learning rate: 0.00234 +2024-09-09 17:15:10.758715: train_loss -0.7384 +2024-09-09 17:15:10.767861: val_loss -0.76 +2024-09-09 17:15:10.769238: Pseudo dice [0.9664, 0.9605, 0.9784, 0.9505, 0.8975, 0.9725, 0.992] +2024-09-09 17:15:10.770209: Epoch time: 287.57 s +2024-09-09 17:15:14.131080: +2024-09-09 17:15:14.132309: Epoch 802 +2024-09-09 17:15:14.134035: Current learning rate: 0.00233 +2024-09-09 17:19:45.439453: train_loss -0.7448 +2024-09-09 17:19:45.441037: val_loss -0.79 +2024-09-09 17:19:45.441815: Pseudo dice [0.9626, 0.9572, 0.9759, 0.9442, 0.8836, 0.9722, 0.9917] +2024-09-09 17:19:45.442911: Epoch time: 271.31 s +2024-09-09 17:19:48.232705: +2024-09-09 17:19:48.233847: Epoch 803 +2024-09-09 17:19:48.234694: Current learning rate: 0.00232 +2024-09-09 17:24:29.729095: train_loss -0.7567 +2024-09-09 17:24:29.741913: val_loss -0.7427 +2024-09-09 17:24:29.743546: Pseudo dice [0.9649, 0.9603, 0.9808, 0.9598, 0.8972, 0.9734, 0.9925] +2024-09-09 17:24:29.744959: Epoch time: 281.5 s +2024-09-09 17:24:33.270544: +2024-09-09 17:24:33.284801: Epoch 804 +2024-09-09 17:24:33.285668: Current learning rate: 0.00231 +2024-09-09 17:29:20.162226: train_loss -0.7566 +2024-09-09 17:29:20.180208: val_loss -0.7231 +2024-09-09 17:29:20.181231: Pseudo dice [0.9663, 0.9614, 0.9782, 0.9529, 0.8727, 0.9696, 0.9933] +2024-09-09 17:29:20.182055: Epoch time: 286.9 s +2024-09-09 17:29:24.805233: +2024-09-09 17:29:24.806460: Epoch 805 +2024-09-09 17:29:24.807480: Current learning rate: 0.0023 +2024-09-09 17:33:49.528651: train_loss -0.7331 +2024-09-09 17:33:49.538492: val_loss -0.7842 +2024-09-09 17:33:49.539347: Pseudo dice [0.9669, 0.9618, 0.981, 0.948, 0.8951, 0.9746, 0.9922] +2024-09-09 17:33:49.540048: Epoch time: 264.73 s +2024-09-09 17:33:52.993056: +2024-09-09 17:33:52.994745: Epoch 806 +2024-09-09 17:33:52.996656: Current learning rate: 0.00229 +2024-09-09 17:38:31.408225: train_loss -0.7585 +2024-09-09 17:38:31.412883: val_loss -0.7663 +2024-09-09 17:38:31.413919: Pseudo dice [0.9669, 0.9604, 0.9795, 0.9526, 0.8984, 0.9727, 0.9929] +2024-09-09 17:38:31.414781: Epoch time: 278.42 s +2024-09-09 17:38:33.954489: +2024-09-09 17:38:33.955472: Epoch 807 +2024-09-09 17:38:33.956214: Current learning rate: 0.00228 +2024-09-09 17:43:06.874306: train_loss -0.7576 +2024-09-09 17:43:06.901098: val_loss -0.7441 +2024-09-09 17:43:06.902111: Pseudo dice [0.9639, 0.9598, 0.975, 0.9552, 0.8992, 0.9766, 0.9932] +2024-09-09 17:43:06.902974: Epoch time: 272.93 s +2024-09-09 17:43:10.204378: +2024-09-09 17:43:10.205280: Epoch 808 +2024-09-09 17:43:10.205984: Current learning rate: 0.00226 +2024-09-09 17:47:49.054107: train_loss -0.7425 +2024-09-09 17:47:49.065388: val_loss -0.7738 +2024-09-09 17:47:49.066304: Pseudo dice [0.9649, 0.9621, 0.9766, 0.9488, 0.8967, 0.9752, 0.9918] +2024-09-09 17:47:49.067075: Epoch time: 278.85 s +2024-09-09 17:47:52.894134: +2024-09-09 17:47:52.902750: Epoch 809 +2024-09-09 17:47:52.904160: Current learning rate: 0.00225 +2024-09-09 17:52:44.342439: train_loss -0.7485 +2024-09-09 17:52:44.358052: val_loss -0.7119 +2024-09-09 17:52:44.359058: Pseudo dice [0.963, 0.9608, 0.9761, 0.9517, 0.8825, 0.9738, 0.9917] +2024-09-09 17:52:44.359951: Epoch time: 291.45 s +2024-09-09 17:52:46.781089: +2024-09-09 17:52:46.790324: Epoch 810 +2024-09-09 17:52:46.791269: Current learning rate: 0.00224 +2024-09-09 17:57:27.791503: train_loss -0.7541 +2024-09-09 17:57:27.793141: val_loss -0.7512 +2024-09-09 17:57:27.794419: Pseudo dice [0.969, 0.9619, 0.9836, 0.9508, 0.8826, 0.9752, 0.9907] +2024-09-09 17:57:27.795689: Epoch time: 281.02 s +2024-09-09 17:57:31.506307: +2024-09-09 17:57:31.507659: Epoch 811 +2024-09-09 17:57:31.508649: Current learning rate: 0.00223 +2024-09-09 18:01:57.861211: train_loss -0.7412 +2024-09-09 18:01:57.863140: val_loss -0.7708 +2024-09-09 18:01:57.864105: Pseudo dice [0.9658, 0.9608, 0.9804, 0.9559, 0.8632, 0.975, 0.9889] +2024-09-09 18:01:57.864982: Epoch time: 266.36 s +2024-09-09 18:02:01.893323: +2024-09-09 18:02:01.894407: Epoch 812 +2024-09-09 18:02:01.895217: Current learning rate: 0.00222 +2024-09-09 18:06:38.379124: train_loss -0.7498 +2024-09-09 18:06:38.392162: val_loss -0.774 +2024-09-09 18:06:38.393158: Pseudo dice [0.9657, 0.9616, 0.9781, 0.9544, 0.8743, 0.9759, 0.991] +2024-09-09 18:06:38.393948: Epoch time: 276.49 s +2024-09-09 18:06:41.221562: +2024-09-09 18:06:41.236970: Epoch 813 +2024-09-09 18:06:41.238118: Current learning rate: 0.00221 +2024-09-09 18:11:13.051727: train_loss -0.7681 +2024-09-09 18:11:13.074414: val_loss -0.7381 +2024-09-09 18:11:13.075333: Pseudo dice [0.9633, 0.9611, 0.9725, 0.949, 0.8917, 0.9769, 0.9929] +2024-09-09 18:11:13.076214: Epoch time: 271.83 s +2024-09-09 18:11:15.900373: +2024-09-09 18:11:15.901569: Epoch 814 +2024-09-09 18:11:15.902400: Current learning rate: 0.0022 +2024-09-09 18:15:29.703058: train_loss -0.7541 +2024-09-09 18:15:29.716939: val_loss -0.8024 +2024-09-09 18:15:29.717891: Pseudo dice [0.9655, 0.9612, 0.9785, 0.9617, 0.895, 0.9754, 0.9924] +2024-09-09 18:15:29.718784: Epoch time: 253.81 s +2024-09-09 18:15:33.324308: +2024-09-09 18:15:33.326552: Epoch 815 +2024-09-09 18:15:33.327745: Current learning rate: 0.00219 +2024-09-09 18:19:49.099740: train_loss -0.7566 +2024-09-09 18:19:49.101369: val_loss -0.7592 +2024-09-09 18:19:49.102389: Pseudo dice [0.9676, 0.9608, 0.9797, 0.9511, 0.8872, 0.9717, 0.9918] +2024-09-09 18:19:49.103532: Epoch time: 255.78 s +2024-09-09 18:19:52.397404: +2024-09-09 18:19:52.405694: Epoch 816 +2024-09-09 18:19:52.406931: Current learning rate: 0.00218 +2024-09-09 18:24:26.920656: train_loss -0.762 +2024-09-09 18:24:26.933388: val_loss -0.7658 +2024-09-09 18:24:26.934273: Pseudo dice [0.9644, 0.9598, 0.9792, 0.9551, 0.8822, 0.9703, 0.9891] +2024-09-09 18:24:26.935090: Epoch time: 274.53 s +2024-09-09 18:24:29.477601: +2024-09-09 18:24:29.478830: Epoch 817 +2024-09-09 18:24:29.479672: Current learning rate: 0.00217 +2024-09-09 18:28:52.891642: train_loss -0.7614 +2024-09-09 18:28:52.893135: val_loss -0.7662 +2024-09-09 18:28:52.893963: Pseudo dice [0.9679, 0.9621, 0.9822, 0.9502, 0.8926, 0.9747, 0.9914] +2024-09-09 18:28:52.894915: Epoch time: 263.42 s +2024-09-09 18:28:56.434371: +2024-09-09 18:28:56.448792: Epoch 818 +2024-09-09 18:28:56.449812: Current learning rate: 0.00216 +2024-09-09 18:33:39.437176: train_loss -0.7606 +2024-09-09 18:33:39.438596: val_loss -0.8093 +2024-09-09 18:33:39.439439: Pseudo dice [0.9678, 0.9615, 0.9809, 0.953, 0.8777, 0.9756, 0.9897] +2024-09-09 18:33:39.440260: Epoch time: 283.01 s +2024-09-09 18:33:43.270755: +2024-09-09 18:33:43.272878: Epoch 819 +2024-09-09 18:33:43.273710: Current learning rate: 0.00215 +2024-09-09 18:38:17.303969: train_loss -0.7438 +2024-09-09 18:38:17.306613: val_loss -0.7413 +2024-09-09 18:38:17.307601: Pseudo dice [0.9658, 0.959, 0.9774, 0.9528, 0.8696, 0.9726, 0.9882] +2024-09-09 18:38:17.308389: Epoch time: 274.04 s +2024-09-09 18:38:20.261799: +2024-09-09 18:38:20.263030: Epoch 820 +2024-09-09 18:38:20.264061: Current learning rate: 0.00214 +2024-09-09 18:42:53.840171: train_loss -0.7405 +2024-09-09 18:42:53.856026: val_loss -0.7624 +2024-09-09 18:42:53.857002: Pseudo dice [0.9662, 0.9598, 0.9806, 0.9538, 0.8805, 0.9724, 0.9919] +2024-09-09 18:42:53.857937: Epoch time: 273.58 s +2024-09-09 18:42:57.187068: +2024-09-09 18:42:57.195681: Epoch 821 +2024-09-09 18:42:57.196845: Current learning rate: 0.00213 +2024-09-09 18:47:12.761520: train_loss -0.7484 +2024-09-09 18:47:12.764336: val_loss -0.7322 +2024-09-09 18:47:12.765439: Pseudo dice [0.9662, 0.9604, 0.9814, 0.9576, 0.859, 0.9712, 0.9901] +2024-09-09 18:47:12.766781: Epoch time: 255.58 s +2024-09-09 18:47:15.946217: +2024-09-09 18:47:15.960174: Epoch 822 +2024-09-09 18:47:15.961761: Current learning rate: 0.00212 +2024-09-09 18:51:46.893339: train_loss -0.7599 +2024-09-09 18:51:46.910064: val_loss -0.7189 +2024-09-09 18:51:46.910965: Pseudo dice [0.9686, 0.9616, 0.9816, 0.9556, 0.9031, 0.9754, 0.9924] +2024-09-09 18:51:46.911749: Epoch time: 270.95 s +2024-09-09 18:51:49.708663: +2024-09-09 18:51:49.710336: Epoch 823 +2024-09-09 18:51:49.711333: Current learning rate: 0.0021 +2024-09-09 18:56:29.505949: train_loss -0.751 +2024-09-09 18:56:29.522167: val_loss -0.78 +2024-09-09 18:56:29.523133: Pseudo dice [0.9648, 0.9598, 0.9775, 0.9469, 0.902, 0.9754, 0.993] +2024-09-09 18:56:29.523975: Epoch time: 279.8 s +2024-09-09 18:56:32.348920: +2024-09-09 18:56:32.350006: Epoch 824 +2024-09-09 18:56:32.350852: Current learning rate: 0.00209 +2024-09-09 19:01:01.959425: train_loss -0.7582 +2024-09-09 19:01:01.968968: val_loss -0.7976 +2024-09-09 19:01:01.970016: Pseudo dice [0.9637, 0.9603, 0.9767, 0.9476, 0.897, 0.9708, 0.9916] +2024-09-09 19:01:01.970967: Epoch time: 269.62 s +2024-09-09 19:01:06.496433: +2024-09-09 19:01:06.497545: Epoch 825 +2024-09-09 19:01:06.498386: Current learning rate: 0.00208 +2024-09-09 19:05:45.926840: train_loss -0.7358 +2024-09-09 19:05:45.937224: val_loss -0.757 +2024-09-09 19:05:45.938095: Pseudo dice [0.9646, 0.9587, 0.9768, 0.9469, 0.8945, 0.9761, 0.9925] +2024-09-09 19:05:45.938897: Epoch time: 279.43 s +2024-09-09 19:05:49.383853: +2024-09-09 19:05:49.385337: Epoch 826 +2024-09-09 19:05:49.386378: Current learning rate: 0.00207 +2024-09-09 19:10:27.993631: train_loss -0.7672 +2024-09-09 19:10:28.024240: val_loss -0.7582 +2024-09-09 19:10:28.025112: Pseudo dice [0.9676, 0.9617, 0.9805, 0.9513, 0.8899, 0.9731, 0.9922] +2024-09-09 19:10:28.025946: Epoch time: 278.62 s +2024-09-09 19:10:31.293652: +2024-09-09 19:10:31.294796: Epoch 827 +2024-09-09 19:10:31.295763: Current learning rate: 0.00206 +2024-09-09 19:14:59.867036: train_loss -0.7455 +2024-09-09 19:14:59.889517: val_loss -0.7421 +2024-09-09 19:14:59.901171: Pseudo dice [0.9662, 0.9623, 0.9779, 0.9527, 0.8985, 0.9739, 0.9934] +2024-09-09 19:14:59.902228: Epoch time: 268.58 s +2024-09-09 19:15:02.241354: +2024-09-09 19:15:02.242692: Epoch 828 +2024-09-09 19:15:02.243490: Current learning rate: 0.00205 +2024-09-09 19:19:44.028049: train_loss -0.7443 +2024-09-09 19:19:44.045301: val_loss -0.7756 +2024-09-09 19:19:44.046100: Pseudo dice [0.969, 0.9614, 0.9825, 0.9506, 0.9018, 0.974, 0.9929] +2024-09-09 19:19:44.046866: Epoch time: 281.79 s +2024-09-09 19:19:47.856164: +2024-09-09 19:19:47.860301: Epoch 829 +2024-09-09 19:19:47.861364: Current learning rate: 0.00204 +2024-09-09 19:24:24.946354: train_loss -0.7392 +2024-09-09 19:24:24.948333: val_loss -0.7333 +2024-09-09 19:24:24.949037: Pseudo dice [0.9653, 0.9608, 0.9774, 0.948, 0.9047, 0.9714, 0.9916] +2024-09-09 19:24:24.949705: Epoch time: 277.1 s +2024-09-09 19:24:27.689301: +2024-09-09 19:24:27.690223: Epoch 830 +2024-09-09 19:24:27.690859: Current learning rate: 0.00203 +2024-09-09 19:29:02.096998: train_loss -0.7461 +2024-09-09 19:29:02.099664: val_loss -0.7819 +2024-09-09 19:29:02.100707: Pseudo dice [0.9686, 0.9597, 0.9816, 0.9505, 0.883, 0.9742, 0.9922] +2024-09-09 19:29:02.101636: Epoch time: 274.41 s +2024-09-09 19:29:05.092595: +2024-09-09 19:29:05.093646: Epoch 831 +2024-09-09 19:29:05.094580: Current learning rate: 0.00202 +2024-09-09 19:33:51.612571: train_loss -0.7451 +2024-09-09 19:33:51.629370: val_loss -0.7313 +2024-09-09 19:33:51.630640: Pseudo dice [0.9645, 0.9601, 0.9766, 0.9496, 0.8912, 0.9743, 0.993] +2024-09-09 19:33:51.631605: Epoch time: 286.52 s +2024-09-09 19:33:54.646191: +2024-09-09 19:33:54.657098: Epoch 832 +2024-09-09 19:33:54.658765: Current learning rate: 0.00201 +2024-09-09 19:38:23.183546: train_loss -0.7631 +2024-09-09 19:38:23.199327: val_loss -0.756 +2024-09-09 19:38:23.200339: Pseudo dice [0.9619, 0.9602, 0.9746, 0.9514, 0.8957, 0.9735, 0.9925] +2024-09-09 19:38:23.201268: Epoch time: 268.54 s +2024-09-09 19:38:26.298481: +2024-09-09 19:38:26.316108: Epoch 833 +2024-09-09 19:38:26.317484: Current learning rate: 0.002 +2024-09-09 19:42:54.327486: train_loss -0.7621 +2024-09-09 19:42:54.344198: val_loss -0.7722 +2024-09-09 19:42:54.345331: Pseudo dice [0.9641, 0.9591, 0.9783, 0.9511, 0.8965, 0.9765, 0.9928] +2024-09-09 19:42:54.346203: Epoch time: 268.03 s +2024-09-09 19:42:56.381948: +2024-09-09 19:42:56.383191: Epoch 834 +2024-09-09 19:42:56.384120: Current learning rate: 0.00199 +2024-09-09 19:47:30.705955: train_loss -0.7428 +2024-09-09 19:47:30.721347: val_loss -0.7756 +2024-09-09 19:47:30.722307: Pseudo dice [0.9664, 0.9617, 0.9802, 0.9586, 0.9104, 0.9781, 0.9942] +2024-09-09 19:47:30.723376: Epoch time: 274.34 s +2024-09-09 19:47:30.724211: Yayy! New best EMA pseudo Dice: 0.9596 +2024-09-09 19:47:38.073862: +2024-09-09 19:47:38.075286: Epoch 835 +2024-09-09 19:47:38.076320: Current learning rate: 0.00198 +2024-09-09 19:52:04.174345: train_loss -0.7666 +2024-09-09 19:52:04.189501: val_loss -0.7617 +2024-09-09 19:52:04.190513: Pseudo dice [0.9656, 0.9616, 0.9786, 0.9539, 0.8999, 0.9705, 0.9919] +2024-09-09 19:52:04.191367: Epoch time: 266.11 s +2024-09-09 19:52:04.192067: Yayy! New best EMA pseudo Dice: 0.9596 +2024-09-09 19:52:09.587914: +2024-09-09 19:52:09.589208: Epoch 836 +2024-09-09 19:52:09.590049: Current learning rate: 0.00196 +2024-09-09 19:56:36.701763: train_loss -0.7405 +2024-09-09 19:56:36.712221: val_loss -0.7616 +2024-09-09 19:56:36.713437: Pseudo dice [0.9668, 0.9608, 0.9776, 0.9587, 0.8966, 0.9761, 0.9931] +2024-09-09 19:56:36.714356: Epoch time: 267.12 s +2024-09-09 19:56:36.715090: Yayy! New best EMA pseudo Dice: 0.9598 +2024-09-09 19:56:42.762268: +2024-09-09 19:56:42.763160: Epoch 837 +2024-09-09 19:56:42.763849: Current learning rate: 0.00195 +2024-09-09 20:01:10.043936: train_loss -0.7421 +2024-09-09 20:01:10.054793: val_loss -0.7495 +2024-09-09 20:01:10.055641: Pseudo dice [0.964, 0.9598, 0.976, 0.9497, 0.8837, 0.9741, 0.9912] +2024-09-09 20:01:10.056755: Epoch time: 267.28 s +2024-09-09 20:01:13.289765: +2024-09-09 20:01:13.290938: Epoch 838 +2024-09-09 20:01:13.291910: Current learning rate: 0.00194 +2024-09-09 20:05:47.545368: train_loss -0.7605 +2024-09-09 20:05:47.547327: val_loss -0.8164 +2024-09-09 20:05:47.548575: Pseudo dice [0.9678, 0.9614, 0.9797, 0.9529, 0.8971, 0.9757, 0.9922] +2024-09-09 20:05:47.550193: Epoch time: 274.26 s +2024-09-09 20:05:49.982061: +2024-09-09 20:05:49.983188: Epoch 839 +2024-09-09 20:05:49.984189: Current learning rate: 0.00193 +2024-09-09 20:10:22.240811: train_loss -0.7559 +2024-09-09 20:10:22.264229: val_loss -0.7596 +2024-09-09 20:10:22.265335: Pseudo dice [0.9664, 0.9622, 0.9805, 0.9546, 0.9022, 0.9747, 0.9932] +2024-09-09 20:10:22.266200: Epoch time: 272.26 s +2024-09-09 20:10:22.266975: Yayy! New best EMA pseudo Dice: 0.9599 +2024-09-09 20:10:28.608987: +2024-09-09 20:10:28.610083: Epoch 840 +2024-09-09 20:10:28.610920: Current learning rate: 0.00192 +2024-09-09 20:14:54.007899: train_loss -0.7569 +2024-09-09 20:14:54.021376: val_loss -0.7381 +2024-09-09 20:14:54.026608: Pseudo dice [0.963, 0.9635, 0.9774, 0.9562, 0.8919, 0.9698, 0.9917] +2024-09-09 20:14:54.028433: Epoch time: 265.4 s +2024-09-09 20:14:56.510797: +2024-09-09 20:14:56.518041: Epoch 841 +2024-09-09 20:14:56.518963: Current learning rate: 0.00191 +2024-09-09 20:19:29.720819: train_loss -0.7456 +2024-09-09 20:19:29.733639: val_loss -0.7543 +2024-09-09 20:19:29.734807: Pseudo dice [0.9679, 0.9611, 0.9799, 0.9477, 0.9008, 0.976, 0.9927] +2024-09-09 20:19:29.735907: Epoch time: 273.22 s +2024-09-09 20:19:29.736966: Yayy! New best EMA pseudo Dice: 0.9599 +2024-09-09 20:19:37.380229: +2024-09-09 20:19:37.389028: Epoch 842 +2024-09-09 20:19:37.390067: Current learning rate: 0.0019 +2024-09-09 20:24:16.999097: train_loss -0.7368 +2024-09-09 20:24:17.001662: val_loss -0.7665 +2024-09-09 20:24:17.002549: Pseudo dice [0.9686, 0.9608, 0.9811, 0.9551, 0.909, 0.9716, 0.9934] +2024-09-09 20:24:17.003710: Epoch time: 279.62 s +2024-09-09 20:24:17.004857: Yayy! New best EMA pseudo Dice: 0.9602 +2024-09-09 20:24:24.268534: +2024-09-09 20:24:24.269805: Epoch 843 +2024-09-09 20:24:24.270710: Current learning rate: 0.00189 +2024-09-09 20:28:44.103852: train_loss -0.767 +2024-09-09 20:28:44.115518: val_loss -0.7743 +2024-09-09 20:28:44.116391: Pseudo dice [0.9663, 0.9607, 0.9791, 0.9519, 0.898, 0.976, 0.9934] +2024-09-09 20:28:44.117235: Epoch time: 259.84 s +2024-09-09 20:28:44.117960: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-09 20:28:51.058629: +2024-09-09 20:28:51.059507: Epoch 844 +2024-09-09 20:28:51.060319: Current learning rate: 0.00188 +2024-09-09 20:33:32.480482: train_loss -0.7673 +2024-09-09 20:33:32.483153: val_loss -0.7594 +2024-09-09 20:33:32.484225: Pseudo dice [0.9695, 0.9625, 0.983, 0.9599, 0.897, 0.9749, 0.9928] +2024-09-09 20:33:32.485319: Epoch time: 281.42 s +2024-09-09 20:33:32.486364: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-09 20:33:38.427200: +2024-09-09 20:33:38.428297: Epoch 845 +2024-09-09 20:33:38.429224: Current learning rate: 0.00187 +2024-09-09 20:38:05.661595: train_loss -0.7712 +2024-09-09 20:38:05.678588: val_loss -0.7609 +2024-09-09 20:38:05.679580: Pseudo dice [0.9621, 0.9611, 0.9759, 0.9495, 0.8971, 0.9747, 0.9922] +2024-09-09 20:38:05.680652: Epoch time: 267.24 s +2024-09-09 20:38:08.842907: +2024-09-09 20:38:08.844179: Epoch 846 +2024-09-09 20:38:08.845128: Current learning rate: 0.00186 +2024-09-09 20:42:46.812222: train_loss -0.7537 +2024-09-09 20:42:46.830135: val_loss -0.7832 +2024-09-09 20:42:46.831082: Pseudo dice [0.9663, 0.9623, 0.9806, 0.9501, 0.8864, 0.9766, 0.9912] +2024-09-09 20:42:46.831865: Epoch time: 277.97 s +2024-09-09 20:42:49.954548: +2024-09-09 20:42:49.955569: Epoch 847 +2024-09-09 20:42:49.956458: Current learning rate: 0.00185 +2024-09-09 20:47:29.288272: train_loss -0.7506 +2024-09-09 20:47:29.306455: val_loss -0.7506 +2024-09-09 20:47:29.307604: Pseudo dice [0.9667, 0.9631, 0.981, 0.9493, 0.903, 0.9739, 0.9933] +2024-09-09 20:47:29.309160: Epoch time: 279.34 s +2024-09-09 20:47:34.117200: +2024-09-09 20:47:34.132880: Epoch 848 +2024-09-09 20:47:34.133790: Current learning rate: 0.00184 +2024-09-09 20:52:16.605805: train_loss -0.7676 +2024-09-09 20:52:16.630465: val_loss -0.7653 +2024-09-09 20:52:16.631712: Pseudo dice [0.9655, 0.9602, 0.9767, 0.9551, 0.9035, 0.976, 0.9932] +2024-09-09 20:52:16.633284: Epoch time: 282.49 s +2024-09-09 20:52:19.992730: +2024-09-09 20:52:19.993853: Epoch 849 +2024-09-09 20:52:19.994682: Current learning rate: 0.00182 +2024-09-09 20:56:31.367463: train_loss -0.7658 +2024-09-09 20:56:31.382410: val_loss -0.7544 +2024-09-09 20:56:31.383415: Pseudo dice [0.9645, 0.96, 0.9765, 0.9524, 0.9028, 0.9777, 0.9936] +2024-09-09 20:56:31.384307: Epoch time: 251.38 s +2024-09-09 20:56:35.370431: Yayy! New best EMA pseudo Dice: 0.9605 +2024-09-09 20:56:40.101567: +2024-09-09 20:56:40.102502: Epoch 850 +2024-09-09 20:56:40.103211: Current learning rate: 0.00181 +2024-09-09 21:01:08.514007: train_loss -0.7466 +2024-09-09 21:01:08.526366: val_loss -0.7493 +2024-09-09 21:01:08.527407: Pseudo dice [0.9667, 0.9605, 0.9791, 0.952, 0.8934, 0.9746, 0.9911] +2024-09-09 21:01:08.528420: Epoch time: 268.42 s +2024-09-09 21:01:11.783787: +2024-09-09 21:01:11.794343: Epoch 851 +2024-09-09 21:01:11.795516: Current learning rate: 0.0018 +2024-09-09 21:05:45.054369: train_loss -0.7584 +2024-09-09 21:05:45.070368: val_loss -0.7467 +2024-09-09 21:05:45.071293: Pseudo dice [0.9633, 0.9611, 0.9744, 0.951, 0.8982, 0.9744, 0.9918] +2024-09-09 21:05:45.072195: Epoch time: 273.28 s +2024-09-09 21:05:47.967085: +2024-09-09 21:05:47.968331: Epoch 852 +2024-09-09 21:05:47.969501: Current learning rate: 0.00179 +2024-09-09 21:09:56.449481: train_loss -0.7653 +2024-09-09 21:09:56.464699: val_loss -0.7768 +2024-09-09 21:09:56.465612: Pseudo dice [0.9673, 0.9615, 0.9823, 0.9571, 0.8744, 0.9761, 0.9907] +2024-09-09 21:09:56.466430: Epoch time: 248.49 s +2024-09-09 21:09:59.453892: +2024-09-09 21:09:59.472778: Epoch 853 +2024-09-09 21:09:59.473777: Current learning rate: 0.00178 +2024-09-09 21:14:44.935987: train_loss -0.7548 +2024-09-09 21:14:44.966096: val_loss -0.7568 +2024-09-09 21:14:44.971714: Pseudo dice [0.9673, 0.9608, 0.9805, 0.9518, 0.8927, 0.9713, 0.9922] +2024-09-09 21:14:44.972776: Epoch time: 285.49 s +2024-09-09 21:14:48.166754: +2024-09-09 21:14:48.168437: Epoch 854 +2024-09-09 21:14:48.169673: Current learning rate: 0.00177 +2024-09-09 21:19:14.772616: train_loss -0.7533 +2024-09-09 21:19:14.802732: val_loss -0.7701 +2024-09-09 21:19:14.803935: Pseudo dice [0.967, 0.9618, 0.9806, 0.9475, 0.8929, 0.9728, 0.9919] +2024-09-09 21:19:14.804902: Epoch time: 266.61 s +2024-09-09 21:19:18.149562: +2024-09-09 21:19:18.156746: Epoch 855 +2024-09-09 21:19:18.157678: Current learning rate: 0.00176 +2024-09-09 21:23:54.265025: train_loss -0.7701 +2024-09-09 21:23:54.267444: val_loss -0.746 +2024-09-09 21:23:54.268454: Pseudo dice [0.9668, 0.9605, 0.9788, 0.9586, 0.8908, 0.9701, 0.9922] +2024-09-09 21:23:54.269424: Epoch time: 276.12 s +2024-09-09 21:23:58.478935: +2024-09-09 21:23:58.488823: Epoch 856 +2024-09-09 21:23:58.489792: Current learning rate: 0.00175 +2024-09-09 21:28:38.917359: train_loss -0.7545 +2024-09-09 21:28:38.920191: val_loss -0.7388 +2024-09-09 21:28:38.921130: Pseudo dice [0.966, 0.9609, 0.9769, 0.9523, 0.8996, 0.9726, 0.9924] +2024-09-09 21:28:38.921995: Epoch time: 280.44 s +2024-09-09 21:28:41.814494: +2024-09-09 21:28:41.820788: Epoch 857 +2024-09-09 21:28:41.821835: Current learning rate: 0.00174 +2024-09-09 21:33:21.281928: train_loss -0.764 +2024-09-09 21:33:21.284558: val_loss -0.7598 +2024-09-09 21:33:21.285720: Pseudo dice [0.9623, 0.9608, 0.9724, 0.9489, 0.893, 0.9739, 0.992] +2024-09-09 21:33:21.286738: Epoch time: 279.47 s +2024-09-09 21:33:24.573908: +2024-09-09 21:33:24.575135: Epoch 858 +2024-09-09 21:33:24.576127: Current learning rate: 0.00173 +2024-09-09 21:37:43.779383: train_loss -0.7715 +2024-09-09 21:37:43.798404: val_loss -0.7564 +2024-09-09 21:37:43.799400: Pseudo dice [0.9694, 0.9635, 0.9811, 0.9583, 0.9008, 0.9771, 0.9937] +2024-09-09 21:37:43.800241: Epoch time: 259.21 s +2024-09-09 21:37:47.065438: +2024-09-09 21:37:47.076768: Epoch 859 +2024-09-09 21:37:47.078164: Current learning rate: 0.00172 +2024-09-09 21:42:12.019081: train_loss -0.7434 +2024-09-09 21:42:12.034230: val_loss -0.7732 +2024-09-09 21:42:12.036252: Pseudo dice [0.9692, 0.9621, 0.9822, 0.9508, 0.9021, 0.9749, 0.9921] +2024-09-09 21:42:12.037179: Epoch time: 264.96 s +2024-09-09 21:42:16.305757: +2024-09-09 21:42:16.307011: Epoch 860 +2024-09-09 21:42:16.307956: Current learning rate: 0.0017 +2024-09-09 21:46:47.663400: train_loss -0.7454 +2024-09-09 21:46:47.665864: val_loss -0.7602 +2024-09-09 21:46:47.666759: Pseudo dice [0.9641, 0.96, 0.9787, 0.947, 0.8952, 0.977, 0.9936] +2024-09-09 21:46:47.667620: Epoch time: 271.36 s +2024-09-09 21:46:49.868639: +2024-09-09 21:46:49.869897: Epoch 861 +2024-09-09 21:46:49.870903: Current learning rate: 0.00169 +2024-09-09 21:51:12.776742: train_loss -0.7609 +2024-09-09 21:51:12.805841: val_loss -0.7492 +2024-09-09 21:51:12.806824: Pseudo dice [0.9684, 0.9621, 0.9826, 0.9518, 0.8895, 0.977, 0.9909] +2024-09-09 21:51:12.807577: Epoch time: 262.91 s +2024-09-09 21:51:15.692913: +2024-09-09 21:51:15.694094: Epoch 862 +2024-09-09 21:51:15.694953: Current learning rate: 0.00168 +2024-09-09 21:55:47.977124: train_loss -0.7488 +2024-09-09 21:55:47.986940: val_loss -0.7411 +2024-09-09 21:55:47.987925: Pseudo dice [0.9658, 0.96, 0.9781, 0.9537, 0.8891, 0.9749, 0.9915] +2024-09-09 21:55:47.988883: Epoch time: 272.29 s +2024-09-09 21:55:50.855149: +2024-09-09 21:55:50.856729: Epoch 863 +2024-09-09 21:55:50.857775: Current learning rate: 0.00167 +2024-09-09 22:00:08.213249: train_loss -0.7578 +2024-09-09 22:00:08.225103: val_loss -0.7276 +2024-09-09 22:00:08.226171: Pseudo dice [0.9594, 0.9595, 0.9729, 0.9562, 0.8834, 0.9739, 0.9932] +2024-09-09 22:00:08.227142: Epoch time: 257.36 s +2024-09-09 22:00:11.455705: +2024-09-09 22:00:11.469172: Epoch 864 +2024-09-09 22:00:11.470525: Current learning rate: 0.00166 +2024-09-09 22:04:36.921490: train_loss -0.7574 +2024-09-09 22:04:36.949711: val_loss -0.7208 +2024-09-09 22:04:36.950597: Pseudo dice [0.9618, 0.9622, 0.9757, 0.9487, 0.8625, 0.9734, 0.9895] +2024-09-09 22:04:36.951470: Epoch time: 265.47 s +2024-09-09 22:04:39.289056: +2024-09-09 22:04:39.304789: Epoch 865 +2024-09-09 22:04:39.305717: Current learning rate: 0.00165 +2024-09-09 22:09:42.282650: train_loss -0.7703 +2024-09-09 22:09:42.284878: val_loss -0.7597 +2024-09-09 22:09:42.285800: Pseudo dice [0.9689, 0.9617, 0.9816, 0.9498, 0.8673, 0.9721, 0.9896] +2024-09-09 22:09:42.286534: Epoch time: 303.0 s +2024-09-09 22:09:45.244187: +2024-09-09 22:09:45.260503: Epoch 866 +2024-09-09 22:09:45.261551: Current learning rate: 0.00164 +2024-09-09 22:14:28.824200: train_loss -0.756 +2024-09-09 22:14:28.838225: val_loss -0.7728 +2024-09-09 22:14:28.839108: Pseudo dice [0.9694, 0.9622, 0.9814, 0.9508, 0.8908, 0.9743, 0.993] +2024-09-09 22:14:28.839969: Epoch time: 283.59 s +2024-09-09 22:14:32.167839: +2024-09-09 22:14:32.185135: Epoch 867 +2024-09-09 22:14:32.186630: Current learning rate: 0.00163 +2024-09-09 22:19:02.073628: train_loss -0.7581 +2024-09-09 22:19:02.077422: val_loss -0.7704 +2024-09-09 22:19:02.078937: Pseudo dice [0.9683, 0.9636, 0.98, 0.9571, 0.897, 0.9755, 0.9928] +2024-09-09 22:19:02.080065: Epoch time: 269.91 s +2024-09-09 22:19:05.378727: +2024-09-09 22:19:05.380049: Epoch 868 +2024-09-09 22:19:05.381070: Current learning rate: 0.00162 +2024-09-09 22:23:54.121591: train_loss -0.7564 +2024-09-09 22:23:54.124115: val_loss -0.7508 +2024-09-09 22:23:54.125127: Pseudo dice [0.9659, 0.9605, 0.9766, 0.9526, 0.892, 0.9736, 0.993] +2024-09-09 22:23:54.126017: Epoch time: 288.75 s +2024-09-09 22:23:55.994949: +2024-09-09 22:23:55.995857: Epoch 869 +2024-09-09 22:23:55.996690: Current learning rate: 0.00161 +2024-09-09 22:28:16.100459: train_loss -0.749 +2024-09-09 22:28:16.102795: val_loss -0.7773 +2024-09-09 22:28:16.103619: Pseudo dice [0.9668, 0.9602, 0.9793, 0.9508, 0.9007, 0.9722, 0.9933] +2024-09-09 22:28:16.104449: Epoch time: 260.11 s +2024-09-09 22:28:18.421622: +2024-09-09 22:28:18.422780: Epoch 870 +2024-09-09 22:28:18.423730: Current learning rate: 0.00159 +2024-09-09 22:32:35.857711: train_loss -0.7515 +2024-09-09 22:32:35.886229: val_loss -0.7811 +2024-09-09 22:32:35.887150: Pseudo dice [0.967, 0.961, 0.98, 0.9533, 0.879, 0.9761, 0.9916] +2024-09-09 22:32:35.887962: Epoch time: 257.44 s +2024-09-09 22:32:39.553273: +2024-09-09 22:32:39.568871: Epoch 871 +2024-09-09 22:32:39.569802: Current learning rate: 0.00158 +2024-09-09 22:37:25.096753: train_loss -0.7577 +2024-09-09 22:37:25.107198: val_loss -0.7456 +2024-09-09 22:37:25.108121: Pseudo dice [0.9572, 0.9608, 0.9702, 0.9555, 0.8942, 0.9759, 0.9937] +2024-09-09 22:37:25.108994: Epoch time: 285.55 s +2024-09-09 22:37:27.611114: +2024-09-09 22:37:27.623774: Epoch 872 +2024-09-09 22:37:27.625233: Current learning rate: 0.00157 +2024-09-09 22:41:41.601307: train_loss -0.749 +2024-09-09 22:41:41.618987: val_loss -0.7557 +2024-09-09 22:41:41.620427: Pseudo dice [0.9685, 0.9618, 0.9818, 0.9531, 0.8953, 0.9716, 0.9908] +2024-09-09 22:41:41.621518: Epoch time: 254.0 s +2024-09-09 22:41:44.332305: +2024-09-09 22:41:44.336279: Epoch 873 +2024-09-09 22:41:44.337238: Current learning rate: 0.00156 +2024-09-09 22:46:19.231078: train_loss -0.7379 +2024-09-09 22:46:19.246523: val_loss -0.7959 +2024-09-09 22:46:19.247746: Pseudo dice [0.968, 0.9604, 0.9819, 0.9537, 0.8898, 0.9732, 0.9922] +2024-09-09 22:46:19.248589: Epoch time: 274.9 s +2024-09-09 22:46:23.289865: +2024-09-09 22:46:23.290984: Epoch 874 +2024-09-09 22:46:23.291830: Current learning rate: 0.00155 +2024-09-09 22:51:03.303374: train_loss -0.7557 +2024-09-09 22:51:03.305991: val_loss -0.7205 +2024-09-09 22:51:03.307035: Pseudo dice [0.9679, 0.9623, 0.9812, 0.9486, 0.8977, 0.9649, 0.9919] +2024-09-09 22:51:03.308966: Epoch time: 280.02 s +2024-09-09 22:51:06.484849: +2024-09-09 22:51:06.486321: Epoch 875 +2024-09-09 22:51:06.487802: Current learning rate: 0.00154 +2024-09-09 22:55:43.482358: train_loss -0.7528 +2024-09-09 22:55:43.496565: val_loss -0.7666 +2024-09-09 22:55:43.497557: Pseudo dice [0.9632, 0.9622, 0.976, 0.948, 0.8913, 0.9776, 0.9909] +2024-09-09 22:55:43.498416: Epoch time: 277.0 s +2024-09-09 22:55:46.290343: +2024-09-09 22:55:46.308126: Epoch 876 +2024-09-09 22:55:46.309696: Current learning rate: 0.00153 +2024-09-09 23:00:23.227072: train_loss -0.744 +2024-09-09 23:00:23.229298: val_loss -0.7163 +2024-09-09 23:00:23.230128: Pseudo dice [0.9668, 0.9621, 0.98, 0.9562, 0.8851, 0.9735, 0.9903] +2024-09-09 23:00:23.231072: Epoch time: 276.94 s +2024-09-09 23:00:26.898338: +2024-09-09 23:00:26.899403: Epoch 877 +2024-09-09 23:00:26.900318: Current learning rate: 0.00152 +2024-09-09 23:05:01.051273: train_loss -0.7667 +2024-09-09 23:05:01.055341: val_loss -0.7755 +2024-09-09 23:05:01.056316: Pseudo dice [0.967, 0.9592, 0.9795, 0.9439, 0.8759, 0.977, 0.9903] +2024-09-09 23:05:01.057636: Epoch time: 274.16 s +2024-09-09 23:05:04.358781: +2024-09-09 23:05:04.376154: Epoch 878 +2024-09-09 23:05:04.391495: Current learning rate: 0.00151 +2024-09-09 23:09:37.095722: train_loss -0.7652 +2024-09-09 23:09:37.110184: val_loss -0.7322 +2024-09-09 23:09:37.111151: Pseudo dice [0.9659, 0.9599, 0.9801, 0.9552, 0.9005, 0.9767, 0.9924] +2024-09-09 23:09:37.112058: Epoch time: 272.74 s +2024-09-09 23:09:39.914527: +2024-09-09 23:09:39.924788: Epoch 879 +2024-09-09 23:09:39.925658: Current learning rate: 0.00149 +2024-09-09 23:14:16.681994: train_loss -0.7566 +2024-09-09 23:14:16.686157: val_loss -0.7668 +2024-09-09 23:14:16.687359: Pseudo dice [0.9665, 0.9615, 0.9803, 0.9536, 0.8937, 0.9741, 0.9911] +2024-09-09 23:14:16.690142: Epoch time: 276.77 s +2024-09-09 23:14:19.709467: +2024-09-09 23:14:19.710701: Epoch 880 +2024-09-09 23:14:19.711577: Current learning rate: 0.00148 +2024-09-09 23:19:02.272506: train_loss -0.7494 +2024-09-09 23:19:02.286216: val_loss -0.7458 +2024-09-09 23:19:02.287054: Pseudo dice [0.9679, 0.9619, 0.9808, 0.9536, 0.8992, 0.9751, 0.9933] +2024-09-09 23:19:02.287805: Epoch time: 282.57 s +2024-09-09 23:19:05.611017: +2024-09-09 23:19:05.612721: Epoch 881 +2024-09-09 23:19:05.614731: Current learning rate: 0.00147 +2024-09-09 23:23:24.993127: train_loss -0.7465 +2024-09-09 23:23:24.995407: val_loss -0.8092 +2024-09-09 23:23:24.996255: Pseudo dice [0.9657, 0.9613, 0.9773, 0.9538, 0.8912, 0.9749, 0.9912] +2024-09-09 23:23:24.997398: Epoch time: 259.39 s +2024-09-09 23:23:27.463378: +2024-09-09 23:23:27.465141: Epoch 882 +2024-09-09 23:23:27.466467: Current learning rate: 0.00146 +2024-09-09 23:28:01.907982: train_loss -0.7468 +2024-09-09 23:28:01.910568: val_loss -0.7788 +2024-09-09 23:28:01.911409: Pseudo dice [0.9668, 0.9609, 0.9811, 0.9512, 0.893, 0.9784, 0.9923] +2024-09-09 23:28:01.912189: Epoch time: 274.45 s +2024-09-09 23:28:04.113703: +2024-09-09 23:28:04.114921: Epoch 883 +2024-09-09 23:28:04.115734: Current learning rate: 0.00145 +2024-09-09 23:32:53.988430: train_loss -0.7505 +2024-09-09 23:32:53.996247: val_loss -0.7483 +2024-09-09 23:32:53.997555: Pseudo dice [0.9657, 0.9622, 0.9751, 0.9504, 0.8914, 0.9732, 0.9915] +2024-09-09 23:32:53.998420: Epoch time: 289.88 s +2024-09-09 23:32:56.690356: +2024-09-09 23:32:56.691682: Epoch 884 +2024-09-09 23:32:56.692549: Current learning rate: 0.00144 +2024-09-09 23:37:15.313509: train_loss -0.7387 +2024-09-09 23:37:15.330348: val_loss -0.7542 +2024-09-09 23:37:15.331508: Pseudo dice [0.9659, 0.962, 0.9773, 0.9513, 0.8937, 0.9739, 0.9924] +2024-09-09 23:37:15.332461: Epoch time: 258.63 s +2024-09-09 23:37:17.923904: +2024-09-09 23:37:17.925127: Epoch 885 +2024-09-09 23:37:17.926166: Current learning rate: 0.00143 +2024-09-09 23:41:38.989959: train_loss -0.7625 +2024-09-09 23:41:38.992378: val_loss -0.7836 +2024-09-09 23:41:38.993340: Pseudo dice [0.9668, 0.9594, 0.9776, 0.956, 0.8796, 0.9771, 0.9912] +2024-09-09 23:41:38.994172: Epoch time: 261.07 s +2024-09-09 23:41:42.817740: +2024-09-09 23:41:42.836104: Epoch 886 +2024-09-09 23:41:42.837412: Current learning rate: 0.00142 +2024-09-09 23:46:16.564774: train_loss -0.7549 +2024-09-09 23:46:16.567703: val_loss -0.7699 +2024-09-09 23:46:16.568810: Pseudo dice [0.9683, 0.9616, 0.9827, 0.9487, 0.8799, 0.9748, 0.9914] +2024-09-09 23:46:16.569679: Epoch time: 273.75 s +2024-09-09 23:46:20.174846: +2024-09-09 23:46:20.192123: Epoch 887 +2024-09-09 23:46:20.193361: Current learning rate: 0.00141 +2024-09-09 23:50:50.008979: train_loss -0.7531 +2024-09-09 23:50:50.011364: val_loss -0.7892 +2024-09-09 23:50:50.012219: Pseudo dice [0.9649, 0.9604, 0.9785, 0.9467, 0.9024, 0.9768, 0.9928] +2024-09-09 23:50:50.013033: Epoch time: 269.84 s +2024-09-09 23:50:52.995898: +2024-09-09 23:50:52.997131: Epoch 888 +2024-09-09 23:50:52.997972: Current learning rate: 0.00139 +2024-09-09 23:55:15.581612: train_loss -0.756 +2024-09-09 23:55:15.584492: val_loss -0.7339 +2024-09-09 23:55:15.585668: Pseudo dice [0.9645, 0.961, 0.9757, 0.9496, 0.8799, 0.9704, 0.9899] +2024-09-09 23:55:15.586727: Epoch time: 262.59 s +2024-09-09 23:55:18.309310: +2024-09-09 23:55:18.310447: Epoch 889 +2024-09-09 23:55:18.311654: Current learning rate: 0.00138 +2024-09-09 23:59:39.493484: train_loss -0.7616 +2024-09-09 23:59:39.495796: val_loss -0.7686 +2024-09-09 23:59:39.511369: Pseudo dice [0.9668, 0.9641, 0.9783, 0.9558, 0.8997, 0.9774, 0.9931] +2024-09-09 23:59:39.512215: Epoch time: 261.19 s +2024-09-09 23:59:42.325793: +2024-09-09 23:59:42.336707: Epoch 890 +2024-09-09 23:59:42.337542: Current learning rate: 0.00137 +2024-09-10 00:04:36.244431: train_loss -0.7631 +2024-09-10 00:04:36.262497: val_loss -0.7694 +2024-09-10 00:04:36.263663: Pseudo dice [0.9632, 0.9593, 0.9752, 0.9537, 0.8952, 0.9748, 0.9932] +2024-09-10 00:04:36.264579: Epoch time: 293.92 s +2024-09-10 00:04:39.345528: +2024-09-10 00:04:39.346674: Epoch 891 +2024-09-10 00:04:39.347498: Current learning rate: 0.00136 +2024-09-10 00:09:21.186635: train_loss -0.7652 +2024-09-10 00:09:21.205597: val_loss -0.7676 +2024-09-10 00:09:21.206485: Pseudo dice [0.9655, 0.9582, 0.9786, 0.9541, 0.8983, 0.9779, 0.9929] +2024-09-10 00:09:21.207218: Epoch time: 281.85 s +2024-09-10 00:09:23.541574: +2024-09-10 00:09:23.542692: Epoch 892 +2024-09-10 00:09:23.543535: Current learning rate: 0.00135 +2024-09-10 00:13:50.451681: train_loss -0.752 +2024-09-10 00:13:50.470563: val_loss -0.7666 +2024-09-10 00:13:50.472062: Pseudo dice [0.9649, 0.9622, 0.977, 0.958, 0.8866, 0.9739, 0.9924] +2024-09-10 00:13:50.473453: Epoch time: 266.91 s +2024-09-10 00:13:53.427069: +2024-09-10 00:13:53.428138: Epoch 893 +2024-09-10 00:13:53.428957: Current learning rate: 0.00134 +2024-09-10 00:18:28.944716: train_loss -0.7419 +2024-09-10 00:18:28.974185: val_loss -0.7577 +2024-09-10 00:18:28.975130: Pseudo dice [0.9669, 0.9611, 0.9791, 0.9528, 0.8994, 0.975, 0.9933] +2024-09-10 00:18:28.975895: Epoch time: 275.52 s +2024-09-10 00:18:31.930270: +2024-09-10 00:18:31.940093: Epoch 894 +2024-09-10 00:18:31.941192: Current learning rate: 0.00133 +2024-09-10 00:22:49.883303: train_loss -0.7608 +2024-09-10 00:22:49.899177: val_loss -0.7321 +2024-09-10 00:22:49.900668: Pseudo dice [0.9662, 0.9625, 0.977, 0.9522, 0.8667, 0.9701, 0.9908] +2024-09-10 00:22:49.901748: Epoch time: 257.96 s +2024-09-10 00:22:53.490147: +2024-09-10 00:22:53.491252: Epoch 895 +2024-09-10 00:22:53.492066: Current learning rate: 0.00132 +2024-09-10 00:27:07.626004: train_loss -0.7639 +2024-09-10 00:27:07.628486: val_loss -0.7656 +2024-09-10 00:27:07.629500: Pseudo dice [0.9676, 0.9604, 0.9792, 0.9554, 0.8865, 0.9756, 0.9916] +2024-09-10 00:27:07.630521: Epoch time: 254.15 s +2024-09-10 00:27:10.640936: +2024-09-10 00:27:10.642128: Epoch 896 +2024-09-10 00:27:10.643059: Current learning rate: 0.0013 +2024-09-10 00:31:52.215976: train_loss -0.7747 +2024-09-10 00:31:52.226322: val_loss -0.7524 +2024-09-10 00:31:52.227349: Pseudo dice [0.9653, 0.9593, 0.9779, 0.9483, 0.8911, 0.9724, 0.9917] +2024-09-10 00:31:52.228165: Epoch time: 281.58 s +2024-09-10 00:31:55.438838: +2024-09-10 00:31:55.440139: Epoch 897 +2024-09-10 00:31:55.441205: Current learning rate: 0.00129 +2024-09-10 00:36:39.918911: train_loss -0.7456 +2024-09-10 00:36:39.938004: val_loss -0.7977 +2024-09-10 00:36:39.938817: Pseudo dice [0.968, 0.9611, 0.9809, 0.9557, 0.8973, 0.9749, 0.9918] +2024-09-10 00:36:39.939615: Epoch time: 284.49 s +2024-09-10 00:36:42.765264: +2024-09-10 00:36:42.772974: Epoch 898 +2024-09-10 00:36:42.774068: Current learning rate: 0.00128 +2024-09-10 00:41:26.609962: train_loss -0.7488 +2024-09-10 00:41:26.626394: val_loss -0.7671 +2024-09-10 00:41:26.627555: Pseudo dice [0.965, 0.963, 0.9778, 0.9591, 0.9009, 0.9766, 0.9931] +2024-09-10 00:41:26.628598: Epoch time: 283.85 s +2024-09-10 00:41:30.392169: +2024-09-10 00:41:30.393643: Epoch 899 +2024-09-10 00:41:30.394516: Current learning rate: 0.00127 +2024-09-10 00:46:25.365973: train_loss -0.7647 +2024-09-10 00:46:25.381842: val_loss -0.752 +2024-09-10 00:46:25.384957: Pseudo dice [0.9617, 0.9611, 0.9711, 0.9528, 0.9074, 0.9754, 0.9928] +2024-09-10 00:46:25.388074: Epoch time: 294.98 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth new file mode 100644 index 0000000000000000000000000000000000000000..1e1fde8b5c32a2e25f307e58be7f89374b786376 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/checkpoint_final.pth @@ -0,0 +1,3 @@ +version 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'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 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]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], '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}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', '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': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}}", + "preprocessed_dataset_folder": "/data2/nnUNet_preprocessed/Dataset611_Tissue/nnUNetPlans_3d_fullres", + "preprocessed_dataset_folder_base": "/data2/nnUNet_preprocessed/Dataset611_Tissue", + "save_every": "50", + "torch_version": "2.3.0+cu121", + "unpack_dataset": "False", + "was_initialized": "True", + "weight_decay": "3e-05" +} \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/progress.png b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/progress.png new file mode 100644 index 0000000000000000000000000000000000000000..7cc9047e750b09da9627d23efbcf9af98a263110 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/progress.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52274fd851730067db78601548e4ae1490545a8e2a493288137cc255451f3721 +size 628640 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_00_53_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_00_53_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..279502b80ba6a7e2399f785fde74efd6f5c4f209 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_00_53_30.txt @@ -0,0 +1,730 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 00:53:40.943702: Using torch.compile... +2024-09-10 00:53:52.305659: do_dummy_2d_data_aug: False +2024-09-10 00:53:52.311562: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 00:53:52.313664: The split file contains 5 splits. +2024-09-10 00:53:52.314133: Desired fold for training: 4 +2024-09-10 00:53:52.314647: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 00:54:03.934689: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 00:54:07.246640: +2024-09-10 00:54:07.252854: Epoch 750 +2024-09-10 00:54:07.254326: Current learning rate: 0.00287 +2024-09-10 01:06:45.519304: train_loss -0.7478 +2024-09-10 01:06:45.521305: val_loss -0.7691 +2024-09-10 01:06:45.521973: Pseudo dice [0.9671, 0.9594, 0.9815, 0.9494, 0.884, 0.9744, 0.9923] +2024-09-10 01:06:45.522525: Epoch time: 758.28 s +2024-09-10 01:06:47.187384: +2024-09-10 01:06:47.188060: Epoch 751 +2024-09-10 01:06:47.188563: Current learning rate: 0.00286 +2024-09-10 01:11:26.286798: train_loss -0.7441 +2024-09-10 01:11:26.302561: val_loss -0.7682 +2024-09-10 01:11:26.303894: Pseudo dice [0.9655, 0.9605, 0.979, 0.9522, 0.8889, 0.9742, 0.9918] +2024-09-10 01:11:26.305009: Epoch time: 279.1 s +2024-09-10 01:11:30.394612: +2024-09-10 01:11:30.395506: Epoch 752 +2024-09-10 01:11:30.396992: Current learning rate: 0.00285 +2024-09-10 01:16:27.950800: train_loss -0.7625 +2024-09-10 01:16:27.952775: val_loss -0.7795 +2024-09-10 01:16:27.953376: Pseudo dice [0.9635, 0.959, 0.9778, 0.9537, 0.8939, 0.973, 0.9916] +2024-09-10 01:16:27.953951: Epoch time: 297.56 s +2024-09-10 01:16:30.898982: +2024-09-10 01:16:30.908467: Epoch 753 +2024-09-10 01:16:30.909155: Current learning rate: 0.00284 +2024-09-10 01:21:21.274019: train_loss -0.7412 +2024-09-10 01:21:21.276181: val_loss -0.7415 +2024-09-10 01:21:21.276960: Pseudo dice [0.964, 0.9586, 0.9769, 0.9474, 0.8694, 0.9704, 0.9891] +2024-09-10 01:21:21.277563: Epoch time: 290.38 s +2024-09-10 01:21:24.615135: +2024-09-10 01:21:24.616095: Epoch 754 +2024-09-10 01:21:24.616951: Current learning rate: 0.00283 +2024-09-10 01:25:55.372012: train_loss -0.7623 +2024-09-10 01:25:55.388489: val_loss -0.7557 +2024-09-10 01:25:55.389211: Pseudo dice [0.9657, 0.9593, 0.9799, 0.951, 0.8835, 0.9728, 0.9917] +2024-09-10 01:25:55.389916: Epoch time: 270.76 s +2024-09-10 01:25:58.130069: +2024-09-10 01:25:58.140732: Epoch 755 +2024-09-10 01:25:58.141489: Current learning rate: 0.00282 +2024-09-10 01:30:17.834099: train_loss -0.7433 +2024-09-10 01:30:17.848226: val_loss -0.7075 +2024-09-10 01:30:17.849210: Pseudo dice [0.9645, 0.9605, 0.9761, 0.9537, 0.8762, 0.9716, 0.9902] +2024-09-10 01:30:17.849817: Epoch time: 259.71 s +2024-09-10 01:30:21.950197: +2024-09-10 01:30:21.951252: Epoch 756 +2024-09-10 01:30:21.952043: Current learning rate: 0.00281 +2024-09-10 01:34:52.088898: train_loss -0.7456 +2024-09-10 01:34:52.090907: val_loss -0.7424 +2024-09-10 01:34:52.091522: Pseudo dice [0.9603, 0.9588, 0.9733, 0.9543, 0.8904, 0.9708, 0.9921] +2024-09-10 01:34:52.108194: Epoch time: 270.14 s +2024-09-10 01:34:55.191463: +2024-09-10 01:34:55.193155: Epoch 757 +2024-09-10 01:34:55.194937: Current learning rate: 0.0028 +2024-09-10 01:39:13.817328: train_loss -0.7507 +2024-09-10 01:39:13.835081: val_loss -0.7826 +2024-09-10 01:39:13.839092: Pseudo dice [0.9638, 0.9591, 0.979, 0.9534, 0.8926, 0.974, 0.9921] +2024-09-10 01:39:13.844695: Epoch time: 258.63 s +2024-09-10 01:39:16.068980: +2024-09-10 01:39:16.069948: Epoch 758 +2024-09-10 01:39:16.070595: Current learning rate: 0.00279 +2024-09-10 01:43:58.106114: train_loss -0.755 +2024-09-10 01:43:58.132378: val_loss -0.7944 +2024-09-10 01:43:58.136561: Pseudo dice [0.9667, 0.9577, 0.9798, 0.9548, 0.8887, 0.9751, 0.9924] +2024-09-10 01:43:58.137832: Epoch time: 282.04 s +2024-09-10 01:44:02.097189: +2024-09-10 01:44:02.105722: Epoch 759 +2024-09-10 01:44:02.106793: Current learning rate: 0.00278 +2024-09-10 01:48:28.386124: train_loss -0.7383 +2024-09-10 01:48:28.402033: val_loss -0.7518 +2024-09-10 01:48:28.402751: Pseudo dice [0.9671, 0.9617, 0.9804, 0.9573, 0.8815, 0.9733, 0.9901] +2024-09-10 01:48:28.403415: Epoch time: 266.29 s +2024-09-10 01:48:31.227092: +2024-09-10 01:48:31.248102: Epoch 760 +2024-09-10 01:48:31.249246: Current learning rate: 0.00277 +2024-09-10 01:52:58.303666: train_loss -0.7566 +2024-09-10 01:52:58.306107: val_loss -0.7536 +2024-09-10 01:52:58.306709: Pseudo dice [0.9665, 0.9609, 0.9786, 0.9496, 0.8763, 0.9704, 0.989] +2024-09-10 01:52:58.307309: Epoch time: 267.08 s +2024-09-10 01:53:01.531184: +2024-09-10 01:53:01.532256: Epoch 761 +2024-09-10 01:53:01.532960: Current learning rate: 0.00276 +2024-09-10 01:57:16.920866: train_loss -0.7618 +2024-09-10 01:57:16.924323: val_loss -0.7402 +2024-09-10 01:57:16.928261: Pseudo dice [0.97, 0.9613, 0.9821, 0.9568, 0.8926, 0.9753, 0.9924] +2024-09-10 01:57:16.929033: Epoch time: 255.4 s +2024-09-10 01:57:20.209853: +2024-09-10 01:57:20.210785: Epoch 762 +2024-09-10 01:57:20.211432: Current learning rate: 0.00275 +2024-09-10 02:01:54.472446: train_loss -0.7492 +2024-09-10 02:01:54.474639: val_loss -0.739 +2024-09-10 02:01:54.475394: Pseudo dice [0.9647, 0.9583, 0.9757, 0.9532, 0.8876, 0.9705, 0.9917] +2024-09-10 02:01:54.476113: Epoch time: 274.27 s +2024-09-10 02:01:56.700225: +2024-09-10 02:01:56.701059: Epoch 763 +2024-09-10 02:01:56.701607: Current learning rate: 0.00274 +2024-09-10 02:06:14.335665: train_loss -0.7608 +2024-09-10 02:06:14.344639: val_loss -0.7514 +2024-09-10 02:06:14.345289: Pseudo dice [0.9621, 0.9605, 0.973, 0.9527, 0.8897, 0.9707, 0.9919] +2024-09-10 02:06:14.345918: Epoch time: 257.64 s +2024-09-10 02:06:17.382644: +2024-09-10 02:06:17.383391: Epoch 764 +2024-09-10 02:06:17.383917: Current learning rate: 0.00273 +2024-09-10 02:10:54.723821: train_loss -0.7439 +2024-09-10 02:10:54.737892: val_loss -0.7337 +2024-09-10 02:10:54.738605: Pseudo dice [0.9666, 0.9599, 0.9799, 0.9532, 0.8695, 0.972, 0.9897] +2024-09-10 02:10:54.739183: Epoch time: 277.35 s +2024-09-10 02:10:57.288056: +2024-09-10 02:10:57.289584: Epoch 765 +2024-09-10 02:10:57.290904: Current learning rate: 0.00272 +2024-09-10 02:15:49.357624: train_loss -0.7738 +2024-09-10 02:15:49.373899: val_loss -0.7459 +2024-09-10 02:15:49.374494: Pseudo dice [0.9677, 0.9609, 0.9811, 0.9476, 0.8757, 0.9717, 0.9913] +2024-09-10 02:15:49.375435: Epoch time: 292.08 s +2024-09-10 02:15:53.813496: +2024-09-10 02:15:53.814449: Epoch 766 +2024-09-10 02:15:53.815049: Current learning rate: 0.00271 +2024-09-10 02:20:15.910524: train_loss -0.7475 +2024-09-10 02:20:15.933715: val_loss -0.7017 +2024-09-10 02:20:15.934468: Pseudo dice [0.965, 0.9612, 0.9764, 0.9516, 0.8754, 0.9727, 0.9901] +2024-09-10 02:20:15.935065: Epoch time: 262.1 s +2024-09-10 02:20:19.527097: +2024-09-10 02:20:19.528076: Epoch 767 +2024-09-10 02:20:19.529144: Current learning rate: 0.0027 +2024-09-10 02:24:42.011856: train_loss -0.7517 +2024-09-10 02:24:42.013969: val_loss -0.7753 +2024-09-10 02:24:42.014637: Pseudo dice [0.9684, 0.9621, 0.9814, 0.9553, 0.8811, 0.9746, 0.9904] +2024-09-10 02:24:42.015310: Epoch time: 262.49 s +2024-09-10 02:24:46.055687: +2024-09-10 02:24:46.065717: Epoch 768 +2024-09-10 02:24:46.067352: Current learning rate: 0.00268 +2024-09-10 02:29:46.415774: train_loss -0.7439 +2024-09-10 02:29:46.429212: val_loss -0.7509 +2024-09-10 02:29:46.429925: Pseudo dice [0.9665, 0.9585, 0.9798, 0.9534, 0.8842, 0.9728, 0.9913] +2024-09-10 02:29:46.430506: Epoch time: 300.37 s +2024-09-10 02:29:49.759403: +2024-09-10 02:29:49.760372: Epoch 769 +2024-09-10 02:29:49.761000: Current learning rate: 0.00267 +2024-09-10 02:34:20.235434: train_loss -0.7547 +2024-09-10 02:34:20.249751: val_loss -0.7457 +2024-09-10 02:34:20.251059: Pseudo dice [0.9643, 0.9612, 0.9782, 0.9508, 0.8814, 0.9727, 0.9915] +2024-09-10 02:34:20.252405: Epoch time: 270.48 s +2024-09-10 02:34:23.515722: +2024-09-10 02:34:23.528350: Epoch 770 +2024-09-10 02:34:23.529122: Current learning rate: 0.00266 +2024-09-10 02:39:05.635377: train_loss -0.768 +2024-09-10 02:39:05.644911: val_loss -0.784 +2024-09-10 02:39:05.645794: Pseudo dice [0.9669, 0.9607, 0.977, 0.9507, 0.8792, 0.9736, 0.9915] +2024-09-10 02:39:05.646559: Epoch time: 282.12 s +2024-09-10 02:39:08.441399: +2024-09-10 02:39:08.442618: Epoch 771 +2024-09-10 02:39:08.443419: Current learning rate: 0.00265 +2024-09-10 02:43:40.970847: train_loss -0.7474 +2024-09-10 02:43:40.996187: val_loss -0.7514 +2024-09-10 02:43:40.997140: Pseudo dice [0.9667, 0.9588, 0.9762, 0.9522, 0.8764, 0.9722, 0.9922] +2024-09-10 02:43:40.997810: Epoch time: 272.53 s +2024-09-10 02:43:43.351728: +2024-09-10 02:43:43.353162: Epoch 772 +2024-09-10 02:43:43.354066: Current learning rate: 0.00264 +2024-09-10 02:48:17.566047: train_loss -0.7492 +2024-09-10 02:48:17.581064: val_loss -0.7358 +2024-09-10 02:48:17.581717: Pseudo dice [0.9659, 0.9597, 0.9784, 0.9476, 0.8806, 0.975, 0.9919] +2024-09-10 02:48:17.582260: Epoch time: 274.22 s +2024-09-10 02:48:22.193829: +2024-09-10 02:48:22.208775: Epoch 773 +2024-09-10 02:48:22.209537: Current learning rate: 0.00263 +2024-09-10 02:53:05.257672: train_loss -0.7703 +2024-09-10 02:53:05.274489: val_loss -0.7343 +2024-09-10 02:53:05.275661: Pseudo dice [0.9639, 0.9611, 0.9768, 0.9472, 0.8807, 0.9729, 0.9913] +2024-09-10 02:53:05.276886: Epoch time: 283.07 s +2024-09-10 02:53:08.246222: +2024-09-10 02:53:08.247079: Epoch 774 +2024-09-10 02:53:08.247634: Current learning rate: 0.00262 +2024-09-10 02:57:36.951064: train_loss -0.7688 +2024-09-10 02:57:36.956042: val_loss -0.7877 +2024-09-10 02:57:36.956738: Pseudo dice [0.9692, 0.9625, 0.9817, 0.9555, 0.8901, 0.9754, 0.9923] +2024-09-10 02:57:36.957434: Epoch time: 268.71 s +2024-09-10 02:57:40.921242: +2024-09-10 02:57:40.930052: Epoch 775 +2024-09-10 02:57:40.930750: Current learning rate: 0.00261 +2024-09-10 03:02:03.706365: train_loss -0.7555 +2024-09-10 03:02:03.708467: val_loss -0.766 +2024-09-10 03:02:03.709141: Pseudo dice [0.9686, 0.9617, 0.9816, 0.9542, 0.8972, 0.9736, 0.993] +2024-09-10 03:02:03.709750: Epoch time: 262.79 s +2024-09-10 03:02:07.277448: +2024-09-10 03:02:07.278304: Epoch 776 +2024-09-10 03:02:07.279018: Current learning rate: 0.0026 +2024-09-10 03:06:34.637501: train_loss -0.7443 +2024-09-10 03:06:34.653693: val_loss -0.761 +2024-09-10 03:06:34.654378: Pseudo dice [0.9655, 0.9621, 0.9764, 0.9554, 0.8902, 0.9752, 0.9914] +2024-09-10 03:06:34.655005: Epoch time: 267.36 s +2024-09-10 03:06:38.473145: +2024-09-10 03:06:38.474101: Epoch 777 +2024-09-10 03:06:38.474742: Current learning rate: 0.00259 +2024-09-10 03:11:19.458625: train_loss -0.7572 +2024-09-10 03:11:19.473655: val_loss -0.795 +2024-09-10 03:11:19.474366: Pseudo dice [0.9672, 0.9606, 0.9811, 0.9546, 0.889, 0.9747, 0.9905] +2024-09-10 03:11:19.474980: Epoch time: 280.99 s +2024-09-10 03:11:24.321293: +2024-09-10 03:11:24.322426: Epoch 778 +2024-09-10 03:11:24.323175: Current learning rate: 0.00258 +2024-09-10 03:16:04.737991: train_loss -0.7587 +2024-09-10 03:16:04.758050: val_loss -0.7797 +2024-09-10 03:16:04.758883: Pseudo dice [0.9672, 0.9625, 0.9811, 0.9509, 0.8963, 0.9742, 0.9934] +2024-09-10 03:16:04.759578: Epoch time: 280.42 s +2024-09-10 03:16:04.760190: Yayy! New best EMA pseudo Dice: 0.9585 +2024-09-10 03:16:12.018608: +2024-09-10 03:16:12.019379: Epoch 779 +2024-09-10 03:16:12.019965: Current learning rate: 0.00257 +2024-09-10 03:21:07.046625: train_loss -0.7553 +2024-09-10 03:21:07.060711: val_loss -0.7387 +2024-09-10 03:21:07.061478: Pseudo dice [0.9635, 0.9604, 0.9757, 0.9531, 0.8929, 0.9726, 0.9917] +2024-09-10 03:21:07.062134: Epoch time: 295.03 s +2024-09-10 03:21:07.062716: Yayy! New best EMA pseudo Dice: 0.9585 +2024-09-10 03:21:13.075191: +2024-09-10 03:21:13.076059: Epoch 780 +2024-09-10 03:21:13.076742: Current learning rate: 0.00256 +2024-09-10 03:25:39.375178: train_loss -0.7537 +2024-09-10 03:25:39.397806: val_loss -0.7804 +2024-09-10 03:25:39.398546: Pseudo dice [0.966, 0.9603, 0.9784, 0.9534, 0.8834, 0.9722, 0.9914] +2024-09-10 03:25:39.399124: Epoch time: 266.3 s +2024-09-10 03:25:43.378358: +2024-09-10 03:25:43.379479: Epoch 781 +2024-09-10 03:25:43.380434: Current learning rate: 0.00255 +2024-09-10 03:30:23.209749: train_loss -0.7544 +2024-09-10 03:30:23.227033: val_loss -0.7355 +2024-09-10 03:30:23.227921: Pseudo dice [0.9659, 0.9591, 0.9804, 0.9522, 0.8778, 0.9743, 0.9919] +2024-09-10 03:30:23.229478: Epoch time: 279.84 s +2024-09-10 03:30:26.592725: +2024-09-10 03:30:26.593528: Epoch 782 +2024-09-10 03:30:26.594209: Current learning rate: 0.00254 +2024-09-10 03:35:08.114816: train_loss -0.7492 +2024-09-10 03:35:08.138072: val_loss -0.7429 +2024-09-10 03:35:08.139236: Pseudo dice [0.9684, 0.9639, 0.9803, 0.9574, 0.8939, 0.9755, 0.9927] +2024-09-10 03:35:08.140145: Epoch time: 281.53 s +2024-09-10 03:35:08.140790: Yayy! New best EMA pseudo Dice: 0.9587 +2024-09-10 03:35:14.765001: +2024-09-10 03:35:14.768102: Epoch 783 +2024-09-10 03:35:14.771485: Current learning rate: 0.00253 +2024-09-10 03:39:39.793137: train_loss -0.7404 +2024-09-10 03:39:39.810018: val_loss -0.7599 +2024-09-10 03:39:39.810772: Pseudo dice [0.9698, 0.962, 0.9809, 0.9545, 0.8897, 0.9741, 0.9929] +2024-09-10 03:39:39.811396: Epoch time: 265.03 s +2024-09-10 03:39:39.811961: Yayy! New best EMA pseudo Dice: 0.9589 +2024-09-10 03:39:47.038182: +2024-09-10 03:39:47.039060: Epoch 784 +2024-09-10 03:39:47.039703: Current learning rate: 0.00252 +2024-09-10 03:44:26.613945: train_loss -0.7577 +2024-09-10 03:44:26.623867: val_loss -0.7517 +2024-09-10 03:44:26.624682: Pseudo dice [0.9647, 0.9595, 0.9752, 0.9544, 0.8879, 0.9719, 0.992] +2024-09-10 03:44:26.625846: Epoch time: 279.58 s +2024-09-10 03:44:29.597083: +2024-09-10 03:44:29.608694: Epoch 785 +2024-09-10 03:44:29.609260: Current learning rate: 0.00251 +2024-09-10 03:49:08.972604: train_loss -0.757 +2024-09-10 03:49:08.973886: val_loss -0.7835 +2024-09-10 03:49:08.974592: Pseudo dice [0.965, 0.9582, 0.9774, 0.9547, 0.8942, 0.972, 0.991] +2024-09-10 03:49:08.975194: Epoch time: 279.38 s +2024-09-10 03:49:13.304306: +2024-09-10 03:49:13.305238: Epoch 786 +2024-09-10 03:49:13.305887: Current learning rate: 0.0025 +2024-09-10 03:53:58.679585: train_loss -0.7675 +2024-09-10 03:53:58.680933: val_loss -0.7474 +2024-09-10 03:53:58.681861: Pseudo dice [0.9682, 0.9629, 0.9818, 0.9558, 0.8967, 0.9744, 0.9912] +2024-09-10 03:53:58.682632: Epoch time: 285.39 s +2024-09-10 03:53:58.683204: Yayy! New best EMA pseudo Dice: 0.9591 +2024-09-10 03:54:05.335188: +2024-09-10 03:54:05.336578: Epoch 787 +2024-09-10 03:54:05.337496: Current learning rate: 0.00249 +2024-09-10 03:58:29.424630: train_loss -0.7548 +2024-09-10 03:58:29.441611: val_loss -0.7489 +2024-09-10 03:58:29.442451: Pseudo dice [0.9683, 0.9601, 0.9788, 0.9533, 0.884, 0.9719, 0.9925] +2024-09-10 03:58:29.443146: Epoch time: 264.1 s +2024-09-10 03:58:32.119442: +2024-09-10 03:58:32.120783: Epoch 788 +2024-09-10 03:58:32.121860: Current learning rate: 0.00248 +2024-09-10 04:03:09.870029: train_loss -0.7401 +2024-09-10 04:03:09.885830: val_loss -0.7146 +2024-09-10 04:03:09.886564: Pseudo dice [0.9633, 0.9579, 0.9753, 0.9367, 0.8786, 0.9725, 0.9902] +2024-09-10 04:03:09.887138: Epoch time: 277.76 s +2024-09-10 04:03:14.941772: +2024-09-10 04:03:14.943165: Epoch 789 +2024-09-10 04:03:14.943867: Current learning rate: 0.00247 +2024-09-10 04:07:53.871953: train_loss -0.7406 +2024-09-10 04:07:53.890228: val_loss -0.7271 +2024-09-10 04:07:53.891052: Pseudo dice [0.9656, 0.957, 0.9804, 0.9469, 0.8778, 0.9723, 0.9928] +2024-09-10 04:07:53.892136: Epoch time: 278.94 s +2024-09-10 04:07:57.579293: +2024-09-10 04:07:57.580643: Epoch 790 +2024-09-10 04:07:57.581628: Current learning rate: 0.00245 +2024-09-10 04:12:42.228540: train_loss -0.7597 +2024-09-10 04:12:42.245873: val_loss -0.7721 +2024-09-10 04:12:42.246471: Pseudo dice [0.965, 0.9555, 0.9774, 0.9443, 0.8845, 0.9736, 0.9916] +2024-09-10 04:12:42.246976: Epoch time: 284.66 s +2024-09-10 04:12:45.684938: +2024-09-10 04:12:45.696734: Epoch 791 +2024-09-10 04:12:45.697455: Current learning rate: 0.00244 +2024-09-10 04:16:58.245255: train_loss -0.7631 +2024-09-10 04:16:58.247635: val_loss -0.7482 +2024-09-10 04:16:58.248662: Pseudo dice [0.9614, 0.9592, 0.9741, 0.9492, 0.8892, 0.9739, 0.9911] +2024-09-10 04:16:58.249686: Epoch time: 252.56 s +2024-09-10 04:17:01.697601: +2024-09-10 04:17:01.699199: Epoch 792 +2024-09-10 04:17:01.700008: Current learning rate: 0.00243 +2024-09-10 04:21:35.006184: train_loss -0.7428 +2024-09-10 04:21:35.029766: val_loss -0.7784 +2024-09-10 04:21:35.030596: Pseudo dice [0.9634, 0.9585, 0.9743, 0.9478, 0.8898, 0.9737, 0.9928] +2024-09-10 04:21:35.031302: Epoch time: 273.31 s +2024-09-10 04:21:38.461416: +2024-09-10 04:21:38.480112: Epoch 793 +2024-09-10 04:21:38.481184: Current learning rate: 0.00242 +2024-09-10 04:26:13.965462: train_loss -0.73 +2024-09-10 04:26:13.967610: val_loss -0.7799 +2024-09-10 04:26:13.968729: Pseudo dice [0.9656, 0.9576, 0.9761, 0.9544, 0.8809, 0.9742, 0.991] +2024-09-10 04:26:13.969707: Epoch time: 275.51 s +2024-09-10 04:26:16.607100: +2024-09-10 04:26:16.607894: Epoch 794 +2024-09-10 04:26:16.608559: Current learning rate: 0.00241 +2024-09-10 04:30:57.278811: train_loss -0.748 +2024-09-10 04:30:57.295739: val_loss -0.7561 +2024-09-10 04:30:57.297584: Pseudo dice [0.9638, 0.9586, 0.9753, 0.9501, 0.8856, 0.9759, 0.9904] +2024-09-10 04:30:57.298924: Epoch time: 280.68 s +2024-09-10 04:31:00.538732: +2024-09-10 04:31:00.539618: Epoch 795 +2024-09-10 04:31:00.540671: Current learning rate: 0.0024 +2024-09-10 04:35:41.130736: train_loss -0.7564 +2024-09-10 04:35:41.133112: val_loss -0.7798 +2024-09-10 04:35:41.136265: Pseudo dice [0.9686, 0.9604, 0.9796, 0.9537, 0.8966, 0.974, 0.9934] +2024-09-10 04:35:41.137017: Epoch time: 280.6 s +2024-09-10 04:35:45.105140: +2024-09-10 04:35:45.106007: Epoch 796 +2024-09-10 04:35:45.106606: Current learning rate: 0.00239 +2024-09-10 04:39:59.377061: train_loss -0.7375 +2024-09-10 04:39:59.379669: val_loss -0.766 +2024-09-10 04:39:59.380481: Pseudo dice [0.9669, 0.9606, 0.9784, 0.9525, 0.8862, 0.9746, 0.9914] +2024-09-10 04:39:59.381166: Epoch time: 254.28 s +2024-09-10 04:40:02.365265: +2024-09-10 04:40:02.377552: Epoch 797 +2024-09-10 04:40:02.378328: Current learning rate: 0.00238 +2024-09-10 04:44:31.764240: train_loss -0.771 +2024-09-10 04:44:31.766386: val_loss -0.7549 +2024-09-10 04:44:31.767100: Pseudo dice [0.9678, 0.9617, 0.9818, 0.9492, 0.8911, 0.9735, 0.993] +2024-09-10 04:44:31.767784: Epoch time: 269.4 s +2024-09-10 04:44:34.726877: +2024-09-10 04:44:34.727723: Epoch 798 +2024-09-10 04:44:34.728312: Current learning rate: 0.00237 +2024-09-10 04:49:01.930004: train_loss -0.7585 +2024-09-10 04:49:01.932857: val_loss -0.7602 +2024-09-10 04:49:01.933736: Pseudo dice [0.9659, 0.9606, 0.9777, 0.9572, 0.8902, 0.9735, 0.9916] +2024-09-10 04:49:01.934587: Epoch time: 267.21 s +2024-09-10 04:49:05.530216: +2024-09-10 04:49:05.531135: Epoch 799 +2024-09-10 04:49:05.531776: Current learning rate: 0.00236 +2024-09-10 04:53:30.850634: train_loss -0.76 +2024-09-10 04:53:30.853012: val_loss -0.7365 +2024-09-10 04:53:30.853675: Pseudo dice [0.9655, 0.9594, 0.9794, 0.9548, 0.8794, 0.9749, 0.9902] +2024-09-10 04:53:30.854337: Epoch time: 265.33 s +2024-09-10 04:53:38.517517: +2024-09-10 04:53:38.518457: Epoch 800 +2024-09-10 04:53:38.519118: Current learning rate: 0.00235 +2024-09-10 04:58:00.599574: train_loss -0.7618 +2024-09-10 04:58:00.613847: val_loss -0.765 +2024-09-10 04:58:00.614596: Pseudo dice [0.9659, 0.9578, 0.9787, 0.9583, 0.8869, 0.976, 0.9923] +2024-09-10 04:58:00.615232: Epoch time: 262.09 s +2024-09-10 04:58:04.043114: +2024-09-10 04:58:04.056118: Epoch 801 +2024-09-10 04:58:04.057030: Current learning rate: 0.00234 +2024-09-10 05:02:33.701188: train_loss -0.7604 +2024-09-10 05:02:33.726355: val_loss -0.7781 +2024-09-10 05:02:33.727426: Pseudo dice [0.9622, 0.9588, 0.9754, 0.946, 0.8781, 0.9732, 0.9904] +2024-09-10 05:02:33.728310: Epoch time: 269.66 s +2024-09-10 05:02:37.173486: +2024-09-10 05:02:37.174437: Epoch 802 +2024-09-10 05:02:37.175037: Current learning rate: 0.00233 +2024-09-10 05:07:32.147740: train_loss -0.7558 +2024-09-10 05:07:32.181494: val_loss -0.7576 +2024-09-10 05:07:32.182335: Pseudo dice [0.9661, 0.9595, 0.9773, 0.9544, 0.8922, 0.9749, 0.9927] +2024-09-10 05:07:32.183137: Epoch time: 294.98 s +2024-09-10 05:07:36.003797: +2024-09-10 05:07:36.016758: Epoch 803 +2024-09-10 05:07:36.017594: Current learning rate: 0.00232 +2024-09-10 05:11:55.021273: train_loss -0.7409 +2024-09-10 05:11:55.023110: val_loss -0.7598 +2024-09-10 05:11:55.023731: Pseudo dice [0.9619, 0.9595, 0.9759, 0.9487, 0.8782, 0.9739, 0.9919] +2024-09-10 05:11:55.024196: Epoch time: 259.02 s +2024-09-10 05:11:57.783079: +2024-09-10 05:11:57.785042: Epoch 804 +2024-09-10 05:11:57.785801: Current learning rate: 0.00231 +2024-09-10 05:16:27.099173: train_loss -0.7585 +2024-09-10 05:16:27.113204: val_loss -0.7638 +2024-09-10 05:16:27.113973: Pseudo dice [0.9659, 0.9608, 0.9796, 0.954, 0.8961, 0.9744, 0.9925] +2024-09-10 05:16:27.114611: Epoch time: 269.32 s +2024-09-10 05:16:30.242874: +2024-09-10 05:16:30.256811: Epoch 805 +2024-09-10 05:16:30.257760: Current learning rate: 0.0023 +2024-09-10 05:20:40.471926: train_loss -0.7594 +2024-09-10 05:20:40.486111: val_loss -0.7744 +2024-09-10 05:20:40.486906: Pseudo dice [0.9647, 0.9622, 0.9795, 0.9499, 0.8902, 0.9727, 0.9913] +2024-09-10 05:20:40.487749: Epoch time: 250.23 s +2024-09-10 05:20:44.624589: +2024-09-10 05:20:44.636622: Epoch 806 +2024-09-10 05:20:44.637316: Current learning rate: 0.00229 +2024-09-10 05:25:19.385474: train_loss -0.7654 +2024-09-10 05:25:19.391839: val_loss -0.7571 +2024-09-10 05:25:19.392828: Pseudo dice [0.9622, 0.9597, 0.9735, 0.9539, 0.8811, 0.975, 0.9913] +2024-09-10 05:25:19.393981: Epoch time: 274.77 s +2024-09-10 05:25:23.665970: +2024-09-10 05:25:23.666826: Epoch 807 +2024-09-10 05:25:23.667497: Current learning rate: 0.00228 +2024-09-10 05:29:53.718723: train_loss -0.7481 +2024-09-10 05:29:53.720901: val_loss -0.7598 +2024-09-10 05:29:53.722136: Pseudo dice [0.9667, 0.9647, 0.9797, 0.9553, 0.8958, 0.9755, 0.9925] +2024-09-10 05:29:53.723044: Epoch time: 270.06 s +2024-09-10 05:29:56.566389: +2024-09-10 05:29:56.576691: Epoch 808 +2024-09-10 05:29:56.592241: Current learning rate: 0.00226 +2024-09-10 05:34:31.989383: train_loss -0.7433 +2024-09-10 05:34:32.002981: val_loss -0.7506 +2024-09-10 05:34:32.003743: Pseudo dice [0.9693, 0.9638, 0.9797, 0.9519, 0.8922, 0.9759, 0.9925] +2024-09-10 05:34:32.004402: Epoch time: 275.43 s +2024-09-10 05:34:36.248542: +2024-09-10 05:34:36.249674: Epoch 809 +2024-09-10 05:34:36.250493: Current learning rate: 0.00225 +2024-09-10 05:38:53.289817: train_loss -0.757 +2024-09-10 05:38:53.302210: val_loss -0.7705 +2024-09-10 05:38:53.303163: Pseudo dice [0.9641, 0.9613, 0.976, 0.9605, 0.8844, 0.9747, 0.9911] +2024-09-10 05:38:53.303988: Epoch time: 257.05 s +2024-09-10 05:38:55.875616: +2024-09-10 05:38:55.876536: Epoch 810 +2024-09-10 05:38:55.877137: Current learning rate: 0.00224 +2024-09-10 05:43:33.499122: train_loss -0.7609 +2024-09-10 05:43:33.513688: val_loss -0.7545 +2024-09-10 05:43:33.515256: Pseudo dice [0.9686, 0.9622, 0.9821, 0.9545, 0.8855, 0.9741, 0.9902] +2024-09-10 05:43:33.517791: Epoch time: 277.63 s +2024-09-10 05:43:36.852524: +2024-09-10 05:43:36.868675: Epoch 811 +2024-09-10 05:43:36.869425: Current learning rate: 0.00223 +2024-09-10 05:48:12.996867: train_loss -0.7619 +2024-09-10 05:48:12.998208: val_loss -0.7644 +2024-09-10 05:48:12.999106: Pseudo dice [0.9662, 0.9612, 0.9782, 0.9589, 0.882, 0.9736, 0.9913] +2024-09-10 05:48:12.999878: Epoch time: 276.15 s +2024-09-10 05:48:16.265205: +2024-09-10 05:48:16.266423: Epoch 812 +2024-09-10 05:48:16.267325: Current learning rate: 0.00222 +2024-09-10 05:52:57.838181: train_loss -0.7592 +2024-09-10 05:52:57.854037: val_loss -0.7762 +2024-09-10 05:52:57.855646: Pseudo dice [0.9683, 0.9599, 0.9819, 0.9527, 0.8835, 0.9757, 0.9911] +2024-09-10 05:52:57.857008: Epoch time: 281.58 s +2024-09-10 05:53:02.776626: +2024-09-10 05:53:02.777785: Epoch 813 +2024-09-10 05:53:02.778566: Current learning rate: 0.00221 +2024-09-10 05:57:33.158924: train_loss -0.7579 +2024-09-10 05:57:33.176171: val_loss -0.7807 +2024-09-10 05:57:33.177120: Pseudo dice [0.9679, 0.9614, 0.9802, 0.9587, 0.8898, 0.9749, 0.9899] +2024-09-10 05:57:33.177758: Epoch time: 270.39 s +2024-09-10 05:57:35.421236: +2024-09-10 05:57:35.422581: Epoch 814 +2024-09-10 05:57:35.423376: Current learning rate: 0.0022 +2024-09-10 06:02:11.541062: train_loss -0.7601 +2024-09-10 06:02:11.543206: val_loss -0.7773 +2024-09-10 06:02:11.543936: Pseudo dice [0.9674, 0.9629, 0.9793, 0.9574, 0.9013, 0.975, 0.9935] +2024-09-10 06:02:11.544585: Epoch time: 276.13 s +2024-09-10 06:02:11.545116: Yayy! New best EMA pseudo Dice: 0.9593 +2024-09-10 06:02:18.475553: +2024-09-10 06:02:18.476401: Epoch 815 +2024-09-10 06:02:18.477076: Current learning rate: 0.00219 +2024-09-10 06:06:40.022482: train_loss -0.7463 +2024-09-10 06:06:40.050822: val_loss -0.7663 +2024-09-10 06:06:40.051703: Pseudo dice [0.9681, 0.9612, 0.9791, 0.9558, 0.8776, 0.9734, 0.99] +2024-09-10 06:06:40.052589: Epoch time: 261.55 s +2024-09-10 06:06:43.439033: +2024-09-10 06:06:43.439899: Epoch 816 +2024-09-10 06:06:43.440586: Current learning rate: 0.00218 +2024-09-10 06:11:13.231237: train_loss -0.7624 +2024-09-10 06:11:13.234243: val_loss -0.7607 +2024-09-10 06:11:13.235070: Pseudo dice [0.9677, 0.9628, 0.9781, 0.9528, 0.8923, 0.9764, 0.9928] +2024-09-10 06:11:13.235747: Epoch time: 269.8 s +2024-09-10 06:11:15.762427: +2024-09-10 06:11:15.763562: Epoch 817 +2024-09-10 06:11:15.764315: Current learning rate: 0.00217 +2024-09-10 06:16:02.423414: train_loss -0.7567 +2024-09-10 06:16:02.437259: val_loss -0.7719 +2024-09-10 06:16:02.438942: Pseudo dice [0.9669, 0.9613, 0.9809, 0.952, 0.8888, 0.9758, 0.9921] +2024-09-10 06:16:02.440538: Epoch time: 286.67 s +2024-09-10 06:16:02.445565: Yayy! New best EMA pseudo Dice: 0.9593 +2024-09-10 06:16:09.280662: +2024-09-10 06:16:09.281490: Epoch 818 +2024-09-10 06:16:09.282197: Current learning rate: 0.00216 +2024-09-10 06:20:36.028023: train_loss -0.739 +2024-09-10 06:20:36.046283: val_loss -0.7493 +2024-09-10 06:20:36.047119: Pseudo dice [0.9647, 0.9613, 0.9774, 0.9512, 0.8944, 0.9762, 0.9932] +2024-09-10 06:20:36.047778: Epoch time: 266.75 s +2024-09-10 06:20:36.048526: Yayy! New best EMA pseudo Dice: 0.9594 +2024-09-10 06:20:43.582325: +2024-09-10 06:20:43.583580: Epoch 819 +2024-09-10 06:20:43.584082: Current learning rate: 0.00215 +2024-09-10 06:25:27.796711: train_loss -0.7642 +2024-09-10 06:25:27.809895: val_loss -0.7531 +2024-09-10 06:25:27.810595: Pseudo dice [0.9657, 0.9614, 0.9785, 0.9536, 0.8959, 0.9761, 0.992] +2024-09-10 06:25:27.811206: Epoch time: 284.22 s +2024-09-10 06:25:27.811745: Yayy! New best EMA pseudo Dice: 0.9595 +2024-09-10 06:25:34.770982: +2024-09-10 06:25:34.771858: Epoch 820 +2024-09-10 06:25:34.772505: Current learning rate: 0.00214 +2024-09-10 06:29:54.126863: train_loss -0.756 +2024-09-10 06:29:54.138693: val_loss -0.7442 +2024-09-10 06:29:54.139876: Pseudo dice [0.966, 0.9617, 0.977, 0.9505, 0.8884, 0.9736, 0.9907] +2024-09-10 06:29:54.140788: Epoch time: 259.36 s +2024-09-10 06:29:57.537215: +2024-09-10 06:29:57.538114: Epoch 821 +2024-09-10 06:29:57.538691: Current learning rate: 0.00213 +2024-09-10 06:34:30.886256: train_loss -0.7545 +2024-09-10 06:34:30.901870: val_loss -0.7506 +2024-09-10 06:34:30.902657: Pseudo dice [0.9655, 0.9591, 0.9795, 0.949, 0.8852, 0.9748, 0.9913] +2024-09-10 06:34:30.903344: Epoch time: 273.35 s +2024-09-10 06:34:33.703069: +2024-09-10 06:34:33.715311: Epoch 822 +2024-09-10 06:34:33.716254: Current learning rate: 0.00212 +2024-09-10 06:39:15.954381: train_loss -0.7353 +2024-09-10 06:39:15.958751: val_loss -0.7549 +2024-09-10 06:39:15.959691: Pseudo dice [0.9634, 0.9561, 0.9754, 0.9529, 0.8798, 0.9695, 0.9901] +2024-09-10 06:39:15.961500: Epoch time: 282.26 s +2024-09-10 06:39:19.817093: +2024-09-10 06:39:19.818152: Epoch 823 +2024-09-10 06:39:19.818888: Current learning rate: 0.0021 +2024-09-10 06:43:54.760879: train_loss -0.7438 +2024-09-10 06:43:54.778070: val_loss -0.7766 +2024-09-10 06:43:54.778819: Pseudo dice [0.9667, 0.9631, 0.9766, 0.9574, 0.8883, 0.9758, 0.992] +2024-09-10 06:43:54.779404: Epoch time: 274.95 s +2024-09-10 06:43:57.085690: +2024-09-10 06:43:57.099147: Epoch 824 +2024-09-10 06:43:57.100113: Current learning rate: 0.00209 +2024-09-10 06:48:35.246105: train_loss -0.7457 +2024-09-10 06:48:35.252856: val_loss -0.7808 +2024-09-10 06:48:35.253620: Pseudo dice [0.9696, 0.9602, 0.983, 0.9547, 0.8884, 0.9739, 0.9917] +2024-09-10 06:48:35.254238: Epoch time: 278.16 s +2024-09-10 06:48:37.867489: +2024-09-10 06:48:37.868713: Epoch 825 +2024-09-10 06:48:37.869390: Current learning rate: 0.00208 +2024-09-10 06:52:47.305068: train_loss -0.749 +2024-09-10 06:52:47.329592: val_loss -0.7624 +2024-09-10 06:52:47.336837: Pseudo dice [0.9641, 0.9617, 0.9757, 0.9547, 0.892, 0.973, 0.9897] +2024-09-10 06:52:47.337502: Epoch time: 249.44 s +2024-09-10 06:52:51.471421: +2024-09-10 06:52:51.472437: Epoch 826 +2024-09-10 06:52:51.473182: Current learning rate: 0.00207 +2024-09-10 06:57:26.726548: train_loss -0.7502 +2024-09-10 06:57:26.733109: val_loss -0.7741 +2024-09-10 06:57:26.733793: Pseudo dice [0.9657, 0.9581, 0.9791, 0.944, 0.8782, 0.9757, 0.9903] +2024-09-10 06:57:26.734428: Epoch time: 275.26 s +2024-09-10 06:57:29.600053: +2024-09-10 06:57:29.611145: Epoch 827 +2024-09-10 06:57:29.611864: Current learning rate: 0.00206 +2024-09-10 07:02:06.664228: train_loss -0.7474 +2024-09-10 07:02:06.678094: val_loss -0.7408 +2024-09-10 07:02:06.678789: Pseudo dice [0.968, 0.9623, 0.9802, 0.9499, 0.8985, 0.975, 0.9921] +2024-09-10 07:02:06.679410: Epoch time: 277.07 s +2024-09-10 07:02:09.858672: +2024-09-10 07:02:09.860369: Epoch 828 +2024-09-10 07:02:09.861211: Current learning rate: 0.00205 +2024-09-10 07:07:08.794255: train_loss -0.7608 +2024-09-10 07:07:08.809840: val_loss -0.7658 +2024-09-10 07:07:08.810587: Pseudo dice [0.9658, 0.96, 0.9788, 0.957, 0.8953, 0.9755, 0.9927] +2024-09-10 07:07:08.811272: Epoch time: 298.94 s +2024-09-10 07:07:12.212805: +2024-09-10 07:07:12.224784: Epoch 829 +2024-09-10 07:07:12.225513: Current learning rate: 0.00204 +2024-09-10 07:11:40.543956: train_loss -0.7516 +2024-09-10 07:11:40.557811: val_loss -0.7458 +2024-09-10 07:11:40.558428: Pseudo dice [0.9677, 0.96, 0.9816, 0.9514, 0.8726, 0.9754, 0.99] +2024-09-10 07:11:40.559035: Epoch time: 268.34 s +2024-09-10 07:11:43.677839: +2024-09-10 07:11:43.692710: Epoch 830 +2024-09-10 07:11:43.693516: Current learning rate: 0.00203 +2024-09-10 07:16:19.509031: train_loss -0.7529 +2024-09-10 07:16:19.541837: val_loss -0.7483 +2024-09-10 07:16:19.542937: Pseudo dice [0.9687, 0.9622, 0.9831, 0.9564, 0.8979, 0.9739, 0.992] +2024-09-10 07:16:19.543640: Epoch time: 275.84 s +2024-09-10 07:16:21.923589: +2024-09-10 07:16:21.936828: Epoch 831 +2024-09-10 07:16:21.937557: Current learning rate: 0.00202 +2024-09-10 07:20:54.285000: train_loss -0.7668 +2024-09-10 07:20:54.287187: val_loss -0.7672 +2024-09-10 07:20:54.287882: Pseudo dice [0.9677, 0.961, 0.9786, 0.9501, 0.8874, 0.9746, 0.9914] +2024-09-10 07:20:54.288921: Epoch time: 272.37 s +2024-09-10 07:20:56.655179: +2024-09-10 07:20:56.656049: Epoch 832 +2024-09-10 07:20:56.656721: Current learning rate: 0.00201 +2024-09-10 07:25:13.993526: train_loss -0.7656 +2024-09-10 07:25:14.003467: val_loss -0.7493 +2024-09-10 07:25:14.004403: Pseudo dice [0.9675, 0.9619, 0.9805, 0.9538, 0.8737, 0.9726, 0.9864] +2024-09-10 07:25:14.005058: Epoch time: 257.34 s +2024-09-10 07:25:17.086675: +2024-09-10 07:25:17.100140: Epoch 833 +2024-09-10 07:25:17.112679: Current learning rate: 0.002 +2024-09-10 07:29:36.278554: train_loss -0.7516 +2024-09-10 07:29:36.280833: val_loss -0.7475 +2024-09-10 07:29:36.281524: Pseudo dice [0.9676, 0.9595, 0.9791, 0.9455, 0.8721, 0.972, 0.9891] +2024-09-10 07:29:36.282172: Epoch time: 259.2 s +2024-09-10 07:29:38.500894: +2024-09-10 07:29:38.501900: Epoch 834 +2024-09-10 07:29:38.502704: Current learning rate: 0.00199 +2024-09-10 07:33:59.264319: train_loss -0.7665 +2024-09-10 07:33:59.266412: val_loss -0.757 +2024-09-10 07:33:59.267109: Pseudo dice [0.9645, 0.9611, 0.9772, 0.9548, 0.8805, 0.973, 0.9886] +2024-09-10 07:33:59.267985: Epoch time: 260.77 s +2024-09-10 07:34:02.096884: +2024-09-10 07:34:02.097829: Epoch 835 +2024-09-10 07:34:02.098501: Current learning rate: 0.00198 +2024-09-10 07:38:31.682658: train_loss -0.7436 +2024-09-10 07:38:31.697976: val_loss -0.7749 +2024-09-10 07:38:31.698644: Pseudo dice [0.9667, 0.9617, 0.9797, 0.9531, 0.8905, 0.9754, 0.9914] +2024-09-10 07:38:31.699268: Epoch time: 269.59 s +2024-09-10 07:38:35.118257: +2024-09-10 07:38:35.132480: Epoch 836 +2024-09-10 07:38:35.133402: Current learning rate: 0.00196 +2024-09-10 07:43:10.949990: train_loss -0.7703 +2024-09-10 07:43:10.963177: val_loss -0.7841 +2024-09-10 07:43:10.964104: Pseudo dice [0.967, 0.9583, 0.9806, 0.9559, 0.8889, 0.9765, 0.9929] +2024-09-10 07:43:10.964940: Epoch time: 275.84 s +2024-09-10 07:43:15.675642: +2024-09-10 07:43:15.676659: Epoch 837 +2024-09-10 07:43:15.677453: Current learning rate: 0.00195 +2024-09-10 07:48:15.149629: train_loss -0.753 +2024-09-10 07:48:15.166202: val_loss -0.7659 +2024-09-10 07:48:15.166963: Pseudo dice [0.9657, 0.9627, 0.9781, 0.9568, 0.8915, 0.9757, 0.9923] +2024-09-10 07:48:15.167602: Epoch time: 299.48 s +2024-09-10 07:48:17.699107: +2024-09-10 07:48:17.712796: Epoch 838 +2024-09-10 07:48:17.713768: Current learning rate: 0.00194 +2024-09-10 07:52:41.746300: train_loss -0.7629 +2024-09-10 07:52:41.748721: val_loss -0.7715 +2024-09-10 07:52:41.749620: Pseudo dice [0.9628, 0.959, 0.9747, 0.9533, 0.8831, 0.9745, 0.991] +2024-09-10 07:52:41.750419: Epoch time: 264.05 s +2024-09-10 07:52:44.592114: +2024-09-10 07:52:44.593122: Epoch 839 +2024-09-10 07:52:44.594355: Current learning rate: 0.00193 +2024-09-10 07:57:07.145963: train_loss -0.7466 +2024-09-10 07:57:07.161053: val_loss -0.772 +2024-09-10 07:57:07.161935: Pseudo dice [0.9659, 0.9589, 0.9753, 0.9508, 0.889, 0.9723, 0.9893] +2024-09-10 07:57:07.162695: Epoch time: 262.56 s +2024-09-10 07:57:09.579605: +2024-09-10 07:57:09.580671: Epoch 840 +2024-09-10 07:57:09.581335: Current learning rate: 0.00192 +2024-09-10 08:01:53.841384: train_loss -0.7487 +2024-09-10 08:01:53.844020: val_loss -0.7233 +2024-09-10 08:01:53.844808: Pseudo dice [0.9597, 0.9589, 0.9713, 0.9524, 0.8817, 0.9714, 0.9916] +2024-09-10 08:01:53.845485: Epoch time: 284.27 s +2024-09-10 08:01:57.165341: +2024-09-10 08:01:57.166396: Epoch 841 +2024-09-10 08:01:57.167021: Current learning rate: 0.00191 +2024-09-10 08:06:39.233339: train_loss -0.7622 +2024-09-10 08:06:39.235850: val_loss -0.729 +2024-09-10 08:06:39.236925: Pseudo dice [0.9662, 0.9617, 0.9799, 0.9567, 0.8772, 0.9761, 0.9903] +2024-09-10 08:06:39.238143: Epoch time: 282.07 s +2024-09-10 08:06:42.085503: +2024-09-10 08:06:42.086343: Epoch 842 +2024-09-10 08:06:42.086920: Current learning rate: 0.0019 +2024-09-10 08:11:06.729095: train_loss -0.7462 +2024-09-10 08:11:06.741810: val_loss -0.7518 +2024-09-10 08:11:06.742476: Pseudo dice [0.9637, 0.9604, 0.9767, 0.955, 0.8898, 0.9747, 0.9923] +2024-09-10 08:11:06.743011: Epoch time: 264.65 s +2024-09-10 08:11:09.590576: +2024-09-10 08:11:09.591390: Epoch 843 +2024-09-10 08:11:09.592039: Current learning rate: 0.00189 +2024-09-10 08:15:57.508762: train_loss -0.7495 +2024-09-10 08:15:57.525258: val_loss -0.7853 +2024-09-10 08:15:57.526262: Pseudo dice [0.9671, 0.9615, 0.9809, 0.9595, 0.8914, 0.9752, 0.9906] +2024-09-10 08:15:57.527026: Epoch time: 287.92 s +2024-09-10 08:16:00.392846: +2024-09-10 08:16:00.393739: Epoch 844 +2024-09-10 08:16:00.394350: Current learning rate: 0.00188 +2024-09-10 08:20:36.071152: train_loss -0.7606 +2024-09-10 08:20:36.093741: val_loss -0.7604 +2024-09-10 08:20:36.095173: Pseudo dice [0.9654, 0.9597, 0.9783, 0.9554, 0.8816, 0.9746, 0.9925] +2024-09-10 08:20:36.096416: Epoch time: 275.68 s +2024-09-10 08:20:39.129287: +2024-09-10 08:20:39.145092: Epoch 845 +2024-09-10 08:20:39.146472: Current learning rate: 0.00187 +2024-09-10 08:25:12.755040: train_loss -0.7572 +2024-09-10 08:25:12.756505: val_loss -0.7671 +2024-09-10 08:25:12.757221: Pseudo dice [0.9675, 0.9599, 0.9805, 0.9586, 0.8757, 0.9747, 0.9916] +2024-09-10 08:25:12.757820: Epoch time: 273.63 s +2024-09-10 08:25:14.722487: +2024-09-10 08:25:14.723370: Epoch 846 +2024-09-10 08:25:14.723984: Current learning rate: 0.00186 +2024-09-10 08:29:37.978577: train_loss -0.7528 +2024-09-10 08:29:37.992289: val_loss -0.7501 +2024-09-10 08:29:37.993301: Pseudo dice [0.9681, 0.9609, 0.9804, 0.9533, 0.8836, 0.967, 0.9903] +2024-09-10 08:29:37.994004: Epoch time: 263.26 s +2024-09-10 08:29:41.602436: +2024-09-10 08:29:41.603410: Epoch 847 +2024-09-10 08:29:41.604149: Current learning rate: 0.00185 +2024-09-10 08:34:17.806872: train_loss -0.7638 +2024-09-10 08:34:17.821921: val_loss -0.76 +2024-09-10 08:34:17.822655: Pseudo dice [0.9655, 0.9617, 0.9757, 0.9489, 0.8813, 0.9731, 0.9921] +2024-09-10 08:34:17.823286: Epoch time: 276.21 s +2024-09-10 08:34:19.927856: +2024-09-10 08:34:19.928922: Epoch 848 +2024-09-10 08:34:19.929951: Current learning rate: 0.00184 +2024-09-10 08:38:26.176839: train_loss -0.757 +2024-09-10 08:38:26.199317: val_loss -0.7419 +2024-09-10 08:38:26.199930: Pseudo dice [0.9654, 0.9605, 0.9757, 0.9541, 0.8741, 0.9766, 0.9914] +2024-09-10 08:38:26.200732: Epoch time: 246.25 s +2024-09-10 08:38:30.025464: +2024-09-10 08:38:30.026544: Epoch 849 +2024-09-10 08:38:30.027237: Current learning rate: 0.00182 +2024-09-10 08:43:12.339715: train_loss -0.7466 +2024-09-10 08:43:12.354710: val_loss -0.7015 +2024-09-10 08:43:12.356020: Pseudo dice [0.9615, 0.9612, 0.969, 0.951, 0.8842, 0.9743, 0.9934] +2024-09-10 08:43:12.357098: Epoch time: 282.32 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_08_52_56.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_08_52_56.txt new file mode 100644 index 0000000000000000000000000000000000000000..312362ce1936398b0fbb1936aa9659efff14d471 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_08_52_56.txt @@ -0,0 +1,380 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 08:53:00.312757: Using torch.compile... +2024-09-10 08:53:10.094110: do_dummy_2d_data_aug: False +2024-09-10 08:53:10.122147: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 08:53:10.141523: The split file contains 5 splits. +2024-09-10 08:53:10.141942: Desired fold for training: 4 +2024-09-10 08:53:10.142310: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 08:53:22.309736: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 08:53:23.529294: +2024-09-10 08:53:23.530203: Epoch 850 +2024-09-10 08:53:23.531511: Current learning rate: 0.00181 +2024-09-10 09:04:09.715130: train_loss -0.7502 +2024-09-10 09:04:09.716590: val_loss -0.7554 +2024-09-10 09:04:09.717315: Pseudo dice [0.9679, 0.9586, 0.9807, 0.9536, 0.891, 0.9697, 0.9917] +2024-09-10 09:04:09.717854: Epoch time: 646.19 s +2024-09-10 09:04:11.704730: +2024-09-10 09:04:11.705461: Epoch 851 +2024-09-10 09:04:11.705948: Current learning rate: 0.0018 +2024-09-10 09:09:02.595794: train_loss -0.7604 +2024-09-10 09:09:02.608476: val_loss -0.7327 +2024-09-10 09:09:02.609116: Pseudo dice [0.9681, 0.9604, 0.9803, 0.9536, 0.8769, 0.9737, 0.9914] +2024-09-10 09:09:02.609736: Epoch time: 290.9 s +2024-09-10 09:09:04.981750: +2024-09-10 09:09:04.999783: Epoch 852 +2024-09-10 09:09:05.002268: Current learning rate: 0.00179 +2024-09-10 09:14:01.926550: train_loss -0.7411 +2024-09-10 09:14:01.927676: val_loss -0.7457 +2024-09-10 09:14:01.928231: Pseudo dice [0.9682, 0.9598, 0.98, 0.9599, 0.8769, 0.9768, 0.9911] +2024-09-10 09:14:01.928672: Epoch time: 296.95 s +2024-09-10 09:14:04.397803: +2024-09-10 09:14:04.398754: Epoch 853 +2024-09-10 09:14:04.399309: Current learning rate: 0.00178 +2024-09-10 09:18:40.736688: train_loss -0.7493 +2024-09-10 09:18:40.759883: val_loss -0.7739 +2024-09-10 09:18:40.760615: Pseudo dice [0.9691, 0.9613, 0.9813, 0.9524, 0.8884, 0.9762, 0.9919] +2024-09-10 09:18:40.761093: Epoch time: 276.34 s +2024-09-10 09:18:44.884358: +2024-09-10 09:18:44.885338: Epoch 854 +2024-09-10 09:18:44.885933: Current learning rate: 0.00177 +2024-09-10 09:23:26.870833: train_loss -0.7543 +2024-09-10 09:23:26.907953: val_loss -0.7583 +2024-09-10 09:23:26.915974: Pseudo dice [0.9686, 0.9618, 0.9797, 0.9466, 0.8843, 0.9754, 0.9922] +2024-09-10 09:23:26.916782: Epoch time: 281.99 s +2024-09-10 09:23:29.486162: +2024-09-10 09:23:29.503750: Epoch 855 +2024-09-10 09:23:29.512426: Current learning rate: 0.00176 +2024-09-10 09:28:43.690158: train_loss -0.7466 +2024-09-10 09:28:43.704656: val_loss -0.7617 +2024-09-10 09:28:43.705255: Pseudo dice [0.9682, 0.9627, 0.9775, 0.9563, 0.8986, 0.9781, 0.994] +2024-09-10 09:28:43.705725: Epoch time: 314.21 s +2024-09-10 09:28:46.363908: +2024-09-10 09:28:46.365085: Epoch 856 +2024-09-10 09:28:46.366085: Current learning rate: 0.00175 +2024-09-10 09:33:58.806468: train_loss -0.7413 +2024-09-10 09:33:58.807789: val_loss -0.76 +2024-09-10 09:33:58.824369: Pseudo dice [0.965, 0.9593, 0.9775, 0.9512, 0.8936, 0.9739, 0.993] +2024-09-10 09:33:58.824944: Epoch time: 312.45 s +2024-09-10 09:34:03.454168: +2024-09-10 09:34:03.464521: Epoch 857 +2024-09-10 09:34:03.465265: Current learning rate: 0.00174 +2024-09-10 09:38:36.664335: train_loss -0.7556 +2024-09-10 09:38:36.665962: val_loss -0.7682 +2024-09-10 09:38:36.667936: Pseudo dice [0.964, 0.9589, 0.9757, 0.9526, 0.8944, 0.9739, 0.9925] +2024-09-10 09:38:36.674232: Epoch time: 273.22 s +2024-09-10 09:38:39.984084: +2024-09-10 09:38:39.985016: Epoch 858 +2024-09-10 09:38:39.985553: Current learning rate: 0.00173 +2024-09-10 09:43:33.238878: train_loss -0.7556 +2024-09-10 09:43:33.244901: val_loss -0.7904 +2024-09-10 09:43:33.245537: Pseudo dice [0.966, 0.9612, 0.9752, 0.9499, 0.8875, 0.9739, 0.9913] +2024-09-10 09:43:33.246133: Epoch time: 293.26 s +2024-09-10 09:43:36.551198: +2024-09-10 09:43:36.552005: Epoch 859 +2024-09-10 09:43:36.552468: Current learning rate: 0.00172 +2024-09-10 09:48:41.988288: train_loss -0.7566 +2024-09-10 09:48:41.989405: val_loss -0.731 +2024-09-10 09:48:41.989935: Pseudo dice [0.9628, 0.9594, 0.9759, 0.9495, 0.8816, 0.9743, 0.9903] +2024-09-10 09:48:41.990417: Epoch time: 305.44 s +2024-09-10 09:48:44.828542: +2024-09-10 09:48:44.830844: Epoch 860 +2024-09-10 09:48:44.843917: Current learning rate: 0.0017 +2024-09-10 09:53:20.986130: train_loss -0.7511 +2024-09-10 09:53:21.000660: val_loss -0.7702 +2024-09-10 09:53:21.001255: Pseudo dice [0.9675, 0.9615, 0.9789, 0.954, 0.8923, 0.973, 0.9917] +2024-09-10 09:53:21.001740: Epoch time: 276.16 s +2024-09-10 09:53:24.896492: +2024-09-10 09:53:24.904337: Epoch 861 +2024-09-10 09:53:24.904929: Current learning rate: 0.00169 +2024-09-10 09:58:12.695406: train_loss -0.7751 +2024-09-10 09:58:12.697358: val_loss -0.7549 +2024-09-10 09:58:12.697914: Pseudo dice [0.9685, 0.9624, 0.9819, 0.9549, 0.8954, 0.972, 0.9918] +2024-09-10 09:58:12.698416: Epoch time: 287.83 s +2024-09-10 09:58:16.198316: +2024-09-10 09:58:16.215786: Epoch 862 +2024-09-10 09:58:16.216535: Current learning rate: 0.00168 +2024-09-10 10:03:00.025791: train_loss -0.761 +2024-09-10 10:03:00.027085: val_loss -0.7644 +2024-09-10 10:03:00.027620: Pseudo dice [0.968, 0.9624, 0.9808, 0.9573, 0.8787, 0.9746, 0.9914] +2024-09-10 10:03:00.028154: Epoch time: 283.83 s +2024-09-10 10:03:04.190488: +2024-09-10 10:03:04.192383: Epoch 863 +2024-09-10 10:03:04.193017: Current learning rate: 0.00167 +2024-09-10 10:08:03.255976: train_loss -0.7683 +2024-09-10 10:08:03.257692: val_loss -0.7185 +2024-09-10 10:08:03.258533: Pseudo dice [0.9681, 0.9627, 0.9816, 0.9553, 0.8798, 0.9754, 0.991] +2024-09-10 10:08:03.259088: Epoch time: 299.07 s +2024-09-10 10:08:06.099123: +2024-09-10 10:08:06.099904: Epoch 864 +2024-09-10 10:08:06.100696: Current learning rate: 0.00166 +2024-09-10 10:12:58.228892: train_loss -0.7425 +2024-09-10 10:12:58.239868: val_loss -0.7893 +2024-09-10 10:12:58.240524: Pseudo dice [0.9678, 0.9616, 0.9824, 0.9539, 0.8802, 0.9739, 0.9917] +2024-09-10 10:12:58.241229: Epoch time: 292.13 s +2024-09-10 10:13:03.409726: +2024-09-10 10:13:03.428717: Epoch 865 +2024-09-10 10:13:03.429377: Current learning rate: 0.00165 +2024-09-10 10:17:56.488888: train_loss -0.759 +2024-09-10 10:17:56.489951: val_loss -0.7951 +2024-09-10 10:17:56.491050: Pseudo dice [0.9641, 0.9596, 0.9746, 0.9525, 0.8912, 0.9748, 0.9922] +2024-09-10 10:17:56.492170: Epoch time: 293.08 s +2024-09-10 10:17:58.605947: +2024-09-10 10:17:58.606921: Epoch 866 +2024-09-10 10:17:58.613433: Current learning rate: 0.00164 +2024-09-10 10:22:50.639483: train_loss -0.7416 +2024-09-10 10:22:50.646374: val_loss -0.7509 +2024-09-10 10:22:50.647081: Pseudo dice [0.9641, 0.961, 0.9748, 0.9483, 0.8644, 0.9741, 0.9913] +2024-09-10 10:22:50.647536: Epoch time: 292.04 s +2024-09-10 10:22:53.981220: +2024-09-10 10:22:54.000414: Epoch 867 +2024-09-10 10:22:54.001041: Current learning rate: 0.00163 +2024-09-10 10:27:41.021785: train_loss -0.763 +2024-09-10 10:27:41.044602: val_loss -0.7921 +2024-09-10 10:27:41.045253: Pseudo dice [0.9688, 0.9601, 0.9813, 0.9564, 0.8904, 0.9756, 0.9919] +2024-09-10 10:27:41.045800: Epoch time: 287.05 s +2024-09-10 10:27:43.487766: +2024-09-10 10:27:43.488633: Epoch 868 +2024-09-10 10:27:43.489212: Current learning rate: 0.00162 +2024-09-10 10:32:53.386775: train_loss -0.7502 +2024-09-10 10:32:53.416675: val_loss -0.7155 +2024-09-10 10:32:53.417932: Pseudo dice [0.9661, 0.9614, 0.979, 0.9483, 0.8763, 0.9759, 0.9908] +2024-09-10 10:32:53.419175: Epoch time: 309.9 s +2024-09-10 10:32:55.966420: +2024-09-10 10:32:55.977328: Epoch 869 +2024-09-10 10:32:55.977966: Current learning rate: 0.00161 +2024-09-10 10:37:46.808841: train_loss -0.758 +2024-09-10 10:37:46.824809: val_loss -0.7561 +2024-09-10 10:37:46.825436: Pseudo dice [0.9682, 0.9607, 0.9813, 0.954, 0.888, 0.977, 0.9909] +2024-09-10 10:37:46.825915: Epoch time: 290.85 s +2024-09-10 10:37:49.238465: +2024-09-10 10:37:49.239376: Epoch 870 +2024-09-10 10:37:49.240716: Current learning rate: 0.00159 +2024-09-10 10:42:48.733545: train_loss -0.7565 +2024-09-10 10:42:48.734818: val_loss -0.7505 +2024-09-10 10:42:48.735548: Pseudo dice [0.9689, 0.9607, 0.9804, 0.9536, 0.891, 0.9756, 0.9924] +2024-09-10 10:42:48.736150: Epoch time: 299.5 s +2024-09-10 10:42:51.708624: +2024-09-10 10:42:51.709638: Epoch 871 +2024-09-10 10:42:51.710786: Current learning rate: 0.00158 +2024-09-10 10:47:46.055905: train_loss -0.7466 +2024-09-10 10:47:46.057557: val_loss -0.7475 +2024-09-10 10:47:46.058367: Pseudo dice [0.9651, 0.9616, 0.9791, 0.9562, 0.881, 0.9758, 0.9914] +2024-09-10 10:47:46.059067: Epoch time: 294.37 s +2024-09-10 10:47:48.370465: +2024-09-10 10:47:48.371737: Epoch 872 +2024-09-10 10:47:48.372621: Current learning rate: 0.00157 +2024-09-10 10:52:47.400580: train_loss -0.751 +2024-09-10 10:52:47.411857: val_loss -0.7164 +2024-09-10 10:52:47.412671: Pseudo dice [0.9684, 0.9618, 0.9794, 0.9552, 0.8886, 0.9725, 0.9919] +2024-09-10 10:52:47.413222: Epoch time: 299.04 s +2024-09-10 10:52:49.761298: +2024-09-10 10:52:49.762340: Epoch 873 +2024-09-10 10:52:49.762974: Current learning rate: 0.00156 +2024-09-10 10:57:36.466559: train_loss -0.7508 +2024-09-10 10:57:36.473291: val_loss -0.7747 +2024-09-10 10:57:36.473802: Pseudo dice [0.9668, 0.9618, 0.9792, 0.9523, 0.8954, 0.9768, 0.9919] +2024-09-10 10:57:36.474282: Epoch time: 286.71 s +2024-09-10 10:57:39.310137: +2024-09-10 10:57:39.310909: Epoch 874 +2024-09-10 10:57:39.311399: Current learning rate: 0.00155 +2024-09-10 11:02:47.888885: train_loss -0.7596 +2024-09-10 11:02:47.897830: val_loss -0.71 +2024-09-10 11:02:47.898406: Pseudo dice [0.9605, 0.9607, 0.971, 0.956, 0.8793, 0.9734, 0.9903] +2024-09-10 11:02:47.898957: Epoch time: 308.58 s +2024-09-10 11:02:50.292749: +2024-09-10 11:02:50.301369: Epoch 875 +2024-09-10 11:02:50.302106: Current learning rate: 0.00154 +2024-09-10 11:07:52.688944: train_loss -0.7607 +2024-09-10 11:07:52.717865: val_loss -0.7471 +2024-09-10 11:07:52.718920: Pseudo dice [0.9648, 0.9623, 0.9775, 0.945, 0.8712, 0.9736, 0.9894] +2024-09-10 11:07:52.719902: Epoch time: 302.4 s +2024-09-10 11:07:55.142664: +2024-09-10 11:07:55.171791: Epoch 876 +2024-09-10 11:07:55.183932: Current learning rate: 0.00153 +2024-09-10 11:12:56.253969: train_loss -0.7399 +2024-09-10 11:12:56.279714: val_loss -0.7581 +2024-09-10 11:12:56.280349: Pseudo dice [0.9643, 0.9615, 0.9755, 0.9492, 0.8807, 0.9748, 0.9908] +2024-09-10 11:12:56.280936: Epoch time: 301.12 s +2024-09-10 11:12:59.864314: +2024-09-10 11:12:59.879241: Epoch 877 +2024-09-10 11:12:59.879877: Current learning rate: 0.00152 +2024-09-10 11:17:41.762139: train_loss -0.7519 +2024-09-10 11:17:41.789326: val_loss -0.7164 +2024-09-10 11:17:41.789964: Pseudo dice [0.9632, 0.9614, 0.9736, 0.9514, 0.8783, 0.9749, 0.9886] +2024-09-10 11:17:41.790468: Epoch time: 281.9 s +2024-09-10 11:17:46.605520: +2024-09-10 11:17:46.616645: Epoch 878 +2024-09-10 11:17:46.617373: Current learning rate: 0.00151 +2024-09-10 11:22:47.944696: train_loss -0.7479 +2024-09-10 11:22:47.958009: val_loss -0.7447 +2024-09-10 11:22:47.958751: Pseudo dice [0.969, 0.9626, 0.9818, 0.9571, 0.8965, 0.9756, 0.9922] +2024-09-10 11:22:47.959359: Epoch time: 301.34 s +2024-09-10 11:22:50.764726: +2024-09-10 11:22:50.765506: Epoch 879 +2024-09-10 11:22:50.766041: Current learning rate: 0.00149 +2024-09-10 11:27:48.175002: train_loss -0.7508 +2024-09-10 11:27:48.185073: val_loss -0.762 +2024-09-10 11:27:48.185782: Pseudo dice [0.9621, 0.9609, 0.9712, 0.9513, 0.8923, 0.9753, 0.9914] +2024-09-10 11:27:48.186255: Epoch time: 297.41 s +2024-09-10 11:27:51.326031: +2024-09-10 11:27:51.327122: Epoch 880 +2024-09-10 11:27:51.327613: Current learning rate: 0.00148 +2024-09-10 11:32:40.948883: train_loss -0.7604 +2024-09-10 11:32:40.951541: val_loss -0.7752 +2024-09-10 11:32:40.952144: Pseudo dice [0.9649, 0.9598, 0.9771, 0.9558, 0.8909, 0.9745, 0.9916] +2024-09-10 11:32:40.952752: Epoch time: 289.63 s +2024-09-10 11:32:44.081951: +2024-09-10 11:32:44.092328: Epoch 881 +2024-09-10 11:32:44.093788: Current learning rate: 0.00147 +2024-09-10 11:37:21.090420: train_loss -0.7424 +2024-09-10 11:37:21.104514: val_loss -0.76 +2024-09-10 11:37:21.105179: Pseudo dice [0.9695, 0.9614, 0.9818, 0.9585, 0.8993, 0.9761, 0.9923] +2024-09-10 11:37:21.105746: Epoch time: 277.01 s +2024-09-10 11:37:23.403032: +2024-09-10 11:37:23.404105: Epoch 882 +2024-09-10 11:37:23.404880: Current learning rate: 0.00146 +2024-09-10 11:42:29.445135: train_loss -0.7429 +2024-09-10 11:42:29.447084: val_loss -0.7719 +2024-09-10 11:42:29.447705: Pseudo dice [0.968, 0.9631, 0.9787, 0.9569, 0.8871, 0.9764, 0.9925] +2024-09-10 11:42:29.448186: Epoch time: 306.05 s +2024-09-10 11:42:35.150412: +2024-09-10 11:42:35.151368: Epoch 883 +2024-09-10 11:42:35.151953: Current learning rate: 0.00145 +2024-09-10 11:47:31.181609: train_loss -0.773 +2024-09-10 11:47:31.197508: val_loss -0.7476 +2024-09-10 11:47:31.198179: Pseudo dice [0.9673, 0.9595, 0.978, 0.9521, 0.8943, 0.976, 0.9928] +2024-09-10 11:47:31.198703: Epoch time: 296.04 s +2024-09-10 11:47:34.274109: +2024-09-10 11:47:34.295761: Epoch 884 +2024-09-10 11:47:34.303959: Current learning rate: 0.00144 +2024-09-10 11:52:30.496832: train_loss -0.7529 +2024-09-10 11:52:30.536905: val_loss -0.7538 +2024-09-10 11:52:30.537443: Pseudo dice [0.9674, 0.9643, 0.9784, 0.9575, 0.8906, 0.9756, 0.9921] +2024-09-10 11:52:30.537917: Epoch time: 296.24 s +2024-09-10 11:52:33.693844: +2024-09-10 11:52:33.695104: Epoch 885 +2024-09-10 11:52:33.695817: Current learning rate: 0.00143 +2024-09-10 11:57:28.509801: train_loss -0.7551 +2024-09-10 11:57:28.527908: val_loss -0.752 +2024-09-10 11:57:28.528651: Pseudo dice [0.9698, 0.9605, 0.9804, 0.9504, 0.8858, 0.9766, 0.9914] +2024-09-10 11:57:28.529083: Epoch time: 294.82 s +2024-09-10 11:57:31.265267: +2024-09-10 11:57:31.266029: Epoch 886 +2024-09-10 11:57:31.266483: Current learning rate: 0.00142 +2024-09-10 12:02:24.689825: train_loss -0.7657 +2024-09-10 12:02:24.705269: val_loss -0.7632 +2024-09-10 12:02:24.705797: Pseudo dice [0.9707, 0.9613, 0.9841, 0.9568, 0.9011, 0.9747, 0.9925] +2024-09-10 12:02:24.706312: Epoch time: 293.43 s +2024-09-10 12:02:24.706712: Yayy! New best EMA pseudo Dice: 0.9596 +2024-09-10 12:02:29.324878: +2024-09-10 12:02:29.336271: Epoch 887 +2024-09-10 12:02:29.337034: Current learning rate: 0.00141 +2024-09-10 12:07:07.932564: train_loss -0.7572 +2024-09-10 12:07:07.949895: val_loss -0.7647 +2024-09-10 12:07:07.950803: Pseudo dice [0.9702, 0.9619, 0.9825, 0.9561, 0.8846, 0.9775, 0.992] +2024-09-10 12:07:07.951668: Epoch time: 278.61 s +2024-09-10 12:07:07.952129: Yayy! New best EMA pseudo Dice: 0.9597 +2024-09-10 12:07:12.984140: +2024-09-10 12:07:12.996553: Epoch 888 +2024-09-10 12:07:12.997359: Current learning rate: 0.00139 +2024-09-10 12:11:45.661820: train_loss -0.7551 +2024-09-10 12:11:45.676674: val_loss -0.7569 +2024-09-10 12:11:45.677344: Pseudo dice [0.9642, 0.9585, 0.975, 0.952, 0.8915, 0.9771, 0.9921] +2024-09-10 12:11:45.677824: Epoch time: 272.68 s +2024-09-10 12:11:48.282558: +2024-09-10 12:11:48.303722: Epoch 889 +2024-09-10 12:11:48.304526: Current learning rate: 0.00138 +2024-09-10 12:16:50.488398: train_loss -0.771 +2024-09-10 12:16:50.494029: val_loss -0.7668 +2024-09-10 12:16:50.494953: Pseudo dice [0.9711, 0.962, 0.9831, 0.955, 0.8951, 0.9754, 0.993] +2024-09-10 12:16:50.495858: Epoch time: 302.21 s +2024-09-10 12:16:50.496836: Yayy! New best EMA pseudo Dice: 0.9599 +2024-09-10 12:16:54.982275: +2024-09-10 12:16:54.990242: Epoch 890 +2024-09-10 12:16:54.991556: Current learning rate: 0.00137 +2024-09-10 12:21:21.568979: train_loss -0.756 +2024-09-10 12:21:21.572941: val_loss -0.7572 +2024-09-10 12:21:21.573817: Pseudo dice [0.9681, 0.9623, 0.9807, 0.9557, 0.8909, 0.9759, 0.9919] +2024-09-10 12:21:21.574735: Epoch time: 266.59 s +2024-09-10 12:21:21.575641: Yayy! New best EMA pseudo Dice: 0.96 +2024-09-10 12:21:25.632620: +2024-09-10 12:21:25.633529: Epoch 891 +2024-09-10 12:21:25.634157: Current learning rate: 0.00136 +2024-09-10 12:26:11.937098: train_loss -0.7712 +2024-09-10 12:26:11.939007: val_loss -0.7639 +2024-09-10 12:26:11.939577: Pseudo dice [0.9664, 0.9611, 0.9778, 0.9588, 0.888, 0.9765, 0.9913] +2024-09-10 12:26:11.940314: Epoch time: 286.31 s +2024-09-10 12:26:11.940742: Yayy! New best EMA pseudo Dice: 0.96 +2024-09-10 12:26:15.881987: +2024-09-10 12:26:15.882828: Epoch 892 +2024-09-10 12:26:15.883424: Current learning rate: 0.00135 +2024-09-10 12:30:47.313087: train_loss -0.767 +2024-09-10 12:30:47.315011: val_loss -0.7642 +2024-09-10 12:30:47.315632: Pseudo dice [0.969, 0.9609, 0.9812, 0.9549, 0.8956, 0.9767, 0.993] +2024-09-10 12:30:47.316235: Epoch time: 271.44 s +2024-09-10 12:30:47.316674: Yayy! New best EMA pseudo Dice: 0.9601 +2024-09-10 12:30:52.593566: +2024-09-10 12:30:52.595367: Epoch 893 +2024-09-10 12:30:52.596877: Current learning rate: 0.00134 +2024-09-10 12:35:44.877273: train_loss -0.7575 +2024-09-10 12:35:44.894261: val_loss -0.7663 +2024-09-10 12:35:44.895411: Pseudo dice [0.9683, 0.9618, 0.9792, 0.9576, 0.8916, 0.9771, 0.9925] +2024-09-10 12:35:44.896008: Epoch time: 292.29 s +2024-09-10 12:35:44.896526: Yayy! New best EMA pseudo Dice: 0.9602 +2024-09-10 12:35:54.000546: +2024-09-10 12:35:54.011800: Epoch 894 +2024-09-10 12:35:54.012661: Current learning rate: 0.00133 +2024-09-10 12:40:32.440898: train_loss -0.7679 +2024-09-10 12:40:32.461252: val_loss -0.7647 +2024-09-10 12:40:32.461838: Pseudo dice [0.9676, 0.9625, 0.9805, 0.9569, 0.8825, 0.9762, 0.9923] +2024-09-10 12:40:32.462374: Epoch time: 278.45 s +2024-09-10 12:40:39.057182: +2024-09-10 12:40:39.058296: Epoch 895 +2024-09-10 12:40:39.058872: Current learning rate: 0.00132 +2024-09-10 12:45:42.774274: train_loss -0.7615 +2024-09-10 12:45:42.782126: val_loss -0.7474 +2024-09-10 12:45:42.783521: Pseudo dice [0.9694, 0.964, 0.981, 0.9572, 0.8883, 0.9766, 0.9913] +2024-09-10 12:45:42.784866: Epoch time: 303.72 s +2024-09-10 12:45:42.786313: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-10 12:45:49.308516: +2024-09-10 12:45:49.309351: Epoch 896 +2024-09-10 12:45:49.310616: Current learning rate: 0.0013 +2024-09-10 12:50:45.558080: train_loss -0.7585 +2024-09-10 12:50:45.573723: val_loss -0.7956 +2024-09-10 12:50:45.574361: Pseudo dice [0.9674, 0.9607, 0.9812, 0.9567, 0.8869, 0.9741, 0.9924] +2024-09-10 12:50:45.574938: Epoch time: 296.25 s +2024-09-10 12:50:47.786899: +2024-09-10 12:50:47.803767: Epoch 897 +2024-09-10 12:50:47.804697: Current learning rate: 0.00129 +2024-09-10 12:55:35.316538: train_loss -0.747 +2024-09-10 12:55:35.333317: val_loss -0.7164 +2024-09-10 12:55:35.333886: Pseudo dice [0.9665, 0.962, 0.9766, 0.9556, 0.88, 0.9774, 0.9913] +2024-09-10 12:55:35.334351: Epoch time: 287.54 s +2024-09-10 12:55:37.322300: +2024-09-10 12:55:37.339044: Epoch 898 +2024-09-10 12:55:37.344392: Current learning rate: 0.00128 +2024-09-10 13:00:24.855252: train_loss -0.7541 +2024-09-10 13:00:24.873656: val_loss -0.7128 +2024-09-10 13:00:24.874511: Pseudo dice [0.9708, 0.9655, 0.9817, 0.961, 0.8928, 0.9766, 0.9913] +2024-09-10 13:00:24.875297: Epoch time: 287.54 s +2024-09-10 13:00:24.875854: Yayy! New best EMA pseudo Dice: 0.9603 +2024-09-10 13:00:29.696389: +2024-09-10 13:00:29.697198: Epoch 899 +2024-09-10 13:00:29.697753: Current learning rate: 0.00127 +2024-09-10 13:05:42.390018: train_loss -0.7665 +2024-09-10 13:05:42.405265: val_loss -0.7743 +2024-09-10 13:05:42.405830: Pseudo dice [0.967, 0.9606, 0.9794, 0.9519, 0.8884, 0.9758, 0.9907] +2024-09-10 13:05:42.406295: Epoch time: 312.7 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_16_52_27.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_16_52_27.txt new file mode 100644 index 0000000000000000000000000000000000000000..5039d5f5faecb6e5b3152143aa3587369bcfc67e --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_16_52_27.txt @@ -0,0 +1,372 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 16:52:31.286041: Using torch.compile... +2024-09-10 16:52:40.795306: do_dummy_2d_data_aug: False +2024-09-10 16:52:40.801819: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 16:52:40.803499: The split file contains 5 splits. +2024-09-10 16:52:40.803888: Desired fold for training: 4 +2024-09-10 16:52:40.804350: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 16:52:52.358509: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 16:52:54.150722: +2024-09-10 16:52:54.167779: Epoch 900 +2024-09-10 16:52:54.169033: Current learning rate: 0.00126 +2024-09-10 17:03:08.096900: train_loss -0.7453 +2024-09-10 17:03:08.099221: val_loss -0.7487 +2024-09-10 17:03:08.099837: Pseudo dice [0.9693, 0.9627, 0.9795, 0.949, 0.8846, 0.9779, 0.9921] +2024-09-10 17:03:08.100378: Epoch time: 613.95 s +2024-09-10 17:03:09.947885: +2024-09-10 17:03:09.949150: Epoch 901 +2024-09-10 17:03:09.949731: Current learning rate: 0.00125 +2024-09-10 17:07:58.683369: train_loss -0.7536 +2024-09-10 17:07:58.697912: val_loss -0.7492 +2024-09-10 17:07:58.698459: Pseudo dice [0.9661, 0.9647, 0.9779, 0.9592, 0.8995, 0.9758, 0.9901] +2024-09-10 17:07:58.698985: Epoch time: 288.74 s +2024-09-10 17:08:01.326470: +2024-09-10 17:08:01.327483: Epoch 902 +2024-09-10 17:08:01.328467: Current learning rate: 0.00124 +2024-09-10 17:13:23.290844: train_loss -0.7536 +2024-09-10 17:13:23.292325: val_loss -0.7406 +2024-09-10 17:13:23.292902: Pseudo dice [0.9684, 0.9602, 0.9826, 0.9529, 0.8942, 0.9768, 0.9914] +2024-09-10 17:13:23.293454: Epoch time: 321.99 s +2024-09-10 17:13:23.293974: Yayy! New best EMA pseudo Dice: 0.9604 +2024-09-10 17:13:29.839237: +2024-09-10 17:13:29.840063: Epoch 903 +2024-09-10 17:13:29.840529: Current learning rate: 0.00122 +2024-09-10 17:18:35.845532: train_loss -0.7571 +2024-09-10 17:18:35.859908: val_loss -0.7441 +2024-09-10 17:18:35.867966: Pseudo dice [0.9688, 0.9612, 0.9819, 0.9533, 0.8875, 0.9733, 0.9915] +2024-09-10 17:18:35.872488: Epoch time: 306.01 s +2024-09-10 17:18:38.733098: +2024-09-10 17:18:38.734093: Epoch 904 +2024-09-10 17:18:38.735027: Current learning rate: 0.00121 +2024-09-10 17:24:12.466005: train_loss -0.7681 +2024-09-10 17:24:12.467208: val_loss -0.7888 +2024-09-10 17:24:12.467767: Pseudo dice [0.9691, 0.961, 0.9795, 0.9501, 0.8867, 0.9767, 0.9922] +2024-09-10 17:24:12.480335: Epoch time: 333.74 s +2024-09-10 17:24:14.957404: +2024-09-10 17:24:14.973881: Epoch 905 +2024-09-10 17:24:14.980363: Current learning rate: 0.0012 +2024-09-10 17:29:11.002646: train_loss -0.7548 +2024-09-10 17:29:11.021470: val_loss -0.7334 +2024-09-10 17:29:11.022246: Pseudo dice [0.968, 0.9643, 0.9806, 0.9547, 0.8905, 0.9748, 0.9912] +2024-09-10 17:29:11.022871: Epoch time: 296.05 s +2024-09-10 17:29:14.212110: +2024-09-10 17:29:14.227815: Epoch 906 +2024-09-10 17:29:14.236502: Current learning rate: 0.00119 +2024-09-10 17:34:27.274041: train_loss -0.7574 +2024-09-10 17:34:27.285711: val_loss -0.7233 +2024-09-10 17:34:27.286570: Pseudo dice [0.9609, 0.9592, 0.9722, 0.9512, 0.8769, 0.9751, 0.991] +2024-09-10 17:34:27.287193: Epoch time: 313.08 s +2024-09-10 17:34:29.963421: +2024-09-10 17:34:29.976640: Epoch 907 +2024-09-10 17:34:29.977611: Current learning rate: 0.00118 +2024-09-10 17:39:48.062870: train_loss -0.7593 +2024-09-10 17:39:48.064750: val_loss -0.7337 +2024-09-10 17:39:48.065274: Pseudo dice [0.9666, 0.9623, 0.9792, 0.9514, 0.8798, 0.972, 0.9901] +2024-09-10 17:39:48.065722: Epoch time: 318.11 s +2024-09-10 17:39:50.801859: +2024-09-10 17:39:50.802734: Epoch 908 +2024-09-10 17:39:50.803312: Current learning rate: 0.00117 +2024-09-10 17:45:08.840568: train_loss -0.7557 +2024-09-10 17:45:08.857884: val_loss -0.7461 +2024-09-10 17:45:08.858963: Pseudo dice [0.9665, 0.9609, 0.98, 0.9534, 0.876, 0.9756, 0.9917] +2024-09-10 17:45:08.859961: Epoch time: 318.04 s +2024-09-10 17:45:12.650499: +2024-09-10 17:45:12.651520: Epoch 909 +2024-09-10 17:45:12.652158: Current learning rate: 0.00116 +2024-09-10 17:50:05.460212: train_loss -0.7494 +2024-09-10 17:50:05.481538: val_loss -0.8033 +2024-09-10 17:50:05.482214: Pseudo dice [0.9659, 0.9612, 0.9787, 0.9547, 0.9, 0.9765, 0.9933] +2024-09-10 17:50:05.482796: Epoch time: 292.82 s +2024-09-10 17:50:08.402499: +2024-09-10 17:50:08.403767: Epoch 910 +2024-09-10 17:50:08.404629: Current learning rate: 0.00115 +2024-09-10 17:55:06.930970: train_loss -0.7496 +2024-09-10 17:55:06.932134: val_loss -0.7313 +2024-09-10 17:55:06.932669: Pseudo dice [0.964, 0.961, 0.9747, 0.956, 0.8857, 0.9754, 0.9914] +2024-09-10 17:55:06.933155: Epoch time: 298.54 s +2024-09-10 17:55:09.540793: +2024-09-10 17:55:09.542269: Epoch 911 +2024-09-10 17:55:09.543105: Current learning rate: 0.00113 +2024-09-10 18:00:19.893301: train_loss -0.7593 +2024-09-10 18:00:19.895093: val_loss -0.7737 +2024-09-10 18:00:19.895591: Pseudo dice [0.9695, 0.9629, 0.9825, 0.958, 0.8904, 0.9775, 0.9917] +2024-09-10 18:00:19.896101: Epoch time: 310.36 s +2024-09-10 18:00:22.173979: +2024-09-10 18:00:22.175297: Epoch 912 +2024-09-10 18:00:22.176443: Current learning rate: 0.00112 +2024-09-10 18:05:29.412374: train_loss -0.7531 +2024-09-10 18:05:29.417396: val_loss -0.7468 +2024-09-10 18:05:29.417956: Pseudo dice [0.9696, 0.9614, 0.9826, 0.9552, 0.8835, 0.9751, 0.9915] +2024-09-10 18:05:29.418386: Epoch time: 307.25 s +2024-09-10 18:05:31.755419: +2024-09-10 18:05:31.756750: Epoch 913 +2024-09-10 18:05:31.757385: Current learning rate: 0.00111 +2024-09-10 18:11:00.841528: train_loss -0.763 +2024-09-10 18:11:00.849207: val_loss -0.7699 +2024-09-10 18:11:00.850547: Pseudo dice [0.9688, 0.9627, 0.982, 0.9501, 0.8922, 0.9774, 0.9933] +2024-09-10 18:11:00.851563: Epoch time: 329.09 s +2024-09-10 18:11:03.859717: +2024-09-10 18:11:03.876568: Epoch 914 +2024-09-10 18:11:03.877492: Current learning rate: 0.0011 +2024-09-10 18:16:08.767130: train_loss -0.7686 +2024-09-10 18:16:08.778905: val_loss -0.7648 +2024-09-10 18:16:08.779608: Pseudo dice [0.9687, 0.9612, 0.9805, 0.9535, 0.8932, 0.9761, 0.9922] +2024-09-10 18:16:08.780095: Epoch time: 304.91 s +2024-09-10 18:16:13.146939: +2024-09-10 18:16:13.147873: Epoch 915 +2024-09-10 18:16:13.148546: Current learning rate: 0.00109 +2024-09-10 18:21:21.111316: train_loss -0.7732 +2024-09-10 18:21:21.125504: val_loss -0.7854 +2024-09-10 18:21:21.126176: Pseudo dice [0.9682, 0.9609, 0.9786, 0.9529, 0.8806, 0.9763, 0.992] +2024-09-10 18:21:21.127146: Epoch time: 307.97 s +2024-09-10 18:21:25.250016: +2024-09-10 18:21:25.250987: Epoch 916 +2024-09-10 18:21:25.251559: Current learning rate: 0.00108 +2024-09-10 18:26:38.605692: train_loss -0.7637 +2024-09-10 18:26:38.625808: val_loss -0.7595 +2024-09-10 18:26:38.626473: Pseudo dice [0.9684, 0.9616, 0.9812, 0.9581, 0.876, 0.9754, 0.9913] +2024-09-10 18:26:38.627016: Epoch time: 313.36 s +2024-09-10 18:26:41.792134: +2024-09-10 18:26:41.798566: Epoch 917 +2024-09-10 18:26:41.799089: Current learning rate: 0.00106 +2024-09-10 18:31:44.693433: train_loss -0.7618 +2024-09-10 18:31:44.709614: val_loss -0.7591 +2024-09-10 18:31:44.710312: Pseudo dice [0.9658, 0.963, 0.9764, 0.9526, 0.897, 0.9762, 0.9922] +2024-09-10 18:31:44.710917: Epoch time: 302.91 s +2024-09-10 18:31:52.033979: +2024-09-10 18:31:52.035586: Epoch 918 +2024-09-10 18:31:52.036478: Current learning rate: 0.00105 +2024-09-10 18:36:56.882549: train_loss -0.7612 +2024-09-10 18:36:56.897206: val_loss -0.7387 +2024-09-10 18:36:56.898005: Pseudo dice [0.9683, 0.9616, 0.9817, 0.9509, 0.8997, 0.9772, 0.9926] +2024-09-10 18:36:56.898617: Epoch time: 304.86 s +2024-09-10 18:36:59.986123: +2024-09-10 18:36:59.987165: Epoch 919 +2024-09-10 18:36:59.988133: Current learning rate: 0.00104 +2024-09-10 18:42:11.553010: train_loss -0.7653 +2024-09-10 18:42:11.569620: val_loss -0.7498 +2024-09-10 18:42:11.570193: Pseudo dice [0.9689, 0.9628, 0.9828, 0.9567, 0.8919, 0.9778, 0.9924] +2024-09-10 18:42:11.570709: Epoch time: 311.57 s +2024-09-10 18:42:14.484461: +2024-09-10 18:42:14.495764: Epoch 920 +2024-09-10 18:42:14.496611: Current learning rate: 0.00103 +2024-09-10 18:47:41.399592: train_loss -0.75 +2024-09-10 18:47:41.417463: val_loss -0.7696 +2024-09-10 18:47:41.418021: Pseudo dice [0.9687, 0.9632, 0.9797, 0.9557, 0.8826, 0.9762, 0.9896] +2024-09-10 18:47:41.418529: Epoch time: 326.94 s +2024-09-10 18:47:43.661495: +2024-09-10 18:47:43.662380: Epoch 921 +2024-09-10 18:47:43.662965: Current learning rate: 0.00102 +2024-09-10 18:52:45.432364: train_loss -0.7552 +2024-09-10 18:52:45.445590: val_loss -0.7623 +2024-09-10 18:52:45.446358: Pseudo dice [0.9684, 0.9628, 0.9797, 0.9551, 0.8841, 0.9769, 0.9916] +2024-09-10 18:52:45.446970: Epoch time: 301.78 s +2024-09-10 18:52:47.785780: +2024-09-10 18:52:47.786741: Epoch 922 +2024-09-10 18:52:47.787221: Current learning rate: 0.00101 +2024-09-10 18:58:08.151526: train_loss -0.7488 +2024-09-10 18:58:08.165842: val_loss -0.777 +2024-09-10 18:58:08.166544: Pseudo dice [0.9671, 0.9632, 0.9785, 0.951, 0.8933, 0.9752, 0.9925] +2024-09-10 18:58:08.167138: Epoch time: 320.37 s +2024-09-10 18:58:10.682339: +2024-09-10 18:58:10.690887: Epoch 923 +2024-09-10 18:58:10.704395: Current learning rate: 0.001 +2024-09-10 19:03:06.311465: train_loss -0.7671 +2024-09-10 19:03:06.313995: val_loss -0.7802 +2024-09-10 19:03:06.314867: Pseudo dice [0.9671, 0.9623, 0.9763, 0.9534, 0.8853, 0.9759, 0.9928] +2024-09-10 19:03:06.315468: Epoch time: 295.64 s +2024-09-10 19:03:09.675740: +2024-09-10 19:03:09.676863: Epoch 924 +2024-09-10 19:03:09.678363: Current learning rate: 0.00098 +2024-09-10 19:08:33.212251: train_loss -0.7749 +2024-09-10 19:08:33.229375: val_loss -0.7549 +2024-09-10 19:08:33.229905: Pseudo dice [0.9681, 0.9612, 0.9812, 0.9543, 0.8951, 0.9778, 0.9912] +2024-09-10 19:08:33.230460: Epoch time: 323.54 s +2024-09-10 19:08:36.025545: +2024-09-10 19:08:36.052413: Epoch 925 +2024-09-10 19:08:36.053092: Current learning rate: 0.00097 +2024-09-10 19:13:41.356983: train_loss -0.7607 +2024-09-10 19:13:41.377986: val_loss -0.7849 +2024-09-10 19:13:41.380341: Pseudo dice [0.9687, 0.9608, 0.9815, 0.9558, 0.8956, 0.9762, 0.9918] +2024-09-10 19:13:41.381693: Epoch time: 305.34 s +2024-09-10 19:13:44.273566: +2024-09-10 19:13:44.283681: Epoch 926 +2024-09-10 19:13:44.284430: Current learning rate: 0.00096 +2024-09-10 19:18:40.743822: train_loss -0.7428 +2024-09-10 19:18:40.757007: val_loss -0.7628 +2024-09-10 19:18:40.757927: Pseudo dice [0.9654, 0.9587, 0.9789, 0.9487, 0.8931, 0.9767, 0.9927] +2024-09-10 19:18:40.758527: Epoch time: 296.48 s +2024-09-10 19:18:43.429370: +2024-09-10 19:18:43.430377: Epoch 927 +2024-09-10 19:18:43.430970: Current learning rate: 0.00095 +2024-09-10 19:23:40.284440: train_loss -0.765 +2024-09-10 19:23:40.308934: val_loss -0.7668 +2024-09-10 19:23:40.309559: Pseudo dice [0.9685, 0.9611, 0.9813, 0.9558, 0.8803, 0.9738, 0.99] +2024-09-10 19:23:40.310054: Epoch time: 296.88 s +2024-09-10 19:23:43.645454: +2024-09-10 19:23:43.680597: Epoch 928 +2024-09-10 19:23:43.681181: Current learning rate: 0.00094 +2024-09-10 19:29:11.170380: train_loss -0.7664 +2024-09-10 19:29:11.185972: val_loss -0.757 +2024-09-10 19:29:11.186742: Pseudo dice [0.9704, 0.9646, 0.981, 0.9581, 0.8907, 0.9771, 0.9927] +2024-09-10 19:29:11.187372: Epoch time: 327.53 s +2024-09-10 19:29:15.775947: +2024-09-10 19:29:15.788362: Epoch 929 +2024-09-10 19:29:15.789073: Current learning rate: 0.00092 +2024-09-10 19:34:19.405514: train_loss -0.7626 +2024-09-10 19:34:19.420369: val_loss -0.7512 +2024-09-10 19:34:19.421016: Pseudo dice [0.963, 0.9628, 0.9742, 0.9519, 0.8748, 0.976, 0.9913] +2024-09-10 19:34:19.421514: Epoch time: 303.64 s +2024-09-10 19:34:21.982623: +2024-09-10 19:34:21.983887: Epoch 930 +2024-09-10 19:34:21.984594: Current learning rate: 0.00091 +2024-09-10 19:39:29.737408: train_loss -0.765 +2024-09-10 19:39:29.749582: val_loss -0.7439 +2024-09-10 19:39:29.751005: Pseudo dice [0.9666, 0.9617, 0.9798, 0.9547, 0.8926, 0.975, 0.9921] +2024-09-10 19:39:29.752107: Epoch time: 307.76 s +2024-09-10 19:39:32.490702: +2024-09-10 19:39:32.491572: Epoch 931 +2024-09-10 19:39:32.492300: Current learning rate: 0.0009 +2024-09-10 19:44:37.408442: train_loss -0.7546 +2024-09-10 19:44:37.417181: val_loss -0.7656 +2024-09-10 19:44:37.417843: Pseudo dice [0.9671, 0.9612, 0.9796, 0.951, 0.8927, 0.9773, 0.9933] +2024-09-10 19:44:37.418349: Epoch time: 304.92 s +2024-09-10 19:44:40.119056: +2024-09-10 19:44:40.144243: Epoch 932 +2024-09-10 19:44:40.152340: Current learning rate: 0.00089 +2024-09-10 19:50:03.481293: train_loss -0.7626 +2024-09-10 19:50:03.497182: val_loss -0.7754 +2024-09-10 19:50:03.497761: Pseudo dice [0.9652, 0.9622, 0.9766, 0.9491, 0.8857, 0.9753, 0.9916] +2024-09-10 19:50:03.498284: Epoch time: 323.37 s +2024-09-10 19:50:09.126287: +2024-09-10 19:50:09.127585: Epoch 933 +2024-09-10 19:50:09.128345: Current learning rate: 0.00088 +2024-09-10 19:55:08.446208: train_loss -0.774 +2024-09-10 19:55:08.458511: val_loss -0.7703 +2024-09-10 19:55:08.459116: Pseudo dice [0.9699, 0.9615, 0.9826, 0.9481, 0.8841, 0.9764, 0.9915] +2024-09-10 19:55:08.459622: Epoch time: 299.33 s +2024-09-10 19:55:11.345934: +2024-09-10 19:55:11.361106: Epoch 934 +2024-09-10 19:55:11.362497: Current learning rate: 0.00087 +2024-09-10 20:00:19.545136: train_loss -0.767 +2024-09-10 20:00:19.554585: val_loss -0.709 +2024-09-10 20:00:19.555329: Pseudo dice [0.9695, 0.961, 0.9817, 0.9545, 0.881, 0.9765, 0.9933] +2024-09-10 20:00:19.555955: Epoch time: 308.21 s +2024-09-10 20:00:22.065004: +2024-09-10 20:00:22.066614: Epoch 935 +2024-09-10 20:00:22.067929: Current learning rate: 0.00085 +2024-09-10 20:05:41.556468: train_loss -0.761 +2024-09-10 20:05:41.558848: val_loss -0.7494 +2024-09-10 20:05:41.559443: Pseudo dice [0.9638, 0.9621, 0.9741, 0.9597, 0.8951, 0.9732, 0.9917] +2024-09-10 20:05:41.560117: Epoch time: 319.5 s +2024-09-10 20:05:44.261942: +2024-09-10 20:05:44.276407: Epoch 936 +2024-09-10 20:05:44.276986: Current learning rate: 0.00084 +2024-09-10 20:10:51.060184: train_loss -0.75 +2024-09-10 20:10:51.062926: val_loss -0.7642 +2024-09-10 20:10:51.063729: Pseudo dice [0.9677, 0.9628, 0.9803, 0.9573, 0.8949, 0.9763, 0.9915] +2024-09-10 20:10:51.064397: Epoch time: 306.79 s +2024-09-10 20:10:53.434384: +2024-09-10 20:10:53.435508: Epoch 937 +2024-09-10 20:10:53.436589: Current learning rate: 0.00083 +2024-09-10 20:16:14.269455: train_loss -0.7489 +2024-09-10 20:16:14.289345: val_loss -0.7603 +2024-09-10 20:16:14.290000: Pseudo dice [0.9698, 0.962, 0.982, 0.9587, 0.8797, 0.9768, 0.9912] +2024-09-10 20:16:14.290579: Epoch time: 320.84 s +2024-09-10 20:16:17.455543: +2024-09-10 20:16:17.475923: Epoch 938 +2024-09-10 20:16:17.484179: Current learning rate: 0.00082 +2024-09-10 20:21:45.999433: train_loss -0.7675 +2024-09-10 20:21:46.013654: val_loss -0.7375 +2024-09-10 20:21:46.014566: Pseudo dice [0.9679, 0.9607, 0.9787, 0.9568, 0.8876, 0.9776, 0.9909] +2024-09-10 20:21:46.015293: Epoch time: 328.55 s +2024-09-10 20:21:48.762558: +2024-09-10 20:21:48.763607: Epoch 939 +2024-09-10 20:21:48.764250: Current learning rate: 0.00081 +2024-09-10 20:26:58.228920: train_loss -0.7698 +2024-09-10 20:26:58.239900: val_loss -0.767 +2024-09-10 20:26:58.240485: Pseudo dice [0.9706, 0.9631, 0.9831, 0.96, 0.8816, 0.9761, 0.9924] +2024-09-10 20:26:58.240995: Epoch time: 309.5 s +2024-09-10 20:27:02.309805: +2024-09-10 20:27:02.327761: Epoch 940 +2024-09-10 20:27:02.328711: Current learning rate: 0.00079 +2024-09-10 20:32:07.119796: train_loss -0.7501 +2024-09-10 20:32:07.122012: val_loss -0.7848 +2024-09-10 20:32:07.122625: Pseudo dice [0.9674, 0.9612, 0.9785, 0.9577, 0.8875, 0.9776, 0.9909] +2024-09-10 20:32:07.123114: Epoch time: 304.82 s +2024-09-10 20:32:13.457279: +2024-09-10 20:32:13.471761: Epoch 941 +2024-09-10 20:32:13.476549: Current learning rate: 0.00078 +2024-09-10 20:37:23.482222: train_loss -0.766 +2024-09-10 20:37:23.485099: val_loss -0.7661 +2024-09-10 20:37:23.486207: Pseudo dice [0.9691, 0.9628, 0.9818, 0.9598, 0.8924, 0.9756, 0.991] +2024-09-10 20:37:23.487200: Epoch time: 310.05 s +2024-09-10 20:37:27.511722: +2024-09-10 20:37:27.518742: Epoch 942 +2024-09-10 20:37:27.519411: Current learning rate: 0.00077 +2024-09-10 20:42:58.697308: train_loss -0.7405 +2024-09-10 20:42:58.700177: val_loss -0.7836 +2024-09-10 20:42:58.700850: Pseudo dice [0.9691, 0.96, 0.9806, 0.9561, 0.8827, 0.9765, 0.9922] +2024-09-10 20:42:58.701508: Epoch time: 331.19 s +2024-09-10 20:43:03.154067: +2024-09-10 20:43:03.168352: Epoch 943 +2024-09-10 20:43:03.169183: Current learning rate: 0.00076 +2024-09-10 20:48:17.777856: train_loss -0.7595 +2024-09-10 20:48:17.792985: val_loss -0.7887 +2024-09-10 20:48:17.793733: Pseudo dice [0.967, 0.9596, 0.9802, 0.9532, 0.8967, 0.9781, 0.9922] +2024-09-10 20:48:17.794370: Epoch time: 314.63 s +2024-09-10 20:48:21.454566: +2024-09-10 20:48:21.468489: Epoch 944 +2024-09-10 20:48:21.470493: Current learning rate: 0.00075 +2024-09-10 20:53:35.997790: train_loss -0.734 +2024-09-10 20:53:35.999967: val_loss -0.7703 +2024-09-10 20:53:36.000651: Pseudo dice [0.9666, 0.9611, 0.9797, 0.9486, 0.8939, 0.9738, 0.9926] +2024-09-10 20:53:36.001239: Epoch time: 314.55 s +2024-09-10 20:53:38.588798: +2024-09-10 20:53:38.589837: Epoch 945 +2024-09-10 20:53:38.590436: Current learning rate: 0.00074 +2024-09-10 20:58:46.987213: train_loss -0.7641 +2024-09-10 20:58:46.999383: val_loss -0.7779 +2024-09-10 20:58:47.000209: Pseudo dice [0.9668, 0.961, 0.9803, 0.9509, 0.8909, 0.9775, 0.993] +2024-09-10 20:58:47.000951: Epoch time: 308.4 s +2024-09-10 20:58:49.762311: +2024-09-10 20:58:49.763526: Epoch 946 +2024-09-10 20:58:49.764175: Current learning rate: 0.00072 +2024-09-10 21:03:55.019174: train_loss -0.7669 +2024-09-10 21:03:55.031104: val_loss -0.759 +2024-09-10 21:03:55.031879: Pseudo dice [0.9691, 0.9618, 0.9809, 0.9562, 0.8882, 0.9768, 0.9906] +2024-09-10 21:03:55.033010: Epoch time: 305.26 s +2024-09-10 21:03:57.553275: +2024-09-10 21:03:57.554618: Epoch 947 +2024-09-10 21:03:57.556089: Current learning rate: 0.00071 +2024-09-10 21:09:15.866409: train_loss -0.764 +2024-09-10 21:09:15.869397: val_loss -0.7634 +2024-09-10 21:09:15.870219: Pseudo dice [0.968, 0.9613, 0.9804, 0.9555, 0.8883, 0.9765, 0.9905] +2024-09-10 21:09:15.871233: Epoch time: 318.32 s +2024-09-10 21:09:19.728071: +2024-09-10 21:09:19.729309: Epoch 948 +2024-09-10 21:09:19.730156: Current learning rate: 0.0007 +2024-09-10 21:14:29.273705: train_loss -0.7619 +2024-09-10 21:14:29.275930: val_loss -0.8013 +2024-09-10 21:14:29.276467: Pseudo dice [0.968, 0.9609, 0.9789, 0.9569, 0.893, 0.9771, 0.9924] +2024-09-10 21:14:29.276945: Epoch time: 309.55 s +2024-09-10 21:14:31.710076: +2024-09-10 21:14:31.724591: Epoch 949 +2024-09-10 21:14:31.725374: Current learning rate: 0.00069 +2024-09-10 21:19:32.110519: train_loss -0.7582 +2024-09-10 21:19:32.129075: val_loss -0.7488 +2024-09-10 21:19:32.129932: Pseudo dice [0.9693, 0.9638, 0.9787, 0.9528, 0.8838, 0.9756, 0.9898] +2024-09-10 21:19:32.130478: Epoch time: 300.41 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_23_53_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_23_53_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..79a70579181d131529b1b93c54e82d6d0a56d0dc --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_10_23_53_16.txt @@ -0,0 +1,1055 @@ + +####################################################################### +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. +####################################################################### + +2024-09-10 23:53:20.843782: Using torch.compile... +2024-09-10 23:53:30.596460: do_dummy_2d_data_aug: False +2024-09-10 23:53:30.622741: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-10 23:53:30.643194: The split file contains 5 splits. +2024-09-10 23:53:30.643559: Desired fold for training: 4 +2024-09-10 23:53:30.643918: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-10 23:53:40.930712: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-10 23:53:42.268401: +2024-09-10 23:53:42.276529: Epoch 950 +2024-09-10 23:53:42.277498: Current learning rate: 0.00067 +2024-09-11 00:03:20.548618: train_loss -0.7665 +2024-09-11 00:03:20.550451: val_loss -0.75 +2024-09-11 00:03:20.551150: Pseudo dice [0.9678, 0.9627, 0.9806, 0.953, 0.8917, 0.977, 0.9907] +2024-09-11 00:03:20.551596: Epoch time: 578.29 s +2024-09-11 00:03:22.045959: +2024-09-11 00:03:22.046803: Epoch 951 +2024-09-11 00:03:22.047313: Current learning rate: 0.00066 +2024-09-11 00:07:54.450068: train_loss -0.7548 +2024-09-11 00:07:54.465457: val_loss -0.7295 +2024-09-11 00:07:54.466102: Pseudo dice [0.967, 0.9574, 0.9807, 0.9528, 0.8763, 0.9763, 0.9909] +2024-09-11 00:07:54.466601: Epoch time: 272.41 s +2024-09-11 00:07:56.906683: +2024-09-11 00:07:56.907678: Epoch 952 +2024-09-11 00:07:56.909325: Current learning rate: 0.00065 +2024-09-11 00:12:20.470751: train_loss -0.7349 +2024-09-11 00:12:20.481540: val_loss -0.7527 +2024-09-11 00:12:20.482023: Pseudo dice [0.9694, 0.9607, 0.9822, 0.9572, 0.8755, 0.9774, 0.9897] +2024-09-11 00:12:20.482475: Epoch time: 263.57 s +2024-09-11 00:12:22.889948: +2024-09-11 00:12:22.890832: Epoch 953 +2024-09-11 00:12:22.891432: Current learning rate: 0.00064 +2024-09-11 00:17:06.456221: train_loss -0.7545 +2024-09-11 00:17:06.470722: val_loss -0.7621 +2024-09-11 00:17:06.472030: Pseudo dice [0.9708, 0.9639, 0.9834, 0.9561, 0.904, 0.9789, 0.9923] +2024-09-11 00:17:06.473119: Epoch time: 283.57 s +2024-09-11 00:17:08.831279: +2024-09-11 00:17:08.832500: Epoch 954 +2024-09-11 00:17:08.833120: Current learning rate: 0.00063 +2024-09-11 00:21:51.054522: train_loss -0.7564 +2024-09-11 00:21:51.065103: val_loss -0.7663 +2024-09-11 00:21:51.065642: Pseudo dice [0.9671, 0.9604, 0.9805, 0.9577, 0.8993, 0.9781, 0.992] +2024-09-11 00:21:51.066097: Epoch time: 282.23 s +2024-09-11 00:21:51.066602: Yayy! New best EMA pseudo Dice: 0.9604 +2024-09-11 00:21:55.049115: +2024-09-11 00:21:55.049938: Epoch 955 +2024-09-11 00:21:55.050494: Current learning rate: 0.00061 +2024-09-11 00:26:37.438297: train_loss -0.7575 +2024-09-11 00:26:37.453339: val_loss -0.7632 +2024-09-11 00:26:37.453932: Pseudo dice [0.9656, 0.9638, 0.9764, 0.9579, 0.9028, 0.9754, 0.9919] +2024-09-11 00:26:37.454389: Epoch time: 282.39 s +2024-09-11 00:26:37.454796: Yayy! New best EMA pseudo Dice: 0.9606 +2024-09-11 00:26:42.140384: +2024-09-11 00:26:42.141268: Epoch 956 +2024-09-11 00:26:42.141862: Current learning rate: 0.0006 +2024-09-11 00:31:18.634132: train_loss -0.7747 +2024-09-11 00:31:18.661217: val_loss -0.7859 +2024-09-11 00:31:18.661793: Pseudo dice [0.9657, 0.9603, 0.9788, 0.9523, 0.884, 0.9758, 0.9915] +2024-09-11 00:31:18.662500: Epoch time: 276.5 s +2024-09-11 00:31:23.076064: +2024-09-11 00:31:23.088350: Epoch 957 +2024-09-11 00:31:23.088990: Current learning rate: 0.00059 +2024-09-11 00:36:26.462435: train_loss -0.7649 +2024-09-11 00:36:26.464662: val_loss -0.7354 +2024-09-11 00:36:26.465596: Pseudo dice [0.9674, 0.9641, 0.9776, 0.9533, 0.9021, 0.9782, 0.9925] +2024-09-11 00:36:26.466598: Epoch time: 303.39 s +2024-09-11 00:36:29.069641: +2024-09-11 00:36:29.070760: Epoch 958 +2024-09-11 00:36:29.071465: Current learning rate: 0.00058 +2024-09-11 00:41:10.056796: train_loss -0.7645 +2024-09-11 00:41:10.068032: val_loss -0.7911 +2024-09-11 00:41:10.068688: Pseudo dice [0.9685, 0.9612, 0.9823, 0.958, 0.8955, 0.9763, 0.9923] +2024-09-11 00:41:10.069208: Epoch time: 280.99 s +2024-09-11 00:41:10.069722: Yayy! New best EMA pseudo Dice: 0.9607 +2024-09-11 00:41:15.005635: +2024-09-11 00:41:15.006637: Epoch 959 +2024-09-11 00:41:15.007096: Current learning rate: 0.00056 +2024-09-11 00:45:58.337101: train_loss -0.7711 +2024-09-11 00:45:58.365413: val_loss -0.7761 +2024-09-11 00:45:58.366031: Pseudo dice [0.9698, 0.9626, 0.9815, 0.9585, 0.8997, 0.978, 0.9921] +2024-09-11 00:45:58.366492: Epoch time: 283.34 s +2024-09-11 00:45:58.367011: Yayy! New best EMA pseudo Dice: 0.9609 +2024-09-11 00:46:03.483044: +2024-09-11 00:46:03.484007: Epoch 960 +2024-09-11 00:46:03.484537: Current learning rate: 0.00055 +2024-09-11 00:50:56.060206: train_loss -0.7615 +2024-09-11 00:50:56.063350: val_loss -0.7716 +2024-09-11 00:50:56.064013: Pseudo dice [0.9707, 0.9628, 0.983, 0.9578, 0.8865, 0.9761, 0.9919] +2024-09-11 00:50:56.064566: Epoch time: 292.58 s +2024-09-11 00:50:56.065233: Yayy! New best EMA pseudo Dice: 0.961 +2024-09-11 00:51:03.386106: +2024-09-11 00:51:03.387157: Epoch 961 +2024-09-11 00:51:03.387783: Current learning rate: 0.00054 +2024-09-11 00:55:44.765003: train_loss -0.7538 +2024-09-11 00:55:44.766929: val_loss -0.7342 +2024-09-11 00:55:44.767566: Pseudo dice [0.9687, 0.9641, 0.9799, 0.9553, 0.896, 0.9785, 0.9917] +2024-09-11 00:55:44.768050: Epoch time: 281.38 s +2024-09-11 00:55:44.768549: Yayy! New best EMA pseudo Dice: 0.9611 +2024-09-11 00:55:49.463508: +2024-09-11 00:55:49.464568: Epoch 962 +2024-09-11 00:55:49.465957: Current learning rate: 0.00053 +2024-09-11 01:00:32.478409: train_loss -0.7567 +2024-09-11 01:00:32.480643: val_loss -0.77 +2024-09-11 01:00:32.481413: Pseudo dice [0.9686, 0.9606, 0.9798, 0.9575, 0.8993, 0.9766, 0.9929] +2024-09-11 01:00:32.481984: Epoch time: 283.02 s +2024-09-11 01:00:32.482792: Yayy! New best EMA pseudo Dice: 0.9612 +2024-09-11 01:00:37.607769: +2024-09-11 01:00:37.608834: Epoch 963 +2024-09-11 01:00:37.609486: Current learning rate: 0.00051 +2024-09-11 01:05:08.180588: train_loss -0.7767 +2024-09-11 01:05:08.197975: val_loss -0.7626 +2024-09-11 01:05:08.198708: Pseudo dice [0.9707, 0.9634, 0.9835, 0.9583, 0.9057, 0.978, 0.9917] +2024-09-11 01:05:08.199602: Epoch time: 270.58 s +2024-09-11 01:05:08.200295: Yayy! New best EMA pseudo Dice: 0.9615 +2024-09-11 01:05:12.676148: +2024-09-11 01:05:12.676980: Epoch 964 +2024-09-11 01:05:12.677510: Current learning rate: 0.0005 +2024-09-11 01:09:51.922550: train_loss -0.7694 +2024-09-11 01:09:51.928802: val_loss -0.7629 +2024-09-11 01:09:51.929401: Pseudo dice [0.9682, 0.9624, 0.9807, 0.9544, 0.9001, 0.9776, 0.9929] +2024-09-11 01:09:51.929952: Epoch time: 279.26 s +2024-09-11 01:09:51.930440: Yayy! New best EMA pseudo Dice: 0.9616 +2024-09-11 01:09:58.014467: +2024-09-11 01:09:58.015712: Epoch 965 +2024-09-11 01:09:58.016437: Current learning rate: 0.00049 +2024-09-11 01:14:33.536039: train_loss -0.7635 +2024-09-11 01:14:33.543215: val_loss -0.7677 +2024-09-11 01:14:33.544178: Pseudo dice [0.9711, 0.963, 0.9838, 0.9589, 0.9033, 0.9787, 0.9928] +2024-09-11 01:14:33.544868: Epoch time: 275.53 s +2024-09-11 01:14:33.545477: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-11 01:14:38.032951: +2024-09-11 01:14:38.034904: Epoch 966 +2024-09-11 01:14:38.035827: Current learning rate: 0.00048 +2024-09-11 01:19:10.055276: train_loss -0.7529 +2024-09-11 01:19:10.069326: val_loss -0.7619 +2024-09-11 01:19:10.069901: Pseudo dice [0.9655, 0.9637, 0.9754, 0.9587, 0.8941, 0.9777, 0.9916] +2024-09-11 01:19:10.070518: Epoch time: 272.03 s +2024-09-11 01:19:13.333451: +2024-09-11 01:19:13.344347: Epoch 967 +2024-09-11 01:19:13.345221: Current learning rate: 0.00046 +2024-09-11 01:24:14.252429: train_loss -0.7677 +2024-09-11 01:24:14.254265: val_loss -0.7892 +2024-09-11 01:24:14.254729: Pseudo dice [0.9689, 0.9617, 0.9804, 0.9554, 0.8935, 0.9773, 0.9929] +2024-09-11 01:24:14.255169: Epoch time: 300.92 s +2024-09-11 01:24:17.184367: +2024-09-11 01:24:17.185932: Epoch 968 +2024-09-11 01:24:17.187455: Current learning rate: 0.00045 +2024-09-11 01:28:45.078977: train_loss -0.7613 +2024-09-11 01:28:45.097287: val_loss -0.7871 +2024-09-11 01:28:45.097817: Pseudo dice [0.9697, 0.9622, 0.9827, 0.9557, 0.8902, 0.978, 0.9928] +2024-09-11 01:28:45.098304: Epoch time: 267.9 s +2024-09-11 01:28:49.419044: +2024-09-11 01:28:49.432558: Epoch 969 +2024-09-11 01:28:49.433549: Current learning rate: 0.00044 +2024-09-11 01:33:42.496591: train_loss -0.7506 +2024-09-11 01:33:42.521339: val_loss -0.7894 +2024-09-11 01:33:42.522012: Pseudo dice [0.9687, 0.9607, 0.9798, 0.9503, 0.8989, 0.9766, 0.993] +2024-09-11 01:33:42.522592: Epoch time: 293.08 s +2024-09-11 01:33:45.655144: +2024-09-11 01:33:45.667451: Epoch 970 +2024-09-11 01:33:45.668464: Current learning rate: 0.00043 +2024-09-11 01:38:38.401110: train_loss -0.7606 +2024-09-11 01:38:38.404176: val_loss -0.7804 +2024-09-11 01:38:38.404941: Pseudo dice [0.9685, 0.9621, 0.981, 0.9592, 0.8926, 0.9762, 0.9917] +2024-09-11 01:38:38.405661: Epoch time: 292.75 s +2024-09-11 01:38:40.723394: +2024-09-11 01:38:40.724206: Epoch 971 +2024-09-11 01:38:40.724826: Current learning rate: 0.00041 +2024-09-11 01:43:12.719750: train_loss -0.7504 +2024-09-11 01:43:12.722150: val_loss -0.7925 +2024-09-11 01:43:12.722737: Pseudo dice [0.9693, 0.9621, 0.9802, 0.9569, 0.8924, 0.9779, 0.9925] +2024-09-11 01:43:12.723362: Epoch time: 272.0 s +2024-09-11 01:43:17.463617: +2024-09-11 01:43:17.464795: Epoch 972 +2024-09-11 01:43:17.465354: Current learning rate: 0.0004 +2024-09-11 01:48:03.918671: train_loss -0.7662 +2024-09-11 01:48:03.938347: val_loss -0.7424 +2024-09-11 01:48:03.938911: Pseudo dice [0.9627, 0.9605, 0.9724, 0.9554, 0.8747, 0.9777, 0.9911] +2024-09-11 01:48:03.939548: Epoch time: 286.46 s +2024-09-11 01:48:06.809136: +2024-09-11 01:48:06.810327: Epoch 973 +2024-09-11 01:48:06.811343: Current learning rate: 0.00039 +2024-09-11 01:52:38.579334: train_loss -0.7575 +2024-09-11 01:52:38.593259: val_loss -0.7407 +2024-09-11 01:52:38.593897: Pseudo dice [0.9674, 0.9598, 0.9796, 0.9542, 0.8977, 0.977, 0.9927] +2024-09-11 01:52:38.594431: Epoch time: 271.78 s +2024-09-11 01:52:41.748705: +2024-09-11 01:52:41.749673: Epoch 974 +2024-09-11 01:52:41.750273: Current learning rate: 0.00037 +2024-09-11 01:57:19.491343: train_loss -0.7619 +2024-09-11 01:57:19.493760: val_loss -0.8052 +2024-09-11 01:57:19.494540: Pseudo dice [0.9679, 0.9605, 0.979, 0.9564, 0.8979, 0.9775, 0.9923] +2024-09-11 01:57:19.495156: Epoch time: 277.75 s +2024-09-11 01:57:22.110520: +2024-09-11 01:57:22.111465: Epoch 975 +2024-09-11 01:57:22.112938: Current learning rate: 0.00036 +2024-09-11 02:02:13.814964: train_loss -0.7739 +2024-09-11 02:02:13.817941: val_loss -0.7854 +2024-09-11 02:02:13.818784: Pseudo dice [0.97, 0.9618, 0.9814, 0.9556, 0.8946, 0.9778, 0.9929] +2024-09-11 02:02:13.819469: Epoch time: 291.74 s +2024-09-11 02:02:16.514518: +2024-09-11 02:02:16.515539: Epoch 976 +2024-09-11 02:02:16.516376: Current learning rate: 0.00035 +2024-09-11 02:07:03.056968: train_loss -0.7718 +2024-09-11 02:07:03.060063: val_loss -0.7407 +2024-09-11 02:07:03.061547: Pseudo dice [0.966, 0.9626, 0.9763, 0.9571, 0.8907, 0.9777, 0.9926] +2024-09-11 02:07:03.063104: Epoch time: 286.55 s +2024-09-11 02:07:05.601009: +2024-09-11 02:07:05.602001: Epoch 977 +2024-09-11 02:07:05.602521: Current learning rate: 0.00034 +2024-09-11 02:11:33.370016: train_loss -0.7574 +2024-09-11 02:11:33.388432: val_loss -0.7606 +2024-09-11 02:11:33.393964: Pseudo dice [0.9681, 0.9579, 0.9794, 0.9545, 0.8816, 0.9782, 0.9923] +2024-09-11 02:11:33.395207: Epoch time: 267.78 s +2024-09-11 02:11:37.952421: +2024-09-11 02:11:37.953715: Epoch 978 +2024-09-11 02:11:37.954415: Current learning rate: 0.00032 +2024-09-11 02:16:22.846525: train_loss -0.7657 +2024-09-11 02:16:22.849610: val_loss -0.7669 +2024-09-11 02:16:22.850524: Pseudo dice [0.9687, 0.9619, 0.9829, 0.9565, 0.8987, 0.9763, 0.9921] +2024-09-11 02:16:22.851334: Epoch time: 284.93 s +2024-09-11 02:16:25.437867: +2024-09-11 02:16:25.438706: Epoch 979 +2024-09-11 02:16:25.439214: Current learning rate: 0.00031 +2024-09-11 02:21:04.027969: train_loss -0.7666 +2024-09-11 02:21:04.047863: val_loss -0.7575 +2024-09-11 02:21:04.048653: Pseudo dice [0.9704, 0.9605, 0.9816, 0.9573, 0.8968, 0.977, 0.9913] +2024-09-11 02:21:04.049788: Epoch time: 278.6 s +2024-09-11 02:21:06.498241: +2024-09-11 02:21:06.499126: Epoch 980 +2024-09-11 02:21:06.499713: Current learning rate: 0.0003 +2024-09-11 02:25:50.866075: train_loss -0.7719 +2024-09-11 02:25:50.877426: val_loss -0.7603 +2024-09-11 02:25:50.878034: Pseudo dice [0.971, 0.9636, 0.9839, 0.9583, 0.8974, 0.9766, 0.9919] +2024-09-11 02:25:50.878506: Epoch time: 284.37 s +2024-09-11 02:25:54.585918: +2024-09-11 02:25:54.586733: Epoch 981 +2024-09-11 02:25:54.587349: Current learning rate: 0.00028 +2024-09-11 02:30:37.182218: train_loss -0.7674 +2024-09-11 02:30:37.201926: val_loss -0.7868 +2024-09-11 02:30:37.202869: Pseudo dice [0.97, 0.9604, 0.9836, 0.9581, 0.9043, 0.9785, 0.9937] +2024-09-11 02:30:37.203544: Epoch time: 282.6 s +2024-09-11 02:30:39.772002: +2024-09-11 02:30:39.772783: Epoch 982 +2024-09-11 02:30:39.773218: Current learning rate: 0.00027 +2024-09-11 02:35:34.396091: train_loss -0.7688 +2024-09-11 02:35:34.399032: val_loss -0.7905 +2024-09-11 02:35:34.399917: Pseudo dice [0.9623, 0.9618, 0.9729, 0.9577, 0.9048, 0.9757, 0.9938] +2024-09-11 02:35:34.400847: Epoch time: 294.63 s +2024-09-11 02:35:37.415482: +2024-09-11 02:35:37.417133: Epoch 983 +2024-09-11 02:35:37.417803: Current learning rate: 0.00026 +2024-09-11 02:40:33.018567: train_loss -0.7692 +2024-09-11 02:40:33.022142: val_loss -0.7628 +2024-09-11 02:40:33.022974: Pseudo dice [0.9661, 0.961, 0.9806, 0.9519, 0.9041, 0.979, 0.9931] +2024-09-11 02:40:33.023986: Epoch time: 295.61 s +2024-09-11 02:40:35.454889: +2024-09-11 02:40:35.456036: Epoch 984 +2024-09-11 02:40:35.456775: Current learning rate: 0.00024 +2024-09-11 02:45:26.091377: train_loss -0.7621 +2024-09-11 02:45:26.093121: val_loss -0.7623 +2024-09-11 02:45:26.093626: Pseudo dice [0.9671, 0.9635, 0.9779, 0.9591, 0.8919, 0.9774, 0.9936] +2024-09-11 02:45:26.094119: Epoch time: 290.64 s +2024-09-11 02:45:29.584770: +2024-09-11 02:45:29.585636: Epoch 985 +2024-09-11 02:45:29.586622: Current learning rate: 0.00023 +2024-09-11 02:50:20.825273: train_loss -0.7618 +2024-09-11 02:50:20.827545: val_loss -0.756 +2024-09-11 02:50:20.828187: Pseudo dice [0.9686, 0.9624, 0.9799, 0.9501, 0.8989, 0.9778, 0.9919] +2024-09-11 02:50:20.828772: Epoch time: 291.25 s +2024-09-11 02:50:23.639734: +2024-09-11 02:50:23.640467: Epoch 986 +2024-09-11 02:50:23.641067: Current learning rate: 0.00021 +2024-09-11 02:55:14.115789: train_loss -0.7651 +2024-09-11 02:55:14.146060: val_loss -0.767 +2024-09-11 02:55:14.146967: Pseudo dice [0.9681, 0.9611, 0.9812, 0.9504, 0.8943, 0.9775, 0.9918] +2024-09-11 02:55:14.147563: Epoch time: 290.48 s +2024-09-11 02:55:17.622746: +2024-09-11 02:55:17.623797: Epoch 987 +2024-09-11 02:55:17.624851: Current learning rate: 0.0002 +2024-09-11 03:00:21.860909: train_loss -0.7666 +2024-09-11 03:00:21.862817: val_loss -0.7561 +2024-09-11 03:00:21.863361: Pseudo dice [0.9681, 0.9639, 0.9799, 0.9567, 0.8784, 0.9782, 0.9911] +2024-09-11 03:00:21.863848: Epoch time: 304.24 s +2024-09-11 03:00:24.130427: +2024-09-11 03:00:24.144493: Epoch 988 +2024-09-11 03:00:24.145309: Current learning rate: 0.00019 +2024-09-11 03:05:09.719083: train_loss -0.7546 +2024-09-11 03:05:09.746293: val_loss -0.8009 +2024-09-11 03:05:09.747229: Pseudo dice [0.9681, 0.9597, 0.9797, 0.9528, 0.9017, 0.9796, 0.9943] +2024-09-11 03:05:09.748692: Epoch time: 285.59 s +2024-09-11 03:05:12.484636: +2024-09-11 03:05:12.485826: Epoch 989 +2024-09-11 03:05:12.486392: Current learning rate: 0.00017 +2024-09-11 03:10:03.141347: train_loss -0.7499 +2024-09-11 03:10:03.157536: val_loss -0.7677 +2024-09-11 03:10:03.158788: Pseudo dice [0.9703, 0.9639, 0.9823, 0.9596, 0.9075, 0.9782, 0.9935] +2024-09-11 03:10:03.159544: Epoch time: 290.66 s +2024-09-11 03:10:05.758432: +2024-09-11 03:10:05.759507: Epoch 990 +2024-09-11 03:10:05.760139: Current learning rate: 0.00016 +2024-09-11 03:14:55.323437: train_loss -0.7737 +2024-09-11 03:14:55.325512: val_loss -0.7558 +2024-09-11 03:14:55.326216: Pseudo dice [0.9698, 0.9636, 0.981, 0.958, 0.8996, 0.9772, 0.9924] +2024-09-11 03:14:55.326764: Epoch time: 289.57 s +2024-09-11 03:14:55.327317: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-11 03:14:59.174623: +2024-09-11 03:14:59.175702: Epoch 991 +2024-09-11 03:14:59.176327: Current learning rate: 0.00014 +2024-09-11 03:19:43.040209: train_loss -0.7503 +2024-09-11 03:19:43.042034: val_loss -0.7925 +2024-09-11 03:19:43.042634: Pseudo dice [0.9684, 0.9624, 0.9788, 0.9584, 0.8857, 0.9762, 0.9922] +2024-09-11 03:19:43.043171: Epoch time: 283.87 s +2024-09-11 03:19:45.373738: +2024-09-11 03:19:45.374773: Epoch 992 +2024-09-11 03:19:45.375307: Current learning rate: 0.00013 +2024-09-11 03:24:22.103224: train_loss -0.7718 +2024-09-11 03:24:22.117386: val_loss -0.7643 +2024-09-11 03:24:22.118566: Pseudo dice [0.9708, 0.9635, 0.9824, 0.955, 0.894, 0.9776, 0.9922] +2024-09-11 03:24:22.119389: Epoch time: 276.74 s +2024-09-11 03:24:26.112139: +2024-09-11 03:24:26.117251: Epoch 993 +2024-09-11 03:24:26.117906: Current learning rate: 0.00011 +2024-09-11 03:28:54.594792: train_loss -0.7626 +2024-09-11 03:28:54.597419: val_loss -0.7923 +2024-09-11 03:28:54.598366: Pseudo dice [0.9688, 0.9604, 0.9833, 0.9556, 0.9027, 0.9777, 0.9931] +2024-09-11 03:28:54.599049: Epoch time: 268.49 s +2024-09-11 03:28:54.599504: Yayy! New best EMA pseudo Dice: 0.9619 +2024-09-11 03:29:00.018118: +2024-09-11 03:29:00.019034: Epoch 994 +2024-09-11 03:29:00.019587: Current learning rate: 0.0001 +2024-09-11 03:33:40.919497: train_loss -0.7837 +2024-09-11 03:33:40.922139: val_loss -0.754 +2024-09-11 03:33:40.923122: Pseudo dice [0.9678, 0.9603, 0.9804, 0.9549, 0.8923, 0.977, 0.9928] +2024-09-11 03:33:40.923955: Epoch time: 280.91 s +2024-09-11 03:33:44.727985: +2024-09-11 03:33:44.728805: Epoch 995 +2024-09-11 03:33:44.729312: Current learning rate: 8e-05 +2024-09-11 03:38:12.368358: train_loss -0.7724 +2024-09-11 03:38:12.386090: val_loss -0.767 +2024-09-11 03:38:12.386883: Pseudo dice [0.9686, 0.9622, 0.9795, 0.9549, 0.892, 0.9791, 0.9927] +2024-09-11 03:38:12.387549: Epoch time: 267.65 s +2024-09-11 03:38:17.127300: +2024-09-11 03:38:17.136430: Epoch 996 +2024-09-11 03:38:17.137170: Current learning rate: 7e-05 +2024-09-11 03:43:11.837437: train_loss -0.7593 +2024-09-11 03:43:11.839557: val_loss -0.7791 +2024-09-11 03:43:11.840117: Pseudo dice [0.9685, 0.9651, 0.9798, 0.9576, 0.9033, 0.9782, 0.9932] +2024-09-11 03:43:11.840585: Epoch time: 294.72 s +2024-09-11 03:43:11.841017: Yayy! New best EMA pseudo Dice: 0.962 +2024-09-11 03:43:17.096313: +2024-09-11 03:43:17.097115: Epoch 997 +2024-09-11 03:43:17.097637: Current learning rate: 5e-05 +2024-09-11 03:48:05.721516: train_loss -0.7675 +2024-09-11 03:48:05.733372: val_loss -0.773 +2024-09-11 03:48:05.733914: Pseudo dice [0.9672, 0.961, 0.9787, 0.9614, 0.8863, 0.9793, 0.9911] +2024-09-11 03:48:05.734359: Epoch time: 288.63 s +2024-09-11 03:48:09.185968: +2024-09-11 03:48:09.188219: Epoch 998 +2024-09-11 03:48:09.189270: Current learning rate: 4e-05 +2024-09-11 03:53:14.270833: train_loss -0.7637 +2024-09-11 03:53:14.291189: val_loss -0.783 +2024-09-11 03:53:14.291896: Pseudo dice [0.9711, 0.963, 0.9839, 0.9616, 0.8984, 0.9788, 0.9931] +2024-09-11 03:53:14.292444: Epoch time: 305.09 s +2024-09-11 03:53:14.293069: Yayy! New best EMA pseudo Dice: 0.9621 +2024-09-11 03:53:21.156385: +2024-09-11 03:53:21.157512: Epoch 999 +2024-09-11 03:53:21.158206: Current learning rate: 2e-05 +2024-09-11 03:58:24.092750: train_loss -0.7632 +2024-09-11 03:58:24.106464: val_loss -0.7845 +2024-09-11 03:58:24.107637: Pseudo dice [0.9687, 0.9623, 0.9786, 0.9593, 0.9047, 0.9788, 0.9932] +2024-09-11 03:58:24.109005: Epoch time: 302.94 s +2024-09-11 03:58:24.110204: Yayy! New best EMA pseudo Dice: 0.9622 +2024-09-11 03:58:31.164522: Training done. +2024-09-11 03:58:31.380250: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-11 03:58:31.383511: The split file contains 5 splits. +2024-09-11 03:58:31.384139: Desired fold for training: 4 +2024-09-11 03:58:31.384651: This split has 1325 training and 331 validation cases. +2024-09-11 03:58:31.394323: predicting saros-004 +2024-09-11 03:58:32.164435: saros-004, shape torch.Size([1, 317, 244, 244]), rank 0 +2024-09-11 03:59:49.021307: predicting saros-008 +2024-09-11 03:59:49.564536: saros-008, shape torch.Size([1, 317, 268, 268]), rank 0 +2024-09-11 03:59:55.155862: predicting saros-016 +2024-09-11 03:59:55.533532: saros-016, shape torch.Size([1, 247, 233, 233]), rank 0 +2024-09-11 03:59:59.275556: predicting saros-017 +2024-09-11 03:59:59.603138: saros-017, shape torch.Size([1, 177, 240, 240]), rank 0 +2024-09-11 04:00:01.541464: predicting saros-026 +2024-09-11 04:00:02.171816: saros-026, shape torch.Size([1, 313, 283, 283]), rank 0 +2024-09-11 04:00:07.801095: predicting saros-035 +2024-09-11 04:00:08.120368: saros-035, shape torch.Size([1, 150, 293, 293]), rank 0 +2024-09-11 04:00:12.474148: predicting saros-036 +2024-09-11 04:00:12.984157: saros-036, shape torch.Size([1, 293, 243, 243]), rank 0 +2024-09-11 04:00:18.657073: predicting saros-037 +2024-09-11 04:00:19.246812: saros-037, shape torch.Size([1, 283, 276, 276]), rank 0 +2024-09-11 04:00:23.126940: predicting saros-038 +2024-09-11 04:00:24.418155: saros-038, shape torch.Size([1, 397, 333, 333]), rank 0 +2024-09-11 04:00:41.121350: predicting saros-045 +2024-09-11 04:00:41.380385: saros-045, shape torch.Size([1, 130, 304, 304]), rank 0 +2024-09-11 04:00:45.669312: predicting saros-048 +2024-09-11 04:00:46.532499: saros-048, shape torch.Size([1, 337, 333, 333]), rank 0 +2024-09-11 04:00:59.069035: predicting saros-053 +2024-09-11 04:00:59.319583: saros-053, shape torch.Size([1, 207, 233, 233]), rank 0 +2024-09-11 04:01:03.130192: predicting saros-085 +2024-09-11 04:01:03.485250: saros-085, shape torch.Size([1, 140, 333, 333]), rank 0 +2024-09-11 04:01:07.892951: predicting saros-095 +2024-09-11 04:01:08.568886: saros-095, shape torch.Size([1, 293, 290, 290]), rank 0 +2024-09-11 04:01:21.036688: predicting saros-103 +2024-09-11 04:01:21.563308: saros-103, shape torch.Size([1, 297, 257, 257]), rank 0 +2024-09-11 04:01:27.314095: predicting saros-111 +2024-09-11 04:01:27.692932: saros-111, shape torch.Size([1, 163, 267, 267]), rank 0 +2024-09-11 04:01:29.698819: predicting saros-112 +2024-09-11 04:01:30.200543: saros-112, shape torch.Size([1, 257, 266, 266]), rank 0 +2024-09-11 04:01:34.018909: predicting saros-115 +2024-09-11 04:01:34.253736: saros-115, shape torch.Size([1, 173, 207, 207]), rank 0 +2024-09-11 04:01:36.199193: predicting saros-119 +2024-09-11 04:01:36.480231: saros-119, shape torch.Size([1, 183, 233, 233]), rank 0 +2024-09-11 04:01:38.403737: predicting saros-122 +2024-09-11 04:01:39.060428: saros-122, shape torch.Size([1, 287, 333, 333]), rank 0 +2024-09-11 04:01:47.501116: predicting saros-127 +2024-09-11 04:01:47.793354: saros-127, shape torch.Size([1, 177, 240, 240]), rank 0 +2024-09-11 04:01:49.847302: predicting saros-129 +2024-09-11 04:01:50.276318: saros-129, shape torch.Size([1, 267, 221, 221]), rank 0 +2024-09-11 04:01:54.055737: predicting saros-143 +2024-09-11 04:01:54.902239: saros-143, shape torch.Size([1, 283, 355, 355]), rank 0 +2024-09-11 04:02:03.840677: predicting saros-146 +2024-09-11 04:02:04.467170: saros-146, shape torch.Size([1, 293, 240, 240]), rank 0 +2024-09-11 04:02:10.156951: predicting saros-147 +2024-09-11 04:02:10.486344: saros-147, shape torch.Size([1, 137, 251, 251]), rank 0 +2024-09-11 04:02:12.491092: predicting saros-150 +2024-09-11 04:02:12.998562: saros-150, shape torch.Size([1, 290, 253, 253]), rank 0 +2024-09-11 04:02:18.615455: predicting saros-151 +2024-09-11 04:02:19.437993: saros-151, shape torch.Size([1, 347, 333, 333]), rank 0 +2024-09-11 04:02:31.944301: predicting saros-159 +2024-09-11 04:02:32.382941: saros-159, shape torch.Size([1, 300, 233, 233]), rank 0 +2024-09-11 04:02:37.977303: predicting saros-170 +2024-09-11 04:02:38.241191: saros-170, shape torch.Size([1, 170, 232, 232]), rank 0 +2024-09-11 04:02:40.188365: predicting saros-183 +2024-09-11 04:02:40.954362: saros-183, shape torch.Size([1, 303, 336, 336]), rank 0 +2024-09-11 04:02:53.491807: predicting saros-186 +2024-09-11 04:02:53.854004: saros-186, shape torch.Size([1, 150, 307, 307]), rank 0 +2024-09-11 04:02:58.370795: predicting saros-197 +2024-09-11 04:02:58.570880: saros-197, shape torch.Size([1, 137, 213, 213]), rank 0 +2024-09-11 04:03:00.570448: predicting saros-202 +2024-09-11 04:03:01.013692: saros-202, shape torch.Size([1, 150, 327, 327]), rank 0 +2024-09-11 04:03:05.412179: predicting saros-207 +2024-09-11 04:03:05.913058: saros-207, shape torch.Size([1, 147, 307, 307]), rank 0 +2024-09-11 04:03:10.168617: predicting saros-219 +2024-09-11 04:03:10.878385: saros-219, shape torch.Size([1, 320, 287, 287]), rank 0 +2024-09-11 04:03:16.651904: predicting saros-224 +2024-09-11 04:03:17.271737: saros-224, shape torch.Size([1, 217, 329, 329]), rank 0 +2024-09-11 04:03:25.627193: predicting saros-226 +2024-09-11 04:03:26.358723: saros-226, shape torch.Size([1, 337, 330, 330]), rank 0 +2024-09-11 04:03:38.851092: predicting saros-232 +2024-09-11 04:03:39.322416: saros-232, shape torch.Size([1, 290, 263, 263]), rank 0 +2024-09-11 04:03:44.950655: predicting saros-233 +2024-09-11 04:03:45.670762: saros-233, shape torch.Size([1, 343, 300, 300]), rank 0 +2024-09-11 04:03:58.195035: predicting saros-240 +2024-09-11 04:03:58.964381: saros-240, shape torch.Size([1, 287, 286, 286]), rank 0 +2024-09-11 04:04:02.888595: predicting saros-242 +2024-09-11 04:04:03.462205: saros-242, shape torch.Size([1, 210, 333, 333]), rank 0 +2024-09-11 04:04:11.919048: predicting saros-246 +2024-09-11 04:04:12.593417: saros-246, shape torch.Size([1, 313, 240, 240]), rank 0 +2024-09-11 04:04:18.271054: predicting saros-253 +2024-09-11 04:04:19.041812: saros-253, shape torch.Size([1, 313, 319, 319]), rank 0 +2024-09-11 04:04:31.530401: predicting saros-258 +2024-09-11 04:04:32.079560: saros-258, shape torch.Size([1, 293, 295, 295]), rank 0 +2024-09-11 04:04:44.504962: predicting saros-262 +2024-09-11 04:04:44.832054: saros-262, shape torch.Size([1, 167, 253, 253]), rank 0 +2024-09-11 04:04:46.815834: predicting saros-267 +2024-09-11 04:04:47.017556: saros-267, shape torch.Size([1, 143, 227, 227]), rank 0 +2024-09-11 04:04:48.954719: predicting saros-279 +2024-09-11 04:04:49.206745: saros-279, shape torch.Size([1, 163, 236, 236]), rank 0 +2024-09-11 04:04:51.160093: predicting saros-282 +2024-09-11 04:04:51.970352: saros-282, shape torch.Size([1, 330, 295, 295]), rank 0 +2024-09-11 04:05:04.400423: predicting saros-293 +2024-09-11 04:05:05.215054: saros-293, shape torch.Size([1, 340, 301, 301]), rank 0 +2024-09-11 04:05:17.680001: predicting saros-297 +2024-09-11 04:05:18.475748: saros-297, shape torch.Size([1, 357, 321, 321]), rank 0 +2024-09-11 04:05:31.096835: predicting saros-301 +2024-09-11 04:05:31.344388: saros-301, shape torch.Size([1, 243, 200, 200]), rank 0 +2024-09-11 04:05:35.168953: predicting saros-309 +2024-09-11 04:05:35.578003: saros-309, shape torch.Size([1, 210, 267, 267]), rank 0 +2024-09-11 04:05:39.348644: predicting saros-317 +2024-09-11 04:05:39.611846: saros-317, shape torch.Size([1, 217, 209, 209]), rank 0 +2024-09-11 04:05:43.344918: predicting saros-325 +2024-09-11 04:05:43.591347: saros-325, shape torch.Size([1, 173, 221, 221]), rank 0 +2024-09-11 04:05:45.573872: predicting saros-335 +2024-09-11 04:05:46.135610: saros-335, shape torch.Size([1, 247, 267, 267]), rank 0 +2024-09-11 04:05:50.007683: predicting saros-347 +2024-09-11 04:05:50.412511: saros-347, shape torch.Size([1, 207, 225, 225]), rank 0 +2024-09-11 04:05:54.215354: predicting saros-348 +2024-09-11 04:05:54.572140: saros-348, shape torch.Size([1, 220, 240, 240]), rank 0 +2024-09-11 04:05:58.478727: predicting saros-358 +2024-09-11 04:05:58.835978: saros-358, shape torch.Size([1, 220, 213, 213]), rank 0 +2024-09-11 04:06:03.257267: predicting saros-364 +2024-09-11 04:06:03.799381: saros-364, shape torch.Size([1, 217, 333, 333]), rank 0 +2024-09-11 04:06:12.263880: predicting saros-373 +2024-09-11 04:06:13.356744: saros-373, shape torch.Size([1, 190, 333, 333]), rank 0 +2024-09-11 04:06:17.735829: predicting saros-374 +2024-09-11 04:06:18.008446: saros-374, shape torch.Size([1, 210, 218, 218]), rank 0 +2024-09-11 04:06:21.784185: predicting saros-384 +2024-09-11 04:06:22.192560: saros-384, shape torch.Size([1, 250, 201, 201]), rank 0 +2024-09-11 04:06:25.976876: predicting saros-388 +2024-09-11 04:06:26.490423: saros-388, shape torch.Size([1, 223, 267, 267]), rank 0 +2024-09-11 04:06:30.298999: predicting saros-400 +2024-09-11 04:06:30.608339: saros-400, shape torch.Size([1, 197, 207, 207]), rank 0 +2024-09-11 04:06:34.360146: predicting saros-402 +2024-09-11 04:06:34.741234: saros-402, shape torch.Size([1, 193, 267, 267]), rank 0 +2024-09-11 04:06:38.572302: predicting saros-403 +2024-09-11 04:06:38.993488: saros-403, shape torch.Size([1, 220, 253, 253]), rank 0 +2024-09-11 04:06:42.843964: predicting saros-413 +2024-09-11 04:06:43.280895: saros-413, shape torch.Size([1, 217, 217, 217]), rank 0 +2024-09-11 04:06:47.030738: predicting saros-414 +2024-09-11 04:06:47.341119: saros-414, shape torch.Size([1, 217, 209, 209]), rank 0 +2024-09-11 04:06:51.112094: predicting saros-415 +2024-09-11 04:06:51.656064: saros-415, shape torch.Size([1, 197, 333, 333]), rank 0 +2024-09-11 04:07:00.021707: predicting saros-419 +2024-09-11 04:07:00.575362: saros-419, shape torch.Size([1, 207, 333, 333]), rank 0 +2024-09-11 04:07:08.940818: predicting saros-421 +2024-09-11 04:07:09.143667: saros-421, shape torch.Size([1, 187, 209, 209]), rank 0 +2024-09-11 04:07:11.111228: predicting saros-437 +2024-09-11 04:07:11.319608: saros-437, shape torch.Size([1, 187, 197, 197]), rank 0 +2024-09-11 04:07:13.252730: predicting saros-438 +2024-09-11 04:07:13.653684: saros-438, shape torch.Size([1, 227, 238, 238]), rank 0 +2024-09-11 04:07:17.420743: predicting saros-439 +2024-09-11 04:07:17.822208: saros-439, shape torch.Size([1, 220, 253, 253]), rank 0 +2024-09-11 04:07:21.653989: predicting saros-441 +2024-09-11 04:07:22.722167: saros-441, shape torch.Size([1, 353, 333, 333]), rank 0 +2024-09-11 04:07:35.368876: predicting saros-452 +2024-09-11 04:07:35.789712: saros-452, shape torch.Size([1, 267, 247, 247]), rank 0 +2024-09-11 04:07:39.605705: predicting saros-456 +2024-09-11 04:07:39.995558: saros-456, shape torch.Size([1, 243, 267, 267]), rank 0 +2024-09-11 04:07:43.853260: predicting saros-460 +2024-09-11 04:07:44.283109: saros-460, shape torch.Size([1, 250, 233, 233]), rank 0 +2024-09-11 04:07:48.090009: predicting saros-463 +2024-09-11 04:07:48.472423: saros-463, shape torch.Size([1, 260, 233, 233]), rank 0 +2024-09-11 04:07:52.225391: predicting saros-472 +2024-09-11 04:07:52.619496: saros-472, shape torch.Size([1, 237, 246, 246]), rank 0 +2024-09-11 04:07:56.391674: predicting saros-488 +2024-09-11 04:07:56.722805: saros-488, shape torch.Size([1, 200, 258, 258]), rank 0 +2024-09-11 04:08:00.468938: predicting saros-489 +2024-09-11 04:08:00.841584: saros-489, shape torch.Size([1, 220, 253, 253]), rank 0 +2024-09-11 04:08:04.641138: predicting saros-496 +2024-09-11 04:08:05.117235: saros-496, shape torch.Size([1, 203, 287, 287]), rank 0 +2024-09-11 04:08:09.193681: predicting saros-502 +2024-09-11 04:08:09.936341: saros-502, shape torch.Size([1, 247, 299, 299]), rank 0 +2024-09-11 04:08:18.388876: predicting saros-503 +2024-09-11 04:08:18.771317: saros-503, shape torch.Size([1, 197, 233, 233]), rank 0 +2024-09-11 04:08:22.513037: predicting saros-505 +2024-09-11 04:08:23.194016: saros-505, shape torch.Size([1, 270, 333, 333]), rank 0 +2024-09-11 04:08:31.593571: predicting saros-509 +2024-09-11 04:08:31.977277: saros-509, shape torch.Size([1, 220, 255, 255]), rank 0 +2024-09-11 04:08:35.862551: predicting saros-524 +2024-09-11 04:08:36.384772: saros-524, shape torch.Size([1, 213, 265, 265]), rank 0 +2024-09-11 04:08:40.204739: predicting saros-540 +2024-09-11 04:08:40.877419: saros-540, shape torch.Size([1, 250, 333, 333]), rank 0 +2024-09-11 04:08:49.335702: predicting saros-546 +2024-09-11 04:08:49.828596: saros-546, shape torch.Size([1, 197, 333, 333]), rank 0 +2024-09-11 04:08:58.237741: predicting saros-550 +2024-09-11 04:08:58.732617: saros-550, shape torch.Size([1, 183, 333, 333]), rank 0 +2024-09-11 04:09:03.114045: predicting saros-553 +2024-09-11 04:09:03.630425: saros-553, shape torch.Size([1, 240, 253, 253]), rank 0 +2024-09-11 04:09:07.416967: predicting saros-556 +2024-09-11 04:09:07.806114: saros-556, shape torch.Size([1, 230, 245, 245]), rank 0 +2024-09-11 04:09:11.567447: predicting saros-563 +2024-09-11 04:09:11.869072: saros-563, shape torch.Size([1, 217, 246, 246]), rank 0 +2024-09-11 04:09:15.665791: predicting saros-573 +2024-09-11 04:09:16.204088: saros-573, shape torch.Size([1, 223, 333, 333]), rank 0 +2024-09-11 04:09:24.611921: predicting saros-574 +2024-09-11 04:09:24.891582: saros-574, shape torch.Size([1, 193, 218, 218]), rank 0 +2024-09-11 04:09:28.625860: predicting saros-577 +2024-09-11 04:09:28.914994: saros-577, shape torch.Size([1, 163, 267, 267]), rank 0 +2024-09-11 04:09:30.892831: predicting saros-579 +2024-09-11 04:09:31.257039: saros-579, shape torch.Size([1, 260, 233, 233]), rank 0 +2024-09-11 04:09:35.020888: predicting saros-586 +2024-09-11 04:09:35.425543: saros-586, shape torch.Size([1, 213, 267, 267]), rank 0 +2024-09-11 04:09:39.368117: predicting saros-592 +2024-09-11 04:09:39.933944: saros-592, shape torch.Size([1, 233, 253, 253]), rank 0 +2024-09-11 04:09:43.713365: predicting saros-607 +2024-09-11 04:09:45.336587: saros-607, shape torch.Size([1, 580, 333, 333]), rank 0 +2024-09-11 04:10:10.286532: predicting saros-609 +2024-09-11 04:10:11.871990: saros-609, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:10:41.026727: predicting saros-619 +2024-09-11 04:10:42.375868: saros-619, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:11:07.252840: predicting saros-624 +2024-09-11 04:11:08.674553: saros-624, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:11:29.554384: predicting saros-627 +2024-09-11 04:11:30.831282: saros-627, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:11:55.777586: predicting saros-637 +2024-09-11 04:11:57.261436: saros-637, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 04:12:22.339606: predicting saros-639 +2024-09-11 04:12:24.490625: saros-639, shape torch.Size([1, 573, 467, 467]), rank 0 +2024-09-11 04:13:02.084464: predicting saros-641 +2024-09-11 04:13:03.724962: saros-641, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:13:28.946705: predicting saros-642 +2024-09-11 04:13:30.351733: saros-642, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:13:55.370438: predicting saros-645 +2024-09-11 04:13:57.264635: saros-645, shape torch.Size([1, 677, 333, 333]), rank 0 +2024-09-11 04:14:26.419109: predicting saros-648 +2024-09-11 04:14:27.581320: saros-648, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-11 04:14:48.575400: predicting saros-654 +2024-09-11 04:14:49.867898: saros-654, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:15:14.893336: predicting saros-671 +2024-09-11 04:15:16.108215: saros-671, shape torch.Size([1, 517, 333, 333]), rank 0 +2024-09-11 04:15:37.041286: predicting saros-676 +2024-09-11 04:15:38.564040: saros-676, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:16:03.543735: predicting saros-677 +2024-09-11 04:16:05.194150: saros-677, shape torch.Size([1, 663, 333, 333]), rank 0 +2024-09-11 04:16:30.213342: predicting saros-685 +2024-09-11 04:16:31.759690: saros-685, shape torch.Size([1, 727, 333, 333]), rank 0 +2024-09-11 04:17:01.020711: predicting saros-690 +2024-09-11 04:17:02.292802: saros-690, shape torch.Size([1, 540, 333, 333]), rank 0 +2024-09-11 04:17:23.359551: predicting saros-694 +2024-09-11 04:17:24.394117: saros-694, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:17:41.359286: predicting saros-698 +2024-09-11 04:17:43.148306: saros-698, shape torch.Size([1, 567, 400, 400]), rank 0 +2024-09-11 04:18:20.034286: predicting saros-704 +2024-09-11 04:18:21.289376: saros-704, shape torch.Size([1, 490, 333, 333]), rank 0 +2024-09-11 04:18:42.142872: predicting saros-706 +2024-09-11 04:18:43.682444: saros-706, shape torch.Size([1, 727, 333, 333]), rank 0 +2024-09-11 04:19:12.773014: predicting saros-707 +2024-09-11 04:19:14.931562: saros-707, shape torch.Size([1, 623, 400, 400]), rank 0 +2024-09-11 04:19:59.200146: predicting saros-718 +2024-09-11 04:20:00.338810: saros-718, shape torch.Size([1, 430, 333, 333]), rank 0 +2024-09-11 04:20:17.130610: predicting saros-733 +2024-09-11 04:20:19.398022: saros-733, shape torch.Size([1, 737, 400, 400]), rank 0 +2024-09-11 04:21:10.928101: predicting saros-736 +2024-09-11 04:21:12.213769: saros-736, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:21:37.273619: predicting saros-753 +2024-09-11 04:21:38.629899: saros-753, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:22:03.735665: predicting saros-754 +2024-09-11 04:22:05.447354: saros-754, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:22:34.680166: predicting saros-757 +2024-09-11 04:22:36.316261: saros-757, shape torch.Size([1, 603, 333, 333]), rank 0 +2024-09-11 04:23:01.280886: predicting saros-759 +2024-09-11 04:23:02.729138: saros-759, shape torch.Size([1, 573, 333, 333]), rank 0 +2024-09-11 04:23:23.778509: predicting saros-760 +2024-09-11 04:23:25.339495: saros-760, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:23:50.328813: predicting saros-765 +2024-09-11 04:23:51.825064: saros-765, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:24:20.957905: predicting saros-767 +2024-09-11 04:24:22.306810: saros-767, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:24:47.286956: predicting saros-780 +2024-09-11 04:24:50.237663: saros-780, shape torch.Size([1, 807, 400, 400]), rank 0 +2024-09-11 04:25:49.374157: predicting saros-795 +2024-09-11 04:25:51.263145: saros-795, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:26:20.523704: predicting saros-796 +2024-09-11 04:26:21.993680: saros-796, shape torch.Size([1, 657, 333, 333]), rank 0 +2024-09-11 04:26:47.063086: predicting saros-810 +2024-09-11 04:26:48.449039: saros-810, shape torch.Size([1, 583, 333, 333]), rank 0 +2024-09-11 04:27:13.374788: predicting saros-817 +2024-09-11 04:27:14.633864: saros-817, shape torch.Size([1, 547, 333, 333]), rank 0 +2024-09-11 04:27:35.541621: predicting saros-819 +2024-09-11 04:27:37.009676: saros-819, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 04:28:02.003861: predicting saros-822 +2024-09-11 04:28:03.519304: saros-822, shape torch.Size([1, 653, 333, 333]), rank 0 +2024-09-11 04:28:28.640356: predicting saros-833 +2024-09-11 04:28:29.942373: saros-833, shape torch.Size([1, 593, 333, 333]), rank 0 +2024-09-11 04:28:55.226920: predicting saros-849 +2024-09-11 04:28:57.017045: saros-849, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:29:26.179466: predicting saros-853 +2024-09-11 04:29:27.956749: saros-853, shape torch.Size([1, 697, 333, 333]), rank 0 +2024-09-11 04:29:57.210740: predicting saros-854 +2024-09-11 04:29:58.706220: saros-854, shape torch.Size([1, 627, 333, 333]), rank 0 +2024-09-11 04:30:23.679247: predicting saros-858 +2024-09-11 04:30:24.973289: saros-858, shape torch.Size([1, 570, 333, 333]), rank 0 +2024-09-11 04:30:45.867432: predicting saros-859 +2024-09-11 04:30:47.426535: saros-859, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:31:16.458422: predicting saros-872 +2024-09-11 04:31:17.991190: saros-872, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:31:47.351753: predicting saros-873 +2024-09-11 04:31:48.583305: saros-873, shape torch.Size([1, 450, 333, 333]), rank 0 +2024-09-11 04:32:05.381424: predicting saros-877 +2024-09-11 04:32:06.699493: saros-877, shape torch.Size([1, 647, 333, 333]), rank 0 +2024-09-11 04:32:31.812461: predicting saros-881 +2024-09-11 04:32:33.548632: saros-881, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:33:02.621214: predicting saros-887 +2024-09-11 04:33:04.065087: saros-887, shape torch.Size([1, 673, 333, 333]), rank 0 +2024-09-11 04:33:33.181540: predicting tseg-s0003 +2024-09-11 04:33:33.263674: tseg-s0003, shape torch.Size([1, 126, 127, 127]), rank 0 +2024-09-11 04:33:33.760550: predicting tseg-s0011 +2024-09-11 04:33:34.599361: tseg-s0011, shape torch.Size([1, 431, 311, 311]), rank 0 +2024-09-11 04:33:51.228011: predicting tseg-s0042 +2024-09-11 04:33:51.745326: tseg-s0042, shape torch.Size([1, 332, 285, 285]), rank 0 +2024-09-11 04:33:57.645463: predicting tseg-s0056 +2024-09-11 04:33:57.749411: tseg-s0056, shape torch.Size([1, 139, 179, 179]), rank 0 +2024-09-11 04:33:58.316754: predicting tseg-s0068 +2024-09-11 04:33:58.388546: tseg-s0068, shape torch.Size([1, 189, 137, 104]), rank 0 +2024-09-11 04:33:58.900012: predicting tseg-s0070 +2024-09-11 04:33:59.376442: tseg-s0070, shape torch.Size([1, 270, 242, 242]), rank 0 +2024-09-11 04:34:03.138383: predicting tseg-s0078 +2024-09-11 04:34:03.458932: tseg-s0078, shape torch.Size([1, 265, 271, 271]), rank 0 +2024-09-11 04:34:07.330416: predicting tseg-s0082 +2024-09-11 04:34:07.695605: tseg-s0082, shape torch.Size([1, 311, 205, 260]), rank 0 +2024-09-11 04:34:13.339556: predicting tseg-s0084 +2024-09-11 04:34:13.400143: tseg-s0084, shape torch.Size([1, 99, 128, 128]), rank 0 +2024-09-11 04:34:13.911988: predicting tseg-s0099 +2024-09-11 04:34:13.968119: tseg-s0099, shape torch.Size([1, 77, 108, 108]), rank 0 +2024-09-11 04:34:14.485947: predicting tseg-s0103 +2024-09-11 04:34:14.681472: tseg-s0103, shape torch.Size([1, 117, 178, 178]), rank 0 +2024-09-11 04:34:15.242576: predicting tseg-s0105 +2024-09-11 04:34:15.412959: tseg-s0105, shape torch.Size([1, 116, 283, 283]), rank 0 +2024-09-11 04:34:17.393984: predicting tseg-s0115 +2024-09-11 04:34:17.939580: tseg-s0115, shape torch.Size([1, 283, 303, 303]), rank 0 +2024-09-11 04:34:26.317826: predicting tseg-s0122 +2024-09-11 04:34:26.358455: tseg-s0122, shape torch.Size([1, 83, 93, 93]), rank 0 +2024-09-11 04:34:26.859876: predicting tseg-s0132 +2024-09-11 04:34:26.952323: tseg-s0132, shape torch.Size([1, 52, 305, 305]), rank 0 +2024-09-11 04:34:31.110015: predicting tseg-s0135 +2024-09-11 04:34:31.297789: tseg-s0135, shape torch.Size([1, 130, 299, 299]), rank 0 +2024-09-11 04:34:35.515071: predicting tseg-s0188 +2024-09-11 04:34:35.935688: tseg-s0188, shape torch.Size([1, 219, 311, 311]), rank 0 +2024-09-11 04:34:44.306667: predicting tseg-s0196 +2024-09-11 04:34:44.892679: tseg-s0196, shape torch.Size([1, 367, 295, 295]), rank 0 +2024-09-11 04:34:57.426273: predicting tseg-s0199 +2024-09-11 04:34:57.546145: tseg-s0199, shape torch.Size([1, 62, 330, 330]), rank 0 +2024-09-11 04:35:01.791608: predicting tseg-s0221 +2024-09-11 04:35:01.858336: tseg-s0221, shape torch.Size([1, 193, 103, 103]), rank 0 +2024-09-11 04:35:43.811573: predicting tseg-s0224 +2024-09-11 04:35:44.881575: tseg-s0224, shape torch.Size([1, 621, 333, 333]), rank 0 +2024-09-11 04:36:09.747162: predicting tseg-s0232 +2024-09-11 04:36:09.946919: tseg-s0232, shape torch.Size([1, 279, 148, 279]), rank 0 +2024-09-11 04:36:11.966038: predicting tseg-s0234 +2024-09-11 04:36:12.071114: tseg-s0234, shape torch.Size([1, 129, 169, 169]), rank 0 +2024-09-11 04:36:12.628101: predicting tseg-s0241 +2024-09-11 04:36:13.020091: tseg-s0241, shape torch.Size([1, 171, 257, 257]), rank 0 +2024-09-11 04:36:15.074501: predicting tseg-s0260 +2024-09-11 04:36:15.755243: tseg-s0260, shape torch.Size([1, 283, 424, 366]), rank 0 +2024-09-11 04:36:27.000629: predicting tseg-s0281 +2024-09-11 04:36:27.100831: tseg-s0281, shape torch.Size([1, 183, 174, 183]), rank 0 +2024-09-11 04:36:27.692148: predicting tseg-s0285 +2024-09-11 04:36:28.220109: tseg-s0285, shape torch.Size([1, 181, 333, 333]), rank 0 +2024-09-11 04:36:32.536342: predicting tseg-s0286 +2024-09-11 04:36:32.754107: tseg-s0286, shape torch.Size([1, 156, 261, 261]), rank 0 +2024-09-11 04:36:34.788380: predicting tseg-s0289 +2024-09-11 04:36:34.890049: tseg-s0289, shape torch.Size([1, 233, 145, 145]), rank 0 +2024-09-11 04:36:35.911028: predicting tseg-s0293 +2024-09-11 04:36:36.257661: tseg-s0293, shape torch.Size([1, 329, 240, 240]), rank 0 +2024-09-11 04:36:41.867844: predicting tseg-s0320 +2024-09-11 04:36:42.237480: tseg-s0320, shape torch.Size([1, 282, 272, 272]), rank 0 +2024-09-11 04:36:46.072252: predicting tseg-s0325 +2024-09-11 04:36:46.591566: tseg-s0325, shape torch.Size([1, 293, 315, 315]), rank 0 +2024-09-11 04:36:59.094831: predicting tseg-s0336 +2024-09-11 04:36:59.149883: tseg-s0336, shape torch.Size([1, 130, 122, 122]), rank 0 +2024-09-11 04:36:59.678662: predicting tseg-s0338 +2024-09-11 04:36:59.783749: tseg-s0338, shape torch.Size([1, 102, 221, 221]), rank 0 +2024-09-11 04:37:01.668611: predicting tseg-s0342 +2024-09-11 04:37:02.081378: tseg-s0342, shape torch.Size([1, 284, 233, 233]), rank 0 +2024-09-11 04:37:05.863049: predicting tseg-s0347 +2024-09-11 04:37:05.908386: tseg-s0347, shape torch.Size([1, 130, 95, 95]), rank 0 +2024-09-11 04:37:06.401282: predicting tseg-s0354 +2024-09-11 04:37:06.687791: tseg-s0354, shape torch.Size([1, 238, 247, 247]), rank 0 +2024-09-11 04:37:10.434472: predicting tseg-s0355 +2024-09-11 04:37:10.825475: tseg-s0355, shape torch.Size([1, 325, 259, 259]), rank 0 +2024-09-11 04:37:16.488419: predicting tseg-s0364 +2024-09-11 04:37:16.618858: tseg-s0364, shape torch.Size([1, 167, 211, 211]), rank 0 +2024-09-11 04:37:18.551950: predicting tseg-s0366 +2024-09-11 04:37:19.037441: tseg-s0366, shape torch.Size([1, 270, 325, 325]), rank 0 +2024-09-11 04:37:27.434386: predicting tseg-s0374 +2024-09-11 04:37:27.592003: tseg-s0374, shape torch.Size([1, 193, 221, 221]), rank 0 +2024-09-11 04:37:31.400049: predicting tseg-s0376 +2024-09-11 04:37:31.454301: tseg-s0376, shape torch.Size([1, 167, 125, 125]), rank 0 +2024-09-11 04:37:31.951080: predicting tseg-s0382 +2024-09-11 04:37:32.243052: tseg-s0382, shape torch.Size([1, 309, 232, 232]), rank 0 +2024-09-11 04:37:37.881444: predicting tseg-s0389 +2024-09-11 04:37:37.971459: tseg-s0389, shape torch.Size([1, 109, 171, 171]), rank 0 +2024-09-11 04:37:38.476352: predicting tseg-s0416 +2024-09-11 04:37:38.762797: tseg-s0416, shape torch.Size([1, 328, 212, 212]), rank 0 +2024-09-11 04:37:44.318514: predicting tseg-s0419 +2024-09-11 04:37:44.559210: tseg-s0419, shape torch.Size([1, 164, 288, 288]), rank 0 +2024-09-11 04:37:46.598037: predicting tseg-s0420 +2024-09-11 04:37:46.870124: tseg-s0420, shape torch.Size([1, 219, 209, 209]), rank 0 +2024-09-11 04:37:50.640377: predicting tseg-s0446 +2024-09-11 04:37:51.041793: tseg-s0446, shape torch.Size([1, 427, 239, 239]), rank 0 +2024-09-11 04:37:58.508027: predicting tseg-s0458 +2024-09-11 04:37:58.848720: tseg-s0458, shape torch.Size([1, 291, 242, 242]), rank 0 +2024-09-11 04:38:04.492560: predicting tseg-s0463 +2024-09-11 04:38:04.683039: tseg-s0463, shape torch.Size([1, 216, 221, 221]), rank 0 +2024-09-11 04:38:08.430676: predicting tseg-s0467 +2024-09-11 04:38:09.281352: tseg-s0467, shape torch.Size([1, 495, 320, 320]), rank 0 +2024-09-11 04:38:30.079719: predicting tseg-s0472 +2024-09-11 04:38:30.703193: tseg-s0472, shape torch.Size([1, 393, 296, 296]), rank 0 +2024-09-11 04:38:47.453852: predicting tseg-s0531 +2024-09-11 04:38:47.611820: tseg-s0531, shape torch.Size([1, 193, 221, 221]), rank 0 +2024-09-11 04:38:51.403675: predicting tseg-s0532 +2024-09-11 04:38:51.432337: tseg-s0532, shape torch.Size([1, 99, 114, 114]), rank 0 +2024-09-11 04:38:51.918446: predicting tseg-s0542 +2024-09-11 04:38:52.406027: tseg-s0542, shape torch.Size([1, 311, 311, 232]), rank 0 +2024-09-11 04:39:00.726227: predicting tseg-s0544 +2024-09-11 04:39:00.800046: tseg-s0544, shape torch.Size([1, 87, 216, 216]), rank 0 +2024-09-11 04:39:02.687097: predicting tseg-s0551 +2024-09-11 04:39:03.047876: tseg-s0551, shape torch.Size([1, 352, 233, 233]), rank 0 +2024-09-11 04:39:08.636870: predicting tseg-s0560 +2024-09-11 04:39:08.741831: tseg-s0560, shape torch.Size([1, 150, 207, 207]), rank 0 +2024-09-11 04:39:10.655183: predicting tseg-s0564 +2024-09-11 04:39:10.831702: tseg-s0564, shape torch.Size([1, 152, 215, 215]), rank 0 +2024-09-11 04:39:12.750483: predicting tseg-s0568 +2024-09-11 04:39:12.810032: tseg-s0568, shape torch.Size([1, 141, 157, 157]), rank 0 +2024-09-11 04:39:13.340686: predicting tseg-s0576 +2024-09-11 04:39:13.490930: tseg-s0576, shape torch.Size([1, 164, 166, 170]), rank 0 +2024-09-11 04:39:14.020231: predicting tseg-s0577 +2024-09-11 04:39:14.508230: tseg-s0577, shape torch.Size([1, 319, 284, 284]), rank 0 +2024-09-11 04:39:20.161468: predicting tseg-s0582 +2024-09-11 04:39:20.335855: tseg-s0582, shape torch.Size([1, 209, 227, 227]), rank 0 +2024-09-11 04:39:24.109073: predicting tseg-s0595 +2024-09-11 04:39:24.150807: tseg-s0595, shape torch.Size([1, 128, 125, 125]), rank 0 +2024-09-11 04:39:24.642276: predicting tseg-s0598 +2024-09-11 04:39:24.917609: tseg-s0598, shape torch.Size([1, 234, 279, 279]), rank 0 +2024-09-11 04:39:28.720819: predicting tseg-s0603 +2024-09-11 04:39:29.220335: tseg-s0603, shape torch.Size([1, 397, 254, 254]), rank 0 +2024-09-11 04:39:36.722109: predicting tseg-s0633 +2024-09-11 04:39:36.874126: tseg-s0633, shape torch.Size([1, 326, 165, 165]), rank 0 +2024-09-11 04:39:38.346759: predicting tseg-s0641 +2024-09-11 04:39:38.610690: tseg-s0641, shape torch.Size([1, 204, 253, 253]), rank 0 +2024-09-11 04:39:42.396858: predicting tseg-s0656 +2024-09-11 04:39:42.829825: tseg-s0656, shape torch.Size([1, 416, 221, 242]), rank 0 +2024-09-11 04:39:50.297212: predicting tseg-s0659 +2024-09-11 04:39:50.498221: tseg-s0659, shape torch.Size([1, 201, 209, 209]), rank 0 +2024-09-11 04:39:54.306072: predicting tseg-s0662 +2024-09-11 04:39:54.895665: tseg-s0662, shape torch.Size([1, 419, 267, 267]), rank 0 +2024-09-11 04:40:02.432095: predicting tseg-s0668 +2024-09-11 04:40:02.595567: tseg-s0668, shape torch.Size([1, 246, 183, 183]), rank 0 +2024-09-11 04:40:03.651380: predicting tseg-s0680 +2024-09-11 04:40:04.264374: tseg-s0680, shape torch.Size([1, 449, 289, 289]), rank 0 +2024-09-11 04:40:20.835927: predicting tseg-s0691 +2024-09-11 04:40:21.406414: tseg-s0691, shape torch.Size([1, 280, 329, 329]), rank 0 +2024-09-11 04:40:29.944072: predicting tseg-s0694 +2024-09-11 04:40:30.306804: tseg-s0694, shape torch.Size([1, 286, 233, 233]), rank 0 +2024-09-11 04:40:34.147610: predicting tseg-s0702 +2024-09-11 04:40:34.494417: tseg-s0702, shape torch.Size([1, 379, 221, 221]), rank 0 +2024-09-11 04:40:40.104658: predicting tseg-s0703 +2024-09-11 04:40:40.606996: tseg-s0703, shape torch.Size([1, 434, 265, 265]), rank 0 +2024-09-11 04:40:48.144127: predicting tseg-s0708 +2024-09-11 04:40:48.554018: tseg-s0708, shape torch.Size([1, 404, 235, 235]), rank 0 +2024-09-11 04:40:56.085410: predicting tseg-s0710 +2024-09-11 04:40:56.172695: tseg-s0710, shape torch.Size([1, 92, 225, 225]), rank 0 +2024-09-11 04:40:58.079633: predicting tseg-s0711 +2024-09-11 04:40:58.454269: tseg-s0711, shape torch.Size([1, 433, 213, 213]), rank 0 +2024-09-11 04:41:05.901142: predicting tseg-s0719 +2024-09-11 04:41:06.313059: tseg-s0719, shape torch.Size([1, 235, 283, 283]), rank 0 +2024-09-11 04:41:10.129911: predicting tseg-s0726 +2024-09-11 04:41:10.672870: tseg-s0726, shape torch.Size([1, 401, 273, 273]), rank 0 +2024-09-11 04:41:18.222632: predicting tseg-s0730 +2024-09-11 04:41:18.304158: tseg-s0730, shape torch.Size([1, 74, 213, 213]), rank 0 +2024-09-11 04:41:20.211694: predicting tseg-s0732 +2024-09-11 04:41:20.416131: tseg-s0732, shape torch.Size([1, 130, 257, 257]), rank 0 +2024-09-11 04:41:22.375427: predicting tseg-s0739 +2024-09-11 04:41:22.886682: tseg-s0739, shape torch.Size([1, 440, 263, 263]), rank 0 +2024-09-11 04:41:30.396574: predicting tseg-s0748 +2024-09-11 04:41:30.428916: tseg-s0748, shape torch.Size([1, 87, 140, 140]), rank 0 +2024-09-11 04:41:30.947052: predicting tseg-s0752 +2024-09-11 04:41:31.291075: tseg-s0752, shape torch.Size([1, 240, 248, 248]), rank 0 +2024-09-11 04:41:35.052960: predicting tseg-s0754 +2024-09-11 04:41:35.835614: tseg-s0754, shape torch.Size([1, 649, 202, 376]), rank 0 +2024-09-11 04:41:52.588049: predicting tseg-s0760 +2024-09-11 04:41:52.875617: tseg-s0760, shape torch.Size([1, 288, 247, 247]), rank 0 +2024-09-11 04:41:56.683089: predicting tseg-s0762 +2024-09-11 04:41:57.225396: tseg-s0762, shape torch.Size([1, 323, 312, 312]), rank 0 +2024-09-11 04:42:09.710026: predicting tseg-s0763 +2024-09-11 04:42:10.475617: tseg-s0763, shape torch.Size([1, 514, 292, 292]), rank 0 +2024-09-11 04:42:31.243167: predicting tseg-s0775 +2024-09-11 04:42:31.406665: tseg-s0775, shape torch.Size([1, 174, 175, 224]), rank 0 +2024-09-11 04:42:32.425511: predicting tseg-s0781 +2024-09-11 04:42:32.568365: tseg-s0781, shape torch.Size([1, 92, 289, 289]), rank 0 +2024-09-11 04:42:36.761524: predicting tseg-s0784 +2024-09-11 04:42:36.984764: tseg-s0784, shape torch.Size([1, 215, 221, 221]), rank 0 +2024-09-11 04:42:40.731084: predicting tseg-s0791 +2024-09-11 04:42:40.780684: tseg-s0791, shape torch.Size([1, 149, 124, 124]), rank 0 +2024-09-11 04:42:41.295857: predicting tseg-s0792 +2024-09-11 04:42:41.343171: tseg-s0792, shape torch.Size([1, 108, 152, 125]), rank 0 +2024-09-11 04:42:41.855013: predicting tseg-s0796 +2024-09-11 04:42:42.411821: tseg-s0796, shape torch.Size([1, 410, 281, 281]), rank 0 +2024-09-11 04:42:49.925630: predicting tseg-s0798 +2024-09-11 04:42:50.087512: tseg-s0798, shape torch.Size([1, 211, 178, 236]), rank 0 +2024-09-11 04:42:52.044011: predicting tseg-s0804 +2024-09-11 04:42:52.584299: tseg-s0804, shape torch.Size([1, 467, 236, 236]), rank 0 +2024-09-11 04:43:00.075997: predicting tseg-s0813 +2024-09-11 04:43:00.146714: tseg-s0813, shape torch.Size([1, 123, 121, 121]), rank 0 +2024-09-11 04:43:00.653479: predicting tseg-s0835 +2024-09-11 04:43:01.164052: tseg-s0835, shape torch.Size([1, 419, 264, 264]), rank 0 +2024-09-11 04:43:08.691212: predicting tseg-s0848 +2024-09-11 04:43:08.755473: tseg-s0848, shape torch.Size([1, 91, 173, 220]), rank 0 +2024-09-11 04:43:09.736367: predicting tseg-s0861 +2024-09-11 04:43:09.967584: tseg-s0861, shape torch.Size([1, 140, 332, 332]), rank 0 +2024-09-11 04:43:14.248763: predicting tseg-s0865 +2024-09-11 04:43:14.294161: tseg-s0865, shape torch.Size([1, 155, 119, 115]), rank 0 +2024-09-11 04:43:14.812261: predicting tseg-s0868 +2024-09-11 04:43:14.939299: tseg-s0868, shape torch.Size([1, 145, 176, 176]), rank 0 +2024-09-11 04:43:15.476890: predicting tseg-s0870 +2024-09-11 04:43:15.619019: tseg-s0870, shape torch.Size([1, 114, 217, 216]), rank 0 +2024-09-11 04:43:17.526726: predicting tseg-s0871 +2024-09-11 04:43:17.666331: tseg-s0871, shape torch.Size([1, 169, 200, 200]), rank 0 +2024-09-11 04:43:19.578587: predicting tseg-s0877 +2024-09-11 04:43:19.661014: tseg-s0877, shape torch.Size([1, 75, 199, 199]), rank 0 +2024-09-11 04:43:21.542392: predicting tseg-s0885 +2024-09-11 04:43:22.039727: tseg-s0885, shape torch.Size([1, 448, 259, 259]), rank 0 +2024-09-11 04:43:29.557940: predicting tseg-s0892 +2024-09-11 04:43:29.771288: tseg-s0892, shape torch.Size([1, 256, 221, 221]), rank 0 +2024-09-11 04:43:33.581392: predicting tseg-s0894 +2024-09-11 04:43:34.225901: tseg-s0894, shape torch.Size([1, 445, 269, 269]), rank 0 +2024-09-11 04:43:41.873455: predicting tseg-s0896 +2024-09-11 04:43:42.356627: tseg-s0896, shape torch.Size([1, 431, 261, 261]), rank 0 +2024-09-11 04:43:49.936549: predicting tseg-s0903 +2024-09-11 04:43:50.624551: tseg-s0903, shape torch.Size([1, 427, 333, 333]), rank 0 +2024-09-11 04:44:07.331573: predicting tseg-s0914 +2024-09-11 04:44:07.560678: tseg-s0914, shape torch.Size([1, 195, 240, 240]), rank 0 +2024-09-11 04:44:11.339555: predicting tseg-s0915 +2024-09-11 04:44:11.841187: tseg-s0915, shape torch.Size([1, 423, 227, 227]), rank 0 +2024-09-11 04:44:19.314629: predicting tseg-s0916 +2024-09-11 04:44:19.829356: tseg-s0916, shape torch.Size([1, 442, 233, 233]), rank 0 +2024-09-11 04:44:27.315056: predicting tseg-s0927 +2024-09-11 04:44:28.104400: tseg-s0927, shape torch.Size([1, 519, 333, 333]), rank 0 +2024-09-11 04:44:48.914030: predicting tseg-s0936 +2024-09-11 04:44:48.969444: tseg-s0936, shape torch.Size([1, 131, 147, 147]), rank 0 +2024-09-11 04:44:49.499067: predicting tseg-s0955 +2024-09-11 04:44:49.513461: tseg-s0955, shape torch.Size([1, 29, 149, 149]), rank 0 +2024-09-11 04:44:49.994599: predicting tseg-s1003 +2024-09-11 04:44:50.028272: tseg-s1003, shape torch.Size([1, 187, 119, 71]), rank 0 +2024-09-11 04:44:50.512716: predicting tseg-s1007 +2024-09-11 04:44:50.860162: tseg-s1007, shape torch.Size([1, 232, 289, 289]), rank 0 +2024-09-11 04:44:59.176534: predicting tseg-s1008 +2024-09-11 04:44:59.622768: tseg-s1008, shape torch.Size([1, 382, 249, 249]), rank 0 +2024-09-11 04:45:05.301458: predicting tseg-s1034 +2024-09-11 04:45:05.434636: tseg-s1034, shape torch.Size([1, 233, 184, 183]), rank 0 +2024-09-11 04:45:06.467908: predicting tseg-s1039 +2024-09-11 04:45:06.751289: tseg-s1039, shape torch.Size([1, 217, 249, 249]), rank 0 +2024-09-11 04:45:10.522849: predicting tseg-s1041 +2024-09-11 04:45:10.821055: tseg-s1041, shape torch.Size([1, 232, 237, 237]), rank 0 +2024-09-11 04:45:14.611033: predicting tseg-s1056 +2024-09-11 04:45:14.693632: tseg-s1056, shape torch.Size([1, 110, 191, 191]), rank 0 +2024-09-11 04:45:15.234047: predicting tseg-s1062 +2024-09-11 04:45:15.819860: tseg-s1062, shape torch.Size([1, 477, 236, 236]), rank 0 +2024-09-11 04:45:23.302575: predicting tseg-s1065 +2024-09-11 04:45:23.403418: tseg-s1065, shape torch.Size([1, 54, 319, 319]), rank 0 +2024-09-11 04:45:27.644915: predicting tseg-s1069 +2024-09-11 04:45:28.084841: tseg-s1069, shape torch.Size([1, 444, 231, 231]), rank 0 +2024-09-11 04:45:35.582513: predicting tseg-s1071 +2024-09-11 04:45:35.691369: tseg-s1071, shape torch.Size([1, 126, 195, 195]), rank 0 +2024-09-11 04:45:37.583345: predicting tseg-s1088 +2024-09-11 04:45:38.549295: tseg-s1088, shape torch.Size([1, 544, 333, 333]), rank 0 +2024-09-11 04:45:59.395836: predicting tseg-s1090 +2024-09-11 04:45:59.976050: tseg-s1090, shape torch.Size([1, 439, 257, 257]), rank 0 +2024-09-11 04:46:07.652484: predicting tseg-s1099 +2024-09-11 04:46:08.272220: tseg-s1099, shape torch.Size([1, 427, 285, 285]), rank 0 +2024-09-11 04:46:15.927636: predicting tseg-s1110 +2024-09-11 04:46:16.393260: tseg-s1110, shape torch.Size([1, 241, 295, 295]), rank 0 +2024-09-11 04:46:24.827758: predicting tseg-s1114 +2024-09-11 04:46:24.933864: tseg-s1114, shape torch.Size([1, 174, 187, 187]), rank 0 +2024-09-11 04:46:25.496602: predicting tseg-s1124 +2024-09-11 04:46:26.268719: tseg-s1124, shape torch.Size([1, 536, 267, 267]), rank 0 +2024-09-11 04:46:35.690179: predicting tseg-s1130 +2024-09-11 04:46:36.093557: tseg-s1130, shape torch.Size([1, 283, 280, 280]), rank 0 +2024-09-11 04:46:39.999107: predicting tseg-s1134 +2024-09-11 04:46:40.051860: tseg-s1134, shape torch.Size([1, 151, 103, 103]), rank 0 +2024-09-11 04:46:40.553751: predicting tseg-s1135 +2024-09-11 04:46:41.232659: tseg-s1135, shape torch.Size([1, 443, 299, 299]), rank 0 +2024-09-11 04:46:57.801148: predicting tseg-s1136 +2024-09-11 04:46:57.955763: tseg-s1136, shape torch.Size([1, 164, 216, 216]), rank 0 +2024-09-11 04:46:59.893005: predicting tseg-s1137 +2024-09-11 04:47:00.132575: tseg-s1137, shape torch.Size([1, 278, 145, 244]), rank 0 +2024-09-11 04:47:02.104217: predicting tseg-s1143 +2024-09-11 04:47:02.658834: tseg-s1143, shape torch.Size([1, 479, 266, 266]), rank 0 +2024-09-11 04:47:10.206206: predicting tseg-s1159 +2024-09-11 04:47:11.064780: tseg-s1159, shape torch.Size([1, 537, 329, 329]), rank 0 +2024-09-11 04:47:32.835250: predicting tseg-s1163 +2024-09-11 04:47:32.984373: tseg-s1163, shape torch.Size([1, 228, 171, 171]), rank 0 +2024-09-11 04:47:34.026810: predicting tseg-s1177 +2024-09-11 04:47:34.052463: tseg-s1177, shape torch.Size([1, 67, 137, 121]), rank 0 +2024-09-11 04:47:34.557674: predicting tseg-s1178 +2024-09-11 04:47:35.475350: tseg-s1178, shape torch.Size([1, 433, 333, 333]), rank 0 +2024-09-11 04:47:52.213923: predicting tseg-s1187 +2024-09-11 04:47:52.598399: tseg-s1187, shape torch.Size([1, 340, 261, 261]), rank 0 +2024-09-11 04:47:58.304707: predicting tseg-s1189 +2024-09-11 04:47:58.543761: tseg-s1189, shape torch.Size([1, 265, 233, 233]), rank 0 +2024-09-11 04:48:02.349065: predicting tseg-s1190 +2024-09-11 04:48:02.617014: tseg-s1190, shape torch.Size([1, 242, 221, 221]), rank 0 +2024-09-11 04:48:06.381750: predicting tseg-s1197 +2024-09-11 04:48:06.546576: tseg-s1197, shape torch.Size([1, 149, 227, 227]), rank 0 +2024-09-11 04:48:08.474873: predicting tseg-s1199 +2024-09-11 04:48:08.724821: tseg-s1199, shape torch.Size([1, 217, 255, 255]), rank 0 +2024-09-11 04:48:12.519518: predicting tseg-s1207 +2024-09-11 04:48:13.202407: tseg-s1207, shape torch.Size([1, 405, 301, 301]), rank 0 +2024-09-11 04:48:29.785380: predicting tseg-s1216 +2024-09-11 04:48:30.341224: tseg-s1216, shape torch.Size([1, 317, 315, 315]), rank 0 +2024-09-11 04:48:42.888819: predicting tseg-s1221 +2024-09-11 04:48:43.139227: tseg-s1221, shape torch.Size([1, 218, 211, 211]), rank 0 +2024-09-11 04:48:46.958563: predicting tseg-s1223 +2024-09-11 04:48:47.297816: tseg-s1223, shape torch.Size([1, 427, 150, 261]), rank 0 +2024-09-11 04:48:51.167723: predicting tseg-s1233 +2024-09-11 04:48:51.641886: tseg-s1233, shape torch.Size([1, 401, 265, 265]), rank 0 +2024-09-11 04:48:59.175170: predicting tseg-s1238 +2024-09-11 04:49:00.018588: tseg-s1238, shape torch.Size([1, 457, 295, 295]), rank 0 +2024-09-11 04:49:16.694840: predicting tseg-s1242 +2024-09-11 04:49:16.789396: tseg-s1242, shape torch.Size([1, 84, 263, 263]), rank 0 +2024-09-11 04:49:18.727858: predicting tseg-s1254 +2024-09-11 04:49:18.817645: tseg-s1254, shape torch.Size([1, 139, 141, 141]), rank 0 +2024-09-11 04:49:19.327615: predicting tseg-s1257 +2024-09-11 04:49:19.496820: tseg-s1257, shape torch.Size([1, 155, 219, 219]), rank 0 +2024-09-11 04:49:21.415160: predicting tseg-s1275 +2024-09-11 04:49:21.463083: tseg-s1275, shape torch.Size([1, 39, 263, 263]), rank 0 +2024-09-11 04:49:23.354815: predicting tseg-s1279 +2024-09-11 04:49:23.391915: tseg-s1279, shape torch.Size([1, 57, 172, 172]), rank 0 +2024-09-11 04:49:23.882921: predicting tseg-s1283 +2024-09-11 04:49:24.414089: tseg-s1283, shape torch.Size([1, 397, 283, 283]), rank 0 +2024-09-11 04:49:31.888312: predicting tseg-s1285 +2024-09-11 04:49:32.121143: tseg-s1285, shape torch.Size([1, 263, 233, 233]), rank 0 +2024-09-11 04:49:35.971585: predicting tseg-s1286 +2024-09-11 04:49:36.014640: tseg-s1286, shape torch.Size([1, 60, 206, 206]), rank 0 +2024-09-11 04:49:37.893756: predicting tseg-s1292 +2024-09-11 04:49:38.158799: tseg-s1292, shape torch.Size([1, 271, 221, 221]), rank 0 +2024-09-11 04:49:41.921203: predicting tseg-s1293 +2024-09-11 04:49:42.309519: tseg-s1293, shape torch.Size([1, 288, 273, 273]), rank 0 +2024-09-11 04:49:46.129020: predicting tseg-s1301 +2024-09-11 04:49:46.515786: tseg-s1301, shape torch.Size([1, 263, 275, 275]), rank 0 +2024-09-11 04:49:50.361680: predicting tseg-s1305 +2024-09-11 04:49:50.623989: tseg-s1305, shape torch.Size([1, 278, 221, 221]), rank 0 +2024-09-11 04:49:54.424677: predicting tseg-s1310 +2024-09-11 04:49:54.778391: tseg-s1310, shape torch.Size([1, 461, 165, 208]), rank 0 +2024-09-11 04:49:58.602496: predicting tseg-s1315 +2024-09-11 04:49:58.656268: tseg-s1315, shape torch.Size([1, 58, 181, 181]), rank 0 +2024-09-11 04:49:59.153052: predicting tseg-s1316 +2024-09-11 04:49:59.232353: tseg-s1316, shape torch.Size([1, 43, 256, 256]), rank 0 +2024-09-11 04:50:01.109045: predicting tseg-s1317 +2024-09-11 04:50:01.175738: tseg-s1317, shape torch.Size([1, 48, 213, 213]), rank 0 +2024-09-11 04:50:03.056577: predicting tseg-s1326 +2024-09-11 04:50:03.470946: tseg-s1326, shape torch.Size([1, 440, 221, 221]), rank 0 +2024-09-11 04:50:10.892225: predicting tseg-s1339 +2024-09-11 04:50:11.231398: tseg-s1339, shape torch.Size([1, 287, 350, 193]), rank 0 +2024-09-11 04:50:16.947402: predicting tseg-s1340 +2024-09-11 04:50:17.546476: tseg-s1340, shape torch.Size([1, 431, 275, 275]), rank 0 +2024-09-11 04:50:25.103143: predicting tseg-s1366 +2024-09-11 04:50:25.690335: tseg-s1366, shape torch.Size([1, 336, 333, 333]), rank 0 +2024-09-11 04:50:38.271699: predicting tseg-s1379 +2024-09-11 04:50:39.072366: tseg-s1379, shape torch.Size([1, 490, 279, 279]), rank 0 +2024-09-11 04:50:48.565842: predicting tseg-s1401 +2024-09-11 04:50:48.748496: tseg-s1401, shape torch.Size([1, 179, 244, 244]), rank 0 +2024-09-11 04:50:50.728159: predicting tseg-s1403 +2024-09-11 04:50:51.154722: tseg-s1403, shape torch.Size([1, 317, 258, 258]), rank 0 +2024-09-11 04:50:56.787326: predicting tseg-s1405 +2024-09-11 04:50:57.116520: tseg-s1405, shape torch.Size([1, 287, 255, 255]), rank 0 +2024-09-11 04:52:19.836109: Validation complete +2024-09-11 04:52:19.836637: Mean Validation Dice: 0.946915680255042 diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_4_16_32_40.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_4_16_32_40.txt new file mode 100644 index 0000000000000000000000000000000000000000..9dd5d9c849adad6410ad55aaa5ddd61fc142fb70 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_4_16_32_40.txt @@ -0,0 +1,421 @@ + +####################################################################### +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. +####################################################################### + +2024-09-04 16:32:40.173767: do_dummy_2d_data_aug: False +2024-09-04 16:32:40.203305: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-04 16:32:40.204947: The split file contains 5 splits. +2024-09-04 16:32:40.205727: Desired fold for training: 4 +2024-09-04 16:32:40.206420: This split has 1325 training and 331 validation cases. +2024-09-04 16:33:03.899059: Using torch.compile... + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-04 16:33:07.271555: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-04 16:33:08.183208: +2024-09-04 16:33:08.192798: Epoch 0 +2024-09-04 16:33:08.193692: Current learning rate: 0.01 +2024-09-04 16:51:24.203965: train_loss 0.8113 +2024-09-04 16:51:24.270070: val_loss 0.4214 +2024-09-04 16:51:24.340770: Pseudo dice [0.5224, 0.6366, 0.5471, 0.0, 0.0, 0.0, 0.8043] +2024-09-04 16:51:24.412388: Epoch time: 1096.03 s +2024-09-04 16:51:24.423833: Yayy! New best EMA pseudo Dice: 0.3586 +2024-09-04 16:51:32.999904: +2024-09-04 16:51:33.001488: Epoch 1 +2024-09-04 16:51:33.002170: Current learning rate: 0.00999 +2024-09-04 16:55:57.605763: train_loss 0.328 +2024-09-04 16:55:57.636886: val_loss 0.1577 +2024-09-04 16:55:57.776264: Pseudo dice [0.7262, 0.6509, 0.7437, 0.1186, 0.0, 0.0, 0.8578] +2024-09-04 16:55:57.838004: Epoch time: 264.61 s +2024-09-04 16:55:57.850106: Yayy! New best EMA pseudo Dice: 0.367 +2024-09-04 16:56:07.072905: +2024-09-04 16:56:07.074223: Epoch 2 +2024-09-04 16:56:07.075863: Current learning rate: 0.00998 +2024-09-04 17:01:02.759638: train_loss 0.0864 +2024-09-04 17:01:02.772911: val_loss -0.0935 +2024-09-04 17:01:02.774901: Pseudo dice [0.7771, 0.7428, 0.8411, 0.6775, 0.0, 0.4156, 0.9062] +2024-09-04 17:01:02.778530: Epoch time: 295.69 s +2024-09-04 17:01:02.780289: Yayy! New best EMA pseudo Dice: 0.3926 +2024-09-04 17:01:13.285104: +2024-09-04 17:01:13.287093: Epoch 3 +2024-09-04 17:01:13.288598: Current learning rate: 0.00997 +2024-09-04 17:06:03.774000: train_loss -0.0998 +2024-09-04 17:06:03.776965: val_loss -0.2484 +2024-09-04 17:06:03.778934: Pseudo dice [0.819, 0.7922, 0.873, 0.7625, 0.2648, 0.5854, 0.9492] +2024-09-04 17:06:03.780826: Epoch time: 290.49 s +2024-09-04 17:06:03.782924: Yayy! New best EMA pseudo Dice: 0.4254 +2024-09-04 17:06:16.953804: +2024-09-04 17:06:16.955679: Epoch 4 +2024-09-04 17:06:16.965967: Current learning rate: 0.00996 +2024-09-04 17:10:54.505831: train_loss -0.2439 +2024-09-04 17:10:54.523236: val_loss -0.296 +2024-09-04 17:10:54.524669: Pseudo dice [0.8313, 0.8191, 0.8703, 0.7725, 0.5544, 0.7048, 0.9557] +2024-09-04 17:10:54.525934: Epoch time: 277.56 s +2024-09-04 17:10:54.528321: Yayy! New best EMA pseudo Dice: 0.4616 +2024-09-04 17:11:03.503073: +2024-09-04 17:11:03.504337: Epoch 5 +2024-09-04 17:11:03.505627: Current learning rate: 0.00995 +2024-09-04 17:15:40.877326: train_loss -0.3163 +2024-09-04 17:15:40.888096: val_loss -0.4384 +2024-09-04 17:15:40.907418: Pseudo dice [0.8609, 0.8538, 0.9124, 0.8355, 0.6174, 0.809, 0.9579] +2024-09-04 17:15:40.923680: Epoch time: 277.38 s +2024-09-04 17:15:40.925647: Yayy! New best EMA pseudo Dice: 0.4989 +2024-09-04 17:15:49.481146: +2024-09-04 17:15:49.482455: Epoch 6 +2024-09-04 17:15:49.483793: Current learning rate: 0.00995 +2024-09-04 17:20:14.977058: train_loss -0.3802 +2024-09-04 17:20:15.006276: val_loss -0.4316 +2024-09-04 17:20:15.008083: Pseudo dice [0.8777, 0.8607, 0.9053, 0.8538, 0.6506, 0.8174, 0.9572] +2024-09-04 17:20:15.010082: Epoch time: 265.5 s +2024-09-04 17:20:15.012199: Yayy! New best EMA pseudo Dice: 0.5337 +2024-09-04 17:20:23.780888: +2024-09-04 17:20:23.782377: Epoch 7 +2024-09-04 17:20:23.785175: Current learning rate: 0.00994 +2024-09-04 17:25:03.872967: train_loss -0.4237 +2024-09-04 17:25:03.887295: val_loss -0.501 +2024-09-04 17:25:03.888621: Pseudo dice [0.8834, 0.8635, 0.9206, 0.8548, 0.7017, 0.8123, 0.9664] +2024-09-04 17:25:03.889937: Epoch time: 280.1 s +2024-09-04 17:25:03.891930: Yayy! New best EMA pseudo Dice: 0.566 +2024-09-04 17:25:13.949597: +2024-09-04 17:25:13.950887: Epoch 8 +2024-09-04 17:25:13.952187: Current learning rate: 0.00993 +2024-09-04 17:29:49.595419: train_loss -0.4547 +2024-09-04 17:29:49.611519: val_loss -0.5434 +2024-09-04 17:29:49.612399: Pseudo dice [0.8908, 0.8851, 0.9238, 0.8666, 0.6983, 0.8511, 0.9698] +2024-09-04 17:29:49.613299: Epoch time: 275.65 s +2024-09-04 17:29:49.614182: Yayy! New best EMA pseudo Dice: 0.5964 +2024-09-04 17:29:59.221276: +2024-09-04 17:29:59.222407: Epoch 9 +2024-09-04 17:29:59.223657: Current learning rate: 0.00992 +2024-09-04 17:34:29.917487: train_loss -0.4749 +2024-09-04 17:34:29.928166: val_loss -0.5184 +2024-09-04 17:34:29.938938: Pseudo dice [0.8891, 0.8875, 0.9245, 0.8706, 0.7389, 0.8685, 0.9747] +2024-09-04 17:34:29.940477: Epoch time: 270.7 s +2024-09-04 17:34:29.942199: Yayy! New best EMA pseudo Dice: 0.6247 +2024-09-04 17:34:42.610559: +2024-09-04 17:34:42.611801: Epoch 10 +2024-09-04 17:34:42.613102: Current learning rate: 0.00991 +2024-09-04 17:39:33.626710: train_loss -0.5179 +2024-09-04 17:39:33.644702: val_loss -0.5296 +2024-09-04 17:39:33.646592: Pseudo dice [0.9049, 0.8992, 0.9305, 0.8826, 0.7265, 0.871, 0.968] +2024-09-04 17:39:33.649851: Epoch time: 291.02 s +2024-09-04 17:39:33.653423: Yayy! New best EMA pseudo Dice: 0.6505 +2024-09-04 17:39:41.706563: +2024-09-04 17:39:41.707955: Epoch 11 +2024-09-04 17:39:41.708647: Current learning rate: 0.0099 +2024-09-04 17:43:58.581145: train_loss -0.5251 +2024-09-04 17:43:58.594685: val_loss -0.5868 +2024-09-04 17:43:58.595956: Pseudo dice [0.9123, 0.9056, 0.9396, 0.8832, 0.7452, 0.8969, 0.9751] +2024-09-04 17:43:58.597373: Epoch time: 256.88 s +2024-09-04 17:43:58.600692: Yayy! New best EMA pseudo Dice: 0.6749 +2024-09-04 17:44:07.954751: +2024-09-04 17:44:07.956272: Epoch 12 +2024-09-04 17:44:07.957377: Current learning rate: 0.00989 +2024-09-04 17:48:35.288700: train_loss -0.5056 +2024-09-04 17:48:35.290721: val_loss -0.5397 +2024-09-04 17:48:35.291877: Pseudo dice [0.9048, 0.8931, 0.9273, 0.8584, 0.7223, 0.8913, 0.9716] +2024-09-04 17:48:35.292994: Epoch time: 267.34 s +2024-09-04 17:48:35.294629: Yayy! New best EMA pseudo Dice: 0.6955 +2024-09-04 17:48:44.845792: +2024-09-04 17:48:44.847599: Epoch 13 +2024-09-04 17:48:44.851414: Current learning rate: 0.00988 +2024-09-04 17:53:34.370907: train_loss -0.5487 +2024-09-04 17:53:34.382585: val_loss -0.5731 +2024-09-04 17:53:34.384303: Pseudo dice [0.9161, 0.9122, 0.9401, 0.8765, 0.7415, 0.8943, 0.9788] +2024-09-04 17:53:34.402497: Epoch time: 289.53 s +2024-09-04 17:53:34.404551: Yayy! New best EMA pseudo Dice: 0.7154 +2024-09-04 17:53:45.108720: +2024-09-04 17:53:45.109713: Epoch 14 +2024-09-04 17:53:45.111018: Current learning rate: 0.00987 +2024-09-04 17:58:27.742827: train_loss -0.5456 +2024-09-04 17:58:27.744574: val_loss -0.5556 +2024-09-04 17:58:27.748271: Pseudo dice [0.9114, 0.9123, 0.9372, 0.8844, 0.7033, 0.8918, 0.9812] +2024-09-04 17:58:27.754526: Epoch time: 282.64 s +2024-09-04 17:58:27.755501: Yayy! New best EMA pseudo Dice: 0.7327 +2024-09-04 17:58:36.417477: +2024-09-04 17:58:36.418590: Epoch 15 +2024-09-04 17:58:36.419919: Current learning rate: 0.00986 +2024-09-04 18:03:13.479480: train_loss -0.5469 +2024-09-04 18:03:13.495457: val_loss -0.574 +2024-09-04 18:03:13.497017: Pseudo dice [0.9104, 0.9045, 0.9362, 0.8984, 0.7699, 0.9132, 0.9795] +2024-09-04 18:03:13.498646: Epoch time: 277.07 s +2024-09-04 18:03:13.499867: Yayy! New best EMA pseudo Dice: 0.7496 +2024-09-04 18:03:24.841192: +2024-09-04 18:03:24.843001: Epoch 16 +2024-09-04 18:03:24.844409: Current learning rate: 0.00986 +2024-09-04 18:08:01.841141: train_loss -0.5559 +2024-09-04 18:08:01.866446: val_loss -0.6161 +2024-09-04 18:08:01.867755: Pseudo dice [0.9167, 0.9056, 0.9439, 0.8838, 0.7397, 0.907, 0.9769] +2024-09-04 18:08:01.869033: Epoch time: 277.0 s +2024-09-04 18:08:01.870192: Yayy! New best EMA pseudo Dice: 0.7643 +2024-09-04 18:08:12.708891: +2024-09-04 18:08:12.710445: Epoch 17 +2024-09-04 18:08:12.711881: Current learning rate: 0.00985 +2024-09-04 18:12:54.632287: train_loss -0.5545 +2024-09-04 18:12:54.660954: val_loss -0.5559 +2024-09-04 18:12:54.662552: Pseudo dice [0.9107, 0.9102, 0.9461, 0.8658, 0.7219, 0.8583, 0.9761] +2024-09-04 18:12:54.663765: Epoch time: 281.93 s +2024-09-04 18:12:54.664649: Yayy! New best EMA pseudo Dice: 0.7763 +2024-09-04 18:13:04.408304: +2024-09-04 18:13:04.409662: Epoch 18 +2024-09-04 18:13:04.411106: Current learning rate: 0.00984 +2024-09-04 18:17:40.343772: train_loss -0.5395 +2024-09-04 18:17:40.355942: val_loss -0.5879 +2024-09-04 18:17:40.357403: Pseudo dice [0.9249, 0.9176, 0.9428, 0.9001, 0.7616, 0.9137, 0.975] +2024-09-04 18:17:40.359000: Epoch time: 275.94 s +2024-09-04 18:17:40.362346: Yayy! New best EMA pseudo Dice: 0.7892 +2024-09-04 18:17:49.771813: +2024-09-04 18:17:49.772866: Epoch 19 +2024-09-04 18:17:49.773630: Current learning rate: 0.00983 +2024-09-04 18:22:34.526834: train_loss -0.6021 +2024-09-04 18:22:34.544459: val_loss -0.636 +2024-09-04 18:22:34.545683: Pseudo dice [0.9288, 0.9209, 0.955, 0.9088, 0.7669, 0.9204, 0.9786] +2024-09-04 18:22:34.546620: Epoch time: 284.76 s +2024-09-04 18:22:34.547749: Yayy! New best EMA pseudo Dice: 0.8014 +2024-09-04 18:22:45.428827: +2024-09-04 18:22:45.429946: Epoch 20 +2024-09-04 18:22:45.430768: Current learning rate: 0.00982 +2024-09-04 18:27:13.543856: train_loss -0.604 +2024-09-04 18:27:13.748393: val_loss -0.6254 +2024-09-04 18:27:13.799753: Pseudo dice [0.9299, 0.925, 0.9589, 0.9084, 0.7767, 0.9146, 0.9838] +2024-09-04 18:27:13.813676: Epoch time: 268.12 s +2024-09-04 18:27:13.933432: Yayy! New best EMA pseudo Dice: 0.8126 +2024-09-04 18:27:22.459034: +2024-09-04 18:27:22.460206: Epoch 21 +2024-09-04 18:27:22.461004: Current learning rate: 0.00981 +2024-09-04 18:32:17.376712: train_loss -0.6299 +2024-09-04 18:32:17.379321: val_loss -0.6425 +2024-09-04 18:32:17.380332: Pseudo dice [0.9265, 0.926, 0.9581, 0.9085, 0.775, 0.9151, 0.9768] +2024-09-04 18:32:17.381227: Epoch time: 294.92 s +2024-09-04 18:32:17.382130: Yayy! New best EMA pseudo Dice: 0.8226 +2024-09-04 18:32:26.947095: +2024-09-04 18:32:26.948571: Epoch 22 +2024-09-04 18:32:26.949336: Current learning rate: 0.0098 +2024-09-04 18:36:55.976203: train_loss -0.6215 +2024-09-04 18:36:55.994845: val_loss -0.6501 +2024-09-04 18:36:55.997325: Pseudo dice [0.9293, 0.9207, 0.9561, 0.9116, 0.7978, 0.9271, 0.9841] +2024-09-04 18:36:55.998827: Epoch time: 269.03 s +2024-09-04 18:36:56.005238: Yayy! New best EMA pseudo Dice: 0.8321 +2024-09-04 18:37:06.498313: +2024-09-04 18:37:06.499322: Epoch 23 +2024-09-04 18:37:06.500053: Current learning rate: 0.00979 +2024-09-04 18:41:47.435128: train_loss -0.604 +2024-09-04 18:41:47.449155: val_loss -0.6086 +2024-09-04 18:41:47.451081: Pseudo dice [0.9272, 0.9226, 0.9468, 0.8962, 0.7484, 0.9087, 0.9639] +2024-09-04 18:41:47.452749: Epoch time: 280.94 s +2024-09-04 18:41:47.457739: Yayy! New best EMA pseudo Dice: 0.8391 +2024-09-04 18:41:57.379441: +2024-09-04 18:41:57.380568: Epoch 24 +2024-09-04 18:41:57.381365: Current learning rate: 0.00978 +2024-09-04 18:46:19.099800: train_loss -0.6218 +2024-09-04 18:46:19.118414: val_loss -0.6286 +2024-09-04 18:46:19.126654: Pseudo dice [0.933, 0.922, 0.9485, 0.9048, 0.7719, 0.921, 0.9795] +2024-09-04 18:46:19.127733: Epoch time: 261.72 s +2024-09-04 18:46:19.128948: Yayy! New best EMA pseudo Dice: 0.8464 +2024-09-04 18:46:29.330645: +2024-09-04 18:46:29.333277: Epoch 25 +2024-09-04 18:46:29.335315: Current learning rate: 0.00977 +2024-09-04 18:51:20.173357: train_loss -0.6399 +2024-09-04 18:51:20.177026: val_loss -0.6611 +2024-09-04 18:51:20.181141: Pseudo dice [0.9346, 0.9303, 0.9588, 0.9149, 0.7945, 0.9073, 0.9849] +2024-09-04 18:51:20.182269: Epoch time: 290.85 s +2024-09-04 18:51:20.183347: Yayy! New best EMA pseudo Dice: 0.8535 +2024-09-04 18:51:29.151200: +2024-09-04 18:51:29.152258: Epoch 26 +2024-09-04 18:51:29.153155: Current learning rate: 0.00977 +2024-09-04 18:55:58.149745: train_loss -0.6217 +2024-09-04 18:55:58.178888: val_loss -0.6457 +2024-09-04 18:55:58.181117: Pseudo dice [0.9333, 0.9351, 0.9514, 0.8866, 0.7873, 0.9369, 0.9811] +2024-09-04 18:55:58.182232: Epoch time: 269.0 s +2024-09-04 18:55:58.183592: Yayy! New best EMA pseudo Dice: 0.8598 +2024-09-04 18:56:06.258479: +2024-09-04 18:56:06.259629: Epoch 27 +2024-09-04 18:56:06.260463: Current learning rate: 0.00976 +2024-09-04 19:00:46.998880: train_loss -0.6411 +2024-09-04 19:00:47.026842: val_loss -0.6627 +2024-09-04 19:00:47.027874: Pseudo dice [0.9388, 0.9268, 0.9635, 0.906, 0.7828, 0.9309, 0.9829] +2024-09-04 19:00:47.028826: Epoch time: 280.75 s +2024-09-04 19:00:47.029656: Yayy! New best EMA pseudo Dice: 0.8657 +2024-09-04 19:00:56.627983: +2024-09-04 19:00:56.629067: Epoch 28 +2024-09-04 19:00:56.629947: Current learning rate: 0.00975 +2024-09-04 19:05:37.827461: train_loss -0.6418 +2024-09-04 19:05:37.839593: val_loss -0.6645 +2024-09-04 19:05:37.840862: Pseudo dice [0.9359, 0.9286, 0.9609, 0.9102, 0.7828, 0.929, 0.9841] +2024-09-04 19:05:37.842238: Epoch time: 281.2 s +2024-09-04 19:05:37.843639: Yayy! New best EMA pseudo Dice: 0.871 +2024-09-04 19:05:47.539607: +2024-09-04 19:05:47.540724: Epoch 29 +2024-09-04 19:05:47.541527: Current learning rate: 0.00974 +2024-09-04 19:10:31.313770: train_loss -0.6448 +2024-09-04 19:10:31.317533: val_loss -0.6252 +2024-09-04 19:10:31.318525: Pseudo dice [0.9321, 0.9332, 0.9514, 0.919, 0.7796, 0.9192, 0.9805] +2024-09-04 19:10:31.319571: Epoch time: 283.78 s +2024-09-04 19:10:31.320744: Yayy! New best EMA pseudo Dice: 0.8755 +2024-09-04 19:10:41.694073: +2024-09-04 19:10:41.696673: Epoch 30 +2024-09-04 19:10:41.697636: Current learning rate: 0.00973 +2024-09-04 19:15:14.714441: train_loss -0.6518 +2024-09-04 19:15:14.727203: val_loss -0.6797 +2024-09-04 19:15:14.728260: Pseudo dice [0.9366, 0.9298, 0.9615, 0.9102, 0.7921, 0.921, 0.9857] +2024-09-04 19:15:14.729170: Epoch time: 273.02 s +2024-09-04 19:15:14.730343: Yayy! New best EMA pseudo Dice: 0.8799 +2024-09-04 19:15:23.543252: +2024-09-04 19:15:23.544285: Epoch 31 +2024-09-04 19:15:23.545066: Current learning rate: 0.00972 +2024-09-04 19:20:09.407885: train_loss -0.6531 +2024-09-04 19:20:09.419533: val_loss -0.6852 +2024-09-04 19:20:09.434546: Pseudo dice [0.9385, 0.9367, 0.9629, 0.9057, 0.8022, 0.9372, 0.9852] +2024-09-04 19:20:09.435728: Epoch time: 285.87 s +2024-09-04 19:20:09.436827: Yayy! New best EMA pseudo Dice: 0.8843 +2024-09-04 19:20:20.063463: +2024-09-04 19:20:20.064466: Epoch 32 +2024-09-04 19:20:20.065244: Current learning rate: 0.00971 +2024-09-04 19:24:59.087967: train_loss -0.6253 +2024-09-04 19:24:59.106990: val_loss -0.6548 +2024-09-04 19:24:59.108001: Pseudo dice [0.9374, 0.931, 0.96, 0.909, 0.7942, 0.9354, 0.9822] +2024-09-04 19:24:59.109404: Epoch time: 279.03 s +2024-09-04 19:24:59.110324: Yayy! New best EMA pseudo Dice: 0.888 +2024-09-04 19:25:08.855661: +2024-09-04 19:25:08.858697: Epoch 33 +2024-09-04 19:25:08.859797: Current learning rate: 0.0097 +2024-09-04 19:29:26.568698: train_loss -0.6634 +2024-09-04 19:29:26.583380: val_loss -0.652 +2024-09-04 19:29:26.584259: Pseudo dice [0.9397, 0.9348, 0.9612, 0.909, 0.7789, 0.9305, 0.984] +2024-09-04 19:29:26.585216: Epoch time: 257.72 s +2024-09-04 19:29:26.586075: Yayy! New best EMA pseudo Dice: 0.8912 +2024-09-04 19:29:36.248986: +2024-09-04 19:29:36.250185: Epoch 34 +2024-09-04 19:29:36.250939: Current learning rate: 0.00969 +2024-09-04 19:34:17.849976: train_loss -0.6633 +2024-09-04 19:34:17.854256: val_loss -0.6411 +2024-09-04 19:34:17.855151: Pseudo dice [0.9395, 0.9371, 0.96, 0.9121, 0.7956, 0.9333, 0.9853] +2024-09-04 19:34:17.856160: Epoch time: 281.6 s +2024-09-04 19:34:17.856926: Yayy! New best EMA pseudo Dice: 0.8944 +2024-09-04 19:34:26.714796: +2024-09-04 19:34:26.715953: Epoch 35 +2024-09-04 19:34:26.717344: Current learning rate: 0.00968 +2024-09-04 19:39:14.007936: train_loss -0.6649 +2024-09-04 19:39:14.025385: val_loss -0.6453 +2024-09-04 19:39:14.026713: Pseudo dice [0.9387, 0.9352, 0.964, 0.9142, 0.7902, 0.9094, 0.9856] +2024-09-04 19:39:14.027446: Epoch time: 287.3 s +2024-09-04 19:39:14.028780: Yayy! New best EMA pseudo Dice: 0.8969 +2024-09-04 19:39:23.609279: +2024-09-04 19:39:23.610320: Epoch 36 +2024-09-04 19:39:23.610965: Current learning rate: 0.00968 +2024-09-04 19:44:04.732991: train_loss -0.6553 +2024-09-04 19:44:04.743302: val_loss -0.6911 +2024-09-04 19:44:04.744873: Pseudo dice [0.9427, 0.9318, 0.9646, 0.9186, 0.7986, 0.9317, 0.9851] +2024-09-04 19:44:04.746037: Epoch time: 281.13 s +2024-09-04 19:44:04.747048: Yayy! New best EMA pseudo Dice: 0.8997 +2024-09-04 19:44:14.248040: +2024-09-04 19:44:14.249168: Epoch 37 +2024-09-04 19:44:14.250018: Current learning rate: 0.00967 +2024-09-04 19:48:58.292471: train_loss -0.6505 +2024-09-04 19:48:58.307500: val_loss -0.679 +2024-09-04 19:48:58.308642: Pseudo dice [0.9446, 0.939, 0.9647, 0.9173, 0.8114, 0.936, 0.9852] +2024-09-04 19:48:58.310401: Epoch time: 284.05 s +2024-09-04 19:48:58.311465: Yayy! New best EMA pseudo Dice: 0.9026 +2024-09-04 19:49:07.456838: +2024-09-04 19:49:07.457936: Epoch 38 +2024-09-04 19:49:07.458842: Current learning rate: 0.00966 +2024-09-04 19:53:36.427083: train_loss -0.6298 +2024-09-04 19:53:36.449251: val_loss -0.6796 +2024-09-04 19:53:36.450341: Pseudo dice [0.9431, 0.936, 0.9618, 0.9193, 0.7924, 0.9372, 0.9831] +2024-09-04 19:53:36.451505: Epoch time: 268.97 s +2024-09-04 19:53:36.452669: Yayy! New best EMA pseudo Dice: 0.9048 +2024-09-04 19:53:45.838205: +2024-09-04 19:53:45.839288: Epoch 39 +2024-09-04 19:53:45.840050: Current learning rate: 0.00965 +2024-09-04 19:58:35.054767: train_loss -0.6494 +2024-09-04 19:58:35.073715: val_loss -0.639 +2024-09-04 19:58:35.075294: Pseudo dice [0.9358, 0.9334, 0.9533, 0.9101, 0.7806, 0.9357, 0.9747] +2024-09-04 19:58:35.076340: Epoch time: 289.22 s +2024-09-04 19:58:35.077208: Yayy! New best EMA pseudo Dice: 0.9061 +2024-09-04 19:58:43.213691: +2024-09-04 19:58:43.215064: Epoch 40 +2024-09-04 19:58:43.215974: Current learning rate: 0.00964 +2024-09-04 20:03:25.980679: train_loss -0.6534 +2024-09-04 20:03:25.999028: val_loss -0.6709 +2024-09-04 20:03:26.000634: Pseudo dice [0.9474, 0.9442, 0.9694, 0.9215, 0.8308, 0.9497, 0.9882] +2024-09-04 20:03:26.001722: Epoch time: 282.77 s +2024-09-04 20:03:26.003229: Yayy! New best EMA pseudo Dice: 0.9091 +2024-09-04 20:03:39.460189: +2024-09-04 20:03:39.461309: Epoch 41 +2024-09-04 20:03:39.462022: Current learning rate: 0.00963 +2024-09-04 20:08:21.799279: train_loss -0.6734 +2024-09-04 20:08:21.815256: val_loss -0.6742 +2024-09-04 20:08:21.816093: Pseudo dice [0.9461, 0.9385, 0.9477, 0.9171, 0.7907, 0.9314, 0.9866] +2024-09-04 20:08:21.816946: Epoch time: 282.34 s +2024-09-04 20:08:21.817676: Yayy! New best EMA pseudo Dice: 0.9104 +2024-09-04 20:08:30.256217: +2024-09-04 20:08:30.257228: Epoch 42 +2024-09-04 20:08:30.257982: Current learning rate: 0.00962 +2024-09-04 20:13:16.094617: train_loss -0.6523 +2024-09-04 20:13:16.111139: val_loss -0.6741 +2024-09-04 20:13:16.112347: Pseudo dice [0.9446, 0.9411, 0.9647, 0.9156, 0.8083, 0.9455, 0.9864] +2024-09-04 20:13:16.113286: Epoch time: 285.84 s +2024-09-04 20:13:16.114245: Yayy! New best EMA pseudo Dice: 0.9123 +2024-09-04 20:13:25.310035: +2024-09-04 20:13:25.311150: Epoch 43 +2024-09-04 20:13:25.311889: Current learning rate: 0.00961 +2024-09-04 20:18:10.642914: train_loss -0.6444 +2024-09-04 20:18:10.661091: val_loss -0.6645 +2024-09-04 20:18:10.662081: Pseudo dice [0.9401, 0.9369, 0.9609, 0.9144, 0.7986, 0.9347, 0.986] +2024-09-04 20:18:10.663245: Epoch time: 285.34 s +2024-09-04 20:18:10.664220: Yayy! New best EMA pseudo Dice: 0.9135 +2024-09-04 20:18:20.390784: +2024-09-04 20:18:20.391921: Epoch 44 +2024-09-04 20:18:20.392771: Current learning rate: 0.0096 +2024-09-04 20:22:47.641408: train_loss -0.6731 +2024-09-04 20:22:47.658307: val_loss -0.6955 +2024-09-04 20:22:47.659445: Pseudo dice [0.9379, 0.9344, 0.9594, 0.9138, 0.8197, 0.95, 0.9854] +2024-09-04 20:22:47.660643: Epoch time: 267.25 s +2024-09-04 20:22:47.661670: Yayy! New best EMA pseudo Dice: 0.915 +2024-09-04 20:22:58.650432: +2024-09-04 20:22:58.651613: Epoch 45 +2024-09-04 20:22:58.652405: Current learning rate: 0.00959 +2024-09-04 20:27:54.590664: train_loss -0.6598 +2024-09-04 20:27:54.608249: val_loss -0.6687 +2024-09-04 20:27:54.616951: Pseudo dice [0.9433, 0.9347, 0.9664, 0.9119, 0.8193, 0.9462, 0.9851] +2024-09-04 20:27:54.618689: Epoch time: 295.94 s +2024-09-04 20:27:54.619920: Yayy! New best EMA pseudo Dice: 0.9165 +2024-09-04 20:28:04.232711: +2024-09-04 20:28:04.233853: Epoch 46 +2024-09-04 20:28:04.234628: Current learning rate: 0.00959 +2024-09-04 20:32:49.975983: train_loss -0.6639 +2024-09-04 20:32:49.992065: val_loss -0.6967 +2024-09-04 20:32:49.993380: Pseudo dice [0.9425, 0.9367, 0.962, 0.9137, 0.8015, 0.9376, 0.9866] +2024-09-04 20:32:49.994688: Epoch time: 285.75 s +2024-09-04 20:32:49.995908: Yayy! New best EMA pseudo Dice: 0.9174 +2024-09-04 20:33:02.677568: +2024-09-04 20:33:02.678794: Epoch 47 +2024-09-04 20:33:02.679681: Current learning rate: 0.00958 +2024-09-04 20:37:43.604329: train_loss -0.6918 +2024-09-04 20:37:43.606353: val_loss -0.6685 +2024-09-04 20:37:43.607578: Pseudo dice [0.9447, 0.9435, 0.962, 0.9285, 0.8226, 0.9436, 0.9886] +2024-09-04 20:37:43.608769: Epoch time: 280.93 s +2024-09-04 20:37:43.610129: Yayy! New best EMA pseudo Dice: 0.919 +2024-09-04 20:37:52.295750: +2024-09-04 20:37:52.296742: Epoch 48 +2024-09-04 20:37:52.297464: Current learning rate: 0.00957 +2024-09-04 20:42:35.975198: train_loss -0.6763 +2024-09-04 20:42:35.978778: val_loss -0.6589 +2024-09-04 20:42:35.979734: Pseudo dice [0.9386, 0.9362, 0.9544, 0.9188, 0.8311, 0.9351, 0.986] +2024-09-04 20:42:35.980656: Epoch time: 283.68 s +2024-09-04 20:42:35.982111: Yayy! New best EMA pseudo Dice: 0.92 +2024-09-04 20:42:45.068394: +2024-09-04 20:42:45.069525: Epoch 49 +2024-09-04 20:42:45.070281: Current learning rate: 0.00956 +2024-09-04 20:47:31.647168: train_loss -0.6664 +2024-09-04 20:47:31.665057: val_loss -0.6867 +2024-09-04 20:47:31.666017: Pseudo dice [0.9441, 0.9411, 0.9663, 0.9257, 0.8189, 0.9455, 0.9874] +2024-09-04 20:47:31.667059: Epoch time: 286.58 s +2024-09-04 20:47:39.065452: Yayy! New best EMA pseudo Dice: 0.9213 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_00_32_01.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_00_32_01.txt new file mode 100644 index 0000000000000000000000000000000000000000..dce5a836d1e786fb02ecb63c221332a3e251f0ed --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_00_32_01.txt @@ -0,0 +1,407 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 00:32:04.973315: Using torch.compile... +2024-09-05 00:32:14.435950: do_dummy_2d_data_aug: False +2024-09-05 00:32:14.441934: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 00:32:14.443220: The split file contains 5 splits. +2024-09-05 00:32:14.443825: Desired fold for training: 4 +2024-09-05 00:32:14.444447: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 00:32:27.321585: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 00:32:30.282242: +2024-09-05 00:32:30.284357: Epoch 50 +2024-09-05 00:32:30.285436: Current learning rate: 0.00955 +2024-09-05 00:50:46.281045: train_loss -0.6671 +2024-09-05 00:50:46.423794: val_loss -0.6688 +2024-09-05 00:50:46.500319: Pseudo dice [0.9494, 0.943, 0.9676, 0.9221, 0.8282, 0.9464, 0.9867] +2024-09-05 00:50:46.580157: Epoch time: 1096.0 s +2024-09-05 00:50:46.591368: Yayy! New best EMA pseudo Dice: 0.9226 +2024-09-05 00:51:03.452987: +2024-09-05 00:51:03.453969: Epoch 51 +2024-09-05 00:51:03.454640: Current learning rate: 0.00954 +2024-09-05 00:55:49.028866: train_loss -0.6727 +2024-09-05 00:55:49.043134: val_loss -0.7119 +2024-09-05 00:55:49.044539: Pseudo dice [0.9513, 0.9415, 0.9725, 0.93, 0.8296, 0.9435, 0.9883] +2024-09-05 00:55:49.045506: Epoch time: 285.58 s +2024-09-05 00:55:49.046485: Yayy! New best EMA pseudo Dice: 0.924 +2024-09-05 00:56:01.204072: +2024-09-05 00:56:01.259882: Epoch 52 +2024-09-05 00:56:01.302214: Current learning rate: 0.00953 +2024-09-05 01:00:46.351682: train_loss -0.6788 +2024-09-05 01:00:46.444247: val_loss -0.7209 +2024-09-05 01:00:46.456299: Pseudo dice [0.9498, 0.943, 0.9694, 0.9171, 0.8217, 0.9416, 0.9878] +2024-09-05 01:00:46.563352: Epoch time: 285.15 s +2024-09-05 01:00:46.628385: Yayy! New best EMA pseudo Dice: 0.9249 +2024-09-05 01:00:55.835351: +2024-09-05 01:00:55.836403: Epoch 53 +2024-09-05 01:00:55.837607: Current learning rate: 0.00952 +2024-09-05 01:05:27.770133: train_loss -0.6244 +2024-09-05 01:05:27.777636: val_loss -0.6598 +2024-09-05 01:05:27.779918: Pseudo dice [0.9345, 0.9324, 0.9582, 0.9232, 0.8029, 0.9338, 0.9792] +2024-09-05 01:05:27.782107: Epoch time: 271.94 s +2024-09-05 01:05:30.184403: +2024-09-05 01:05:30.185508: Epoch 54 +2024-09-05 01:05:30.187947: Current learning rate: 0.00951 +2024-09-05 01:10:08.280730: train_loss -0.6454 +2024-09-05 01:10:08.300779: val_loss -0.6806 +2024-09-05 01:10:08.303504: Pseudo dice [0.9441, 0.9417, 0.9632, 0.9279, 0.8051, 0.9447, 0.9857] +2024-09-05 01:10:08.304695: Epoch time: 278.1 s +2024-09-05 01:10:08.305753: Yayy! New best EMA pseudo Dice: 0.9253 +2024-09-05 01:10:17.425145: +2024-09-05 01:10:17.426567: Epoch 55 +2024-09-05 01:10:17.428274: Current learning rate: 0.0095 +2024-09-05 01:14:56.965958: train_loss -0.6653 +2024-09-05 01:14:56.987843: val_loss -0.7005 +2024-09-05 01:14:56.989682: Pseudo dice [0.9466, 0.9386, 0.9652, 0.9216, 0.8074, 0.9415, 0.9859] +2024-09-05 01:14:56.991737: Epoch time: 279.54 s +2024-09-05 01:14:56.993505: Yayy! New best EMA pseudo Dice: 0.9257 +2024-09-05 01:15:06.250482: +2024-09-05 01:15:06.251465: Epoch 56 +2024-09-05 01:15:06.252457: Current learning rate: 0.00949 +2024-09-05 01:19:46.582939: train_loss -0.666 +2024-09-05 01:19:46.599659: val_loss -0.6802 +2024-09-05 01:19:46.601279: Pseudo dice [0.9391, 0.938, 0.9643, 0.915, 0.8285, 0.943, 0.9861] +2024-09-05 01:19:46.602834: Epoch time: 280.34 s +2024-09-05 01:19:46.603587: Yayy! New best EMA pseudo Dice: 0.9262 +2024-09-05 01:19:57.466064: +2024-09-05 01:19:57.467243: Epoch 57 +2024-09-05 01:19:57.468563: Current learning rate: 0.00949 +2024-09-05 01:24:31.228616: train_loss -0.6696 +2024-09-05 01:24:31.235795: val_loss -0.687 +2024-09-05 01:24:31.236659: Pseudo dice [0.9442, 0.941, 0.9607, 0.9221, 0.7929, 0.9217, 0.983] +2024-09-05 01:24:31.238064: Epoch time: 273.77 s +2024-09-05 01:24:35.326167: +2024-09-05 01:24:35.327344: Epoch 58 +2024-09-05 01:24:35.328964: Current learning rate: 0.00948 +2024-09-05 01:29:09.008828: train_loss -0.6717 +2024-09-05 01:29:09.019162: val_loss -0.6965 +2024-09-05 01:29:09.020243: Pseudo dice [0.9481, 0.9404, 0.9677, 0.9289, 0.7951, 0.9428, 0.9817] +2024-09-05 01:29:09.021242: Epoch time: 273.69 s +2024-09-05 01:29:09.022225: Yayy! New best EMA pseudo Dice: 0.9263 +2024-09-05 01:29:17.881023: +2024-09-05 01:29:17.882110: Epoch 59 +2024-09-05 01:29:17.883218: Current learning rate: 0.00947 +2024-09-05 01:33:41.411705: train_loss -0.659 +2024-09-05 01:33:41.427354: val_loss -0.6225 +2024-09-05 01:33:41.429318: Pseudo dice [0.9418, 0.9379, 0.9637, 0.9194, 0.7982, 0.9326, 0.9825] +2024-09-05 01:33:41.430784: Epoch time: 263.53 s +2024-09-05 01:33:45.265781: +2024-09-05 01:33:45.282658: Epoch 60 +2024-09-05 01:33:45.284543: Current learning rate: 0.00946 +2024-09-05 01:38:44.884982: train_loss -0.658 +2024-09-05 01:38:44.899367: val_loss -0.7199 +2024-09-05 01:38:44.901010: Pseudo dice [0.9493, 0.9436, 0.9692, 0.9237, 0.8254, 0.9405, 0.9867] +2024-09-05 01:38:44.902050: Epoch time: 299.62 s +2024-09-05 01:38:44.902865: Yayy! New best EMA pseudo Dice: 0.927 +2024-09-05 01:38:55.326507: +2024-09-05 01:38:55.327526: Epoch 61 +2024-09-05 01:38:55.328205: Current learning rate: 0.00945 +2024-09-05 01:43:34.375296: train_loss -0.6774 +2024-09-05 01:43:34.389414: val_loss -0.7004 +2024-09-05 01:43:34.390762: Pseudo dice [0.9514, 0.9433, 0.9705, 0.9288, 0.8229, 0.9456, 0.9857] +2024-09-05 01:43:34.392004: Epoch time: 279.05 s +2024-09-05 01:43:34.398986: Yayy! New best EMA pseudo Dice: 0.9278 +2024-09-05 01:43:46.352009: +2024-09-05 01:43:46.353153: Epoch 62 +2024-09-05 01:43:46.353888: Current learning rate: 0.00944 +2024-09-05 01:48:45.668679: train_loss -0.673 +2024-09-05 01:48:45.684192: val_loss -0.6994 +2024-09-05 01:48:45.685195: Pseudo dice [0.9531, 0.9445, 0.9693, 0.9294, 0.8163, 0.9477, 0.9881] +2024-09-05 01:48:45.686040: Epoch time: 299.32 s +2024-09-05 01:48:45.686792: Yayy! New best EMA pseudo Dice: 0.9286 +2024-09-05 01:48:54.627822: +2024-09-05 01:48:54.628958: Epoch 63 +2024-09-05 01:48:54.629636: Current learning rate: 0.00943 +2024-09-05 01:54:08.612260: train_loss -0.6892 +2024-09-05 01:54:08.628078: val_loss -0.7362 +2024-09-05 01:54:08.628979: Pseudo dice [0.9538, 0.9471, 0.972, 0.9396, 0.8411, 0.9489, 0.9888] +2024-09-05 01:54:08.629790: Epoch time: 313.99 s +2024-09-05 01:54:08.630772: Yayy! New best EMA pseudo Dice: 0.9299 +2024-09-05 01:54:17.401487: +2024-09-05 01:54:17.402273: Epoch 64 +2024-09-05 01:54:17.402934: Current learning rate: 0.00942 +2024-09-05 01:59:02.617384: train_loss -0.6806 +2024-09-05 01:59:02.637782: val_loss -0.7057 +2024-09-05 01:59:02.638782: Pseudo dice [0.9501, 0.9476, 0.968, 0.9314, 0.829, 0.9534, 0.9879] +2024-09-05 01:59:02.639804: Epoch time: 285.22 s +2024-09-05 01:59:02.640675: Yayy! New best EMA pseudo Dice: 0.9307 +2024-09-05 01:59:13.399294: +2024-09-05 01:59:13.400443: Epoch 65 +2024-09-05 01:59:13.401486: Current learning rate: 0.00941 +2024-09-05 02:03:50.662711: train_loss -0.6856 +2024-09-05 02:03:50.677450: val_loss -0.6947 +2024-09-05 02:03:50.678751: Pseudo dice [0.9525, 0.9449, 0.9691, 0.9212, 0.8193, 0.9501, 0.9867] +2024-09-05 02:03:50.680032: Epoch time: 277.27 s +2024-09-05 02:03:50.680907: Yayy! New best EMA pseudo Dice: 0.9311 +2024-09-05 02:03:59.639314: +2024-09-05 02:03:59.640329: Epoch 66 +2024-09-05 02:03:59.641758: Current learning rate: 0.0094 +2024-09-05 02:08:43.300671: train_loss -0.6919 +2024-09-05 02:08:43.316547: val_loss -0.6906 +2024-09-05 02:08:43.318591: Pseudo dice [0.9507, 0.9463, 0.9654, 0.9345, 0.8411, 0.9519, 0.9906] +2024-09-05 02:08:43.320185: Epoch time: 283.67 s +2024-09-05 02:08:43.322589: Yayy! New best EMA pseudo Dice: 0.932 +2024-09-05 02:08:51.907338: +2024-09-05 02:08:51.908350: Epoch 67 +2024-09-05 02:08:51.909433: Current learning rate: 0.00939 +2024-09-05 02:13:14.807268: train_loss -0.6988 +2024-09-05 02:13:14.821779: val_loss -0.6842 +2024-09-05 02:13:14.822806: Pseudo dice [0.9499, 0.9461, 0.9694, 0.9315, 0.8166, 0.9503, 0.9851] +2024-09-05 02:13:14.823734: Epoch time: 262.9 s +2024-09-05 02:13:14.825143: Yayy! New best EMA pseudo Dice: 0.9324 +2024-09-05 02:13:24.833615: +2024-09-05 02:13:24.834875: Epoch 68 +2024-09-05 02:13:24.835551: Current learning rate: 0.00939 +2024-09-05 02:18:12.467193: train_loss -0.6867 +2024-09-05 02:18:12.533021: val_loss -0.7231 +2024-09-05 02:18:12.573664: Pseudo dice [0.9485, 0.9402, 0.9667, 0.9258, 0.8329, 0.9483, 0.9863] +2024-09-05 02:18:12.638060: Epoch time: 287.64 s +2024-09-05 02:18:12.648910: Yayy! New best EMA pseudo Dice: 0.9327 +2024-09-05 02:18:22.951016: +2024-09-05 02:18:22.952125: Epoch 69 +2024-09-05 02:18:22.952810: Current learning rate: 0.00938 +2024-09-05 02:23:02.416120: train_loss -0.6634 +2024-09-05 02:23:02.417654: val_loss -0.6739 +2024-09-05 02:23:02.418469: Pseudo dice [0.9459, 0.9381, 0.9648, 0.93, 0.8184, 0.9365, 0.9833] +2024-09-05 02:23:02.419162: Epoch time: 279.47 s +2024-09-05 02:23:05.042977: +2024-09-05 02:23:05.044061: Epoch 70 +2024-09-05 02:23:05.044687: Current learning rate: 0.00937 +2024-09-05 02:27:38.138090: train_loss -0.672 +2024-09-05 02:27:38.155448: val_loss -0.6962 +2024-09-05 02:27:38.156412: Pseudo dice [0.951, 0.9431, 0.9658, 0.9297, 0.8174, 0.9491, 0.9851] +2024-09-05 02:27:38.157386: Epoch time: 273.1 s +2024-09-05 02:27:38.158556: Yayy! New best EMA pseudo Dice: 0.9327 +2024-09-05 02:27:46.623930: +2024-09-05 02:27:46.624884: Epoch 71 +2024-09-05 02:27:46.625626: Current learning rate: 0.00936 +2024-09-05 02:32:28.448218: train_loss -0.6884 +2024-09-05 02:32:28.449984: val_loss -0.7428 +2024-09-05 02:32:28.452617: Pseudo dice [0.9534, 0.946, 0.971, 0.9334, 0.8363, 0.9502, 0.9854] +2024-09-05 02:32:28.454113: Epoch time: 281.83 s +2024-09-05 02:32:28.454803: Yayy! New best EMA pseudo Dice: 0.9334 +2024-09-05 02:32:38.317943: +2024-09-05 02:32:38.318844: Epoch 72 +2024-09-05 02:32:38.319495: Current learning rate: 0.00935 +2024-09-05 02:37:12.448748: train_loss -0.6866 +2024-09-05 02:37:12.466140: val_loss -0.7287 +2024-09-05 02:37:12.467186: Pseudo dice [0.9498, 0.9479, 0.9703, 0.9318, 0.8261, 0.9515, 0.9872] +2024-09-05 02:37:12.468405: Epoch time: 274.14 s +2024-09-05 02:37:12.468924: Yayy! New best EMA pseudo Dice: 0.9338 +2024-09-05 02:37:20.736644: +2024-09-05 02:37:20.737796: Epoch 73 +2024-09-05 02:37:20.738786: Current learning rate: 0.00934 +2024-09-05 02:42:14.508003: train_loss -0.6921 +2024-09-05 02:42:14.523937: val_loss -0.7148 +2024-09-05 02:42:14.524649: Pseudo dice [0.9533, 0.9482, 0.9723, 0.9299, 0.822, 0.9452, 0.9838] +2024-09-05 02:42:14.525301: Epoch time: 293.77 s +2024-09-05 02:42:14.525881: Yayy! New best EMA pseudo Dice: 0.9341 +2024-09-05 02:42:25.331862: +2024-09-05 02:42:25.391534: Epoch 74 +2024-09-05 02:42:25.405781: Current learning rate: 0.00933 +2024-09-05 02:47:07.780315: train_loss -0.6832 +2024-09-05 02:47:07.786218: val_loss -0.7569 +2024-09-05 02:47:07.787888: Pseudo dice [0.95, 0.9455, 0.9674, 0.9338, 0.8219, 0.953, 0.9891] +2024-09-05 02:47:07.789437: Epoch time: 282.45 s +2024-09-05 02:47:07.790646: Yayy! New best EMA pseudo Dice: 0.9344 +2024-09-05 02:47:17.489353: +2024-09-05 02:47:17.490294: Epoch 75 +2024-09-05 02:47:17.491210: Current learning rate: 0.00932 +2024-09-05 02:51:44.340425: train_loss -0.6954 +2024-09-05 02:51:44.348528: val_loss -0.7012 +2024-09-05 02:51:44.349739: Pseudo dice [0.9499, 0.9444, 0.9678, 0.9402, 0.8263, 0.9509, 0.9902] +2024-09-05 02:51:44.351171: Epoch time: 266.86 s +2024-09-05 02:51:44.352127: Yayy! New best EMA pseudo Dice: 0.9348 +2024-09-05 02:51:55.451908: +2024-09-05 02:51:55.452890: Epoch 76 +2024-09-05 02:51:55.453969: Current learning rate: 0.00931 +2024-09-05 02:56:53.341409: train_loss -0.6888 +2024-09-05 02:56:53.343350: val_loss -0.7278 +2024-09-05 02:56:53.344696: Pseudo dice [0.9558, 0.9487, 0.9712, 0.9381, 0.8346, 0.9525, 0.9889] +2024-09-05 02:56:53.345706: Epoch time: 297.89 s +2024-09-05 02:56:53.346914: Yayy! New best EMA pseudo Dice: 0.9355 +2024-09-05 02:57:02.389209: +2024-09-05 02:57:02.390176: Epoch 77 +2024-09-05 02:57:02.390866: Current learning rate: 0.0093 +2024-09-05 03:01:40.829627: train_loss -0.7064 +2024-09-05 03:01:40.851373: val_loss -0.7308 +2024-09-05 03:01:40.852802: Pseudo dice [0.9548, 0.9481, 0.9713, 0.931, 0.8304, 0.9529, 0.9876] +2024-09-05 03:01:40.854233: Epoch time: 278.44 s +2024-09-05 03:01:40.856251: Yayy! New best EMA pseudo Dice: 0.9359 +2024-09-05 03:01:51.049490: +2024-09-05 03:01:51.050502: Epoch 78 +2024-09-05 03:01:51.051274: Current learning rate: 0.0093 +2024-09-05 03:06:13.784502: train_loss -0.6949 +2024-09-05 03:06:13.803955: val_loss -0.6962 +2024-09-05 03:06:13.805719: Pseudo dice [0.9486, 0.9409, 0.9698, 0.9368, 0.825, 0.9521, 0.9862] +2024-09-05 03:06:13.806996: Epoch time: 262.74 s +2024-09-05 03:06:13.808679: Yayy! New best EMA pseudo Dice: 0.936 +2024-09-05 03:06:23.270738: +2024-09-05 03:06:23.272020: Epoch 79 +2024-09-05 03:06:23.273342: Current learning rate: 0.00929 +2024-09-05 03:11:15.014634: train_loss -0.6898 +2024-09-05 03:11:15.035076: val_loss -0.6998 +2024-09-05 03:11:15.037121: Pseudo dice [0.9532, 0.9461, 0.9724, 0.9244, 0.8331, 0.9476, 0.9867] +2024-09-05 03:11:15.038393: Epoch time: 291.75 s +2024-09-05 03:11:15.039529: Yayy! New best EMA pseudo Dice: 0.9361 +2024-09-05 03:11:24.800644: +2024-09-05 03:11:24.801723: Epoch 80 +2024-09-05 03:11:24.802548: Current learning rate: 0.00928 +2024-09-05 03:16:11.492538: train_loss -0.6908 +2024-09-05 03:16:11.506637: val_loss -0.7329 +2024-09-05 03:16:11.509050: Pseudo dice [0.9538, 0.9452, 0.9741, 0.9277, 0.8317, 0.9519, 0.9865] +2024-09-05 03:16:11.510798: Epoch time: 286.7 s +2024-09-05 03:16:11.512049: Yayy! New best EMA pseudo Dice: 0.9364 +2024-09-05 03:16:20.912363: +2024-09-05 03:16:20.913461: Epoch 81 +2024-09-05 03:16:20.914518: Current learning rate: 0.00927 +2024-09-05 03:20:57.535024: train_loss -0.6969 +2024-09-05 03:20:57.551159: val_loss -0.6919 +2024-09-05 03:20:57.552145: Pseudo dice [0.9489, 0.9439, 0.9677, 0.933, 0.8386, 0.9562, 0.9898] +2024-09-05 03:20:57.553193: Epoch time: 276.63 s +2024-09-05 03:20:57.554128: Yayy! New best EMA pseudo Dice: 0.9367 +2024-09-05 03:21:07.649339: +2024-09-05 03:21:07.650622: Epoch 82 +2024-09-05 03:21:07.651321: Current learning rate: 0.00926 +2024-09-05 03:25:51.460794: train_loss -0.701 +2024-09-05 03:25:51.462433: val_loss -0.7487 +2024-09-05 03:25:51.463284: Pseudo dice [0.9534, 0.9454, 0.9738, 0.9362, 0.8189, 0.9541, 0.9899] +2024-09-05 03:25:51.464211: Epoch time: 283.81 s +2024-09-05 03:25:51.464922: Yayy! New best EMA pseudo Dice: 0.9369 +2024-09-05 03:26:00.150874: +2024-09-05 03:26:00.152140: Epoch 83 +2024-09-05 03:26:00.153009: Current learning rate: 0.00925 +2024-09-05 03:30:33.143934: train_loss -0.6854 +2024-09-05 03:30:33.155339: val_loss -0.7281 +2024-09-05 03:30:33.156406: Pseudo dice [0.9537, 0.9442, 0.9724, 0.9349, 0.8313, 0.9426, 0.9889] +2024-09-05 03:30:33.157285: Epoch time: 273.0 s +2024-09-05 03:30:33.158123: Yayy! New best EMA pseudo Dice: 0.9371 +2024-09-05 03:30:42.018631: +2024-09-05 03:30:42.019564: Epoch 84 +2024-09-05 03:30:42.020622: Current learning rate: 0.00924 +2024-09-05 03:35:05.573079: train_loss -0.6981 +2024-09-05 03:35:05.575215: val_loss -0.7089 +2024-09-05 03:35:05.576268: Pseudo dice [0.9541, 0.944, 0.9673, 0.9314, 0.8389, 0.9525, 0.9899] +2024-09-05 03:35:05.577176: Epoch time: 263.56 s +2024-09-05 03:35:05.578362: Yayy! New best EMA pseudo Dice: 0.9373 +2024-09-05 03:35:14.824181: +2024-09-05 03:35:14.825083: Epoch 85 +2024-09-05 03:35:14.825839: Current learning rate: 0.00923 +2024-09-05 03:39:51.828353: train_loss -0.6652 +2024-09-05 03:39:51.843274: val_loss -0.6899 +2024-09-05 03:39:51.844277: Pseudo dice [0.9491, 0.9439, 0.9671, 0.9319, 0.8016, 0.9461, 0.9845] +2024-09-05 03:39:51.845625: Epoch time: 277.01 s +2024-09-05 03:39:55.658889: +2024-09-05 03:39:55.660112: Epoch 86 +2024-09-05 03:39:55.661359: Current learning rate: 0.00922 +2024-09-05 03:44:52.162103: train_loss -0.6908 +2024-09-05 03:44:52.179118: val_loss -0.7016 +2024-09-05 03:44:52.179976: Pseudo dice [0.9521, 0.9441, 0.9721, 0.9359, 0.8108, 0.9523, 0.9861] +2024-09-05 03:44:52.180827: Epoch time: 296.51 s +2024-09-05 03:44:55.268441: +2024-09-05 03:44:55.278619: Epoch 87 +2024-09-05 03:44:55.279598: Current learning rate: 0.00921 +2024-09-05 03:49:32.671864: train_loss -0.6916 +2024-09-05 03:49:32.683387: val_loss -0.7227 +2024-09-05 03:49:32.684571: Pseudo dice [0.9488, 0.9466, 0.9652, 0.9271, 0.8342, 0.9583, 0.9891] +2024-09-05 03:49:32.685843: Epoch time: 277.41 s +2024-09-05 03:49:35.592674: +2024-09-05 03:49:35.606737: Epoch 88 +2024-09-05 03:49:35.607722: Current learning rate: 0.0092 +2024-09-05 03:54:33.837677: train_loss -0.6849 +2024-09-05 03:54:33.855227: val_loss -0.7167 +2024-09-05 03:54:33.856074: Pseudo dice [0.9537, 0.9471, 0.9676, 0.9407, 0.8346, 0.9566, 0.9895] +2024-09-05 03:54:33.856864: Epoch time: 298.25 s +2024-09-05 03:54:33.857652: Yayy! New best EMA pseudo Dice: 0.9374 +2024-09-05 03:54:42.117138: +2024-09-05 03:54:42.118146: Epoch 89 +2024-09-05 03:54:42.118985: Current learning rate: 0.0092 +2024-09-05 03:59:34.740976: train_loss -0.7015 +2024-09-05 03:59:34.742766: val_loss -0.7194 +2024-09-05 03:59:34.743580: Pseudo dice [0.9555, 0.9505, 0.9709, 0.9384, 0.835, 0.9553, 0.9885] +2024-09-05 03:59:34.744394: Epoch time: 292.63 s +2024-09-05 03:59:34.745283: Yayy! New best EMA pseudo Dice: 0.9378 +2024-09-05 03:59:43.010936: +2024-09-05 03:59:43.011844: Epoch 90 +2024-09-05 03:59:43.012568: Current learning rate: 0.00919 +2024-09-05 04:04:10.683610: train_loss -0.6844 +2024-09-05 04:04:10.695153: val_loss -0.7171 +2024-09-05 04:04:10.696362: Pseudo dice [0.9509, 0.9515, 0.969, 0.9252, 0.8474, 0.9568, 0.9898] +2024-09-05 04:04:10.704710: Epoch time: 267.68 s +2024-09-05 04:04:10.705531: Yayy! New best EMA pseudo Dice: 0.9382 +2024-09-05 04:04:20.484995: +2024-09-05 04:04:20.485975: Epoch 91 +2024-09-05 04:04:20.486583: Current learning rate: 0.00918 +2024-09-05 04:08:58.812582: train_loss -0.7054 +2024-09-05 04:08:58.823331: val_loss -0.7046 +2024-09-05 04:08:58.824474: Pseudo dice [0.9553, 0.9535, 0.9719, 0.9354, 0.8501, 0.9574, 0.9884] +2024-09-05 04:08:58.825499: Epoch time: 278.33 s +2024-09-05 04:08:58.826892: Yayy! New best EMA pseudo Dice: 0.9388 +2024-09-05 04:09:08.102994: +2024-09-05 04:09:08.104393: Epoch 92 +2024-09-05 04:09:08.105277: Current learning rate: 0.00917 +2024-09-05 04:13:54.687767: train_loss -0.6978 +2024-09-05 04:13:54.707136: val_loss -0.7222 +2024-09-05 04:13:54.708482: Pseudo dice [0.9546, 0.9444, 0.9748, 0.9347, 0.8347, 0.9587, 0.9887] +2024-09-05 04:13:54.709733: Epoch time: 286.59 s +2024-09-05 04:13:54.711128: Yayy! New best EMA pseudo Dice: 0.9391 +2024-09-05 04:14:02.979905: +2024-09-05 04:14:02.981087: Epoch 93 +2024-09-05 04:14:02.981834: Current learning rate: 0.00916 +2024-09-05 04:18:47.548112: train_loss -0.7088 +2024-09-05 04:18:47.556100: val_loss -0.7028 +2024-09-05 04:18:47.557607: Pseudo dice [0.9427, 0.941, 0.9651, 0.9254, 0.8203, 0.9432, 0.9891] +2024-09-05 04:18:47.559367: Epoch time: 284.57 s +2024-09-05 04:18:50.408362: +2024-09-05 04:18:50.409667: Epoch 94 +2024-09-05 04:18:50.410371: Current learning rate: 0.00915 +2024-09-05 04:23:40.440267: train_loss -0.662 +2024-09-05 04:23:40.444535: val_loss -0.6992 +2024-09-05 04:23:40.445508: Pseudo dice [0.9522, 0.9448, 0.968, 0.9397, 0.8339, 0.9568, 0.9905] +2024-09-05 04:23:40.446706: Epoch time: 290.04 s +2024-09-05 04:23:43.748145: +2024-09-05 04:23:43.811944: Epoch 95 +2024-09-05 04:23:43.821850: Current learning rate: 0.00914 +2024-09-05 04:28:10.968873: train_loss -0.7021 +2024-09-05 04:28:10.984816: val_loss -0.7002 +2024-09-05 04:28:10.986737: Pseudo dice [0.9489, 0.9468, 0.9659, 0.9375, 0.8274, 0.948, 0.9886] +2024-09-05 04:28:10.988773: Epoch time: 267.23 s +2024-09-05 04:28:14.112067: +2024-09-05 04:28:14.113071: Epoch 96 +2024-09-05 04:28:14.114377: Current learning rate: 0.00913 +2024-09-05 04:32:38.601240: train_loss -0.6946 +2024-09-05 04:32:38.616652: val_loss -0.7299 +2024-09-05 04:32:38.617468: Pseudo dice [0.9524, 0.9475, 0.9699, 0.939, 0.824, 0.9473, 0.9878] +2024-09-05 04:32:38.618495: Epoch time: 264.49 s +2024-09-05 04:32:41.659636: +2024-09-05 04:32:41.670783: Epoch 97 +2024-09-05 04:32:41.672038: Current learning rate: 0.00912 +2024-09-05 04:37:35.813766: train_loss -0.6733 +2024-09-05 04:37:35.830057: val_loss -0.6665 +2024-09-05 04:37:35.831276: Pseudo dice [0.946, 0.9443, 0.9501, 0.9339, 0.8193, 0.9537, 0.9884] +2024-09-05 04:37:35.832635: Epoch time: 294.16 s +2024-09-05 04:37:38.772938: +2024-09-05 04:37:38.774112: Epoch 98 +2024-09-05 04:37:38.775043: Current learning rate: 0.00911 +2024-09-05 04:42:29.285207: train_loss -0.6792 +2024-09-05 04:42:29.297668: val_loss -0.7269 +2024-09-05 04:42:29.299549: Pseudo dice [0.9472, 0.9451, 0.9646, 0.9299, 0.8369, 0.956, 0.987] +2024-09-05 04:42:29.301110: Epoch time: 290.52 s +2024-09-05 04:42:33.665346: +2024-09-05 04:42:33.666614: Epoch 99 +2024-09-05 04:42:33.669087: Current learning rate: 0.0091 +2024-09-05 04:47:14.867587: train_loss -0.6871 +2024-09-05 04:47:14.883224: val_loss -0.7362 +2024-09-05 04:47:14.884154: Pseudo dice [0.9497, 0.9416, 0.9678, 0.9377, 0.8273, 0.9509, 0.9883] +2024-09-05 04:47:14.885314: Epoch time: 281.21 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_08_31_30.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_08_31_30.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f694a19161114f888b40c9245ffaf8ba5664b18 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_08_31_30.txt @@ -0,0 +1,393 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 08:31:33.208848: Using torch.compile... +2024-09-05 08:31:41.987125: do_dummy_2d_data_aug: False +2024-09-05 08:31:41.993165: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 08:31:41.994796: The split file contains 5 splits. +2024-09-05 08:31:41.995496: Desired fold for training: 4 +2024-09-05 08:31:41.996219: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 08:31:55.917622: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 08:31:57.857160: +2024-09-05 08:31:57.996643: Epoch 100 +2024-09-05 08:31:57.998321: Current learning rate: 0.0091 +2024-09-05 08:50:26.399457: train_loss -0.6933 +2024-09-05 08:50:26.400795: val_loss -0.7053 +2024-09-05 08:50:26.401423: Pseudo dice [0.9536, 0.944, 0.9691, 0.932, 0.8276, 0.9525, 0.9889] +2024-09-05 08:50:26.402338: Epoch time: 1108.55 s +2024-09-05 08:50:28.009291: +2024-09-05 08:50:28.010729: Epoch 101 +2024-09-05 08:50:28.011358: Current learning rate: 0.00909 +2024-09-05 08:54:52.707263: train_loss -0.7052 +2024-09-05 08:54:52.723186: val_loss -0.7465 +2024-09-05 08:54:52.724522: Pseudo dice [0.9549, 0.9473, 0.9716, 0.9279, 0.8313, 0.9554, 0.9874] +2024-09-05 08:54:52.725850: Epoch time: 264.7 s +2024-09-05 08:54:56.161548: +2024-09-05 08:54:56.162609: Epoch 102 +2024-09-05 08:54:56.164051: Current learning rate: 0.00908 +2024-09-05 08:59:27.506498: train_loss -0.6953 +2024-09-05 08:59:27.522923: val_loss -0.6941 +2024-09-05 08:59:27.524642: Pseudo dice [0.9533, 0.9479, 0.9691, 0.9379, 0.8342, 0.9583, 0.9884] +2024-09-05 08:59:27.525935: Epoch time: 271.35 s +2024-09-05 08:59:31.092454: +2024-09-05 08:59:31.093707: Epoch 103 +2024-09-05 08:59:31.094634: Current learning rate: 0.00907 +2024-09-05 09:04:20.403252: train_loss -0.6993 +2024-09-05 09:04:20.404696: val_loss -0.7347 +2024-09-05 09:04:20.406168: Pseudo dice [0.9545, 0.947, 0.9731, 0.9354, 0.8414, 0.9541, 0.9892] +2024-09-05 09:04:20.407418: Epoch time: 289.32 s +2024-09-05 09:04:24.790452: +2024-09-05 09:04:24.791494: Epoch 104 +2024-09-05 09:04:24.792967: Current learning rate: 0.00906 +2024-09-05 09:08:54.951428: train_loss -0.7095 +2024-09-05 09:08:54.967489: val_loss -0.6891 +2024-09-05 09:08:54.968784: Pseudo dice [0.9577, 0.9524, 0.9744, 0.9379, 0.834, 0.9558, 0.9891] +2024-09-05 09:08:54.969752: Epoch time: 270.17 s +2024-09-05 09:08:54.970896: Yayy! New best EMA pseudo Dice: 0.9393 +2024-09-05 09:09:04.542419: +2024-09-05 09:09:04.543401: Epoch 105 +2024-09-05 09:09:04.544115: Current learning rate: 0.00905 +2024-09-05 09:13:33.858522: train_loss -0.6999 +2024-09-05 09:13:33.863998: val_loss -0.7192 +2024-09-05 09:13:33.864732: Pseudo dice [0.9589, 0.9511, 0.9733, 0.9316, 0.8535, 0.9577, 0.9907] +2024-09-05 09:13:33.865417: Epoch time: 269.32 s +2024-09-05 09:13:33.865987: Yayy! New best EMA pseudo Dice: 0.9399 +2024-09-05 09:13:42.686337: +2024-09-05 09:13:42.687346: Epoch 106 +2024-09-05 09:13:42.688194: Current learning rate: 0.00904 +2024-09-05 09:18:28.622828: train_loss -0.705 +2024-09-05 09:18:28.643055: val_loss -0.703 +2024-09-05 09:18:28.644897: Pseudo dice [0.9531, 0.9479, 0.9699, 0.9236, 0.831, 0.9543, 0.9861] +2024-09-05 09:18:28.646913: Epoch time: 285.94 s +2024-09-05 09:18:31.692240: +2024-09-05 09:18:31.702022: Epoch 107 +2024-09-05 09:18:31.710738: Current learning rate: 0.00903 +2024-09-05 09:23:36.545626: train_loss -0.6995 +2024-09-05 09:23:36.547441: val_loss -0.7129 +2024-09-05 09:23:36.548443: Pseudo dice [0.9563, 0.9472, 0.9725, 0.9323, 0.8424, 0.9566, 0.9895] +2024-09-05 09:23:36.549210: Epoch time: 304.86 s +2024-09-05 09:23:36.549970: Yayy! New best EMA pseudo Dice: 0.9399 +2024-09-05 09:23:44.917922: +2024-09-05 09:23:44.919049: Epoch 108 +2024-09-05 09:23:44.919769: Current learning rate: 0.00902 +2024-09-05 09:28:41.946540: train_loss -0.7037 +2024-09-05 09:28:41.948395: val_loss -0.7571 +2024-09-05 09:28:41.949314: Pseudo dice [0.9573, 0.9494, 0.9736, 0.9394, 0.8448, 0.9607, 0.9896] +2024-09-05 09:28:41.950566: Epoch time: 297.03 s +2024-09-05 09:28:41.951312: Yayy! New best EMA pseudo Dice: 0.9405 +2024-09-05 09:28:50.205616: +2024-09-05 09:28:50.206687: Epoch 109 +2024-09-05 09:28:50.207384: Current learning rate: 0.00901 +2024-09-05 09:33:43.828933: train_loss -0.7102 +2024-09-05 09:33:43.863657: val_loss -0.7286 +2024-09-05 09:33:43.874826: Pseudo dice [0.9597, 0.9509, 0.9739, 0.9389, 0.8457, 0.9602, 0.9908] +2024-09-05 09:33:43.875638: Epoch time: 293.63 s +2024-09-05 09:33:43.876250: Yayy! New best EMA pseudo Dice: 0.941 +2024-09-05 09:33:53.173218: +2024-09-05 09:33:53.174155: Epoch 110 +2024-09-05 09:33:53.175416: Current learning rate: 0.009 +2024-09-05 09:38:52.297734: train_loss -0.7142 +2024-09-05 09:38:52.300240: val_loss -0.7101 +2024-09-05 09:38:52.301296: Pseudo dice [0.959, 0.9502, 0.9703, 0.936, 0.8312, 0.9548, 0.9877] +2024-09-05 09:38:52.302050: Epoch time: 299.13 s +2024-09-05 09:38:52.302685: Yayy! New best EMA pseudo Dice: 0.941 +2024-09-05 09:39:00.751228: +2024-09-05 09:39:00.752165: Epoch 111 +2024-09-05 09:39:00.753210: Current learning rate: 0.009 +2024-09-05 09:43:25.299289: train_loss -0.6994 +2024-09-05 09:43:25.313209: val_loss -0.6878 +2024-09-05 09:43:25.314182: Pseudo dice [0.9553, 0.9501, 0.9702, 0.9345, 0.8332, 0.9533, 0.9895] +2024-09-05 09:43:25.315021: Epoch time: 264.55 s +2024-09-05 09:43:28.618601: +2024-09-05 09:43:28.619815: Epoch 112 +2024-09-05 09:43:28.621606: Current learning rate: 0.00899 +2024-09-05 09:48:17.099793: train_loss -0.7116 +2024-09-05 09:48:17.101588: val_loss -0.7207 +2024-09-05 09:48:17.102417: Pseudo dice [0.9564, 0.9501, 0.9699, 0.939, 0.8349, 0.961, 0.9889] +2024-09-05 09:48:17.103248: Epoch time: 288.49 s +2024-09-05 09:48:17.103952: Yayy! New best EMA pseudo Dice: 0.9412 +2024-09-05 09:48:24.989398: +2024-09-05 09:48:24.990449: Epoch 113 +2024-09-05 09:48:24.991206: Current learning rate: 0.00898 +2024-09-05 09:53:18.988886: train_loss -0.7149 +2024-09-05 09:53:19.005594: val_loss -0.716 +2024-09-05 09:53:19.006514: Pseudo dice [0.9594, 0.9533, 0.9751, 0.9445, 0.8493, 0.9624, 0.9905] +2024-09-05 09:53:19.007496: Epoch time: 294.0 s +2024-09-05 09:53:19.008379: Yayy! New best EMA pseudo Dice: 0.9418 +2024-09-05 09:53:27.796534: +2024-09-05 09:53:27.797683: Epoch 114 +2024-09-05 09:53:27.798383: Current learning rate: 0.00897 +2024-09-05 09:58:15.828614: train_loss -0.7038 +2024-09-05 09:58:15.834845: val_loss -0.6875 +2024-09-05 09:58:15.836709: Pseudo dice [0.9545, 0.9499, 0.9695, 0.9273, 0.8263, 0.9565, 0.9901] +2024-09-05 09:58:15.837706: Epoch time: 288.04 s +2024-09-05 09:58:19.489381: +2024-09-05 09:58:19.490508: Epoch 115 +2024-09-05 09:58:19.491159: Current learning rate: 0.00896 +2024-09-05 10:02:59.575806: train_loss -0.709 +2024-09-05 10:02:59.577221: val_loss -0.6977 +2024-09-05 10:02:59.578068: Pseudo dice [0.9575, 0.9526, 0.9747, 0.9364, 0.8334, 0.9523, 0.9883] +2024-09-05 10:02:59.578797: Epoch time: 280.09 s +2024-09-05 10:03:02.314463: +2024-09-05 10:03:02.316903: Epoch 116 +2024-09-05 10:03:02.317712: Current learning rate: 0.00895 +2024-09-05 10:07:46.777229: train_loss -0.7089 +2024-09-05 10:07:46.791254: val_loss -0.7438 +2024-09-05 10:07:46.792319: Pseudo dice [0.956, 0.9508, 0.973, 0.9431, 0.8454, 0.9612, 0.9887] +2024-09-05 10:07:46.793376: Epoch time: 284.47 s +2024-09-05 10:07:46.794533: Yayy! New best EMA pseudo Dice: 0.942 +2024-09-05 10:07:57.178772: +2024-09-05 10:07:57.179833: Epoch 117 +2024-09-05 10:07:57.181033: Current learning rate: 0.00894 +2024-09-05 10:12:44.073680: train_loss -0.7071 +2024-09-05 10:12:44.093731: val_loss -0.7053 +2024-09-05 10:12:44.095339: Pseudo dice [0.9584, 0.9509, 0.9717, 0.9401, 0.8415, 0.9574, 0.9881] +2024-09-05 10:12:44.097703: Epoch time: 286.9 s +2024-09-05 10:12:44.098617: Yayy! New best EMA pseudo Dice: 0.9422 +2024-09-05 10:12:52.647337: +2024-09-05 10:12:52.648363: Epoch 118 +2024-09-05 10:12:52.649093: Current learning rate: 0.00893 +2024-09-05 10:17:33.615312: train_loss -0.7189 +2024-09-05 10:17:33.623873: val_loss -0.706 +2024-09-05 10:17:33.624867: Pseudo dice [0.959, 0.95, 0.9749, 0.9387, 0.8476, 0.9596, 0.985] +2024-09-05 10:17:33.625777: Epoch time: 280.97 s +2024-09-05 10:17:33.626998: Yayy! New best EMA pseudo Dice: 0.9425 +2024-09-05 10:17:43.298550: +2024-09-05 10:17:43.299704: Epoch 119 +2024-09-05 10:17:43.300348: Current learning rate: 0.00892 +2024-09-05 10:22:25.312725: train_loss -0.7151 +2024-09-05 10:22:25.327417: val_loss -0.7141 +2024-09-05 10:22:25.328893: Pseudo dice [0.9591, 0.952, 0.9719, 0.94, 0.8601, 0.9594, 0.9914] +2024-09-05 10:22:25.329845: Epoch time: 282.02 s +2024-09-05 10:22:25.331354: Yayy! New best EMA pseudo Dice: 0.943 +2024-09-05 10:22:32.942094: +2024-09-05 10:22:32.943103: Epoch 120 +2024-09-05 10:22:32.943763: Current learning rate: 0.00891 +2024-09-05 10:26:58.785181: train_loss -0.688 +2024-09-05 10:26:58.797009: val_loss -0.6563 +2024-09-05 10:26:58.798710: Pseudo dice [0.9388, 0.9427, 0.9595, 0.9233, 0.8251, 0.9464, 0.9874] +2024-09-05 10:26:58.799721: Epoch time: 265.85 s +2024-09-05 10:27:01.506039: +2024-09-05 10:27:01.507380: Epoch 121 +2024-09-05 10:27:01.508337: Current learning rate: 0.0089 +2024-09-05 10:31:59.820786: train_loss -0.7064 +2024-09-05 10:31:59.835422: val_loss -0.7268 +2024-09-05 10:31:59.836764: Pseudo dice [0.9489, 0.9461, 0.963, 0.9261, 0.8499, 0.9622, 0.9899] +2024-09-05 10:31:59.838241: Epoch time: 298.32 s +2024-09-05 10:32:02.248506: +2024-09-05 10:32:02.249583: Epoch 122 +2024-09-05 10:32:02.250756: Current learning rate: 0.00889 +2024-09-05 10:37:06.451734: train_loss -0.6988 +2024-09-05 10:37:06.454245: val_loss -0.7064 +2024-09-05 10:37:06.455005: Pseudo dice [0.956, 0.9504, 0.9723, 0.936, 0.8345, 0.9589, 0.9882] +2024-09-05 10:37:06.455705: Epoch time: 304.21 s +2024-09-05 10:37:08.904326: +2024-09-05 10:37:08.905849: Epoch 123 +2024-09-05 10:37:08.906900: Current learning rate: 0.00889 +2024-09-05 10:41:54.934162: train_loss -0.711 +2024-09-05 10:41:54.936602: val_loss -0.7298 +2024-09-05 10:41:54.937296: Pseudo dice [0.9485, 0.9489, 0.9646, 0.9307, 0.8414, 0.96, 0.9889] +2024-09-05 10:41:54.938021: Epoch time: 286.04 s +2024-09-05 10:41:57.486381: +2024-09-05 10:41:57.488211: Epoch 124 +2024-09-05 10:41:57.492272: Current learning rate: 0.00888 +2024-09-05 10:46:52.655605: train_loss -0.72 +2024-09-05 10:46:52.673049: val_loss -0.7326 +2024-09-05 10:46:52.674010: Pseudo dice [0.9543, 0.9509, 0.9755, 0.9348, 0.8402, 0.9598, 0.9889] +2024-09-05 10:46:52.675006: Epoch time: 295.18 s +2024-09-05 10:46:55.356164: +2024-09-05 10:46:55.357763: Epoch 125 +2024-09-05 10:46:55.358767: Current learning rate: 0.00887 +2024-09-05 10:51:46.475138: train_loss -0.7072 +2024-09-05 10:51:46.492784: val_loss -0.733 +2024-09-05 10:51:46.493772: Pseudo dice [0.9562, 0.95, 0.9749, 0.9413, 0.8297, 0.9566, 0.9892] +2024-09-05 10:51:46.494670: Epoch time: 291.13 s +2024-09-05 10:51:49.099882: +2024-09-05 10:51:49.101037: Epoch 126 +2024-09-05 10:51:49.102200: Current learning rate: 0.00886 +2024-09-05 10:56:45.788230: train_loss -0.6876 +2024-09-05 10:56:45.802507: val_loss -0.7272 +2024-09-05 10:56:45.803571: Pseudo dice [0.959, 0.9505, 0.9769, 0.9374, 0.8495, 0.9599, 0.9894] +2024-09-05 10:56:45.804432: Epoch time: 296.7 s +2024-09-05 10:56:49.015158: +2024-09-05 10:56:49.016225: Epoch 127 +2024-09-05 10:56:49.017060: Current learning rate: 0.00885 +2024-09-05 11:01:34.710553: train_loss -0.7045 +2024-09-05 11:01:34.724384: val_loss -0.7526 +2024-09-05 11:01:34.725733: Pseudo dice [0.9583, 0.9499, 0.9718, 0.9317, 0.85, 0.9633, 0.9908] +2024-09-05 11:01:34.726674: Epoch time: 285.7 s +2024-09-05 11:01:37.314422: +2024-09-05 11:01:37.315470: Epoch 128 +2024-09-05 11:01:37.316234: Current learning rate: 0.00884 +2024-09-05 11:06:32.082983: train_loss -0.7075 +2024-09-05 11:06:32.095026: val_loss -0.731 +2024-09-05 11:06:32.096134: Pseudo dice [0.9552, 0.95, 0.9708, 0.926, 0.8519, 0.9574, 0.9904] +2024-09-05 11:06:32.096873: Epoch time: 294.77 s +2024-09-05 11:06:34.853001: +2024-09-05 11:06:34.853958: Epoch 129 +2024-09-05 11:06:34.854654: Current learning rate: 0.00883 +2024-09-05 11:11:43.361523: train_loss -0.7152 +2024-09-05 11:11:43.376806: val_loss -0.7112 +2024-09-05 11:11:43.377605: Pseudo dice [0.9559, 0.9524, 0.9718, 0.9366, 0.8571, 0.9581, 0.9875] +2024-09-05 11:11:43.378676: Epoch time: 308.51 s +2024-09-05 11:11:46.428410: +2024-09-05 11:11:46.429568: Epoch 130 +2024-09-05 11:11:46.430621: Current learning rate: 0.00882 +2024-09-05 11:16:40.410879: train_loss -0.7084 +2024-09-05 11:16:40.432343: val_loss -0.7288 +2024-09-05 11:16:40.433475: Pseudo dice [0.9559, 0.947, 0.9716, 0.9343, 0.8504, 0.952, 0.9909] +2024-09-05 11:16:40.434485: Epoch time: 293.99 s +2024-09-05 11:16:44.182437: +2024-09-05 11:16:44.183914: Epoch 131 +2024-09-05 11:16:44.185045: Current learning rate: 0.00881 +2024-09-05 11:21:42.306970: train_loss -0.7084 +2024-09-05 11:21:42.309247: val_loss -0.7119 +2024-09-05 11:21:42.310668: Pseudo dice [0.9538, 0.9478, 0.9711, 0.9386, 0.8387, 0.9546, 0.9868] +2024-09-05 11:21:42.311481: Epoch time: 298.13 s +2024-09-05 11:21:44.795953: +2024-09-05 11:21:44.797158: Epoch 132 +2024-09-05 11:21:44.798672: Current learning rate: 0.0088 +2024-09-05 11:26:36.456504: train_loss -0.7165 +2024-09-05 11:26:36.472849: val_loss -0.6986 +2024-09-05 11:26:36.474085: Pseudo dice [0.9577, 0.953, 0.9724, 0.9373, 0.8554, 0.9571, 0.9886] +2024-09-05 11:26:36.475073: Epoch time: 291.67 s +2024-09-05 11:26:36.475995: Yayy! New best EMA pseudo Dice: 0.9432 +2024-09-05 11:26:45.324401: +2024-09-05 11:26:45.414147: Epoch 133 +2024-09-05 11:26:45.475767: Current learning rate: 0.00879 +2024-09-05 11:31:33.946777: train_loss -0.7078 +2024-09-05 11:31:33.963815: val_loss -0.7338 +2024-09-05 11:31:33.965427: Pseudo dice [0.9567, 0.9499, 0.9737, 0.9368, 0.8462, 0.9539, 0.9888] +2024-09-05 11:31:33.967028: Epoch time: 288.63 s +2024-09-05 11:31:33.968162: Yayy! New best EMA pseudo Dice: 0.9432 +2024-09-05 11:31:42.670502: +2024-09-05 11:31:42.671329: Epoch 134 +2024-09-05 11:31:42.672193: Current learning rate: 0.00879 +2024-09-05 11:36:46.754985: train_loss -0.7011 +2024-09-05 11:36:46.762226: val_loss -0.7177 +2024-09-05 11:36:46.763110: Pseudo dice [0.9562, 0.9482, 0.9739, 0.937, 0.8419, 0.9579, 0.9863] +2024-09-05 11:36:46.763767: Epoch time: 304.09 s +2024-09-05 11:36:50.237945: +2024-09-05 11:36:50.245979: Epoch 135 +2024-09-05 11:36:50.254767: Current learning rate: 0.00878 +2024-09-05 11:41:55.119341: train_loss -0.7027 +2024-09-05 11:41:55.121178: val_loss -0.7044 +2024-09-05 11:41:55.121966: Pseudo dice [0.9568, 0.9515, 0.9718, 0.9364, 0.8375, 0.9577, 0.988] +2024-09-05 11:41:55.123206: Epoch time: 304.89 s +2024-09-05 11:41:57.671821: +2024-09-05 11:41:57.673198: Epoch 136 +2024-09-05 11:41:57.673953: Current learning rate: 0.00877 +2024-09-05 11:46:41.907846: train_loss -0.7017 +2024-09-05 11:46:41.919086: val_loss -0.6841 +2024-09-05 11:46:41.920176: Pseudo dice [0.9598, 0.9542, 0.9753, 0.9394, 0.8378, 0.9589, 0.987] +2024-09-05 11:46:41.921114: Epoch time: 284.24 s +2024-09-05 11:46:41.922077: Yayy! New best EMA pseudo Dice: 0.9433 +2024-09-05 11:46:51.749876: +2024-09-05 11:46:51.750782: Epoch 137 +2024-09-05 11:46:51.751464: Current learning rate: 0.00876 +2024-09-05 11:51:39.641618: train_loss -0.709 +2024-09-05 11:51:39.670110: val_loss -0.7417 +2024-09-05 11:51:39.671371: Pseudo dice [0.9577, 0.9495, 0.9713, 0.9354, 0.8451, 0.9589, 0.9899] +2024-09-05 11:51:39.672490: Epoch time: 287.9 s +2024-09-05 11:51:39.673985: Yayy! New best EMA pseudo Dice: 0.9434 +2024-09-05 11:51:50.658661: +2024-09-05 11:51:50.659746: Epoch 138 +2024-09-05 11:51:50.660446: Current learning rate: 0.00875 +2024-09-05 11:57:01.666031: train_loss -0.7058 +2024-09-05 11:57:01.679199: val_loss -0.7418 +2024-09-05 11:57:01.680222: Pseudo dice [0.9568, 0.9516, 0.9685, 0.9409, 0.8596, 0.9632, 0.9898] +2024-09-05 11:57:01.681102: Epoch time: 311.01 s +2024-09-05 11:57:01.681970: Yayy! New best EMA pseudo Dice: 0.9438 +2024-09-05 11:57:11.089004: +2024-09-05 11:57:11.090175: Epoch 139 +2024-09-05 11:57:11.090962: Current learning rate: 0.00874 +2024-09-05 12:01:53.837976: train_loss -0.7089 +2024-09-05 12:01:53.839646: val_loss -0.7197 +2024-09-05 12:01:53.840648: Pseudo dice [0.955, 0.9493, 0.9695, 0.9311, 0.839, 0.9548, 0.985] +2024-09-05 12:01:53.841617: Epoch time: 282.75 s +2024-09-05 12:01:56.513507: +2024-09-05 12:01:56.515674: Epoch 140 +2024-09-05 12:01:56.517249: Current learning rate: 0.00873 +2024-09-05 12:06:52.033353: train_loss -0.7228 +2024-09-05 12:06:52.045919: val_loss -0.7182 +2024-09-05 12:06:52.046918: Pseudo dice [0.9567, 0.9513, 0.974, 0.9442, 0.8606, 0.9596, 0.9896] +2024-09-05 12:06:52.047671: Epoch time: 295.53 s +2024-09-05 12:06:52.048385: Yayy! New best EMA pseudo Dice: 0.9439 +2024-09-05 12:07:01.163798: +2024-09-05 12:07:01.165209: Epoch 141 +2024-09-05 12:07:01.165875: Current learning rate: 0.00872 +2024-09-05 12:11:41.755405: train_loss -0.7187 +2024-09-05 12:11:41.775191: val_loss -0.6949 +2024-09-05 12:11:41.776465: Pseudo dice [0.957, 0.9531, 0.9717, 0.9412, 0.8502, 0.9626, 0.9901] +2024-09-05 12:11:41.777377: Epoch time: 280.6 s +2024-09-05 12:11:41.778527: Yayy! New best EMA pseudo Dice: 0.9442 +2024-09-05 12:11:51.142560: +2024-09-05 12:11:51.143686: Epoch 142 +2024-09-05 12:11:51.144419: Current learning rate: 0.00871 +2024-09-05 12:16:41.358440: train_loss -0.7193 +2024-09-05 12:16:41.384407: val_loss -0.7175 +2024-09-05 12:16:41.385262: Pseudo dice [0.957, 0.9505, 0.9732, 0.9321, 0.8522, 0.9602, 0.9884] +2024-09-05 12:16:41.386262: Epoch time: 290.22 s +2024-09-05 12:16:41.387206: Yayy! New best EMA pseudo Dice: 0.9442 +2024-09-05 12:16:50.973878: +2024-09-05 12:16:50.974794: Epoch 143 +2024-09-05 12:16:50.975374: Current learning rate: 0.0087 +2024-09-05 12:21:50.675764: train_loss -0.7083 +2024-09-05 12:21:50.686975: val_loss -0.7253 +2024-09-05 12:21:50.687726: Pseudo dice [0.9545, 0.9458, 0.9686, 0.9275, 0.843, 0.952, 0.9906] +2024-09-05 12:21:50.688363: Epoch time: 299.71 s +2024-09-05 12:21:53.823565: +2024-09-05 12:21:53.824629: Epoch 144 +2024-09-05 12:21:53.825410: Current learning rate: 0.00869 +2024-09-05 12:26:41.709201: train_loss -0.6933 +2024-09-05 12:26:41.713146: val_loss -0.6843 +2024-09-05 12:26:41.714683: Pseudo dice [0.9576, 0.9508, 0.9734, 0.9445, 0.8447, 0.9506, 0.9885] +2024-09-05 12:26:41.716970: Epoch time: 287.89 s +2024-09-05 12:26:44.728096: +2024-09-05 12:26:44.729263: Epoch 145 +2024-09-05 12:26:44.730140: Current learning rate: 0.00868 +2024-09-05 12:31:32.819788: train_loss -0.7088 +2024-09-05 12:31:32.821523: val_loss -0.6973 +2024-09-05 12:31:32.823053: Pseudo dice [0.9555, 0.9514, 0.9674, 0.9378, 0.8485, 0.9599, 0.9891] +2024-09-05 12:31:32.824847: Epoch time: 288.1 s +2024-09-05 12:31:35.814490: +2024-09-05 12:31:35.815900: Epoch 146 +2024-09-05 12:31:35.817061: Current learning rate: 0.00868 +2024-09-05 12:36:26.066411: train_loss -0.7025 +2024-09-05 12:36:26.077172: val_loss -0.7443 +2024-09-05 12:36:26.078484: Pseudo dice [0.9544, 0.9454, 0.9747, 0.9312, 0.8577, 0.9612, 0.9907] +2024-09-05 12:36:26.079463: Epoch time: 290.26 s +2024-09-05 12:36:29.053782: +2024-09-05 12:36:29.087571: Epoch 147 +2024-09-05 12:36:29.096684: Current learning rate: 0.00867 +2024-09-05 12:41:20.507456: train_loss -0.7161 +2024-09-05 12:41:20.523254: val_loss -0.7365 +2024-09-05 12:41:20.524509: Pseudo dice [0.9588, 0.9521, 0.9735, 0.9409, 0.8587, 0.9604, 0.9912] +2024-09-05 12:41:20.525774: Epoch time: 291.46 s +2024-09-05 12:41:20.526817: Yayy! New best EMA pseudo Dice: 0.9444 +2024-09-05 12:41:30.074561: +2024-09-05 12:41:30.075533: Epoch 148 +2024-09-05 12:41:30.076283: Current learning rate: 0.00866 +2024-09-05 12:46:20.508111: train_loss -0.7142 +2024-09-05 12:46:20.532422: val_loss -0.7035 +2024-09-05 12:46:20.533360: Pseudo dice [0.9524, 0.9507, 0.9693, 0.9421, 0.8462, 0.9567, 0.989] +2024-09-05 12:46:20.534460: Epoch time: 290.44 s +2024-09-05 12:46:25.963256: +2024-09-05 12:46:25.964599: Epoch 149 +2024-09-05 12:46:25.965954: Current learning rate: 0.00865 +2024-09-05 12:51:14.850443: train_loss -0.7153 +2024-09-05 12:51:14.853160: val_loss -0.7414 +2024-09-05 12:51:14.854385: Pseudo dice [0.9588, 0.9515, 0.977, 0.9412, 0.857, 0.9606, 0.9861] +2024-09-05 12:51:14.855154: Epoch time: 288.89 s +2024-09-05 12:51:22.173386: Yayy! New best EMA pseudo Dice: 0.9447 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_16_30_44.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_16_30_44.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba2b0f381f0304e2c6c6871ca5b159c281c31604 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_5_16_30_44.txt @@ -0,0 +1,383 @@ + +####################################################################### +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. +####################################################################### + +2024-09-05 16:30:47.824330: Using torch.compile... +2024-09-05 16:30:56.878169: do_dummy_2d_data_aug: False +2024-09-05 16:30:56.884317: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-05 16:30:56.885675: The split file contains 5 splits. +2024-09-05 16:30:56.886271: Desired fold for training: 4 +2024-09-05 16:30:56.886878: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-05 16:31:08.310054: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-05 16:31:09.725539: +2024-09-05 16:31:09.727430: Epoch 150 +2024-09-05 16:31:09.730137: Current learning rate: 0.00864 +2024-09-05 16:49:45.197304: train_loss -0.7094 +2024-09-05 16:49:45.199080: val_loss -0.6946 +2024-09-05 16:49:45.200007: Pseudo dice [0.9574, 0.9499, 0.9753, 0.9405, 0.8454, 0.961, 0.9837] +2024-09-05 16:49:45.200701: Epoch time: 1115.48 s +2024-09-05 16:49:45.201404: Yayy! New best EMA pseudo Dice: 0.9447 +2024-09-05 16:49:57.886505: +2024-09-05 16:49:57.887260: Epoch 151 +2024-09-05 16:49:57.887944: Current learning rate: 0.00863 +2024-09-05 16:54:32.857258: train_loss -0.7086 +2024-09-05 16:54:32.871101: val_loss -0.7477 +2024-09-05 16:54:32.872193: Pseudo dice [0.958, 0.9501, 0.9769, 0.9386, 0.8447, 0.9594, 0.9883] +2024-09-05 16:54:32.873178: Epoch time: 274.97 s +2024-09-05 16:54:32.874359: Yayy! New best EMA pseudo Dice: 0.9447 +2024-09-05 16:54:41.866123: +2024-09-05 16:54:41.866995: Epoch 152 +2024-09-05 16:54:41.867586: Current learning rate: 0.00862 +2024-09-05 16:59:12.174812: train_loss -0.7258 +2024-09-05 16:59:12.187205: val_loss -0.6818 +2024-09-05 16:59:12.188113: Pseudo dice [0.9547, 0.9558, 0.963, 0.9384, 0.8435, 0.9593, 0.9873] +2024-09-05 16:59:12.188804: Epoch time: 270.31 s +2024-09-05 16:59:15.267719: +2024-09-05 16:59:15.268706: Epoch 153 +2024-09-05 16:59:15.269516: Current learning rate: 0.00861 +2024-09-05 17:04:08.653519: train_loss -0.7222 +2024-09-05 17:04:08.654951: val_loss -0.7166 +2024-09-05 17:04:08.655843: Pseudo dice [0.9558, 0.9522, 0.9726, 0.9401, 0.866, 0.9617, 0.9907] +2024-09-05 17:04:08.656632: Epoch time: 293.39 s +2024-09-05 17:04:08.657502: Yayy! New best EMA pseudo Dice: 0.9449 +2024-09-05 17:04:16.530138: +2024-09-05 17:04:16.531070: Epoch 154 +2024-09-05 17:04:16.531794: Current learning rate: 0.0086 +2024-09-05 17:08:36.442608: train_loss -0.7121 +2024-09-05 17:08:36.623563: val_loss -0.7327 +2024-09-05 17:08:36.755042: Pseudo dice [0.9553, 0.9536, 0.9712, 0.9414, 0.865, 0.964, 0.9901] +2024-09-05 17:08:36.886168: Epoch time: 259.92 s +2024-09-05 17:08:36.990217: Yayy! New best EMA pseudo Dice: 0.9453 +2024-09-05 17:08:47.823387: +2024-09-05 17:08:47.824422: Epoch 155 +2024-09-05 17:08:47.825412: Current learning rate: 0.00859 +2024-09-05 17:13:24.608424: train_loss -0.7053 +2024-09-05 17:13:24.623150: val_loss -0.7351 +2024-09-05 17:13:24.624755: Pseudo dice [0.9554, 0.948, 0.9719, 0.9394, 0.8287, 0.947, 0.9876] +2024-09-05 17:13:24.626139: Epoch time: 276.79 s +2024-09-05 17:13:27.182533: +2024-09-05 17:13:27.183611: Epoch 156 +2024-09-05 17:13:27.184520: Current learning rate: 0.00858 +2024-09-05 17:18:30.409827: train_loss -0.7042 +2024-09-05 17:18:30.429123: val_loss -0.7508 +2024-09-05 17:18:30.430720: Pseudo dice [0.9574, 0.9514, 0.9716, 0.9394, 0.8447, 0.9558, 0.9869] +2024-09-05 17:18:30.432031: Epoch time: 303.23 s +2024-09-05 17:18:34.624249: +2024-09-05 17:18:34.625309: Epoch 157 +2024-09-05 17:18:34.626234: Current learning rate: 0.00858 +2024-09-05 17:23:51.047524: train_loss -0.7048 +2024-09-05 17:23:51.071706: val_loss -0.7099 +2024-09-05 17:23:51.072822: Pseudo dice [0.9567, 0.9483, 0.9675, 0.9326, 0.8396, 0.9511, 0.9894] +2024-09-05 17:23:51.073465: Epoch time: 316.43 s +2024-09-05 17:23:55.523241: +2024-09-05 17:23:55.524537: Epoch 158 +2024-09-05 17:23:55.525484: Current learning rate: 0.00857 +2024-09-05 17:29:17.024537: train_loss -0.7047 +2024-09-05 17:29:17.039184: val_loss -0.7046 +2024-09-05 17:29:17.039911: Pseudo dice [0.9541, 0.9472, 0.9676, 0.9366, 0.8426, 0.9601, 0.9889] +2024-09-05 17:29:17.040638: Epoch time: 321.51 s +2024-09-05 17:29:20.166975: +2024-09-05 17:29:20.168884: Epoch 159 +2024-09-05 17:29:20.170243: Current learning rate: 0.00856 +2024-09-05 17:34:39.067236: train_loss -0.6951 +2024-09-05 17:34:39.083100: val_loss -0.7201 +2024-09-05 17:34:39.083829: Pseudo dice [0.9572, 0.9505, 0.969, 0.9393, 0.8463, 0.9556, 0.9911] +2024-09-05 17:34:39.084487: Epoch time: 318.91 s +2024-09-05 17:34:44.063214: +2024-09-05 17:34:44.064060: Epoch 160 +2024-09-05 17:34:44.064796: Current learning rate: 0.00855 +2024-09-05 17:39:55.141363: train_loss -0.7182 +2024-09-05 17:39:55.166707: val_loss -0.7135 +2024-09-05 17:39:55.167497: Pseudo dice [0.9588, 0.9554, 0.9696, 0.9398, 0.8443, 0.9594, 0.9906] +2024-09-05 17:39:55.168221: Epoch time: 311.08 s +2024-09-05 17:39:59.199381: +2024-09-05 17:39:59.200348: Epoch 161 +2024-09-05 17:39:59.201232: Current learning rate: 0.00854 +2024-09-05 17:45:23.312195: train_loss -0.7066 +2024-09-05 17:45:23.325477: val_loss -0.6611 +2024-09-05 17:45:23.326356: Pseudo dice [0.9556, 0.9519, 0.9241, 0.9388, 0.8409, 0.9616, 0.9902] +2024-09-05 17:45:23.327369: Epoch time: 324.12 s +2024-09-05 17:45:26.780561: +2024-09-05 17:45:26.781927: Epoch 162 +2024-09-05 17:45:26.782825: Current learning rate: 0.00853 +2024-09-05 17:50:35.905339: train_loss -0.7172 +2024-09-05 17:50:35.937731: val_loss -0.7228 +2024-09-05 17:50:35.939473: Pseudo dice [0.959, 0.9527, 0.9723, 0.9415, 0.8422, 0.9637, 0.9889] +2024-09-05 17:50:35.940192: Epoch time: 309.13 s +2024-09-05 17:50:39.322322: +2024-09-05 17:50:39.323265: Epoch 163 +2024-09-05 17:50:39.324268: Current learning rate: 0.00852 +2024-09-05 17:55:54.841519: train_loss -0.7266 +2024-09-05 17:55:54.870919: val_loss -0.7162 +2024-09-05 17:55:54.871986: Pseudo dice [0.9558, 0.9498, 0.9712, 0.932, 0.8457, 0.9638, 0.991] +2024-09-05 17:55:54.872640: Epoch time: 315.53 s +2024-09-05 17:55:58.360628: +2024-09-05 17:55:58.361836: Epoch 164 +2024-09-05 17:55:58.362559: Current learning rate: 0.00851 +2024-09-05 18:01:24.686827: train_loss -0.7305 +2024-09-05 18:01:24.703216: val_loss -0.7278 +2024-09-05 18:01:24.703930: Pseudo dice [0.9581, 0.9528, 0.9705, 0.9421, 0.8462, 0.9611, 0.991] +2024-09-05 18:01:24.705441: Epoch time: 326.33 s +2024-09-05 18:01:28.952222: +2024-09-05 18:01:28.953328: Epoch 165 +2024-09-05 18:01:28.954013: Current learning rate: 0.0085 +2024-09-05 18:06:34.240678: train_loss -0.7248 +2024-09-05 18:06:34.253362: val_loss -0.7352 +2024-09-05 18:06:34.254422: Pseudo dice [0.9592, 0.9544, 0.9708, 0.9441, 0.8497, 0.9584, 0.987] +2024-09-05 18:06:34.255178: Epoch time: 305.29 s +2024-09-05 18:06:38.297891: +2024-09-05 18:06:38.299015: Epoch 166 +2024-09-05 18:06:38.299767: Current learning rate: 0.00849 +2024-09-05 18:11:44.692227: train_loss -0.7212 +2024-09-05 18:11:44.693786: val_loss -0.7049 +2024-09-05 18:11:44.694710: Pseudo dice [0.958, 0.951, 0.9729, 0.9383, 0.8475, 0.9613, 0.9906] +2024-09-05 18:11:44.695525: Epoch time: 306.4 s +2024-09-05 18:11:47.178309: +2024-09-05 18:11:47.179207: Epoch 167 +2024-09-05 18:11:47.179901: Current learning rate: 0.00848 +2024-09-05 18:17:00.895800: train_loss -0.7174 +2024-09-05 18:17:00.922174: val_loss -0.7214 +2024-09-05 18:17:00.923677: Pseudo dice [0.957, 0.9549, 0.9688, 0.9378, 0.8707, 0.9627, 0.9876] +2024-09-05 18:17:00.925956: Epoch time: 313.72 s +2024-09-05 18:17:04.972841: +2024-09-05 18:17:04.982479: Epoch 168 +2024-09-05 18:17:04.983438: Current learning rate: 0.00847 +2024-09-05 18:22:16.396921: train_loss -0.7321 +2024-09-05 18:22:16.411096: val_loss -0.7638 +2024-09-05 18:22:16.411821: Pseudo dice [0.9612, 0.9518, 0.9753, 0.9434, 0.8716, 0.9663, 0.9903] +2024-09-05 18:22:16.412526: Epoch time: 311.43 s +2024-09-05 18:22:16.413104: Yayy! New best EMA pseudo Dice: 0.9455 +2024-09-05 18:22:25.135513: +2024-09-05 18:22:25.136565: Epoch 169 +2024-09-05 18:22:25.137254: Current learning rate: 0.00847 +2024-09-05 18:27:48.917960: train_loss -0.7245 +2024-09-05 18:27:48.934072: val_loss -0.7869 +2024-09-05 18:27:48.935295: Pseudo dice [0.9617, 0.9525, 0.9751, 0.9396, 0.8635, 0.965, 0.9907] +2024-09-05 18:27:48.936715: Epoch time: 323.79 s +2024-09-05 18:27:48.938242: Yayy! New best EMA pseudo Dice: 0.9459 +2024-09-05 18:27:59.583993: +2024-09-05 18:27:59.585830: Epoch 170 +2024-09-05 18:27:59.586593: Current learning rate: 0.00846 +2024-09-05 18:32:44.929965: train_loss -0.7285 +2024-09-05 18:32:44.943531: val_loss -0.7031 +2024-09-05 18:32:44.944477: Pseudo dice [0.9577, 0.9532, 0.9712, 0.9366, 0.8597, 0.9641, 0.99] +2024-09-05 18:32:44.945242: Epoch time: 285.35 s +2024-09-05 18:32:44.945988: Yayy! New best EMA pseudo Dice: 0.9461 +2024-09-05 18:32:56.141564: +2024-09-05 18:32:56.142507: Epoch 171 +2024-09-05 18:32:56.143360: Current learning rate: 0.00845 +2024-09-05 18:38:03.607418: train_loss -0.7214 +2024-09-05 18:38:03.609485: val_loss -0.7273 +2024-09-05 18:38:03.611577: Pseudo dice [0.9596, 0.9528, 0.9753, 0.939, 0.8576, 0.9629, 0.9899] +2024-09-05 18:38:03.613813: Epoch time: 307.47 s +2024-09-05 18:38:03.615134: Yayy! New best EMA pseudo Dice: 0.9463 +2024-09-05 18:38:14.334177: +2024-09-05 18:38:14.335376: Epoch 172 +2024-09-05 18:38:14.336378: Current learning rate: 0.00844 +2024-09-05 18:43:31.168499: train_loss -0.7342 +2024-09-05 18:43:31.218074: val_loss -0.7232 +2024-09-05 18:43:31.219038: Pseudo dice [0.9586, 0.9507, 0.9754, 0.944, 0.8523, 0.9614, 0.9876] +2024-09-05 18:43:31.220400: Epoch time: 316.84 s +2024-09-05 18:43:31.220979: Yayy! New best EMA pseudo Dice: 0.9464 +2024-09-05 18:43:41.932484: +2024-09-05 18:43:41.933679: Epoch 173 +2024-09-05 18:43:41.934765: Current learning rate: 0.00843 +2024-09-05 18:48:57.431447: train_loss -0.7091 +2024-09-05 18:48:57.434136: val_loss -0.7117 +2024-09-05 18:48:57.435353: Pseudo dice [0.9566, 0.9518, 0.9717, 0.9346, 0.8608, 0.9634, 0.9825] +2024-09-05 18:48:57.436392: Epoch time: 315.5 s +2024-09-05 18:49:01.061773: +2024-09-05 18:49:01.062706: Epoch 174 +2024-09-05 18:49:01.063427: Current learning rate: 0.00842 +2024-09-05 18:54:04.032732: train_loss -0.7157 +2024-09-05 18:54:04.050928: val_loss -0.7396 +2024-09-05 18:54:04.053031: Pseudo dice [0.9588, 0.9523, 0.9747, 0.9374, 0.8526, 0.9606, 0.9865] +2024-09-05 18:54:04.055108: Epoch time: 302.98 s +2024-09-05 18:54:07.368411: +2024-09-05 18:54:07.370449: Epoch 175 +2024-09-05 18:54:07.372406: Current learning rate: 0.00841 +2024-09-05 18:59:03.250557: train_loss -0.7262 +2024-09-05 18:59:03.267033: val_loss -0.7379 +2024-09-05 18:59:03.267884: Pseudo dice [0.9586, 0.9536, 0.9707, 0.9416, 0.8601, 0.9602, 0.9893] +2024-09-05 18:59:03.268632: Epoch time: 295.89 s +2024-09-05 18:59:03.269318: Yayy! New best EMA pseudo Dice: 0.9464 +2024-09-05 18:59:13.464255: +2024-09-05 18:59:13.465257: Epoch 176 +2024-09-05 18:59:13.466163: Current learning rate: 0.0084 +2024-09-05 19:04:07.997064: train_loss -0.7123 +2024-09-05 19:04:08.012110: val_loss -0.7174 +2024-09-05 19:04:08.013153: Pseudo dice [0.9569, 0.9517, 0.9706, 0.9419, 0.8479, 0.9621, 0.9888] +2024-09-05 19:04:08.014688: Epoch time: 294.54 s +2024-09-05 19:04:11.679140: +2024-09-05 19:04:11.687007: Epoch 177 +2024-09-05 19:04:11.687790: Current learning rate: 0.00839 +2024-09-05 19:09:35.290141: train_loss -0.7082 +2024-09-05 19:09:35.308108: val_loss -0.7405 +2024-09-05 19:09:35.308918: Pseudo dice [0.9581, 0.9523, 0.9747, 0.9448, 0.8643, 0.962, 0.9895] +2024-09-05 19:09:35.309941: Epoch time: 323.62 s +2024-09-05 19:09:35.310786: Yayy! New best EMA pseudo Dice: 0.9467 +2024-09-05 19:09:46.053557: +2024-09-05 19:09:46.055041: Epoch 178 +2024-09-05 19:09:46.055974: Current learning rate: 0.00838 +2024-09-05 19:15:01.231167: train_loss -0.7099 +2024-09-05 19:15:01.233497: val_loss -0.744 +2024-09-05 19:15:01.234215: Pseudo dice [0.9565, 0.9498, 0.9752, 0.9403, 0.8487, 0.9612, 0.9895] +2024-09-05 19:15:01.234962: Epoch time: 315.18 s +2024-09-05 19:15:04.253046: +2024-09-05 19:15:04.265683: Epoch 179 +2024-09-05 19:15:04.266806: Current learning rate: 0.00837 +2024-09-05 19:20:04.760613: train_loss -0.72 +2024-09-05 19:20:04.770134: val_loss -0.7027 +2024-09-05 19:20:04.771507: Pseudo dice [0.9569, 0.9524, 0.971, 0.9347, 0.8281, 0.9592, 0.9894] +2024-09-05 19:20:04.772781: Epoch time: 300.52 s +2024-09-05 19:20:08.421538: +2024-09-05 19:20:08.422512: Epoch 180 +2024-09-05 19:20:08.423340: Current learning rate: 0.00836 +2024-09-05 19:25:03.233018: train_loss -0.7196 +2024-09-05 19:25:03.263515: val_loss -0.7369 +2024-09-05 19:25:03.265032: Pseudo dice [0.9567, 0.9538, 0.9602, 0.9439, 0.8528, 0.9589, 0.9912] +2024-09-05 19:25:03.266103: Epoch time: 294.82 s +2024-09-05 19:25:06.297819: +2024-09-05 19:25:06.299447: Epoch 181 +2024-09-05 19:25:06.301105: Current learning rate: 0.00836 +2024-09-05 19:30:12.100416: train_loss -0.7117 +2024-09-05 19:30:12.102865: val_loss -0.7202 +2024-09-05 19:30:12.104092: Pseudo dice [0.9572, 0.9545, 0.9714, 0.9376, 0.8478, 0.9622, 0.9908] +2024-09-05 19:30:12.105131: Epoch time: 305.81 s +2024-09-05 19:30:14.800620: +2024-09-05 19:30:14.802528: Epoch 182 +2024-09-05 19:30:14.803665: Current learning rate: 0.00835 +2024-09-05 19:35:02.455344: train_loss -0.7179 +2024-09-05 19:35:02.467131: val_loss -0.7424 +2024-09-05 19:35:02.468145: Pseudo dice [0.9603, 0.9528, 0.9779, 0.9472, 0.8653, 0.9637, 0.9896] +2024-09-05 19:35:02.468984: Epoch time: 287.66 s +2024-09-05 19:35:05.493794: +2024-09-05 19:35:05.495538: Epoch 183 +2024-09-05 19:35:05.497195: Current learning rate: 0.00834 +2024-09-05 19:40:01.683511: train_loss -0.7074 +2024-09-05 19:40:01.699054: val_loss -0.7348 +2024-09-05 19:40:01.699786: Pseudo dice [0.9548, 0.9526, 0.9641, 0.9353, 0.8455, 0.9609, 0.9905] +2024-09-05 19:40:01.700459: Epoch time: 296.2 s +2024-09-05 19:40:05.114229: +2024-09-05 19:40:05.122098: Epoch 184 +2024-09-05 19:40:05.123140: Current learning rate: 0.00833 +2024-09-05 19:44:45.020462: train_loss -0.7245 +2024-09-05 19:44:45.022464: val_loss -0.7064 +2024-09-05 19:44:45.023612: Pseudo dice [0.9543, 0.9531, 0.9703, 0.936, 0.8661, 0.9654, 0.9901] +2024-09-05 19:44:45.024592: Epoch time: 279.91 s +2024-09-05 19:44:48.370089: +2024-09-05 19:44:48.371369: Epoch 185 +2024-09-05 19:44:48.372589: Current learning rate: 0.00832 +2024-09-05 19:49:45.722538: train_loss -0.7219 +2024-09-05 19:49:45.728655: val_loss -0.7148 +2024-09-05 19:49:45.729250: Pseudo dice [0.9571, 0.9509, 0.9725, 0.9364, 0.8484, 0.9621, 0.9877] +2024-09-05 19:49:45.729960: Epoch time: 297.36 s +2024-09-05 19:49:49.060980: +2024-09-05 19:49:49.061893: Epoch 186 +2024-09-05 19:49:49.062628: Current learning rate: 0.00831 +2024-09-05 19:54:38.888208: train_loss -0.7131 +2024-09-05 19:54:38.904608: val_loss -0.7353 +2024-09-05 19:54:38.905410: Pseudo dice [0.9554, 0.9523, 0.9733, 0.9363, 0.8464, 0.9618, 0.9869] +2024-09-05 19:54:38.906202: Epoch time: 289.83 s +2024-09-05 19:54:42.963749: +2024-09-05 19:54:42.964702: Epoch 187 +2024-09-05 19:54:42.965337: Current learning rate: 0.0083 +2024-09-05 19:59:38.368394: train_loss -0.7124 +2024-09-05 19:59:38.370933: val_loss -0.7172 +2024-09-05 19:59:38.371652: Pseudo dice [0.9564, 0.953, 0.9743, 0.9421, 0.8677, 0.959, 0.9901] +2024-09-05 19:59:38.372495: Epoch time: 295.41 s +2024-09-05 19:59:41.276868: +2024-09-05 19:59:41.293912: Epoch 188 +2024-09-05 19:59:41.294872: Current learning rate: 0.00829 +2024-09-05 20:05:03.870704: train_loss -0.71 +2024-09-05 20:05:03.888712: val_loss -0.7488 +2024-09-05 20:05:03.889886: Pseudo dice [0.9571, 0.9532, 0.9744, 0.9356, 0.8517, 0.9627, 0.9892] +2024-09-05 20:05:03.890712: Epoch time: 322.6 s +2024-09-05 20:05:06.651683: +2024-09-05 20:05:06.656037: Epoch 189 +2024-09-05 20:05:06.656759: Current learning rate: 0.00828 +2024-09-05 20:10:14.999340: train_loss -0.7151 +2024-09-05 20:10:15.001952: val_loss -0.7264 +2024-09-05 20:10:15.003029: Pseudo dice [0.9545, 0.9527, 0.9688, 0.9362, 0.8589, 0.9574, 0.9899] +2024-09-05 20:10:15.003965: Epoch time: 308.35 s +2024-09-05 20:10:17.608095: +2024-09-05 20:10:17.609072: Epoch 190 +2024-09-05 20:10:17.609900: Current learning rate: 0.00827 +2024-09-05 20:15:09.880084: train_loss -0.7288 +2024-09-05 20:15:09.882903: val_loss -0.7595 +2024-09-05 20:15:09.884121: Pseudo dice [0.9595, 0.9566, 0.9735, 0.9463, 0.8587, 0.9651, 0.9904] +2024-09-05 20:15:09.884819: Epoch time: 292.28 s +2024-09-05 20:15:12.840332: +2024-09-05 20:15:12.841293: Epoch 191 +2024-09-05 20:15:12.842489: Current learning rate: 0.00826 +2024-09-05 20:20:07.950098: train_loss -0.7198 +2024-09-05 20:20:07.965099: val_loss -0.7328 +2024-09-05 20:20:07.966668: Pseudo dice [0.9613, 0.954, 0.9774, 0.9398, 0.8656, 0.963, 0.9914] +2024-09-05 20:20:07.967477: Epoch time: 295.11 s +2024-09-05 20:20:07.968173: Yayy! New best EMA pseudo Dice: 0.947 +2024-09-05 20:20:17.467331: +2024-09-05 20:20:17.468585: Epoch 192 +2024-09-05 20:20:17.469241: Current learning rate: 0.00825 +2024-09-05 20:25:02.501168: train_loss -0.7414 +2024-09-05 20:25:02.503829: val_loss -0.7496 +2024-09-05 20:25:02.504598: Pseudo dice [0.9601, 0.9514, 0.972, 0.9373, 0.856, 0.96, 0.9898] +2024-09-05 20:25:02.505411: Epoch time: 285.04 s +2024-09-05 20:25:07.239320: +2024-09-05 20:25:07.240251: Epoch 193 +2024-09-05 20:25:07.240992: Current learning rate: 0.00824 +2024-09-05 20:30:03.286353: train_loss -0.7153 +2024-09-05 20:30:03.304914: val_loss -0.7178 +2024-09-05 20:30:03.305925: Pseudo dice [0.9583, 0.9547, 0.9718, 0.9436, 0.8551, 0.9588, 0.9801] +2024-09-05 20:30:03.307348: Epoch time: 296.05 s +2024-09-05 20:30:08.668954: +2024-09-05 20:30:08.670131: Epoch 194 +2024-09-05 20:30:08.670832: Current learning rate: 0.00824 +2024-09-05 20:34:46.989166: train_loss -0.7261 +2024-09-05 20:34:47.003292: val_loss -0.703 +2024-09-05 20:34:47.004112: Pseudo dice [0.9594, 0.953, 0.9721, 0.9393, 0.8495, 0.9588, 0.9887] +2024-09-05 20:34:47.004800: Epoch time: 278.33 s +2024-09-05 20:34:51.337080: +2024-09-05 20:34:51.338270: Epoch 195 +2024-09-05 20:34:51.339351: Current learning rate: 0.00823 +2024-09-05 20:40:01.573319: train_loss -0.7323 +2024-09-05 20:40:01.575200: val_loss -0.7303 +2024-09-05 20:40:01.576874: Pseudo dice [0.958, 0.9529, 0.9747, 0.9379, 0.8494, 0.9606, 0.9888] +2024-09-05 20:40:01.578079: Epoch time: 310.24 s +2024-09-05 20:40:05.463562: +2024-09-05 20:40:05.465015: Epoch 196 +2024-09-05 20:40:05.466110: Current learning rate: 0.00822 +2024-09-05 20:45:03.258408: train_loss -0.716 +2024-09-05 20:45:03.283597: val_loss -0.7151 +2024-09-05 20:45:03.284490: Pseudo dice [0.9582, 0.9512, 0.9713, 0.9361, 0.8575, 0.9597, 0.9898] +2024-09-05 20:45:03.285390: Epoch time: 297.8 s +2024-09-05 20:45:08.158273: +2024-09-05 20:45:08.171531: Epoch 197 +2024-09-05 20:45:08.172413: Current learning rate: 0.00821 +2024-09-05 20:50:18.887197: train_loss -0.7258 +2024-09-05 20:50:18.904623: val_loss -0.7097 +2024-09-05 20:50:18.905939: Pseudo dice [0.9565, 0.9552, 0.9736, 0.9361, 0.8645, 0.9604, 0.9893] +2024-09-05 20:50:18.907491: Epoch time: 310.73 s +2024-09-05 20:50:21.994991: +2024-09-05 20:50:21.998303: Epoch 198 +2024-09-05 20:50:22.000366: Current learning rate: 0.0082 +2024-09-05 20:55:20.238691: train_loss -0.7251 +2024-09-05 20:55:20.240184: val_loss -0.7285 +2024-09-05 20:55:20.241568: Pseudo dice [0.9578, 0.9549, 0.971, 0.9437, 0.852, 0.9607, 0.9905] +2024-09-05 20:55:20.242367: Epoch time: 298.25 s +2024-09-05 20:55:24.004655: +2024-09-05 20:55:24.005911: Epoch 199 +2024-09-05 20:55:24.006757: Current learning rate: 0.00819 +2024-09-05 21:00:16.045925: train_loss -0.7088 +2024-09-05 21:00:16.059522: val_loss -0.7179 +2024-09-05 21:00:16.061052: Pseudo dice [0.96, 0.9518, 0.9751, 0.9348, 0.8538, 0.9603, 0.9891] +2024-09-05 21:00:16.062064: Epoch time: 292.05 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_00_30_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_00_30_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..8d08930787b7ca7efd51a6490ede6c8b4ba85b1d --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_00_30_16.txt @@ -0,0 +1,739 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 00:30:19.077829: Using torch.compile... +2024-09-06 00:30:30.047762: do_dummy_2d_data_aug: False +2024-09-06 00:30:30.053798: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 00:30:30.055234: The split file contains 5 splits. +2024-09-06 00:30:30.055965: Desired fold for training: 4 +2024-09-06 00:30:30.056727: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 00:30:40.893051: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 00:30:41.936303: +2024-09-06 00:30:41.937964: Epoch 200 +2024-09-06 00:30:41.948261: Current learning rate: 0.00818 +2024-09-06 00:49:17.781717: train_loss -0.7223 +2024-09-06 00:49:17.784045: val_loss -0.7423 +2024-09-06 00:49:17.784996: Pseudo dice [0.9608, 0.9538, 0.9765, 0.9431, 0.8547, 0.9543, 0.9896] +2024-09-06 00:49:17.786469: Epoch time: 1115.85 s +2024-09-06 00:49:19.883762: +2024-09-06 00:49:19.885012: Epoch 201 +2024-09-06 00:49:19.886151: Current learning rate: 0.00817 +2024-09-06 00:53:56.149357: train_loss -0.6985 +2024-09-06 00:53:56.151919: val_loss -0.705 +2024-09-06 00:53:56.154849: Pseudo dice [0.9549, 0.9499, 0.9681, 0.9373, 0.8459, 0.9607, 0.9882] +2024-09-06 00:53:56.157369: Epoch time: 276.27 s +2024-09-06 00:53:58.820120: +2024-09-06 00:53:58.822110: Epoch 202 +2024-09-06 00:53:58.825307: Current learning rate: 0.00816 +2024-09-06 00:58:38.604644: train_loss -0.7176 +2024-09-06 00:58:38.625221: val_loss -0.7072 +2024-09-06 00:58:38.627613: Pseudo dice [0.9582, 0.9512, 0.9736, 0.9387, 0.8529, 0.9542, 0.9902] +2024-09-06 00:58:38.629928: Epoch time: 279.79 s +2024-09-06 00:58:41.602818: +2024-09-06 00:58:41.604127: Epoch 203 +2024-09-06 00:58:41.604983: Current learning rate: 0.00815 +2024-09-06 01:03:22.162904: train_loss -0.7157 +2024-09-06 01:03:22.179139: val_loss -0.7322 +2024-09-06 01:03:22.180671: Pseudo dice [0.9598, 0.9557, 0.976, 0.9455, 0.8559, 0.9623, 0.9872] +2024-09-06 01:03:22.182668: Epoch time: 280.57 s +2024-09-06 01:03:25.227210: +2024-09-06 01:03:25.231669: Epoch 204 +2024-09-06 01:03:25.232821: Current learning rate: 0.00814 +2024-09-06 01:08:09.811889: train_loss -0.7109 +2024-09-06 01:08:09.850902: val_loss -0.741 +2024-09-06 01:08:09.851926: Pseudo dice [0.9621, 0.9538, 0.9776, 0.947, 0.8664, 0.9675, 0.9898] +2024-09-06 01:08:09.853267: Epoch time: 284.59 s +2024-09-06 01:08:09.855422: Yayy! New best EMA pseudo Dice: 0.9472 +2024-09-06 01:08:18.718395: +2024-09-06 01:08:18.719422: Epoch 205 +2024-09-06 01:08:18.720356: Current learning rate: 0.00813 +2024-09-06 01:13:07.920099: train_loss -0.7162 +2024-09-06 01:13:07.921828: val_loss -0.7361 +2024-09-06 01:13:07.922854: Pseudo dice [0.9595, 0.956, 0.9728, 0.9445, 0.8647, 0.9626, 0.9898] +2024-09-06 01:13:07.923940: Epoch time: 289.2 s +2024-09-06 01:13:07.925103: Yayy! New best EMA pseudo Dice: 0.9475 +2024-09-06 01:13:14.646184: +2024-09-06 01:13:14.647198: Epoch 206 +2024-09-06 01:13:14.648135: Current learning rate: 0.00813 +2024-09-06 01:17:47.759396: train_loss -0.7174 +2024-09-06 01:17:47.776259: val_loss -0.7553 +2024-09-06 01:17:47.778787: Pseudo dice [0.9599, 0.9518, 0.976, 0.9355, 0.865, 0.9543, 0.9897] +2024-09-06 01:17:47.779784: Epoch time: 273.12 s +2024-09-06 01:17:50.719496: +2024-09-06 01:17:50.720755: Epoch 207 +2024-09-06 01:17:50.721913: Current learning rate: 0.00812 +2024-09-06 01:22:11.649340: train_loss -0.7223 +2024-09-06 01:22:11.659735: val_loss -0.7311 +2024-09-06 01:22:11.660830: Pseudo dice [0.9586, 0.9538, 0.9728, 0.9368, 0.8546, 0.9633, 0.9903] +2024-09-06 01:22:11.661800: Epoch time: 260.94 s +2024-09-06 01:22:15.498149: +2024-09-06 01:22:15.499190: Epoch 208 +2024-09-06 01:22:15.500022: Current learning rate: 0.00811 +2024-09-06 01:26:46.219346: train_loss -0.7017 +2024-09-06 01:26:46.233781: val_loss -0.739 +2024-09-06 01:26:46.236559: Pseudo dice [0.9621, 0.9568, 0.9766, 0.9416, 0.8705, 0.9632, 0.9893] +2024-09-06 01:26:46.238134: Epoch time: 270.73 s +2024-09-06 01:26:46.239514: Yayy! New best EMA pseudo Dice: 0.9479 +2024-09-06 01:26:54.899502: +2024-09-06 01:26:54.901754: Epoch 209 +2024-09-06 01:26:54.902590: Current learning rate: 0.0081 +2024-09-06 01:31:33.812066: train_loss -0.7143 +2024-09-06 01:31:33.833086: val_loss -0.7118 +2024-09-06 01:31:33.834466: Pseudo dice [0.9603, 0.954, 0.9761, 0.9404, 0.8472, 0.9647, 0.9903] +2024-09-06 01:31:33.835753: Epoch time: 278.92 s +2024-09-06 01:31:37.006447: +2024-09-06 01:31:37.007825: Epoch 210 +2024-09-06 01:31:37.013370: Current learning rate: 0.00809 +2024-09-06 01:36:03.117740: train_loss -0.7137 +2024-09-06 01:36:03.121151: val_loss -0.6941 +2024-09-06 01:36:03.122686: Pseudo dice [0.9491, 0.9503, 0.9663, 0.9358, 0.8265, 0.954, 0.9886] +2024-09-06 01:36:03.123768: Epoch time: 266.12 s +2024-09-06 01:36:06.615696: +2024-09-06 01:36:06.617270: Epoch 211 +2024-09-06 01:36:06.618702: Current learning rate: 0.00808 +2024-09-06 01:40:42.869944: train_loss -0.7209 +2024-09-06 01:40:42.895965: val_loss -0.7235 +2024-09-06 01:40:42.897170: Pseudo dice [0.9549, 0.9533, 0.9724, 0.9421, 0.862, 0.9662, 0.9895] +2024-09-06 01:40:42.898250: Epoch time: 276.26 s +2024-09-06 01:40:46.209469: +2024-09-06 01:40:46.218429: Epoch 212 +2024-09-06 01:40:46.219843: Current learning rate: 0.00807 +2024-09-06 01:44:50.714614: train_loss -0.71 +2024-09-06 01:44:50.733689: val_loss -0.7407 +2024-09-06 01:44:50.737676: Pseudo dice [0.959, 0.952, 0.9756, 0.9372, 0.8517, 0.9659, 0.9881] +2024-09-06 01:44:50.739480: Epoch time: 244.51 s +2024-09-06 01:44:53.656308: +2024-09-06 01:44:53.657693: Epoch 213 +2024-09-06 01:44:53.658734: Current learning rate: 0.00806 +2024-09-06 01:49:23.497720: train_loss -0.7197 +2024-09-06 01:49:23.519788: val_loss -0.7172 +2024-09-06 01:49:23.520927: Pseudo dice [0.9566, 0.9567, 0.9738, 0.9398, 0.8527, 0.9649, 0.9873] +2024-09-06 01:49:23.522464: Epoch time: 269.85 s +2024-09-06 01:49:26.444156: +2024-09-06 01:49:26.445222: Epoch 214 +2024-09-06 01:49:26.446121: Current learning rate: 0.00805 +2024-09-06 01:53:54.072051: train_loss -0.7117 +2024-09-06 01:53:54.087491: val_loss -0.7147 +2024-09-06 01:53:54.088541: Pseudo dice [0.9558, 0.952, 0.9723, 0.9357, 0.8494, 0.9558, 0.9882] +2024-09-06 01:53:54.089407: Epoch time: 267.63 s +2024-09-06 01:53:56.728210: +2024-09-06 01:53:56.729378: Epoch 215 +2024-09-06 01:53:56.730214: Current learning rate: 0.00804 +2024-09-06 01:58:32.621697: train_loss -0.721 +2024-09-06 01:58:32.624805: val_loss -0.7327 +2024-09-06 01:58:32.626307: Pseudo dice [0.9622, 0.9541, 0.9778, 0.9437, 0.8505, 0.9672, 0.989] +2024-09-06 01:58:32.628032: Epoch time: 275.9 s +2024-09-06 01:58:34.998784: +2024-09-06 01:58:35.000432: Epoch 216 +2024-09-06 01:58:35.002391: Current learning rate: 0.00803 +2024-09-06 02:03:02.175663: train_loss -0.7242 +2024-09-06 02:03:02.180732: val_loss -0.7169 +2024-09-06 02:03:02.183472: Pseudo dice [0.9592, 0.9515, 0.975, 0.9465, 0.858, 0.9637, 0.9898] +2024-09-06 02:03:02.185395: Epoch time: 267.18 s +2024-09-06 02:03:04.583644: +2024-09-06 02:03:04.587817: Epoch 217 +2024-09-06 02:03:04.590786: Current learning rate: 0.00802 +2024-09-06 02:07:31.775681: train_loss -0.7344 +2024-09-06 02:07:31.793070: val_loss -0.7633 +2024-09-06 02:07:31.794193: Pseudo dice [0.9634, 0.9544, 0.9782, 0.9412, 0.8603, 0.9636, 0.9904] +2024-09-06 02:07:31.795333: Epoch time: 267.2 s +2024-09-06 02:07:34.883154: +2024-09-06 02:07:34.894741: Epoch 218 +2024-09-06 02:07:34.896184: Current learning rate: 0.00801 +2024-09-06 02:12:11.121891: train_loss -0.7037 +2024-09-06 02:12:11.133740: val_loss -0.706 +2024-09-06 02:12:11.135262: Pseudo dice [0.9562, 0.9556, 0.9615, 0.9378, 0.8401, 0.9612, 0.9877] +2024-09-06 02:12:11.141801: Epoch time: 276.25 s +2024-09-06 02:12:13.252053: +2024-09-06 02:12:13.253408: Epoch 219 +2024-09-06 02:12:13.254416: Current learning rate: 0.00801 +2024-09-06 02:16:49.283255: train_loss -0.718 +2024-09-06 02:16:49.286303: val_loss -0.7204 +2024-09-06 02:16:49.287327: Pseudo dice [0.9599, 0.9552, 0.9756, 0.936, 0.8564, 0.9606, 0.989] +2024-09-06 02:16:49.288334: Epoch time: 276.04 s +2024-09-06 02:16:51.743006: +2024-09-06 02:16:51.744651: Epoch 220 +2024-09-06 02:16:51.746032: Current learning rate: 0.008 +2024-09-06 02:21:03.526942: train_loss -0.7224 +2024-09-06 02:21:03.544113: val_loss -0.7583 +2024-09-06 02:21:03.545022: Pseudo dice [0.9622, 0.9559, 0.9769, 0.9442, 0.8549, 0.9653, 0.9896] +2024-09-06 02:21:03.545840: Epoch time: 251.79 s +2024-09-06 02:21:05.864142: +2024-09-06 02:21:05.865281: Epoch 221 +2024-09-06 02:21:05.866215: Current learning rate: 0.00799 +2024-09-06 02:25:27.109015: train_loss -0.7262 +2024-09-06 02:25:27.131508: val_loss -0.7584 +2024-09-06 02:25:27.134364: Pseudo dice [0.9587, 0.957, 0.974, 0.9447, 0.8672, 0.9662, 0.9915] +2024-09-06 02:25:27.135843: Epoch time: 261.25 s +2024-09-06 02:25:29.689929: +2024-09-06 02:25:29.706817: Epoch 222 +2024-09-06 02:25:29.707958: Current learning rate: 0.00798 +2024-09-06 02:29:59.672324: train_loss -0.726 +2024-09-06 02:29:59.685261: val_loss -0.7219 +2024-09-06 02:29:59.686554: Pseudo dice [0.9628, 0.9568, 0.9795, 0.9483, 0.8691, 0.9628, 0.987] +2024-09-06 02:29:59.687519: Epoch time: 269.99 s +2024-09-06 02:29:59.688559: Yayy! New best EMA pseudo Dice: 0.9483 +2024-09-06 02:30:08.020429: +2024-09-06 02:30:08.022156: Epoch 223 +2024-09-06 02:30:08.023057: Current learning rate: 0.00797 +2024-09-06 02:34:25.690776: train_loss -0.7126 +2024-09-06 02:34:25.692836: val_loss -0.7435 +2024-09-06 02:34:25.695415: Pseudo dice [0.9569, 0.9501, 0.9679, 0.9451, 0.8584, 0.9622, 0.9883] +2024-09-06 02:34:25.696494: Epoch time: 257.67 s +2024-09-06 02:34:30.315897: +2024-09-06 02:34:30.317270: Epoch 224 +2024-09-06 02:34:30.318074: Current learning rate: 0.00796 +2024-09-06 02:38:58.603790: train_loss -0.7092 +2024-09-06 02:38:58.620265: val_loss -0.6957 +2024-09-06 02:38:58.621299: Pseudo dice [0.9528, 0.9504, 0.9688, 0.9354, 0.8524, 0.9577, 0.9861] +2024-09-06 02:38:58.622227: Epoch time: 268.29 s +2024-09-06 02:39:01.129270: +2024-09-06 02:39:01.130570: Epoch 225 +2024-09-06 02:39:01.131725: Current learning rate: 0.00795 +2024-09-06 02:43:15.703332: train_loss -0.695 +2024-09-06 02:43:15.706225: val_loss -0.7021 +2024-09-06 02:43:15.708827: Pseudo dice [0.9577, 0.9541, 0.9592, 0.9423, 0.8543, 0.9565, 0.9894] +2024-09-06 02:43:15.711526: Epoch time: 254.58 s +2024-09-06 02:43:17.841681: +2024-09-06 02:43:17.855493: Epoch 226 +2024-09-06 02:43:17.863891: Current learning rate: 0.00794 +2024-09-06 02:47:45.686017: train_loss -0.7145 +2024-09-06 02:47:45.703304: val_loss -0.6998 +2024-09-06 02:47:45.704760: Pseudo dice [0.9596, 0.9544, 0.9745, 0.9435, 0.8374, 0.9577, 0.9897] +2024-09-06 02:47:45.705937: Epoch time: 267.85 s +2024-09-06 02:47:49.869087: +2024-09-06 02:47:49.870490: Epoch 227 +2024-09-06 02:47:49.871420: Current learning rate: 0.00793 +2024-09-06 02:52:18.562670: train_loss -0.6946 +2024-09-06 02:52:18.575729: val_loss -0.7134 +2024-09-06 02:52:18.577742: Pseudo dice [0.9556, 0.9519, 0.9677, 0.9376, 0.8432, 0.9605, 0.9904] +2024-09-06 02:52:18.579547: Epoch time: 268.7 s +2024-09-06 02:52:21.633424: +2024-09-06 02:52:21.634931: Epoch 228 +2024-09-06 02:52:21.636053: Current learning rate: 0.00792 +2024-09-06 02:56:40.695759: train_loss -0.6995 +2024-09-06 02:56:40.712281: val_loss -0.7137 +2024-09-06 02:56:40.713314: Pseudo dice [0.9557, 0.9484, 0.9716, 0.9325, 0.8589, 0.9598, 0.9899] +2024-09-06 02:56:40.714290: Epoch time: 259.07 s +2024-09-06 02:56:43.151709: +2024-09-06 02:56:43.153141: Epoch 229 +2024-09-06 02:56:43.154390: Current learning rate: 0.00791 +2024-09-06 03:01:00.859150: train_loss -0.7186 +2024-09-06 03:01:00.861764: val_loss -0.7124 +2024-09-06 03:01:00.862737: Pseudo dice [0.9568, 0.9521, 0.9691, 0.9291, 0.8324, 0.9563, 0.9861] +2024-09-06 03:01:00.863775: Epoch time: 257.71 s +2024-09-06 03:01:03.944860: +2024-09-06 03:01:03.946322: Epoch 230 +2024-09-06 03:01:03.947514: Current learning rate: 0.0079 +2024-09-06 03:05:46.712950: train_loss -0.6716 +2024-09-06 03:05:46.740156: val_loss -0.7212 +2024-09-06 03:05:46.741130: Pseudo dice [0.9545, 0.9502, 0.9589, 0.9377, 0.8329, 0.9572, 0.9873] +2024-09-06 03:05:46.742136: Epoch time: 282.77 s +2024-09-06 03:05:49.474131: +2024-09-06 03:05:49.476059: Epoch 231 +2024-09-06 03:05:49.485304: Current learning rate: 0.00789 +2024-09-06 03:10:07.461631: train_loss -0.6696 +2024-09-06 03:10:07.477218: val_loss -0.666 +2024-09-06 03:10:07.478280: Pseudo dice [0.9465, 0.9452, 0.9653, 0.921, 0.7885, 0.944, 0.9801] +2024-09-06 03:10:07.479687: Epoch time: 258.02 s +2024-09-06 03:10:10.223735: +2024-09-06 03:10:10.234665: Epoch 232 +2024-09-06 03:10:10.235959: Current learning rate: 0.00789 +2024-09-06 03:14:30.933892: train_loss -0.6793 +2024-09-06 03:14:30.949375: val_loss -0.7068 +2024-09-06 03:14:30.950983: Pseudo dice [0.9513, 0.9508, 0.9672, 0.9397, 0.8555, 0.9545, 0.9881] +2024-09-06 03:14:30.953276: Epoch time: 260.71 s +2024-09-06 03:14:34.955388: +2024-09-06 03:14:34.956570: Epoch 233 +2024-09-06 03:14:34.957815: Current learning rate: 0.00788 +2024-09-06 03:19:06.419318: train_loss -0.7146 +2024-09-06 03:19:06.431222: val_loss -0.7423 +2024-09-06 03:19:06.432439: Pseudo dice [0.9602, 0.9517, 0.9733, 0.9455, 0.8506, 0.9601, 0.989] +2024-09-06 03:19:06.434079: Epoch time: 271.47 s +2024-09-06 03:19:09.147072: +2024-09-06 03:19:09.149848: Epoch 234 +2024-09-06 03:19:09.151100: Current learning rate: 0.00787 +2024-09-06 03:23:52.110795: train_loss -0.7058 +2024-09-06 03:23:52.125824: val_loss -0.736 +2024-09-06 03:23:52.127193: Pseudo dice [0.9574, 0.9508, 0.9704, 0.9403, 0.8562, 0.9584, 0.9903] +2024-09-06 03:23:52.128718: Epoch time: 282.97 s +2024-09-06 03:23:55.008348: +2024-09-06 03:23:55.009629: Epoch 235 +2024-09-06 03:23:55.010653: Current learning rate: 0.00786 +2024-09-06 03:28:29.730009: train_loss -0.6894 +2024-09-06 03:28:29.731954: val_loss -0.7074 +2024-09-06 03:28:29.732958: Pseudo dice [0.9565, 0.9514, 0.973, 0.9408, 0.8628, 0.9646, 0.9909] +2024-09-06 03:28:29.733970: Epoch time: 274.73 s +2024-09-06 03:28:32.188019: +2024-09-06 03:28:32.201578: Epoch 236 +2024-09-06 03:28:32.207588: Current learning rate: 0.00785 +2024-09-06 03:33:02.748606: train_loss -0.7238 +2024-09-06 03:33:02.759546: val_loss -0.7287 +2024-09-06 03:33:02.762100: Pseudo dice [0.9563, 0.9537, 0.9662, 0.9419, 0.8471, 0.9587, 0.989] +2024-09-06 03:33:02.764797: Epoch time: 270.61 s +2024-09-06 03:33:06.533222: +2024-09-06 03:33:06.534852: Epoch 237 +2024-09-06 03:33:06.536028: Current learning rate: 0.00784 +2024-09-06 03:37:22.458835: train_loss -0.7021 +2024-09-06 03:37:22.462461: val_loss -0.7367 +2024-09-06 03:37:22.464452: Pseudo dice [0.9518, 0.9518, 0.9671, 0.9365, 0.8544, 0.9623, 0.9886] +2024-09-06 03:37:22.465667: Epoch time: 255.93 s +2024-09-06 03:37:25.107359: +2024-09-06 03:37:25.108465: Epoch 238 +2024-09-06 03:37:25.109323: Current learning rate: 0.00783 +2024-09-06 03:41:29.009627: train_loss -0.7204 +2024-09-06 03:41:29.013589: val_loss -0.7269 +2024-09-06 03:41:29.014786: Pseudo dice [0.9604, 0.9533, 0.9657, 0.9495, 0.8541, 0.9616, 0.9888] +2024-09-06 03:41:29.015900: Epoch time: 243.91 s +2024-09-06 03:41:31.307616: +2024-09-06 03:41:31.308944: Epoch 239 +2024-09-06 03:41:31.310313: Current learning rate: 0.00782 +2024-09-06 03:45:49.452131: train_loss -0.7159 +2024-09-06 03:45:49.454836: val_loss -0.701 +2024-09-06 03:45:49.455783: Pseudo dice [0.9603, 0.9569, 0.969, 0.946, 0.8528, 0.9623, 0.9888] +2024-09-06 03:45:49.456734: Epoch time: 258.15 s +2024-09-06 03:45:52.636966: +2024-09-06 03:45:52.638304: Epoch 240 +2024-09-06 03:45:52.639579: Current learning rate: 0.00781 +2024-09-06 03:50:23.257629: train_loss -0.7243 +2024-09-06 03:50:23.270567: val_loss -0.7129 +2024-09-06 03:50:23.271574: Pseudo dice [0.9577, 0.9523, 0.9746, 0.9434, 0.8644, 0.9622, 0.9897] +2024-09-06 03:50:23.272834: Epoch time: 270.63 s +2024-09-06 03:50:26.307970: +2024-09-06 03:50:26.309119: Epoch 241 +2024-09-06 03:50:26.310066: Current learning rate: 0.0078 +2024-09-06 03:55:00.680861: train_loss -0.7193 +2024-09-06 03:55:00.696892: val_loss -0.7225 +2024-09-06 03:55:00.698380: Pseudo dice [0.9593, 0.9526, 0.9749, 0.9382, 0.8495, 0.9595, 0.9885] +2024-09-06 03:55:00.699696: Epoch time: 274.38 s +2024-09-06 03:55:03.398803: +2024-09-06 03:55:03.400328: Epoch 242 +2024-09-06 03:55:03.401463: Current learning rate: 0.00779 +2024-09-06 03:59:20.209581: train_loss -0.7197 +2024-09-06 03:59:20.225563: val_loss -0.7226 +2024-09-06 03:59:20.227412: Pseudo dice [0.9525, 0.9551, 0.9703, 0.9421, 0.8583, 0.9614, 0.9896] +2024-09-06 03:59:20.229031: Epoch time: 256.82 s +2024-09-06 03:59:22.696741: +2024-09-06 03:59:22.697783: Epoch 243 +2024-09-06 03:59:22.699306: Current learning rate: 0.00778 +2024-09-06 04:03:23.648618: train_loss -0.6947 +2024-09-06 04:03:23.664391: val_loss -0.6712 +2024-09-06 04:03:23.671010: Pseudo dice [0.9452, 0.9446, 0.9652, 0.9385, 0.8357, 0.9561, 0.9867] +2024-09-06 04:03:23.672185: Epoch time: 240.96 s +2024-09-06 04:03:26.743806: +2024-09-06 04:03:26.745049: Epoch 244 +2024-09-06 04:03:26.745978: Current learning rate: 0.00777 +2024-09-06 04:07:44.764560: train_loss -0.6811 +2024-09-06 04:07:44.779622: val_loss -0.7056 +2024-09-06 04:07:44.780787: Pseudo dice [0.9479, 0.9469, 0.9449, 0.9343, 0.8355, 0.9504, 0.9878] +2024-09-06 04:07:44.782120: Epoch time: 258.03 s +2024-09-06 04:07:47.179845: +2024-09-06 04:07:47.190941: Epoch 245 +2024-09-06 04:07:47.192529: Current learning rate: 0.00777 +2024-09-06 04:11:57.394003: train_loss -0.6813 +2024-09-06 04:11:57.412231: val_loss -0.7016 +2024-09-06 04:11:57.413150: Pseudo dice [0.9514, 0.9488, 0.9634, 0.9346, 0.8275, 0.9405, 0.9865] +2024-09-06 04:11:57.415260: Epoch time: 250.22 s +2024-09-06 04:12:00.811335: +2024-09-06 04:12:00.812898: Epoch 246 +2024-09-06 04:12:00.814265: Current learning rate: 0.00776 +2024-09-06 04:16:17.253955: train_loss -0.6903 +2024-09-06 04:16:17.256758: val_loss -0.703 +2024-09-06 04:16:17.257810: Pseudo dice [0.9516, 0.9444, 0.9702, 0.9354, 0.8292, 0.9482, 0.9835] +2024-09-06 04:16:17.258777: Epoch time: 256.45 s +2024-09-06 04:16:20.322817: +2024-09-06 04:16:20.324075: Epoch 247 +2024-09-06 04:16:20.324956: Current learning rate: 0.00775 +2024-09-06 04:20:42.556529: train_loss -0.6979 +2024-09-06 04:20:42.572666: val_loss -0.6951 +2024-09-06 04:20:42.573789: Pseudo dice [0.9519, 0.947, 0.9675, 0.9376, 0.8506, 0.9549, 0.9839] +2024-09-06 04:20:42.574977: Epoch time: 262.24 s +2024-09-06 04:20:45.897159: +2024-09-06 04:20:45.898377: Epoch 248 +2024-09-06 04:20:45.899195: Current learning rate: 0.00774 +2024-09-06 04:25:12.619198: train_loss -0.6805 +2024-09-06 04:25:12.622157: val_loss -0.6784 +2024-09-06 04:25:12.623398: Pseudo dice [0.9533, 0.9487, 0.9724, 0.9354, 0.8448, 0.9598, 0.9878] +2024-09-06 04:25:12.624273: Epoch time: 266.73 s +2024-09-06 04:25:14.947830: +2024-09-06 04:25:14.961940: Epoch 249 +2024-09-06 04:25:14.963033: Current learning rate: 0.00773 +2024-09-06 04:29:24.004116: train_loss -0.6984 +2024-09-06 04:29:24.022998: val_loss -0.7038 +2024-09-06 04:29:24.024200: Pseudo dice [0.9566, 0.9518, 0.9708, 0.9329, 0.8467, 0.9574, 0.9895] +2024-09-06 04:29:24.025344: Epoch time: 249.06 s +2024-09-06 04:29:32.465942: +2024-09-06 04:29:32.466902: Epoch 250 +2024-09-06 04:29:32.467757: Current learning rate: 0.00772 +2024-09-06 04:33:57.157333: train_loss -0.7099 +2024-09-06 04:33:57.175082: val_loss -0.6809 +2024-09-06 04:33:57.176202: Pseudo dice [0.9558, 0.9544, 0.9723, 0.9497, 0.8567, 0.9597, 0.9906] +2024-09-06 04:33:57.177451: Epoch time: 264.69 s +2024-09-06 04:34:00.102120: +2024-09-06 04:34:00.103877: Epoch 251 +2024-09-06 04:34:00.105549: Current learning rate: 0.00771 +2024-09-06 04:38:21.745553: train_loss -0.7039 +2024-09-06 04:38:21.764380: val_loss -0.7193 +2024-09-06 04:38:21.765930: Pseudo dice [0.957, 0.9552, 0.971, 0.9473, 0.8661, 0.9606, 0.9899] +2024-09-06 04:38:21.767473: Epoch time: 261.65 s +2024-09-06 04:38:24.571028: +2024-09-06 04:38:24.574051: Epoch 252 +2024-09-06 04:38:24.576909: Current learning rate: 0.0077 +2024-09-06 04:43:01.505611: train_loss -0.7278 +2024-09-06 04:43:01.541541: val_loss -0.778 +2024-09-06 04:43:01.543553: Pseudo dice [0.9606, 0.9523, 0.9768, 0.941, 0.8617, 0.9606, 0.9891] +2024-09-06 04:43:01.545711: Epoch time: 276.94 s +2024-09-06 04:43:03.852543: +2024-09-06 04:43:03.853868: Epoch 253 +2024-09-06 04:43:03.855058: Current learning rate: 0.00769 +2024-09-06 04:47:24.874402: train_loss -0.7185 +2024-09-06 04:47:24.887437: val_loss -0.7117 +2024-09-06 04:47:24.888478: Pseudo dice [0.9579, 0.9541, 0.9722, 0.9427, 0.862, 0.9624, 0.992] +2024-09-06 04:47:24.889457: Epoch time: 261.03 s +2024-09-06 04:47:27.979991: +2024-09-06 04:47:27.981088: Epoch 254 +2024-09-06 04:47:27.982130: Current learning rate: 0.00768 +2024-09-06 04:51:37.849409: train_loss -0.7183 +2024-09-06 04:51:37.865418: val_loss -0.7434 +2024-09-06 04:51:37.866762: Pseudo dice [0.9617, 0.952, 0.975, 0.9356, 0.8669, 0.966, 0.99] +2024-09-06 04:51:37.868526: Epoch time: 249.87 s +2024-09-06 04:51:41.827322: +2024-09-06 04:51:41.842817: Epoch 255 +2024-09-06 04:51:41.843938: Current learning rate: 0.00767 +2024-09-06 04:56:04.138469: train_loss -0.7277 +2024-09-06 04:56:04.155786: val_loss -0.721 +2024-09-06 04:56:04.156975: Pseudo dice [0.9609, 0.9572, 0.9738, 0.9437, 0.8621, 0.9627, 0.99] +2024-09-06 04:56:04.158451: Epoch time: 262.32 s +2024-09-06 04:56:07.067943: +2024-09-06 04:56:07.069236: Epoch 256 +2024-09-06 04:56:07.070426: Current learning rate: 0.00766 +2024-09-06 05:00:36.315516: train_loss -0.7353 +2024-09-06 05:00:36.322514: val_loss -0.7414 +2024-09-06 05:00:36.323992: Pseudo dice [0.9604, 0.9557, 0.9756, 0.9459, 0.8694, 0.9671, 0.9925] +2024-09-06 05:00:36.325289: Epoch time: 269.25 s +2024-09-06 05:00:38.831112: +2024-09-06 05:00:38.832130: Epoch 257 +2024-09-06 05:00:38.832999: Current learning rate: 0.00765 +2024-09-06 05:04:57.947940: train_loss -0.7307 +2024-09-06 05:04:57.952293: val_loss -0.748 +2024-09-06 05:04:57.966952: Pseudo dice [0.9611, 0.9562, 0.9775, 0.9468, 0.8658, 0.9641, 0.9899] +2024-09-06 05:04:57.967920: Epoch time: 259.12 s +2024-09-06 05:05:00.555199: +2024-09-06 05:05:00.556548: Epoch 258 +2024-09-06 05:05:00.557638: Current learning rate: 0.00764 +2024-09-06 05:09:19.753711: train_loss -0.7211 +2024-09-06 05:09:19.758053: val_loss -0.7075 +2024-09-06 05:09:19.759804: Pseudo dice [0.9579, 0.9539, 0.9738, 0.9453, 0.8592, 0.9642, 0.9897] +2024-09-06 05:09:19.761050: Epoch time: 259.2 s +2024-09-06 05:09:22.972925: +2024-09-06 05:09:22.974314: Epoch 259 +2024-09-06 05:09:22.975745: Current learning rate: 0.00764 +2024-09-06 05:14:13.663144: train_loss -0.7453 +2024-09-06 05:14:13.669572: val_loss -0.7203 +2024-09-06 05:14:13.670567: Pseudo dice [0.9593, 0.9555, 0.9748, 0.9461, 0.8631, 0.9628, 0.9897] +2024-09-06 05:14:13.671498: Epoch time: 290.7 s +2024-09-06 05:14:16.434931: +2024-09-06 05:14:16.436415: Epoch 260 +2024-09-06 05:14:16.437800: Current learning rate: 0.00763 +2024-09-06 05:18:35.479437: train_loss -0.735 +2024-09-06 05:18:35.491421: val_loss -0.7465 +2024-09-06 05:18:35.492704: Pseudo dice [0.9615, 0.9544, 0.9766, 0.949, 0.8687, 0.967, 0.9902] +2024-09-06 05:18:35.493888: Epoch time: 259.05 s +2024-09-06 05:18:37.950449: +2024-09-06 05:18:37.956001: Epoch 261 +2024-09-06 05:18:37.956973: Current learning rate: 0.00762 +2024-09-06 05:22:49.689372: train_loss -0.7333 +2024-09-06 05:22:49.704586: val_loss -0.7543 +2024-09-06 05:22:49.705697: Pseudo dice [0.9594, 0.9549, 0.9748, 0.9462, 0.8658, 0.9665, 0.9901] +2024-09-06 05:22:49.707179: Epoch time: 251.74 s +2024-09-06 05:22:49.708328: Yayy! New best EMA pseudo Dice: 0.9483 +2024-09-06 05:22:58.241111: +2024-09-06 05:22:58.242167: Epoch 262 +2024-09-06 05:22:58.243209: Current learning rate: 0.00761 +2024-09-06 05:27:35.833991: train_loss -0.7378 +2024-09-06 05:27:35.836091: val_loss -0.7456 +2024-09-06 05:27:35.842164: Pseudo dice [0.9583, 0.9552, 0.9752, 0.9409, 0.8717, 0.9668, 0.9898] +2024-09-06 05:27:35.843537: Epoch time: 277.6 s +2024-09-06 05:27:35.844763: Yayy! New best EMA pseudo Dice: 0.9486 +2024-09-06 05:27:46.223442: +2024-09-06 05:27:46.224594: Epoch 263 +2024-09-06 05:27:46.225511: Current learning rate: 0.0076 +2024-09-06 05:32:10.905085: train_loss -0.7312 +2024-09-06 05:32:10.923752: val_loss -0.7581 +2024-09-06 05:32:10.925914: Pseudo dice [0.9637, 0.9548, 0.9773, 0.9476, 0.8644, 0.9685, 0.9918] +2024-09-06 05:32:10.927443: Epoch time: 264.68 s +2024-09-06 05:32:10.928592: Yayy! New best EMA pseudo Dice: 0.949 +2024-09-06 05:32:19.458449: +2024-09-06 05:32:19.459470: Epoch 264 +2024-09-06 05:32:19.461380: Current learning rate: 0.00759 +2024-09-06 05:36:47.040277: train_loss -0.737 +2024-09-06 05:36:47.044546: val_loss -0.7421 +2024-09-06 05:36:47.046134: Pseudo dice [0.9622, 0.958, 0.9777, 0.9474, 0.8587, 0.9669, 0.9911] +2024-09-06 05:36:47.047383: Epoch time: 267.58 s +2024-09-06 05:36:47.050696: Yayy! New best EMA pseudo Dice: 0.9493 +2024-09-06 05:36:54.219122: +2024-09-06 05:36:54.220284: Epoch 265 +2024-09-06 05:36:54.221257: Current learning rate: 0.00758 +2024-09-06 05:41:06.708179: train_loss -0.7355 +2024-09-06 05:41:06.711680: val_loss -0.7475 +2024-09-06 05:41:06.712806: Pseudo dice [0.9611, 0.9555, 0.9767, 0.9425, 0.8476, 0.9632, 0.9896] +2024-09-06 05:41:06.713779: Epoch time: 252.49 s +2024-09-06 05:41:09.507316: +2024-09-06 05:41:09.508903: Epoch 266 +2024-09-06 05:41:09.510233: Current learning rate: 0.00757 +2024-09-06 05:45:32.891002: train_loss -0.7233 +2024-09-06 05:45:32.894324: val_loss -0.7649 +2024-09-06 05:45:32.895388: Pseudo dice [0.9651, 0.9557, 0.9817, 0.9475, 0.874, 0.9652, 0.9915] +2024-09-06 05:45:32.896334: Epoch time: 263.39 s +2024-09-06 05:45:32.897361: Yayy! New best EMA pseudo Dice: 0.9497 +2024-09-06 05:45:40.042038: +2024-09-06 05:45:40.043013: Epoch 267 +2024-09-06 05:45:40.044010: Current learning rate: 0.00756 +2024-09-06 05:50:10.278499: train_loss -0.7192 +2024-09-06 05:50:10.282200: val_loss -0.7548 +2024-09-06 05:50:10.283284: Pseudo dice [0.962, 0.9566, 0.9754, 0.9419, 0.8679, 0.9672, 0.9913] +2024-09-06 05:50:10.284582: Epoch time: 270.24 s +2024-09-06 05:50:10.285595: Yayy! New best EMA pseudo Dice: 0.9499 +2024-09-06 05:50:19.474096: +2024-09-06 05:50:19.475256: Epoch 268 +2024-09-06 05:50:19.476315: Current learning rate: 0.00755 +2024-09-06 05:54:36.268779: train_loss -0.7247 +2024-09-06 05:54:36.270672: val_loss -0.7467 +2024-09-06 05:54:36.271651: Pseudo dice [0.9582, 0.9551, 0.9745, 0.942, 0.8688, 0.9663, 0.9892] +2024-09-06 05:54:36.272600: Epoch time: 256.8 s +2024-09-06 05:54:36.273543: Yayy! New best EMA pseudo Dice: 0.95 +2024-09-06 05:54:44.293760: +2024-09-06 05:54:44.295101: Epoch 269 +2024-09-06 05:54:44.296061: Current learning rate: 0.00754 +2024-09-06 05:59:02.846680: train_loss -0.7254 +2024-09-06 05:59:02.864480: val_loss -0.7478 +2024-09-06 05:59:02.865517: Pseudo dice [0.9589, 0.9569, 0.9692, 0.9436, 0.8682, 0.9666, 0.9905] +2024-09-06 05:59:02.866502: Epoch time: 258.56 s +2024-09-06 05:59:02.867504: Yayy! New best EMA pseudo Dice: 0.95 +2024-09-06 05:59:11.045156: +2024-09-06 05:59:11.046395: Epoch 270 +2024-09-06 05:59:11.047392: Current learning rate: 0.00753 +2024-09-06 06:03:30.218526: train_loss -0.7323 +2024-09-06 06:03:30.221181: val_loss -0.7272 +2024-09-06 06:03:30.222157: Pseudo dice [0.9597, 0.9545, 0.9736, 0.9441, 0.8596, 0.9626, 0.9879] +2024-09-06 06:03:30.223219: Epoch time: 259.18 s +2024-09-06 06:03:32.595072: +2024-09-06 06:03:32.596140: Epoch 271 +2024-09-06 06:03:32.597051: Current learning rate: 0.00752 +2024-09-06 06:08:01.661696: train_loss -0.7127 +2024-09-06 06:08:01.666436: val_loss -0.7084 +2024-09-06 06:08:01.669406: Pseudo dice [0.956, 0.9531, 0.9681, 0.9332, 0.8572, 0.9648, 0.991] +2024-09-06 06:08:01.671199: Epoch time: 269.07 s +2024-09-06 06:08:04.120307: +2024-09-06 06:08:04.135273: Epoch 272 +2024-09-06 06:08:04.136815: Current learning rate: 0.00751 +2024-09-06 06:12:20.430492: train_loss -0.7123 +2024-09-06 06:12:20.432945: val_loss -0.7571 +2024-09-06 06:12:20.435473: Pseudo dice [0.958, 0.955, 0.9731, 0.9365, 0.8691, 0.9646, 0.9891] +2024-09-06 06:12:20.437785: Epoch time: 256.32 s +2024-09-06 06:12:24.472820: +2024-09-06 06:12:24.474786: Epoch 273 +2024-09-06 06:12:24.475981: Current learning rate: 0.00751 +2024-09-06 06:16:46.888700: train_loss -0.7327 +2024-09-06 06:16:46.898048: val_loss -0.727 +2024-09-06 06:16:46.899338: Pseudo dice [0.9625, 0.9544, 0.9757, 0.9467, 0.8682, 0.9634, 0.9915] +2024-09-06 06:16:46.900581: Epoch time: 262.42 s +2024-09-06 06:16:52.239078: +2024-09-06 06:16:52.247874: Epoch 274 +2024-09-06 06:16:52.250044: Current learning rate: 0.0075 +2024-09-06 06:21:33.191822: train_loss -0.7287 +2024-09-06 06:21:33.207536: val_loss -0.7165 +2024-09-06 06:21:33.208596: Pseudo dice [0.9632, 0.9599, 0.9761, 0.9514, 0.8759, 0.9695, 0.9922] +2024-09-06 06:21:33.209521: Epoch time: 280.96 s +2024-09-06 06:21:33.210630: Yayy! New best EMA pseudo Dice: 0.9503 +2024-09-06 06:21:40.266578: +2024-09-06 06:21:40.267461: Epoch 275 +2024-09-06 06:21:40.268255: Current learning rate: 0.00749 +2024-09-06 06:25:51.866778: train_loss -0.7427 +2024-09-06 06:25:51.868389: val_loss -0.7523 +2024-09-06 06:25:51.869346: Pseudo dice [0.9643, 0.9562, 0.9795, 0.9494, 0.8729, 0.9682, 0.9908] +2024-09-06 06:25:51.870233: Epoch time: 251.6 s +2024-09-06 06:25:51.871129: Yayy! New best EMA pseudo Dice: 0.9507 +2024-09-06 06:26:00.678865: +2024-09-06 06:26:00.679986: Epoch 276 +2024-09-06 06:26:00.680957: Current learning rate: 0.00748 +2024-09-06 06:30:19.255775: train_loss -0.7349 +2024-09-06 06:30:19.259118: val_loss -0.7939 +2024-09-06 06:30:19.260277: Pseudo dice [0.963, 0.9547, 0.9804, 0.9447, 0.873, 0.9687, 0.9913] +2024-09-06 06:30:19.261347: Epoch time: 258.58 s +2024-09-06 06:30:19.264371: Yayy! New best EMA pseudo Dice: 0.951 +2024-09-06 06:30:27.165914: +2024-09-06 06:30:27.166830: Epoch 277 +2024-09-06 06:30:27.167680: Current learning rate: 0.00747 +2024-09-06 06:35:03.373560: train_loss -0.7264 +2024-09-06 06:35:03.399711: val_loss -0.7327 +2024-09-06 06:35:03.401698: Pseudo dice [0.9628, 0.954, 0.9767, 0.9481, 0.8572, 0.9655, 0.9914] +2024-09-06 06:35:03.403366: Epoch time: 276.21 s +2024-09-06 06:35:05.739281: +2024-09-06 06:35:05.740758: Epoch 278 +2024-09-06 06:35:05.742437: Current learning rate: 0.00746 +2024-09-06 06:39:49.569729: train_loss -0.7282 +2024-09-06 06:39:49.577817: val_loss -0.7564 +2024-09-06 06:39:49.578925: Pseudo dice [0.9592, 0.9537, 0.973, 0.9464, 0.8591, 0.964, 0.9874] +2024-09-06 06:39:49.579976: Epoch time: 283.83 s +2024-09-06 06:39:52.363322: +2024-09-06 06:39:52.364467: Epoch 279 +2024-09-06 06:39:52.365349: Current learning rate: 0.00745 +2024-09-06 06:44:22.739891: train_loss -0.7379 +2024-09-06 06:44:22.864470: val_loss -0.7485 +2024-09-06 06:44:22.918850: Pseudo dice [0.9578, 0.9572, 0.9691, 0.9408, 0.8739, 0.9683, 0.9913] +2024-09-06 06:44:23.042374: Epoch time: 270.38 s +2024-09-06 06:44:25.822027: +2024-09-06 06:44:25.823089: Epoch 280 +2024-09-06 06:44:25.824029: Current learning rate: 0.00744 +2024-09-06 06:48:54.910581: train_loss -0.7302 +2024-09-06 06:48:54.922128: val_loss -0.7453 +2024-09-06 06:48:54.923559: Pseudo dice [0.9634, 0.9561, 0.9779, 0.9434, 0.8638, 0.9672, 0.9895] +2024-09-06 06:48:54.924858: Epoch time: 269.09 s +2024-09-06 06:48:57.324452: +2024-09-06 06:48:57.327282: Epoch 281 +2024-09-06 06:48:57.328304: Current learning rate: 0.00743 +2024-09-06 06:53:25.867927: train_loss -0.7288 +2024-09-06 06:53:25.870461: val_loss -0.7417 +2024-09-06 06:53:25.871606: Pseudo dice [0.961, 0.9552, 0.9732, 0.9418, 0.871, 0.9647, 0.9921] +2024-09-06 06:53:25.872591: Epoch time: 268.55 s +2024-09-06 06:53:28.399822: +2024-09-06 06:53:28.400952: Epoch 282 +2024-09-06 06:53:28.401970: Current learning rate: 0.00742 +2024-09-06 06:57:54.021727: train_loss -0.7267 +2024-09-06 06:57:54.030630: val_loss -0.7498 +2024-09-06 06:57:54.031761: Pseudo dice [0.9634, 0.9574, 0.9756, 0.9458, 0.8673, 0.9655, 0.9907] +2024-09-06 06:57:54.032758: Epoch time: 265.63 s +2024-09-06 06:57:54.033633: Yayy! New best EMA pseudo Dice: 0.9511 +2024-09-06 06:58:02.328940: +2024-09-06 06:58:02.330341: Epoch 283 +2024-09-06 06:58:02.331986: Current learning rate: 0.00741 +2024-09-06 07:02:29.415686: train_loss -0.7336 +2024-09-06 07:02:29.426081: val_loss -0.7764 +2024-09-06 07:02:29.427632: Pseudo dice [0.9616, 0.958, 0.975, 0.949, 0.8918, 0.9696, 0.9912] +2024-09-06 07:02:29.428947: Epoch time: 267.09 s +2024-09-06 07:02:29.430014: Yayy! New best EMA pseudo Dice: 0.9516 +2024-09-06 07:02:38.472762: +2024-09-06 07:02:38.473797: Epoch 284 +2024-09-06 07:02:38.474702: Current learning rate: 0.0074 +2024-09-06 07:07:01.199036: train_loss -0.7385 +2024-09-06 07:07:01.201670: val_loss -0.7508 +2024-09-06 07:07:01.218810: Pseudo dice [0.9613, 0.9553, 0.9767, 0.9458, 0.8728, 0.9651, 0.9915] +2024-09-06 07:07:01.219971: Epoch time: 262.73 s +2024-09-06 07:07:01.221153: Yayy! New best EMA pseudo Dice: 0.9517 +2024-09-06 07:07:10.239452: +2024-09-06 07:07:10.240289: Epoch 285 +2024-09-06 07:07:10.241101: Current learning rate: 0.00739 +2024-09-06 07:11:57.916086: train_loss -0.7282 +2024-09-06 07:11:57.917753: val_loss -0.7651 +2024-09-06 07:11:57.918713: Pseudo dice [0.9573, 0.9542, 0.9739, 0.9438, 0.8675, 0.9652, 0.9898] +2024-09-06 07:11:57.919647: Epoch time: 287.68 s +2024-09-06 07:12:01.081021: +2024-09-06 07:12:01.082189: Epoch 286 +2024-09-06 07:12:01.083079: Current learning rate: 0.00738 +2024-09-06 07:16:26.581167: train_loss -0.7123 +2024-09-06 07:16:26.646336: val_loss -0.7211 +2024-09-06 07:16:26.771588: Pseudo dice [0.9601, 0.9551, 0.9735, 0.949, 0.8606, 0.9641, 0.9874] +2024-09-06 07:16:26.852450: Epoch time: 265.51 s +2024-09-06 07:16:29.292969: +2024-09-06 07:16:29.294190: Epoch 287 +2024-09-06 07:16:29.295121: Current learning rate: 0.00738 +2024-09-06 07:20:56.426336: train_loss -0.7207 +2024-09-06 07:20:56.428482: val_loss -0.6886 +2024-09-06 07:20:56.429337: Pseudo dice [0.9583, 0.9574, 0.9688, 0.938, 0.8443, 0.9628, 0.99] +2024-09-06 07:20:56.430188: Epoch time: 267.14 s +2024-09-06 07:20:58.712065: +2024-09-06 07:20:58.713123: Epoch 288 +2024-09-06 07:20:58.714008: Current learning rate: 0.00737 +2024-09-06 07:25:23.405005: train_loss -0.7188 +2024-09-06 07:25:23.422064: val_loss -0.7397 +2024-09-06 07:25:23.423254: Pseudo dice [0.9626, 0.9528, 0.9778, 0.9484, 0.8726, 0.9624, 0.9893] +2024-09-06 07:25:23.424169: Epoch time: 264.7 s +2024-09-06 07:25:26.918593: +2024-09-06 07:25:26.919720: Epoch 289 +2024-09-06 07:25:26.920678: Current learning rate: 0.00736 +2024-09-06 07:29:55.854898: train_loss -0.729 +2024-09-06 07:29:55.859126: val_loss -0.7169 +2024-09-06 07:29:55.864341: Pseudo dice [0.9614, 0.9562, 0.977, 0.9455, 0.8763, 0.9664, 0.9905] +2024-09-06 07:29:55.870838: Epoch time: 268.94 s +2024-09-06 07:29:58.059072: +2024-09-06 07:29:58.060584: Epoch 290 +2024-09-06 07:29:58.062034: Current learning rate: 0.00735 +2024-09-06 07:34:13.480098: train_loss -0.7212 +2024-09-06 07:34:13.481999: val_loss -0.7048 +2024-09-06 07:34:13.483091: Pseudo dice [0.9604, 0.9528, 0.976, 0.946, 0.8676, 0.9665, 0.9917] +2024-09-06 07:34:13.484104: Epoch time: 255.43 s +2024-09-06 07:34:15.912668: +2024-09-06 07:34:15.914313: Epoch 291 +2024-09-06 07:34:15.915646: Current learning rate: 0.00734 +2024-09-06 07:38:43.675439: train_loss -0.7234 +2024-09-06 07:38:43.683098: val_loss -0.6943 +2024-09-06 07:38:43.684215: Pseudo dice [0.9626, 0.9555, 0.9741, 0.9395, 0.838, 0.9602, 0.9889] +2024-09-06 07:38:43.685264: Epoch time: 267.77 s +2024-09-06 07:38:46.297464: +2024-09-06 07:38:46.298570: Epoch 292 +2024-09-06 07:38:46.299364: Current learning rate: 0.00733 +2024-09-06 07:43:20.869125: train_loss -0.7226 +2024-09-06 07:43:20.894101: val_loss -0.7348 +2024-09-06 07:43:20.895539: Pseudo dice [0.9588, 0.951, 0.9736, 0.9446, 0.8632, 0.9658, 0.9896] +2024-09-06 07:43:20.896575: Epoch time: 274.58 s +2024-09-06 07:43:24.348319: +2024-09-06 07:43:24.363350: Epoch 293 +2024-09-06 07:43:24.364821: Current learning rate: 0.00732 +2024-09-06 07:47:46.782495: train_loss -0.718 +2024-09-06 07:47:46.784648: val_loss -0.7374 +2024-09-06 07:47:46.785901: Pseudo dice [0.9563, 0.9544, 0.9707, 0.9478, 0.8638, 0.9644, 0.9914] +2024-09-06 07:47:46.787031: Epoch time: 262.44 s +2024-09-06 07:47:49.133104: +2024-09-06 07:47:49.134322: Epoch 294 +2024-09-06 07:47:49.135184: Current learning rate: 0.00731 +2024-09-06 07:52:10.966929: train_loss -0.7221 +2024-09-06 07:52:10.979245: val_loss -0.7419 +2024-09-06 07:52:10.980895: Pseudo dice [0.9637, 0.9572, 0.9779, 0.9415, 0.8673, 0.9651, 0.9898] +2024-09-06 07:52:10.982160: Epoch time: 261.84 s +2024-09-06 07:52:13.318587: +2024-09-06 07:52:13.322763: Epoch 295 +2024-09-06 07:52:13.323880: Current learning rate: 0.0073 +2024-09-06 07:56:46.386478: train_loss -0.7429 +2024-09-06 07:56:46.448021: val_loss -0.7538 +2024-09-06 07:56:46.492529: Pseudo dice [0.9642, 0.9567, 0.9799, 0.9493, 0.8818, 0.9681, 0.9922] +2024-09-06 07:56:46.495727: Epoch time: 273.07 s +2024-09-06 07:56:48.929904: +2024-09-06 07:56:48.931138: Epoch 296 +2024-09-06 07:56:48.932250: Current learning rate: 0.00729 +2024-09-06 08:01:03.556771: train_loss -0.7226 +2024-09-06 08:01:03.569520: val_loss -0.7344 +2024-09-06 08:01:03.570658: Pseudo dice [0.9624, 0.9573, 0.9777, 0.9492, 0.8685, 0.9664, 0.9883] +2024-09-06 08:01:03.571631: Epoch time: 254.63 s +2024-09-06 08:01:05.857118: +2024-09-06 08:01:05.870012: Epoch 297 +2024-09-06 08:01:05.871412: Current learning rate: 0.00728 +2024-09-06 08:05:34.117197: train_loss -0.7379 +2024-09-06 08:05:34.128298: val_loss -0.7636 +2024-09-06 08:05:34.129587: Pseudo dice [0.9615, 0.953, 0.975, 0.9435, 0.8648, 0.9654, 0.9907] +2024-09-06 08:05:34.130785: Epoch time: 268.27 s +2024-09-06 08:05:36.647809: +2024-09-06 08:05:36.649131: Epoch 298 +2024-09-06 08:05:36.650128: Current learning rate: 0.00727 +2024-09-06 08:10:03.702073: train_loss -0.7331 +2024-09-06 08:10:03.728251: val_loss -0.7295 +2024-09-06 08:10:03.730498: Pseudo dice [0.959, 0.9525, 0.9691, 0.9387, 0.8545, 0.9657, 0.9889] +2024-09-06 08:10:03.732316: Epoch time: 267.06 s +2024-09-06 08:10:06.344141: +2024-09-06 08:10:06.345531: Epoch 299 +2024-09-06 08:10:06.346517: Current learning rate: 0.00726 +2024-09-06 08:14:31.142192: train_loss -0.7158 +2024-09-06 08:14:31.150262: val_loss -0.7218 +2024-09-06 08:14:31.157732: Pseudo dice [0.9602, 0.9564, 0.9714, 0.9447, 0.8589, 0.9649, 0.989] +2024-09-06 08:14:31.159074: Epoch time: 264.8 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_08_29_45.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_08_29_45.txt new file mode 100644 index 0000000000000000000000000000000000000000..552c720f0649e362ddea0594f55adf6f98d7d8a9 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_08_29_45.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 08:29:48.783543: Using torch.compile... +2024-09-06 08:29:57.197958: do_dummy_2d_data_aug: False +2024-09-06 08:29:57.203871: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 08:29:57.205261: The split file contains 5 splits. +2024-09-06 08:29:57.205820: Desired fold for training: 4 +2024-09-06 08:29:57.206424: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 08:30:08.783134: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 08:30:10.804809: +2024-09-06 08:30:10.806277: Epoch 300 +2024-09-06 08:30:10.807941: Current learning rate: 0.00725 +2024-09-06 08:48:39.005703: train_loss -0.7276 +2024-09-06 08:48:39.008049: val_loss -0.7442 +2024-09-06 08:48:39.008667: Pseudo dice [0.9597, 0.9525, 0.9732, 0.9469, 0.8611, 0.9663, 0.9903] +2024-09-06 08:48:39.009374: Epoch time: 1108.21 s +2024-09-06 08:48:41.009036: +2024-09-06 08:48:41.009893: Epoch 301 +2024-09-06 08:48:41.010719: Current learning rate: 0.00724 +2024-09-06 08:52:57.924264: train_loss -0.7135 +2024-09-06 08:52:57.936394: val_loss -0.7522 +2024-09-06 08:52:57.937749: Pseudo dice [0.9602, 0.9578, 0.9751, 0.9499, 0.8744, 0.9683, 0.9898] +2024-09-06 08:52:57.940590: Epoch time: 256.92 s +2024-09-06 08:53:01.673280: +2024-09-06 08:53:01.674767: Epoch 302 +2024-09-06 08:53:01.677159: Current learning rate: 0.00724 +2024-09-06 08:57:49.702112: train_loss -0.7386 +2024-09-06 08:57:49.720889: val_loss -0.7359 +2024-09-06 08:57:49.724245: Pseudo dice [0.9602, 0.9551, 0.9738, 0.9485, 0.8552, 0.9676, 0.9901] +2024-09-06 08:57:49.725931: Epoch time: 288.04 s +2024-09-06 08:57:53.723704: +2024-09-06 08:57:53.725487: Epoch 303 +2024-09-06 08:57:53.727381: Current learning rate: 0.00723 +2024-09-06 09:02:24.774901: train_loss -0.7386 +2024-09-06 09:02:24.794262: val_loss -0.7624 +2024-09-06 09:02:24.795406: Pseudo dice [0.96, 0.9536, 0.9761, 0.9433, 0.876, 0.9646, 0.9912] +2024-09-06 09:02:24.796218: Epoch time: 271.06 s +2024-09-06 09:02:30.927047: +2024-09-06 09:02:30.928683: Epoch 304 +2024-09-06 09:02:30.930594: Current learning rate: 0.00722 +2024-09-06 09:07:14.762312: train_loss -0.7306 +2024-09-06 09:07:14.766904: val_loss -0.744 +2024-09-06 09:07:14.767856: Pseudo dice [0.9624, 0.9557, 0.9767, 0.9457, 0.8619, 0.9692, 0.9903] +2024-09-06 09:07:14.768788: Epoch time: 283.84 s +2024-09-06 09:07:16.944445: +2024-09-06 09:07:16.945596: Epoch 305 +2024-09-06 09:07:16.946894: Current learning rate: 0.00721 +2024-09-06 09:12:04.607888: train_loss -0.7282 +2024-09-06 09:12:04.626301: val_loss -0.7448 +2024-09-06 09:12:04.628198: Pseudo dice [0.9635, 0.9573, 0.9752, 0.9455, 0.8716, 0.9655, 0.9903] +2024-09-06 09:12:04.629687: Epoch time: 287.67 s +2024-09-06 09:12:08.520520: +2024-09-06 09:12:08.521924: Epoch 306 +2024-09-06 09:12:08.523627: Current learning rate: 0.0072 +2024-09-06 09:16:47.869189: train_loss -0.7253 +2024-09-06 09:16:47.884573: val_loss -0.7592 +2024-09-06 09:16:47.885540: Pseudo dice [0.9625, 0.9553, 0.9779, 0.9418, 0.8695, 0.9654, 0.9896] +2024-09-06 09:16:47.886553: Epoch time: 279.35 s +2024-09-06 09:16:51.503605: +2024-09-06 09:16:51.518595: Epoch 307 +2024-09-06 09:16:51.519362: Current learning rate: 0.00719 +2024-09-06 09:21:28.272789: train_loss -0.7375 +2024-09-06 09:21:28.277474: val_loss -0.7203 +2024-09-06 09:21:28.278251: Pseudo dice [0.9621, 0.9555, 0.9769, 0.9463, 0.8632, 0.9688, 0.9898] +2024-09-06 09:21:28.278947: Epoch time: 276.77 s +2024-09-06 09:21:31.695333: +2024-09-06 09:21:31.696353: Epoch 308 +2024-09-06 09:21:31.696999: Current learning rate: 0.00718 +2024-09-06 09:26:22.290192: train_loss -0.7236 +2024-09-06 09:26:22.299819: val_loss -0.7208 +2024-09-06 09:26:22.300580: Pseudo dice [0.9586, 0.954, 0.9733, 0.948, 0.864, 0.9652, 0.9908] +2024-09-06 09:26:22.301359: Epoch time: 290.6 s +2024-09-06 09:26:25.596525: +2024-09-06 09:26:25.597790: Epoch 309 +2024-09-06 09:26:25.599052: Current learning rate: 0.00717 +2024-09-06 09:31:01.081009: train_loss -0.7397 +2024-09-06 09:31:01.087907: val_loss -0.7339 +2024-09-06 09:31:01.088850: Pseudo dice [0.9615, 0.9564, 0.9751, 0.9451, 0.8642, 0.9676, 0.9902] +2024-09-06 09:31:01.089735: Epoch time: 275.49 s +2024-09-06 09:31:03.677664: +2024-09-06 09:31:03.678695: Epoch 310 +2024-09-06 09:31:03.679315: Current learning rate: 0.00716 +2024-09-06 09:35:42.143708: train_loss -0.724 +2024-09-06 09:35:42.153710: val_loss -0.7571 +2024-09-06 09:35:42.154545: Pseudo dice [0.9618, 0.9543, 0.9766, 0.946, 0.8536, 0.9631, 0.9888] +2024-09-06 09:35:42.155428: Epoch time: 278.47 s +2024-09-06 09:35:44.881274: +2024-09-06 09:35:44.895051: Epoch 311 +2024-09-06 09:35:44.896125: Current learning rate: 0.00715 +2024-09-06 09:40:14.092987: train_loss -0.7388 +2024-09-06 09:40:14.094964: val_loss -0.6931 +2024-09-06 09:40:14.095621: Pseudo dice [0.9614, 0.9549, 0.9743, 0.9472, 0.8741, 0.9634, 0.9907] +2024-09-06 09:40:14.096864: Epoch time: 269.22 s +2024-09-06 09:40:17.967486: +2024-09-06 09:40:17.968310: Epoch 312 +2024-09-06 09:40:17.969010: Current learning rate: 0.00714 +2024-09-06 09:44:58.367942: train_loss -0.7391 +2024-09-06 09:44:58.376861: val_loss -0.7343 +2024-09-06 09:44:58.377651: Pseudo dice [0.9621, 0.9537, 0.9781, 0.947, 0.8731, 0.9632, 0.9867] +2024-09-06 09:44:58.378378: Epoch time: 280.41 s +2024-09-06 09:45:01.815699: +2024-09-06 09:45:01.816735: Epoch 313 +2024-09-06 09:45:01.817402: Current learning rate: 0.00713 +2024-09-06 09:49:52.555048: train_loss -0.7345 +2024-09-06 09:49:52.557607: val_loss -0.7456 +2024-09-06 09:49:52.558521: Pseudo dice [0.9591, 0.9544, 0.9723, 0.9397, 0.8677, 0.9619, 0.9905] +2024-09-06 09:49:52.559263: Epoch time: 290.74 s +2024-09-06 09:49:55.364712: +2024-09-06 09:49:55.366497: Epoch 314 +2024-09-06 09:49:55.367295: Current learning rate: 0.00712 +2024-09-06 09:54:36.297178: train_loss -0.7285 +2024-09-06 09:54:36.310481: val_loss -0.7523 +2024-09-06 09:54:36.311250: Pseudo dice [0.9619, 0.9558, 0.9768, 0.9444, 0.875, 0.9672, 0.9916] +2024-09-06 09:54:36.312004: Epoch time: 280.94 s +2024-09-06 09:54:39.637281: +2024-09-06 09:54:39.638227: Epoch 315 +2024-09-06 09:54:39.638889: Current learning rate: 0.00711 +2024-09-06 09:59:32.200233: train_loss -0.7084 +2024-09-06 09:59:32.202424: val_loss -0.754 +2024-09-06 09:59:32.203134: Pseudo dice [0.9623, 0.9551, 0.9764, 0.9441, 0.8607, 0.9583, 0.9893] +2024-09-06 09:59:32.204086: Epoch time: 292.57 s +2024-09-06 09:59:34.469258: +2024-09-06 09:59:34.470225: Epoch 316 +2024-09-06 09:59:34.470915: Current learning rate: 0.0071 +2024-09-06 10:04:20.122296: train_loss -0.7441 +2024-09-06 10:04:20.136223: val_loss -0.7348 +2024-09-06 10:04:20.137140: Pseudo dice [0.9653, 0.9554, 0.9788, 0.9498, 0.8566, 0.9649, 0.9902] +2024-09-06 10:04:20.138029: Epoch time: 285.66 s +2024-09-06 10:04:25.118148: +2024-09-06 10:04:25.119823: Epoch 317 +2024-09-06 10:04:25.121241: Current learning rate: 0.0071 +2024-09-06 10:09:02.787838: train_loss -0.7265 +2024-09-06 10:09:02.790206: val_loss -0.714 +2024-09-06 10:09:02.791057: Pseudo dice [0.9599, 0.956, 0.9741, 0.9431, 0.8526, 0.9588, 0.9894] +2024-09-06 10:09:02.791776: Epoch time: 277.68 s +2024-09-06 10:09:05.275010: +2024-09-06 10:09:05.276078: Epoch 318 +2024-09-06 10:09:05.276955: Current learning rate: 0.00709 +2024-09-06 10:13:50.232093: train_loss -0.7243 +2024-09-06 10:13:50.248058: val_loss -0.7255 +2024-09-06 10:13:50.249447: Pseudo dice [0.9636, 0.9587, 0.9738, 0.9516, 0.8534, 0.9668, 0.9897] +2024-09-06 10:13:50.250629: Epoch time: 284.96 s +2024-09-06 10:13:53.414987: +2024-09-06 10:13:53.416184: Epoch 319 +2024-09-06 10:13:53.417217: Current learning rate: 0.00708 +2024-09-06 10:18:45.017700: train_loss -0.7362 +2024-09-06 10:18:45.033641: val_loss -0.7565 +2024-09-06 10:18:45.034911: Pseudo dice [0.961, 0.956, 0.9757, 0.9459, 0.8668, 0.961, 0.9902] +2024-09-06 10:18:45.036791: Epoch time: 291.61 s +2024-09-06 10:18:48.449021: +2024-09-06 10:18:48.450095: Epoch 320 +2024-09-06 10:18:48.450797: Current learning rate: 0.00707 +2024-09-06 10:23:40.643255: train_loss -0.7292 +2024-09-06 10:23:40.660283: val_loss -0.7369 +2024-09-06 10:23:40.660999: Pseudo dice [0.9549, 0.9565, 0.9673, 0.9376, 0.8598, 0.9599, 0.99] +2024-09-06 10:23:40.661704: Epoch time: 292.2 s +2024-09-06 10:23:43.572414: +2024-09-06 10:23:43.573736: Epoch 321 +2024-09-06 10:23:43.574542: Current learning rate: 0.00706 +2024-09-06 10:28:21.406859: train_loss -0.7323 +2024-09-06 10:28:21.420929: val_loss -0.7439 +2024-09-06 10:28:21.422422: Pseudo dice [0.9632, 0.9574, 0.9777, 0.945, 0.8699, 0.9655, 0.991] +2024-09-06 10:28:21.423571: Epoch time: 277.84 s +2024-09-06 10:28:24.111866: +2024-09-06 10:28:24.126593: Epoch 322 +2024-09-06 10:28:24.127541: Current learning rate: 0.00705 +2024-09-06 10:33:23.281473: train_loss -0.728 +2024-09-06 10:33:23.282935: val_loss -0.7575 +2024-09-06 10:33:23.283800: Pseudo dice [0.9624, 0.9591, 0.9769, 0.9373, 0.8713, 0.969, 0.9916] +2024-09-06 10:33:23.284596: Epoch time: 299.18 s +2024-09-06 10:33:25.574815: +2024-09-06 10:33:25.575834: Epoch 323 +2024-09-06 10:33:25.576542: Current learning rate: 0.00704 +2024-09-06 10:38:23.595693: train_loss -0.7274 +2024-09-06 10:38:23.597084: val_loss -0.76 +2024-09-06 10:38:23.597958: Pseudo dice [0.9614, 0.9555, 0.9761, 0.948, 0.873, 0.9639, 0.9901] +2024-09-06 10:38:23.598637: Epoch time: 298.03 s +2024-09-06 10:38:27.230215: +2024-09-06 10:38:27.243239: Epoch 324 +2024-09-06 10:38:27.245204: Current learning rate: 0.00703 +2024-09-06 10:43:19.565357: train_loss -0.7485 +2024-09-06 10:43:19.580707: val_loss -0.7469 +2024-09-06 10:43:19.581829: Pseudo dice [0.9643, 0.9591, 0.98, 0.9479, 0.8904, 0.971, 0.9931] +2024-09-06 10:43:19.582672: Epoch time: 292.34 s +2024-09-06 10:43:22.738064: +2024-09-06 10:43:22.739069: Epoch 325 +2024-09-06 10:43:22.739816: Current learning rate: 0.00702 +2024-09-06 10:48:04.834470: train_loss -0.7462 +2024-09-06 10:48:04.854089: val_loss -0.7519 +2024-09-06 10:48:04.855446: Pseudo dice [0.9602, 0.958, 0.9754, 0.9476, 0.8715, 0.9694, 0.9893] +2024-09-06 10:48:04.856370: Epoch time: 282.1 s +2024-09-06 10:48:04.857234: Yayy! New best EMA pseudo Dice: 0.9518 +2024-09-06 10:48:14.826176: +2024-09-06 10:48:14.827172: Epoch 326 +2024-09-06 10:48:14.827850: Current learning rate: 0.00701 +2024-09-06 10:52:45.636664: train_loss -0.7447 +2024-09-06 10:52:45.639284: val_loss -0.7572 +2024-09-06 10:52:45.640131: Pseudo dice [0.9637, 0.9545, 0.9792, 0.9496, 0.8562, 0.9657, 0.9894] +2024-09-06 10:52:45.640860: Epoch time: 270.82 s +2024-09-06 10:52:48.353154: +2024-09-06 10:52:48.354045: Epoch 327 +2024-09-06 10:52:48.354787: Current learning rate: 0.007 +2024-09-06 10:57:24.288243: train_loss -0.7438 +2024-09-06 10:57:24.295450: val_loss -0.7425 +2024-09-06 10:57:24.296316: Pseudo dice [0.9648, 0.9579, 0.9738, 0.9534, 0.8686, 0.9642, 0.9893] +2024-09-06 10:57:24.297173: Epoch time: 275.94 s +2024-09-06 10:57:24.297863: Yayy! New best EMA pseudo Dice: 0.9519 +2024-09-06 10:57:33.493166: +2024-09-06 10:57:33.494489: Epoch 328 +2024-09-06 10:57:33.495426: Current learning rate: 0.00699 +2024-09-06 11:02:11.504092: train_loss -0.729 +2024-09-06 11:02:11.522559: val_loss -0.7299 +2024-09-06 11:02:11.523585: Pseudo dice [0.9605, 0.9565, 0.9772, 0.9439, 0.8706, 0.9662, 0.9917] +2024-09-06 11:02:11.524628: Epoch time: 278.02 s +2024-09-06 11:02:11.525784: Yayy! New best EMA pseudo Dice: 0.952 +2024-09-06 11:02:21.868171: +2024-09-06 11:02:21.869041: Epoch 329 +2024-09-06 11:02:21.869755: Current learning rate: 0.00698 +2024-09-06 11:07:06.317146: train_loss -0.7356 +2024-09-06 11:07:06.348654: val_loss -0.7423 +2024-09-06 11:07:06.356577: Pseudo dice [0.9619, 0.9561, 0.9771, 0.941, 0.8668, 0.9673, 0.9899] +2024-09-06 11:07:06.358685: Epoch time: 284.45 s +2024-09-06 11:07:10.362418: +2024-09-06 11:07:10.363507: Epoch 330 +2024-09-06 11:07:10.364292: Current learning rate: 0.00697 +2024-09-06 11:11:46.675490: train_loss -0.7216 +2024-09-06 11:11:46.684970: val_loss -0.7301 +2024-09-06 11:11:46.685946: Pseudo dice [0.9602, 0.9529, 0.976, 0.9499, 0.8743, 0.9685, 0.9892] +2024-09-06 11:11:46.686877: Epoch time: 276.32 s +2024-09-06 11:11:46.688110: Yayy! New best EMA pseudo Dice: 0.952 +2024-09-06 11:11:54.947168: +2024-09-06 11:11:54.948013: Epoch 331 +2024-09-06 11:11:54.948626: Current learning rate: 0.00696 +2024-09-06 11:16:34.917022: train_loss -0.7292 +2024-09-06 11:16:34.919308: val_loss -0.7438 +2024-09-06 11:16:34.920593: Pseudo dice [0.9627, 0.9537, 0.9787, 0.9438, 0.8716, 0.9682, 0.9892] +2024-09-06 11:16:34.921903: Epoch time: 279.97 s +2024-09-06 11:16:34.923049: Yayy! New best EMA pseudo Dice: 0.9521 +2024-09-06 11:16:42.806026: +2024-09-06 11:16:42.807091: Epoch 332 +2024-09-06 11:16:42.807786: Current learning rate: 0.00696 +2024-09-06 11:21:15.650980: train_loss -0.7408 +2024-09-06 11:21:15.652292: val_loss -0.7543 +2024-09-06 11:21:15.653024: Pseudo dice [0.9602, 0.9551, 0.9735, 0.941, 0.868, 0.9673, 0.9905] +2024-09-06 11:21:15.653659: Epoch time: 272.85 s +2024-09-06 11:21:18.086053: +2024-09-06 11:21:18.087136: Epoch 333 +2024-09-06 11:21:18.088008: Current learning rate: 0.00695 +2024-09-06 11:25:53.541291: train_loss -0.7307 +2024-09-06 11:25:53.555751: val_loss -0.7081 +2024-09-06 11:25:53.556721: Pseudo dice [0.9598, 0.9556, 0.9717, 0.9479, 0.8437, 0.9655, 0.9886] +2024-09-06 11:25:53.557626: Epoch time: 275.46 s +2024-09-06 11:25:56.265131: +2024-09-06 11:25:56.265986: Epoch 334 +2024-09-06 11:25:56.267077: Current learning rate: 0.00694 +2024-09-06 11:30:36.143722: train_loss -0.7168 +2024-09-06 11:30:36.145228: val_loss -0.7446 +2024-09-06 11:30:36.147899: Pseudo dice [0.9606, 0.9588, 0.9716, 0.9444, 0.8637, 0.966, 0.9916] +2024-09-06 11:30:36.148722: Epoch time: 279.88 s +2024-09-06 11:30:38.474931: +2024-09-06 11:30:38.475887: Epoch 335 +2024-09-06 11:30:38.476778: Current learning rate: 0.00693 +2024-09-06 11:35:23.612104: train_loss -0.69 +2024-09-06 11:35:23.620605: val_loss -0.7512 +2024-09-06 11:35:23.621393: Pseudo dice [0.9501, 0.9416, 0.9643, 0.9272, 0.839, 0.9604, 0.9892] +2024-09-06 11:35:23.621959: Epoch time: 285.14 s +2024-09-06 11:35:26.864481: +2024-09-06 11:35:26.865599: Epoch 336 +2024-09-06 11:35:26.866424: Current learning rate: 0.00692 +2024-09-06 11:39:55.035477: train_loss -0.6995 +2024-09-06 11:39:55.058113: val_loss -0.7373 +2024-09-06 11:39:55.059164: Pseudo dice [0.9553, 0.952, 0.9722, 0.9368, 0.8571, 0.9613, 0.9894] +2024-09-06 11:39:55.059870: Epoch time: 268.18 s +2024-09-06 11:39:57.859062: +2024-09-06 11:39:57.860218: Epoch 337 +2024-09-06 11:39:57.861407: Current learning rate: 0.00691 +2024-09-06 11:44:31.636653: train_loss -0.704 +2024-09-06 11:44:31.651681: val_loss -0.7257 +2024-09-06 11:44:31.652560: Pseudo dice [0.9593, 0.9504, 0.9755, 0.9385, 0.8605, 0.9636, 0.9919] +2024-09-06 11:44:31.653389: Epoch time: 273.78 s +2024-09-06 11:44:34.943798: +2024-09-06 11:44:34.955333: Epoch 338 +2024-09-06 11:44:34.957079: Current learning rate: 0.0069 +2024-09-06 11:49:18.458713: train_loss -0.7025 +2024-09-06 11:49:18.471127: val_loss -0.7457 +2024-09-06 11:49:18.472272: Pseudo dice [0.9615, 0.9545, 0.9771, 0.9382, 0.8583, 0.9677, 0.9903] +2024-09-06 11:49:18.473279: Epoch time: 283.53 s +2024-09-06 11:49:21.664507: +2024-09-06 11:49:21.665489: Epoch 339 +2024-09-06 11:49:21.666375: Current learning rate: 0.00689 +2024-09-06 11:54:06.178982: train_loss -0.718 +2024-09-06 11:54:06.181478: val_loss -0.7161 +2024-09-06 11:54:06.182617: Pseudo dice [0.963, 0.9584, 0.9753, 0.9506, 0.8736, 0.9632, 0.9905] +2024-09-06 11:54:06.184058: Epoch time: 284.52 s +2024-09-06 11:54:08.879844: +2024-09-06 11:54:08.881376: Epoch 340 +2024-09-06 11:54:08.883405: Current learning rate: 0.00688 +2024-09-06 11:58:49.675189: train_loss -0.731 +2024-09-06 11:58:49.687729: val_loss -0.7358 +2024-09-06 11:58:49.689278: Pseudo dice [0.9607, 0.9566, 0.9753, 0.9466, 0.8733, 0.9656, 0.992] +2024-09-06 11:58:49.690431: Epoch time: 280.8 s +2024-09-06 11:58:52.854685: +2024-09-06 11:58:52.855827: Epoch 341 +2024-09-06 11:58:52.857028: Current learning rate: 0.00687 +2024-09-06 12:03:38.272189: train_loss -0.7377 +2024-09-06 12:03:38.275643: val_loss -0.7808 +2024-09-06 12:03:38.277081: Pseudo dice [0.9609, 0.9546, 0.9756, 0.94, 0.8671, 0.9684, 0.99] +2024-09-06 12:03:38.278200: Epoch time: 285.42 s +2024-09-06 12:03:40.947649: +2024-09-06 12:03:40.950051: Epoch 342 +2024-09-06 12:03:40.952022: Current learning rate: 0.00686 +2024-09-06 12:08:14.121638: train_loss -0.7262 +2024-09-06 12:08:14.140901: val_loss -0.7627 +2024-09-06 12:08:14.142226: Pseudo dice [0.9599, 0.9545, 0.975, 0.9472, 0.8682, 0.9665, 0.9876] +2024-09-06 12:08:14.144622: Epoch time: 273.18 s +2024-09-06 12:08:18.547982: +2024-09-06 12:08:18.549181: Epoch 343 +2024-09-06 12:08:18.550332: Current learning rate: 0.00685 +2024-09-06 12:13:19.870620: train_loss -0.7351 +2024-09-06 12:13:19.886390: val_loss -0.7506 +2024-09-06 12:13:19.888019: Pseudo dice [0.9633, 0.957, 0.979, 0.9396, 0.866, 0.9603, 0.9902] +2024-09-06 12:13:19.889699: Epoch time: 301.33 s +2024-09-06 12:13:22.814858: +2024-09-06 12:13:22.815835: Epoch 344 +2024-09-06 12:13:22.816514: Current learning rate: 0.00684 +2024-09-06 12:17:55.184978: train_loss -0.7111 +2024-09-06 12:17:55.203007: val_loss -0.7335 +2024-09-06 12:17:55.204143: Pseudo dice [0.9605, 0.9541, 0.9741, 0.9445, 0.8652, 0.9669, 0.991] +2024-09-06 12:17:55.205244: Epoch time: 272.38 s +2024-09-06 12:17:58.282665: +2024-09-06 12:17:58.299623: Epoch 345 +2024-09-06 12:17:58.300411: Current learning rate: 0.00683 +2024-09-06 12:22:21.012345: train_loss -0.7213 +2024-09-06 12:22:21.037205: val_loss -0.721 +2024-09-06 12:22:21.038357: Pseudo dice [0.9629, 0.9565, 0.9776, 0.9422, 0.865, 0.9639, 0.9913] +2024-09-06 12:22:21.039079: Epoch time: 262.73 s +2024-09-06 12:22:25.068305: +2024-09-06 12:22:25.069395: Epoch 346 +2024-09-06 12:22:25.070105: Current learning rate: 0.00682 +2024-09-06 12:26:48.904992: train_loss -0.7183 +2024-09-06 12:26:48.907486: val_loss -0.7514 +2024-09-06 12:26:48.908226: Pseudo dice [0.958, 0.9523, 0.9747, 0.9417, 0.8611, 0.9655, 0.9905] +2024-09-06 12:26:48.908930: Epoch time: 263.84 s +2024-09-06 12:26:51.495684: +2024-09-06 12:26:51.496853: Epoch 347 +2024-09-06 12:26:51.497487: Current learning rate: 0.00681 +2024-09-06 12:31:41.735297: train_loss -0.7044 +2024-09-06 12:31:41.738091: val_loss -0.7038 +2024-09-06 12:31:41.738924: Pseudo dice [0.9564, 0.9525, 0.9633, 0.941, 0.8543, 0.9636, 0.9872] +2024-09-06 12:31:41.739725: Epoch time: 290.24 s +2024-09-06 12:31:44.274525: +2024-09-06 12:31:44.276107: Epoch 348 +2024-09-06 12:31:44.277138: Current learning rate: 0.0068 +2024-09-06 12:36:12.832036: train_loss -0.7204 +2024-09-06 12:36:12.844846: val_loss -0.6918 +2024-09-06 12:36:12.845813: Pseudo dice [0.9587, 0.9567, 0.9738, 0.9474, 0.8617, 0.9656, 0.9918] +2024-09-06 12:36:12.846800: Epoch time: 268.56 s +2024-09-06 12:36:16.240768: +2024-09-06 12:36:16.257634: Epoch 349 +2024-09-06 12:36:16.258977: Current learning rate: 0.0068 +2024-09-06 12:40:54.201499: train_loss -0.7078 +2024-09-06 12:40:54.218132: val_loss -0.7646 +2024-09-06 12:40:54.219582: Pseudo dice [0.9575, 0.9558, 0.9721, 0.9407, 0.8756, 0.967, 0.988] +2024-09-06 12:40:54.220710: Epoch time: 277.97 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_17_12_24.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_17_12_24.txt new file mode 100644 index 0000000000000000000000000000000000000000..59f6b8f723231d413984671fc9190aa627707d47 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_6_17_12_24.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-06 17:12:27.278076: Using torch.compile... +2024-09-06 17:12:32.441337: do_dummy_2d_data_aug: False +2024-09-06 17:12:32.447213: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-06 17:12:32.448589: The split file contains 5 splits. +2024-09-06 17:12:32.452561: Desired fold for training: 4 +2024-09-06 17:12:32.453172: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-06 17:12:45.612316: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-06 17:12:46.868299: +2024-09-06 17:12:46.879360: Epoch 350 +2024-09-06 17:12:46.880290: Current learning rate: 0.00679 +2024-09-06 17:23:59.959363: train_loss -0.7353 +2024-09-06 17:23:59.960541: val_loss -0.7661 +2024-09-06 17:23:59.961449: Pseudo dice [0.9634, 0.9575, 0.9771, 0.9476, 0.8592, 0.9672, 0.9902] +2024-09-06 17:23:59.963558: Epoch time: 673.1 s +2024-09-06 17:24:01.761758: +2024-09-06 17:24:01.762513: Epoch 351 +2024-09-06 17:24:01.763139: Current learning rate: 0.00678 +2024-09-06 17:28:37.488122: train_loss -0.7388 +2024-09-06 17:28:37.507687: val_loss -0.7221 +2024-09-06 17:28:37.509122: Pseudo dice [0.9628, 0.9566, 0.9786, 0.9484, 0.8743, 0.969, 0.9904] +2024-09-06 17:28:37.510544: Epoch time: 275.73 s +2024-09-06 17:28:41.495666: +2024-09-06 17:28:41.496774: Epoch 352 +2024-09-06 17:28:41.498230: Current learning rate: 0.00677 +2024-09-06 17:33:43.916291: train_loss -0.7276 +2024-09-06 17:33:43.938053: val_loss -0.7325 +2024-09-06 17:33:43.939619: Pseudo dice [0.9621, 0.9547, 0.9759, 0.9452, 0.8715, 0.9655, 0.991] +2024-09-06 17:33:43.940618: Epoch time: 302.43 s +2024-09-06 17:33:46.528925: +2024-09-06 17:33:46.529974: Epoch 353 +2024-09-06 17:33:46.530908: Current learning rate: 0.00676 +2024-09-06 17:38:34.889995: train_loss -0.7269 +2024-09-06 17:38:34.893133: val_loss -0.728 +2024-09-06 17:38:34.894958: Pseudo dice [0.9631, 0.957, 0.9766, 0.944, 0.8462, 0.9645, 0.9902] +2024-09-06 17:38:34.896794: Epoch time: 288.37 s +2024-09-06 17:38:38.224046: +2024-09-06 17:38:38.228681: Epoch 354 +2024-09-06 17:38:38.229545: Current learning rate: 0.00675 +2024-09-06 17:43:09.454658: train_loss -0.7309 +2024-09-06 17:43:09.460982: val_loss -0.7133 +2024-09-06 17:43:09.462771: Pseudo dice [0.9583, 0.9553, 0.973, 0.9457, 0.8673, 0.9665, 0.9898] +2024-09-06 17:43:09.464308: Epoch time: 271.24 s +2024-09-06 17:43:13.617833: +2024-09-06 17:43:13.618930: Epoch 355 +2024-09-06 17:43:13.620210: Current learning rate: 0.00674 +2024-09-06 17:48:12.697938: train_loss -0.7417 +2024-09-06 17:48:12.702062: val_loss -0.707 +2024-09-06 17:48:12.706535: Pseudo dice [0.9599, 0.9574, 0.9694, 0.9445, 0.8607, 0.9674, 0.9912] +2024-09-06 17:48:12.707321: Epoch time: 299.08 s +2024-09-06 17:48:15.300340: +2024-09-06 17:48:15.301367: Epoch 356 +2024-09-06 17:48:15.302391: Current learning rate: 0.00673 +2024-09-06 17:53:12.883965: train_loss -0.7422 +2024-09-06 17:53:12.905964: val_loss -0.7355 +2024-09-06 17:53:12.906839: Pseudo dice [0.9618, 0.9552, 0.9771, 0.9384, 0.8627, 0.9664, 0.9906] +2024-09-06 17:53:12.908216: Epoch time: 297.59 s +2024-09-06 17:53:16.603680: +2024-09-06 17:53:16.604779: Epoch 357 +2024-09-06 17:53:16.605660: Current learning rate: 0.00672 +2024-09-06 17:59:02.275902: train_loss -0.7275 +2024-09-06 17:59:02.281412: val_loss -0.7491 +2024-09-06 17:59:02.282837: Pseudo dice [0.9632, 0.955, 0.9772, 0.9439, 0.8573, 0.9652, 0.9888] +2024-09-06 17:59:02.283967: Epoch time: 345.68 s +2024-09-06 17:59:05.747630: +2024-09-06 17:59:05.748673: Epoch 358 +2024-09-06 17:59:05.749528: Current learning rate: 0.00671 +2024-09-06 18:04:07.051946: train_loss -0.7398 +2024-09-06 18:04:07.055628: val_loss -0.7656 +2024-09-06 18:04:07.056548: Pseudo dice [0.9643, 0.9579, 0.9776, 0.9506, 0.8733, 0.9682, 0.9911] +2024-09-06 18:04:07.057325: Epoch time: 301.31 s +2024-09-06 18:04:09.241811: +2024-09-06 18:04:09.242827: Epoch 359 +2024-09-06 18:04:09.243862: Current learning rate: 0.0067 +2024-09-06 18:09:33.982489: train_loss -0.7315 +2024-09-06 18:09:33.984555: val_loss -0.7716 +2024-09-06 18:09:33.985489: Pseudo dice [0.9671, 0.9612, 0.9777, 0.9505, 0.8733, 0.9678, 0.9917] +2024-09-06 18:09:33.986255: Epoch time: 324.75 s +2024-09-06 18:09:37.641477: +2024-09-06 18:09:37.642840: Epoch 360 +2024-09-06 18:09:37.643455: Current learning rate: 0.00669 +2024-09-06 18:15:19.248241: train_loss -0.7414 +2024-09-06 18:15:19.263040: val_loss -0.7726 +2024-09-06 18:15:19.263850: Pseudo dice [0.9626, 0.9548, 0.9774, 0.9458, 0.877, 0.9696, 0.9914] +2024-09-06 18:15:19.264677: Epoch time: 341.61 s +2024-09-06 18:15:22.817773: +2024-09-06 18:15:22.819108: Epoch 361 +2024-09-06 18:15:22.820354: Current learning rate: 0.00668 +2024-09-06 18:21:04.550943: train_loss -0.7508 +2024-09-06 18:21:04.552443: val_loss -0.76 +2024-09-06 18:21:04.553257: Pseudo dice [0.9588, 0.9602, 0.9707, 0.9492, 0.878, 0.9684, 0.99] +2024-09-06 18:21:04.554206: Epoch time: 341.74 s +2024-09-06 18:21:08.078788: +2024-09-06 18:21:08.079842: Epoch 362 +2024-09-06 18:21:08.080943: Current learning rate: 0.00667 +2024-09-06 18:26:48.661996: train_loss -0.7479 +2024-09-06 18:26:48.672874: val_loss -0.7283 +2024-09-06 18:26:48.675884: Pseudo dice [0.9648, 0.9573, 0.9786, 0.9472, 0.8631, 0.9706, 0.9909] +2024-09-06 18:26:48.678033: Epoch time: 340.59 s +2024-09-06 18:26:51.654448: +2024-09-06 18:26:51.655621: Epoch 363 +2024-09-06 18:26:51.656569: Current learning rate: 0.00666 +2024-09-06 18:32:51.384512: train_loss -0.7231 +2024-09-06 18:32:51.385911: val_loss -0.7264 +2024-09-06 18:32:51.386683: Pseudo dice [0.9602, 0.9565, 0.9755, 0.9429, 0.8561, 0.9652, 0.9885] +2024-09-06 18:32:51.387403: Epoch time: 359.74 s +2024-09-06 18:32:55.274437: +2024-09-06 18:32:55.275802: Epoch 364 +2024-09-06 18:32:55.277034: Current learning rate: 0.00665 +2024-09-06 18:38:33.406928: train_loss -0.7374 +2024-09-06 18:38:33.408883: val_loss -0.7359 +2024-09-06 18:38:33.410043: Pseudo dice [0.9602, 0.9533, 0.9748, 0.9399, 0.8716, 0.9667, 0.9894] +2024-09-06 18:38:33.411168: Epoch time: 338.14 s +2024-09-06 18:38:35.947389: +2024-09-06 18:38:35.948383: Epoch 365 +2024-09-06 18:38:35.949651: Current learning rate: 0.00665 +2024-09-06 18:44:32.289824: train_loss -0.7115 +2024-09-06 18:44:32.291667: val_loss -0.7128 +2024-09-06 18:44:32.292675: Pseudo dice [0.956, 0.9583, 0.9717, 0.9434, 0.8776, 0.9674, 0.9919] +2024-09-06 18:44:32.294510: Epoch time: 356.35 s +2024-09-06 18:44:35.543117: +2024-09-06 18:44:35.544214: Epoch 366 +2024-09-06 18:44:35.545152: Current learning rate: 0.00664 +2024-09-06 18:51:04.828915: train_loss -0.7337 +2024-09-06 18:51:04.831974: val_loss -0.7428 +2024-09-06 18:51:04.832886: Pseudo dice [0.9603, 0.9576, 0.9752, 0.9481, 0.8594, 0.9669, 0.989] +2024-09-06 18:51:04.834101: Epoch time: 389.29 s +2024-09-06 18:51:08.512300: +2024-09-06 18:51:08.513412: Epoch 367 +2024-09-06 18:51:08.514512: Current learning rate: 0.00663 +2024-09-06 18:57:35.352969: train_loss -0.7399 +2024-09-06 18:57:35.360077: val_loss -0.763 +2024-09-06 18:57:35.361008: Pseudo dice [0.9617, 0.9552, 0.9617, 0.9381, 0.8638, 0.9667, 0.9897] +2024-09-06 18:57:35.361784: Epoch time: 386.85 s +2024-09-06 18:57:38.575710: +2024-09-06 18:57:38.579309: Epoch 368 +2024-09-06 18:57:38.580558: Current learning rate: 0.00662 +2024-09-06 19:03:44.219983: train_loss -0.7381 +2024-09-06 19:03:44.223916: val_loss -0.7271 +2024-09-06 19:03:44.224936: Pseudo dice [0.9581, 0.9533, 0.974, 0.9394, 0.8683, 0.9633, 0.991] +2024-09-06 19:03:44.225923: Epoch time: 365.65 s +2024-09-06 19:03:47.452384: +2024-09-06 19:03:47.453452: Epoch 369 +2024-09-06 19:03:47.454721: Current learning rate: 0.00661 +2024-09-06 19:10:04.547491: train_loss -0.734 +2024-09-06 19:10:04.551280: val_loss -0.7514 +2024-09-06 19:10:04.552696: Pseudo dice [0.9612, 0.9557, 0.9759, 0.95, 0.8703, 0.9677, 0.99] +2024-09-06 19:10:04.555603: Epoch time: 377.1 s +2024-09-06 19:10:08.673383: +2024-09-06 19:10:08.674558: Epoch 370 +2024-09-06 19:10:08.675262: Current learning rate: 0.0066 +2024-09-06 19:16:38.091399: train_loss -0.7445 +2024-09-06 19:16:38.108601: val_loss -0.7452 +2024-09-06 19:16:38.109459: Pseudo dice [0.962, 0.9574, 0.974, 0.9401, 0.8688, 0.9674, 0.99] +2024-09-06 19:16:38.110645: Epoch time: 389.42 s +2024-09-06 19:16:41.755264: +2024-09-06 19:16:41.756793: Epoch 371 +2024-09-06 19:16:41.757946: Current learning rate: 0.00659 +2024-09-06 19:23:22.746921: train_loss -0.7225 +2024-09-06 19:23:22.750319: val_loss -0.7423 +2024-09-06 19:23:22.751205: Pseudo dice [0.9581, 0.9548, 0.9762, 0.9461, 0.8713, 0.968, 0.9903] +2024-09-06 19:23:22.751892: Epoch time: 401.0 s +2024-09-06 19:23:25.099142: +2024-09-06 19:23:25.106461: Epoch 372 +2024-09-06 19:23:25.107512: Current learning rate: 0.00658 +2024-09-06 19:29:44.549490: train_loss -0.7359 +2024-09-06 19:29:44.562860: val_loss -0.7753 +2024-09-06 19:29:44.564612: Pseudo dice [0.9641, 0.9603, 0.9777, 0.9507, 0.8875, 0.9675, 0.9925] +2024-09-06 19:29:44.565587: Epoch time: 379.46 s +2024-09-06 19:29:48.260062: +2024-09-06 19:29:48.261278: Epoch 373 +2024-09-06 19:29:48.262981: Current learning rate: 0.00657 +2024-09-06 19:36:22.122205: train_loss -0.734 +2024-09-06 19:36:22.125581: val_loss -0.7443 +2024-09-06 19:36:22.126561: Pseudo dice [0.9609, 0.9551, 0.977, 0.9468, 0.8587, 0.9646, 0.9882] +2024-09-06 19:36:22.127451: Epoch time: 393.87 s +2024-09-06 19:36:24.937311: +2024-09-06 19:36:24.939675: Epoch 374 +2024-09-06 19:36:24.941092: Current learning rate: 0.00656 +2024-09-06 19:42:41.048535: train_loss -0.7373 +2024-09-06 19:42:41.064975: val_loss -0.7332 +2024-09-06 19:42:41.065840: Pseudo dice [0.9621, 0.9584, 0.9775, 0.9471, 0.8673, 0.9659, 0.9894] +2024-09-06 19:42:41.066540: Epoch time: 376.12 s +2024-09-06 19:42:44.585160: +2024-09-06 19:42:44.586359: Epoch 375 +2024-09-06 19:42:44.587280: Current learning rate: 0.00655 +2024-09-06 19:49:31.248469: train_loss -0.7264 +2024-09-06 19:49:31.265414: val_loss -0.757 +2024-09-06 19:49:31.266249: Pseudo dice [0.9604, 0.9557, 0.9753, 0.9421, 0.8554, 0.9623, 0.9903] +2024-09-06 19:49:31.267023: Epoch time: 406.67 s +2024-09-06 19:49:36.452613: +2024-09-06 19:49:36.453798: Epoch 376 +2024-09-06 19:49:36.454550: Current learning rate: 0.00654 +2024-09-06 19:55:59.673106: train_loss -0.7256 +2024-09-06 19:55:59.676838: val_loss -0.762 +2024-09-06 19:55:59.678112: Pseudo dice [0.9593, 0.9556, 0.9718, 0.9423, 0.8622, 0.9684, 0.9917] +2024-09-06 19:55:59.679130: Epoch time: 383.23 s +2024-09-06 19:56:03.474084: +2024-09-06 19:56:03.475228: Epoch 377 +2024-09-06 19:56:03.475930: Current learning rate: 0.00653 +2024-09-06 20:02:31.219985: train_loss -0.7155 +2024-09-06 20:02:31.228097: val_loss -0.7467 +2024-09-06 20:02:31.229844: Pseudo dice [0.9614, 0.9544, 0.975, 0.9436, 0.8586, 0.9608, 0.9905] +2024-09-06 20:02:31.230930: Epoch time: 387.75 s +2024-09-06 20:02:35.685767: +2024-09-06 20:02:35.687679: Epoch 378 +2024-09-06 20:02:35.690188: Current learning rate: 0.00652 +2024-09-06 20:08:53.926895: train_loss -0.7213 +2024-09-06 20:08:53.929687: val_loss -0.7313 +2024-09-06 20:08:53.930966: Pseudo dice [0.9584, 0.9554, 0.9745, 0.9462, 0.8637, 0.9614, 0.9869] +2024-09-06 20:08:53.932073: Epoch time: 378.25 s +2024-09-06 20:08:56.445280: +2024-09-06 20:08:56.452105: Epoch 379 +2024-09-06 20:08:56.460809: Current learning rate: 0.00651 +2024-09-06 20:15:12.987996: train_loss -0.7109 +2024-09-06 20:15:12.997938: val_loss -0.6992 +2024-09-06 20:15:12.998729: Pseudo dice [0.9559, 0.9549, 0.9704, 0.9441, 0.8651, 0.9636, 0.9887] +2024-09-06 20:15:12.999860: Epoch time: 376.55 s +2024-09-06 20:15:17.219695: +2024-09-06 20:15:17.228228: Epoch 380 +2024-09-06 20:15:17.229195: Current learning rate: 0.0065 +2024-09-06 20:21:48.910105: train_loss -0.6868 +2024-09-06 20:21:48.920873: val_loss -0.6678 +2024-09-06 20:21:48.927678: Pseudo dice [0.9377, 0.9389, 0.9586, 0.9215, 0.8009, 0.9202, 0.9857] +2024-09-06 20:21:48.942945: Epoch time: 391.69 s +2024-09-06 20:21:52.202156: +2024-09-06 20:21:52.203222: Epoch 381 +2024-09-06 20:21:52.204285: Current learning rate: 0.00649 +2024-09-06 20:28:44.358585: train_loss -0.6369 +2024-09-06 20:28:44.381202: val_loss -0.6723 +2024-09-06 20:28:44.383249: Pseudo dice [0.9438, 0.9381, 0.9615, 0.9319, 0.824, 0.9509, 0.9789] +2024-09-06 20:28:44.384723: Epoch time: 412.16 s +2024-09-06 20:28:48.373448: +2024-09-06 20:28:48.374849: Epoch 382 +2024-09-06 20:28:48.375811: Current learning rate: 0.00648 +2024-09-06 20:35:43.711991: train_loss -0.6774 +2024-09-06 20:35:43.716694: val_loss -0.7324 +2024-09-06 20:35:43.718350: Pseudo dice [0.9516, 0.9454, 0.9684, 0.9385, 0.8307, 0.9522, 0.9859] +2024-09-06 20:35:43.719350: Epoch time: 415.34 s +2024-09-06 20:35:47.088498: +2024-09-06 20:35:47.089700: Epoch 383 +2024-09-06 20:35:47.091020: Current learning rate: 0.00648 +2024-09-06 20:42:31.345004: train_loss -0.7045 +2024-09-06 20:42:31.353484: val_loss -0.7395 +2024-09-06 20:42:31.354462: Pseudo dice [0.9581, 0.9529, 0.9737, 0.9394, 0.8525, 0.9571, 0.9854] +2024-09-06 20:42:31.355269: Epoch time: 404.26 s +2024-09-06 20:42:34.625766: +2024-09-06 20:42:34.627116: Epoch 384 +2024-09-06 20:42:34.628002: Current learning rate: 0.00647 +2024-09-06 20:49:31.674439: train_loss -0.706 +2024-09-06 20:49:31.684668: val_loss -0.7411 +2024-09-06 20:49:31.685461: Pseudo dice [0.9599, 0.9554, 0.9712, 0.9445, 0.8681, 0.9638, 0.9901] +2024-09-06 20:49:31.686648: Epoch time: 417.05 s +2024-09-06 20:49:35.310856: +2024-09-06 20:49:35.312122: Epoch 385 +2024-09-06 20:49:35.313716: Current learning rate: 0.00646 +2024-09-06 20:56:34.535505: train_loss -0.7128 +2024-09-06 20:56:34.547771: val_loss -0.7549 +2024-09-06 20:56:34.548683: Pseudo dice [0.9602, 0.9546, 0.9752, 0.9394, 0.8519, 0.9658, 0.9893] +2024-09-06 20:56:34.549447: Epoch time: 419.23 s +2024-09-06 20:56:38.727412: +2024-09-06 20:56:38.728678: Epoch 386 +2024-09-06 20:56:38.729674: Current learning rate: 0.00645 +2024-09-06 21:03:31.866282: train_loss -0.727 +2024-09-06 21:03:31.873914: val_loss -0.7511 +2024-09-06 21:03:31.874749: Pseudo dice [0.9585, 0.9544, 0.9748, 0.9458, 0.86, 0.9583, 0.9894] +2024-09-06 21:03:31.875707: Epoch time: 413.14 s +2024-09-06 21:03:35.380849: +2024-09-06 21:03:35.382075: Epoch 387 +2024-09-06 21:03:35.383194: Current learning rate: 0.00644 +2024-09-06 21:10:44.384444: train_loss -0.7266 +2024-09-06 21:10:44.416466: val_loss -0.7447 +2024-09-06 21:10:44.417384: Pseudo dice [0.9586, 0.9536, 0.974, 0.946, 0.8394, 0.9586, 0.9904] +2024-09-06 21:10:44.418342: Epoch time: 429.01 s +2024-09-06 21:10:50.576830: +2024-09-06 21:10:50.586816: Epoch 388 +2024-09-06 21:10:50.594663: Current learning rate: 0.00643 +2024-09-06 21:18:23.914598: train_loss -0.7181 +2024-09-06 21:18:23.932606: val_loss -0.7781 +2024-09-06 21:18:23.933513: Pseudo dice [0.959, 0.9504, 0.9743, 0.9399, 0.8659, 0.9661, 0.9894] +2024-09-06 21:18:23.934381: Epoch time: 453.34 s +2024-09-06 21:18:26.907532: +2024-09-06 21:18:26.917409: Epoch 389 +2024-09-06 21:18:26.918619: Current learning rate: 0.00642 +2024-09-06 21:25:16.283830: train_loss -0.7115 +2024-09-06 21:25:16.286487: val_loss -0.7328 +2024-09-06 21:25:16.287727: Pseudo dice [0.9621, 0.9544, 0.9775, 0.9406, 0.8473, 0.9659, 0.9887] +2024-09-06 21:25:16.288481: Epoch time: 409.38 s +2024-09-06 21:25:19.970035: +2024-09-06 21:25:19.971727: Epoch 390 +2024-09-06 21:25:19.973190: Current learning rate: 0.00641 +2024-09-06 21:32:48.700763: train_loss -0.7094 +2024-09-06 21:32:48.712112: val_loss -0.7192 +2024-09-06 21:32:48.713113: Pseudo dice [0.9553, 0.9512, 0.9724, 0.9352, 0.8548, 0.9634, 0.9901] +2024-09-06 21:32:48.713825: Epoch time: 448.74 s +2024-09-06 21:32:51.351083: +2024-09-06 21:32:51.352321: Epoch 391 +2024-09-06 21:32:51.353438: Current learning rate: 0.0064 +2024-09-06 21:39:53.980895: train_loss -0.6864 +2024-09-06 21:39:53.987713: val_loss -0.6754 +2024-09-06 21:39:53.989908: Pseudo dice [0.9506, 0.9447, 0.9688, 0.9271, 0.8374, 0.9578, 0.9892] +2024-09-06 21:39:53.992053: Epoch time: 422.63 s +2024-09-06 21:39:58.812257: +2024-09-06 21:39:58.819901: Epoch 392 +2024-09-06 21:39:58.823043: Current learning rate: 0.00639 +2024-09-06 21:46:54.328077: train_loss -0.7018 +2024-09-06 21:46:54.336950: val_loss -0.7435 +2024-09-06 21:46:54.339322: Pseudo dice [0.9595, 0.9529, 0.9757, 0.9396, 0.863, 0.9658, 0.9903] +2024-09-06 21:46:54.341365: Epoch time: 415.52 s +2024-09-06 21:46:57.680576: +2024-09-06 21:46:57.681668: Epoch 393 +2024-09-06 21:46:57.682887: Current learning rate: 0.00638 +2024-09-06 21:53:56.573371: train_loss -0.7155 +2024-09-06 21:53:56.590168: val_loss -0.7408 +2024-09-06 21:53:56.591094: Pseudo dice [0.9566, 0.9528, 0.968, 0.9415, 0.862, 0.9647, 0.9875] +2024-09-06 21:53:56.591987: Epoch time: 418.9 s +2024-09-06 21:53:59.935671: +2024-09-06 21:53:59.937028: Epoch 394 +2024-09-06 21:53:59.938527: Current learning rate: 0.00637 +2024-09-06 22:00:57.482838: train_loss -0.7162 +2024-09-06 22:00:57.495358: val_loss -0.7341 +2024-09-06 22:00:57.496659: Pseudo dice [0.9625, 0.9522, 0.9773, 0.9407, 0.8705, 0.968, 0.9899] +2024-09-06 22:00:57.497602: Epoch time: 417.55 s +2024-09-06 22:01:00.055364: +2024-09-06 22:01:00.056379: Epoch 395 +2024-09-06 22:01:00.057135: Current learning rate: 0.00636 +2024-09-06 22:07:16.545029: train_loss -0.7187 +2024-09-06 22:07:16.549793: val_loss -0.7069 +2024-09-06 22:07:16.550885: Pseudo dice [0.9567, 0.9553, 0.9702, 0.9369, 0.8733, 0.9674, 0.9905] +2024-09-06 22:07:16.551779: Epoch time: 376.49 s +2024-09-06 22:07:20.289849: +2024-09-06 22:07:20.290785: Epoch 396 +2024-09-06 22:07:20.291681: Current learning rate: 0.00635 +2024-09-06 22:14:33.283298: train_loss -0.7301 +2024-09-06 22:14:33.305724: val_loss -0.7437 +2024-09-06 22:14:33.307814: Pseudo dice [0.9618, 0.9573, 0.9752, 0.9479, 0.8624, 0.9679, 0.9911] +2024-09-06 22:14:33.309253: Epoch time: 433.0 s +2024-09-06 22:14:37.708351: +2024-09-06 22:14:37.709957: Epoch 397 +2024-09-06 22:14:37.711272: Current learning rate: 0.00634 +2024-09-06 22:21:42.927294: train_loss -0.7256 +2024-09-06 22:21:42.953233: val_loss -0.7608 +2024-09-06 22:21:42.954668: Pseudo dice [0.9604, 0.9546, 0.9745, 0.9449, 0.8712, 0.967, 0.9904] +2024-09-06 22:21:42.955915: Epoch time: 425.22 s +2024-09-06 22:21:46.981781: +2024-09-06 22:21:46.983719: Epoch 398 +2024-09-06 22:21:46.985186: Current learning rate: 0.00633 +2024-09-06 22:28:44.040740: train_loss -0.7417 +2024-09-06 22:28:44.058131: val_loss -0.7377 +2024-09-06 22:28:44.059500: Pseudo dice [0.9625, 0.9567, 0.9757, 0.94, 0.866, 0.9681, 0.9911] +2024-09-06 22:28:44.060770: Epoch time: 417.07 s +2024-09-06 22:28:49.284526: +2024-09-06 22:28:49.286400: Epoch 399 +2024-09-06 22:28:49.287697: Current learning rate: 0.00632 +2024-09-06 22:35:55.813262: train_loss -0.736 +2024-09-06 22:35:55.830185: val_loss -0.7015 +2024-09-06 22:35:55.845664: Pseudo dice [0.9619, 0.9577, 0.9763, 0.944, 0.86, 0.9668, 0.9911] +2024-09-06 22:35:55.847022: Epoch time: 426.53 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_14_05_16.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_14_05_16.txt new file mode 100644 index 0000000000000000000000000000000000000000..7f44c3ea1d2530e2be793d554fc29f5ea68eb38f --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_14_05_16.txt @@ -0,0 +1,377 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 14:05:20.135945: Using torch.compile... +2024-09-07 14:05:26.560047: do_dummy_2d_data_aug: False +2024-09-07 14:05:26.565605: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 14:05:26.566894: The split file contains 5 splits. +2024-09-07 14:05:26.567472: Desired fold for training: 4 +2024-09-07 14:05:26.568047: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 14:05:42.797204: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 14:05:44.220720: +2024-09-07 14:05:44.232437: Epoch 400 +2024-09-07 14:05:44.233446: Current learning rate: 0.00631 +2024-09-07 14:23:07.901074: train_loss -0.7194 +2024-09-07 14:23:07.907047: val_loss -0.731 +2024-09-07 14:23:07.910391: Pseudo dice [0.9611, 0.9567, 0.9743, 0.9471, 0.8597, 0.9662, 0.9892] +2024-09-07 14:23:07.915658: Epoch time: 1043.68 s +2024-09-07 14:23:09.787485: +2024-09-07 14:23:09.788800: Epoch 401 +2024-09-07 14:23:09.789678: Current learning rate: 0.0063 +2024-09-07 14:28:32.126314: train_loss -0.7327 +2024-09-07 14:28:32.140091: val_loss -0.7211 +2024-09-07 14:28:32.141266: Pseudo dice [0.9603, 0.9579, 0.9745, 0.9369, 0.8662, 0.9645, 0.9904] +2024-09-07 14:28:32.142134: Epoch time: 322.34 s +2024-09-07 14:28:34.653753: +2024-09-07 14:28:34.654809: Epoch 402 +2024-09-07 14:28:34.655801: Current learning rate: 0.0063 +2024-09-07 14:33:34.604556: train_loss -0.7386 +2024-09-07 14:33:34.618947: val_loss -0.7491 +2024-09-07 14:33:34.620122: Pseudo dice [0.9602, 0.9558, 0.9769, 0.9519, 0.8773, 0.9714, 0.9904] +2024-09-07 14:33:34.621220: Epoch time: 299.96 s +2024-09-07 14:33:37.662067: +2024-09-07 14:33:37.663402: Epoch 403 +2024-09-07 14:33:37.664201: Current learning rate: 0.00629 +2024-09-07 14:38:43.257990: train_loss -0.7215 +2024-09-07 14:38:43.263882: val_loss -0.7317 +2024-09-07 14:38:43.265015: Pseudo dice [0.964, 0.9594, 0.978, 0.9468, 0.8707, 0.9696, 0.9907] +2024-09-07 14:38:43.265891: Epoch time: 305.6 s +2024-09-07 14:38:46.130210: +2024-09-07 14:38:46.133462: Epoch 404 +2024-09-07 14:38:46.135092: Current learning rate: 0.00628 +2024-09-07 14:43:47.598736: train_loss -0.7287 +2024-09-07 14:43:47.605977: val_loss -0.7491 +2024-09-07 14:43:47.606728: Pseudo dice [0.9648, 0.9569, 0.9794, 0.9464, 0.8713, 0.9712, 0.9911] +2024-09-07 14:43:47.607464: Epoch time: 301.48 s +2024-09-07 14:43:50.067159: +2024-09-07 14:43:50.068219: Epoch 405 +2024-09-07 14:43:50.069441: Current learning rate: 0.00627 +2024-09-07 14:48:46.233679: train_loss -0.7414 +2024-09-07 14:48:46.250429: val_loss -0.7338 +2024-09-07 14:48:46.252449: Pseudo dice [0.9568, 0.9544, 0.9681, 0.9414, 0.8606, 0.9649, 0.989] +2024-09-07 14:48:46.253614: Epoch time: 296.17 s +2024-09-07 14:48:49.935317: +2024-09-07 14:48:49.936964: Epoch 406 +2024-09-07 14:48:49.938189: Current learning rate: 0.00626 +2024-09-07 14:55:59.056870: train_loss -0.7288 +2024-09-07 14:55:59.068579: val_loss -0.6975 +2024-09-07 14:55:59.069601: Pseudo dice [0.9627, 0.9575, 0.9766, 0.9457, 0.85, 0.9658, 0.9876] +2024-09-07 14:55:59.072346: Epoch time: 429.13 s +2024-09-07 14:56:02.798609: +2024-09-07 14:56:02.813090: Epoch 407 +2024-09-07 14:56:02.814730: Current learning rate: 0.00625 +2024-09-07 15:03:34.232286: train_loss -0.7348 +2024-09-07 15:03:34.235914: val_loss -0.7484 +2024-09-07 15:03:34.278584: Pseudo dice [0.9652, 0.9597, 0.9793, 0.9488, 0.8575, 0.9686, 0.9903] +2024-09-07 15:03:34.279989: Epoch time: 451.45 s +2024-09-07 15:03:38.664162: +2024-09-07 15:03:38.665224: Epoch 408 +2024-09-07 15:03:38.666484: Current learning rate: 0.00624 +2024-09-07 15:09:43.441863: train_loss -0.743 +2024-09-07 15:09:43.443764: val_loss -0.7484 +2024-09-07 15:09:43.444666: Pseudo dice [0.9599, 0.9571, 0.9707, 0.9394, 0.8577, 0.9682, 0.9908] +2024-09-07 15:09:43.445542: Epoch time: 364.8 s +2024-09-07 15:09:47.375611: +2024-09-07 15:09:47.377014: Epoch 409 +2024-09-07 15:09:47.377980: Current learning rate: 0.00623 +2024-09-07 15:15:37.022274: train_loss -0.7321 +2024-09-07 15:15:37.036977: val_loss -0.7275 +2024-09-07 15:15:37.037851: Pseudo dice [0.9625, 0.9549, 0.9764, 0.9491, 0.8617, 0.9703, 0.9909] +2024-09-07 15:15:37.038704: Epoch time: 349.65 s +2024-09-07 15:15:41.671318: +2024-09-07 15:15:41.684611: Epoch 410 +2024-09-07 15:15:41.685489: Current learning rate: 0.00622 +2024-09-07 15:21:15.882112: train_loss -0.7255 +2024-09-07 15:21:15.884810: val_loss -0.7815 +2024-09-07 15:21:15.885852: Pseudo dice [0.9627, 0.9574, 0.9752, 0.952, 0.8732, 0.97, 0.9894] +2024-09-07 15:21:15.887098: Epoch time: 334.22 s +2024-09-07 15:21:19.097038: +2024-09-07 15:21:19.098584: Epoch 411 +2024-09-07 15:21:19.099990: Current learning rate: 0.00621 +2024-09-07 15:26:07.428585: train_loss -0.7502 +2024-09-07 15:26:07.430065: val_loss -0.7259 +2024-09-07 15:26:07.430882: Pseudo dice [0.9582, 0.957, 0.9707, 0.9453, 0.8833, 0.9689, 0.9899] +2024-09-07 15:26:07.431664: Epoch time: 288.34 s +2024-09-07 15:26:10.948289: +2024-09-07 15:26:10.949593: Epoch 412 +2024-09-07 15:26:10.952450: Current learning rate: 0.0062 +2024-09-07 15:30:54.734767: train_loss -0.7347 +2024-09-07 15:30:54.741362: val_loss -0.7098 +2024-09-07 15:30:54.742892: Pseudo dice [0.9657, 0.9577, 0.9772, 0.946, 0.8699, 0.9686, 0.9912] +2024-09-07 15:30:54.743901: Epoch time: 283.8 s +2024-09-07 15:30:57.372887: +2024-09-07 15:30:57.374079: Epoch 413 +2024-09-07 15:30:57.374931: Current learning rate: 0.00619 +2024-09-07 15:35:56.552142: train_loss -0.7431 +2024-09-07 15:35:56.570864: val_loss -0.7748 +2024-09-07 15:35:56.572563: Pseudo dice [0.9647, 0.9597, 0.979, 0.9514, 0.8711, 0.9679, 0.9899] +2024-09-07 15:35:56.573881: Epoch time: 299.18 s +2024-09-07 15:36:04.755023: +2024-09-07 15:36:04.756328: Epoch 414 +2024-09-07 15:36:04.757053: Current learning rate: 0.00618 +2024-09-07 15:41:33.894904: train_loss -0.7459 +2024-09-07 15:41:33.901982: val_loss -0.7805 +2024-09-07 15:41:33.903122: Pseudo dice [0.9599, 0.9583, 0.9768, 0.952, 0.8844, 0.9715, 0.9911] +2024-09-07 15:41:33.904017: Epoch time: 329.15 s +2024-09-07 15:41:33.904703: Yayy! New best EMA pseudo Dice: 0.9522 +2024-09-07 15:41:41.435447: +2024-09-07 15:41:41.437205: Epoch 415 +2024-09-07 15:41:41.438215: Current learning rate: 0.00617 +2024-09-07 15:46:34.185121: train_loss -0.7367 +2024-09-07 15:46:34.199893: val_loss -0.7202 +2024-09-07 15:46:34.200658: Pseudo dice [0.9531, 0.9542, 0.9654, 0.941, 0.8638, 0.9641, 0.9902] +2024-09-07 15:46:34.201602: Epoch time: 292.76 s +2024-09-07 15:46:37.402398: +2024-09-07 15:46:37.403675: Epoch 416 +2024-09-07 15:46:37.404574: Current learning rate: 0.00616 +2024-09-07 15:51:39.109857: train_loss -0.7084 +2024-09-07 15:51:39.112737: val_loss -0.7064 +2024-09-07 15:51:39.113540: Pseudo dice [0.9512, 0.9509, 0.9665, 0.944, 0.8519, 0.9652, 0.9896] +2024-09-07 15:51:39.114230: Epoch time: 301.71 s +2024-09-07 15:51:42.296616: +2024-09-07 15:51:42.297844: Epoch 417 +2024-09-07 15:51:42.298613: Current learning rate: 0.00615 +2024-09-07 15:56:20.304588: train_loss -0.7271 +2024-09-07 15:56:20.307666: val_loss -0.7556 +2024-09-07 15:56:20.309225: Pseudo dice [0.9613, 0.9515, 0.9773, 0.944, 0.8707, 0.9654, 0.9912] +2024-09-07 15:56:20.310446: Epoch time: 278.03 s +2024-09-07 15:56:23.821959: +2024-09-07 15:56:23.835729: Epoch 418 +2024-09-07 15:56:23.836638: Current learning rate: 0.00614 +2024-09-07 16:01:03.847891: train_loss -0.7212 +2024-09-07 16:01:03.861003: val_loss -0.7476 +2024-09-07 16:01:03.863691: Pseudo dice [0.9545, 0.9541, 0.9678, 0.9428, 0.8664, 0.9689, 0.9911] +2024-09-07 16:01:03.865424: Epoch time: 280.03 s +2024-09-07 16:01:07.387671: +2024-09-07 16:01:07.405138: Epoch 419 +2024-09-07 16:01:07.407442: Current learning rate: 0.00613 +2024-09-07 16:05:51.630500: train_loss -0.7377 +2024-09-07 16:05:51.636587: val_loss -0.7498 +2024-09-07 16:05:51.637337: Pseudo dice [0.9581, 0.957, 0.973, 0.9439, 0.8685, 0.9669, 0.9878] +2024-09-07 16:05:51.638243: Epoch time: 284.25 s +2024-09-07 16:05:53.606507: +2024-09-07 16:05:53.608604: Epoch 420 +2024-09-07 16:05:53.610372: Current learning rate: 0.00612 +2024-09-07 16:10:33.160364: train_loss -0.733 +2024-09-07 16:10:33.162564: val_loss -0.7324 +2024-09-07 16:10:33.163773: Pseudo dice [0.9588, 0.9578, 0.9749, 0.9499, 0.8694, 0.9652, 0.9926] +2024-09-07 16:10:33.165123: Epoch time: 279.56 s +2024-09-07 16:10:36.412534: +2024-09-07 16:10:36.413697: Epoch 421 +2024-09-07 16:10:36.414435: Current learning rate: 0.00612 +2024-09-07 16:14:54.595313: train_loss -0.7311 +2024-09-07 16:14:54.610199: val_loss -0.7417 +2024-09-07 16:14:54.611461: Pseudo dice [0.9578, 0.9574, 0.9663, 0.9368, 0.865, 0.9676, 0.9911] +2024-09-07 16:14:54.612596: Epoch time: 258.19 s +2024-09-07 16:14:57.178344: +2024-09-07 16:14:57.181067: Epoch 422 +2024-09-07 16:14:57.199826: Current learning rate: 0.00611 +2024-09-07 16:19:39.906554: train_loss -0.7261 +2024-09-07 16:19:39.922382: val_loss -0.7703 +2024-09-07 16:19:39.923725: Pseudo dice [0.9612, 0.9541, 0.9763, 0.9429, 0.8527, 0.9634, 0.9897] +2024-09-07 16:19:39.924820: Epoch time: 282.73 s +2024-09-07 16:19:42.778473: +2024-09-07 16:19:42.779575: Epoch 423 +2024-09-07 16:19:42.796436: Current learning rate: 0.0061 +2024-09-07 16:24:44.173741: train_loss -0.742 +2024-09-07 16:24:44.178200: val_loss -0.7225 +2024-09-07 16:24:44.179133: Pseudo dice [0.9644, 0.9575, 0.9759, 0.9512, 0.8718, 0.9684, 0.99] +2024-09-07 16:24:44.179894: Epoch time: 301.4 s +2024-09-07 16:24:47.273289: +2024-09-07 16:24:47.274332: Epoch 424 +2024-09-07 16:24:47.275628: Current learning rate: 0.00609 +2024-09-07 16:29:23.657366: train_loss -0.7287 +2024-09-07 16:29:23.673414: val_loss -0.7132 +2024-09-07 16:29:23.674166: Pseudo dice [0.9612, 0.9545, 0.9763, 0.948, 0.8711, 0.97, 0.991] +2024-09-07 16:29:23.674887: Epoch time: 276.39 s +2024-09-07 16:29:26.543668: +2024-09-07 16:29:26.552076: Epoch 425 +2024-09-07 16:29:26.553032: Current learning rate: 0.00608 +2024-09-07 16:34:05.311182: train_loss -0.7301 +2024-09-07 16:34:05.329922: val_loss -0.7327 +2024-09-07 16:34:05.330772: Pseudo dice [0.9656, 0.9586, 0.9802, 0.9457, 0.8799, 0.97, 0.9907] +2024-09-07 16:34:05.331526: Epoch time: 278.77 s +2024-09-07 16:34:08.944175: +2024-09-07 16:34:08.960402: Epoch 426 +2024-09-07 16:34:08.961649: Current learning rate: 0.00607 +2024-09-07 16:38:39.842803: train_loss -0.7458 +2024-09-07 16:38:39.868571: val_loss -0.737 +2024-09-07 16:38:39.869514: Pseudo dice [0.9632, 0.9565, 0.9778, 0.9455, 0.8862, 0.9727, 0.9928] +2024-09-07 16:38:39.870157: Epoch time: 270.9 s +2024-09-07 16:38:42.961761: +2024-09-07 16:38:42.962841: Epoch 427 +2024-09-07 16:38:42.963915: Current learning rate: 0.00606 +2024-09-07 16:43:29.115551: train_loss -0.7186 +2024-09-07 16:43:29.121621: val_loss -0.7361 +2024-09-07 16:43:29.122530: Pseudo dice [0.9624, 0.9562, 0.9774, 0.9431, 0.8838, 0.9706, 0.9935] +2024-09-07 16:43:29.123244: Epoch time: 286.16 s +2024-09-07 16:43:29.123905: Yayy! New best EMA pseudo Dice: 0.9525 +2024-09-07 16:43:37.058562: +2024-09-07 16:43:37.059624: Epoch 428 +2024-09-07 16:43:37.060382: Current learning rate: 0.00605 +2024-09-07 16:48:35.529924: train_loss -0.7172 +2024-09-07 16:48:35.546452: val_loss -0.737 +2024-09-07 16:48:35.547454: Pseudo dice [0.9596, 0.9528, 0.9731, 0.95, 0.8706, 0.9644, 0.9888] +2024-09-07 16:48:35.548498: Epoch time: 298.48 s +2024-09-07 16:48:39.257631: +2024-09-07 16:48:39.258888: Epoch 429 +2024-09-07 16:48:39.259959: Current learning rate: 0.00604 +2024-09-07 16:53:08.773230: train_loss -0.7327 +2024-09-07 16:53:08.789124: val_loss -0.7305 +2024-09-07 16:53:08.801517: Pseudo dice [0.9567, 0.9556, 0.9713, 0.9414, 0.8761, 0.9648, 0.9921] +2024-09-07 16:53:08.803086: Epoch time: 269.52 s +2024-09-07 16:53:12.100252: +2024-09-07 16:53:12.116229: Epoch 430 +2024-09-07 16:53:12.128462: Current learning rate: 0.00603 +2024-09-07 16:57:47.991295: train_loss -0.7315 +2024-09-07 16:57:48.004006: val_loss -0.7278 +2024-09-07 16:57:48.004798: Pseudo dice [0.9632, 0.959, 0.9755, 0.9471, 0.8597, 0.9705, 0.9895] +2024-09-07 16:57:48.005482: Epoch time: 275.9 s +2024-09-07 16:57:50.599958: +2024-09-07 16:57:50.611422: Epoch 431 +2024-09-07 16:57:50.613658: Current learning rate: 0.00602 +2024-09-07 17:02:26.676132: train_loss -0.7317 +2024-09-07 17:02:26.698873: val_loss -0.7563 +2024-09-07 17:02:26.700001: Pseudo dice [0.9662, 0.9577, 0.9815, 0.9484, 0.8599, 0.9661, 0.9904] +2024-09-07 17:02:26.701347: Epoch time: 276.09 s +2024-09-07 17:02:30.316856: +2024-09-07 17:02:30.317923: Epoch 432 +2024-09-07 17:02:30.318774: Current learning rate: 0.00601 +2024-09-07 17:07:15.270179: train_loss -0.7441 +2024-09-07 17:07:15.289324: val_loss -0.7617 +2024-09-07 17:07:15.290401: Pseudo dice [0.9633, 0.9581, 0.9779, 0.9506, 0.8698, 0.9657, 0.9904] +2024-09-07 17:07:15.291130: Epoch time: 284.97 s +2024-09-07 17:07:17.942477: +2024-09-07 17:07:17.943878: Epoch 433 +2024-09-07 17:07:17.959890: Current learning rate: 0.006 +2024-09-07 17:12:16.681847: train_loss -0.7446 +2024-09-07 17:12:16.699900: val_loss -0.7664 +2024-09-07 17:12:16.701571: Pseudo dice [0.9644, 0.9581, 0.9773, 0.9458, 0.8713, 0.9674, 0.9916] +2024-09-07 17:12:16.703173: Epoch time: 298.75 s +2024-09-07 17:12:16.704787: Yayy! New best EMA pseudo Dice: 0.9526 +2024-09-07 17:12:21.582712: +2024-09-07 17:12:21.583425: Epoch 434 +2024-09-07 17:12:21.584029: Current learning rate: 0.00599 +2024-09-07 17:16:51.719883: train_loss -0.7458 +2024-09-07 17:16:51.740154: val_loss -0.7354 +2024-09-07 17:16:51.741723: Pseudo dice [0.9614, 0.9575, 0.9761, 0.9501, 0.8838, 0.972, 0.991] +2024-09-07 17:16:51.743843: Epoch time: 270.14 s +2024-09-07 17:16:51.744607: Yayy! New best EMA pseudo Dice: 0.9529 +2024-09-07 17:16:57.355769: +2024-09-07 17:16:57.356791: Epoch 435 +2024-09-07 17:16:57.357619: Current learning rate: 0.00598 +2024-09-07 17:21:29.449881: train_loss -0.7302 +2024-09-07 17:21:29.465961: val_loss -0.7363 +2024-09-07 17:21:29.467502: Pseudo dice [0.9638, 0.9568, 0.9766, 0.9433, 0.8607, 0.9687, 0.9922] +2024-09-07 17:21:29.468897: Epoch time: 272.1 s +2024-09-07 17:21:32.417042: +2024-09-07 17:21:32.435861: Epoch 436 +2024-09-07 17:21:32.440119: Current learning rate: 0.00597 +2024-09-07 17:26:19.509067: train_loss -0.7372 +2024-09-07 17:26:19.511470: val_loss -0.7234 +2024-09-07 17:26:19.512259: Pseudo dice [0.9611, 0.9574, 0.9737, 0.9428, 0.8626, 0.9618, 0.9904] +2024-09-07 17:26:19.512941: Epoch time: 287.1 s +2024-09-07 17:26:22.090931: +2024-09-07 17:26:22.092739: Epoch 437 +2024-09-07 17:26:22.095507: Current learning rate: 0.00596 +2024-09-07 17:31:10.783182: train_loss -0.7193 +2024-09-07 17:31:10.796216: val_loss -0.7215 +2024-09-07 17:31:10.797511: Pseudo dice [0.9554, 0.9551, 0.9687, 0.9407, 0.8707, 0.9649, 0.9906] +2024-09-07 17:31:10.799142: Epoch time: 288.7 s +2024-09-07 17:31:13.501534: +2024-09-07 17:31:13.502749: Epoch 438 +2024-09-07 17:31:13.504149: Current learning rate: 0.00595 +2024-09-07 17:36:13.997110: train_loss -0.718 +2024-09-07 17:36:14.009094: val_loss -0.743 +2024-09-07 17:36:14.009897: Pseudo dice [0.961, 0.956, 0.9751, 0.944, 0.8698, 0.9642, 0.99] +2024-09-07 17:36:14.010585: Epoch time: 300.5 s +2024-09-07 17:36:17.004400: +2024-09-07 17:36:17.020325: Epoch 439 +2024-09-07 17:36:17.021084: Current learning rate: 0.00594 +2024-09-07 17:41:00.283607: train_loss -0.7266 +2024-09-07 17:41:00.293694: val_loss -0.7335 +2024-09-07 17:41:00.295156: Pseudo dice [0.9557, 0.958, 0.9626, 0.9493, 0.8626, 0.9699, 0.9926] +2024-09-07 17:41:00.296861: Epoch time: 283.28 s +2024-09-07 17:41:04.304085: +2024-09-07 17:41:04.316407: Epoch 440 +2024-09-07 17:41:04.317136: Current learning rate: 0.00593 +2024-09-07 17:45:32.783856: train_loss -0.7393 +2024-09-07 17:45:32.792381: val_loss -0.7213 +2024-09-07 17:45:32.793931: Pseudo dice [0.9593, 0.956, 0.9724, 0.9408, 0.8619, 0.9642, 0.9888] +2024-09-07 17:45:32.795696: Epoch time: 268.48 s +2024-09-07 17:45:37.996921: +2024-09-07 17:45:37.999168: Epoch 441 +2024-09-07 17:45:38.012803: Current learning rate: 0.00592 +2024-09-07 17:50:29.836643: train_loss -0.7365 +2024-09-07 17:50:29.856939: val_loss -0.7356 +2024-09-07 17:50:29.857778: Pseudo dice [0.9613, 0.9565, 0.9782, 0.948, 0.8755, 0.9679, 0.9902] +2024-09-07 17:50:29.858670: Epoch time: 291.85 s +2024-09-07 17:50:33.187555: +2024-09-07 17:50:33.188889: Epoch 442 +2024-09-07 17:50:33.189837: Current learning rate: 0.00592 +2024-09-07 17:55:18.221020: train_loss -0.7269 +2024-09-07 17:55:18.226326: val_loss -0.7656 +2024-09-07 17:55:18.227769: Pseudo dice [0.9633, 0.9592, 0.977, 0.9432, 0.8722, 0.9694, 0.9909] +2024-09-07 17:55:18.229419: Epoch time: 285.04 s +2024-09-07 17:55:21.519209: +2024-09-07 17:55:21.520244: Epoch 443 +2024-09-07 17:55:21.521406: Current learning rate: 0.00591 +2024-09-07 17:59:53.275119: train_loss -0.7396 +2024-09-07 17:59:53.301690: val_loss -0.7622 +2024-09-07 17:59:53.303784: Pseudo dice [0.965, 0.9576, 0.9794, 0.9478, 0.8727, 0.9634, 0.9897] +2024-09-07 17:59:53.305255: Epoch time: 271.76 s +2024-09-07 17:59:56.588620: +2024-09-07 17:59:56.589645: Epoch 444 +2024-09-07 17:59:56.590347: Current learning rate: 0.0059 +2024-09-07 18:04:53.184896: train_loss -0.7374 +2024-09-07 18:04:53.201714: val_loss -0.747 +2024-09-07 18:04:53.209329: Pseudo dice [0.963, 0.9584, 0.978, 0.9545, 0.875, 0.9686, 0.9911] +2024-09-07 18:04:53.211256: Epoch time: 296.6 s +2024-09-07 18:04:57.327956: +2024-09-07 18:04:57.344639: Epoch 445 +2024-09-07 18:04:57.346319: Current learning rate: 0.00589 +2024-09-07 18:09:47.678915: train_loss -0.7358 +2024-09-07 18:09:47.696721: val_loss -0.7347 +2024-09-07 18:09:47.697560: Pseudo dice [0.9637, 0.9591, 0.9759, 0.9493, 0.8808, 0.9701, 0.9925] +2024-09-07 18:09:47.699725: Epoch time: 290.36 s +2024-09-07 18:09:50.066368: +2024-09-07 18:09:50.067551: Epoch 446 +2024-09-07 18:09:50.068477: Current learning rate: 0.00588 +2024-09-07 18:14:29.877090: train_loss -0.741 +2024-09-07 18:14:29.893157: val_loss -0.7611 +2024-09-07 18:14:29.894870: Pseudo dice [0.9627, 0.9572, 0.9787, 0.9506, 0.8509, 0.9671, 0.9873] +2024-09-07 18:14:29.896810: Epoch time: 279.82 s +2024-09-07 18:14:32.916367: +2024-09-07 18:14:32.928888: Epoch 447 +2024-09-07 18:14:32.940789: Current learning rate: 0.00587 +2024-09-07 18:19:18.293890: train_loss -0.7404 +2024-09-07 18:19:18.299185: val_loss -0.7616 +2024-09-07 18:19:18.299926: Pseudo dice [0.9601, 0.9568, 0.975, 0.9431, 0.8731, 0.9639, 0.9901] +2024-09-07 18:19:18.300698: Epoch time: 285.38 s +2024-09-07 18:19:20.781975: +2024-09-07 18:19:20.783171: Epoch 448 +2024-09-07 18:19:20.783880: Current learning rate: 0.00586 +2024-09-07 18:23:51.136840: train_loss -0.738 +2024-09-07 18:23:51.151163: val_loss -0.7315 +2024-09-07 18:23:51.151946: Pseudo dice [0.9662, 0.959, 0.9796, 0.9474, 0.8854, 0.9706, 0.9919] +2024-09-07 18:23:51.152676: Epoch time: 270.36 s +2024-09-07 18:23:51.153273: Yayy! New best EMA pseudo Dice: 0.953 +2024-09-07 18:23:56.098520: +2024-09-07 18:23:56.099662: Epoch 449 +2024-09-07 18:23:56.100432: Current learning rate: 0.00585 +2024-09-07 18:28:31.283517: train_loss -0.7465 +2024-09-07 18:28:31.304827: val_loss -0.7397 +2024-09-07 18:28:31.305883: Pseudo dice [0.9614, 0.9572, 0.9764, 0.9504, 0.8697, 0.9671, 0.9896] +2024-09-07 18:28:31.306934: Epoch time: 275.19 s +2024-09-07 18:28:34.417731: Yayy! New best EMA pseudo Dice: 0.953 \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_22_04_48.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_22_04_48.txt new file mode 100644 index 0000000000000000000000000000000000000000..cdfc21d838982181bf3a4c5bf4741d5a6198487f --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_7_22_04_48.txt @@ -0,0 +1,380 @@ + +####################################################################### +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. +####################################################################### + +2024-09-07 22:04:50.986536: Using torch.compile... +2024-09-07 22:04:57.855917: do_dummy_2d_data_aug: False +2024-09-07 22:04:57.867567: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-07 22:04:57.868685: The split file contains 5 splits. +2024-09-07 22:04:57.869095: Desired fold for training: 4 +2024-09-07 22:04:57.869452: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-07 22:05:10.874029: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-07 22:05:11.760533: +2024-09-07 22:05:11.774254: Epoch 450 +2024-09-07 22:05:11.775374: Current learning rate: 0.00584 +2024-09-07 22:23:13.190535: train_loss -0.7312 +2024-09-07 22:23:13.192669: val_loss -0.7446 +2024-09-07 22:23:13.193215: Pseudo dice [0.9625, 0.9587, 0.975, 0.9442, 0.8723, 0.9701, 0.9904] +2024-09-07 22:23:13.193786: Epoch time: 1081.43 s +2024-09-07 22:23:13.194237: Yayy! New best EMA pseudo Dice: 0.9531 +2024-09-07 22:23:16.657381: +2024-09-07 22:23:16.658161: Epoch 451 +2024-09-07 22:23:16.658618: Current learning rate: 0.00583 +2024-09-07 22:28:10.107502: train_loss -0.7312 +2024-09-07 22:28:10.109963: val_loss -0.7714 +2024-09-07 22:28:10.110689: Pseudo dice [0.9656, 0.9582, 0.9787, 0.9501, 0.8843, 0.9722, 0.9922] +2024-09-07 22:28:10.111230: Epoch time: 293.45 s +2024-09-07 22:28:10.113029: Yayy! New best EMA pseudo Dice: 0.9535 +2024-09-07 22:28:17.794645: +2024-09-07 22:28:17.795573: Epoch 452 +2024-09-07 22:28:17.796117: Current learning rate: 0.00582 +2024-09-07 22:33:07.911388: train_loss -0.7468 +2024-09-07 22:33:07.940817: val_loss -0.7635 +2024-09-07 22:33:07.943114: Pseudo dice [0.9626, 0.9588, 0.9754, 0.9504, 0.8591, 0.9656, 0.9912] +2024-09-07 22:33:07.944444: Epoch time: 290.12 s +2024-09-07 22:33:11.532655: +2024-09-07 22:33:11.544435: Epoch 453 +2024-09-07 22:33:11.546668: Current learning rate: 0.00581 +2024-09-07 22:38:06.329442: train_loss -0.7245 +2024-09-07 22:38:06.331808: val_loss -0.7187 +2024-09-07 22:38:06.332427: Pseudo dice [0.9589, 0.9559, 0.9735, 0.9436, 0.8401, 0.9704, 0.9896] +2024-09-07 22:38:06.332990: Epoch time: 294.8 s +2024-09-07 22:38:10.325478: +2024-09-07 22:38:10.340485: Epoch 454 +2024-09-07 22:38:10.341066: Current learning rate: 0.0058 +2024-09-07 22:43:31.636971: train_loss -0.7369 +2024-09-07 22:43:31.645243: val_loss -0.7573 +2024-09-07 22:43:31.646154: Pseudo dice [0.9638, 0.9592, 0.9797, 0.9484, 0.8882, 0.9717, 0.9915] +2024-09-07 22:43:31.646800: Epoch time: 321.32 s +2024-09-07 22:43:35.129329: +2024-09-07 22:43:35.140481: Epoch 455 +2024-09-07 22:43:35.142226: Current learning rate: 0.00579 +2024-09-07 22:49:18.090684: train_loss -0.7254 +2024-09-07 22:49:18.105017: val_loss -0.7566 +2024-09-07 22:49:18.105724: Pseudo dice [0.9637, 0.9609, 0.9787, 0.9462, 0.8728, 0.9691, 0.9905] +2024-09-07 22:49:18.106240: Epoch time: 342.97 s +2024-09-07 22:49:20.971268: +2024-09-07 22:49:20.972254: Epoch 456 +2024-09-07 22:49:20.972860: Current learning rate: 0.00578 +2024-09-07 22:54:01.290193: train_loss -0.7257 +2024-09-07 22:54:01.306583: val_loss -0.7219 +2024-09-07 22:54:01.307371: Pseudo dice [0.9589, 0.9508, 0.9692, 0.9395, 0.8437, 0.9616, 0.9875] +2024-09-07 22:54:01.308679: Epoch time: 280.33 s +2024-09-07 22:54:04.660523: +2024-09-07 22:54:04.671766: Epoch 457 +2024-09-07 22:54:04.672783: Current learning rate: 0.00577 +2024-09-07 22:58:39.326661: train_loss -0.7274 +2024-09-07 22:58:39.340596: val_loss -0.75 +2024-09-07 22:58:39.341308: Pseudo dice [0.9626, 0.9563, 0.9743, 0.9441, 0.868, 0.9679, 0.9907] +2024-09-07 22:58:39.342076: Epoch time: 274.67 s +2024-09-07 22:58:42.282849: +2024-09-07 22:58:42.283942: Epoch 458 +2024-09-07 22:58:42.284921: Current learning rate: 0.00576 +2024-09-07 23:03:18.539228: train_loss -0.725 +2024-09-07 23:03:18.553327: val_loss -0.7466 +2024-09-07 23:03:18.554529: Pseudo dice [0.9634, 0.9554, 0.9769, 0.9494, 0.8743, 0.9699, 0.991] +2024-09-07 23:03:18.555604: Epoch time: 276.26 s +2024-09-07 23:03:21.459397: +2024-09-07 23:03:21.472504: Epoch 459 +2024-09-07 23:03:21.473580: Current learning rate: 0.00575 +2024-09-07 23:08:06.589683: train_loss -0.7445 +2024-09-07 23:08:06.609713: val_loss -0.7305 +2024-09-07 23:08:06.610508: Pseudo dice [0.9612, 0.9594, 0.9741, 0.9512, 0.8738, 0.9694, 0.9919] +2024-09-07 23:08:06.611164: Epoch time: 285.14 s +2024-09-07 23:08:09.706374: +2024-09-07 23:08:09.707267: Epoch 460 +2024-09-07 23:08:09.707812: Current learning rate: 0.00574 +2024-09-07 23:12:50.031551: train_loss -0.724 +2024-09-07 23:12:50.051890: val_loss -0.7428 +2024-09-07 23:12:50.053234: Pseudo dice [0.9638, 0.9572, 0.9774, 0.9463, 0.8794, 0.9657, 0.992] +2024-09-07 23:12:50.054592: Epoch time: 280.33 s +2024-09-07 23:12:53.305032: +2024-09-07 23:12:53.316371: Epoch 461 +2024-09-07 23:12:53.317338: Current learning rate: 0.00573 +2024-09-07 23:17:46.976728: train_loss -0.7395 +2024-09-07 23:17:46.978719: val_loss -0.7447 +2024-09-07 23:17:46.979866: Pseudo dice [0.9634, 0.9603, 0.9776, 0.9435, 0.8574, 0.9617, 0.9894] +2024-09-07 23:17:46.981107: Epoch time: 293.68 s +2024-09-07 23:17:50.329456: +2024-09-07 23:17:50.332663: Epoch 462 +2024-09-07 23:17:50.333282: Current learning rate: 0.00572 +2024-09-07 23:22:41.421077: train_loss -0.7195 +2024-09-07 23:22:41.422278: val_loss -0.7441 +2024-09-07 23:22:41.422844: Pseudo dice [0.9615, 0.9558, 0.9751, 0.9463, 0.8823, 0.9695, 0.9913] +2024-09-07 23:22:41.423579: Epoch time: 291.1 s +2024-09-07 23:22:44.926314: +2024-09-07 23:22:44.927452: Epoch 463 +2024-09-07 23:22:44.928209: Current learning rate: 0.00571 +2024-09-07 23:27:34.944713: train_loss -0.7318 +2024-09-07 23:27:34.949027: val_loss -0.7631 +2024-09-07 23:27:34.949617: Pseudo dice [0.9568, 0.9532, 0.9764, 0.943, 0.8759, 0.9693, 0.9917] +2024-09-07 23:27:34.950135: Epoch time: 290.02 s +2024-09-07 23:27:37.670043: +2024-09-07 23:27:37.671126: Epoch 464 +2024-09-07 23:27:37.671773: Current learning rate: 0.0057 +2024-09-07 23:32:22.196526: train_loss -0.7416 +2024-09-07 23:32:22.200903: val_loss -0.7296 +2024-09-07 23:32:22.202038: Pseudo dice [0.9594, 0.9569, 0.9708, 0.9459, 0.8727, 0.9714, 0.9911] +2024-09-07 23:32:22.203141: Epoch time: 284.53 s +2024-09-07 23:32:25.684864: +2024-09-07 23:32:25.685911: Epoch 465 +2024-09-07 23:32:25.686627: Current learning rate: 0.0057 +2024-09-07 23:36:59.998538: train_loss -0.7436 +2024-09-07 23:37:00.000456: val_loss -0.7875 +2024-09-07 23:37:00.001153: Pseudo dice [0.9632, 0.9561, 0.9767, 0.9524, 0.8821, 0.9706, 0.9916] +2024-09-07 23:37:00.001971: Epoch time: 274.32 s +2024-09-07 23:37:03.747823: +2024-09-07 23:37:03.749579: Epoch 466 +2024-09-07 23:37:03.750099: Current learning rate: 0.00569 +2024-09-07 23:41:48.005393: train_loss -0.7369 +2024-09-07 23:41:48.006747: val_loss -0.7784 +2024-09-07 23:41:48.007312: Pseudo dice [0.9666, 0.9581, 0.9809, 0.9504, 0.8786, 0.9697, 0.9909] +2024-09-07 23:41:48.011691: Epoch time: 284.26 s +2024-09-07 23:41:52.399820: +2024-09-07 23:41:52.400708: Epoch 467 +2024-09-07 23:41:52.401212: Current learning rate: 0.00568 +2024-09-07 23:46:36.647121: train_loss -0.7432 +2024-09-07 23:46:36.662857: val_loss -0.7831 +2024-09-07 23:46:36.664278: Pseudo dice [0.9625, 0.9556, 0.9778, 0.9529, 0.8793, 0.971, 0.9914] +2024-09-07 23:46:36.665285: Epoch time: 284.27 s +2024-09-07 23:46:36.665779: Yayy! New best EMA pseudo Dice: 0.9537 +2024-09-07 23:46:43.547436: +2024-09-07 23:46:43.548748: Epoch 468 +2024-09-07 23:46:43.549299: Current learning rate: 0.00567 +2024-09-07 23:51:27.358926: train_loss -0.7352 +2024-09-07 23:51:27.372828: val_loss -0.7542 +2024-09-07 23:51:27.373491: Pseudo dice [0.9621, 0.9608, 0.9745, 0.9517, 0.8767, 0.9694, 0.9925] +2024-09-07 23:51:27.374036: Epoch time: 283.82 s +2024-09-07 23:51:27.374555: Yayy! New best EMA pseudo Dice: 0.9539 +2024-09-07 23:51:32.303117: +2024-09-07 23:51:32.304318: Epoch 469 +2024-09-07 23:51:32.305215: Current learning rate: 0.00566 +2024-09-07 23:56:06.976470: train_loss -0.7468 +2024-09-07 23:56:07.005857: val_loss -0.7661 +2024-09-07 23:56:07.006771: Pseudo dice [0.9669, 0.9607, 0.9796, 0.9574, 0.8744, 0.9715, 0.992] +2024-09-07 23:56:07.007407: Epoch time: 274.68 s +2024-09-07 23:56:07.007897: Yayy! New best EMA pseudo Dice: 0.9542 +2024-09-07 23:56:13.451531: +2024-09-07 23:56:13.452679: Epoch 470 +2024-09-07 23:56:13.453548: Current learning rate: 0.00565 +2024-09-08 00:01:13.995486: train_loss -0.7474 +2024-09-08 00:01:14.017278: val_loss -0.7469 +2024-09-08 00:01:14.017906: Pseudo dice [0.9655, 0.9585, 0.9802, 0.9492, 0.888, 0.9681, 0.9913] +2024-09-08 00:01:14.018414: Epoch time: 300.55 s +2024-09-08 00:01:14.018903: Yayy! New best EMA pseudo Dice: 0.9545 +2024-09-08 00:01:20.438493: +2024-09-08 00:01:20.451228: Epoch 471 +2024-09-08 00:01:20.451879: Current learning rate: 0.00564 +2024-09-08 00:06:18.778933: train_loss -0.7508 +2024-09-08 00:06:18.804593: val_loss -0.7873 +2024-09-08 00:06:18.805447: Pseudo dice [0.9638, 0.9587, 0.9762, 0.9414, 0.8857, 0.9706, 0.9918] +2024-09-08 00:06:18.806327: Epoch time: 298.35 s +2024-09-08 00:06:18.807201: Yayy! New best EMA pseudo Dice: 0.9546 +2024-09-08 00:06:24.722396: +2024-09-08 00:06:24.723884: Epoch 472 +2024-09-08 00:06:24.725305: Current learning rate: 0.00563 +2024-09-08 00:11:04.656432: train_loss -0.7361 +2024-09-08 00:11:04.669965: val_loss -0.7198 +2024-09-08 00:11:04.670731: Pseudo dice [0.9531, 0.9555, 0.9678, 0.9407, 0.8565, 0.9615, 0.9894] +2024-09-08 00:11:04.671287: Epoch time: 279.94 s +2024-09-08 00:11:08.659917: +2024-09-08 00:11:08.660899: Epoch 473 +2024-09-08 00:11:08.661611: Current learning rate: 0.00562 +2024-09-08 00:15:56.369083: train_loss -0.7352 +2024-09-08 00:15:56.371340: val_loss -0.6784 +2024-09-08 00:15:56.371921: Pseudo dice [0.9546, 0.9571, 0.9662, 0.9459, 0.8708, 0.9664, 0.9906] +2024-09-08 00:15:56.372392: Epoch time: 287.72 s +2024-09-08 00:15:58.621457: +2024-09-08 00:15:58.637114: Epoch 474 +2024-09-08 00:15:58.638343: Current learning rate: 0.00561 +2024-09-08 00:20:32.152855: train_loss -0.7498 +2024-09-08 00:20:32.172679: val_loss -0.753 +2024-09-08 00:20:32.175099: Pseudo dice [0.9624, 0.9599, 0.9752, 0.9467, 0.8769, 0.9662, 0.992] +2024-09-08 00:20:32.177171: Epoch time: 273.54 s +2024-09-08 00:20:36.167485: +2024-09-08 00:20:36.169069: Epoch 475 +2024-09-08 00:20:36.171346: Current learning rate: 0.0056 +2024-09-08 00:25:15.769614: train_loss -0.7465 +2024-09-08 00:25:15.783735: val_loss -0.7549 +2024-09-08 00:25:15.785025: Pseudo dice [0.9634, 0.9593, 0.9783, 0.9551, 0.8806, 0.9717, 0.991] +2024-09-08 00:25:15.786231: Epoch time: 279.61 s +2024-09-08 00:25:18.511431: +2024-09-08 00:25:18.528373: Epoch 476 +2024-09-08 00:25:18.529068: Current learning rate: 0.00559 +2024-09-08 00:29:52.094850: train_loss -0.7412 +2024-09-08 00:29:52.120717: val_loss -0.7588 +2024-09-08 00:29:52.121296: Pseudo dice [0.9622, 0.9571, 0.9744, 0.9459, 0.8791, 0.9696, 0.9911] +2024-09-08 00:29:52.121782: Epoch time: 273.59 s +2024-09-08 00:29:55.210673: +2024-09-08 00:29:55.211772: Epoch 477 +2024-09-08 00:29:55.212475: Current learning rate: 0.00558 +2024-09-08 00:34:29.326380: train_loss -0.7474 +2024-09-08 00:34:29.340973: val_loss -0.7279 +2024-09-08 00:34:29.342032: Pseudo dice [0.9641, 0.9571, 0.9796, 0.9486, 0.8768, 0.9664, 0.9902] +2024-09-08 00:34:29.343478: Epoch time: 274.12 s +2024-09-08 00:34:32.848649: +2024-09-08 00:34:32.849542: Epoch 478 +2024-09-08 00:34:32.850139: Current learning rate: 0.00557 +2024-09-08 00:39:09.630492: train_loss -0.7398 +2024-09-08 00:39:09.633874: val_loss -0.7311 +2024-09-08 00:39:09.634780: Pseudo dice [0.9638, 0.9581, 0.9786, 0.9494, 0.883, 0.9697, 0.9903] +2024-09-08 00:39:09.635742: Epoch time: 276.79 s +2024-09-08 00:39:12.701030: +2024-09-08 00:39:12.719971: Epoch 479 +2024-09-08 00:39:12.721865: Current learning rate: 0.00556 +2024-09-08 00:43:39.261947: train_loss -0.7446 +2024-09-08 00:43:39.280535: val_loss -0.7398 +2024-09-08 00:43:39.281247: Pseudo dice [0.962, 0.9571, 0.9757, 0.9446, 0.875, 0.9631, 0.9887] +2024-09-08 00:43:39.282425: Epoch time: 266.57 s +2024-09-08 00:43:42.708770: +2024-09-08 00:43:42.716732: Epoch 480 +2024-09-08 00:43:42.717938: Current learning rate: 0.00555 +2024-09-08 00:48:24.641081: train_loss -0.7417 +2024-09-08 00:48:24.654092: val_loss -0.7729 +2024-09-08 00:48:24.654753: Pseudo dice [0.9638, 0.9581, 0.9784, 0.9504, 0.8747, 0.971, 0.9905] +2024-09-08 00:48:24.655331: Epoch time: 281.94 s +2024-09-08 00:48:27.984570: +2024-09-08 00:48:27.985496: Epoch 481 +2024-09-08 00:48:27.986168: Current learning rate: 0.00554 +2024-09-08 00:53:05.704468: train_loss -0.7362 +2024-09-08 00:53:05.719740: val_loss -0.7273 +2024-09-08 00:53:05.721310: Pseudo dice [0.9621, 0.9559, 0.976, 0.9462, 0.8636, 0.9703, 0.9881] +2024-09-08 00:53:05.723586: Epoch time: 277.74 s +2024-09-08 00:53:09.224624: +2024-09-08 00:53:09.226802: Epoch 482 +2024-09-08 00:53:09.228113: Current learning rate: 0.00553 +2024-09-08 00:57:43.227264: train_loss -0.7422 +2024-09-08 00:57:43.261312: val_loss -0.734 +2024-09-08 00:57:43.263008: Pseudo dice [0.9597, 0.9593, 0.9727, 0.9505, 0.877, 0.9686, 0.9912] +2024-09-08 00:57:43.264528: Epoch time: 274.01 s +2024-09-08 00:57:47.190082: +2024-09-08 00:57:47.204782: Epoch 483 +2024-09-08 00:57:47.206422: Current learning rate: 0.00552 +2024-09-08 01:02:21.437906: train_loss -0.7477 +2024-09-08 01:02:21.440471: val_loss -0.7654 +2024-09-08 01:02:21.441264: Pseudo dice [0.9614, 0.9595, 0.9743, 0.9451, 0.8773, 0.9721, 0.9912] +2024-09-08 01:02:21.441868: Epoch time: 274.25 s +2024-09-08 01:02:24.564393: +2024-09-08 01:02:24.576500: Epoch 484 +2024-09-08 01:02:24.584346: Current learning rate: 0.00551 +2024-09-08 01:07:02.534924: train_loss -0.7541 +2024-09-08 01:07:02.549829: val_loss -0.7502 +2024-09-08 01:07:02.550828: Pseudo dice [0.9672, 0.9582, 0.9816, 0.9496, 0.8851, 0.9702, 0.9912] +2024-09-08 01:07:02.551600: Epoch time: 277.97 s +2024-09-08 01:07:05.454758: +2024-09-08 01:07:05.456295: Epoch 485 +2024-09-08 01:07:05.457512: Current learning rate: 0.0055 +2024-09-08 01:11:49.617350: train_loss -0.7473 +2024-09-08 01:11:49.634219: val_loss -0.7588 +2024-09-08 01:11:49.634950: Pseudo dice [0.9636, 0.9579, 0.9754, 0.9481, 0.8746, 0.9721, 0.9895] +2024-09-08 01:11:49.635484: Epoch time: 284.17 s +2024-09-08 01:11:52.248252: +2024-09-08 01:11:52.249041: Epoch 486 +2024-09-08 01:11:52.249681: Current learning rate: 0.00549 +2024-09-08 01:16:26.395377: train_loss -0.7307 +2024-09-08 01:16:26.433532: val_loss -0.7653 +2024-09-08 01:16:26.434174: Pseudo dice [0.963, 0.9579, 0.9778, 0.9507, 0.8783, 0.9702, 0.9909] +2024-09-08 01:16:26.434725: Epoch time: 274.15 s +2024-09-08 01:16:29.761353: +2024-09-08 01:16:29.762318: Epoch 487 +2024-09-08 01:16:29.763360: Current learning rate: 0.00548 +2024-09-08 01:21:10.668715: train_loss -0.7396 +2024-09-08 01:21:10.684351: val_loss -0.7698 +2024-09-08 01:21:10.686424: Pseudo dice [0.9659, 0.9591, 0.9783, 0.9484, 0.8657, 0.9723, 0.9897] +2024-09-08 01:21:10.687963: Epoch time: 280.91 s +2024-09-08 01:21:13.852590: +2024-09-08 01:21:13.864434: Epoch 488 +2024-09-08 01:21:13.866516: Current learning rate: 0.00547 +2024-09-08 01:26:00.415111: train_loss -0.7371 +2024-09-08 01:26:00.428533: val_loss -0.7607 +2024-09-08 01:26:00.429670: Pseudo dice [0.961, 0.958, 0.9742, 0.9505, 0.8929, 0.9722, 0.9915] +2024-09-08 01:26:00.430717: Epoch time: 286.57 s +2024-09-08 01:26:00.432343: Yayy! New best EMA pseudo Dice: 0.9547 +2024-09-08 01:26:05.620333: +2024-09-08 01:26:05.621381: Epoch 489 +2024-09-08 01:26:05.621964: Current learning rate: 0.00546 +2024-09-08 01:31:01.512697: train_loss -0.7267 +2024-09-08 01:31:01.516672: val_loss -0.7323 +2024-09-08 01:31:01.517725: Pseudo dice [0.96, 0.9589, 0.9725, 0.9465, 0.8832, 0.9685, 0.9912] +2024-09-08 01:31:01.518219: Epoch time: 295.9 s +2024-09-08 01:31:04.532825: +2024-09-08 01:31:04.547630: Epoch 490 +2024-09-08 01:31:04.549033: Current learning rate: 0.00546 +2024-09-08 01:35:52.891026: train_loss -0.7388 +2024-09-08 01:35:52.907812: val_loss -0.7104 +2024-09-08 01:35:52.909226: Pseudo dice [0.9659, 0.9577, 0.9785, 0.9426, 0.8715, 0.9682, 0.9919] +2024-09-08 01:35:52.910706: Epoch time: 288.36 s +2024-09-08 01:35:56.592233: +2024-09-08 01:35:56.603857: Epoch 491 +2024-09-08 01:35:56.605241: Current learning rate: 0.00545 +2024-09-08 01:40:33.519490: train_loss -0.7348 +2024-09-08 01:40:33.548431: val_loss -0.7787 +2024-09-08 01:40:33.550271: Pseudo dice [0.9605, 0.9578, 0.9748, 0.9505, 0.884, 0.9719, 0.9921] +2024-09-08 01:40:33.552350: Epoch time: 276.93 s +2024-09-08 01:40:33.555063: Yayy! New best EMA pseudo Dice: 0.9547 +2024-09-08 01:40:38.463018: +2024-09-08 01:40:38.464973: Epoch 492 +2024-09-08 01:40:38.465497: Current learning rate: 0.00544 +2024-09-08 01:45:18.735689: train_loss -0.7285 +2024-09-08 01:45:18.754264: val_loss -0.7561 +2024-09-08 01:45:18.756147: Pseudo dice [0.96, 0.9555, 0.9761, 0.9496, 0.8708, 0.9657, 0.9913] +2024-09-08 01:45:18.757536: Epoch time: 280.28 s +2024-09-08 01:45:24.874331: +2024-09-08 01:45:24.875006: Epoch 493 +2024-09-08 01:45:24.875464: Current learning rate: 0.00543 +2024-09-08 01:49:52.182678: train_loss -0.7317 +2024-09-08 01:49:52.200996: val_loss -0.7431 +2024-09-08 01:49:52.203867: Pseudo dice [0.9634, 0.9577, 0.975, 0.947, 0.8808, 0.9718, 0.9907] +2024-09-08 01:49:52.206284: Epoch time: 267.31 s +2024-09-08 01:49:55.686024: +2024-09-08 01:49:55.686980: Epoch 494 +2024-09-08 01:49:55.687533: Current learning rate: 0.00542 +2024-09-08 01:54:35.348404: train_loss -0.7354 +2024-09-08 01:54:35.355142: val_loss -0.7412 +2024-09-08 01:54:35.356400: Pseudo dice [0.9619, 0.9571, 0.9761, 0.9464, 0.8662, 0.967, 0.9901] +2024-09-08 01:54:35.358968: Epoch time: 279.67 s +2024-09-08 01:54:39.120290: +2024-09-08 01:54:39.136546: Epoch 495 +2024-09-08 01:54:39.137400: Current learning rate: 0.00541 +2024-09-08 01:59:29.525133: train_loss -0.7482 +2024-09-08 01:59:29.544965: val_loss -0.7227 +2024-09-08 01:59:29.546835: Pseudo dice [0.9622, 0.9546, 0.9717, 0.9395, 0.8582, 0.9656, 0.9903] +2024-09-08 01:59:29.548058: Epoch time: 290.41 s +2024-09-08 01:59:32.757017: +2024-09-08 01:59:32.757737: Epoch 496 +2024-09-08 01:59:32.758183: Current learning rate: 0.0054 +2024-09-08 02:04:32.394107: train_loss -0.7311 +2024-09-08 02:04:32.410179: val_loss -0.7238 +2024-09-08 02:04:32.411542: Pseudo dice [0.9572, 0.9558, 0.9745, 0.9455, 0.8668, 0.9706, 0.9901] +2024-09-08 02:04:32.412568: Epoch time: 299.64 s +2024-09-08 02:04:35.656746: +2024-09-08 02:04:35.657852: Epoch 497 +2024-09-08 02:04:35.659627: Current learning rate: 0.00539 +2024-09-08 02:09:14.932717: train_loss -0.7392 +2024-09-08 02:09:14.934991: val_loss -0.7508 +2024-09-08 02:09:14.935625: Pseudo dice [0.9572, 0.957, 0.9681, 0.9417, 0.8764, 0.9698, 0.9912] +2024-09-08 02:09:14.936140: Epoch time: 279.28 s +2024-09-08 02:09:18.737810: +2024-09-08 02:09:18.738817: Epoch 498 +2024-09-08 02:09:18.740737: Current learning rate: 0.00538 +2024-09-08 02:14:09.132523: train_loss -0.7355 +2024-09-08 02:14:09.135931: val_loss -0.7624 +2024-09-08 02:14:09.137155: Pseudo dice [0.9623, 0.957, 0.9754, 0.943, 0.8698, 0.9684, 0.9896] +2024-09-08 02:14:09.138033: Epoch time: 290.4 s +2024-09-08 02:14:12.154061: +2024-09-08 02:14:12.155062: Epoch 499 +2024-09-08 02:14:12.155741: Current learning rate: 0.00537 +2024-09-08 02:18:46.378985: train_loss -0.7465 +2024-09-08 02:18:46.389874: val_loss -0.759 +2024-09-08 02:18:46.390438: Pseudo dice [0.9584, 0.9549, 0.9736, 0.9399, 0.859, 0.9697, 0.9903] +2024-09-08 02:18:46.391060: Epoch time: 274.23 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_06_04_18.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_06_04_18.txt new file mode 100644 index 0000000000000000000000000000000000000000..49b55430646ae1c2f7ad29062e9440a6448ea43e --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_06_04_18.txt @@ -0,0 +1,374 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 06:04:21.367316: Using torch.compile... +2024-09-08 06:04:27.583812: do_dummy_2d_data_aug: False +2024-09-08 06:04:27.599167: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 06:04:27.610585: The split file contains 5 splits. +2024-09-08 06:04:27.610974: Desired fold for training: 4 +2024-09-08 06:04:27.611366: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 06:04:41.515792: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 06:04:42.310771: +2024-09-08 06:04:42.331727: Epoch 500 +2024-09-08 06:04:42.333425: Current learning rate: 0.00536 +2024-09-08 06:19:29.955379: train_loss -0.7406 +2024-09-08 06:19:29.958015: val_loss -0.7301 +2024-09-08 06:19:29.958628: Pseudo dice [0.9641, 0.9573, 0.9772, 0.9563, 0.8767, 0.9721, 0.9899] +2024-09-08 06:19:29.959144: Epoch time: 887.65 s +2024-09-08 06:19:31.706437: +2024-09-08 06:19:31.707209: Epoch 501 +2024-09-08 06:19:31.707728: Current learning rate: 0.00535 +2024-09-08 06:24:01.099357: train_loss -0.7359 +2024-09-08 06:24:01.106471: val_loss -0.7472 +2024-09-08 06:24:01.107960: Pseudo dice [0.9617, 0.9577, 0.9772, 0.9479, 0.8596, 0.9677, 0.9889] +2024-09-08 06:24:01.108941: Epoch time: 269.4 s +2024-09-08 06:24:04.172509: +2024-09-08 06:24:04.173260: Epoch 502 +2024-09-08 06:24:04.174975: Current learning rate: 0.00534 +2024-09-08 06:28:35.558567: train_loss -0.7445 +2024-09-08 06:28:35.576642: val_loss -0.7577 +2024-09-08 06:28:35.577525: Pseudo dice [0.9652, 0.9596, 0.978, 0.9476, 0.8877, 0.9713, 0.9918] +2024-09-08 06:28:35.580729: Epoch time: 271.39 s +2024-09-08 06:28:41.112297: +2024-09-08 06:28:41.113247: Epoch 503 +2024-09-08 06:28:41.114351: Current learning rate: 0.00533 +2024-09-08 06:33:41.032177: train_loss -0.732 +2024-09-08 06:33:41.040021: val_loss -0.76 +2024-09-08 06:33:41.040711: Pseudo dice [0.963, 0.956, 0.9795, 0.9474, 0.8778, 0.9685, 0.9919] +2024-09-08 06:33:41.041804: Epoch time: 299.92 s +2024-09-08 06:33:44.369462: +2024-09-08 06:33:44.370441: Epoch 504 +2024-09-08 06:33:44.371562: Current learning rate: 0.00532 +2024-09-08 06:38:37.942145: train_loss -0.7172 +2024-09-08 06:38:37.946358: val_loss -0.7278 +2024-09-08 06:38:37.947797: Pseudo dice [0.9598, 0.957, 0.9749, 0.9476, 0.8575, 0.9646, 0.9901] +2024-09-08 06:38:37.948438: Epoch time: 293.58 s +2024-09-08 06:38:41.255162: +2024-09-08 06:38:41.267976: Epoch 505 +2024-09-08 06:38:41.269588: Current learning rate: 0.00531 +2024-09-08 06:43:17.338887: train_loss -0.722 +2024-09-08 06:43:17.356001: val_loss -0.7564 +2024-09-08 06:43:17.357129: Pseudo dice [0.9551, 0.9516, 0.97, 0.9457, 0.8667, 0.958, 0.9857] +2024-09-08 06:43:17.358204: Epoch time: 276.09 s +2024-09-08 06:43:20.445121: +2024-09-08 06:43:20.446213: Epoch 506 +2024-09-08 06:43:20.446854: Current learning rate: 0.0053 +2024-09-08 06:47:57.230476: train_loss -0.7265 +2024-09-08 06:47:57.251851: val_loss -0.7592 +2024-09-08 06:47:57.252856: Pseudo dice [0.9595, 0.9568, 0.9744, 0.9494, 0.8677, 0.9647, 0.99] +2024-09-08 06:47:57.253772: Epoch time: 276.79 s +2024-09-08 06:47:59.889720: +2024-09-08 06:47:59.890715: Epoch 507 +2024-09-08 06:47:59.891373: Current learning rate: 0.00529 +2024-09-08 06:52:42.227083: train_loss -0.7389 +2024-09-08 06:52:42.242126: val_loss -0.747 +2024-09-08 06:52:42.244108: Pseudo dice [0.9624, 0.9578, 0.9785, 0.9503, 0.8705, 0.9603, 0.9889] +2024-09-08 06:52:42.245712: Epoch time: 282.34 s +2024-09-08 06:52:45.779746: +2024-09-08 06:52:45.804005: Epoch 508 +2024-09-08 06:52:45.806687: Current learning rate: 0.00528 +2024-09-08 06:57:24.350423: train_loss -0.7269 +2024-09-08 06:57:24.366554: val_loss -0.7548 +2024-09-08 06:57:24.368182: Pseudo dice [0.9637, 0.9559, 0.979, 0.9428, 0.8811, 0.9679, 0.9906] +2024-09-08 06:57:24.369938: Epoch time: 278.58 s +2024-09-08 06:57:27.671870: +2024-09-08 06:57:27.673711: Epoch 509 +2024-09-08 06:57:27.674801: Current learning rate: 0.00527 +2024-09-08 07:02:11.199925: train_loss -0.7332 +2024-09-08 07:02:11.213900: val_loss -0.7492 +2024-09-08 07:02:11.215543: Pseudo dice [0.9602, 0.9564, 0.9723, 0.9458, 0.8756, 0.9652, 0.9887] +2024-09-08 07:02:11.216761: Epoch time: 283.54 s +2024-09-08 07:02:14.706309: +2024-09-08 07:02:14.707805: Epoch 510 +2024-09-08 07:02:14.709517: Current learning rate: 0.00526 +2024-09-08 07:06:42.825918: train_loss -0.7348 +2024-09-08 07:06:42.837111: val_loss -0.7181 +2024-09-08 07:06:42.838618: Pseudo dice [0.9619, 0.9558, 0.976, 0.9481, 0.8904, 0.973, 0.9907] +2024-09-08 07:06:42.840020: Epoch time: 268.13 s +2024-09-08 07:06:46.171887: +2024-09-08 07:06:46.184371: Epoch 511 +2024-09-08 07:06:46.185655: Current learning rate: 0.00525 +2024-09-08 07:11:51.639113: train_loss -0.7279 +2024-09-08 07:11:51.644582: val_loss -0.7406 +2024-09-08 07:11:51.646128: Pseudo dice [0.9601, 0.9572, 0.9727, 0.9481, 0.8751, 0.9685, 0.9917] +2024-09-08 07:11:51.646675: Epoch time: 305.47 s +2024-09-08 07:11:54.376599: +2024-09-08 07:11:54.389261: Epoch 512 +2024-09-08 07:11:54.390148: Current learning rate: 0.00524 +2024-09-08 07:16:47.747863: train_loss -0.7419 +2024-09-08 07:16:47.755363: val_loss -0.7661 +2024-09-08 07:16:47.756286: Pseudo dice [0.9618, 0.9582, 0.977, 0.9482, 0.8814, 0.9718, 0.9914] +2024-09-08 07:16:47.757202: Epoch time: 293.38 s +2024-09-08 07:16:51.274758: +2024-09-08 07:16:51.275796: Epoch 513 +2024-09-08 07:16:51.276352: Current learning rate: 0.00523 +2024-09-08 07:21:26.955551: train_loss -0.7299 +2024-09-08 07:21:26.980492: val_loss -0.7369 +2024-09-08 07:21:26.981532: Pseudo dice [0.9629, 0.9585, 0.9746, 0.9522, 0.8796, 0.9718, 0.9918] +2024-09-08 07:21:26.982256: Epoch time: 275.69 s +2024-09-08 07:21:29.960925: +2024-09-08 07:21:29.961913: Epoch 514 +2024-09-08 07:21:29.962989: Current learning rate: 0.00522 +2024-09-08 07:26:13.776355: train_loss -0.7436 +2024-09-08 07:26:13.792897: val_loss -0.7563 +2024-09-08 07:26:13.793811: Pseudo dice [0.9618, 0.9569, 0.9755, 0.9423, 0.8759, 0.9668, 0.9908] +2024-09-08 07:26:13.794706: Epoch time: 283.82 s +2024-09-08 07:26:16.485562: +2024-09-08 07:26:16.486488: Epoch 515 +2024-09-08 07:26:16.487271: Current learning rate: 0.00521 +2024-09-08 07:30:48.490879: train_loss -0.7132 +2024-09-08 07:30:48.508777: val_loss -0.7391 +2024-09-08 07:30:48.512540: Pseudo dice [0.9605, 0.9526, 0.9749, 0.9468, 0.8766, 0.9623, 0.9905] +2024-09-08 07:30:48.518073: Epoch time: 272.01 s +2024-09-08 07:30:51.464977: +2024-09-08 07:30:51.466845: Epoch 516 +2024-09-08 07:30:51.468307: Current learning rate: 0.0052 +2024-09-08 07:35:36.327676: train_loss -0.73 +2024-09-08 07:35:36.354013: val_loss -0.7363 +2024-09-08 07:35:36.355916: Pseudo dice [0.9635, 0.9579, 0.9755, 0.9511, 0.8745, 0.9707, 0.992] +2024-09-08 07:35:36.357677: Epoch time: 284.89 s +2024-09-08 07:35:40.187366: +2024-09-08 07:35:40.188728: Epoch 517 +2024-09-08 07:35:40.190389: Current learning rate: 0.00519 +2024-09-08 07:40:30.110944: train_loss -0.7477 +2024-09-08 07:40:30.140679: val_loss -0.7557 +2024-09-08 07:40:30.141371: Pseudo dice [0.9587, 0.9524, 0.9605, 0.9469, 0.878, 0.9705, 0.9923] +2024-09-08 07:40:30.142088: Epoch time: 289.93 s +2024-09-08 07:40:35.958794: +2024-09-08 07:40:35.972587: Epoch 518 +2024-09-08 07:40:35.973297: Current learning rate: 0.00518 +2024-09-08 07:45:24.743198: train_loss -0.7186 +2024-09-08 07:45:24.758192: val_loss -0.6841 +2024-09-08 07:45:24.759167: Pseudo dice [0.9621, 0.9593, 0.9696, 0.9449, 0.871, 0.9643, 0.9915] +2024-09-08 07:45:24.760146: Epoch time: 288.79 s +2024-09-08 07:45:28.016667: +2024-09-08 07:45:28.017998: Epoch 519 +2024-09-08 07:45:28.028003: Current learning rate: 0.00518 +2024-09-08 07:50:02.213761: train_loss -0.7391 +2024-09-08 07:50:02.231459: val_loss -0.7685 +2024-09-08 07:50:02.233361: Pseudo dice [0.9627, 0.9582, 0.9755, 0.952, 0.8853, 0.9701, 0.9912] +2024-09-08 07:50:02.235596: Epoch time: 274.2 s +2024-09-08 07:50:05.007678: +2024-09-08 07:50:05.020299: Epoch 520 +2024-09-08 07:50:05.021569: Current learning rate: 0.00517 +2024-09-08 07:54:58.021972: train_loss -0.7274 +2024-09-08 07:54:58.050062: val_loss -0.7796 +2024-09-08 07:54:58.052112: Pseudo dice [0.9619, 0.9581, 0.9744, 0.9508, 0.8617, 0.9699, 0.9897] +2024-09-08 07:54:58.054920: Epoch time: 293.02 s +2024-09-08 07:55:01.635535: +2024-09-08 07:55:01.636998: Epoch 521 +2024-09-08 07:55:01.637741: Current learning rate: 0.00516 +2024-09-08 07:59:38.391368: train_loss -0.7594 +2024-09-08 07:59:38.403080: val_loss -0.7546 +2024-09-08 07:59:38.403814: Pseudo dice [0.9633, 0.9588, 0.9742, 0.9507, 0.8748, 0.9714, 0.9908] +2024-09-08 07:59:38.404604: Epoch time: 276.76 s +2024-09-08 07:59:40.950710: +2024-09-08 07:59:40.951711: Epoch 522 +2024-09-08 07:59:40.952645: Current learning rate: 0.00515 +2024-09-08 08:04:29.434965: train_loss -0.7536 +2024-09-08 08:04:29.448813: val_loss -0.745 +2024-09-08 08:04:29.450107: Pseudo dice [0.965, 0.9588, 0.9771, 0.9529, 0.8855, 0.9728, 0.9909] +2024-09-08 08:04:29.451440: Epoch time: 288.49 s +2024-09-08 08:04:31.893566: +2024-09-08 08:04:31.894593: Epoch 523 +2024-09-08 08:04:31.895655: Current learning rate: 0.00514 +2024-09-08 08:09:24.838370: train_loss -0.7537 +2024-09-08 08:09:24.856625: val_loss -0.772 +2024-09-08 08:09:24.858213: Pseudo dice [0.9609, 0.9562, 0.9738, 0.9458, 0.8817, 0.969, 0.9906] +2024-09-08 08:09:24.859172: Epoch time: 292.95 s +2024-09-08 08:09:27.214305: +2024-09-08 08:09:27.217877: Epoch 524 +2024-09-08 08:09:27.220103: Current learning rate: 0.00513 +2024-09-08 08:14:22.767226: train_loss -0.7328 +2024-09-08 08:14:22.781553: val_loss -0.7225 +2024-09-08 08:14:22.782201: Pseudo dice [0.9628, 0.9585, 0.9756, 0.9461, 0.8599, 0.9744, 0.9908] +2024-09-08 08:14:22.782758: Epoch time: 295.56 s +2024-09-08 08:14:26.064790: +2024-09-08 08:14:26.080569: Epoch 525 +2024-09-08 08:14:26.081275: Current learning rate: 0.00512 +2024-09-08 08:19:10.274018: train_loss -0.7426 +2024-09-08 08:19:10.300902: val_loss -0.7737 +2024-09-08 08:19:10.301502: Pseudo dice [0.9661, 0.957, 0.9789, 0.9458, 0.8697, 0.969, 0.9903] +2024-09-08 08:19:10.302102: Epoch time: 284.21 s +2024-09-08 08:19:13.686836: +2024-09-08 08:19:13.700915: Epoch 526 +2024-09-08 08:19:13.702218: Current learning rate: 0.00511 +2024-09-08 08:24:05.314023: train_loss -0.7412 +2024-09-08 08:24:05.329638: val_loss -0.7082 +2024-09-08 08:24:05.330430: Pseudo dice [0.9616, 0.9578, 0.9757, 0.9428, 0.8585, 0.9693, 0.9906] +2024-09-08 08:24:05.332573: Epoch time: 291.65 s +2024-09-08 08:24:09.375597: +2024-09-08 08:24:09.377938: Epoch 527 +2024-09-08 08:24:09.378819: Current learning rate: 0.0051 +2024-09-08 08:28:51.051881: train_loss -0.7484 +2024-09-08 08:28:51.058851: val_loss -0.7609 +2024-09-08 08:28:51.061823: Pseudo dice [0.9639, 0.9553, 0.9783, 0.9449, 0.869, 0.9691, 0.9887] +2024-09-08 08:28:51.062744: Epoch time: 281.68 s +2024-09-08 08:28:54.860378: +2024-09-08 08:28:54.862197: Epoch 528 +2024-09-08 08:28:54.863204: Current learning rate: 0.00509 +2024-09-08 08:33:40.919717: train_loss -0.7316 +2024-09-08 08:33:40.921910: val_loss -0.7605 +2024-09-08 08:33:40.922538: Pseudo dice [0.961, 0.9536, 0.9773, 0.9376, 0.8768, 0.9721, 0.9919] +2024-09-08 08:33:40.923071: Epoch time: 286.07 s +2024-09-08 08:33:45.462221: +2024-09-08 08:33:45.476372: Epoch 529 +2024-09-08 08:33:45.477325: Current learning rate: 0.00508 +2024-09-08 08:38:32.545169: train_loss -0.7472 +2024-09-08 08:38:32.559512: val_loss -0.7586 +2024-09-08 08:38:32.560700: Pseudo dice [0.9644, 0.9572, 0.9739, 0.9493, 0.8774, 0.9686, 0.9916] +2024-09-08 08:38:32.561592: Epoch time: 287.09 s +2024-09-08 08:38:35.777678: +2024-09-08 08:38:35.779245: Epoch 530 +2024-09-08 08:38:35.780473: Current learning rate: 0.00507 +2024-09-08 08:43:23.696410: train_loss -0.7202 +2024-09-08 08:43:23.706160: val_loss -0.7751 +2024-09-08 08:43:23.707658: Pseudo dice [0.9652, 0.9564, 0.9801, 0.9507, 0.8767, 0.9692, 0.9918] +2024-09-08 08:43:23.708163: Epoch time: 287.93 s +2024-09-08 08:43:26.556562: +2024-09-08 08:43:26.557427: Epoch 531 +2024-09-08 08:43:26.558121: Current learning rate: 0.00506 +2024-09-08 08:48:34.047949: train_loss -0.7606 +2024-09-08 08:48:34.050108: val_loss -0.7313 +2024-09-08 08:48:34.050792: Pseudo dice [0.9636, 0.9586, 0.9776, 0.947, 0.8881, 0.9718, 0.9918] +2024-09-08 08:48:34.051304: Epoch time: 307.5 s +2024-09-08 08:48:39.007798: +2024-09-08 08:48:39.008992: Epoch 532 +2024-09-08 08:48:39.009669: Current learning rate: 0.00505 +2024-09-08 08:53:24.920512: train_loss -0.7481 +2024-09-08 08:53:24.938901: val_loss -0.7763 +2024-09-08 08:53:24.939880: Pseudo dice [0.9592, 0.9571, 0.9749, 0.9552, 0.888, 0.9706, 0.991] +2024-09-08 08:53:24.941439: Epoch time: 285.92 s +2024-09-08 08:53:28.021114: +2024-09-08 08:53:28.022058: Epoch 533 +2024-09-08 08:53:28.022694: Current learning rate: 0.00504 +2024-09-08 08:58:15.887513: train_loss -0.7497 +2024-09-08 08:58:15.888913: val_loss -0.7382 +2024-09-08 08:58:15.889589: Pseudo dice [0.9633, 0.9597, 0.9743, 0.947, 0.8662, 0.968, 0.9897] +2024-09-08 08:58:15.890242: Epoch time: 287.87 s +2024-09-08 08:58:18.214387: +2024-09-08 08:58:18.227726: Epoch 534 +2024-09-08 08:58:18.228581: Current learning rate: 0.00503 +2024-09-08 09:03:03.023839: train_loss -0.7618 +2024-09-08 09:03:03.026967: val_loss -0.7814 +2024-09-08 09:03:03.027954: Pseudo dice [0.9646, 0.9599, 0.9783, 0.957, 0.8866, 0.9687, 0.9925] +2024-09-08 09:03:03.028682: Epoch time: 284.81 s +2024-09-08 09:03:05.543726: +2024-09-08 09:03:05.544789: Epoch 535 +2024-09-08 09:03:05.545626: Current learning rate: 0.00502 +2024-09-08 09:07:37.668909: train_loss -0.7484 +2024-09-08 09:07:37.686538: val_loss -0.7856 +2024-09-08 09:07:37.687388: Pseudo dice [0.9662, 0.9601, 0.9809, 0.9513, 0.8831, 0.971, 0.9903] +2024-09-08 09:07:37.688125: Epoch time: 272.13 s +2024-09-08 09:07:37.688567: Yayy! New best EMA pseudo Dice: 0.9548 +2024-09-08 09:07:43.721169: +2024-09-08 09:07:43.722178: Epoch 536 +2024-09-08 09:07:43.722885: Current learning rate: 0.00501 +2024-09-08 09:12:33.395346: train_loss -0.7451 +2024-09-08 09:12:33.408772: val_loss -0.7488 +2024-09-08 09:12:33.410470: Pseudo dice [0.9669, 0.9605, 0.9782, 0.9537, 0.8798, 0.9718, 0.9914] +2024-09-08 09:12:33.411885: Epoch time: 289.68 s +2024-09-08 09:12:33.412718: Yayy! New best EMA pseudo Dice: 0.9551 +2024-09-08 09:12:38.611084: +2024-09-08 09:12:38.612337: Epoch 537 +2024-09-08 09:12:38.613099: Current learning rate: 0.005 +2024-09-08 09:17:25.941105: train_loss -0.7392 +2024-09-08 09:17:25.950982: val_loss -0.7474 +2024-09-08 09:17:25.952219: Pseudo dice [0.9672, 0.9608, 0.9822, 0.948, 0.8862, 0.9732, 0.9923] +2024-09-08 09:17:25.953183: Epoch time: 287.34 s +2024-09-08 09:17:25.954099: Yayy! New best EMA pseudo Dice: 0.9555 +2024-09-08 09:17:31.662198: +2024-09-08 09:17:31.662864: Epoch 538 +2024-09-08 09:17:31.663351: Current learning rate: 0.00499 +2024-09-08 09:22:21.436658: train_loss -0.7253 +2024-09-08 09:22:21.458281: val_loss -0.7416 +2024-09-08 09:22:21.459085: Pseudo dice [0.9639, 0.9561, 0.9763, 0.9483, 0.8558, 0.9701, 0.9897] +2024-09-08 09:22:21.459938: Epoch time: 289.78 s +2024-09-08 09:22:24.220659: +2024-09-08 09:22:24.221536: Epoch 539 +2024-09-08 09:22:24.222151: Current learning rate: 0.00498 +2024-09-08 09:26:50.609444: train_loss -0.7271 +2024-09-08 09:26:50.626128: val_loss -0.7256 +2024-09-08 09:26:50.629226: Pseudo dice [0.9585, 0.9553, 0.9674, 0.9405, 0.8396, 0.9644, 0.9915] +2024-09-08 09:26:50.632275: Epoch time: 266.39 s +2024-09-08 09:26:54.147334: +2024-09-08 09:26:54.148161: Epoch 540 +2024-09-08 09:26:54.148885: Current learning rate: 0.00497 +2024-09-08 09:31:33.437696: train_loss -0.7264 +2024-09-08 09:31:33.439932: val_loss -0.7136 +2024-09-08 09:31:33.440576: Pseudo dice [0.9634, 0.9569, 0.9772, 0.9478, 0.8616, 0.9663, 0.9867] +2024-09-08 09:31:33.441052: Epoch time: 279.3 s +2024-09-08 09:31:35.485678: +2024-09-08 09:31:35.486754: Epoch 541 +2024-09-08 09:31:35.487327: Current learning rate: 0.00496 +2024-09-08 09:36:17.564342: train_loss -0.726 +2024-09-08 09:36:17.577414: val_loss -0.7235 +2024-09-08 09:36:17.578982: Pseudo dice [0.9559, 0.9551, 0.9658, 0.9434, 0.8512, 0.9681, 0.989] +2024-09-08 09:36:17.579542: Epoch time: 282.08 s +2024-09-08 09:36:20.733650: +2024-09-08 09:36:20.734590: Epoch 542 +2024-09-08 09:36:20.735169: Current learning rate: 0.00495 +2024-09-08 09:40:57.907608: train_loss -0.7364 +2024-09-08 09:40:57.930111: val_loss -0.7547 +2024-09-08 09:40:57.931697: Pseudo dice [0.9615, 0.9549, 0.9758, 0.9469, 0.8623, 0.9678, 0.9883] +2024-09-08 09:40:57.933966: Epoch time: 277.18 s +2024-09-08 09:41:00.834003: +2024-09-08 09:41:00.835918: Epoch 543 +2024-09-08 09:41:00.837353: Current learning rate: 0.00494 +2024-09-08 09:45:52.313781: train_loss -0.7218 +2024-09-08 09:45:52.315266: val_loss -0.752 +2024-09-08 09:45:52.315938: Pseudo dice [0.9578, 0.9526, 0.9749, 0.9418, 0.8537, 0.956, 0.9907] +2024-09-08 09:45:52.316432: Epoch time: 291.48 s +2024-09-08 09:45:54.961562: +2024-09-08 09:45:54.962512: Epoch 544 +2024-09-08 09:45:54.963149: Current learning rate: 0.00493 +2024-09-08 09:50:47.253572: train_loss -0.7203 +2024-09-08 09:50:47.255823: val_loss -0.7246 +2024-09-08 09:50:47.256407: Pseudo dice [0.9611, 0.9583, 0.977, 0.9439, 0.8668, 0.9633, 0.9918] +2024-09-08 09:50:47.256923: Epoch time: 292.3 s +2024-09-08 09:50:51.123945: +2024-09-08 09:50:51.125080: Epoch 545 +2024-09-08 09:50:51.125687: Current learning rate: 0.00492 +2024-09-08 09:55:49.481514: train_loss -0.7301 +2024-09-08 09:55:49.492652: val_loss -0.7521 +2024-09-08 09:55:49.493536: Pseudo dice [0.9643, 0.9595, 0.9785, 0.9437, 0.8785, 0.9691, 0.9901] +2024-09-08 09:55:49.494395: Epoch time: 298.36 s +2024-09-08 09:55:53.245915: +2024-09-08 09:55:53.255530: Epoch 546 +2024-09-08 09:55:53.257765: Current learning rate: 0.00491 +2024-09-08 10:00:38.290061: train_loss -0.7342 +2024-09-08 10:00:38.328650: val_loss -0.7361 +2024-09-08 10:00:38.330008: Pseudo dice [0.9619, 0.9573, 0.9726, 0.9383, 0.8746, 0.9628, 0.9902] +2024-09-08 10:00:38.331114: Epoch time: 285.05 s +2024-09-08 10:00:41.525663: +2024-09-08 10:00:41.526602: Epoch 547 +2024-09-08 10:00:41.527236: Current learning rate: 0.0049 +2024-09-08 10:05:27.520084: train_loss -0.753 +2024-09-08 10:05:27.541317: val_loss -0.7787 +2024-09-08 10:05:27.541847: Pseudo dice [0.9608, 0.9563, 0.9741, 0.9488, 0.8776, 0.9665, 0.9916] +2024-09-08 10:05:27.542356: Epoch time: 286.0 s +2024-09-08 10:05:31.508051: +2024-09-08 10:05:31.521583: Epoch 548 +2024-09-08 10:05:31.523570: Current learning rate: 0.00489 +2024-09-08 10:10:06.254998: train_loss -0.7345 +2024-09-08 10:10:06.268748: val_loss -0.7656 +2024-09-08 10:10:06.269374: Pseudo dice [0.9607, 0.9587, 0.9734, 0.9499, 0.8606, 0.9699, 0.9891] +2024-09-08 10:10:06.269968: Epoch time: 274.75 s +2024-09-08 10:10:09.040588: +2024-09-08 10:10:09.041609: Epoch 549 +2024-09-08 10:10:09.042570: Current learning rate: 0.00488 +2024-09-08 10:14:51.785695: train_loss -0.733 +2024-09-08 10:14:51.788051: val_loss -0.7158 +2024-09-08 10:14:51.788790: Pseudo dice [0.9591, 0.959, 0.9711, 0.9502, 0.8774, 0.9704, 0.991] +2024-09-08 10:14:51.789329: Epoch time: 282.75 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_14_03_50.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_14_03_50.txt new file mode 100644 index 0000000000000000000000000000000000000000..f90c2fa1e44603f802586afe08c600cf20bc7650 --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_8_14_03_50.txt @@ -0,0 +1,371 @@ + +####################################################################### +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. +####################################################################### + +2024-09-08 14:03:53.565303: Using torch.compile... +2024-09-08 14:03:59.791460: do_dummy_2d_data_aug: False +2024-09-08 14:03:59.796792: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-08 14:03:59.797882: The split file contains 5 splits. +2024-09-08 14:03:59.798224: Desired fold for training: 4 +2024-09-08 14:03:59.798601: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-08 14:04:13.396133: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-08 14:04:14.616564: +2024-09-08 14:04:14.628297: Epoch 550 +2024-09-08 14:04:14.629484: Current learning rate: 0.00487 +2024-09-08 14:19:06.147789: train_loss -0.7513 +2024-09-08 14:19:06.149634: val_loss -0.7639 +2024-09-08 14:19:06.150207: Pseudo dice [0.9642, 0.9583, 0.9771, 0.9507, 0.8649, 0.9666, 0.9876] +2024-09-08 14:19:06.150680: Epoch time: 891.54 s +2024-09-08 14:19:07.632291: +2024-09-08 14:19:07.633148: Epoch 551 +2024-09-08 14:19:07.633656: Current learning rate: 0.00486 +2024-09-08 14:23:50.878324: train_loss -0.7259 +2024-09-08 14:23:50.893682: val_loss -0.7144 +2024-09-08 14:23:50.894381: Pseudo dice [0.9593, 0.9559, 0.9695, 0.9447, 0.8737, 0.971, 0.9922] +2024-09-08 14:23:50.895052: Epoch time: 283.25 s +2024-09-08 14:23:53.420205: +2024-09-08 14:23:53.432547: Epoch 552 +2024-09-08 14:23:53.434313: Current learning rate: 0.00485 +2024-09-08 14:28:47.751087: train_loss -0.7438 +2024-09-08 14:28:47.752717: val_loss -0.7667 +2024-09-08 14:28:47.753813: Pseudo dice [0.967, 0.96, 0.9797, 0.9507, 0.8917, 0.972, 0.9914] +2024-09-08 14:28:47.754903: Epoch time: 294.34 s +2024-09-08 14:28:50.495289: +2024-09-08 14:28:50.516326: Epoch 553 +2024-09-08 14:28:50.517503: Current learning rate: 0.00484 +2024-09-08 14:33:31.312333: train_loss -0.7559 +2024-09-08 14:33:31.314157: val_loss -0.7442 +2024-09-08 14:33:31.314879: Pseudo dice [0.9619, 0.9595, 0.9714, 0.947, 0.8674, 0.9678, 0.9894] +2024-09-08 14:33:31.315773: Epoch time: 280.82 s +2024-09-08 14:33:34.926491: +2024-09-08 14:33:34.927556: Epoch 554 +2024-09-08 14:33:34.928125: Current learning rate: 0.00484 +2024-09-08 14:38:13.426824: train_loss -0.7278 +2024-09-08 14:38:13.428424: val_loss -0.6858 +2024-09-08 14:38:13.429312: Pseudo dice [0.9653, 0.958, 0.9803, 0.9501, 0.8752, 0.9587, 0.991] +2024-09-08 14:38:13.430104: Epoch time: 278.51 s +2024-09-08 14:38:16.512316: +2024-09-08 14:38:16.513046: Epoch 555 +2024-09-08 14:38:16.513610: Current learning rate: 0.00483 +2024-09-08 14:42:48.262008: train_loss -0.7381 +2024-09-08 14:42:48.274222: val_loss -0.7209 +2024-09-08 14:42:48.276028: Pseudo dice [0.9645, 0.9584, 0.9759, 0.9505, 0.8536, 0.9655, 0.9909] +2024-09-08 14:42:48.278867: Epoch time: 271.75 s +2024-09-08 14:42:50.860671: +2024-09-08 14:42:50.861650: Epoch 556 +2024-09-08 14:42:50.862167: Current learning rate: 0.00482 +2024-09-08 14:47:24.454534: train_loss -0.7559 +2024-09-08 14:47:24.469010: val_loss -0.7432 +2024-09-08 14:47:24.470235: Pseudo dice [0.9642, 0.9577, 0.9781, 0.9489, 0.8781, 0.9725, 0.9919] +2024-09-08 14:47:24.471362: Epoch time: 273.6 s +2024-09-08 14:47:28.829716: +2024-09-08 14:47:28.832036: Epoch 557 +2024-09-08 14:47:28.833631: Current learning rate: 0.00481 +2024-09-08 14:52:34.113690: train_loss -0.7379 +2024-09-08 14:52:34.132906: val_loss -0.7575 +2024-09-08 14:52:34.133863: Pseudo dice [0.9657, 0.9597, 0.978, 0.954, 0.8881, 0.9732, 0.9927] +2024-09-08 14:52:34.134381: Epoch time: 305.29 s +2024-09-08 14:52:37.296544: +2024-09-08 14:52:37.312403: Epoch 558 +2024-09-08 14:52:37.313117: Current learning rate: 0.0048 +2024-09-08 14:57:16.069855: train_loss -0.7369 +2024-09-08 14:57:16.077576: val_loss -0.7071 +2024-09-08 14:57:16.080485: Pseudo dice [0.961, 0.9568, 0.9688, 0.9417, 0.8684, 0.9656, 0.9891] +2024-09-08 14:57:16.083800: Epoch time: 278.78 s +2024-09-08 14:57:19.308489: +2024-09-08 14:57:19.309968: Epoch 559 +2024-09-08 14:57:19.311440: Current learning rate: 0.00479 +2024-09-08 15:02:04.701917: train_loss -0.7374 +2024-09-08 15:02:04.733070: val_loss -0.7164 +2024-09-08 15:02:04.734018: Pseudo dice [0.9637, 0.9578, 0.9766, 0.95, 0.8712, 0.9633, 0.9881] +2024-09-08 15:02:04.734672: Epoch time: 285.4 s +2024-09-08 15:02:07.962999: +2024-09-08 15:02:07.964263: Epoch 560 +2024-09-08 15:02:07.965874: Current learning rate: 0.00478 +2024-09-08 15:06:45.741466: train_loss -0.7331 +2024-09-08 15:06:45.764764: val_loss -0.7501 +2024-09-08 15:06:45.765399: Pseudo dice [0.9614, 0.9596, 0.9721, 0.9406, 0.8744, 0.9428, 0.986] +2024-09-08 15:06:45.766119: Epoch time: 277.78 s +2024-09-08 15:06:48.301662: +2024-09-08 15:06:48.313892: Epoch 561 +2024-09-08 15:06:48.315264: Current learning rate: 0.00477 +2024-09-08 15:11:24.922374: train_loss -0.7384 +2024-09-08 15:11:24.924863: val_loss -0.7351 +2024-09-08 15:11:24.926092: Pseudo dice [0.9625, 0.958, 0.9706, 0.9451, 0.8655, 0.9711, 0.991] +2024-09-08 15:11:24.926795: Epoch time: 276.63 s +2024-09-08 15:11:27.235639: +2024-09-08 15:11:27.244323: Epoch 562 +2024-09-08 15:11:27.245714: Current learning rate: 0.00476 +2024-09-08 15:16:13.595778: train_loss -0.7395 +2024-09-08 15:16:13.612924: val_loss -0.7203 +2024-09-08 15:16:13.614583: Pseudo dice [0.9643, 0.9587, 0.9754, 0.9496, 0.8775, 0.9704, 0.9916] +2024-09-08 15:16:13.615810: Epoch time: 286.36 s +2024-09-08 15:16:16.897418: +2024-09-08 15:16:16.898863: Epoch 563 +2024-09-08 15:16:16.900651: Current learning rate: 0.00475 +2024-09-08 15:20:59.206893: train_loss -0.7541 +2024-09-08 15:20:59.223166: val_loss -0.7469 +2024-09-08 15:20:59.224008: Pseudo dice [0.9659, 0.9606, 0.9773, 0.9539, 0.8793, 0.9708, 0.9911] +2024-09-08 15:20:59.224947: Epoch time: 282.31 s +2024-09-08 15:21:01.757895: +2024-09-08 15:21:01.758963: Epoch 564 +2024-09-08 15:21:01.759954: Current learning rate: 0.00474 +2024-09-08 15:25:52.724288: train_loss -0.7334 +2024-09-08 15:25:52.725832: val_loss -0.7569 +2024-09-08 15:25:52.726618: Pseudo dice [0.9678, 0.9603, 0.9819, 0.9518, 0.8685, 0.9699, 0.9901] +2024-09-08 15:25:52.727237: Epoch time: 290.97 s +2024-09-08 15:25:55.673025: +2024-09-08 15:25:55.674139: Epoch 565 +2024-09-08 15:25:55.674680: Current learning rate: 0.00473 +2024-09-08 15:30:46.404215: train_loss -0.7292 +2024-09-08 15:30:46.419046: val_loss -0.7862 +2024-09-08 15:30:46.419703: Pseudo dice [0.9672, 0.9577, 0.9823, 0.9531, 0.8803, 0.9679, 0.9906] +2024-09-08 15:30:46.420837: Epoch time: 290.74 s +2024-09-08 15:30:49.925760: +2024-09-08 15:30:49.940592: Epoch 566 +2024-09-08 15:30:49.941853: Current learning rate: 0.00472 +2024-09-08 15:35:45.414736: train_loss -0.7541 +2024-09-08 15:35:45.431373: val_loss -0.7571 +2024-09-08 15:35:45.432202: Pseudo dice [0.965, 0.9583, 0.9788, 0.9522, 0.8805, 0.9693, 0.9908] +2024-09-08 15:35:45.433708: Epoch time: 295.5 s +2024-09-08 15:35:47.912909: +2024-09-08 15:35:47.914103: Epoch 567 +2024-09-08 15:35:47.915061: Current learning rate: 0.00471 +2024-09-08 15:40:14.229429: train_loss -0.7486 +2024-09-08 15:40:14.242330: val_loss -0.7534 +2024-09-08 15:40:14.243900: Pseudo dice [0.965, 0.9591, 0.9782, 0.9547, 0.8827, 0.97, 0.9918] +2024-09-08 15:40:14.245159: Epoch time: 266.32 s +2024-09-08 15:40:17.685350: +2024-09-08 15:40:17.686808: Epoch 568 +2024-09-08 15:40:17.687549: Current learning rate: 0.0047 +2024-09-08 15:45:09.950429: train_loss -0.705 +2024-09-08 15:45:09.966640: val_loss -0.7355 +2024-09-08 15:45:09.967475: Pseudo dice [0.9634, 0.9538, 0.9791, 0.9447, 0.8481, 0.972, 0.9893] +2024-09-08 15:45:09.968366: Epoch time: 292.27 s +2024-09-08 15:45:12.824775: +2024-09-08 15:45:12.832675: Epoch 569 +2024-09-08 15:45:12.833828: Current learning rate: 0.00469 +2024-09-08 15:49:43.303561: train_loss -0.7401 +2024-09-08 15:49:43.307579: val_loss -0.7792 +2024-09-08 15:49:43.308306: Pseudo dice [0.9648, 0.9566, 0.9779, 0.9487, 0.8689, 0.9724, 0.9904] +2024-09-08 15:49:43.308897: Epoch time: 270.49 s +2024-09-08 15:49:46.218879: +2024-09-08 15:49:46.220101: Epoch 570 +2024-09-08 15:49:46.221622: Current learning rate: 0.00468 +2024-09-08 15:54:33.614379: train_loss -0.7503 +2024-09-08 15:54:33.630365: val_loss -0.7659 +2024-09-08 15:54:33.632506: Pseudo dice [0.9651, 0.9571, 0.9776, 0.9435, 0.8744, 0.9725, 0.9911] +2024-09-08 15:54:33.635308: Epoch time: 287.4 s +2024-09-08 15:54:36.613829: +2024-09-08 15:54:36.626493: Epoch 571 +2024-09-08 15:54:36.627268: Current learning rate: 0.00467 +2024-09-08 15:58:56.937057: train_loss -0.7278 +2024-09-08 15:58:56.956371: val_loss -0.7565 +2024-09-08 15:58:56.957079: Pseudo dice [0.9644, 0.9569, 0.9775, 0.9513, 0.8643, 0.969, 0.991] +2024-09-08 15:58:56.957823: Epoch time: 260.33 s +2024-09-08 15:59:00.320781: +2024-09-08 15:59:00.321669: Epoch 572 +2024-09-08 15:59:00.322148: Current learning rate: 0.00466 +2024-09-08 16:03:51.352414: train_loss -0.7433 +2024-09-08 16:03:51.354681: val_loss -0.7598 +2024-09-08 16:03:51.355194: Pseudo dice [0.9647, 0.9581, 0.9765, 0.9559, 0.8682, 0.9679, 0.9919] +2024-09-08 16:03:51.355714: Epoch time: 291.04 s +2024-09-08 16:03:54.664968: +2024-09-08 16:03:54.666073: Epoch 573 +2024-09-08 16:03:54.666737: Current learning rate: 0.00465 +2024-09-08 16:08:40.192010: train_loss -0.7404 +2024-09-08 16:08:40.212317: val_loss -0.768 +2024-09-08 16:08:40.213189: Pseudo dice [0.9645, 0.9592, 0.9762, 0.9446, 0.8756, 0.9704, 0.9909] +2024-09-08 16:08:40.213821: Epoch time: 285.53 s +2024-09-08 16:08:43.005395: +2024-09-08 16:08:43.006722: Epoch 574 +2024-09-08 16:08:43.008054: Current learning rate: 0.00464 +2024-09-08 16:13:43.749873: train_loss -0.7297 +2024-09-08 16:13:43.752547: val_loss -0.7452 +2024-09-08 16:13:43.753217: Pseudo dice [0.9604, 0.958, 0.9695, 0.9466, 0.8649, 0.9652, 0.9904] +2024-09-08 16:13:43.753783: Epoch time: 300.75 s +2024-09-08 16:13:46.481277: +2024-09-08 16:13:46.496371: Epoch 575 +2024-09-08 16:13:46.497199: Current learning rate: 0.00463 +2024-09-08 16:18:15.610814: train_loss -0.7321 +2024-09-08 16:18:15.626550: val_loss -0.7406 +2024-09-08 16:18:15.627280: Pseudo dice [0.9643, 0.9579, 0.9774, 0.9448, 0.8769, 0.9705, 0.992] +2024-09-08 16:18:15.627823: Epoch time: 269.13 s +2024-09-08 16:18:19.016466: +2024-09-08 16:18:19.028358: Epoch 576 +2024-09-08 16:18:19.029373: Current learning rate: 0.00462 +2024-09-08 16:23:10.955954: train_loss -0.7397 +2024-09-08 16:23:10.974512: val_loss -0.731 +2024-09-08 16:23:10.975740: Pseudo dice [0.9658, 0.959, 0.9783, 0.9493, 0.866, 0.9681, 0.9901] +2024-09-08 16:23:10.976652: Epoch time: 291.94 s +2024-09-08 16:23:13.388249: +2024-09-08 16:23:13.389878: Epoch 577 +2024-09-08 16:23:13.390699: Current learning rate: 0.00461 +2024-09-08 16:27:41.764588: train_loss -0.7346 +2024-09-08 16:27:41.794060: val_loss -0.7966 +2024-09-08 16:27:41.794868: Pseudo dice [0.9656, 0.9579, 0.9792, 0.9533, 0.8897, 0.9744, 0.9919] +2024-09-08 16:27:41.795292: Epoch time: 268.38 s +2024-09-08 16:27:45.600782: +2024-09-08 16:27:45.602609: Epoch 578 +2024-09-08 16:27:45.604023: Current learning rate: 0.0046 +2024-09-08 16:32:22.290439: train_loss -0.7379 +2024-09-08 16:32:22.304947: val_loss -0.7377 +2024-09-08 16:32:22.306053: Pseudo dice [0.9636, 0.9568, 0.9764, 0.9509, 0.8551, 0.9711, 0.9897] +2024-09-08 16:32:22.307149: Epoch time: 276.7 s +2024-09-08 16:32:25.473046: +2024-09-08 16:32:25.484817: Epoch 579 +2024-09-08 16:32:25.485989: Current learning rate: 0.00459 +2024-09-08 16:37:12.730473: train_loss -0.7384 +2024-09-08 16:37:12.747488: val_loss -0.7438 +2024-09-08 16:37:12.748265: Pseudo dice [0.9679, 0.9594, 0.9813, 0.9526, 0.8736, 0.973, 0.9916] +2024-09-08 16:37:12.748828: Epoch time: 287.28 s +2024-09-08 16:37:16.220093: +2024-09-08 16:37:16.221300: Epoch 580 +2024-09-08 16:37:16.222475: Current learning rate: 0.00458 +2024-09-08 16:41:59.983807: train_loss -0.7214 +2024-09-08 16:42:00.005138: val_loss -0.7172 +2024-09-08 16:42:00.007498: Pseudo dice [0.9603, 0.9573, 0.9733, 0.943, 0.8757, 0.9663, 0.9911] +2024-09-08 16:42:00.009329: Epoch time: 283.77 s +2024-09-08 16:42:03.218973: +2024-09-08 16:42:03.220207: Epoch 581 +2024-09-08 16:42:03.221030: Current learning rate: 0.00457 +2024-09-08 16:46:50.676880: train_loss -0.7211 +2024-09-08 16:46:50.678540: val_loss -0.7427 +2024-09-08 16:46:50.680178: Pseudo dice [0.9669, 0.9599, 0.9768, 0.9539, 0.8809, 0.9689, 0.9895] +2024-09-08 16:46:50.682460: Epoch time: 287.46 s +2024-09-08 16:46:53.038619: +2024-09-08 16:46:53.050674: Epoch 582 +2024-09-08 16:46:53.051411: Current learning rate: 0.00456 +2024-09-08 16:51:29.136930: train_loss -0.7424 +2024-09-08 16:51:29.153809: val_loss -0.7519 +2024-09-08 16:51:29.154685: Pseudo dice [0.9662, 0.9603, 0.9785, 0.9466, 0.8746, 0.9723, 0.9916] +2024-09-08 16:51:29.155370: Epoch time: 276.1 s +2024-09-08 16:51:31.585573: +2024-09-08 16:51:31.586670: Epoch 583 +2024-09-08 16:51:31.588083: Current learning rate: 0.00455 +2024-09-08 16:56:24.240015: train_loss -0.7312 +2024-09-08 16:56:24.249087: val_loss -0.7563 +2024-09-08 16:56:24.256340: Pseudo dice [0.9612, 0.9578, 0.9777, 0.9474, 0.8841, 0.9723, 0.9905] +2024-09-08 16:56:24.257086: Epoch time: 292.66 s +2024-09-08 16:56:26.801736: +2024-09-08 16:56:26.802808: Epoch 584 +2024-09-08 16:56:26.806926: Current learning rate: 0.00454 +2024-09-08 17:01:03.520098: train_loss -0.7446 +2024-09-08 17:01:03.541690: val_loss -0.7583 +2024-09-08 17:01:03.543469: Pseudo dice [0.9666, 0.9605, 0.9807, 0.9568, 0.87, 0.9728, 0.9906] +2024-09-08 17:01:03.544997: Epoch time: 276.73 s +2024-09-08 17:01:07.495748: +2024-09-08 17:01:07.507753: Epoch 585 +2024-09-08 17:01:07.510223: Current learning rate: 0.00453 +2024-09-08 17:05:59.956936: train_loss -0.742 +2024-09-08 17:05:59.977825: val_loss -0.7476 +2024-09-08 17:05:59.978615: Pseudo dice [0.9616, 0.9581, 0.9763, 0.9436, 0.8631, 0.9693, 0.989] +2024-09-08 17:05:59.979117: Epoch time: 292.47 s +2024-09-08 17:06:02.358299: +2024-09-08 17:06:02.364218: Epoch 586 +2024-09-08 17:06:02.371316: Current learning rate: 0.00452 +2024-09-08 17:10:36.840920: train_loss -0.7484 +2024-09-08 17:10:36.860794: val_loss -0.7693 +2024-09-08 17:10:36.861366: Pseudo dice [0.9661, 0.9599, 0.9808, 0.9502, 0.8746, 0.9712, 0.9902] +2024-09-08 17:10:36.862026: Epoch time: 274.49 s +2024-09-08 17:10:40.272850: +2024-09-08 17:10:40.273689: Epoch 587 +2024-09-08 17:10:40.274201: Current learning rate: 0.00451 +2024-09-08 17:15:44.016606: train_loss -0.7437 +2024-09-08 17:15:44.020881: val_loss -0.7364 +2024-09-08 17:15:44.021473: Pseudo dice [0.9589, 0.9567, 0.9714, 0.9515, 0.8784, 0.9706, 0.9916] +2024-09-08 17:15:44.021999: Epoch time: 303.75 s +2024-09-08 17:15:47.232960: +2024-09-08 17:15:47.233943: Epoch 588 +2024-09-08 17:15:47.234512: Current learning rate: 0.0045 +2024-09-08 17:20:44.463397: train_loss -0.7444 +2024-09-08 17:20:44.475828: val_loss -0.7875 +2024-09-08 17:20:44.476424: Pseudo dice [0.9617, 0.9591, 0.9759, 0.95, 0.8812, 0.9719, 0.9909] +2024-09-08 17:20:44.476903: Epoch time: 297.23 s +2024-09-08 17:20:47.768555: +2024-09-08 17:20:47.784435: Epoch 589 +2024-09-08 17:20:47.786079: Current learning rate: 0.00449 +2024-09-08 17:25:34.630910: train_loss -0.7548 +2024-09-08 17:25:34.652603: val_loss -0.7376 +2024-09-08 17:25:34.656589: Pseudo dice [0.967, 0.9611, 0.9772, 0.9505, 0.8703, 0.9735, 0.9911] +2024-09-08 17:25:34.661492: Epoch time: 286.87 s +2024-09-08 17:25:38.235560: +2024-09-08 17:25:38.237058: Epoch 590 +2024-09-08 17:25:38.237935: Current learning rate: 0.00448 +2024-09-08 17:30:31.027272: train_loss -0.7496 +2024-09-08 17:30:31.029716: val_loss -0.7215 +2024-09-08 17:30:31.030355: Pseudo dice [0.9645, 0.9576, 0.9766, 0.9452, 0.8868, 0.9718, 0.9919] +2024-09-08 17:30:31.030861: Epoch time: 292.81 s +2024-09-08 17:30:34.164527: +2024-09-08 17:30:34.176336: Epoch 591 +2024-09-08 17:30:34.177073: Current learning rate: 0.00447 +2024-09-08 17:35:25.229268: train_loss -0.7422 +2024-09-08 17:35:25.250699: val_loss -0.7465 +2024-09-08 17:35:25.251749: Pseudo dice [0.9629, 0.9612, 0.9752, 0.9553, 0.8664, 0.9735, 0.99] +2024-09-08 17:35:25.252464: Epoch time: 291.07 s +2024-09-08 17:35:29.297301: +2024-09-08 17:35:29.298251: Epoch 592 +2024-09-08 17:35:29.298983: Current learning rate: 0.00446 +2024-09-08 17:39:57.208260: train_loss -0.7512 +2024-09-08 17:39:57.228990: val_loss -0.7653 +2024-09-08 17:39:57.230192: Pseudo dice [0.962, 0.9565, 0.9767, 0.9496, 0.8752, 0.9703, 0.9919] +2024-09-08 17:39:57.231085: Epoch time: 267.92 s +2024-09-08 17:40:00.846530: +2024-09-08 17:40:00.847471: Epoch 593 +2024-09-08 17:40:00.849505: Current learning rate: 0.00445 +2024-09-08 17:44:58.120139: train_loss -0.7273 +2024-09-08 17:44:58.122776: val_loss -0.7546 +2024-09-08 17:44:58.123430: Pseudo dice [0.9613, 0.9584, 0.9747, 0.9502, 0.8697, 0.9671, 0.9917] +2024-09-08 17:44:58.123942: Epoch time: 297.28 s +2024-09-08 17:45:01.105817: +2024-09-08 17:45:01.106667: Epoch 594 +2024-09-08 17:45:01.107249: Current learning rate: 0.00444 +2024-09-08 17:49:43.290385: train_loss -0.7419 +2024-09-08 17:49:43.312732: val_loss -0.7357 +2024-09-08 17:49:43.313464: Pseudo dice [0.9624, 0.9602, 0.9743, 0.9514, 0.8631, 0.9698, 0.9894] +2024-09-08 17:49:43.314065: Epoch time: 282.19 s +2024-09-08 17:49:46.570191: +2024-09-08 17:49:46.571054: Epoch 595 +2024-09-08 17:49:46.572777: Current learning rate: 0.00443 +2024-09-08 17:54:19.531115: train_loss -0.7457 +2024-09-08 17:54:19.545303: val_loss -0.7014 +2024-09-08 17:54:19.546866: Pseudo dice [0.9609, 0.9565, 0.9701, 0.9385, 0.8739, 0.9659, 0.9882] +2024-09-08 17:54:19.548542: Epoch time: 272.97 s +2024-09-08 17:54:23.703242: +2024-09-08 17:54:23.704618: Epoch 596 +2024-09-08 17:54:23.706336: Current learning rate: 0.00442 +2024-09-08 17:59:02.583762: train_loss -0.727 +2024-09-08 17:59:02.587890: val_loss -0.7268 +2024-09-08 17:59:02.588769: Pseudo dice [0.9641, 0.9608, 0.9762, 0.9467, 0.8722, 0.9725, 0.992] +2024-09-08 17:59:02.589324: Epoch time: 278.89 s +2024-09-08 17:59:06.570299: +2024-09-08 17:59:06.573008: Epoch 597 +2024-09-08 17:59:06.573881: Current learning rate: 0.00441 +2024-09-08 18:03:51.612360: train_loss -0.7415 +2024-09-08 18:03:51.618697: val_loss -0.7129 +2024-09-08 18:03:51.619546: Pseudo dice [0.9608, 0.9595, 0.974, 0.9499, 0.8837, 0.973, 0.9924] +2024-09-08 18:03:51.620591: Epoch time: 285.05 s +2024-09-08 18:03:54.498136: +2024-09-08 18:03:54.499149: Epoch 598 +2024-09-08 18:03:54.500095: Current learning rate: 0.0044 +2024-09-08 18:08:41.018147: train_loss -0.7403 +2024-09-08 18:08:41.034173: val_loss -0.7541 +2024-09-08 18:08:41.034695: Pseudo dice [0.9654, 0.9592, 0.9776, 0.9463, 0.8844, 0.9719, 0.9915] +2024-09-08 18:08:41.035163: Epoch time: 286.53 s +2024-09-08 18:08:44.681027: +2024-09-08 18:08:44.692487: Epoch 599 +2024-09-08 18:08:44.693539: Current learning rate: 0.00439 +2024-09-08 18:13:28.777770: train_loss -0.7421 +2024-09-08 18:13:28.784587: val_loss -0.7614 +2024-09-08 18:13:28.786522: Pseudo dice [0.9633, 0.9585, 0.9786, 0.9554, 0.8796, 0.971, 0.9883] +2024-09-08 18:13:28.788191: Epoch time: 284.1 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_06_03_47.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_06_03_47.txt new file mode 100644 index 0000000000000000000000000000000000000000..342d04a28b4e4215d796ceb46dc4f85bac69207b --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_06_03_47.txt @@ -0,0 +1,376 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 06:03:49.946725: Using torch.compile... +2024-09-09 06:03:56.087053: do_dummy_2d_data_aug: False +2024-09-09 06:03:56.092802: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 06:03:56.094554: The split file contains 5 splits. +2024-09-09 06:03:56.094923: Desired fold for training: 4 +2024-09-09 06:03:56.095267: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 06:04:08.912320: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 06:04:23.095742: +2024-09-09 06:04:23.096476: Epoch 600 +2024-09-09 06:04:23.097072: Current learning rate: 0.00438 +2024-09-09 06:17:52.694244: train_loss -0.7464 +2024-09-09 06:17:52.702421: val_loss -0.7604 +2024-09-09 06:17:52.703114: Pseudo dice [0.9609, 0.9569, 0.9734, 0.9466, 0.871, 0.9678, 0.9909] +2024-09-09 06:17:52.703612: Epoch time: 809.6 s +2024-09-09 06:17:54.630153: +2024-09-09 06:17:54.631427: Epoch 601 +2024-09-09 06:17:54.632371: Current learning rate: 0.00437 +2024-09-09 06:22:25.406349: train_loss -0.7564 +2024-09-09 06:22:25.417641: val_loss -0.7589 +2024-09-09 06:22:25.418181: Pseudo dice [0.9635, 0.9576, 0.9772, 0.9457, 0.8828, 0.973, 0.9925] +2024-09-09 06:22:25.418654: Epoch time: 270.78 s +2024-09-09 06:22:29.268107: +2024-09-09 06:22:29.270097: Epoch 602 +2024-09-09 06:22:29.272721: Current learning rate: 0.00436 +2024-09-09 06:27:08.944952: train_loss -0.7577 +2024-09-09 06:27:08.962047: val_loss -0.789 +2024-09-09 06:27:08.975767: Pseudo dice [0.9645, 0.9583, 0.9801, 0.943, 0.8787, 0.9709, 0.9911] +2024-09-09 06:27:08.980571: Epoch time: 279.68 s +2024-09-09 06:27:12.948413: +2024-09-09 06:27:12.955026: Epoch 603 +2024-09-09 06:27:12.956022: Current learning rate: 0.00435 +2024-09-09 06:31:56.495852: train_loss -0.7589 +2024-09-09 06:31:56.506334: val_loss -0.7874 +2024-09-09 06:31:56.507125: Pseudo dice [0.9643, 0.9585, 0.9777, 0.9524, 0.8984, 0.9736, 0.9924] +2024-09-09 06:31:56.507853: Epoch time: 283.55 s +2024-09-09 06:31:59.381905: +2024-09-09 06:31:59.383268: Epoch 604 +2024-09-09 06:31:59.383880: Current learning rate: 0.00434 +2024-09-09 06:36:38.402739: train_loss -0.7499 +2024-09-09 06:36:38.405222: val_loss -0.7389 +2024-09-09 06:36:38.405997: Pseudo dice [0.9626, 0.9574, 0.9769, 0.9416, 0.8803, 0.972, 0.992] +2024-09-09 06:36:38.406552: Epoch time: 279.03 s +2024-09-09 06:36:41.725736: +2024-09-09 06:36:41.726721: Epoch 605 +2024-09-09 06:36:41.727841: Current learning rate: 0.00433 +2024-09-09 06:41:27.965194: train_loss -0.7438 +2024-09-09 06:41:27.976909: val_loss -0.7474 +2024-09-09 06:41:27.977949: Pseudo dice [0.9639, 0.9587, 0.979, 0.9502, 0.8759, 0.9716, 0.9905] +2024-09-09 06:41:27.979096: Epoch time: 286.24 s +2024-09-09 06:41:32.157162: +2024-09-09 06:41:32.175744: Epoch 606 +2024-09-09 06:41:32.176586: Current learning rate: 0.00432 +2024-09-09 06:46:14.095090: train_loss -0.7582 +2024-09-09 06:46:14.097913: val_loss -0.7169 +2024-09-09 06:46:14.098636: Pseudo dice [0.9655, 0.9577, 0.9777, 0.9471, 0.8666, 0.9712, 0.9882] +2024-09-09 06:46:14.099121: Epoch time: 281.94 s +2024-09-09 06:46:18.020942: +2024-09-09 06:46:18.035797: Epoch 607 +2024-09-09 06:46:18.044291: Current learning rate: 0.00431 +2024-09-09 06:50:44.112678: train_loss -0.7509 +2024-09-09 06:50:44.119108: val_loss -0.764 +2024-09-09 06:50:44.119706: Pseudo dice [0.9638, 0.9627, 0.9774, 0.9502, 0.8838, 0.9739, 0.9919] +2024-09-09 06:50:44.120206: Epoch time: 266.1 s +2024-09-09 06:50:46.916626: +2024-09-09 06:50:46.920955: Epoch 608 +2024-09-09 06:50:46.921793: Current learning rate: 0.0043 +2024-09-09 06:55:33.232899: train_loss -0.7491 +2024-09-09 06:55:33.234886: val_loss -0.7732 +2024-09-09 06:55:33.235437: Pseudo dice [0.9651, 0.9585, 0.9788, 0.951, 0.8842, 0.9734, 0.9923] +2024-09-09 06:55:33.236023: Epoch time: 286.32 s +2024-09-09 06:55:33.236526: Yayy! New best EMA pseudo Dice: 0.9556 +2024-09-09 06:55:38.357304: +2024-09-09 06:55:38.358506: Epoch 609 +2024-09-09 06:55:38.360562: Current learning rate: 0.00429 +2024-09-09 07:00:26.535580: train_loss -0.7243 +2024-09-09 07:00:26.538476: val_loss -0.7571 +2024-09-09 07:00:26.539226: Pseudo dice [0.9618, 0.9616, 0.9714, 0.9567, 0.8847, 0.9699, 0.9915] +2024-09-09 07:00:26.540148: Epoch time: 288.18 s +2024-09-09 07:00:26.540934: Yayy! New best EMA pseudo Dice: 0.9557 +2024-09-09 07:00:32.118265: +2024-09-09 07:00:32.119032: Epoch 610 +2024-09-09 07:00:32.119761: Current learning rate: 0.00429 +2024-09-09 07:05:01.555482: train_loss -0.7309 +2024-09-09 07:05:01.557953: val_loss -0.7602 +2024-09-09 07:05:01.558533: Pseudo dice [0.964, 0.9593, 0.9759, 0.9478, 0.8791, 0.9715, 0.9885] +2024-09-09 07:05:01.559149: Epoch time: 269.44 s +2024-09-09 07:05:05.989437: +2024-09-09 07:05:05.998477: Epoch 611 +2024-09-09 07:05:05.999303: Current learning rate: 0.00428 +2024-09-09 07:09:35.117353: train_loss -0.7474 +2024-09-09 07:09:35.119164: val_loss -0.7469 +2024-09-09 07:09:35.120326: Pseudo dice [0.9626, 0.9604, 0.9758, 0.949, 0.8819, 0.9699, 0.9911] +2024-09-09 07:09:35.121143: Epoch time: 269.13 s +2024-09-09 07:09:38.609384: +2024-09-09 07:09:38.610595: Epoch 612 +2024-09-09 07:09:38.611405: Current learning rate: 0.00427 +2024-09-09 07:14:30.974580: train_loss -0.7423 +2024-09-09 07:14:30.989433: val_loss -0.7599 +2024-09-09 07:14:30.989931: Pseudo dice [0.9617, 0.9605, 0.9753, 0.9514, 0.8705, 0.9683, 0.9892] +2024-09-09 07:14:30.992222: Epoch time: 292.37 s +2024-09-09 07:14:34.509640: +2024-09-09 07:14:34.510689: Epoch 613 +2024-09-09 07:14:34.511499: Current learning rate: 0.00426 +2024-09-09 07:19:13.178501: train_loss -0.7562 +2024-09-09 07:19:13.181867: val_loss -0.727 +2024-09-09 07:19:13.191867: Pseudo dice [0.9641, 0.9572, 0.9764, 0.9451, 0.8628, 0.973, 0.9916] +2024-09-09 07:19:13.200341: Epoch time: 278.67 s +2024-09-09 07:19:16.996158: +2024-09-09 07:19:16.997047: Epoch 614 +2024-09-09 07:19:16.997822: Current learning rate: 0.00425 +2024-09-09 07:24:13.675415: train_loss -0.7377 +2024-09-09 07:24:13.704573: val_loss -0.7809 +2024-09-09 07:24:13.706730: Pseudo dice [0.9638, 0.9558, 0.976, 0.9507, 0.8755, 0.971, 0.9909] +2024-09-09 07:24:13.707936: Epoch time: 296.7 s +2024-09-09 07:24:17.350660: +2024-09-09 07:24:17.351954: Epoch 615 +2024-09-09 07:24:17.353721: Current learning rate: 0.00424 +2024-09-09 07:28:40.288074: train_loss -0.7343 +2024-09-09 07:28:40.312484: val_loss -0.7722 +2024-09-09 07:28:40.313274: Pseudo dice [0.9574, 0.9555, 0.9752, 0.9474, 0.8766, 0.9714, 0.9905] +2024-09-09 07:28:40.313907: Epoch time: 262.94 s +2024-09-09 07:28:44.896652: +2024-09-09 07:28:44.897616: Epoch 616 +2024-09-09 07:28:44.898329: Current learning rate: 0.00423 +2024-09-09 07:33:32.345155: train_loss -0.7288 +2024-09-09 07:33:32.357922: val_loss -0.7473 +2024-09-09 07:33:32.358716: Pseudo dice [0.965, 0.9585, 0.9814, 0.9552, 0.8859, 0.9715, 0.9903] +2024-09-09 07:33:32.359473: Epoch time: 287.45 s +2024-09-09 07:33:36.224195: +2024-09-09 07:33:36.225682: Epoch 617 +2024-09-09 07:33:36.226635: Current learning rate: 0.00422 +2024-09-09 07:38:19.517545: train_loss -0.7423 +2024-09-09 07:38:19.536744: val_loss -0.7223 +2024-09-09 07:38:19.537494: Pseudo dice [0.9646, 0.9596, 0.979, 0.9481, 0.864, 0.9658, 0.9911] +2024-09-09 07:38:19.538114: Epoch time: 283.3 s +2024-09-09 07:38:23.747270: +2024-09-09 07:38:23.748206: Epoch 618 +2024-09-09 07:38:23.749141: Current learning rate: 0.00421 +2024-09-09 07:43:02.904654: train_loss -0.7548 +2024-09-09 07:43:02.922313: val_loss -0.7662 +2024-09-09 07:43:02.923105: Pseudo dice [0.9666, 0.9593, 0.9789, 0.9466, 0.8737, 0.9704, 0.9905] +2024-09-09 07:43:02.927844: Epoch time: 279.16 s +2024-09-09 07:43:06.468624: +2024-09-09 07:43:06.480441: Epoch 619 +2024-09-09 07:43:06.481168: Current learning rate: 0.0042 +2024-09-09 07:47:43.216216: train_loss -0.7331 +2024-09-09 07:47:43.234010: val_loss -0.7758 +2024-09-09 07:47:43.234721: Pseudo dice [0.9639, 0.9601, 0.9768, 0.9583, 0.8836, 0.9689, 0.9909] +2024-09-09 07:47:43.235467: Epoch time: 276.75 s +2024-09-09 07:47:46.950065: +2024-09-09 07:47:46.950859: Epoch 620 +2024-09-09 07:47:46.951537: Current learning rate: 0.00419 +2024-09-09 07:52:22.578768: train_loss -0.7408 +2024-09-09 07:52:22.601243: val_loss -0.7391 +2024-09-09 07:52:22.601909: Pseudo dice [0.9649, 0.9596, 0.9773, 0.9483, 0.8796, 0.9721, 0.9911] +2024-09-09 07:52:22.602499: Epoch time: 275.63 s +2024-09-09 07:52:25.926550: +2024-09-09 07:52:25.936794: Epoch 621 +2024-09-09 07:52:25.937721: Current learning rate: 0.00418 +2024-09-09 07:57:00.633941: train_loss -0.743 +2024-09-09 07:57:00.661758: val_loss -0.7449 +2024-09-09 07:57:00.662570: Pseudo dice [0.9659, 0.9599, 0.9794, 0.9519, 0.8825, 0.9719, 0.9913] +2024-09-09 07:57:00.663253: Epoch time: 274.71 s +2024-09-09 07:57:03.792650: +2024-09-09 07:57:03.806707: Epoch 622 +2024-09-09 07:57:03.807375: Current learning rate: 0.00417 +2024-09-09 08:01:39.001677: train_loss -0.7436 +2024-09-09 08:01:39.016230: val_loss -0.7293 +2024-09-09 08:01:39.016916: Pseudo dice [0.9646, 0.9579, 0.9751, 0.9483, 0.8603, 0.9719, 0.9897] +2024-09-09 08:01:39.017637: Epoch time: 275.21 s +2024-09-09 08:01:42.337905: +2024-09-09 08:01:42.348495: Epoch 623 +2024-09-09 08:01:42.350919: Current learning rate: 0.00416 +2024-09-09 08:06:20.931125: train_loss -0.7299 +2024-09-09 08:06:20.933644: val_loss -0.7757 +2024-09-09 08:06:20.934312: Pseudo dice [0.9645, 0.9588, 0.9763, 0.9503, 0.8701, 0.9739, 0.9893] +2024-09-09 08:06:20.935000: Epoch time: 278.6 s +2024-09-09 08:06:23.284915: +2024-09-09 08:06:23.286164: Epoch 624 +2024-09-09 08:06:23.287023: Current learning rate: 0.00415 +2024-09-09 08:11:03.458450: train_loss -0.7353 +2024-09-09 08:11:03.472874: val_loss -0.7482 +2024-09-09 08:11:03.473403: Pseudo dice [0.9662, 0.9614, 0.9795, 0.9518, 0.8819, 0.9725, 0.9908] +2024-09-09 08:11:03.473859: Epoch time: 280.18 s +2024-09-09 08:11:07.009109: +2024-09-09 08:11:07.011435: Epoch 625 +2024-09-09 08:11:07.012398: Current learning rate: 0.00414 +2024-09-09 08:15:59.879910: train_loss -0.7491 +2024-09-09 08:15:59.900133: val_loss -0.7327 +2024-09-09 08:15:59.901983: Pseudo dice [0.9664, 0.9599, 0.9788, 0.9492, 0.8686, 0.9716, 0.9906] +2024-09-09 08:15:59.902886: Epoch time: 292.88 s +2024-09-09 08:16:03.642744: +2024-09-09 08:16:03.647053: Epoch 626 +2024-09-09 08:16:03.660359: Current learning rate: 0.00413 +2024-09-09 08:21:10.374783: train_loss -0.7473 +2024-09-09 08:21:10.388236: val_loss -0.7778 +2024-09-09 08:21:10.389652: Pseudo dice [0.964, 0.9607, 0.9758, 0.9487, 0.8795, 0.9751, 0.9924] +2024-09-09 08:21:10.390614: Epoch time: 306.74 s +2024-09-09 08:21:14.909900: +2024-09-09 08:21:14.925092: Epoch 627 +2024-09-09 08:21:14.926102: Current learning rate: 0.00412 +2024-09-09 08:25:58.902766: train_loss -0.722 +2024-09-09 08:25:58.907008: val_loss -0.7474 +2024-09-09 08:25:58.911858: Pseudo dice [0.9643, 0.9579, 0.9779, 0.9492, 0.8721, 0.9716, 0.9912] +2024-09-09 08:25:58.920538: Epoch time: 284.0 s +2024-09-09 08:26:01.344231: +2024-09-09 08:26:01.345890: Epoch 628 +2024-09-09 08:26:01.346649: Current learning rate: 0.00411 +2024-09-09 08:30:43.272359: train_loss -0.7496 +2024-09-09 08:30:43.273889: val_loss -0.7121 +2024-09-09 08:30:43.274514: Pseudo dice [0.965, 0.9574, 0.9802, 0.9537, 0.8852, 0.968, 0.9932] +2024-09-09 08:30:43.275176: Epoch time: 281.94 s +2024-09-09 08:30:43.275696: Yayy! New best EMA pseudo Dice: 0.9557 +2024-09-09 08:30:47.204957: +2024-09-09 08:30:47.205640: Epoch 629 +2024-09-09 08:30:47.206213: Current learning rate: 0.0041 +2024-09-09 08:35:37.435490: train_loss -0.7407 +2024-09-09 08:35:37.437455: val_loss -0.6962 +2024-09-09 08:35:37.437971: Pseudo dice [0.9601, 0.9563, 0.97, 0.9457, 0.8378, 0.9665, 0.9892] +2024-09-09 08:35:37.438520: Epoch time: 290.23 s +2024-09-09 08:35:41.391346: +2024-09-09 08:35:41.392557: Epoch 630 +2024-09-09 08:35:41.393837: Current learning rate: 0.00409 +2024-09-09 08:40:22.342311: train_loss -0.7355 +2024-09-09 08:40:22.345256: val_loss -0.7264 +2024-09-09 08:40:22.346046: Pseudo dice [0.9654, 0.9593, 0.9798, 0.953, 0.8827, 0.9708, 0.9904] +2024-09-09 08:40:22.346546: Epoch time: 280.96 s +2024-09-09 08:40:24.642492: +2024-09-09 08:40:24.643561: Epoch 631 +2024-09-09 08:40:24.644254: Current learning rate: 0.00408 +2024-09-09 08:45:21.297287: train_loss -0.7312 +2024-09-09 08:45:21.298923: val_loss -0.7193 +2024-09-09 08:45:21.300399: Pseudo dice [0.9622, 0.9581, 0.9739, 0.9535, 0.8893, 0.9733, 0.9912] +2024-09-09 08:45:21.301113: Epoch time: 296.66 s +2024-09-09 08:45:23.623511: +2024-09-09 08:45:23.624399: Epoch 632 +2024-09-09 08:45:23.624986: Current learning rate: 0.00407 +2024-09-09 08:50:12.502527: train_loss -0.753 +2024-09-09 08:50:12.506187: val_loss -0.7357 +2024-09-09 08:50:12.508250: Pseudo dice [0.9655, 0.9601, 0.9763, 0.9502, 0.8844, 0.9722, 0.9913] +2024-09-09 08:50:12.508772: Epoch time: 288.88 s +2024-09-09 08:50:14.347373: +2024-09-09 08:50:14.348125: Epoch 633 +2024-09-09 08:50:14.349686: Current learning rate: 0.00406 +2024-09-09 08:55:04.260141: train_loss -0.7455 +2024-09-09 08:55:04.265253: val_loss -0.7244 +2024-09-09 08:55:04.265833: Pseudo dice [0.9634, 0.9604, 0.974, 0.949, 0.8765, 0.9672, 0.9916] +2024-09-09 08:55:04.266321: Epoch time: 289.92 s +2024-09-09 08:55:05.873966: +2024-09-09 08:55:05.874714: Epoch 634 +2024-09-09 08:55:05.875297: Current learning rate: 0.00405 +2024-09-09 08:59:57.512938: train_loss -0.7331 +2024-09-09 08:59:57.517517: val_loss -0.757 +2024-09-09 08:59:57.518351: Pseudo dice [0.9648, 0.9574, 0.9783, 0.9498, 0.8814, 0.9704, 0.9905] +2024-09-09 08:59:57.520183: Epoch time: 291.64 s +2024-09-09 08:59:59.159416: +2024-09-09 08:59:59.163411: Epoch 635 +2024-09-09 08:59:59.164237: Current learning rate: 0.00404 +2024-09-09 09:04:43.326657: train_loss -0.7517 +2024-09-09 09:04:43.333323: val_loss -0.7759 +2024-09-09 09:04:43.335022: Pseudo dice [0.9665, 0.963, 0.9785, 0.9572, 0.8887, 0.9746, 0.9914] +2024-09-09 09:04:43.335573: Epoch time: 284.17 s +2024-09-09 09:04:43.336739: Yayy! New best EMA pseudo Dice: 0.9559 +2024-09-09 09:04:47.797840: +2024-09-09 09:04:47.799129: Epoch 636 +2024-09-09 09:04:47.801056: Current learning rate: 0.00403 +2024-09-09 09:09:39.538496: train_loss -0.7469 +2024-09-09 09:09:39.540618: val_loss -0.7378 +2024-09-09 09:09:39.541280: Pseudo dice [0.9624, 0.9587, 0.9765, 0.9554, 0.8796, 0.9734, 0.9912] +2024-09-09 09:09:39.541803: Epoch time: 291.75 s +2024-09-09 09:09:39.542218: Yayy! New best EMA pseudo Dice: 0.956 +2024-09-09 09:09:43.589991: +2024-09-09 09:09:43.590591: Epoch 637 +2024-09-09 09:09:43.590976: Current learning rate: 0.00402 +2024-09-09 09:14:28.926908: train_loss -0.7356 +2024-09-09 09:14:28.931002: val_loss -0.7572 +2024-09-09 09:14:28.931926: Pseudo dice [0.9638, 0.9558, 0.9782, 0.9508, 0.8709, 0.9713, 0.9919] +2024-09-09 09:14:28.932564: Epoch time: 285.34 s +2024-09-09 09:14:30.693113: +2024-09-09 09:14:30.693746: Epoch 638 +2024-09-09 09:14:30.694181: Current learning rate: 0.00401 +2024-09-09 09:19:25.353311: train_loss -0.7319 +2024-09-09 09:19:25.357405: val_loss -0.7491 +2024-09-09 09:19:25.358288: Pseudo dice [0.964, 0.9583, 0.9735, 0.9513, 0.8784, 0.9709, 0.9907] +2024-09-09 09:19:25.359253: Epoch time: 294.66 s +2024-09-09 09:19:27.513869: +2024-09-09 09:19:27.517350: Epoch 639 +2024-09-09 09:19:27.520605: Current learning rate: 0.004 +2024-09-09 09:24:13.445387: train_loss -0.732 +2024-09-09 09:24:13.447538: val_loss -0.7749 +2024-09-09 09:24:13.448096: Pseudo dice [0.9638, 0.9565, 0.9733, 0.9518, 0.8764, 0.9729, 0.9916] +2024-09-09 09:24:13.448797: Epoch time: 285.94 s +2024-09-09 09:24:15.394772: +2024-09-09 09:24:15.400138: Epoch 640 +2024-09-09 09:24:15.400767: Current learning rate: 0.00399 +2024-09-09 09:28:59.713134: train_loss -0.7331 +2024-09-09 09:28:59.716243: val_loss -0.7617 +2024-09-09 09:28:59.717633: Pseudo dice [0.9654, 0.9613, 0.9773, 0.9503, 0.8715, 0.9717, 0.9907] +2024-09-09 09:28:59.718679: Epoch time: 284.32 s +2024-09-09 09:29:01.342849: +2024-09-09 09:29:01.343545: Epoch 641 +2024-09-09 09:29:01.343995: Current learning rate: 0.00398 +2024-09-09 09:33:24.843608: train_loss -0.7582 +2024-09-09 09:33:24.847150: val_loss -0.7898 +2024-09-09 09:33:24.847720: Pseudo dice [0.9604, 0.9573, 0.9729, 0.9443, 0.871, 0.9685, 0.9897] +2024-09-09 09:33:24.848213: Epoch time: 263.5 s +2024-09-09 09:33:27.055300: +2024-09-09 09:33:27.056792: Epoch 642 +2024-09-09 09:33:27.057521: Current learning rate: 0.00397 +2024-09-09 09:37:57.044658: train_loss -0.7494 +2024-09-09 09:37:57.046465: val_loss -0.764 +2024-09-09 09:37:57.047061: Pseudo dice [0.9576, 0.9594, 0.9668, 0.947, 0.8723, 0.9711, 0.9893] +2024-09-09 09:37:57.047732: Epoch time: 270.0 s +2024-09-09 09:38:00.744960: +2024-09-09 09:38:00.745716: Epoch 643 +2024-09-09 09:38:00.746249: Current learning rate: 0.00396 +2024-09-09 09:42:33.966831: train_loss -0.7438 +2024-09-09 09:42:33.968811: val_loss -0.7608 +2024-09-09 09:42:33.969591: Pseudo dice [0.9653, 0.9606, 0.9789, 0.9468, 0.8703, 0.9716, 0.9911] +2024-09-09 09:42:33.971550: Epoch time: 273.23 s +2024-09-09 09:42:35.496862: +2024-09-09 09:42:35.498157: Epoch 644 +2024-09-09 09:42:35.499827: Current learning rate: 0.00395 +2024-09-09 09:47:29.331323: train_loss -0.7206 +2024-09-09 09:47:29.335899: val_loss -0.667 +2024-09-09 09:47:29.337016: Pseudo dice [0.9564, 0.9539, 0.9707, 0.9436, 0.8207, 0.9619, 0.9878] +2024-09-09 09:47:29.337906: Epoch time: 293.84 s +2024-09-09 09:47:31.571574: +2024-09-09 09:47:31.575794: Epoch 645 +2024-09-09 09:47:31.577133: Current learning rate: 0.00394 +2024-09-09 09:51:56.788088: train_loss -0.723 +2024-09-09 09:51:56.790824: val_loss -0.7667 +2024-09-09 09:51:56.793675: Pseudo dice [0.9622, 0.9569, 0.9764, 0.9492, 0.8796, 0.97, 0.9886] +2024-09-09 09:51:56.794369: Epoch time: 265.22 s +2024-09-09 09:51:58.872565: +2024-09-09 09:51:58.873563: Epoch 646 +2024-09-09 09:51:58.874265: Current learning rate: 0.00393 +2024-09-09 09:56:59.641709: train_loss -0.7279 +2024-09-09 09:56:59.642736: val_loss -0.7299 +2024-09-09 09:56:59.643315: Pseudo dice [0.9623, 0.9574, 0.9764, 0.9431, 0.8738, 0.9715, 0.9898] +2024-09-09 09:56:59.643889: Epoch time: 300.78 s +2024-09-09 09:57:01.158592: +2024-09-09 09:57:01.159382: Epoch 647 +2024-09-09 09:57:01.159938: Current learning rate: 0.00392 +2024-09-09 10:01:35.422937: train_loss -0.7262 +2024-09-09 10:01:35.426787: val_loss -0.732 +2024-09-09 10:01:35.428483: Pseudo dice [0.9635, 0.9592, 0.9765, 0.9521, 0.8726, 0.9696, 0.9905] +2024-09-09 10:01:35.429103: Epoch time: 274.27 s +2024-09-09 10:01:37.771367: +2024-09-09 10:01:37.773887: Epoch 648 +2024-09-09 10:01:37.777008: Current learning rate: 0.00391 +2024-09-09 10:06:18.156483: train_loss -0.7506 +2024-09-09 10:06:18.158863: val_loss -0.7303 +2024-09-09 10:06:18.161258: Pseudo dice [0.9594, 0.9572, 0.9729, 0.9392, 0.8747, 0.9715, 0.9898] +2024-09-09 10:06:18.163311: Epoch time: 280.39 s +2024-09-09 10:06:20.233776: +2024-09-09 10:06:20.235304: Epoch 649 +2024-09-09 10:06:20.237410: Current learning rate: 0.0039 +2024-09-09 10:10:51.518754: train_loss -0.7417 +2024-09-09 10:10:51.520050: val_loss -0.7546 +2024-09-09 10:10:51.520959: Pseudo dice [0.9634, 0.9559, 0.9765, 0.9486, 0.8798, 0.9702, 0.9909] +2024-09-09 10:10:51.521831: Epoch time: 271.29 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_16_54_15.txt b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_16_54_15.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f63f9e9addf0a2ffd544cda77467e5533df108a --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_4/logs/training_log_2024_9_9_16_54_15.txt @@ -0,0 +1,741 @@ + +####################################################################### +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. +####################################################################### + +2024-09-09 16:54:18.774984: Using torch.compile... +2024-09-09 16:54:25.095811: do_dummy_2d_data_aug: False +2024-09-09 16:54:25.148124: Using splits from existing split file: /data2/nnUNet_preprocessed/Dataset611_Tissue/splits_final.json +2024-09-09 16:54:25.164634: The split file contains 5 splits. +2024-09-09 16:54:25.165289: Desired fold for training: 4 +2024-09-09 16:54:25.165890: This split has 1325 training and 331 validation cases. + +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': [192, 192, 192], 'median_image_size_in_voxels': [250.0, 257.0, 260.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], '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.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False} + +These are the global plan.json settings: + {'dataset_name': 'Dataset611_Tissue', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [170, 309, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 9580.0, 'mean': -115.16296809491125, 'median': -61.0, 'min': -3048.0, 'percentile_00_5': -948.0, 'percentile_99_5': 816.0, 'std': 324.74287592532374}}} + +2024-09-09 16:54:37.870827: Unable to plot network architecture: nnUNet_compile is enabled! +2024-09-09 16:54:38.974728: +2024-09-09 16:54:38.996155: Epoch 650 +2024-09-09 16:54:39.006343: Current learning rate: 0.00389 +2024-09-09 17:10:27.078643: train_loss -0.7289 +2024-09-09 17:10:27.079800: val_loss -0.7251 +2024-09-09 17:10:27.081021: Pseudo dice [0.9637, 0.9572, 0.9746, 0.9482, 0.8582, 0.9698, 0.9907] +2024-09-09 17:10:27.081680: Epoch time: 948.11 s +2024-09-09 17:10:28.648083: +2024-09-09 17:10:28.648901: Epoch 651 +2024-09-09 17:10:28.649494: Current learning rate: 0.00388 +2024-09-09 17:14:55.941852: train_loss -0.7339 +2024-09-09 17:14:55.952491: val_loss -0.7365 +2024-09-09 17:14:55.953196: Pseudo dice [0.96, 0.9549, 0.974, 0.9415, 0.8754, 0.9666, 0.9909] +2024-09-09 17:14:55.953795: Epoch time: 267.3 s +2024-09-09 17:14:58.443746: +2024-09-09 17:14:58.445380: Epoch 652 +2024-09-09 17:14:58.447381: Current learning rate: 0.00387 +2024-09-09 17:19:12.505871: train_loss -0.7612 +2024-09-09 17:19:12.507853: val_loss -0.7523 +2024-09-09 17:19:12.508664: Pseudo dice [0.9638, 0.9583, 0.9748, 0.9474, 0.8742, 0.9714, 0.9918] +2024-09-09 17:19:12.509460: Epoch time: 254.07 s +2024-09-09 17:19:15.482666: +2024-09-09 17:19:15.492722: Epoch 653 +2024-09-09 17:19:15.493540: Current learning rate: 0.00386 +2024-09-09 17:23:30.318016: train_loss -0.7422 +2024-09-09 17:23:30.332217: val_loss -0.7597 +2024-09-09 17:23:30.333001: Pseudo dice [0.962, 0.9556, 0.9752, 0.9417, 0.8673, 0.9697, 0.9909] +2024-09-09 17:23:30.333648: Epoch time: 254.84 s +2024-09-09 17:23:32.823460: +2024-09-09 17:23:32.824360: Epoch 654 +2024-09-09 17:23:32.825021: Current learning rate: 0.00385 +2024-09-09 17:28:07.277631: train_loss -0.7458 +2024-09-09 17:28:07.280488: val_loss -0.7863 +2024-09-09 17:28:07.281335: Pseudo dice [0.9662, 0.9602, 0.981, 0.9515, 0.8743, 0.9723, 0.99] +2024-09-09 17:28:07.282159: Epoch time: 274.46 s +2024-09-09 17:28:09.776210: +2024-09-09 17:28:09.777289: Epoch 655 +2024-09-09 17:28:09.778075: Current learning rate: 0.00384 +2024-09-09 17:32:49.131334: train_loss -0.7448 +2024-09-09 17:32:49.144266: val_loss -0.7677 +2024-09-09 17:32:49.146921: Pseudo dice [0.9665, 0.9596, 0.9808, 0.9494, 0.8694, 0.9747, 0.9895] +2024-09-09 17:32:49.149710: Epoch time: 279.36 s +2024-09-09 17:32:52.099133: +2024-09-09 17:32:52.099957: Epoch 656 +2024-09-09 17:32:52.100516: Current learning rate: 0.00383 +2024-09-09 17:37:30.268575: train_loss -0.7408 +2024-09-09 17:37:30.289394: val_loss -0.7552 +2024-09-09 17:37:30.289967: Pseudo dice [0.9608, 0.9591, 0.9732, 0.9457, 0.8923, 0.9693, 0.9912] +2024-09-09 17:37:30.290439: Epoch time: 278.17 s +2024-09-09 17:37:32.983690: +2024-09-09 17:37:32.984678: Epoch 657 +2024-09-09 17:37:32.985305: Current learning rate: 0.00382 +2024-09-09 17:42:22.750564: train_loss -0.7503 +2024-09-09 17:42:22.765944: val_loss -0.7399 +2024-09-09 17:42:22.766767: Pseudo dice [0.9633, 0.9608, 0.976, 0.9486, 0.8895, 0.9725, 0.9913] +2024-09-09 17:42:22.767574: Epoch time: 289.77 s +2024-09-09 17:42:25.759961: +2024-09-09 17:42:25.772720: Epoch 658 +2024-09-09 17:42:25.773505: Current learning rate: 0.00381 +2024-09-09 17:46:54.886900: train_loss -0.7446 +2024-09-09 17:46:54.889076: val_loss -0.7539 +2024-09-09 17:46:54.889869: Pseudo dice [0.9654, 0.9597, 0.9781, 0.9497, 0.871, 0.9705, 0.9907] +2024-09-09 17:46:54.890547: Epoch time: 269.13 s +2024-09-09 17:46:58.625995: +2024-09-09 17:46:58.626973: Epoch 659 +2024-09-09 17:46:58.627615: Current learning rate: 0.0038 +2024-09-09 17:51:31.691477: train_loss -0.741 +2024-09-09 17:51:31.705929: val_loss -0.7508 +2024-09-09 17:51:31.706523: Pseudo dice [0.9666, 0.9593, 0.9767, 0.9472, 0.8791, 0.9709, 0.9913] +2024-09-09 17:51:31.707224: Epoch time: 273.07 s +2024-09-09 17:51:35.112714: +2024-09-09 17:51:35.124599: Epoch 660 +2024-09-09 17:51:35.125336: Current learning rate: 0.00379 +2024-09-09 17:56:28.013898: train_loss -0.7374 +2024-09-09 17:56:28.033753: val_loss -0.7702 +2024-09-09 17:56:28.034497: Pseudo dice [0.9622, 0.9588, 0.9756, 0.9459, 0.8816, 0.9711, 0.9917] +2024-09-09 17:56:28.035045: Epoch time: 292.91 s +2024-09-09 17:56:31.714449: +2024-09-09 17:56:31.715362: Epoch 661 +2024-09-09 17:56:31.716146: Current learning rate: 0.00378 +2024-09-09 18:01:00.066092: train_loss -0.756 +2024-09-09 18:01:00.088081: val_loss -0.7665 +2024-09-09 18:01:00.088972: Pseudo dice [0.9689, 0.9611, 0.9822, 0.9564, 0.8894, 0.9751, 0.9914] +2024-09-09 18:01:00.089590: Epoch time: 268.36 s +2024-09-09 18:01:03.835051: +2024-09-09 18:01:03.848658: Epoch 662 +2024-09-09 18:01:03.849411: Current learning rate: 0.00377 +2024-09-09 18:05:18.803311: train_loss -0.7389 +2024-09-09 18:05:18.826117: val_loss -0.7393 +2024-09-09 18:05:18.827114: Pseudo dice [0.9663, 0.9611, 0.9772, 0.9422, 0.8915, 0.9759, 0.9928] +2024-09-09 18:05:18.827941: Epoch time: 254.97 s +2024-09-09 18:05:22.037629: +2024-09-09 18:05:22.038646: Epoch 663 +2024-09-09 18:05:22.039332: Current learning rate: 0.00376 +2024-09-09 18:09:53.048728: train_loss -0.7404 +2024-09-09 18:09:53.063774: val_loss -0.7391 +2024-09-09 18:09:53.064492: Pseudo dice [0.9659, 0.9599, 0.9789, 0.9522, 0.8886, 0.9719, 0.9918] +2024-09-09 18:09:53.065156: Epoch time: 271.02 s +2024-09-09 18:09:56.412311: +2024-09-09 18:09:56.424685: Epoch 664 +2024-09-09 18:09:56.425359: Current learning rate: 0.00375 +2024-09-09 18:14:32.431124: train_loss -0.7415 +2024-09-09 18:14:32.453864: val_loss -0.7265 +2024-09-09 18:14:32.454696: Pseudo dice [0.9658, 0.9586, 0.9777, 0.9469, 0.8845, 0.9735, 0.992] +2024-09-09 18:14:32.455384: Epoch time: 276.02 s +2024-09-09 18:14:35.226075: +2024-09-09 18:14:35.227068: Epoch 665 +2024-09-09 18:14:35.227654: Current learning rate: 0.00374 +2024-09-09 18:19:18.706479: train_loss -0.7636 +2024-09-09 18:19:18.721341: val_loss -0.7726 +2024-09-09 18:19:18.722046: Pseudo dice [0.9653, 0.9578, 0.9798, 0.9536, 0.8823, 0.9723, 0.9904] +2024-09-09 18:19:18.722950: Epoch time: 283.48 s +2024-09-09 18:19:18.723709: Yayy! New best EMA pseudo Dice: 0.9561 +2024-09-09 18:19:25.451512: +2024-09-09 18:19:25.452298: Epoch 666 +2024-09-09 18:19:25.452995: Current learning rate: 0.00373 +2024-09-09 18:23:48.277412: train_loss -0.7501 +2024-09-09 18:23:48.301949: val_loss -0.7299 +2024-09-09 18:23:48.302687: Pseudo dice [0.9594, 0.9599, 0.9718, 0.9499, 0.8945, 0.971, 0.9914] +2024-09-09 18:23:48.303415: Epoch time: 262.83 s +2024-09-09 18:23:48.304048: Yayy! New best EMA pseudo Dice: 0.9562 +2024-09-09 18:23:55.322130: +2024-09-09 18:23:55.322914: Epoch 667 +2024-09-09 18:23:55.323524: Current learning rate: 0.00372 +2024-09-09 18:28:23.027203: train_loss -0.7527 +2024-09-09 18:28:23.044960: val_loss -0.7239 +2024-09-09 18:28:23.045788: Pseudo dice [0.9668, 0.9594, 0.9767, 0.9478, 0.8724, 0.9731, 0.9908] +2024-09-09 18:28:23.046572: Epoch time: 267.71 s +2024-09-09 18:28:25.698797: +2024-09-09 18:28:25.700099: Epoch 668 +2024-09-09 18:28:25.701026: Current learning rate: 0.00371 +2024-09-09 18:32:42.812239: train_loss -0.7613 +2024-09-09 18:32:42.825907: val_loss -0.7559 +2024-09-09 18:32:42.826576: Pseudo dice [0.9653, 0.9587, 0.978, 0.9476, 0.8745, 0.9744, 0.9905] +2024-09-09 18:32:42.827240: Epoch time: 257.12 s +2024-09-09 18:32:45.408443: +2024-09-09 18:32:45.409259: Epoch 669 +2024-09-09 18:32:45.409895: Current learning rate: 0.0037 +2024-09-09 18:37:01.225252: train_loss -0.7265 +2024-09-09 18:37:01.242368: val_loss -0.7623 +2024-09-09 18:37:01.243045: Pseudo dice [0.9655, 0.9587, 0.976, 0.9511, 0.8678, 0.9711, 0.9885] +2024-09-09 18:37:01.243669: Epoch time: 255.82 s +2024-09-09 18:37:03.582675: +2024-09-09 18:37:03.583575: Epoch 670 +2024-09-09 18:37:03.584265: Current learning rate: 0.00369 +2024-09-09 18:41:03.946579: train_loss -0.7543 +2024-09-09 18:41:03.962054: val_loss -0.7565 +2024-09-09 18:41:03.962724: Pseudo dice [0.961, 0.9603, 0.9753, 0.9546, 0.8929, 0.97, 0.9907] +2024-09-09 18:41:03.963404: Epoch time: 240.37 s +2024-09-09 18:41:07.278284: +2024-09-09 18:41:07.285516: Epoch 671 +2024-09-09 18:41:07.286218: Current learning rate: 0.00368 +2024-09-09 18:45:38.299157: train_loss -0.7307 +2024-09-09 18:45:38.301130: val_loss -0.784 +2024-09-09 18:45:38.301823: Pseudo dice [0.962, 0.9602, 0.974, 0.9506, 0.8773, 0.9707, 0.9923] +2024-09-09 18:45:38.302412: Epoch time: 271.03 s +2024-09-09 18:45:41.144986: +2024-09-09 18:45:41.156987: Epoch 672 +2024-09-09 18:45:41.158095: Current learning rate: 0.00367 +2024-09-09 18:50:31.013255: train_loss -0.7509 +2024-09-09 18:50:31.015356: val_loss -0.7893 +2024-09-09 18:50:31.016118: Pseudo dice [0.9686, 0.9623, 0.9808, 0.9512, 0.8837, 0.9736, 0.9921] +2024-09-09 18:50:31.016744: Epoch time: 289.87 s +2024-09-09 18:50:31.017400: Yayy! New best EMA pseudo Dice: 0.9563 +2024-09-09 18:50:39.132471: +2024-09-09 18:50:39.133486: Epoch 673 +2024-09-09 18:50:39.134322: Current learning rate: 0.00366 +2024-09-09 18:55:15.318485: train_loss -0.7228 +2024-09-09 18:55:15.334157: val_loss -0.7544 +2024-09-09 18:55:15.335132: Pseudo dice [0.9666, 0.9597, 0.9789, 0.9533, 0.8861, 0.974, 0.9924] +2024-09-09 18:55:15.336339: Epoch time: 276.19 s +2024-09-09 18:55:15.337028: Yayy! New best EMA pseudo Dice: 0.9565 +2024-09-09 18:55:22.714303: +2024-09-09 18:55:22.715201: Epoch 674 +2024-09-09 18:55:22.715761: Current learning rate: 0.00365 +2024-09-09 18:59:55.100191: train_loss -0.7379 +2024-09-09 18:59:55.112441: val_loss -0.7758 +2024-09-09 18:59:55.113217: Pseudo dice [0.9692, 0.964, 0.9816, 0.956, 0.8892, 0.975, 0.9929] +2024-09-09 18:59:55.113964: Epoch time: 272.39 s +2024-09-09 18:59:55.114535: Yayy! New best EMA pseudo Dice: 0.957 +2024-09-09 19:00:02.603469: +2024-09-09 19:00:02.604545: Epoch 675 +2024-09-09 19:00:02.605191: Current learning rate: 0.00364 +2024-09-09 19:04:37.555782: train_loss -0.7471 +2024-09-09 19:04:37.564113: val_loss -0.7585 +2024-09-09 19:04:37.564973: Pseudo dice [0.9656, 0.9605, 0.9791, 0.9484, 0.8797, 0.973, 0.9902] +2024-09-09 19:04:37.565670: Epoch time: 274.96 s +2024-09-09 19:04:40.831593: +2024-09-09 19:04:40.832480: Epoch 676 +2024-09-09 19:04:40.833170: Current learning rate: 0.00363 +2024-09-09 19:09:15.831951: train_loss -0.7427 +2024-09-09 19:09:15.834326: val_loss -0.7786 +2024-09-09 19:09:15.835013: Pseudo dice [0.9662, 0.9591, 0.9812, 0.9517, 0.8827, 0.9743, 0.9906] +2024-09-09 19:09:15.835629: Epoch time: 275.0 s +2024-09-09 19:09:15.836157: Yayy! New best EMA pseudo Dice: 0.957 +2024-09-09 19:09:23.508043: +2024-09-09 19:09:23.509046: Epoch 677 +2024-09-09 19:09:23.509828: Current learning rate: 0.00362 +2024-09-09 19:14:02.285481: train_loss -0.7463 +2024-09-09 19:14:02.299254: val_loss -0.7361 +2024-09-09 19:14:02.300881: Pseudo dice [0.9624, 0.9584, 0.9736, 0.9522, 0.8928, 0.9732, 0.9926] +2024-09-09 19:14:02.304252: Epoch time: 278.78 s +2024-09-09 19:14:02.307024: Yayy! New best EMA pseudo Dice: 0.9571 +2024-09-09 19:14:08.318058: +2024-09-09 19:14:08.318807: Epoch 678 +2024-09-09 19:14:08.319338: Current learning rate: 0.00361 +2024-09-09 19:18:44.416346: train_loss -0.756 +2024-09-09 19:18:44.430979: val_loss -0.7543 +2024-09-09 19:18:44.431589: Pseudo dice [0.968, 0.9608, 0.9814, 0.9511, 0.8779, 0.9708, 0.9919] +2024-09-09 19:18:44.432174: Epoch time: 276.1 s +2024-09-09 19:18:44.432723: Yayy! New best EMA pseudo Dice: 0.9572 +2024-09-09 19:18:49.947075: +2024-09-09 19:18:49.947747: Epoch 679 +2024-09-09 19:18:49.948323: Current learning rate: 0.0036 +2024-09-09 19:23:16.638056: train_loss -0.745 +2024-09-09 19:23:16.639881: val_loss -0.7595 +2024-09-09 19:23:16.640476: Pseudo dice [0.9627, 0.9593, 0.9755, 0.9517, 0.8814, 0.9733, 0.9909] +2024-09-09 19:23:16.640970: Epoch time: 266.69 s +2024-09-09 19:23:19.419875: +2024-09-09 19:23:19.428082: Epoch 680 +2024-09-09 19:23:19.428840: Current learning rate: 0.00359 +2024-09-09 19:27:43.517943: train_loss -0.7369 +2024-09-09 19:27:43.537814: val_loss -0.7689 +2024-09-09 19:27:43.538578: Pseudo dice [0.9678, 0.9612, 0.9806, 0.9529, 0.8895, 0.9739, 0.992] +2024-09-09 19:27:43.539270: Epoch time: 264.1 s +2024-09-09 19:27:43.539886: Yayy! New best EMA pseudo Dice: 0.9573 +2024-09-09 19:27:50.900071: +2024-09-09 19:27:50.901000: Epoch 681 +2024-09-09 19:27:50.901670: Current learning rate: 0.00358 +2024-09-09 19:32:21.659206: train_loss -0.7542 +2024-09-09 19:32:21.674177: val_loss -0.7772 +2024-09-09 19:32:21.674908: Pseudo dice [0.9665, 0.9607, 0.9781, 0.9552, 0.8857, 0.9712, 0.9916] +2024-09-09 19:32:21.675600: Epoch time: 270.76 s +2024-09-09 19:32:21.676176: Yayy! New best EMA pseudo Dice: 0.9574 +2024-09-09 19:32:28.729717: +2024-09-09 19:32:28.730564: Epoch 682 +2024-09-09 19:32:28.731269: Current learning rate: 0.00357 +2024-09-09 19:37:10.569615: train_loss -0.7521 +2024-09-09 19:37:10.586835: val_loss -0.754 +2024-09-09 19:37:10.587542: Pseudo dice [0.9611, 0.9571, 0.977, 0.9484, 0.8812, 0.9735, 0.9908] +2024-09-09 19:37:10.588254: Epoch time: 281.84 s +2024-09-09 19:37:14.507377: +2024-09-09 19:37:14.508545: Epoch 683 +2024-09-09 19:37:14.509305: Current learning rate: 0.00356 +2024-09-09 19:42:02.304556: train_loss -0.7474 +2024-09-09 19:42:02.306693: val_loss -0.7868 +2024-09-09 19:42:02.307565: Pseudo dice [0.9683, 0.9598, 0.9814, 0.9494, 0.8763, 0.9708, 0.9925] +2024-09-09 19:42:02.308182: Epoch time: 287.8 s +2024-09-09 19:42:06.125216: +2024-09-09 19:42:06.140708: Epoch 684 +2024-09-09 19:42:06.141320: Current learning rate: 0.00355 +2024-09-09 19:46:54.855746: train_loss -0.7516 +2024-09-09 19:46:54.869830: val_loss -0.7636 +2024-09-09 19:46:54.870494: Pseudo dice [0.9624, 0.9593, 0.9735, 0.9527, 0.8948, 0.9735, 0.9936] +2024-09-09 19:46:54.871091: Epoch time: 288.74 s +2024-09-09 19:46:57.969430: +2024-09-09 19:46:57.984726: Epoch 685 +2024-09-09 19:46:57.985506: Current learning rate: 0.00354 +2024-09-09 19:51:30.166551: train_loss -0.7408 +2024-09-09 19:51:30.168631: val_loss -0.7442 +2024-09-09 19:51:30.169298: Pseudo dice [0.9667, 0.9619, 0.9791, 0.953, 0.8768, 0.973, 0.9909] +2024-09-09 19:51:30.169934: Epoch time: 272.2 s +2024-09-09 19:51:33.818347: +2024-09-09 19:51:33.836236: Epoch 686 +2024-09-09 19:51:33.837684: Current learning rate: 0.00353 +2024-09-09 19:56:21.284809: train_loss -0.7484 +2024-09-09 19:56:21.302694: val_loss -0.7698 +2024-09-09 19:56:21.303478: Pseudo dice [0.9639, 0.9581, 0.9782, 0.9439, 0.8846, 0.971, 0.9896] +2024-09-09 19:56:21.304197: Epoch time: 287.47 s +2024-09-09 19:56:23.929232: +2024-09-09 19:56:23.950285: Epoch 687 +2024-09-09 19:56:23.951519: Current learning rate: 0.00352 +2024-09-09 20:01:02.308302: train_loss -0.7574 +2024-09-09 20:01:02.311125: val_loss -0.7764 +2024-09-09 20:01:02.311987: Pseudo dice [0.9636, 0.9595, 0.9779, 0.9504, 0.8937, 0.9738, 0.991] +2024-09-09 20:01:02.312843: Epoch time: 278.39 s +2024-09-09 20:01:06.357221: +2024-09-09 20:01:06.358147: Epoch 688 +2024-09-09 20:01:06.358830: Current learning rate: 0.00351 +2024-09-09 20:05:09.039482: train_loss -0.738 +2024-09-09 20:05:09.065795: val_loss -0.7634 +2024-09-09 20:05:09.066497: Pseudo dice [0.9665, 0.9603, 0.9805, 0.9549, 0.887, 0.9717, 0.9905] +2024-09-09 20:05:09.067140: Epoch time: 242.69 s +2024-09-09 20:05:09.067668: Yayy! New best EMA pseudo Dice: 0.9575 +2024-09-09 20:05:15.324820: +2024-09-09 20:05:15.325661: Epoch 689 +2024-09-09 20:05:15.326280: Current learning rate: 0.0035 +2024-09-09 20:09:42.353695: train_loss -0.7395 +2024-09-09 20:09:42.355885: val_loss -0.7574 +2024-09-09 20:09:42.356577: Pseudo dice [0.9651, 0.9588, 0.9765, 0.9483, 0.8741, 0.9729, 0.9904] +2024-09-09 20:09:42.357162: Epoch time: 267.03 s +2024-09-09 20:09:45.549407: +2024-09-09 20:09:45.550303: Epoch 690 +2024-09-09 20:09:45.550944: Current learning rate: 0.00349 +2024-09-09 20:14:20.608427: train_loss -0.7579 +2024-09-09 20:14:20.611294: val_loss -0.7434 +2024-09-09 20:14:20.612392: Pseudo dice [0.9667, 0.9614, 0.9799, 0.9554, 0.8852, 0.9711, 0.9926] +2024-09-09 20:14:20.613191: Epoch time: 275.06 s +2024-09-09 20:14:23.729341: +2024-09-09 20:14:23.748062: Epoch 691 +2024-09-09 20:14:23.748976: Current learning rate: 0.00348 +2024-09-09 20:18:40.923666: train_loss -0.7462 +2024-09-09 20:18:40.925579: val_loss -0.79 +2024-09-09 20:18:40.926226: Pseudo dice [0.9684, 0.9609, 0.9823, 0.9541, 0.8921, 0.9737, 0.9912] +2024-09-09 20:18:40.926871: Epoch time: 257.2 s +2024-09-09 20:18:40.927756: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 20:18:47.313366: +2024-09-09 20:18:47.314124: Epoch 692 +2024-09-09 20:18:47.314673: Current learning rate: 0.00346 +2024-09-09 20:23:09.346247: train_loss -0.7498 +2024-09-09 20:23:09.370141: val_loss -0.7626 +2024-09-09 20:23:09.370973: Pseudo dice [0.9658, 0.9596, 0.9796, 0.9516, 0.8859, 0.9726, 0.9918] +2024-09-09 20:23:09.371645: Epoch time: 262.04 s +2024-09-09 20:23:09.372251: Yayy! New best EMA pseudo Dice: 0.9577 +2024-09-09 20:23:15.234082: +2024-09-09 20:23:15.234926: Epoch 693 +2024-09-09 20:23:15.235537: Current learning rate: 0.00345 +2024-09-09 20:27:34.468879: train_loss -0.7265 +2024-09-09 20:27:34.489463: val_loss -0.7748 +2024-09-09 20:27:34.490170: Pseudo dice [0.9664, 0.9594, 0.9795, 0.9543, 0.885, 0.9727, 0.9916] +2024-09-09 20:27:34.490919: Epoch time: 259.24 s +2024-09-09 20:27:34.491450: Yayy! New best EMA pseudo Dice: 0.9578 +2024-09-09 20:27:40.908537: +2024-09-09 20:27:40.909344: Epoch 694 +2024-09-09 20:27:40.909922: Current learning rate: 0.00344 +2024-09-09 20:32:09.184787: train_loss -0.7463 +2024-09-09 20:32:09.205666: val_loss -0.7463 +2024-09-09 20:32:09.206390: Pseudo dice [0.9659, 0.9598, 0.9793, 0.957, 0.8833, 0.9734, 0.9911] +2024-09-09 20:32:09.206988: Epoch time: 268.28 s +2024-09-09 20:32:09.207562: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 20:32:14.869820: +2024-09-09 20:32:14.870809: Epoch 695 +2024-09-09 20:32:14.871531: Current learning rate: 0.00343 +2024-09-09 20:36:38.424122: train_loss -0.7419 +2024-09-09 20:36:38.441801: val_loss -0.7648 +2024-09-09 20:36:38.442503: Pseudo dice [0.9664, 0.9606, 0.9782, 0.9544, 0.8857, 0.9713, 0.992] +2024-09-09 20:36:38.443083: Epoch time: 263.56 s +2024-09-09 20:36:38.443835: Yayy! New best EMA pseudo Dice: 0.9579 +2024-09-09 20:36:44.599053: +2024-09-09 20:36:44.599822: Epoch 696 +2024-09-09 20:36:44.600422: Current learning rate: 0.00342 +2024-09-09 20:40:58.225673: train_loss -0.7324 +2024-09-09 20:40:58.227729: val_loss -0.7621 +2024-09-09 20:40:58.228339: Pseudo dice [0.9658, 0.9618, 0.9776, 0.9535, 0.8926, 0.9752, 0.9924] +2024-09-09 20:40:58.228977: Epoch time: 253.63 s +2024-09-09 20:40:58.229585: Yayy! New best EMA pseudo Dice: 0.9581 +2024-09-09 20:41:05.858722: +2024-09-09 20:41:05.872068: Epoch 697 +2024-09-09 20:41:05.873045: Current learning rate: 0.00341 +2024-09-09 20:45:36.089445: train_loss -0.7546 +2024-09-09 20:45:36.105920: val_loss -0.7231 +2024-09-09 20:45:36.106781: Pseudo dice [0.9634, 0.9604, 0.9756, 0.9526, 0.8706, 0.9727, 0.9912] +2024-09-09 20:45:36.107382: Epoch time: 270.24 s +2024-09-09 20:45:41.869488: +2024-09-09 20:45:41.881196: Epoch 698 +2024-09-09 20:45:41.882855: Current learning rate: 0.0034 +2024-09-09 20:50:13.086580: train_loss -0.7513 +2024-09-09 20:50:13.102523: val_loss -0.7773 +2024-09-09 20:50:13.103363: Pseudo dice [0.9655, 0.9616, 0.9768, 0.9457, 0.8776, 0.957, 0.9883] +2024-09-09 20:50:13.104086: Epoch time: 271.22 s +2024-09-09 20:50:16.485598: +2024-09-09 20:50:16.486616: Epoch 699 +2024-09-09 20:50:16.487568: Current learning rate: 0.00339 +2024-09-09 20:54:57.871330: train_loss -0.7541 +2024-09-09 20:54:57.885837: val_loss -0.7786 +2024-09-09 20:54:57.886561: Pseudo dice [0.9626, 0.9599, 0.9737, 0.9497, 0.8829, 0.9716, 0.9924] +2024-09-09 20:54:57.887244: Epoch time: 281.39 s +2024-09-09 20:55:03.996881: +2024-09-09 20:55:03.997758: Epoch 700 +2024-09-09 20:55:03.998460: Current learning rate: 0.00338 +2024-09-09 20:59:30.880719: train_loss -0.7404 +2024-09-09 20:59:30.883156: val_loss -0.7117 +2024-09-09 20:59:30.884097: Pseudo dice [0.9616, 0.9584, 0.9726, 0.956, 0.8756, 0.9727, 0.9916] +2024-09-09 20:59:30.884964: Epoch time: 266.89 s +2024-09-09 20:59:33.973346: +2024-09-09 20:59:33.988156: Epoch 701 +2024-09-09 20:59:33.989129: Current learning rate: 0.00337 +2024-09-09 21:04:07.510696: train_loss -0.731 +2024-09-09 21:04:07.521914: val_loss -0.735 +2024-09-09 21:04:07.523580: Pseudo dice [0.9647, 0.9595, 0.9797, 0.9455, 0.8769, 0.9715, 0.9904] +2024-09-09 21:04:07.524713: Epoch time: 273.54 s +2024-09-09 21:04:10.985500: +2024-09-09 21:04:10.986428: Epoch 702 +2024-09-09 21:04:10.987219: Current learning rate: 0.00336 +2024-09-09 21:08:46.454842: train_loss -0.7556 +2024-09-09 21:08:46.457049: val_loss -0.7285 +2024-09-09 21:08:46.457802: Pseudo dice [0.9633, 0.959, 0.9765, 0.951, 0.8751, 0.9752, 0.9913] +2024-09-09 21:08:46.458474: Epoch time: 275.47 s +2024-09-09 21:08:50.014139: +2024-09-09 21:08:50.028743: Epoch 703 +2024-09-09 21:08:50.029408: Current learning rate: 0.00335 +2024-09-09 21:13:19.578228: train_loss -0.7603 +2024-09-09 21:13:19.594013: val_loss -0.7644 +2024-09-09 21:13:19.594728: Pseudo dice [0.9654, 0.9611, 0.9787, 0.9536, 0.8846, 0.9717, 0.9903] +2024-09-09 21:13:19.595351: Epoch time: 269.57 s +2024-09-09 21:13:22.980809: +2024-09-09 21:13:22.992960: Epoch 704 +2024-09-09 21:13:22.994094: Current learning rate: 0.00334 +2024-09-09 21:17:59.527768: train_loss -0.7674 +2024-09-09 21:17:59.542274: val_loss -0.7219 +2024-09-09 21:17:59.543721: Pseudo dice [0.9634, 0.9605, 0.9758, 0.9494, 0.889, 0.9731, 0.99] +2024-09-09 21:17:59.544892: Epoch time: 276.55 s +2024-09-09 21:18:02.374477: +2024-09-09 21:18:02.375510: Epoch 705 +2024-09-09 21:18:02.376207: Current learning rate: 0.00333 +2024-09-09 21:22:23.936786: train_loss -0.7514 +2024-09-09 21:22:23.939588: val_loss -0.751 +2024-09-09 21:22:23.940236: Pseudo dice [0.9675, 0.9589, 0.9791, 0.9528, 0.8728, 0.975, 0.9905] +2024-09-09 21:22:23.940903: Epoch time: 261.57 s +2024-09-09 21:22:27.386770: +2024-09-09 21:22:27.397127: Epoch 706 +2024-09-09 21:22:27.398045: Current learning rate: 0.00332 +2024-09-09 21:27:05.361423: train_loss -0.7579 +2024-09-09 21:27:05.370375: val_loss -0.7618 +2024-09-09 21:27:05.371104: Pseudo dice [0.966, 0.9596, 0.9793, 0.9529, 0.8766, 0.9712, 0.9915] +2024-09-09 21:27:05.371779: Epoch time: 277.98 s +2024-09-09 21:27:07.604290: +2024-09-09 21:27:07.605483: Epoch 707 +2024-09-09 21:27:07.606134: Current learning rate: 0.00331 +2024-09-09 21:31:40.901113: train_loss -0.7432 +2024-09-09 21:31:40.911097: val_loss -0.7471 +2024-09-09 21:31:40.911827: Pseudo dice [0.9611, 0.9592, 0.9775, 0.9516, 0.8957, 0.9744, 0.9926] +2024-09-09 21:31:40.913108: Epoch time: 273.3 s +2024-09-09 21:31:43.637420: +2024-09-09 21:31:43.638503: Epoch 708 +2024-09-09 21:31:43.639201: Current learning rate: 0.0033 +2024-09-09 21:36:22.534182: train_loss -0.7531 +2024-09-09 21:36:22.552107: val_loss -0.7456 +2024-09-09 21:36:22.552847: Pseudo dice [0.9625, 0.9594, 0.9753, 0.9524, 0.8894, 0.9728, 0.9922] +2024-09-09 21:36:22.553509: Epoch time: 278.9 s +2024-09-09 21:36:26.442100: +2024-09-09 21:36:26.443159: Epoch 709 +2024-09-09 21:36:26.443791: Current learning rate: 0.00329 +2024-09-09 21:40:46.214607: train_loss -0.7483 +2024-09-09 21:40:46.230998: val_loss -0.7612 +2024-09-09 21:40:46.232261: Pseudo dice [0.9627, 0.9594, 0.9697, 0.9502, 0.8864, 0.9742, 0.9929] +2024-09-09 21:40:46.233403: Epoch time: 259.78 s +2024-09-09 21:40:48.697528: +2024-09-09 21:40:48.704285: Epoch 710 +2024-09-09 21:40:48.708140: Current learning rate: 0.00328 +2024-09-09 21:45:04.699904: train_loss -0.7662 +2024-09-09 21:45:04.702037: val_loss -0.7708 +2024-09-09 21:45:04.703076: Pseudo dice [0.9628, 0.9611, 0.9771, 0.9488, 0.8925, 0.9746, 0.992] +2024-09-09 21:45:04.703704: Epoch time: 256.01 s +2024-09-09 21:45:08.700411: +2024-09-09 21:45:08.714029: Epoch 711 +2024-09-09 21:45:08.714799: Current learning rate: 0.00327 +2024-09-09 21:49:21.588274: train_loss -0.736 +2024-09-09 21:49:21.590418: val_loss -0.7513 +2024-09-09 21:49:21.591143: Pseudo dice [0.9659, 0.9601, 0.9772, 0.9471, 0.8758, 0.9729, 0.991] +2024-09-09 21:49:21.591787: Epoch time: 252.89 s +2024-09-09 21:49:25.219155: +2024-09-09 21:49:25.220603: Epoch 712 +2024-09-09 21:49:25.221635: Current learning rate: 0.00326 +2024-09-09 21:54:06.546794: train_loss -0.7511 +2024-09-09 21:54:06.550851: val_loss -0.7728 +2024-09-09 21:54:06.551612: Pseudo dice [0.9638, 0.9619, 0.9739, 0.9571, 0.8849, 0.9762, 0.9921] +2024-09-09 21:54:06.552971: Epoch time: 281.33 s +2024-09-09 21:54:08.662838: +2024-09-09 21:54:08.663851: Epoch 713 +2024-09-09 21:54:08.664537: Current learning rate: 0.00325 +2024-09-09 21:58:35.907461: train_loss -0.7604 +2024-09-09 21:58:35.909825: val_loss -0.7783 +2024-09-09 21:58:35.910756: Pseudo dice [0.9675, 0.96, 0.9823, 0.9534, 0.8723, 0.9729, 0.9895] +2024-09-09 21:58:35.911446: Epoch time: 267.25 s +2024-09-09 21:58:38.705949: +2024-09-09 21:58:38.706891: Epoch 714 +2024-09-09 21:58:38.707495: Current learning rate: 0.00324 +2024-09-09 22:03:03.282865: train_loss -0.7697 +2024-09-09 22:03:03.298128: val_loss -0.7522 +2024-09-09 22:03:03.299117: Pseudo dice [0.9659, 0.962, 0.9797, 0.9587, 0.8867, 0.9723, 0.9907] +2024-09-09 22:03:03.299791: Epoch time: 264.58 s +2024-09-09 22:03:06.194628: +2024-09-09 22:03:06.195853: Epoch 715 +2024-09-09 22:03:06.196568: Current learning rate: 0.00323 +2024-09-09 22:07:48.878177: train_loss -0.7538 +2024-09-09 22:07:48.895003: val_loss -0.7552 +2024-09-09 22:07:48.897228: Pseudo dice [0.966, 0.9602, 0.9787, 0.9505, 0.8735, 0.9746, 0.99] +2024-09-09 22:07:48.899628: Epoch time: 282.69 s +2024-09-09 22:07:51.862839: +2024-09-09 22:07:51.876637: Epoch 716 +2024-09-09 22:07:51.877368: Current learning rate: 0.00322 +2024-09-09 22:12:12.630432: train_loss -0.7477 +2024-09-09 22:12:12.632652: val_loss -0.7256 +2024-09-09 22:12:12.633334: Pseudo dice [0.9678, 0.9605, 0.9783, 0.9553, 0.8706, 0.9724, 0.9923] +2024-09-09 22:12:12.634002: Epoch time: 260.77 s +2024-09-09 22:12:16.504856: +2024-09-09 22:12:16.524096: Epoch 717 +2024-09-09 22:12:16.525091: Current learning rate: 0.00321 +2024-09-09 22:16:38.314270: train_loss -0.7486 +2024-09-09 22:16:38.328194: val_loss -0.777 +2024-09-09 22:16:38.330215: Pseudo dice [0.9667, 0.9616, 0.9809, 0.9543, 0.8925, 0.9741, 0.9926] +2024-09-09 22:16:38.331851: Epoch time: 261.81 s +2024-09-09 22:16:41.426231: +2024-09-09 22:16:41.427145: Epoch 718 +2024-09-09 22:16:41.427765: Current learning rate: 0.0032 +2024-09-09 22:20:55.261117: train_loss -0.7452 +2024-09-09 22:20:55.277567: val_loss -0.7401 +2024-09-09 22:20:55.278183: Pseudo dice [0.9672, 0.9619, 0.98, 0.9579, 0.8806, 0.971, 0.9897] +2024-09-09 22:20:55.278721: Epoch time: 253.84 s +2024-09-09 22:20:58.387914: +2024-09-09 22:20:58.404864: Epoch 719 +2024-09-09 22:20:58.405832: Current learning rate: 0.00319 +2024-09-09 22:25:38.814210: train_loss -0.7622 +2024-09-09 22:25:38.829886: val_loss -0.7626 +2024-09-09 22:25:38.830584: Pseudo dice [0.9619, 0.9598, 0.9706, 0.9504, 0.8816, 0.9706, 0.9917] +2024-09-09 22:25:38.831255: Epoch time: 280.43 s +2024-09-09 22:25:41.865039: +2024-09-09 22:25:41.865955: Epoch 720 +2024-09-09 22:25:41.866675: Current learning rate: 0.00318 +2024-09-09 22:30:26.212425: train_loss -0.7557 +2024-09-09 22:30:26.214742: val_loss -0.7638 +2024-09-09 22:30:26.215443: Pseudo dice [0.9682, 0.9636, 0.9804, 0.9544, 0.8797, 0.9741, 0.9918] +2024-09-09 22:30:26.216099: Epoch time: 284.35 s +2024-09-09 22:30:30.729914: +2024-09-09 22:30:30.740072: Epoch 721 +2024-09-09 22:30:30.740883: Current learning rate: 0.00317 +2024-09-09 22:34:48.754834: train_loss -0.7457 +2024-09-09 22:34:48.777864: val_loss -0.7637 +2024-09-09 22:34:48.778615: Pseudo dice [0.9669, 0.9596, 0.9802, 0.9574, 0.8972, 0.9753, 0.9929] +2024-09-09 22:34:48.779229: Epoch time: 258.03 s +2024-09-09 22:34:52.564563: +2024-09-09 22:34:52.576729: Epoch 722 +2024-09-09 22:34:52.577489: Current learning rate: 0.00316 +2024-09-09 22:39:31.130851: train_loss -0.7537 +2024-09-09 22:39:31.138636: val_loss -0.7863 +2024-09-09 22:39:31.139627: Pseudo dice [0.9679, 0.9629, 0.9821, 0.9552, 0.8912, 0.9742, 0.9923] +2024-09-09 22:39:31.140395: Epoch time: 278.57 s +2024-09-09 22:39:31.141178: Yayy! New best EMA pseudo Dice: 0.9582 +2024-09-09 22:39:41.153314: +2024-09-09 22:39:41.154222: Epoch 723 +2024-09-09 22:39:41.154898: Current learning rate: 0.00315 +2024-09-09 22:44:13.834788: train_loss -0.7523 +2024-09-09 22:44:13.854669: val_loss -0.806 +2024-09-09 22:44:13.856287: Pseudo dice [0.9663, 0.9578, 0.9793, 0.9546, 0.8871, 0.9745, 0.9906] +2024-09-09 22:44:13.857637: Epoch time: 272.69 s +2024-09-09 22:44:13.858544: Yayy! New best EMA pseudo Dice: 0.9582 +2024-09-09 22:44:20.811447: +2024-09-09 22:44:20.812276: Epoch 724 +2024-09-09 22:44:20.812887: Current learning rate: 0.00314 +2024-09-09 22:48:50.121397: train_loss -0.7568 +2024-09-09 22:48:50.123561: val_loss -0.7839 +2024-09-09 22:48:50.129186: Pseudo dice [0.9636, 0.9594, 0.9763, 0.954, 0.8821, 0.9732, 0.9917] +2024-09-09 22:48:50.130044: Epoch time: 269.31 s +2024-09-09 22:48:53.616905: +2024-09-09 22:48:53.617814: Epoch 725 +2024-09-09 22:48:53.618441: Current learning rate: 0.00313 +2024-09-09 22:53:13.394618: train_loss -0.7387 +2024-09-09 22:53:13.397083: val_loss -0.7641 +2024-09-09 22:53:13.397858: Pseudo dice [0.9647, 0.9597, 0.9782, 0.9522, 0.876, 0.9732, 0.9918] +2024-09-09 22:53:13.398593: Epoch time: 259.78 s +2024-09-09 22:53:17.125518: +2024-09-09 22:53:17.126587: Epoch 726 +2024-09-09 22:53:17.127351: Current learning rate: 0.00312 +2024-09-09 22:57:36.255643: train_loss -0.7455 +2024-09-09 22:57:36.265923: val_loss -0.7885 +2024-09-09 22:57:36.266959: Pseudo dice [0.9639, 0.9594, 0.9771, 0.9511, 0.8895, 0.9723, 0.9908] +2024-09-09 22:57:36.267694: Epoch time: 259.13 s +2024-09-09 22:57:39.517941: +2024-09-09 22:57:39.519070: Epoch 727 +2024-09-09 22:57:39.519677: Current learning rate: 0.00311 +2024-09-09 23:02:02.688951: train_loss -0.7569 +2024-09-09 23:02:02.691096: val_loss -0.7291 +2024-09-09 23:02:02.691679: Pseudo dice [0.9666, 0.961, 0.9791, 0.9487, 0.8776, 0.9738, 0.9918] +2024-09-09 23:02:02.692433: Epoch time: 263.18 s +2024-09-09 23:02:05.929811: +2024-09-09 23:02:05.941043: Epoch 728 +2024-09-09 23:02:05.942116: Current learning rate: 0.0031 +2024-09-09 23:06:25.318637: train_loss -0.7466 +2024-09-09 23:06:25.321103: val_loss -0.7261 +2024-09-09 23:06:25.321984: Pseudo dice [0.9628, 0.9586, 0.9774, 0.9542, 0.8699, 0.9701, 0.9873] +2024-09-09 23:06:25.322649: Epoch time: 259.4 s +2024-09-09 23:06:28.650681: +2024-09-09 23:06:28.651630: Epoch 729 +2024-09-09 23:06:28.652337: Current learning rate: 0.00309 +2024-09-09 23:10:52.580324: train_loss -0.7555 +2024-09-09 23:10:52.582422: val_loss -0.7503 +2024-09-09 23:10:52.583081: Pseudo dice [0.9689, 0.9601, 0.9806, 0.9539, 0.8822, 0.9728, 0.9908] +2024-09-09 23:10:52.583640: Epoch time: 263.94 s +2024-09-09 23:10:55.094393: +2024-09-09 23:10:55.095306: Epoch 730 +2024-09-09 23:10:55.095971: Current learning rate: 0.00308 +2024-09-09 23:15:20.764637: train_loss -0.7536 +2024-09-09 23:15:20.779649: val_loss -0.7509 +2024-09-09 23:15:20.780416: Pseudo dice [0.9671, 0.961, 0.9809, 0.9554, 0.8922, 0.9744, 0.9926] +2024-09-09 23:15:20.781051: Epoch time: 265.68 s +2024-09-09 23:15:24.239566: +2024-09-09 23:15:24.251765: Epoch 731 +2024-09-09 23:15:24.252572: Current learning rate: 0.00307 +2024-09-09 23:19:59.551842: train_loss -0.7523 +2024-09-09 23:19:59.586523: val_loss -0.7734 +2024-09-09 23:19:59.587775: Pseudo dice [0.9696, 0.9604, 0.9831, 0.9574, 0.8792, 0.9736, 0.9908] +2024-09-09 23:19:59.588804: Epoch time: 275.32 s +2024-09-09 23:20:02.858043: +2024-09-09 23:20:02.859804: Epoch 732 +2024-09-09 23:20:02.860396: Current learning rate: 0.00306 +2024-09-09 23:24:31.844913: train_loss -0.7559 +2024-09-09 23:24:31.861967: val_loss -0.7629 +2024-09-09 23:24:31.862727: Pseudo dice [0.9689, 0.9599, 0.9816, 0.9515, 0.8883, 0.9743, 0.9921] +2024-09-09 23:24:31.863359: Epoch time: 268.99 s +2024-09-09 23:24:35.207467: +2024-09-09 23:24:35.213741: Epoch 733 +2024-09-09 23:24:35.214541: Current learning rate: 0.00305 +2024-09-09 23:29:01.973487: train_loss -0.7511 +2024-09-09 23:29:01.975432: val_loss -0.7865 +2024-09-09 23:29:01.975986: Pseudo dice [0.9668, 0.9598, 0.9805, 0.955, 0.8898, 0.9764, 0.9916] +2024-09-09 23:29:01.976567: Epoch time: 266.77 s +2024-09-09 23:29:01.977042: Yayy! New best EMA pseudo Dice: 0.9583 +2024-09-09 23:29:09.705345: +2024-09-09 23:29:09.706256: Epoch 734 +2024-09-09 23:29:09.706931: Current learning rate: 0.00304 +2024-09-09 23:33:35.958028: train_loss -0.7518 +2024-09-09 23:33:35.981151: val_loss -0.743 +2024-09-09 23:33:35.982001: Pseudo dice [0.9646, 0.958, 0.9781, 0.9472, 0.8706, 0.9728, 0.9899] +2024-09-09 23:33:35.982705: Epoch time: 266.26 s +2024-09-09 23:33:38.657300: +2024-09-09 23:33:38.658316: Epoch 735 +2024-09-09 23:33:38.659032: Current learning rate: 0.00303 +2024-09-09 23:38:08.969827: train_loss -0.7492 +2024-09-09 23:38:08.973760: val_loss -0.7016 +2024-09-09 23:38:08.975720: Pseudo dice [0.9651, 0.9631, 0.9758, 0.9451, 0.8721, 0.9702, 0.9913] +2024-09-09 23:38:08.977560: Epoch time: 270.32 s +2024-09-09 23:38:13.649170: +2024-09-09 23:38:13.650218: Epoch 736 +2024-09-09 23:38:13.652505: Current learning rate: 0.00302 +2024-09-09 23:42:54.474362: train_loss -0.76 +2024-09-09 23:42:54.489999: val_loss -0.7899 +2024-09-09 23:42:54.490902: Pseudo dice [0.9669, 0.9596, 0.9818, 0.9545, 0.8812, 0.9745, 0.9905] +2024-09-09 23:42:54.491596: Epoch time: 280.84 s +2024-09-09 23:42:57.844212: +2024-09-09 23:42:57.845273: Epoch 737 +2024-09-09 23:42:57.845882: Current learning rate: 0.00301 +2024-09-09 23:47:26.200981: train_loss -0.7481 +2024-09-09 23:47:26.203563: val_loss -0.738 +2024-09-09 23:47:26.204257: Pseudo dice [0.9655, 0.9591, 0.9763, 0.9477, 0.8754, 0.9751, 0.9918] +2024-09-09 23:47:26.205066: Epoch time: 268.36 s +2024-09-09 23:47:29.083533: +2024-09-09 23:47:29.084605: Epoch 738 +2024-09-09 23:47:29.085375: Current learning rate: 0.003 +2024-09-09 23:52:02.915918: train_loss -0.7442 +2024-09-09 23:52:02.919861: val_loss -0.723 +2024-09-09 23:52:02.920567: Pseudo dice [0.9686, 0.9622, 0.9814, 0.954, 0.8774, 0.9744, 0.9891] +2024-09-09 23:52:02.921153: Epoch time: 273.84 s +2024-09-09 23:52:07.123781: +2024-09-09 23:52:07.125135: Epoch 739 +2024-09-09 23:52:07.126177: Current learning rate: 0.00299 +2024-09-09 23:56:56.845532: train_loss -0.7495 +2024-09-09 23:56:56.858001: val_loss -0.7386 +2024-09-09 23:56:56.858689: Pseudo dice [0.9693, 0.9628, 0.9829, 0.9529, 0.8742, 0.9693, 0.989] +2024-09-09 23:56:56.859271: Epoch time: 289.73 s +2024-09-09 23:57:01.597420: +2024-09-09 23:57:01.598368: Epoch 740 +2024-09-09 23:57:01.599057: Current learning rate: 0.00297 +2024-09-10 00:01:42.511602: train_loss -0.7449 +2024-09-10 00:01:42.513977: val_loss -0.7811 +2024-09-10 00:01:42.514796: Pseudo dice [0.9638, 0.9602, 0.9743, 0.9501, 0.8871, 0.9724, 0.9913] +2024-09-10 00:01:42.515538: Epoch time: 280.92 s +2024-09-10 00:01:44.962341: +2024-09-10 00:01:44.963399: Epoch 741 +2024-09-10 00:01:44.964237: Current learning rate: 0.00296 +2024-09-10 00:06:07.650256: train_loss -0.748 +2024-09-10 00:06:07.652361: val_loss -0.7525 +2024-09-10 00:06:07.653112: Pseudo dice [0.9664, 0.9592, 0.9786, 0.951, 0.8973, 0.9746, 0.9932] +2024-09-10 00:06:07.653793: Epoch time: 262.69 s +2024-09-10 00:06:11.217913: +2024-09-10 00:06:11.219038: Epoch 742 +2024-09-10 00:06:11.219718: Current learning rate: 0.00295 +2024-09-10 00:10:38.340889: train_loss -0.7329 +2024-09-10 00:10:38.343300: val_loss -0.7405 +2024-09-10 00:10:38.344108: Pseudo dice [0.9652, 0.958, 0.9787, 0.9507, 0.8826, 0.9663, 0.9929] +2024-09-10 00:10:38.344919: Epoch time: 267.13 s +2024-09-10 00:10:41.886844: +2024-09-10 00:10:41.887846: Epoch 743 +2024-09-10 00:10:41.888572: Current learning rate: 0.00294 +2024-09-10 00:15:16.228575: train_loss -0.7388 +2024-09-10 00:15:16.242189: val_loss -0.7121 +2024-09-10 00:15:16.242968: Pseudo dice [0.9655, 0.9604, 0.9778, 0.9505, 0.8818, 0.9684, 0.9919] +2024-09-10 00:15:16.243677: Epoch time: 274.35 s +2024-09-10 00:15:19.693210: +2024-09-10 00:15:19.694169: Epoch 744 +2024-09-10 00:15:19.694876: Current learning rate: 0.00293 +2024-09-10 00:19:46.507893: train_loss -0.7343 +2024-09-10 00:19:46.522193: val_loss -0.7405 +2024-09-10 00:19:46.522842: Pseudo dice [0.9665, 0.962, 0.9796, 0.9546, 0.8835, 0.9752, 0.9911] +2024-09-10 00:19:46.523441: Epoch time: 266.82 s +2024-09-10 00:19:50.304636: +2024-09-10 00:19:50.305610: Epoch 745 +2024-09-10 00:19:50.306216: Current learning rate: 0.00292 +2024-09-10 00:24:21.901468: train_loss -0.7528 +2024-09-10 00:24:21.921535: val_loss -0.7222 +2024-09-10 00:24:21.923577: Pseudo dice [0.9659, 0.9589, 0.9785, 0.955, 0.8705, 0.9681, 0.9887] +2024-09-10 00:24:21.925778: Epoch time: 271.6 s +2024-09-10 00:24:25.648306: +2024-09-10 00:24:25.649346: Epoch 746 +2024-09-10 00:24:25.650017: Current learning rate: 0.00291 +2024-09-10 00:29:02.846255: train_loss -0.7559 +2024-09-10 00:29:02.848864: val_loss -0.7695 +2024-09-10 00:29:02.849630: Pseudo dice [0.9691, 0.9621, 0.9822, 0.9521, 0.8768, 0.9753, 0.9906] +2024-09-10 00:29:02.850497: Epoch time: 277.2 s +2024-09-10 00:29:07.885435: +2024-09-10 00:29:07.886604: Epoch 747 +2024-09-10 00:29:07.887601: Current learning rate: 0.0029 +2024-09-10 00:33:37.896878: train_loss -0.7513 +2024-09-10 00:33:37.898988: val_loss -0.7603 +2024-09-10 00:33:37.899640: Pseudo dice [0.9686, 0.9614, 0.9831, 0.9549, 0.883, 0.9682, 0.9906] +2024-09-10 00:33:37.901047: Epoch time: 270.02 s +2024-09-10 00:33:41.664593: +2024-09-10 00:33:41.665527: Epoch 748 +2024-09-10 00:33:41.666497: Current learning rate: 0.00289 +2024-09-10 00:38:09.304243: train_loss -0.7425 +2024-09-10 00:38:09.306746: val_loss -0.7594 +2024-09-10 00:38:09.307648: Pseudo dice [0.9657, 0.9604, 0.9749, 0.9458, 0.8771, 0.9718, 0.9909] +2024-09-10 00:38:09.308398: Epoch time: 267.64 s +2024-09-10 00:38:13.405613: +2024-09-10 00:38:13.406664: Epoch 749 +2024-09-10 00:38:13.407326: Current learning rate: 0.00288 +2024-09-10 00:43:03.257471: train_loss -0.7558 +2024-09-10 00:43:03.273629: val_loss -0.7455 +2024-09-10 00:43:03.274372: Pseudo dice [0.965, 0.964, 0.9763, 0.9489, 0.8805, 0.9754, 0.9917] +2024-09-10 00:43:03.275055: Epoch time: 289.86 s \ No newline at end of file diff --git a/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json new file mode 100644 index 0000000000000000000000000000000000000000..f2c3b7720a251a9b33f33f45b6efa0d074625fcd --- /dev/null +++ b/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json @@ -0,0 +1,356 @@ +{ + "dataset_name": "Dataset611_Tissue", + "plans_name": "nnUNetResEncUNetLPlans", + "original_median_spacing_after_transp": [ + 1.5, + 1.5, + 1.5 + ], + "original_median_shape_after_transp": [ + 170, + 309, + 309 + ], + "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": 89, + "patch_size": [ + 320, + 320 + ], + "median_image_size_in_voxels": [ + 257.0, + 260.0 + ], + "spacing": [ + 1.5, + 1.5 + ], + "normalization_schemes": [ + 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