北京网站建设itcask,企业展示型网站,公众号如何做网站,设计产品目录 报错解决办法 报错 笔者在使用 nnunetv2 进行 KiTS19肾脏肿瘤分割实验的训练步骤中
使用 2d 和3d_lowres 训练都没有问题
nnUNetv2_train 40 2d 0nnUNetv2_train 40 3d_lowres 0但是使用 3d_cascade_fullres 和 3d_fullres 训练
nnUNetv2_train 40 3d_cascade_fullres … 目录 报错解决办法 报错 笔者在使用 nnunetv2 进行 KiTS19肾脏肿瘤分割实验的训练步骤中
使用 2d 和3d_lowres 训练都没有问题
nnUNetv2_train 40 2d 0nnUNetv2_train 40 3d_lowres 0但是使用 3d_cascade_fullres 和 3d_fullres 训练
nnUNetv2_train 40 3d_cascade_fullres 0nnUNetv2_train 40 3d_fullres 0都会报这个异常 ValueError: mmap length is greater than file size
具体报错内容如下
rootautodl-container-fdb34f8e52-02177b7e:~# nnUNetv2_train 40 3d_cascade_fullres 0
Using device: cuda:0#######################################################################
Please cite the following paper when using nnU-Net:
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
#######################################################################This is the configuration used by this training:
Configuration name: 3d_cascade_fullres{data_identifier: nnUNetPlans_3d_fullres, preprocessor_name: DefaultPreprocessor, batch_size: 2, patch_size: [128, 128, 128], median_image_size_in_voxels: [525.5, 512.0, 512.0], spacing: [0.78126, 0.78125, 0.78125], normalization_schemes: [CTNormalization], use_mask_for_norm: [False], UNet_class_name: PlainConvUNet, UNet_base_num_features: 32, n_conv_per_stage_encoder: [2, 2, 2, 2, 2, 2], n_conv_per_stage_decoder: [2, 2, 2, 2, 2], num_pool_per_axis: [5, 5, 5], pool_op_kernel_sizes: [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], conv_kernel_sizes: [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], unet_max_num_features: 320, resampling_fn_data: resample_data_or_seg_to_shape, resampling_fn_seg: resample_data_or_seg_to_shape, resampling_fn_data_kwargs: {is_seg: False, order: 3, order_z: 0, force_separate_z: None}, resampling_fn_seg_kwargs: {is_seg: True, order: 1, order_z: 0, force_separate_z: None}, resampling_fn_probabilities: resample_data_or_seg_to_shape, resampling_fn_probabilities_kwargs: {is_seg: False, order: 1, order_z: 0, force_separate_z: None}, batch_dice: True, inherits_from: 3d_fullres, previous_stage: 3d_lowres}These are the global plan.json settings:{dataset_name: Dataset040_KiTS, plans_name: nnUNetPlans, original_median_spacing_after_transp: [3.0, 0.78125, 0.78125], original_median_shape_after_transp: [108, 512, 512], image_reader_writer: SimpleITKIO, transpose_forward: [2, 0, 1], transpose_backward: [1, 2, 0], experiment_planner_used: ExperimentPlanner, label_manager: LabelManager, foreground_intensity_properties_per_channel: {0: {max: 3071.0, mean: 102.5714111328125, median: 103.0, min: -1015.0, percentile_00_5: -75.0, percentile_99_5: 295.0, std: 73.64986419677734}}}2023-10-13 17:22:36.747343: unpacking dataset...
2023-10-13 17:22:40.991390: unpacking done...
2023-10-13 17:22:40.992978: do_dummy_2d_data_aug: False
2023-10-13 17:22:40.997410: Using splits from existing split file: /root/autodl-tmp/nnUNet-master/dataset/nnUNet_preprocessed/Dataset040_KiTS/splits_final.json
2023-10-13 17:22:40.998125: The split file contains 5 splits.
2023-10-13 17:22:40.998262: Desired fold for training: 0
2023-10-13 17:22:40.998355: This split has 168 training and 42 validation cases.
/root/miniconda3/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator instance_norm is set to trainTrue. Exporting with trainTrue.warnings.warn(
2023-10-13 17:22:45.383066:
2023-10-13 17:22:45.383146: Epoch 0
2023-10-13 17:22:45.383244: Current learning rate: 0.01
Exception in background worker 4:mmap length is greater than file size
Traceback (most recent call last):File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py, line 53, in produceritem next(data_loader)File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/data_loader.py, line 126, in __next__return self.generate_train_batch()File /root/autodl-tmp/nnUNet-master/nnunetv2/training/dataloading/data_loader_3d.py, line 19, in generate_train_batchdata, seg, properties self._data.load_case(i)File /root/autodl-tmp/nnUNet-master/nnunetv2/training/dataloading/nnunet_dataset.py, line 86, in load_casedata np.load(entry[data_file][:-4] .npy, r)File /root/miniconda3/lib/python3.10/site-packages/numpy/lib/npyio.py, line 429, in loadreturn format.open_memmap(file, modemmap_mode,File /root/miniconda3/lib/python3.10/site-packages/numpy/lib/format.py, line 937, in open_memmapmarray numpy.memmap(filename, dtypedtype, shapeshape, orderorder,File /root/miniconda3/lib/python3.10/site-packages/numpy/core/memmap.py, line 267, in __new__mm mmap.mmap(fid.fileno(), bytes, accessacc, offsetstart)
ValueError: mmap length is greater than file size
Exception in background worker 2:mmap length is greater than file size
Traceback (most recent call last):File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py, line 53, in produceritem next(data_loader)File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/data_loader.py, line 126, in __next__return self.generate_train_batch()File /root/autodl-tmp/nnUNet-master/nnunetv2/training/dataloading/data_loader_3d.py, line 19, in generate_train_batchdata, seg, properties self._data.load_case(i)File /root/autodl-tmp/nnUNet-master/nnunetv2/training/dataloading/nnunet_dataset.py, line 86, in load_casedata np.load(entry[data_file][:-4] .npy, r)File /root/miniconda3/lib/python3.10/site-packages/numpy/lib/npyio.py, line 429, in loadreturn format.open_memmap(file, modemmap_mode,File /root/miniconda3/lib/python3.10/site-packages/numpy/lib/format.py, line 937, in open_memmapmarray numpy.memmap(filename, dtypedtype, shapeshape, orderorder,File /root/miniconda3/lib/python3.10/site-packages/numpy/core/memmap.py, line 267, in __new__mm mmap.mmap(fid.fileno(), bytes, accessacc, offsetstart)
ValueError: mmap length is greater than file size
using pin_memory on device 0
Traceback (most recent call last):File /root/miniconda3/bin/nnUNetv2_train, line 8, in modulesys.exit(run_training_entry())File /root/autodl-tmp/nnUNet-master/nnunetv2/run/run_training.py, line 268, in run_training_entryrun_training(args.dataset_name_or_id, args.configuration, args.fold, args.tr, args.p, args.pretrained_weights,File /root/autodl-tmp/nnUNet-master/nnunetv2/run/run_training.py, line 204, in run_trainingnnunet_trainer.run_training()File /root/autodl-tmp/nnUNet-master/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py, line 1237, in run_trainingtrain_outputs.append(self.train_step(next(self.dataloader_train)))File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py, line 196, in __next__item self.__get_next_item()File /root/miniconda3/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py, line 181, in __get_next_itemraise RuntimeError(One or more background workers are no longer alive. Exiting. Please check the
RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message解决办法 nnunet 作者给出的解决办法详情请戳 进入指定文件夹中执行
rm *.npy
