h5py\_init_.py:26：FutureWarning: Conversion of the second argument of issubdtype from `float` to

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原创 最新推荐文章于 2023-10-10 14:00:45 发布 · 1.4k 阅读 · 1 ·
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#tensorflow #h5py

博客围绕TensorFlow开发展开，开发时h5py包出现错误，出错位置在h5py_init_.py:26。解决办法是对h5py进行更新升级。同时介绍了h5py是Python操作HDF5的模块，HDF5是用于存储和组织大量数据的文件格式。

错误

在做tensorflow开发时，总是提示错误：

h5py\_init_.py:26：FutureWarning: Conversion of the second argument of issubdtype from `float` to

解决错误

出错位置h5py_init_.py:26
包内出错，是h5py包

对h5py进行更新升级

pip install --upgrade h5py

感谢https://blog.youkuaiyun.com/qq_41185868/article/details/80276847#t0

h5py是什么

h5py是Python语言用来操作HDF5的模块
官方网站：http://www.h5py.org/
感谢：https://blog.youkuaiyun.com/jclian91/article/details/83033834

HDF5是什么

HDF（Hierarchical Data Format）是一种设计用于存储和组织大量数据的文件格式，最开始由美国国家超算中心研发

感谢：https://blog.youkuaiyun.com/u010945683/article/details/79931702

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(nnunet_env) jzuser@vpc87-3:~/Work_dir/Gn/pystudy/nnUNet/nnUNet$ ls -R .: documentation LICENSE nnunetv2 nnunetv2.egg-info pyproject.toml readme.md setup.py UNKNOWN.egg-info ./documentation: assets dataset_format.md __init__.py run_inference_with_pretrained_models.md benchmarking.md explanation_normalization.md installation_instructions.md set_environment_variables.md changelog.md explanation_plans_files.md manual_data_splits.md setting_up_paths.md competitions extending_nnunet.md pretraining_and_finetuning.md tldr_migration_guide_from_v1.md convert_msd_dataset.md how_to_use_nnunet.md region_based_training.md dataset_format_inference.md ignore_label.md resenc_presets.md ./documentation/assets: amos2022_sparseseg10_2d.png dkfz_logo.png nnUNetMagician.png regions_vs_labels.png sparse_annotation_amos.png amos2022_sparseseg10.png HI_Logo.png nnU-Net_overview.png scribble_example.png ./documentation/competitions: AortaSeg24.md AutoPETII.md FLARE24 __init__.py Toothfairy2 ./documentation/competitions/FLARE24: __init__.py Task_1 Task_2 ./documentation/competitions/FLARE24/Task_1: inference_flare_task1.py __init__.py readme.md ./documentation/competitions/FLARE24/Task_2: inference_flare_task2.py __init__.py readme.md ./documentation/competitions/Toothfairy2: inference_script_semseg_only_customInf2.py __init__.py readme.md ./nnunetv2: batch_running ensembling imageio model_sharing preprocessing training configuration.py evaluation inference paths.py run utilities dataset_conversion experiment_planning __init__.py postprocessing tests ./nnunetv2/batch_running: benchmarking collect_results_custom_Decathlon.py __init__.py release_trainings collect_results_custom_Decathlon_2d.py generate_lsf_runs_customDecathlon.py jobs.sh ./nnunetv2/batch_running/benchmarking: generate_benchmarking_commands.py __init__.py summarize_benchmark_results.py ./nnunetv2/batch_running/release_trainings: __init__.py nnunetv2_v1 ./nnunetv2/batch_running/release_trainings/nnunetv2_v1: collect_results.py generate_lsf_commands.py __init__.py ./nnunetv2/dataset_conversion: convert_MSD_dataset.py Dataset114_MNMs.py Dataset223_AMOS2022postChallenge.py convert_raw_dataset_from_old_nnunet_format.py Dataset115_EMIDEC.py Dataset224_AbdomenAtlas1.0.py Dataset015_018_RibFrac_RibSeg.py Dataset119_ToothFairy2_All.py Dataset226_BraTS2024-BraTS-GLI.py Dataset021_CTAAorta.py Dataset120_RoadSegmentation.py Dataset227_TotalSegmentatorMRI.py Dataset023_AbdomenAtlas1_1Mini.py Dataset137_BraTS21.py Dataset987_dummyDataset4.py Dataset027_ACDC.py Dataset218_Amos2022_task1.py Dataset989_dummyDataset4_2.py Dataset042_BraTS18.py Dataset219_Amos2022_task2.py datasets_for_integration_tests Dataset043_BraTS19.py Dataset220_KiTS2023.py generate_dataset_json.py Dataset073_Fluo_C3DH_A549_SIM.py Dataset221_AutoPETII_2023.py __init__.py ./nnunetv2/dataset_conversion/datasets_for_integration_tests: Dataset996_IntegrationTest_Hippocampus_regions_ignore.py Dataset998_IntegrationTest_Hippocampus_ignore.py __init__.py Dataset997_IntegrationTest_Hippocampus_regions.py Dataset999_IntegrationTest_Hippocampus.py ./nnunetv2/ensembling: ensemble.py __init__.py ./nnunetv2/evaluation: accumulate_cv_results.py evaluate_predictions.py find_best_configuration.py __init__.py ./nnunetv2/experiment_planning: dataset_fingerprint __init__.py plan_and_preprocess_entrypoints.py verify_dataset_integrity.py experiment_planners plan_and_preprocess_api.py plans_for_pretraining ./nnunetv2/experiment_planning/dataset_fingerprint: fingerprint_extractor.py __init__.py ./nnunetv2/experiment_planning/experiment_planners: default_experiment_planner.py __init__.py network_topology.py resampling resencUNet_planner.py residual_unets ./nnunetv2/experiment_planning/experiment_planners/resampling: __init__.py planners_no_resampling.py resample_with_torch.py ./nnunetv2/experiment_planning/experiment_planners/residual_unets: __init__.py residual_encoder_unet_planners.py ./nnunetv2/experiment_planning/plans_for_pretraining: __init__.py move_plans_between_datasets.py ./nnunetv2/imageio: base_reader_writer.py natural_image_reader_writer.py reader_writer_registry.py simpleitk_reader_writer.py __init__.py nibabel_reader_writer.py readme.md tif_reader_writer.py ./nnunetv2/inference: data_iterators.py export_prediction.py JHU_inference.py readme.md examples.py __init__.py predict_from_raw_data.py sliding_window_prediction.py ./nnunetv2/model_sharing: entry_points.py __init__.py model_download.py model_export.py model_import.py ./nnunetv2/postprocessing: __init__.py remove_connected_components.py ./nnunetv2/preprocessing: cropping __init__.py normalization preprocessors resampling ./nnunetv2/preprocessing/cropping: cropping.py __init__.py ./nnunetv2/preprocessing/normalization: default_normalization_schemes.py __init__.py map_channel_name_to_normalization.py readme.md ./nnunetv2/preprocessing/preprocessors: default_preprocessor.py __init__.py ./nnunetv2/preprocessing/resampling: default_resampling.py __init__.py no_resampling.py resample_torch.py utils.py ./nnunetv2/run: __init__.py load_pretrained_weights.py run_training.py ./nnunetv2/tests: example_data __init__.py integration_tests ./nnunetv2/tests/example_data: example_ct_sm.nii.gz example_ct_sm_T300_output.nii.gz ./nnunetv2/tests/integration_tests: add_lowres_and_cascade.py lsf_commands.sh run_integration_test_bestconfig_inference.py run_nnunet_inference.py cleanup_integration_test.py prepare_integration_tests.sh run_integration_test.sh __init__.py readme.md run_integration_test_trainingOnly_DDP.sh ./nnunetv2/training: data_augmentation dataloading __init__.py logging loss lr_scheduler nnUNetTrainer ./nnunetv2/training/data_augmentation: compute_initial_patch_size.py custom_transforms __init__.py ./nnunetv2/training/data_augmentation/custom_transforms: cascade_transforms.py __init__.py region_based_training.py deep_supervision_donwsampling.py masking.py transforms_for_dummy_2d.py ./nnunetv2/training/dataloading: data_loader.py __init__.py nnunet_dataset.py utils.py ./nnunetv2/training/logging: __init__.py nnunet_logger.py ./nnunetv2/training/loss: compound_losses.py deep_supervision.py dice.py __init__.py robust_ce_loss.py ./nnunetv2/training/lr_scheduler: __init__.py polylr.py warmup.py ./nnunetv2/training/nnUNetTrainer: __init__.py nnUNetTrainer.py primus variants ./nnunetv2/training/nnUNetTrainer/primus: __init__.py primus_trainers.py ./nnunetv2/training/nnUNetTrainer/variants: benchmarking data_augmentation loss network_architecture sampling competitions __init__.py lr_schedule optimizer training_length ./nnunetv2/training/nnUNetTrainer/variants/benchmarking: __init__.py nnUNetTrainerBenchmark_5epochs_noDataLoading.py nnUNetTrainerBenchmark_5epochs.py ./nnunetv2/training/nnUNetTrainer/variants/competitions: aortaseg24.py __init__.py ./nnunetv2/training/nnUNetTrainer/variants/data_augmentation: __init__.py nnUNetTrainerDAOrd0.py nnUNetTrainer_noDummy2DDA.py nnUNetTrainerDA5.py nnUNetTrainerNoDA.py nnUNetTrainerNoMirroring.py ./nnunetv2/training/nnUNetTrainer/variants/loss: __init__.py nnUNetTrainerCELoss.py nnUNetTrainerDiceLoss.py nnUNetTrainerTopkLoss.py ./nnunetv2/training/nnUNetTrainer/variants/lr_schedule: __init__.py nnUNetTrainerCosAnneal.py nnUNetTrainer_warmup.py ./nnunetv2/training/nnUNetTrainer/variants/network_architecture: __init__.py nnUNetTrainerBN.py nnUNetTrainerNoDeepSupervision.py ./nnunetv2/training/nnUNetTrainer/variants/optimizer: __init__.py nnUNetTrainerAdam.py nnUNetTrainerAdan.py ./nnunetv2/training/nnUNetTrainer/variants/sampling: __init__.py nnUNetTrainer_probabilisticOversampling.py ./nnunetv2/training/nnUNetTrainer/variants/training_length: __init__.py nnUNetTrainer_Xepochs_NoMirroring.py nnUNetTrainer_Xepochs.py ./nnunetv2/utilities: collate_outputs.py default_n_proc_DA.py helpers.py network_initialization.py crossval_split.py file_path_utilities.py __init__.py overlay_plots.py dataset_name_id_conversion.py find_class_by_name.py json_export.py plans_handling ddp_allgather.py get_network_from_plans.py label_handling utils.py ./nnunetv2/utilities/label_handling: __init__.py label_handling.py ./nnunetv2/utilities/plans_handling: __init__.py plans_handler.py ./nnunetv2.egg-info: dependency_links.txt entry_points.txt PKG-INFO requires.txt SOURCES.txt top_level.txt ./UNKNOWN.egg-info: dependency_links.txt PKG-INFO SOURCES.txt top_level.txt (nnunet_env) jzuser@vpc87-3:~/Work_dir/Gn/pystudy/nnUNet/nnUNet$
最新发布
08-15
你运行了 `ls -R`，显示了当前目录的完整结构，看起来你已经进入了 `nnUNet/nnUNet` 目录，并且： - 存在 `setup.py` 文件（虽然你之前看到它内容为空） - 存在完整的 `nnunetv2/` 源码目录 - 存在 `nnunetv2_plan_and_preprocess` 的模块路径：`nnunetv2/experiment_planning/plan_and_preprocess_entrypoints.py` --- ## ✅ 问题定位 你之前运行 `cat setup.py` 显示内容为空，这说明： - 当前目录下的 `setup.py` 是空的，无法通过 `pip install -e .` 生成命令行脚本（如 `nnunetv2_plan_and_preprocess`） - 但是你拥有完整的源码结构，说明你**可以手动运行模块** --- ## ✅ 解决方案：绕过命令行工具，直接调用 Python 模块 你可以使用如下方式直接运行 `nnunetv2_plan_and_preprocess` 的功能： ### ✅ 手动运行预处理模块（Python 模块方式） ```bash cd ~/Work_dir/Gn/pystudy/nnUNet/nnUNet PYTHONPATH=/home/jzuser/Work_dir/Gn/pystudy/nnUNet/nnUNet python -m nnunetv2.experiment_planning.plan_and_preprocess_entrypoints.nnunetv2_plan_and_preprocess -d Task003_Liver --configuration 3d_fullres ``` ### 🔍 参数说明： - `PYTHONPATH=...`：告诉 Python 到哪里去找 `nnunetv2` 模块 - `-m ...`：运行 `plan_and_preprocess_entrypoints.py` 中的 `nnunetv2_plan_and_preprocess` 模块 - `-d Task003_Liver`：指定你要预处理的数据集 - `--configuration 3d_fullres`：指定配置（3D 全分辨率） --- ## ✅ 设置环境变量（非常重要） 确保你设置了以下环境变量： ```bash export nnUNet_raw=/home/jzuser/Work_dir/Gn export nnUNet_preprocessed=/home/jzuser/Work_dir/Gn/preprocessed export nnUNet_results=/home/jzuser/Work_dir/Gn/results ``` 这些路径用于告诉 `nnUNet` 数据放在哪里。 --- ## ✅ 示例完整命令流程 ```bash # 设置环境变量 export nnUNet_raw=/home/jzuser/Work_dir/Gn export nnUNet_preprocessed=/home/jzuser/Work_dir/Gn/preprocessed export nnUNet_results=/home/jzuser/Work_dir/Gn/results # 设置 PYTHONPATH export PYTHONPATH=/home/jzuser/Work_dir/Gn/pystudy/nnUNet/nnUNet:$PYTHONPATH # 进入项目目录 cd ~/Work_dir/Gn/pystudy/nnUNet/nnUNet # 手动运行预处理 python -m nnunetv2.experiment_planning.plan_and_preprocess_entrypoints.nnunetv2_plan_and_preprocess -d Task003_Liver --configuration 3d_fullres ``` --- ## ✅ 如果你希望修复 `setup.py`（可选） 你可以将下面的内容保存为 `setup.py` 来修复它： ```python from setuptools import setup, find_packages setup( name="nnunetv2", version="2.1", packages=find_packages(), entry_points={ 'console_scripts': [ 'nnunetv2_plan_and_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:nnunetv2_plan_and_preprocess', 'nnunetv2_train = nnunetv2.run.run_training:main', 'nnunetv2_predict = nnunetv2.inference.predict:main', 'nnunetv2_convert_decathlon_task = nnunetv2.dataset_conversion.convert_Decathlon_dataset:main', ], }, ) ``` 保存后重新运行： ```bash pip install -e . ``` 然后你就可以使用命令： ```bash nnunetv2_plan_and_preprocess -d Task003_Liver --configuration 3d_fullres ``` --- ## ✅ 总结 | 目标 | 方法 | |------|------| | 运行预处理 | 使用 `python -m` 手动调用模块 | | 设置路径 | 设置 `nnUNet_raw`, `nnUNet_preprocessed`, `nnUNet_results` | | 修复 `setup.py` | 替换为空文件 → 使用上面的完整 `setup.py` 内容 | | 生成命令行脚本 | 安装修复后的 `setup.py` | --- ##
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