pytorch-3dunet is a cross-platform package and runs on Windows and OS X as well.InstallationThe easiest way to install pytorch-3dunet package is via conda/mamba: conda install -c conda-forge mamba mamba create -
3D U-Net model for volumetric semantic segmentation written in pytorch - pytorch-3dunet/LICENSE at master · three-san/pytorch-3dunet
setup.py fromsetuptoolsimportsetup,find_packages# 获得__version__.py里的内容,使得获取到__version__exec(open('pytorch3dunet/__version__.py').read())setup(name="pytorch3dunet",# 包名称---生成的egg名称# 自动动态获取packages,默认在和setup.py同一目录下搜索各个含有 init.py的包。exclude:打包的...
master pytorch-3dunet/tests/ Go to file This branch is 285 commits behind wolny:master. Contribute Latest commit Git stats History Files Type Name Latest commit message Commit time . . __init__.py 3D U-Net implementation Sep 21, 2018 test_criterion.py Fix MeanIoU for a single ...
3D U-Net model for volumetric semantic segmentation written in pytorch - pytorch-3dunet/resources at master · Gofinge/pytorch-3dunet
3D U-Net model for volumetric semantic segmentation written in pytorch - pytorch-3dunet/resources/3DUnet_denoising/train_config_regression.yaml at master · Lycas/pytorch-3dunet
3D U-Net model for volumetric semantic segmentation written in pytorch - pytorch-3dunet/resources/3DUnet_confocal_boundary/test_config.yml at master · three-san/pytorch-3dunet
master pytorch-3dunet/train.py/ Jump to executable file128 lines (105 sloc)5.63 KB RawBlame importimportlib importtorch importtorch.optimasoptim fromtorch.optim.lr_schedulerimportReduceLROnPlateau fromdatasets.hdf5importget_train_loaders fromunet3d.configimportload_config ...
3D U-Net model for volumetric semantic segmentation written in pytorch - pytorch-3dunet/resources/3DUnet_multiclass/train_config.yaml at master · three-san/pytorch-3dunet
import_module('unet3d.predictor') predictor_class = getattr(m, class_name) return predictor_class(model, loader, output_file, config, **predictor_config) def main(): # Load configuration config = load_config() # Create the model model = get_model(config) # Load model state model_path ...