Improve conda build

.gitignore

@@ -11,4 +11,8 @@ __pycache__/
 logs/
 
 # PyTorch models
-*.pytorch
+*.pytorch
+
+# Python packages
+pytorch3dunet.egg-info/
+dist/

conda-recipe/meta.yaml

@@ -1,15 +1,14 @@
 package:
   name: pytorch-3dunet
-  version: 1.0.2
+  version: {{ GIT_DESCRIBE_TAG }}
 
 source:
-  git_rev: v1.0.2
-  git_url: https://github.com/wolny/pytorch-3dunet.git
+  path: ..
 
 build:
   entry_points:
-    - predict3dunet = main.predict:main
-    - train3dunet = main.train:main
+    - predict3dunet = pytorch3dunet.predict:main
+    - train3dunet = pytorch3dunet.train:main
 
 requirements:
   build:
@@ -37,4 +36,5 @@ test:
     - pytest tests/
 
 about:
-  home: https://github.com/wolny/pytorch-3dunet
+  home: https://github.com/wolny/pytorch-3dunet
+  license: MIT

pytorch3dunet/__init__.py

@@ -0,0 +1 @@
+from .__version__ import __version__

pytorch3dunet/__version__.py

@@ -0,0 +1 @@
+__version__ = 'v1.0.3'

augment/transforms.py → pytorch3dunet/augment/transforms.py

@@ -577,7 +577,7 @@ def weight_transform(self):
 
     @staticmethod
     def _transformer_class(class_name):
-        m = importlib.import_module('augment.transforms')
+        m = importlib.import_module('pytorch3dunet.augment.transforms')
         clazz = getattr(m, class_name)
         return clazz
 

datasets/hdf5.py → pytorch3dunet/datasets/hdf5.py

@@ -6,8 +6,8 @@
 import torch
 from torch.utils.data import Dataset, DataLoader, ConcatDataset
 
-import augment.transforms as transforms
-from unet3d.utils import get_logger
+import pytorch3dunet.augment.transforms as transforms
+from pytorch3dunet.unet3d.utils import get_logger
 
 logger = get_logger('HDF5Dataset')
 
@@ -362,7 +362,7 @@ def _check_dimensionality(raws, labels):
 
 
 def _get_slice_builder_cls(class_name):
-    m = importlib.import_module('datasets.hdf5')
+    m = importlib.import_module('pytorch3dunet.datasets.hdf5')
     clazz = getattr(m, class_name)
     return clazz
 

embeddings/contrastive_loss.py → pytorch3dunet/embeddings/contrastive_loss.py

@@ -1,7 +1,7 @@
 import torch
 from torch import nn
 
-from unet3d.utils import expand_as_one_hot
+from pytorch3dunet.unet3d.utils import expand_as_one_hot
 
 
 class ContrastiveLoss(nn.Module):

main/predict.py → pytorch3dunet/predict.py

@@ -1,10 +1,10 @@
 import importlib
 import os
 
-from datasets.hdf5 import get_test_loaders
-from unet3d import utils
-from unet3d.config import load_config
-from unet3d.model import get_model
+from pytorch3dunet.datasets.hdf5 import get_test_loaders
+from pytorch3dunet.unet3d import utils
+from pytorch3dunet.unet3d.config import load_config
+from pytorch3dunet.unet3d.model import get_model
 
 logger = utils.get_logger('UNet3DPredictor')
 
@@ -31,7 +31,7 @@ def _get_predictor(model, loader, output_file, config):
     predictor_config = config.get('predictor', {})
     class_name = predictor_config.get('name', 'StandardPredictor')
 
-    m = importlib.import_module('unet3d.predictor')
+    m = importlib.import_module('pytorch3dunet.unet3d.predictor')
     predictor_class = getattr(m, class_name)
 
     return predictor_class(model, loader, output_file, config, **predictor_config)

main/train.py → pytorch3dunet/train.py

@@ -4,14 +4,14 @@
 import torch.optim as optim
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
-from datasets.hdf5 import get_train_loaders
-from unet3d.config import load_config
-from unet3d.losses import get_loss_criterion
-from unet3d.metrics import get_evaluation_metric
-from unet3d.model import get_model
-from unet3d.trainer import UNet3DTrainer
-from unet3d.utils import get_logger, get_tensorboard_formatter
-from unet3d.utils import get_number_of_learnable_parameters
+from pytorch3dunet.datasets.hdf5 import get_train_loaders
+from pytorch3dunet.unet3d.config import load_config
+from pytorch3dunet.unet3d.losses import get_loss_criterion
+from pytorch3dunet.unet3d.metrics import get_evaluation_metric
+from pytorch3dunet.unet3d.model import get_model
+from pytorch3dunet.unet3d.trainer import UNet3DTrainer
+from pytorch3dunet.unet3d.utils import get_logger, get_tensorboard_formatter
+from pytorch3dunet.unet3d.utils import get_number_of_learnable_parameters
 
 
 def _create_trainer(config, model, optimizer, lr_scheduler, loss_criterion, eval_criterion, loaders, logger):

unet3d/losses.py → pytorch3dunet/unet3d/losses.py

@@ -4,8 +4,8 @@
 from torch.autograd import Variable
 from torch.nn import MSELoss, SmoothL1Loss, L1Loss
 
-from embeddings.contrastive_loss import ContrastiveLoss
-from unet3d.utils import expand_as_one_hot
+from pytorch3dunet.embeddings.contrastive_loss import ContrastiveLoss
+from pytorch3dunet.unet3d.utils import expand_as_one_hot
 
 
 def compute_per_channel_dice(input, target, epsilon=1e-5, ignore_index=None, weight=None):

unet3d/metrics.py → pytorch3dunet/unet3d/metrics.py

@@ -9,8 +9,8 @@
 from skimage import measure
 from sklearn.cluster import MeanShift
 
-from unet3d.losses import compute_per_channel_dice
-from unet3d.utils import get_logger, adapted_rand, expand_as_one_hot, plot_segm
+from pytorch3dunet.unet3d.losses import compute_per_channel_dice
+from pytorch3dunet.unet3d.utils import get_logger, adapted_rand, expand_as_one_hot, plot_segm
 
 LOGGER = get_logger('EvalMetric')
 
@@ -688,7 +688,7 @@ def get_evaluation_metric(config):
     """
 
     def _metric_class(class_name):
-        m = importlib.import_module('unet3d.metrics')
+        m = importlib.import_module('pytorch3dunet.unet3d.metrics')
         clazz = getattr(m, class_name)
         return clazz
 

unet3d/model.py → pytorch3dunet/unet3d/model.py

@@ -2,8 +2,8 @@
 
 import torch.nn as nn
 
-from unet3d.buildingblocks import Encoder, Decoder, DoubleConv, ExtResNetBlock, SingleConv
-from unet3d.utils import create_feature_maps
+from pytorch3dunet.unet3d.buildingblocks import Encoder, Decoder, DoubleConv, ExtResNetBlock, SingleConv
+from pytorch3dunet.unet3d.utils import create_feature_maps
 
 
 class UNet3D(nn.Module):
@@ -299,7 +299,7 @@ def forward(self, x):
 
 def get_model(config):
     def _model_class(class_name):
-        m = importlib.import_module('unet3d.model')
+        m = importlib.import_module('pytorch3dunet.unet3d.model')
         clazz = getattr(m, class_name)
         return clazz
 

unet3d/predictor.py → pytorch3dunet/unet3d/predictor.py

@@ -6,9 +6,9 @@
 import torch
 from sklearn.cluster import MeanShift
 
-from datasets.hdf5 import SliceBuilder
-from unet3d.utils import get_logger
-from unet3d.utils import unpad
+from pytorch3dunet.datasets.hdf5 import SliceBuilder
+from pytorch3dunet.unet3d.utils import get_logger
+from pytorch3dunet.unet3d.utils import unpad
 
 logger = get_logger('UNet3DTrainer')
 

unet3d/utils.py → pytorch3dunet/unet3d/utils.py

@@ -391,7 +391,7 @@ def get_tensorboard_formatter(config):
         return DefaultTensorboardFormatter()
 
     class_name = config['name']
-    m = importlib.import_module('unet3d.utils')
+    m = importlib.import_module('pytorch3dunet.unet3d.utils')
     clazz = getattr(m, class_name)
     return clazz(**config)
 

resources/test_config_4d_input.yaml

@@ -33,7 +33,7 @@ loaders:
   test:
     # paths to the test datasets
     file_paths:
-      - 'resources/random_raw4D.h5'
+      - '../resources/random_raw4D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:

resources/test_config_ce.yaml

@@ -33,7 +33,7 @@ loaders:
   test:
     # paths to the test datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:

resources/test_config_dice.yaml

@@ -32,7 +32,7 @@ loaders:
   test:
     # paths to the test datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:

resources/train_config_4d_input.yaml

@@ -69,7 +69,7 @@ loaders:
   train:
     # paths to the training datasets
     file_paths:
-      - 'resources/random_raw4D.h5'
+      - '../resources/random_raw4D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:
@@ -116,7 +116,7 @@ loaders:
   val:
     # paths to the validation datasets
     file_paths:
-      - 'resources/random_raw4D.h5'
+      - '../resources/random_raw4D.h5'
 
     # SliceBuilder configuration
     slice_builder:

resources/train_config_ce.yaml

@@ -69,7 +69,7 @@ loaders:
   train:
     # paths to the training datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:
@@ -116,7 +116,7 @@ loaders:
   val:
     # paths to the validation datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration
     slice_builder:

resources/train_config_dice.yaml

@@ -69,7 +69,7 @@ loaders:
   train:
     # paths to the training datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
     slice_builder:
@@ -116,7 +116,7 @@ loaders:
   val:
     # paths to the validation datasets
     file_paths:
-      - 'resources/random_label3D.h5'
+      - '../resources/random_label3D.h5'
 
     # SliceBuilder configuration
     slice_builder:

setup.py

@@ -1,11 +1,10 @@
 from setuptools import setup, find_packages
 
-_version_ = "1.0.2"
-
+exec(open('pytorch3dunet/__version__.py').read())
 setup(
     name="pytorch3dunet",
     packages=find_packages(exclude=["tests"]),
-    version=_version_,
+    version=__version__,
     author="Adrian Wolny, Lorenzo Cerrone",
     url="https://github.com/wolny/pytorch-3dunet",
     license="MIT",

tests/test_criterion.py

@@ -5,14 +5,13 @@
 import torch.nn as nn
 from skimage import measure
 
-from augment.transforms import LabelToAffinities, StandardLabelToBoundary
-from unet3d.losses import GeneralizedDiceLoss, WeightedCrossEntropyLoss, BCELossWrapper, \
+from pytorch3dunet.augment.transforms import LabelToAffinities, StandardLabelToBoundary
+from pytorch3dunet.embeddings.contrastive_loss import ContrastiveLoss
+from pytorch3dunet.unet3d.losses import GeneralizedDiceLoss, WeightedCrossEntropyLoss, BCELossWrapper, \
     DiceLoss, TagsAngularLoss
-from unet3d.metrics import DiceCoefficient, MeanIoU, BoundaryAveragePrecision, AdaptedRandError, \
+from pytorch3dunet.unet3d.metrics import DiceCoefficient, MeanIoU, BoundaryAveragePrecision, AdaptedRandError, \
     BoundaryAdaptedRandError, EmbeddingsAdaptedRandError
 
-from embeddings.contrastive_loss import ContrastiveLoss
-
 
 def _compute_criterion(criterion, n_times=100):
     shape = [1, 0, 30, 30, 30]

tests/test_dataset.py

@@ -4,7 +4,7 @@
 import numpy as np
 from torch.utils.data import DataLoader
 
-from datasets.hdf5 import HDF5Dataset
+from pytorch3dunet.datasets.hdf5 import HDF5Dataset
 
 
 class TestHDF5Dataset: