Code quality

- Refactored with recent version of black
- Ran isort
- Run pre-commit hooks, make sure they all run properly (except pylint/mypy for now)
- Remove requirements.txt
- Add requirements to setup.py
- Excluded generated modules in pyproject.toml for pylint

.github/labeler.yml

@@ -0,0 +1,15 @@
+python:
+  - "**/*.py"
+  - "*.py"
+
+documentation:
+  - "**/*.md"
+  - "*.md"
+  - "**/*.rst"
+  - "*.rst"
+
+docker:
+  - "docker/Dockerfile"
+
+ci:
+  - ".github/**"

.github/workflows/pylint.yml

@@ -15,6 +15,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pylint
+        pip install -e .
     - name: Analysing the code with pylint
       run: |
         pylint direct --errors-only

direct/checkpointer.py

@@ -1,21 +1,19 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+import datetime
 import logging
 import pathlib
-import torch
-import datetime
-import warnings
 import re
-import torch.nn as nn
-
-
+import warnings
 from pickle import UnpicklingError
-from typing import Union, Optional, Dict, Mapping, get_args
-
-from direct.types import PathOrString, HasStateDict
+from typing import Dict, Mapping, Optional, Union, get_args
 
-from torch.nn.parallel import DistributedDataParallel
+import torch
+import torch.nn as nn
 from torch.nn import DataParallel
+from torch.nn.parallel import DistributedDataParallel
+
+from direct.types import HasStateDict, PathOrString
 
 # TODO: Rewrite Checkpointer
 # There are too many issues with typing and mypy in the checkpointer.

direct/common/subsample.py

@@ -5,18 +5,17 @@
 # https://github.com/facebookresearch/fastMRI/
 # The code can have been adjusted to our needs.
 
-import numpy as np
-import torch
+import contextlib
+import logging
 import pathlib
-
-from typing import Tuple, Optional, List
 from abc import abstractmethod
+from typing import List, Optional, Tuple
 
-from direct.utils import str_to_class
-from direct.types import Number
+import numpy as np
+import torch
 
-import logging
-import contextlib
+from direct.types import Number
+from direct.utils import str_to_class
 
 logger = logging.getLogger(__name__)
 

direct/common/subsample_config.py

@@ -1,7 +1,8 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
 from dataclasses import dataclass
-from typing import Tuple, Optional, Union, Any
+from typing import Any, Optional, Tuple, Union
+
 from omegaconf import MISSING
 
 from direct.config.defaults import BaseConfig

direct/config/defaults.py

@@ -2,14 +2,13 @@
 # Copyright (c) DIRECT Contributors
 
 from dataclasses import dataclass, field
-from omegaconf import MISSING
+from typing import Any, List, Optional
 
+from omegaconf import MISSING
 
 from direct.config import BaseConfig
 from direct.data.datasets_config import DatasetConfig
 
-from typing import Optional, List, Any
-
 
 @dataclass
 class TensorboardConfig(BaseConfig):

direct/data/bbox.py

@@ -1,10 +1,10 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+from typing import List, Union
+
 import numpy as np
 import torch
 
-from typing import List, Union
-
 
 def crop_to_bbox(
     data: Union[np.ndarray, torch.Tensor], bbox: List[int], pad_value: int = 0

direct/data/datasets.py

@@ -1,19 +1,16 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
-import numpy as np
-import pathlib
 import bisect
-
-from typing import Callable, Dict, Optional, Any, List, Union
+import pathlib
 from functools import lru_cache
+from typing import Any, Callable, Dict, List, Optional, Union
 
-from direct.data.h5_data import H5SliceData
-from direct.utils import str_to_class, remove_keys
-from direct.types import PathOrString
-
-
+import numpy as np
 from torch.utils.data import Dataset, IterableDataset
 
+from direct.data.h5_data import H5SliceData
+from direct.types import PathOrString
+from direct.utils import remove_keys, str_to_class
 
 try:
     import ismrmrd

direct/data/datasets_config.py

@@ -1,13 +1,13 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
 from dataclasses import dataclass, field
-from typing import Tuple, Optional, List
-
-from direct.config.defaults import BaseConfig
-from direct.common.subsample_config import MaskingConfig
+from typing import List, Optional, Tuple
 
 from omegaconf import MISSING
 
+from direct.common.subsample_config import MaskingConfig
+from direct.config.defaults import BaseConfig
+
 
 @dataclass
 class TransformsConfig(BaseConfig):

direct/data/h5_data.py

@@ -1,17 +1,16 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+import logging
 import pathlib
-import numpy as np
-import h5py
 import re
+from typing import Any, Dict, List, Optional, Tuple, Union
 
+import h5py
+import numpy as np
 from torch.utils.data import Dataset
-from typing import Dict, Optional, Any, Tuple, List, Union
 
-from direct.utils import cast_as_path
 from direct.types import PathOrString
-
-import logging
+from direct.utils import cast_as_path
 
 logger = logging.getLogger(__name__)
 

direct/data/lr_scheduler.py

@@ -9,12 +9,12 @@
 # - Calls to other subroutines which do not exist in DIRECT.
 # - Stylistic changes.
 
+import logging
 import math
 from bisect import bisect_right
 from typing import List
-import torch
-import logging
 
+import torch
 
 # NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
 # only on epoch boundaries. We typically use iteration based schedules instead.

direct/data/mri_transforms.py

@@ -1,19 +1,17 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
-import torch
-import numpy as np
-import warnings
 import functools
-import torch.nn as nn
+import logging
+import warnings
+from typing import Any, Callable, Dict, Iterable, Optional
 
-from typing import Dict, Any, Callable, Optional, Iterable
+import numpy as np
+import torch
+import torch.nn as nn
 
 from direct.data import transforms as T
 from direct.utils import DirectModule, DirectTransform
 
-
-import logging
-
 logger = logging.getLogger(__name__)
 
 

direct/data/samplers.py

@@ -5,19 +5,17 @@
 # - Docstring to match the rest of the library
 # - Calls to other subroutines which do not exist in DIRECT.
 
-import torch
 import itertools
-import random
-import numpy as np
 import logging
 import math
+import random
+from typing import List, Optional
 
-from typing import Optional
+import numpy as np
+import torch
 from torch.utils.data.sampler import Sampler
 
-from direct.utils import communication, chunks
-
-from typing import List
+from direct.utils import chunks, communication
 
 
 class DistributedSampler(Sampler):

direct/data/tests/test_transforms.py

@@ -7,6 +7,7 @@
 import numpy as np
 import pytest
 import torch
+
 from direct.data import transforms
 from direct.data.transforms import tensor_to_complex_numpy
 

direct/data/transforms.py

@@ -5,17 +5,16 @@
 # https://github.com/facebookresearch/fastMRI/
 # The code can have been adjusted to our needs.
 
+from typing import Any, Callable, List, Optional, Tuple, Union
+
 import numpy as np
 import torch
+from packaging import version
 
-from typing import Union, Optional, List, Tuple, Callable, Any
-
-from direct.utils import is_power_of_two, ensure_list
 from direct.data.bbox import crop_to_bbox
+from direct.utils import ensure_list, is_power_of_two
 from direct.utils.asserts import assert_complex, assert_named, assert_same_shape
 
-from packaging import version
-
 if version.parse(torch.__version__) >= version.parse("1.7.0"):
     import torch.fft
 

direct/engine.py

@@ -1,57 +1,46 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+import functools
+import gc
 import logging
 import pathlib
+import signal
 import sys
+import warnings
+from abc import ABC, abstractmethod
 from collections import namedtuple
+from typing import Callable, Dict, List, Optional, TypedDict, Union
 
-import torch
-import signal
-import direct
 import numpy as np
-import warnings
-import functools
-import gc
-
-from typing import Optional, Dict, List, Union, Callable, TypedDict
-from abc import abstractmethod, ABC
-
+import torch
 from torch import nn
+from torch.cuda.amp import GradScaler
 from torch.nn import DataParallel
 from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, Dataset, Sampler
-from torch.cuda.amp import GradScaler
-
+from torchvision.utils import make_grid
 
-from direct.data.mri_transforms import AddNames
+import direct
+from direct.checkpointer import Checkpointer
+from direct.config.defaults import BaseConfig
+from direct.data import transforms as T
+from direct.data.bbox import crop_to_largest
 from direct.data.datasets import ConcatDataset
+from direct.data.mri_transforms import AddNames
 from direct.data.samplers import ConcatDatasetBatchSampler
-from direct.checkpointer import Checkpointer
-from direct.utils.collate import named_collate
+from direct.exceptions import ProcessKilledException, TrainingException
+from direct.types import PathOrString
 from direct.utils import (
     communication,
-    prefix_dict_keys,
     evaluate_dict,
     normalize_image,
-    str_to_class,
+    prefix_dict_keys,
     reduce_list_of_dicts,
+    str_to_class,
 )
-from direct.data.bbox import crop_to_largest
+from direct.utils.collate import named_collate
+from direct.utils.events import CommonMetricPrinter, EventStorage, JSONWriter, TensorboardWriter, get_event_storage
 from direct.utils.io import write_json
-from direct.utils.events import (
-    get_event_storage,
-    EventStorage,
-    JSONWriter,
-    CommonMetricPrinter,
-    TensorboardWriter,
-)
-from direct.data import transforms as T
-from direct.config.defaults import BaseConfig
-from direct.exceptions import ProcessKilledException, TrainingException
-from direct.types import PathOrString
-
-from torchvision.utils import make_grid
-
 
 DoIterationOutput = namedtuple(
     "DoIterationOutput",