CI: pre-commit (#2843)

* add pre-commit

Signed-off-by: Jirka <[email protected]>

.deepsource.toml

@@ -24,4 +24,4 @@ enabled = true
 
 [[analyzers]]
 name = "shell"
-enabled = true
+enabled = true

.dockerignore

@@ -11,4 +11,3 @@ coverage.xml
 *.toml
 
 !README.md
-

.github/ISSUE_TEMPLATE/question.md

@@ -9,4 +9,4 @@ assignees: ''
 **Please use MONAI's Discussions tab**
 For questions relating to MONAI usage, please do not create an issue.
 
-Instead, use [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). This can be found next to Issues and Pull Requests along the top of our repository.
+Instead, use [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). This can be found next to Issues and Pull Requests along the top of our repository.

.github/workflows/weekly-preview.yml

@@ -43,4 +43,3 @@ jobs:
       with:
         user: __token__
         password: ${{ secrets.PYPI_TOKEN }}
-

.pre-commit-config.yaml

@@ -0,0 +1,63 @@
+default_language_version:
+  python: python3.8
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: check-docstring-first
+      - id: check-executables-have-shebangs
+      - id: check-toml
+      - id: check-case-conflict
+      - id: check-added-large-files
+        args: ['--maxkb=1024']
+      - id: detect-private-key
+
+  #- repo: https://github.com/asottile/pyupgrade
+  #  rev: v2.23.2
+  #  hooks:
+  #    - id: pyupgrade
+  #      args: [--py36-plus]
+  #      name: Upgrade code
+
+  #- repo: https://github.com/asottile/yesqa
+  #  rev: v1.2.3
+  #  hooks:
+  #    - id: yesqa
+  #      name: Unused noqa
+
+  #- repo: https://github.com/PyCQA/isort
+  #  rev: 5.9.3
+  #  hooks:
+  #    - id: isort
+  #      name: Format imports
+
+  - repo: https://github.com/psf/black
+    rev: 21.7b0
+    hooks:
+      - id: black
+        name: Format code
+
+  #- repo: https://github.com/executablebooks/mdformat
+  #  rev: 0.7.8
+  #  hooks:
+  #    - id: mdformat
+  #      additional_dependencies:
+  #        - mdformat-gfm
+  #        - mdformat_frontmatter
+  #      exclude: CHANGELOG.md
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+        name: Check PEP8

docs/source/metrics.rst

@@ -83,4 +83,4 @@ Metrics
 `Peak signal to noise ratio`
 ----------------------------
 .. autoclass:: PSNRMetric
-    :members:
+    :members:

monai/config/type_definitions.py

@@ -14,69 +14,55 @@
 import numpy as np
 import torch
 
+# Commonly used concepts
+# This module provides naming and type specifications for commonly used concepts
+# within the MONAI package. The intent is to explicitly identify information
+# that should be used consistently throughout the entire MONAI package.
+#
+# A type would be named as type_definitions.KeysCollection
+# which includes a meaningful name for the consent in the name itself. The
+# definitions in this file map context meaningful names to the underlying
+# object properties that define the expected API.
+#
+# A conceptual type is represented by a new type name but is also one which
+# can be different depending on an environment (i.e. differences for python 3.6 vs 3.9
+# may be implemented). Consistent use of the concept and recorded documentation of
+# the rationale and convention behind it lowers the learning curve for new
+# developers. For readability, short names are preferred.
 __all__ = ["KeysCollection", "IndexSelection", "DtypeLike", "NdarrayTensor", "NdarrayOrTensor", "TensorOrList"]
 
-"""Commonly used concepts
-This module provides naming and type specifications for commonly used concepts
-within the MONAI package. The intent is to explicitly identify information
-that should be used consistently throughout the entire MONAI package.
-
-A type would be named as type_definitions.KeysCollection
-which includes a meaningful name for the consent in the name itself. The
-definitions in this file map context meaningful names to the underlying
-object properties that define the expected API.
-
-A conceptual type is represented by a new type name but is also one which
-can be different depending on an environment (i.e. differences for python 3.6 vs 3.9
-may be implemented). Consistent use of the concept and recorded documentation of
-the rationale and convention behind it lowers the learning curve for new
-developers. For readability, short names are preferred.
-"""
 
+#: KeysCollection
+#
+# The KeyCollection type is used to for defining variables
+# that store a subset of keys to select items from a dictionary.
+# The container of keys must contain hashable elements.
+# NOTE:  `Hashable` is not a collection, but is provided as a
+#        convenience to end-users.  All supplied values will be
+#        internally converted to a tuple of `Hashable`'s before
+#        use
 KeysCollection = Union[Collection[Hashable], Hashable]
-"""KeysCollection
-
-The KeyCollection type is used to for defining variables
-that store a subset of keys to select items from a dictionary.
-The container of keys must contain hashable elements.
-NOTE:  `Hashable` is not a collection, but is provided as a
-       convenience to end-users.  All supplied values will be
-       internally converted to a tuple of `Hashable`'s before
-       use
-"""
-
 
+#: IndexSelection
+#
+# The IndexSelection type is used to for defining variables
+# that store a subset of indices to select items from a List or Array like objects.
+# The indices must be integers, and if a container of indices is specified, the
+# container must be iterable.
 IndexSelection = Union[Iterable[int], int]
-"""IndexSelection
-
-The IndexSelection type is used to for defining variables
-that store a subset of indices to select items from a List or Array like objects.
-The indices must be integers, and if a container of indices is specified, the
-container must be iterable.
-"""
-
 
+#: Type of datatypes: Adapted from https://github.com/numpy/numpy/blob/master/numpy/typing/_dtype_like.py
 DtypeLike = Union[np.dtype, type, None]
-"""Type of datatypes
-
-Adapted from https://github.com/numpy/numpy/blob/master/numpy/typing/_dtype_like.py
-"""
 
+#: NdarrayTensor
+#
+# Generic type which can represent either a numpy.ndarray or a torch.Tensor
+# Unlike Union can create a dependence between parameter(s) / return(s)
 NdarrayTensor = TypeVar("NdarrayTensor", np.ndarray, torch.Tensor)
-"""NdarrayTensor
 
-Generic type which can represent either a numpy.ndarray or a torch.Tensor
-Unlike Union can create a dependence between parameter(s) / return(s)
-"""
 
+#: NdarrayOrTensor: Union of numpy.ndarray and torch.Tensor to be used for typing
 NdarrayOrTensor = Union[np.ndarray, torch.Tensor]
-"""NdarrayOrTensor
-
-Union of numpy.ndarray and torch.Tensor to be used for typing
-"""
 
+#: TensorOrList: The TensorOrList type is used for defining `batch-first Tensor` or `list of channel-first Tensor`.
 TensorOrList = Union[torch.Tensor, Sequence[torch.Tensor]]
-"""TensorOrList
-
-The TensorOrList type is used for defining `batch-first Tensor` or `list of channel-first Tensor`.
-"""

monai/csrc/filtering/bilateral/bilateralfilter_cpu.cpp

@@ -164,4 +164,4 @@ torch::Tensor BilateralFilterCpu(torch::Tensor inputTensor, float spatialSigma,
                                       }));
 
   return outputTensor;
-}
+}

monai/csrc/filtering/filtering.h

@@ -14,4 +14,4 @@ limitations under the License.
 #pragma once
 
 #include "bilateral/bilateral.h"
-#include "permutohedral/permutohedral.h"
+#include "permutohedral/permutohedral.h"

monai/csrc/filtering/permutohedral/hash_table.cuh

@@ -257,4 +257,4 @@ __device__ static int hashTableRetrieve(signed short* key) {
     if (h == table_capacity * 2)
       h = 0;
   }
-}
+}

monai/csrc/filtering/permutohedral/permutohedral.h

@@ -25,4 +25,4 @@ template <typename scalar_t, int dc, int fc>
 void PermutohedralCuda(scalar_t* data, scalar_t* features, int elementCount, bool accurate);
 #endif
 
-torch::Tensor PermutohedralFilter(torch::Tensor input, torch::Tensor features);
+torch::Tensor PermutohedralFilter(torch::Tensor input, torch::Tensor features);

monai/csrc/filtering/permutohedral/permutohedral_cpu.cpp

@@ -499,4 +499,4 @@ void PermutohedralCPU(scalar_t* data, scalar_t* features, int dataChannels, int
 }
 
 template void PermutohedralCPU(float* data, float* features, int dataChannels, int featureChannels, int elementCount);
-template void PermutohedralCPU(double* data, double* features, int dataChannels, int featureChannels, int elementCount);
+template void PermutohedralCPU(double* data, double* features, int dataChannels, int featureChannels, int elementCount);