Add CircleCI job for python lint (#2056)

* Add CircleCI job for python lint

* Break lint

* Fix

* Fix lint

* Re-enable all tests and remove travis python lint

Author: fmassa

.circleci/config.yml

@@ -76,6 +76,17 @@ jobs:
             python .circleci/regenerate.py
             git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1)
 
+
+  python_lint:
+    docker:
+      - image: circleci/python:3.7
+    steps:
+      - checkout
+      - run:
+          command: |
+            pip install --user --progress-bar off flake8 typing
+            flake8 .
+
   binary_linux_wheel:
     <<: *binary_common
     docker:
@@ -509,6 +520,8 @@ workflows:
           name: torchvision_win_py3.6_cu101
           python_version: "3.6"
           cu_version: "cu101"
+      - python_lint
+
 
   nightly:
     jobs:

.circleci/config.yml.in

@@ -76,6 +76,17 @@ jobs:
             python .circleci/regenerate.py
             git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1)
 
+
+  python_lint:
+    docker:
+      - image: circleci/python:3.7
+    steps:
+      - checkout
+      - run:
+          command: |
+            pip install --user --progress-bar off flake8 typing
+            flake8 .
+
   binary_linux_wheel:
     <<: *binary_common
     docker:
@@ -311,6 +322,8 @@ workflows:
           name: torchvision_win_py3.6_cu101
           python_version: "3.6"
           cu_version: "cu101"
+      - python_lint
+
 
   nightly:
 {%- endif %}

.travis.yml

@@ -15,11 +15,6 @@ matrix:
       before_install: skip
       install: skip
       script: ./travis-scripts/run-clang-format/run-clang-format.py -r torchvision/csrc
-    - env: LINT_CHECK
-      python: "3.6"
-      install: pip install flake8 typing
-      script: flake8 .circleci
-      after_success: []
     - python: "3.6"
       env: IMAGE_BACKEND=Pillow-SIMD
     - python: "3.6"

setup.py

@@ -95,9 +95,9 @@ def get_extensions():
             includes="torchvision/csrc/cuda/*",
             show_detailed=True,
             is_pytorch_extension=True,
-            )
+        )
         source_cuda = glob.glob(os.path.join(extensions_dir, 'hip', '*.hip'))
-        ## Copy over additional files
+        # Copy over additional files
         shutil.copy("torchvision/csrc/cuda/cuda_helpers.h", "torchvision/csrc/hip/cuda_helpers.h")
         shutil.copy("torchvision/csrc/cuda/vision_cuda.h", "torchvision/csrc/hip/vision_cuda.h")
 
@@ -122,7 +122,8 @@ def get_extensions():
     define_macros = []
 
     extra_compile_args = {}
-    if (torch.cuda.is_available() and ((CUDA_HOME is not None)  or is_rocm_pytorch)) or os.getenv('FORCE_CUDA', '0') == '1':
+    if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) \
+            or os.getenv('FORCE_CUDA', '0') == '1':
         extension = CUDAExtension
         sources += source_cuda
         if not is_rocm_pytorch:

test/fakedata_generation.py

@@ -253,13 +253,14 @@ def _make_mat(file):
 
         yield root
 
+
 @contextlib.contextmanager
 def voc_root():
     with get_tmp_dir() as tmp_dir:
         voc_dir = os.path.join(tmp_dir, 'VOCdevkit',
-                'VOC2012','ImageSets','Main')
+                               'VOC2012', 'ImageSets', 'Main')
         os.makedirs(voc_dir)
-        train_file = os.path.join(voc_dir,'train.txt')
+        train_file = os.path.join(voc_dir, 'train.txt')
         with open(train_file, 'w') as f:
             f.write('test')
 

test/test_onnx.py

@@ -415,13 +415,14 @@ def test_heatmaps_to_keypoints(self):
 
         assert torch.all(out2[0].eq(out_trace2[0]))
         assert torch.all(out2[1].eq(out_trace2[1]))
-        
+
     @unittest.skip("Disable test until export of interpolate script module to ONNX is fixed")
     def test_keypoint_rcnn(self):
         class KeyPointRCNN(torch.nn.Module):
             def __init__(self):
                 super(KeyPointRCNN, self).__init__()
-                self.model = models.detection.keypoint_rcnn.keypointrcnn_resnet50_fpn(pretrained=True, min_size=200, max_size=300)
+                self.model = models.detection.keypoint_rcnn.keypointrcnn_resnet50_fpn(
+                    pretrained=True, min_size=200, max_size=300)
 
             def forward(self, images):
                 output = self.model(images)

test/test_transforms.py

@@ -202,7 +202,7 @@ def test_randomperspective_fill(self):
             startpoints, endpoints = transforms.RandomPerspective.get_params(width, height, 0.5)
             tr_img = F.perspective(img_conv, startpoints, endpoints, fill=fill)
             pixel = tr_img.getpixel((0, 0))
-        
+
             if not isinstance(pixel, tuple):
                 pixel = (pixel,)
             self.assertTupleEqual(pixel, tuple([fill] * num_bands))
@@ -896,7 +896,6 @@ def test_normalize_3d_tensor(self):
         assert_array_almost_equal(target, result1.numpy())
         assert_array_almost_equal(target, result2.numpy())
 
-
     def test_adjust_brightness(self):
         x_shape = [2, 2, 3]
         x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]

torchvision/models/detection/transform.py

@@ -192,8 +192,11 @@ def __repr__(self):
 
 def resize_keypoints(keypoints, original_size, new_size):
     # type: (Tensor, List[int], List[int])
-    ratios = [torch.tensor(s, dtype=torch.float32, device=keypoints.device) / torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device)
-              for s, s_orig in zip(new_size, original_size)]
+    ratios = [
+        torch.tensor(s, dtype=torch.float32, device=keypoints.device) /
+        torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device)
+        for s, s_orig in zip(new_size, original_size)
+    ]
     ratio_h, ratio_w = ratios
     resized_data = keypoints.clone()
     if torch._C._get_tracing_state():
@@ -208,8 +211,11 @@ def resize_keypoints(keypoints, original_size, new_size):
 
 def resize_boxes(boxes, original_size, new_size):
     # type: (Tensor, List[int], List[int])
-    ratios = [torch.tensor(s, dtype=torch.float32, device=boxes.device) / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
-              for s, s_orig in zip(new_size, original_size)]
+    ratios = [
+        torch.tensor(s, dtype=torch.float32, device=boxes.device) /
+        torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
+        for s, s_orig in zip(new_size, original_size)
+    ]
     ratio_height, ratio_width = ratios
     xmin, ymin, xmax, ymax = boxes.unbind(1)
 

torchvision/ops/misc.py

@@ -76,7 +76,7 @@ def _check_size_scale_factor(dim, size, scale_factor):
         raise ValueError("either size or scale_factor should be defined")
     if size is not None and scale_factor is not None:
         raise ValueError("only one of size or scale_factor should be defined")
-    if scale_factor is not None: 
+    if scale_factor is not None:
         if isinstance(scale_factor, (list, tuple)):
             if len(scale_factor) != dim:
                 raise ValueError(

torchvision/transforms/functional.py

@@ -188,7 +188,7 @@ def normalize(tensor, mean, std, inplace=False):
     """
     if not torch.is_tensor(tensor):
         raise TypeError('tensor should be a torch tensor. Got {}.'.format(type(tensor)))
-    
+
     if tensor.ndimension() != 3:
         raise ValueError('Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = '
                          '{}.'.format(tensor.size()))
@@ -424,7 +424,7 @@ def _parse_fill(fill, img, min_pil_version):
             image. If int or float, the value is used for all bands respectively.
             Defaults to 0 for all bands.
         img (PIL Image): Image to be filled.
-        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option 
+        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option
             was first introduced in the calling function. (e.g. rotate->5.2.0, perspective->5.0.0)
 
     Returns: