feat(ci): use new ci to check code format (#78)

* feat(models): update ci

* fix(models): fix flake8 and isort in vision

* fix(models): fix pylint error and refine gitignore

Author: Feng Wang

.github/workflows/ci.yml

@@ -6,9 +6,7 @@ name: CI
 # events but only for the master branch
 on:
   push:
-    branches: [ master ]
   pull_request:
-    branches: [ master ]
 
 # A workflow run is made up of one or more jobs that can run sequentially or in parallel
 jobs:
@@ -34,16 +32,25 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt
-        pip install megengine -f https://megengine.org.cn/whl/mge.html
 
     # Runs a set of commands using the runners shell
-    - name: Pylint check
+    - name: Format check
       run: |
         export PYTHONPATH=$PWD:$PYTHONPATH
+
+        CHECK_DIR=official/vision/
         pip install pylint==2.5.2
-        pylint official/vision --rcfile=.pylintrc || pylint_ret=$?
+        pylint $CHECK_DIR --rcfile=.pylintrc || pylint_ret=$?
         echo test, and deploy your project.
         if [ "$pylint_ret" ]; then
           exit $pylint_ret
         fi
         echo "All lint steps passed!"
+
+        pip3 install flake8==3.7.9
+        flake8 official
+        echo "All flake check passed!"
+
+        pip3 install isort==4.3.21
+        isort --check-only -rc official
+        echo "All isort check passed!"

.gitignore

@@ -1,4 +1,31 @@
-__pycache__/
 *log*/
+*.jpg
+*.png
+*.txt
+
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
 *.so
+build/
+dist/
+wheels/
+
+# pytorch/python/numpy formats
+*.pth
 *.pkl
+*.npy
+
+# ipython/jupyter notebooks
+*.ipynb
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# Pycharm editor settings
+.idea

official/vision/classification/resnet/inference.py

@@ -9,7 +9,8 @@
 import argparse
 import json
 
-import model as resnet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as resnet_model
 
 import cv2
 import numpy as np

official/vision/classification/resnet/model.py

@@ -229,8 +229,9 @@ def __init__(
                     M.init.uniform_(m.bias, -bound, bound)
 
         # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        # so that the residual branch starts with zeros, and each residual block
+        # behaves like an identity. According to https://arxiv.org/abs/1706.02677
+        # This improves the model by 0.2~0.3%.
         if zero_init_residual:
             for m in self.modules():
                 if isinstance(m, Bottleneck):
@@ -356,7 +357,8 @@ def resnet152(**kwargs):
 )
 def resnext50_32x4d(**kwargs):
     r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    `"Aggregated Residual Transformation for Deep Neural Networks"
+    <https://arxiv.org/pdf/1611.05431.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -372,7 +374,8 @@ def resnext50_32x4d(**kwargs):
 )
 def resnext101_32x8d(**kwargs):
     r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    `"Aggregated Residual Transformation for Deep Neural Networks"
+    <https://arxiv.org/pdf/1611.05431.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet

official/vision/classification/resnet/test.py

@@ -10,7 +10,8 @@
 import multiprocessing
 import time
 
-import model as resnet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as resnet_model
 
 import megengine
 import megengine.data as data

official/vision/classification/resnet/train.py

@@ -12,7 +12,8 @@
 import os
 import time
 
-import model as resnet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as resnet_model
 
 import megengine
 import megengine.autodiff as autodiff
@@ -117,6 +118,7 @@ def main():
 
 
 def worker(rank, world_size, ngpus_per_node, args):
+    # pylint: disable=too-many-statements
     if rank == 0:
         os.makedirs(os.path.join(args.save, args.arch), exist_ok=True)
         megengine.logger.set_log_file(os.path.join(args.save, args.arch, "log.txt"))

official/vision/classification/shufflenet/inference.py

@@ -9,7 +9,8 @@
 import argparse
 import json
 
-import model as snet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as snet_model
 
 import cv2
 import numpy as np

official/vision/classification/shufflenet/model.py

@@ -58,7 +58,10 @@ def __init__(self, inp, oup, mid_channels, *, ksize, stride):
             M.BatchNorm2d(mid_channels),
             M.ReLU(),
             # dw
-            M.Conv2d(mid_channels, mid_channels, ksize, stride, pad, groups=mid_channels, bias=False,),
+            M.Conv2d(
+                mid_channels, mid_channels, ksize, stride, pad,
+                groups=mid_channels, bias=False,
+            ),
             M.BatchNorm2d(mid_channels),
             # pw-linear
             M.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),
@@ -135,13 +138,15 @@ def __init__(self, num_classes=1000, model_size="1.5x"):
                 if i == 0:
                     self.features.append(
                         ShuffleV2Block(
-                            input_channel, output_channel, mid_channels=output_channel // 2, ksize=3, stride=2,
+                            input_channel, output_channel,
+                            mid_channels=output_channel // 2, ksize=3, stride=2,
                         )
                     )
                 else:
                     self.features.append(
                         ShuffleV2Block(
-                            input_channel // 2, output_channel, mid_channels=output_channel // 2, ksize=3, stride=1,
+                            input_channel // 2, output_channel,
+                            mid_channels=output_channel // 2, ksize=3, stride=1,
                         )
                     )
 
@@ -157,7 +162,9 @@ def __init__(self, num_classes=1000, model_size="1.5x"):
         self.globalpool = M.AvgPool2d(7)
         if self.model_size == "2.0x":
             self.dropout = M.Dropout(0.2)
-        self.classifier = M.Sequential(M.Linear(self.stage_out_channels[-1], num_classes, bias=False))
+        self.classifier = M.Sequential(
+            M.Linear(self.stage_out_channels[-1], num_classes, bias=False)
+        )
         self._initialize_weights()
 
     def forward(self, x):

official/vision/classification/shufflenet/test.py

@@ -10,7 +10,8 @@
 import multiprocessing
 import time
 
-import model as snet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as snet_model
 
 import megengine
 import megengine.data as data

official/vision/classification/shufflenet/train.py

@@ -11,7 +11,8 @@
 import os
 import time
 
-import model as snet_model  # pylint-disable=import-error
+# pylint: disable=import-error
+import model as snet_model
 
 import megengine
 import megengine.autodiff as autodiff
@@ -116,6 +117,7 @@ def main():
 
 
 def worker(rank, world_size, ngpus_per_node, args):
+    # pylint: disable=too-many-statements
     if rank == 0:
         os.makedirs(os.path.join(args.save, args.arch), exist_ok=True)
         megengine.logger.set_log_file(os.path.join(args.save, args.arch, "log.txt"))
@@ -162,7 +164,7 @@ def worker(rank, world_size, ngpus_per_node, args):
             print("NOT include ", n, p.shape)
             params_nwd.append(p)
     opt = optim.SGD(
-        [{"params": params_wd}, {"params": params_nwd, "weight_decay": 0},],
+        [{"params": params_wd}, {"params": params_nwd, "weight_decay": 0}, ],
         lr=args.lr,
         momentum=args.momentum,
         weight_decay=args.weight_decay * world_size,  # scale weight decay in "SUM" mode