Add pyupgrade pre-commit hook (#1937)

* add pyupgrade

* add options for pyupgrade

* minor refinement

.dev_scripts/visualize_lr.py

@@ -42,7 +42,7 @@ def parse_args():
 class SimpleModel(nn.Module):
 
     def __init__(self):
-        super(SimpleModel, self).__init__()
+        super().__init__()
         self.conv = nn.Conv2d(1, 1, 1)
 
     def train_step(self, *args, **kwargs):
@@ -159,13 +159,13 @@ def run(cfg, logger):
 def plot_lr_curve(json_file, cfg):
     data_dict = dict(LearningRate=[], Momentum=[])
     assert os.path.isfile(json_file)
-    with open(json_file, 'r') as f:
+    with open(json_file) as f:
         for line in f:
             log = json.loads(line.strip())
             data_dict['LearningRate'].append(log['lr'])
             data_dict['Momentum'].append(log['momentum'])
 
-    wind_w, wind_h = [int(size) for size in cfg.window_size.split('*')]
+    wind_w, wind_h = (int(size) for size in cfg.window_size.split('*'))
     # if legend is None, use {filename}_{key} as legend
     fig, axes = plt.subplots(2, 1, figsize=(wind_w, wind_h))
     plt.subplots_adjust(hspace=0.5)

.pre-commit-config.yaml

@@ -43,7 +43,11 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
-
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.32.1
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
   - repo: https://github.com/open-mmlab/pre-commit-hooks
     rev: v0.2.0  # Use the ref you want to point at
     hooks:

docs/en/conf.py

@@ -20,7 +20,7 @@ from sphinx.builders.html import StandaloneHTMLBuilder
 sys.path.insert(0, os.path.abspath('../..'))
 
 version_file = '../../mmcv/version.py'
-with open(version_file, 'r') as f:
+with open(version_file) as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']
 

docs/zh_cn/conf.py

@@ -20,7 +20,7 @@ from sphinx.builders.html import StandaloneHTMLBuilder
 sys.path.insert(0, os.path.abspath('../..'))
 
 version_file = '../../mmcv/version.py'
-with open(version_file, 'r') as f:
+with open(version_file) as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']
 

examples/train.py

@@ -14,7 +14,7 @@ from mmcv.utils import get_logger
 class Model(nn.Module):
 
     def __init__(self):
-        super(Model, self).__init__()
+        super().__init__()
         self.conv1 = nn.Conv2d(3, 6, 5)
         self.pool = nn.MaxPool2d(2, 2)
         self.conv2 = nn.Conv2d(6, 16, 5)

mmcv/cnn/alexnet.py

@@ -12,7 +12,7 @@ class AlexNet(nn.Module):
     """
 
     def __init__(self, num_classes=-1):
-        super(AlexNet, self).__init__()
+        super().__init__()
         self.num_classes = num_classes
         self.features = nn.Sequential(
             nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),

mmcv/cnn/bricks/activation.py

@@ -29,7 +29,7 @@ class Clamp(nn.Module):
     """
 
     def __init__(self, min=-1., max=1.):
-        super(Clamp, self).__init__()
+        super().__init__()
         self.min = min
         self.max = max
 

mmcv/cnn/bricks/context_block.py

@@ -38,7 +38,7 @@ class ContextBlock(nn.Module):
                  ratio,
                  pooling_type='att',
                  fusion_types=('channel_add', )):
-        super(ContextBlock, self).__init__()
+        super().__init__()
         assert pooling_type in ['avg', 'att']
         assert isinstance(fusion_types, (list, tuple))
         valid_fusion_types = ['channel_add', 'channel_mul']

mmcv/cnn/bricks/conv_module.py

@@ -83,7 +83,7 @@ class ConvModule(nn.Module):
                  with_spectral_norm=False,
                  padding_mode='zeros',
                  order=('conv', 'norm', 'act')):
-        super(ConvModule, self).__init__()
+        super().__init__()
         assert conv_cfg is None or isinstance(conv_cfg, dict)
         assert norm_cfg is None or isinstance(norm_cfg, dict)
         assert act_cfg is None or isinstance(act_cfg, dict)
@@ -96,7 +96,7 @@ class ConvModule(nn.Module):
         self.with_explicit_padding = padding_mode not in official_padding_mode
         self.order = order
         assert isinstance(self.order, tuple) and len(self.order) == 3
-        assert set(order) == set(['conv', 'norm', 'act'])
+        assert set(order) == {'conv', 'norm', 'act'}
 
         self.with_norm = norm_cfg is not None
         self.with_activation = act_cfg is not None

mmcv/cnn/bricks/conv_ws.py

@@ -35,7 +35,7 @@ class ConvWS2d(nn.Conv2d):
                  groups=1,
                  bias=True,
                  eps=1e-5):
-        super(ConvWS2d, self).__init__(
+        super().__init__(
             in_channels,
             out_channels,
             kernel_size,

mmcv/cnn/bricks/depthwise_separable_conv_module.py

@@ -59,7 +59,7 @@ class DepthwiseSeparableConvModule(nn.Module):
                  pw_norm_cfg='default',
                  pw_act_cfg='default',
                  **kwargs):
-        super(DepthwiseSeparableConvModule, self).__init__()
+        super().__init__()
         assert 'groups' not in kwargs, 'groups should not be specified'
 
         # if norm/activation config of depthwise/pointwise ConvModule is not

mmcv/cnn/bricks/drop.py

@@ -37,7 +37,7 @@ class DropPath(nn.Module):
     """
 
     def __init__(self, drop_prob=0.1):
-        super(DropPath, self).__init__()
+        super().__init__()
         self.drop_prob = drop_prob
 
     def forward(self, x):

mmcv/cnn/bricks/generalized_attention.py

@@ -54,7 +54,7 @@ class GeneralizedAttention(nn.Module):
                  q_stride=1,
                  attention_type='1111'):
 
-        super(GeneralizedAttention, self).__init__()
+        super().__init__()
 
         # hard range means local range for non-local operation
         self.position_embedding_dim = (

mmcv/cnn/bricks/hsigmoid.py

@@ -27,7 +27,7 @@ class HSigmoid(nn.Module):
     """
 
     def __init__(self, bias=3.0, divisor=6.0, min_value=0.0, max_value=1.0):
-        super(HSigmoid, self).__init__()
+        super().__init__()
         warnings.warn(
             'In MMCV v1.4.4, we modified the default value of args to align '
             'with PyTorch official. Previous Implementation: '

mmcv/cnn/bricks/hswish.py

@@ -22,7 +22,7 @@ class HSwish(nn.Module):
     """
 
     def __init__(self, inplace=False):
-        super(HSwish, self).__init__()
+        super().__init__()
         self.act = nn.ReLU6(inplace)
 
     def forward(self, x):

mmcv/cnn/bricks/non_local.py

@@ -40,7 +40,7 @@ class _NonLocalNd(nn.Module, metaclass=ABCMeta):
                  norm_cfg=None,
                  mode='embedded_gaussian',
                  **kwargs):
-        super(_NonLocalNd, self).__init__()
+        super().__init__()
         self.in_channels = in_channels
         self.reduction = reduction
         self.use_scale = use_scale
@@ -228,8 +228,7 @@ class NonLocal1d(_NonLocalNd):
                  sub_sample=False,
                  conv_cfg=dict(type='Conv1d'),
                  **kwargs):
-        super(NonLocal1d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
+        super().__init__(in_channels, conv_cfg=conv_cfg, **kwargs)
 
         self.sub_sample = sub_sample
 
@@ -262,8 +261,7 @@ class NonLocal2d(_NonLocalNd):
                  sub_sample=False,
                  conv_cfg=dict(type='Conv2d'),
                  **kwargs):
-        super(NonLocal2d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
+        super().__init__(in_channels, conv_cfg=conv_cfg, **kwargs)
 
         self.sub_sample = sub_sample
 
@@ -293,8 +291,7 @@ class NonLocal3d(_NonLocalNd):
                  sub_sample=False,
                  conv_cfg=dict(type='Conv3d'),
                  **kwargs):
-        super(NonLocal3d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
+        super().__init__(in_channels, conv_cfg=conv_cfg, **kwargs)
         self.sub_sample = sub_sample
 
         if sub_sample:

mmcv/cnn/bricks/scale.py

@@ -14,7 +14,7 @@ class Scale(nn.Module):
     """
 
     def __init__(self, scale=1.0):
-        super(Scale, self).__init__()
+        super().__init__()
         self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
 
     def forward(self, x):

mmcv/cnn/bricks/swish.py

@@ -19,7 +19,7 @@ class Swish(nn.Module):
     """
 
     def __init__(self):
-        super(Swish, self).__init__()
+        super().__init__()
 
     def forward(self, x):
         return x * torch.sigmoid(x)

mmcv/cnn/bricks/transformer.py

@@ -96,7 +96,7 @@ class AdaptivePadding(nn.Module):
     """
 
     def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
-        super(AdaptivePadding, self).__init__()
+        super().__init__()
         assert padding in ('same', 'corner')
 
         kernel_size = to_2tuple(kernel_size)
@@ -190,7 +190,7 @@ class PatchEmbed(BaseModule):
                  norm_cfg=None,
                  input_size=None,
                  init_cfg=None):
-        super(PatchEmbed, self).__init__(init_cfg=init_cfg)
+        super().__init__(init_cfg=init_cfg)
 
         self.embed_dims = embed_dims
         if stride is None:
@@ -435,7 +435,7 @@ class MultiheadAttention(BaseModule):
                  init_cfg=None,
                  batch_first=False,
                  **kwargs):
-        super(MultiheadAttention, self).__init__(init_cfg)
+        super().__init__(init_cfg)
         if 'dropout' in kwargs:
             warnings.warn(
                 'The arguments `dropout` in MultiheadAttention '
@@ -590,7 +590,7 @@ class FFN(BaseModule):
                  add_identity=True,
                  init_cfg=None,
                  **kwargs):
-        super(FFN, self).__init__(init_cfg)
+        super().__init__(init_cfg)
         assert num_fcs >= 2, 'num_fcs should be no less ' \
             f'than 2. got {num_fcs}.'
         self.embed_dims = embed_dims
@@ -694,12 +694,12 @@ class BaseTransformerLayer(BaseModule):
                     f'to a dict named `ffn_cfgs`. ', DeprecationWarning)
                 ffn_cfgs[new_name] = kwargs[ori_name]
 
-        super(BaseTransformerLayer, self).__init__(init_cfg)
+        super().__init__(init_cfg)
 
         self.batch_first = batch_first
 
-        assert set(operation_order) & set(
-            ['self_attn', 'norm', 'ffn', 'cross_attn']) == \
+        assert set(operation_order) & {
+            'self_attn', 'norm', 'ffn', 'cross_attn'} == \
             set(operation_order), f'The operation_order of' \
             f' {self.__class__.__name__} should ' \
             f'contains all four operation type ' \
@@ -880,7 +880,7 @@ class TransformerLayerSequence(BaseModule):
     """
 
     def __init__(self, transformerlayers=None, num_layers=None, init_cfg=None):
-        super(TransformerLayerSequence, self).__init__(init_cfg)
+        super().__init__(init_cfg)
         if isinstance(transformerlayers, dict):
             transformerlayers = [
                 copy.deepcopy(transformerlayers) for _ in range(num_layers)

mmcv/cnn/bricks/upsample.py

@@ -26,7 +26,7 @@ class PixelShufflePack(nn.Module):
 
     def __init__(self, in_channels, out_channels, scale_factor,
                  upsample_kernel):
-        super(PixelShufflePack, self).__init__()
+        super().__init__()
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.scale_factor = scale_factor