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

mmcv/cnn/resnet.py

@@ -30,7 +30,7 @@ class BasicBlock(nn.Module):
                  downsample=None,
                  style='pytorch',
                  with_cp=False):
-        super(BasicBlock, self).__init__()
+        super().__init__()
         assert style in ['pytorch', 'caffe']
         self.conv1 = conv3x3(inplanes, planes, stride, dilation)
         self.bn1 = nn.BatchNorm2d(planes)
@@ -77,7 +77,7 @@ class Bottleneck(nn.Module):
         If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
         it is "caffe", the stride-two layer is the first 1x1 conv layer.
         """
-        super(Bottleneck, self).__init__()
+        super().__init__()
         assert style in ['pytorch', 'caffe']
         if style == 'pytorch':
             conv1_stride = 1
@@ -218,7 +218,7 @@ class ResNet(nn.Module):
                  bn_eval=True,
                  bn_frozen=False,
                  with_cp=False):
-        super(ResNet, self).__init__()
+        super().__init__()
         if depth not in self.arch_settings:
             raise KeyError(f'invalid depth {depth} for resnet')
         assert num_stages >= 1 and num_stages <= 4
@@ -293,7 +293,7 @@ class ResNet(nn.Module):
             return tuple(outs)
 
     def train(self, mode=True):
-        super(ResNet, self).train(mode)
+        super().train(mode)
         if self.bn_eval:
             for m in self.modules():
                 if isinstance(m, nn.BatchNorm2d):

mmcv/cnn/utils/flops_counter.py

@@ -277,10 +277,10 @@ def print_model_with_flops(model,
         return ', '.join([
             params_to_string(
                 accumulated_num_params, units='M', precision=precision),
-            '{:.3%} Params'.format(accumulated_num_params / total_params),
+            f'{accumulated_num_params / total_params:.3%} Params',
             flops_to_string(
                 accumulated_flops_cost, units=units, precision=precision),
-            '{:.3%} FLOPs'.format(accumulated_flops_cost / total_flops),
+            f'{accumulated_flops_cost / total_flops:.3%} FLOPs',
             self.original_extra_repr()
         ])
 

mmcv/cnn/utils/weight_init.py

@@ -129,7 +129,7 @@ def _get_bases_name(m):
     return [b.__name__ for b in m.__class__.__bases__]
 
 
-class BaseInit(object):
+class BaseInit:
 
     def __init__(self, *, bias=0, bias_prob=None, layer=None):
         self.wholemodule = False
@@ -461,7 +461,7 @@ class Caffe2XavierInit(KaimingInit):
 
 
 @INITIALIZERS.register_module(name='Pretrained')
-class PretrainedInit(object):
+class PretrainedInit:
     """Initialize module by loading a pretrained model.
 
     Args:

mmcv/cnn/vgg.py

@@ -70,7 +70,7 @@ class VGG(nn.Module):
                  bn_frozen=False,
                  ceil_mode=False,
                  with_last_pool=True):
-        super(VGG, self).__init__()
+        super().__init__()
         if depth not in self.arch_settings:
             raise KeyError(f'invalid depth {depth} for vgg')
         assert num_stages >= 1 and num_stages <= 5
@@ -157,7 +157,7 @@ class VGG(nn.Module):
             return tuple(outs)
 
     def train(self, mode=True):
-        super(VGG, self).train(mode)
+        super().train(mode)
         if self.bn_eval:
             for m in self.modules():
                 if isinstance(m, nn.BatchNorm2d):

mmcv/device/mlu/data_parallel.py

@@ -33,7 +33,7 @@ class MLUDataParallel(MMDataParallel):
     """
 
     def __init__(self, *args, dim=0, **kwargs):
-        super(MLUDataParallel, self).__init__(*args, dim=dim, **kwargs)
+        super().__init__(*args, dim=dim, **kwargs)
         self.device_ids = [0]
         self.src_device_obj = torch.device('mlu:0')
 

mmcv/fileio/file_client.py

@@ -210,9 +210,9 @@ class PetrelBackend(BaseStorageBackend):
         """
         if not has_method(self._client, 'delete'):
             raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `delete` method, please use a higher version or dev'
-                 ' branch instead.'))
+                'Current version of Petrel Python SDK has not supported '
+                'the `delete` method, please use a higher version or dev'
+                ' branch instead.')
 
         filepath = self._map_path(filepath)
         filepath = self._format_path(filepath)
@@ -230,9 +230,9 @@ class PetrelBackend(BaseStorageBackend):
         if not (has_method(self._client, 'contains')
                 and has_method(self._client, 'isdir')):
             raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` and `isdir` methods, please use a higher'
-                 'version or dev branch instead.'))
+                'Current version of Petrel Python SDK has not supported '
+                'the `contains` and `isdir` methods, please use a higher'
+                'version or dev branch instead.')
 
         filepath = self._map_path(filepath)
         filepath = self._format_path(filepath)
@@ -251,9 +251,9 @@ class PetrelBackend(BaseStorageBackend):
         """
         if not has_method(self._client, 'isdir'):
             raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `isdir` method, please use a higher version or dev'
-                 ' branch instead.'))
+                'Current version of Petrel Python SDK has not supported '
+                'the `isdir` method, please use a higher version or dev'
+                ' branch instead.')
 
         filepath = self._map_path(filepath)
         filepath = self._format_path(filepath)
@@ -271,9 +271,9 @@ class PetrelBackend(BaseStorageBackend):
         """
         if not has_method(self._client, 'contains'):
             raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` method, please use a higher version or '
-                 'dev branch instead.'))
+                'Current version of Petrel Python SDK has not supported '
+                'the `contains` method, please use a higher version or '
+                'dev branch instead.')
 
         filepath = self._map_path(filepath)
         filepath = self._format_path(filepath)
@@ -366,9 +366,9 @@ class PetrelBackend(BaseStorageBackend):
         """
         if not has_method(self._client, 'list'):
             raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `list` method, please use a higher version or dev'
-                 ' branch instead.'))
+                'Current version of Petrel Python SDK has not supported '
+                'the `list` method, please use a higher version or dev'
+                ' branch instead.')
 
         dir_path = self._map_path(dir_path)
         dir_path = self._format_path(dir_path)
@@ -549,7 +549,7 @@ class HardDiskBackend(BaseStorageBackend):
         Returns:
             str: Expected text reading from ``filepath``.
         """
-        with open(filepath, 'r', encoding=encoding) as f:
+        with open(filepath, encoding=encoding) as f:
             value_buf = f.read()
         return value_buf
 

mmcv/fileio/handlers/pickle_handler.py

@@ -12,8 +12,7 @@ class PickleHandler(BaseFileHandler):
         return pickle.load(file, **kwargs)
 
     def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(
-            filepath, mode='rb', **kwargs)
+        return super().load_from_path(filepath, mode='rb', **kwargs)
 
     def dump_to_str(self, obj, **kwargs):
         kwargs.setdefault('protocol', 2)
@@ -24,5 +23,4 @@ class PickleHandler(BaseFileHandler):
         pickle.dump(obj, file, **kwargs)
 
     def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(
-            obj, filepath, mode='wb', **kwargs)
+        super().dump_to_path(obj, filepath, mode='wb', **kwargs)

mmcv/image/geometric.py

@@ -157,7 +157,7 @@ def imresize_to_multiple(img,
         size = _scale_size((w, h), scale_factor)
 
     divisor = to_2tuple(divisor)
-    size = tuple([int(np.ceil(s / d)) * d for s, d in zip(size, divisor)])
+    size = tuple(int(np.ceil(s / d)) * d for s, d in zip(size, divisor))
     resized_img, w_scale, h_scale = imresize(
         img,
         size,

mmcv/onnx/onnx_utils/symbolic_helper.py

@@ -59,7 +59,7 @@ def _parse_arg(value, desc):
             raise RuntimeError(
                 "ONNX symbolic doesn't know to interpret ListConstruct node")
 
-    raise RuntimeError('Unexpected node type: {}'.format(value.node().kind()))
+    raise RuntimeError(f'Unexpected node type: {value.node().kind()}')
 
 
 def _maybe_get_const(value, desc):

mmcv/ops/border_align.py

@@ -86,7 +86,7 @@ class BorderAlign(nn.Module):
     """
 
     def __init__(self, pool_size):
-        super(BorderAlign, self).__init__()
+        super().__init__()
         self.pool_size = pool_size
 
     def forward(self, input, boxes):

mmcv/ops/box_iou_rotated.py

@@ -131,7 +131,7 @@ def box_iou_rotated(bboxes1,
     if aligned:
         ious = bboxes1.new_zeros(rows)
     else:
-        ious = bboxes1.new_zeros((rows * cols))
+        ious = bboxes1.new_zeros(rows * cols)
     if not clockwise:
         flip_mat = bboxes1.new_ones(bboxes1.shape[-1])
         flip_mat[-1] = -1

mmcv/ops/carafe.py

@@ -85,7 +85,7 @@ carafe_naive = CARAFENaiveFunction.apply
 class CARAFENaive(Module):
 
     def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFENaive, self).__init__()
+        super().__init__()
 
         assert isinstance(kernel_size, int) and isinstance(
             group_size, int) and isinstance(scale_factor, int)
@@ -195,7 +195,7 @@ class CARAFE(Module):
     """
 
     def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFE, self).__init__()
+        super().__init__()
 
         assert isinstance(kernel_size, int) and isinstance(
             group_size, int) and isinstance(scale_factor, int)
@@ -238,7 +238,7 @@ class CARAFEPack(nn.Module):
                  encoder_kernel=3,
                  encoder_dilation=1,
                  compressed_channels=64):
-        super(CARAFEPack, self).__init__()
+        super().__init__()
         self.channels = channels
         self.scale_factor = scale_factor
         self.up_kernel = up_kernel

mmcv/ops/corner_pool.py

@@ -125,7 +125,7 @@ class CornerPool(nn.Module):
     }
 
     def __init__(self, mode):
-        super(CornerPool, self).__init__()
+        super().__init__()
         assert mode in self.pool_functions
         self.mode = mode
         self.corner_pool = self.pool_functions[mode]

mmcv/ops/deform_conv.py

@@ -236,7 +236,7 @@ class DeformConv2d(nn.Module):
                  deform_groups: int = 1,
                  bias: bool = False,
                  im2col_step: int = 32) -> None:
-        super(DeformConv2d, self).__init__()
+        super().__init__()
 
         assert not bias, \
             f'bias={bias} is not supported in DeformConv2d.'
@@ -356,7 +356,7 @@ class DeformConv2dPack(DeformConv2d):
     _version = 2
 
     def __init__(self, *args, **kwargs):
-        super(DeformConv2dPack, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
         self.conv_offset = nn.Conv2d(
             self.in_channels,
             self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1],

mmcv/ops/deform_roi_pool.py

@@ -96,7 +96,7 @@ class DeformRoIPool(nn.Module):
                  spatial_scale=1.0,
                  sampling_ratio=0,
                  gamma=0.1):
-        super(DeformRoIPool, self).__init__()
+        super().__init__()
         self.output_size = _pair(output_size)
         self.spatial_scale = float(spatial_scale)
         self.sampling_ratio = int(sampling_ratio)
@@ -117,8 +117,7 @@ class DeformRoIPoolPack(DeformRoIPool):
                  spatial_scale=1.0,
                  sampling_ratio=0,
                  gamma=0.1):
-        super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale,
-                                                sampling_ratio, gamma)
+        super().__init__(output_size, spatial_scale, sampling_ratio, gamma)
 
         self.output_channels = output_channels
         self.deform_fc_channels = deform_fc_channels
@@ -158,8 +157,7 @@ class ModulatedDeformRoIPoolPack(DeformRoIPool):
                  spatial_scale=1.0,
                  sampling_ratio=0,
                  gamma=0.1):
-        super(ModulatedDeformRoIPoolPack,
-              self).__init__(output_size, spatial_scale, sampling_ratio, gamma)
+        super().__init__(output_size, spatial_scale, sampling_ratio, gamma)
 
         self.output_channels = output_channels
         self.deform_fc_channels = deform_fc_channels

mmcv/ops/focal_loss.py

@@ -89,7 +89,7 @@ sigmoid_focal_loss = SigmoidFocalLossFunction.apply
 class SigmoidFocalLoss(nn.Module):
 
     def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SigmoidFocalLoss, self).__init__()
+        super().__init__()
         self.gamma = gamma
         self.alpha = alpha
         self.register_buffer('weight', weight)
@@ -195,7 +195,7 @@ softmax_focal_loss = SoftmaxFocalLossFunction.apply
 class SoftmaxFocalLoss(nn.Module):
 
     def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SoftmaxFocalLoss, self).__init__()
+        super().__init__()
         self.gamma = gamma
         self.alpha = alpha
         self.register_buffer('weight', weight)

mmcv/ops/fused_bias_leakyrelu.py

@@ -212,7 +212,7 @@ class FusedBiasLeakyReLU(nn.Module):
     """
 
     def __init__(self, num_channels, negative_slope=0.2, scale=2**0.5):
-        super(FusedBiasLeakyReLU, self).__init__()
+        super().__init__()
 
         self.bias = nn.Parameter(torch.zeros(num_channels))
         self.negative_slope = negative_slope

mmcv/ops/masked_conv.py

@@ -98,13 +98,12 @@ class MaskedConv2d(nn.Conv2d):
                  dilation=1,
                  groups=1,
                  bias=True):
-        super(MaskedConv2d,
-              self).__init__(in_channels, out_channels, kernel_size, stride,
-                             padding, dilation, groups, bias)
+        super().__init__(in_channels, out_channels, kernel_size, stride,
+                         padding, dilation, groups, bias)
 
     def forward(self, input, mask=None):
         if mask is None:  # fallback to the normal Conv2d
-            return super(MaskedConv2d, self).forward(input)
+            return super().forward(input)
         else:
             return masked_conv2d(input, mask, self.weight, self.bias,
                                  self.padding)

mmcv/ops/merge_cells.py

@@ -53,7 +53,7 @@ class BaseMergeCell(nn.Module):
                  input_conv_cfg=None,
                  input_norm_cfg=None,
                  upsample_mode='nearest'):
-        super(BaseMergeCell, self).__init__()
+        super().__init__()
         assert upsample_mode in ['nearest', 'bilinear']
         self.with_out_conv = with_out_conv
         self.with_input1_conv = with_input1_conv
@@ -121,7 +121,7 @@ class BaseMergeCell(nn.Module):
 class SumCell(BaseMergeCell):
 
     def __init__(self, in_channels, out_channels, **kwargs):
-        super(SumCell, self).__init__(in_channels, out_channels, **kwargs)
+        super().__init__(in_channels, out_channels, **kwargs)
 
     def _binary_op(self, x1, x2):
         return x1 + x2
@@ -130,8 +130,7 @@ class SumCell(BaseMergeCell):
 class ConcatCell(BaseMergeCell):
 
     def __init__(self, in_channels, out_channels, **kwargs):
-        super(ConcatCell, self).__init__(in_channels * 2, out_channels,
-                                         **kwargs)
+        super().__init__(in_channels * 2, out_channels, **kwargs)
 
     def _binary_op(self, x1, x2):
         ret = torch.cat([x1, x2], dim=1)

mmcv/ops/modulated_deform_conv.py

@@ -168,7 +168,7 @@ class ModulatedDeformConv2d(nn.Module):
                  groups=1,
                  deform_groups=1,
                  bias=True):
-        super(ModulatedDeformConv2d, self).__init__()
+        super().__init__()
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.kernel_size = _pair(kernel_size)
@@ -227,7 +227,7 @@ class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
     _version = 2
 
     def __init__(self, *args, **kwargs):
-        super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
         self.conv_offset = nn.Conv2d(
             self.in_channels,
             self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
@@ -239,7 +239,7 @@ class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
         self.init_weights()
 
     def init_weights(self):
-        super(ModulatedDeformConv2dPack, self).init_weights()
+        super().init_weights()
         if hasattr(self, 'conv_offset'):
             self.conv_offset.weight.data.zero_()
             self.conv_offset.bias.data.zero_()

mmcv/ops/point_sample.py

@@ -296,7 +296,7 @@ class SimpleRoIAlign(nn.Module):
                 If True, align the results more perfectly.
         """
 
-        super(SimpleRoIAlign, self).__init__()
+        super().__init__()
         self.output_size = _pair(output_size)
         self.spatial_scale = float(spatial_scale)
         # to be consistent with other RoI ops

mmcv/ops/psa_mask.py

@@ -72,7 +72,7 @@ psa_mask = PSAMaskFunction.apply
 class PSAMask(nn.Module):
 
     def __init__(self, psa_type, mask_size=None):
-        super(PSAMask, self).__init__()
+        super().__init__()
         assert psa_type in ['collect', 'distribute']
         if psa_type == 'collect':
             psa_type_enum = 0

mmcv/ops/riroi_align_rotated.py

@@ -116,7 +116,7 @@ class RiRoIAlignRotated(nn.Module):
                  num_samples=0,
                  num_orientations=8,
                  clockwise=False):
-        super(RiRoIAlignRotated, self).__init__()
+        super().__init__()
 
         self.out_size = out_size
         self.spatial_scale = float(spatial_scale)

mmcv/ops/roi_align.py

@@ -181,7 +181,7 @@ class RoIAlign(nn.Module):
                  pool_mode='avg',
                  aligned=True,
                  use_torchvision=False):
-        super(RoIAlign, self).__init__()
+        super().__init__()
 
         self.output_size = _pair(output_size)
         self.spatial_scale = float(spatial_scale)

mmcv/ops/roi_align_rotated.py

@@ -156,7 +156,7 @@ class RoIAlignRotated(nn.Module):
                  sampling_ratio=0,
                  aligned=True,
                  clockwise=False):
-        super(RoIAlignRotated, self).__init__()
+        super().__init__()
 
         self.output_size = _pair(output_size)
         self.spatial_scale = float(spatial_scale)

mmcv/ops/roi_pool.py

@@ -71,7 +71,7 @@ roi_pool = RoIPoolFunction.apply
 class RoIPool(nn.Module):
 
     def __init__(self, output_size, spatial_scale=1.0):
-        super(RoIPool, self).__init__()
+        super().__init__()
 
         self.output_size = _pair(output_size)
         self.spatial_scale = float(spatial_scale)

mmcv/ops/sparse_conv.py

@@ -64,7 +64,7 @@ class SparseConvolution(SparseModule):
                  inverse=False,
                  indice_key=None,
                  fused_bn=False):
-        super(SparseConvolution, self).__init__()
+        super().__init__()
         assert groups == 1
         if not isinstance(kernel_size, (list, tuple)):
             kernel_size = [kernel_size] * ndim
@@ -217,7 +217,7 @@ class SparseConv2d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SparseConv2d, self).__init__(
+        super().__init__(
             2,
             in_channels,
             out_channels,
@@ -243,7 +243,7 @@ class SparseConv3d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SparseConv3d, self).__init__(
+        super().__init__(
             3,
             in_channels,
             out_channels,
@@ -269,7 +269,7 @@ class SparseConv4d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SparseConv4d, self).__init__(
+        super().__init__(
             4,
             in_channels,
             out_channels,
@@ -295,7 +295,7 @@ class SparseConvTranspose2d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SparseConvTranspose2d, self).__init__(
+        super().__init__(
             2,
             in_channels,
             out_channels,
@@ -322,7 +322,7 @@ class SparseConvTranspose3d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SparseConvTranspose3d, self).__init__(
+        super().__init__(
             3,
             in_channels,
             out_channels,
@@ -345,7 +345,7 @@ class SparseInverseConv2d(SparseConvolution):
                  kernel_size,
                  indice_key=None,
                  bias=True):
-        super(SparseInverseConv2d, self).__init__(
+        super().__init__(
             2,
             in_channels,
             out_channels,
@@ -364,7 +364,7 @@ class SparseInverseConv3d(SparseConvolution):
                  kernel_size,
                  indice_key=None,
                  bias=True):
-        super(SparseInverseConv3d, self).__init__(
+        super().__init__(
             3,
             in_channels,
             out_channels,
@@ -387,7 +387,7 @@ class SubMConv2d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SubMConv2d, self).__init__(
+        super().__init__(
             2,
             in_channels,
             out_channels,
@@ -414,7 +414,7 @@ class SubMConv3d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SubMConv3d, self).__init__(
+        super().__init__(
             3,
             in_channels,
             out_channels,
@@ -441,7 +441,7 @@ class SubMConv4d(SparseConvolution):
                  groups=1,
                  bias=True,
                  indice_key=None):
-        super(SubMConv4d, self).__init__(
+        super().__init__(
             4,
             in_channels,
             out_channels,

mmcv/ops/sparse_modules.py

@@ -86,7 +86,7 @@ class SparseSequential(SparseModule):
     """
 
     def __init__(self, *args, **kwargs):
-        super(SparseSequential, self).__init__()
+        super().__init__()
         if len(args) == 1 and isinstance(args[0], OrderedDict):
             for key, module in args[0].items():
                 self.add_module(key, module)
@@ -103,7 +103,7 @@ class SparseSequential(SparseModule):
 
     def __getitem__(self, idx):
         if not (-len(self) <= idx < len(self)):
-            raise IndexError('index {} is out of range'.format(idx))
+            raise IndexError(f'index {idx} is out of range')
         if idx < 0:
             idx += len(self)
         it = iter(self._modules.values())

mmcv/ops/sparse_pool.py

@@ -29,7 +29,7 @@ class SparseMaxPool(SparseModule):
                  padding=0,
                  dilation=1,
                  subm=False):
-        super(SparseMaxPool, self).__init__()
+        super().__init__()
         if not isinstance(kernel_size, (list, tuple)):
             kernel_size = [kernel_size] * ndim
         if not isinstance(stride, (list, tuple)):
@@ -77,12 +77,10 @@ class SparseMaxPool(SparseModule):
 class SparseMaxPool2d(SparseMaxPool):
 
     def __init__(self, kernel_size, stride=1, padding=0, dilation=1):
-        super(SparseMaxPool2d, self).__init__(2, kernel_size, stride, padding,
-                                              dilation)
+        super().__init__(2, kernel_size, stride, padding, dilation)
 
 
 class SparseMaxPool3d(SparseMaxPool):
 
     def __init__(self, kernel_size, stride=1, padding=0, dilation=1):
-        super(SparseMaxPool3d, self).__init__(3, kernel_size, stride, padding,
-                                              dilation)
+        super().__init__(3, kernel_size, stride, padding, dilation)

mmcv/ops/sparse_structure.py

@@ -18,7 +18,7 @@ def scatter_nd(indices, updates, shape):
     return ret
 
 
-class SparseConvTensor(object):
+class SparseConvTensor:
 
     def __init__(self,
                  features,

mmcv/ops/sync_bn.py

@@ -198,7 +198,7 @@ class SyncBatchNorm(Module):
                  track_running_stats=True,
                  group=None,
                  stats_mode='default'):
-        super(SyncBatchNorm, self).__init__()
+        super().__init__()
         self.num_features = num_features
         self.eps = eps
         self.momentum = momentum

mmcv/parallel/data_parallel.py

@@ -32,7 +32,7 @@ class MMDataParallel(DataParallel):
     """
 
     def __init__(self, *args, dim=0, **kwargs):
-        super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs)
+        super().__init__(*args, dim=dim, **kwargs)
         self.dim = dim
 
     def forward(self, *inputs, **kwargs):

mmcv/parallel/distributed_deprecated.py

@@ -18,7 +18,7 @@ class MMDistributedDataParallel(nn.Module):
                  dim=0,
                  broadcast_buffers=True,
                  bucket_cap_mb=25):
-        super(MMDistributedDataParallel, self).__init__()
+        super().__init__()
         self.module = module
         self.dim = dim
         self.broadcast_buffers = broadcast_buffers

mmcv/runner/base_module.py

@@ -35,7 +35,7 @@ class BaseModule(nn.Module, metaclass=ABCMeta):
         # NOTE init_cfg can be defined in different levels, but init_cfg
         # in low levels has a higher priority.
 
-        super(BaseModule, self).__init__()
+        super().__init__()
         # define default value of init_cfg instead of hard code
         # in init_weights() function
         self._is_init = False

mmcv/runner/hooks/checkpoint.py

@@ -83,8 +83,8 @@ class CheckpointHook(Hook):
             basename = osp.basename(runner.work_dir.rstrip(osp.sep))
             self.out_dir = self.file_client.join_path(self.out_dir, basename)
 
-        runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
-                            f'{self.file_client.name}.'))
+        runner.logger.info(f'Checkpoints will be saved to {self.out_dir} by '
+                           f'{self.file_client.name}.')
 
         # disable the create_symlink option because some file backends do not
         # allow to create a symlink
@@ -93,9 +93,9 @@ class CheckpointHook(Hook):
                     'create_symlink'] and not self.file_client.allow_symlink:
                 self.args['create_symlink'] = False
                 warnings.warn(
-                    ('create_symlink is set as True by the user but is changed'
-                     'to be False because creating symbolic link is not '
-                     f'allowed in {self.file_client.name}'))
+                    'create_symlink is set as True by the user but is changed'
+                    'to be False because creating symbolic link is not '
+                    f'allowed in {self.file_client.name}')
         else:
             self.args['create_symlink'] = self.file_client.allow_symlink
 

mmcv/runner/hooks/evaluation.py

@@ -214,8 +214,8 @@ class EvalHook(Hook):
             basename = osp.basename(runner.work_dir.rstrip(osp.sep))
             self.out_dir = self.file_client.join_path(self.out_dir, basename)
             runner.logger.info(
-                (f'The best checkpoint will be saved to {self.out_dir} by '
-                 f'{self.file_client.name}'))
+                f'The best checkpoint will be saved to {self.out_dir} by '
+                f'{self.file_client.name}')
 
         if self.save_best is not None:
             if runner.meta is None:
@@ -335,8 +335,8 @@ class EvalHook(Hook):
                     self.best_ckpt_path):
                 self.file_client.remove(self.best_ckpt_path)
                 runner.logger.info(
-                    (f'The previous best checkpoint {self.best_ckpt_path} was '
-                     'removed'))
+                    f'The previous best checkpoint {self.best_ckpt_path} was '
+                    'removed')
 
             best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
             self.best_ckpt_path = self.file_client.join_path(

mmcv/runner/hooks/logger/clearml.py

@@ -34,8 +34,7 @@ class ClearMLLoggerHook(LoggerHook):
                  ignore_last=True,
                  reset_flag=False,
                  by_epoch=True):
-        super(ClearMLLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.import_clearml()
         self.init_kwargs = init_kwargs
 
@@ -49,7 +48,7 @@ class ClearMLLoggerHook(LoggerHook):
 
     @master_only
     def before_run(self, runner):
-        super(ClearMLLoggerHook, self).before_run(runner)
+        super().before_run(runner)
         task_kwargs = self.init_kwargs if self.init_kwargs else {}
         self.task = self.clearml.Task.init(**task_kwargs)
         self.task_logger = self.task.get_logger()

mmcv/runner/hooks/logger/mlflow.py

@@ -40,8 +40,7 @@ class MlflowLoggerHook(LoggerHook):
                  ignore_last=True,
                  reset_flag=False,
                  by_epoch=True):
-        super(MlflowLoggerHook, self).__init__(interval, ignore_last,
-                                               reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.import_mlflow()
         self.exp_name = exp_name
         self.tags = tags
@@ -59,7 +58,7 @@ class MlflowLoggerHook(LoggerHook):
 
     @master_only
     def before_run(self, runner):
-        super(MlflowLoggerHook, self).before_run(runner)
+        super().before_run(runner)
         if self.exp_name is not None:
             self.mlflow.set_experiment(self.exp_name)
         if self.tags is not None:

mmcv/runner/hooks/logger/neptune.py

@@ -49,8 +49,7 @@ class NeptuneLoggerHook(LoggerHook):
                  with_step=True,
                  by_epoch=True):
 
-        super(NeptuneLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.import_neptune()
         self.init_kwargs = init_kwargs
         self.with_step = with_step

mmcv/runner/hooks/logger/pavi.py

@@ -40,8 +40,7 @@ class PaviLoggerHook(LoggerHook):
                  reset_flag=False,
                  by_epoch=True,
                  img_key='img_info'):
-        super(PaviLoggerHook, self).__init__(interval, ignore_last, reset_flag,
-                                             by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.init_kwargs = init_kwargs
         self.add_graph = add_graph
         self.add_last_ckpt = add_last_ckpt
@@ -49,7 +48,7 @@ class PaviLoggerHook(LoggerHook):
 
     @master_only
     def before_run(self, runner):
-        super(PaviLoggerHook, self).before_run(runner)
+        super().before_run(runner)
         try:
             from pavi import SummaryWriter
         except ImportError:

mmcv/runner/hooks/logger/segmind.py

@@ -27,8 +27,7 @@ class SegmindLoggerHook(LoggerHook):
                  ignore_last=True,
                  reset_flag=False,
                  by_epoch=True):
-        super(SegmindLoggerHook, self).__init__(interval, ignore_last,
-                                                reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.import_segmind()
 
     def import_segmind(self):

mmcv/runner/hooks/logger/tensorboard.py

@@ -28,13 +28,12 @@ class TensorboardLoggerHook(LoggerHook):
                  ignore_last=True,
                  reset_flag=False,
                  by_epoch=True):
-        super(TensorboardLoggerHook, self).__init__(interval, ignore_last,
-                                                    reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.log_dir = log_dir
 
     @master_only
     def before_run(self, runner):
-        super(TensorboardLoggerHook, self).before_run(runner)
+        super().before_run(runner)
         if (TORCH_VERSION == 'parrots'
                 or digit_version(TORCH_VERSION) < digit_version('1.1')):
             try:

mmcv/runner/hooks/logger/text.py

@@ -62,8 +62,7 @@ class TextLoggerHook(LoggerHook):
                  out_suffix=('.log.json', '.log', '.py'),
                  keep_local=True,
                  file_client_args=None):
-        super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag,
-                                             by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.by_epoch = by_epoch
         self.time_sec_tot = 0
         self.interval_exp_name = interval_exp_name
@@ -87,7 +86,7 @@ class TextLoggerHook(LoggerHook):
                                                        self.out_dir)
 
     def before_run(self, runner):
-        super(TextLoggerHook, self).before_run(runner)
+        super().before_run(runner)
 
         if self.out_dir is not None:
             self.file_client = FileClient.infer_client(self.file_client_args,
@@ -97,8 +96,8 @@ class TextLoggerHook(LoggerHook):
             basename = osp.basename(runner.work_dir.rstrip(osp.sep))
             self.out_dir = self.file_client.join_path(self.out_dir, basename)
             runner.logger.info(
-                (f'Text logs will be saved to {self.out_dir} by '
-                 f'{self.file_client.name} after the training process.'))
+                f'Text logs will be saved to {self.out_dir} by '
+                f'{self.file_client.name} after the training process.')
 
         self.start_iter = runner.iter
         self.json_log_path = osp.join(runner.work_dir,
@@ -242,15 +241,15 @@ class TextLoggerHook(LoggerHook):
                 local_filepath = osp.join(runner.work_dir, filename)
                 out_filepath = self.file_client.join_path(
                     self.out_dir, filename)
-                with open(local_filepath, 'r') as f:
+                with open(local_filepath) as f:
                     self.file_client.put_text(f.read(), out_filepath)
 
                 runner.logger.info(
-                    (f'The file {local_filepath} has been uploaded to '
-                     f'{out_filepath}.'))
+                    f'The file {local_filepath} has been uploaded to '
+                    f'{out_filepath}.')
 
                 if not self.keep_local:
                     os.remove(local_filepath)
                     runner.logger.info(
-                        (f'{local_filepath} was removed due to the '
-                         '`self.keep_local=False`'))
+                        f'{local_filepath} was removed due to the '
+                        '`self.keep_local=False`')

mmcv/runner/hooks/logger/wandb.py

@@ -57,8 +57,7 @@ class WandbLoggerHook(LoggerHook):
                  with_step=True,
                  log_artifact=True,
                  out_suffix=('.log.json', '.log', '.py')):
-        super(WandbLoggerHook, self).__init__(interval, ignore_last,
-                                              reset_flag, by_epoch)
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
         self.import_wandb()
         self.init_kwargs = init_kwargs
         self.commit = commit
@@ -76,7 +75,7 @@ class WandbLoggerHook(LoggerHook):
 
     @master_only
     def before_run(self, runner):
-        super(WandbLoggerHook, self).before_run(runner)
+        super().before_run(runner)
         if self.wandb is None:
             self.import_wandb()
         if self.init_kwargs:

mmcv/runner/hooks/lr_updater.py

@@ -157,7 +157,7 @@ class LrUpdaterHook(Hook):
 class FixedLrUpdaterHook(LrUpdaterHook):
 
     def __init__(self, **kwargs):
-        super(FixedLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         return base_lr
@@ -188,7 +188,7 @@ class StepLrUpdaterHook(LrUpdaterHook):
         self.step = step
         self.gamma = gamma
         self.min_lr = min_lr
-        super(StepLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         progress = runner.epoch if self.by_epoch else runner.iter
@@ -215,7 +215,7 @@ class ExpLrUpdaterHook(LrUpdaterHook):
 
     def __init__(self, gamma, **kwargs):
         self.gamma = gamma
-        super(ExpLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         progress = runner.epoch if self.by_epoch else runner.iter
@@ -228,7 +228,7 @@ class PolyLrUpdaterHook(LrUpdaterHook):
     def __init__(self, power=1., min_lr=0., **kwargs):
         self.power = power
         self.min_lr = min_lr
-        super(PolyLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         if self.by_epoch:
@@ -247,7 +247,7 @@ class InvLrUpdaterHook(LrUpdaterHook):
     def __init__(self, gamma, power=1., **kwargs):
         self.gamma = gamma
         self.power = power
-        super(InvLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         progress = runner.epoch if self.by_epoch else runner.iter
@@ -269,7 +269,7 @@ class CosineAnnealingLrUpdaterHook(LrUpdaterHook):
         assert (min_lr is None) ^ (min_lr_ratio is None)
         self.min_lr = min_lr
         self.min_lr_ratio = min_lr_ratio
-        super(CosineAnnealingLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         if self.by_epoch:
@@ -317,7 +317,7 @@ class FlatCosineAnnealingLrUpdaterHook(LrUpdaterHook):
         self.start_percent = start_percent
         self.min_lr = min_lr
         self.min_lr_ratio = min_lr_ratio
-        super(FlatCosineAnnealingLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         if self.by_epoch:
@@ -367,7 +367,7 @@ class CosineRestartLrUpdaterHook(LrUpdaterHook):
         self.restart_weights = restart_weights
         assert (len(self.periods) == len(self.restart_weights)
                 ), 'periods and restart_weights should have the same length.'
-        super(CosineRestartLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
         self.cumulative_periods = [
             sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
@@ -484,10 +484,10 @@ class CyclicLrUpdaterHook(LrUpdaterHook):
 
         assert not by_epoch, \
             'currently only support "by_epoch" = False'
-        super(CyclicLrUpdaterHook, self).__init__(by_epoch, **kwargs)
+        super().__init__(by_epoch, **kwargs)
 
     def before_run(self, runner):
-        super(CyclicLrUpdaterHook, self).before_run(runner)
+        super().before_run(runner)
         # initiate lr_phases
         # total lr_phases are separated as up and down
         self.max_iter_per_phase = runner.max_iters // self.cyclic_times
@@ -598,7 +598,7 @@ class OneCycleLrUpdaterHook(LrUpdaterHook):
         self.final_div_factor = final_div_factor
         self.three_phase = three_phase
         self.lr_phases = []  # init lr_phases
-        super(OneCycleLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def before_run(self, runner):
         if hasattr(self, 'total_steps'):
@@ -668,7 +668,7 @@ class LinearAnnealingLrUpdaterHook(LrUpdaterHook):
         assert (min_lr is None) ^ (min_lr_ratio is None)
         self.min_lr = min_lr
         self.min_lr_ratio = min_lr_ratio
-        super(LinearAnnealingLrUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_lr(self, runner, base_lr):
         if self.by_epoch:

mmcv/runner/hooks/momentum_updater.py

@@ -176,7 +176,7 @@ class StepMomentumUpdaterHook(MomentumUpdaterHook):
         self.step = step
         self.gamma = gamma
         self.min_momentum = min_momentum
-        super(StepMomentumUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_momentum(self, runner, base_momentum):
         progress = runner.epoch if self.by_epoch else runner.iter
@@ -214,7 +214,7 @@ class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
         assert (min_momentum is None) ^ (min_momentum_ratio is None)
         self.min_momentum = min_momentum
         self.min_momentum_ratio = min_momentum_ratio
-        super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_momentum(self, runner, base_momentum):
         if self.by_epoch:
@@ -247,7 +247,7 @@ class LinearAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
         assert (min_momentum is None) ^ (min_momentum_ratio is None)
         self.min_momentum = min_momentum
         self.min_momentum_ratio = min_momentum_ratio
-        super(LinearAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def get_momentum(self, runner, base_momentum):
         if self.by_epoch:
@@ -328,10 +328,10 @@ class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
         # currently only support by_epoch=False
         assert not by_epoch, \
             'currently only support "by_epoch" = False'
-        super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
+        super().__init__(by_epoch, **kwargs)
 
     def before_run(self, runner):
-        super(CyclicMomentumUpdaterHook, self).before_run(runner)
+        super().before_run(runner)
         # initiate momentum_phases
         # total momentum_phases are separated as up and down
         max_iter_per_phase = runner.max_iters // self.cyclic_times
@@ -439,7 +439,7 @@ class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
             self.anneal_func = annealing_linear
         self.three_phase = three_phase
         self.momentum_phases = []  # init momentum_phases
-        super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def before_run(self, runner):
         if isinstance(runner.optimizer, dict):

mmcv/runner/hooks/optimizer.py

@@ -110,7 +110,7 @@ class GradientCumulativeOptimizerHook(OptimizerHook):
     """
 
     def __init__(self, cumulative_iters=1, **kwargs):
-        super(GradientCumulativeOptimizerHook, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
         assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \
             f'cumulative_iters only accepts positive int, but got ' \
@@ -297,8 +297,7 @@ if (TORCH_VERSION != 'parrots'
         """
 
         def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
+            super().__init__(*args, **kwargs)
 
         def after_train_iter(self, runner):
             if not self.initialized:
@@ -490,8 +489,7 @@ else:
         iters gradient cumulating."""
 
         def __init__(self, *args, **kwargs):
-            super(GradientCumulativeFp16OptimizerHook,
-                  self).__init__(*args, **kwargs)
+            super().__init__(*args, **kwargs)
 
         def after_train_iter(self, runner):
             if not self.initialized:

mmcv/runner/iter_based_runner.py

@@ -263,7 +263,7 @@ class IterBasedRunner(BaseRunner):
         if log_config is not None:
             for info in log_config['hooks']:
                 info.setdefault('by_epoch', False)
-        super(IterBasedRunner, self).register_training_hooks(
+        super().register_training_hooks(
             lr_config=lr_config,
             momentum_config=momentum_config,
             optimizer_config=optimizer_config,

mmcv/tensorrt/tensorrt_utils.py

@@ -54,7 +54,7 @@ def onnx2trt(onnx_model: Union[str, onnx.ModelProto],
     msg += reset_style
     warnings.warn(msg)
 
-    device = torch.device('cuda:{}'.format(device_id))
+    device = torch.device(f'cuda:{device_id}')
     # create builder and network
     logger = trt.Logger(log_level)
     builder = trt.Builder(logger)
@@ -209,7 +209,7 @@ class TRTWrapper(torch.nn.Module):
         msg += reset_style
         warnings.warn(msg)
 
-        super(TRTWrapper, self).__init__()
+        super().__init__()
         self.engine = engine
         if isinstance(self.engine, str):
             self.engine = load_trt_engine(engine)

mmcv/utils/config.py

@@ -39,7 +39,7 @@ class ConfigDict(Dict):
 
     def __getattr__(self, name):
         try:
-            value = super(ConfigDict, self).__getattr__(name)
+            value = super().__getattr__(name)
         except KeyError:
             ex = AttributeError(f"'{self.__class__.__name__}' object has no "
                                 f"attribute '{name}'")
@@ -96,7 +96,7 @@ class Config:
 
     @staticmethod
     def _validate_py_syntax(filename):
-        with open(filename, 'r', encoding='utf-8') as f:
+        with open(filename, encoding='utf-8') as f:
             # Setting encoding explicitly to resolve coding issue on windows
             content = f.read()
         try:
@@ -116,7 +116,7 @@ class Config:
             fileBasename=file_basename,
             fileBasenameNoExtension=file_basename_no_extension,
             fileExtname=file_extname)
-        with open(filename, 'r', encoding='utf-8') as f:
+        with open(filename, encoding='utf-8') as f:
             # Setting encoding explicitly to resolve coding issue on windows
             config_file = f.read()
         for key, value in support_templates.items():
@@ -130,7 +130,7 @@ class Config:
     def _pre_substitute_base_vars(filename, temp_config_name):
         """Substitute base variable placehoders to string, so that parsing
         would work."""
-        with open(filename, 'r', encoding='utf-8') as f:
+        with open(filename, encoding='utf-8') as f:
             # Setting encoding explicitly to resolve coding issue on windows
             config_file = f.read()
         base_var_dict = {}
@@ -183,7 +183,7 @@ class Config:
         check_file_exist(filename)
         fileExtname = osp.splitext(filename)[1]
         if fileExtname not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
+            raise OSError('Only py/yml/yaml/json type are supported now!')
 
         with tempfile.TemporaryDirectory() as temp_config_dir:
             temp_config_file = tempfile.NamedTemporaryFile(
@@ -236,7 +236,7 @@ class Config:
             warnings.warn(warning_msg, DeprecationWarning)
 
         cfg_text = filename + '\n'
-        with open(filename, 'r', encoding='utf-8') as f:
+        with open(filename, encoding='utf-8') as f:
             # Setting encoding explicitly to resolve coding issue on windows
             cfg_text += f.read()
 
@@ -356,7 +356,7 @@ class Config:
             :obj:`Config`: Config obj.
         """
         if file_format not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
+            raise OSError('Only py/yml/yaml/json type are supported now!')
         if file_format != '.py' and 'dict(' in cfg_str:
             # check if users specify a wrong suffix for python
             warnings.warn(
@@ -396,16 +396,16 @@ class Config:
         if isinstance(filename, Path):
             filename = str(filename)
 
-        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
-        super(Config, self).__setattr__('_filename', filename)
+        super().__setattr__('_cfg_dict', ConfigDict(cfg_dict))
+        super().__setattr__('_filename', filename)
         if cfg_text:
             text = cfg_text
         elif filename:
-            with open(filename, 'r') as f:
+            with open(filename) as f:
                 text = f.read()
         else:
             text = ''
-        super(Config, self).__setattr__('_text', text)
+        super().__setattr__('_text', text)
 
     @property
     def filename(self):
@@ -556,9 +556,9 @@ class Config:
 
     def __setstate__(self, state):
         _cfg_dict, _filename, _text = state
-        super(Config, self).__setattr__('_cfg_dict', _cfg_dict)
-        super(Config, self).__setattr__('_filename', _filename)
-        super(Config, self).__setattr__('_text', _text)
+        super().__setattr__('_cfg_dict', _cfg_dict)
+        super().__setattr__('_filename', _filename)
+        super().__setattr__('_text', _text)
 
     def dump(self, file=None):
         """Dumps config into a file or returns a string representation of the
@@ -584,7 +584,7 @@ class Config:
                 will be dumped. Defaults to None.
         """
         import mmcv
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict()
+        cfg_dict = super().__getattribute__('_cfg_dict').to_dict()
         if file is None:
             if self.filename is None or self.filename.endswith('.py'):
                 return self.pretty_text
@@ -638,8 +638,8 @@ class Config:
             subkey = key_list[-1]
             d[subkey] = v
 
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-        super(Config, self).__setattr__(
+        cfg_dict = super().__getattribute__('_cfg_dict')
+        super().__setattr__(
             '_cfg_dict',
             Config._merge_a_into_b(
                 option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys))

mmcv/utils/timer.py

@@ -6,7 +6,7 @@ class TimerError(Exception):
 
     def __init__(self, message):
         self.message = message
-        super(TimerError, self).__init__(message)
+        super().__init__(message)
 
 
 class Timer:

mmcv/video/optflow.py

@@ -40,10 +40,10 @@ def flowread(flow_or_path: Union[np.ndarray, str],
             try:
                 header = f.read(4).decode('utf-8')
             except Exception:
-                raise IOError(f'Invalid flow file: {flow_or_path}')
+                raise OSError(f'Invalid flow file: {flow_or_path}')
             else:
                 if header != 'PIEH':
-                    raise IOError(f'Invalid flow file: {flow_or_path}, '
+                    raise OSError(f'Invalid flow file: {flow_or_path}, '
                                   'header does not contain PIEH')
 
             w = np.fromfile(f, np.int32, 1).squeeze()
@@ -53,7 +53,7 @@ def flowread(flow_or_path: Union[np.ndarray, str],
         assert concat_axis in [0, 1]
         cat_flow = imread(flow_or_path, flag='unchanged')
         if cat_flow.ndim != 2:
-            raise IOError(
+            raise OSError(
                 f'{flow_or_path} is not a valid quantized flow file, '
                 f'its dimension is {cat_flow.ndim}.')
         assert cat_flow.shape[concat_axis] % 2 == 0
@@ -86,7 +86,7 @@ def flowwrite(flow: np.ndarray,
     """
     if not quantize:
         with open(filename, 'wb') as f:
-            f.write('PIEH'.encode('utf-8'))
+            f.write(b'PIEH')
             np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
             flow = flow.astype(np.float32)
             flow.tofile(f)
@@ -146,7 +146,7 @@ def dequantize_flow(dx: np.ndarray,
     assert dx.shape == dy.shape
     assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
 
-    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
+    dx, dy = (dequantize(d, -max_val, max_val, 255) for d in [dx, dy])
 
     if denorm:
         dx *= dx.shape[1]

mmcv/visualization/optflow.py

@@ -1,5 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
 from typing import Optional, Union
 
 import numpy as np

setup.py

@@ -39,7 +39,7 @@ def choose_requirement(primary, secondary):
 
 def get_version():
     version_file = 'mmcv/version.py'
-    with open(version_file, 'r', encoding='utf-8') as f:
+    with open(version_file, encoding='utf-8') as f:
         exec(compile(f.read(), version_file, 'exec'))
     return locals()['__version__']
 
@@ -94,12 +94,11 @@ def parse_requirements(fname='requirements/runtime.txt', with_version=True):
             yield info
 
     def parse_require_file(fpath):
-        with open(fpath, 'r') as f:
+        with open(fpath) as f:
             for line in f.readlines():
                 line = line.strip()
                 if line and not line.startswith('#'):
-                    for info in parse_line(line):
-                        yield info
+                    yield from parse_line(line)
 
     def gen_packages_items():
         if exists(require_fpath):

tests/test_arraymisc.py

@@ -1,5 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
 
 import numpy as np
 import pytest

tests/test_cnn/test_conv_module.py

@@ -23,7 +23,7 @@ class ExampleConv(nn.Module):
                  groups=1,
                  bias=True,
                  norm_cfg=None):
-        super(ExampleConv, self).__init__()
+        super().__init__()
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.kernel_size = kernel_size

tests/test_fileclient.py

@@ -202,21 +202,22 @@ class TestFileClient:
         # test `list_dir_or_file`
         with build_temporary_directory() as tmp_dir:
             # 1. list directories and files
-            assert set(disk_backend.list_dir_or_file(tmp_dir)) == set(
-                ['dir1', 'dir2', 'text1.txt', 'text2.txt'])
+            assert set(disk_backend.list_dir_or_file(tmp_dir)) == {
+                'dir1', 'dir2', 'text1.txt', 'text2.txt'
+            }
             # 2. list directories and files recursively
             assert set(disk_backend.list_dir_or_file(
-                tmp_dir, recursive=True)) == set([
+                tmp_dir, recursive=True)) == {
                     'dir1',
                     osp.join('dir1', 'text3.txt'), 'dir2',
                     osp.join('dir2', 'dir3'),
                     osp.join('dir2', 'dir3', 'text4.txt'),
                     osp.join('dir2', 'img.jpg'), 'text1.txt', 'text2.txt'
-                ])
+                }
             # 3. only list directories
             assert set(
                 disk_backend.list_dir_or_file(
-                    tmp_dir, list_file=False)) == set(['dir1', 'dir2'])
+                    tmp_dir, list_file=False)) == {'dir1', 'dir2'}
             with pytest.raises(
                     TypeError,
                     match='`suffix` should be None when `list_dir` is True'):
@@ -227,30 +228,30 @@ class TestFileClient:
             # 4. only list directories recursively
             assert set(
                 disk_backend.list_dir_or_file(
-                    tmp_dir, list_file=False, recursive=True)) == set(
-                        ['dir1', 'dir2',
-                         osp.join('dir2', 'dir3')])
+                    tmp_dir, list_file=False, recursive=True)) == {
+                        'dir1', 'dir2',
+                        osp.join('dir2', 'dir3')
+                    }
             # 5. only list files
             assert set(disk_backend.list_dir_or_file(
-                tmp_dir, list_dir=False)) == set(['text1.txt', 'text2.txt'])
+                tmp_dir, list_dir=False)) == {'text1.txt', 'text2.txt'}
             # 6. only list files recursively
             assert set(
                 disk_backend.list_dir_or_file(
-                    tmp_dir, list_dir=False, recursive=True)) == set([
+                    tmp_dir, list_dir=False, recursive=True)) == {
                         osp.join('dir1', 'text3.txt'),
                         osp.join('dir2', 'dir3', 'text4.txt'),
                         osp.join('dir2', 'img.jpg'), 'text1.txt', 'text2.txt'
-                    ])
+                    }
             # 7. only list files ending with suffix
             assert set(
                 disk_backend.list_dir_or_file(
                     tmp_dir, list_dir=False,
-                    suffix='.txt')) == set(['text1.txt', 'text2.txt'])
+                    suffix='.txt')) == {'text1.txt', 'text2.txt'}
             assert set(
                 disk_backend.list_dir_or_file(
                     tmp_dir, list_dir=False,
-                    suffix=('.txt',
-                            '.jpg'))) == set(['text1.txt', 'text2.txt'])
+                    suffix=('.txt', '.jpg'))) == {'text1.txt', 'text2.txt'}
             with pytest.raises(
                     TypeError,
                     match='`suffix` must be a string or tuple of strings'):
@@ -260,22 +261,22 @@ class TestFileClient:
             assert set(
                 disk_backend.list_dir_or_file(
                     tmp_dir, list_dir=False, suffix='.txt',
-                    recursive=True)) == set([
+                    recursive=True)) == {
                         osp.join('dir1', 'text3.txt'),
                         osp.join('dir2', 'dir3', 'text4.txt'), 'text1.txt',
                         'text2.txt'
-                    ])
+                    }
             # 7. only list files ending with suffix
             assert set(
                 disk_backend.list_dir_or_file(
                     tmp_dir,
                     list_dir=False,
                     suffix=('.txt', '.jpg'),
-                    recursive=True)) == set([
+                    recursive=True)) == {
                         osp.join('dir1', 'text3.txt'),
                         osp.join('dir2', 'dir3', 'text4.txt'),
                         osp.join('dir2', 'img.jpg'), 'text1.txt', 'text2.txt'
-                    ])
+                    }
 
     @patch('ceph.S3Client', MockS3Client)
     def test_ceph_backend(self):
@@ -463,21 +464,21 @@ class TestFileClient:
 
         with build_temporary_directory() as tmp_dir:
             # 1. list directories and files
-            assert set(petrel_backend.list_dir_or_file(tmp_dir)) == set(
-                ['dir1', 'dir2', 'text1.txt', 'text2.txt'])
+            assert set(petrel_backend.list_dir_or_file(tmp_dir)) == {
+                'dir1', 'dir2', 'text1.txt', 'text2.txt'
+            }
             # 2. list directories and files recursively
             assert set(
-                petrel_backend.list_dir_or_file(
-                    tmp_dir, recursive=True)) == set([
-                        'dir1', '/'.join(('dir1', 'text3.txt')), 'dir2',
-                        '/'.join(('dir2', 'dir3')), '/'.join(
+                petrel_backend.list_dir_or_file(tmp_dir, recursive=True)) == {
+                    'dir1', '/'.join(('dir1', 'text3.txt')), 'dir2', '/'.join(
+                        ('dir2', 'dir3')), '/'.join(
                             ('dir2', 'dir3', 'text4.txt')), '/'.join(
                                 ('dir2', 'img.jpg')), 'text1.txt', 'text2.txt'
-                    ])
+                }
             # 3. only list directories
             assert set(
                 petrel_backend.list_dir_or_file(
-                    tmp_dir, list_file=False)) == set(['dir1', 'dir2'])
+                    tmp_dir, list_file=False)) == {'dir1', 'dir2'}
             with pytest.raises(
                     TypeError,
                     match=('`list_dir` should be False when `suffix` is not '
@@ -489,31 +490,30 @@ class TestFileClient:
             # 4. only list directories recursively
             assert set(
                 petrel_backend.list_dir_or_file(
-                    tmp_dir, list_file=False, recursive=True)) == set(
-                        ['dir1', 'dir2', '/'.join(('dir2', 'dir3'))])
+                    tmp_dir, list_file=False, recursive=True)) == {
+                        'dir1', 'dir2', '/'.join(('dir2', 'dir3'))
+                    }
             # 5. only list files
             assert set(
-                petrel_backend.list_dir_or_file(tmp_dir,
-                                                list_dir=False)) == set(
-                                                    ['text1.txt', 'text2.txt'])
+                petrel_backend.list_dir_or_file(
+                    tmp_dir, list_dir=False)) == {'text1.txt', 'text2.txt'}
             # 6. only list files recursively
             assert set(
                 petrel_backend.list_dir_or_file(
-                    tmp_dir, list_dir=False, recursive=True)) == set([
+                    tmp_dir, list_dir=False, recursive=True)) == {
                         '/'.join(('dir1', 'text3.txt')), '/'.join(
                             ('dir2', 'dir3', 'text4.txt')), '/'.join(
                                 ('dir2', 'img.jpg')), 'text1.txt', 'text2.txt'
-                    ])
+                    }
             # 7. only list files ending with suffix
             assert set(
                 petrel_backend.list_dir_or_file(
                     tmp_dir, list_dir=False,
-                    suffix='.txt')) == set(['text1.txt', 'text2.txt'])
+                    suffix='.txt')) == {'text1.txt', 'text2.txt'}
             assert set(
                 petrel_backend.list_dir_or_file(
                     tmp_dir, list_dir=False,
-                    suffix=('.txt',
-                            '.jpg'))) == set(['text1.txt', 'text2.txt'])
+                    suffix=('.txt', '.jpg'))) == {'text1.txt', 'text2.txt'}
             with pytest.raises(
                     TypeError,
                     match='`suffix` must be a string or tuple of strings'):
@@ -523,22 +523,22 @@ class TestFileClient:
             assert set(
                 petrel_backend.list_dir_or_file(
                     tmp_dir, list_dir=False, suffix='.txt',
-                    recursive=True)) == set([
+                    recursive=True)) == {
                         '/'.join(('dir1', 'text3.txt')), '/'.join(
                             ('dir2', 'dir3', 'text4.txt')), 'text1.txt',
                         'text2.txt'
-                    ])
+                    }
             # 7. only list files ending with suffix
             assert set(
                 petrel_backend.list_dir_or_file(
                     tmp_dir,
                     list_dir=False,
                     suffix=('.txt', '.jpg'),
-                    recursive=True)) == set([
+                    recursive=True)) == {
                         '/'.join(('dir1', 'text3.txt')), '/'.join(
                             ('dir2', 'dir3', 'text4.txt')), '/'.join(
                                 ('dir2', 'img.jpg')), 'text1.txt', 'text2.txt'
-                    ])
+                    }
 
     @patch('mc.MemcachedClient.GetInstance', MockMemcachedClient)
     @patch('mc.pyvector', MagicMock)

tests/test_fileio.py

@@ -128,7 +128,7 @@ def test_register_handler():
     assert content == '1.jpg\n2.jpg\n3.jpg\n4.jpg\n5.jpg'
     tmp_filename = osp.join(tempfile.gettempdir(), 'mmcv_test.txt2')
     mmcv.dump(content, tmp_filename)
-    with open(tmp_filename, 'r') as f:
+    with open(tmp_filename) as f:
         written = f.read()
     os.remove(tmp_filename)
     assert written == '\n' + content

tests/test_ops/test_bbox.py

@@ -6,7 +6,7 @@ import torch
 from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
 
 
-class TestBBox(object):
+class TestBBox:
 
     def _test_bbox_overlaps(self, device='cpu', dtype=torch.float):
         from mmcv.ops import bbox_overlaps

tests/test_ops/test_bilinear_grid_sample.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn.functional as F
 
 
-class TestBilinearGridSample(object):
+class TestBilinearGridSample:
 
     def _test_bilinear_grid_sample(self,
                                    dtype=torch.float,

tests/test_ops/test_box_iou_rotated.py

@@ -4,7 +4,7 @@ import pytest
 import torch
 
 
-class TestBoxIoURotated(object):
+class TestBoxIoURotated:
 
     def test_box_iou_rotated_cpu(self):
         from mmcv.ops import box_iou_rotated

tests/test_ops/test_carafe.py

@@ -3,7 +3,7 @@ import torch
 from torch.autograd import gradcheck
 
 
-class TestCarafe(object):
+class TestCarafe:
 
     def test_carafe_naive_gradcheck(self):
         if not torch.cuda.is_available():

tests/test_ops/test_cc_attention.py

@@ -15,7 +15,7 @@ class Loss(nn.Module):
         return torch.mean(input - target)
 
 
-class TestCrissCrossAttention(object):
+class TestCrissCrossAttention:
 
     def test_cc_attention(self):
         device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

tests/test_ops/test_deform_conv.py

@@ -35,7 +35,7 @@ gt_offset_bias_grad = [1.44, -0.72, 0., 0., -0.10, -0.08, -0.54, -0.54],
 gt_deform_weight_grad = [[[[3.62, 0.], [0.40, 0.18]]]]
 
 
-class TestDeformconv(object):
+class TestDeformconv:
 
     def _test_deformconv(self,
                          dtype=torch.float,

tests/test_ops/test_deform_roi_pool.py

@@ -35,7 +35,7 @@ outputs = [([[[[1, 1.25], [1.5, 1.75]]]], [[[[3.0625, 0.4375],
                                 0.00390625]]]])]
 
 
-class TestDeformRoIPool(object):
+class TestDeformRoIPool:
 
     def test_deform_roi_pool_gradcheck(self):
         if not torch.cuda.is_available():

tests/test_ops/test_focal_loss.py

@@ -37,7 +37,7 @@ sigmoid_outputs = [(0.13562961, [[-0.00657264, 0.11185755],
                                  [-0.02462499, 0.08277918, 0.18050370]])]
 
 
-class Testfocalloss(object):
+class Testfocalloss:
 
     def _test_softmax(self, dtype=torch.float):
         if not torch.cuda.is_available():

tests/test_ops/test_fused_bias_leakyrelu.py

@@ -10,7 +10,7 @@ except ImportError:
     _USING_PARROTS = False
 
 
-class TestFusedBiasLeakyReLU(object):
+class TestFusedBiasLeakyReLU:
 
     @classmethod
     def setup_class(cls):

tests/test_ops/test_info.py

@@ -2,7 +2,7 @@
 import torch
 
 
-class TestInfo(object):
+class TestInfo:
 
     def test_info(self):
         if not torch.cuda.is_available():

tests/test_ops/test_masked_conv2d.py

@@ -2,7 +2,7 @@
 import torch
 
 
-class TestMaskedConv2d(object):
+class TestMaskedConv2d:
 
     def test_masked_conv2d(self):
         if not torch.cuda.is_available():

tests/test_ops/test_modulated_deform_conv.py

@@ -37,7 +37,7 @@ dcn_offset_b_grad = [
 ]
 
 
-class TestMdconv(object):
+class TestMdconv:
 
     def _test_mdconv(self, dtype=torch.float, device='cuda'):
         if not torch.cuda.is_available() and device == 'cuda':

tests/test_ops/test_ms_deformable_attn.py

@@ -55,7 +55,7 @@ def test_forward_multi_scale_deformable_attn_pytorch():
     N, M, D = 1, 2, 2
     Lq, L, P = 2, 2, 2
     shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long)
-    S = sum([(H * W).item() for H, W in shapes])
+    S = sum((H * W).item() for H, W in shapes)
 
     torch.manual_seed(3)
     value = torch.rand(N, S, M, D) * 0.01
@@ -78,7 +78,7 @@ def test_forward_equal_with_pytorch_double():
     shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
     level_start_index = torch.cat((shapes.new_zeros(
         (1, )), shapes.prod(1).cumsum(0)[:-1]))
-    S = sum([(H * W).item() for H, W in shapes])
+    S = sum((H * W).item() for H, W in shapes)
 
     torch.manual_seed(3)
     value = torch.rand(N, S, M, D).cuda() * 0.01
@@ -111,7 +111,7 @@ def test_forward_equal_with_pytorch_float():
     shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
     level_start_index = torch.cat((shapes.new_zeros(
         (1, )), shapes.prod(1).cumsum(0)[:-1]))
-    S = sum([(H * W).item() for H, W in shapes])
+    S = sum((H * W).item() for H, W in shapes)
 
     torch.manual_seed(3)
     value = torch.rand(N, S, M, D).cuda() * 0.01
@@ -155,7 +155,7 @@ def test_gradient_numerical(channels,
     shapes = torch.as_tensor([(3, 2), (2, 1)], dtype=torch.long).cuda()
     level_start_index = torch.cat((shapes.new_zeros(
         (1, )), shapes.prod(1).cumsum(0)[:-1]))
-    S = sum([(H * W).item() for H, W in shapes])
+    S = sum((H * W).item() for H, W in shapes)
 
     value = torch.rand(N, S, M, channels).cuda() * 0.01
     sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()

tests/test_ops/test_nms.py

@@ -6,7 +6,7 @@ import torch
 from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
 
 
-class Testnms(object):
+class Testnms:
 
     @pytest.mark.parametrize('device', [
         pytest.param(
@@ -129,8 +129,7 @@ class Testnms(object):
         scores = tensor_dets[:, 4]
         nms_keep_inds = nms(boxes.contiguous(), scores.contiguous(),
                             iou_thr)[1]
-        assert set([g[0].item()
-                    for g in np_groups]) == set(nms_keep_inds.tolist())
+        assert {g[0].item() for g in np_groups} == set(nms_keep_inds.tolist())
 
         # non empty tensor input
         tensor_dets = torch.from_numpy(np_dets)

tests/test_ops/test_onnx.py

@@ -33,7 +33,7 @@ def run_before_and_after_test():
 class WrapFunction(nn.Module):
 
     def __init__(self, wrapped_function):
-        super(WrapFunction, self).__init__()
+        super().__init__()
         self.wrapped_function = wrapped_function
 
     def forward(self, *args, **kwargs):
@@ -662,7 +662,7 @@ def test_cummax_cummin(key, opset=11):
     input_list = [
         # arbitrary shape, e.g. 1-D, 2-D, 3-D, ...
         torch.rand((2, 3, 4, 1, 5)),
-        torch.rand((1)),
+        torch.rand(1),
         torch.rand((2, 0, 1)),  # tensor.numel() is 0
         torch.FloatTensor(),  # empty tensor
     ]

tests/test_ops/test_psa_mask.py

@@ -15,7 +15,7 @@ class Loss(nn.Module):
         return torch.mean(input - target)
 
 
-class TestPSAMask(object):
+class TestPSAMask:
 
     def test_psa_mask_collect(self):
         if not torch.cuda.is_available():

tests/test_ops/test_roi_pool.py

@@ -29,7 +29,7 @@ outputs = [([[[[1., 2.], [3., 4.]]]], [[[[1., 1.], [1., 1.]]]]),
                                                               1.]]]])]
 
 
-class TestRoiPool(object):
+class TestRoiPool:
 
     def test_roipool_gradcheck(self):
         if not torch.cuda.is_available():

tests/test_ops/test_syncbn.py

@@ -14,7 +14,7 @@ else:
     import re
 
 
-class TestSyncBN(object):
+class TestSyncBN:
 
     def dist_init(self):
         rank = int(os.environ['SLURM_PROCID'])

tests/test_ops/test_tensorrt.py

@@ -30,7 +30,7 @@ if not is_tensorrt_plugin_loaded():
 class WrapFunction(nn.Module):
 
     def __init__(self, wrapped_function):
-        super(WrapFunction, self).__init__()
+        super().__init__()
         self.wrapped_function = wrapped_function
 
     def forward(self, *args, **kwargs):
@@ -576,7 +576,7 @@ def test_cummin_cummax(func: Callable):
     input_list = [
         # arbitrary shape, e.g. 1-D, 2-D, 3-D, ...
         torch.rand((2, 3, 4, 1, 5)).cuda(),
-        torch.rand((1)).cuda()
+        torch.rand(1).cuda()
     ]
 
     input_names = ['input']
@@ -756,7 +756,7 @@ def test_corner_pool(mode):
     class CornerPoolWrapper(CornerPool):
 
         def __init__(self, mode):
-            super(CornerPoolWrapper, self).__init__(mode)
+            super().__init__(mode)
 
         def forward(self, x):
             # no use `torch.cummax`, instead `corner_pool` is used

tests/test_ops/test_upfirdn2d.py

@@ -10,7 +10,7 @@ except ImportError:
     _USING_PARROTS = False
 
 
-class TestUpFirDn2d(object):
+class TestUpFirDn2d:
     """Unit test for UpFirDn2d.
 
     Here, we just test the basic case of upsample version. More gerneal tests

tests/test_ops/test_voxelization.py

@@ -96,8 +96,8 @@ def test_voxelization_nondeterministic():
     coors_all = dynamic_voxelization.forward(points)
     coors_all = coors_all.cpu().detach().numpy().tolist()
 
-    coors_set = set([tuple(c) for c in coors])
-    coors_all_set = set([tuple(c) for c in coors_all])
+    coors_set = {tuple(c) for c in coors}
+    coors_all_set = {tuple(c) for c in coors_all}
 
     assert len(coors_set) == len(coors)
     assert len(coors_set - coors_all_set) == 0
@@ -112,7 +112,7 @@ def test_voxelization_nondeterministic():
 
     for c, ps, n in zip(coors, voxels, num_points_per_voxel):
         ideal_voxel_points_set = coors_points_dict[tuple(c)]
-        voxel_points_set = set([tuple(p) for p in ps[:n]])
+        voxel_points_set = {tuple(p) for p in ps[:n]}
         assert len(voxel_points_set) == n
         if n < max_num_points:
             assert voxel_points_set == ideal_voxel_points_set
@@ -133,7 +133,7 @@ def test_voxelization_nondeterministic():
     voxels, coors, num_points_per_voxel = hard_voxelization.forward(points)
     coors = coors.cpu().detach().numpy().tolist()
 
-    coors_set = set([tuple(c) for c in coors])
-    coors_all_set = set([tuple(c) for c in coors_all])
+    coors_set = {tuple(c) for c in coors}
+    coors_all_set = {tuple(c) for c in coors_all}
 
     assert len(coors_set) == len(coors) == len(coors_all_set)

tests/test_parallel.py

@@ -63,7 +63,7 @@ def test_is_module_wrapper():
 
     # test module wrapper registry
     @MODULE_WRAPPERS.register_module()
-    class ModuleWrapper(object):
+    class ModuleWrapper:
 
         def __init__(self, module):
             self.module = module