Add pyupgrade pre-commit hook (#1937)

* add pyupgrade

* add options for pyupgrade

* minor refinement

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 '
+                'Current version of Petrel Python SDK has not supported '
                 'the `delete` method, please use a higher version or dev'
-                 ' branch instead.'))
+                ' 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 '
+                'Current version of Petrel Python SDK has not supported '
                 'the `contains` and `isdir` methods, please use a higher'
-                 'version or dev branch instead.'))
+                '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 '
+                'Current version of Petrel Python SDK has not supported '
                 'the `isdir` method, please use a higher version or dev'
-                 ' branch instead.'))
+                ' 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 '
+                'Current version of Petrel Python SDK has not supported '
                 'the `contains` method, please use a higher version or '
-                 'dev branch instead.'))
+                '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 '
+                'Current version of Petrel Python SDK has not supported '
                 'the `list` method, please use a higher version or dev'
-                 ' branch instead.'))
+                ' 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,
+        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_()