From e0859f749831b504cc490d77d03023c2be7d6ef9 Mon Sep 17 00:00:00 2001 From: weishengyu Date: Thu, 22 Oct 2020 17:43:33 +0800 Subject: [PATCH] formatting --- ppcls/modeling/architectures/shufflenet_v2.py | 96 +++++++------------ 1 file changed, 37 insertions(+), 59 deletions(-) diff --git a/ppcls/modeling/architectures/shufflenet_v2.py b/ppcls/modeling/architectures/shufflenet_v2.py index a051e440..9e06c955 100644 --- a/ppcls/modeling/architectures/shufflenet_v2.py +++ b/ppcls/modeling/architectures/shufflenet_v2.py @@ -21,7 +21,6 @@ from paddle.nn import Layer, Conv2d, MaxPool2d, AdaptiveAvgPool2d, BatchNorm, Li from paddle.nn.initializer import MSRA from paddle.nn.functional import swish - __all__ = [ "ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5", "ShuffleNetV2", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0", @@ -34,7 +33,8 @@ def channel_shuffle(x, groups): channels_per_group = num_channels // groups # reshape - x = reshape(x=x, shape=[batch_size, groups, channels_per_group, height, width]) + x = reshape( + x=x, shape=[batch_size, groups, channels_per_group, height, width]) # transpose x = transpose(x=x, perm=[0, 2, 1, 3, 4]) @@ -54,8 +54,7 @@ class ConvBNLayer(Layer): padding, groups=1, act=None, - name=None, - ): + name=None, ): super(ConvBNLayer, self).__init__() self._conv = Conv2d( in_channels=in_channels, @@ -64,9 +63,9 @@ class ConvBNLayer(Layer): stride=stride, padding=padding, groups=groups, - weight_attr=ParamAttr(initializer=MSRA(), name=name + "_weights"), - bias_attr=False - ) + weight_attr=ParamAttr( + initializer=MSRA(), name=name + "_weights"), + bias_attr=False) self._batch_norm = BatchNorm( out_channels, @@ -74,8 +73,7 @@ class ConvBNLayer(Layer): bias_attr=ParamAttr(name=name + "_bn_offset"), act=act, moving_mean_name=name + "_bn_mean", - moving_variance_name=name + "_bn_variance" - ) + moving_variance_name=name + "_bn_variance") def forward(self, inputs): y = self._conv(inputs) @@ -84,14 +82,12 @@ class ConvBNLayer(Layer): class InvertedResidual(Layer): - def __init__( - self, - in_channels, - out_channels, - stride, - act="relu", - name=None - ): + def __init__(self, + in_channels, + out_channels, + stride, + act="relu", + name=None): super(InvertedResidual, self).__init__() self._conv_pw = ConvBNLayer( in_channels=in_channels // 2, @@ -101,8 +97,7 @@ class InvertedResidual(Layer): padding=0, groups=1, act=act, - name='stage_' + name + '_conv1' - ) + name='stage_' + name + '_conv1') self._conv_dw = ConvBNLayer( in_channels=out_channels // 2, out_channels=out_channels // 2, @@ -111,8 +106,7 @@ class InvertedResidual(Layer): padding=1, groups=out_channels // 2, act=None, - name='stage_' + name + '_conv2' - ) + name='stage_' + name + '_conv2') self._conv_linear = ConvBNLayer( in_channels=out_channels // 2, out_channels=out_channels // 2, @@ -121,11 +115,13 @@ class InvertedResidual(Layer): padding=0, groups=1, act=act, - name='stage_' + name + '_conv3' - ) + name='stage_' + name + '_conv3') def forward(self, inputs): - x1, x2 = split(inputs, num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2], axis=1) + x1, x2 = split( + inputs, + num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2], + axis=1) x2 = self._conv_pw(x2) x2 = self._conv_dw(x2) x2 = self._conv_linear(x2) @@ -134,14 +130,12 @@ class InvertedResidual(Layer): class InvertedResidualDS(Layer): - def __init__( - self, - in_channels, - out_channels, - stride, - act="relu", - name=None - ): + def __init__(self, + in_channels, + out_channels, + stride, + act="relu", + name=None): super(InvertedResidualDS, self).__init__() # branch1 @@ -153,8 +147,7 @@ class InvertedResidualDS(Layer): padding=1, groups=in_channels, act=None, - name='stage_' + name + '_conv4' - ) + name='stage_' + name + '_conv4') self._conv_linear_1 = ConvBNLayer( in_channels=in_channels, out_channels=out_channels // 2, @@ -163,8 +156,7 @@ class InvertedResidualDS(Layer): padding=0, groups=1, act=act, - name='stage_' + name + '_conv5' - ) + name='stage_' + name + '_conv5') # branch2 self._conv_pw_2 = ConvBNLayer( in_channels=in_channels, @@ -174,8 +166,7 @@ class InvertedResidualDS(Layer): padding=0, groups=1, act=act, - name='stage_' + name + '_conv1' - ) + name='stage_' + name + '_conv1') self._conv_dw_2 = ConvBNLayer( in_channels=out_channels // 2, out_channels=out_channels // 2, @@ -184,8 +175,7 @@ class InvertedResidualDS(Layer): padding=1, groups=out_channels // 2, act=None, - name='stage_' + name + '_conv2' - ) + name='stage_' + name + '_conv2') self._conv_linear_2 = ConvBNLayer( in_channels=out_channels // 2, out_channels=out_channels // 2, @@ -194,8 +184,7 @@ class InvertedResidualDS(Layer): padding=0, groups=1, act=act, - name='stage_' + name + '_conv3' - ) + name='stage_' + name + '_conv3') def forward(self, inputs): x1 = self._conv_dw_1(inputs) @@ -238,13 +227,8 @@ class ShuffleNet(Layer): stride=2, padding=1, act=act, - name='stage1_conv' - ) - self._max_pool = MaxPool2d( - kernel_size=3, - stride=2, - padding=1 - ) + name='stage1_conv') + self._max_pool = MaxPool2d(kernel_size=3, stride=2, padding=1) # 2. bottleneck sequences self._block_list = [] @@ -258,9 +242,7 @@ class ShuffleNet(Layer): out_channels=stage_out_channels[stage_id + 2], stride=2, act=act, - name=str(stage_id + 2) + '_' + str(i + 1) - ) - ) + name=str(stage_id + 2) + '_' + str(i + 1))) else: block = self.add_sublayer( name=str(stage_id + 2) + '_' + str(i + 1), @@ -269,9 +251,7 @@ class ShuffleNet(Layer): out_channels=stage_out_channels[stage_id + 2], stride=1, act=act, - name=str(stage_id + 2) + '_' + str(i + 1) - ) - ) + name=str(stage_id + 2) + '_' + str(i + 1))) self._block_list.append(block) # 3. last_conv self._last_conv = ConvBNLayer( @@ -281,8 +261,7 @@ class ShuffleNet(Layer): stride=1, padding=0, act=act, - name='conv5' - ) + name='conv5') # 4. pool self._pool2d_avg = AdaptiveAvgPool2d(1) self._out_c = stage_out_channels[-1] @@ -291,8 +270,7 @@ class ShuffleNet(Layer): stage_out_channels[-1], class_dim, weight_attr=ParamAttr(name='fc6_weights'), - bias_attr=ParamAttr(name='fc6_offset') - ) + bias_attr=ParamAttr(name='fc6_offset')) def forward(self, inputs): y = self._conv1(inputs) -- GitLab