formatting

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)