remove fpn name

ppocr/modeling/heads/det_db_head.py

@@ -23,10 +23,10 @@ import paddle.nn.functional as F
 from paddle import ParamAttr
 
 
-def get_bias_attr(k, name):
+def get_bias_attr(k):
     stdv = 1.0 / math.sqrt(k * 1.0)
     initializer = paddle.nn.initializer.Uniform(-stdv, stdv)
-    bias_attr = ParamAttr(initializer=initializer, name=name + "_b_attr")
+    bias_attr = ParamAttr(initializer=initializer)
     return bias_attr
 
 
@@ -38,18 +38,14 @@ class Head(nn.Layer):
             out_channels=in_channels // 4,
             kernel_size=3,
             padding=1,
-            weight_attr=ParamAttr(name=name_list[0] + '.w_0'),
+            weight_attr=ParamAttr(),
             bias_attr=False)
         self.conv_bn1 = nn.BatchNorm(
             num_channels=in_channels // 4,
             param_attr=ParamAttr(
-                name=name_list[1] + '.w_0',
                 initializer=paddle.nn.initializer.Constant(value=1.0)),
             bias_attr=ParamAttr(
-                name=name_list[1] + '.b_0',
                 initializer=paddle.nn.initializer.Constant(value=1e-4)),
-            moving_mean_name=name_list[1] + '.w_1',
-            moving_variance_name=name_list[1] + '.w_2',
             act='relu')
         self.conv2 = nn.Conv2DTranspose(
             in_channels=in_channels // 4,
@@ -57,19 +53,14 @@ class Head(nn.Layer):
             kernel_size=2,
             stride=2,
             weight_attr=ParamAttr(
-                name=name_list[2] + '.w_0',
                 initializer=paddle.nn.initializer.KaimingUniform()),
-            bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv2"))
+            bias_attr=get_bias_attr(in_channels // 4))
         self.conv_bn2 = nn.BatchNorm(
             num_channels=in_channels // 4,
             param_attr=ParamAttr(
-                name=name_list[3] + '.w_0',
                 initializer=paddle.nn.initializer.Constant(value=1.0)),
             bias_attr=ParamAttr(
-                name=name_list[3] + '.b_0',
                 initializer=paddle.nn.initializer.Constant(value=1e-4)),
-            moving_mean_name=name_list[3] + '.w_1',
-            moving_variance_name=name_list[3] + '.w_2',
             act="relu")
         self.conv3 = nn.Conv2DTranspose(
             in_channels=in_channels // 4,
@@ -77,10 +68,8 @@ class Head(nn.Layer):
             kernel_size=2,
             stride=2,
             weight_attr=ParamAttr(
-                name=name_list[4] + '.w_0',
                 initializer=paddle.nn.initializer.KaimingUniform()),
-            bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv3"),
-        )
+            bias_attr=get_bias_attr(in_channels // 4), )
 
     def forward(self, x):
         x = self.conv1(x)

ppocr/modeling/necks/db_fpn.py

@@ -32,61 +32,53 @@ class DBFPN(nn.Layer):
             in_channels=in_channels[0],
             out_channels=self.out_channels,
             kernel_size=1,
-            weight_attr=ParamAttr(
-                name='conv2d_51.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.in3_conv = nn.Conv2D(
             in_channels=in_channels[1],
             out_channels=self.out_channels,
             kernel_size=1,
-            weight_attr=ParamAttr(
-                name='conv2d_50.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.in4_conv = nn.Conv2D(
             in_channels=in_channels[2],
             out_channels=self.out_channels,
             kernel_size=1,
-            weight_attr=ParamAttr(
-                name='conv2d_49.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.in5_conv = nn.Conv2D(
             in_channels=in_channels[3],
             out_channels=self.out_channels,
             kernel_size=1,
-            weight_attr=ParamAttr(
-                name='conv2d_48.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.p5_conv = nn.Conv2D(
             in_channels=self.out_channels,
             out_channels=self.out_channels // 4,
             kernel_size=3,
             padding=1,
-            weight_attr=ParamAttr(
-                name='conv2d_52.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.p4_conv = nn.Conv2D(
             in_channels=self.out_channels,
             out_channels=self.out_channels // 4,
             kernel_size=3,
             padding=1,
-            weight_attr=ParamAttr(
-                name='conv2d_53.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.p3_conv = nn.Conv2D(
             in_channels=self.out_channels,
             out_channels=self.out_channels // 4,
             kernel_size=3,
             padding=1,
-            weight_attr=ParamAttr(
-                name='conv2d_54.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
         self.p2_conv = nn.Conv2D(
             in_channels=self.out_channels,
             out_channels=self.out_channels // 4,
             kernel_size=3,
             padding=1,
-            weight_attr=ParamAttr(
-                name='conv2d_55.w_0', initializer=weight_attr),
+            weight_attr=ParamAttr(initializer=weight_attr),
             bias_attr=False)
 
     def forward(self, x):