delete norm_decay in resnet

ppcls/arch/backbone/legendary_models/resnet.py

@@ -117,7 +117,6 @@ class ConvBNLayer(TheseusLayer):
                  is_vd_mode=False,
                  act=None,
                  lr_mult=1.0,
-                 norm_decay=0.,
                  data_format="NCHW"):
         super().__init__()
         self.is_vd_mode = is_vd_mode
@@ -135,14 +134,8 @@ class ConvBNLayer(TheseusLayer):
             bias_attr=False,
             data_format=data_format)
 
-        weight_attr = ParamAttr(
+        weight_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
-            learning_rate=lr_mult,
+        bias_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
-            regularizer=L2Decay(norm_decay),
-            trainable=True)
-        bias_attr = ParamAttr(
-            learning_rate=lr_mult,
-            regularizer=L2Decay(norm_decay),
-            trainable=True)
 
         self.bn = BatchNorm2D(
             num_filters, weight_attr=weight_attr, bias_attr=bias_attr)
@@ -166,7 +159,6 @@ class BottleneckBlock(TheseusLayer):
                  shortcut=True,
                  if_first=False,
                  lr_mult=1.0,
-                 norm_decay=0.,
                  data_format="NCHW"):
         super().__init__()
 
@@ -176,7 +168,6 @@ class BottleneckBlock(TheseusLayer):
             filter_size=1,
             act="relu",
             lr_mult=lr_mult,
-            norm_decay=norm_decay,
             data_format=data_format)
         self.conv1 = ConvBNLayer(
             num_channels=num_filters,
@@ -185,7 +176,6 @@ class BottleneckBlock(TheseusLayer):
             stride=stride,
             act="relu",
             lr_mult=lr_mult,
-            norm_decay=norm_decay,
             data_format=data_format)
         self.conv2 = ConvBNLayer(
             num_channels=num_filters,
@@ -193,7 +183,6 @@ class BottleneckBlock(TheseusLayer):
             filter_size=1,
             act=None,
             lr_mult=lr_mult,
-            norm_decay=norm_decay,
             data_format=data_format)
 
         if not shortcut:
@@ -204,7 +193,6 @@ class BottleneckBlock(TheseusLayer):
                 stride=stride if if_first else 1,
                 is_vd_mode=False if if_first else True,
                 lr_mult=lr_mult,
-                norm_decay=norm_decay,
                 data_format=data_format)
 
         self.relu = nn.ReLU()
@@ -233,7 +221,6 @@ class BasicBlock(TheseusLayer):
                  shortcut=True,
                  if_first=False,
                  lr_mult=1.0,
-                 norm_decay=0.,
                  data_format="NCHW"):
         super().__init__()
 
@@ -245,7 +232,6 @@ class BasicBlock(TheseusLayer):
             stride=stride,
             act="relu",
             lr_mult=lr_mult,
-            norm_decay=norm_decay,
             data_format=data_format)
         self.conv1 = ConvBNLayer(
             num_channels=num_filters,
@@ -253,7 +239,6 @@ class BasicBlock(TheseusLayer):
             filter_size=3,
             act=None,
             lr_mult=lr_mult,
-            norm_decay=norm_decay,
             data_format=data_format)
         if not shortcut:
             self.short = ConvBNLayer(
@@ -263,7 +248,6 @@ class BasicBlock(TheseusLayer):
                 stride=stride if if_first else 1,
                 is_vd_mode=False if if_first else True,
                 lr_mult=lr_mult,
-                norm_decay=norm_decay,
                 data_format=data_format)
         self.shortcut = shortcut
         self.relu = nn.ReLU()
@@ -300,7 +284,6 @@ class ResNet(TheseusLayer):
                  stem_act="relu",
                  class_num=1000,
                  lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
-                 norm_decay=0.,
                  data_format="NCHW",
                  input_image_channel=3,
                  return_patterns=None,
@@ -340,7 +323,6 @@ class ResNet(TheseusLayer):
                 stride=s,
                 act=stem_act,
                 lr_mult=self.lr_mult_list[0],
-                norm_decay=norm_decay,
                 data_format=data_format)
             for in_c, out_c, k, s in self.stem_cfg[version]
         ])
@@ -359,7 +341,6 @@ class ResNet(TheseusLayer):
                     shortcut=shortcut,
                     if_first=block_idx == i == 0 if version == "vd" else True,
                     lr_mult=self.lr_mult_list[block_idx + 1],
-                    norm_decay=norm_decay,
                     data_format=data_format))
                 shortcut = True
         self.blocks = nn.Sequential(*block_list)

ppcls/configs/Attr/StrongBaselineAttr.yaml

@@ -20,7 +20,6 @@ Arch:
   name: "ResNet50"
   pretrained: True
   class_num: 26
-  norm_decay: 0.0005
 
 # loss function config for traing/eval process
 Loss: