remove transition

ppcls/arch/backbone/legendary_models/hrnet.py

@@ -94,47 +94,6 @@ class Layer1(TheseusLayer):
         return y
 
 
-class TransitionLayer(TheseusLayer):
-    def __init__(self, in_channels, out_channels, name=None):
-        super(TransitionLayer, self).__init__()
-
-        num_in = len(in_channels)
-        num_out = len(out_channels)
-        out = []
-        self.conv_bn_func_list = []
-        for i in range(num_out):
-            residual = None
-            if i < num_in:
-                if in_channels[i] != out_channels[i]:
-                    residual = self.add_sublayer(
-                        "transition_{}_layer_{}".format(name, i + 1),
-                        ConvBNLayer(
-                            num_channels=in_channels[i],
-                            num_filters=out_channels[i],
-                            filter_size=3))
-            else:
-                residual = self.add_sublayer(
-                    "transition_{}_layer_{}".format(name, i + 1),
-                    ConvBNLayer(
-                        num_channels=in_channels[-1],
-                        num_filters=out_channels[i],
-                        filter_size=3,
-                        stride=2))
-            self.conv_bn_func_list.append(residual)
-
-    def forward(self, x, res_dict=None):
-        outs = []
-        for idx, conv_bn_func in enumerate(self.conv_bn_func_list):
-            if conv_bn_func is None:
-                outs.append(x[idx])
-            else:
-                if idx < len(x):
-                    outs.append(conv_bn_func(x[idx]))
-                else:
-                    outs.append(conv_bn_func(x[-1]))
-        return outs
-
-
 class Branches(TheseusLayer):
     def __init__(self,
                  block_num,
@@ -537,8 +496,16 @@ class HRNet(TheseusLayer):
 
         self.la1 = Layer1(num_channels=64, has_se=has_se, name="layer2")
 
-        self.tr1 = TransitionLayer(
+        self.tr1_1 = BasicBlock(
-            in_channels=[256], out_channels=channels_2, name="tr1")
+            num_channels=256,
+            num_filters=width,
+            has_se=has_se,
+            name="tr1_1")
+        self.tr1_2 = BasicBlock(
+            num_channels=width,
+            num_filters=width * 2,
+            has_se=has_se,
+            name="tr1_2")
 
         self.st2 = Stage(
             num_channels=channels_2,
@@ -547,8 +514,11 @@ class HRNet(TheseusLayer):
             has_se=self.has_se,
             name="st2")
 
-        self.tr2 = TransitionLayer(
+        self.tr2 = BasicBlock(
-            in_channels=channels_2, out_channels=channels_3, name="tr2")
+            num_channels=width * 2,
+            num_filters=width * 4,
+            has_se=has_se,
+            name="tr2")
         self.st3 = Stage(
             num_channels=channels_3,
             num_modules=num_modules_3,
@@ -556,8 +526,12 @@ class HRNet(TheseusLayer):
             has_se=self.has_se,
             name="st3")
 
-        self.tr3 = TransitionLayer(
+        self.tr3 = BasicBlock(
-            in_channels=channels_3, out_channels=channels_4, name="tr3")
+            num_channels=width * 4,
+            num_filters=width * 8,
+            has_se=has_se,
+            name="tr3")
+
         self.st4 = Stage(
             num_channels=channels_4,
             num_modules=num_modules_4,