the resnet.py is inconstant with the standard model. modify the basicblock func

03.image_classification/resnet.py

@@ -32,30 +32,30 @@ def conv_bn_layer(input,
         stride=stride,
         padding=padding,
         act=paddle.activation.Linear(),
-        bias_attr=False)
+        bias_attr=False,
+		)
     return paddle.layer.batch_norm(input=tmp, act=active_type)
 
 
-def shortcut(ipt, n_in, n_out, stride):
-    if n_in != n_out:
-        return conv_bn_layer(ipt, n_out, 1, stride, 0,
+def shortcut(ipt, ch_in, ch_out, stride):
+    if ch_in != ch_out:
+        return conv_bn_layer(ipt, ch_out, 1, stride, 0,
                              paddle.activation.Linear())
     else:
         return ipt
 
 
-def basicblock(ipt, ch_out, stride):
-    ch_in = ch_out * 2
+def basicblock(ipt, ch_in, ch_out, stride):
     tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
     tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())
     short = shortcut(ipt, ch_in, ch_out, stride)
     return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())
 
 
-def layer_warp(block_func, ipt, features, count, stride):
-    tmp = block_func(ipt, features, stride)
+def layer_warp(block_func, ipt, ch_in, ch_out, count, stride):
+    tmp = block_func(ipt, ch_in, ch_out, stride)
     for i in range(1, count):
-        tmp = block_func(tmp, features, 1)
+        tmp = block_func(tmp, ch_out, ch_out, 1)
     return tmp
 
 
@@ -66,9 +66,9 @@ def resnet_cifar10(ipt, depth=32):
     nStages = {16, 64, 128}
     conv1 = conv_bn_layer(
         ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
-    res1 = layer_warp(basicblock, conv1, 16, n, 1)
-    res2 = layer_warp(basicblock, res1, 32, n, 2)
-    res3 = layer_warp(basicblock, res2, 64, n, 2)
+    res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
+    res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
+    res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
     pool = paddle.layer.img_pool(
         input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
     return pool