cleanup the use of preact

examples/ResNet/resnet_model.py

@@ -23,7 +23,6 @@ def resnet_shortcut(l, n_out, stride, nl=tf.identity):
 
 def apply_preactivation(l, preact):
     if preact == 'bnrelu':
-        # this is used only for preact-resnet
         shortcut = l    # preserve identity mapping
         l = BNReLU('preact', l)
     else:
@@ -70,26 +69,26 @@ def preresnet_group(l, name, block_func, features, count, stride):
     return l
 
 
-def resnet_basicblock(l, ch_out, stride, preact):
-    l, shortcut = apply_preactivation(l, preact)
+def resnet_basicblock(l, ch_out, stride):
+    shortcut = l
     l = Conv2D('conv1', l, ch_out, 3, stride=stride, nl=BNReLU)
     l = Conv2D('conv2', l, ch_out, 3, nl=get_bn(zero_init=True))
     return l + resnet_shortcut(shortcut, ch_out, stride, nl=get_bn(zero_init=False))
 
 
-def resnet_bottleneck(l, ch_out, stride, preact, stride_first=False):
+def resnet_bottleneck(l, ch_out, stride, stride_first=False):
     """
     stride_first: original resnet put stride on first conv. fb.resnet.torch put stride on second conv.
     """
-    l, shortcut = apply_preactivation(l, preact)
+    shortcut = l
     l = Conv2D('conv1', l, ch_out, 1, stride=stride if stride_first else 1, nl=BNReLU)
     l = Conv2D('conv2', l, ch_out, 3, stride=1 if stride_first else stride, nl=BNReLU)
     l = Conv2D('conv3', l, ch_out * 4, 1, nl=get_bn(zero_init=True))
     return l + resnet_shortcut(shortcut, ch_out * 4, stride, nl=get_bn(zero_init=False))
 
 
-def se_resnet_bottleneck(l, ch_out, stride, preact):
-    l, shortcut = apply_preactivation(l, preact)
+def se_resnet_bottleneck(l, ch_out, stride):
+    shortcut = l
     l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
     l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
     l = Conv2D('conv3', l, ch_out * 4, 1, nl=get_bn(zero_init=True))
@@ -105,8 +104,7 @@ def resnet_group(l, name, block_func, features, count, stride):
     with tf.variable_scope(name):
         for i in range(0, count):
             with tf.variable_scope('block{}'.format(i)):
-                l = block_func(l, features,
-                               stride if i == 0 else 1, 'no_preact')
+                l = block_func(l, features, stride if i == 0 else 1)
                 # end of each block need an activation
                 l = tf.nn.relu(l)
     return l