[ResNet] add non-preact resnet

examples/ResNet/imagenet_resnet_utils.py

@@ -139,9 +139,9 @@ def apply_preactivation(l, preact):
 
 def get_bn(zero_init=False):
     if zero_init:
-        return lambda x, _: BatchNorm('bn', x, gamma_init=tf.zeros_initializer())
+        return lambda x, name: BatchNorm('bn', x, gamma_init=tf.zeros_initializer())
     else:
-        return lambda x, _: BatchNorm('bn', x)
+        return lambda x, name: BatchNorm('bn', x)
 
 
 def preresnet_basicblock(l, ch_out, stride, preact):
@@ -172,6 +172,27 @@ def preresnet_group(l, name, block_func, features, count, stride):
     return l
 
 
+def resnet_bottleneck(l, ch_out, stride, preact):
+    l, shortcut = apply_preactivation(l, preact)
+    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))
+    return l + resnet_shortcut(shortcut, ch_out * 4, stride, nl=get_bn(zero_init=False))
+
+
+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)):
+                # first block doesn't need activation
+                l = block_func(l, features,
+                               stride if i == 0 else 1,
+                               'no_preact' if i == 0 else 'relu')
+        # end of each group need an extra activation
+        l = tf.nn.relu(l)
+    return l
+
+
 def resnet_backbone(image, num_blocks, group_func, block_func):
     with argscope(Conv2D, nl=tf.identity, use_bias=False,
                   W_init=variance_scaling_initializer(mode='FAN_OUT')):