simplify resnet BNReLU

examples/ResNet/README.md

@@ -44,7 +44,4 @@ The validation error here is computed on test set.
 
 ![cifar10](cifar10-resnet.png)
 
-Download model:
-[Cifar10 ResNet-110 (n=18)](https://drive.google.com/open?id=0B9IPQTvr2BBkTXBlZmh1cmlnQ0k)
-
 Also see an implementation of [DenseNet](https://github.com/YixuanLi/densenet-tensorflow) from [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993).

examples/ResNet/cifar10-resnet.py

@@ -54,16 +54,9 @@ class Model(ModelDesc):
                 stride1 = 1
 
             with tf.variable_scope(name) as scope:
-                if not first:
-                    b1 = BatchNorm('bn1', l)
-                    b1 = tf.nn.relu(b1)
-                else:
-                    b1 = l
-                c1 = Conv2D('conv1', b1, out_channel, stride=stride1)
-                b2 = BatchNorm('bn2', c1)
-                b2 = tf.nn.relu(b2)
-                c2 = Conv2D('conv2', b2, out_channel)
-
+                b1 = l if first else BNReLU(l)
+                c1 = Conv2D('conv1', b1, out_channel, stride=stride1, nl=BNReLU)
+                c2 = Conv2D('conv2', c1, out_channel)
                 if increase_dim:
                     l = AvgPooling('pool', l, 2)
                     l = tf.pad(l, [[0,0], [0,0], [0,0], [in_channel//2, in_channel//2]])
@@ -73,9 +66,7 @@ class Model(ModelDesc):
 
         with argscope(Conv2D, nl=tf.identity, use_bias=False, kernel_shape=3,
                     W_init=variance_scaling_initializer(mode='FAN_OUT')):
-            l = Conv2D('conv0', image, 16)
-            l = BatchNorm('bn0', l)
-            l = tf.nn.relu(l)
+            l = Conv2D('conv0', image, 16, nl=BNReLU)
             l = residual('res1.0', l, first=True)
             for k in range(1, self.n):
                 l = residual('res1.{}'.format(k), l)
@@ -89,8 +80,7 @@ class Model(ModelDesc):
             l = residual('res3.0', l, increase_dim=True)
             for k in range(1, self.n):
                 l = residual('res3.' + str(k), l)
-            l = BatchNorm('bnlast', l)
-            l = tf.nn.relu(l)
+            l = BNReLU('bnlast', l)
             # 8,c=64
             l = GlobalAvgPooling('gap', l)
 

examples/ResNet/imagenet-resnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
-# File: imagenet-resnet.py
+# File: imagenet-resnet-short.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import cv2
@@ -42,36 +42,30 @@ class Model(ModelDesc):
 
         def basicblock(l, ch_out, stride, preact):
             ch_in = l.get_shape().as_list()[-1]
-            input = l
             if preact == 'both_preact':
-                l = BatchNorm('preact', l)
-                l = tf.nn.relu(l, name='preact-relu')
+                l = BNReLU('preact', l)
                 input = l
             elif preact != 'no_preact':
-                l = BatchNorm('preact', l)
-                l = tf.nn.relu(l, name='preact-relu')
-            l = Conv2D('conv1', l, ch_out, 3, stride=stride)
-            l = BatchNorm('bn', l)
-            l = tf.nn.relu(l)
+                input = l
+                l = BNReLU('preact', l)
+            else:
+                input = l
+            l = Conv2D('conv1', l, ch_out, 3, stride=stride, nl=BNReLU)
             l = Conv2D('conv2', l, ch_out, 3)
             return l + shortcut(input, ch_in, ch_out, stride)
 
         def bottleneck(l, ch_out, stride, preact):
             ch_in = l.get_shape().as_list()[-1]
-            input = l
             if preact == 'both_preact':
-                l = BatchNorm('preact', l)
-                l = tf.nn.relu(l, name='preact-relu')
+                l = BNReLU('preact', l)
                 input = l
             elif preact != 'no_preact':
-                l = BatchNorm('preact', l)
-                l = tf.nn.relu(l, name='preact-relu')
-            l = Conv2D('conv1', l, ch_out, 1)
-            l = BatchNorm('bn1', l)
-            l = tf.nn.relu(l)
-            l = Conv2D('conv2', l, ch_out, 3, stride=stride)
-            l = BatchNorm('bn2', l)
-            l = tf.nn.relu(l)
+                input = l
+                l = BNReLU('preact', l)
+            else:
+                input = 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)
             return l + shortcut(input, ch_in, ch_out * 4, stride)
 
@@ -102,8 +96,7 @@ class Model(ModelDesc):
                 .apply(layer, 'group1', block_func, 128, defs[1], 2)
                 .apply(layer, 'group2', block_func, 256, defs[2], 2)
                 .apply(layer, 'group3', block_func, 512, defs[3], 2)
-                .BatchNorm('bnlast')
-                .tf.nn.relu()
+                .BNReLU('bnlast')
                 .GlobalAvgPooling('gap')
                 .FullyConnected('linear', 1000, nl=tf.identity)())
 

tensorpack/models/__init__.py

@@ -57,7 +57,7 @@ class LinearWrap(object):
                     return LinearWrap(ret)
             else:
                 def f(*args, **kwargs):
-                    if isinstance(args[0], six.string_types):
+                    if len(args) and isinstance(args[0], six.string_types):
                         name, args = args[0], args[1:]
                         ret = layer(name, self._t, *args, **kwargs)
                     else: