fix bn bug

examples/Atari2600/README.md

@@ -43,4 +43,4 @@ To visualize the agent:
 ./DQN.py --rom breakout.bin --task play --load pretrained.model
 ```
 
-A3C code will be released at the end of August.
+A3C code will be released in about a week.

examples/cifar-convnet.py

@@ -32,15 +32,16 @@ class Model(ModelDesc):
                 InputVar(tf.int32, [None], 'label')
                ]
 
-    def _build_graph(self, input_vars, is_training):
+    def _build_graph(self, input_vars):
         image, label = input_vars
+        is_training = get_current_tower_context().is_training
         keep_prob = tf.constant(0.5 if is_training else 1.0)
 
         if is_training:
             tf.image_summary("train_image", image, 10)
 
         image = image / 4.0     # just to make range smaller
-        with argscope(Conv2D, nl=BNReLU(is_training), use_bias=False, kernel_shape=3):
+        with argscope(Conv2D, nl=BNReLU(), use_bias=False, kernel_shape=3):
             logits = LinearWrap(image) \
                     .Conv2D('conv1.1', out_channel=64) \
                     .Conv2D('conv1.2', out_channel=64) \

examples/mnist-convnet.py

@@ -22,8 +22,8 @@ class Model(ModelDesc):
         return [InputVar(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
                 InputVar(tf.int32, (None,), 'label') ]
 
-    def _build_graph(self, input_vars, is_training):
-        is_training = bool(is_training)
+    def _build_graph(self, input_vars):
+        is_training = get_current_tower_context().is_training
         keep_prob = tf.constant(0.5 if is_training else 1.0)
 
         image, label = input_vars

tensorpack/models/batch_norm.py

@@ -72,20 +72,20 @@ def BatchNorm(x, use_local_stat=None, decay=0.9, epsilon=1e-5):
                 tf.add_to_collection(EXTRA_SAVE_VARS_KEY, ema_var)
     else:
         assert not use_local_stat
-        with tf.name_scope(None):
-            ema = tf.train.ExponentialMovingAverage(decay=decay, name=emaname)
-
         if ctx.is_main_tower:
             # not training, but main tower. need to create the vars
             with tf.name_scope(None):
+                ema = tf.train.ExponentialMovingAverage(decay=decay, name=emaname)
                 ema_apply_op = ema.apply([batch_mean, batch_var])
                 ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)
         else:
             # use statistics in another tower
             G = tf.get_default_graph()
             # figure out the var name
-            mean_var_name = ema.average_name(batch_mean) + ':0'
-            var_var_name = ema.average_name(batch_var) + ':0'
+            with tf.name_scope(None):
+                ema = tf.train.ExponentialMovingAverage(decay=decay, name=emaname)
+                mean_var_name = ema.average_name(batch_mean) + ':0'
+                var_var_name = ema.average_name(batch_var) + ':0'
             ema_mean = ctx.find_tensor_in_main_tower(G, mean_var_name)
             ema_var = ctx.find_tensor_in_main_tower(G, var_var_name)
             #logger.info("In prediction, using {} instead of {} for {}".format(

tensorpack/models/model_desc.py

@@ -4,6 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 from abc import ABCMeta, abstractmethod
+import re
 import tensorflow as tf
 from collections import namedtuple
 import inspect

tensorpack/models/nonlin.py

@@ -64,14 +64,14 @@ def LeakyReLU(x, alpha, name=None):
     return tf.mul(x, 0.5, name=name)
 
 # I'm not a layer, but I return a nonlinearity.
-def BNReLU(is_training, **kwargs):
+def BNReLU(is_training=None, **kwargs):
     """
     :param is_traning: boolean
     :param kwargs: args for BatchNorm
     :returns: an activation function that performs BN + ReLU (a too common combination)
     """
     def BNReLU(x, name=None):
-        x = BatchNorm('bn', x, is_training, **kwargs)
+        x = BatchNorm('bn', x, use_local_stat=is_training, **kwargs)
         x = tf.nn.relu(x, name=name)
         return x
     return BNReLU