Support internal_update in BN

examples/FasterRCNN/data.py

@@ -8,7 +8,7 @@ import copy
 
 from tensorpack.utils.argtools import memoized, log_once
 from tensorpack.dataflow import (
-    MapData, imgaug, TestDataSpeed, PrefetchDataZMQ,
+    imgaug, TestDataSpeed, PrefetchDataZMQ, MultiProcessMapData,
     MapDataComponent, DataFromList)
 # import tensorpack.utils.viz as tpviz
 
@@ -251,8 +251,7 @@ def get_train_dataflow(add_mask=False):
             # tpviz.interactive_imshow(viz)
         return ret
 
-    ds = MapData(ds, preprocess)
-    ds = PrefetchDataZMQ(ds, 1)
+    ds = MultiProcessMapData(ds, 3, preprocess)
     return ds
 
 

tensorpack/models/batch_norm.py

@@ -37,7 +37,8 @@ def get_bn_variables(n_out, use_scale, use_bias, gamma_init):
     return beta, gamma, moving_mean, moving_var
 
 
-def update_bn_ema(xn, batch_mean, batch_var, moving_mean, moving_var, decay):
+def update_bn_ema(xn, batch_mean, batch_var,
+                  moving_mean, moving_var, decay, internal_update):
     # TODO is there a way to use zero_debias in multi-GPU?
     update_op1 = moving_averages.assign_moving_average(
         moving_mean, batch_mean, decay, zero_debias=False,
@@ -46,9 +47,10 @@ def update_bn_ema(xn, batch_mean, batch_var, moving_mean, moving_var, decay):
         moving_var, batch_var, decay, zero_debias=False,
         name='var_ema_op')
 
-    # TODO add an option, and maybe enable it for replica mode?
-    # with tf.control_dependencies([update_op1, update_op2]):
-    # return tf.identity(xn, name='output')
+    if internal_update:
+        with tf.control_dependencies([update_op1, update_op2]):
+            return tf.identity(xn, name='output')
+    else:
         tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_op1)
         tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_op2)
         return xn
@@ -65,7 +67,8 @@ def reshape_for_bn(param, ndims, chan, data_format):
 @layer_register()
 def BatchNorm(x, use_local_stat=None, decay=0.9, epsilon=1e-5,
               use_scale=True, use_bias=True,
-              gamma_init=tf.constant_initializer(1.0), data_format='NHWC'):
+              gamma_init=tf.constant_initializer(1.0), data_format='NHWC',
+              internal_update=False):
     """
     Batch Normalization layer, as described in the paper:
     `Batch Normalization: Accelerating Deep Network Training by
@@ -79,6 +82,9 @@ def BatchNorm(x, use_local_stat=None, decay=0.9, epsilon=1e-5,
         epsilon (float): epsilon to avoid divide-by-zero.
         use_scale, use_bias (bool): whether to use the extra affine transformation or not.
         gamma_init: initializer for gamma (the scale).
+        internal_update (bool): if False, add EMA update ops to
+            `tf.GraphKeys.UPDATE_OPS`. If True, update EMA inside the layer
+            which will be slightly slower.
 
     Returns:
         tf.Tensor: a tensor named ``output`` with the same shape of x.
@@ -161,7 +167,7 @@ def BatchNorm(x, use_local_stat=None, decay=0.9, epsilon=1e-5,
         add_model_variable(moving_mean)
         add_model_variable(moving_var)
     if ctx.is_main_training_tower and use_local_stat:
-        ret = update_bn_ema(xn, batch_mean, batch_var, moving_mean, moving_var, decay)
+        ret = update_bn_ema(xn, batch_mean, batch_var, moving_mean, moving_var, decay, internal_update)
     else:
         ret = tf.identity(xn, name='output')