widely use add_moving_summary

examples/Atari2600/DQN.py

@@ -16,6 +16,7 @@ from collections import deque
 from tensorpack import *
 from tensorpack.utils.concurrency import *
 from tensorpack.tfutils import symbolic_functions as symbf
+from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.RL import *
 import common
 from common import play_model, Evaluator, eval_model_multithread
@@ -95,7 +96,7 @@ class Model(ModelDesc):
         pred_action_value = tf.reduce_sum(self.predict_value * action_onehot, 1)    #N,
         max_pred_reward = tf.reduce_mean(tf.reduce_max(
             self.predict_value, 1), name='predict_reward')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, max_pred_reward)
+        add_moving_summary(max_pred_reward)
         self.greedy_choice = tf.argmax(self.predict_value, 1)   # N,
 
         with tf.variable_scope('target'):

examples/Atari2600/README.md

@@ -15,10 +15,8 @@ Claimed performance in the paper can be reproduced, on several games I've tested
 
 A demo trained with Double-DQN on breakout game is available at [youtube](https://youtu.be/o21mddZtE5Y).
 
-<!--
-   -DQN would typically take 2~3 days of training to reach a score of 400 on breakout, but my A3C implementation only takes <4 hours on 1 GPU.
-   -This is probably the fastest RL trainer you'd find.
-	 -->
+DQN would typically take 2~3 days of training to reach a score of 400 on breakout, but my A3C implementation only takes <2 hours on 1 GPU.
+This is probably the fastest RL trainer you'd find.
 
 ## How to use
 

examples/DisturbLabel/mnist-disturb.py

@@ -42,22 +42,19 @@ class Model(mnist_example.Model):
                     .FullyConnected('fc1', out_dim=10, nl=tf.identity)())
         prob = tf.nn.softmax(logits, name='prob')
 
-        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
-        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
-
         # compute the number of failed samples, for ClassificationError to use at test time
         wrong = symbolic_functions.prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
+        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
+        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
         # weight decay on all W of fc layers
         wd_cost = tf.mul(1e-5,
                          regularize_cost('fc.*/W', tf.nn.l2_loss),
                          name='regularize_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         self.cost = tf.add_n([wd_cost, cost], name='cost')
 

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -151,20 +151,17 @@ class Model(ModelDesc):
         tf.get_variable = old_get_variable
         prob = tf.nn.softmax(logits, name='output')
 
-        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
-        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
-
         # compute the number of failed samples
         wrong = prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY,
-                tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
+        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
+        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
         # weight decay on all W of fc layers
         wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         add_param_summary([('.*/W', ['histogram', 'rms'])])
         self.cost = tf.add_n([cost, wd_cost], name='cost')

examples/Inception/inception-bn.py

@@ -98,24 +98,21 @@ class Model(ModelDesc):
         loss3 = tf.reduce_mean(loss3, name='loss3')
 
         cost = tf.add_n([loss3, 0.3 * loss2, 0.3 * loss1], name='weighted_cost')
-        for k in [cost, loss1, loss2, loss3]:
-            tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, k)
+        add_moving_summary([cost, loss1, loss2, loss3])
 
         wrong = prediction_incorrect(logits, label, 1)
         nr_wrong = tf.reduce_sum(wrong, name='wrong-top1')
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error_top1'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error_top1'))
 
         wrong = prediction_incorrect(logits, label, 5)
         nr_wrong = tf.reduce_sum(wrong, name='wrong-top5')
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error_top5'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error_top5'))
 
         # weight decay on all W of fc layers
         wd_w = tf.train.exponential_decay(0.0002, get_global_step_var(),
                                           80000, 0.7, True)
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(wd_cost)
 
         add_param_summary([('.*/W', ['histogram'])])   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
@@ -144,7 +141,7 @@ def get_data(train_or_test):
     ds = AugmentImageComponent(ds, augmentors)
     ds = BatchData(ds, BATCH_SIZE, remainder=not isTrain)
     if isTrain:
-        ds = PrefetchDataZMQ(ds, 5)
+        ds = PrefetchDataZMQ(ds, 6)
     return ds
 
 

examples/ResNet/cifar10-resnet.py

@@ -100,19 +100,17 @@ class Model(ModelDesc):
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         wrong = prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
         # weight decay on all W of fc layers
         wd_w = tf.train.exponential_decay(0.0002, get_global_step_var(),
                                           480000, 0.2, True)
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         add_param_summary([('.*/W', ['histogram'])])   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')

examples/cifar-convnet.py

@@ -60,20 +60,18 @@ class Model(ModelDesc):
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ClassificationError to use at test time
         wrong = symbf.prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
         # weight decay on all W of fc layers
         wd_cost = tf.mul(0.004,
                          regularize_cost('fc.*/W', tf.nn.l2_loss),
                          name='regularize_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         add_param_summary([('.*/W', ['histogram'])])   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')

examples/mnist-convnet.py

@@ -9,6 +9,7 @@ import os, sys
 import argparse
 
 from tensorpack import *
+from tensorpack.tfutils.summary import add_moving_summary
 
 """
 MNIST ConvNet example.
@@ -46,20 +47,18 @@ class Model(ModelDesc):
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ClassificationError to use at test time
         wrong = symbolic_functions.prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
         # weight decay on all W of fc layers
         wd_cost = tf.mul(1e-5,
                          regularize_cost('fc.*/W', tf.nn.l2_loss),
                          name='regularize_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
         self.cost = tf.add_n([wd_cost, cost], name='cost')

examples/svhn-digit-convnet.py

@@ -41,20 +41,18 @@ class Model(ModelDesc):
                 .FullyConnected('linear', out_dim=10, nl=tf.identity)())
         prob = tf.nn.softmax(logits, name='output')
 
-        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
-        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
-
         # compute the number of failed samples, for ClassificationError to use at test time
         wrong = prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
+        add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
+
+        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
+        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
         # weight decay on all W of fc layers
         wd_cost = regularize_cost('fc.*/W', l2_regularizer(0.00001))
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
+        add_moving_summary(cost, wd_cost)
 
         add_param_summary([('.*/W', ['histogram', 'rms'])])   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')

tensorpack/dataflow/dataset/ilsvrc.py

@@ -107,9 +107,8 @@ class ILSVRC12(RNGDataFlow):
         command to build the above structure for `train/`:
 
         .. code-block:: none
-
-            find -type f | parallel -P 10 'mkdir -p {/.} && tar xf {} -C {/.}'
-
+            tar xvf ILSVRC12_img_train.tar -C train && cd train
+            find -type f -name '*.tar' | parallel -P 10 'echo {} && mkdir -p {/.} && tar xf {} -C {/.}'
             Or:
             for i in *.tar; do dir=${i%.tar}; echo $dir; mkdir -p $dir; tar xf $i -C $dir; done
         """

tensorpack/tfutils/gradproc.py

@@ -6,7 +6,8 @@
 import tensorflow as tf
 from abc import ABCMeta, abstractmethod
 import re
-from ..utils import logger, MOVING_SUMMARY_VARS_KEY
+from ..utils import logger
+from .summary import add_moving_summary
 
 __all__ = ['GradientProcessor', 'SummaryGradient', 'CheckGradient',
            'ScaleGradient', 'MapGradient']
@@ -41,8 +42,8 @@ class SummaryGradient(GradientProcessor):
                 continue
             _summaried_gradient.add(name)
             tf.histogram_summary(name + '/grad', grad)
-            tf.add_to_collection(MOVING_SUMMARY_VARS_KEY,
-                                 tf.sqrt(tf.reduce_mean(tf.square(grad)),
+            add_moving_summary(tf.sqrt(
+                tf.reduce_mean(tf.square(grad)),
                 name=name + '/gradRMS'))
         return grads
 

tensorpack/tfutils/summary.py

@@ -79,8 +79,15 @@ def add_param_summary(summary_lists):
                 for act in actions:
                     perform(p, act)
 
-def add_moving_summary(v):
-    tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, v)
+def add_moving_summary(v, *args):
+    """
+    :param v: tensor or list of tensor to summary
+    """
+    if not isinstance(v, list):
+        v = [v]
+    v.extend(args)
+    for x in v:
+        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, x)
 
 def summary_moving_average():
     """ Create a MovingAverage op and summary for all variables in