misc small changes

docs/tutorial/performance-tuning.md

@@ -38,14 +38,13 @@ Benchmark your DataFlow with modifications to understand which part is the bottl
 2. Gradually add some pre-processing and see how the performance changes.
 3. Change the number of parallel processes or threads.
 
-A DataFlow could be blocked by CPU/hard disk/network/IPC bandwidth. Only by benchmarking will you
+A DataFlow could be blocked by CPU/disk/network/IPC bandwidth. Only by benchmarking will you
 know the reason and improve it accordingly, e.g.:
 
 1. Use single-file database to avoid random read on hard disk.
-2. Write faster pre-processing with whatever tools you have.
+2. Use fewer pre-processings or write faster ones with whatever tools you have.
 3. Move certain pre-processing (e.g. mean/std normalization) to the graph, if TF has fast implementation of it.
-4. Compress your data (e.g. use uint8 images, or JPEG-compressed images) before sending them through
-	 anything (network, ZMQ pipe, Python-TF copy etc.)
+4. Compress your data (e.g. use uint8 images, or JPEG-compressed images) before sending them through anything (network, ZMQ pipe, Python-TF copy etc.)
 5. Use distributed data preprocessing, with `send_dataflow_zmq` and `RemoteDataZMQ`.
 
 ## Investigate TensorFlow

examples/ResNet/README.md

@@ -28,7 +28,7 @@ To train, first decompress ImageNet data into [this structure](http://tensorpack
 You should be able to see good GPU utilization (95%~99%), if your data is fast enough.
 It can finish training [within 20 hours](http://dawn.cs.stanford.edu/benchmark/ImageNet/train.html) on AWS p3.16xlarge.
 
-The default data pipeline is probably OK for most SSD systems.
+The default data pipeline is probably OK for machines with SSD + E5 CPUs.
 See the [tutorial](http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html) on other options to speed up your data.
 
 ![imagenet](imagenet-resnet.png)

examples/ResNet/imagenet_utils.py

@@ -58,7 +58,7 @@ def fbresnet_augmentor(isTrain):
     if isTrain:
         augmentors = [
             GoogleNetResize(),
-            imgaug.RandomOrderAug(
+            imgaug.RandomOrderAug(      # Remove these augs if your CPU is not fast enough
                 [imgaug.BrightnessScale((0.6, 1.4), clip=False),
                  imgaug.Contrast((0.6, 1.4), clip=False),
                  imgaug.Saturation(0.4, rgb=False),

examples/SuperResolution/enet-pat.py

@@ -30,7 +30,7 @@ GAN_FACTOR_PARAMETER = 2.
 def normalize(v):
     assert isinstance(v, tf.Tensor)
     v.get_shape().assert_has_rank(4)
-    return v / tf.reduce_mean(v, axis=[1, 2, 3], keep_dims=True)
+    return v / tf.reduce_mean(v, axis=[1, 2, 3], keepdims=True)
 
 
 def gram_matrix(v):

tensorpack/callbacks/param.py

@@ -336,7 +336,13 @@ class StatMonitorParamSetter(HyperParamSetter):
         self.last_changed_epoch = 0
 
     def _get_value_to_set(self):
+        try:
             hist = self.trainer.monitors.get_history(self.stat_name)
+        except KeyError:
+            logger.warn(
+                "[StatMonitorParamSetter] Key {} not found in monitor history! Ignore it.".format(self.stat_name))
+            return None
+
         if len(hist) < self.last_k + 1 or \
                 self.epoch_num - self.last_changed_epoch < self.last_k:
             return None

tensorpack/tfutils/collection.py

@@ -81,24 +81,36 @@ class CollectionGuard(object):
 
     def __init__(self, name, check_diff,
                  freeze_keys=[],
-                 diff_whitelist=[
-                     tf.GraphKeys.TRAINABLE_VARIABLES,
-                     tf.GraphKeys.GLOBAL_VARIABLES,
-                     tf.GraphKeys.QUEUE_RUNNERS,
-                     tf.GraphKeys.LOCAL_VARIABLES]):
+                 diff_whitelist=None):
         """
         Args:
            name (str): name of the tower
-           check_diff (bool): whether to test and print about collection change
+           check_diff (bool): whether to check and print about collection change
+                when leaving this guard.
            freeze_keys (list): list of keys to freeze
-           diff_whitelist (list): list of keys to not print, when check_diff is True
+           diff_whitelist (list): list of keys to ignore, when check_diff is True.
+                Defaults to some collections that are normally changed,
+                including variables, losses, contexts, queue runners.
         """
         self._name = name
         self._check_diff = check_diff
+        if diff_whitelist is None:
+            diff_whitelist = CollectionGuard._default_diff_whitelist()
         self._whitelist = set(diff_whitelist)
         self._freeze_keys = freeze_keys
         self._inverse_graphkeys = get_inverse_graphkeys()
 
+    @staticmethod
+    def _default_diff_whitelist():
+        ret = [tf.GraphKeys.TRAINABLE_VARIABLES,
+               tf.GraphKeys.GLOBAL_VARIABLES,
+               tf.GraphKeys.QUEUE_RUNNERS,
+               tf.GraphKeys.LOCAL_VARIABLES]
+        for newkey in ['COND_CONTEXT', 'WHILE_CONTEXT', 'LOSSES']:
+            if hasattr(tf.GraphKeys, newkey):
+                ret.append(getattr(tf.GraphKeys, newkey))
+        return ret
+
     def _key_name(self, name):
         return self._inverse_graphkeys.get(name, name)
 

tensorpack/tfutils/tower.py

@@ -8,7 +8,7 @@ from six.moves import zip
 
 from ..utils import logger
 from ..utils.argtools import call_only_once
-from ..utils.naming import TRAIN_TOWER_FREEZE_KEYS, PREDICT_TOWER_FREEZE_KEYS
+from ..utils.naming import MOVING_SUMMARY_OPS_KEY
 from ..utils.develop import HIDE_DOC
 from .collection import CollectionGuard
 from .common import get_tf_version_number, get_op_or_tensor_by_name, get_op_tensor_name
@@ -122,8 +122,9 @@ class TowerContext(object):
         if self.is_main_training_tower:
             return []
         if self.is_training:
-            return TRAIN_TOWER_FREEZE_KEYS
-        return PREDICT_TOWER_FREEZE_KEYS
+            return [tf.GraphKeys.SUMMARIES, MOVING_SUMMARY_OPS_KEY]
+        # freeze UPDATE_OPS during inference because they should never be used
+        return [tf.GraphKeys.SUMMARIES, MOVING_SUMMARY_OPS_KEY, tf.GraphKeys.UPDATE_OPS]
 
     def __enter__(self):
         global _CurrentTowerContext

tensorpack/utils/naming.py

@@ -2,17 +2,7 @@
 # File: naming.py
 
 
-import tensorflow as tf
-
 GLOBAL_STEP_INCR_OP_NAME = 'global_step_incr'
-GLOBAL_STEP_INCR_VAR_NAME = 'global_step_incr:0'
 
 # extra variables to summarize during training in a moving-average way
 MOVING_SUMMARY_OPS_KEY = 'MOVING_SUMMARY_OPS'
-
-SUMMARY_BACKUP_KEYS = [tf.GraphKeys.SUMMARIES, MOVING_SUMMARY_OPS_KEY]
-
-TRAIN_TOWER_FREEZE_KEYS = SUMMARY_BACKUP_KEYS
-PREDICT_TOWER_FREEZE_KEYS = SUMMARY_BACKUP_KEYS + [tf.GraphKeys.UPDATE_OPS]
-# also freeze UPDATE_OPS in inference, because they should never be used
-# TODO a better way to log and warn about collection change during build_graph.