fix bug in ProxyCallback when before_run is used

examples/ResNet/imagenet-resnet.py

@@ -79,9 +79,8 @@ class Model(ModelDesc):
 def get_data(name, batch):
     isTrain = name == 'train'
     augmentors = fbresnet_augmentor(isTrain)
-    datadir = args.data
     return get_imagenet_dataflow(
-        datadir, name, batch, augmentors, dir_structure='original')
+        args.data, name, batch, augmentors, dir_structure='original')
 
 
 def get_config(model, fake=False):
@@ -106,8 +105,10 @@ def get_config(model, fake=False):
         infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                 ClassificationError('wrong-top5', 'val-error-top5')]
         if nr_tower == 1:
+            # single-GPU inference with queue prefetch
             callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
         else:
+            # multi-GPU inference (with mandatory queue prefetch)
             callbacks.append(DataParallelInferenceRunner(
                 dataset_val, infs, list(range(nr_tower))))
 

tensorpack/callbacks/base.py

@@ -250,7 +250,7 @@ class ProxyCallback(Callback):
         self.cb.after_epoch()
 
     def _before_run(self, ctx):
-        self.cb._before_run(ctx)
+        return self.cb._before_run(ctx)
 
     def _after_run(self, ctx, run_values):
         self.cb._after_run(ctx, run_values)

tensorpack/dataflow/dataset/ilsvrc.py

@@ -146,8 +146,7 @@ class ILSVRC12Files(RNGDataFlow):
 
 class ILSVRC12(ILSVRC12Files):
     """
-    Produces uint8 ILSVRC12 images of shape [h, w, 3(BGR)], and a label between [0, 999],
-    and optionally a bounding box of [xmin, ymin, xmax, ymax].
+    Produces uint8 ILSVRC12 images of shape [h, w, 3(BGR)], and a label between [0, 999].
     """
     def __init__(self, dir, name, meta_dir=None,
                  shuffle=None, dir_structure='original'):

tensorpack/dataflow/dataset/svhn.py

@@ -41,7 +41,7 @@ class SVHNDigit(RNGDataFlow):
         if not os.path.isfile(filename):
             url = SVHN_URL + os.path.basename(filename)
             logger.info("File {} not found!".format(filename))
-            logger.info("Downloading from {}.".format(url))
+            logger.info("Downloading from {} ...".format(url))
             download(url, os.path.dirname(filename))
         logger.info("Loading {} ...".format(filename))
         data = scipy.io.loadmat(filename)

tensorpack/models/regularize.py

@@ -24,8 +24,9 @@ l1_regularizer = tf.contrib.layers.l1_regularizer
 
 def regularize_cost(regex, func, name='regularize_cost'):
     """
-    Apply a regularizer on trainable variables matching the regex.
-    In replicated mode, will only regularize variables within the current tower.
+    Apply a regularizer on trainable variables matching the regex, and print
+    the matched variables (only print once in multi-tower training).
+    In replicated mode, it will only regularize variables within the current tower.
 
     Args:
         regex (str): a regex to match variable names, e.g. "conv.*/W"