update about version info

README.md

@@ -2,7 +2,7 @@
 Neural Network Toolbox on TensorFlow
 
 See some [examples](examples) to learn about the framework.
-You can train them and reproduce the performance... not just to see how to write code.
+They're not only for demonstration of the framework -- you can train them and reproduce the results in papers.
 
 + [DoReFa-Net: training binary / low bitwidth CNN on ImageNet](examples/DoReFa-Net)
 + [ResNet for ImageNet/Cifar10/SVHN classification](examples/ResNet)
@@ -44,7 +44,7 @@ The components are designed to be independent. You can use Model or DataFlow in
 ## Dependencies:
 
 + Python 2 or 3
-+ TensorFlow >= 0.10
++ TensorFlow >= 0.11
 + Python bindings for OpenCV
 + other requirements:
 ```

examples/GAN/Image2Image.py

@@ -19,7 +19,7 @@ from GAN import GANTrainer, RandomZData, build_GAN_losses
 """
 To train:
     ./Image2Image.py --data /path/to/datadir --mode {AtoB,BtoA}
-    # datadir should contain 512x256 images formed by A and B
+    # datadir should contain images of shpae 2s x s, formed by A and B
     # training visualization will appear be in tensorboard
 
 To visualize on test set:
@@ -128,7 +128,8 @@ def split_input(img):
     img: an 512x256x3 image
     :return: [input, output]
     """
-    input, output = img[:,:256,:], img[:,256:,:]
+    s = img.shape[0]
+    input, output = img[:,:s,:], img[:,s:,:]
     if args.mode == 'BtoA':
         input, output = output, input
     if IN_CH == 1:

tensorpack/callbacks/stats.py

@@ -40,9 +40,6 @@ class StatHolder(object):
         :param k: name
         :param v: value
         """
-        suffix = '-summary'
-        if k.endswith(suffix):
-            k = k[:-len(suffix)]
         self.stat_now[k] = float(v)
 
     def set_print_tag(self, print_tag):

tensorpack/tfutils/summary.py

@@ -34,6 +34,7 @@ def add_activation_summary(x, name=None):
     if ctx is not None and not ctx.is_main_training_tower:
         return
     ndim = x.get_shape().ndims
+    # TODO use scalar if found ndim == 1
     assert ndim >= 2, \
         "Summary a scalar with histogram? Maybe use scalar instead. FIXME!"
     if name is None:

tensorpack/train/base.py

@@ -91,6 +91,9 @@ class Trainer(object):
         for val in summary.value:
             if val.WhichOneof('value') == 'simple_value':
                 val.tag = re.sub('tower[p0-9]+/', '', val.tag)   # TODO move to subclasses
+                suffix = '-summary' # issue#6150
+                if val.tag.endswith(suffix):
+                    val.tag = va.tag[:-len(suffix)]
                 self.stat_holder.add_stat(val.tag, val.simple_value)
         self.summary_writer.add_summary(summary, get_global_step())