docs update and fix bug in ResNet-SE

docs/tutorial/summary.md

@@ -47,3 +47,5 @@ are likely to have too much variance. You can:
 Besides TensorFlow summaries,
 a callback is free to log any other types of data to the monitor backend,
 anytime after the training has started.
+As long as the type of data is supported, it will be logged by each monitor.
+In other words, tensorboard can show not only summaries in the graph, but also your custom data.

examples/ResNet/README.md

@@ -11,7 +11,7 @@ Models can be [downloaded here](https://goo.gl/6XjK9V).
 | ResNet18           |     10.55%  |      29.73% |
 | ResNet34           |     8.51%   |      26.50% |
 | ResNet50           |     7.24%   |      23.91% |
-| ResNet50-SE        |     6.42%   |      22.94% |
+| ResNet50-SE        |  TRAINING   |  TRAINING   |
 | ResNet101          |     6.26%   |      22.53% |
 
 To train, just run:

examples/ResNet/imagenet-resnet-se.py

@@ -48,7 +48,7 @@ class Model(ModelDesc):
             l = Conv2D('conv3', l, ch_out * 4, 1)
 
             squeeze = GlobalAvgPooling('gap', l)
-            squeeze = FullyConnected('fc1', squeeze, ch_out // 4, nl=tf.identity)
+            squeeze = FullyConnected('fc1', squeeze, ch_out // 4, nl=tf.nn.relu)
             squeeze = FullyConnected('fc2', squeeze, ch_out * 4, nl=tf.nn.sigmoid)
             l = l * tf.reshape(squeeze, [-1, ch_out * 4, 1, 1])
             return l + resnet_shortcut(shortcut, ch_out * 4, stride)

tensorpack/tfutils/sesscreate.py

@@ -32,7 +32,7 @@ class NewSessionCreator(tf.train.SessionCreator):
         sess = tf.Session(target=self.target, graph=self.graph, config=self.config)
         sess.run(tf.global_variables_initializer())
         sess.run(tf.local_variables_initializer())
-        logger.info("Global variables initialized.")
+        logger.info("Global and local variables initialized.")
         return sess
 
 

tensorpack/utils/viz.py

@@ -118,7 +118,7 @@ class Canvas(object):
         self.nr_col = nr_col
 
         if border is None:
-            border = int(0.1 * min(ph, pw))
+            border = int(0.05 * min(ph, pw))
         self.border = border
 
         if isinstance(bgcolor, int):
@@ -169,9 +169,9 @@ def stack_patches(
     Args:
         patch_list(list[ndarray] or ndarray): NHW or NHWC images in [0,255].
         nr_row(int), nr_col(int): rows and cols of the grid.
-            ``nr_col * nr_row`` must be equal to ``len(patch_list)``.
+            ``nr_col * nr_row`` must be no less than ``len(patch_list)``.
         border(int): border length between images.
-            Defaults to ``0.1 * min(patch_width, patch_height)``.
+            Defaults to ``0.05 * min(patch_width, patch_height)``.
         pad (boolean): when `patch_list` is a list, pad all patches to the maximum height and width.
             This option allows stacking patches of different shapes together.
         bgcolor(int or 3-tuple): background color in [0, 255]. Either an int
@@ -235,7 +235,7 @@ def gen_stack_patches(patch_list,
     ph, pw = patch_list.shape[1:3]
 
     if border is None:
-        border = int(0.1 * min(ph, pw))
+        border = int(0.05 * min(ph, pw))
     if nr_row is None:
         nr_row = int(max_height / (ph + border))
     if nr_col is None: