[MaskRCNN] padding bugfix

README.md

@@ -22,9 +22,9 @@ It's Yet Another TF high-level API, with __speed__, __readability__ and __flexib
       some benchmark scripts.
 
 2. Focus on __large datasets__.
-	+ [You don't need `tf.data`](http://tensorpack.readthedocs.io/tutorial/input-source.html#tensorflow-reader-cons).
- It's unnecessary and painful to process data with a new language called TF.
-	  Tensorpack helps you efficiently load large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
+	+ [You don't usually need `tf.data`](http://tensorpack.readthedocs.io/tutorial/input-source.html#tensorflow-reader-cons).
+      Symbolic programming often makes data processing harder.
+	  Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
 
 3. It's not a model wrapper.
 	+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.

docs/tutorial/input-source.md

@@ -58,7 +58,7 @@ The disadvantage of TF reader is obvious and it's huge: it's __too complicated__
 
 Unlike running a mathematical model, data processing is a complicated and poorly-structured task.
 You need to handle different formats, handle corner cases, noisy data, combination of data.
-Doing these require condition operations, loops, data structures, sometimes even exception handling. 
+Doing these requires condition operations, loops, data structures, sometimes even exception handling. 
 These operations are __naturally not the right task for a symbolic graph__.
 
 Let's take a look at what users are asking for `tf.data`:

examples/FasterRCNN/basemodel.py

@@ -130,10 +130,11 @@ def resnet_fpn_backbone(image, num_blocks, freeze_c2=True):
     assert len(num_blocks) == 4, num_blocks
     with resnet_argscope():
         chan = image.shape[1]
+        pad_base = maybe_reverse_pad(2, 3)
         l = tf.pad(image, tf.stack(
             [[0, 0], [0, 0],
-             maybe_reverse_pad(2, 3 + pad_shape2d[0]),
-             maybe_reverse_pad(2, 3 + pad_shape2d[1])]))
+             [pad_base[0], pad_base[1] + pad_shape2d[0]],
+             [pad_base[0], pad_base[1] + pad_shape2d[1]]]))
         l.set_shape([None, chan, None, None])
         l = Conv2D('conv0', l, 64, 7, strides=2, activation=BNReLU, padding='VALID')
         l = tf.pad(l, [[0, 0], [0, 0], maybe_reverse_pad(0, 1), maybe_reverse_pad(0, 1)])