super() in transforms. update docs

CHANGES.md

@@ -6,9 +6,9 @@ The backward compatibilty will be __preserved for at least several months__, wit
 so you don't need to look at here very often.
 
 Here are a list of things that were changed, starting from an early version.
-TensorFlow itself also APIs changes and those are not listed here.
+TensorFlow itself also changes API and those are not listed here.
 
-+ [2018/04/05] msgpack is replaced by pyarrow. If you want compatibility with old serialized data,
++ [2018/04/05] msgpack is replaced by pyarrow. If you want old behavior,
 	`export TENSORPACK_SERIALIZE=msgpack`.
 + [2018/03/20] `ModelDesc` starts to use simplified interfaces:
 	+ `_get_inputs()` renamed to `inputs()` and returns `tf.placeholder`s.

docs/tutorial/inference.md

@@ -23,8 +23,8 @@ You can use this predicate to choose a different code path in inference mode.
 
 Tensorpack is a training interface -- it doesn't care what happened after training.
 It saves models to standard checkpoint format.
-You can build the graph for inference, load the checkpoint, and then use whatever deployment methods TensorFlow supports.
-But you'll need to read TF docs and do it on your own.
+So you can build the graph for inference, load the checkpoint, and then use whatever deployment methods TensorFlow supports.
+But you'll need to read TF docs and __do it on your own__.
 
 ### Don't Use Training Metagraph for Inference
 
@@ -50,6 +50,11 @@ with TowerContext('', is_training=False):
 ### OfflinePredictor
 The only tool tensorpack has for after-training inference is [OfflinePredictor](../modules/predict.html#tensorpack.predict.OfflinePredictor),
 a simple function to build the graph and return a callable for you.
-It is mainly for quick demo purposes.
-It only runs inference on numpy arrays, therefore may not be the most efficient way.
 Check out examples and docs for its usage.
+
+
+OfflinePredictor is only for quick demo purposes.
+It runs inference on numpy arrays, therefore may not be the most efficient way.
+It also has very limited functionalities.
+If you need anything more complicated, please __do it on your own__ because Tensorpack
+doesn't care what happened after training.

examples/FasterRCNN/common.py

@@ -38,6 +38,7 @@ class CustomResize(transform.TransformAugmentorBase):
             size (int): the size to resize the shortest edge to.
             max_size (int): maximum allowed longest edge.
         """
+        super(CustomResize, self).__init__()
         self._init(locals())
 
     def _get_augment_params(self, img):

examples/README.md

@@ -23,29 +23,29 @@ These are all the toy examples in tensorpack. They are supposed to be just demos
 + [A boilerplate file to start with, for your own tasks](boilerplate.py)
 
 ## Vision:
-| Name | Performance |
-| ---  | --- |
-|	Train [ResNet](ResNet), [ShuffleNet and other models](ImageNetModels) on ImageNet		| reproduce paper	|
-|	[Train Faster-RCNN / Mask-RCNN on COCO](FasterRCNN)				|	reproduce paper		|
+| Name                                                                                                                                                  | Performance        |
+| ---                                                                                                                                                   | ---                |
+| Train [ResNet](ResNet), [ShuffleNet and other models](ImageNetModels) on ImageNet                                                                     | reproduce paper    |
+| [Train Faster-RCNN / Mask-RCNN on COCO](FasterRCNN)                                                                                                   | reproduce paper    |
 | [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, <br/> Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN | visually reproduce |
-| [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net) | reproduce paper |
-| [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED) | visually reproduce |
-| [Spatial Transformer Networks on MNIST addition](SpatialTransformer) | reproduce paper |
-| [Visualize CNN saliency maps](Saliency) | visually reproduce |
-| [Similarity learning on MNIST](SimilarityLearning) | |
-| Single-image super-resolution using [EnhanceNet](SuperResolution) |  |
-| Learn steering filters with [Dynamic Filter Networks](DynamicFilterNetwork) | visually reproduce |
-| Load a pre-trained [AlexNet, VGG, or Convolutional Pose Machines](CaffeModels) | |
+| [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net)                                                                              | reproduce paper    |
+| [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED)                                                                        | visually reproduce |
+| [Spatial Transformer Networks on MNIST addition](SpatialTransformer)                                                                                  | reproduce paper    |
+| [Visualize CNN saliency maps](Saliency)                                                                                                               | visually reproduce |
+| [Similarity learning on MNIST](SimilarityLearning)                                                                                                    |                    |
+| Single-image super-resolution using [EnhanceNet](SuperResolution)                                                                                     |                    |
+| Learn steering filters with [Dynamic Filter Networks](DynamicFilterNetwork)                                                                           | visually reproduce |
+| Load a pre-trained [AlexNet, VGG, or Convolutional Pose Machines](CaffeModels)                                                                        |                    |
 
 ## Reinforcement Learning:
-| Name | Performance |
-| ---  | --- |
-| [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including <br/> DQN, DoubleDQN, DuelingDQN.  | reproduce paper |
-| [Asynchronous Advantage Actor-Critic(A3C) on Atari games](A3C-Gym) | reproduce paper |
+| Name                                                                                                     | Performance     |
+| ---                                                                                                      | ---             |
+| [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including <br/> DQN, DoubleDQN, DuelingDQN. | reproduce paper |
+| [Asynchronous Advantage Actor-Critic(A3C) on Atari games](A3C-Gym)                                       | reproduce paper |
 
 ## Speech / NLP:
-| Name | Performance |
-| ---  | --- |
-| [LSTM-CTC for speech recognition](CTC-TIMIT) | reproduce paper |
-| [char-rnn for fun](Char-RNN) | fun |
+| Name                                                | Performance              |
+| ---                                                 | ---                      |
+| [LSTM-CTC for speech recognition](CTC-TIMIT)        | reproduce paper          |
+| [char-rnn for fun](Char-RNN)                        | fun                      |
 | [LSTM language model on PennTreebank](PennTreebank) | reproduce reference code |

tensorpack/dataflow/imgaug/transform.py

@@ -56,6 +56,7 @@ class ImageTransform(object):
 
 class ResizeTransform(ImageTransform):
     def __init__(self, h, w, newh, neww, interp):
+        super(ResizeTransform, self).__init__()
         self._init(locals())
 
     def apply_image(self, img):
@@ -75,6 +76,7 @@ class ResizeTransform(ImageTransform):
 
 class CropTransform(ImageTransform):
     def __init__(self, h0, w0, h, w):
+        super(CropTransform, self).__init__()
         self._init(locals())
 
     def apply_image(self, img):
@@ -89,6 +91,7 @@ class CropTransform(ImageTransform):
 class WarpAffineTransform(ImageTransform):
     def __init__(self, mat, dsize, interp=cv2.INTER_LINEAR,
                  borderMode=cv2.BORDER_CONSTANT, borderValue=0):
+        super(WarpAffineTransform, self).__init__()
         self._init(locals())
 
     def apply_image(self, img):

tensorpack/utils/serialize.py

@@ -10,8 +10,9 @@ __all__ = ['loads', 'dumps']
 def dumps_msgpack(obj):
     """
     Serialize an object.
+
     Returns:
-        Implementation-dependent bytes-like object
+        Implementation-dependent bytes-like object.
     """
     return msgpack.dumps(obj, use_bin_type=True)
 
@@ -29,7 +30,8 @@ def dumps_pyarrow(obj):
     Serialize an object.
 
     Returns:
-        Implementation-dependent bytes-like object
+        Implementation-dependent bytes-like object.
+        May not be compatible across different versions of pyarrow.
     """
     return pa.serialize(obj).to_buffer()