update readme

README.md

@@ -5,6 +5,7 @@ In development but usable. API might change a bit.
 
 See some interesting [examples](https://github.com/ppwwyyxx/tensorpack/tree/master/examples) to learn about the framework:
 
++ [DoReFa-Net: low bitwidth CNN](https://github.com/ppwwyyxx/tensorpack/tree/master/examples/DoReFa-Net)
 + [Double-DQN for playing Atari games](https://github.com/ppwwyyxx/tensorpack/tree/master/examples/Atari2600)
 + [ResNet for Cifar10 classification](https://github.com/ppwwyyxx/tensorpack/tree/master/examples/ResNet)
 + [char-rnn language model](https://github.com/ppwwyyxx/tensorpack/tree/master/examples/char-rnn)

examples/DoReFa-Net/README.md

@@ -2,9 +2,7 @@ This is the official script to load and run pretrained model for the paper:
 
 [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](http://arxiv.org/abs/1606.06160), by Zhou et al.
 
-(Work in Progress. More instructions to come soon)
-
-This is an AlexNet model with 1 bit weights, 2 bit activations, trained with 4 bit gradients.
+The provided model is an AlexNet with 1 bit weights, 2 bit activations, trained with 4 bit gradients.
 
 ## Preparation:
 
@@ -12,25 +10,26 @@ To use the script. You'll need:
 
 + [TensorFlow](tensorflow.org) >= 0.8
 
-+ [tensorpack](https://github.com/ppwwyyxx/tensorpack) and pyzmq:
++ [tensorpack](https://github.com/ppwwyyxx/tensorpack):
 
 ```
 git clone https://github.com/ppwwyyxx/tensorpack
 pip install --user -r tensorpack/requirements.txt
-pip install --user pyzmq
 export PYTHONPATH=$PYTHONPATH:`readlink -f tensorpack`
 ```
 
++ Download the model at [google drive](https://drive.google.com/drive/u/2/folders/0B308TeQzmFDLa0xOeVQwcXg1ZjQ)
+
 ## Load and run the model
-We provide two format for the model:
+We publish the model in two file formats:
 
-1. alexnet.npy. It's simply a numpy dict of {param name: value}. To load:
+1. `alexnet.npy`. It's simply a numpy dict of {param name: value}. Use it with:
 
 ```
 ./alexnet.py --load alexnet.npy [--input img.jpg] [--data path/to/data]
 ```
 
-2. alexnet.meta + alexnet.tfmodel. A TensorFlow MetaGraph proto and a saved checkpoint.
+2. `alexnet.meta` + `alexnet.tfmodel`. A TensorFlow MetaGraph proto and a saved checkpoint.
 
 ```
 ./alexnet.py --graph alexnet.meta --load alexnet.tfmodel [--input path/to/img.jpg] [--data path/to/ILSVRC12]
@@ -38,3 +37,8 @@ We provide two format for the model:
 
 One of `--data` or `--input` must be present, to either run classification on some input images, or run evaluation on ILSVRC12 validation set.
 To eval on ILSVRC12, `path/to/ILSVRC12` must have a subdirectory named 'val' containing all the validation images.
+
+## Support
+
+Please use [github issues](https://github.com/ppwwyyxx/tensorpack/issues) for any issues related to the code.
+Send email to the authors for other questions related to the paper.

examples/DoReFa-Net/alexnet.py

@@ -8,15 +8,6 @@ import argparse
 import numpy as np
 import os
 
-"""
-Run the pretrained model of paper:
-DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
-http://arxiv.org/abs/1606.06160
-
-Model can be downloaded at:
-https://drive.google.com/drive/u/2/folders/0B308TeQzmFDLa0xOeVQwcXg1ZjQ
-"""
-
 from tensorpack import *
 from tensorpack.utils.stat import RatioCounter
 from tensorpack.tfutils.symbolic_functions import prediction_incorrect
@@ -76,7 +67,7 @@ def eval_on_ILSVRC12(model, sess_init, data_dir):
     ]
     ds = AugmentImageComponent(ds, transformers)
     ds = BatchData(ds, 128, remainder=True)
-    ds = PrefetchDataZMQ(ds, 10)    # TODO use PrefetchData as fallback
+    ds = PrefetchData(ds, 10, nr_proc=1)
 
     cfg = PredictConfig(
         model=model,
@@ -155,6 +146,7 @@ op/variable names")
         raise RuntimeError("Unsupported model type!")
 
     if args.data:
+        assert os.path.isdir(os.path.join(args.data, 'val'))
         eval_on_ILSVRC12(M, sess_init, args.data)
     elif args.input:
         run_test(M, sess_init, args.input)