update readme

examples/DoReFa-Net/README.md

@@ -13,7 +13,7 @@ It's provided in the format of numpy dictionary, so it should be very easy to po
 
 To use the script. You'll need:
 
-+ [TensorFlow](https://tensorflow.org) >= 0.10
++ TensorFlow 0.10,0.11rc1,0.11rc2. 0.11 is not supported due to [TF bug](https://github.com/tensorflow/tensorflow/issues/5888)
 
 + OpenCV bindings for Python
 
@@ -22,7 +22,7 @@ To use the script. You'll need:
 ```
 git clone https://github.com/ppwwyyxx/tensorpack
 pip install --user -r tensorpack/requirements.txt
-pip install --user pyzmq scipy
+pip install --user scipy
 export PYTHONPATH=$PYTHONPATH:`readlink -f tensorpack`
 ```
 

examples/OpenAIGym/README.md

@@ -6,6 +6,8 @@ Use A3C in [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.o
 
 `./train-atari.py --env Breakout-v0 --gpu 0`
 
+The pre-trained models are all trained with 4 GPUs.
+
 ### To run a pretrained Atari model for 100 episodes:
 
 1. Download models from [model zoo](https://drive.google.com/open?id=0B9IPQTvr2BBkS0VhX0xmS1c5aFk)

tensorpack/tfutils/modelutils.py

@@ -3,6 +3,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
+from termcolor import colored
 
 from ..utils import logger
 
@@ -20,8 +21,9 @@ def describe_model():
         msg.append("{}: shape={}, dim={}".format(
             v.name, shape.as_list(), ele))
     size_mb = total * 4 / 1024.0**2
-    msg.append("Total param={} ({:01f} MB assuming all float32)".format(total, size_mb))
-    logger.info("Model Parameters: {}".format('\n'.join(msg)))
+    msg.append(colored(
+        "Total param={} ({:01f} MB assuming all float32)".format(total, size_mb), 'cyan'))
+    logger.info(colored("Model Parameters: ", 'cyan') + '\n'.join(msg))
 
 
 def get_shape_str(tensors):

tensorpack/tfutils/sessinit.py

@@ -197,8 +197,8 @@ def get_model_loader(filename):
     Get a corresponding model loader by looking at the file name
     :return: either a ParamRestore or SaverRestore
     """
-    assert os.path.isfile(filename), filename
     if filename.endswith('.npy'):
+        assert os.path.isfile(filename), filename
         return ParamRestore(np.load(filename, encoding='latin1').item())
     else:
         return SaverRestore(filename)

tensorpack/utils/fs.py

@@ -42,6 +42,7 @@ def download(url, dir):
         raise
     assert size > 0, "Download an empty file!"
     sys.stdout.write('\n')
+    # TODO human-readable size
     print('Succesfully downloaded ' + fname + " " + str(size) + ' bytes.')
     return fpath