add infogan models, and some small changes.

examples/DeepQNetwork/README.md

@@ -19,7 +19,7 @@ Claimed performance in the paper can be reproduced, on several games I've tested
 
 ![DQN](curve-breakout.png)
 
-DQN typically took 2 days of training to reach a score of 400 on breakout game (same as the paper).
+DQN typically took 1.5 days of training to reach a score of 400 on breakout game (same as the paper).
 My Batch-A3C implementation only took <2 hours.
 Both were trained on one GPU with an extra GPU for simulation.
 

examples/DeepQNetwork/expreplay.py

@@ -102,7 +102,7 @@ class ExpReplay(DataFlow, Callback):
         # self.mem.append(deepcopy(self.mem[0]))
         # return
         old_s = self.player.current_state()
-        if self.rng.rand() <= self.exploration:
+        if self.rng.rand() <= self.exploration or len(self.mem) < 5:
             act = self.rng.choice(range(self.num_actions))
         else:
             # build a history state

examples/GAN/ConditionalGAN-mnist.py

@@ -21,6 +21,8 @@ To train:
 
 To visualize:
     ./ConditionalGAN-mnist.py --sample --load path/to/model
+
+A pretrained model is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
 """
 
 BATCH = 128

examples/GAN/DCGAN-CelebA.py

@@ -23,6 +23,8 @@ from GAN import GANTrainer, RandomZData, GANModelDesc
 
 You can also train on other images (just use any directory of jpg files in
 `--data`). But you may need to change the preprocessing steps in `get_data()`.
+
+A pretrained model on CelebA is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
 """
 
 SHAPE = 64

examples/GAN/InfoGAN-mnist.py

@@ -23,6 +23,8 @@ To train:
 
 To visualize:
     ./InfoGAN-mnist.py --sample --load path/to/model
+
+A pretrained model is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
 """
 
 BATCH = 128

examples/GAN/README.md

@@ -12,14 +12,12 @@ Reproduce the following GAN-related methods:
 
 + [Wasserstein GAN](https://arxiv.org/abs/1701.07875)
 
-Please see the __docstring__ in each script for detailed usage.
+Please see the __docstring__ in each script for detailed usage and pretrained models.
 
 ## DCGAN-CelebA.py
 
 Reproduce DCGAN following the setup in [dcgan.torch](https://github.com/soumith/dcgan.torch).
 
-Play with the [pretrained model](https://drive.google.com/drive/folders/0B9IPQTvr2BBkLUF2M0RXU1NYSkE?usp=sharing) on CelebA face dataset:
-
 + Generated samples
 
 ![sample](demo/CelebA-samples.jpg)

tensorpack/dataflow/imgaug/noname.py

@@ -73,7 +73,7 @@ class ResizeShortestEdge(ImageAugmentor):
     keeping the aspect ratio.
     """
 
-    def __init__(self, size):
+    def __init__(self, size, interp=cv2.INTER_LINEAR):
         """
         Args:
             size (int): the size to resize the shortest edge to.
@@ -85,7 +85,7 @@ class ResizeShortestEdge(ImageAugmentor):
         h, w = img.shape[:2]
         scale = self.size / min(h, w)
         desSize = map(int, [scale * w, scale * h])
-        ret = cv2.resize(img, tuple(desSize), interpolation=cv2.INTER_LINEAR)
+        ret = cv2.resize(img, tuple(desSize), interpolation=self.interp)
         if img.ndim == 3 and ret.ndim == 2:
             ret = ret[:, :, np.newaxis]
         return ret

tensorpack/models/batch_norm.py

@@ -241,11 +241,11 @@ def BatchRenorm(x, rmax, dmax, decay=0.9, epsilon=1e-5,
     if use_local_stat:
         xn, batch_mean, batch_var = tf.nn.fused_batch_norm(x, gamma, beta,
                                                            epsilon=epsilon, is_training=True)
-        moving_sigma = tf.sqrt(moving_var, 'sigma')
+        inv_sigma = tf.rsqrt(moving_var, 'inv_sigma')
         r = tf.stop_gradient(tf.clip_by_value(
-            tf.sqrt(batch_var / moving_var), 1.0 / rmax, rmax))
+            tf.sqrt(batch_var) * inv_sigma, 1.0 / rmax, rmax))
         d = tf.stop_gradient(tf.clip_by_value(
-            (batch_mean - moving_mean) / moving_sigma,
+            (batch_mean - moving_mean) * inv_sigma,
             -dmax, dmax))
         xn = xn * r + d
     else:

tensorpack/tfutils/modelutils.py

@@ -77,7 +77,7 @@ def apply_slim_collections(cost):
     if ctx is not None and ctx.is_main_training_tower:
         non_grad_updates = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
         if non_grad_updates:
-            logger.info("Applying UPDATE_OPS collection on cost.")
+            logger.info("Applying UPDATE_OPS collection from the first tower on cost.")
             with tf.control_dependencies(non_grad_updates):
                 cost = tf.identity(cost, name='cost_with_update')
     return cost

tensorpack/train/trainer.py

@@ -40,6 +40,7 @@ class PredictorFactory(object):
 
         def get_name_in_tower(name):
             return PREDICT_TOWER + str(tower) + '/' + name
+
         def maybe_inside_tower(name):
             name = get_op_tensor_name(name)[0]
             if name in placeholder_names: