small fix in RandomApplyAug & A3C

examples/A3C-Gym/train-atari.py

@@ -80,12 +80,12 @@ class MySimulatorWorker(SimulatorProcess):
 class Model(ModelDesc):
     def _get_inputs(self):
         assert NUM_ACTIONS is not None
-        return [InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
+        return [InputDesc(tf.uint8, (None,) + IMAGE_SHAPE3, 'state'),
                 InputDesc(tf.int64, (None,), 'action'),
                 InputDesc(tf.float32, (None,), 'futurereward')]
 
     def _get_NN_prediction(self, image):
-        image = image / 255.0
+        image = tf.cast(image, tf.float32) / 255.0
         with argscope(Conv2D, nl=tf.nn.relu):
             l = Conv2D('conv0', image, out_channel=32, kernel_shape=5)
             l = MaxPooling('pool0', l, 2)
@@ -220,7 +220,7 @@ def get_config():
         dataflow=dataflow,
         callbacks=[
             ModelSaver(),
-            ScheduledHyperParamSetter('learning_rate', [(80, 0.0003), (120, 0.0001)]),
+            ScheduledHyperParamSetter('learning_rate', [(20, 0.0003), (120, 0.0001)]),
             ScheduledHyperParamSetter('entropy_beta', [(80, 0.005)]),
             ScheduledHyperParamSetter('explore_factor',
                                       [(80, 2), (100, 3), (120, 4), (140, 5)]),
@@ -230,7 +230,7 @@ def get_config():
             StartProcOrThread(master),
             PeriodicTrigger(Evaluator(
                 EVAL_EPISODE, ['state'], ['policy'], get_player),
-                every_k_epochs=2),
+                every_k_epochs=3),
         ],
         session_creator=sesscreate.NewSessionCreator(
             config=get_default_sess_config(0.5)),
@@ -264,7 +264,7 @@ if __name__ == '__main__':
     if args.task != 'train':
         cfg = PredictConfig(
             model=Model(),
-            session_init=SaverRestore(args.load),
+            session_init=get_model_loader(args.load),
             input_names=['state'],
             output_names=['policy'])
         if args.task == 'play':
@@ -296,7 +296,7 @@ if __name__ == '__main__':
             trainer = QueueInputTrainer
         config = get_config()
         if args.load:
-            config.session_init = SaverRestore(args.load)
+            config.session_init = get_model_loader(args.load)
         config.tower = train_tower
         config.predict_tower = predict_tower
         trainer(config).train()

examples/DeepQNetwork/common.py

@@ -120,4 +120,4 @@ def play_n_episodes(player, predfunc, nr):
         if k != 0:
             player.restart_episode()
         score = play_one_episode(player, predfunc)
-        print("Score:", score)
+        print("{}/{}, score=", k, nr, score)

tensorpack/dataflow/image.py

@@ -109,7 +109,10 @@ class AugmentImageComponents(MapData):
                 to keep the original images not modified.
                 Turn it off to save time when you know it's OK.
         """
-        self.augs = AugmentorList(augmentors)
+        if isinstance(augmentors, AugmentorList):
+            self.augs = augmentors
+        else:
+            self.augs = AugmentorList(augmentors)
         self.ds = ds
         self._nr_error = 0
 

tensorpack/dataflow/imgaug/meta.py

@@ -38,6 +38,14 @@ class RandomApplyAug(ImageAugmentor):
         else:
             return (False, None)
 
+    def _augment_return_params(self, img):
+        p = self.rng.rand()
+        if p < self.prob:
+            img, prms = self.aug._augment_return_params(img)
+            return img, (True, prms)
+        else:
+            return img, (False, None)
+
     def reset_state(self):
         super(RandomApplyAug, self).reset_state()
         self.aug.reset_state()

tensorpack/models/fc.py

@@ -31,7 +31,7 @@ def FullyConnected(x, out_dim,
 
     Variable Names:
 
-    * ``W``: weights
+    * ``W``: weights of shape [in_dim, out_dim]
     * ``b``: bias
     """
     x = symbf.batch_flatten(x)

tensorpack/predict/concurrency.py

@@ -99,9 +99,9 @@ class PredictorWorkerThread(StoppableThread, ShareSessionThread):
                 #         self.id, len(futures), self.queue.qsize())
                 #  debug, for speed testing
                 # if not hasattr(self, 'xxx'):
-                #     self.xxx = outputs = self.func(batched)
+                    # self.xxx = outputs = self.func(batched)
                 # else:
-                #     outputs = [[self.xxx[0][0]] * len(batched[0]), [self.xxx[1][0]] * len(batched[0])]
+                    # outputs = [[self.xxx[0][0]] * len(batched[0]), [self.xxx[1][0]] * len(batched[0])]
 
                 for idx, f in enumerate(futures):
                     f.set_result([k[idx] for k in outputs])