fix deprecate use of periodiccallback

docs/casestudies/colorize.md

@@ -299,7 +299,7 @@ def get_config():
     return TrainConfig(
         dataflow=dataset,
         optimizer=tf.train.AdamOptimizer(lr),
-        callbacks=[PeriodicCallback(ModelSaver(), 3)],
+        callbacks=[PeriodicTrigger(ModelSaver(), every_k_epochs=3)],
         model=Model(),
         steps_per_epoch=dataset.size(),
         max_epoch=100,

examples/A3C-Gym/train-atari.py

@@ -220,7 +220,7 @@ def get_config():
                                       [(80, 2), (100, 3), (120, 4), (140, 5)]),
             master,
             StartProcOrThread(master),
-            PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['policy']), 2),
+            PeriodicTrigger(Evaluator(EVAL_EPISODE, ['state'], ['policy']), every_k_epochs=2),
         ],
         session_creator=sesscreate.NewSessionCreator(
             config=get_default_sess_config(0.5)),

examples/Char-RNN/char-rnn.py

@@ -73,7 +73,7 @@ class Model(ModelDesc):
     def _build_graph(self, inputs):
         input, nextinput = inputs
 
-        cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(num_units=param.rnn_size)
+        cell = rnn.MultiRNNCell([rnn.LSTMBlockCell(num_units=param.rnn_size)
                                 for _ in range(param.num_rnn_layer)])
 
         def get_v(n):
@@ -91,7 +91,6 @@ class Model(ModelDesc):
 
         input_list = tf.unstack(input_feature, axis=1)  # seqlen x (Bxrnnsize)
 
-        # seqlen is 1 in inference. don't need loop_function
         outputs, last_state = rnn.static_rnn(cell, input_list, initial, scope='rnnlm')
         self.last_state = tf.identity(last_state, 'last_state')
 

tensorpack/RL/gymenv.py

@@ -84,7 +84,10 @@ except ImportError:
 
 
 if __name__ == '__main__':
-    env = GymEnv('Breakout-v0', viz=0.1)
+    import gym_ple, cv2  # noqa
+    import os
+    os.environ["SDL_VIDEODRIVER"] = "dummy"
+    env = GymEnv('FlappyBird-v0', viz=0.1)
     num = env.get_action_space().num_actions()
 
     from ..utils import get_rng
@@ -93,6 +96,10 @@ if __name__ == '__main__':
         act = rng.choice(range(num))
         # print act
         r, o = env.action(act)
-        env.current_state()
+        state = env.current_state()
+        state = cv2.resize(state[:450], (84, 84))
+        cv2.imshow("aa", state)
+        cv2.waitKey(3)
+        print(state.shape)
         if r != 0 or o:
             print(r, o)