play_n_episodes for gym submission

examples/A3C-Gym/train-atari.py

@@ -28,7 +28,8 @@ from tensorpack.tfutils.gradproc import MapGradient, SummaryGradient
 from tensorpack.RL import *
 from simulator import *
 import common
-from common import (play_model, Evaluator, eval_model_multithread, play_one_episode)
+from common import (play_model, Evaluator, eval_model_multithread,
+                    play_one_episode, play_n_episodes)
 
 if six.PY3:
     from concurrent import futures
@@ -238,18 +239,6 @@ def get_config():
     )
 
 
-def run_submission(cfg, output, nr):
-    player = get_player(train=False, dumpdir=output)
-    predfunc = OfflinePredictor(cfg)
-    logger.info("Start evaluation: ")
-    for k in range(nr):
-        if k != 0:
-            player.restart_episode()
-        score = play_one_episode(player, predfunc)
-        print("Score:", score)
-    # gym.upload(output, api_key='xxx')
-
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
@@ -283,7 +272,10 @@ if __name__ == '__main__':
         elif args.task == 'eval':
             eval_model_multithread(cfg, args.episode, get_player)
         elif args.task == 'gen_submit':
-            run_submission(cfg, args.output, args.episode)
+            play_n_episodes(
+                get_player(train=False, dumpdir=args.output),
+                OfflinePredictor(cfg), args.episode)
+            # gym.upload(output, api_key='xxx')
     else:
         nr_gpu = get_nr_gpu()
         if nr_gpu > 0:

examples/DeepQNetwork/DQN.py

@@ -62,6 +62,9 @@ def get_player(viz=False, train=False):
 
 
 class Model(DQNModel):
+    def __init__(self):
+        super(Model, self).__init__(IMAGE_SIZE, CHANNEL, METHOD, NUM_ACTIONS, GAMMA)
+
     def _get_DQN_prediction(self, image):
         """ image: [0,255]"""
         image = image / 255.0
@@ -95,7 +98,7 @@ class Model(DQNModel):
 def get_config():
     logger.auto_set_dir()
 
-    M = Model(IMAGE_SIZE, CHANNEL, METHOD, NUM_ACTIONS, GAMMA)
+    M = Model()
     expreplay = ExpReplay(
         predictor_io_names=(['state'], ['Qvalue']),
         player=get_player(train=True),

examples/DeepQNetwork/DQNModel.py

@@ -93,4 +93,3 @@ class Model(ModelDesc):
                 logger.info("{} <- {}".format(target_name, new_name))
                 ops.append(v.assign(G.get_tensor_by_name(new_name + ':0')))
         return tf.group(*ops, name='update_target_network')
-

examples/DeepQNetwork/common.py

@@ -112,3 +112,12 @@ class Evaluator(Triggerable):
             self.eval_episode = int(self.eval_episode * 0.94)
         self.trainer.monitors.put('mean_score', mean)
         self.trainer.monitors.put('max_score', max)
+
+
+def play_n_episodes(player, predfunc, nr):
+    logger.info("Start evaluation: ")
+    for k in range(nr):
+        if k != 0:
+            player.restart_episode()
+        score = play_one_episode(player, predfunc)
+        print("Score:", score)

tests/dev/git-hooks/pre-commit

@@ -5,5 +5,6 @@ cd examples
 GIT_ARG="--git-dir ../.git --work-tree .."
 
 # find out modified python files
-MOD=$(git $GIT_ARG status -s | grep -E '\.py$'  | grep -E '^\b+M\b+' | cut -c 4-)
+MOD=$(git $GIT_ARG status -s | grep -E '\.py$'  | grep -E '^ *M|^ *A ' | cut -c 4-)
+# git $GIT_ARG status -s | grep -E '\.py$'
 flake8 $MOD