small changes in __main__

examples/A3C-Gym/train-atari.py

@@ -60,10 +60,6 @@ ENV_NAME = None
 def get_player(viz=False, train=False, dumpdir=None):
     pl = GymEnv(ENV_NAME, viz=viz, dumpdir=dumpdir)
     pl = MapPlayerState(pl, lambda img: cv2.resize(img, IMAGE_SIZE[::-1]))
-
-    global NUM_ACTIONS
-    NUM_ACTIONS = pl.get_action_space().num_actions()
-
     pl = HistoryFramePlayer(pl, FRAME_HISTORY)
     if not train:
         pl = PreventStuckPlayer(pl, 30, 1)
@@ -201,8 +197,6 @@ class MySimulatorMaster(SimulatorMaster, Callback):
 
 
 def get_config():
-    dirname = os.path.join('train_log', 'train-atari-{}'.format(ENV_NAME))
-    logger.set_logger_dir(dirname)
     M = Model()
 
     name_base = str(uuid.uuid1())[:6]
@@ -251,17 +245,15 @@ if __name__ == '__main__':
     args = parser.parse_args()
 
     ENV_NAME = args.env
-    assert ENV_NAME
     logger.info("Environment Name: {}".format(ENV_NAME))
-    p = get_player()
-    del p    # set NUM_ACTIONS
+    NUM_ACTIONS = get_player().get_action_space().num_actions()
+    logger.info("Number of actions: {}".format(NUM_ACTIONS))
 
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    if args.task != 'train':
-        assert args.load is not None
 
     if args.task != 'train':
+        assert args.load is not None
         cfg = PredictConfig(
             model=Model(),
             session_init=get_model_loader(args.load),
@@ -277,7 +269,11 @@ if __name__ == '__main__':
                 OfflinePredictor(cfg), args.episode)
             # gym.upload(output, api_key='xxx')
     else:
+        dirname = os.path.join('train_log', 'train-atari-{}'.format(ENV_NAME))
+        logger.set_logger_dir(dirname)
+
         nr_gpu = get_nr_gpu()
+        trainer = QueueInputTrainer
         if nr_gpu > 0:
             if nr_gpu > 1:
                 predict_tower = list(range(nr_gpu))[-nr_gpu // 2:]
@@ -285,12 +281,12 @@ if __name__ == '__main__':
                 predict_tower = [0]
             PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
             train_tower = list(range(nr_gpu))[:-nr_gpu // 2] or [0]
-            logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
+            logger.info("[Batch-A3C] Train on gpu {} and infer on gpu {}".format(
                 ','.join(map(str, train_tower)), ','.join(map(str, predict_tower))))
-            trainer = AsyncMultiGPUTrainer
+            if len(train_tower) > 1:
+                trainer = AsyncMultiGPUTrainer
         else:
             logger.warn("Without GPU this model will never learn! CPU is only useful for debug.")
-            nr_gpu = 0
             PREDICTOR_THREAD = 1
             predict_tower, train_tower = [0], [0]
             trainer = QueueInputTrainer

examples/DeepQNetwork/DQN.py

@@ -149,17 +149,14 @@ if __name__ == '__main__':
 
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    if args.task != 'train':
-        assert args.load is not None
     ROM_FILE = args.rom
     METHOD = args.algo
-
     # set num_actions
-    pl = AtariPlayer(ROM_FILE, viz=False)
-    NUM_ACTIONS = pl.get_action_space().num_actions()
-    del pl
+    NUM_ACTIONS = AtariPlayer(ROM_FILE).get_action_space().num_actions()
+    logger.info("ROM: {}, Num Actions: {}".format(ROM_FILE, NUM_ACTIONS))
 
     if args.task != 'train':
+        assert args.load is not None
         cfg = PredictConfig(
             model=Model(),
             session_init=get_model_loader(args.load),
@@ -171,8 +168,8 @@ if __name__ == '__main__':
             eval_model_multithread(cfg, EVAL_EPISODE, get_player)
     else:
         logger.set_logger_dir(
-            'train_log/DQN-{}'.format(
-                os.path.basename(ROM_FILE).split('.')[0]))
+            os.path.join('train_log', 'DQN-{}'.format(
+                os.path.basename(ROM_FILE).split('.')[0])))
         config = get_config()
         if args.load:
             config.session_init = SaverRestore(args.load)

examples/cifar-convnet.py

@@ -112,8 +112,6 @@ def get_data(train_or_test, cifar_classnum):
 
 
 def get_config(cifar_classnum):
-    logger.auto_set_dir()
-
     # prepare dataset
     dataset_train = get_data('train', cifar_classnum)
     dataset_test = get_data('test', cifar_classnum)
@@ -145,10 +143,9 @@ if __name__ == '__main__':
 
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    else:
-        os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
     with tf.Graph().as_default():
+        logger.set_logger_dir(os.path.join('train_log', 'cifar' + str(args.classnum)))
         config = get_config(args.classnum)
         if args.load:
             config.session_init = SaverRestore(args.load)
@@ -156,7 +153,7 @@ if __name__ == '__main__':
         if args.gpu:
             config.nr_tower = len(args.gpu.split(','))
         nr_gpu = get_nr_gpu()
-        if nr_gpu == 1:
+        if nr_gpu <= 1:
             QueueInputTrainer(config).train()
         else:
             SyncMultiGPUTrainer(config).train()

examples/mnist-convnet.py

@@ -102,9 +102,6 @@ def get_data():
 
 
 def get_config():
-    # automatically setup the directory train_log/mnist-convnet for logging
-    logger.auto_set_dir()
-
     dataset_train, dataset_test = get_data()
     # How many iterations you want in each epoch.
     # This is the default value, don't actually need to set it in the config
@@ -136,9 +133,12 @@ if __name__ == '__main__':
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
+    # automatically setup the directory train_log/mnist-convnet for logging
+    logger.auto_set_dir()
+
     config = get_config()
     if args.load:
         config.session_init = SaverRestore(args.load)
     # SimpleTrainer is slow, this is just a demo.
-    SimpleTrainer(config).train()
     # You can use QueueInputTrainer instead
+    SimpleTrainer(config).train()

examples/svhn-digit-convnet.py

@@ -94,7 +94,6 @@ def get_data():
 
 
 def get_config():
-    logger.auto_set_dir()
     data_train, data_test = get_data()
 
     return TrainConfig(
@@ -120,6 +119,7 @@ if __name__ == '__main__':
     else:
         os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
+    logger.auto_set_dir()
     with tf.Graph().as_default():
         config = get_config()
         if args.load:

tensorpack/RL/history.py

@@ -40,6 +40,9 @@ class HistoryBuffer(object):
 class HistoryFramePlayer(ProxyPlayer):
     """ Include history frames in state, or use black images.
         It assumes the underlying player will do auto-restart.
+
+        Map the original frames into (H, W, HIST x channels).
+        Oldest frames first.
     """
 
     def __init__(self, player, hist_len):