update atari/common

examples/Atari2600/common.py

@@ -75,14 +75,15 @@ def eval_model_multithread(cfg, nr_eval):
     logger.info("Average Score: {}; Max Score: {}".format(mean, max))
 
 class Evaluator(Callback):
-    def __init__(self, nr_eval, output_name):
+    def __init__(self, nr_eval, input_names, output_names):
         self.eval_episode = nr_eval
-        self.output_name = output_name
+        self.input_names = input_names
+        self.output_names = output_names
 
     def _before_train(self):
         NR_PROC = min(multiprocessing.cpu_count() // 2, 8)
         self.pred_funcs = [self.trainer.get_predict_func(
-           ['state'], [self.output_name])] * NR_PROC
+            self.input_names, self.output_names)] * NR_PROC
 
     def _trigger_epoch(self):
         t = time.time()

tensorpack/dataflow/dataset/bsds500.py

@@ -15,7 +15,7 @@ try:
     from scipy.io import loadmat
     __all__ = ['BSDS500']
 except ImportError:
-    logger.error("Cannot import scipy. BSDS500 dataset won't be available!")
+    logger.warn("Cannot import scipy. BSDS500 dataset won't be available!")
     __all__ = []
 
 DATA_URL = "http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz"

tensorpack/dataflow/dataset/svhn.py

@@ -15,7 +15,7 @@ try:
     import scipy.io
     __all__ = ['SVHNDigit']
 except ImportError:
-    logger.error("Cannot import scipy. SVHNDigit dataset won't be available!")
+    logger.warn("Cannot import scipy. SVHNDigit dataset won't be available!")
     __all__ = []
 
 SVHN_URL = "http://ufldl.stanford.edu/housenumbers/"

tensorpack/train/base.py

@@ -122,7 +122,9 @@ class Trainer(object):
                         for step in tqdm.trange(
                                 self.config.step_per_epoch,
                                 leave=True, mininterval=0.5,
-                                dynamic_ncols=True, ascii=True):
+                                smoothing=0.5,
+                                dynamic_ncols=True,
+                                ascii=True):
                                 #bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt} [{elapsed}<{remaining},{rate_noinv_fmt}]'):
                             if self.coord.should_stop():
                                 return

tensorpack/utils/logger.py

@@ -16,7 +16,7 @@ __all__ = []
 
 class MyFormatter(logging.Formatter):
     def format(self, record):
-        date = colored('[%(asctime)s %(lineno)d@%(filename)s:%(name)s]', 'green')
+        date = colored('[%(asctime)s @%(filename)s:%(lineno)d]', 'green')
         msg = '%(message)s'
         if record.levelno == logging.WARNING:
             fmt = date + ' ' + colored('WRN', 'red', attrs=['blink']) + ' ' + msg
@@ -28,24 +28,21 @@ class MyFormatter(logging.Formatter):
             # Python3 compatibilty
             self._style._fmt = fmt
         self._fmt = fmt
-        else:
-            self._fmt = fmt
         return super(MyFormatter, self).format(record)
 
 def getlogger():
     logger = logging.getLogger('tensorpack')
     logger.propagate = False
     logger.setLevel(logging.INFO)
-    handler = logging.StreamHandler()
+    handler = logging.StreamHandler(sys.stdout)
     handler.setFormatter(MyFormatter(datefmt='%d %H:%M:%S'))
     logger.addHandler(handler)
     return logger
+logger = getlogger()
+
 
 def get_time_str():
     return datetime.now().strftime('%m%d-%H%M%S')
-
-logger = getlogger()
-
 # logger file and directory:
 global LOG_FILE, LOG_DIR
 def _set_file(path):