execute_only_once and maxsaver

docs/update.sh

+#!/bin/bash -e
+# File: update.sh
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
+
+make clean
+sphinx-apidoc -o modules ../tensorpack -f -d 10
+make html

examples/Atari2600/atari.py

@@ -10,7 +10,8 @@ from collections import deque
 import threading
 import six
 from six.moves import range
-from tensorpack.utils import get_rng, logger, memoized, get_dataset_path
+from tensorpack.utils import (get_rng, logger, memoized,
+        get_dataset_path, execute_only_once)
 from tensorpack.utils.stat import StatCounter
 
 from tensorpack.RL.envbase import RLEnvironment, DiscreteActionSpace
@@ -19,10 +20,6 @@ from ale_python_interface import ALEInterface
 
 __all__ = ['AtariPlayer']
 
-@memoized
-def log_once():
-    logger.warn("https://github.com/mgbellemare/Arcade-Learning-Environment/pull/171 is not merged!")
-
 ROM_URL = "https://github.com/openai/atari-py/tree/master/atari_py/atari_roms"
 _ALE_LOCK = threading.Lock()
 
@@ -56,7 +53,8 @@ class AtariPlayer(RLEnvironment):
         try:
             ALEInterface.setLoggerMode(ALEInterface.Logger.Warning)
         except AttributeError:
-            log_once()
+            if execute_only_once():
+                logger.warn("https://github.com/mgbellemare/Arcade-Learning-Environment/pull/171 is not merged!")
 
         # avoid simulator bugs: https://github.com/mgbellemare/Arcade-Learning-Environment/issues/86
         with _ALE_LOCK:

tensorpack/callbacks/common.py

@@ -88,25 +88,30 @@ class MinSaver(Callback):
         self.min = None
 
     def _get_stat(self):
-        return self.trainer.stat_holder.get_stat_now(self.monitor_stat)
+        try:
+            v = self.trainer.stat_holder.get_stat_now(self.monitor_stat)
+        except KeyError:
+            v = None
+        return v
 
     def _need_save(self):
-        if self.reverse:
-            return self._get_stat() > self.min
-        else:
-            return self._get_stat() < self.min
+        v = self._get_stat()
+        if not v:
+            return False
+        return v > self.min if self.reverse else v < self.min
 
     def _trigger_epoch(self):
         if self.min is None or self._need_save():
             self.min = self._get_stat()
-            self._save()
+            if self.min:
+                self._save()
 
     def _save(self):
         ckpt = tf.train.get_checkpoint_state(logger.LOG_DIR)
         if ckpt is None:
             raise RuntimeError(
                 "Cannot find a checkpoint state. Do you forget to use ModelSaver?")
-        path = chpt.model_checkpoint_path
+        path = ckpt.model_checkpoint_path
         newname = os.path.join(logger.LOG_DIR,
                 self.filename or
                 ('max-' if self.reverse else 'min-' + self.monitor_stat + '.tfmodel'))

tensorpack/utils/utils.py

@@ -15,23 +15,10 @@ __all__ = ['change_env',
         'map_arg',
         'get_rng', 'memoized',
         'get_dataset_path',
-        'get_tqdm_kwargs'
+        'get_tqdm_kwargs',
+        'execute_only_once'
         ]
 
-#def expand_dim_if_necessary(var, dp):
-#    """
-#    Args:
-#        var: a tensor
-#        dp: a numpy array
-#    Return a reshaped version of dp, if that makes it match the valid dimension of var
-#    """
-#    shape = var.get_shape().as_list()
-#    valid_shape = [k for k in shape if k]
-#    if dp.shape == tuple(valid_shape):
-#        new_shape = [k if k else 1 for k in shape]
-#        dp = dp.reshape(new_shape)
-#    return dp
-
 @contextmanager
 def change_env(name, val):
     oldval = os.environ.get(name, None)
@@ -104,13 +91,24 @@ def get_rng(obj=None):
             int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295
     return np.random.RandomState(seed)
 
+
+_EXECUTE_HISTORY = set()
+def execute_only_once():
+    f = inspect.currentframe().f_back
+    ident =  (f.f_code.co_filename, f.f_lineno)
+    if ident in _EXECUTE_HISTORY:
+        return False
+    _EXECUTE_HISTORY.add(ident)
+    return True
+
 def get_dataset_path(*args):
-    from . import logger
     d = os.environ.get('TENSORPACK_DATASET', None)
     if d is None:
         d = os.path.abspath(os.path.join(
                     os.path.dirname(__file__), '..', 'dataflow', 'dataset'))
-        logger.info("TENSORPACK_DATASET not set, using {} for dataset.".format(d))
+        if execute_only_once():
+            from . import logger
+            logger.info("TENSORPACK_DATASET not set, using {} for dataset.".format(d))
     assert os.path.isdir(d), d
     return os.path.join(d, *args)