Use default session config in predict

examples/DeepQNetwork/README.md

@@ -15,14 +15,15 @@ Reproduce the following reinforcement learning methods:
 + A3C in [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783). (I
 used a modified version where each batch contains transitions from different simulators, which I called "Batch-A3C".)
 
+## Performance & Speed
 Claimed performance in the paper can be reproduced, on several games I've tested with.
 
 ![DQN](curve-breakout.png)
 
 On one TitanX, Double-DQN took 1 day of training to reach a score of 400 on breakout game.
-Batch-A3C implementation only took <2 hours. (Both are trained with a larger network noted in the code).
+Batch-A3C implementation only took <2 hours.
 
-Double-DQN runs at 60 batches (3840 trained frames, 240 seen frames, 960 game frames) per second on TitanX.
+Double-DQN runs at 60 batches (3840 trained frames, 240 seen frames, 960 game frames) per second on (Maxwell) TitanX.
 
 ## How to use
 

examples/ResNet/imagenet-resnet-se.py

@@ -98,7 +98,7 @@ def get_config():
                 ClassificationError('wrong-top1', 'val-error-top1'),
                 ClassificationError('wrong-top5', 'val-error-top5')]),
             ScheduledHyperParamSetter('learning_rate',
-                                      [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5)]),
+                                      [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5), (105, 1e-6)]),
         ],
         steps_per_epoch=5000,
         max_epoch=110,
@@ -112,7 +112,7 @@ if __name__ == '__main__':
     parser.add_argument('--data', help='ILSVRC dataset dir')
     parser.add_argument('--load', help='load model')
     parser.add_argument('-d', '--depth', help='resnet depth',
-                        type=int, default=18, choices=[18, 34, 50, 101])
+                        type=int, default=50, choices=[50, 101])
     parser.add_argument('--eval', action='store_true')
     args = parser.parse_args()
 
@@ -121,7 +121,7 @@ if __name__ == '__main__':
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
     if args.eval:
-        BATCH_SIZE = 64  # something that can run on one gpu
+        BATCH_SIZE = 128  # something that can run on one gpu
         ds = get_data('val')
         eval_on_ILSVRC12(Model(), args.load, ds)
         sys.exit()

tensorpack/predict/config.py

@@ -6,7 +6,6 @@ import tensorflow as tf
 import six
 
 from ..graph_builder import ModelDesc
-from ..utils.develop import log_deprecated
 from ..tfutils import get_default_sess_config
 from ..tfutils.sessinit import SessionInit, JustCurrentSession
 from ..tfutils.sesscreate import NewSessionCreator
@@ -22,7 +21,6 @@ class PredictConfig(object):
                  output_names=None,
                  return_input=False,
                  create_graph=True,
-                 session_config=None,   # deprecated
                  ):
         """
         Args:
@@ -50,18 +48,13 @@ class PredictConfig(object):
         assert_type(self.session_init, SessionInit)
 
         if session_creator is None:
-            if session_config is not None:
-                log_deprecated("PredictConfig(session_config=)", "Use session_creator instead!", "2017-04-20")
-                self.session_creator = NewSessionCreator(config=session_config)
-            else:
-                self.session_creator = NewSessionCreator(config=get_default_sess_config(0.4))
+            self.session_creator = NewSessionCreator(config=get_default_sess_config())
         else:
             self.session_creator = session_creator
 
         # inputs & outputs
         self.input_names = input_names
         if self.input_names is None:
-            # neither options is set, assume all inputs
             raw_tensors = self.model.get_inputs_desc()
             self.input_names = [k.name for k in raw_tensors]
         self.output_names = output_names