fix import in DQN. add auto_reuse_vs in DQN.

examples/DeepQNetwork/DQNModel.py

@@ -10,6 +10,7 @@ from tensorpack.utils import logger
 from tensorpack.tfutils import (
     collection, summary, get_current_tower_context, optimizer, gradproc)
 from tensorpack.tfutils import symbolic_functions as symbf
+from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 
 
 class Model(ModelDesc):
@@ -34,11 +35,16 @@ class Model(ModelDesc):
     def _get_DQN_prediction(self, image):
         pass
 
+    # decorate the function
+    @auto_reuse_variable_scope
+    def get_DQN_prediction(self, image):
+        return self._get_DQN_prediction(image)
+
     def _build_graph(self, inputs):
         comb_state, action, reward, isOver = inputs
         comb_state = tf.cast(comb_state, tf.float32)
         state = tf.slice(comb_state, [0, 0, 0, 0], [-1, -1, -1, self.channel], name='state')
-        self.predict_value = self._get_DQN_prediction(state)
+        self.predict_value = self.get_DQN_prediction(state)
         if not get_current_tower_context().is_training:
             return
 
@@ -51,18 +57,15 @@ class Model(ModelDesc):
             self.predict_value, 1), name='predict_reward')
         summary.add_moving_summary(max_pred_reward)
 
-        with tf.variable_scope('target'), \
-                collection.freeze_collection([tf.GraphKeys.TRAINABLE_VARIABLES]):
-            targetQ_predict_value = self._get_DQN_prediction(next_state)    # NxA
+        with tf.variable_scope('target'):
+            targetQ_predict_value = self.get_DQN_prediction(next_state)    # NxA
 
         if self.method != 'Double':
             # DQN
             best_v = tf.reduce_max(targetQ_predict_value, 1)    # N,
         else:
             # Double-DQN
-            sc = tf.get_variable_scope()
-            with tf.variable_scope(sc, reuse=True):
-                next_predict_value = self._get_DQN_prediction(next_state)
+            next_predict_value = self.get_DQN_prediction(next_state)
             self.greedy_choice = tf.argmax(next_predict_value, 1)   # N,
             predict_onehot = tf.one_hot(self.greedy_choice, self.num_actions, 1.0, 0.0)
             best_v = tf.reduce_sum(targetQ_predict_value * predict_onehot, 1)

examples/DeepQNetwork/common.py

@@ -13,6 +13,7 @@ from six.moves import queue
 from tensorpack import *
 from tensorpack.utils.concurrency import *
 from tensorpack.utils.stats import *
+from tensorpack.utils.utils import get_tqdm_kwargs
 
 
 def play_one_episode(player, func, verbose=False):

tensorpack/tfutils/scope_utils.py

@@ -26,10 +26,8 @@ def auto_reuse_variable_scope(func):
         h = hash((tf.get_default_graph(), scope.name))
         # print("Entering " + scope.name + " reuse: " + str(h in used_scope))
         if h in used_scope:
-            ns = scope.original_name_scope
             with tf.variable_scope(scope, reuse=True):
-                with tf.name_scope(ns):
-                    return func(*args, **kwargs)
+                return func(*args, **kwargs)
         else:
             used_scope.add(h)
             return func(*args, **kwargs)