Patch f1 (https://github.com/ebonyclock/vizdoom_cig2017) to work under latest TensorFlow + latest tensorpack

f1.diff

diff --git i/f1/F1_track1/agent.py w/f1/F1_track1/agent.py
index a717842..4ae8405 100644
--- i/f1/F1_track1/agent.py
+++ w/f1/F1_track1/agent.py
@@ -140,15 +140,15 @@ class FinalEnv(RLEnvironment):
 
 
 class Model(ModelDesc):
-    def _get_input_vars(self):
-        return [InputVar(tf.float32, (None,) + IMAGE_SHAPE3, 'image'),
-                InputVar(tf.float32, (None, 5), 'vars')]
+    def _get_inputs(self):
+        return [InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'image'),
+                InputDesc(tf.float32, (None, 5), 'vars')]
 
-    def _get_NN_prediction(self, state, is_training):
+    def _get_NN_prediction(self, state):
         """ image: [0,255]"""
         image, vars = state
         image = image / 255.0
-        with argscope(Conv2D, nl=PReLU.f):
+        with argscope(Conv2D, nl=PReLU.symbolic_function):
             l = (LinearWrap(image)
                 .Conv2D('conv0', out_channel=32, kernel_shape=7, stride=2)
             # 60
@@ -160,16 +160,16 @@ class Model(ModelDesc):
             # 7
                 .Conv2D('conv4', out_channel=192, kernel_shape=3, padding='VALID')
             # 5
-                .FullyConnected('fcimage', 1024, nl=PReLU.f)())
+                .FullyConnected('fcimage', 1024, nl=PReLU.symbolic_function)())
 
         vars = tf.tile(vars, [1, 10], name='tiled_vars')
-        feat = tf.concat(1, [l, vars])
+        feat = tf.concat([l, vars], 1)
 
         policy = FullyConnected('fc-pi-m', feat, out_dim=NUM_ACTIONS, nl=tf.identity) * 0.1
         return policy
 
-    def _build_graph(self, inputs, is_training):
-        policy = self._get_NN_prediction(inputs, is_training)
+    def _build_graph(self, inputs):
+        policy = self._get_NN_prediction(inputs)
         self.logits = tf.nn.softmax(policy, name='logits')
 
 class Model2(ModelDesc):
@@ -213,9 +213,9 @@ class Runner(object):
         cfg = PredictConfig(
                 model=Model(),
                 session_init=SaverRestore('model.tfmodel'),
-                input_var_names=['image', 'vars'],
-                output_var_names=['logits'])
-        self._pred_func = get_predict_func(cfg)
+                input_names=['image', 'vars'],
+                output_names=['logits'])
+        self._pred_func = OfflinePredictor(cfg)
 
     def action_func(self, inputs):
         f = self._pred_func
diff --git i/f1/F1_track1/history.py w/f1/F1_track1/history.py
index 5049412..511cef6 100644
--- i/f1/F1_track1/history.py
+++ w/f1/F1_track1/history.py
@@ -11,12 +11,12 @@ __all__ = ['HistoryPlayerWithVar']
 class HistoryPlayerWithVar(HistoryFramePlayer):
     def current_state(self):
         assert len(self.history) != 0
-        assert len(self.history[0]) == 2, "state needs to be like [img, vars]"
-        diff_len = self.history.maxlen - len(self.history)
-        zeros = [np.zeros_like(self.history[0][0]) for k in range(diff_len)]
-        for k in self.history:
+        assert len(self.history.buf[0]) == 2, "state needs to be a 2-list like [img, vars]"
+        diff_len = self.history.buf.maxlen - len(self.history)
+        zeros = [np.zeros_like(self.history.buf[0][0]) for k in range(diff_len)]
+        for k in self.history.buf:
             zeros.append(k[0])
         img = np.concatenate(zeros, axis=2)
-        gvar = self.history[-1][1]
+        gvar = self.history.buf[-1][1]
         return img, gvar
 

Thanks. The diff is updated.