fix PredictConfig. use inputs_desc adn tower_func for load-cpm

examples/ConvolutionalPoseMachines/load-cpm.py

@@ -44,14 +44,7 @@ def get_gaussian_map():
     return gaussian_map.reshape((1, 368, 368, 1))
 
 
-class Model(ModelDesc):
-    def _get_inputs(self):
-        return [InputDesc(tf.float32, (None, 368, 368, 3), 'input'),
-                InputDesc(tf.float32, (None, 368, 368, 15), 'label'),
-                ]
-
-    def _build_graph(self, inputs):
-        image, label = inputs
+def CPM(image):
     image = image / 256.0 - 0.5
 
     gmap = tf.constant(get_gaussian_map())
@@ -108,7 +101,8 @@ class Model(ModelDesc):
 def run_test(model_path, img_file):
     param_dict = np.load(model_path, encoding='latin1').item()
     predict_func = OfflinePredictor(PredictConfig(
-        model=Model(),
+        inputs_desc=[InputDesc(tf.float32, (None, 368, 368, 3), 'input')],
+        tower_func=CPM,
         session_init=DictRestore(param_dict),
         input_names=['input'],
         output_names=['resized_map']

tensorpack/predict/config.py

@@ -27,7 +27,7 @@ class PredictConfig(object):
                  ):
         """
         Args:
-            model (ModelDescBase): the model to be used to obtain inputs_desc and tower_func.
+            model (ModelDescBase): to be used to obtain inputs_desc and tower_func.
             inputs_desc ([InputDesc]):
             tower_func: a callable which takes input tensors
 
@@ -35,8 +35,7 @@ class PredictConfig(object):
                 session. Defaults to :class:`tf.train.ChiefSessionCreator()`.
             session_init (SessionInit): how to initialize variables of the session.
                 Defaults to do nothing.
-            input_names (list): a list of input tensor names. Defaults to all
-                inputs of the model.
+            input_names (list): a list of input tensor names. Defaults to match inputs_desc.
             output_names (list): a list of names of the output tensors to predict, the
                 tensors can be any computable tensor in the graph.
             return_input (bool): same as in :attr:`PredictorBase.return_input`.
@@ -70,8 +69,7 @@ class PredictConfig(object):
         # inputs & outputs
         self.input_names = input_names
         if self.input_names is None:
-            raw_tensors = self.model.get_inputs_desc()
-            self.input_names = [k.name for k in raw_tensors]
+            self.input_names = [k.name for k in self.inputs_desc]
         self.output_names = output_names
         assert_type(self.output_names, list)
         assert_type(self.input_names, list)