require output_var_name in predict.

tensorpack/predict.py

@@ -78,11 +78,8 @@ def get_predict_func(config):
         input_map = [input_vars[k] for k in config.input_data_mapping]
 
     # check output_var_names against output_vars
-    if output_var_names is not None:
-        output_vars = [tf.get_default_graph().get_tensor_by_name(get_op_var_name(n)[1])
-                       for n in output_var_names]
-    else:
-        output_vars = []
+    output_vars = [tf.get_default_graph().get_tensor_by_name(get_op_var_name(n)[1])
+                   for n in output_var_names]
 
     describe_model()
 
@@ -94,31 +91,29 @@ def run_input(dp):
             "Graph has {} inputs but dataset only gives {} components!".format(
                     len(input_map), len(dp))
         feed = dict(zip(input_map, dp))
-        if output_var_names is not None:
-            results = sess.run(output_vars, feed_dict=feed)
-            return results
+
+        results = sess.run(output_vars, feed_dict=feed)
+        if len(output_vars) == 1:
+            return results[0]
         else:
-            results = sess.run([cost_var], feed_dict=feed)
-            cost = results[0]
-            return cost
+            return results
     return run_input
 
 PredictResult = namedtuple('PredictResult', ['input', 'output'])
 
+# TODO mutligpu predictor
+
 class DatasetPredictor(object):
     """
     Run the predict_config on a given `DataFlow`.
     """
-    def __init__(self, predict_config, dataset, batch=0):
+    def __init__(self, predict_config, dataset):
         """
         :param predict_config: a `PredictConfig` instance.
         :param dataset: a `DataFlow` instance.
-        :param batch: if batch > zero, will batch the dataset before running.
         """
         assert isinstance(dataset, DataFlow)
         self.ds = dataset
-        if batch > 0:
-            self.ds = BatchData(self.ds, batch, remainder=True)
         self.predict_func = get_predict_func(predict_config)
 
     def get_result(self):
@@ -133,3 +128,4 @@ def get_all_result(self):
         Run over the dataset and return a list of all predictions.
         """
         return list(self.get_result())
+