fix bug that was resulted by add object detection task (#577)

paddlehub/finetune/task/base_task.py

@@ -344,10 +344,6 @@ class BaseTask(object):
         # set default phase
         self.enter_phase("train")
 
-    @property
-    def base_main_program(self):
-        return self._base_main_program
-
     @contextlib.contextmanager
     def phase_guard(self, phase):
         self.enter_phase(phase)
@@ -397,7 +393,7 @@ class BaseTask(object):
         self._build_env_start_event()
         self.env.is_inititalized = True
         self.env.main_program = clone_program(
-            self.base_main_program, for_test=False)
+            self._base_main_program, for_test=False)
 
         self.env.startup_program = fluid.Program()
         with fluid.program_guard(self.env.main_program,
@@ -410,7 +406,6 @@ class BaseTask(object):
                     self.env.metrics = self._add_metrics()
 
         if self.is_predict_phase or self.is_test_phase:
-            # Todo: paddle.fluid.core_avx.EnforceNotMet: Getting 'tensor_desc' is not supported by the type of var kCUDNNFwdAlgoCache. at
             self.env.main_program = clone_program(
                 self.env.main_program, for_test=True)
             hub.common.paddle_helper.set_op_attr(
@@ -1063,10 +1058,8 @@ class BaseTask(object):
                     capacity=64,
                     use_double_buffer=True,
                     iterable=True)
-                data_reader = data_loader.set_sample_list_generator(
-                    self.reader, self.places)
-                # data_reader = data_loader.set_batch_generator(
-                #     self.reader, places=self.places)
+                data_reader = data_loader.set_batch_generator(
+                    self.reader, places=self.places)
             else:
                 data_feeder = fluid.DataFeeder(
                     feed_list=self.feed_list, place=self.place)
@@ -1083,28 +1076,12 @@ class BaseTask(object):
                 step_run_state.run_step = 1
                 num_batch_examples = len(batch)
 
-                if self.return_numpy == 2:
-                    fetch_result = self.exe.run(
-                        self.main_program_to_be_run,
-                        feed=batch,
-                        fetch_list=self.fetch_list,
-                        return_numpy=False)
-                    # fetch_result = [x if isinstance(x,fluid.LoDTensor) else np.array(x) for x in fetch_result]
-                    fetch_result = [
-                        x if hasattr(x, 'recursive_sequence_lengths') else
-                        np.array(x) for x in fetch_result
-                    ]
-                elif self.return_numpy:
-                    fetch_result = self.exe.run(
-                        self.main_program_to_be_run,
-                        feed=batch,
-                        fetch_list=self.fetch_list)
-                else:
-                    fetch_result = self.exe.run(
-                        self.main_program_to_be_run,
-                        feed=batch,
-                        fetch_list=self.fetch_list,
-                        return_numpy=False)
+                fetch_result = self.exe.run(
+                    self.main_program_to_be_run,
+                    feed=batch,
+                    fetch_list=self.fetch_list,
+                    return_numpy=self.return_numpy)
+                if not self.return_numpy:
                     fetch_result = [np.array(x) for x in fetch_result]
 
                 for index, result in enumerate(fetch_result):