Fixed the bug where multiple call contexts caused parameter names to alternate in retinanet module

hub_module/modules/image/object_detection/retinanet_resnet50_fpn_coco2017/module.py

@@ -69,7 +69,7 @@ class RetinaNetResNet50FPN(hub.Module):
                 num_classes=81,
                 trainable=True,
                 pretrained=True,
-                get_prediction=False):
+                phase='train'):
         """
         Distill the Head Features, so as to perform transfer learning.
 
@@ -77,7 +77,7 @@ class RetinaNetResNet50FPN(hub.Module):
             num_classes (int): number of classes.
             trainable (bool): whether to set parameters trainable.
             pretrained (bool): whether to load default pretrained model.
-            get_prediction (bool): whether to get prediction.
+            phase (str): optional choices are 'train' and 'predict'.
 
         Returns:
              inputs(dict): the input variables.
@@ -87,6 +87,7 @@ class RetinaNetResNet50FPN(hub.Module):
         context_prog = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(context_prog, startup_program):
+            with fluid.unique_name.guard():
                 var_prefix = '@HUB_{}@'.format(self.name)
                 # image
                 image = fluid.layers.data(
@@ -139,14 +140,16 @@ class RetinaNetResNet50FPN(hub.Module):
                     'image': var_prefix + image.name,
                     'im_info': var_prefix + im_info.name
                 }
-            if get_prediction:
+                if phase == 'predict':
                     pred = retina_head.get_prediction(body_feats, spatial_scale,
                                                       im_info)
                     outputs = {'bbox_out': var_prefix + pred.name}
                 else:
                     outputs = {
-                    'body_features':
-                    [var_prefix + var.name for key, var in body_feats.items()]
+                        'body_features': [
+                            var_prefix + var.name
+                            for key, var in body_feats.items()
+                        ]
                     }
 
                 # add_vars_prefix
@@ -154,7 +157,10 @@ class RetinaNetResNet50FPN(hub.Module):
                 add_vars_prefix(fluid.default_startup_program(), var_prefix)
 
                 global_vars = context_prog.global_block().vars
-            inputs = {key: global_vars[value] for key, value in inputs.items()}
+                inputs = {
+                    key: global_vars[value]
+                    for key, value in inputs.items()
+                }
                 outputs = {
                     key: global_vars[value] if not isinstance(value, list) else
                     [global_vars[var] for var in value]