Remove leftover `Switch` ops (#240)

* Replace `switch` ops with `cond`

* Use func instead of lambda

ppdet/modeling/architectures/cascade_mask_rcnn.py

@@ -321,14 +321,14 @@ class CascadeMaskRCNN(object):
             dtype='float32',
             persistable=False,
             name=mask_name)
-        with fluid.layers.control_flow.Switch() as switch:
-            with switch.case(cond):
+
+        def noop():
             fluid.layers.assign(input=bbox_pred, output=mask_pred)
-            with switch.default():
+
+        def process_boxes():
             bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6])
 
-                im_scale = fluid.layers.slice(
-                    im_info, [1], starts=[2], ends=[3])
+            im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3])
             im_scale = fluid.layers.sequence_expand(im_scale, bbox)
 
             mask_rois = bbox * im_scale
@@ -341,6 +341,8 @@ class CascadeMaskRCNN(object):
 
             mask_out = self.mask_head.get_prediction(mask_feat, bbox)
             fluid.layers.assign(input=mask_out, output=mask_pred)
+
+        fluid.layers.cond(cond, noop, process_boxes)
         return mask_pred, bbox_pred
 
     def _input_check(self, require_fields, feed_vars):

ppdet/modeling/architectures/mask_rcnn.py

@@ -240,14 +240,14 @@ class MaskRCNN(object):
             dtype='float32',
             persistable=False,
             name=mask_name)
-        with fluid.layers.control_flow.Switch() as switch:
-            with switch.case(cond):
+
+        def noop():
             fluid.layers.assign(input=bbox_pred, output=mask_pred)
-            with switch.default():
+
+        def process_boxes():
             bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6])
 
-                im_scale = fluid.layers.slice(
-                    im_info, [1], starts=[2], ends=[3])
+            im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3])
             im_scale = fluid.layers.sequence_expand(im_scale, bbox)
 
             mask_rois = bbox * im_scale
@@ -261,6 +261,8 @@ class MaskRCNN(object):
 
             mask_out = self.mask_head.get_prediction(mask_feat, bbox)
             fluid.layers.assign(input=mask_out, output=mask_pred)
+
+        fluid.layers.cond(cond, noop, process_boxes)
         return mask_pred, bbox_pred
 
     def _input_check(self, require_fields, feed_vars):