fix fastrcnn accuracy

model_zoo/faster_rcnn/src/dataset.py

@@ -441,6 +441,7 @@ def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, devi
     hwc_to_chw = C.HWC2CHW()
     normalize_op = C.Normalize((123.675, 116.28, 103.53), (58.395, 57.12, 57.375))
     horizontally_op = C.RandomHorizontalFlip(1)
+    type_cast0 = CC.TypeCast(mstype.float32)
     type_cast1 = CC.TypeCast(mstype.float16)
     type_cast2 = CC.TypeCast(mstype.int32)
     type_cast3 = CC.TypeCast(mstype.bool_)
@@ -453,13 +454,15 @@ def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, devi
 
         flip = (np.random.rand() < config.flip_ratio)
         if flip:
-            ds = ds.map(input_columns=["image"], operations=[normalize_op, horizontally_op, hwc_to_chw, type_cast1],
-                        num_parallel_workers=24)
+            ds = ds.map(input_columns=["image"], operations=[normalize_op, type_cast0, horizontally_op],
+                        num_parallel_workers=12)
             ds = ds.map(input_columns=["image", "image_shape", "box", "label", "valid_num"],
                         operations=flipped_generation, num_parallel_workers=num_parallel_workers)
         else:
-            ds = ds.map(input_columns=["image"], operations=[normalize_op, hwc_to_chw, type_cast1],
-                        num_parallel_workers=24)
+            ds = ds.map(input_columns=["image"], operations=[normalize_op, type_cast0],
+                        num_parallel_workers=12)
+        ds = ds.map(input_columns=["image"], operations=[hwc_to_chw, type_cast1],
+                    num_parallel_workers=12)
 
     else:
         ds = ds.map(input_columns=["image", "annotation"],