optimize fastrcnn training process

model_zoo/faster_rcnn/src/dataset.py

@@ -318,10 +318,6 @@ def preprocess_fn(image, box, is_training):
         else:
             input_data = resize_column(*input_data)
 
-        photo = (np.random.rand() < config.photo_ratio)
-        if photo:
-            input_data = photo_crop_column(*input_data)
-
         input_data = image_bgr_rgb(*input_data)
 
         output_data = input_data
@@ -432,19 +428,19 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fast
         writer.write_raw_data([row])
     writer.commit()
 
+
 def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, device_num=1, rank_id=0,
-                              is_training=True, num_parallel_workers=8):
+                              is_training=True, num_parallel_workers=4):
     """Creatr FasterRcnn dataset with MindDataset."""
     ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank_id,
-                        num_parallel_workers=num_parallel_workers, shuffle=is_training)
+                        num_parallel_workers=1, shuffle=is_training)
     decode = C.Decode()
-    ds = ds.map(input_columns=["image"], operations=decode)
+    ds = ds.map(input_columns=["image"], operations=decode, num_parallel_workers=1)
     compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
 
     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_)
@@ -453,17 +449,18 @@ def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, devi
         ds = ds.map(input_columns=["image", "annotation"],
                     output_columns=["image", "image_shape", "box", "label", "valid_num"],
                     columns_order=["image", "image_shape", "box", "label", "valid_num"],
-                    operations=compose_map_func, num_parallel_workers=4)
-
-        ds = ds.map(input_columns=["image"], operations=[normalize_op, type_cast0],
-                    num_parallel_workers=num_parallel_workers)
+                    operations=compose_map_func, num_parallel_workers=num_parallel_workers)
 
         flip = (np.random.rand() < config.flip_ratio)
         if flip:
-            ds = ds.map(input_columns=["image"], operations=[horizontally_op],
-                        num_parallel_workers=num_parallel_workers)
+            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", "image_shape", "box", "label", "valid_num"],
-                        operations=flipped_generation, num_parallel_workers=4)
+                        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)
+
     else:
         ds = ds.map(input_columns=["image", "annotation"],
                     output_columns=["image", "image_shape", "box", "label", "valid_num"],
@@ -471,11 +468,10 @@ def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, devi
                     operations=compose_map_func,
                     num_parallel_workers=num_parallel_workers)
 
-        ds = ds.map(input_columns=["image"], operations=[normalize_op, type_cast0],
-                    num_parallel_workers=num_parallel_workers)
+        ds = ds.map(input_columns=["image"], operations=[normalize_op, hwc_to_chw, type_cast1],
+                    num_parallel_workers=24)
 
     # transpose_column from python to c
-    ds = ds.map(input_columns=["image"], operations=[hwc_to_chw, type_cast1])
     ds = ds.map(input_columns=["image_shape"], operations=[type_cast1])
     ds = ds.map(input_columns=["box"], operations=[type_cast1])
     ds = ds.map(input_columns=["label"], operations=[type_cast2])