fix fasterrcnn performance issue

model_zoo/official/cv/faster_rcnn/src/config.py

@@ -129,6 +129,10 @@ config = ed({
     "keep_checkpoint_max": 10,
     "save_checkpoint_path": "./",
 
+    # Number of threads used to process the dataset in parallel
+    "num_parallel_workers": 8,
+    # Parallelize Python operations with multiple worker processes
+    "python_multiprocessing": True,
     "mindrecord_dir": "../MindRecord_COCO_TRAIN",
     "coco_root": "./cocodataset/",
     "train_data_type": "train2017",

model_zoo/official/cv/faster_rcnn/src/dataset.py

@@ -457,7 +457,7 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fast
 
 
 def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_id=0, is_training=True,
-                              num_parallel_workers=8):
+                              num_parallel_workers=8, python_multiprocessing=False):
     """Create FasterRcnn dataset with MindDataset."""
     cv2.setNumThreads(0)
     de.config.set_prefetch_size(8)
@@ -471,7 +471,7 @@ def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_
         ds = ds.map(input_columns=["image", "annotation"],
                     output_columns=["image", "image_shape", "box", "label", "valid_num"],
                     column_order=["image", "image_shape", "box", "label", "valid_num"],
-                    operations=compose_map_func, python_multiprocessing=False,
+                    operations=compose_map_func, python_multiprocessing=python_multiprocessing,
                     num_parallel_workers=num_parallel_workers)
         ds = ds.batch(batch_size, drop_remainder=True)
     else:

model_zoo/official/cv/faster_rcnn/train.py

@@ -113,7 +113,9 @@ if __name__ == '__main__':
 
     # When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
     dataset = create_fasterrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
-                                        device_num=device_num, rank_id=rank)
+                                        device_num=device_num, rank_id=rank,
+                                        num_parallel_workers=config.num_parallel_workers,
+                                        python_multiprocessing=config.python_multiprocessing)
 
     dataset_size = dataset.get_dataset_size()
     print("Create dataset done!")

model_zoo/official/cv/unet/scripts/run_infer_310.sh

@@ -70,7 +70,7 @@ function preprocess_data()
 
 function compile_app()
 {
-    cd ../ascend310_infer/ || exit
+    cd ../ascend310_infer/src || exit
     if [ -f "Makefile" ]; then
         make clean
     fi

model_zoo/research/cv/FaceRecognition/eval.py

@@ -328,6 +328,6 @@ if __name__ == '__main__':
     log_path = os.path.join(arg.ckpt_path, 'logs')
     arg.logger = get_logger(log_path, arg.local_rank)
 
-    arg.logger.info('Config: {}'.format(pformat(arg)))
+    arg.logger.info('Config: %s', pformat(arg))
 
     main(arg)

model_zoo/research/cv/FaceRecognitionForTracking/train.py

@@ -90,13 +90,13 @@ def main():
     cfg.logger.info('start create dataloader')
     de_dataset, steps_per_epoch, class_num = get_de_dataset(cfg)
     cfg.steps_per_epoch = steps_per_epoch
-    cfg.logger.info('step per epoch: {}'.format(cfg.steps_per_epoch))
+    cfg.logger.info('step per epoch: %s', cfg.steps_per_epoch)
     de_dataloader = de_dataset.create_tuple_iterator()
-    cfg.logger.info('class num original: {}'.format(class_num))
+    cfg.logger.info('class num original: %s', class_num)
     if class_num % 16 != 0:
         class_num = (class_num // 16 + 1) * 16
     cfg.class_num = class_num
-    cfg.logger.info('change the class num to: {}'.format(cfg.class_num))
+    cfg.logger.info('change the class num to: %s', cfg.class_num)
     cfg.logger.info('end create dataloader')
 
     # backbone and loss