some words misspelled,it has modified

model_zoo/official/cv/yolov3_darknet53/eval.py

@@ -87,7 +87,7 @@ class DetectionEngine:
 
     def _nms(self, predicts, threshold):
         """Calculate NMS."""
-        # conver xywh -> xmin ymin xmax ymax
+        # convert xywh -> xmin ymin xmax ymax
         x1 = predicts[:, 0]
         y1 = predicts[:, 1]
         x2 = x1 + predicts[:, 2]
@@ -111,8 +111,8 @@ class DetectionEngine:
             intersect_area = intersect_w * intersect_h
             ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
 
-            indexs = np.where(ovr <= threshold)[0]
+            indexes = np.where(ovr <= threshold)[0]
-            order = order[indexs + 1]
+            order = order[indexes + 1]
         return reserved_boxes
 
     def write_result(self):
@@ -179,7 +179,7 @@ class DetectionEngine:
 
                 x_top_left = x - w / 2.
                 y_top_left = y - h / 2.
-                # creat all False
+                # create all False
                 flag = np.random.random(cls_emb.shape) > sys.maxsize
                 for i in range(flag.shape[0]):
                     c = cls_argmax[i]

model_zoo/official/cv/yolov3_darknet53/scripts/run_eval.sh

@@ -58,7 +58,7 @@ cp ../*.py ./eval
 cp -r ../src ./eval
 cd ./eval || exit
 env > env.log
-echo "start infering for device $DEVICE_ID"
+echo "start inferring for device $DEVICE_ID"
 python eval.py \
     --data_dir=$DATASET_PATH \
     --pretrained=$CHECKPOINT_PATH \

model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh

@@ -58,7 +58,7 @@ cp ../*.py ./eval
 cp -r ../src ./eval
 cd ./eval || exit
 env > env.log
-echo "start infering for device $DEVICE_ID"
+echo "start inferring for device $DEVICE_ID"
 python eval.py \
     --device_target="GPU" \
     --data_dir=$DATASET_PATH \

model_zoo/official/cv/yolov3_darknet53/src/convert_weight.py

@@ -39,7 +39,7 @@ def build_network():
 
 
 def convert(weights_file, output_file):
-    """Conver weight to mindspore ckpt."""
+    """Convert weight to mindspore ckpt."""
     params = build_network()
     weights = load_weight(weights_file)
     index = 0

model_zoo/official/cv/yolov3_darknet53/src/yolo.py

@@ -59,7 +59,7 @@ class YoloBlock(nn.Cell):
 
     Args:
         in_channels: Integer. Input channel.
-        out_chls: Interger. Middle channel.
+        out_chls: Integer. Middle channel.
         out_channels: Integer. Output channel.
 
     Returns:
@@ -108,7 +108,7 @@ class YOLOv3(nn.Cell):
      Args:
          backbone_shape: List. Darknet output channels shape.
          backbone: Cell. Backbone Network.
-         out_channel: Interger. Output channel.
+         out_channel: Integer. Output channel.
 
      Returns:
          Tensor, output tensor.

model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py

@@ -45,7 +45,7 @@ def has_valid_annotation(anno):
     # if all boxes have close to zero area, there is no annotation
     if _has_only_empty_bbox(anno):
         return False
-    # keypoints task have a slight different critera for considering
+    # keypoints task have a slight different criteria for considering
     # if an annotation is valid
     if "keypoints" not in anno[0]:
         return True

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -131,9 +131,7 @@ def conver_training_shape(args):
     return training_shape
 
 
-def train():
+def network_init(args):
-    """Train function."""
-    args = parse_args()
     devid = int(os.getenv('DEVICE_ID', '0'))
     context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
                         device_target=args.device_target, save_graphs=True, device_id=devid)
@@ -145,26 +143,21 @@ def train():
             init("nccl")
         args.rank = get_rank()
         args.group_size = get_group_size()
-    # select for master rank save ckpt or all rank save, compatiable for model parallel
+    # select for master rank save ckpt or all rank save, compatible for model parallel
     args.rank_save_ckpt_flag = 0
     if args.is_save_on_master:
         if args.rank == 0:
             args.rank_save_ckpt_flag = 1
     else:
         args.rank_save_ckpt_flag = 1
-
     # logger
     args.outputs_dir = os.path.join(args.ckpt_path,
                                     datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
     args.logger = get_logger(args.outputs_dir, args.rank)
     args.logger.save_args(args)
 
-    if args.need_profiler:
-        from mindspore.profiler.profiling import Profiler
-        profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
-
-    loss_meter = AverageMeter('loss')
 
+def parallel_init(args):
     context.reset_auto_parallel_context()
     parallel_mode = ParallelMode.STAND_ALONE
     degree = 1
@@ -173,6 +166,17 @@ def train():
         degree = get_group_size()
     context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=degree)
 
+def train():
+    """Train function."""
+    args = parse_args()
+    network_init(args)
+    if args.need_profiler:
+        from mindspore.profiler.profiling import Profiler
+        profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
+
+    loss_meter = AverageMeter('loss')
+    parallel_init(args)
+
     network = YOLOV3DarkNet53(is_training=True)
     # default is kaiming-normal
     default_recurisive_init(network)
@@ -182,7 +186,6 @@ def train():
     args.logger.info('finish get network')
 
     config = ConfigYOLOV3DarkNet53()
-
     config.label_smooth = args.label_smooth
     config.label_smooth_factor = args.label_smooth_factor
 
@@ -202,7 +205,6 @@ def train():
         args.ckpt_interval = args.steps_per_epoch
 
     lr = get_lr(args)
-
     opt = Momentum(params=get_param_groups(network),
                    learning_rate=Tensor(lr),
                    momentum=args.momentum,
@@ -281,7 +283,6 @@ def train():
             if i == 10:
                 profiler.analyse()
                 break
-
     args.logger.info('==========end training===============')