fix network spell error.

model_zoo/official/cv/inceptionv4/README.md

@@ -152,7 +152,7 @@ sh scripts/run_distribute_train_gpu.sh DATA_PATH
 
 ### Result
 
-Training result will be stored in the example path. Checkpoints will be stored at `ckpt_path` by default, and training log  will be redirected to `./log.txt` like followings.
+Training result will be stored in the example path. Checkpoints will be stored at `ckpt_path` by default, and training log  will be redirected to `./log.txt` like following.
 
 - Ascend
 
@@ -209,7 +209,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 
 ### Result
 
-Evaluation result will be stored in the example path, you can find result like the followings in `eval.log`.
+Evaluation result will be stored in the example path, you can find result like the following in `eval.log`.
 
 - Ascend
 

model_zoo/official/cv/yolov4/test.py

@@ -243,7 +243,7 @@ class DetectionEngine():
                     self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
 
 
-def conver_testing_shape(args_test):
+def convert_testing_shape(args_test):
     testing_shape = [int(args_test.testing_shape), int(args_test.testing_shape)]
     return testing_shape
 
@@ -296,7 +296,7 @@ def test():
 
     config = ConfigYOLOV4CspDarkNet53()
     if args.testing_shape:
-        config.test_img_shape = conver_testing_shape(args)
+        config.test_img_shape = convert_testing_shape(args)
 
     data_txt = os.path.join(args.data_dir, 'testdev2017.txt')
     ds, data_size = create_yolo_datasetv2(data_root, data_txt=data_txt, batch_size=args.per_batch_size,

model_zoo/research/cv/FaceDetection/src/FaceDetection/voc_wrapper.py

@@ -70,8 +70,8 @@ def nms(boxes, threshold=0.5):
         intersect_area = intersect_w * intersect_h
         ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
 
-        indexes = np.where(ovr <= threshold)[0]
-        order = order[indexes + 1]
+        indices = np.where(ovr <= threshold)[0]
+        order = order[indices + 1]
 
     return reserved_boxes