fix doc and comment spell mistakes

model_zoo/official/cv/cnnctc/README_CN.md

@@ -261,7 +261,7 @@ bash scripts/run_eval_ascend.sh $TRAINED_CKPT
 
 ### 训练性能
 
-| 参数 | Faster R-CNN |
+| 参数 | CNNCTC |
 | -------------------------- | ----------------------------------------------------------- |
 | 模型版本 | V1 |
 | 资源 | Ascend 910；CPU 2.60GHz，192核；内存：755G |
@@ -278,7 +278,7 @@ bash scripts/run_eval_ascend.sh $TRAINED_CKPT
 
 ### 评估性能
 
-| 参数 | Faster R-CNN |
+| 参数 | CNNCTC |
 | ------------------- | --------------------------- |
 | 模型版本 | V1 |
 | 资源 | Ascend 910 |

model_zoo/official/cv/densenet121/scripts/run_distribute_eval.sh

@@ -15,13 +15,13 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh scripts/run_distribute_eval.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_PATH"
 echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json /path/to/dataset /path/to/ckpt"
 echo "It is better to use absolute path."
 echo "================================================================================================================="
 
-echo "After running the scipt, the network runs in the background. The log will be generated in eval_x/log.txt"
+echo "After running the script, the network runs in the background. The log will be generated in eval_x/log.txt"
 
 export RANK_SIZE=$1
 export RANK_TABLE_FILE=$2
@@ -37,7 +37,7 @@ do
     cp -r ./src ./eval_$i
     cd ./eval_$i || exit
     export RANK_ID=$i
-    echo "start infering for rank $i, device $DEVICE_ID"
+    echo "start inferring for rank $i, device $DEVICE_ID"
     env > env.log
     python eval.py  \
     --data_dir=$DATASET  \

model_zoo/official/cv/densenet121/scripts/run_distribute_train.sh

@@ -15,13 +15,13 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh scripts/run_distribute_train.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_FILE"
 echo "for example: sh scripts/run_distribute_train.sh 8 /data/hccl.json /path/to/dataset ckpt_file"
 echo "It is better to use absolute path."
 echo "================================================================================================================="
 
-echo "After running the scipt, the network runs in the background. The log will be generated in train_x/log.txt"
+echo "After running the script, the network runs in the background. The log will be generated in train_x/log.txt"
 
 export RANK_SIZE=$1
 export RANK_TABLE_FILE=$2

model_zoo/official/cv/densenet121/src/network/densenet.py

@@ -121,7 +121,7 @@ class _DenseBlock(nn.Cell):
 
 class _Transition(nn.Cell):
     """
-    the transiton layer
+    the transition layer
     """
     def __init__(self, num_input_features, num_output_features):
         super(_Transition, self).__init__()
@@ -203,7 +203,7 @@ def _densenet201(**kwargs):
 
 class DenseNet121(nn.Cell):
     """
-    the densenet121 architectur
+    the densenet121 architecture
     """
     def __init__(self, num_classes, include_top=True):
         super(DenseNet121, self).__init__()

model_zoo/official/cv/inceptionv4/README.md

@@ -139,7 +139,7 @@ sh scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR
 
 ### 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.
 
 ```python
 epoch: 1 step: 1251, loss is 5.4833196
@@ -175,7 +175,7 @@ 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`.
 
 ```python
 metric: {'Loss': 0.9849, 'Top1-Acc':0.7985, 'Top5-Acc':0.9460}

model_zoo/official/cv/yolov4/eval.py

@@ -115,7 +115,7 @@ 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]
@@ -139,13 +139,13 @@ class DetectionEngine:
             intersect_area = intersect_w * intersect_h
             ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
 
-            indexs = np.where(ovr <= threshold)[0]
-            order = order[indexs + 1]
+            indexes = np.where(ovr <= threshold)[0]
+            order = order[indexes + 1]
         return reserved_boxes
 
     def _diou_nms(self, dets, thresh=0.5):
         """
-        conver xywh -> xmin ymin xmax ymax
+        convert xywh -> xmin ymin xmax ymax
         """
         x1 = dets[:, 0]
         y1 = dets[:, 1]
@@ -248,7 +248,7 @@ 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/yolov4/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/yolov4/scripts/run_test.sh

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

model_zoo/official/cv/yolov4/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:
@@ -111,7 +111,7 @@ class YOLOv4(nn.Cell):
          feature_shape: List. Input image shape, [N,C,H,W].
          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/yolov4/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/yolov4/test.py

@@ -107,7 +107,7 @@ class DetectionEngine():
 
     def _nms(self, dets, thresh):
         """nms function"""
-        # conver xywh -> xmin ymin xmax ymax
+        # convert xywh -> xmin ymin xmax ymax
         x1 = dets[:, 0]
         y1 = dets[:, 1]
         x2 = x1 + dets[:, 2]
@@ -137,7 +137,7 @@ class DetectionEngine():
 
 
     def _diou_nms(self, dets, thresh=0.5):
-        """conver xywh -> xmin ymin xmax ymax"""
+        """convert xywh -> xmin ymin xmax ymax"""
         x1 = dets[:, 0]
         y1 = dets[:, 1]
         x2 = x1 + dets[:, 2]
@@ -223,7 +223,7 @@ 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/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)
 
-        indexs = np.where(ovr <= threshold)[0]
-        order = order[indexs + 1]
+        indexes = np.where(ovr <= threshold)[0]
+        order = order[indexes + 1]
 
     return reserved_boxes
 

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

@@ -40,7 +40,7 @@ def prepare_file_paths():
     image_names = []
     for dataset_root in dataset_root_list:
         if not os.path.isdir(dataset_root):
-            raise ValueError("dataset root is unvalid!")
+            raise ValueError("dataset root is invalid!")
         anno_dir = os.path.join(dataset_root, "Annotations")
         image_dir = os.path.join(dataset_root, "JPEGImages")
         if is_train:

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

@@ -38,7 +38,7 @@ def prepare_file_paths():
     anno_files = []
     for dataset_root in dataset_root_list:
         if not os.path.isdir(dataset_root):
-            raise ValueError("dataset root is unvalid!")
+            raise ValueError("dataset root is invalid!")
         anno_dir = os.path.join(dataset_root, "Annotations")
         image_dir = os.path.join(dataset_root, "JPEGImages")
         if is_train:

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

@@ -39,7 +39,7 @@ def prepare_file_paths():
     anno_files = []
     for dataset_root in dataset_root_list:
         if not os.path.isdir(dataset_root):
-            raise ValueError("dataset root is unvalid!")
+            raise ValueError("dataset root is invalid!")
         anno_dir = os.path.join(dataset_root, "Annotations")
         image_dir = os.path.join(dataset_root, "JPEGImages")
         if is_train:

model_zoo/research/cv/FaceRecognition/eval.py

@@ -139,7 +139,7 @@ def get_model(args):
         load_param_into_net(net, param_dict_new)
         args.logger.info('INFO, ------------- load model success--------------')
     else:
-        args.logger.info('ERROR, not supprot file:{}, please check weight in config.py'.format(args.weight))
+        args.logger.info('ERROR, not support file:{}, please check weight in config.py'.format(args.weight))
         return 0
     net.set_train(False)
     return net