!13815 fixed the code spell errors

From: @oacjiewen
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian

model_zoo/official/cv/FCN8s/scripts/run_eval.sh

@@ -16,7 +16,7 @@
 
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_distribute_eval.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_PATH"
 echo "for example: sh run_eval.sh [RANK_TABLE_FILE] /path/to/dataset /path/to/ckpt device_id"
 echo "It is better to use absolute path."

model_zoo/official/cv/alexnet/eval.py

@@ -76,7 +76,7 @@ if __name__ == "__main__":
         model = Model(network, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
 
     else:
-        raise ValueError("Unsupport dataset.")
+        raise ValueError("Unsupported dataset.")
 
     if ds_eval.get_dataset_size() == 0:
         raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")

model_zoo/official/cv/alexnet/train.py

@@ -64,7 +64,7 @@ if __name__ == "__main__":
     elif args.dataset_name == "imagenet":
         cfg = alexnet_imagenet_cfg
     else:
-        raise ValueError("Unsupport dataset.")
+        raise ValueError("Unsupported dataset.")
 
     device_target = args.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
@@ -92,7 +92,7 @@ if __name__ == "__main__":
     elif args.dataset_name == "imagenet":
         ds_train = create_dataset_imagenet(args.data_path, cfg.batch_size)
     else:
-        raise ValueError("Unsupport dataset.")
+        raise ValueError("Unsupported dataset.")
 
     if ds_train.get_dataset_size() == 0:
         raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
@@ -124,7 +124,7 @@ if __name__ == "__main__":
             loss_scale_manager = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
 
     else:
-        raise ValueError("Unsupport dataset.")
+        raise ValueError("Unsupported dataset.")
 
     if device_target == "Ascend":
         model = Model(network, loss_fn=loss, optimizer=opt, metrics=metrics, amp_level="O2", keep_batchnorm_fp32=False,

model_zoo/official/cv/centerface/src/var_init.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""weight initilization"""
+"""weight initialization"""
 
 import math
 import numpy as np

model_zoo/official/cv/psenet/src/ETSNET/pse/adaptor.cpp

@@ -35,21 +35,21 @@ using cv::Point;
 namespace py = pybind11;
 
 namespace pse_adaptor {
-    void get_kernals(const int *data, vector<int64> data_shape, vector<Mat> *kernals) {
+    void get_kernels(const int *data, vector<int64> data_shape, vector<Mat> *kernels) {
         for (int i = 0; i < data_shape[0]; ++i) {
-            Mat kernal = Mat::zeros(data_shape[1], data_shape[2], CV_8UC1);
-            for (int x = 0; x < kernal.rows; ++x) {
-                for (int y = 0; y < kernal.cols; ++y) {
-                    kernal.at<char>(x, y) = data[i * data_shape[1] * data_shape[2] + x * data_shape[2] + y];
+            Mat kernel = Mat::zeros(data_shape[1], data_shape[2], CV_8UC1);
+            for (int x = 0; x < kernel.rows; ++x) {
+                for (int y = 0; y < kernel.cols; ++y) {
+                    kernel.at<char>(x, y) = data[i * data_shape[1] * data_shape[2] + x * data_shape[2] + y];
                 }
             }
-            kernals->emplace_back(kernal);
+            kernels->emplace_back(kernel);
         }
     }
 
-    void growing_text_line(const vector<Mat> &kernals, vector<vector<int>> *text_line, float min_area) {
+    void growing_text_line(const vector<Mat> &kernels, vector<vector<int>> *text_line, float min_area) {
         Mat label_mat;
-        int label_num = connectedComponents(kernals[kernals.size() - 1], label_mat, 4);
+        int label_num = connectedComponents(kernels[kernels.size() - 1], label_mat, 4);
         vector<int> area(label_num + 1, 0);
         for (int x = 0; x < label_mat.rows; ++x) {
             for (int y = 0; y < label_mat.cols; ++y) {
@@ -76,7 +76,7 @@ namespace pse_adaptor {
         int dx[] = {-1, 1, 0, 0};
         int dy[] = {0, 0, -1, 1};
 
-        for (int kernal_id = kernals.size() - 2; kernal_id >= 0; --kernal_id) {
+        for (int kernal_id = kernels.size() - 2; kernal_id >= 0; --kernal_id) {
             while (!queue.empty()) {
                 Point point = queue.front();
                 queue.pop();
@@ -90,7 +90,7 @@ namespace pse_adaptor {
 
                     if (tmp_x < 0 || tmp_x >= static_cast<int>(text_line->size())) continue;
                     if (tmp_y < 0 || tmp_y >= static_cast<int>(text_line->at(1).size())) continue;
-                    if (kernals[kernal_id].at<char>(tmp_x, tmp_y) == 0) continue;
+                    if (kernels[kernal_id].at<char>(tmp_x, tmp_y) == 0) continue;
                     if (text_line->at(tmp_x)[tmp_y] > 0) continue;
 
                     Point point_tmp(tmp_x, tmp_y);
@@ -110,10 +110,10 @@ namespace pse_adaptor {
     vector<vector<int>> pse(py::array_t<int, py::array::c_style | py::array::forcecast> quad_n9, float min_area) {
         auto buf = quad_n9.request();
         auto data = static_cast<int *>(buf.ptr);
-        vector<Mat> kernals;
-        get_kernals(data, buf.shape, &kernals);
+        vector<Mat> kernels;
+        get_kernels(data, buf.shape, &kernels);
         vector<vector<int>> text_line;
-        growing_text_line(kernals, &text_line, min_area);
+        growing_text_line(kernels, &text_line, min_area);
 
         return text_line;
     }

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

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "for example: sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json /opt/ssd-300.ckpt(optional) 200(optional)"
 echo "It is better to use absolute path."
@@ -33,7 +33,7 @@ BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
 cd $BASE_PATH/../ || exit
 python train.py --only_create_dataset=True --dataset=$4
 
-echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
+echo "After running the script, the network runs in the background. The log will be generated in LOGx/log.txt"
 
 export RANK_SIZE=$1
 EPOCH_SIZE=$2

model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_distribute_train_gpu.sh DEVICE_NUM EPOCH_SIZE LR DATASET PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "for example: sh run_distribute_train_gpu.sh 8 500 0.2 coco /opt/ssd-300.ckpt(optional) 200(optional)"
 echo "It is better to use absolute path."
@@ -33,7 +33,7 @@ BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
 cd $BASE_PATH/../ || exit
 python train.py --only_create_dataset=True --run_platform="GPU" --dataset=$4
 
-echo "After running the scipt, the network runs in the background. The log will be generated in LOG/log.txt"
+echo "After running the script, the network runs in the background. The log will be generated in LOG/log.txt"
 
 export RANK_SIZE=$1
 EPOCH_SIZE=$2

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

@@ -57,7 +57,7 @@ cp ./*.py ./eval$3
 cp -r ./src ./eval$3
 cd ./eval$3 || exit
 env > env.log
-echo "start infering for device $DEVICE_ID"
+echo "start inferring for device $DEVICE_ID"
 python eval.py \
     --dataset=$DATASET \
     --checkpoint_path=$CHECKPOINT_PATH \

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

@@ -57,7 +57,7 @@ cp ./*.py ./eval$3
 cp -r ./src ./eval$3
 cd ./eval$3 || exit
 env > env.log
-echo "start infering for device $DEVICE_ID"
+echo "start inferring for device $DEVICE_ID"
 python eval.py \
     --dataset=$DATASET \
     --checkpoint_path=$CHECKPOINT_PATH \

model_zoo/official/nlp/bert_thor/scripts/run_distribute_pretrain.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR RANK_TABLE_FILE"
 echo "for example: bash run_distribute_pretrain.sh 8 1 /path/zh-wiki/ /path/Schema.json /path/hccl.json"
 echo "It is better to use absolute path."

model_zoo/official/nlp/bert_thor/scripts/run_standalone_pretrain.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR"
 echo "for example: bash run_standalone_pretrain.sh 0 40 /path/zh-wiki/ /path/Schema.json"
 echo "=============================================================================================================="

model_zoo/official/nlp/bert_thor/src/model_thor.py

@@ -585,7 +585,7 @@ class Model:
                                      returned and passed to the network. Otherwise, a tuple (data, label) should
                                      be returned, and the data and label are passed to the network and loss
                                      function respectively.
-            callbacks (list): List of callback object. Callbacks which should be excuted while training. Default: None.
+            callbacks (list): List of callback object. Callbacks which should be executed while training. Default: None.
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
                                       Configure pynative mode, the training process will be performed with
                                       dataset not sink.
@@ -704,7 +704,7 @@ class Model:
 
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.
-            callbacks (list): List of callback object. Callbacks which should be excuted
+            callbacks (list): List of callback object. Callbacks which should be executed
                               while training. Default: None.
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
 

model_zoo/official/nlp/bert_thor/src/utils.py

@@ -65,7 +65,7 @@ def make_directory(path: str):
     """Make directory."""
     if path is None or not isinstance(path, str) or path.strip() == "":
         logger.error("The path(%r) is invalid type.", path)
-        raise TypeError("Input path is invaild type")
+        raise TypeError("Input path is invalid type")
 
     # convert the relative paths
     path = os.path.realpath(path)

model_zoo/official/nlp/fasttext/README.md

@@ -25,9 +25,9 @@
 # [FastText](#contents)
 
 FastText is a fast text classification algorithm, which is simple and efficient. It was proposed by Armand
-Joulin, Tomas Mikolov etc. in the artical "Bag of Tricks for Efficient Text Classification" in 2016. It is similar to
+Joulin, Tomas Mikolov etc. in the article "Bag of Tricks for Efficient Text Classification" in 2016. It is similar to
 CBOW in model architecture, where the middle word is replace by a label. FastText adopts ngram feature as addition feature
-to get some information about words. It speeds up training and testing while maintaining high percision, and widly used
+to get some information about words. It speeds up training and testing while maintaining high precision, and widly used
 in various tasks of text classification.
 
 [Paper](https://arxiv.org/pdf/1607.01759.pdf): "Bag of Tricks for Efficient Text Classification", 2016, A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov

model_zoo/official/nlp/fasttext/scripts/create_dataset.sh

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ============================================================================
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh create_dataset.sh SOURCE_DATASET_PATH DATASET_NAME"
 echo "for example: sh create_dataset.sh /home/workspace/ag_news_csv ag"
 echo "DATASET_NAME including ag, dbpedia, and yelp_p"

model_zoo/official/nlp/fasttext/scripts/run_distribute_train_8p.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_distributed_train.sh DATASET_PATH RANK_TABLE_PATH"
 echo "for example: sh run_distributed_train.sh /home/workspace/ag /home/workspace/rank_table_file.json"
 echo "It is better to use absolute path."

model_zoo/official/nlp/fasttext/scripts/run_eval.sh

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ============================================================================
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_eval.sh DATASET_PATH DATASET_NAME MODEL_CKPT"
 echo "for example: sh run_eval.sh /home/workspace/ag/test*.mindrecord ag device0/ckpt0/fasttext-5-118.ckpt"
 echo "It is better to use absolute path."

model_zoo/official/nlp/fasttext/scripts/run_standalone_train.sh

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ============================================================================
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "sh run_standalone_train.sh DATASET_PATH"
 echo "for example: sh run_standalone_train.sh /home/workspace/ag"
 echo "It is better to use absolute path."

model_zoo/official/nlp/fasttext/src/fasttext_model.py

@@ -62,7 +62,7 @@ class FastText(nn.Cell):
         embeding = self.realdiv(embeding, src_token_length)
 
         embeding = self.cast(embeding, mstype.float16)
-        classifer = self.fc(embeding)
-        classifer = self.cast(classifer, mstype.float32)
+        classifier = self.fc(embeding)
+        classifier = self.cast(classifier, mstype.float32)
 
-        return classifer
+        return classifier

model_zoo/official/nlp/fasttext/train.py

@@ -184,7 +184,7 @@ def train_paralle(input_file_path):
         input_file_path: preprocessed dataset path
     """
     set_parallel_env()
-    print("Starting traning on mutiple devices. |~ _ ~| |~ _ ~| |~ _ ~| |~ _ ~|")
+    print("Starting traning on multiple devices. |~ _ ~| |~ _ ~| |~ _ ~| |~ _ ~|")
     preprocessed_data = load_dataset(dataset_path=input_file_path,
                                      batch_size=config.batch_size,
                                      epoch_count=config.epoch_count,

model_zoo/official/nlp/gpt/scripts/pre_process.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash script/pre_process.sh \"INPUT_GLOB\" DATASET_TYPE OUTPUT_FILE"
 echo "for example: bash script/pre_process.sh \"dataset/*.output\" openwebtext ./output/openwebtext.mindrecord"
 echo "=============================================================================================================="

model_zoo/official/nlp/gpt/scripts/run_distribute_train.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_distributed_pretrain_ascend.sh DATA_DIR RANK_TABLE_FILE DEVICE_NUM"
 echo "for example: bash run_distributed_pretrain_ascend.sh /path/dataset /path/hccl.json 8"
 echo "It is better to use absolute path."

model_zoo/official/nlp/gpt/scripts/run_evaluation.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash scripts/run_evaluation.sh TASK_TYPE CKPT_PATH DATA_PATH METRICS"
 echo "for example: bash scripts/run_evaluation.sh lambada /your/ckpt /your/data acc"
 echo "=============================================================================================================="

model_zoo/official/nlp/gpt/scripts/run_standalone_train.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_pretrain_ascend.sh DEVICE_ID EPOCH_SIZE DATA_DIR"
 echo "for example: bash run_standalone_pretrain_ascend.sh 0 40 /path/zh-wiki/"
 echo "=============================================================================================================="

model_zoo/official/nlp/gpt/src/dataset.py

@@ -14,7 +14,7 @@
 # ============================================================================
 
 """
-Create dataset for training and evaluting
+Create dataset for training and evaluating
 """
 
 import os