!12208 fix typos in README for maskrcnn and fastercnn

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

model_zoo/official/cv/faster_rcnn/README.md

@@ -90,7 +90,7 @@ Dataset used: [COCO2017](<https://cocodataset.org/>)
         train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
         ```
 
-        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `IMAGE_DIR`(dataset directory) and the relative path in `ANNO_PATH`(the TXT file path), `IMAGE_DIR` and `ANNO_PATH` are setting in `config.py`.
+        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class information of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `IMAGE_DIR`(dataset directory) and the relative path in `ANNO_PATH`(the TXT file path), `IMAGE_DIR` and `ANNO_PATH` are setting in `config.py`.
 
 # Quick Start
 
@@ -242,7 +242,7 @@ Notes:
 
 ### Result
 
-Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in loss_rankid.log.
+Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the following in loss_rankid.log.
 
 ```log
 # distribute training result(8p)
@@ -265,10 +265,12 @@ sh run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
 ```
 
 > checkpoint can be produced in training process.
+>
+> Images size in dataset should be equal to the annotation size in VALIDATION_JSON_FILE, otherwise the evaluation result cannot be displayed properly.
 
 ### Result
 
-Eval result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
+Eval result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the following in log.
 
 ```log
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.360

model_zoo/official/cv/faster_rcnn/README_CN.md

@@ -268,6 +268,8 @@ sh run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
 ```
 
 > 在训练过程中生成检查点。
+>
+> 数据集中图片的数量要和VALIDATION_JSON_FILE文件中标记数量一致，否则精度结果展示格式可能出现异常。
 
 ### 结果
 

model_zoo/official/cv/faster_rcnn/ascend310_infer/src/DvppCommon.cpp

@@ -59,7 +59,7 @@ int DvppCommon::DeInit(void) {
 
     ret = acldvppDestroyChannel(dvppChannelDesc_);
     if (ret != OK) {
-        std::cout << "Failed to destory dvpp channel, ret = " << ret << "." << std::endl;
+        std::cout << "Failed to destroy dvpp channel, ret = " << ret << "." << std::endl;
         return ret;
     }
 
@@ -646,7 +646,7 @@ int DvppCommon::CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat
         return ret;
     }
 
-    // In TransferImageH2D function, device buffer will be alloced to store the input image
+    // In TransferImageH2D function, device buffer will be allocated to store the input image
     // Need to pay attention to release of the buffer
     ret = TransferImageH2D(imageInfo, inputImage_);
     if (ret != OK) {

model_zoo/official/cv/faster_rcnn/src/FasterRcnn/roi_align.py

@@ -24,7 +24,7 @@ from mindspore.common.tensor import Tensor
 
 class ROIAlign(nn.Cell):
     """
-    Extract RoI features from mulitple feature map.
+    Extract RoI features from multiple feature map.
 
     Args:
         out_size_h (int) - RoI height.
@@ -59,7 +59,7 @@ class SingleRoIExtractor(nn.Cell):
     """
     Extract RoI features from a single level feature map.
 
-    If there are mulitple input feature levels, each RoI is mapped to a level
+    If there are multiple input feature levels, each RoI is mapped to a level
     according to its scale.
 
     Args:

model_zoo/official/cv/maskrcnn/README.md

@@ -431,7 +431,7 @@ bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
 
 ### [Training Result](#content)
 
-Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in loss_rankid.log.
+Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the following in loss_rankid.log.
 
 ```bash
 # distribute training result(8p)
@@ -457,10 +457,12 @@ bash run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]
 
 > As for the COCO2017 dataset, VALIDATION_ANN_FILE_JSON is refer to the annotations/instances_val2017.json in the dataset directory.  
 > checkpoint can be produced and saved in training process, whose folder name begins with "train/checkpoint" or "train_parallel*/checkpoint".
+>
+> Images size in dataset should be equal to the annotation size in VALIDATION_ANN_FILE_JSON, otherwise the evaluation result cannot be displayed properly.
 
 ### [Evaluation result](#content)
 
-Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
+Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the following in log.
 
 ```bash
 Evaluate annotation type *bbox*

model_zoo/official/cv/maskrcnn/README_CN.md

@@ -455,6 +455,8 @@ sh run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]
 
 > 关于COCO2017数据集，VALIDATION_ANN_FILE_JSON参考数据集目录下的annotations/instances_val2017.json文件。  
 > 检查点可在训练过程中生成并保存，其文件夹名称以“train/checkpoint”或“train_parallel*/checkpoint”开头。
+>
+> 数据集中图片的数量要和VALIDATION_ANN_FILE_JSON文件中标记数量一致，否则精度结果展示格式可能出现异常。
 
 ### 评估结果
 

model_zoo/official/cv/maskrcnn/ascend310_infer/src/DvppCommon.cpp

@@ -59,7 +59,7 @@ int DvppCommon::DeInit(void) {
 
     ret = acldvppDestroyChannel(dvppChannelDesc_);
     if (ret != OK) {
-        std::cout << "Failed to destory dvpp channel, ret = " << ret << "." << std::endl;
+        std::cout << "Failed to destroy dvpp channel, ret = " << ret << "." << std::endl;
         return ret;
     }
 
@@ -646,7 +646,7 @@ int DvppCommon::CombineJpegdProcess(const RawData& imageInfo, acldvppPixelFormat
         return ret;
     }
 
-    // In TransferImageH2D function, device buffer will be alloced to store the input image
+    // In TransferImageH2D function, device buffer will be allocated to store the input image
     // Need to pay attention to release of the buffer
     ret = TransferImageH2D(imageInfo, inputImage_);
     if (ret != OK) {

model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample.py

@@ -23,7 +23,7 @@ import mindspore.common.dtype as mstype
 
 class BboxAssignSample(nn.Cell):
     """
-    Bbox assigner and sampler defination.
+    Bbox assigner and sampler definition.
 
     Args:
         config (dict): Config.

model_zoo/official/cv/maskrcnn/src/maskrcnn/roi_align.py

@@ -24,7 +24,7 @@ from mindspore.common.tensor import Tensor
 
 class ROIAlign(nn.Cell):
     """
-    Extract RoI features from mulitple feature map.
+    Extract RoI features from mulitiple feature map.
 
     Args:
         out_size_h (int) - RoI height.
@@ -61,7 +61,7 @@ class SingleRoIExtractor(nn.Cell):
     """
     Extract RoI features from a single level feature map.
 
-    If there are mulitple input feature levels, each RoI is mapped to a level
+    If there are multiple input feature levels, each RoI is mapped to a level
     according to its scale.
 
     Args:

model_zoo/official/cv/maskrcnn_mobilenetv1/README.md

@@ -208,7 +208,7 @@ Usage: sh run_standalone_train.sh [PRETRAINED_MODEL]
 "neg_iou_thr": 0.3,                                                      # negative sample threshold after IOU
 "pos_iou_thr": 0.7,                                                      # positive sample threshold after IOU
 "min_pos_iou": 0.3,                                                      # minimal positive sample threshold after IOU
-"num_bboxes": 245520,                                                    # total bbox numner
+"num_bboxes": 245520,                                                    # total bbox number
 "num_gts": 128,                                                          # total ground truth number
 "num_expected_neg": 256,                                                 # negative sample number
 "num_expected_pos": 128,                                                 # positive sample number
@@ -220,7 +220,7 @@ Usage: sh run_standalone_train.sh [PRETRAINED_MODEL]
 # roi_alignj
 "roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2), # ROIAlign parameters
 "roi_align_out_channels": 256,                                                  # ROIAlign out channels size
-"roi_align_featmap_strides": [4, 8, 16, 32],                                    # stride size for differnt level of ROIAling feature map
+"roi_align_featmap_strides": [4, 8, 16, 32],                                    # stride size for different level of ROIAling feature map
 "roi_align_finest_scale": 56,                                                   # finest scale ofr ROIAlign
 "roi_sample_num": 640,                                                          # sample number in ROIAling layer
 
@@ -338,7 +338,7 @@ sh run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
 
 ### [Training Result](#content)
 
-Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in loss_rankid.log.
+Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the following in loss_rankid.log.
 
 ```bash
 # distribute training result(8p)
@@ -369,7 +369,7 @@ sh run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]
 
 ### [Evaluation result](#content)
 
-Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
+Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the following in log.
 
 ```bash
 Evaluate annotation type *bbox*

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/bbox_assign_sample.py

@@ -23,7 +23,7 @@ import mindspore.common.dtype as mstype
 
 class BboxAssignSample(nn.Cell):
     """
-    Bbox assigner and sampler defination.
+    Bbox assigner and sampler definition.
 
     Args:
         config (dict): Config.

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/bbox_assign_sample_stage2.py

@@ -22,7 +22,7 @@ from mindspore.common.tensor import Tensor
 
 class BboxAssignSampleForRcnn(nn.Cell):
     """
-    Bbox assigner and sampler defination.
+    Bbox assigner and sampler definition.
 
     Args:
         config (dict): Config.

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/roi_align.py

@@ -24,7 +24,7 @@ from mindspore.common.tensor import Tensor
 
 class ROIAlign(nn.Cell):
     """
-    Extract RoI features from mulitple feature map.
+    Extract RoI features from multiple feature map.
 
     Args:
         out_size_h (int) - RoI height.
@@ -61,7 +61,7 @@ class SingleRoIExtractor(nn.Cell):
     """
     Extract RoI features from a single level feature map.
 
-    If there are mulitple input feature levels, each RoI is mapped to a level
+    If there are multiple input feature levels, each RoI is mapped to a level
     according to its scale.
 
     Args: