[] (#3205)

Signed-off-by: Yulv-git <yulvchi@qq.com>

Author: Yulv-git

EISeg/docs/remote_sensing.md

@@ -41,7 +41,7 @@ conda install gdal
 
 ### 2.1 数据加载
 
-目前EISeg仅支持了*.tif/tiff图像后缀的的遥感影像读取，由于训练数据都是来自于RGB三通道的遥感图像切片，因此交互分割也仅在RGB三通道上完成，也就表示EISeg支持多波段数据的波段选择。
+目前EISeg仅支持了*.tif/tiff图像后缀的遥感影像读取，由于训练数据都是来自于RGB三通道的遥感图像切片，因此交互分割也仅在RGB三通道上完成，也就表示EISeg支持多波段数据的波段选择。
 
 当使用EISeg打开GTiff图像时，会获取当前波段数，可通过波段设置的下拉列表进行设置。默认为[b1, b1, b1]。下例展示的是天宫一号多光谱数据设置真彩色：
 
@@ -63,7 +63,7 @@ conda install gdal
 当打开标注的GTiff图像带有地理参考，可设置EISeg保存时保存为带有地理参考的GTiff和ESRI Shapefile。
 
 - GTiff：已成为GIS和卫星遥感应用的行业图像标准文件。
-- ESRI Shapefile：是最常见的的矢量数据格式，Shapefile文件是美国环境系统研究所（ESRI）所研制的GIS文件系统格式文件，是工业标准的矢量数据文件。 所有的商业和开源GIS软件都支持。无处不在的它已成为行业标准。
+- ESRI Shapefile：是最常见的矢量数据格式，Shapefile文件是美国环境系统研究所（ESRI）所研制的GIS文件系统格式文件，是工业标准的矢量数据文件。 所有的商业和开源GIS软件都支持。无处不在的它已成为行业标准。
 
 ![82jlu-no59o](https://user-images.githubusercontent.com/71769312/141137726-76457454-5e9c-4ad0-85d6-d03f658ee63c.gif)
 

Matting/deploy/human_matting_android_demo/README.md

@@ -63,7 +63,7 @@ This demo uses the MODNet with HRNet_W18 backbone to perform human matting. Plea
 
 In order to be able to infer on Android phones, the dynamic graph model needs to be exported as a static graph model, and the input size of the image should be fixed when exporting.
 
-First, update the [PaddleSeg](https://github.com/paddlepaddle/paddleseg/tree/develop) repository. Then `cd` to the `PaddleSeg/contrib/Matting` directory. Then put the downloaded modnet-hrnet_w18.pdparams (traing by youself is ok) on current directory（`PaddleSeg/contrib/Matting`). After that, fix the config file `configs/modnet_mobilenetv2.yml`(note: hrnet18 is used, but the config file `modnet_hrnet_w18.yml` is based on `modnet_mobilenetv2.yml`), where,modify the val_dataset field as follows:
+First, update the [PaddleSeg](https://github.com/paddlepaddle/paddleseg/tree/develop) repository. Then `cd` to the `PaddleSeg/contrib/Matting` directory. Then put the downloaded modnet-hrnet_w18.pdparams (training by youself is ok) on current directory（`PaddleSeg/contrib/Matting`). After that, fix the config file `configs/modnet_mobilenetv2.yml`(note: hrnet18 is used, but the config file `modnet_hrnet_w18.yml` is based on `modnet_mobilenetv2.yml`), where,modify the val_dataset field as follows:
 
 ``` yml
 val_dataset:

Matting/ppmatting/models/backbone/resnet_vd.py

@@ -272,7 +272,7 @@ def __init__(self,
                         block] if dilation_dict and block in dilation_dict else 1
 
                     # Actually block here is 'stage', and i is 'block' in 'stage'
-                    # At the stage 4, expand the the dilation_rate if given multi_grid
+                    # At the stage 4, expand the dilation_rate if given multi_grid
                     if block == 3:
                         dilation_rate = dilation_rate * multi_grid[i]
                     ###############################################################################

configs/pssl/README.md

@@ -24,7 +24,7 @@ Here we show the configuration files of two lightweight models, [STDC2](https://
 
 There is no need to do this step if [STDC2](https://paddleseg.bj.bcebos.com/dygraph/pssl/stdc2_pssl_pretrained/model.pdparams) and [PPLite-Seg-B](https://paddleseg.bj.bcebos.com/dygraph/pssl/pp_liteseg_stdc2_pssl_pretrained/model.pdparams) are used.
 
-Otherwise, checking the following steps for preparing the enviroment and the datasets:
+Otherwise, checking the following steps for preparing the environment and the datasets:
 
 1. Make sure that PaddleSeg is well installed. See the [installation guide](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/install.md) for details.
 

contrib/DomainAdaptation/models/gscnn.py

@@ -154,7 +154,7 @@ def forward(self, x, feat_list, s_input):
             input_shape[2:],
             mode='bilinear',
             align_corners=self.align_corners)
-        cs = F.sigmoid(cs)  # Ouput of shape stream
+        cs = F.sigmoid(cs)  # Output of shape stream
 
         return [cs, ]
 

contrib/LaneSeg/README.md

@@ -217,7 +217,7 @@ python export.py --help
 python deploy/python/infer.py \
     --config output/export/deploy.yaml \
     --image_path data/test_images/3.jpg \
-    --save_dir ouput/results
+    --save_dir output/results
 ```
 
 Run the following command to view more parameters.

contrib/LaneSeg/README_CN.md

@@ -220,7 +220,7 @@ python export.py --help
 python deploy/python/infer.py \
     --config output/export/deploy.yaml \
     --image_path  data/test_images/3.jpg \
-    --save_dir ouput/results
+    --save_dir output/results
 ```
 
 更多参数信息请运行如下命令进行查看:

contrib/MedicalSeg/medicalseg/utils/utils.py

@@ -149,7 +149,7 @@ def get_image_list(image_path, valid_suffix=None, filter_key=None):
     Args:
     image_path(str): the image or image folder where you want to get a image list from.
     valid_suffix(tuple): Contain only the suffix you want to include.
-    filter_key(dict): the key(ignore case) and whether you want to include it. e.g.:{"segmentation": True} will futher filter the imagename with segmentation in it.
+    filter_key(dict): the key(ignore case) and whether you want to include it. e.g.:{"segmentation": True} will further filter the imagename with segmentation in it.
 
     """
     if valid_suffix is None:
@@ -213,18 +213,18 @@ def save_array(save_path, save_content, form, image_infor):
     """
     if not isinstance(save_content, dict):
         raise TypeError(
-            'The save_content need to be dict which the key is the save name and the value is the numpy array to be saved, but recieved {}'
+            'The save_content need to be dict which the key is the save name and the value is the numpy array to be saved, but received {}'
             .format(type(save_content)))
 
     for (key, val) in save_content.items():
         if not isinstance(val, np.ndarray):
-            raise TypeError('We only save numpy array, but recieved {}'.format(
+            raise TypeError('We only save numpy array, but received {}'.format(
                 type(val)))
         if len(val.shape) > 3:
             save_content[key] = np.squeeze(val)
 
     if not isinstance(form, Iterable):
-        raise TypeError('The form need be iterable, but recieved {}'.format(
+        raise TypeError('The form need be iterable, but received {}'.format(
             type(form)))
 
     if save_path is not None:

contrib/MedicalSeg/nnunet/datasets/dataset.py

@@ -343,7 +343,7 @@ def setup_DA_params(self):
         if self.stage == 1 and self.cascade:
             self.data_aug_params["num_cached_per_thread"] = 2
 
-            self.data_aug_params['move_last_seg_chanel_to_data'] = True
+            self.data_aug_params['move_last_seg_channel_to_data'] = True
             self.data_aug_params['cascade_do_cascade_augmentations'] = True
 
             self.data_aug_params['cascade_random_binary_transform_p'] = 0.4

contrib/MedicalSeg/nnunet/transforms/augmentation.py

@@ -150,8 +150,8 @@ def get_moreDA_augmentation(dataloader_train,
 
     tr_transforms.append(RemoveLabelTransform(-1, 0))
 
-    if params.get("move_last_seg_chanel_to_data") is not None and params.get(
-            "move_last_seg_chanel_to_data"):
+    if params.get("move_last_seg_channel_to_data") is not None and params.get(
+            "move_last_seg_channel_to_data"):
         tr_transforms.append(
             MoveSegAsOneHotToData(
                 1, params.get("all_segmentation_labels"), 'seg', 'data'))
@@ -224,8 +224,8 @@ def get_moreDA_augmentation(dataloader_train,
         val_transforms.append(
             SegChannelSelectionTransform(params.get("selected_seg_channels")))
 
-    if params.get("move_last_seg_chanel_to_data") is not None and params.get(
-            "move_last_seg_chanel_to_data"):
+    if params.get("move_last_seg_channel_to_data") is not None and params.get(
+            "move_last_seg_channel_to_data"):
         val_transforms.append(
             MoveSegAsOneHotToData(
                 1, params.get("all_segmentation_labels"), 'seg', 'data'))