remove word face in docs

docs/api/api_python/dataset/mindspore.dataset.CelebADataset.rst

@@ -62,13 +62,13 @@ mindspore.dataset.CelebADataset
 
     **关于CelebA数据集：**
 
-    CelebFaces Attributes Dataset（CelebA）数据集是一个大规模的人脸属性数据集，拥有超过20万张名人图像，每个图像都有40个属性标注。此数据集包含了大量不同姿态、各种背景的人脸图像，种类丰富、数量庞大、标注充分。数据集总体包含：
+    CelebFaces Attributes Dataset（CelebA）数据集是一个大规模数据集，拥有超过20万张名人图像，每个图像都有40个属性标注。此数据集包含了大量不同姿态、各种背景的图像，种类丰富、数量庞大、标注充分。数据集总体包含：
 
     - 10177个不同的身份
-    - 202599张人脸图像
+    - 202599张图像
     - 每张图像拥有5个五官位置标注，40个属性标签
 
-    此数据集可用于各种计算机视觉任务的训练和测试，包括人脸识别、人脸检测、五官定位、人脸编辑和合成等。
+    此数据集可用于各种计算机视觉任务的训练和测试，包括属性识别、检测和五官定位等。
 
     原始CelebA数据集结构：
 
@@ -108,7 +108,7 @@ mindspore.dataset.CelebADataset
 
         @article{DBLP:journals/corr/LiuLWT14,
         author        = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
-        title         = {Deep Learning Face Attributes in the Wild},
+        title         = {Deep Learning Attributes in the Wild},
         journal       = {CoRR},
         volume        = {abs/1411.7766},
         year          = {2014},

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h

@@ -133,10 +133,10 @@ enum {
   // define the idrecognition web error code
   APP_ERROR_FACE_WEB_USE_BASE = 10000,
   APP_ERROR_FACE_WEB_USE_SYSTEM_ERROR = APP_ERROR_FACE_WEB_USE_BASE + 1,  // Web: system error
-  APP_ERROR_FACE_WEB_USE_MUL_FACE = APP_ERROR_FACE_WEB_USE_BASE + 2,      // Web: multiple faces
+  APP_ERROR_FACE_WEB_USE_MUL_FACE = APP_ERROR_FACE_WEB_USE_BASE + 2,      // Web: multiple cheeks
   APP_ERROR_FACE_WEB_USE_REPEAT_REG = APP_ERROR_FACE_WEB_USE_BASE + 3,    // Web: repeat registration
   APP_ERROR_FACE_WEB_USE_PART_SUCCESS = APP_ERROR_FACE_WEB_USE_BASE + 4,  // Web: partial search succeeded
-  APP_ERROR_FACE_WEB_USE_NO_FACE = APP_ERROR_FACE_WEB_USE_BASE + 5,       // Web: no face detected
+  APP_ERROR_FACE_WEB_USE_NO_FACE = APP_ERROR_FACE_WEB_USE_BASE + 5,       // Web: no cheek detected
   APP_ERR_QUEUE_END,  // Not an error code, define the range of blocking queue
   // error code
 };

mindspore/python/mindspore/dataset/engine/datasets_vision.py

@@ -418,7 +418,7 @@ class CelebADataset(MappableDataset, VisionBaseDataset):
         usage (str, optional): Specify the 'train', 'valid', 'test' part or 'all' parts of dataset
             (default= 'all', will read all samples).
         sampler (Sampler, optional): Object used to choose samples from the dataset (default=None).
-        decode (bool, optional): decode the images after reading (default=False).
+        decode (bool, optional): Whether to decode the images after reading (default=False).
         extensions (list[str], optional): List of file extensions to be included in the dataset (default=None).
         num_samples (int, optional): The number of images to be included in the dataset
             (default=None, will include all images).
@@ -480,19 +480,19 @@ class CelebADataset(MappableDataset, VisionBaseDataset):
 
     About CelebA dataset:
 
-    CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset
+    CelebFaces Attributes Dataset (CelebA) is a large-scale dataset
     with more than 200K celebrity images, each with 40 attribute annotations.
 
     The images in this dataset cover large pose variations and background clutter.
     CelebA has large diversities, large quantities, and rich annotations, including
 
     * 10,177 number of identities,
-    * 202,599 number of face images,
+    * 202,599 number of images,
     * 5 landmark locations, 40 binary attributes annotations per image.
 
     The dataset can be employed as the training and test sets for the following computer
-    vision tasks: face attribute recognition, face detection, landmark (or facial part)
-    localization, and face editing & synthesis.
+    vision tasks: attribute recognition, detection, landmark (or facial part) and
+    localization.
 
     Original CelebA dataset structure:
 
@@ -532,7 +532,7 @@ class CelebADataset(MappableDataset, VisionBaseDataset):
 
         @article{DBLP:journals/corr/LiuLWT14,
         author        = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
-        title         = {Deep Learning Face Attributes in the Wild},
+        title         = {Deep Learning Attributes in the Wild},
         journal       = {CoRR},
         volume        = {abs/1411.7766},
         year          = {2014},
@@ -2648,10 +2648,10 @@ class LFWDataset(MappableDataset, VisionBaseDataset):
 
     About LFW dataset:
 
-    Labeled Faces in the Wild (LFW) is a database of face photographs designed for studying the problem of
-    unconstrained face recognition. This database was created and maintained by researchers at the University
+    LFW is a database of photographs designed for studying the problem of
+    unconstrained recognition. This database was created and maintained by researchers at the University
     of Massachusetts, Amherst (specific references are in Acknowledgments section). 13,233 images of 5,749
-    people were detected and centered by the Viola Jones face detector and collected from the web. 1,680 of the
+    people were detected and centered by the Viola Jones detector and collected from the web. 1,680 of the
     people pictured have two or more distinct photos in the dataset.
 
     You can unzip the original LFW dataset files into this directory structure and read by MindSpore's API.
@@ -2696,7 +2696,7 @@ class LFWDataset(MappableDataset, VisionBaseDataset):
     .. code-block::
 
         @TechReport{LFWTech,
-            title={Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments},
+            title={LFW: A Database for Studying Recognition in Unconstrained Environments},
             author={Gary B. Huang and Manu Ramesh and Tamara Berg and Erik Learned-Miller},
             institution ={University of Massachusetts, Amherst},
             year={2007}
@@ -4785,7 +4785,7 @@ class WIDERFaceDataset(MappableDataset, VisionBaseDataset):
 
     About WIDERFace dataset:
 
-    The WIDERFace database of people faces has a training set of 12,880 samples, a testing set of 16,097 examples
+    The WIDERFace database has a training set of 12,880 samples, a testing set of 16,097 examples
     and a validating set of 3,226 examples. It is a subset of a larger set available from WIDER. The digits have
     been size-normalized and centered in a fixed-size image.
 
@@ -4827,7 +4827,7 @@ class WIDERFaceDataset(MappableDataset, VisionBaseDataset):
     .. code-block::
 
         @inproceedings{2016WIDER,
-          title={WIDER FACE: A Face Detection Benchmark},
+          title={WIDERFACE: A Detection Benchmark},
           author={Yang, S. and Luo, P. and Loy, C. C. and Tang, X.},
           booktitle={IEEE},
           pages={5525-5533},

tests/ut/data/mindrecord/testAclImdbData/pos/10001_10.txt

@@ -1 +1 @@
-Brilliant over-acting by Lesley Ann Warren. Best dramatic hobo lady I have ever seen, and love scenes in clothes warehouse are second to none. The corn on face is a classic, as good as anything in Blazing Saddles. The take on lawyers is also superb. After being accused of being a turncoat, selling out his boss, and being dishonest the lawyer of Pepto Bolt shrugs indifferently "I'm a lawyer" he says. Three funny words. Jeffrey Tambor, a favorite from the later Larry Sanders show, is fantastic here too as a mad millionaire who wants to crush the ghetto. His character is more malevolent than usual. The hospital scene, and the scene where the homeless invade a demolition site, are all-time classics. Look for the legs scene and the two big diggers fighting (one bleeds). This movie gets better each time I see it (which is quite often).
+Brilliant over-acting by Lesley Ann Warren. Best dramatic hobo lady I have ever seen, and love scenes in clothes warehouse are second to none. The corn on cheek is a classic, as good as anything in Blazing Saddles. The take on lawyers is also superb. After being accused of being a turncoat, selling out his boss, and being dishonest the lawyer of Pepto Bolt shrugs indifferently "I'm a lawyer" he says. Three funny words. Jeffrey Tambor, a favorite from the later Larry Sanders show, is fantastic here too as a mad millionaire who wants to crush the ghetto. His character is more malevolent than usual. The hospital scene, and the scene where the homeless invade a demolition site, are all-time classics. Look for the legs scene and the two big diggers fighting (one bleeds). This movie gets better each time I see it (which is quite often).