diff --git a/docs/source_en/network_list.md b/docs/source_en/network_list.md index 9de3b4a806d0ce4d2b05614734c86d7cca44e951..d2e4f1f9c9781eb35ffa45dc0d760aa8317bc512 100644 --- a/docs/source_en/network_list.md +++ b/docs/source_en/network_list.md @@ -10,7 +10,7 @@ - + ## Model Zoo @@ -53,7 +53,7 @@ Domain | Sub Domain| Network | Dataset | CPU | GPU | Ascend | 0.5.0-beta* |Computer Vision (CV) | Image Classification| [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)| MNIST | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/lenet/lenet_ascend_0.5.0_mnist_official_classification_20200716.tar.gz) |Computer Vision (CV) | Image Classification| [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)| CIFAR-10 | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/vgg/vgg16_ascend_0.5.0_cifar10_official_classification_20200715.tar.gz) |Computer Vision (CV) | Image Classification| [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) | CIFAR-10| | | ✓ |[Download](http://download.mindspore.cn/model_zoo/official/cv/resnet/resnet50_v1.5_ascend_0.3.0_cifar10_official_classification_20200718.tar.gz) -|Computer Vision (CV) | Targets Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) +|Computer Vision (CV) | Targets Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_Base](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | zhwiki | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_base_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_NEZHA](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)| zhwiki| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_nezha_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)| WMT English-German| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/transformer/transformer_ascend_0.5.0_wmtende_official_machine_translation_20200713.tar.gz) diff --git a/docs/source_zh_cn/network_list.md b/docs/source_zh_cn/network_list.md index 0f196eb8136604e6573a5ecedee818c90ead7ebf..06c400157e60d09f8d667e6fec590ef7e32ede3d 100644 --- a/docs/source_zh_cn/network_list.md +++ b/docs/source_zh_cn/network_list.md @@ -10,7 +10,7 @@ - + ## Model Zoo diff --git a/lite/tutorials/source_zh_cn/quick_start/quick_start.md b/lite/tutorials/source_zh_cn/quick_start/quick_start.md index 165388673ee6753c21fd0116a797562e73b19c2e..a50c7b52bbd354063a85e824965def2cb0f0f542 100644 --- a/lite/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/lite/tutorials/source_zh_cn/quick_start/quick_start.md @@ -28,7 +28,7 @@ 2. 将模型转换成MindSpore Lite模型格式。 3. 在端侧使用MindSpore Lite推理模型。详细说明如何在端侧利用MindSpore Lite C++ API（Android JNI）和MindSpore Lite图像分类模型完成端侧推理，实现对设备摄像头捕获的内容进行分类，并在APP图像预览界面中，显示出最可能的分类结果。 -> 你可以在这里找到[Android图像分类模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite)和[示例代码](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/lite/image_classification)。 +> 你可以在这里找到[Android图像分类模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite)和[示例代码](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/image_classification)。 ## 选择模型 diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index 0ff68ce380132d8618de67ad3c7d4ac6ae98399d..13fa54ac4c57c24d0cf5c8becf909ba0bc369355 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -61,7 +61,7 @@ Next, let's use MindSpore to solve the image classification task. The overall pr 5. Call the high-level `Model` API to train and save the model file. 6. Load the saved model for inference. -> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： . +> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： . The key parts of the task process code are explained below. diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md index 24c12287b21e6f720aea801536016955daa05322..0e5e4fd8845dbd76f498d6963f1ef13718b063e3 100644 --- a/tutorials/source_en/advanced_use/network_migration.md +++ b/tutorials/source_en/advanced_use/network_migration.md @@ -19,7 +19,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index 4c36e6d4b05fbd981ea673b388eaf922f30c5664..e42856478bac7fa3fe53405003e1e517bab00fca 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -88,7 +88,7 @@ Currently, MindSpore GPU and CPU supports SentimentNet network based on the long Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used for processing and predicting an important event with a long interval and delay in a time sequence. For details, refer to online documentation. 3. After the model is obtained, use the validation dataset to check the accuracy of model. -> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at： +> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at： > - `src/config.py`：some configurations on the network, including the batch size and number of training epochs. > - `src/dataset.py`：dataset related definition，include MindRecord file convert and data-preprocess, etc. > - `src/imdb.py`： the util class for parsing IMDB dataset. diff --git a/tutorials/source_en/use/custom_operator.md b/tutorials/source_en/use/custom_operator.md index 7e58182b495e22cdc8cb8ae825ffb6ec0f5bf92f..cec453c2db665078b8ff20c3bb3d4819bd5f418c 100644 --- a/tutorials/source_en/use/custom_operator.md +++ b/tutorials/source_en/use/custom_operator.md @@ -38,7 +38,7 @@ This section takes a Square operator as an example to describe how to customize The primitive of an operator is a subclass inherited from `PrimitiveWithInfer`. The type name of the subclass is the operator name. The definition of the custom operator primitive is the same as that of the built-in operator primitive. -- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code. +- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/blob/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code. - The input and output names are defined by the `init_prim_io_names` function. - The shape inference method of the output tensor is defined in the `infer_shape` function, and the dtype inference method of the output tensor is defined in the `infer_dtype` function. diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index a2d6143cd2468b4dd7246146575e40acc8539d03..b40d13a7fa7902d980e836074462d76ae3732081 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -63,7 +63,7 @@ MindSpore当前支持的图像分类网络包括：典型网络LeNet、AlexNet 6. 加载保存的模型进行推理 -> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码： +> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码： 下面对任务流程中各个环节及代码关键片段进行解释说明。 diff --git a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md index 5ea2290e4647d865fdef6ddaf50032efb22a5fe8..f2eff3fc572d1743fd7bd91ccd92bd20ad41403c 100644 --- a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md +++ b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md @@ -59,11 +59,11 @@ from model_zoo.official.cv.lenet.src.lenet import LeNet5 ### 加载数据集 -利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py]()导入。 +利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py]()导入。 ### 定义网络 -这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py]()导入。 +这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py]()导入。 ### 定义训练模型 将训练流程拆分为正向反向训练、参数更新和累积梯度清理三个部分： @@ -253,7 +253,7 @@ if __name__ == "__main__": **验证模型** -通过model_zoo中lenet目录下的[eval.py]()，使用保存的CheckPoint文件，加载验证数据集，进行验证。 +通过model_zoo中lenet目录下的[eval.py]()，使用保存的CheckPoint文件，加载验证数据集，进行验证。 ```shell $ python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index f6e70eb436668bbc35d24f47979cfc02064c724c..3b8d9e3c48329f9eaa6832ad6fd0aee2c54400f3 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md @@ -89,7 +89,7 @@ $F1分数 = (2 * Precision * Recall) / (Precision + Recall)$ > LSTM（Long short-term memory，长短期记忆）网络是一种时间循环神经网络，适合于处理和预测时间序列中间隔和延迟非常长的重要事件。具体介绍可参考网上资料，在此不再赘述。 3. 得到模型之后，使用验证数据集，查看模型精度情况。 -> 本例面向GPU或CPU硬件平台，你可以在这里下载完整的样例代码： +> 本例面向GPU或CPU硬件平台，你可以在这里下载完整的样例代码： > - `src/config.py`：网络中的一些配置，包括`batch size`、进行几次epoch训练等。 > - `src/dataset.py`：数据集相关，包括转换成MindRecord文件，数据预处理等。 > - `src/imdb.py`： 解析IMDB数据集的工具。 diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md index febbca921a2c20e784b0f3481933ddf4c7b62b06..4916ebc1a6af26640ba1a4232249f1ef44d4d0e6 100644 --- a/tutorials/source_zh_cn/use/custom_operator.md +++ b/tutorials/source_zh_cn/use/custom_operator.md @@ -38,7 +38,7 @@ 每个算子的原语是一个继承于`PrimitiveWithInfer`的子类，其类型名称即是算子名称。 自定义算子原语与内置算子原语的接口定义完全一致： -- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。 +- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/blob/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。 - 输入输出的名称通过`init_prim_io_names`函数定义。 - 输出Tensor的shape推理方法在`infer_shape`函数中定义，输出Tensor的dtype推理方法在`infer_dtype`函数中定义。