From ac9864745d90480127e3c956709c834fcfadc78c Mon Sep 17 00:00:00 2001 From: gaotingquan Date: Tue, 1 Nov 2022 13:18:49 +0000 Subject: [PATCH] docs: fix --- docs/en/PPShiTu/PPShiTuV2_introduction.md | 2 +- docs/en/models/PP-HGNet_en.md | 19 ++++++------------- docs/zh_CN/models/PP-ShiTu/README.md | 2 +- 3 files changed, 8 insertions(+), 15 deletions(-) diff --git a/docs/en/PPShiTu/PPShiTuV2_introduction.md b/docs/en/PPShiTu/PPShiTuV2_introduction.md index e420bd71..b0a9aef3 100644 --- a/docs/en/PPShiTu/PPShiTuV2_introduction.md +++ b/docs/en/PPShiTu/PPShiTuV2_introduction.md @@ -163,7 +163,7 @@ cd deploy/models wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar # Download the feature extraction inference model and unzip it -wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1. +wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.tar ``` ### 4.2 Test data preparation diff --git a/docs/en/models/PP-HGNet_en.md b/docs/en/models/PP-HGNet_en.md index 2babd1fd..3c664351 100644 --- a/docs/en/models/PP-HGNet_en.md +++ b/docs/en/models/PP-HGNet_en.md @@ -153,13 +153,7 @@ result = clas.predict(infer_imgs) print(next(result)) ``` -**Note**: The result returned by model.predict() is a `generator`, so you need to use the `next()` function to call it or `for loop` to loop it. And it will predict with batch_size size batch and return the prediction results when called. The default batch_size is 1, and you also specify the batch_size when instantiating, such as model = paddleclas.PaddleClas(model_name="PPHGNet_small", batch_size=2). The result of demo above: - - -``` ->>> result -[{'class_ids': [8, 7, 86, 82, 81], 'scores': [0.71479, 0.08682, 0.00806, 0.0023, 0.00121], 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'ptarmigan'], 'filename': 'docs/images/inference_deployment/whl_demo.jpg'}] -``` +**Note**: The result returned by model.predict() is a `generator`, so you need to use the `next()` function to call it or `for loop` to loop it. And it will predict with batch_size size batch and return the prediction results when called. The default batch_size is 1, and you also specify the batch_size when instantiating, such as `model = paddleclas.PaddleClas(model_name="PPHGNet_small", batch_size=2)`. @@ -212,7 +206,7 @@ where `train/` and `val/` are the training set and validation set, respectively. #### 3.3.1 Train ImageNet -The PPHGNet_small training configuration is provided in `ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml`, which can be started with the following script: +The PPHGNet_small training configuration is provided in `ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml`, which can be started with the following script: ```shell export CUDA_VISIBLE_DEVICES=0,1,2,3 @@ -226,12 +220,12 @@ python3 -m paddle.distributed.launch \ **Note:** * The current model with the best accuracy will be saved in `output/PPHGNet_small/best_model.pdparams` - + #### 3.3.2 Fine-tuning based on ImageNet weights -If you are not training an ImageNet task, you need to change the configuration file and training method, such as reducing the learning rate, reducing the number of epochs, etc. +If you are not training an ImageNet task, you need to change the configuration file and training method, such as reducing the learning rate, reducing the number of epochs, etc. @@ -265,13 +259,13 @@ The results: **Note**: -* Among the above command, argument `-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` specify the path of the best model weight file. You can specify other path if needed. +* Among the above command, argument `-o Global.pretrained_model="output/PPHGNet_small/best_model"` specify the path of the best model weight file. You can specify other path if needed. * The default test image is `docs/images/inference_deployment/whl_demo.jpg` ，And you can test other image, only need to specify the argument `-o Infer.infer_imgs=path_to_test_image`. * The default output is the value of Top-5. If you want to output the value of Top-k, you can specify `-o Infer.PostProcess.topk=k`, where `k` is the value you specify. - + * The default label mapping is based on the ImageNet dataset. If you change the dataset, you need to re-specify `Infer.PostProcess.class_id_map_file`. For the method of making the mapping file, please refer to `ppcls/utils/imagenet1k_label_list.txt` @@ -410,4 +404,3 @@ PaddleClas provides an example of how to deploy on mobile by Paddle-Lite. Please Paddle2ONNX support convert Paddle Inference model to ONNX model. And you can deploy with ONNX model on different inference engine, such as TensorRT, OpenVINO, MNN/TNN, NCNN and so on. About Paddle2ONNX details, please refer to [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX). PaddleClas provides an example of how to convert Paddle Inference model to ONNX model by paddle2onnx toolkit and predict by ONNX model. You can refer to [paddle2onnx](../../../deploy/paddle2onnx/readme_en.md) for deployment details. - diff --git a/docs/zh_CN/models/PP-ShiTu/README.md b/docs/zh_CN/models/PP-ShiTu/README.md index a031e8ef..01b64a1b 100644 --- a/docs/zh_CN/models/PP-ShiTu/README.md +++ b/docs/zh_CN/models/PP-ShiTu/README.md @@ -167,7 +167,7 @@ cd deploy/models wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar # 下载特征提取inference模型并解压 -wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1. +wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.tar ``` ### 4.2 测试数据准备 -- GitLab