diff --git a/README.md b/README.md deleted file mode 100644 index 88965dc0f98f30bb91bf1f009cb740e0828499c3..0000000000000000000000000000000000000000 --- a/README.md +++ /dev/null @@ -1,148 +0,0 @@ -简体中文 | [English](README_en.md) - -# PaddleClas - -## 简介 - -飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 - -**近期更新** - -- 2021.06.22,23,24 PaddleClas官方研发团队带来技术深入解读三日直播课,6月22日、23日、24日晚上20:30,[直播地址](https://live.bilibili.com/21689802) -- 🔥🔥🔥: 2021.06.16 PaddleClas v2.2版本升级,集成Metric learning,向量检索等组件。新增商品识别、动漫人物识别、车辆识别和logo识别等4个图像识别应用。新增LeViT、Twins、TNT、DLA、HarDNet、RedNet系列30个预训练模型。 -- 2021.05.14 添加`SwinTransformer` 系列模型。 -- 2021.04.15 添加`MixNet_L`和`ReXNet_3_0`系列模型。 - -[more](./docs/zh_CN/update_history.md) - -## 特性 - -- 实用的图像识别系统:集成了目标检测、特征学习、图像检索等模块,广泛适用于各类图像识别任务。 -提供商品识别、车辆识别、logo识别和动漫人物识别等4个场景应用示例。 - -- 丰富的预训练模型库:提供了35个系列共164个ImageNet预训练模型,其中6个精选系列模型支持结构快速修改。 - -- 全面易用的特征学习组件:集成arcmargin, triplet loss等12度量学习方法,通过配置文件即可随意组合切换。 - -- SSLD知识蒸馏:14个分类预训练模型,精度普遍提升3%以上;其中ResNet50_vd模型在ImageNet-1k数据集上的Top-1精度达到了84.0%, -Res2Net200_vd预训练模型Top-1精度高达85.1%。 - -- 数据增广:支持AutoAugment、Cutout、Cutmix等8种数据增广算法详细介绍、代码复现和在统一实验环境下的效果评估。 - -## 图像识别系统效果展示 -
- -
- -更多效果图请见:[识别效果展示页面](./docs/zh_CN/more_demo.md) - -## 欢迎加入技术交流群 - -* 您可以扫描下面的微信群二维码, 加入PaddleClas 微信交流群。获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。 - -
- -
- -## 快速体验 -图像识别快速体验:[点击这里](./docs/zh_CN/tutorials/quick_start_recognition.md) - -## 文档教程 - -- [快速安装](./docs/zh_CN/tutorials/install.md) -- [图像识别快速体验](./docs/zh_CN/tutorials/quick_start_recognition.md) -- [图像识别系统介绍](#图像识别系统介绍) -- [识别效果展示](#识别效果展示) -- 算法介绍 - - [骨干网络和预训练模型库](./docs/zh_CN/ImageNet_models_cn.md) - - [主体检测](./docs/zh_CN/application/mainbody_detection.md) - - [图像分类](./docs/zh_CN/tutorials/image_classification.md) - - [特征学习](./docs/zh_CN/application/feature_learning.md) - - [商品识别](./docs/zh_CN/application/product_recognition.md) - - [车辆识别](./docs/zh_CN/application/vehicle_recognition.md) - - [logo识别](./docs/zh_CN/application/logo_recognition.md) - - [动漫人物识别](./docs/zh_CN/application/cartoon_character_recognition.md) - - [向量检索](./deploy/vector_search/README.md) -- 模型训练/评估 - - [图像分类任务](./docs/zh_CN/tutorials/getting_started.md) - - [特征学习任务](./docs/zh_CN/tutorials/getting_started_retrieval.md) -- 模型预测 - - [基于Python预测引擎预测推理](./docs/zh_CN/inference.md) - - [基于C++预测引擎预测推理](./deploy/cpp/readme.md)(当前只支持图像分类任务,图像识别更新中) -- 模型部署(当前只支持图像分类任务,图像识别更新中) - - [服务化部署](./deploy/hubserving/readme.md) - - [端侧部署](./deploy/lite/readme.md) - - [whl包预测](./docs/zh_CN/whl.md) -- 高阶使用 - - [知识蒸馏](./docs/zh_CN/advanced_tutorials/distillation/distillation.md) - - [模型量化](./docs/zh_CN/extension/paddle_quantization.md) - - [数据增广](./docs/zh_CN/advanced_tutorials/image_augmentation/ImageAugment.md) -- FAQ(暂停更新) - - [图像分类任务FAQ](docs/zh_CN/faq.md) -- [许可证书](#许可证书) -- [贡献代码](#贡献代码) - - - -## 图像识别系统介绍 - - -
- -
- -整个图像识别系统分为三步:(1)通过一个目标检测模型,检测图像物体候选区域(2)对每个候选区域进行特征提取(3)与检索库中图像进行特征匹配,提取识别结果。 - -对于新的未知类别,无需重新训练模型,只需要在检索库补入该类别图像,重新建立检索库,就可以识别该类别。 - - - -## 识别效果展示 [more](./docs/zh_CN/more_demo.md) -- 商品识别 -
- -
-
- -
- -- 动漫人物识别 -
- -
-
- -
- -- logo识别 -
- -
-
- -
- -- 车辆识别 -
- -
-
- -
- - - -## 许可证书 -本项目的发布受Apache 2.0 license许可认证。 - - - -## 贡献代码 -我们非常欢迎你为PaddleClas贡献代码,也十分感谢你的反馈。 - -- 非常感谢[nblib](https://github.com/nblib)修正了PaddleClas中RandErasing的数据增广配置文件。 -- 非常感谢[chenpy228](https://github.com/chenpy228)修正了PaddleClas文档中的部分错别字。 -- 非常感谢[jm12138](https://github.com/jm12138)为PaddleClas添加ViT,DeiT系列模型和RepVGG系列模型。 -- 非常感谢[FutureSI](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/76563)对PaddleClas代码的解析与总结。 - -我们非常欢迎你为PaddleClas贡献代码,也十分感谢你的反馈。 diff --git a/README.md b/README.md new file mode 120000 index 0000000000000000000000000000000000000000..13c4f964bb9063f28d6e08dfb8c6b828a81d2536 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +README_en.md \ No newline at end of file diff --git a/README_ch.md b/README_ch.md new file mode 100644 index 0000000000000000000000000000000000000000..7971be16d356f4b7ecae79097a60acd897b0f152 --- /dev/null +++ b/README_ch.md @@ -0,0 +1,145 @@ +简体中文 | [English](README_en.md) + +# PaddleClas + +## 简介 + +飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 + +**近期更新** + +- 2021.06.22,23,24 PaddleClas官方研发团队带来技术深入解读三日直播课,6月22日、23日、24日晚上20:30,[直播地址](https://live.bilibili.com/21689802) +- 🔥🔥🔥: 2021.06.16 PaddleClas v2.2版本升级,集成Metric learning,向量检索等组件。新增商品识别、动漫人物识别、车辆识别和logo识别等4个图像识别应用。新增LeViT、Twins、TNT、DLA、HarDNet、RedNet系列30个预训练模型。 +- [more](./docs/zh_CN/update_history.md) + +## 特性 + +- 实用的图像识别系统:集成了目标检测、特征学习、图像检索等模块,广泛适用于各类图像识别任务。 +提供商品识别、车辆识别、logo识别和动漫人物识别等4个场景应用示例。 + +- 丰富的预训练模型库:提供了35个系列共164个ImageNet预训练模型,其中6个精选系列模型支持结构快速修改。 + +- 全面易用的特征学习组件:集成arcmargin, triplet loss等12度量学习方法,通过配置文件即可随意组合切换。 + +- SSLD知识蒸馏:14个分类预训练模型,精度普遍提升3%以上;其中ResNet50_vd模型在ImageNet-1k数据集上的Top-1精度达到了84.0%, +Res2Net200_vd预训练模型Top-1精度高达85.1%。 + +- 数据增广:支持AutoAugment、Cutout、Cutmix等8种数据增广算法详细介绍、代码复现和在统一实验环境下的效果评估。 + +## 图像识别系统效果展示 +
+ +
+ +更多效果图请见:[识别效果展示页面](./docs/zh_CN/more_demo.md) + +## 欢迎加入技术交流群 + +* 您可以扫描下面的微信群二维码, 加入PaddleClas 微信交流群。获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。 + +
+ +
+ +## 快速体验 +图像识别快速体验:[点击这里](./docs/zh_CN/tutorials/quick_start_recognition.md) + +## 文档教程 + +- [快速安装](./docs/zh_CN/tutorials/install.md) +- [图像识别快速体验](./docs/zh_CN/tutorials/quick_start_recognition.md) +- [图像识别系统介绍](#图像识别系统介绍) +- [识别效果展示](#识别效果展示) +- 算法介绍 + - [骨干网络和预训练模型库](./docs/zh_CN/ImageNet_models_cn.md) + - [主体检测](./docs/zh_CN/application/mainbody_detection.md) + - [图像分类](./docs/zh_CN/tutorials/image_classification.md) + - [特征学习](./docs/zh_CN/application/feature_learning.md) + - [商品识别](./docs/zh_CN/application/product_recognition.md) + - [车辆识别](./docs/zh_CN/application/vehicle_recognition.md) + - [logo识别](./docs/zh_CN/application/logo_recognition.md) + - [动漫人物识别](./docs/zh_CN/application/cartoon_character_recognition.md) + - [向量检索](./deploy/vector_search/README.md) +- 模型训练/评估 + - [图像分类任务](./docs/zh_CN/tutorials/getting_started.md) + - [特征学习任务](./docs/zh_CN/tutorials/getting_started_retrieval.md) +- 模型预测 + - [基于Python预测引擎预测推理](./docs/zh_CN/inference.md) + - [基于C++预测引擎预测推理](./deploy/cpp/readme.md)(当前只支持图像分类任务,图像识别更新中) +- 模型部署(当前只支持图像分类任务,图像识别更新中) + - [服务化部署](./deploy/hubserving/readme.md) + - [端侧部署](./deploy/lite/readme.md) + - [whl包预测](./docs/zh_CN/whl.md) +- 高阶使用 + - [知识蒸馏](./docs/zh_CN/advanced_tutorials/distillation/distillation.md) + - [模型量化](./docs/zh_CN/extension/paddle_quantization.md) + - [数据增广](./docs/zh_CN/advanced_tutorials/image_augmentation/ImageAugment.md) +- FAQ(暂停更新) + - [图像分类任务FAQ](docs/zh_CN/faq.md) +- [许可证书](#许可证书) +- [贡献代码](#贡献代码) + + + +## 图像识别系统介绍 + + +
+ +
+ +整个图像识别系统分为三步:(1)通过一个目标检测模型,检测图像物体候选区域(2)对每个候选区域进行特征提取(3)与检索库中图像进行特征匹配,提取识别结果。 + +对于新的未知类别,无需重新训练模型,只需要在检索库补入该类别图像,重新建立检索库,就可以识别该类别。 + + + +## 识别效果展示 [more](./docs/zh_CN/more_demo.md) +- 商品识别 +
+ +
+
+ +
+ +- 动漫人物识别 +
+ +
+
+ +
+ +- logo识别 +
+ +
+
+ +
+ +- 车辆识别 +
+ +
+
+ +
+ + + +## 许可证书 +本项目的发布受Apache 2.0 license许可认证。 + + + +## 贡献代码 +我们非常欢迎你为PaddleClas贡献代码,也十分感谢你的反馈。 + +- 非常感谢[nblib](https://github.com/nblib)修正了PaddleClas中RandErasing的数据增广配置文件。 +- 非常感谢[chenpy228](https://github.com/chenpy228)修正了PaddleClas文档中的部分错别字。 +- 非常感谢[jm12138](https://github.com/jm12138)为PaddleClas添加ViT,DeiT系列模型和RepVGG系列模型。 +- 非常感谢[FutureSI](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/76563)对PaddleClas代码的解析与总结。 + +我们非常欢迎你为PaddleClas贡献代码,也十分感谢你的反馈。 diff --git a/README_en.md b/README_en.md index 8815bb6d472bc28c6db7b77d7dbf3be15553d510..1caef7e4cbfbf4b24bdf8f8eb818341adca3dfff 100644 --- a/README_en.md +++ b/README_en.md @@ -1,4 +1,4 @@ -[简体中文](README.md) | English +[简体中文](README_ch.md) | English # PaddleClas @@ -8,17 +8,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us **Recent updates** -- 🔥🔥🔥: 2021.06.16 PaddleClas release/2.2. - - Add metric learning and vector search modules. - - Add product recognition, animation character recognition, vehicle recognition and logo recognition. - - Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper. - -- 2021.05.14 - - Add `SwinTransformer` series pretrained models, whose Top-1 Acc on ImageNet-1k dataset reaches 87.19%. - -- 2021.04.15 - - Add `MixNet` and `ReXNet` pretrained models, `MixNet_L`'s Top-1 Acc on ImageNet-1k reaches 78.6% and `ReXNet_3_0` reaches 82.09%. - +- 🔥🔥🔥: 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper. - [more](./docs/en/update_history_en.md) ## Features @@ -34,27 +24,29 @@ Four sample solutions are provided, including product recognition, vehicle recog - Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. with detailed introduction, code replication and evaluation of effectiveness in a unified experimental environment. - + ## Image Recognition System Effect Demonstration
- +
+For more effect pictures, please see [Demo images](./docs/en/more_demo.md). + ## Welcome to Join the Technical Exchange Group * You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
- +
-## Quick Start +## Quick Start Quick experience of image recognition:[Link](./docs/en/tutorials/quick_start_recognition_en.md) ## Tutorials -- [Quick Installatiopn](./docs/en/tutorials/install_en.md) +- [Quick Installation](./docs/en/tutorials/install_en.md) - [Quick Start of Recognition](./docs/en/tutorials/quick_start_recognition_en.md) - [Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems) - [Demo images](#Demo_images) @@ -89,7 +81,7 @@ Quick experience of image recognition:[Link](./docs/en/tutorials/quick_start_r ## Introduction to Image Recognition Systems
- +
Image recognition can be divided into three steps: @@ -103,34 +95,34 @@ For a new unknown category, there is no need to retrain the model, just prepare ## Demo images [more](./docs/en/more_demo.md) - Product recognition
- +
- +
- Cartoon character recognition
- +
- +
- Logo recognition
- +
- +
- Car recognition
- +
- +
diff --git a/deploy/utils/draw_bbox.py b/deploy/utils/draw_bbox.py index 7c8044d1b84a996a4ce4bbe6c62d14a8e15b56ee..9c0bcf61bfc84d5dbb217f1f317dc084bbd5f975 100644 --- a/deploy/utils/draw_bbox.py +++ b/deploy/utils/draw_bbox.py @@ -39,7 +39,8 @@ def draw_bbox_results(image, xmin, ymin, xmax, ymax = result["bbox"] text = "{}, {:.2f}".format(result["rec_docs"], result["rec_scores"]) th = font_size - tw = int(len(result["rec_docs"]) * font_size) + 60 + tw = font.getsize(text)[0] + # tw = int(len(result["rec_docs"]) * font_size) + 60 start_y = max(0, ymin - th) draw.rectangle( diff --git a/docs/en/more_demo.md b/docs/en/more_demo.md index d338a023d9a34f2fa27be78bac78ba245a2950a0..575ddc98a425d42b7cb842c15136a4cd3c0389f5 100644 --- a/docs/en/more_demo.md +++ b/docs/en/more_demo.md @@ -1,61 +1,61 @@ ## Demo images - Product recognition
- +
- +
- +
- +
- +
- +
- +
- +
- Cartoon character recognition
- +
- +
- +
- Logo recognition
- +
- +
- +
- Car recognition
- +
- +
- +
[More demo images](../images/recognition/more_demo_images) diff --git a/docs/en/tutorials/install_en.md b/docs/en/tutorials/install_en.md index 36739dd65f634097708ea35ec4e03c389fd38ed9..f3710b6abaffb02bc0d7ba98216ec66e5c98858a 100644 --- a/docs/en/tutorials/install_en.md +++ b/docs/en/tutorials/install_en.md @@ -41,7 +41,7 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags ``` # use ctrl+P+Q to exit docker, to re-enter docker using the following command: -sudo docker container exec -it ppcls /bin/bash +sudo docker exec -it ppcls /bin/bash ``` ### 1.3 Install PaddlePaddle using pip diff --git a/docs/en/tutorials/quick_start_recognition_en.md b/docs/en/tutorials/quick_start_recognition_en.md index 7d9925ceb002631a1f3907e6e2b9b2104d3daac2..60ae04f005be72a3115f0509d8ab474cc125c70c 100644 --- a/docs/en/tutorials/quick_start_recognition_en.md +++ b/docs/en/tutorials/quick_start_recognition_en.md @@ -40,10 +40,10 @@ The detection model with the recognition inference model for the 4 directions (L | Logo Recognition Model | Logo Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/logo_rec_ResNet50_Logo3K_v1.0_infer.tar) | [inference_logo.yaml](../../../deploy/configs/inference_logo.yaml) | [build_logo.yaml](../../../deploy/configs/build_logo.yaml) | | Cartoon Face Recognition Model| Cartoon Face Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) | | Vehicle Subclassification Model | Vehicle Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) | -| Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_inshop.yaml](../../../deploy/configs/) | [build_inshop.yaml](../../../deploy/configs/build_inshop.yaml) | +| Product Recignition Model | Product Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) | -Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar). +Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar). **Attention** @@ -89,7 +89,7 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/infere cd .. # Download the demo data and unzip it -wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar && tar -xf recognition_demo_data_v1.0.tar +wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar && tar -xf recognition_demo_data_en_v1.0.tar ``` Once unpacked, the `recognition_demo_data_v1.0` folder should have the following file structure. @@ -123,7 +123,7 @@ The `models` folder should have the following file structure. ``` -### 2.2 Product Recognition and Retrival +### 2.2 Product Recognition and Retrieval Take the product recognition demo as an example to show the recognition and retrieval process (if you wish to try other scenarios of recognition and retrieval, replace the corresponding configuration file after downloading and unzipping the corresponding demo data and model to complete the prediction)。 @@ -137,7 +137,7 @@ Run the following command to identify and retrieve the image `./recognition_demo # use the following command to predict using GPU. python3.7 python/predict_system.py -c configs/inference_product.yaml # use the following command to predict using CPU -python3.7 python/predict_system.py -c configs/inference_product.yaml +python3.7 python/predict_system.py -c configs/inference_product.yaml -o Global.use_gpu=False ``` **Note:** Program lib used to build index is compliled on our machine, if error occured because of the environment, you can refer to [vector search tutorial](../../../deploy/vector_search/README.md) to rebuild the lib. @@ -153,7 +153,7 @@ The image to be retrieved is shown below. The final output is shown below. ``` -[{'bbox': [287, 129, 497, 326], 'rec_docs': '稻香村金猪饼', 'rec_scores': 0.8309420943260193}, {'bbox': [99, 242, 313, 426], 'rec_docs': '稻香村金猪饼', 'rec_scores': 0.7245652079582214}] +[{'bbox': [287, 129, 497, 326], 'rec_docs': 'Daoxaingcun Golden Piggie Cake', 'rec_scores': 0.8309420347213745}, {'bbox': [99, 242, 313, 426], 'rec_docs': 'Daoxaingcun Golden Piggie Cake', 'rec_scores': 0.7245651483535767}] ``` @@ -163,7 +163,7 @@ where bbox indicates the location of the detected object, rec_docs indicates the The detection result is also saved in the folder `output`, for this image, the visualization result is as follows.
- +
@@ -182,13 +182,12 @@ The results on the screen are shown as following. ``` ... -[{'bbox': [37, 29, 123, 89], 'rec_docs': '香奈儿包', 'rec_scores': 0.6163763999938965}, {'bbox': [153, 96, 235, 175], 'rec_docs': '香奈儿包', 'rec_scores': 0.5279821157455444}] -[{'bbox': [735, 562, 1133, 851], 'rec_docs': '香奈儿包', 'rec_scores': 0.5588355660438538}] -[{'bbox': [124, 50, 230, 129], 'rec_docs': '香奈儿包', 'rec_scores': 0.6980369687080383}] -[{'bbox': [0, 0, 275, 183], 'rec_docs': '香奈儿包', 'rec_scores': 0.5818190574645996}] -[{'bbox': [400, 1179, 905, 1537], 'rec_docs': '香奈儿包', 'rec_scores': 0.9814301133155823}] -[{'bbox': [544, 4, 1482, 932], 'rec_docs': '香奈儿包', 'rec_scores': 0.5143815279006958}] -[{'bbox': [29, 42, 194, 183], 'rec_docs': '香奈儿包', 'rec_scores': 0.9543638229370117}] +[{'bbox': [37, 29, 123, 89], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.6163763999938965}, {'bbox': [153, 96, 235, 175], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5279821157455444}] +[{'bbox': [735, 562, 1133, 851], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5588355660438538}] +[{'bbox': [124, 50, 230, 129], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.6980369687080383}] +[{'bbox': [0, 0, 275, 183], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5818190574645996}] +[{'bbox': [400, 1179, 905, 1537], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.9814301133155823}, {'bbox': [295, 713, 820, 1046], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.9496176242828369}, {'bbox': [153, 236, 694, 614], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.8395382761955261}] +[{'bbox': [544, 4, 1482, 932], 'rec_docs': 'Chanel Handbag', 'rec_scores': 0.5143815279006958}] ... ``` @@ -238,12 +237,12 @@ cp recognition_demo_data_v1.0/gallery_product/data_file.txt recognition_demo_dat Then add some new lines into the new label file, which is shown as follows. ``` -gallery/anmuxi/001.jpg 安慕希酸奶 -gallery/anmuxi/002.jpg 安慕希酸奶 -gallery/anmuxi/003.jpg 安慕希酸奶 -gallery/anmuxi/004.jpg 安慕希酸奶 -gallery/anmuxi/005.jpg 安慕希酸奶 -gallery/anmuxi/006.jpg 安慕希酸奶 +gallery/anmuxi/001.jpg Anmuxi Ambrosial Yogurt +gallery/anmuxi/002.jpg Anmuxi Ambrosial Yogurt +gallery/anmuxi/003.jpg Anmuxi Ambrosial Yogurt +gallery/anmuxi/004.jpg Anmuxi Ambrosial Yogurt +gallery/anmuxi/005.jpg Anmuxi Ambrosial Yogurt +gallery/anmuxi/006.jpg Anmuxi Ambrosial Yogurt ``` Each line can be splited into two fields. The first field denotes the relative image path, and the second field denotes its label. The `delimiter` is `tab` here. @@ -274,11 +273,11 @@ python3.7 python/predict_system.py -c configs/inference_product.yaml -o Global.i The output is as follows: ``` -[{'bbox': [243, 80, 523, 522], 'rec_docs': '安慕希酸奶', 'rec_scores': 0.5570770502090454}] +[{'bbox': [243, 80, 523, 522], 'rec_docs': 'Anmuxi Ambrosial Yogurt', 'rec_scores': 0.5570770502090454}] ``` -The final recognition result is `安慕希酸奶`, which is corrrect, the visualization result is as follows. +The final recognition result is `Anmuxi Ambrosial Yogurt`, which is corrrect, the visualization result is as follows.
- +
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Binary files /dev/null and b/docs/images/recognition_en.gif differ diff --git a/docs/images/wx_group.jpeg b/docs/images/wx_group.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..a898ecd0b1a224618fdc1c1076281b272c3baca4 Binary files /dev/null and b/docs/images/wx_group.jpeg differ diff --git a/docs/zh_CN/ImageNet_models_cn.md b/docs/zh_CN/ImageNet_models_cn.md index 890dcb5c9fc2107ea5883ac0647a4dbfebaf1135..3732959923540ac8169dead5b29d646d181e6fd0 100644 --- a/docs/zh_CN/ImageNet_models_cn.md +++ b/docs/zh_CN/ImageNet_models_cn.md @@ -6,7 +6,7 @@ ### 模型库概览图 -基于ImageNet1k分类数据集,PaddleClas支持24种系列分类网络结构以及对应的122个图像分类预训练模型,训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现,下面所有的速度指标评估环境如下: +基于ImageNet1k分类数据集,PaddleClas支持35个系列分类网络结构以及对应的164个图像分类预训练模型,训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现,下面所有的速度指标评估环境如下: * CPU的评估环境基于骁龙855(SD855)。 * GPU评估环境基于T4机器,在FP32+TensorRT配置下运行500次测得(去除前10次的warmup时间)。 diff --git a/docs/zh_CN/tutorials/install.md b/docs/zh_CN/tutorials/install.md index 0b554fddece8509d2d5ccfdc4b1c425f45e5ddad..fad6d90e097ec9ddef8890b682768cdcf8c6f2c2 100644 --- a/docs/zh_CN/tutorials/install.md +++ b/docs/zh_CN/tutorials/install.md @@ -44,7 +44,7 @@ sudo nvidia-docker run --name ppcls -v $PWD:/paddle --shm-size=8G --network=host ``` # ctrl+P+Q可退出docker 容器,重新进入docker 容器使用如下命令 -sudo docker container exec -it ppcls /bin/bash +sudo docker exec -it ppcls /bin/bash ``` ### 1.3 通过pip安装PaddlePaddle diff --git a/hubconf.py b/hubconf.py index 84ae3a3edff1089ce6c524c2b790038813e07ccf..eb114bc20d0b7954ec5b8fd3206cfffe5ef0407b 100644 --- a/hubconf.py +++ b/hubconf.py @@ -43,8 +43,8 @@ class _SysPathG(object): with _SysPathG( os.path.join( - os.path.dirname(os.path.abspath(__file__)), 'ppcls', 'modeling')): - import architectures + os.path.dirname(os.path.abspath(__file__)), 'ppcls', 'arch')): + import backbone def _load_pretrained_parameters(model, name): url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format( @@ -63,7 +63,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `AlexNet` model depends on args. """ - model = architectures.AlexNet(**kwargs) + model = backbone.AlexNet(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'AlexNet') @@ -80,7 +80,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `VGG11` model depends on args. """ - model = architectures.VGG11(**kwargs) + model = backbone.VGG11(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG11') @@ -97,7 +97,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `VGG13` model depends on args. """ - model = architectures.VGG13(**kwargs) + model = backbone.VGG13(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG13') @@ -114,7 +114,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `VGG16` model depends on args. """ - model = architectures.VGG16(**kwargs) + model = backbone.VGG16(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG16') @@ -131,7 +131,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `VGG19` model depends on args. """ - model = architectures.VGG19(**kwargs) + model = backbone.VGG19(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG19') @@ -149,7 +149,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNet18` model depends on args. """ - model = architectures.ResNet18(**kwargs) + model = backbone.ResNet18(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet18') @@ -167,7 +167,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNet34` model depends on args. """ - model = architectures.ResNet34(**kwargs) + model = backbone.ResNet34(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet34') @@ -185,7 +185,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNet50` model depends on args. """ - model = architectures.ResNet50(**kwargs) + model = backbone.ResNet50(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet50') @@ -203,7 +203,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNet101` model depends on args. """ - model = architectures.ResNet101(**kwargs) + model = backbone.ResNet101(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet101') @@ -221,7 +221,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNet152` model depends on args. """ - model = architectures.ResNet152(**kwargs) + model = backbone.ResNet152(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet152') @@ -237,7 +237,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `SqueezeNet1_0` model depends on args. """ - model = architectures.SqueezeNet1_0(**kwargs) + model = backbone.SqueezeNet1_0(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'SqueezeNet1_0') @@ -253,7 +253,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `SqueezeNet1_1` model depends on args. """ - model = architectures.SqueezeNet1_1(**kwargs) + model = backbone.SqueezeNet1_1(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'SqueezeNet1_1') @@ -271,7 +271,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `DenseNet121` model depends on args. """ - model = architectures.DenseNet121(**kwargs) + model = backbone.DenseNet121(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet121') @@ -289,7 +289,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `DenseNet161` model depends on args. """ - model = architectures.DenseNet161(**kwargs) + model = backbone.DenseNet161(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet161') @@ -307,7 +307,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `DenseNet169` model depends on args. """ - model = architectures.DenseNet169(**kwargs) + model = backbone.DenseNet169(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet169') @@ -325,7 +325,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `DenseNet201` model depends on args. """ - model = architectures.DenseNet201(**kwargs) + model = backbone.DenseNet201(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet201') @@ -343,7 +343,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `DenseNet264` model depends on args. """ - model = architectures.DenseNet264(**kwargs) + model = backbone.DenseNet264(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet264') @@ -359,7 +359,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `InceptionV3` model depends on args. """ - model = architectures.InceptionV3(**kwargs) + model = backbone.InceptionV3(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'InceptionV3') @@ -375,7 +375,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `InceptionV4` model depends on args. """ - model = architectures.InceptionV4(**kwargs) + model = backbone.InceptionV4(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'InceptionV4') @@ -391,7 +391,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `GoogLeNet` model depends on args. """ - model = architectures.GoogLeNet(**kwargs) + model = backbone.GoogLeNet(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'GoogLeNet') @@ -407,7 +407,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ShuffleNetV2_x0_25` model depends on args. """ - model = architectures.ShuffleNetV2_x0_25(**kwargs) + model = backbone.ShuffleNetV2_x0_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ShuffleNetV2_x0_25') @@ -423,7 +423,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV1` model depends on args. """ - model = architectures.MobileNetV1(**kwargs) + model = backbone.MobileNetV1(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1') @@ -439,7 +439,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args. """ - model = architectures.MobileNetV1_x0_25(**kwargs) + model = backbone.MobileNetV1_x0_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_25') @@ -455,7 +455,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args. """ - model = architectures.MobileNetV1_x0_5(**kwargs) + model = backbone.MobileNetV1_x0_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_5') @@ -471,7 +471,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args. """ - model = architectures.MobileNetV1_x0_75(**kwargs) + model = backbone.MobileNetV1_x0_75(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_75') @@ -487,7 +487,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV2_x0_25` model depends on args. """ - model = architectures.MobileNetV2_x0_25(**kwargs) + model = backbone.MobileNetV2_x0_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x0_25') @@ -503,7 +503,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV2_x0_5` model depends on args. """ - model = architectures.MobileNetV2_x0_5(**kwargs) + model = backbone.MobileNetV2_x0_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x0_5') @@ -519,7 +519,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV2_x0_75` model depends on args. """ - model = architectures.MobileNetV2_x0_75(**kwargs) + model = backbone.MobileNetV2_x0_75(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x0_75') @@ -535,7 +535,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV2_x1_5` model depends on args. """ - model = architectures.MobileNetV2_x1_5(**kwargs) + model = backbone.MobileNetV2_x1_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x1_5') @@ -551,7 +551,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV2_x2_0` model depends on args. """ - model = architectures.MobileNetV2_x2_0(**kwargs) + model = backbone.MobileNetV2_x2_0(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x2_0') @@ -567,7 +567,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_large_x0_35` model depends on args. """ - model = architectures.MobileNetV3_large_x0_35(**kwargs) + model = backbone.MobileNetV3_large_x0_35(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_35') @@ -584,7 +584,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_large_x0_5` model depends on args. """ - model = architectures.MobileNetV3_large_x0_5(**kwargs) + model = backbone.MobileNetV3_large_x0_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_5') @@ -601,7 +601,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_large_x0_75` model depends on args. """ - model = architectures.MobileNetV3_large_x0_75(**kwargs) + model = backbone.MobileNetV3_large_x0_75(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_75') @@ -618,7 +618,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_large_x1_0` model depends on args. """ - model = architectures.MobileNetV3_large_x1_0(**kwargs) + model = backbone.MobileNetV3_large_x1_0(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_large_x1_0') @@ -635,7 +635,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_large_x1_25` model depends on args. """ - model = architectures.MobileNetV3_large_x1_25(**kwargs) + model = backbone.MobileNetV3_large_x1_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_large_x1_25') @@ -652,7 +652,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_small_x0_35` model depends on args. """ - model = architectures.MobileNetV3_small_x0_35(**kwargs) + model = backbone.MobileNetV3_small_x0_35(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_35') @@ -669,7 +669,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_small_x0_5` model depends on args. """ - model = architectures.MobileNetV3_small_x0_5(**kwargs) + model = backbone.MobileNetV3_small_x0_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_5') @@ -686,7 +686,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_small_x0_75` model depends on args. """ - model = architectures.MobileNetV3_small_x0_75(**kwargs) + model = backbone.MobileNetV3_small_x0_75(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_75') @@ -703,7 +703,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_small_x1_0` model depends on args. """ - model = architectures.MobileNetV3_small_x1_0(**kwargs) + model = backbone.MobileNetV3_small_x1_0(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_small_x1_0') @@ -720,7 +720,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `MobileNetV3_small_x1_25` model depends on args. """ - model = architectures.MobileNetV3_small_x1_25(**kwargs) + model = backbone.MobileNetV3_small_x1_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV3_small_x1_25') @@ -737,7 +737,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt101_32x4d` model depends on args. """ - model = architectures.ResNeXt101_32x4d(**kwargs) + model = backbone.ResNeXt101_32x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt101_32x4d') @@ -753,7 +753,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt101_64x4d` model depends on args. """ - model = architectures.ResNeXt101_64x4d(**kwargs) + model = backbone.ResNeXt101_64x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt101_64x4d') @@ -769,7 +769,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt152_32x4d` model depends on args. """ - model = architectures.ResNeXt152_32x4d(**kwargs) + model = backbone.ResNeXt152_32x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt152_32x4d') @@ -785,7 +785,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt152_64x4d` model depends on args. """ - model = architectures.ResNeXt152_64x4d(**kwargs) + model = backbone.ResNeXt152_64x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt152_64x4d') @@ -801,7 +801,7 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt50_32x4d` model depends on args. """ - model = architectures.ResNeXt50_32x4d(**kwargs) + model = backbone.ResNeXt50_32x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt50_32x4d') @@ -817,8 +817,24 @@ with _SysPathG( Returns: model: nn.Layer. Specific `ResNeXt50_64x4d` model depends on args. """ - model = architectures.ResNeXt50_64x4d(**kwargs) + model = backbone.ResNeXt50_64x4d(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNeXt50_64x4d') return model + + def darknet53(pretrained=False, **kwargs): + """ + DarkNet53 + Args: + pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise. + kwargs: + class_dim: int=1000. Output dim of last fc layer. + Returns: + model: nn.Layer. Specific `ResNeXt50_64x4d` model depends on args. + """ + model = backbone.DarkNet53(**kwargs) + if pretrained: + model = _load_pretrained_parameters(model, 'DarkNet53') + + return model diff --git a/ppcls/arch/__init__.py b/ppcls/arch/__init__.py index 23bcc630dfa0c22ea958b7c1999f5e7fc7056864..0117def4cea0fabf559305dbc21fb6928d0d4230 100644 --- a/ppcls/arch/__init__.py +++ b/ppcls/arch/__init__.py @@ -16,11 +16,14 @@ import copy import importlib import paddle.nn as nn +from paddle.jit import to_static +from paddle.static import InputSpec from . import backbone, gears from .backbone import * from .gears import build_gear from .utils import * +from ppcls.utils import logger from ppcls.utils.save_load import load_dygraph_pretrain __all__ = ["build_model", "RecModel", "DistillationModel"] @@ -34,6 +37,19 @@ def build_model(config): return arch +def apply_to_static(config, model): + support_to_static = config['Global'].get('to_static', False) + + if support_to_static: + specs = None + if 'image_shape' in config['Global']: + specs = [InputSpec([None] + config['Global']['image_shape'])] + model = to_static(model, input_spec=specs) + logger.info("Successfully to apply @to_static with specs: {}".format( + specs)) + return model + + class RecModel(nn.Layer): def __init__(self, **config): super().__init__() diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml index e49e8366e40a6e6a278b5c1ab3b82aae686e5b54..8eeb507b29252536478ea947ba0ac94e8bfdbfcf 100644 --- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml +++ b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml @@ -14,6 +14,8 @@ Global: # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference + # training model under @to_static + to_static: False # model architecture Arch: diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml index 3046f26f091a34a92c4c8d53d066f126ba760d16..6c58dd3ae1b3aca01fe4efbf3b8ccb19c36712bf 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml +++ b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml @@ -14,6 +14,8 @@ Global: # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference + # training model under @to_static + to_static: False # model architecture Arch: diff --git a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml b/ppcls/configs/ImageNet/ResNet/ResNet101.yaml index 1c00203e9f6729af59cfa8be8a8d6147feba7d23..9bfcdecb6c8efbdc4e81499652d2d97200aa63f6 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml +++ b/ppcls/configs/ImageNet/ResNet/ResNet101.yaml @@ -14,6 +14,8 @@ Global: # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference + # training model under @to_static + to_static: False # model architecture Arch: diff --git a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml b/ppcls/configs/ImageNet/ResNet/ResNet152.yaml index d88c665b5826f5e11c6d1faed3703f582ac48e0e..95ca0d80d6ae6990a364bbea6e7fc0a0d1d5c394 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml +++ b/ppcls/configs/ImageNet/ResNet/ResNet152.yaml @@ -14,6 +14,8 @@ Global: # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference + # training model under @to_static + to_static: False # model architecture Arch: diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50.yaml index 9b725141432bf65c454f54649e06b0397cd7f14a..8e63061e3e3fedda3bf1e54980f71bc7151d5cec 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml +++ b/ppcls/configs/ImageNet/ResNet/ResNet50.yaml @@ -14,6 +14,8 @@ Global: # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference + # training model under @to_static + to_static: False # model architecture Arch: diff --git a/ppcls/engine/trainer.py b/ppcls/engine/trainer.py index b70793f3b4dbbeb7363c59fc7a45e3af58d6a772..7848ae6fb09155add1ec2f16d04dfcb6fa6b0cee 100644 --- a/ppcls/engine/trainer.py +++ b/ppcls/engine/trainer.py @@ -34,6 +34,7 @@ from ppcls.utils.logger import init_logger from ppcls.utils.config import print_config from ppcls.data import build_dataloader from ppcls.arch import build_model +from ppcls.arch import apply_to_static from ppcls.loss import build_loss from ppcls.metric import build_metrics from ppcls.optimizer import build_optimizer @@ -73,6 +74,8 @@ class Trainer(object): self.is_rec = False self.model = build_model(self.config["Arch"]) + # set @to_static for benchmark, skip this by default. + apply_to_static(self.config, self.model) if self.config["Global"]["pretrained_model"] is not None: load_dygraph_pretrain(self.model,