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Merge branch 'develop' of https://github.com/PaddlePaddle/FastDeploy into huawei

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25
examples/vision/detection/scaledyolov4/README.md
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## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了ScaledYOLOv4导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了ScaledYOLOv4导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [ScaledYOLOv4-P5-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5-896.onnx) | 271MB | 51.2% | | [ScaledYOLOv4-P5-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5-896.onnx) | 271MB | 51.2% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P5+BoF-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_-896.onnx) | 271MB | 51.7% | | [ScaledYOLOv4-P5+BoF-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_-896.onnx) | 271MB | 51.7% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6-1280.onnx) | 487MB | 53.9% | | [ScaledYOLOv4-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6-1280.onnx) | 487MB | 53.9% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P6+BoF-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_-1280.onnx) | 487MB | 54.4% | | [ScaledYOLOv4-P6+BoF-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_-1280.onnx) | 487MB | 54.4% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P7-1536](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7-1536.onnx) | 1.1GB | 55.0% | | [ScaledYOLOv4-P7-1536](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7-1536.onnx) | 1.1GB | 55.0% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P5](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5.onnx) | 271MB | - | | [ScaledYOLOv4-P5](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5.onnx) | 271MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P5+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_.onnx) | 271MB | -| | [ScaledYOLOv4-P5+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_.onnx) | 271MB | -| 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P6](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6.onnx) | 487MB | - | | [ScaledYOLOv4-P6](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6.onnx) | 487MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P6+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_.onnx) | 487MB | - | | [ScaledYOLOv4-P6+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_.onnx) | 487MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
| [ScaledYOLOv4-P7](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7.onnx) | 1.1GB | - | | [ScaledYOLOv4-P7](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7.onnx) | 1.1GB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolor/README.md
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## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOR导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOR导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% | | [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-W6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-1280-1280.onnx) | 305MB | 55.5% | | [YOLOR-W6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-1280-1280.onnx) | 305MB | 55.5% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-E6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-1280-1280.onnx ) | 443MB | 56.4% | | [YOLOR-E6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-1280-1280.onnx ) | 443MB | 56.4% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-1280-1280.onnx) | 580MB | 57.0% | | [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-1280-1280.onnx) | 580MB | 57.0% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-1280-1280.onnx) | 580MB | 57.3% | | [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-1280-1280.onnx) | 580MB | 57.3% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-P6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-640-640.onnx) | 143MB | - | | [YOLOR-P6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-640-640.onnx) | 143MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-W6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-640-640.onnx) | 305MB | - | | [YOLOR-W6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-640-640.onnx) | 305MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-E6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-640-640.onnx ) | 443MB | - | | [YOLOR-E6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-640-640.onnx ) | 443MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-640-640.onnx) | 580MB | - | | [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-640-640.onnx) | 580MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-640-640.onnx) | 580MB | - | | [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-640-640.onnx) | 580MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolov5/README.md
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## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv5导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv5导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:---- |
| [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.6MB | 28.0% | | [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.6MB | 28.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28MB | 37.4% | | [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28MB | 37.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 82MB | 45.4% | | [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 82MB | 45.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 178MB | 49.0% | | [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 178MB | 49.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 332MB | 50.7% | | [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 332MB | 50.7% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolov5lite/README.md
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## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv5Lite导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv5Lite导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [YOLOv5Lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-e-sim-320.onnx) | 3.1MB | 35.1% | | [YOLOv5Lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-e-sim-320.onnx) | 3.1MB | 35.1% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License ｜
| [YOLOv5Lite-s](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-s-sim-416.onnx) | 6.3MB | 42.0% | | [YOLOv5Lite-s](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-s-sim-416.onnx) | 6.3MB | 42.0% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
| [YOLOv5Lite-c](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-c-sim-512.onnx) | 18MB | 50.9% | | [YOLOv5Lite-c](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-c-sim-512.onnx) | 18MB | 50.9% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
| [YOLOv5Lite-g](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-g-sim-640.onnx) | 21MB | 57.6% | | [YOLOv5Lite-g](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-g-sim-640.onnx) | 21MB | 57.6% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolov6/README.md
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@@ -11,13 +11,12 @@
## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv6导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv6导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [YOLOv6s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx) | 66MB | 43.1% | | [YOLOv6s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx) | 66MB | 43.1% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
| [YOLOv6s_640](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s-640x640.onnx) | 66MB | 43.1% | | [YOLOv6s_640](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s-640x640.onnx) | 66MB | 43.1% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
| [YOLOv6t](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6t.onnx) | 58MB | 41.3% | | [YOLOv6t](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6t.onnx) | 58MB | 41.3% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
| [YOLOv6n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6n.onnx) | 17MB | 35.0% | | [YOLOv6n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6n.onnx) | 17MB | 35.0% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolov7/README.md
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@@ -27,16 +27,14 @@ python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv7导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv7导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- | :----- |
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | | [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | | [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | | [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | | [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | | [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | | [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

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examples/vision/detection/yolov7/README_EN.md
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@@ -24,14 +24,14 @@ python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
To facilitate testing for developers, we provide below the models exported by YOLOv7, which developers can download and use directly. (The accuracy of the models in the table is sourced from the official library) To facilitate testing for developers, we provide below the models exported by YOLOv7, which developers can download and use directly. (The accuracy of the models in the table is sourced from the official library)
| Model | Size | Accuracy | | Model | Size | Accuracy | Note |
| ------------------------------------------------------------------------ | ----- | -------- | | ------------------------------------------------------------------------ | ----- | -------- | -------- |
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | | [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | | [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | | [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | | [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | | [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | | [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
## Detailed Deployment Tutorials ## Detailed Deployment Tutorials

16
examples/vision/detection/yolov7end2end_ort/README.md
View File

@@ -20,14 +20,14 @@ python export.py --weights yolov7.pt --grid --end2end --simplify --topk-all 100
## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv7End2EndORT导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv7End2EndORT导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [yolov7-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-ort-nms.onnx) | 141MB | 51.4% | | [yolov7-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-ort-nms.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7x-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-ort-nms.onnx) | 273MB | 53.1% | | [yolov7x-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-ort-nms.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-w6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-ort-nms.onnx) | 269MB | 54.9% | | [yolov7-w6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-ort-nms.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-e6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-ort-nms.onnx) | 372MB | 56.0% | | [yolov7-e6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-ort-nms.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-d6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-ort-nms.onnx) | 511MB | 56.6% | | [yolov7-d6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-ort-nms.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-e6e-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-ort-nms.onnx) | 579MB | 56.8% | | [yolov7-e6e-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-ort-nms.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

16
examples/vision/detection/yolov7end2end_trt/README.md
View File

@@ -22,14 +22,14 @@ python export.py --weights yolov7.pt --grid --end2end --simplify --topk-all 100
## 下载预训练ONNX模型 ## 下载预训练ONNX模型
为了方便开发者的测试，下面提供了YOLOv7End2EndTRT 导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） 为了方便开发者的测试，下面提供了YOLOv7End2EndTRT 导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
| 模型 | 大小 | 精度 | | 模型 | 大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | |:---------------------------------------------------------------- |:----- |:----- |:----- |
| [yolov7-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx) | 141MB | 51.4% | | [yolov7-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7x-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-trt-nms.onnx) | 273MB | 53.1% | | [yolov7x-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-trt-nms.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-w6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-trt-nms.onnx) | 269MB | 54.9% | | [yolov7-w6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-trt-nms.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-e6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-trt-nms.onnx) | 372MB | 56.0% | | [yolov7-e6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-trt-nms.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-d6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-trt-nms.onnx) | 511MB | 56.6% | | [yolov7-d6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-trt-nms.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
| [yolov7-e6e-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-trt-nms.onnx) | 579MB | 56.8% | | [yolov7-e6e-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-trt-nms.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
## 详细部署文档 ## 详细部署文档

8
examples/vision/matting/rvm/README.md
View File

@@ -16,10 +16,10 @@
| 模型 | 参数大小 | 精度 | 备注 | | 模型 | 参数大小 | 精度 | 备注 |
|:---------------------------------------------------------------- |:----- |:----- | :------ | |:---------------------------------------------------------------- |:----- |:----- | :------ |
| [rvm_mobilenetv3_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx) | 15MB | - | | [rvm_mobilenetv3_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx) | 15MB ||exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
| [rvm_resnet50_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_fp32.onnx) | 103MB | - | | [rvm_resnet50_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_fp32.onnx) | 103MB | |exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
| [rvm_mobilenetv3_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx) | 15MB | - | | [rvm_mobilenetv3_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx) | 15MB | |exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
| [rvm_resnet50_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_trt.onnx) | 103MB | - | | [rvm_resnet50_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_trt.onnx) | 103MB | | exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
**Note**： **Note**：
- 如果要使用 TensorRT 进行推理，需要下载后缀为 trt 的 onnx 模型文件 - 如果要使用 TensorRT 进行推理，需要下载后缀为 trt 的 onnx 模型文件

11
serving/Dockerfile
View File

@@ -41,8 +41,12 @@ RUN apt-get update \
RUN apt-get update \ RUN apt-get update \
&& apt-get install -y --no-install-recommends libre2-5 libb64-0d python3 python3-pip libarchive-dev ffmpeg libsm6 libxext6 \ && apt-get install -y --no-install-recommends libre2-5 libb64-0d python3 python3-pip libarchive-dev ffmpeg libsm6 libxext6 \
&& python3 -m pip install -U pip \ && python3 -m pip install -U pip \
&& python3 -m pip install paddlenlp fast-tokenizer-python \ && python3 -m pip install paddlenlp fast-tokenizer-python
&& python3 -m pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
# unset proxy
ENV http_proxy=
ENV https_proxy=
python3 -m pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
COPY python/dist/*.whl /opt/fastdeploy/ COPY python/dist/*.whl /opt/fastdeploy/
RUN python3 -m pip install /opt/fastdeploy/*.whl \ RUN python3 -m pip install /opt/fastdeploy/*.whl \
@@ -53,6 +57,3 @@ COPY build/fastdeploy_install /opt/fastdeploy/
ENV LD_LIBRARY_PATH="/opt/TensorRT-8.4.1.5/lib/:/opt/fastdeploy/lib:/opt/fastdeploy/third_libs/install/onnxruntime/lib:/opt/fastdeploy/third_libs/install/paddle2onnx/lib:/opt/fastdeploy/third_libs/install/tensorrt/lib:/opt/fastdeploy/third_libs/install/paddle_inference/paddle/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mkldnn/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mklml/lib:/opt/fastdeploy/third_libs/install/openvino/runtime/lib:$LD_LIBRARY_PATH" ENV LD_LIBRARY_PATH="/opt/TensorRT-8.4.1.5/lib/:/opt/fastdeploy/lib:/opt/fastdeploy/third_libs/install/onnxruntime/lib:/opt/fastdeploy/third_libs/install/paddle2onnx/lib:/opt/fastdeploy/third_libs/install/tensorrt/lib:/opt/fastdeploy/third_libs/install/paddle_inference/paddle/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mkldnn/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mklml/lib:/opt/fastdeploy/third_libs/install/openvino/runtime/lib:$LD_LIBRARY_PATH"
ENV PATH="/opt/tritonserver/bin:$PATH" ENV PATH="/opt/tritonserver/bin:$PATH"
# unset proxy
ENV http_proxy=
ENV https_proxy=

40
serving/scripts/build.sh
View File

@@ -12,7 +12,41 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
WITH_GPU=${1:-ON}
ARGS=`getopt -a -o w:n:h:hs -l WITH_GPU:,docker_name:,http_proxy:,https_proxy: -- "$@"`
eval set -- "${ARGS}"
echo "parse start"
while true
do
case "$1" in
-w|--WITH_GPU)
WITH_GPU="$2"
shift;;
-n|--docker_name)
docker_name="$2"
shift;;
-h|--http_proxy)
http_proxy="$2"
shift;;
-hs|--https_proxy)
https_proxy="$2"
shift;;
--)
shift
break;;
esac
shift
done
if [ -z $WITH_GPU ];then
WITH_GPU="ON"
fi
if [ -z $docker_name ];then
docker_name="build_fd"
fi
if [ $WITH_GPU == "ON" ]; then if [ $WITH_GPU == "ON" ]; then
@@ -30,7 +64,7 @@ if [ ! -d "./TensorRT-8.4.1.5/" ]; then
rm -rf TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz rm -rf TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz
fi fi
nvidia-docker run -i --rm --name build_fd \ nvidia-docker run -i --rm --name ${docker_name} \
-v`pwd`/..:/workspace/fastdeploy \ -v`pwd`/..:/workspace/fastdeploy \
-e "http_proxy=${http_proxy}" \ -e "http_proxy=${http_proxy}" \
-e "https_proxy=${https_proxy}" \ -e "https_proxy=${https_proxy}" \
@@ -68,7 +102,7 @@ else
echo "start build FD CPU library" echo "start build FD CPU library"
docker run -i --rm --name build_fd \ docker run -i --rm --name ${docker_name} \
-v`pwd`/..:/workspace/fastdeploy \ -v`pwd`/..:/workspace/fastdeploy \
-e "http_proxy=${http_proxy}" \ -e "http_proxy=${http_proxy}" \
-e "https_proxy=${https_proxy}" \ -e "https_proxy=${https_proxy}" \