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[Model] Support Insightface model inferenceing on RKNPU (#1113)
* 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * Update issues.md * Update fastdeploy_init.sh * 更新交叉编译 * 更新insightface系列模型的rknpu2支持 * 更新insightface系列模型的rknpu2支持 * 更新说明文档 * 更新insightface * 尝试解决pybind问题 Co-authored-by: Jason <928090362@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
Zheng-Bicheng
@@ -13,14 +13,22 @@ ONNX模型不能直接调用RK芯片中的NPU进行运算，需要把ONNX模型
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* ARM CPU使用ONNX框架进行测试
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* NPU均使用单核进行测试
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| 任务场景 | 模型 | 模型版本(表示已经测试的版本) | ARM CPU/RKNN速度(ms) |
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|----------------|------------------------------------------------------------------------------------------|--------------------------|--------------------|
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| Detection | [Picodet](../../../../examples/vision/detection/paddledetection/rknpu2/README.md) | Picodet-s | 162/112 |
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| Detection | [RKYOLOV5](../../../../examples/vision/detection/rkyolo/README.md) | YOLOV5-S-Relu(int8) | -/57 |
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| Detection | [RKYOLOX](../../../../examples/vision/detection/rkyolo/README.md) | - | -/- |
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| Detection | [RKYOLOV7](../../../../examples/vision/detection/rkyolo/README.md) | - | -/- |
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| Segmentation | [Unet](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | Unet-cityscapes | -/- |
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| Segmentation | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | portrait(int8) | 133/43 |
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| Segmentation | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | human(int8) | 133/43 |
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| Face Detection | [SCRFD](../../../../examples/vision/facedet/scrfd/rknpu2/README.md) | SCRFD-2.5G-kps-640(int8) | 108/42 |
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| Classification | [ResNet](../../../../examples/vision/classification/paddleclas/rknpu2/README.md) | ResNet50_vd | -/33 |
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| 任务场景 | 模型 | 模型版本(表示已经测试的版本) | ARM CPU/RKNN速度(ms) |
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|----------------------|------------------------------------------------------------------------------------------|--------------------------|--------------------|
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| Detection | [Picodet](../../../../examples/vision/detection/paddledetection/rknpu2/README.md) | Picodet-s | 162/112 |
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| Detection | [RKYOLOV5](../../../../examples/vision/detection/rkyolo/README.md) | YOLOV5-S-Relu(int8) | -/57 |
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| Detection | [RKYOLOX](../../../../examples/vision/detection/rkyolo/README.md) | - | -/- |
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| Detection | [RKYOLOV7](../../../../examples/vision/detection/rkyolo/README.md) | - | -/- |
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| Segmentation | [Unet](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | Unet-cityscapes | -/- |
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| Segmentation | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | portrait(int8) | 133/43 |
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| Segmentation | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | human(int8) | 133/43 |
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| Face Detection | [SCRFD](../../../../examples/vision/facedet/scrfd/rknpu2/README.md) | SCRFD-2.5G-kps-640(int8) | 108/42 |
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| Face FaceRecognition | [InsightFace](../../../../examples/vision/faceid/insightface/rknpu2/README_CN.md) | ms1mv3_arcface_r18(int8) | 81/12 |
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| Classification | [ResNet](../../../../examples/vision/classification/paddleclas/rknpu2/README.md) | ResNet50_vd | -/33 |
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## 预编译库下载
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为了方便大家进行开发，这里提供1.0.2版本的FastDeploy给大家使用
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- [FastDeploy RK356X c++ SDK](https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-aarch64-rk356X-1.0.2.tgz)
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- [FastDeploy RK3588 c++ SDK](https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-aarch64-rk3588-1.0.2.tgz)
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@@ -101,7 +101,7 @@ VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。
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#### Predict函数
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> ```c++
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> ArcFace::Predict(cv::Mat* im, FaceRecognitionResult* result)
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> ArcFace::Predict(const cv::Mat& im, FaceRecognitionResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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@@ -121,8 +121,6 @@ VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。
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通过InsightFaceRecognitionPreprocessor::SetAlpha(std::vector<float>& alpha)来进行修改
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> > * **beta**(vector<float>): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f],
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通过InsightFaceRecognitionPreprocessor::SetBeta(std::vector<float>& beta)来进行修改
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> > * **permute**(bool): 预处理是否将BGR转换成RGB，默认true,
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通过InsightFaceRecognitionPreprocessor::SetPermute(bool permute)来进行修改
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#### InsightFaceRecognitionPostprocessor成员变量(后处理参数)
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> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false,
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@@ -100,7 +100,6 @@ ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式
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> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[112, 112]
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> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
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> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
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> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
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#### AdaFacePostprocessor的成员变量
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以下变量为AdaFacePostprocessor的成员变量
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@@ -3,7 +3,6 @@ import cv2
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import numpy as np
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# 余弦相似度
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def cosine_similarity(a, b):
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a = np.array(a)
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b = np.array(b)
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@@ -56,24 +55,17 @@ def build_option(args):
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.faceid.ArcFace(args.model, runtime_option=runtime_option)
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# 加载图片
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face0 = cv2.imread(args.face) # 0,1 同一个人
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face1 = cv2.imread(args.face_positive)
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face2 = cv2.imread(args.face_negative) # 0,2 不同的人
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# 设置 l2 normalize
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model.postprocessor.l2_normalize = True
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# 预测图片检测结果
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result0 = model.predict(face0)
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result1 = model.predict(face1)
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result2 = model.predict(face2)
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# 计算余弦相似度
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embedding0 = result0.embedding
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embedding1 = result1.embedding
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embedding2 = result2.embedding
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@@ -81,7 +73,6 @@ embedding2 = result2.embedding
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cosine01 = cosine_similarity(embedding0, embedding1)
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cosine02 = cosine_similarity(embedding0, embedding2)
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# 打印结果
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print(result0, end="")
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print(result1, end="")
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print(result2, end="")
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@@ -3,7 +3,6 @@ import cv2
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import numpy as np
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# 余弦相似度
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def cosine_similarity(a, b):
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a = np.array(a)
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b = np.array(b)
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@@ -56,24 +55,17 @@ def build_option(args):
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.faceid.CosFace(args.model, runtime_option=runtime_option)
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# 加载图片
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face0 = cv2.imread(args.face) # 0,1 同一个人
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face0 = cv2.imread(args.face)
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face1 = cv2.imread(args.face_positive)
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face2 = cv2.imread(args.face_negative) # 0,2 不同的人
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face2 = cv2.imread(args.face_negative)
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# 设置 l2 normalize
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model.postprocessor.l2_normalize = True
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# 预测图片检测结果
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result0 = model.predict(face0)
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result1 = model.predict(face1)
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result2 = model.predict(face2)
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# 计算余弦相似度
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embedding0 = result0.embedding
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embedding1 = result1.embedding
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embedding2 = result2.embedding
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@@ -81,7 +73,6 @@ embedding2 = result2.embedding
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cosine01 = cosine_similarity(embedding0, embedding1)
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cosine02 = cosine_similarity(embedding0, embedding2)
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# 打印结果
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print(result0, end="")
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print(result1, end="")
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print(result2, end="")
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@@ -3,7 +3,6 @@ import cv2
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import numpy as np
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# 余弦相似度
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def cosine_similarity(a, b):
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a = np.array(a)
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b = np.array(b)
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@@ -56,24 +55,18 @@ def build_option(args):
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.faceid.PartialFC(args.model, runtime_option=runtime_option)
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# 加载图片
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face0 = cv2.imread(args.face) # 0,1 同一个人
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face0 = cv2.imread(args.face)
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face1 = cv2.imread(args.face_positive)
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face2 = cv2.imread(args.face_negative) # 0,2 不同的人
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face2 = cv2.imread(args.face_negative)
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# 设置 l2 normalize
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model.postprocessor.l2_normalize = True
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# 预测图片检测结果
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result0 = model.predict(face0)
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result1 = model.predict(face1)
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result2 = model.predict(face2)
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# 计算余弦相似度
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embedding0 = result0.embedding
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embedding1 = result1.embedding
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embedding2 = result2.embedding
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@@ -81,7 +74,6 @@ embedding2 = result2.embedding
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cosine01 = cosine_similarity(embedding0, embedding1)
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cosine02 = cosine_similarity(embedding0, embedding2)
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# 打印结果
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print(result0, end="")
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print(result1, end="")
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print(result2, end="")
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@@ -3,7 +3,6 @@ import cv2
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import numpy as np
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# 余弦相似度
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def cosine_similarity(a, b):
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a = np.array(a)
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b = np.array(b)
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@@ -56,24 +55,17 @@ def build_option(args):
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.faceid.VPL(args.model, runtime_option=runtime_option)
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# 加载图片
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face0 = cv2.imread(args.face) # 0,1 同一个人
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face1 = cv2.imread(args.face_positive)
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face2 = cv2.imread(args.face_negative) # 0,2 不同的人
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# 设置 l2 normalize
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model.postprocessor.l2_normalize = True
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# 预测图片检测结果
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result0 = model.predict(face0)
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result1 = model.predict(face1)
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result2 = model.predict(face2)
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# 计算余弦相似度
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embedding0 = result0.embedding
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embedding1 = result1.embedding
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embedding2 = result2.embedding
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@@ -81,7 +73,6 @@ embedding2 = result2.embedding
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cosine01 = cosine_similarity(embedding0, embedding1)
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cosine02 = cosine_similarity(embedding0, embedding2)
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# 打印结果
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print(result0, end="")
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print(result1, end="")
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print(result2, end="")
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54
examples/vision/faceid/insightface/rknpu2/README.md
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54
examples/vision/faceid/insightface/rknpu2/README.md
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@@ -0,0 +1,54 @@
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[English](README.md) | 简体中文
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# InsightFace RKNPU准备部署模型
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本教程提供InsightFace模型在RKNPU2环境下的部署，模型的详细介绍已经ONNX模型的下载请查看[模型介绍文档](../README.md)。
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## 支持模型列表
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目前FastDeploy支持如下模型的部署
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- ArcFace
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- CosFace
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- PartialFC
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- VPL
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## 下载预训练ONNX模型
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为了方便开发者的测试，下面提供了InsightFace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）其中精度指标来源于InsightFace中对各模型的介绍，详情各参考InsightFace中的说明
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| 模型 | 大小 | 精度 (AgeDB_30) |
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|:-------------------------------------------------------------------------------------------|:------|:--------------|
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| [CosFace-r18](https://bj.bcebos.com/paddlehub/fastdeploy/glint360k_cosface_r18.onnx) | 92MB | 97.7 |
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| [CosFace-r34](https://bj.bcebos.com/paddlehub/fastdeploy/glint360k_cosface_r34.onnx) | 131MB | 98.3 |
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| [CosFace-r50](https://bj.bcebos.com/paddlehub/fastdeploy/glint360k_cosface_r50.onnx) | 167MB | 98.3 |
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| [CosFace-r100](https://bj.bcebos.com/paddlehub/fastdeploy/glint360k_cosface_r100.onnx) | 249MB | 98.4 |
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| [ArcFace-r18](https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r18.onnx) | 92MB | 97.7 |
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| [ArcFace-r34](https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r34.onnx) | 131MB | 98.1 |
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| [ArcFace-r50](https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r50.onnx) | 167MB | - |
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| [ArcFace-r100](https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx) | 249MB | 98.4 |
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| [ArcFace-r100_lr0.1](https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_r100_lr01.onnx) | 249MB | 98.4 |
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| [PartialFC-r34](https://bj.bcebos.com/paddlehub/fastdeploy/partial_fc_glint360k_r50.onnx) | 167MB | - |
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| [PartialFC-r50](https://bj.bcebos.com/paddlehub/fastdeploy/partial_fc_glint360k_r100.onnx) | 249MB | - |
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## 转换为RKNPU模型
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```bash
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r18.onnx
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python -m paddle2onnx.optimize --input_model ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx \
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--output_model ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx \
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--input_shape_dict "{'data':[1,3,112,112]}"
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python /Path/To/FastDeploy/tools/rknpu2/export.py \
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--config_path tools/rknpu2/config/arcface_unquantized.yaml \
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--target_platform rk3588
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```
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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## 版本说明
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- 本版本文档和代码基于[InsightFace CommitID:babb9a5](https://github.com/deepinsight/insightface/commit/babb9a5) 编写
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11
examples/vision/faceid/insightface/rknpu2/cpp/CMakeLists.txt
Normal file
11
examples/vision/faceid/insightface/rknpu2/cpp/CMakeLists.txt
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@@ -0,0 +1,11 @@
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_arcface_demo ${PROJECT_SOURCE_DIR}/infer_arcface.cc)
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target_link_libraries(infer_arcface_demo ${FASTDEPLOY_LIBS})
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136
examples/vision/faceid/insightface/rknpu2/cpp/README.md
Normal file
136
examples/vision/faceid/insightface/rknpu2/cpp/README.md
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@@ -0,0 +1,136 @@
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[English](README.md) | 简体中文
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# InsightFace C++部署示例
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FastDeploy支持在RKNPU上部署包括ArcFace\CosFace\VPL\Partial_FC在内的InsightFace系列模型。
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本目录下提供`infer_arcface.cc`快速完成InsighFace模型包括ArcFace在CPU/RKNPU加速部署的示例。
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在部署前，需确认以下两个步骤:
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1. 软硬件环境满足要求
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2. 根据开发环境，下载预编译部署库或者从头编译FastDeploy仓库
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以上步骤请参考[RK2代NPU部署库编译](../../../../../../docs/cn/build_and_install/rknpu2.md)实现
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在本目录执行如下命令即可完成编译测试
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```bash
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mkdir build
|
||||
cd build
|
||||
# FastDeploy version need >=1.0.3
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
||||
make -j
|
||||
|
||||
# 下载官方转换好的ArcFace模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r18.onnx
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
|
||||
unzip face_demo.zip
|
||||
|
||||
# CPU推理
|
||||
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 0
|
||||
# RKNPU推理
|
||||
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 1
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<div width="700">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
|
||||
</div>
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
## InsightFace C++接口
|
||||
|
||||
### ArcFace类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::faceid::ArcFace(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
```
|
||||
|
||||
ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||||
|
||||
### CosFace类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::faceid::CosFace(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
```
|
||||
|
||||
CosFace模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||||
|
||||
### PartialFC类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::faceid::PartialFC(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
```
|
||||
|
||||
PartialFC模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||||
|
||||
### VPL类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::faceid::VPL(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
```
|
||||
|
||||
VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为ONNX格式
|
||||
|
||||
#### Predict函数
|
||||
|
||||
> ```c++
|
||||
> ArcFace::Predict(const cv::Mat& im, FaceRecognitionResult* result)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||||
> > * **result**: 检测结果，包括检测框，各个框的置信度, FaceRecognitionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
### 修改预处理以及后处理的参数
|
||||
预处理和后处理的参数的需要通过修改InsightFaceRecognitionPostprocessor，InsightFaceRecognitionPreprocessor的成员变量来进行修改。
|
||||
|
||||
#### InsightFaceRecognitionPreprocessor成员变量(预处理参数)
|
||||
> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[112, 112],
|
||||
通过InsightFaceRecognitionPreprocessor::SetSize(std::vector<int>& size)来进行修改
|
||||
> > * **alpha**(vector<float>): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5],
|
||||
通过InsightFaceRecognitionPreprocessor::SetAlpha(std::vector<float>& alpha)来进行修改
|
||||
> > * **beta**(vector<float>): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f],
|
||||
通过InsightFaceRecognitionPreprocessor::SetBeta(std::vector<float>& beta)来进行修改
|
||||
|
||||
#### InsightFaceRecognitionPostprocessor成员变量(后处理参数)
|
||||
> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false,
|
||||
InsightFaceRecognitionPostprocessor::SetL2Normalize(bool& l2_normalize)来进行修改
|
||||
|
||||
- [模型介绍](../../../)
|
||||
- [Python部署](../python)
|
||||
- [视觉模型预测结果](../../../../../../docs/api/vision_results/README.md)
|
||||
- [如何切换模型推理后端引擎](../../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
123
examples/vision/faceid/insightface/rknpu2/cpp/infer_arcface.cc
Normal file
123
examples/vision/faceid/insightface/rknpu2/cpp/infer_arcface.cc
Normal file
@@ -0,0 +1,123 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "fastdeploy/vision.h"
|
||||
|
||||
void CpuInfer(const std::string& model_file,
|
||||
const std::vector<std::string>& image_file) {
|
||||
auto model = fastdeploy::vision::faceid::ArcFace(model_file, "");
|
||||
|
||||
cv::Mat face0 = cv::imread(image_file[0]);
|
||||
fastdeploy::vision::FaceRecognitionResult res0;
|
||||
if (!model.Predict(face0, &res0)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
}
|
||||
|
||||
cv::Mat face1 = cv::imread(image_file[1]);
|
||||
fastdeploy::vision::FaceRecognitionResult res1;
|
||||
if (!model.Predict(face1, &res1)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
}
|
||||
|
||||
cv::Mat face2 = cv::imread(image_file[2]);
|
||||
fastdeploy::vision::FaceRecognitionResult res2;
|
||||
if (!model.Predict(face2, &res2)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << "Prediction Done!" << std::endl;
|
||||
|
||||
std::cout << "--- [Face 0]:" << res0.Str();
|
||||
std::cout << "--- [Face 1]:" << res1.Str();
|
||||
std::cout << "--- [Face 2]:" << res2.Str();
|
||||
|
||||
float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
|
||||
res0.embedding, res1.embedding,
|
||||
model.GetPostprocessor().GetL2Normalize());
|
||||
float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
|
||||
res0.embedding, res2.embedding,
|
||||
model.GetPostprocessor().GetL2Normalize());
|
||||
std::cout << "Detect Done! Cosine 01: " << cosine01
|
||||
<< ", Cosine 02:" << cosine02 << std::endl;
|
||||
}
|
||||
|
||||
void RKNPUInfer(const std::string& model_file,
|
||||
const std::vector<std::string>& image_file) {
|
||||
std::string params_file;
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseRKNPU2();
|
||||
auto format = fastdeploy::ModelFormat::RKNN;
|
||||
auto model = fastdeploy::vision::faceid::ArcFace(model_file, params_file,
|
||||
option, format);
|
||||
model.GetPreprocessor().DisableNormalize();
|
||||
model.GetPreprocessor().DisablePermute();
|
||||
|
||||
cv::Mat face0 = cv::imread(image_file[0]);
|
||||
fastdeploy::vision::FaceRecognitionResult res0;
|
||||
if (!model.Predict(face0, &res0)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
cv::Mat face1 = cv::imread(image_file[1]);
|
||||
fastdeploy::vision::FaceRecognitionResult res1;
|
||||
if (!model.Predict(face1, &res1)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
cv::Mat face2 = cv::imread(image_file[2]);
|
||||
fastdeploy::vision::FaceRecognitionResult res2;
|
||||
if (!model.Predict(face2, &res2)) {
|
||||
std::cerr << "Prediction Failed." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << "Prediction Done!" << std::endl;
|
||||
|
||||
std::cout << "--- [Face 0]:" << res0.Str();
|
||||
std::cout << "--- [Face 1]:" << res1.Str();
|
||||
std::cout << "--- [Face 2]:" << res2.Str();
|
||||
|
||||
float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
|
||||
res0.embedding, res1.embedding,
|
||||
model.GetPostprocessor().GetL2Normalize());
|
||||
float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
|
||||
res0.embedding, res2.embedding,
|
||||
model.GetPostprocessor().GetL2Normalize());
|
||||
std::cout << "Detect Done! Cosine 01: " << cosine01
|
||||
<< ", Cosine 02:" << cosine02 << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 6) {
|
||||
std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
|
||||
"e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
|
||||
"face_0.jpg face_1.jpg face_2.jpg 0"
|
||||
<< std::endl;
|
||||
std::cout << "The data type of run_option is int, "
|
||||
"0: run with cpu; 1: run with rknpu2."
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
std::vector<std::string> image_files = {argv[2], argv[3], argv[4]};
|
||||
if (std::atoi(argv[5]) == 0) {
|
||||
CpuInfer(argv[1], image_files);
|
||||
} else if (std::atoi(argv[5]) == 1) {
|
||||
RKNPUInfer(argv[1], image_files);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
108
examples/vision/faceid/insightface/rknpu2/python/README_CN.md
Normal file
108
examples/vision/faceid/insightface/rknpu2/python/README_CN.md
Normal file
@@ -0,0 +1,108 @@
|
||||
[English](README.md) | 简体中文
|
||||
# InsightFace Python部署示例
|
||||
|
||||
FastDeploy支持在RKNPU上部署包括ArcFace\CosFace\VPL\Partial_FC在内的InsightFace系列模型。
|
||||
|
||||
本目录下提供`infer_arcface.py`快速完成InsighFace模型包括ArcFace在CPU/RKNPU加速部署的示例。
|
||||
|
||||
|
||||
在部署前，需确认以下步骤:
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/rknpu2.md)
|
||||
|
||||
```bash
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd examples/vision/faceid/insightface/python/
|
||||
|
||||
#下载ArcFace模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
|
||||
unzip face_demo.zip
|
||||
|
||||
# CPU推理
|
||||
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
|
||||
--face face_0.jpg \
|
||||
--face_positive face_1.jpg \
|
||||
--face_negative face_2.jpg \
|
||||
--device cpu
|
||||
# GPU推理
|
||||
python infer_arcface.py --model ms1mv3_arcface_r100.onnx \
|
||||
--face face_0.jpg \
|
||||
--face_positive face_1.jpg \
|
||||
--face_negative face_2.jpg \
|
||||
--device gpu
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<div width="700">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
|
||||
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
|
||||
</div>
|
||||
|
||||
```bash
|
||||
Prediction Done!
|
||||
--- [Face 0]:FaceRecognitionResult: [Dim(512), Min(-2.309220), Max(2.372197), Mean(0.016987)]
|
||||
--- [Face 1]:FaceRecognitionResult: [Dim(512), Min(-2.288258), Max(1.995104), Mean(-0.003400)]
|
||||
--- [Face 2]:FaceRecognitionResult: [Dim(512), Min(-3.243411), Max(3.875866), Mean(-0.030682)]
|
||||
Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388
|
||||
|
||||
```
|
||||
|
||||
## InsightFace Python接口
|
||||
|
||||
```python
|
||||
fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
fastdeploy.vision.faceid.CosFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
fastdeploy.vision.faceid.VPL(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
||||
```
|
||||
|
||||
ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为ONNX
|
||||
|
||||
### predict函数
|
||||
|
||||
> ```python
|
||||
> ArcFace.predict(image_data)
|
||||
> ```
|
||||
>
|
||||
> 模型预测结口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
|
||||
> **返回**
|
||||
>
|
||||
> > 返回`fastdeploy.vision.FaceRecognitionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
#### AdaFacePreprocessor的成员变量
|
||||
以下变量为AdaFacePreprocessor的成员变量
|
||||
> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[112, 112]
|
||||
> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
|
||||
> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
|
||||
|
||||
#### AdaFacePostprocessor的成员变量
|
||||
以下变量为AdaFacePostprocessor的成员变量
|
||||
> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认False
|
||||
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [InsightFace 模型介绍](..)
|
||||
- [InsightFace C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
@@ -0,0 +1,76 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
def cosine_similarity(a, b):
|
||||
a = np.array(a)
|
||||
b = np.array(b)
|
||||
mul_a = np.linalg.norm(a, ord=2)
|
||||
mul_b = np.linalg.norm(b, ord=2)
|
||||
mul_ab = np.dot(a, b)
|
||||
return mul_ab / (mul_a * mul_b)
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", required=True, help="Path of insgihtface onnx model.")
|
||||
parser.add_argument(
|
||||
"--face", required=True, help="Path of test face image file.")
|
||||
parser.add_argument(
|
||||
"--face_positive",
|
||||
required=True,
|
||||
help="Path of test face_positive image file.")
|
||||
parser.add_argument(
|
||||
"--face_negative",
|
||||
required=True,
|
||||
help="Path of test face_negative image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu' or 'gpu'.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "npu":
|
||||
option.use_rknpu2()
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
runtime_option = fd.RuntimeOption()
|
||||
model = fd.vision.faceid.ArcFace(args.model, runtime_option=runtime_option)
|
||||
if args.device.lower() == "npu":
|
||||
runtime_option.use_rknpu2()
|
||||
model.preprocessor.disable_normalize()
|
||||
model.preprocessor.disable_permute()
|
||||
|
||||
face0 = cv2.imread(args.face)
|
||||
face1 = cv2.imread(args.face_positive)
|
||||
face2 = cv2.imread(args.face_negative)
|
||||
|
||||
result0 = model.predict(face0)
|
||||
result1 = model.predict(face1)
|
||||
result2 = model.predict(face2)
|
||||
|
||||
embedding0 = result0.embedding
|
||||
embedding1 = result1.embedding
|
||||
embedding2 = result2.embedding
|
||||
|
||||
cosine01 = cosine_similarity(embedding0, embedding1)
|
||||
cosine02 = cosine_similarity(embedding0, embedding2)
|
||||
|
||||
print(result0, end="")
|
||||
print(result1, end="")
|
||||
print(result2, end="")
|
||||
print("Cosine 01: ", cosine01)
|
||||
print("Cosine 02: ", cosine02)
|
||||
print(model.runtime_option)
|
||||
12
fastdeploy/vision/faceid/contrib/insightface/base.cc
Executable file → Normal file
12
fastdeploy/vision/faceid/contrib/insightface/base.cc
Executable file → Normal file
@@ -22,7 +22,6 @@ InsightFaceRecognitionBase::InsightFaceRecognitionBase(
|
||||
const std::string& model_file, const std::string& params_file,
|
||||
const fastdeploy::RuntimeOption& custom_option,
|
||||
const fastdeploy::ModelFormat& model_format) {
|
||||
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
@@ -31,6 +30,7 @@ InsightFaceRecognitionBase::InsightFaceRecognitionBase(
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
valid_kunlunxin_backends = {Backend::LITE};
|
||||
}
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
runtime_option = custom_option;
|
||||
runtime_option.model_format = model_format;
|
||||
runtime_option.model_file = model_file;
|
||||
@@ -55,8 +55,9 @@ bool InsightFaceRecognitionBase::Predict(const cv::Mat& im,
|
||||
return true;
|
||||
}
|
||||
|
||||
bool InsightFaceRecognitionBase::BatchPredict(const std::vector<cv::Mat>& images,
|
||||
std::vector<FaceRecognitionResult>* results){
|
||||
bool InsightFaceRecognitionBase::BatchPredict(
|
||||
const std::vector<cv::Mat>& images,
|
||||
std::vector<FaceRecognitionResult>* results) {
|
||||
std::vector<FDMat> fd_images = WrapMat(images);
|
||||
FDASSERT(images.size() == 1, "Only support batch = 1 now.");
|
||||
if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
|
||||
@@ -70,8 +71,9 @@ bool InsightFaceRecognitionBase::BatchPredict(const std::vector<cv::Mat>& images
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!postprocessor_.Run(reused_output_tensors_, results)){
|
||||
FDERROR << "Failed to postprocess the inference results by runtime." << std::endl;
|
||||
if (!postprocessor_.Run(reused_output_tensors_, results)) {
|
||||
FDERROR << "Failed to postprocess the inference results by runtime."
|
||||
<< std::endl;
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
||||
@@ -19,83 +19,120 @@ void BindInsightFace(pybind11::module& m) {
|
||||
pybind11::class_<vision::faceid::InsightFaceRecognitionPreprocessor>(
|
||||
m, "InsightFaceRecognitionPreprocessor")
|
||||
.def(pybind11::init())
|
||||
.def("run", [](vision::faceid::InsightFaceRecognitionPreprocessor& self,
|
||||
std::vector<pybind11::array>& im_list) {
|
||||
std::vector<vision::FDMat> images;
|
||||
for (size_t i = 0; i < im_list.size(); ++i) {
|
||||
images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
|
||||
}
|
||||
std::vector<FDTensor> outputs;
|
||||
if (!self.Run(&images, &outputs)) {
|
||||
throw std::runtime_error("Failed to preprocess the input data in InsightFaceRecognitionPreprocessor.");
|
||||
}
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
outputs[i].StopSharing();
|
||||
}
|
||||
return outputs;
|
||||
})
|
||||
.def_property("permute", &vision::faceid::InsightFaceRecognitionPreprocessor::GetPermute,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetPermute)
|
||||
.def_property("alpha", &vision::faceid::InsightFaceRecognitionPreprocessor::GetAlpha,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetAlpha)
|
||||
.def_property("beta", &vision::faceid::InsightFaceRecognitionPreprocessor::GetBeta,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetBeta)
|
||||
.def_property("size", &vision::faceid::InsightFaceRecognitionPreprocessor::GetSize,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetSize);
|
||||
.def("run",
|
||||
[](vision::faceid::InsightFaceRecognitionPreprocessor& self,
|
||||
std::vector<pybind11::array>& im_list) {
|
||||
std::vector<vision::FDMat> images;
|
||||
for (size_t i = 0; i < im_list.size(); ++i) {
|
||||
images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
|
||||
}
|
||||
std::vector<FDTensor> outputs;
|
||||
if (!self.Run(&images, &outputs)) {
|
||||
throw std::runtime_error(
|
||||
"Failed to preprocess the input data in "
|
||||
"InsightFaceRecognitionPreprocessor.");
|
||||
}
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
outputs[i].StopSharing();
|
||||
}
|
||||
return outputs;
|
||||
})
|
||||
.def(
|
||||
"disable_normalize",
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::DisableNormalize)
|
||||
.def("disable_permute",
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::DisablePermute)
|
||||
.def_property(
|
||||
"alpha",
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::GetAlpha,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetAlpha)
|
||||
.def_property(
|
||||
"beta", &vision::faceid::InsightFaceRecognitionPreprocessor::GetBeta,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetBeta)
|
||||
.def_property(
|
||||
"size", &vision::faceid::InsightFaceRecognitionPreprocessor::GetSize,
|
||||
&vision::faceid::InsightFaceRecognitionPreprocessor::SetSize);
|
||||
|
||||
pybind11::class_<vision::faceid::InsightFaceRecognitionPostprocessor>(
|
||||
m, "InsightFaceRecognitionPostprocessor")
|
||||
.def(pybind11::init())
|
||||
.def("run", [](vision::faceid::InsightFaceRecognitionPostprocessor& self, std::vector<FDTensor>& inputs) {
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error("Failed to postprocess the runtime result in InsightFaceRecognitionPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def("run", [](vision::faceid::InsightFaceRecognitionPostprocessor& self, std::vector<pybind11::array>& input_array) {
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
std::vector<FDTensor> inputs;
|
||||
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error("Failed to postprocess the runtime result in InsightFaceRecognitionPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def_property("l2_normalize", &vision::faceid::InsightFaceRecognitionPostprocessor::GetL2Normalize,
|
||||
&vision::faceid::InsightFaceRecognitionPostprocessor::SetL2Normalize);
|
||||
.def("run",
|
||||
[](vision::faceid::InsightFaceRecognitionPostprocessor& self,
|
||||
std::vector<FDTensor>& inputs) {
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error(
|
||||
"Failed to postprocess the runtime result in "
|
||||
"InsightFaceRecognitionPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def("run",
|
||||
[](vision::faceid::InsightFaceRecognitionPostprocessor& self,
|
||||
std::vector<pybind11::array>& input_array) {
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
std::vector<FDTensor> inputs;
|
||||
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
||||
if (!self.Run(inputs, &results)) {
|
||||
throw std::runtime_error(
|
||||
"Failed to postprocess the runtime result in "
|
||||
"InsightFaceRecognitionPostprocessor.");
|
||||
}
|
||||
return results;
|
||||
})
|
||||
.def_property(
|
||||
"l2_normalize",
|
||||
&vision::faceid::InsightFaceRecognitionPostprocessor::GetL2Normalize,
|
||||
&vision::faceid::InsightFaceRecognitionPostprocessor::SetL2Normalize);
|
||||
|
||||
pybind11::class_<vision::faceid::InsightFaceRecognitionBase, FastDeployModel>(
|
||||
m, "InsightFaceRecognitionBase")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
|
||||
.def("predict", [](vision::faceid::InsightFaceRecognitionBase& self, pybind11::array& data) {
|
||||
cv::Mat im = PyArrayToCvMat(data);
|
||||
vision::FaceRecognitionResult result;
|
||||
self.Predict(im, &result);
|
||||
return result;
|
||||
})
|
||||
.def("batch_predict", [](vision::faceid::InsightFaceRecognitionBase& self, std::vector<pybind11::array>& data) {
|
||||
std::vector<cv::Mat> images;
|
||||
for (size_t i = 0; i < data.size(); ++i) {
|
||||
images.push_back(PyArrayToCvMat(data[i]));
|
||||
}
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
self.BatchPredict(images, &results);
|
||||
return results;
|
||||
})
|
||||
.def_property_readonly("preprocessor", &vision::faceid::InsightFaceRecognitionBase::GetPreprocessor)
|
||||
.def_property_readonly("postprocessor", &vision::faceid::InsightFaceRecognitionBase::GetPostprocessor);
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>())
|
||||
.def("predict",
|
||||
[](vision::faceid::InsightFaceRecognitionBase& self,
|
||||
pybind11::array& data) {
|
||||
cv::Mat im = PyArrayToCvMat(data);
|
||||
vision::FaceRecognitionResult result;
|
||||
self.Predict(im, &result);
|
||||
return result;
|
||||
})
|
||||
.def("batch_predict",
|
||||
[](vision::faceid::InsightFaceRecognitionBase& self,
|
||||
std::vector<pybind11::array>& data) {
|
||||
std::vector<cv::Mat> images;
|
||||
for (size_t i = 0; i < data.size(); ++i) {
|
||||
images.push_back(PyArrayToCvMat(data[i]));
|
||||
}
|
||||
std::vector<vision::FaceRecognitionResult> results;
|
||||
self.BatchPredict(images, &results);
|
||||
return results;
|
||||
})
|
||||
.def_property_readonly(
|
||||
"preprocessor",
|
||||
&vision::faceid::InsightFaceRecognitionBase::GetPreprocessor)
|
||||
.def_property_readonly(
|
||||
"postprocessor",
|
||||
&vision::faceid::InsightFaceRecognitionBase::GetPostprocessor);
|
||||
|
||||
pybind11::class_<vision::faceid::ArcFace, vision::faceid::InsightFaceRecognitionBase>(m, "ArcFace")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
|
||||
pybind11::class_<vision::faceid::ArcFace,
|
||||
vision::faceid::InsightFaceRecognitionBase>(m, "ArcFace")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>());
|
||||
|
||||
pybind11::class_<vision::faceid::CosFace, vision::faceid::InsightFaceRecognitionBase>(m, "CosFace")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
|
||||
pybind11::class_<vision::faceid::CosFace,
|
||||
vision::faceid::InsightFaceRecognitionBase>(m, "CosFace")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>());
|
||||
|
||||
pybind11::class_<vision::faceid::PartialFC, vision::faceid::InsightFaceRecognitionBase>(m, "PartialFC")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
|
||||
pybind11::class_<vision::faceid::PartialFC,
|
||||
vision::faceid::InsightFaceRecognitionBase>(m, "PartialFC")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>());
|
||||
|
||||
pybind11::class_<vision::faceid::VPL, vision::faceid::InsightFaceRecognitionBase>(m, "VPL")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,ModelFormat>());
|
||||
pybind11::class_<vision::faceid::VPL,
|
||||
vision::faceid::InsightFaceRecognitionBase>(m, "VPL")
|
||||
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
||||
ModelFormat>());
|
||||
}
|
||||
} // namespace fastdeploy
|
||||
|
||||
@@ -35,6 +35,8 @@ class FASTDEPLOY_DECL ArcFace : public InsightFaceRecognitionBase {
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
} else if (model_format == ModelFormat::RKNN) {
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
@@ -63,6 +65,8 @@ class FASTDEPLOY_DECL CosFace : public InsightFaceRecognitionBase {
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
} else if (model_format == ModelFormat::RKNN) {
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
@@ -83,13 +87,15 @@ class FASTDEPLOY_DECL PartialFC : public InsightFaceRecognitionBase {
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format
|
||||
*/
|
||||
PartialFC(const std::string& model_file, const std::string& params_file = "",
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
: InsightFaceRecognitionBase(model_file, params_file, custom_option,
|
||||
model_format) {
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
} else if (model_format == ModelFormat::RKNN) {
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
@@ -110,13 +116,15 @@ class FASTDEPLOY_DECL VPL : public InsightFaceRecognitionBase {
|
||||
* \param[in] model_format Model format of the loaded model, default is Paddle format
|
||||
*/
|
||||
VPL(const std::string& model_file, const std::string& params_file = "",
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
const RuntimeOption& custom_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||||
: InsightFaceRecognitionBase(model_file, params_file, custom_option,
|
||||
model_format) {
|
||||
if (model_format == ModelFormat::ONNX) {
|
||||
valid_cpu_backends = {Backend::ORT};
|
||||
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
||||
} else if (model_format == ModelFormat::RKNN) {
|
||||
valid_rknpu_backends = {Backend::RKNPU2};
|
||||
} else {
|
||||
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::LITE};
|
||||
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
||||
|
||||
19
fastdeploy/vision/faceid/contrib/insightface/preprocessor.cc
Executable file → Normal file
19
fastdeploy/vision/faceid/contrib/insightface/preprocessor.cc
Executable file → Normal file
@@ -23,11 +23,10 @@ InsightFaceRecognitionPreprocessor::InsightFaceRecognitionPreprocessor() {
|
||||
size_ = {112, 112};
|
||||
alpha_ = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
|
||||
beta_ = {-1.f, -1.f, -1.f}; // RGB
|
||||
permute_ = true;
|
||||
}
|
||||
|
||||
bool InsightFaceRecognitionPreprocessor::Preprocess(FDMat * mat, FDTensor* output) {
|
||||
|
||||
bool InsightFaceRecognitionPreprocessor::Preprocess(FDMat* mat,
|
||||
FDTensor* output) {
|
||||
// face recognition model's preprocess steps in insightface
|
||||
// reference: insightface/recognition/arcface_torch/inference.py
|
||||
// 1. Resize
|
||||
@@ -39,13 +38,16 @@ bool InsightFaceRecognitionPreprocessor::Preprocess(FDMat * mat, FDTensor* outpu
|
||||
if (resize_h != mat->Height() || resize_w != mat->Width()) {
|
||||
Resize::Run(mat, resize_w, resize_h);
|
||||
}
|
||||
if (permute_) {
|
||||
|
||||
if (!disable_permute_) {
|
||||
BGR2RGB::Run(mat);
|
||||
}
|
||||
|
||||
Convert::Run(mat, alpha_, beta_);
|
||||
HWC2CHW::Run(mat);
|
||||
Cast::Run(mat, "float");
|
||||
if (!disable_normalize_) {
|
||||
Convert::Run(mat, alpha_, beta_);
|
||||
HWC2CHW::Run(mat);
|
||||
Cast::Run(mat, "float");
|
||||
}
|
||||
|
||||
mat->ShareWithTensor(output);
|
||||
output->ExpandDim(0); // reshape to n, h, w, c
|
||||
@@ -55,7 +57,8 @@ bool InsightFaceRecognitionPreprocessor::Preprocess(FDMat * mat, FDTensor* outpu
|
||||
bool InsightFaceRecognitionPreprocessor::Run(std::vector<FDMat>* images,
|
||||
std::vector<FDTensor>* outputs) {
|
||||
if (images->empty()) {
|
||||
FDERROR << "The size of input images should be greater than 0." << std::endl;
|
||||
FDERROR << "The size of input images should be greater than 0."
|
||||
<< std::endl;
|
||||
return false;
|
||||
}
|
||||
FDASSERT(images->size() == 1, "Only support batch = 1 now.");
|
||||
|
||||
@@ -54,10 +54,11 @@ class FASTDEPLOY_DECL InsightFaceRecognitionPreprocessor {
|
||||
/// Set beta.
|
||||
void SetBeta(std::vector<float>& beta) { beta_ = beta; }
|
||||
|
||||
bool GetPermute() { return permute_; }
|
||||
/// This function will disable normalize and hwc2chw in preprocessing step.
|
||||
void DisableNormalize() { disable_normalize_ = true; }
|
||||
|
||||
/// Set permute.
|
||||
void SetPermute(bool permute) { permute_ = permute; }
|
||||
/// This function will disable hwc2chw in preprocessing step.
|
||||
void DisablePermute() { disable_permute_ = true; }
|
||||
|
||||
protected:
|
||||
bool Preprocess(FDMat* mat, FDTensor* output);
|
||||
@@ -70,9 +71,11 @@ class FASTDEPLOY_DECL InsightFaceRecognitionPreprocessor {
|
||||
// Argument for image preprocessing step, beta values for normalization,
|
||||
// default beta = {-1.f, -1.f, -1.f}
|
||||
std::vector<float> beta_;
|
||||
// for recording the switch of normalize
|
||||
bool disable_normalize_ = false;
|
||||
// Argument for image preprocessing step, whether to swap the B and R channel,
|
||||
// such as BGR->RGB, default true.
|
||||
bool permute_;
|
||||
bool disable_permute_ = false;
|
||||
};
|
||||
|
||||
} // namespace faceid
|
||||
|
||||
@@ -56,13 +56,17 @@ class InsightFaceRecognitionPreprocessor:
|
||||
"""
|
||||
return self._preprocessor.beta
|
||||
|
||||
@property
|
||||
def permute(self):
|
||||
def disable_normalize(self):
|
||||
"""
|
||||
Argument for image preprocessing step, whether to swap the B and R channel,
|
||||
such as BGR->RGB, default true.
|
||||
This function will disable normalize in preprocessing step.
|
||||
"""
|
||||
return self._preprocessor.permute
|
||||
self._preprocessor.disable_normalize()
|
||||
|
||||
def disable_permute(self):
|
||||
"""
|
||||
This function will disable hwc2chw in preprocessing step.
|
||||
"""
|
||||
self._preprocessor.disable_permute()
|
||||
|
||||
|
||||
class InsightFaceRecognitionPostprocessor:
|
||||
|
||||
15
tools/rknpu2/config/arcface_quantized.yaml
Normal file
15
tools/rknpu2/config/arcface_quantized.yaml
Normal file
@@ -0,0 +1,15 @@
|
||||
mean:
|
||||
-
|
||||
- 127.5
|
||||
- 127.5
|
||||
- 127.5
|
||||
std:
|
||||
-
|
||||
- 127.5
|
||||
- 127.5
|
||||
- 127.5
|
||||
model_path: ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx
|
||||
outputs_nodes:
|
||||
do_quantization: True
|
||||
dataset: "./ms1mv3_arcface_r18/datasets.txt"
|
||||
output_folder: "./ms1mv3_arcface_r18"
|
||||
15
tools/rknpu2/config/arcface_unquantized.yaml
Normal file
15
tools/rknpu2/config/arcface_unquantized.yaml
Normal file
@@ -0,0 +1,15 @@
|
||||
mean:
|
||||
-
|
||||
- 127.5
|
||||
- 127.5
|
||||
- 127.5
|
||||
std:
|
||||
-
|
||||
- 127.5
|
||||
- 127.5
|
||||
- 127.5
|
||||
model_path: ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx
|
||||
outputs_nodes:
|
||||
do_quantization: False
|
||||
dataset: "./ms1mv3_arcface_r18/datasets.txt"
|
||||
output_folder: "./ms1mv3_arcface_r18"
|
||||
Reference in New Issue
Block a user