[Left TODO] Support PaddleSeg deployment #39
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felixhjh
commented
Jul 25, 2022
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felixhjh
commented
- 支持 PaddleSeg 六个模型的部署（Deeplabv3-ResNet50, hrnet-w18, pp-humanseg, pp-humanseg-server, PP-LiteSeg, unet）
- 支持三种模型导出形式（logits matrix, logits matrix with argmax, logits matrix with argmax and with softmax）
- 支持visualization
Fix bug while the inference result is empty with YOLOv5 (PaddlePaddle#29)
…into PaddlePaddle-develop
felixhjh
changed the title
jiangjiajun
requested changes
Aug 3, 2022
| std::map<std::string, std::array<int, 2>>* im_info) { | ||
| FDASSERT(infer_result.dtype == FDDataType::INT64 || | ||
| infer_result.dtype == FDDataType::FP32, | ||
| "Require the data type of output is int64 or fp32, but now it's " + | ||
| Str(const_cast<fastdeploy::FDDataType&>(infer_result.dtype)) + |
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这里是不是可以直接写成Str(infer_result.dtype)
| } | ||
| } | ||
| } | ||
| } |
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这个hwc2chw是不是可以直接改成opencv的操作
| for (int i = 0; i < height; i++) { | ||
| for (int j = 0; j < width; j++) { | ||
| temp_mat.at<float_t>(i, j) = | ||
| static_cast<float_t>(infer_result_buffer[index++]); |
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processor的这个函数建议按照功能模块拆一下，提升可读性
jiangjiajun
requested changes
Aug 3, 2022
| int64_t chw = channel * height * width; | ||
| int64_t* infer_result_buffer = static_cast<int64_t*>(infer_result.Data()); | ||
| std::vector<float_t> float_result_buffer(chw); | ||
| temp_mat = cv::Mat(height, width, CV_32FC(channel)); |
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int64出来的结果，转为了float的来进行后面的resize处理，这个部署的结果是否跟PaddleSeg对齐了？
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这么做的原因有二：
- int64的结果出来的是类别的mask矩阵，对于pp-human系列也就是0,1矩阵（其他也就是0-num_classes的矩阵）。opencv没有64位的矩阵变量，因此必须要转。
- 转为float是因为，resize的插值方式双线性策略（inter=1，也是前处理所使用的策略）只支持float类型opencv mat（不支持int、uint8精度缺失）。为保持前后处理一致所以统一转为float。
- PaddleSeg给到的三张示例图片，正常人像、竖屏人像、横屏人像fastdeploy结果与PaddleSeg是一致的，后续evaluation模块开发完成（正在去除Paddle依赖）会跑全量
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第3点， 先确认输出的值(numpy) 与 PaddleSeg是一致的，并做到与Seg结果一致
jiangjiajun
approved these changes
Aug 5, 2022
jiangjiajun
changed the title
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