Modify PPMatting backend and docs #182
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DefTruth
requested changes
Sep 1, 2022
| @@ -50,39 +50,6 @@ void CpuInfer(const std::string& model_dir, const std::string& image_file, | |||
| << std::endl; | |||
| } | |||
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| void GpuInfer(const std::string& model_dir, const std::string& image_file, | |||
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这里保留GpuInfer，但是设置一下backend为paddle
option.UsePaddleBackend()| @@ -131,8 +98,6 @@ int main(int argc, char* argv[]) { | |||
| } | |||
| if (std::atoi(argv[4]) == 0) { | |||
| @@ -19,8 +19,6 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg | |||
| wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg | |||
| # CPU推理 | |||
| python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu | |||
| # GPU推理 (TODO: ORT-GPU 推理会报错) | |||
| python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu | |||
| @@ -27,8 +27,6 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg | |||
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| # CPU推理 | |||
| ./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 0 | |||
| # GPU推理 (TODO: ORT-GPU 推理会报错) | |||
| ./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1 | |||
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这里保留GpuInfer，但是设置一下backend为paddle
DefTruth
approved these changes
Sep 1, 2022
jiangjiajun
requested changes
Sep 3, 2022
| @@ -92,6 +96,22 @@ bool PPMatting::BuildPreprocessPipelineFromConfig() { | |||
| std = op["std"].as<std::vector<float>>(); | |||
| } | |||
| processors_.push_back(std::make_shared<Normalize>(mean, std)); | |||
| } else if (op["type"].as<std::string>() == "ResizeByLong") { | |||
| int target_size = 512; | |||
| std::cout << "If LimintShort in yaml file, you may transfer PPMatting " | ||
| "model by yourself, please make sure your input image's " | ||
| "width==hight and not smaller than " | ||
| << max_short << std::endl; |
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日志输出改为FDINFO
LimitShort, height 单词拼错了
改为Detected LimitShort processing step in yaml file, if the model is exported from PaddleSeg, please make sure the input of your model is fixed with a square shape, and equal to " << max_short << "." << std::endl;
| } else if (op["type"].as<std::string>() == "Pad") { | ||
| // size: (w, h) | ||
| auto size = op["size"].as<std::vector<int>>(); | ||
| std::vector<float> value = {114, 114, 114}; |
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为啥是114， 114的这个默认值是PaddleSeg里面默认的吗？
DefTruth
requested changes
Sep 5, 2022
| if (processors_[i]->Name().compare("LimitShort") == 0) { | ||
| int input_h = static_cast<int>(mat->Height()); | ||
| int input_w = static_cast<int>(mat->Width()); | ||
| FDASSERT(input_h == input_w, |
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这里用FDASSERT，用户输入的如果不是方形的，是不是会直接报错了？我看到这个input_h/w，是从输入的mat获取的，并不是从模型输入的shape获取的，但是报错提示的是"model"的input_shape必须是方形的
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if (option.backend == Backend::PDINFER) {
if (option.device == Device::CPU) {
FDWARN << "" << std::endl;
// some ops
}
} else {
FDWARN << "" << std::endl;
// resize op
}| if (pad_to_size != im_info.end() && resize_by_long != im_info.end()) { | ||
| int resize_h = resize_by_long->second[0]; | ||
| int resize_w = resize_by_long->second[1]; | ||
| int pad_h = pad_to_size->second[0]; |
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这里pad_h和pad_w获取后并没有用上，所以这两个值的具体作用是什么
| "sure the input of your model is fixed with a square shape"); | ||
| auto processor = dynamic_cast<LimitShort*>(processors_[i].get()); | ||
| int max_short = processor->GetMaxShort(); | ||
| FDASSERT(input_h >= max_short && input_w >= max_short, |
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同上，这里获取的是输入的input_h和input_w，并不是模型的
| if args.device.lower() == "gpu": | ||
| option.use_gpu() | ||
| if args.use_trt: |
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这个paddle gpu的判断，应该不用缩进一个层级，可以这样(gpu下，如果不指定trt，默认就用paddle推)
def build_option(args):
option = fd.RuntimeOption()
if args.device.lower() == "gpu":
option.use_gpu()
option.use_paddle_backend()
if args.use_trt:
option.use_trt_backend()
option.set_trt_input_shape("img", [1, 3, 512, 512])
return option| ProcLib lib = ProcLib::OPENCV_CPU); | ||
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| private: | ||
| double GenerateScale(const int origin_w, const int origin_h); |
DefTruth
approved these changes
Sep 6, 2022
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