mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
Update evaluation function to support calculate average inference time (#106)
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
@@ -58,8 +58,6 @@ void OrtBackend::BuildOption(const OrtBackendOption& option) {
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<< std::endl;
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option_.use_gpu = false;
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} else {
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FDASSERT(option.gpu_id == 0, "Requires gpu_id == 0, but now gpu_id = " +
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std::to_string(option.gpu_id) + ".");
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OrtCUDAProviderOptions cuda_options;
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cuda_options.device_id = option.gpu_id;
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session_options_.AppendExecutionProvider_CUDA(cuda_options);
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@@ -20,12 +20,14 @@ namespace fastdeploy {
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namespace vision {
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namespace classification {
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PaddleClasModel::PaddleClasModel(const std::string& model_file, const std::string& params_file,
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const std::string& config_file, const RuntimeOption& custom_option,
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const Frontend& model_format) {
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PaddleClasModel::PaddleClasModel(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const Frontend& model_format) {
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config_file_ = config_file;
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valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
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valid_gpu_backends = {Backend::ORT, Backend::PDINFER};
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valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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@@ -109,8 +111,8 @@ bool PaddleClasModel::Preprocess(Mat* mat, FDTensor* output) {
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return true;
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}
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bool PaddleClasModel::Postprocess(const FDTensor& infer_result, ClassifyResult* result,
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int topk) {
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bool PaddleClasModel::Postprocess(const FDTensor& infer_result,
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ClassifyResult* result, int topk) {
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int num_classes = infer_result.shape[1];
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const float* infer_result_buffer =
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reinterpret_cast<const float*>(infer_result.data.data());
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@@ -148,6 +150,6 @@ bool PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
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return true;
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}
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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@@ -12,11 +12,11 @@
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision/utils/utils.h"
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#include "fastdeploy/vision/segmentation/ppseg/model.h"
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namespace fastdeploy {
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namespace vision {
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namespace utils {
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namespace segmentation {
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void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
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bool contain_score_map) {
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@@ -54,6 +54,6 @@ void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
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}
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}
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} // namespace utils
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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@@ -143,8 +143,7 @@ bool PaddleSegModel::Postprocess(
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Mat* mat = nullptr;
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if (is_resized) {
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cv::Mat temp_mat;
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utils::FDTensor2FP32CVMat(temp_mat, infer_result,
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result->contain_score_map);
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FDTensor2FP32CVMat(temp_mat, infer_result, result->contain_score_map);
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// original image shape
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auto iter_ipt = (*im_info).find("input_shape");
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@@ -38,6 +38,9 @@ class FASTDEPLOY_DECL PaddleSegModel : public FastDeployModel {
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std::vector<std::shared_ptr<Processor>> processors_;
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std::string config_file_;
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};
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void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
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bool contain_score_map);
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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@@ -115,9 +115,6 @@ void NCHW2NHWC(FDTensor& infer_result) {
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infer_result.shape = {num, height, width, channel};
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}
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void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
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bool contain_score_map);
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void NMS(DetectionResult* output, float iou_threshold = 0.5);
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void NMS(FaceDetectionResult* result, float iou_threshold = 0.5);
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@@ -57,7 +57,6 @@ void BindVision(pybind11::module& m) {
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.def_readwrite("label_map", &vision::SegmentationResult::label_map)
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.def_readwrite("score_map", &vision::SegmentationResult::score_map)
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.def_readwrite("shape", &vision::SegmentationResult::shape)
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.def_readwrite("shape", &vision::SegmentationResult::shape)
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.def("__repr__", &vision::SegmentationResult::Str)
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.def("__str__", &vision::SegmentationResult::Str);
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@@ -2,25 +2,53 @@
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## 模型版本说明
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- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas)
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- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
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目前FastDeploy支持如下模型的部署
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- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
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- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
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- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
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- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
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- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
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- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
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- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
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- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/ResNet_and_vd.md)
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## 准备PaddleClas部署模型
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PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数。
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注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
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## 下载预训练模型
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为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
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| 模型 | 大小 |输入Shape | 精度 |
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|:---------------------------------------------------------------- |:----- |:----- | :----- |
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| [PPLCNet]() | 141MB | 224x224 |51.4% |
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| [PPLCNetv2]() | 10MB | 224x224 |51.4% |
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| [EfficientNet]() | | 224x224 | |
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| 模型 | 参数文件大小 |输入Shape | Top1 | Top5 |
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|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
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| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
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| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz) | 26MB | 224x224 |77.04% | 93.27% |
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| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) | 255MB | 600x600 | 84.3% | 96.9% |
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| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)| 18MB | 224x224 | 75.8% | 75.8% |
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| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) | 29MB | 224x224 | 75.7% | 92.5% |
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| [GhostNet_x0_5_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) | 10MB | 224x224 | 66.8% | 86.9% |
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| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) | 1.9MB | 224x224 | 51.4% | 75.5% |
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| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) | 17MB | 224x224 | 77.9% | 93.9% |
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| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) | 5.9MB | 224x224 | 53.2% | 76.5% |
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| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) | 14MB | 224x224 | 76.74% | 93.39% |
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| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) | 6.4MB | 224x224 | 55.55% | 77.71% |
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| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) | 22MB | 224x224 | 78.96% | 94.48% |
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| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) | 2.4MB | 224x224 | 49.9% | 73.79% |
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| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) | 29MB | 224x224 | 73.15% | 91.2% |
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| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) | 4.8MB | 224x224 | 60.1% | 81.9% |
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| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) | 92MB | 299x299 | 79.14% | 94.59% |
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| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) | 57MB | 224x224 | 81.95% | 96.12% |
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| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) | 274MB | 224x224 | 85.0% | 97.35% |
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| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) | 98MB | 224x224 | 79.12% | 94.44% |
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## 详细部署文档
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@@ -5,67 +5,71 @@
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
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本目录下提供`infer.py`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```
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# 下载yolov7模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 下载ResNet50_vd模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vison/detection/yolov7/python/
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cd examples/vision/classification/paddleclas/python
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# CPU推理
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device cpu
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu
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# GPU推理
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu --use_trt True
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True
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```
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运行完成可视化结果如下图所示
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## YOLOv7 Python接口
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运行完成后返回结果如下所示
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```
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fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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YOLOv7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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## PaddleClasModel Python接口
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```
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fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
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```
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PaddleClas模型加载和初始化，其中model_file, params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **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
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(Frontend): 模型格式，默认为ONNX
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> * **model_format**(Frontend): 模型格式，默认为Paddle格式
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### predict函数
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> ```
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> YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
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> PaddleClasModel.predict(input_image, topk=1)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **conf_threshold**(float): 检测框置信度过滤阈值
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> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> > * **topk**(int):返回预测概率最高的topk个分类结果
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> **返回**
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>
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> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员属性
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> > * **size**(list | tuple): 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[640, 640]
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## 其它文档
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||||
- [YOLOv7 模型介绍](..)
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- [YOLOv7 C++部署](../cpp)
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- [PaddleClas 模型介绍](..)
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- [PaddleClas C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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@@ -23,14 +23,14 @@ class PaddleClasModel(FastDeployModel):
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model_file,
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params_file,
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config_file,
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backend_option=None,
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||||
runtime_option=None,
|
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model_format=Frontend.PADDLE):
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super(PaddleClasModel, self).__init__(backend_option)
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super(PaddleClasModel, self).__init__(runtime_option)
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|
||||
assert model_format == Frontend.PADDLE, "PaddleClasModel only support model format of Frontend.Paddle now."
|
||||
self._model = C.vision.classification.PaddleClasModel(model_file, params_file,
|
||||
config_file, self._runtime_option,
|
||||
model_format)
|
||||
self._model = C.vision.classification.PaddleClasModel(
|
||||
model_file, params_file, config_file, self._runtime_option,
|
||||
model_format)
|
||||
assert self.initialized, "PaddleClas model initialize failed."
|
||||
|
||||
def predict(self, input_image, topk=1):
|
||||
|
||||
@@ -14,6 +14,8 @@
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import collections
|
||||
|
||||
|
||||
def topk_accuracy(topk_list, label_list):
|
||||
@@ -25,6 +27,7 @@ def topk_accuracy(topk_list, label_list):
|
||||
def eval_classify(model, image_file_path, label_file_path, topk=5):
|
||||
from tqdm import trange
|
||||
import cv2
|
||||
import math
|
||||
|
||||
result_list = []
|
||||
label_list = []
|
||||
@@ -36,6 +39,7 @@ def eval_classify(model, image_file_path, label_file_path, topk=5):
|
||||
label_file_path), "The label_file_path:{} is not a file.".format(
|
||||
label_file_path)
|
||||
assert isinstance(topk, int), "The tok:{} is not int type".format(topk)
|
||||
|
||||
with open(label_file_path, 'r') as file:
|
||||
lines = file.readlines()
|
||||
for line in lines:
|
||||
@@ -44,14 +48,30 @@ def eval_classify(model, image_file_path, label_file_path, topk=5):
|
||||
label = items[1]
|
||||
image_label_dict[image_name] = int(label)
|
||||
images_num = len(image_label_dict)
|
||||
twenty_percent_images_num = math.ceil(images_num * 0.2)
|
||||
start_time = 0
|
||||
end_time = 0
|
||||
average_inference_time = 0
|
||||
scores = collections.OrderedDict()
|
||||
for (image, label), i in zip(image_label_dict.items(),
|
||||
trange(
|
||||
images_num, desc='Inference Progress')):
|
||||
if i == twenty_percent_images_num:
|
||||
start_time = time.time()
|
||||
|
||||
label_list.append([label])
|
||||
image_path = os.path.join(image_file_path, image)
|
||||
im = cv2.imread(image_path)
|
||||
result = model.predict(im, topk)
|
||||
result_list.append(result.label_ids)
|
||||
|
||||
if i == images_num - 1:
|
||||
end_time = time.time()
|
||||
average_inference_time = round(
|
||||
(end_time - start_time) / (images_num - twenty_percent_images_num), 4)
|
||||
topk_acc_score = topk_accuracy(np.array(result_list), np.array(label_list))
|
||||
return topk_acc_score
|
||||
if topk == 1:
|
||||
scores.update({'topk1': topk_acc_score})
|
||||
elif topk == 5:
|
||||
scores.update({'topk5': topk_acc_score})
|
||||
scores.update({'average_inference_time': average_inference_time})
|
||||
return scores
|
||||
|
||||
@@ -15,6 +15,7 @@
|
||||
import numpy as np
|
||||
import copy
|
||||
import collections
|
||||
import math
|
||||
|
||||
|
||||
def eval_detection(model,
|
||||
@@ -48,9 +49,15 @@ def eval_detection(model,
|
||||
eval_metric = COCOMetric(
|
||||
coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False)
|
||||
scores = collections.OrderedDict()
|
||||
twenty_percent_image_num = math.ceil(image_num * 0.2)
|
||||
start_time = 0
|
||||
end_time = 0
|
||||
average_inference_time = 0
|
||||
for image_info, i in zip(all_image_info,
|
||||
trange(
|
||||
image_num, desc="Inference Progress")):
|
||||
if i == twenty_percent_image_num:
|
||||
start_time = time.time()
|
||||
im = cv2.imread(image_info["image"])
|
||||
im_id = image_info["im_id"]
|
||||
if conf_threshold is None and nms_iou_threshold is None:
|
||||
@@ -66,8 +73,13 @@ def eval_detection(model,
|
||||
'im_id': im_id
|
||||
}
|
||||
eval_metric.update(im_id, pred)
|
||||
if i == image_num - 1:
|
||||
end_time = time.time()
|
||||
average_inference_time = round(
|
||||
(end_time - start_time) / (image_num - twenty_percent_image_num), 4)
|
||||
eval_metric.accumulate()
|
||||
eval_details = eval_metric.details
|
||||
scores.update(eval_metric.get())
|
||||
scores.update({'average_inference_time': average_inference_time})
|
||||
eval_metric.reset()
|
||||
return scores
|
||||
|
||||
Reference in New Issue
Block a user