diff --git a/csrc/fastdeploy/backends/ort/ort_backend.cc b/csrc/fastdeploy/backends/ort/ort_backend.cc index c17890109..5aa14448c 100644 --- a/csrc/fastdeploy/backends/ort/ort_backend.cc +++ b/csrc/fastdeploy/backends/ort/ort_backend.cc @@ -58,8 +58,6 @@ void OrtBackend::BuildOption(const OrtBackendOption& option) { << std::endl; option_.use_gpu = false; } else { - FDASSERT(option.gpu_id == 0, "Requires gpu_id == 0, but now gpu_id = " + - std::to_string(option.gpu_id) + "."); OrtCUDAProviderOptions cuda_options; cuda_options.device_id = option.gpu_id; session_options_.AppendExecutionProvider_CUDA(cuda_options); diff --git a/csrc/fastdeploy/vision/classification/ppcls/model.cc b/csrc/fastdeploy/vision/classification/ppcls/model.cc index 607d1114e..2ea6d846f 100644 --- a/csrc/fastdeploy/vision/classification/ppcls/model.cc +++ b/csrc/fastdeploy/vision/classification/ppcls/model.cc @@ -20,12 +20,14 @@ namespace fastdeploy { namespace vision { namespace classification { -PaddleClasModel::PaddleClasModel(const std::string& model_file, const std::string& params_file, - const std::string& config_file, const RuntimeOption& custom_option, - const Frontend& model_format) { +PaddleClasModel::PaddleClasModel(const std::string& model_file, + const std::string& params_file, + const std::string& config_file, + const RuntimeOption& custom_option, + const Frontend& model_format) { config_file_ = config_file; valid_cpu_backends = {Backend::ORT, Backend::PDINFER}; - valid_gpu_backends = {Backend::ORT, Backend::PDINFER}; + valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT}; runtime_option = custom_option; runtime_option.model_format = model_format; runtime_option.model_file = model_file; @@ -109,8 +111,8 @@ bool PaddleClasModel::Preprocess(Mat* mat, FDTensor* output) { return true; } -bool PaddleClasModel::Postprocess(const FDTensor& infer_result, ClassifyResult* result, - int topk) { +bool PaddleClasModel::Postprocess(const FDTensor& infer_result, + ClassifyResult* result, int topk) { int num_classes = infer_result.shape[1]; const float* infer_result_buffer = reinterpret_cast(infer_result.data.data()); @@ -148,6 +150,6 @@ bool PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk) { return true; } -} // namespace classification -} // namespace vision -} // namespace fastdeploy +} // namespace classification +} // namespace vision +} // namespace fastdeploy diff --git a/csrc/fastdeploy/vision/utils/FDTensor2CVMat.cc b/csrc/fastdeploy/vision/segmentation/ppseg/FDTensor2CVMat.cc similarity index 95% rename from csrc/fastdeploy/vision/utils/FDTensor2CVMat.cc rename to csrc/fastdeploy/vision/segmentation/ppseg/FDTensor2CVMat.cc index fdd110cb8..3e7a58366 100644 --- a/csrc/fastdeploy/vision/utils/FDTensor2CVMat.cc +++ b/csrc/fastdeploy/vision/segmentation/ppseg/FDTensor2CVMat.cc @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "fastdeploy/vision/utils/utils.h" +#include "fastdeploy/vision/segmentation/ppseg/model.h" namespace fastdeploy { namespace vision { -namespace utils { +namespace segmentation { void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result, bool contain_score_map) { @@ -54,6 +54,6 @@ void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result, } } -} // namespace utils +} // namespace segmentation } // namespace vision } // namespace fastdeploy diff --git a/csrc/fastdeploy/vision/segmentation/ppseg/model.cc b/csrc/fastdeploy/vision/segmentation/ppseg/model.cc index 2c37a6079..be5c241f7 100644 --- a/csrc/fastdeploy/vision/segmentation/ppseg/model.cc +++ b/csrc/fastdeploy/vision/segmentation/ppseg/model.cc @@ -143,8 +143,7 @@ bool PaddleSegModel::Postprocess( Mat* mat = nullptr; if (is_resized) { cv::Mat temp_mat; - utils::FDTensor2FP32CVMat(temp_mat, infer_result, - result->contain_score_map); + FDTensor2FP32CVMat(temp_mat, infer_result, result->contain_score_map); // original image shape auto iter_ipt = (*im_info).find("input_shape"); diff --git a/csrc/fastdeploy/vision/segmentation/ppseg/model.h b/csrc/fastdeploy/vision/segmentation/ppseg/model.h index a180f5c5b..c1491cb0b 100644 --- a/csrc/fastdeploy/vision/segmentation/ppseg/model.h +++ b/csrc/fastdeploy/vision/segmentation/ppseg/model.h @@ -38,6 +38,9 @@ class FASTDEPLOY_DECL PaddleSegModel : public FastDeployModel { std::vector> processors_; std::string config_file_; }; + +void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result, + bool contain_score_map); } // namespace segmentation } // namespace vision } // namespace fastdeploy diff --git a/csrc/fastdeploy/vision/utils/utils.h b/csrc/fastdeploy/vision/utils/utils.h index 02cf16e9c..3fa36e7f1 100644 --- a/csrc/fastdeploy/vision/utils/utils.h +++ b/csrc/fastdeploy/vision/utils/utils.h @@ -115,9 +115,6 @@ void NCHW2NHWC(FDTensor& infer_result) { infer_result.shape = {num, height, width, channel}; } -void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result, - bool contain_score_map); - void NMS(DetectionResult* output, float iou_threshold = 0.5); void NMS(FaceDetectionResult* result, float iou_threshold = 0.5); diff --git a/csrc/fastdeploy/vision/vision_pybind.cc b/csrc/fastdeploy/vision/vision_pybind.cc index 5d4ba00fc..09a043756 100644 --- a/csrc/fastdeploy/vision/vision_pybind.cc +++ b/csrc/fastdeploy/vision/vision_pybind.cc @@ -57,7 +57,6 @@ void BindVision(pybind11::module& m) { .def_readwrite("label_map", &vision::SegmentationResult::label_map) .def_readwrite("score_map", &vision::SegmentationResult::score_map) .def_readwrite("shape", &vision::SegmentationResult::shape) - .def_readwrite("shape", &vision::SegmentationResult::shape) .def("__repr__", &vision::SegmentationResult::Str) .def("__str__", &vision::SegmentationResult::Str); diff --git a/examples/vision/classification/paddleclas/README.md b/examples/vision/classification/paddleclas/README.md index a834a25e9..1f8377616 100644 --- a/examples/vision/classification/paddleclas/README.md +++ b/examples/vision/classification/paddleclas/README.md @@ -2,25 +2,53 @@ ## 模型版本说明 -- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas) +- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4) + +目前FastDeploy支持如下模型的部署 + +- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md) +- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md) +- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md) +- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md) +- [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) +- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md) +- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md) +- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md) +- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md) +- [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) ## 准备PaddleClas部署模型 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) -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。 +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。 ## 下载预训练模型 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。 -| 模型 | 大小 |输入Shape | 精度 | -|:---------------------------------------------------------------- |:----- |:----- | :----- | -| [PPLCNet]() | 141MB | 224x224 |51.4% | -| [PPLCNetv2]() | 10MB | 224x224 |51.4% | -| [EfficientNet]() | | 224x224 | | - +| 模型 | 参数文件大小 |输入Shape | Top1 | Top5 | +|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- | +| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% | +| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz) | 26MB | 224x224 |77.04% | 93.27% | +| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) | 255MB | 600x600 | 84.3% | 96.9% | +| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)| 18MB | 224x224 | 75.8% | 75.8% | +| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) | 29MB | 224x224 | 75.7% | 92.5% | +| [GhostNet_x0_5_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) | 10MB | 224x224 | 66.8% | 86.9% | +| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) | 1.9MB | 224x224 | 51.4% | 75.5% | +| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) | 17MB | 224x224 | 77.9% | 93.9% | +| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) | 5.9MB | 224x224 | 53.2% | 76.5% | +| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) | 14MB | 224x224 | 76.74% | 93.39% | +| [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% | +| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) | 22MB | 224x224 | 78.96% | 94.48% | +| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) | 2.4MB | 224x224 | 49.9% | 73.79% | +| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) | 29MB | 224x224 | 73.15% | 91.2% | +| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) | 4.8MB | 224x224 | 60.1% | 81.9% | +| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) | 92MB | 299x299 | 79.14% | 94.59% | +| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) | 57MB | 224x224 | 81.95% | 96.12% | +| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) | 274MB | 224x224 | 85.0% | 97.35% | +| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) | 98MB | 224x224 | 79.12% | 94.44% | ## 详细部署文档 diff --git a/examples/vision/classification/paddleclas/python/README.md b/examples/vision/classification/paddleclas/python/README.md index 972ff15eb..19b96491b 100644 --- a/examples/vision/classification/paddleclas/python/README.md +++ b/examples/vision/classification/paddleclas/python/README.md @@ -5,67 +5,71 @@ - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md) - 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md) -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 +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 ``` -# 下载yolov7模型文件和测试图片 -wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx -wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg +# 下载ResNet50_vd模型文件和测试图片 +wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz +tar -xvf ResNet50_vd_infer.tgz +wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git -cd examples/vison/detection/yolov7/python/ +cd examples/vision/classification/paddleclas/python # CPU推理 -python infer.py --model yolov7.onnx --image 000000087038.jpg --device cpu +python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu # GPU推理 -python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu +python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu # 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） -python infer.py --model yolov7.onnx --image 000000087038.jpg --device gpu --use_trt True +python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True ``` -运行完成可视化结果如下图所示 - -## YOLOv7 Python接口 - +运行完成后返回结果如下所示 ``` -fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) +ClassifyResult( +label_ids: 153, +scores: 0.686229, +) ``` -YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式 +## PaddleClasModel Python接口 + +``` +fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) +``` + +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) **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 +> * **params_file**(str): 参数文件路径 +> * **config_file**(str): 推理部署配置文件 > * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置 -> * **model_format**(Frontend): 模型格式，默认为ONNX +> * **model_format**(Frontend): 模型格式，默认为Paddle格式 ### predict函数 > ``` -> YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) +> PaddleClasModel.predict(input_image, topk=1) > ``` > > 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 -> > * **conf_threshold**(float): 检测框置信度过滤阈值 -> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值 +> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式 +> > * **topk**(int):返回预测概率最高的topk个分类结果 > **返回** > -> > 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/) +> > 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/) -### 类成员属性 - -> > * **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] ## 其它文档 -- [YOLOv7 模型介绍](..) -- [YOLOv7 C++部署](../cpp) +- [PaddleClas 模型介绍](..) +- [PaddleClas C++部署](../cpp) - [模型预测结果说明](../../../../../docs/api/vision_results/) diff --git a/fastdeploy/vision/classification/ppcls/__init__.py b/fastdeploy/vision/classification/ppcls/__init__.py index 3207e1e5c..ade0f7a99 100644 --- a/fastdeploy/vision/classification/ppcls/__init__.py +++ b/fastdeploy/vision/classification/ppcls/__init__.py @@ -23,14 +23,14 @@ class PaddleClasModel(FastDeployModel): model_file, params_file, config_file, - backend_option=None, + runtime_option=None, model_format=Frontend.PADDLE): - super(PaddleClasModel, self).__init__(backend_option) + super(PaddleClasModel, self).__init__(runtime_option) 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): diff --git a/fastdeploy/vision/evaluation/classify.py b/fastdeploy/vision/evaluation/classify.py index 2d591f744..1e44f2368 100644 --- a/fastdeploy/vision/evaluation/classify.py +++ b/fastdeploy/vision/evaluation/classify.py @@ -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 diff --git a/fastdeploy/vision/evaluation/detection.py b/fastdeploy/vision/evaluation/detection.py index 4750756b7..dc958c861 100644 --- a/fastdeploy/vision/evaluation/detection.py +++ b/fastdeploy/vision/evaluation/detection.py @@ -15,6 +15,7 @@ import numpy as np import copy import collections +import math def eval_detection(model, @@ -26,7 +27,7 @@ def eval_detection(model, from .utils import CocoDetection from .utils import COCOMetric import cv2 - from tqdm import trange + from tqdm import trange if conf_threshold is not None or nms_iou_threshold is not None: assert conf_threshold is not None and nms_iou_threshold is not None, "The conf_threshold and nms_iou_threshold should be setted at the same time" @@ -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