diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc b/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc index 7018fd015..952a4dd00 100644 --- a/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc +++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_picodet.cc @@ -31,7 +31,7 @@ void SophgoInfer(const std::string& model_dir, const std::string& image_file) { auto model = fastdeploy::vision::detection::PicoDet( model_file, params_file, config_file, option, format); - model.GetPostprocessor().ApplyDecodeAndNMS(); + model.GetPostprocessor().ApplyNMS(); auto im = cv::imread(image_file); diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc b/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc index 5844468cb..ffabbfa86 100644 --- a/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc +++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe.cc @@ -31,7 +31,7 @@ void SophgoInfer(const std::string& model_dir, const std::string& image_file) { auto model = fastdeploy::vision::detection::PPYOLOE( model_file, params_file, config_file, option, format); - model.GetPostprocessor().ApplyDecodeAndNMS(); + model.GetPostprocessor().ApplyNMS(); auto im = cv::imread(image_file); diff --git a/examples/vision/detection/paddledetection/sophgo/cpp/infer_yolov8.cc b/examples/vision/detection/paddledetection/sophgo/cpp/infer_yolov8.cc index 2f0132a60..65f4ce268 100644 --- a/examples/vision/detection/paddledetection/sophgo/cpp/infer_yolov8.cc +++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_yolov8.cc @@ -30,7 +30,7 @@ void SophgoInfer(const std::string& model_dir, const std::string& image_file) { auto model = fastdeploy::vision::detection::PaddleYOLOv8(model_file, params_file, config_file, option, format); - model.GetPostprocessor().ApplyDecodeAndNMS(); + model.GetPostprocessor().ApplyNMS(); auto im = cv::imread(image_file); diff --git a/examples/vision/detection/paddledetection/sophgo/python/README.md b/examples/vision/detection/paddledetection/sophgo/python/README.md index f849cc622..82ac5a9fc 100644 --- a/examples/vision/detection/paddledetection/sophgo/python/README.md +++ b/examples/vision/detection/paddledetection/sophgo/python/README.md @@ -15,13 +15,21 @@ cd FastDeploy/examples/vision/detection/paddledetection/sophgo/python wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg # 推理 -#ppyoloe推理示例 -python3 infer_ppyoloe.py --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg +# ppyoloe推理示例 +# 指定--auto True，自动完成模型准备、转换和推理，需要指定PaddleDetection路径 +python3 infer_ppyoloe.py --auto True --pp_detect_path {Path to PaddleDetection} --model_file '' --config_file '' --image '' -#picodet推理示例 -python3 infer_picodet.py --model_file model/picodet_s_416_coco_lcnet_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg +# 指定--auto False，需要用户指定模型、配置文件和图片路径，不需要指定PaddleDetection路径。 +python3 infer_ppyoloe.py --auto False --pp_detect_path '' --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg -#yolov8推理示例 +# picodet推理示例 +# 指定--auto True，自动完成模型准备、转换和推理，需要指定PaddleDetection路径 +python3 infer_picodet.py --auto True --pp_detect_path {Path to PaddleDetection} --model_file '' --config_file '' --image '' + +# 指定--auto False，需要用户指定模型、配置文件和图片路径，不需要指定PaddleDetection路径。 +python3 infer_picodet.py --auto False --pp_detect_path '' --model_file model/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg + +# yolov8推理示例 python3 infer_yolov8.py --model_file model/yolov8s_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg # 运行完成后返回结果如下所示 可视化结果存储在sophgo_result.jpg中 diff --git a/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py b/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py index fd079e16c..256929cc6 100644 --- a/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py +++ b/examples/vision/detection/paddledetection/sophgo/python/infer_picodet.py @@ -14,12 +14,101 @@ import fastdeploy as fd import cv2 import os +from subprocess import run +from prepare_npz import prepare + +def export_model(args): + PPDetection_path = args.pp_detect_path + + export_str = 'python3 tools/export_model.py \ + -c configs/picodet/picodet_s_320_coco_lcnet.yml \ + --output_dir=output_inference \ + -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams' + cur_path = os.getcwd() + os.chdir(PPDetection_path) + print(export_str) + run(export_str, shell=True) + cp_str = 'cp -r ./output_inference/picodet_s_320_coco_lcnet ' + cur_path + print(cp_str) + run(cp_str, shell=True) + os.chdir(cur_path) + +def paddle2onnx(): + convert_str = 'paddle2onnx --model_dir picodet_s_320_coco_lcnet/ \ + --model_filename model.pdmodel \ + --params_filename model.pdiparams \ + --save_file picodet_s_320_coco_lcnet.onnx \ + --enable_dev_version True' + print(convert_str) + run(convert_str, shell=True) + fix_shape_str = 'python3 -m paddle2onnx.optimize \ + --input_model picodet_s_320_coco_lcnet.onnx \ + --output_model picodet_s_320_coco_lcnet.onnx \ + --input_shape_dict "{\'image\':[1,3,640,640]}"' + print(fix_shape_str) + run(fix_shape_str, shell=True) + +def mlir_prepare(): + mlir_path = os.getenv("MODEL_ZOO_PATH") + mlir_path = mlir_path[:-13] + regression_path = os.path.join(mlir_path, 'regression') + mv_str_list = ['mkdir picodet', + 'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./picodet', + 'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./picodet', + 'cp picodet_s_320_coco_lcnet.onnx ./picodet', + 'mkdir ./picodet/workspace'] + for str in mv_str_list: + print(str) + run(str, shell=True) + +def image_prepare(): + img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg' + if not os.path.exists('000000014439.jpg'): + print(img_str) + run(img_str, shell=True) + prepare('000000014439.jpg', [320, 320]) + cp_npz_str = 'cp ./inputs.npz ./picodet' + print(cp_npz_str) + run(cp_npz_str, shell=True) + +def onnx2mlir(): + transform_str = 'model_transform.py \ + --model_name picodet_s_320_coco_lcnet \ + --model_def ../picodet_s_320_coco_lcnet.onnx \ + --input_shapes [[1,3,320,320],[1,2]] \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names p2o.Div.79,p2o.Concat.9 \ + --test_input ../inputs.npz \ + --test_result picodet_s_320_coco_lcnet_top_outputs.npz \ + --mlir picodet_s_320_coco_lcnet.mlir' + os.chdir('./picodet/workspace') + print(transform_str) + run(transform_str, shell=True) + os.chdir('../../') + +def mlir2bmodel(): + deploy_str = 'model_deploy.py \ + --mlir picodet_s_320_coco_lcnet.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input picodet_s_320_coco_lcnet_in_f32.npz \ + --test_reference picodet_s_320_coco_lcnet_top_outputs.npz \ + --model picodet_s_320_coco_lcnet_1684x_f32.bmodel' + os.chdir('./picodet/workspace') + print(deploy_str) + run(deploy_str, shell=True) + os.chdir('../../') def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() + parser.add_argument( + "--auto", required=True, help="Auto download, convert, compile and infer if True") + parser.add_argument( + "--pp_detect_path", default='/workspace/PaddleDetection', help="Path of PaddleDetection folder") parser.add_argument( "--model_file", required=True, help="Path of sophgo model.") parser.add_argument("--config_file", required=True, help="Path of config.") @@ -31,10 +120,18 @@ def parse_arguments(): if __name__ == "__main__": args = parse_arguments() - model_file = args.model_file - params_file = "" - config_file = args.config_file + if args.auto: + export_model() + paddle2onnx() + mlir_prepare() + image_prepare() + onnx2mlir() + mlir2bmodel() + model_file = './picodet/workspace/picodet_s_320_coco_lcnet_1684x_f32.bmodel' if args.auto else args.model_file + params_file = "" + config_file = './picodet_s_320_coco_lcnet/infer_cfg.yml' if args.auto else args.config_file + img_file = './000000014439.jpg' if args.auto else args.image # 配置runtime，加载模型 runtime_option = fd.RuntimeOption() runtime_option.use_sophgo() @@ -46,14 +143,14 @@ if __name__ == "__main__": runtime_option=runtime_option, model_format=fd.ModelFormat.SOPHGO) - model.postprocessor.apply_decode_and_nms() + model.postprocessor.apply_nms() # 预测图片分割结果 - im = cv2.imread(args.image) + im = cv2.imread(img_file) result = model.predict(im) print(result) # 可视化结果 vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5) cv2.imwrite("sophgo_result.jpg", vis_im) - print("Visualized result save in ./sophgo_result.jpg") + print("Visualized result save in ./sophgo_result_picodet.jpg") diff --git a/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py b/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py index 597a77a5f..845f2d386 100644 --- a/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py +++ b/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe.py @@ -14,12 +14,17 @@ import fastdeploy as fd import cv2 import os - +from subprocess import run +from prepare_npz import prepare def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() + parser.add_argument( + "--auto", required=True, help="Auto download, convert, compile and infer if True") + parser.add_argument( + "--pp_detect_path", default='/workspace/PaddleDetection', help="Path of PaddleDetection folder") parser.add_argument( "--model_file", required=True, help="Path of sophgo model.") parser.add_argument("--config_file", required=True, help="Path of config.") @@ -27,13 +32,102 @@ def parse_arguments(): "--image", type=str, required=True, help="Path of test image file.") return parser.parse_args() +def export_model(args): + PPDetection_path = args.pp_detect_path + export_str = 'python3 tools/export_model.py \ + -c configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml \ + -output_dir=output_inference \ + -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams' + cur_path = os.getcwd() + os.chdir(PPDetection_path) + print(export_str) + run(export_str, shell=True) + cp_str = 'cp -r ./output_inference/ppyoloe_crn_s_300e_coco ' + cur_path + print(cp_str) + run(cp_str, shell=True) + os.chdir(cur_path) + +def paddle2onnx(): + convert_str = 'paddle2onnx --model_dir ppyoloe_crn_s_300e_coco \ + --model_filename model.pdmodel \ + --params_filename model.pdiparams \ + --save_file ppyoloe_crn_s_300e_coco.onnx \ + --enable_dev_version True' + print(convert_str) + run(convert_str, shell=True) + fix_shape_str = 'python3 -m paddle2onnx.optimize --input_model ppyoloe_crn_s_300e_coco.onnx \ + --output_model ppyoloe_crn_s_300e_coco.onnx \ + --input_shape_dict "{\'image\':[1,3,640,640]}"' + print(fix_shape_str) + run(fix_shape_str, shell=True) + +def mlir_prepare(): + mlir_path = os.getenv("MODEL_ZOO_PATH") + mlir_path = mlir_path[:-13] + regression_path = os.path.join(mlir_path, 'regression') + mv_str_list = ['mkdir ppyoloe', + 'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./ppyoloe', + 'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ppyoloe', + 'cp ppyoloe_crn_s_300e_coco.onnx ./ppyoloe', + 'mkdir ./ppyoloe/workspace'] + for str in mv_str_list: + print(str) + run(str, shell=True) + +def image_prepare(): + img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg' + if not os.path.exists('000000014439.jpg'): + print(img_str) + run(img_str, shell=True) + prepare('000000014439.jpg', [640, 640]) + cp_npz_str = 'cp ./inputs.npz ./ppyoloe' + print(cp_npz_str) + run(cp_npz_str, shell=True) + +def onnx2mlir(): + transform_str = 'model_transform.py \ + --model_name ppyoloe_crn_s_300e_coco \ + --model_def ../ppyoloe_crn_s_300e_coco.onnx \ + --input_shapes [[1,3,640,640],[1,2]] \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names p2o.Div.1,p2o.Concat.29 \ + --test_input ../inputs.npz \ + --test_result ppyoloe_crn_s_300e_coco_top_outputs.npz \ + --mlir ppyoloe_crn_s_300e_coco.mlir' + os.chdir('./ppyoloe/workspace') + print(transform_str) + run(transform_str, shell=True) + os.chdir('../../') + +def mlir2bmodel(): + deploy_str = 'model_deploy.py \ + --mlir ppyoloe_crn_s_300e_coco.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ppyoloe_crn_s_300e_coco_in_f32.npz \ + --test_reference ppyoloe_crn_s_300e_coco_top_outputs.npz \ + --model ppyoloe_crn_s_300e_coco_1684x_f32.bmodel' + os.chdir('./ppyoloe/workspace') + print(deploy_str) + run(deploy_str, shell=True) + os.chdir('../../') if __name__ == "__main__": args = parse_arguments() - model_file = args.model_file + if args.auto: + export_model(args) + paddle2onnx() + mlir_prepare() + image_prepare() + onnx2mlir() + mlir2bmodel() + + model_file = './ppyoloe/workspace/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel' if args.auto else args.model_file params_file = "" - config_file = args.config_file + config_file = './ppyoloe_crn_s_300e_coco/infer_cfg.yml' if args.auto else args.config_file + image_file = './000000014439.jpg' if args.auto else args.image # 配置runtime，加载模型 runtime_option = fd.RuntimeOption() @@ -46,14 +140,14 @@ if __name__ == "__main__": runtime_option=runtime_option, model_format=fd.ModelFormat.SOPHGO) - model.postprocessor.apply_decode_and_nms() + model.postprocessor.apply_nms() # 预测图片分割结果 - im = cv2.imread(args.image) + im = cv2.imread(image_file) result = model.predict(im) print(result) # 可视化结果 vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5) cv2.imwrite("sophgo_result.jpg", vis_im) - print("Visualized result save in ./sophgo_result.jpg") + print("Visualized result save in ./sophgo_result_ppyoloe.jpg") diff --git a/examples/vision/detection/paddledetection/sophgo/python/prepare_npz.py b/examples/vision/detection/paddledetection/sophgo/python/prepare_npz.py new file mode 100644 index 000000000..33b1ef151 --- /dev/null +++ b/examples/vision/detection/paddledetection/sophgo/python/prepare_npz.py @@ -0,0 +1,17 @@ +import cv2 +import numpy as np + +def prepare(img_path, sz): + im = cv2.imread(img_path) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + im = cv2.resize(im, sz) + im = im.transpose((2,0,1)) + im = im[np.newaxis,...] + im_scale_y = sz[0] / float(im.shape[2]) + im_scale_x = sz[1] / float(im.shape[3]) + inputs = {} + inputs['image'] = np.array(im).astype('float32') + # scale = np.array([im_scale_y, im_scale_x]) + # scale = scale[np.newaxis,...] + inputs['scale_factor'] = np.array(([im_scale_y, im_scale_x], )).astype('float32') + np.savez('inputs.npz', image=inputs['image'], scale_factor=inputs['scale_factor']) \ No newline at end of file diff --git a/examples/vision/detection/yolov5/sophgo/python/README.md b/examples/vision/detection/yolov5/sophgo/python/README.md index 3f876ccca..0602bac02 100644 --- a/examples/vision/detection/yolov5/sophgo/python/README.md +++ b/examples/vision/detection/yolov5/sophgo/python/README.md @@ -15,7 +15,10 @@ cd FastDeploy/examples/vision/detection/yolov5/sophgo/python # Download images. wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg -# Inference. +# Set --auto True, automatic inference. +python3 infer.py --auto True + +# Set --auto False, need to set the model path and image path manually. python3 infer.py --model_file ./bmodel/yolov5s_1684x_f32.bmodel --image 000000014439.jpg # The returned result. diff --git a/examples/vision/detection/yolov5/sophgo/python/README_CN.md b/examples/vision/detection/yolov5/sophgo/python/README_CN.md index 69a2ed4af..37e73513a 100644 --- a/examples/vision/detection/yolov5/sophgo/python/README_CN.md +++ b/examples/vision/detection/yolov5/sophgo/python/README_CN.md @@ -15,8 +15,11 @@ cd FastDeploy/examples/vision/detection/yolov5/sophgo/python # 下载图片 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg -# 推理 -python3 infer.py --model_file ./bmodel/yolov5s_1684x_f32.bmodel --image 000000014439.jpg +# 指定--auto True，自动完成下载数据、模型转换、推理 +python3 infer.py --auto True + +# 指定--auto False，则需要设置模型目录、图片目录 +python3 infer.py --auto False --model_file ./bmodel/yolov5s_1684x_f32.bmodel --image 000000014439.jpg # 运行完成后返回结果如下所示 DetectionResult: [xmin, ymin, xmax, ymax, score, label_id] diff --git a/examples/vision/detection/yolov5/sophgo/python/infer.py b/examples/vision/detection/yolov5/sophgo/python/infer.py index d1ea190c5..52d2d275f 100644 --- a/examples/vision/detection/yolov5/sophgo/python/infer.py +++ b/examples/vision/detection/yolov5/sophgo/python/infer.py @@ -1,27 +1,94 @@ import fastdeploy as fd import cv2 import os - +from subprocess import run def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() - parser.add_argument("--model", required=True, help="Path of model.") parser.add_argument( - "--image", type=str, required=True, help="Path of test image file.") + "--auto", required=True, help="Auto download, convert, compile and infer if True") + parser.add_argument("--model", help="Path of model.") + parser.add_argument( + "--image", type=str, help="Path of test image file.") return parser.parse_args() +def download(): + download_model_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx' + if not os.path.exists('yolov5s.onnx'): + print(download_model_str) + run(download_model_str, shell=True) + download_img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg' + if not os.path.exists('000000014439.jpg'): + print(download_img_str) + run(download_img_str, shell=True) + + +def mlir_prepare(): + mlir_path = os.getenv("MODEL_ZOO_PATH") + mlir_path = mlir_path[:-13] + regression_path = os.path.join(mlir_path, 'regression') + mv_str_list = ['mkdir YOLOv5s', + 'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./YOLOv5s', + 'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./YOLOv5s', + 'cp yolov5s.onnx ./YOLOv5s', + 'mkdir ./YOLOv5s/workspace'] + for str in mv_str_list: + print(str) + run(str, shell=True) + + +def onnx2mlir(): + transform_str = 'model_transform.py \ + --model_name yolov5s \ + --model_def ../yolov5s.onnx \ + --input_shapes [[1,3,640,640]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names output0 \ + --test_input ../image/dog.jpg \ + --test_result yolov5s_top_outputs.npz \ + --mlir yolov5s.mlir' + os.chdir('./YOLOv5s/workspace') + print(transform_str) + run(transform_str, shell=True) + os.chdir('../../') + +def mlir2bmodel(): + deploy_str = 'model_deploy.py \ + --mlir yolov5s.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input yolov5s_in_f32.npz \ + --test_reference yolov5s_top_outputs.npz \ + --model yolov5s_1684x_f32.bmodel' + + os.chdir('./YOLOv5s/workspace') + print(deploy_str) + run(deploy_str, shell=True) + os.chdir('../../') + + args = parse_arguments() +if args.auto: + download() + mlir_prepare() + onnx2mlir() + mlir2bmodel() + # 配置runtime，加载模型 runtime_option = fd.RuntimeOption() runtime_option.use_sophgo() -model_file = args.model +model_file = './YOLOv5s/workspace/yolov5s_1684x_f32.bmodel' if args.auto else args.model params_file = "" +img_file = './000000014439.jpg' if args.auto else args.image model = fd.vision.detection.YOLOv5( model_file, @@ -29,8 +96,9 @@ model = fd.vision.detection.YOLOv5( runtime_option=runtime_option, model_format=fd.ModelFormat.SOPHGO) + # 预测图片分类结果 -im = cv2.imread(args.image) +im = cv2.imread(img_file) result = model.predict(im) print(result) diff --git a/examples/vision/ocr/PP-OCR/sophgo/python/README.md b/examples/vision/ocr/PP-OCR/sophgo/python/README.md index 27dbe2694..4db4198f3 100644 --- a/examples/vision/ocr/PP-OCR/sophgo/python/README.md +++ b/examples/vision/ocr/PP-OCR/sophgo/python/README.md @@ -27,11 +27,16 @@ cd PaddleOCR/deploy/fastdeploy/sophgo/python # 下载图片 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg -#下载字典文件 +# 下载字典文件 wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt # 推理 -python3 infer.py --det_model ocr_bmodel/ch_PP-OCRv3_det_1684x_f32.bmodel \ +# --auto True，自动完成下载数据、模型转换、推理 +python3 infer.py --auto True --det_model '' --cls_model '' --rec_model '' --rec_label_file '' --image '' + +# --auto False，需要用户设置模型、字典、图片路径，进行推理 +python3 infer.py --auto False \ + --det_model ocr_bmodel/ch_PP-OCRv3_det_1684x_f32.bmodel \ --cls_model ocr_bmodel/ch_ppocr_mobile_v2.0_cls_1684x_f32.bmodel \ --rec_model ocr_bmodel/ch_PP-OCRv3_rec_1684x_f32.bmodel \ --rec_label_file ../ppocr_keys_v1.txt \ diff --git a/examples/vision/ocr/PP-OCR/sophgo/python/infer.py b/examples/vision/ocr/PP-OCR/sophgo/python/infer.py index 356317099..3b3eb9151 100644 --- a/examples/vision/ocr/PP-OCR/sophgo/python/infer.py +++ b/examples/vision/ocr/PP-OCR/sophgo/python/infer.py @@ -1,12 +1,15 @@ import fastdeploy as fd import cv2 import os +from subprocess import run def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() + parser.add_argument( + "--auto", required=True, help="Auto download, convert, compile and infer if True") parser.add_argument( "--det_model", required=True, help="Path of Detection model of PPOCR.") parser.add_argument( @@ -27,22 +30,210 @@ def parse_arguments(): return parser.parse_args() +def getPPOCRv3(): + cmd_str_det = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar' + tar_str_det = 'tar xvf ch_PP-OCRv3_det_infer.tar' + cmd_str_cls = 'wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar' + tar_str_cls = 'tar xvf ch_ppocr_mobile_v2.0_cls_infer.tar' + cmd_str_rec = 'wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar' + tar_str_rec = 'tar xvf ch_PP-OCRv3_rec_infer.tar' + cmd_str_img = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg' + cmd_str_label = 'wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt' + script_str = 'wget https://raw.githubusercontent.com/PaddlePaddle/Paddle2ONNX/develop/tools/paddle/paddle_infer_shape.py' + if not os.path.exists('ch_PP-OCRv3_det_infer.tar'): + print(cmd_str_det, tar_str_det) + run(cmd_str_det, shell=True) + run(tar_str_det, shell=True) + if not os.path.exists('ch_ppocr_mobile_v2.0_cls_infer.tar'): + print(cmd_str_cls, tar_str_cls) + run(cmd_str_cls, shell=True) + run(tar_str_cls, shell=True) + if not os.path.exists('ch_PP-OCRv3_rec_infer.tar'): + print(cmd_str_rec, tar_str_rec) + run(cmd_str_rec, shell=True) + run(tar_str_rec, shell=True) + if not os.path.exists('12.jpg'): + print(cmd_str_img) + run(cmd_str_img, shell=True) + if not os.path.exists('ppocr_keys_v1.txt'): + print(cmd_str_label) + run(cmd_str_label, shell=True) + if not os.path.exists('paddle_infer_shape.py'): + print(script_str) + run(script_str, shell=True) + +def fix_input_shape(): + fix_det_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_det_infer \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_dir ch_PP-OCRv3_det_infer_fix \ + --input_shape_dict="{\'x\':[1,3,960,608]}"' + fix_rec_str = 'python paddle_infer_shape.py --model_dir ch_PP-OCRv3_rec_infer \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_dir ch_PP-OCRv3_rec_infer_fix \ + --input_shape_dict="{\'x\':[1,3,48,320]}"' + fix_cls_str = 'python paddle_infer_shape.py --model_dir ch_ppocr_mobile_v2.0_cls_infer \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_dir ch_PP-OCRv3_cls_infer_fix \ + --input_shape_dict="{\'x\':[1,3,48,192]}"' + print(fix_det_str) + run(fix_det_str, shell=True) + print(fix_rec_str) + run(fix_rec_str, shell=True) + print(fix_cls_str) + run(fix_cls_str, shell=True) + + +def paddle2onnx(): + cmd_str_det = 'paddle2onnx --model_dir ch_PP-OCRv3_det_infer_fix \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_file ch_PP-OCRv3_det_infer.onnx \ + --enable_dev_version True' + cmd_str_cls = 'paddle2onnx --model_dir ch_PP-OCRv3_cls_infer_fix \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_file ch_PP-OCRv3_cls_infer.onnx \ + --enable_dev_version True' + cmd_str_rec = 'paddle2onnx --model_dir ch_PP-OCRv3_rec_infer_fix \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --save_file ch_PP-OCRv3_rec_infer.onnx \ + --enable_dev_version True' + print(cmd_str_det) + run(cmd_str_det, shell=True) + print(cmd_str_cls) + run(cmd_str_cls, shell=True) + print(cmd_str_rec) + run(cmd_str_rec, shell=True) + +def mlir_prepare(): + mlir_path = os.getenv("MODEL_ZOO_PATH") + mlir_path = mlir_path[:-13] + regression_path = os.path.join(mlir_path, 'regression') + mv_str_list = ['mkdir ch_PP-OCRv3', + 'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./ch_PP-OCRv3', + 'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ch_PP-OCRv3', + 'mv ch_PP-OCRv3_det_infer.onnx ./ch_PP-OCRv3', + 'mv ch_PP-OCRv3_rec_infer.onnx ./ch_PP-OCRv3', + 'mv ch_PP-OCRv3_cls_infer.onnx ./ch_PP-OCRv3', + 'mkdir ./ch_PP-OCRv3/workspace'] + for str in mv_str_list: + print(str) + run(str, shell=True) + + +def onnx2mlir(): + transform_str_det = 'model_transform.py \ + --model_name ch_PP-OCRv3_det \ + --model_def ../ch_PP-OCRv3_det_infer.onnx \ + --input_shapes [[1,3,960,608]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names sigmoid_0.tmp_0 \ + --test_input ../image/dog.jpg \ + --test_result ch_PP-OCRv3_det_top_outputs.npz \ + --mlir ./ch_PP-OCRv3_det.mlir' + transform_str_rec = 'model_transform.py \ + --model_name ch_PP-OCRv3_rec \ + --model_def ../ch_PP-OCRv3_rec_infer.onnx \ + --input_shapes [[1,3,48,320]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names softmax_5.tmp_0 \ + --test_input ../image/dog.jpg \ + --test_result ch_PP-OCRv3_rec_top_outputs.npz \ + --mlir ./ch_PP-OCRv3_rec.mlir' + transform_str_cls = 'model_transform.py \ + --model_name ch_PP-OCRv3_cls \ + --model_def ../ch_PP-OCRv3_cls_infer.onnx \ + --input_shapes [[1,3,48,192]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names softmax_0.tmp_0 \ + --test_input ../image/dog.jpg \ + --test_result ch_PP-OCRv3_cls_top_outputs.npz \ + --mlir ./ch_PP-OCRv3_cls.mlir' + + os.chdir('./ch_PP-OCRv3/workspace/') + + print(transform_str_det) + run(transform_str_det, shell=True) + + print(transform_str_rec) + run(transform_str_rec, shell=True) + + print(transform_str_cls) + run(transform_str_cls, shell=True) + + os.chdir('../../') + +def mlir2bmodel(): + det_str = 'model_deploy.py \ + --mlir ./ch_PP-OCRv3_det.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ./ch_PP-OCRv3_det_in_f32.npz \ + --test_reference ./ch_PP-OCRv3_det_top_outputs.npz \ + --model ./ch_PP-OCRv3_det_1684x_f32.bmodel' + rec_str = 'model_deploy.py \ + --mlir ./ch_PP-OCRv3_rec.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ./ch_PP-OCRv3_rec_in_f32.npz \ + --test_reference ./ch_PP-OCRv3_rec_top_outputs.npz \ + --model ./ch_PP-OCRv3_rec_1684x_f32.bmodel' + cls_str = 'model_deploy.py \ + --mlir ./ch_PP-OCRv3_cls.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input ./ch_PP-OCRv3_cls_in_f32.npz \ + --test_reference ./ch_PP-OCRv3_cls_top_outputs.npz \ + --model ./ch_PP-OCRv3_cls_1684x_f32.bmodel' + os.chdir('./ch_PP-OCRv3/workspace/') + print(det_str) + run(det_str, shell=True) + + print(rec_str) + run(rec_str, shell=True) + + print(cls_str) + run(cls_str, shell=True) + os.chdir('../../') + args = parse_arguments() +if (args.auto): + getPPOCRv3() + fix_input_shape() + paddle2onnx() + mlir_prepare() + onnx2mlir() + mlir2bmodel() + # 配置runtime，加载模型 runtime_option = fd.RuntimeOption() runtime_option.use_sophgo() # Detection模型, 检测文字框 -det_model_file = args.det_model +det_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_det_1684x_f32.bmodel' if args.auto else args.det_model det_params_file = "" # Classification模型，方向分类，可选 -cls_model_file = args.cls_model +cls_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_cls_1684x_f32.bmodel' if args.auto else args.cls_model cls_params_file = "" # Recognition模型，文字识别模型 -rec_model_file = args.rec_model +rec_model_file = './ch_PP-OCRv3/workspace/ch_PP-OCRv3_rec_1684x_f32.bmodel' if args.auto else args.rec_model rec_params_file = "" -rec_label_file = args.rec_label_file +rec_label_file = './ppocr_keys_v1.txt' if args.auto else args.rec_label_file +image_file = './12.jpg' if args.auto else args.image # PPOCR的cls和rec模型现在已经支持推理一个Batch的数据 # 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置 @@ -92,9 +283,9 @@ rec_model = fd.vision.ocr.Recognizer( ppocr_v3 = fd.vision.ocr.PPOCRv3( det_model=det_model, cls_model=cls_model, rec_model=rec_model) -# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 584] +# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 320] rec_model.preprocessor.static_shape_infer = True -rec_model.preprocessor.rec_image_shape = [3, 48, 584] +rec_model.preprocessor.rec_image_shape = [3, 48, 320] # 给cls和rec模型设置推理时的batch size # 此值能为-1, 和1到正无穷 @@ -103,7 +294,7 @@ ppocr_v3.cls_batch_size = cls_batch_size ppocr_v3.rec_batch_size = rec_batch_size # 预测图片准备 -im = cv2.imread(args.image) +im = cv2.imread(image_file) #预测并打印结果 result = ppocr_v3.predict(im) diff --git a/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/README.md b/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/README.md index 5de41d8c0..f2c4d3874 100644 --- a/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/README.md +++ b/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/README.md @@ -28,7 +28,11 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png 将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README_CN.md) # 推理 -python3 infer.py --model_file ./bmodel/pp_liteseg_1684x_f32.bmodel --config_file ./bmodel/deploy.yaml --image cityscapes_demo.png +# --auto True: 自动完成下载数据、模型转换、推理 +python3 infer.py --auto True --model '' --config_file '' --image '' + +# --auto False，需要用户设置模型、配置文件、图片路径，进行推理 +python3 infer.py --auto False --model_file ./bmodel/pp_liteseg_1684x_f32.bmodel --config_file ./bmodel/deploy.yaml --image cityscapes_demo.png # 运行完成后返回结果如下所示 运行结果保存在sophgo_img.png中 diff --git a/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py b/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py index 1b294da60..13dfd3f4d 100644 --- a/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py +++ b/examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py @@ -1,12 +1,14 @@ import fastdeploy as fd import cv2 import os - +from subprocess import run def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() + parser.add_argument( + "--auto", required=True, help="Auto download, convert, compile and infer if True") parser.add_argument("--model", required=True, help="Path of model.") parser.add_argument( "--config_file", required=True, help="Path of config file.") @@ -16,15 +18,102 @@ def parse_arguments(): return parser.parse_args() +def download(): + download_model_str = 'wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz' + if not os.path.exists('PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz'): + print(download_model_str) + run(download_model_str, shell=True) + tar_str = 'tar xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz' + if not os.path.exists('./PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer'): + print(tar_str) + run(tar_str, shell=True) + download_script_str = 'wget https://raw.githubusercontent.com/PaddlePaddle/Paddle2ONNX/develop/tools/paddle/paddle_infer_shape.py' + if not os.path.exists('paddle_infer_shape.py'): + print(download_script_str) + run(download_script_str, shell=True) + download_img_str = 'wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png' + if not os.path.exists('cityscapes_demo.png'): + print(download_img_str) + run(download_img_str, shell=True) + +def paddle2onnx(): + paddle_infer_shape_str = 'python3 paddle_infer_shape.py --model_dir PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer \ + --model_filename model.pdmodel \ + --params_filename model.pdiparams \ + --save_dir pp_liteseg_fix \ + --input_shape_dict="{\'x\':[1,3,512,512]}"' + print(paddle_infer_shape_str) + run(paddle_infer_shape_str, shell=True) + pp2onnx_str = 'paddle2onnx --model_dir pp_liteseg_fix \ + --model_filename model.pdmodel \ + --params_filename model.pdiparams \ + --save_file pp_liteseg.onnx \ + --enable_dev_version True' + print(pp2onnx_str) + run(pp2onnx_str, shell=True) + +def mlir_prepare(): + mlir_path = os.getenv("MODEL_ZOO_PATH") + mlir_path = mlir_path[:-13] + regression_path = os.path.join(mlir_path, 'regression') + mv_str_list = ['mkdir pp_liteseg', + 'cp -rf ' + os.path.join(regression_path, 'dataset/COCO2017/') + ' ./pp_liteseg', + 'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./pp_liteseg', + 'mv pp_liteseg.onnx ./pp_liteseg', + 'mkdir ./pp_liteseg/workspace'] + for str in mv_str_list: + print(str) + run(str, shell=True) + +def onnx2mlir(): + transform_str = 'model_transform.py \ + --model_name pp_liteseg \ + --model_def ../pp_liteseg.onnx \ + --input_shapes [[1,3,512,512]] \ + --mean 0.0,0.0,0.0 \ + --scale 0.0039216,0.0039216,0.0039216 \ + --keep_aspect_ratio \ + --pixel_format rgb \ + --output_names bilinear_interp_v2_6.tmp_0 \ + --test_input ../image/dog.jpg \ + --test_result pp_liteseg_top_outputs.npz \ + --mlir pp_liteseg.mlir' + print(transform_str) + os.chdir('./pp_liteseg/workspace') + run(transform_str, shell=True) + os.chdir('../../') + +def mlir2bmodel(): + deploy_str = 'model_deploy.py \ + --mlir pp_liteseg.mlir \ + --quantize F32 \ + --chip bm1684x \ + --test_input pp_liteseg_in_f32.npz \ + --test_reference pp_liteseg_top_outputs.npz \ + --model pp_liteseg_1684x_f32.bmodel' + print(deploy_str) + os.chdir('./pp_liteseg/workspace') + run(deploy_str, shell=True) + os.chdir('../../') + args = parse_arguments() + +if args.auto: + download() + paddle2onnx() + mlir_prepare() + onnx2mlir() + mlir2bmodel() + # 配置runtime，加载模型 runtime_option = fd.RuntimeOption() runtime_option.use_sophgo() -model_file = args.model +model_file = './pp_liteseg/workspace/pp_liteseg_1684x_f32.bmodel' if args.auto else args.model params_file = "" -config_file = args.config_file +config_file = './PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer/deploy.yaml' if args.auto else args.config_file +img_file = './cityscapes_demo.png' if args.auto else args.image model = fd.vision.segmentation.PaddleSegModel( model_file, @@ -34,7 +123,7 @@ model = fd.vision.segmentation.PaddleSegModel( model_format=fd.ModelFormat.SOPHGO) # 预测图片分类结果 -im_org = cv2.imread(args.image) +im_org = cv2.imread(img_file) #bmodel 是静态模型，模型输入固定，这里设置为[512, 512] im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR) result = model.predict(im) @@ -42,4 +131,5 @@ print(result) # 预测结果可视化 vis_im = fd.vision.vis_segmentation(im, result, weight=0.5) +vis_im = cv2.resize(vis_im, [im_org.shape[1], im_org.shape[0]], interpolation=cv2.INTER_LINEAR) cv2.imwrite("sophgo_img.png", vis_im) diff --git a/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc b/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc index 39b2770f4..9be9e79cb 100644 --- a/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc +++ b/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc @@ -225,6 +225,13 @@ bool SophgoBackend::Infer(std::vector& inputs, free(temp_out); } + for (int i = 0; i < input_size; i++) { + bm_free_device(handle_, input_tensors[i].device_mem); + } + for (int i = 0; i < output_size; i++) { + bm_free_device(handle_, output_tensors[i].device_mem); + } + return true; }