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[Other] Set TRT dynamic input shape for PPOCR examples. (#566)
* Imporve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Make all the model links come from PaddleOCR * Improve OCR readme * Improve OCR readme * Improve OCR readme * Improve OCR readme * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add comments to create API docs * Improve OCR comments * Rename OCR and add comments * Make sure previous python example works * Make sure previous python example works * Fix Rec model bug * Fix Rec model bug * Fix rec model bug * Add SetTrtMaxBatchSize function for TensorRT * Add SetTrtMaxBatchSize Pybind * Add set_trt_max_batch_size python function * Set TRT dynamic shape in PPOCR examples * Set TRT dynamic shape in PPOCR examples * Set TRT dynamic shape in PPOCR examples Co-authored-by: Jason <jiangjiajun@baidu.com>
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yunyaoXYY
@@ -29,9 +29,25 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
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auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
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auto det_option = option;
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auto cls_option = option;
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auto rec_option = option;
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// If use TRT backend, the dynamic shape will be set as follow.
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det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
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{1, 3, 1536, 1536});
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cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320}, {1, 3, 48, 1024});
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rec_option.SetTrtInputShape("x", {1, 3, 32, 10}, {1, 3, 32, 320},
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{1, 3, 32, 2304});
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// Users could save TRT cache file to disk as follow.
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// det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
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// cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
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// rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
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auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
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auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
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assert(det_model.Initialized());
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assert(cls_model.Initialized());
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@@ -96,19 +96,29 @@ rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
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rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
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rec_label_file = args.rec_label_file
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# 对于三个模型，均采用同样的部署配置
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# 用户也可根据自行需求分别配置
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runtime_option = build_option(args)
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det_option = runtime_option
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cls_option = runtime_option
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rec_option = runtime_option
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# 当使用TRT时，分别给三个Runtime设置动态shape
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det_option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
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[1, 3, 1536, 1536])
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
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[1, 3, 48, 1024])
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rec_option.set_trt_input_shape("x", [1, 3, 32, 10], [1, 3, 32, 320],
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[1, 3, 32, 2304])
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# 用户可以把TRT引擎文件保存至本地
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# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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det_model = fd.vision.ocr.DBDetector(
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det_model_file, det_params_file, runtime_option=runtime_option)
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det_model_file, det_params_file, runtime_option=det_option)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=runtime_option)
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cls_model_file, cls_params_file, runtime_option=cls_option)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=runtime_option)
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rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
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# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
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ppocr_v2 = fd.vision.ocr.PPOCRv2(
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@@ -29,9 +29,25 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
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auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
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auto det_option = option;
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auto cls_option = option;
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auto rec_option = option;
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// If use TRT backend, the dynamic shape will be set as follow.
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det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
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{1, 3, 1536, 1536});
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cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320}, {1, 3, 48, 1024});
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rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
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{1, 3, 48, 2304});
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// Users could save TRT cache file to disk as follow.
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// det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
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// cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
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// rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
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auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
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auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
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assert(det_model.Initialized());
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assert(cls_model.Initialized());
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@@ -100,15 +100,29 @@ rec_label_file = args.rec_label_file
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# 用户也可根据自行需求分别配置
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runtime_option = build_option(args)
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det_option = runtime_option
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cls_option = runtime_option
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rec_option = runtime_option
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# 当使用TRT时，分别给三个Runtime设置动态shape
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det_option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
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[1, 3, 1536, 1536])
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
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[1, 3, 48, 1024])
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
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[1, 3, 48, 2304])
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# 用户可以把TRT引擎文件保存至本地
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# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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det_model = fd.vision.ocr.DBDetector(
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det_model_file, det_params_file, runtime_option=runtime_option)
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det_model_file, det_params_file, runtime_option=det_option)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=runtime_option)
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cls_model_file, cls_params_file, runtime_option=cls_option)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=runtime_option)
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rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
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# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
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ppocr_v3 = fd.vision.ocr.PPOCRv3(
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