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Improve PPOCR example
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yunyaoXYY
@@ -35,8 +35,8 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
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# 昆仑芯XPU推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
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# 华为昇腾推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
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# 华为昇腾推理,需要使用静态shape脚本, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
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python infer_static_shape.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
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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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@@ -58,43 +58,113 @@ def parse_arguments():
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type=int,
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default=9,
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help="Number of threads while inference on CPU.")
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parser.add_argument(
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"--cls_bs",
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type=int,
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default=1,
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help="Classification model inference batch size.")
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parser.add_argument(
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"--rec_bs",
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type=int,
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default=6,
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help="Recognition model inference batch size")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu(0)
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option.set_cpu_thread_num(args.cpu_thread_num)
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det_option = fd.RuntimeOption()
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cls_option = fd.RuntimeOption()
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rec_option = fd.RuntimeOption()
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det_option.set_cpu_thread_num(args.cpu_thread_num)
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cls_option.set_cpu_thread_num(args.cpu_thread_num)
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rec_option.set_cpu_thread_num(args.cpu_thread_num)
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if args.device.lower() == "gpu":
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det_option.use_gpu(args.device_id)
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cls_option.use_gpu(args.device_id)
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rec_option.use_gpu(args.device_id)
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if args.device.lower() == "kunlunxin":
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option.use_kunlunxin()
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return option
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det_option.use_kunlunxin()
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cls_option.use_kunlunxin()
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rec_option.use_kunlunxin()
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if args.device.lower() == "ascend":
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option.use_ascend()
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return option
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return det_option, cls_option, rec_option
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if args.backend.lower() == "trt":
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assert args.device.lower(
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) == "gpu", "TensorRT backend require inference on device GPU."
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option.use_trt_backend()
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det_option.use_trt_backend()
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cls_option.use_trt_backend()
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rec_option.use_trt_backend()
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# 设置trt input shape
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
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[1, 3, 960, 960])
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[args.cls_bs, 3, 48, 320],
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[args.cls_bs, 3, 48, 1024])
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[args.rec_bs, 3, 48, 320],
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[args.rec_bs, 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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elif args.backend.lower() == "pptrt":
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assert args.device.lower(
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) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
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option.use_trt_backend()
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option.enable_paddle_trt_collect_shape()
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option.enable_paddle_to_trt()
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det_option.use_trt_backend()
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det_option.enable_paddle_trt_collect_shape()
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det_option.enable_paddle_to_trt()
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cls_option.use_trt_backend()
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cls_option.enable_paddle_trt_collect_shape()
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cls_option.enable_paddle_to_trt()
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rec_option.use_trt_backend()
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rec_option.enable_paddle_trt_collect_shape()
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rec_option.enable_paddle_to_trt()
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# 设置trt input shape
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
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[1, 3, 960, 960])
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[args.cls_bs, 3, 48, 320],
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[args.cls_bs, 3, 48, 1024])
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[args.rec_bs, 3, 48, 320],
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[args.rec_bs, 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)
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cls_option.set_trt_cache_file(args.cls_model)
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rec_option.set_trt_cache_file(args.rec_model)
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elif args.backend.lower() == "ort":
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option.use_ort_backend()
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det_option.use_ort_backend()
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cls_option.use_ort_backend()
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rec_option.use_ort_backend()
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elif args.backend.lower() == "paddle":
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option.use_paddle_infer_backend()
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det_option.use_paddle_infer_backend()
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cls_option.use_paddle_infer_backend()
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rec_option.use_paddle_infer_backend()
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elif args.backend.lower() == "openvino":
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assert args.device.lower(
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) == "cpu", "OpenVINO backend require inference on device CPU."
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option.use_openvino_backend()
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return option
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det_option.use_openvino_backend()
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cls_option.use_openvino_backend()
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rec_option.use_openvino_backend()
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return det_option, cls_option, rec_option
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args = parse_arguments()
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@@ -111,49 +181,18 @@ 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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# 用户也可根据自己的需求,个性化配置
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det_option, cls_option, rec_option = build_option(args)
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# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
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# 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
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# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
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# 需要把cls_batch_size和rec_batch_size都设置为1.
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cls_batch_size = 1
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rec_batch_size = 6
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# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
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# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option = runtime_option
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
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[1, 3, 960, 960])
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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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det_model = fd.vision.ocr.DBDetector(
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det_model_file, det_params_file, runtime_option=det_option)
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cls_option = runtime_option
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[cls_batch_size, 3, 48, 320],
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[cls_batch_size, 3, 48, 1024])
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# 用户可以把TRT引擎文件保存至本地
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=cls_option)
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rec_option = runtime_option
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[rec_batch_size, 3, 48, 320],
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[rec_batch_size, 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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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
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# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
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# 需要使用下行代码, 来启用rec模型的静态shape推理.
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# rec_model.preprocessor.static_shape_infer = True
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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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det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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@@ -161,8 +200,8 @@ ppocr_v3 = fd.vision.ocr.PPOCRv3(
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# 给cls和rec模型设置推理时的batch size
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# 此值能为-1, 和1到正无穷
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# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
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ppocr_v3.cls_batch_size = cls_batch_size
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ppocr_v3.rec_batch_size = rec_batch_size
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ppocr_v3.cls_batch_size = args.cls_bs
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ppocr_v3.rec_batch_size = args.rec_bs
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# 预测图片准备
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im = cv2.imread(args.image)
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114
examples/vision/ocr/PP-OCRv3/python/infer_static_shape.py
Executable file
114
examples/vision/ocr/PP-OCRv3/python/infer_static_shape.py
Executable file
@@ -0,0 +1,114 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--det_model", required=True, help="Path of Detection model of PPOCR.")
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parser.add_argument(
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"--cls_model",
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required=True,
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help="Path of Classification model of PPOCR.")
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parser.add_argument(
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"--rec_model",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--rec_label_file",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
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parser.add_argument(
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"--cpu_thread_num",
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type=int,
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default=9,
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help="Number of threads while inference on CPU.")
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return parser.parse_args()
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def build_option(args):
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det_option = fd.RuntimeOption()
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cls_option = fd.RuntimeOption()
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rec_option = fd.RuntimeOption()
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# 当前需要对PP-OCR启用静态shape推理的硬件只有昇腾.
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if args.device.lower() == "ascend":
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det_option.use_ascend()
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cls_option.use_ascend()
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rec_option.use_ascend()
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return det_option, cls_option, rec_option
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args = parse_arguments()
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# Detection模型, 检测文字框
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det_model_file = os.path.join(args.det_model, "inference.pdmodel")
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det_params_file = os.path.join(args.det_model, "inference.pdiparams")
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# Classification模型，方向分类，可选
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cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
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cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
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# Recognition模型，文字识别模型
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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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det_option, cls_option, rec_option = build_option(args)
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det_model = fd.vision.ocr.DBDetector(
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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=cls_option)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
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# Rec模型启用静态shape推理
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rec_model.preprocessor.static_shape_infer = True
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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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det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# Cls模型和Rec模型的batch size 必须设置为1, 开启静态shape推理
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ppocr_v3.cls_batch_size = 1
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ppocr_v3.rec_batch_size = 1
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# 预测图片准备
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im = cv2.imread(args.image)
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#预测并打印结果
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result = ppocr_v3.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_ppocr(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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