在使用paddleocr转onnx后，推理遇到的问题解决

报错

if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
TypeError: '>' not supported between instances of 'str' and 'int'

定位一下问题可以看到

import onnxruntime as ort
sess = ort.InferenceSession("inference/det_onnx/model.onnx")
sess.get_inputs()[0].shape
获取的输入维度如下，不是固定的输入
['p2o.DynamicDimension.0', 3, 'p2o.DynamicDimension.1', 'p2o.DynamicDimension.2']

解决办法：

1、删掉代码

predict_det.py line144

        # if self.use_onnx:
        #     img_h, img_w = self.input_tensor.shape[2:]
        #     if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
        #         pre_process_list[0] = {
        #             'DetResizeForTest': {
        #                 'image_shape': [img_h, img_w]
        #             }
        #         }

predict_rec.py line174

        # if self.use_onnx:
        #     w = self.input_tensor.shape[3:][0]
        #     if w is not None and w > 0:
        #         imgW = w

对以上报错的部分代码进行注释掉，再运行就正常运行了。

2、将动态输入维度转固定输入维度

在转onnx之前对原paddle模型进行处理，处理成固定输入， 原输入是动态维度，只要运行paddle_infer_shape.py，固定模型输入之后，再转onnx并且进行推理就不会出错了。

具体自己操作一下吧，这两种办法我都试过，都可以。

看到有人问我这个脚本paddle_infer_shape.py找不到，可能不知道这个脚本是个链接，不知道点，所以我还是把这个代码复制过来吧，直接复制下面的脚本，就是paddle_infer_shape.py代码。

import argparse
 
def process_old_ops_desc(program):
    for i in range(len(program.blocks[0].ops)):
        if program.blocks[0].ops[i].type == "matmul":
            if not program.blocks[0].ops[i].has_attr("head_number"):
                program.blocks[0].ops[i]._set_attr("head_number", 1)
 
def infer_shape(program, input_shape_dict):
    import paddle
    paddle.enable_static()
    import paddle.fluid as fluid
 
    OP_WITHOUT_KERNEL_SET = {
        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
        'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
        'gen_bkcl_id', 'c_gen_bkcl_id', 'gen_nccl_id', 'c_gen_nccl_id',
        'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream',
        'queue_generator', 'dequeue', 'enqueue', 'heter_listen_and_serv',
        'c_wait_comm', 'c_wait_compute', 'c_gen_hccl_id', 'c_comm_init_hccl',
        'copy_cross_scope'
    }
    model_version = program.desc._version()
    paddle_version = paddle.__version__
    major_ver = model_version // 1000000
    minor_ver = (model_version - major_ver * 1000000) // 1000
    patch_ver = model_version - major_ver * 1000000 - minor_ver * 1000
    model_version = "{}.{}.{}".format(major_ver, minor_ver, patch_ver)
    if model_version != paddle_version:
        print("[WARNING] The model is saved by paddlepaddle v{}, but now your paddlepaddle is version of {}, this difference may cause error, it is recommend you reinstall a same version of paddlepaddle for this model".format(model_version, paddle_version))
    for k, v in input_shape_dict.items():
        program.blocks[0].var(k).desc.set_shape(v)
    for i in range(len(program.blocks)):
        for j in range(len(program.blocks[0].ops)):
            if program.blocks[i].ops[j].type in OP_WITHOUT_KERNEL_SET:
                continue
            program.blocks[i].ops[j].desc.infer_shape(program.blocks[i].desc)
    
def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', required=True, help='Path of directory saved the input model.')
    parser.add_argument('--model_filename', required=True, help='The input model file name.')
    parser.add_argument('--params_filename', required=True, help='The parameters file name.')
    parser.add_argument('--save_dir', required=True,
                        help='Path of directory to save the new exported model.')
    parser.add_argument('--input_shape_dict', required=True, help="The new shape information.")
    return parser.parse_args()
 
if __name__ == '__main__':
    args = parse_arguments()
    import paddle
    paddle.enable_static()
    import paddle.fluid as fluid
    input_shape_dict_str = args.input_shape_dict
    input_shape_dict = eval(input_shape_dict_str)
    print("Start to load paddle model...")
    exe = fluid.Executor(fluid.CPUPlace())
    [prog, ipts, outs] = fluid.io.load_inference_model(args.model_dir, exe, model_filename=args.model_filename, params_filename=args.params_filename)
    process_old_ops_desc(prog)
    infer_shape(prog, input_shape_dict)
    fluid.io.save_inference_model(args.save_dir, ipts, outs, exe, prog, model_filename=args.model_filename, params_filename=args.params_filename)

真是为了小白们煞费苦心啊，要面面俱到