AIlib2/ocrUtils2/pth2onnx.py

#import  crnn_model.vgg_model as  vgg

import sys
from ocrTrt import toONNX,ONNXtoTrt
from collections import OrderedDict
import torch
import argparse
def crnnModel(opt):
    input_height=opt.mHeight
    input_width=opt.mWidth
    mode=opt.mode.strip()
    ##生成识别模型
    device='cuda:0'
    model_path = opt.weights

    if mode=='en':
        import crnn_model 
        model = crnn_model.CRNN(32, 1, 93, 256 )
    else:
        import crnnCh as crnn
        model = crnn.CRNN(3, 256, 7935, 32)
    

    print('####line24:',mode)
    checkpoint = torch.load(model_path)
    if 'state_dict' in checkpoint.keys():
        model.load_state_dict(checkpoint['state_dict'])
    else:
        try:
            model.load_state_dict(checkpoint)   
        except:
            ##修正模型参数的名字
            state_dict = torch.load(model_path)
            # create new OrderedDict that does not contain `module.`
            from collections import OrderedDict
            new_state_dict = OrderedDict()
            for k, v in state_dict.items():
                name = k[7:] # remove `module.`
                new_state_dict[name] = v
            # load params
            model.load_state_dict(new_state_dict)    

                
    model = model.to(device)    
    

    return model
    
   
if __name__=='__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='english_g2.onnx', help='model path(s)')
    parser.add_argument('--mWidth', type=int, default=640, help='segmodel mWdith')
    parser.add_argument('--mHeight', type=int, default=360, help='segmodel mHeight')
    parser.add_argument('--mode', type=str, default='en', help='segmodel mHeight')
    
    opt = parser.parse_args()
    
    pthmodel = crnnModel(opt)
    
    ###转换TRT模型
    onnxFile=opt.weights.replace('.pth','_%dX%d.onnx'%(opt.mWidth,opt.mHeight))
    trtFile=opt.weights.replace('.pth','_%dX%d.engine'%(opt.mWidth,opt.mHeight))

    print('#'*20, ' begin to toONNX')
    if opt.mode=='en':inputShape=(1,1,opt.mHeight, opt.mWidth)
    else: inputShape=(1,3,opt.mHeight, opt.mWidth)
    toONNX(pthmodel,onnxFile,inputShape=inputShape,device='cuda:0')  
    print('#'*20, ' begin to TRT')
    ONNXtoTrt(onnxFile,trtFile,half=False)