tw/convert_pytorch.py

import torchvision
import torch
import argparse
from torch.autograd import Variable
import onnx
print(torch.__version__)

parser = argparse.ArgumentParser(description='MindSpore Lenet Example')

parser.add_argument('--model',
                    type=str,
                    help='path to training/inference dataset folder'
                    )
parser.add_argument('--n',
                    type=int,
                    default=256,
                    help='batch size for input shape type'
                    )
parser.add_argument('--c',
                    type=int,
                    default=1,
                    help='channel for input shape type'
                    )
parser.add_argument('--h',
                    type=int,
                    default=28,
                    help='height for input shape type'
                    )
parser.add_argument('--w',
                    type=int,
                    default=28,
                    help='width for input shape type'
                    )
                    
if __name__ == "__main__":
    args = parser.parse_args()
    print('args:')
    print(args)

    model_file = '/dataset/' + args.model
    print(model_file)
    model = torch.load(model_file)
    print(model)
    print(type(model))
    for k, v in model.named_parameters():
	    print("k:",k)
	    print("v:",v.shape)

    suffix = args.model.rindex(".")
    out_file = '/model/' + args.model + ".onnx"
    if suffix!=-1 :
        out_file = '/model/' + args.model[0:suffix] + ".onnx"
    print(out_file)
    input_name = ['input']
    output_name = ['output']
    input = Variable(torch.randn(args.n, args.c, args.h, args.w))
    torch.onnx.export(model, input, out_file, input_names=input_name, output_names=output_name, verbose=True)