classification-main/export_onnx.py

import os
import torch
import argparse
from model.layer import deploy

# -------------------------------------------------------------------------------------------------------------------- #
parser = argparse.ArgumentParser(description='|将pt模型转为onnx，同时导出类别信息|')
parser.add_argument('--weight', default='best.pt', type=str, help='|模型位置|')
parser.add_argument('--input_size', default=32, type=int, help='|输入图片大小|')
parser.add_argument('--normalization', default='sigmoid', type=str, help='|选择sigmoid或softmax归一化，单类别一定要选sigmoid|')
parser.add_argument('--batch', default=0, type=int, help='|输入图片批量，0为动态|')
parser.add_argument('--sim', default=True, type=bool, help='|使用onnxsim压缩简化模型|')
parser.add_argument('--device', default='cuda', type=str, help='|在哪个设备上加载模型|')
parser.add_argument('--float16', default=True, type=bool, help='|转换的onnx模型数据类型，需要GPU，False时为float32|')
parser.add_argument('--save_path', default='best.onnx', type=str, help='|移动存储位置|')
args = parser.parse_args()
args.weight = args.weight.split('.')[0] + '.pt'
args.save_name = args.weight.split('.')[0] + '.onnx'
# -------------------------------------------------------------------------------------------------------------------- #
assert os.path.exists(args.weight), f'! 没有找到模型{args.weight} !'
if args.float16:
    assert torch.cuda.is_available(), '! cuda不可用，无法使用float16 !'


# -------------------------------------------------------------------------------------------------------------------- #
def export_onnx():
    model_dict = torch.load(args.weight, map_location='cpu')
    model = model_dict['model']
    model = deploy(model, args.normalization)
    model = model.eval().half().to(args.device) if args.float16 else model.eval().float().to(args.device)
    input_shape = torch.rand(1, args.input_size, args.input_size, 3,
                             dtype=torch.float16 if args.float16 else torch.float32).to(args.device)
    torch.onnx.export(model, input_shape, args.save_name,
                      opset_version=12, input_names=['input'], output_names=['output'],
                      dynamic_axes={'input': {args.batch: 'batch_size'}, 'output': {args.batch: 'batch_size'}})
    print(f'| 转为onnx模型成功:{args.save_name} |')
    if args.sim:
        import onnx
        import onnxsim

        model_onnx = onnx.load(args.save_name)
        model_simplify, check = onnxsim.simplify(model_onnx)
        onnx.save(model_simplify, args.save_name)
        print(f'| 使用onnxsim简化模型成功:{args.save_name} |')


if __name__ == '__main__':
    export_onnx()
    # 移动生成的 ONNX 文件到指定文件夹
    destination_folder = args.save_path
    shutil.move(args.save_name, os.path.join(destination_folder, args.save_name))
    print(f'| 已将 {args.save_name} 移动到 {destination_folder} 中 |')