海思开发：mobilefacenet 模型： pytorch -＞ onnx -＞ caffe -＞ nnie_mobilefacenet onnx csdn

一、前言

最近有空，把之前的项目梳理记录一下，惠已惠人。我的demo代码

二、详情

人脸模型是在 pytorch 下训练的，工程文件用的是这个：MobileFaceNet_Tutorial_Pytorch
训练完成之后，先转为onnx模型并做简化，代码如下：

def export_onnx():
    import onnx
    parser = argparse.ArgumentParser()
    #parser.add_argument('--weights', type=str, default=r'F:\demo\Pytorch_demo\yolov5_fruit\weights\fruit_last.pt', help='weights path')
    parser.add_argument('--img-size', nargs='+', type=int, default=[112, 112], help='image size')
    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
    opt = parser.parse_args()
    print(opt)

    # Parameters
    f = "zeng_mobileface_model\zeng_insightface.onnx"
    sim_onnx_path = "zeng_mobileface_model\zeng_insightface_sim.onnx"
    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size, (1, 3, 320, 192) iDetection

    # Load pytorch model
    #google_utils.attempt_download(opt.weights)
    #model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()

    #model.eval()
    device = "cpu"
    model = MobileFaceNet(512).to(device)  # embeding size is 512 (feature vector)
    model.load_state_dict(
        torch.load('F:\demo\Pytorch_demo\MobileFaceNet_Tutorial_Pytorch\zeng_mobileface_model\Iter_455000_model.ckpt',
                   map_location='cpu')['net_state_dict'])
    print('MobileFaceNet face detection model generated')

    model.eval()

    _ = model(img)  # dry run
    torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
                      output_names=['output'])  # output_names=['classes', 'boxes']

    # Check onnx model
    model = onnx.load(f)  # load onnx model
    onnx.checker.check_model(model)  # check onnx model
    print(onnx.helper.printable_graph(model.graph))  # print a hum
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