without using GPU

[heltru]

Good day. Can only use face-parsing with CPU?

[hfnvbh]

Hi @heltru

I successfully ran test.py in this repo without GPU. For that i changed a little evaluate function.

def evaluate(respth='./res/test_res', dspth='./data', cp='model_final_diss.pth'):

    if not os.path.exists(respth):
        os.makedirs(respth)

    n_classes = 19
    net = BiSeNet(n_classes=n_classes)
#     net.cuda()
    save_pth = osp.join('res/cp', cp)
    net.load_state_dict(torch.load(save_pth, map_location=torch.device('cpu')))
    net.eval()

    to_tensor = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])
    with torch.no_grad():
        for image_path in os.listdir(dspth):
            img = Image.open(osp.join(dspth, image_path))
            image = img.resize((512, 512), Image.BILINEAR)
            img = to_tensor(image)
            img = torch.unsqueeze(img, 0)
#             img = img.cuda()
            out = net(img)[0]
            parsing = out.squeeze(0).cpu().numpy().argmax(0)
            # print(parsing)
            print(np.unique(parsing))

            vis_parsing_maps(image, parsing, stride=1, save_im=True, save_path=osp.join(respth, image_path))