How can we get result for new images, not from test datasets? about cssr HOT 2 CLOSED

def test(args, cfg):
    device = torch.device(cfg.DEVICE)
    if cfg.MODEL.SR_SEG_INV:
        model = InvModel(cfg).to(device)
        print(f'------------Model Architecture-------------\n\n<Network SS>\n{model.segmentation_model}\n\n<Network SR>\n{model.sr_model}')
    else:
        model = Model(cfg).to(device)
        print(f'------------Model Architecture-------------\n\n<Network SR>\n{model.sr_model}\n\n<Network SS>\n{model.segmentation_model}')

    model.load_state_dict(fix_model_state_dict(torch.load(args.trained_model, map_location=lambda storage, loc:storage)))
    model.eval()

class TestTransforms:
    def __init__(self, cfg):
        self.augment = Compose([
            ConvertFromInts(),
            ToTensor(),
        ])

    def __call__(self, image, mask):
        image, mask = self.augment(image, mask)
        if mask is not None:
            return image/255, mask/255
        
        return image/255, None

sr_preds, segment_preds = model(imgs)

# SR evaluation
if not cfg.MODEL.SR_SEG_INV and cfg.MODEL.SCALE_FACTOR != 1:
    sr_preds[sr_preds>1] = 1 # clipping
    sr_preds[sr_preds<0] = 0 # clipping        
    psnr_scores = np.append(psnr_scores, psnr(sr_preds, sr_targets.to("cuda"))) 
    ssim_scores = np.append(ssim_scores, ssim(sr_preds, sr_targets.to("cuda"))) 
    save_img(args.output_dirname, sr_preds, fname)