c6a079c70368b41873f3288b05b74d38f74b6680,run_inference.py,,main,#,36

Before Change

        // compute output
        output = model(input_var)
        rgb_flow = flow2rgb(args.div_flow * output.data[].cpu().numpy(), max_value=args.max_flow)
        to_save = (rgb_flow * 255).astype(np.uint8)
        imsave(save_path/(img1_file.namebase[:-2] + "_flow.png"), to_save)

After Change

        if args.bidirectional:
            // feed inverted pair along with normal pair
            inverted_input_var = torch.autograd.Variable(torch.cat([img2, img1],).cuda(), volatile=True).unsqueeze(0)
            input_var = torch.cat([input_var, inverted_input_var])

        // compute output
        output = model(input_var)
        if not args.no_resize:

Instances