7ce6642f777e9da6249bd5b05330d57fa09ea37a,example/mpii.py,,validate,#Any#Any#Any#Any#Any#Any#,206

Before Change

        target = target.cuda(async=True)

        input_var = torch.autograd.Variable(inputs.cuda(), volatile=True)
        target_var = torch.autograd.Variable(target, volatile=True)

        // compute output
        output = model(input_var)
        score_map = output[-1].data.cpu()
        if flip:
            flip_input_var = torch.autograd.Variable(
                    torch.from_numpy(fliplr(inputs.clone().numpy())).float().cuda(), 
                    volatile=True
                )
            flip_output_var = model(flip_input_var)
            flip_output = flip_back(flip_output_var[-1].data.cpu())
            score_map += flip_output



        loss = 0
        for o in output:
            loss += criterion(o, target_var)
        acc = accuracy(score_map, target.cpu(), idx)

        // generate predictions
        preds = final_preds(score_map, meta["center"], meta["scale"], [64, 64])
        for n in range(score_map.size(0)):
            predictions[meta["index"][n], :, :] = preds[n, :, :]


        if debug:
            gt_batch_img = batch_with_heatmap(inputs, target)
            pred_batch_img = batch_with_heatmap(inputs, score_map)
            if not gt_win or not pred_win:
                plt.subplot(121)
                gt_win = plt.imshow(gt_batch_img)
                plt.subplot(122)
                pred_win = plt.imshow(pred_batch_img)
            else:
                gt_win.set_data(gt_batch_img)
                pred_win.set_data(pred_batch_img)
            plt.pause(.05)
            plt.draw()

        // measure accuracy and record loss
        losses.update(loss.data[0], inputs.size(0))
        acces.update(acc[0], inputs.size(0))

        // measure elapsed time

After Change

        target = target.cuda(async=True)

        input = input.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        // compute output
        output = model(input)

Instances