585303417c3f4641f61ac5a916a51505a67cc507,example/mpii.py,,validate,#,207

Before Change

        output = model(input)
        score_map = output[-1].cpu()
        if flip:
            flip_input = torch.autograd.Variable(
                    torch.from_numpy(fliplr(input.clone().numpy())).float().to(device),
                    volatile=True
                )
            flip_output_var = model(flip_input)
            flip_output = flip_back(flip_output_var[-1].cpu())
            score_map += flip_output



        loss = 0
        for o in output:
            loss += criterion(o, target)
        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(input, target)
            pred_batch_img = batch_with_heatmap(input, 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)

After Change

        output = model(input)[-1]
        score_map = output[-1].cpu()
        if flip:
            flip_input = torch.from_numpy(fliplr(input.clone().numpy())).float().to(device)
            flip_output_var = model(flip_input)
            flip_output = flip_back(flip_output_var[-1].cpu())
            score_map += flip_output



        loss = 0
        for o in output:
            loss += criterion(o, target)
        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(input, target)
            pred_batch_img = batch_with_heatmap(input, 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)

Instances