Random image cropping and patching (RICAP)

1.RICAP

RICAP [1] randomly cut portion of the four images, and then splice them into a picture, four pictures while mixing label.

2.implementation

    for i, (input, target) in tqdm(enumerate(train_loader), total=len(train_loader)):
        # from original paper's appendix
        if args.ricap:
            I_x, I_y = input.size()[2:]

            w = int(np.round(I_x * np.random.beta(args.ricap_beta, args.ricap_beta)))
            h = int(np.round(I_y * np.random.beta(args.ricap_beta, args.ricap_beta)))
            w_ = [w, I_x - w, w, I_x - w]
            h_ = [h, h, I_y - h, I_y - h]

            cropped_images = {}
            c_ = {}
            W_ = {}
            for k in range(4):
                idx = torch.randperm(input.size(0))
                x_k = np.random.randint(0, I_x - w_[k] + 1)
                y_k = np.random.randint(0, I_y - h_[k] + 1)
                cropped_images[k] = input[idx][:, :, x_k:x_k + w_[k], y_k:y_k + h_[k]]
                c_[k] = target[idx].cuda()
                W_[k] = w_[k] * h_[k] / (I_x * I_y)

            patched_images = torch.cat(
                (torch.cat((cropped_images[0], cropped_images[1]), 2),
                torch.cat((cropped_images[2], cropped_images[3]), 2)),
            3)
            patched_images = patched_images.cuda()

            output = model(patched_images)
            loss = sum([W_[k] * criterion(output, c_[k]) for k in range(4)])

            acc = sum([W_[k] * accuracy(output, c_[k])[0] for k in range(4)])

3.Reference

[1] https://arxiv.org/pdf/1811.09030.pdf
[2] https://github.com/4uiiurz1/pytorch-ricap