def METHOD_NAME(self):
    import torchvision.models as models
    import torchvision.transforms as T
    from PIL import Image
    from scipy.io import loadmat
    category = self.category.capitalize()
    names = glob.glob(osp.join(self.raw_dir, category, '*.png'))
    names = sorted([name[:-4] for name in names])
    vgg16_outputs = []
    def hook(module, x, y):
        vgg16_outputs.append(y.to('cpu'))
    vgg16 = models.vgg16(pretrained=True).to(self.device)
    vgg16.eval()
    vgg16.features[20].register_forward_hook(hook)  # relu4_2
    vgg16.features[25].register_forward_hook(hook)  # relu5_1
    transform = T.Compose([
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    data_list = []
    for name in names:
        pos = loadmat(f'{name}.mat')['pts_coord']
        x, y = torch.from_numpy(pos).to(torch.float)
        pos = torch.stack([x, y], dim=1)
        # The "face" category contains a single image with less than 10
        # keypoints, so we need to skip it.
        if pos.size(0) != 10:
            continue
        with open(f'{name}.png', 'rb') as f:
            img = Image.open(f).convert('RGB')
        # Rescale keypoints.
        pos[:, 0] = pos[:, 0] * 256.0 / (img.size[0])
        pos[:, 1] = pos[:, 1] * 256.0 / (img.size[1])
        img = img.resize((256, 256), resample=Image.BICUBIC)
        img = transform(img)
        data = Data(img=img, pos=pos, name=name)
        data_list.append(data)
    imgs = [data.img for data in data_list]
    loader = DataLoader(imgs, self.batch_size, shuffle=False)
    for i, batch_img in enumerate(loader):
        vgg16_outputs.clear()
        with torch.no_grad():
            vgg16(batch_img.to(self.device))
        out1 = F.interpolate(vgg16_outputs[0], (256, 256), mode='bilinear',
                             align_corners=False)
        out2 = F.interpolate(vgg16_outputs[1], (256, 256), mode='bilinear',
                             align_corners=False)
        for j in range(out1.size(0)):
            data = data_list[i * self.batch_size + j]
            idx = data.pos.round().long().clamp(0, 255)
            x_1 = out1[j, :, idx[:, 1], idx[:, 0]].to('cpu')
            x_2 = out2[j, :, idx[:, 1], idx[:, 0]].to('cpu')
            data.img = None
            data.x = torch.cat([x_1.t(), x_2.t()], dim=-1)
        del out1
        del out2
    if self.pre_filter is not None:
        data_list = [data for data in data_list if self.pre_filter(data)]
    if self.pre_transform is not None:
        data_list = [self.pre_transform(data) for data in data_list]
    torch.save(self.collate(data_list), self.processed_paths[0])