import torch def convert_flow_to_deformation(flow): r"""convert flow fields to deformations. Args: flow (tensor): Flow field obtained by the model Returns: deformation (tensor): The deformation used for warping """ b,c,h,w = flow.shape flow_norm = 2 * torch.cat([flow[:,:1,...]/(w-1),flow[:,1:,...]/(h-1)], 1) grid = make_coordinate_grid(flow) deformation = grid + flow_norm.permute(0,2,3,1) return deformation def make_coordinate_grid(flow): r"""obtain coordinate grid with the same size as the flow filed. Args: flow (tensor): Flow field obtained by the model Returns: grid (tensor): The grid with the same size as the input flow """ b,c,h,w = flow.shape x = torch.arange(w).to(flow) y = torch.arange(h).to(flow) x = (2 * (x / (w - 1)) - 1) y = (2 * (y / (h - 1)) - 1) yy = y.view(-1, 1).repeat(1, w) xx = x.view(1, -1).repeat(h, 1) meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) meshed = meshed.expand(b, -1, -1, -1) return meshed def warp_image(source_image, deformation): r"""warp the input image according to the deformation Args: source_image (tensor): source images to be warped deformation (tensor): deformations used to warp the images; value in range (-1, 1) Returns: output (tensor): the warped images """ _, h_old, w_old, _ = deformation.shape _, _, h, w = source_image.shape if h_old != h or w_old != w: deformation = deformation.permute(0, 3, 1, 2) deformation = torch.nn.functional.interpolate(deformation, size=(h, w), mode='bilinear') deformation = deformation.permute(0, 2, 3, 1) return torch.nn.functional.grid_sample(source_image, deformation)