import torch import torch.nn.functional as F import numpy as np from scipy import interpolate class InputPadder: """ Pads images such that dimensions are divisible by 8 """ def __init__(self, dims, mode='sintel'): self.ht, self.wd = dims[-2:] pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8 pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8 if mode == 'sintel': self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2] else: self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht] def pad(self, *inputs): return [F.pad(x, self._pad, mode='replicate') for x in inputs] def unpad(self,x): ht, wd = x.shape[-2:] c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]] return x[..., c[0]:c[1], c[2]:c[3]] def forward_interpolate(flow): flow = flow.detach().cpu().numpy() dx, dy = flow[0], flow[1] ht, wd = dx.shape x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht)) x1 = x0 + dx y1 = y0 + dy x1 = x1.reshape(-1) y1 = y1.reshape(-1) dx = dx.reshape(-1) dy = dy.reshape(-1) valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht) x1 = x1[valid] y1 = y1[valid] dx = dx[valid] dy = dy[valid] flow_x = interpolate.griddata( (x1, y1), dx, (x0, y0), method='nearest', fill_value=0) flow_y = interpolate.griddata( (x1, y1), dy, (x0, y0), method='nearest', fill_value=0) flow = np.stack([flow_x, flow_y], axis=0) return torch.from_numpy(flow).float() def bilinear_sampler(img, coords, mode='bilinear', mask=False): """ Wrapper for grid_sample, uses pixel coordinates """ H, W = img.shape[-2:] xgrid, ygrid = coords.split([1,1], dim=-1) xgrid = 2*xgrid/(W-1) - 1 ygrid = 2*ygrid/(H-1) - 1 grid = torch.cat([xgrid, ygrid], dim=-1) img = F.grid_sample(img, grid, align_corners=True) if mask: mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1) return img, mask.float() return img def coords_grid(batch, ht, wd): coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) coords = torch.stack(coords[::-1], dim=0).float() return coords[None].repeat(batch, 1, 1, 1) def upflow8(flow, mode='bilinear'): new_size = (8 * flow.shape[2], 8 * flow.shape[3]) return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)