| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from scipy import interpolate | |
| def coords_grid2(batch, ht, wd, device): | |
| coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device)) | |
| coords = torch.stack(coords[::-1], dim=0).float() | |
| return coords[None].repeat(batch, 1, 1, 1) | |
| 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) | |