''' This code is partially borrowed from RAFT (https://github.com/princeton-vl/RAFT). ''' import torch import torch.nn as nn import torch.nn.functional as F def resize(x, scale_factor): return F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False) def bilinear_sampler(img, coords, 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, device): coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device), indexing='ij') coords = torch.stack(coords[::-1], dim=0).float() return coords[None].repeat(batch, 1, 1, 1) class SmallUpdateBlock(nn.Module): def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, fc_dim, corr_levels=4, radius=3, scale_factor=None): super(SmallUpdateBlock, self).__init__() cor_planes = corr_levels * (2 * radius + 1) **2 self.scale_factor = scale_factor self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0) self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3) self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1) self.conv = nn.Conv2d(corr_dim+flow_dim, fc_dim, 3, padding=1) self.gru = nn.Sequential( nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), ) self.feat_head = nn.Sequential( nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, cdim, 3, padding=1), ) self.flow_head = nn.Sequential( nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, 4, 3, padding=1), ) self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) def forward(self, net, flow, corr): net = resize(net, 1 / self.scale_factor ) if self.scale_factor is not None else net cor = self.lrelu(self.convc1(corr)) flo = self.lrelu(self.convf1(flow)) flo = self.lrelu(self.convf2(flo)) cor_flo = torch.cat([cor, flo], dim=1) inp = self.lrelu(self.conv(cor_flo)) inp = torch.cat([inp, flow, net], dim=1) out = self.gru(inp) delta_net = self.feat_head(out) delta_flow = self.flow_head(out) if self.scale_factor is not None: delta_net = resize(delta_net, scale_factor=self.scale_factor) delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor) return delta_net, delta_flow class BasicUpdateBlock(nn.Module): def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, corr_dim2, fc_dim, corr_levels=4, radius=3, scale_factor=None, out_num=1): super(BasicUpdateBlock, self).__init__() cor_planes = corr_levels * (2 * radius + 1) **2 self.scale_factor = scale_factor self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0) self.convc2 = nn.Conv2d(corr_dim, corr_dim2, 3, padding=1) self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3) self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1) self.conv = nn.Conv2d(flow_dim+corr_dim2, fc_dim, 3, padding=1) self.gru = nn.Sequential( nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), ) self.feat_head = nn.Sequential( nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, cdim, 3, padding=1), ) self.flow_head = nn.Sequential( nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(hidden_dim, 4*out_num, 3, padding=1), ) self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) def forward(self, net, flow, corr): net = resize(net, 1 / self.scale_factor ) if self.scale_factor is not None else net cor = self.lrelu(self.convc1(corr)) cor = self.lrelu(self.convc2(cor)) flo = self.lrelu(self.convf1(flow)) flo = self.lrelu(self.convf2(flo)) cor_flo = torch.cat([cor, flo], dim=1) inp = self.lrelu(self.conv(cor_flo)) inp = torch.cat([inp, flow, net], dim=1) out = self.gru(inp) delta_net = self.feat_head(out) delta_flow = self.flow_head(out) if self.scale_factor is not None: delta_net = resize(delta_net, scale_factor=self.scale_factor) delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor) return delta_net, delta_flow class BidirCorrBlock: def __init__(self, fmap1, fmap2, num_levels=4, radius=4): self.num_levels = num_levels self.radius = radius self.corr_pyramid = [] self.corr_pyramid_T = [] corr = BidirCorrBlock.corr(fmap1, fmap2) batch, h1, w1, dim, h2, w2 = corr.shape corr_T = corr.clone().permute(0, 4, 5, 3, 1, 2) corr = corr.reshape(batch*h1*w1, dim, h2, w2) corr_T = corr_T.reshape(batch*h2*w2, dim, h1, w1) self.corr_pyramid.append(corr) self.corr_pyramid_T.append(corr_T) for _ in range(self.num_levels-1): corr = F.avg_pool2d(corr, 2, stride=2) corr_T = F.avg_pool2d(corr_T, 2, stride=2) self.corr_pyramid.append(corr) self.corr_pyramid_T.append(corr_T) def __call__(self, coords0, coords1): r = self.radius coords0 = coords0.permute(0, 2, 3, 1) coords1 = coords1.permute(0, 2, 3, 1) assert coords0.shape == coords1.shape, f"coords0 shape: [{coords0.shape}] is not equal to [{coords1.shape}]" batch, h1, w1, _ = coords0.shape out_pyramid = [] out_pyramid_T = [] for i in range(self.num_levels): corr = self.corr_pyramid[i] corr_T = self.corr_pyramid_T[i] dx = torch.linspace(-r, r, 2*r+1, device=coords0.device) dy = torch.linspace(-r, r, 2*r+1, device=coords0.device) delta = torch.stack(torch.meshgrid(dy, dx, indexing='ij'), axis=-1) delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2) centroid_lvl_0 = coords0.reshape(batch*h1*w1, 1, 1, 2) / 2**i centroid_lvl_1 = coords1.reshape(batch*h1*w1, 1, 1, 2) / 2**i coords_lvl_0 = centroid_lvl_0 + delta_lvl coords_lvl_1 = centroid_lvl_1 + delta_lvl corr = bilinear_sampler(corr, coords_lvl_0) corr_T = bilinear_sampler(corr_T, coords_lvl_1) corr = corr.view(batch, h1, w1, -1) corr_T = corr_T.view(batch, h1, w1, -1) out_pyramid.append(corr) out_pyramid_T.append(corr_T) out = torch.cat(out_pyramid, dim=-1) out_T = torch.cat(out_pyramid_T, dim=-1) return out.permute(0, 3, 1, 2).contiguous().float(), out_T.permute(0, 3, 1, 2).contiguous().float() @staticmethod def corr(fmap1, fmap2): batch, dim, ht, wd = fmap1.shape fmap1 = fmap1.view(batch, dim, ht*wd) fmap2 = fmap2.view(batch, dim, ht*wd) corr = torch.matmul(fmap1.transpose(1,2), fmap2) corr = corr.view(batch, ht, wd, 1, ht, wd) return corr / torch.sqrt(torch.tensor(dim).float())