import torch import torch.nn as nn from src.networks.blocks.raft import coords_grid, SmallUpdateBlock, BidirCorrBlock from src.networks.blocks.feat_enc import SmallEncoder from src.networks.blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder from src.networks.blocks.multi_flow import multi_flow_combine, MultiFlowDecoder class Model(nn.Module): def __init__( self, corr_radius=3, corr_lvls=4, num_flows=3, channels=[20, 32, 44, 56], skip_channels=20, ): super(Model, self).__init__() self.radius = corr_radius self.corr_levels = corr_lvls self.num_flows = num_flows self.channels = channels self.skip_channels = skip_channels self.feat_encoder = SmallEncoder(output_dim=84, norm_fn="instance", dropout=0.0) self.encoder = Encoder(channels) self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels) self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels) self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels) self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows) self.update4 = self._get_updateblock(44) self.update3 = self._get_updateblock(32, 2) self.update2 = self._get_updateblock(20, 4) self.comb_block = nn.Sequential( nn.Conv2d(3 * num_flows, 6 * num_flows, 3, 1, 1), nn.PReLU(6 * num_flows), nn.Conv2d(6 * num_flows, 3, 3, 1, 1), ) def _get_updateblock(self, cdim, scale_factor=None): return SmallUpdateBlock( cdim=cdim, hidden_dim=76, flow_dim=20, corr_dim=64, fc_dim=68, scale_factor=scale_factor, corr_levels=self.corr_levels, radius=self.radius, ) def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1): # convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0 # based on linear assumption t1_scale = 1.0 / embt t0_scale = 1.0 / (1.0 - embt) if downsample != 1: inv = 1 / downsample flow0 = inv * resize(flow0, scale_factor=inv) flow1 = inv * resize(flow1, scale_factor=inv) corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale) corr = torch.cat([corr0, corr1], dim=1) flow = torch.cat([flow0, flow1], dim=1) return corr, flow def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs): mean_ = ( torch.cat([img0, img1], 2) .mean(1, keepdim=True) .mean(2, keepdim=True) .mean(3, keepdim=True) ) img0 = img0 - mean_ img1 = img1 - mean_ img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0 img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1 b, _, h, w = img0_.shape coord = coords_grid(b, h // 8, w // 8, img0.device) fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8] corr_fn = BidirCorrBlock( fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels ) # f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4] # f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16] f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_) f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_) ######################################### the 4th decoder ######################################### up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt) corr_4, flow_4 = self._corr_scale_lookup( corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1 ) # residue update with lookup corr delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4) delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1) up_flow0_4 = up_flow0_4 + delta_flow0_4 up_flow1_4 = up_flow1_4 + delta_flow1_4 ft_3_ = ft_3_ + delta_ft_3_ ######################################### the 3rd decoder ######################################### up_flow0_3, up_flow1_3, ft_2_ = self.decoder3( ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4 ) corr_3, flow_3 = self._corr_scale_lookup( corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2 ) # residue update with lookup corr delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3) delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1) up_flow0_3 = up_flow0_3 + delta_flow0_3 up_flow1_3 = up_flow1_3 + delta_flow1_3 ft_2_ = ft_2_ + delta_ft_2_ ######################################### the 2nd decoder ######################################### up_flow0_2, up_flow1_2, ft_1_ = self.decoder2( ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3 ) corr_2, flow_2 = self._corr_scale_lookup( corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4 ) # residue update with lookup corr delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2) delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1) up_flow0_2 = up_flow0_2 + delta_flow0_2 up_flow1_2 = up_flow1_2 + delta_flow1_2 ft_1_ = ft_1_ + delta_ft_1_ ######################################### the 1st decoder ######################################### up_flow0_1, up_flow1_1, mask, img_res = self.decoder1( ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2 ) if scale_factor != 1.0: up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * ( 1.0 / scale_factor ) up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * ( 1.0 / scale_factor ) mask = resize(mask, scale_factor=(1.0 / scale_factor)) img_res = resize(img_res, scale_factor=(1.0 / scale_factor)) # Merge multiple predictions imgt_pred = multi_flow_combine( self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_ ) imgt_pred = torch.clamp(imgt_pred, 0, 1) if eval: return { "imgt_pred": imgt_pred, } else: up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w) up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w) return { "imgt_pred": imgt_pred, "flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4], "flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4], "ft_pred": [ft_1_, ft_2_, ft_3_], }