import torch import torch.nn as nn from networks.blocks.raft import ( coords_grid, BasicUpdateBlock, BidirCorrBlock ) from networks.blocks.feat_enc import ( BasicEncoder ) from networks.blocks.ifrnet import ( resize, Encoder, InitDecoder, IntermediateDecoder ) from networks.blocks.multi_flow import ( multi_flow_combine, MultiFlowDecoder ) class Model(nn.Module): def __init__(self, corr_radius=3, corr_lvls=4, num_flows=5, channels=[48, 64, 72, 128], skip_channels=48 ): super(Model, self).__init__() self.radius = corr_radius self.corr_levels = corr_lvls self.num_flows = num_flows self.feat_encoder = BasicEncoder(output_dim=128, norm_fn='instance', dropout=0.) self.encoder = Encoder([48, 64, 72, 128], large=True) 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(72, None) self.update3 = self._get_updateblock(64, 2.0) self.update2 = self._get_updateblock(48, 4.0) self.comb_block = nn.Sequential( nn.Conv2d(3*self.num_flows, 6*self.num_flows, 7, 1, 3), nn.PReLU(6*self.num_flows), nn.Conv2d(6*self.num_flows, 3, 7, 1, 3), ) def _get_updateblock(self, cdim, scale_factor=None): return BasicUpdateBlock(cdim=cdim, hidden_dim=128, flow_dim=48, corr_dim=256, corr_dim2=160, fc_dim=124, 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. / embt t0_scale = 1. / (1. - 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)) 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_], }