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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from networks.blocks.raft import ( | |
| coords_grid, | |
| BasicUpdateBlock, BidirCorrBlock | |
| ) | |
| from networks.blocks.feat_enc import ( | |
| LargeEncoder | |
| ) | |
| 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=[84, 96, 112, 128], | |
| skip_channels=84): | |
| super(Model, self).__init__() | |
| self.radius = corr_radius | |
| self.corr_levels = corr_lvls | |
| self.num_flows = num_flows | |
| self.feat_encoder = LargeEncoder(output_dim=128, norm_fn='instance', dropout=0.) | |
| self.encoder = Encoder(channels, 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(112, None) | |
| self.update3_low = self._get_updateblock(96, 2.0) | |
| self.update2_low = self._get_updateblock(84, 4.0) | |
| self.update3_high = self._get_updateblock(96, None) | |
| self.update2_high = self._get_updateblock(84, None) | |
| 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=192, flow_dim=64, | |
| corr_dim=256, corr_dim2=192, fc_dim=188, | |
| 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_low(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_ | |
| # residue update with lookup corr (hr) | |
| corr_3 = resize(corr_3, scale_factor=2.0) | |
| up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1) | |
| delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3) | |
| ft_2_ += delta_ft_2_ | |
| up_flow0_3 += delta_up_flow_3[:, 0:2] | |
| up_flow1_3 += delta_up_flow_3[:, 2:4] | |
| ######################################### 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_low(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_ | |
| # residue update with lookup corr (hr) | |
| corr_2 = resize(corr_2, scale_factor=4.0) | |
| up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1) | |
| delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2) | |
| ft_1_ += delta_ft_1_ | |
| up_flow0_2 += delta_up_flow_2[:, 0:2] | |
| up_flow1_2 += delta_up_flow_2[:, 2:4] | |
| ######################################### 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_], | |
| } | |