from torch import nn import torch.nn.functional as F import torch from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian import pdb from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d class DenseMotionNetwork(nn.Module): """ Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving """ def __init__(self, block_expansion, num_blocks, max_features, num_kp, num_channels, estimate_occlusion_map=False, scale_factor=1, kp_variance=0.01): super(DenseMotionNetwork, self).__init__() self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp + 1) * (num_channels + 1), max_features=max_features, num_blocks=num_blocks) self.mask = nn.Conv2d(self.hourglass.out_filters, num_kp + 1, kernel_size=(7, 7), padding=(3, 3)) if estimate_occlusion_map: self.occlusion = nn.Conv2d(self.hourglass.out_filters, 1, kernel_size=(7, 7), padding=(3, 3)) else: self.occlusion = None self.num_kp = num_kp self.scale_factor = scale_factor self.kp_variance = kp_variance if self.scale_factor != 1: self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) def create_heatmap_representations(self, source_image, kp_driving, kp_source): """ Eq 6. in the paper H_k(z) """ spatial_size = source_image.shape[2:] gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=self.kp_variance) gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=self.kp_variance) heatmap = gaussian_driving - gaussian_source #adding background feature zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()) heatmap = torch.cat([zeros, heatmap], dim=1) heatmap = heatmap.unsqueeze(2) return heatmap def create_sparse_motions(self, source_image, kp_driving, kp_source): """ Eq 4. in the paper T_{s<-d}(z) """ bs, _, h, w = source_image.shape identity_grid = make_coordinate_grid((h, w), type=kp_source['value'].type()) identity_grid = identity_grid.view(1, 1, h, w, 2) coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 2) if 'jacobian' in kp_driving: jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian'])) jacobian = jacobian.unsqueeze(-3).unsqueeze(-3) jacobian = jacobian.repeat(1, 1, h, w, 1, 1) coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)) coordinate_grid = coordinate_grid.squeeze(-1) driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 2) #adding background feature identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1) sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) #bs, num_kp+1,w,h,2 return sparse_motions def create_deformed_source_image(self, source_image, sparse_motions): """ Eq 7. in the paper \hat{T}_{s<-d}(z) """ bs, _, h, w = source_image.shape source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp + 1, 1, 1, 1, 1) source_repeat = source_repeat.view(bs * (self.num_kp + 1), -1, h, w) sparse_motions = sparse_motions.view((bs * (self.num_kp + 1), h, w, -1)) sparse_deformed = F.grid_sample(source_repeat, sparse_motions) sparse_deformed = sparse_deformed.view((bs, self.num_kp + 1, -1, h, w)) return sparse_deformed def forward(self, source_image, kp_driving, kp_source): if self.scale_factor != 1: source_image = self.down(source_image) bs, _, h, w = source_image.shape out_dict = dict() heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source) sparse_motion = self.create_sparse_motions(source_image, kp_driving, kp_source) deformed_source = self.create_deformed_source_image(source_image, sparse_motion) out_dict['sparse_deformed'] = deformed_source input = torch.cat([heatmap_representation, deformed_source], dim=2) input = input.view(bs, -1, h, w) prediction = self.hourglass(input) mask = self.mask(prediction) mask = F.softmax(mask, dim=1) out_dict['mask'] = mask mask = mask.unsqueeze(2) sparse_motion = sparse_motion.permute(0, 1, 4, 2, 3) deformation = (sparse_motion * mask).sum(dim=1) deformation = deformation.permute(0, 2, 3, 1) out_dict['deformation'] = deformation # Sec. 3.2 in the paper if self.occlusion: occlusion_map = torch.sigmoid(self.occlusion(prediction)) out_dict['occlusion_map'] = occlusion_map return out_dict