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| from torch import nn | |
| import torch.nn.functional as F | |
| import torch | |
| from src.facerender.modules.util import Hourglass, make_coordinate_grid, kp2gaussian | |
| from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d | |
| 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, feature_channel, reshape_depth, compress, | |
| estimate_occlusion_map=False): | |
| super(DenseMotionNetwork, self).__init__() | |
| # self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(feature_channel+1), max_features=max_features, num_blocks=num_blocks) | |
| self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks) | |
| self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3) | |
| self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1) | |
| self.norm = BatchNorm3d(compress, affine=True) | |
| if estimate_occlusion_map: | |
| # self.occlusion = nn.Conv2d(reshape_channel*reshape_depth, 1, kernel_size=7, padding=3) | |
| self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3) | |
| else: | |
| self.occlusion = None | |
| self.num_kp = num_kp | |
| def create_sparse_motions(self, feature, kp_driving, kp_source): | |
| bs, _, d, h, w = feature.shape | |
| identity_grid = make_coordinate_grid((d, h, w), type=kp_source['value'].type()) | |
| identity_grid = identity_grid.view(1, 1, d, h, w, 3) | |
| coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 1, 3) | |
| # if 'jacobian' in kp_driving: | |
| if 'jacobian' in kp_driving and kp_driving['jacobian'] is not None: | |
| jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian'])) | |
| jacobian = jacobian.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3) | |
| jacobian = jacobian.repeat(1, 1, d, 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, 1, 3) # (bs, num_kp, d, h, w, 3) | |
| #adding background feature | |
| identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1) | |
| sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) #bs num_kp+1 d h w 3 | |
| # sparse_motions = driving_to_source | |
| return sparse_motions | |
| def create_deformed_feature(self, feature, sparse_motions): | |
| bs, _, d, h, w = feature.shape | |
| feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1) # (bs, num_kp+1, 1, c, d, h, w) | |
| feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w) # (bs*(num_kp+1), c, d, h, w) | |
| sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1)) # (bs*(num_kp+1), d, h, w, 3) !!!! | |
| sparse_deformed = F.grid_sample(feature_repeat, sparse_motions) | |
| sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w)) # (bs, num_kp+1, c, d, h, w) | |
| return sparse_deformed | |
| def create_heatmap_representations(self, feature, kp_driving, kp_source): | |
| spatial_size = feature.shape[3:] | |
| gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01) | |
| gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01) | |
| heatmap = gaussian_driving - gaussian_source | |
| # adding background feature | |
| zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type()) | |
| heatmap = torch.cat([zeros, heatmap], dim=1) | |
| heatmap = heatmap.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w) | |
| return heatmap | |
| def forward(self, feature, kp_driving, kp_source): | |
| bs, _, d, h, w = feature.shape | |
| feature = self.compress(feature) | |
| feature = self.norm(feature) | |
| feature = F.relu(feature) | |
| out_dict = dict() | |
| sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source) | |
| deformed_feature = self.create_deformed_feature(feature, sparse_motion) | |
| heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source) | |
| input_ = torch.cat([heatmap, deformed_feature], dim=2) | |
| input_ = input_.view(bs, -1, d, h, w) | |
| # input = deformed_feature.view(bs, -1, d, h, w) # (bs, num_kp+1 * c, d, h, w) | |
| prediction = self.hourglass(input_) | |
| mask = self.mask(prediction) | |
| mask = F.softmax(mask, dim=1) | |
| out_dict['mask'] = mask | |
| mask = mask.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w) | |
| sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4) # (bs, num_kp+1, 3, d, h, w) | |
| deformation = (sparse_motion * mask).sum(dim=1) # (bs, 3, d, h, w) | |
| deformation = deformation.permute(0, 2, 3, 4, 1) # (bs, d, h, w, 3) | |
| out_dict['deformation'] = deformation | |
| if self.occlusion: | |
| bs, c, d, h, w = prediction.shape | |
| prediction = prediction.view(bs, -1, h, w) | |
| occlusion_map = torch.sigmoid(self.occlusion(prediction)) | |
| out_dict['occlusion_map'] = occlusion_map | |
| return out_dict | |