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| # coding: utf-8 | |
| """ | |
| The module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving | |
| """ | |
| from torch import nn | |
| import torch.nn.functional as F | |
| import torch | |
| from .util import Hourglass, make_coordinate_grid, kp2gaussian | |
| class DenseMotionNetwork(nn.Module): | |
| def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress, estimate_occlusion_map=True): | |
| super(DenseMotionNetwork, self).__init__() | |
| self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks) # ~60+G | |
| self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3) # 65G! NOTE: computation cost is large | |
| self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1) # 0.8G | |
| self.norm = nn.BatchNorm3d(compress, affine=True) | |
| self.num_kp = num_kp | |
| self.flag_estimate_occlusion_map = estimate_occlusion_map | |
| if self.flag_estimate_occlusion_map: | |
| self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3) | |
| else: | |
| self.occlusion = None | |
| def create_sparse_motions(self, feature, kp_driving, kp_source): | |
| bs, _, d, h, w = feature.shape # (bs, 4, 16, 64, 64) | |
| identity_grid = make_coordinate_grid((d, h, w), ref=kp_source) # (16, 64, 64, 3) | |
| identity_grid = identity_grid.view(1, 1, d, h, w, 3) # (1, 1, d=16, h=64, w=64, 3) | |
| coordinate_grid = identity_grid - kp_driving.view(bs, self.num_kp, 1, 1, 1, 3) | |
| k = coordinate_grid.shape[1] | |
| # NOTE: there lacks an one-order flow | |
| driving_to_source = coordinate_grid + kp_source.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, 1+num_kp, d, h, w, 3) | |
| 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, align_corners=False) | |
| 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:] # (d=16, h=64, w=64) | |
| gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01) # (bs, num_kp, d, h, w) | |
| gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01) # (bs, num_kp, d, h, w) | |
| heatmap = gaussian_driving - gaussian_source # (bs, num_kp, d, h, w) | |
| # adding background feature | |
| zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type()).to(heatmap.device) | |
| heatmap = torch.cat([zeros, heatmap], dim=1) | |
| heatmap = heatmap.unsqueeze(2) # (bs, 1+num_kp, 1, d, h, w) | |
| return heatmap | |
| def forward(self, feature, kp_driving, kp_source): | |
| bs, _, d, h, w = feature.shape # (bs, 32, 16, 64, 64) | |
| feature = self.compress(feature) # (bs, 4, 16, 64, 64) | |
| feature = self.norm(feature) # (bs, 4, 16, 64, 64) | |
| feature = F.relu(feature) # (bs, 4, 16, 64, 64) | |
| out_dict = dict() | |
| # 1. deform 3d feature | |
| sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source) # (bs, 1+num_kp, d, h, w, 3) | |
| deformed_feature = self.create_deformed_feature(feature, sparse_motion) # (bs, 1+num_kp, c=4, d=16, h=64, w=64) | |
| # 2. (bs, 1+num_kp, d, h, w) | |
| heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source) # (bs, 1+num_kp, 1, d, h, w) | |
| input = torch.cat([heatmap, deformed_feature], dim=2) # (bs, 1+num_kp, c=5, d=16, h=64, w=64) | |
| input = input.view(bs, -1, d, h, w) # (bs, (1+num_kp)*c=105, d=16, h=64, w=64) | |
| prediction = self.hourglass(input) | |
| mask = self.mask(prediction) | |
| mask = F.softmax(mask, dim=1) # (bs, 1+num_kp, d=16, h=64, w=64) | |
| 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) mask take effect in this place | |
| deformation = deformation.permute(0, 2, 3, 4, 1) # (bs, d, h, w, 3) | |
| out_dict['deformation'] = deformation | |
| if self.flag_estimate_occlusion_map: | |
| bs, _, d, h, w = prediction.shape | |
| prediction_reshape = prediction.view(bs, -1, h, w) | |
| occlusion_map = torch.sigmoid(self.occlusion(prediction_reshape)) # Bx1x64x64 | |
| out_dict['occlusion_map'] = occlusion_map | |
| return out_dict | |