import torch from torch import nn import torch.nn.functional as F from src.facerender.modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d, ResBlock3d, SPADEResnetBlock from src.facerender.modules.dense_motion import DenseMotionNetwork class OcclusionAwareGenerator(nn.Module): """ Generator follows NVIDIA architecture. """ def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth, num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): super(OcclusionAwareGenerator, self).__init__() if dense_motion_params is not None: self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel, estimate_occlusion_map=estimate_occlusion_map, **dense_motion_params) else: self.dense_motion_network = None self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(7, 7), padding=(3, 3)) down_blocks = [] for i in range(num_down_blocks): in_features = min(max_features, block_expansion * (2 ** i)) out_features = min(max_features, block_expansion * (2 ** (i + 1))) down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) self.down_blocks = nn.ModuleList(down_blocks) self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1) self.reshape_channel = reshape_channel self.reshape_depth = reshape_depth self.resblocks_3d = torch.nn.Sequential() for i in range(num_resblocks): self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1)) out_features = block_expansion * (2 ** (num_down_blocks)) self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True) self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1) self.resblocks_2d = torch.nn.Sequential() for i in range(num_resblocks): self.resblocks_2d.add_module('2dr' + str(i), ResBlock2d(out_features, kernel_size=3, padding=1)) up_blocks = [] for i in range(num_down_blocks): in_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i))) out_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i - 1))) up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) self.up_blocks = nn.ModuleList(up_blocks) self.final = nn.Conv2d(block_expansion, image_channel, kernel_size=(7, 7), padding=(3, 3)) self.estimate_occlusion_map = estimate_occlusion_map self.image_channel = image_channel def deform_input(self, inp, deformation): _, d_old, h_old, w_old, _ = deformation.shape _, _, d, h, w = inp.shape if d_old != d or h_old != h or w_old != w: deformation = deformation.permute(0, 4, 1, 2, 3) deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear') deformation = deformation.permute(0, 2, 3, 4, 1) return F.grid_sample(inp, deformation) def forward(self, source_image, kp_driving, kp_source): # Encoding (downsampling) part out = self.first(source_image) for i in range(len(self.down_blocks)): out = self.down_blocks[i](out) out = self.second(out) bs, c, h, w = out.shape # print(out.shape) feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) feature_3d = self.resblocks_3d(feature_3d) # Transforming feature representation according to deformation and occlusion output_dict = {} if self.dense_motion_network is not None: dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving, kp_source=kp_source) output_dict['mask'] = dense_motion['mask'] if 'occlusion_map' in dense_motion: occlusion_map = dense_motion['occlusion_map'] output_dict['occlusion_map'] = occlusion_map else: occlusion_map = None deformation = dense_motion['deformation'] out = self.deform_input(feature_3d, deformation) bs, c, d, h, w = out.shape out = out.view(bs, c*d, h, w) out = self.third(out) out = self.fourth(out) if occlusion_map is not None: if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') out = out * occlusion_map # output_dict["deformed"] = self.deform_input(source_image, deformation) # 3d deformation cannot deform 2d image # Decoding part out = self.resblocks_2d(out) for i in range(len(self.up_blocks)): out = self.up_blocks[i](out) out = self.final(out) out = F.sigmoid(out) output_dict["prediction"] = out return output_dict class SPADEDecoder(nn.Module): def __init__(self): super().__init__() ic = 256 oc = 64 norm_G = 'spadespectralinstance' label_nc = 256 self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1) self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc) self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc) self.conv_img = nn.Conv2d(oc, 3, 3, padding=1) self.up = nn.Upsample(scale_factor=2) def forward(self, feature): seg = feature x = self.fc(feature) x = self.G_middle_0(x, seg) x = self.G_middle_1(x, seg) x = self.G_middle_2(x, seg) x = self.G_middle_3(x, seg) x = self.G_middle_4(x, seg) x = self.G_middle_5(x, seg) x = self.up(x) x = self.up_0(x, seg) # 256, 128, 128 x = self.up(x) x = self.up_1(x, seg) # 64, 256, 256 x = self.conv_img(F.leaky_relu(x, 2e-1)) # x = torch.tanh(x) x = F.sigmoid(x) return x class OcclusionAwareSPADEGenerator(nn.Module): def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth, num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): super(OcclusionAwareSPADEGenerator, self).__init__() if dense_motion_params is not None: self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel, estimate_occlusion_map=estimate_occlusion_map, **dense_motion_params) else: self.dense_motion_network = None self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1)) down_blocks = [] for i in range(num_down_blocks): in_features = min(max_features, block_expansion * (2 ** i)) out_features = min(max_features, block_expansion * (2 ** (i + 1))) down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) self.down_blocks = nn.ModuleList(down_blocks) self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1) self.reshape_channel = reshape_channel self.reshape_depth = reshape_depth self.resblocks_3d = torch.nn.Sequential() for i in range(num_resblocks): self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1)) out_features = block_expansion * (2 ** (num_down_blocks)) self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True) self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1) self.estimate_occlusion_map = estimate_occlusion_map self.image_channel = image_channel self.decoder = SPADEDecoder() def deform_input(self, inp, deformation): _, d_old, h_old, w_old, _ = deformation.shape _, _, d, h, w = inp.shape if d_old != d or h_old != h or w_old != w: deformation = deformation.permute(0, 4, 1, 2, 3) deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear') deformation = deformation.permute(0, 2, 3, 4, 1) return F.grid_sample(inp, deformation) def forward(self, source_image, kp_driving, kp_source): # Encoding (downsampling) part out = self.first(source_image) for i in range(len(self.down_blocks)): out = self.down_blocks[i](out) out = self.second(out) bs, c, h, w = out.shape # print(out.shape) feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) feature_3d = self.resblocks_3d(feature_3d) # Transforming feature representation according to deformation and occlusion output_dict = {} if self.dense_motion_network is not None: dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving, kp_source=kp_source) output_dict['mask'] = dense_motion['mask'] if 'occlusion_map' in dense_motion: occlusion_map = dense_motion['occlusion_map'] output_dict['occlusion_map'] = occlusion_map else: occlusion_map = None deformation = dense_motion['deformation'] out = self.deform_input(feature_3d, deformation) bs, c, d, h, w = out.shape out = out.view(bs, c*d, h, w) out = self.third(out) out = self.fourth(out) if occlusion_map is not None: if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') out = out * occlusion_map # Decoding part out = self.decoder(out) output_dict["prediction"] = out return output_dict