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A10G
Running
on
A10G
| 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 |