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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d,SPADEResnetBlock | |
| from modules.dense_motion import * | |
| import pdb | |
| from modules.AdaIN import calc_mean_std,adaptive_instance_normalization | |
| from modules.dynamic_conv import Dynamic_conv2d | |
| class SPADEGenerator(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| ic = 256 | |
| cc = 4 | |
| oc = 64 | |
| norm_G = 'spadespectralinstance' | |
| label_nc = 3 + cc | |
| self.compress = nn.Conv2d(ic, cc, 3, padding=1) | |
| 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, image): | |
| cp = self.compress(feature) | |
| seg = torch.cat((F.interpolate(cp, size=(image.shape[2], image.shape[3])), image), dim=1) # 7, 256, 256 | |
| x = feature # 256, 64, 64 | |
| x = self.fc(x) # 512, 64, 64 | |
| 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) # 256, 128, 128 | |
| x = self.up_0(x, seg) | |
| x = self.up(x) # 64, 256, 256 | |
| x = self.up_1(x, seg) | |
| x = self.conv_img(F.leaky_relu(x, 2e-1)) | |
| # x = torch.tanh(x) | |
| x = F.sigmoid(x) | |
| return x | |
| class DepthAwareAttention(nn.Module): | |
| """ depth-aware attention Layer""" | |
| def __init__(self,in_dim,activation): | |
| super(DepthAwareAttention,self).__init__() | |
| self.chanel_in = in_dim | |
| self.activation = activation | |
| self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) | |
| self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) | |
| self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1) | |
| self.gamma = nn.Parameter(torch.zeros(1)) | |
| self.softmax = nn.Softmax(dim=-1) # | |
| def forward(self,source,feat): | |
| """ | |
| inputs : | |
| source : input feature maps( B X C X W X H) 256,64,64 | |
| driving : input feature maps( B X C X W X H) 256,64,64 | |
| returns : | |
| out : self attention value + input feature | |
| attention: B X N X N (N is Width*Height) | |
| """ | |
| m_batchsize,C,width ,height = source.size() | |
| proj_query = self.activation(self.query_conv(source)).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N) [bz,32,64,64] | |
| proj_key = self.activation(self.key_conv(feat)).view(m_batchsize,-1,width*height) # B X C x (*W*H) | |
| energy = torch.bmm(proj_query,proj_key) # transpose check | |
| attention = self.softmax(energy) # BX (N) X (N) | |
| proj_value = self.activation(self.value_conv(feat)).view(m_batchsize,-1,width*height) # B X C X N | |
| out = torch.bmm(proj_value,attention.permute(0,2,1) ) | |
| out = out.view(m_batchsize,C,width,height) | |
| out = self.gamma*out + feat | |
| return out,attention | |
| #### main #### | |
| class DepthAwareGenerator(nn.Module): | |
| """ | |
| Generator that given source image and and keypoints try to transform image according to movement trajectories | |
| induced by keypoints. Generator follows Johnson architecture. | |
| """ | |
| def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks, | |
| num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): | |
| super(DepthAwareGenerator, self).__init__() | |
| if dense_motion_params is not None: | |
| self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels, | |
| estimate_occlusion_map=estimate_occlusion_map, | |
| **dense_motion_params) | |
| else: | |
| self.dense_motion_network = None | |
| self.first = SameBlock2d(num_channels, 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) | |
| #source depth | |
| self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) | |
| src_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))) | |
| src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) | |
| self.src_down_blocks = nn.ModuleList(src_down_blocks) | |
| # #driving depth | |
| # self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) | |
| # dst_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))) | |
| # dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) | |
| # self.dst_down_blocks = nn.ModuleList(dst_down_blocks) | |
| self.AttnModule = DepthAwareAttention(out_features,nn.ReLU()) | |
| up_blocks = [] | |
| for i in range(num_down_blocks): | |
| in_features = min(max_features, block_expansion * (2 ** (num_down_blocks - i))) | |
| out_features = min(max_features, 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.bottleneck = torch.nn.Sequential() | |
| in_features = min(max_features, block_expansion * (2 ** num_down_blocks)) | |
| for i in range(num_bottleneck_blocks): | |
| self.bottleneck.add_module('r' + str(i), ResBlock2d(in_features, kernel_size=(3, 3), padding=(1, 1))) | |
| self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3)) | |
| self.estimate_occlusion_map = estimate_occlusion_map | |
| self.num_channels = num_channels | |
| def deform_input(self, inp, deformation): | |
| _, h_old, w_old, _ = deformation.shape | |
| _, _, h, w = inp.shape | |
| if h_old != h or w_old != w: | |
| deformation = deformation.permute(0, 3, 1, 2) | |
| deformation = F.interpolate(deformation, size=(h, w), mode='bilinear') | |
| deformation = deformation.permute(0, 2, 3, 1) | |
| return F.grid_sample(inp, deformation) | |
| def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth): | |
| # Encoding (downsampling) part | |
| out = self.first(source_image) | |
| for i in range(len(self.down_blocks)): | |
| out = self.down_blocks[i](out) | |
| src_out = self.src_first(source_depth) | |
| for i in range(len(self.src_down_blocks)): | |
| src_out = self.src_down_blocks[i](src_out) | |
| # dst_out = self.dst_first(driving_depth) | |
| # for i in range(len(self.down_blocks)): | |
| # dst_out = self.dst_down_blocks[i](dst_out) | |
| # Transforming feature representation according to deformation and occlusion | |
| output_dict = {} | |
| if self.dense_motion_network is not None: | |
| dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving, | |
| kp_source=kp_source) | |
| output_dict['mask'] = dense_motion['mask'] | |
| output_dict['sparse_deformed'] = dense_motion['sparse_deformed'] | |
| 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(out, deformation) | |
| 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 | |
| out,attention = self.AttnModule(src_out,out) | |
| output_dict["deformed"] = self.deform_input(source_image, deformation) | |
| output_dict["attention"] = attention | |
| # Decoding part | |
| out = self.bottleneck(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 SPADEDepthAwareGenerator(nn.Module): | |
| """ | |
| Generator that given source image and and keypoints try to transform image according to movement trajectories | |
| induced by keypoints. Generator follows Johnson architecture. | |
| """ | |
| def __init__(self, num_channels, num_kp, block_expansion, max_features, num_down_blocks, | |
| num_bottleneck_blocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): | |
| super(SPADEDepthAwareGenerator, self).__init__() | |
| if dense_motion_params is not None: | |
| self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, num_channels=num_channels, | |
| estimate_occlusion_map=estimate_occlusion_map, | |
| **dense_motion_params) | |
| else: | |
| self.dense_motion_network = None | |
| self.first = SameBlock2d(num_channels, 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) | |
| #source depth | |
| self.src_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) | |
| src_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))) | |
| src_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) | |
| self.src_down_blocks = nn.ModuleList(src_down_blocks) | |
| # #driving depth | |
| # self.dst_first = SameBlock2d(1, block_expansion, kernel_size=(7, 7), padding=(3, 3)) | |
| # dst_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))) | |
| # dst_down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) | |
| # self.dst_down_blocks = nn.ModuleList(dst_down_blocks) | |
| self.AttnModule = DepthAwareAttention(out_features,nn.ReLU()) | |
| self.decoder = SPADEGenerator() | |
| self.estimate_occlusion_map = estimate_occlusion_map | |
| self.num_channels = num_channels | |
| def deform_input(self, inp, deformation): | |
| _, h_old, w_old, _ = deformation.shape | |
| _, _, h, w = inp.shape | |
| if h_old != h or w_old != w: | |
| deformation = deformation.permute(0, 3, 1, 2) | |
| deformation = F.interpolate(deformation, size=(h, w), mode='bilinear') | |
| deformation = deformation.permute(0, 2, 3, 1) | |
| return F.grid_sample(inp, deformation) | |
| def forward(self, source_image, kp_driving, kp_source, source_depth, driving_depth): | |
| # Encoding (downsampling) part | |
| out = self.first(source_image) | |
| for i in range(len(self.down_blocks)): | |
| out = self.down_blocks[i](out) | |
| src_out = self.src_first(source_depth) | |
| for i in range(len(self.src_down_blocks)): | |
| src_out = self.src_down_blocks[i](src_out) | |
| # dst_out = self.dst_first(driving_depth) | |
| # for i in range(len(self.down_blocks)): | |
| # dst_out = self.dst_down_blocks[i](dst_out) | |
| # Transforming feature representation according to deformation and occlusion | |
| output_dict = {} | |
| if self.dense_motion_network is not None: | |
| dense_motion = self.dense_motion_network(source_image=source_image, kp_driving=kp_driving, | |
| kp_source=kp_source) | |
| output_dict['mask'] = dense_motion['mask'] | |
| output_dict['sparse_deformed'] = dense_motion['sparse_deformed'] | |
| 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(out, deformation) | |
| 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 | |
| out,attention = self.AttnModule(src_out,out) | |
| deformed_image = self.deform_input(source_image, deformation) | |
| output_dict["deformed"] = deformed_image | |
| output_dict["attention"] = attention | |
| if occlusion_map is not None: | |
| if deformed_image.shape[2] != occlusion_map.shape[2] or deformed_image.shape[3] != occlusion_map.shape[3]: | |
| occlusion_map = F.interpolate(occlusion_map, size=deformed_image.shape[2:], mode='bilinear') | |
| deformed_image = deformed_image * occlusion_map | |
| out = self.decoder(out, deformed_image) | |
| # # Decoding part | |
| # out = self.bottleneck(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 | |