import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.nn.utils.spectral_norm as SpectralNorm from basicsr.utils.registry import ARCH_REGISTRY from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization from .vgg_arch import VGGFeatureExtractor class SFTUpBlock(nn.Module): """Spatial feature transform (SFT) with upsampling block.""" def __init__(self, in_channel, out_channel, kernel_size=3, padding=1): super(SFTUpBlock, self).__init__() self.conv1 = nn.Sequential( Blur(in_channel), SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)), nn.LeakyReLU(0.04, True), # The official codes use two LeakyReLU here, so 0.04 for equivalent ) self.convup = nn.Sequential( nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)), nn.LeakyReLU(0.2, True), ) # for SFT scale and shift self.scale_block = nn.Sequential( SpectralNorm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))) self.shift_block = nn.Sequential( SpectralNorm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid()) # The official codes use sigmoid for shift block, do not know why def forward(self, x, updated_feat): out = self.conv1(x) # SFT scale = self.scale_block(updated_feat) shift = self.shift_block(updated_feat) out = out * scale + shift # upsample out = self.convup(out) return out @ARCH_REGISTRY.register() class DFDNet(nn.Module): """DFDNet: Deep Face Dictionary Network. It only processes faces with 512x512 size. """ def __init__(self, num_feat, dict_path): super().__init__() self.parts = ['left_eye', 'right_eye', 'nose', 'mouth'] # part_sizes: [80, 80, 50, 110] channel_sizes = [128, 256, 512, 512] self.feature_sizes = np.array([256, 128, 64, 32]) self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4'] self.flag_dict_device = False # dict self.dict = torch.load(dict_path) # vgg face extractor self.vgg_extractor = VGGFeatureExtractor( layer_name_list=self.vgg_layers, vgg_type='vgg19', use_input_norm=True, range_norm=True, requires_grad=False) # attention block for fusing dictionary features and input features self.attn_blocks = nn.ModuleDict() for idx, feat_size in enumerate(self.feature_sizes): for name in self.parts: self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx]) # multi scale dilation block self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1]) # upsampling and reconstruction self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8) self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4) self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2) self.upsample3 = SFTUpBlock(num_feat * 2, num_feat) self.upsample4 = nn.Sequential( SpectralNorm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat), UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh()) def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size): """swap the features from the dictionary.""" # get the original vgg features part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone() # resize original vgg features part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False) # use adaptive instance normalization to adjust color and illuminations dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat) # get similarity scores similarity_score = F.conv2d(part_resize_feat, dict_feat) similarity_score = F.softmax(similarity_score.view(-1), dim=0) # select the most similar features in the dict (after norm) select_idx = torch.argmax(similarity_score) swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4]) # attention attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat) attn_feat = attn * swap_feat # update features updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat return updated_feat def put_dict_to_device(self, x): if self.flag_dict_device is False: for k, v in self.dict.items(): for kk, vv in v.items(): self.dict[k][kk] = vv.to(x) self.flag_dict_device = True def forward(self, x, part_locations): """ Now only support testing with batch size = 0. Args: x (Tensor): Input faces with shape (b, c, 512, 512). part_locations (list[Tensor]): Part locations. """ self.put_dict_to_device(x) # extract vggface features vgg_features = self.vgg_extractor(x) # update vggface features using the dictionary for each part updated_vgg_features = [] batch = 0 # only supports testing with batch size = 0 for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes): dict_features = self.dict[f'{f_size}'] vgg_feat = vgg_features[vgg_layer] updated_feat = vgg_feat.clone() # swap features from dictionary for part_idx, part_name in enumerate(self.parts): location = (part_locations[part_idx][batch] // (512 / f_size)).int() updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name, f_size) updated_vgg_features.append(updated_feat) vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4']) # use updated vgg features to modulate the upsampled features with # SFT (Spatial Feature Transform) scaling and shifting manner. upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3]) upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2]) upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1]) upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0]) out = self.upsample4(upsampled_feat) return out