import torch from torch import nn from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d class Wav2LipV2(nn.Module): def __init__(self): super(Wav2LipV2, self).__init__() self.face_encoder_blocks = nn.ModuleList([ nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=2, padding=1), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2d(512, 512, kernel_size=4, stride=1, padding=0), Conv2d(512, 512, kernel_size=1, stride=1, padding=0)), ]) self.audio_encoder = nn.Sequential( Conv2d(1, 32, kernel_size=3, stride=1, padding=1), Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(64, 128, kernel_size=3, stride=3, padding=1), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(256, 512, kernel_size=3, stride=1, padding=0), Conv2d(512, 512, kernel_size=1, stride=1, padding=0), ) self.face_decoder_blocks = nn.ModuleList([ nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0), ), nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=4, stride=1, padding=0), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), ), nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), ), ]) self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) def audio_forward(self, audio_sequences, a_alpha=1.): audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 if a_alpha != 1.: audio_embedding *= a_alpha return audio_embedding def inference(self, audio_embedding, face_sequences): feats = [] x = face_sequences for f in self.face_encoder_blocks: x = f(x) feats.append(x) x = audio_embedding for f in self.face_decoder_blocks: x = f(x) try: x = torch.cat((x, feats[-1]), dim=1) except Exception as e: print(x.size()) print(feats[-1].size()) raise e feats.pop() x = self.output_block(x) outputs = x return outputs def forward(self, audio_sequences, face_sequences, a_alpha=1.): # audio_sequences = (B, T, 1, 80, 16) B = audio_sequences.size(0) input_dim_size = len(face_sequences.size()) if input_dim_size > 4: audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)#[bz, 5, 1, 80, 16]->[bz*5, 1, 80, 16] face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)#[bz, 6, 5, 256, 256]->[bz*5, 6, 256, 256] audio_embedding = self.audio_encoder(audio_sequences) # [bz*5, 1, 80, 16]->[bz*5, 512, 1, 1] if a_alpha != 1.: audio_embedding *= a_alpha #放大音频强度 feats = [] x = face_sequences for f in self.face_encoder_blocks: x = f(x) feats.append(x) x = audio_embedding for f in self.face_decoder_blocks: x = f(x) try: x = torch.cat((x, feats[-1]), dim=1) except Exception as e: print(x.size()) print(feats[-1].size()) raise e feats.pop() x = self.output_block(x) #[bz*5, 80, 256, 256]->[bz*5, 3, 256, 256] if input_dim_size > 4: #[bz*5, 3, 256, 256]->[B, 3, 5, 256, 256] x = torch.split(x, B, dim=0) outputs = torch.stack(x, dim=2) else: outputs = x return outputs class Wav2Lip_disc_qual(nn.Module): def __init__(self): super(Wav2Lip_disc_qual, self).__init__() self.face_encoder_blocks = nn.ModuleList([ nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1), ), nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1), ), nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=4, stride=1, padding=0), nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)), ]) self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) self.label_noise = .0 def get_lower_half(self, face_sequences): #取得输入图片的下半部分。 return face_sequences[:, :, face_sequences.size(2) // 2:] def to_2d(self, face_sequences): #将输入的图片序列连接起来，形成一个二维的tensor。 B = face_sequences.size(0) face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) return face_sequences def perceptual_forward(self, false_face_sequences): #前传生成图像 false_face_sequences = self.to_2d(false_face_sequences) #[bz, 3, 5, 256, 256]->[bz*5, 3, 256, 256] false_face_sequences = self.get_lower_half(false_face_sequences)#[bz*5, 3, 256, 256]->[bz*5, 3, 128, 256] false_feats = false_face_sequences for f in self.face_encoder_blocks: #[bz*5, 3, 128, 256]->[bz*5, 512, 1, 1] false_feats = f(false_feats) return self.binary_pred(false_feats).view(len(false_feats), -1) #[bz*5, 512, 1, 1]->[bz*5, 1, 1] def forward(self, face_sequences): #前传真值图像 face_sequences = self.to_2d(face_sequences) face_sequences = self.get_lower_half(face_sequences) x = face_sequences for f in self.face_encoder_blocks: x = f(x) return self.binary_pred(x).view(len(x), -1)