import torch from torch import nn from torch.nn import functional as F import math from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d class Wav2Lip(nn.Module): def __init__(self): super(Wav2Lip, self).__init__() self.face_encoder_blocks = nn.ModuleList([ nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 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), # 24,24 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), # 12,12 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), # 6,6 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), # 3,3 Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 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=3, stride=1, padding=0), # 3,3 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),), # 6, 6 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),), # 12, 12 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),), # 24, 24 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),), # 48, 48 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),),]) # 96,96 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 forward(self, audio_sequences, face_sequences): # 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) face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 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) #每一次,音频编码, concat 视频编码. 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) if input_dim_size > 4: x = torch.split(x, B, dim=0) # [(B, C, H, W)] outputs = torch.stack(x, dim=2) # (B, C, T, H, W) 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)), # 48,96 nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48 nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), # 24,24 nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), # 12,12 nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 6,6 nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), # 3,3 nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),), nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 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): return face_sequences[:, :, face_sequences.size(2)//2:] def to_2d(self, face_sequences): 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): false_face_sequences = self.to_2d(false_face_sequences) false_face_sequences = self.get_lower_half(false_face_sequences) false_feats = false_face_sequences for f in self.face_encoder_blocks: false_feats = f(false_feats) false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), torch.ones((len(false_feats), 1)).cuda()) return false_pred_loss def forward(self, face_sequences): 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)