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| 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) | |
| 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) | |