wav2lip.py
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import torch
from torch import nn
from torch.nn import functional as F
from .conv_384 import Conv2dTranspose, Conv2d, nonorm_Conv2d
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return self.sigmoid(x)
class SAM(nn.Module):
def __init__(self):
super(SAM, self).__init__()
self.sa = SpatialAttention()
def forward(self, sp, se):
sp_att = self.sa(sp)
out = se * sp_att + se
return out
class Wav2Lip(nn.Module):
def __init__(self, audio_encoder=None):
super(Wav2Lip, self).__init__()
self.sam = SAM()
self.face_encoder_blocks = nn.ModuleList([
nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3),
Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True)), # 192, 192
nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 96, 96
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, 32, kernel_size=3, stride=1, padding=1, residual=True)),
nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 48, 48
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), # 24, 24
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, 128, kernel_size=3, stride=1, padding=1, residual=True)),
nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 12, 12
Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
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), # 6, 6
Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
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(Conv2d(512, 1024, kernel_size=3, stride=2, padding=1), # 3, 3
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True)),
nn.Sequential(Conv2d(1024, 1024, kernel_size=3, stride=1, padding=0), # 1, 1
Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0),
Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)), ])
if audio_encoder is None:
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, 256, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(256, 512, kernel_size=3, stride=1, 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),
Conv2d(512, 1024, kernel_size=3, stride=1, padding=0),
Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0))
else:
self.audio_encoder = audio_encoder
for p in self.audio_encoder.parameters():
p.requires_grad = False
self.audio_refine = nn.Sequential(
Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0),
Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0))
self.face_decoder_blocks = nn.ModuleList([
nn.Sequential(Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0), ), # + 1024
nn.Sequential(Conv2dTranspose(2048, 1024, kernel_size=3, stride=1, padding=0), # 3,3
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True), ), # + 1024
nn.Sequential(Conv2dTranspose(2048, 1024, kernel_size=3, stride=2, padding=1, output_padding=1),
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True), ), # 6, 6 + 512
nn.Sequential(Conv2dTranspose(1536, 768, kernel_size=3, stride=2, padding=1, output_padding=1),
Conv2d(768, 768, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(768, 768, kernel_size=3, stride=1, padding=1, residual=True), ), # 12, 12 + 256
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), ), # 24, 24 + 128
nn.Sequential(Conv2dTranspose(640, 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), ), # 48, 48 + 64
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), ), # 96, 96 + 32
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), ), ]) # 192, 192 + 16
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 freeze_audio_encoder(self):
for p in self.audio_encoder.parameters():
p.requires_grad = False
def forward(self, audio_sequences, face_sequences):
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 = self.sam(feats[-1], x)
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