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import torch
import torch.nn.functional as F
from torch import nn
class Conv2d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv2d(cin, cout, kernel_size, stride, padding),
nn.BatchNorm2d(cout)
)
self.act = nn.ReLU()
self.residual = residual
self.use_act = use_act
def forward(self, x):
out = self.conv_block(x)
if self.residual:
out += x
if self.use_act:
return self.act(out)
else:
return out
class SimpleWrapperV2(nn.Module):
def __init__(self) -> None:
super().__init__()
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),
)
#### load the pre-trained audio_encoder
#self.audio_encoder = self.audio_encoder.to(device)
'''
wav2lip_state_dict = torch.load('/apdcephfs_cq2/share_1290939/wenxuazhang/checkpoints/wav2lip.pth')['state_dict']
state_dict = self.audio_encoder.state_dict()
for k,v in wav2lip_state_dict.items():
if 'audio_encoder' in k:
print('init:', k)
state_dict[k.replace('module.audio_encoder.', '')] = v
self.audio_encoder.load_state_dict(state_dict)
'''
self.mapping1 = nn.Linear(512+64+1, 64)
#self.mapping2 = nn.Linear(30, 64)
#nn.init.constant_(self.mapping1.weight, 0.)
nn.init.constant_(self.mapping1.bias, 0.)
def forward(self, x, ref, ratio):
x = self.audio_encoder(x).view(x.size(0), -1)
ref_reshape = ref.reshape(x.size(0), -1)
ratio = ratio.reshape(x.size(0), -1)
y = self.mapping1(torch.cat([x, ref_reshape, ratio], dim=1))
out = y.reshape(ref.shape[0], ref.shape[1], -1) #+ ref # resudial
return out
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