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A10G
Running
on
A10G
| 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 | |