Delete animation/SadTalker directory

animation/SadTalker/src/audio2exp_models/audio2exp.py

@@ -1,41 +0,0 @@
-from tqdm import tqdm
-import torch
-from torch import nn
-
-
-class Audio2Exp(nn.Module):
-    def __init__(self, netG, cfg, device, prepare_training_loss=False):
-        super(Audio2Exp, self).__init__()
-        self.cfg = cfg
-        self.device = device
-        self.netG = netG.to(device)
-
-    def test(self, batch):
-
-        mel_input = batch['indiv_mels']                         # bs T 1 80 16
-        bs = mel_input.shape[0]
-        T = mel_input.shape[1]
-
-        exp_coeff_pred = []
-
-        for i in tqdm(range(0, T, 10),'audio2exp:'): # every 10 frames
-            
-            current_mel_input = mel_input[:,i:i+10]
-
-            #ref = batch['ref'][:, :, :64].repeat((1,current_mel_input.shape[1],1))           #bs T 64
-            ref = batch['ref'][:, :, :64][:, i:i+10]
-            ratio = batch['ratio_gt'][:, i:i+10]                               #bs T
-
-            audiox = current_mel_input.view(-1, 1, 80, 16)                  # bs*T 1 80 16
-
-            curr_exp_coeff_pred  = self.netG(audiox, ref, ratio)         # bs T 64 
-
-            exp_coeff_pred += [curr_exp_coeff_pred]
-
-        # BS x T x 64
-        results_dict = {
-            'exp_coeff_pred': torch.cat(exp_coeff_pred, axis=1)
-            }
-        return results_dict
-
-

animation/SadTalker/src/audio2exp_models/networks.py

@@ -1,74 +0,0 @@
-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