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import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
import math
class Conv1d(nn.Module):
def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv_block = nn.Sequential(
nn.Conv1d(cin, cout, kernel_size, stride, padding),
nn.BatchNorm1d(cout)
)
self.act = nn.ReLU()
self.residual = residual
def forward(self, x):
out = self.conv_block(x)
if self.residual:
out += x
return self.act(out)
class LossScale(nn.Module):
def __init__(self, init_w=10.0, init_b=-5.0):
super(LossScale, self).__init__()
self.wC = nn.Parameter(torch.tensor(init_w))
self.bC = nn.Parameter(torch.tensor(init_b))
class CLIPLoss(nn.Module):
def __init__(self,):
super().__init__()
def forward(self, audio_features, motion_features, logit_scale, clip_mask=None):
logits_per_audio = logit_scale * audio_features @ motion_features.T # [b,c]
logits_per_motion = logit_scale * motion_features @ audio_features.T # [b,c]
if clip_mask is not None:
logits_per_audio += clip_mask
logits_per_motion += clip_mask
labels = torch.arange(logits_per_motion.shape[0]).to(logits_per_motion.device)
motion_loss = F.cross_entropy(logits_per_motion, labels)
audio_loss = F.cross_entropy(logits_per_audio, labels)
clip_loss = (motion_loss + audio_loss) / 2
ret = {
"audio_loss": audio_loss,
"motion_loss": motion_loss,
"clip_loss": clip_loss
}
return ret
def compute_sync_conf(self, audio_features, motion_features, return_matrix=False):
logits_per_audio = audio_features @ motion_features.T # [b,c]
if return_matrix:
return logits_per_audio
return logits_per_audio[range(len(audio_features)), range(len(audio_features))]
class LandmarkHubertSyncNet(nn.Module):
def __init__(self, lm_dim=60, audio_dim=1024, num_layers_per_block=3, base_hid_size=128, out_dim=512):
super(LandmarkHubertSyncNet, self).__init__()
self.clip_loss_fn = CLIPLoss()
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) * 0
self.logit_scale_2 = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) * 0
self.logit_scale_max = math.log(1. / 0.01)
# hubert = torch.rand(B, 1024, t=10)
hubert_layers = [
Conv1d(audio_dim, base_hid_size, kernel_size=3, stride=1, padding=1)
]
hubert_layers.append(
Conv1d(base_hid_size, base_hid_size, kernel_size=3, stride=1, padding=1),
)
hubert_layers += [
Conv1d(base_hid_size, base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
hubert_layers.append(
Conv1d(base_hid_size, 2*base_hid_size, kernel_size=3, stride=2, padding=1),
)
hubert_layers += [
Conv1d(2*base_hid_size, 2*base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
hubert_layers.append(
Conv1d(2*base_hid_size, 4*base_hid_size, kernel_size=3, stride=2, padding=1),
)
hubert_layers += [
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
hubert_layers += [
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=1),
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=0),
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=1, stride=1, padding=0),
Conv1d(4*base_hid_size, out_dim, kernel_size=1, stride=1, padding=0),
]
self.hubert_encoder = nn.Sequential(*hubert_layers)
# mouth = torch.rand(B, 20*3, t=5)
mouth_layers = [
Conv1d(lm_dim, 96, kernel_size=3, stride=1, padding=1)
]
mouth_layers.append(
Conv1d(96, base_hid_size, kernel_size=3, stride=1, padding=1),
)
mouth_layers += [
Conv1d(base_hid_size, base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
mouth_layers.append(
Conv1d(base_hid_size, 2*base_hid_size, kernel_size=3, stride=2, padding=1),
)
mouth_layers += [
Conv1d(2*base_hid_size, 2*base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
mouth_layers.append(
Conv1d(2*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=1),
)
mouth_layers += [
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=1, residual=True) for _ in range(num_layers_per_block-1)
]
mouth_layers += [
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=1),
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=3, stride=1, padding=0),
Conv1d(4*base_hid_size, 4*base_hid_size, kernel_size=1, stride=1, padding=0),
Conv1d(4*base_hid_size, out_dim, kernel_size=1, stride=1, padding=0),
]
self.mouth_encoder = nn.Sequential(*mouth_layers)
self.lm_dim = lm_dim
self.audio_dim = audio_dim
self.logloss = nn.BCELoss()
def forward(self, hubert, mouth_lm):
# hubert := (B, T=10, C=1024)
# mouth_lm3d := (B, T=5, C=60)
hubert = hubert.transpose(1,2)
mouth_lm = mouth_lm.transpose(1,2)
mouth_embedding = self.mouth_encoder(mouth_lm)
audio_embedding = self.hubert_encoder(hubert)
audio_embedding = audio_embedding.view(audio_embedding.size(0), -1)
mouth_embedding = mouth_embedding.view(mouth_embedding.size(0), -1)
audio_embedding = F.normalize(audio_embedding, p=2, dim=1)
mouth_embedding = F.normalize(mouth_embedding, p=2, dim=1)
return audio_embedding, mouth_embedding
def cal_sync_loss(self, audio_embedding, mouth_embedding, label, reduction='none'):
if isinstance(label, torch.Tensor): # finegrained label
gt_d = label.float().view(-1).to(audio_embedding.device)
else: # int to represent global label, 1 denotes positive, and 0 denotes negative, used when calculate sync loss for other models
gt_d = (torch.ones([audio_embedding.shape[0]]) * label).float().to(audio_embedding.device) # int
d = F.cosine_similarity(audio_embedding, mouth_embedding) # [B]
loss = F.binary_cross_entropy(d.reshape([audio_embedding.shape[0],]), gt_d, reduction=reduction)
return loss, d
def cal_clip_loss(self, audio_embedding, mouth_embedding, clip_mask=None):
# logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
logit_scale = 1
clip_ret = self.clip_loss_fn(audio_embedding, mouth_embedding, logit_scale, clip_mask=clip_mask)
loss = clip_ret['clip_loss']
return loss
def cal_clip_loss_local(self, audio_embedding, mouth_embedding, clip_mask=None):
# logit_scale = torch.clamp(self.logit_scale_2, max=self.logit_scale_max).exp()
logit_scale = 1
clip_ret = self.clip_loss_fn(audio_embedding, mouth_embedding, logit_scale, clip_mask=clip_mask)
loss = clip_ret['clip_loss']
return loss
def compute_sync_conf(self, audio_embedding, mouth_embedding, return_matrix=False):
# logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
logit_scale = 1
clip_ret = self.clip_loss_fn.compute_sync_conf(audio_embedding, mouth_embedding, return_matrix)
return clip_ret
if __name__ == '__main__':
syncnet = LandmarkHubertSyncNet(lm_dim=204)
hubert = torch.rand(2, 10, 1024)
lm = torch.rand(2, 5, 204)
mel_embedding, exp_embedding = syncnet(hubert, lm)
label = torch.tensor([1., 0.])
loss = syncnet.cal_sync_loss(mel_embedding, exp_embedding, label)
print(" ") |