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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
# from util import util
from lib.models.networks.audio_network import ResNetSE, SEBasicBlock
from lib.models.networks.FAN_feature_extractor import FAN_use
from lib.models.networks.vision_network import ResNeXt50
from torchvision.models.vgg import vgg19_bn
from lib.models.networks.swin_transformer import SwinTransformer
from lib.models.networks.wavlm.wavlm import WavLM, WavLMConfig
class ResSEAudioEncoder(nn.Module):
def __init__(self, opt, nOut=2048, n_mel_T=None):
super(ResSEAudioEncoder, self).__init__()
self.nOut = nOut
self.opt = opt
pose_dim = self.opt.model.net_nonidentity.pose_dim
eye_dim = self.opt.model.net_nonidentity.eye_dim
# Number of filters
num_filters = [32, 64, 128, 256]
if n_mel_T is None: # use it when use audio identity
n_mel_T = opt.model.net_audio.n_mel_T
self.model = ResNetSE(SEBasicBlock, [3, 4, 6, 3], num_filters, self.nOut, n_mel_T=n_mel_T)
if opt.audio_only:
self.mouth_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 512-pose_dim))
else:
self.mouth_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 512-pose_dim-eye_dim))
# def forward_feature(self, x):
def forward(self, x, _type=None):
input_size = x.size()
if len(input_size) == 5:
bz, clip_len, c, f, t = input_size
x = x.view(bz * clip_len, c, f, t)
out = self.model(x)
if _type == "to_mouth_embed":
out = out.view(-1, out.shape[-1])
mouth_embed = self.mouth_embed(out)
return out, mouth_embed
return out
# def forward(self, x):
# out = self.forward_feature(x)
# score = self.fc(out)
# return out, score
class ResSESyncEncoder(ResSEAudioEncoder):
def __init__(self, opt):
super(ResSESyncEncoder, self).__init__(opt, nOut=512, n_mel_T=1)
class ResNeXtEncoder(ResNeXt50):
def __init__(self, opt):
super(ResNeXtEncoder, self).__init__(opt)
class VGGEncoder(nn.Module):
def __init__(self, opt):
super(VGGEncoder, self).__init__()
self.model = vgg19_bn(num_classes=opt.data.num_classes)
def forward(self, x):
return self.model(x)
class FanEncoder(nn.Module):
def __init__(self, opt):
super(FanEncoder, self).__init__()
self.opt = opt
pose_dim = self.opt.model.net_nonidentity.pose_dim
eye_dim = self.opt.model.net_nonidentity.eye_dim
self.model = FAN_use()
# self.classifier = nn.Sequential(nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, opt.data.num_classes))
# mapper to mouth subspace
### revised version1
# self.to_mouth = nn.Sequential(nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512))
# self.mouth_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 512-pose_dim-eye_dim))
# mapper to head pose subspace
### revised version1
self.to_headpose = nn.Sequential(nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512))
self.headpose_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, pose_dim))
self.to_eye = nn.Sequential(nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512))
self.eye_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, eye_dim))
self.to_emo = nn.Sequential(nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512))
self.emo_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 30))
# self.feature_fuse = nn.Sequential(nn.ReLU(), nn.Linear(1036, 512))
# self.feature_fuse = nn.Sequential(nn.ReLU(), nn.Linear(1036, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512))
def forward_feature(self, x):
net = self.model(x)
return net
def forward(self, x, _type="feature"):
if _type == "feature":
return self.forward_feature(x)
elif _type == "feature_embed":
x = self.model(x)
# mouth_feat = self.to_mouth(x)
# mouth_emb = self.mouth_embed(mouth_feat)
headpose_feat = self.to_headpose(x)
headpose_emb = self.headpose_embed(headpose_feat)
eye_feat = self.to_eye(x)
eye_embed = self.eye_embed(eye_feat)
emo_feat = self.to_emo(x)
emo_embed = self.emo_embed(emo_feat)
# return headpose_emb, eye_embed, emo_feat
return headpose_emb, eye_embed, emo_embed
elif _type == "to_headpose":
x = self.model(x)
headpose_feat = self.to_headpose(x)
headpose_emb = self.headpose_embed(headpose_feat)
return headpose_emb
# class WavlmEncoder(nn.Module):
# def __init__(self, opt):
# super(WavlmEncoder, self).__init__()
# wavlm_checkpoint = torch.load(opt.model.net_audio.official_pretrain)
# wavlm_cfg = WavLMConfig(wavlm_checkpoint['cfg'])
# # pose_dim = opt.model.net_nonidentity.pose_dim
# self.model = WavLM(wavlm_cfg)
# # self.mouth_embed = nn.Sequential(nn.ReLU(), nn.Linear(768, 512-pose_dim))
# def forward(self, x):
# feature = self.model.extract_features(x)[0]
# # audio_feat = self.mouth_embed(feature.mean(1))
# return feature.mean(1)
class WavlmEncoder(nn.Module):
def __init__(self, opt):
super(WavlmEncoder, self).__init__()
self.input_wins = opt.audio.num_frames_per_clip
self.s = (self.input_wins - 5) // 2 * 2
self.e = self.s + 5 * 2 - 1
wavlm_checkpoint = torch.load(opt.model.net_audio.official_pretrain)
wavlm_cfg = WavLMConfig(wavlm_checkpoint['cfg'])
pose_dim = opt.model.net_nonidentity.pose_dim
self.model = WavLM(wavlm_cfg)
self.mouth_feat = nn.Sequential(nn.Linear(768, 512), nn.ReLU(), nn.Linear(512, 512))
self.mouth_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 512-pose_dim))
def forward(self, x):
feature = self.model.extract_features(x)[0]
feature = self.mouth_feat(feature[:, self.s:self.e].mean(1))
audio_feat = self.mouth_embed(feature)
return feature, audio_feat
class SwinEncoder(nn.Module):
def __init__(self, cfg):
super(SwinEncoder, self).__init__()
self.encoder = SwinTransformer(
num_classes = 0,
img_size = cfg.model.net_nonidentity.img_size,
patch_size = cfg.model.net_nonidentity.patch_size,
in_chans = cfg.model.net_nonidentity.in_chans,
embed_dim = cfg.model.net_nonidentity.embed_dim,
depths = cfg.model.net_nonidentity.depths,
num_heads = cfg.model.net_nonidentity.num_heads,
window_size = cfg.model.net_nonidentity.window_size,
mlp_ratio = cfg.model.net_nonidentity.mlp_ratio,
qkv_bias = cfg.model.net_nonidentity.qkv_bias,
qk_scale = None if not cfg.model.net_nonidentity.qk_scale else 0.1,
drop_rate = cfg.model.net_nonidentity.drop_rate,
drop_path_rate = cfg.model.net_nonidentity.drop_path_rate,
ape = cfg.model.net_nonidentity.ape,
patch_norm = cfg.model.net_nonidentity.patch_norm,
use_checkpoint = False
)
# self.audio_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.eye_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.landmark_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.exp_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
def forward(self, img):
feature = self.encoder(img)
# audio_embed = self.audio_mlp(feature)
# eye_embed = self.eye_mlp(feature)
# ldmk_embed = self.landmark_mlp(feature)
# exp_embed = self.exp_mlp(feature)
# return feature, audio_embed, eye_embed, ldmk_embed, exp_embed
return feature
class ResEncoder(nn.Module):
def __init__(self, opt):
super(ResEncoder, self).__init__()
self.opt = opt
self.model = resnet50(num_classes=512, include_top=True)
def forward(self, x):
feature = self.model(x)
# print(feature.shape)
return feature
class FansEncoder(nn.Module):
def __init__(self, cfg):
super(FansEncoder, self).__init__()
self.encoder = FAN_use(out_dim=768)
# self.audio_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.eye_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.landmark_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.exp_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
def forward(self, img):
feature = self.encoder(img)
# audio_embed = self.audio_mlp(feature)
# eye_embed = self.eye_mlp(feature)
# ldmk_embed = self.landmark_mlp(feature)
# exp_embed = self.exp_mlp(feature)
# return feature, audio_embed, eye_embed, ldmk_embed, exp_embed
return feature
class SwinEncoder(nn.Module):
def __init__(self, cfg):
super(SwinEncoder, self).__init__()
self.encoder = SwinTransformer(
num_classes = 0,
img_size = cfg.model.net_nonidentity.img_size,
patch_size = cfg.model.net_nonidentity.patch_size,
in_chans = cfg.model.net_nonidentity.in_chans,
embed_dim = cfg.model.net_nonidentity.embed_dim,
depths = cfg.model.net_nonidentity.depths,
num_heads = cfg.model.net_nonidentity.num_heads,
window_size = cfg.model.net_nonidentity.window_size,
mlp_ratio = cfg.model.net_nonidentity.mlp_ratio,
qkv_bias = cfg.model.net_nonidentity.qkv_bias,
qk_scale = None if not cfg.model.net_nonidentity.qk_scale else 0.1,
drop_rate = cfg.model.net_nonidentity.drop_rate,
drop_path_rate = cfg.model.net_nonidentity.drop_path_rate,
ape = cfg.model.net_nonidentity.ape,
patch_norm = cfg.model.net_nonidentity.patch_norm,
use_checkpoint = False
)
# self.audio_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.eye_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.landmark_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.exp_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
def forward(self, img):
feature = self.encoder(img)
# audio_embed = self.audio_mlp(feature)
# eye_embed = self.eye_mlp(feature)
# ldmk_embed = self.landmark_mlp(feature)
# exp_embed = self.exp_mlp(feature)
# return feature, audio_embed, eye_embed, ldmk_embed, exp_embed
return feature
class ResEncoder(nn.Module):
def __init__(self, opt):
super(ResEncoder, self).__init__()
self.opt = opt
self.model = resnet50(num_classes=512, include_top=True)
def forward(self, x):
feature = self.model(x)
# print(feature.shape)
return feature
class FansEncoder(nn.Module):
def __init__(self, cfg):
super(FansEncoder, self).__init__()
self.encoder = FAN_use(out_dim=768)
# self.audio_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.eye_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.landmark_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
# self.exp_mlp = nn.Sequential(nn.Linear(768, 768), nn.ReLU(), nn.LayerNorm(768), nn.Linear(768, 768))
def forward(self, img):
feature = self.encoder(img)
# audio_embed = self.audio_mlp(feature)
# eye_embed = self.eye_mlp(feature)
# ldmk_embed = self.landmark_mlp(feature)
# exp_embed = self.exp_mlp(feature)
# return feature, audio_embed, eye_embed, ldmk_embed, exp_embed
return feature |