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av_model.py
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av_model.py
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import torch
import models.networks as networks
from models.networks.architecture import VGGFace19
import util.util as util
from models.networks.loss import CrossEntropyLoss
import os
class AvModel(torch.nn.Module):
@staticmethod
def modify_commandline_options(parser, is_train):
networks.modify_commandline_options(parser, is_train)
return parser
def __init__(self, opt):
super(AvModel, self).__init__()
self.opt = opt
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
self.FloatTensor = torch.cuda.FloatTensor if self.use_gpu() \
else torch.FloatTensor
self.ByteTensor = torch.cuda.ByteTensor if self.use_gpu() \
else torch.ByteTensor
self.netG, self.netD, self.netA, self.netA_sync, self.netV, self.netE = \
self.initialize_networks(opt)
# set loss functions
if opt.isTrain:
self.loss_cls = CrossEntropyLoss()
self.criterionFeat = torch.nn.L1Loss()
if opt.softmax_contrastive:
self.criterionSoftmaxContrastive = networks.SoftmaxContrastiveLoss()
if opt.train_recognition or opt.train_sync:
pass
else:
self.criterionGAN = networks.GANLoss(
opt.gan_mode, tensor=self.FloatTensor, opt=self.opt)
if not opt.no_vgg_loss:
self.criterionVGG = networks.VGGLoss(self.opt)
if opt.vgg_face:
self.VGGFace = VGGFace19(self.opt)
self.criterionVGGFace = networks.VGGLoss(self.opt, self.VGGFace)
if opt.disentangle:
self.criterionLogSoftmax = networks.L2SoftmaxLoss()
# Entry point for all calls involving forward pass
# of deep networks. We used this approach since DataParallel module
# can't parallelize custom functions, we branch to different
# routines based on |mode|.
# |data|: dictionary of the input data
def preprocessing(self, data):
target_images = data['target'].cuda()
input_image = data['input'].cuda()
augmented = data['augmented'].cuda()
spectrogram = data['spectrograms'].cuda() if self.opt.use_audio else None
target_images = target_images.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
augmented = augmented.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
return input_image, target_images, augmented, spectrogram
def forward(self, data, mode):
labels = data['label']
input_image, target_images, augmentated, spectrogram = self.preprocessing(data)
if mode == 'generator':
g_loss, generated, id_scores = self.compute_generator_loss(
input_image, target_images, augmentated, spectrogram,
netD=self.netD, labels=labels, no_ganFeat_loss=self.opt.no_ganFeat_loss,
no_vgg_loss=self.opt.no_vgg_loss, lambda_D=self.opt.lambda_D)
return g_loss, generated, id_scores
if mode == 'encoder':
g_loss, cls_score = self.compute_encoder_loss(
input_image, target_images, spectrogram, labels)
return g_loss, cls_score
if mode == 'sync':
g_loss = self.sync(augmentated, spectrogram)
return g_loss
if mode == 'sync_D':
d_loss = self.sync_D(spectrogram, labels)
return d_loss
elif mode == 'discriminator':
d_loss = self.compute_discriminator_loss(
input_image, target_images, augmentated, spectrogram, netD=self.netD, labels=labels, lambda_D=self.opt.lambda_D)
return d_loss
elif mode == 'inference':
assert self.opt.use_audio, 'must use audio driven strategy.'
driving_pose_frames = data['driving_pose_frames'].cuda()
with torch.no_grad():
fake_image_ref_pose_a, fake_image_driven_pose_a = self.inference(input_image, spectrogram,
driving_pose_frames)
return fake_image_ref_pose_a, fake_image_driven_pose_a
else:
raise ValueError("|mode| is invalid")
def create_optimizers(self, opt):
optimizer_D = None
if opt.no_TTUR:
beta1, beta2 = opt.beta1, opt.beta2
G_lr, D_lr = opt.lr, opt.lr
else:
beta1, beta2 = 0, 0.9
G_lr, D_lr = opt.lr / 2, opt.lr * 2
if opt.train_recognition:
util.freeze_model(self.netV)
for param in self.netV.fc.parameters():
param.requires_grad = True
netV_params = list(self.netV.fc.parameters())
netA_params = list(self.netA.parameters())
G_params = netV_params + netA_params
elif opt.train_sync:
netA_sync_params = list(self.netA_sync.model.parameters())
# netE_params = list(self.netE.model.parameters())
netE_mouth_params = list(self.netE.to_mouth.parameters())
G_params = netA_sync_params + netE_mouth_params
D_params = list(self.netA_sync.fc.parameters()) + list(self.netE.classifier.parameters())
optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2))
elif opt.train_dis_pose:
netE_pure_pose_params = list(self.netE.pure_pose.parameters())+list(self.netE.headpose_embed.parameters())
netG_params = list(self.netG.parameters())
netV_params = list(self.netV.parameters())
netE_params = list(self.netE.model.parameters())
netA_sync_params = list(self.netA_sync.parameters()) if self.opt.use_audio else None
netE_mouth_all_params = list(self.netE.to_mouth.parameters()) + list(self.netE.mouth_fc.parameters())
G_params = []
if not opt.fix_netE_mouth:
G_params = G_params + netE_mouth_all_params
else:
util.freeze_model(self.netE.to_mouth)
util.freeze_model(self.netE.mouth_fc)
if not opt.fix_netE_headpose:
G_params = G_params + netE_pure_pose_params
else:
util.freeze_model(self.netE.pure_pose)
util.freeze_model(self.netE.headpose_embed)
if not opt.fix_netG:
G_params = G_params + netG_params
else:
util.freeze_model(self.netG)
if not opt.fix_netV:
G_params = G_params + netV_params
else:
util.freeze_model(self.netV)
if not opt.fix_netE:
G_params = G_params + netE_params
else:
util.freeze_model(self.netE.model)
if self.opt.use_audio:
if not opt.fix_netA_sync:
G_params = G_params + netA_sync_params
else:
util.freeze_model(self.netA_sync)
if opt.isTrain:
D_params = list(self.netD.parameters())
if opt.disentangle:
if not opt.fix_netE_headpose:
D_params = list(self.netE.headpose_fc.parameters()) + D_params
else:
util.freeze_model(self.netE.headpose_fc)
if not opt.fix_netD:
optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2))
else:
util.freeze_model(self.netD)
else:
netG_params = list(self.netG.parameters())
netA_sync_params = list(self.netA_sync.model.parameters()) if opt.use_audio else 0
netE_mouth_params = list(self.netE.to_mouth.parameters())
netV_params = list(self.netV.parameters())
netE_params = list(self.netE.model.parameters())
G_params = netA_sync_params + netE_mouth_params
if not opt.fix_netV:
G_params = G_params + netV_params
else:
util.freeze_model(self.netV)
if not opt.fix_netE:
G_params = G_params + netE_params
else:
util.freeze_model(self.netE)
if not opt.fix_netG:
G_params = G_params + netG_params
else:
util.freeze_model(self.netG)
if opt.isTrain:
D_params = list(self.netD.parameters())
if opt.disentangle:
D_params = list(self.netE.classifier.parameters()) + D_params
if not opt.fix_netD:
optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2))
else:
util.freeze_model(self.netD)
if opt.optimizer == 'sgd':
optimizer_G = torch.optim.SGD(G_params, lr=G_lr, momentum=0.9, nesterov=True)
else:
optimizer_G = torch.optim.Adam(G_params, lr=G_lr, betas=(beta1, beta2), amsgrad=True)
return optimizer_G, optimizer_D
def save(self, epoch):
if self.opt.train_recognition:
util.save_network(self.netV, 'V', epoch, self.opt)
elif self.opt.train_sync:
util.save_network(self.netE, 'E', epoch, self.opt)
if self.opt.use_audio:
util.save_network(self.netA_sync, 'A_sync', epoch, self.opt)
else:
util.save_network(self.netG, 'G', epoch, self.opt)
# util.save_network(self.netD, 'D', epoch, self.opt)
if self.opt.use_audio:
if self.opt.use_audio_id:
util.save_network(self.netA, 'A', epoch, self.opt)
util.save_network(self.netA_sync, 'A_sync', epoch, self.opt)
util.save_network(self.netV, 'V', epoch, self.opt)
util.save_network(self.netE, 'E', epoch, self.opt)
############################################################################
# Private helper methods
############################################################################
def initialize_networks(self, opt):
netG = None
netD = None
netE = None
netV = None
netA = None
netA_sync = None
if opt.train_recognition:
netV = networks.define_V(opt)
elif opt.train_sync:
netA_sync = networks.define_A_sync(opt) if opt.use_audio else None
netE = networks.define_E(opt)
else:
netG = networks.define_G(opt)
netA = networks.define_A(opt) if opt.use_audio and opt.use_audio_id else None
netA_sync = networks.define_A_sync(opt) if opt.use_audio else None
netE = networks.define_E(opt)
netV = networks.define_V(opt)
if opt.isTrain:
netD = networks.define_D(opt)
if not opt.isTrain or opt.continue_train:
self.load_network(netG, 'G', opt.which_epoch)
self.load_network(netV, 'V', opt.which_epoch)
self.load_network(netE, 'E', opt.which_epoch)
if opt.use_audio:
if opt.use_audio_id:
self.load_network(netA, 'A', opt.which_epoch)
self.load_network(netA_sync, 'A_sync', opt.which_epoch)
if opt.isTrain and not opt.noload_D:
self.load_network(netD, 'D', opt.which_epoch)
# self.load_network(netD_rotate, 'D_rotate', opt.which_epoch, pretrained_path)
else:
if self.opt.pretrain:
if opt.netE == 'fan':
netE.load_pretrain()
netV.load_pretrain()
if opt.load_separately:
netG = self.load_separately(netG, 'G', opt)
netA = self.load_separately(netA, 'A', opt) if opt.use_audio and opt.use_audio_id else None
netA_sync = self.load_separately(netA_sync, 'A_sync', opt) if opt.use_audio else None
netV = self.load_separately(netV, 'V', opt)
netE = self.load_separately(netE, 'E', opt)
if not opt.noload_D:
netD = self.load_separately(netD, 'D', opt)
return netG, netD, netA, netA_sync, netV, netE
def compute_encoder_loss(self, input_img, real_image, spectrogram, labels):
G_losses = {}
real_image = real_image.view(-1, self.opt.clip_len, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
[image_feature, net_V_feature], cls_score_V = self.netV.forward(real_image)
audio_feature, cls_score_A_2 = self.netA.forward(spectrogram)
audio_feature = audio_feature.view(-1, self.opt.clip_len, audio_feature.shape[-1])
audio_feature = torch.mean(audio_feature, 1)
G_losses['loss_cls_V'] = self.loss_cls(cls_score_V, labels)
cls_score_A = self.netV.fc.forward(audio_feature)
G_losses['loss_cls_A'] = self.loss_cls(cls_score_A, labels)
# G_losses['loss_cls_A_2'] = self.loss_cls(cls_score_A_2, labels)
if not self.opt.no_cross_modal:
G_losses['CrossModal'] = self.criterionFeat(image_feature.detach(), audio_feature) * self.opt.lambda_crossmodal
if self.opt.softmax_contrastive:
G_losses['SoftmaxContrastive'] = self.criterionSoftmaxContrastive(image_feature.detach(), audio_feature) * self.opt.lambda_contrastive
return G_losses, cls_score_A
def sync_D(self, spectrogram, labels):
D_losses = {}
with torch.no_grad():
audio_content_feature = self.netA_sync.forward_feature(spectrogram)
audio_content_feature = audio_content_feature.detach()
audio_content_feature.requires_grad_()
cls_score_A = self.netA_sync.fc.forward(audio_content_feature)
labels = labels.unsqueeze(1)
labels_expand = labels.expand(-1, self.opt.clip_len)
labels_expand = labels_expand.contiguous().view(-1)
D_losses['loss_cls_A'] = self.loss_cls(cls_score_A, labels_expand)
return D_losses
def encode_audiosync_feature(self, spectrogram):
audio_content_feature = self.netA_sync.forward_feature(spectrogram)
audio_content_feature = audio_content_feature.view(-1, self.opt.clip_len, audio_content_feature.shape[-1])
return audio_content_feature
def sync(self, augmented, spectrogram):
G_losses = {}
pose_feature = self.encode_noid_feature(augmented)
audio_content_feature = self.encode_audiosync_feature(spectrogram)
G_losses = self.compute_sync_loss(pose_feature, audio_content_feature, G_losses)
return G_losses
def compute_sync_loss(self, image_content_feature, audio_content_feature, G_losses, name=''):
audio_content_feature_all = audio_content_feature.view(audio_content_feature.shape[0], -1)
image_content_feature_all = image_content_feature.view(image_content_feature.shape[0], -1)
if not self.opt.no_cross_modal:
G_losses['CrossModal{}'.format(name)] = self.criterionFeat(image_content_feature_all.detach(),
audio_content_feature_all) * self.opt.lambda_crossmodal
if self.opt.softmax_contrastive:
G_losses['SoftmaxContrastive{}'.format(name)] = self.criterionSoftmaxContrastive(image_content_feature_all.detach(), audio_content_feature_all) * self.opt.lambda_contrastive
G_losses['SoftmaxContrastive_v2a'] = self.criterionSoftmaxContrastive(audio_content_feature_all.detach(), image_content_feature_all) * self.opt.lambda_contrastive
return G_losses
def audio_identity_feature(self, id_mel, no_grad=True):
id_mel = id_mel.view(-1, 1, id_mel.shape[-2], id_mel.shape[-1])
if no_grad:
with torch.no_grad():
id_feature, id_scores = self.netA(id_mel)
else:
id_feature, id_scores = self.netA(id_mel)
return id_feature, id_scores
def encode_identity_feature(self, input_img):
input_img = input_img.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
if not self.opt.isTrain or self.opt.fix_netV:
with torch.no_grad():
id_feature, id_scores = self.netV(input_img)
else:
id_feature, id_scores = self.netV(input_img)
id_feature[0] = id_feature[0].unsqueeze(1).repeat(1, self.opt.clip_len, 1).view(-1, *id_feature[0].shape[1:])
id_feature[1] = id_feature[1].unsqueeze(1).repeat(1, self.opt.clip_len, 1, 1, 1).view(-1, *id_feature[1].shape[1:])
return id_feature, id_scores
def encode_ref_noid(self, input_img):
input_img = input_img.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
with torch.no_grad():
ref_noid_feature = self.netE.forward_feature(input_img)
ref_noid_feature = ref_noid_feature.view(-1, self.opt.num_inputs, ref_noid_feature.shape[-1])
ref_noid_feature = ref_noid_feature.mean(1).unsqueeze(1).repeat(1, self.opt.clip_len, 1)
return ref_noid_feature
def compute_pose_diff(self, pose_feature, ref_noid_feature):
pose_feature = pose_feature.view(-1, self.opt.clip_len, pose_feature.shape[-1])
pose_differences = pose_feature - ref_noid_feature
return pose_differences
def compute_diff_loss(self, input_img, pose_feature, pose_feature_audio, G_losses):
pose_feature_audio = pose_feature_audio.view(-1, self.opt.clip_len, pose_feature_audio.shape[-1])
ref_noid_feature = self.encode_ref_noid(input_img)
pose_differences = self.compute_pose_diff(pose_feature, ref_noid_feature)
self.compute_sync_loss(pose_differences, pose_feature_audio, G_losses)
pose_feature_audio = ref_noid_feature + pose_feature_audio
return pose_feature_audio
def encode_noid_feature(self, augmented):
augmented = augmented.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
if (not self.opt.isTrain) or self.opt.train_sync or self.opt.fix_netE:
with torch.no_grad():
noid_feature = self.netE.forward_feature(augmented)
else:
noid_feature = self.netE.forward_feature(augmented)
noid_feature = noid_feature.view(-1, self.opt.clip_len, noid_feature.shape[-1])
return noid_feature
def select_frames(self, in_obj_ts):
if len(in_obj_ts.shape) == 2:
obj_ts = in_obj_ts.view(-1, self.opt.clip_len, in_obj_ts.shape[-1])
obj_ts = obj_ts[:, ::self.opt.generate_interval, :].contiguous()
obj_ts = obj_ts.view(-1, obj_ts.shape[-1])
elif len(in_obj_ts.shape) == 3:
obj_ts = in_obj_ts[:, ::self.opt.generate_interval, :].contiguous()
elif len(in_obj_ts.shape) == 4:
obj_ts = in_obj_ts.view(-1, self.opt.clip_len, *in_obj_ts.shape[1:])
obj_ts = obj_ts[:, ::self.opt.generate_interval, :].contiguous()
obj_ts = obj_ts.view(-1, *obj_ts.shape[2:])
elif len(in_obj_ts.shape) == 5:
obj_ts = in_obj_ts[:, ::self.opt.generate_interval, :].contiguous()
else:
raise ValueError
return obj_ts
def generate_fake(self, id_feature, pose_feature):
pose_feature = pose_feature.view(-1, pose_feature.shape[-1])
style = torch.cat([id_feature[0], pose_feature], 1)
style = [style]
if self.opt.input_id_feature:
fake_image, style_rgb = self.netG(style, identity_style=id_feature[1])
else:
fake_image, style_rgb = self.netG(style)
fake_image = fake_image.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
return fake_image, style_rgb
def merge_mouthpose(self, mouth_feature, headpose_feature, embed_headpose=False):
mouth_feature = self.netE.mouth_embed(mouth_feature)
if not embed_headpose:
headpose_feature = self.netE.headpose_embed(headpose_feature)
pose_feature = torch.cat((mouth_feature, headpose_feature), dim=2)
return pose_feature
def inference(self, input_img, spectrogram,
driving_pose_frames, mouth_feature_weight=1.2):
##### ***************** encode image feature and generate ******************************
id_feature, _ = self.encode_identity_feature(input_img)
fake_image_pose_driven_a = None
if self.opt.generate_from_audio_only:
assert self.opt.use_audio, 'must use audio in this case'
A_mouth_feature = self.encode_audiosync_feature(spectrogram)
A_mouth_feature = A_mouth_feature * mouth_feature_weight
sel_id_feature = []
sel_id_feature.append(self.select_frames(id_feature[0]))
sel_id_feature.append(self.select_frames(id_feature[1]))
V_noid_ref_feature = self.encode_ref_noid(input_img)
V_headpose_ref_feature = self.netE.to_headpose(V_noid_ref_feature)
ref_merge_feature_a = self.select_frames(self.merge_mouthpose(A_mouth_feature, V_headpose_ref_feature))
fake_image_ref_pose_a, _ = self.generate_fake(sel_id_feature, ref_merge_feature_a)
if self.opt.driving_pose:
V_noid_driving_feature = self.encode_noid_feature(driving_pose_frames)
V_headpose_feature = self.netE.to_headpose(V_noid_driving_feature)
driven_merge_feature_a = self.merge_mouthpose(A_mouth_feature, V_headpose_feature)
sel_driven_pose_feature_a = self.select_frames(driven_merge_feature_a)
fake_image_pose_driven_a, _ = self.generate_fake(sel_id_feature, sel_driven_pose_feature_a)
return fake_image_ref_pose_a, fake_image_pose_driven_a
def compute_generator_loss(self, input_img, real_image, augmented, spectrogram,
netD, labels, no_ganFeat_loss=False, no_vgg_loss=False, lambda_D=1):
G_losses = {}
real_image = real_image.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
##### ***************** encode image feature and generate ******************************
V_noid_feature = self.encode_noid_feature(augmented)
V_mouth_feature = self.netE.to_mouth(V_noid_feature)
V_headpose_feature = self.netE.to_headpose(V_noid_feature)
id_feature, id_scores = self.encode_identity_feature(input_img)
sel_id_feature = []
sel_id_feature.append(self.select_frames(id_feature[0]))
sel_id_feature.append(self.select_frames(id_feature[1]))
sel_real_image = self.select_frames(real_image)
fake_image_A, fake_image_V = None, None
if self.opt.generate_from_audio_only:
assert self.opt.use_audio, 'must use audio in this case'
V_merge_feature = self.merge_mouthpose(V_mouth_feature, V_headpose_feature)
sel_V_merge_feature = self.select_frames(V_merge_feature)
if self.opt.use_audio: # use audio pose feature
A_mouth_feature = self.encode_audiosync_feature(spectrogram)
self.compute_sync_loss(V_mouth_feature, A_mouth_feature, G_losses)
A_merge_feature = self.merge_mouthpose(A_mouth_feature, V_headpose_feature)
sel_A_merge_feature = self.select_frames(A_merge_feature)
fake_image_A, style_rgb_a = self.generate_fake(sel_id_feature, sel_A_merge_feature)
pred_fake_audio = self.discriminate_single(fake_image_A, netD)
if not self.opt.generate_from_audio_only: # use both audio and image pose feature
fake_image_V, style_rgb_v = self.generate_fake(sel_id_feature, sel_V_merge_feature)
else: # only use image pose feature
fake_image_V, style_rgb_v = self.generate_fake(sel_id_feature, sel_V_merge_feature)
pred_real = self.discriminate_single(sel_real_image, netD)
##### ****************************************************************************
if (not self.opt.generate_from_audio_only) or (not self.opt.use_audio):
pred_fake = self.discriminate_single(fake_image_V, netD)
if not no_ganFeat_loss:
if not self.opt.generate_from_audio_only:
G_losses['GAN_Feat'] = self.compute_GAN_Feat_loss(pred_fake, pred_real)
if self.opt.use_audio:
G_losses['GAN_Feat_audio'] = self.compute_GAN_Feat_loss(pred_fake_audio, pred_real)
if not self.opt.fix_netD:
if not self.opt.generate_from_audio_only:
G_losses['GANv'] = self.criterionGAN(pred_fake, True,
for_discriminator=False) * lambda_D
if self.opt.use_audio:
G_losses['GANa'] = self.criterionGAN(pred_fake_audio, True,
for_discriminator=False) * lambda_D
if not no_vgg_loss:
if not self.opt.generate_from_audio_only:
G_losses['VGGv'] = self.criterionVGG(fake_image_V, sel_real_image) \
* self.opt.lambda_vgg
if self.opt.use_audio:
G_losses['VGGa'] = self.criterionVGG(fake_image_A, sel_real_image) \
* self.opt.lambda_vgg
if self.opt.vgg_face:
if not self.opt.generate_from_audio_only:
G_losses['VGGFace_v'] = self.criterionVGGFace(fake_image_V, sel_real_image, layer=2) \
* self.opt.lambda_vggface
if self.opt.use_audio:
G_losses['VGGFace_a'] = self.criterionVGGFace(fake_image_A, sel_real_image, layer=2) \
* self.opt.lambda_vggface
if not self.opt.no_id_loss or not self.fix_netV:
G_losses['loss_cls'] = self.loss_cls(id_scores, labels)
if self.opt.disentangle and self.opt.clip_len*self.opt.frame_interval >= 20:
V_headpose_embed = self.netE.headpose_embed(V_headpose_feature)
with torch.no_grad():
V_all_headpose_embed = V_headpose_embed.view(-1, self.opt.clip_len * V_headpose_embed.shape[-1])
headpose_word_scores = self.netE.headpose_fc(V_all_headpose_embed)
G_losses['logSoftmax_v'] = self.criterionLogSoftmax(headpose_word_scores) * self.opt.lambda_softmax
return G_losses, [sel_real_image, fake_image_V, fake_image_A,
], id_scores
# Given fake and real image, return the prediction of discriminator
# for each fake and real image.
def compute_GAN_Feat_loss(self, pred_fake, pred_real):
num_D = len(pred_fake)
GAN_Feat_loss = self.FloatTensor(1).fill_(0)
for i in range(num_D): # for each discriminator
# last output is the final prediction, so we exclude it
num_intermediate_outputs = len(pred_fake[i]) - 1
for j in range(num_intermediate_outputs): # for each layer output
unweighted_loss = self.criterionFeat(
pred_fake[i][j], pred_real[i][j].detach())
if j == 0:
unweighted_loss *= self.opt.lambda_image
GAN_Feat_loss += unweighted_loss * self.opt.lambda_feat / num_D
return GAN_Feat_loss
def compute_discriminator_loss(self, input_img, real_image, augmented, spectrogram, netD, labels, lambda_D=1):
D_losses = {}
with torch.no_grad():
##### ***************** encode feature and generate ******************************
id_feature, _ = self.encode_identity_feature(input_img)
sel_id_feature = []
sel_id_feature.append(self.select_frames(id_feature[0]))
sel_id_feature.append(self.select_frames(id_feature[1]))
sel_real_image = self.select_frames(real_image)
sel_input_img = self.select_frames(input_img)
V_noid_feature = self.encode_noid_feature(augmented)
V_noid_feature = V_noid_feature.detach()
V_noid_feature.requires_grad_()
V_mouth_feature = self.netE.to_mouth(V_noid_feature)
V_headpose_feature = self.netE.to_headpose(V_noid_feature)
fake_image_audio, fake_image = None, None
if self.opt.generate_from_audio_only:
assert self.opt.use_audio, 'must use audio in this case'
if not self.opt.generate_from_audio_only:
V_merge_feature = self.merge_mouthpose(V_mouth_feature, V_headpose_feature)
sel_V_merge_feature = self.select_frames(V_merge_feature)
if self.opt.use_audio:
A_mouth_feature = self.encode_audiosync_feature(spectrogram)
A_pose_feature = self.merge_mouthpose(A_mouth_feature, V_headpose_feature)
sel_A_pose_feature = self.select_frames(A_pose_feature)
fake_image_audio, style_rgb_a = self.generate_fake(sel_id_feature, sel_A_pose_feature)
fake_image = fake_image_audio
if not self.opt.generate_from_audio_only: # use both audio and image pose feature
fake_image, style_rgb_v = self.generate_fake(sel_id_feature, sel_V_merge_feature)
fake_image = torch.cat([fake_image_audio, fake_image], 0)
else: # only use image pose feature
fake_image, style_rgb_v = self.generate_fake(sel_id_feature, sel_V_merge_feature)
sel_real_image = torch.cat([sel_real_image,]*(len(fake_image)//len(sel_real_image)), 0)
sel_input_img = torch.cat([sel_input_img,]*(len(fake_image)//len(sel_input_img)), 0)
if fake_image is not None:
fake_image = fake_image.detach()
fake_image.requires_grad_()
if fake_image_audio is not None:
fake_image_audio = fake_image_audio.detach()
fake_image_audio.requires_grad_()
if self.opt.disentangle:
V_headpose_embed = self.netE.headpose_embed(V_headpose_feature)
V_headpose_embed = V_headpose_embed.detach()
V_headpose_embed.requires_grad_()
pred_fake, pred_real = self.discriminate(
sel_input_img, fake_image, sel_real_image, netD)
if self.opt.stylegan_D:
pred_fake_styleGAN, pred_real_styleGAN = self.discriminate(
sel_input_img, fake_image, sel_real_image, self.net_styleGAN_D)
if type(pred_fake) == list and type(pred_real) == list:
pred_fake.append(pred_fake_styleGAN)
pred_real.append(pred_real_styleGAN)
else:
pred_fake = [pred_fake]
pred_fake.append(pred_fake_styleGAN)
pred_real = [pred_real]
pred_real.append(pred_real_styleGAN)
D_losses['D_Fake'] = self.criterionGAN(pred_fake, False,
for_discriminator=True) * lambda_D
D_losses['D_real'] = self.criterionGAN(pred_real, True,
for_discriminator=True) * lambda_D
if self.opt.disentangle and self.opt.clip_len*self.opt.frame_interval >= 20:
V_all_headpose_embed = V_headpose_embed.view(-1, self.opt.clip_len * V_headpose_embed.shape[-1])
headpose_word_scores = self.netE.headpose_fc(V_all_headpose_embed)
D_losses['headpose_feature_cls'] = self.loss_cls(headpose_word_scores, labels)
return D_losses
def discriminate(self, input, fake_image, real_image, netD):
if self.opt.D_input == "concat":
fake_concat = torch.cat([input, fake_image], dim=1)
real_concat = torch.cat([input, real_image], dim=1)
else:
fake_concat = fake_image
real_concat = real_image
fake_and_real = torch.cat([fake_concat, real_concat], dim=0)
discriminator_out = netD(fake_and_real)
pred_fake, pred_real = self.divide_pred(discriminator_out)
return pred_fake, pred_real
def discriminate_single(self, single_image, netD):
if single_image.dim() == 5:
single_image = single_image.view(-1, self.opt.output_nc, self.opt.crop_size, self.opt.crop_size)
pred_single = netD(single_image)
return pred_single
# Take the prediction of fake and real images from the combined batch
def divide_pred(self, pred):
# the prediction contains the intermediate outputs of multiscale GAN,
# so it's usually a list
if type(pred) == list:
fake = []
real = []
for p in pred:
fake.append([tensor[:tensor.size(0) // 2] for tensor in p])
real.append([tensor[tensor.size(0) // 2:] for tensor in p])
else:
fake = pred[:pred.size(0) // 2]
# rotate_fake = pred[pred.size(0) // 3: pred.size(0) * 2 // 3]
real = pred[pred.size(0)//2 :]
return fake, real
def load_separately(self, network, network_label, opt):
load_path = None
if network_label == 'G':
load_path = opt.G_pretrain_path
elif network_label == 'D':
load_path = opt.D_pretrain_path
elif network_label == 'D_rotate':
load_path = opt.D_rotate_pretrain_path
elif network_label == 'E':
load_path = opt.E_pretrain_path
elif network_label == 'A':
load_path = opt.A_pretrain_path
elif network_label == 'A_sync':
load_path = opt.A_sync_pretrain_path
elif network_label == 'V':
load_path = opt.V_pretrain_path
if load_path is not None:
if os.path.isfile(load_path):
print("=> loading checkpoint '{}'".format(load_path))
checkpoint = torch.load(load_path)
util.copy_state_dict(checkpoint, network, strip='MobileNet', replace='model')
else:
print("no load_path")
return network
def load_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_dir = self.save_dir
save_path = os.path.join(save_dir, save_filename)
if not os.path.isfile(save_path):
if not self.opt.train_recognition:
print('%s not exists yet!' % save_path)
if network_label == 'G':
raise ('Generator must exist!')
else:
# network.load_state_dict(torch.load(save_path))
try:
network.load_state_dict(torch.load(save_path))
except:
pretrained_dict = torch.load(save_path)
model_dict = network.state_dict()
try:
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
network.load_state_dict(pretrained_dict)
if self.opt.verbose:
print(
'Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
except:
print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
for k, v in pretrained_dict.items():
if v.size() == model_dict[k].size():
model_dict[k] = v
not_initialized = set()
for k, v in model_dict.items():
if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
not_initialized.add(k.split('.')[0])
print(sorted(not_initialized))
network.load_state_dict(model_dict)
def use_gpu(self):
return len(self.opt.gpu_ids) > 0