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trainer_Mouth_Pose_decouple.py
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import torch
from networks_Lip_NonLip.discriminator import Discriminator
from networks_Lip_NonLip.generator import Generator
import torch.nn.functional as F
from torch import nn, optim
import os
from vgg19 import VGGLoss
from collections import OrderedDict
def requires_grad(net, flag=True):
for p in net.parameters():
p.requires_grad = flag
class Trainer(nn.Module):
def __init__(self, args, device):
super(Trainer, self).__init__()
self.args = args
self.batch_size = args.batch_size
self.gen = Generator(args.size, args.latent_dim_style, args.latent_dim_lip, args.latent_dim_pose, args.channel_multiplier).to(
device)
self.dis = Discriminator(args.size, args.channel_multiplier).to(device)
# requires_grad(self.gen.enc, False)
# requires_grad(self.gen.dec, False)
# requires_grad(self.gen.fc, False)
if args.distributed:
self.gen = nn.parallel.DistributedDataParallel(
self.gen,
device_ids=[args.local_rank],
output_device=args.local_rank,
broadcast_buffers=False,
find_unused_parameters=True,
)
self.dis = nn.parallel.DistributedDataParallel(
self.dis,
device_ids=[args.local_rank],
output_device=args.local_rank,
broadcast_buffers=False,
find_unused_parameters=True,
)
# distributed computing
# self.gen = DDP(self.gen, device_ids=[rank], find_unused_parameters=True)
# self.dis = DDP(self.dis, device_ids=[rank], find_unused_parameters=True)
g_reg_ratio = args.g_reg_every / (args.g_reg_every + 1)
d_reg_ratio = args.d_reg_every / (args.d_reg_every + 1)
if args.distributed:
self.gen = self.gen.module
self.dis = self.dis.module
net_parameters = filter(lambda p: p.requires_grad, self.gen.parameters())
self.g_optim = optim.Adam(
net_parameters,
lr=args.lr * g_reg_ratio,
betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio)
)
self.d_optim = optim.Adam(
self.dis.parameters(),
lr=args.lr * d_reg_ratio,
betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio)
)
self.criterion_vgg = VGGLoss().to('cuda')
self.criterion_vgg = VGGLoss().to('cuda')
self.start_iter = 0
def g_nonsaturating_loss(self, fake_pred):
return F.softplus(-fake_pred).mean()
def d_nonsaturating_loss(self, fake_pred, real_pred):
real_loss = F.softplus(-real_pred)
fake_loss = F.softplus(fake_pred)
return real_loss.mean() + fake_loss.mean()
def gen_update(self, img_a_identity, img_b_identity, img_a, img_b, imagea_b, imageb_a):
self.gen.train()
self.gen.zero_grad()
G_losses = {}
requires_grad(self.gen, True)
# requires_grad(self.gen.enc, False)
# requires_grad(self.gen.dec, False)
# requires_grad(self.gen.fc, False)
requires_grad(self.dis, False)
# img_a_identity, img_b_identity, img_a, img_b, imagea_b, imageb_a = bi['img_a_identity'],bi['img_b_identity'],bi['img_a'],bi['img_b'],bi['imagea_b'],bi['imageb_a']
# img_a_identity, img_b_identity, img_a, img_b, imagea_b, imageb_a = img_a_identity.cuda(), img_b_identity.cuda(), img_a.cuda(), img_b.cuda(), imagea_b.cuda(), imageb_a.cuda()
wa_a_identity, wa_b_identity, feats_a_identity, feats_b_identity = self.gen.enc(img_a_identity, img_b_identity)
wa_a, wa_b, _, _ = self.gen.enc(img_a, img_b)
wa_a_b, wa_b_a, _, _ = self.gen.enc(imagea_b, imageb_a)
# recon: a,b
shared_fc_a = self.gen.fc(wa_a)
lip_a = self.gen.lip_fc(shared_fc_a) # torch.Size([12, 20])
pose_a = self.gen.pose_fc(shared_fc_a) # torch.Size([12, 6])
alpha_a = torch.cat([lip_a, pose_a], dim=-1)
directions_a_share = self.gen.direction_lipnonlip.get_shared_out(alpha_a)
directions_a_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_a_share)
directions_a_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_a_share)
directions_a = directions_a_lip+directions_a_pose # torch.Size([12, 512])
latent_a = wa_a_identity + directions_a
recon_a = self.gen.dec(latent_a, None, feats_a_identity)
# recon_a_pred = self.dis(recon_a)
shared_fc_b = self.gen.fc(wa_b)
lip_b = self.gen.lip_fc(shared_fc_b)
pose_b = self.gen.pose_fc(shared_fc_b)
alpha_b = torch.cat([lip_b, pose_b], dim=-1)
directions_b_share = self.gen.direction_lipnonlip.get_shared_out(alpha_b)
directions_b_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_b_share)
directions_b_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_b_share)
directions_b = directions_b_lip+directions_b_pose
# directions_b = self.gen.direction_lipnonlip(alpha_b)
latent_b = wa_b_identity + directions_b
recon_b = self.gen.dec(latent_b, None, feats_b_identity)
# recon_b_pred = self.dis(recon_b)
G_losses['recon_vgg_loss'] = self.criterion_vgg(recon_a, img_a).mean() + self.criterion_vgg(recon_b, img_b).mean()
G_losses['recon_l1_loss'] = F.l1_loss(recon_a, img_a) + F.l1_loss(recon_b, img_b)
# G_losses['recon_gan_g_loss'] = self.g_nonsaturating_loss(recon_a_pred) + self.g_nonsaturating_loss(recon_b_pred)
# cross_recon: a,b
shared_fc_a_b = self.gen.fc(wa_a_b)
lip_a_b = self.gen.lip_fc(shared_fc_a_b) # b的lip
pose_a_b = self.gen.pose_fc(shared_fc_a_b) # a的pose
shared_fc_b_a = self.gen.fc(wa_b_a)
lip_b_a = self.gen.lip_fc(shared_fc_b_a) # a的lip
pose_b_a = self.gen.pose_fc(shared_fc_b_a) # b的pose
# start cross
alpha_a_cross = torch.cat([lip_b_a, pose_a_b], dim=-1) # cross recon a
# directions_a_cross = self.gen.direction_lipnonlip(alpha_a_cross)
directions_a_cross_share = self.gen.direction_lipnonlip.get_shared_out(alpha_a_cross)
directions_a_cross_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_a_cross_share)
directions_a_cross_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_a_cross_share)
directions_a_cross = directions_a_cross_lip+directions_a_cross_pose
latent_a_corss = wa_a_identity + directions_a_cross
recon_a_cross = self.gen.dec(latent_a_corss, None, feats_a_identity)
# recon_a_pred_cross = self.dis(recon_a_cross)
alpha_b_cross = torch.cat([lip_a_b, pose_b_a], dim=-1) # cross recon b
# directions_b_cross = self.gen.direction_lipnonlip(alpha_b_cross)
directions_b_cross_share = self.gen.direction_lipnonlip.get_shared_out(alpha_b_cross)
directions_b_cross_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_b_cross_share)
directions_b_cross_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_b_cross_share)
directions_b_cross = directions_b_cross_lip+directions_b_cross_pose
latent_b_corss = wa_b_identity + directions_b_cross
recon_b_cross = self.gen.dec(latent_b_corss, None, feats_b_identity)
# recon_b_pred_cross = self.dis(recon_b_cross)
recon_pred_total = self.dis(torch.cat([recon_a, recon_b, recon_a_cross, recon_b_cross]))
G_losses['cross_recon_vgg_loss'] = self.criterion_vgg(recon_a_cross, img_a).mean() + self.criterion_vgg(recon_b_cross, img_b).mean()
G_losses['cross_recon_l1_loss'] = F.l1_loss(recon_a_cross, img_a) + F.l1_loss(recon_b_cross, img_b)
G_losses['cross_recon_gan_g_loss'] = self.g_nonsaturating_loss(recon_pred_total)
# latent loss
# lip space
G_losses['lip_space'] = torch.exp(-F.cosine_similarity(directions_b_cross_lip, directions_b_lip))+torch.exp(-F.cosine_similarity(directions_a_cross_lip, directions_a_lip))
G_losses['pose_space'] = torch.exp(-F.cosine_similarity(directions_b_cross_pose, directions_b_pose))+torch.exp(-F.cosine_similarity(directions_a_cross_pose, directions_a_pose))
G_losses['lip_space'] = G_losses['lip_space'].sum()
G_losses['pose_space'] = G_losses['pose_space'].sum()
# img_target_recon = self.gen(img_a_identity, img_a)
# img_recon_pred = self.dis(img_target_recon)
# vgg_loss = self.criterion_vgg(img_target_recon, img_target).mean()
# l1_loss = F.l1_loss(img_target_recon, img_target)
# gan_g_loss = self.g_nonsaturating_loss(img_recon_pred)
G_losses_values = [val.mean() for val in G_losses.values()]
g_loss = sum(G_losses_values)
g_loss.backward()
self.g_optim.step()
return G_losses, recon_a_cross, recon_b_cross, g_loss
def dis_update(self, img_real, img_recon):
self.dis.zero_grad()
requires_grad(self.gen, False)
requires_grad(self.dis, True)
real_img_pred = self.dis(img_real)
recon_img_pred = self.dis(img_recon.detach())
d_loss = self.d_nonsaturating_loss(recon_img_pred, real_img_pred)
d_loss.backward()
self.d_optim.step()
return d_loss
def sample(self, img_a_identity, img_b_identity, img_a, img_b, imagea_b, imageb_a):
with torch.no_grad():
self.gen.eval()
G_losses = {}
wa_a_identity, wa_b_identity, feats_a_identity, feats_b_identity = self.gen.enc(img_a_identity, img_b_identity)
wa_a, wa_b, _, _ = self.gen.enc(img_a, img_b)
wa_a_b, wa_b_a, _, _ = self.gen.enc(imagea_b, imageb_a)
# recon: a,b
shared_fc_a = self.gen.fc(wa_a)
lip_a = self.gen.lip_fc(shared_fc_a) # torch.Size([12, 20])
pose_a = self.gen.pose_fc(shared_fc_a) # torch.Size([12, 6])
alpha_a = torch.cat([lip_a, pose_a], dim=-1)
directions_a_share = self.gen.direction_lipnonlip.get_shared_out(alpha_a)
directions_a_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_a_share)
directions_a_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_a_share)
directions_a = directions_a_lip+directions_a_pose # torch.Size([12, 512])
latent_a = wa_a_identity + directions_a
recon_a = self.gen.dec(latent_a, None, feats_a_identity)
# recon_a_pred = self.dis(recon_a)
shared_fc_b = self.gen.fc(wa_b)
lip_b = self.gen.lip_fc(shared_fc_b)
pose_b = self.gen.pose_fc(shared_fc_b)
alpha_b = torch.cat([lip_b, pose_b], dim=-1)
directions_b_share = self.gen.direction_lipnonlip.get_shared_out(alpha_b)
directions_b_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_b_share)
directions_b_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_b_share)
directions_b = directions_b_lip+directions_b_pose
# directions_b = self.gen.direction_lipnonlip(alpha_b)
latent_b = wa_b_identity + directions_b
recon_b = self.gen.dec(latent_b, None, feats_b_identity)
# recon_b_pred = self.dis(recon_b)
G_losses['recon_vgg_loss'] = self.criterion_vgg(recon_a, img_a).mean() + self.criterion_vgg(recon_b, img_b).mean()
G_losses['recon_l1_loss'] = F.l1_loss(recon_a, img_a) + F.l1_loss(recon_b, img_b)
# G_losses['recon_gan_g_loss'] = self.g_nonsaturating_loss(recon_a_pred) + self.g_nonsaturating_loss(recon_b_pred)
# cross_recon: a,b
shared_fc_a_b = self.gen.fc(wa_a_b)
lip_a_b = self.gen.lip_fc(shared_fc_a_b) # b的lip
pose_a_b = self.gen.pose_fc(shared_fc_a_b) # a的pose
shared_fc_b_a = self.gen.fc(wa_b_a)
lip_b_a = self.gen.lip_fc(shared_fc_b_a) # a的lip
pose_b_a = self.gen.pose_fc(shared_fc_b_a) # b的pose
# start cross
alpha_a_cross = torch.cat([lip_b_a, pose_a_b], dim=-1) # cross recon a
# directions_a_cross = self.gen.direction_lipnonlip(alpha_a_cross)
directions_a_cross_share = self.gen.direction_lipnonlip.get_shared_out(alpha_a_cross)
directions_a_cross_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_a_cross_share)
directions_a_cross_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_a_cross_share)
directions_a_cross = directions_a_cross_lip+directions_a_cross_pose
latent_a_corss = wa_a_identity + directions_a_cross
recon_a_cross = self.gen.dec(latent_a_corss, None, feats_a_identity)
# recon_a_pred_cross = self.dis(recon_a_cross)
alpha_b_cross = torch.cat([lip_a_b, pose_b_a], dim=-1) # cross recon a
# directions_b_cross = self.gen.direction_lipnonlip(alpha_b_cross)
directions_b_cross_share = self.gen.direction_lipnonlip.get_shared_out(alpha_b_cross)
directions_b_cross_lip = self.gen.direction_lipnonlip.get_lip_latent(directions_b_cross_share)
directions_b_cross_pose = self.gen.direction_lipnonlip.get_pose_latent(directions_b_cross_share)
directions_b_cross = directions_b_cross_lip+directions_b_cross_pose
latent_b_corss = wa_b_identity + directions_b_cross
recon_b_cross = self.gen.dec(latent_b_corss, None, feats_b_identity)
# recon_b_pred_cross = self.dis(recon_b_cross)
recon_pred_total = self.dis(torch.cat([recon_a, recon_b, recon_a_cross, recon_b_cross]))
G_losses['cross_recon_vgg_loss'] = self.criterion_vgg(recon_a_cross, img_a).mean() + self.criterion_vgg(recon_b_cross, img_b).mean()
G_losses['cross_recon_l1_loss'] = F.l1_loss(recon_a_cross, img_a) + F.l1_loss(recon_b_cross, img_b)
G_losses['cross_recon_gan_g_loss'] = self.g_nonsaturating_loss(recon_pred_total)
# latent loss
# lip space
G_losses['lip_space'] = torch.exp(-F.cosine_similarity(directions_b_cross_lip, directions_b_lip))+torch.exp(-F.cosine_similarity(directions_a_cross_lip, directions_a_lip))
G_losses['pose_space'] = torch.exp(-F.cosine_similarity(directions_b_cross_pose, directions_b_pose))+torch.exp(-F.cosine_similarity(directions_a_cross_pose, directions_a_pose))
G_losses['lip_space'] = G_losses['lip_space'].sum()
G_losses['pose_space'] = G_losses['pose_space'].sum()
# img_target_recon = self.gen(img_a_identity, img_a)
# img_recon_pred = self.dis(img_target_recon)
# vgg_loss = self.criterion_vgg(img_target_recon, img_target).mean()
# l1_loss = F.l1_loss(img_target_recon, img_target)
# gan_g_loss = self.g_nonsaturating_loss(img_recon_pred)
G_losses_values = [val.mean() for val in G_losses.values()]
g_loss = sum(G_losses_values)
return G_losses, recon_a_cross, recon_b_cross, g_loss
def resume(self, resume_ckpt):
print("load model:", resume_ckpt)
ckpt = torch.load(resume_ckpt, map_location=torch.device('cpu'))
ckpt_name = os.path.basename(resume_ckpt)
# try:
# self.start_iter = ckpt["start_iter"] #int(os.path.splitext(ckpt_name)[0])
# except:
# self.start_iter = 0
try:
start_iter = int(os.path.splitext(ckpt_name)[0])
except:
start_iter = 0
# self.gen.load_state_dict(ckpt["gen"])
checkpoint = ckpt['gen']
# new_state_dict = OrderedDict()
# for key, value in checkpoint.items():
# if 'enc.fc.' in key:
# if 'enc.fc.4' in key:
# continue
# name = key.split('enc.fc.')[1]
# new_state_dict[name] = value
self.gen.load_state_dict(checkpoint)
# new_state_dict = OrderedDict()
# for key, value in checkpoint.items():
# if 'enc.net_app.' in key:
# name = key.split('enc.')[1]
# new_state_dict[name] = value
# self.gen.enc.load_state_dict(new_state_dict)
# new_state_dict = OrderedDict()
# for key, value in checkpoint.items():
# if 'dec.' in key:
# if 'dec.direc' in key:
# continue
# name = key.split('dec.')[1]
# new_state_dict[name] = value
# self.gen.dec.load_state_dict(new_state_dict)
self.dis.load_state_dict(ckpt["dis"])
try:
self.g_optim.load_state_dict(ckpt["g_optim"])
except:
print('cannot load pretrained g_optim')
self.d_optim.load_state_dict(ckpt["d_optim"])
return start_iter
def save(self, idx, checkpoint_path):
torch.save(
{
"gen": self.gen.state_dict(),
"dis": self.dis.state_dict(),
"g_optim": self.g_optim.state_dict(),
"d_optim": self.d_optim.state_dict(),
"args": self.args
},
f"{checkpoint_path}/{str(idx).zfill(6)}.pt"
)