G, opt_G = amp.initialize(G, opt_G, opt_level=optim_level)
D, opt_D = amp.initialize(D, opt_D, opt_level=optim_level)
try:
p_G = './saved_mask_models/G_latest.pth'
p_D = './saved_mask_models/D_latest.pth'
G.load_state_dict(torch.load(p_G, map_location=torch.device('cpu')), strict=False)
D.load_state_dict(torch.load(p_D, map_location=torch.device('cpu')), strict=False)
print('p_G : ',p_G)
print('p_D : ',p_D)
except Exception as e:
print(e)
dataset = FaceEmbed(['./train_datasets/Foreign-2020-09-06/'], same_prob=0.35)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
# print(torch.backends.cudnn.benchmark)
torch.backends.cudnn.benchmark = True
for epoch in range(0, max_epoch):
# torch.cuda.empty_cache()
for iteration, data in enumerate(dataloader):
start_time = time.time()
Xs, Xt, same_person = data
Xs = Xs.to(device)
Xt = Xt.to(device)
G.load_state_dict(torch.load(os.path.join(args.saved_models,
'G_latest.pth'),
map_location=torch.device('cpu')),
strict=False)
D.load_state_dict(torch.load(os.path.join(args.saved_models,
'D_latest.pth'),
map_location=torch.device('cpu')),
strict=False)
except Exception as e:
print(e)
# if not fine_tune_with_identity:
# dataset = FaceEmbed(['../celeb-aligned-256_0.85/', '../ffhq_256_0.85/', '../vgg_256_0.85/', '../stars_256_0.85/'], same_prob=0.5)
# else:
# dataset = With_Identity('../washed_img/', 0.8)
dataset = FaceEmbed([args.images_path], same_prob=args.same_prob)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
return torch.relu(1 - X).mean()
else:
print(e)
try:
with open('./saved_models/AEI_niter.pkl', 'rb') as f:
min_iter = pickle.load(f)
except Exception as e:
print(e)
writer = SummaryWriter('runs/FaceShifterAEInet', purge_step=min_iter)
TrainFaceSources = [
'/home/olivier/Images/FaceShifter/celeba-256/',
'/home/olivier/Images/FaceShifter/Perso/',
'/home/olivier/Images/FaceShifter/VGGFaceTrain/',
'/home/olivier/Images/FaceShifter/FFHQ/',
'/home/olivier/Images/FaceShifter/Others/'
]
train_dataset = FaceEmbed(TrainFaceSources, same_prob=0.2)
train_dataloader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
train_loader = iter(train_dataloader)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
G.load_state_dict(torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')),
strict=False)
D.load_state_dict(torch.load('./saved_models/D_latest.pth',
map_location=torch.device('cpu')),
strict=False)
except Exception as e:
print(e)
# if not fine_tune_with_identity:
# dataset = FaceEmbed(['../celeb-aligned-256_0.85/', '../ffhq_256_0.85/', '../vgg_256_0.85/', '../stars_256_0.85/'], same_prob=0.5)
# else:
# dataset = With_Identity('../washed_img/', 0.8)
dataset = FaceEmbed([
'../celeb-aligned-256_0.85/', '../ffhq_256_0.85/', '../vgg_256_0.85/',
'../stars_256_0.85/'
],
same_prob=0.8)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
opt_D = optim.Adam(D.parameters(), lr=lr_D, betas=(0, 0.999))
G, opt_G = amp.initialize(G, opt_G, opt_level=optim_level)
D, opt_D = amp.initialize(D, opt_D, opt_level=optim_level)
try:
G.load_state_dict(torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')),
strict=False)
D.load_state_dict(torch.load('./saved_models/D_latest.pth',
map_location=torch.device('cpu')),
strict=False)
except Exception as e:
print(e)
dataset = FaceEmbed(['../celeba_64/'], same_prob=0.8)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
return torch.relu(1 - X).mean()
else:
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth', map_location=device), strict=False)
opt_G = optim.Adam(G.parameters(), lr=lr_G, betas=(0, 0.999), weight_decay=1e-4)
opt_D = optim.Adam(D.parameters(), lr=lr_D, betas=(0, 0.999), weight_decay=1e-4)
G, opt_G = amp.initialize(G, opt_G, opt_level=optim_level)
D, opt_D = amp.initialize(D, opt_D, opt_level=optim_level)
try:
G.load_state_dict(torch.load('./saved_models/G_latest.pth', map_location=torch.device('cpu')), strict=False)
D.load_state_dict(torch.load('./saved_models/D_latest.pth', map_location=torch.device('cpu')), strict=False)
except Exception as e:
print(e)
dataset = FaceEmbed([dataset_path], same_prob=0.5)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
return torch.relu(1-X)
else:
return torch.relu(X+1)
strict=False)
print('Finetune_G_Model : ', Finetune_G_Model)
print('Finetune_D_Model : ', Finetune_D_Model)
except Exception as e:
print(e)
# if not fine_tune_with_identity:
# dataset = FaceEmbed(['../celeb-aligned-256_0.85/', '../ffhq_256_0.85/', '../vgg_256_0.85/', '../stars_256_0.85/'], same_prob=0.5)
# else:
# dataset = With_Identity('../washed_img/', 0.8)
# dataset = FaceEmbed(['../celeb-aligned-256_0.85/', '../ffhq_256_0.85/', '../vgg_256_0.85/', '../stars_256_0.85/'], same_prob=0.8)
dataset = FaceEmbed(['./train_datasets/Foreign-2020-09-06/'],
same_prob=0.5,
Flag_256=Flag_256)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=3,
drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
# print(torch.backends.cudnn.benchmark)
torch.backends.cudnn.benchmark = True
for epoch in range(0, max_epoch):
# torch.cuda.empty_cache()
for iteration, data in enumerate(dataloader):
try:
print('load pretrained model')
# G.load_state_dict(torch.load('./saved_models/G_latest_Heonozis_original.pth', map_location=torch.device('cpu')), strict=False)
# D.load_state_dict(torch.load('./saved_models/D_latest_Heonozis_original.pth', map_location=torch.device('cpu')), strict=False)
G.load_state_dict(torch.load('./saved_models/G_train.pth',
map_location=torch.device('cpu')),
strict=False)
D.load_state_dict(torch.load('./saved_models/D_train.pth',
map_location=torch.device('cpu')),
strict=False)
except Exception as e:
print(e)
dataset = FaceEmbed(['../datasets/img_align_celeba_64/'], same_prob=0.2)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
MSE = torch.nn.MSELoss()
L1 = torch.nn.L1Loss()
def hinge_loss(X, positive=True):
if positive:
return torch.relu(1 - X).mean()
else: