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model.py
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model.py
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import itertools
import functools
import os
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import utils
from arch import define_Gen, define_Dis, set_grad
from torch.optim import lr_scheduler
'''
Class for CycleGAN with train() as a member function
'''
class cycleGAN(object):
def __init__(self,args):
# Define the network
#####################################################
self.Gab = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG=args.gen_net, norm=args.norm,
use_dropout= not args.no_dropout, gpu_ids=args.gpu_ids)
self.Gba = define_Gen(input_nc=3, output_nc=3, ngf=args.ngf, netG=args.gen_net, norm=args.norm,
use_dropout= not args.no_dropout, gpu_ids=args.gpu_ids)
self.Da = define_Dis(input_nc=3, ndf=args.ndf, netD= args.dis_net, n_layers_D=3, norm=args.norm, gpu_ids=args.gpu_ids)
self.Db = define_Dis(input_nc=3, ndf=args.ndf, netD= args.dis_net, n_layers_D=3, norm=args.norm, gpu_ids=args.gpu_ids)
utils.print_networks([self.Gab,self.Gba,self.Da,self.Db], ['Gab','Gba','Da','Db'])
# Define Loss criterias
self.MSE = nn.MSELoss()
self.L1 = nn.L1Loss()
# Optimizers
#####################################################
self.g_optimizer = torch.optim.Adam(itertools.chain(self.Gab.parameters(),self.Gba.parameters()), lr=args.lr, betas=(0.5, 0.999))
self.d_optimizer = torch.optim.Adam(itertools.chain(self.Da.parameters(),self.Db.parameters()), lr=args.lr, betas=(0.5, 0.999))
self.g_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(self.g_optimizer, lr_lambda=utils.LambdaLR(args.epochs, 0, args.decay_epoch).step)
self.d_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(self.d_optimizer, lr_lambda=utils.LambdaLR(args.epochs, 0, args.decay_epoch).step)
# Try loading checkpoint
#####################################################
if not os.path.isdir(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
try:
ckpt = utils.load_checkpoint('%s/latest.ckpt' % (args.checkpoint_dir))
self.start_epoch = ckpt['epoch']
self.Da.load_state_dict(ckpt['Da'])
self.Db.load_state_dict(ckpt['Db'])
self.Gab.load_state_dict(ckpt['Gab'])
self.Gba.load_state_dict(ckpt['Gba'])
self.d_optimizer.load_state_dict(ckpt['d_optimizer'])
self.g_optimizer.load_state_dict(ckpt['g_optimizer'])
except:
print(' [*] No checkpoint!')
self.start_epoch = 0
def train(self,args):
# For transforming the input image
transform = transforms.Compose(
[transforms.RandomHorizontalFlip(),
transforms.Resize((args.load_height,args.load_width)),
transforms.RandomCrop((args.crop_height,args.crop_width)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
dataset_dirs = utils.get_traindata_link(args.dataset_dir)
# Pytorch dataloader
a_loader = torch.utils.data.DataLoader(dsets.ImageFolder(dataset_dirs['trainA'], transform=transform),
batch_size=args.batch_size, shuffle=True, num_workers=4)
b_loader = torch.utils.data.DataLoader(dsets.ImageFolder(dataset_dirs['trainB'], transform=transform),
batch_size=args.batch_size, shuffle=True, num_workers=4)
a_fake_sample = utils.Sample_from_Pool()
b_fake_sample = utils.Sample_from_Pool()
for epoch in range(self.start_epoch, args.epochs):
lr = self.g_optimizer.param_groups[0]['lr']
print('learning rate = %.7f' % lr)
for i, (a_real, b_real) in enumerate(zip(a_loader, b_loader)):
# step
step = epoch * min(len(a_loader), len(b_loader)) + i + 1
# Generator Computations
##################################################
set_grad([self.Da, self.Db], False)
self.g_optimizer.zero_grad()
a_real = Variable(a_real[0])
b_real = Variable(b_real[0])
a_real, b_real = utils.cuda([a_real, b_real])
# Forward pass through generators
##################################################
a_fake = self.Gab(b_real)
b_fake = self.Gba(a_real)
a_recon = self.Gab(b_fake)
b_recon = self.Gba(a_fake)
a_idt = self.Gab(a_real)
b_idt = self.Gba(b_real)
# Identity losses
###################################################
a_idt_loss = self.L1(a_idt, a_real) * args.lamda * args.idt_coef
b_idt_loss = self.L1(b_idt, b_real) * args.lamda * args.idt_coef
# Adversarial losses
###################################################
a_fake_dis = self.Da(a_fake)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(a_fake_dis.size())))
a_gen_loss = self.MSE(a_fake_dis, real_label)
b_gen_loss = self.MSE(b_fake_dis, real_label)
# Cycle consistency losses
###################################################
a_cycle_loss = self.L1(a_recon, a_real) * args.lamda
b_cycle_loss = self.L1(b_recon, b_real) * args.lamda
# Total generators losses
###################################################
gen_loss = a_gen_loss + b_gen_loss + a_cycle_loss + b_cycle_loss + a_idt_loss + b_idt_loss
# Update generators
###################################################
gen_loss.backward()
self.g_optimizer.step()
# Discriminator Computations
#################################################
set_grad([self.Da, self.Db], True)
self.d_optimizer.zero_grad()
# Sample from history of generated images
#################################################
a_fake = Variable(torch.Tensor(a_fake_sample([a_fake.cpu().data.numpy()])[0]))
b_fake = Variable(torch.Tensor(b_fake_sample([b_fake.cpu().data.numpy()])[0]))
a_fake, b_fake = utils.cuda([a_fake, b_fake])
# Forward pass through discriminators
#################################################
a_real_dis = self.Da(a_real)
a_fake_dis = self.Da(a_fake)
b_real_dis = self.Db(b_real)
b_fake_dis = self.Db(b_fake)
real_label = utils.cuda(Variable(torch.ones(a_real_dis.size())))
fake_label = utils.cuda(Variable(torch.zeros(a_fake_dis.size())))
# Discriminator losses
##################################################
a_dis_real_loss = self.MSE(a_real_dis, real_label)
a_dis_fake_loss = self.MSE(a_fake_dis, fake_label)
b_dis_real_loss = self.MSE(b_real_dis, real_label)
b_dis_fake_loss = self.MSE(b_fake_dis, fake_label)
# Total discriminators losses
a_dis_loss = (a_dis_real_loss + a_dis_fake_loss)*0.5
b_dis_loss = (b_dis_real_loss + b_dis_fake_loss)*0.5
# Update discriminators
##################################################
a_dis_loss.backward()
b_dis_loss.backward()
self.d_optimizer.step()
print("Epoch: (%3d) (%5d/%5d) | Gen Loss:%.2e | Dis Loss:%.2e" %
(epoch, i + 1, min(len(a_loader), len(b_loader)),
gen_loss,a_dis_loss+b_dis_loss))
# Override the latest checkpoint
#######################################################
utils.save_checkpoint({'epoch': epoch + 1,
'Da': self.Da.state_dict(),
'Db': self.Db.state_dict(),
'Gab': self.Gab.state_dict(),
'Gba': self.Gba.state_dict(),
'd_optimizer': self.d_optimizer.state_dict(),
'g_optimizer': self.g_optimizer.state_dict()},
'%s/latest.ckpt' % (args.checkpoint_dir))
# Update learning rates
########################
self.g_lr_scheduler.step()
self.d_lr_scheduler.step()