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import numpy as np
import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import util.util as util
from collections import OrderedDict
from torch.autograd import Variable
import itertools
import util.util as util
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import sys
class PairModel(BaseModel):
def name(self):
return 'CycleGANModel'
def initialize(self, opt):
BaseModel.initialize(self, opt)
nb = opt.batchSize
size = opt.fineSize
self.opt = opt
self.input_A = self.Tensor(nb, opt.input_nc, size, size)
self.input_B = self.Tensor(nb, opt.output_nc, size, size)
if opt.vgg > 0:
self.vgg_loss = networks.PerceptualLoss()
self.vgg_loss.cuda()
self.vgg = networks.load_vgg16("./model")
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
# load/define networks
# The naming conversion is different from those used in the paper
# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
skip = True if opt.skip > 0 else False
self.netG_A = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, self.gpu_ids, skip=skip, opt=opt)
self.netG_B = networks.define_G(opt.output_nc, opt.input_nc,
opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, self.gpu_ids, skip=False, opt=opt)
if self.isTrain:
use_sigmoid = opt.no_lsgan
self.netD_A = networks.define_D(opt.output_nc, opt.ndf,
opt.which_model_netD,
opt.n_layers_D, opt.norm, use_sigmoid, self.gpu_ids)
self.netD_B = networks.define_D(opt.input_nc, opt.ndf,
opt.which_model_netD,
opt.n_layers_D, opt.norm, use_sigmoid, self.gpu_ids)
if not self.isTrain or opt.continue_train:
which_epoch = opt.which_epoch
self.load_network(self.netG_A, 'G_A', which_epoch)
self.load_network(self.netG_B, 'G_B', which_epoch)
if self.isTrain:
self.load_network(self.netD_A, 'D_A', which_epoch)
self.load_network(self.netD_B, 'D_B', which_epoch)
if self.isTrain:
self.old_lr = opt.lr
self.fake_A_pool = ImagePool(opt.pool_size)
self.fake_B_pool = ImagePool(opt.pool_size)
# define loss functions
if opt.use_wgan:
self.criterionGAN = networks.DiscLossWGANGP()
else:
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)
if opt.use_mse:
self.criterionCycle = torch.nn.MSELoss()
else:
self.criterionCycle = torch.nn.L1Loss()
self.criterionL1 = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()),
lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D_A = torch.optim.Adam(self.netD_A.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D_B = torch.optim.Adam(self.netD_B.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
print('---------- Networks initialized -------------')
networks.print_network(self.netG_A)
networks.print_network(self.netG_B)
if self.isTrain:
networks.print_network(self.netD_A)
networks.print_network(self.netD_B)
if opt.isTrain:
self.netG_A.train()
self.netG_B.train()
else:
self.netG_A.eval()
self.netG_B.eval()
print('-----------------------------------------------')
def set_input(self, input):
AtoB = self.opt.which_direction == 'AtoB'
input_A = input['A' if AtoB else 'B']
input_B = input['B' if AtoB else 'A']
self.input_A.resize_(input_A.size()).copy_(input_A)
self.input_B.resize_(input_B.size()).copy_(input_B)
self.image_paths = input['A_paths' if AtoB else 'B_paths']
def forward(self):
self.real_A = Variable(self.input_A)
self.real_B = Variable(self.input_B)
def test(self):
self.real_A = Variable(self.input_A, volatile=True)
# print(np.transpose(self.real_A.data[0].cpu().float().numpy(),(1,2,0))[:2][:2][:])
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(self.real_A)
else:
self.fake_B = self.netG_A.forward(self.real_A)
self.rec_A = self.netG_B.forward(self.fake_B)
self.real_B = Variable(self.input_B, volatile=True)
self.fake_A = self.netG_B.forward(self.real_B)
if self.opt.skip == 1:
self.rec_B, self.latent_fake_A = self.netG_A.forward(self.fake_A)
else:
self.rec_B = self.netG_A.forward(self.fake_A)
def predict(self):
self.real_A = Variable(self.input_A, volatile=True)
# print(np.transpose(self.real_A.data[0].cpu().float().numpy(),(1,2,0))[:2][:2][:])
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(self.real_A)
else:
self.fake_B = self.netG_A.forward(self.real_A)
self.rec_A = self.netG_B.forward(self.fake_B)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
rec_A = util.tensor2im(self.rec_A.data)
if self.opt.skip == 1:
latent_real_A = util.tensor2im(self.latent_real_A.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ("latent_real_A", latent_real_A), ("rec_A", rec_A)])
else:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ("rec_A", rec_A)])
# get image paths
def get_image_paths(self):
return self.image_paths
def backward_D_basic(self, netD, real, fake):
# Real
pred_real = netD.forward(real)
if self.opt.use_wgan:
loss_D_real = pred_real.mean()
else:
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD.forward(fake.detach())
if self.opt.use_wgan:
loss_D_fake = pred_fake.mean()
else:
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss
if self.opt.use_wgan:
loss_D = loss_D_fake - loss_D_real + self.criterionGAN.calc_gradient_penalty(netD, real.data, fake.data)
else:
loss_D = (loss_D_real + loss_D_fake) * 0.5
# backward
loss_D.backward()
return loss_D
def backward_D_A(self):
fake_B = self.fake_B_pool.query(self.fake_B)
self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
def backward_D_B(self):
fake_A = self.fake_A_pool.query(self.fake_A)
self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
def backward_G(self):
lambda_idt = self.opt.identity
lambda_A = self.opt.lambda_A
lambda_B = self.opt.lambda_B
# Identity loss
if lambda_idt > 0:
# G_A should be identity if real_B is fed.
if self.opt.skip == 1:
self.idt_A, _ = self.netG_A.forward(self.real_B)
else:
self.idt_A = self.netG_A.forward(self.real_B)
self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt
# G_B should be identity if real_A is fed.
self.idt_B = self.netG_B.forward(self.real_A)
self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt
else:
self.loss_idt_A = 0
self.loss_idt_B = 0
# GAN loss
# D_A(G_A(A))
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(self.real_A)
else:
self.fake_B = self.netG_A.forward(self.real_A)
# = self.latent_real_A + self.opt.skip * self.real_A
pred_fake = self.netD_A.forward(self.fake_B)
if self.opt.use_wgan:
self.loss_G_A = -pred_fake.mean()
else:
self.loss_G_A = self.criterionGAN(pred_fake, True)
self.L1_AB = self.criterionL1(self.fake_B, self.real_B) * self.opt.l1
# D_B(G_B(B))
self.fake_A = self.netG_B.forward(self.real_B)
pred_fake = self.netD_B.forward(self.fake_A)
self.L1_BA = self.criterionL1(self.fake_A, self.real_A) * self.opt.l1
if self.opt.use_wgan:
self.loss_G_B = -pred_fake.mean()
else:
self.loss_G_B = self.criterionGAN(pred_fake, True)
# Forward cycle loss
if lambda_A > 0:
self.rec_A = self.netG_B.forward(self.fake_B)
self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A
else:
self.loss_cycle_A = 0
# Backward cycle loss
# = self.latent_fake_A + self.opt.skip * self.fake_A
if lambda_B > 0:
if self.opt.skip == 1:
self.rec_B, self.latent_fake_A = self.netG_A.forward(self.fake_A)
else:
self.rec_B = self.netG_A.forward(self.fake_A)
self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B
else:
self.loss_cycle_B = 0
self.loss_vgg_a = self.vgg_loss.compute_vgg_loss(self.vgg, self.fake_A, self.real_B) * self.opt.vgg if self.opt.vgg > 0 else 0
self.loss_vgg_b = self.vgg_loss.compute_vgg_loss(self.vgg, self.fake_B, self.real_A) * self.opt.vgg if self.opt.vgg > 0 else 0
# combined loss
self.loss_G = self.loss_G_A + self.loss_G_B + self.L1_AB + self.L1_BA + self.loss_cycle_A + self.loss_cycle_B + \
self.loss_vgg_a + self.loss_vgg_b + \
self.loss_idt_A + self.loss_idt_B
# self.loss_G = self.L1_AB + self.L1_BA
self.loss_G.backward()
def optimize_parameters(self):
# forward
self.forward()
# G_A and G_B
self.optimizer_G.zero_grad()
self.backward_G()
self.optimizer_G.step()
# D_A
self.optimizer_D_A.zero_grad()
self.backward_D_A()
self.optimizer_D_A.step()
# D_B
self.optimizer_D_B.zero_grad()
self.backward_D_B()
self.optimizer_D_B.step()
def get_current_errors(self):
D_A = self.loss_D_A.data[0]
G_A = self.loss_G_A.data[0]
L1 = (self.L1_AB + self.L1_BA).data[0]
Cyc_A = self.loss_cycle_A.data[0]
D_B = self.loss_D_B.data[0]
G_B = self.loss_G_B.data[0]
Cyc_B = self.loss_cycle_B.data[0]
vgg = (self.loss_vgg_a.data[0] + self.loss_vgg_b.data[0])/self.opt.vgg if self.opt.vgg > 0 else 0
if self.opt.identity > 0:
idt = self.loss_idt_A.data[0] + self.loss_idt_B.data[0]
if self.opt.lambda_A > 0.0:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('L1', L1), ('Cyc_A', Cyc_A),
('D_B', D_B), ('G_B', G_B), ('Cyc_B', Cyc_B), ("vgg", vgg), ("idt", idt)])
else:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('L1', L1),
('D_B', D_B), ('G_B', G_B)], ("vgg", vgg), ("idt", idt))
else:
if self.opt.lambda_A > 0.0:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('L1', L1), ('Cyc_A', Cyc_A),
('D_B', D_B), ('G_B', G_B), ('Cyc_B', Cyc_B), ("vgg", vgg)])
else:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('L1', L1),
('D_B', D_B), ('G_B', G_B)], ("vgg", vgg))
def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
if self.opt.skip > 0:
latent_real_A = util.tensor2im(self.latent_real_A.data)
real_B = util.tensor2im(self.real_B.data)
fake_A = util.tensor2im(self.fake_A.data)
if self.opt.identity > 0:
idt_A = util.tensor2im(self.idt_A.data)
idt_B = util.tensor2im(self.idt_B.data)
if self.opt.lambda_A > 0.0:
rec_A = util.tensor2im(self.rec_A.data)
rec_B = util.tensor2im(self.rec_B.data)
if self.opt.skip > 0:
latent_fake_A = util.tensor2im(self.latent_fake_A.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A), ('rec_A', rec_A),
('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B), ('latent_fake_A', latent_fake_A),
("idt_A", idt_A), ("idt_B", idt_B)])
else:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('rec_A', rec_A),
('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B), ("idt_A", idt_A), ("idt_B", idt_B)])
else:
if self.opt.skip > 0:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('real_B', real_B), ('fake_A', fake_A), ("idt_A", idt_A), ("idt_B", idt_B)])
else:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B),
('real_B', real_B), ('fake_A', fake_A), ("idt_A", idt_A), ("idt_B", idt_B)])
else:
if self.opt.lambda_A > 0.0:
rec_A = util.tensor2im(self.rec_A.data)
rec_B = util.tensor2im(self.rec_B.data)
if self.opt.skip > 0:
latent_fake_A = util.tensor2im(self.latent_fake_A.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A), ('rec_A', rec_A),
('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B), ('latent_fake_A', latent_fake_A)])
else:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('rec_A', rec_A),
('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B)])
else:
if self.opt.skip > 0:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('real_B', real_B), ('fake_A', fake_A)])
else:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B),
('real_B', real_B), ('fake_A', fake_A)])
def save(self, label):
self.save_network(self.netG_A, 'G_A', label, self.gpu_ids)
self.save_network(self.netD_A, 'D_A', label, self.gpu_ids)
self.save_network(self.netG_B, 'G_B', label, self.gpu_ids)
self.save_network(self.netD_B, 'D_B', label, self.gpu_ids)
def update_learning_rate(self):
if self.opt.new_lr:
lr = self.old_lr/2
else:
lrd = self.opt.lr / self.opt.niter_decay
lr = self.old_lr - lrd
for param_group in self.optimizer_D_A.param_groups:
param_group['lr'] = lr
for param_group in self.optimizer_D_B.param_groups:
param_group['lr'] = lr
for param_group in self.optimizer_G.param_groups:
param_group['lr'] = lr
print('update learning rate: %f -> %f' % (self.old_lr, lr))
self.old_lr = lr
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