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import numpy as np
import torch
import os
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 CycleGANModel(BaseModel):
def name(self):
return 'CycleGANModel'
def initialize(self, opt):
BaseModel.initialize(self, opt)
nb = opt.batchSize
size = opt.fineSize
self.input_A = self.Tensor(nb, opt.input_nc, size, size)
self.input_B = self.Tensor(nb, opt.output_nc, size, size)
# 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)
self.netG_A = networks.define_G(opt.input_nc, opt.output_nc,
opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)
self.netG_B = networks.define_G(opt.output_nc, opt.input_nc,
opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)
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, opt.init_type, 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, opt.init_type, 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
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)
self.criterionCycle = 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))
self.optimizers = []
self.schedulers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D_A)
self.optimizers.append(self.optimizer_D_B)
for optimizer in self.optimizers:
self.schedulers.append(networks.get_scheduler(optimizer, opt))
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)
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)
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)
self.rec_B = self.netG_A.forward(self.fake_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)
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD.forward(fake.detach())
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss
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.
idt_A = self.netG_A.forward(self.real_B)
loss_idt_A = self.criterionIdt(idt_A, self.real_B) * lambda_B * lambda_idt
# G_B should be identity if real_A is fed.
idt_B = self.netG_B.forward(self.real_A)
loss_idt_B = self.criterionIdt(idt_B, self.real_A) * lambda_A * lambda_idt
self.idt_A = idt_A.data
self.idt_B = idt_B.data
self.loss_idt_A = loss_idt_A.data[0]
self.loss_idt_B = loss_idt_B.data[0]
else:
loss_idt_A = 0
loss_idt_B = 0
self.loss_idt_A = 0
self.loss_idt_B = 0
# GAN loss
# D_A(G_A(A))
fake_B = self.netG_A.forward(self.real_A)
pred_fake = self.netD_A.forward(fake_B)
loss_G_A = self.criterionGAN(pred_fake, True)
# D_B(G_B(B))
fake_A = self.netG_B.forward(self.real_B)
pred_fake = self.netD_B.forward(fake_A)
loss_G_B = self.criterionGAN(pred_fake, True)
# Forward cycle loss
rec_A = self.netG_B.forward(fake_B)
loss_cycle_A = self.criterionCycle(rec_A, self.real_A) * lambda_A
# Backward cycle loss
rec_B = self.netG_A.forward(fake_A)
loss_cycle_B = self.criterionCycle(rec_B, self.real_B) * lambda_B
# combined loss
loss_G = loss_G_A + loss_G_B + loss_cycle_A + loss_cycle_B + loss_idt_A + loss_idt_B
loss_G.backward()
self.fake_B = fake_B.data
self.fake_A = fake_A.data
self.rec_A = rec_A.data
self.rec_B = rec_B.data
self.loss_G_A = loss_G_A.data[0]
self.loss_G_B = loss_G_B.data[0]
self.loss_cycle_A = loss_cycle_A.data[0]
self.loss_cycle_B = loss_cycle_B.data[0]
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
Cyc_A = self.loss_cycle_A
D_B = self.loss_D_B.data[0]
G_B = self.loss_G_B
Cyc_B = self.loss_cycle_B
if self.opt.identity > 0.0:
idt_A = self.loss_idt_A
idt_B = self.loss_idt_B
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('Cyc_A', Cyc_A), ('idt_A', idt_A),
('D_B', D_B), ('G_B', G_B), ('Cyc_B', Cyc_B), ('idt_B', idt_B)])
else:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('Cyc_A', Cyc_A),
('D_B', D_B), ('G_B', G_B), ('Cyc_B', Cyc_B)])
def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
rec_A = util.tensor2im(self.rec_A.data)
real_B = util.tensor2im(self.real_B.data)
fake_A = util.tensor2im(self.fake_A.data)
rec_B = util.tensor2im(self.rec_B.data)
if self.opt.isTrain and self.opt.identity > 0.0:
idt_A = util.tensor2im(self.idt_A)
idt_B = util.tensor2im(self.idt_B)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('rec_A', rec_A), ('idt_B', idt_B),
('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B), ('idt_A', idt_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)])
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)
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