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) rec_A = util.tensor2im(self.rec_A) real_B = util.tensor2im(self.real_B.data) fake_A = util.tensor2im(self.fake_A) rec_B = util.tensor2im(self.rec_B) 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)