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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 util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
def name(self):
return 'Pix2PixModel'
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.isTrain = opt.isTrain
# define tensors
self.input_A = self.Tensor(opt.batchSize, opt.input_nc,
opt.fineSize, opt.fineSize)
self.input_B = self.Tensor(opt.batchSize, opt.output_nc,
opt.fineSize, opt.fineSize)
# load/define networks
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf,
opt.which_model_netG, opt.norm, not opt.no_dropout, self.gpu_ids)
if self.isTrain:
use_sigmoid = opt.no_lsgan
self.netD = networks.define_D(opt.input_nc + opt.output_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:
self.load_network(self.netG, 'G', opt.which_epoch)
if self.isTrain:
self.load_network(self.netD, 'D', opt.which_epoch)
if self.isTrain:
self.fake_AB_pool = ImagePool(opt.pool_size)
self.old_lr = opt.lr
# define loss functions
self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)
self.criterionL1 = torch.nn.L1Loss()
# initialize optimizers
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=opt.lr, betas=(opt.beta1, 0.999))
print('---------- Networks initialized -------------')
networks.print_network(self.netG)
if self.isTrain:
networks.print_network(self.netD)
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.fake_B = self.netG.forward(self.real_A)
self.real_B = Variable(self.input_B)
# no backprop gradients
def test(self):
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
self.real_B = Variable(self.input_B, volatile=True)
# get image paths
def get_image_paths(self):
return self.image_paths
def backward_D(self):
# Fake
# stop backprop to the generator by detaching fake_B
fake_AB = self.fake_AB_pool.query(torch.cat((self.real_A, self.fake_B), 1))
self.pred_fake = self.netD.forward(fake_AB.detach())
self.loss_D_fake = self.criterionGAN(self.pred_fake, False)
# Real
real_AB = torch.cat((self.real_A, self.real_B), 1)
self.pred_real = self.netD.forward(real_AB)
self.loss_D_real = self.criterionGAN(self.pred_real, True)
# Combined loss
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
self.loss_D.backward()
def backward_G(self):
# First, G(A) should fake the discriminator
fake_AB = torch.cat((self.real_A, self.fake_B), 1)
pred_fake = self.netD.forward(fake_AB)
self.loss_G_GAN = self.criterionGAN(pred_fake, True)
# Second, G(A) = B
self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_A
self.loss_G = self.loss_G_GAN + self.loss_G_L1
self.loss_G.backward()
def optimize_parameters(self):
self.forward()
self.optimizer_D.zero_grad()
self.backward_D()
self.optimizer_D.step()
self.optimizer_G.zero_grad()
self.backward_G()
self.optimizer_G.step()
def get_current_errors(self):
return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),
('G_L1', self.loss_G_L1.data[0]),
('D_real', self.loss_D_real.data[0]),
('D_fake', self.loss_D_fake.data[0])
])
def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
real_B = util.tensor2im(self.real_B.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B)])
def save(self, label):
self.save_network(self.netG, 'G', label, self.gpu_ids)
self.save_network(self.netD, 'D', label, self.gpu_ids)
def update_learning_rate(self):
lrd = self.opt.lr / self.opt.niter_decay
lr = self.old_lr - lrd
for param_group in self.optimizer_D.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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