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model.py
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import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import networks
class BaseModel():
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt['gpu_ids']
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
self.save_dir = opt['outf']
def set_input(self, input):
self.input = input
def forward(self):
pass
# used in test time, no backprop
def test(self):
pass
def get_image_paths(self):
pass
def optimize_parameters(self):
pass
def get_current_visuals(self):
return self.input
def get_current_errors(self):
return {}
def save(self, label):
pass
# helper saving function that can be used by subclasses
def save_network(self, network, network_label, epoch_label, gpu_ids):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.cpu().state_dict(), save_path)
if len(gpu_ids) and torch.cuda.is_available():
network.cuda(device_id=gpu_ids[0])
# helper loading function that can be used by subclasses
def load_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
network.load_state_dict(torch.load(save_path))
def update_learning_rate():
pass
class netModel(BaseModel):
def name(self):
return 'netModel'
def initialize(self, opt, train_mode=True):
# Model transforms from A --> B and uses Adv as the
# adversarial example.
BaseModel.initialize(self, opt)
self.train_mode = train_mode
# define tensors
self.input_B = self.Tensor(opt['batchSize'], opt['input_nc'],
opt['B_height'], opt['B_width'])
self.input_A = self.Tensor(opt['batchSize'], opt['output_nc'],
opt['A_height'], opt['A_width'])
# load/define networks
self.netG = networks.define_G(opt['input_nc'], opt['output_nc'], opt['ngf'],
opt['norm'], self.gpu_ids)
if self.train_mode:
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'], use_sigmoid, self.gpu_ids)
if self.train_mode:
# 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.content_loss = torch.nn.MSELoss()
# 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)
networks.print_network(self.netD)
print('-----------------------------------------------')
def set_input(self, input):
if self.train_mode:
input_B = input[0][0]
input_A = input[1][0]
self.input_B.resize_(input_B.size()).copy_(input_B)
self.input_A.resize_(input_A.size()).copy_(input_A)
else:
input_A = input[0]
self.input_A.resize_(input_A.size()).copy_(input_A)
def forward(self):
if self.train_mode:
self.real_A = Variable(self.input_A)
self.fake_B = self.netG.forward(self.real_A)
self.real_B = Variable(self.input_B)
else:
# Do not backprop gradients
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
def backward_D(self):
# stop backprop to the generator by detaching fake_B
fake_AB = 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)
self.loss_D_fake.backward()
# 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)
self.loss_D_real.backward()
# Combined loss
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
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_content = self.content_loss(self.fake_B, self.real_B)
self.loss_G = self.loss_G_content + self.loss_G_GAN * self.opt['L1lambda']
self.loss_G.backward()
def optimize_parameters(self):
'''
Run forward and backward pathds and apply optimization step
'''
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):
if self.train_mode:
return OrderedDict([
('G_GAN', self.loss_G_GAN.data[0]),
('G_L1', self.loss_G_content.data[0]),
('D_real', self.loss_D_real.data[0]),
('D_fake', self.loss_D_fake.data[0]),
])
raise UnboundLocalError('Errors are only computed in when train_mode is True')
def get_current_visuals(self, test=False):
# fake_in = util.tensor2im(self.fake_in.data)
# fake_out = util.tensor2im(self.fake_out.data)
# real_out = util.tensor2im(self.real_out.data)
if test or not self.train_mode:
return OrderedDict([('fake_out', self.fake_B),
('fake_in', self.real_A)])
return OrderedDict([('fake_in', self.real_A),
('fake_out', self.fake_B),
('real_out', self.real_B)])
def update_learning_rate(self):
lr = self.old_lr / 10.
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