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import numpy as np
import torch
from torch import nn
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
import random
from . import networks
import sys
class TempModel(BaseModel):
def name(self):
return 'TempModel'
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)
self.input_img = self.Tensor(nb, opt.input_nc, size, size)
self.input_A_gray = self.Tensor(nb, 1, size, size)
if opt.vgg > 0:
self.vgg_loss = networks.PerceptualLoss(opt)
# if self.opt.IN_vgg:
# self.vgg_patch_loss = networks.PerceptualLoss(opt)
# self.vgg_patch_loss.cuda()
self.vgg_loss.cuda()
self.vgg = networks.load_vgg16("./model", self.gpu_ids)
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
elif opt.fcn > 0:
self.fcn_loss = networks.SemanticLoss(opt)
self.fcn_loss.cuda()
self.fcn = networks.load_fcn("./model")
self.fcn.eval()
for param in self.fcn.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, False)
if self.opt.patchD:
self.netD_P = networks.define_D(opt.input_nc, opt.ndf,
opt.which_model_netD,
opt.n_layers_patchD, opt.norm, use_sigmoid, self.gpu_ids, True)
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)
if self.opt.patchD:
self.load_network(self.netD_P, 'D_P', 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(self.netG_A.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))
# if self.opt.patchD:
# self.optimizer_D_P = torch.optim.Adam(self.netD_P.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)
# if self.opt.patchD:
# networks.print_network(self.netD_P)
# 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']
input_img = input['input_img']
input_A_gray = input['A_gray']
self.input_A.resize_(input_A.size()).copy_(input_A)
self.input_A_gray.resize_(input_A_gray.size()).copy_(input_A_gray)
self.input_B.resize_(input_B.size()).copy_(input_B)
self.input_img.resize_(input_img.size()).copy_(input_img)
self.image_paths = input['A_paths' if AtoB else 'B_paths']
def test(self):
self.real_A = Variable(self.input_A, volatile=True)
self.real_A_gray = Variable(self.input_A_gray, volatile=True)
if self.opt.noise > 0:
self.noise = Variable(torch.cuda.FloatTensor(self.real_A.size()).normal_(mean=0, std=self.opt.noise/255.))
self.real_A = self.real_A + self.noise
if self.opt.input_linear:
self.real_A = (self.real_A - torch.min(self.real_A))/(torch.max(self.real_A) - torch.min(self.real_A))
# 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, self.real_A_gray)
else:
self.fake_B = self.netG_A.forward(self.real_A, self.real_A_gray)
# self.rec_A = self.netG_B.forward(self.fake_B)
self.real_B = Variable(self.input_B, volatile=True)
def predict(self):
self.real_A = Variable(self.input_A, volatile=True)
self.real_A_gray = Variable(self.input_A_gray, volatile=True)
# if self.opt.noise > 0:
# self.noise = Variable(torch.cuda.FloatTensor(self.real_A.size()).normal_(mean=0, std=self.opt.noise/255.))
# self.real_A = self.real_A + self.noise
# if self.opt.input_linear:
# self.real_A = (self.real_A - torch.min(self.real_A))/(torch.max(self.real_A) - torch.min(self.real_A))
# 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, self.real_A_gray)
else:
self.fake_B = self.netG_A.forward(self.real_A, self.real_A_gray)
# 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)
A_gray = util.atten2im(self.real_A_gray.data)
# rec_A = util.tensor2im(self.rec_A.data)
# if self.opt.skip == 1:
# latent_real_A = util.tensor2im(self.latent_real_A.data)
# latent_show = util.latent2im(self.latent_real_A.data)
# max_image = util.max2im(self.fake_B.data, self.latent_real_A.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('max_image', max_image), ('A_gray', A_gray)])
# else:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
# return OrderedDict([('fake_B', fake_B)])
return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
# get image paths
def get_image_paths(self):
return self.image_paths
def backward_D_basic(self, netD, real, fake, use_ragan):
# Real
pred_real = netD.forward(real)
pred_fake = netD.forward(fake.detach())
if self.opt.use_wgan:
loss_D_real = pred_real.mean()
loss_D_fake = pred_fake.mean()
loss_D = loss_D_fake - loss_D_real + self.criterionGAN.calc_gradient_penalty(netD,
real.data, fake.data)
elif self.opt.use_ragan and use_ragan:
loss_D = (self.criterionGAN(pred_real - torch.mean(pred_fake), True) +
self.criterionGAN(pred_fake - torch.mean(pred_real), False)) / 2
else:
loss_D_real = self.criterionGAN(pred_real, True)
loss_D_fake = self.criterionGAN(pred_fake, False)
loss_D = (loss_D_real + loss_D_fake) * 0.5
# loss_D.backward()
return loss_D
def backward_D_A(self):
fake_B = self.fake_B_pool.query(self.fake_B)
fake_B = self.fake_B
self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B, True)
self.loss_D_A.backward()
def backward_D_P(self):
if self.opt.hybrid_loss:
loss_D_P = self.backward_D_basic(self.netD_P, self.real_patch, self.fake_patch, False)
if self.opt.patchD_3 > 0:
for i in range(self.opt.patchD_3):
loss_D_P += self.backward_D_basic(self.netD_P, self.real_patch_1[i], self.fake_patch_1[i], False)
self.loss_D_P = loss_D_P/float(self.opt.patchD_3 + 1)
else:
self.loss_D_P = loss_D_P
else:
loss_D_P = self.backward_D_basic(self.netD_P, self.real_patch, self.fake_patch, True)
if self.opt.patchD_3 > 0:
for i in range(self.opt.patchD_3):
loss_D_P += self.backward_D_basic(self.netD_P, self.real_patch_1[i], self.fake_patch_1[i], True)
self.loss_D_P = loss_D_P/float(self.opt.patchD_3 + 1)
else:
self.loss_D_P = loss_D_P
if self.opt.D_P_times2:
self.loss_D_P = self.loss_D_P*2
self.loss_D_P.backward()
# 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 forward(self):
self.real_A = Variable(self.input_A)
self.real_B = Variable(self.input_B)
self.real_A_gray = Variable(self.input_A_gray)
self.real_img = Variable(self.input_img)
if self.opt.noise > 0:
self.noise = Variable(torch.cuda.FloatTensor(self.real_A.size()).normal_(mean=0, std=self.opt.noise/255.))
self.real_A = self.real_A + self.noise
if self.opt.input_linear:
self.real_A = (self.real_A - torch.min(self.real_A))/(torch.max(self.real_A) - torch.min(self.real_A))
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(self.real_img, self.real_A_gray)
else:
self.fake_B = self.netG_A.forward(self.real_img, self.real_A_gray)
if self.opt.patchD:
w = self.real_A.size(3)
h = self.real_A.size(2)
w_offset = random.randint(0, max(0, w - self.opt.patchSize - 1))
h_offset = random.randint(0, max(0, h - self.opt.patchSize - 1))
self.fake_patch = self.fake_B[:,:, h_offset:h_offset + self.opt.patchSize,
w_offset:w_offset + self.opt.patchSize]
self.real_patch = self.real_B[:,:, h_offset:h_offset + self.opt.patchSize,
w_offset:w_offset + self.opt.patchSize]
self.input_patch = self.real_A[:,:, h_offset:h_offset + self.opt.patchSize,
w_offset:w_offset + self.opt.patchSize]
if self.opt.patchD_3 > 0:
self.fake_patch_1 = []
self.real_patch_1 = []
self.input_patch_1 = []
w = self.real_A.size(3)
h = self.real_A.size(2)
for i in range(self.opt.patchD_3):
w_offset_1 = random.randint(0, max(0, w - self.opt.patchSize - 1))
h_offset_1 = random.randint(0, max(0, h - self.opt.patchSize - 1))
self.fake_patch_1.append(self.fake_B[:,:, h_offset_1:h_offset_1 + self.opt.patchSize,
w_offset_1:w_offset_1 + self.opt.patchSize])
self.real_patch_1.append(self.real_B[:,:, h_offset_1:h_offset_1 + self.opt.patchSize,
w_offset_1:w_offset_1 + self.opt.patchSize])
self.input_patch_1.append(self.real_A[:,:, h_offset_1:h_offset_1 + self.opt.patchSize,
w_offset_1:w_offset_1 + self.opt.patchSize])
w_offset_2 = random.randint(0, max(0, w - self.opt.patchSize - 1))
h_offset_2 = random.randint(0, max(0, h - self.opt.patchSize - 1))
self.fake_patch_2 = self.fake_B[:,:, h_offset_2:h_offset_2 + self.opt.patchSize,
w_offset_2:w_offset_2 + self.opt.patchSize]
self.real_patch_2 = self.real_B[:,:, h_offset_2:h_offset_2 + self.opt.patchSize,
w_offset_2:w_offset_2 + self.opt.patchSize]
self.input_patch_2 = self.real_A[:,:, h_offset_2:h_offset_2 + self.opt.patchSize,
w_offset_2:w_offset_2 + self.opt.patchSize]
def backward_G(self, epoch):
pred_fake = self.netD_A.forward(self.fake_B)
if self.opt.use_wgan:
self.loss_G_A = -pred_fake.mean()
elif self.opt.use_ragan:
pred_real = self.netD_A.forward(self.real_B)
self.loss_G_A = (self.criterionGAN(pred_real - torch.mean(pred_fake), False) +
self.criterionGAN(pred_fake - torch.mean(pred_real), True)) / 2
else:
self.loss_G_A = self.criterionGAN(pred_fake, True)
loss_G_A = 0
if self.opt.patchD:
pred_fake_patch = self.netD_P.forward(self.fake_patch)
if self.opt.hybrid_loss:
loss_G_A += self.criterionGAN(pred_fake_patch, True)
else:
pred_real_patch = self.netD_P.forward(self.real_patch)
loss_G_A += (self.criterionGAN(pred_real_patch - torch.mean(pred_fake_patch), False) +
self.criterionGAN(pred_fake_patch - torch.mean(pred_real_patch), True)) / 2
self.loss_G_A += loss_G_A
if self.opt.patchD_3 > 0:
for i in range(self.opt.patchD_3):
pred_fake_patch_1 = self.netD_P.forward(self.fake_patch_1[i])
if self.opt.hybrid_loss:
loss_G_A += self.criterionGAN(pred_fake_patch_1, True)
else:
pred_real_patch_1 = self.netD_P.forward(self.real_patch_1[i])
loss_G_A += (self.criterionGAN(pred_real_patch_1 - torch.mean(pred_fake_patch_1), False) +
self.criterionGAN(pred_fake_patch_1 - torch.mean(pred_real_patch_1), True)) / 2
if not self.opt.D_P_times2:
self.loss_G_A += loss_G_A/float(self.opt.patchD_3 + 1)
else:
self.loss_G_A += loss_G_A/float(self.opt.patchD_3 + 1)*2
else:
if not self.opt.D_P_times2:
self.loss_G_A += loss_G_A
else:
self.loss_G_A += loss_G_A*2
if epoch < 0:
vgg_w = 0
else:
vgg_w = 1
if self.opt.vgg > 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
if self.opt.patch_vgg:
if not self.opt.IN_vgg:
loss_vgg_patch = self.vgg_loss.compute_vgg_loss(self.vgg,
self.fake_patch, self.input_patch) * self.opt.vgg
else:
loss_vgg_patch = self.vgg_patch_loss.compute_vgg_loss(self.vgg,
self.fake_patch, self.input_patch) * self.opt.vgg
if self.opt.patchD_3 > 0:
for i in range(self.opt.patchD_3):
if not self.opt.IN_vgg:
loss_vgg_patch += self.vgg_loss.compute_vgg_loss(self.vgg,
self.fake_patch_1[i], self.input_patch_1[i]) * self.opt.vgg
else:
loss_vgg_patch += self.vgg_patch_loss.compute_vgg_loss(self.vgg,
self.fake_patch_1[i], self.input_patch_1[i]) * self.opt.vgg
self.loss_vgg_b += loss_vgg_patch/float(self.opt.patchD_3 + 1)
else:
self.loss_vgg_b += loss_vgg_patch
self.loss_G = self.loss_G_A + self.loss_vgg_b*vgg_w
elif self.opt.fcn > 0:
self.loss_fcn_b = self.fcn_loss.compute_fcn_loss(self.fcn,
self.fake_B, self.real_A) * self.opt.fcn if self.opt.fcn > 0 else 0
if self.opt.patchD:
loss_fcn_patch = self.fcn_loss.compute_vgg_loss(self.fcn,
self.fake_patch, self.input_patch) * self.opt.fcn
if self.opt.patchD_3 > 0:
for i in range(self.opt.patchD_3):
loss_fcn_patch += self.fcn_loss.compute_vgg_loss(self.fcn,
self.fake_patch_1[i], self.input_patch_1[i]) * self.opt.fcn
self.loss_fcn_b += loss_fcn_patch/float(self.opt.patchD_3 + 1)
else:
self.loss_fcn_b += loss_fcn_patch
self.loss_G = self.loss_G_A + self.loss_fcn_b*vgg_w
# self.loss_G = self.L1_AB + self.L1_BA
self.loss_G.backward()
# def optimize_parameters(self, epoch):
# # forward
# self.forward()
# # G_A and G_B
# self.optimizer_G.zero_grad()
# self.backward_G(epoch)
# self.optimizer_G.step()
# # D_A
# self.optimizer_D_A.zero_grad()
# self.backward_D_A()
# self.optimizer_D_A.step()
# if self.opt.patchD:
# self.forward()
# self.optimizer_D_P.zero_grad()
# self.backward_D_P()
# self.optimizer_D_P.step()
# D_B
# self.optimizer_D_B.zero_grad()
# self.backward_D_B()
# self.optimizer_D_B.step()
def optimize_parameters(self, epoch):
# forward
self.forward()
# G_A and G_B
self.optimizer_G.zero_grad()
self.backward_G(epoch)
self.optimizer_G.step()
# D_A
self.optimizer_D_A.zero_grad()
self.backward_D_A()
if not self.opt.patchD:
self.optimizer_D_A.step()
else:
# self.forward()
self.optimizer_D_P.zero_grad()
self.backward_D_P()
self.optimizer_D_A.step()
self.optimizer_D_P.step()
def get_current_errors(self, epoch):
D_A = self.loss_D_A.data[0]
D_P = self.loss_D_P.data[0] if self.opt.patchD else 0
G_A = self.loss_G_A.data[0]
if self.opt.vgg > 0:
vgg = self.loss_vgg_b.data[0]/self.opt.vgg if self.opt.vgg > 0 else 0
return OrderedDict([('D_A', D_A), ('G_A', G_A), ("vgg", vgg)])
elif self.opt.fcn > 0:
fcn = self.loss_fcn_b.data[0]/self.opt.fcn if self.opt.fcn > 0 else 0
return OrderedDict([('D_A', D_A), ('G_A', G_A), ("fcn", fcn)])
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)
if self.opt.skip > 0:
latent_real_A = util.tensor2im(self.latent_real_A.data)
latent_show = util.latent2im(self.latent_real_A.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B)])
# if self.opt.patchD:
# fake_patch = util.tensor2im(self.fake_patch.data)
# real_patch = util.tensor2im(self.real_patch.data)
# if self.opt.patch_vgg:
# input_patch = util.tensor2im(self.input_patch.data)
# if not self.opt.self_attention:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
# ('fake_patch', fake_patch), ('input_patch', input_patch)])
# else:
# self_attention = util.atten2im(self.real_A_gray.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
# ('fake_patch', fake_patch), ('input_patch', input_patch), ('self_attention', self_attention)])
# else:
# if not self.opt.self_attention:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
# ('fake_patch', fake_patch)])
# else:
# self_attention = util.atten2im(self.real_A_gray.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
# ('fake_patch', fake_patch), ('self_attention', self_attention)])
# else:
# if not self.opt.self_attention:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('real_B', real_B)])
# else:
# self_attention = util.atten2im(self.real_A_gray.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
# ('latent_real_A', latent_real_A), ('latent_show', latent_show),
# ('self_attention', self_attention)])
# else:
# if not self.opt.self_attention:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B)])
# else:
# self_attention = util.atten2im(self.real_A_gray.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
# ('self_attention', self_attention)])
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)
if self.opt.patchD:
self.save_network(self.netD_P, 'D_P', 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
if self.opt.patchD:
for param_group in self.optimizer_D_P.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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