File size: 12,822 Bytes
aba0e05 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 |
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.util import weights_init, get_model_list, vgg_preprocess, load_vgg16, get_scheduler
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
from .unit_network import *
import sys
def get_config(config):
import yaml
with open(config, 'r') as stream:
return yaml.load(stream)
class UNITModel(BaseModel):
def name(self):
return 'UNITModel'
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.config = get_config(opt.config)
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.gen_a = VAEGen(self.config['input_dim_a'], self.config['gen'])
self.gen_b = VAEGen(self.config['input_dim_a'], self.config['gen'])
if self.isTrain:
self.dis_a = MsImageDis(self.config['input_dim_a'], self.config['dis']) # discriminator for domain a
self.dis_b = MsImageDis(self.config['input_dim_b'], self.config['dis']) # discriminator for domain b
if not self.isTrain or opt.continue_train:
which_epoch = opt.which_epoch
self.load_network(self.gen_a, 'G_A', which_epoch)
self.load_network(self.gen_b, 'G_B', which_epoch)
if self.isTrain:
self.load_network(self.dis_a, 'D_A', which_epoch)
self.load_network(self.dis_b, 'D_B', which_epoch)
if self.isTrain:
self.old_lr = self.config['lr']
self.fake_A_pool = ImagePool(opt.pool_size)
self.fake_B_pool = ImagePool(opt.pool_size)
# define loss functions
# Setup the optimizers
beta1 = self.config['beta1']
beta2 = self.config['beta2']
dis_params = list(self.dis_a.parameters()) + list(self.dis_b.parameters())
gen_params = list(self.gen_a.parameters()) + list(self.gen_b.parameters())
self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
lr=self.config['lr'], betas=(beta1, beta2), weight_decay=self.config['weight_decay'])
self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
lr=self.config['lr'], betas=(beta1, beta2), weight_decay=self.config['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, self.config)
self.gen_scheduler = get_scheduler(self.gen_opt, self.config)
# Network weight initialization
# self.apply(weights_init(self.config['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
# Load VGG model if needed
if 'vgg_w' in self.config.keys() and self.config['vgg_w'] > 0:
self.vgg = load_vgg16(self.config['vgg_model_path'] + '/models')
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
self.gen_a.cuda()
self.gen_b.cuda()
self.dis_a.cuda()
self.dis_b.cuda()
print('---------- Networks initialized -------------')
networks.print_network(self.gen_a)
networks.print_network(self.gen_b)
if self.isTrain:
networks.print_network(self.dis_a)
networks.print_network(self.dis_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']
self.real_A = Variable(self.input_A.cuda())
self.real_B = Variable(self.input_B.cuda())
# 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.cuda(), volatile=True)
self.real_B = Variable(self.input_B.cuda(), volatile=True)
h_a, n_a = self.gen_a.encode(self.real_A)
h_b, n_b = self.gen_b.encode(self.real_B)
x_a_recon = self.gen_a.decode(h_a + n_a) + x_a*1
x_b_recon = self.gen_b.decode(h_b + n_b) + x_b*1
x_ba = self.gen_a.decode(h_b + n_b) + x_b*1
x_ab = self.gen_b.decode(h_a + n_a) + x_a*1
h_b_recon, n_b_recon = self.gen_a.encode(x_ba)
h_a_recon, n_a_recon = self.gen_b.encode(x_ab)
x_aba = self.gen_a.decode(h_a_recon + n_a_recon) + x_ab*1 if self.config['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode(h_b_recon + n_b_recon) + x_ba*1 if self.config['recon_x_cyc_w'] > 0 else None
self.x_a_recon, self.x_ab, self.x_aba = x_a_recon, x_ab, x_aba
self.x_b_recon, self.x_ba, self.x_bab = x_b_recon, x_ba, x_bab
# get image paths
def get_image_paths(self):
return self.image_paths
def optimize_parameters(self):
self.gen_update(self.real_A, self.real_B)
self.dis_update(self.real_A, self.real_B)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(h_b)
x_ab = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def gen_update(self, x_a, x_b):
self.gen_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (within domain)
x_a_recon = self.gen_a.decode(h_a + n_a) + 0*x_a
x_b_recon = self.gen_b.decode(h_b + n_b) + 0*x_b
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b) + 0*x_b
x_ab = self.gen_b.decode(h_a + n_a) + 0*x_a
# encode again
h_b_recon, n_b_recon = self.gen_a.encode(x_ba)
h_a_recon, n_a_recon = self.gen_b.encode(x_ab)
# decode again (if needed)
x_aba = self.gen_a.decode(h_a_recon + n_a_recon) + 0*x_ab if self.config['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode(h_b_recon + n_b_recon) + 0*x_ba if self.config['recon_x_cyc_w'] > 0 else None
# reconstruction loss
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b)
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
# GAN loss
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
# domain-invariant perceptual loss
self.loss_gen_vgg_a = self.compute_vgg_loss(self.vgg, x_ba, x_b) if self.config['vgg_w'] > 0 else 0
self.loss_gen_vgg_b = self.compute_vgg_loss(self.vgg, x_ab, x_a) if self.config['vgg_w'] > 0 else 0
# total loss
self.loss_gen_total = self.config['gan_w'] * self.loss_gen_adv_a + \
self.config['gan_w'] * self.loss_gen_adv_b + \
self.config['recon_x_w'] * self.loss_gen_recon_x_a + \
self.config['recon_kl_w'] * self.loss_gen_recon_kl_a + \
self.config['recon_x_w'] * self.loss_gen_recon_x_b + \
self.config['recon_kl_w'] * self.loss_gen_recon_kl_b + \
self.config['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
self.config['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
self.config['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
self.config['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab + \
self.config['vgg_w'] * self.loss_gen_vgg_a + \
self.config['vgg_w'] * self.loss_gen_vgg_b
self.loss_gen_total.backward()
self.gen_opt.step()
self.x_a_recon, self.x_ab, self.x_aba = x_a_recon, x_ab, x_aba
self.x_b_recon, self.x_ba, self.x_bab = x_b_recon, x_ba, x_bab
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean((self.instancenorm(img_fea) - self.instancenorm(target_fea)) ** 2)
def dis_update(self, x_a, x_b):
self.dis_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b)
x_ab = self.gen_b.decode(h_a + n_a)
# D loss
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_ab.detach(), x_b)
self.loss_dis_total = self.config['gan_w'] * self.loss_dis_a + self.config['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def get_current_errors(self):
D_A = self.loss_dis_a.data[0]
G_A = self.loss_gen_adv_a.data[0]
kl_A = self.loss_gen_recon_kl_a.data[0]
Cyc_A = self.loss_gen_cyc_x_a.data[0]
D_B = self.loss_dis_b.data[0]
G_B = self.loss_gen_adv_b.data[0]
kl_B = self.loss_gen_recon_kl_b.data[0]
Cyc_B = self.loss_gen_cyc_x_b.data[0]
if self.config['vgg_w'] > 0:
vgg_A = self.loss_gen_vgg_a
vgg_B = self.loss_gen_vgg_b
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('Cyc_A', Cyc_A), ('kl_A', kl_A), ('vgg_A', vgg_A),
('D_B', D_B), ('G_B', G_B), ('Cyc_B', Cyc_B), ('kl_B', kl_B), ('vgg_B', vgg_B)])
else:
return OrderedDict([('D_A', D_A), ('G_A', G_A), ('kl_A', kl_A), ('Cyc_A', Cyc_A),
('D_B', D_B), ('G_B', G_B), ('kl_B', kl_B), ('Cyc_B', Cyc_B)])
def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
recon_A = util.tensor2im(self.x_a_recon.data)
A_B = util.tensor2im(self.x_ab.data)
ABA = util.tensor2im(self.x_aba.data)
real_B = util.tensor2im(self.real_B.data)
recon_B = util.tensor2im(self.x_b_recon.data)
B_A = util.tensor2im(self.x_ba.data)
BAB = util.tensor2im(self.x_b_recon.data)
return OrderedDict([('real_A', real_A), ('A_B', A_B), ('recon_A', recon_A), ('ABA', ABA),
('real_B', real_B), ('B_A', B_A), ('recon_B', recon_B), ('BAB', BAB)])
def save(self, label):
self.save_network(self.gen_a, 'G_A', label, self.gpu_ids)
self.save_network(self.dis_a, 'D_A', label, self.gpu_ids)
self.save_network(self.gen_b, 'G_B', label, self.gpu_ids)
self.save_network(self.dis_b, 'D_B', label, self.gpu_ids)
def update_learning_rate(self):
lrd = self.config['lr'] / self.opt.niter_decay
lr = self.old_lr - lrd
for param_group in self.gen_a.param_groups:
param_group['lr'] = lr
for param_group in self.gen_b.param_groups:
param_group['lr'] = lr
for param_group in self.dis_a.param_groups:
param_group['lr'] = lr
for param_group in self.dis_b.param_groups:
param_group['lr'] = lr
print('update learning rate: %f -> %f' % (self.old_lr, lr))
self.old_lr = lr |