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UGATIT.py
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import time, itertools
from dataset import ImageFolder
from paddorch.vision import transforms
from paddorch.utils.data import DataLoader
from networks import *
from utils import *
from glob import glob
import paddorch as torch
from paddle import fluid
from visualdl import LogWriter
class UGATIT(object) :
def __init__(self, args):
self.light = args.light
self.is_parallel=args.parallel
self.strategy=args.strategy
self.n_gpu=1
if self.strategy is None:
print("Parallel not success,change to single mode")
self.is_parallel=False
else:
self.n_gpu=self.strategy.nranks
if self.light :
self.model_name = 'UGATIT_light'
else :
self.model_name = 'UGATIT'
self.result_dir = args.result_dir
self.dataset = args.dataset
self.iteration = args.iteration
self.decay_flag = args.decay_flag
self.batch_size = args.batch_size
self.print_freq = args.print_freq
self.save_freq = args.save_freq
self.lr = args.lr
self.weight_decay = args.weight_decay
self.ch = args.ch
""" Weight """
self.adv_weight = args.adv_weight
self.cycle_weight = args.cycle_weight
self.identity_weight = args.identity_weight
self.cam_weight = args.cam_weight
""" Generator """
self.n_res = args.n_res
""" Discriminator """
self.n_dis = args.n_dis
self.img_size = args.img_size
self.img_ch = args.img_ch
self.device = args.device
self.benchmark_flag = args.benchmark_flag
self.resume = args.resume
print()
print("##### Information #####")
print("# light : ", self.light)
print("# dataset : ", self.dataset)
print("# batch_size : ", self.batch_size)
print("# iteration per epoch : ", self.iteration)
print()
print("##### Generator #####")
print("# residual blocks : ", self.n_res)
print()
print("##### Discriminator #####")
print("# discriminator layer : ", self.n_dis)
print()
print("##### Weight #####")
print("# adv_weight : ", self.adv_weight)
print("# cycle_weight : ", self.cycle_weight)
print("# identity_weight : ", self.identity_weight)
print("# cam_weight : ", self.cam_weight)
##################################################################################
# Model
##################################################################################
def build_model(self):
""" DataLoader """
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize((self.img_size + 30, self.img_size+30)),
transforms.RandomCrop(self.img_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
test_transform = transforms.Compose([
transforms.Resize((self.img_size, self.img_size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
self.trainA = ImageFolder(os.path.join('dataset', self.dataset, 'trainA'), train_transform)
self.trainB = ImageFolder(os.path.join('dataset', self.dataset, 'trainB'), train_transform)
self.testA = ImageFolder(os.path.join('dataset', self.dataset, 'testA'), test_transform)
self.testB = ImageFolder(os.path.join('dataset', self.dataset, 'testB'), test_transform)
if self.is_parallel:
from paddorch.utils.data.sampler import DistributedBatchSampler
batch_sampler_trainA=DistributedBatchSampler( self.trainA,self.batch_size)
batch_sampler_trainB = DistributedBatchSampler(self.trainB, self.batch_size)
batch_sampler_testA= DistributedBatchSampler( self.testA,self.batch_size)
batch_sampler_testB = DistributedBatchSampler(self.testB, self.batch_size)
self.trainA_loader = DataLoader(self.trainA, batch_size=1,batch_sampler=batch_sampler_trainA)
self.trainB_loader = DataLoader(self.trainB ,batch_size=1,batch_sampler=batch_sampler_trainB)
self.testA_loader = DataLoader(self.testA ,batch_size=1,batch_sampler=batch_sampler_testA)
self.testB_loader = DataLoader(self.testB ,batch_size=1,batch_sampler=batch_sampler_testB)
else:
self.trainA_loader = DataLoader(self.trainA, batch_size=self.batch_size, shuffle=True )
self.trainB_loader = DataLoader(self.trainB, batch_size=self.batch_size, shuffle=True )
self.testA_loader = DataLoader(self.testA, batch_size=1, shuffle=False )
self.testB_loader = DataLoader(self.testB, batch_size=1, shuffle=False )
""" Define Generator, Discriminator """
self.genA2B = ResnetGenerator(input_nc=3, output_nc=3, ngf=self.ch, n_blocks=self.n_res, img_size=self.img_size, light=self.light)
self.genB2A = ResnetGenerator(input_nc=3, output_nc=3, ngf=self.ch, n_blocks=self.n_res, img_size=self.img_size, light=self.light)
self.disGA = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disGB = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disLA = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
self.disLB = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
if self.is_parallel:
from paddle.fluid.dygraph import DataParallel
self.genA2B=DataParallel(self.genA2B,self.strategy)
self.genB2A = DataParallel(self.genB2A,self.strategy)
self.disGA = DataParallel(self.disGA,self.strategy)
self.disGB = DataParallel(self.disGB,self.strategy)
self.disLA = DataParallel (self.disLA,self.strategy)
self.disLB = DataParallel(self.disLB,self.strategy)
""" Define Loss """
self.L1_loss = nn.L1Loss()
self.MSE_loss = nn.MSELoss()
self.BCE_loss = nn.BCEWithLogitsLoss()
""" Trainer """
self.G_optim = torch.optim.Adam( self.genA2B.parameters()+self.genB2A.parameters() , lr=self.lr, betas=(0.5, 0.999), weight_decay=self.weight_decay)
self.D_optim = torch.optim.Adam( self.disGA.parameters()+self.disGB.parameters()+self.disLA.parameters()+self.disLB.parameters() , lr=self.lr, betas=(0.5, 0.999), weight_decay=self.weight_decay)
# self.G_optim = fluid.contrib.mixed_precision.decorator.decorate(self.G_optim)
# self.D_optim = fluid.contrib.mixed_precision.decorator.decorate(self.D_optim)
""" Define Rho clipper to constraint the value of rho in AdaILN and ILN"""
self.Rho_clipper = RhoClipper(0, 1)
def train(self):
if not self.is_parallel:
writer=LogWriter(logdir=self.result_dir+"/log/")
self.genA2B.train(), self.genB2A.train(), self.disGA.train(), self.disGB.train(), self.disLA.train(), self.disLB.train()
start_iter = 1
if self.resume:
print(self.result_dir, self.dataset ,os.path.join(self.result_dir, self.dataset, 'model', '*.pt'))
model_list = glob(os.path.join(self.result_dir, self.dataset, 'model', '*.pt'))
print("resuming, model_list",model_list)
if not len(model_list) == 0:
model_list.sort()
start_iter = int(model_list[-1].split('_')[-1].split('.')[0])
print("resuming, start_iter",start_iter)
self.load(os.path.join(self.result_dir, self.dataset, 'model'), start_iter)
print(" [*] Load SUCCESS")
if self.decay_flag and start_iter > (self.iteration // 2):
self.G_optim._learning_rate -= (self.lr / (self.iteration // 2)) * (start_iter - self.iteration // 2)
self.D_optim._learning_rate -= (self.lr / (self.iteration // 2)) * (start_iter - self.iteration // 2)
# training loop
print('training start !')
start_time = time.time()
for step in range(start_iter, self.iteration + 1):
if self.decay_flag and step > (self.iteration // 2):
self.G_optim._learning_rate -= (self.lr / (self.iteration // 2))
self.D_optim._learning_rate -= (self.lr / (self.iteration // 2))
try:
real_A, _ = trainA_iter.next()
except:
trainA_iter = iter(self.trainA_loader)
real_A, _ = trainA_iter.next()
try:
real_B, _ = trainB_iter.next()
except:
trainB_iter = iter(self.trainB_loader)
real_B, _ = trainB_iter.next()
real_A=real_A[0]
real_B=real_B[0] ##some handling needed using paddle dataloader
# Update D
if hasattr(self.D_optim, "_optimizer"): # support meta optimizer
self.D_optim._optimizer.clear_gradients()
else:
self.D_optim.clear_gradients()
fake_A2B, _, _ = self.genA2B(real_A)
fake_B2A, _, _ = self.genB2A(real_B)
real_GA_logit, real_GA_cam_logit, _ = self.disGA(real_A)
real_LA_logit, real_LA_cam_logit, _ = self.disLA(real_A)
real_GB_logit, real_GB_cam_logit, _ = self.disGB(real_B)
real_LB_logit, real_LB_cam_logit, _ = self.disLB(real_B)
fake_GA_logit, fake_GA_cam_logit, _ = self.disGA(fake_B2A)
fake_LA_logit, fake_LA_cam_logit, _ = self.disLA(fake_B2A)
fake_GB_logit, fake_GB_cam_logit, _ = self.disGB(fake_A2B)
fake_LB_logit, fake_LB_cam_logit, _ = self.disLB(fake_A2B)
D_ad_loss_GA = self.MSE_loss(real_GA_logit, torch.ones_like(real_GA_logit).to(self.device)) + self.MSE_loss(fake_GA_logit, torch.zeros_like(fake_GA_logit).to(self.device))
D_ad_cam_loss_GA = self.MSE_loss(real_GA_cam_logit, torch.ones_like(real_GA_cam_logit).to(self.device)) + self.MSE_loss(fake_GA_cam_logit, torch.zeros_like(fake_GA_cam_logit).to(self.device))
D_ad_loss_LA = self.MSE_loss(real_LA_logit, torch.ones_like(real_LA_logit).to(self.device)) + self.MSE_loss(fake_LA_logit, torch.zeros_like(fake_LA_logit).to(self.device))
D_ad_cam_loss_LA = self.MSE_loss(real_LA_cam_logit, torch.ones_like(real_LA_cam_logit).to(self.device)) + self.MSE_loss(fake_LA_cam_logit, torch.zeros_like(fake_LA_cam_logit).to(self.device))
D_ad_loss_GB = self.MSE_loss(real_GB_logit, torch.ones_like(real_GB_logit).to(self.device)) + self.MSE_loss(fake_GB_logit, torch.zeros_like(fake_GB_logit).to(self.device))
D_ad_cam_loss_GB = self.MSE_loss(real_GB_cam_logit, torch.ones_like(real_GB_cam_logit).to(self.device)) + self.MSE_loss(fake_GB_cam_logit, torch.zeros_like(fake_GB_cam_logit).to(self.device))
D_ad_loss_LB = self.MSE_loss(real_LB_logit, torch.ones_like(real_LB_logit).to(self.device)) + self.MSE_loss(fake_LB_logit, torch.zeros_like(fake_LB_logit).to(self.device))
D_ad_cam_loss_LB = self.MSE_loss(real_LB_cam_logit, torch.ones_like(real_LB_cam_logit).to(self.device)) + self.MSE_loss(fake_LB_cam_logit, torch.zeros_like(fake_LB_cam_logit).to(self.device))
D_loss_A = self.adv_weight * (D_ad_loss_GA + D_ad_cam_loss_GA + D_ad_loss_LA + D_ad_cam_loss_LA)/self.n_gpu
D_loss_B = self.adv_weight * (D_ad_loss_GB + D_ad_cam_loss_GB + D_ad_loss_LB + D_ad_cam_loss_LB)/self.n_gpu
Discriminator_loss = D_loss_A + D_loss_B
Discriminator_loss.backward()
if self.is_parallel:
self.disGA.apply_collective_grads()
self.disGB.apply_collective_grads()
self.disLA.apply_collective_grads()
self.disLB.apply_collective_grads()
self.genA2B.apply_collective_grads()
self.genB2A.apply_collective_grads()
self.D_optim.minimize(Discriminator_loss)
# Update G
if hasattr(self.G_optim, "_optimizer"): # support meta optimizer
self.G_optim._optimizer.clear_gradients()
else:
self.G_optim.clear_gradients()
fake_A2B, fake_A2B_cam_logit, _ = self.genA2B(real_A)
fake_B2A, fake_B2A_cam_logit, _ = self.genB2A(real_B)
fake_A2B2A, _, _ = self.genB2A(fake_A2B)
fake_B2A2B, _, _ = self.genA2B(fake_B2A)
fake_A2A, fake_A2A_cam_logit, _ = self.genB2A(real_A)
fake_B2B, fake_B2B_cam_logit, _ = self.genA2B(real_B)
fake_GA_logit, fake_GA_cam_logit, _ = self.disGA(fake_B2A)
fake_LA_logit, fake_LA_cam_logit, _ = self.disLA(fake_B2A)
fake_GB_logit, fake_GB_cam_logit, _ = self.disGB(fake_A2B)
fake_LB_logit, fake_LB_cam_logit, _ = self.disLB(fake_A2B)
G_ad_loss_GA = self.MSE_loss(fake_GA_logit, torch.ones_like(fake_GA_logit).to(self.device))
G_ad_cam_loss_GA = self.MSE_loss(fake_GA_cam_logit, torch.ones_like(fake_GA_cam_logit).to(self.device))
G_ad_loss_LA = self.MSE_loss(fake_LA_logit, torch.ones_like(fake_LA_logit).to(self.device))
G_ad_cam_loss_LA = self.MSE_loss(fake_LA_cam_logit, torch.ones_like(fake_LA_cam_logit).to(self.device))
G_ad_loss_GB = self.MSE_loss(fake_GB_logit, torch.ones_like(fake_GB_logit).to(self.device))
G_ad_cam_loss_GB = self.MSE_loss(fake_GB_cam_logit, torch.ones_like(fake_GB_cam_logit).to(self.device))
G_ad_loss_LB = self.MSE_loss(fake_LB_logit, torch.ones_like(fake_LB_logit).to(self.device))
G_ad_cam_loss_LB = self.MSE_loss(fake_LB_cam_logit, torch.ones_like(fake_LB_cam_logit).to(self.device))
G_recon_loss_A = self.L1_loss(fake_A2B2A, real_A)
G_recon_loss_B = self.L1_loss(fake_B2A2B, real_B)
G_identity_loss_A = self.L1_loss(fake_A2A, real_A)
G_identity_loss_B = self.L1_loss(fake_B2B, real_B)
G_cam_loss_A = self.BCE_loss(fake_B2A_cam_logit, torch.ones_like(fake_B2A_cam_logit).to(self.device)) + self.BCE_loss(fake_A2A_cam_logit, torch.zeros_like(fake_A2A_cam_logit).to(self.device))
G_cam_loss_B = self.BCE_loss(fake_A2B_cam_logit, torch.ones_like(fake_A2B_cam_logit).to(self.device)) + self.BCE_loss(fake_B2B_cam_logit, torch.zeros_like(fake_B2B_cam_logit).to(self.device))
G_loss_A = (self.adv_weight * (G_ad_loss_GA + G_ad_cam_loss_GA + G_ad_loss_LA + G_ad_cam_loss_LA) + self.cycle_weight * G_recon_loss_A + self.identity_weight * G_identity_loss_A + self.cam_weight * G_cam_loss_A)/self.n_gpu
G_loss_B = (self.adv_weight * (G_ad_loss_GB + G_ad_cam_loss_GB + G_ad_loss_LB + G_ad_cam_loss_LB) + self.cycle_weight * G_recon_loss_B + self.identity_weight * G_identity_loss_B + self.cam_weight * G_cam_loss_B)/self.n_gpu
Generator_loss = G_loss_A + G_loss_B
Generator_loss.backward()
if self.is_parallel:
self.disGA.apply_collective_grads()
self.disGB.apply_collective_grads()
self.disLA.apply_collective_grads()
self.disLB.apply_collective_grads()
self.genA2B.apply_collective_grads()
self.genB2A.apply_collective_grads()
self.G_optim.minimize(Generator_loss)
# clip parameter of AdaILN and ILN, applied after optimizer step
self.Rho_clipper(self.genA2B)
self.Rho_clipper(self.genB2A)
if not self.is_parallel:
writer.add_scalar(tag="G/G_loss_A", step=step, value=G_loss_A.numpy())
writer.add_scalar(tag="G/G_loss_B", step=step, value=G_loss_B.numpy())
writer.add_scalar(tag="D/D_loss_A", step=step, value=D_loss_A.numpy())
writer.add_scalar(tag="D/D_loss_B", step=step, value=D_loss_B.numpy())
writer.add_scalar(tag="D/Discriminator_loss", step=step, value=Discriminator_loss.numpy())
writer.add_scalar(tag="D/Generator_loss", step=step, value=Generator_loss.numpy())
if step%10==9:
writer.add_image("fake_A2B", (porch.Tensor(fake_A2B[0]*255)).clamp_(0, 255).numpy().transpose([1,2,0]).astype(np.uint8), step )
writer.add_image("fake_B2A", (porch.Tensor(fake_B2A[0]*255)).clamp_(0, 255).numpy().transpose([1,2,0]).astype(np.uint8),step )
writer.add_image("fake_A2B2A", (porch.Tensor(fake_A2B[0]*255)).clamp_(0, 255).numpy().transpose([1,2,0]).astype(np.uint8), step )
writer.add_image("fake_B2A2B", (porch.Tensor(fake_B2A[0]*255)).clamp_(0, 255).numpy().transpose([1,2,0]).astype(np.uint8),step )
print("[%5d/%5d] time: %4.4f d_loss: %.8f, g_loss: %.8f" % (step, self.iteration, time.time() - start_time, Discriminator_loss, Generator_loss))
if step % self.print_freq == 0:
train_sample_num = 5
test_sample_num = 5
A2B = np.zeros((self.img_size * 7, 0, 3))
B2A = np.zeros((self.img_size * 7, 0, 3))
self.genA2B.eval(), self.genB2A.eval(), self.disGA.eval(), self.disGB.eval(), self.disLA.eval(), self.disLB.eval()
for _ in range(train_sample_num):
try:
real_A, _ = trainA_iter.next()
except:
trainA_iter = iter(self.trainA_loader)
real_A, _ = trainA_iter.next()
try:
real_B, _ = trainB_iter.next()
except:
trainB_iter = iter(self.trainB_loader)
real_B, _ = trainB_iter.next()
real_A,real_B=real_A[0],real_B[0]
fake_A2B, _, fake_A2B_heatmap = self.genA2B(real_A)
fake_B2A, _, fake_B2A_heatmap = self.genB2A(real_B)
fake_A2B2A, _, fake_A2B2A_heatmap = self.genB2A(fake_A2B)
fake_B2A2B, _, fake_B2A2B_heatmap = self.genA2B(fake_B2A)
fake_A2A, _, fake_A2A_heatmap = self.genB2A(real_A)
fake_B2B, _, fake_B2B_heatmap = self.genA2B(real_B)
A2B = np.concatenate((A2B, np.concatenate((RGB2BGR(tensor2numpy(denorm(real_A[0]))),
cam(tensor2numpy(fake_A2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2A[0]))),
cam(tensor2numpy(fake_A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B[0]))),
cam(tensor2numpy(fake_A2B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B2A[0])))), 0)), 1)
B2A = np.concatenate((B2A, np.concatenate((RGB2BGR(tensor2numpy(denorm(real_B[0]))),
cam(tensor2numpy(fake_B2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2B[0]))),
cam(tensor2numpy(fake_B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A[0]))),
cam(tensor2numpy(fake_B2A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A2B[0])))), 0)), 1)
for _ in range(test_sample_num):
try:
real_A, _ = testA_iter.next()
except:
testA_iter = iter(self.testA_loader)
real_A, _ = testA_iter.next()
try:
real_B, _ = testB_iter.next()
except:
testB_iter = iter(self.testB_loader)
real_B, _ = testB_iter.next()
real_A, real_B = real_A[0], real_B[0]
fake_A2B, _, fake_A2B_heatmap = self.genA2B(real_A)
fake_B2A, _, fake_B2A_heatmap = self.genB2A(real_B)
fake_A2B2A, _, fake_A2B2A_heatmap = self.genB2A(fake_A2B)
fake_B2A2B, _, fake_B2A2B_heatmap = self.genA2B(fake_B2A)
fake_A2A, _, fake_A2A_heatmap = self.genB2A(real_A)
fake_B2B, _, fake_B2B_heatmap = self.genA2B(real_B)
A2B = np.concatenate((A2B, np.concatenate((RGB2BGR(tensor2numpy(denorm(real_A[0]))),
cam(tensor2numpy(fake_A2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2A[0]))),
cam(tensor2numpy(fake_A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B[0]))),
cam(tensor2numpy(fake_A2B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B2A[0])))), 0)), 1)
B2A = np.concatenate((B2A, np.concatenate((RGB2BGR(tensor2numpy(denorm(real_B[0]))),
cam(tensor2numpy(fake_B2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2B[0]))),
cam(tensor2numpy(fake_B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A[0]))),
cam(tensor2numpy(fake_B2A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A2B[0])))), 0)), 1)
if (not self.is_parallel) or fluid.dygraph.parallel.Env().local_rank == 0:
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'img', 'A2B_%07d.png' % step), A2B * 255.0)
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'img', 'B2A_%07d.png' % step), B2A * 255.0)
self.genA2B.train(), self.genB2A.train(), self.disGA.train(), self.disGB.train(), self.disLA.train(), self.disLB.train()
if step % self.save_freq == 0:
if (not self.is_parallel) or fluid.dygraph.parallel.Env().local_rank == 0 :
self.save(os.path.join(self.result_dir, self.dataset, 'model'), step)
if step % 1000 == 0:
params = {}
params['genA2B'] = self.genA2B.state_dict()
params['genB2A'] = self.genB2A.state_dict()
params['disGA'] = self.disGA.state_dict()
params['disGB'] = self.disGB.state_dict()
params['disLA'] = self.disLA.state_dict()
params['disLB'] = self.disLB.state_dict()
if (not self.is_parallel) or fluid.dygraph.parallel.Env().local_rank == 0:
torch.save(params, os.path.join(self.result_dir, self.dataset + '_params_latest.pt'))
def save(self, dir, step):
params = {}
params['genA2B'] = self.genA2B.state_dict()
params['genB2A'] = self.genB2A.state_dict()
params['disGA'] = self.disGA.state_dict()
params['disGB'] = self.disGB.state_dict()
params['disLA'] = self.disLA.state_dict()
params['disLB'] = self.disLB.state_dict()
torch.save(params, os.path.join(dir, self.dataset + '_params_%07d.pt' % step))
def load(self, dir, step):
params = torch.load(os.path.join(dir, self.dataset + '_params_%07d.pt' % step))
self.genA2B.set_dict(params['genA2B'])
self.genB2A.set_dict(params['genB2A'])
self.disGA.set_dict(params['disGA'])
self.disGB.set_dict(params['disGB'])
self.disLA.set_dict(params['disLA'])
self.disLB.set_dict(params['disLB'])
def test(self):
model_list = glob(os.path.join(self.result_dir, self.dataset, 'model', '*.pt'))
if not len(model_list) == 0:
model_list.sort()
iter = int(model_list[-1].split('_')[-1].split('.')[0])
self.load(os.path.join(self.result_dir, self.dataset, 'model'), iter)
print(" [*] Load SUCCESS")
else:
print(" [*] Load FAILURE")
return
self.genA2B.eval(), self.genB2A.eval()
for n, (real_A, _) in enumerate(self.testA_loader):
real_A = real_A[0]
fake_A2B, _, fake_A2B_heatmap = self.genA2B(real_A)
fake_A2B2A, _, fake_A2B2A_heatmap = self.genB2A(fake_A2B)
fake_A2A, _, fake_A2A_heatmap = self.genB2A(real_A)
A2B = np.concatenate((RGB2BGR(tensor2numpy(denorm(real_A[0]))),
cam(tensor2numpy(fake_A2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2A[0]))),
cam(tensor2numpy(fake_A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B[0]))),
cam(tensor2numpy(fake_A2B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_A2B2A[0])))), 0)
A2B_single=RGB2BGR(tensor2numpy(denorm(fake_A2B[0])))
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'test', 'A2B_%d.png' % (n + 1)), A2B * 255.0)
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'test', 'A2B_%d_single.png' % (n + 1)), A2B_single * 255.0)
for n, (real_B, _) in enumerate(self.testB_loader):
real_B = real_B[0]
fake_B2A, _, fake_B2A_heatmap = self.genB2A(real_B)
fake_B2A2B, _, fake_B2A2B_heatmap = self.genA2B(fake_B2A)
fake_B2B, _, fake_B2B_heatmap = self.genA2B(real_B)
B2A = np.concatenate((RGB2BGR(tensor2numpy(denorm(real_B[0]))),
cam(tensor2numpy(fake_B2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2B[0]))),
cam(tensor2numpy(fake_B2A_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A[0]))),
cam(tensor2numpy(fake_B2A2B_heatmap[0]), self.img_size),
RGB2BGR(tensor2numpy(denorm(fake_B2A2B[0])))), 0)
B2A_single=RGB2BGR(tensor2numpy(denorm(fake_B2A[0])))
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'test', 'B2A_%d.png' % (n + 1)), B2A * 255.0)
cv2.imwrite(os.path.join(self.result_dir, self.dataset, 'test', 'B2A_%d_single.png' % (n + 1)), B2A_single * 255.0)