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Train_Guide.py
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Train_Guide.py
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import os
import cv2
import glob
import skimage
import skimage.io
import skimage.metrics
import numpy as np
import torch
import shutil
from tensorboardX import SummaryWriter
from Options import BaseOptions
from Dataset import CreateDataLoader
from Models import CreateModel
from Optimizers import Get_Optimizer
from Losses import Get_LossFunction
from Utils import GetLogger, Get_Time_Stamp, AverageMeter, Accuracy
os.environ['CUDA_VISIBLE_DEVICES'] = '4'
torch.backends.cudnn.benchmark = True
def Setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
class Mainer(object):
def __init__(self, cfgs):
self.cfgs = cfgs
self.train_dataloader, self.T1_Val_dataloader, self.T2_Val_dataloader, self.T1_Test_dataloader, self.T2_Test_dataloader= CreateDataLoader(self.cfgs)
self.epoch_batchs = len(self.train_dataloader)
self.model = CreateModel(self.cfgs)
self.model.cuda()
if(self.cfgs.preTrained != ''):
self.model.load_params(self.cfgs.preTrained)
self.model = torch.nn.DataParallel(self.model)
self.optim_schedulers = Get_Optimizer(self.cfgs, self.model, self.epoch_batchs)
self.loss_meter = Get_LossFunction(self.cfgs)
self.writer = SummaryWriter(self.cfgs.Save_dir)
shutil.copyfile('Options/options.py', self.cfgs.Save_dir + '/config.py')
shutil.copyfile('Train_Guide.py', self.cfgs.Save_dir + '/Train_Guide.py')
def train(self,):
best_PSNR = 0
best_SSIM = 0
best_epoch = 0
for epoch in range(self.cfgs.max_epoch):
self.train_epoch(epoch)
if(epoch >0 and epoch %3 ==0):
self.T1_Test(epoch)
self.T2_Test(epoch)
PSNR_T1, SSIM_T1 = self.T1_Val(epoch)
PSNR_T2, SSIM_T2 = self.T2_Val(epoch)
SSIM = (SSIM_T2 + SSIM_T1)/2.0
PSNR = (PSNR_T2 + PSNR_T1)/2.0
if(SSIM > best_SSIM):
best_SSIM = SSIM
best_PSNR = PSNR
best_epoch = epoch
save_file = os.path.join(self.cfgs.Save_dir, 'Model/Epoch_best.pth')
torch.save(self.model.state_dict(),save_file)
files = glob.glob(self.cfgs.Save_dir + '/T2/Pair*.png')
for file in files:
shutil.copyfile(file, file.replace('/T2/', '/T2/B_'))
files = glob.glob(self.cfgs.Save_dir + '/T1/Image*.png')
for file in files:
shutil.copyfile(file, file.replace('/T1/', '/T1/B_'))
if(epoch >20 and epoch %2 ==0):
save_file = os.path.join(self.cfgs.Save_dir, f'Model/Epoch_{epoch}.pth')
torch.save(self.model.state_dict(),save_file)
logger.info(f"Best_Epoch: {best_epoch}, best_PSNR: {best_PSNR:.5f}, best_SSIM: {best_SSIM:.5f} \r")
def train_epoch(self, epoch):
log_Loss = AverageMeter()
log_SSIM = AverageMeter()
log_PSNR = AverageMeter()
log_DirAcc = AverageMeter()
log_TmpAcc = AverageMeter()
self.model.train()
for step, data in enumerate(self.train_dataloader):
Img_Input = data['Img_Input'].cuda()
Depth_Input = data['Depth_Input'].cuda()
InTmp = data['InTmp'].cuda()
InDir = data['InDir'].cuda()
Img_Guide = data['Img_Guide'].cuda()
Depth_Guide = data['Depth_Guide'].cuda()
GdTmp = data['GdTmp'].cuda()
GdDir = data['GdDir'].cuda()
Img_Target = data['Img_Target'].cuda()
GtTmp = data['GtTmp'].cuda()
GtDir = data['GtDir'].cuda()
CVaild = data['CVaild'].cuda()
FValid = data['FValid'].cuda()
fake_img, pinlight, pgdlight, pinTmp, pgdTmp = self.model(Img_Input, Depth_Input, Img_Guide, Depth_Guide)
loss_L1 = self.loss_meter['criterionMSE'](fake_img, Img_Target,FValid)
loss_SSIM = self.loss_meter['criterionSSIM'](fake_img, Img_Target,FValid)
if(self.cfgs.useGrad):
loss_Grad = self.loss_meter['criterionGrad'](fake_img, Img_Target,FValid)
if(self.cfgs.useLPIPS):
loss_LPIPS = self.loss_meter['criterionLPIPS'](fake_img, Img_Target,FValid)
loss_input_dir = self.loss_meter['criterionFDIR'](pinlight, InDir, CVaild)
loss_guide_dir = self.loss_meter['criterionFDIR'](pgdlight, GdDir, CVaild)
loss_input_tmp = self.loss_meter['criterionFTMP'](pinTmp, InTmp, CVaild)
loss_guide_tmp = self.loss_meter['criterionFTMP'](pgdTmp, GdTmp, CVaild)
loss = loss_L1 + 1.0*(1-loss_SSIM) + loss_input_dir + loss_guide_dir + loss_input_tmp + loss_guide_tmp
if(self.cfgs.useLPIPS):
loss += 0.01*loss_LPIPS.mean()
if(self.cfgs.useGrad):
loss += loss_Grad
self.optim_schedulers['optimizer'].zero_grad()
loss.backward()
self.optim_schedulers['optimizer'].step()
Indir_acc1 = Accuracy(pinlight, InDir, topk=(1,))
Gddir_acc1 = Accuracy(pgdlight, GdDir, topk=(1,))
Intmp_acc1 = Accuracy(pinTmp, InTmp, topk=(1,))
Gdtmp_acc1 = Accuracy(pgdTmp, GdTmp, topk=(1,))
log_DirAcc.update((Indir_acc1[0].item() + Gddir_acc1[0].item())/2,len(GtDir))
log_TmpAcc.update((Intmp_acc1[0].item() + Gdtmp_acc1[0].item())/2,len(GtDir))
log_Loss.update(loss.item(), self.cfgs.batchSize)
log_SSIM.update(loss_SSIM.item(), self.cfgs.batchSize)
# log_PSNR.update(loss_PSNR.item(), self.cfgs.batchSize)
if step%50 == 0:
self.writer.add_scalar('log_Loss:', log_Loss.avg, self.epoch_batchs*epoch + step//10)
self.writer.add_scalar('log_SSIM:', log_SSIM.avg, self.epoch_batchs*epoch + step//10)
logger.info(f"iter: {step}/{self.epoch_batchs}/{epoch}, log_Loss: {log_Loss.avg:.5f}, log_DirAcc: {log_DirAcc.avg:.5f}, "
f"log_TmpAcc: {log_TmpAcc.avg:.5f}, loss_SSIM: {log_SSIM.avg:.5f}, log_PSNR: {log_PSNR.avg:.5f}, LR: {'%e'%self.optim_schedulers['optimizer'].param_groups[0]['lr']}\r")
self.optim_schedulers['scheduler'].step()
def T1_Val(self, epoch):
self.model.eval()
with torch.no_grad():
Rc_Lists = []
TargetPaths = []
for step, data in enumerate(self.T1_Val_dataloader):
Depth_Input = data['Depth_Input'].cuda()
Img_Input = data['Img_Input'].cuda()
InTmp = data['InTmp'].cuda()
InDir = data['InDir'].cuda()
Depth_Guide = data['Depth_Guide'].cuda()
Img_Guide = data['Img_Guide'].cuda()
GdTmp = data['GdTmp'].cuda()
GdDir = data['GdDir'].cuda()
Img_Target = data['Img_Target'].cuda()
GtPath = data['GtPath'][0]
TargetPaths.append(GtPath)
fake_img, _, _, _, _ = self.model(Img_Input, Depth_Input, Img_Guide, Depth_Guide)
fake_img = fake_img[0].cpu().data.numpy()
fake_img = fake_img.transpose((1,2,0))
fake_img = np.squeeze(fake_img)
fake_img = (fake_img*255.0).astype(np.uint8)
fake_img = cv2.resize(fake_img, (1024, 1024))
Rc_Lists.append(fake_img)
cv2.imwrite(os.path.join(self.cfgs.Save_dir, 'V1/'+GtPath.split('/')[-1]), fake_img)
PSNRs = []
SSIMs = []
for rc, gtpath in zip(Rc_Lists, TargetPaths):
gt = cv2.imread(gtpath)
gt = cv2.cvtColor(gt,cv2.COLOR_BGR2RGB)
rc = cv2.cvtColor(rc,cv2.COLOR_BGR2RGB)
PSNRs.append(skimage.metrics.peak_signal_noise_ratio(gt, rc))
SSIMs.append(skimage.metrics.structural_similarity(gt, rc, multichannel=True))
PSNR = np.mean(PSNRs)
SSIM = np.mean(SSIMs)
self.Rc_Lists = []
logger.info(f"T1--Epoch: {epoch}, PSNR: {PSNR:.5f}, SSIM: {SSIM:.5f} \r")
return PSNR, SSIM
def T2_Val(self, epoch):
self.model.eval()
with torch.no_grad():
Rc_Lists = []
TargetPaths = []
for step, data in enumerate(self.T2_Val_dataloader):
Depth_Input = data['Depth_Input'].cuda()
Img_Input = data['Img_Input'].cuda()
InTmp = data['InTmp'].cuda()
InDir = data['InDir'].cuda()
Depth_Guide = data['Depth_Guide'].cuda()
Img_Guide = data['Img_Guide'].cuda()
GdTmp = data['GdTmp'].cuda()
GdDir = data['GdDir'].cuda()
Img_Target = data['Img_Target'].cuda()
GtPath = data['GtPath'][0]
TargetPaths.append(GtPath)
fake_img, _, _, _, _ = self.model(Img_Input, Depth_Input, Img_Guide, Depth_Guide)
fake_img = fake_img[0].cpu().data.numpy()
fake_img = fake_img.transpose((1,2,0))
fake_img = np.squeeze(fake_img)
fake_img = (fake_img*255.0).astype(np.uint8)
fake_img = cv2.resize(fake_img, (512, 512))
Rc_Lists.append(fake_img)
cv2.imwrite(os.path.join(self.cfgs.Save_dir, 'V2/'+GtPath.split('/')[-1]), fake_img)
PSNRs = []
SSIMs = []
for rc, gtpath in zip(Rc_Lists, TargetPaths):
gt = cv2.imread(gtpath)
gt = cv2.cvtColor(gt,cv2.COLOR_BGR2RGB)
rc = cv2.cvtColor(rc,cv2.COLOR_BGR2RGB)
PSNRs.append(skimage.metrics.peak_signal_noise_ratio(gt, rc))
SSIMs.append(skimage.metrics.structural_similarity(gt, rc, multichannel=True))
PSNR = np.mean(PSNRs)
SSIM = np.mean(SSIMs)
self.Rc_Lists = []
logger.info(f"T2--Epoch: {epoch}, PSNR: {PSNR:.5f}, SSIM: {SSIM:.5f} \r")
return PSNR, SSIM
def T1_Test(self, epoch):
self.model.eval()
with torch.no_grad():
for step, data in enumerate(self.T1_Test_dataloader):
Depth_Input = data['Depth_Input'].cuda()
Img_Input = data['Img_Input'].cuda()
InTmp = data['InTmp'].cuda()
InDir = data['InDir'].cuda()
Depth_Guide = data['Depth_Guide'].cuda()
Img_Guide = data['Img_Guide'].cuda()
GdTmp = data['GdTmp'].cuda()
GdDir = data['GdDir'].cuda()
InFile = data['InPath'][0]
fake_img, _, _, _, _ = self.model(Img_Input, Depth_Input, Img_Guide, Depth_Guide)
fake_img = fake_img[0].cpu().data.numpy()
fake_img = fake_img.transpose((1,2,0))
fake_img = np.squeeze(fake_img)
fake_img = (fake_img*255.0).astype(np.uint8)
fake_img = cv2.resize(fake_img, (1024, 1024))
cv2.imwrite(os.path.join(self.cfgs.Save_dir, 'T1/'+InFile.split('/')[-1]), fake_img)
logger.info(f"T1--Epoch: {epoch}, Test \r")
def T2_Test(self, epoch):
self.model.eval()
with torch.no_grad():
for step, data in enumerate(self.T2_Test_dataloader):
Depth_Input = data['Depth_Input'].cuda()
Img_Input = data['Img_Input'].cuda()
InTmp = data['InTmp'].cuda()
InDir = data['InDir'].cuda()
Depth_Guide = data['Depth_Guide'].cuda()
Img_Guide = data['Img_Guide'].cuda()
GdTmp = data['GdTmp'].cuda()
GdDir = data['GdDir'].cuda()
InFile = data['InPath'][0]
fake_img, _, _, _, _ = self.model(Img_Input, Depth_Input, Img_Guide, Depth_Guide)
fake_img = fake_img[0].cpu().data.numpy()
fake_img = fake_img.transpose((1,2,0))
fake_img = np.squeeze(fake_img)
fake_img = (fake_img*255.0).astype(np.uint8)
fake_img = cv2.resize(fake_img, (1024, 1024))
cv2.imwrite(os.path.join(self.cfgs.Save_dir, 'T2/'+InFile.split('/')[-1]), fake_img)
logger.info(f"T2--Epoch: {epoch}, Test \r")
if __name__ == '__main__':
# Setup_seed(233)
train_opts = BaseOptions()
train_opts.Save_dir = os.path.join(train_opts.Save_dir, train_opts.model +'_'+Get_Time_Stamp())
if(not os.path.exists(train_opts.Save_dir)):
os.makedirs(train_opts.Save_dir)
if(not os.path.exists(train_opts.Save_dir + '/T1')):
os.makedirs(train_opts.Save_dir + '/T1')
if(not os.path.exists(train_opts.Save_dir + '/T2')):
os.makedirs(train_opts.Save_dir + '/T2')
if(not os.path.exists(train_opts.Save_dir + '/V1')):
os.makedirs(train_opts.Save_dir + '/V1')
if(not os.path.exists(train_opts.Save_dir + '/V2')):
os.makedirs(train_opts.Save_dir + '/V2')
if(not os.path.exists(train_opts.Save_dir + '/Model')):
os.makedirs(train_opts.Save_dir + '/Model')
logger = GetLogger("Relight" , train_opts.Save_dir + '/' + train_opts.model +'.log')
logger.info("startup... \r")
mainer = Mainer(train_opts)
mainer.train()