import torch import os import shutil os.environ["CUDA_VISIBLE_DEVICES"] = '3' def set_learning_rate(lr): for optimizer in engine.model.optimizers: util.set_opt_param(optimizer, 'lr', lr) # python train_errnet_unaligned.py --name my_errnet --hyper --unaligned_loss vgg --save_epoch_freq 10 # python train_errnet_unaligned.py --name origin_errnet --hyper --unaligned_loss vgg --save_epoch_freq 10 if __name__ == "__main__": mp.set_start_method('spawn') opt = TrainOptions().parse() cudnn.benchmark = True opt.display_freq = 10 # copy from train_errnet.py datadir = opt.root_dir # datadir = '/media/kaixuan/DATA/Papers/Code/Data/Reflection/' # -------------delete previous model checkpoints -------------- checkpoint_path = join(opt.checkpoints_dir, opt.name) if os.path.exists(checkpoint_path): shutil.rmtree(checkpoint_path) # ----------------------- data preparation ------------------- # datadir_syn = join(datadir, 'VOCdevkit/VOC2012/PNGImages') # datadir_real = join(datadir, 'real_train') datadir_unaligned = join(datadir, 'unaligned', 'unaligned_train400') # train_dataset = datasets.CEILDataset(datadir_syn, read_fns('VOC2012_224_train_png.txt'), size=opt.max_dataset_size) # train_dataset_real = datasets.CEILTestDataset(datadir_real, enable_transforms=True)
from os.path import join from options.errnet.train_options import TrainOptions from engine import Engine from data.image_folder import read_fns import torch.backends.cudnn as cudnn import data.reflect_dataset as datasets import util.util as util import data opt = TrainOptions().parse() cudnn.benchmark = True # modify the following code to datadir = '/media/kaixuan/DATA/Papers/Code/Data/Reflection/' datadir_syn = join(datadir, 'VOCdevkit/VOC2012/PNGImages') datadir_real = join(datadir, 'real_train') datadir_unaligned = join(datadir, 'unaligned', 'unaligned_train250') train_dataset = datasets.CEILDataset(datadir_syn, read_fns('VOC2012_224_train_png.txt'), size=opt.max_dataset_size) train_dataset_real = datasets.CEILTestDataset(datadir_real, enable_transforms=True) train_dataset_unaligned = datasets.CEILTestDataset(datadir_unaligned, enable_transforms=True, flag={'unaligned':True}, size=None) train_dataset_fusion = datasets.FusionDataset([train_dataset, train_dataset_unaligned, train_dataset_real], [0.25,0.5,0.25]) train_dataloader_fusion = datasets.DataLoader( train_dataset_fusion, batch_size=opt.batchSize, shuffle=not opt.serial_batches, num_workers=opt.nThreads, pin_memory=True)
from os.path import join from options.errnet.train_options import TrainOptions from engine import Engine from data.image_folder import read_fns import torch.backends.cudnn as cudnn import data.reflect_dataset as datasets import util.util as util import data opt = TrainOptions().parse() cudnn.benchmark = True opt.display_freq = 10 if opt.debug: opt.display_id = 1 opt.display_freq = 20 opt.print_freq = 20 opt.nEpochs = 40 opt.max_dataset_size = 100 opt.no_log = False opt.nThreads = 0 opt.decay_iter = 0 opt.serial_batches = True opt.no_flip = True # modify the following code to datadir = '/home/centos/reflection_removal/train_dataset/' #'/media/kaixuan/DATA/Papers/Code/Data/Reflection/'
from os.path import join, basename from options.errnet.train_options import TrainOptions from engine import Engine from data.image_folder import read_fns from data.transforms import __scale_width import torch.backends.cudnn as cudnn import data.reflect_dataset as datasets import util.util as util opt = TrainOptions().parse() opt.isTrain = False cudnn.benchmark = True opt.no_log = True opt.display_id = 0 opt.verbose = False datadir = opt.root_dir # datadir = '/media/kaixuan/DATA/Papers/Code/Data/Reflection/' # Define evaluation/test dataset eval_dataset_ceilnet = datasets.CEILTestDataset( join(datadir, 'testdata_CEILNET_table2')) eval_dataset_sir2 = datasets.CEILTestDataset(join(datadir, 'sir2_withgt')) eval_dataset_real = datasets.CEILTestDataset(join(datadir, 'real20'), fns=read_fns('real_test.txt'), size=20) eval_dataset_postcard = datasets.CEILTestDataset(join(datadir, 'postcard')) eval_dataset_solidobject = datasets.CEILTestDataset(
import torch.backends.cudnn as cudnn import data.reflect_dataset as datasets import util.util as util import data import torch.multiprocessing as mp import torch import os os.environ["CUDA_VISIBLE_DEVICES"] = '3' # python eval_best.py --name my_errnet --hyper -r --unaligned_loss vgg # python eval_best.py --name origin_errnet --hyper -r --unaligned_loss vgg if __name__ == "__main__": mp.set_start_method('spawn') opt = TrainOptions().parse() opt.isTrain = True cudnn.benchmark = True opt.no_log = True opt.display_id = 0 opt.verbose = False datadir = opt.root_dir # ----------------------- data preparation ------------------- eval_dataset_ceilnet = datasets.CEILTestDataset( join(datadir, 'testdata_CEILNET_table2')) eval_dataset_real = datasets.CEILTestDataset(join(datadir, 'real20'), fns=read_fns('real_test.txt'))