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)
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_unaligned, batch_size=opt.batchSize, shuffle=not opt.serial_batches, # train_dataset_fusion
num_workers=opt.nThreads, pin_memory=True)
eval_dataset_ceilnet = datasets.CEILTestDataset(join(datadir, 'testdata_CEILNET_table2'))
eval_dataset_real = datasets.CEILTestDataset(
join(datadir, 'real20'),
fns=read_fns('real_test.txt'))
eval_dataloader_ceilnet = datasets.DataLoader(
eval_dataset_ceilnet, batch_size=1, shuffle=False,
num_workers=opt.nThreads, pin_memory=True)
eval_dataloader_real = datasets.DataLoader(
eval_dataset_real, batch_size=1, shuffle=False,
num_workers=opt.nThreads, pin_memory=True)
# -------------------- engine start ---------------------------
engine = Engine(opt)
# ----------------------Main Loop for direct training---------------------------
engine.model.opt.lambda_gan = 0
# engine.model.opt.lambda_gan = 0.01
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)
engine = Engine(opt)
"""Main Loop"""
def set_learning_rate(lr):
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.serial_batches = True
opt.serial_batches = True
opt.no_flip = True
datadir_syn = '/content/gdrive/My Drive/Datasets/SIRR/voc_reshaped_224x224/'
datadir_real = '/content/gdrive/My Drive/Datasets/SIRR/real_dataset_CEILNet_Berkley/real/'
train_dataset = datasets.CEILDataset(datadir_syn,
read_fns('VOC17k_train.txt'),
size=opt.max_dataset_size,
enable_transforms=True,
low_sigma=opt.low_sigma,
high_sigma=opt.high_sigma,
low_gamma=opt.low_gamma,
high_gamma=opt.high_gamma)
train_dataset_real = datasets.CEILTestDataset(datadir_real,
fns=read_fns('real_train.txt'),
enable_transforms=True)
# train_dataset_fusion = datasets.FusionDataset([train_dataset, train_dataset_real], [0.7, 0.3])
train_dataset_fusion = train_dataset
train_dataloader_fusion = datasets.DataLoader(train_dataset_fusion,
batch_size=opt.batchSize,
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/'
datadir_syn = join(datadir, 'VOCdevkit/VOC2012/PNGImages')
datadir_real = join(datadir, 'real_train')
train_dataset = datasets.CEILDataset(datadir_syn,
read_fns('VOC2012_224_train_png.txt'),
size=opt.max_dataset_size,
enable_transforms=True,
low_sigma=opt.low_sigma,
high_sigma=opt.high_sigma,
low_gamma=opt.low_gamma,
high_gamma=opt.high_gamma)
train_dataset_real = datasets.CEILTestDataset(datadir_real,
enable_transforms=True)
train_dataset_fusion = datasets.FusionDataset(
[train_dataset, train_dataset_real], [0.7, 0.3])
train_dataloader_fusion = datasets.DataLoader(train_dataset_fusion,
batch_size=opt.batchSize,
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 = '/media/kaixuan/DATA/Papers/Code/Data/Reflection/'
datadir_syn = join(datadir, 'VOCdevkit/VOC2012/PNGImages')
datadir_real = join(datadir, 'real_train')
train_dataset = datasets.CEILDataset(
datadir_syn, read_fns('VOC2012_224_train_png.txt'), size=opt.max_dataset_size, enable_transforms=True,
low_sigma=opt.low_sigma, high_sigma=opt.high_sigma,
low_gamma=opt.low_gamma, high_gamma=opt.high_gamma)
train_dataset_real = datasets.CEILTestDataset(datadir_real, enable_transforms=True)
train_dataset_fusion = datasets.FusionDataset([train_dataset, train_dataset_real], [0.7, 0.3])
train_dataloader_fusion = datasets.DataLoader(
train_dataset_fusion, batch_size=opt.batchSize, shuffle=not opt.serial_batches,
num_workers=opt.nThreads, pin_memory=True)
eval_dataset_ceilnet = datasets.CEILTestDataset(join(datadir, 'testdata_CEILNET_table2'))
eval_dataset_real = datasets.CEILTestDataset(
join(datadir, 'real20'),