# -------------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)
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)
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):
for optimizer in engine.model.optimizers:
util.set_opt_param(optimizer, 'lr', lr)
set_learning_rate(1e-4)
while engine.epoch < 80:
if engine.epoch == 65:
set_learning_rate(5e-5)
if engine.epoch == 70:
fns=read_fns('real_test.txt'),
size=20)
eval_dataset_postcard = datasets.CEILTestDataset(join(datadir, 'postcard'))
eval_dataset_solidobject = datasets.CEILTestDataset(
join(datadir, 'solidobject'))
# test_dataset_internet = datasets.RealDataset(join(datadir, 'internet'))
# test_dataset_unaligned300 = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned300/blended'))
# test_dataset_unaligned150 = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned150/blended'))
# test_dataset_unaligned_dynamic = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned_dynamic/blended'))
# test_dataset_sir2 = datasets.RealDataset(join(datadir, 'sir2_wogt/blended'))
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)
eval_dataloader_sir2 = datasets.DataLoader(eval_dataset_sir2,
batch_size=1,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
if __name__ == "__main__":
mp.set_start_method('spawn')
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/'
test_dataset_unaligned = datasets.RealDataset(
join(datadir, 'unaligned/unaligned_test50/blended'))
test_dataloader_unaligned = datasets.DataLoader(test_dataset_unaligned,
batch_size=1,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
engine = Engine(opt)
"""Main Loop"""
result_dir = join(datadir, 'results/' + opt.name)
if os.path.exists(result_dir): # delete previous model results
shutil.rmtree(result_dir)
print(result_dir, 'removed')
os.mkdir(result_dir)
res = engine.test(test_dataloader_unaligned,
savedir=join(result_dir, 'unaligned_test50'))
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'))
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 ---------------------------
best_val_loss = 1e-6
for root, _, fnames in sorted(os.walk(join(opt.checkpoints_dir,
opt.name))):
for fname in fnames:
if fname.endswith('.pt') and 'best' not in fname:
join(datadir, 'real20'),
fns=read_fns('real_test.txt'),
size=20)
eval_dataset_postcard = datasets.CEILTestDataset(join(datadir, 'postcard'))
eval_dataset_solidobject = datasets.CEILTestDataset(join(datadir, 'solidobject'))
test_dataset_internet = datasets.RealDataset(join(datadir, 'internet'))
test_dataset_unaligned300 = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned300/blended'))
test_dataset_unaligned150 = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned150/blended'))
test_dataset_unaligned_dynamic = datasets.RealDataset(join(datadir, 'refined_unaligned_data/unaligned_dynamic/blended'))
test_dataset_sir2 = datasets.RealDataset(join(datadir, 'sir2_wogt/blended'))
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)
eval_dataloader_sir2 = datasets.DataLoader(
eval_dataset_sir2, batch_size=1, shuffle=False,
num_workers=opt.nThreads, pin_memory=True)
eval_dataloader_solidobject = datasets.DataLoader(
eval_dataset_solidobject, batch_size=1, shuffle=False,
num_workers=opt.nThreads, pin_memory=True)
eval_dataloader_postcard = datasets.DataLoader(
