def main():
# data files
test_files, _ = data_util.get_train_files(args.input_data_path,
args.test_file_list, '')
if len(test_files) > args.max_to_vis:
test_files = test_files[:args.max_to_vis]
else:
args.max_to_vis = len(test_files)
random.seed(42)
random.shuffle(test_files)
print('#test files = ', len(test_files))
test_dataset = scene_dataloader.SceneDataset(test_files, args.input_dim,
args.truncation,
args.num_hierarchy_levels,
args.max_input_height, 0,
args.target_data_path)
test_dataloader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=2,
collate_fn=scene_dataloader.collate)
if os.path.exists(args.output):
raw_input(
'warning: output dir %s exists, press key to overwrite and continue'
% args.output)
if not os.path.exists(args.output):
os.makedirs(args.output)
# start testing
print('starting testing...')
loss_weights = np.ones(args.num_hierarchy_levels + 1, dtype=np.float32)
test(loss_weights, test_dataloader, args.output, args.max_to_vis)
def val_dataloader(self):
log.info('Validation data loader called.')
data_path, train_files, val_files = self._get_train_files()
input_dim = tuple(self.hparams.model.input_dim)
num_hierarchy_levels = self.hparams.train.num_hierarchy_levels
truncation = self.hparams.train.truncation
batch_size = self.hparams.train.batch_size
num_workers_valid = self.hparams.train.num_workers_valid
num_overfit_val = self.hparams.train.num_overfit_val
if len(val_files) > 0:
val_dataset = scene_dataloader.SceneDataset(
val_files, input_dim, truncation, num_hierarchy_levels, 0,
num_overfit_val)
print('val_dataset', len(val_dataset))
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers_valid,
collate_fn=scene_dataloader.collate)
return val_dataloader
def train_dataloader(self):
log.info('Training data loader called.')
data_path, train_files, val_files = self._get_train_files()
input_dim = self.hparams.input_dim
num_hierarchy_levels = self.hparams.num_hierarchy_levels
truncation = self.hparams.truncation
batch_size = self.hparams.batch_size
num_workers_train = self.hparams.num_workers_train
_OVERFIT = False
if len(train_files) == 1:
_OVERFIT = True
# TODO:
#args.use_loss_masking = False
num_overfit_train = 0 if not _OVERFIT else 640
num_overfit_val = 0 if not _OVERFIT else 160
print('#train files = ', len(train_files))
print('#val files = ', len(val_files))
train_dataset = scene_dataloader.SceneDataset(train_files, input_dim, truncation, num_hierarchy_levels, 0, num_overfit_train)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers_train,
collate_fn=scene_dataloader.collate)
self._iter_counter = self.hparams.start_epoch * (len(train_dataset) // self.hparams.batch_size)
return train_dataloader
def main():
# data files
test_files, _, _ = data_util.get_train_files(args.input_data_path, args.test_file_list, '', 0)
if len(test_files) > args.max_to_process:
test_files = test_files[:args.max_to_process]
else:
args.max_to_process = len(test_files)
random.seed(42)
random.shuffle(test_files)
print('#test files = ', len(test_files))
test_dataset = scene_dataloader.SceneDataset(test_files, args.input_dim, args.truncation, True, args.augment_rgb_scaling, (args.augment_scale_min, args.augment_scale_max), args.color_truncation, args.color_space, target_path=args.target_data_path, max_input_height=args.max_input_height)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=2, collate_fn=scene_dataloader.collate_voxels)
if os.path.exists(args.output):
if args.vis_only:
print('warning: output dir %s exists, will overwrite any existing files')
else:
input('warning: output dir %s exists, press key to delete and continue' % args.output)
shutil.rmtree(args.output)
if not os.path.exists(args.output):
os.makedirs(args.output)
output_vis_path = os.path.join(args.output, 'vis')
if not os.path.exists(output_vis_path):
os.makedirs(output_vis_path)
# start testing
print('starting testing...')
test(test_dataloader, output_vis_path, args.num_to_vis)
last_epoch=last_epoch)
# data files
train_files, val_files = data_util.get_train_files(args.data_path,
args.train_file_list,
args.val_file_list)
_OVERFIT = False
if len(train_files) == 1:
_OVERFIT = True
args.use_loss_masking = False
num_overfit_train = 0 if not _OVERFIT else 640
num_overfit_val = 0 if not _OVERFIT else 160
print('#train files = ', len(train_files))
print('#val files = ', len(val_files))
train_dataset = scene_dataloader.SceneDataset(train_files, args.input_dim,
args.truncation,
args.num_hierarchy_levels, 0,
num_overfit_train)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
collate_fn=scene_dataloader.collate)
if len(val_files) > 0:
val_dataset = scene_dataloader.SceneDataset(val_files, args.input_dim,
args.truncation,
args.num_hierarchy_levels, 0,
num_overfit_val)
print('val_dataset', len(val_dataset))
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
