def test(config):
"""Test point cloud data loader.
"""
from torch.utils.data import DataLoader
from lib.utils import Timer
timer = Timer()
DatasetClass = StanfordVoxelization2cmDataset
transformations = [
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
t.HueSaturationTranslation(config.data_aug_hue_max,
config.data_aug_saturation_max),
]
dataset = DatasetClass(config,
input_transform=t.Compose(transformations),
augment_data=True,
cache=True,
elastic_distortion=True)
data_loader = DataLoader(
dataset=dataset,
collate_fn=t.cfl_collate_fn_factory(limit_numpoints=False),
batch_size=4,
shuffle=True)
# Start from index 1
iter = data_loader.__iter__()
for i in range(100):
timer.tic()
data = iter.next()
print(timer.toc())
def test(config, intensity=False):
"""Test point cloud data loader.
"""
from torch.utils.data import DataLoader
from lib.utils import Timer
import open3d as o3d
def make_pcd(coords, feats):
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(coords[:, :3].float().numpy())
pcd.colors = o3d.utility.Vector3dVector(feats[:, :3].numpy() / 255)
if intensity:
pcd.intensities = o3d.utility.Vector3dVector(feats[:, 3:3].numpy())
return pcd
timer = Timer()
DatasetClass = FacilityArea5Dataset
transformations = [
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
]
dataset = DatasetClass(config,
prevoxel_transform=t.ElasticDistortion(
DatasetClass.ELASTIC_DISTORT_PARAMS),
input_transform=t.Compose(transformations),
augment_data=True,
cache=True,
elastic_distortion=True)
data_loader = DataLoader(
dataset=dataset,
collate_fn=t.cfl_collate_fn_factory(limit_numpoints=False),
batch_size=1,
shuffle=True)
# Start from index 1
iter = data_loader.__iter__()
for i in range(100):
timer.tic()
coords, feats, labels = iter.next()
pcd = make_pcd(coords, feats)
o3d.visualization.draw_geometries([pcd])
print(timer.toc())
def initialize_data_loader(DatasetClass,
config,
phase,
threads,
shuffle,
repeat,
augment_data,
batch_size,
limit_numpoints,
elastic_distortion=False,
input_transform=None,
target_transform=None):
if isinstance(phase, str):
phase = str2datasetphase_type(phase)
if config.return_transformation:
collate_fn = t.cflt_collate_fn_factory(limit_numpoints)
else:
collate_fn = t.cfl_collate_fn_factory(limit_numpoints)
input_transforms = []
if input_transform is not None:
input_transforms += input_transform
if augment_data:
input_transforms += [
t.RandomDropout(0.2),
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
# t.HueSaturationTranslation(config.data_aug_hue_max, config.data_aug_saturation_max),
]
if len(input_transforms) > 0:
input_transforms = t.Compose(input_transforms)
else:
input_transforms = None
dataset = DatasetClass(config,
input_transform=input_transforms,
target_transform=target_transform,
cache=config.cache_data,
augment_data=augment_data,
elastic_distortion=elastic_distortion,
phase=phase)
if repeat:
# Use the inf random sampler
data_loader = DataLoader(dataset=dataset,
num_workers=threads,
batch_size=batch_size,
collate_fn=collate_fn,
sampler=InfSampler(dataset, shuffle))
else:
# Default shuffle=False
data_loader = DataLoader(dataset=dataset,
num_workers=threads,
batch_size=batch_size,
collate_fn=collate_fn,
shuffle=shuffle)
return data_loader
def initialize_data_loader(DatasetClass,
config,
phase,
num_workers,
shuffle,
repeat,
augment_data,
batch_size,
limit_numpoints,
input_transform=None,
target_transform=None):
if isinstance(phase, str):
phase = str2datasetphase_type(phase)
if config.return_transformation:
collate_fn = t.cflt_collate_fn_factory(limit_numpoints)
else:
collate_fn = t.cfl_collate_fn_factory(limit_numpoints)
prevoxel_transform_train = []
if augment_data:
prevoxel_transform_train.append(
t.ElasticDistortion(DatasetClass.ELASTIC_DISTORT_PARAMS))
if len(prevoxel_transform_train) > 0:
prevoxel_transforms = t.Compose(prevoxel_transform_train)
else:
prevoxel_transforms = None
input_transforms = []
if input_transform is not None:
input_transforms += input_transform
if augment_data:
input_transforms += [
t.RandomDropout(0.2),
t.RandomHorizontalFlip(DatasetClass.ROTATION_AXIS,
DatasetClass.IS_TEMPORAL),
t.ChromaticAutoContrast(),
t.ChromaticTranslation(config.data_aug_color_trans_ratio),
t.ChromaticJitter(config.data_aug_color_jitter_std),
# t.HueSaturationTranslation(config.data_aug_hue_max, config.data_aug_saturation_max),
]
if len(input_transforms) > 0:
input_transforms = t.Compose(input_transforms)
else:
input_transforms = None
dataset = DatasetClass(config,
prevoxel_transform=prevoxel_transforms,
input_transform=input_transforms,
target_transform=target_transform,
cache=config.cache_data,
augment_data=augment_data,
phase=phase)
data_args = {
'dataset': dataset,
'num_workers': num_workers,
'batch_size': batch_size,
'collate_fn': collate_fn,
}
if repeat:
data_args['sampler'] = InfSampler(dataset, shuffle)
else:
data_args['shuffle'] = shuffle
data_loader = DataLoader(**data_args)
return data_loader
