def build_dataset(dataset_config,
data_dir,
grid_size=[480, 360, 32],
demo_label_dir=None):
if demo_label_dir == '':
imageset = "demo"
else:
imageset = "val"
label_mapping = dataset_config["label_mapping"]
SemKITTI_demo = get_pc_model_class('SemKITTI_demo')
demo_pt_dataset = SemKITTI_demo(data_dir,
imageset=imageset,
return_ref=True,
label_mapping=label_mapping,
demo_label_path=demo_label_dir)
demo_dataset = get_model_class(dataset_config['dataset_type'])(
demo_pt_dataset,
grid_size=grid_size,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
)
demo_dataset_loader = torch.utils.data.DataLoader(
dataset=demo_dataset,
batch_size=1,
collate_fn=collate_fn_BEV,
shuffle=False,
num_workers=4)
return demo_dataset_loader
def build(dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=[480, 360, 32]):
data_path = train_dataloader_config["data_path"]
train_imageset = train_dataloader_config["imageset"]
val_imageset = val_dataloader_config["imageset"]
train_ref = train_dataloader_config["return_ref"]
val_ref = val_dataloader_config["return_ref"]
label_mapping = dataset_config["label_mapping"]
SemKITTI = get_pc_model_class(dataset_config['pc_dataset_type'])
nusc=None
if "nusc" in dataset_config['pc_dataset_type']:
from nuscenes import NuScenes
nusc = NuScenes(version='v1.0-trainval', dataroot=data_path, verbose=True)
train_pt_dataset = SemKITTI(data_path, imageset=train_imageset,
return_ref=train_ref, label_mapping=label_mapping, nusc=nusc)
val_pt_dataset = SemKITTI(data_path, imageset=val_imageset,
return_ref=val_ref, label_mapping=label_mapping, nusc=nusc)
train_dataset = get_model_class(dataset_config['dataset_type'])(
train_pt_dataset,
grid_size=grid_size,
flip_aug=True,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
rotate_aug=True,
scale_aug=True,
transform_aug=True
)
val_dataset = get_model_class(dataset_config['dataset_type'])(
val_pt_dataset,
grid_size=grid_size,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
)
train_dataset_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=train_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=train_dataloader_config["shuffle"],
num_workers=train_dataloader_config["num_workers"])
val_dataset_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=val_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=val_dataloader_config["shuffle"],
num_workers=val_dataloader_config["num_workers"])
return train_dataset_loader, val_dataset_loader
def build(dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=[480, 360, 32]):
data_path = train_dataloader_config["data_path"]
train_imageset = train_dataloader_config["imageset"]
val_imageset = val_dataloader_config["imageset"]
train_ref = train_dataloader_config["return_ref"]
val_ref = val_dataloader_config["return_ref"]
label_mapping = dataset_config["label_mapping"]
SemKITTI = get_pc_model_class(dataset_config['pc_dataset_type'])
train_pt_dataset = SemKITTI(data_path + '/sequences/',
imageset=train_imageset,
return_ref=train_ref,
label_mapping=label_mapping)
val_pt_dataset = SemKITTI(data_path + '/sequences/',
imageset=val_imageset,
return_ref=val_ref,
label_mapping=label_mapping)
train_dataset = get_model_class(dataset_config['dataset_type'])(
train_pt_dataset,
grid_size=grid_size,
flip_aug=True,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
rotate_aug=True,
scale_aug=True,
transform_aug=True)
val_dataset = get_model_class(dataset_config['dataset_type'])(
val_pt_dataset,
grid_size=grid_size,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
)
train_dataset_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=train_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=train_dataloader_config["shuffle"],
num_workers=train_dataloader_config["num_workers"])
val_dataset_loader = torch.utils.data.DataLoader(
dataset=val_dataset,
batch_size=val_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=val_dataloader_config["shuffle"],
num_workers=val_dataloader_config["num_workers"])
return train_dataset_loader, val_dataset_loader
def build(dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=[480, 360, 32]):
data_path = train_dataloader_config["data_path"]
train_imageset = train_dataloader_config["imageset"]
val_imageset = val_dataloader_config["imageset"]
train_ref = train_dataloader_config["return_ref"]
val_ref = val_dataloader_config["return_ref"]
label_mapping = dataset_config["label_mapping"]
SemKITTI = get_pc_model_class(dataset_config['pc_dataset_type'])
nusc = None
if "nusc" in dataset_config['pc_dataset_type']:
from nuscenes import NuScenes
nusc = NuScenes(version='v1.0-trainval',
dataroot=data_path,
verbose=True)
# nusc = NuScenes(version='v1.0-mini', dataroot=data_path, verbose=True)
# print("list_lidarseg_categories：")
# nusc.list_lidarseg_categories(sort_by='count')
# print("lidarseg_idx2name_mapping：")
# nusc.lidarseg_idx2name_mapping
# print("lidarseg_name2idx_mapping：")
# nusc.lidarseg_name2idx_mapping
# my_sample = nusc.sample[87]
# print("get_sample_lidarseg_stats：")
# nusc.get_sample_lidarseg_stats(my_sample['token'], sort_by='count')
train_pt_dataset = SemKITTI(data_path,
imageset=train_imageset,
return_ref=train_ref,
label_mapping=label_mapping,
nusc=nusc)
val_pt_dataset = SemKITTI(data_path,
imageset=val_imageset,
return_ref=val_ref,
label_mapping=label_mapping,
nusc=nusc)
train_dataset = get_model_class(dataset_config['dataset_type'])(
train_pt_dataset,
grid_size=grid_size,
flip_aug=True,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
rotate_aug=True,
scale_aug=True,
transform_aug=True)
val_dataset = get_model_class(dataset_config['dataset_type'])(
val_pt_dataset,
grid_size=grid_size,
fixed_volume_space=dataset_config['fixed_volume_space'],
max_volume_space=dataset_config['max_volume_space'],
min_volume_space=dataset_config['min_volume_space'],
ignore_label=dataset_config["ignore_label"],
)
train_dataset_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=train_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=train_dataloader_config["shuffle"],
num_workers=train_dataloader_config["num_workers"])
val_dataset_loader = torch.utils.data.DataLoader(
dataset=val_dataset,
batch_size=val_dataloader_config["batch_size"],
collate_fn=collate_fn_BEV,
shuffle=val_dataloader_config["shuffle"],
num_workers=val_dataloader_config["num_workers"])
return train_dataset_loader, val_dataset_loader