def make_data_loader(config, phase, batch_size, num_threads=0, shuffle=None):
assert phase in ['train', 'trainval', 'val', 'test']
if shuffle is None:
# 如果是训练则默认打乱
shuffle = phase != 'test'
if config.dataset not in dataset_str_mapping.keys():
logging.error(f'Dataset {config.dataset}, does not exists in ' +
', '.join(dataset_str_mapping.keys()))
Dataset = dataset_str_mapping[config.dataset]
use_random_scale = False
use_random_rotation = False
transforms = []
if phase in ['train', 'trainval']:
use_random_rotation = config.use_random_rotation #True
use_random_scale = config.use_random_scale #False
transforms += [t.Jitter()]
dset = Dataset(
phase,
transform=t.Compose(transforms),
random_scale=use_random_scale,
random_rotation=use_random_rotation,
config=config)
loader = torch.utils.data.DataLoader(
dset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_threads,
collate_fn=collate_pair_fn,
pin_memory=False,
drop_last=True)
return loader
def make_data_loader(config, batch_size, num_threads=0):
if config.data.dataset not in dataset_str_mapping.keys():
logging.error(f'Dataset {config.data.dataset}, does not exists in ' +
', '.join(dataset_str_mapping.keys()))
Dataset = dataset_str_mapping[config.data.dataset]
transforms = []
use_random_rotation = config.trainer.use_random_rotation
use_random_scale = config.trainer.use_random_scale
transforms += [t.Jitter()]
dset = Dataset(phase="train",
transform=t.Compose(transforms),
random_scale=use_random_scale,
random_rotation=use_random_rotation,
config=config)
collate_pair_fn = default_collate_pair_fn
batch_size = batch_size // config.misc.num_gpus
if config.misc.num_gpus > 1:
sampler = DistributedInfSampler(dset)
else:
sampler = None
loader = torch.utils.data.DataLoader(dset,
batch_size=batch_size,
shuffle=False if sampler else True,
num_workers=num_threads,
collate_fn=collate_pair_fn,
pin_memory=False,
sampler=sampler,
drop_last=True)
return loader
def make_data_loader(config, phase, batch_size, num_threads=0, shuffle=None):
assert phase in ['train', 'trainval', 'val', 'test']
if shuffle is None:
shuffle = phase != 'test'
if config.dataset not in dataset_str_mapping.keys():
logging.error(f'Dataset {config.dataset}, does not exists in ' +
', '.join(dataset_str_mapping.keys()))
Dataset = dataset_str_mapping[config.dataset]
use_random_scale = False
use_random_rotation = False
transforms = []
if phase in ['train', 'trainval']:
use_random_rotation = config.use_random_rotation
use_random_scale = config.use_random_scale
transforms += [t.Jitter()]
if config.dataset == "KITTIMapDataset":
#import sys
import importlib
split = phase
cfg = importlib.import_module("ext.benchmark_tools.configs.config")
dset = Dataset(split, cfg,
phase,
transform=t.Compose(transforms),
random_scale=use_random_scale,
random_rotation=use_random_rotation,
config=config)
elif config.dataset == "ArgoverseMapDataset":
#import sys
import importlib
split = phase
cfg = importlib.import_module("ext.benchmark_tools.configs.config")
dset = Dataset(split, cfg,
phase,
transform=t.Compose(transforms),
random_scale=use_random_scale,
random_rotation=use_random_rotation,
config=config)
else:
dset = Dataset(
phase,
transform=t.Compose(transforms),
random_scale=use_random_scale,
random_rotation=use_random_rotation,
config=config)
loader = torch.utils.data.DataLoader(
dset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_threads,
collate_fn=collate_pair_fn,
pin_memory=False,
drop_last=True)
return loader