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import importlib
import torch.utils.data
from data.base_dataset import BaseDataset
from data.sampler import InfiniteSamplerWrapper
def find_dataset_using_name(dataset_name):
# Given the option --dataset [datasetname],
# the file "datasets/datasetname_dataset.py"
# will be imported.
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
# In the file, the class called DatasetNameDataset() will
# be instantiated. It has to be a subclass of BaseDataset,
# and it is case-insensitive.
dataset = None
target_dataset_name = dataset_name.replace('_', '') + 'dataset'
for name, cls in datasetlib.__dict__.items():
if name.lower() == target_dataset_name.lower() \
and issubclass(cls, BaseDataset):
dataset = cls
if dataset is None:
raise ValueError("In %s.py, there should be a subclass of BaseDataset "
"with class name that matches %s in lowercase." %
(dataset_filename, target_dataset_name))
return dataset
def get_option_setter(dataset_name):
dataset_class = find_dataset_using_name(dataset_name)
return dataset_class.modify_commandline_options
def create_dataloader(opt):
if opt.phase=='test':
dataset = find_dataset_using_name(opt.dataset_mode)
instance = dataset()
instance.initialize(opt)
print("dataset [%s] of size %d was created" %
(type(instance).__name__, len(instance)))
dataloader = torch.utils.data.DataLoader(
instance,
batch_size=opt.batchSize,
shuffle=not opt.serial_batches,
num_workers=int(opt.nThreads),
drop_last=opt.isTrain
)
return dataloader
else:
dataset = find_dataset_using_name(opt.dataset_mode)
instance = dataset()
instance.initialize(opt)
print("dataset [%s] of size %d was created" %
(type(instance).__name__, len(instance)))
# Use InfiniteSamplerWrapper for an infinite sampler
sampler = InfiniteSamplerWrapper(instance) if opt.use_infinite_sampler else None
dataloader = torch.utils.data.DataLoader(
instance,
batch_size=opt.batchSize,
sampler=sampler,
shuffle=not opt.serial_batches if sampler is None else False,
num_workers=int(opt.nThreads),
drop_last=opt.isTrain
)
return dataloader
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