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"""This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
-- <__len__>: return the size of dataset.
-- <__getitem__>: get a data point from data loader.
-- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
See our template dataset class 'template_dataset.py' for more details.
"""
import numpy as np
import importlib
import torch.utils.data
from face3d.data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
"""Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
"""
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
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 NotImplementedError("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):
"""Return the static method <modify_commandline_options> of the dataset class."""
dataset_class = find_dataset_using_name(dataset_name)
return dataset_class.modify_commandline_options
def create_dataset(opt, rank=0):
"""Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
"""
data_loader = CustomDatasetDataLoader(opt, rank=rank)
dataset = data_loader.load_data()
return dataset
class CustomDatasetDataLoader():
"""Wrapper class of Dataset class that performs multi-threaded data loading"""
def __init__(self, opt, rank=0):
"""Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
"""
self.opt = opt
dataset_class = find_dataset_using_name(opt.dataset_mode)
self.dataset = dataset_class(opt)
self.sampler = None
print("rank %d %s dataset [%s] was created" % (rank, self.dataset.name, type(self.dataset).__name__))
if opt.use_ddp and opt.isTrain:
world_size = opt.world_size
self.sampler = torch.utils.data.distributed.DistributedSampler(
self.dataset,
num_replicas=world_size,
rank=rank,
shuffle=not opt.serial_batches
)
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
sampler=self.sampler,
num_workers=int(opt.num_threads / world_size),
batch_size=int(opt.batch_size / world_size),
drop_last=True)
else:
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=opt.batch_size,
shuffle=(not opt.serial_batches) and opt.isTrain,
num_workers=int(opt.num_threads),
drop_last=True
)
def set_epoch(self, epoch):
self.dataset.current_epoch = epoch
if self.sampler is not None:
self.sampler.set_epoch(epoch)
def load_data(self):
return self
def __len__(self):
"""Return the number of data in the dataset"""
return min(len(self.dataset), self.opt.max_dataset_size)
def __iter__(self):
"""Return a batch of data"""
for i, data in enumerate(self.dataloader):
if i * self.opt.batch_size >= self.opt.max_dataset_size:
break
yield data
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