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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 | |