""" Base class for dataset. See mnist.py for an example of dataset. """ import collections import logging from abc import ABCMeta, abstractmethod import omegaconf import torch from omegaconf import OmegaConf from torch.utils.data import DataLoader, Sampler, get_worker_info from torch.utils.data._utils.collate import ( default_collate_err_msg_format, np_str_obj_array_pattern, ) from ..utils.tensor import string_classes from ..utils.tools import set_num_threads, set_seed logger = logging.getLogger(__name__) class LoopSampler(Sampler): def __init__(self, loop_size, total_size=None): self.loop_size = loop_size self.total_size = total_size - (total_size % loop_size) def __iter__(self): return (i % self.loop_size for i in range(self.total_size)) def __len__(self): return self.total_size def worker_init_fn(i): info = get_worker_info() if hasattr(info.dataset, "conf"): conf = info.dataset.conf set_seed(info.id + conf.seed) set_num_threads(conf.num_threads) else: set_num_threads(1) def collate(batch): """Difference with PyTorch default_collate: it can stack of other objects.""" if not isinstance(batch, list): # no batching return batch elem = batch[0] elem_type = type(elem) if isinstance(elem, torch.Tensor): if torch.utils.data.get_worker_info() is not None: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel = sum([x.numel() for x in batch]) try: storage = elem.untyped_storage()._new_shared(numel) # noqa: F841 except AttributeError: storage = elem.storage()._new_shared(numel) # noqa: F841 return torch.stack(batch, dim=0) elif ( elem_type.__module__ == "numpy" and elem_type.__name__ != "str_" and elem_type.__name__ != "string_" ): if elem_type.__name__ == "ndarray" or elem_type.__name__ == "memmap": # array of string classes and object if np_str_obj_array_pattern.search(elem.dtype.str) is not None: raise TypeError(default_collate_err_msg_format.format(elem.dtype)) return collate([torch.as_tensor(b) for b in batch]) elif elem.shape == (): # scalars return torch.as_tensor(batch) elif isinstance(elem, float): return torch.tensor(batch, dtype=torch.float64) elif isinstance(elem, int): return torch.tensor(batch) elif isinstance(elem, string_classes): return batch elif isinstance(elem, collections.abc.Mapping): return {key: collate([d[key] for d in batch]) for key in elem} elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple return elem_type(*(collate(samples) for samples in zip(*batch))) elif isinstance(elem, collections.abc.Sequence): # check to make sure that the elements in batch have consistent size it = iter(batch) elem_size = len(next(it)) if not all(len(elem) == elem_size for elem in it): raise RuntimeError("each element in list of batch should be of equal size") transposed = zip(*batch) return [collate(samples) for samples in transposed] elif elem is None: return elem else: # try to stack anyway in case the object implements stacking. return torch.stack(batch, 0) class BaseDataset(metaclass=ABCMeta): """ What the dataset model is expect to declare: default_conf: dictionary of the default configuration of the dataset. It overwrites base_default_conf in BaseModel, and it is overwritten by the user-provided configuration passed to __init__. Configurations can be nested. _init(self, conf): initialization method, where conf is the final configuration object (also accessible with `self.conf`). Accessing unknown configuration entries will raise an error. get_dataset(self, split): method that returns an instance of torch.utils.data.Dataset corresponding to the requested split string, which can be `'train'`, `'val'`, or `'test'`. """ base_default_conf = { "name": "???", "num_workers": "???", "train_batch_size": "???", "val_batch_size": "???", "test_batch_size": "???", "shuffle_training": True, "batch_size": 1, "num_threads": 1, "seed": 0, "prefetch_factor": 2, } default_conf = {} def __init__(self, conf): """Perform some logic and call the _init method of the child model.""" default_conf = OmegaConf.merge( OmegaConf.create(self.base_default_conf), OmegaConf.create(self.default_conf), ) OmegaConf.set_struct(default_conf, True) if isinstance(conf, dict): conf = OmegaConf.create(conf) self.conf = OmegaConf.merge(default_conf, conf) OmegaConf.set_readonly(self.conf, True) logger.info(f"Creating dataset {self.__class__.__name__}") self._init(self.conf) @abstractmethod def _init(self, conf): """To be implemented by the child class.""" raise NotImplementedError @abstractmethod def get_dataset(self, split): """To be implemented by the child class.""" raise NotImplementedError def get_data_loader(self, split, shuffle=None, pinned=False, distributed=False): """Return a data loader for a given split.""" assert split in ["train", "val", "test"] dataset = self.get_dataset(split) try: batch_size = self.conf[split + "_batch_size"] except omegaconf.MissingMandatoryValue: batch_size = self.conf.batch_size num_workers = self.conf.get("num_workers", batch_size) if distributed: shuffle = False sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: sampler = None if shuffle is None: shuffle = split == "train" and self.conf.shuffle_training return DataLoader( dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler, pin_memory=pinned, collate_fn=collate, num_workers=num_workers, worker_init_fn=worker_init_fn, prefetch_factor=self.conf.prefetch_factor, drop_last=True if split == "train" else False, ) def get_overfit_loader(self, split): """Return an overfit data loader. The training set is composed of a single duplicated batch, while the validation and test sets contain a single copy of this same batch. This is useful to debug a model and make sure that losses and metrics correlate well. """ assert split in ["train", "val", "test"] dataset = self.get_dataset("train") sampler = LoopSampler( self.conf.batch_size, len(dataset) if split == "train" else self.conf.batch_size, ) num_workers = self.conf.get("num_workers", self.conf.batch_size) return DataLoader( dataset, batch_size=self.conf.batch_size, pin_memory=True, num_workers=num_workers, sampler=sampler, worker_init_fn=worker_init_fn, collate_fn=collate, )