87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from typing import Any, Iterable, Mapping, Union
|
|
|
|
import torch
|
|
from torch.utils.data import DataLoader, IterableDataset
|
|
|
|
from pytorch_lightning.utilities import rank_zero_warn
|
|
|
|
BType = Union[torch.Tensor, str, Mapping[Any, "BType"], Iterable["BType"]]
|
|
|
|
|
|
def extract_batch_size(batch: BType) -> int:
|
|
"""
|
|
Recursively unpack a batch to find a torch.Tensor.
|
|
|
|
Returns:
|
|
``len(tensor)`` when found, or ``1`` when it hits an empty or non iterable.
|
|
"""
|
|
if isinstance(batch, torch.Tensor):
|
|
return batch.size(0)
|
|
if isinstance(batch, str):
|
|
return len(batch)
|
|
if isinstance(batch, dict):
|
|
sample = next(iter(batch.values()), 1)
|
|
return extract_batch_size(sample)
|
|
if isinstance(batch, Iterable):
|
|
sample = next(iter(batch), 1)
|
|
return extract_batch_size(sample)
|
|
|
|
return 1
|
|
|
|
|
|
def has_iterable_dataset(dataloader: DataLoader) -> bool:
|
|
return hasattr(dataloader, "dataset") and isinstance(dataloader.dataset, IterableDataset)
|
|
|
|
|
|
def has_len(dataloader: DataLoader) -> bool:
|
|
"""
|
|
Checks if a given Dataloader has ``__len__`` method implemented i.e. if
|
|
it is a finite dataloader or infinite dataloader.
|
|
|
|
Raises:
|
|
ValueError:
|
|
If the length of Dataloader is 0, as it requires at least one batch
|
|
"""
|
|
|
|
try:
|
|
# try getting the length
|
|
if len(dataloader) == 0:
|
|
raise ValueError("`Dataloader` returned 0 length. Please make sure that it returns at least 1 batch")
|
|
has_len = True
|
|
except TypeError:
|
|
has_len = False
|
|
except NotImplementedError: # e.g. raised by torchtext if a batch_size_fn is used
|
|
has_len = False
|
|
|
|
if has_len and has_iterable_dataset(dataloader):
|
|
rank_zero_warn(
|
|
"Your `IterableDataset` has `__len__` defined."
|
|
" In combination with multi-process data loading (when num_workers > 1),"
|
|
" `__len__` could be inaccurate if each worker is not configured independently"
|
|
" to avoid having duplicate data."
|
|
)
|
|
return has_len
|
|
|
|
|
|
def get_len(dataloader: DataLoader) -> Union[int, float]:
|
|
"""Return the length of the given DataLoader. If ``__len__`` method is not implemented, return float('inf')."""
|
|
|
|
if has_len(dataloader):
|
|
return len(dataloader)
|
|
|
|
return float("inf")
|