Source code for composer.core.types

# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0

"""Reference for common types used throughout the composer library.

    Batch (Any): Alias to type Any.
        A batch of data can be represented in several formats, depending on the application.
    JSON (str | float | int | None | list['JSON'] | dict[str, 'JSON']): JSON Data.
    Dataset ([Batch]): Alias for :class:``.

from __future__ import annotations

from typing import Any, Union

import torch

from composer.utils import StringEnum

__all__ = ['Batch', 'JSON', 'MemoryFormat', 'TrainerMode']

Batch = Any

Dataset =[Batch]

JSON = Union[str, float, int, None, list['JSON'], dict[str, 'JSON']]

[docs]class TrainerMode(StringEnum): """Enum to represent which mode the Trainer is in. Attributes: TRAIN: In training mode. EVAL: In evaluation mode. PREDICT: In predict mode. """ TRAIN = 'train' EVAL = 'eval' PREDICT = 'predict'
[docs]class MemoryFormat(StringEnum): """Enum class to represent different memory formats. See :class:`torch.torch.memory_format` for more details. Attributes: CONTIGUOUS_FORMAT: Default PyTorch memory format represnting a tensor allocated with consecutive dimensions sequential in allocated memory. CHANNELS_LAST: This is also known as NHWC. Typically used for images with 2 spatial dimensions (i.e., Height and Width) where channels next to each other in indexing are next to each other in allocated memory. For example, if C[0] is at memory location M_0 then C[1] is at memory location M_1, etc. CHANNELS_LAST_3D: This can also be referred to as NTHWC. Same as :attr:`CHANNELS_LAST` but for videos with 3 spatial dimensions (i.e., Time, Height and Width). PRESERVE_FORMAT: A way to tell operations to make the output tensor to have the same memory format as the input tensor. """ CONTIGUOUS_FORMAT = 'contiguous_format' CHANNELS_LAST = 'channels_last' CHANNELS_LAST_3D = 'channels_last_3d' PRESERVE_FORMAT = 'preserve_format'