external/cv/mmcv/transforms/formatting.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import Sequence, Union

import mmengine
import numpy as np
import torch

from .base import BaseTransform
from .builder import TRANSFORMS


def to_tensor(
    data: Union[torch.Tensor, np.ndarray, Sequence, int,
                float]) -> torch.Tensor:
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.

    Args:
        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
            be converted.

    Returns:
        torch.Tensor: the converted data.
    """

    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmengine.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError(f'type {type(data)} cannot be converted to tensor.')


@TRANSFORMS.register_module()
class ToTensor(BaseTransform):
    """Convert some results to :obj:`torch.Tensor` by given keys.

    Required keys:

    - all these keys in `keys`

    Modified Keys:

    - all these keys in `keys`

    Args:
        keys (Sequence[str]): Keys that need to be converted to Tensor.
    """

    def __init__(self, keys: Sequence[str]) -> None:
        self.keys = keys

    def transform(self, results: dict) -> dict:
        """Transform function to convert data to `torch.Tensor`.

        Args:
            results (dict): Result dict from loading pipeline.
        Returns:
            dict: `keys` in results will be updated.
        """
        for key in self.keys:

            key_list = key.split('.')
            cur_item = results
            for i in range(len(key_list)):
                if key_list[i] not in cur_item:
                    raise KeyError(f'Can not find key {key}')
                if i == len(key_list) - 1:
                    cur_item[key_list[i]] = to_tensor(cur_item[key_list[i]])
                    break
                cur_item = cur_item[key_list[i]]

        return results

    def __repr__(self) -> str:
        return self.__class__.__name__ + f'(keys={self.keys})'


@TRANSFORMS.register_module()
class ImageToTensor(BaseTransform):
    """Convert image to :obj:`torch.Tensor` by given keys.

    The dimension order of input image is (H, W, C). The pipeline will convert
    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
    (1, H, W).

    Required keys:

    - all these keys in `keys`

    Modified Keys:

    - all these keys in `keys`

    Args:
        keys (Sequence[str]): Key of images to be converted to Tensor.
    """

    def __init__(self, keys: dict) -> None:
        self.keys = keys

    def transform(self, results: dict) -> dict:
        """Transform function to convert image in results to
        :obj:`torch.Tensor` and transpose the channel order.
        Args:
            results (dict): Result dict contains the image data to convert.
        Returns:
            dict: The result dict contains the image converted
            to :obj:``torch.Tensor`` and transposed to (C, H, W) order.
        """
        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = (to_tensor(img.transpose(2, 0, 1))).contiguous()
        return results

    def __repr__(self) -> str:
        return self.__class__.__name__ + f'(keys={self.keys})'