Source code for compressai.transforms.point.to_dict

# Copyright (c) 2021-2024, InterDigital Communications, Inc
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted (subject to the limitations in the disclaimer
# below) provided that the following conditions are met:

# * Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
# * Neither the name of InterDigital Communications, Inc nor the names of its
#   contributors may be used to endorse or promote products derived from this
#   software without specific prior written permission.

# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

from typing import Any, Dict

import torch

from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform

from compressai.registry import register_transform




@functional_transform("to_dict")
@register_transform("ToDict")
class ToDict(BaseTransform):
    r"""Convert :obj:`Mapping[str, Any]`
    (functional name: :obj:`to_dict`).
    """

    def __init__(self, *, wrapper="dict"):
        if wrapper == "dict":
            self.wrap = dict
        elif wrapper == "torch_geometric.data.Data":
            self.wrap = Data
        else:
            raise ValueError(f"Unknown wrapper: {wrapper}")

    def __call__(self, data) -> Dict[str, Any]:
        data = {
            k: v if isinstance(v, torch.Tensor) else torch.tensor(v)
            for k, v in data.items()
        }
        return self.wrap(**data)