# 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, List, Mapping, Optional, Tuple
from torch import Tensor
from compressai.registry import register_module
from .base import LatentCodec
from .gaussian_conditional import GaussianConditionalLatentCodec
from .hyper import HyperLatentCodec
__all__ = [
"HyperpriorLatentCodec",
]
[docs]
@register_module("HyperpriorLatentCodec")
class HyperpriorLatentCodec(LatentCodec):
"""Hyperprior codec constructed from latent codec for ``y`` that
compresses ``y`` using ``params`` from ``hyper`` branch.
Hyperprior entropy modeling introduced in
`"Variational Image Compression with a Scale Hyperprior"
<https://arxiv.org/abs/1802.01436>`_,
by J. Balle, D. Minnen, S. Singh, S.J. Hwang, and N. Johnston,
International Conference on Learning Representations (ICLR), 2018.
.. code-block:: none
┌──────────┐
┌─►──┤ lc_hyper ├──►─┐
│ └──────────┘ │
│ ▼ params
│ │
│ ┌──┴───┐
y ──┴───────►─────────┤ lc_y ├───►── y_hat
└──────┘
By default, the following codec is constructed:
.. code-block:: none
┌───┐ z ┌───┐ z_hat z_hat ┌───┐
┌─►──┤h_a├──►──┤ Q ├───►───····───►───┤h_s├──►─┐
│ └───┘ └───┘ EB └───┘ │
│ │
│ ┌──────────────◄────────────┘
│ │ params
│ ┌──┴──┐
│ │ EP │
│ └──┬──┘
│ │
│ ┌───┐ y_hat ▼
y ──┴─►─┤ Q ├────►────····────►── y_hat
└───┘ GC
Common configurations of latent codecs include:
- entropy bottleneck ``hyper`` (default) and gaussian conditional ``y`` (default)
- entropy bottleneck ``hyper`` (default) and autoregressive ``y``
"""
latent_codec: Mapping[str, LatentCodec]
def __init__(
self, latent_codec: Optional[Mapping[str, LatentCodec]] = None, **kwargs
):
super().__init__()
self._set_group_defaults(
"latent_codec",
latent_codec,
defaults={
"y": GaussianConditionalLatentCodec,
"hyper": HyperLatentCodec,
},
save_direct=True,
)
def __getitem__(self, key: str) -> LatentCodec:
return self.latent_codec[key]
def forward(self, y: Tensor) -> Dict[str, Any]:
hyper_out = self.latent_codec["hyper"](y)
y_out = self.latent_codec["y"](y, hyper_out["params"])
return {
"likelihoods": {
"y": y_out["likelihoods"]["y"],
"z": hyper_out["likelihoods"]["z"],
},
"y_hat": y_out["y_hat"],
}
def compress(self, y: Tensor) -> Dict[str, Any]:
hyper_out = self.latent_codec["hyper"].compress(y)
y_out = self.latent_codec["y"].compress(y, hyper_out["params"])
[z_strings] = hyper_out["strings"]
return {
"strings": [*y_out["strings"], z_strings],
"shape": {"y": y_out["shape"], "hyper": hyper_out["shape"]},
"y_hat": y_out["y_hat"],
}
def decompress(
self, strings: List[List[bytes]], shape: Dict[str, Tuple[int, ...]], **kwargs
) -> Dict[str, Any]:
*y_strings_, z_strings = strings
assert all(len(y_strings) == len(z_strings) for y_strings in y_strings_)
hyper_out = self.latent_codec["hyper"].decompress([z_strings], shape["hyper"])
y_out = self.latent_codec["y"].decompress(
y_strings_, shape["y"], hyper_out["params"]
)
return {"y_hat": y_out["y_hat"]}
