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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 GainHyperLatentCodec
__all__ = [
"GainHyperpriorLatentCodec",
]
[docs]
@register_module("GainHyperpriorLatentCodec")
class GainHyperpriorLatentCodec(LatentCodec):
"""Hyperprior codec constructed from latent codec for ``y`` that
compresses ``y`` using ``params`` from ``hyper`` branch.
Gain-controlled hyperprior introduced in
`"Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation"
<https://arxiv.org/abs/2003.02012>`_, by Ze Cui, Jing Wang,
Shangyin Gao, Bo Bai, Tiansheng Guo, and Yihui Feng, CVPR, 2021.
.. code-block:: none
z_gain z_gain_inv
│ │
▼ ▼
┌┴────────┴┐
┌──►──┤ lc_hyper ├──►─┐
│ └──────────┘ │
│ │
│ y_gain ▼ params y_gain_inv
│ │ │ │
│ ▼ │ ▼
│ │ ┌──┴───┐ │
y ──┴────►───×───►─────┤ lc_y ├────►─────×─────►── y_hat
└──────┘
By default, the following codec is constructed:
.. code-block:: none
z_gain z_gain_inv
│ │
▼ ▼
┌───┐ z │ z_g ┌───┐ z_hat z_hat │ ┌───┐
┌─►──┤h_a├──►──×──►──┤ Q ├───►───····───►────×────►──┤h_s├──┐
│ └───┘ └───┘ EB └───┘ │
│ │
│ ┌──────────────◄─────────────┘
│ │ params
│ ┌──┴──┐
│ y_gain │ EP │ y_gain_inv
│ │ └──┬──┘ │
│ ▼ │ ▼
│ │ ┌───┐ ▼ │
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": GainHyperLatentCodec,
},
save_direct=True,
)
def __getitem__(self, key: str) -> LatentCodec:
return self.latent_codec[key]
def forward(
self,
y: Tensor,
y_gain: Tensor,
z_gain: Tensor,
y_gain_inv: Tensor,
z_gain_inv: Tensor,
) -> Dict[str, Any]:
hyper_out = self.latent_codec["hyper"](y, z_gain, z_gain_inv)
y_out = self.latent_codec["y"](y * y_gain, hyper_out["params"])
y_hat = y_out["y_hat"] * y_gain_inv
return {
"likelihoods": {
"y": y_out["likelihoods"]["y"],
"z": hyper_out["likelihoods"]["z"],
},
"y_hat": y_hat,
}
def compress(
self,
y: Tensor,
y_gain: Tensor,
z_gain: Tensor,
y_gain_inv: Tensor,
z_gain_inv: Tensor,
) -> Dict[str, Any]:
hyper_out = self.latent_codec["hyper"].compress(y, z_gain, z_gain_inv)
y_out = self.latent_codec["y"].compress(y * y_gain, hyper_out["params"])
y_hat = y_out["y_hat"] * y_gain_inv
return {
"strings": [*y_out["strings"], *hyper_out["strings"]],
"shape": {"y": y_out["shape"], "hyper": hyper_out["shape"]},
"y_hat": y_hat,
}
def decompress(
self,
strings: List[List[bytes]],
shape: Dict[str, Tuple[int, ...]],
y_gain_inv: Tensor,
z_gain_inv: Tensor,
**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"], z_gain_inv
)
y_out = self.latent_codec["y"].decompress(
y_strings_, shape["y"], hyper_out["params"]
)
y_hat = y_out["y_hat"] * y_gain_inv
return {"y_hat": y_hat}
