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from typing import Any, Dict, List, Optional, Tuple
from torch import Tensor
from compressai.entropy_models import EntropyBottleneck
from compressai.registry import register_module
from .base import LatentCodec
__all__ = [
"EntropyBottleneckLatentCodec",
]
[docs]
@register_module("EntropyBottleneckLatentCodec")
class EntropyBottleneckLatentCodec(LatentCodec):
"""Entropy bottleneck codec.
Factorized prior "entropy bottleneck" 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
┌───┐ y_hat
y ──►──┤ Q ├───►───····───►─── y_hat
└───┘ EB
"""
entropy_bottleneck: EntropyBottleneck
def __init__(
self,
entropy_bottleneck: Optional[EntropyBottleneck] = None,
**kwargs,
):
super().__init__()
self.entropy_bottleneck = entropy_bottleneck or EntropyBottleneck(**kwargs)
def forward(self, y: Tensor) -> Dict[str, Any]:
y_hat, y_likelihoods = self.entropy_bottleneck(y)
return {"likelihoods": {"y": y_likelihoods}, "y_hat": y_hat}
def compress(self, y: Tensor) -> Dict[str, Any]:
shape = y.size()[-2:]
y_strings = self.entropy_bottleneck.compress(y)
y_hat = self.entropy_bottleneck.decompress(y_strings, shape)
return {"strings": [y_strings], "shape": shape, "y_hat": y_hat}
def decompress(
self, strings: List[List[bytes]], shape: Tuple[int, int], **kwargs
) -> Dict[str, Any]:
(y_strings,) = strings
y_hat = self.entropy_bottleneck.decompress(y_strings, shape)
return {"y_hat": y_hat}
