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from typing import Any, Dict, List, Optional, Tuple
import torch.nn as nn
from torch import Tensor
from compressai.entropy_models import EntropyBottleneck
from compressai.ops import quantize_ste
from compressai.registry import register_module
from .base import LatentCodec
__all__ = [
"HyperLatentCodec",
]
[docs]
@register_module("HyperLatentCodec")
class HyperLatentCodec(LatentCodec):
"""Entropy bottleneck codec with surrounding `h_a` and `h_s` transforms.
"Hyper" side-information branch 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.
.. note:: ``HyperLatentCodec`` should be used inside
``HyperpriorLatentCodec`` to construct a full hyperprior.
.. code-block:: none
┌───┐ z ┌───┐ z_hat z_hat ┌───┐
y ──►──┤h_a├──►──┤ Q ├───►───····───►───┤h_s├──►── params
└───┘ └───┘ EB └───┘
"""
entropy_bottleneck: EntropyBottleneck
h_a: nn.Module
h_s: nn.Module
def __init__(
self,
entropy_bottleneck: Optional[EntropyBottleneck] = None,
h_a: Optional[nn.Module] = None,
h_s: Optional[nn.Module] = None,
quantizer: str = "noise",
**kwargs,
):
super().__init__()
assert entropy_bottleneck is not None
self.entropy_bottleneck = entropy_bottleneck
self.h_a = h_a or nn.Identity()
self.h_s = h_s or nn.Identity()
self.quantizer = quantizer
def forward(self, y: Tensor) -> Dict[str, Any]:
z = self.h_a(y)
z_hat, z_likelihoods = self.entropy_bottleneck(z)
if self.quantizer == "ste":
z_medians = self.entropy_bottleneck._get_medians()
z_hat = quantize_ste(z - z_medians) + z_medians
params = self.h_s(z_hat)
return {"likelihoods": {"z": z_likelihoods}, "params": params}
def compress(self, y: Tensor) -> Dict[str, Any]:
z = self.h_a(y)
shape = z.size()[-2:]
z_strings = self.entropy_bottleneck.compress(z)
z_hat = self.entropy_bottleneck.decompress(z_strings, shape)
params = self.h_s(z_hat)
return {"strings": [z_strings], "shape": shape, "params": params}
def decompress(
self, strings: List[List[bytes]], shape: Tuple[int, int], **kwargs
) -> Dict[str, Any]:
(z_strings,) = strings
z_hat = self.entropy_bottleneck.decompress(z_strings, shape)
params = self.h_s(z_hat)
return {"params": params}
