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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.registry import register_module
from ..base import LatentCodec
__all__ = [
"GainHyperLatentCodec",
]
[docs]
@register_module("GainHyperLatentCodec")
class GainHyperLatentCodec(LatentCodec):
"""Entropy bottleneck codec with surrounding `h_a` and `h_s` transforms.
Gain-controlled side branch for 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.
.. note:: ``GainHyperLatentCodec`` should be used inside
``GainHyperpriorLatentCodec`` to construct a full hyperprior.
.. code-block:: none
gain gain_inv
│ │
▼ ▼
┌───┐ 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,
**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()
def forward(self, y: Tensor, gain: Tensor, gain_inv: Tensor) -> Dict[str, Any]:
z = self.h_a(y)
z = z * gain
z_hat, z_likelihoods = self.entropy_bottleneck(z)
z_hat = z_hat * gain_inv
params = self.h_s(z_hat)
return {"likelihoods": {"z": z_likelihoods}, "params": params}
def compress(self, y: Tensor, gain: Tensor, gain_inv: Tensor) -> Dict[str, Any]:
z = self.h_a(y)
z = z * gain
shape = z.size()[-2:]
z_strings = self.entropy_bottleneck.compress(z)
z_hat = self.entropy_bottleneck.decompress(z_strings, shape)
z_hat = z_hat * gain_inv
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],
gain_inv: Tensor,
**kwargs,
) -> Dict[str, Any]:
(z_strings,) = strings
z_hat = self.entropy_bottleneck.decompress(z_strings, shape)
z_hat = z_hat * gain_inv
params = self.h_s(z_hat)
return {"params": params}
