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from itertools import accumulate
from typing import Any, Dict, List, Mapping, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from compressai.registry import register_module
from .base import LatentCodec
__all__ = [
"ChannelGroupsLatentCodec",
]
[docs]
@register_module("ChannelGroupsLatentCodec")
class ChannelGroupsLatentCodec(LatentCodec):
"""Reconstructs groups of channels using previously decoded groups.
Context model from [Minnen2020] and [He2022].
Also known as a "channel-conditional" (CC) entropy model.
See :py:class:`~compressai.models.sensetime.Elic2022Official`
for example usage.
[Minnen2020]: `"Channel-wise Autoregressive Entropy Models for
Learned Image Compression" <https://arxiv.org/abs/2007.08739>`_, by
David Minnen, and Saurabh Singh, ICIP 2020.
[He2022]: `"ELIC: Efficient Learned Image Compression with
Unevenly Grouped Space-Channel Contextual Adaptive Coding"
<https://arxiv.org/abs/2203.10886>`_, by Dailan He, Ziming Yang,
Weikun Peng, Rui Ma, Hongwei Qin, and Yan Wang, CVPR 2022.
"""
latent_codec: Mapping[str, LatentCodec]
channel_context: Mapping[str, nn.Module]
def __init__(
self,
latent_codec: Optional[Mapping[str, LatentCodec]] = None,
channel_context: Optional[Mapping[str, nn.Module]] = None,
*,
groups: List[int],
**kwargs,
):
super().__init__()
self._kwargs = kwargs
self.groups = list(groups)
self.groups_acc = list(accumulate(self.groups, initial=0))
self.channel_context = nn.ModuleDict(channel_context)
self.latent_codec = nn.ModuleDict(latent_codec)
def __getitem__(self, key: str) -> LatentCodec:
return self.latent_codec[key]
def forward(self, y: Tensor, side_params: Tensor) -> Dict[str, Any]:
y_ = torch.split(y, self.groups, dim=1)
y_out_ = [{}] * len(self.groups)
y_hat_ = [Tensor()] * len(self.groups)
y_likelihoods_ = [Tensor()] * len(self.groups)
for k in range(len(self.groups)):
params = self._get_ctx_params(k, side_params, y_hat_)
y_out_[k] = self.latent_codec[f"y{k}"](y_[k], params)
y_hat_[k] = y_out_[k]["y_hat"]
y_likelihoods_[k] = y_out_[k]["likelihoods"]["y"]
y_hat = torch.cat(y_hat_, dim=1)
y_likelihoods = torch.cat(y_likelihoods_, dim=1)
return {
"likelihoods": {
"y": y_likelihoods,
},
"y_hat": y_hat,
}
def compress(self, y: Tensor, side_params: Tensor) -> Dict[str, Any]:
y_ = torch.split(y, self.groups, dim=1)
y_out_ = [{}] * len(self.groups)
y_hat = torch.zeros_like(y)
y_hat_ = y_hat.split(self.groups, dim=1)
for k in range(len(self.groups)):
params = self._get_ctx_params(k, side_params, y_hat_)
y_out_[k] = self.latent_codec[f"y{k}"].compress(y_[k], params)
y_hat_[k][:] = y_out_[k]["y_hat"]
y_strings_groups = [y_out["strings"] for y_out in y_out_]
assert all(len(y_strings_groups[0]) == len(ss) for ss in y_strings_groups)
return {
"strings": [s for ss in y_strings_groups for s in ss],
"shape": [y_out["shape"] for y_out in y_out_],
"y_hat": y_hat,
}
def decompress(
self,
strings: List[List[bytes]],
shape: List[Tuple[int, ...]],
side_params: Tensor,
**kwargs,
) -> Dict[str, Any]:
n = len(strings[0])
assert all(len(ss) == n for ss in strings)
strings_per_group = len(strings) // len(self.groups)
y_out_ = [{}] * len(self.groups)
y_shape = (sum(s[0] for s in shape), *shape[0][1:])
y_hat = torch.zeros((n, *y_shape), device=side_params.device)
y_hat_ = y_hat.split(self.groups, dim=1)
for k in range(len(self.groups)):
params = self._get_ctx_params(k, side_params, y_hat_)
y_out_[k] = self.latent_codec[f"y{k}"].decompress(
strings[strings_per_group * k : strings_per_group * (k + 1)],
shape[k],
params,
)
y_hat_[k][:] = y_out_[k]["y_hat"]
return {
"y_hat": y_hat,
}
def merge_y(self, *args):
return torch.cat(args, dim=1)
def merge_params(self, *args):
return torch.cat(args, dim=1)
def _get_ctx_params(
self, k: int, side_params: Tensor, y_hat_: List[Tensor]
) -> Tensor:
if k == 0:
return side_params
ch_ctx_params = self.channel_context[f"y{k}"](self.merge_y(*y_hat_[:k]))
return self.merge_params(ch_ctx_params, side_params)
