Skip to content

refactor: simplify forward() permutation logic for compile-friendly execution #344

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
fracape merged 2 commits into from
Oct 23, 2025
Merged

refactor: simplify forward() permutation logic for compile-friendly execution #344

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
06354e2
refactor: simplify forward() permutation logic for compile-friendly e…
studyingeugene Oct 22, 2025
5c95297
refactor: fix lint errors
studyingeugene Oct 22, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
33 changes: 9 additions & 24 deletions compressai/entropy_models/entropy_models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,9 +54,7 @@ def __init__(self, method):

if method not in available_entropy_coders():
methods = ", ".join(available_entropy_coders())
raise ValueError(
f'Unknown entropy coder "{method}"' f" (available: {methods})"
)
raise ValueError(f'Unknown entropy coder "{method}" (available: {methods})')

if method == "ans":
from compressai import ans
Expand Down Expand Up @@ -474,28 +472,18 @@ def forward(
if training is None:
training = self.training

if not torch.jit.is_scripting():
# x from B x C x ... to C x B x ...
perm = torch.cat(
(
torch.tensor([1, 0], dtype=torch.long, device=x.device),
torch.arange(2, x.ndim, dtype=torch.long, device=x.device),
)
)
inv_perm = perm
else:
raise NotImplementedError()
# TorchScript in 2D for static inference
# Convert to (channels, ... , batch) format
# perm = (1, 2, 3, 0)
# inv_perm = (3, 0, 1, 2)
D = x.dim()
# B C ... -> C B ...
perm = [1, 0] + list(range(2, D))
inv_perm = [0] * D
for i, p in enumerate(perm):
inv_perm[p] = i

x = x.permute(*perm).contiguous()
shape = x.size()
values = x.reshape(x.size(0), 1, -1)

# Add noise or quantize

outputs = self.quantize(
values, "noise" if training else "dequantize", self._get_medians()
)
Expand All @@ -510,11 +498,8 @@ def forward(
# likelihood = torch.zeros_like(outputs)

# Convert back to input tensor shape
outputs = outputs.reshape(shape)
outputs = outputs.permute(*inv_perm).contiguous()

likelihood = likelihood.reshape(shape)
likelihood = likelihood.permute(*inv_perm).contiguous()
outputs = outputs.reshape(shape).permute(*inv_perm).contiguous()
likelihood = likelihood.reshape(shape).permute(*inv_perm).contiguous()

return outputs, likelihood

Expand Down