jaxlpips 1.2.0

LPIPS perceptual loss for JAX

JAX LPIPS

LPIPS perceptual loss implementation for JAX.

Information on the metric is available in the original repo.
Pretrained network and LPIPS linear weights are available on Hugging Face.

Installation

Install jaxlpips with:

pip install jaxlpips

Why?

There are already some LPIPS versions for JAX.

This implementation provides:

• Alexnet and VGG support
• trimmed pretrained network parameters and calculations
• safetensors for all parameters

Example

Supports "alexnet" and "vgg16" for pretrained_network to perform feature extraction.

Minimal example using dummy data:

import numpy as np
from jaxlpips import LPIPS

rng = np.random.default_rng(2781)

ref = rng.normal(size=(4, 64, 64, 3)).astype(np.float32)
tgt = rng.normal(size=(4, 64, 64, 3)).astype(np.float32)

lpips_loss_fn = LPIPS(pretrained_network="alexnet")

loss = lpips_loss_fn(ref, tgt)

References

[1] Zhang, Richard, et al. "The unreasonable effectiveness of deep features as a perceptual metric." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.