carolussolis / contextual_loss_pytorch

Contextual Loss (CX) and Contextual Bilateral Loss (CoBi).

Contextual Loss

PyTorch implementation of Contextual Loss (CX) and Contextual Bilateral Loss (CoBi).

Introduction

There are many image transformation tasks whose spatially aligned data is hard to capture in the wild. Pixel-to-pixel or global loss functions can NOT be directly applied such unaligned data. CX is a loss function to defeat the problem. The key idea of CX is interpreting images as sets of feature points that don't have spatial coordinates. If you want to know more about CX, please refer the original paper, repo and examples in ./doc directory.

Requirements

• Python3.7+
• torch & torchvision

Installation

pip install git+https://github.com/S-aiueo32/contextual_loss_pytorch.git

Usage

You can use it like PyTorch APIs.

import torch

import contextual_loss as cl
import contextual_loss.fuctional as F


# input features
img1 = torch.rand(1, 3, 96, 96)
img2 = torch.rand(1, 3, 96, 96)

# contextual loss
criterion = cl.ContextualLoss()
loss = criterion(img1, img2)

# functional call
loss = F.contextual_loss(img1, img2, band_width=0.1, loss_type='cosine')

# comparing with VGG features
# if `use_vgg` is set, VGG model will be created inside of the criterion
criterion = cl.ContextualLoss(use_vgg=True, vgg_layer='relu5_4')
loss = criterion(img1, img2)

Reference

Papers

1. Mechrez, Roey, Itamar Talmi, and Lihi Zelnik-Manor. "The contextual loss for image transformation with non-aligned data." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
2. Mechrez, Roey, et al. "Maintaining natural image statistics with the contextual loss." Asian Conference on Computer Vision. Springer, Cham, 2018.

Implementations

Thanks to the owners of the following awesome implementations.

• Original Repository: https://github.com/roimehrez/contextualLoss
• Simple PyTorch Implemantation: https://gist.github.com/yunjey/3105146c736f9c1055463c33b4c989da
• CoBi: https://github.com/ceciliavision/zoom-learn-zoom