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https://github.com/garcinc/noised-topk
[ICML2022] Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
https://github.com/garcinc/noised-topk
deep-learning pytorch smooth topk
Last synced: 9 days ago
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[ICML2022] Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
- Host: GitHub
- URL: https://github.com/garcinc/noised-topk
- Owner: garcinc
- License: mit
- Created: 2022-06-16T15:14:50.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-30T08:57:56.000Z (10 months ago)
- Last Synced: 2024-11-04T09:17:12.482Z (12 days ago)
- Topics: deep-learning, pytorch, smooth, topk
- Language: Python
- Homepage: https://proceedings.mlr.press/v162/garcin22a/garcin22a.pdf
- Size: 28.3 KB
- Stars: 11
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
This is the code of the paper [Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification](https://proceedings.mlr.press/v162/garcin22a/garcin22a.pdf) published at ICML2022.
If you use this code for your work, please cite the paper:
```
@inproceedings{pmlr-v162-garcin22a,
title = {Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification},
author = {Garcin, Camille and Servajean, Maximilien and Joly, Alexis and Salmon, Joseph},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {7208--7222},
year = {2022},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
}
```
## The pytopk package
The pytopk package contains the code for the balanced and imbalanced top-k losses as well as a differentiable top-k function.
### Installation
It can be installed as follows:
```console
pip install pytopk
```
### Top-k losses
Our losses can be used as standard pytorch loss functions:
```python
import torch
from pytopk import BalNoisedTopK, ImbalNoisedTopK
scores = torch.tensor([[2.0, 1.5, -3.0],
[7.5, 4.0, -1.5]])
labels = torch.tensor([0, 2])
k = 2
criteria_bal = BalNoisedTopK(k=k, epsilon=1.0)
criteria_imbal = ImbalNoisedTopK(k=k, epsilon=0.01, max_m=0.3, cls_num_list=[17, 23, 55])
loss_batch_bal = criteria_bal(scores, labels)
loss_batch_imbal = criteria_imbal(scores, labels)
```
### Smooth top-k function
We also provide a differentiable top-k function for tensors of any size that can be plugged into any neural network architecture:
```python
import torch
from pytopk import NoisedTopK
smooth_topk = NoisedTopK(k=3, dim=-1)
x = torch.tensor([[-1.5, 2.0, 0.7, 3.8],
[-1.1, -5.4, 0.1, 2.3]], requires_grad=True)
out = smooth_topk(x)
print(out)
>> tensor([ 0.4823, -1.4710], grad_fn=<_NoisedTopKBackward>)
out.sum().backward()
print(x.grad)
>> tensor([[0.0000, 0.4000, 0.6000, 0.0000],
[0.8000, 0.0000, 0.2000, 0.0000]])
```
## Experiments
For running the experiments, you can download the required packages running:
```console
pip install -r requirements.txt
```
For running the experiments on Pl@ntNet-300K, you first need to download the dataset [here](https://zenodo.org/record/5645731#.YfMvN-rMJPY)
and then place it as *plantnet* inside a folder named *data*.
Command lines that were run for obtaining the results can be found in the *cmd_lines.txt* file.