PyTorch implementation of Normalizer-Free Networks and Adaptive Gradient Clipping

Paper: https://arxiv.org/abs/2102.06171.pdf

Original code: https://github.com/deepmind/deepmind-research/tree/master/nfnets

Blog post: https://tourdeml.github.io/blog/posts/2021-03-31-adaptive-gradient-clipping/. Feel free to subscribe to the newsletter, and leave a comment if you have anything to add/suggest publicly.

Do star this repository if it helps your work, and don't forget to cite if you use this code in your research!

Installation

Install from PyPi:

pip3 install nfnets-pytorch

or install the latest code using:

pip3 install git+https://github.com/vballoli/nfnets-pytorch

Usage

WSConv2d

Use WSConv1d, WSConv2d, ScaledStdConv2d(timm) and WSConvTranspose2d like any other torch.nn.Conv2d or torch.nn.ConvTranspose2d modules.

import torch
from torch import nn
from nfnets import WSConv2d, WSConvTranspose2d, ScaledStdConv2d

conv = nn.Conv2d(3,6,3)
w_conv = WSConv2d(3,6,3)

conv_t = nn.ConvTranspose2d(3,6,3)
w_conv_t = WSConvTranspose2d(3,6,3)

Generic AGC (recommended)

import torch
from torch import nn, optim
from torchvision.models import resnet18

from nfnets import WSConv2d
from nfnets.agc import AGC # Needs testing

conv = nn.Conv2d(3,6,3)
w_conv = WSConv2d(3,6,3)

optim = optim.SGD(conv.parameters(), 1e-3)
optim_agc = AGC(conv.parameters(), optim) # Needs testing

# Ignore fc of a model while applying AGC.
model = resnet18()
optim = torch.optim.SGD(model.parameters(), 1e-3)
optim = AGC(model.parameters(), optim, model=model, ignore_agc=['fc'])

SGD - Adaptive Gradient Clipping

Similarly, use SGD_AGC like torch.optim.SGD

# The generic AGC is preferable since the paper recommends not applying AGC to the last fc layer.
import torch
from torch import nn, optim
from nfnets import WSConv2d, SGD_AGC

conv = nn.Conv2d(3,6,3)
w_conv = WSConv2d(3,6,3)

optim = optim.SGD(conv.parameters(), 1e-3)
optim_agc = SGD_AGC(conv.parameters(), 1e-3)

Using it within any non-residual PyTorch model (with non-residual connections)

replace_conv replaces the convolution in your (non-residual) model with the convolution class and replaces the batchnorm with identity. While the identity is not ideal, it shouldn't cause a major difference in the latency.

Note that as per the paper, replace_conv is only valid for non-residual models(vgg, mobilenetv1, etc.). See the above mentioned blog post for more information regarding the details.

import torch
from torch import nn
from torchvision.models import vgg16

from nfnets import replace_conv, WSConv2d, ScaledStdConv2d

model = vgg16()
replace_conv(model, WSConv2d) # This repo's original implementation
replace_conv(model, ScaledStdConv2d) # From timm

"""
class YourCustomClass(nn.Conv2d):
  ...
replace_conv(model, YourCustomClass)
"""

Docs

Find the docs at readthedocs

Cite Original Work

To cite the original paper, use:

@article{brock2021high,
  author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
  title={High-Performance Large-Scale Image Recognition Without Normalization},
  journal={arXiv preprint arXiv:},
  year={2021}
}

Cite this repository

To cite this repository, use:

@misc{nfnets2021pytorch,
  author = {Vaibhav Balloli},
  title = {A PyTorch implementation of NFNets and Adaptive Gradient Clipping},
  year = {2021},
  howpublished = {\url{https://github.com/vballoli/nfnets-pytorch}}
}