Training Very Deep Neural Networks Without Skip-Connections

Last update: Oct 12, 2022

Related tags

Overview

DiracNets

v2 update (January 2018):

The code was updated for DiracNets-v2 in which we removed NCReLU by adding per-channel a and b multipliers without weight decay. This allowed us to significantly simplify the network, which is now folds into a simple chain of convolution-ReLU layers, like VGG. On ImageNet DiracNet-18 and DiracNet-34 closely match corresponding ResNet with the same number of parameters.

See v1 branch for DiracNet-v1.

PyTorch code and models for DiracNets: Training Very Deep Neural Networks Without Skip-Connections

https://arxiv.org/abs/1706.00388

Networks with skip-connections like ResNet show excellent performance in image recognition benchmarks, but do not benefit from increased depth, we are thus still interested in learning actually deep representations, and the benefits they could bring. We propose a simple weight parameterization, which improves training of deep plain (without skip-connections) networks, and allows training plain networks with hundreds of layers. Accuracy of our proposed DiracNets is close to Wide ResNet (although DiracNets need more parameters to achieve it), and we are able to match ResNet-1000 accuracy with plain DiracNet with only 28 layers. Also, the proposed Dirac weight parameterization can be folded into one filter for inference, leading to easily interpretable VGG-like network.

DiracNets on ImageNet:

TL;DR

In a nutshell, Dirac parameterization is a sum of filters and scaled Dirac delta function:

conv2d(x, alpha * delta + W)

Here is simplified PyTorch-like pseudocode for the function we use to train plain DiracNets (with weight normalization):

def dirac_conv2d(input, W, alpha, beta)
    return F.conv2d(input, alpha * dirac(W) + beta * normalize(W))

where alpha and beta are per-channel scaling multipliers, and normalize does l_2 normalization over each feature plane.

Code

Code structure:

├── README.md # this file
├── diracconv.py # modular DiracConv definitions
├── test.py # unit tests
├── diracnet-export.ipynb # ImageNet pretrained models
├── diracnet.py # functional model definitions
└── train.py # CIFAR and ImageNet training code

Requirements

First install PyTorch, then install torchnet:

pip install git+https://github.com/pytorch/[email protected]

Install other Python packages:

pip install -r requirements.txt

To train DiracNet-34-2 on CIFAR do:

python train.py --save ./logs/diracnets_$RANDOM$RANDOM --depth 34 --width 2

To train DiracNet-18 on ImageNet do:

python train.py --dataroot ~/ILSVRC2012/ --dataset ImageNet --depth 18 --save ./logs/diracnet_$RANDOM$RANDOM \
                --batchSize 256 --epoch_step [30,60,90] --epochs 100 --weightDecay 0.0001 --lr_decay_ratio 0.1

nn.Module code

We provide DiracConv1d, DiracConv2d, DiracConv3d, which work like nn.Conv1d, nn.Conv2d, nn.Conv3d, but have Dirac-parametrization inside (our training code doesn't use these modules though).

Pretrained models

We fold batch normalization and Dirac parameterization into F.conv2d weight and bias tensors for simplicity. Resulting models are as simple as VGG or AlexNet, having only nonlinearity+conv2d as a basic block.

See diracnets.ipynb for functional and modular model definitions.

There is also folded DiracNet definition in diracnet.py, which uses code from PyTorch model_zoo and downloads pretrained model from Amazon S3:

from diracnet import diracnet18
model = diracnet18(pretrained=True)

Printout of the model above:

DiracNet(
  (features): Sequential(
    (conv): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
    (max_pool0): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), ceil_mode=False)
    (group0.block0.relu): ReLU()
    (group0.block0.conv): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group0.block1.relu): ReLU()
    (group0.block1.conv): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group0.block2.relu): ReLU()
    (group0.block2.conv): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group0.block3.relu): ReLU()
    (group0.block3.conv): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (max_pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
    (group1.block0.relu): ReLU()
    (group1.block0.conv): Conv2d (64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group1.block1.relu): ReLU()
    (group1.block1.conv): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group1.block2.relu): ReLU()
    (group1.block2.conv): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group1.block3.relu): ReLU()
    (group1.block3.conv): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (max_pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
    (group2.block0.relu): ReLU()
    (group2.block0.conv): Conv2d (128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group2.block1.relu): ReLU()
    (group2.block1.conv): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group2.block2.relu): ReLU()
    (group2.block2.conv): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group2.block3.relu): ReLU()
    (group2.block3.conv): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (max_pool3): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
    (group3.block0.relu): ReLU()
    (group3.block0.conv): Conv2d (256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group3.block1.relu): ReLU()
    (group3.block1.conv): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group3.block2.relu): ReLU()
    (group3.block2.conv): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (group3.block3.relu): ReLU()
    (group3.block3.conv): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (last_relu): ReLU()
    (avg_pool): AvgPool2d(kernel_size=7, stride=7, padding=0, ceil_mode=False, count_include_pad=True)
  )
  (fc): Linear(in_features=512, out_features=1000)
)

The models were trained with OpenCV, so you need to use it too to reproduce stated accuracy.

Pretrained weights for DiracNet-18 and DiracNet-34:
https://s3.amazonaws.com/modelzoo-networks/diracnet18v2folded-a2174e15.pth
https://s3.amazonaws.com/modelzoo-networks/diracnet34v2folded-dfb15d34.pth

Pretrained weights for the original (not folded) model, functional definition only:
https://s3.amazonaws.com/modelzoo-networks/diracnet18-v2_checkpoint.pth
https://s3.amazonaws.com/modelzoo-networks/diracnet34-v2_checkpoint.pth

We plan to add more pretrained models later.

Bibtex

@inproceedings{Zagoruyko2017diracnets,
    author = {Sergey Zagoruyko and Nikos Komodakis},
    title = {DiracNets: Training Very Deep Neural Networks Without Skip-Connections},
    url = {https://arxiv.org/abs/1706.00388},
    year = {2017}}

Training Very Deep Neural Networks Without Skip-Connections

Related tags

Overview

DiracNets

v2 update (January 2018):

TL;DR

Code

Requirements

nn.Module code

Pretrained models

Bibtex

Owner

Sergey Zagoruyko

Benchmarks for Model-Based Optimization

PyArmadillo: an alternative approach to linear algebra in Python

It's final year project of Diploma Engineering. This project is based on Computer Vision.

Localized representation learning from Vision and Text (LoVT)

Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

Implementation of the HMAX model of vision in PyTorch

Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing

A high performance implementation of HDBSCAN clustering.

Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

A Dying Light 2 (DL2) PAKFile Utility for Modders and Mod Makers.

https://sites.google.com/cornell.edu/recsys2021tutorial

Creative Applications of Deep Learning w/ Tensorflow

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

GestureSSD CBAM - A gesture recognition web system based on SSD and CBAM, using pytorch, flask and node.js

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”