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 outperform ResNet-1000 with plain DiracNet with only 34 layers. Also, the proposed Dirac weight parameterization can be folded into one filter for inference, leading to easily interpretable VGG-like network.
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 scaling scalars, and normalize does l_2 normalization over each feature plane.
We also use NCReLU (negative CReLU) nonlinearity:
def ncrelu(x):
return torch.cat([x.clamp(min=0), x.clamp(max=0)], dim=1)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
First install PyTorch, then install torchnet:
pip install git+https://github.com/pytorch/tnt.git@master
Install OpenCV with Python bindings (e.g. conda install -c menpo opencv3), and torchvision
with OpenCV transforms:
pip install git+https://github.com/szagoruyko/vision.git@opencv
Finally, 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 0.1We 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).
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.
We provide printout of DiracNet-18-0.75 sequential model for reference:
Sequential (
(conv): Conv2d(3, 48, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
(max_pool0): MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
(group0.block0.ncrelu): NCReLU()
(group0.block0.conv): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group0.block1.ncrelu): NCReLU()
(group0.block1.conv): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group0.block2.ncrelu): NCReLU()
(group0.block2.conv): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group0.block3.ncrelu): NCReLU()
(group0.block3.conv): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(max_pool1): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(group1.block0.ncrelu): NCReLU()
(group1.block0.conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group1.block1.ncrelu): NCReLU()
(group1.block1.conv): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group1.block2.ncrelu): NCReLU()
(group1.block2.conv): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group1.block3.ncrelu): NCReLU()
(group1.block3.conv): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(max_pool2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(group2.block0.ncrelu): NCReLU()
(group2.block0.conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group2.block1.ncrelu): NCReLU()
(group2.block1.conv): Conv2d(384, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group2.block2.ncrelu): NCReLU()
(group2.block2.conv): Conv2d(384, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group2.block3.ncrelu): NCReLU()
(group2.block3.conv): Conv2d(384, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(max_pool3): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
(group3.block0.ncrelu): NCReLU()
(group3.block0.conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group3.block1.ncrelu): NCReLU()
(group3.block1.conv): Conv2d(768, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group3.block2.ncrelu): NCReLU()
(group3.block2.conv): Conv2d(768, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(group3.block3.ncrelu): NCReLU()
(group3.block3.conv): Conv2d(768, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu): ReLU ()
(avg_pool): AvgPool2d ()
(view): Flatten()
(fc): Linear (384 -> 1000)
)
Pretrained weights for this model: https://www.dropbox.com/s/0j1dnixlzdr5byc/diracnet-18-0.75-br-export.hkl?dl=0
Pretrained weights for the original (not folded) model: https://www.dropbox.com/s/cyp2dhqhffdtlmo/diracnet-18-0.75-br.hkl?dl=0 (functional definition only)
We plan to add more pretrained models later.
@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}}