In recent times, convolutional neural networks (ConvNets) have continued to evolve, pushing the boundaries of what is possible in the field of deep learning. The Sparse Large Kernel Network (SLaK) stands at the forefront of these advancements, allowing us to upscale convolutional kernels beyond the traditional limits. This blog will walk you through the installation, training, and results using SLaK in PyTorch.

Table of Contents

• Installation
• Results of SLaK
• Training of SLaK
• Troubleshooting

Installation

Before diving into the intricate world of SLaK, let’s set up your environment.

# Create a conda virtual environment
conda create -n slak python=3.8 -y
conda activate slak

# Install Pytorch
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge

# Clone the repository and install required packages
git clone https://github.com/Shiweiliuiiiiiii/SLaK.git
pip install timm tensorboardX six

Results of SLaK

The SLaK model leverages large kernel sizes (up to 51×51) to boost its accuracy and efficiency across various tasks.

For instance, the SLaK-T model, trained on ImageNet-1K, achieved notable results compared to traditional ConvNeXt models:

• SLaK-T with 51×51 kernels: Accuracy (acc@1) = 82.5% with 30M parameters.
• SLaK-S with 51×51 kernels: Accuracy (acc@1) = 83.8% with 55M parameters.
• SLaK-B with 51×51 kernels: Accuracy (acc@1) = 84.0% with 95M parameters.

These figures illustrate how increasing kernel size through SLaK can yield better performance. Imagine SLaK as a camera lens with a wider aperture; it allows more light to come in, thus capturing clearer images in low illumination. Similarly, large kernels capture more contextual information, hence improving the model’s understanding of images.

Training of SLaK

The training process involves a few command-line instructions according to your specific model. Here’s how you can initiate the training:

# SLaK-T training command
python -m torch.distributed.launch --nproc_per_node=4 main.py \
  --Decom True --sparse --width_factor 1.3 -u 2000 \
  --sparsity 0.4 --sparse_init snip --prune_rate 0.5 \
  --growth random --epochs 300 --model SLaK_tiny \
  --drop_path 0.1 --batch_size 128 --lr 4e-3 --update_freq 8 \
  --data_path pathtoimagenet-1k --num_workers 40 \
  --kernel_size 51 49 47 13 5 --output_dir pathtosave_results

Troubleshooting

While setting up and running SLaK, you may encounter some common issues that can be easily resolved.

• CUDA Issues: Make sure you have the correct version of CUDA installed (tested with CUDA 11.3.1). Check your CUDA_HOME environment variable.
• Memory Errors: If you face out-of-memory (OOM) errors, consider adjusting the batch size or update frequency. The effective batch size can be calculated as:
  effective_batch_size = num_nodes * num_gpus * batch_size * update_freq.
• Installation Errors: If Python cannot find the required packages, ensure to modify your PYTHONPATH correctly, particularly for the depthwise convolution implementation.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.