This implementation of dilated convolutional layer was accomplished during my internship in Zerotech Inc., Beijing. The need for dilated convolutional layer comes from training CSRNet for crowd detection.
Darknet is an open source neural network framework written in C and CUDA by Joseph Redmon. It is fast, easy to install, and supports CPU and GPU computation. For more information please refer to the Darknet project website.
Darknet has only two dependencies, and they are optional:
* OpenCV - for video I/O and more image types support
* CUDA - for GPU acceleration
This dilated convolutional layer implementation on GPU was written with CUDA Toolkit 9.0
The Makefile is provided in the main folder, to install Darknet with dilated convolutional layer, type:
make
The added layer was tested by comparing with the output of Caffe's implementation of dilated conv layer, and it was also tested by the training of CSRNet composed of it.
Darknet uses .cfg file as the configuration of Neural Network. In the .cfg file, the dilated convolutional layer is configured with:
[dilated_convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
dilate_rate=1
Here's a demonstration of dilated convolution and dilate rate:
Dilated convolution expands its convolutional kernel according to dilate rate. In this demonstration, dilate rate = 1. If dilate rate = 0, dilated convolution is simply normal convolution.
This configuration file is included in ./cfg/yolo-d-tiny.cfg