This repository contains code that implements filter combination learning.
- python : 3.6
- torch : 1.5.0
- torchvision : 0.6.0
python mnist_main.py --save_dir [PATH]vgg16
python cifar10_main.py --model_name vgg16 -e conv -t normal -o normal --save_dir [PATH]resnet18
python cifar10_main.py --model_name resnet18 -e conv -t normal -o normal --save_dir [PATH]vgg16
python cifar100_main.py --model_name vgg16 -e conv -t normal -o normal --save_dir [PATH]resnet18
python cifar100_main.py --model_name resnet18 -e conv -t normal -o normal --save_dir [PATH]- VGG16 example
- GF Layer -> Conv Layer
for i, (name, module)in enumerate(model.features.named_modules()):
if isinstance(module, GFLayer):
current_layer += 1
in_channels = module.in_ch
out_channels = module.out_ch
groups = module.groups
stride = module.stride
padding = module.padding
if current_layer <= 8:
f = middle_filters
else:
f = last_filters
new_weights = f.view(1, 1, 3, 3, 3) * \
module.weights.view(out_channels, in_channels // groups, 3, 1, 1).repeat(1, 1, 1, 3, 3)
new_weights = new_weights.sum(2)
new_conv = torch.nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
padding=padding,
groups=groups,
bias=(module.bias is not None)).to(device)
new_conv.weight.data = new_weights
model.features[i-1] = new_conv@article{jeong2021filter,
title={Filter combination learning for CNN model compression},
author={Jeong, Jaemin and Cho, Ji-Ho and Lee, Jeong-Gun},
journal={ICT Express},
volume={7},
number={1},
pages={5--9},
year={2021},
publisher={Elsevier}
}