Multiception

Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy

A new convolution type to boost the performance of depthwise seperable convolution (DSConv)

class Multiception(nn.Module):
    def __init__(self, in_channel, out_channel, stride, kernels):
        super(Multiception, self).__init__()
        padding_dict = {1:0, 3:1, 5:2, 7:3}
        self.seps = nn.ModuleList()
        for kernel in kernels:
            sep = nn.Conv2d(in_channel,in_channel, kernel_size = kernel,stride =1,padding = padding_dict[kernel],dilation=1,groups=in_channel, bias=False)
            self.seps.append(sep)
        self.bn1 = nn.BatchNorm2d(in_channel*len(kernels)) 
        self.pointwise = nn.Conv2d(in_channel*len(kernels), out_channel, 1, stride, 0, 1, 1)
        self.bn2 = nn.BatchNorm2d(out_channel)       

    def forward(self, x):
        seps = []
        for sep in self.seps:
            seps.append(sep(x))
        out_seq = torch.cat(seps, dim=1)
        out = self.pointwise(self.bn1(out_seq))
        out = self.bn2(out)
        return out 

Citation

Guoqing Bao, Manuel B. Graeber, Xiuying Wang, "Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy," 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2020, pp. 747-752, doi: 10.1109/ICARCV50220.2020.9305369.

School of Computer Science, The University of Sydney

Experiments

Run Experiments

For example:

Run experiment on stl-10 and imagenet32x32 datasets using resnet50 model

python3 main.py --label 10 --dataset stl --model resnet50 --batch_size 64

python3 main.py --label 1000 --dataset imagenet32 --model resnet50 --epochs 50 

Run experiment on cifar-10 and cifar-100 datasets using shakenet model

python3 main.py --label 10 --dataset cifar --model shake

python3 main.py --label 100 --dataset cifar --model shake 

replace parameters dataset and/or model to explore other experiments

Performance comparison with depthwise seperable convolution (DSConv)

Cifar 10

Cifar 100

STL 10

Performance comparison with standard convolution