Paper: Dynamic Convolution: Attention over Convolution Kernels
Implementation with reference to [1] https://github.com/kaijieshi7/Dynamic-convolution-Pytorch
The training time is about 7 times faster than [1] upper link on the cifar10 dataset.
python dyconv2d.py
python train.py --device 0 #'cuda device, i.e. 0 or 0,1,2,3 or cpu' --training_optim #training more faster
just call model.inference_mode()
model = DyMobileNetV2(num_classes=opt.num_classes, input_size=32, width_mult=1.) model.inference_mode()