Conditional convolution (Dynamic convolution) in tensorflow2.2.0. This depository implements the method described in the paper:
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Brandon Yang, Gabriel Bender, Quoc V.Le, Jiquan Ngiam
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Meanwhile, the softmax with a large temperature for kernel attention introduced by Dynamic Convolution: Attention Over Convolution Kernels is adopted.
Another similar paper: DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks.
You can start according to the default arguments by python main.py. Or specify the arguments:
python main.py --arch cond_cifar_resnet --num_layers 56 --num_experts 3 --dataset cifar10 --num_classes 10