senet.pytorch

PyTorch implementation of SENet

An implementation of SENet, proposed in Squeeze-and-Excitation Networks by Jie Hu, Li Shen and Gang Sun, who are the winners of ILSVRC 2017 classification competition.

Now SE-ResNet (18, 34, 50, 101, 152/20, 32) and SE-Inception-v3 are implemented.

  • python cifar.py runs SE-ResNet20 with Cifar10 dataset.

  • python imagenet.py and python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} imagenet.py run SE-ResNet50 with ImageNet(2012) dataset,

    • You need to prepare dataset by yourself in ~/.torch/data or set an enviroment variable IMAGENET_ROOT=${PATH_TO_YOUR_IMAGENET}
    • First download files and then follow the instruction.
    • The number of workers and some hyper parameters are fixed so check and change them if you need.
    • This script uses all GPUs available. To specify GPUs, use CUDA_VISIBLE_DEVICES variable. (e.g. CUDA_VISIBLE_DEVICES=1,2 to use GPU 1 and 2)

For SE-Inception-v3, the input size is required to be 299x299 as the original Inception.