This codebase requires PyTorch 1.0. Please refer to net.py for PyTorch layer definitions. Custom CUDA kernals for quantization (log and linear) and shift layers are in kernels/. This codebase uses Label Refinery for improved model performance.
We use a custom data loader which loads the entire ImageNet into memory (~200GB) before training (required due to network drives). This can be swapped out for the standard PyTorch dataloader and should lead to similar model performance.
python train.py --dataset-root <dataset folder> --dataset imagenet --batch-size 1024 \
--epochs 140 --lr-type cosine --lr 0.256 --aug + --save-path <save path> \
--teacher-path <pretrained resnet50 path> --filters 128 512 512 512 1024 \
--layers 4 4 4 4 1 --strides 1 2 2 2 1 --groups 2 8 8 8 8 --max-weight-exp 0 \
--weight-levels 8 --layer-type quant --bn-type quant-bn --n-class 1000 \
--input-size 224 --reshape-stride 4 --gamma 0.4 --in-memory
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