lsq.md

LSQ

LSQ	fp32	w4a4	w3a3	w2a2	w8a8(1epoch, quantize data)
AlexNet	56.55, 79.09	56.96, 79.46 √	55.31, 78.59	51.18, 75.38
ResNet18	69.76, 89.08	70.26, 89.34 √	69.45, 88.85		69.68 88.92 √

bash

AlexNet_LSQ_w4a4

python examples/classifier_imagenet/main.py ~/datasets/data.imagenet \
    -a alexnet_lsq -j 10 --pretrained -b 2048 --log-name $2 \
    --lr 0.01 --wd 1e-4 --warmup-epoch -1 \
    --gpu $1 --epochs 90 --lr-scheduler CosineAnnealingLR \
    --qw 4 --qa 4 --q-mode layer_wise \
    --debug

AlexNet_LSQ_w3a3

python examples/classifier_imagenet/main.py ~/datasets/data.imagenet \
    -a alexnet_lsq -j 10 --pretrained -b 2048 --log-name $2 \
    --lr 0.01 --wd 1e-4 --warmup-epoch -1 \
    --gpu $1 --epochs 90 --lr-scheduler CosineAnnealingLR \
    --qw 3 --qa 3 --q-mode layer_wise \
    --debug

AlexNet_LSQ_w2a2

python examples/classifier_imagenet/main.py ~/datasets/data.imagenet \
    -a alexnet_lsq -j 10 --pretrained -b 2048 --log-name $2 \
    --lr 0.01 --wd 1e-4 --warmup-epoch -1 \
    --gpu $1 --epochs 90 --lr-scheduler CosineAnnealingLR \
    --qw 3 --qa 3 --q-mode layer_wise \
    --debug

ResNet18_LSQ_w4a4

python examples/classifier_imagenet/main.py ~/datasets/data.imagenet \
    -a resnet18_lsq -j 10 -b 512 --pretrained \
    --lr 0.01 --wd 1e-4 --warmup-epoch -1 \
    --gpu $1 --log-name $2 --epochs 90 --lr-scheduler CosineAnnealingLR \
    --qw 4 --qa 4 --q-mode layer_wise \
    --debug