download .pth files to qmodels/:
googlenet : https://download.pytorch.org/models/quantized/googlenet_fbgemm-c00238cf.pth
inception_v3 : https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-71447a44.pth
mobilenet_v2 : https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth
mobilenet_v3_large : https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth
resnet18 : https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth
resnet50 : https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth
resnext101 : https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth
shufflenetv2_x1.0 : https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pth
ghostnet : https://1drv.ms/u/s!Ahqo_6nBJPIHhloNb-Rg2uXs38MU?e=Smakww
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Ghostnet can do some operations on the feature maps of the inter-layer by manipulating the Class ConvX in the operations.py
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And other works shoud modifies the Class Comp in operations.py
python validate.py --data --model --actbit <8 or 16 for ghostnet>
# python validate.py --model mobilenet_v3_large --data ./imagenet
# python validate.py --model resnet_18 --data ./imagenet
# python validate.py --model resnet_50 --data ./imagenet
# python validate.py --model resnext_101 --data ./imagenet
# python validate.py --model googlenet --data ./imagenet
# python validate.py --model shufflenet_v2 --data ./imagenet
# python validate.py --model inception_v3 --data ./imagenet
# python validate.py --model mobilenet_v2 --data ./imagenet
# python validate.py --model ghostnet --data ./imagenet --actbit 8