Code for IntraQ, PyTorch implementation of our paper under review

Overview

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper

Requirements

Python >= 3.7.10

Pytorch == 1.7.1

Reproduce results

Stage1: Generate data.

cd data_generate

Please install all required package in requirements.txt.

"--save_path_head" in run_generate_cifar10.sh/run_generate_cifar100.sh is the path where you want to save your generated data pickle.

For cifar10/100

bash run_generate_cifar10.sh
bash run_generate_cifar100.sh

For ImageNet

"--save_path_head" in run_generate.sh is the path where you want to save your generated data pickle.

"--model" in run_generate.sh is the pre-trained model you want (also is the quantized model). You can use resnet18/mobilenet_w1/mobilenetv2_w1.

bash run_generate.sh

Stage2: Train the quantized network

cd ..
  1. Modify "qw" and "qa" in cifar10_resnet20.hocon/cifar100_resnet20.hocon/imagenet.hocon to select desired bit-width.

  2. Modify "dataPath" in cifar10_resnet20.hocon/cifar100_resnet20.hocon/imagenet.hocon to the real dataset path (for construct the test dataloader).

  3. Modify the "Path_to_data_pickle" in main_direct.py (line 122 and line 135) to the data_path and label_path you just generate from Stage1.

  4. Use the below commands to train the quantized network. Please note that the model that generates the data and the quantized model should be the same.

For cifar10/100

python main_direct.py --model_name resnet20_cifar10 --conf_path cifar10_resnet20.hocon --id=0

python main_direct.py --model_name resnet20_cifar100 --conf_path cifar100_resnet20.hocon --id=0

For ImageNet, you can choose the model by modifying "--model_name" (resnet18/mobilenet_w1/mobilenetv2_w1)

python main_direct.py --model_name resnet18 --conf_path imagenet.hocon --id=0

Evaluate pre-trained models

The pre-trained models and corresponding logs can be downloaded here

Please make sure the "qw" and "qa" in *.hocon, *.hocon, "--model_name" and "--model_path" are correct.

For cifar10/100

python test.py --model_name resnet20_cifar10 --model_path path_to_pre-trained model --conf_path cifar10_resnet20.hocon

python test.py --model_name resnet20_cifar100 --model_path path_to_pre-trained model --conf_path cifar100_resnet20.hocon

For ImageNet

python test.py --model_name resnet18/mobilenet_w1/mobilenetv2_w1 --model_path path_to_pre-trained model --conf_path imagenet.hocon

Results of pre-trained models are shown below:

Model Bit-width Dataset Top-1 Acc.
resnet18 W4A4 ImageNet 66.47%
resnet18 W5A5 ImageNet 69.94%
mobilenetv1 W4A4 ImageNet 51.36%
mobilenetv1 W5A5 ImageNet 68.17%
mobilenetv2 W4A4 ImageNet 65.10%
mobilenetv2 W5A5 ImageNet 71.28%
resnet-20 W3A3 cifar10 77.07%
resnet-20 W4A4 cifar10 91.49%
resnet-20 W3A3 cifar100 64.98%
resnet-20 W4A4 cifar100 48.25%
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