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Tutorial 5: How to Extract Inception State for FID Evaluation

In MMGeneration, we provide a script to extract the inception state of the dataset. In this doc, we provide a brief introduction on how to use this script.

Load Images

We provide two ways to load real data, namely, pass the path of directory that contains real images and pass the dataset config file you want to use.

Load from Directory

If you want to pass the path of real images, you can use --imgsdir arguments as the follow command.

python tools/utils/inception_stat.py --imgsdir ${IMGS_PATH} --pklname ${PKLNAME} --size ${SIZE} --flip ${FLIP}

Then a pre-defined pipeline will be used to load images in ${IMGS_PATH}.

pipeline = [
    dict(type='LoadImageFromFile', key='real_img'),
    dict(
        type='Resize', keys=['real_img'], scale=SIZE,
        keep_ratio=False),
    dict(
        type='Normalize',
        keys=['real_img'],
        mean=[127.5] * 3,
        std=[127.5] * 3,
        to_rgb=True),  # default to RGB images
    dict(type='Collect', keys=['real_img'], meta_keys=[]),
    dict(type='ImageToTensor', keys=['real_img'])
]

If ${FLIP} is set as True, the following config of horizontal flip operation would be added to the end of the pipeline.

dict(type='Flip', keys=['real_img'], direction='horizontal')

If you want to use a specific pipeline otherwise the pre-defined ones, you can use --pipeline-cfg to pass a config file contains the data pipeline you want to use.

python tools/utils/inception_stat.py --imgsdir ${IMGS_PATH} --pklname ${PKLNAME} --pipeline-cfg ${PIPELINE}

To be noted that, the name of the pipeline dict in ${PIPELINE} should be fixed as inception_pipeline. For example,

# an example of ${PIPELINE}
inception_pipeline = [
    dict(type='LoadImageFromFile', key='real_img'),
    ...
]

Load with Dataset Config

If you want to use a dataset config, you can use --data-config arguments as the following command.

python tools/utils/inception_stat.py --data-config ${CONFIG} --pklname ${PKLNAME} --subset ${SUBSET}

Then a dataset will be instantiated following the ${SUBSET} in the configs, and defaults to test. Take the following dataset config as example,

# from `imagenet_128x128_inception_stat.py`
data = dict(
    samples_per_gpu=None,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_prefix='data/imagenet/train',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=test_pipeline))

If not defined, the config in data['test'] would be used in data loading process. If you want to extract the inception state of the training set, you can set --subset train in the command. Then the dataset would be built under the guidance of config in data['train'] and images under data/imagenet/train and process pipeline of train_pipeline would be used.

Define the Version of Inception Net

In the aforementioned command, the script will take the PyTorch InceptionV3 by default. If you want the Tero's InceptionV3, you will need to switch to the script module:

python tools/utils/inception_stat.py --imgsdir ${IMGS_PATH} --pklname ${PKLNAME} --size ${SIZE} \
    --inception-style stylegan --inception-pth ${PATH_SCRIPT_MODULE}

Control Number of Images to Calculate Inception State

In inception_stat.py, we provide --num-samples argument to control the number of images used to calculate inception state.

python tools/utils/inception_stat.py --data-config ${CONFIG} --pklname ${PKLNAME} --num-samples ${NUMS}

If ${NUMS} is set as -1, all images in the defined dataset would be used.

Control the Shuffle Operation in Data Loading

In inception_stat.py, we provide --no-shuffle argument to avoid the shuffle operation in images loading process. For example, you can use the following command:

python tools/utils/inception_stat.py --data-config ${CONFIG} --pklname ${PKLNAME} --no-shuffle

Note on Inception State Extraction between Various Code Bases

For FID evaluation, differences between PyTorch Studio GAN and ours are mainly on the selection of real samples. In MMGen, we follow the pipeline of BigGAN, where the whole training set is adopted to extract inception statistics. Besides, we also use Tero's Inception for feature extraction.

You can download the preprocessed inception state by the following url:

You can use following commands to extract those inception states by yourself as well.

# For CIFAR10
python tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/cifar10_inception_stat.py --pklname cifar10.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train

# For ImageNet1k
python tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/imagenet_128x128_inception_stat.py --pklname imagenet.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train

# For ImageNet1k-64x64
python tools/utils/inception_stat.py --data-cfg configs/_base_/datasets/imagenet_64x64_inception_stat.py --pklname imagenet_64x64.pkl --no-shuffle --inception-style stylegan --num-samples -1 --subset train