The MegEngine implementation of LightCNN(Light CNN for Deep Face Recognition)

pip install -r requirements.txt

If you don't want to compare the ouput error between the MegEngine implementation and PyTorch one, just ignore requirements.txt and install MegEngine from the command line:

python3 -m pip install --upgrade pip 
python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

Convert trained weights from torch to megengine, the converted weights will be saved in ./pretained/ , you need to specify the convert model architecture and path to checkpoint offered by official repo.

python convert_weights.py -m 9 -c /path/to/ckpt

Use python compare.py .

By default, the compare script will convert the torch state_dict to the format that megengine need.

If you want to compare the error by checkpoints, you neet load them manually.

Import from megengine.hub:

Way 1:

from megengine import hub

modelhub = hub.import_module(
    repo_info='asthestarsfalll/LightCNN-MegEngine:main', git_host='github.com')

# load pretrained model
pretrained_model = modelhub.LightCNN_9Layers(pretrained=True)

Way 2:

from  megengine import hub

# load pretrained model 
model_name = 'LightCNN_9Layers'
pretrained_model = hub.load(
    repo_info='asthestarsfalll/LightCNN-MegEngine:main', entry=model_name, git_host='github.com', pretrained=True)

For those models which do not have pretrained model online, you need to convert weights mannaly, and load the model without pretrained weights like this:

model = modelhub.LightCNN_29Layers_v2()
# or
model_name = 'LightCNN_29Layers_v2'
model = hub.load(
    repo_info='asthestarsfalll/LightCNN-MegEngine:main', entry=model_name, git_host='github.com')

The official pytorch implementation of LightCNN