Compatibility with Other Libraries¶

VISSL provides several helpful scripts to convert VISSL models to models that are compatible with other libraries like Detectron2 and ClassyVision compatible models.
VISSL also provides scripts to convert models from other sources like Caffe2 models in the paper to VISSL compatible models.
TorchVision models are directly compatible with VISSL and don’t require any conversion.

Converting Models VISSL -> {Detectron2, ClassyVision, TorchVision}¶

We provide scripts to convert VISSL models to Detectron2 and ClassyVision compatible models.

Converting to Detectron2¶

All the ResNe(X)t models in VISSL can be converted to Detectron2 weights using following command:

python extra_scripts/convert_vissl_to_detectron2.py \
    --input_model_file <input_model>.pth  \
    --output_model <d2_model>.torch \
    --weights_type torch \
    --state_dict_key_name classy_state_dict

Converting to ClassyVision¶

All the ResNe(X)t models in VISSL can be converted to Detectron2 weights using following command:

python extra_scripts/convert_vissl_to_classy_vision.py \
    --input_model_file <input_model>.pth  \
    --output_model <d2_model>.torch \
    --state_dict_key_name classy_state_dict

Converting to TorchVision¶

All the ResNe(X)t models in VISSL can be converted to Torchvision weights using following command:

python extra_scripts/convert_vissl_to_torchvision.py \
    --model_url_or_file <input_model>.pth  \
    --output_dir /path/to/output/dir/ \
    --output_name <my_converted_model>.torch

Converting Caffe2 models -> VISSL¶

We provide conversion of all the Caffe2 models in the paper.

ResNet-50 models to VISSL¶

Jigsaw model:

python extra_scripts/convert_caffe2_to_torchvision_resnet.py \
    --c2_model <model>.pkl \
    --output_model <pth_model>.torch \
    --jigsaw True --bgr2rgb True

Colorization model:

python extra_scripts/convert_caffe2_to_torchvision_resnet.py \
    --c2_model <model>.pkl \
    --output_model <pth_model>.torch \
    --bgr2rgb False

Supervised model:

python extra_scripts/convert_caffe2_to_pytorch_rn50.py \
    --c2_model <model>.pkl \
    --output_model <pth_model>.torch \
    --bgr2rgb True

AlexNet models to VISSL¶

AlexNet Jigsaw models:

python extra_scripts/convert_caffe2_to_vissl_alexnet.py \
    --weights_type caffe2 \
    --model_name jigsaw \
    --bgr2rgb True \
    --input_model_weights <model.pkl> \
    --output_model <pth_model>.torch

AlexNet Colorization models:

python extra_scripts/convert_caffe2_to_vissl_alexnet.py \
    --weights_type caffe2 \
    --model_name colorization \
    --input_model_weights <model.pkl> \
    --output_model <pth_model>.torch

AlexNet Supervised models:

python extra_scripts/convert_caffe2_to_vissl_alexnet.py \
    --weights_type caffe2 \
    --model_name supervised \
    --bgr2rgb True \
    --input_model_weights <model.pkl> \
    --output_model <pth_model>.torch

Converting Models ClassyVision -> VISSL¶

We provide scripts to convert ClassyVision models to VISSL compatible models.

python extra_scripts/convert_classy_vision_to_vissl_resnet.py \
    --input_model_file <input_model>.pth  \
    --output_model <d2_model>.torch \
    --depth 50

Converting Official RotNet and DeepCluster models -> VISSL¶

AlexNet RotNet model:

python extra_scripts/convert_caffe2_to_vissl_alexnet.py \
    --weights_type torch \
    --model_name rotnet \
    --input_model_weights <model> \
    --output_model <pth_model>.torch

AlexNet DeepCluster model:

python extra_scripts/convert_alexnet_models.py \
    --weights_type torch \
    --model_name deepcluster \
    --input_model_weights <model> \
    --output_model <pth_model>.torch