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Project description
PyDLmeta
Features: identify the model type belong to which deep learning framework and extract the meta data if possible. The meta data includes input/output name and shape of the model.
The supported formats are:
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Tensorflow: frozen model *.pb, *.h5, SavedModel directory, *.tflite
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Pytorch: *.pt, TorchScript
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ONNX: *.onnx
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Caffe model directory: *.caffemodel/ *.prototxt
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Openvino IR directory: *.xml/ *.bin
Installation
- Create a Python 3.8 environment and activate it.
git clone --depth 1 -b develop --recursive https://github.com/skymizer/pydlmeta.git
(cd pydlmeta && python3 -m pip install -e .)
Usage
— Retrieve the metadata of the model
from pydlmeta.meta import retrieve_model_metadata
res = retrieve_model_metadata("/path/to/your/model")
- Identify model format
from pydlmeta.identifier.model import identify
model_format = identify(model_path)
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