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dbdd28207c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42421 Previously, we can only feed shape info from Python with float dtype, and batch based dim type when we do onnxifi from Python. This diff removes this limitation and uses TensorBoundShapes protobuf as a generic shape info struct. This will make the onnxifi interface in Python more flexible. Reviewed By: ChunliF Differential Revision: D22889781 fbshipit-source-id: 1a89f3a68c215a0409738c425b4e0d0617d58245 |
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| .. | ||
| bin | ||
| tests | ||
| __init__.py | ||
| backend.py | ||
| backend_cpp_rep.py | ||
| backend_rep.py | ||
| error.py | ||
| frontend.py | ||
| helper.py | ||
| onnxifi.py | ||
| ONNXOpCoverage.md | ||
| README.md | ||
| test_onnxifi.py | ||
| workspace.py | ||
Caffe2 implementation of Open Neural Network Exchange (ONNX)
Usage
Installation
onnx-caffe2 is installed as a part of Caffe2. Please follow the instructions to install Caffe2.
Folder Structure
- ./: the main folder that all code lies under
- frontend.py: translate from caffe2 model to onnx model
- backend.py: execution engine that runs onnx on caffe2
- tests/: test files
Testing
onnx-caffe2 uses pytest as test driver. In order to run tests, first you need to install pytest:
pip install pytest-cov
After installing pytest, do
pytest
to run tests.
Testing coverage issues/status: https://github.com/caffe2/caffe2/blob/master/caffe2/python/onnx/ONNXOpCoverage.md
Development
During development it's convenient to install caffe2 in development mode:
cd /path/to/caffe2
pip install -e caffe2/