https://github.com/albanie/pytorch-mcn
Convert models from MatConvNet to PyTorch
https://github.com/albanie/pytorch-mcn
Last synced: 6 months ago
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Convert models from MatConvNet to PyTorch
- Host: GitHub
- URL: https://github.com/albanie/pytorch-mcn
- Owner: albanie
- License: mit
- Created: 2017-12-21T13:39:54.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-01-06T11:22:54.000Z (almost 7 years ago)
- Last Synced: 2024-11-14T05:33:53.639Z (12 months ago)
- Language: Python
- Homepage:
- Size: 172 KB
- Stars: 94
- Watchers: 4
- Forks: 13
- Open Issues: 3
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
pytorch-mcn
---
A tool for importing trained MatConvNet models into Pytorch (if you were hoping to go the other way, try [mcnPyTorch](https://github.com/albanie/mcnPyTorch)).
### Demo
To run the importer, set the path to the MatConvNet model and supply an output directory (where the imported PyTorch models will be stored) in the `importer.sh` script. There are a few examples in the script which can be commented/uncommented to run as a demo. Then run `bash importer.sh`.
### Imported Models
A number of standard models have been imported and verified, and can be found [here](http://www.robots.ox.ac.uk/~albanie/pytorch-models.html).
### Verification
Verifying an imported model requires MATLAB and a copy of MatConvNet (the specific dependencies are given in `compare/startup.m`). The process is as follows:
1. Run the `compare/featureDumper.m` script to dump intermediate features from the original MatConvNet model to disk.
2. Import model to PyTorch in `debug_mode` (an option that can be set in `importer.sh`. This will generate additional source code in the PyTorch model definition that stores every intermediate tensor computed by the network.
3. Run the `compare/compare_models.py` script, which will perform a numerical comparison between the tensors.
### Notes
Model conversion between frameworks can be challenging, because the layers and modules of each framework do not have an exact correspondence. As a result, there is often a bit of work involved in the conversion process, particularly for non-standard architectures.
This tool requires at least Python 3.5 and PyTorch 0.3.0 (by default, `ipython` will be used, but you can switch to standard python by changing a config variable in `importer.sh`). The tool expects the MatConvNet models to be in `dagnn` format (the [ensure_dags.m](compare/ensure_dags.m) script converts models to this format from SimpleNN if required).
Ideally in future the model conversion process will run via onnx but it seems that currently quite a lot of support is missing for required functionality. The plan is therefore to update the converter when possible.