I have been investigating a bit exporting fastai models to formats that can be run browserside.
There are some caveats but it would be interesting to make a straightforward export path that would require little effort from the user.
The main motivation is to reduce HTTP requests in vision tasks. Not so much for the server load but in order to do rapid predictions from camera feed without having a lot of networking going on, uploading images is quite slow sometimes.
I have looked into ONNX and WebDNN (which also uses ONNX it seems) and Pytorch versions seems to be one.
Here are some related topics:
- Converting A PyTorch Model to Tensorflow or Keras for Production
- https://anaconda.org/conda-forge/onnx (I was able to export resnet50 using this)
- https://github.com/onnx/onnx-tensorflow (but not importing it into tensorflow yet, plan is to make it into tensorflow.js)
- https://mil-tokyo.github.io/webdnn/docs/tutorial/pytorch.html (This needs a lower pytorch version)
cls = ['wolf', 'not_wolf'] empty_data = ImageDataBunch.single_from_classes(path, cls, tfms=get_transforms(), size=224).normalize(imagenet_stats) learn = create_cnn(empty_data, models.resnet50) learn.load(model_name) learn.model.cpu() dummy_input = Variable(torch.randn(1, 3, 224, 224).cpu()) # one RGB 224 x 224 picture will be the input to the model graph = PyTorchConverter().convert(learn.model, dummy_input)
This generates output (when not using the nightly pytorch/torchvision) but complains about missing features:
RuntimeError: ONNX export failed: Couldn't export operator aten::adaptive_max_pool2d I plan to investigate if this could be overcome, and what needs to be implemented.
Here is a thread related to that: https://github.com/pytorch/pytorch/issues/5310#issuecomment-383900481
This works to generate an ONNX from the same torch version as fastai uses:
from torch.autograd import Variable path = Path('/home/toffe/data/wolf_detector') cls = ['wolf', 'not_wolf'] empty_data = ImageDataBunch.single_from_classes(path, cls, tfms=get_transforms(), size=224).normalize(imagenet_stats) learn = create_cnn(empty_data, models.resnet50) learn.load('wolf_not_wolf__res50___stage-2') learn.model.cpu() # Export the trained model to ONNX dummy_input = Variable(torch.randn(1, 3, 224, 224).cpu()) # one RGB 224 x 224 picture will be the input to the model torch.onnx.export(learn.model, dummy_input, path/"models/wolf.onnx")
Importing the onnx model to onnx-tensorflow should be straightforward like so:
import onnx from onnx_tf.backend import prepare onnx_model = onnx.load(path/"models/wolf.onnx") # load onnx model output = prepare(onnx_model).run(input) # run the loaded model
but currently there is this issue:
According to documentation you need to install protbuf before installing onnx if you are using pip, the conda version seemed to not work
pip uninstall onnx sudo apt-get install protobuf-compiler libprotoc-dev pip install onnx
Currently the onnx_tf prepare command doesn’t result in an exception, but it just seems to stall and not finish.
It is also possible to run onnx models in the browser using tfjs-onnx, will do some tests with that: