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| author | Edward Z. Yang <ezyang@fb.com> | 2017-09-05 21:06:30 -0700 |
|---|---|---|
| committer | Adam Paszke <adam.paszke@gmail.com> | 2017-09-06 21:35:50 -0400 |
| commit | fbb8f13499e43f1fbf4557c7a73e708646ac8e55 (patch) | |
| tree | ed606eb2cd8c4e7add0f0c5ea6d55d6ce02fdef8 /docs | |
| parent | a2e5224847aa63b598e76aa749d3721234910d27 (diff) | |
| download | pytorch-fbb8f13499e43f1fbf4557c7a73e708646ac8e55.tar.gz pytorch-fbb8f13499e43f1fbf4557c7a73e708646ac8e55.tar.bz2 pytorch-fbb8f13499e43f1fbf4557c7a73e708646ac8e55.zip | |
Docs now finally run with ToffeeIR master.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/source/onnx.rst | 10 |
1 files changed, 7 insertions, 3 deletions
diff --git a/docs/source/onnx.rst b/docs/source/onnx.rst index c122924200..86f6c2f3b3 100644 --- a/docs/source/onnx.rst +++ b/docs/source/onnx.rst @@ -68,15 +68,17 @@ To run the exported script with Caffe2, you will need to install the backend for Caffe2:: # ...continuing from above - import onnx.backend.c2 as backend + import onnx.backend.caffe2 as backend import numpy as np rep = backend.prepare(graph, device="CUDA:0") # or "CPU" # For the Caffe2 backend: # rep.predict_net is the Caffe2 protobuf for the network # rep.workspace is the Caffe2 workspace for the network - # (see the class toffee.backend.c2.Workspace) - outputs = backend.run(rep, np.rand(10, 3, 224, 224)) + # (see the class onnx.backend.c2.Workspace) + outputs = rep.run(np.random.randn(10, 3, 224, 224).astype(np.float32)) + # To run networks with more than one input, pass a tuple + # rather than a single numpy ndarray. print(outputs[0]) In the future, there will be backends for other frameworks as well. @@ -124,6 +126,7 @@ In this tech preview, only the following operators are supported: * Slice (only integer indexing is supported) * Dropout (inplace is discarded) * Relu (inplace is discarded) +* PReLU (inplace is discarded, sharing a single weight among all channels is not supported) * LeakyRelu (inplace is discarded) * MaxPool1d (ceil_mode must be False) * MaxPool2d (ceil_mode must be False @@ -141,6 +144,7 @@ list. The operator set above is sufficient to export the following models: * SqueezeNet * SuperResolution * VGG +* `word_language_model <https://github.com/pytorch/examples/tree/master/word_language_model>`_ The interface for specifying operator definitions is highly experimental and undocumented; adventurous users should note that the APIs will probably |