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Update Readme with examples (#336)

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powderluv 2021-09-28 21:43:38 -07:00 committed by GitHub
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@ -73,7 +73,7 @@ cmake --build build
## Setup ENV
```
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir
```
### TorchScript
@ -81,17 +81,25 @@ Running execution (end-to-end) tests:
```
# Run E2E TorchScript tests. These compile and run the TorchScript program
# through torch-mlir with a simplified linalg-on-tensors based backend we call
# RefBackend (more production-grade backends at this same abstraction layer
# exist in the MLIR community, such as IREE).
./tools/torchscript_e2e_test.sh --filter Conv2d --verbose
# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
python -m e2e_testing.torchscript.main --filter Conv2d --verbose
```
Standalone script to generate and run a ResNet18 model:
Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:
```
# Run ResNet18 as a standalone script.
python examples/torchscript_resnet18_e2e.py
(mlir_venv) mlir@torch-mlir:~$ python examples/torchscript_resnet18_e2e.py
load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/anush/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100.0%
PyTorch prediction
[('Labrador retriever', 70.66319274902344), ('golden retriever', 4.956596374511719), ('Chesapeake Bay retriever', 4.195662975311279)]
torch-mlir prediction
[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]
```
Jupyter notebook: