Update PyTorch readme (#101)

* update pt readme

* Update README.md

Co-authored-by: Alisa Chen <[email protected]>

models/pytorch2onnx/README.md

@@ -1,7 +1,33 @@
-# Freeze ONNX model from PyTorch
+# PyTorch2ONNX
 
-If you want to test NNFusion against PyTorch model, we recommend ONNX as intermediary, because PyTorch [onnx_exporter](https://pytorch.org/docs/stable/onnx.html) handles many dynamics and we don't want to reimplement them.
+*Note:* In this section, we assume nnfusion cli has been installed as [Build Guide](https://github.com/microsoft/nnfusion/wiki/Build-Guide).
 
-On PyTorch onnx_exporter, we build a simple wrapper to freeze and check(not fully implemented) whether NNFusion could accept the model. We provide a [VGG example](./vgg16_model.py) for this tool.
+NNFusion leverages ONNX to support PyTorch. So this section focuses on how to freeze an ONNX model from PyTorch source code. You could get NNFusion supported ONNX ops [here](../../thirdparty/ngraph/src/nnfusion/frontend/onnx_import/ops_bridge.cpp).
 
-Of course, you could freeze ONNX model from thirdparties, like the [transformer example](./bert_model.py).
+## Freeze model by PyTorch ONNX exporter
+
+Please refer to PyTorch [onnx section](https://pytorch.org/docs/stable/onnx.html) to convert a PyTorch model to ONNX format, currently it already supports a great majority of deeplearning workloads.
+
+## Freeze model by NNFusion pt_freezer
+
+On PyTorch onnx_exporter, we build a simple wrapper called [pt_freezer](./pytorch_freezer.py), it wraps PyTorch onnx_exporter with control flow and op availability(not implemented yet) check. We provide a well self-explanatory [VGG example](./vgg16_model.py) for this tool:
+
+```bash
+# step0: install prerequisites
+apt update && sudo apt install python3-pip
+pip3 install onnx torch torchvision
+
+# step1: freeze vgg16 model
+python3 vgg16_model.py
+```
+
+## Freeze model from thirdparties
+
+Of course, you could freeze ONNX model from thirdparties, like [huggingface transformer](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb), which supports exporting to ONNX format.
+
+## Freezed ONNX models
+
+| model         | nnf codegen flags                 | download link |
+| -----------   | -----------                       | -----------   |
+| VGG16         | -f onnx                           | [vgg16.onnx](https://nnfusion.blob.core.windows.net/models/onnx/vgg16.onnx) |
+| BERT_base     | -f onnx -p 'batch:3;sequence:512' | [pt-bert-base-cased.onnx](https://nnfusion.blob.core.windows.net/models/onnx/bert/pt-bert-base-cased.onnx) |

models/pytorch2onnx/vgg16_model.py

@@ -8,10 +8,13 @@
 
 
 def main():
+    # define pytorch module
     model = torchvision.models.vgg16()
+    # define the pytorch model input/output description
     input_desc = [IODescription("data", [1, 3, 224, 224], torch.float32)]
     output_desc = [IODescription("logits", [1, 1000], torch.float32)]
     model_desc = ModelDescription(input_desc, output_desc)
+    # save the onnx model somewhere
     freezer = PTFreezer(model, model_desc)
     freezer.execute("./vgg16.onnx")