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Update rnnt conversion instruction with named outputs (#7585)

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* Update rnnt conversion instruction with named outputs

* fix missed comma

* Update docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
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Ekaterina Aidova 2021-09-23 01:05:11 +03:00 committed by GitHub
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docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RNNT.md
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@ -73,19 +73,21 @@ inp = torch.randn([seq_length, batch_size, feature_length])
feature_length = torch.LongTensor([seq_length])
x_padded, x_lens = model.encoder(inp, feature_length)
torch.onnx.export(model.encoder, (inp, feature_length), "rnnt_encoder.onnx", opset_version=12,
input_names=['input.1', '1'], dynamic_axes={'input.1': {0: 'seq_len', 1: 'batch'}})
input_names=['input', 'feature_length'], output_names=['x_padded', 'x_lens'],
dynamic_axes={'input': {0: 'seq_len', 1: 'batch'}})
symbol = torch.LongTensor([[20]])
hidden = torch.randn([2, batch_size, 320]), torch.randn([2, batch_size, 320])
g, hidden = model.prediction.forward(symbol, hidden)
torch.onnx.export(model.prediction, (symbol, hidden), "rnnt_prediction.onnx", opset_version=12,
input_names=['input.1', '1', '2'],
dynamic_axes={'input.1': {0: 'batch'}, '1': {1: 'batch'}, '2': {1: 'batch'}})
input_names=['symbol', 'hidden_in_1', 'hidden_in_2'],
output_names=['g', 'hidden_out_1', 'hidden_out_2'],
dynamic_axes={'symbol': {0: 'batch'}, 'hidden_in_1': {1: 'batch'}, 'hidden_in_2': {1: 'batch'}})
f = torch.randn([batch_size, 1, 1024])
model.joint.forward(f, g)
torch.onnx.export(model.joint, (f, g), "rnnt_joint.onnx", opset_version=12,
input_names=['0', '1'], dynamic_axes={'0': {0: 'batch'}, '1': {0: 'batch'}})
input_names=['0', '1'], output_names=['result'], dynamic_axes={'0': {0: 'batch'}, '1': {0: 'batch'}})
```
```bash
@ -97,10 +99,10 @@ After completing this step, the files `rnnt_encoder.onnx`, `rnnt_prediction.onnx
**Step 6**. Run the conversion command:
```bash
python3 {path_to_openvino}/mo.py --input_model rnnt_encoder.onnx --input "input.1[157 1 240],1->157"
python3 {path_to_openvino}/mo.py --input_model rnnt_prediction.onnx --input "input.1[1 1],1[2 1 320],2[2 1 320]"
python3 {path_to_openvino}/mo.py --input_model rnnt_encoder.onnx --input "input[157 1 240],feature_length->157"
python3 {path_to_openvino}/mo.py --input_model rnnt_prediction.onnx --input "symbol[1 1],hidden_in_1[2 1 320],hidden_in_2[2 1 320]"
python3 {path_to_openvino}/mo.py --input_model rnnt_joint.onnx --input "0[1 1 1024],1[1 1 320]"
```
Please note that hardcoded value for sequence length = 157 was taken from the MLCommons but conversion to IR preserves
network [reshapeability](../../../../IE_DG/ShapeInference.md), this means you can change input shapes manually to any value either during conversion or
inference.
inference.