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| import types |
| import torch |
| from funasr.register import tables |
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| def export_rebuild_model(model, **kwargs): |
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| is_onnx = kwargs.get("type", "onnx") == "onnx" |
| encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export") |
| model.encoder = encoder_class(model.encoder, onnx=is_onnx) |
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|
| model.forward = types.MethodType(export_forward, model) |
| model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model) |
| model.export_input_names = types.MethodType(export_input_names, model) |
| model.export_output_names = types.MethodType(export_output_names, model) |
| model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model) |
| model.export_name = types.MethodType(export_name, model) |
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| return model |
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| def export_forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor): |
| """Compute loss value from buffer sequences. |
| |
| Args: |
| input (torch.Tensor): Input ids. (batch, len) |
| hidden (torch.Tensor): Target ids. (batch, len) |
| |
| """ |
| x = self.embed(inputs) |
| h, _ = self.encoder(x, text_lengths) |
| y = self.decoder(h) |
| return y |
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| def export_dummy_inputs(self): |
| length = 120 |
| text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length)).type(torch.int32) |
| text_lengths = torch.tensor([length - 20, length], dtype=torch.int32) |
| return (text_indexes, text_lengths) |
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| def export_input_names(self): |
| return ["inputs", "text_lengths"] |
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| def export_output_names(self): |
| return ["logits"] |
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| def export_dynamic_axes(self): |
| return { |
| "inputs": {0: "batch_size", 1: "feats_length"}, |
| "text_lengths": { |
| 0: "batch_size", |
| }, |
| "logits": {0: "batch_size", 1: "logits_length"}, |
| } |
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| def export_name(self): |
| return "model.onnx" |
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|
