export onnx (#1457)

zhifu gao

2024-03-11 9d48230c4f8f25bf88c5d6105f97370a36c9cf43

 funasr/models/ct_transformer_streaming/model.py

@@ -173,3 +173,68 @@
    
        return results, meta_data

    def export(
        self,
        **kwargs,
    ):
    
        is_onnx = kwargs.get("type", "onnx") == "onnx"
        encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
        self.encoder = encoder_class(self.encoder, onnx=is_onnx)
    
        self.forward = self._export_forward
    
        return self

    def _export_forward(self, inputs: torch.Tensor,
                text_lengths: torch.Tensor,
                vad_indexes: torch.Tensor,
                sub_masks: 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)
        # mask = self._target_mask(input)
        h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
        y = self.decoder(h)
        return y

    def export_dummy_inputs(self):
        length = 120
        text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
        text_lengths = torch.tensor([length], dtype=torch.int32)
        vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
        sub_masks = torch.ones(length, length, dtype=torch.float32)
        sub_masks = torch.tril(sub_masks).type(torch.float32)
        return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])

    def export_input_names(self):
        return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']

    def export_output_names(self):
        return ['logits']

    def export_dynamic_axes(self):
        return {
            'inputs': {
                1: 'feats_length'
            },
            'vad_masks': {
                2: 'feats_length1',
                3: 'feats_length2'
            },
            'sub_masks': {
                2: 'feats_length1',
                3: 'feats_length2'
            },
            'logits': {
                1: 'logits_length'
            },
        }
    def export_name(self):
        return "model.onnx"