| | |
| | | |
| | | return model |
| | | |
| | | def export_forward(self, inputs: torch.Tensor, |
| | | |
| | | def export_forward( |
| | | self, |
| | | inputs: torch.Tensor, |
| | | text_lengths: torch.Tensor, |
| | | vad_indexes: torch.Tensor, |
| | | sub_masks: torch.Tensor, |
| | |
| | | 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) |
| | |
| | | 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'] |
| | | return ["inputs", "text_lengths", "vad_masks", "sub_masks"] |
| | | |
| | | |
| | | def export_output_names(self): |
| | | return ['logits'] |
| | | 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' |
| | | }, |
| | | "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" |