| | |
| | | |
| | | def get_dummy_inputs(self): |
| | | length = 120 |
| | | text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length)) |
| | | 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) |
| | | |
| | |
| | | |
| | | def get_dummy_inputs(self): |
| | | length = 120 |
| | | text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)) |
| | | 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) |