| 11 | return logits |
| 12 | |
| 13 | class DPRReaderFinalMixin(BaseMixin): |
| 14 | def __init__(self, hidden_size, projection_dim): |
| 15 | super().__init__() |
| 16 | if projection_dim > 0: |
| 17 | embeddings_size = projection_dim |
| 18 | else: |
| 19 | embeddings_size = hidden_size |
| 20 | self.qa_outputs = nn.Linear(embeddings_size, 2) |
| 21 | self.qa_classifier = nn.Linear(embeddings_size, 1) |
| 22 | |
| 23 | def final_forward(self, logits, **kwargs): |
| 24 | # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length |
| 25 | print("Before final_forward: logits = ", logits) |
| 26 | n_passages, sequence_length = logits.size()[:2] |
| 27 | sequence_output = logits |
| 28 | |
| 29 | # compute logits |
| 30 | logits = self.qa_outputs(sequence_output) |
| 31 | start_logits, end_logits = logits.split(1, dim=-1) |
| 32 | start_logits = start_logits.squeeze(-1).contiguous() |
| 33 | end_logits = end_logits.squeeze(-1).contiguous() |
| 34 | relevance_logits = self.qa_classifier(sequence_output[:, 0, :]) |
| 35 | |
| 36 | # resize |
| 37 | start_logits = start_logits.view(n_passages, sequence_length) |
| 38 | end_logits = end_logits.view(n_passages, sequence_length) |
| 39 | relevance_logits = relevance_logits.view(n_passages) |
| 40 | |
| 41 | return (start_logits, end_logits, relevance_logits) |
| 42 | |
| 43 | class DPRTypeMixin(BaseMixin): |
| 44 | def __init__(self, num_types, hidden_size): |