(sparse_embedding_list, dense_value_list)
| 124 | |
| 125 | |
| 126 | def combined_dnn_input(sparse_embedding_list, dense_value_list): |
| 127 | if len(sparse_embedding_list) > 0 and len(dense_value_list) > 0: |
| 128 | sparse_dnn_input = torch.flatten( |
| 129 | torch.cat(sparse_embedding_list, dim=-1), start_dim=1) |
| 130 | dense_dnn_input = torch.flatten( |
| 131 | torch.cat(dense_value_list, dim=-1), start_dim=1) |
| 132 | return concat_fun([sparse_dnn_input, dense_dnn_input]) |
| 133 | elif len(sparse_embedding_list) > 0: |
| 134 | return torch.flatten(torch.cat(sparse_embedding_list, dim=-1), start_dim=1) |
| 135 | elif len(dense_value_list) > 0: |
| 136 | return torch.flatten(torch.cat(dense_value_list, dim=-1), start_dim=1) |
| 137 | else: |
| 138 | raise NotImplementedError |
| 139 | |
| 140 | |
| 141 | def get_varlen_pooling_list(embedding_dict, features, feature_index, varlen_sparse_feature_columns, device): |
no test coverage detected