| |
| |
| @@ -341,17 +341,16 @@ |
| compute_loss=compute_loss, |
| ensemble_representations=ensemble_representations) |
| |
| + emb_config = self.config.embeddings_and_evoformer |
| + prev = { |
| + 'prev_pos': jnp.zeros( |
| + [num_residues, residue_constants.atom_type_num, 3]), |
| + 'prev_msa_first_row': jnp.zeros( |
| + [num_residues, emb_config.msa_channel]), |
| + 'prev_pair': jnp.zeros( |
| + [num_residues, num_residues, emb_config.pair_channel]), |
| + } |
| if self.config.num_recycle: |
| - emb_config = self.config.embeddings_and_evoformer |
| - prev = { |
| - 'prev_pos': jnp.zeros( |
| - [num_residues, residue_constants.atom_type_num, 3]), |
| - 'prev_msa_first_row': jnp.zeros( |
| - [num_residues, emb_config.msa_channel]), |
| - 'prev_pair': jnp.zeros( |
| - [num_residues, num_residues, emb_config.pair_channel]), |
| - } |
| - |
| if 'num_iter_recycling' in batch: |
| # Training time: num_iter_recycling is in batch. |
| # The value for each ensemble batch is the same, so arbitrarily taking |
| @@ -378,7 +377,6 @@ |
| body, |
| (0, prev)) |
| else: |
| - prev = {} |
| num_iter = 0 |
| |
| ret = do_call(prev=prev, recycle_idx=num_iter) |
|
|
