update the test input to be A2B3

tests/test_permutation.py

@@ -21,9 +21,9 @@ import unittest
 from openfold.config import model_config
 from openfold.data import data_transforms
 from openfold.model.model import AlphaFold
+from openfold.utils.loss import AlphaFoldMultimerLoss
 from openfold.utils.tensor_utils import tensor_tree_map
 from tests.config import consts
-from .unifold_permutation import multi_chain_perm_align
 import logging
 logger = logging.getLogger(__name__)
 import os
@@ -40,12 +40,12 @@ class TestPermutation(unittest.TestCase):
         In the test case, use PDB ID 1e4k as the label
         """
         self.test_data_dir = os.path.join(os.getcwd(),"tests/test_data")
-        self.label_ids = ['label_1','label_2','label_2']
-
+        self.label_ids = ['label_1','label_1','label_2','label_2','label_2']
+        self.asym_id = [1]*9+[2]*9+[3]*13+[4]*13 + [5]*13
     def test_dry_run(self):
         n_seq = consts.n_seq
         n_templ = consts.n_templ
-        n_res = consts.n_res +9
+        n_res = len(self.asym_id)
         n_extra_seq = consts.n_extra
 
         c = model_config(consts.model, train=True)
@@ -54,6 +54,7 @@ class TestPermutation(unittest.TestCase):
         # deepspeed for this test
 
         model = AlphaFold(c)
+        multimer_loss = AlphaFoldMultimerLoss(c)
         example_label = [pickle.load(open(os.path.join(self.test_data_dir,f"{i}.pkl"),'rb')) 
                          for i in self.label_ids]
         batch = {}
@@ -62,8 +63,6 @@ class TestPermutation(unittest.TestCase):
             tf, c.model.input_embedder.tf_dim
         ).float()
         batch["aatype"] = torch.argmax(batch["target_feat"], dim=-1)
-        print(f"target_feat shape is {batch['target_feat'].size()}")
-        print(f"batch_dim is {batch['target_feat'].shape[:-2]}")
         batch["residue_index"] = torch.arange(n_res)
 
         batch["msa_feat"] = torch.rand((n_seq, n_res, c.model.input_embedder.msa_dim))
@@ -83,23 +82,19 @@ class TestPermutation(unittest.TestCase):
             # Modify asym_id, entity_id and sym_id so that it encodes 
             # 2 chains
             # #
-            asym_id = [1]*9+[2]*9+[3]*13
+            asym_id = self.asym_id
             batch["asym_id"] = torch.tensor(asym_id,dtype=torch.float64)
             # batch["entity_id"] = torch.randint(0, 1, size=(n_res,))
-            batch['entity_id'] = torch.tensor([1]*18+[2]*13,dtype=torch.float64)
+            batch['entity_id'] = torch.tensor([1]*18+[2]*39,dtype=torch.float64)
             batch["sym_id"] = torch.tensor(asym_id,dtype=torch.float64)
-            batch["num_sym"] = torch.tensor([1]*18+[2]*13,dtype=torch.int64) # currently there are just 2 chains
+            # batch["num_sym"] = torch.tensor([1]*18+[2]*13,dtype=torch.int64) # currently there are just 2 chains
             batch["extra_deletion_matrix"] = torch.randint(0, 2, size=(n_extra_seq, n_res))
         add_recycling_dims = lambda t: (
             t.unsqueeze(-1).expand(*t.shape, c.data.common.max_recycling_iters)
         )
-        print(f"max_recycling_iters is {c.data.common.max_recycling_iters}")
-        input_batch = tensor_tree_map(add_recycling_dims, batch)
+        batch = tensor_tree_map(add_recycling_dims, batch)
 
         with torch.no_grad():
-            out = model(input_batch)
-            print("finished running multimer forward")
-            print(f"out is {type(out)} and has keys {out.keys()}")
-            print(f"final_atom_positions is {out['final_atom_positions'].shape}")
+            out = model(batch)
+            permutated_labels = multimer_loss(out,(batch,example_label))
+            print(f"permuated_labels is {type(permutated_labels)} and keys are:\n {permutated_labels.keys()}")
\ No newline at end of file
-            print(f"out itpm score is {out['iptm_score']}")
-            multi_chain_perm_align(out,batch,example_label)
\ No newline at end of file