Added tests for squeeze_features.

openfold/data/data_transforms.py

@@ -145,7 +145,10 @@ def squeeze_features(protein):
         if k in protein:
             final_dim = protein[k].shape[-1]
             if isinstance(final_dim, int) and final_dim == 1:
+                if torch.is_tensor(protein[k]):
                     protein[k] = torch.squeeze(protein[k], dim=-1)
+                else:
+                    protein[k] = np.squeeze(protein[k], axis=-1)
 
     for k in ["seq_length", "num_alignments"]:
         if k in protein:

tests/test_data_transforms.py

@@ -5,12 +5,12 @@ import os
 
 import pickle
 
-import numpy
+import numpy as np
 import torch
 import unittest
 
 from data.data_transforms import make_seq_mask, add_distillation_flag, make_all_atom_aatype, fix_templates_aatype, \
-    correct_msa_restypes
+    correct_msa_restypes, squeeze_features
 from openfold.config import model_config
 
 
@@ -65,6 +65,38 @@ class TestDataTransforms(unittest.TestCase):
         print(protein)
         assert torch.all(torch.eq(torch.tensor(features['msa'].shape), torch.tensor(protein['msa'].shape)))
 
+    def test_squeeze_features(self):
+        with open("../test_data/features.pkl", "rb") as file:
+            features = pickle.load(file)
+            print(os.path.realpath(file.name), 'Keys: ', features.keys())
+
+        features_list = [
+            'domain_name', 'msa', 'num_alignments', 'seq_length', 'sequence',
+            'superfamily', 'deletion_matrix', 'resolution',
+            'between_segment_residues', 'residue_index', 'template_all_atom_mask']
+
+        protein = {'aatype': torch.tensor(features['aatype'])}
+        for k in features_list:
+            if k in features:
+                print(k, features[k].dtype)
+                if k in ['domain_name', 'sequence']:
+                    protein[k] = np.expand_dims(features[k], -1)
+                else:
+                    protein[k] = torch.tensor(features[k]).unsqueeze(-1)
+
+        for k in ['seq_length', 'num_alignments']:
+            if k in protein:
+                protein[k] = torch.tensor(protein[k]).unsqueeze(0)
+
+        protein_squeezed = squeeze_features(protein)
+        print(protein)
+        for k in features_list:
+            if k in protein:
+                print(k, protein_squeezed[k].shape, features[k].shape)
+                assert protein_squeezed[k].shape == features[k].shape
+
+
+
 
 if __name__ == '__main__':
     unittest.main()