v1.5.1

Author: sokrypton

README.md

@@ -1,9 +1,10 @@
-# ColabFold - v1.5.0
+# ColabFold - v1.5.1
 
 ```diff
-+ 04Feb2023: ColabFold updated to use AlphaFold v2.3.1!
++ 04Feb2023: v1.5.0 - ColabFold updated to use AlphaFold v2.3.1!
++ 06Feb2023: v1.5.1 - fixing --save-all/--save-recycles option
 ```
-For details of what was changed in v1.5.0, see [change log](https://github.com/sokrypton/ColabFold/wiki/v1.5.0)!
+For details of what was changed in v1.5, see [change log](https://github.com/sokrypton/ColabFold/wiki/v1.5.0)!
 
 <p align="center"><img src="https://github.com/sokrypton/ColabFold/raw/main/.github/ColabFold_Marv_Logo.png" height="250"/></p>
 

colabfold/batch.py

@@ -446,7 +446,6 @@ def callback(prediction_result, recycles):
             prediction_result, recycles = \
             model_runner.predict(input_features, random_seed=seed, prediction_callback=callback)
             prediction_result = _jnp_to_np(prediction_result)
-            prediction_result["representations"] = prediction_result.pop("prev")
             prediction_times.append(time.time() - start)
 
             ########################
@@ -480,23 +479,23 @@ def callback(prediction_result, recycles):
 
             #########################
             # save results
-            #########################            
+            #########################      
+
             # save pdb
             protein_lines = protein.to_pdb(unrelaxed_protein)
             files.get("unrelaxed","pdb").write_text(protein_lines)
             unrelaxed_pdb_lines.append(protein_lines)
 
-
             # save raw outputs
             if save_all:
                 with files.get("all","pickle").open("wb") as handle:
                     pickle.dump(prediction_result, handle)
-            if save_single_representations or save_pair_representations:
-                rep = prediction_result["representations"]
-                if save_single_representations:
-                    np.save(files.get("single_repr","npy"), rep["prev_msa_first_row"])
-                if save_pair_representations:
-                    np.save(files.get("pair_repr","npy"), rep["prev_pair"])
+            if save_single_representations:
+                np.save(files.get("single_repr","npy"),
+                    prediction_result["prev"]["prev_msa_first_row"])
+            if save_pair_representations:
+                np.save(files.get("pair_repr","npy"),
+                    prediction_result["prev"]["prev_pair"])
 
             # write an easy-to-use format (pAE and pLDDT)
             with files.get("scores","json").open("w") as handle: