Merge branch 'main' of github.com:aqlaboratory/openfold into main

README.md

 ![header ](imgs/of_banner.png)
-_Figure: Comparison of OpenFold, AlphaFold2, and experimental structure of Streptomyces tokunonesis TokK protein (pdb code 7KDX), related to novel antibiotics used for rare infections including during COVID-19 infection._
+_Figure: Comparison of OpenFold and AlphaFold2 predictions to the experimental structure of PDB 7KDX, chain B._
 
 
 # OpenFold

run_pretrained_openfold.py

@@ -208,36 +208,57 @@ def generate_feature_dict(
 
     return feature_dict
 
+def get_model_basename(model_path):
+    return os.path.splitext(
+                os.path.basename(
+                    os.path.normpath(model_path)
+                )
+            )[0]
+
+def make_output_directory(output_dir, model_name, multiple_model_mode):
+    if multiple_model_mode:
+        prediction_dir = os.path.join(output_dir, "predictions", model_name)
+    else:
+        prediction_dir = os.path.join(output_dir, "predictions")
+    os.makedirs(prediction_dir, exist_ok=True)
+    return prediction_dir
+
+def count_models_to_evaluate(openfold_checkpoint_path, jax_param_path):
+    model_count = 0
+    if openfold_checkpoint_path:
+        model_count += len(openfold_checkpoint_path.split(","))
+    if jax_param_path:
+        model_count += len(jax_param_path.split(","))
+    return model_count
 
 def load_models_from_command_line(args, config):
     # Create the output directory
-    os.makedirs(args.output_dir, exist_ok=True)
+
+    multiple_model_mode = count_models_to_evaluate(args.openfold_checkpoint_path, args.jax_param_path) > 1
+    if multiple_model_mode:
+        logger.info(f"evaluating multiple models")
+
     if args.jax_param_path:
         for path in args.jax_param_path.split(","):
+            model_basename = get_model_basename(path)
+            model_version = "_".join(model_basename.split("_")[1:])
             model = AlphaFold(config)
             model = model.eval()
             import_jax_weights_(
-                model, path, version=args.config_preset
+                model, path, version=model_version
             )
             model = model.to(args.model_device)
             logger.info(
-                f"Successfully loaded JAX parameters at {args.jax_param_path}..."
-            )
-            model_version = os.path.basename(
-                os.path.normpath(args.jax_param_path),
+                f"Successfully loaded JAX parameters at {path}..."
             )
-            model_version = os.path.splitext(model_version)[0]
-            yield model, model_version
+            output_directory = make_output_directory(args.output_dir, model_basename, multiple_model_mode)
+            yield model, output_directory
     
     if args.openfold_checkpoint_path:
         for path in args.openfold_checkpoint_path.split(","):
             model = AlphaFold(config)
             model = model.eval()
-            checkpoint_basename = os.path.splitext(
-                os.path.basename(
-                    os.path.normpath(path)
-                )
-            )[0]
+            checkpoint_basename = get_model_basename(path)
             if os.path.isdir(path):
                 # A DeepSpeed checkpoint
                 ckpt_path = os.path.join(
@@ -256,17 +277,17 @@ def load_models_from_command_line(args, config):
                 ckpt_path = path
                 d = torch.load(ckpt_path)
 
-                if ("ema" in d):
+                if "ema" in d:
                     # The public weights have had this done to them already
                     d = d["ema"]["params"]
                 model.load_state_dict(d)
             
             model = model.to(args.model_device)
             logger.info(
-                f"Loaded OpenFold parameters at {args.openfold_checkpoint_path}..."
+                f"Loaded OpenFold parameters at {path}..."
             )
-            
-            yield model, checkpoint_basename
+            output_directory = make_output_directory(args.output_dir, checkpoint_basename, multiple_model_mode)
+            yield model, output_directory
     
     if not args.jax_param_path and not args.openfold_checkpoint_path:
         raise ValueError(
@@ -308,9 +329,6 @@ def main(args):
         alignment_dir = args.use_precomputed_alignments
         logger.info(f"Using precomputed alignments at {alignment_dir}...")
 
-    prediction_dir = os.path.join(args.output_dir, "predictions")
-    os.makedirs(prediction_dir, exist_ok=True)
-
     for fasta_file in list_files_with_extensions(args.fasta_dir, (".fasta", ".fa")):
         # Gather input sequences
         with open(os.path.join(args.fasta_dir, fasta_file), "r") as fp:
@@ -323,14 +341,6 @@ def main(args):
         output_name = f'{tag}_{args.config_preset}'
         if args.output_postfix is not None:
             output_name = f'{output_name}_{args.output_postfix}'
-    
-        unrelaxed_output_path = os.path.join(
-            prediction_dir, f'{output_name}_unrelaxed.pdb'
-        )
-   
-        # Output already exists
-        if os.path.exists(unrelaxed_output_path):
-            continue
 
         precompute_alignments(tags, seqs, alignment_dir, args)
     
@@ -346,7 +356,7 @@ def main(args):
             feature_dict, mode='predict',
         )
 
-        for model, model_version in load_models_from_command_line(args, config):
+        for model, output_directory in load_models_from_command_line(args, config):
             working_batch = deepcopy(processed_feature_dict)
             out = run_model(model, working_batch, tag, args)
 
@@ -358,6 +368,14 @@ def main(args):
                 out, working_batch, feature_dict, feature_processor, args
             )
 
+            unrelaxed_output_path = os.path.join(
+                output_directory, f'{output_name}_unrelaxed.pdb'
+            )
+
+            # Output already exists
+            if os.path.exists(unrelaxed_output_path):
+                continue
+
             with open(unrelaxed_output_path, 'w') as fp:
                 fp.write(protein.to_pdb(unrelaxed_protein))
 
@@ -382,7 +400,7 @@ def main(args):
 
                 # Save the relaxed PDB.
                 relaxed_output_path = os.path.join(
-                    prediction_dir, f'{output_name}_relaxed.pdb'
+                    output_directory, f'{output_name}_relaxed.pdb'
                 )
                 with open(relaxed_output_path, 'w') as fp:
                     fp.write(relaxed_pdb_str)
@@ -391,7 +409,7 @@ def main(args):
 
             if args.save_outputs:
                 output_dict_path = os.path.join(
-                    args.output_dir, f'{output_name}_output_dict.pkl'
+                    output_directory, f'{output_name}_output_dict.pkl'
                 )
                 with open(output_dict_path, "wb") as fp:
                     pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)