Write the inference and relax times to file

run_pretrained_openfold.py

@@ -120,6 +120,7 @@ def run_model(model, batch, tag, args):
         out = model(batch)
         inference_time = time.perf_counter() - t
         logger.info(f"Inference time: {inference_time}")
+        update_timings({"inference": inference_time}, os.path.join(args.output_dir, "timings.json"))
    
         model.config.template.enabled = template_enabled
 
@@ -480,7 +481,10 @@ def main(args):
                     os.environ["CUDA_VISIBLE_DEVICES"] = device_no
                 relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
                 os.environ["CUDA_VISIBLE_DEVICES"] = visible_devices
-                logger.info(f"Relaxation time: {time.perf_counter() - t}")
+                relaxation_time = time.perf_counter() - t
+
+                logger.info(f"Relaxation time: {relaxation_time}")
+                update_timings({"relaxation": relaxation_time}, os.path.join(args.output_dir, "timings.json"))
 
                 # Save the relaxed PDB.
                 relaxed_output_path = os.path.join(
@@ -500,6 +504,18 @@ def main(args):
 
                 logger.info(f"Model output written to {output_dict_path}...")
 
+def update_timings(dict, output_file=os.path.join(os.getcwd(), "timings.json")):
+    """Write dictionary of one or more run step times to a file"""
+    import json
+    if os.path.exists(output_file):
+        with open(output_file, "r") as f:
+            timings = json.loads(f)
+    else:
+        timings = {}
+    timings.update(dict)
+    with open(output_file, "w") as f:
+        timings.dumps(f)
+    return output_file
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()