Replace time.time() with time.perf_counter()

openfold/data/tools/utils.py

@@ -36,9 +36,9 @@ def tmpdir_manager(base_dir: Optional[str] = None):
 @contextlib.contextmanager
 def timing(msg: str):
     logging.info("Started %s", msg)
-    tic = time.time()
+    tic = time.perf_counter()
     yield
-    toc = time.time()
+    toc = time.perf_counter()
     logging.info("Finished %s in %.3f seconds", msg, toc - tic)
 
 

openfold/np/relax/amber_minimize.py

@@ -435,7 +435,7 @@ def _run_one_iteration(
     tolerance = tolerance * ENERGY
     stiffness = stiffness * ENERGY / (LENGTH ** 2)
 
-    start = time.time()
+    start = time.perf_counter()
     minimized = False
     attempts = 0
     while not minimized and attempts < max_attempts:
@@ -457,7 +457,7 @@ def _run_one_iteration(
             logging.info(e)
     if not minimized:
         raise ValueError(f"Minimization failed after {max_attempts} attempts.")
-    ret["opt_time"] = time.time() - start
+    ret["opt_time"] = time.perf_counter() - start
     ret["min_attempts"] = attempts
     return ret
 

run_pretrained_openfold.py

@@ -138,9 +138,9 @@ def main(args):
                 for k,v in batch.items()
             }
         
-            t = time.time()
+            t = time.perf_counter()
             out = model(batch)
-            logging.info(f"Inference time: {time.time() - t}")
+            logging.info(f"Inference time: {time.perf_counter() - t}")
        
         # Toss out the recycling dimensions --- we don't need them anymore
         batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
@@ -164,9 +164,9 @@ def main(args):
         )
         
         # Relax the prediction.
-        t = time.time()
+        t = time.perf_counter()
         relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
-        logging.info(f"Relaxation time: {time.time() - t}")
+        logging.info(f"Relaxation time: {time.perf_counter() - t}")
         
         # Save the relaxed PDB.
         relaxed_output_path = os.path.join(