Update time to monotonic

lerobot/common/policies/act/policy.py

@@ -54,7 +54,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
     def update(self, replay_buffer, step):
         del step
 
-        start_time = time.time()
+        start_time = time.monotonic()
 
         self.train()
 
@@ -104,7 +104,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         batch = replay_buffer.sample(batch_size)
         batch = process_batch(batch, self.cfg.horizon, num_slices)
 
-        data_s = time.time() - start_time
+        data_s = time.monotonic() - start_time
 
         loss = self.compute_loss(batch)
         loss.backward()
@@ -125,7 +125,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
             # "lr": self.lr_scheduler.get_last_lr()[0],
             "lr": self.cfg.lr,
             "data_s": data_s,
-            "update_s": time.time() - start_time,
+            "update_s": time.monotonic() - start_time,
         }
 
         return info

lerobot/common/policies/act/utils.py

@@ -188,8 +188,8 @@ class MetricLogger:
     def log_every(self, iterable, print_freq, header=None):
         if not header:
             header = ""
-        start_time = time.time()
-        end = time.time()
+        start_time = time.monotonic()
+        end = time.monotonic()
         iter_time = SmoothedValue(fmt="{avg:.4f}")
         data_time = SmoothedValue(fmt="{avg:.4f}")
         space_fmt = ":" + str(len(str(len(iterable)))) + "d"
@@ -218,9 +218,9 @@ class MetricLogger:
             )
         mega_b = 1024.0 * 1024.0
         for i, obj in enumerate(iterable):
-            data_time.update(time.time() - end)
+            data_time.update(time.monotonic() - end)
             yield obj
-            iter_time.update(time.time() - end)
+            iter_time.update(time.monotonic() - end)
             if i % print_freq == 0 or i == len(iterable) - 1:
                 eta_seconds = iter_time.global_avg * (len(iterable) - i)
                 eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
@@ -247,8 +247,8 @@ class MetricLogger:
                             data=str(data_time),
                         )
                     )
-            end = time.time()
-        total_time = time.time() - start_time
+            end = time.monotonic()
+        total_time = time.monotonic() - start_time
         total_time_str = str(datetime.timedelta(seconds=int(total_time)))
         print("{} Total time: {} ({:.4f} s / it)".format(header, total_time_str, total_time / len(iterable)))
 

lerobot/common/policies/diffusion/policy.py

@@ -111,7 +111,7 @@ class DiffusionPolicy(nn.Module):
         return action
 
     def update(self, replay_buffer, step):
-        start_time = time.time()
+        start_time = time.monotonic()
 
         self.diffusion.train()
 
@@ -158,7 +158,7 @@ class DiffusionPolicy(nn.Module):
         batch = replay_buffer.sample(batch_size)
         batch = process_batch(batch, self.cfg.horizon, num_slices)
 
-        data_s = time.time() - start_time
+        data_s = time.monotonic() - start_time
 
         loss = self.diffusion.compute_loss(batch)
         loss.backward()
@@ -181,7 +181,7 @@ class DiffusionPolicy(nn.Module):
             "grad_norm": float(grad_norm),
             "lr": self.lr_scheduler.get_last_lr()[0],
             "data_s": data_s,
-            "update_s": time.time() - start_time,
+            "update_s": time.monotonic() - start_time,
         }
 
         # TODO(rcadene): remove hardcoding

lerobot/common/policies/tdmpc/policy.py

@@ -291,7 +291,7 @@ class TDMPC(nn.Module):
 
     def update(self, replay_buffer, step, demo_buffer=None):
         """Main update function. Corresponds to one iteration of the model learning."""
-        start_time = time.time()
+        start_time = time.monotonic()
 
         num_slices = self.cfg.batch_size
         batch_size = self.cfg.horizon * num_slices
@@ -405,7 +405,7 @@ class TDMPC(nn.Module):
         self.std = h.linear_schedule(self.cfg.std_schedule, step)
         self.model.train()
 
-        data_s = time.time() - start_time
+        data_s = time.monotonic() - start_time
 
         # Compute targets
         with torch.no_grad():
@@ -501,7 +501,7 @@ class TDMPC(nn.Module):
             "grad_norm": float(grad_norm),
             "lr": self.cfg.lr,
             "data_s": data_s,
-            "update_s": time.time() - start_time,
+            "update_s": time.monotonic() - start_time,
         }
         info["demo_batch_size"] = demo_batch_size
         info["expectile"] = expectile

lerobot/scripts/eval.py

@@ -32,7 +32,7 @@ def eval_policy(
     fps: int = 15,
     return_first_video: bool = False,
 ):
-    start = time.time()
+    start = time.monotonic()
     sum_rewards = []
     max_rewards = []
     successes = []
@@ -85,8 +85,8 @@ def eval_policy(
         "avg_sum_reward": np.nanmean(sum_rewards),
         "avg_max_reward": np.nanmean(max_rewards),
         "pc_success": np.nanmean(successes) * 100,
-        "eval_s": time.time() - start,
-        "eval_ep_s": (time.time() - start) / num_episodes,
+        "eval_s": time.monotonic() - start,
+        "eval_ep_s": (time.monotonic() - start) / num_episodes,
     }
     if return_first_video:
         return info, first_video