Merge remote-tracking branch 'upstream/main' into unify_policy_api

examples/3_train_policy.py

@@ -53,16 +53,10 @@ step = 0
 done = False
 while not done:
     for batch in dataloader:
-        for k in batch:
-            batch[k] = batch[k].to(device, non_blocking=True)
+        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
         info = policy.update(batch)
         if step % log_freq == 0:
-            num_samples = (step + 1) * cfg.batch_size
-            loss = info["loss"]
-            update_s = info["update_s"]
-            print(
-                f"step: {step} samples: {num_samples} loss: {loss:.3f} update_time: {update_s:.3f} (seconds)"
-            )
+            print(f"step: {step} loss: {info['loss']:.3f} update_time: {info['update_s']:.3f} (seconds)")
         step += 1
         if step >= training_steps:
             done = True

lerobot/common/policies/act/modeling_act.py

@@ -65,12 +65,16 @@ class ActionChunkingTransformerPolicy(nn.Module):
         "ActionChunkingTransformerPolicy does not handle multiple observation steps."
     )
 
-    def __init__(self, cfg: ActionChunkingTransformerConfig):
+    def __init__(self, cfg: ActionChunkingTransformerConfig | None = None):
         """
-        TODO(alexander-soare): Add documentation for all parameters once we have model configs established.
+        Args:
+            cfg: Policy configuration class instance or None, in which case the default instantiation of the
+                 configuration class is used.
         """
         super().__init__()
-        if getattr(cfg, "n_obs_steps", 1) != 1:
+        if cfg is None:
+            cfg = ActionChunkingTransformerConfig()
+        if cfg.n_obs_steps != 1:
             raise ValueError(self._multiple_obs_steps_not_handled_msg)
         self.cfg = cfg
 
@@ -161,8 +165,8 @@ class ActionChunkingTransformerPolicy(nn.Module):
         if self.cfg.n_action_steps is not None:
             self._action_queue = deque([], maxlen=self.cfg.n_action_steps)
 
-    def forward(self, batch: dict[str, Tensor], **_) -> Tensor:
-        return self.select_action(self, batch)
+    # def forward(self, batch: dict[str, Tensor], **_) -> Tensor:
+    #     return self.select_action(self, batch)
 
     @torch.no_grad
     def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -33,8 +33,6 @@ from lerobot.common.policies.utils import (
     populate_queues,
 )
 
-logger = logging.getLogger(__name__)
-
 
 class DiffusionPolicy(nn.Module):
     """
@@ -44,8 +42,17 @@ class DiffusionPolicy(nn.Module):
 
     name = "diffusion"
 
-    def __init__(self, cfg: DiffusionConfig, lr_scheduler_num_training_steps: int):
+    def __init__(self, cfg: DiffusionConfig | None, lr_scheduler_num_training_steps: int = 0):
         super().__init__()
+        """
+        Args:
+            cfg: Policy configuration class instance or None, in which case the default instantiation of the
+                 configuration class is used.
+        """
+        # TODO(alexander-soare): LR scheduler will be removed.
+        assert lr_scheduler_num_training_steps > 0
+        if cfg is None:
+            cfg = DiffusionConfig()
         self.cfg = cfg
 
         # queues are populated during rollout of the policy, they contain the n latest observations and actions

tests/test_examples.py

@@ -40,7 +40,8 @@ def test_examples_3_and_2():
         ],
     )
 
-    exec(file_contents)
+    # Pass empty globals to allow dictionary comprehension https://stackoverflow.com/a/32897127/4391249.
+    exec(file_contents, {})
 
     for file_name in ["model.pt", "stats.pth", "config.yaml"]:
         assert Path(f"outputs/train/example_pusht_diffusion/{file_name}").exists()