ready for review

examples/3_train_policy.py

@@ -32,8 +32,6 @@ policy = DiffusionPolicy(
     cfg=cfg.policy,
     cfg_device=cfg.device,
     cfg_noise_scheduler=cfg.noise_scheduler,
-    cfg_rgb_model=cfg.rgb_model,
-    cfg_obs_encoder=cfg.obs_encoder,
     cfg_optimizer=cfg.optimizer,
     cfg_ema=cfg.ema,
     **cfg.policy,

lerobot/common/policies/act/policy.py

@@ -213,7 +213,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         return action[: self.n_action_steps]
 
     def __call__(self, *args, **kwargs) -> dict:
-        # TODO(now): Temporary bridge until we know what to do about the `update` method.
+        # TODO(alexander-soare): Temporary bridge until we know what to do about the `update` method.
         return self.update(*args, **kwargs)
 
     def _preprocess_batch(

lerobot/common/policies/diffusion/model/conditional_unet1d.py

@@ -10,7 +10,6 @@ logger = logging.getLogger(__name__)
 
 
 class _SinusoidalPosEmb(nn.Module):
-    # TODO(now): consolidate?
     def __init__(self, dim):
         super().__init__()
         self.dim = dim

lerobot/common/policies/diffusion/model/diffusion_unet_image_policy.py

@@ -10,18 +10,6 @@ from lerobot.common.policies.utils import get_device_from_parameters, get_dtype_
 
 
 class DiffusionUnetImagePolicy(nn.Module):
-    """
-    TODO(now): Add DDIM scheduler.
-
-    Changes:  TODO(now)
-      - Use single image encoder for now instead of generic obs_encoder. We may generalize again when/if
-        needed. Code for a general observation encoder can be found at:
-        https://github.com/huggingface/lerobot/blob/920e0d118b493e4cc3058a9b1b764f38ae145d8e/lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py
-      - Uses the observation as global conditioning for the Unet by default.
-      - Does not do any inpainting (which would be applicable if the observation were not used to condition
-        the Unet).
-    """
-
     def __init__(
         self,
         cfg,
@@ -87,7 +75,7 @@ class DiffusionUnetImagePolicy(nn.Module):
                 torch.full(sample.shape[:1], t, dtype=torch.long, device=sample.device),
                 global_cond=global_cond,
             )
-            # Compute previous image: x_t -> x_t-1  # TODO(now): Is this right?
+            # Compute previous image: x_t -> x_t-1
             sample = self.noise_scheduler.step(model_output, t, sample, generator=generator).prev_sample
 
         return sample

lerobot/common/policies/diffusion/policy.py

@@ -6,7 +6,7 @@ from collections import deque
 import hydra
 import torch
 from diffusers.optimization import get_scheduler
-from torch import Tensor, nn
+from torch import nn
 
 from lerobot.common.policies.diffusion.model.diffusion_unet_image_policy import DiffusionUnetImagePolicy
 from lerobot.common.policies.utils import populate_queues
@@ -43,7 +43,6 @@ class DiffusionPolicy(nn.Module):
         # queues are populated during rollout of the policy, they contain the n latest observations and actions
         self._queues = None
 
-        # TODO(now): In-house this.
         noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
 
         self.diffusion = DiffusionUnetImagePolicy(
@@ -103,45 +102,35 @@ class DiffusionPolicy(nn.Module):
             "action": deque(maxlen=self.n_action_steps),
         }
 
-    def forward(self, batch: dict[str, Tensor], **_) -> Tensor:
-        """A forward pass through the DNN part of this policy with optional loss computation."""
-        return self.select_action(batch)
-
     @torch.no_grad
     def select_action(self, batch, **_):
         """
         Note: this uses the ema model weights if self.training == False, otherwise the non-ema model weights.
-        # TODO(now): Handle a batch
         """
         assert "observation.image" in batch
         assert "observation.state" in batch
-        assert len(batch) == 2  # TODO(now): Does this not have a batch dim?
+        assert len(batch) == 2
 
         self._queues = populate_queues(self._queues, batch)
 
         if len(self._queues["action"]) == 0:
             # stack n latest observations from the queue
             batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
-            actions = self._generate_actions(batch)
+            if not self.training and self.ema_diffusion is not None:
+                actions = self.ema_diffusion.generate_actions(batch)
+            else:
+                actions = self.diffusion.generate_actions(batch)
             self._queues["action"].extend(actions.transpose(0, 1))
 
         action = self._queues["action"].popleft()
         return action
 
-    def _generate_actions(self, batch):
-        if not self.training and self.ema_diffusion is not None:
-            return self.ema_diffusion.generate_actions(batch)
-        else:
-            return self.diffusion.generate_actions(batch)
-
-    def update(self, batch, **_):
-        """Run the model in train mode, compute the loss, and do an optimization step."""
+    def forward(self, batch, **_):
         start_time = time.time()
 
         self.diffusion.train()
 
-        loss = self.compute_loss(batch)
-
+        loss = self.diffusion.compute_loss(batch)
         loss.backward()
 
         grad_norm = torch.nn.utils.clip_grad_norm_(
@@ -166,9 +155,6 @@ class DiffusionPolicy(nn.Module):
 
         return info
 
-    def compute_loss(self, batch: dict[str, Tensor]) -> Tensor:
-        return self.diffusion.compute_loss(batch)
-
     def save(self, fp):
         torch.save(self.state_dict(), fp)
 

lerobot/common/policies/utils.py

@@ -18,7 +18,6 @@ def get_device_from_parameters(module: nn.Module) -> torch.device:
     """Get a module's device by checking one of its parameters.
 
     Note: assumes that all parameters have the same device
-    TODO(now): Add test.
     """
     return next(iter(module.parameters())).device
 
@@ -27,6 +26,5 @@ def get_dtype_from_parameters(module: nn.Module) -> torch.dtype:
     """Get a module's parameter dtype by checking one of its parameters.
 
     Note: assumes that all parameters have the same dtype.
-    TODO(now): Add test.
     """
     return next(iter(module.parameters())).dtype

lerobot/scripts/train.py

@@ -251,7 +251,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
         for key in batch:
             batch[key] = batch[key].to(cfg.device, non_blocking=True)
 
-        train_info = policy.update(batch, step=step)
+        train_info = policy(batch, step=step)
 
         # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
         if step % cfg.log_freq == 0: