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

lerobot/common/policies/abstract.py

@@ -1,11 +1,10 @@
-from abc import ABC, abstractmethod
 from collections import deque
 
 import torch
 from torch import Tensor, nn
 
 
-class AbstractPolicy(nn.Module, ABC):
+class AbstractPolicy(nn.Module):
     """Base policy which all policies should be derived from.
 
     The forward method should generally not be overriden as it plays the role of handling multi-step policies. See its
@@ -22,9 +21,9 @@ class AbstractPolicy(nn.Module, ABC):
         self.n_action_steps = n_action_steps
         self.clear_action_queue()
 
-    @abstractmethod
     def update(self, replay_buffer, step):
         """One step of the policy's learning algorithm."""
+        raise NotImplementedError("Abstract method")
 
     def save(self, fp):
         torch.save(self.state_dict(), fp)
@@ -33,13 +32,13 @@ class AbstractPolicy(nn.Module, ABC):
         d = torch.load(fp)
         self.load_state_dict(d)
 
-    @abstractmethod
     def select_actions(self, observation) -> Tensor:
         """Select an action (or trajectory of actions) based on an observation during rollout.
 
         If n_action_steps was provided at initialization, this should return a (batch_size, n_action_steps, *) tensor of
         actions. Otherwise if n_actions_steps is None, this should return a (batch_size, *) tensor of actions.
         """
+        raise NotImplementedError("Abstract method")
 
     def clear_action_queue(self):
         """This should be called whenever the environment is reset."""

lerobot/common/policies/act/policy.py

@@ -7,6 +7,7 @@ import torchvision.transforms as transforms
 
 from lerobot.common.policies.abstract import AbstractPolicy
 from lerobot.common.policies.act.detr_vae import build
+from lerobot.common.utils import get_safe_torch_device
 
 
 def build_act_model_and_optimizer(cfg):
@@ -45,7 +46,7 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
         super().__init__(n_action_steps)
         self.cfg = cfg
         self.n_action_steps = n_action_steps
-        self.device = device
+        self.device = get_safe_torch_device(device)
         self.model, self.optimizer = build_act_model_and_optimizer(cfg)
         self.kl_weight = self.cfg.kl_weight
         logging.info(f"KL Weight {self.kl_weight}")

lerobot/common/policies/diffusion/policy.py

@@ -9,6 +9,7 @@ from lerobot.common.policies.abstract import AbstractPolicy
 from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
 from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
 from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder, RgbEncoder
+from lerobot.common.utils import get_safe_torch_device
 
 
 class DiffusionPolicy(AbstractPolicy):
@@ -66,9 +67,8 @@ class DiffusionPolicy(AbstractPolicy):
             **kwargs,
         )
 
-        self.device = torch.device(cfg_device)
-        if torch.cuda.is_available() and cfg_device == "cuda":
-            self.diffusion.cuda()
+        self.device = get_safe_torch_device(cfg_device)
+        self.diffusion.to(self.device)
 
         self.ema_diffusion = None
         self.ema = None

lerobot/common/policies/tdmpc/policy.py

@@ -10,6 +10,7 @@ import torch.nn as nn
 
 import lerobot.common.policies.tdmpc.helper as h
 from lerobot.common.policies.abstract import AbstractPolicy
+from lerobot.common.utils import get_safe_torch_device
 
 FIRST_FRAME = 0
 
@@ -94,9 +95,10 @@ class TDMPC(AbstractPolicy):
         self.action_dim = cfg.action_dim
 
         self.cfg = cfg
-        self.device = torch.device(device)
+        self.device = get_safe_torch_device(device)
         self.std = h.linear_schedule(cfg.std_schedule, 0)
-        self.model = TOLD(cfg).cuda() if torch.cuda.is_available() and device == "cuda" else TOLD(cfg)
+        self.model = TOLD(cfg)
+        self.model.to(self.device)
         self.model_target = deepcopy(self.model)
         self.optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
         self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr)