Merge remote-tracking branch 'upstream/user/rcadene/2024_03_31_remove_torchrl' into refactor_act

.github/poetry/cpu/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -889,6 +889,69 @@ files = [
 [package.extras]
 protobuf = ["grpcio-tools (>=1.62.1)"]
 
+[[package]]
+name = "gym-aloha"
+version = "0.1.0"
+description = "A gym environment for ALOHA"
+optional = true
+python-versions = "^3.10"
+files = []
+develop = false
+
+[package.dependencies]
+dm-control = "1.0.14"
+gymnasium = "^0.29.1"
+mujoco = "^2.3.7"
+
+[package.source]
+type = "git"
+url = "git@github.com:huggingface/gym-aloha.git"
+reference = "HEAD"
+resolved_reference = "c636f05ba0d1760df94537da84c860be1487e17f"
+
+[[package]]
+name = "gym-pusht"
+version = "0.1.0"
+description = "A gymnasium environment for PushT."
+optional = true
+python-versions = "^3.10"
+files = []
+develop = false
+
+[package.dependencies]
+gymnasium = "^0.29.1"
+opencv-python = "^4.9.0.80"
+pygame = "^2.5.2"
+pymunk = "^6.6.0"
+scikit-image = "^0.22.0"
+shapely = "^2.0.3"
+
+[package.source]
+type = "git"
+url = "git@github.com:huggingface/gym-pusht.git"
+reference = "HEAD"
+resolved_reference = "6c9893504f670ff069d0f759a733e971ea1efdbf"
+
+[[package]]
+name = "gym-xarm"
+version = "0.1.0"
+description = "A gym environment for xArm"
+optional = true
+python-versions = "^3.10"
+files = []
+develop = false
+
+[package.dependencies]
+gymnasium = "^0.29.1"
+gymnasium-robotics = "^1.2.4"
+mujoco = "^2.3.7"
+
+[package.source]
+type = "git"
+url = "git@github.com:huggingface/gym-xarm.git"
+reference = "HEAD"
+resolved_reference = "08ddd5a9400783a6898bbf3c3014fc5da3961b9d"
+
 [[package]]
 name = "gymnasium"
 version = "0.29.1"
@@ -2988,31 +3051,6 @@ numpy = "*"
 packaging = "*"
 protobuf = ">=3.20"
 
-[[package]]
-name = "tensordict"
-version = "0.4.0+b4c91e8"
-description = ""
-optional = false
-python-versions = "*"
-files = []
-develop = false
-
-[package.dependencies]
-cloudpickle = "*"
-numpy = "*"
-torch = ">=2.1.0"
-
-[package.extras]
-checkpointing = ["torchsnapshot-nightly"]
-h5 = ["h5py (>=3.8)"]
-tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures", "pyyaml"]
-
-[package.source]
-type = "git"
-url = "https://github.com/pytorch/tensordict"
-reference = "HEAD"
-resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
-
 [[package]]
 name = "termcolor"
 version = "2.4.0"
@@ -3091,40 +3129,6 @@ type = "legacy"
 url = "https://download.pytorch.org/whl/cpu"
 reference = "torch-cpu"
 
-[[package]]
-name = "torchrl"
-version = "0.4.0+13bef42"
-description = ""
-optional = false
-python-versions = "*"
-files = []
-develop = false
-
-[package.dependencies]
-cloudpickle = "*"
-numpy = "*"
-packaging = "*"
-tensordict = ">=0.4.0"
-torch = ">=2.1.0"
-
-[package.extras]
-all = ["ale-py", "atari-py", "dm_control", "git", "gym", "gym[accept-rom-license]", "gymnasium", "h5py", "huggingface_hub", "hydra-core (>=1.1)", "hydra-submitit-launcher", "minari", "moviepy", "mujoco", "pandas", "pettingzoo (>=1.24.1)", "pillow", "pygame", "pytest", "pytest-instafail", "pyyaml", "requests", "scikit-learn", "scipy", "tensorboard", "torchsnapshot", "torchvision", "tqdm", "vmas (>=1.2.10)", "wandb"]
-atari = ["ale-py", "atari-py", "gym", "gym[accept-rom-license]", "pygame"]
-checkpointing = ["torchsnapshot"]
-dm-control = ["dm_control"]
-gym-continuous = ["gymnasium", "mujoco"]
-marl = ["pettingzoo (>=1.24.1)", "vmas (>=1.2.10)"]
-offline-data = ["h5py", "huggingface_hub", "minari", "pandas", "pillow", "requests", "scikit-learn", "torchvision", "tqdm"]
-rendering = ["moviepy"]
-tests = ["pytest", "pytest-instafail", "pyyaml", "scipy"]
-utils = ["git", "hydra-core (>=1.1)", "hydra-submitit-launcher", "tensorboard", "tqdm", "wandb"]
-
-[package.source]
-type = "git"
-url = "https://github.com/pytorch/rl"
-reference = "13bef426dcfa5887c6e5034a6e9697993fa92c37"
-resolved_reference = "13bef426dcfa5887c6e5034a6e9697993fa92c37"
-
 [[package]]
 name = "torchvision"
 version = "0.17.1+cpu"
@@ -3330,4 +3334,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "8800bb8b24312d17b765cd2ce2799f49436171dd5fbf1bec3b07f853cfa9befd"
+content-hash = "32cd6caa01276a90b37cb177204e5b1511e92838f3f0268391034042d56f3bd6"

.github/poetry/cpu/pyproject.toml

@@ -39,8 +39,6 @@ scikit-image = "^0.22.0"
 numba = "^0.59.0"
 mpmath = "^1.3.0"
 torch = {version = "^2.2.1", source = "torch-cpu"}
-tensordict = {git = "https://github.com/pytorch/tensordict"}
-torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
 mujoco = "^2.3.7"
 opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"
@@ -53,7 +51,12 @@ huggingface-hub = "^0.21.4"
 gymnasium-robotics = "^1.2.4"
 gymnasium = "^0.29.1"
 cmake = "^3.29.0.1"
-
+gym-pusht = { git = "git@github.com:huggingface/gym-pusht.git", optional = true}
+gym-xarm = { git = "git@github.com:huggingface/gym-xarm.git", optional = true}
+gym-aloha = { git = "git@github.com:huggingface/gym-aloha.git", optional = true}
+# gym-pusht = { path = "../gym-pusht", develop = true, optional = true}
+# gym-xarm = { path = "../gym-xarm", develop = true, optional = true}
+# gym-aloha = { path = "../gym-aloha", develop = true, optional = true}
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^3.6.2"

lerobot/common/policies/abstract.py

@@ -1,82 +0,0 @@
-from collections import deque
-
-import torch
-from torch import Tensor, nn
-
-
-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
-    documentation for more information.
-
-    Note:
-        When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
-            1. set the required class attributes:
-                - for classes inheriting from `AbstractDataset`: `available_datasets`
-                - for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
-                - for classes inheriting from `AbstractPolicy`: `name`
-            2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
-            3. update variables in `tests/test_available.py` by importing your new class
-    """
-
-    name: str | None = None  # same name should be used to instantiate the policy in factory.py
-
-    def __init__(self, n_action_steps: int | None):
-        """
-        n_action_steps: Sets the cache size for storing action trajectories. If None, it is assumed that a single
-            action is returned by `select_actions` and that doesn't have a horizon dimension. The `forward` method then
-            adds that dimension.
-        """
-        super().__init__()
-        assert self.name is not None, "Subclasses of `AbstractPolicy` should set the `name` class attribute."
-        self.n_action_steps = n_action_steps
-        self.clear_action_queue()
-
-    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)
-
-    def load(self, fp):
-        d = torch.load(fp)
-        self.load_state_dict(d)
-
-    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."""
-        if self.n_action_steps is not None:
-            self._action_queue = deque([], maxlen=self.n_action_steps)
-
-    def forward(self, *args, **kwargs) -> Tensor:
-        """Inference step that makes multi-step policies compatible with their single-step environments.
-
-        WARNING: In general, this should not be overriden.
-
-        Consider a "policy" that observes the environment then charts a course of N actions to take. To make this fit
-        into the formalism of a TorchRL environment, we view it as being effectively a policy that (1) makes an
-        observation and prepares a queue of actions, (2) consumes that queue when queried, regardless of the environment
-        observation, (3) repopulates the action queue when empty. This method handles the aforementioned logic so that
-        the subclass doesn't have to.
-
-        This method effectively wraps the `select_actions` method of the subclass. The following assumptions are made:
-        1. The `select_actions` method returns a Tensor of actions with shape (B, H, *) where B is the batch size, H is
-           the action trajectory horizon and * is the action dimensions.
-        2. Prior to the `select_actions` method being called, theres is an `n_action_steps` instance attribute defined.
-        """
-        if self.n_action_steps is None:
-            return self.select_actions(*args, **kwargs)
-        if len(self._action_queue) == 0:
-            # `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has shape
-            # (n_action_steps, batch_size, *), hence the transpose.
-            self._action_queue.extend(self.select_actions(*args, **kwargs).transpose(0, 1))
-        return self._action_queue.popleft()

lerobot/common/policies/act/policy.py

@@ -65,7 +65,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         "ActionChunkingTransformerPolicy does not handle multiple observation steps."
     )
 
-    def __init__(self, cfg, device, n_action_steps=1):
+    def __init__(self, cfg, device):
         """
         TODO(alexander-soare): Add documentation for all parameters once we have model configs established.
         """
@@ -73,7 +73,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         if getattr(cfg, "n_obs_steps", 1) != 1:
             raise ValueError(self._multiple_obs_steps_not_handled_msg)
         self.cfg = cfg
-        self.n_action_steps = n_action_steps
+        self.n_action_steps = cfg.n_action_steps
         self.device = get_safe_torch_device(device)
         self.camera_names = cfg.camera_names
         self.use_vae = cfg.use_vae
@@ -176,7 +176,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         if self.n_action_steps is not None:
             self._action_queue = deque([], maxlen=self.n_action_steps)
 
-    def select_action(self, batch: dict[str, Tensor], *_) -> Tensor:
+    def select_action(self, batch: dict[str, Tensor], *_, **__) -> Tensor:
         """
         This method wraps `select_actions` in order to return one action at a time for execution in the
         environment. It works by managing the actions in a queue and only calling `select_actions` when the
@@ -244,7 +244,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         # Note: no squeeze is required for "observation.images.top" because then we'd have to unsqueeze to get
         # the image index dimension.
 
-    def update(self, batch, *_) -> dict:
+    def update(self, batch, *_, **__) -> dict:
         start_time = time.time()
         self._preprocess_batch(batch)
 
@@ -311,7 +311,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
 
     def _forward(
         self, robot_state: Tensor, image: Tensor, actions: Tensor | None = None
-    ) -> tuple[Tensor, tuple[Tensor, Tensor]]:
+    ) -> tuple[Tensor, tuple[Tensor | None, Tensor | None]]:
         """
         Args:
             robot_state: (B, J) batch of robot joint configurations.
@@ -344,16 +344,16 @@ class ActionChunkingTransformerPolicy(nn.Module):
             # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
             pos_embed = self.vae_encoder_pos_enc.clone().detach()  # (1, S+2, D)
 
-            # Forward pass through VAE encoder.
+            # Forward pass through VAE encoder to get the latent PDF parameters.
             cls_token_out = self.vae_encoder(
                 vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
             )[0]  # select the class token, with shape (B, D)
             latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
-
-            # Sample the latent with the reparameterization trick.
             mu = latent_pdf_params[:, : self.latent_dim]
             # This is 2log(sigma). Done this way to match the original implementation.
             log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
+
+            # Sample the latent with the reparameterization trick.
             latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
         else:
             # When not using the VAE encoder, we set the latent to be all zeros.

lerobot/common/policies/factory.py

@@ -16,18 +16,15 @@ def make_policy(cfg):
             cfg_obs_encoder=cfg.obs_encoder,
             cfg_optimizer=cfg.optimizer,
             cfg_ema=cfg.ema,
-            n_obs_steps=cfg.n_obs_steps,
+            # n_obs_steps=cfg.n_obs_steps,
-            n_action_steps=cfg.n_action_steps,
+            # n_action_steps=cfg.n_action_steps,
             **cfg.policy,
         )
     elif cfg.policy.name == "act":
         from lerobot.common.policies.act.policy import ActionChunkingTransformerPolicy
 
-        policy = ActionChunkingTransformerPolicy(
+        policy = ActionChunkingTransformerPolicy(cfg.policy, cfg.device)
-            cfg.policy,
+        policy.to(cfg.device)
-            cfg.device,
-            n_action_steps=cfg.n_action_steps,
-        )
     else:
         raise ValueError(cfg.policy.name)
 

lerobot/common/policies/tdmpc/policy.py

@@ -110,7 +110,6 @@ class TDMPCPolicy(nn.Module):
         # self.bc_optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
         self.model.eval()
         self.model_target.eval()
-        self.batch_size = cfg.batch_size
 
         self.register_buffer("step", torch.zeros(1))
 
@@ -151,6 +150,8 @@ class TDMPCPolicy(nn.Module):
 
         t0 = step == 0
 
+        self.eval()
+
         if len(self._queues["action"]) == 0:
             batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
 
@@ -172,7 +173,7 @@ class TDMPCPolicy(nn.Module):
                 actions.append(action)
             action = torch.stack(actions)
 
-            # self.act returns an action for 1 timestep only, so we copy it over `n_action_steps` time
+            # tdmpc returns an action for 1 timestep only, so we copy it over `n_action_steps` time
             if i in range(self.n_action_steps):
                 self._queues["action"].append(action)
 
@@ -325,7 +326,7 @@ class TDMPCPolicy(nn.Module):
     def _td_target(self, next_z, reward, mask):
         """Compute the TD-target from a reward and the observation at the following time step."""
         next_v = self.model.V(next_z)
-        td_target = reward + self.cfg.discount * mask * next_v
+        td_target = reward + self.cfg.discount * mask * next_v.squeeze(2)
         return td_target
 
     def forward(self, batch, step):
@@ -420,6 +421,8 @@ class TDMPCPolicy(nn.Module):
         #     idxs = torch.cat([idxs, demo_idxs])
         #     weights = torch.cat([weights, demo_weights])
 
+        batch_size = batch["index"].shape[0]
+
         # TODO(rcadene): convert tdmpc with (batch size, time/horizon, channels)
         # instead of currently (time/horizon, batch size, channels) which is not the pytorch convention
         # batch size b = 256, time/horizon t = 5
@@ -433,7 +436,7 @@ class TDMPCPolicy(nn.Module):
         #  idxs = batch["index"]  # TODO(rcadene): use idxs to update sampling weights
         done = torch.zeros_like(reward, dtype=torch.bool, device=reward.device)
         mask = torch.ones_like(reward, dtype=torch.bool, device=reward.device)
-        weights = torch.ones_like(reward, dtype=torch.bool, device=reward.device)
+        weights = torch.ones(batch_size, dtype=torch.bool, device=reward.device)
 
         obses = {
             "rgb": batch["observation.image"],
@@ -476,7 +479,7 @@ class TDMPCPolicy(nn.Module):
             td_targets = self._td_target(next_z, reward, mask)
 
         # Latent rollout
-        zs = torch.empty(horizon + 1, self.batch_size, self.cfg.latent_dim, device=self.device)
+        zs = torch.empty(horizon + 1, batch_size, self.cfg.latent_dim, device=self.device)
         reward_preds = torch.empty_like(reward, device=self.device)
         assert reward.shape[0] == horizon
         z = self.model.encode(obs)
@@ -485,22 +488,21 @@ class TDMPCPolicy(nn.Module):
         for t in range(horizon):
             z, reward_pred = self.model.next(z, action[t])
             zs[t + 1] = z
-            reward_preds[t] = reward_pred
+            reward_preds[t] = reward_pred.squeeze(1)
 
         with torch.no_grad():
             v_target = self.model_target.Q(zs[:-1].detach(), action, return_type="min")
 
         # Predictions
         qs = self.model.Q(zs[:-1], action, return_type="all")
+        qs = qs.squeeze(3)
         value_info["Q"] = qs.mean().item()
         v = self.model.V(zs[:-1])
         value_info["V"] = v.mean().item()
 
         # Losses
-        rho = torch.pow(self.cfg.rho, torch.arange(horizon, device=self.device)).view(-1, 1, 1)
+        rho = torch.pow(self.cfg.rho, torch.arange(horizon, device=self.device)).view(-1, 1)
-        consistency_loss = (rho * torch.mean(h.mse(zs[1:], z_targets), dim=2, keepdim=True) * loss_mask).sum(
+        consistency_loss = (rho * torch.mean(h.mse(zs[1:], z_targets), dim=2) * loss_mask).sum(dim=0)
-            dim=0
-        )
         reward_loss = (rho * h.mse(reward_preds, reward) * loss_mask).sum(dim=0)
         q_value_loss, priority_loss = 0, 0
         for q in range(self.cfg.num_q):
@@ -508,7 +510,9 @@ class TDMPCPolicy(nn.Module):
             priority_loss += (rho * h.l1(qs[q], td_targets) * loss_mask).sum(dim=0)
 
         expectile = h.linear_schedule(self.cfg.expectile, step)
-        v_value_loss = (rho * h.l2_expectile(v_target - v, expectile=expectile) * loss_mask).sum(dim=0)
+        v_value_loss = (rho * h.l2_expectile(v_target - v, expectile=expectile).squeeze(2) * loss_mask).sum(
+            dim=0
+        )
 
         total_loss = (
             self.cfg.consistency_coef * consistency_loss
@@ -517,7 +521,7 @@ class TDMPCPolicy(nn.Module):
             + self.cfg.value_coef * v_value_loss
         )
 
-        weighted_loss = (total_loss.squeeze(1) * weights).mean()
+        weighted_loss = (total_loss * weights).mean()
         weighted_loss.register_hook(lambda grad: grad * (1 / self.cfg.horizon))
         has_nan = torch.isnan(weighted_loss).item()
         if has_nan:

lerobot/configs/policy/act.yaml

@@ -51,6 +51,7 @@ policy:
   utd: 1
 
   n_obs_steps: ${n_obs_steps}
+  n_action_steps: ${n_action_steps}
 
   temporal_agg: false
 

lerobot/configs/policy/diffusion.yaml

@@ -38,6 +38,7 @@ policy:
 
   horizon: ${horizon}
   n_obs_steps: ${n_obs_steps}
+  n_action_steps: ${n_action_steps}
   num_inference_steps: 100
   obs_as_global_cond: ${obs_as_global_cond}
   # crop_shape: null

lerobot/configs/policy/tdmpc.yaml

@@ -36,7 +36,6 @@ policy:
   log_std_max: 2
 
   # learning
-  batch_size: 256
   max_buffer_size: 10000
   horizon: 5
   reward_coef: 0.5

lerobot/scripts/eval.py

@@ -86,14 +86,10 @@ def eval_policy(
     def maybe_render_frame(env):
         if save_video:  # noqa: B023
             if return_first_video:
-                # TODO(now): Put mode back in.
                 visu = env.envs[0].render()
-                # visu = env.envs[0].render(mode="visualization")
                 visu = visu[None, ...]  # add batch dim
             else:
-                # TODO(now): Put mode back in.
                 visu = np.stack([env.render() for env in env.envs])
-                # visu = np.stack([env.render(mode="visualization") for env in env.envs])
             ep_frames.append(visu)  # noqa: B023
 
     for _ in range(num_episodes):

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -898,7 +898,7 @@ mujoco = "^2.3.7"
 type = "git"
 url = "git@github.com:huggingface/gym-aloha.git"
 reference = "HEAD"
-resolved_reference = "ec7200831e36c14e343cf7d275c6b047f2fe9d11"
+resolved_reference = "c636f05ba0d1760df94537da84c860be1487e17f"
 
 [[package]]
 name = "gym-pusht"
@@ -3339,31 +3339,6 @@ numpy = "*"
 packaging = "*"
 protobuf = ">=3.20"
 
-[[package]]
-name = "tensordict"
-version = "0.4.0+f622b2f"
-description = ""
-optional = false
-python-versions = "*"
-files = []
-develop = false
-
-[package.dependencies]
-cloudpickle = "*"
-numpy = "*"
-torch = ">=2.1.0"
-
-[package.extras]
-checkpointing = ["torchsnapshot-nightly"]
-h5 = ["h5py (>=3.8)"]
-tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures", "pyyaml"]
-
-[package.source]
-type = "git"
-url = "https://github.com/pytorch/tensordict"
-reference = "HEAD"
-resolved_reference = "f622b2f973320f769b6c09793ca827f27e47d603"
-
 [[package]]
 name = "termcolor"
 version = "2.4.0"
@@ -3464,40 +3439,6 @@ typing-extensions = ">=4.8.0"
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
-[[package]]
-name = "torchrl"
-version = "0.4.0+13bef42"
-description = ""
-optional = false
-python-versions = "*"
-files = []
-develop = false
-
-[package.dependencies]
-cloudpickle = "*"
-numpy = "*"
-packaging = "*"
-tensordict = ">=0.4.0"
-torch = ">=2.1.0"
-
-[package.extras]
-all = ["ale-py", "atari-py", "dm_control", "git", "gym", "gym[accept-rom-license]", "gymnasium", "h5py", "huggingface_hub", "hydra-core (>=1.1)", "hydra-submitit-launcher", "minari", "moviepy", "mujoco", "pandas", "pettingzoo (>=1.24.1)", "pillow", "pygame", "pytest", "pytest-instafail", "pyyaml", "requests", "scikit-learn", "scipy", "tensorboard", "torchsnapshot", "torchvision", "tqdm", "vmas (>=1.2.10)", "wandb"]
-atari = ["ale-py", "atari-py", "gym", "gym[accept-rom-license]", "pygame"]
-checkpointing = ["torchsnapshot"]
-dm-control = ["dm_control"]
-gym-continuous = ["gymnasium", "mujoco"]
-marl = ["pettingzoo (>=1.24.1)", "vmas (>=1.2.10)"]
-offline-data = ["h5py", "huggingface_hub", "minari", "pandas", "pillow", "requests", "scikit-learn", "torchvision", "tqdm"]
-rendering = ["moviepy"]
-tests = ["pytest", "pytest-instafail", "pyyaml", "scipy"]
-utils = ["git", "hydra-core (>=1.1)", "hydra-submitit-launcher", "tensorboard", "tqdm", "wandb"]
-
-[package.source]
-type = "git"
-url = "https://github.com/pytorch/rl"
-reference = "13bef426dcfa5887c6e5034a6e9697993fa92c37"
-resolved_reference = "13bef426dcfa5887c6e5034a6e9697993fa92c37"
-
 [[package]]
 name = "torchvision"
 version = "0.17.2"
@@ -3741,4 +3682,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "cb450ac7186e004536d75409edd42cd96062f7b1fd47822a5460d12eab8762f9"
+content-hash = "bf4627c62a45764931729ce373f1038fe289b6caebb01e66d878f6f278c54518"

pyproject.toml

@@ -39,8 +39,6 @@ scikit-image = "^0.22.0"
 numba = "^0.59.0"
 mpmath = "^1.3.0"
 torch = "^2.2.1"
-tensordict = {git = "https://github.com/pytorch/tensordict"}
-torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
 mujoco = "^2.3.7"
 opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"

tests/test_policies.py

@@ -15,10 +15,10 @@ from .utils import DEVICE, DEFAULT_CONFIG_PATH
         ("xarm", "tdmpc", ["policy.mpc=true"]),
         ("pusht", "tdmpc", ["policy.mpc=false"]),
         ("pusht", "diffusion", []),
-        # ("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_human"]),
+        ("aloha", "act", ["env.task=AlohaInsertion-v0", "dataset_id=aloha_sim_insertion_human"]),
-        #("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_scripted"]),
+        ("aloha", "act", ["env.task=AlohaInsertion-v0", "dataset_id=aloha_sim_insertion_scripted"]),
-        #("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_human"]),
+        ("aloha", "act", ["env.task=AlohaTransferCube-v0", "dataset_id=aloha_sim_transfer_cube_human"]),
-        #("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_scripted"]),
+        ("aloha", "act", ["env.task=AlohaTransferCube-v0", "dataset_id=aloha_sim_transfer_cube_scripted"]),
         # TODO(aliberts): xarm not working with diffusion
         # ("xarm", "diffusion", []),
     ],
@@ -49,7 +49,7 @@ def test_policy(env_name, policy_name, extra_overrides):
     dataloader = torch.utils.data.DataLoader(
         dataset,
         num_workers=4,
-        batch_size=cfg.policy.batch_size,
+        batch_size=2,
         shuffle=True,
         pin_memory=DEVICE != "cpu",
         drop_last=True,