From cb3978b5f3ac78fd03bc3398257f7ce18ad4345e Mon Sep 17 00:00:00 2001
From: Alexander Soare <alexander.soare159@gmail.com>
Date: Tue, 16 Apr 2024 18:12:39 +0100
Subject: [PATCH] backup wip
---
lerobot/common/policies/act/modeling_act.py | 107 +++++++++++---------
lerobot/common/policies/policy_protocol.py | 45 ++++++++
2 files changed, 102 insertions(+), 50 deletions(-)
create mode 100644 lerobot/common/policies/policy_protocol.py
diff --git a/lerobot/common/policies/act/modeling_act.py b/lerobot/common/policies/act/modeling_act.py
index 143d1459..e1576777 100644
--- a/lerobot/common/policies/act/modeling_act.py
+++ b/lerobot/common/policies/act/modeling_act.py
@@ -165,8 +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:
@@ -186,20 +186,28 @@ class ActionChunkingTransformerPolicy(nn.Module):
def _select_actions(self, batch: dict[str, Tensor]) -> Tensor:
"""Use the action chunking transformer to generate a sequence of actions."""
self.eval()
- self._preprocess_batch(batch, add_obs_steps_dim=True)
- action = self.forward(batch, return_loss=False)
- return action[: self.cfg.n_action_steps]
+ batch = self._reshape_batch(batch, add_obs_steps_dim=True)
+ actions, _ = self._forward(
+ batch["observation.state"], self.image_normalizer(batch["observation.images.top"])
+ )
+ return actions[: self.cfg.n_action_steps]
+
+ def _reshape_batch(self, batch: dict[str, Tensor], add_obs_steps_dim: bool = False) -> dict[str, Tensor]:
+ """Reshapes the batch items to account for various requirements of this policy.
- def _preprocess_batch(
- self, batch: dict[str, Tensor], add_obs_steps_dim: bool = False
- ) -> dict[str, Tensor]:
- """
This function expects `batch` to have (at least):
{
"observation.state": (B, 1, J) OR (B, J) tensor of robot states (joint configuration).
"observation.images.top": (B, 1, C, H, W) OR (B, C, H, W) tensor of images.
}
+
+ TODO(alexander-soare): Right now this method does and undoes reshaping operations. This is just to
+ separate out the core logic from the temporary logic. See comments below.
"""
+ # Create a shallow copy.
+ batch = dict(batch)
+
+ # Add a dimension for observation steps.
if add_obs_steps_dim:
# Add a dimension for the observations steps. Since n_obs_steps > 1 is not supported right now,
# this just amounts to an unsqueeze.
@@ -207,18 +215,29 @@ class ActionChunkingTransformerPolicy(nn.Module):
if k.startswith("observation."):
batch[k] = batch[k].unsqueeze(1)
- if batch["observation.state"].shape[1] != 1:
+ # Temporary logic to remove the observation step dimension as the policy does not yet handle it.
+ # TODO(alexander-soare): generalize this to multiple observations steps.
+ # Check that there is only 1 observation step (policy does not yet handle more).
+ if not all(batch[k].shape[1] == 1 for k in batch if k.startswith("observation.")):
raise ValueError(self._multiple_obs_steps_not_handled_msg)
- batch["observation.state"] = batch["observation.state"].squeeze(1)
- # TODO(alexander-soare): generalize this to multiple images.
+ # Remove observation steps dimension.
+ for k in batch:
+ if k.startswith("observation."):
+ batch[k] = batch[k].squeeze(1)
+
+ # Temporary logic to add the multiple image dimension back in.
+ # TODO(alexander-soare): generalize this to multiple images. Once resolved, this logic will stack all
+ # images.
assert (
sum(k.startswith("observation.images.") and not k.endswith("is_pad") for k in batch) == 1
- ), "ACT only handles one image for now."
- # Note: no squeeze is required for "observation.images.top" because then we'd have to unsqueeze to get
- # the image index dimension.
+ ), f"{self.__class__.__name__} only handles one image for now."
+ # Since we only handle one image, just unsqueeze instead of stacking.
+ batch["observation.images.top"] = batch["observation.images.top"].unsqueeze(1)
+
+ return batch
def compute_loss(self, batch, **_) -> float:
- self._preprocess_batch(batch)
+ batch = self._reshape_batch(batch)
self.train()
@@ -228,7 +247,28 @@ class ActionChunkingTransformerPolicy(nn.Module):
assert batch_size % self.cfg.chunk_size == 0
assert batch_size % num_slices == 0
- loss = self.forward(batch, return_loss=True)["loss"]
+ actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(
+ batch["observation.state"],
+ self.image_normalizer(batch["observation.images.top"]),
+ batch["action"],
+ )
+
+ l1_loss = (
+ F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
+ ).mean()
+
+ if self.cfg.use_vae:
+ # Calculate Dāā(latent_pdf || standard_normal). Note: After computing the KL-divergence for
+ # each dimension independently, we sum over the latent dimension to get the total
+ # KL-divergence per batch element, then take the mean over the batch.
+ # (See App. B of https://arxiv.org/abs/1312.6114 for more details).
+ mean_kld = (
+ (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
+ )
+ loss = l1_loss + mean_kld * self.cfg.kl_weight
+ else:
+ loss = l1_loss
+
return loss
def update(self, batch, **_) -> dict:
@@ -255,39 +295,6 @@ class ActionChunkingTransformerPolicy(nn.Module):
return info
- def forward(self, batch: dict[str, Tensor], return_loss: bool = False) -> dict | Tensor:
- """A forward pass through the DNN part of this policy with optional loss computation."""
- images = self.image_normalizer(batch["observation.images.top"])
-
- if return_loss: # training time
- actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(
- batch["observation.state"], images, batch["action"]
- )
-
- l1_loss = (
- F.l1_loss(batch["action"], actions_hat, reduction="none")
- * ~batch["action_is_pad"].unsqueeze(-1)
- ).mean()
-
- loss_dict = {}
- loss_dict["l1"] = l1_loss
- if self.cfg.use_vae:
- # Calculate Dāā(latent_pdf || standard_normal). Note: After computing the KL-divergence for
- # each dimension independently, we sum over the latent dimension to get the total
- # KL-divergence per batch element, then take the mean over the batch.
- # (See App. B of https://arxiv.org/abs/1312.6114 for more details).
- mean_kld = (
- (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
- )
- loss_dict["kl"] = mean_kld
- loss_dict["loss"] = loss_dict["l1"] + loss_dict["kl"] * self.cfg.kl_weight
- else:
- loss_dict["loss"] = loss_dict["l1"]
- return loss_dict
- else:
- action, _ = self._forward(batch["observation.state"], images)
- return action
-
def _forward(
self, robot_state: Tensor, image: Tensor, actions: Tensor | None = None
) -> tuple[Tensor, tuple[Tensor | None, Tensor | None]]:
diff --git a/lerobot/common/policies/policy_protocol.py b/lerobot/common/policies/policy_protocol.py
new file mode 100644
index 00000000..3a396e84
--- /dev/null
+++ b/lerobot/common/policies/policy_protocol.py
@@ -0,0 +1,45 @@
+"""A protocol that all policies should follow.
+
+This provides a mechanism for type-hinting and isinstance checks without requiring the policies classes
+subclass a base class.
+
+The protocol structure, method signatures, and docstrings should be used by developers as a reference for
+how to implement new policies.
+"""
+
+from typing import Protocol, runtime_checkable
+
+from torch import Tensor
+
+
+@runtime_checkable
+class Policy(Protocol):
+ """The required interface for implementing a policy."""
+
+ name: str
+
+ def reset(self):
+ """To be called whenever the environment is reset.
+
+ Does things like clearing caches.
+ """
+
+ def forward(self, batch: dict[str, Tensor], **kwargs):
+ """Wired to `select_action`."""
+
+ def select_action(self, batch: dict[str, Tensor], **kwargs):
+ """Return one action to run in the environment (potentially in batch mode).
+
+ When the model uses a history of observations, or outputs a sequence of actions, this method deals
+ with caching.
+ """
+
+ def compute_loss(self, batch: dict[str, Tensor], **kwargs):
+ """Runs the batch through the model and computes the loss for training or validation."""
+
+ def update(self, batch, **kwargs):
+ """Does compute_loss then an optimization step.
+
+ TODO(alexander-soare): We will move the optimization step back into the training loop, so this will
+ disappear.
+ """