582 lines
26 KiB
Python
582 lines
26 KiB
Python
"""Action Chunking Transformer Policy
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As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://arxiv.org/abs/2304.13705).
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The majority of changes here involve removing unused code, unifying naming, and adding helpful comments.
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"""
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import math
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from collections import deque
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from itertools import chain
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from typing import Callable
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import einops
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import numpy as np
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import torch
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import torch.nn.functional as F # noqa: N812
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import torchvision
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from huggingface_hub import PyTorchModelHubMixin
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from torch import Tensor, nn
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from torchvision.models._utils import IntermediateLayerGetter
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from torchvision.ops.misc import FrozenBatchNorm2d
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from lerobot.common.policies.act.configuration_act import ACTConfig
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from lerobot.common.policies.normalize import Normalize, Unnormalize
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class ACTPolicy(nn.Module, PyTorchModelHubMixin):
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"""
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Action Chunking Transformer Policy as per Learning Fine-Grained Bimanual Manipulation with Low-Cost
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Hardware (paper: https://arxiv.org/abs/2304.13705, code: https://github.com/tonyzhaozh/act)
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"""
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name = "act"
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def __init__(
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self,
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config: ACTConfig | None = None,
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dataset_stats: dict[str, dict[str, Tensor]] | None = None,
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):
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"""
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Args:
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config: Policy configuration class instance or None, in which case the default instantiation of
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the configuration class is used.
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dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
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that they will be passed with a call to `load_state_dict` before the policy is used.
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"""
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super().__init__()
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if config is None:
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config = ACTConfig()
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self.config = config
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self.normalize_inputs = Normalize(
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config.input_shapes, config.input_normalization_modes, dataset_stats
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)
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self.normalize_targets = Normalize(
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config.output_shapes, config.output_normalization_modes, dataset_stats
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)
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self.unnormalize_outputs = Unnormalize(
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config.output_shapes, config.output_normalization_modes, dataset_stats
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)
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self.model = ACT(config)
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def reset(self):
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"""This should be called whenever the environment is reset."""
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if self.config.n_action_steps is not None:
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self._action_queue = deque([], maxlen=self.config.n_action_steps)
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@torch.no_grad
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def select_action(self, batch: dict[str, Tensor]) -> Tensor:
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"""Select a single action given environment observations.
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This method wraps `select_actions` in order to return one action at a time for execution in the
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environment. It works by managing the actions in a queue and only calling `select_actions` when the
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queue is empty.
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"""
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assert "observation.images.top" in batch
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assert "observation.state" in batch
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self.eval()
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batch = self.normalize_inputs(batch)
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self._stack_images(batch)
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if len(self._action_queue) == 0:
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# `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
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# effectively has shape (n_action_steps, batch_size, *), hence the transpose.
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actions = self.model(batch)[0][: self.config.n_action_steps]
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# TODO(rcadene): make _forward return output dictionary?
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actions = self.unnormalize_outputs({"action": actions})["action"]
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self._action_queue.extend(actions.transpose(0, 1))
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return self._action_queue.popleft()
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def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
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"""Run the batch through the model and compute the loss for training or validation."""
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batch = self.normalize_inputs(batch)
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batch = self.normalize_targets(batch)
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self._stack_images(batch)
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actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
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l1_loss = (
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F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
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).mean()
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loss_dict = {"l1_loss": l1_loss.item()}
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if self.config.use_vae:
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# Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
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# each dimension independently, we sum over the latent dimension to get the total
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# KL-divergence per batch element, then take the mean over the batch.
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# (See App. B of https://arxiv.org/abs/1312.6114 for more details).
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mean_kld = (
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(-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
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)
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loss_dict["kld_loss"] = mean_kld.item()
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loss_dict["loss"] = l1_loss + mean_kld * self.config.kl_weight
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else:
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loss_dict["loss"] = l1_loss
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return loss_dict
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def _stack_images(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
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"""Stacks all the images in a batch and puts them in a new key: "observation.images".
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This function expects `batch` to have (at least):
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{
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"observation.state": (B, state_dim) batch of robot states.
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"observation.images.{name}": (B, C, H, W) tensor of images.
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}
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"""
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# Stack images in the order dictated by input_shapes.
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batch["observation.images"] = torch.stack(
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[batch[k] for k in self.config.input_shapes if k.startswith("observation.images.")],
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dim=-4,
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)
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class ACT(nn.Module):
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"""Action Chunking Transformer: The underlying neural network for ACTPolicy.
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Note: In this code we use the terms `vae_encoder`, 'encoder', `decoder`. The meanings are as follows.
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- The `vae_encoder` is, as per the literature around variational auto-encoders (VAE), the part of the
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model that encodes the target data (a sequence of actions), and the condition (the robot
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joint-space).
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- A transformer with an `encoder` (not the VAE encoder) and `decoder` (not the VAE decoder) with
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cross-attention is used as the VAE decoder. For these terms, we drop the `vae_` prefix because we
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have an option to train this model without the variational objective (in which case we drop the
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`vae_encoder` altogether, and nothing about this model has anything to do with a VAE).
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Transformer
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Used alone for inference
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(acts as VAE decoder
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during training)
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┌───────────────────────┐
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│ Outputs │
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│ ▲ │
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│ ┌─────►┌───────┐ │
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┌──────┐ │ │ │Transf.│ │
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│ │ │ ├─────►│decoder│ │
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┌────┴────┐ │ │ │ │ │ │
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│ │ │ │ ┌───┴───┬─►│ │ │
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│ VAE │ │ │ │ │ └───────┘ │
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│ encoder │ │ │ │Transf.│ │
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│ │ │ │ │encoder│ │
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└───▲─────┘ │ │ │ │ │
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│ │ │ └───▲───┘ │
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│ │ │ │ │
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inputs └─────┼─────┘ │
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│ │
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└───────────────────────┘
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"""
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def __init__(self, config: ACTConfig):
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super().__init__()
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self.config = config
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# BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
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# The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
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if self.config.use_vae:
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self.vae_encoder = ACTEncoder(config)
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self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model)
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# Projection layer for joint-space configuration to hidden dimension.
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self.vae_encoder_robot_state_input_proj = nn.Linear(
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config.input_shapes["observation.state"][0], config.dim_model
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)
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# Projection layer for action (joint-space target) to hidden dimension.
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self.vae_encoder_action_input_proj = nn.Linear(
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config.input_shapes["observation.state"][0], config.dim_model
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)
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self.latent_dim = config.latent_dim
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# Projection layer from the VAE encoder's output to the latent distribution's parameter space.
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self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, self.latent_dim * 2)
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# Fixed sinusoidal positional embedding the whole input to the VAE encoder. Unsqueeze for batch
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# dimension.
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self.register_buffer(
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"vae_encoder_pos_enc",
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create_sinusoidal_pos_embedding(1 + 1 + config.chunk_size, config.dim_model).unsqueeze(0),
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)
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# Backbone for image feature extraction.
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backbone_model = getattr(torchvision.models, config.vision_backbone)(
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replace_stride_with_dilation=[False, False, config.replace_final_stride_with_dilation],
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weights=config.pretrained_backbone_weights,
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norm_layer=FrozenBatchNorm2d,
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)
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# Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final feature
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# map).
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# Note: The forward method of this returns a dict: {"feature_map": output}.
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self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
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# Transformer (acts as VAE decoder when training with the variational objective).
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self.encoder = ACTEncoder(config)
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self.decoder = ACTDecoder(config)
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# Transformer encoder input projections. The tokens will be structured like
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# [latent, robot_state, image_feature_map_pixels].
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self.encoder_robot_state_input_proj = nn.Linear(
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config.input_shapes["observation.state"][0], config.dim_model
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)
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self.encoder_latent_input_proj = nn.Linear(self.latent_dim, config.dim_model)
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self.encoder_img_feat_input_proj = nn.Conv2d(
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backbone_model.fc.in_features, config.dim_model, kernel_size=1
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)
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# Transformer encoder positional embeddings.
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self.encoder_robot_and_latent_pos_embed = nn.Embedding(2, config.dim_model)
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self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
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# Transformer decoder.
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# Learnable positional embedding for the transformer's decoder (in the style of DETR object queries).
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self.decoder_pos_embed = nn.Embedding(config.chunk_size, config.dim_model)
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# Final action regression head on the output of the transformer's decoder.
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self.action_head = nn.Linear(config.dim_model, config.output_shapes["action"][0])
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self._reset_parameters()
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def _reset_parameters(self):
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"""Xavier-uniform initialization of the transformer parameters as in the original code."""
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for p in chain(self.encoder.parameters(), self.decoder.parameters()):
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]:
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"""A forward pass through the Action Chunking Transformer (with optional VAE encoder).
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`batch` should have the following structure:
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{
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"observation.state": (B, state_dim) batch of robot states.
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"observation.images": (B, n_cameras, C, H, W) batch of images.
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"action" (optional, only if training with VAE): (B, chunk_size, action dim) batch of actions.
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}
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Returns:
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(B, chunk_size, action_dim) batch of action sequences
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Tuple containing the latent PDF's parameters (mean, log(σ²)) both as (B, L) tensors where L is the
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latent dimension.
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"""
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if self.config.use_vae and self.training:
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assert (
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"action" in batch
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), "actions must be provided when using the variational objective in training mode."
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batch_size = batch["observation.state"].shape[0]
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# Prepare the latent for input to the transformer encoder.
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if self.config.use_vae and "action" in batch:
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# Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence].
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cls_embed = einops.repeat(
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self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
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) # (B, 1, D)
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robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]).unsqueeze(
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1
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) # (B, 1, D)
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action_embed = self.vae_encoder_action_input_proj(batch["action"]) # (B, S, D)
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vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1) # (B, S+2, D)
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# Prepare fixed positional embedding.
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# Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
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pos_embed = self.vae_encoder_pos_enc.clone().detach() # (1, S+2, D)
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# Forward pass through VAE encoder to get the latent PDF parameters.
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cls_token_out = self.vae_encoder(
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vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
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)[0] # select the class token, with shape (B, D)
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latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
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mu = latent_pdf_params[:, : self.latent_dim]
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# This is 2log(sigma). Done this way to match the original implementation.
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log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
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# Sample the latent with the reparameterization trick.
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latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
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else:
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# When not using the VAE encoder, we set the latent to be all zeros.
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mu = log_sigma_x2 = None
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latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
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batch["observation.state"].device
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)
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# Prepare all other transformer encoder inputs.
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# Camera observation features and positional embeddings.
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all_cam_features = []
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all_cam_pos_embeds = []
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images = batch["observation.images"]
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for cam_index in range(images.shape[-4]):
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cam_features = self.backbone(images[:, cam_index])["feature_map"]
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cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
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cam_features = self.encoder_img_feat_input_proj(cam_features) # (B, C, h, w)
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all_cam_features.append(cam_features)
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all_cam_pos_embeds.append(cam_pos_embed)
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# Concatenate camera observation feature maps and positional embeddings along the width dimension.
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encoder_in = torch.cat(all_cam_features, axis=3)
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cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=3)
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# Get positional embeddings for robot state and latent.
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robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])
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latent_embed = self.encoder_latent_input_proj(latent_sample)
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# Stack encoder input and positional embeddings moving to (S, B, C).
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encoder_in = torch.cat(
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[
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torch.stack([latent_embed, robot_state_embed], axis=0),
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encoder_in.flatten(2).permute(2, 0, 1),
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]
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)
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pos_embed = torch.cat(
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[
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self.encoder_robot_and_latent_pos_embed.weight.unsqueeze(1),
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cam_pos_embed.flatten(2).permute(2, 0, 1),
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],
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axis=0,
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)
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# Forward pass through the transformer modules.
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encoder_out = self.encoder(encoder_in, pos_embed=pos_embed)
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decoder_in = torch.zeros(
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(self.config.chunk_size, batch_size, self.config.dim_model),
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dtype=pos_embed.dtype,
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device=pos_embed.device,
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)
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decoder_out = self.decoder(
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decoder_in,
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encoder_out,
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encoder_pos_embed=pos_embed,
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decoder_pos_embed=self.decoder_pos_embed.weight.unsqueeze(1),
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)
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# Move back to (B, S, C).
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decoder_out = decoder_out.transpose(0, 1)
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actions = self.action_head(decoder_out)
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return actions, (mu, log_sigma_x2)
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class ACTEncoder(nn.Module):
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"""Convenience module for running multiple encoder layers, maybe followed by normalization."""
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def __init__(self, config: ACTConfig):
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super().__init__()
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self.layers = nn.ModuleList([ACTEncoderLayer(config) for _ in range(config.n_encoder_layers)])
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self.norm = nn.LayerNorm(config.dim_model) if config.pre_norm else nn.Identity()
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def forward(self, x: Tensor, pos_embed: Tensor | None = None) -> Tensor:
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for layer in self.layers:
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x = layer(x, pos_embed=pos_embed)
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x = self.norm(x)
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return x
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class ACTEncoderLayer(nn.Module):
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def __init__(self, config: ACTConfig):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
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# Feed forward layers.
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self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward)
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self.dropout = nn.Dropout(config.dropout)
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self.linear2 = nn.Linear(config.dim_feedforward, config.dim_model)
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self.norm1 = nn.LayerNorm(config.dim_model)
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self.norm2 = nn.LayerNorm(config.dim_model)
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self.dropout1 = nn.Dropout(config.dropout)
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self.dropout2 = nn.Dropout(config.dropout)
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self.activation = get_activation_fn(config.feedforward_activation)
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self.pre_norm = config.pre_norm
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def forward(self, x, pos_embed: Tensor | None = None) -> Tensor:
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skip = x
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if self.pre_norm:
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x = self.norm1(x)
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q = k = x if pos_embed is None else x + pos_embed
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x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights
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x = skip + self.dropout1(x)
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if self.pre_norm:
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skip = x
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x = self.norm2(x)
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else:
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x = self.norm1(x)
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skip = x
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x = self.linear2(self.dropout(self.activation(self.linear1(x))))
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x = skip + self.dropout2(x)
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if not self.pre_norm:
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x = self.norm2(x)
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return x
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class ACTDecoder(nn.Module):
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def __init__(self, config: ACTConfig):
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"""Convenience module for running multiple decoder layers followed by normalization."""
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super().__init__()
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self.layers = nn.ModuleList([ACTDecoderLayer(config) for _ in range(config.n_decoder_layers)])
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self.norm = nn.LayerNorm(config.dim_model)
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def forward(
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self,
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x: Tensor,
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encoder_out: Tensor,
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decoder_pos_embed: Tensor | None = None,
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encoder_pos_embed: Tensor | None = None,
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) -> Tensor:
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for layer in self.layers:
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x = layer(
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x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
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)
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if self.norm is not None:
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x = self.norm(x)
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return x
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class ACTDecoderLayer(nn.Module):
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def __init__(self, config: ACTConfig):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
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self.multihead_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout)
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|
|
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# Feed forward layers.
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self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward)
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|
self.dropout = nn.Dropout(config.dropout)
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|
self.linear2 = nn.Linear(config.dim_feedforward, config.dim_model)
|
|
|
|
self.norm1 = nn.LayerNorm(config.dim_model)
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|
self.norm2 = nn.LayerNorm(config.dim_model)
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self.norm3 = nn.LayerNorm(config.dim_model)
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|
self.dropout1 = nn.Dropout(config.dropout)
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|
self.dropout2 = nn.Dropout(config.dropout)
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|
self.dropout3 = nn.Dropout(config.dropout)
|
|
|
|
self.activation = get_activation_fn(config.feedforward_activation)
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|
self.pre_norm = config.pre_norm
|
|
|
|
def maybe_add_pos_embed(self, tensor: Tensor, pos_embed: Tensor | None) -> Tensor:
|
|
return tensor if pos_embed is None else tensor + pos_embed
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
encoder_out: Tensor,
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|
decoder_pos_embed: Tensor | None = None,
|
|
encoder_pos_embed: Tensor | None = None,
|
|
) -> Tensor:
|
|
"""
|
|
Args:
|
|
x: (Decoder Sequence, Batch, Channel) tensor of input tokens.
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|
encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are
|
|
cross-attending with.
|
|
decoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder).
|
|
encoder_pos_embed: (DS, 1, C) Positional_embedding for the queries (from the decoder).
|
|
Returns:
|
|
(DS, B, C) tensor of decoder output features.
|
|
"""
|
|
skip = x
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|
if self.pre_norm:
|
|
x = self.norm1(x)
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|
q = k = self.maybe_add_pos_embed(x, decoder_pos_embed)
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|
x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights
|
|
x = skip + self.dropout1(x)
|
|
if self.pre_norm:
|
|
skip = x
|
|
x = self.norm2(x)
|
|
else:
|
|
x = self.norm1(x)
|
|
skip = x
|
|
x = self.multihead_attn(
|
|
query=self.maybe_add_pos_embed(x, decoder_pos_embed),
|
|
key=self.maybe_add_pos_embed(encoder_out, encoder_pos_embed),
|
|
value=encoder_out,
|
|
)[0] # select just the output, not the attention weights
|
|
x = skip + self.dropout2(x)
|
|
if self.pre_norm:
|
|
skip = x
|
|
x = self.norm3(x)
|
|
else:
|
|
x = self.norm2(x)
|
|
skip = x
|
|
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
|
|
x = skip + self.dropout3(x)
|
|
if not self.pre_norm:
|
|
x = self.norm3(x)
|
|
return x
|
|
|
|
|
|
def create_sinusoidal_pos_embedding(num_positions: int, dimension: int) -> Tensor:
|
|
"""1D sinusoidal positional embeddings as in Attention is All You Need.
|
|
|
|
Args:
|
|
num_positions: Number of token positions required.
|
|
Returns: (num_positions, dimension) position embeddings (the first dimension is the batch dimension).
|
|
|
|
"""
|
|
|
|
def get_position_angle_vec(position):
|
|
return [position / np.power(10000, 2 * (hid_j // 2) / dimension) for hid_j in range(dimension)]
|
|
|
|
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(num_positions)])
|
|
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
|
|
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
|
|
return torch.from_numpy(sinusoid_table).float()
|
|
|
|
|
|
class ACTSinusoidalPositionEmbedding2d(nn.Module):
|
|
"""2D sinusoidal positional embeddings similar to what's presented in Attention Is All You Need.
|
|
|
|
The variation is that the position indices are normalized in [0, 2π] (not quite: the lower bound is 1/H
|
|
for the vertical direction, and 1/W for the horizontal direction.
|
|
"""
|
|
|
|
def __init__(self, dimension: int):
|
|
"""
|
|
Args:
|
|
dimension: The desired dimension of the embeddings.
|
|
"""
|
|
super().__init__()
|
|
self.dimension = dimension
|
|
self._two_pi = 2 * math.pi
|
|
self._eps = 1e-6
|
|
# Inverse "common ratio" for the geometric progression in sinusoid frequencies.
|
|
self._temperature = 10000
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
"""
|
|
Args:
|
|
x: A (B, C, H, W) batch of 2D feature map to generate the embeddings for.
|
|
Returns:
|
|
A (1, C, H, W) batch of corresponding sinusoidal positional embeddings.
|
|
"""
|
|
not_mask = torch.ones_like(x[0, :1]) # (1, H, W)
|
|
# Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations
|
|
# they would be range(0, H) and range(0, W). Keeping it at as is to match the original code.
|
|
y_range = not_mask.cumsum(1, dtype=torch.float32)
|
|
x_range = not_mask.cumsum(2, dtype=torch.float32)
|
|
|
|
# "Normalize" the position index such that it ranges in [0, 2π].
|
|
# Note: Adding epsilon on the denominator should not be needed as all values of y_embed and x_range
|
|
# are non-zero by construction. This is an artifact of the original code.
|
|
y_range = y_range / (y_range[:, -1:, :] + self._eps) * self._two_pi
|
|
x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi
|
|
|
|
inverse_frequency = self._temperature ** (
|
|
2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension
|
|
)
|
|
|
|
x_range = x_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1)
|
|
y_range = y_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1)
|
|
|
|
# Note: this stack then flatten operation results in interleaved sine and cosine terms.
|
|
# pos_embed_x and pos_embed_y are (1, H, W, C // 2).
|
|
pos_embed_x = torch.stack((x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1).flatten(3)
|
|
pos_embed_y = torch.stack((y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1).flatten(3)
|
|
pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(0, 3, 1, 2) # (1, C, H, W)
|
|
|
|
return pos_embed
|
|
|
|
|
|
def get_activation_fn(activation: str) -> Callable:
|
|
"""Return an activation function given a string."""
|
|
if activation == "relu":
|
|
return F.relu
|
|
if activation == "gelu":
|
|
return F.gelu
|
|
if activation == "glu":
|
|
return F.glu
|
|
raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.")
|