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Refactor TD-MPC (#103)

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Co-authored-by: Cadene <re.cadene@gmail.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
This commit is contained in:
Alexander Soare
2024-05-01 16:40:04 +01:00
committed by GitHub
parent a4891095e4
commit d1855a202a
17 changed files with 1105 additions and 1205 deletions
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17
lerobot/common/policies/diffusion/configuration_diffusion.py
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@@ -3,7 +3,7 @@ from dataclasses import dataclass, field
@dataclass
class DiffusionConfig:
"""Configuration class for Diffusion Policy.
"""Configuration class for DiffusionPolicy.
Defaults are configured for training with PushT providing proprioceptive and single camera observations.
@@ -25,11 +25,12 @@ class DiffusionConfig:
The key represents the output data name, and the value is a list indicating the dimensions
of the corresponding data. For example, "action" refers to an output shape of [14], indicating
14-dimensional actions. Importantly, shapes doesnt include batch dimension or temporal dimension.
normalize_input_modes: A dictionary with key represents the modality (e.g. "observation.state"),
and the value specifies the normalization mode to apply. The two availables
modes are "mean_std" which substracts the mean and divide by the standard
deviation and "min_max" which rescale in a [-1, 1] range.
unnormalize_output_modes: Similar dictionary as `normalize_input_modes`, but to unormalize in original scale.
input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
and the value specifies the normalization mode to apply. The two available modes are "mean_std"
which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
[-1, 1] range.
output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
original scale. Note that this is also used for normalizing the training targets.
vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
within the image size. If None, no cropping is done.
@@ -70,13 +71,13 @@ class DiffusionConfig:
horizon: int = 16
n_action_steps: int = 8
input_shapes: dict[str, list[str]] = field(
input_shapes: dict[str, list[int]] = field(
default_factory=lambda: {
"observation.image": [3, 96, 96],
"observation.state": [2],
}
)
output_shapes: dict[str, list[str]] = field(
output_shapes: dict[str, list[int]] = field(
default_factory=lambda: {
"action": [2],
}

11
lerobot/common/policies/diffusion/modeling_diffusion.py
View File

@@ -43,15 +43,16 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
def __init__(
self,
config: DiffusionConfig | None = None,
dataset_stats=None,
dataset_stats: dict[str, dict[str, Tensor]] | None = None,
):
"""
Args:
config: Policy configuration class instance or None, in which case the default instantiation of
the configuration class is used.
the configuration class is used.
dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
that they will be passed with a call to `load_state_dict` before the policy is used.
"""
super().__init__()
# TODO(alexander-soare): LR scheduler will be removed.
if config is None:
config = DiffusionConfig()
self.config = config
@@ -88,7 +89,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
}
@torch.no_grad
def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:
def select_action(self, batch: dict[str, Tensor]) -> Tensor:
"""Select a single action given environment observations.
This method handles caching a history of observations and an action trajectory generated by the
@@ -136,7 +137,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
action = self._queues["action"].popleft()
return action
def forward(self, batch: dict[str, Tensor], **_) -> dict[str, Tensor]:
def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
"""Run the batch through the model and compute the loss for training or validation."""
batch = self.normalize_inputs(batch)
batch = self.normalize_targets(batch)