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Simplify configs (#550)

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Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: HUANG TZU-CHUN <137322177+tc-huang@users.noreply.github.com>
This commit is contained in:
Simon Alibert
2025-01-31 13:57:37 +01:00
committed by GitHub
parent 1ee1acf8ad
commit 3c0a209f9f
119 changed files with 5761 additions and 5466 deletions
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114
lerobot/common/policies/diffusion/configuration_diffusion.py
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@@ -16,9 +16,15 @@
# limitations under the License.
from dataclasses import dataclass, field
from lerobot.common.optim.optimizers import AdamConfig
from lerobot.common.optim.schedulers import DiffuserSchedulerConfig
from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.types import NormalizationMode
@PreTrainedConfig.register_subclass("diffusion")
@dataclass
class DiffusionConfig:
class DiffusionConfig(PreTrainedConfig):
"""Configuration class for DiffusionPolicy.
Defaults are configured for training with PushT providing proprioceptive and single camera observations.
@@ -102,26 +108,17 @@ class DiffusionConfig:
horizon: int = 16
n_action_steps: int = 8
input_shapes: dict[str, list[int]] = field(
normalization_mapping: dict[str, NormalizationMode] = field(
default_factory=lambda: {
"observation.image": [3, 96, 96],
"observation.state": [2],
}
)
output_shapes: dict[str, list[int]] = field(
default_factory=lambda: {
"action": [2],
"VISUAL": NormalizationMode.MEAN_STD,
"STATE": NormalizationMode.MIN_MAX,
"ACTION": NormalizationMode.MIN_MAX,
}
)
# Normalization / Unnormalization
input_normalization_modes: dict[str, str] = field(
default_factory=lambda: {
"observation.image": "mean_std",
"observation.state": "min_max",
}
)
output_normalization_modes: dict[str, str] = field(default_factory=lambda: {"action": "min_max"})
# The original implementation doesn't sample frames for the last 7 steps,
# which avoids excessive padding and leads to improved training results.
drop_n_last_frames: int = 7 # horizon - n_action_steps - n_obs_steps + 1
# Architecture / modeling.
# Vision backbone.
@@ -154,39 +151,23 @@ class DiffusionConfig:
# Loss computation
do_mask_loss_for_padding: bool = False
# Training presets
optimizer_lr: float = 1e-4
optimizer_betas: tuple = (0.95, 0.999)
optimizer_eps: float = 1e-8
optimizer_weight_decay: float = 1e-6
scheduler_name: str = "cosine"
scheduler_warmup_steps: int = 500
def __post_init__(self):
super().__post_init__()
"""Input validation (not exhaustive)."""
if not self.vision_backbone.startswith("resnet"):
raise ValueError(
f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
)
image_keys = {k for k in self.input_shapes if k.startswith("observation.image")}
if len(image_keys) == 0 and "observation.environment_state" not in self.input_shapes:
raise ValueError("You must provide at least one image or the environment state among the inputs.")
if len(image_keys) > 0:
if self.crop_shape is not None:
for image_key in image_keys:
if (
self.crop_shape[0] > self.input_shapes[image_key][1]
or self.crop_shape[1] > self.input_shapes[image_key][2]
):
raise ValueError(
f"`crop_shape` should fit within `input_shapes[{image_key}]`. Got {self.crop_shape} "
f"for `crop_shape` and {self.input_shapes[image_key]} for "
"`input_shapes[{image_key}]`."
)
# Check that all input images have the same shape.
first_image_key = next(iter(image_keys))
for image_key in image_keys:
if self.input_shapes[image_key] != self.input_shapes[first_image_key]:
raise ValueError(
f"`input_shapes[{image_key}]` does not match `input_shapes[{first_image_key}]`, but we "
"expect all image shapes to match."
)
supported_prediction_types = ["epsilon", "sample"]
if self.prediction_type not in supported_prediction_types:
raise ValueError(
@@ -207,3 +188,50 @@ class DiffusionConfig:
"The horizon should be an integer multiple of the downsampling factor (which is determined "
f"by `len(down_dims)`). Got {self.horizon=} and {self.down_dims=}"
)
def get_optimizer_preset(self) -> AdamConfig:
return AdamConfig(
lr=self.optimizer_lr,
betas=self.optimizer_betas,
eps=self.optimizer_eps,
weight_decay=self.optimizer_weight_decay,
)
def get_scheduler_preset(self) -> DiffuserSchedulerConfig:
return DiffuserSchedulerConfig(
name=self.scheduler_name,
num_warmup_steps=self.scheduler_warmup_steps,
)
def validate_features(self) -> None:
if len(self.image_features) == 0 and self.env_state_feature is None:
raise ValueError("You must provide at least one image or the environment state among the inputs.")
if self.crop_shape is not None:
for key, image_ft in self.image_features.items():
if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
raise ValueError(
f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
f"for `crop_shape` and {image_ft.shape} for "
f"`{key}`."
)
# Check that all input images have the same shape.
first_image_key, first_image_ft = next(iter(self.image_features.items()))
for key, image_ft in self.image_features.items():
if image_ft.shape != first_image_ft.shape:
raise ValueError(
f"`{key}` does not match `{first_image_key}`, but we " "expect all image shapes to match."
)
@property
def observation_delta_indices(self) -> list:
return list(range(1 - self.n_obs_steps, 1))
@property
def action_delta_indices(self) -> list:
return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.horizon))
@property
def reward_delta_indices(self) -> None:
return None