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AdilZouitine
2025-04-25 17:22:02 +02:00
parent 1d4f660075
commit 50e9a8ed6a
3 changed files with 24 additions and 63 deletions
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12
lerobot/common/datasets/utils.py
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@@ -225,10 +225,7 @@ def load_episodes(local_dir: Path) -> dict:
def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path): def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path):
# We wrap episode_stats in a dictionary since `episode_stats["episode_index"]` # We wrap episode_stats in a dictionary since `episode_stats["episode_index"]`
# is a dictionary of stats and not an integer. # is a dictionary of stats and not an integer.
episode_stats = { episode_stats = {"episode_index": episode_index, "stats": serialize_dict(episode_stats)}
"episode_index": episode_index,
"stats": serialize_dict(episode_stats),
}
append_jsonlines(episode_stats, local_dir / EPISODES_STATS_PATH) append_jsonlines(episode_stats, local_dir / EPISODES_STATS_PATH)
@@ -412,7 +409,7 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
names = ft["names"] names = ft["names"]
# Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets. # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
if names is not None and names[2] in ["channel", "channels"]: # (h, w, c) -> (c, h, w) if names[2] in ["channel", "channels"]: # (h, w, c) -> (c, h, w)
shape = (shape[2], shape[0], shape[1]) shape = (shape[2], shape[0], shape[1])
elif key == "observation.environment_state": elif key == "observation.environment_state":
type = FeatureType.ENV type = FeatureType.ENV
@@ -543,10 +540,7 @@ def check_timestamps_sync(
def check_delta_timestamps( def check_delta_timestamps(
delta_timestamps: dict[str, list[float]], delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True
fps: int,
tolerance_s: float,
raise_value_error: bool = True,
) -> bool: ) -> bool:
"""This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance. """This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance.
This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be

55
lerobot/common/policies/normalize.py
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@@ -79,46 +79,28 @@ def create_stats_buffers(
) )
# TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch) # TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch)
if stats and key in stats: if stats:
if norm_mode is NormalizationMode.MEAN_STD: if isinstance(stats[key]["mean"], np.ndarray):
if "mean" not in stats[key] or "std" not in stats[key]: if norm_mode is NormalizationMode.MEAN_STD:
raise ValueError(
f"Missing 'mean' or 'std' in stats for key {key} with MEAN_STD normalization"
)
if isinstance(stats[key]["mean"], np.ndarray):
buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32) buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32)
buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32) buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32)
elif isinstance(stats[key]["mean"], torch.Tensor): elif norm_mode is NormalizationMode.MIN_MAX:
# Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
# tensors anywhere (for example, when we use the same stats for normalization and
# unnormalization). See the logic here
# https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32)
buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
else:
type_ = type(stats[key]["mean"])
raise ValueError(
f"np.ndarray or torch.Tensor expected for 'mean', but type is '{type_}' instead."
)
elif norm_mode is NormalizationMode.MIN_MAX:
if "min" not in stats[key] or "max" not in stats[key]:
raise ValueError(
f"Missing 'min' or 'max' in stats for key {key} with MIN_MAX normalization"
)
if isinstance(stats[key]["min"], np.ndarray):
buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32) buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32)
buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32) buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32)
elif isinstance(stats[key]["min"], torch.Tensor): elif isinstance(stats[key]["mean"], torch.Tensor):
# Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
# tensors anywhere (for example, when we use the same stats for normalization and
# unnormalization). See the logic here
# https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
if norm_mode is NormalizationMode.MEAN_STD:
buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32)
buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
elif norm_mode is NormalizationMode.MIN_MAX:
buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32) buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32)
buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32) buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
else: else:
type_ = type(stats[key]["min"]) type_ = type(stats[key]["mean"])
raise ValueError( raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.")
f"np.ndarray or torch.Tensor expected for 'min', but type is '{type_}' instead."
)
stats_buffers[key] = buffer stats_buffers[key] = buffer
return stats_buffers return stats_buffers
@@ -166,14 +148,11 @@ class Normalize(nn.Module):
for key, buffer in stats_buffers.items(): for key, buffer in stats_buffers.items():
setattr(self, "buffer_" + key.replace(".", "_"), buffer) setattr(self, "buffer_" + key.replace(".", "_"), buffer)
# TODO(rcadene): should we remove torch.no_grad?
# @torch.no_grad
def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
batch = dict(batch) # shallow copy avoids mutating the input batch batch = dict(batch) # shallow copy avoids mutating the input batch
for key, ft in self.features.items(): for key, ft in self.features.items():
if key not in batch: if key not in batch:
# FIXME(aliberts, rcadene): This might lead to silent fail! # FIXME(aliberts, rcadene): This might lead to silent fail!
# NOTE: (azouitine) This continues help us for instantiation SACPolicy
continue continue
norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
@@ -241,8 +220,6 @@ class Unnormalize(nn.Module):
for key, buffer in stats_buffers.items(): for key, buffer in stats_buffers.items():
setattr(self, "buffer_" + key.replace(".", "_"), buffer) setattr(self, "buffer_" + key.replace(".", "_"), buffer)
# TODO(rcadene): should we remove torch.no_grad?
# @torch.no_grad
def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
batch = dict(batch) # shallow copy avoids mutating the input batch batch = dict(batch) # shallow copy avoids mutating the input batch
for key, ft in self.features.items(): for key, ft in self.features.items():

20
lerobot/common/robot_devices/robots/manipulator.py
View File

@@ -28,22 +28,14 @@ import numpy as np
import torch import torch
from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
from lerobot.common.robot_devices.motors.utils import ( from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
MotorsBus,
make_motors_buses_from_configs,
)
from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig
from lerobot.common.robot_devices.robots.utils import get_arm_id from lerobot.common.robot_devices.robots.utils import get_arm_id
from lerobot.common.robot_devices.utils import ( from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
RobotDeviceAlreadyConnectedError,
RobotDeviceNotConnectedError,
)
def ensure_safe_goal_position( def ensure_safe_goal_position(
goal_pos: torch.Tensor, goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float]
present_pos: torch.Tensor,
max_relative_target: float | list[float],
): ):
# Cap relative action target magnitude for safety. # Cap relative action target magnitude for safety.
diff = goal_pos - present_pos diff = goal_pos - present_pos
@@ -53,7 +45,7 @@ def ensure_safe_goal_position(
safe_goal_pos = present_pos + safe_diff safe_goal_pos = present_pos + safe_diff
if not torch.allclose(goal_pos, safe_goal_pos): if not torch.allclose(goal_pos, safe_goal_pos):
logging.debug( logging.warning(
"Relative goal position magnitude had to be clamped to be safe.\n" "Relative goal position magnitude had to be clamped to be safe.\n"
f" requested relative goal position target: {diff}\n" f" requested relative goal position target: {diff}\n"
f" clamped relative goal position target: {safe_diff}" f" clamped relative goal position target: {safe_diff}"
@@ -317,9 +309,7 @@ class ManipulatorRobot:
print(f"Missing calibration file '{arm_calib_path}'") print(f"Missing calibration file '{arm_calib_path}'")
if self.robot_type in ["koch", "koch_bimanual", "aloha"]: if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
from lerobot.common.robot_devices.robots.dynamixel_calibration import ( from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration
run_arm_calibration,
)
calibration = run_arm_calibration(arm, self.robot_type, name, arm_type) calibration = run_arm_calibration(arm, self.robot_type, name, arm_type)