code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def reward_track_body_position_extended(
self,
body_state: BodyState,
ref_motion_state: ReferenceMotionState,
**kwargs,
) -> torch.Tensor:
"""
Computes a reward based on the difference between the body's extended position and the reference motion's
extended po... |
Computes a reward based on the difference between the body's extended position and the reference motion's
extended position.
This function is rewritten from _reward_teleop_body_position_extend of legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) represe... | reward_track_body_position_extended | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def reward_track_body_position_vr_key_points(
self,
body_state: BodyState,
ref_motion_state: ReferenceMotionState,
**kwargs,
) -> torch.Tensor:
"""
Computes a reward based on the difference between selected key points of the body's extended position
and the re... |
Computes a reward based on the difference between selected key points of the body's extended position
and the reference motion's extended position.
This function is rewritten from _reward_teleop_body_position_vr_3keypoints of legged_gym.
Returns:
torch.Tensor: A float tens... | reward_track_body_position_vr_key_points | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def reward_track_body_rotation(
self,
body_state: BodyState,
ref_motion_state: ReferenceMotionState,
**kwargs,
) -> torch.Tensor:
"""
Computes a reward based on the difference between the body's rotation and the reference motion's rotation.
This function is r... |
Computes a reward based on the difference between the body's rotation and the reference motion's rotation.
This function is rewritten from _reward_teleop_body_rotation of legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed reward for eac... | reward_track_body_rotation | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_torques(
self,
articulation_data: ArticulationData,
**kwargs,
) -> torch.Tensor:
"""
Computes the penalty on applied torques to minimize energy consumption.
This function is adapted from _reward_torques in legged_gym.
Returns:
torch.... |
Computes the penalty on applied torques to minimize energy consumption.
This function is adapted from _reward_torques in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_torques | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_joint_accelerations(
self,
articulation_data: ArticulationData,
**kwargs,
) -> torch.Tensor:
"""
Computes the penalty on joint acceleration of each motor.
This function is adapted from _reward_dof_acc in legged_gym.
Returns:
torch.Te... |
Computes the penalty on joint acceleration of each motor.
This function is adapted from _reward_dof_acc in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_joint_accelerations | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_joint_velocities(
self,
articulation_data: ArticulationData,
**kwargs,
) -> torch.Tensor:
"""
Computes the penalty on joint velocity of each motor.
This function is adapted from _reward_dof_vel in legged_gym.
Returns:
torch.Tensor: A... |
Computes the penalty on joint velocity of each motor.
This function is adapted from _reward_dof_vel in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_joint_velocities | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_lower_body_action_changes(
self,
previous_actions: torch.Tensor,
actions: torch.Tensor,
**kwargs,
) -> torch.Tensor:
"""
Computes the penalty for action changes in the lower body.
This function is adapted from _reward_lower_action_rate in legged_... |
Computes the penalty for action changes in the lower body.
This function is adapted from _reward_lower_action_rate in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_lower_body_action_changes | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_upper_body_action_changes(
self,
previous_actions: torch.Tensor,
actions: torch.Tensor,
**kwargs,
) -> torch.Tensor:
"""
Computes the penalty for action changes in the upper body.
This function is adapted from _reward_upper_action_rate in legged_... |
Computes the penalty for action changes in the upper body.
This function is adapted from _reward_upper_action_rate in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_upper_body_action_changes | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_early_termination(self, reset_buf: torch.Tensor, timeout_buf: torch.Tensor, **kwargs):
"""
Computes the penalty for episodes that terminate before timeout.
This function is adapted from `_reward_termination` in `legged_gym`.
Returns:
torch.Tensor: A tensor of s... |
Computes the penalty for episodes that terminate before timeout.
This function is adapted from `_reward_termination` in `legged_gym`.
Returns:
torch.Tensor: A tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_early_termination | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_stumble(self, **kwargs):
"""
Computes the penalty for stumbling.
This function is adapted from _reward_stumble in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
"""
f... |
Computes the penalty for stumbling.
This function is adapted from _reward_stumble in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_stumble | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_slippage(self, body_state: BodyState, **kwargs):
"""
Computes the penalty for slippage.
This function is adapted from _reward_slippage in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environmen... |
Computes the penalty for slippage.
This function is adapted from _reward_slippage in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_slippage | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_feet_orientation(self, body_state: BodyState, **kwargs):
"""
Computes the penalty on feet orientation to make no x and y projected gravity.
This function is adapted from _reward_feet_ori in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) r... |
Computes the penalty on feet orientation to make no x and y projected gravity.
This function is adapted from _reward_feet_ori in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_feet_orientation | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_feet_air_time(self, ref_motion_state: ReferenceMotionState, **kwargs):
"""
Computes the penalty for the time that the feet are in the air before the newest contact with the terrain.
This function is adapted from _reward_feet_air_time_teleop in legged_gym.
Returns:
... |
Computes the penalty for the time that the feet are in the air before the newest contact with the terrain.
This function is adapted from _reward_feet_air_time_teleop in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each... | penalize_feet_air_time | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_both_feet_in_air(self, **kwargs):
"""
Computes the penalty for both feet being in the air.
This function is adapted from _reward_in_the_air in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each envir... |
Computes the penalty for both feet being in the air.
This function is adapted from _reward_in_the_air in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_both_feet_in_air | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_orientation(self, articulation_data: ArticulationData, **kwarg):
"""
Computes the penalty based on a non-flat base orientation.
This function is adapted from _reward_orientation in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representi... |
Computes the penalty based on a non-flat base orientation.
This function is adapted from _reward_orientation in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for each environment.
| penalize_orientation | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def penalize_max_feet_height_before_contact(self, body_state: BodyState, **kwargs):
"""
Computes the penalty based on the maximum height of the feet in the air before the current contact.
This function is adapted from _reward_feet_max_height_for_this_air in legged_gym.
Returns:
... |
Computes the penalty based on the maximum height of the feet in the air before the current contact.
This function is adapted from _reward_feet_max_height_for_this_air in legged_gym.
Returns:
torch.Tensor: A float tensor of shape (num_envs) representing the computed penalty for eac... | penalize_max_feet_height_before_contact | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/rewards/rewards.py | Apache-2.0 |
def deep_compare_dicts(dict1, dict2):
"""Recursively compare two dictionaries, including torch tensors."""
if dict1.keys() != dict2.keys():
return False
for key in dict1:
value1 = dict1[key]
value2 = dict2[key]
if isinstance(value1, dict) and isinstance(value2, dict):
... | Recursively compare two dictionaries, including torch tensors. | deep_compare_dicts | python | NVlabs/HOVER | neural_wbc/isaac_lab_wrapper/tests/test_neural_wbc_env_cfg.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/isaac_lab_wrapper/tests/test_neural_wbc_env_cfg.py | Apache-2.0 |
def position_pd_control(env: NeuralWBCEnv, pos_actions: torch.Tensor, joint_ids=None):
"""Calculates the PD control torque based on the network output position actions"""
robot = env.robot
joint_pos = robot.joint_positions
joint_vel = robot.joint_velocities
if joint_ids:
joint_pos = joint_p... | Calculates the PD control torque based on the network output position actions | position_pd_control | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/control.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/control.py | Apache-2.0 |
def update(self, obs_dict: dict[str, torch.Tensor | list[str] | None]) -> None:
"""Update the underlying model based on the observations from the environment/real robot.
Args:
obs_dict (dict[str, torch.Tensor]): A dictionary containing the latest robot observations.
"""
if "... | Update the underlying model based on the observations from the environment/real robot.
Args:
obs_dict (dict[str, torch.Tensor]): A dictionary containing the latest robot observations.
| update | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_robot.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_robot.py | Apache-2.0 |
def reset(self, **kwargs) -> None:
"""Resets the wrapper
Args:
kwargs (dict[str, Any], optional): key-word arguments to pass to underlying models. Defaults to None.
"""
qpos = kwargs.get("qpos")
qvel = kwargs.get("qvel")
self._sim.reset(qpos=qpos, qvel=qvel)
... | Resets the wrapper
Args:
kwargs (dict[str, Any], optional): key-word arguments to pass to underlying models. Defaults to None.
| reset | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_robot.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_robot.py | Apache-2.0 |
def __init__(
self,
model_path: str,
sim_dt: float = 0.005,
enable_viewer: bool = False,
num_instances: int = 1,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
) -> None:
"""Initialize the underlying mujoco simulator
Args:
... | Initialize the underlying mujoco simulator
Args:
model_path: Path to the Mujoco model xml file
sim_dt: Simulation timestep
enable_viewer: Whether to enable the viewer
num_instances: Number of instances to simulate
device: torch device to use for the t... | __init__ | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def joint_names(self) -> list[str]:
"""Get the names of all joints in the model except the free floating joint.
Returns:
list[str]: List of joint names
"""
offset = 0
if self.has_free_joint:
offset = 1 # base/world joint
return [get_entity_name(s... | Get the names of all joints in the model except the free floating joint.
Returns:
list[str]: List of joint names
| joint_names | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def joint_positions(self) -> torch.Tensor:
"""Get the joint positions of the robot as tensor
Returns:
torch.Tensor: Tensor of joint positions
"""
return (
torch.from_numpy(self._data.qpos[self.joint_pos_offset :].copy())
.to(dtype=torch.float32, devic... | Get the joint positions of the robot as tensor
Returns:
torch.Tensor: Tensor of joint positions
| joint_positions | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def joint_velocities(self) -> torch.Tensor:
"""Get the joint velocities of the robot as tensor
Returns:
torch.Tensor: Tensor of joint velocities
"""
return (
torch.from_numpy(self._data.qvel[self.joint_vel_offset :].copy())
.to(dtype=torch.float32, de... | Get the joint velocities of the robot as tensor
Returns:
torch.Tensor: Tensor of joint velocities
| joint_velocities | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def body_positions(self) -> torch.Tensor:
"""Get the body positions of the robot as tensor
Returns:
torch.Tensor: Tensor of body positions
"""
# NOTE: Global frame, https://mujoco.readthedocs.io/en/stable/APIreference/APItypes.html
# Get the body positions, excluding... | Get the body positions of the robot as tensor
Returns:
torch.Tensor: Tensor of body positions
| body_positions | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def body_rotations(self) -> torch.Tensor:
"""Get the body rotations of the robot as tensor
Returns:
torch.Tensor: Tensor of body rotations
"""
# NOTE: Global frame, https://mujoco.readthedocs.io/en/stable/APIreference/APItypes.html
# Get the body positions, excluding... | Get the body rotations of the robot as tensor
Returns:
torch.Tensor: Tensor of body rotations
| body_rotations | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def body_velocities(self) -> tuple[torch.Tensor, torch.Tensor]:
"""Get the body linear and angular velocities of the robot as a pair of tensors
Returns:
tuple[torch.Tensor, torch.Tensor]: Tuple of linear and angular body velocities
"""
linear_velocities = torch.zeros(self.nu... | Get the body linear and angular velocities of the robot as a pair of tensors
Returns:
tuple[torch.Tensor, torch.Tensor]: Tuple of linear and angular body velocities
| body_velocities | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def step(self, actions: np.ndarray | None = None, nsteps: int = 1) -> None:
"""Step the simulation forward nsteps with the given action.
Args:
actions (np.ndarray | None, optional): Action to apply to the robot. Defaults to None.
nsteps (int, optional): Number of steps to take. ... | Step the simulation forward nsteps with the given action.
Args:
actions (np.ndarray | None, optional): Action to apply to the robot. Defaults to None.
nsteps (int, optional): Number of steps to take. Defaults to 1.
| step | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def reset(
self,
qpos: np.ndarray | torch.Tensor | None = None,
qvel: np.ndarray | torch.Tensor | None = None,
) -> None:
"""Reset the model to its initial state
Args:
qpos (np.ndarray | torch.Tensor | None, optional): Positions of the generalized coordinates. De... | Reset the model to its initial state
Args:
qpos (np.ndarray | torch.Tensor | None, optional): Positions of the generalized coordinates. Defaults to None.
qvel (np.ndarray | torch.Tensor | None, optional): Velocities of the generalized coordinates. Defaults to None.
| reset | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def set_robot_state(
self, qpos: np.ndarray | torch.Tensor | None = None, qvel: np.ndarray | torch.Tensor | None = None
):
"""
Set robot state including positions and velocities of the generalized coordinates.
Args:
qpos (np.ndarray | torch.Tensor | None, optional): Posi... |
Set robot state including positions and velocities of the generalized coordinates.
Args:
qpos (np.ndarray | torch.Tensor | None, optional): Positions of the generalized coordinates. Defaults to None.
qvel (np.ndarray | torch.Tensor | None, optional): Velocities of the generaliz... | set_robot_state | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_body_ids(self, body_names: list[str] | None = None, free_joint_offset: int = 1) -> dict[str, int]:
"""Get the IDs of all bodies in the model, indexed after removing the world body.
Args:
body_names (list[str] | None, optional): Names of the bodies. Defaults to None.
free... | Get the IDs of all bodies in the model, indexed after removing the world body.
Args:
body_names (list[str] | None, optional): Names of the bodies. Defaults to None.
free_joint_offset (int, optional): Offset to remove the free joint. Defaults to 1.
Returns:
dict[str,... | get_body_ids | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_joint_ids(self, joint_names: list[str] | None = None, free_joint_offset: int = 1) -> dict[str, int]:
"""Get the IDs of all joints in the model, indexed after removing the free joint.
Args:
joint_names (list[str] | None, optional): Names of the joints. Defaults to None.
f... | Get the IDs of all joints in the model, indexed after removing the free joint.
Args:
joint_names (list[str] | None, optional): Names of the joints. Defaults to None.
free_joint_offset (int, optional): Offset to remove the free joint. Defaults to 1.
Returns:
dict[str... | get_joint_ids | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_body_pose(self, body_name: str = "pelvis") -> tuple[torch.Tensor, torch.Tensor]:
"""Get the position and quaternion of the base
Args:
body_name (str, optional): Name of the body. Defaults to 'pelvis'.
Returns:
tuple[torch.Tensor, torch.Tensor]: Position and quat... | Get the position and quaternion of the base
Args:
body_name (str, optional): Name of the body. Defaults to 'pelvis'.
Returns:
tuple[torch.Tensor, torch.Tensor]: Position and quaternion of the base
| get_body_pose | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_base_projected_gravity(self, base_name: str = "pelvis") -> torch.Tensor:
"""Get the projection of the gravity vector to the base frame
Args:
base_name (str, optional): Name of the base. Defaults to 'pelvis'.
Returns:
torch.Tensor: Projection of the gravity vecto... | Get the projection of the gravity vector to the base frame
Args:
base_name (str, optional): Name of the base. Defaults to 'pelvis'.
Returns:
torch.Tensor: Projection of the gravity vector to the base frame
| get_base_projected_gravity | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_contact_forces_with_floor(self, body_name: str) -> torch.Tensor:
"""Get the contact forces on a given body with the floor
Args:
body_name (str): Name of the body
Returns:
torch.Tensor: Contact forces, shape (num_envs, 3), i.e. normal and two tangent directions
... | Get the contact forces on a given body with the floor
Args:
body_name (str): Name of the body
Returns:
torch.Tensor: Contact forces, shape (num_envs, 3), i.e. normal and two tangent directions
Notes:
Only checks contacts with the floor, and thus assumes the... | get_contact_forces_with_floor | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def print_actuator_info(self, actuators_id: list[int] | None = None):
"""Utility function to print out actuator types in the model.
Args:
actuators_id (list[int] | None, optional): Actuator ids. Defaults to None.
"""
actuators_id_ = range(self.model.nu) if actuators_id is No... | Utility function to print out actuator types in the model.
Args:
actuators_id (list[int] | None, optional): Actuator ids. Defaults to None.
| print_actuator_info | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def _check_actuator_consistency(self):
"""Check whether all the actuators share the same control mode."""
actuator_type_system = None
for actuator_id in range(self.model.nu):
actuator_type = self.model.actuator_trntype[actuator_id]
if actuator_type_system is None:
... | Check whether all the actuators share the same control mode. | _check_actuator_consistency | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_simulator.py | Apache-2.0 |
def get_entity_name(model: mj.MjModel, entity_type: str, entity_id: int) -> str:
"""Gets name of an entity based on ID
Args:
model (mj.MjModel): model
entity_type (str): entity type
entity_id (int): entity id
Returns:
str: entity name
"""
if entity_type == "body":
... | Gets name of an entity based on ID
Args:
model (mj.MjModel): model
entity_type (str): entity type
entity_id (int): entity id
Returns:
str: entity name
| get_entity_name | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_utils.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/mujoco_utils.py | Apache-2.0 |
def to_numpy(x):
"""
Check if input is a PyTorch tensor and convert to numpy array if true.
Otherwise return the input unchanged.
Args:
x: Input to check and potentially convert
Returns:
numpy array if input was torch tensor, otherwise original input
"""
if isinstance(x, to... |
Check if input is a PyTorch tensor and convert to numpy array if true.
Otherwise return the input unchanged.
Args:
x: Input to check and potentially convert
Returns:
numpy array if input was torch tensor, otherwise original input
| to_numpy | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/utils.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/utils.py | Apache-2.0 |
def squeeze_if_tensor(x, dim: int = 0):
"""
Check if input is a PyTorch tensor and squeeze along the given dim if true.
Args:
x: Input to check and potentially convert
dim: Dimension to squeeze
Returns:
numpy array if input was torch tensor, otherwise original input
"""
... |
Check if input is a PyTorch tensor and squeeze along the given dim if true.
Args:
x: Input to check and potentially convert
dim: Dimension to squeeze
Returns:
numpy array if input was torch tensor, otherwise original input
| squeeze_if_tensor | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/utils.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/utils.py | Apache-2.0 |
def draw_reference_state(self, state: ReferenceMotionState):
"""Visualize the reference state in Mujoco."""
body_pos_np = np.squeeze(state.body_pos.detach().cpu().numpy())
body_pos_extend_np = np.squeeze(state.body_pos_extend.detach().cpu().numpy())
body_pos = np.vstack([body_pos_np, bo... | Visualize the reference state in Mujoco. | draw_reference_state | python | NVlabs/HOVER | neural_wbc/mujoco_wrapper/mujoco_wrapper/visualization.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/mujoco_wrapper/mujoco_wrapper/visualization.py | Apache-2.0 |
def _produce_actions(self, obs: dict) -> torch.Tensor:
"""
Roll out the environment with either the expert policy or the student policy, depending on
the current state of training.
"""
if self._iterations + self.start_iteration >= self._cfg.student_rollout_iteration:
... |
Roll out the environment with either the expert policy or the student policy, depending on
the current state of training.
| _produce_actions | python | NVlabs/HOVER | neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer.py | Apache-2.0 |
def save(self, file_path: str):
"""
Save the dataclass fields to a JSON file.
Args:
file_path (str): The path to the file where the JSON will be saved.
"""
# Convert the dataclass to a dictionary
data_dict = {
field_info.name: getattr(self, field_... |
Save the dataclass fields to a JSON file.
Args:
file_path (str): The path to the file where the JSON will be saved.
| save | python | NVlabs/HOVER | neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer_cfg.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer_cfg.py | Apache-2.0 |
def add_args_to_parser(parser: argparse.ArgumentParser, default_overwrites: dict = {}):
"""
Add the fields of the dataclass (except for `teacher_policy`) to an ArgumentParser.
This method iterates over the fields of the StudentPolicyTrainerCfg dataclass and adds them as arguments
to the... |
Add the fields of the dataclass (except for `teacher_policy`) to an ArgumentParser.
This method iterates over the fields of the StudentPolicyTrainerCfg dataclass and adds them as arguments
to the provided ArgumentParser. The `teacher_policy` field is skipped. If a field has a default value or
... | add_args_to_parser | python | NVlabs/HOVER | neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer_cfg.py | https://github.com/NVlabs/HOVER/blob/master/neural_wbc/student_policy/neural_wbc/student_policy/student_policy_trainer_cfg.py | Apache-2.0 |
def load(filepath: str) -> "TeacherPolicyCfg":
"""Loads the configuration from a YAML file."""
with open(filepath, encoding="utf-8") as file:
data = json.load(file)
cfg = fromdict(TeacherPolicyCfg, data)
return cfg | Loads the configuration from a YAML file. | load | python | NVlabs/HOVER | scripts/rsl_rl/teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/teacher_policy_cfg.py | Apache-2.0 |
def add_args_to_parser(parser: argparse.ArgumentParser, default_overwrites: dict = {}):
"""Adds configuration fields to an ArgumentParser."""
group = parser.add_argument_group("Teacher policy configurations (RSL RL)")
def add_fields_to_parser(fields):
for field_info in fields:
... | Adds configuration fields to an ArgumentParser. | add_args_to_parser | python | NVlabs/HOVER | scripts/rsl_rl/teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/teacher_policy_cfg.py | Apache-2.0 |
def from_argparse_args(args: argparse.Namespace) -> "TeacherPolicyCfg":
"""Creates an instance from argparse arguments."""
def extract_fields(cls: Type) -> dict:
"""Helper function to extract fields for a given dataclass type."""
extracted_fields = {
field.name: ... | Creates an instance from argparse arguments. | from_argparse_args | python | NVlabs/HOVER | scripts/rsl_rl/teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/teacher_policy_cfg.py | Apache-2.0 |
def extract_fields(cls: Type) -> dict:
"""Helper function to extract fields for a given dataclass type."""
extracted_fields = {
field.name: getattr(args, TeacherPolicyCfg.args_prefix() + field.name)
for field in fields(cls)
if hasattr(args, Teacher... | Helper function to extract fields for a given dataclass type. | extract_fields | python | NVlabs/HOVER | scripts/rsl_rl/teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/teacher_policy_cfg.py | Apache-2.0 |
def resolve_student_policy_path(root_path: str, teacher_policy: NeuralWBCTeacherPolicy, append_timestamp: bool):
"""
Generates a path for storing student policy configurations based on the root path and teacher policy.
This function appends a timestamp and the teacher policy name to the given root path to ... |
Generates a path for storing student policy configurations based on the root path and teacher policy.
This function appends a timestamp and the teacher policy name to the given root path to create a unique path
for storing student policy configurations.
Args:
root_path (str): The root directo... | resolve_student_policy_path | python | NVlabs/HOVER | scripts/rsl_rl/train_student_policy.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/train_student_policy.py | Apache-2.0 |
def get_config_values_from_argparser(args: argparse.Namespace, teacher_policy: NeuralWBCTeacherPolicy):
"""
Extracts configuration values from command-line arguments based on a predefined prefix and field names.
This function processes the command-line arguments, filters out those that match a specified pr... |
Extracts configuration values from command-line arguments based on a predefined prefix and field names.
This function processes the command-line arguments, filters out those that match a specified prefix,
and returns a dictionary of configuration values that are relevant to the StudentPolicyTrainerCfg dat... | get_config_values_from_argparser | python | NVlabs/HOVER | scripts/rsl_rl/train_student_policy.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/train_student_policy.py | Apache-2.0 |
def get_student_trainer_cfg(args: argparse.Namespace, env: NeuralWBCEnv, teacher_policy: NeuralWBCTeacherPolicy):
"""
Create an instance of StudentPolicyTrainerCfg from command-line arguments, environment configuration, and a teacher policy.
This function processes the command-line arguments, extracts nece... |
Create an instance of StudentPolicyTrainerCfg from command-line arguments, environment configuration, and a teacher policy.
This function processes the command-line arguments, extracts necessary values, fills in any missing values using
the environment configuration, and creates an instance of the Student... | get_student_trainer_cfg | python | NVlabs/HOVER | scripts/rsl_rl/train_student_policy.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/train_student_policy.py | Apache-2.0 |
def load_student_policy_trainer_cfg(args: argparse.Namespace, teacher_policy: NeuralWBCTeacherPolicy):
"""
Loads the student policy trainer configuration from a specified resume path.
This function checks if a resume path is provided in the command-line arguments. If so, it loads the configuration
from... |
Loads the student policy trainer configuration from a specified resume path.
This function checks if a resume path is provided in the command-line arguments. If so, it loads the configuration
from a config.json at the resume path, updates it with any new arguments provided, and returns an instance of
... | load_student_policy_trainer_cfg | python | NVlabs/HOVER | scripts/rsl_rl/train_student_policy.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/train_student_policy.py | Apache-2.0 |
def get_customized_rsl_rl():
"""Helper function to ensure the correct version of rsl_rl is imported.
This function does the following:
1. Gets the installed rsl_rl package location and adds it to sys.path
2. Removes any existing rsl_rl and submodules from sys.modules to force reimporting
"""
im... | Helper function to ensure the correct version of rsl_rl is imported.
This function does the following:
1. Gets the installed rsl_rl package location and adds it to sys.path
2. Removes any existing rsl_rl and submodules from sys.modules to force reimporting
| get_customized_rsl_rl | python | NVlabs/HOVER | scripts/rsl_rl/utils.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/utils.py | Apache-2.0 |
def __init__(self, env: NeuralWBCEnv):
"""Initializes the wrapper.
Note:
The wrapper calls :meth:`reset` at the start since the RSL-RL runner does not call reset.
Args:
env: The environment to wrap around.
"""
super().__init__(mode=env.cfg.mode)
... | Initializes the wrapper.
Note:
The wrapper calls :meth:`reset` at the start since the RSL-RL runner does not call reset.
Args:
env: The environment to wrap around.
| __init__ | python | NVlabs/HOVER | scripts/rsl_rl/vecenv_wrapper.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/vecenv_wrapper.py | Apache-2.0 |
def test_save_and_load(self):
"""Test saving and loading the configuration to/from a file."""
# Create a temporary file to save the configuration
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
file_path = tmp_file.name
# Save the configuration to the file
... | Test saving and loading the configuration to/from a file. | test_save_and_load | python | NVlabs/HOVER | scripts/rsl_rl/tests/test_teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/tests/test_teacher_policy_cfg.py | Apache-2.0 |
def test_add_args_to_parser(self):
"""Test adding configuration fields to an argument parser."""
parser = argparse.ArgumentParser(description="RSL-RL configuration")
TeacherPolicyCfg.add_args_to_parser(parser)
# Create sample arguments
args = [
"--teacher_policy.seed... | Test adding configuration fields to an argument parser. | test_add_args_to_parser | python | NVlabs/HOVER | scripts/rsl_rl/tests/test_teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/tests/test_teacher_policy_cfg.py | Apache-2.0 |
def test_default_values(self):
"""Test the default values of the configuration."""
# Verify default values
self.assertEqual(self.config.seed, 1)
self.assertEqual(self.config.policy.init_noise_std, 1.0)
self.assertEqual(self.config.policy.actor_hidden_dims, [512, 256, 128])
... | Test the default values of the configuration. | test_default_values | python | NVlabs/HOVER | scripts/rsl_rl/tests/test_teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/tests/test_teacher_policy_cfg.py | Apache-2.0 |
def test_add_args_to_parser_with_default_overwrites(self):
"""Test adding configuration fields to an argument parser with default overwrites."""
parser = argparse.ArgumentParser(description="RSL-RL configuration")
default_overwrites = {
"seed": 10,
"init_noise_std": 0.5,
... | Test adding configuration fields to an argument parser with default overwrites. | test_add_args_to_parser_with_default_overwrites | python | NVlabs/HOVER | scripts/rsl_rl/tests/test_teacher_policy_cfg.py | https://github.com/NVlabs/HOVER/blob/master/scripts/rsl_rl/tests/test_teacher_policy_cfg.py | Apache-2.0 |
def format_seconds_to_human_readable(self, total_seconds):
"""Formats seconds into a human readable string with hours, minutes and seconds.
Args:
total_seconds (float): Number of seconds to format
Returns:
str: Formatted string in the format "Xh, Ym, Zs" where X=hours, ... | Formats seconds into a human readable string with hours, minutes and seconds.
Args:
total_seconds (float): Number of seconds to format
Returns:
str: Formatted string in the format "Xh, Ym, Zs" where X=hours, Y=minutes, Z=seconds
| format_seconds_to_human_readable | python | NVlabs/HOVER | third_party/rsl_rl/rsl_rl/runners/on_policy_runner.py | https://github.com/NVlabs/HOVER/blob/master/third_party/rsl_rl/rsl_rl/runners/on_policy_runner.py | Apache-2.0 |
def split_and_pad_trajectories(tensor, dones):
""" Splits trajectories at done indices. Then concatenates them and padds with zeros up to the length og the longest trajectory.
Returns masks corresponding to valid parts of the trajectories
Example:
Input: [ [a1, a2, a3, a4 | a5, a6],
... | Splits trajectories at done indices. Then concatenates them and padds with zeros up to the length og the longest trajectory.
Returns masks corresponding to valid parts of the trajectories
Example:
Input: [ [a1, a2, a3, a4 | a5, a6],
[b1, b2 | b3, b4, b5 | b6]
]
... | split_and_pad_trajectories | python | NVlabs/HOVER | third_party/rsl_rl/rsl_rl/utils/utils.py | https://github.com/NVlabs/HOVER/blob/master/third_party/rsl_rl/rsl_rl/utils/utils.py | Apache-2.0 |
def setup(self) -> None:
"""Load the model into memory to make running multiple predictions efficient"""
# if checkpoints folder does not exist, create it
if not os.path.exists(MODEL_CACHE):
download(MODEL_URL, MODEL_CACHE)
disable_verbosity()
cv2.setNumThrea... | Load the model into memory to make running multiple predictions efficient | setup | python | ali-vilab/AnyDoor | predict.py | https://github.com/ali-vilab/AnyDoor/blob/master/predict.py | MIT |
def predict(
self,
reference_image_path: Path = Input(description="Source Image"),
reference_image_mask: Path = Input(description="Source Image"),
bg_image_path: Path = Input(description="Target Image"),
bg_mask_path: Path = Input(description="Target Image mask"),
control... | Run a single prediction on the model | predict | python | ali-vilab/AnyDoor | predict.py | https://github.com/ali-vilab/AnyDoor/blob/master/predict.py | MIT |
def mask_score(mask):
'''Scoring the mask according to connectivity.'''
mask = mask.astype(np.uint8)
if mask.sum() < 10:
return 0
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnt_area = [cv2.contourArea(cnt) for cnt in contours]
conc_score = np.max(cnt_a... | Scoring the mask according to connectivity. | mask_score | python | ali-vilab/AnyDoor | datasets/data_utils.py | https://github.com/ali-vilab/AnyDoor/blob/master/datasets/data_utils.py | MIT |
def resize_and_pad(image, box):
'''Fitting an image to the box region while keeping the aspect ratio.'''
y1,y2,x1,x2 = box
H,W = y2-y1, x2-x1
h,w = image.shape[0], image.shape[1]
r_box = W / H
r_image = w / h
if r_box >= r_image:
h_target = H
w_target = int(w * H / h)
... | Fitting an image to the box region while keeping the aspect ratio. | resize_and_pad | python | ali-vilab/AnyDoor | datasets/data_utils.py | https://github.com/ali-vilab/AnyDoor/blob/master/datasets/data_utils.py | MIT |
def q_x(x_0,t=65):
'''Adding noise for and given image.'''
x_0 = torch.from_numpy(x_0).float() / 127.5 - 1
num_steps = 100
betas = torch.linspace(-6,6,num_steps)
betas = torch.sigmoid(betas)*(0.5e-2 - 1e-5)+1e-5
alphas = 1-betas
alphas_prod = torch.cumprod(alphas,0)
alphas_pro... | Adding noise for and given image. | q_x | python | ali-vilab/AnyDoor | datasets/data_utils.py | https://github.com/ali-vilab/AnyDoor/blob/master/datasets/data_utils.py | MIT |
def make_dataset(
*,
dataset_str: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
):
"""
Creates a dataset with the specified parameters.
Args:
dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).
transform: A transfo... |
Creates a dataset with the specified parameters.
Args:
dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).
transform: A transform to apply to images.
target_transform: A transform to apply to targets.
Returns:
The created dataset.
| make_dataset | python | ali-vilab/AnyDoor | dinov2/dinov2/data/loaders.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/data/loaders.py | MIT |
def make_data_loader(
*,
dataset,
batch_size: int,
num_workers: int,
shuffle: bool = True,
seed: int = 0,
sampler_type: Optional[SamplerType] = SamplerType.INFINITE,
sampler_size: int = -1,
sampler_advance: int = 0,
drop_last: bool = True,
persistent_workers: bool = False,
... |
Creates a data loader with the specified parameters.
Args:
dataset: A dataset (third party, LaViDa or WebDataset).
batch_size: The size of batches to generate.
num_workers: The number of workers to use.
shuffle: Whether to shuffle samples.
seed: The random seed to use.
... | make_data_loader | python | ali-vilab/AnyDoor | dinov2/dinov2/data/loaders.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/data/loaders.py | MIT |
def _generate_randperm_indices(*, size: int, generator: torch.Generator):
"""Generate the indices of a random permutation."""
dtype = _get_torch_dtype(size)
# This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#... | Generate the indices of a random permutation. | _generate_randperm_indices | python | ali-vilab/AnyDoor | dinov2/dinov2/data/samplers.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/data/samplers.py | MIT |
def __call__(self, pic):
"""
Args:
pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if isinstance(pic, torch.Tensor):
return pic
return super().__call__(pic) |
Args:
pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor.
Returns:
Tensor: Converted image.
| __call__ | python | ali-vilab/AnyDoor | dinov2/dinov2/data/transforms.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/data/transforms.py | MIT |
def get_local_rank() -> int:
"""
Returns:
The rank of the current process within the local (per-machine) process group.
"""
if not is_enabled():
return 0
assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
return _LOCAL_RANK |
Returns:
The rank of the current process within the local (per-machine) process group.
| get_local_rank | python | ali-vilab/AnyDoor | dinov2/dinov2/distributed/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/distributed/__init__.py | MIT |
def get_local_size() -> int:
"""
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
"""
if not is_enabled():
return 1
assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
return _LOCAL_WORLD_SIZE |
Returns:
The size of the per-machine process group,
i.e. the number of processes per machine.
| get_local_size | python | ali-vilab/AnyDoor | dinov2/dinov2/distributed/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/distributed/__init__.py | MIT |
def _restrict_print_to_main_process() -> None:
"""
This function disables printing when not in the main process
"""
import builtins as __builtin__
builtin_print = __builtin__.print
def print(*args, **kwargs):
force = kwargs.pop("force", False)
if is_main_process() or force:
... |
This function disables printing when not in the main process
| _restrict_print_to_main_process | python | ali-vilab/AnyDoor | dinov2/dinov2/distributed/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/distributed/__init__.py | MIT |
def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False):
"""Enable distributed mode
Args:
set_cuda_current_device: If True, call torch.cuda.set_device() to set the
current PyTorch CUDA device to the one matching the local rank.
... | Enable distributed mode
Args:
set_cuda_current_device: If True, call torch.cuda.set_device() to set the
current PyTorch CUDA device to the one matching the local rank.
overwrite: If True, overwrites already set variables. Else fails.
| enable | python | ali-vilab/AnyDoor | dinov2/dinov2/distributed/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/distributed/__init__.py | MIT |
def forward(self, features_rank):
"""
Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k`
"""
assert all(k <= self.max_k for k in self.nb_knn)
topk_sims, neighbors_labels = self.compute_neighbors(features_rank)
batch_size = neighbors_l... |
Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k`
| forward | python | ali-vilab/AnyDoor | dinov2/dinov2/eval/knn.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/eval/knn.py | MIT |
def eval_log_regression(
*,
model,
train_dataset,
val_dataset,
finetune_dataset,
metric_type,
batch_size,
num_workers,
finetune_on_val=False,
train_dtype=torch.float64,
train_features_device=_CPU_DEVICE,
max_train_iters=DEFAULT_MAX_ITER,
):
"""
Implements the "sta... |
Implements the "standard" process for log regression evaluation:
The value of C is chosen by training on train_dataset and evaluating on
finetune_dataset. Then, the final model is trained on a concatenation of
train_dataset and finetune_dataset, and is evaluated on val_dataset.
If there is no finet... | eval_log_regression | python | ali-vilab/AnyDoor | dinov2/dinov2/eval/log_regression.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/eval/log_regression.py | MIT |
def save(self, name: str, **kwargs: Any) -> None:
"""
Dump model and checkpointables to a file.
Args:
name (str): name of the file.
kwargs (dict): extra arbitrary data to save.
"""
if not self.save_dir or not self.save_to_disk:
return
... |
Dump model and checkpointables to a file.
Args:
name (str): name of the file.
kwargs (dict): extra arbitrary data to save.
| save | python | ali-vilab/AnyDoor | dinov2/dinov2/fsdp/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/fsdp/__init__.py | MIT |
def get_checkpoint_file(self) -> str:
"""
Returns:
str: The latest checkpoint file in target directory.
"""
save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
try:
with self.path_manager.open(save_file, "r") as f:
last_... |
Returns:
str: The latest checkpoint file in target directory.
| get_checkpoint_file | python | ali-vilab/AnyDoor | dinov2/dinov2/fsdp/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/fsdp/__init__.py | MIT |
def tag_last_checkpoint(self, last_filename_basename: str) -> None:
"""
Tag the last checkpoint.
Args:
last_filename_basename (str): the basename of the last filename.
"""
if distributed.is_enabled():
torch.distributed.barrier()
save_file = os.pat... |
Tag the last checkpoint.
Args:
last_filename_basename (str): the basename of the last filename.
| tag_last_checkpoint | python | ali-vilab/AnyDoor | dinov2/dinov2/fsdp/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/fsdp/__init__.py | MIT |
def get_attn_bias_and_cat(x_list, branges=None):
"""
this will perform the index select, cat the tensors, and provide the attn_bias from cache
"""
batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
all_shapes = tuple((b, x.shape[1]) for b, x in zip(b... |
this will perform the index select, cat the tensors, and provide the attn_bias from cache
| get_attn_bias_and_cat | python | ali-vilab/AnyDoor | dinov2/dinov2/layers/block.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/layers/block.py | MIT |
def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
"""
x_list contains a list of tensors to nest together and run
"""
assert isinstance(self.attn, MemEffAttention)
if self.training and self.sample_drop_ratio > 0.0:
def attn_residual_func(x: Tensor, attn... |
x_list contains a list of tensors to nest together and run
| forward_nested | python | ali-vilab/AnyDoor | dinov2/dinov2/layers/block.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/layers/block.py | MIT |
def synchronize_between_processes(self):
"""
Distributed synchronization of the metric
Warning: does not synchronize the deque!
"""
if not distributed.is_enabled():
return
t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
t... |
Distributed synchronization of the metric
Warning: does not synchronize the deque!
| synchronize_between_processes | python | ali-vilab/AnyDoor | dinov2/dinov2/logging/helpers.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/logging/helpers.py | MIT |
def _configure_logger(
name: Optional[str] = None,
*,
level: int = logging.DEBUG,
output: Optional[str] = None,
):
"""
Configure a logger.
Adapted from Detectron2.
Args:
name: The name of the logger to configure.
level: The logging level to use.
output: A file n... |
Configure a logger.
Adapted from Detectron2.
Args:
name: The name of the logger to configure.
level: The logging level to use.
output: A file name or a directory to save log. If None, will not save log file.
If ends with ".txt" or ".log", assumed to be a file name.
... | _configure_logger | python | ali-vilab/AnyDoor | dinov2/dinov2/logging/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/logging/__init__.py | MIT |
def setup_logging(
output: Optional[str] = None,
*,
name: Optional[str] = None,
level: int = logging.DEBUG,
capture_warnings: bool = True,
) -> None:
"""
Setup logging.
Args:
output: A file name or a directory to save log files. If None, log
files will not be saved. ... |
Setup logging.
Args:
output: A file name or a directory to save log files. If None, log
files will not be saved. If output ends with ".txt" or ".log", it
is assumed to be a file name.
Otherwise, logs will be saved to `output/log.txt`.
name: The name of the l... | setup_logging | python | ali-vilab/AnyDoor | dinov2/dinov2/logging/__init__.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/logging/__init__.py | MIT |
def forward(self, student_output_list, teacher_out_softmaxed_centered_list):
"""
Cross-entropy between softmax outputs of the teacher and student networks.
"""
# TODO: Use cross_entropy_distribution here
total_loss = 0
for s in student_output_list:
lsm = F.log... |
Cross-entropy between softmax outputs of the teacher and student networks.
| forward | python | ali-vilab/AnyDoor | dinov2/dinov2/loss/dino_clstoken_loss.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/loss/dino_clstoken_loss.py | MIT |
def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat):
"""
Cross-entropy between softmax outputs of the teacher and student networks.
student_patch_tokens: (B, N, D) tensor
teacher_patch_tokens: (B, N, D) tensor
student_masks_flat: (B, N) tensor
... |
Cross-entropy between softmax outputs of the teacher and student networks.
student_patch_tokens: (B, N, D) tensor
teacher_patch_tokens: (B, N, D) tensor
student_masks_flat: (B, N) tensor
| forward | python | ali-vilab/AnyDoor | dinov2/dinov2/loss/ibot_patch_loss.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/loss/ibot_patch_loss.py | MIT |
def pairwise_NNs_inner(self, x):
"""
Pairwise nearest neighbors for L2-normalized vectors.
Uses Torch rather than Faiss to remain on GPU.
"""
# parwise dot products (= inverse distance)
dots = torch.mm(x, x.t())
n = x.shape[0]
dots.view(-1)[:: (n + 1)].fil... |
Pairwise nearest neighbors for L2-normalized vectors.
Uses Torch rather than Faiss to remain on GPU.
| pairwise_NNs_inner | python | ali-vilab/AnyDoor | dinov2/dinov2/loss/koleo_loss.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/loss/koleo_loss.py | MIT |
def forward(self, student_output, eps=1e-8):
"""
Args:
student_output (BxD): backbone output of student
"""
with torch.cuda.amp.autocast(enabled=False):
student_output = F.normalize(student_output, eps=eps, p=2, dim=-1)
I = self.pairwise_NNs_inner(stud... |
Args:
student_output (BxD): backbone output of student
| forward | python | ali-vilab/AnyDoor | dinov2/dinov2/loss/koleo_loss.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/loss/koleo_loss.py | MIT |
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
ffn_bias=True,
proj_bias=True,
drop_path_rate=0.0,
drop_path_uniform=False,
in... |
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
... | __init__ | python | ali-vilab/AnyDoor | dinov2/dinov2/models/vision_transformer.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/models/vision_transformer.py | MIT |
def init_weights_vit_timm(module: nn.Module, name: str = ""):
"""ViT weight initialization, original timm impl (for reproducibility)"""
if isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias) | ViT weight initialization, original timm impl (for reproducibility) | init_weights_vit_timm | python | ali-vilab/AnyDoor | dinov2/dinov2/models/vision_transformer.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/models/vision_transformer.py | MIT |
def vit_giant2(patch_size=16, **kwargs):
"""
Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
"""
model = DinoVisionTransformer(
patch_size=patch_size,
embed_dim=1536,
depth=40,
num_heads=24,
mlp_ratio=4,
block_fn=partial(Block... |
Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
| vit_giant2 | python | ali-vilab/AnyDoor | dinov2/dinov2/models/vision_transformer.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/models/vision_transformer.py | MIT |
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg_from_args(args)
os.makedirs(args.output_dir, exist_ok=True)
default_setup(args)
apply_scaling_rules_to_cfg(cfg)
write_config(cfg, args.output_dir)
return cfg |
Create configs and perform basic setups.
| setup | python | ali-vilab/AnyDoor | dinov2/dinov2/utils/config.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/utils/config.py | MIT |
def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False):
"""
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
lr_decay_rate (float): base lr decay rate.
num_layers (int): number of ViT bloc... |
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
lr_decay_rate (float): base lr decay rate.
num_layers (int): number of ViT blocks.
Returns:
lr decay rate for the given parameter.
| get_vit_lr_decay_rate | python | ali-vilab/AnyDoor | dinov2/dinov2/utils/param_groups.py | https://github.com/ali-vilab/AnyDoor/blob/master/dinov2/dinov2/utils/param_groups.py | MIT |
def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
ema_power=1., param_names=()):
"""AdamW that saves EMA versions of the param... | AdamW that saves EMA versions of the parameters. | __init__ | python | ali-vilab/AnyDoor | ldm/util.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/util.py | MIT |
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
... | Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | ali-vilab/AnyDoor | ldm/util.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/util.py | MIT |
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's... |
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
| q_mean_variance | python | ali-vilab/AnyDoor | ldm/models/diffusion/ddpm.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/ddpm.py | MIT |
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = ... |
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
| delta_border | python | ali-vilab/AnyDoor | ldm/models/diffusion/ddpm.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/ddpm.py | MIT |
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# number of crops in i... |
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
| get_fold_unfold | python | ali-vilab/AnyDoor | ldm/models/diffusion/ddpm.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/ddpm.py | MIT |
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in b... |
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
| _prior_bpd | python | ali-vilab/AnyDoor | ldm/models/diffusion/ddpm.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/ddpm.py | MIT |
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} ... | Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py | append_dims | python | ali-vilab/AnyDoor | ldm/models/diffusion/sampling_util.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/sampling_util.py | MIT |
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
):
"""Create a wrapper class for the forward SDE (VP type).
***
Update: We support discrete-time dif... | Create a wrapper class for the forward SDE (VP type).
***
Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution imag... | __init__ | python | ali-vilab/AnyDoor | ldm/models/diffusion/dpm_solver/dpm_solver.py | https://github.com/ali-vilab/AnyDoor/blob/master/ldm/models/diffusion/dpm_solver/dpm_solver.py | MIT |
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