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fixed typo

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This commit is contained in:
Cheng Chi
2023-04-05 16:03:18 -04:00
parent 4eed454514
commit 27395b7500
29 changed files with 49 additions and 49 deletions
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4
diffusion_policy/common/pose_trajectory_interpolator.py
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@@ -28,7 +28,7 @@ class PoseTrajectoryInterpolator:
poses = np.array(poses)
if len(times) == 1:
# special treatement for single step interpolation
# special treatment for single step interpolation
self.single_step = True
self._times = times
self._poses = poses
@@ -69,7 +69,7 @@ class PoseTrajectoryInterpolator:
should_keep = (start_t < times) & (times < end_t)
keep_times = times[should_keep]
all_times = np.concatenate([[start_t], keep_times, [end_t]])
# remove duplicates, Slerp requries strictly increasing x
# remove duplicates, Slerp requires strictly increasing x
all_times = np.unique(all_times)
# interpolate
all_poses = self(all_times)

4
diffusion_policy/common/pymunk_override.py
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@@ -96,7 +96,7 @@ class DrawOptions(pymunk.SpaceDebugDrawOptions):
See pygame_util.demo.py for a full example
Since pygame uses a coordiante system where y points down (in contrast
Since pygame uses a coordinate system where y points down (in contrast
to many other cases), you either have to make the physics simulation
with Pymunk also behave in that way, or flip everything when you draw.
@@ -226,7 +226,7 @@ def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int
"""Convenience method to convert pymunk coordinates to pygame surface
local coordinates.
Note that in case positive_y_is_up is False, this function wont actually do
Note that in case positive_y_is_up is False, this function won't actually do
anything except converting the point to integers.
"""
if positive_y_is_up:

4
diffusion_policy/common/replay_buffer.py
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@@ -84,7 +84,7 @@ def get_optimal_chunks(shape, dtype,
class ReplayBuffer:
"""
Zarr-based temporal datastructure.
Assumes first dimention to be time. Only chunk in time dimension.
Assumes first dimension to be time. Only chunk in time dimension.
"""
def __init__(self,
root: Union[zarr.Group,
@@ -216,7 +216,7 @@ class ReplayBuffer:
Recommended
"""
if backend == 'numpy':
print('backend argument is depreacted!')
print('backend argument is deprecated!')
store = None
group = zarr.open(os.path.expanduser(zarr_path), 'r')
return cls.copy_from_store(src_store=group.store, store=store,

6
diffusion_policy/common/timestamp_accumulator.py
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@@ -13,7 +13,7 @@ def get_accumulate_timestamp_idxs(
) -> Tuple[List[int], List[int], int]:
"""
For each dt window, choose the first timestamp in the window.
Assumes timestamps sorted. One timestamp might be chosen mulitple times due to dropped frames.
Assumes timestamps sorted. One timestamp might be chosen multiple times due to dropped frames.
next_global_idx should start at 0 normally, and then use the returned next_global_idx.
However, when overwiting previous values are desired, set last_global_idx to None.
@@ -60,7 +60,7 @@ def align_timestamps(
allow_negative=True
)
if len(global_idxs) > len(target_global_idxs):
# if more steps avaliable, truncate
# if more steps available, truncate
global_idxs = global_idxs[:len(target_global_idxs)]
local_idxs = local_idxs[:len(target_global_idxs)]
@@ -216,7 +216,7 @@ class TimestampActionAccumulator:
self.action_buffer = np.resize(self.action_buffer, new_shape)
self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size,))
# potentally rewrite old data (as expected)
# potentially rewrite old data (as expected)
self.action_buffer[global_idxs] = actions[local_idxs]
self.timestamp_buffer[global_idxs] = timestamps[local_idxs]
self.size = max(self.size, this_max_size)

2
diffusion_policy/config/train_diffusion_unet_video_workspace.yaml
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@@ -39,7 +39,7 @@ policy:
norm_groups: 8
input_channel: 3
model_depth: 50 # ResNet 50 (18,34 not yet avaliable)
model_depth: 50 # ResNet 50 (18,34 not yet available)
pool:
_target_: diffusion_policy.model.ibc.global_avgpool.GlobalAvgpool

6
diffusion_policy/dataset/real_pusht_image_dataset.py
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@@ -84,7 +84,7 @@ class RealPushTImageDataset(BaseImageDataset):
if delta_action:
# replace action as relative to previous frame
actions = replay_buffer['action'][:]
# suport positions only at this time
# support positions only at this time
assert actions.shape[1] <= 3
actions_diff = np.zeros_like(actions)
episode_ends = replay_buffer.episode_ends[:]
@@ -93,8 +93,8 @@ class RealPushTImageDataset(BaseImageDataset):
if i > 0:
start = episode_ends[i-1]
end = episode_ends[i]
# delta action is the difference between previous desired postion and the current
# it should be scheduled at the previous timestep for the curren timestep
# delta action is the difference between previous desired position and the current
# it should be scheduled at the previous timestep for the current timestep
# to ensure consistency with positional mode
actions_diff[start+1:end] = np.diff(actions[start:end], axis=0)
replay_buffer['action'][:] = actions_diff

2
diffusion_policy/env/block_pushing/block_pushing.py vendored
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@@ -745,7 +745,7 @@ class BlockPush(gym.Env):
WARNING: py_environment wrapper assumes environments aren't reset in their
constructor and will often reset the environment unintentionally. It is
always recommeneded that you call env.reset on the tfagents wrapper before
always recommended that you call env.reset on the tfagents wrapper before
playback (replaying pybullet_state).
Args:

2
diffusion_policy/env/block_pushing/block_pushing_multimodal.py vendored
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@@ -615,7 +615,7 @@ class BlockPushMultimodal(block_pushing.BlockPush):
WARNING: py_environment wrapper assumes environments aren't reset in their
constructor and will often reset the environment unintentionally. It is
always recommeneded that you call env.reset on the tfagents wrapper before
always recommended that you call env.reset on the tfagents wrapper before
playback (replaying pybullet_state).
Args:

2
diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_robot.py vendored
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@@ -24,7 +24,7 @@ import click
from adept_envs import base_robot
from adept_envs.utils.config import (get_config_root_node, read_config_from_node)
# obervations structure
# observations structure
from collections import namedtuple
observation = namedtuple('observation', ['time', 'qpos_robot', 'qvel_robot', 'qpos_object', 'qvel_object'])

2
diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/robot_env.py vendored
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@@ -40,7 +40,7 @@ class RobotEnv(mujoco_env.MujocoEnv):
# Mapping of device path to the calibration file to use. If the device path
# is not found, the 'default' key is used.
# This can be overriden by subclasses.
# This can be overridden by subclasses.
CALIBRATION_PATHS = {}
def __init__(self,

2
diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/config.py vendored
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@@ -87,7 +87,7 @@ if __name__ == '__main__':
float))
print("data:type \t", read_config_from_node(root, "data", "type", str))
# read straight from xml (dum the XML data as duh.xml for this test)
# read straight from xml (dumb the XML data as duh.xml for this test)
root, root_name = get_config_root_node(config_file_name="duh.xml")
print("Read from xml --------------------------------")
print("limit:low \t", read_config_from_xml("duh.xml", "limits", "low",

4
diffusion_policy/env/kitchen/relay_policy_learning/third_party/franka/README.md vendored
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@@ -4,6 +4,6 @@ Franka panda mujoco models
# Environment
franka_panda.xml | comming soon
franka_panda.xml | coming soon
:-------------------------:|:-------------------------:
![Alt text](franka_panda.png?raw=false "sawyer") | comming soon
![Alt text](franka_panda.png?raw=false "sawyer") | coming soon

12
diffusion_policy/env/pusht/pusht_env.py vendored
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@@ -44,7 +44,7 @@ class PushTEnv(gym.Env):
# Local controller params.
self.k_p, self.k_v = 100, 20 # PD control.z
self.control_hz = self.metadata['video.frames_per_second']
# legcay set_state for data compatiblity
# legcay set_state for data compatibility
self.legacy = legacy
# agent_pos, block_pos, block_angle
@@ -92,7 +92,7 @@ class PushTEnv(gym.Env):
if self.damping is not None:
self.space.damping = self.damping
# use legacy RandomState for compatiblity
# use legacy RandomState for compatibility
state = self.reset_to_state
if state is None:
rs = np.random.RandomState(seed=seed)
@@ -163,7 +163,7 @@ class PushTEnv(gym.Env):
inertia = pymunk.moment_for_box(mass, (50, 100))
body = pymunk.Body(mass, inertia)
# preserving the legacy assignment order for compatibility
# the order here dosn't matter somehow, maybe because CoM is aligned with body origin
# the order here doesn't matter somehow, maybe because CoM is aligned with body origin
body.position = pose[:2].tolist()
body.angle = pose[2]
return body
@@ -210,7 +210,7 @@ class PushTEnv(gym.Env):
pygame.event.pump()
pygame.display.update()
# the clock is aleady ticked during in step for "human"
# the clock is already ticked during in step for "human"
img = np.transpose(
@@ -255,7 +255,7 @@ class PushTEnv(gym.Env):
# if not the same as CoM
# therefore should be modified first.
if self.legacy:
# for compatiblity with legacy data
# for compatibility with legacy data
self.block.position = pos_block
self.block.angle = rot_block
else:
@@ -307,7 +307,7 @@ class PushTEnv(gym.Env):
self.goal_color = pygame.Color('LightGreen')
self.goal_pose = np.array([256,256,np.pi/4]) # x, y, theta (in radians)
# Add collision handeling
# Add collision handling
self.collision_handeler = self.space.add_collision_handler(0, 0)
self.collision_handeler.post_solve = self._handle_collision
self.n_contact_points = 0

4
diffusion_policy/env/pusht/pusht_keypoints_env.py vendored
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@@ -27,7 +27,7 @@ class PushTKeypointsEnv(PushTEnv):
ws = self.window_size
if local_keypoint_map is None:
# create default keypoint definiton
# create default keypoint definition
kp_kwargs = self.genenerate_keypoint_manager_params()
local_keypoint_map = kp_kwargs['local_keypoint_map']
color_map = kp_kwargs['color_map']
@@ -107,7 +107,7 @@ class PushTKeypointsEnv(PushTEnv):
obs = kps.flatten()
obs_mask = kps_mask.flatten()
if not self.agent_keypoints:
# passing agent position when keypoints are not avaiable
# passing agent position when keypoints are not available
agent_pos = np.array(self.agent.position)
obs = np.concatenate([
obs, agent_pos

4
diffusion_policy/env/pusht/pymunk_override.py vendored
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@@ -96,7 +96,7 @@ class DrawOptions(pymunk.SpaceDebugDrawOptions):
See pygame_util.demo.py for a full example
Since pygame uses a coordiante system where y points down (in contrast
Since pygame uses a coordinate system where y points down (in contrast
to many other cases), you either have to make the physics simulation
with Pymunk also behave in that way, or flip everything when you draw.
@@ -226,7 +226,7 @@ def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int
"""Convenience method to convert pymunk coordinates to pygame surface
local coordinates.
Note that in case positive_y_is_up is False, this function wont actually do
Note that in case positive_y_is_up is False, this function won't actually do
anything except converting the point to integers.
"""
if positive_y_is_up:

2
diffusion_policy/env_runner/pusht_keypoints_runner.py
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@@ -45,7 +45,7 @@ class PushTKeypointsRunner(BaseLowdimRunner):
n_envs = n_train + n_test
# handle latency step
# to mimic latency, we request n_latency_steps addtional steps
# to mimic latency, we request n_latency_steps additional steps
# of past observations, and the discard the last n_latency_steps
env_n_obs_steps = n_obs_steps + n_latency_steps
env_n_action_steps = n_action_steps

2
diffusion_policy/env_runner/robomimic_lowdim_runner.py
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@@ -91,7 +91,7 @@ class RobomimicLowdimRunner(BaseLowdimRunner):
n_envs = n_train + n_test
# handle latency step
# to mimic latency, we request n_latency_steps addtional steps
# to mimic latency, we request n_latency_steps additional steps
# of past observations, and the discard the last n_latency_steps
env_n_obs_steps = n_obs_steps + n_latency_steps
env_n_action_steps = n_action_steps

4
diffusion_policy/gym_util/multistep_wrapper.py
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@@ -39,10 +39,10 @@ def dict_take_last_n(x, n):
def aggregate(data, method='max'):
if method == 'max':
# equivelent to any
# equivalent to any
return np.max(data)
elif method == 'min':
# equivelent to all
# equivalent to all
return np.min(data)
elif method == 'mean':
return np.mean(data)

2
diffusion_policy/model/bet/action_ae/discretizers/k_means.py
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@@ -41,7 +41,7 @@ class KMeansDiscretizer(DictOfTensorMixin):
@classmethod
def _kmeans(cls, x: torch.Tensor, ncluster: int = 512, niter: int = 50):
"""
Simple k-means clustering algorithm adapted from Karpathy's minGPT libary
Simple k-means clustering algorithm adapted from Karpathy's minGPT library
https://github.com/karpathy/minGPT/blob/master/play_image.ipynb
"""
N, D = x.size()

2
diffusion_policy/model/common/rotation_transformer.py
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@@ -21,7 +21,7 @@ class RotationTransformer:
"""
Valid representations
Alwasy use matrix as intermediate representation.
Always use matrix as intermediate representation.
"""
assert from_rep != to_rep
assert from_rep in self.valid_reps

6
diffusion_policy/real_world/rtde_interpolation_controller.py
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@@ -21,8 +21,8 @@ class Command(enum.Enum):
class RTDEInterpolationController(mp.Process):
"""
To ensure sending commnd to the robot with predictable latency
this controller need its seperate process (due to python GIL)
To ensure sending command to the robot with predictable latency
this controller need its separate process (due to python GIL)
"""
@@ -54,7 +54,7 @@ class RTDEInterpolationController(mp.Process):
tcp_offset_pose: 6d pose
payload_mass: float
payload_cog: 3d position, center of gravity
soft_real_time: enables round-robbin scheduling and real-time priority
soft_real_time: enables round-robin scheduling and real-time priority
requires running scripts/rtprio_setup.sh before hand.
"""

2
diffusion_policy/real_world/spacemouse.py
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@@ -8,7 +8,7 @@ import time
class Spacemouse(Thread):
def __init__(self, max_value=500, deadzone=(0,0,0,0,0,0), dtype=np.float32):
"""
Continously listen to 3D connecion space naviagtor events
Continuously listen to 3D connection space naviagtor events
and update the latest state.
max_value: {300, 500} 300 for wired version and 500 for wireless

4
diffusion_policy/real_world/spacemouse_shared_memory.py
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@@ -15,7 +15,7 @@ class Spacemouse(mp.Process):
n_buttons=2,
):
"""
Continously listen to 3D connecion space naviagtor events
Continuously listen to 3D connection space naviagtor events
and update the latest state.
max_value: {300, 500} 300 for wired version and 500 for wireless
@@ -36,7 +36,7 @@ class Spacemouse(mp.Process):
deadzone = np.array(deadzone, dtype=dtype)
assert (deadzone >= 0).all()
# copied varaibles
# copied variables
self.frequency = frequency
self.max_value = max_value
self.dtype = dtype

2
diffusion_policy/real_world/video_recorder.py
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@@ -128,7 +128,7 @@ class VideoRecorder:
dt=1/self.fps,
next_global_idx=self.next_global_idx
)
# number of apperance means repeats
# number of appearance means repeats
n_repeats = len(local_idxs)
if self.shape is None:

4
diffusion_policy/scripts/episode_lengths.py
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@@ -16,8 +16,8 @@ from diffusion_policy.common.replay_buffer import ReplayBuffer
@click.option('--dt', default=0.1, type=float)
def main(input, dt):
buffer = ReplayBuffer.create_from_path(input)
lenghts = buffer.episode_lengths
durations = lenghts * dt
lengths = buffer.episode_lengths
durations = lengths * dt
result = {
'duration/mean': np.mean(durations)
}

2
diffusion_policy/scripts/real_pusht_successrate.py
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@@ -16,7 +16,7 @@ import json
@click.command()
@click.option(
'--reference', '-r', required=True,
help='Reference metrics_raw.json from demostration dataset.'
help='Reference metrics_raw.json from demonstration dataset.'
)
@click.option(
'--input', '-i', required=True,

2
diffusion_policy/shared_memory/shared_memory_ring_buffer.py
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@@ -25,7 +25,7 @@ class SharedMemoryRingBuffer:
):
"""
shm_manager: Manages the life cycle of share memories
across processes. Remeber to run .start() before passing.
across processes. Remember to run .start() before passing.
array_specs: Name, shape and type of arrays for a single time step.
get_max_k: The maxmum number of items can be queried at once.
get_time_budget: The maxmum amount of time spent copying data from