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6c74ee643589a48ea028e071c8ab35c1aa0e063a | gautams3/deformable-ravens | ravens/gripper.py | [
"Apache-2.0"
] | Python | release | null | def release(self):
"""
If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects.
"""
if self.activated:
self.activated = False
# Release gripped rigid object (if any).
if... |
If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects.
| If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects. | [
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if self.activated:
self.activated = False
if self.contact_constraint is not None:
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p.removeConstraint(self.contact_constraint)
self.contact_constraint = None
except:
pass
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6c74ee643589a48ea028e071c8ab35c1aa0e063a | gautams3/deformable-ravens | ravens/gripper.py | [
"Apache-2.0"
] | Python | detect_contact | <not_specific> | def detect_contact(self, def_IDs):
"""
If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects.
:def_IDs: a list of IDs of deformable objects. Since it may be
computationally heavy to check, if we have any... |
If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects.
:def_IDs: a list of IDs of deformable objects. Since it may be
computationally heavy to check, if we have any contact points with
rigid we just ret... | If suction off, detect contact between gripper and objects.
If suction on, detect contact between picked object and other objects. | [
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] | def detect_contact(self, def_IDs):
body, link = self.body, 0
if self.activated and self.contact_constraint is not None:
try:
info = p.getConstraintInfo(self.contact_constraint)
body, link = info[2], info[3]
except:
self.contact_cons... | [
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6c74ee643589a48ea028e071c8ab35c1aa0e063a | gautams3/deformable-ravens | ravens/gripper.py | [
"Apache-2.0"
] | Python | detect_contact_def | <not_specific> | def detect_contact_def(self, gripper_position, defId):
"""Detect contact, when dealing with deformables.
We may want to speed this up if it is a bottleneck. Returns a binary
signal of whether there exists _any_ vertex within the threshold.
Note with collisionMargin=0.004, I am getting m... | Detect contact, when dealing with deformables.
We may want to speed this up if it is a bottleneck. Returns a binary
signal of whether there exists _any_ vertex within the threshold.
Note with collisionMargin=0.004, I am getting most cloth vertices
(for ClothFlat) to settle at ~0.004m hi... | Detect contact, when dealing with deformables.
We may want to speed this up if it is a bottleneck. Returns a binary
signal of whether there exists _any_ vertex within the threshold.
Note with collisionMargin=0.004, I am getting most cloth vertices
(for ClothFlat) to settle at ~0.004m high. | [
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_, vert_pos_l = p.getMeshData(defId, -1, flags=p.MESH_DATA_SIMULATION_MESH)
distances_np = gripper_position - np.array(vert_pos_l)
assert len(distances_np.shape) == 2, distances_np.shape
distances_L2 = np.linalg.norm(distances_np, ax... | [
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6c74ee643589a48ea028e071c8ab35c1aa0e063a | gautams3/deformable-ravens | ravens/gripper.py | [
"Apache-2.0"
] | Python | check_grasp | <not_specific> | def check_grasp(self):
"""Check a grasp for picking success.
If picking fails, then robot doesn't do the place action. For rigid
items: index 2 in getConstraintInfo returns childBodyUniqueId. For
deformables, check the length of the anchors.
"""
pick_deformable = False
... | Check a grasp for picking success.
If picking fails, then robot doesn't do the place action. For rigid
items: index 2 in getConstraintInfo returns childBodyUniqueId. For
deformables, check the length of the anchors.
| Check a grasp for picking success.
If picking fails, then robot doesn't do the place action. For rigid
items: index 2 in getConstraintInfo returns childBodyUniqueId. For
deformables, check the length of the anchors. | [
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pick_deformable = False
if self.def_grip_anchors is not None:
pick_deformable = len(self.def_grip_anchors) > 0
return (not self.contact_constraint is None) or pick_deformable | [
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1a5169920c0452072c78502e23d3486d19b0c06a | gautams3/deformable-ravens | load.py | [
"Apache-2.0"
] | Python | load | <not_specific> | def load(path, iepisode, field):
"""Adapted from `dataset.py` so we can sample goal images. Just including
some logic to extract the episode automatically based on the index
`iepisode`, so we don't need to know the length in advance.
"""
field_path = os.path.join(path, field)
data_list = [os.pat... | Adapted from `dataset.py` so we can sample goal images. Just including
some logic to extract the episode automatically based on the index
`iepisode`, so we don't need to know the length in advance.
| Adapted from `dataset.py` so we can sample goal images. Just including
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field_path = os.path.join(path, field)
data_list = [os.path.join(field_path, x) for x in os.listdir(field_path)]
fname = [x for x in data_list if f'{iepisode:06d}' in x]
assert len(fname) == 1, fname
fname = fname[0]
return pickle.load(open(fname, 'rb')) | [
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1a5169920c0452072c78502e23d3486d19b0c06a | gautams3/deformable-ravens | load.py | [
"Apache-2.0"
] | Python | debug_time_step | null | def debug_time_step(t, epidx, obs, act, extras, goal=None):
"""Save images and other stuff from time `t` in episode `epidx`."""
pth = 'tmp'
tt = str(t).zfill(2)
# Convert from BGR to RGB to match what we see in the GUI.
def save(fname, c_img):
cv2.imwrite(fname, img=cv2.cvtColor(c_img, cv2.... | Save images and other stuff from time `t` in episode `epidx`. | Save images and other stuff from time `t` in episode `epidx`. | [
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pth = 'tmp'
tt = str(t).zfill(2)
def save(fname, c_img):
cv2.imwrite(fname, img=cv2.cvtColor(c_img, cv2.COLOR_BGR2RGB))
for img_idx, c_img in enumerate(obs['color']):
fname = join(pth, f'ep_{epidx}_t{tt}_cimg_{img_idx}.png')
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1a5169920c0452072c78502e23d3486d19b0c06a | gautams3/deformable-ravens | load.py | [
"Apache-2.0"
] | Python | rollout | <not_specific> | def rollout(agent, env, task, goal_conditioned, args, num_finished, debug=False):
"""Standard gym environment rollout.
Adding more debugging options (enable with debug=True), such as printing
the pose and saving the images and heatmaps. We can also run `dataset.py`
and see goal images in the `goals_out... | Standard gym environment rollout.
Adding more debugging options (enable with debug=True), such as printing
the pose and saving the images and heatmaps. We can also run `dataset.py`
and see goal images in the `goals_out` directory.
:goal_conditioned: a boolean to check if we have goal-conditioning.
... | Standard gym environment rollout.
Adding more debugging options (enable with debug=True), such as printing
the pose and saving the images and heatmaps. We can also run `dataset.py`
and see goal images in the `goals_out` directory. | [
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if debug:
if not os.path.exists('tmp/'):
os.makedirs('tmp/')
print('')
start_t = 0
if args.agent in ['gt_state', 'gt_state_2_step']:
start_t = 1
episode = []
total_reward = 0
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1a5169920c0452072c78502e23d3486d19b0c06a | gautams3/deformable-ravens | load.py | [
"Apache-2.0"
] | Python | ignore_this_demo | <not_specific> | def ignore_this_demo(args, reward, t, last_extras):
"""In some cases, we should filter out demonstrations.
Filter for if t == 0, which means the initial state was a success, and
also if we have exit_gracefully, which means for the bag-items tasks, it
may not have had visible item(s) at the start, for s... | In some cases, we should filter out demonstrations.
Filter for if t == 0, which means the initial state was a success, and
also if we have exit_gracefully, which means for the bag-items tasks, it
may not have had visible item(s) at the start, for some reason.
| In some cases, we should filter out demonstrations.
Filter for if t == 0, which means the initial state was a success, and
also if we have exit_gracefully, which means for the bag-items tasks, it
may not have had visible item(s) at the start, for some reason. | [
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ignore = (t == 0)
if 'exit_gracefully' in last_extras:
assert last_extras['exit_gracefully']
return True
return ignore | [
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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"""Adding optional hertz parameter for better cloth physics.
From our discussion with Erwin, we should just set time.sleep(0.001),
or even consider removing it all together. It's mainly for us to
visualize PyBullet with the GUI to make it not move too fast
... | Adding optional hertz parameter for better cloth physics.
From our discussion with Erwin, we should just set time.sleep(0.001),
or even consider removing it all together. It's mainly for us to
visualize PyBullet with the GUI to make it not move too fast
| Adding optional hertz parameter for better cloth physics.
From our discussion with Erwin, we should just set time.sleep(0.001),
or even consider removing it all together. It's mainly for us to
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p.setTimeStep(1.0 / self.hz)
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p.stepSimulation()
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | is_static | <not_specific> | def is_static(self):
"""Checks if env is static, used for checking if action finished.
However, this won't work in PyBullet (at least v2.8.4) since soft
bodies cause this code to hang. Therefore, look at the task's
`def_IDs` list, which by design will have all IDs of soft bodies.
... | Checks if env is static, used for checking if action finished.
However, this won't work in PyBullet (at least v2.8.4) since soft
bodies cause this code to hang. Therefore, look at the task's
`def_IDs` list, which by design will have all IDs of soft bodies.
Furthermore, for the bag tasks... | Checks if env is static, used for checking if action finished.
However, this won't work in PyBullet (at least v2.8.4) since soft
bodies cause this code to hang. Therefore, look at the task's
`def_IDs` list, which by design will have all IDs of soft bodies.
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
"Apache-2.0"
] | Python | reset | <not_specific> | def reset(self, task, last_info=None, disable_render_load=True):
"""Sets up PyBullet, loads models, resets the specific task.
We do a step() call with act=None at the end. This will only return
an empty obs dict, obs={}. For some tasks where the reward could be
nonzero at the start, we ... | Sets up PyBullet, loads models, resets the specific task.
We do a step() call with act=None at the end. This will only return
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nonzero at the start, we can report the reward shown here.
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We do a step() call with act=None at the end. This will only return
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if self.use_new_deformable:
p.resetSimulation(p.RESET_USE_DEFORMABLE_WORLD)
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p.resetSimulation()
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
"Apache-2.0"
] | Python | step | <not_specific> | def step(self, act=None):
"""Execute action with specified primitive.
For each episode (training, loading, etc.), this is normally called the first
time from `env.reset()` above, with NO action, and returns an EMPTY observation.
Then, it's called a SECOND time with an action that lacks ... | Execute action with specified primitive.
For each episode (training, loading, etc.), this is normally called the first
time from `env.reset()` above, with NO action, and returns an EMPTY observation.
Then, it's called a SECOND time with an action that lacks a primitive (set to
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For each episode (training, loading, etc.), this is normally called the first
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if act and act['primitive']:
success = self.primitives[act['primitive']](**act['params'])
if (not success) or self.task.exit_gracefully:
_, reward_extras = self.task.reward()
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
"Apache-2.0"
] | Python | render | <not_specific> | def render(self, config):
"""Render RGB-D image with specified configuration."""
# Compute OpenGL camera settings.
lookdir = np.array([0, 0, 1]).reshape(3, 1)
updir = np.array([0, -1, 0]).reshape(3, 1)
rotation = p.getMatrixFromQuaternion(config['rotation'])
rotm = np.ar... | Render RGB-D image with specified configuration. | Render RGB-D image with specified configuration. | [
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lookdir = np.array([0, 0, 1]).reshape(3, 1)
updir = np.array([0, -1, 0]).reshape(3, 1)
rotation = p.getMatrixFromQuaternion(config['rotation'])
rotm = np.array(rotation).reshape(3, 3)
lookdir = (rotm @ lookdir).reshape(-1)
updir = (rotm @ updir).... | [
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | movej | <not_specific> | def movej(self, targj, speed=0.01, t_lim=20):
"""Move UR5 to target joint configuration."""
t0 = time.time()
while (time.time() - t0) < t_lim:
currj = [p.getJointState(self.ur5, i)[0] for i in self.joints]
currj = np.array(currj)
diffj = targj - currj
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t0 = time.time()
while (time.time() - t0) < t_lim:
currj = [p.getJointState(self.ur5, i)[0] for i in self.joints]
currj = np.array(currj)
diffj = targj - currj
if all(np.abs(diffj) < 1e-2):
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | movep | <not_specific> | def movep(self, pose, speed=0.01):
"""Move UR5 to target end effector pose."""
# # Keep joint angles between -180/+180
# targj[5] = ((targj[5] + np.pi) % (2 * np.pi) - np.pi)
targj = self.solve_IK(pose)
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | pick_place | <not_specific> | def pick_place(self, pose0, pose1):
"""Execute pick and place primitive.
Standard ravens tasks use the `delta` vector to lower the gripper
until it makes contact with something. With deformables, however, we
need to consider cases when the gripper could detect a rigid OR a
soft ... | Execute pick and place primitive.
Standard ravens tasks use the `delta` vector to lower the gripper
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | is_new_cable_env | <not_specific> | def is_new_cable_env(self):
"""I want a way to track new cable-related stuff alone."""
return (isinstance(self.task, tasks.names['cable-shape']) or
isinstance(self.task, tasks.names['cable-shape-notarget']) or
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
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] | Python | is_cloth_env | <not_specific> | def is_cloth_env(self):
"""Keep this updated when I adjust environment names."""
return (isinstance(self.task, tasks.names['cloth-flat']) or
isinstance(self.task, tasks.names['cloth-flat-notarget']) or
isinstance(self.task, tasks.names['cloth-cover'])) | Keep this updated when I adjust environment names. | Keep this updated when I adjust environment names. | [
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return (isinstance(self.task, tasks.names['cloth-flat']) or
isinstance(self.task, tasks.names['cloth-flat-notarget']) or
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7f8815b3761deea061ba8bffc2da724c866803d5 | gautams3/deformable-ravens | ravens/environment.py | [
"Apache-2.0"
] | Python | is_bag_env | <not_specific> | def is_bag_env(self):
"""Keep this updated when I adjust environment names."""
return (isinstance(self.task, tasks.names['bag-alone-open']) or
isinstance(self.task, tasks.names['bag-items-easy']) or
isinstance(self.task, tasks.names['bag-items-hard']) or
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9552039ba941bc587c0bb9d09d5ea50879693890 | gautams3/deformable-ravens | ravens/tasks/task.py | [
"Apache-2.0"
] | Python | params_no_rots | <not_specific> | def params_no_rots(self, vertex_pos, target_pos, overshoot):
"""Helper to handle common pick-place code for the oracle policy.
We often have this patten: vertex positions and target positions in
2D, and then potentially slightly overshoot the target. For example,
with cloth it's helpful... | Helper to handle common pick-place code for the oracle policy.
We often have this patten: vertex positions and target positions in
2D, and then potentially slightly overshoot the target. For example,
with cloth it's helpful to do this since otherwise the physics will
favor the cloth 're... | Helper to handle common pick-place code for the oracle policy.
We often have this patten: vertex positions and target positions in
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p0 = (vertex_pos[0], vertex_pos[1], 0.001)
p1 = (target_pos[0], target_pos[1], 0.001)
direction = np.float32(p0) - np.float32(p1)
length = np.linalg.norm(direction)
direction = direction / length
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9552039ba941bc587c0bb9d09d5ea50879693890 | gautams3/deformable-ravens | ravens/tasks/task.py | [
"Apache-2.0"
] | Python | done | <not_specific> | def done(self):
"""Check if the task is done AND has not failed.
To be clear: for normal Ravens envs, `self.total_rewards` represents
the sum of deltas, or the sum of the `reward` returned by `reward()`
defined above. Think of it as the "true reward at this moment." I
follow thi... | Check if the task is done AND has not failed.
To be clear: for normal Ravens envs, `self.total_rewards` represents
the sum of deltas, or the sum of the `reward` returned by `reward()`
defined above. Think of it as the "true reward at this moment." I
follow this convention for custom env... | Check if the task is done AND has not failed.
For multi-step tasks such as `cloth-cover-item`, one must need to
accomplish all stages correctly.
(08 Sept 2020): I added self.exit_gracefully, which will help us
quickly exit if the demo has failed, but this is not handled here.
(14 Sept 2020) We want self.done to retu... | [
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zone_done, goal_done, cable_done, cov_done, bag_done = \
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if self.metric == 'zone':
zone_done = self.total_rewards == 1
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9552039ba941bc587c0bb9d09d5ea50879693890 | gautams3/deformable-ravens | ravens/tasks/task.py | [
"Apache-2.0"
] | Python | fill_template | <not_specific> | def fill_template(self, template, replace):
"""Read template file and replace string keys.
The replace[field] needs to be a tuple. Use (item,) for a single
item, but you can combine keys with NAMEX where X starts from 0.
"""
filepath = os.path.dirname(os.path.abspath(__file__))
... | Read template file and replace string keys.
The replace[field] needs to be a tuple. Use (item,) for a single
item, but you can combine keys with NAMEX where X starts from 0.
| Read template file and replace string keys.
The replace[field] needs to be a tuple. Use (item,) for a single
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9552039ba941bc587c0bb9d09d5ea50879693890 | gautams3/deformable-ravens | ravens/tasks/task.py | [
"Apache-2.0"
] | Python | random_pose | <not_specific> | def random_pose(self, env, object_size, hard_code=False):
"""Get random collision-free pose in workspace bounds for object.
For tasks like sweeping to a zone target, generates a target zone at
random, then later generates items to be swept in it. The second step
requires free space to a... | Get random collision-free pose in workspace bounds for object.
For tasks like sweeping to a zone target, generates a target zone at
random, then later generates items to be swept in it. The second step
requires free space to avoid being within the zone.
The `mask` defines a distributio... | Get random collision-free pose in workspace bounds for object.
For tasks like sweeping to a zone target, generates a target zone at
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max_size = np.sqrt(object_size[0]**2 + object_size[1]**2)
erode_size = int(np.round(max_size / self.pixel_size))
colormap, heightmap, object_mask = self.get_object_masks(env)
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9552039ba941bc587c0bb9d09d5ea50879693890 | gautams3/deformable-ravens | ravens/tasks/task.py | [
"Apache-2.0"
] | Python | apply | <not_specific> | def apply(self, pose, position):
"""Daniel: apply a rigid body transformation on `position` by `pose`.
The `pose` has the rotation matrix and translation vector. Apply the
rotation matrix on `position` and then translate. Example: used in
cables env to sequentially translate each bead i... | Daniel: apply a rigid body transformation on `position` by `pose`.
The `pose` has the rotation matrix and translation vector. Apply the
rotation matrix on `position` and then translate. Example: used in
cables env to sequentially translate each bead in the cable.
Returns a *position*, ... | apply a rigid body transformation on `position` by `pose`.
The `pose` has the rotation matrix and translation vector. Apply the
rotation matrix on `position` and then translate. Example: used in
cables env to sequentially translate each bead in the cable.
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rotation = np.float32(p.getMatrixFromQuaternion(pose[1])).reshape(3, 3)
translation = np.float32(pose[0]).reshape(3, 1)
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
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] | Python | extract_x_y_theta | <not_specific> | def extract_x_y_theta(self, object_info, t_worldaug_world=None, preserve_theta=False, softbody=False):
"""Given either object OR action pose info, return stuff to put in GT observation.
Note: only called from within this class and 2-step case via subclassing.
During training, there is normally ... | Given either object OR action pose info, return stuff to put in GT observation.
Note: only called from within this class and 2-step case via subclassing.
During training, there is normally data augmentation applied, so t_worldaug_world
is NOT None, and augmentation is applied as if the 'image' ... | Given either object OR action pose info, return stuff to put in GT observation.
Note: only called from within this class and 2-step case via subclassing.
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
"Apache-2.0"
] | Python | info_to_gt_obs | <not_specific> | def info_to_gt_obs(self, info, t_worldaug_world=None, goal=None):
"""Daniel: from info dict of IDs, create the observation for GT models.
Assumes `info` consists of just PyBullet object IDs. Creates a numpy array
from combining the `object_x_y_theta` from all IDs, and potentially add more
... | Daniel: from info dict of IDs, create the observation for GT models.
Assumes `info` consists of just PyBullet object IDs. Creates a numpy array
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info based on if using box dimensions or colors; see `__init__()` above.
... | from info dict of IDs, create the observation for GT models.
Assumes `info` consists of just PyBullet object IDs. Creates a numpy array
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
"Apache-2.0"
] | Python | act_to_gt_act | <not_specific> | def act_to_gt_act(self, act, t_worldaug_world=None, transform_params=None):
"""Daniel: similarly, from action, create the appropriate ground truth action.
This may involve a transformation if doing data augmentation.
Comment from Andy/Pete: dont update theta due to suction invariance to theta
... | Daniel: similarly, from action, create the appropriate ground truth action.
This may involve a transformation if doing data augmentation.
Comment from Andy/Pete: dont update theta due to suction invariance to theta
| similarly, from action, create the appropriate ground truth action.
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
"Apache-2.0"
] | Python | init_model | null | def init_model(self, dataset):
"""Initialize self.model, including normalization parameters."""
self.set_max_obs_vector_length(dataset)
# Get obs dim and action dim (3 for pick, 3 for place), initialize model.
if self.goal_conditioned:
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self.set_max_obs_vector_length(dataset)
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_, _, info, goal = dataset.random_sample(goal_images=True)
obs_vector = self.info_to_gt_obs(info, goal=goal)
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
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] | Python | train | <not_specific> | def train(self, dataset, num_iter, writer, validation_dataset=None):
"""Train on dataset for a specific number of iterations.
Daniel: not testing with validation, argument copied over from ravens. Naively,
we can train with MSE, but better to use a mixture model (MDN) since the output
s... | Train on dataset for a specific number of iterations.
Daniel: not testing with validation, argument copied over from ravens. Naively,
we can train with MSE, but better to use a mixture model (MDN) since the output
should be multi-modal; could be several pick points, and several placing points
... | Train on dataset for a specific number of iterations.
Daniel: not testing with validation, argument copied over from ravens. Naively,
we can train with MSE, but better to use a mixture model (MDN) since the output
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loss_criterion = mdn_utils.mdn_loss
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fa696e30f5de82c9e1f7c6cb76a250b9a299197f | gautams3/deformable-ravens | ravens/agents/gt_state.py | [
"Apache-2.0"
] | Python | act | <not_specific> | def act(self, obs, info, goal=None):
"""Run inference and return best action."""
act = {'camera_config': self.camera_config, 'primitive': None}
# Get observations and run predictions (second part just for visualization).
if self.goal_conditioned:
gt_obs = self.info_to_gt_obs... | Run inference and return best action. | Run inference and return best action. | [
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act = {'camera_config': self.camera_config, 'primitive': None}
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gt_obs = self.info_to_gt_obs(info, goal=goal)
gt_act_center = self.info_to_gt_obs(info, goal=goal)
else:
gt_obs = self.info_to_gt_obs(... | [
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
"Apache-2.0"
] | Python | add_cable | null | def add_cable(self, env, size_range, info={}, cable_idx=0,
direction='z', max_force=100):
"""Add a cable like Andy does it in his cable environment.
Add each `part_id` to (a) env.objects, (b) object_points, (c) _IDs,
and (d) cable_bead_IDs. For (b) it is because, like the sweeping e... | Add a cable like Andy does it in his cable environment.
Add each `part_id` to (a) env.objects, (b) object_points, (c) _IDs,
and (d) cable_bead_IDs. For (b) it is because, like the sweeping env,
the demonstrator detects the one farthest from the goal to use as the
pick, and `object_point... | Add a cable like Andy does it in his cable environment.
When iterating through the number of parts, ensure that the given
cable is _separate_ from prior cables, in case there are more than
one. ALL beads are put in the `env.objects` list.
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num_parts = self.num_parts
radius = self.radius
length = self.length
color = self.colors[cable_idx] + [1]
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
"Apache-2.0"
] | Python | add_cable_ring | <not_specific> | def add_cable_ring(self, env, info={}, cable_idx=0):
"""Add a cable, but make it connected at both ends to form a ring.
For consistency, add each `part_id` to various information tracking
lists and dictionaries (see `add_cable` documentation).
:cable_idx: Used for environments with mor... | Add a cable, but make it connected at both ends to form a ring.
For consistency, add each `part_id` to various information tracking
lists and dictionaries (see `add_cable` documentation).
:cable_idx: Used for environments with more than one cable.
:info: Stores relevant stuff, such as ... | Add a cable, but make it connected at both ends to form a ring.
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
"Apache-2.0"
] | Python | add_random_box | <not_specific> | def add_random_box(self, env):
"""Generate randomly shaped box, from aligning env."""
box_size = self.random_size(0.05, 0.15, 0.05, 0.15, 0.01, 0.06)
box_pose = self.random_pose(env, box_size)
box_template = 'assets/box/box-template.urdf'
box_urdf = self.fill_template(box_templat... | Generate randomly shaped box, from aligning env. | Generate randomly shaped box, from aligning env. | [
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box_size = self.random_size(0.05, 0.15, 0.05, 0.15, 0.01, 0.06)
box_pose = self.random_pose(env, box_size)
box_template = 'assets/box/box-template.urdf'
box_urdf = self.fill_template(box_template, {'DIM': box_size})
box_id = env.add_object(box_urdf,... | [
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
"Apache-2.0"
] | Python | area_thresh | <not_specific> | def area_thresh(self):
"""Only if we are using the cable-ring environment...
So far I think using 0.8 or higher might be too hard because moving
the ring to the target can cause other areas to decrease.
"""
return 0.75 | Only if we are using the cable-ring environment...
So far I think using 0.8 or higher might be too hard because moving
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
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"""Reset to start an episode.
If generating training data for goal-conditioned Transporters with
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superclass. The code already puts the bead poses inside `info`. For
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this env it's IDs 4 through 27 (for 24 beads) but I scale i... | Reset to start an episode.
If generating training data for goal-conditioned Transporters with
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34f37ec93172cf68794b7788dbe41e68d9959cf8 | gautams3/deformable-ravens | ravens/tasks/defs_cables.py | [
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] | Python | _get_goal_info | <not_specific> | def _get_goal_info(self, last_info):
"""Used to determine the goal given the last `info` dict."""
start_ID = 4
end_ID = start_ID + self.num_parts
places = {}
for ID in range(start_ID, end_ID):
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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Args:
points: HxWx3 float array of 3D points in camera coordinates.
transform: 4x4 float array representing a rigid transformation matrix.
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padding = ((0, 0), (0, 0), (0, 1))
homogen_points = np.pad(points.copy(), padding,
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for i in range(3):
points[..., i] = np.sum(transform[i, :] * homogen_points, axis=-1)
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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The color and depth are np.arrays or lists, where the leading dimension
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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] | Python | pixel_to_position | <not_specific> | def pixel_to_position(pixel, height, bounds, pixel_size, skip_height=False):
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u, v = pixel
x = bounds[0, 0] + v * pixel_size
y = bounds[1, 0] + u * pixel_size
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y = bounds[1, 0] + u * pixel_size
if not skip_height:
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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distortion: np.ndarray) -> np.ndarray:
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uv_coordinates: pixel coordinates to unproject of shape (n, 2)... | Vectorized version of unproject(), for N points.
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cam_mtx = intrinsic
cam_dist = np.array(distortion)
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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] | Python | unproject_depth_vectorized | np.ndarray | def unproject_depth_vectorized(im_depth: np.ndarray, depth_dist: np.ndarray,
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camera_dist: np.ndarray) -> np.ndarray:
"""Unproject depth image into 3D point cloud, using calibration.
Args:
im_depth: raw depth image, pre-calibr... | Unproject depth image into 3D point cloud, using calibration.
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im_depth: raw depth image, pre-calibration of shape (height, width).
depth_dist: depth distortion parameters of shape (8,)
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h, w = im_depth.shape
u_map, v_map = np.meshgrid(np.linspace(0, w - 1, w), np.linspace(0, h - 1, h))
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
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] | Python | sample_distribution | <not_specific> | def sample_distribution(prob, n_samples=1):
"""Sample data point from a custom distribution."""
flat_prob = np.ndarray.flatten(prob) / np.sum(prob)
rand_ind = np.random.choice(
np.arange(len(flat_prob)), n_samples, p=flat_prob, replace=False)
rand_ind_coords = np.array(np.unravel_index(rand_ind,... | Sample data point from a custom distribution. | Sample data point from a custom distribution. | [
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flat_prob = np.ndarray.flatten(prob) / np.sum(prob)
rand_ind = np.random.choice(
np.arange(len(flat_prob)), n_samples, p=flat_prob, replace=False)
rand_ind_coords = np.array(np.unravel_index(rand_ind, prob.shape)).T
return np.int32(rand_ind_coords.sque... | [
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cb75751c2dd46752fb45c7d29f397d2f9b0239f7 | gautams3/deformable-ravens | ravens/utils.py | [
"Apache-2.0"
] | Python | check_transform | <not_specific> | def check_transform(image, pixel, transform):
"""Valid transform only if pixel locations are still in FoV after transform."""
new_pixel = np.flip(np.int32(np.round(np.dot(transform, np.float32(
[pixel[1], pixel[0], 1.]).reshape(3, 1))))[:2].squeeze())
valid = np.all(new_pixel >= 0) and new_pixel[0] ... | Valid transform only if pixel locations are still in FoV after transform. | Valid transform only if pixel locations are still in FoV after transform. | [
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new_pixel = np.flip(np.int32(np.round(np.dot(transform, np.float32(
[pixel[1], pixel[0], 1.]).reshape(3, 1))))[:2].squeeze())
valid = np.all(new_pixel >= 0) and new_pixel[0] < image.shape[
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a1d8f38472c9af4a84de9b198e8ea30a6c30c1c0 | gautams3/deformable-ravens | ravens/agents/regression.py | [
"Apache-2.0"
] | Python | train | <not_specific> | def train(self, dataset, num_iter, writer):
"""Train on dataset for a specific number of iterations."""
@tf.function
def pick_train_step(model, optim, in_tensor, yxtheta, loss_criterion):
with tf.GradientTape() as tape:
output = model(in_tensor)
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@tf.function
def pick_train_step(model, optim, in_tensor, yxtheta, loss_criterion):
with tf.GradientTape() as tape:
output = model(in_tensor)
loss = loss_criterion(yxtheta, output)
grad = tape.gradient(lo... | [
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a1d8f38472c9af4a84de9b198e8ea30a6c30c1c0 | gautams3/deformable-ravens | ravens/agents/regression.py | [
"Apache-2.0"
] | Python | act | <not_specific> | def act(self, obs, info):
"""Run inference and return best action given visual observations."""
self.pick_regression_model.set_batch_size(1)
self.place_regression_model.set_batch_size(1)
act = {'camera_config': self.camera_config, 'primitive': None}
if not obs:
return... | Run inference and return best action given visual observations. | Run inference and return best action given visual observations. | [
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self.pick_regression_model.set_batch_size(1)
self.place_regression_model.set_batch_size(1)
act = {'camera_config': self.camera_config, 'primitive': None}
if not obs:
return act
colormap, heightmap = self.get_heightmap(obs, self.camera_config)... | [
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0aafbd728ded3c727fb9518d2ce6a2c0e234ddaa | gautams3/deformable-ravens | ravens/models/transport_goal.py | [
"Apache-2.0"
] | Python | forward | <not_specific> | def forward(self, in_img, goal_img, p, apply_softmax=True):
"""Forward pass of our goal-conditioned Transporter.
Relevant shapes and info:
in_img and goal_img: (320,160,6)
p: integer pixels on in_img, e.g., [158, 30]
self.padding: [[32,32],[32,32],0,0]], with shape ... | Forward pass of our goal-conditioned Transporter.
Relevant shapes and info:
in_img and goal_img: (320,160,6)
p: integer pixels on in_img, e.g., [158, 30]
self.padding: [[32,32],[32,32],0,0]], with shape (3,2)
Run input through all three networks, to get output of t... | Forward pass of our goal-conditioned Transporter.
Relevant shapes and info.
Run input through all three networks, to get output of the same
shape, except that the last channel is 3 (output_dim). Then, the
output for one stream has the convolutional kernels for another.
I actually think cropping after the query netw... | [
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assert in_img.shape == goal_img.shape, f'{in_img.shape}, {goal_img.shape}'
input_unproc = np.pad(in_img, self.padding, mode='constant')
input_data = self.preprocess(input_unproc.copy())
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0aafbd728ded3c727fb9518d2ce6a2c0e234ddaa | gautams3/deformable-ravens | ravens/models/transport_goal.py | [
"Apache-2.0"
] | Python | visualize_logits | null | def visualize_logits(self, logits, name):
"""Given logits (BEFORE tf.nn.convolution), get a heatmap.
Here we apply a softmax to make it more human-readable. However, the
tf.nn.convolution with the learned kernels happens without a softmax
on the logits. [Update: wait, then why should we... | Given logits (BEFORE tf.nn.convolution), get a heatmap.
Here we apply a softmax to make it more human-readable. However, the
tf.nn.convolution with the learned kernels happens without a softmax
on the logits. [Update: wait, then why should we have a softmax,
then? I forgot why we did th... | Given logits (BEFORE tf.nn.convolution), get a heatmap.
Here we apply a softmax to make it more human-readable. However, the
tf.nn.convolution with the learned kernels happens without a softmax
on the logits. [Update: wait, then why should we have a softmax,
then. I forgot why we did this ...] | [
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original_shape = logits.shape
logits = tf.reshape(logits, (1, np.prod(original_shape)))
vis_transport = np.float32(logits).reshape(original_shape)
vis_transport = vis_transport[0]
vis_transport = vis_transport - np.min(vis_transport)
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5b9dd52f7225cee1ee08ef3b0e7fc62d4576b9e7 | gautams3/deformable-ravens | ravens/models/resnet.py | [
"Apache-2.0"
] | Python | identity_block | <not_specific> | def identity_block(input_tensor, kernel_size, filters, stage, block, activation=True, include_batchnorm=False):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer ... | The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, curren... | The identity block is the block that has no conv layer at shortcut.
Returns
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batchnorm_axis = 3
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
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5b9dd52f7225cee1ee08ef3b0e7fc62d4576b9e7 | gautams3/deformable-ravens | ravens/models/resnet.py | [
"Apache-2.0"
] | Python | ResNet43_8s | <not_specific> | def ResNet43_8s(input_shape, output_dim, include_batchnorm=False, batchnorm_axis=3, prefix='', cutoff_early=False):
"""Daniel: produces an hourglass FCN network, adapted to CoRL submission size.
Regarding shapes, look at: https://www.tensorflow.org/api_docs/python/tf/keras/Input [excludes batch size]
Here ... | Daniel: produces an hourglass FCN network, adapted to CoRL submission size.
Regarding shapes, look at: https://www.tensorflow.org/api_docs/python/tf/keras/Input [excludes batch size]
Here are the shape patterns, where I print shapes for the input, and after each conv_block or Conv2D call.
Attention:
(... | produces an hourglass FCN network, adapted to CoRL submission size.
Transport, query module, assumes cropping beforehand.
Here I ignore output after identity blocks, which produce tensors of the same size.
Parameters | [
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x = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same', kernel_initializer='glorot_uniform', name=prefix + 'conv1')(input_data)... | [
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a793edf751328666028af50cf3fa666f38d140d3 | gautams3/deformable-ravens | ravens/dataset.py | [
"Apache-2.0"
] | Python | _change_name | <not_specific> | def _change_name(self, suff, info_extra):
"""Depending on the env, make changes to image suffix `suff`."""
if 'cable-ring' in self.path:
i1 = info_extra['convex_hull_area']
i2 = info_extra['best_possible_area']
f = i1 / i2
suff = suff.replace('.png',
... | Depending on the env, make changes to image suffix `suff`. | Depending on the env, make changes to image suffix `suff`. | [
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if 'cable-ring' in self.path:
i1 = info_extra['convex_hull_area']
i2 = info_extra['best_possible_area']
f = i1 / i2
suff = suff.replace('.png',
f'-area-{i1:0.3f}-best-{i2:0.3f}-FRAC-{f:0.3f}.png')
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a793edf751328666028af50cf3fa666f38d140d3 | gautams3/deformable-ravens | ravens/dataset.py | [
"Apache-2.0"
] | Python | _save_images | null | def _save_images(self, episode_len, color_l, depth_l, info_l, outdir, i_ep):
"""For each item (timestep) in this episode, save relevant images."""
for t in range(episode_len):
assert color_l[t].shape == (3, 480, 640, 3), color_l[t].shape
assert depth_l[t].shape == (3, 480, 640),... | For each item (timestep) in this episode, save relevant images. | For each item (timestep) in this episode, save relevant images. | [
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
"Apache-2.0"
] | Python | compute_pixel_IoU_coverage | <not_specific> | def compute_pixel_IoU_coverage(self):
"""Computes IoU and coverage based on pixels.
Use: `self.target_hull` and `self.current_hull`. For the former:
values of 255 refer to the area contained within the zone (and
includes the zone itself, FWIW). For the latter: segment to detect
... | Computes IoU and coverage based on pixels.
Use: `self.target_hull` and `self.current_hull`. For the former:
values of 255 refer to the area contained within the zone (and
includes the zone itself, FWIW). For the latter: segment to detect
the workspace OR the zone ID. Then use numpy and/... | Computes IoU and coverage based on pixels.
NOTE I: assumes cloth can be segmented by detecting the workspace and
zone lines, and that any pixel OTHER than those belongs to cloth.
NOTE II: IoU and coverage are computed in the same way, except that
the former divides by the union, the latter divides by just the goal. | [
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mask = np.isin(object_mask, test_elements=IDs)
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mask[idx_0s] = 1
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
"Apache-2.0"
] | Python | is_item_covered | <not_specific> | def is_item_covered(self):
"""For cloth-cover, if it's covered, it should NOT be in the mask."""
_, _, object_mask = self.get_object_masks(self.env)
assert len(self.block_IDs) == 1, self.block_IDs
block = self.block_IDs[0]
return 1 - float(block in object_mask) | For cloth-cover, if it's covered, it should NOT be in the mask. | For cloth-cover, if it's covered, it should NOT be in the mask. | [
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
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"""Adds a square target (green) zone.
To handle goal-conditioned cloth flattening, we save `zone_pose` and
provide it as input, to avoid re-sampling. This means starting cloth
states are sampled at a correct distance, and that the same IoU
... | Adds a square target (green) zone.
To handle goal-conditioned cloth flattening, we save `zone_pose` and
provide it as input, to avoid re-sampling. This means starting cloth
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metric can be used as the reward.
| Adds a square target (green) zone.
To handle goal-conditioned cloth flattening, we save `zone_pose` and
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zone_urdf = self.fill_template(zone_template, replace)
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
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] | Python | add_cloth | <not_specific> | def add_cloth(self, env, base_pos, base_orn):
"""Adding a cloth from an .obj file."""
cloth_id = p.loadSoftBody(
fileName=self._f_cloth,
basePosition=base_pos,
baseOrientation=base_orn,
collisionMargin=self._collisionMargin,
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
"Apache-2.0"
] | Python | _sample_cloth_orientation | <not_specific> | def _sample_cloth_orientation(self):
"""Sample the bag (and let it drop) to get interesting starting states."""
orn = [self._base_orn[0] + np.random.normal(loc=0.0, scale=self._scalex),
self._base_orn[1] + np.random.normal(loc=0.0, scale=self._scaley),
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6407ae1b005c3df54361642a0c9341bdab9e01b5 | gautams3/deformable-ravens | ravens/tasks/defs_cloth.py | [
"Apache-2.0"
] | Python | reset | null | def reset(self, env, last_info=None):
"""Reset to start an episode.
Call the superclass to generate as usual, and then remove the zone
here. Requires care in `environment.py` to avoid iterating over
invalid IDs, and we need the zone in the superclass for many reasons.
If loadin... | Reset to start an episode.
Call the superclass to generate as usual, and then remove the zone
here. Requires care in `environment.py` to avoid iterating over
invalid IDs, and we need the zone in the superclass for many reasons.
If loading goal images, we cannot just override self.targe... | Reset to start an episode.
Call the superclass to generate as usual, and then remove the zone
here. Requires care in `environment.py` to avoid iterating over
invalid IDs, and we need the zone in the superclass for many reasons.
If loading goal images, we cannot just override self.target_hull_bool
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zone_pose = None
if last_info is not None:
zone_pose = last_info['sampled_zone_pose']
super().reset(env, zone_pose=zone_pose)
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dc734e3854df3efa40d9312c1e9d2a749225cd04 | gautams3/deformable-ravens | generate_goals.py | [
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] | Python | rollout | <not_specific> | def rollout(agent, env, task):
"""Standard gym environment rollout, following as in main.py."""
episode = []
total_reward = 0
obs = env.reset(task)
info = env.info
for t in range(task.max_steps):
act = agent.act(obs, info)
if len(obs) > 0 and act['primitive']:
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episode = []
total_reward = 0
obs = env.reset(task)
info = env.info
for t in range(task.max_steps):
act = agent.act(obs, info)
if len(obs) > 0 and act['primitive']:
episode.append((obs, act, info))
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dc734e3854df3efa40d9312c1e9d2a749225cd04 | gautams3/deformable-ravens | generate_goals.py | [
"Apache-2.0"
] | Python | is_goal_conditioned | <not_specific> | def is_goal_conditioned(args):
"""
Be careful with checking this condition. See `load.py`.
Here, we just check the task name.
"""
goal_tasks = ['insertion-goal', 'cable-shape-notarget', 'cable-line-notarget',
'cloth-flat-notarget', 'bag-color-goal']
return (args.task in goal_tasks) |
Be careful with checking this condition. See `load.py`.
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goal_tasks = ['insertion-goal', 'cable-shape-notarget', 'cable-line-notarget',
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return (args.task in goal_tasks) | [
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dc734e3854df3efa40d9312c1e9d2a749225cd04 | gautams3/deformable-ravens | generate_goals.py | [
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"""In some cases, we should filter out demonstrations.
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Also, for the bag envs, if we end up in a catastrophic state, I exit
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Also, for the bag envs, if we end up in a catastrophic state, I exit
gracefully and we should avoid those demos (they won't have images we
need for the dataset anyway).
| In some cases, we should filter out demonstrations.
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assert last_extras['exit_gracefully']
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756ed8495366dd61bb7762b99d09dfc77248b91f | gautams3/deformable-ravens | ravens/models/transport.py | [
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756ed8495366dd61bb7762b99d09dfc77248b91f | gautams3/deformable-ravens | ravens/models/transport.py | [
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99cd2f14d228db24ac2914bb61df8d4193c17bca | gautams3/deformable-ravens | ravens/models/regression.py | [
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] | Python | train_place_conditioned_on_pick | <not_specific> | def train_place_conditioned_on_pick(self, in_img, p, q, theta):
"""
Regress pixel p to pixel q.
Args:
input:
p: pixel (y, x)
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delta_pixel = np.array(q) - np.array(p)
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d3f56d0a421fe53cb37137680db81252b7576991 | gautams3/deformable-ravens | ravens/agents/conv_mlp.py | [
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] | Python | train | <not_specific> | def train(self, dataset, num_iter, writer, validation_dataset):
"""Train on dataset for a specific number of iterations."""
VALIDATION_RATE = 100
@tf.function
def pick_train_step(model, optim, in_tensor, yxtheta, loss_criterion):
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d3f56d0a421fe53cb37137680db81252b7576991 | gautams3/deformable-ravens | ravens/agents/conv_mlp.py | [
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] | Python | act | <not_specific> | def act(self, obs, gt_act, info):
"""Run inference and return best action given visual observations."""
self.regression_model.set_batch_size(1)
act = {'camera_config': self.camera_config, 'primitive': None}
if not obs:
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# Get heightmap from RGB-D images.
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if not obs:
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colormap, heightmap = self.get_heightmap(obs, self.camera_config)
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
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This should lead to better physics and will make it easy for an
algorithm to see the bag's top ring. Please see the cable-ring env
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
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"""Adding a bag from an .obj file."""
bag_id = p.loadSoftBody(
fileName=self._f_bag,
basePosition=base_pos,
baseOrientation=base_orn,
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
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] | Python | _sample_bag_orientation | <not_specific> | def _sample_bag_orientation(self):
"""Sample the bag (and let it drop) to get interesting starting states."""
orn = [self._base_orn[0] + np.random.normal(loc=0.0, scale=self._scale),
self._base_orn[1] + np.random.normal(loc=0.0, scale=self._scale),
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | understand_bag_top_ring | <not_specific> | def understand_bag_top_ring(self, env, base_pos):
"""By our circular bag design, there exists a top ring file.
Reading it gives us several important pieces of information. We assign to:
_top_ring_idxs: indices of the vertices (out of entire bag).
_top_ring_posi: their starting ... | By our circular bag design, there exists a top ring file.
Reading it gives us several important pieces of information. We assign to:
_top_ring_idxs: indices of the vertices (out of entire bag).
_top_ring_posi: their starting xyz positions (BEFORE simulation
or applying ... | By our circular bag design, there exists a top ring file.
Reading it gives us several important pieces of information. We assign to.
indices of the vertices (out of entire bag).
_top_ring_posi: their starting xyz positions (BEFORE simulation
or applying pose transformations). This way we can get the
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self._top_ring_f = (self._f_bag).replace('.obj', '_top_ring.txt')
self._top_ring_f = os.path.join('ravens', self._top_ring_f)
self._top_ring_idxs = []
self._top_ring_posi = []
with open(self._top_ring_f, 'r') as fh:
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | _apply_small_force | null | def _apply_small_force(self, num_iters, fx=10, fy=10, fz=8):
"""A small force to perturb the starting bag."""
# First bag. Assume that 32 beads are in one bag.
bead_idx = np.random.randint(len(self.cable_bead_IDs))
bead_id = self.cable_bead_IDs[bead_idx]
fx_1 = np.random.randint... | A small force to perturb the starting bag. | A small force to perturb the starting bag. | [
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bead_idx = np.random.randint(len(self.cable_bead_IDs))
bead_id = self.cable_bead_IDs[bead_idx]
fx_1 = np.random.randint(low=-fx, high=fx + 1)
fy_1 = np.random.randint(low=-fy, high=fy + 1)
if len(self.cable_bead_IDs) > ... | [
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | determine_task_stage | <not_specific> | def determine_task_stage(self, colormap=None, heightmap=None,
object_mask=None, visible_beads=None):
"""Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because... | Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because of this task's particular quirks in
requiring different action parameters (particularly height of the
pull) for each stage. ... | Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because of this task's particular quirks in
requiring different action parameters (particularly height of the
pull) for each stage. One option is to use this method ... | [
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object_mask=None, visible_beads=None):
if self.task_stage == 2 and (len(self.items_in_bag_IDs) == len(self.item_IDs)):
self.task_stage = 3
return (True, None)
elif self.task_stage == 3:
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | add_random_box | <not_specific> | def add_random_box(self, env, max_total_dims):
"""Generate randomly shaped box, from aligning env.
Make rand_x and rand_y add up to the max_total. Also, the aligning
env uses a box with mass 0.1, but we can make ours lighter. But, will
it cause the block to bounce too much when inserted... | Generate randomly shaped box, from aligning env.
Make rand_x and rand_y add up to the max_total. Also, the aligning
env uses a box with mass 0.1, but we can make ours lighter. But, will
it cause the block to bounce too much when inserted in the bag?
Also returning the object size, so w... | Generate randomly shaped box, from aligning env.
Also returning the object size, so we can use it for later.
Use to control how long we can make boxes. I would
keep this value at a level making these boxes comparable to the
cubes, if not smaller, used in bag-items-easy. | [
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min_val = 0.015
assert min_val*2 <= max_total_dims, min_val
rand_x = np.random.uniform(min_val, max_total_dims - min_val)
rand_y = max_total_dims - rand_x
box_size = (rand_x, rand_y, 0.03)
box_pose = self.random_pose(env, box... | [
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | determine_task_stage | <not_specific> | def determine_task_stage(self, colormap=None, heightmap=None,
object_mask=None, visible_beads=None):
"""Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because... | Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because of this task's particular quirks in
requiring different action parameters (particularly height of the
pull) for each stage. ... | Get the task stage in a consistent manner among different policies.
When training an oracle policy, we can determine the training stage,
which is critical because of this task's particular quirks in
requiring different action parameters (particularly height of the
pull) for each stage. One option is to use this method ... | [
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if self.task_stage == 2 and (len(self.items_in_bag_IDs) == len(self.item_IDs)):
self.task_stage = 3
return (True, None)
elif self.task_stage == 3:
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | add_block | <not_specific> | def add_block(self, env, block_name, max_total_dims):
"""Generate randomly shaped block for the goal based task.
Similar to the random block method used for bag-items-hard.
env uses a box with mass 0.1, but we can make ours lighter. But, will
it cause the block to bounce too much when i... | Generate randomly shaped block for the goal based task.
Similar to the random block method used for bag-items-hard.
env uses a box with mass 0.1, but we can make ours lighter. But, will
it cause the block to bounce too much when inserted in the bag?
Also returning the object size, so w... | Generate randomly shaped block for the goal based task.
Similar to the random block method used for bag-items-hard.
env uses a box with mass 0.1, but we can make ours lighter.
Also returning the object size, so we can use it for later. | [
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box_pose = self.random_pose(env, box_size)
box_template = 'assets/box/box-template.urdf'
box_urdf = self.fill_template(box_template, {'DIM': box_size})
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892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | is_item_in_bag | <not_specific> | def is_item_in_bag(self):
"""Used to determine reward. We want item to be inside the bag hull.
Actually, first detect bead visibility. If we can't see any then we should
just return 0 because that means we've messed up somewhere.
Returns an info dict, which has 'reward' keyword.
... | Used to determine reward. We want item to be inside the bag hull.
Actually, first detect bead visibility. If we can't see any then we should
just return 0 because that means we've messed up somewhere.
Returns an info dict, which has 'reward' keyword.
| Used to determine reward. We want item to be inside the bag hull.
Actually, first detect bead visibility. If we can't see any then we should
just return 0 because that means we've messed up somewhere.
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colormap, heightmap, object_mask = self.get_object_masks(self.env)
visible_beads = []
for bead in self.cable_bead_target_bag_IDs:
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"others": []
} |
892b8903a295a2c434c6cb4740082433af7abfdb | gautams3/deformable-ravens | ravens/tasks/defs_bags.py | [
"Apache-2.0"
] | Python | determine_task_stage | <not_specific> | def determine_task_stage(self, colormap=None, heightmap=None,
object_mask=None, visible_beads=None):
"""Get the task stage in a consistent manner among different policies.
This should be easier as compared to the bag-items since we only handle
two task stages (and w... | Get the task stage in a consistent manner among different policies.
This should be easier as compared to the bag-items since we only handle
two task stages (and we don't have drastically different action params).
| Get the task stage in a consistent manner among different policies.
This should be easier as compared to the bag-items since we only handle
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object_mask=None, visible_beads=None):
if self.task_stage == 2:
print('ON TASK STAGE 2, still here, should not normally happen...')
return (True, None)
BUF = 0.025
cable_IDs = np.ar... | [
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502c83dde41cd58ae0dfcc7819f5689315b1fecd | jtz/Bitcoin-Trading-Simulator | SimulatedTrading.py | [
"MIT"
] | Python | record_into_file | null | def record_into_file(self, btc_price):
"""Record account status into file."""
# total bitcoin value according to current price
btc_value = btc_price * self.__current_amount
# total asset value according to current price
total_value = btc_value + self.__current_cash
... | Record account status into file. | Record account status into file. | [
"Record",
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"file",
"."
] | def record_into_file(self, btc_price):
btc_value = btc_price * self.__current_amount
total_value = btc_value + self.__current_cash
record = [datetime.now().strftime("%Y-%m-%d,%H:%M:%S"),\
str(total_value), str(self.__current_cash), str(btc_value),\
str(self.__cu... | [
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] | [
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502c83dde41cd58ae0dfcc7819f5689315b1fecd | jtz/Bitcoin-Trading-Simulator | SimulatedTrading.py | [
"MIT"
] | Python | simulated_trading_main | null | def simulated_trading_main(self):
"""Prompt user to select actions in the submenu."""
simulated_trading = SimulatedTrading()
# check if need to initialize the trading account
file_exist = simulated_trading.check_file_exist()
if not file_exist:
simulated_trading... | Prompt user to select actions in the submenu. | Prompt user to select actions in the submenu. | [
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"to",
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"."
] | def simulated_trading_main(self):
simulated_trading = SimulatedTrading()
file_exist = simulated_trading.check_file_exist()
if not file_exist:
simulated_trading.setup_account()
while True:
select_str = input("Here is the Simulated Trading Menu: \n"
... | [
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} |
4d4f47503d58ae5c242a31467a7c59bfbdea9a51 | jtz/Bitcoin-Trading-Simulator | PriceHistory.py | [
"MIT"
] | Python | view_historical_price | <not_specific> | def view_historical_price(self, interval):
"""Return text description of the historical price."""
self.interval = interval
# view history for one day
if self.interval == "one":
self.df_one = self.get_historical_price("one")
# get a summary info... | Return text description of the historical price. | Return text description of the historical price. | [
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] | def view_historical_price(self, interval):
self.interval = interval
if self.interval == "one":
self.df_one = self.get_historical_price("one")
df_info = self.df_one.describe().round(2)
return ("\nPrice History on {}: \n"
... | [
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4d4f47503d58ae5c242a31467a7c59bfbdea9a51 | jtz/Bitcoin-Trading-Simulator | PriceHistory.py | [
"MIT"
] | Python | price_chart | null | def price_chart(self, interval):
"""Get candlestick chart of historical price."""
self.interval = interval
# get candlestick chart from one day dataframe
if self.interval == "one":
view_history = self.view_historical_price("one") ... | Get candlestick chart of historical price. | Get candlestick chart of historical price. | [
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] | def price_chart(self, interval):
self.interval = interval
if self.interval == "one":
view_history = self.view_historical_price("one")
mpf.plot(self.df_one, type="candle", style="yahoo",\
title=" Bitcoi... | [
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4d4f47503d58ae5c242a31467a7c59bfbdea9a51 | jtz/Bitcoin-Trading-Simulator | PriceHistory.py | [
"MIT"
] | Python | price_history_main | null | def price_history_main(self):
"""Prompt user to select actions in the submenu."""
while True:
# prompt user to select actions in the submenu
select_str = input("Here is the Historical Price Menu: \n"
"1 - View Historical Price For A Day \n"
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] | def price_history_main(self):
while True:
select_str = input("Here is the Historical Price Menu: \n"
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} |
ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
"MIT"
] | Python | hashhex | <not_specific> | def hashhex(s):
"""Returns a heximal formated SHA1 hash of the input string."""
s = s.encode('utf-8')
h = hashlib.sha1()
h.update(s)
return h.hexdigest() | Returns a heximal formated SHA1 hash of the input string. | Returns a heximal formated SHA1 hash of the input string. | [
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s = s.encode('utf-8')
h = hashlib.sha1()
h.update(s)
return h.hexdigest() | [
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
"MIT"
] | Python | read_csv_data | <not_specific> | def read_csv_data(csv_file):
""" Reader to parse the csv file"""
data = []
with open(args.input_file_path, encoding='utf-8', errors='ignore') as f:
csv_reader = csv.reader(f, delimiter=',')
for ind, row in enumerate(csv_reader):
if ind == 0:
header = row
... | Reader to parse the csv file | Reader to parse the csv file | [
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] | def read_csv_data(csv_file):
data = []
with open(args.input_file_path, encoding='utf-8', errors='ignore') as f:
csv_reader = csv.reader(f, delimiter=',')
for ind, row in enumerate(csv_reader):
if ind == 0:
header = row
else:
data.append(row... | [
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
"MIT"
] | Python | read_generic_file | <not_specific> | def read_generic_file(filepath):
""" reads any generic text file into
list containing one line as element
"""
text = []
with open(filepath, 'r') as f:
for line in f.read().splitlines():
text.append(line.strip())
return text | reads any generic text file into
list containing one line as element
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text = []
with open(filepath, 'r') as f:
for line in f.read().splitlines():
text.append(line.strip())
return text | [
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} |
ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
"MIT"
] | Python | calculate_metric_scores | <not_specific> | def calculate_metric_scores(cands, refs):
""" calculate Rouge-1 precision, Bert precision
and Entailment Scores
"""
# calculate rouge-1 precision
rouge = Rouge()
rouge1_p = []
for r, c in tqdm(zip(refs, cands)):
r = " ".join(list(nlp_parser.tokenize(r))).lower()
c = " ".join(... | calculate Rouge-1 precision, Bert precision
and Entailment Scores
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] | def calculate_metric_scores(cands, refs):
rouge = Rouge()
rouge1_p = []
for r, c in tqdm(zip(refs, cands)):
r = " ".join(list(nlp_parser.tokenize(r))).lower()
c = " ".join(list(nlp_parser.tokenize(c))).lower()
scores = rouge.get_scores(c, r)[0]
rouge1_p.append(round(scores['r... | [
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
"MIT"
] | Python | generate_scu | <not_specific> | def generate_scu(sentence, max_scus=5):
""" Given a scu sentence retrieve SCUs"""
srl = predictor.predict(sentence=sentence['scuSentence'])
# ipdb.set_trace()
scus = srl['verbs']
scu_list = []
tokens = srl['words']
for scu in scus:
tags = scu['tags']
words = []
if no... | Given a scu sentence retrieve SCUs | Given a scu sentence retrieve SCUs | [
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] | def generate_scu(sentence, max_scus=5):
srl = predictor.predict(sentence=sentence['scuSentence'])
scus = srl['verbs']
scu_list = []
tokens = srl['words']
for scu in scus:
tags = scu['tags']
words = []
if not ("B-ARG1" in tags or "B-ARG2" in tags or "B-ARG0" in tags):
... | [
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
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""" Given a scu sentence retrieve SCUs"""
if doc_summ=='summ':
KEY_sent = 'scuSentence'
KEY_sent_char_idx = 'scuSentCharIdx'
KEY_scu_text = 'scuText'
KEY_scu_offset = 'scuOffsets'
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KEY_sent_char_idx = 'scuSentCharIdx'
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KEY_scu_offset = 'scuOffsets'
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
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""" Given a scu sentence retrieve SCUs"""
if doc_summ=='summ':
KEY_sent = 'scuSentence'
KEY_sent_char_idx = 'scuSentCharIdx'
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ad6f0cb3eb34ef6aad08821e5982cb6876daec11 | oriern/SuperPAL | utils.py | [
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] | Python | word_aligner | <not_specific> | def word_aligner(sent1, sent2):
""" wrapper which calls the monolingual
word aligner and gives the alignment scores between
sent1 and sent2
"""
## tokenize
sent1_tok = " ".join(list(nlp_parser.tokenize(sent1)))
sent2_tok = " ".join(list(nlp_parser.tokenize(sent2)))
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sent1_tok = " ".join(list(nlp_parser.tokenize(sent1)))
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process = subprocess.Popen(['python2', 'predict_align.py', '--s1', sent1_tok,
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23d99ad3f669d5fa3e975165921c0678aaacef69 | WildMeOrg/wbia-plugin-lca | setup.py | [
"Apache-2.0"
] | Python | parse_version | <not_specific> | def parse_version(fpath='wbia_lca/__init__.py'):
"""
Statically parse the version number from a python file
"""
import ast
if not exists(fpath):
raise ValueError('fpath={!r} does not exist'.format(fpath))
with open(fpath, 'r') as file_:
sourcecode = file_.read()
pt = ast.p... |
Statically parse the version number from a python file
| Statically parse the version number from a python file | [
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] | def parse_version(fpath='wbia_lca/__init__.py'):
import ast
if not exists(fpath):
raise ValueError('fpath={!r} does not exist'.format(fpath))
with open(fpath, 'r') as file_:
sourcecode = file_.read()
pt = ast.parse(sourcecode)
class VersionVisitor(ast.NodeVisitor):
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728c7dbf236b851027fba228628b4be61ca27366 | atreyasha/memory-daemon | mem_daemon_mail.py | [
"MIT"
] | Python | sendMail | null | def sendMail(receiver, sender, password, text, smtp, port, threshold, col_log,
selfie):
"""
Function to send out email based on output of mem_daemon
Args:
receiver (str): comma-separated receiver email address(es)
sender (str): sender email adress
password (str): plaint... |
Function to send out email based on output of mem_daemon
Args:
receiver (str): comma-separated receiver email address(es)
sender (str): sender email adress
password (str): plaintext password for sender (unsafe)
text (str): proc string to display for process termination
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selfie):
subject_1 = "[Process Termination Notification] "
subject_0 = "[RAM Threshold Notification] "
pre_text_1 = ('Dear User,\n\n`{}` was terminated on {} as the total used '
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68315ff74f5476cf3242794e0083c0b9336c67ab | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode4.py | [
"Apache-2.0"
] | Python | start_stream | <not_specific> | def start_stream(queries):
"Asynchronously start a new Twitter stream"
stream = Stream(auth, RawTweetsListener())
stream.filter(track=queries, async=True)
return stream | Asynchronously start a new Twitter stream | Asynchronously start a new Twitter stream | [
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6b265c7c4ba7a60a1de1ab90b63e7f850a2efb4b | DTAIEB/Thoughtful-Data-Science | chapter 5/sampleCode6.py | [
"Apache-2.0"
] | Python | doGetNextData | null | def doGetNextData(self):
"""Return the next batch of data from the underlying stream.
Accepted return values are:
1. (x,y): tuple of list/numpy arrays representing the x and y axis
2. pandas dataframe
3. y: list/numpy array representing the y axis. In this case, the x axis is automatically created
... | Return the next batch of data from the underlying stream.
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f854662f0a97cb28a17cd5869b6efad3bf2ebe36 | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode17.py | [
"Apache-2.0"
] | Python | start_streaming_dataframe | <not_specific> | def start_streaming_dataframe(output_dir):
"Start a Spark Streaming DataFrame from a file source"
schema = StructType(
[StructField(f["name"], f["type"], True) for f in field_metadata]
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return spark.readStream \
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1a0aae4684fdc8a4e95c539d2103a6ccb6746187 | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode33.py | [
"Apache-2.0"
] | Python | start_streaming_dataframe | <not_specific> | def start_streaming_dataframe():
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schema = StructType(
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kafka_options = {
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dcada2d2a62be09e92fc5640296db9fe3d650817 | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode18.py | [
"Apache-2.0"
] | Python | start_parquet_streaming_query | <not_specific> | def start_parquet_streaming_query(csv_sdf):
"""
Create and run a streaming query from a Structured DataFrame
outputing the results into a parquet database
"""
streaming_query = csv_sdf \
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Create and run a streaming query from a Structured DataFrame
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fda17cb91688d92c692982803b574cd2ad815012 | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode1.py | [
"Apache-2.0"
] | Python | flush_buffer_if_needed | null | def flush_buffer_if_needed(self):
"Check the buffer capacity and write to a new file if needed"
length = len(self.buffered_data)
if length > 0 and length % 10 == 0:
with open(os.path.join( output_dir, "tweets{}.csv".format(self.counter)), "w") as fs:
self.counter += 1... | Check the buffer capacity and write to a new file if needed | Check the buffer capacity and write to a new file if needed | [
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] | def flush_buffer_if_needed(self):
length = len(self.buffered_data)
if length > 0 and length % 10 == 0:
with open(os.path.join( output_dir, "tweets{}.csv".format(self.counter)), "w") as fs:
self.counter += 1
csv_writer = csv.DictWriter( fs, fieldnames = fieldna... | [
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94e1d7c7ccff98c0726cf62631ddb3ad4f159f0d | DTAIEB/Thoughtful-Data-Science | chapter 7/sampleCode16.py | [
"Apache-2.0"
] | Python | start_stream | <not_specific> | def start_stream(queries):
"Asynchronously start a new Twitter stream"
stream = Stream(auth, RawTweetsListener())
stream.filter(track=queries, languages=["en"], async=True)
return stream | Asynchronously start a new Twitter stream | Asynchronously start a new Twitter stream | [
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] | def start_stream(queries):
stream = Stream(auth, RawTweetsListener())
stream.filter(track=queries, languages=["en"], async=True)
return stream | [
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f648fa7f2db64201644eb5a37143ea7885e9852d | vliz-be-opsci/pykg2tbl | pykg2tbl/service.py | [
"MIT"
] | Python | as_csv | null | def as_csv(self, fileoutputlocation:str, sep:str=","):
"""
convert and outputs csv file from result query
:param fileoutputlocation: location + filename where the csv should be written to.
:param sep: delimiter that should be used for writing the csv file.
"""
#... |
convert and outputs csv file from result query
:param fileoutputlocation: location + filename where the csv should be written to.
:param sep: delimiter that should be used for writing the csv file.
| convert and outputs csv file from result query | [
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f = open(fileoutputlocation, 'w', newline="")
writer = csv.DictWriter(f, self._data[0].keys(),delimiter=sep)
for row in self._data:
writer.writerow(row)
f.close() | [
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