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Fix a number of tests

This commit is contained in:
hang-yin 2024-07-17 16:22:27 -07:00
parent 1dfb2cd6fb
commit 1f5b918b10
14 changed files with 74 additions and 43 deletions
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10
omnigibson/examples/object_states/dicing_demo.py
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@ -95,15 +95,17 @@ def main(random_selection=False, headless=False, short_exec=False):
orientation=T.euler2quat([-math.pi / 2, 0, 0]),
)
input("The knife will fall on the apple and dice it. Press [ENTER] to continue.")
if short_exec == False:
input("The knife will fall on the apple and dice it. Press [ENTER] to continue.")
# Step simulation for a bit so that apple is diced
for i in range(1000):
for _ in range(1000):
env.step(th.empty(0))
input("Apple has been diced! Press [ENTER] to terminate the demo.")
if short_exec == False:
input("Apple has been diced! Press [ENTER] to terminate the demo.")
# Always close environment at the end
# Always close simulator at the end
env.close()

14
omnigibson/examples/object_states/heat_source_or_sink_demo.py
View File

@ -8,7 +8,7 @@ from omnigibson.macros import gm
gm.ENABLE_OBJECT_STATES = True
def main():
def main(random_selection=False, headless=False, short_exec=False):
# Create the scene config to load -- empty scene with a stove object added
cfg = {
"scene": {
@ -56,7 +56,8 @@ def main():
assert not heat_source_state
# Toggle on stove, notify user
input("Heat source will now turn ON: Press ENTER to continue.")
if not short_exec:
input("Heat source will now turn ON: Press ENTER to continue.")
stove.states[object_states.ToggledOn].set_value(True)
assert stove.states[object_states.ToggledOn].get_value()
@ -71,20 +72,23 @@ def main():
env.step(th.empty(0))
# Toggle off stove, notify user
input("Heat source will now turn OFF: Press ENTER to continue.")
if not short_exec:
input("Heat source will now turn OFF: Press ENTER to continue.")
stove.states[object_states.ToggledOn].set_value(False)
assert not stove.states[object_states.ToggledOn].get_value()
for _ in range(200):
env.step(th.empty(0))
# Move stove, notify user
input("Heat source is now moving: Press ENTER to continue.")
if not short_exec:
input("Heat source is now moving: Press ENTER to continue.")
stove.set_position(th.tensor([0, 1.0, 0.61]))
for i in range(100):
env.step(th.empty(0))
# Toggle on stove again, notify user
input("Heat source will now turn ON: Press ENTER to continue.")
if not short_exec:
input("Heat source will now turn ON: Press ENTER to continue.")
stove.states[object_states.ToggledOn].set_value(True)
assert stove.states[object_states.ToggledOn].get_value()
for i in range(500):

16
omnigibson/examples/object_states/heated_state_demo.py
View File

@ -8,7 +8,7 @@ from omnigibson.macros import gm
gm.ENABLE_OBJECT_STATES = True
def main():
def main(random_selection=False, headless=False, short_exec=False):
# Define object configurations for objects to load -- we want to load a light and three bowls
obj_configs = []
@ -70,8 +70,9 @@ def main():
print("==== Initial state ====")
report_states(objs)
# Notify user that we're about to heat the object
input("Objects will be heated, and steam will slowly rise. Press ENTER to continue.")
if not short_exec:
# Notify user that we're about to heat the object
input("Objects will be heated, and steam will slowly rise. Press ENTER to continue.")
# Heated.
for obj in objs:
@ -83,7 +84,8 @@ def main():
# After a while, objects will be below the Steam temperature threshold.
print("==== Objects are now heated... ====")
print()
for _ in range(2000):
max_iterations = 2000 if not short_exec else 100
for _ in range(max_iterations):
env.step(th.empty(0))
# Also print temperatures
temps = [f"{obj.states[object_states.Temperature].get_value():>7.2f}" for obj in objs]
@ -94,8 +96,10 @@ def main():
print("==== Objects are no longer heated... ====")
report_states(objs)
# Close environment at the end
input("Demo completed. Press ENTER to shutdown environment.")
if not short_exec:
# Close environment at the end
input("Demo completed. Press ENTER to shutdown environment.")
env.close()

23
omnigibson/examples/object_states/object_state_texture_demo.py
View File

@ -11,7 +11,7 @@ gm.USE_GPU_DYNAMICS = True
gm.ENABLE_HQ_RENDERING = True
def main():
def main(random_selection=False, headless=False, short_exec=False):
# Create the scene config to load -- empty scene plus a cabinet
cfg = {
"scene": {
@ -87,38 +87,45 @@ def main():
report_states()
# Notify user that we're about to freeze the object, and then freeze the object
input("\nObject will be frozen. Press ENTER to continue.")
if not short_exec:
input("\nObject will be frozen. Press ENTER to continue.")
obj.states[object_states.Temperature].set_value(-50)
report_states()
# Notify user that we're about to cook the object, and then cook the object
input("\nObject will be cooked. Press ENTER to continue.")
if not short_exec:
input("\nObject will be cooked. Press ENTER to continue.")
obj.states[object_states.Temperature].set_value(100)
report_states()
# Notify user that we're about to burn the object, and then burn the object
input("\nObject will be burned. Press ENTER to continue.")
if not short_exec:
input("\nObject will be burned. Press ENTER to continue.")
obj.states[object_states.Temperature].set_value(250)
report_states()
# Notify user that we're about to reset the object to its default state, and then reset state
input("\nObject will be reset to default state. Press ENTER to continue.")
if not short_exec:
input("\nObject will be reset to default state. Press ENTER to continue.")
obj.states[object_states.Temperature].set_value(macros.object_states.temperature.DEFAULT_TEMPERATURE)
obj.states[object_states.MaxTemperature].set_value(macros.object_states.temperature.DEFAULT_TEMPERATURE)
report_states()
# Notify user that we're about to soak the object, and then soak the object
input("\nObject will be saturated with water. Press ENTER to continue.")
if not short_exec:
input("\nObject will be saturated with water. Press ENTER to continue.")
obj.states[object_states.Saturated].set_value(env.scene.get_system("water"), True)
report_states()
# Notify user that we're about to unsoak the object, and then unsoak the object
input("\nObject will be unsaturated with water. Press ENTER to continue.")
if not short_exec:
input("\nObject will be unsaturated with water. Press ENTER to continue.")
obj.states[object_states.Saturated].set_value(env.scene.get_system("water"), False)
report_states()
# Close environment at the end
input("Demo completed. Press ENTER to shutdown environment.")
if not short_exec:
input("Demo completed. Press ENTER to shutdown environment.")
env.close()

1
omnigibson/examples/object_states/overlaid_demo.py
View File

@ -68,6 +68,7 @@ def main(random_selection=False, headless=False, short_exec=False):
while steps != max_steps:
print(f"Overlaid {carpet.states[Overlaid].get_value(breakfast_table)} ", end="\r")
env.step(th.empty(0))
steps += 1
# Shut down env at the end
env.close()

4
omnigibson/examples/objects/draw_bounding_box.py
View File

@ -76,7 +76,9 @@ def main(random_selection=False, headless=False, short_exec=False):
if "3d" not in bbox_modality:
from omnigibson.utils.deprecated_utils import colorize_bboxes
colorized_img = colorize_bboxes(bboxes_2d_data=obs[bbox_modality], bboxes_2d_rgb=obs["rgb"], num_channels=4)
colorized_img = colorize_bboxes(
bboxes_2d_data=obs[bbox_modality], bboxes_2d_rgb=obs["rgb"].cpu().numpy(), num_channels=4
)
fpath = f"{bbox_modality}_img.png"
plt.imsave(fpath, colorized_img)
og.log.info(f"Saving modality [{bbox_modality}] image to: {fpath}")

2
omnigibson/objects/object_base.py
View File

@ -327,7 +327,7 @@ class BaseObject(EntityPrim, Registerable, metaclass=ABCMeta):
}
material.enable_emission = True if enabled else self._highlight_cached_values[material]["enable_emission"]
material.emissive_color = (
m.HIGHLIGHT_RGB if enabled else self._highlight_cached_values[material]["emissive_color"]
m.HIGHLIGHT_RGB if enabled else self._highlight_cached_values[material]["emissive_color"].tolist()
)
material.emissive_intensity = (
m.HIGHLIGHT_INTENSITY if enabled else self._highlight_cached_values[material]["emissive_intensity"]

13
omnigibson/objects/primitive_object.py
View File

@ -184,7 +184,11 @@ class PrimitiveObject(StatefulObject):
raise ValueError("Prim type must either be PrimType.RIGID or PrimType.CLOTH for loading a primitive object")
visual_geom_prim.color = self._load_config["color"]
visual_geom_prim.opacity = self._load_config["opacity"].item()
visual_geom_prim.opacity = (
self._load_config["opacity"].item()
if isinstance(self._load_config["opacity"], th.Tensor)
else self._load_config["opacity"]
)
@property
def radius(self):
@ -326,10 +330,13 @@ class PrimitiveObject(StatefulObject):
for attr in (geom.GetPointsAttr(), geom.GetNormalsAttr()):
# Scale all three axes by the scaling factor
vals = th.tensor(attr.Get()).double() * scaling_factor
attr.Set(lazy.pxr.Vt.Vec3fArray([lazy.pxr.Gf.Vec3f(*v) for v in vals]))
attr.Set(lazy.pxr.Vt.Vec3fArray([lazy.pxr.Gf.Vec3f(*v.tolist()) for v in vals]))
geom.GetExtentAttr().Set(
lazy.pxr.Vt.Vec3fArray(
[lazy.pxr.Gf.Vec3f(*(-self._extents / 2.0)), lazy.pxr.Gf.Vec3f(*(self._extents / 2.0))]
[
lazy.pxr.Gf.Vec3f(*(-self._extents / 2.0).tolist()),
lazy.pxr.Gf.Vec3f(*(self._extents / 2.0).tolist()),
]
)
)

9
omnigibson/sensors/vision_sensor.py
View File

@ -294,7 +294,8 @@ class VisionSensor(BaseSensor):
# All segmentation modalities return uint32 warp arrays, but PyTorch doesn't support it
if modality in ["seg_semantic", "seg_instance", "seg_instance_id"]:
obs[modality] = obs[modality].view(lazy.warp.int32)
obs[modality] = lazy.warp.to_torch(obs[modality])
if not modality in ["bbox_2d_tight", "bbox_2d_loose", "bbox_3d"]:
obs[modality] = lazy.warp.to_torch(obs[modality])
if modality == "seg_semantic":
id_to_labels = raw_obs["info"]["idToLabels"]
obs[modality], info[modality] = self._remap_semantic_segmentation(obs[modality], id_to_labels)
@ -341,7 +342,7 @@ class VisionSensor(BaseSensor):
replicator_mapping[key] = categories[0]
assert (
replicator_mapping[key] in semantic_class_id_to_name(self._scene).values()
replicator_mapping[key] in semantic_class_id_to_name().values()
), f"Class {val['class']} does not exist in the semantic class name to id mapping!"
image_keys = th.unique(img)
@ -349,9 +350,7 @@ class VisionSensor(BaseSensor):
set(replicator_mapping.keys())
), "Semantic segmentation image does not match the original id_to_labels mapping."
return VisionSensor.SEMANTIC_REMAPPER.remap(
replicator_mapping, semantic_class_id_to_name(self._scene), img, image_keys
)
return VisionSensor.SEMANTIC_REMAPPER.remap(replicator_mapping, semantic_class_id_to_name(), img, image_keys)
def _remap_instance_segmentation(self, img, id_to_labels, semantic_img, semantic_labels, id=False):
"""

1
omnigibson/utils/asset_utils.py
View File

@ -163,6 +163,7 @@ def get_all_system_categories():
og_categories_path = os.path.join(og_dataset_path, "systems")
categories = [f for f in os.listdir(og_categories_path) if not is_dot_file(f)]
categories.append("cloth")
return sorted(categories)

10
omnigibson/utils/constants.py
View File

@ -11,7 +11,7 @@ import torch as th
import omnigibson as og
from omnigibson.macros import gm
from omnigibson.utils.asset_utils import get_all_object_categories, get_og_avg_category_specs
from omnigibson.utils.asset_utils import get_all_object_categories, get_all_system_categories
MAX_INSTANCE_COUNT = th.iinfo(th.int32).max
MAX_CLASS_COUNT = th.iinfo(th.int32).max
@ -170,7 +170,7 @@ UNDER_OBJECTS = [
@cache
def semantic_class_name_to_id(scene):
def semantic_class_name_to_id():
"""
Get mapping from semantic class name to class id
@ -178,8 +178,8 @@ def semantic_class_name_to_id(scene):
dict: class name to class id
"""
categories = get_all_object_categories()
systems = get_all_system_categories()
systems = sorted(scene.system_registry.object_names)
all_semantics = sorted(set(categories + systems + ["background", "unlabelled", "object", "light", "agent"]))
# Assign a unique class id to each class name with hashing, the upper limit here is the max of int32
@ -189,11 +189,11 @@ def semantic_class_name_to_id(scene):
@cache
def semantic_class_id_to_name(scene):
def semantic_class_id_to_name():
"""
Get mapping from semantic class id to class name
Returns:
dict: class id to class name
"""
return {v: k for k, v in semantic_class_name_to_id(scene).items()}
return {v: k for k, v in semantic_class_name_to_id().items()}

7
omnigibson/utils/deprecated_utils.py
View File

@ -10,6 +10,7 @@ import omni
import omni.graph.core as ogc
import omni.timeline
import omni.usd as ou
import numpy as np
import torch
import torch as th
import warp as wp
@ -681,10 +682,10 @@ def colorize_bboxes(bboxes_2d_data, bboxes_2d_rgb, num_channels=3):
for bbox_2d in bboxes_2d_data:
semantic_id_list.append(bbox_2d["semanticId"])
bbox_2d_list.append(bbox_2d)
semantic_id_list_np = th.unique(th.tensor(semantic_id_list))
semantic_id_list_np = np.unique(np.array(semantic_id_list))
color_list = random_colours(len(semantic_id_list_np.tolist()), True, num_channels)
for bbox_2d in bbox_2d_list:
index = th.where(semantic_id_list_np == bbox_2d["semanticId"])[0][0]
index = np.where(semantic_id_list_np == bbox_2d["semanticId"])[0][0]
bbox_color = color_list[index]
outline = (bbox_color[0], bbox_color[1], bbox_color[2])
if num_channels == 4:
@ -697,5 +698,5 @@ def colorize_bboxes(bboxes_2d_data, bboxes_2d_rgb, num_channels=3):
rgb_img_draw.rectangle(
[(bbox_2d["x_min"], bbox_2d["y_min"]), (bbox_2d["x_max"], bbox_2d["y_max"])], outline=outline, width=2
)
bboxes_2d_rgb = th.tensor(rgb_img)
bboxes_2d_rgb = np.array(rgb_img)
return bboxes_2d_rgb

2
omnigibson/utils/vision_utils.py
View File

@ -153,7 +153,7 @@ class Remapper:
f"We do not have semantic information about bounding box semantic id {semantic_id} yet. Marking as unlabelled."
)
self.warning_printed.add(semantic_id)
return semantic_class_name_to_id(scene)["unlabelled"]
return semantic_class_name_to_id()["unlabelled"]
return self.key_array[semantic_id]

5
tests/create_tests_of_examples.py
View File

@ -17,7 +17,10 @@ EXAMPLES_TO_SKIP = [
"learning.navigation_policy_demo",
"teleoperation.robot_teleoperate_demo",
# TODO: Temporarily skip the following examples
"robots.all_robots_visualizer",
"robots.all_robots_visualizer", # waiting for base link bug to be fixed
"object_states.attachment_demo", # seg fualt??
"environments.behavior_env_demo", # This only works with pre-sampled cached BEHAVIOR activity scene
"robots.advanced.ik_example", # waiting for base link bug to be fixed; Fetch is fix base in this example
]