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test_ai2thor_mapping.py
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test_ai2thor_mapping.py
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import os
import platform
import random
import sys
import urllib
import urllib.request
import warnings
from collections import defaultdict
# noinspection PyUnresolvedReferences
from tempfile import mkdtemp
from typing import Dict, List, Tuple, cast
# noinspection PyUnresolvedReferences
import ai2thor
# noinspection PyUnresolvedReferences
import ai2thor.wsgi_server
import compress_pickle
import numpy as np
import torch
from allenact.algorithms.onpolicy_sync.storage import RolloutBlockStorage
from allenact.base_abstractions.misc import Memory, ActorCriticOutput
from allenact.embodiedai.mapping.mapping_utils.map_builders import SemanticMapBuilder
from allenact.utils.experiment_utils import set_seed
from allenact.utils.system import get_logger
from allenact.utils.tensor_utils import batch_observations
from allenact_plugins.ithor_plugin.ithor_sensors import (
RelativePositionChangeTHORSensor,
ReachableBoundsTHORSensor,
BinnedPointCloudMapTHORSensor,
SemanticMapTHORSensor,
)
from allenact_plugins.ithor_plugin.ithor_util import get_open_x_displays
from allenact_plugins.robothor_plugin.robothor_sensors import DepthSensorThor
from constants import ABS_PATH_OF_TOP_LEVEL_DIR
class TestAI2THORMapSensors(object):
def setup_path_for_use_with_rearrangement_project(self) -> bool:
if platform.system() != "Darwin" and len(get_open_x_displays()) == 0:
wrn_msg = "Cannot run tests as there seem to be no open displays!"
warnings.warn(wrn_msg)
get_logger().warning(wrn_msg)
return False
os.chdir(ABS_PATH_OF_TOP_LEVEL_DIR)
sys.path.append(
os.path.join(ABS_PATH_OF_TOP_LEVEL_DIR, "projects/ithor_rearrangement")
)
try:
import rearrange
except ImportError:
wrn_msg = (
"Could not import `rearrange`. Is it possible you have"
" not initialized the submodules (i.e. by running"
" `git submodule init; git submodule update;`)?"
)
warnings.warn(wrn_msg)
get_logger().warning(wrn_msg)
return False
return True
def test_binned_and_semantic_mapping(self, tmpdir):
try:
if not self.setup_path_for_use_with_rearrangement_project():
return
from baseline_configs.rearrange_base import RearrangeBaseExperimentConfig
from baseline_configs.walkthrough.walkthrough_rgb_base import (
WalkthroughBaseExperimentConfig,
)
from rearrange.constants import (
FOV,
PICKUPABLE_OBJECTS,
OPENABLE_OBJECTS,
)
from datagen.datagen_utils import get_scenes
ORDERED_OBJECT_TYPES = list(sorted(PICKUPABLE_OBJECTS + OPENABLE_OBJECTS))
map_range_sensor = ReachableBoundsTHORSensor(margin=1.0)
map_info = dict(
map_range_sensor=map_range_sensor,
vision_range_in_cm=40 * 5,
map_size_in_cm=1050,
resolution_in_cm=5,
)
map_sensors = [
RelativePositionChangeTHORSensor(),
map_range_sensor,
DepthSensorThor(
height=224,
width=224,
use_normalization=False,
uuid="depth",
),
BinnedPointCloudMapTHORSensor(
fov=FOV,
ego_only=False,
**map_info,
),
SemanticMapTHORSensor(
fov=FOV,
ego_only=False,
ordered_object_types=ORDERED_OBJECT_TYPES,
**map_info,
),
]
all_sensors = [*WalkthroughBaseExperimentConfig.SENSORS, *map_sensors]
open_x_displays = []
try:
open_x_displays = get_open_x_displays()
except (AssertionError, IOError):
pass
walkthrough_task_sampler = WalkthroughBaseExperimentConfig.make_sampler_fn(
stage="train",
sensors=all_sensors,
scene_to_allowed_rearrange_inds={s: [0] for s in get_scenes("train")},
force_cache_reset=True,
allowed_scenes=None,
seed=1,
x_display=open_x_displays[0] if len(open_x_displays) != 0 else None,
thor_controller_kwargs={
**RearrangeBaseExperimentConfig.THOR_CONTROLLER_KWARGS,
# "server_class": ai2thor.wsgi_server.WsgiServer, # Only for debugging
},
)
targets_path = os.path.join(tmpdir, "rearrange_mapping_examples.pkl.gz")
urllib.request.urlretrieve(
"https://ai2-prior-allenact-public-test.s3-us-west-2.amazonaws.com/ai2thor_mapping/rearrange_mapping_examples.pkl.gz",
targets_path,
)
goal_obs_dict = compress_pickle.load(targets_path)
def compare_recursive(obs, goal_obs, key_list: List):
if isinstance(obs, Dict):
for k in goal_obs:
compare_recursive(
obs=obs[k], goal_obs=goal_obs[k], key_list=key_list + [k]
)
elif isinstance(obs, (List, Tuple)):
for i in range(len(goal_obs)):
compare_recursive(
obs=obs[i], goal_obs=goal_obs[i], key_list=key_list + [i]
)
else:
# Should be a numpy array at this point
assert isinstance(obs, np.ndarray) and isinstance(
goal_obs, np.ndarray
), f"After {key_list}, not numpy arrays, obs={obs}, goal_obs={goal_obs}"
obs = 1.0 * obs
goal_obs = 1.0 * goal_obs
goal_where_nan = np.isnan(goal_obs)
obs_where_nan = np.isnan(obs)
where_nan_not_equal = (goal_where_nan != obs_where_nan).sum()
# assert (
# where_nan_not_equal.sum() <= 1
# and where_nan_not_equal.mean() < 1e3
# )
where_nan = np.logical_or(goal_where_nan, obs_where_nan)
obs[where_nan] = 0.0
goal_obs[where_nan] = 0.0
def special_mean(v):
while len(v.shape) > 2:
v = v.sum(-1)
return v.mean()
numer = np.abs(obs - goal_obs)
denom = np.abs(
np.stack((obs, goal_obs, np.ones_like(obs)), axis=0)
).max(0)
difference = special_mean(numer / denom)
# assert (
# difference < 1.2e-3
# ), f"Difference of {np.abs(obs - goal_obs).mean()} at {key_list}."
if (
len(obs.shape) >= 2
and obs.shape[0] == obs.shape[1]
and obs.shape[0] > 1
):
# Sanity check that rotating the observations makes them not-equal
rot_obs = np.rot90(obs)
numer = np.abs(rot_obs - goal_obs)
denom = np.abs(
np.stack((rot_obs, goal_obs, np.ones_like(obs)), axis=0)
).max(0)
rot_difference = special_mean(numer / denom)
assert (
difference < rot_difference or (obs == rot_obs).all()
), f"Too small a difference ({(numer / denom).mean()})."
observations_dict = defaultdict(lambda: [])
for i in range(5): # Why 5, why not 5?
set_seed(i)
task = walkthrough_task_sampler.next_task()
obs_list = observations_dict[i]
obs_list.append(task.get_observations())
k = 0
compare_recursive(
obs=obs_list[0], goal_obs=goal_obs_dict[i][0], key_list=[i, k]
)
while not task.is_done():
obs = task.step(
action=task.action_names().index(
random.choice(
3
* [
"move_ahead",
"rotate_right",
"rotate_left",
"look_up",
"look_down",
]
+ ["done"]
)
)
).observation
k += 1
obs_list.append(obs)
compare_recursive(
obs=obs,
goal_obs=goal_obs_dict[i][task.num_steps_taken()],
key_list=[i, k],
)
# Free space metric map in RGB using pointclouds coming from depth images. This
# is built iteratively after every step.
# R - is used to encode points at a height < 0.02m (i.e. the floor)
# G - is used to encode points at a height between 0.02m and 2m, i.e. objects the agent would run into
# B - is used to encode points higher than 2m, i.e. ceiling
# Uncomment if you wish to visualize the observations:
import matplotlib.pyplot as plt
plt.imshow(
np.flip(255 * (obs["binned_pc_map"]["map"] > 0), 0)
) # np.flip because we expect "up" to be -row
plt.title("Free space map")
plt.show()
plt.close()
# See also `obs["binned_pc_map"]["egocentric_update"]` to see the
# the metric map from the point of view of the agent before it is
# rotated into the world-space coordinates and merged with past observations.
# Semantic map in RGB which is iteratively revealed using depth maps to figure out what
# parts of the scene the agent has seen so far.
# This map has shape 210x210x72 with the 72 channels corresponding to the 72
# object types in `ORDERED_OBJECT_TYPES`
semantic_map = obs["semantic_map"]["map"]
# We can't display all 72 channels in an RGB image so instead we randomly assign
# each object a color and then just allow them to overlap each other
colored_semantic_map = (
SemanticMapBuilder.randomly_color_semantic_map(semantic_map)
)
# Here's the full semantic map with nothing masked out because the agent
# hasn't seen it yet
colored_semantic_map_no_fog = (
SemanticMapBuilder.randomly_color_semantic_map(
map_sensors[
-1
].semantic_map_builder.ground_truth_semantic_map
)
)
# Uncomment if you wish to visualize the observations:
# import matplotlib.pyplot as plt
# plt.imshow(
# np.flip( # np.flip because we expect "up" to be -row
# np.concatenate(
# (
# colored_semantic_map,
# 255 + 0 * colored_semantic_map[:, :10, :],
# colored_semantic_map_no_fog,
# ),
# axis=1,
# ),
# 0,
# )
# )
# plt.title("Semantic map with and without exploration fog")
# plt.show()
# plt.close()
# See also
# * `obs["semantic_map"]["egocentric_update"]`
# * `obs["semantic_map"]["explored_mask"]`
# * `obs["semantic_map"]["egocentric_mask"]`
# To save observations for comparison against future runs, uncomment the below.
# os.makedirs("tmp_out", exist_ok=True)
# compress_pickle.dump(
# {**observations_dict}, "tmp_out/rearrange_mapping_examples.pkl.gz"
# )
finally:
try:
walkthrough_task_sampler.close()
except NameError:
pass
def test_pretrained_rearrange_walkthrough_mapping_agent(self, tmpdir):
try:
if not self.setup_path_for_use_with_rearrangement_project():
return
from baseline_configs.rearrange_base import RearrangeBaseExperimentConfig
from baseline_configs.walkthrough.walkthrough_rgb_mapping_ppo import (
WalkthroughRGBMappingPPOExperimentConfig,
)
from rearrange.constants import (
FOV,
PICKUPABLE_OBJECTS,
OPENABLE_OBJECTS,
)
from datagen.datagen_utils import get_scenes
open_x_displays = []
try:
open_x_displays = get_open_x_displays()
except (AssertionError, IOError):
pass
walkthrough_task_sampler = (
WalkthroughRGBMappingPPOExperimentConfig.make_sampler_fn(
stage="train",
scene_to_allowed_rearrange_inds={
s: [0] for s in get_scenes("train")
},
force_cache_reset=True,
allowed_scenes=None,
seed=2,
x_display=open_x_displays[0] if len(open_x_displays) != 0 else None,
)
)
named_losses = (
WalkthroughRGBMappingPPOExperimentConfig.training_pipeline()._named_losses
)
ckpt_path = os.path.join(
tmpdir, "pretrained_walkthrough_mapping_agent_75mil.pt"
)
if not os.path.exists(ckpt_path):
urllib.request.urlretrieve(
"https://prior-model-weights.s3.us-east-2.amazonaws.com/embodied-ai/rearrangement/walkthrough/pretrained_walkthrough_mapping_agent_75mil.pt",
ckpt_path,
)
state_dict = torch.load(
ckpt_path,
map_location="cpu",
)
walkthrough_model = WalkthroughRGBMappingPPOExperimentConfig.create_model()
walkthrough_model.load_state_dict(state_dict["model_state_dict"])
memory = RolloutBlockStorage.create_memory(
spec=walkthrough_model.recurrent_memory_specification, num_samplers=1
).step_squeeze(0)
masks = torch.FloatTensor([0]).view(1, 1, 1)
binned_map_losses = []
semantic_map_losses = []
for i in range(5):
masks = 0 * masks
set_seed(i + 1)
task = walkthrough_task_sampler.next_task()
def add_step_dim(input):
if isinstance(input, torch.Tensor):
return input.unsqueeze(0)
elif isinstance(input, Dict):
return {k: add_step_dim(v) for k, v in input.items()}
else:
raise NotImplementedError
batch = add_step_dim(batch_observations([task.get_observations()]))
while not task.is_done():
# noinspection PyTypeChecker
ac_out, memory = cast(
Tuple[ActorCriticOutput, Memory],
walkthrough_model.forward(
observations=batch,
memory=memory,
prev_actions=None,
masks=masks,
),
)
binned_map_losses.append(
named_losses["binned_map_loss"]
.loss(
step_count=0, # Not used in this loss
batch={"observations": batch},
actor_critic_output=ac_out,
)[0]
.item()
)
assert (
binned_map_losses[-1] < 0.16
), f"Binned map loss to large at ({i}, {task.num_steps_taken()})"
semantic_map_losses.append(
named_losses["semantic_map_loss"]
.loss(
step_count=0, # Not used in this loss
batch={"observations": batch},
actor_critic_output=ac_out,
)[0]
.item()
)
assert (
semantic_map_losses[-1] < 0.004
), f"Semantic map loss to large at ({i}, {task.num_steps_taken()})"
masks = masks.fill_(1.0)
obs = task.step(
action=ac_out.distributions.sample().item()
).observation
batch = add_step_dim(batch_observations([obs]))
if task.num_steps_taken() >= 10:
break
# To save observations for comparison against future runs, uncomment the below.
# os.makedirs("tmp_out", exist_ok=True)
# compress_pickle.dump(
# {**observations_dict}, "tmp_out/rearrange_mapping_examples.pkl.gz"
# )
finally:
try:
walkthrough_task_sampler.close()
except NameError:
pass
if __name__ == "__main__":
TestAI2THORMapSensors().test_binned_and_semantic_mapping(mkdtemp()) # type:ignore
# TestAI2THORMapSensors().test_binned_and_semantic_mapping("tmp_out") # Used for local debugging
# TestAI2THORMapSensors().test_pretrained_rearrange_walkthrough_mapping_agent(
# mkdtemp() # "tmp_out"
# ) # Used for local debugging