def faux_dataset_random_pt(eval_envs):
print("Generating faux dataset")
units = UnrealUnits(scale=1.0)
dataset = []
for env_id in eval_envs:
segment_dataset = []
config = load_env_config(env_id)
template = load_template(env_id)
start_pt = np.asarray(config["startPos"])
second_pt = np.asarray(config["startHeading"])
end_x = random.uniform(0, 1000)
end_y = random.uniform(0, 1000)
end_pt = np.asarray([end_x, end_y])
state1 = DroneState(None, start_pt)
state2 = DroneState(None, second_pt)
state3 = DroneState(None, end_pt)
segment_dataset.append(bare_min_sample(state1, False, env_id))
segment_dataset.append(bare_min_sample(state2, False, env_id))
segment_dataset.append(bare_min_sample(state3, True, env_id))
dataset.append(segment_dataset)
return dataset
def get_landmark_pos(self, env_id):
template = load_template(env_id)
config = load_env_config(env_id)
landmark_idx = config["landmarkName"].index(template["landmark1"])
pos_x = config["xPos"][landmark_idx]
pos_y = config["zPos"][landmark_idx]
landmark_pos = np.asarray([pos_x, pos_y])
return landmark_pos
def faux_dataset_random_landmark(eval_envs):
print("Generating faux dataset")
units = UnrealUnits(scale=1.0)
dataset = []
for env_id in eval_envs:
segment_dataset = []
config = load_env_config(env_id)
template = load_template(env_id)
path = load_path(env_id)
landmark_radii = config["radius"]
start_pt = np.asarray(config["startPos"])
second_pt = np.asarray(config["startHeading"])
landmark_choice_ids = list(range(len(config["landmarkName"])))
choice_id = random.choice(landmark_choice_ids)
target_x = config["xPos"][choice_id]
target_y = config["zPos"][choice_id]
target_lm_pos = np.asarray([target_x, target_y])
landmark_dir = target_lm_pos - start_pt
method = template["side"]
theta = math.atan2(landmark_dir[1], landmark_dir[0]) + math.pi
if method == "infront":
theta = theta
elif method == "random":
theta = random.random() * 2 * math.pi
elif method == "behind":
theta = theta + math.pi
elif method == "left":
theta = theta - math.pi / 2
elif method == "right":
theta = theta + math.pi / 2
x, z = target_lm_pos[0], target_lm_pos[1]
landmark_radius = landmark_radii[choice_id]
sample_point = np.array([
x + math.cos(theta) * landmark_radius,
z + math.sin(theta) * landmark_radius
])
state1 = DroneState(None, start_pt)
state2 = DroneState(None, second_pt)
state3 = DroneState(None, sample_point)
segment_dataset.append(bare_min_sample(state1, False, env_id))
segment_dataset.append(bare_min_sample(state2, False, env_id))
segment_dataset.append(bare_min_sample(state3, True, env_id))
dataset.append(segment_dataset)
return dataset
def get_mentioned_landmark_side_tplt(env_id):
template = load_template(env_id)
mentioned_lm = template["landmark1"]
lm_name2index = get_landmark_name_to_index()
mentioned_index = lm_name2index[mentioned_lm]
mentioned_side = template["side"]
side_name2index = get_side_name2idx()
side_idx = side_name2index[mentioned_side]
return mentioned_lm, mentioned_index, mentioned_side, side_idx
def __add_aux_data_template(self, labels, env_id):
template_json = load_template(env_id)
# Template data is unavailable - means this is probably real natural language
if template_json is None:
return False
mentioned_landmark_idx, mentioned_side_idx = self.__get_goal_landmark_idx(template_json)
labels[:, :, 6] = mentioned_landmark_idx
labels[:, :, 7] = mentioned_side_idx
for landmark_idx in range(labels.size(1)):
labels[:, landmark_idx, 8] = 1.0 if landmark_idx == mentioned_landmark_idx else 0
return True
def is_ambiguous(env_id):
template = load_template(env_id)
config = load_env_config(env_id)
#TODO: Handle the case where instructions refer to multiple landmarks
ref_landmark = template["landmark1"]
occ_count = 0
for landmark_name in config["landmarkName"]:
if has_ambiguous_noun_phrase(landmark_name, ref_landmark):
occ_count += 1
if occ_count == 0:
print("Error! Referred to landmark that's not found in env!")
exit(-1)
# More than one such landmark occurs in the test set
if occ_count > 1:
return True
else:
return False
def correct_side(self, rollout, env_id):
template = load_template(env_id)
landmark_pos = self.get_landmark_pos(env_id)
last_pos = rollout[-1].state.get_pos()
first_pos = rollout[0].state.get_pos()
dir_landmark = landmark_pos - first_pos
if len(N_SIDES) == 4:
dir_lm_to_last = last_pos - landmark_pos
dir_landmark_norm = dir_landmark / (np.linalg.norm(dir_landmark) +
1e-18)
dir_ortho_norm = np.asarray(
[dir_landmark_norm[1], -dir_landmark_norm[0]])
proj = np.dot(dir_lm_to_last, dir_landmark_norm)
opp_proj = np.dot(dir_lm_to_last, dir_ortho_norm)
angle = math.atan2(proj, opp_proj)
DEG45 = 0.785398
if template["side"] == "right":
return -DEG45 < angle < DEG45
elif template["side"] == "back":
return DEG45 < angle < 3 * DEG45
elif template["side"] == "left":
return 3 * DEG45 < angle < math.pi or -math.pi < angle < -3 * DEG45
elif template["side"] == "front":
return -3 * DEG45 < angle < -DEG45
else:
print("Unknown side: ", template["side"])
print("Angle: ", angle)
else: # len(N_SIDES) = 2
dir_end = last_pos - first_pos
z = np.cross(dir_landmark, dir_end)
if template["side"] == "left":
return z > 0
else:
return z < 0
