def setUp(self):
super(NDHEnvTest, self).setUp()
self.data_dir = FLAGS.test_srcdir + ('valan/r2r/testdata')
self.reward_fn_type = 'distance_to_goal'
self._env_config = hparam.HParams(
problem='NDH',
history='all',
path_type='trusted_path',
max_goal_room_panos=4,
base_path=self.data_dir,
problem_path=os.path.join(self.data_dir, 'NDH'),
vocab_file='vocab.txt',
images_per_pano=36,
max_conns=14,
image_encoding_dim=2052,
image_features_dir=os.path.join(self.data_dir, 'image_features'),
instruction_len=50,
max_agent_actions=6,
reward_fn_type=self.reward_fn_type,
reward_fn=env_config.RewardFunction.get_reward_fn(
self.reward_fn_type))
self._runtime_config = common.RuntimeConfig(task_id=0, num_tasks=1)
self._env = env_ndh.NDHEnv(data_sources=['small_split'],
runtime_config=self._runtime_config,
env_config=self._env_config)
# For deterministic behavior in test
np.random.seed(0)
def get_environment(self):
if not self._env:
assert self._data_sources, 'data_sources must be non-empty.'
self._env = env_ndh.NDHEnv(
data_sources=self._data_sources,
runtime_config=self._runtime_config,
env_config=env_ndh_config.get_ndh_env_config())
return self._env
def testStepToGoalRoom(self):
self.reward_fn_type = 'distance_to_room'
self._env_config = hparam.HParams(
problem='NDH',
history='all',
path_type='trusted_path',
max_goal_room_panos=4,
base_path=self.data_dir,
problem_path=os.path.join(self.data_dir, 'NDH'),
vocab_file='vocab.txt',
images_per_pano=36,
max_conns=14,
image_encoding_dim=2052,
image_features_dir=os.path.join(self.data_dir, 'image_features'),
instruction_len=50,
max_agent_actions=6,
reward_fn_type=self.reward_fn_type,
reward_fn=env_config.RewardFunction.get_reward_fn(
self.reward_fn_type))
self._runtime_config = common.RuntimeConfig(task_id=0, num_tasks=1)
self._env = env_ndh.NDHEnv(data_sources=['small_split'],
runtime_config=self._runtime_config,
env_config=self._env_config)
scan_id = 0 # testdata only has single scan 'gZ6f7yhEvPG'
_ = self._env.reset()
golden_path = [
'ba27da20782d4e1a825f0a133ad84da9',
'47d8a8282c1c4a7fb3eeeacc45e9d959', # in the goal room
'0ee20663dfa34b438d48750ddcd7366c' # in the goal room
]
# Step through the trajectory and verify the env_output.
for i, action in enumerate(
[self._get_pano_id(p, scan_id) for p in golden_path]):
expected_time_step = i + 1
expected_heading, expected_pitch = self._env._get_heading_pitch(
action, scan_id, expected_time_step)
if i + 1 < len(golden_path):
expected_oracle_action = self._get_pano_id(
golden_path[i + 1], scan_id)
else:
expected_oracle_action = constants.STOP_NODE_ID
expected_reward = 1 if i <= 1 else 0
env_test.verify_env_output(
self,
self._env.step(action),
expected_reward=expected_reward, # Moving towards goal.
expected_done=False,
expected_info='',
expected_time_step=expected_time_step,
expected_path_id=318,
expected_pano_name=golden_path[i],
expected_heading=expected_heading,
expected_pitch=expected_pitch,
expected_scan_id=scan_id,
expected_oracle_action=expected_oracle_action)
# Stop at goal pano. Terminating the episode results in resetting the
# observation to next episode.
env_test.verify_env_output(
self,
self._env.step(constants.STOP_NODE_ID),
expected_reward=4, # reached goal and stopped
expected_done=True, # end of episode
expected_info='',
# observation for next episode.
expected_time_step=0,
expected_path_id=1304,
expected_pano_name='80929af5cf234ae38ac3a2a4e60e4342',
expected_heading=6.101,
expected_pitch=0.,
expected_scan_id=scan_id,
expected_oracle_action=self._get_pano_id(
'ba27da20782d4e1a825f0a133ad84da9', scan_id))