def __init__(self, runtime_config, mode, data_sources):
self._runtime_config = runtime_config
self._mode = mode
self._data_sources = data_sources
self._agent = agent.R2RAgent(agent_config.get_ndh_agent_config())
self._prob_ac = 0.5
self._env = None
self._loss_type = None
self._eval_dict = self._get_eval_dict()
def test_call_ndh(self):
self._agent = agent.R2RAgent(agent_config.get_ndh_agent_config())
self.data_dir = FLAGS.test_srcdir + (
'valan/r2r/testdata')
self._env_config = hparam.HParams(
problem='NDH',
history='all',
path_type='trusted_path',
max_goal_room_panos=4,
scan_base_dir=self.data_dir,
data_base_dir=self.data_dir,
vocab_dir=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=64,
direction_encoding_dim=256,
image_features_dir=os.path.join(self.data_dir, 'image_features'),
instruction_len=50,
max_agent_actions=6,
reward_fn=env_config.RewardFunction.get_reward_fn('distance_to_goal'))
self._runtime_config = common.RuntimeConfig(task_id=0, num_tasks=1)
self._env = env.R2REnv(
data_sources=['R2R_small_split'],
runtime_config=self._runtime_config,
env_config=self._env_config)
env_output = self._env.reset()
observation = tf.nest.map_structure(lambda t: tf.expand_dims(t, 0),
env_output.observation)
initial_agent_state = self._agent.get_initial_state(
observation, batch_size=1)
# Agent always expects time,batch dimensions. First add and then remove.
env_output = utils.add_time_batch_dim(env_output)
agent_output, _ = self._agent(env_output, initial_agent_state)
self.assertEqual(agent_output.policy_logits.shape, [1, 1, 14])
self.assertEqual(agent_output.baseline.shape, [1, 1])
initial_agent_state = ([
(tf.random.normal([self.batch_size,
512]), tf.random.normal([self.batch_size, 512])),
(tf.random.normal([self.batch_size,
512]), tf.random.normal([self.batch_size, 512]))
], tf.random.normal([self.batch_size, 5, 512]))
agent_output, _ = self._agent(self._test_environment, initial_agent_state)
self.assertEqual(agent_output.policy_logits.shape,
[self.time_step, self.batch_size, 14])
self.assertEqual(agent_output.baseline.shape,
[self.time_step, self.batch_size])
def __init__(self, runtime_config, mode, data_sources, agent_type='r2r'):
self._runtime_config = runtime_config
self._mode = mode
self._data_sources = data_sources
if agent_type.lower() == 'r2r':
self._agent = agent.R2RAgent(agent_config.get_ndh_agent_config(),
mode=mode)
elif agent_type.lower() == 'mt':
self._agent = mt_agent.MTEnvAgAgent(
mt_agent_config.get_agent_config(), mode=mode)
else:
raise ValueError('Invalid agent_type: {}'.format(agent_type))
self._prob_ac = 0.5
self._env = None
self._loss_type = None
self._eval_dict = self._get_eval_dict()