def valid_task_sampler_args(
self,
process_ind: int,
total_processes: int,
devices: Optional[List[int]] = None,
seeds: Optional[List[int]] = None,
deterministic_cudnn: bool = False,
) -> Dict[str, Any]:
if total_processes != 1:
raise NotImplementedError(
"In validation, `total_processes` must equal 1 for habitat tasks"
)
config = self.CONFIG.clone()
config.defrost()
config.DATASET.DATA_PATH = self.VALID_SCENES
config.MODE = "validate"
config.freeze()
return {
"env_config":
config,
"max_steps":
self.MAX_STEPS,
"sensors":
self.SENSORS,
"action_space":
gym.spaces.Discrete(len(PointNavTask.class_action_names())),
"distance_to_goal":
self.DISTANCE_TO_GOAL,
}
class PointNavHabitatRGBDeterministiSimpleConvGRUImitationExperimentConfig(
DebugPointNavHabitatBaseConfig, PointNavMixInSimpleConvGRUConfig):
"""An Point Navigation experiment configuration in Habitat with Depth
input."""
SENSORS = [
RGBSensorHabitat(
height=DebugPointNavHabitatBaseConfig.SCREEN_SIZE,
width=DebugPointNavHabitatBaseConfig.SCREEN_SIZE,
use_resnet_normalization=True,
),
TargetCoordinatesSensorHabitat(coordinate_dims=2),
ExpertActionSensor(nactions=len(PointNavTask.class_action_names())),
]
@classmethod
def tag(cls):
return "Debug-Pointnav-Habitat-RGB-SimpleConv-BC"
@classmethod
def training_pipeline(cls, **kwargs):
imitate_steps = int(75000000)
lr = 3e-4
num_mini_batch = 1
update_repeats = 3
num_steps = 30
save_interval = 5000000
log_interval = 10000 if torch.cuda.is_available() else 1
gamma = 0.99
use_gae = True
gae_lambda = 0.95
max_grad_norm = 0.5
return TrainingPipeline(
save_interval=save_interval,
metric_accumulate_interval=log_interval,
optimizer_builder=Builder(optim.Adam, dict(lr=lr)),
num_mini_batch=num_mini_batch,
update_repeats=update_repeats,
max_grad_norm=max_grad_norm,
num_steps=num_steps,
named_losses={"imitation_loss": Imitation()},
gamma=gamma,
use_gae=use_gae,
gae_lambda=gae_lambda,
advance_scene_rollout_period=cls.ADVANCE_SCENE_ROLLOUT_PERIOD,
pipeline_stages=[
PipelineStage(
loss_names=["imitation_loss"],
max_stage_steps=imitate_steps,
# teacher_forcing=LinearDecay(steps=int(1e5), startp=1.0, endp=0.0,),
),
],
lr_scheduler_builder=Builder(
LambdaLR, {"lr_lambda": LinearDecay(steps=imitate_steps)}),
)
def create_model(cls, **kwargs) -> nn.Module:
return PointNavActorCriticSimpleConvRNN(
action_space=gym.spaces.Discrete(
len(PointNavTask.class_action_names())),
observation_space=kwargs["observation_set"].observation_spaces,
goal_sensor_uuid="target_coordinates_ind",
hidden_size=512,
embed_coordinates=False,
coordinate_dims=2,
num_rnn_layers=1,
rnn_type="GRU",
)
def test_task_sampler_args(
self,
process_ind: int,
total_processes: int,
devices: Optional[List[int]] = None,
seeds: Optional[List[int]] = None,
deterministic_cudnn: bool = False,
) -> Dict[str, Any]:
config = self.TEST_CONFIG_PER_PROCESS[process_ind]
return {
"env_config":
config,
"max_steps":
self.MAX_STEPS,
"sensors":
self.SENSORS,
"action_space":
gym.spaces.Discrete(len(PointNavTask.class_action_names())),
"distance_to_goal":
self.DISTANCE_TO_GOAL,
}
def train_task_sampler_args(
self,
process_ind: int,
total_processes: int,
devices: Optional[List[int]] = None,
seeds: Optional[List[int]] = None,
deterministic_cudnn: bool = False,
) -> Dict[str, Any]:
config = self.TRAIN_CONFIGS[process_ind]
return {
"env_config":
config,
"max_steps":
self.MAX_STEPS,
"sensors":
self.SENSORS,
"action_space":
gym.spaces.Discrete(len(PointNavTask.class_action_names())),
"distance_to_goal":
self.DISTANCE_TO_GOAL,
# "filter_dataset_func": DebugPointNavHabitatBaseConfig.make_easy_dataset,
}
def __init__(self):
super().__init__()
self.SENSORS = [
RGBSensorHabitat(
height=self.SCREEN_SIZE,
width=self.SCREEN_SIZE,
use_resnet_normalization=True,
),
TargetCoordinatesSensorHabitat(coordinate_dims=2),
ExpertActionSensor(
nactions=len(PointNavTask.class_action_names())),
]
self.PREPROCESSORS = []
self.OBSERVATIONS = ["rgb", "target_coordinates_ind", "expert_action"]
self.CONFIG = self.CONFIG.clone()
self.CONFIG.SIMULATOR.AGENT_0.SENSORS = ["RGB_SENSOR"]
self.TRAIN_CONFIGS = construct_env_configs(config=self.CONFIG,
allow_scene_repeat=True)