def __init__(self):
super().__init__()
self.SENSORS = [
RGBSensorHabitat(
height=self.SCREEN_SIZE,
width=self.SCREEN_SIZE,
use_resnet_normalization=True,
),
DepthSensorHabitat(
height=self.SCREEN_SIZE,
width=self.SCREEN_SIZE,
use_normalization=True,
),
TargetCoordinatesSensorHabitat(coordinate_dims=2),
]
self.PREPROCESSORS = []
self.OBSERVATIONS = [
"rgb",
"depth",
"target_coordinates_ind",
]
self.CONFIG = self.CONFIG.clone()
self.CONFIG.SIMULATOR.AGENT_0.SENSORS = ["DEPTH_SENSOR", "RGB_SENSOR"]
self.TRAIN_CONFIGS = construct_env_configs(config=self.CONFIG)
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)}),
)
class PointNavHabitatDepthDeterministiSimpleConvGRUDDPPOExperimentConfig(
DebugPointNavHabitatBaseConfig, PointNavHabitatMixInPPOConfig,
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),
]
@classmethod
def tag(cls):
return "Debug-Pointnav-Habitat-RGB-SimpleConv-DDPPO"
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)
class PointNavHabitatRGBPPOTutorialExperimentConfig(ExperimentConfig):
"""A Point Navigation experiment configuration in Habitat."""
# Task Parameters
MAX_STEPS = 500
REWARD_CONFIG = {
"step_penalty": -0.01,
"goal_success_reward": 10.0,
"failed_stop_reward": 0.0,
"shaping_weight": 1.0,
}
DISTANCE_TO_GOAL = 0.2
# Simulator Parameters
CAMERA_WIDTH = 640
CAMERA_HEIGHT = 480
SCREEN_SIZE = 224
# Training Engine Parameters
ADVANCE_SCENE_ROLLOUT_PERIOD: Optional[int] = None
NUM_PROCESSES = max(5 * torch.cuda.device_count() - 1, 4)
TRAINING_GPUS = list(range(torch.cuda.device_count()))
VALIDATION_GPUS = [torch.cuda.device_count() - 1]
TESTING_GPUS = [torch.cuda.device_count() - 1]
task_data_dir_template = os.path.join(
HABITAT_DATASETS_DIR, "pointnav/gibson/v1/{}/{}.json.gz"
)
TRAIN_SCENES = task_data_dir_template.format(*(["train"] * 2))
VALID_SCENES = task_data_dir_template.format(*(["val"] * 2))
TEST_SCENES = task_data_dir_template.format(*(["test"] * 2))
CONFIG = get_habitat_config(
os.path.join(HABITAT_CONFIGS_DIR, "tasks/pointnav_gibson.yaml")
)
CONFIG.defrost()
CONFIG.NUM_PROCESSES = NUM_PROCESSES
CONFIG.SIMULATOR_GPU_IDS = TRAINING_GPUS
CONFIG.DATASET.SCENES_DIR = "habitat/habitat-api/data/scene_datasets/"
CONFIG.DATASET.POINTNAVV1.CONTENT_SCENES = ["*"]
CONFIG.DATASET.DATA_PATH = TRAIN_SCENES
CONFIG.SIMULATOR.AGENT_0.SENSORS = ["RGB_SENSOR"]
CONFIG.SIMULATOR.RGB_SENSOR.WIDTH = CAMERA_WIDTH
CONFIG.SIMULATOR.RGB_SENSOR.HEIGHT = CAMERA_HEIGHT
CONFIG.SIMULATOR.TURN_ANGLE = 30
CONFIG.SIMULATOR.FORWARD_STEP_SIZE = 0.25
CONFIG.ENVIRONMENT.MAX_EPISODE_STEPS = MAX_STEPS
CONFIG.TASK.TYPE = "Nav-v0"
CONFIG.TASK.SUCCESS_DISTANCE = DISTANCE_TO_GOAL
CONFIG.TASK.SENSORS = ["POINTGOAL_WITH_GPS_COMPASS_SENSOR"]
CONFIG.TASK.POINTGOAL_WITH_GPS_COMPASS_SENSOR.GOAL_FORMAT = "POLAR"
CONFIG.TASK.POINTGOAL_WITH_GPS_COMPASS_SENSOR.DIMENSIONALITY = 2
CONFIG.TASK.GOAL_SENSOR_UUID = "pointgoal_with_gps_compass"
CONFIG.TASK.MEASUREMENTS = ["DISTANCE_TO_GOAL", "SUCCESS", "SPL"]
CONFIG.TASK.SPL.TYPE = "SPL"
CONFIG.TASK.SPL.SUCCESS_DISTANCE = DISTANCE_TO_GOAL
CONFIG.TASK.SUCCESS.SUCCESS_DISTANCE = DISTANCE_TO_GOAL
CONFIG.MODE = "train"
SENSORS = [
RGBSensorHabitat(
height=SCREEN_SIZE, width=SCREEN_SIZE, use_resnet_normalization=True,
),
TargetCoordinatesSensorHabitat(coordinate_dims=2),
]
PREPROCESSORS = [
Builder(
ResNetPreprocessor,
{
"input_height": SCREEN_SIZE,
"input_width": SCREEN_SIZE,
"output_width": 7,
"output_height": 7,
"output_dims": 512,
"pool": False,
"torchvision_resnet_model": models.resnet18,
"input_uuids": ["rgb_lowres"],
"output_uuid": "rgb_resnet",
},
),
]
OBSERVATIONS = [
"rgb_resnet",
"target_coordinates_ind",
]
TRAIN_CONFIGS = construct_env_configs(CONFIG)
@classmethod
def tag(cls):
return "PointNavHabitatRGBPPO"
@classmethod
def training_pipeline(cls, **kwargs):
ppo_steps = int(250000000)
lr = 3e-4
num_mini_batch = 1
update_repeats = 3
num_steps = 30
save_interval = 5000000
log_interval = 10000
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={"ppo_loss": PPO(**PPOConfig)},
gamma=gamma,
use_gae=use_gae,
gae_lambda=gae_lambda,
advance_scene_rollout_period=cls.ADVANCE_SCENE_ROLLOUT_PERIOD,
pipeline_stages=[
PipelineStage(loss_names=["ppo_loss"], max_stage_steps=ppo_steps)
],
lr_scheduler_builder=Builder(
LambdaLR, {"lr_lambda": LinearDecay(steps=ppo_steps)}
),
)
def machine_params(self, mode="train", **kwargs):
if mode == "train":
workers_per_device = 1
gpu_ids = (
[]
if not torch.cuda.is_available()
else self.TRAINING_GPUS * workers_per_device
)
nprocesses = (
1
if not torch.cuda.is_available()
else evenly_distribute_count_into_bins(self.NUM_PROCESSES, len(gpu_ids))
)
elif mode == "valid":
nprocesses = 1
gpu_ids = [] if not torch.cuda.is_available() else self.VALIDATION_GPUS
elif mode == "test":
nprocesses = 1
gpu_ids = [] if not torch.cuda.is_available() else self.TESTING_GPUS
else:
raise NotImplementedError("mode must be 'train', 'valid', or 'test'.")
sensor_preprocessor_graph = (
SensorPreprocessorGraph(
source_observation_spaces=SensorSuite(self.SENSORS).observation_spaces,
preprocessors=self.PREPROCESSORS,
)
if mode == "train"
or (
(isinstance(nprocesses, int) and nprocesses > 0)
or (isinstance(nprocesses, Sequence) and sum(nprocesses) > 0)
)
else None
)
return MachineParams(
nprocesses=nprocesses,
devices=gpu_ids,
sensor_preprocessor_graph=sensor_preprocessor_graph,
)
# Define Model
@classmethod
def create_model(cls, **kwargs) -> nn.Module:
return ResnetTensorPointNavActorCritic(
action_space=gym.spaces.Discrete(len(PointNavTask.class_action_names())),
observation_space=kwargs["sensor_preprocessor_graph"].observation_spaces,
goal_sensor_uuid="target_coordinates_ind",
rgb_resnet_preprocessor_uuid="rgb_resnet",
hidden_size=512,
goal_dims=32,
)
# Define Task Sampler
@classmethod
def make_sampler_fn(cls, **kwargs) -> TaskSampler:
return PointNavTaskSampler(**kwargs)
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, # type:ignore
}
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]:
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, # type:ignore
}
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]:
raise NotImplementedError("Testing not implemented for this tutorial.")
class ObjectNavRoboThorRGBPPOExperimentConfig(ExperimentConfig):
"""A Point Navigation experiment configuration in RoboThor."""
# Task Parameters
MAX_STEPS = 500
REWARD_CONFIG = {
"step_penalty": -0.01,
"goal_success_reward": 10.0,
"failed_stop_reward": 0.0,
"shaping_weight": 1.0,
}
# Simulator Parameters
CAMERA_WIDTH = 640
CAMERA_HEIGHT = 480
SCREEN_SIZE = 224
# Training Engine Parameters
ADVANCE_SCENE_ROLLOUT_PERIOD = 10000000000000
NUM_PROCESSES = 60
TRAINING_GPUS = [0, 1, 2, 3, 4, 5, 6]
VALIDATION_GPUS = [7]
TESTING_GPUS = [7]
TRAIN_SCENES = (
"habitat/habitat-api/data/datasets/pointnav/gibson/v1/train/train.json.gz"
)
VALID_SCENES = (
"habitat/habitat-api/data/datasets/pointnav/gibson/v1/val/val.json.gz")
TEST_SCENES = (
"habitat/habitat-api/data/datasets/pointnav/gibson/v1/test/test.json.gz"
)
TRAIN_GPUS = [0, 1, 2, 3, 4, 5, 6, 7]
VALIDATION_GPUS = [7]
TESTING_GPUS = [7]
NUM_PROCESSES = 80
CONFIG = habitat.get_config("configs/gibson.yaml")
CONFIG.defrost()
CONFIG.NUM_PROCESSES = NUM_PROCESSES
CONFIG.SIMULATOR_GPU_IDS = TRAIN_GPUS
CONFIG.DATASET.SCENES_DIR = "habitat/habitat-api/data/scene_datasets/"
CONFIG.DATASET.POINTNAVV1.CONTENT_SCENES = ["*"]
CONFIG.DATASET.DATA_PATH = TRAIN_SCENES
CONFIG.SIMULATOR.AGENT_0.SENSORS = ["RGB_SENSOR"]
CONFIG.SIMULATOR.RGB_SENSOR.WIDTH = CAMERA_WIDTH
CONFIG.SIMULATOR.RGB_SENSOR.HEIGHT = CAMERA_HEIGHT
CONFIG.SIMULATOR.TURN_ANGLE = 30
CONFIG.SIMULATOR.FORWARD_STEP_SIZE = 0.25
CONFIG.ENVIRONMENT.MAX_EPISODE_STEPS = MAX_STEPS
CONFIG.TASK.TYPE = "Nav-v0"
CONFIG.TASK.SUCCESS_DISTANCE = 0.2
CONFIG.TASK.SENSORS = ["POINTGOAL_WITH_GPS_COMPASS_SENSOR"]
CONFIG.TASK.POINTGOAL_WITH_GPS_COMPASS_SENSOR.GOAL_FORMAT = "POLAR"
CONFIG.TASK.POINTGOAL_WITH_GPS_COMPASS_SENSOR.DIMENSIONALITY = 2
CONFIG.TASK.GOAL_SENSOR_UUID = "pointgoal_with_gps_compass"
CONFIG.TASK.MEASUREMENTS = ["DISTANCE_TO_GOAL", "SPL"]
CONFIG.TASK.SPL.TYPE = "SPL"
CONFIG.TASK.SPL.SUCCESS_DISTANCE = 0.2
CONFIG.MODE = "train"
SENSORS = [
RGBSensorHabitat(
height=SCREEN_SIZE,
width=SCREEN_SIZE,
use_resnet_normalization=True,
),
TargetCoordinatesSensorHabitat(coordinate_dims=2),
]
PREPROCESSORS = [
Builder(
ResnetPreProcessorHabitat,
{
"input_height": SCREEN_SIZE,
"input_width": SCREEN_SIZE,
"output_width": 7,
"output_height": 7,
"output_dims": 512,
"pool": False,
"torchvision_resnet_model": models.resnet18,
"input_uuids": ["rgb_lowres"],
"output_uuid": "rgb_resnet",
"parallel": False, # TODO False for debugging
},
),
]
OBSERVATIONS = [
"rgb_resnet",
"target_coordinates_ind",
]
TRAIN_CONFIGS = construct_env_configs(CONFIG)
@classmethod
def tag(cls):
return "PointNavHabitatRGBPPO"
@classmethod
def training_pipeline(cls, **kwargs):
ppo_steps = int(250000000)
lr = 3e-4
num_mini_batch = 1
update_repeats = 3
num_steps = 30
save_interval = 5000000
log_interval = 10000
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={
"ppo_loss": Builder(
PPO,
kwargs={},
default=PPOConfig,
)
},
gamma=gamma,
use_gae=use_gae,
gae_lambda=gae_lambda,
advance_scene_rollout_period=cls.ADVANCE_SCENE_ROLLOUT_PERIOD,
pipeline_stages=[
PipelineStage(loss_names=["ppo_loss"],
max_stage_steps=ppo_steps)
],
lr_scheduler_builder=Builder(
LambdaLR, {"lr_lambda": LinearDecay(steps=ppo_steps)}),
)
def split_num_processes(self, ndevices):
assert self.NUM_PROCESSES >= ndevices, "NUM_PROCESSES {} < ndevices {}".format(
self.NUM_PROCESSES, ndevices)
res = [0] * ndevices
for it in range(self.NUM_PROCESSES):
res[it % ndevices] += 1
return res
def machine_params(self, mode="train", **kwargs):
if mode == "train":
workers_per_device = 1
gpu_ids = ([] if not torch.cuda.is_available() else
self.TRAINING_GPUS * workers_per_device)
nprocesses = (1 if not torch.cuda.is_available() else
self.split_num_processes(len(gpu_ids)))
sampler_devices = self.TRAINING_GPUS
render_video = False
elif mode == "valid":
nprocesses = 1
gpu_ids = [] if not torch.cuda.is_available(
) else self.VALIDATION_GPUS
render_video = False
elif mode == "test":
nprocesses = 1
gpu_ids = [] if not torch.cuda.is_available(
) else self.TESTING_GPUS
render_video = False
else:
raise NotImplementedError(
"mode must be 'train', 'valid', or 'test'.")
# Disable parallelization for validation process
if mode == "valid":
for prep in self.PREPROCESSORS:
prep.kwargs["parallel"] = False
observation_set = (Builder(
ObservationSet,
kwargs=dict(
source_ids=self.OBSERVATIONS,
all_preprocessors=self.PREPROCESSORS,
all_sensors=self.SENSORS,
),
) if mode == "train" or nprocesses > 0 else None)
return {
"nprocesses": nprocesses,
"gpu_ids": gpu_ids,
"observation_set": observation_set,
"render_video": render_video,
}
# Define Model
@classmethod
def create_model(cls, **kwargs) -> nn.Module:
return ResnetTensorPointNavActorCritic(
action_space=gym.spaces.Discrete(
len(PointNavTask.class_action_names())),
observation_space=kwargs["observation_set"].observation_spaces,
goal_sensor_uuid="target_coordinates_ind",
rgb_resnet_preprocessor_uuid="rgb_resnet",
hidden_size=512,
goal_dims=32,
)
# Define Task Sampler
@classmethod
def make_sampler_fn(cls, **kwargs) -> TaskSampler:
return PointNavTaskSampler(**kwargs)
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, # type:ignore
}
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]:
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, # type:ignore
}
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_CONFIGS[process_ind] # type:ignore
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, # type:ignore
}