def train_vae(variant, return_data=False):
from rlkit.util.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import ConvVAE as conv_vae
from rlkit.torch.vae.conv_vae import ConvVAE
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
from rlkit.pythonplusplus import identity
import torch
from rlkit.torch.vae.conv_vae import imsize48_default_architecture
beta = variant["beta"]
representation_size = variant.get("representation_size", 4)
train_dataset, test_dataset = generate_vae_data(variant)
decoder_activation = identity
# train_dataset = train_dataset.cuda()
# test_dataset = test_dataset.cuda()
architecture = variant.get('vae_architecture',
imsize48_default_architecture)
image_size = variant.get('image_size', 48)
input_channels = variant.get('input_channels', 1)
vae_model = ConvVAE(representation_size,
decoder_output_activation=decoder_activation,
architecture=architecture,
imsize=image_size,
input_channels=input_channels,
decoder_distribution='gaussian_identity_variance')
vae_model.cuda()
vae_trainner = ConvVAETrainer(train_dataset,
test_dataset,
vae_model,
beta=beta,
beta_schedule=None)
save_period = variant['save_period']
dump_skew_debug_plots = variant.get('dump_skew_debug_plots', False)
for epoch in range(variant['num_epochs']):
vae_trainner.train_epoch(epoch)
vae_trainner.test_epoch(epoch)
if epoch % save_period == 0:
vae_trainner.dump_samples(epoch)
vae_trainner.update_train_weights()
# logger.save_extra_data(vae_model, 'vae.pkl', mode='pickle')
project_path = osp.abspath(os.curdir)
save_dir = osp.join(project_path + str('/saved_model/'), 'vae_model.pkl')
torch.save(vae_model.state_dict(), save_dir)
# torch.save(vae_model.state_dict(), \
# '/mnt/manh/project/visual_RL_imaged_goal/saved_model/vae_model.pkl')
if return_data:
return vae_model, train_dataset, test_dataset
return vae_model
def experiment(variant):
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data, info = generate_vae_dataset(
**variant['get_data_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
m = ConvVAE(representation_size,
input_channels=3,
**variant['conv_vae_kwargs'])
if ptu.gpu_enabled():
m.to(ptu.device)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
save_period = variant['save_period']
for epoch in range(variant['num_epochs']):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.test_epoch(epoch,
save_reconstruction=should_save_imgs,
save_scatterplot=should_save_imgs)
if should_save_imgs:
t.dump_samples(epoch)
def experiment(variant):
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data = get_data(10000)
m = ConvVAE(representation_size)
t = ConvVAETrainer(train_data, test_data, m, beta=beta, use_cuda=False)
for epoch in range(10):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
def train_vae(variant, return_data=False):
beta = variant["beta"]
representation_size = variant["representation_size"]
generate_vae_dataset_fctn = variant.get('generate_vae_data_fctn',
generate_vae_dataset)
train_data, test_data, info = generate_vae_dataset_fctn(
variant['generate_vae_dataset_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if variant.get('decoder_activation', None) == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = variant['vae_kwargs'].get('architecture', None)
if not architecture and variant.get('imsize') == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and variant.get('imsize') == 48:
architecture = conv_vae.imsize48_default_architecture
variant['vae_kwargs']['architecture'] = architecture
variant['vae_kwargs']['imsize'] = variant.get('imsize')
m = ConvVAE(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
m.to(ptu.device)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
**variant['algo_kwargs'])
save_period = variant['save_period']
for epoch in range(variant['num_epochs']):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.test_epoch(
epoch,
save_reconstruction=should_save_imgs,
)
if should_save_imgs:
t.dump_samples(epoch)
logger.save_extra_data(m, 'vae.pkl', mode='pickle')
if return_data:
return m, train_data, test_data
return m
def experiment(variant):
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data, info = get_data(**variant['get_data_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
beta_schedule = PiecewiseLinearSchedule(**variant['beta_schedule_kwargs'])
m = ConvVAE(representation_size, input_channels=3)
if ptu.gpu_enabled():
m.to(ptu.device)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
for epoch in range(variant['num_epochs']):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
def tdm_td3_experiment_online_vae(variant):
import rlkit.samplers.rollout_functions as rf
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.exploration_strategies.base import (
PolicyWrappedWithExplorationStrategy)
from rlkit.state_distance.tdm_networks import TdmQf, TdmPolicy
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.torch.online_vae.online_vae_tdm_td3 import OnlineVaeTdmTd3
preprocess_rl_variant(variant)
env = get_envs(variant)
es = get_exploration_strategy(variant, env)
vae_trainer_kwargs = variant.get('vae_trainer_kwargs')
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size)
goal_dim = (env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
vectorized = 'vectorized' in env.reward_type
variant['algo_kwargs']['tdm_td3_kwargs']['tdm_kwargs'][
'vectorized'] = vectorized
norm_order = env.norm_order
# variant['algo_kwargs']['tdm_td3_kwargs']['tdm_kwargs'][
# 'norm_order'] = norm_order
qf1 = TdmQf(env=env,
vectorized=vectorized,
norm_order=norm_order,
observation_dim=obs_dim,
goal_dim=goal_dim,
action_dim=action_dim,
**variant['qf_kwargs'])
qf2 = TdmQf(env=env,
vectorized=vectorized,
norm_order=norm_order,
observation_dim=obs_dim,
goal_dim=goal_dim,
action_dim=action_dim,
**variant['qf_kwargs'])
policy = TdmPolicy(env=env,
observation_dim=obs_dim,
goal_dim=goal_dim,
action_dim=action_dim,
**variant['policy_kwargs'])
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs'])
algo_kwargs = variant['algo_kwargs']['tdm_td3_kwargs']
td3_kwargs = algo_kwargs['td3_kwargs']
td3_kwargs['training_env'] = env
tdm_kwargs = algo_kwargs['tdm_kwargs']
tdm_kwargs['observation_key'] = observation_key
tdm_kwargs['desired_goal_key'] = desired_goal_key
algo_kwargs["replay_buffer"] = replay_buffer
t = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'],
vae,
beta=variant['online_vae_beta'],
**vae_trainer_kwargs)
render = variant["render"]
assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs"
algorithm = OnlineVaeTdmTd3(
online_vae_kwargs=dict(vae=vae,
vae_trainer=t,
**variant['algo_kwargs']['online_vae_kwargs']),
tdm_td3_kwargs=dict(env=env,
qf1=qf1,
qf2=qf2,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_kwargs']['tdm_td3_kwargs']),
)
algorithm.to(ptu.device)
vae.to(ptu.device)
if variant.get("save_video", True):
policy.train(False)
rollout_function = rf.create_rollout_function(
rf.tdm_rollout,
init_tau=algorithm._sample_max_tau_for_rollout(),
decrement_tau=algorithm.cycle_taus_for_rollout,
cycle_tau=algorithm.cycle_taus_for_rollout,
max_path_length=algorithm.max_path_length,
observation_key=algorithm.observation_key,
desired_goal_key=algorithm.desired_goal_key,
)
video_func = get_video_save_func(
rollout_function,
env,
algorithm.eval_policy,
variant,
)
algorithm.post_train_funcs.append(video_func)
algorithm.to(ptu.device)
if not variant.get("do_state_exp", False):
env.vae.to(ptu.device)
algorithm.train()
def td3_experiment_online_vae_exploring(variant):
import rlkit.samplers.rollout_functions as rf
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.exploration_strategies.base import (
PolicyWrappedWithExplorationStrategy)
from rlkit.torch.her.online_vae_joint_algo import OnlineVaeHerJointAlgo
from rlkit.torch.networks import ConcatMlp, TanhMlpPolicy
from rlkit.torch.td3.td3 import TD3
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
preprocess_rl_variant(variant)
env = get_envs(variant)
es = get_exploration_strategy(variant, env)
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
qf1 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'],
)
qf2 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'],
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'],
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
exploring_qf1 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'],
)
exploring_qf2 = ConcatMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs'],
)
exploring_policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'],
)
exploring_exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=exploring_policy,
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs'])
variant["algo_kwargs"]["replay_buffer"] = replay_buffer
if variant.get('use_replay_buffer_goals', False):
env.replay_buffer = replay_buffer
env.use_replay_buffer_goals = True
vae_trainer_kwargs = variant.get('vae_trainer_kwargs')
t = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'],
vae,
beta=variant['online_vae_beta'],
**vae_trainer_kwargs)
control_algorithm = TD3(env=env,
training_env=env,
qf1=qf1,
qf2=qf2,
policy=policy,
exploration_policy=exploration_policy,
**variant['algo_kwargs'])
exploring_algorithm = TD3(env=env,
training_env=env,
qf1=exploring_qf1,
qf2=exploring_qf2,
policy=exploring_policy,
exploration_policy=exploring_exploration_policy,
**variant['algo_kwargs'])
assert 'vae_training_schedule' not in variant,\
"Just put it in joint_algo_kwargs"
algorithm = OnlineVaeHerJointAlgo(vae=vae,
vae_trainer=t,
env=env,
training_env=env,
policy=policy,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
algo1=control_algorithm,
algo2=exploring_algorithm,
algo1_prefix="Control_",
algo2_prefix="VAE_Exploration_",
observation_key=observation_key,
desired_goal_key=desired_goal_key,
**variant['joint_algo_kwargs'])
algorithm.to(ptu.device)
vae.to(ptu.device)
if variant.get("save_video", True):
policy.train(False)
rollout_function = rf.create_rollout_function(
rf.multitask_rollout,
max_path_length=algorithm.max_path_length,
observation_key=algorithm.observation_key,
desired_goal_key=algorithm.desired_goal_key,
)
video_func = get_video_save_func(
rollout_function,
env,
algorithm.eval_policy,
variant,
)
algorithm.post_train_funcs.append(video_func)
algorithm.train()
def train_vae(variant, other_variant, return_data=False):
from rlkit.util.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import (
ConvVAE, )
import rlkit.torch.vae.conv_vae as conv_vae
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
from rlkit.pythonplusplus import identity
import torch
beta = variant["beta"]
representation_size = variant["representation_size"]
generate_vae_dataset_fctn = variant.get('generate_vae_data_fctn',
generate_vae_dataset)
train_data, test_data, info = generate_vae_dataset_fctn(
variant['generate_vae_dataset_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
if variant.get('decoder_activation', None) == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = variant['vae_kwargs'].get('architecture', None)
if not architecture and variant.get('imsize') == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and variant.get('imsize') == 48:
architecture = conv_vae.imsize48_default_architecture
variant['vae_kwargs']['architecture'] = architecture
variant['vae_kwargs']['imsize'] = variant.get('imsize')
m = ConvVAE(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
m.to(ptu.device)
t = ConvVAETrainer(train_data,
test_data,
m,
other_variant,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
save_period = variant['save_period']
dump_skew_debug_plots = variant.get('dump_skew_debug_plots', False)
for epoch in range(variant['num_epochs']):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.test_epoch(
epoch,
save_reconstruction=should_save_imgs,
# save_vae=False,
)
if should_save_imgs:
t.dump_samples(epoch)
t.update_train_weights()
logger.save_extra_data(m, 'vae.pkl', mode='pickle')
# torch.save(m, other_variant['vae_pkl_path']+'/online_vae.pkl') # easy way:load momdel for via bonus
if return_data:
return m, train_data, test_data
return m
def skewfit_experiment(variant, other_variant):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
skewfit_preprocess_variant(variant)
env = get_envs(variant)
uniform_dataset_fn = variant.get('generate_uniform_dataset_fn', None)
if uniform_dataset_fn:
uniform_dataset = uniform_dataset_fn(
**variant['generate_uniform_dataset_kwargs'])
else:
uniform_dataset = None
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = variant.get('hidden_sizes', [400, 300])
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=hidden_sizes,
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=env.vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs'])
vae_trainer = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'], env.vae,
other_variant,
**variant['online_vae_trainer_kwargs'])
assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs"
max_path_length = variant['max_path_length']
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['twin_sac_trainer_kwargs'])
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
variant['evaluation_goal_sampling_mode'],
env,
MakeDeterministic(policy),
max_path_length,
other_variant=other_variant,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
variant['exploration_goal_sampling_mode'],
env,
policy,
max_path_length,
other_variant=other_variant,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
uniform_dataset=uniform_dataset,
max_path_length=max_path_length,
**variant['algo_kwargs'])
if variant['custom_goal_sampler'] == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()
def train_vae(beta,
representation_size,
imsize,
num_epochs,
save_period,
generate_vae_dataset_fctn=None,
beta_schedule_kwargs=None,
decoder_activation=None,
vae_kwargs=None,
generate_vae_dataset_kwargs=None,
algo_kwargs=None,
use_spatial_auto_encoder=False,
vae_class=None,
dump_skew_debug_plots=False):
from rlkit.misc.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import (
ConvVAE,
SpatialAutoEncoder,
AutoEncoder,
)
import rlkit.torch.vae.conv_vae as conv_vae
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.torch.vae.vae_experiment import VAEExperiment
from rlkit.pythonplusplus import identity
from rlkit.torch.grill.launcher import generate_vae_dataset
import torch
if vae_kwargs is None:
vae_kwargs = {}
if generate_vae_dataset_kwargs is None:
generate_vae_dataset_kwargs = {}
if algo_kwargs is None:
algo_kwargs = {}
if generate_vae_dataset_fctn is None:
generate_vae_dataset_fctn = generate_vae_dataset
if vae_class is None:
vae_class = ConvVAE
if beta_schedule_kwargs is not None:
beta_schedule = PiecewiseLinearSchedule(**beta_schedule_kwargs)
else:
beta_schedule = None
if decoder_activation == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = vae_kwargs.get('architecture', None)
if not architecture and imsize == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and imsize == 48:
architecture = conv_vae.imsize48_default_architecture
vae_kwargs['architecture'] = architecture
vae_kwargs['imsize'] = imsize
if algo_kwargs.get('is_auto_encoder', False):
m = AutoEncoder(representation_size,
decoder_output_activation=decoder_activation,
**vae_kwargs)
elif use_spatial_auto_encoder:
m = SpatialAutoEncoder(representation_size,
decoder_output_activation=decoder_activation,
**vae_kwargs)
else:
m = vae_class(representation_size,
decoder_output_activation=decoder_activation,
**vae_kwargs)
train_data, test_data, info = generate_vae_dataset_fctn(
generate_vae_dataset_kwargs)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**algo_kwargs)
vae_exp = VAEExperiment(t, num_epochs, save_period, dump_skew_debug_plots)
return vae_exp, train_data, test_data
def skewfit_experiment(variant):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer \
import OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
import rlkit.torch.vae.vae_schedules as vae_schedules
#### getting parameter for training VAE and RIG
env = get_envs(variant)
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = variant.get('hidden_sizes', [400, 300])
replay_buffer_kwargs = variant.get(
'replay_buffer_kwargs',
dict(
start_skew_epoch=10,
max_size=int(100000),
fraction_goals_rollout_goals=0.2,
fraction_goals_env_goals=0.5,
exploration_rewards_type='None',
vae_priority_type='vae_prob',
priority_function_kwargs=dict(
sampling_method='importance_sampling',
decoder_distribution='gaussian_identity_variance',
num_latents_to_sample=10,
),
power=0,
relabeling_goal_sampling_mode='vae_prior',
))
online_vae_trainer_kwargs = variant.get('online_vae_trainer_kwargs',
dict(beta=20, lr=1e-3))
max_path_length = variant.get('max_path_length', 50)
algo_kwargs = variant.get(
'algo_kwargs',
dict(
batch_size=1024,
num_epochs=1000,
num_eval_steps_per_epoch=500,
num_expl_steps_per_train_loop=500,
num_trains_per_train_loop=1000,
min_num_steps_before_training=10000,
vae_training_schedule=vae_schedules.custom_schedule_2,
oracle_data=False,
vae_save_period=50,
parallel_vae_train=False,
))
twin_sac_trainer_kwargs = variant.get(
'twin_sac_trainer_kwargs',
dict(
discount=0.99,
reward_scale=1,
soft_target_tau=1e-3,
target_update_period=1, # 1
use_automatic_entropy_tuning=True,
))
############################################################################
qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=hidden_sizes)
vae = variant['vae_model']
# create a replay buffer for training an online VAE
replay_buffer = OnlineVaeRelabelingBuffer(
vae=vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**replay_buffer_kwargs)
# create an online vae_trainer to train vae on the fly
vae_trainer = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'], vae,
**online_vae_trainer_kwargs)
# create a SACTrainer to learn a soft Q-function and appropriate policy
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**twin_sac_trainer_kwargs)
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
variant.get('evaluation_goal_sampling_mode', 'reset_of_env'),
env,
MakeDeterministic(policy),
max_path_length,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
variant.get('exploration_goal_sampling_mode', 'vae_prior'),
env,
policy,
max_path_length,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
max_path_length=max_path_length,
**algo_kwargs)
if variant['custom_goal_sampler'] == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()
def get_n_train_vae(latent_dim,
env,
vae_train_epochs,
num_image_examples,
vae_kwargs,
vae_trainer_kwargs,
vae_architecture,
vae_save_period=10,
vae_test_p=.9,
decoder_activation='sigmoid',
vae_class='VAE',
**kwargs):
env.goal_sampling_mode = 'test'
image_examples = unnormalize_image(
env.sample_goals(num_image_examples)['desired_goal'])
n = int(num_image_examples * vae_test_p)
train_dataset = ImageObservationDataset(image_examples[:n, :])
test_dataset = ImageObservationDataset(image_examples[n:, :])
if decoder_activation == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
vae_class = vae_class.lower()
if vae_class == 'VAE'.lower():
vae_class = ConvVAE
elif vae_class == 'SpatialVAE'.lower():
vae_class = SpatialAutoEncoder
else:
raise RuntimeError("Invalid VAE Class: {}".format(vae_class))
vae = vae_class(latent_dim,
architecture=vae_architecture,
decoder_output_activation=decoder_activation,
**vae_kwargs)
trainer = ConvVAETrainer(vae, **vae_trainer_kwargs)
logger.remove_tabular_output('progress.csv', relative_to_snapshot_dir=True)
logger.add_tabular_output('vae_progress.csv',
relative_to_snapshot_dir=True)
for epoch in range(vae_train_epochs):
should_save_imgs = (epoch % vae_save_period == 0)
trainer.train_epoch(epoch, train_dataset)
trainer.test_epoch(epoch, test_dataset)
if should_save_imgs:
trainer.dump_reconstructions(epoch)
trainer.dump_samples(epoch)
stats = trainer.get_diagnostics()
for k, v in stats.items():
logger.record_tabular(k, v)
logger.dump_tabular()
trainer.end_epoch(epoch)
if epoch % 50 == 0:
logger.save_itr_params(epoch, vae)
logger.save_extra_data(vae, 'vae.pkl', mode='pickle')
logger.remove_tabular_output('vae_progress.csv',
relative_to_snapshot_dir=True)
logger.add_tabular_output('progress.csv', relative_to_snapshot_dir=True)
return vae
def train_vae(cfgs, return_data=False):
from rlkit.util.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import (
ConvVAE, )
import rlkit.torch.vae.conv_vae as conv_vae
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
from rlkit.pythonplusplus import identity
import torch
train_data, test_data, info = generate_vae_dataset(cfgs)
logger.save_extra_data(info)
logger.get_snapshot_dir()
# FIXME default gaussian
if cfgs.VAE.get('decoder_activation', None) == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = cfgs.VAE.get('architecture', None)
if not architecture and cfgs.ENV.get('img_size') == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and cfgs.ENV.get('img_size') == 48:
architecture = conv_vae.imsize48_default_architecture
vae_model = ConvVAE(
representation_size=cfgs.VAE.representation_size,
architecture=architecture,
decoder_output_activation=decoder_activation,
input_channels=cfgs.VAE.input_channels,
decoder_distribution=cfgs.VAE.decoder_distribution,
imsize=cfgs.VAE.img_size,
)
vae_model.to(ptu.device)
# FIXME the function of beta_schedule?
if 'beta_schedule_kwargs' in cfgs.VAE_TRAINER:
beta_schedule = PiecewiseLinearSchedule(
**cfgs.VAE_TRAINER.beta_schedule_kwargs)
else:
beta_schedule = None
t = ConvVAETrainer(train_data,
test_data,
vae_model,
lr=cfgs.VAE_TRAINER.lr,
beta=cfgs.VAE_TRAINER.beta,
beta_schedule=beta_schedule)
save_period = cfgs.VAE_TRAINER.save_period
for epoch in range(cfgs.VAE_TRAINER.num_epochs):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.test_epoch(
epoch,
save_reconstruction=should_save_imgs,
# save_vae=False,
)
if should_save_imgs:
t.dump_samples(epoch)
t.update_train_weights()
logger.save_extra_data(vae_model, 'vae.pkl', mode='pickle')
if return_data:
return vae_model, train_data, test_data
return vae_model
def skewfit_experiment(cfgs):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
skewfit_preprocess_variant(cfgs)
env = get_envs(cfgs)
# TODO
uniform_dataset_fn = cfgs.GENERATE_VAE_DATASET.get(
'uniform_dataset_generator', None)
if uniform_dataset_fn:
uniform_dataset = uniform_dataset_fn(
**cfgs.GENERATE_VAE_DATASET.generate_uniform_dataset_kwargs)
else:
uniform_dataset = None
observation_key = cfgs.SKEW_FIT.get('observation_key',
'latent_observation')
desired_goal_key = cfgs.SKEW_FIT.get('desired_goal_key',
'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = cfgs.Q_FUNCTION.get('hidden_sizes', [400, 300])
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=cfgs.POLICY.get('hidden_sizes', [400, 300]),
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=env.vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
priority_function_kwargs=cfgs.PRIORITY_FUNCTION,
**cfgs.REPLAY_BUFFER)
vae_trainer = ConvVAETrainer(
cfgs.VAE_TRAINER.train_data,
cfgs.VAE_TRAINER.test_data,
env.vae,
beta=cfgs.VAE_TRAINER.beta,
lr=cfgs.VAE_TRAINER.lr,
)
# assert 'vae_training_schedule' not in cfgs, "Just put it in algo_kwargs"
max_path_length = cfgs.SKEW_FIT.max_path_length
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**cfgs.TWIN_SAC_TRAINER)
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
cfgs.SKEW_FIT.evaluation_goal_sampling_mode,
env,
MakeDeterministic(policy),
decode_goals=True, # TODO check this
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
cfgs.SKEW_FIT.exploration_goal_sampling_mode,
env,
policy,
decode_goals=True,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
uniform_dataset=uniform_dataset, # TODO used in test vae
max_path_length=max_path_length,
parallel_vae_train=cfgs.VAE_TRAINER.parallel_train,
**cfgs.ALGORITHM)
if cfgs.SKEW_FIT.custom_goal_sampler == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()
def train_vae(variant, return_data=False):
from rlkit.util.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import ConvVAE
import rlkit.torch.vae.conv_vae as conv_vae
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.core import logger
import rlkit.torch.pytorch_util as ptu
from rlkit.pythonplusplus import identity
import torch
beta = variant["beta"]
representation_size = variant["representation_size"]
generate_vae_dataset_fctn = variant.get("generate_vae_data_fctn",
generate_vae_dataset)
train_data, test_data, info = generate_vae_dataset_fctn(
variant["generate_vae_dataset_kwargs"])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if "beta_schedule_kwargs" in variant:
beta_schedule = PiecewiseLinearSchedule(
**variant["beta_schedule_kwargs"])
else:
beta_schedule = None
if variant.get("decoder_activation", None) == "sigmoid":
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = variant["vae_kwargs"].get("architecture", None)
if not architecture and variant.get("imsize") == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and variant.get("imsize") == 48:
architecture = conv_vae.imsize48_default_architecture
variant["vae_kwargs"]["architecture"] = architecture
variant["vae_kwargs"]["imsize"] = variant.get("imsize")
m = ConvVAE(representation_size,
decoder_output_activation=decoder_activation,
**variant["vae_kwargs"])
m.to(ptu.device)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant["algo_kwargs"])
save_period = variant["save_period"]
dump_skew_debug_plots = variant.get("dump_skew_debug_plots", False)
for epoch in range(variant["num_epochs"]):
should_save_imgs = epoch % save_period == 0
t.train_epoch(epoch)
t.test_epoch(
epoch,
save_reconstruction=should_save_imgs,
# save_vae=False,
)
if should_save_imgs:
t.dump_samples(epoch)
t.update_train_weights()
logger.save_extra_data(m, "vae.pkl", mode="pickle")
if return_data:
return m, train_data, test_data
return m
def train_vae(
variant,
return_data=False,
skewfit_variant=None): # acutally train both the vae and the lstm
from rlkit.util.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.conv_vae import (
ConvVAE, )
import rlkit.torch.vae.conv_vae as conv_vae
import ROLL.LSTM_model as LSTM_model
from ROLL.LSTM_model import ConvLSTM2
from ROLL.LSTM_trainer import ConvLSTMTrainer
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
import rlkit.torch.pytorch_util as ptu
from rlkit.pythonplusplus import identity
import torch
seg_pretrain = variant['seg_pretrain']
ori_pretrain = variant['ori_pretrain']
generate_vae_dataset_fctn = variant.get('generate_vae_data_fctn',
generate_vae_dataset)
generate_lstm_dataset_fctn = variant.get('generate_lstm_data_fctn')
assert generate_lstm_dataset_fctn is not None, "Must provide a custom generate lstm pretraining dataset function!"
train_data_lstm, test_data_lstm, info_lstm = generate_lstm_dataset_fctn(
variant['generate_lstm_dataset_kwargs'],
segmented=True,
segmentation_method=skewfit_variant['segmentation_method'])
train_data_ori, test_data_ori, info_ori = generate_vae_dataset_fctn(
variant['generate_vae_dataset_kwargs'])
if 'beta_schedule_kwargs' in variant:
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
if variant.get('decoder_activation', None) == 'sigmoid':
decoder_activation = torch.nn.Sigmoid()
else:
decoder_activation = identity
architecture = variant['vae_kwargs'].get('architecture', None)
if not architecture and variant.get('imsize') == 84:
architecture = conv_vae.imsize84_default_architecture
elif not architecture and variant.get('imsize') == 48:
architecture = conv_vae.imsize48_default_architecture
variant['vae_kwargs']['architecture'] = architecture
variant['vae_kwargs']['imsize'] = variant.get('imsize')
architecture = variant['lstm_kwargs'].get('architecture', None)
if not architecture and variant.get('imsize') == 84:
architecture = None # TODO LSTM: wrap a 84 lstm architecutre
elif not architecture and variant.get('imsize') == 48:
architecture = LSTM_model.imsize48_default_architecture
variant['lstm_kwargs']['architecture'] = architecture
variant['lstm_kwargs']['imsize'] = variant.get('imsize')
train_datas = [
train_data_lstm,
train_data_ori,
]
test_datas = [
test_data_lstm,
test_data_ori,
]
names = [
'lstm_seg_pretrain',
'vae_ori_pretrain',
]
vaes = []
env_id = variant['generate_lstm_dataset_kwargs'].get('env_id')
assert env_id is not None
lstm_pretrain_vae_only = variant.get('lstm_pretrain_vae_only', False)
for idx in range(2):
train_data, test_data, name = train_datas[idx], test_datas[idx], names[
idx]
logger.add_tabular_output('{}_progress.csv'.format(name),
relative_to_snapshot_dir=True)
if idx == 1: # train the original vae
representation_size = variant.get(
"vae_representation_size", variant.get('representation_size'))
beta = variant.get('vae_beta', variant.get('beta'))
m = ConvVAE(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
else: # train the segmentation lstm
lstm_version = variant.get('lstm_version', 2)
if lstm_version == 2:
lstm_class = ConvLSTM2
representation_size = variant.get(
"lstm_representation_size", variant.get('representation_size'))
beta = variant.get('lstm_beta', variant.get('beta'))
m = lstm_class(representation_size,
decoder_output_activation=decoder_activation,
**variant['lstm_kwargs'])
t = ConvLSTMTrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
m.to(ptu.device)
vaes.append(m)
print("test data len: ", len(test_data))
print("train data len: ", len(train_data))
save_period = variant['save_period']
pjhome = os.environ['PJHOME']
if env_id == 'SawyerPushHurdle-v0' and osp.exists(
osp.join(
pjhome,
'data/local/pre-train-lstm', '{}-{}-{}-0.3-0.5.npy'.format(
'SawyerPushHurdle-v0', 'seg-color', '500'))):
data_file_path = osp.join(
pjhome, 'data/local/pre-train-lstm',
'{}-{}-{}-0.3-0.5.npy'.format(env_id, 'seg-color', 500))
puck_pos_path = osp.join(
pjhome, 'data/local/pre-train-lstm',
'{}-{}-{}-0.3-0.5-puck-pos.npy'.format(env_id, 'seg-color',
500))
all_data = np.load(data_file_path)
puck_pos = np.load(puck_pos_path)
all_data = normalize_image(all_data.copy())
obj_states = puck_pos
else:
all_data = np.concatenate([train_data_lstm, test_data_lstm],
axis=0)
all_data = normalize_image(all_data.copy())
obj_states = info_lstm['obj_state']
obj = 'door' if 'Door' in env_id else 'puck'
num_epochs = variant['num_lstm_epochs'] if idx == 0 else variant[
'num_vae_epochs']
if (idx == 0 and seg_pretrain) or (idx == 1 and ori_pretrain):
for epoch in range(num_epochs):
should_save_imgs = (epoch % save_period == 0)
if idx == 0: # only LSTM trainer has 'only_train_vae' argument
t.train_epoch(epoch, only_train_vae=lstm_pretrain_vae_only)
t.test_epoch(epoch,
save_reconstruction=should_save_imgs,
save_prefix='r_' + name,
only_train_vae=lstm_pretrain_vae_only)
else:
t.train_epoch(epoch)
t.test_epoch(
epoch,
save_reconstruction=should_save_imgs,
save_prefix='r_' + name,
)
if should_save_imgs:
t.dump_samples(epoch, save_prefix='s_' + name)
if idx == 0:
compare_latent_distance(
m,
all_data,
obj_states,
obj_name=obj,
save_dir=logger.get_snapshot_dir(),
save_name='lstm_latent_distance_{}.png'.format(
epoch))
test_lstm_traj(
env_id,
m,
save_path=logger.get_snapshot_dir(),
save_name='lstm_test_traj_{}.png'.format(epoch))
test_masked_traj_lstm(
env_id,
m,
save_dir=logger.get_snapshot_dir(),
save_name='masked_test_{}.png'.format(epoch))
t.update_train_weights()
logger.save_extra_data(m, '{}.pkl'.format(name), mode='pickle')
logger.remove_tabular_output('{}_progress.csv'.format(name),
relative_to_snapshot_dir=True)
if idx == 0 and variant.get("only_train_lstm", False):
exit()
if return_data:
return vaes, train_datas, test_datas
return m
