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 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 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):
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 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 __init__(
self,
representation_size,
architecture,
action_dim,
dynamics_type=None,
encoder_class=CNN,
decoder_class=DCNN,
decoder_output_activation=identity,
decoder_distribution='bernoulli',
input_channels=1,
num_labels=0,
imsize=48,
init_w=1e-3,
min_variance=1e-3,
hidden_init=nn.init.xavier_uniform_,
add_labels_to_latents=False,
reconstruction_channels=3,
base_depth=32,
weight_init_gain=1.0,
):
super().__init__(representation_size, architecture, encoder_class,
decoder_class, decoder_output_activation,
decoder_distribution, num_labels, input_channels,
imsize, init_w, min_variance, add_labels_to_latents,
hidden_init, reconstruction_channels, base_depth,
weight_init_gain)
self.CDVAE = CDVAE(representation_size, architecture, action_dim,
dynamics_type, encoder_class, decoder_class,
decoder_output_activation, decoder_distribution,
input_channels, num_labels, imsize, init_w,
min_variance, hidden_init, add_labels_to_latents,
reconstruction_channels, base_depth,
weight_init_gain)
self.adversary = ConvVAE(representation_size[0], architecture,
encoder_class, decoder_class,
decoder_output_activation,
decoder_distribution, input_channels, imsize,
init_w, min_variance, hidden_init)
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 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 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
def _pointmass_fixed_goal_experiment(
vae_latent_size,
replay_buffer_size,
cnn_kwargs,
vae_kwargs,
policy_kwargs,
qf_kwargs,
e2e_trainer_kwargs,
sac_trainer_kwargs,
algorithm_kwargs,
eval_path_collector_kwargs=None,
expl_path_collector_kwargs=None,
**kwargs
):
if expl_path_collector_kwargs is None:
expl_path_collector_kwargs = {}
if eval_path_collector_kwargs is None:
eval_path_collector_kwargs = {}
from multiworld.core.image_env import ImageEnv
from multiworld.envs.pygame.point2d import Point2DEnv
from multiworld.core.flat_goal_env import FlatGoalEnv
env = Point2DEnv(
images_are_rgb=True,
render_onscreen=False,
show_goal=False,
ball_radius=2,
render_size=48,
fixed_goal=(0, 0),
)
env = ImageEnv(env, imsize=env.render_size, transpose=True, normalize=True)
env = FlatGoalEnv(env)#, append_goal_to_obs=True)
input_width, input_height = env.image_shape
action_dim = int(np.prod(env.action_space.shape))
vae = ConvVAE(
representation_size=vae_latent_size,
input_channels=3,
imsize=input_width,
decoder_output_activation=nn.Sigmoid(),
# decoder_distribution='gaussian_identity_variance',
**vae_kwargs)
encoder = Vae2Encoder(vae)
def make_cnn():
return networks.CNN(
input_width=input_width,
input_height=input_height,
input_channels=3,
output_conv_channels=True,
output_size=None,
**cnn_kwargs
)
def make_qf():
return networks.MlpQfWithObsProcessor(
obs_processor=nn.Sequential(
encoder,
networks.Flatten(),
),
output_size=1,
input_size=action_dim+vae_latent_size,
**qf_kwargs
)
qf1 = make_qf()
qf2 = make_qf()
target_qf1 = make_qf()
target_qf2 = make_qf()
action_dim = int(np.prod(env.action_space.shape))
policy_cnn = make_cnn()
policy = TanhGaussianPolicyAdapter(
nn.Sequential(policy_cnn, networks.Flatten()),
policy_cnn.conv_output_flat_size,
action_dim,
**policy_kwargs
)
eval_env = expl_env = env
eval_policy = MakeDeterministic(policy)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
**eval_path_collector_kwargs
)
replay_buffer = EnvReplayBuffer(
replay_buffer_size,
expl_env,
)
vae_trainer = VAETrainer(vae)
sac_trainer = SACTrainer(
env=eval_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**sac_trainer_kwargs
)
trainer = End2EndSACTrainer(
sac_trainer=sac_trainer,
vae_trainer=vae_trainer,
**e2e_trainer_kwargs,
)
expl_path_collector = MdpPathCollector(
expl_env,
policy,
**expl_path_collector_kwargs
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**algorithm_kwargs
)
algorithm.to(ptu.device)
algorithm.train()