def experiment(variant):
from railrl.core import logger
import railrl.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 = SpatialAutoEncoder(representation_size,
num_feat_points=representation_size // 2,
input_channels=3)
if ptu.gpu_enabled():
m.to(ptu.device)
gpu_id = variant.get("gpu_id", None)
if gpu_id is not None:
ptu.set_device(gpu_id)
t = ConvVAETrainer(train_data,
test_data,
m,
beta=0,
**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):
num_feat_points=variant['feat_points']
from railrl.core import logger
beta = variant["beta"]
print('collecting data')
train_data, test_data, info = get_data(**variant['get_data_kwargs'])
print('finish collecting data')
logger.save_extra_data(info)
logger.get_snapshot_dir()
m = SpatialAutoEncoder(2 * num_feat_points, num_feat_points, input_channels=3)
# m = ConvVAE(2*num_feat_points, input_channels=3)
t = ConvVAETrainer(train_data, test_data, m, lr=variant['lr'], beta=beta)
for epoch in range(variant['num_epochs']):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
def get_vae(variant, action_dim):
from railrl.torch.vae.conv_vae import (
ConvVAE,
SpatialAutoEncoder,
AutoEncoder,
)
import railrl.torch.vae.conv_vae as conv_vae
import railrl.torch.pytorch_util as ptu
from railrl.pythonplusplus import identity
import torch
representation_size = variant["representation_size"]
use_linear_dynamics = variant.get('use_linear_dynamics', False)
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')
if variant['algo_kwargs'].get('is_auto_encoder', False):
model = AutoEncoder(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
elif variant.get('use_spatial_auto_encoder', False):
model = SpatialAutoEncoder(
representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
else:
vae_class = variant.get('vae_class', ConvVAE)
if use_linear_dynamics:
model = vae_class(representation_size,
decoder_output_activation=decoder_activation,
action_dim=action_dim,
**variant['vae_kwargs'])
else:
model = vae_class(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
model.to(ptu.device)
return model
def train_vae(variant, return_data=False):
from railrl.misc.ml_util import PiecewiseLinearSchedule
from railrl.torch.vae.conv_vae import (
ConvVAE,
SpatialAutoEncoder,
AutoEncoder,
)
import railrl.torch.vae.conv_vae as conv_vae
from railrl.torch.vae.vae_trainer import ConvVAETrainer
from railrl.core import logger
import railrl.torch.pytorch_util as ptu
from railrl.pythonplusplus import identity
import torch
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data, info = generate_vae_dataset_from_demos(
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')
if variant['algo_kwargs'].get('is_auto_encoder', False):
m = AutoEncoder(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
elif variant.get('use_spatial_auto_encoder', False):
raise NotImplementedError(
'This is currently broken, please update SpatialAutoEncoder then remove this line'
)
m = SpatialAutoEncoder(representation_size,
int(representation_size / 2))
else:
vae_class = variant.get('vae_class', ConvVAE)
m = vae_class(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_scatterplot=should_save_imgs,
# save_vae=False,
)
if should_save_imgs:
t.dump_samples(epoch)
if dump_skew_debug_plots:
t.dump_best_reconstruction(epoch)
t.dump_worst_reconstruction(epoch)
t.dump_sampling_histogram(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):
from railrl.misc.ml_util import PiecewiseLinearSchedule, ConstantSchedule
from railrl.torch.vae.conv_vae import (
ConvVAE,
SpatialAutoEncoder,
AutoEncoder,
)
import railrl.torch.vae.conv_vae as conv_vae
from railrl.torch.vae.vae_trainer import ConvVAETrainer
from railrl.core import logger
import railrl.torch.pytorch_util as ptu
from railrl.pythonplusplus import identity
import torch
beta = variant["beta"]
representation_size = variant.get("representation_size",
variant.get("latent_sizes", None))
use_linear_dynamics = variant.get('use_linear_dynamics', False)
generate_vae_dataset_fctn = variant.get('generate_vae_data_fctn',
generate_vae_dataset)
variant['generate_vae_dataset_kwargs'][
'use_linear_dynamics'] = use_linear_dynamics
variant['generate_vae_dataset_kwargs']['batch_size'] = variant[
'algo_kwargs']['batch_size']
train_dataset, test_dataset, info = generate_vae_dataset_fctn(
variant['generate_vae_dataset_kwargs'])
if use_linear_dynamics:
action_dim = train_dataset.data['actions'].shape[2]
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 'context_schedule' in variant:
schedule = variant['context_schedule']
if type(schedule) is dict:
context_schedule = PiecewiseLinearSchedule(**schedule)
else:
context_schedule = ConstantSchedule(schedule)
variant['algo_kwargs']['context_schedule'] = context_schedule
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')
if variant['algo_kwargs'].get('is_auto_encoder', False):
model = AutoEncoder(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
elif variant.get('use_spatial_auto_encoder', False):
model = SpatialAutoEncoder(
representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
else:
vae_class = variant.get('vae_class', ConvVAE)
if use_linear_dynamics:
model = vae_class(representation_size,
decoder_output_activation=decoder_activation,
action_dim=action_dim,
**variant['vae_kwargs'])
else:
model = vae_class(representation_size,
decoder_output_activation=decoder_activation,
**variant['vae_kwargs'])
model.to(ptu.device)
vae_trainer_class = variant.get('vae_trainer_class', ConvVAETrainer)
trainer = vae_trainer_class(model,
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)
trainer.train_epoch(epoch, train_dataset)
trainer.test_epoch(epoch, test_dataset)
if should_save_imgs:
trainer.dump_reconstructions(epoch)
trainer.dump_samples(epoch)
if dump_skew_debug_plots:
trainer.dump_best_reconstruction(epoch)
trainer.dump_worst_reconstruction(epoch)
trainer.dump_sampling_histogram(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, model)
logger.save_extra_data(model, 'vae.pkl', mode='pickle')
if return_data:
return model, train_dataset, test_dataset
return model
