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 = ConvVAE(representation_size, 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=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):
from railrl.core import logger
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data, info = generate_dataset(
)
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
kwargs = variant['beta_schedule_kwargs']
kwargs['y_values'][2] = variant['beta']
kwargs['x_values'][1] = variant['flat_x']
kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
beta_schedule = PiecewiseLinearSchedule(**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
output_scale=1
if variant['algo_kwargs']['is_auto_encoder']:
m = AutoEncoder(representation_size,
train_data.shape[1],
output_scale=output_scale,
**variant['vae_kwargs']
)
else:
m = VAE(representation_size,
train_data.shape[1],
output_scale=output_scale,
**variant['vae_kwargs']
)
t = VAETrainer(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)
def train_vae(variant, return_data=False):
from railrl.misc.ml_util import PiecewiseLinearSchedule
from railrl.torch.vae.vae_trainer import ConvVAETrainer
from railrl.core import logger
beta = variant["beta"]
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
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]
else:
action_dim = 0
model = get_vae(variant, action_dim)
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
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
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 = variant['generate_vae_dataset_fn'](
variant['generate_vae_dataset_kwargs'])
uniform_dataset = load_local_or_remote_file(
variant['uniform_dataset_path']).item()
uniform_dataset = unormalize_image(uniform_dataset['image_desired_goal'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
# kwargs = variant['beta_schedule_kwargs']
# kwargs['y_values'][2] = variant['beta']
# kwargs['x_values'][1] = variant['flat_x']
# kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
variant['beta_schedule_kwargs']['y_values'][-1] = variant['beta']
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
m = variant['vae'](representation_size,
decoder_output_activation=nn.Sigmoid(),
**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']
for epoch in range(variant['num_epochs']):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.log_loss_under_uniform(
m, uniform_dataset,
variant['algo_kwargs']['priority_function_kwargs'])
t.test_epoch(epoch,
save_reconstruction=should_save_imgs,
save_scatterplot=should_save_imgs)
if should_save_imgs:
t.dump_samples(epoch)
if variant['dump_skew_debug_plots']:
t.dump_best_reconstruction(epoch)
t.dump_worst_reconstruction(epoch)
t.dump_sampling_histogram(epoch)
t.dump_uniform_imgs_and_reconstructions(
dataset=uniform_dataset, epoch=epoch)
t.update_train_weights()
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']
# )
num_divisions = 5
images = np.zeros((num_divisions * 10000, 21168))
for i in range(num_divisions):
imgs = np.load(
'/home/murtaza/vae_data/sawyer_torque_control_images100000_' +
str(i + 1) + '.npy')
images[i * 10000:(i + 1) * 10000] = imgs
print(i)
mid = int(num_divisions * 10000 * .9)
train_data, test_data = images[:mid], images[mid:]
info = dict()
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
kwargs = variant['beta_schedule_kwargs']
kwargs['y_values'][2] = variant['beta']
kwargs['x_values'][1] = variant['flat_x']
kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
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.cuda()
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):
from railrl.core import logger
import railrl.torch.pytorch_util as ptu
beta = variant["beta"]
representation_size = variant["representation_size"]
data = joblib.load(variant['file'])
obs = data['obs']
size = int(data['size'])
dataset = obs[:size, :]
n = int(size * .9)
train_data = dataset[:n, :]
test_data = dataset[n:, :]
logger.get_snapshot_dir()
print('SIZE: ', size)
uniform_dataset = generate_uniform_dataset_door(
**variant['generate_uniform_dataset_kwargs']
)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
# kwargs = variant['beta_schedule_kwargs']
# kwargs['y_values'][2] = variant['beta']
# kwargs['x_values'][1] = variant['flat_x']
# kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
variant['beta_schedule_kwargs']['y_values'][-1] = variant['beta']
beta_schedule = PiecewiseLinearSchedule(**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
m = variant['vae'](representation_size, decoder_output_activation=nn.Sigmoid(), **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']
for epoch in range(variant['num_epochs']):
should_save_imgs = (epoch % save_period == 0)
t.train_epoch(epoch)
t.log_loss_under_uniform(uniform_dataset)
t.test_epoch(epoch, save_reconstruction=should_save_imgs,
save_scatterplot=should_save_imgs)
if should_save_imgs:
t.dump_samples(epoch)
if variant['dump_skew_debug_plots']:
t.dump_best_reconstruction(epoch)
t.dump_worst_reconstruction(epoch)
t.dump_sampling_histogram(epoch)
t.dump_uniform_imgs_and_reconstructions(dataset=uniform_dataset, epoch=epoch)
t.update_train_weights()
def experiment(variant):
from railrl.core import logger
import railrl.torch.pytorch_util as ptu
beta = variant["beta"]
representation_size = variant["representation_size"]
#this has both states and images so can't use generate vae dataset
X = np.load(
'/home/murtaza/vae_data/sawyer_torque_control_ou_imgs_zoomed_out10000.npy'
)
Y = np.load(
'/home/murtaza/vae_data/sawyer_torque_control_ou_states_zoomed_out10000.npy'
)
Y = np.concatenate((Y[:, :7], Y[:, 14:]), axis=1)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.1)
info = dict()
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,
state_sim_debug=True,
state_size=Y.shape[1],
**variant['conv_vae_kwargs'])
if ptu.gpu_enabled():
m.cuda()
t = ConvVAETrainer((X_train, Y_train), (X_test, Y_test),
m,
beta=beta,
beta_schedule=beta_schedule,
state_sim_debug=True,
**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):
if variant["use_gpu"]:
gpu_id = variant["gpu_id"]
ptu.set_gpu_mode(True)
ptu.set_device(gpu_id)
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data = get_data(10000)
m = ConvVAE(representation_size, input_channels=3)
t = ConvVAETrainer(train_data,
test_data,
m,
beta_schedule=PiecewiseLinearSchedule([0, 400, 800],
[0.5, 0.5, beta]))
for epoch in range(1001):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
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 = variant['generate_vae_dataset_fn'](
variant['generate_vae_dataset_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
# kwargs = variant['beta_schedule_kwargs']
# kwargs['y_values'][2] = variant['beta']
# kwargs['x_values'][1] = variant['flat_x']
# kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
variant['beta_schedule_kwargs']['y_values'][-1] = variant['beta']
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
m = variant['vae'](representation_size, **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']
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)
if variant['dump_skew_debug_plots']:
t.dump_best_reconstruction(epoch)
t.dump_worst_reconstruction(epoch)
t.dump_sampling_histogram(epoch)
t.update_train_weights()
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 = 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 experiment(variant):
lmbda = variant['lmbda']
gamma = variant['gamma']
mu = variant['mu']
beta = PiecewiseLinearSchedule([0, 2500, 3500], [0, 0, variant['beta']])
representation_size = variant["representation_size"]
train_data, test_data, info = generate_vae_dataset(
**variant['generate_vae_dataset_kwargs'])
m = ACAI(representation_size, input_channels=3)
t = ACAITrainer(train_data,
test_data,
m,
beta_schedule=beta,
gamma=gamma,
mu=mu,
lmbda=lmbda)
for epoch in range(6001):
t.train_epoch(epoch)
t.test_epoch(epoch)
if epoch % variant['save_period'] == 0:
t.dump_samples(epoch)
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['generate_vae_dataset_kwargs'])
logger.save_extra_data(info)
logger.get_snapshot_dir()
if 'beta_schedule_kwargs' in variant:
# kwargs = variant['beta_schedule_kwargs']
# kwargs['y_values'][2] = variant['beta']
# kwargs['x_values'][1] = variant['flat_x']
# kwargs['x_values'][2] = variant['ramp_x'] + variant['flat_x']
variant['beta_schedule_kwargs']['y_values'][-1] = variant['beta']
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.cuda()
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 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):
from railrl.misc.ml_util import PiecewiseLinearSchedule
from railrl.torch.vae.conv_vae import ConvVAE
from railrl.torch.vae.conv_vae_trainer import ConvVAETrainer
from railrl.core import logger
import railrl.torch.pytorch_util as ptu
from multiworld.core.image_env import ImageEnv
from railrl.envs.vae_wrappers import VAEWrappedEnv
from railrl.misc.asset_loader import local_path_from_s3_or_local_path
logger.remove_tabular_output('progress.csv', relative_to_snapshot_dir=True)
logger.add_tabular_output('vae_progress.csv',
relative_to_snapshot_dir=True)
env_id = variant['generate_vae_dataset_kwargs'].get('env_id', None)
if env_id is not None:
import gym
env = gym.make(env_id)
else:
env_class = variant['generate_vae_dataset_kwargs']['env_class']
env_kwargs = variant['generate_vae_dataset_kwargs']['env_kwargs']
env = env_class(**env_kwargs)
representation_size = variant["representation_size"]
beta = variant["beta"]
if 'beta_schedule_kwargs' in variant:
beta_schedule = PiecewiseLinearSchedule(
**variant['beta_schedule_kwargs'])
else:
beta_schedule = None
# obtain training and testing data
dataset_path = variant['generate_vae_dataset_kwargs'].get(
'dataset_path', None)
test_p = variant['generate_vae_dataset_kwargs'].get('test_p', 0.9)
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename, allow_pickle=True).item()
N = dataset['obs'].shape[0]
n = int(N * test_p)
train_data = {}
test_data = {}
for k in dataset.keys():
train_data[k] = dataset[k][:n, :]
test_data[k] = dataset[k][n:, :]
# setup vae
variant['vae_kwargs']['action_dim'] = train_data['actions'].shape[1]
if variant.get('vae_type', None) == "VAE-state":
from railrl.torch.vae.vae import VAE
input_size = train_data['obs'].shape[1]
variant['vae_kwargs']['input_size'] = input_size
m = VAE(representation_size, **variant['vae_kwargs'])
elif variant.get('vae_type', None) == "VAE2":
from railrl.torch.vae.conv_vae2 import ConvVAE2
variant['vae_kwargs']['imsize'] = variant['imsize']
m = ConvVAE2(representation_size, **variant['vae_kwargs'])
else:
variant['vae_kwargs']['imsize'] = variant['imsize']
m = ConvVAE(representation_size, **variant['vae_kwargs'])
if ptu.gpu_enabled():
m.cuda()
# setup vae trainer
if variant.get('vae_type', None) == "VAE-state":
from railrl.torch.vae.vae_trainer import VAETrainer
t = VAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
else:
t = ConvVAETrainer(train_data,
test_data,
m,
beta=beta,
beta_schedule=beta_schedule,
**variant['algo_kwargs'])
# visualization
vis_variant = variant.get('vis_kwargs', {})
save_video = vis_variant.get('save_video', False)
if isinstance(env, ImageEnv):
image_env = env
else:
image_env = ImageEnv(
env,
variant['generate_vae_dataset_kwargs'].get('imsize'),
init_camera=variant['generate_vae_dataset_kwargs'].get(
'init_camera'),
transpose=True,
normalize=True,
)
render = variant.get('render', False)
reward_params = variant.get("reward_params", dict())
vae_env = VAEWrappedEnv(image_env,
m,
imsize=image_env.imsize,
decode_goals=render,
render_goals=render,
render_rollouts=render,
reward_params=reward_params,
**variant.get('vae_wrapped_env_kwargs', {}))
vae_env.reset()
vae_env.add_mode("video_env", 'video_env')
vae_env.add_mode("video_vae", 'video_vae')
if save_video:
import railrl.samplers.rollout_functions as rf
from railrl.policies.simple import RandomPolicy
random_policy = RandomPolicy(vae_env.action_space)
rollout_function = rf.create_rollout_function(
rf.multitask_rollout,
max_path_length=100,
observation_key='latent_observation',
desired_goal_key='latent_desired_goal',
vis_list=vis_variant.get('vis_list', []),
dont_terminate=True,
)
dump_video_kwargs = variant.get("dump_video_kwargs", dict())
dump_video_kwargs['imsize'] = vae_env.imsize
dump_video_kwargs['vis_list'] = [
'image_observation',
'reconstr_image_observation',
'image_latent_histogram_2d',
'image_latent_histogram_mu_2d',
'image_plt',
'image_rew',
'image_rew_euclidean',
'image_rew_mahalanobis',
'image_rew_logp',
'image_rew_kl',
'image_rew_kl_rev',
]
def visualization_post_processing(save_vis, save_video, epoch):
vis_list = vis_variant.get('vis_list', [])
if save_vis:
if vae_env.vae_input_key_prefix == 'state':
vae_env.dump_reconstructions(epoch,
n_recon=vis_variant.get(
'n_recon', 16))
vae_env.dump_samples(epoch,
n_samples=vis_variant.get('n_samples', 64))
if 'latent_representation' in vis_list:
vae_env.dump_latent_plots(epoch)
if any(elem in vis_list for elem in [
'latent_histogram', 'latent_histogram_mu',
'latent_histogram_2d', 'latent_histogram_mu_2d'
]):
vae_env.compute_latent_histogram()
if not save_video and ('latent_histogram' in vis_list):
vae_env.dump_latent_histogram(epoch=epoch,
noisy=True,
use_true_prior=True)
if not save_video and ('latent_histogram_mu' in vis_list):
vae_env.dump_latent_histogram(epoch=epoch,
noisy=False,
use_true_prior=True)
if save_video and save_vis:
from railrl.envs.vae_wrappers import temporary_mode
from railrl.misc.video_gen import dump_video
from railrl.core import logger
vae_env.compute_goal_encodings()
logdir = logger.get_snapshot_dir()
filename = osp.join(logdir,
'video_{epoch}.mp4'.format(epoch=epoch))
variant['dump_video_kwargs']['epoch'] = epoch
temporary_mode(vae_env,
mode='video_env',
func=dump_video,
args=(vae_env, random_policy, filename,
rollout_function),
kwargs=variant['dump_video_kwargs'])
if not vis_variant.get('save_video_env_only', True):
filename = osp.join(
logdir, 'video_{epoch}_vae.mp4'.format(epoch=epoch))
temporary_mode(vae_env,
mode='video_vae',
func=dump_video,
args=(vae_env, random_policy, filename,
rollout_function),
kwargs=variant['dump_video_kwargs'])
# train vae
for epoch in range(variant['num_epochs']):
#for epoch in range(2000):
save_vis = (epoch % vis_variant['save_period'] == 0
or epoch == variant['num_epochs'] - 1)
save_vae = (epoch % variant['snapshot_gap'] == 0
or epoch == variant['num_epochs'] - 1)
t.train_epoch(epoch)
'''if epoch % 500 == 0 or epoch == variant['num_epochs']-1:
t.test_epoch(
epoch,
save_reconstruction=save_vis,
save_interpolation=save_vis,
save_vae=save_vae,
)
if epoch % 200 == 0 or epoch == variant['num_epochs']-1:
visualization_post_processing(save_video, save_video, epoch)'''
t.test_epoch(
epoch,
save_reconstruction=save_vis,
save_interpolation=save_vis,
save_vae=save_vae,
)
if epoch % 300 == 0 or epoch == variant['num_epochs'] - 1:
visualization_post_processing(save_vis, save_video, epoch)
logger.save_extra_data(m, '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,
)
print("finished --------------------!!!!!!!!!!!!!!!")
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
def __init__(
self,
vae,
*args,
decoded_obs_key='image_observation',
decoded_achieved_goal_key='image_achieved_goal',
decoded_desired_goal_key='image_desired_goal',
exploration_rewards_type='None',
exploration_rewards_scale=1.0,
vae_priority_type='None',
start_skew_epoch=0,
power=1.0,
internal_keys=None,
exploration_schedule_kwargs=None,
priority_function_kwargs=None,
exploration_counter_kwargs=None,
relabeling_goal_sampling_mode='vae_prior',
decode_vae_goals=False,
**kwargs
):
if internal_keys is None:
internal_keys = []
for key in [
decoded_obs_key,
decoded_achieved_goal_key,
decoded_desired_goal_key
]:
if key not in internal_keys:
internal_keys.append(key)
super().__init__(internal_keys=internal_keys, *args, **kwargs)
# assert isinstance(self.env, VAEWrappedEnv)
self.vae = vae
self.decoded_obs_key = decoded_obs_key
self.decoded_desired_goal_key = decoded_desired_goal_key
self.decoded_achieved_goal_key = decoded_achieved_goal_key
self.exploration_rewards_type = exploration_rewards_type
self.exploration_rewards_scale = exploration_rewards_scale
self.start_skew_epoch = start_skew_epoch
self.vae_priority_type = vae_priority_type
self.power = power
self._relabeling_goal_sampling_mode = relabeling_goal_sampling_mode
self.decode_vae_goals = decode_vae_goals
if exploration_schedule_kwargs is None:
self.explr_reward_scale_schedule = \
ConstantSchedule(self.exploration_rewards_scale)
else:
self.explr_reward_scale_schedule = \
PiecewiseLinearSchedule(**exploration_schedule_kwargs)
self._give_explr_reward_bonus = (
exploration_rewards_type != 'None'
and exploration_rewards_scale != 0.
)
self._exploration_rewards = np.zeros((self.max_size, 1), dtype=np.float32)
self._prioritize_vae_samples = (
vae_priority_type != 'None'
and power != 0.
)
self._vae_sample_priorities = np.zeros((self.max_size, 1), dtype=np.float32)
self._vae_sample_probs = None
self.use_dynamics_model = (
self.exploration_rewards_type == 'forward_model_error'
)
if self.use_dynamics_model:
self.initialize_dynamics_model()
type_to_function = {
'reconstruction_error': self.reconstruction_mse,
'bce': self.binary_cross_entropy,
'latent_distance': self.latent_novelty,
'latent_distance_true_prior': self.latent_novelty_true_prior,
'forward_model_error': self.forward_model_error,
'gaussian_inv_prob': self.gaussian_inv_prob,
'bernoulli_inv_prob': self.bernoulli_inv_prob,
'vae_prob': self.vae_prob,
'hash_count': self.hash_count_reward,
'None': self.no_reward,
}
self.exploration_reward_func = (
type_to_function[self.exploration_rewards_type]
)
self.vae_prioritization_func = (
type_to_function[self.vae_priority_type]
)
if priority_function_kwargs is None:
self.priority_function_kwargs = dict()
else:
self.priority_function_kwargs = priority_function_kwargs
if self.exploration_rewards_type == 'hash_count':
if exploration_counter_kwargs is None:
exploration_counter_kwargs = dict()
self.exploration_counter = CountExploration(env=self.env, **exploration_counter_kwargs)
self.epoch = 0
