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.misc.ml_util import PiecewiseLinearSchedule
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
from rlkit.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 save_paths(algo, epoch):
expl_paths = algo.expl_data_collector.get_epoch_paths()
filename = osp.join(logger.get_snapshot_dir(),
'video_{epoch}_vae.p'.format(epoch=epoch))
pickle.dump(expl_paths, open(filename, "wb"))
print("saved", filename)
eval_paths = algo.eval_data_collector.get_epoch_paths()
filename = osp.join(logger.get_snapshot_dir(),
'video_{epoch}_env.p'.format(epoch=epoch))
pickle.dump(eval_paths, open(filename, "wb"))
print("saved", filename)
def plot_scattered(self, z, epoch):
try:
import matplotlib.pyplot as plt
except ImportError:
logger.log(__file__ + ": Unable to load matplotlib. Consider "
"setting do_scatterplot to False")
return
dim_and_stds = [(i, np.std(z[:, i])) for i in range(z.shape[1])]
dim_and_stds = sorted(dim_and_stds, key=lambda x: x[1])
dim1 = dim_and_stds[-1][0]
dim2 = dim_and_stds[-2][0]
plt.figure(figsize=(8, 8))
plt.scatter(z[:, dim1], z[:, dim2], marker='o', edgecolor='none')
if self.model.dist_mu is not None:
x1 = self.model.dist_mu[dim1:dim1 + 1]
y1 = self.model.dist_mu[dim2:dim2 + 1]
x2 = self.model.dist_mu[dim1:dim1 +
1] + self.model.dist_std[dim1:dim1 + 1]
y2 = self.model.dist_mu[dim2:dim2 +
1] + self.model.dist_std[dim2:dim2 + 1]
plt.plot([x1, x2], [y1, y2], color='k', linestyle='-', linewidth=2)
axes = plt.gca()
axes.set_xlim([-6, 6])
axes.set_ylim([-6, 6])
axes.set_title('dim {} vs dim {}'.format(dim1, dim2))
plt.grid(True)
save_file = osp.join(logger.get_snapshot_dir(),
'scatter%d.png' % epoch)
plt.savefig(save_file)
def experiment(user_variant):
variant = default_variant.copy()
variant.update(user_variant)
if ptu.gpu_enabled():
enable_gpus("0")
env_id = variant["env"]
env = build_env(env_id)
agent_configs = variant["agent_configs"]
agent = build_agent(env, env_id, agent_configs)
agent.visualize = variant["visualize"]
model_file = variant.get("model_file")
if (model_file is not ""):
agent.load_model(model_file)
log_dir = logger.get_snapshot_dir()
if (variant["train"]):
agent.train(max_iter=variant["max_iter"],
test_episodes=variant["test_episodes"],
output_dir=log_dir,
output_iters=variant["output_iters"])
else:
agent.eval(num_episodes=variant["test_episodes"])
return
def test_iql():
logger.reset()
# make tests small by mutating variant
iql.variant["algo_kwargs"]["start_epoch"] = -2
iql.variant["algo_kwargs"]["num_epochs"] = 2
iql.variant["algo_kwargs"]["batch_size"] = 2
iql.variant["algo_kwargs"]["num_eval_steps_per_epoch"] = 2
iql.variant["algo_kwargs"]["num_expl_steps_per_train_loop"] = 2
iql.variant["algo_kwargs"]["num_trains_per_train_loop"] = 100
iql.variant["algo_kwargs"]["min_num_steps_before_training"] = 2
iql.variant["qf_kwargs"] = dict(hidden_sizes=[2, 2])
iql.variant["seed"] = 25580
iql.main()
reference_csv = "tests/regression/iql/halfcheetah_online_progress.csv"
output_csv = os.path.join(logger.get_snapshot_dir(), "progress.csv")
print("comparing reference %s against output %s" %
(reference_csv, output_csv))
output = csv_util.get_exp(output_csv)
reference = csv_util.get_exp(reference_csv)
keys = [
"epoch",
"expl/num steps total",
"expl/Average Returns",
"trainer/Q1 Predictions Mean",
]
csv_util.check_equal(reference, output, keys)
def create_save_h_vs_state_distance_fn(save_period, initial_save_period,
encoder, encoder_input_key):
import matplotlib.pyplot as plt
from rlkit.core import logger
import os.path as osp
logdir = logger.get_snapshot_dir()
def save_h_vs_state_distance(algo, epoch):
if ((epoch < save_period and epoch % initial_save_period == 0)
or epoch % save_period == 0 or epoch >= algo.num_epochs - 1):
filename = osp.join(
logdir,
'h_vs_distance_scatterplot_{epoch}.png'.format(epoch=epoch))
replay_buffer = algo.replay_buffer
size = min(1024, replay_buffer._size)
idxs1 = replay_buffer._sample_indices(size)
idxs2 = replay_buffer._sample_indices(size)
encoder_obs = replay_buffer._obs[encoder_input_key]
x1 = encoder_obs[idxs1]
x2 = encoder_obs[idxs2]
z1 = encoder.encode(x1)
z2 = encoder.encode(x2)
state_obs = replay_buffer._obs['state_observation']
states1 = state_obs[idxs1]
states2 = state_obs[idxs2]
state_deltas = np.linalg.norm(states1 - states2, axis=1, ord=1)
encoder_deltas = np.linalg.norm(z1 - z2, axis=1, ord=1)
plt.clf()
plt.scatter(state_deltas, encoder_deltas, alpha=0.2)
plt.savefig(filename)
return save_h_vs_state_distance
def __init__(self,
model,
data_collector,
tag,
save_video_period,
goal_image_key=None,
decode_goal_image_key=None,
reconstruction_key=None,
**kwargs):
self.model = model
self.data_collector = data_collector
self.tag = tag
self.goal_image_key = goal_image_key
self.decode_goal_image_key = decode_goal_image_key
self.reconstruction_key = reconstruction_key
self.dump_video_kwargs = kwargs
self.save_video_period = save_video_period
self.keys = []
if goal_image_key:
self.keys.append(goal_image_key)
if decode_goal_image_key:
self.keys.append(decode_goal_image_key)
self.keys.append("image_observation")
if reconstruction_key:
self.keys.append(reconstruction_key)
self.logdir = logger.get_snapshot_dir()
def dump_samples(self, epoch):
self.model.eval()
# set gpu device explicitly
label = torch.tensor(np.zeros(64)).long().to(ptu.device)
#sample1 = ptu.randn(64, self.representation_size)
#sample2 = self.model.decode(sample1)[0].cpu()
sample = self.model.pixelcnn.generate(label, (3, 3), 64).cpu()
e_indices = torch.tensor(sample).reshape(-1, 1).long().to(ptu.device)
min_encodings = torch.zeros(e_indices.shape[0], 64).to(ptu.device)
min_encodings.scatter_(1, e_indices, 1)
e_weights = self.model.vector_quantization.embedding.weight
z_q = torch.matmul(min_encodings, e_weights).view((64, 3, 3, 2))
z_q = z_q.permute(0, 3, 1, 2).contiguous()
z_q = self.model.pre_dequantization_conv(z_q)
x_recon = self.model.decoder(z_q)
sample = x_recon.view(64, -1)
#print(sample1.shape, sample2.shape, sample.shape, x_recon.shape)
#assert False
save_dir = osp.join(logger.get_snapshot_dir(), 's%d.png' % epoch)
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize).transpose(2, 3), save_dir)
def save(self):
path = logger.get_snapshot_dir()
trainer = self.trainer
expl_data_collector = self.expl_data_collector
eval_data_collector = self.eval_data_collector
replay_buffer = self.replay_buffer
expl_env = self.expl_env
eval_env = self.eval_env
pretrain_policy = self.pretrain_policy
delattr(self, "trainer")
delattr(self, "expl_data_collector")
delattr(self, "eval_data_collector")
delattr(self, "replay_buffer")
delattr(self, "expl_env")
delattr(self, "eval_env")
delattr(self, "pretrain_policy")
pickle.dump(self, open(os.path.join(path, "algorithm.pkl"), "wb"))
trainer.save(path, "trainer.pkl")
expl_data_collector.save(path, "expl_data_collector.pkl")
eval_data_collector.save(path, "eval_data_collector.pkl")
replay_buffer.save(path, "replay_buffer.pkl")
expl_env.save(path, "expl_env.pkl")
eval_env.save(path, "eval_env.pkl")
pretrain_policy.save(path, "pretrain_policy.pkl")
self.trainer = trainer
self.expl_data_collector = expl_data_collector
self.eval_data_collector = eval_data_collector
self.replay_buffer = replay_buffer
self.expl_env = expl_env
self.eval_env = eval_env
self.pretrain_policy = pretrain_policy
def post_epoch_visualize_func(algorithm, epoch):
if epoch % 10 == 0:
visualize_rollout(
algorithm.eval_env.envs[0],
algorithm.trainer.world_model,
logger.get_snapshot_dir(),
algorithm.max_path_length,
low_level_primitives=algorithm.low_level_primitives,
policy=algorithm.eval_data_collector._policy,
use_raps_obs=False,
use_true_actions=True,
num_rollouts=6,
)
if algorithm.low_level_primitives:
visualize_rollout(
algorithm.eval_env.envs[0],
algorithm.trainer.world_model,
logger.get_snapshot_dir(),
algorithm.max_path_length,
low_level_primitives=algorithm.low_level_primitives,
policy=algorithm.eval_data_collector._policy,
use_raps_obs=True,
use_true_actions=True,
num_rollouts=2,
)
visualize_rollout(
algorithm.eval_env.envs[0],
algorithm.trainer.world_model,
logger.get_snapshot_dir(),
algorithm.max_path_length,
low_level_primitives=algorithm.low_level_primitives,
policy=algorithm.eval_data_collector._policy,
use_raps_obs=True,
use_true_actions=False,
num_rollouts=2,
)
visualize_rollout(
algorithm.eval_env.envs[0],
algorithm.trainer.world_model,
logger.get_snapshot_dir(),
algorithm.max_path_length,
low_level_primitives=algorithm.low_level_primitives,
policy=algorithm.eval_data_collector._policy,
use_raps_obs=False,
use_true_actions=False,
num_rollouts=2,
)
def train(self):
epoch = -1
for t in range(self.num_update_loops_per_train_call):
epoch += 1
for _ in range(self.num_disc_updates_per_loop_iter):
self._do_reward_training(epoch)
self.discriminator.eval()
logits = self.discriminator(self.xy_var, None)
rewards = self._convert_logits_to_reward(logits)
self.discriminator.train()
logit_bound = 10.0
if self.train_objective == 'airl':
rew_bound = 10.0
elif self.train_objective == 'fairl':
rew_bound = 100.0
elif self.train_objective == 'gail':
rew_bound = 10.0
elif self.train_objective == 'w1':
rew_bound = 10.0
else:
raise Exception()
# plot the logits of the discriminator
# print(logit_bound)
# print(rew_bound)
logits = ptu.get_numpy(logits)
logits = np.reshape(logits, (int(self._d_len), int(self._d_len))).T
plot_seaborn_grid(
logits, -logit_bound, logit_bound,
'Disc Logits Epoch %d' % epoch,
osp.join(logger.get_snapshot_dir(),
'disc_logits_epoch_%d.png' % epoch))
# plot the rewards given by the discriminator
rewards = ptu.get_numpy(rewards)
rewards = np.reshape(rewards,
(int(self._d_len), int(self._d_len))).T
plot_seaborn_grid(
rewards, -rew_bound, rew_bound,
'Disc Rewards Epoch %d' % epoch,
osp.join(logger.get_snapshot_dir(),
'disc_rewards_epoch_%d.png' % epoch))
logger.dump_tabular(with_prefix=False, with_timestamp=False)
self.rewardf_eval_statistics = None
def test_epoch(
self,
epoch,
save_reconstruction=True,
save_vae=True,
from_rl=False,
):
self.model.eval()
losses = []
log_probs = []
kles = []
zs = []
beta = float(self.beta_schedule.get_value(epoch))
for batch_idx in range(10):
next_obs = self.get_batch(train=False)
reconstructions, obs_distribution_params, latent_distribution_params = self.model(next_obs)
log_prob = self.model.logprob(next_obs, obs_distribution_params)
kle = self.model.kl_divergence(latent_distribution_params)
loss = -1 * log_prob + beta * kle
encoder_mean = latent_distribution_params[0]
z_data = ptu.get_numpy(encoder_mean.cpu())
for i in range(len(z_data)):
zs.append(z_data[i, :])
losses.append(loss.item())
log_probs.append(log_prob.item())
kles.append(kle.item())
if batch_idx == 0 and save_reconstruction:
n = min(next_obs.size(0), 8)
comparison = torch.cat([
next_obs[:n].narrow(start=0, length=self.imlength, dim=1)
.contiguous().view(
-1, self.input_channels, self.imsize, self.imsize
).transpose(2, 3),
reconstructions.view(
self.batch_size,
self.input_channels,
self.imsize,
self.imsize,
)[:n].transpose(2, 3)
])
save_dir = osp.join(logger.get_snapshot_dir(),
'r%d.png' % epoch)
save_image(comparison.data.cpu(), save_dir, nrow=n)
zs = np.array(zs)
self.eval_statistics['epoch'] = epoch
self.eval_statistics['test/log prob'] = np.mean(log_probs)
self.eval_statistics['test/KL'] = np.mean(kles)
self.eval_statistics['test/loss'] = np.mean(losses)
self.eval_statistics['beta'] = beta
if not from_rl:
for k, v in self.eval_statistics.items():
logger.record_tabular(k, v)
logger.dump_tabular()
if save_vae:
logger.save_itr_params(epoch, self.model)
def run_task(variant):
from rlkit.core import logger
print(variant)
logger.log("Hello from script")
logger.log("variant: " + str(variant))
logger.record_tabular("value", 1)
logger.dump_tabular()
logger.log("snapshot_dir:", logger.get_snapshot_dir())
def render_video(self, tag, counter):
import numpy as np
import pdb
# log.debug("{}".format("render_video_and_add_to_tensorboard"))
path = self.eval_data_collector.collect_new_paths(
self.max_path_length,
self.num_eval_steps_per_epoch,
discard_incomplete_paths=True
)
if ("vae_reconstruction" in path[0]['env_infos'][0]):
video = np.array([ [y['vae_reconstruction'] for y in x['env_infos']] for x in path])
display_gif(images=video, logdir=logger.get_snapshot_dir()+"/"+tag+"_reconstruction" , fps=15, counter=counter)
video = np.array([ [y['rendering'] for y in x['env_infos']] for x in path])
display_gif(images=video, logdir=logger.get_snapshot_dir()+"/"+tag , fps=15, counter=counter)
def dump_samples(self, epoch):
self.model.eval()
sample = ptu.Variable(torch.randn(64, self.representation_size))
sample = self.model.decode(sample).cpu()
save_dir = osp.join(logger.get_snapshot_dir(), 's%d.png' % epoch)
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize), save_dir)
def train_vae(variant):
#train_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_train.hdf5'
#test_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_test.hdf5'
# train_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorBigTwoBallSmall.h5'
# test_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorBigTwoBallSmall.h5'
train_path = '/home/jcoreyes/objects/RailResearch/BlocksGeneration/rendered/fiveBlock10kActions.h5'
test_path = '/home/jcoreyes/objects/RailResearch/BlocksGeneration/rendered/fiveBlock10kActions.h5'
train_feats, train_actions = load_dataset(train_path, train=True)
test_feats, test_actions = load_dataset(test_path, train=False)
K = variant['vae_kwargs']['K']
rep_size = variant['vae_kwargs']['representation_size']
logger.get_snapshot_dir()
variant['vae_kwargs']['architecture'] = iodine.imsize64_large_iodine_architecture
variant['vae_kwargs']['decoder_class'] = BroadcastCNN
refinement_net = RefinementNetwork(**iodine.imsize64_large_iodine_architecture['refine_args'],
hidden_activation=nn.ELU())
physics_net = PhysicsNetwork(K, rep_size, train_actions.shape[-1])
m = IodineVAE(
**variant['vae_kwargs'],
refinement_net=refinement_net,
dynamic=True,
physics_net=physics_net,
)
m.to(ptu.device)
t = IodineTrainer(train_feats, test_feats, m, variant['train_seedsteps'], variant['test_seedsteps'],
train_actions=train_actions, test_actions=test_actions,
**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, batches=train_feats.shape[0]//variant['algo_kwargs']['batch_size'])
t.test_epoch(epoch, save_vae=True, train=False, record_stats=True, batches=1,
save_reconstruction=should_save_imgs)
t.test_epoch(epoch, save_vae=False, train=True, record_stats=False, batches=1,
save_reconstruction=should_save_imgs)
logger.save_extra_data(m, 'vae.pkl', mode='pickle')
def dump_samples(self, epoch, save_prefix='s'):
self.model.eval()
sample = ptu.randn(64, self.representation_size)
sample = self.model.decode(sample)[0].cpu()
save_dir = osp.join(logger.get_snapshot_dir(),
'{}{}.png'.format(save_prefix, epoch))
save_image(
sample.data.view(64, self.input_channels, self.imsize,
self.imsize).transpose(2, 3), save_dir)
def dump_debug_images(
self,
epoch,
dump_images=True,
num_recons=10,
num_samples=25,
debug_period=10,
unnormalize_images=False,
):
"""
:param epoch:
:param dump_images: Set to False to not dump any images.
:param num_recons:
:param num_samples:
:param debug_period: How often do you dump debug images?
:param unnormalize_images: Should your unnormalize images before
dumping them? Set to True if images are floats in [0, 1].
:return:
"""
if not dump_images or epoch % debug_period != 0:
return
example_obs_batch_np = ptu.get_numpy(self.example_obs_batch)
recon_examples_np = ptu.get_numpy(
self.vae.reconstruct(self.example_obs_batch))
top_row_example = example_obs_batch_np[:num_recons]
bottom_row_recon = np.clip(recon_examples_np, 0, 1)[:num_recons]
recon_vis = combine_images_into_grid(
imgs=list(top_row_example) + list(bottom_row_recon),
imwidth=example_obs_batch_np.shape[2],
imheight=example_obs_batch_np.shape[3],
max_num_cols=len(top_row_example),
image_format='CWH',
unnormalize=unnormalize_images,
)
logdir = logger.get_snapshot_dir()
cv2.imwrite(
osp.join(logdir, '{}_recons.png'.format(epoch)),
cv2.cvtColor(recon_vis, cv2.COLOR_RGB2BGR),
)
raw_samples = ptu.get_numpy(self.vae.sample(num_samples))
vae_samples = np.clip(raw_samples, 0, 1)
vae_sample_vis = combine_images_into_grid(
imgs=list(vae_samples),
imwidth=example_obs_batch_np.shape[2],
imheight=example_obs_batch_np.shape[3],
image_format='CWH',
unnormalize=unnormalize_images,
)
cv2.imwrite(
osp.join(logdir, '{}_vae_samples.png'.format(epoch)),
cv2.cvtColor(vae_sample_vis, cv2.COLOR_RGB2BGR),
)
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 _log_stats(self, epoch):
logger.log("Epoch {} finished".format(epoch), with_timestamp=True)
"""
Replay Buffer
"""
logger.record_dict(self.replay_buffer.get_diagnostics(),
prefix='replay_buffer/')
# If you want to save replay buffer as a whole, use this
snap_shot_dir = logger.get_snapshot_dir()
self.replay_buffer.save_buffer(snap_shot_dir + '/online_buffer.hdf5')
"""
Trainer
"""
logger.record_dict(self.trainer.get_diagnostics(), prefix='trainer/')
"""
Exploration
"""
logger.record_dict(self.expl_data_collector.get_diagnostics(),
prefix='exploration/')
expl_paths = self.expl_data_collector.get_epoch_paths()
# import ipdb; ipdb.set_trace()
if hasattr(self.expl_env, 'get_diagnostics'):
logger.record_dict(
self.expl_env.get_diagnostics(expl_paths),
prefix='exploration/',
)
if not self.batch_rl or self.eval_both:
logger.record_dict(
eval_util.get_generic_path_information(expl_paths),
prefix="exploration/",
)
"""
Evaluation
"""
logger.record_dict(
self.eval_data_collector.get_diagnostics(),
prefix='evaluation/',
)
eval_paths = self.eval_data_collector.get_epoch_paths()
if hasattr(self.eval_env, 'get_diagnostics'):
logger.record_dict(
self.eval_env.get_diagnostics(eval_paths),
prefix='evaluation/',
)
logger.record_dict(
eval_util.get_generic_path_information(eval_paths),
prefix="evaluation/",
)
"""
Misc
"""
gt.stamp('logging')
logger.record_dict(_get_epoch_timings())
logger.record_tabular('Epoch', epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
def train_set_vae(create_vae_kwargs,
vae_trainer_kwargs,
algo_kwargs,
data_loader_kwargs,
generate_set_kwargs,
num_ungrouped_images,
env_id=None,
env_class=None,
env_kwargs=None,
beta_schedule_kwargs=None,
env=None,
renderer=None,
sets=None) -> VAE:
if beta_schedule_kwargs is None:
beta_schedule_kwargs = {}
print("vae_launcher:train_set_vae: device", ptu.device)
eval_set_imgs, renderer, set_imgs, set_imgs_iterator, ungrouped_imgs = create_dataset(
env_id,
env_class,
env_kwargs,
generate_set_kwargs,
num_ungrouped_images,
env=env,
renderer=renderer,
sets=sets,
)
set_imgs_flat = set_imgs.view((-1, *set_imgs.shape[-3:]))
all_imgs = torch.cat([ungrouped_imgs, set_imgs_flat], dim=0)
all_imgs_iterator = data.DataLoader(all_imgs, **data_loader_kwargs)
vae = create_image_vae(img_chw=renderer.image_chw, **create_vae_kwargs)
set_key = 'set'
data_key = 'data'
dict_loader = DictLoader({
data_key: all_imgs_iterator,
set_key: infinite(set_imgs_iterator),
})
beta_schedule = create_beta_schedule(**beta_schedule_kwargs)
vae_trainer = SetVAETrainer(vae=vae,
set_key=set_key,
data_key=data_key,
train_sets=set_imgs,
eval_sets=eval_set_imgs[:2],
beta_schedule=beta_schedule,
**vae_trainer_kwargs)
algorithm = UnsupervisedTorchAlgorithm(
vae_trainer,
dict_loader,
**algo_kwargs,
)
algorithm.to(ptu.device)
algorithm.run()
print(logger.get_snapshot_dir())
return vae
def plot_encoder_function(variant, encoder, tag=""):
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from rlkit.core import logger
logdir = logger.get_snapshot_dir()
def plot_encoder(algo, epoch, is_x=False):
save_period = variant.get('save_video_period', 50)
if epoch % save_period == 0 or epoch == algo.num_epochs:
filename = osp.join(
logdir,
'encoder_{}_{}_{epoch}_env.gif'.format(tag,
"x" if is_x else "y",
epoch=epoch))
vary = np.arange(-4, 4, .1)
static = np.zeros(len(vary))
points_x = np.c_[vary.reshape(-1, 1), static.reshape(-1, 1)]
points_y = np.c_[static.reshape(-1, 1), vary.reshape(-1, 1)]
encoded_points_x = ptu.get_numpy(
encoder.forward(ptu.from_numpy(points_x)))
encoded_points_y = ptu.get_numpy(
encoder.forward(ptu.from_numpy(points_y)))
plt.clf()
fig = plt.figure()
plt.xlim(
min(min(encoded_points_x[:, 0]), min(encoded_points_y[:, 0])),
max(max(encoded_points_x[:, 0]), max(encoded_points_y[:, 0])))
plt.ylim(
min(min(encoded_points_x[:, 1]), min(encoded_points_y[:, 1])),
max(max(encoded_points_x[:, 1]), max(encoded_points_y[:, 1])))
colors = ["red", "blue"]
lines = [
plt.plot([], [], 'o', color=colors[i], alpha=0.4)[0]
for i in range(2)
]
def animate(i):
lines[0].set_data(encoded_points_x[:i + 1, 0],
encoded_points_x[:i + 1, 1])
lines[1].set_data(encoded_points_y[:i + 1, 0],
encoded_points_y[:i + 1, 1])
return lines
ani = FuncAnimation(fig, animate, frames=len(vary), interval=40)
ani.save(filename, writer='imagemagick', fps=60)
# def plot_encoder_x_and_y(algo, epoch):
# plot_encoder(algo, epoch, is_x=True)
# plot_encoder(algo, epoch, is_x=False)
return plot_encoder
def visualize_representation(algo, epoch):
if ((epoch < save_period and epoch % initial_save_period == 0)
or epoch % save_period == 0 or epoch >= algo.num_epochs - 1):
logdir = logger.get_snapshot_dir()
filename = osp.join(
logdir,
'obj{obj_id}_sweep_visualization_{epoch}.png'.format(
obj_id=obj_to_sweep, epoch=epoch),
)
visualizations = []
for goal_dict in goal_dicts:
start_img = goal_dict['image_observation']
new_states = goal_dict['new_states']
encoded = encoder.encode(new_states)
# img_format = renderer.output_image_format
images_to_stack = [start_img]
for i in range(encoded.shape[1]):
values = encoded[:, i]
value_image = values.reshape(env_renderer.image_chw[1:])
# TODO: fix hardcoding of CHW
value_img_rgb = np.repeat(value_image[None, :, :],
3,
axis=0)
value_img_rgb = (
(value_img_rgb - value_img_rgb.min()) /
(value_img_rgb.max() - value_img_rgb.min() + 1e-9))
images_to_stack.append(value_img_rgb)
visualizations.append(
combine_images_into_grid(
images_to_stack,
imwidth=renderer.image_chw[2],
imheight=renderer.image_chw[1],
max_num_cols=len(start_states),
pad_length=1,
pad_color=0,
subpad_length=1,
subpad_color=128,
image_format=renderer.output_image_format,
unnormalize=True,
))
final_image = combine_images_into_grid(
visualizations,
imwidth=visualizations[0].shape[1],
imheight=visualizations[0].shape[0],
max_num_cols=3,
image_format='HWC',
pad_length=0,
subpad_length=0,
)
cv2.imwrite(filename, final_image)
print("Saved visualization image to to ", filename)
def create_and_save_dict(list_of_waypoints, goals):
dataset = {
'list_of_waypoints': list_of_waypoints,
'goals': goals,
}
from rlkit.core import logger
logdir = logger.get_snapshot_dir()
np.save(
os.path.join(logdir, 'example_dataset.npy'),
dataset
)
return dataset
def get_video_save_func(rollout_function, env, policy, variant):
from multiworld.core.image_env import ImageEnv
from rlkit.core import logger
from rlkit.envs.vae_wrappers import temporary_mode
from rlkit.visualization.video import dump_video
logdir = logger.get_snapshot_dir()
save_period = variant.get('save_video_period', 50)
do_state_exp = variant.get("do_state_exp", False)
dump_video_kwargs = variant.get("dump_video_kwargs", dict())
dump_video_kwargs['horizon'] = variant['max_path_length']
if do_state_exp:
imsize = variant.get('imsize')
dump_video_kwargs['imsize'] = imsize
image_env = ImageEnv(
env,
imsize,
init_camera=variant.get('init_camera', None),
transpose=True,
normalize=True,
)
def save_video(algo, epoch):
if epoch % save_period == 0 or epoch == algo.num_epochs:
filename = osp.join(
logdir, 'video_{epoch}_env.mp4'.format(epoch=epoch))
dump_video(image_env, policy, filename, rollout_function,
**dump_video_kwargs)
else:
image_env = env
dump_video_kwargs['imsize'] = env.imsize
def save_video(algo, epoch):
if epoch % save_period == 0 or epoch == algo.num_epochs:
filename = osp.join(
logdir, 'video_{epoch}_env.mp4'.format(epoch=epoch))
temporary_mode(image_env,
mode='video_env',
func=dump_video,
args=(image_env, policy, filename,
rollout_function),
kwargs=dump_video_kwargs)
filename = osp.join(
logdir, 'video_{epoch}_vae.mp4'.format(epoch=epoch))
temporary_mode(image_env,
mode='video_vae',
func=dump_video,
args=(image_env, policy, filename,
rollout_function),
kwargs=dump_video_kwargs)
return save_video
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 train_vae(variant):
#train_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_train_10000.hdf5'
#test_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_test.hdf5'
train_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorTwoBallSmall.h5'
test_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorTwoBallSmall.h5'
train_data = load_dataset(train_path, train=True)
test_data = load_dataset(test_path, train=False)
train_data = train_data.reshape((train_data.shape[0], -1))
test_data = test_data.reshape((test_data.shape[0], -1))
#logger.save_extra_data(info)
logger.get_snapshot_dir()
variant['vae_kwargs'][
'architecture'] = monet.imsize64_monet_architecture #monet.imsize84_monet_architecture
variant['vae_kwargs']['decoder_output_activation'] = identity
variant['vae_kwargs']['decoder_class'] = BroadcastCNN
attention_net = UNet(in_channels=4,
n_classes=1,
up_mode='upsample',
depth=3,
padding=True)
m = MonetVAE(**variant['vae_kwargs'], attention_net=attention_net)
m.to(ptu.device)
t = MonetTrainer(train_data, test_data, m, **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')
def train_vae(variant):
train_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_train.hdf5'
test_path = '/home/jcoreyes/objects/rlkit/examples/monet/clevr_test.hdf5'
#train_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorBigTwoBallSmall.h5'
#test_path = '/home/jcoreyes/objects/RailResearch/DataGeneration/ColorBigTwoBallSmall.h5'
train_data = load_dataset(train_path, train=True)
test_data = load_dataset(test_path, train=False)
train_data = train_data.reshape((train_data.shape[0], -1))[:500]
#train_data = train_data.reshape((train_data.shape[0], -1))[0]
#train_data = np.reshape(train_data[:2], (2, -1)).repeat(100, 0)
test_data = test_data.reshape((test_data.shape[0], -1))[:10]
#logger.save_extra_data(info)
logger.get_snapshot_dir()
variant['vae_kwargs']['architecture'] = iodine.imsize84_iodine_architecture
variant['vae_kwargs']['decoder_class'] = BroadcastCNN
refinement_net = RefinementNetwork(
**iodine.imsize84_iodine_architecture['refine_args'],
hidden_activation=nn.ELU())
m = IodineVAE(**variant['vae_kwargs'], refinement_net=refinement_net)
m.to(ptu.device)
t = IodineTrainer(train_data, test_data, m, **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,
batches=train_data.shape[0] //
variant['algo_kwargs']['batch_size'])
t.test_epoch(epoch,
save_reconstruction=should_save_imgs,
save_vae=False)
if should_save_imgs:
t.dump_samples(epoch)
logger.save_extra_data(m, 'vae.pkl', mode='pickle')
def evaluate(self, epoch):
"""
Evaluate the policy, e.g. save/print progress.
:param epoch:
:return:
"""
statistics = OrderedDict()
try:
statistics.update(self.eval_statistics)
self.eval_statistics = None
except:
print('No Stats to Eval')
logger.log("Collecting samples for evaluation")
test_paths = self.eval_sampler.obtain_samples()
statistics.update(
eval_util.get_generic_path_information(
test_paths,
stat_prefix="Test",
))
statistics.update(
eval_util.get_generic_path_information(
self._exploration_paths,
stat_prefix="Exploration",
))
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(test_paths)
if hasattr(self.env, "log_statistics"):
statistics.update(self.env.log_statistics(test_paths))
if epoch % self.freq_log_visuals == 0:
if hasattr(self.env, "log_visuals"):
self.env.log_visuals(test_paths, epoch,
logger.get_snapshot_dir())
average_returns = eval_util.get_average_returns(test_paths)
statistics['AverageReturn'] = average_returns
for key, value in statistics.items():
logger.record_tabular(key, value)
best_statistic = statistics[self.best_key]
if best_statistic > self.best_statistic_so_far:
self.best_statistic_so_far = best_statistic
if self.save_best and epoch >= self.save_best_starting_from_epoch:
data_to_save = {'epoch': epoch, 'statistics': statistics}
data_to_save.update(self.get_epoch_snapshot(epoch))
logger.save_extra_data(data_to_save, 'best.pkl')
print('\n\nSAVED BEST\n\n')