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import numpy as np
import rlkit.torch.pytorch_util as ptu
from multiworld.core.image_env import normalize_image
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.data_management.obs_dict_replay_buffer import flatten_dict
from rlkit.data_management.shared_obs_dict_replay_buffer import \
SharedObsDictRelabelingBuffer
from rlkit.envs.vae_wrapper import VAEWrappedEnv
from rlkit.torch.vae.vae_trainer import (
compute_p_x_np_to_np,
relative_probs_from_log_probs,
)
class OnlineVaeRelabelingBuffer(SharedObsDictRelabelingBuffer):
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,
priority_function_kwargs=None,
relabeling_goal_sampling_mode='vae_prior',
**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._give_explr_reward_bonus = (
exploration_rewards_type != 'None'
and exploration_rewards_scale != 0.
)
self._exploration_rewards = np.zeros((self.max_size, 1))
self._prioritize_vae_samples = (
vae_priority_type != 'None'
and power != 0.
)
self._vae_sample_priorities = np.zeros((self.max_size, 1))
self._vae_sample_probs = None
type_to_function = {
'vae_prob': self.vae_prob,
'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
self.epoch = 0
self._register_mp_array("_exploration_rewards")
self._register_mp_array("_vae_sample_priorities")
def add_path(self, path):
self.add_decoded_vae_goals_to_path(path)
super().add_path(path)
def add_decoded_vae_goals_to_path(self, path):
# decoding the self-sampled vae images should be done in batch (here)
# rather than in the env for efficiency
desired_goals = flatten_dict(
path['observations'],
[self.desired_goal_key]
)[self.desired_goal_key]
desired_decoded_goals = self.env._decode(desired_goals)
desired_decoded_goals = desired_decoded_goals.reshape(
len(desired_decoded_goals),
-1
)
for idx, next_obs in enumerate(path['observations']):
path['observations'][idx][self.decoded_desired_goal_key] = \
desired_decoded_goals[idx]
path['next_observations'][idx][self.decoded_desired_goal_key] = \
desired_decoded_goals[idx]
def get_diagnostics(self):
if self._vae_sample_probs is None or self._vae_sample_priorities is None:
stats = create_stats_ordered_dict(
'VAE Sample Weights',
np.zeros(self._size),
)
stats.update(create_stats_ordered_dict(
'VAE Sample Probs',
np.zeros(self._size),
))
else:
vae_sample_priorities = self._vae_sample_priorities[:self._size]
vae_sample_probs = self._vae_sample_probs[:self._size]
stats = create_stats_ordered_dict(
'VAE Sample Weights',
vae_sample_priorities,
)
stats.update(create_stats_ordered_dict(
'VAE Sample Probs',
vae_sample_probs,
))
return stats
def refresh_latents(self, epoch):
self.epoch = epoch
self.skew = (self.epoch > self.start_skew_epoch)
batch_size = 512
next_idx = min(batch_size, self._size)
if self.exploration_rewards_type == 'hash_count':
# you have to count everything then compute exploration rewards
cur_idx = 0
next_idx = min(batch_size, self._size)
while cur_idx < self._size:
idxs = np.arange(cur_idx, next_idx)
normalized_imgs = (
normalize_image(self._next_obs[self.decoded_obs_key][idxs])
)
cur_idx = next_idx
next_idx += batch_size
next_idx = min(next_idx, self._size)
cur_idx = 0
obs_sum = np.zeros(self.vae.representation_size)
obs_square_sum = np.zeros(self.vae.representation_size)
while cur_idx < self._size:
idxs = np.arange(cur_idx, next_idx)
self._obs[self.observation_key][idxs] = \
self.env._encode(
normalize_image(self._obs[self.decoded_obs_key][idxs])
)
self._next_obs[self.observation_key][idxs] = \
self.env._encode(
normalize_image(self._next_obs[self.decoded_obs_key][idxs])
)
# WARNING: we only refresh the desired/achieved latents for
# "next_obs". This means that obs[desired/achieve] will be invalid,
# so make sure there's no code that references this.
self._next_obs[self.desired_goal_key][idxs] = \
self.env._encode(
normalize_image(self._next_obs[self.decoded_desired_goal_key][idxs])
)
self._next_obs[self.achieved_goal_key][idxs] = \
self.env._encode(
normalize_image(self._next_obs[self.decoded_achieved_goal_key][idxs])
)
normalized_imgs = (
normalize_image(self._next_obs[self.decoded_obs_key][idxs])
)
if self._give_explr_reward_bonus:
rewards = self.exploration_reward_func(
normalized_imgs,
idxs,
**self.priority_function_kwargs
)
self._exploration_rewards[idxs] = rewards.reshape(-1, 1)
if self._prioritize_vae_samples:
if (
self.exploration_rewards_type == self.vae_priority_type
and self._give_explr_reward_bonus
):
self._vae_sample_priorities[idxs] = (
self._exploration_rewards[idxs]
)
else:
self._vae_sample_priorities[idxs] = (
self.vae_prioritization_func(
normalized_imgs,
idxs,
**self.priority_function_kwargs
).reshape(-1, 1)
)
obs_sum+= self._obs[self.observation_key][idxs].sum(axis=0)
obs_square_sum+= np.power(self._obs[self.observation_key][idxs], 2).sum(axis=0)
cur_idx = next_idx
next_idx += batch_size
next_idx = min(next_idx, self._size)
self.vae.dist_mu = obs_sum/self._size
self.vae.dist_std = np.sqrt(obs_square_sum/self._size - np.power(self.vae.dist_mu, 2))
if self._prioritize_vae_samples:
"""
priority^power is calculated in the priority function
for image_bernoulli_prob or image_gaussian_inv_prob and
directly here if not.
"""
if self.vae_priority_type == 'vae_prob':
self._vae_sample_priorities[:self._size] = relative_probs_from_log_probs(
self._vae_sample_priorities[:self._size]
)
self._vae_sample_probs = self._vae_sample_priorities[:self._size]
else:
self._vae_sample_probs = self._vae_sample_priorities[:self._size] ** self.power
p_sum = np.sum(self._vae_sample_probs)
assert p_sum > 0, "Unnormalized p sum is {}".format(p_sum)
self._vae_sample_probs /= np.sum(self._vae_sample_probs)
self._vae_sample_probs = self._vae_sample_probs.flatten()
def sample_weighted_indices(self, batch_size):
if (
self._prioritize_vae_samples and
self._vae_sample_probs is not None and
self.skew
):
indices = np.random.choice(
len(self._vae_sample_probs),
batch_size,
p=self._vae_sample_probs,
)
assert (
np.max(self._vae_sample_probs) <= 1 and
np.min(self._vae_sample_probs) >= 0
)
else:
indices = self._sample_indices(batch_size)
return indices
def _sample_goals_from_env(self, batch_size):
self.env.goal_sampling_mode = self._relabeling_goal_sampling_mode
return self.env.sample_goals(batch_size)
def sample_buffer_goals(self, batch_size):
"""
Samples goals from weighted replay buffer for relabeling or exploration.
Returns None if replay buffer is empty.
Example of what might be returned:
dict(
image_desired_goals: image_achieved_goals[weighted_indices],
latent_desired_goals: latent_desired_goals[weighted_indices],
)
"""
if self._size == 0:
return None
weighted_idxs = self.sample_weighted_indices(
batch_size,
)
next_image_obs = normalize_image(
self._next_obs[self.decoded_obs_key][weighted_idxs]
)
next_latent_obs = self._next_obs[self.achieved_goal_key][weighted_idxs]
return {
self.decoded_desired_goal_key: next_image_obs,
self.desired_goal_key: next_latent_obs
}
def random_vae_training_data(self, batch_size, epoch):
# epoch no longer needed. Using self.skew in sample_weighted_indices
# instead.
weighted_idxs = self.sample_weighted_indices(
batch_size,
)
next_image_obs = normalize_image(
self._next_obs[self.decoded_obs_key][weighted_idxs]
)
return dict(
next_obs=ptu.from_numpy(next_image_obs)
)
def vae_prob(self, next_vae_obs, indices, **kwargs):
return compute_p_x_np_to_np(
self.vae,
next_vae_obs,
power=self.power,
**kwargs
)
def no_reward(self, next_vae_obs, indices):
return np.zeros((len(next_vae_obs), 1))
def _get_sorted_idx_and_train_weights(self):
idx_and_weights = zip(range(len(self._vae_sample_probs)),
self._vae_sample_probs)
return sorted(idx_and_weights, key=lambda x: x[1])
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