def generate_vae_dataset(
N=10000, test_p=0.9, use_cached=True, imsize=84, show=False,
dataset_path=None, policy_path=None, action_space_sampling=False, env_class=SawyerDoorEnv, env_kwargs=None,
init_camera=sawyer_door_env_camera_v2,
):
if policy_path is not None:
filename = "/tmp/sawyer_door_pull_open_oracle+random_policy_data_closer_zoom_action_limited" + str(N) + ".npy"
elif action_space_sampling:
filename = "/tmp/sawyer_door_pull_open_zoomed_in_action_space_sampling" + str(N) + ".npy"
else:
filename = "/tmp/sawyer_door_pull_open" + str(N) + ".npy"
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
else:
now = time.time()
env = env_class(**env_kwargs)
env = ImageEnv(
env, imsize,
transpose=True,
init_camera=init_camera,
normalize=True,
)
info['env'] = env
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
env.wrapped_env.reset()
dataset = np.zeros((N, imsize * imsize * 3), dtype=np.uint8)
for i in range(N):
if i % 20==0:
env.reset_model()
exploration_policy.reset()
for _ in range(10):
action = exploration_policy.get_action()[0]
env.wrapped_env.step(
action
)
# env.set_to_goal_angle(env.get_goal()['state_desired_goal'])
img = env._get_flat_img()
dataset[i, :] = unormalize_image(img)
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
print("done making training data", filename, time.time() - now)
np.save(filename, dataset)
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset, info
def generate_goal_data_set(env=None, num_goals=1000, use_cached_dataset=False,
action_scale=1 / 10):
if use_cached_dataset and osp.isfile(
'/tmp/goals' + str(num_goals) + '.npy'):
goal_dict = np.load('/tmp/goals' + str(num_goals) + '.npy').item()
print("loaded data from saved file")
return goal_dict
cached_goal_keys = [
'latent_desired_goal',
'image_desired_goal',
'state_desired_goal',
'joint_desired_goal',
]
goal_sizes = [
env.observation_space.spaces['latent_desired_goal'].low.size,
env.observation_space.spaces['image_desired_goal'].low.size,
env.observation_space.spaces['state_desired_goal'].low.size,
7
]
observation_keys = [
'latent_observation',
'image_observation',
'state_observation',
'state_observation',
]
goal_generation_dict = dict()
for goal_key, goal_size, obs_key in zip(
cached_goal_keys,
goal_sizes,
observation_keys,
):
goal_generation_dict[goal_key] = [goal_size, obs_key]
goal_dict = dict()
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
for goal_key in goal_generation_dict:
goal_size, obs_key = goal_generation_dict[goal_key]
goal_dict[goal_key] = np.zeros((num_goals, goal_size))
print('Generating Random Goals')
for i in range(num_goals):
if i % 50 == 0:
print('Reset')
env.reset_model()
exploration_policy.reset()
action = exploration_policy.get_action()[0] * action_scale
obs, _, _, _ = env.step(
action
)
print(i)
for goal_key in goal_generation_dict:
goal_size, obs_key = goal_generation_dict[goal_key]
goal_dict[goal_key][i, :] = obs[obs_key]
np.save('/tmp/goals' + str(num_goals) + '.npy', goal_dict)
return goal_dict
def visualize_policy_error(qf, env, args):
model = NumpyModelExtractor(qf, args.cheat, num_steps_left=args.tau)
policy = RandomPolicy(env.action_space)
actual_state = env.reset()
predicted_states = []
actual_states = []
predicted_state = actual_state
for _ in range(args.H):
predicted_states.append(predicted_state.copy())
actual_states.append(actual_state.copy())
action, _ = policy.get_action(actual_state)
predicted_state = model.next_state(predicted_state, action)
actual_state = env.step(action)[0]
predicted_states = np.array(predicted_states)
actual_states = np.array(actual_states)
times = np.arange(args.H)
num_state_dims = env.observation_space.low.size
dims = list(range(num_state_dims))
norm = colors.Normalize(vmin=0, vmax=num_state_dims)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.hsv)
for dim in dims:
plt.plot(
times,
predicted_states[:, dim],
'--',
label='Predicted, Dim {}'.format(dim),
color=mapper.to_rgba(dim),
)
plt.plot(
times,
actual_states[:, dim],
'-',
label='Actual, Dim {}'.format(dim),
color=mapper.to_rgba(dim),
)
plt.xlabel("Time Steps")
plt.ylabel("Observation Value")
plt.legend(loc='best')
plt.show()
def pretrain(self):
if (self.num_paths_for_normalization == 0 or
(self.obs_normalizer is None and self.action_normalizer is None)):
return
pretrain_paths = []
random_policy = RandomPolicy(self.env.action_space)
while len(pretrain_paths) < self.num_paths_for_normalization:
path = rollout(self.env, random_policy, self.max_path_length)
pretrain_paths.append(path)
ob_mean, ob_std, delta_mean, delta_std, ac_mean, ac_std = (
compute_normalization(pretrain_paths))
if self.obs_normalizer is not None:
self.obs_normalizer.set_mean(ob_mean)
self.obs_normalizer.set_std(ob_std)
if self.delta_normalizer is not None:
self.delta_normalizer.set_mean(delta_mean)
self.delta_normalizer.set_std(delta_std)
if self.action_normalizer is not None:
self.action_normalizer.set_mean(ac_mean)
self.action_normalizer.set_std(ac_std)
from railrl.state_distance.rollout_util import multitask_rollout
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('file', type=str,
help='path to the snapshot file')
parser.add_argument('--pause', action='store_true')
args = parser.parse_args()
if args.pause:
import ipdb; ipdb.set_trace()
data = joblib.load(args.file)
env = data['env']
qf = data['qf']
policy = data['policy']
tdm_policy = data['trained_policy']
random_policy = RandomPolicy(env.action_space)
vf = data['vf']
path = multitask_rollout(
env,
random_policy,
init_tau=0,
max_path_length=100,
animated=True,
)
goal = env.sample_goal_for_rollout()
import ipdb; ipdb.set_trace()
agent_infos = path['agent_infos']
def generate_vae_dataset(
N=10000,
test_p=0.9,
use_cached=True,
imsize=84,
show=False,
dataset_path=None,
action_space_sampling=False,
init_camera=None,
env_class=None,
env_kwargs=None,
):
filename = "/tmp/sawyer_xyz_pos_control_new_zoom_cam" + str(N) + '.npy'
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
else:
now = time.time()
if env_kwargs == None:
env_kwargs = dict()
env = env_class(**env_kwargs)
env = ImageEnv(
env,
imsize,
transpose=True,
init_camera=init_camera,
normalize=True,
)
dataset = np.zeros((N, imsize * imsize * 3), dtype=np.uint8)
if action_space_sampling:
action_space = Box(np.array([-.1, .5, 0]), np.array([.1, .7, .5]))
for i in range(N):
env.set_to_goal(env.sample_goal())
img = env._get_flat_img()
dataset[i, :] = unormalize_image(img)
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
info['env'] = env
else:
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
for i in range(N):
# Move the goal out of the image
env.wrapped_env.set_goal(np.array([100, 100, 100]))
if i % 50 == 0:
print('Reset')
env.reset()
exploration_policy.reset()
for _ in range(1):
action = exploration_policy.get_action()[0] * 10
env.wrapped_env.step(action)
img = env.step(env.action_space.sample())[0]
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
print("done making training data", time.time() - now)
np.save(filename, dataset)
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset, info
def generate_vae_dataset(
N=10000,
test_p=0.9,
use_cached=True,
imsize=84,
show=False,
dataset_path=None,
policy_path=None,
ratio_oracle_policy_data_to_random=1 / 2,
action_space_sampling=False,
env_class=None,
env_kwargs=None,
action_plus_random_sampling=False,
init_camera=sawyer_door_env_camera,
):
if policy_path is not None:
filename = "/tmp/sawyer_door_push_open_oracle+random_policy_data_closer_zoom_action_limited" + str(
N) + ".npy"
elif action_space_sampling:
filename = "/tmp/sawyer_door_push_open_zoomed_in_action_space_sampling" + str(
N) + ".npy"
else:
filename = "/tmp/sawyer_door_push_open" + str(N) + ".npy"
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
elif action_space_sampling:
env = SawyerDoorPushOpenEnv(**env_kwargs)
env = ImageEnv(
env,
imsize,
transpose=False,
init_camera=sawyer_door_env_camera,
normalize=False,
)
action_space = Box(np.array([-env.max_x_pos, .5, .06]),
np.array([env.max_x_pos, env.max_y_pos, .06]))
dataset = np.zeros((N, imsize * imsize * 3))
for i in range(N):
env.set_to_goal_pos(action_space.sample()) #move arm to spot
goal = env.sample_goal()
env.set_to_goal(goal)
img = env.get_image().flatten()
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
info['env'] = env
elif action_plus_random_sampling:
env = env_class(**env_kwargs)
env = ImageEnv(
env,
imsize,
transpose=True,
init_camera=init_camera,
normalize=True,
)
action_space = Box(np.array([-env.max_x_pos, .5, .06]),
np.array([env.max_x_pos, .6, .06]))
action_sampled_data = int(N / 2)
dataset = np.zeros((N, imsize * imsize * 3))
print('Action Space Sampling')
for i in range(action_sampled_data):
env.set_to_goal_pos(action_space.sample()) # move arm to spot
goal = env.sample_goal()
env.set_to_goal(goal)
img = env._get_flat_img()
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
env._wrapped_env.min_y_pos = .6
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
print('Random Sampling')
for i in range(action_sampled_data, N):
if i % 20 == 0:
env.reset()
exploration_policy.reset()
for _ in range(10):
action = exploration_policy.get_action()[0]
env.wrapped_env.step(action)
img = env._get_flat_img()
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
env._wrapped_env.min_y_pos = .5
info['env'] = env
else:
now = time.time()
env = SawyerDoorPushOpenEnv(max_angle=.5)
env = ImageEnv(
env,
imsize,
transpose=True,
init_camera=sawyer_door_env_camera,
normalize=True,
)
info['env'] = env
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
dataset = np.zeros((N, imsize * imsize * 3))
for i in range(N):
if i % 100 == 0:
env.reset()
exploration_policy.reset()
for _ in range(25):
# env.wrapped_env.step(
# env.wrapped_env.action_space.sample()
# )
action = exploration_policy.get_action()[0]
env.wrapped_env.step(action)
goal = env.sample_goal_for_rollout()
env.set_to_goal(goal)
img = env.step(env.action_space.sample())[0]
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
print("done making training data", filename, time.time() - now)
np.save(filename, dataset)
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset, info
def generate_vae_dataset(
N=10000, test_p=0.9, use_cached=True, imsize=84, show=False,
dataset_path=None, policy_path=None, action_space_sampling=False, env_class=SawyerPushAndPullDoorEnv, env_kwargs=None,
action_plus_random_sampling=False, init_camera=sawyer_door_env_camera, ratio_action_sample_to_random=1 / 2, env_id=None,
):
if policy_path is not None:
filename = "/tmp/sawyer_door_push_and_pull_open_oracle+random_policy_data_closer_zoom_action_limited" + str(N) + ".npy"
elif action_space_sampling:
filename = "/tmp/sawyer_door_push_and_pull_open_zoomed_in_action_space_sampling" + str(N) + ".npy"
else:
filename = "/tmp/sawyer_door_push_and_pull_open" + str(N) + ".npy"
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
elif action_plus_random_sampling:
if env_id is not None:
import gym
env = gym.make(env_id)
else:
env = env_class(**env_kwargs)
env = ImageEnv(
env, imsize,
transpose=True,
init_camera=init_camera,
normalize=True,
)
action_sampled_data = int(N*ratio_action_sample_to_random)
dataset = np.zeros((N, imsize * imsize * 3), dtype=np.uint8)
print('Action Space Sampling')
for i in range(action_sampled_data):
goal = env.sample_goal()
env.set_to_goal(goal)
img = env._get_flat_img()
dataset[i, :] = unormalize_image(img)
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
print('Random Sampling')
for i in range(action_sampled_data, N):
if i % 20==0:
env.reset()
exploration_policy.reset()
for _ in range(10):
action = exploration_policy.get_action()[0]
env.wrapped_env.step(
action
)
goal = env.sample_goal()
env.set_to_goal_angle(goal['state_desired_goal'])
img = env._get_flat_img()
dataset[i, :] = unormalize_image(img)
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
env._wrapped_env.min_y_pos = .5
info['env'] = env
else:
raise NotImplementedError()
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset, info
def main():
model_data = joblib.load(MODEL_PATH)
model = model_data['model']
tdm_data = joblib.load(TDM_PATH)
env = tdm_data['env']
qf = tdm_data['qf']
variant_path = Path(TDM_PATH).parents[0] / 'variant.json'
variant = json.load(variant_path.open())
reward_scale = variant['sac_tdm_kwargs']['base_kwargs']['reward_scale']
tdm = ImplicitModel(qf, None)
random_policy = RandomPolicy(env.action_space)
H = 10
path = rollout(env, random_policy, max_path_length=H)
model_distance_preds = []
tdm_distance_preds = []
for ob, action, next_ob in zip(
path['observations'],
path['actions'],
path['next_observations'],
):
obs = ob[None]
actions = action[None]
next_feature = env.convert_ob_to_goal(next_ob)
model_next_ob_pred = ob + model.eval_np(obs, actions)[0]
model_distance_pred = np.abs(
env.convert_ob_to_goal(model_next_ob_pred) - next_feature)
tdm_next_feature_pred = get_feasible_goal(env, tdm, ob, action)
tdm_distance_pred = np.abs(tdm_next_feature_pred - next_feature)
model_distance_preds.append(model_distance_pred)
tdm_distance_preds.append(tdm_distance_pred)
model_distances = np.array(model_distance_preds)
tdm_distances = np.array(tdm_distance_preds)
ts = np.arange(len(model_distance_preds))
num_dim = model_distances[0].size
ind = np.arange(num_dim)
width = 0.35
fig, ax = plt.subplots()
means = model_distances.mean(axis=0)
stds = model_distances.std(axis=0)
rects1 = ax.bar(ind, means, width, color='r', yerr=stds)
means = tdm_distances.mean(axis=0)
stds = tdm_distances.std(axis=0)
rects2 = ax.bar(ind + width, means, width, color='y', yerr=stds)
ax.legend((rects1[0], rects2[0]), ('Model', 'TDM'))
ax.set_xlabel("Dimension")
ax.set_ylabel("Absolute Error")
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(list(map(str, ind)))
plt.show()
plt.subplot(2, 1, 1)
for i in range(num_dim):
plt.plot(
ts,
model_distances[:, i],
label=str(i),
)
plt.xlabel("Time")
plt.ylabel("Absolute Error")
plt.title("Model")
plt.legend()
plt.subplot(2, 1, 2)
for i in range(num_dim):
plt.plot(
ts,
tdm_distances[:, i],
label=str(i),
)
plt.xlabel("Time")
plt.ylabel("Absolute Error")
plt.title("TDM")
plt.legend()
plt.show()
goal = env.convert_ob_to_goal(path['observations'][H // 2].copy())
path = rollout(env, random_policy, max_path_length=H)
model_distance_preds = []
tdm_distance_preds = []
for ob, action, next_ob in zip(
path['observations'],
path['actions'],
path['next_observations'],
):
model_next_ob_pred = ob + model.eval_np(ob[None], action[None])[0]
model_distance_pred = np.linalg.norm(
env.convert_ob_to_goal(model_next_ob_pred) - goal)
tdm_distance_pred = tdm.eval_np(
ob[None],
goal[None],
np.zeros((1, 1)),
action[None],
)[0] / reward_scale
model_distance_preds.append(model_distance_pred)
tdm_distance_preds.append(tdm_distance_pred)
fig, ax = plt.subplots()
means = model_distances.mean(axis=0)
stds = model_distances.std(axis=0)
rects1 = ax.bar(ind, means, width, color='r', yerr=stds)
means = tdm_distances.mean(axis=0)
stds = tdm_distances.std(axis=0)
rects2 = ax.bar(ind + width, means, width, color='y', yerr=stds)
ax.legend((rects1[0], rects2[0]), ('Model', 'TDM'))
ax.set_xlabel("Dimension")
ax.set_ylabel("Error To Random Goal State")
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(list(map(str, ind)))
plt.show()
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 visualize_policy_error(model, env, horizon):
policy = RandomPolicy(env.action_space)
actual_state = env.reset()
predicted_states = []
actual_states = []
predicted_state = actual_state
for _ in range(horizon):
predicted_states.append(predicted_state.copy())
actual_states.append(actual_state.copy())
action, _ = policy.get_action(actual_state)
delta = get_np_prediction(model, predicted_state, action)
predicted_state += delta
actual_state = env.step(action)[0]
predicted_states = np.array(predicted_states)
actual_states = np.array(actual_states)
times = np.arange(horizon)
num_state_dims = env.observation_space.low.size
dims = list(range(num_state_dims))
norm = colors.Normalize(vmin=0, vmax=num_state_dims)
mapper = cm.ScalarMappable(norm=norm, cmap=cm.hsv)
# Plot the predicted and actual values
plt.subplot(2, 1, 1)
for dim in dims:
plt.plot(
times,
predicted_states[:, dim],
'--',
label='Predicted, Dim {}'.format(dim),
color=mapper.to_rgba(dim),
)
plt.plot(
times,
actual_states[:, dim],
'-',
label='Actual, Dim {}'.format(dim),
color=mapper.to_rgba(dim),
)
plt.xlabel("Time Steps")
plt.ylabel("Observation Value")
plt.legend(loc='best')
# Plot the predicted and actual value errors
plt.subplot(2, 1, 2)
for dim in dims:
plt.plot(
times,
np.abs(predicted_states[:, dim] - actual_states[:, dim]),
'-',
label='Dim {}'.format(dim),
color=mapper.to_rgba(dim),
)
plt.xlabel("Time Steps")
plt.ylabel("|Predicted - Actual| - Absolute Error")
plt.legend(loc='best')
plt.show()
nrows = min(5, num_state_dims)
ncols = math.ceil(num_state_dims / nrows)
fig = plt.figure()
for dim in dims:
ax = fig.add_subplot(nrows, ncols, dim+1)
ax.plot(
times,
predicted_states[:, dim],
'--',
label='Predicted, Dim {}'.format(dim),
)
ax.plot(
times,
actual_states[:, dim],
'-',
label='Actual, Dim {}'.format(dim),
)
ax.set_ylabel("Observation Value")
ax.set_xlabel("Time Steps")
ax.set_title("Dim {}".format(dim))
ax_error = ax.twinx()
ax_error.plot(
times,
np.abs(predicted_states[:, dim] - actual_states[:, dim]),
'.',
label='Error, Dim {}'.format(dim),
color='r',
)
ax_error.set_ylabel("Error", color='r')
ax_error.tick_params('y', colors='r')
ax.legend(loc='best')
plt.show()
def generate_vae_dataset(
N=10000,
test_p=0.9,
use_cached=True,
imsize=84,
show=False,
dataset_path=None,
env_class=None,
env_kwargs=None,
init_camera=sawyer_door_env_camera,
):
filename = "/tmp/sawyer_door_push_open_and_reach" + str(N) + ".npy"
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
else:
env = env_class(**env_kwargs)
env = ImageEnv(
env,
imsize,
transpose=True,
init_camera=init_camera,
normalize=True,
)
oracle_sampled_data = int(N / 2)
dataset = np.zeros((N, imsize * imsize * 3))
print('Goal Space Sampling')
for i in range(oracle_sampled_data):
goal = env.sample_goal()
env.set_to_goal(goal)
img = env._get_flat_img()
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
env._wrapped_env.min_y_pos = .6
policy = RandomPolicy(env.action_space)
es = OUStrategy(action_space=env.action_space, theta=0)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
print('Random Sampling')
for i in range(oracle_sampled_data, N):
if i % 20 == 0:
env.reset()
exploration_policy.reset()
for _ in range(10):
action = exploration_policy.get_action()[0]
env.wrapped_env.step(action)
img = env._get_flat_img()
dataset[i, :] = img
if show:
cv2.imshow('img', img.reshape(3, 84, 84).transpose())
cv2.waitKey(1)
print(i)
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset, info