def preprocess_obs_dict(obs_dict):
"""
Apply internal replay buffer representation changes: save images as bytes
"""
for obs_key, obs in obs_dict.items():
if 'image' in obs_key and obs is not None:
obs_dict[obs_key] = unormalize_image(obs)
return obs_dict
def generate_vae_data(variant):
env_id = variant.get('env_id', None)
N = variant.get('N', 1000)
test_p = variant.get('test_p', 0.9)
image_size = variant.get('image_size', 84)
num_channels = variant.get('num_channels', 3)
init_camera = variant.get('init_camera', None)
oracle_dataset_using_set_to_goal = variant.get(
'oracle_dataset_using_set_to_goal', False)
random_rollout_data = variant.get('random_rollout_data', False)
random_and_oracle_policy_data = variant.get(
'random_and_oracle_policy_data', False)
random_and_oracle_policy_data_split = variant.get(
'random_and_oracle_policy_data_split', 0)
n_random_steps = variant.get('n_random_steps', 100)
show = variant.get('show', False)
import rlkit.torch.pytorch_util as ptu
info = {}
env = gym.make(env_id)
env = ImageEnv(env,
image_size,
init_camera=init_camera,
transpose=True,
normalize=True,
non_presampled_goal_img_is_garbage=None)
dataset = np.zeros((N, image_size * image_size * num_channels),
dtype=np.uint8)
# print('aa')
for i in range(N):
if oracle_dataset_using_set_to_goal:
goal = env.sample_goal()
print(goal)
env.set_to_goal(goal)
obs = env._get_obs()
# print(obs)
img = obs['image_observation']
print('length of image arr:', len(img))
### this block to test image #############################
if show:
# print(obs['image_observation'])
img = img.reshape(3, image_size, image_size).transpose()
print(img.size)
img = img[::-1, :, ::-1]
img = (img * 255).astype(np.uint8)
img = Image.fromarray(img, 'RGB')
print(img.size)
# print(len(img))
# img.save('/home/manhlt/extra_disk/data/RIG_data/image_'+str(i)+'.png')
#############################################################
dataset[i:] = unormalize_image(img)
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
return train_dataset, test_dataset
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 generate_uniform_dataset_pick_and_place(env_class=None,
env_kwargs=None,
num_imgs=1000,
use_cached_dataset=False,
init_camera=None,
imsize=48,
save_file_prefix=None,
env_id=None,
tag='',
dataset_path=None):
if dataset_path is not None:
dataset = load_local_or_remote_file(dataset_path)
return dataset
import gym
from gym.envs import registration
# trigger registration
import multiworld.envs.pygame
import multiworld.envs.mujoco
if not env_class or not env_kwargs:
env = gym.make(env_id)
else:
env = env_class(**env_kwargs)
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
)
env.non_presampled_goal_img_is_garbage = True
if save_file_prefix is None and env_id is not None:
save_file_prefix = env_id
filename = "/tmp/{}_N{}_imsize{}uniform_images_{}.npy".format(
save_file_prefix,
str(num_imgs),
env.imsize,
tag,
)
if use_cached_dataset and osp.isfile(filename):
images = np.load(filename)
print("Loaded data from {}".format(filename))
return images
print('Sampling Uniform Dataset')
dataset = unormalize_image(
get_image_presampled_goals(env, num_imgs)['image_desired_goal'])
np.save(filename, dataset)
print("Saving file to {}".format(filename))
return dataset
def generate_vae_dataset(variant):
env_class = variant.get('env_class', None)
env_kwargs = variant.get('env_kwargs', None)
env_id = variant.get('env_id', None)
N = variant.get('N', 10000)
test_p = variant.get('test_p', 0.9)
use_cached = variant.get('use_cached', True)
imsize = variant.get('imsize', 84)
num_channels = variant.get('num_channels', 3)
show = variant.get('show', False)
init_camera = variant.get('init_camera', None)
dataset_path = variant.get('dataset_path', None)
oracle_dataset_using_set_to_goal = variant.get(
'oracle_dataset_using_set_to_goal', False)
random_rollout_data = variant.get('random_rollout_data', False)
random_and_oracle_policy_data = variant.get(
'random_and_oracle_policy_data', False)
random_and_oracle_policy_data_split = variant.get(
'random_and_oracle_policy_data_split', 0)
policy_file = variant.get('policy_file', None)
n_random_steps = variant.get('n_random_steps', 100)
vae_dataset_specific_env_kwargs = variant.get(
'vae_dataset_specific_env_kwargs', None)
save_file_prefix = variant.get('save_file_prefix', None)
non_presampled_goal_img_is_garbage = variant.get(
'non_presampled_goal_img_is_garbage', None)
tag = variant.get('tag', '')
from multiworld.core.image_env import ImageEnv, unormalize_image
import rlkit.torch.pytorch_util as ptu
info = {}
if dataset_path is not None:
dataset = load_local_or_remote_file(dataset_path)
N = dataset.shape[0]
else:
if env_kwargs is None:
env_kwargs = {}
if save_file_prefix is None:
save_file_prefix = env_id
if save_file_prefix is None:
save_file_prefix = env_class.__name__
filename = "/tmp/{}_N{}_{}_imsize{}_random_oracle_split_{}{}.npy".format(
save_file_prefix,
str(N),
init_camera.__name__ if init_camera else '',
imsize,
random_and_oracle_policy_data_split,
tag,
)
if use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
else:
now = time.time()
if env_id is not None:
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
else:
if vae_dataset_specific_env_kwargs is None:
vae_dataset_specific_env_kwargs = {}
for key, val in env_kwargs.items():
if key not in vae_dataset_specific_env_kwargs:
vae_dataset_specific_env_kwargs[key] = val
env = env_class(**vae_dataset_specific_env_kwargs)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
non_presampled_goal_img_is_garbage=
non_presampled_goal_img_is_garbage,
)
else:
imsize = env.imsize
env.non_presampled_goal_img_is_garbage = non_presampled_goal_img_is_garbage
env.reset()
info['env'] = env
if random_and_oracle_policy_data:
policy_file = load_local_or_remote_file(policy_file)
policy = policy_file['policy']
policy.to(ptu.device)
if random_rollout_data:
from rlkit.exploration_strategies.ou_strategy import OUStrategy
policy = OUStrategy(env.action_space)
dataset = np.zeros((N, imsize * imsize * num_channels),
dtype=np.uint8)
for i in range(N):
if random_and_oracle_policy_data:
num_random_steps = int(N *
random_and_oracle_policy_data_split)
if i < num_random_steps:
env.reset()
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
else:
obs = env.reset()
policy.reset()
for _ in range(n_random_steps):
policy_obs = np.hstack((
obs['state_observation'],
obs['state_desired_goal'],
))
action, _ = policy.get_action(policy_obs)
obs, _, _, _ = env.step(action)
elif oracle_dataset_using_set_to_goal:
print(i)
goal = env.sample_goal()
env.set_to_goal(goal)
obs = env._get_obs()
elif random_rollout_data:
if i % n_random_steps == 0:
g = dict(
state_desired_goal=env.sample_goal_for_rollout())
env.set_to_goal(g)
policy.reset()
# env.reset()
u = policy.get_action_from_raw_action(
env.action_space.sample())
obs = env.step(u)[0]
else:
env.reset()
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
img = obs['image_observation']
dataset[i, :] = unormalize_image(img)
if show:
img = img.reshape(3, imsize, imsize).transpose()
img = img[::-1, :, ::-1]
cv2.imshow('img', img)
cv2.waitKey(1)
# radius = input('waiting...')
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_LSTM_vae_only_dataset(variant,
segmented=False,
segmentation_method='color'):
from multiworld.core.image_env import ImageEnv, unormalize_image
env_id = variant.get('env_id', None)
N = variant.get('N', 500)
test_p = variant.get('test_p', 0.9)
imsize = variant.get('imsize', 48)
num_channels = variant.get('num_channels', 3)
init_camera = variant.get('init_camera', None)
occlusion_prob = variant.get('occlusion_prob', 0)
occlusion_level = variant.get('occlusion_level', 0.5)
segmentation_kwargs = variant.get('segmentation_kwargs', {})
if segmentation_kwargs.get('segment') is not None:
segmented = segmentation_kwargs.get('segment')
assert env_id is not None, 'you must provide an env id!'
obj = 'puck-pos'
if env_id == 'SawyerDoorHookResetFreeEnv-v1':
obj = 'door-angle'
pjhome = os.environ['PJHOME']
if segmented:
if 'unet' in segmentation_method:
seg_name = 'seg-unet'
else:
seg_name = 'seg-' + segmentation_method
else:
seg_name = 'no-seg'
if env_id == 'SawyerDoorHookResetFreeEnv-v1':
seg_name += '-2'
data_file_path = osp.join(
pjhome, 'data/local/pre-train-lstm',
'vae-only-{}-{}-{}-{}-{}.npy'.format(env_id, seg_name, N,
occlusion_prob, occlusion_level))
obj_state_path = osp.join(
pjhome, 'data/local/pre-train-lstm',
'vae-only-{}-{}-{}-{}-{}-{}.npy'.format(env_id, seg_name, N,
occlusion_prob,
occlusion_level, obj))
print(data_file_path)
if osp.exists(data_file_path):
all_data = np.load(data_file_path)
if len(all_data) >= N:
print("load stored data at: ", data_file_path)
n = int(len(all_data) * test_p)
train_dataset = all_data[:n]
test_dataset = all_data[n:]
obj_states = np.load(obj_state_path)
info = {'obj_state': obj_states}
return train_dataset, test_dataset, info
if segmented:
print(
"generating lstm vae pretrain only dataset with segmented images using method: ",
segmentation_method)
if segmentation_method == 'unet':
segment_func = segment_image_unet
else:
raise NotImplementedError
else:
print("generating lstm vae pretrain only dataset with original images")
info = {}
dataset = np.zeros((N, imsize * imsize * num_channels), dtype=np.uint8)
imgs = []
obj_states = None
if env_id == 'SawyerDoorHookResetFreeEnv-v1':
from rlkit.util.io import load_local_or_remote_file
pjhome = os.environ['PJHOME']
pre_sampled_goal_path = osp.join(
pjhome, 'data/local/pre-train-vae/door_original_dataset.npy')
goal_dict = np.load(pre_sampled_goal_path, allow_pickle=True).item()
imgs = goal_dict['image_desired_goal']
door_angles = goal_dict['state_desired_goal'][:, -1]
obj_states = door_angles[:, np.newaxis]
elif env_id == 'SawyerPickupEnvYZEasy-v0':
from rlkit.util.io import load_local_or_remote_file
pjhome = os.environ['PJHOME']
pre_sampled_goal_path = osp.join(
pjhome, 'data/local/pre-train-vae/pickup-original-dataset.npy')
goal_dict = load_local_or_remote_file(pre_sampled_goal_path).item()
imgs = goal_dict['image_desired_goal']
puck_pos = goal_dict['state_desired_goal'][:, 3:]
obj_states = puck_pos
else:
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
)
env.reset()
info['env'] = env
puck_pos = np.zeros((N, 2), dtype=np.float)
for i in range(N):
print("lstm vae pretrain only dataset generation, number: ", i)
if env_id == 'SawyerPushHurdle-v0':
obs, puck_p = _generate_sawyerhurdle_dataset(
env, return_puck_pos=True, segmented=segmented)
elif env_id == 'SawyerPushHurdleMiddle-v0':
obs, puck_p = _generate_sawyerhurdlemiddle_dataset(
env, return_puck_pos=True)
elif env_id == 'SawyerPushNIPSEasy-v0':
obs, puck_p = _generate_sawyerpushnipseasy_dataset(
env, return_puck_pos=True)
elif env_id == 'SawyerPushHurdleResetFreeEnv-v0':
obs, puck_p = _generate_sawyerhurldeblockresetfree_dataset(
env, return_puck_pos=True)
else:
raise NotImplementedError
img = obs[
'image_observation'] # NOTE: this is already normalized image, of detype np.float64.
imgs.append(img)
puck_pos[i] = puck_p
obj_states = puck_pos
# now we segment the images
for i in range(N):
print("segmenting image ", i)
img = imgs[i]
if segmented:
dataset[i, :] = segment_func(img,
normalize=False,
**segmentation_kwargs)
p = np.random.rand(
) # manually drop some images, so as to make occlusions
if p < occlusion_prob:
mask = (np.random.uniform(low=0, high=1, size=(imsize, imsize))
> occlusion_level).astype(np.uint8)
img = dataset[i].reshape(3, imsize, imsize).transpose()
img[mask < 1] = 0
dataset[i] = img.transpose().flatten()
else:
dataset[i, :] = unormalize_image(img)
# add the trajectory dimension
dataset = dataset[:, np.newaxis, :] # batch_size x traj_len = 1 x imlen
obj_states = obj_states[:,
np.newaxis, :] # batch_size x traj_len = 1 x imlen
info['obj_state'] = obj_states
n = int(N * test_p)
train_dataset = dataset[:n]
test_dataset = dataset[n:]
if N >= 500:
print('save data to: ', data_file_path)
all_data = np.concatenate([train_dataset, test_dataset], axis=0)
np.save(data_file_path, all_data)
np.save(obj_state_path, obj_states)
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 generate_vae_dataset_from_params(
env_class=None,
env_kwargs=None,
env_id=None,
N=10000,
test_p=0.9,
use_cached=True,
imsize=84,
num_channels=1,
show=False,
init_camera=None,
dataset_path=None,
oracle_dataset=False,
n_random_steps=100,
vae_dataset_specific_env_kwargs=None,
save_file_prefix=None,
):
from multiworld.core.image_env import ImageEnv, unormalize_image
import time
assert oracle_dataset == True
if env_kwargs is None:
env_kwargs = {}
if save_file_prefix is None:
save_file_prefix = env_id
if save_file_prefix is None:
save_file_prefix = env_class.__name__
filename = "/tmp/{}_N{}_{}_imsize{}_oracle{}.npy".format(
save_file_prefix,
str(N),
init_camera.__name__ if init_camera else '',
imsize,
oracle_dataset,
)
info = {}
if dataset_path is not None:
filename = local_path_from_s3_or_local_path(dataset_path)
dataset = np.load(filename)
np.random.shuffle(dataset)
N = dataset.shape[0]
elif use_cached and osp.isfile(filename):
dataset = np.load(filename)
np.random.shuffle(dataset)
print("loaded data from saved file", filename)
else:
now = time.time()
if env_id is not None:
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
else:
if vae_dataset_specific_env_kwargs is None:
vae_dataset_specific_env_kwargs = {}
for key, val in env_kwargs.items():
if key not in vae_dataset_specific_env_kwargs:
vae_dataset_specific_env_kwargs[key] = val
env = env_class(**vae_dataset_specific_env_kwargs)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
)
setup_pickup_image_env(env, num_presampled_goals=N)
env.reset()
info['env'] = env
dataset = np.zeros((N, imsize * imsize * num_channels), dtype=np.uint8)
for i in range(N):
img = env._presampled_goals['image_desired_goal'][i]
dataset[i, :] = unormalize_image(img)
if show:
img = img.reshape(3, imsize, imsize).transpose()
img = img[::-1, :, ::-1]
cv2.imshow('img', img)
cv2.waitKey(1)
time.sleep(.2)
# radius = input('waiting...')
print("done making training data", filename, time.time() - now)
np.random.shuffle(dataset)
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,
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(variant):
print(variant)
from tqdm import tqdm
env_class = variant.get('env_class', None)
env_kwargs = variant.get('env_kwargs', None)
env_id = variant.get('env_id', None)
N = variant.get('N', 10000)
batch_size = variant.get('batch_size', 128)
test_p = variant.get('test_p', 0.9)
use_cached = variant.get('use_cached', True)
imsize = variant.get('imsize', 84)
num_channels = variant.get('num_channels', 3)
show = variant.get('show', False)
init_camera = variant.get('init_camera', None)
dataset_path = variant.get('dataset_path', None)
augment_data = variant.get('augment_data', False)
data_filter_fn = variant.get('data_filter_fn', lambda x: x)
delete_after_loading = variant.get('delete_after_loading', False)
oracle_dataset_using_set_to_goal = variant.get(
'oracle_dataset_using_set_to_goal', False)
random_rollout_data = variant.get('random_rollout_data', False)
random_rollout_data_set_to_goal = variant.get(
'random_rollout_data_set_to_goal', True)
random_and_oracle_policy_data = variant.get(
'random_and_oracle_policy_data', False)
random_and_oracle_policy_data_split = variant.get(
'random_and_oracle_policy_data_split', 0)
policy_file = variant.get('policy_file', None)
n_random_steps = variant.get('n_random_steps', 100)
vae_dataset_specific_env_kwargs = variant.get(
'vae_dataset_specific_env_kwargs', None)
save_file_prefix = variant.get('save_file_prefix', None)
non_presampled_goal_img_is_garbage = variant.get(
'non_presampled_goal_img_is_garbage', None)
conditional_vae_dataset = variant.get('conditional_vae_dataset', False)
use_env_labels = variant.get('use_env_labels', False)
use_linear_dynamics = variant.get('use_linear_dynamics', False)
enviorment_dataset = variant.get('enviorment_dataset', False)
save_trajectories = variant.get('save_trajectories', False)
save_trajectories = save_trajectories or use_linear_dynamics or conditional_vae_dataset
tag = variant.get('tag', '')
assert N % n_random_steps == 0, "Fix N/horizon or dataset generation will fail"
from multiworld.core.image_env import ImageEnv, unormalize_image
import rlkit.torch.pytorch_util as ptu
from rlkit.util.io import load_local_or_remote_file
from rlkit.data_management.dataset import (
TrajectoryDataset, ImageObservationDataset, InitialObservationDataset,
EnvironmentDataset, ConditionalDynamicsDataset,
InitialObservationNumpyDataset, InfiniteBatchLoader,
InitialObservationNumpyJitteringDataset)
info = {}
use_test_dataset = False
if dataset_path is not None:
if type(dataset_path) == str:
dataset = load_local_or_remote_file(
dataset_path, delete_after_loading=delete_after_loading)
dataset = dataset.item()
N = dataset['observations'].shape[0] * dataset[
'observations'].shape[1]
n_random_steps = dataset['observations'].shape[1]
if isinstance(dataset_path, list):
dataset = concatenate_datasets(dataset_path)
N = dataset['observations'].shape[0] * dataset[
'observations'].shape[1]
n_random_steps = dataset['observations'].shape[1]
if isinstance(dataset_path, dict):
if type(dataset_path['train']) == str:
dataset = load_local_or_remote_file(
dataset_path['train'],
delete_after_loading=delete_after_loading)
dataset = dataset.item()
elif isinstance(dataset_path['train'], list):
dataset = concatenate_datasets(dataset_path['train'])
if type(dataset_path['test']) == str:
test_dataset = load_local_or_remote_file(
dataset_path['test'],
delete_after_loading=delete_after_loading)
test_dataset = test_dataset.item()
elif isinstance(dataset_path['test'], list):
test_dataset = concatenate_datasets(dataset_path['test'])
N = dataset['observations'].shape[0] * dataset[
'observations'].shape[1]
n_random_steps = dataset['observations'].shape[1]
use_test_dataset = True
else:
if env_kwargs is None:
env_kwargs = {}
if save_file_prefix is None:
save_file_prefix = env_id
if save_file_prefix is None:
save_file_prefix = env_class.__name__
filename = "/tmp/{}_N{}_{}_imsize{}_random_oracle_split_{}{}.npy".format(
save_file_prefix,
str(N),
init_camera.__name__
if init_camera and hasattr(init_camera, '__name__') else '',
imsize,
random_and_oracle_policy_data_split,
tag,
)
if use_cached and osp.isfile(filename):
dataset = load_local_or_remote_file(
filename, delete_after_loading=delete_after_loading)
if conditional_vae_dataset:
dataset = dataset.item()
print("loaded data from saved file", filename)
else:
now = time.time()
if env_id is not None:
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
else:
if vae_dataset_specific_env_kwargs is None:
vae_dataset_specific_env_kwargs = {}
for key, val in env_kwargs.items():
if key not in vae_dataset_specific_env_kwargs:
vae_dataset_specific_env_kwargs[key] = val
env = env_class(**vae_dataset_specific_env_kwargs)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
non_presampled_goal_img_is_garbage=
non_presampled_goal_img_is_garbage,
)
else:
imsize = env.imsize
env.non_presampled_goal_img_is_garbage = non_presampled_goal_img_is_garbage
env.reset()
info['env'] = env
if random_and_oracle_policy_data:
policy_file = load_local_or_remote_file(policy_file)
policy = policy_file['policy']
policy.to(ptu.device)
if random_rollout_data:
from rlkit.exploration_strategies.ou_strategy import OUStrategy
policy = OUStrategy(env.action_space)
if save_trajectories:
dataset = {
'observations':
np.zeros((N // n_random_steps, n_random_steps,
imsize * imsize * num_channels),
dtype=np.uint8),
'actions':
np.zeros((N // n_random_steps, n_random_steps,
env.action_space.shape[0]),
dtype=np.float),
'env':
np.zeros(
(N // n_random_steps, imsize * imsize * num_channels),
dtype=np.uint8),
}
else:
dataset = np.zeros((N, imsize * imsize * num_channels),
dtype=np.uint8)
labels = []
for i in tqdm(range(N)):
if random_and_oracle_policy_data:
num_random_steps = int(N *
random_and_oracle_policy_data_split)
if i < num_random_steps:
env.reset()
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
else:
obs = env.reset()
policy.reset()
for _ in range(n_random_steps):
policy_obs = np.hstack((
obs['state_observation'],
obs['state_desired_goal'],
))
action, _ = policy.get_action(policy_obs)
obs, _, _, _ = env.step(action)
elif random_rollout_data: #ADD DATA WHERE JUST PUCK MOVES
if i % n_random_steps == 0:
env.reset()
policy.reset()
env_img = env._get_obs()['image_observation']
if random_rollout_data_set_to_goal:
env.set_to_goal(env.get_goal())
obs = env._get_obs()
u = policy.get_action_from_raw_action(
env.action_space.sample())
env.step(u)
elif oracle_dataset_using_set_to_goal:
print(i)
goal = env.sample_goal()
env.set_to_goal(goal)
obs = env._get_obs()
else:
env.reset()
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
img = obs['image_observation']
if use_env_labels:
labels.append(obs['label'])
if save_trajectories:
dataset['observations'][
i // n_random_steps,
i % n_random_steps, :] = unormalize_image(img)
dataset['actions'][i // n_random_steps,
i % n_random_steps, :] = u
dataset['env'][i // n_random_steps, :] = unormalize_image(
env_img)
else:
dataset[i, :] = unormalize_image(img)
if show:
img = img.reshape(3, imsize, imsize).transpose()
img = img[::-1, :, ::-1]
cv2.imshow('img', img)
cv2.waitKey(1)
# radius = input('waiting...')
print("done making training data", filename, time.time() - now)
np.save(filename, dataset)
#np.save(filename[:-4] + 'labels.npy', np.array(labels))
info['train_labels'] = []
info['test_labels'] = []
dataset = data_filter_fn(dataset)
if use_linear_dynamics and conditional_vae_dataset:
num_trajectories = N // n_random_steps
n = int(num_trajectories * test_p)
train_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][:n, :, :],
'actions':
dataset['actions'][:n, :, :],
'env':
dataset['env'][:n, :]
})
test_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][n:, :, :],
'actions':
dataset['actions'][n:, :, :],
'env':
dataset['env'][n:, :]
})
num_trajectories = N // n_random_steps
n = int(num_trajectories * test_p)
indices = np.arange(num_trajectories)
np.random.shuffle(indices)
train_i, test_i = indices[:n], indices[n:]
try:
train_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][train_i, :, :],
'actions':
dataset['actions'][train_i, :, :],
'env':
dataset['env'][train_i, :]
})
test_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][test_i, :, :],
'actions':
dataset['actions'][test_i, :, :],
'env':
dataset['env'][test_i, :]
})
except:
train_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][train_i, :, :],
'actions':
dataset['actions'][train_i, :, :],
})
test_dataset = ConditionalDynamicsDataset({
'observations':
dataset['observations'][test_i, :, :],
'actions':
dataset['actions'][test_i, :, :],
})
elif use_linear_dynamics:
num_trajectories = N // n_random_steps
n = int(num_trajectories * test_p)
train_dataset = TrajectoryDataset({
'observations':
dataset['observations'][:n, :, :],
'actions':
dataset['actions'][:n, :, :]
})
test_dataset = TrajectoryDataset({
'observations':
dataset['observations'][n:, :, :],
'actions':
dataset['actions'][n:, :, :]
})
elif enviorment_dataset:
n = int(n_random_steps * test_p)
train_dataset = EnvironmentDataset({
'observations':
dataset['observations'][:, :n, :],
})
test_dataset = EnvironmentDataset({
'observations':
dataset['observations'][:, n:, :],
})
elif conditional_vae_dataset:
num_trajectories = N // n_random_steps
n = int(num_trajectories * test_p)
indices = np.arange(num_trajectories)
np.random.shuffle(indices)
train_i, test_i = indices[:n], indices[n:]
if augment_data:
dataset_class = InitialObservationNumpyJitteringDataset
else:
dataset_class = InitialObservationNumpyDataset
if 'env' not in dataset:
dataset['env'] = dataset['observations'][:, 0]
if use_test_dataset and ('env' not in test_dataset):
test_dataset['env'] = test_dataset['observations'][:, 0]
if use_test_dataset:
train_dataset = dataset_class({
'observations':
dataset['observations'],
'env':
dataset['env']
})
test_dataset = dataset_class({
'observations':
test_dataset['observations'],
'env':
test_dataset['env']
})
else:
train_dataset = dataset_class({
'observations':
dataset['observations'][train_i, :, :],
'env':
dataset['env'][train_i, :]
})
test_dataset = dataset_class({
'observations':
dataset['observations'][test_i, :, :],
'env':
dataset['env'][test_i, :]
})
train_batch_loader_kwargs = variant.get(
'train_batch_loader_kwargs',
dict(
batch_size=batch_size,
num_workers=0,
))
test_batch_loader_kwargs = variant.get(
'test_batch_loader_kwargs',
dict(
batch_size=batch_size,
num_workers=0,
))
train_data_loader = data.DataLoader(train_dataset,
shuffle=True,
drop_last=True,
**train_batch_loader_kwargs)
test_data_loader = data.DataLoader(test_dataset,
shuffle=True,
drop_last=True,
**test_batch_loader_kwargs)
train_dataset = InfiniteBatchLoader(train_data_loader)
test_dataset = InfiniteBatchLoader(test_data_loader)
else:
n = int(N * test_p)
train_dataset = ImageObservationDataset(dataset[:n, :])
test_dataset = ImageObservationDataset(dataset[n:, :])
return train_dataset, test_dataset, info
def generate_vae_dataset(variant):
"""
If not provided a pre-train vae dataset generation function, this function will be used to collect
the dataset for training vae.
"""
import rlkit.torch.pytorch_util as ptu
import gym
import multiworld
multiworld.register_all_envs()
print("generating vae dataset with original images")
env_class = variant.get('env_class', None)
env_kwargs = variant.get('env_kwargs', None)
env_id = variant.get('env_id', None)
N = variant.get('N', 10000)
test_p = variant.get('test_p', 0.9)
use_cached = variant.get('use_cached', True)
imsize = variant.get('imsize', 84)
num_channels = variant.get('num_channels', 3)
show = variant.get('show', False)
init_camera = variant.get('init_camera', None)
dataset_path = variant.get('dataset_path', None)
oracle_dataset_using_set_to_goal = variant.get(
'oracle_dataset_using_set_to_goal', False)
random_rollout_data = variant.get('random_rollout_data', False)
random_and_oracle_policy_data = variant.get(
'random_and_oracle_policy_data', False)
random_and_oracle_policy_data_split = variant.get(
'random_and_oracle_policy_data_split', 0)
policy_file = variant.get('policy_file', None)
n_random_steps = variant.get('n_random_steps', 100)
vae_dataset_specific_env_kwargs = variant.get(
'vae_dataset_specific_env_kwargs', None)
save_file_prefix = variant.get('save_file_prefix', None)
non_presampled_goal_img_is_garbage = variant.get(
'non_presampled_goal_img_is_garbage', None)
tag = variant.get('tag', '')
info = {}
if dataset_path is not None:
print('load vae training dataset from: ', dataset_path)
pjhome = os.environ['PJHOME']
dataset = np.load(osp.join(pjhome, dataset_path),
allow_pickle=True).item()
if isinstance(dataset, dict):
dataset = dataset['image_desired_goal']
dataset = unormalize_image(dataset)
N = dataset.shape[0]
else:
if env_kwargs is None:
env_kwargs = {}
if save_file_prefix is None:
save_file_prefix = env_id
if save_file_prefix is None:
save_file_prefix = env_class.__name__
filename = "/tmp/{}_N{}_{}_imsize{}_random_oracle_split_{}{}.npy".format(
save_file_prefix,
str(N),
init_camera.__name__ if init_camera else '',
imsize,
random_and_oracle_policy_data_split,
tag,
)
if use_cached and osp.isfile(filename):
dataset = np.load(filename)
print("loaded data from saved file", filename)
else:
now = time.time()
if env_id is not None:
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
else:
if vae_dataset_specific_env_kwargs is None:
vae_dataset_specific_env_kwargs = {}
for key, val in env_kwargs.items():
if key not in vae_dataset_specific_env_kwargs:
vae_dataset_specific_env_kwargs[key] = val
env = env_class(**vae_dataset_specific_env_kwargs)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
non_presampled_goal_img_is_garbage=
non_presampled_goal_img_is_garbage,
)
else:
imsize = env.imsize
env.non_presampled_goal_img_is_garbage = non_presampled_goal_img_is_garbage
env.reset()
info['env'] = env
if random_and_oracle_policy_data:
policy_file = load_local_or_remote_file(policy_file)
policy = policy_file['policy']
policy.to(ptu.device)
if random_rollout_data:
from rlkit.exploration_strategies.ou_strategy import OUStrategy
policy = OUStrategy(env.action_space)
dataset = np.zeros((N, imsize * imsize * num_channels),
dtype=np.uint8)
for i in range(N):
if random_and_oracle_policy_data:
num_random_steps = int(N *
random_and_oracle_policy_data_split)
if i < num_random_steps:
env.reset()
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
else:
obs = env.reset()
policy.reset()
for _ in range(n_random_steps):
policy_obs = np.hstack((
obs['state_observation'],
obs['state_desired_goal'],
))
action, _ = policy.get_action(policy_obs)
obs, _, _, _ = env.step(action)
elif oracle_dataset_using_set_to_goal:
print(i)
goal = env.sample_goal()
env.set_to_goal(goal)
obs = env._get_obs()
elif random_rollout_data:
if i % n_random_steps == 0:
g = dict(
state_desired_goal=env.sample_goal_for_rollout())
env.set_to_goal(g)
policy.reset()
# env.reset()
u = policy.get_action_from_raw_action(
env.action_space.sample())
obs = env.step(u)[0]
else:
print("using totally random rollouts")
for _ in range(n_random_steps):
obs = env.step(env.action_space.sample())[0]
img = obs[
'image_observation'] # NOTE yufei: this is already normalized image, of detype np.float64.
dataset[i, :] = unormalize_image(img)
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=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_uniform_dataset_door(num_imgs=1000,
use_cached_dataset=False,
init_camera=None,
imsize=48,
policy_file=None,
show=False,
path_length=100,
save_file_prefix=None,
env_id=None,
tag='',
dataset_path=None):
if dataset_path is not None:
dataset = load_local_or_remote_file(dataset_path)
return dataset
import gym
from gym.envs import registration
# trigger registration
import multiworld.envs.pygame
import multiworld.envs.mujoco
env = gym.make(env_id)
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
)
env.non_presampled_goal_img_is_garbage = True
if save_file_prefix is None and env_id is not None:
save_file_prefix = env_id
filename = "/tmp/{}_N{}_imsize{}uniform_images_{}.npy".format(
save_file_prefix,
str(num_imgs),
env.imsize,
tag,
)
if use_cached_dataset and osp.isfile(filename):
images = np.load(filename)
print("Loaded data from {}".format(filename))
return images
policy_file = load_local_or_remote_file(policy_file)
policy = policy_file['policy']
policy.to(ptu.device)
print('Sampling Uniform Dataset')
dataset = np.zeros((num_imgs, 3 * env.imsize**2), dtype=np.uint8)
for j in range(num_imgs):
obs = env.reset()
policy.reset()
for i in range(path_length):
policy_obs = np.hstack((
obs['state_observation'],
obs['state_desired_goal'],
))
action, _ = policy.get_action(policy_obs)
obs, _, _, _ = env.step(action)
img_f = obs['image_observation']
if show:
img = obs['image_observation']
img = img.reshape(3, env.imsize, env.imsize).transpose()
img = img[::-1, :, ::-1]
cv2.imshow('img', img)
cv2.waitKey(1)
print(j)
dataset[j, :] = unormalize_image(img_f)
temp = env.reset_free
env.reset_free = True
env.reset()
env.reset_free = temp
np.save(filename, dataset)
print("Saving file to {}".format(filename))
return dataset
def generate_vae_dataset(variant):
import cv2
env_class = variant.get('env_class', None)
env_kwargs = variant.get('env_kwargs', None)
env_id = variant.get('env_id', None)
N = variant.get('N', 10000)
use_images = variant.get('use_images', True)
imsize = variant.get('imsize', 84)
show = variant.get('show', False)
init_camera = variant.get('init_camera', None)
oracle_dataset = variant.get('oracle_dataset', False)
if 'n_random_steps' in variant:
n_random_steps = variant['n_random_steps']
else:
if oracle_dataset:
n_random_steps = 3
else:
n_random_steps = 100
vae_dataset_specific_env_kwargs = variant.get(
'vae_dataset_specific_env_kwargs', None)
non_presampled_goal_img_is_garbage = variant.get(
'non_presampled_goal_img_is_garbage', None)
from multiworld.core.image_env import ImageEnv, unormalize_image
info = {}
from railrl.core import logger
logdir = logger.get_snapshot_dir()
filename = osp.join(logdir, "vae_dataset.npy")
now = time.time()
if env_id is not None:
import gym
env = gym.make(env_id)
else:
if vae_dataset_specific_env_kwargs is None:
vae_dataset_specific_env_kwargs = {}
for key, val in env_kwargs.items():
if key not in vae_dataset_specific_env_kwargs:
vae_dataset_specific_env_kwargs[key] = val
env = env_class(**vae_dataset_specific_env_kwargs)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
non_presampled_goal_img_is_garbage=
non_presampled_goal_img_is_garbage,
)
else:
imsize = env.imsize
env.non_presampled_goal_img_is_garbage = non_presampled_goal_img_is_garbage
env.reset()
info['env'] = env
if use_images:
data_size = len(env.observation_space.spaces['image_observation'].low)
dtype = np.uint8
else:
data_size = len(env.observation_space.spaces['state_observation'].low)
dtype = np.float32
state_size = len(env.observation_space.spaces['state_observation'].low)
dataset = {
'obs': np.zeros((N, data_size), dtype=dtype),
'actions': np.zeros((N, len(env.action_space.low)), dtype=np.float32),
'next_obs': np.zeros((N, data_size), dtype=dtype),
'obs_state': np.zeros((N, state_size), dtype=np.float32),
'next_obs_state': np.zeros((N, state_size), dtype=np.float32),
}
for i in range(N):
if i % (N / 50) == 0:
print(i)
if oracle_dataset:
if i % 100 == 0:
env.reset()
goal = env.sample_goal()
env.set_to_goal(goal)
for _ in range(n_random_steps):
env.step(env.action_space.sample())
else:
env.reset()
for _ in range(n_random_steps):
env.step(env.action_space.sample())
obs = env._get_obs()
if use_images:
dataset['obs'][i, :] = unormalize_image(obs['image_observation'])
else:
dataset['obs'][i, :] = obs['state_observation']
dataset['obs_state'][i, :] = obs['state_observation']
action = env.action_space.sample()
dataset['actions'][i, :] = action
obs = env.step(action)[0]
img = obs['image_observation']
if use_images:
dataset['next_obs'][i, :] = unormalize_image(img)
else:
dataset['next_obs'][i, :] = obs['state_observation']
dataset['next_obs_state'][i, :] = obs['state_observation']
show = True #todo del own
if show:
img = img.reshape(3, imsize, imsize).transpose((1, 2, 0))
img = img[::, :, ::-1]
cv2.imshow('img', img)
cv2.waitKey(1000)
print("keys and shapes:")
for k in dataset.keys():
print(k, dataset[k].shape)
print("done making training data", filename, time.time() - now)
np.save(filename, dataset)
def generate_sawyerhurdle_dataset(variant,
segmented=False,
segmentation_method='unet'):
from multiworld.core.image_env import ImageEnv, unormalize_image
env_id = variant.get('env_id', None)
N = variant.get('N', 10000)
test_p = variant.get('test_p', 0.9)
imsize = variant.get('imsize', 84)
num_channels = variant.get('num_channels', 3)
init_camera = variant.get('init_camera', None)
segmentation_kwargs = variant.get('segmentation_kwargs', {})
pjhome = os.environ['PJHOME']
seg_name = 'seg-' + segmentation_method if segmented else 'no-seg'
data_file_path = osp.join(pjhome, 'data/local/pre-train-vae',
'{}-{}-{}.npy'.format(env_id, seg_name, N))
puck_pos_path = osp.join(
pjhome, 'data/local/pre-train-vae',
'{}-{}-{}-puck-pos.npy'.format(env_id, seg_name, N))
if osp.exists(data_file_path):
all_data = np.load(data_file_path)
if len(all_data) >= N:
print("load stored data at: ", data_file_path)
n = int(len(all_data) * test_p)
train_dataset = all_data[:n]
test_dataset = all_data[n:]
puck_pos = np.load(puck_pos_path)
info = {'puck_pos': puck_pos}
return train_dataset, test_dataset, info
if segmented:
print("generating vae dataset with segmented images using method: ",
segmentation_method)
if segmentation_method == 'unet':
segment_func = segment_image_unet
else:
raise NotImplementedError
else:
print("generating vae dataset with original images")
assert env_id is not None
import gym
import multiworld
multiworld.register_all_envs()
env = gym.make(env_id)
if not isinstance(env, ImageEnv):
env = ImageEnv(
env,
imsize,
init_camera=init_camera,
transpose=True,
normalize=True,
)
info = {}
env.reset()
info['env'] = env
dataset = np.zeros((N, imsize * imsize * num_channels), dtype=np.uint8)
puck_pos = np.zeros((N, 2), dtype=np.float)
for i in range(N):
print("sawyer hurdle custom vae data set generation, number: ", i)
if env_id == 'SawyerPushHurdle-v0':
obs, puck_p = _generate_sawyerhurdle_dataset(env,
return_puck_pos=True)
elif env_id == 'SawyerPushHurdleMiddle-v0':
obs, puck_p = _generate_sawyerhurdlemiddle_dataset(
env, return_puck_pos=True)
else:
raise NotImplementedError
img = obs[
'image_observation'] # NOTE yufei: this is already normalized image, of detype np.float64.
if segmented:
dataset[i, :] = segment_func(img,
normalize=False,
**segmentation_kwargs)
else:
dataset[i, :] = unormalize_image(img)
puck_pos[i] = puck_p
n = int(N * test_p)
train_dataset = dataset[:n, :]
test_dataset = dataset[n:, :]
info['puck_pos'] = puck_pos
if N >= 2000:
print('save data to: ', data_file_path)
all_data = np.concatenate([train_dataset, test_dataset], axis=0)
np.save(data_file_path, all_data)
np.save(puck_pos_path, puck_pos)
return train_dataset, test_dataset, info
