def __init__(self, args):
self.args = args
self.env = make_env(args)
self.env_test = make_env(args)
self.info = []
if args.save_acc:
make_dir('log/accs', clear=False)
self.test_rollouts = 100
self.env_List = []
self.env_test_List = []
for _ in range(self.test_rollouts):
self.env_List.append(make_env(args))
self.env_test_List.append(make_env(args))
self.acc_record = {}
self.acc_record[self.args.goal] = []
for key in self.acc_record.keys():
self.info.append('Success/' + key + '@blue')
def __init__(self, args, test_rollouts=100, after_train_test=False):
self.args = args
self.env = make_env(args)
self.env_test = make_env(args)
self.info = []
self.calls = 0
self.after_train_test = after_train_test
if args.save_acc:
make_dir('log/accs', clear=False)
self.test_rollouts = test_rollouts
self.env_List = []
for _ in range(self.test_rollouts):
self.env_List.append(make_env(args))
self.acc_record = {}
self.acc_record[self.args.goal] = []
for key in self.acc_record.keys():
self.info.append('Success'+'@blue')
self.info.append('MaxDistance')
self.info.append('MinDistance')
self.coll_tol = 0 #this attribute is just used for tests after training
def __init__(self, args):
self.args = args
self.env = make_env(args)
self.info = []
if args.save_rews:
make_dir('log/rews', clear=False)
self.rews_record = {}
self.rews_record[args.env] = []
if args.save_Q:
make_dir('log/Q_std', clear=False)
make_dir('log/Q_net', clear=False)
make_dir('log/Q_ground', clear=False)
self.Q_std_record, self.Q_net_record, self.Q_ground_record = {}, {}, {}
self.Q_std_record[args.env], self.Q_net_record[
args.env], self.Q_ground_record[args.env] = [], [], []
self.info += ['Q_error/mean', 'Q_error/std']
[ppairs[inds1][:, 0, :], ppairs_around_obstacle[inds2][:, 0, :]],
axis=1)
return new_ppairs
def flat_entries(bboxes_list, ppair):
return np.concatenate([bboxes_list.ravel(), ppair.ravel()])
if __name__ == "__main__":
args = get_args()
# create data folder if it does not exist, corresponding folders, and files where to store data
this_file_dir = os.path.dirname(os.path.abspath(__file__)) + '/'
base_data_dir = this_file_dir + 'data/'
env_data_dir = base_data_dir + args.env + '/'
make_dir(env_data_dir, clear=False)
if args.vae_dist_help:
load_vaes(args)
load_field_parameters(args)
env = make_temp_env(args)
field_names = ['ppair', 'bbox', 'distance']
csv_file_path = env_data_dir + 'distances.csv'
csv_file_path_val = env_data_dir + 'distances_val.csv'
csv_file_path_test = env_data_dir + 'distances_test.csv'
for csv_path in [csv_file_path, csv_file_path_val, csv_file_path_test]:
if os.path.exists(csv_path):
os.remove(csv_path)
with open(csv_path, 'w') as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=field_names)
default=6)
parser.add_argument('--beta',
help='beta val for the reconstruction loss',
type=np.float,
default=8.) #5#8
parser.add_argument('--gamma',
help='gamma val for the mask loss',
type=np.float,
default=5.) #2.)#5
parser.add_argument('--bg_sigma', help='', type=np.float, default=0.09)
parser.add_argument('--fg_sigma', help='', type=np.float, default=0.11)
args = parser.parse_args()
# get names corresponding folders, and files where to store data
make_dir(this_file_dir + 'results/', clear=False)
base_data_dir = this_file_dir + '../data/'
data_dir = base_data_dir + args.env + '/'
train_file = data_dir + 'all_set.npy'
weights_path = data_dir + 'all_sb_model'
if args.task == 'train':
train_Vae(epochs=args.train_epochs,
batch_size=args.batch_size,
img_size=args.img_size,
latent_size=args.latent_size,
train_file=train_file,
vae_weights_path=weights_path,
beta=args.beta,
gamma=args.gamma,
parser.add_argument('--task', help='the task for the generation of data', type=str,
default='generate', choices=['generate', 'mix', 'show'], required=True)
#CURRENTLY using just FetchGenerativeEnv-v1; argument is left since it is used in different parts of code
parser.add_argument('--env', help='gym env id', type=str, default='FetchGenerativeEnv-v1', choices=Robotics_envs_id)
args, _ = parser.parse_known_args()
if args.task == 'mix':
parser.add_argument('--file_1', help='first file to mix', type=str)
parser.add_argument('--file_2', help='second file to mix', type=str)
parser.add_argument('--output_file', help='name of output file for mixed dataset', type=str)
args = parser.parse_args()
this_file_dir = os.path.dirname(os.path.abspath(__file__)) + '/'
base_data_dir = this_file_dir + '../data/'
env_data_dir = base_data_dir + args.env + '/'
make_dir(env_data_dir, clear=False)
data_file_1 = env_data_dir + args.file_1
data_file_2 = env_data_dir + args.file_2
data_1 = np.load(data_file_1)
data_2 = np.load(data_file_2)
mixed_data = np.concatenate([data_1, data_2], axis=0)
np.random.shuffle(mixed_data)
output_file = env_data_dir + args.output_file
np.save(output_file, mixed_data)
else:
if args.env == 'HandReach-v0':
parser.add_argument('--goal', help='method of goal generation', type=str, default='reach',
choices=['vanilla', 'reach'])