def generate(args):
num_files = args.num_files
validation = args.validation
seq_len = args.seq_len
vae = VAE()
weights = "final.h5"
try:
vae.set_weights("./vae/" + weights)
except:
print("./vae/" + weights + " does not exist")
raise FileNotFoundError
for file_id in range(num_files):
print("Generating file {}...".format(file_id))
obs_data = []
action_data = []
for env_name in config.train_envs:
try:
if validation:
new_obs_data = np.load("./data/obs_valid_" + env_name + ".npz")["arr_0"]
new_action_data = np.load("./data/action_valid_" + env_name + ".npz")["arr_0"]
else:
new_obs_data = np.load("./data/obs_data_" + env_name + "_" + str(file_id) + ".npz")["arr_0"]
new_action_data = np.load("./data/action_data_" + env_name + "_" + str(file_id) + ".npz")["arr_0"]
index = 0
for episode in new_obs_data:
# print(len(episode))
if len(episode) != seq_len:
new_obs_data = np.delete(new_obs_data, index)
new_action_data = np.delete(new_action_data, index)
else:
index += 1
obs_data = np.append(obs_data, new_obs_data)
action_data = np.append(action_data, new_action_data)
print("Found {}...current data size = {} episodes".format(env_name, len(obs_data)))
except Exception as e:
print(e)
pass
if validation:
rnn_input, rnn_output = vae.generate_rnn_data(obs_data, action_data)
np.savez_compressed("./data/rnn_input_" + env_name + "_valid", rnn_input)
np.savez_compressed("./data/rnn_output_" + env_name + "_valid", rnn_output)
else:
rnn_input, rnn_output = vae.generate_rnn_data(obs_data, action_data)
np.savez_compressed("./data/rnn_input_" + env_name + "_" + str(file_id), rnn_input)
np.savez_compressed("./data/rnn_output_" + env_name + "_" + str(file_id), rnn_output)
if validation:
break
def main(args):
start_batch = args.start_batch
max_batch = args.max_batch
vae = VAE()
try:
vae.set_weights('./vae/weights.h5')
except:
print(
"./vae/weights.h5 does not exist - ensure you have run 02_train_vae.py first"
)
raise
for i in range(start_batch, max_batch + 1):
print('Generating batch {}...'.format(i))
vae.generate_rnn_data(i)
def main(args):
start_batch = args.start_batch
max_batch = args.max_batch
vae = VAE()
try:
vae.set_weights('./vae/weights.h5')
except:
print(
"./vae/weights.h5 does not exist - ensure you have run 02_train_vae.py first"
)
raise
for batch_num in range(start_batch, max_batch + 1):
first_item = True
print('Generating batch {}...'.format(batch_num))
for env_name in config.train_envs:
try:
new_obs_data = np.load('./data/obs_data_' + env_name + '_' +
str(batch_num) + '.npy')
new_action_data = np.load('./data/action_data_' + env_name +
'_' + str(batch_num) + '.npy')
if first_item:
obs_data = new_obs_data
action_data = new_action_data
first_item = False
else:
obs_data = np.concatenate([obs_data, new_obs_data])
action_data = np.concatenate(
[action_data, new_action_data])
print('Found {}...current data size = {} episodes'.format(
env_name, len(obs_data)))
except:
pass
if first_item == False:
rnn_input, rnn_output = vae.generate_rnn_data(
obs_data, action_data)
np.save('./data/rnn_input_' + str(batch_num), rnn_input)
np.save('./data/rnn_output_' + str(batch_num), rnn_output)
else:
print('no data found for batch number {}'.format(batch_num))
def main(args):
start_batch = args.start_batch
max_batch = args.max_batch
vae = VAE()
try:
vae.set_weights('./vae/weights.h5')
except:
print("./vae/weights.h5 does not exist - ensure you have run 02_train_vae.py first")
raise
for batch_num in range(start_batch, max_batch + 1):
first_item = True
print('Generating batch {}...'.format(batch_num))
for env_name in config.train_envs:
try:
new_obs_data = np.load('./data/obs_data_' + env_name + '_' + str(batch_num) + '.npy')
new_action_data = np.load('./data/action_data_' + env_name + '_' + str(batch_num) + '.npy')
if first_item:
obs_data = new_obs_data
action_data = new_action_data
first_item = False
else:
obs_data = np.concatenate([obs_data, new_obs_data])
action_data = np.concatenate([action_data, new_action_data])
print('Found {}...current data size = {} episodes'.format(env_name, len(obs_data)))
except:
pass
if first_item == False:
rnn_input, rnn_output = vae.generate_rnn_data(obs_data, action_data)
np.save('./data/rnn_input_' + str(batch_num), rnn_input)
np.save('./data/rnn_output_' + str(batch_num), rnn_output)
else:
print('no data found for batch number {}'.format(batch_num))
