path_matching=config.path_matching_val,
img_prepro=greyscale,
form_prepro=get_form_prepro(config.vocab),
max_len=config.max_length_formula,
max_iter=config.max_iter)
test_set = Dataset(path_formulas=config.path_formulas,
dir_images=config.dir_images,
path_matching=config.path_matching_test,
img_prepro=greyscale,
form_prepro=get_form_prepro(config.vocab),
max_len=config.max_length_formula,
max_iter=config.max_iter)
test_set = train_set = val_set # for test purposes
n_batches_epoch = (len(train_set) + config.batch_size -
1) // config.batch_size
lr_schedule = LRSchedule(lr_init=config.lr_init,
lr_min=config.lr_min,
start_decay=config.start_decay * n_batches_epoch,
end_decay=config.end_decay * n_batches_epoch,
lr_warm=config.lr_warm,
end_warm=config.end_warm * n_batches_epoch)
# Build model
model = Model(config)
model.build()
model.train(train_set, val_set, lr_schedule)
model.evaluate(test_set, config.model_output, config.path_results_final)
class A2C:
def __init__(self, sess, args):
self.args = args
self.model = Model(sess,
optimizer_params={
'learning_rate': args.learning_rate, 'alpha': 0.99, 'epsilon': 1e-5}, args=self.args)
self.trainer = Trainer(sess, self.model, args=self.args)
self.env_class = A2C.env_name_parser(self.args.env_class)
def train(self):
env = A2C.make_all_environments(self.args.num_envs, self.env_class, self.args.env_name,
self.args.env_seed)
print("\n\nBuilding the model...")
self.model.build(env.observation_space.shape, env.action_space.n)
print("Model is built successfully\n\n")
with open(self.args.experiment_dir + self.args.env_name + '.pkl', 'wb') as f:
pickle.dump((env.observation_space.shape, env.action_space.n), f, pickle.HIGHEST_PROTOCOL)
print('Training...')
try:
# Produce video only if monitor method is implemented.
try:
if self.args.record_video_every != -1:
env.monitor(is_monitor=True, is_train=True, experiment_dir=self.args.experiment_dir,
record_video_every=self.args.record_video_every)
except:
pass
self.trainer.train(env)
except KeyboardInterrupt:
print('Error occured..\n')
self.trainer.save()
env.close()
def test(self, total_timesteps):
observation_space_shape, action_space_n = None, None
try:
with open(self.args.experiment_dir + self.args.env_name + '.pkl', 'rb') as f:
observation_space_shape, action_space_n = pickle.load(f)
except:
print(
"Environment or checkpoint data not found. Make sure that env_data.pkl is present in the experiment by running training first.\n")
exit(1)
env = self.make_all_environments(num_envs=1, env_class=self.env_class, env_name=self.args.env_name,
seed=self.args.env_seed)
self.model.build(observation_space_shape, action_space_n)
print('Testing...')
try:
# Produce video only if monitor method is implemented.
try:
if self.args.record_video_every != -1:
env.monitor(is_monitor=True, is_train=False, experiment_dir=self.args.experiment_dir,
record_video_every=self.args.record_video_every)
else:
env.monitor(is_monitor=True, is_train=False, experiment_dir=self.args.experiment_dir,
record_video_every=20)
except:
pass
self.trainer.test(total_timesteps=total_timesteps, env=env)
except KeyboardInterrupt:
print('Error occured..\n')
env.close()
def infer(self, observation):
"""Used for inference.
:param observation: (tf.tensor) having the shape (None,img_height,img_width,num_classes*num_stack)
:return action after noise and argmax
:return value function of the state
"""
states = self.model.step_policy.initial_state
dones = []
# states and dones are for LSTM, leave them for now!
action, value, states = self.model.step_policy.step(observation, states, dones)
return action, value___
# The reason behind this design pattern is to pass the function handler when required after serialization.
@staticmethod
def __env_maker(env_class, env_name, i, seed):
def __make_env():
return env_class(env_name, i, seed)
return __make_env
@staticmethod
def make_all_environments(num_envs=4, env_class=None, env_name="SpaceInvaders", seed=42):
set_all_global_seeds(seed)
return SubprocVecEnv(
[A2C.__env_maker(env_class, env_name, i, seed) for i in range(num_envs)])
@staticmethod
def env_name_parser(env_name):
from envs.gym_env import GymEnv
envs_to_class = {'GymEnv': GymEnv}
if env_name in envs_to_class:
return envs_to_class[env_name]
raise ValueError("There is no environment with this name. Make sure that the environment exists.")
