def rankcaptions(filenames, top_n=5):
# n_captions = top_n # the captions it ranks as highest should all be relevant
n_captions = 1 # RCL: image caption mismatch when n_captions is not just one
batch_size = 128
image_features, captions = loadFeaturesTargets(filenames, 'val2014', n_captions=n_captions)
stream = DataETL.getFinalStream(
image_features
, captions
, ("image_vects", "word_vects")
, ("image_vects_k", "word_vects_k")
, batch_size=batch_size
)
f_emb = ModelIO.load('/home/luke/datasets/coco/predict/fullencoder_maxfeatures.50000')
im_emb, s_emb = None, None
print "Computing Image and Text Embeddings"
for batch in stream.get_epoch_iterator():
im_vects = batch[0]
s_vects = batch[1]
batch_im_emb, batch_s_emb = f_emb(im_vects, s_vects)
im_emb = vStackMatrices(im_emb, batch_im_emb)
s_emb = vStackMatrices(s_emb, batch_s_emb)
# account for make sure theres matching fns for each of the n_captions
image_fns = fillOutFilenames(filenames, n_captions=n_captions)
print "Computing Cosine Distances and Ranking Captions"
relevant_captions = ModelEval.getRelevantCaptions(
im_emb, s_emb, image_fns, captions, z=n_captions, top_n=top_n
)
dict2json(relevant_captions, "rankcaptions_fullencoder_maxfeatures.50000.json", cls=DecimalEncoder)
return relevant_captions
def rank_function(self=None):
teX, teY, _ = cocoXYFilenames(n_captions=5)
sources = ('X', 'Y')
sources_k = ('X_k', 'Y_k')
stream = DataETL.getFinalStream(teX, teY, sources=sources,
sources_k=sources_k, batch_size=1000,
shuffle=False)
images, captions, _0, _1 = stream.get_epoch_iterator().next()
predict_dir = '/home/luke/datasets/coco/predict/'
# encoder_name = '+coco_encoder_lstm_dim.300'
encoder_name = 'sbu.100000+coco_encoder_lstm_dim.300_adadelta'
# encoder_name = 'fullencoder_maxfeatures.50000_epochsampler'
f_emb = ModelIO.load(predict_dir + encoder_name)
image_embs, caption_embs = f_emb(images, captions)
ModelEval.ImageSentenceRanking(image_embs, caption_embs)
def rank_function(self=None):
teX, teY, _ = cocoXYFilenames(n_captions=5)
sources = ('X', 'Y')
sources_k = ('X_k', 'Y_k')
stream = DataETL.getFinalStream(teX,
teY,
sources=sources,
sources_k=sources_k,
batch_size=1000,
shuffle=False)
images, captions, _0, _1 = stream.get_epoch_iterator().next()
predict_dir = '/home/luke/datasets/coco/predict/'
# encoder_name = '+coco_encoder_lstm_dim.300'
encoder_name = 'sbu.100000+coco_encoder_lstm_dim.300_adadelta'
# encoder_name = 'fullencoder_maxfeatures.50000_epochsampler'
f_emb = ModelIO.load(predict_dir + encoder_name)
image_embs, caption_embs = f_emb(images, captions)
ModelEval.ImageSentenceRanking(image_embs, caption_embs)
def rankcaptions(filenames, top_n=5):
# n_captions = top_n # the captions it ranks as highest should all be relevant
n_captions = 1 # RCL: image caption mismatch when n_captions is not just one
batch_size = 128
image_features, captions = loadFeaturesTargets(filenames,
'val2014',
n_captions=n_captions)
stream = DataETL.getFinalStream(image_features,
captions,
("image_vects", "word_vects"),
("image_vects_k", "word_vects_k"),
batch_size=batch_size)
f_emb = ModelIO.load(
'/home/luke/datasets/coco/predict/fullencoder_maxfeatures.50000')
im_emb, s_emb = None, None
print "Computing Image and Text Embeddings"
for batch in stream.get_epoch_iterator():
im_vects = batch[0]
s_vects = batch[1]
batch_im_emb, batch_s_emb = f_emb(im_vects, s_vects)
im_emb = vStackMatrices(im_emb, batch_im_emb)
s_emb = vStackMatrices(s_emb, batch_s_emb)
# account for make sure theres matching fns for each of the n_captions
image_fns = fillOutFilenames(filenames, n_captions=n_captions)
print "Computing Cosine Distances and Ranking Captions"
relevant_captions = ModelEval.getRelevantCaptions(im_emb,
s_emb,
image_fns,
captions,
z=n_captions,
top_n=top_n)
dict2json(relevant_captions,
"rankcaptions_fullencoder_maxfeatures.50000.json",
cls=DecimalEncoder)
return relevant_captions
class REINFORCE:
def __init__(self,
state_dim,
action_dim,
gamma=0.99,
hidden_dim=64,
policy_lr=0.001,
baseline_lr=0.001):
self._V = StateValueFunction(state_dim, hidden_dim=hidden_dim)
self._pi = Policy(state_dim, action_dim, hidden_dim=hidden_dim)
# self._V.cuda()
# self._pi.cuda()
self._gamma = gamma
self._loss_function = nn.MSELoss()
self._V_optimizer = optim.Adam(self._V.parameters(), lr=baseline_lr)
self._pi_optimizer = optim.Adam(self._pi.parameters(), lr=policy_lr)
self._action_dim = action_dim
# --- ModelIO ---
self._modelio = ModelIO(model_path=Path(__file__).resolve().parent /
'models')
def get_action(self, s):
mu_action = self._pi(tt(s))
# mu_action = self._pi(tt(s)).detach().numpy()
action_sampled = np.random.normal(loc=mu_action.detach().numpy(),
scale=0.1,
size=1)
action_sampled = np.clip(action_sampled, a_min=-1.0, a_max=1.0)
log_prob = torch.log(mu_action + torch.normal(mean=mu_action))
return action_sampled, log_prob
def train(self, env, episodes, time_steps):
stats = EpisodeStats(episode_lengths=np.zeros(episodes),
episode_rewards=np.zeros(episodes))
for i_episode in range(1, episodes + 1):
# Generate an episode.
# An episode is an array of (state, action, reward) tuples
episode = []
s = env.reset()
for t in range(time_steps):
a, log_prob_a = self.get_action(s)
ns, r, d, _ = env.step(a)
stats.episode_rewards[i_episode - 1] += r
stats.episode_lengths[i_episode - 1] = t
episode.append((s, a, log_prob_a, r))
if d:
break
s = ns
# collect all rewards at one place
T = len(episode)
G = 0.0
for t in reversed(range(T)):
s, a, log_prob, r = episode[t]
G = self._gamma * G + r
baseline = self._V(tt(s))
advantage = G - baseline
self._train_baseline(G, baseline)
self._train_policy(advantage, t, log_prob)
print("\r{} Steps in Episode {}/{}. Reward {}".format(
len(episode), i_episode, episodes,
sum([e[3] for i, e in enumerate(episode)])))
return stats
def _train_baseline(self, G, baseline):
self._V_optimizer.zero_grad()
loss = self._loss_function(tt(np.array([G])), baseline)
loss.backward(retain_graph=True)
self._V_optimizer.step()
def _train_policy(self, error, t, log_prob_a):
self._pi_optimizer.zero_grad()
neg_log_prob_a = -log_prob_a
target = np.power(self._gamma, t) * error * neg_log_prob_a
target.backward(retain_graph=True)
self._pi_optimizer.step()
def save_models(self, model_name):
self._modelio.save(model=self._pi,
model_name=f'r_c_policy_{model_name}.pt')
self._modelio.save(model=self._V,
model_name=f'r_c_baseline_{model_name}.pt')
def load_models(self, model_name):
# if self._model
self._modelio.load(model=self._pi,
model_name=f'r_c_policy_{model_name}.pt')
self._modelio.load(model=self._V,
model_name=f'r_c_baseline_{model_name}.pt')
mlb = MultiLabelBinarizer()
mlb.fit(vect.transform(captions))
return vect, mlb
# if not multilabel:
return vect
dataset_name = 'coco_train2014'
n_sbu=None
if n_sbu:
dataset_name += "+sbu%d" % n_sbu
# global vectorizer
vect_name = 'tokenizer_%s' % dataset_name
mlb_name = 'mlb_%s' % dataset_name
try:
if mlb_name:
mlb = ModelIO.load(mlb_name)
print "MLB loaded from file"
vect = ModelIO.load(vect_name)
# vect = ModelIO.load('tokenizer_reddit') # gloveglove
print "Tokenizer loaded from file."
except:
if mlb_name:
vect, mlb = prepVect(n_sbu=n_sbu, n_captions=1, multilabel=True)
ModelIO.save(vect, vect_name)
ModelIO.save(mlb, mlb_name)
print "Saved %s, %s for future use." % (vect_name, mlb_name)
else:
vect = prepVect(n_sbu=n_sbu, n_captions=1)
ModelIO.save(vect, vect_name)
print "Saved %s for future use." % vect_name
class DDPG:
def __init__(self,
state_dim,
action_dim,
gamma=0.99,
noise_std=0.02,
hidden_dim=64,
actor_lr=0.001,
critic_lr=0.001,
verbose=False):
self.gamma = gamma
# self.tau = 0.01
self.tau = 0.001
self.actor = Actor(state_dim,
noise_std=noise_std,
hidden_dim=hidden_dim)
self.actor_target = Actor(state_dim,
noise_std=noise_std,
hidden_dim=hidden_dim)
self.critic = Critic(state_dim,
action_dim,
hidden_dim=hidden_dim)
self.critic_target = Critic(state_dim,
action_dim,
hidden_dim=hidden_dim)
self.actor_target.load_state_dict(self.actor.state_dict())
self.critic_target.load_state_dict(self.critic.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),
lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
lr=critic_lr)
self.buffer = ReplayBuffer(max_size=1e5)
self.logging_period = 10 if verbose else 100
# --- ModelIO ---
self.modelio = ModelIO(model_path=Path(__file__).resolve().parent /
'models')
def update_target(self, target, source):
for target_param, param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(param.data * self.tau + target_param.data *
(1.0 - self.tau))
def get_action(self, state, is_testing=False):
"""
used for test time (not training)
"""
action = self.actor(state, is_testing).detach()
# env_action = torch.clamp(action, min=-1.0, max=1.0).detach().numpy()
action = self.actor(state, is_testing).detach().numpy()
return action
def train(self, env, episodes, timesteps):
stats = EpisodeStats(episode_lengths=np.zeros(episodes),
episode_rewards=np.zeros(episodes))
for i_episode in range(1, episodes + 1):
state = env.reset()
for t in range(timesteps):
# --- choose action
action = self.actor(state).detach()
env_action = torch.clamp(action, min = -2.0,
max = 2.0).detach().numpy()
next_state, reward, done, _, _ = env.step(env_action)
# --- saving stats
stats.episode_rewards[i_episode - 1] += reward
stats.episode_lengths[i_episode - 1] = t
# --- save the transision
self.buffer.add_transition(
state=torch.from_numpy(state).float().to(device),
action=action,
next_state=torch.from_numpy(next_state).float().to(device),
reward=reward,
done=done)
# --- sample a batch of transitions
batch = self.buffer.next_random_batch(batch_size=32)
# --- train
self.train_batch(batch)
# --- update target networks
self.update_target(target=self.actor_target, source=self.actor)
self.update_target(target=self.critic_target,
source=self.critic)
if done:
break
state = next_state
# logging
if i_episode % self.logging_period == 0:
print((f"{int(stats.episode_lengths[i_episode - 1])} Steps in"
f"Episode {i_episode}/{episodes}. "
f"Reward {stats.episode_rewards[i_episode-1]}"))
# snapshot instants
snaps_moments = np.array([400, 800, 1200, 1600])
for snap in snaps_moments:
if (i_episode == snap):
self.save_models(model_name=snap)
return stats
def train_batch(self, batch):
states, actions, next_states, rewards, dones = batch
batch_rewards = torch.FloatTensor(rewards).to(device)
batch_states = torch.stack(states).to(device)
batch_actions = torch.stack(actions).to(device)
batch_next_states = torch.stack(next_states).to(device)
batch_na = self.actor_target(batch_next_states)
batch_q_ns_na = self.critic_target(batch_next_states,
batch_na.detach().view(-1, 1))
update_targets = batch_rewards.view(-1, 1) + self.gamma * batch_q_ns_na
batch_q_s_a = self.critic(batch_states, batch_actions.view(-1, 1))
critic_loss = F.mse_loss(batch_q_s_a, update_targets)
actor_loss = -self.critic(batch_states,
self.actor(batch_states).view(-1, 1)).mean()
# actor_loss = self.critic(batch_states,
# self.actor(batch_states).view(-1, 1)).mean()
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
def save_models(self, model_name):
self.modelio.save(model=self.actor,
model_name=f'ddpg_c_actor_{model_name}.pt')
self.modelio.save(model=self.critic,
model_name=f'ddpg_c_critic_{model_name}.pt')
def load_models(self, model_name):
# if self._model
self.modelio.load(model=self.actor,
model_name=f'ddpg_c_actor_{model_name}.pt')
self.modelio.load(model=self.actor_target,
model_name=f'ddpg_c_actor_{model_name}.pt')
self.modelio.load(model=self.critic,
model_name=f'ddpg_c_critic_{model_name}.pt')
self.modelio.load(model=self.critic_target,
model_name=f'ddpg_c_critic_{model_name}.pt')
class ActorCritic:
def __init__(self, state_dim, action_dim, gamma, d2c=None):
self._V = StateValueFunction(state_dim)
self._pi = Policy(state_dim, action_dim)
self.d2c = d2c # discrete to continuous actions
# self._V.cuda()
# self._pi.cuda()
self._gamma = gamma
self._loss_function = nn.MSELoss()
self._V_optimizer = optim.Adam(self._V.parameters(), lr=0.001)
self._pi_optimizer = optim.Adam(self._pi.parameters(), lr=0.0001)
self._action_dim = action_dim
# --- ModelIO ---
self._modelio = ModelIO(model_path=Path(__file__).resolve().parent /
'models')
self._baseline_model_name = 'ac_baseline.pt'
self._policy_model_name = 'ac_policy.pt'
def get_action(self, s):
probs = self._pi(tt(s))
action = np.random.choice(a=self._action_dim,
p=np.squeeze(probs.detach().numpy()))
log_prob = torch.log(probs.squeeze(0)[action])
# converting the discrete action [0,1,2,...]
# to an action in the continuous
# range (actionspace.low <--> actionspace.high)
if self.d2c:
action = self.d2c(action)
return action, log_prob
def train(self, env, episodes, time_steps):
stats = EpisodeStats(episode_lengths=np.zeros(episodes),
episode_rewards=np.zeros(episodes))
for i_episode in range(1, episodes + 1):
# Generate an episode.
# An episode is an array of (state, action, reward) tuples
s = env.reset()
comounded_decay = 1
for t in range(time_steps):
a, log_prob_a = self.get_action(s)
ns, r, d, _ = env.step(a)
stats.episode_rewards[i_episode - 1] += r
stats.episode_lengths[i_episode - 1] = t
target = r
if not d:
target = target + self._gamma * self._V(
tt(ns)).cpu().detach()
baseline = self._V(tt(s))
advantage = target - baseline
comounded_decay *= self._gamma
self._train_baseline(target, baseline)
self._train_policy(advantage, comounded_decay, log_prob_a)
if d:
break
s = ns
print(
f"{stats.episode_lengths[i_episode-1]} Steps in Episode {i_episode}/{episodes}. Reward {stats.episode_rewards[i_episode-1]}"
)
return stats
def _train_policy(self, advantage, comp_decay, log_prob_a):
self._pi_optimizer.zero_grad()
neg_log_prob_a = -log_prob_a
target_objective = comp_decay * advantage * neg_log_prob_a
target_objective.backward()
self._pi_optimizer.step()
def _train_baseline(self, target, baseline):
self._V_optimizer.zero_grad()
loss = self._loss_function(tt(np.array([target])), baseline)
loss.backward(retain_graph=True)
self._V_optimizer.step()
def save_models(self):
self._modelio.save(model=self._pi, model_name=self._policy_model_name)
self._modelio.save(model=self._V, model_name=self._baseline_model_name)
def load_models(self):
# if self._model
self._modelio.load(model=self._pi, model_name=self._policy_model_name)
self._modelio.load(model=self._V, model_name=self._baseline_model_name)
class DQN:
def __init__(self, state_dim, action_dim, gamma, d2c=None):
self._q = Q(state_dim, action_dim)
self._q_target = Q(state_dim, action_dim)
# self._q.cuda()
# self._q_target.cuda()
self._gamma = gamma
self._loss_function = nn.MSELoss()
self._q_optimizer = optim.Adam(self._q.parameters(), lr=0.0001)
self._action_dim = action_dim
self._replay_buffer = ReplayBuffer(5000)
self._d2c = d2c
# --- ModelIO ---
self._modelio = ModelIO(model_path=Path(__file__).resolve().parent /
'models')
self._q_model_name = 'q.pt'
self._target_model_name = 'target.pt'
def get_action(self, x, epsilon):
u = np.argmax(self._q(tt(x)).cpu().detach().numpy())
r = np.random.uniform()
if r < epsilon:
u = np.random.randint(self._action_dim)
if self._d2c:
u = self._d2c(u)
return u
def train(self, env, episodes, time_steps, epsilon):
stats = EpisodeStats(episode_lengths=np.zeros(episodes),
episode_rewards=np.zeros(episodes))
for i_episode in range(1, episodes + 1):
state = env.reset()
for t in range(time_steps):
action = self.get_action(state, epsilon)
next_state, reward, done, _ = env.step(action)
stats.episode_rewards[i_episode - 1] += reward
stats.episode_lengths[i_episode - 1] = t
# calculate priority of the transition (td-error)
q_s_a = self._q(tt(state)).cpu().detach().numpy()[int(
np.squeeze(action))]
target = reward + self._gamma * np.max(
self._q_target(tt(next_state)).cpu().detach().numpy())
priority = -abs(target - q_s_a) + np.random.randn() * 1e-2
# add the experience into the buffer
self._replay_buffer.add_transition(state=state,
action=action,
next_state=next_state,
reward=reward,
done=done,
priority=priority)
# sample a batch of experiences
samples = self._replay_buffer.next_batch(batch_size=64)
# update q network parameters
self._train_batch(samples)
# update target network periodically/slowly
soft_update(target=self._q_target, source=self._q, tau=0.01)
if done:
break
state = next_state
print(
f"{int(stats.episode_lengths[i_episode-1])} Steps in Episode {i_episode}/{episodes}. Reward {stats.episode_rewards[i_episode-1]}"
)
return stats
def _train_batch(self, batch):
states, actions, next_states, rewards, dones = batch
batch_size = len(rewards)
# calculating q(s,a)
batch_actions = np.array(actions).squeeze()
batch_qs = self._q(tt(np.array(states)))
batch_qs = batch_qs[np.arange(batch_size), batch_actions]
# calculating r + gamma * max_a' q(s', a')
targets = tt(np.array(rewards))
non_terminal_idx = np.array(dones)
batch_next_qs = self._q_target(tt(np.array(next_states)))
batch_max_next_qs, batch_argmax_next_qs = batch_next_qs.max(1)
targets[non_terminal_idx] = targets[
non_terminal_idx] + self._gamma * batch_max_next_qs[
non_terminal_idx]
self._q_optimizer.zero_grad()
loss = self._loss_function(batch_qs, targets)
loss.backward()
self._q_optimizer.step()
def save_models(self):
self._modelio.save(model=self._q, model_name=self._q_model_name)
self._modelio.save(model=self._q_target,
model_name=self._target_model_name)
def load_models(self):
# if self._model
self._modelio.load(model=self._q, model_name=self._q_model_name)
self._modelio.load(model=self._q_target,
model_name=self._target_model_name)
class PPO:
def __init__(self,
state_dim,
action_dim,
action_std=0.1,
gamma=0.99,
hidden_dim=64,
actor_lr=0.001,
critic_lr=0.001,
K_epochs=5,
eps_clip=0.2,
entropy_coeff=0.02,
verbose=False):
self.gamma = gamma
self.eps_clip = eps_clip
self.K_epochs = K_epochs
self.entropy_coeff = entropy_coeff
self.verbose = verbose
self.critic = Critic(state_dim, hidden_dim=hidden_dim).to(device)
self.actor = Actor(state_dim,
action_dim,
action_std=action_std,
hidden_dim=hidden_dim).to(device)
self.actor_old = Actor(state_dim,
action_dim,
action_std=action_std,
hidden_dim=hidden_dim).to(device)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),
lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
lr=critic_lr)
self.actor_old.load_state_dict(self.actor.state_dict())
self.buffer = Buffer()
# --- ModelIO ---
self.modelio = ModelIO(model_path=Path(__file__).resolve().parent /
'models')
def get_action(self, state):
"""
used for test time (not training)
"""
action, _ = self.actor_old(state)
action = torch.clamp(action, min=-1.0, max=1.0).detach().numpy()
return action
def train(self, env, episodes, timesteps, update_timestep):
stats = EpisodeStats(episode_lengths=np.zeros(episodes),
episode_rewards=np.zeros(episodes))
timestep = 0
for i_episode in range(1, episodes + 1):
state = env.reset()
for t in range(timesteps):
timestep += 1
# Running policy_old:
action, action_logprob = self.actor_old(state)
env_action = torch.clamp(action, min=-1.0,
max=1.0).detach().numpy()
next_state, reward, done, _ = env.step(env_action)
# saving stats
stats.episode_rewards[i_episode - 1] += reward
stats.episode_lengths[i_episode - 1] = t
# Saving the experience in buffer:
self.buffer.states.append(
torch.from_numpy(state).float().to(device))
self.buffer.actions.append(action)
self.buffer.logprobs.append(action_logprob)
self.buffer.rewards.append(reward)
self.buffer.is_terminals.append(done)
# update if its time
if timestep % update_timestep == 0:
self.update()
self.buffer.clear_buffer()
timestep = 0
if done:
break
state = next_state
# logging
if self.verbose:
if i_episode % 10 == 0:
print((
f"{int(stats.episode_lengths[i_episode - 1])} Steps in"
f"Episode {i_episode}/{episodes}. "
f"Reward {stats.episode_rewards[i_episode-1]}"))
else:
if i_episode % 1000 == 0:
print((
f"{int(stats.episode_lengths[i_episode - 1])} Steps in"
f"Episode {i_episode}/{episodes}. "
f"Reward {stats.episode_rewards[i_episode-1]}"))
return stats
def update(self):
# Monte Carlo estimate of the return over all steps
# (possibly across episodes):
rewards = np.zeros_like(self.buffer.rewards, dtype=np.float32)
discounted_reward = 0
for i, (reward, is_terminal) in enumerate(
zip(reversed(self.buffer.rewards),
reversed(self.buffer.is_terminals))):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + (self.gamma * discounted_reward)
rewards[-(i + 1)] = discounted_reward
# Normalizing the rewards:
rewards = torch.tensor(rewards).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)
# convert list to tensor
old_states = torch.stack(self.buffer.states).to(device).detach()
old_actions = torch.stack(self.buffer.actions).to(device).detach()
old_logprobs = torch.stack(self.buffer.logprobs).to(device).detach()
# Optimize policy for K epochs:
for _ in range(self.K_epochs):
# Evaluating old actions and values:
logprobs, dist_entropy = self.actor.evaluate(
old_states, old_actions)
# getting the state_values from the critic
state_values = self.critic(old_states)
# Finding the ratio (pi_theta / pi_theta__old):
ratios = torch.exp(logprobs - old_logprobs.detach())
# Finding Surrogate Loss:
advantages = rewards - state_values.detach()
surr1 = ratios * advantages
surr2 = torch.clamp(ratios, 1 - self.eps_clip,
1 + self.eps_clip) * advantages
actor_loss = -torch.min(surr1,
surr2) - self.entropy_coeff * dist_entropy
critic_loss = 0.5 * F.mse_loss(state_values, rewards)
# take gradient step (actor)
self.actor_optimizer.zero_grad()
actor_loss.mean().backward()
self.actor_optimizer.step()
# take gradient step (critic)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# Copy new weights into old policy:
self.actor_old.load_state_dict(self.actor.state_dict())
def save_models(self, model_name):
self.modelio.save(model=self.actor,
model_name=f'ppo_c_actor_{model_name}.pt')
self.modelio.save(model=self.critic,
model_name=f'ppo_c_critic_{model_name}.pt')
def load_models(self, model_name):
# if self._model
self.modelio.load(model=self.actor,
model_name=f'ppo_c_actor_{model_name}.pt')
self.modelio.load(model=self.actor_old,
model_name=f'ppo_c_actor_{model_name}.pt')
self.modelio.load(model=self.critic,
model_name=f'ppo_c_critic_{model_name}.pt')
mlb.fit(vect.transform(captions))
return vect, mlb
# if not multilabel:
return vect
dataset_name = 'coco_train2014'
n_sbu = None
if n_sbu:
dataset_name += "+sbu%d" % n_sbu
# global vectorizer
vect_name = 'tokenizer_%s' % dataset_name
mlb_name = 'mlb_%s' % dataset_name
try:
if mlb_name:
mlb = ModelIO.load(mlb_name)
print "MLB loaded from file"
vect = ModelIO.load(vect_name)
# vect = ModelIO.load('tokenizer_reddit') # gloveglove
print "Tokenizer loaded from file."
except:
if mlb_name:
vect, mlb = prepVect(n_sbu=n_sbu, n_captions=1, multilabel=True)
ModelIO.save(vect, vect_name)
ModelIO.save(mlb, mlb_name)
print "Saved %s, %s for future use." % (vect_name, mlb_name)
else:
vect = prepVect(n_sbu=n_sbu, n_captions=1)
ModelIO.save(vect, vect_name)
print "Saved %s for future use." % vect_name