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Unverified Commit 6b9d5fba authored by Toby Boyd's avatar Toby Boyd Committed by GitHub
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Merge branch 'master' into patch-1

parents 5fd687c5 5fa2a4e6
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Showing with 191 additions and 103 deletions
research/pcl_rl/full_episode_objective.py
View file @ 6b9d5fba
......@@ -42,7 +42,8 @@ class Reinforce(objective.Objective):
def get(self, rewards, pads, values, final_values,
log_probs, prev_log_probs, target_log_probs,
entropies, logits):
entropies, logits,
target_values, final_target_values):
seq_length = tf.shape(rewards)[0]
not_pad = tf.reshape(1 - pads, [seq_length, -1, self.num_samples])
......
research/pcl_rl/gym_wrapper.py
View file @ 6b9d5fba
......@@ -22,6 +22,7 @@ import gym
import numpy as np
import random
from six.moves import xrange
import env_spec
......@@ -92,14 +93,14 @@ class GymWrapper(object):
def step(self, actions):
def env_step(action):
def env_step(env, action):
action = self.env_spec.convert_action_to_gym(action)
obs, reward, done, tt = env.step(action)
obs = self.env_spec.convert_obs_to_list(obs)
return obs, reward, done, tt
actions = zip(*actions)
outputs = [env_step(action)
outputs = [env_step(env, action)
if not done else (self.env_spec.initial_obs(None), 0, True, None)
for action, env, done in zip(actions, self.envs, self.dones)]
for i, (_, _, done, _) in enumerate(outputs):
......
research/pcl_rl/model.py
View file @ 6b9d5fba
......@@ -57,6 +57,8 @@ class Model(object):
# summary placeholder
self.avg_episode_reward = tf.placeholder(
tf.float32, [], 'avg_episode_reward')
self.greedy_episode_reward = tf.placeholder(
tf.float32, [], 'greedy_episode_reward')
# sampling placeholders
self.internal_state = tf.placeholder(tf.float32,
......@@ -118,12 +120,13 @@ class Model(object):
self.prev_log_probs = tf.placeholder(tf.float32, [None, None],
'prev_log_probs')
def setup(self):
def setup(self, train=True):
"""Setup Tensorflow Graph."""
self.setup_placeholders()
tf.summary.scalar('avg_episode_reward', self.avg_episode_reward)
tf.summary.scalar('greedy_episode_reward', self.greedy_episode_reward)
with tf.variable_scope('model', reuse=None):
# policy network
......@@ -174,45 +177,46 @@ class Model(object):
target_p.assign(aa * target_p + (1 - aa) * online_p)
for online_p, target_p in zip(online_vars, target_vars)])
# evaluate objective
(self.loss, self.raw_loss, self.regression_target,
self.gradient_ops, self.summary) = self.objective.get(
self.rewards, self.pads,
self.values[:-1, :],
self.values[-1, :] * (1 - self.terminated),
self.log_probs, self.prev_log_probs, self.target_log_probs,
self.entropies,
self.logits)
self.regression_target = tf.reshape(self.regression_target, [-1])
self.policy_vars = [
v for v in tf.trainable_variables()
if '/policy_net' in v.name]
self.value_vars = [
v for v in tf.trainable_variables()
if '/value_net' in v.name]
# trust region optimizer
if self.trust_region_policy_opt is not None:
with tf.variable_scope('trust_region_policy', reuse=None):
avg_self_kl = (
tf.reduce_sum(sum(self.self_kls) * (1 - self.pads)) /
tf.reduce_sum(1 - self.pads))
self.trust_region_policy_opt.setup(
self.policy_vars, self.raw_loss, avg_self_kl,
self.avg_kl)
# value optimizer
if self.value_opt is not None:
with tf.variable_scope('trust_region_value', reuse=None):
self.value_opt.setup(
self.value_vars,
tf.reshape(self.values[:-1, :], [-1]),
self.regression_target,
tf.reshape(self.pads, [-1]),
self.regression_input, self.regression_weight)
if train:
# evaluate objective
(self.loss, self.raw_loss, self.regression_target,
self.gradient_ops, self.summary) = self.objective.get(
self.rewards, self.pads,
self.values[:-1, :],
self.values[-1, :] * (1 - self.terminated),
self.log_probs, self.prev_log_probs, self.target_log_probs,
self.entropies, self.logits, self.target_values[:-1, :],
self.target_values[-1, :] * (1 - self.terminated))
self.regression_target = tf.reshape(self.regression_target, [-1])
self.policy_vars = [
v for v in tf.trainable_variables()
if '/policy_net' in v.name]
self.value_vars = [
v for v in tf.trainable_variables()
if '/value_net' in v.name]
# trust region optimizer
if self.trust_region_policy_opt is not None:
with tf.variable_scope('trust_region_policy', reuse=None):
avg_self_kl = (
tf.reduce_sum(sum(self.self_kls) * (1 - self.pads)) /
tf.reduce_sum(1 - self.pads))
self.trust_region_policy_opt.setup(
self.policy_vars, self.raw_loss, avg_self_kl,
self.avg_kl)
# value optimizer
if self.value_opt is not None:
with tf.variable_scope('trust_region_value', reuse=None):
self.value_opt.setup(
self.value_vars,
tf.reshape(self.values[:-1, :], [-1]),
self.regression_target,
tf.reshape(self.pads, [-1]),
self.regression_input, self.regression_weight)
# we re-use variables for the sampling operations
with tf.variable_scope('model', reuse=True):
......@@ -249,32 +253,42 @@ class Model(object):
def train_step(self, sess,
observations, internal_state, actions,
rewards, terminated, pads,
avg_episode_reward=0):
avg_episode_reward=0, greedy_episode_reward=0):
"""Train network using standard gradient descent."""
outputs = [self.raw_loss, self.gradient_ops, self.summary]
feed_dict = {self.internal_state: internal_state,
self.rewards: rewards,
self.terminated: terminated,
self.pads: pads,
self.avg_episode_reward: avg_episode_reward}
self.avg_episode_reward: avg_episode_reward,
self.greedy_episode_reward: greedy_episode_reward}
time_len = None
for action_place, action in zip(self.actions, actions):
if time_len is None:
time_len = len(action)
assert time_len == len(action)
feed_dict[action_place] = action
for obs_place, obs in zip(self.observations, observations):
assert time_len == len(obs)
feed_dict[obs_place] = obs
assert len(rewards) == time_len - 1
return sess.run(outputs, feed_dict=feed_dict)
def trust_region_step(self, sess,
observations, internal_state, actions,
rewards, terminated, pads,
avg_episode_reward=0):
avg_episode_reward=0,
greedy_episode_reward=0):
"""Train policy using trust region step."""
feed_dict = {self.internal_state: internal_state,
self.rewards: rewards,
self.terminated: terminated,
self.pads: pads,
self.avg_episode_reward: avg_episode_reward}
self.avg_episode_reward: avg_episode_reward,
self.greedy_episode_reward: greedy_episode_reward}
for action_place, action in zip(self.actions, actions):
feed_dict[action_place] = action
for obs_place, obs in zip(self.observations, observations):
......
research/pcl_rl/objective.py
View file @ 6b9d5fba
......@@ -46,7 +46,8 @@ class Objective(object):
def get(self, rewards, pads, values, final_values,
log_probs, prev_log_probs, target_log_probs,
entropies, logits):
entropies, logits,
target_values, final_target_values):
"""Get objective calculations."""
raise NotImplementedError()
......@@ -101,7 +102,8 @@ class ActorCritic(Objective):
def __init__(self, learning_rate, clip_norm=5,
policy_weight=1.0, critic_weight=0.1,
tau=0.1, gamma=1.0, rollout=10,
eps_lambda=0.0, clip_adv=None):
eps_lambda=0.0, clip_adv=None,
use_target_values=False):
super(ActorCritic, self).__init__(learning_rate, clip_norm=clip_norm)
self.policy_weight = policy_weight
self.critic_weight = critic_weight
......@@ -111,14 +113,17 @@ class ActorCritic(Objective):
self.clip_adv = clip_adv
self.eps_lambda = tf.get_variable( # TODO: need a better way
'eps_lambda', [], initializer=tf.constant_initializer(eps_lambda))
'eps_lambda', [], initializer=tf.constant_initializer(eps_lambda),
trainable=False)
self.new_eps_lambda = tf.placeholder(tf.float32, [])
self.assign_eps_lambda = self.eps_lambda.assign(
0.95 * self.eps_lambda + 0.05 * self.new_eps_lambda)
0.99 * self.eps_lambda + 0.01 * self.new_eps_lambda)
self.use_target_values = use_target_values
def get(self, rewards, pads, values, final_values,
log_probs, prev_log_probs, target_log_probs,
entropies, logits):
entropies, logits,
target_values, final_target_values):
not_pad = 1 - pads
batch_size = tf.shape(rewards)[1]
......@@ -126,10 +131,17 @@ class ActorCritic(Objective):
rewards = not_pad * rewards
value_estimates = not_pad * values
log_probs = not_pad * sum(log_probs)
target_values = not_pad * tf.stop_gradient(target_values)
final_target_values = tf.stop_gradient(final_target_values)
sum_rewards = discounted_future_sum(rewards, self.gamma, self.rollout)
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
if self.use_target_values:
last_values = shift_values(
target_values, self.gamma, self.rollout,
final_target_values)
else:
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
future_values = sum_rewards + last_values
baseline_values = value_estimates
......@@ -183,7 +195,8 @@ class PCL(ActorCritic):
def get(self, rewards, pads, values, final_values,
log_probs, prev_log_probs, target_log_probs,
entropies, logits):
entropies, logits,
target_values, final_target_values):
not_pad = 1 - pads
batch_size = tf.shape(rewards)[1]
......@@ -192,6 +205,8 @@ class PCL(ActorCritic):
log_probs = not_pad * sum(log_probs)
target_log_probs = not_pad * tf.stop_gradient(sum(target_log_probs))
relative_log_probs = not_pad * (log_probs - target_log_probs)
target_values = not_pad * tf.stop_gradient(target_values)
final_target_values = tf.stop_gradient(final_target_values)
# Prepend.
not_pad = tf.concat([tf.ones([self.rollout - 1, batch_size]),
......@@ -210,14 +225,26 @@ class PCL(ActorCritic):
prev_log_probs], 0)
relative_log_probs = tf.concat([tf.zeros([self.rollout - 1, batch_size]),
relative_log_probs], 0)
target_values = tf.concat(
[self.gamma ** tf.expand_dims(
tf.range(float(self.rollout - 1), 0, -1), 1) *
tf.ones([self.rollout - 1, batch_size]) *
target_values[0:1, :],
target_values], 0)
sum_rewards = discounted_future_sum(rewards, self.gamma, self.rollout)
sum_log_probs = discounted_future_sum(log_probs, self.gamma, self.rollout)
sum_prev_log_probs = discounted_future_sum(prev_log_probs, self.gamma, self.rollout)
sum_relative_log_probs = discounted_future_sum(
relative_log_probs, self.gamma, self.rollout)
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
if self.use_target_values:
last_values = shift_values(
target_values, self.gamma, self.rollout,
final_target_values)
else:
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
future_values = (
- self.tau * sum_log_probs
......@@ -272,7 +299,8 @@ class TRPO(ActorCritic):
def get(self, rewards, pads, values, final_values,
log_probs, prev_log_probs, target_log_probs,
entropies, logits):
entropies, logits,
target_values, final_target_values):
not_pad = 1 - pads
batch_size = tf.shape(rewards)[1]
......@@ -280,10 +308,18 @@ class TRPO(ActorCritic):
value_estimates = not_pad * values
log_probs = not_pad * sum(log_probs)
prev_log_probs = not_pad * prev_log_probs
target_values = not_pad * tf.stop_gradient(target_values)
final_target_values = tf.stop_gradient(final_target_values)
sum_rewards = discounted_future_sum(rewards, self.gamma, self.rollout)
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
if self.use_target_values:
last_values = shift_values(
target_values, self.gamma, self.rollout,
final_target_values)
else:
last_values = shift_values(value_estimates, self.gamma, self.rollout,
final_values)
future_values = sum_rewards + last_values
baseline_values = value_estimates
......
research/pcl_rl/optimizers.py
View file @ 6b9d5fba
......@@ -25,6 +25,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
import tensorflow as tf
import numpy as np
import scipy.optimize
......
research/pcl_rl/replay_buffer.py
View file @ 6b9d5fba
......@@ -20,6 +20,7 @@ Implements replay buffer in Python.
import random
import numpy as np
from six.moves import xrange
class ReplayBuffer(object):
......@@ -150,7 +151,7 @@ class PrioritizedReplayBuffer(ReplayBuffer):
def get_batch(self, n):
"""Get batch of episodes to train on."""
p = self.sampling_distribution()
idxs = np.random.choice(self.cur_size, size=n, replace=False, p=p)
idxs = np.random.choice(self.cur_size, size=int(n), replace=False, p=p)
self.last_batch = idxs
return [self.buffer[idx] for idx in idxs], p[idxs]
......
research/pcl_rl/trainer.py
View file @ 6b9d5fba
......@@ -25,6 +25,7 @@ import random
import os
import pickle
from six.moves import xrange
import controller
import model
import policy
......@@ -92,6 +93,8 @@ flags.DEFINE_bool('update_eps_lambda', False,
'Update lambda automatically based on last 100 episodes.')
flags.DEFINE_float('gamma', 1.0, 'discount')
flags.DEFINE_integer('rollout', 10, 'rollout')
flags.DEFINE_bool('use_target_values', False,
'use target network for value estimates')
flags.DEFINE_bool('fixed_std', True,
'fix the std in Gaussian distributions')
flags.DEFINE_bool('input_prev_actions', True,
......@@ -152,6 +155,10 @@ class Trainer(object):
self.env = gym_wrapper.GymWrapper(self.env_str,
distinct=FLAGS.batch_size // self.num_samples,
count=self.num_samples)
self.eval_env = gym_wrapper.GymWrapper(
self.env_str,
distinct=FLAGS.batch_size // self.num_samples,
count=self.num_samples)
self.env_spec = env_spec.EnvSpec(self.env.get_one())
self.max_step = FLAGS.max_step
......@@ -169,7 +176,8 @@ class Trainer(object):
self.value_opt = FLAGS.value_opt
assert not self.trust_region_p or self.objective in ['pcl', 'trpo']
assert self.objective != 'trpo' or self.trust_region_p
assert self.value_opt is None or self.critic_weight == 0.0
assert self.value_opt is None or self.value_opt == 'None' or \
self.critic_weight == 0.0
self.max_divergence = FLAGS.max_divergence
self.learning_rate = FLAGS.learning_rate
......@@ -182,6 +190,7 @@ class Trainer(object):
self.update_eps_lambda = FLAGS.update_eps_lambda
self.gamma = FLAGS.gamma
self.rollout = FLAGS.rollout
self.use_target_values = FLAGS.use_target_values
self.fixed_std = FLAGS.fixed_std
self.input_prev_actions = FLAGS.input_prev_actions
self.recurrent = FLAGS.recurrent
......@@ -208,8 +217,7 @@ class Trainer(object):
self.value_hidden_layers = FLAGS.value_hidden_layers
self.tf_seed = FLAGS.tf_seed
self.save_trajectories_dir = (
FLAGS.save_trajectories_dir or FLAGS.save_dir)
self.save_trajectories_dir = FLAGS.save_trajectories_dir
self.save_trajectories_file = (
os.path.join(
self.save_trajectories_dir, self.env_str.replace('-', '_'))
......@@ -244,7 +252,8 @@ class Trainer(object):
policy_weight=policy_weight,
critic_weight=self.critic_weight,
tau=tau, gamma=self.gamma, rollout=self.rollout,
eps_lambda=self.eps_lambda, clip_adv=self.clip_adv)
eps_lambda=self.eps_lambda, clip_adv=self.clip_adv,
use_target_values=self.use_target_values)
elif self.objective in ['reinforce', 'urex']:
cls = (full_episode_objective.Reinforce
if self.objective == 'reinforce' else
......@@ -322,10 +331,10 @@ class Trainer(object):
self.num_expert_paths, self.env_str, self.env_spec,
load_trajectories_file=self.load_trajectories_file)
def get_controller(self):
def get_controller(self, env):
"""Get controller."""
cls = controller.Controller
return cls(self.env, self.env_spec, self.internal_dim,
return cls(env, self.env_spec, self.internal_dim,
use_online_batch=self.use_online_batch,
batch_by_steps=self.batch_by_steps,
unify_episodes=self.unify_episodes,
......@@ -334,7 +343,7 @@ class Trainer(object):
cutoff_agent=self.cutoff_agent,
save_trajectories_file=self.save_trajectories_file,
use_trust_region=self.trust_region_p,
use_value_opt=self.value_opt is not None,
use_value_opt=self.value_opt not in [None, 'None'],
update_eps_lambda=self.update_eps_lambda,
prioritize_by=self.prioritize_by,
get_model=self.get_model,
......@@ -359,16 +368,19 @@ class Trainer(object):
saver.restore(sess, ckpt.model_checkpoint_path)
elif FLAGS.load_path:
logging.info('restoring from %s', FLAGS.load_path)
with gfile.AsUser('distbelief-brain-gpu'):
saver.restore(sess, FLAGS.load_path)
saver.restore(sess, FLAGS.load_path)
if FLAGS.supervisor:
with tf.device(tf.ReplicaDeviceSetter(FLAGS.ps_tasks, merge_devices=True)):
self.global_step = tf.train.get_or_create_global_step()
self.global_step = tf.contrib.framework.get_or_create_global_step()
tf.set_random_seed(FLAGS.tf_seed)
self.controller = self.get_controller()
self.controller = self.get_controller(self.env)
self.model = self.controller.model
self.controller.setup()
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
self.eval_controller = self.get_controller(self.eval_env)
self.eval_controller.setup(train=False)
saver = tf.train.Saver(max_to_keep=10)
step = self.model.global_step
sv = tf.Supervisor(logdir=FLAGS.save_dir,
......@@ -382,10 +394,14 @@ class Trainer(object):
sess = sv.PrepareSession(FLAGS.master)
else:
tf.set_random_seed(FLAGS.tf_seed)
self.global_step = tf.train.get_or_create_global_step()
self.controller = self.get_controller()
self.global_step = tf.contrib.framework.get_or_create_global_step()
self.controller = self.get_controller(self.env)
self.model = self.controller.model
self.controller.setup()
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
self.eval_controller = self.get_controller(self.eval_env)
self.eval_controller.setup(train=False)
saver = tf.train.Saver(max_to_keep=10)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
......@@ -414,21 +430,25 @@ class Trainer(object):
(loss, summary,
total_rewards, episode_rewards) = self.controller.train(sess)
_, greedy_episode_rewards = self.eval_controller.eval(sess)
self.controller.greedy_episode_rewards = greedy_episode_rewards
losses.append(loss)
rewards.append(total_rewards)
all_ep_rewards.extend(episode_rewards)
if random.random() < 1 and is_chief and sv and sv._summary_writer:
if (random.random() < 0.1 and summary and episode_rewards and
is_chief and sv and sv._summary_writer):
sv.summary_computed(sess, summary)
model_step = sess.run(self.model.global_step)
if is_chief and step % self.validation_frequency == 0:
logging.info('at training step %d, model step %d: '
'avg loss %f, avg reward %f, '
'episode rewards: %f',
'episode rewards: %f, greedy rewards: %f',
step, model_step,
np.mean(losses), np.mean(rewards),
np.mean(all_ep_rewards))
np.mean(all_ep_rewards),
np.mean(greedy_episode_rewards))
losses = []
rewards = []
......
research/pcl_rl/trust_region.py
View file @ 6b9d5fba
......@@ -24,6 +24,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
import tensorflow as tf
import numpy as np
......
research/ptn/metrics.py
View file @ 6b9d5fba
......@@ -19,6 +19,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
import tensorflow as tf
slim = tf.contrib.slim
......@@ -108,5 +109,3 @@ def add_volume_iou_metrics(inputs, outputs):
names_to_values['volume_iou'] = tmp_values * 3.0
names_to_updates['volume_iou'] = tmp_updates
return names_to_values, names_to_updates
research/ptn/model_ptn.py
View file @ 6b9d5fba
......@@ -21,6 +21,7 @@ from __future__ import print_function
import os
import numpy as np
from six.moves import xrange
import tensorflow as tf
import losses
......
research/ptn/model_rotator.py
View file @ 6b9d5fba
......@@ -21,6 +21,7 @@ from __future__ import print_function
import os
import numpy as np
from six.moves import xrange
import tensorflow as tf
import input_generator
......@@ -191,19 +192,19 @@ def get_train_op_for_scope(loss, optimizer, scopes, params):
def get_metrics(inputs, outputs, params):
"""Aggregate the metrics for rotator model.
Args:
inputs: Input dictionary of the rotator model.
outputs: Output dictionary returned by the rotator model.
params: Hyperparameters of the rotator model.
Returns:
names_to_values: metrics->values (dict).
names_to_updates: metrics->ops (dict).
"""
names_to_values = dict()
names_to_updates = dict()
tmp_values, tmp_updates = metrics.add_image_pred_metrics(
inputs, outputs, params.num_views, 3*params.image_size**2)
names_to_values.update(tmp_values)
......@@ -217,7 +218,7 @@ def get_metrics(inputs, outputs, params):
for name, value in names_to_values.iteritems():
slim.summaries.add_scalar_summary(
value, name, prefix='eval', print_summary=True)
return names_to_values, names_to_updates
......
research/ptn/model_voxel_generation.py
View file @ 6b9d5fba
......@@ -22,6 +22,7 @@ import abc
import os
import numpy as np
from six.moves import xrange
import tensorflow as tf
import input_generator
......
research/ptn/pretrain_rotator.py
View file @ 6b9d5fba
......@@ -21,6 +21,7 @@ from __future__ import print_function
import os
import numpy as np
from six.moves import xrange
import tensorflow as tf
from tensorflow import app
......
research/ptn/utils.py
View file @ 6b9d5fba
......@@ -28,6 +28,7 @@ from mpl_toolkits.mplot3d import axes3d as p3 # pylint:disable=unused-import
import numpy as np
from PIL import Image
from skimage import measure
from six.moves import xrange
import tensorflow as tf
......@@ -116,4 +117,3 @@ def visualize_voxel_scatter(points, vis_size=128):
vis_size, vis_size, 3)
p.close('all')
return data
research/real_nvp/celeba_formatting.py
View file @ 6b9d5fba
......@@ -30,6 +30,8 @@ python celeba_formatting.py \
"""
from __future__ import print_function
import os
import os.path
......@@ -70,7 +72,7 @@ def main():
writer = tf.python_io.TFRecordWriter(file_out)
for example_idx, img_fn in enumerate(img_fn_list):
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
research/real_nvp/imnet_formatting.py
View file @ 6b9d5fba
......@@ -34,6 +34,8 @@ done
"""
from __future__ import print_function
import os
import os.path
......@@ -73,10 +75,10 @@ def main():
file_out = "%s_%05d.tfrecords"
file_out = file_out % (FLAGS.file_out,
example_idx // n_examples_per_file)
print "Writing on:", file_out
print("Writing on:", file_out)
writer = tf.python_io.TFRecordWriter(file_out)
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = scipy.ndimage.imread(os.path.join(fn_root, img_fn))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
research/real_nvp/lsun_formatting.py
View file @ 6b9d5fba
......@@ -29,6 +29,7 @@ python lsun_formatting.py \
--fn_root [LSUN_FOLDER]
"""
from __future__ import print_function
import os
import os.path
......@@ -68,10 +69,10 @@ def main():
file_out = "%s_%05d.tfrecords"
file_out = file_out % (FLAGS.file_out,
example_idx // n_examples_per_file)
print "Writing on:", file_out
print("Writing on:", file_out)
writer = tf.python_io.TFRecordWriter(file_out)
if example_idx % 1000 == 0:
print example_idx, "/", num_examples
print(example_idx, "/", num_examples)
image_raw = numpy.array(Image.open(os.path.join(fn_root, img_fn)))
rows = image_raw.shape[0]
cols = image_raw.shape[1]
......
research/real_nvp/real_nvp_multiscale_dataset.py
View file @ 6b9d5fba
......@@ -23,11 +23,14 @@ $ python real_nvp_multiscale_dataset.py \
--data_path [DATA_PATH]
"""
from __future__ import print_function
import time
from datetime import datetime
import os
import numpy
from six.moves import xrange
import tensorflow as tf
from tensorflow import gfile
......@@ -1435,10 +1438,10 @@ class RealNVP(object):
n_equal = int(n_equal)
n_dash = bar_len - n_equal
progress_bar = "[" + "=" * n_equal + "-" * n_dash + "]\r"
print progress_bar,
print(progress_bar, end=' ')
cost = self.bit_per_dim.eval()
eval_costs.append(cost)
print ""
print("")
return float(numpy.mean(eval_costs))
......@@ -1467,7 +1470,7 @@ def train_model(hps, logdir):
ckpt_state = tf.train.get_checkpoint_state(logdir)
if ckpt_state and ckpt_state.model_checkpoint_path:
print "Loading file %s" % ckpt_state.model_checkpoint_path
print("Loading file %s" % ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
# Start the queue runners.
......@@ -1499,8 +1502,8 @@ def train_model(hps, logdir):
format_str = ('%s: step %d, loss = %.2f '
'(%.1f examples/sec; %.3f '
'sec/batch)')
print format_str % (datetime.now(), global_step_val, loss,
examples_per_sec, duration)
print(format_str % (datetime.now(), global_step_val, loss,
examples_per_sec, duration))
if should_eval_summaries:
summary_str = outputs[-1]
......@@ -1542,24 +1545,24 @@ def evaluate(hps, logdir, traindir, subset="valid", return_val=False):
while True:
ckpt_state = tf.train.get_checkpoint_state(traindir)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
print "No model to eval yet at %s" % traindir
print("No model to eval yet at %s" % traindir)
time.sleep(30)
continue
print "Loading file %s" % ckpt_state.model_checkpoint_path
print("Loading file %s" % ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
current_step = tf.train.global_step(sess, eval_model.step)
if current_step == previous_global_step:
print "Waiting for the checkpoint to be updated."
print("Waiting for the checkpoint to be updated.")
time.sleep(30)
continue
previous_global_step = current_step
print "Evaluating..."
print("Evaluating...")
bit_per_dim = eval_model.eval_epoch(hps)
print ("Epoch: %d, %s -> %.3f bits/dim"
% (current_step, subset, bit_per_dim))
print "Writing summary..."
print("Epoch: %d, %s -> %.3f bits/dim"
% (current_step, subset, bit_per_dim))
print("Writing summary...")
summary = tf.Summary()
summary.value.extend(
[tf.Summary.Value(
......@@ -1597,7 +1600,7 @@ def sample_from_model(hps, logdir, traindir):
ckpt_state = tf.train.get_checkpoint_state(traindir)
if not (ckpt_state and ckpt_state.model_checkpoint_path):
if not initialized:
print "No model to eval yet at %s" % traindir
print("No model to eval yet at %s" % traindir)
time.sleep(30)
continue
else:
......@@ -1607,7 +1610,7 @@ def sample_from_model(hps, logdir, traindir):
current_step = tf.train.global_step(sess, eval_model.step)
if current_step == previous_global_step:
print "Waiting for the checkpoint to be updated."
print("Waiting for the checkpoint to be updated.")
time.sleep(30)
continue
previous_global_step = current_step
......
research/real_nvp/real_nvp_utils.py
View file @ 6b9d5fba
......@@ -19,6 +19,7 @@ r"""Utility functions for Real NVP.
# pylint: disable=dangerous-default-value
import numpy
from six.moves import xrange
import tensorflow as tf
from tensorflow.python.framework import ops
......
research/slim/datasets/build_imagenet_data.py
View file @ 6b9d5fba
......@@ -94,6 +94,7 @@ import threading
import google3
import numpy as np
from six.moves import xrange
import tensorflow as tf
tf.app.flags.DEFINE_string('train_directory', '/tmp/',
......
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