def process_single_frame(prev_outputs, inputs):
"""Process a single frame of the video."""
cur_image, input_reward, action = inputs
time_step, prev_image, prev_reward, frame_buf, lstm_states = prev_outputs
generated_items = [prev_image]
groundtruth_items = [cur_image]
done_warm_start = tf.greater(time_step, context_frames - 1)
input_image, = self.get_scheduled_sample_inputs(
done_warm_start, groundtruth_items, generated_items, ss_func)
# Prediction
pred_image, lstm_states = self.construct_predictive_tower(
input_image, None, action, lstm_states, latent)
if self.hparams.reward_prediction:
reward_input_image = pred_image
if self.hparams.reward_prediction_stop_gradient:
reward_input_image = tf.stop_gradient(reward_input_image)
with tf.control_dependencies([time_step]):
frame_buf = [reward_input_image] + frame_buf[:-1]
pred_reward = self.reward_prediction(frame_buf, None, action,
latent)
pred_reward = common_video.decode_to_shape(
pred_reward, common_layers.shape_list(input_reward),
"reward_dec")
else:
pred_reward = prev_reward
time_step += 1
outputs = (time_step, pred_image, pred_reward, frame_buf,
lstm_states)
return outputs
def reward_prediction(self, input_image, input_reward, action, latent):
"""Builds a reward prediction network."""
del action
del latent
conv_size = self.tinyify([32, 32, 16, 4])
with tf.variable_scope("reward_pred", reuse=tf.AUTO_REUSE):
x = input_image
x = tfcl.batch_norm(x, updates_collections=None,
is_training=self.is_training, scope="reward_bn0")
x = tfl.conv2d(x, conv_size[1], [3, 3], strides=(2, 2),
padding="SAME", activation=tf.nn.relu, name="reward_conv1")
x = tfcl.batch_norm(x, updates_collections=None,
is_training=self.is_training, scope="reward_bn1")
x = tfl.conv2d(x, conv_size[2], [3, 3], strides=(2, 2),
padding="SAME", activation=tf.nn.relu, name="reward_conv2")
x = tfcl.batch_norm(x, updates_collections=None,
is_training=self.is_training, scope="reward_bn2")
x = tfl.conv2d(x, conv_size[3], [3, 3], strides=(2, 2),
padding="SAME", activation=tf.nn.relu, name="reward_conv3")
pred_reward = common_video.decode_to_shape(
x, input_reward.shape, "reward_dec")
return pred_reward
def process_single_frame(prev_outputs, inputs):
"""Process a single frame of the video."""
cur_image, input_reward, action = inputs
time_step, prev_image, prev_reward, frame_buf, lstm_states = prev_outputs
# sample from softmax (by argmax). this is noop for non-softmax loss.
prev_image = self.get_sampled_frame(prev_image)
generated_items = [prev_image]
groundtruth_items = [cur_image]
done_warm_start = tf.greater(time_step, context_frames - 1)
input_image, = self.get_scheduled_sample_inputs(
done_warm_start, groundtruth_items, generated_items, ss_func)
# Prediction
pred_image, lstm_states, _ = self.construct_predictive_tower(
input_image, None, action, lstm_states, latent)
if self.hparams.reward_prediction:
reward_input_image = self.get_sampled_frame(pred_image)
if self.hparams.reward_prediction_stop_gradient:
reward_input_image = tf.stop_gradient(reward_input_image)
with tf.control_dependencies([time_step]):
frame_buf = [reward_input_image] + frame_buf[:-1]
pred_reward = self.reward_prediction(frame_buf, None, action, latent)
pred_reward = common_video.decode_to_shape(
pred_reward, common_layers.shape_list(input_reward), "reward_dec")
else:
pred_reward = prev_reward
time_step += 1
outputs = (time_step, pred_image, pred_reward, frame_buf, lstm_states)
return outputs
def construct_model(self, images, actions, rewards):
images = tf.unstack(images, axis=0)
actions = tf.unstack(actions, axis=0)
rewards = tf.unstack(rewards, axis=0)
batch_size = common_layers.shape_list(images[0])[0]
context_frames = self.hparams.video_num_input_frames
# Predicted images and rewards.
gen_rewards, gen_images, latent_means, latent_stds = [], [], [], []
# LSTM states.
lstm_state = [None] * 7
# Create scheduled sampling function
ss_func = self.get_scheduled_sample_func(batch_size)
pred_image = tf.zeros_like(images[0])
pred_reward = tf.zeros_like(rewards[0])
latent = None
for timestep, image, action, reward in zip(range(len(images) - 1),
images[:-1], actions[:-1],
rewards[:-1]):
# Scheduled Sampling
done_warm_start = timestep > context_frames - 1
groundtruth_items = [image, reward]
generated_items = [pred_image, pred_reward]
input_image, input_reward = self.get_scheduled_sample_inputs(
done_warm_start, groundtruth_items, generated_items, ss_func)
# Latent
# TODO(mbz): should we use input_image iunstead of image?
latent_images = tf.stack([image, images[timestep + 1]], axis=0)
latent_mean, latent_std = self.construct_latent_tower(
latent_images, time_axis=0)
latent = common_video.get_gaussian_tensor(latent_mean, latent_std)
latent_means.append(latent_mean)
latent_stds.append(latent_std)
# Prediction
pred_image, lstm_state = self.construct_predictive_tower(
input_image, input_reward, action, lstm_state, latent)
if self.hparams.reward_prediction:
pred_reward = self.reward_prediction(pred_image, input_reward,
action, latent)
pred_reward = common_video.decode_to_shape(
pred_reward, common_layers.shape_list(input_reward),
"reward_dec")
else:
pred_reward = input_reward
gen_images.append(pred_image)
gen_rewards.append(pred_reward)
gen_images = tf.stack(gen_images, axis=0)
gen_rewards = tf.stack(gen_rewards, axis=0)
return gen_images, gen_rewards, latent_means, latent_stds
def construct_model(self, images, actions, rewards):
images = tf.unstack(images, axis=0)
actions = tf.unstack(actions, axis=0)
rewards = tf.unstack(rewards, axis=0)
batch_size = common_layers.shape_list(images[0])[0]
context_frames = self.hparams.video_num_input_frames
# Predicted images and rewards.
gen_rewards, gen_images, latent_means, latent_stds = [], [], [], []
# LSTM states.
lstm_state = [None] * 7
# Create scheduled sampling function
ss_func = self.get_scheduled_sample_func(batch_size)
pred_image = tf.zeros_like(images[0])
pred_reward = tf.zeros_like(rewards[0])
latent = None
for timestep, image, action, reward in zip(
range(len(images)-1), images[:-1], actions[:-1], rewards[:-1]):
# Scheduled Sampling
done_warm_start = timestep > context_frames - 1
groundtruth_items = [image, reward]
generated_items = [pred_image, pred_reward]
input_image, input_reward = self.get_scheduled_sample_inputs(
done_warm_start, groundtruth_items, generated_items, ss_func)
# Latent
# TODO(mbz): should we use input_image iunstead of image?
latent_images = tf.stack([image, images[timestep+1]], axis=0)
latent_mean, latent_std = self.construct_latent_tower(
latent_images, time_axis=0)
latent = common_video.get_gaussian_tensor(latent_mean, latent_std)
latent_means.append(latent_mean)
latent_stds.append(latent_std)
# Prediction
pred_image, lstm_state, _ = self.construct_predictive_tower(
input_image, input_reward, action, lstm_state, latent)
if self.hparams.reward_prediction:
pred_reward = self.reward_prediction(
pred_image, input_reward, action, latent)
pred_reward = common_video.decode_to_shape(
pred_reward, common_layers.shape_list(input_reward), "reward_dec")
else:
pred_reward = input_reward
gen_images.append(pred_image)
gen_rewards.append(pred_reward)
gen_images = tf.stack(gen_images, axis=0)
gen_rewards = tf.stack(gen_rewards, axis=0)
return gen_images, gen_rewards, latent_means, latent_stds