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prediction_input.py
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prediction_input.py
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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Code for building the input for the prediction model."""
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import flags
from tensorflow.python.platform import gfile
FLAGS = flags.FLAGS
# Original image dimensions
ORIGINAL_WIDTH = 640
ORIGINAL_HEIGHT = 512
COLOR_CHAN = 3
# Default image dimensions.
IMG_WIDTH = 64
IMG_HEIGHT = 64
# Dimension of the state and action.
STATE_DIM = 5
def build_tfrecord_input(training=True):
"""Create input tfrecord tensors.
Args:
training: training or validation data.
Returns:
list of tensors corresponding to images, actions, and states. The images
tensor is 5D, batch x time x height x width x channels. The state and
action tensors are 3D, batch x time x dimension.
Raises:
RuntimeError: if no files found.
"""
filenames = gfile.Glob(os.path.join(FLAGS.data_dir, '*'))
if not filenames:
raise RuntimeError('No data files found.')
index = int(np.floor(FLAGS.train_val_split * len(filenames)))
if training:
filenames = filenames[:index]
else:
filenames = filenames[index:]
filename_queue = tf.train.string_input_producer(filenames, shuffle=True)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
image_seq, state_seq, action_seq = [], [], []
for i in range(FLAGS.sequence_length):
image_name = 'move/' + str(i) + '/image/encoded'
action_name = 'move/' + str(i) + '/commanded_pose/vec_pitch_yaw'
state_name = 'move/' + str(i) + '/endeffector/vec_pitch_yaw'
if FLAGS.use_state:
features = {image_name: tf.FixedLenFeature([1], tf.string),
action_name: tf.FixedLenFeature([STATE_DIM], tf.float32),
state_name: tf.FixedLenFeature([STATE_DIM], tf.float32)}
else:
features = {image_name: tf.FixedLenFeature([1], tf.string)}
features = tf.parse_single_example(serialized_example, features=features)
image_buffer = tf.reshape(features[image_name], shape=[])
image = tf.image.decode_jpeg(image_buffer, channels=COLOR_CHAN)
image.set_shape([ORIGINAL_HEIGHT, ORIGINAL_WIDTH, COLOR_CHAN])
if IMG_HEIGHT != IMG_WIDTH:
raise ValueError('Unequal height and width unsupported')
crop_size = min(ORIGINAL_HEIGHT, ORIGINAL_WIDTH)
image = tf.image.resize_image_with_crop_or_pad(image, crop_size, crop_size)
image = tf.reshape(image, [1, crop_size, crop_size, COLOR_CHAN])
image = tf.image.resize_bicubic(image, [IMG_HEIGHT, IMG_WIDTH])
image = tf.cast(image, tf.float32) / 255.0
image_seq.append(image)
if FLAGS.use_state:
state = tf.reshape(features[state_name], shape=[1, STATE_DIM])
state_seq.append(state)
action = tf.reshape(features[action_name], shape=[1, STATE_DIM])
action_seq.append(action)
image_seq = tf.concat(0, image_seq)
if FLAGS.use_state:
state_seq = tf.concat(0, state_seq)
action_seq = tf.concat(0, action_seq)
[image_batch, action_batch, state_batch] = tf.train.batch(
[image_seq, action_seq, state_seq],
FLAGS.batch_size,
num_threads=FLAGS.batch_size,
capacity=100 * FLAGS.batch_size)
return image_batch, action_batch, state_batch
else:
image_batch = tf.train.batch(
[image_seq],
FLAGS.batch_size,
num_threads=FLAGS.batch_size,
capacity=100 * FLAGS.batch_size)
zeros_batch = tf.zeros([FLAGS.batch_size, FLAGS.sequence_length, STATE_DIM])
return image_batch, zeros_batch, zeros_batch