def build_inputs(self):
if self.mode=="inference":
image_feed=tf.placeholder(dtype=tf.string,shape=[],name="image_feed")
word_prob_feed=tf.placeholder(dtype=tf.float32,shape=[319],name="word_prob_feed")
input_feed=tf.placeholder(dtype=tf.int64,shape=[None],name="input_feed")
images=tf.expand_dims(self.process_image(image_feed),0)
word_prob=tf.expand_dims(word_prob_feed,0)
input_seqs=tf.expand_dims(input_feed,1)
target_seqs=None
input_mask=None
else:
input_queue=input_ops.prefetch_input_data(self.reader,self.config.input_file_pattern,is_training=self.is_training(),batch_size=self.config.batch_size,values_per_shard=self.config.values_per_input_shard,input_queue_capacity_factor=self.config.input_queue_capacity_factor,num_reader_threads=self.config.num_input_reader_threads)
assert self.config.num_preprocess_threads%2==0
images_word_prob_and_captions=[]
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example=input_queue.dequeue()
encoded_image,caption,word_prob=input_ops.parse_sequence_example(serialized_sequence_example,image_feature=self.config.image_feature_name,word_prob_feature=self.config.word_prob_feature_name,caption_feature=self.config.caption_feature_name)
image=self.process_image(encoded_image,thread_id=thread_id)
images_word_prob_and_captions.append([image,word_prob,caption])
queue_capacity=(2*self.config.num_preprocess_threads*self.config.batch_size)
images,word_prob,input_seqs,target_seqs,input_mask=(input_ops.batch_with_dynamic_pad(images_word_prob_and_captions,batch_size=self.config.batch_size,queue_capacity=queue_capacity))
self.images=images
self.word_prob=word_prob
self.input_seqs=input_seqs
self.target_seqs=target_seqs
self.input_mask=input_mask
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(
dtype=tf.string, shape=[], name="image_feed")
input_feed = tf.placeholder(dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(input_feed, 1)
# No target sequences or input mask in inference mode.
target_seqs = None
input_mask = None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, caption = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
caption_feature=self.config.caption_feature_name)
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_captions.append([image, caption])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, input_seqs, target_seqs, input_mask = (
input_ops.batch_with_dynamic_pad(images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
self.images = images
self.input_seqs = input_seqs
self.target_seqs = target_seqs
self.input_mask = input_mask
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_feed")
input_feed = tf.placeholder(dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(input_feed, 1)
# No target sequences or input mask in inference mode.
target_seqs = None
input_mask = None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, caption = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
caption_feature=self.config.caption_feature_name)
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_captions.append([image, caption])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, input_seqs, target_seqs, input_mask = (
input_ops.batch_with_dynamic_pad(images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
self.images = images
self.input_seqs = input_seqs
self.target_seqs = target_seqs
self.input_mask = input_mask
def build_inputs(self):
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
# approximate values nums for all shard
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
# queue_capacity_factor for shards
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different color distortions.
assert self.config.num_preprocess_threads % 2 == 0
images_and_label = []
for thread_id in range(self.config.num_preprocess_threads):
# thread
serialized_sequence_example = input_queue.dequeue()
encoded_image, image_label, image_name = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
label_feature=self.config.label_feature_name,
filename_feature=self.config.filename_feature_name)
# preprocessing, for different thread_id use different distortion function
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_label.append([image, image_name])
# mutil threads preprocessing the image
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, image_names = tf.train.batch_join(
images_and_label,
batch_size=self.config.batch_size,
capacity=queue_capacity,
dynamic_pad=True,
name="batch")
self.images = images # batch_size, 224, 224 , 3
#self.images = tf.cast(self.images, tf.float32)
tf.summary.image("raw_images", self.images)
self.images = tf.cast(self.images, tf.float32) / 255.
if self.mode == "train":
#with tf.variable_scope('noised'):
# self.inputs = tf.add(self.images, self.scale * tf.random_normal(shape=self.images.shape))
self.inputs = self.images
tf.summary.image("noised_images", self.images)
elif self.mode == "inference":
self.inputs = self.images
self.image_names = image_names
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.targets (training and eval only)
"""
if self.mode == "inference":
with tf.name_scope("image_input"):
"In inference mode, images are fed via placeholders."
image_feed = tf.placeholder(dtype=tf.string,
shape=[],
name="image_feed")
# Process image and insert batch dimensions
images, old_h, old_w = self.process_image(image_feed)
self.old_img_height = old_h
self.old_img_width = old_w
self.new_img_height = tf.shape(images)[1]
self.new_img_width = tf.shape(images)[2]
# No targets in inference mode.
targets = None
else:
with tf.name_scope("load_tf_records"), tf.device("/cpu:0"):
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Decode data from sequence example.
serialized_sequence_example = input_queue.dequeue()
image, targets, img_file = input_ops.parse_sequence_example(
serialized_sequence_example)
images = tf.expand_dims(self.process_image(image), 0)
self.images = images
self.targets = targets
def build_inputs(self):
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
# approximate values nums for all shard
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
# queue_capacity_factor for shards
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different color distortions.
assert self.config.num_preprocess_threads % 2 == 0
images_and_label = []
for thread_id in range(self.config.num_preprocess_threads):
# thread
serialized_sequence_example = input_queue.dequeue()
encoded_image, image_label, image_name = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
label_feature=self.config.label_feature_name,
filename_feature=self.config.filename_feature_name)
# preprocessing, for different thread_id use different distortion function
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_label.append([image, image_label, image_name])
# mutil threads preprocessing the image
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, labels, image_names = tf.train.batch_join(
images_and_label,
batch_size=self.config.batch_size,
capacity=queue_capacity,
dynamic_pad=True,
name="batch")
self.images = images
self.labels = labels
self.image_names = image_names
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.targets (training and eval only)
"""
if self.mode == "inference":
with tf.name_scope("image_input"):
"In inference mode, images are fed via placeholders."
image_feed = tf.placeholder(dtype=tf.string,
shape=[],
name="image_feed")
# Process image and insert batch dimensions
images = tf.expand_dims(self.process_image(image_feed), 0)
# No target word in inference mode.
targets = None
# No sequence length in inference mode.
seq_lengths = None
else:
with tf.name_scope("load_tf_records"):
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
assert self.config.num_preprocess_threads % 2 == 0
images_and_words = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, word = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
word_feature=self.config.word_feature_name)
image = self.process_image(encoded_image,
thread_id=thread_id)
images_and_words.append([image, word])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, targets, seq_lengths = input_ops.make_batch(
images_and_words,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity,
vocab_size=self.config.vocab_size)
self.images = images
self.targets = targets
self.seq_lengths = seq_lengths
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
images and seqs
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string,
shape=[],
name="image_feed")
# Process image and insert batch dimensions.
image_feed = self.process_image(image_feed)
# Process image and insert batch dimensions.
image_seqs = tf.expand_dims(image_feed, 0)
# No target sequences or input mask in inference mode.
input_mask = tf.placeholder(
dtype=tf.int64,
shape=[1, 8], # batch_size
name="input_mask")
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
# assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
set_id, encoded_images, image_ids, captions, likes = (
input_ops.parse_sequence_example(
serialized_sequence_example,
set_id=self.config.set_id_name,
image_feature=self.config.image_feature_name,
image_index=self.config.image_index_name,
caption_feature=self.config.caption_feature_name,
number_set_images=self.config.number_set_images))
images = []
for i in range(self.config.number_set_images):
images.append(
self.process_image(encoded_images[i], image_idx=i))
images_and_captions.append(
[set_id, images, image_ids, captions, likes])
# Batch inputs.
queue_capacity = (5 * self.config.num_preprocess_threads *
self.config.batch_size)
#(set_ids, image_seqs, image_ids, f_input_seqs, f_target_seqs,
# b_input_seqs, b_target_seqs, input_mask, cap_seqs, cap_mask) = (
(set_ids, image_seqs, image_ids, input_mask, loss_mask, cap_seqs,
cap_mask, likes) = (input_ops.batch_with_dynamic_pad(
images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
self.images = image_seqs
self.input_mask = input_mask
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.input_mask (training and eval only)
"""
# in prediction mode, we use a batch size of one
batch_size = self.config.batch_size
if self.mode == "prediction":
batch_size = 1
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(
dtype=tf.string, shape=[],
name="image_feed") # shape: scalar value
#image_fn_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_fn_feed")
#image_filename_queue = tf.train.string_input_producer([image_fn_feed]) # list of files to read
#reader = tf.WholeFileReader()
#_, image_feed = reader.read(image_filename_queue)
text_feed = tf.placeholder(
dtype=tf.int64,
shape=[
None, self.config.sentence_length
], # shape 2D tensor - variable size (first dimension sentence sequence, second dimension token sequence (actually fixed size))
name="text_feed")
# arbitrary labels (not used)
mi_label = tf.constant(-1, dtype=tf.int64)
sc_label = tf.constant(-1.0, dtype=tf.float32)
image = self.process_image(image_feed)
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(text_feed, 0)
mi_labels = tf.expand_dims(mi_label, 0)
sc_labels = tf.expand_dims(sc_label, 0)
input_mask = tf.expand_dims(tf.constant([1], dtype=tf.int32), 0)
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads,
mode=self.mode)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_texts = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, text, mi, sc = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
sentences_feature=self.config.sentences_feature_name,
sentence_length=self.config.sentence_length,
mi_feature=self.config.mi_feature_name,
sc_feature=self.config.sc_feature_name)
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_texts.append([image, text, mi, sc])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
batch_size)
images, input_seqs, mi_labels, sc_labels, input_mask = (
input_ops.batch_with_dynamic_pad(
images_and_texts,
batch_size=batch_size,
queue_capacity=queue_capacity))
#print('Shapes')
#print('Shape images: ' + str(images.get_shape()))
#print('Shape input_seqs: ' + str(input_seqs.get_shape()))
#print('Shape input_mask: ' + str(input_mask.get_shape()))
self.images = images
self.input_seqs = input_seqs
if self.mode == "prediction":
self.mi_labels = None
self.sc_labels = None
else:
self.mi_labels = mi_labels
self.sc_labels = sc_labels
self.input_mask = input_mask
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
Inputs of the model.
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string,
shape=[],
name="image_feed")
# Process image and insert batch dimensions.
image_feed = self.process_image(image_feed)
input_feed = tf.placeholder(
dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
image_seqs = tf.expand_dims(image_feed, 0)
cap_seqs = tf.expand_dims(input_feed, 1)
# No target sequences or input mask in inference mode.
input_mask = tf.placeholder(
dtype=tf.int64,
shape=[1, 6], # batch_size
name="input_mask")
# input_mask = None
cap_mask = None
loss_mask = None
set_ids = None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion. But we only
# use one thread in our Polyvore model. weights are not used.
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
global set_id
set_id, encoded_images, image_ids, captions, weights, votes = (
input_ops.parse_sequence_example(
serialized_sequence_example,
set_id=self.config.set_id_name,
image_feature=self.config.image_feature_name,
image_index=self.config.image_index_name,
caption_feature=self.config.caption_feature_name,
number_set_images=self.config.number_set_images))
set_id = tf.Print(set_id, [set_id], message="This is set_id: ")
# weights = tf.Print(weights, [weights], message="This is weights: ")
# votes = tf.to_float(votes, name='ToFloat')
# votes = tf.Print(votes, [votes], message="This is votes: ")
# votes = np.ceil(votes/100)+1
print("go inside the loop")
images = []
for i in range(self.config.number_set_images):
print('hello this is a debugging information')
images.append(
self.process_image(encoded_images[i], image_idx=i))
images_and_captions.append(
[set_id, images, image_ids, captions, weights, votes])
# Batch inputs.
queue_capacity = (5 * self.config.num_preprocess_threads *
self.config.batch_size)
(set_ids, image_seqs, image_ids, input_mask, loss_mask, cap_seqs,
cap_mask, weights, votes) = (input_ops.batch_with_dynamic_pad(
images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
# set_ids = tf.Print(set_ids, [set_ids], message="This is set_id: ")
# weights = tf.Print(weights, [weights], message="This is weights: ")
self.images = image_seqs
self.input_mask = input_mask
self.loss_mask = loss_mask
self.cap_seqs = cap_seqs
self.cap_mask = cap_mask
self.set_ids = set_ids
self.weights = weights
self.votes = votes
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.center_words
self.context_words
"""
if self.mode == "train":
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
assert self.config.num_preprocess_threads % 2 == 0
# create a queue for samples (center_word, context_word) from multiple sequence examples
min_queue_examples = self.config.tokens_per_sequence_example * self.config.sample_queue_capacity_factor * self.config.num_skips
capacity = min_queue_examples + 100 * self.config.batch_size # the extra capacity allows the Queue to have enough time to enqueue new samples, since it should always hold at least min_queue_examples
samples_queue = tf.RandomShuffleQueue(
capacity=capacity,
min_after_dequeue=min_queue_examples,
dtypes=[tf.int64],
shapes=[2],
name="random_samples_queue")
enqueue_ops = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
sample_center_words, sample_context_words = input_ops.parse_sequence_example(
serialized_sequence_example)
sample_center_words = tf.expand_dims(sample_center_words, 1)
sample_context_words = tf.expand_dims(sample_context_words, 1)
samples = tf.concat(
1, [sample_center_words, sample_context_words])
enqueue_ops.append(samples_queue.enqueue_many(samples))
tf.train.queue_runner.add_queue_runner(
tf.train.queue_runner.QueueRunner(samples_queue, enqueue_ops)
) # this op will start the actual threads as soon as the session runs
random_samples = []
for thread_id in range(self.config.num_preprocess_threads):
sample = samples_queue.dequeue()
random_samples.append(sample)
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
center_words, context_words = (input_ops.batch_samples(
random_samples,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
#print('Shapes')
#print('Shape center_words: ' + str(center_words.get_shape()))
#print('Shape context_words: ' + str(context_words.get_shape()))
self.center_words = center_words
self.context_words = context_words
else:
valid_size = 16
valid_dataset = tf.placeholder(tf.int64, [valid_size],
name="valid_dataset")
self.valid_dataset = valid_dataset
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string,
shape=[],
name="image_feed")
input_feed = tf.placeholder(
dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(input_feed, 1)
#TODO
# senti_feed_0= tf.Variable([1],dtype=tf.int64)
# senti_feed_0= tf.get_variable("senti_input_0",[1],initializer = tf.zeros_initializer,dtype=tf.int64)
# senti_feed_0= tf.Variable([FLAGS.sentiment_input],dtype=tf.int64,name="senti_input_0")
senti_feed_0 = tf.cast([FLAGS.sentiment_input], tf.int64)
out = tf.expand_dims(senti_feed_0, 1)
# No target sequences or input mask in inference mode.
target_seqs = None
input_mask = None
# out= None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.
input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
captions_co = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, caption = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
caption_feature=self.config.caption_feature_name)
# print(caption.get_shape().as_list())
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_captions.append([image, caption])
captions_co.append(caption)
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, input_seqs, target_seqs, input_mask, out = (
input_ops.batch_with_dynamic_pad(
images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
# print(out.get_shape().as_list())
# print("out")
# keys = self.sentiment_dict.keys()
# values = self.sentiment_dict.values()
# dic_value = tf.cast(values, dtype=tf.float32)
# dic_keys = tf.cast(keys, dtype=tf.int64)
# self.table = tf.contrib.lookup.HashTable(
# tf.contrib.lookup.KeyValueTensorInitializer(dic_keys, dic_value), -1
# )
self.images = images
self.input_seqs = input_seqs
self.target_seqs = target_seqs
self.input_mask = input_mask
self.sentiments = out