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run_inference.py
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run_inference.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.
# ==============================================================================
r"""Generate captions for images using default beam search parameters."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import tensorflow as tf
import configuration
import inference_wrapper
from inference_utils import caption_generator
from inference_utils import vocabulary
from googletrans import Translator
"""FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string("checkpoint_path", "",
"Model checkpoint file or directory containing a "
"model checkpoint file.")
tf.flags.DEFINE_string("vocab_file", "", "Text file containing the vocabulary.")
tf.flags.DEFINE_string("input_files", "",
"File pattern or comma-separated list of file patterns "
"of image files.")"""
tf.logging.set_verbosity(tf.logging.INFO)
checkpoint_path = "data/model/Hugh/train/newmodel.ckpt-2000000"
vocab_file = "data/Hugh/word_counts.txt"
input_files = "static/client/img/"
def rename_model_ckpt():
BASE_DIR = '/Users/wlku/Documents/Developing/TensorFlow/Im2txt/'
vars_to_rename = {
"lstm/BasicLSTMCell/Linear/Matrix": "lstm/basic_lstm_cell/kernel",
"lstm/BasicLSTMCell/Linear/Bias": "lstm/basic_lstm_cell/bias",
}
new_checkpoint_vars = {}
reader = tf.train.NewCheckpointReader(checkpoint_path)
for old_name in reader.get_variable_to_shape_map():
if old_name in vars_to_rename:
new_name = vars_to_rename[old_name]
else:
new_name = old_name
print(old_name)
new_checkpoint_vars[new_name] = tf.Variable(reader.get_tensor(old_name))
init = tf.global_variables_initializer()
saver = tf.train.Saver(new_checkpoint_vars)
print("BASE_DIR = ", BASE_DIR)
with tf.Session() as sess:
sess.run(init)
saver.save(sess, os.path.join(BASE_DIR, "data/model/Hugh/train/newmodel.ckpt-2000000"))
print("checkpoint file rename successful... ")
def mainfunction(currentimagename):
# Change tensor name
#rename_model_ckpt()
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(vocab_file)
imageName = input_files + currentimagename
filenames = []
for file_pattern in imageName.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), imageName)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "rb") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
translator = Translator()
result = translator.translate(sentence, src='en', dest='ja')
#print(" %d) %s (p=%f)" % (i, result, math.exp(caption.logprob)))
if i==0:
displayCaption = result.text
print(displayCaption)
return displayCaption
return displayCaption
if __name__ == "__main__":
tf.app.run()