def convert_test(tfrecords_path):
inception_graph = tf.Graph()
inception_sess = tf.Session(graph=inception_graph)
with inception_graph.as_default(), inception_sess.as_default():
incept_model = inception.inception_model()
with tf.Graph().as_default() as g, tf.Session().as_default() as sess:
labels_path = tf.placeholder(dtype=tf.string)
images_ds = tf.contrib.data.Dataset.from_tensor_slices(tf.constant(tf.gfile.ListDirectory(paths.TEST_DIR))) \
.map(read_test_example)
labels_iter = images_ds.make_initializable_iterator()
next_label = labels_iter.get_next()
sess.run(labels_iter.initializer,
feed_dict={labels_path: paths.LABELS})
print('Writing ', tfrecords_path)
with tf.python_io.TFRecordWriter(
tfrecords_path,
tf.python_io.TFRecordCompressionType.NONE) as writer:
try:
while True:
img, id = sess.run(next_label)
with inception_graph.as_default():
inception_output = incept_model(
inception_sess, img).reshape(-1).tolist()
# print(inception_output.shape)
print('writing %s - %s' % (len(img), id))
example = tf.train.Example(features=tf.train.Features(
feature={
'id':
bytes_feature(id.encode()),
consts.IMAGE_RAW_FIELD:
bytes_feature(img),
consts.INCEPTION_OUTPUT_FIELD:
float_feature(inception_output)
}))
writer.write(example.SerializeToString())
except tf.errors.OutOfRangeError:
print('End of the dataset')
writer.flush()
print('Finished')
def convert_train(tfrecords_path):
one_hot_encoder, _ = dataset.one_hot_label_encoder()
inception_graph = tf.Graph()
inception_sess = tf.Session(graph=inception_graph)
with inception_graph.as_default(), inception_sess.as_default():
incept_model = inception.inception_model()
with tf.Graph().as_default(), tf.Session().as_default() as sess:
labels_path = tf.placeholder(dtype=tf.string)
images_ds = tf.contrib.data.TextLineDataset(labels_path) \
.skip(1) \
.map(parse_row) \
.map(read_example)
labels_iter = images_ds.make_initializable_iterator()
next_label = labels_iter.get_next()
sess.run(labels_iter.initializer,
feed_dict={labels_path: paths.LABELS})
print('Writing ', tfrecords_path)
bar = pyprind.ProgBar(13000, update_interval=1, width=60)
augmenter = image_augmenter()
with tf.python_io.TFRecordWriter(
tfrecords_path,
tf.python_io.TFRecordCompressionType.NONE) as writer:
try:
while True:
id, img, breed_label = sess.run(next_label)
one_hot_label = one_hot_encoder([breed_label
]).reshape(-1).tolist()
def get_inception_ouput(img):
with inception_graph.as_default():
inception_output = incept_model(
inception_sess, img).reshape(-1).tolist()
return inception_output
# print(inception_output.shape)
# print('writing %s - %s' % (len(img), breed_label))
images = [img]
images.extend(augmenter(img))
for image in images:
example = build_train_example(
image, one_hot_label, breed_label,
get_inception_ouput(image))
writer.write(example.SerializeToString())
bar.update()
except tf.errors.OutOfRangeError:
print('End of the dataset')
writer.flush()
writer.close()
print('Finished')
consts.LABEL_ONE_HOT_FIELD: float_feature(one_hot_label),
consts.INCEPTION_OUTPUT_FIELD: float_feature(inception_output)
}))
return example
if __name__ == '__main__':
one_hot_encoder, _ = dataset.one_hot_label_encoder()
with tf.Graph().as_default(), \
tf.Session().as_default() as sess, \
tf.python_io.TFRecordWriter(paths.STANFORD_DS_TF_RECORDS,
tf.python_io.TFRecordCompressionType.NONE) as writer:
incept_model = inception.inception_model()
def get_inception_ouput(img):
inception_output = incept_model(sess, img).reshape(-1).tolist()
return inception_output
for breed_dir in [d for d in os.listdir(annotations_root_dir)]:
print(breed_dir)
for annotation_file in [
f for f in os.listdir(
os.path.join(annotations_root_dir, breed_dir))
]:
print(annotation_file)
annotation = parse_annotation(
os.path.join(annotations_root_dir, breed_dir,
annotation_file))