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# Copyright 2020 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.
# ==============================================================================
"""Tests for model_export."""
import os
import numpy as np
from absl.testing import flagsaver
import tensorflow as tf
from tensorflow.compat.v1 import flags
import common_flags
import model_export
_CHECKPOINT = 'model.ckpt-399731'
_CHECKPOINT_URL = (
'http://download.tensorflow.org/models/attention_ocr_2017_08_09.tar.gz')
def _clean_up():
tf.io.gfile.rmtree(tf.compat.v1.test.get_temp_dir())
def _create_tf_example_string(image):
"""Create a serialized tf.Example proto for feeding the model."""
example = tf.train.Example()
example.features.feature['image/encoded'].float_list.value.extend(
list(np.reshape(image, (-1))))
return example.SerializeToString()
class AttentionOcrExportTest(tf.test.TestCase):
"""Tests for model_export.export_model."""
def setUp(self):
for suffix in ['.meta', '.index', '.data-00000-of-00001']:
filename = _CHECKPOINT + suffix
self.assertTrue(
tf.io.gfile.exists(filename),
msg='Missing checkpoint file %s. '
'Please download and extract it from %s' %
(filename, _CHECKPOINT_URL))
flags.FLAGS.dataset_name = 'fsns'
flags.FLAGS.checkpoint = _CHECKPOINT
flags.FLAGS.dataset_dir = os.path.join(
os.path.dirname(__file__), 'datasets/testdata/fsns')
tf.test.TestCase.setUp(self)
_clean_up()
self.export_dir = os.path.join(
tf.compat.v1.test.get_temp_dir(), 'exported_model')
self.minimal_output_signature = {
'predictions': 'AttentionOcr_v1/predicted_chars:0',
'scores': 'AttentionOcr_v1/predicted_scores:0',
'predicted_length': 'AttentionOcr_v1/predicted_length:0',
'predicted_text': 'AttentionOcr_v1/predicted_text:0',
'predicted_conf': 'AttentionOcr_v1/predicted_conf:0',
'normalized_seq_conf': 'AttentionOcr_v1/normalized_seq_conf:0'
}
def create_input_feed(self, graph_def, serving):
"""Returns the input feed for the model.
Creates random images, according to the size specified by dataset_name,
format it in the correct way depending on whether the model was exported
for serving, and return the correctly keyed feed_dict for inference.
Args:
graph_def: Graph definition of the loaded model.
serving: Whether the model was exported for Serving.
Returns:
The feed_dict suitable for model inference.
"""
# Creates a dataset based on FLAGS.dataset_name.
self.dataset = common_flags.create_dataset('test')
# Create some random images to test inference for any dataset.
self.images = {
'img1':
np.random.uniform(low=64, high=192,
size=self.dataset.image_shape).astype('uint8'),
'img2':
np.random.uniform(low=32, high=224,
size=self.dataset.image_shape).astype('uint8'),
}
signature_def = graph_def.signature_def[
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
if serving:
input_name = signature_def.inputs[
tf.saved_model.CLASSIFY_INPUTS].name
# Model for serving takes input: inputs['inputs'] = 'tf_example:0'
feed_dict = {
input_name: [
_create_tf_example_string(self.images['img1']),
_create_tf_example_string(self.images['img2'])
]
}
else:
input_name = signature_def.inputs['images'].name
# Model for direct use takes input: inputs['images'] = 'original_image:0'
feed_dict = {
input_name: np.stack([self.images['img1'], self.images['img2']])
}
return feed_dict
def verify_export_load_and_inference(self, export_for_serving=False):
"""Verify exported model can be loaded and inference can run successfully.
This function will load the exported model in self.export_dir, then create
some fake images according to the specification of FLAGS.dataset_name.
It then feeds the input through the model, and verify the minimal set of
output signatures are present.
Note: Model and dataset creation in the underlying library depends on the
following commandline flags:
FLAGS.dataset_name
Args:
export_for_serving: True if the model was exported for Serving. This
affects how input is fed into the model.
"""
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
graph_def = tf.compat.v1.saved_model.loader.load(
sess=sess,
tags=[tf.saved_model.SERVING],
export_dir=self.export_dir)
feed_dict = self.create_input_feed(graph_def, export_for_serving)
results = sess.run(self.minimal_output_signature, feed_dict=feed_dict)
out_shape = (2,)
self.assertEqual(np.shape(results['predicted_conf']), out_shape)
self.assertEqual(np.shape(results['predicted_text']), out_shape)
self.assertEqual(np.shape(results['predicted_length']), out_shape)
self.assertEqual(np.shape(results['normalized_seq_conf']), out_shape)
out_shape = (2, self.dataset.max_sequence_length)
self.assertEqual(np.shape(results['scores']), out_shape)
self.assertEqual(np.shape(results['predictions']), out_shape)
@flagsaver.flagsaver
def test_fsns_export_for_serving_and_load_inference(self):
model_export.export_model(self.export_dir, True)
self.verify_export_load_and_inference(True)
@flagsaver.flagsaver
def test_fsns_export_and_load_inference(self):
model_export.export_model(self.export_dir, False, batch_size=2)
self.verify_export_load_and_inference(False)
if __name__ == '__main__':
tf.test.main()
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