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AttentionOCR partial TF2 migration (#8843) 

* update readme according to template

* initial upgrades

* demo_inference updated, need to check tf.flags

* issues with tf.image

* manual checks needed for tf.contrib

* more tf2 updates, need to fix tf.contrib and tf.flags
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research/attention_ocr/README.md
View file @ 50fa3eb9
## Attention-based Extraction of Structured Information from Street View Imagery
# Attention-based Extraction of Structured Information from Street View Imagery
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-extraction-of-structured/optical-character-recognition-on-fsns-test)](https://paperswithcode.com/sota/optical-character-recognition-on-fsns-test?p=attention-based-extraction-of-structured)
[![Paper](http://img.shields.io/badge/paper-arXiv.1704.03549-B3181B.svg)](https://arxiv.org/abs/1704.03549)
......@@ -7,14 +7,20 @@
*A TensorFlow model for real-world image text extraction problems.*
This folder contains the code needed to train a new Attention OCR model on the
[FSNS dataset][FSNS] dataset to transcribe street names in France. You can
also use it to train it on your own data.
[FSNS dataset][FSNS] to transcribe street names in France. You can also train the code on your own data.
More details can be found in our paper:
["Attention-based Extraction of Structured Information from Street View
Imagery"](https://arxiv.org/abs/1704.03549)
## Description
* Paper presents a model based on ConvNets, RNN's and a novel attention mechanism.
Achieves **84.2%** on FSNS beating the previous benchmark (**72.46%**). Also studies
the speed/accuracy tradeoff that results from using CNN feature extractors of
different depths.
## Contacts
Authors
......@@ -22,7 +28,18 @@ Authors
* Zbigniew Wojna (zbigniewwojna@gmail.com)
* Alexander Gorban (gorban@google.com)
Maintainer: Xavier Gibert [@xavigibert](https://github.com/xavigibert)
Maintainer
* Xavier Gibert ([@xavigibert](https://github.com/xavigibert))
## Table of Contents
* [Requirements](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#requirements)
* [Dataset](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#dataset)
* [How to use this code](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#how-to-use-this-code)
* [Using your own image data](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#using-your-own-image-data)
* [How to use a pre-trained model](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#how-to-use-a-pre-trained-model)
* [Disclaimer](https://github.com/tensorflow/models/blob/master/research/attention_ocr/README.md#disclaimer)
## Requirements
......@@ -49,6 +66,42 @@ cd ..
[TF]: https://www.tensorflow.org/install/
[FSNS]: https://github.com/tensorflow/models/tree/master/research/street
## Dataset
The French Street Name Signs (FSNS) dataset is split into subsets,
each of which is composed of multiple files. Note that these datasets
are very large. The approximate sizes are:
* Train: 512 files of 300MB each.
* Validation: 64 files of 40MB each.
* Test: 64 files of 50MB each.
* The datasets download includes a directory `testdata` that contains
some small datasets that are big enough to test that models can
actually learn something.
* Total: around 158GB
The download paths are in the following list:
```
https://download.tensorflow.org/data/fsns-20160927/charset_size=134.txt
https://download.tensorflow.org/data/fsns-20160927/test/test-00000-of-00064
...
https://download.tensorflow.org/data/fsns-20160927/test/test-00063-of-00064
https://download.tensorflow.org/data/fsns-20160927/testdata/arial-32-00000-of-00001
https://download.tensorflow.org/data/fsns-20160927/testdata/fsns-00000-of-00001
https://download.tensorflow.org/data/fsns-20160927/testdata/mnist-sample-00000-of-00001
https://download.tensorflow.org/data/fsns-20160927/testdata/numbers-16-00000-of-00001
https://download.tensorflow.org/data/fsns-20160927/train/train-00000-of-00512
...
https://download.tensorflow.org/data/fsns-20160927/train/train-00511-of-00512
https://download.tensorflow.org/data/fsns-20160927/validation/validation-00000-of-00064
...
https://download.tensorflow.org/data/fsns-20160927/validation/validation-00063-of-00064
```
All URLs are stored in the [research/street](https://github.com/tensorflow/models/tree/master/research/street)
repository in the text file `python/fsns_urls.txt`.
## How to use this code
To run all unit tests:
......@@ -80,7 +133,7 @@ tar xf attention_ocr_2017_08_09.tar.gz
python train.py --checkpoint=model.ckpt-399731
```
## How to use your own image data to train the model
## Using your own image data
You need to define a new dataset. There are two options:
......
research/attention_ocr/python/data_provider.py
View file @ 50fa3eb9
......@@ -56,14 +56,14 @@ def augment_image(image):
Returns:
Distorted Tensor image of the same shape.
"""
with tf.variable_scope('AugmentImage'):
with tf.compat.v1.variable_scope('AugmentImage'):
height = image.get_shape().dims[0].value
width = image.get_shape().dims[1].value
# Random crop cut from the street sign image, resized to the same size.
# Assures that the crop is covers at least 0.8 area of the input image.
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image),
image_size=tf.shape(input=image),
bounding_boxes=tf.zeros([0, 0, 4]),
min_object_covered=0.8,
aspect_ratio_range=[0.8, 1.2],
......@@ -74,7 +74,7 @@ def augment_image(image):
# Randomly chooses one of the 4 interpolation methods
distorted_image = inception_preprocessing.apply_with_random_selector(
distorted_image,
lambda x, method: tf.image.resize_images(x, [height, width], method),
lambda x, method: tf.image.resize(x, [height, width], method),
num_cases=4)
distorted_image.set_shape([height, width, 3])
......@@ -99,9 +99,10 @@ def central_crop(image, crop_size):
Returns:
A tensor of shape [crop_height, crop_width, channels].
"""
with tf.variable_scope('CentralCrop'):
with tf.compat.v1.variable_scope('CentralCrop'):
target_width, target_height = crop_size
image_height, image_width = tf.shape(image)[0], tf.shape(image)[1]
image_height, image_width = tf.shape(
input=image)[0], tf.shape(input=image)[1]
assert_op1 = tf.Assert(
tf.greater_equal(image_height, target_height),
['image_height < target_height', image_height, target_height])
......@@ -129,7 +130,7 @@ def preprocess_image(image, augment=False, central_crop_size=None,
A float32 tensor of shape [H x W x 3] with RGB values in the required
range.
"""
with tf.variable_scope('PreprocessImage'):
with tf.compat.v1.variable_scope('PreprocessImage'):
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
if augment or central_crop_size:
if num_towers == 1:
......@@ -182,7 +183,7 @@ def get_data(dataset,
image_orig, augment, central_crop_size, num_towers=dataset.num_of_views)
label_one_hot = slim.one_hot_encoding(label, dataset.num_char_classes)
images, images_orig, labels, labels_one_hot = (tf.train.shuffle_batch(
images, images_orig, labels, labels_one_hot = (tf.compat.v1.train.shuffle_batch(
[image, image_orig, label, label_one_hot],
batch_size=batch_size,
num_threads=shuffle_config.num_batching_threads,
......
research/attention_ocr/python/datasets/fsns.py
View file @ 50fa3eb9
......@@ -72,7 +72,7 @@ def read_charset(filename, null_character=u'\u2591'):
"""
pattern = re.compile(r'(\d+)\t(.+)')
charset = {}
with tf.gfile.GFile(filename) as f:
with tf.io.gfile.GFile(filename) as f:
for i, line in enumerate(f):
m = pattern.match(line)
if m is None:
......@@ -96,9 +96,9 @@ class _NumOfViewsHandler(slim.tfexample_decoder.ItemHandler):
self._num_of_views = num_of_views
def tensors_to_item(self, keys_to_tensors):
return tf.to_int64(
return tf.cast(
self._num_of_views * keys_to_tensors[self._original_width_key] /
keys_to_tensors[self._width_key])
keys_to_tensors[self._width_key], dtype=tf.int64)
def get_split(split_name, dataset_dir=None, config=None):
......@@ -133,19 +133,19 @@ def get_split(split_name, dataset_dir=None, config=None):
zero = tf.zeros([1], dtype=tf.int64)
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
tf.io.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature((), tf.string, default_value='png'),
tf.io.FixedLenFeature((), tf.string, default_value='png'),
'image/width':
tf.FixedLenFeature([1], tf.int64, default_value=zero),
tf.io.FixedLenFeature([1], tf.int64, default_value=zero),
'image/orig_width':
tf.FixedLenFeature([1], tf.int64, default_value=zero),
tf.io.FixedLenFeature([1], tf.int64, default_value=zero),
'image/class':
tf.FixedLenFeature([config['max_sequence_length']], tf.int64),
tf.io.FixedLenFeature([config['max_sequence_length']], tf.int64),
'image/unpadded_class':
tf.VarLenFeature(tf.int64),
tf.io.VarLenFeature(tf.int64),
'image/text':
tf.FixedLenFeature([1], tf.string, default_value=''),
tf.io.FixedLenFeature([1], tf.string, default_value=''),
}
items_to_handlers = {
'image':
......@@ -171,7 +171,7 @@ def get_split(split_name, dataset_dir=None, config=None):
config['splits'][split_name]['pattern'])
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
reader=tf.compat.v1.TFRecordReader,
decoder=decoder,
num_samples=config['splits'][split_name]['size'],
items_to_descriptions=config['items_to_descriptions'],
......
research/attention_ocr/python/datasets/fsns_test.py
View file @ 50fa3eb9
......@@ -91,7 +91,7 @@ class FsnsTest(tf.test.TestCase):
image_tf, label_tf = provider.get(['image', 'label'])
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())
with slim.queues.QueueRunners(sess):
image_np, label_np = sess.run([image_tf, label_tf])
......
research/attention_ocr/python/datasets/testdata/fsns/download_data.py
View file @ 50fa3eb9
......@@ -10,7 +10,8 @@ KEEP_NUM_RECORDS = 5
print('Downloading %s ...' % URL)
urllib.request.urlretrieve(URL, DST_ORIG)
print('Writing %d records from %s to %s ...' % (KEEP_NUM_RECORDS, DST_ORIG, DST))
print('Writing %d records from %s to %s ...' %
(KEEP_NUM_RECORDS, DST_ORIG, DST))
with tf.io.TFRecordWriter(DST) as writer:
for raw_record in itertools.islice(tf.python_io.tf_record_iterator(DST_ORIG), KEEP_NUM_RECORDS):
for raw_record in itertools.islice(tf.compat.v1.python_io.tf_record_iterator(DST_ORIG), KEEP_NUM_RECORDS):
writer.write(raw_record)
research/attention_ocr/python/demo_inference.py
View file @ 50fa3eb9
......@@ -49,7 +49,7 @@ def load_images(file_pattern, batch_size, dataset_name):
for i in range(batch_size):
path = file_pattern % i
print("Reading %s" % path)
pil_image = PIL.Image.open(tf.gfile.GFile(path, 'rb'))
pil_image = PIL.Image.open(tf.io.gfile.GFile(path, 'rb'))
images_actual_data[i, ...] = np.asarray(pil_image)
return images_actual_data
......@@ -58,12 +58,13 @@ def create_model(batch_size, dataset_name):
width, height = get_dataset_image_size(dataset_name)
dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
model = common_flags.create_model(
num_char_classes=dataset.num_char_classes,
seq_length=dataset.max_sequence_length,
num_views=dataset.num_of_views,
null_code=dataset.null_code,
charset=dataset.charset)
raw_images = tf.placeholder(tf.uint8, shape=[batch_size, height, width, 3])
num_char_classes=dataset.num_char_classes,
seq_length=dataset.max_sequence_length,
num_views=dataset.num_of_views,
null_code=dataset.null_code,
charset=dataset.charset)
raw_images = tf.compat.v1.placeholder(
tf.uint8, shape=[batch_size, height, width, 3])
images = tf.map_fn(data_provider.preprocess_image, raw_images,
dtype=tf.float32)
endpoints = model.create_base(images, labels_one_hot=None)
......@@ -76,9 +77,9 @@ def run(checkpoint, batch_size, dataset_name, image_path_pattern):
images_data = load_images(image_path_pattern, batch_size,
dataset_name)
session_creator = monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint)
checkpoint_filename_with_path=checkpoint)
with monitored_session.MonitoredSession(
session_creator=session_creator) as sess:
session_creator=session_creator) as sess:
predictions = sess.run(endpoints.predicted_text,
feed_dict={images_placeholder: images_data})
return [pr_bytes.decode('utf-8') for pr_bytes in predictions.tolist()]
......@@ -87,10 +88,10 @@ def run(checkpoint, batch_size, dataset_name, image_path_pattern):
def main(_):
print("Predicted strings:")
predictions = run(FLAGS.checkpoint, FLAGS.batch_size, FLAGS.dataset_name,
FLAGS.image_path_pattern)
FLAGS.image_path_pattern)
for line in predictions:
print(line)
if __name__ == '__main__':
tf.app.run()
tf.compat.v1.app.run()
research/attention_ocr/python/demo_inference_test.py
View file @ 50fa3eb9
......@@ -14,12 +14,13 @@ class DemoInferenceTest(tf.test.TestCase):
super(DemoInferenceTest, self).setUp()
for suffix in ['.meta', '.index', '.data-00000-of-00001']:
filename = _CHECKPOINT + suffix
self.assertTrue(tf.gfile.Exists(filename),
self.assertTrue(tf.io.gfile.exists(filename),
msg='Missing checkpoint file %s. '
'Please download and extract it from %s' %
(filename, _CHECKPOINT_URL))
self._batch_size = 32
tf.flags.FLAGS.dataset_dir = os.path.join(os.path.dirname(__file__), 'datasets/testdata/fsns')
tf.flags.FLAGS.dataset_dir = os.path.join(
os.path.dirname(__file__), 'datasets/testdata/fsns')
def test_moving_variables_properly_loaded_from_a_checkpoint(self):
batch_size = 32
......@@ -30,15 +31,15 @@ class DemoInferenceTest(tf.test.TestCase):
images_data = demo_inference.load_images(image_path_pattern, batch_size,
dataset_name)
tensor_name = 'AttentionOcr_v1/conv_tower_fn/INCE/InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean'
moving_mean_tf = tf.get_default_graph().get_tensor_by_name(
tensor_name + ':0')
reader = tf.train.NewCheckpointReader(_CHECKPOINT)
moving_mean_tf = tf.compat.v1.get_default_graph().get_tensor_by_name(
tensor_name + ':0')
reader = tf.compat.v1.train.NewCheckpointReader(_CHECKPOINT)
moving_mean_expected = reader.get_tensor(tensor_name)
session_creator = monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=_CHECKPOINT)
checkpoint_filename_with_path=_CHECKPOINT)
with monitored_session.MonitoredSession(
session_creator=session_creator) as sess:
session_creator=session_creator) as sess:
moving_mean_np = sess.run(moving_mean_tf,
feed_dict={images_placeholder: images_data})
......@@ -50,38 +51,38 @@ class DemoInferenceTest(tf.test.TestCase):
'fsns',
image_path_pattern)
self.assertEqual([
u'Boulevard de Lunel░░░░░░░░░░░░░░░░░░░',
'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
'Rue de Port Maria░░░░░░░░░░░░░░░░░░░░',
'Avenue Charles Gounod░░░░░░░░░░░░░░░░',
'Rue de l‘Aurore░░░░░░░░░░░░░░░░░░░░░░',
'Rue de Beuzeville░░░░░░░░░░░░░░░░░░░░',
'Rue d‘Orbey░░░░░░░░░░░░░░░░░░░░░░░░░░',
'Rue Victor Schoulcher░░░░░░░░░░░░░░░░',
'Rue de la Gare░░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Tulipes░░░░░░░░░░░░░░░░░░░░░░',
'Rue André Maginot░░░░░░░░░░░░░░░░░░░░',
'Route de Pringy░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Landelles░░░░░░░░░░░░░░░░░░░░',
'Rue des Ilettes░░░░░░░░░░░░░░░░░░░░░░',
'Avenue de Maurin░░░░░░░░░░░░░░░░░░░░░',
'Rue Théresa░░░░░░░░░░░░░░░░░░░░░░░░░░', # GT='Rue Thérésa'
'Route de la Balme░░░░░░░░░░░░░░░░░░░░',
'Rue Hélène Roederer░░░░░░░░░░░░░░░░░░',
'Rue Emile Bernard░░░░░░░░░░░░░░░░░░░░',
'Place de la Mairie░░░░░░░░░░░░░░░░░░░',
'Rue des Perrots░░░░░░░░░░░░░░░░░░░░░░',
'Rue de la Libération░░░░░░░░░░░░░░░░░',
'Impasse du Capcir░░░░░░░░░░░░░░░░░░░░',
'Avenue de la Grand Mare░░░░░░░░░░░░░░',
'Rue Pierre Brossolette░░░░░░░░░░░░░░░',
'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
'Rue du Docteur Mourre░░░░░░░░░░░░░░░░',
'Rue d‘Ortheuil░░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Sarments░░░░░░░░░░░░░░░░░░░░░',
'Rue du Centre░░░░░░░░░░░░░░░░░░░░░░░░',
'Impasse Pierre Mourgues░░░░░░░░░░░░░░',
'Rue Marcel Dassault░░░░░░░░░░░░░░░░░░'
u'Boulevard de Lunel░░░░░░░░░░░░░░░░░░░',
'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
'Rue de Port Maria░░░░░░░░░░░░░░░░░░░░',
'Avenue Charles Gounod░░░░░░░░░░░░░░░░',
'Rue de l‘Aurore░░░░░░░░░░░░░░░░░░░░░░',
'Rue de Beuzeville░░░░░░░░░░░░░░░░░░░░',
'Rue d‘Orbey░░░░░░░░░░░░░░░░░░░░░░░░░░',
'Rue Victor Schoulcher░░░░░░░░░░░░░░░░',
'Rue de la Gare░░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Tulipes░░░░░░░░░░░░░░░░░░░░░░',
'Rue André Maginot░░░░░░░░░░░░░░░░░░░░',
'Route de Pringy░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Landelles░░░░░░░░░░░░░░░░░░░░',
'Rue des Ilettes░░░░░░░░░░░░░░░░░░░░░░',
'Avenue de Maurin░░░░░░░░░░░░░░░░░░░░░',
'Rue Théresa░░░░░░░░░░░░░░░░░░░░░░░░░░', # GT='Rue Thérésa'
'Route de la Balme░░░░░░░░░░░░░░░░░░░░',
'Rue Hélène Roederer░░░░░░░░░░░░░░░░░░',
'Rue Emile Bernard░░░░░░░░░░░░░░░░░░░░',
'Place de la Mairie░░░░░░░░░░░░░░░░░░░',
'Rue des Perrots░░░░░░░░░░░░░░░░░░░░░░',
'Rue de la Libération░░░░░░░░░░░░░░░░░',
'Impasse du Capcir░░░░░░░░░░░░░░░░░░░░',
'Avenue de la Grand Mare░░░░░░░░░░░░░░',
'Rue Pierre Brossolette░░░░░░░░░░░░░░░',
'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
'Rue du Docteur Mourre░░░░░░░░░░░░░░░░',
'Rue d‘Ortheuil░░░░░░░░░░░░░░░░░░░░░░░',
'Rue des Sarments░░░░░░░░░░░░░░░░░░░░░',
'Rue du Centre░░░░░░░░░░░░░░░░░░░░░░░░',
'Impasse Pierre Mourgues░░░░░░░░░░░░░░',
'Rue Marcel Dassault░░░░░░░░░░░░░░░░░░'
], predictions)
......
research/attention_ocr/python/eval.py
View file @ 50fa3eb9
......@@ -45,8 +45,8 @@ flags.DEFINE_integer('number_of_steps', None,
def main(_):
if not tf.gfile.Exists(FLAGS.eval_log_dir):
tf.gfile.MakeDirs(FLAGS.eval_log_dir)
if not tf.io.gfile.exists(FLAGS.eval_log_dir):
tf.io.gfile.makedirs(FLAGS.eval_log_dir)
dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
model = common_flags.create_model(dataset.num_char_classes,
......@@ -62,7 +62,7 @@ def main(_):
eval_ops = model.create_summaries(
data, endpoints, dataset.charset, is_training=False)
slim.get_or_create_global_step()
session_config = tf.ConfigProto(device_count={"GPU": 0})
session_config = tf.compat.v1.ConfigProto(device_count={"GPU": 0})
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.train_log_dir,
......
research/attention_ocr/python/inception_preprocessing.py
View file @ 50fa3eb9
......@@ -38,7 +38,7 @@ def apply_with_random_selector(x, func, num_cases):
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
sel = tf.random.uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
......@@ -64,7 +64,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
Raises:
ValueError: if color_ordering not in [0, 3]
"""
with tf.name_scope(scope, 'distort_color', [image]):
with tf.compat.v1.name_scope(scope, 'distort_color', [image]):
if fast_mode:
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
......@@ -131,7 +131,7 @@ def distorted_bounding_box_crop(image,
Returns:
A tuple, a 3-D Tensor cropped_image and the distorted bbox
"""
with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
with tf.compat.v1.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
......@@ -143,7 +143,7 @@ def distorted_bounding_box_crop(image,
# bounding box. If no box is supplied, then we assume the bounding box is
# the entire image.
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.shape(image),
image_size=tf.shape(input=image),
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
......@@ -188,7 +188,7 @@ def preprocess_for_train(image,
Returns:
3-D float Tensor of distorted image used for training with range [-1, 1].
"""
with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):
with tf.compat.v1.name_scope(scope, 'distort_image', [image, height, width, bbox]):
if bbox is None:
bbox = tf.constant(
[0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
......@@ -198,7 +198,7 @@ def preprocess_for_train(image,
# the coordinates are ordered [ymin, xmin, ymax, xmax].
image_with_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), bbox)
tf.summary.image('image_with_bounding_boxes', image_with_box)
tf.compat.v1.summary.image('image_with_bounding_boxes', image_with_box)
distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
# Restore the shape since the dynamic slice based upon the bbox_size loses
......@@ -206,8 +206,8 @@ def preprocess_for_train(image,
distorted_image.set_shape([None, None, 3])
image_with_distorted_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), distorted_bbox)
tf.summary.image('images_with_distorted_bounding_box',
image_with_distorted_box)
tf.compat.v1.summary.image('images_with_distorted_bounding_box',
image_with_distorted_box)
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
......@@ -218,11 +218,11 @@ def preprocess_for_train(image,
num_resize_cases = 1 if fast_mode else 4
distorted_image = apply_with_random_selector(
distorted_image,
lambda x, method: tf.image.resize_images(x, [height, width], method=method),
lambda x, method: tf.image.resize(x, [height, width], method=method),
num_cases=num_resize_cases)
tf.summary.image('cropped_resized_image',
tf.expand_dims(distorted_image, 0))
tf.compat.v1.summary.image('cropped_resized_image',
tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
......@@ -233,8 +233,8 @@ def preprocess_for_train(image,
lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4)
tf.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0))
tf.compat.v1.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0))
distorted_image = tf.subtract(distorted_image, 0.5)
distorted_image = tf.multiply(distorted_image, 2.0)
return distorted_image
......@@ -265,7 +265,7 @@ def preprocess_for_eval(image,
Returns:
3-D float Tensor of prepared image.
"""
with tf.name_scope(scope, 'eval_image', [image, height, width]):
with tf.compat.v1.name_scope(scope, 'eval_image', [image, height, width]):
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Crop the central region of the image with an area containing 87.5% of
......@@ -276,8 +276,8 @@ def preprocess_for_eval(image,
if height and width:
# Resize the image to the specified height and width.
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(
image, [height, width], align_corners=False)
image = tf.image.resize(
image, [height, width], method=tf.image.ResizeMethod.BILINEAR)
image = tf.squeeze(image, [0])
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
......
research/attention_ocr/python/metrics.py
View file @ 50fa3eb9
......@@ -34,20 +34,21 @@ def char_accuracy(predictions, targets, rej_char, streaming=False):
a update_ops for execution and value tensor whose value on evaluation
returns the total character accuracy.
"""
with tf.variable_scope('CharAccuracy'):
with tf.compat.v1.variable_scope('CharAccuracy'):
predictions.get_shape().assert_is_compatible_with(targets.get_shape())
targets = tf.to_int32(targets)
targets = tf.cast(targets, dtype=tf.int32)
const_rej_char = tf.constant(rej_char, shape=targets.get_shape())
weights = tf.to_float(tf.not_equal(targets, const_rej_char))
correct_chars = tf.to_float(tf.equal(predictions, targets))
accuracy_per_example = tf.div(
tf.reduce_sum(tf.multiply(correct_chars, weights), 1),
tf.reduce_sum(weights, 1))
weights = tf.cast(tf.not_equal(targets, const_rej_char), dtype=tf.float32)
correct_chars = tf.cast(tf.equal(predictions, targets), dtype=tf.float32)
accuracy_per_example = tf.compat.v1.div(
tf.reduce_sum(input_tensor=tf.multiply(
correct_chars, weights), axis=1),
tf.reduce_sum(input_tensor=weights, axis=1))
if streaming:
return tf.contrib.metrics.streaming_mean(accuracy_per_example)
else:
return tf.reduce_mean(accuracy_per_example)
return tf.reduce_mean(input_tensor=accuracy_per_example)
def sequence_accuracy(predictions, targets, rej_char, streaming=False):
......@@ -66,25 +67,26 @@ def sequence_accuracy(predictions, targets, rej_char, streaming=False):
returns the total sequence accuracy.
"""
with tf.variable_scope('SequenceAccuracy'):
with tf.compat.v1.variable_scope('SequenceAccuracy'):
predictions.get_shape().assert_is_compatible_with(targets.get_shape())
targets = tf.to_int32(targets)
targets = tf.cast(targets, dtype=tf.int32)
const_rej_char = tf.constant(
rej_char, shape=targets.get_shape(), dtype=tf.int32)
include_mask = tf.not_equal(targets, const_rej_char)
include_predictions = tf.to_int32(
tf.where(include_mask, predictions,
tf.zeros_like(predictions) + rej_char))
correct_chars = tf.to_float(tf.equal(include_predictions, targets))
include_predictions = tf.cast(
tf.compat.v1.where(include_mask, predictions,
tf.zeros_like(predictions) + rej_char), dtype=tf.int32)
correct_chars = tf.cast(
tf.equal(include_predictions, targets), dtype=tf.float32)
correct_chars_counts = tf.cast(
tf.reduce_sum(correct_chars, reduction_indices=[1]), dtype=tf.int32)
tf.reduce_sum(input_tensor=correct_chars, axis=[1]), dtype=tf.int32)
target_length = targets.get_shape().dims[1].value
target_chars_counts = tf.constant(
target_length, shape=correct_chars_counts.get_shape())
accuracy_per_example = tf.to_float(
tf.equal(correct_chars_counts, target_chars_counts))
accuracy_per_example = tf.cast(
tf.equal(correct_chars_counts, target_chars_counts), dtype=tf.float32)
if streaming:
return tf.contrib.metrics.streaming_mean(accuracy_per_example)
else:
return tf.reduce_mean(accuracy_per_example)
return tf.reduce_mean(input_tensor=accuracy_per_example)
research/attention_ocr/python/metrics_test.py
View file @ 50fa3eb9
......@@ -38,8 +38,8 @@ class AccuracyTest(tf.test.TestCase):
A session object that should be used as a context manager.
"""
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.local_variables_initializer())
yield sess
def _fake_labels(self):
......@@ -55,7 +55,7 @@ class AccuracyTest(tf.test.TestCase):
return incorrect
def test_sequence_accuracy_identical_samples(self):
labels_tf = tf.convert_to_tensor(self._fake_labels())
labels_tf = tf.convert_to_tensor(value=self._fake_labels())
accuracy_tf = metrics.sequence_accuracy(labels_tf, labels_tf,
self.rej_char)
......@@ -66,9 +66,9 @@ class AccuracyTest(tf.test.TestCase):
def test_sequence_accuracy_one_char_difference(self):
ground_truth_np = self._fake_labels()
ground_truth_tf = tf.convert_to_tensor(ground_truth_np)
ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
prediction_tf = tf.convert_to_tensor(
self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
accuracy_tf = metrics.sequence_accuracy(prediction_tf, ground_truth_tf,
self.rej_char)
......@@ -80,9 +80,9 @@ class AccuracyTest(tf.test.TestCase):
def test_char_accuracy_one_char_difference_with_padding(self):
ground_truth_np = self._fake_labels()
ground_truth_tf = tf.convert_to_tensor(ground_truth_np)
ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
prediction_tf = tf.convert_to_tensor(
self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
accuracy_tf = metrics.char_accuracy(prediction_tf, ground_truth_tf,
self.rej_char)
......
research/attention_ocr/python/model.py
View file @ 50fa3eb9
......@@ -92,8 +92,8 @@ class CharsetMapper(object):
Args:
ids: a tensor with shape [batch_size, max_sequence_length]
"""
return tf.reduce_join(
self.table.lookup(tf.to_int64(ids)), reduction_indices=1)
return tf.strings.reduce_join(
inputs=self.table.lookup(tf.cast(ids, dtype=tf.int64)), axis=1)
def get_softmax_loss_fn(label_smoothing):
......@@ -110,7 +110,7 @@ def get_softmax_loss_fn(label_smoothing):
def loss_fn(labels, logits):
return (tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
logits=logits, labels=tf.stop_gradient(labels)))
else:
def loss_fn(labels, logits):
......@@ -140,7 +140,7 @@ def get_tensor_dimensions(tensor):
raise ValueError(
'Incompatible shape: len(tensor.get_shape().dims) != 4 (%d != 4)' %
len(tensor.get_shape().dims))
batch_size = tf.shape(tensor)[0]
batch_size = tf.shape(input=tensor)[0]
height = tensor.get_shape().dims[1].value
width = tensor.get_shape().dims[2].value
num_features = tensor.get_shape().dims[3].value
......@@ -161,8 +161,8 @@ def lookup_indexed_value(indices, row_vecs):
A tensor of shape (batch, ) formed by row_vecs[i, indices[i]].
"""
gather_indices = tf.stack((tf.range(
tf.shape(row_vecs)[0], dtype=tf.int32), tf.cast(indices, tf.int32)),
axis=1)
tf.shape(input=row_vecs)[0], dtype=tf.int32), tf.cast(indices, tf.int32)),
axis=1)
return tf.gather_nd(row_vecs, gather_indices)
......@@ -181,7 +181,7 @@ def max_char_logprob_cumsum(char_log_prob):
so the same function can be used regardless whether use_length_predictions
is true or false.
"""
max_char_log_prob = tf.reduce_max(char_log_prob, reduction_indices=2)
max_char_log_prob = tf.reduce_max(input_tensor=char_log_prob, axis=2)
# For an input array [a, b, c]) tf.cumsum returns [a, a + b, a + b + c] if
# exclusive set to False (default).
return tf.cumsum(max_char_log_prob, axis=1, exclusive=False)
......@@ -203,7 +203,7 @@ def find_length_by_null(predicted_chars, null_code):
A [batch, ] tensor which stores the sequence length for each sample.
"""
return tf.reduce_sum(
tf.cast(tf.not_equal(null_code, predicted_chars), tf.int32), axis=1)
input_tensor=tf.cast(tf.not_equal(null_code, predicted_chars), tf.int32), axis=1)
def axis_pad(tensor, axis, before=0, after=0, constant_values=0.0):
......@@ -248,7 +248,8 @@ def null_based_length_prediction(chars_log_prob, null_code):
element #seq_length - is the probability of length=seq_length.
predicted_length is a tensor with shape [batch].
"""
predicted_chars = tf.to_int32(tf.argmax(chars_log_prob, axis=2))
predicted_chars = tf.cast(
tf.argmax(input=chars_log_prob, axis=2), dtype=tf.int32)
# We do right pad to support sequences with seq_length elements.
text_log_prob = max_char_logprob_cumsum(
axis_pad(chars_log_prob, axis=1, after=1))
......@@ -334,9 +335,9 @@ class Model(object):
"""
mparams = self._mparams['conv_tower_fn']
logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
with tf.variable_scope('conv_tower_fn/INCE'):
with tf.compat.v1.variable_scope('conv_tower_fn/INCE'):
if reuse:
tf.get_variable_scope().reuse_variables()
tf.compat.v1.get_variable_scope().reuse_variables()
with slim.arg_scope(inception.inception_v3_arg_scope()):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
......@@ -372,7 +373,7 @@ class Model(object):
def sequence_logit_fn(self, net, labels_one_hot):
mparams = self._mparams['sequence_logit_fn']
# TODO(gorban): remove /alias suffixes from the scopes.
with tf.variable_scope('sequence_logit_fn/SQLR'):
with tf.compat.v1.variable_scope('sequence_logit_fn/SQLR'):
layer_class = sequence_layers.get_layer_class(mparams.use_attention,
mparams.use_autoregression)
layer = layer_class(net, labels_one_hot, self._params, mparams)
......@@ -392,7 +393,7 @@ class Model(object):
]
xy_flat_shape = (batch_size, 1, height * width, num_features)
nets_for_merge = []
with tf.variable_scope('max_pool_views', values=nets_list):
with tf.compat.v1.variable_scope('max_pool_views', values=nets_list):
for net in nets_list:
nets_for_merge.append(tf.reshape(net, xy_flat_shape))
merged_net = tf.concat(nets_for_merge, 1)
......@@ -413,10 +414,11 @@ class Model(object):
Returns:
A tensor of shape [batch_size, seq_length, features_size].
"""
with tf.variable_scope('pool_views_fn/STCK'):
with tf.compat.v1.variable_scope('pool_views_fn/STCK'):
net = tf.concat(nets, 1)
batch_size = tf.shape(net)[0]
image_size = net.get_shape().dims[1].value * net.get_shape().dims[2].value
batch_size = tf.shape(input=net)[0]
image_size = net.get_shape().dims[1].value * \
net.get_shape().dims[2].value
feature_size = net.get_shape().dims[3].value
return tf.reshape(net, tf.stack([batch_size, image_size, feature_size]))
......@@ -438,11 +440,13 @@ class Model(object):
with shape [batch_size x seq_length].
"""
log_prob = utils.logits_to_log_prob(chars_logit)
ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
ids = tf.cast(tf.argmax(input=log_prob, axis=2),
name='predicted_chars', dtype=tf.int32)
mask = tf.cast(
slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
all_scores = tf.nn.softmax(chars_logit)
selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
selected_scores = tf.boolean_mask(
tensor=all_scores, mask=mask, name='char_scores')
scores = tf.reshape(
selected_scores,
shape=(-1, self._params.seq_length),
......@@ -499,7 +503,7 @@ class Model(object):
images = tf.subtract(images, 0.5)
images = tf.multiply(images, 2.5)
with tf.variable_scope(scope, reuse=reuse):
with tf.compat.v1.variable_scope(scope, reuse=reuse):
views = tf.split(
value=images, num_or_size_splits=self._params.num_views, axis=2)
logging.debug('Views=%d single view: %s', len(views), views[0])
......@@ -566,7 +570,7 @@ class Model(object):
# multiple losses including regularization losses.
self.sequence_loss_fn(endpoints.chars_logit, data.labels)
total_loss = slim.losses.get_total_loss()
tf.summary.scalar('TotalLoss', total_loss)
tf.compat.v1.summary.scalar('TotalLoss', total_loss)
return total_loss
def label_smoothing_regularization(self, chars_labels, weight=0.1):
......@@ -605,7 +609,7 @@ class Model(object):
A Tensor with shape [batch_size] - the log-perplexity for each sequence.
"""
mparams = self._mparams['sequence_loss_fn']
with tf.variable_scope('sequence_loss_fn/SLF'):
with tf.compat.v1.variable_scope('sequence_loss_fn/SLF'):
if mparams.label_smoothing > 0:
smoothed_one_hot_labels = self.label_smoothing_regularization(
chars_labels, mparams.label_smoothing)
......@@ -625,7 +629,7 @@ class Model(object):
shape=(batch_size, seq_length),
dtype=tf.int64)
known_char = tf.not_equal(chars_labels, reject_char)
weights = tf.to_float(known_char)
weights = tf.cast(known_char, dtype=tf.float32)
logits_list = tf.unstack(chars_logits, axis=1)
weights_list = tf.unstack(weights, axis=1)
......@@ -635,7 +639,7 @@ class Model(object):
weights_list,
softmax_loss_function=get_softmax_loss_fn(mparams.label_smoothing),
average_across_timesteps=mparams.average_across_timesteps)
tf.losses.add_loss(loss)
tf.compat.v1.losses.add_loss(loss)
return loss
def create_summaries(self, data, endpoints, charset, is_training):
......@@ -665,13 +669,14 @@ class Model(object):
# tf.summary.text(sname('text/pr'), pr_text)
# gt_text = charset_mapper.get_text(data.labels[:max_outputs,:])
# tf.summary.text(sname('text/gt'), gt_text)
tf.summary.image(sname('image'), data.images, max_outputs=max_outputs)
tf.compat.v1.summary.image(
sname('image'), data.images, max_outputs=max_outputs)
if is_training:
tf.summary.image(
tf.compat.v1.summary.image(
sname('image/orig'), data.images_orig, max_outputs=max_outputs)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
for var in tf.compat.v1.trainable_variables():
tf.compat.v1.summary.histogram(var.op.name, var)
return None
else:
......@@ -700,7 +705,8 @@ class Model(object):
for name, value in names_to_values.items():
summary_name = 'eval/' + name
tf.summary.scalar(summary_name, tf.Print(value, [value], summary_name))
tf.compat.v1.summary.scalar(
summary_name, tf.compat.v1.Print(value, [value], summary_name))
return list(names_to_updates.values())
def create_init_fn_to_restore(self,
......@@ -733,9 +739,9 @@ class Model(object):
logging.info('variables_to_restore:\n%s',
utils.variables_to_restore().keys())
logging.info('moving_average_variables:\n%s',
[v.op.name for v in tf.moving_average_variables()])
[v.op.name for v in tf.compat.v1.moving_average_variables()])
logging.info('trainable_variables:\n%s',
[v.op.name for v in tf.trainable_variables()])
[v.op.name for v in tf.compat.v1.trainable_variables()])
if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(), master_checkpoint)
......
research/attention_ocr/python/model_export.py
View file @ 50fa3eb9
......@@ -42,7 +42,8 @@ flags.DEFINE_integer(
'image_height', None,
'Image height used during training(or crop height if used)'
' If not set, the dataset default is used instead.')
flags.DEFINE_string('work_dir', '/tmp', 'A directory to store temporary files.')
flags.DEFINE_string('work_dir', '/tmp',
'A directory to store temporary files.')
flags.DEFINE_integer('version_number', 1, 'Version number of the model')
flags.DEFINE_bool(
'export_for_serving', True,
......@@ -116,7 +117,7 @@ def export_model(export_dir,
image_height = crop_image_height or dataset_image_height
if export_for_serving:
images_orig = tf.placeholder(
images_orig = tf.compat.v1.placeholder(
tf.string, shape=[batch_size], name='tf_example')
images_orig_float = model_export_lib.generate_tfexample_image(
images_orig,
......@@ -126,22 +127,23 @@ def export_model(export_dir,
name='float_images')
else:
images_shape = (batch_size, image_height, image_width, image_depth)
images_orig = tf.placeholder(
images_orig = tf.compat.v1.placeholder(
tf.uint8, shape=images_shape, name='original_image')
images_orig_float = tf.image.convert_image_dtype(
images_orig, dtype=tf.float32, name='float_images')
endpoints = model.create_base(images_orig_float, labels_one_hot=None)
sess = tf.Session()
saver = tf.train.Saver(slim.get_variables_to_restore(), sharded=True)
sess = tf.compat.v1.Session()
saver = tf.compat.v1.train.Saver(
slim.get_variables_to_restore(), sharded=True)
saver.restore(sess, get_checkpoint_path())
tf.logging.info('Model restored successfully.')
tf.compat.v1.logging.info('Model restored successfully.')
# Create model signature.
if export_for_serving:
input_tensors = {
tf.saved_model.signature_constants.CLASSIFY_INPUTS: images_orig
tf.saved_model.CLASSIFY_INPUTS: images_orig
}
else:
input_tensors = {'images': images_orig}
......@@ -163,21 +165,21 @@ def export_model(export_dir,
dataset.max_sequence_length)):
output_tensors['attention_mask_%d' % i] = t
signature_outputs = model_export_lib.build_tensor_info(output_tensors)
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
signature_def = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
signature_inputs, signature_outputs,
tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME)
tf.saved_model.CLASSIFY_METHOD_NAME)
# Save model.
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
sess, [tf.saved_model.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def
},
main_op=tf.tables_initializer(),
main_op=tf.compat.v1.tables_initializer(),
strip_default_attrs=True)
builder.save()
tf.logging.info('Model has been exported to %s' % export_dir)
tf.compat.v1.logging.info('Model has been exported to %s' % export_dir)
return signature_def
......
research/attention_ocr/python/model_export_lib.py
View file @ 50fa3eb9
......@@ -36,7 +36,7 @@ def normalize_image(image, original_minval, original_maxval, target_minval,
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('NormalizeImage', values=[image]):
with tf.compat.v1.name_scope('NormalizeImage', values=[image]):
original_minval = float(original_minval)
original_maxval = float(original_maxval)
target_minval = float(target_minval)
......@@ -68,16 +68,17 @@ def generate_tfexample_image(input_example_strings,
A tensor with shape [batch_size, height, width, channels] of type float32
with values in the range [0..1]
"""
batch_size = tf.shape(input_example_strings)[0]
batch_size = tf.shape(input=input_example_strings)[0]
images_shape = tf.stack(
[batch_size, image_height, image_width, image_channels])
tf_example_image_key = 'image/encoded'
feature_configs = {
tf_example_image_key:
tf.FixedLenFeature(
tf.io.FixedLenFeature(
image_height * image_width * image_channels, dtype=tf.float32)
}
feature_tensors = tf.parse_example(input_example_strings, feature_configs)
feature_tensors = tf.io.parse_example(
serialized=input_example_strings, features=feature_configs)
float_images = tf.reshape(
normalize_image(
feature_tensors[tf_example_image_key],
......@@ -97,11 +98,11 @@ def attention_ocr_attention_masks(num_characters):
names = ['%s/Softmax:0' % (prefix)]
for i in range(1, num_characters):
names += ['%s_%d/Softmax:0' % (prefix, i)]
return [tf.get_default_graph().get_tensor_by_name(n) for n in names]
return [tf.compat.v1.get_default_graph().get_tensor_by_name(n) for n in names]
def build_tensor_info(tensor_dict):
return {
k: tf.saved_model.utils.build_tensor_info(t)
k: tf.compat.v1.saved_model.utils.build_tensor_info(t)
for k, t in tensor_dict.items()
}
research/attention_ocr/python/model_export_test.py
View file @ 50fa3eb9
......@@ -29,7 +29,7 @@ _CHECKPOINT_URL = (
def _clean_up():
tf.gfile.DeleteRecursively(tf.test.get_temp_dir())
tf.io.gfile.rmtree(tf.compat.v1.test.get_temp_dir())
def _create_tf_example_string(image):
......@@ -47,7 +47,7 @@ class AttentionOcrExportTest(tf.test.TestCase):
for suffix in ['.meta', '.index', '.data-00000-of-00001']:
filename = _CHECKPOINT + suffix
self.assertTrue(
tf.gfile.Exists(filename),
tf.io.gfile.exists(filename),
msg='Missing checkpoint file %s. '
'Please download and extract it from %s' %
(filename, _CHECKPOINT_URL))
......@@ -57,7 +57,8 @@ class AttentionOcrExportTest(tf.test.TestCase):
os.path.dirname(__file__), 'datasets/testdata/fsns')
tf.test.TestCase.setUp(self)
_clean_up()
self.export_dir = os.path.join(tf.test.get_temp_dir(), 'exported_model')
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',
......@@ -93,10 +94,10 @@ class AttentionOcrExportTest(tf.test.TestCase):
size=self.dataset.image_shape).astype('uint8'),
}
signature_def = graph_def.signature_def[
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
if serving:
input_name = signature_def.inputs[
tf.saved_model.signature_constants.CLASSIFY_INPUTS].name
tf.saved_model.CLASSIFY_INPUTS].name
# Model for serving takes input: inputs['inputs'] = 'tf_example:0'
feed_dict = {
input_name: [
......@@ -126,11 +127,11 @@ class AttentionOcrExportTest(tf.test.TestCase):
export_for_serving: True if the model was exported for Serving. This
affects how input is fed into the model.
"""
tf.reset_default_graph()
sess = tf.Session()
graph_def = tf.saved_model.loader.load(
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.tag_constants.SERVING],
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)
......
research/attention_ocr/python/model_test.py
View file @ 50fa3eb9
......@@ -52,7 +52,7 @@ class ModelTest(tf.test.TestCase):
self.num_char_classes)
self.length_logit_shape = (self.batch_size, self.seq_length + 1)
# Placeholder knows image dimensions, but not batch size.
self.input_images = tf.placeholder(
self.input_images = tf.compat.v1.placeholder(
tf.float32,
shape=(None, self.image_height, self.image_width, 3),
name='input_node')
......@@ -89,8 +89,8 @@ class ModelTest(tf.test.TestCase):
with self.test_session() as sess:
endpoints_tf = ocr_model.create_base(
images=self.input_images, labels_one_hot=None)
sess.run(tf.global_variables_initializer())
tf.tables_initializer().run()
sess.run(tf.compat.v1.global_variables_initializer())
tf.compat.v1.tables_initializer().run()
endpoints = sess.run(
endpoints_tf, feed_dict={self.input_images: self.fake_images})
......@@ -127,7 +127,7 @@ class ModelTest(tf.test.TestCase):
ocr_model = self.create_model()
conv_tower = ocr_model.conv_tower_fn(self.input_images)
sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())
conv_tower_np = sess.run(
conv_tower, feed_dict={self.input_images: self.fake_images})
......@@ -141,9 +141,9 @@ class ModelTest(tf.test.TestCase):
ocr_model = self.create_model()
ocr_model.create_base(images=self.input_images, labels_one_hot=None)
with self.test_session() as sess:
tfprof_root = tf.profiler.profile(
tfprof_root = tf.compat.v1.profiler.profile(
sess.graph,
options=tf.profiler.ProfileOptionBuilder
options=tf.compat.v1.profiler.ProfileOptionBuilder
.trainable_variables_parameter())
model_size_bytes = 4 * tfprof_root.total_parameters
......@@ -163,9 +163,9 @@ class ModelTest(tf.test.TestCase):
summaries = ocr_model.create_summaries(
data, endpoints, charset, is_training=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
tf.tables_initializer().run()
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.local_variables_initializer())
tf.compat.v1.tables_initializer().run()
sess.run(summaries) # just check it is runnable
def test_sequence_loss_function_without_label_smoothing(self):
......@@ -188,7 +188,7 @@ class ModelTest(tf.test.TestCase):
Returns:
a list of tensors with encoded image coordinates in them.
"""
batch_size = tf.shape(net)[0]
batch_size = tf.shape(input=net)[0]
_, h, w, _ = net.shape.as_list()
h_loc = [
tf.tile(
......@@ -200,7 +200,8 @@ class ModelTest(tf.test.TestCase):
h_loc = tf.concat([tf.expand_dims(t, 2) for t in h_loc], 2)
w_loc = [
tf.tile(
tf.contrib.layers.one_hot_encoding(tf.constant([i]), num_classes=w),
tf.contrib.layers.one_hot_encoding(
tf.constant([i]), num_classes=w),
[h, 1]) for i in range(w)
]
w_loc = tf.concat([tf.expand_dims(t, 2) for t in w_loc], 2)
......@@ -272,8 +273,8 @@ class ModelTest(tf.test.TestCase):
endpoints_tf = ocr_model.create_base(
images=self.fake_images, labels_one_hot=None)
sess.run(tf.global_variables_initializer())
tf.tables_initializer().run()
sess.run(tf.compat.v1.global_variables_initializer())
tf.compat.v1.tables_initializer().run()
endpoints = sess.run(endpoints_tf)
self.assertEqual(endpoints.predicted_text.shape, (self.batch_size,))
......@@ -289,7 +290,7 @@ class CharsetMapperTest(tf.test.TestCase):
charset_mapper = model.CharsetMapper(charset)
with self.test_session() as sess:
tf.tables_initializer().run()
tf.compat.v1.tables_initializer().run()
text = sess.run(charset_mapper.get_text(ids))
self.assertAllEqual(text, [b'hello', b'world'])
......
research/attention_ocr/python/sequence_layers.py
View file @ 50fa3eb9
......@@ -111,12 +111,12 @@ class SequenceLayerBase(object):
self._mparams = method_params
self._net = net
self._labels_one_hot = labels_one_hot
self._batch_size = tf.shape(net)[0]
self._batch_size = tf.shape(input=net)[0]
# Initialize parameters for char logits which will be computed on the fly
# inside an LSTM decoder.
self._char_logits = {}
regularizer = slim.l2_regularizer(self._mparams.weight_decay)
regularizer = tf.keras.regularizers.l2(0.5 * (self._mparams.weight_decay))
self._softmax_w = slim.model_variable(
'softmax_w',
[self._mparams.num_lstm_units, self._params.num_char_classes],
......@@ -124,7 +124,7 @@ class SequenceLayerBase(object):
regularizer=regularizer)
self._softmax_b = slim.model_variable(
'softmax_b', [self._params.num_char_classes],
initializer=tf.zeros_initializer(),
initializer=tf.compat.v1.zeros_initializer(),
regularizer=regularizer)
@abc.abstractmethod
......@@ -203,8 +203,8 @@ class SequenceLayerBase(object):
A tensor with shape [batch_size, num_char_classes]
"""
if char_index not in self._char_logits:
self._char_logits[char_index] = tf.nn.xw_plus_b(inputs, self._softmax_w,
self._softmax_b)
self._char_logits[char_index] = tf.compat.v1.nn.xw_plus_b(inputs, self._softmax_w,
self._softmax_b)
return self._char_logits[char_index]
def char_one_hot(self, logit):
......@@ -216,7 +216,7 @@ class SequenceLayerBase(object):
Returns:
A tensor with shape [batch_size, num_char_classes]
"""
prediction = tf.argmax(logit, axis=1)
prediction = tf.argmax(input=logit, axis=1)
return slim.one_hot_encoding(prediction, self._params.num_char_classes)
def get_input(self, prev, i):
......@@ -244,10 +244,10 @@ class SequenceLayerBase(object):
Returns:
A tensor with shape [batch_size, seq_length, num_char_classes].
"""
with tf.variable_scope('LSTM'):
with tf.compat.v1.variable_scope('LSTM'):
first_label = self.get_input(prev=None, i=0)
decoder_inputs = [first_label] + [None] * (self._params.seq_length - 1)
lstm_cell = tf.contrib.rnn.LSTMCell(
lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
self._mparams.num_lstm_units,
use_peepholes=False,
cell_clip=self._mparams.lstm_state_clip_value,
......@@ -259,9 +259,9 @@ class SequenceLayerBase(object):
loop_function=self.get_input,
cell=lstm_cell)
with tf.variable_scope('logits'):
with tf.compat.v1.variable_scope('logits'):
logits_list = [
tf.expand_dims(self.char_logit(logit, i), dim=1)
tf.expand_dims(self.char_logit(logit, i), axis=1)
for i, logit in enumerate(lstm_outputs)
]
......
research/attention_ocr/python/sequence_layers_test.py
View file @ 50fa3eb9
......@@ -29,13 +29,13 @@ import sequence_layers
def fake_net(batch_size, num_features, feature_size):
return tf.convert_to_tensor(
np.random.uniform(size=(batch_size, num_features, feature_size)),
value=np.random.uniform(size=(batch_size, num_features, feature_size)),
dtype=tf.float32)
def fake_labels(batch_size, seq_length, num_char_classes):
labels_np = tf.convert_to_tensor(
np.random.randint(
value=np.random.randint(
low=0, high=num_char_classes, size=(batch_size, seq_length)))
return slim.one_hot_encoding(labels_np, num_classes=num_char_classes)
......
research/attention_ocr/python/train.py
View file @ 50fa3eb9
......@@ -96,16 +96,16 @@ def get_training_hparams():
def create_optimizer(hparams):
"""Creates optimized based on the specified flags."""
if hparams.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(
optimizer = tf.compat.v1.train.MomentumOptimizer(
hparams.learning_rate, momentum=hparams.momentum)
elif hparams.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(hparams.learning_rate)
optimizer = tf.compat.v1.train.AdamOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(hparams.learning_rate)
optimizer = tf.compat.v1.train.AdadeltaOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(hparams.learning_rate)
optimizer = tf.compat.v1.train.AdagradOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
optimizer = tf.compat.v1.train.RMSPropOptimizer(
hparams.learning_rate, momentum=hparams.momentum)
return optimizer
......@@ -154,14 +154,14 @@ def train(loss, init_fn, hparams):
def prepare_training_dir():
if not tf.gfile.Exists(FLAGS.train_log_dir):
if not tf.io.gfile.exists(FLAGS.train_log_dir):
logging.info('Create a new training directory %s', FLAGS.train_log_dir)
tf.gfile.MakeDirs(FLAGS.train_log_dir)
tf.io.gfile.makedirs(FLAGS.train_log_dir)
else:
if FLAGS.reset_train_dir:
logging.info('Reset the training directory %s', FLAGS.train_log_dir)
tf.gfile.DeleteRecursively(FLAGS.train_log_dir)
tf.gfile.MakeDirs(FLAGS.train_log_dir)
tf.io.gfile.rmtree(FLAGS.train_log_dir)
tf.io.gfile.makedirs(FLAGS.train_log_dir)
else:
logging.info('Use already existing training directory %s',
FLAGS.train_log_dir)
......@@ -169,7 +169,7 @@ def prepare_training_dir():
def calculate_graph_metrics():
param_stats = model_analyzer.print_model_analysis(
tf.get_default_graph(),
tf.compat.v1.get_default_graph(),
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
return param_stats.total_parameters
......@@ -186,7 +186,7 @@ def main(_):
# If ps_tasks is zero, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices.
device_setter = tf.train.replica_device_setter(
device_setter = tf.compat.v1.train.replica_device_setter(
FLAGS.ps_tasks, merge_devices=True)
with tf.device(device_setter):
data = data_provider.get_data(
......
research/attention_ocr/python/utils.py
View file @ 50fa3eb9
......@@ -37,16 +37,16 @@ def logits_to_log_prob(logits):
probabilities.
"""
with tf.variable_scope('log_probabilities'):
with tf.compat.v1.variable_scope('log_probabilities'):
reduction_indices = len(logits.shape.as_list()) - 1
max_logits = tf.reduce_max(
logits, reduction_indices=reduction_indices, keep_dims=True)
input_tensor=logits, axis=reduction_indices, keepdims=True)
safe_logits = tf.subtract(logits, max_logits)
sum_exp = tf.reduce_sum(
tf.exp(safe_logits),
reduction_indices=reduction_indices,
keep_dims=True)
log_probs = tf.subtract(safe_logits, tf.log(sum_exp))
input_tensor=tf.exp(safe_logits),
axis=reduction_indices,
keepdims=True)
log_probs = tf.subtract(safe_logits, tf.math.log(sum_exp))
return log_probs
......@@ -91,7 +91,7 @@ def ConvertAllInputsToTensors(func):
"""
def FuncWrapper(*args):
tensors = [tf.convert_to_tensor(a) for a in args]
tensors = [tf.convert_to_tensor(value=a) for a in args]
return func(*tensors)
return FuncWrapper
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