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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) [![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) [![Paper](http://img.shields.io/badge/paper-arXiv.1704.03549-B3181B.svg)](https://arxiv.org/abs/1704.03549)
...@@ -7,14 +7,20 @@ ...@@ -7,14 +7,20 @@
*A TensorFlow model for real-world image text extraction problems.* *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 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 [FSNS dataset][FSNS] to transcribe street names in France. You can also train the code on your own data.
also use it to train it on your own data.
More details can be found in our paper: More details can be found in our paper:
["Attention-based Extraction of Structured Information from Street View ["Attention-based Extraction of Structured Information from Street View
Imagery"](https://arxiv.org/abs/1704.03549) 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 ## Contacts
Authors Authors
...@@ -22,7 +28,18 @@ Authors ...@@ -22,7 +28,18 @@ Authors
* Zbigniew Wojna (zbigniewwojna@gmail.com) * Zbigniew Wojna (zbigniewwojna@gmail.com)
* Alexander Gorban (gorban@google.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 ## Requirements
...@@ -49,6 +66,42 @@ cd .. ...@@ -49,6 +66,42 @@ cd ..
[TF]: https://www.tensorflow.org/install/ [TF]: https://www.tensorflow.org/install/
[FSNS]: https://github.com/tensorflow/models/tree/master/research/street [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 ## How to use this code
To run all unit tests: To run all unit tests:
...@@ -80,7 +133,7 @@ tar xf attention_ocr_2017_08_09.tar.gz ...@@ -80,7 +133,7 @@ tar xf attention_ocr_2017_08_09.tar.gz
python train.py --checkpoint=model.ckpt-399731 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: 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): ...@@ -56,14 +56,14 @@ def augment_image(image):
Returns: Returns:
Distorted Tensor image of the same shape. 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 height = image.get_shape().dims[0].value
width = image.get_shape().dims[1].value width = image.get_shape().dims[1].value
# Random crop cut from the street sign image, resized to the same size. # 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. # 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( 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]), bounding_boxes=tf.zeros([0, 0, 4]),
min_object_covered=0.8, min_object_covered=0.8,
aspect_ratio_range=[0.8, 1.2], aspect_ratio_range=[0.8, 1.2],
...@@ -74,7 +74,7 @@ def augment_image(image): ...@@ -74,7 +74,7 @@ def augment_image(image):
# Randomly chooses one of the 4 interpolation methods # Randomly chooses one of the 4 interpolation methods
distorted_image = inception_preprocessing.apply_with_random_selector( distorted_image = inception_preprocessing.apply_with_random_selector(
distorted_image, 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) num_cases=4)
distorted_image.set_shape([height, width, 3]) distorted_image.set_shape([height, width, 3])
...@@ -99,9 +99,10 @@ def central_crop(image, crop_size): ...@@ -99,9 +99,10 @@ def central_crop(image, crop_size):
Returns: Returns:
A tensor of shape [crop_height, crop_width, channels]. 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 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( assert_op1 = tf.Assert(
tf.greater_equal(image_height, target_height), tf.greater_equal(image_height, target_height),
['image_height < target_height', 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, ...@@ -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 A float32 tensor of shape [H x W x 3] with RGB values in the required
range. range.
""" """
with tf.variable_scope('PreprocessImage'): with tf.compat.v1.variable_scope('PreprocessImage'):
image = tf.image.convert_image_dtype(image, dtype=tf.float32) image = tf.image.convert_image_dtype(image, dtype=tf.float32)
if augment or central_crop_size: if augment or central_crop_size:
if num_towers == 1: if num_towers == 1:
...@@ -182,7 +183,7 @@ def get_data(dataset, ...@@ -182,7 +183,7 @@ def get_data(dataset,
image_orig, augment, central_crop_size, num_towers=dataset.num_of_views) image_orig, augment, central_crop_size, num_towers=dataset.num_of_views)
label_one_hot = slim.one_hot_encoding(label, dataset.num_char_classes) 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], [image, image_orig, label, label_one_hot],
batch_size=batch_size, batch_size=batch_size,
num_threads=shuffle_config.num_batching_threads, num_threads=shuffle_config.num_batching_threads,
......
research/attention_ocr/python/datasets/fsns.py
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...@@ -72,7 +72,7 @@ def read_charset(filename, null_character=u'\u2591'): ...@@ -72,7 +72,7 @@ def read_charset(filename, null_character=u'\u2591'):
""" """
pattern = re.compile(r'(\d+)\t(.+)') pattern = re.compile(r'(\d+)\t(.+)')
charset = {} charset = {}
with tf.gfile.GFile(filename) as f: with tf.io.gfile.GFile(filename) as f:
for i, line in enumerate(f): for i, line in enumerate(f):
m = pattern.match(line) m = pattern.match(line)
if m is None: if m is None:
...@@ -96,9 +96,9 @@ class _NumOfViewsHandler(slim.tfexample_decoder.ItemHandler): ...@@ -96,9 +96,9 @@ class _NumOfViewsHandler(slim.tfexample_decoder.ItemHandler):
self._num_of_views = num_of_views self._num_of_views = num_of_views
def tensors_to_item(self, keys_to_tensors): 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] / 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): def get_split(split_name, dataset_dir=None, config=None):
...@@ -133,19 +133,19 @@ 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) zero = tf.zeros([1], dtype=tf.int64)
keys_to_features = { keys_to_features = {
'image/encoded': 'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''), tf.io.FixedLenFeature((), tf.string, default_value=''),
'image/format': 'image/format':
tf.FixedLenFeature((), tf.string, default_value='png'), tf.io.FixedLenFeature((), tf.string, default_value='png'),
'image/width': 'image/width':
tf.FixedLenFeature([1], tf.int64, default_value=zero), tf.io.FixedLenFeature([1], tf.int64, default_value=zero),
'image/orig_width': 'image/orig_width':
tf.FixedLenFeature([1], tf.int64, default_value=zero), tf.io.FixedLenFeature([1], tf.int64, default_value=zero),
'image/class': 'image/class':
tf.FixedLenFeature([config['max_sequence_length']], tf.int64), tf.io.FixedLenFeature([config['max_sequence_length']], tf.int64),
'image/unpadded_class': 'image/unpadded_class':
tf.VarLenFeature(tf.int64), tf.io.VarLenFeature(tf.int64),
'image/text': 'image/text':
tf.FixedLenFeature([1], tf.string, default_value=''), tf.io.FixedLenFeature([1], tf.string, default_value=''),
} }
items_to_handlers = { items_to_handlers = {
'image': 'image':
...@@ -171,7 +171,7 @@ def get_split(split_name, dataset_dir=None, config=None): ...@@ -171,7 +171,7 @@ def get_split(split_name, dataset_dir=None, config=None):
config['splits'][split_name]['pattern']) config['splits'][split_name]['pattern'])
return slim.dataset.Dataset( return slim.dataset.Dataset(
data_sources=file_pattern, data_sources=file_pattern,
reader=tf.TFRecordReader, reader=tf.compat.v1.TFRecordReader,
decoder=decoder, decoder=decoder,
num_samples=config['splits'][split_name]['size'], num_samples=config['splits'][split_name]['size'],
items_to_descriptions=config['items_to_descriptions'], 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): ...@@ -91,7 +91,7 @@ class FsnsTest(tf.test.TestCase):
image_tf, label_tf = provider.get(['image', 'label']) image_tf, label_tf = provider.get(['image', 'label'])
with self.test_session() as sess: 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): with slim.queues.QueueRunners(sess):
image_np, label_np = sess.run([image_tf, label_tf]) 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 ...@@ -10,7 +10,8 @@ KEEP_NUM_RECORDS = 5
print('Downloading %s ...' % URL) print('Downloading %s ...' % URL)
urllib.request.urlretrieve(URL, DST_ORIG) 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: 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) 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): ...@@ -49,7 +49,7 @@ def load_images(file_pattern, batch_size, dataset_name):
for i in range(batch_size): for i in range(batch_size):
path = file_pattern % i path = file_pattern % i
print("Reading %s" % path) 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) images_actual_data[i, ...] = np.asarray(pil_image)
return images_actual_data return images_actual_data
...@@ -63,7 +63,8 @@ def create_model(batch_size, dataset_name): ...@@ -63,7 +63,8 @@ def create_model(batch_size, dataset_name):
num_views=dataset.num_of_views, num_views=dataset.num_of_views,
null_code=dataset.null_code, null_code=dataset.null_code,
charset=dataset.charset) charset=dataset.charset)
raw_images = tf.placeholder(tf.uint8, shape=[batch_size, height, width, 3]) raw_images = tf.compat.v1.placeholder(
tf.uint8, shape=[batch_size, height, width, 3])
images = tf.map_fn(data_provider.preprocess_image, raw_images, images = tf.map_fn(data_provider.preprocess_image, raw_images,
dtype=tf.float32) dtype=tf.float32)
endpoints = model.create_base(images, labels_one_hot=None) endpoints = model.create_base(images, labels_one_hot=None)
...@@ -93,4 +94,4 @@ def main(_): ...@@ -93,4 +94,4 @@ def main(_):
if __name__ == '__main__': 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): ...@@ -14,12 +14,13 @@ class DemoInferenceTest(tf.test.TestCase):
super(DemoInferenceTest, self).setUp() super(DemoInferenceTest, self).setUp()
for suffix in ['.meta', '.index', '.data-00000-of-00001']: for suffix in ['.meta', '.index', '.data-00000-of-00001']:
filename = _CHECKPOINT + suffix filename = _CHECKPOINT + suffix
self.assertTrue(tf.gfile.Exists(filename), self.assertTrue(tf.io.gfile.exists(filename),
msg='Missing checkpoint file %s. ' msg='Missing checkpoint file %s. '
'Please download and extract it from %s' % 'Please download and extract it from %s' %
(filename, _CHECKPOINT_URL)) (filename, _CHECKPOINT_URL))
self._batch_size = 32 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): def test_moving_variables_properly_loaded_from_a_checkpoint(self):
batch_size = 32 batch_size = 32
...@@ -30,9 +31,9 @@ class DemoInferenceTest(tf.test.TestCase): ...@@ -30,9 +31,9 @@ class DemoInferenceTest(tf.test.TestCase):
images_data = demo_inference.load_images(image_path_pattern, batch_size, images_data = demo_inference.load_images(image_path_pattern, batch_size,
dataset_name) dataset_name)
tensor_name = 'AttentionOcr_v1/conv_tower_fn/INCE/InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean' 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( moving_mean_tf = tf.compat.v1.get_default_graph().get_tensor_by_name(
tensor_name + ':0') tensor_name + ':0')
reader = tf.train.NewCheckpointReader(_CHECKPOINT) reader = tf.compat.v1.train.NewCheckpointReader(_CHECKPOINT)
moving_mean_expected = reader.get_tensor(tensor_name) moving_mean_expected = reader.get_tensor(tensor_name)
session_creator = monitored_session.ChiefSessionCreator( session_creator = monitored_session.ChiefSessionCreator(
......
research/attention_ocr/python/eval.py
View file @ 50fa3eb9
...@@ -45,8 +45,8 @@ flags.DEFINE_integer('number_of_steps', None, ...@@ -45,8 +45,8 @@ flags.DEFINE_integer('number_of_steps', None,
def main(_): def main(_):
if not tf.gfile.Exists(FLAGS.eval_log_dir): if not tf.io.gfile.exists(FLAGS.eval_log_dir):
tf.gfile.MakeDirs(FLAGS.eval_log_dir) tf.io.gfile.makedirs(FLAGS.eval_log_dir)
dataset = common_flags.create_dataset(split_name=FLAGS.split_name) dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
model = common_flags.create_model(dataset.num_char_classes, model = common_flags.create_model(dataset.num_char_classes,
...@@ -62,7 +62,7 @@ def main(_): ...@@ -62,7 +62,7 @@ def main(_):
eval_ops = model.create_summaries( eval_ops = model.create_summaries(
data, endpoints, dataset.charset, is_training=False) data, endpoints, dataset.charset, is_training=False)
slim.get_or_create_global_step() 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( slim.evaluation.evaluation_loop(
master=FLAGS.master, master=FLAGS.master,
checkpoint_dir=FLAGS.train_log_dir, 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): ...@@ -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 The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically. 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. # Pass the real x only to one of the func calls.
return control_flow_ops.merge([ return control_flow_ops.merge([
func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case) 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): ...@@ -64,7 +64,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
Raises: Raises:
ValueError: if color_ordering not in [0, 3] 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 fast_mode:
if color_ordering == 0: if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.) image = tf.image.random_brightness(image, max_delta=32. / 255.)
...@@ -131,7 +131,7 @@ def distorted_bounding_box_crop(image, ...@@ -131,7 +131,7 @@ def distorted_bounding_box_crop(image,
Returns: Returns:
A tuple, a 3-D Tensor cropped_image and the distorted bbox 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 # Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax]. # the coordinates are ordered [ymin, xmin, ymax, xmax].
...@@ -143,7 +143,7 @@ def distorted_bounding_box_crop(image, ...@@ -143,7 +143,7 @@ def distorted_bounding_box_crop(image,
# bounding box. If no box is supplied, then we assume the bounding box is # bounding box. If no box is supplied, then we assume the bounding box is
# the entire image. # the entire image.
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box( sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.shape(image), image_size=tf.shape(input=image),
bounding_boxes=bbox, bounding_boxes=bbox,
min_object_covered=min_object_covered, min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range, aspect_ratio_range=aspect_ratio_range,
...@@ -188,7 +188,7 @@ def preprocess_for_train(image, ...@@ -188,7 +188,7 @@ def preprocess_for_train(image,
Returns: Returns:
3-D float Tensor of distorted image used for training with range [-1, 1]. 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: if bbox is None:
bbox = tf.constant( bbox = tf.constant(
[0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
...@@ -198,7 +198,7 @@ def preprocess_for_train(image, ...@@ -198,7 +198,7 @@ def preprocess_for_train(image,
# the coordinates are ordered [ymin, xmin, ymax, xmax]. # the coordinates are ordered [ymin, xmin, ymax, xmax].
image_with_box = tf.image.draw_bounding_boxes( image_with_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), bbox) 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) distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
# Restore the shape since the dynamic slice based upon the bbox_size loses # Restore the shape since the dynamic slice based upon the bbox_size loses
...@@ -206,7 +206,7 @@ def preprocess_for_train(image, ...@@ -206,7 +206,7 @@ def preprocess_for_train(image,
distorted_image.set_shape([None, None, 3]) distorted_image.set_shape([None, None, 3])
image_with_distorted_box = tf.image.draw_bounding_boxes( image_with_distorted_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), distorted_bbox) tf.expand_dims(image, 0), distorted_bbox)
tf.summary.image('images_with_distorted_bounding_box', tf.compat.v1.summary.image('images_with_distorted_bounding_box',
image_with_distorted_box) image_with_distorted_box)
# This resizing operation may distort the images because the aspect # This resizing operation may distort the images because the aspect
...@@ -218,10 +218,10 @@ def preprocess_for_train(image, ...@@ -218,10 +218,10 @@ def preprocess_for_train(image,
num_resize_cases = 1 if fast_mode else 4 num_resize_cases = 1 if fast_mode else 4
distorted_image = apply_with_random_selector( distorted_image = apply_with_random_selector(
distorted_image, 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) num_cases=num_resize_cases)
tf.summary.image('cropped_resized_image', tf.compat.v1.summary.image('cropped_resized_image',
tf.expand_dims(distorted_image, 0)) tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally. # Randomly flip the image horizontally.
...@@ -233,7 +233,7 @@ def preprocess_for_train(image, ...@@ -233,7 +233,7 @@ def preprocess_for_train(image,
lambda x, ordering: distort_color(x, ordering, fast_mode), lambda x, ordering: distort_color(x, ordering, fast_mode),
num_cases=4) num_cases=4)
tf.summary.image('final_distorted_image', tf.compat.v1.summary.image('final_distorted_image',
tf.expand_dims(distorted_image, 0)) tf.expand_dims(distorted_image, 0))
distorted_image = tf.subtract(distorted_image, 0.5) distorted_image = tf.subtract(distorted_image, 0.5)
distorted_image = tf.multiply(distorted_image, 2.0) distorted_image = tf.multiply(distorted_image, 2.0)
...@@ -265,7 +265,7 @@ def preprocess_for_eval(image, ...@@ -265,7 +265,7 @@ def preprocess_for_eval(image,
Returns: Returns:
3-D float Tensor of prepared image. 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: if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(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 # Crop the central region of the image with an area containing 87.5% of
...@@ -276,8 +276,8 @@ def preprocess_for_eval(image, ...@@ -276,8 +276,8 @@ def preprocess_for_eval(image,
if height and width: if height and width:
# Resize the image to the specified height and width. # Resize the image to the specified height and width.
image = tf.expand_dims(image, 0) image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear( image = tf.image.resize(
image, [height, width], align_corners=False) image, [height, width], method=tf.image.ResizeMethod.BILINEAR)
image = tf.squeeze(image, [0]) image = tf.squeeze(image, [0])
image = tf.subtract(image, 0.5) image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0) 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): ...@@ -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 a update_ops for execution and value tensor whose value on evaluation
returns the total character accuracy. 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()) 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()) const_rej_char = tf.constant(rej_char, shape=targets.get_shape())
weights = tf.to_float(tf.not_equal(targets, const_rej_char)) weights = tf.cast(tf.not_equal(targets, const_rej_char), dtype=tf.float32)
correct_chars = tf.to_float(tf.equal(predictions, targets)) correct_chars = tf.cast(tf.equal(predictions, targets), dtype=tf.float32)
accuracy_per_example = tf.div( accuracy_per_example = tf.compat.v1.div(
tf.reduce_sum(tf.multiply(correct_chars, weights), 1), tf.reduce_sum(input_tensor=tf.multiply(
tf.reduce_sum(weights, 1)) correct_chars, weights), axis=1),
tf.reduce_sum(input_tensor=weights, axis=1))
if streaming: if streaming:
return tf.contrib.metrics.streaming_mean(accuracy_per_example) return tf.contrib.metrics.streaming_mean(accuracy_per_example)
else: 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): def sequence_accuracy(predictions, targets, rej_char, streaming=False):
...@@ -66,25 +67,26 @@ 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. 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()) 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( const_rej_char = tf.constant(
rej_char, shape=targets.get_shape(), dtype=tf.int32) rej_char, shape=targets.get_shape(), dtype=tf.int32)
include_mask = tf.not_equal(targets, const_rej_char) include_mask = tf.not_equal(targets, const_rej_char)
include_predictions = tf.to_int32( include_predictions = tf.cast(
tf.where(include_mask, predictions, tf.compat.v1.where(include_mask, predictions,
tf.zeros_like(predictions) + rej_char)) tf.zeros_like(predictions) + rej_char), dtype=tf.int32)
correct_chars = tf.to_float(tf.equal(include_predictions, targets)) correct_chars = tf.cast(
tf.equal(include_predictions, targets), dtype=tf.float32)
correct_chars_counts = tf.cast( 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_length = targets.get_shape().dims[1].value
target_chars_counts = tf.constant( target_chars_counts = tf.constant(
target_length, shape=correct_chars_counts.get_shape()) target_length, shape=correct_chars_counts.get_shape())
accuracy_per_example = tf.to_float( accuracy_per_example = tf.cast(
tf.equal(correct_chars_counts, target_chars_counts)) tf.equal(correct_chars_counts, target_chars_counts), dtype=tf.float32)
if streaming: if streaming:
return tf.contrib.metrics.streaming_mean(accuracy_per_example) return tf.contrib.metrics.streaming_mean(accuracy_per_example)
else: 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): ...@@ -38,8 +38,8 @@ class AccuracyTest(tf.test.TestCase):
A session object that should be used as a context manager. A session object that should be used as a context manager.
""" """
with self.cached_session() as sess: with self.cached_session() as sess:
sess.run(tf.global_variables_initializer()) sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.local_variables_initializer()) sess.run(tf.compat.v1.local_variables_initializer())
yield sess yield sess
def _fake_labels(self): def _fake_labels(self):
...@@ -55,7 +55,7 @@ class AccuracyTest(tf.test.TestCase): ...@@ -55,7 +55,7 @@ class AccuracyTest(tf.test.TestCase):
return incorrect return incorrect
def test_sequence_accuracy_identical_samples(self): 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, accuracy_tf = metrics.sequence_accuracy(labels_tf, labels_tf,
self.rej_char) self.rej_char)
...@@ -66,9 +66,9 @@ class AccuracyTest(tf.test.TestCase): ...@@ -66,9 +66,9 @@ class AccuracyTest(tf.test.TestCase):
def test_sequence_accuracy_one_char_difference(self): def test_sequence_accuracy_one_char_difference(self):
ground_truth_np = self._fake_labels() 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( 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, accuracy_tf = metrics.sequence_accuracy(prediction_tf, ground_truth_tf,
self.rej_char) self.rej_char)
...@@ -80,9 +80,9 @@ class AccuracyTest(tf.test.TestCase): ...@@ -80,9 +80,9 @@ class AccuracyTest(tf.test.TestCase):
def test_char_accuracy_one_char_difference_with_padding(self): def test_char_accuracy_one_char_difference_with_padding(self):
ground_truth_np = self._fake_labels() 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( 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, accuracy_tf = metrics.char_accuracy(prediction_tf, ground_truth_tf,
self.rej_char) self.rej_char)
......
research/attention_ocr/python/model.py
View file @ 50fa3eb9
...@@ -92,8 +92,8 @@ class CharsetMapper(object): ...@@ -92,8 +92,8 @@ class CharsetMapper(object):
Args: Args:
ids: a tensor with shape [batch_size, max_sequence_length] ids: a tensor with shape [batch_size, max_sequence_length]
""" """
return tf.reduce_join( return tf.strings.reduce_join(
self.table.lookup(tf.to_int64(ids)), reduction_indices=1) inputs=self.table.lookup(tf.cast(ids, dtype=tf.int64)), axis=1)
def get_softmax_loss_fn(label_smoothing): def get_softmax_loss_fn(label_smoothing):
...@@ -110,7 +110,7 @@ def get_softmax_loss_fn(label_smoothing): ...@@ -110,7 +110,7 @@ def get_softmax_loss_fn(label_smoothing):
def loss_fn(labels, logits): def loss_fn(labels, logits):
return (tf.nn.softmax_cross_entropy_with_logits( return (tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=labels)) logits=logits, labels=tf.stop_gradient(labels)))
else: else:
def loss_fn(labels, logits): def loss_fn(labels, logits):
...@@ -140,7 +140,7 @@ def get_tensor_dimensions(tensor): ...@@ -140,7 +140,7 @@ def get_tensor_dimensions(tensor):
raise ValueError( raise ValueError(
'Incompatible shape: len(tensor.get_shape().dims) != 4 (%d != 4)' % 'Incompatible shape: len(tensor.get_shape().dims) != 4 (%d != 4)' %
len(tensor.get_shape().dims)) 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 height = tensor.get_shape().dims[1].value
width = tensor.get_shape().dims[2].value width = tensor.get_shape().dims[2].value
num_features = tensor.get_shape().dims[3].value num_features = tensor.get_shape().dims[3].value
...@@ -161,7 +161,7 @@ def lookup_indexed_value(indices, row_vecs): ...@@ -161,7 +161,7 @@ def lookup_indexed_value(indices, row_vecs):
A tensor of shape (batch, ) formed by row_vecs[i, indices[i]]. A tensor of shape (batch, ) formed by row_vecs[i, indices[i]].
""" """
gather_indices = tf.stack((tf.range( gather_indices = tf.stack((tf.range(
tf.shape(row_vecs)[0], dtype=tf.int32), tf.cast(indices, tf.int32)), tf.shape(input=row_vecs)[0], dtype=tf.int32), tf.cast(indices, tf.int32)),
axis=1) axis=1)
return tf.gather_nd(row_vecs, gather_indices) return tf.gather_nd(row_vecs, gather_indices)
...@@ -181,7 +181,7 @@ def max_char_logprob_cumsum(char_log_prob): ...@@ -181,7 +181,7 @@ def max_char_logprob_cumsum(char_log_prob):
so the same function can be used regardless whether use_length_predictions so the same function can be used regardless whether use_length_predictions
is true or false. 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 # For an input array [a, b, c]) tf.cumsum returns [a, a + b, a + b + c] if
# exclusive set to False (default). # exclusive set to False (default).
return tf.cumsum(max_char_log_prob, axis=1, exclusive=False) return tf.cumsum(max_char_log_prob, axis=1, exclusive=False)
...@@ -203,7 +203,7 @@ def find_length_by_null(predicted_chars, null_code): ...@@ -203,7 +203,7 @@ def find_length_by_null(predicted_chars, null_code):
A [batch, ] tensor which stores the sequence length for each sample. A [batch, ] tensor which stores the sequence length for each sample.
""" """
return tf.reduce_sum( 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): 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): ...@@ -248,7 +248,8 @@ def null_based_length_prediction(chars_log_prob, null_code):
element #seq_length - is the probability of length=seq_length. element #seq_length - is the probability of length=seq_length.
predicted_length is a tensor with shape [batch]. 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. # We do right pad to support sequences with seq_length elements.
text_log_prob = max_char_logprob_cumsum( text_log_prob = max_char_logprob_cumsum(
axis_pad(chars_log_prob, axis=1, after=1)) axis_pad(chars_log_prob, axis=1, after=1))
...@@ -334,9 +335,9 @@ class Model(object): ...@@ -334,9 +335,9 @@ class Model(object):
""" """
mparams = self._mparams['conv_tower_fn'] mparams = self._mparams['conv_tower_fn']
logging.debug('Using final_endpoint=%s', mparams.final_endpoint) 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: 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(inception.inception_v3_arg_scope()):
with slim.arg_scope([slim.batch_norm, slim.dropout], with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training): is_training=is_training):
...@@ -372,7 +373,7 @@ class Model(object): ...@@ -372,7 +373,7 @@ class Model(object):
def sequence_logit_fn(self, net, labels_one_hot): def sequence_logit_fn(self, net, labels_one_hot):
mparams = self._mparams['sequence_logit_fn'] mparams = self._mparams['sequence_logit_fn']
# TODO(gorban): remove /alias suffixes from the scopes. # 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, layer_class = sequence_layers.get_layer_class(mparams.use_attention,
mparams.use_autoregression) mparams.use_autoregression)
layer = layer_class(net, labels_one_hot, self._params, mparams) layer = layer_class(net, labels_one_hot, self._params, mparams)
...@@ -392,7 +393,7 @@ class Model(object): ...@@ -392,7 +393,7 @@ class Model(object):
] ]
xy_flat_shape = (batch_size, 1, height * width, num_features) xy_flat_shape = (batch_size, 1, height * width, num_features)
nets_for_merge = [] 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: for net in nets_list:
nets_for_merge.append(tf.reshape(net, xy_flat_shape)) nets_for_merge.append(tf.reshape(net, xy_flat_shape))
merged_net = tf.concat(nets_for_merge, 1) merged_net = tf.concat(nets_for_merge, 1)
...@@ -413,10 +414,11 @@ class Model(object): ...@@ -413,10 +414,11 @@ class Model(object):
Returns: Returns:
A tensor of shape [batch_size, seq_length, features_size]. 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) net = tf.concat(nets, 1)
batch_size = tf.shape(net)[0] batch_size = tf.shape(input=net)[0]
image_size = net.get_shape().dims[1].value * net.get_shape().dims[2].value image_size = net.get_shape().dims[1].value * \
net.get_shape().dims[2].value
feature_size = net.get_shape().dims[3].value feature_size = net.get_shape().dims[3].value
return tf.reshape(net, tf.stack([batch_size, image_size, feature_size])) return tf.reshape(net, tf.stack([batch_size, image_size, feature_size]))
...@@ -438,11 +440,13 @@ class Model(object): ...@@ -438,11 +440,13 @@ class Model(object):
with shape [batch_size x seq_length]. with shape [batch_size x seq_length].
""" """
log_prob = utils.logits_to_log_prob(chars_logit) 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( mask = tf.cast(
slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool) slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
all_scores = tf.nn.softmax(chars_logit) 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( scores = tf.reshape(
selected_scores, selected_scores,
shape=(-1, self._params.seq_length), shape=(-1, self._params.seq_length),
...@@ -499,7 +503,7 @@ class Model(object): ...@@ -499,7 +503,7 @@ class Model(object):
images = tf.subtract(images, 0.5) images = tf.subtract(images, 0.5)
images = tf.multiply(images, 2.5) 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( views = tf.split(
value=images, num_or_size_splits=self._params.num_views, axis=2) value=images, num_or_size_splits=self._params.num_views, axis=2)
logging.debug('Views=%d single view: %s', len(views), views[0]) logging.debug('Views=%d single view: %s', len(views), views[0])
...@@ -566,7 +570,7 @@ class Model(object): ...@@ -566,7 +570,7 @@ class Model(object):
# multiple losses including regularization losses. # multiple losses including regularization losses.
self.sequence_loss_fn(endpoints.chars_logit, data.labels) self.sequence_loss_fn(endpoints.chars_logit, data.labels)
total_loss = slim.losses.get_total_loss() total_loss = slim.losses.get_total_loss()
tf.summary.scalar('TotalLoss', total_loss) tf.compat.v1.summary.scalar('TotalLoss', total_loss)
return total_loss return total_loss
def label_smoothing_regularization(self, chars_labels, weight=0.1): def label_smoothing_regularization(self, chars_labels, weight=0.1):
...@@ -605,7 +609,7 @@ class Model(object): ...@@ -605,7 +609,7 @@ class Model(object):
A Tensor with shape [batch_size] - the log-perplexity for each sequence. A Tensor with shape [batch_size] - the log-perplexity for each sequence.
""" """
mparams = self._mparams['sequence_loss_fn'] 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: if mparams.label_smoothing > 0:
smoothed_one_hot_labels = self.label_smoothing_regularization( smoothed_one_hot_labels = self.label_smoothing_regularization(
chars_labels, mparams.label_smoothing) chars_labels, mparams.label_smoothing)
...@@ -625,7 +629,7 @@ class Model(object): ...@@ -625,7 +629,7 @@ class Model(object):
shape=(batch_size, seq_length), shape=(batch_size, seq_length),
dtype=tf.int64) dtype=tf.int64)
known_char = tf.not_equal(chars_labels, reject_char) 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) logits_list = tf.unstack(chars_logits, axis=1)
weights_list = tf.unstack(weights, axis=1) weights_list = tf.unstack(weights, axis=1)
...@@ -635,7 +639,7 @@ class Model(object): ...@@ -635,7 +639,7 @@ class Model(object):
weights_list, weights_list,
softmax_loss_function=get_softmax_loss_fn(mparams.label_smoothing), softmax_loss_function=get_softmax_loss_fn(mparams.label_smoothing),
average_across_timesteps=mparams.average_across_timesteps) average_across_timesteps=mparams.average_across_timesteps)
tf.losses.add_loss(loss) tf.compat.v1.losses.add_loss(loss)
return loss return loss
def create_summaries(self, data, endpoints, charset, is_training): def create_summaries(self, data, endpoints, charset, is_training):
...@@ -665,13 +669,14 @@ class Model(object): ...@@ -665,13 +669,14 @@ class Model(object):
# tf.summary.text(sname('text/pr'), pr_text) # tf.summary.text(sname('text/pr'), pr_text)
# gt_text = charset_mapper.get_text(data.labels[:max_outputs,:]) # gt_text = charset_mapper.get_text(data.labels[:max_outputs,:])
# tf.summary.text(sname('text/gt'), gt_text) # 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: if is_training:
tf.summary.image( tf.compat.v1.summary.image(
sname('image/orig'), data.images_orig, max_outputs=max_outputs) sname('image/orig'), data.images_orig, max_outputs=max_outputs)
for var in tf.trainable_variables(): for var in tf.compat.v1.trainable_variables():
tf.summary.histogram(var.op.name, var) tf.compat.v1.summary.histogram(var.op.name, var)
return None return None
else: else:
...@@ -700,7 +705,8 @@ class Model(object): ...@@ -700,7 +705,8 @@ class Model(object):
for name, value in names_to_values.items(): for name, value in names_to_values.items():
summary_name = 'eval/' + name 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()) return list(names_to_updates.values())
def create_init_fn_to_restore(self, def create_init_fn_to_restore(self,
...@@ -733,9 +739,9 @@ class Model(object): ...@@ -733,9 +739,9 @@ class Model(object):
logging.info('variables_to_restore:\n%s', logging.info('variables_to_restore:\n%s',
utils.variables_to_restore().keys()) utils.variables_to_restore().keys())
logging.info('moving_average_variables:\n%s', 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', 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: if master_checkpoint:
assign_from_checkpoint(utils.variables_to_restore(), 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( ...@@ -42,7 +42,8 @@ flags.DEFINE_integer(
'image_height', None, 'image_height', None,
'Image height used during training(or crop height if used)' 'Image height used during training(or crop height if used)'
' If not set, the dataset default is used instead.') ' 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_integer('version_number', 1, 'Version number of the model')
flags.DEFINE_bool( flags.DEFINE_bool(
'export_for_serving', True, 'export_for_serving', True,
...@@ -116,7 +117,7 @@ def export_model(export_dir, ...@@ -116,7 +117,7 @@ def export_model(export_dir,
image_height = crop_image_height or dataset_image_height image_height = crop_image_height or dataset_image_height
if export_for_serving: if export_for_serving:
images_orig = tf.placeholder( images_orig = tf.compat.v1.placeholder(
tf.string, shape=[batch_size], name='tf_example') tf.string, shape=[batch_size], name='tf_example')
images_orig_float = model_export_lib.generate_tfexample_image( images_orig_float = model_export_lib.generate_tfexample_image(
images_orig, images_orig,
...@@ -126,22 +127,23 @@ def export_model(export_dir, ...@@ -126,22 +127,23 @@ def export_model(export_dir,
name='float_images') name='float_images')
else: else:
images_shape = (batch_size, image_height, image_width, image_depth) 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') tf.uint8, shape=images_shape, name='original_image')
images_orig_float = tf.image.convert_image_dtype( images_orig_float = tf.image.convert_image_dtype(
images_orig, dtype=tf.float32, name='float_images') images_orig, dtype=tf.float32, name='float_images')
endpoints = model.create_base(images_orig_float, labels_one_hot=None) endpoints = model.create_base(images_orig_float, labels_one_hot=None)
sess = tf.Session() sess = tf.compat.v1.Session()
saver = tf.train.Saver(slim.get_variables_to_restore(), sharded=True) saver = tf.compat.v1.train.Saver(
slim.get_variables_to_restore(), sharded=True)
saver.restore(sess, get_checkpoint_path()) saver.restore(sess, get_checkpoint_path())
tf.logging.info('Model restored successfully.') tf.compat.v1.logging.info('Model restored successfully.')
# Create model signature. # Create model signature.
if export_for_serving: if export_for_serving:
input_tensors = { input_tensors = {
tf.saved_model.signature_constants.CLASSIFY_INPUTS: images_orig tf.saved_model.CLASSIFY_INPUTS: images_orig
} }
else: else:
input_tensors = {'images': images_orig} input_tensors = {'images': images_orig}
...@@ -163,21 +165,21 @@ def export_model(export_dir, ...@@ -163,21 +165,21 @@ def export_model(export_dir,
dataset.max_sequence_length)): dataset.max_sequence_length)):
output_tensors['attention_mask_%d' % i] = t output_tensors['attention_mask_%d' % i] = t
signature_outputs = model_export_lib.build_tensor_info(output_tensors) 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, signature_inputs, signature_outputs,
tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME) tf.saved_model.CLASSIFY_METHOD_NAME)
# Save model. # 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( builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING], sess, [tf.saved_model.SERVING],
signature_def_map={ signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def signature_def
}, },
main_op=tf.tables_initializer(), main_op=tf.compat.v1.tables_initializer(),
strip_default_attrs=True) strip_default_attrs=True)
builder.save() 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 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, ...@@ -36,7 +36,7 @@ def normalize_image(image, original_minval, original_maxval, target_minval,
Returns: Returns:
image: image which is the same shape as input image. 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_minval = float(original_minval)
original_maxval = float(original_maxval) original_maxval = float(original_maxval)
target_minval = float(target_minval) target_minval = float(target_minval)
...@@ -68,16 +68,17 @@ def generate_tfexample_image(input_example_strings, ...@@ -68,16 +68,17 @@ def generate_tfexample_image(input_example_strings,
A tensor with shape [batch_size, height, width, channels] of type float32 A tensor with shape [batch_size, height, width, channels] of type float32
with values in the range [0..1] 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( images_shape = tf.stack(
[batch_size, image_height, image_width, image_channels]) [batch_size, image_height, image_width, image_channels])
tf_example_image_key = 'image/encoded' tf_example_image_key = 'image/encoded'
feature_configs = { feature_configs = {
tf_example_image_key: tf_example_image_key:
tf.FixedLenFeature( tf.io.FixedLenFeature(
image_height * image_width * image_channels, dtype=tf.float32) 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( float_images = tf.reshape(
normalize_image( normalize_image(
feature_tensors[tf_example_image_key], feature_tensors[tf_example_image_key],
...@@ -97,11 +98,11 @@ def attention_ocr_attention_masks(num_characters): ...@@ -97,11 +98,11 @@ def attention_ocr_attention_masks(num_characters):
names = ['%s/Softmax:0' % (prefix)] names = ['%s/Softmax:0' % (prefix)]
for i in range(1, num_characters): for i in range(1, num_characters):
names += ['%s_%d/Softmax:0' % (prefix, i)] 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): def build_tensor_info(tensor_dict):
return { 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() for k, t in tensor_dict.items()
} }
research/attention_ocr/python/model_export_test.py
View file @ 50fa3eb9
...@@ -29,7 +29,7 @@ _CHECKPOINT_URL = ( ...@@ -29,7 +29,7 @@ _CHECKPOINT_URL = (
def _clean_up(): 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): def _create_tf_example_string(image):
...@@ -47,7 +47,7 @@ class AttentionOcrExportTest(tf.test.TestCase): ...@@ -47,7 +47,7 @@ class AttentionOcrExportTest(tf.test.TestCase):
for suffix in ['.meta', '.index', '.data-00000-of-00001']: for suffix in ['.meta', '.index', '.data-00000-of-00001']:
filename = _CHECKPOINT + suffix filename = _CHECKPOINT + suffix
self.assertTrue( self.assertTrue(
tf.gfile.Exists(filename), tf.io.gfile.exists(filename),
msg='Missing checkpoint file %s. ' msg='Missing checkpoint file %s. '
'Please download and extract it from %s' % 'Please download and extract it from %s' %
(filename, _CHECKPOINT_URL)) (filename, _CHECKPOINT_URL))
...@@ -57,7 +57,8 @@ class AttentionOcrExportTest(tf.test.TestCase): ...@@ -57,7 +57,8 @@ class AttentionOcrExportTest(tf.test.TestCase):
os.path.dirname(__file__), 'datasets/testdata/fsns') os.path.dirname(__file__), 'datasets/testdata/fsns')
tf.test.TestCase.setUp(self) tf.test.TestCase.setUp(self)
_clean_up() _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 = { self.minimal_output_signature = {
'predictions': 'AttentionOcr_v1/predicted_chars:0', 'predictions': 'AttentionOcr_v1/predicted_chars:0',
'scores': 'AttentionOcr_v1/predicted_scores:0', 'scores': 'AttentionOcr_v1/predicted_scores:0',
...@@ -93,10 +94,10 @@ class AttentionOcrExportTest(tf.test.TestCase): ...@@ -93,10 +94,10 @@ class AttentionOcrExportTest(tf.test.TestCase):
size=self.dataset.image_shape).astype('uint8'), size=self.dataset.image_shape).astype('uint8'),
} }
signature_def = graph_def.signature_def[ 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: if serving:
input_name = signature_def.inputs[ 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' # Model for serving takes input: inputs['inputs'] = 'tf_example:0'
feed_dict = { feed_dict = {
input_name: [ input_name: [
...@@ -126,11 +127,11 @@ class AttentionOcrExportTest(tf.test.TestCase): ...@@ -126,11 +127,11 @@ class AttentionOcrExportTest(tf.test.TestCase):
export_for_serving: True if the model was exported for Serving. This export_for_serving: True if the model was exported for Serving. This
affects how input is fed into the model. affects how input is fed into the model.
""" """
tf.reset_default_graph() tf.compat.v1.reset_default_graph()
sess = tf.Session() sess = tf.compat.v1.Session()
graph_def = tf.saved_model.loader.load( graph_def = tf.compat.v1.saved_model.loader.load(
sess=sess, sess=sess,
tags=[tf.saved_model.tag_constants.SERVING], tags=[tf.saved_model.SERVING],
export_dir=self.export_dir) export_dir=self.export_dir)
feed_dict = self.create_input_feed(graph_def, export_for_serving) feed_dict = self.create_input_feed(graph_def, export_for_serving)
results = sess.run(self.minimal_output_signature, feed_dict=feed_dict) 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): ...@@ -52,7 +52,7 @@ class ModelTest(tf.test.TestCase):
self.num_char_classes) self.num_char_classes)
self.length_logit_shape = (self.batch_size, self.seq_length + 1) self.length_logit_shape = (self.batch_size, self.seq_length + 1)
# Placeholder knows image dimensions, but not batch size. # Placeholder knows image dimensions, but not batch size.
self.input_images = tf.placeholder( self.input_images = tf.compat.v1.placeholder(
tf.float32, tf.float32,
shape=(None, self.image_height, self.image_width, 3), shape=(None, self.image_height, self.image_width, 3),
name='input_node') name='input_node')
...@@ -89,8 +89,8 @@ class ModelTest(tf.test.TestCase): ...@@ -89,8 +89,8 @@ class ModelTest(tf.test.TestCase):
with self.test_session() as sess: with self.test_session() as sess:
endpoints_tf = ocr_model.create_base( endpoints_tf = ocr_model.create_base(
images=self.input_images, labels_one_hot=None) images=self.input_images, labels_one_hot=None)
sess.run(tf.global_variables_initializer()) sess.run(tf.compat.v1.global_variables_initializer())
tf.tables_initializer().run() tf.compat.v1.tables_initializer().run()
endpoints = sess.run( endpoints = sess.run(
endpoints_tf, feed_dict={self.input_images: self.fake_images}) endpoints_tf, feed_dict={self.input_images: self.fake_images})
...@@ -127,7 +127,7 @@ class ModelTest(tf.test.TestCase): ...@@ -127,7 +127,7 @@ class ModelTest(tf.test.TestCase):
ocr_model = self.create_model() ocr_model = self.create_model()
conv_tower = ocr_model.conv_tower_fn(self.input_images) 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_np = sess.run(
conv_tower, feed_dict={self.input_images: self.fake_images}) conv_tower, feed_dict={self.input_images: self.fake_images})
...@@ -141,9 +141,9 @@ class ModelTest(tf.test.TestCase): ...@@ -141,9 +141,9 @@ class ModelTest(tf.test.TestCase):
ocr_model = self.create_model() ocr_model = self.create_model()
ocr_model.create_base(images=self.input_images, labels_one_hot=None) ocr_model.create_base(images=self.input_images, labels_one_hot=None)
with self.test_session() as sess: with self.test_session() as sess:
tfprof_root = tf.profiler.profile( tfprof_root = tf.compat.v1.profiler.profile(
sess.graph, sess.graph,
options=tf.profiler.ProfileOptionBuilder options=tf.compat.v1.profiler.ProfileOptionBuilder
.trainable_variables_parameter()) .trainable_variables_parameter())
model_size_bytes = 4 * tfprof_root.total_parameters model_size_bytes = 4 * tfprof_root.total_parameters
...@@ -163,9 +163,9 @@ class ModelTest(tf.test.TestCase): ...@@ -163,9 +163,9 @@ class ModelTest(tf.test.TestCase):
summaries = ocr_model.create_summaries( summaries = ocr_model.create_summaries(
data, endpoints, charset, is_training=False) data, endpoints, charset, is_training=False)
with self.test_session() as sess: with self.test_session() as sess:
sess.run(tf.global_variables_initializer()) sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.local_variables_initializer()) sess.run(tf.compat.v1.local_variables_initializer())
tf.tables_initializer().run() tf.compat.v1.tables_initializer().run()
sess.run(summaries) # just check it is runnable sess.run(summaries) # just check it is runnable
def test_sequence_loss_function_without_label_smoothing(self): def test_sequence_loss_function_without_label_smoothing(self):
...@@ -188,7 +188,7 @@ class ModelTest(tf.test.TestCase): ...@@ -188,7 +188,7 @@ class ModelTest(tf.test.TestCase):
Returns: Returns:
a list of tensors with encoded image coordinates in them. 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, w, _ = net.shape.as_list()
h_loc = [ h_loc = [
tf.tile( tf.tile(
...@@ -200,7 +200,8 @@ class ModelTest(tf.test.TestCase): ...@@ -200,7 +200,8 @@ class ModelTest(tf.test.TestCase):
h_loc = tf.concat([tf.expand_dims(t, 2) for t in h_loc], 2) h_loc = tf.concat([tf.expand_dims(t, 2) for t in h_loc], 2)
w_loc = [ w_loc = [
tf.tile( 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) [h, 1]) for i in range(w)
] ]
w_loc = tf.concat([tf.expand_dims(t, 2) for t in w_loc], 2) w_loc = tf.concat([tf.expand_dims(t, 2) for t in w_loc], 2)
...@@ -272,8 +273,8 @@ class ModelTest(tf.test.TestCase): ...@@ -272,8 +273,8 @@ class ModelTest(tf.test.TestCase):
endpoints_tf = ocr_model.create_base( endpoints_tf = ocr_model.create_base(
images=self.fake_images, labels_one_hot=None) images=self.fake_images, labels_one_hot=None)
sess.run(tf.global_variables_initializer()) sess.run(tf.compat.v1.global_variables_initializer())
tf.tables_initializer().run() tf.compat.v1.tables_initializer().run()
endpoints = sess.run(endpoints_tf) endpoints = sess.run(endpoints_tf)
self.assertEqual(endpoints.predicted_text.shape, (self.batch_size,)) self.assertEqual(endpoints.predicted_text.shape, (self.batch_size,))
...@@ -289,7 +290,7 @@ class CharsetMapperTest(tf.test.TestCase): ...@@ -289,7 +290,7 @@ class CharsetMapperTest(tf.test.TestCase):
charset_mapper = model.CharsetMapper(charset) charset_mapper = model.CharsetMapper(charset)
with self.test_session() as sess: with self.test_session() as sess:
tf.tables_initializer().run() tf.compat.v1.tables_initializer().run()
text = sess.run(charset_mapper.get_text(ids)) text = sess.run(charset_mapper.get_text(ids))
self.assertAllEqual(text, [b'hello', b'world']) self.assertAllEqual(text, [b'hello', b'world'])
......
research/attention_ocr/python/sequence_layers.py
View file @ 50fa3eb9
...@@ -111,12 +111,12 @@ class SequenceLayerBase(object): ...@@ -111,12 +111,12 @@ class SequenceLayerBase(object):
self._mparams = method_params self._mparams = method_params
self._net = net self._net = net
self._labels_one_hot = labels_one_hot 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 # Initialize parameters for char logits which will be computed on the fly
# inside an LSTM decoder. # inside an LSTM decoder.
self._char_logits = {} 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( self._softmax_w = slim.model_variable(
'softmax_w', 'softmax_w',
[self._mparams.num_lstm_units, self._params.num_char_classes], [self._mparams.num_lstm_units, self._params.num_char_classes],
...@@ -124,7 +124,7 @@ class SequenceLayerBase(object): ...@@ -124,7 +124,7 @@ class SequenceLayerBase(object):
regularizer=regularizer) regularizer=regularizer)
self._softmax_b = slim.model_variable( self._softmax_b = slim.model_variable(
'softmax_b', [self._params.num_char_classes], 'softmax_b', [self._params.num_char_classes],
initializer=tf.zeros_initializer(), initializer=tf.compat.v1.zeros_initializer(),
regularizer=regularizer) regularizer=regularizer)
@abc.abstractmethod @abc.abstractmethod
...@@ -203,7 +203,7 @@ class SequenceLayerBase(object): ...@@ -203,7 +203,7 @@ class SequenceLayerBase(object):
A tensor with shape [batch_size, num_char_classes] A tensor with shape [batch_size, num_char_classes]
""" """
if char_index not in self._char_logits: if char_index not in self._char_logits:
self._char_logits[char_index] = tf.nn.xw_plus_b(inputs, self._softmax_w, self._char_logits[char_index] = tf.compat.v1.nn.xw_plus_b(inputs, self._softmax_w,
self._softmax_b) self._softmax_b)
return self._char_logits[char_index] return self._char_logits[char_index]
...@@ -216,7 +216,7 @@ class SequenceLayerBase(object): ...@@ -216,7 +216,7 @@ class SequenceLayerBase(object):
Returns: Returns:
A tensor with shape [batch_size, num_char_classes] 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) return slim.one_hot_encoding(prediction, self._params.num_char_classes)
def get_input(self, prev, i): def get_input(self, prev, i):
...@@ -244,10 +244,10 @@ class SequenceLayerBase(object): ...@@ -244,10 +244,10 @@ class SequenceLayerBase(object):
Returns: Returns:
A tensor with shape [batch_size, seq_length, num_char_classes]. 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) first_label = self.get_input(prev=None, i=0)
decoder_inputs = [first_label] + [None] * (self._params.seq_length - 1) 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, self._mparams.num_lstm_units,
use_peepholes=False, use_peepholes=False,
cell_clip=self._mparams.lstm_state_clip_value, cell_clip=self._mparams.lstm_state_clip_value,
...@@ -259,9 +259,9 @@ class SequenceLayerBase(object): ...@@ -259,9 +259,9 @@ class SequenceLayerBase(object):
loop_function=self.get_input, loop_function=self.get_input,
cell=lstm_cell) cell=lstm_cell)
with tf.variable_scope('logits'): with tf.compat.v1.variable_scope('logits'):
logits_list = [ 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) 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 ...@@ -29,13 +29,13 @@ import sequence_layers
def fake_net(batch_size, num_features, feature_size): def fake_net(batch_size, num_features, feature_size):
return tf.convert_to_tensor( 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) dtype=tf.float32)
def fake_labels(batch_size, seq_length, num_char_classes): def fake_labels(batch_size, seq_length, num_char_classes):
labels_np = tf.convert_to_tensor( labels_np = tf.convert_to_tensor(
np.random.randint( value=np.random.randint(
low=0, high=num_char_classes, size=(batch_size, seq_length))) low=0, high=num_char_classes, size=(batch_size, seq_length)))
return slim.one_hot_encoding(labels_np, num_classes=num_char_classes) 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(): ...@@ -96,16 +96,16 @@ def get_training_hparams():
def create_optimizer(hparams): def create_optimizer(hparams):
"""Creates optimized based on the specified flags.""" """Creates optimized based on the specified flags."""
if hparams.optimizer == 'momentum': if hparams.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer( optimizer = tf.compat.v1.train.MomentumOptimizer(
hparams.learning_rate, momentum=hparams.momentum) hparams.learning_rate, momentum=hparams.momentum)
elif hparams.optimizer == 'adam': elif hparams.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(hparams.learning_rate) optimizer = tf.compat.v1.train.AdamOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adadelta': elif hparams.optimizer == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(hparams.learning_rate) optimizer = tf.compat.v1.train.AdadeltaOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adagrad': elif hparams.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(hparams.learning_rate) optimizer = tf.compat.v1.train.AdagradOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'rmsprop': elif hparams.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer( optimizer = tf.compat.v1.train.RMSPropOptimizer(
hparams.learning_rate, momentum=hparams.momentum) hparams.learning_rate, momentum=hparams.momentum)
return optimizer return optimizer
...@@ -154,14 +154,14 @@ def train(loss, init_fn, hparams): ...@@ -154,14 +154,14 @@ def train(loss, init_fn, hparams):
def prepare_training_dir(): 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) 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: else:
if FLAGS.reset_train_dir: if FLAGS.reset_train_dir:
logging.info('Reset the training directory %s', FLAGS.train_log_dir) logging.info('Reset the training directory %s', FLAGS.train_log_dir)
tf.gfile.DeleteRecursively(FLAGS.train_log_dir) tf.io.gfile.rmtree(FLAGS.train_log_dir)
tf.gfile.MakeDirs(FLAGS.train_log_dir) tf.io.gfile.makedirs(FLAGS.train_log_dir)
else: else:
logging.info('Use already existing training directory %s', logging.info('Use already existing training directory %s',
FLAGS.train_log_dir) FLAGS.train_log_dir)
...@@ -169,7 +169,7 @@ def prepare_training_dir(): ...@@ -169,7 +169,7 @@ def prepare_training_dir():
def calculate_graph_metrics(): def calculate_graph_metrics():
param_stats = model_analyzer.print_model_analysis( 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) tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
return param_stats.total_parameters return param_stats.total_parameters
...@@ -186,7 +186,7 @@ def main(_): ...@@ -186,7 +186,7 @@ def main(_):
# If ps_tasks is zero, the local device is used. When using multiple # If ps_tasks is zero, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables # (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices. # 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) FLAGS.ps_tasks, merge_devices=True)
with tf.device(device_setter): with tf.device(device_setter):
data = data_provider.get_data( data = data_provider.get_data(
......
research/attention_ocr/python/utils.py
View file @ 50fa3eb9
...@@ -37,16 +37,16 @@ def logits_to_log_prob(logits): ...@@ -37,16 +37,16 @@ def logits_to_log_prob(logits):
probabilities. probabilities.
""" """
with tf.variable_scope('log_probabilities'): with tf.compat.v1.variable_scope('log_probabilities'):
reduction_indices = len(logits.shape.as_list()) - 1 reduction_indices = len(logits.shape.as_list()) - 1
max_logits = tf.reduce_max( 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) safe_logits = tf.subtract(logits, max_logits)
sum_exp = tf.reduce_sum( sum_exp = tf.reduce_sum(
tf.exp(safe_logits), input_tensor=tf.exp(safe_logits),
reduction_indices=reduction_indices, axis=reduction_indices,
keep_dims=True) keepdims=True)
log_probs = tf.subtract(safe_logits, tf.log(sum_exp)) log_probs = tf.subtract(safe_logits, tf.math.log(sum_exp))
return log_probs return log_probs
...@@ -91,7 +91,7 @@ def ConvertAllInputsToTensors(func): ...@@ -91,7 +91,7 @@ def ConvertAllInputsToTensors(func):
""" """
def FuncWrapper(*args): 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 func(*tensors)
return FuncWrapper return FuncWrapper
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