def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading cifar10.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if not reader:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature((), tf.string, default_value='png'),
'image/class/label':
tf.FixedLenFeature([],
tf.int64,
default_value=tf.zeros([], dtype=tf.int64)),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image(shape=[32, 32, 3]),
'label': slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)
def classify_image(image_path, train_dir, label_dir):
image_size = 299
with tf.Graph().as_default():
image_string = tf.gfile.FastGFile(image_path, 'r').read()
_, image_ext = os.path.splitext(image_path)
if image_ext in ['.jpg', '.jpeg']:
image = tf.image.decode_jpeg(image_string, channels=3)
elif image_ext == '.png':
image = tf.image.decode_png(image_string, channels=3)
else:
raise ValueError('image format not supported, must be jpg or png')
processed_image = inception_preprocessing.preprocess_image(
image, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
labels_to_names = dataset_utils.read_label_file(label_dir)
# Create the model, use the default arg scope to
# configure the batch norm parameters.
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits, endpoints = inception_v4.inception_v4(
processed_images,
num_classes=len(labels_to_names),
is_training=False)
probabilities = endpoints['Predictions']
checkpoint_path = tf.train.latest_checkpoint(train_dir)
saver = tf.train.Saver(tf.model_variables())
with tf.Session() as sess:
saver.restore(sess, checkpoint_path)
probabilities = sess.run(probabilities)
probabilities = probabilities[0, 0:]
sorted_inds = [
i[0]
for i in sorted(enumerate(-probabilities), key=lambda x: x[1])
]
for i in range(5):
index = sorted_inds[i]
print('Probability %0.2f%% => [%s]' %
(probabilities[index] * 100, labels_to_names[index]))
def get_split_lips(split_name,
tfrecord_dir,
file_pattern=None,
reader=None,
num_frames=12):
"""Gets a dataset tuple for reading lip movement videos.
Args:
split_name: A 'train'/'validation' split name.
tfrecord_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the
dataset sources. It is assumed that the pattern contains
a '%s' string so that the split name can be inserted.
reader: The TensorFlow reader type.
num_frames: The number of frames contained in each video sample
(this is determined when we create the TFRecord files).
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/validation split.
"""
if split_name not in ['train', 'validation']:
raise ValueError(
'The split_name %s is not recognized.' % (split_name)
+ 'Please input either train or validation as the split_name')
if not file_pattern:
file_pattern = _FILE_PATTERN
# Compute the number of samples contained in the dataset.
num_samples = 0
tfrecords_to_count = [
os.path.join(tfrecord_dir, file)
for file in os.listdir(tfrecord_dir)
if fnmatch.fnmatch(file, file_pattern % split_name)]
for tfrecord_file in tfrecords_to_count:
for record in tf.python_io.tf_record_iterator(tfrecord_file):
num_samples += 1
file_pattern = os.path.join(tfrecord_dir, file_pattern % split_name)
if reader is None:
reader = tf.TFRecordReader
# Create the keys_to_features dictionary for the decoder
keys_to_features = {
'video/data': tf.FixedLenFeature((60, 80, num_frames), tf.float32),
'video/label': tf.FixedLenFeature(
(), tf.int64, default_value=tf.zeros((), dtype=tf.int64)),
}
items_to_handlers = {
'video': slim.tfexample_decoder.Tensor(
'video/data', shape=(60, 80, num_frames, 1)),
'label': slim.tfexample_decoder.Tensor('video/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
labels_to_names = None
num_classes = None
if dataset_utils.has_labels(tfrecord_dir):
labels_to_names = dataset_utils.read_label_file(tfrecord_dir)
num_classes = len(labels_to_names)
return slim.dataset.Dataset(
data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=num_classes,
labels_to_names=labels_to_names)
def get_split_color_depth(split_name,
tfrecord_dir,
file_pattern=None,
reader=None,
color_channels=3,
depth_channels=3):
"""Gets a dataset tuple for reading color-depth image pairs.
Args:
split_name: A 'train'/'validation' split name.
tfrecord_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the
dataset sources. It is assumed that the pattern contains
a '%s' string so that the split name can be inserted.
reader: The TensorFlow reader type.
color_channels: The number of channels contained in the color
images of the output dataset.
depth_channels: The number of channels contained in the depth
images of the output dataset.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/validation split.
"""
if split_name not in ['train', 'validation']:
raise ValueError(
'The split_name %s is not recognized.' % (split_name) +
'Please input either train or validation as the split_name')
if not file_pattern:
file_pattern = _FILE_PATTERN
num_samples = 0
tfrecords_to_count = [
os.path.join(tfrecord_dir, file) for file in os.listdir(tfrecord_dir)
if fnmatch.fnmatch(file, file_pattern % split_name)
]
for tfrecord_file in tfrecords_to_count:
for record in tf.python_io.tf_record_iterator(tfrecord_file):
num_samples += 1
file_pattern = os.path.join(tfrecord_dir, file_pattern % split_name)
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'image/color/encoded':
tf.FixedLenFeature((), tf.string),
'image/color/format':
tf.FixedLenFeature((), tf.string),
'image/depth/encoded':
tf.FixedLenFeature((), tf.string),
'image/depth/format':
tf.FixedLenFeature((), tf.string),
'image/class/label':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros((), dtype=tf.int64)),
}
items_to_handlers = {
'image/color':
slim.tfexample_decoder.Image(image_key='image/color/encoded',
format_key='image/color/format',
channels=color_channels),
'image/depth':
slim.tfexample_decoder.Image(image_key='image/depth/encoded',
format_key='image/depth/format',
channels=depth_channels),
'label':
slim.tfexample_decoder.Tensor('image/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
num_classes = None
if dataset_utils.has_labels(tfrecord_dir):
labels_to_names = dataset_utils.read_label_file(tfrecord_dir)
num_classes = len(labels_to_names)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=num_classes,
labels_to_names=labels_to_names)
def get_split(split_name, dataset_dir, file_pattern, reader,
items_to_descriptions, num_classes):
"""Gets a dataset tuple with instructions for reading Pascal VOC dataset.
Args:
split_name: A trainval/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in ['trainval', 'test']:
raise ValueError('split name %s was not recognized.' % split_name)
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
# Features in Pascal VOC TFRecords.
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string,
default_value='jpeg'),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
}
items_to_handlers = {
'image':
slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'shape':
slim.tfexample_decoder.Tensor('image/shape'),
'object/bbox':
slim.tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
'image/object/bbox/'),
'object/label':
slim.tfexample_decoder.Tensor('image/object/bbox/label'),
'object/difficult':
slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
'object/truncated':
slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
# else:
# labels_to_names = create_readable_names_for_imagenet_labels()
# dataset_utils.write_label_file(labels_to_names, dataset_dir)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=split_to_sizes[split_name],
items_to_descriptions=items_to_descriptions,
num_classes=num_classes,
labels_to_names=labels_to_names)
def get_split_mfcc_lips(split_name,
tfrecord_dir,
file_pattern=None,
reader=None,
num_frames_audio=24,
num_frames_video=12):
"""Gets a dataset tuple for reading mfcc features and videos.
Args:
split_name: 'train_all'/'trainAT'/'trainUZ'/'validaion'/
'valdationAT'/'validationUZ'.
tfrecord_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the
dataset sources. It is assumed that the pattern contains
a '%s' string so that the split name can be inserted.
reader: The TensorFlow reader type.
num_frames_audio: The number of frames of the stored audios.
Thie is fixed once the TFRecords are generated.
num_frames_video: The number of frames of the stored videos.
Thie is fixed once the TFRecords are generated.
Returns:
A `Dataset` namedtuple.
"""
if not file_pattern:
file_pattern = _FILE_PATTERN
num_samples = 0
tfrecords_to_count = [
os.path.join(tfrecord_dir, file) for file in os.listdir(tfrecord_dir)
if fnmatch.fnmatch(file, file_pattern % split_name)
]
for tfrecord_file in tfrecords_to_count:
for record in tf.python_io.tf_record_iterator(tfrecord_file):
num_samples += 1
file_pattern = os.path.join(tfrecord_dir, file_pattern % split_name)
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'audio/mfcc':
tf.FixedLenFeature((26, num_frames_audio), tf.float32),
'video/data':
tf.FixedLenFeature((60, 80, num_frames_video), tf.float32),
'label':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros((), dtype=tf.int64)),
}
items_to_handlers = {
'mfcc':
slim.tfexample_decoder.Tensor('audio/mfcc',
shape=(26, num_frames_audio, 1)),
'video':
slim.tfexample_decoder.Tensor('video/data',
shape=(60, 80, num_frames_video, 1)),
'label':
slim.tfexample_decoder.Tensor('label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
num_classes = None
if dataset_utils.has_labels(tfrecord_dir):
labels_to_names = dataset_utils.read_label_file(tfrecord_dir)
num_classes = len(labels_to_names)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=num_classes,
labels_to_names=labels_to_names)
def get_split_avicar(split_name,
tfrecord_dir,
file_pattern=None,
reader=None,
num_frames=20):
if not file_pattern:
file_pattern = _FILE_PATTERN
num_samples = 0
tfrecords_to_count = [
os.path.join(tfrecord_dir, file) for file in os.listdir(tfrecord_dir)
if fnmatch.fnmatch(file, file_pattern % split_name)
]
for tfrecord_file in tfrecords_to_count:
for record in tf.python_io.tf_record_iterator(tfrecord_file):
num_samples += 1
file_pattern = os.path.join(tfrecord_dir, file_pattern % split_name)
if reader is None:
reader = tf.TFRecordReader
# Create the keys_to_features dictionary for the decoder
keys_to_features = {
'audio/wav/data':
tf.VarLenFeature(tf.float32),
'audio/wav/length':
tf.FixedLenFeature((), tf.int64),
'audio/mfcc':
tf.FixedLenFeature((26, num_frames), tf.float32),
'audio/label':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros((), dtype=tf.int64)),
}
items_to_handlers = {
'wav':
slim.tfexample_decoder.Tensor('audio/wav/data',
shape_keys='audio/wav/length'),
'mfcc':
slim.tfexample_decoder.Tensor('audio/mfcc', shape=(26, num_frames, 1)),
'label':
slim.tfexample_decoder.Tensor('audio/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
num_classes = None
if dataset_utils.has_labels(tfrecord_dir):
labels_to_names = dataset_utils.read_label_file(tfrecord_dir)
num_classes = len(labels_to_names)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=num_classes,
labels_to_names=labels_to_names)
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
"""Gets a dataset tuple with instructions for reading ImageNet.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
file_pattern: The file pattern to use when matching the dataset sources.
It is assumed that the pattern contains a '%s' string so that the split
name can be inserted.
reader: The TensorFlow reader type.
Returns:
A `Dataset` namedtuple.
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/format':
tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/class/label':
tf.FixedLenFeature([], dtype=tf.int64, default_value=-1),
'image/class/text':
tf.FixedLenFeature([], dtype=tf.string, default_value=''),
'image/object/bbox/xmin':
tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin':
tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/xmax':
tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymax':
tf.VarLenFeature(dtype=tf.float32),
'image/object/class/label':
tf.VarLenFeature(dtype=tf.int64),
}
items_to_handlers = {
'image':
slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'label':
slim.tfexample_decoder.Tensor('image/class/label'),
'label_text':
slim.tfexample_decoder.Tensor('image/class/text'),
'object/bbox':
slim.tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
'image/object/bbox/'),
'object/label':
slim.tfexample_decoder.Tensor('image/object/class/label'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
labels_to_names = None
if dataset_utils.has_labels(dataset_dir):
labels_to_names = dataset_utils.read_label_file(dataset_dir)
else:
labels_to_names = create_readable_names_for_imagenet_labels()
dataset_utils.write_label_file(labels_to_names, dataset_dir)
return slim.dataset.Dataset(data_sources=file_pattern,
reader=reader,
decoder=decoder,
num_samples=_SPLITS_TO_SIZES[split_name],
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
num_classes=_NUM_CLASSES,
labels_to_names=labels_to_names)