Python tensorflow.parse_single_example方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def _mapper(example_proto):
  features = {
      'samples': tf.FixedLenSequenceFeature([1], tf.float32, allow_missing=True),
      'label': tf.FixedLenSequenceFeature([], tf.string, allow_missing=True)
  }
  example = tf.parse_single_example(example_proto, features)

  wav = example['samples'][:, 0]

  wav = wav[:16384]
  wav_len = tf.shape(wav)[0]
  wav = tf.pad(wav, [[0, 16384 - wav_len]])

  label = tf.reduce_join(example['label'], 0)

  return wav, label 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def read_from_tfrecord(filenames):
    tfrecord_file_queue = tf.train.string_input_producer(filenames, name='queue')
    reader = tf.TFRecordReader()
    _, tfrecord_serialized = reader.read(tfrecord_file_queue)

    tfrecord_features = tf.parse_single_example(tfrecord_serialized, features={
        'label': tf.FixedLenFeature([],tf.int64),
        'shape': tf.FixedLenFeature([],tf.string),
        'image': tf.FixedLenFeature([],tf.string),
    }, name='features')

    image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
    shape = tf.decode_raw(tfrecord_features['shape'], tf.int32)

    image = tf.reshape(image, shape)
    label = tfrecord_features['label']
    return label, shape, image 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.reshape(image, [227, 227, 6])

  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    return tf.split(image, 2, 2) # 3rd dimension two parts 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def read_and_decode_aug(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
      # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
        })

    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image = tf.image.random_flip_left_right(tf.reshape(image, [227, 227, 6]))
  # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
    image = tf.image.random_brightness(image, 0.01)
    image = tf.image.random_contrast(image, 0.95, 1.05)
    return tf.split(image, 2, 2) # 3rd dimension two parts 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def parse_fn(self, serialized_example):
        features={
            'image/id_name': tf.FixedLenFeature([], tf.string),
            'image/height' : tf.FixedLenFeature([], tf.int64),
            'image/width'  : tf.FixedLenFeature([], tf.int64),
            'image/encoded': tf.FixedLenFeature([], tf.string),
        }
        for name in self.feature_list:
            features[name] = tf.FixedLenFeature([], tf.int64)

        example = tf.parse_single_example(serialized_example, features=features)
        image = tf.decode_raw(example['image/encoded'], tf.uint8)
        raw_height = tf.cast(example['image/height'], tf.int32)
        raw_width = tf.cast(example['image/width'], tf.int32)
        image = tf.reshape(image, [raw_height, raw_width, 3])
        image = tf.image.resize_images(image, size=[self.height, self.width])
        # from IPython import embed; embed(); exit()

        feature_val_list = [tf.cast(example[name], tf.float32) for name in self.feature_list]
        return image, feature_val_list 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def parse_fun(serialized_example):
    """ Data parsing function.
    """
    features = tf.parse_single_example(serialized_example,
                                       features={'image': tf.FixedLenFeature([], tf.string),
                                                 'label': tf.FixedLenFeature([], tf.int64),
                                                 'height': tf.FixedLenFeature([], tf.int64),
                                                 'width': tf.FixedLenFeature([], tf.int64),
                                                 'depth': tf.FixedLenFeature([], tf.int64)})
    height = tf.cast(features['height'], tf.int32)
    width = tf.cast(features['width'], tf.int32)
    depth = tf.cast(features['depth'], tf.int32)
    image = tf.decode_raw(features['image'], tf.float32)
    image = tf.reshape(image, shape=[height * width * depth])
    image.set_shape([28 * 28 * 1])
    image = tf.cast(image, tf.float32) * (1. / 255)
    label = tf.cast(features['label'], tf.int32)
    features = {'images': image, 'labels': label}
    return(features) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def parse_color_data(example_proto):
    features = {"img_raw": tf.FixedLenFeature([], tf.string),
                "label": tf.FixedLenFeature([], tf.string),
                "width": tf.FixedLenFeature([], tf.int64),
                "height": tf.FixedLenFeature([], tf.int64)}
    parsed_features = tf.parse_single_example(example_proto, features)
    img = parsed_features["img_raw"]
    img = tf.decode_raw(img, tf.uint8)
    width = parsed_features["width"]
    height = parsed_features["height"]
    img = tf.reshape(img, [height, width, 3])
    img = tf.cast(img, tf.float32) * (1. / 255.) - 0.5
    label = parsed_features["label"]
    label = tf.decode_raw(label, tf.float32)

    return img, label 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def _read_raw(self):
        """Read raw data from TFRecord.

        Returns:
            :return: data list [input_raw, label_raw].
        """
        self._reader = tf.TFRecordReader()

        _, serialized_example = self._reader.read(self._queue)

        features = tf.parse_single_example(serialized_example,
                                           features={
                                               'name': tf.FixedLenFeature([], tf.string),
                                               'block': tf.FixedLenFeature([], tf.string)
                                           })

        input_raw, label_raw = decode_block(features['block'], tensor_size=self._raw_size)

        if self._with_key:
            return input_raw, label_raw, features['name']
        return input_raw, label_raw 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)

  features = tf.parse_single_example(
      serialized_example,
      features={
          'image_raw': tf.FixedLenFeature([], tf.string),
          'label': tf.FixedLenFeature([], tf.int64),
      })

  image = tf.decode_raw(features['image_raw'], tf.uint8)
  image.set_shape([784])
  image = tf.cast(image, tf.float32) * (1. / 255)
  label = tf.cast(features['label'], tf.int32)

  return image, label 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def load_patch_coordinates_from_filename_queue(filename_queue):
  """Loads coordinates and volume names from filename queue.

  Args:
    filename_queue: Tensorflow queue created from create_filename_queue()

  Returns:
    Tuple of coordinates (shape `[1, 3]`) and volume name (shape `[1]`) tensors.
  """
  record_options = tf.python_io.TFRecordOptions(
      tf.python_io.TFRecordCompressionType.GZIP)
  keys, protos = tf.TFRecordReader(options=record_options).read(filename_queue)
  examples = tf.parse_single_example(protos, features=dict(
      center=tf.FixedLenFeature(shape=[1, 3], dtype=tf.int64),
      label_volume_name=tf.FixedLenFeature(shape=[1], dtype=tf.string),
  ))
  coord = examples['center']
  volname = examples['label_volume_name']
  return coord, volname 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example, features = {
        "image/encoded": tf.FixedLenFeature([], tf.string),
        "image/height": tf.FixedLenFeature([], tf.int64),
        "image/width": tf.FixedLenFeature([], tf.int64),
        "image/filename": tf.FixedLenFeature([], tf.string),
        "image/class/label": tf.FixedLenFeature([], tf.int64),})

    image_encoded = features["image/encoded"]
    image_raw = tf.image.decode_jpeg(image_encoded, channels=3)

    current_image_object = image_object()

    current_image_object.image = tf.image.resize_image_with_crop_or_pad(image_raw, FLAGS.image_height, FLAGS.image_width) # cropped image with size 299x299
#    current_image_object.image = tf.cast(image_crop, tf.float32) * (1./255) - 0.5
    current_image_object.height = features["image/height"] # height of the raw image
    current_image_object.width = features["image/width"] # width of the raw image
    current_image_object.filename = features["image/filename"] # filename of the raw image
    current_image_object.label = tf.cast(features["image/class/label"], tf.int32) # label of the raw image
    
    return current_image_object 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def dataset_parser(value):
    keys_to_features = {
        'image/encoded': tf.FixedLenFeature((), tf.string, ''),
        'image/format': tf.FixedLenFeature((), tf.string, 'jpeg'),
        'image/class/label': tf.FixedLenFeature([], tf.int64, -1)
    }

    parsed = tf.parse_single_example(value, keys_to_features)
    image_bytes = tf.reshape(parsed['image/encoded'], shape=[])

    # Preprocess the images.
    image = tf.image.decode_jpeg(image_bytes)
    image = tf.image.random_flip_left_right(image)
    image = tf.image.resize_images(image, [IMAGE_SIZE, IMAGE_SIZE])
    image = tf.image.convert_image_dtype(
      image, dtype=tf.bfloat16)

    # Subtract one so that labels are in [0, 1000).
    label = tf.cast(
        tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32) - 1

    return image, label 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def decode_pred(serialized_example):
	"""Parses prediction data from the given `serialized_example`."""

	features = tf.parse_single_example(
					serialized_example,
					features={
						'T1':tf.FixedLenFeature([],tf.string),
						'T2':tf.FixedLenFeature([], tf.string)
					})

	patch_shape = [conf.patch_size, conf.patch_size, conf.patch_size]

	# Convert from a scalar string tensor
	image_T1 = tf.decode_raw(features['T1'], tf.int16)
	image_T1 = tf.reshape(image_T1, patch_shape)
	image_T2 = tf.decode_raw(features['T2'], tf.int16)
	image_T2 = tf.reshape(image_T2, patch_shape)

	# Convert dtype.
	image_T1 = tf.cast(image_T1, tf.float32)
	image_T2 = tf.cast(image_T2, tf.float32)
	label = tf.zeros(image_T1.shape) # pseudo label

	return image_T1, image_T2, label 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def test_parse_spec():
    fc = FeatureColumns(
        True,
        False,
        VOCAB_FILE,
        VOCAB_SIZE,
        10,
        10,
        1000,
        10)
    parse_spec = tf.feature_column.make_parse_example_spec(fc)
    print parse_spec
    reader = tf.python_io.tf_record_iterator(INPUT_FILE)
    sess = tf.Session()
    for record in reader:
        example = tf.parse_single_example(
            record,
            parse_spec)
        print sess.run(example)
        break 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import parse_single_example [as 别名]
def parse_tfrecord_tf(record, res, rnd_crop):
    features = tf.parse_single_example(record, features={
        'shape': tf.FixedLenFeature([3], tf.int64),
        'data': tf.FixedLenFeature([], tf.string),
        'label': tf.FixedLenFeature([1], tf.int64)})
    # label is always 0 if uncondtional
    # to get CelebA attr, add 'attr': tf.FixedLenFeature([40], tf.int64)
    data, label, shape = features['data'], features['label'], features['shape']
    label = tf.cast(tf.reshape(label, shape=[]), dtype=tf.int32)
    img = tf.decode_raw(data, tf.uint8)
    if rnd_crop:
        # For LSUN Realnvp only - random crop
        img = tf.reshape(img, shape)
        img = tf.random_crop(img, [res, res, 3])
    img = tf.reshape(img, [res, res, 3])
    return img, label  # to get CelebA attr, also return attr