def read_single_example(example_string):
"""Parses the record string."""
return tf.parse_single_example(
example_string,
features={
'image': tf.FixedLenFeature([], dtype=tf.string),
'label': tf.FixedLenFeature([], tf.int64)
})
def process_example(example_string, image_size, data_augmentation=None):
"""Processes a single example string.
Extracts and processes the image, and ignores the label. We assume that the
image has three channels.
Args:
example_string: str, an Example protocol buffer.
image_size: int, desired image size. The extracted image will be resized to
`[image_size, image_size]`.
data_augmentation: A DataAugmentation object with parameters for perturbing
the images.
Returns:
image_rescaled: the image, resized to `image_size x image_size` and rescaled
to [-1, 1]. Note that Gaussian data augmentation may cause values to
go beyond this range.
"""
image_string = tf.parse_single_example(example_string,
features={
'image':
tf.FixedLenFeature(
[], dtype=tf.string),
'label':
tf.FixedLenFeature([], tf.int64)
})['image']
image_decoded = tf.image.decode_jpeg(image_string, channels=3)
image_resized = tf.image.resize_images(
image_decoded, [image_size, image_size],
method=tf.image.ResizeMethod.BILINEAR,
align_corners=True)
image = 2 * (image_resized / 255.0 - 0.5) # Rescale to [-1, 1].
if data_augmentation is not None:
if data_augmentation.enable_gaussian_noise:
image = image + tf.random_normal(
tf.shape(image)) * data_augmentation.gaussian_noise_std
if data_augmentation.enable_jitter:
j = data_augmentation.jitter_amount
paddings = tf.constant([[j, j], [j, j], [0, 0]])
image = tf.pad(image, paddings, 'REFLECT')
image = tf.image.random_crop(image, [image_size, image_size, 3])
return image
def __call__(self, example_string):
"""Processes a single example string.
Extracts and processes the image, and ignores the label. We assume that the
image has three channels.
Args:
example_string: str, an Example protocol buffer.
Returns:
image_rescaled: the image, resized to `image_size x image_size` and
rescaled to [-1, 1]. Note that Gaussian data augmentation may cause values
to go beyond this range.
"""
image_string = tf.parse_single_example(
example_string,
features={
'image': tf.FixedLenFeature([], dtype=tf.string),
'label': tf.FixedLenFeature([], tf.int64)
})['image']
image_decoded = tf.image.decode_image(image_string, channels=3)
image_decoded.set_shape([None, None, 3])
image_resized = tf.image.resize_images(
image_decoded, [self.image_size, self.image_size],
method=tf.image.ResizeMethod.BILINEAR,
align_corners=True)
image_resized = tf.cast(image_resized, tf.float32)
image = 2 * (image_resized / 255.0 - 0.5) # Rescale to [-1, 1].
if self.data_augmentation is not None:
if self.data_augmentation.enable_gaussian_noise:
image = image + tf.random_normal(tf.shape(
image)) * self.data_augmentation.gaussian_noise_std
if self.data_augmentation.enable_jitter:
j = self.data_augmentation.jitter_amount
paddings = tf.constant([[j, j], [j, j], [0, 0]])
image = tf.pad(image, paddings, 'REFLECT')
image = tf.image.random_crop(
image, [self.image_size, self.image_size, 3])
return image
def __call__(self, example_string):
"""Processes a single example string.
Extracts and processes the feature, and ignores the label.
Args:
example_string: str, an Example protocol buffer.
Returns:
feat: The feature tensor.
"""
feat_string = tf.parse_single_example(
example_string,
features={
'image/embedding': tf.FixedLenFeature([], dtype=tf.string),
'image/class/label': tf.FixedLenFeature([], tf.int64)
})['image/embedding']
feat = tf.io.parse_tensor(feat_string, tf.float32)
return feat
