Python tensorflow.contrib.slim.python.slim.nets.inception_v3.inception_v3_arg_scope() Examples

The following are 6 code examples of tensorflow.contrib.slim.python.slim.nets.inception_v3.inception_v3_arg_scope(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.contrib.slim.python.slim.nets.inception_v3 , or try the search function .
Example #1
Source File: preprocess.py    From cloudml-edge-automation with Apache License 2.0 5 votes vote down vote up 
def build_graph(self):
    """Forms the core by building a wrapper around the inception graph.

      Here we add the necessary input & output tensors, to decode jpegs,
      serialize embeddings, restore from checkpoint etc.

      To use other Inception models modify this file. Note that to use other
      models beside Inception, you should make sure input_shape matches
      their input. Resizing or other modifications may be necessary as well.
      See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
      details about InceptionV3.

    Returns:
      input_jpeg: A tensor containing raw image bytes as the input layer.
      embedding: The embeddings tensor, that will be materialized later.
    """

    input_jpeg = tf.placeholder(tf.string, shape=None)
    image = tf.image.decode_jpeg(input_jpeg, channels=self.CHANNELS)

    # Note resize expects a batch_size, but we are feeding a single image.
    # So we have to expand then squeeze.  Resize returns float32 in the
    # range [0, uint8_max]
    image = tf.expand_dims(image, 0)

    # convert_image_dtype also scales [0, uint8_max] -> [0 ,1).
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    image = tf.image.resize_bilinear(
        image, [self.HEIGHT, self.WIDTH], align_corners=False)

    # Then rescale range to [-1, 1) for Inception.
    image = tf.subtract(image, 0.5)
    inception_input = tf.multiply(image, 2.0)

    # Build Inception layers, which expect a tensor of type float from [-1, 1)
    # and shape [batch_size, height, width, channels].
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      _, end_points = inception.inception_v3(inception_input, is_training=False)

    embedding = end_points['PreLogits']
    return input_jpeg, embedding
Example #2
Source File: preprocess.py    From cloudml-samples with Apache License 2.0 5 votes vote down vote up 
def build_graph(self):
    """Forms the core by building a wrapper around the inception graph.

      Here we add the necessary input & output tensors, to decode jpegs,
      serialize embeddings, restore from checkpoint etc.

      To use other Inception models modify this file. Note that to use other
      models beside Inception, you should make sure input_shape matches
      their input. Resizing or other modifications may be necessary as well.
      See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
      details about InceptionV3.

    Returns:
      input_jpeg: A tensor containing raw image bytes as the input layer.
      embedding: The embeddings tensor, that will be materialized later.
    """

    input_jpeg = tf.placeholder(tf.string, shape=None)
    image = tf.image.decode_jpeg(input_jpeg, channels=self.CHANNELS)

    # Note resize expects a batch_size, but we are feeding a single image.
    # So we have to expand then squeeze.  Resize returns float32 in the
    # range [0, uint8_max]
    image = tf.expand_dims(image, 0)

    # convert_image_dtype also scales [0, uint8_max] -> [0 ,1).
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    image = tf.image.resize_bilinear(
        image, [self.HEIGHT, self.WIDTH], align_corners=False)

    # Then rescale range to [-1, 1) for Inception.
    image = tf.subtract(image, 0.5)
    inception_input = tf.multiply(image, 2.0)

    # Build Inception layers, which expect a tensor of type float from [-1, 1)
    # and shape [batch_size, height, width, channels].
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      _, end_points = inception.inception_v3(inception_input, is_training=False)

    embedding = end_points['PreLogits']
    return input_jpeg, embedding
Example #3
Source File: inception_v3_test.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 299, 299
    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    with arg_scope(inception_v3.inception_v3_arg_scope()):
      inception_v3.inception_v3_base(inputs)
    total_params, _ = model_analyzer.analyze_vars(
        variables_lib.get_model_variables())
    self.assertAlmostEqual(21802784, total_params)
Example #4
Source File: inception_v3_test.py    From keras-lambda with MIT License 5 votes vote down vote up 
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 299, 299
    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    with arg_scope(inception_v3.inception_v3_arg_scope()):
      inception_v3.inception_v3_base(inputs)
    total_params, _ = model_analyzer.analyze_vars(
        variables_lib.get_model_variables())
    self.assertAlmostEqual(21802784, total_params)
Example #5
Source File: model.py    From cloudml-edge-automation with Apache License 2.0 4 votes vote down vote up 
def build_inception_graph(self):
    """Builds an inception graph and add the necessary input & output tensors.

      To use other Inception models modify this file. Also preprocessing must be
      modified accordingly.

      See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
      details about InceptionV3.

    Returns:
      input_jpeg: A placeholder for jpeg string batch that allows feeding the
                  Inception layer with image bytes for prediction.
      inception_embeddings: The embeddings tensor.
    """

    # These constants are set by Inception v3's expectations.
    height = 299
    width = 299
    channels = 3

    image_str_tensor = tf.placeholder(tf.string, shape=[None])

    # The CloudML Prediction API always "feeds" the Tensorflow graph with
    # dynamic batch sizes e.g. (?,).  decode_jpeg only processes scalar
    # strings because it cannot guarantee a batch of images would have
    # the same output size.  We use tf.map_fn to give decode_jpeg a scalar
    # string from dynamic batches.
    def decode_and_resize(image_str_tensor):
      """Decodes jpeg string, resizes it and returns a uint8 tensor."""
      image = tf.image.decode_jpeg(image_str_tensor, channels=channels)
      # Note resize expects a batch_size, but tf_map supresses that index,
      # thus we have to expand then squeeze.  Resize returns float32 in the
      # range [0, uint8_max]
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(
          image, [height, width], align_corners=False)
      image = tf.squeeze(image, squeeze_dims=[0])
      image = tf.cast(image, dtype=tf.uint8)
      return image

    image = tf.map_fn(
        decode_and_resize, image_str_tensor, back_prop=False, dtype=tf.uint8)
    # convert_image_dtype, also scales [0, uint8_max] -> [0 ,1).
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)

    # Then shift images to [-1, 1) for Inception.
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)

    # Build Inception layers, which expect A tensor of type float from [-1, 1)
    # and shape [batch_size, height, width, channels].
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      _, end_points = inception.inception_v3(image, is_training=False)

    inception_embeddings = end_points['PreLogits']
    inception_embeddings = tf.squeeze(
        inception_embeddings, [1, 2], name='SpatialSqueeze')
    return image_str_tensor, inception_embeddings
Example #6
Source File: model.py    From cloudml-samples with Apache License 2.0 4 votes vote down vote up 
def build_inception_graph(self):
    """Builds an inception graph and add the necessary input & output tensors.

      To use other Inception models modify this file. Also preprocessing must be
      modified accordingly.

      See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
      details about InceptionV3.

    Returns:
      input_jpeg: A placeholder for jpeg string batch that allows feeding the
                  Inception layer with image bytes for prediction.
      inception_embeddings: The embeddings tensor.
    """

    # These constants are set by Inception v3's expectations.
    height = 299
    width = 299
    channels = 3

    image_str_tensor = tf.placeholder(tf.string, shape=[None])

    # The CloudML Prediction API always "feeds" the Tensorflow graph with
    # dynamic batch sizes e.g. (?,).  decode_jpeg only processes scalar
    # strings because it cannot guarantee a batch of images would have
    # the same output size.  We use tf.map_fn to give decode_jpeg a scalar
    # string from dynamic batches.
    def decode_and_resize(image_str_tensor):
      """Decodes jpeg string, resizes it and returns a uint8 tensor."""
      image = tf.image.decode_jpeg(image_str_tensor, channels=channels)
      # Note resize expects a batch_size, but tf_map supresses that index,
      # thus we have to expand then squeeze.  Resize returns float32 in the
      # range [0, uint8_max]
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(
          image, [height, width], align_corners=False)
      image = tf.squeeze(image, squeeze_dims=[0])
      image = tf.cast(image, dtype=tf.uint8)
      return image

    image = tf.map_fn(
        decode_and_resize, image_str_tensor, back_prop=False, dtype=tf.uint8)
    # convert_image_dtype, also scales [0, uint8_max] -> [0 ,1).
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)

    # Then shift images to [-1, 1) for Inception.
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)

    # Build Inception layers, which expect A tensor of type float from [-1, 1)
    # and shape [batch_size, height, width, channels].
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      _, end_points = inception.inception_v3(image, is_training=False)

    inception_embeddings = end_points['PreLogits']
    inception_embeddings = tf.squeeze(
        inception_embeddings, [1, 2], name='SpatialSqueeze')
    return image_str_tensor, inception_embeddings