Python inception.inception_v3_arg_scope方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception import inception_v3_arg_scope [as 别名]
def build_inceptionv3_graph(images, endpoint, is_training, checkpoint,
                            reuse=False):
  """Builds an InceptionV3 model graph.

  Args:
    images: A 4-D float32 `Tensor` of batch images.
    endpoint: String, name of the InceptionV3 endpoint.
    is_training: Boolean, whether or not to build a training or inference graph.
    checkpoint: String, path to the pretrained model checkpoint.
    reuse: Boolean, whether or not we are reusing the embedder.
  Returns:
    inception_output: `Tensor` holding the InceptionV3 output.
    inception_variables: List of inception variables.
    init_fn: Function to initialize the weights (if not reusing, then None).
  """
  with slim.arg_scope(inception.inception_v3_arg_scope()):
    _, endpoints = inception.inception_v3(
        images, num_classes=1001, is_training=is_training)
    inception_output = endpoints[endpoint]
    inception_variables = slim.get_variables_to_restore()
    inception_variables = [
        i for i in inception_variables if 'global_step' not in i.name]
    if is_training and not reuse:
      init_saver = tf.train.Saver(inception_variables)
      def init_fn(scaffold, sess):
        del scaffold
        init_saver.restore(sess, checkpoint)
    else:
      init_fn = None
    return inception_output, inception_variables, init_fn 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception import inception_v3_arg_scope [as 别名]
def main(args):
  if not os.path.exists(FLAGS.checkpoint):
    tf.logging.fatal(
        'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
        FLAGS.checkpoint)
  g = tf.Graph()
  with g.as_default():
    input_image = tf.placeholder(tf.string)
    processed_image = PreprocessImage(input_image)

    with slim.arg_scope(inception.inception_v3_arg_scope()):
      logits, end_points = inception.inception_v3(
          processed_image, num_classes=FLAGS.num_classes, is_training=False)

    predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
        logits, name='multi_predictions')
    saver = tf_saver.Saver()
    sess = tf.Session()
    saver.restore(sess, FLAGS.checkpoint)

    # Run the evaluation on the images
    for image_path in FLAGS.image_path:
      if not os.path.exists(image_path):
        tf.logging.fatal('Input image does not exist %s', FLAGS.image_path[0])
      img_data = tf.gfile.FastGFile(image_path, "rb").read()
      print(image_path)
      predictions_eval = np.squeeze(sess.run(predictions,
                                             {input_image: img_data}))

      # Print top(n) results
      labelmap, label_dict = LoadLabelMaps(FLAGS.num_classes, FLAGS.labelmap, FLAGS.dict)

      top_k = predictions_eval.argsort()[-FLAGS.n:][::-1]
      for idx in top_k:
        mid = labelmap[idx]
        display_name = label_dict.get(mid, 'unknown')
        score = predictions_eval[idx]
        print('{}: {} - {} (score = {:.2f})'.format(idx, mid, display_name, score))
      print() 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception import inception_v3_arg_scope [as 别名]
def main(args):
  if not os.path.exists(FLAGS.checkpoint):
    tf.logging.fatal(
        'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
        FLAGS.checkpoint)
  g = tf.Graph()
  with g.as_default():
    input_image = PreprocessImage(FLAGS.image_path[0])

    with slim.arg_scope(inception.inception_v3_arg_scope()):
      logits, end_points = inception.inception_v3(
          input_image, num_classes=FLAGS.num_classes, is_training=False)

    bottleneck = end_points['PreLogits']
    init_op = tf.group(tf.global_variables_initializer(),
                       tf.local_variables_initializer(),
                       tf.tables_initializer())
    saver = tf_saver.Saver()
    sess = tf.Session()
    saver.restore(sess, FLAGS.checkpoint)

    # Run the evaluation on the image
    bottleneck_eval = np.squeeze(sess.run(bottleneck))

  first = True
  for val in bottleneck_eval:
    if not first:
      sys.stdout.write(",")
    first = False
    sys.stdout.write('{:.3f}'.format(val))
  sys.stdout.write('\n')