fa19bd7fc3a5ddb4583312448fe750031c145d5f,models/experimental/inception/inception_v2.py,InputPipeline,dataset_parser,#InputPipeline#Any#,267

Before Change

      bbox = tf.transpose(bbox, [0, 2, 1])

    image = features["image/encoded"]
    image = tf.image.decode_jpeg(image, channels=3)
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)

    if FLAGS.preprocessing == "vgg":
      image = vgg_preprocessing.preprocess_image(
          image=image,
          output_height=FLAGS.height,
          output_width=FLAGS.width,
          is_training=self.is_training,
          resize_side_min=_RESIZE_SIDE_MIN,
          resize_side_max=_RESIZE_SIDE_MAX)
    elif FLAGS.preprocessing == "inception":
      image = inception_preprocessing.preprocess_image(
          image=image,
          output_height=FLAGS.height,
          output_width=FLAGS.width,
          is_training=self.is_training,
          bbox=bbox)

    label = tf.cast(
        tf.reshape(features["image/class/label"], shape=[]), dtype=tf.int32)

    return image, label

After Change

      bbox = tf.transpose(bbox, [0, 2, 1])

    image = features["image/encoded"]
    image = preprocess_raw_bytes(image, is_training=self.is_training, bbox=bbox)
    label = tf.cast(
        tf.reshape(features["image/class/label"], shape=[]), dtype=tf.int32)

    return image, label

Instances