b13e6b6d19ba633e4f55f7ab5910f675d203e38a,models/official/resnet/imagenet_input.py,ImageNetTFExampleInput,dataset_parser,#ImageNetTFExampleInput#Any#,86

Before Change

    Returns:
      Returns a tuple of (image, label) from the TFExample.
    
    keys_to_features = {
        "image/encoded": tf.FixedLenFeature((), tf.string, ""),
        "image/format": tf.FixedLenFeature((), tf.string, "jpeg"),
        "image/class/label": tf.FixedLenFeature([], tf.int64, -1),
        "image/class/text": tf.FixedLenFeature([], tf.string, ""),
        "image/object/bbox/xmin": tf.VarLenFeature(dtype=tf.float32),
        "image/object/bbox/ymin": tf.VarLenFeature(dtype=tf.float32),
        "image/object/bbox/xmax": tf.VarLenFeature(dtype=tf.float32),
        "image/object/bbox/ymax": tf.VarLenFeature(dtype=tf.float32),
        "image/object/class/label": tf.VarLenFeature(dtype=tf.int64),
    }

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

After Change

    Returns:
      Returns a tuple of (image, label) from the TFExample.
    
    keys_to_features = {
        "image/encoded": tf.FixedLenFeature((), tf.string, ""),
        "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=[])

Instances