train.py 文件源码

def sample_k_fids_for_pid(pid, all_fids, all_pids, batch_k):
    """ Given a PID, select K FIDs of that specific PID. """
    possible_fids = tf.boolean_mask(all_fids, tf.equal(all_pids, pid))

    # The following simply uses a subset of K of the possible FIDs
    # if more than, or exactly K are available. Otherwise, we first
    # create a padded list of indices which contain a multiple of the
    # original FID count such that all of them will be sampled equally likely.
    count = tf.shape(possible_fids)[0]
    padded_count = tf.cast(tf.ceil(batch_k / count), tf.int32) * count
    full_range = tf.mod(tf.range(padded_count), count)

    # Sampling is always performed by shuffling and taking the first k.
    shuffled = tf.random_shuffle(full_range)
    selected_fids = tf.gather(possible_fids, shuffled[:batch_k])

    return selected_fids, tf.fill([batch_k], pid)