def attention()

in utils.py [0:0]

• 64 lines of code
• 4 McCabe index (conditional complexity)

def attention(
        inp,
        q,
        q_b,
        k,
        k_b,
        v,
        v_b,
        gamma,
        reuse,
        scope,
        stop_at_grad=False,
        seperate=False,
        scale=False,
        train=False,
        dropout=0.0):
    conv_q = conv_block(
        inp,
        q,
        q_b,
        reuse=reuse,
        scope=scope,
        use_stride=False,
        activation=None,
        use_bias=True,
        pn=False,
        bn=False,
        gn=False)
    conv_k = conv_block(
        inp,
        k,
        k_b,
        reuse=reuse,
        scope=scope,
        use_stride=False,
        activation=None,
        use_bias=True,
        pn=False,
        bn=False,
        gn=False)

    conv_v = conv_block(
        inp,
        v,
        v_b,
        reuse=reuse,
        scope=scope,
        use_stride=False,
        pn=False,
        bn=False,
        gn=False)

    c_num = float(conv_q.get_shape().as_list()[-1])
    s = tf.matmul(hw_flatten(conv_q), hw_flatten(conv_k), transpose_b=True)

    if scale:
        s = s / (c_num) ** 0.5

    if train:
        s = tf.nn.dropout(s, 0.9)

    beta = tf.nn.softmax(s, axis=-1)
    o = tf.matmul(beta, hw_flatten(conv_v))
    o = tf.reshape(o, shape=tf.shape(inp))
    inp = inp + gamma * o

    if not seperate:
        return inp
    else:
        return gamma * o