def separable_conv2d()

in tfops.py [0:0]

• 21 lines of code
• 3 McCabe index (conditional complexity)

def separable_conv2d(name, x, width, filter_size=[3, 3], stride=[1, 1], padding="SAME", do_actnorm=True, std=0.05):
    n_in = int(x.get_shape()[3])
    with tf.variable_scope(name):
        assert filter_size[0] % 2 == 1 and filter_size[1] % 2 == 1
        strides = [1] + stride + [1]
        w1_shape = filter_size + [n_in, 1]
        w1_init = np.zeros(w1_shape, dtype='float32')
        w1_init[(filter_size[0]-1)//2, (filter_size[1]-1)//2, :,
                :] = 1.  # initialize depthwise conv as identity
        w1 = tf.get_variable("W1", dtype=tf.float32, initializer=w1_init)
        w2_shape = [1, 1, n_in, width]
        w2 = tf.get_variable("W2", w2_shape, tf.float32,
                             initializer=default_initializer(std))
        x = tf.nn.separable_conv2d(
            x, w1, w2, strides, padding, data_format='NHWC')
        if do_actnorm:
            x = actnorm("actnorm", x)
        else:
            x += tf.get_variable("b", [1, 1, 1, width],
                                 initializer=tf.zeros_initializer(std))

    return x