def conv2d()

in tfops.py [0:0]

• 26 lines of code
• 8 McCabe index (conditional complexity)

def conv2d(name, x, width, filter_size=[3, 3], stride=[1, 1], pad="SAME", do_weightnorm=False, do_actnorm=True, context1d=None, skip=1, edge_bias=True):
    with tf.variable_scope(name):
        if edge_bias and pad == "SAME":
            x = add_edge_padding(x, filter_size)
            pad = 'VALID'

        n_in = int(x.get_shape()[3])

        stride_shape = [1] + stride + [1]
        filter_shape = filter_size + [n_in, width]
        w = tf.get_variable("W", filter_shape, tf.float32,
                            initializer=default_initializer())
        if do_weightnorm:
            w = tf.nn.l2_normalize(w, [0, 1, 2])
        if skip == 1:
            x = tf.nn.conv2d(x, w, stride_shape, pad, data_format='NHWC')
        else:
            assert stride[0] == 1 and stride[1] == 1
            x = tf.nn.atrous_conv2d(x, w, skip, pad)
        if do_actnorm:
            x = actnorm("actnorm", x)
        else:
            x += tf.get_variable("b", [1, 1, 1, width],
                                 initializer=tf.zeros_initializer())

        if context1d != None:
            x += tf.reshape(linear("context", context1d,
                                   width), [-1, 1, 1, width])
    return x