filterbank.py

def filterbank(gx, gy, sigma2,delta, N):
    grid_i = tf.reshape(tf.cast(tf.range(N), tf.float32), [1, -1])
    mu_x = gx + (grid_i - N / 2 - 0.5) * delta # eq 19
    mu_y = gy + (grid_i - N / 2 - 0.5) * delta # eq 20
    a = tf.reshape(tf.cast(tf.range(A), tf.float32), [1, 1, -1])
    b = tf.reshape(tf.cast(tf.range(B), tf.float32), [1, 1, -1])
    mu_x = tf.reshape(mu_x, [-1, N, 1])
    mu_y = tf.reshape(mu_y, [-1, N, 1])
    sigma2 = tf.reshape(sigma2, [-1, 1, 1])
    Fx = tf.exp(-tf.square((a - mu_x) / (2*sigma2))) # 2*sigma2?
    Fy = tf.exp(-tf.square((b - mu_y) / (2*sigma2))) # batch x N x B
    # normalize, sum over A and B dims
    Fx=Fx/tf.maximum(tf.reduce_sum(Fx,2,keep_dims=True),eps)
    Fy=Fy/tf.maximum(tf.reduce_sum(Fy,2,keep_dims=True),eps)
    return Fx,Fy