Algorithm 1 Tensorflow-like implementation of SimAM

class SimAM(tf.keras.layers.Layer):      def __init__(self, lambda = $1\times {10}^{-7}$, **kwargs):         super(SimAM, self).__init__(**kwargs)         self.lambda = lambda      def call(self, X, **kwargs):         # X Input feature [N, H, W, C]         height, width = X.shape[1:2]         # spatial size         n = width * height − 1         # square of (X − u)         u = tf.math.reduce_mean (X, axis = (1,2), True)         d = tf.math.square (X − u)         # d.sum()/n is channel variance         v = tf.math.reduce_sum (d, axis = (1,2), True)/n         # E_inv groups all importance of X         E_inv = d/(4 * tf.maximum (v, self.lambda) + 0.5)         # return attended features         return X * tf.keras.activations.sigmoid(E_inv)