c23aaeec82f5d1d5f5876d15f7adfc18ba399da5,pytorch2keras/normalization_layers.py,,convert_instancenorm,#Any#Any#Any#Any#Any#Any#Any#,64

Before Change

    assert(len(inputs) == 3)

    // Use previously taken constants
    assert(inputs[-2] + "_np" in layers)
    assert(inputs[-1] + "_np" in layers)
    gamma = layers[inputs[-2] + "_np"]
    beta = layers[inputs[-1] + "_np"]

After Change

    if inputs[-1] + "_np" in layers:
        beta = layers[inputs[-1] + "_np"]
    else:
        beta = weights[bias_name].numpy()

    def target_layer(x, epsilon=params["epsilon"], gamma=gamma, beta=beta):
        layer = tf.contrib.layers.instance_norm(
            x,

Instances