import numpy as np

import tensorflow as tf

from tensorflow.python.ops import gen_nn_ops

from tensorflow.python.framework import ops

def unpool_with_argmax(pool, ind, name = None, ksize=[1, 2, 2, 1]):

    """

       Unpooling layer after max_pool_with_argmax.

       Args:

           pool:   max pooled output tensor

           ind:      argmax indices

           ksize:     ksize is the same as for the pool

       Return:

           unpool:    unpooling tensor

    """

    with tf.variable_scope(name):

        input_shape = pool.get_shape().as_list()

        output_shape = (input_shape[0], input_shape[1] * ksize[1], input_shape[2] * ksize[2], input_shape[3])

        flat_input_size = np.prod(input_shape)

        flat_output_shape = [output_shape[0], output_shape[1] * output_shape[2] * output_shape[3]]

        pool_ = tf.reshape(pool, [flat_input_size])

        batch_range = tf.reshape(tf.range(output_shape[0], dtype=ind.dtype), shape=[input_shape[0], 1, 1, 1])

        b = tf.ones_like(ind) * batch_range

        b = tf.reshape(b, [flat_input_size, 1])

        ind_ = tf.reshape(ind, [flat_input_size, 1])

        ind_ = tf.concat([b, ind_], 1) #交换了两个参数--remove!!

        ret = tf.scatter_nd(ind_, pool_, shape=flat_output_shape)

        ret = tf.reshape(ret, output_shape)

        return ret

try:

    @ops.RegisterGradient("MaxPoolWithArgmax")

    def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad):

        return gen_nn_ops._max_pool_grad_with_argmax(op.inputs[0],

                                                     grad,

                                                     op.outputs[1],

                                                     op.get_attr("ksize"),

                                                     op.get_attr("strides"),

                                                     padding=op.get_attr("padding"))

except Exception as e:

    print(f"Could not add gradient for MaxPoolWithArgMax, Likely installed already (tf 1.4)")

    print(e)