<reponame>llv22/MatchZoo
from keras.layers import Input
from keras import backend as K
from keras.engine.topology import Layer
import numpy as np

class DynamicMaxPooling(Layer):

    def __init__(self, psize1, psize2, **kwargs):
        # for psize1, psize2 [3, 10]
        self.psize1 = psize1
        self.psize2 = psize2
        super(DynamicMaxPooling, self).__init__(**kwargs)

    def build(self, input_shape):
        # for input data dimension layer : (None, 60, 60, 32)
        input_shape_one = input_shape[0]
        self.msize1 = input_shape_one[1]
        self.msize2 = input_shape_one[2]
        super(DynamicMaxPooling, self).build(input_shape)  

    def call(self, data):
        x, self.dpool_index = data
        x_expand = K.tf.gather_nd(x, self.dpool_index)
        stride1 = self.msize1 / self.psize1
        stride2 = self.msize2 / self.psize2
        
        suggestion1 = self.msize1 / stride1
        suggestion2 = self.msize2 / stride2

        # should be consistent with kernel pooling size
        if suggestion1 != self.psize1 or suggestion2 != self.psize2:
            print("DynamicMaxPooling Layer can not "
                  "generate ({} x {}) output feature map,"
                  "please use ({} x {} instead.)".format(self.psize1, self.psize2, 
                                                       suggestion1, suggestion2))
            exit()

        x_pool = K.tf.nn.max_pool(x_expand, 
                    [1, self.msize1 / self.psize1, self.msize2 / self.psize2, 1], 
                    [1, self.msize1 / self.psize1, self.msize2 / self.psize2, 1], 
                    "VALID")
        return x_pool

    def compute_output_shape(self, input_shape):
        input_shape_one = input_shape[0]
        return (None, self.psize1, self.psize2, input_shape_one[3])

    @staticmethod
    def dynamic_pooling_index(len1, len2, max_len1, max_len2, 
                              compress_ratio1 = 1, compress_ratio2 = 1):
        def dpool_index_(batch_idx, len1_one, len2_one, max_len1, max_len2):
            '''
            TODO: Here is the check of sentences length to be positive.
            To make sure that the lenght of the input sentences are positive. 
            if len1_one == 0:
                print("[Error:DynamicPooling] len1 = 0 at batch_idx = {}".format(batch_idx))
                exit()
            if len2_one == 0:
                print("[Error:DynamicPooling] len2 = 0 at batch_idx = {}".format(batch_idx))
                exit()
            '''
            # decide stride for len1 and len2 based on current len1 and len1 of sentence
            if len1_one == 0:
                stride1 = max_len1
            else:
                stride1 = 1.0 * max_len1 / len1_one

            if len2_one == 0:
                stride2 = max_len2
            else:
                stride2 = 1.0 * max_len2 / len2_one

            # generate <len1 index for stride>, <len2 index for stride>
            idx1_one = [int(i / stride1) for i in range(max_len1)]
            idx2_one = [int(i / stride2) for i in range(max_len2)]
            mesh1, mesh2 = np.meshgrid(idx1_one, idx2_one)
            assert mesh1.shape == mesh2.shape
            index_one = np.transpose(np.stack([np.ones(mesh1.shape) * batch_idx,
                                      mesh1, mesh2]), (2,1,0))
            return index_one
        index = []
        dpool_bias1 = dpool_bias2 = 0
        # shifting up for dpool_bias1 and dpool_bias2
        if max_len1 % compress_ratio1 != 0:
            dpool_bias1 = 1
        if max_len2 % compress_ratio2 != 0:
            dpool_bias2 = 1
        cur_max_len1 = max_len1 // compress_ratio1 + dpool_bias1
        cur_max_len2 = max_len2 // compress_ratio2 + dpool_bias2
        assert len(len1) == len(len2)
        for i in range(len(len1)):
            # enumerate all batch size from [0, len1-1], generate convd parameter (0..len1-1, <len1 index for stride>, <len2 index for stride>)
            index.append(dpool_index_(i, len1[i] // compress_ratio1, 
                         len2[i] // compress_ratio2, cur_max_len1, cur_max_len2))
        return np.array(index)

from keras.layers import Input
from keras import backend as K
from keras.engine.topology import Layer
import numpy as np

class DynamicMaxPooling(Layer):

    def __init__(self, psize1, psize2, **kwargs):
        # for psize1, psize2 [3, 10]
        self.psize1 = psize1
        self.psize2 = psize2
        super(DynamicMaxPooling, self).__init__(**kwargs)

    def build(self, input_shape):
        # for input data dimension layer : (None, 60, 60, 32)
        input_shape_one = input_shape[0]
        self.msize1 = input_shape_one[1]
        self.msize2 = input_shape_one[2]
        super(DynamicMaxPooling, self).build(input_shape)  

    def call(self, data):
        x, self.dpool_index = data
        x_expand = K.tf.gather_nd(x, self.dpool_index)
        stride1 = self.msize1 / self.psize1
        stride2 = self.msize2 / self.psize2
        
        suggestion1 = self.msize1 / stride1
        suggestion2 = self.msize2 / stride2

        # should be consistent with kernel pooling size
        if suggestion1 != self.psize1 or suggestion2 != self.psize2:
            print("DynamicMaxPooling Layer can not "
                  "generate ({} x {}) output feature map,"
                  "please use ({} x {} instead.)".format(self.psize1, self.psize2, 
                                                       suggestion1, suggestion2))
            exit()

        x_pool = K.tf.nn.max_pool(x_expand, 
                    [1, self.msize1 / self.psize1, self.msize2 / self.psize2, 1], 
                    [1, self.msize1 / self.psize1, self.msize2 / self.psize2, 1], 
                    "VALID")
        return x_pool

    def compute_output_shape(self, input_shape):
        input_shape_one = input_shape[0]
        return (None, self.psize1, self.psize2, input_shape_one[3])

    @staticmethod
    def dynamic_pooling_index(len1, len2, max_len1, max_len2, 
                              compress_ratio1 = 1, compress_ratio2 = 1):
        def dpool_index_(batch_idx, len1_one, len2_one, max_len1, max_len2):
            '''
            TODO: Here is the check of sentences length to be positive.
            To make sure that the lenght of the input sentences are positive. 
            if len1_one == 0:
                print("[Error:DynamicPooling] len1 = 0 at batch_idx = {}".format(batch_idx))
                exit()
            if len2_one == 0:
                print("[Error:DynamicPooling] len2 = 0 at batch_idx = {}".format(batch_idx))
                exit()
            '''
            # decide stride for len1 and len2 based on current len1 and len1 of sentence
            if len1_one == 0:
                stride1 = max_len1
            else:
                stride1 = 1.0 * max_len1 / len1_one

            if len2_one == 0:
                stride2 = max_len2
            else:
                stride2 = 1.0 * max_len2 / len2_one

            # generate <len1 index for stride>, <len2 index for stride>
            idx1_one = [int(i / stride1) for i in range(max_len1)]
            idx2_one = [int(i / stride2) for i in range(max_len2)]
            mesh1, mesh2 = np.meshgrid(idx1_one, idx2_one)
            assert mesh1.shape == mesh2.shape
            index_one = np.transpose(np.stack([np.ones(mesh1.shape) * batch_idx,
                                      mesh1, mesh2]), (2,1,0))
            return index_one
        index = []
        dpool_bias1 = dpool_bias2 = 0
        # shifting up for dpool_bias1 and dpool_bias2
        if max_len1 % compress_ratio1 != 0:
            dpool_bias1 = 1
        if max_len2 % compress_ratio2 != 0:
            dpool_bias2 = 1
        cur_max_len1 = max_len1 // compress_ratio1 + dpool_bias1
        cur_max_len2 = max_len2 // compress_ratio2 + dpool_bias2
        assert len(len1) == len(len2)
        for i in range(len(len1)):
            # enumerate all batch size from [0, len1-1], generate convd parameter (0..len1-1, <len1 index for stride>, <len2 index for stride>)
            index.append(dpool_index_(i, len1[i] // compress_ratio1, 
                         len2[i] // compress_ratio2, cur_max_len1, cur_max_len2))
        return np.array(index)
# for psize1, psize2 [3, 10] # for input data dimension layer : (None, 60, 60, 32) # should be consistent with kernel pooling size TODO: Here is the check of sentences length to be positive.             To make sure that the lenght of the input sentences are positive.              if len1_one == 0:                 print("[Error:DynamicPooling] len1 = 0 at batch_idx = {}".format(batch_idx))                 exit()             if len2_one == 0:                 print("[Error:DynamicPooling] len2 = 0 at batch_idx = {}".format(batch_idx))                 exit() # decide stride for len1 and len2 based on current len1 and len1 of sentence # generate <len1 index for stride>, <len2 index for stride> # shifting up for dpool_bias1 and dpool_bias2 # enumerate all batch size from [0, len1-1], generate convd parameter (0..len1-1, <len1 index for stride>, <len2 index for stride>)