model_utils.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_sequence
import numpy as np, itertools, random, copy, math


class DiffLoss(nn.Module):

    def __init__(self, args):
        super(DiffLoss, self).__init__()

    def forward(self, input1, input2):

        # input1 (B,N,D)    input2 (B,N,D)

        batch_size = input1.size(0)
        N = input1.size(1)
        input1 = input1.view(batch_size, -1)  # (B,N*D)
        input2 = input2.view(batch_size, -1)  # (B, N*D)
     #   print('input1:', input1)
     #   print('input2:', input2)
        # Zero mean
        input1_mean = torch.mean(input1, dim=0, keepdim=True) # (1,N*D)
        input2_mean = torch.mean(input2, dim=0, keepdim=True) # (1,N*D)
        input1 = input1 - input1_mean     # (B,N*D)
        input2 = input2 - input2_mean     # (B,N*D)

        input1_l2_norm = torch.norm(input1, p=2, dim=1, keepdim=True) # (B,1)
        input1_l2 = input1.div(input1_l2_norm.expand_as(input1) + 1e-6) # (B,N*D)
        

        input2_l2_norm = torch.norm(input2, p=2, dim=1, keepdim=True) # (B,1)
        input2_l2 = input2.div(input2_l2_norm.expand_as(input2) + 1e-6) # (B,N*D)
     #  print("input1_l2_norm:", input1_l2_norm.detach().cpu().numpy())
     #   print("input2_l2_norm:", input2_l2_norm.detach().cpu().numpy())
     #   print("input1_l2:", input1_l2.detach().cpu().numpy())
     #   print("input2_l2:", input2_l2.detach().cpu().numpy())
        norm_diff = torch.mean(torch.norm(input1_l2 - input2_l2, p=2, dim=1))
        if norm_diff.item() == 0:
            return torch.tensor(float('inf'), device=input1.device)
        diff_loss = 1.0 / norm_diff
        
     #   print('loss:', diff_loss)
  
        return diff_loss


class MaskedNLLLoss(nn.Module):

    def __init__(self, weight=None):
        super(MaskedNLLLoss, self).__init__()
        self.weight = weight
        self.loss = nn.NLLLoss(weight=weight,
                               reduction='sum')

    def forward(self, pred, target, mask):
        """
        pred -> batch*seq_len, n_classes
        target -> batch*seq_len
        mask -> batch, seq_len
        """
        mask_ = mask.view(-1, 1)  # batch*seq_len, 1
        if type(self.weight) == type(None):
            loss = self.loss(pred * mask_, target) / torch.sum(mask)
        else:
            loss = self.loss(pred * mask_, target) \
                   / torch.sum(self.weight[target] * mask_.squeeze())
        return loss


class MaskedMSELoss(nn.Module):

    def __init__(self):
        super(MaskedMSELoss, self).__init__()
        self.loss = nn.MSELoss(reduction='sum')

    def forward(self, pred, target, mask):
        """
        pred -> batch*seq_len
        target -> batch*seq_len
        mask -> batch*seq_len
        """
        loss = self.loss(pred * mask, target) / torch.sum(mask)
        return loss


class UnMaskedWeightedNLLLoss(nn.Module):

    def __init__(self, weight=None):
        super(UnMaskedWeightedNLLLoss, self).__init__()
        self.weight = weight
        self.loss = nn.NLLLoss(weight=weight,
                               reduction='sum')

    def forward(self, pred, target):
        """
        pred -> batch*seq_len, n_classes
        target -> batch*seq_len
        """
        if type(self.weight) == type(None):
            loss = self.loss(pred, target)
        else:
            loss = self.loss(pred, target) \
                   / torch.sum(self.weight[target])
        return loss

class GatedSelection(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.context_trans = nn.Linear(hidden_size, hidden_size)
        self.linear1 = nn.Linear(hidden_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.fc = nn.Linear(hidden_size, hidden_size)
        self.sigmoid = nn.Sigmoid()
        self.relu = nn.ReLU()

    def forward(self, x1, x2):
        x2 = self.context_trans(x2)
        s = self.sigmoid(self.linear1(x1)+self.linear2(x2))
        h = s * x1 + (1 - s) * x2
        return self.relu(self.fc(h))

def mask_logic(alpha, adj):
    '''
    performing mask logic with adj
    :param alpha:
    :param adj:
    :return:
    '''
    return alpha - (1 - adj) * 1e30

class GatLinear(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.linear = nn.Linear(hidden_size * 2, 1)


    def forward(self, Q, K, V, adj):
        '''
        imformation gatherer with linear attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :return:
        '''
        N = K.size()[1]
        # print('Q',Q.size())
        Q = Q.unsqueeze(1).expand(-1, N, -1) # (B, N, D)
        # print('K',K.size())
        X = torch.cat((Q,K), dim = 2) # (B, N, 2D)
        # print('X',X.size())
        alpha = self.linear(X).permute(0,2,1) #(B, 1, N)
        # print('alpha',alpha.size())
        # print(alpha)
        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj) # (B, 1, N)
        # print('alpha after mask',alpha.size())
        # print(alpha)

        attn_weight = F.softmax(alpha, dim = 2) # (B, 1, N)
        # print('attn_weight',attn_weight.size())
        # print(attn_weight)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1) # (B, D)
        # print('attn_sum',attn_sum.size())

        return attn_weight, attn_sum

class GatDot(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.linear1 = nn.Linear(hidden_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)

    def forward(self, Q, K, V, adj):
        '''
        imformation gatherer with dot product attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :return:
        '''
        N = K.size()[1]


        Q = self.linear1(Q).unsqueeze(2) # (B,D,1)
        # K = self.linear2(Q) # (B, N, D)
        K = self.linear2(K) # (B, N, D)

        alpha = torch.bmm(K, Q).permute(0, 2, 1)  # (B, 1, N)

        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj)  # (B, 1, N)

        attn_weight = F.softmax(alpha, dim=2)  # (B, 1, N)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1)  # (B,  D)

        return attn_weight, attn_sum

class GatLinear_rel(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.linear = nn.Linear(hidden_size * 3, 1)
        self.rel_emb = nn.Embedding(2, hidden_size)


    def forward(self, Q, K, V, adj, s_mask):
        '''
        imformation gatherer with linear attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :param s_mask: (B,  N) #
        :return:
        '''
        rel_emb = self.rel_emb(s_mask) # (B, N, D)
        N = K.size()[1]
        # print('Q',Q.size())
        Q = Q.unsqueeze(1).expand(-1, N, -1) # (B, N, D)
        # print('K',K.size())
        # print('rel_emb', rel_emb.size())
        X = torch.cat((Q,K, rel_emb), dim = 2) # (B, N, 2D)?   (B, N, 3D)
        # print('X',X.size())
        alpha = self.linear(X).permute(0,2,1) #(B, 1, N)
        # print('alpha',alpha.size())
        # print(alpha)
        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj) # (B, 1, N)
        # print('alpha after mask',alpha.size())
        # print(alpha)

        attn_weight = F.softmax(alpha, dim = 2) # (B, 1, N)
        # print('attn_weight',attn_weight.size())
        # print(attn_weight)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1) # (B, D)
        # print('attn_sum',attn_sum.size())

        return attn_weight, attn_sum


class GatDot_rel(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.linear1 = nn.Linear(hidden_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, 1)
        self.rel_emb = nn.Embedding(2, hidden_size)

    def forward(self, Q, K, V, adj, s_mask):
        '''
        imformation gatherer with dot product attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :param s_mask: (B,  N) #  relation mask
        :return:
        '''
        N = K.size()[1]

        rel_emb = self.rel_emb(s_mask)
        Q = self.linear1(Q).unsqueeze(2) # (B,D,1)
        K = self.linear2(K) # (B, N, D)
        y = self.linear3(rel_emb) # (B, N, 1)

        alpha = (torch.bmm(K, Q) + y).permute(0, 2, 1)  # (B, 1, N)

        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj)  # (B, 1, N)

        attn_weight = F.softmax(alpha, dim=2)  # (B, 1, N)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1)  # (B,  D)

        return attn_weight, attn_sum


class GAT_dialoggcn(nn.Module):
    '''
    H_i = alpha_ij(W_rH_j)
    alpha_ij = attention(H_i, H_j)
    '''
    def __init__(self, hidden_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.linear = nn.Linear(hidden_size * 2, 1)
        self.rel_emb = nn.Parameter(torch.randn(2, hidden_size, hidden_size))

    def forward(self, Q, K, V, adj, s_mask_onehot):
        '''
        imformation gatherer with linear attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :param s_mask: (B,  N, 2) #
        :return:
        '''
        B = K.size()[0]
        N = K.size()[1]
        # print('Q',Q.size())
        Q = Q.unsqueeze(1).expand(-1, N, -1) # (B, N, D)；
        # print('K',K.size())
        X = torch.cat((Q,K), dim = 2) # (B, N, 2D)
        # print('X',X.size())
        alpha = self.linear(X).permute(0,2,1) #(B, 1, N)
        # print('alpha',alpha.size())
        # print(alpha)
        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj) # (B, 1, N)
        # print('alpha after mask',alpha.size())
        # print(alpha)

        attn_weight = F.softmax(alpha, dim = 2) # (B, 1, N)
        # print('attn_weight',attn_weight.size())
        # print(attn_weight)

        # print('s_mask_onehot', s_mask_onehot.size())
        D = self.rel_emb.size()[2]
        # print('rel_emb', self.rel_emb.size())
        rel_emb = self.rel_emb.unsqueeze(0).expand(B,-1,-1,-1)
        # rel_emb = self.rel_emb.unsqueeze(0).repeat(B, 1, 1, 1)
        # print('rel_emb expand', rel_emb.size())

        rel_emb = rel_emb.reshape((B, 2, D*D))
        # print('rel_emb resize', rel_emb.size())
        Wr = torch.bmm(s_mask_onehot, rel_emb).reshape((B, N, D, D)) # (B, N, D, D)
        # print('Wr', Wr.size()) # (B, N, D, D)

        Wr = Wr.reshape((B*N, D, D))
        # print('Wr after reshape', Wr.size())

        V = V.unsqueeze(2).reshape((B*N, 1, -1)) # (B*N, 1, D)
        # print('V after reshape', V.size())
        V = torch.bmm(V, Wr).unsqueeze(1) #(B * N,  D)
        # print('V after transform', V.size())
        V = V.reshape((B,N,-1))
        # print('Final V', V.size())

        attn_sum = torch.bmm(attn_weight, V).squeeze(1) # (B, D)
        # print('attn_sum',attn_sum.size())

        return attn_weight, attn_sum


class GAT_dialoggcn_v1(nn.Module):
    '''
    use linear to avoid OOM
    H_i = alpha_ij(W_rH_j)
    alpha_ij = attention(H_i, H_j)
    '''
    def __init__(self, hidden_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.linear = nn.Linear(hidden_size * 2, 1)
        self.Wr0 = nn.Linear(hidden_size, hidden_size, bias = False)
        self.Wr1 = nn.Linear(hidden_size, hidden_size, bias = False)

    def forward(self, Q, K, V, adj, s_mask):
        '''
        imformation gatherer with linear attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :param s_mask: (B,  N) #
        :return:
        '''
        B = K.size()[0]
        N = K.size()[1]
        # print('Q',Q.size())
        Q = Q.unsqueeze(1).expand(-1, N, -1) # (B, N, D)；
        # print('K',K.size())
        X = torch.cat((Q,K), dim = 2) # (B, N, 2D)
        # print('X',X.size())
        alpha = self.linear(X).permute(0,2,1) #(B, 1, N)
        #alpha = F.leaky_relu(alpha)
        # print('alpha',alpha.size())
        # print(alpha)
        adj = adj.unsqueeze(1)  # (B, 1, N)
        alpha = mask_logic(alpha, adj) # (B, 1, N)
        # print('alpha after mask',alpha.size())
        # print(alpha)

        attn_weight = F.softmax(alpha, dim = 2) # (B, 1, N)
        # print('attn_weight',attn_weight.size())
        # print(attn_weight)

        V0 = self.Wr0(V) # (B, N, D)
        V1 = self.Wr1(V) # (B, N, D)

        s_mask = s_mask.unsqueeze(2).float()   # (B, N, 1)
        V = V0 * s_mask + V1 * (1 - s_mask)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1) # (B, D)
        # print('attn_sum',attn_sum.size())

        return attn_weight, attn_sum


class GAT_dialoggcn_v2(nn.Module):
    '''
    use linear to avoid OOM
    H_i = alpha_ij(W_rH_j)
    alpha_ij = attention(H_i, H_j, rel)
    '''
    def __init__(self, hidden_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.linear = nn.Linear(hidden_size * 3, 1)
        self.Wr0 = nn.Linear(hidden_size, hidden_size, bias = False)
        self.Wr1 = nn.Linear(hidden_size, hidden_size, bias = False)
        self.rel_emb = nn.Embedding(2, hidden_size)

    def forward(self, Q, K, V, adj, s_mask):
        '''
        imformation gatherer with linear attention
        :param Q: (B, D) # query utterance
        :param K: (B, N, D) # context
        :param V: (B, N, D) # context
        :param adj: (B,  N) # the adj matrix of the i th node
        :param s_mask: (B,  N) #
        :return:
        '''
        rel_emb = self.rel_emb(s_mask) # (B, N, D)
        B = K.size()[0]
        N = K.size()[1]
        # print('Q',Q.size())
        Q = Q.unsqueeze(1).expand(-1, N, -1) # (B, N, D)；
        # print('K',K.size())
        X = torch.cat((Q,K,rel_emb), dim = 2) # (B, N, 3D)
        # print('X',X.size())
        alpha = self.linear(X).permute(0,2,1) #(B, 1, N)
        # print('alpha',alpha.size())
        # print(alpha)
        adj = adj.unsqueeze(1)
        alpha = mask_logic(alpha, adj) # (B, 1, N)
        # print('alpha after mask',alpha.size())
        # print(alpha)

        attn_weight = F.softmax(alpha, dim = 2) # (B, 1, N)
        # print('attn_weight',attn_weight.size())
        # print(attn_weight)

        V0 = self.Wr0(V) # (B, N,D)
        V1 = self.Wr1(V) # (B, N, D)

        s_mask = s_mask.unsqueeze(2).float()
        V = V0 * s_mask + V1 * (1 - s_mask)

        attn_sum = torch.bmm(attn_weight, V).squeeze(1) # (B, D)
        # print('attn_sum',attn_sum.size())

        return attn_weight, attn_sum


class attentive_node_features(nn.Module):
    '''
    Method to obtain attentive node features over the graph convoluted features
    '''
    def __init__(self, hidden_size):
        super().__init__()
        self.transform = nn.Linear(hidden_size, hidden_size)

    def forward(self,features, lengths, nodal_att_type):
        '''
        features : (B, N, V)
        lengths : (B, )
        nodal_att_type : type of the final nodal attention
        '''

        if nodal_att_type==None:
            return features

        batch_size = features.size(0)
        max_seq_len = features.size(1)
        padding_mask = [l*[1]+(max_seq_len-l)*[0] for l in lengths]
        padding_mask = torch.tensor(padding_mask).to(features)    # (B, N)
        causal_mask = torch.ones(max_seq_len, max_seq_len).to(features)  # (N, N)
        causal_mask = torch.tril(causal_mask).unsqueeze(0)  # (1, N, N)

        if nodal_att_type=='global':
            mask = padding_mask.unsqueeze(1)
        elif nodal_att_type=='past':
            mask = padding_mask.unsqueeze(1)*causal_mask

        x = self.transform(features)  # (B, N, V)
        temp = torch.bmm(x, features.permute(0,2,1))
        #print(temp)
        alpha = F.softmax(torch.tanh(temp), dim=2)  # (B, N, N)
        alpha_masked = alpha*mask  # (B, N, N)
        
        alpha_sum = torch.sum(alpha_masked, dim=2, keepdim=True)  # (B, N, 1)
        #print(alpha_sum)
        alpha = alpha_masked / alpha_sum    # (B, N, N)
        attn_pool = torch.bmm(alpha, features)  # (B, N, V)

        return attn_pool