net.py

import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
import dgl
import networkx as nx
import dgl.function as fn
from dgl.data import MiniGCDataset
import dgl.function as fn
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from dgl.nn.pytorch import SumPooling
import numpy as np
from dgl.data.utils import save_graphs, get_download_dir, load_graphs
# from dgl.subgraph import DGLSubGraph
from torch.utils.data import Dataset, DataLoader
from dset import dgraph, collate
from dgl.nn.pytorch.conv import GraphConv
from torch.nn import Linear
from dgl.nn.pytorch.conv import GraphConv
from torch.nn import Linear
from layers import three_gcn, cross_sim, att_layer, NTN


class sub_GMN(torch.nn.Module):
    def __init__(self, GCN_in_size, GCN_out_size, q_size, da_size, NTN_k, mask=False):
        super(sub_GMN, self).__init__()
        self.GCN_in_size = GCN_in_size
        self.GCN_out_size = GCN_out_size  # D
        self.q_size = q_size
        self.da_size = da_size
        self.NTN_k = NTN_k
        self.mask = mask
        # layers
        self.GCN = three_gcn(in_size=self.GCN_in_size, out_size=self.GCN_out_size)
        self.NTN1 = NTN(q_size=self.q_size, da_size=self.da_size, D=self.GCN_out_size, k=self.NTN_k)
        self.NTN2 = NTN(q_size=self.q_size, da_size=self.da_size, D=self.GCN_out_size, k=self.NTN_k)
        self.NTN3 = NTN(q_size=self.q_size, da_size=self.da_size, D=self.GCN_out_size, k=self.NTN_k)
        self.Con1 = nn.Conv2d(self.NTN_k + 1, 1, [1, 1])
        self.Con2 = nn.Conv2d(self.NTN_k + 1, 1, [1, 1])
        self.Con3 = nn.Conv2d(self.NTN_k + 1, 1, [1, 1])
        self.con_end = nn.Conv2d(self.NTN_k * 3 + 6, 1, [1, 1])

    def forward(self, bg_da, bg_q, b_same):  # b_same bx5x8
        b_same_adddim = torch.unsqueeze(b_same, 1)  # b_same bx1x5x8
        da1, da2, da3 = self.GCN(bg_da, self.da_size)
        q1, q2, q3 = self.GCN(bg_q, self.q_size)
        # 1
        att1 = att_layer(batch_q_em=q1, batch_da_em=da1)  # att bx1x5x18
        N1_16 = self.NTN1(batch_q_em=q1, batch_da_em=da1)  # N1_16 bxkx5x18
        N1_16 = N1_16 * att1  # N1_16 bxkx5x18
        he_1 = torch.cat([b_same_adddim, N1_16], dim=1)  # he_1 bx(k+1)x5x18
        end1 = self.Con1(he_1)  # end1 bx1x5x18
        end1 = torch.softmax(end1, dim=3)
        # 2
        att2 = att_layer(batch_q_em=q2, batch_da_em=da2)  # att bx1x5x18
        N2_16 = self.NTN2(batch_q_em=q2, batch_da_em=da2)  # N_16 bxkx5x18
        N2_16 = N2_16 * att2  # N_16 bxkx5x18
        he_2 = torch.cat([b_same_adddim, N2_16], dim=1)  # he bx(k+1)x5x18
        end2 = self.Con2(he_2)  # end bx1x5x18
        end2 = torch.softmax(end2, dim=3)
        # 3
        att3 = att_layer(batch_q_em=q3, batch_da_em=da3)  # att bx1x5x18
        N3_16 = self.NTN3(batch_q_em=q3, batch_da_em=da3)  # N_16 bxkx5x18
        N3_16 = N3_16 * att3  # N_16 bxkx5x18
        he_3 = torch.cat([b_same_adddim, N3_16], dim=1)  # he bx(k+1)x5x18
        end3 = self.Con3(he_3)  # end bx1x5x18
        end3 = torch.softmax(end3, dim=3)
        # end
        end = torch.cat([end1, end2, end3, he_1, he_2, he_3], dim=1)  # end bx(3k+6)x5x18
        end = self.con_end(end)  # end bx1x5x18
        end = end.reshape(-1, self.q_size, self.da_size)  # end bx5x18
        if self.mask:
            end = torch.softmax(end, dim=2)
            end = end * b_same  # end bx5x18
            # end = torch.softmax(end, dim=2)
        else:
            end = torch.softmax(end, dim=2)
        return end