그림 주의력 네트워크pyGAT 논문 코드 주석

논문 주소:https://arxiv.org/abs/1710.10903
코드 주소:https://github.com/Diego999/pyGAT
인용문 네트워크cora를 사용하기 때문에 대부분의 작업이 유사하기 때문에 여기서 코드에 대해 상세한 설명을 하지 않았습니다. 데이터 불러오기, 매트릭스 정규화, 원-hot 인코딩 변환 부분은 참고할 수 있습니다.https://blog.csdn.net/qq_44015059/article/details/105968961
인접 행렬의 처리에 있어 그림 주의력 모델인 GAT는 주의력 메커니즘으로 GCN에서 고정된 표준화 조작을 대체했다.본질적으로 GAT는 원래 GCN의 표준화 함수를 주의력이 중요한 이웃 노드 특징 집합 함수로 바꿀 뿐이다

train.py

from __future__ import division
from __future__ import print_function

import os
import glob
import time
import random
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

from utils import load_data, accuracy
from models import GAT, SpGAT

# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.')
parser.add_argument('--fastmode', action='store_true', default=False, help='Validate during training pass.')
parser.add_argument('--sparse', action='store_true', default=False, help='GAT with sparse version or not.')
parser.add_argument('--seed', type=int, default=72, help='Random seed.')
parser.add_argument('--epochs', type=int, default=10000, help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.005, help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=4, help='Number of hidden units.')
parser.add_argument('--nb_heads', type=int, default=8, help='Number of head attentions.')
parser.add_argument('--dropout', type=float, default=0.6, help='Dropout rate (1 - keep probability).')
parser.add_argument('--alpha', type=float, default=0.2, help='Alpha for the leaky_relu.')
parser.add_argument('--patience', type=int, default=100, help='Patience')

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

# Load data
adj, features, labels, idx_train, idx_val, idx_test = load_data()

# Model and optimizer
if args.sparse:
    model = SpGAT(nfeat=features.shape[1], 
                nhid=args.hidden, 
                nclass=int(labels.max()) + 1, 
                dropout=args.dropout, 
                nheads=args.nb_heads, 
                alpha=args.alpha)
else:
    model = GAT(nfeat=features.shape[1], 
                nhid=args.hidden, 
                nclass=int(labels.max()) + 1, 
                dropout=args.dropout, 
                nheads=args.nb_heads, 
                alpha=args.alpha)
optimizer = optim.Adam(model.parameters(), 
                       lr=args.lr, 
                       weight_decay=args.weight_decay)

if args.cuda:
    model.cuda()
    features = features.cuda()
    adj = adj.cuda()
    labels = labels.cuda()
    idx_train = idx_train.cuda()
    idx_val = idx_val.cuda()
    idx_test = idx_test.cuda()

features, adj, labels = Variable(features), Variable(adj), Variable(labels)


def train(epoch):
    t = time.time()
    model.train()
    optimizer.zero_grad()
    output = model(features, adj)
    loss_train = F.nll_loss(output[idx_train], labels[idx_train])
    acc_train = accuracy(output[idx_train], labels[idx_train])
    loss_train.backward()
    optimizer.step()

    if not args.fastmode:
        # Evaluate validation set performance separately,
        # deactivates dropout during validation run.
        model.eval()
        output = model(features, adj)

    loss_val = F.nll_loss(output[idx_val], labels[idx_val])
    acc_val = accuracy(output[idx_val], labels[idx_val])
    print('Epoch: {:04d}'.format(epoch+1),
          'loss_train: {:.4f}'.format(loss_train.data.item()),
          'acc_train: {:.4f}'.format(acc_train.data.item()),
          'loss_val: {:.4f}'.format(loss_val.data.item()),
          'acc_val: {:.4f}'.format(acc_val.data.item()),
          'time: {:.4f}s'.format(time.time() - t))

    return loss_val.data.item()


def compute_test():
    model.eval()
    output = model(features, adj)
    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
    acc_test = accuracy(output[idx_test], labels[idx_test])
    print("Test set results:",
          "loss= {:.4f}".format(loss_test.data[0]),
          "accuracy= {:.4f}".format(acc_test.data[0]))

# Train model
t_total = time.time()
loss_values = []
bad_counter = 0
best = args.epochs + 1
best_epoch = 0
for epoch in range(args.epochs):
    loss_values.append(train(epoch))

    torch.save(model.state_dict(), '{}.pkl'.format(epoch))
    if loss_values[-1] < best:
        best = loss_values[-1]
        best_epoch = epoch
        bad_counter = 0
    else:
        bad_counter += 1

    if bad_counter == args.patience:
        break

    files = glob.glob('*.pkl')
    for file in files:
        epoch_nb = int(file.split('.')[0])
        if epoch_nb < best_epoch:
            os.remove(file)

files = glob.glob('*.pkl')
for file in files:
    epoch_nb = int(file.split('.')[0])
    if epoch_nb > best_epoch:
        os.remove(file)

print("Optimization Finished!")
print("Total time elapsed: {:.4f}s".format(time.time() - t_total))

# Restore best model
print('Loading {}th epoch'.format(best_epoch))
model.load_state_dict(torch.load('{}.pkl'.format(best_epoch)))

# Testing
compute_test()

models.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphAttentionLayer, SpGraphAttentionLayer


class GAT(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        """Dense version of GAT."""
        super(GAT, self).__init__()
        self.dropout = dropout
        # print('nfeat:',nfeat)   #              :           1433
        # print('nhid:',nhid)
        # print('nclass:',nclass) # 7   
        # print('dropout:',dropout)   # 0.6
        # print('alpha:',alpha)       # 0.2
        # print('nheads:',nheads)     # 8
        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]
        #   8 GAT
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)
        # print('self.add_module:',self.add_module)
        #     8        ，    1433->4
        #          ，                  
        #      4*8=32，          7 
        # nhid * nheads 32
        # nclass    7
        # dropout   0.6
        # alpha     0.2
        self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)

    def forward(self, x, adj):
        print('adj.shape:',adj.shape)
        x = F.dropout(x, self.dropout, training=self.training)
        #               
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        # ELU    
        x = F.elu(self.out_att(x, adj))
        return F.log_softmax(x, dim=1)


#   
class SpGAT(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        """Sparse version of GAT."""
        super(SpGAT, self).__init__()
        self.dropout = dropout

        self.attentions = [SpGraphAttentionLayer(nfeat,
                                                 nhid,
                                                 dropout=dropout,
                                                 alpha=alpha,
                                                 concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)

        self.out_att = SpGraphAttentionLayer(nhid * nheads,
                                             nclass,
                                             dropout=dropout,
                                             alpha=alpha,
                                             concat=False)

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        x = F.elu(self.out_att(x, adj))
        return F.log_softmax(x, dim=1)

utils.py

import numpy as np
import scipy.sparse as sp
import torch


def encode_onehot(labels):
    classes = set(labels)
    classes_dict = {c: np.identity(len(classes))[i, :] for i, c in enumerate(classes)}
    labels_onehot = np.array(list(map(classes_dict.get, labels)), dtype=np.int32)
    return labels_onehot


def load_data(path="./data/cora/", dataset="cora"):
    """Load citation network dataset (cora only for now)"""
    print('Loading {} dataset...'.format(dataset))

    idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str))
    features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
    labels = encode_onehot(idx_features_labels[:, -1])

    # build graph
    idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
    idx_map = {j: i for i, j in enumerate(idx)}
    edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32)
    edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)
    adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)

    # build symmetric adjacency matrix
    adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)

    features = normalize_features(features)
    adj = normalize_adj(adj + sp.eye(adj.shape[0]))

    idx_train = range(140)
    idx_val = range(200, 500)
    idx_test = range(500, 1500)

    adj = torch.FloatTensor(np.array(adj.todense()))
    features = torch.FloatTensor(np.array(features.todense()))
    labels = torch.LongTensor(np.where(labels)[1])

    idx_train = torch.LongTensor(idx_train)
    idx_val = torch.LongTensor(idx_val)
    idx_test = torch.LongTensor(idx_test)

    return adj, features, labels, idx_train, idx_val, idx_test


def normalize_adj(mx):
    """Row-normalize sparse matrix"""
    rowsum = np.array(mx.sum(1))
    r_inv_sqrt = np.power(rowsum, -0.5).flatten()
    r_inv_sqrt[np.isinf(r_inv_sqrt)] = 0.
    r_mat_inv_sqrt = sp.diags(r_inv_sqrt)
    return mx.dot(r_mat_inv_sqrt).transpose().dot(r_mat_inv_sqrt)


def normalize_features(mx):
    """Row-normalize sparse matrix"""
    rowsum = np.array(mx.sum(1))
    r_inv = np.power(rowsum, -1).flatten()
    r_inv[np.isinf(r_inv)] = 0.
    r_mat_inv = sp.diags(r_inv)
    mx = r_mat_inv.dot(mx)
    return mx


def accuracy(output, labels):
    preds = output.max(1)[1].type_as(labels)
    correct = preds.eq(labels).double()
    correct = correct.sum()
    return correct / len(labels)

layers.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


class GraphAttentionLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(GraphAttentionLayer, self).__init__()
        # print('in_features:',in_features)   #              :           1433(8)     32
        # print('out_features:',out_features)     #                                 4(8)        7
        # print('dropout:',dropout)   # 0.6
        # print('alpha:',alpha)       # 0.2
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat

        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        # nn.init.xavier_uniform_                 ，                
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        # print('self.W.shape:',self.W.shape)     # torch.Size([1433, 4])(8)  torch.Size([32, 7])
        self.a = nn.Parameter(torch.zeros(size=(2*out_features, 1)))
        # self.a             i    j    ，          (                             k     )
        #        https://blog.csdn.net/qq_44015059/article/details/105749213
        # nn.init.xavier_uniform_                 ，                
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
        # print('self.a.shape:',self.a.shape)     # torch.Size([8, 1])(8)     torch.Size([14, 1])

        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, input, adj):
        h = torch.mm(input, self.W)
        N = h.size()[0]
        # 2708 cora        
        # print('h.shape:',h.shape)       # 2708,4(8)    2708,7
        a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)], dim=1).view(N, -1, 2 * self.out_features)
        # print('a_input.shape:',a_input.shape)       # torch.Size([2708, 2708, 8])(8)    torch.Size([2708, 2708, 14])
        e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
        # e     (2708,2708,8)*(8,1) ->(2708,2708,1)
        zero_vec = -9e15*torch.ones_like(e)
        # torch.where       ，        ，            ，             
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)

        if self.concat:
            return F.elu(h_prime)
        else:
            return h_prime
    #      ，__repr__()            “  +object at+    ”  
    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'


class SpecialSpmmFunction(torch.autograd.Function):
    """Special function for only sparse region backpropataion layer."""
    @staticmethod
    def forward(ctx, indices, values, shape, b):
        assert indices.requires_grad == False
        a = torch.sparse_coo_tensor(indices, values, shape)
        ctx.save_for_backward(a, b)
        ctx.N = shape[0]
        return torch.matmul(a, b)

    @staticmethod
    def backward(ctx, grad_output):
        a, b = ctx.saved_tensors
        grad_values = grad_b = None
        if ctx.needs_input_grad[1]:
            grad_a_dense = grad_output.matmul(b.t())
            edge_idx = a._indices()[0, :] * ctx.N + a._indices()[1, :]
            grad_values = grad_a_dense.view(-1)[edge_idx]
        if ctx.needs_input_grad[3]:
            grad_b = a.t().matmul(grad_output)
        return None, grad_values, None, grad_b


class SpecialSpmm(nn.Module):
    def forward(self, indices, values, shape, b):
        return SpecialSpmmFunction.apply(indices, values, shape, b)

    
class SpGraphAttentionLayer(nn.Module):
    """
    Sparse version GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(SpGraphAttentionLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat

        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_normal_(self.W.data, gain=1.414)
                
        self.a = nn.Parameter(torch.zeros(size=(1, 2*out_features)))
        nn.init.xavier_normal_(self.a.data, gain=1.414)

        self.dropout = nn.Dropout(dropout)
        self.leakyrelu = nn.LeakyReLU(self.alpha)
        self.special_spmm = SpecialSpmm()

    def forward(self, input, adj):
        dv = 'cuda' if input.is_cuda else 'cpu'

        N = input.size()[0]
        edge = adj.nonzero().t()

        h = torch.mm(input, self.W)
        # h: N x out
        assert not torch.isnan(h).any()

        # Self-attention on the nodes - Shared attention mechanism
        edge_h = torch.cat((h[edge[0, :], :], h[edge[1, :], :]), dim=1).t()
        # edge: 2*D x E

        edge_e = torch.exp(-self.leakyrelu(self.a.mm(edge_h).squeeze()))
        assert not torch.isnan(edge_e).any()
        # edge_e: E

        e_rowsum = self.special_spmm(edge, edge_e, torch.Size([N, N]), torch.ones(size=(N,1), device=dv))
        # e_rowsum: N x 1

        edge_e = self.dropout(edge_e)
        # edge_e: E

        h_prime = self.special_spmm(edge, edge_e, torch.Size([N, N]), h)
        assert not torch.isnan(h_prime).any()
        # h_prime: N x out
        
        h_prime = h_prime.div(e_rowsum)
        # h_prime: N x out
        assert not torch.isnan(h_prime).any()

        if self.concat:
            # if this layer is not last layer,
            return F.elu(h_prime)
        else:
            # if this layer is last layer,
            return h_prime

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'