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f74dd01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | """multiple transformaiton and multiple propagation"""
import torch.nn as nn
import torch.nn.functional as F
import math
import torch
import torch.optim as optim
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from deeprobust.graph import utils
from copy import deepcopy
from sklearn.metrics import f1_score
from torch.nn import init
import torch_sparse
class SGC(nn.Module):
def __init__(self, nfeat, nhid, nclass, nlayers=2, dropout=0.5, lr=0.01, weight_decay=5e-4,
ntrans=2, with_bias=True, with_bn=False, device=None):
"""nlayers indicates the number of propagations"""
super(SGC, self).__init__()
assert device is not None, "Please specify 'device'!"
self.device = device
self.nfeat = nfeat
self.nclass = nclass
self.layers = nn.ModuleList([])
if ntrans == 1:
self.layers.append(MyLinear(nfeat, nclass))
else:
self.layers.append(MyLinear(nfeat, nhid))
if with_bn:
self.bns = torch.nn.ModuleList()
self.bns.append(nn.BatchNorm1d(nhid))
for i in range(ntrans-2):
if with_bn:
self.bns.append(nn.BatchNorm1d(nhid))
self.layers.append(MyLinear(nhid, nhid))
self.layers.append(MyLinear(nhid, nclass))
self.nlayers = nlayers
self.dropout = dropout
self.lr = lr
self.with_bn = with_bn
self.with_bias = with_bias
self.weight_decay = weight_decay
self.output = None
self.best_model = None
self.best_output = None
self.adj_norm = None
self.features = None
self.multi_label = None
def forward(self, x, adj):
for ix, layer in enumerate(self.layers):
x = layer(x)
if ix != len(self.layers) - 1:
x = self.bns[ix](x) if self.with_bn else x
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
for i in range(self.nlayers):
x = torch.spmm(adj, x)
if self.multi_label:
return torch.sigmoid(x)
else:
return F.log_softmax(x, dim=1)
def forward_sampler(self, x, adjs):
for ix, layer in enumerate(self.layers):
x = layer(x)
if ix != len(self.layers) - 1:
x = self.bns[ix](x) if self.with_bn else x
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
for ix, (adj, _, size) in enumerate(adjs):
# x_target = x[: size[1]]
# x = self.layers[ix]((x, x_target), edge_index)
# adj = adj.to(self.device)
x = torch_sparse.matmul(adj, x)
if self.multi_label:
return torch.sigmoid(x)
else:
return F.log_softmax(x, dim=1)
def forward_sampler_syn(self, x, adjs):
for ix, layer in enumerate(self.layers):
x = layer(x)
if ix != len(self.layers) - 1:
x = self.bns[ix](x) if self.with_bn else x
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
for ix, (adj) in enumerate(adjs):
if type(adj) == torch.Tensor:
x = adj @ x
else:
x = torch_sparse.matmul(adj, x)
if self.multi_label:
return torch.sigmoid(x)
else:
return F.log_softmax(x, dim=1)
def initialize(self):
"""Initialize parameters of GCN.
"""
for layer in self.layers:
layer.reset_parameters()
if self.with_bn:
for bn in self.bns:
bn.reset_parameters()
def fit_with_val(self, features, adj, labels, data, train_iters=200, initialize=True, verbose=False, normalize=True, patience=None, noval=False, **kwargs):
'''data: full data class'''
if initialize:
self.initialize()
# features, adj, labels = data.feat_train, data.adj_train, data.labels_train
if type(adj) is not torch.Tensor:
features, adj, labels = utils.to_tensor(features, adj, labels, device=self.device)
else:
features = features.to(self.device)
adj = adj.to(self.device)
labels = labels.to(self.device)
if normalize:
if utils.is_sparse_tensor(adj):
adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
adj_norm = utils.normalize_adj_tensor(adj)
else:
adj_norm = adj
if 'feat_norm' in kwargs and kwargs['feat_norm']:
from utils import row_normalize_tensor
features = row_normalize_tensor(features-features.min())
self.adj_norm = adj_norm
self.features = features
if len(labels.shape) > 1:
self.multi_label = True
self.loss = torch.nn.BCELoss()
else:
self.multi_label = False
self.loss = F.nll_loss
labels = labels.float() if self.multi_label else labels
self.labels = labels
if noval:
self._train_with_val(labels, data, train_iters, verbose, adj_val=True)
else:
self._train_with_val(labels, data, train_iters, verbose)
def _train_with_val(self, labels, data, train_iters, verbose, adj_val=False):
if adj_val:
feat_full, adj_full = data.feat_val, data.adj_val
else:
feat_full, adj_full = data.feat_full, data.adj_full
feat_full, adj_full = utils.to_tensor(feat_full, adj_full, device=self.device)
adj_full_norm = utils.normalize_adj_tensor(adj_full, sparse=True)
labels_val = torch.LongTensor(data.labels_val).to(self.device)
if verbose:
print('=== training gcn model ===')
optimizer = optim.Adam(self.parameters(), lr=self.lr, weight_decay=self.weight_decay)
best_acc_val = 0
for i in range(train_iters):
if i == train_iters // 2:
lr = self.lr*0.1
optimizer = optim.Adam(self.parameters(), lr=lr, weight_decay=self.weight_decay)
self.train()
optimizer.zero_grad()
output = self.forward(self.features, self.adj_norm)
loss_train = self.loss(output, labels)
loss_train.backward()
optimizer.step()
if verbose and i % 100 == 0:
print('Epoch {}, training loss: {}'.format(i, loss_train.item()))
with torch.no_grad():
self.eval()
output = self.forward(feat_full, adj_full_norm)
if adj_val:
loss_val = F.nll_loss(output, labels_val)
acc_val = utils.accuracy(output, labels_val)
else:
loss_val = F.nll_loss(output[data.idx_val], labels_val)
acc_val = utils.accuracy(output[data.idx_val], labels_val)
if acc_val > best_acc_val:
best_acc_val = acc_val
self.output = output
weights = deepcopy(self.state_dict())
if verbose:
print('=== picking the best model according to the performance on validation ===')
self.load_state_dict(weights)
def test(self, idx_test):
"""Evaluate GCN performance on test set.
Parameters
----------
idx_test :
node testing indices
"""
self.eval()
output = self.predict()
# output = self.output
loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])
acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])
print("Test set results:",
"loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
return acc_test.item()
@torch.no_grad()
def predict(self, features=None, adj=None):
"""By default, the inputs should be unnormalized adjacency
Parameters
----------
features :
node features. If `features` and `adj` are not given, this function will use previous stored `features` and `adj` from training to make predictions.
adj :
adjcency matrix. If `features` and `adj` are not given, this function will use previous stored `features` and `adj` from training to make predictions.
Returns
-------
torch.FloatTensor
output (log probabilities) of GCN
"""
self.eval()
if features is None and adj is None:
return self.forward(self.features, self.adj_norm)
else:
if type(adj) is not torch.Tensor:
features, adj = utils.to_tensor(features, adj, device=self.device)
self.features = features
if utils.is_sparse_tensor(adj):
self.adj_norm = utils.normalize_adj_tensor(adj, sparse=True)
else:
self.adj_norm = utils.normalize_adj_tensor(adj)
return self.forward(self.features, self.adj_norm)
@torch.no_grad()
def predict_unnorm(self, features=None, adj=None):
self.eval()
if features is None and adj is None:
return self.forward(self.features, self.adj_norm)
else:
if type(adj) is not torch.Tensor:
features, adj = utils.to_tensor(features, adj, device=self.device)
self.features = features
self.adj_norm = adj
return self.forward(self.features, self.adj_norm)
class MyLinear(Module):
"""Simple Linear layer, modified from https://github.com/tkipf/pygcn
"""
def __init__(self, in_features, out_features, with_bias=True):
super(MyLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if with_bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
# stdv = 1. / math.sqrt(self.weight.size(1))
stdv = 1. / math.sqrt(self.weight.T.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
if input.data.is_sparse:
support = torch.spmm(input, self.weight)
else:
support = torch.mm(input, self.weight)
output = support
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')'
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