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execute_aug.py
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execute_aug.py
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import time
import numpy as np
import pickle as pkl
import tensorflow as tf
from augs import aug
from augs import dgi
from models import GeomGAT
from utils import process
checkpt_file = 'pre_trained/cora/mod_cora.ckpt'
dataset = 'cora'
# augmentation params
aug_type = 'angle'
aug_percent = 0.2 # dropping or masking ratio of data augmentation
# training params
batch_size = 1
nb_epochs = 100000
patience = 100
lr = 0.005 # learning rate
l2_coef = 0.0005 # weight decay
hid_units = [8] # numbers of hidden units per each attention head in each layer
n_heads = [4, 1] # additional entry for the output layer
residual = False
nonlinearity = tf.nn.elu
lam = 0.1 # coefficient of harmonic energy
mu = 1 # coefficient of contrastive loss
model = GeomGAT
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('lambda: ' + str(lam))
print('mu: ' + str(mu))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(dataset)
features, spars = process.preprocess_features(features)
B, angle_vertex = process.compute_vertex(adj, features)
K = process.spring_constants(angle_vertex, features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = np.array(features)
adj = np.array(adj.todense())
B = np.array(B.todense())
features = features[np.newaxis]
adj = adj[np.newaxis]
B = B[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=1)
biases_B = process.adj_to_bias(B, [nb_nodes], nhood=1)
# augmentation: edge, mask, angle, subgraph. positive samples
if aug_type == 'edge': # GraphCL
aug_features = features[0]
aug_adj = aug.aug_random_edge(adj[0], aug_percent)
elif aug_type == 'mask':
aug_features = aug.aug_mask_ftr(features[0], aug_percent)
aug_adj = adj[0]
elif aug_type == 'angle':
aug_features, aug_adj, aug_B, aug_angle = aug.aug_drop_angle(features[0], adj[0], B[0], angle_vertex, aug_percent)
elif aug_type == 'subgraph':
aug_features, aug_adj = aug.aug_subgraph(features[0], adj[0], aug_percent)
else:
assert False
# shuffle: negative samples
idx = np.random.permutation(nb_nodes)
features_shuf = features[:, idx, :]
aug_features = aug_features[np.newaxis]
aug_adj = aug_adj[np.newaxis]
aug_B = aug_B[np.newaxis]
biases_aug = process.adj_to_bias(aug_adj, [nb_nodes], nhood=1)
biases_B_aug = process.adj_to_bias(aug_B, [nb_nodes], nhood=1)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
ftr_shuf = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
ftr_aug = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
bias_aug = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
bias_B_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
bias_B_aug = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
lbl_1 = tf.ones([batch_size, nb_nodes])
lbl_2 = tf.zeros([batch_size, nb_nodes])
lbl_pre = tf.concat([lbl_1, lbl_2], axis=1)
logits_pre = dgi.DGI_forward(model, ftr_in, ftr_shuf, ftr_aug, bias_in, bias_B_in,
angle_vertex, bias_aug, bias_B_aug, aug_angle, nb_classes, nb_nodes,
attn_drop, ffd_drop, hid_units, n_heads)
loss_pre = dgi.DGI_loss(logits_pre, lbl_pre)
logits, _, affine_trans, harmonic_maps = model.inference(ftr_in, nb_classes,
nb_nodes, is_train, attn_drop, ffd_drop,
bias_mat=bias_in,
bias_mat_B=bias_B_in,
angle_vertex=angle_vertex,
hid_units=hid_units, n_heads=n_heads,
residual=residual, activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss_harm = process.harmonic_loss(K, harmonic_maps)
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss + lam*loss_harm + mu*loss_pre, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
with tf.Session() as sess:
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
train_loss_harm_avg = 0
train_loss_pre_avg = 0
val_loss_avg = 0
val_acc_avg = 0
val_loss_harm_avg = 0
val_loss_pre_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
_, loss_value_tr, loss_harm_tr, loss_pre_tr, acc_tr = sess.run([train_op, loss, loss_harm, loss_pre, accuracy],
feed_dict={
ftr_in: features[tr_step*batch_size:(tr_step+1)*batch_size],
ftr_shuf: features_shuf[tr_step*batch_size:(tr_step+1)*batch_size],
ftr_aug: aug_features[tr_step*batch_size:(tr_step+1)*batch_size],
bias_in: biases[tr_step*batch_size:(tr_step+1)*batch_size],
bias_aug: biases_aug[tr_step*batch_size:(tr_step+1)*batch_size],
bias_B_in: biases_B[tr_step*batch_size:(tr_step+1)*batch_size],
bias_B_aug: biases_B_aug[tr_step*batch_size:(tr_step+1)*batch_size],
lbl_in: y_train[tr_step*batch_size:(tr_step+1)*batch_size],
msk_in: train_mask[tr_step*batch_size:(tr_step+1)*batch_size],
is_train: True,
attn_drop: 0.5, ffd_drop: 0.5})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
train_loss_harm_avg += loss_harm_tr
train_loss_pre_avg += loss_pre_tr
tr_step += 1
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
loss_value_vl, loss_harm_vl, loss_pre_vl, acc_vl = sess.run([loss, loss_harm, loss_pre, accuracy],
feed_dict={
ftr_in: features[vl_step*batch_size:(vl_step+1)*batch_size],
ftr_shuf: features_shuf[vl_step*batch_size:(vl_step+1)*batch_size],
ftr_aug: aug_features[vl_step*batch_size:(vl_step+1)*batch_size],
bias_in: biases[vl_step*batch_size:(vl_step+1)*batch_size],
bias_aug: biases_aug[vl_step*batch_size:(vl_step+1)*batch_size],
bias_B_in: biases_B[vl_step*batch_size:(vl_step+1)*batch_size],
bias_B_aug: biases_B_aug[vl_step*batch_size:(vl_step+1)*batch_size],
lbl_in: y_val[vl_step*batch_size:(vl_step+1)*batch_size],
msk_in: val_mask[vl_step*batch_size:(vl_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
val_loss_harm_avg += loss_harm_vl
val_loss_pre_avg += loss_pre_vl
vl_step += 1
print('Training: loss = %.5f, acc = %.5f, harm = %.5f, pre = %.5f | Val: loss = %.5f, acc = %.5f, harm = %.5f, pre = %.5f' %
(train_loss_avg/tr_step, train_acc_avg/tr_step, train_loss_harm_avg/tr_step, train_loss_pre_avg/tr_step,
val_loss_avg/vl_step, val_acc_avg/vl_step, val_loss_harm_avg/vl_step, val_loss_pre_avg/vl_step))
# early stop
if val_acc_avg/vl_step >= vacc_mx or val_loss_avg/vl_step <= vlss_mn:
if val_acc_avg/vl_step >= vacc_mx and val_loss_avg/vl_step <= vlss_mn:
vacc_early_model = val_acc_avg/vl_step
vlss_early_model = val_loss_avg/vl_step
saver.save(sess, checkpt_file)
vacc_mx = np.max((val_acc_avg/vl_step, vacc_mx)) # max accuracy on validation set
vlss_mn = np.min((val_loss_avg/vl_step, vlss_mn)) # min loss on validation set
curr_step = 0 # note that once the model ability improves, "curr_step" will be cleared and restart counting
else:
curr_step += 1 # model ability didn't improve in successive rounds
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn, ', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ', vlss_early_model, ', accuracy: ', vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
train_loss_harm_avg = 0
train_loss_pre_avg = 0
val_loss_avg = 0
val_acc_avg = 0
val_loss_harm_avg = 0
val_loss_pre_avg = 0
saver.restore(sess, checkpt_file)
ts_step = 0
ts_size = features.shape[0]
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
loss_value_ts, acc_ts, affine, harmonic = sess.run([loss, accuracy, affine_trans, harmonic_maps],
feed_dict={
ftr_in: features[ts_step*batch_size:(ts_step+1)*batch_size],
ftr_shuf: features_shuf[ts_step*batch_size:(ts_step+1)*batch_size],
ftr_aug: aug_features[ts_step*batch_size:(ts_step+1)*batch_size],
bias_in: biases[ts_step*batch_size:(ts_step+1)*batch_size],
bias_aug: biases_aug[ts_step*batch_size:(ts_step+1)*batch_size],
bias_B_in: biases_B[ts_step*batch_size:(ts_step+1)*batch_size],
bias_B_aug: biases_B_aug[ts_step*batch_size:(ts_step+1)*batch_size],
lbl_in: y_test[ts_step*batch_size:(ts_step+1)*batch_size],
msk_in: test_mask[ts_step*batch_size:(ts_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
with open("map_result.pickle", 'wb') as f:
pkl.dump([affine, harmonic], f)
print('Test loss:', ts_loss/ts_step, '; Test accuracy:', ts_acc/ts_step)
sess.close()