def main():
with tf.device('/gpu:' + str(GPU_ID)):
# dis_param = cPickle.load(open(DIS_MODEL_NEWEST_FILE))
# discriminator = DIS(IMAGE_DIM, TEXT_DIM, HIDDEN_DIM, OUTPUT_DIM, WEIGHT_DECAY, D_LEARNING_RATE, BETA, GAMMA, loss = 'svm', param = dis_param)
discriminator = DIS(IMAGE_DIM,
TEXT_DIM,
HIDDEN_DIM,
OUTPUT_DIM,
WEIGHT_DECAY,
D_LEARNING_RATE,
BETA,
GAMMA,
loss='svm',
param=None)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
print('start adversarial training')
map_best_val_gen = 0.0
map_best_val_dis = 0.0
average_map = 0
#for epoch in range(WHOLE_EPOCH):
print('Training D ...')
for d_epoch in range(D_EPOCH):
print('d_epoch: ' + str(d_epoch))
if d_epoch % GS_EPOCH == 0:
print('negative text sampling for d using g ...')
dis_train_i2t_list = generate_samples(train_i2t_pos,
train_i2t_neg, 'i2t')
print('negative image sampling for d using g ...')
dis_train_t2i_list = generate_samples(train_t2i_pos,
train_t2i_neg, 't2i')
discriminator = train_discriminator(sess, discriminator,
dis_train_i2t_list, 'i2t')
discriminator = train_discriminator(sess, discriminator,
dis_train_t2i_list, 't2i')
if (d_epoch + 1) % (D_DISPLAY) == 0:
i2t_test_map, t2i_test_map, i2i_test_map, t2t_test_map = MAP(
sess, discriminator)
print('I2T_Test_MAP: %.4f' % i2t_test_map)
print('T2I_Test_MAP: %.4f' % t2i_test_map)
# print('I2I_Test_MAP: %.4f' % i2i_test_map)
# print('T2T_Test_MAP: %.4f' % t2t_test_map)
average_map = 0.5 * (i2t_test_map + t2i_test_map)
if average_map > map_best_val_dis:
map_best_val_dis = average_map
discriminator.save_model(sess, DIS_MODEL_BEST_FILE)
#discriminator.save_model(sess, DIS_MODEL_NEWEST_FILE)
sess.close()
def test():
discriminator_param = cPickle.load(open(DIS_MODEL_BEST_FILE))
discriminator = DIS(IMAGE_DIM,
TEXT_DIM,
HIDDEN_DIM,
OUTPUT_DIM,
WEIGHT_DECAY,
D_LEARNING_RATE,
BETA,
GAMMA,
loss='svm',
param=discriminator_param)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
I_db = extract_feature(sess, discriminator, database_img)
T_db = extract_feature(sess, discriminator, database_txt)
I_te = extract_feature(sess, discriminator, test_img)
T_te = extract_feature(sess, discriminator, test_txt)
sio.savemat('./result/DIS_mir_' + str(OUTPUT_DIM) + '.mat', {
'B_I_db': I_db,
'B_T_db': T_db,
'B_I_te': I_te,
'B_T_te': T_te
})
sess.close()
def main():
discriminator = DIS(I_DIM, T_DIM, A_DIM, V_DIM, D_DIM, HIDDEN_DIM,
OUTPUT_DIM, WEIGHT_DECAY, D_LEARNING_RATE, BETA, GAMMA)
#generator = GEN(I_DIM, T_DIM, A_DIM, V_DIM, D_DIM, HIDDEN_DIM, OUTPUT_DIM, WEIGHT_DECAY, D_LEARNING_RATE, BETA, GAMMA)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
# pdb.set_trace()
saver = tf.train.Saver(var_list=[var for var in tf.trainable_variables()])
# saver.restore(sess, DIS_MODEL_NEWEST_FILE)
print('start adversarial training')
#map_best_val_gen = 0.0
map_best_val_dis = 0.0
for epoch in range(WHOLE_EPOCH):
print('Training D ...')
for d_epoch in range(D_EPOCH):
print('d_epoch: ' + str(d_epoch))
if d_epoch == 0:
print('negative sampling for d using g ...')
dis_train_list_I = generate_samples(0)
dis_train_list_T = generate_samples(1)
dis_train_list_D = generate_samples(2)
dis_train_list_V = generate_samples(3)
dis_train_list_A = generate_samples(4)
discriminator = train_discriminator(sess, discriminator,
dis_train_list_I, 'I')
discriminator = train_discriminator(sess, discriminator,
dis_train_list_T, 'T')
discriminator = train_discriminator(sess, discriminator,
dis_train_list_D, 'D')
discriminator = train_discriminator(sess, discriminator,
dis_train_list_V, 'V')
discriminator = train_discriminator(sess, discriminator,
dis_train_list_A, 'A')
if (d_epoch + 1) % (D_DISPLAY) == 0:
test_map = MAP_ARGV(sess, discriminator, test_feature,
database_feature, test_label,
database_label, OUTPUT_DIM)
print('Test_MAP: %.4f' % test_map)
if test_map > map_best_val_dis:
map_best_val_dis = test_map
saver.save(sess, DIS_MODEL_BEST_FILE)
saver.save(sess, DIS_MODEL_NEWEST_FILE)
sess.close()
def test(): discriminator_param = pickle.load(open(DIS_MODEL_BEST_I2I_FILE, 'rb')) discriminator = DIS(IMAGE_DIM, TEXT_DIM, HIDDEN_DIM, OUTPUT_DIM, WEIGHT_DECAY, D_LEARNING_RATE, BETA, GAMMA, loss ='svm', param=discriminator_param) config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.initialize_all_variables()) I_db = extract_feature(sess, discriminator, train_img, 'image') T_db = extract_feature(sess, discriminator, train_txt, 'text') knn_img, knn_txt = get_knn(I_db, T_db) pdb.set_trace() result_dir = '/home/huhengtong/UKD/data/' np.save(result_dir + 'teacher_KNN_img.npy', knn_img) np.save(result_dir + 'teacher_KNN_txt.npy', knn_txt)
def main():
with tf.device('/gpu:' + str(GPU_ID)):
dis_param = pickle.load(open(DIS_MODEL_PRETRAIN_FILE, 'rb'))
# gen_param = cPickle.load(open(GEN_MODEL_PRETRAIN_FILE))
discriminator = DIS(IMAGE_DIM,
TEXT_DIM,
HIDDEN_DIM,
OUTPUT_DIM,
WEIGHT_DECAY,
D_LEARNING_RATE,
BETA,
GAMMA,
param=dis_param)
generator = GEN(IMAGE_DIM,
TEXT_DIM,
HIDDEN_DIM,
OUTPUT_DIM,
CLASS_DIM,
WEIGHT_DECAY,
G_LEARNING_RATE,
param=None)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.initialize_all_variables())
print('start adversarial training')
map_best_val_gen = 0.0
map_best_val_dis = 0.0
for epoch in range(WHOLE_EPOCH):
print('Training D ...')
for d_epoch in range(D_EPOCH):
print('d_epoch: ' + str(d_epoch))
if d_epoch % GS_EPOCH == 0:
print('negative text sampling for d using g ...')
dis_train_i2t_list = generate_samples(
sess, generator, train_i2t, train_i2t_pos,
train_i2t_neg, 'i2t')
print('negative image sampling for d using g ...')
dis_train_t2i_list = generate_samples(
sess, generator, train_t2i, train_t2i_pos,
train_t2i_neg, 't2i')
# dis_train_i2i_list = generate_samples_pretrain(train_i2i_pos, train_i2i_neg, 'i2i')
# dis_train_t2t_list = generate_samples_pretrain(train_t2t_pos, train_t2t_neg, 't2i')
discriminator = train_discriminator(sess, discriminator,
dis_train_i2t_list, 'i2t')
discriminator = train_discriminator(sess, discriminator,
dis_train_t2i_list, 't2i')
# discriminator = train_discriminator(sess, discriminator, dis_train_i2i_list, 'i2i')
# discriminator = train_discriminator(sess, discriminator, dis_train_t2t_list, 't2t')
if (d_epoch + 1) % (D_DISPLAY) == 0:
i2t_test_map, t2i_test_map = MAP(sess, discriminator)
print(
'---------------------------------------------------------------'
)
print('train_I2T_Test_MAP: %.4f' % i2t_test_map)
print('train_T2I_Test_MAP: %.4f' % t2i_test_map)
# average_map = 0.5 * (i2t_test_map + t2i_test_map)
# if average_map > map_best_val_dis:
# map_best_val_dis = average_map
# discriminator.save_model(sess, DIS_MODEL_BEST_FILE)
# discriminator.save_model(sess, DIS_MODEL_NEWEST_FILE)
print('Training G ...')
for g_epoch in range(G_EPOCH):
print('g_epoch: ' + str(g_epoch))
generator = train_generator(sess, generator, discriminator,
train_i2t, train_i2t_pos, 'i2t')
generator = train_generator(sess, generator, discriminator,
train_t2i, train_t2i_pos, 't2i')
if (g_epoch + 1) % (G_DISPLAY) == 0:
i2t_test_map, t2i_test_map = MAP(sess, generator,
test_i2t_pos, test_i2t,
test_t2i_pos, test_t2i,
feature_dict, label_dict)
print(
'---------------------------------------------------------------'
)
print('train_I2T_Test_MAP: %.4f' % i2t_test_map)
print('train_T2I_Test_MAP: %.4f' % t2i_test_map)
sess.close()