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 main():
with tf.device('/gpu:' + str(GPU_ID)):
dis_param = cPickle.load(open(DIS_MODEL_PRETRAIN_FILE))
# 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 = gen_param)
# discriminator = DIS(IMAGE_DIM, TEXT_DIM, HIDDEN_DIM, OUTPUT_DIM, WEIGHT_DECAY, D_LEARNING_RATE, BETA, GAMMA, param = None)
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')
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 = MAP(sess, discriminator, test_i2t_pos,
test_i2t, feature_dict, 'i2t')
print('E%d D%d I2T_Test_MAP: %.4f' %
(epoch, d_epoch, i2t_test_map))
t2i_test_map = MAP(sess, discriminator, test_t2i_pos,
test_t2i, feature_dict, 't2i')
print('E%d D%d T2I_Test_MAP: %.4f' %
(epoch, d_epoch, t2i_test_map))
with open('record.txt', 'a') as record_file:
record_file.write('E%d D%d I2T_Test_MAP: %.4f\n' %
(epoch, d_epoch, i2t_test_map))
record_file.write('E%d D%d T2I_Test_MAP: %.4f\n' %
(epoch, d_epoch, 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 = MAP(sess, generator, test_i2t_pos, test_i2t,
feature_dict, 'i2t')
print('E%d G%d I2T_Test_MAP: %.4f' %
(epoch, g_epoch, i2t_test_map))
t2i_test_map = MAP(sess, generator, test_t2i_pos, test_t2i,
feature_dict, 't2i')
print('E%d G%d T2I_Test_MAP: %.4f' %
(epoch, g_epoch, t2i_test_map))
with open('record.txt', 'a') as record_file:
record_file.write('E%d G%d I2T_Test_MAP: %.4f\n' %
(epoch, g_epoch, i2t_test_map))
record_file.write('E%d G%d T2I_Test_MAP: %.4f\n' %
(epoch, g_epoch, t2i_test_map))
average_map = 0.5 * (i2t_test_map + t2i_test_map)
if average_map > map_best_val_gen:
map_best_val_gen = average_map
generator.save_model(sess, GEN_MODEL_BEST_FILE)
generator.save_model(sess, GEN_MODEL_NEWEST_FILE)
sess.close()
