def main(_):
print('#label={}'.format(config.num_label))
gen_t = GEN(flags, is_training=True)
scope = tf.get_variable_scope()
scope.reuse_variables()
gen_v = GEN(flags, is_training=False)
# train_filename = 'yfcc10k_{}.train.tfrecord'.format(flags.model_name)
# train_tfrecord = path.join(config.tfrecord_dir, train_filename)
# valid_filename = 'yfcc10k_{}.valid.tfrecord'.format(flags.model_name)
# valid_tfrecord = path.join(config.tfrecord_dir, valid_filename)
data_sources_t = utils.get_data_sources(flags, config.train_file, 500)
data_sources_v = utils.get_data_sources(flags, config.valid_file, 1)
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
# check_ts_list(ts_list_t)
# check_ts_list(ts_list_v)
bt_list_t = utils.generate_batch(ts_list_t, config.train_batch_size)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
# check_bt_list(bt_list_t, config.train_batch_size)
# check_bt_list(bt_list_v, config.valid_batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, image_file_bt_t = bt_list_t
user_bt_v, image_bt_v, text_bt_v, label_bt_v, image_file_bt_v = bt_list_v
best_hit_v = -np.inf
init_op = tf.global_variables_initializer()
start = time.time()
with tf.Session() as sess:
writer = tf.summary.FileWriter(config.logs_dir, graph=tf.get_default_graph())
sess.run(init_op)
with slim.queues.QueueRunners(sess):
for batch_t in range(num_batch_t):
image_np_t, label_np_t = sess.run([image_bt_t, label_bt_t])
feed_dict = {gen_t.image_ph:image_np_t, gen_t.label_ph:label_np_t}
_, summary = sess.run([gen_t.train_op, gen_t.summary_op], feed_dict=feed_dict)
writer.add_summary(summary, batch_t)
if (batch_t + 1) % int(config.train_data_size / config.train_batch_size) != 0:
continue
hit_v = []
image_file_v = set()
for batch_v in range(num_batch_v):
image_np_v, label_np_v, image_file_np_v = sess.run([image_bt_v, label_bt_v, image_file_bt_v])
feed_dict = {gen_v.image_ph:image_np_v}
logit_np_v, = sess.run([gen_v.logits], feed_dict=feed_dict)
for image_file in image_file_np_v:
image_file_v.add(image_file)
hit_bt = compute_hit(logit_np_v, label_np_v, flags.cutoff)
hit_v.append(hit_bt)
hit_v = np.mean(hit_v)
total_time = time.time() - start
avg_batch = total_time / (batch_t + 1)
avg_epoch = avg_batch * (config.train_data_size / config.train_batch_size)
s = '{0} hit={1:.4f} tot={2:.0f}s avg={3:.0f}s'
s = s.format(batch_t, hit_v, total_time, avg_epoch)
print(s)
def main(_):
global_step = tf.train.create_global_step()
tch_t = TCH(flags, is_training=True)
scope = tf.get_variable_scope()
scope.reuse_variables()
tch_v = TCH(flags, is_training=False)
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('{}\t({} params)'.format(variable.name, num_params))
data_sources_t = utils.get_data_sources(flags, is_training=True, single=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' % (len(data_sources_t), len(data_sources_v)))
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_t = utils.generate_batch(ts_list_t, config.train_batch_size)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, file_bt_t = bt_list_t
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
best_hit_v = -np.inf
init_op = tf.global_variables_initializer()
start = time.time()
with tf.Session() as sess:
writer = tf.summary.FileWriter(config.logs_dir, graph=tf.get_default_graph())
sess.run(init_op)
with slim.queues.QueueRunners(sess):
for batch_t in range(num_batch_t):
text_np_t, label_np_t = sess.run([text_bt_t, label_bt_t])
feed_dict = {tch_t.text_ph:text_np_t, tch_t.label_ph:label_np_t}
_, summary = sess.run([tch_t.train_op, tch_t.summary_op], feed_dict=feed_dict)
writer.add_summary(summary, batch_t)
if (batch_t + 1) % eval_interval != 0:
continue
hit_v = []
for batch_v in range(num_batch_v):
text_np_v, label_np_v = sess.run([text_bt_v, label_bt_v])
feed_dict = {tch_v.text_ph:text_np_v}
logit_np_v, = sess.run([tch_v.logits], feed_dict=feed_dict)
hit_bt = metric.compute_hit(logit_np_v, label_np_v, flags.cutoff)
hit_v.append(hit_bt)
hit_v = np.mean(hit_v)
tot_time = time.time() - start
print('#{0} hit={1:.4f} {2:.0f}s'.format(batch_t, hit_v, tot_time))
if hit_v < best_hit_v:
continue
best_hit_v = hit_v
ckpt_file = path.join(config.ckpt_dir, '%s.ckpt' % flags.tch_model)
tch_t.saver.save(sess, ckpt_file)
print('best hit={0:.4f}'.format(best_hit_v))
def get_batch(self, flags, is_training):
if is_training:
single = False
stage = 'train'
shuffle = True
else:
single = True
stage = 'valid'
shuffle = False
data_sources = utils.get_data_sources(flags,
is_training=is_training,
single=single)
print('#tfrecord=%d for %s' % (len(data_sources), stage))
ts_list = utils.decode_tfrecord(flags, data_sources, shuffle=shuffle)
bt_list = utils.generate_batch(ts_list, flags.batch_size)
return bt_list
def main(_):
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('{}\t({} params)'.format(variable.name, num_params))
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_d = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_d = utils.generate_batch(ts_list_d, config.train_batch_size)
user_bt_d, image_bt_d, text_bt_d, label_bt_d, file_bt_d = bt_list_d
ts_list_g = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_g = utils.generate_batch(ts_list_g, config.train_batch_size)
user_bt_g, image_bt_g, text_bt_g, label_bt_g, file_bt_g = bt_list_g
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
best_hit_v = -np.inf
start = time.time()
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
dis_t.saver.restore(sess, flags.dis_model_ckpt)
gen_t.saver.restore(sess, flags.gen_model_ckpt)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with slim.queues.QueueRunners(sess):
image_hit_v = utils.evaluate(flags, sess, gen_v, bt_list_v)
print('init\thit={0:.4f}'.format(image_hit_v))
batch_d, batch_g = -1, -1
for epoch in range(flags.num_epoch):
for dis_epoch in range(flags.num_dis_epoch):
print('epoch %03d dis_epoch %03d' % (epoch, dis_epoch))
num_batch_d = math.ceil(train_data_size /
config.train_batch_size)
for _ in range(num_batch_d):
batch_d += 1
image_np_d, label_dat_d = sess.run(
[image_bt_d, label_bt_d])
# print(image_np_d.shape, label_dat_d.shape)
feed_dict = {gen_t.image_ph: image_np_d}
label_gen_d, = sess.run([gen_t.labels],
feed_dict=feed_dict)
# print(label_gen_d.shape, type(label_gen_d))
sample_np_d, label_np_d = gan_dis_sample(
label_dat_d, label_gen_d)
feed_dict = {
dis_t.image_ph: image_np_d,
dis_t.sample_ph: sample_np_d,
dis_t.label_ph: label_np_d,
}
sess.run(dis_t.train_op, feed_dict=feed_dict)
# _, summary = sess.run([dis_t.train_op, dis_t.summary_op], feed_dict=feed_dict)
# writer.add_summary(summary, batch_d)
# if (batch_d + 1) % eval_interval != 0:
# continue
# image_hit_v = utils.evaluate(flags, sess, dis_v, bt_list_v)
# tot_time = time.time() - start
# print('#%d hit=%.4f (%.0fs)' % (batch_d, image_hit_v, tot_time))
for gen_epoch in range(flags.num_gen_epoch):
print('epoch %03d gen_epoch %03d' % (epoch, gen_epoch))
num_batch_g = math.ceil(train_data_size /
config.train_batch_size)
for _ in range(num_batch_g):
batch_g += 1
image_np_g, label_dat_g = sess.run(
[image_bt_g, label_bt_g])
# print(image_np_g.shape, label_dat_g.shape)
feed_dict = {gen_t.image_ph: image_np_g}
label_gen_g, = sess.run([gen_t.labels],
feed_dict=feed_dict)
sample_np_g = generate_label(label_dat_g, label_gen_g)
# for sample in sample_np_g:
# print(sample)
feed_dict = {
dis_t.image_ph: image_np_g,
dis_t.sample_ph: sample_np_g,
}
reward_np_g, = sess.run([dis_t.rewards],
feed_dict=feed_dict)
# for sample, reward in zip(sample_np_g, reward_np_g):
# batch = sample[0]
# label = [i for i, l in enumerate(label_dat_g[batch]) if l != 0.0]
# print(sample, reward, label)
# print('%.2f %.2f' % (reward_np_g.min(), reward_np_g.max()))
# input()
feed_dict = {
gen_t.image_ph: image_np_g,
gen_t.sample_ph: sample_np_g,
gen_t.reward_ph: reward_np_g,
}
sess.run([gen_t.gan_train_op], feed_dict=feed_dict)
if (batch_g + 1) % eval_interval != 0:
continue
image_hit_v = utils.evaluate(flags, sess, gen_v,
bt_list_v)
tot_time = time.time() - start
print('#%d hit=%.4f (%.0fs)' %
(batch_g, image_hit_v, tot_time))
if image_hit_v > best_hit_v:
best_hit_v = image_hit_v
print('best hit=%.4f (%.0fs)' %
(image_hit_v, tot_time))
# break
print('best\thit={0:.4f}'.format(best_hit_v))
def main(_):
gen_t = GEN(flags, is_training=True)
scope = tf.get_variable_scope()
scope.reuse_variables()
gen_v = GEN(flags, is_training=False)
tf.summary.scalar(gen_t.learning_rate.name, gen_t.learning_rate)
tf.summary.scalar(gen_t.pre_loss.name, gen_t.pre_loss)
summary_op = tf.summary.merge_all()
init_op = tf.global_variables_initializer()
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('%-50s (%d params)' % (variable.name, num_params))
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_t = utils.generate_batch(ts_list_t, flags.batch_size)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, file_bt_t = bt_list_t
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
figure_data = []
best_hit_v = -np.inf
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with slim.queues.QueueRunners(sess):
for batch_t in range(num_batch_t):
image_np_t, label_np_t = sess.run([image_bt_t, label_bt_t])
feed_dict = {
gen_t.image_ph: image_np_t,
gen_t.hard_label_ph: label_np_t
}
_, summary = sess.run([gen_t.pre_update, summary_op],
feed_dict=feed_dict)
writer.add_summary(summary, batch_t)
batch = batch_t + 1
remain = (batch * flags.batch_size) % train_data_size
epoch = (batch * flags.batch_size) // train_data_size
if remain == 0:
pass
# print('%d\t%d\t%d' % (epoch, batch, remain))
elif (train_data_size - remain) < flags.batch_size:
epoch = epoch + 1
# print('%d\t%d\t%d' % (epoch, batch, remain))
else:
continue
# if (batch_t + 1) % eval_interval != 0:
# continue
hit_v = []
for batch_v in range(num_batch_v):
image_np_v, label_np_v = sess.run([image_bt_v, label_bt_v])
feed_dict = {gen_v.image_ph: image_np_v}
logit_np_v, = sess.run([gen_v.logits], feed_dict=feed_dict)
hit_bt = metric.compute_hit(logit_np_v, label_np_v,
flags.cutoff)
hit_v.append(hit_bt)
hit_v = np.mean(hit_v)
figure_data.append((epoch, hit_v, batch_t))
if hit_v < best_hit_v:
continue
tot_time = time.time() - start
best_hit_v = hit_v
print('#%03d curbst=%.4f time=%.0fs' %
(epoch, hit_v, tot_time))
gen_t.saver.save(sess, flags.gen_model_ckpt)
print('bsthit=%.4f' % (best_hit_v))
utils.create_if_nonexist(os.path.dirname(flags.gen_figure_data))
fout = open(flags.gen_figure_data, 'w')
for epoch, hit_v, batch_t in figure_data:
fout.write('%d\t%.4f\t%d\n' % (epoch, hit_v, batch_t))
fout.close()
def main(_):
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('%-50s (%d params)' % (variable.name, num_params))
tf.summary.scalar(gen_t.learning_rate.name, gen_t.learning_rate)
tf.summary.scalar(gen_t.kd_loss.name, gen_t.kd_loss)
summary_op = tf.summary.merge_all()
init_op = tf.global_variables_initializer()
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_t = utils.generate_batch(ts_list_t, config.train_batch_size)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, file_bt_t = bt_list_t
best_hit_v = -np.inf
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
gen_t.saver.restore(sess, flags.gen_model_ckpt)
tch_t.saver.restore(sess, flags.tch_model_ckpt)
with slim.queues.QueueRunners(sess):
hit_v = utils.evaluate(flags, sess, gen_v, bt_list_v)
print('init hit=%.4f' % (hit_v))
for batch_t in range(num_batch_t):
image_np_t, text_np_t, hard_labels = sess.run(
[image_bt_t, text_bt_t, label_bt_t])
# print('hard labels:\t{}'.format(hard_labels.shape))
# print(np.argsort(-hard_labels[0,:])[:10])
feed_dict = {tch_t.text_ph: text_np_t}
soft_labels, = sess.run([tch_t.labels], feed_dict=feed_dict)
# print('soft labels:\t{}'.format(soft_labels.shape))
# print(np.argsort(-soft_labels[0,:])[:10])
feed_dict = {
gen_t.image_ph: image_np_t,
gen_t.hard_label_ph: hard_labels,
gen_t.soft_label_ph: soft_labels,
}
_, summary = sess.run([gen_t.kd_update, summary_op],
feed_dict=feed_dict)
writer.add_summary(summary, batch_t)
if (batch_t + 1) % eval_interval != 0:
continue
hit_v = utils.evaluate(flags, sess, gen_v, bt_list_v)
tot_time = time.time() - start
print('#%08d hit=%.4f %06ds' % (batch_t, hit_v, int(tot_time)))
if hit_v <= best_hit_v:
continue
best_hit_v = hit_v
print('best hit=%.4f' % (best_hit_v))
print('best hit=%.4f' % (best_hit_v))
def main(_):
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('%-50s (%d params)' % (variable.name, num_params))
dis_summary_op = tf.summary.merge([
tf.summary.scalar(dis_t.learning_rate.name, dis_t.learning_rate),
tf.summary.scalar(dis_t.gan_loss.name, dis_t.gan_loss),
])
gen_summary_op = tf.summary.merge([
tf.summary.scalar(gen_t.learning_rate.name, gen_t.learning_rate),
tf.summary.scalar(gen_t.gan_loss.name, gen_t.gan_loss),
])
print(type(dis_summary_op), type(gen_summary_op))
init_op = tf.global_variables_initializer()
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_d = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_d = utils.generate_batch(ts_list_d, flags.batch_size)
user_bt_d, image_bt_d, text_bt_d, label_bt_d, file_bt_d = bt_list_d
ts_list_g = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_g = utils.generate_batch(ts_list_g, flags.batch_size)
user_bt_g, image_bt_g, text_bt_g, label_bt_g, file_bt_g = bt_list_g
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
figure_data = []
best_hit_v = -np.inf
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
dis_t.saver.restore(sess, flags.dis_model_ckpt)
gen_t.saver.restore(sess, flags.gen_model_ckpt)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with slim.queues.QueueRunners(sess):
hit_v = utils.evaluate_image(flags, sess, gen_v, bt_list_v)
print('init hit=%.4f' % (hit_v))
batch_d, batch_g = -1, -1
for epoch in range(flags.num_epoch):
for dis_epoch in range(flags.num_dis_epoch):
print('epoch %03d dis_epoch %03d' % (epoch, dis_epoch))
num_batch_d = math.ceil(train_data_size / flags.batch_size)
for _ in range(num_batch_d):
batch_d += 1
image_np_d, label_dat_d = sess.run(
[image_bt_d, label_bt_d])
feed_dict = {gen_t.image_ph: image_np_d}
label_gen_d, = sess.run([gen_t.labels],
feed_dict=feed_dict)
sample_np_d, label_np_d = utils.gan_dis_sample(
flags, label_dat_d, label_gen_d)
feed_dict = {
dis_t.image_ph: image_np_d,
dis_t.sample_ph: sample_np_d,
dis_t.dis_label_ph: label_np_d,
}
_, summary_d = sess.run(
[dis_t.gan_update, dis_summary_op],
feed_dict=feed_dict)
writer.add_summary(summary_d, batch_d)
for gen_epoch in range(flags.num_gen_epoch):
print('epoch %03d gen_epoch %03d' % (epoch, gen_epoch))
num_batch_g = math.ceil(train_data_size / flags.batch_size)
for _ in range(num_batch_g):
batch_g += 1
image_np_g, label_dat_g = sess.run(
[image_bt_g, label_bt_g])
feed_dict = {gen_t.image_ph: image_np_g}
label_gen_g, = sess.run([gen_t.labels],
feed_dict=feed_dict)
sample_np_g = utils.generate_label(
flags, label_dat_g, label_gen_g)
feed_dict = {
dis_t.image_ph: image_np_g,
dis_t.sample_ph: sample_np_g,
}
reward_np_g, = sess.run([dis_t.rewards],
feed_dict=feed_dict)
feed_dict = {
gen_t.image_ph: image_np_g,
gen_t.sample_ph: sample_np_g,
gen_t.reward_ph: reward_np_g,
}
_, summary_g = sess.run(
[gen_t.gan_update, gen_summary_op],
feed_dict=feed_dict)
writer.add_summary(summary_g, batch_g)
# if (batch_g + 1) % eval_interval != 0:
# continue
# hit_v = utils.evaluate(flags, sess, gen_v, bt_list_v)
# tot_time = time.time() - start
# print('#%08d hit=%.4f %06ds' % (batch_g, hit_v, int(tot_time)))
# if hit_v <= best_hit_v:
# continue
# best_hit_v = hit_v
# print('best hit=%.4f' % (best_hit_v))
hit_v = utils.evaluate_image(flags, sess, gen_v, bt_list_v)
tot_time = time.time() - start
print('#%03d curbst=%.4f %.0fs' % (epoch, hit_v, tot_time))
figure_data.append((epoch, hit_v))
if hit_v <= best_hit_v:
continue
best_hit_v = hit_v
print('bsthit=%.4f' % (best_hit_v))
utils.create_if_nonexist(os.path.dirname(flags.gan_figure_data))
fout = open(flags.gan_figure_data, 'w')
for epoch, hit_v in figure_data:
fout.write('%d\t%.4f\n' % (epoch, hit_v))
fout.close()
def main(_):
dis_t = DIS(flags, is_training=True)
scope = tf.get_variable_scope()
scope.reuse_variables()
dis_v = DIS(flags, is_training=False)
tf.summary.scalar(dis_t.learning_rate.name, dis_t.learning_rate)
tf.summary.scalar(dis_t.pre_loss.name, dis_t.pre_loss)
summary_op = tf.summary.merge_all()
init_op = tf.global_variables_initializer()
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('%-50s (%d params)' % (variable.name, num_params))
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_t = utils.generate_batch(ts_list_t, flags.batch_size)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, file_bt_t = bt_list_t
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
start = time.time()
best_hit_v = -np.inf
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with slim.queues.QueueRunners(sess):
for batch_t in range(num_batch_t):
image_np_t, label_np_t = sess.run([image_bt_t, label_bt_t])
feed_dict = {
dis_t.image_ph: image_np_t,
dis_t.hard_label_ph: label_np_t
}
_, summary = sess.run([dis_t.pre_update, summary_op],
feed_dict=feed_dict)
writer.add_summary(summary, batch_t)
if (batch_t + 1) % eval_interval != 0:
continue
hit_v = []
for batch_v in range(num_batch_v):
image_np_v, label_np_v = sess.run([image_bt_v, label_bt_v])
feed_dict = {dis_v.image_ph: image_np_v}
logit_np_v, = sess.run([dis_v.logits], feed_dict=feed_dict)
hit_bt = metric.compute_hit(logit_np_v, label_np_v,
flags.cutoff)
hit_v.append(hit_bt)
hit_v = np.mean(hit_v)
tot_time = time.time() - start
print('#%08d hit=%.4f %06ds' % (batch_t, hit_v, int(tot_time)))
if hit_v < best_hit_v:
continue
best_hit_v = hit_v
dis_t.saver.save(sess, flags.dis_model_ckpt)
print('best hit=%.4f' % (best_hit_v))
def main(_):
for variable in tf.trainable_variables():
num_params = 1
for dim in variable.shape:
num_params *= dim.value
print('%-50s (%d params)' % (variable.name, num_params))
dis_summary_op = tf.summary.merge([
tf.summary.scalar(dis_t.learning_rate.name, dis_t.learning_rate),
tf.summary.scalar(dis_t.gan_loss.name, dis_t.gan_loss),
])
gen_summary_op = tf.summary.merge([
tf.summary.scalar(gen_t.learning_rate.name, gen_t.learning_rate),
tf.summary.scalar(gen_t.kdgan_loss.name, gen_t.kdgan_loss),
])
tch_summary_op = tf.summary.merge([
tf.summary.scalar(tch_t.learning_rate.name, tch_t.learning_rate),
tf.summary.scalar(tch_t.kdgan_loss.name, tch_t.kdgan_loss),
])
init_op = tf.global_variables_initializer()
data_sources_t = utils.get_data_sources(flags, is_training=True)
data_sources_v = utils.get_data_sources(flags, is_training=False)
print('tn: #tfrecord=%d\nvd: #tfrecord=%d' %
(len(data_sources_t), len(data_sources_v)))
ts_list_d = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_d = utils.generate_batch(ts_list_d, flags.batch_size)
user_bt_d, image_bt_d, text_bt_d, label_bt_d, file_bt_d = bt_list_d
ts_list_g = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_g = utils.generate_batch(ts_list_g, flags.batch_size)
user_bt_g, image_bt_g, text_bt_g, label_bt_g, file_bt_g = bt_list_g
ts_list_t = utils.decode_tfrecord(flags, data_sources_t, shuffle=True)
bt_list_t = utils.generate_batch(ts_list_t, flags.batch_size)
user_bt_t, image_bt_t, text_bt_t, label_bt_t, file_bt_t = bt_list_t
ts_list_v = utils.decode_tfrecord(flags, data_sources_v, shuffle=False)
bt_list_v = utils.generate_batch(ts_list_v, config.valid_batch_size)
figure_data = []
best_hit_v = -np.inf
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
dis_t.saver.restore(sess, flags.dis_model_ckpt)
gen_t.saver.restore(sess, flags.gen_model_ckpt)
tch_t.saver.restore(sess, flags.tch_model_ckpt)
writer = tf.summary.FileWriter(config.logs_dir,
graph=tf.get_default_graph())
with slim.queues.QueueRunners(sess):
gen_hit = utils.evaluate_image(flags, sess, gen_v, bt_list_v)
tch_hit = utils.evaluate_text(flags, sess, tch_v, bt_list_v)
print('hit gen=%.4f tch=%.4f' % (gen_hit, tch_hit))
batch_d, batch_g, batch_t = -1, -1, -1
for epoch in range(flags.num_epoch):
for dis_epoch in range(flags.num_dis_epoch):
print('epoch %03d dis_epoch %03d' % (epoch, dis_epoch))
for _ in range(num_batch_per_epoch):
#continue
batch_d += 1
image_d, text_d, label_dat_d = sess.run(
[image_bt_d, text_bt_d, label_bt_d])
feed_dict = {gen_t.image_ph: image_d}
label_gen_d, = sess.run([gen_t.labels],
feed_dict=feed_dict)
# print('gen label', label_gen_d.shape)
feed_dict = {
tch_t.text_ph: text_d,
tch_t.image_ph: image_d
}
label_tch_d, = sess.run([tch_t.labels],
feed_dict=feed_dict)
# print('tch label', label_tch_d.shape)
sample_d, label_d = utils.kdgan_dis_sample(
flags, label_dat_d, label_gen_d, label_tch_d)
# print(sample_d.shape, label_d.shape)
feed_dict = {
dis_t.image_ph: image_d,
dis_t.sample_ph: sample_d,
dis_t.dis_label_ph: label_d,
}
_, summary_d = sess.run(
[dis_t.gan_update, dis_summary_op],
feed_dict=feed_dict)
writer.add_summary(summary_d, batch_d)
for tch_epoch in range(flags.num_tch_epoch):
print('epoch %03d tch_epoch %03d' % (epoch, tch_epoch))
for _ in range(num_batch_per_epoch):
#continue
batch_t += 1
image_t, text_t, label_dat_t = sess.run(
[image_bt_t, text_bt_t, label_bt_t])
feed_dict = {
tch_t.text_ph: text_t,
tch_t.image_ph: image_t
}
label_tch_t, = sess.run([tch_t.labels],
feed_dict=feed_dict)
sample_t = utils.generate_label(
flags, label_dat_t, label_tch_t)
feed_dict = {
dis_t.image_ph: image_t,
dis_t.sample_ph: sample_t,
}
reward_t, = sess.run([dis_t.rewards],
feed_dict=feed_dict)
feed_dict = {
gen_t.image_ph: image_t,
}
label_gen_g = sess.run(gen_t.logits,
feed_dict=feed_dict)
#print(len(label_dat_t), len(label_dat_t[0]))
#exit()
feed_dict = {
tch_t.text_ph: text_t,
tch_t.image_ph: image_t,
tch_t.sample_ph: sample_t,
tch_t.reward_ph: reward_t,
tch_t.hard_label_ph: label_dat_t,
tch_t.soft_label_ph: label_gen_g,
}
_, summary_t, tch_kdgan_loss = sess.run(
[
tch_t.kdgan_update, tch_summary_op,
tch_t.kdgan_loss
],
feed_dict=feed_dict)
writer.add_summary(summary_t, batch_t)
#print("teacher kdgan loss:", tch_kdgan_loss)
for gen_epoch in range(flags.num_gen_epoch):
print('epoch %03d gen_epoch %03d' % (epoch, gen_epoch))
for _ in range(num_batch_per_epoch):
batch_g += 1
image_g, text_g, label_dat_g = sess.run(
[image_bt_g, text_bt_g, label_bt_g])
feed_dict = {
tch_t.text_ph: text_g,
tch_t.image_ph: image_g
}
label_tch_g, = sess.run([tch_t.labels],
feed_dict=feed_dict)
# print('tch label {}'.format(label_tch_g.shape))
feed_dict = {gen_t.image_ph: image_g}
label_gen_g, = sess.run([gen_t.labels],
feed_dict=feed_dict)
sample_g = utils.generate_label(
flags, label_dat_g, label_gen_g)
feed_dict = {
dis_t.image_ph: image_g,
dis_t.sample_ph: sample_g,
}
reward_g, = sess.run([dis_t.rewards],
feed_dict=feed_dict)
feed_dict = {
gen_t.image_ph: image_g,
gen_t.hard_label_ph: label_dat_g,
gen_t.soft_label_ph: label_tch_g,
gen_t.sample_ph: sample_g,
gen_t.reward_ph: reward_g,
}
_, summary_g = sess.run(
[gen_t.kdgan_update, gen_summary_op],
feed_dict=feed_dict)
writer.add_summary(summary_g, batch_g)
# if (batch_g + 1) % eval_interval != 0:
# continue
# gen_hit = utils.evaluate_image(flags, sess, gen_v, bt_list_v)
# tot_time = time.time() - start
# print('#%08d hit=%.4f %06ds' % (batch_g, gen_hit, int(tot_time)))
# if gen_hit <= best_hit_v:
# continue
# best_hit_v = gen_hit
# print('best hit=%.4f' % (best_hit_v))
gen_hit = utils.evaluate_image(flags, sess, gen_v, bt_list_v)
tch_hit = utils.evaluate_text(flags, sess, tch_v, bt_list_v)
tot_time = time.time() - start
print('#%03d curgen=%.4f curtch=%.4f %.0fs' %
(epoch, gen_hit, tch_hit, tot_time))
figure_data.append((epoch, gen_hit, tch_hit))
if gen_hit <= best_hit_v:
continue
best_hit_v = gen_hit
print("epoch ", epoch + 1, ":, new best validation hit:",
best_hit_v, "saving...")
gen_t.saver.save(sess,
flags.kdgan_model_ckpt,
global_step=epoch + 1)
print("finish saving")
print('best hit=%.4f' % (best_hit_v))
utils.create_if_nonexist(os.path.dirname(flags.kdgan_figure_data))
fout = open(flags.kdgan_figure_data, 'w')
for epoch, gen_hit, tch_hit in figure_data:
fout.write('%d\t%.4f\t%.4f\n' % (epoch, gen_hit, tch_hit))
fout.close()