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 eval(sess, bt_list_v):
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
image_hit_v, text_hit_v = [], []
for batch_v in range(num_batch_v):
image_np_v, text_np_v, label_np_v = sess.run(
[image_bt_v, text_bt_v, label_bt_v])
feed_dict = {gen_v.image_ph: image_np_v, tch_v.text_ph: text_np_v}
image_logit_v, = sess.run([gen_v.logits], feed_dict=feed_dict)
image_hit_bt = metric.compute_hit(image_logit_v, label_np_v,
flags.cutoff)
image_hit_v.append(image_hit_bt)
text_logit_v, = sess.run([tch_v.logits], feed_dict=feed_dict)
text_hit_bt = metric.compute_hit(text_logit_v, label_np_v,
flags.cutoff)
text_hit_v.append(text_hit_bt)
image_hit_v = np.mean(image_hit_v)
text_hit_v = np.mean(text_hit_v)
# print('img:\thit=%.4f\ntxt:\thit=%.4f' % (image_hit_v, text_hit_v))
return image_hit_v
def evaluate(flags, sess, gen_v, bt_list_v):
num_batch_v = int(config.valid_data_size / config.valid_batch_size)
# print('vd:\t#batch=%d\n' % num_batch_v)
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
image_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}
image_logit_v, = sess.run([gen_v.logits], feed_dict=feed_dict)
image_hit_bt = metric.compute_hit(image_logit_v, label_np_v,
flags.cutoff)
image_hit_v.append(image_hit_bt)
image_hit_v = np.mean(image_hit_v)
return image_hit_v
def evaluate_text(flags, sess, tch_v, bt_list_v):
vd_size = get_vd_size(flags.dataset)
num_batch_v = int(vd_size / config.valid_batch_size)
# print('vd:\t#batch=%d\n' % num_batch_v)
user_bt_v, image_bt_v, text_bt_v, label_bt_v, file_bt_v = bt_list_v
text_hit_v = []
for batch_v in range(num_batch_v):
text_np_v, label_np_v, image_np_v = sess.run(
[text_bt_v, label_bt_v, image_bt_v])
feed_dict = {tch_v.text_ph: text_np_v, tch_v.image_ph: image_np_v}
text_logit_v, = sess.run([tch_v.logits], feed_dict=feed_dict)
text_hit_bt = metric.compute_hit(text_logit_v, label_np_v,
flags.cutoff)
text_hit_v.append(text_hit_bt)
text_hit_v = np.mean(text_hit_v)
return text_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(_):
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))