def autotrain(train_data_list, val_data_list, test_data_list, vgg_features_neb, bow_neb):
import model
BATCH_SIZE = 128
LAMBDA = 1
#data_list = np.load('data_list.npy')
bow = np.load('bow.npy')
labels = np.load('label.npy')
vgg_features = np.load('feature.npy')
vgg_features = np.tanh((vgg_features) / np.std(vgg_features))
images = tf.placeholder(tf.float32, [None, 4096])
target_embedding = tf.placeholder(tf.float32, [None, 64])
# images_target = tf.placeholder(tf.float32, [BATCH_SIZE, 4096])
images_drop = tf.nn.dropout(images, 0.5)
texts = tf.placeholder(tf.float32, [None, 2000])
# texts_target = tf.placeholder(tf.float32, [BATCH_SIZE, 1386])
texts_drop = tf.nn.dropout(texts, 0.5)
model = model.luo(BATCH_SIZE, 64)
text_embedding = model.txt_encoder(texts_drop)
image_embedding = model.image_encoder(images_drop)
text_embedding_t = model.txt_encoder(texts, reuse=True)
image_embedding_t = model.image_encoder(images, reuse=True)
mixed_embedding = (text_embedding + image_embedding) / 2
mixed_embedding = tf.nn.l2_normalize(mixed_embedding, 1)
pre_distance = []
for i in range(BATCH_SIZE):
pre_distance.append(tf.reduce_sum(mixed_embedding[i] * mixed_embedding, 1))
pre_distance = tf.stack(pre_distance)
mean = tf.reduce_mean(pre_distance)
var = tf.reduce_mean(tf.square(pre_distance - mean))
var_loss = tf.square(tf.clip_by_value(0.04- var, 0, 0.04))
re_image_text = model.txt_decoder_image(text_embedding, 4096)
re_text_image = model.image_decoder_text(image_embedding, 2000)
re_image_text_drop = tf.nn.dropout(re_image_text, 0.5)
re_text_image_drop = tf.nn.dropout(re_text_image, 0.5)
# disc_real = model.disc(images, texts)
disc_fake_image = model.disc(re_image_text_drop, texts, reuse=False , drop_rate=1.0)
disc_fake_text = model.disc(images, re_text_image_drop, reuse=True, drop_rate=1.0)
loss_image = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake_image, labels=tf.ones_like(disc_fake_image)))
loss_text = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake_text, labels=tf.ones_like(disc_fake_text)))
gen_cost = loss_image + loss_text
consistency_loss = tf.reduce_mean(tf.square(text_embedding - image_embedding))
vars = tf.trainable_variables()
gen_params = [v for v in vars if 'coder' in v.name]
disc_params = [v for v in vars if 'disc' in v.name]
gen_train_op = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9).minimize(
consistency_loss + gen_cost + 100 * var_loss, var_list=gen_params)
def test():
mAP_n = 10000
P_n = 1000
def check(x, y):
flag = bool(np.sum(np.logical_and(x, y)))
return flag
def countMAP(result, train_label, po):
AP = 0
total_relevant = 0
buffer_yes = np.zeros(mAP_n)
Ns = np.arange(1, mAP_n + 1, 1)
for i in range(mAP_n):
if check(train_label[result[i]], po):
buffer_yes[i] = 1
total_relevant += 1
P = np.cumsum(buffer_yes) / Ns
if sum(buffer_yes) != 0:
AP += sum(P * buffer_yes) / sum(buffer_yes)
return AP
def countP(result, train_label, po):
P = 0
for i in range(P_n):
if check(train_label[result[i]], po):
P += 1
return P / P_n
def eval_total(binary_codes_train, binary_codes_test, train_label, test_label):
lens1 = len(binary_codes_test)
lens2 = len(binary_codes_train)
P = 0
mAP = 0
dist = np.zeros((lens1, lens2), dtype=np.float32)
binary_codes_train_norm = np.linalg.norm(binary_codes_train, axis=1)
binary_codes_test_norm = np.linalg.norm(binary_codes_test, axis=1)
for i in range(lens1):
dist[i] = -np.sum(binary_codes_test[i] * binary_codes_train, 1) / binary_codes_train_norm / \
binary_codes_test_norm[i]
results = []
for i in range(lens1):
results.append(np.argsort(dist[i]))
for i in range(lens1):
mAP += countMAP(results[i], train_label, test_label[i])
P += countP(results[i], train_label, test_label[i])
return P / lens1, mAP / lens1
total_train_embedding = []
total_test_embedding = []
for i in range(int(len(train_data_list) / 1000)):
train_images = vgg_features[train_data_list[i * 1000:min((i + 1) * 1000, len(train_data_list))]]
_image_embedding = session.run(image_embedding_t, feed_dict={images: train_images})
total_train_embedding.append(_image_embedding)
total_train_embedding = np.concatenate(total_train_embedding, axis=0)
for i in range(int(len(val_data_list) / 1000)):
test_texts = bow[val_data_list[i * 1000:min((i + 1) * 1000, len(val_data_list))]]
_text_embedding = session.run(text_embedding_t, feed_dict={texts: test_texts})
total_test_embedding.append(_text_embedding)
total_test_embedding = np.concatenate(total_test_embedding, axis=0)
P, mAP = eval_total(total_train_embedding, total_test_embedding, labels[train_data_list],
labels[val_data_list])
print('mAP')
print(mAP)
print('P')
print(P)
return mAP
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as session:
session.run(tf.initialize_all_variables())
saver = tf.train.Saver()
saver2 = tf.train.Saver(disc_params)
saver2.restore(session, 'disc_block')
old_map = 0
for i in range(50001):
train_list = np.random.randint(0, len(train_data_list), BATCH_SIZE)
train_images = vgg_features[train_data_list[train_list]]
# _target_embedding = np.random.uniform(-1, 1, [BATCH_SIZE, 64])
changer = np.random.randint(0, 2, [BATCH_SIZE, 64], np.int8)
_target_embedding = np.clip(
changer * np.random.normal(0.7, 0.2, [BATCH_SIZE, 64]) + (1 - changer) * np.random.normal(-0.7, 0.2,
[BATCH_SIZE,
64]), -1, 1)
train_texts = bow[train_data_list[train_list]]
# train_images_list = np.zeros_like(train_list, np.int32)
session.run(gen_train_op,
feed_dict={images: train_images, texts: train_texts, target_embedding: _target_embedding})
#session.run(disc_train_op,
# feed_dict={images: train_images, texts: train_texts, target_embedding: _target_embedding})
if (i % 2000 == 0 and i >= 30000):
new_map = test()
if new_map > old_map:
old_map = new_map
saver.save(session, './referee_advKNN')
if (i % 100 == 0):
_closs, _gloss, _var, _t, _i, _d_loss = session.run(
[consistency_loss, gen_cost, var, loss_text, loss_image, var_loss],
feed_dict={images: train_images, texts: train_texts, target_embedding: _target_embedding})
print("consistency_loss:%f, gen_loss:%f, var:%f, var_loss:%f" % (_closs, _gloss, _var, _d_loss))
print('image:%f, text:%f' % (_i, _t))
print('best map')
print(old_map)
session.close()
return old_map
def binary_train(code_len, rounds):
import model
BATCH_SIZE = 128
tf_idf = np.load('bow.npy')
vgg_features = np.load('feature.npy')
vgg_features = np.tanh((vgg_features) / np.std(vgg_features))
images = tf.placeholder(tf.float32, [None, 4096])
texts = tf.placeholder(tf.float32, [None, 2000])
embeddings = tf.placeholder(tf.float32, [BATCH_SIZE, 64])
dist = tf.placeholder(tf.float32, [BATCH_SIZE, BATCH_SIZE])
model = model.luo(BATCH_SIZE, 64)
text_embedding = model.txt_encoder(texts)
image_embedding = model.image_encoder(images)
embeddings_drop = tf.nn.dropout(embeddings, 0.5)
binary_vec_drop = model.binary_layer(embeddings_drop, code_len, rate=0.5)
loss = 0
for i in range(BATCH_SIZE):
loss += tf.reduce_mean(tf.square(dist[i, :] - tf.reduce_mean(binary_vec_drop[i] * binary_vec_drop, 1)))
loss /= BATCH_SIZE
lossb = tf.square(1 - tf.reduce_mean(binary_vec_drop * binary_vec_drop))
vars = tf.trainable_variables()
b_params = [v for v in vars if 'binary' in v.name]
o_params = [v for v in vars if 'binary' not in v.name]
b_train_op = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss, var_list=b_params)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
train_data_list = np.load('train_data_list.npy')
total_train_embedding_images = []
total_train_embedding_texts = []
with tf.Session(config=config) as session:
saver_old = tf.train.Saver(o_params)
saver = tf.train.Saver()
session.run(tf.initialize_all_variables())
saver_old.restore(session, './referee_advKNN')
for i in range(int(len(train_data_list) / 1000)):
train_images = vgg_features[train_data_list[i * 1000:min((i + 1) * 1000, len(train_data_list))]]
_image_embedding = session.run(image_embedding, feed_dict={images: train_images})
total_train_embedding_images.append(_image_embedding)
total_train_embedding_images = np.concatenate(total_train_embedding_images, axis=0)
for i in range(int(len(train_data_list) / 1000)):
train_texts = tf_idf[train_data_list[i * 1000:min((i + 1) * 1000, len(train_data_list))]]
_text_embedding = session.run(text_embedding, feed_dict={texts: train_texts})
total_train_embedding_texts.append(_text_embedding)
total_train_embedding_texts = np.concatenate(total_train_embedding_texts, axis=0)
total_train_embedding = (total_train_embedding_texts + total_train_embedding_images) / 2
total_dist = np.zeros((len(train_data_list), len(train_data_list)), dtype=np.float32)
norm = np.linalg.norm(total_train_embedding, axis=1)
for i in range(len(train_data_list)):
total_dist[i, i:] = np.sum(total_train_embedding[i] * total_train_embedding[i:], 1) / norm[i:] / norm[i]
total_dist[i:, i] = total_dist[i, i:]
tempstd2 = total_dist.std()
total_dist = np.clip((-2/np.mean(np.min(total_dist-1, 1))) * (total_dist - 1) + 1, -1, 1)
tempstd = total_dist.std()
for i in range(rounds):
train_list = np.random.randint(0, len(train_data_list), BATCH_SIZE)
train_embeddings = total_train_embedding[train_list]
train_dist = np.zeros([BATCH_SIZE, BATCH_SIZE], np.float32)
for k in range(BATCH_SIZE):
train_dist[k] = total_dist[train_list[k]][train_list]
session.run(b_train_op, feed_dict={embeddings: train_embeddings, dist: train_dist})
if (i % 10000 == 0):
saver.save(session, './referee_advKNNB')
if (i % 100 == 0):
_loss, _lossB = session.run([loss, lossb], feed_dict={embeddings: train_embeddings, dist: train_dist})
print(_loss)
print(_lossB)
session.close()
return tempstd, tempstd2
def embeddingB(code_len):
import model
mAP_n = 10000
P_n = 1000
def HammingDistance(a, b):
# c = np.logical_xor((np.sign(a)+1).astype(np.bool_),(np.sign(b)+1).astype(np.bool_))
# dis = np.sum(c.astype(np.int32))
dis = np.sum(np.square(a - b))
return dis
def check(x, y):
flag = bool(np.sum(np.logical_and(x, y)))
return flag
def countMAP(result, train_label, po):
AP = 0
total_relevant = 0
buffer_yes = np.zeros(mAP_n)
Ns = np.arange(1, mAP_n + 1, 1)
for i in range(mAP_n):
if check(train_label[result[i]], po):
buffer_yes[i] = 1
total_relevant += 1
P = np.cumsum(buffer_yes) / Ns
if sum(buffer_yes) != 0:
AP += sum(P * buffer_yes) / sum(buffer_yes)
return AP
def countP(result, train_label, po):
P = 0
for i in range(P_n):
if check(train_label[result[i]], po):
P += 1
return P / P_n
def eval_total(binary_codes_train, binary_codes_test, train_label, test_label):
lens1 = len(binary_codes_test)
lens2 = len(binary_codes_train)
P = 0
mAP = 0
dist = np.zeros((lens1, lens2), dtype=np.float32)
binary_codes_train_norm = np.linalg.norm(binary_codes_train, axis=1)
binary_codes_test_norm = np.linalg.norm(binary_codes_test, axis=1)
for i in range(lens1):
dist[i] = -np.sum(binary_codes_test[i] * binary_codes_train, 1) / binary_codes_train_norm / \
binary_codes_test_norm[i]
results = []
for i in range(lens1):
results.append(np.argsort(dist[i]))
for i in range(lens1):
mAP += countMAP(results[i], train_label, test_label[i])
P += countP(results[i], train_label, test_label[i])
return P / lens1, mAP / lens1
BATCH_SIZE = 128
bow = np.load('bow.npy')
labels = np.load('label.npy')
vgg_features = np.load('feature.npy')
vgg_features = np.tanh((vgg_features) / np.std(vgg_features))
images = tf.placeholder(tf.float32, [None, 4096])
texts = tf.placeholder(tf.float32, [None, 2000])
model = model.luo(BATCH_SIZE, 64)
text_embedding = model.txt_encoder(texts)
text_embedding = tf.sign(model.binary_layer(text_embedding, code_len))
image_embedding = model.image_encoder(images)
image_embedding = tf.sign(model.binary_layer(image_embedding, code_len, reuse=True))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
train_data_list = np.load('train_data_list.npy')
test_data_list = np.load('test_data_list.npy')
total_train_embedding = []
total_test_embedding = []
with tf.Session(config=config) as session:
saver = tf.train.Saver()
saver.restore(session, './referee_advKNNB')
for i in range(int(len(train_data_list) / 1000)):
train_images = vgg_features[train_data_list[i * 1000:min((i + 1) * 1000, len(train_data_list))]]
_image_embedding = session.run(image_embedding, feed_dict={images: train_images})
total_train_embedding.append(_image_embedding)
total_train_embedding = np.concatenate(total_train_embedding, axis=0)
for i in range(int(len(test_data_list) / 1000)):
test_texts = bow[test_data_list[i * 1000:min((i + 1) * 1000, len(test_data_list))]]
_text_embedding = session.run(text_embedding, feed_dict={texts: test_texts})
total_test_embedding.append(_text_embedding)
total_test_embedding = np.concatenate(total_test_embedding, axis=0)
session.close()
P, mAP = eval_total(total_train_embedding, total_test_embedding, labels[train_data_list], labels[test_data_list])
print('mAP')
print(mAP)
print('P')
print(P)
return P, mAP
for i in range(lens1):
results.append(np.argsort(dist[i]))
for i in range(lens1):
mAP += countMAP(results[i], train_label, test_label[i])
P += countP(results[i], train_label, test_label[i])
return P/lens1, mAP/lens1
BATCH_SIZE = 128
tf_idf = np.load('bow.npy')
labels = np.load('label.npy')
vgg_features = np.load('feature.npy')
vgg_features = np.tanh((vgg_features)/np.std(vgg_features))
images = tf.placeholder(tf.float32, [None, 4096])
texts = tf.placeholder(tf.float32, [None, 2000])
model = model.luo(BATCH_SIZE,64)
text_embedding = model.txt_encoder(texts)
image_embedding = model.image_encoder(images)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
train_data_list = np.load('train_data_list.npy')
test_data_list = np.load('test_data_list.npy')
total_train_embedding = []
total_test_embedding = []
with tf.Session(config=config) as session:
saver = tf.train.Saver()
saver.restore(session, './referee_ae1.50_0.05')
for i in range(int(len(train_data_list)/1000)):
train_images = vgg_features[train_data_list[i*1000:min((i+1)*1000, len(train_data_list))]]
_image_embedding = session.run(image_embedding, feed_dict={images:train_images})
total_train_embedding.append(_image_embedding)
total_train_embedding = np.concatenate(total_train_embedding, axis=0)