def train(**kwargs):
opt.parse(kwargs)
# Load data
images, tags, labels = load_data(opt.data_path)
y_dim = tags.shape[1]
X, Y, L = split_data(images, tags, labels, opt)
print('Loading and splitting data finished.')
# Init module
img_model = ImgModule(opt.bit)
txt_model = TxtModule(y_dim, opt.bit)
cls_model = ClassifierModule(opt.bit, opt.num_class)
if opt.use_gpu:
img_model = img_model.cuda()
txt_model = txt_model.cuda()
cls_model = cls_model.cuda()
# Data to torch tensor
train_L = torch.from_numpy(L['train'])
train_x = torch.from_numpy(X['train'])
train_y = torch.from_numpy(Y['train'])
query_L = torch.from_numpy(L['query'])
query_x = torch.from_numpy(X['query'])
query_y = torch.from_numpy(Y['query'])
retrieval_L = torch.from_numpy(L['retrieval'])
retrieval_x = torch.from_numpy(X['retrieval'])
retrieval_y = torch.from_numpy(Y['retrieval'])
num_train = train_x.shape[0]
F_buffer = torch.randn(num_train, opt.bit) # tensor (num_train, bit)
G_buffer = torch.randn(num_train, opt.bit) # tensor (num_train, bit)
if opt.use_gpu:
train_L = train_L.float().cuda()
F_buffer = F_buffer.cuda()
G_buffer = G_buffer.cuda()
B = torch.sign(F_buffer + G_buffer) # tensor (num_train, bit)
lr = opt.lr
optimizer_img = Adam(img_model.parameters(), lr=lr)
optimizer_txt = Adam(txt_model.parameters(), lr=lr)
optimizer_cls = Adam(cls_model.parameters(), lr=lr)
cls_criterion = nn.BCEWithLogitsLoss() # Multi-Label Classification(Binary Cross Entropy Loss)
best_mapi2t = 0.0
print('...training procedure starts')
ones = torch.ones(opt.batch_size, 1)
ones_ = torch.ones(num_train - opt.batch_size, 1)
if opt.use_gpu:
ones = ones.cuda()
ones_ = ones_.cuda()
for epoch in range(opt.max_epoch):
# Part 1: train image net & update F & classifier
for i in tqdm(range(num_train // opt.batch_size)):
# Random samples
index = np.random.permutation(num_train)
ind = index[0: opt.batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :]) # (batch_size, num_class)
image = Variable(train_x[ind].type(torch.float))
if opt.use_gpu:
image = image.cuda()
sample_L = sample_L.cuda()
cur_f = img_model(image) # cur_f: (batch_size, bit)
F_buffer[ind, :] = cur_f.data # update F
pred_x = cls_model(cur_f)
cls_x = cls_criterion(pred_x, sample_L)
quantization_x = torch.sum(torch.pow(B[ind, :] - cur_f, 2)) / (opt.batch_size * num_train) # ||B-f||_2^F
loss_x = cls_x + quantization_x
optimizer_img.zero_grad()
optimizer_cls.zero_grad()
loss_x.backward()
optimizer_img.step()
optimizer_cls.zero_grad()
# Part 2: train txt net & update G & classifier
for i in tqdm(range(num_train // opt.batch_size)):
index = np.random.permutation(num_train)
ind = index[0: opt.batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
text = train_y[ind, :].unsqueeze(1).unsqueeze(-1).type(torch.float)
text = Variable(text)
if opt.use_gpu:
text = text.cuda()
sample_L = sample_L.cuda()
cur_g = txt_model(text) # cur_g: (batch_size, bit)
G_buffer[ind, :] = cur_g.data # update G
pred_y = cls_model(cur_g)
cls_y = cls_criterion(pred_y, sample_L)
quantization_y = torch.sum(torch.pow(B[ind, :] - cur_g, 2)) / (num_train * opt.batch_size) # ||B-g||_2^F
loss_y = cls_y + quantization_y
optimizer_txt.zero_grad()
optimizer_cls.zero_grad()
loss_y.backward()
optimizer_txt.step()
optimizer_cls.step()
# Update B
B = torch.sign(F_buffer + G_buffer)
if opt.valid and True:
mapi2t, mapt2i = evaluate(img_model, txt_model, query_x, query_y, retrieval_x, retrieval_y, query_L, retrieval_L, opt.bit)
# save best model
if mapi2t > best_mapi2t:
print("best mapi2t, save model...")
best_mapi2t = mapi2t
txt_model.save(opt.load_txt_path)
img_model.save(opt.load_img_path)
print("{}".format(datetime.now()))
print('%d...eval map: map(i->t): \033[1;32;40m%3.3f\033[0m, map(t->i): \033[1;32;40m%3.3f\033[0m' % (
epoch, mapi2t, mapt2i))
print('...training procedure finish')
mapi2t, mapt2i = evaluate(img_model, txt_model, query_x, query_y, retrieval_x, retrieval_y, query_L, retrieval_L, opt.bit)
print('...test map: map(i->t): %3.3f, map(t->i): %3.3f' % (mapi2t, mapt2i))
def train(**kwargs):
opt.parse(kwargs)
alpha = [0.2,0.5,0.8,1.0,1.3.1.5.1.8.2.0,2.5]
images, tags, labels = load_data(opt.data_path)
pretrain_model = load_pretrain_model(opt.pretrain_model_path)
y_dim = tags.shape[1]
label_num = labels.shape[1]
X, Y, L = split_data(images, tags, labels)
print('...loading and splitting data finish')
img_model = ImgModule(opt.bit, pretrain_model)
txt_model = TxtModule(y_dim, opt.bit)
hash_model = HashModule(opt.bit)
label_model = LabModule(label_num)
if opt.use_gpu:
img_model = img_model.cuda()
txt_model = txt_model.cuda()
hash_model = hash_model.cuda()
label_model = label_model.cuda()
train_L = torch.from_numpy(L['train'])
train_x = torch.from_numpy(X['train'])
train_y = torch.from_numpy(Y['train'])
query_L = torch.from_numpy(L['query'])
query_x = torch.from_numpy(X['query'])
query_y = torch.from_numpy(Y['query'])
retrieval_L = torch.from_numpy(L['retrieval'])
retrieval_x = torch.from_numpy(X['retrieval'])
retrieval_y = torch.from_numpy(Y['retrieval'])
num_train = train_x.shape[0]
F_buffer = torch.randn(num_train, opt.bit)
G_buffer = torch.randn(num_train, opt.bit)
X_fea_buffer = torch.randn(num_train, opt.X_fea_nums)
Y_fea_buffer = torch.randn(num_train,opt.Y_fea_nums)
X_label_buffer = torch.randn(num_train, label_num)
Y_label_buffer = torch.randn(num_train, label_num)
Label_buffer = torch.randn(num_train, label_num)
Label_hash_buffer = torch.randn(num_train, opt.bit)
Label_label_buffer = torch.randn(num_train, label_num)
if opt.use_gpu:
train_L = train_L.cuda()
F_buffer = F_buffer.cuda()
G_buffer = G_buffer.cuda()
X_fea_buffer = X_fea_buffer.cuda()
Y_fea_buffer = Y_fea_buffer.cuda()
Label_buffer = Label_buffer.cuda()
X_label_buffer = X_label_buffer.cuda()
Y_label_buffer = Y_label_buffer.cuda()
Label_hash_buffer = Label_hash_buffer.cuda()
Label_label_buffer = Label_label_buffer.cuda()
Sim = calc_neighbor(train_L, train_L)
###############ddddddd
B = torch.sign(F_buffer + G_buffer)
B_buffer = torch.sign(F_buffer + G_buffer)
batch_size = opt.batch_size
lr = opt.lr
optimizer_img = SGD(img_model.parameters(), lr=lr)
optimizer_txt = SGD(txt_model.parameters(), lr=lr)
optimizer_hash = SGD(hash_model.parameters(), lr=lr)
optimizer_label = SGD(label_model.parameters(), lr=lr)
learning_rate = np.linspace(opt.lr, np.power(10, -6.), opt.max_epoch + 1)
result = {
'loss': [],
'hash_loss' : [],
'total_loss' : []
}
ones = torch.ones(batch_size, 1)
ones_ = torch.ones(num_train - batch_size, 1)
unupdated_size = num_train - batch_size
max_mapi2t = max_mapt2i = 0.
for epoch in range(opt.max_epoch):
# train label net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
label = Variable(train_L[ind,:].unsqueeze(1).unsqueeze(-1).type(torch.float))
if opt.use_gpu:
label = label.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L)
label_hash, label_label = label_model(label) #
Label_hash_buffer[ind, :] = label_hash.data
Label_label_buffer[ind, :] = label_label.data
Label = Variable(train_L)
Label_B = torch.sign(label_hash)
Label_H = Variable(Label_hash_buffer)
theta_l = 1.0 / 2 * torch.matmul(label_hash, Label_H.t())
logloss_l = -torch.sum(S * theta_l - torch.log(1.0 + torch.exp(theta_l)))
quantization_l = torch.sum(torch.pow(Label_hash_buffer[ind, :] - Label_B, 2))
labelloss_l = torch.sum(torch.pow(Label[ind, :].float() - label_label, 2))
loss_label = logloss_l + opt.beta * quantization_l + opt.alpha * labelloss_l # + logloss_x_fea
loss_label /= (batch_size * num_train)
optimizer_label.zero_grad()
loss_label.backward()
optimizer_label.step()
# train image net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
image = Variable(train_x[ind].type(torch.float))
if opt.use_gpu:
image = image.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
image_fea, cur_f, image_label = img_model(image) # cur_f: (batch_size, bit)
X_fea_buffer[ind, :] = image_fea.data
F_buffer[ind, :] = cur_f.data
X_label_buffer[ind, :] = image_label.data
G = Variable(G_buffer)
H_l = Variable(Label_hash_buffer)
B_x = torch.sign(F_buffer)
theta_x = 1.0 / 2 * torch.matmul(cur_f, H_l.t())
logloss_x = -torch.sum(S * theta_x - torch.log(1.0 + torch.exp(theta_x)))
quantization_xh = torch.sum(torch.pow(B_buffer[ind, :] - cur_f, 2))
quantization_xb = torch.sum(torch.pow(B_x[ind, :]- cur_f, 2))
labelloss_x = torch.sum(torch.pow(train_L[ind, :].float() - image_label,2))
loss_x = logloss_x + opt.beta * quantization_xh + opt.alpha * labelloss_x + opt.gamma * quantization_xb# + logloss_x_fea
loss_x /= (batch_size * num_train)
optimizer_img.zero_grad()
loss_x.backward()
optimizer_img.step()
# train txt net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
text = train_y[ind, :].unsqueeze(1).unsqueeze(-1).type(torch.float)
text = Variable(text)
if opt.use_gpu:
text = text.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
txt_fea, cur_g, txt_label = txt_model(text) # cur_f: (batch_size, bit)
Y_fea_buffer[ind, :] = txt_fea.data
G_buffer[ind, :] = cur_g.data
Y_label_buffer[ind, :] = txt_label.data
F = Variable(F_buffer)
H_l = Variable(Label_hash_buffer)
B_y = torch.sign(F)
# calculate loss
# theta_y: (batch_size, num_train)
theta_y = 1.0 / 2 * torch.matmul(cur_g, H_l.t())
logloss_y = -torch.sum(S * theta_y - torch.log(1.0 + torch.exp(theta_y)))
quantization_yh = torch.sum(torch.pow(B_buffer[ind, :] - cur_g, 2))
quantization_yb = torch.sum(torch.pow(B_y[ind, :] - cur_g, 2))
labelloss_y = torch.sum(torch.pow(train_L[ind, :].float() - txt_label, 2))
loss_y = logloss_y + opt.beta * quantization_yh + opt.alpha * labelloss_y + opt.gamma * quantization_yb# + logloss_y_fea
loss_y /= (num_train * batch_size)
optimizer_txt.zero_grad()
loss_y.backward()
optimizer_txt.step()
#train hash net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
#W = norm(X_fea_buffer[ind, :], Y_fea_buffer[ind, :])
#fea = 1.0 / 2 * (torch.matmul(W, X_fea_buffer[ind, :]) + torch.matmul(W, Y_fea_buffer[ind, :]))
fea = torch.cat([X_fea_buffer[ind, :], Y_fea_buffer[ind, :]], dim=1)
fea = Variable(fea)
if opt.use_gpu:
fea = fea.cuda()
sample_L = sample_L.cuda()
S = calc_neighbor(sample_L, train_L)
A = caculateAdj(sample_L, sample_L)
cur_B, label_hash = hash_model(fea, A)
B_buffer[ind, :] = cur_B.data
#caculate loss
B = Variable(torch.sign(B_buffer))
theta_hash = 1.0 / 2 * torch.matmul(cur_B, B_buffer.t())
logloss_hash = -torch.sum(S * theta_hash - torch.log(1.0 + torch.exp(theta_hash)))
label_loss = torch.sum(torch.pow(train_L[ind, :].float() - label_hash, 2))
hashloss = torch.sum(torch.pow(B[ind, :] - cur_B, 2))
loss_hash = logloss_hash + opt.alpha * label_loss + opt.beta * hashloss
optimizer_hash.zero_grad()
loss_hash.backward()
optimizer_hash.step()
# train image net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
image = Variable(train_x[ind].type(torch.float))
if opt.use_gpu:
image = image.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
image_fea, cur_f, image_label = img_model(image) # cur_f: (batch_size, bit)
X_fea_buffer[ind, :] = image_fea.data
F_buffer[ind, :] = cur_f.data
X_label_buffer[ind, :] = image_label.data
G = Variable(G_buffer)
H_l = Variable(Label_hash_buffer)
B_x = torch.sign(F_buffer)
theta_x = 1.0 / 2 * torch.matmul(cur_f, H_l.t())
logloss_x = -torch.sum(S * theta_x - torch.log(1.0 + torch.exp(theta_x)))
quantization_xh = torch.sum(torch.pow(B_buffer[ind, :] - cur_f, 2))
quantization_xb = torch.sum(torch.pow(B_x[ind, :] - cur_f, 2))
labelloss_x = torch.sum(torch.pow(train_L[ind, :].float() - image_label, 2))
loss_x = logloss_x + opt.gamma * quantization_xh + opt.alpha * labelloss_x + opt.beta * quantization_xb # + logloss_x_fea
loss_x /= (batch_size * num_train)
optimizer_img.zero_grad()
loss_x.backward()
optimizer_img.step()
# train txt net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0: batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
text = train_y[ind, :].unsqueeze(1).unsqueeze(-1).type(torch.float)
text = Variable(text)
if opt.use_gpu:
text = text.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
txt_fea, cur_g, txt_label = txt_model(text) # cur_f: (batch_size, bit)
Y_fea_buffer[ind, :] = txt_fea.data
G_buffer[ind, :] = cur_g.data
Y_label_buffer[ind, :] = txt_label.data
F = Variable(F_buffer)
H_l = Variable(Label_hash_buffer)
B_y = torch.sign(F)
# calculate loss
# theta_y: (batch_size, num_train)
theta_y = 1.0 / 2 * torch.matmul(cur_g, H_l.t())
logloss_y = -torch.sum(S * theta_y - torch.log(1.0 + torch.exp(theta_y)))
quantization_yh = torch.sum(torch.pow(B_buffer[ind, :] - cur_g, 2))
quantization_yb = torch.sum(torch.pow(B_y[ind, :] - cur_g, 2))
labelloss_y = torch.sum(torch.pow(train_L[ind, :].float() - txt_label, 2))
loss_y = logloss_y + opt.gamma * quantization_yh + opt.alpha * labelloss_y + opt.beta * quantization_yb # + logloss_y_fea
loss_y /= (num_train * batch_size)
optimizer_txt.zero_grad()
loss_y.backward()
optimizer_txt.step()
# calculate total loss
loss, hash_loss, total_loss = calc_loss(B, F, G, Variable(Sim), opt.alpha, opt.beta,Label_buffer, train_L, X_label_buffer,Y_label_buffer)
print('...epoch: %3d, loss: %3.3f, lr: %f' % (epoch + 1, loss.data, lr))
print('...epoch: %3d, hash_loss: %3.3f, lr: %f' % (epoch + 1, hash_loss.data, lr))
print('...epoch: %3d, total_loss: %3.3f, lr: %f' % (epoch + 1, total_loss.data, lr))
result['loss'].append(float(loss.data))
result['hash_loss'].append(float(hash_loss.data))
result['total_loss'].append(float(total_loss.data))
if opt.valid:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x, query_y, retrieval_y,
query_L, retrieval_L)
print('...epoch: %3d, valid MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' % (epoch + 1, mapi2t, mapt2i))
if mapt2i >= max_mapt2i and mapi2t >= max_mapi2t:
max_mapi2t = mapi2t
max_mapt2i = mapt2i
img_model.save(img_model.module_name + '.pth')
txt_model.save(txt_model.module_name + '.pth')
hash_model.save(hash_model.module_name+'.pth')
lr = learning_rate[epoch + 1]
# set learning rate
for param in optimizer_img.param_groups:
param['lr'] = lr
for param in optimizer_txt.param_groups:
param['lr'] = lr
print('...training procedure finish')
if opt.valid:
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' % (max_mapi2t, max_mapt2i))
result['mapi2t'] = max_mapi2t
result['mapt2i'] = max_mapt2i
else:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x, query_y, retrieval_y,
query_L, retrieval_L)
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' % (mapi2t, mapt2i))
result['mapi2t'] = mapi2t
result['mapt2i'] = mapt2i
write_result(result)
def train(**kwargs):
opt.parse(kwargs)
images, tags, labels = load_data(opt.data_path)
pretrain_model = load_pretrain_model(opt.pretrain_model_path)
y_dim = tags.shape[1]
X, Y, L = split_data(images, tags, labels)
print('...loading and splitting data finish')
img_model = ImgModule(opt.bit, pretrain_model)
txt_model = TxtModule(y_dim, opt.bit)
if opt.use_gpu:
img_model = img_model.cuda()
txt_model = txt_model.cuda()
train_L = torch.from_numpy(L['train'])
train_x = torch.from_numpy(X['train'])
train_y = torch.from_numpy(Y['train'])
query_L = torch.from_numpy(L['query'])
query_x = torch.from_numpy(X['query'])
query_y = torch.from_numpy(Y['query'])
retrieval_L = torch.from_numpy(L['retrieval'])
retrieval_x = torch.from_numpy(X['retrieval'])
retrieval_y = torch.from_numpy(Y['retrieval'])
num_train = train_x.shape[0]
F_buffer = torch.randn(num_train, opt.bit)
G_buffer = torch.randn(num_train, opt.bit)
if opt.use_gpu:
train_L = train_L.cuda()
F_buffer = F_buffer.cuda()
G_buffer = G_buffer.cuda()
Sim = calc_neighbor(train_L, train_L)
B = torch.sign(F_buffer + G_buffer)
batch_size = opt.batch_size
lr = opt.lr
optimizer_img = SGD(img_model.parameters(), lr=lr)
optimizer_txt = SGD(txt_model.parameters(), lr=lr)
learning_rate = np.linspace(opt.lr, np.power(10, -6.), opt.max_epoch + 1)
result = {'loss': []}
ones = torch.ones(batch_size, 1)
ones_ = torch.ones(num_train - batch_size, 1)
unupdated_size = num_train - batch_size
max_mapi2t = max_mapt2i = 0.
for epoch in range(opt.max_epoch):
# train image net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0:batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
image = Variable(train_x[ind].type(torch.float))
if opt.use_gpu:
image = image.cuda()
sample_L = sample_L.cuda()
ones = ones.cuda()
ones_ = ones_.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
cur_f = img_model(image) # cur_f: (batch_size, bit)
F_buffer[ind, :] = cur_f.data
F = Variable(F_buffer)
G = Variable(G_buffer)
theta_x = 1.0 / 2 * torch.matmul(cur_f, G.t())
logloss_x = -torch.sum(S * theta_x -
torch.log(1.0 + torch.exp(theta_x)))
quantization_x = torch.sum(torch.pow(B[ind, :] - cur_f, 2))
balance_x = torch.sum(
torch.pow(cur_f.t().mm(ones) + F[unupdated_ind].t().mm(ones_),
2))
loss_x = logloss_x + opt.gamma * quantization_x + opt.eta * balance_x
loss_x /= (batch_size * num_train)
optimizer_img.zero_grad()
loss_x.backward()
optimizer_img.step()
# train txt net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0:batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
text = train_y[ind, :].unsqueeze(1).unsqueeze(-1).type(torch.float)
text = Variable(text)
if opt.use_gpu:
text = text.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
cur_g = txt_model(text) # cur_f: (batch_size, bit)
G_buffer[ind, :] = cur_g.data
F = Variable(F_buffer)
G = Variable(G_buffer)
# calculate loss
# theta_y: (batch_size, num_train)
theta_y = 1.0 / 2 * torch.matmul(cur_g, F.t())
logloss_y = -torch.sum(S * theta_y -
torch.log(1.0 + torch.exp(theta_y)))
quantization_y = torch.sum(torch.pow(B[ind, :] - cur_g, 2))
balance_y = torch.sum(
torch.pow(cur_g.t().mm(ones) + G[unupdated_ind].t().mm(ones_),
2))
loss_y = logloss_y + opt.gamma * quantization_y + opt.eta * balance_y
loss_y /= (num_train * batch_size)
optimizer_txt.zero_grad()
loss_y.backward()
optimizer_txt.step()
# update B
B = torch.sign(F_buffer + G_buffer)
# calculate total loss
loss = calc_loss(B, F, G, Variable(Sim), opt.gamma, opt.eta)
print('...epoch: %3d, loss: %3.3f, lr: %f' %
(epoch + 1, loss.data, lr))
result['loss'].append(float(loss.data))
if opt.valid:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x,
query_y, retrieval_y, query_L, retrieval_L)
print(
'...epoch: %3d, valid MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f'
% (epoch + 1, mapi2t, mapt2i))
if mapt2i >= max_mapt2i and mapi2t >= max_mapi2t:
max_mapi2t = mapi2t
max_mapt2i = mapt2i
img_model.save(img_model.module_name + '.pth')
txt_model.save(txt_model.module_name + '.pth')
lr = learning_rate[epoch + 1]
# set learning rate
for param in optimizer_img.param_groups:
param['lr'] = lr
for param in optimizer_txt.param_groups:
param['lr'] = lr
print('...training procedure finish')
if opt.valid:
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' %
(max_mapi2t, max_mapt2i))
result['mapi2t'] = max_mapi2t
result['mapt2i'] = max_mapt2i
else:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x,
query_y, retrieval_y, query_L, retrieval_L)
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' %
(mapi2t, mapt2i))
result['mapi2t'] = mapi2t
result['mapt2i'] = mapt2i
write_result(result)