def main():
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
nclasses = 10
epochs = args.epochs
learning_rate = 0.1
model_name = args.model_name
trainloader, testloader = data_loader(model_name)
model = cnn_model.CNNNet(model_name,
nclasses=nclasses,
pretrained=args.pretrained)
model = model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
record = {}
record['train loss'] = []
record['train acc'] = []
record['test loss'] = []
record['test acc'] = []
record['epoch time'] = []
record['batch size'] = args.batch_size
record['base lr'] = learning_rate
record['pretrain'] = args.pretrained
record['filename'] = 'log/' + model_name + '_' + datetime.now().strftime(
"%y-%m-%d-%H-%M-%S") + '.pkl'
record['description'] = 'Model: base model\n'
for epoch in range(epochs):
epoch_time = time.time()
train_loss, train_acc = train(trainloader, model, criterion, optimizer)
train_time = time.time() - epoch_time
print(
'[Epoch: %3d/%3d][Train Loss: %5.5f][Train Acc: %5.5f][Epoch Time: %3.3f]'
% (epoch, epochs, train_loss, train_acc, train_time))
test_loss, test_acc = test(testloader, model, criterion)
print('[Epoch: %3d/%3d][Test Loss: %5.5f][Test Acc: %5.5f]' %
(epoch, epochs, test_loss, test_acc))
optimizer, learning_rate = adjust_learning_rate(
optimizer, learning_rate, epoch)
record['epoch time'].append(train_time)
record['train loss'].append(train_loss)
record['train acc'].append(train_acc)
record['test loss'].append(test_loss)
record['test acc'].append(test_acc)
fp = open(record['filename'], 'wb')
pickle.dump(record, fp)
fp.close()
def create_dataset(dataset_name):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transformations = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
if dataset_name == 'NUS-WIDE':
dset_test = dp.DatasetProcessingNUS_WIDE('data/NUS-WIDE',
'test_img.txt',
transformations)
return dset_test
if dataset_name == 'CIFAR-10':
dset_test = dp.DatasetProcessingCIFAR_10('data/CIFAR-10',
'test_img.txt',
transformations)
return dset_test
if dataset_name == 'Project':
if not os.path.exists('dcodes/adch-project-48bits-record.pkl'):
record = {}
dset_database = dp.DatasetProcessingPorject(
'data/Project', 'database_img.txt', transformations)
databaseloader = DataLoader(dset_database,
batch_size=1,
shuffle=False,
num_workers=4)
model = cnn_model.CNNNet('resnet50', 48)
model.load_state_dict(
torch.load('dict/adch-nuswide-48bits.pth',
map_location=torch.device('cpu')))
model.eval()
rB = encode(model, databaseloader, 4985, 48)
record['rB'] = rB
with open('dcodes/adch-project-48bits-record.pkl', 'wb') as fp:
pickle.dump(record, fp)
dset_test = dp.DatasetProcessingPorject('data/Project', 'test_img.txt',
transformations)
return dset_test
def DCDH_algo(code_length):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
torch.manual_seed(0)
torch.cuda.manual_seed(0)
'''
parameter setting
'''
max_iter = opt.max_iter
epochs = opt.epochs
batch_size = opt.batch_size
learning_rate = opt.learning_rate
weight_decay = 5 * 10**-4
num_samples = opt.num_samples
gamma = opt.gamma
record['param']['topk'] = 5000
record['param']['opt'] = opt
record['param']['description'] = '[Comment: learning rate decay]'
logger.info(opt)
logger.info(code_length)
logger.info(record['param']['description'])
'''
dataset preprocessing
'''
nums, dsets, labels = _dataset()
num_database, num_test = nums
dset_database, dset_test = dsets
database_labels, test_labels = labels
'''
model construction
'''
model = cnn_model.CNNNet(opt.arch, code_length)
model.cuda()
DCDH_loss = al.DCDHLoss(gamma, code_length, num_database)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
labelNet = cnn_model.MLP(code_length, 38)
labelNet.cuda()
label_loss = al.DCDHLoss(gamma, code_length, num_database)
optimizer2 = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
product_model = cnn_model.ConcatMLP(code_length, 38)
product_model.cuda()
product_loss = al.ProductLoss(gamma, code_length, num_samples)
optimizer3 = optim.Adam(product_model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
V = np.zeros((num_database, code_length))
model.train()
labelNet.train()
product_model.train()
for iter in range(max_iter):
iter_time = time.time()
'''
sampling and construct similarity matrix
'''
select_index = list(np.random.permutation(
range(num_database)))[0:num_samples]
_sampler = subsetsampler.SubsetSampler(select_index)
trainloader = DataLoader(dset_database,
batch_size=batch_size,
sampler=_sampler,
shuffle=False,
num_workers=4)
'''
learning deep neural network: feature learning
'''
sample_label = database_labels.index_select(
0, torch.from_numpy(np.array(select_index)))
Sim = calc_sim(sample_label)
U = np.zeros((num_samples, code_length), dtype=np.float)
L = np.zeros((num_samples, code_length), dtype=np.float)
I = np.zeros((num_samples, code_length), dtype=np.float)
for epoch in range(epochs):
for iteration, (train_input, train_label,
batch_ind) in enumerate(trainloader):
batch_size_ = train_label.size(0)
u_ind = np.linspace(iteration * batch_size,
np.min((num_samples,
(iteration + 1) * batch_size)) - 1,
batch_size_,
dtype=int)
train_input = Variable(train_input.cuda())
output = model(train_input)
outputL = labelNet(train_label.type(torch.FloatTensor).cuda())
S = calc_sim(train_label)
U[u_ind, :] = output.cpu().data.numpy()
L[u_ind, :] = outputL.cpu().data.numpy()
#
semanCode = outputL.clone().detach().requires_grad_(True)
imgCode = output.clone().detach().requires_grad_(True)
product = torch.einsum('bi,bj->bij', semanCode, imgCode)
product = product.reshape(batch_size_,
code_length * code_length)
hashcode, classify = product_model(product.cuda())
I[u_ind, :] = hashcode.cpu().data.numpy()
model.zero_grad()
labelNet.zero_grad()
product_model.zero_grad()
loss3 = product_loss(hashcode, V, S,
V[batch_ind.cpu().numpy(), :], classify,
train_label, imgCode, semanCode)
loss2 = label_loss(output, V, S, V[batch_ind.cpu().numpy(), :])
loss = DCDH_loss(output, V, S, V[batch_ind.cpu().numpy(), :]
) + opt.lamda * loss2 + opt.mu * loss3
loss.backward()
optimizer.step()
optimizer2.step()
optimizer3.step()
adjusting_learning_rate(optimizer, iter)
'''
learning binary codes: discrete coding
'''
Q = -2 * code_length * Sim.cpu().numpy().transpose().dot(
U) - 2 * gamma * U
for k in range(code_length):
sel_ind = np.setdiff1d([ii for ii in range(code_length)], k)
V_ = V[:, sel_ind]
V_ = V_[select_index, :]
Uk = U[:, k]
U_ = U[:, sel_ind]
V[select_index,
k] = -np.sign((Q[:, k] + 2 * V_.dot(U_.transpose().dot(Uk))) +
opt.lamda * L[:, k] + opt.mu * I[:, k])
iter_time = time.time() - iter_time
loss_ = calc_loss(V, U,
Sim.cpu().numpy(), code_length, select_index, gamma)
logger.info('[Iteration: %3d/%3d][Train Loss: %.4f]', iter, max_iter,
loss_)
record['train loss'].append(loss_)
record['iter time'].append(iter_time)
'''
training procedure finishes, evaluation
'''
if iter % 10 == 9:
model.eval()
testloader = DataLoader(dset_test,
batch_size=1,
shuffle=False,
num_workers=4)
qB = encode(model, testloader, num_test, code_length)
rB = V
map = calc_hr.calc_map(qB, rB, test_labels.numpy(),
database_labels.numpy())
topkmap = calc_hr.calc_topMap(qB, rB, test_labels.numpy(),
database_labels.numpy(),
record['param']['topk'])
logger.info('[Evaluation: mAP: %.4f, top-%d mAP: %.4f]', map,
record['param']['topk'], topkmap)
record['rB'] = rB
record['qB'] = qB
record['map'] = map
record['topkmap'] = topkmap
filename = os.path.join(logdir,
str(code_length) + 'bits-record.pkl')
_save_record(record, filename)
def adsh_algo(code_length):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
torch.manual_seed(0)
torch.cuda.manual_seed(0)
'''
parameter setting
'''
max_iter = opt.max_iter
epochs = opt.epochs
batch_size = opt.batch_size
learning_rate = opt.learning_rate
weight_decay = 5 * 10**-4
num_samples = opt.num_samples
alpha = opt.alpha
gamma = opt.gamma
lamda = opt.lamda
model_save_path = opt.model_save_path
record['param']['opt'] = opt
record['param']['description'] = '[Comment: learning rate decay]'
logger.info(opt)
logger.info(code_length)
logger.info(record['param']['description'])
'''
dataset preprocessing
'''
nums, dsets, labels = _dataset()
num_database, num_test = nums
dset_database, dset_test = dsets
database_labels, test_labels = labels
'''
model construction
'''
model = cnn_model.CNNNet(opt.arch, code_length)
model.cuda()
adsh_loss = al.ADSHLoss(alpha, gamma, lamda, code_length, num_database)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
V = np.zeros((num_database, code_length))
model.train()
for iter in range(max_iter):
iter_time = time.time()
'''
sampling and construct similarity matrix
'''
select_index = list(np.random.permutation(
range(num_database)))[0:num_samples]
_sampler = subsetsampler.SubsetSampler(select_index)
trainloader = DataLoader(dset_database,
batch_size=batch_size,
sampler=_sampler,
shuffle=False,
num_workers=4)
'''
learning deep neural network: feature learning
'''
sample_label = database_labels.index_select(
0, torch.from_numpy(np.array(select_index)))
Sim = calc_sim(sample_label, database_labels)
U = np.zeros((num_samples, code_length), dtype=np.float)
for epoch in range(epochs):
for iteration, (train_input, train_label,
batch_ind) in enumerate(trainloader):
batch_size_ = train_label.size(0)
u_ind = np.linspace(iteration * batch_size,
np.min((num_samples,
(iteration + 1) * batch_size)) - 1,
batch_size_,
dtype=int)
#是一个batch_size
train_input = Variable(train_input.cuda())
output = model(train_input)
S = Sim.index_select(0, torch.from_numpy(u_ind))
S_query = calc_sim(sample_label[u_ind, :],
sample_label[u_ind, :])
U[u_ind, :] = output.cpu().data.numpy()
model.zero_grad()
loss = adsh_loss(output, V, S, S_query,
V[batch_ind.cpu().numpy(), :], 1)
loss.backward()
optimizer.step()
#print optimizer.state_dict()
adjusting_learning_rate(optimizer, iter)
'''
learning binary codes: discrete coding
'''
barU = np.zeros((num_database, code_length))
barU[select_index, :] = U
Q = -2 * code_length * Sim.cpu().numpy().transpose().dot(
U) - 2 * gamma * barU
for k in range(code_length):
sel_ind = np.setdiff1d([ii for ii in range(code_length)], k)
V_ = V[:, sel_ind]
Uk = U[:, k]
U_ = U[:, sel_ind]
V[:, k] = -np.sign(Q[:, k] + 2 * V_.dot(U_.transpose().dot(Uk)))
iter_time = time.time() - iter_time
S_query = calc_sim(sample_label, sample_label)
square_loss_, quanty_loss_, triplet_loss_, loss_ = calc_loss(
V, U,
Sim.cpu().numpy(), code_length, select_index, gamma, lamda)
logger.info(
'[Iteration: %3d/%3d][square Loss: %.4f][quanty Loss: %.4f][triplet Loss: %.4f][train Loss: %.4f]',
iter, max_iter, square_loss_, quanty_loss_, triplet_loss_, loss_)
record['train loss'].append(loss_)
record['iter time'].append(iter_time)
'''
training procedure finishes, evaluation
'''
torch.save(model, model_save_path)
print "model saved!"
model.eval()
testloader = DataLoader(dset_test,
batch_size=1,
shuffle=False,
num_workers=4)
qB = encode(model, testloader, num_test, code_length)
rB = V
#计算有序性
map = calc_hr.calc_map(qB, rB, test_labels.numpy(),
database_labels.numpy())
logger.info('[Evaluation: mAP: %.4f]', map)
record['rB'] = rB
record['qB'] = qB
record['map'] = map
filename = os.path.join(logdir, str(code_length) + 'bits-record.pkl')
_save_record(record, filename)
def create_retrieval_result_fig(model_name, dataset_name):
if model_name == 'adch':
result_dir = '-'.join([
'result/result-ADCH', dataset_name,
datetime.now().strftime("%y-%m-%d-%H-%M-%S")
])
else:
result_dir = '-'.join([
'result/result-ADSH', dataset_name,
datetime.now().strftime("%y-%m-%d-%H-%M-%S")
])
os.mkdir(result_dir)
model = cnn_model.CNNNet('resnet50', 48)
dimg_paths = []
timg_paths = []
timg_ori_paths = []
if dataset_name == 'CIFAR-10':
rLfile = open('data/' + dataset_name + '/database_label.txt')
qLfile = open('data/' + dataset_name + '/test_label.txt')
rlables = [int(x.strip()) for x in rLfile]
qLables = [int(x.strip()) for x in qLfile]
rL = np.zeros([len(rlables), 10])
for i in range(len(rlables)):
rL[i, rlables[i]] = 1
qL = np.zeros([len(qLables), 10])
for i in range(len(qLables)):
qL[i, qLables[i]] = 1
rLfile.close()
qLfile.close()
elif dataset_name == 'NUS-WIDE':
rL = np.loadtxt('data/NUS-WIDE/database_label.txt', dtype=np.int64)
qL = np.loadtxt('data/NUS-WIDE/test_label.txt', dtype=np.int64)
if dataset_name == 'Project':
dfile = open('data/' + dataset_name + '/database_img.txt',
'r',
encoding='utf-8')
tfile = open('data/' + dataset_name + '/test_img.txt',
'r',
encoding='utf-8')
tfile_ori = open('data/' + dataset_name + '/test_img_bak.txt',
'r',
encoding='utf-8')
else:
dfile = open('data/' + dataset_name + '/database_img.txt', 'r')
tfile = open('data/' + dataset_name + '/test_img.txt', 'r')
tfile_ori = open('data/' + dataset_name + '/test_img_bak.txt', 'r')
if model_name == 'adch':
if dataset_name == 'CIFAR-10':
model.load_state_dict(
torch.load('dict/adch-cifar10-48bits.pth',
map_location=torch.device('cpu')))
else:
model.load_state_dict(
torch.load('dict/adch-nuswide-48bits.pth',
map_location=torch.device('cpu')))
else:
if dataset_name == 'CIFAR-10':
model.load_state_dict(
torch.load('dict/adsh-cifar10-48bits.pth',
map_location=torch.device('cpu')))
else:
model.load_state_dict(
torch.load('dict/adsh-nuswide-48bits.pth',
map_location=torch.device('cpu')))
for line in dfile.readlines():
dimg_paths.append(line.strip())
for line in tfile.readlines():
timg_paths.append(line.strip())
for line in tfile_ori.readlines():
timg_ori_paths.append(line.strip())
dfile.close()
tfile.close()
tfile_ori.close()
tind = []
for i in range(5):
tind.append(timg_ori_paths.index(timg_paths[i]))
model.eval()
dset_test = create_dataset(dataset_name)
testloader = DataLoader(dset_test,
batch_size=1,
shuffle=False,
num_workers=4)
if dataset_name == 'CIFAR-10':
f = open('dcodes/adch-cifar10-48bits-record.pkl', 'rb')
elif dataset_name == 'NUS-WIDE':
f = open('dcodes/adch-nuswide-48bits-record.pkl', 'rb')
else:
f = open('dcodes/adch-project-48bits-record.pkl', 'rb')
record = pickle.load(f)
f.close()
qB = encode(model, testloader, len(dset_test), 48)
rB = record['rB']
qimgs = []
rimgs = []
accuracies = []
for i in range(5):
# accuracy = []
# gnd = (np.dot(qL[tind[i], :], rL.transpose()) > 0).astype(np.float32)
hamm = calc_hamming_dist(qB[i], rB)
ind = np.argsort(hamm)
rimg_dir = os.path.join(result_dir,
timg_paths[i].split('/')[1].split('.')[0])
os.mkdir(rimg_dir)
for j in range(24):
rimg_path = os.path.join('data/' + dataset_name,
dimg_paths[ind[j]])
dest_path = os.path.join(rimg_dir,
dimg_paths[ind[j]].split('/')[1])
shutil.copy(rimg_path, dest_path)
qimg = Image.open(os.path.join('data/' + dataset_name, timg_paths[i]))
qimg = qimg.resize((200, 100))
qimgs.append(qimg)
for j in range(10):
# accuracy.append(gnd[ind[j]])
rimg = Image.open(
os.path.join('data/' + dataset_name, dimg_paths[ind[j]]))
rimg = rimg.resize((200, 100))
rimgs.append(rimg)
# accuracies.append(accuracy)
plt.figure(figsize=(24, 5.5))
for i in range(5):
plt.subplot(5, 11, i * 11 + 1)
plt.imshow(qimgs[i])
plt.axis('off')
for j in range(10):
plt.subplot(5, 11, i * 11 + j + 2)
image = rimgs[i * 10 + j]
# if accuracies[i][j] < 1:
# draw = ImageDraw.Draw(image)
# draw.line(
# [(2, 4), (2, image.size[1] - 4), (image.size[0] - 3, image.size[1] - 4), (image.size[0] - 3, 4),
# (2, 4)], width=4, fill='red')
plt.imshow(image)
plt.axis('off')
plt.subplots_adjust(left=0,
top=0.8,
right=0.8,
bottom=0,
wspace=0.02,
hspace=0.02)
plt.savefig(os.path.join(result_dir, 'result'), bbox_inches='tight')
plt.show()
def algo(code_length):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
torch.manual_seed(0)
torch.cuda.manual_seed(0)
'''
parameter setting
'''
epochs = opt.epochs
batch_size = opt.batch_size
learning_rate = opt.learning_rate
temp1 = opt.temp1
temp2 = opt.temp2
alpha = opt.alpha
beta = opt.beta
img_path = opt.img_path
record['param']['topk'] = 50
record['param']['topp'] = 100
record['param']['opt'] = opt
logger.info(opt)
logger.info(code_length)
'''
dataset preprocessing
'''
nums, dsets, labels = _dataset(img_path)
num_database, num_test = nums
dset_database, dset_test = dsets
database_labels, test_labels = labels
'''
model construction
'''
model = cnn_model.CNNNet(temp1, code_length, opt.n_class)
model.cuda()
proploss = al.proposed(alpha, code_length, temp1, temp2)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=beta)
trainloader = DataLoader(dset_database,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
model.train()
for epoch in range(epochs):
iter_time = time.time()
for iteration, (train_input, train_label,
batch_ind) in enumerate(trainloader):
train_input = Variable(train_input.cuda(non_blocking=True))
train_label = Variable(train_label.float().cuda(non_blocking=True),
requires_grad=False)
output, z, m = model(train_input)
model.zero_grad()
loss = proploss(output, z, m, train_label)
loss.backward()
optimizer.step()
'''
learning binary codes: discrete coding
'''
iter_time = time.time() - iter_time
loss_ = loss.cpu().data.numpy()
logger.info('[Iteration: %3d/%3d][Train Loss: %.4f][Time: %.3f secs]',
epoch + 1, epochs, loss_, iter_time)
record['train loss'].append(loss_)
record['iter time'].append(iter_time)
record['model'] = model
'''
training procedure finishes, evaluation
'''
model = cnn_model.CNNExtractNet(model)
model.eval()
trainloader = DataLoader(dset_database,
batch_size=batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
testloader = DataLoader(dset_test,
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True)
qB = encode(model, testloader, num_test, code_length)
rB = encode(model, trainloader, num_database, code_length)
mapka, mapkb, topp_ndcg, topp_acg = calc_hr.calc_metrics(
qB, rB, test_labels.numpy(), database_labels.numpy(),
record['param']['topk'], record['param']['topp'],
record['param']['topp'])
logger.info(
'[Evaluation: mAP@%d (A): %.4f, mAP@%d (B): %.4f, top-%d NDCG: %.4f, top-%d ACG: %.4f]',
record['param']['topk'], mapka, record['param']['topk'], mapkb,
record['param']['topp'], topp_ndcg, record['param']['topp'], topp_acg)
record['rB'] = rB
record['qB'] = qB
record['mapka'] = mapka
record['mapkb'] = mapkb
record['topp_ndcg'] = topp_ndcg
record['topp_acg'] = topp_acg
filename = os.path.join(logdir, str(code_length) + 'bits-record.pkl')
_save_record(record, filename)
import os
from PIL import Image
import pandas as pd
import torch
import torchvision.transforms as transforms
import utils.cnn_model as cnn_model
image_infos = []
image_codes = []
dir_path = "data/Project/images"
model = cnn_model.CNNNet('resnet50', 48)
model.load_state_dict(
torch.load('dict/adch-nuswide-48bits.pth',
map_location=torch.device('cpu')))
model.eval()
Image.MAX_IMAGE_PIXELS = 1000000000
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transformations = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
for img_name in os.listdir(dir_path):
img_path = os.path.join(dir_path, img_name)
img = Image.open(img_path)
width = img.size[0]
height = img.size[1]
img_info = [img_name, width, height]
image_infos.append(img_info)
data = image_infos
def dchuc_algo(code_length):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
torch.manual_seed(0)
torch.cuda.manual_seed(0)
'''
parameter setting
'''
max_iter = opt.max_iter
epochs = opt.epochs
batch_size = opt.batch_size
learning_rate = opt.learning_rate
learning_rate_txt = opt.learning_rate_txt
weight_decay = 5 * 10 ** -4
num_samples = opt.num_samples
gamma = opt.gamma
alpha = opt.alpha
beta = opt.beta
yita = opt.yita
mu = opt.mu
y_dim =opt.y_dim
path = '/data/nus'
'''
dataset preprocessing
'''
nums, dsets, labels = _dataset(path)
num_database, num_test = nums
dset_database, dset_database_txt, dset_test, dset_test_txt = dsets
database_labels, test_labels = labels
n_class = test_labels.size()[1]
testloader = DataLoader(dset_test, batch_size=1,
shuffle=False,
num_workers=4)
testloader_txt = DataLoader(dset_test_txt, batch_size=1,
shuffle=False,
num_workers=4)
'''
model construction
'''
model = cnn_model.CNNNet(opt.arch, code_length)
model.cuda()
model_txt = txt_model.TxtModule(y_dim, code_length)
model_txt.cuda()
adsh_loss = dl.DCHUCLoss(gamma, code_length, num_database, alpha, mu)
adsh_loss_txt = dl.DCHUCLoss(gamma, code_length, num_database, alpha, mu)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
optimizer_txt = optim.SGD(model_txt.parameters(), lr=learning_rate_txt, weight_decay=weight_decay)
V = np.zeros((num_database, code_length))
W = np.random.normal(loc=0.0, scale=0.01, size=(code_length, n_class))
model.train()
for iter in range(max_iter):
'''
sampling and construct similarity matrix
'''
select_index = list(np.random.permutation(range(num_database)))[0: num_samples]
_sampler = subsetsampler.SubsetSampler(select_index)
trainloader = DataLoader(dset_database, batch_size=batch_size,
sampler=_sampler,
shuffle=False,
num_workers=4)
trainloader_txt = DataLoader(dset_database_txt, batch_size=batch_size,
sampler=_sampler,
shuffle=False,
num_workers=4)
'''
learning deep neural network: feature learning
'''
sample_label = database_labels.index_select(0, torch.from_numpy(np.array(select_index)))
Sim = calc_sim(sample_label, database_labels)
S1 = calc_sim(sample_label, sample_label)
U = np.zeros((num_samples, code_length), dtype=np.float)
G = np.zeros((num_samples, code_length), dtype=np.float)
for epoch in range(epochs):
for zz in range(1):
for iteration, (train_input, train_label, batch_ind) in enumerate(trainloader):
batch_size_ = train_label.size(0)
u_ind = np.linspace(iteration * batch_size, np.min((num_samples, (iteration + 1) * batch_size)) - 1,
batch_size_, dtype=int)
train_input = Variable(train_input.cuda())
output = model(train_input)
S = Sim.index_select(0, torch.from_numpy(u_ind))
S_1 = S.index_select(1, torch.from_numpy(u_ind))
U[u_ind, :] = output.cpu().data.numpy()
model.zero_grad()
loss = adsh_loss(output, G[u_ind, :], V, S, S_1, V[batch_ind.cpu().numpy(), :],
Variable(torch.from_numpy(W).type(torch.FloatTensor).cuda()),
Variable(train_label.type(torch.FloatTensor).cuda()))
loss.backward()
optimizer.step()
for zz in range(1):
for iteration, (train_input, train_label, batch_ind) in enumerate(trainloader_txt):
batch_size_ = train_label.size(0)
u_ind = np.linspace(iteration * batch_size, np.min((num_samples, (iteration + 1) * batch_size)) - 1,
batch_size_, dtype=int)
train_input = train_input.unsqueeze(1).unsqueeze(-1).type(torch.FloatTensor)
train_input = Variable(train_input.cuda())
output = model_txt(train_input)
S = Sim.index_select(0, torch.from_numpy(u_ind))
S_1 = S.index_select(1, torch.from_numpy(u_ind))
G[u_ind, :] = output.cpu().data.numpy()
model_txt.zero_grad()
loss = adsh_loss_txt(output, U[u_ind, :], V, S, S_1, V[batch_ind.cpu().numpy(), :],
Variable(torch.from_numpy(W).type(torch.FloatTensor).cuda()),
Variable(train_label.type(torch.FloatTensor).cuda()))
loss.backward()
optimizer_txt.step()
adjusting_learning_rate(optimizer, iter)
adjusting_learning_rate(optimizer_txt, iter)
'''
learning binary codes: discrete coding
'''
barU = np.zeros((num_database, code_length))
barG = np.zeros((num_database, code_length))
barU[select_index, :] = U
barG[select_index, :] = G
Q = -2 * code_length * Sim.cpu().numpy().transpose().dot(U + G) - gamma * (barU + barG)\
- 2 * beta * database_labels.numpy().dot(W.transpose())
for k in range(code_length):
sel_ind = np.setdiff1d([ii for ii in range(code_length)], k)
V_ = V[:, sel_ind]
W_ = W.transpose()[:, sel_ind]
Wk = W.transpose()[:, k]
Uk = U[:, k]
Gk = G[:, k]
U_ = U[:, sel_ind]
G_ = G[:, sel_ind]
V[:, k] = -np.sign(Q[:, k] + 2 * V_.dot(U_.transpose().dot(Uk) + 2 * G_.transpose().dot(Gk)
+ beta * 2 * W_.transpose().dot(Wk)))
I = np.eye(code_length)
P = np.matrix(U.transpose().dot(U)) * alpha + alpha * np.matrix(G.transpose().dot(G))\
+ beta * np.matrix(V.transpose().dot(V)) + yita * I
PN = np.linalg.pinv(P)
BL = (alpha * barU + alpha * barG + beta * V).transpose().dot(database_labels.numpy())
W = np.asarray(PN.dot(BL))
lossx = calc_loss(W, sample_label, V, U, G, Sim.cpu().numpy(), S1.cpu().numpy(), code_length, select_index, gamma, yita, alpha, mu)
print('[Iteration: %3d/%3d][Train Loss: %.4f]' % (iter, max_iter, lossx))
'''
training procedure finishes, evaluation
'''
model.eval()
model_txt.eval()
qB = encode(model, testloader, num_test, code_length)
qB_txt = encode(model_txt, testloader_txt, num_test, code_length, istxt=True)
rB = V
map = calc_hr.calc_map(qB, rB, test_labels.numpy(), database_labels.numpy())
map_txt2img = calc_hr.calc_map(qB_txt, rB, test_labels.numpy(), database_labels.numpy())
print('[Evaluation: mAP_i2t: %.4f, mAP_txt2img: %.4f]' % (map, map_txt2img))