help='hyper parameter, default=50')
opt = parser.parse_args()
print(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpuid)
transformations = transforms.Compose([
transforms.Resize(opt.imagesize),
transforms.CenterCrop(opt.imagesize),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
print('pre-process data...')
dset_database = DP.DatasetProcessingCIFAR_10(opt.dataroot, opt.database,
opt.database_label,
transformations)
dset_test = DP.DatasetProcessingCIFAR_10(opt.dataroot, opt.test_file,
opt.test_label, transformations)
print('loading test data...')
num_database, num_test = len(dset_database), len(dset_test)
database_loader = DataLoader(dset_database,
batch_size=opt.batchsize,
shuffle=False,
num_workers=4)
test_loader = DataLoader(dset_test,
batch_size=opt.batchsize,
shuffle=False,
num_workers=4)
def DPSH_algo(bit, param, gpu_ind=0):
# parameters setting
# os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_ind)
DATA_DIR = '/home/gpuadmin/datasets/CIFAR-10'
DATABASE_FILE = 'database_img.txt'
TRAIN_FILE = 'train_img.txt'
TEST_FILE = 'test_img.txt'
DATABASE_LABEL = 'database_label.txt'
TRAIN_LABEL = 'train_label.txt'
TEST_LABEL = 'test_label.txt'
batch_size = 64
# batch_size = 256
# batch_size = 150
epochs = 200
learning_rate = 0.05
weight_decay = 10**-5
# model_name = 'APN'
# model_name = 'alexnet'
# model_name = 'resnet18'
# model_name = 'vgg11'
# model_name = 'resnet34'
model_name = 'resnet34resnet34'
# model_name = 'resnet50'
# model_name = 'dpn92'
# model_name = 'resnet50'
# model_name = 'resnet50_cbam'
# model_name = 'resnet18_cbam'
# model_name = 'resnet34_cbam'
# model_name = 'resnet18_pc'
# model_name = 'resnet34_pc'
# model_name = 'resnet101_cbam'
# model_name = 'resnet152_cbam'
print(model_name)
nclasses = 10
use_gpu = torch.cuda.is_available()
filename = param['filename']
lamda = param['lambda']
param['bit'] = bit
param['epochs'] = epochs
param['learning rate'] = learning_rate
param['model'] = model_name
### data processing
transformations = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dset_database = DP.DatasetProcessingCIFAR_10(DATA_DIR, DATABASE_FILE,
DATABASE_LABEL,
transformations)
dset_train = DP.DatasetProcessingCIFAR_10(DATA_DIR, TRAIN_FILE,
TRAIN_LABEL, transformations)
dset_test = DP.DatasetProcessingCIFAR_10(DATA_DIR, TEST_FILE, TEST_LABEL,
transformations)
num_database, num_train, num_test = len(dset_database), len(
dset_train), len(dset_test)
database_loader = DataLoader(dset_database,
batch_size=batch_size,
shuffle=False,
num_workers=4)
train_loader = DataLoader(dset_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
test_loader = DataLoader(dset_test,
batch_size=batch_size,
shuffle=False,
num_workers=4)
### create model
model = CreateModel(model_name, bit, use_gpu)
model = torch.nn.DataParallel(model).cuda()
# model = AlexNetPlusLatent(bit).cuda()
print(model)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
### training phase
# parameters setting
B = torch.zeros(num_train, bit)
U = torch.zeros(num_train, bit)
train_labels = LoadLabel(TRAIN_LABEL, DATA_DIR)
train_labels_onehot = EncodingOnehot(train_labels, nclasses)
test_labels = LoadLabel(TEST_LABEL, DATA_DIR)
test_labels_onehot = EncodingOnehot(test_labels, nclasses)
train_loss = []
map_record = []
totloss_record = []
totl1_record = []
totl2_record = []
t1_record = []
softmaxloss = torch.nn.CrossEntropyLoss().cuda()
Sim = CalcSim(train_labels_onehot, train_labels_onehot)
for epoch in range(epochs):
epoch_loss = 0.0
## training epoch
for iter, traindata in enumerate(train_loader, 0):
train_input, train_label, batch_ind = traindata
train_label = torch.squeeze(train_label)
if use_gpu:
train_label_onehot = EncodingOnehot(train_label, nclasses)
train_input, train_label = Variable(
train_input.cuda()), Variable(train_label.cuda())
S = CalcSim(train_label_onehot, train_labels_onehot)
else:
train_label_onehot = EncodingOnehot(train_label, nclasses)
train_input, train_label = Variable(train_input), Variable(
train_label)
S = CalcSim(train_label_onehot, train_labels_onehot)
model.zero_grad()
train_outputs = model(train_input)
for i, ind in enumerate(batch_ind):
U[ind, :] = train_outputs.data[i]
B[ind, :] = torch.sign(train_outputs.data[i])
Bbatch = torch.sign(train_outputs)
# Bbatch1 = torch.sign(ym)
if use_gpu:
theta_x = train_outputs.mm(Variable(U.cuda()).t()) / 2
logloss = (Variable(S.cuda())*theta_x - Logtrick(theta_x, use_gpu)).sum() \
/ (num_train * len(train_label))
regterm = (Bbatch - train_outputs).pow(2).sum() / (
num_train * len(train_label)
) #+(Bbatch1-ym).pow(2).sum() / (num_train * len(train_label))
else:
theta_x = train_outputs.mm(Variable(U).t()) / 2
logloss = (Variable(S)*theta_x - Logtrick(theta_x, use_gpu)).sum() \
/ (num_train * len(train_label))
regterm = (Bbatch - train_outputs).pow(2).sum() / (
num_train * len(train_label))
# l2loss = softmaxloss(c,train_label)#+ softmaxloss(cm,train_label)
# Qloss = torch.mean((torch.abs(train_outputs) - 1) ** 2)
# loss1 = nn.BCELoss()
# output1 = loss1(nn.Sigmoid(c), train_label)
# loss2 = nn.L1Loss()
# output2 = loss2(c.cuda(), train_label)
# loss3 = nn.MSELoss()
# output3 = loss3(c.cuda(), train_label)
# criterion_xent = nn.CrossEntropyLoss()
# # criterion_cent = CenterLoss(nclasses, feat_dim=bit, use_gpu=use_gpu)
# FocalLoss_loss = FocalLoss(nclasses, use_gpu)
# loss_xent = criterion_xent(c, train_label)
# loss_cent = criterion_cent(train_outputs, train_label)
Qloss = torch.mean((torch.abs(train_outputs) - 1)**2) + torch.mean(
(torch.abs(Bbatch) - 1)**2
) #+torch.mean((torch.abs(ym) - 1) ** 2)+torch.mean((torch.abs(Bbatch1) - 1) ** 2) # +torch.mean((torch.abs(Bbatch - train_outputs) ) ** 2)
loss = -logloss + lamda * regterm #+l2loss+Qloss # + FocalLoss_loss # # +loss_xent+loss_cent#+loss2+loss3#+loss1#
loss.backward()
optimizer.step()
epoch_loss += loss.item()
# print('[Training Phase][Epoch: %3d/%3d][Iteration: %3d/%3d] Loss: %3.5f' % \
# (epoch + 1, epochs, iter + 1, np.ceil(num_train / batch_size),loss.data[0]))
print('[Train Phase][Epoch: %3d/%3d][Loss: %3.5f]' %
(epoch + 1, epochs, epoch_loss / len(train_loader)),
end='')
optimizer = AdjustLearningRate(optimizer, epoch, learning_rate)
l, l1, l2, t1 = Totloss(U, B, Sim, lamda, num_train)
totloss_record.append(l)
totl1_record.append(l1)
totl2_record.append(l2)
t1_record.append(t1)
print(
'[Total Loss: %10.5f][total L1: %10.5f][total L2: %10.5f][norm theta: %3.5f]'
% (l, l1, l2, t1),
end='')
### testing during epoch
qB = GenerateCode(model, test_loader, num_test, bit, use_gpu)
#tB = torch.sign(B).numpy()
tB = GenerateCode(model, train_loader, num_train, bit, use_gpu)
map_ = CalcHR.CalcTopMap(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy(), 5000)
train_loss.append(epoch_loss / len(train_loader))
map_record.append(map_)
print('[Test Phase ][Epoch: %3d/%3d] MAP(retrieval train): %3.5f' %
(epoch + 1, epochs, map_))
print(len(train_loader))
### evaluation phase
## create binary code
model.eval()
database_labels = LoadLabel(DATABASE_LABEL, DATA_DIR)
database_labels_onehot = EncodingOnehot(database_labels, nclasses)
qB = GenerateCode(model, test_loader, num_test, bit, use_gpu)
dB = GenerateCode(model, database_loader, num_database, bit, use_gpu)
map = CalcHR.CalcTopMap(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy(), 5000)
print('[Retrieval Phase] MAP(retrieval database): %3.5f' % map)
# print('[Retrieval Phase] MAP(retrieval database): %3.5f' % map)
test_binary = 0.5 * (qB + 1)
database_binary = 0.5 * (dB + 1)
test_labels = LoadLabel(TEST_LABEL, DATA_DIR)
database_labels = LoadLabel(DATABASE_LABEL, DATA_DIR)
sum_p, sum_r = precision(database_binary, database_labels, test_binary,
test_labels)
if not os.path.isdir('result/' + str(bit)):
os.mkdir('result/' + str(bit))
np.savetxt('./result' + '/' + str(bit) + '/' + 'sum_p.txt',
sum_p,
fmt='%3.5f')
np.savetxt('./result' + '/' + str(bit) + '/' + 'sum_r.txt',
sum_r,
fmt='%3.5f')
np.savetxt('./result' + '/' + str(bit) + '/' + 'map',
map.reshape(1, -1),
fmt='%3f')
result = {}
result['qB'] = qB
result['dB'] = dB
result['train loss'] = train_loss
result['map record'] = map_record
result['map'] = map
result['param'] = param
result['total loss'] = totloss_record
result['l1 loss'] = totl1_record
result['l2 loss'] = totl2_record
result['norm theta'] = t1_record
result['filename'] = filename
# if not os.path.isdir('result'):
# os.mkdir('result')
# torch.save(qB, './result/qB.txt')
# torch.save(dB, './result/dB.txt')
# torch.save(map, './result/map.txt')
# np.savetxt('./result/qB.txt', qB,fmt='%d',delimiter=',')
return result
def DPSH_algo(bit, param, gpu_ind=0):
# parameters setting
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_ind)
DATA_DIR = 'data/CIFAR-10'
DATABASE_FILE = 'database_img.txt'
TRAIN_FILE = 'train_img.txt'
TEST_FILE = 'test_img.txt'
DATABASE_LABEL = 'database_label.txt'
TRAIN_LABEL = 'train_label.txt'
TEST_LABEL = 'test_label.txt'
batch_size = 128
epochs = 150
learning_rate = 0.05
weight_decay = 10**-5
model_name = 'alexnet'
nclasses = 10
use_gpu = torch.cuda.is_available()
filename = param['filename']
lamda = param['lambda']
param['bit'] = bit
param['epochs'] = epochs
param['learning rate'] = learning_rate
param['model'] = model_name
### data processing
transformations = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dset_database = DP.DatasetProcessingCIFAR_10(DATA_DIR, DATABASE_FILE,
DATABASE_LABEL,
transformations)
dset_train = DP.DatasetProcessingCIFAR_10(DATA_DIR, TRAIN_FILE,
TRAIN_LABEL, transformations)
dset_test = DP.DatasetProcessingCIFAR_10(DATA_DIR, TEST_FILE, TEST_LABEL,
transformations)
num_database, num_train, num_test = len(dset_database), len(
dset_train), len(dset_test)
database_loader = DataLoader(dset_database,
batch_size=batch_size,
shuffle=False,
num_workers=4)
train_loader = DataLoader(dset_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
test_loader = DataLoader(dset_test,
batch_size=batch_size,
shuffle=False,
num_workers=4)
### create model
model = CreateModel(model_name, bit, use_gpu)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
### training phase
# parameters setting
B = torch.zeros(num_train, bit)
U = torch.zeros(num_train, bit)
train_labels = LoadLabel(TRAIN_LABEL, DATA_DIR)
train_labels_onehot = EncodingOnehot(train_labels, nclasses)
test_labels = LoadLabel(TEST_LABEL, DATA_DIR)
test_labels_onehot = EncodingOnehot(test_labels, nclasses)
train_loss = []
map_record = []
totloss_record = []
totl1_record = []
totl2_record = []
t1_record = []
Sim = CalcSim(train_labels_onehot, train_labels_onehot)
for epoch in range(epochs):
epoch_loss = 0.0
## training epoch
for iter, traindata in enumerate(train_loader, 0):
train_input, train_label, batch_ind = traindata
train_label = torch.squeeze(train_label)
if use_gpu:
train_label_onehot = EncodingOnehot(train_label, nclasses)
train_input, train_label = Variable(
train_input.cuda()), Variable(train_label.cuda())
S = CalcSim(train_label_onehot, train_labels_onehot)
else:
train_label_onehot = EncodingOnehot(train_label, nclasses)
train_input, train_label = Variable(train_input), Variable(
train_label)
S = CalcSim(train_label_onehot, train_labels_onehot)
model.zero_grad()
train_outputs = model(train_input)
for i, ind in enumerate(batch_ind):
U[ind, :] = train_outputs.data[i]
B[ind, :] = torch.sign(train_outputs.data[i])
Bbatch = torch.sign(train_outputs)
if use_gpu:
theta_x = train_outputs.mm(Variable(U.cuda()).t()) / 2
logloss = (Variable(S.cuda())*theta_x - Logtrick(theta_x, use_gpu)).sum() \
/ (num_train * len(train_label))
regterm = (Bbatch - train_outputs).pow(2).sum() / (
num_train * len(train_label))
else:
theta_x = train_outputs.mm(Variable(U).t()) / 2
logloss = (Variable(S)*theta_x - Logtrick(theta_x, use_gpu)).sum() \
/ (num_train * len(train_label))
regterm = (Bbatch - train_outputs).pow(2).sum() / (
num_train * len(train_label))
loss = -logloss + lamda * regterm
loss.backward()
optimizer.step()
epoch_loss += loss.data[0]
# print('[Training Phase][Epoch: %3d/%3d][Iteration: %3d/%3d] Loss: %3.5f' % \
# (epoch + 1, epochs, iter + 1, np.ceil(num_train / batch_size),loss.data[0]))
print('[Train Phase][Epoch: %3d/%3d][Loss: %3.5f]' %
(epoch + 1, epochs, epoch_loss / len(train_loader)),
end='')
optimizer = AdjustLearningRate(optimizer, epoch, learning_rate)
l, l1, l2, t1 = Totloss(U, B, Sim, lamda, num_train)
totloss_record.append(l)
totl1_record.append(l1)
totl2_record.append(l2)
t1_record.append(t1)
print(
'[Total Loss: %10.5f][total L1: %10.5f][total L2: %10.5f][norm theta: %3.5f]'
% (l, l1, l2, t1),
end='')
### testing during epoch
qB = GenerateCode(model, test_loader, num_test, bit, use_gpu)
tB = torch.sign(B).numpy()
map_ = CalcHR.CalcMap(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy())
train_loss.append(epoch_loss / len(train_loader))
map_record.append(map_)
print('[Test Phase ][Epoch: %3d/%3d] MAP(retrieval train): %3.5f' %
(epoch + 1, epochs, map_))
print(len(train_loader))
### evaluation phase
## create binary code
model.eval()
database_labels = LoadLabel(DATABASE_LABEL, DATA_DIR)
database_labels_onehot = EncodingOnehot(database_labels, nclasses)
qB = GenerateCode(model, test_loader, num_test, bit, use_gpu)
dB = GenerateCode(model, database_loader, num_database, bit, use_gpu)
map = CalcHR.CalcMap(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy())
print('[Retrieval Phase] MAP(retrieval database): %3.5f' % map)
result = {}
result['qB'] = qB
result['dB'] = dB
result['train loss'] = train_loss
result['map record'] = map_record
result['map'] = map
result['param'] = param
result['total loss'] = totloss_record
result['l1 loss'] = totl1_record
result['l2 loss'] = totl2_record
result['norm theta'] = t1_record
result['filename'] = filename
return result
def DenseHash_RF_algo(bit, param, gpu_ind=0):
# parameters setting
#os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_indi)
#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
#DATA_DIR = 'data/CIFAR-10'
DATA_DIR = '/home/zhangjingyi/res/DPSH-pytorch/data/CIFAR-10'
DATABASE_FILE = 'database_img.txt'
TRAIN_FILE = 'train_img.txt'
TEST_FILE = 'test_img.txt'
DATABASE_LABEL = 'database_label.txt'
TRAIN_LABEL = 'train_label.txt'
TEST_LABEL = 'test_label.txt'
batch_size = 64
epochs = 30
learning_rate = 0.003
weight_decay = 10**-5
model_name = 'vgg16'
#model_name = 'alexnet'
nclasses = 10
use_gpu = torch.cuda.is_available()
filename = param['filename']
print('pkl file name %s' % filename)
lamda = param['lambda']
param['bit'] = bit
param['epochs'] = epochs
param['learning rate'] = learning_rate
param['model'] = model_name
print('**********************************')
print('Solver Settings:')
print(param)
### data processing
transformations = transforms.Compose([
transforms.Scale(256),
#transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dset_database = DP.DatasetProcessingCIFAR_10(DATA_DIR, DATABASE_FILE,
DATABASE_LABEL,
transformations)
dset_train = DP.DatasetProcessingCIFAR_10(DATA_DIR, TRAIN_FILE,
TRAIN_LABEL, transformations)
dset_test = DP.DatasetProcessingCIFAR_10(DATA_DIR, TEST_FILE, TEST_LABEL,
transformations)
num_database, num_train, num_test = len(dset_database), len(
dset_train), len(dset_test)
database_loader = DataLoader(dset_database,
batch_size=batch_size,
shuffle=False,
num_workers=4)
train_loader = DataLoader(dset_train,
batch_size=batch_size,
shuffle=True,
num_workers=4)
test_loader = DataLoader(dset_test,
batch_size=batch_size,
shuffle=False,
num_workers=4)
### create model
model = CreateModel(model_name, bit, use_gpu)
optimizer = optim.SGD(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
### training phase
# parameters setting
B = torch.sign(torch.randn(num_train, bit))
U = torch.sign(torch.randn(num_train, bit))
train_labels = LoadLabel(TRAIN_LABEL, DATA_DIR)
#print(train_labels)
train_labels_onehot = EncodingOnehot(train_labels, nclasses)
test_labels = LoadLabel(TEST_LABEL, DATA_DIR)
test_labels_onehot = EncodingOnehot(test_labels, nclasses)
Y = train_labels_onehot
#Sim = CalcSim(train_labels_onehot, train_labels_onehot)
#file = open(filename.replace('snapshot/','log/').replace('.pkl','.log'),'a')
for epoch in range(epochs):
model.train()
start_time = time.time()
epoch_loss = 0.0
# D step
temp1 = Y.t().mm(Y) + torch.eye(nclasses)
temp1 = temp1.inverse()
temp1 = temp1.mm(Y.t())
D = temp1.mm(B)
# B step
B = torch.sign(Y.mm(D) + 1e-5 * U)
print('[Epoch %3d B step time cost: %3.5fs]' %
(epoch + 1, time.time() - start_time))
# F step
## training epoch
ave_iter_loss = 0.0
for iter, traindata in enumerate(train_loader, 0):
iter_timer = time.time()
train_input, train_label, batch_ind = traindata
train_input = Variable(train_input.cuda())
model.zero_grad()
train_outputs, f = model(train_input)
#print(train_outputs.size())
temp = torch.zeros(train_outputs.data.size())
for i, ind in enumerate(batch_ind):
temp[i, :] = B[ind, :]
U[ind, :] = train_outputs.data[i]
temp = Variable(temp.cuda())
loss = (temp - train_outputs).pow(2).sum() / (batch_size)
loss.backward()
optimizer.step()
epoch_loss += loss.data[0]
ave_iter_loss += loss.data[0]
if iter % 20 == 0:
print('[Iteration %d][%3.2fs/iter][Iter Loss: %3.5f]' %
(iter + epoch * len(train_loader),
time.time() - iter_timer, ave_iter_loss / 20))
ave_iter_loss = 0
print('[Train Phase][Epoch: %3d/%3d][Loss: %3.5f]' %
(epoch + 1, epochs, epoch_loss / len(train_loader)))
optimizer = AdjustLearningRate(optimizer, epoch, learning_rate)
### testing during epoch
test_timer = time.time()
model.eval()
qB, f = GenerateCode(model, test_loader, num_test, bit, use_gpu)
tB = torch.sign(U).numpy()
f = f.cpu().data.numpy()
np.save('f.npy', f)
map_ = CalcHR.CalcMap(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy())
print('[Test Phase ][Epoch: %3d/%3d] MAP(retrieval train): %3.5f' %
(epoch + 1, epochs, map_))
map_topk = CalcHR.CalcTopMap(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy(), 50)
print(
'[Test Phase ][Epoch: %3d/%3d] MAP@top50(retrieval train): %3.5f' %
(epoch + 1, epochs, map_topk))
map_topk = CalcHR.CalcTopMap(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy(), 500)
print(
'[Test Phase ][Epoch: %3d/%3d] MAP@top500(retrieval train): %3.5f'
% (epoch + 1, epochs, map_topk))
acc_topk = CalcHR.CalcTopAcc(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy(), 50)
print(
'[Test Phase ][Epoch: %3d/%3d] Precision@top50(retrieval train): %3.5f'
% (epoch + 1, epochs, acc_topk))
acc_topk = CalcHR.CalcTopAcc(qB, tB, test_labels_onehot.numpy(),
train_labels_onehot.numpy(), 500)
print(
'[Test Phase ][Epoch: %3d/%3d] Precision@top500(retrieval train): %3.5f'
% (epoch + 1, epochs, acc_topk))
print('[Test time cost: %d]' % (time.time() - test_timer))
print('[Epoch %3d time cost: %ds]' %
(epoch + 1, time.time() - start_time))
### evaluation phase
## create binary code
if (epoch + 1) % epochs == 0:
eval_timer = time.time()
model.eval()
database_labels = LoadLabel(DATABASE_LABEL, DATA_DIR)
database_labels_onehot = EncodingOnehot(database_labels, nclasses)
qB, _ = GenerateCode(model, test_loader, num_test, bit, use_gpu)
dB, _ = GenerateCode(model, database_loader, num_database, bit,
use_gpu)
map = CalcHR.CalcMap(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy())
print('[Retrieval Phase] MAP(retrieval database): %3.5f' % map)
map_topk = CalcHR.CalcTopMap(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy(), 500)
print('[Retrieval Phase] MAP@500(retrieval database): %3.5f' %
map_topk)
map_topk = CalcHR.CalcTopMap(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy(), 5000)
print('[Retrieval Phase] MAP@5000(retrieval database): %3.5f' %
map_topk)
acc_topk = CalcHR.CalcTopAcc(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy(), 500)
print(
'[Retrieval Phase] Precision@500(retrieval database): %3.5f' %
acc_topk)
acc_topk = CalcHR.CalcTopAcc(qB, dB, test_labels_onehot.numpy(),
database_labels_onehot.numpy(), 5000)
print(
'[Retrieval Phase] Precision@5000(retrieval database): %3.5f' %
acc_topk)
print('[Eval time: %ds]' % (time.time() - eval_timer))
## save trained model
torch.save(model.state_dict(), filename)
result = {}
result['qB'] = qB
result['dB'] = dB
result['map'] = map
return result