def _dataset():
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
])
dset_database = dp.DatasetProcessingCIFAR_10('data/CIFAR-10',
'database_img.txt',
'database_label.txt',
transformations)
dset_test = dp.DatasetProcessingCIFAR_10('data/CIFAR-10', 'test_img.txt',
'test_label.txt', transformations)
num_database, num_test = len(dset_database), len(dset_test)
def load_label(filename, DATA_DIR):
path = os.path.join(DATA_DIR, filename)
fp = open(path, 'r')
labels = [x.strip() for x in fp]
fp.close()
return torch.LongTensor(list(map(int, labels)))
testlabels = load_label('test_label.txt', 'data/CIFAR-10')
databaselabels = load_label('database_label.txt', 'data/CIFAR-10')
testlabels = encoding_onehot(testlabels)
databaselabels = encoding_onehot(databaselabels)
dsets = (dset_database, dset_test)
nums = (num_database, num_test)
labels = (databaselabels, testlabels)
return nums, dsets, labels
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