def get_attr_ims(attr, num=10):
ids = prep.get_attr(attr_map, id_attr_map, attr)
dataset = prep.ImageDiskLoader(ids)
indices = np.random.randint(0, len(dataset), num)
ims = [dataset[i] for i in indices]
idx_ids = [dataset.im_ids[i] for i in indices]
return ims, idx_ids
LOG_PATH = './logs/log.pkl'
MODEL_PATH = './checkpoints/'
COMPARE_PATH = './comparisons/'
use_cuda = USE_CUDA and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print('Using device', device)
print('num cpus:', multiprocessing.cpu_count())
print(torch.cuda.is_available())
# training code
train_ids, test_ids = prep.split_dataset()
print('num train_images:', len(train_ids))
print('num test_images:', len(test_ids))
data_train = prep.ImageDiskLoader(train_ids)
data_test = prep.ImageDiskLoader(test_ids)
print(data_train)
kwargs = {
'num_workers': multiprocessing.cpu_count(),
'pin_memory': True
} if use_cuda else {}
#train_loader = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=True, **kwargs)
train_loader = torch.utils.data.DataLoader(x_train_new,
batch_size=BATCH_SIZE,
shuffle=True,
**kwargs)
#test_loader = torch.utils.data.DataLoader(data_test, batch_size=TEST_BATCH_SIZE, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(x_test_new,