def load_syn(scale=True, usps=False, all_use=False): syn_data = loadmat(base_dir + '/syn_number.mat') syn_train = syn_data['train_data'] syn_test = syn_data['test_data'] syn_train = syn_train.transpose(0, 3, 1, 2).astype(np.float32) syn_test = syn_test.transpose(0, 3, 1, 2).astype(np.float32) syn_labels_train = syn_data['train_label'] syn_labels_test = syn_data['test_label'] train_label = syn_labels_train inds = np.random.permutation(syn_train.shape[0]) syn_train = syn_train[inds] train_label = train_label[inds] test_label = syn_labels_test # syn_train = syn_train[:25000] # train_label = train_label[:25000] # syn_test = syn_test[:9000] # test_label = test_label[:9000] train_label = dense_to_one_hot(train_label) test_label = dense_to_one_hot(test_label) print('syn number train X shape->', syn_train.shape) print('syn number train y shape->', train_label.shape) print('syn number test X shape->', syn_test.shape) print('syn number test y shape->', test_label.shape) return syn_train, train_label, syn_test, test_label
def load_syn(base_dir, scale=32): syn_train = loadmat(base_dir + '/synth_train_{}x{}.mat'.format(scale, scale)) syn_test = loadmat(base_dir + '/synth_test_{}x{}.mat'.format(scale, scale)) # train syn_train_im = syn_train['X'] syn_train_im = syn_train_im.transpose(3, 2, 0, 1).astype(np.float32) train_label = dense_to_one_hot(syn_train['y'].squeeze()) # idx = np.random.permutation(syn_train_im.shape[0]) # syn_train_im = syn_train_im[idx] # train_label = train_label[idx] # syn_train_im = syn_train_im[:25000] # train_label = train_label[:25000] # test syn_test_im = syn_test['X'] syn_test_im = syn_test_im.transpose(3, 2, 0, 1).astype(np.float32) test_label = dense_to_one_hot(syn_test['y'].squeeze()) # syn_test_im = syn_test_im[:9000] # test_label = test_label[:9000] print('syn number train X shape->', syn_train_im.shape) print('syn number train y shape->', train_label.shape) print('syn number test X shape->', syn_test_im.shape) print('syn number test y shape->', test_label.shape) print("====================") return syn_train_im, train_label, syn_test_im, test_label
def load_usps(base_dir): dataset = loadmat(base_dir + '/usps_28x28.mat') data_set = dataset['dataset'] img_train = data_set[0][0] label_train = data_set[0][1] img_test = data_set[1][0] label_test = data_set[1][1] inds = np.random.permutation(img_train.shape[0]) img_train = img_train[inds] label_train = label_train[inds] img_train = img_train * 255 img_test = img_test * 255 img_train = img_train.reshape((img_train.shape[0], 1, 28, 28)) img_test = img_test.reshape((img_test.shape[0], 1, 28, 28)) label_train = dense_to_one_hot(label_train) label_test = dense_to_one_hot(label_test) img_train = np.concatenate([img_train, img_train, img_train], 1) img_test = np.concatenate([img_test, img_test, img_test], 1) print('usps train X shape->', img_train.shape) print('usps train y shape->', label_train.shape) print('usps test X shape->', img_test.shape) print('usps test y shape->', label_test.shape) print("====================") return img_train, label_train, img_test, label_test
def load_svhn(base_dir, scale=32): svhn_train = loadmat(base_dir + '/svhn_train_{}x{}.mat'.format(scale, scale)) svhn_test = loadmat(base_dir + '/svhn_test_{}x{}.mat'.format(scale, scale)) svhn_train_im = svhn_train['X'] svhn_train_im = svhn_train_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label = dense_to_one_hot(svhn_train['y']) # sample train set idx = np.random.permutation(svhn_train_im.shape[0]) svhn_train_im = svhn_train_im[idx] svhn_label = svhn_label[idx] svhn_train_im = svhn_train_im[:25000] svhn_label = svhn_label[:25000] svhn_test_im = svhn_test['X'] svhn_test_im = svhn_test_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label_test = dense_to_one_hot(svhn_test['y']) # sample test set svhn_test_im = svhn_test_im[:14459] svhn_label_test = svhn_label_test[:14459] print('svhn train X shape->', svhn_train_im.shape) print('svhn train y shape->', svhn_label.shape) print('svhn test X shape->', svhn_test_im.shape) print('svhn test y shape->', svhn_label_test.shape) print("====================") return svhn_train_im, svhn_label, svhn_test_im, svhn_label_test
def load_usps(directory,all_use=False): base_dir = directory #f = gzip.open('data/usps_28x28.pkl', 'rb') #data_set = pickle.load(f) #f.close() dataset = loadmat(base_dir + '/usps_28x28.mat') data_set = dataset['dataset'] img_train = data_set[0][0] label_train = data_set[0][1] img_test = data_set[1][0] label_test = data_set[1][1] inds = np.random.permutation(img_train.shape[0]) img_train = img_train[inds] label_train = label_train[inds] img_train = img_train * 255 img_test = img_test * 255 img_train = img_train.reshape((img_train.shape[0], 1, 28, 28)) img_test = img_test.reshape((img_test.shape[0], 1, 28, 28)) #img_test = dense_to_one_hot(img_test) label_train = dense_to_one_hot(label_train) label_test = dense_to_one_hot(label_test) img_train = np.concatenate([img_train, img_train, img_train, img_train], 0) label_train = np.concatenate([label_train, label_train, label_train, label_train], 0) print('usps train X shape->', img_train.shape) print('usps train y shape->', label_train.shape) print('usps test X shape->', img_test.shape) print('usps test y shape->', label_test.shape) return img_train, label_train, img_test, label_test
def load_svhn(): svhn_train = loadmat('../data/train_32x32.mat') svhn_test = loadmat('../data/test_32x32.mat') svhn_train_im = svhn_train['X'] svhn_train_im = svhn_train_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label = dense_to_one_hot(svhn_train['y']) svhn_test_im = svhn_test['X'] svhn_test_im = svhn_test_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label_test = dense_to_one_hot(svhn_test['y']) return svhn_train_im, svhn_label, svhn_test_im, svhn_label_test
def load_syn(scale=True, usps=False, all_use=False): syn_train = loadmat(base_dir + '/synth_train_28x28.mat') syn_test = loadmat(base_dir + '/synth_test_28x28.mat') syn_train_im = syn_train['X'] syn_train_im = syn_train_im.transpose(3, 2, 0, 1).astype(np.float32) train_label = dense_to_one_hot(syn_train['y']) syn_test_im = syn_test['X'] syn_test_im = syn_test_im.transpose(3, 2, 0, 1).astype(np.float32) test_label = dense_to_one_hot(syn_test['y']) print('syn number train X shape->', syn_train_im.shape) print('syn number train y shape->', train_label.shape) print('syn number test X shape->', syn_test_im.shape) print('syn number test y shape->', test_label.shape) return syn_train_im, train_label, syn_test_im, test_label
def load_svhn(): svhn_train = loadmat(base_dir + '/svhn_train_28x28.mat') svhn_test = loadmat(base_dir + '/svhn_test_28x28.mat') svhn_train_im = svhn_train['X'] svhn_train_im = svhn_train_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label = dense_to_one_hot(svhn_train['y']) svhn_test_im = svhn_test['X'] svhn_test_im = svhn_test_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label_test = dense_to_one_hot(svhn_test['y']) print('svhn train X shape->', svhn_train_im.shape) print('svhn train y shape->', svhn_label.shape) print('svhn test X shape->', svhn_test_im.shape) print('svhn test y shape->', svhn_label_test.shape) return svhn_train_im, svhn_label, svhn_test_im, svhn_label_test
def load_svhn(): svhn_train = loadmat(base_dir + '/train_32x32.mat') svhn_test = loadmat(base_dir + '/test_32x32.mat') svhn_train_im = svhn_train['X'] svhn_train_im = svhn_train_im.transpose(3, 2, 0, 1).astype(np.float32) print('svhn train y shape before dense_to_one_hot->', svhn_train['y'].shape) svhn_label = dense_to_one_hot(svhn_train['y']) print('svhn train y shape after dense_to_one_hot->', svhn_label.shape) svhn_test_im = svhn_test['X'] svhn_test_im = svhn_test_im.transpose(3, 2, 0, 1).astype(np.float32) svhn_label_test = dense_to_one_hot(svhn_test['y']) svhn_train_im = svhn_train_im[:25000] svhn_label = svhn_label[:25000] svhn_test_im = svhn_test_im[:9000] svhn_label_test = svhn_label_test[:9000] print('svhn train X shape->', svhn_train_im.shape) print('svhn train y shape->', svhn_label.shape) print('svhn test X shape->', svhn_test_im.shape) print('svhn test y shape->', svhn_label_test.shape) return svhn_train_im, svhn_label, svhn_test_im, svhn_label_test