def test_bath_afterPadded(): data1 = [{ 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }] data2 = [{ 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(1, np.uint8) }] ds1 = ds.PaddedDataset(data1) ds2 = ds.PaddedDataset(data2) ds3 = ds1 + ds2 testsampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=False, num_samples=None) ds3.use_sampler(testsampler) ds4 = ds3.batch(2) assert sum([1 for _ in ds4]) == 2
def test_raise_error(): data1 = [{'image': np.zeros(1, np.uint8)}, {'image': np.zeros(2, np.uint8)}, {'image': np.zeros(3, np.uint8)}, {'image': np.zeros(4, np.uint8)}, {'image': np.zeros(5, np.uint8)}] data2 = [{'image': np.zeros(6, np.uint8)}, {'image': np.zeros(7, np.uint8)}, {'image': np.zeros(8, np.uint8)}] ds1 = ds.PaddedDataset(data1) ds4 = ds1.batch(2) ds2 = ds.PaddedDataset(data2) ds3 = ds4 + ds2 with pytest.raises(TypeError) as excinfo: testsampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=False, num_samples=None) ds3.use_sampler(testsampler) assert excinfo.type == 'TypeError' with pytest.raises(TypeError) as excinfo: otherSampler = ds.SequentialSampler() ds3.use_sampler(otherSampler) assert excinfo.type == 'TypeError' with pytest.raises(ValueError) as excinfo: testsampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=True, num_samples=None) ds3.use_sampler(testsampler) assert excinfo.type == 'ValueError' with pytest.raises(ValueError) as excinfo: testsampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=False, num_samples=5) ds3.use_sampler(testsampler) assert excinfo.type == 'ValueError'
def test_Mindrecord_Padded(remove_mindrecord_file): result_list = [] verify_list = [[1, 2], [3, 4], [5, 11], [6, 12], [7, 13], [8, 14], [9], [10]] num_readers = 4 data_set = ds.MindDataset(CV_FILE_NAME + "0", ['file_name'], num_readers, shuffle=False) data1 = [{ 'file_name': np.array(b'image_00011.jpg', dtype='|S15') }, { 'file_name': np.array(b'image_00012.jpg', dtype='|S15') }, { 'file_name': np.array(b'image_00013.jpg', dtype='|S15') }, { 'file_name': np.array(b'image_00014.jpg', dtype='|S15') }] ds1 = ds.PaddedDataset(data1) ds2 = data_set + ds1 shard_num = 8 for i in range(shard_num): testsampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None) ds2.use_sampler(testsampler) tem_list = [] for ele in ds2.create_dict_iterator(): tem_list.append( int(ele['file_name'].tostring().decode().lstrip( 'image_').rstrip('.jpg'))) result_list.append(tem_list) assert result_list == verify_list
def test_imagefolder_padded_with_decode_and_get_dataset_size(): num_shards = 5 count = 0 for shard_id in range(num_shards): DATA_DIR = "../data/dataset/testPK/data" data = ds.ImageFolderDatasetV2(DATA_DIR) white_io = BytesIO() Image.new('RGB', (224, 224), (255, 255, 255)).save(white_io, 'JPEG') padded_sample = {} padded_sample['image'] = np.array(bytearray(white_io.getvalue()), dtype='uint8') padded_sample['label'] = np.array(-1, np.int32) white_samples = [ padded_sample, padded_sample, padded_sample, padded_sample ] data2 = ds.PaddedDataset(white_samples) data3 = data + data2 testsampler = ds.DistributedSampler(num_shards=num_shards, shard_id=shard_id, shuffle=False, num_samples=None) data3.use_sampler(testsampler) shard_dataset_size = data3.get_dataset_size() data3 = data3.map(input_columns="image", operations=V_C.Decode()) shard_sample_count = 0 for ele in data3.create_dict_iterator(): print("label: {}".format(ele['label'])) count += 1 shard_sample_count += 1 assert shard_sample_count in (9, 10) assert shard_dataset_size == shard_sample_count assert count == 48
def test_imagefolder_error(): DATA_DIR = "../data/dataset/testPK/data" data = ds.ImageFolderDataset(DATA_DIR, num_samples=14) data1 = [{ 'image': np.zeros(1, np.uint8), 'label': np.array(0, np.int32) }, { 'image': np.zeros(2, np.uint8), 'label': np.array(1, np.int32) }, { 'image': np.zeros(3, np.uint8), 'label': np.array(0, np.int32) }, { 'image': np.zeros(4, np.uint8), 'label': np.array(1, np.int32) }, { 'image': np.zeros(5, np.uint8), 'label': np.array(0, np.int32) }, { 'image': np.zeros(6, np.uint8), 'label': np.array(1, np.int32) }] data2 = ds.PaddedDataset(data1) data3 = data + data2 with pytest.raises(ValueError) as excinfo: testsampler = ds.DistributedSampler(num_shards=5, shard_id=4, shuffle=False, num_samples=None) data3.use_sampler(testsampler) assert excinfo.type == 'ValueError'
def test_more_shard_padded(): result_list = [] for i in range(8): result_list.append(1) result_list.append(0) data1 = ds.GeneratorDataset(generator_5, ["col1"]) data2 = ds.GeneratorDataset(generator_8, ["col1"]) data3 = data1 + data2 vertifyList = [] numShard = 9 for i in range(numShard): tem_list = [] testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None) data3.use_sampler(testsampler) for item in data3.create_dict_iterator(): tem_list.append(item['col1']) vertifyList.append(tem_list) assert [len(ele) for ele in vertifyList] == result_list vertifyList1 = [] result_list1 = [] for i in range(8): result_list1.append([i+1]) result_list1.append([]) data1 = [{'image': np.zeros(1, np.uint8)}, {'image': np.zeros(2, np.uint8)}, {'image': np.zeros(3, np.uint8)}, {'image': np.zeros(4, np.uint8)}, {'image': np.zeros(5, np.uint8)}] data2 = [{'image': np.zeros(6, np.uint8)}, {'image': np.zeros(7, np.uint8)}, {'image': np.zeros(8, np.uint8)}] ds1 = ds.PaddedDataset(data1) ds2 = ds.PaddedDataset(data2) ds3 = ds1 + ds2 for i in range(numShard): tem_list = [] testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None) ds3.use_sampler(testsampler) for item in ds3.create_dict_iterator(): tem_list.append(len(item['image'])) vertifyList1.append(tem_list) assert vertifyList1 == result_list1
def test_Unevenly_distributed(): result_list = [[1, 4, 7], [2, 5, 8], [3, 6]] verify_list = [] data1 = [{ 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(2, np.uint8) }, { 'image': np.zeros(3, np.uint8) }, { 'image': np.zeros(4, np.uint8) }, { 'image': np.zeros(5, np.uint8) }] data2 = [{ 'image': np.zeros(6, np.uint8) }, { 'image': np.zeros(7, np.uint8) }, { 'image': np.zeros(8, np.uint8) }] testsampler = ds.DistributedSampler(num_shards=4, shard_id=0, shuffle=False, num_samples=None, offset=1) ds1 = ds.PaddedDataset(data1) ds2 = ds.PaddedDataset(data2) ds3 = ds1 + ds2 numShard = 3 for i in range(numShard): tem_list = [] testsampler = ds.DistributedSampler(num_shards=numShard, shard_id=i, shuffle=False, num_samples=None) ds3.use_sampler(testsampler) for item in ds3.create_dict_iterator(): tem_list.append(len(item['image'])) verify_list.append(tem_list) assert verify_list == result_list
def test_Reapeat_afterPadded(): result_list = [1, 3, 5, 7] verify_list = [] data1 = [{'image': np.zeros(1, np.uint8)}, {'image': np.zeros(2, np.uint8)}, {'image': np.zeros(3, np.uint8)}, {'image': np.zeros(4, np.uint8)}, {'image': np.zeros(5, np.uint8)}] data2 = [{'image': np.zeros(6, np.uint8)}, {'image': np.zeros(7, np.uint8)}, {'image': np.zeros(8, np.uint8)}] ds1 = ds.PaddedDataset(data1) ds2 = ds.PaddedDataset(data2) ds3 = ds1 + ds2 testsampler = ds.DistributedSampler(num_shards=2, shard_id=0, shuffle=False, num_samples=None) ds3.use_sampler(testsampler) repeat_num = 2 ds3 = ds3.repeat(repeat_num) for item in ds3.create_dict_iterator(): verify_list.append(len(item['image'])) assert verify_list == result_list * repeat_num
def test_celeba_padded(): data = ds.CelebADataset("../data/dataset/testCelebAData/") padded_samples = [{'image': np.zeros(1, np.uint8), 'attr': np.zeros(1, np.uint32)}] padded_ds = ds.PaddedDataset(padded_samples) data = data + padded_ds dis_sampler = ds.DistributedSampler(num_shards=2, shard_id=1, shuffle=False, num_samples=None) data.use_sampler(dis_sampler) data = data.repeat(2) count = 0 for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True): count = count + 1 assert count == 4
def test_manifest_dataset_get_num_class(): data = ds.ManifestDataset(DATA_FILE, decode=True, shuffle=False) assert data.num_classes() == 3 padded_samples = [{ 'image': np.zeros(1, np.uint8), 'label': np.array(1, np.int32) }] padded_ds = ds.PaddedDataset(padded_samples) data = data.repeat(2) padded_ds = padded_ds.repeat(2) data1 = data + padded_ds assert data1.num_classes() == 3
def test_clue_padded_and_skip_with_0_samples(): """ Test num_samples param of CLUE dataset """ TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json' data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train') count = 0 for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True): count += 1 assert count == 3 data_copy1 = copy.deepcopy(data) sample = { "label": np.array(1, np.string_), "sentence1": np.array(1, np.string_), "sentence2": np.array(1, np.string_) } samples = [sample] padded_ds = ds.PaddedDataset(samples) dataset = data + padded_ds testsampler = ds.DistributedSampler(num_shards=2, shard_id=1, shuffle=False, num_samples=None) dataset.use_sampler(testsampler) assert dataset.get_dataset_size() == 2 count = 0 for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): count += 1 assert count == 2 dataset = dataset.skip(count=2) # dataset2 has none samples count = 0 for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): count += 1 assert count == 0 with pytest.raises(ValueError, match="There is no samples in the "): dataset = dataset.concat(data_copy1) count = 0 for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): count += 1 assert count == 2
def test_TFRecord_Padded(): DATA_DIR = [ "../data/dataset/test_tf_file_3_images/train-0000-of-0001.data" ] SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json" result_list = [[159109, 2], [192607, 3], [179251, 4], [1, 5]] verify_list = [] shard_num = 4 for i in range(shard_num): data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False, shard_equal_rows=True) padded_samples = [{ 'image': np.zeros(1, np.uint8) }, { 'image': np.zeros(2, np.uint8) }, { 'image': np.zeros(3, np.uint8) }, { 'image': np.zeros(4, np.uint8) }, { 'image': np.zeros(5, np.uint8) }] padded_ds = ds.PaddedDataset(padded_samples) concat_ds = data + padded_ds testsampler = ds.DistributedSampler(num_shards=shard_num, shard_id=i, shuffle=False, num_samples=None) concat_ds.use_sampler(testsampler) shard_list = [] for item in concat_ds.create_dict_iterator(num_epochs=1, output_numpy=True): shard_list.append(len(item['image'])) verify_list.append(shard_list) assert verify_list == result_list
def test_imagefolder_padded(): DATA_DIR = "../data/dataset/testPK/data" data = ds.ImageFolderDatasetV2(DATA_DIR) data1 = [{'image': np.zeros(1, np.uint8), 'label': np.array(0, np.int32)}, {'image': np.zeros(2, np.uint8), 'label': np.array(1, np.int32)}, {'image': np.zeros(3, np.uint8), 'label': np.array(0, np.int32)}, {'image': np.zeros(4, np.uint8), 'label': np.array(1, np.int32)}, {'image': np.zeros(5, np.uint8), 'label': np.array(0, np.int32)}, {'image': np.zeros(6, np.uint8), 'label': np.array(1, np.int32)}] data2 = ds.PaddedDataset(data1) data3 = data + data2 testsampler = ds.DistributedSampler(num_shards=5, shard_id=4, shuffle=False, num_samples=None) data3.use_sampler(testsampler) assert sum([1 for _ in data3]) == 10 verify_list = [] for ele in data3.create_dict_iterator(): verify_list.append(len(ele['image'])) assert verify_list[8] == 1 assert verify_list[9] == 6
def test_padded_dataset_size(): dataset = ds.PaddedDataset([{"data": [1, 2, 3]}, {"data": [1, 0, 1]}]) assert dataset.get_dataset_size() == 2