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
