def util_check_serialize_deserialize_file(data_orig, filename,
remove_json_files):
"""
Utility function for testing serdes files. It is to check if a json file is indeed created with correct name
after serializing and if it remains the same after repeatedly saving and loading.
:param data_orig: original data pipeline to be serialized
:param filename: filename to be saved as json format
:param remove_json_files: whether to remove the json file after testing
:return: The data pipeline after serializing and deserializing using the original pipeline
"""
file1 = filename + ".json"
file2 = filename + "_1.json"
ds.serialize(data_orig, file1)
assert validate_jsonfile(file1) is True
assert validate_jsonfile("wrong_name.json") is False
data_changed = ds.deserialize(json_filepath=file1)
ds.serialize(data_changed, file2)
assert validate_jsonfile(file2) is True
assert filecmp.cmp(file1, file2)
# Remove the generated json file
if remove_json_files:
delete_json_files()
return data_changed
def test_serdes_exception():
"""
Test exception case in serdes
"""
data_dir = [
"../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"
]
schema_file = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
data1 = ds.TFRecordDataset(data_dir,
schema_file,
columns_list=["image", "label"],
shuffle=False)
data1 = data1.filter(input_columns=["image", "label"],
predicate=lambda data: data < 11,
num_parallel_workers=4)
data1_json = ds.serialize(data1)
with pytest.raises(RuntimeError) as msg:
ds.deserialize(input_dict=data1_json)
assert "Filter is not yet supported by ds.engine.deserialize" in str(msg)
def test_serdes_zip_dataset(remove_json_files=True):
"""
Test serdes on zip dataset pipeline.
"""
files = ["../data/dataset/testTFTestAllTypes/test.data"]
schema_file = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
ds.config.set_seed(1)
ds0 = ds.TFRecordDataset(files,
schema=schema_file,
shuffle=ds.Shuffle.GLOBAL)
data1 = ds.TFRecordDataset(files,
schema=schema_file,
shuffle=ds.Shuffle.GLOBAL)
data2 = ds.TFRecordDataset(files,
schema=schema_file,
shuffle=ds.Shuffle.FILES)
data2 = data2.shuffle(10000)
data2 = data2.rename(input_columns=[
"col_sint16", "col_sint32", "col_sint64", "col_float", "col_1d",
"col_2d", "col_3d", "col_binary"
],
output_columns=[
"column_sint16", "column_sint32", "column_sint64",
"column_float", "column_1d", "column_2d",
"column_3d", "column_binary"
])
data3 = ds.zip((data1, data2))
ds.serialize(data3, "zip_dataset_pipeline.json")
assert validate_jsonfile("zip_dataset_pipeline.json") is True
assert validate_jsonfile("zip_dataset_pipeline_typo.json") is False
data4 = ds.deserialize(json_filepath="zip_dataset_pipeline.json")
ds.serialize(data4, "zip_dataset_pipeline_1.json")
assert validate_jsonfile("zip_dataset_pipeline_1.json") is True
assert filecmp.cmp('zip_dataset_pipeline.json',
'zip_dataset_pipeline_1.json')
rows = 0
for d0, d3, d4 in zip(ds0.create_tuple_iterator(output_numpy=True),
data3.create_tuple_iterator(output_numpy=True),
data4.create_tuple_iterator(output_numpy=True)):
num_cols = len(d0)
offset = 0
for t1 in d0:
np.testing.assert_array_equal(t1, d3[offset])
np.testing.assert_array_equal(t1, d3[offset + num_cols])
np.testing.assert_array_equal(t1, d4[offset])
np.testing.assert_array_equal(t1, d4[offset + num_cols])
offset += 1
rows += 1
assert rows == 12
if remove_json_files:
delete_json_files()
def test_serdes_mnist_dataset(remove_json_files=True):
"""
Test serdes on mnist dataset pipeline.
"""
data_dir = "../data/dataset/testMnistData"
ds.config.set_seed(1)
data1 = ds.MnistDataset(data_dir, num_samples=100)
one_hot_encode = c.OneHot(10) # num_classes is input argument
data1 = data1.map(operations=one_hot_encode, input_columns="label")
# batch_size is input argument
data1 = data1.batch(batch_size=10, drop_remainder=True)
ds.serialize(data1, "mnist_dataset_pipeline.json")
assert validate_jsonfile("mnist_dataset_pipeline.json") is True
data2 = ds.deserialize(json_filepath="mnist_dataset_pipeline.json")
ds.serialize(data2, "mnist_dataset_pipeline_1.json")
assert validate_jsonfile("mnist_dataset_pipeline_1.json") is True
assert filecmp.cmp('mnist_dataset_pipeline.json',
'mnist_dataset_pipeline_1.json')
data3 = ds.deserialize(json_filepath="mnist_dataset_pipeline_1.json")
num = 0
for data1, data2, data3 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True),
data3.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(data1['image'], data2['image'])
np.testing.assert_array_equal(data1['image'], data3['image'])
np.testing.assert_array_equal(data1['label'], data2['label'])
np.testing.assert_array_equal(data1['label'], data3['label'])
num += 1
logger.info("mnist total num samples is {}".format(str(num)))
assert num == 10
if remove_json_files:
delete_json_files()
def test_mnist_dataset(remove_json_files=True):
data_dir = "../data/dataset/testMnistData"
ds.config.set_seed(1)
data1 = ds.MnistDataset(data_dir, 100)
one_hot_encode = c.OneHot(10) # num_classes is input argument
data1 = data1.map(input_columns="label", operations=one_hot_encode)
# batch_size is input argument
data1 = data1.batch(batch_size=10, drop_remainder=True)
ds.serialize(data1, "mnist_dataset_pipeline.json")
assert (validate_jsonfile("mnist_dataset_pipeline.json") is True)
data2 = ds.deserialize(json_filepath="mnist_dataset_pipeline.json")
ds.serialize(data2, "mnist_dataset_pipeline_1.json")
assert (validate_jsonfile("mnist_dataset_pipeline_1.json") is True)
assert (filecmp.cmp('mnist_dataset_pipeline.json',
'mnist_dataset_pipeline_1.json'))
data3 = ds.deserialize(json_filepath="mnist_dataset_pipeline_1.json")
num = 0
for data1, data2, data3 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator(),
data3.create_dict_iterator()):
assert (np.array_equal(data1['image'], data2['image']))
assert (np.array_equal(data1['image'], data3['image']))
assert (np.array_equal(data1['label'], data2['label']))
assert (np.array_equal(data1['label'], data3['label']))
num += 1
logger.info("mnist total num samples is {}".format(str(num)))
assert (num == 10)
if remove_json_files:
delete_json_files()
def test_serdes_random_crop():
"""
Test serdes on RandomCrop pipeline.
"""
logger.info("test_random_crop")
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"
original_seed = config_get_set_seed(1)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
decode_op = vision.Decode()
random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200])
data1 = data1.map(operations=decode_op, input_columns="image")
data1 = data1.map(operations=random_crop_op, input_columns="image")
# Serializing into python dictionary
ds1_dict = ds.serialize(data1)
# Serializing into json object
_ = json.dumps(ds1_dict, indent=2)
# Reconstruct dataset pipeline from its serialized form
data1_1 = ds.deserialize(input_dict=ds1_dict)
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
data2 = data2.map(operations=decode_op, input_columns="image")
for item1, item1_1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data1_1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(item1['image'], item1_1['image'])
_ = item2["image"]
# Restore configuration num_parallel_workers
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_serdes_imagefolder_dataset(remove_json_files=True):
"""
Test simulating resnet50 dataset pipeline.
"""
data_dir = "../data/dataset/testPK/data"
ds.config.set_seed(1)
# define data augmentation parameters
rescale = 1.0 / 255.0
shift = 0.0
resize_height, resize_width = 224, 224
weights = [
1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 1.1
]
# Constructing DE pipeline
sampler = ds.WeightedRandomSampler(weights, 11)
child_sampler = ds.SequentialSampler()
sampler.add_child(child_sampler)
data1 = ds.ImageFolderDataset(data_dir, sampler=sampler)
data1 = data1.repeat(1)
data1 = data1.map(operations=[vision.Decode(True)],
input_columns=["image"])
rescale_op = vision.Rescale(rescale, shift)
resize_op = vision.Resize((resize_height, resize_width), Inter.LINEAR)
data1 = data1.map(operations=[rescale_op, resize_op],
input_columns=["image"])
data1 = data1.batch(2)
# Serialize the dataset pre-processing pipeline.
# data1 should still work after saving.
ds.serialize(data1, "imagenet_dataset_pipeline.json")
ds1_dict = ds.serialize(data1)
assert validate_jsonfile("imagenet_dataset_pipeline.json") is True
# Print the serialized pipeline to stdout
ds.show(data1)
# Deserialize the serialized json file
data2 = ds.deserialize(json_filepath="imagenet_dataset_pipeline.json")
# Serialize the pipeline we just deserialized.
# The content of the json file should be the same to the previous serialize.
ds.serialize(data2, "imagenet_dataset_pipeline_1.json")
assert validate_jsonfile("imagenet_dataset_pipeline_1.json") is True
assert filecmp.cmp('imagenet_dataset_pipeline.json',
'imagenet_dataset_pipeline_1.json')
# Deserialize the latest json file again
data3 = ds.deserialize(json_filepath="imagenet_dataset_pipeline_1.json")
data4 = ds.deserialize(input_dict=ds1_dict)
num_samples = 0
# Iterate and compare the data in the original pipeline (data1) against the deserialized pipeline (data2)
for item1, item2, item3, item4 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True),
data3.create_dict_iterator(num_epochs=1, output_numpy=True),
data4.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(item1['image'], item2['image'])
np.testing.assert_array_equal(item1['image'], item3['image'])
np.testing.assert_array_equal(item1['label'], item2['label'])
np.testing.assert_array_equal(item1['label'], item3['label'])
np.testing.assert_array_equal(item3['image'], item4['image'])
np.testing.assert_array_equal(item3['label'], item4['label'])
num_samples += 1
logger.info("Number of data in data1: {}".format(num_samples))
assert num_samples == 6
# Remove the generated json file
if remove_json_files:
delete_json_files()
