def test_folder_class_data():
path = get_test_dataset_path(DATASET_PATHS.FOLDER_CLASS_DATA)
expected_items = [str(p) for p in Path(path).glob("*/*.jpg")]
ds = loaders.from_folder_class_data(path)
found_items = [i[0] for i in ds]
assert set(expected_items) == set(found_items)
def test_string_image_conversion():
path = get_test_dataset_path(DATASET_PATHS.FOLDER_DATA)
ds_str = loaders.from_folder_data(path)
ds_img = ds_str.image()
items_img = [x for x in ds_img]
for data in items_img:
data = data[0]
assert(issubclass(type(data), Image.Image))
def test_folder_data():
path = get_test_dataset_path(DATASET_PATHS.FOLDER_DATA)
expected_items = [
str(Path(path) / "frame_000{}.jpg".format(i)) for i in range(1, 7)
]
ds = loaders.from_folder_data(path)
found_items = [i[0] for i in ds]
assert set(expected_items) == set(found_items)
def test_readme_example_1():
path = (
Path(get_test_dataset_path(DATASET_PATHS.FOLDER_DATASET_CLASS_DATA)) / "amazon"
)
train, val, test = (
do.from_folder_class_data(path)
.named("data", "label")
.image_resize((240, 240))
.one_hot("label")
.shuffle(seed=42)
.split([0.6, 0.2, 0.2])
)
def test_domain_adaptation():
p = Path(get_test_dataset_path(DATASET_PATHS.FOLDER_DATASET_CLASS_DATA))
train, val, test = domain_adaptation_office31(
source_data_path=p / "amazon", target_data_path=p / "dslr", seed=1
)
# prepare for tensorflow
train, val, test = [
d.to_tensorflow().batch(16).prefetch(2) for d in [train, val, test]
]
# take an item from each and make sure it doesn't raise
for d in [train, val, test]:
next(iter(d))
def test_folder_dataset_class_data():
path = get_test_dataset_path(DATASET_PATHS.FOLDER_DATASET_CLASS_DATA)
sets = Path(path).glob("[!._]*")
sets_of_expected_items = [
set([str(p) for p in Path(s).glob("*/*.jpg")]) for s in sets
]
datasets = loaders.from_folder_dataset_class_data(path)
sets_of_found_items = [set([i[0] for i in ds]) for ds in datasets]
for expected_items_set in sets_of_expected_items:
assert any([
expected_items_set == found_items
for found_items in sets_of_found_items
])
def test_image_resize():
ds = from_dummy_numpy_data().reshape(DUMMY_NUMPY_DATA_SHAPE_2D)
for tpl in ds:
data = tpl[0]
assert(data.shape == DUMMY_NUMPY_DATA_SHAPE_2D)
NEW_SIZE = (5,5)
# works directly on numpy arrays (ints)
ds_resized = ds.image_resize(NEW_SIZE)
for tpl in ds_resized:
data = tpl[0]
assert(data.size == NEW_SIZE)
assert(data.mode == 'L') # grayscale int
# also if they are floats
ds_resized_float = ds.transform([custom(np.float32)]).image_resize(NEW_SIZE)
for tpl in ds_resized_float:
data = tpl[0]
assert(data.size == NEW_SIZE)
assert(data.mode == 'F') # grayscale float
# works directly on strings
ds_str = loaders.from_folder_data(get_test_dataset_path(DATASET_PATHS.FOLDER_DATA))
ds_resized_from_str = ds_str.image_resize(NEW_SIZE)
for tpl in ds_resized_from_str:
data = tpl[0]
assert(data.size == NEW_SIZE)
# works on other images (scaling down)
ds_resized_again = ds_resized.image_resize(DUMMY_NUMPY_DATA_SHAPE_2D)
for tpl in ds_resized_again:
data = tpl[0]
assert(data.size == DUMMY_NUMPY_DATA_SHAPE_2D)
# Test error scenarios
with pytest.raises(ValueError):
ds.image_resize() # No args
with pytest.raises(ValueError):
ds.image_resize(NEW_SIZE, NEW_SIZE, NEW_SIZE) # Too many args
with pytest.raises(AssertionError):
ds.image_resize((4,4,4)) # Invalid size
def test_mat_single_with_multi_data():
path = get_test_dataset_path(DATASET_PATHS.MAT_SINGLE_WITH_MULTI_DATA)
datasets = loaders.from_mat_single_mult_data(path)
for ds in datasets:
# check dataset sizes and names
if ds.name == "src":
assert len(ds) == 2000
elif ds.name == "tar":
assert len(ds) == 1800
else:
assert False
# randomly check some samples for their dimension
ids = random.sample(range(len(ds)), 42)
for i in ids:
data, label = ds[i]
assert data.shape == (256, )
assert int(label) in range(10)
def test_reshape():
ds = from_dummy_numpy_data().named('data','label')
items = list(ds)
s = ds.shape
assert(ds.shape == (DUMMY_NUMPY_DATA_SHAPE_1D, _DEFAULT_SHAPE) )
assert(ds[0][0].shape == DUMMY_NUMPY_DATA_SHAPE_1D)
# reshape adding extra dim
ds_r = ds.reshape(DUMMY_NUMPY_DATA_SHAPE_2D)
ds_r_alt = ds.reshape(data=DUMMY_NUMPY_DATA_SHAPE_2D)
items_r = list(ds_r)
items_r_alt = list(ds_r_alt)
assert(ds_r.shape == ( DUMMY_NUMPY_DATA_SHAPE_2D, _DEFAULT_SHAPE) )
assert(ds_r[0][0].shape == DUMMY_NUMPY_DATA_SHAPE_2D)
for (old_data, l), (new_data, ln), (new_data_alt, lna) in zip(items, items_r, items_r_alt):
assert(set(old_data) == set(new_data.flatten()) == set(new_data_alt.flatten()))
assert(old_data.shape != new_data.shape == new_data_alt.shape)
assert(l == ln == lna)
# use wildcard
ds_wild = ds.reshape((-1,DUMMY_NUMPY_DATA_SHAPE_2D[1]))
items_wild = list(ds_wild)
for (old_data, _), (new_data, _) in zip(items_r, items_wild):
assert(np.array_equal(old_data, new_data))
# reshape back, alternative syntax
ds_back = ds_r.reshape(DUMMY_NUMPY_DATA_SHAPE_1D, None)
items_back = [x for x in ds_back]
for (old_data, _), (new_data, _) in zip(items, items_back):
assert(np.array_equal(old_data, new_data))
# yet another syntax
ds_trans = ds.transform([reshape(DUMMY_NUMPY_DATA_SHAPE_2D)])
items_trans = [x for x in ds_trans]
for (old_data, _), (new_data, _) in zip(items_r, items_trans):
assert(np.array_equal(old_data, new_data))
# doing nothing also works
ds_dummy = ds.reshape(None, None)
items_dummy = [x for x in ds_dummy]
for (old_data, _), (new_data, _) in zip(items, items_dummy):
assert(np.array_equal(old_data, new_data))
# TODO test reshape on string data
ds_str = loaders.from_folder_data(get_test_dataset_path(DATASET_PATHS.FOLDER_DATA))
with pytest.raises(ValueError):
# string has no shape
ds_str.reshape((1,2))
with pytest.raises(ValueError):
# No input
ds.reshape()
with pytest.raises(TypeError):
# bad input
ds.reshape('whazzagh')
with pytest.raises(ValueError):
# Too many inputs
ds.reshape(None, None, None)
with pytest.raises(ValueError):
# Dimensions don't match
ds.reshape((13,13))
