def test_iter(self):
images = {str(idx): data.LocalImage(str(idx)) for idx in range(3)}
collection = data.LocalImageCollection(images)
actual = {name: image for name, image in collection}
desired = images
assert actual == desired
def test_getitem(self):
images = {str(idx): data.LocalImage(str(idx)) for idx in range(3)}
collection = data.LocalImageCollection(images)
actual = {str(idx): collection[str(idx)] for idx in range(len(collection))}
desired = images
assert actual == desired
def test_len(self):
num_images = 3
images = {str(idx): data.LocalImage(str(idx)) for idx in range(num_images)}
collection = data.LocalImageCollection(images)
actual = len(collection)
desired = num_images
assert actual == desired
def test_read(self, tmpdir):
def create_images(root):
torch.manual_seed(0)
files = {}
for idx in range(3):
name = str(idx)
image = torch.rand(1, 3, 32, 32)
file = path.join(root, f"{name}.png")
write_image(image, file)
files[name] = file
return files
files = create_images(tmpdir)
collection = data.LocalImageCollection(
{name: data.LocalImage(file) for name, file in files.items()}
)
actual = collection.read()
desired = {name: read_image(file) for name, file in files.items()}
ptu.assert_allclose(actual, desired)
def test_LocalImageCollection_read(self):
def create_images(root):
torch.manual_seed(0)
files = {}
for idx in range(3):
name = str(idx)
image = torch.rand(1, 3, 32, 32)
file = path.join(root, f"{name}.png")
write_image(image, file)
files[name] = file
return files
with get_tmp_dir() as root:
files = create_images(root)
collection = data.LocalImageCollection(
{name: data.LocalImage(file) for name, file in files.items()}
)
actual = collection.read()
desired = {name: read_image(file) for name, file in files.items()}
self.assertTensorDictAlmostEqual(actual, desired)
def test_repr_smoke(self):
images = {str(idx): data.LocalImage(str(idx)) for idx in range(3)}
collection = data.LocalImageCollection(images)
assert isinstance(repr(collection), str)