def test_filedataset_segmentation(self):
target_trans = Compose([
default_image_load_fn,
Resize(60),
RandomRotation(90),
ToTensor()
])
file_dataset = FileDataset(self.paths,
self.paths,
self.transform,
target_trans,
seed=1337)
x, y = file_dataset[0]
assert np.allclose(x.numpy(), y.numpy())
out1 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
out2 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
assert all([
np.allclose(x1.numpy(), x2.numpy())
for (x1, _), (x2, _) in zip(out1, out2)
])
file_dataset = FileDataset(self.paths,
self.paths,
self.transform,
target_trans,
seed=None)
x, y = file_dataset[0]
assert np.allclose(x.numpy(), y.numpy())
out1 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
out2 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
assert not all([
np.allclose(x1.numpy(), x2.numpy())
for (x1, _), (x2, _) in zip(out1, out2)
])
def setUp(self):
self.lbls = None
self.transform = Compose([Resize(60), RandomRotation(90), ToTensor()])
testtransform = Compose([Resize(32), ToTensor()])
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.lbls = self.generate_labels(len(self.paths), 10)
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.active = ActiveLearningDataset(
self.dataset,
labelled=(np.array(self.lbls) != -1),
pool_specifics={'transform': testtransform})
def setUp(self):
self.lbls = None
self.transform = Compose([Resize(60), RandomRotation(90), ToTensor()])
testtransform = Compose([Resize(32), ToTensor()])
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.lbls = self.generate_labels(len(self.paths), 10)
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.active = ActiveLearningDataset(
self.dataset,
eval_transform=testtransform,
labelled=torch.from_numpy(
(np.array(self.lbls) != -1).astype(np.uint8)))
def test_segmentation_pipeline(self):
class DrawSquare:
def __init__(self, side):
self.side = side
def __call__(self, x, **kwargs):
x, canvas = x # x is a [int, ndarray]
canvas[:self.side, :self.side] = x
return canvas
target_trans = BaaLCompose([
GetCanvas(),
DrawSquare(3),
ToPILImage(mode=None),
Resize(60, interpolation=0),
RandomRotation(10, resample=NEAREST, fill=0.0),
PILToLongTensor()
])
file_dataset = FileDataset(self.paths, [1] * len(self.paths),
self.transform, target_trans)
x, y = file_dataset[0]
assert np.allclose(np.unique(y), [0, 1])
assert y.shape[1:] == x.shape[1:]
def test_default_label(self):
dataset = FileDataset(self.paths)
assert dataset.lbls == [-1] * len(self.paths)
class FileDatasetTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
tmp_dir = tempfile.gettempdir()
paths = []
for idx in range(100):
path = os.path.join(tmp_dir, "{}.png".format(idx))
Image.fromarray(np.random.randint(0, 100, [10, 10, 3],
np.uint8)).save(path)
paths.append(path)
cls.paths = paths
def setUp(self):
self.lbls = None
self.transform = Compose([Resize(60), RandomRotation(90), ToTensor()])
testtransform = Compose([Resize(32), ToTensor()])
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.lbls = self.generate_labels(len(self.paths), 10)
self.dataset = FileDataset(self.paths,
self.lbls,
transform=self.transform)
self.active = ActiveLearningDataset(
self.dataset,
labelled=torch.from_numpy(
(np.array(self.lbls) != -1).astype(np.uint8)),
pool_specifics={'transform': testtransform})
def generate_labels(self, n, init_lbls):
lbls = [-1] * n
for i in random.sample(range(n), init_lbls):
lbls[i] = i % 10
return lbls
def test_default_label(self):
dataset = FileDataset(self.paths)
assert dataset.lbls == [-1] * len(self.paths)
def test_labelling(self):
actually_labelled = [i for i, j in enumerate(self.lbls) if j >= 0]
actually_not_labelled = [i for i, j in enumerate(self.lbls) if j < 0]
with pytest.warns(UserWarning):
self.dataset.label(actually_labelled[0], 1)
self.dataset.label(actually_not_labelled[0], 1)
assert sum([1 for i, j in enumerate(self.dataset.lbls)
if j >= 0]) == 11
def test_filedataset_segmentation(self):
target_trans = Compose([
default_image_load_fn,
Resize(60),
RandomRotation(90),
ToTensor()
])
file_dataset = FileDataset(self.paths,
self.paths,
self.transform,
target_trans,
seed=1337)
x, y = file_dataset[0]
assert np.allclose(x.numpy(), y.numpy())
out1 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
out2 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
assert all([
np.allclose(x1.numpy(), x2.numpy())
for (x1, _), (x2, _) in zip(out1, out2)
])
file_dataset = FileDataset(self.paths,
self.paths,
self.transform,
target_trans,
seed=None)
x, y = file_dataset[0]
assert np.allclose(x.numpy(), y.numpy())
out1 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
out2 = list(
DataLoader(file_dataset,
batch_size=1,
num_workers=3,
shuffle=False))
assert not all([
np.allclose(x1.numpy(), x2.numpy())
for (x1, _), (x2, _) in zip(out1, out2)
])
def test_segmentation_pipeline(self):
class DrawSquare:
def __init__(self, side):
self.side = side
def __call__(self, x, **kwargs):
x, canvas = x # x is a [int, ndarray]
canvas[:self.side, :self.side] = x
return canvas
target_trans = BaaLCompose([
GetCanvas(),
DrawSquare(3),
ToPILImage(mode=None),
Resize(60, interpolation=0),
RandomRotation(10, resample=NEAREST, fill=0.0),
PILToLongTensor()
])
file_dataset = FileDataset(self.paths, [1] * len(self.paths),
self.transform, target_trans)
x, y = file_dataset[0]
assert np.allclose(np.unique(y), [0, 1])
assert y.shape[1:] == x.shape[1:]