def test_jit(self, device, dtype):
scale_factor = torch.tensor([[0.5, 0.5]], device=device, dtype=dtype)
batch_size, channels, height, width = 2, 3, 64, 64
img = torch.ones(batch_size, channels, height, width, device=device, dtype=dtype)
trans = kornia.Scale(scale_factor)
trans_traced = torch.jit.trace(kornia.Scale(scale_factor), img)
assert_close(trans(img), trans_traced(img), atol=1e-4, rtol=1e-4)
def test_jit(self, device):
scale_factor = torch.tensor([[0.5, 0.5]]).to(device)
batch_size, channels, height, width = 2, 3, 64, 64
img = torch.ones(batch_size, channels, height, width).to(device)
trans = kornia.Scale(scale_factor)
trans_traced = torch.jit.trace(kornia.Scale(scale_factor), img)
assert_allclose(trans(img), trans_traced(img))
def test_scale_factor_2(self, device):
# prepare input data
inp = torch.tensor([[[0., 0., 0., 0.], [0., 1., 1., 0.],
[0., 1., 1., 0.], [0., 0., 0., 0.]]]).to(device)
# prepare transformation
scale_factor = torch.tensor([[2., 2.]]).to(device)
transform = kornia.Scale(scale_factor)
assert_allclose(transform(inp).sum().item(), 12.25)
def test_scale_factor_05(self):
# prepare input data
inp = torch.tensor([[[1., 1., 1., 1.], [1., 1., 1., 1.],
[1., 1., 1., 1.], [1., 1., 1., 1.]]])
expected = torch.tensor([[[0., 0., 0., 0.], [0., 1., 1., 0.],
[0., 1., 1., 0.], [0., 0., 0., 0.]]])
# prepare transformation
scale_factor = torch.tensor([0.5])
transform = kornia.Scale(scale_factor)
assert_allclose(transform(inp), expected)
def test_scale_factor_2(self, device, dtype):
# prepare input data
inp = torch.tensor(
[[[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]],
device=device,
dtype=dtype,
)
# prepare transformation
scale_factor = torch.tensor([[2.0, 2.0]], device=device, dtype=dtype)
transform = kornia.Scale(scale_factor)
assert_close(transform(inp).sum().item(), 12.25, atol=1e-4, rtol=1e-4)
def test_scale_factor_05_batch2_broadcast(self, device):
# prepare input data
inp = torch.tensor([[[1., 1., 1., 1.], [1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]).repeat(2, 1, 1, 1).to(device)
expected = torch.tensor([[[0., 0., 0., 0.], [0., 1., 1., 0.],
[0., 1., 1., 0.], [0., 0., 0.,
0.]]]).to(device)
# prepare transformation
scale_factor = torch.tensor([[0.5, 0.5]]).to(device)
transform = kornia.Scale(scale_factor)
assert_allclose(transform(inp), expected)
def test_scale_factor_05_batch2(self, device, dtype):
# prepare input data
inp = torch.tensor([[[1., 1., 1., 1.], [1., 1., 1., 1.],
[1., 1., 1., 1.], [1., 1., 1., 1.]]],
device=device,
dtype=dtype).repeat(2, 1, 1, 1)
expected = torch.tensor([[[0., 0., 0., 0.], [0., 1., 1., 0.],
[0., 1., 1., 0.], [0., 0., 0., 0.]]],
device=device,
dtype=dtype).repeat(2, 1, 1, 1)
# prepare transformation
scale_factor = torch.tensor([[0.5, 0.5]], device=device, dtype=dtype)
transform = kornia.Scale(scale_factor)
assert_allclose(transform(inp), expected, atol=1e-4, rtol=1e-4)
def test_scale_factor_05(self, device, dtype):
# prepare input data
inp = torch.tensor(
[[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]],
device=device,
dtype=dtype,
)
expected = torch.tensor(
[[[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]],
device=device,
dtype=dtype,
)
# prepare transformation
scale_factor = torch.tensor([[0.5, 0.5]], device=device, dtype=dtype)
transform = kornia.Scale(scale_factor)
assert_close(transform(inp), expected, atol=1e-4, rtol=1e-4)