def test_random_mixup_num2(self, device, dtype):
torch.manual_seed(76)
f = RandomCutMix(width=4, height=3, num_mix=5, p=1.)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
expected = torch.tensor([[[[0., 0., 0., 1.],
[0., 0., 0., 1.],
[1., 1., 1., 1.]]],
[[[1., 1., 0., 0.],
[1., 1., 0., 0.],
[0., 0., 0., 0.]]]], device=device, dtype=dtype)
out_image, out_label = f(input, label)
assert_allclose(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[:, :, 0] == label).all()
assert (out_label[:, :, 1] == torch.tensor(
[[1, 0], [1, 0], [1, 0], [1, 0], [0, 1]], device=device)).all()
assert_allclose(out_label[:, :, 2], torch.tensor(
[[0.0833, 0.3333], [0., 0.1667], [0.5, 0.0833], [0.0833, 0.], [0.5, 0.3333]],
device=device, dtype=dtype), rtol=1e-4, atol=1e-4)
def test_random_mixup_same_on_batch(self, device, dtype):
torch.manual_seed(0)
f = RandomCutMix(same_on_batch=True, width=4, height=3, p=1.)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
lam = torch.tensor([0.0885, 0.0885], device=device, dtype=dtype)
expected = torch.tensor([[[[0., 0., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]],
[[[1., 1., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]]], device=device, dtype=dtype)
out_image, out_label = f(input, label)
assert_allclose(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[0, :, 0] == label).all()
assert (out_label[0, :, 1] == torch.tensor([0, 1], device=device, dtype=dtype)).all()
assert_allclose(out_label[0, :, 2],
torch.tensor([0.1667, 0.1667], device=device, dtype=dtype), rtol=1e-4, atol=1e-4)
def test_random_mixup_same_on_batch(self, device, dtype):
torch.manual_seed(42)
f = RandomCutMix(same_on_batch=True, width=4, height=3, p=1.0)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
# TODO(jian): where is it used ?
# lam = torch.tensor([0.0885, 0.0885], device=device, dtype=dtype)
expected = torch.tensor(
[
[[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]],
[[[1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]],
],
device=device,
dtype=dtype,
)
out_image, out_label = f(input, label)
assert_close(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[0, :, 0] == label).all()
assert (out_label[0, :, 1] == torch.tensor([0, 1],
device=device,
dtype=dtype)).all()
assert_close(out_label[0, :, 2],
torch.tensor([0.5000, 0.5000], device=device,
dtype=dtype),
rtol=1e-4,
atol=1e-4)
def test_random_mixup_beta0(self, device, dtype):
torch.manual_seed(76)
# beta 0 => resample 0.5 area
f = RandomCutMix(beta=0., width=4, height=3, p=1.)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
expected = torch.tensor([[[[1., 0., 0., 1.],
[1., 0., 0., 1.],
[1., 1., 1., 1.]]],
[[[1., 1., 0., 0.],
[1., 1., 0., 0.],
[0., 0., 0., 0.]]]], device=device, dtype=dtype)
out_image, out_label = f(input, label)
assert_allclose(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[0, :, 0] == label).all()
assert (out_label[0, :, 1] == torch.tensor([0, 1])).all()
# cut area = 4 / 12
assert_allclose(out_label[0, :, 2], torch.tensor([0.33333, 0.33333], dtype=dtype))
def test_random_mixup_p1(self, device, dtype):
torch.manual_seed(76)
f = RandomCutMix(width=4, height=3, p=1.0)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
lam = torch.tensor([0.1320, 0.3074], device=device, dtype=dtype)
expected = torch.tensor(
[
[[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]],
[[[1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]],
],
device=device,
dtype=dtype,
)
out_image, out_label = f(input, label)
assert_allclose(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[0, :, 0] == label).all()
assert (out_label[0, :, 1] == torch.tensor([0, 1],
device=device,
dtype=dtype)).all()
assert (out_label[0, :, 2] == torch.tensor([0.5, 0.5],
device=device,
dtype=dtype)).all()
def test_smoke(self, device, dtype):
f = RandomCutMix(width=3, height=3)
repr = (
"RandomCutMix(num_mix=1, beta=None, cut_size=None, height=3, width=3, p=1.0, "
"p_batch=1.0, same_on_batch=False)"
)
assert str(f) == repr
def test_random_mixup_beta0(self, device, dtype):
torch.manual_seed(76)
# beta 0 => resample 0.5 area
# beta cannot be 0 after torch 1.8.0
f = RandomCutMix(beta=1e-7, p=1.0)
input = torch.stack([
torch.ones(1, 3, 4, device=device, dtype=dtype),
torch.zeros(1, 3, 4, device=device, dtype=dtype)
])
label = torch.tensor([1, 0], device=device)
expected = torch.tensor(
[
[[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]],
[[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]],
],
device=device,
dtype=dtype,
)
out_image, out_label = f(input, label)
assert_close(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label[0, :, 0] == label).all()
assert (out_label[0, :, 1] == torch.tensor([0, 1],
device=device,
dtype=dtype)).all()
# cut area = 4 / 12
assert_close(
out_label[0, :, 2],
torch.tensor([0.33333, 0.33333], device=device, dtype=dtype))
def test_random_mixup_p0(self, device, dtype):
torch.manual_seed(76)
f = RandomCutMix(p=0.0, width=4, height=3)
input = torch.stack(
[torch.ones(1, 3, 4, device=device, dtype=dtype), torch.zeros(1, 3, 4, device=device, dtype=dtype)]
)
label = torch.tensor([1, 0], device=device)
expected = input.clone()
out_image, out_label = f(input, label)
assert_allclose(out_image, expected, rtol=1e-4, atol=1e-4)
assert (out_label == label).all()
def smoke_test(self, device, dtype):
f = RandomCutMix(width=3, height=3)
repr = "RandomCutMix(p=1, num_mix=1, beta=tensor(0.),, width=3, height=3, same_on_batch=False"
assert str(f) == repr
def test_smoke(self, device, dtype):
f = RandomCutMix(width=3, height=3)
repr = "RandomCutMix(num_mix=1, beta=1.0, cut_size=tensor([0., 1.]), , p=1.0, p_batch=1.0, same_on_batch=False)"
assert str(f) == repr
def test_smoke(self):
f = RandomCutMix()
repr = (
"RandomCutMix(cut_size=None, beta=None, num_mix=1, p=1.0, p_batch=1.0, same_on_batch=False)"
)
assert str(f) == repr