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import math
import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
class TestBinaryFocalLossWithLogits:
def test_smoke_none(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
assert kornia.losses.binary_focal_loss_with_logits(
logits, labels, alpha=0.5, gamma=2.0, reduction="none"
).shape == (2, 3, 2)
def test_smoke_sum(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
assert (
kornia.losses.binary_focal_loss_with_logits(logits, labels, alpha=0.5, gamma=2.0, reduction="sum").shape
== ()
)
def test_smoke_mean(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
assert (
kornia.losses.binary_focal_loss_with_logits(logits, labels, alpha=0.5, gamma=2.0, reduction="mean").shape
== ()
)
def test_smoke_mean_flat(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
assert (
kornia.losses.binary_focal_loss_with_logits(logits, labels, alpha=0.5, gamma=2.0, reduction="mean").shape
== ()
)
def test_jit(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
op = kornia.losses.binary_focal_loss_with_logits
op_script = torch.jit.script(op)
actual = op_script(logits, labels, alpha=0.5, gamma=2.0, reduction="none")
expected = op(logits, labels, alpha=0.5, gamma=2.0, reduction="none")
assert_close(actual, expected)
def test_gradcheck(self, device):
alpha, gamma = 0.5, 2.0 # for focal loss with logits
logits = torch.rand(2, 3, 2).to(device)
labels = torch.rand(2, 1, 3, 2)
labels = labels.to(device).long()
logits = utils.tensor_to_gradcheck_var(logits) # to var
assert gradcheck(
kornia.losses.binary_focal_loss_with_logits, (logits, labels, alpha, gamma), raise_exception=True
)
def test_same_output(self, device, dtype):
logits = torch.rand(2, 3, 2, dtype=dtype, device=device)
labels = torch.rand(2, 3, 2, dtype=dtype, device=device)
kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
assert kornia.losses.binary_focal_loss_with_logits(
logits, labels, **kwargs
) == kornia.losses.BinaryFocalLossWithLogits(**kwargs)(logits, labels)
class TestFocalLoss:
def test_smoke_none(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
assert kornia.losses.focal_loss(logits, labels, alpha=0.5, gamma=2.0, reduction="none").shape == (2, 3, 2)
def test_smoke_sum(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
assert kornia.losses.focal_loss(logits, labels, alpha=0.5, gamma=2.0, reduction="sum").shape == ()
def test_smoke_mean(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
assert kornia.losses.focal_loss(logits, labels, alpha=0.5, gamma=2.0, reduction="mean").shape == ()
def test_smoke_mean_flat(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, device=device, dtype=dtype)
labels = torch.rand(2) * num_classes
labels = labels.to(device).long()
assert kornia.losses.focal_loss(logits, labels, alpha=0.5, gamma=2.0, reduction="mean").shape == ()
def test_gradcheck(self, device, dtype):
num_classes = 3
alpha, gamma = 0.5, 2.0 # for focal loss
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
logits = utils.tensor_to_gradcheck_var(logits) # to var
assert gradcheck(kornia.losses.focal_loss, (logits, labels, alpha, gamma), raise_exception=True)
def test_jit(self, device, dtype):
num_classes = 3
params = (0.5, 2.0)
logits = torch.rand(2, num_classes, device=device, dtype=dtype)
labels = torch.rand(2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.focal_loss
op_script = torch.jit.script(op)
actual = op_script(logits, labels, *params)
expected = op(logits, labels, *params)
assert_close(actual, expected)
def test_module(self, device, dtype):
num_classes = 3
params = (0.5, 2.0)
logits = torch.rand(2, num_classes, device=device, dtype=dtype)
labels = torch.rand(2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.focal_loss
op_module = kornia.losses.FocalLoss(*params)
actual = op_module(logits, labels)
expected = op(logits, labels, *params)
assert_close(actual, expected)
class TestTverskyLoss:
def test_smoke(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
criterion = kornia.losses.TverskyLoss(alpha=0.5, beta=0.5)
assert criterion(logits, labels) is not None
def test_all_zeros(self, device, dtype):
num_classes = 3
logits = torch.zeros(2, num_classes, 1, 2, device=device, dtype=dtype)
logits[:, 0] = 10.0
logits[:, 1] = 1.0
logits[:, 2] = 1.0
labels = torch.zeros(2, 1, 2, device=device, dtype=torch.int64)
criterion = kornia.losses.TverskyLoss(alpha=0.5, beta=0.5)
loss = criterion(logits, labels)
assert_close(loss, torch.zeros_like(loss), atol=1e-3, rtol=1e-3)
def test_gradcheck(self, device, dtype):
num_classes = 3
alpha, beta = 0.5, 0.5 # for tversky loss
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
logits = utils.tensor_to_gradcheck_var(logits) # to var
assert gradcheck(kornia.losses.tversky_loss, (logits, labels, alpha, beta), raise_exception=True)
def test_jit(self, device, dtype):
num_classes = 3
params = (0.5, 0.05)
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.tversky_loss
op_script = torch.jit.script(op)
actual = op_script(logits, labels, *params)
expected = op(logits, labels, *params)
assert_close(actual, expected)
def test_module(self, device, dtype):
num_classes = 3
params = (0.5, 0.5)
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.tversky_loss
op_module = kornia.losses.TverskyLoss(*params)
actual = op_module(logits, labels)
expected = op(logits, labels, *params)
assert_close(actual, expected)
class TestDiceLoss:
def test_smoke(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
criterion = kornia.losses.DiceLoss()
assert criterion(logits, labels) is not None
def test_all_zeros(self, device, dtype):
num_classes = 3
logits = torch.zeros(2, num_classes, 1, 2, device=device, dtype=dtype)
logits[:, 0] = 10.0
logits[:, 1] = 1.0
logits[:, 2] = 1.0
labels = torch.zeros(2, 1, 2, device=device, dtype=torch.int64)
criterion = kornia.losses.DiceLoss()
loss = criterion(logits, labels)
assert_close(loss, torch.zeros_like(loss), rtol=1e-3, atol=1e-3)
def test_gradcheck(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 3, 2, device=device, dtype=dtype)
labels = torch.rand(2, 3, 2) * num_classes
labels = labels.to(device).long()
logits = utils.tensor_to_gradcheck_var(logits) # to var
assert gradcheck(kornia.losses.dice_loss, (logits, labels), raise_exception=True)
def test_jit(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 1, 2, device=device, dtype=dtype)
labels = torch.rand(2, 1, 2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.dice_loss
op_script = torch.jit.script(op)
assert_close(op(logits, labels), op_script(logits, labels))
def test_module(self, device, dtype):
num_classes = 3
logits = torch.rand(2, num_classes, 1, 2, device=device, dtype=dtype)
labels = torch.rand(2, 1, 2) * num_classes
labels = labels.to(device).long()
op = kornia.losses.dice_loss
op_module = kornia.losses.DiceLoss()
assert_close(op(logits, labels), op_module(logits, labels))
class TestDepthSmoothnessLoss:
@pytest.mark.parametrize("data_shape", [(1, 1, 10, 16), (2, 4, 8, 15)])
def test_smoke(self, device, dtype, data_shape):
image = torch.rand(data_shape, device=device, dtype=dtype)
depth = torch.rand(data_shape, device=device, dtype=dtype)
criterion = kornia.losses.InverseDepthSmoothnessLoss()
assert criterion(depth, image) is not None
def test_jit(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
depth = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
op = kornia.losses.inverse_depth_smoothness_loss
op_script = torch.jit.script(op)
assert_close(op(image, depth), op_script(image, depth))
def test_module(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
depth = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
op = kornia.losses.inverse_depth_smoothness_loss
op_module = kornia.losses.InverseDepthSmoothnessLoss()
assert_close(op(image, depth), op_module(image, depth))
def test_gradcheck(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
depth = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
depth = utils.tensor_to_gradcheck_var(depth) # to var
image = utils.tensor_to_gradcheck_var(image) # to var
assert gradcheck(kornia.losses.inverse_depth_smoothness_loss, (depth, image), raise_exception=True)
class TestSSIMLoss:
def test_ssim_equal_none(self, device, dtype):
# input data
img1 = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
img2 = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
ssim1 = kornia.losses.ssim_loss(img1, img1, window_size=5, reduction="none")
ssim2 = kornia.losses.ssim_loss(img2, img2, window_size=5, reduction="none")
tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4)
assert_close(ssim1, torch.zeros_like(img1), rtol=tol_val, atol=tol_val)
assert_close(ssim2, torch.zeros_like(img2), rtol=tol_val, atol=tol_val)
@pytest.mark.parametrize("window_size", [5, 11])
@pytest.mark.parametrize("reduction_type", ["mean", "sum"])
@pytest.mark.parametrize("batch_shape", [(1, 1, 10, 16), (2, 4, 8, 15)])
def test_ssim(self, device, dtype, batch_shape, window_size, reduction_type):
if device.type == 'xla':
pytest.skip("test highly unstable with tpu")
# input data
img = torch.rand(batch_shape, device=device, dtype=dtype)
loss = kornia.losses.ssim_loss(img, img, window_size, reduction=reduction_type)
tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4)
assert_close(loss.item(), 0.0, rtol=tol_val, atol=tol_val)
def test_jit(self, device, dtype):
img1 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
img2 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
args = (img1, img2, 5, 1.0, 1e-6, 'mean')
op = kornia.losses.ssim_loss
op_script = torch.jit.script(op)
assert_close(op(*args), op_script(*args))
def test_module(self, device, dtype):
img1 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
img2 = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
args = (img1, img2, 5, 1.0, 1e-12, 'mean')
op = kornia.losses.ssim_loss
op_module = kornia.losses.SSIMLoss(*args[2:])
assert_close(op(*args), op_module(*args[:2]))
def test_gradcheck(self, device, dtype):
# input data
window_size = 3
img1 = torch.rand(1, 1, 5, 4, device=device, dtype=dtype)
img2 = torch.rand(1, 1, 5, 4, device=device, dtype=dtype)
# evaluate function gradient
img1 = utils.tensor_to_gradcheck_var(img1) # to var
img2 = utils.tensor_to_gradcheck_var(img2) # to var
# TODO: review method since it needs `nondet_tol` in cuda sometimes.
assert gradcheck(kornia.losses.ssim_loss, (img1, img2, window_size), raise_exception=True, nondet_tol=1e-8)
class TestDivergenceLoss:
@pytest.mark.parametrize(
'input,target,expected',
[
(torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.zeros((1, 7, 2, 4)), 0.346574),
(torch.zeros((1, 7, 2, 4)), torch.full((1, 7, 2, 4), 0.125), 0.346574),
],
)
def test_js_div_loss_2d(self, device, dtype, input, target, expected):
actual = kornia.losses.js_div_loss_2d(input.to(device, dtype), target.to(device, dtype))
assert_close(actual.item(), expected)
@pytest.mark.parametrize(
'input,target,expected',
[
(torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.zeros((1, 7, 2, 4)), 0.0),
(torch.zeros((1, 7, 2, 4)), torch.full((1, 7, 2, 4), 0.125), math.inf),
],
)
def test_kl_div_loss_2d(self, device, dtype, input, target, expected):
actual = kornia.losses.kl_div_loss_2d(input.to(device, dtype), target.to(device, dtype))
assert_close(actual.item(), expected)
@pytest.mark.parametrize(
'input,target,expected',
[
(torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1), 0.0)),
(torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7), 0.0)),
(torch.full((1, 7, 2, 4), 0.125), torch.zeros((1, 7, 2, 4)), torch.full((1, 7), 0.0)),
(torch.zeros((1, 7, 2, 4)), torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7), math.inf)),
],
)
def test_kl_div_loss_2d_without_reduction(self, device, dtype, input, target, expected):
actual = kornia.losses.kl_div_loss_2d(input.to(device, dtype), target.to(device, dtype), reduction='none')
assert_close(actual, expected.to(device, dtype))
@pytest.mark.parametrize(
'input,target,expected',
[
(torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.zeros((1, 7, 2, 4)), 0.0),
(torch.zeros((1, 7, 2, 4)), torch.full((1, 7, 2, 4), 0.125), math.inf),
],
)
def test_noncontiguous_kl(self, device, dtype, input, target, expected):
input = input.to(device, dtype).view(input.shape[::-1]).T
target = target.to(device, dtype).view(target.shape[::-1]).T
actual = kornia.losses.kl_div_loss_2d(input, target).item()
assert_close(actual, expected)
@pytest.mark.parametrize(
'input,target,expected',
[
(torch.full((1, 1, 2, 4), 0.125), torch.full((1, 1, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.full((1, 7, 2, 4), 0.125), 0.0),
(torch.full((1, 7, 2, 4), 0.125), torch.zeros((1, 7, 2, 4)), 0.346574),
(torch.zeros((1, 7, 2, 4)), torch.full((1, 7, 2, 4), 0.125), 0.346574),
],
)
def test_noncontiguous_js(self, device, dtype, input, target, expected):
input = input.to(device, dtype).view(input.shape[::-1]).T
target = target.to(device, dtype).view(target.shape[::-1]).T
actual = kornia.losses.js_div_loss_2d(input, target).item()
assert_close(actual, expected)
def test_gradcheck_kl(self, device, dtype):
input = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
target = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
# evaluate function gradient
input = utils.tensor_to_gradcheck_var(input) # to var
target = utils.tensor_to_gradcheck_var(target) # to var
assert gradcheck(kornia.losses.kl_div_loss_2d, (input, target), raise_exception=True)
def test_gradcheck_js(self, device, dtype):
input = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
target = torch.rand(1, 1, 10, 16, device=device, dtype=dtype)
# evaluate function gradient
input = utils.tensor_to_gradcheck_var(input) # to var
target = utils.tensor_to_gradcheck_var(target) # to var
assert gradcheck(kornia.losses.js_div_loss_2d, (input, target), raise_exception=True)
def test_jit_kl(self, device, dtype):
input = torch.randn((2, 4, 10, 16), dtype=dtype, device=device)
target = torch.randn((2, 4, 10, 16), dtype=dtype, device=device)
args = (input, target)
op = kornia.losses.kl_div_loss_2d
op_jit = torch.jit.script(op)
assert_close(op(*args), op_jit(*args), rtol=0, atol=1e-5)
def test_jit_js(self, device, dtype):
input = torch.randn((2, 4, 10, 16), dtype=dtype, device=device)
target = torch.randn((2, 4, 10, 16), dtype=dtype, device=device)
args = (input, target)
op = kornia.losses.js_div_loss_2d
op_jit = torch.jit.script(op)
assert_close(op(*args), op_jit(*args), rtol=0, atol=1e-5)
class TestTotalVariation:
# Total variation of constant vectors is 0
@pytest.mark.parametrize(
'input, expected', [(torch.ones(3, 4, 5), torch.zeros(())), (2 * torch.ones(2, 3, 4, 5), torch.zeros(2))]
)
def test_tv_on_constant(self, device, dtype, input, expected):
actual = kornia.losses.total_variation(input.to(device, dtype))
assert_close(actual, expected.to(device, dtype))
# Total variation for 3D tensors
@pytest.mark.parametrize(
'input, expected',
[
(
torch.tensor(
[
[
[0.11747694, 0.5717714, 0.89223915, 0.2929412, 0.63556224],
[0.5371079, 0.13416398, 0.7782737, 0.21392655, 0.1757018],
[0.62360305, 0.8563448, 0.25304103, 0.68539226, 0.6956515],
[0.9350611, 0.01694632, 0.78724295, 0.4760313, 0.73099905],
],
[
[0.4788819, 0.45253807, 0.932798, 0.5721999, 0.7612051],
[0.5455887, 0.8836531, 0.79551977, 0.6677338, 0.74293613],
[0.4830376, 0.16420758, 0.15784949, 0.21445751, 0.34168917],
[0.8675162, 0.5468113, 0.6117004, 0.01305223, 0.17554593],
],
[
[0.6423703, 0.5561105, 0.54304767, 0.20339686, 0.8553698],
[0.98024786, 0.31562763, 0.10122144, 0.17686582, 0.26260805],
[0.20522952, 0.14523649, 0.8601968, 0.02593213, 0.7382898],
[0.71935296, 0.9625162, 0.42287344, 0.07979459, 0.9149871],
],
]
),
torch.tensor(33.001236),
),
(
torch.tensor([[[0.09094203, 0.32630223, 0.8066123], [0.10921168, 0.09534764, 0.48588026]]]),
torch.tensor(1.6900232),
),
],
)
def test_tv_on_3d(self, device, dtype, input, expected):
actual = kornia.losses.total_variation(input.to(device, dtype))
assert_close(actual, expected.to(device, dtype))
# Total variation for 4D tensors
@pytest.mark.parametrize(
'input, expected',
[
(
torch.tensor(
[
[
[[0.8756, 0.0920], [0.8034, 0.3107]],
[[0.3069, 0.2981], [0.9399, 0.7944]],
[[0.6269, 0.1494], [0.2493, 0.8490]],
],
[
[[0.3256, 0.9923], [0.2856, 0.9104]],
[[0.4107, 0.4387], [0.2742, 0.0095]],
[[0.7064, 0.3674], [0.6139, 0.2487]],
],
]
),
torch.tensor([5.0054283, 3.1870906]),
),
(
torch.tensor(
[
[[[0.1104, 0.2284, 0.4371], [0.4569, 0.1906, 0.8035]]],
[[[0.0552, 0.6831, 0.8310], [0.3589, 0.5044, 0.0802]]],
[[[0.5078, 0.5703, 0.9110], [0.4765, 0.8401, 0.2754]]],
]
),
torch.tensor([1.9565653, 2.5786452, 2.2681699]),
),
],
)
def test_tv_on_4d(self, device, dtype, input, expected):
actual = kornia.losses.total_variation(input.to(device, dtype))
assert_close(actual, expected.to(device, dtype), rtol=1e-4, atol=1e-4)
# Expect ValueError to be raised when tensors of ndim != 3 or 4 are passed
@pytest.mark.parametrize('input', [torch.rand(2, 3, 4, 5, 3), torch.rand(3, 1)])
def test_tv_on_invalid_dims(self, device, dtype, input):
with pytest.raises(ValueError):
kornia.losses.total_variation(input.to(device, dtype))
# Expect TypeError to be raised when non-torch tensors are passed
@pytest.mark.parametrize('input', [1, [1, 2]])
def test_tv_on_invalid_types(self, device, dtype, input):
with pytest.raises(TypeError):
kornia.losses.total_variation(input)
def test_jit(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
op = kornia.losses.total_variation
op_script = torch.jit.script(op)
assert_close(op(image), op_script(image))
def test_module(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
op = kornia.losses.total_variation
op_module = kornia.losses.TotalVariation()
assert_close(op(image), op_module(image))
def test_gradcheck(self, device, dtype):
image = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
image = utils.tensor_to_gradcheck_var(image) # to var
assert gradcheck(kornia.losses.total_variation, (image,), raise_exception=True)
class TestPSNRLoss:
def test_smoke(self, device, dtype):
input = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
target = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
criterion = kornia.losses.PSNRLoss(1.0)
loss = criterion(input, target)
assert loss is not None
def test_type(self, device, dtype):
# Expecting an exception
# since we pass integers instead of torch tensors
criterion = kornia.losses.PSNRLoss(1.0).to(device, dtype)
with pytest.raises(Exception):
criterion(1, 2)
def test_shape(self, device, dtype):
# Expecting an exception
# since we pass tensors of different shapes
criterion = kornia.losses.PSNRLoss(1.0).to(device, dtype)
with pytest.raises(Exception):
criterion(torch.rand(2, 3, 3, 2), torch.rand(2, 3, 3))
def test_loss(self, device, dtype):
input = torch.ones(1, device=device, dtype=dtype)
expected = torch.tensor(-20.0, device=device, dtype=dtype)
actual = kornia.losses.psnr_loss(input, 1.2 * input, 2.0)
assert_close(actual, expected)
def test_jit(self, device, dtype):
input = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
target = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
args = (input, target, 1.0)
op = kornia.losses.psnr_loss
op_script = torch.jit.script(op)
assert_close(op(*args), op_script(*args))
def test_module(self, device, dtype):
input = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
target = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
args = (input, target, 1.0)
op = kornia.losses.psnr_loss
op_module = kornia.losses.PSNRLoss(1.0)
assert_close(op(*args), op_module(input, target))
def test_gradcheck(self, device, dtype):
input = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
target = torch.rand(2, 3, 3, 2, device=device, dtype=dtype)
input = utils.tensor_to_gradcheck_var(input) # to var
target = utils.tensor_to_gradcheck_var(target) # to var
assert gradcheck(kornia.losses.psnr_loss, (input, target, 1.0), raise_exception=True)
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