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import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
class TestNMS2d:
def test_shape(self, device):
inp = torch.ones(1, 3, 4, 4, device=device)
nms = kornia.geometry.subpix.NonMaximaSuppression2d((3, 3)).to(device)
assert nms(inp).shape == inp.shape
def test_shape_batch(self, device):
inp = torch.ones(4, 3, 4, 4, device=device)
nms = kornia.geometry.subpix.NonMaximaSuppression2d((3, 3)).to(device)
assert nms(inp).shape == inp.shape
def test_nms(self, device):
inp = torch.tensor(
[
[
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.1, 1.0, 0.0, 1.0, 1.0, 0.0],
[0.0, 0.7, 1.1, 0.0, 1.0, 2.0, 0.0],
[0.0, 0.8, 1.0, 0.0, 1.0, 1.0, 0.0],
]
]
],
device=device,
).float()
expected = torch.tensor(
[
[
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0, 0, 0.0, 0, 0.0, 0.0],
[0.0, 0, 1.1, 0.0, 0.0, 2.0, 0.0],
[0.0, 0, 0, 0.0, 0.0, 0.0, 0.0],
]
]
],
device=device,
).float()
nms = kornia.geometry.subpix.NonMaximaSuppression2d((3, 3)).to(device)
scores = nms(inp)
assert_close(scores, expected, atol=1e-4, rtol=1e-3)
def test_gradcheck(self, device):
batch_size, channels, height, width = 1, 2, 5, 4
img = torch.rand(batch_size, channels, height, width, device=device)
img = utils.tensor_to_gradcheck_var(img) # to var
assert gradcheck(kornia.geometry.subpix.nms2d, (img, (3, 3)), raise_exception=True, nondet_tol=1e-4)
class TestNMS3d:
def test_shape(self, device):
inp = torch.ones(1, 1, 3, 4, 4, device=device)
nms = kornia.geometry.subpix.NonMaximaSuppression3d((3, 3, 3)).to(device)
assert nms(inp).shape == inp.shape
def test_shape_batch(self, device):
inp = torch.ones(4, 1, 3, 4, 4, device=device)
nms = kornia.geometry.subpix.NonMaximaSuppression3d((3, 3, 3)).to(device)
assert nms(inp).shape == inp.shape
def test_nms(self, device):
inp = torch.tensor(
[
[
[
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 1.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
]
]
]
).to(device)
expected = torch.tensor(
[
[
[
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
]
]
]
).to(device)
nms = kornia.geometry.subpix.NonMaximaSuppression3d((3, 3, 3)).to(device)
scores = nms(inp)
assert_close(scores, expected, atol=1e-4, rtol=1e-3)
def test_gradcheck(self, device):
batch_size, channels, depth, height, width = 1, 1, 4, 5, 4
img = torch.rand(batch_size, channels, depth, height, width, device=device)
img = utils.tensor_to_gradcheck_var(img) # to var
assert gradcheck(kornia.geometry.subpix.nms3d, (img, (3, 3, 3)), raise_exception=True, nondet_tol=1e-4)
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