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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
from packaging import version
class TestImageHistogram2d:
fcn = kornia.enhance.image_histogram2d
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
def test_shape(self, device, dtype, kernel):
input = torch.ones(32, 32, device=device, dtype=dtype)
hist, pdf = TestImageHistogram2d.fcn(input, 0.0, 1.0, 256, kernel=kernel)
assert hist.shape == (256,) and pdf.shape == (256,)
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
def test_shape_channels(self, device, dtype, kernel):
input = torch.ones(3, 32, 32, device=device, dtype=dtype)
hist, pdf = TestImageHistogram2d.fcn(input, 0.0, 1.0, 256, kernel=kernel)
assert hist.shape == (3, 256) and pdf.shape == (3, 256)
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
def test_shape_batch(self, device, dtype, kernel):
input = torch.ones(8, 3, 32, 32, device=device, dtype=dtype)
hist, pdf = TestImageHistogram2d.fcn(input, 0.0, 1.0, 256, kernel=kernel)
assert hist.shape == (8, 3, 256) and pdf.shape == (8, 3, 256)
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
def test_gradcheck(self, device, dtype, kernel):
input = torch.ones(32, 32, device=device, dtype=dtype)
input = utils.tensor_to_gradcheck_var(input) # to var
centers = torch.linspace(0, 255, 8, device=device, dtype=dtype)
centers = utils.tensor_to_gradcheck_var(centers)
assert gradcheck(
TestImageHistogram2d.fcn, (input, 0.0, 255.0, 256, None, centers, True, kernel), raise_exception=True
)
@pytest.mark.skipif(
version.parse(torch.__version__) < version.parse("1.9"), reason="Tuple cannot be jitted with PyTorch < v1.9"
)
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
def test_jit(self, device, dtype, kernel):
input = torch.linspace(0, 255, 10, device=device, dtype=dtype)
input_x, _ = torch.meshgrid(input, input)
inputs = (input_x, 0.0, 255.0, 10, None, None, False, kernel)
op = TestImageHistogram2d.fcn
op_script = torch.jit.script(op)
assert_close(op(*inputs), op_script(*inputs))
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
@pytest.mark.parametrize("size", [(1, 1), (3, 1, 1), (8, 3, 1, 1)])
def test_uniform_hist(self, device, dtype, kernel, size):
input = torch.linspace(0, 255, 10, device=device, dtype=dtype)
input_x, _ = torch.meshgrid(input, input)
input_x = input_x.repeat(*size)
if kernel == "gaussian":
bandwidth = 2 * 0.4 ** 2
else:
bandwidth = None
hist, _ = TestImageHistogram2d.fcn(input_x, 0.0, 255.0, 10, bandwidth=bandwidth, centers=input, kernel=kernel)
ans = 10 * torch.ones_like(hist)
assert_close(ans, hist)
@pytest.mark.parametrize("kernel", ["triangular", "gaussian", "uniform", "epanechnikov"])
@pytest.mark.parametrize("size", [(1, 1), (3, 1, 1), (8, 3, 1, 1)])
def test_uniform_dist(self, device, dtype, kernel, size):
input = torch.linspace(0, 255, 10, device=device, dtype=dtype)
input_x, _ = torch.meshgrid(input, input)
input_x = input_x.repeat(*size)
if kernel == "gaussian":
bandwidth = 2 * 0.4 ** 2
else:
bandwidth = None
hist, pdf = TestImageHistogram2d.fcn(
input_x, 0.0, 255.0, 10, bandwidth=bandwidth, centers=input, kernel=kernel, return_pdf=True
)
ans = 0.1 * torch.ones_like(hist)
assert_close(ans, pdf)
class TestHistogram2d:
fcn = kornia.enhance.histogram2d
def test_shape(self, device, dtype):
inp1 = torch.ones(1, 32, device=device, dtype=dtype)
inp2 = torch.ones(1, 32, device=device, dtype=dtype)
bins = torch.linspace(0, 255, 128, device=device, dtype=dtype)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
pdf = TestHistogram2d.fcn(inp1, inp2, bins, bandwidth)
assert pdf.shape == (1, 128, 128)
def test_shape_batch(self, device, dtype):
inp1 = torch.ones(8, 32, device=device, dtype=dtype)
inp2 = torch.ones(8, 32, device=device, dtype=dtype)
bins = torch.linspace(0, 255, 128, device=device, dtype=dtype)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
pdf = TestHistogram2d.fcn(inp1, inp2, bins, bandwidth)
assert pdf.shape == (8, 128, 128)
def test_gradcheck(self, device, dtype):
inp1 = torch.ones(1, 8, device=device, dtype=dtype)
inp2 = torch.ones(1, 8, device=device, dtype=dtype)
inp1 = utils.tensor_to_gradcheck_var(inp1) # to var
inp2 = utils.tensor_to_gradcheck_var(inp2) # to var
bins = torch.linspace(0, 255, 8, device=device, dtype=dtype)
bins = utils.tensor_to_gradcheck_var(bins)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
bandwidth = utils.tensor_to_gradcheck_var(bandwidth)
assert gradcheck(TestHistogram2d.fcn, (inp1, inp2, bins, bandwidth), raise_exception=True)
def test_jit(self, device, dtype):
input1 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
input2 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
bins = torch.linspace(0, 255, 10, device=device, dtype=dtype)
bandwidth = torch.tensor(2 * 0.4 ** 2, device=device, dtype=dtype)
inputs = (input1, input2, bins, bandwidth)
op = TestHistogram2d.fcn
op_script = torch.jit.script(op)
assert_close(op(*inputs), op_script(*inputs))
def test_uniform_dist(self, device, dtype):
input1 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
input2 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
bins = torch.linspace(0, 255, 10, device=device, dtype=dtype)
bandwidth = torch.tensor(2 * 0.4 ** 2, device=device, dtype=dtype)
pdf = TestHistogram2d.fcn(input1, input2, bins, bandwidth)
ans = 0.1 * kornia.eye_like(10, pdf)
assert_close(ans, pdf)
class TestHistogram:
fcn = kornia.enhance.histogram
def test_shape(self, device, dtype):
inp = torch.ones(1, 32, device=device, dtype=dtype)
bins = torch.linspace(0, 255, 128, device=device, dtype=dtype)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
pdf = TestHistogram.fcn(inp, bins, bandwidth)
assert pdf.shape == (1, 128)
def test_shape_batch(self, device, dtype):
inp = torch.ones(8, 32, device=device, dtype=dtype)
bins = torch.linspace(0, 255, 128, device=device, dtype=dtype)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
pdf = TestHistogram.fcn(inp, bins, bandwidth)
assert pdf.shape == (8, 128)
def test_gradcheck(self, device, dtype):
inp = torch.ones(1, 8, device=device, dtype=dtype)
inp = utils.tensor_to_gradcheck_var(inp) # to var
bins = torch.linspace(0, 255, 8, device=device, dtype=dtype)
bins = utils.tensor_to_gradcheck_var(bins)
bandwidth = torch.tensor(0.9, device=device, dtype=dtype)
bandwidth = utils.tensor_to_gradcheck_var(bandwidth)
assert gradcheck(TestHistogram.fcn, (inp, bins, bandwidth), raise_exception=True)
def test_jit(self, device, dtype):
input1 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
bins = torch.linspace(0, 255, 10, device=device, dtype=dtype)
bandwidth = torch.tensor(2 * 0.4 ** 2, device=device, dtype=dtype)
inputs = (input1, bins, bandwidth)
op = TestHistogram.fcn
op_script = torch.jit.script(op)
assert_close(op(*inputs), op_script(*inputs))
def test_uniform_dist(self, device, dtype):
input1 = torch.linspace(0, 255, 10, device=device, dtype=dtype).unsqueeze(0)
input2 = torch.linspace(0, 255, 10, device=device, dtype=dtype)
bandwidth = torch.tensor(2 * 0.4 ** 2, device=device, dtype=dtype)
pdf = TestHistogram.fcn(input1, input2, bandwidth)
ans = 0.1 * torch.ones(1, 10, device=device, dtype=dtype)
assert_close(ans, pdf)
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