def test_logSumExp_kernels_feature(self):
############################################################
from pykeops.torch import Kernel, kernel_product
params = {'gamma': 1. / self.sigmac**2, 'mode': 'lse'}
if pykeops.config.gpu_available:
backend_to_test = ['auto', 'GPU_1D', 'GPU_2D', 'pytorch']
else:
backend_to_test = ['auto', 'pytorch']
for k, b in itertools.product(
['gaussian', 'laplacian', 'cauchy', 'inverse_multiquadric'],
backend_to_test):
with self.subTest(k=k, b=b):
params['id'] = Kernel(k + '(x,y)')
params['backend'] = b
# Call cuda kernel
gamma = kernel_product(params, self.xc, self.yc, self.gc).cpu()
# Numpy version
log_K = log_np_kernel(self.x, self.y, self.sigma, kernel=k)
log_KP = log_K + self.g.T
gamma_py = log_sum_exp(log_KP, axis=1)
# compare output
self.assertTrue(
np.allclose(gamma.data.numpy().ravel(),
gamma_py,
atol=1e-6))
def test_conv_kernels_feature(self):
############################################################
from pykeops.torch.kernel_product.kernels import Kernel, kernel_product
params = {
'gamma': 1. / self.sigmac**2,
'mode': 'sum',
}
if pykeops.config.gpu_available:
backend_to_test = ['auto', 'GPU_1D', 'GPU_2D', 'pytorch']
else:
backend_to_test = ['auto', 'pytorch']
for k, b in itertools.product(
['gaussian', 'laplacian', 'cauchy', 'inverse_multiquadric'],
backend_to_test):
with self.subTest(k=k, b=b):
params['id'] = Kernel(k + '(x,y)')
params['backend'] = b
# Call cuda kernel
gamma = kernel_product(params, self.xc, self.yc, self.bc).cpu()
# Numpy version
gamma_py = np.matmul(
np_kernel(self.x, self.y, self.sigma, kernel=k), self.b)
# compare output
self.assertTrue(np.allclose(gamma.cpu().data.numpy(),
gamma_py))
def test_grad1conv_kernels_feature(self):
############################################################
import torch
from pykeops.torch import Kernel, kernel_product
params = {
'gamma': 1. / self.sigmac**2,
'mode': 'sum',
}
if pykeops.config.gpu_available:
backend_to_test = ['auto', 'GPU_1D', 'GPU_2D', 'pytorch']
else:
backend_to_test = ['auto', 'pytorch']
for k, b in itertools.product(
['gaussian', 'laplacian', 'cauchy', 'inverse_multiquadric'],
backend_to_test):
with self.subTest(k=k, b=b):
params['id'] = Kernel(k + '(x,y)')
params['backend'] = b
# Call cuda kernel
aKxy_b = torch.dot(
self.ac.view(-1),
kernel_product(params, self.xc, self.yc, self.bc).view(-1))
gamma_keops = torch.autograd.grad(aKxy_b,
self.xc,
create_graph=False)[0].cpu()
# Numpy version
A = differences(self.x, self.y) * grad_np_kernel(
self.x, self.y, self.sigma, kernel=k)
gamma_py = 2 * (np.sum(self.a *
(np.matmul(A, self.b)), axis=2)).T
# compare output
self.assertTrue(
np.allclose(gamma_keops.cpu().data.numpy(),
gamma_py,
atol=1e-6))