How to use the diffusion.gauss2DNp function in diffusion
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opveclib / opveclib / opveclib / examples / test_gauss.py View on Github 
def test(self):
"""
Creates Gauss kernels on the CPU/GPU and in numpy and compares these.
"""
print('*** Running Test: ' + self.__class__.__name__ + ' function: ' + _getframe().f_code.co_name)
# this test fails with mX = 9 and mY = 8 due to a bug in nvcc that needs more investigation
# for now, skip this test to the build can pass.
# for mY in [1, 2, 3, 8]:
# for mX in [1, 2, 3, 9]:
for mY in [1, 2, 3, 9]:
for mX in [1, 2, 3, 8]:
print "Test case mX = %d and mY = %d." % (mX, mY) # Print parameters of the test case.
op = Gauss2DOp(dimOut=[mY,mX], clear_cache=True)
dataCPU = op.evaluate_c()
dataNPY = gauss2DNp([mY,mX])
assert np.allclose(dataCPU, dataNPY)
if ops.local.cuda_enabled:
dataGPU = op.evaluate_cuda()
assert np.allclose(dataGPU, dataNPY)
