def test_cross_map_norm_noncontiguous_grad():
# Check the case reported at https://groups.google.com/d/topic/pylearn-users/KxIYc3hczf4/discussion
x = cuda_ftensor4('x')
x_shuffled = x.dimshuffle(1, 2, 3, 0)
x_shuffled = gpu_contiguous(x_shuffled)
response_norm = CrossMapNorm(size_f=16,
add_scale=(15. / 16.),
pow_scale=1,
blocked=True)
output_shuffled = response_norm(x_shuffled)[0]
output = output_shuffled.dimshuffle(3, 0, 1, 2)
cost = output.sum()
cost.name = 'cost'
grad_x = theano.grad(cost, x)
f = theano.function([x], grad_x, mode=mode_with_gpu)
x_val = CudaNdarray(numpy.ones((2, 16, 2, 2), dtype='float32'))
f(x_val)
def test_cross_map_norm_simple():
op = CrossMapNorm(16, 15. / 16., 1., True)
x = CudaNdarray(numpy.ones((16, 2, 2, 2), dtype='float32'))
x_ = theano.tensor.TensorVariable(CudaNdarrayType([False] * 4))
f = theano.function([x_], op(x_)[0])
numpy.testing.assert_allclose(f(x), 0.0625)
