Comments (4)
What we often do is temp.numpy()
and then reshape to get a 2D image from a enoki array in Python. This might be faster than torch()
.
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I believe you can get a pointer accessible from the CPU with the following code:
auto my_cpu_data_ptr = my_gpu_data.managed().data();
From this pointer you should be able to create your CPU array. Does this answer your question?
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I am not copying the pointer but want to access the value for a GPU array, for example I want to read the value of a FloatD and save its value and the gradient into an .exr file. However, since I used monte carlo on that FloatD which makes the graph of that variable really complex, I am wondering if enoki has a solution for this. Just to be clear, if I have
FloatD temp = zero<FloatD>(1000000)
pdf = 1/1000000
for i in range(0, 1000000):
temp += function(temp) * pdf
after this I can try
image = temp.torch().cpu() // really slow
loadtoOpenEXR(image)
so I am curious if mitsuba2 have this problem or there is any way to directly save a GPU enoki array into openEXR
from enoki.
Also, I don't think there would be a reason for the size of the graph associated to your FloatD
to influence the time it takes to copy it to the CPU (at least when using an Enoki-only method).
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