Multiple Backend Support¶
The NumPy backend is the reference implementation of the transform. All algorithms and transforms will have a NumPy backend. NumPy implementations are written to be efficient but also clear in their operation.
Some transforms and algorithms implement an OpenCL backend. This backend, if present, will provide an identical API to the NumPy backend. NumPy-based input may be passed in and out of the backends but if OpenCL-based input is passed in, a copy back to the host may be avoided in some cases. Not all transforms or algorithms have an OpenCL-based implementation and the implementation itself may not be full-featured.
OpenCL support depends on the PyOpenCL package being installed and an OpenCL implementation being installed on your machine. Attempting to use an OpenCL backen without both of these being present will result in a runtime (but not import-time) exception.
Which backend should I use?¶
The top-level transform routines, such as :py:class`dtcwt.Transform2d`, will automatically use the NumPy backend. If you are not primarily focussed on speed, this is the correct choice since the NumPy backend has the fullest feature support, is the best tested and behaves correctly given single- and double-precision input.
If you care about speed and need only single-precision calculations, the OpenCL backend can provide significant speed-up. On the author’s system, the 2D transform sees around a times 10 speed improvement.
Using a backend¶
Access to the 2D transform is via a
dtcwt.Transform2d instance. For
example, to compute the 2D DT-CWT of the 2D real array in X:
>>> from dtcwt.numpy import Transform2d >>> trans = Transform2d() # You may optionally specify which wavelets to use here >>> Y = trans.forward(X, nlevels=4) # Perform a 4-level transform of X >>> imshow(Y.lowpass) # Show coarsest scale low-pass image >>> imshow(Y.highpasses[-1][:,:,0]) # Show first coarsest scale subband
In this case Y is an instance of a class which behaves like
dtcwt.Pyramid. Backends are free to
return whatever result they like as long as the result can be used like this
base class. (For example, the OpenCL backend returns a
dtcwt.opencl.Pyramid instance which
keeps the device-side results available.)
dtcwt.push_backend('opencl') # ... Transform2d, etc now use OpenCL ...
# Run benchmark with NumPy my_benchmarking_function() # Run benchmark with OpenCL dtcwt.push_backend('opencl') my_benchmarking_function() dtcwt.pop_backend()
It is safer to use the
dtcwt.preserve_backend_stack() function. This
returns a guard object which can be used with the
with statement to save
the state of the backend stack
with dtcwt.preserve_backend_stack(): dtcwt.push_backend('opencl') my_benchmarking_function() # Outside of the 'with' clause the backend is reset to numpy.
Finally the default backend may be set via the
variable. This is useful to run scripts with different backends without having
to modify their source.