Multiple Backend Support

The dtcwt library currently provides two backends for computing the wavelet transform: a NumPy based implementation and an OpenCL implementation which uses the PyOpenCL bindings for Python.


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

The NumPy and OpenCL backends live in the dtcwt.numpy and dtcwt.opencl modules respectively. Both provide implementations of some subset of the DTCWT library functionality.

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.)

The default backend used by dtcwt.Transform2d, etc can be manipulated using the dtcwt.push_backend() function. For example, to switch to the OpenCL backend

# ... Transform2d, etc now use OpenCL ...

As is suggested by the name, changing the backend manipulates a stack behind the scenes and so one can temporarily switch backend using dtcwt.push_backend() and dtcwt.pop_backend()

# Run benchmark with NumPy

# Run benchmark with OpenCL

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():

# Outside of the 'with' clause the backend is reset to numpy.

Finally the default backend may be set via the DTCWT_BACKEND environment variable. This is useful to run scripts with different backends without having to modify their source.