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Add illustration of using numpy arrays directly in Cython
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‎source-code/cython/Numpy/Convolution/Makefile

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VERSION = cpython-311-x86_64-linux-gnu
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CONVOLUTION_LIB = convolution_cython.$(VERSION).so
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CONVOLUTION_INDEXED_LIB = convolution_cython_indexed.$(VERSION).so
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CONVOLUTION_NO_CHECKS_LIB = convolution_cython_no_checks.$(VERSION).so
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all: $(CONVOLUTION_LIB) $(CONVOLUTION_INDEXED_LIB) $(CONVOLUTION_NO_CHECKS_LIB)
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$(CONVOLUTION_LIB): convolution.pyx
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python setup.py build_ext --inplace
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$(CONVOLUTION_LIB): convolution_indexed.pyx
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python setup.py build_ext --inplace
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$(CONVOLUTION_LIB): convolution_no_checks.pyx
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python setup.py build_ext --inplace
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clean:
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python setup.py clean
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$(RM) $(wildcard *.c) $(wildcard *.so)
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$(RM) -r build

‎source-code/cython/Numpy/Convolution/README.md

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# Convolution
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Illustration of how to improve perfomance when using numpy arrays in Cython
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by doing indexing right. The algorithm used is a naive implementation of
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convolution.
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## What is it?
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1. `convolution.py`: Python implementation of the algorithm (uses numpy).
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1. `convolution.pyx`: Cython implementation that adds types.
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1. `convolution_indexed.pyx`: Cython implementation that adds
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numpy array element types and indexing information.
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1. `convolution_no_checks.pyx`: Cython implementation that adds
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function decorator to avoid index checks for numpy arrays.
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1. `setup.py`: script to build the extensions.
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1. `Makefile`: make file to conveniently build the extensions.
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1. `driver.py`: Python scripts to run benchmark tests on the various
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implementations.

‎source-code/cython/Numpy/Convolution/convolution.py

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import numpy as np
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def convolve(input, kernel):
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'''Naive convolution of matrices input and kernel
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Parameters
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----------
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input : numpy.ndarray
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input matrix
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kernel : numpy.ndarray
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filter kernel matrix, dimensions must be odd
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Returns
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-------
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output : numpy.ndarray
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output matrix, it is not cropped
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Raises
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------
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ValueError
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if dimensions of kernel are not odd
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'''
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if kernel.shape[0] % 2 != 1 or kernel.shape[1] % 2 != 1:
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raise ValueError("Only odd dimensions on filter supported")
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# s_mid and t_mid are number of pixels between the center pixel
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# and the edge, ie for a 5x5 filter they will be 2.
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#
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# The output size is calculated by adding s_mid, t_mid to each
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# side of the dimensions of the input image.
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s_mid = kernel.shape[0] // 2
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t_mid = kernel.shape[1] // 2
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x_max = input.shape[0] + 2 * s_mid
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y_max = input.shape[1] + 2 * t_mid
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# Allocate result image.
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output = np.zeros([x_max, y_max], dtype=input.dtype)
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# Do convolution
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for x in range(x_max):
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for y in range(y_max):
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# Calculate pixel value for h at (x,y). Sum one component
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# for each pixel (s, t) of the filter kernel.
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s_from = max(s_mid - x, -s_mid)
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s_to = min((x_max - x) - s_mid, s_mid + 1)
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t_from = max(t_mid - y, -t_mid)
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t_to = min((y_max - y) - t_mid, t_mid + 1)
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value = 0
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for s in range(s_from, s_to):
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for t in range(t_from, t_to):
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v = x - s_mid + s
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w = y - t_mid + t
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value += kernel[s_mid - s, t_mid - t]*input[v, w]
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output[x, y] = value
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return output

‎source-code/cython/Numpy/Convolution/convolution.pyx

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import numpy as np
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cimport numpy as cnp
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cnp.import_array()
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DTYPE = np.float64
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ctypedef cnp.float64_t DTYPE_t
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def convolve(cnp.ndarray input, cnp.ndarray kernel):
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'''Naive convolution of matrices input and kernel
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Parameters
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----------
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input : numpy.ndarray
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input matrix
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kernel : numpy.ndarray
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filter kernel matrix, dimensions must be odd
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Returns
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-------
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output : numpy.ndarray
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output matrix, it is not cropped
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Raises
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------
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ValueError
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if dimensions of kernel are not odd
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'''
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if kernel.shape[0] % 2 != 1 or kernel.shape[1] % 2 != 1:
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raise ValueError("Only odd dimensions on filter supported")
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assert input.dtype == DTYPE and kernel.dtype == DTYPE
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# s_mid and t_mid are number of pixels between the center pixel
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# and the edge, ie for a 5x5 filter they will be 2.
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#
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# The output size is calculated by adding s_mid, t_mid to each
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# side of the dimensions of the input image.
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cdef int s_mid = kernel.shape[0] // 2
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cdef int t_mid = kernel.shape[1] // 2
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cdef int x_max = input.shape[0] + 2 * s_mid
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cdef int y_max = input.shape[1] + 2 * t_mid
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# Allocate result image.
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cdef cnp.ndarray output = np.zeros([x_max, y_max], dtype=input.dtype)
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# Do convolution
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cdef int x, y, s, t, v, w
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cdef int s_from, s_to, t_from, t_to
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cdef DTYPE_t value
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for x in range(x_max):
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for y in range(y_max):
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# Calculate pixel value for h at (x,y). Sum one component
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# for each pixel (s, t) of the filter kernel.
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s_from = max(s_mid - x, -s_mid)
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s_to = min((x_max - x) - s_mid, s_mid + 1)
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t_from = max(t_mid - y, -t_mid)
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t_to = min((y_max - y) - t_mid, t_mid + 1)
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value = 0
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for s in range(s_from, s_to):
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for t in range(t_from, t_to):
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v = x - s_mid + s
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w = y - t_mid + t
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value += kernel[s_mid - s, t_mid - t]*input[v, w]
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output[x, y] = value
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return output

‎source-code/cython/Numpy/Convolution/convolution_indexed.pyx

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import numpy as np
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cimport numpy as cnp
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cnp.import_array()
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DTYPE = np.float64
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ctypedef cnp.float64_t DTYPE_t
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def convolve(cnp.ndarray[DTYPE_t, ndim=2] input, cnp.ndarray[DTYPE_t, ndim=2] kernel):
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'''Naive convolution of matrices input and kernel
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Parameters
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----------
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input : numpy.ndarray
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input matrix
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kernel : numpy.ndarray
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filter kernel matrix, dimensions must be odd
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Returns
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-------
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output : numpy.ndarray
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output matrix, it is not cropped
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Raises
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------
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ValueError
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if dimensions of kernel are not odd
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'''
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if kernel.shape[0] % 2 != 1 or kernel.shape[1] % 2 != 1:
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raise ValueError("Only odd dimensions on filter supported")
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assert input.dtype == DTYPE and kernel.dtype == DTYPE
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# s_mid and t_mid are number of pixels between the center pixel
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# and the edge, ie for a 5x5 filter they will be 2.
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#
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# The output size is calculated by adding s_mid, t_mid to each
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# side of the dimensions of the input image.
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cdef int s_mid = kernel.shape[0] // 2
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cdef int t_mid = kernel.shape[1] // 2
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cdef int x_max = input.shape[0] + 2 * s_mid
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cdef int y_max = input.shape[1] + 2 * t_mid
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# Allocate result image.
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cdef cnp.ndarray[DTYPE_t, ndim=2] output = np.zeros([x_max, y_max], dtype=input.dtype)
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# Do convolution
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cdef int x, y, s, t, v, w
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cdef int s_from, s_to, t_from, t_to
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cdef DTYPE_t value
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for x in range(x_max):
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for y in range(y_max):
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# Calculate pixel value for h at (x,y). Sum one component
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# for each pixel (s, t) of the filter kernel.
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s_from = max(s_mid - x, -s_mid)
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s_to = min((x_max - x) - s_mid, s_mid + 1)
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t_from = max(t_mid - y, -t_mid)
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t_to = min((y_max - y) - t_mid, t_mid + 1)
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value = 0
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for s in range(s_from, s_to):
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for t in range(t_from, t_to):
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v = x - s_mid + s
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w = y - t_mid + t
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value += kernel[s_mid - s, t_mid - t]*input[v, w]
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output[x, y] = value
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return output

‎source-code/cython/Numpy/Convolution/convolution_no_checks.pyx

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cimport cython
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import numpy as np
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cimport numpy as cnp
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cnp.import_array()
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DTYPE = np.float64
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ctypedef cnp.float64_t DTYPE_t
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def convolve(cnp.ndarray[DTYPE_t, ndim=2] input, cnp.ndarray[DTYPE_t, ndim=2] kernel):
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'''Naive convolution of matrices input and kernel
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Parameters
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----------
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input : numpy.ndarray
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input matrix
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kernel : numpy.ndarray
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filter kernel matrix, dimensions must be odd
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Returns
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-------
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output : numpy.ndarray
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output matrix, it is not cropped
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Raises
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------
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ValueError
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if dimensions of kernel are not odd
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'''
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if kernel.shape[0] % 2 != 1 or kernel.shape[1] % 2 != 1:
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raise ValueError("Only odd dimensions on filter supported")
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assert input.dtype == DTYPE and kernel.dtype == DTYPE
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# s_mid and t_mid are number of pixels between the center pixel
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# and the edge, ie for a 5x5 filter they will be 2.
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#
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# The output size is calculated by adding s_mid, t_mid to each
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# side of the dimensions of the input image.
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cdef int s_mid = kernel.shape[0] // 2
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cdef int t_mid = kernel.shape[1] // 2
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cdef int x_max = input.shape[0] + 2 * s_mid
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cdef int y_max = input.shape[1] + 2 * t_mid
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# Allocate result image.
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cdef cnp.ndarray[DTYPE_t, ndim=2] output = np.zeros([x_max, y_max], dtype=input.dtype)
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# Do convolution
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cdef int x, y, s, t, v, w
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cdef int s_from, s_to, t_from, t_to
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cdef DTYPE_t value
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for x in range(x_max):
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for y in range(y_max):
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# Calculate pixel value for h at (x,y). Sum one component
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# for each pixel (s, t) of the filter kernel.
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s_from = max(s_mid - x, -s_mid)
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s_to = min((x_max - x) - s_mid, s_mid + 1)
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t_from = max(t_mid - y, -t_mid)
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t_to = min((y_max - y) - t_mid, t_mid + 1)
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value = 0
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for s in range(s_from, s_to):
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for t in range(t_from, t_to):
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v = x - s_mid + s
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w = y - t_mid + t
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value += kernel[s_mid - s, t_mid - t]*input[v, w]
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output[x, y] = value
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return output

‎source-code/cython/Numpy/Convolution/driver.py

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#!/usr/bin/env python
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# script to run various implementations of the convolution filter
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# to compare their performance.
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# The script has the following input parameters:
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# --input_x: width of the input matrix
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# --input_y: height of the input matrix
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# --kernel_x: width of the kernel matrix
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# --kernel_y: height of the kernel matrix
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# --iterations: number of iterations to run
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# --implementation: which implementation to run
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import argparse
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import numpy as np
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import timeit
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import sys
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def main():
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parser = argparse.ArgumentParser(description='Run convolution filter')
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parser.add_argument('--input_x', type=int, default=100,
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help='width of the input matrix')
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parser.add_argument('--input_y', type=int, default=100,
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help='height of the input matrix')
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parser.add_argument('--kernel_x', type=int, default=9,
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help='width of the kernel matrix')
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parser.add_argument('--kernel_y', type=int, default=9,
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help='height of the kernel matrix')
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parser.add_argument('--iterations', type=int, default=1,
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help='number of iterations to run')
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parser.add_argument('--implementation', type=str,
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choices=['python', 'cython', 'cython_indexed', 'cython_no_checks'],
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default='python',
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help='which implementation to run')
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parser.add_argument('--check', action='store_true',
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help='check the results')
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args = parser.parse_args()
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# Create input and kernel matrices
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input = np.random.rand(args.input_x, args.input_y)
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kernel = np.random.rand(args.kernel_x, args.kernel_y)
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# Run the requested implementation
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if args.implementation == 'python':
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from convolution import convolve
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elif args.implementation == 'cython':
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from convolution_cython import convolve
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elif args.implementation == 'cython_indexed':
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from convolution_cython_indexed import convolve
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elif args.implementation == 'cython_no_checks':
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from convolution_cython_no_checks import convolve
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# Measure the execution time
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time = timeit.timeit(lambda: convolve(input, kernel), number=args.iterations)
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print(f'Time: {time/args.iterations} s per iteration, {args.iterations} iterations')
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# Check the results
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if args.check:
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from convolution import convolve as convolve_python
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result_python = convolve_python(input, kernel)
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result = convolve(input, kernel)
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if np.allclose(result_python, result):
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print('Results are correct')
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else:
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print('Results are incorrect')
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sys.exit(1)
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if __name__ == '__main__':
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main()

‎source-code/cython/Numpy/Convolution/setup.py

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#!/usr/bin/env python
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from distutils.core import setup
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from Cython.Build import cythonize
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from pathlib import Path
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from distutils.extension import Extension
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home_dir = str(Path.home())
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extensions = [
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Extension(
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"convolution_cython",
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["convolution.pyx"],
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include_dirs=[f'{home_dir}/mambaforge/envs/python_for_hpc/lib/python3.11/site-packages/numpy/core/include/'],
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),
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Extension(
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"convolution_cython_indexed",
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["convolution_indexed.pyx"],
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include_dirs=[f'{home_dir}/mambaforge/envs/python_for_hpc/lib/python3.11/site-packages/numpy/core/include/'],
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),
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Extension(
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"convolution_cython_no_checks",
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["convolution_no_checks.pyx"],
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include_dirs=[f'{home_dir}/mambaforge/envs/python_for_hpc/lib/python3.11/site-packages/numpy/core/include/'],
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),
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]
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setup(
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ext_modules=cythonize(extensions,
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language_level='3str')
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)

‎source-code/cython/Numpy/README.md

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the pure Python Cython implementation of the array sum.
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1. `Makefile`: make file for building the cython extension and profiling
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the three implementations.
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1. `Convolution`: illustration of using numpy arrays directly in Cython
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code.

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