mirror of
https://github.com/wassname/scikit-image.git
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François Boulogne
68 lines
2.0 KiB
Plaintext
Executable File
68 lines
2.0 KiB
Plaintext
Executable File
=========================
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How to parallelize loops?
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=========================
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In image processing, we frequently apply the same algorithm
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on a large batch of images. Let us define an example.
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.. code-block:: python
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from skimage import data, color
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from skimage.restoration import denoise_tv_chambolle
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from skimage.feature import hog
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def task(image):
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"""
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Apply some functions and return an image.
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"""
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image = denoise_tv_chambolle(image, weight=0.1, multichannel=True)
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fd, hog_image = hog(color.rgb2gray(image), orientations=8,
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pixels_per_cell=(16, 16), cells_per_block=(1, 1),
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visualise=True)
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return hog_image
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# Prepare images
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hubble = data.hubble_deep_field()
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width = 10
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pics = [hubble[:,slice:slice+width] for slice in range(0, 1000, width)]
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To call the function ``task`` on each element of the list ``pics``, it is
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usual to write a for loop. To measure the execution time of this loop, a function
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is defined and called with ``timeit``.
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.. code-block:: python
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def classic_loop():
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for image in pics:
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task(image)
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import timeit
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print("classic_loop():", timeit.timeit("classic_loop()", setup="from __main__ import (classic_loop, task, pics)", number=1))
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Alternatively, you can use ipython and measure the execution time with ``%timeit``.
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.. code-block:: python
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%timeit classic_loop()
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Another equivalent way to code this loop is to use a comprehension list which has the same efficiency.
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.. code-block:: python
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def comprehension_loop():
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[task(image) for image in pics]
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%timeit comprehension_loop()
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``joblib`` is a library providing an easy way to parallelize for loops once we have a comprehension list.
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The number of jobs can be specified.
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.. code-block:: python
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from joblib import Parallel, delayed
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def joblib_loop():
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Parallel(n_jobs=4)(delayed(task)(i) for i in pics)
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%timeit joblib_loop()
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