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# See "Writing benchmarks" in the asv docs for more information.
# https://asv.readthedocs.io/en/latest/writing_benchmarks.html
import numpy as np
from skimage import transform
class InterpolationResize:
param_names = ['new_shape', 'order', 'mode', 'dtype', 'anti_aliasing']
params = [
((500, 800), (2000, 4000), (80, 80, 80), (150, 150, 150)), # new_shape
(0, 1, 3, 5), # order
('symmetric',), # mode
(np.float64,), # dtype
(True,), # anti_aliasing
]
"""Benchmark for filter routines in scikit-image."""
def setup(self, new_shape, order, mode, dtype, anti_aliasing):
ndim = len(new_shape)
if ndim == 2:
image = np.random.random((1000, 1000))
else:
image = np.random.random((100, 100, 100))
self.image = image.astype(dtype, copy=False)
def time_resize(self, new_shape, order, mode, dtype, anti_aliasing):
transform.resize(
self.image, new_shape, order=order, mode=mode, anti_aliasing=anti_aliasing
)
def time_rescale(self, new_shape, order, mode, dtype, anti_aliasing):
scale = tuple(s2 / s1 for s2, s1 in zip(new_shape, self.image.shape))
transform.rescale(
self.image, scale, order=order, mode=mode, anti_aliasing=anti_aliasing
)
def peakmem_resize(self, new_shape, order, mode, dtype, anti_aliasing):
transform.resize(
self.image, new_shape, order=order, mode=mode, anti_aliasing=anti_aliasing
)
def peakmem_reference(self, *args):
"""Provide reference for memory measurement with empty benchmark.
Peakmem benchmarks measure the maximum amount of RAM used by a
function. However, this maximum also includes the memory used
during the setup routine (as of asv 0.2.1; see [1]_).
Measuring an empty peakmem function might allow us to disambiguate
between the memory used by setup and the memory used by target (see
other ``peakmem_`` functions below).
References
----------
.. [1]: https://asv.readthedocs.io/en/stable/writing_benchmarks.html#peak-memory
"""
pass
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