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# ============================================================================================= #
# Author: Samuel Šuľan, Lucia Hradecká, Filip Lux #
# Copyright: Lucia Hradecká : lucia.d.hradecka@gmail.com #
# Filip Lux : lux.filip@gmail.com #
# #
# MIT License. #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# ============================================================================================= #
from bio_volumentations.core.composition import *
from bio_volumentations.augmentations import *
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import time
size_sample = [(1, 64, 65, 66), (1, 128, 128, 128), (1, 256, 256, 256), (1, 512, 512, 64)]
size_sample = [(1, 64, 65, 66), (1, 128, 128, 128), (1, 256, 256, 256)]
# None are placeholders
augmentations_to_check = [
RandomAffineTransform(angle_limit=[22.5, 22.5, 22.5], p=1),
RandomAffineTransform(angle_limit=[22.5, 22.5, 22.5],
translation_limit=[10, 10, 10],
scaling_limit=[.2, .2, .2],
spacing=[1, 0.5, 2],
p=1),
RandomScale(scaling_limit=(0.75, 1), p=1),
RandomScale(scaling_limit=(1, 1.5), p=1),
Scale(scales=0.75, p=1),
Scale(scales=1.5, p=1)
# Flip(axes=[1, 2, 3], p=1),
# GaussianBlur(sigma=0, p=1),
# GaussianNoise(var_limit=(0.001, 0.1), mean=0, p=1),
# HistogramEqualization(bins=256, p=1),
# Normalize(mean=0, std=1, p=1),
# NormalizeMeanStd(mean=0.1, std=1, p=1),
# RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.1),
# RandomFlip(axes_to_choose=None, p=1),
# RandomGamma(gamma_limit=(0.8, 1.2), p=1),
# RandomGaussianBlur(max_sigma=0.8, p=1),
# RandomRotate90(axes=[1, 2, 3] , p=1),
# Scale(scale_factor=1.5, p=1),
# Scale(scale_factor=0.75, p=1),"""
]
def get_augmentations(number: int, shape):
bigger_shape = (shape[1] * 1.5, shape[2] * 1.5, shape[3] * 1.5)
smaller_shape = (shape[1] * 0.75, shape[2] * 0.75, shape[3] * 0.75)
augmentations = [
CenterCrop(shape=bigger_shape, p=1),
CenterCrop(shape=smaller_shape, p=1),
RandomCrop(shape=bigger_shape, p=1),
RandomCrop(shape=smaller_shape, p=1),
Resize(bigger_shape, p=1),
Resize(smaller_shape, p=1),
]
return augmentations[number]
def single_transform(iterations, size, augumentation):
cummulative = 0
maximum = 0
for i in range(iterations):
test = np.random.uniform(low=0, high=1, size=size)
aug = Compose(transforms=[augumentation], p=1)
data = {'image': test}
second_time = time.time()
aug_data = aug(**data)
_ = aug_data['image'].shape
time_spent = time.time() - second_time
cummulative += time_spent
if time_spent > maximum:
maximum = time_spent
return maximum, cummulative
def augmentation_getter(augmentations, iteration):
if augmentations[iteration] is not None:
return augmentations[iteration]
def transformation_speed_benchmark(iterations):
f = open("./100iterationCorrections_aff.txt", "w")
for i, augmentation in enumerate(augmentations_to_check): # random_scale_transform
for size in size_sample:
# augumentation = augumentation_getter(augmentations_to_check, i, size)
aug_name = augmentation.__class__.__name__
print(aug_name)
test_sample = np.random.uniform(low=0, high=1, size=size)
test = test_sample.copy()
aug = Compose(transforms=[augmentation], p=1)
data = {'image': test}
first_time = time.time()
aug_data = aug(**data)
first_result = time.time() - first_time
maximum, cumulative = single_transform(iterations, size, augmentation)
result_time = cumulative / iterations
f.write(f"Transform: {aug_name}, Size: {size}, FirstRun: {first_result:.3f}, " +
f"Average: {result_time:.3f}, Iterations: {iterations}, maximum: {maximum:.3f} \n")
print(f"Transform: {aug_name}, Size: {size}, FirstRun: {first_result:.3f}, " +
f"Average: {result_time:.3f}, Iterations: {iterations}, maximum: {maximum:.3f} \n")
f.close()
if __name__ == '__main__':
transformation_speed_benchmark(10)