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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from mmdet.datasets.transforms import (AutoAugment, AutoContrast, Brightness,
Color, Contrast, Equalize, Invert,
Posterize, RandAugment, Rotate,
Sharpness, ShearX, ShearY, Solarize,
SolarizeAdd, TranslateX, TranslateY)
from mmdet.utils import register_all_modules
from .utils import check_result_same, construct_toy_data
register_all_modules()
class TestAutoAugment(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which are used in every test method.
TestCase calls functions in this order: setUp() -> testMethod() ->
tearDown() -> cleanUp()
"""
self.check_keys = ('img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_masks',
'gt_ignore_flags', 'gt_seg_map',
'homography_matrix')
self.results_mask = construct_toy_data(poly2mask=True)
self.img_fill_val = (104, 116, 124)
self.seg_ignore_label = 255
def test_autoaugment(self):
# test AutoAugment equipped with Shear
policies = [[
dict(type='ShearX', prob=1.0, level=3, reversal_prob=0.0),
dict(type='ShearY', prob=1.0, level=7, reversal_prob=1.0)
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_shearx = ShearX(prob=1.0, level=3, reversal_prob=0.0)
transform_sheary = ShearY(prob=1.0, level=7, reversal_prob=1.0)
results_sheared = transform_sheary(
transform_shearx(copy.deepcopy(self.results_mask)))
check_result_same(results_sheared, results_auto, self.check_keys)
# test AutoAugment equipped with Rotate
policies = [[
dict(type='Rotate', prob=1.0, level=10, reversal_prob=0.0),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_rotate = Rotate(prob=1.0, level=10, reversal_prob=0.0)
results_rotated = transform_rotate(copy.deepcopy(self.results_mask))
check_result_same(results_rotated, results_auto, self.check_keys)
# test AutoAugment equipped with Translate
policies = [[
dict(
type='TranslateX',
prob=1.0,
level=10,
max_mag=1.0,
reversal_prob=0.0),
dict(
type='TranslateY',
prob=1.0,
level=10,
max_mag=1.0,
reversal_prob=1.0)
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_translatex = TranslateX(
prob=1.0, level=10, max_mag=1.0, reversal_prob=0.0)
transform_translatey = TranslateY(
prob=1.0, level=10, max_mag=1.0, reversal_prob=1.0)
results_translated = transform_translatey(
transform_translatex(copy.deepcopy(self.results_mask)))
check_result_same(results_translated, results_auto, self.check_keys)
# test AutoAugment equipped with Brightness
policies = [[
dict(type='Brightness', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_brightness = Brightness(prob=1.0, level=3)
results_brightness = transform_brightness(
copy.deepcopy(self.results_mask))
check_result_same(results_brightness, results_auto, self.check_keys)
# test AutoAugment equipped with Color
policies = [[
dict(type='Color', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_color = Color(prob=1.0, level=3)
results_colored = transform_color(copy.deepcopy(self.results_mask))
check_result_same(results_colored, results_auto, self.check_keys)
# test AutoAugment equipped with Contrast
policies = [[
dict(type='Contrast', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_contrast = Contrast(prob=1.0, level=3)
results_contrasted = transform_contrast(
copy.deepcopy(self.results_mask))
check_result_same(results_contrasted, results_auto, self.check_keys)
# test AutoAugment equipped with Sharpness
policies = [[
dict(type='Sharpness', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_sharpness = Sharpness(prob=1.0, level=3)
results_sharpness = transform_sharpness(
copy.deepcopy(self.results_mask))
check_result_same(results_sharpness, results_auto, self.check_keys)
# test AutoAugment equipped with Solarize
policies = [[
dict(type='Solarize', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_solarize = Solarize(prob=1.0, level=3)
results_solarized = transform_solarize(
copy.deepcopy(self.results_mask))
check_result_same(results_solarized, results_auto, self.check_keys)
# test AutoAugment equipped with SolarizeAdd
policies = [[
dict(type='SolarizeAdd', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_solarizeadd = SolarizeAdd(prob=1.0, level=3)
results_solarizeadded = transform_solarizeadd(
copy.deepcopy(self.results_mask))
check_result_same(results_solarizeadded, results_auto, self.check_keys)
# test AutoAugment equipped with Posterize
policies = [[
dict(type='Posterize', prob=1.0, level=3),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_posterize = Posterize(prob=1.0, level=3)
results_posterized = transform_posterize(
copy.deepcopy(self.results_mask))
check_result_same(results_posterized, results_auto, self.check_keys)
# test AutoAugment equipped with Equalize
policies = [[
dict(type='Equalize', prob=1.0),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_equalize = Equalize(prob=1.0)
results_equalized = transform_equalize(
copy.deepcopy(self.results_mask))
check_result_same(results_equalized, results_auto, self.check_keys)
# test AutoAugment equipped with AutoContrast
policies = [[
dict(type='AutoContrast', prob=1.0),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_autocontrast = AutoContrast(prob=1.0)
results_autocontrast = transform_autocontrast(
copy.deepcopy(self.results_mask))
check_result_same(results_autocontrast, results_auto, self.check_keys)
# test AutoAugment equipped with Invert
policies = [[
dict(type='Invert', prob=1.0),
]]
transform_auto = AutoAugment(policies=policies)
results_auto = transform_auto(copy.deepcopy(self.results_mask))
transform_invert = Invert(prob=1.0)
results_inverted = transform_invert(copy.deepcopy(self.results_mask))
check_result_same(results_inverted, results_auto, self.check_keys)
# test AutoAugment equipped with default policies
transform_auto = AutoAugment()
transform_auto(copy.deepcopy(self.results_mask))
def test_repr(self):
policies = [[
dict(type='Rotate', prob=1.0, level=10, reversal_prob=0.0),
dict(type='Invert', prob=1.0),
]]
transform = AutoAugment(policies=policies)
self.assertEqual(
repr(transform), ('AutoAugment('
'policies=[['
"{'type': 'Rotate', 'prob': 1.0, "
"'level': 10, 'reversal_prob': 0.0}, "
"{'type': 'Invert', 'prob': 1.0}]], "
'prob=None)'))
class TestRandAugment(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which are used in every test method.
TestCase calls functions in this order: setUp() -> testMethod() ->
tearDown() -> cleanUp()
"""
self.check_keys = ('img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_masks',
'gt_ignore_flags', 'gt_seg_map',
'homography_matrix')
self.results_mask = construct_toy_data(poly2mask=True)
self.img_fill_val = (104, 116, 124)
self.seg_ignore_label = 255
def test_randaugment(self):
# test RandAugment equipped with Rotate
aug_space = [[
dict(type='Rotate', prob=1.0, level=10, reversal_prob=0.0)
]]
transform_rand = RandAugment(aug_space=aug_space, aug_num=1)
results_rand = transform_rand(copy.deepcopy(self.results_mask))
transform_rotate = Rotate(prob=1.0, level=10, reversal_prob=0.0)
results_rotated = transform_rotate(copy.deepcopy(self.results_mask))
check_result_same(results_rotated, results_rand, self.check_keys)
# test RandAugment equipped with default augmentation space
transform_rand = RandAugment()
transform_rand(copy.deepcopy(self.results_mask))
def test_repr(self):
aug_space = [
[dict(type='Rotate')],
[dict(type='Invert')],
]
transform = RandAugment(aug_space=aug_space)
self.assertEqual(
repr(transform), ('RandAugment('
'aug_space=['
"[{'type': 'Rotate'}], "
"[{'type': 'Invert'}]], "
'aug_num=2, '
'prob=None)'))
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