=================================== FAILURES ===================================
___________________ TestBatchLoader.test_basic_functionality ___________________

self = <test_multicore.TestBatchLoader testMethod=test_basic_functionality>

    def test_basic_functionality(self):
        def _load_func():
            for _ in sm.xrange(20):
                yield ia.Batch(images=np.zeros((2, 4, 4, 3), dtype=np.uint8))
    
        warnings.simplefilter("always")
        with warnings.catch_warnings(record=True) as caught_warnings:
            for nb_workers in [1, 2]:
                # repeat these tests many times to catch rarer race conditions
                for _ in sm.xrange(5):
                    loader = multicore.BatchLoader(
                        _load_func, queue_size=2, nb_workers=nb_workers,
                        threaded=True)
                    loaded = []
                    counter = 0
                    while ((not loader.all_finished()
                            or not loader.queue.empty())
                            and counter < 1000):
                        try:
                            batch = loader.queue.get(timeout=0.001)
                            loaded.append(batch)
                        except:
                            pass
                        counter += 1
                    assert len(loaded) == 20*nb_workers, \
                        "Expected %d to be loaded by threads, got %d for %d " \
                        "workers at counter %d." % (
                            20*nb_workers, len(loaded), nb_workers, counter
                        )
    
                    loader = multicore.BatchLoader(
                        _load_func, queue_size=200, nb_workers=nb_workers,
                        threaded=True)
                    loader.terminate()
                    assert loader.all_finished()
    
                    loader = multicore.BatchLoader(
                        _load_func, queue_size=2, nb_workers=nb_workers,
>                       threaded=False)

test/test_multicore.py:820: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
imgaug/imgaug.py:183: in wrapped
    return func(*args, **kwargs)
imgaug/multicore.py:558: in __init__
    worker.start()
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/process.py:112: in start
    self._popen = self._Popen(self)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/context.py:223: in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/context.py:284: in _Popen
    return Popen(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_spawn_posix.py:32: in __init__
    super().__init__(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_fork.py:20: in __init__
    self._launch(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_spawn_posix.py:47: in _launch
    reduction.dump(process_obj, fp)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = <Process(Process-38, initial daemon)>
file = <_io.BytesIO object at 0x7fffc8b21110>, protocol = None

    def dump(obj, file, protocol=None):
        '''Replacement for pickle.dump() using ForkingPickler.'''
>       ForkingPickler(file, protocol).dump(obj)
E       AttributeError: Can't pickle local object 'TestBatchLoader.test_basic_functionality.<locals>._load_func'

/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/reduction.py:60: AttributeError
_ TestAugmenter_augment_batches.test_augment_batches_with_many_different_augmenters _

self = <test_meta.TestAugmenter_augment_batches testMethod=test_augment_batches_with_many_different_augmenters>

    def test_augment_batches_with_many_different_augmenters(self):
        image = np.array([[0, 0, 1, 1, 1],
                          [0, 0, 1, 1, 1],
                          [0, 1, 1, 1, 1]], dtype=np.uint8)
        keypoint = ia.Keypoint(x=2, y=1)
        keypoints = [ia.KeypointsOnImage([keypoint], shape=image.shape + (1,))]
    
        def _lambda_func_images(images, random_state, parents, hooks):
            return images
    
        def _lambda_func_keypoints(keypoints_on_images, random_state,
                                   parents, hooks):
            return keypoints_on_images
    
        def _assertlambda_func_images(images, random_state, parents, hooks):
            return True
    
        def _assertlambda_func_keypoints(keypoints_on_images, random_state, parents, hooks):
            return True
    
        augs = [
            iaa.Sequential([iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
            iaa.SomeOf(1, [iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
            iaa.OneOf([iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
            iaa.Sometimes(1.0, iaa.Fliplr(1)),
            iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
            iaa.WithChannels([0], iaa.Add((-50, 50))),
            iaa.Identity(name="Identity-nochange"),
            iaa.Lambda(
                func_images=_lambda_func_images,
                func_keypoints=_lambda_func_keypoints,
                name="Lambda-nochange"
            ),
            iaa.AssertLambda(
                func_images=_assertlambda_func_images,
                func_keypoints=_assertlambda_func_keypoints,
                name="AssertLambda-nochange"
            ),
            iaa.AssertShape(
                (None, 64, 64, 3),
                check_keypoints=False,
                name="AssertShape-nochange"
            ),
            iaa.Resize((0.5, 0.9)),
            iaa.CropAndPad(px=(-50, 50)),
            iaa.Pad(px=(1, 50)),
            iaa.Crop(px=(1, 50)),
            iaa.Fliplr(1.0),
            iaa.Flipud(1.0),
            iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
            iaa.ChangeColorspace(to_colorspace="GRAY"),
            iaa.Grayscale(alpha=(0.1, 1.0)),
            iaa.GaussianBlur(1.0),
            iaa.AverageBlur(5),
            iaa.MedianBlur(5),
            iaa.Convolve(np.array([[0, 1, 0],
                                   [1, -4, 1],
                                   [0, 1, 0]])),
            iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
            iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
            iaa.EdgeDetect(alpha=(0.1, 1.0)),
            iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
            iaa.Add((-50, 50)),
            iaa.AddElementwise((-50, 50)),
            iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
            iaa.Multiply((0.6, 1.4)),
            iaa.MultiplyElementwise((0.6, 1.4)),
            iaa.Dropout((0.3, 0.5)),
            iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
            iaa.Invert(0.5),
            iaa.Affine(
                scale=(0.7, 1.3),
                translate_percent=(-0.1, 0.1),
                rotate=(-20, 20),
                shear=(-20, 20),
                order=ia.ALL,
                mode=ia.ALL,
                cval=(0, 255)),
            iaa.PiecewiseAffine(scale=(0.1, 0.3)),
            iaa.ElasticTransformation(alpha=0.5)
        ]
    
        nb_iterations = 100
        image = ia.quokka(size=(64, 64))
        batches = [ia.Batch(images=[np.copy(image)],
                            keypoints=[keypoints[0].deepcopy()])
                   for _ in sm.xrange(nb_iterations)]
        for aug in augs:
            nb_changed = 0
>           batches_aug = list(aug.augment_batches(batches, background=True))

test/augmenters/test_meta.py:2846: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
imgaug/augmenters/meta.py:506: in augment_batches
    with multicore.Pool(self) as pool:
imgaug/multicore.py:362: in __enter__
    _ = self.pool  # initialize internal multiprocessing pool instance
imgaug/multicore.py:177: in pool
    maxtasksperchild=self.maxtasksperchild)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/context.py:119: in Pool
    context=self.get_context())
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/pool.py:176: in __init__
    self._repopulate_pool()
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/pool.py:241: in _repopulate_pool
    w.start()
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/process.py:112: in start
    self._popen = self._Popen(self)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/context.py:284: in _Popen
    return Popen(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_spawn_posix.py:32: in __init__
    super().__init__(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_fork.py:20: in __init__
    self._launch(process_obj)
/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/popen_spawn_posix.py:47: in _launch
    reduction.dump(process_obj, fp)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = <SpawnProcess(SpawnPoolWorker-216, initial daemon)>
file = <_io.BytesIO object at 0x7fffc7d23470>, protocol = None

    def dump(obj, file, protocol=None):
        '''Replacement for pickle.dump() using ForkingPickler.'''
>       ForkingPickler(file, protocol).dump(obj)
E       AttributeError: Can't pickle local object 'TestAugmenter_augment_batches.test_augment_batches_with_many_different_augmenters.<locals>._lambda_func_images'

/nix/store/drr8qcgiccfc5by09r5zc30flgwh1mbx-python3-3.7.5/lib/python3.7/multiprocessing/reduction.py:60: AttributeError
=============================== warnings summary ===============================
test/test_dtypes.py::Test_change_dtype_::test_clip_and_round
  /build/imgaug-58fac47309fb094269fe581412746f236a848f12/imgaug/imgaug.py:308: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
    return isinstance(val, collections.Iterable)