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Fixes #1559

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merged 2 commits into from
Mar 4, 2024
Merged

Fixes #1559

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c2f2449
Fixes
ternaus Mar 4, 2024
bf49148
updated version
ternaus Mar 4, 2024
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20 changes: 14 additions & 6 deletions albumentations/augmentations/mixing/transforms.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import random
from typing import Any, Callable, Dict, Generator, Iterable, Iterator, Optional, Sequence, Tuple, Union
import types
from typing import Any, Callable, Dict, Generator, Iterable, Iterator, List, Optional, Sequence, Tuple, Union
from warnings import warn

import numpy as np
Expand Down Expand Up @@ -61,8 +62,8 @@ class MixUp(ReferenceBasedTransform):

def __init__(
self,
reference_data: Optional[Union[Generator[ReferenceImage, None, None], Sequence[ReferenceImage]]] = None,
read_fn: Callable[[ReferenceImage], Dict[str, Any]] = lambda x: {"image": x, "mask": None, "class_label": None},
reference_data: Optional[Union[Generator[ReferenceImage, None, None], Sequence[Any]]] = None,
read_fn: Callable[[ReferenceImage], Any] = lambda x: {"image": x, "mask": None, "class_label": None},
alpha: float = 0.4,
always_apply: bool = False,
p: float = 0.5,
Expand All @@ -79,8 +80,13 @@ def __init__(
if reference_data is None:
warn("No reference data provided for MixUp. This transform will act as a no-op.")
# Create an empty generator
elif isinstance(reference_data, Iterable) and not isinstance(reference_data, str):
self.reference_data = reference_data
self.reference_data: List[Any] = []
elif (
isinstance(reference_data, types.GeneratorType)
or isinstance(reference_data, Iterable)
and not isinstance(reference_data, str)
):
self.reference_data = reference_data # type: ignore[assignment]
else:
msg = "reference_data must be a list, tuple, generator, or None."
raise TypeError(msg)
Expand Down Expand Up @@ -132,7 +138,9 @@ def get_params(self) -> Dict[str, Union[None, float, Dict[str, Any]]]:
"Further mixing augmentations will not be applied.",
RuntimeWarning,
)
return {"mix_data": None, "mix_coef": 1}
return {"mix_data": {}, "mix_coef": 1}
else:
return {"mix_data": {}, "mix_coef": 1}
mix_coef = beta(self.alpha, self.alpha) if mix_data else 1

return {"mix_data": self.read_fn(mix_data) if mix_data else None, "mix_coef": mix_coef}
28 changes: 22 additions & 6 deletions tests/test_mixing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,14 +22,19 @@ def complex_read_fn_image(x):
[(A.MixUp, {
"reference_data": [{"image": np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)}],
"read_fn": lambda x: x}),
(A.MixUp, {
"reference_data": [1],
"read_fn": lambda x: {"image": np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)}},
),
(A.MixUp, {
"reference_data": None,
}),
(A.MixUp, {
"reference_data": image_generator(),
"read_fn": lambda x: x}),
(A.MixUp, {
"reference_data": complex_image_generator(),
"read_fn": complex_read_fn_image})]

)
"read_fn": complex_read_fn_image})] )
def test_image_only(augmentation_cls, params, image):
aug = augmentation_cls(p=1, **params)
data = aug(image=image)
Expand All @@ -40,7 +45,13 @@ def test_image_only(augmentation_cls, params, image):
[(A.MixUp, {
"reference_data": [{"image": np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8),
"global_label": np.array([0, 0, 1])}],
"read_fn": lambda x: x})]
"read_fn": lambda x: x}),
(A.MixUp, {
"reference_data": [1],
"read_fn": lambda x: {"image": np.ones((100, 100, 3)).astype(np.uint8),
"global_label": np.array([0, 0, 1])}},
),
]
)
def test_image_global_label(augmentation_cls, params, image, global_label):
aug = augmentation_cls(p=1, **params)
Expand All @@ -49,8 +60,13 @@ def test_image_global_label(augmentation_cls, params, image, global_label):

assert data["image"].dtype == np.uint8

mix_coeff_image = find_mix_coef(data["image"], image, aug.reference_data[0]["image"])
mix_coeff_label = find_mix_coef(data["global_label"], global_label, aug.reference_data[0]["global_label"])
reference_item = params["read_fn"](aug.reference_data[0])

reference_image = reference_item["image"]
reference_global_label = reference_item["global_label"]

mix_coeff_image = find_mix_coef(data["image"], image, reference_image)
mix_coeff_label = find_mix_coef(data["global_label"], global_label, reference_global_label)

assert math.isclose(mix_coeff_image, mix_coeff_label, abs_tol=0.01)
assert 0 <= mix_coeff_image <= 1
Expand Down
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