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[Image Transformers] to_pil fix float edge cases (#20406)

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* Correct type checking

* up
This commit is contained in:
Patrick von Platen
2022-11-23 13:47:59 +01:00
committed by GitHub
parent 1c6309bf79
commit 81c46679bd
2 changed files with 24 additions and 1 deletions
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2
src/transformers/image_transforms.py
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@@ -145,7 +145,7 @@ def to_pil_image(
image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image
# PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed. # PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed.
do_rescale = isinstance(image.flat[0], float) if do_rescale is None else do_rescale do_rescale = isinstance(image.flat[0], (float, np.float32, np.float64)) if do_rescale is None else do_rescale
if do_rescale: if do_rescale:
image = rescale(image, 255) image = rescale(image, 255)
image = image.astype(np.uint8) image = image.astype(np.uint8)

23
tests/test_image_transforms.py
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@@ -61,6 +61,8 @@ class ImageTransformsTester(unittest.TestCase):
[ [
("numpy_float_channels_first", (3, 4, 5), np.float32), ("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float32), ("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float64),
("numpy_int_channels_first", (3, 4, 5), np.int32), ("numpy_int_channels_first", (3, 4, 5), np.int32),
("numpy_uint_channels_first", (3, 4, 5), np.uint8), ("numpy_uint_channels_first", (3, 4, 5), np.uint8),
] ]
@@ -72,6 +74,27 @@ class ImageTransformsTester(unittest.TestCase):
self.assertIsInstance(pil_image, PIL.Image.Image) self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4)) self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
@parameterized.expand(
[
("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float64),
]
)
@require_vision
def test_to_pil_image_from_float(self, name, image_shape, dtype):
image = np.random.rand(*image_shape).astype(dtype)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
@require_tf @require_tf
def test_to_pil_image_from_tensorflow(self): def test_to_pil_image_from_tensorflow(self):
# channels_first # channels_first