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Refactor image processor testers (#25450)

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* Refactor image processor test mixin

- Move test_call_numpy, test_call_pytorch, test_call_pil to mixin
- Rename mixin to reflect handling of logic more than saving
- Add prepare_image_inputs, expected_image_outputs for tests

* Fix for oneformer
This commit is contained in:
amyeroberts
2023-08-11 11:30:18 +01:00
committed by GitHub
parent 454957c9bb
commit 41d56ea6dd
42 changed files with 993 additions and 3763 deletions
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27
tests/models/donut/test_image_processing_donut.py
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@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
@@ -78,10 +78,24 @@ class DonutImageProcessingTester(unittest.TestCase):
"image_std": self.image_std,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
class DonutImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DonutImageProcessor if is_vision_available() else None
def setUp(self):
@@ -113,15 +127,12 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=(42, 84))
self.assertEqual(image_processor.size, {"height": 84, "width": 42})
def test_batch_feature(self):
pass
@is_flaky()
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
@@ -154,7 +165,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
@@ -187,7 +198,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)