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Update tests: replace feature extractor tests with image processor (#20768)

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* Update imports and test fetcher

* Revert but keep test fetcher update

* Fix imports

* Fix all imports

* Replace fe with ip names

* Add generate kwargs to `AutomaticSpeechRecognitionPipeline` (#20952)

* Add generate kwargs to AutomaticSpeechRecognitionPipeline

* Add test for generation kwargs

* Update image processor parameters if creating with kwargs (#20866)

* Update parameters if creating with kwargs

* Shallow copy to prevent mutating input

* Pass all args in constructor dict - warnings in init

* Fix typo

* Rename tester class

* Rebase and tidy up

* Fixup

* Use ImageProcessingSavingTestMixin

* Update property ref in tests

* Update property ref in tests

* Update recently merged in models

* Small fix

Co-authored-by: bofeng huang <bofenghuang7@gmail.com>
This commit is contained in:
amyeroberts
2023-01-23 17:25:41 +00:00
committed by GitHub
parent 354ea44340
commit e2bd7f80d0
31 changed files with 1974 additions and 2002 deletions
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148
tests/models/blip/test_image_processing_blip.py
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@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
@@ -65,7 +65,7 @@ class BlipImageProcessingTester(unittest.TestCase):
self.do_pad = do_pad
self.do_convert_rgb = do_convert_rgb
def prepare_feat_extract_dict(self):
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
@@ -109,180 +109,180 @@ class BlipImageProcessingTester(unittest.TestCase):
@require_torch
@require_vision
class BlipImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
class BlipImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
feature_extraction_class = BlipImageProcessor if is_vision_available() else None
image_processing_class = BlipImageProcessor if is_vision_available() else None
def setUp(self):
self.feature_extract_tester = BlipImageProcessingTester(self)
self.image_processor_tester = BlipImageProcessingTester(self)
@property
def feat_extract_dict(self):
return self.feature_extract_tester.prepare_feat_extract_dict()
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_feat_extract_properties(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
self.assertTrue(hasattr(feature_extractor, "do_resize"))
self.assertTrue(hasattr(feature_extractor, "size"))
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
self.assertTrue(hasattr(feature_extractor, "image_mean"))
self.assertTrue(hasattr(feature_extractor, "image_std"))
self.assertTrue(hasattr(feature_extractor, "do_convert_rgb"))
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False)
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.feature_extract_tester.batch_size,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, numpify=True)
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.feature_extract_tester.batch_size,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, torchify=True)
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.feature_extract_tester.batch_size,
self.feature_extract_tester.num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
@require_torch
@require_vision
class BlipImageProcessingTestFourChannels(FeatureExtractionSavingTestMixin, unittest.TestCase):
class BlipImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unittest.TestCase):
feature_extraction_class = BlipImageProcessor if is_vision_available() else None
image_processing_class = BlipImageProcessor if is_vision_available() else None
def setUp(self):
self.feature_extract_tester = BlipImageProcessingTester(self, num_channels=4)
self.image_processor_tester = BlipImageProcessingTester(self, num_channels=4)
self.expected_encoded_image_num_channels = 3
@property
def feat_extract_dict(self):
return self.feature_extract_tester.prepare_feat_extract_dict()
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_feat_extract_properties(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
self.assertTrue(hasattr(feature_extractor, "do_resize"))
self.assertTrue(hasattr(feature_extractor, "size"))
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
self.assertTrue(hasattr(feature_extractor, "image_mean"))
self.assertTrue(hasattr(feature_extractor, "image_std"))
self.assertTrue(hasattr(feature_extractor, "do_convert_rgb"))
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_batch_feature(self):
pass
def test_call_pil_four_channels(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False)
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.expected_encoded_image_num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.feature_extract_tester.batch_size,
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.feature_extract_tester.size["height"],
self.feature_extract_tester.size["width"],
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)