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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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103
tests/models/efficientformer/test_image_processing_efficientformer.py
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@@ -21,8 +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 prepare_image_inputs
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
@@ -31,7 +30,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import ViTFeatureExtractor
from transformers import ViTImageProcessor
class EfficientFormerImageProcessorTester(unittest.TestCase):
@@ -62,7 +61,7 @@ class EfficientFormerImageProcessorTester(unittest.TestCase):
self.image_mean = image_mean
self.image_std = image_std
def prepare_feat_extract_dict(self):
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
@@ -74,120 +73,120 @@ class EfficientFormerImageProcessorTester(unittest.TestCase):
@require_torch
@require_vision
class EfficientFormerImageProcessorTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
class EfficientFormerImageProcessorTest(ImageProcessingSavingTestMixin, unittest.TestCase):
feature_extraction_class = ViTFeatureExtractor if is_vision_available() else None
image_processing_class = ViTImageProcessor if is_vision_available() else None
def setUp(self):
self.feature_extract_tester = EfficientFormerImageProcessorTester(self)
self.image_proc_tester = EfficientFormerImageProcessorTester(self)
@property
def feat_extract_dict(self):
return self.feature_extract_tester.prepare_feat_extract_dict()
def image_processor_dict(self):
return self.image_proc_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, "image_mean"))
self.assertTrue(hasattr(feature_extractor, "image_std"))
self.assertTrue(hasattr(feature_extractor, "do_normalize"))
self.assertTrue(hasattr(feature_extractor, "do_resize"))
self.assertTrue(hasattr(feature_extractor, "size"))
def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
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 = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
image_inputs = prepare_image_inputs(self.image_proc_tester, 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_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_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_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_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 = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
image_inputs = prepare_image_inputs(self.image_proc_tester, 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_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_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_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_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 = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
image_inputs = prepare_image_inputs(self.image_proc_tester, 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_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_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_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)