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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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73
tests/models/glpn/test_image_processing_glpn.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():
@@ -31,10 +30,10 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import GLPNFeatureExtractor
from transformers import GLPNImageProcessor
class GLPNFeatureExtractionTester(unittest.TestCase):
class GLPNImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
@@ -57,7 +56,7 @@ class GLPNFeatureExtractionTester(unittest.TestCase):
self.size_divisor = size_divisor
self.do_rescale = do_rescale
def prepare_feat_extract_dict(self):
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
@@ -67,62 +66,62 @@ class GLPNFeatureExtractionTester(unittest.TestCase):
@require_torch
@require_vision
class GLPNFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
feature_extraction_class = GLPNFeatureExtractor if is_vision_available() else None
image_processing_class = GLPNImageProcessor if is_vision_available() else None
def setUp(self):
self.feature_extract_tester = GLPNFeatureExtractionTester(self)
self.image_processor_tester = GLPNImageProcessingTester(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_divisor"))
self.assertTrue(hasattr(feature_extractor, "resample"))
self.assertTrue(hasattr(feature_extractor, "do_rescale"))
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size_divisor"))
self.assertTrue(hasattr(image_processing, "resample"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
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_processing
image_processing = 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_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input (GLPNFeatureExtractor doesn't support batching)
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.feature_extract_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.feature_extract_tester.size_divisor == 0)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def test_call_numpy(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processing
image_processing = 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_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input (GLPNFeatureExtractor doesn't support batching)
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.feature_extract_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.feature_extract_tester.size_divisor == 0)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def test_call_pytorch(self):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# Initialize image_processing
image_processing = 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_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input (GLPNFeatureExtractor doesn't support batching)
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.feature_extract_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.feature_extract_tester.size_divisor == 0)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)