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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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29
tests/models/oneformer/test_image_processing_oneformer.py
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@@ -23,8 +23,7 @@ from huggingface_hub import hf_hub_download
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():
@@ -100,7 +99,7 @@ class OneFormerImageProcessorTester(unittest.TestCase):
self.do_reduce_labels = do_reduce_labels
self.ignore_index = ignore_index
def prepare_feat_extract_dict(self):
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
@@ -156,20 +155,20 @@ class OneFormerImageProcessorTester(unittest.TestCase):
@require_torch
@require_vision
class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
class OneFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_feat_extracttion_common.test_feat_extract_to_json_string
feature_extraction_class = image_processing_class
# only for test_image_processing_common.test_image_proc_to_json_string
image_processing_class = image_processing_class
def setUp(self):
self.image_processing_tester = OneFormerImageProcessorTester(self)
@property
def feat_extract_dict(self):
return self.image_processing_tester.prepare_feat_extract_dict()
def image_processor_dict(self):
return self.image_processing_tester.prepare_image_processor_dict()
def test_feat_extract_properties(self):
image_processor = self.image_processing_class(**self.feat_extract_dict)
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"))
@@ -187,7 +186,7 @@ class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.Te
def test_call_pil(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.feat_extract_dict)
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processing_tester, equal_resolution=False)
for image in image_inputs:
@@ -221,7 +220,7 @@ class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.Te
def test_call_numpy(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.feat_extract_dict)
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processing_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
@@ -255,7 +254,7 @@ class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.Te
def test_call_pytorch(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.feat_extract_dict)
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processing_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
@@ -289,7 +288,7 @@ class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.Te
def test_equivalence_pad_and_create_pixel_mask(self):
# Initialize image_processors
image_processor_1 = self.image_processing_class(**self.feat_extract_dict)
image_processor_1 = self.image_processing_class(**self.image_processor_dict)
image_processor_2 = self.image_processing_class(
do_resize=False,
do_normalize=False,
@@ -320,7 +319,7 @@ class OneFormerImageProcessingTest(FeatureExtractionSavingTestMixin, unittest.Te
def comm_get_image_processor_inputs(
self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
):
image_processor = self.image_processing_class(**self.feat_extract_dict)
image_processor = self.image_processing_class(**self.image_processor_dict)
# prepare image and target
num_labels = self.image_processing_tester.num_labels
annotations = None