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Vision processors - replace FE with IPs (#20590)

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* Replace FE references with IPs

* Update processor tests

* Update src/transformers/models/clip/processing_clip.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/clip/processing_clip.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update warning messages v4.27 -> v5

* Fixup

* Update Chinese CLIP processor

* Add feature_extractor property

* Add attributes

* Add tests

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
amyeroberts
2022-12-09 10:48:34 +00:00
committed by GitHub
parent 704027f0ef
commit a95fd35426
22 changed files with 681 additions and 375 deletions
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72
tests/models/layoutlmv2/test_processor_layoutlmv2.py
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@@ -31,7 +31,7 @@ from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytes
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMv2FeatureExtractor, LayoutLMv2Processor
from transformers import LayoutLMv2ImageProcessor, LayoutLMv2Processor
@require_pytesseract
@@ -59,7 +59,7 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
"lowest",
]
feature_extractor_map = {
image_processor_map = {
"do_resize": True,
"size": 224,
"apply_ocr": True,
@@ -69,9 +69,9 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(feature_extractor_map) + "\n")
self.image_processing_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
with open(self.image_processing_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(image_processor_map) + "\n")
def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
@@ -82,8 +82,8 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]:
return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
def get_feature_extractor(self, **kwargs):
return LayoutLMv2FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
def get_image_processor(self, **kwargs):
return LayoutLMv2ImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
@@ -100,10 +100,10 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
return image_inputs
def test_save_load_pretrained_default(self):
feature_extractor = self.get_feature_extractor()
image_processor = self.get_image_processor()
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
processor.save_pretrained(self.tmpdirname)
processor = LayoutLMv2Processor.from_pretrained(self.tmpdirname)
@@ -111,16 +111,16 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, (LayoutLMv2Tokenizer, LayoutLMv2TokenizerFast))
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, LayoutLMv2FeatureExtractor)
self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor.image_processor, LayoutLMv2ImageProcessor)
def test_save_load_pretrained_additional_features(self):
processor = LayoutLMv2Processor(feature_extractor=self.get_feature_extractor(), tokenizer=self.get_tokenizer())
processor = LayoutLMv2Processor(image_processor=self.get_image_processor(), tokenizer=self.get_tokenizer())
processor.save_pretrained(self.tmpdirname)
# slow tokenizer
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30)
image_processor_add_kwargs = self.get_image_processor(do_resize=False, size=30)
processor = LayoutLMv2Processor.from_pretrained(
self.tmpdirname, use_fast=False, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30
@@ -129,12 +129,12 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, LayoutLMv2Tokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, LayoutLMv2FeatureExtractor)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, LayoutLMv2ImageProcessor)
# fast tokenizer
tokenizer_add_kwargs = self.get_rust_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30)
image_processor_add_kwargs = self.get_image_processor(do_resize=False, size=30)
processor = LayoutLMv2Processor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30
@@ -143,14 +143,14 @@ class LayoutLMv2ProcessorTest(unittest.TestCase):
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, LayoutLMv2TokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, LayoutLMv2FeatureExtractor)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, LayoutLMv2ImageProcessor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = LayoutLMv2Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor = LayoutLMv2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
@@ -220,15 +220,15 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
def test_processor_case_1(self):
# case 1: document image classification (training, inference) + token classification (inference), apply_ocr = True
feature_extractor = LayoutLMv2FeatureExtractor()
image_processor = LayoutLMv2ImageProcessor()
tokenizers = self.get_tokenizers
images = self.get_images
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
# not batched
input_feat_extract = feature_extractor(images[0], return_tensors="pt")
input_image_proc = image_processor(images[0], return_tensors="pt")
input_processor = processor(images[0], return_tensors="pt")
# verify keys
@@ -237,9 +237,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
self.assertListEqual(actual_keys, expected_keys)
# verify image
self.assertAlmostEqual(
input_feat_extract["pixel_values"].sum(), input_processor["image"].sum(), delta=1e-2
)
self.assertAlmostEqual(input_image_proc["pixel_values"].sum(), input_processor["image"].sum(), delta=1e-2)
# verify input_ids
# this was obtained with Tesseract 4.1.1
@@ -250,7 +248,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
self.assertSequenceEqual(decoding, expected_decoding)
# batched
input_feat_extract = feature_extractor(images, return_tensors="pt")
input_image_proc = image_processor(images, return_tensors="pt")
input_processor = processor(images, padding=True, return_tensors="pt")
# verify keys
@@ -259,9 +257,7 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
self.assertListEqual(actual_keys, expected_keys)
# verify images
self.assertAlmostEqual(
input_feat_extract["pixel_values"].sum(), input_processor["image"].sum(), delta=1e-2
)
self.assertAlmostEqual(input_image_proc["pixel_values"].sum(), input_processor["image"].sum(), delta=1e-2)
# verify input_ids
# this was obtained with Tesseract 4.1.1
@@ -275,12 +271,12 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
def test_processor_case_2(self):
# case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
image_processor = LayoutLMv2ImageProcessor(apply_ocr=False)
tokenizers = self.get_tokenizers
images = self.get_images
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
# not batched
words = ["hello", "world"]
@@ -329,12 +325,12 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
def test_processor_case_3(self):
# case 3: token classification (training), apply_ocr=False
feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
image_processor = LayoutLMv2ImageProcessor(apply_ocr=False)
tokenizers = self.get_tokenizers
images = self.get_images
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
# not batched
words = ["weirdly", "world"]
@@ -394,12 +390,12 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
def test_processor_case_4(self):
# case 4: visual question answering (inference), apply_ocr=True
feature_extractor = LayoutLMv2FeatureExtractor()
image_processor = LayoutLMv2ImageProcessor()
tokenizers = self.get_tokenizers
images = self.get_images
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
# not batched
question = "What's his name?"
@@ -445,12 +441,12 @@ class LayoutLMv2ProcessorIntegrationTests(unittest.TestCase):
def test_processor_case_5(self):
# case 5: visual question answering (inference), apply_ocr=False
feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
image_processor = LayoutLMv2ImageProcessor(apply_ocr=False)
tokenizers = self.get_tokenizers
images = self.get_images
for tokenizer in tokenizers:
processor = LayoutLMv2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)
# not batched
question = "What's his name?"