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Fix all torch pipeline failures except one (#30290)

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* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2024-04-18 10:35:43 +02:00
committed by GitHub
parent 7915a25976
commit 28a22834bf
4 changed files with 25 additions and 23 deletions
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30
tests/pipelines/test_pipelines_document_question_answering.py
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@@ -103,7 +103,9 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
@require_detectron2
@require_pytesseract
def test_small_model_pt(self):
dqa_pipeline = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2")
dqa_pipeline = pipeline(
"document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test"
)
image = INVOICE_URL
question = "How many cats are there?"
@@ -253,19 +255,19 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
outputs = dqa_pipeline(image=image, question=question, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4),
nested_simplify(outputs, decimals=3),
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
],
)
outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4),
nested_simplify(outputs, decimals=3),
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
],
)
@@ -273,11 +275,11 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
nested_simplify(outputs, decimals=3),
[
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2,
@@ -288,10 +290,10 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
# This model should also work if `image` is set to None
outputs = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=4),
nested_simplify(outputs, decimals=3),
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
],
)
@@ -355,7 +357,7 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
"document-question-answering",
model="naver-clova-ix/donut-base-finetuned-docvqa",
tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"),
feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa",
image_processor="naver-clova-ix/donut-base-finetuned-docvqa",
)
image = INVOICE_URL