wehub-resource-sync
e06fe8e8c6
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Waiting to run
New model PR merged notification / Notify new model (push) Waiting to run
Update Transformers metadata / build_and_package (push) Waiting to run
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
89 lines
4.1 KiB
Python
89 lines
4.1 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# http://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
|
||
import unittest
|
||
|
||
from transformers import GotOcr2Processor
|
||
from transformers.testing_utils import require_vision
|
||
|
||
from ...test_processing_common import ProcessorTesterMixin
|
||
|
||
|
||
@require_vision
|
||
class GotOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||
processor_class = GotOcr2Processor
|
||
# Tiny processor created with make_tiny_processor.py from "stepfun-ai/GOT-OCR-2.0-hf"
|
||
tiny_model_id = "hf-internal-testing/tiny-processor-got_ocr2"
|
||
|
||
@classmethod
|
||
def _setup_image_processor(cls):
|
||
# Instantiate directly to avoid loading the full 384×384 image processor from Hub.
|
||
image_processor_class = cls._get_component_class_from_processor("image_processor")
|
||
return image_processor_class()
|
||
|
||
@unittest.skip("GotOcr2Processor pop the image processor output 'num_patches'")
|
||
def test_image_processor_defaults(self):
|
||
pass
|
||
|
||
def test_ocr_queries(self):
|
||
processor = self.get_processor()
|
||
image_input = self.prepare_image_inputs()
|
||
inputs = processor(image_input, return_tensors="pt")
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 324))
|
||
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
|
||
|
||
inputs = processor(image_input, return_tensors="pt", format=True)
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 328))
|
||
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
|
||
|
||
inputs = processor(image_input, return_tensors="pt", color="red")
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 329))
|
||
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
|
||
|
||
inputs = processor(image_input, return_tensors="pt", box=[0, 0, 100, 100])
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 341))
|
||
self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
|
||
|
||
inputs = processor([image_input, image_input], return_tensors="pt", multi_page=True, format=True)
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 595))
|
||
self.assertEqual(inputs["pixel_values"].shape, (2, 3, 384, 384))
|
||
|
||
inputs = processor(image_input, return_tensors="pt", crop_to_patches=True, max_patches=6)
|
||
self.assertEqual(inputs["input_ids"].shape, (1, 1872))
|
||
self.assertEqual(inputs["pixel_values"].shape, (7, 3, 384, 384))
|
||
|
||
def test_processor_text_has_no_visual(self):
|
||
# Overwritten: requires `multi_page` kwarg to process nested vision inputs
|
||
processor = self.get_processor()
|
||
|
||
text = self.prepare_text_inputs(batch_size=3, modalities="image")
|
||
image_inputs = self.prepare_image_inputs(batch_size=3)
|
||
processing_kwargs = {"return_tensors": "pt", "padding": True, "multi_page": True}
|
||
|
||
# Call with nested list of vision inputs
|
||
image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
|
||
inputs_dict_nested = {"text": text, "images": image_inputs_nested}
|
||
inputs = processor(**inputs_dict_nested, **processing_kwargs)
|
||
self.assertTrue(self.text_input_name in inputs)
|
||
|
||
# Call with one of the samples with no associated vision input
|
||
plain_text = "lower newer"
|
||
image_inputs_nested[0] = []
|
||
text[0] = plain_text
|
||
inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
|
||
inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
|
||
self.assertListEqual(
|
||
inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
|
||
)
|