mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
4d0de5f73a
* More limited setup -> setupclass conversion * make fixup * Trigger tests * Fixup UDOP * Missed a spot * tearDown -> tearDownClass where appropriate * Couple more class fixes * Fixups for UDOP and VisionTextDualEncoder * Ignore errors when removing the tmpdir, in case it already got cleaned up somewhere * CLIP fixes * More correct classmethods * Wav2Vec2Bert fixes * More methods become static * More class methods * More class methods * Revert changes for integration tests / modeling files * Use a different tempdir for tests that actually write to it * Remove addClassCleanup and just use teardownclass * Remove changes in modeling files * Cleanup get_processor_dict() for got_ocr2 * Fix regression on Wav2Vec2BERT test that was masked by this before * Rework tests that modify the tmpdir * make fix-copies * revert clvp modeling test changes * Fix CLIP processor test * make fix-copies
515 lines
24 KiB
Python
515 lines
24 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import shutil
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import tempfile
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import unittest
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from transformers import (
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PreTrainedTokenizer,
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PreTrainedTokenizerBase,
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PreTrainedTokenizerFast,
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UdopProcessor,
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UdopTokenizer,
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UdopTokenizerFast,
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)
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from transformers.testing_utils import (
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require_pytesseract,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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)
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from transformers.utils import cached_property, is_pytesseract_available, is_torch_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_torch_available():
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import torch
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if is_pytesseract_available():
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from PIL import Image
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from transformers import LayoutLMv3ImageProcessor
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@require_pytesseract
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@require_sentencepiece
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@require_tokenizers
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class UdopProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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tokenizer_class = UdopTokenizer
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rust_tokenizer_class = UdopTokenizerFast
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processor_class = UdopProcessor
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maxDiff = None
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = LayoutLMv3ImageProcessor(
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do_resize=True,
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size=224,
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apply_ocr=True,
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)
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tokenizer = UdopTokenizer.from_pretrained("microsoft/udop-large")
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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cls.tokenizer_pretrained_name = "microsoft/udop-large"
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image_processor = cls.get_image_processor()
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tokenizer = cls.get_tokenizers()[0]
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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@classmethod
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def get_tokenizer(cls, **kwargs) -> PreTrainedTokenizer:
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return cls.tokenizer_class.from_pretrained(cls.tokenizer_pretrained_name, **kwargs)
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@classmethod
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def get_image_processor(cls, **kwargs):
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return LayoutLMv3ImageProcessor.from_pretrained(cls.tmpdirname, **kwargs)
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@classmethod
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def get_rust_tokenizer(cls, **kwargs) -> PreTrainedTokenizerFast:
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return cls.rust_tokenizer_class.from_pretrained(cls.tokenizer_pretrained_name, **kwargs)
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@classmethod
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def get_tokenizers(cls, **kwargs) -> list[PreTrainedTokenizerBase]:
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return [cls.get_tokenizer(**kwargs), cls.get_rust_tokenizer(**kwargs)]
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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def test_save_load_pretrained_default(self):
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image_processor = self.get_image_processor()
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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with tempfile.TemporaryDirectory() as tmpdir:
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processor.save_pretrained(tmpdir)
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processor = UdopProcessor.from_pretrained(tmpdir)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.tokenizer, (UdopTokenizer, UdopTokenizerFast))
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self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
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self.assertIsInstance(processor.image_processor, LayoutLMv3ImageProcessor)
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def test_save_load_pretrained_additional_features(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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processor = UdopProcessor(image_processor=self.get_image_processor(), tokenizer=self.get_tokenizer())
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processor.save_pretrained(tmpdir)
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# slow tokenizer
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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image_processor_add_kwargs = self.get_image_processor(do_resize=False, size=30)
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processor = UdopProcessor.from_pretrained(
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tmpdir,
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use_fast=False,
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bos_token="(BOS)",
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eos_token="(EOS)",
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do_resize=False,
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size=30,
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, UdopTokenizer)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, LayoutLMv3ImageProcessor)
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# fast tokenizer
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tokenizer_add_kwargs = self.get_rust_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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image_processor_add_kwargs = self.get_image_processor(do_resize=False, size=30)
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processor = UdopProcessor.from_pretrained(
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self.tmpdirname, use_xlm=True, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, UdopTokenizerFast)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, LayoutLMv3ImageProcessor)
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def test_model_input_names(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = UdopProcessor(tokenizer=tokenizer, image_processor=image_processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(images=image_input, text=input_str)
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self.assertListEqual(list(inputs.keys()), processor.model_input_names)
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def test_text_target(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = UdopProcessor(tokenizer=tokenizer, image_processor=image_processor)
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text = "hello world"
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expected_decoding = "hello world</s>"
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encoding_processor = processor(text_target=text)
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encoding_tokenizer = tokenizer(text_target=text)
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self.assertListEqual(encoding_processor["input_ids"], [21820, 296, 1])
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self.assertListEqual(encoding_processor["attention_mask"], [1, 1, 1])
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self.assertDictEqual(dict(encoding_processor), dict(encoding_tokenizer))
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self.assertEqual(tokenizer.decode(encoding_processor["input_ids"]), expected_decoding)
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@slow
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def test_overflowing_tokens(self):
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# In the case of overflowing tokens, test that we still have 1-to-1 mapping between the images and input_ids (sequences that are too long are broken down into multiple sequences).
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from datasets import load_dataset
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# set up
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datasets = load_dataset("nielsr/funsd", trust_remote_code=True)
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processor = UdopProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
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def preprocess_data(examples):
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images = [Image.open(path).convert("RGB") for path in examples["image_path"]]
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words = examples["words"]
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boxes = examples["bboxes"]
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word_labels = examples["ner_tags"]
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encoded_inputs = processor(
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images,
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words,
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boxes=boxes,
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word_labels=word_labels,
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max_length=512,
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padding="max_length",
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truncation=True,
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return_overflowing_tokens=True,
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stride=50,
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return_offsets_mapping=True,
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return_tensors="pt",
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)
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return encoded_inputs
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train_data = preprocess_data(datasets["train"])
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self.assertEqual(len(train_data["pixel_values"]), len(train_data["input_ids"]))
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# different use cases tests
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@require_sentencepiece
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@require_torch
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@require_pytesseract
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class UdopProcessorIntegrationTests(unittest.TestCase):
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@cached_property
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def get_images(self):
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# we verify our implementation on 2 document images from the DocVQA dataset
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from datasets import load_dataset
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ds = load_dataset("hf-internal-testing/fixtures_docvqa", split="test", trust_remote_code=True)
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image_1 = Image.open(ds[0]["file"]).convert("RGB")
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image_2 = Image.open(ds[1]["file"]).convert("RGB")
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return image_1, image_2
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@cached_property
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def get_tokenizers(self):
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slow_tokenizer = UdopTokenizer.from_pretrained("microsoft/udop-large")
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fast_tokenizer = UdopTokenizerFast.from_pretrained("microsoft/udop-large")
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return [slow_tokenizer, fast_tokenizer]
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@slow
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def test_processor_case_1(self):
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# case 1: document image classification (training, inference) + token classification (inference), apply_ocr = True
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image_processor = LayoutLMv3ImageProcessor()
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tokenizers = self.get_tokenizers
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images = self.get_images
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for tokenizer in tokenizers:
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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# not batched
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input_image_processor = image_processor(images[0], return_tensors="pt")
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input_processor = processor(images[0], return_tensors="pt")
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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self.assertListEqual(actual_keys, expected_keys)
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# verify pixel_values
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self.assertTrue(
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torch.allclose(input_image_processor["pixel_values"], input_processor["pixel_values"], atol=1e-2)
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)
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# verify input_ids
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# this was obtained with Tesseract 4.1.1
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# fmt: off
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expected_decoding = "11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231
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# fmt: on
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decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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# batched
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input_image_processor = image_processor(images, return_tensors="pt")
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input_processor = processor(images, padding=True, return_tensors="pt")
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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self.assertListEqual(actual_keys, expected_keys)
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# verify pixel_values
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self.assertTrue(
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torch.allclose(input_image_processor["pixel_values"], input_processor["pixel_values"], atol=1e-2)
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)
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# verify input_ids
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# this was obtained with Tesseract 4.1.1
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# fmt: off
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expected_decoding = "7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>" # noqa: E231
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# fmt: on
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decoding = processor.decode(input_processor.input_ids[1].tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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@slow
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def test_processor_case_2(self):
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# case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
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image_processor = LayoutLMv3ImageProcessor(apply_ocr=False)
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tokenizers = self.get_tokenizers
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images = self.get_images
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for tokenizer in tokenizers:
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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# not batched
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words = ["hello", "world"]
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boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
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input_processor = processor(images[0], words, boxes=boxes, return_tensors="pt")
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
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actual_keys = list(input_processor.keys())
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for key in expected_keys:
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self.assertIn(key, actual_keys)
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# verify input_ids
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expected_decoding = "hello world</s>"
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decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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# batched
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words = [["hello", "world"], ["my", "name", "is", "niels"]]
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boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]]
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input_processor = processor(images, words, boxes=boxes, padding=True, return_tensors="pt")
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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self.assertListEqual(actual_keys, expected_keys)
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# verify input_ids
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expected_decoding = "hello world</s><pad><pad><pad><pad>"
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decoding = processor.decode(input_processor.input_ids[0].tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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# verify bbox
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expected_bbox = [
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[3, 2, 5, 1],
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[6, 7, 4, 2],
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[3, 9, 2, 4],
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[1, 1, 2, 3],
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[1, 1, 2, 3],
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[1, 1, 2, 3],
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[1000, 1000, 1000, 1000],
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]
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self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox)
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@slow
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def test_processor_case_3(self):
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# case 3: token classification (training), apply_ocr=False
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image_processor = LayoutLMv3ImageProcessor(apply_ocr=False)
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tokenizers = self.get_tokenizers
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images = self.get_images
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for tokenizer in tokenizers:
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processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
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# not batched
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words = ["weirdly", "world"]
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boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
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word_labels = [1, 2]
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input_processor = processor(images[0], words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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self.assertListEqual(actual_keys, expected_keys)
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# verify input_ids
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expected_decoding = "weirdly world</s>"
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decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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# verify labels
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expected_labels = [1, -100, 2, -100]
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self.assertListEqual(input_processor.labels.squeeze().tolist(), expected_labels)
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# batched
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words = [["hello", "world"], ["my", "name", "is", "niels"]]
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boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]]
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word_labels = [[1, 2], [6, 3, 10, 2]]
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input_processor = processor(
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images, words, boxes=boxes, word_labels=word_labels, padding=True, return_tensors="pt"
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)
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# verify keys
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expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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self.assertListEqual(actual_keys, expected_keys)
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# verify input_ids
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expected_decoding = "my name is niels</s>"
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decoding = processor.decode(input_processor.input_ids[1].tolist())
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self.assertSequenceEqual(decoding, expected_decoding)
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# verify bbox
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expected_bbox = [
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[3, 2, 5, 1],
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[6, 7, 4, 2],
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[3, 9, 2, 4],
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[1, 1, 2, 3],
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[1, 1, 2, 3],
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[1, 1, 2, 3],
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[1000, 1000, 1000, 1000],
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]
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self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox)
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# verify labels
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expected_labels = [6, 3, 10, 2, -100, -100, -100]
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self.assertListEqual(input_processor.labels[1].tolist(), expected_labels)
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@slow
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def test_processor_case_4(self):
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# case 4: visual question answering (inference), apply_ocr=True
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||
image_processor = LayoutLMv3ImageProcessor()
|
||
tokenizers = self.get_tokenizers
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||
images = self.get_images
|
||
|
||
for tokenizer in tokenizers:
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||
processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
|
||
|
||
# not batched
|
||
question = "What's his name?"
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||
input_processor = processor(images[0], question, return_tensors="pt")
|
||
|
||
# verify keys
|
||
expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
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actual_keys = sorted(input_processor.keys())
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||
self.assertListEqual(actual_keys, expected_keys)
|
||
|
||
# verify input_ids
|
||
# this was obtained with Tesseract 4.1.1
|
||
# fmt: off
|
||
expected_decoding = "What's his name?</s> 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231
|
||
# fmt: on
|
||
decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
|
||
self.assertSequenceEqual(decoding, expected_decoding)
|
||
|
||
# batched
|
||
questions = ["How old is he?", "what's the time"]
|
||
input_processor = processor(
|
||
images, questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt"
|
||
)
|
||
|
||
# verify keys
|
||
expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
|
||
actual_keys = sorted(input_processor.keys())
|
||
self.assertListEqual(actual_keys, expected_keys)
|
||
|
||
# verify input_ids
|
||
# this was obtained with Tesseract 4.1.1
|
||
expected_decoding = "what's the time</s> 7 ITC Limited REPORT AND ACCOUNTS 2013 I</s>"
|
||
decoding = processor.decode(input_processor.input_ids[1].tolist())
|
||
self.assertSequenceEqual(decoding, expected_decoding)
|
||
|
||
# verify bbox
|
||
# fmt: off
|
||
expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [1000, 1000, 1000, 1000]] # noqa: E231
|
||
# fmt: on
|
||
self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox)
|
||
|
||
@slow
|
||
def test_processor_case_5(self):
|
||
# case 5: visual question answering (inference), apply_ocr=False
|
||
|
||
image_processor = LayoutLMv3ImageProcessor(apply_ocr=False)
|
||
tokenizers = self.get_tokenizers
|
||
images = self.get_images
|
||
|
||
for tokenizer in tokenizers:
|
||
processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer)
|
||
|
||
# not batched
|
||
question = "What's his name?"
|
||
words = ["hello", "world"]
|
||
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]
|
||
input_processor = processor(images[0], question, text_pair=words, boxes=boxes, return_tensors="pt")
|
||
|
||
# verify keys
|
||
expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
|
||
actual_keys = sorted(input_processor.keys())
|
||
self.assertListEqual(actual_keys, expected_keys)
|
||
|
||
# verify input_ids
|
||
expected_decoding = "What's his name?</s> hello world</s>"
|
||
decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
|
||
self.assertSequenceEqual(decoding, expected_decoding)
|
||
|
||
# batched
|
||
questions = ["How old is he?", "what's the time"]
|
||
words = [["hello", "world"], ["my", "name", "is", "niels"]]
|
||
boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]]
|
||
input_processor = processor(
|
||
images, questions, text_pair=words, boxes=boxes, padding=True, return_tensors="pt"
|
||
)
|
||
|
||
# verify keys
|
||
expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"]
|
||
actual_keys = sorted(input_processor.keys())
|
||
self.assertListEqual(actual_keys, expected_keys)
|
||
|
||
# verify input_ids
|
||
expected_decoding = "How old is he?</s> hello world</s><pad><pad><pad>"
|
||
decoding = processor.decode(input_processor.input_ids[0].tolist())
|
||
self.assertSequenceEqual(decoding, expected_decoding)
|
||
|
||
expected_decoding = "what's the time</s> my name is niels</s>"
|
||
decoding = processor.decode(input_processor.input_ids[1].tolist())
|
||
self.assertSequenceEqual(decoding, expected_decoding)
|
||
|
||
# verify bbox
|
||
expected_bbox = [[3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [1, 1, 2, 3], [1000, 1000, 1000, 1000]]
|
||
self.assertListEqual(input_processor.bbox[1].tolist()[-5:], expected_bbox)
|