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https://github.com/huggingface/transformers.git
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89d7bf584f
* Make kwargs uniform for TrOCR * Add tests * Put back current_processor * Remove args * Add todo comment * Code review - breaking change
130 lines
5.4 KiB
Python
130 lines
5.4 KiB
Python
import os
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import shutil
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import tempfile
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import unittest
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import pytest
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from transformers.models.xlm_roberta.tokenization_xlm_roberta import VOCAB_FILES_NAMES
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from transformers.testing_utils import (
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require_sentencepiece,
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require_tokenizers,
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require_vision,
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)
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import TrOCRProcessor, ViTImageProcessor, XLMRobertaTokenizerFast
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@require_sentencepiece
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@require_tokenizers
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@require_vision
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class TrOCRProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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text_input_name = "labels"
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processor_class = TrOCRProcessor
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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image_processor = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
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tokenizer = XLMRobertaTokenizerFast.from_pretrained("FacebookAI/xlm-roberta-base")
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processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(self.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def get_tokenizer(self, **kwargs):
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return XLMRobertaTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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def get_image_processor(self, **kwargs):
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return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
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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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tokenizer = self.get_tokenizer()
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processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(self.tmpdirname)
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processor = TrOCRProcessor.from_pretrained(self.tmpdirname)
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self.assertIsInstance(processor.tokenizer, XLMRobertaTokenizerFast)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.image_processor, ViTImageProcessor)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
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def test_save_load_pretrained_additional_features(self):
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processor = TrOCRProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
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processor.save_pretrained(self.tmpdirname)
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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_normalize=False, padding_value=1.0)
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processor = TrOCRProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertIsInstance(processor.tokenizer, XLMRobertaTokenizerFast)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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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, ViTImageProcessor)
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def test_image_processor(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = TrOCRProcessor(tokenizer=tokenizer, image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = image_processor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = TrOCRProcessor(tokenizer=tokenizer, image_processor=image_processor)
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input_str = "lower newer"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_processor_text(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = TrOCRProcessor(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(text=input_str, images=image_input)
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self.assertListEqual(list(inputs.keys()), ["pixel_values", "labels"])
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_tokenizer_decode(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = TrOCRProcessor(tokenizer=tokenizer, image_processor=image_processor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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