186 lines
6.9 KiB
Python
186 lines
6.9 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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import pytest
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torchvision_available, 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 (
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AutoProcessor,
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BertTokenizerFast,
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GPT2Tokenizer,
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InstructBlipVideoProcessor,
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PreTrainedTokenizerFast,
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)
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if is_torchvision_available():
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from transformers import InstructBlipVideoVideoProcessor
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@require_vision
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@require_torch
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class InstructBlipVideoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = InstructBlipVideoProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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video_processor = InstructBlipVideoVideoProcessor()
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tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
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qformer_tokenizer = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")
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processor = InstructBlipVideoProcessor(video_processor, tokenizer, qformer_tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
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def get_qformer_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).qformer_tokenizer
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def prepare_processor_dict(self):
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return {"num_query_tokens": 1}
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def get_video_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor
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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_additional_features(self):
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processor = InstructBlipVideoProcessor(
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tokenizer=self.get_tokenizer(),
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video_processor=self.get_video_processor(),
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qformer_tokenizer=self.get_qformer_tokenizer(),
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)
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with tempfile.TemporaryDirectory() as tmpdir:
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processor.save_pretrained(tmpdir)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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video_processor_add_kwargs = self.get_video_processor(do_normalize=False, padding_value=1.0)
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processor = InstructBlipVideoProcessor.from_pretrained(
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tmpdir, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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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, PreTrainedTokenizerFast)
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self.assertEqual(processor.video_processor.to_json_string(), video_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.video_processor, InstructBlipVideoVideoProcessor)
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self.assertIsInstance(processor.qformer_tokenizer, BertTokenizerFast)
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def test_video_processor(self):
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video_processor = self.get_video_processor()
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tokenizer = self.get_tokenizer()
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qformer_tokenizer = self.get_qformer_tokenizer()
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processor_kwargs = self.prepare_processor_dict()
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processor = InstructBlipVideoProcessor(
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tokenizer=tokenizer,
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video_processor=video_processor,
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qformer_tokenizer=qformer_tokenizer,
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**processor_kwargs,
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)
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image_input = self.prepare_image_inputs()
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input_feat_extract = video_processor(image_input, return_tensors="pt")
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input_processor = processor(images=image_input, return_tensors="pt")
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for key in input_feat_extract:
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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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video_processor = self.get_video_processor()
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tokenizer = self.get_tokenizer()
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qformer_tokenizer = self.get_qformer_tokenizer()
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processor_kwargs = self.prepare_processor_dict()
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processor = InstructBlipVideoProcessor(
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tokenizer=tokenizer,
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video_processor=video_processor,
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qformer_tokenizer=qformer_tokenizer,
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**processor_kwargs,
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)
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input_str = ["lower newer"]
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encoded_processor = processor(text=input_str)
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encoded_tokens = tokenizer(input_str, return_token_type_ids=False)
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encoded_tokens_qformer = qformer_tokenizer(input_str, return_token_type_ids=False)
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for key in encoded_tokens:
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self.assertListEqual(encoded_tokens[key], encoded_processor[key])
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for key in encoded_tokens_qformer:
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self.assertListEqual(encoded_tokens_qformer[key], encoded_processor["qformer_" + key])
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def test_processor(self):
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video_processor = self.get_video_processor()
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tokenizer = self.get_tokenizer()
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qformer_tokenizer = self.get_qformer_tokenizer()
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processor_kwargs = self.prepare_processor_dict()
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processor = InstructBlipVideoProcessor(
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tokenizer=tokenizer,
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video_processor=video_processor,
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qformer_tokenizer=qformer_tokenizer,
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**processor_kwargs,
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)
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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(
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list(inputs.keys()),
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["qformer_input_ids", "qformer_attention_mask", "input_ids", "attention_mask", "pixel_values"],
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)
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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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video_processor = self.get_video_processor()
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tokenizer = self.get_tokenizer()
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qformer_tokenizer = self.get_qformer_tokenizer()
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processor_kwargs = self.prepare_processor_dict()
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processor = InstructBlipVideoProcessor(
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tokenizer=tokenizer,
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video_processor=video_processor,
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qformer_tokenizer=qformer_tokenizer,
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**processor_kwargs,
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)
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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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