Add GIT (GenerativeImage2Text) (#20295)
* First draft * Make model instantiation work * Fix copied from statement * More fixes * Add correct output head * Improve configuration * Add conversion script * Improve conversion script * Remove token_type_ids * Fix conversion of projection layers * Convert all weights * Use cats image * Make logits match * Generate caption on cats image * Add GITProcessor * Update conversion script * Add support for more checkpoints * Fix conversion script * Add initial tests * Remove cross-attention * More improvements * Remove is_decoder * Improve model tests * Improve tests * Improve model outputs * Fix model outputs equivalence * Fix more tests * Remove unused code * Use generate to generate text, no use of cache for now * Use generate more appropriately * Fix config tests * Fix style * Add support for use_cache Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Fix style * Fix GIT vision encoder * Update README * Fix integration test * Set bos and eos token ids * Improve docs * Improve code * Add support for provided attention_mask * Add copied from statement * Fix gradient checkpointing test * Set model_input_names * Investigate model_input_names * Remove script * Fix model inputs * Fix docstring * Rename GIT to Git * Support more models * Add support for textvqa model * Add video support * Extend conversion script for video * Add support for large variant * Add support for more models * Fix config archive map * Update integration test * Fix README * Fix CLIP mean and std * Update processor * Fix use_cache for video, thanks @gante * Remove print statements * Remove assertion * Add processor tests * Fix model_input_names * Use Auto API for processor * Fix processor tests * Fix integration test * Fix pipeline test * Make tests faster * Update conversion script * Update conversion script * Convert more checkpoints * Update conversion script * Fix typo * Update docstrings * Improve code snippets * Fix doc tests * Add more code examplesé * Fix doc tests * Add integration tests * Fix unused variable * revert * Add GIT to Japanese README Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
NielsRogge
153
tests/models/git/test_processor_git.py
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153
tests/models/git/test_processor_git.py
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# Copyright 2022 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 numpy as np
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import pytest
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from transformers.testing_utils import require_vision
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from transformers.utils import is_vision_available
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if is_vision_available():
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from PIL import Image
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from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
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@require_vision
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class GitProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = CLIPImageProcessor()
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tokenizer = BertTokenizer.from_pretrained(
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"hf-internal-testing/tiny-random-BertModel", model_input_names=["input_ids", "attention_mask"]
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)
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processor = GitProcessor(image_processor, tokenizer)
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processor.save_pretrained(self.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_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def test_save_load_pretrained_additional_features(self):
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processor = GitProcessor(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 = GitProcessor.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.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.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, CLIPImageProcessor)
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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 = GitProcessor(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 = GitProcessor(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, return_token_type_ids=False)
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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(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = GitProcessor(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()), ["input_ids", "attention_mask", "pixel_values"])
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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 = GitProcessor(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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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 = GitProcessor(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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# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
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