mirror of
https://github.com/huggingface/transformers.git
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32eca7197a
* update * apply suggestion * fix tests for main branch * remove unused logger * add special tokens in tests * nit * fix more tests * fix test * pg also
335 lines
14 KiB
Python
335 lines
14 KiB
Python
# Copyright 2024 HuggingFace Inc.
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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 io import BytesIO
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import requests
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from transformers import Idefics2Processor
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from transformers.testing_utils import require_torch, require_vision
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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 PIL import Image
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from transformers import (
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AutoProcessor,
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Idefics2Processor,
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)
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@require_torch
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@require_vision
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class Idefics2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Idefics2Processor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2)
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processor.save_pretrained(cls.tmpdirname)
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cls.image1 = Image.open(
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BytesIO(
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requests.get(
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"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
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).content
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)
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)
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cls.image2 = Image.open(
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BytesIO(requests.get("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg").content)
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)
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cls.image3 = Image.open(
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BytesIO(
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requests.get(
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"https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
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).content
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)
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)
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cls.bos_token = processor.tokenizer.bos_token
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cls.image_token = processor.image_token
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cls.fake_image_token = processor.fake_image_token
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cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_token)
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cls.image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.image_token)
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cls.fake_image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.fake_image_token)
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cls.image_seq_len = processor.image_seq_len
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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 get_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
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@staticmethod
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def prepare_processor_dict():
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return {"image_seq_len": 2}
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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_process_interleaved_images_prompts_no_image_splitting(self):
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tokenizer = self.get_tokenizer()
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processor = self.get_processor()
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processor.image_processor.do_image_splitting = False
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# Test that a single image is processed correctly
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inputs = processor(images=self.image1)
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self.assertEqual(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
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self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
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# fmt: on
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# Test a single sample with image and text
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image_str = "<image>"
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text_str = "In this image, we see"
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text = image_str + text_str
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inputs = processor(text=text, images=self.image1)
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# fmt: off
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tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
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expected_input_ids = [[self.bos_token_id] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
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self.assertEqual(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
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self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
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# fmt: on
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# Test that batch is correctly processed
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "bla, bla"
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text = [
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image_str + text_str_1,
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text_str_2 + image_str + image_str,
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]
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images = [[self.image1], [self.image2, self.image3]]
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inputs = processor(text=text, images=images, padding=True)
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# fmt: off
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tokenized_sentence_1 = tokenizer(text_str_1, add_special_tokens=False)
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tokenized_sentence_2 = tokenizer(text_str_2, add_special_tokens=False)
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expected_input_ids_1 = [self.bos_token_id] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
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expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id]
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# Pad the first input to match the second input
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pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
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padded_expected_input_ids_1 = [0] * pad_len + expected_input_ids_1
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self.assertEqual(
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inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
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)
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self.assertEqual(
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inputs["attention_mask"],
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[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
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)
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self.assertEqual(inputs['pixel_values'].shape, (2, 2, 3, 767, 980))
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self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 2, 767, 980))
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# fmt: on
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def test_process_interleaved_images_prompts_image_splitting(self):
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processor = self.get_processor()
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tokenizer = self.get_tokenizer()
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processor.image_processor.do_image_splitting = True
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# Test that a single image is processed correctly
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inputs = processor(images=self.image1)
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self.assertEqual(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
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self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
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# fmt: on
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# Test a single sample with image and text
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image_str = "<image>"
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text_str = "In this image, we see"
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text = image_str + text_str
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inputs = processor(text=text, images=self.image1)
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# fmt: off
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tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
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expected_input_ids = [[self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
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self.assertEqual(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
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self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
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# fmt: on
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# Test that batch is correctly processed
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "bla, bla"
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text = [
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image_str + text_str_1,
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text_str_2 + image_str + image_str,
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]
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images = [[self.image1], [self.image2, self.image3]]
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inputs = processor(text=text, images=images, padding=True)
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# fmt: off
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tokenized_sentence_1 = tokenizer(text_str_1, add_special_tokens=False)
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tokenized_sentence_2 = tokenizer(text_str_2, add_special_tokens=False)
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expected_input_ids_1 = [self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
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expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id]
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# Pad the first input to match the second input
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pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
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padded_expected_input_ids_1 = [0] * pad_len + expected_input_ids_1
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self.assertEqual(
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inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
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)
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self.assertEqual(
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inputs["attention_mask"],
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[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
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)
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self.assertEqual(inputs['pixel_values'].shape, (2, 10, 3, 767, 980))
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self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 10, 767, 980))
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# fmt: on
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def test_add_special_tokens_processor(self):
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processor = self.get_processor()
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tokenizer = self.get_tokenizer()
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image_str = "<image>"
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text_str = "In this image, we see"
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text = text_str + image_str
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n_image_repeat = 5 if processor.image_processor.do_image_splitting else 1
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# fmt: off
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inputs = processor(text=text, images=self.image1, add_special_tokens=False)
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tokenized_sentence = tokenizer(text_str, add_special_tokens=False)
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expected_input_ids = [tokenized_sentence["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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inputs = processor(text=text, images=self.image1)
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expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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# fmt: on
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def test_non_nested_images_with_batched_text(self):
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processor = self.get_processor()
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processor.image_processor.do_image_splitting = False
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "bla, bla"
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text = [
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image_str + text_str_1,
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text_str_2 + image_str + image_str,
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]
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images = [self.image1, self.image2, self.image3]
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inputs = processor(text=text, images=images, padding=True)
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self.assertEqual(inputs["pixel_values"].shape, (2, 2, 3, 767, 980))
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self.assertEqual(inputs["pixel_attention_mask"].shape, (2, 2, 767, 980))
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def test_process_interleaved_images_prompts_image_error(self):
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processor = self.get_processor()
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text = [
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"This is a test sentence.",
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"In this other sentence we try some good things",
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]
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images = [[self.image1], [self.image2]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [[self.image1], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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text = [
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"This is a test sentence.<image>",
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"In this other sentence we try some good things<image>",
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]
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images = [[self.image1], [self.image2, self.image3]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [[], [self.image2]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1, self.image2, self.image3]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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text = [
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"This is a test sentence.",
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"In this other sentence we try some good things<image>",
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]
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images = [[self.image1], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [[], [self.image2]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1, self.image2]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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def test_apply_chat_template(self):
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# Message contains content which a mix of lists with images and image urls and string
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What do these images show?"},
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{"type": "image"},
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{"type": "image"},
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"What do these images show?",
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],
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},
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{
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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": "The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.",
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}
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],
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},
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{"role": "user", "content": [{"type": "text", "text": "And who is that?"}]},
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]
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processor = self.get_processor()
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# Make short sequence length to test that the fake tokens are added correctly
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rendered = processor.apply_chat_template(messages, add_generation_prompt=True)
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expected_rendered = (
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"User: What do these images show?<image><image><end_of_utterance>\n"
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"Assistant: The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.<end_of_utterance>\n"
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"User: And who is that?<end_of_utterance>\n"
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"Assistant:"
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)
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self.assertEqual(rendered, expected_rendered)
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