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
300 lines
12 KiB
Python
300 lines
12 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 numpy as np
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import requests
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from transformers import AriaProcessor
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from transformers.models.auto.processing_auto import AutoProcessor
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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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@require_torch
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@require_vision
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class AriaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = AriaProcessor
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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 = AriaProcessor.from_pretrained("m-ric/Aria_hf_2", size_conversion={490: 2, 980: 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 = "<|im_start|>"
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cls.eos_token = "<|im_end|>"
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cls.image_token = processor.tokenizer.image_token
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cls.fake_image_token = "o"
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cls.global_img_token = "<|img|>"
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cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_token)
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cls.eos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.eos_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.global_img_tokens_id = processor.tokenizer(cls.global_img_token, add_special_tokens=False)["input_ids"]
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cls.padding_token_id = processor.tokenizer.pad_token_id
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cls.image_seq_len = 2
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@staticmethod
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def prepare_processor_dict():
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return {
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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}{% elif message['content'] is iterable %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<fim_prefix><|img|><fim_suffix>{% endif %}{% endfor %}{% endif %}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
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"size_conversion": {490: 2, 980: 2},
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} # fmt: skip
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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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@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_image_splitting(self):
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processor = self.get_processor()
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processor.image_processor.split_image = True
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# Test that a single image is processed correctly
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inputs = processor(images=self.image1, text="Ok<|img|>", images_kwargs={"split_image": True})
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (2, 3, 980, 980))
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self.assertEqual(np.array(inputs["pixel_mask"]).shape, (2, 980, 980))
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def test_process_interleaved_images_prompts_no_image_splitting(self):
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processor = self.get_processor()
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processor.image_processor.split_image = False
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# Test that a single image is processed correctly
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inputs = processor(images=self.image1, text="Ok<|img|>")
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image1_expected_size = (980, 980)
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 3, *image1_expected_size))
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self.assertEqual(np.array(inputs["pixel_mask"]).shape, (1, *image1_expected_size))
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# fmt: on
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# Test a single sample with image and text
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image_str = "<|img|>"
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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 = processor.tokenizer(text_str, add_special_tokens=False)
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expected_input_ids = [[self.image_token_id] * self.image_seq_len + tokenized_sentence["input_ids"]]
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# self.assertEqual(len(inputs["input_ids"]), len(expected_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(np.array(inputs["pixel_values"]).shape, (1, 3, *image1_expected_size))
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self.assertEqual(np.array(inputs["pixel_mask"]).shape, (1, *image1_expected_size))
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# fmt: on
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# Test that batch is correctly processed
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image_str = "<|img|>"
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text_str_1 = "In this image, we see"
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text_str_2 = "In this image, we see"
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text = [
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image_str + text_str_1,
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image_str + image_str + text_str_2,
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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 = processor.tokenizer(text_str_1, add_special_tokens=False)
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tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False)
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image_tokens = [self.image_token_id] * self.image_seq_len
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expected_input_ids_1 = image_tokens + tokenized_sentence_1["input_ids"]
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expected_input_ids_2 = 2 * image_tokens + tokenized_sentence_2["input_ids"]
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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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expected_attention_mask = [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * (len(expected_input_ids_2))]
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self.assertEqual(
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inputs["attention_mask"],
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expected_attention_mask
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)
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self.assertEqual(np.array(inputs['pixel_values']).shape, (3, 3, 980, 980))
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self.assertEqual(np.array(inputs['pixel_mask']).shape, (3, 980, 980))
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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 = "<|img|>"
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text_str_1 = "In this image, we see"
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text_str_2 = "In this image, we see"
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text = [
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image_str + text_str_1,
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image_str + image_str + text_str_2,
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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(np.array(inputs["pixel_values"]).shape, (3, 3, 980, 980))
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self.assertEqual(np.array(inputs["pixel_mask"]).shape, (3, 980, 980))
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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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print(rendered)
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expected_rendered = """<|im_start|>user
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What do these images show?<fim_prefix><|img|><fim_suffix><fim_prefix><|img|><fim_suffix><|im_end|>
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<|im_start|>assistant
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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.<|im_end|>
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<|im_start|>user
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And who is that?<|im_end|>
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<|im_start|>assistant
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"""
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self.assertEqual(rendered, expected_rendered)
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def test_image_chat_template_accepts_processing_kwargs(self):
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processor = self.get_processor()
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if processor.chat_template is None:
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self.skipTest("Processor has no chat template")
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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],
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},
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]
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]
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formatted_prompt_tokenized = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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padding="max_length",
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max_length=50,
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)
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self.assertEqual(len(formatted_prompt_tokenized[0]), 50)
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formatted_prompt_tokenized = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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truncation=True,
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max_length=5,
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)
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self.assertEqual(len(formatted_prompt_tokenized[0]), 5)
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# Now test the ability to return dict
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messages[0][0]["content"].append(
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
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)
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out_dict = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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max_image_size=980,
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return_tensors="np",
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)
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self.assertListEqual(list(out_dict[self.images_input_name].shape), [1, 3, 980, 980])
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def test_special_mm_token_truncation(self):
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"""Tests that special vision tokens do not get truncated when `truncation=True` is set."""
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processor = self.get_processor()
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input_str = self.prepare_text_inputs(batch_size=2, modality="image")
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image_input = self.prepare_image_inputs(batch_size=2)
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=None,
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padding=True,
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)
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with self.assertRaises(ValueError):
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=3,
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)
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