mirror of
https://github.com/huggingface/transformers.git
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d8500cd229
* add uniformized pixtral and kwargs * update doc * fix _validate_images_text_input_order * nit
392 lines
18 KiB
Python
392 lines
18 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 requests
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import torch
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from transformers.testing_utils import (
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require_torch,
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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 PIL import Image
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from transformers import AutoTokenizer, PixtralImageProcessor, PixtralProcessor
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@require_vision
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class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = PixtralProcessor
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@classmethod
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def setUpClass(cls):
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cls.url_0 = "https://www.ilankelman.org/stopsigns/australia.jpg"
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cls.image_0 = Image.open(requests.get(cls.url_0, stream=True).raw)
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cls.url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
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cls.image_1 = Image.open(requests.get(cls.url_1, stream=True).raw)
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cls.url_2 = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
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cls.image_2 = Image.open(requests.get(cls.url_2, stream=True).raw)
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# FIXME - just load the processor directly from the checkpoint
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/pixtral-12b")
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image_processor = PixtralImageProcessor()
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processor = PixtralProcessor(tokenizer=tokenizer, image_processor=image_processor)
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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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@unittest.skip("No chat template was set for this model (yet)")
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def test_chat_template(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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expected_prompt = "USER: [IMG]\nWhat is shown in this image? ASSISTANT:"
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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": "image"},
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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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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(expected_prompt, formatted_prompt)
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@unittest.skip("No chat template was set for this model (yet)")
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def test_image_token_filling(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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# Important to check with non square image
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image = torch.randint(0, 2, (3, 500, 316))
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expected_image_tokens = 1526
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image_token_index = 32000
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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": "image"},
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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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inputs = processor(
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text=[processor.apply_chat_template(messages)],
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images=[image],
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return_tensors="pt",
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)
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image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
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self.assertEqual(expected_image_tokens, image_tokens)
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def test_processor_with_single_image(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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prompt_string = "USER: [IMG]\nWhat's the content of the image? ASSISTANT:"
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# Make small for checking image token expansion
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processor.image_processor.size = {"longest_edge": 30}
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processor.image_processor.patch_size = {"height": 2, "width": 2}
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# Test passing in an image
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inputs_image = processor(text=prompt_string, images=self.image_0, return_tensors="pt")
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self.assertIn("input_ids", inputs_image)
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self.assertTrue(len(inputs_image["input_ids"]) == 1)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test passing in a url
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inputs_url = processor(text=prompt_string, images=self.url_0, return_tensors="pt")
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self.assertIn("input_ids", inputs_url)
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self.assertTrue(len(inputs_url["input_ids"]) == 1)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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def test_processor_with_multiple_images_single_list(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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prompt_string = "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:"
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# Make small for checking image token expansion
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processor.image_processor.size = {"longest_edge": 30}
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processor.image_processor.patch_size = {"height": 2, "width": 2}
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# Test passing in an image
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inputs_image = processor(text=prompt_string, images=[self.image_0, self.image_1], return_tensors="pt")
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self.assertIn("input_ids", inputs_image)
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self.assertTrue(len(inputs_image["input_ids"]) == 1)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test passing in a url
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inputs_url = processor(text=prompt_string, images=[self.url_0, self.url_1], return_tensors="pt")
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self.assertIn("input_ids", inputs_url)
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self.assertTrue(len(inputs_url["input_ids"]) == 1)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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def test_processor_with_multiple_images_multiple_lists(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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prompt_string = [
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"USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:",
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"USER: [IMG]\nWhat's the content of the image? ASSISTANT:",
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]
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processor.tokenizer.pad_token = "</s>"
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image_inputs = [[self.image_0, self.image_1], [self.image_2]]
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# Make small for checking image token expansion
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processor.image_processor.size = {"longest_edge": 30}
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processor.image_processor.patch_size = {"height": 2, "width": 2}
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# Test passing in an image
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inputs_image = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
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self.assertIn("input_ids", inputs_image)
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self.assertTrue(len(inputs_image["input_ids"]) == 2)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test passing in a url
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inputs_url = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
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self.assertIn("input_ids", inputs_url)
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self.assertTrue(len(inputs_url["input_ids"]) == 2)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Override all tests requiring shape as returning tensor batches is not supported by PixtralProcessor
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@require_torch
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@require_vision
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", size={"height": 240, "width": 240})
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tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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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# Added dimension by pixtral image processor
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self.assertEqual(len(inputs["pixel_values"][0][0][0][0]), 240)
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@require_torch
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@require_vision
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def test_kwargs_overrides_default_image_processor_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", size={"height": 400, "width": 400})
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tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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, size={"height": 240, "width": 240})
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self.assertEqual(len(inputs["pixel_values"][0][0][0][0]), 240)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"size": {"height": 240, "width": 240}},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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self.assertEqual(inputs["pixel_values"][0][0].shape[-1], 240)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested_from_dict(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"size": {"height": 240, "width": 240}},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.assertEqual(inputs["pixel_values"][0][0].shape[-1], 240)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_unstructured_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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(
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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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size={"height": 240, "width": 240},
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padding="max_length",
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max_length=76,
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)
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self.assertEqual(inputs["pixel_values"][0][0].shape[-1], 240)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_unstructured_kwargs_batched(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = ["lower newer", "upper older longer string"]
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# images needs to be nested to detect multiple prompts
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image_input = [self.prepare_image_inputs()] * 2
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inputs = 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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size={"height": 240, "width": 240},
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padding="longest",
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max_length=76,
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)
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self.assertEqual(inputs["pixel_values"][0][0].shape[-1], 240)
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self.assertEqual(len(inputs["input_ids"][0]), 4)
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