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112 lines
5.0 KiB
Python
112 lines
5.0 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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from transformers import GemmaTokenizer, PaliGemmaProcessor
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from transformers.testing_utils import get_tests_dir, 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 transformers import SiglipImageProcessor
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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@require_vision
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class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = PaliGemmaProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
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image_processor.image_seq_length = 0 # TODO: raushan fix me in #37342
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tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
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processor = PaliGemmaProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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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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@require_torch
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@require_vision
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def test_image_seq_length(self):
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length")
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image_processor.image_seq_length = 14
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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inputs = processor(
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text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
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)
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self.assertEqual(len(inputs["input_ids"][0]), 112)
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@require_torch
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def test_call_with_suffix(self):
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input_str = "lower newer"
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suffix = "upper older longer string"
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image_input = self.prepare_image_inputs()
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processor = self.get_processor()
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inputs = processor(text=input_str, images=image_input, suffix=suffix)
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self.assertTrue("labels" in inputs)
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self.assertEqual(len(inputs["labels"][0]), len(inputs["input_ids"][0]))
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inputs = processor(text=input_str, images=image_input, suffix=suffix, return_tensors="pt")
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self.assertTrue("labels" in inputs)
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self.assertEqual(len(inputs["labels"][0]), len(inputs["input_ids"][0]))
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def test_text_with_image_tokens(self):
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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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text_multi_images = "<image><image>Dummy text!"
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text_single_image = "<image>Dummy text!"
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text_no_image = "Dummy text!"
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image = self.prepare_image_inputs()
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out_noimage = processor(text=text_no_image, images=image, return_tensors="np")
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out_singlimage = processor(text=text_single_image, images=image, return_tensors="np")
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for k in out_noimage:
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self.assertTrue(out_noimage[k].tolist() == out_singlimage[k].tolist())
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out_multiimages = processor(text=text_multi_images, images=[image, image], return_tensors="np")
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out_noimage = processor(text=text_no_image, images=[[image, image]], return_tensors="np")
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# We can't be sure what is users intention, whether user want "one text + two images" or user forgot to add the second text
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with self.assertRaises(ValueError):
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out_noimage = processor(text=text_no_image, images=[image, image], return_tensors="np")
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for k in out_noimage:
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self.assertTrue(out_noimage[k].tolist() == out_multiimages[k].tolist())
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text_batched = ["Dummy text!", "Dummy text!"]
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text_batched_with_image = ["<image>Dummy text!", "<image>Dummy text!"]
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out_images = processor(text=text_batched_with_image, images=[image, image], return_tensors="np")
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out_noimage_nested = processor(text=text_batched, images=[[image], [image]], return_tensors="np")
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out_noimage = processor(text=text_batched, images=[image, image], return_tensors="np")
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for k in out_noimage:
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self.assertTrue(out_noimage[k].tolist() == out_images[k].tolist() == out_noimage_nested[k].tolist())
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