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296 lines
12 KiB
Python
296 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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"""Testing suite for the ColPali processor."""
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import unittest
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import torch
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from transformers.models.colpali.processing_colpali import ColPaliProcessor
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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 ColPaliProcessor, GemmaTokenizer, 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 ColPaliProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = ColPaliProcessor
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@classmethod
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def _setup_tokenizer(cls):
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return GemmaTokenizer.from_pretrained(SAMPLE_VOCAB, keep_accents=True)
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@classmethod
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def _setup_image_processor(cls):
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# Use 64×64 instead of the default 384×384 from google/siglip-so400m-patch14-384 to avoid
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# large tensors. image_seq_length=0 matches the processor attribute so token-count tests pass.
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image_processor = SiglipImageProcessor(size={"height": 64, "width": 64})
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image_processor.image_seq_length = 0
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return image_processor
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@unittest.skip("ColpaliProcessor can only process one of text or images at a time")
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def test_processor_with_multiple_inputs(self):
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pass
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@unittest.skip("ColpaliProcessor adds a prefix and suffix to the text")
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def test_tokenizer_defaults(self):
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pass
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def test_get_num_vision_tokens(self):
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"Tests general functionality of the helper used internally in vLLM"
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processor = self.get_processor()
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output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
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self.assertTrue("num_image_tokens" in output)
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self.assertEqual(len(output["num_image_tokens"]), 3)
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self.assertTrue("num_image_patches" in output)
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self.assertEqual(len(output["num_image_patches"]), 3)
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@require_torch
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@require_vision
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def test_process_images(self):
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# Processor configuration
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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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# Get the processor
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processor = self.processor_class(
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tokenizer=tokenizer,
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image_processor=image_processor,
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)
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# Process the image
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batch_feature = processor.process_images(images=image_input, return_tensors="pt")
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# Assertions
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self.assertIn("pixel_values", batch_feature)
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self.assertEqual(batch_feature["pixel_values"].shape, torch.Size([1, 3, 64, 64]))
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@require_torch
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@require_vision
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def test_process_queries(self):
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# Inputs
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queries = [
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"Is attention really all you need?",
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"Are Benjamin, Antoine, Merve, and Jo best friends?",
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]
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# Processor configuration
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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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# Get the processor
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processor = self.processor_class(
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tokenizer=tokenizer,
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image_processor=image_processor,
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)
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# Process the image
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batch_feature = processor.process_queries(text=queries, return_tensors="pt")
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# Assertions
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self.assertIn("input_ids", batch_feature)
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self.assertIsInstance(batch_feature["input_ids"], torch.Tensor)
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self.assertEqual(batch_feature["input_ids"].shape[0], len(queries))
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# The following tests override the parent tests because ColPaliProcessor can only take one of images or text as input at a time.
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def test_tokenizer_defaults_preserved_by_kwargs(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = self.prepare_text_inputs()
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inputs = processor(text=input_str, return_tensors="pt")
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self.assertEqual(inputs[self.text_input_name].shape[-1], 117)
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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"""
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We use do_rescale=True, rescale_factor=-1.0 to ensure that image_processor kwargs are preserved in the processor.
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We then check that the mean of the pixel_values is less than or equal to 0 after processing.
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Since the original pixel_values are in [0, 255], this is a good indicator that the rescale_factor is indeed applied.
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"""
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_components["image_processor"] = self.get_component(
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"image_processor", do_rescale=True, rescale_factor=-1.0
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)
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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image_input = self.prepare_image_inputs()
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inputs = processor(images=image_input, return_tensors="pt")
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self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
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def test_kwargs_overrides_default_tokenizer_kwargs(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_components["tokenizer"] = self.get_component("tokenizer", padding="longest")
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = self.prepare_text_inputs()
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inputs = processor(text=input_str, return_tensors="pt", max_length=112, padding="max_length")
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self.assertEqual(inputs[self.text_input_name].shape[-1], 112)
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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.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_components["image_processor"] = self.get_component(
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"image_processor", do_rescale=True, rescale_factor=1
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)
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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image_input = self.prepare_image_inputs()
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inputs = processor(images=image_input, do_rescale=True, rescale_factor=-1.0, return_tensors="pt")
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self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
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def test_unstructured_kwargs(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = self.prepare_text_inputs()
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inputs = processor(
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text=input_str,
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return_tensors="pt",
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do_rescale=True,
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rescale_factor=-1.0,
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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[self.text_input_name].shape[-1], 76)
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def test_unstructured_kwargs_batched(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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image_input = self.prepare_image_inputs(batch_size=2)
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inputs = processor(
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images=image_input,
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return_tensors="pt",
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do_rescale=True,
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rescale_factor=-1.0,
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padding="longest",
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max_length=76,
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)
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self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
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def test_doubly_passed_kwargs(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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image_input = self.prepare_image_inputs()
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with self.assertRaises(ValueError):
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_ = processor(
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images=image_input,
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images_kwargs={"do_rescale": True, "rescale_factor": -1.0},
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do_rescale=True,
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return_tensors="pt",
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)
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def test_structured_kwargs_nested(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = self.prepare_text_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": {"do_rescale": True, "rescale_factor": -1.0},
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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, **all_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
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def test_structured_kwargs_nested_from_dict(self):
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if "image_processor" not in self.processor_class.get_attributes():
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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self.skip_processor_without_typed_kwargs(processor)
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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": {"do_rescale": True, "rescale_factor": -1.0},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(images=image_input, **all_kwargs)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
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# Can process only text or images at a time
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def test_model_input_names(self):
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processor = self.get_processor()
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image_input = self.prepare_image_inputs()
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inputs = processor(images=image_input)
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self.assertSetEqual(set(inputs.keys()), set(processor.model_input_names))
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@unittest.skip("ColPali can't process text+image inputs at the same time")
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def test_processor_text_has_no_visual(self):
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pass
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@unittest.skip("ColPaliProcessor can't process text+image inputs at the same time")
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def test_get_num_multimodal_tokens_matches_processor_call(self):
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pass
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@unittest.skip("ColPaliProcessor can't process text+image inputs at the same time")
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def test_flat_kwarg_applied_when_modality_dict_lacks_it(self):
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pass
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