# Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import tempfile import unittest import numpy as np from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, load_image from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs, prepare_video_inputs from ...test_processing_common import url_to_local_path if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import Qwen2VLImageProcessor if is_torchvision_available(): from transformers import Qwen2VLImageProcessorFast class Qwen2VLImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, num_frames=10, min_resolution=56, max_resolution=1024, min_pixels=56 * 56, max_pixels=28 * 28 * 1280, do_normalize=True, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD, do_resize=True, patch_size=14, temporal_patch_size=2, merge_size=2, do_convert_rgb=True, ): self.parent = parent self.batch_size = batch_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.num_channels = num_channels self.num_frames = num_frames self.image_mean = OPENAI_CLIP_MEAN self.image_std = OPENAI_CLIP_STD self.min_pixels = min_pixels self.max_pixels = max_pixels self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.merge_size = merge_size self.do_resize = do_resize self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "image_mean": self.image_mean, "image_std": self.image_std, "min_pixels": self.min_pixels, "max_pixels": self.max_pixels, "patch_size": self.patch_size, "temporal_patch_size": self.temporal_patch_size, "merge_size": self.merge_size, } def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): images = prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) return [[image] for image in images] def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_video_inputs( batch_size=self.batch_size, num_channels=self.num_channels, num_frames=self.num_frames, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class Qwen2VLImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = Qwen2VLImageProcessor if is_vision_available() else None fast_image_processing_class = Qwen2VLImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = Qwen2VLImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "min_pixels")) self.assertTrue(hasattr(image_processing, "max_pixels")) self.assertTrue(hasattr(image_processing, "do_convert_rgb")) self.assertTrue(hasattr(image_processing, "patch_size")) self.assertTrue(hasattr(image_processing, "temporal_patch_size")) self.assertTrue(hasattr(image_processing, "merge_size")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.min_pixels, 56 * 56) self.assertEqual(image_processor.max_pixels, 28 * 28 * 1280) image_processor = image_processing_class.from_dict( self.image_processor_dict, min_pixels=256 * 256, max_pixels=640 * 640 ) self.assertEqual(image_processor.min_pixels, 256 * 256) self.assertEqual(image_processor.max_pixels, 640 * 640) def test_select_best_resolution(self): # Test with a final resize resolution best_resolution = smart_resize(561, 278, factor=28) self.assertEqual(best_resolution, (560, 280)) def test_call_pil(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for image in image_inputs: self.assertIsInstance(image[0], Image.Image) # Test not batched input process_out = image_processing(image_inputs[0], return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (4900, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]]) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) # Test batched process_out = image_processing(image_inputs, return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (34300, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) def test_call_numpy(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True) for image in image_inputs: self.assertIsInstance(image[0], np.ndarray) # Test not batched input process_out = image_processing(image_inputs[0], return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (4900, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]]) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) # Test batched process_out = image_processing(image_inputs, return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (34300, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) def test_call_pytorch(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) for image in image_inputs: self.assertIsInstance(image[0], torch.Tensor) # Test not batched input process_out = image_processing(image_inputs[0], return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (4900, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]]) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) # Test batched process_out = image_processing(image_inputs, return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (34300, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) @unittest.skip(reason="Qwen2VLImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") def test_call_numpy_4_channels(self): pass def test_nested_input(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) # Test batched as a list of images process_out = image_processing(image_inputs, return_tensors="pt") encoded_images = process_out.pixel_values image_grid_thws = process_out.image_grid_thw expected_output_image_shape = (34300, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) # Test batched as a nested list of images, where each sublist is one batch image_inputs_nested = image_inputs[:3] + image_inputs[3:] process_out = image_processing(image_inputs_nested, return_tensors="pt") encoded_images_nested = process_out.pixel_values image_grid_thws_nested = process_out.image_grid_thw expected_output_image_shape = (34300, 1176) expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7) self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape) self.assertTrue((image_grid_thws == expected_image_grid_thws).all()) # Image processor should return same pixel values, independently of ipnut format self.assertTrue((encoded_images_nested == encoded_images).all()) self.assertTrue((image_grid_thws_nested == expected_image_grid_thws).all()) def test_custom_image_size(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: image_processing.save_pretrained(tmpdirname) image_processor_loaded = image_processing_class.from_pretrained( tmpdirname, max_pixels=56 * 56, min_pixels=28 * 28 ) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) process_out = image_processor_loaded(image_inputs, return_tensors="pt") expected_output_video_shape = [112, 1176] self.assertListEqual(list(process_out.pixel_values.shape), expected_output_video_shape) def test_custom_pixels(self): pixel_choices = frozenset(itertools.product((100, 150, 200, 20000), (100, 150, 200, 20000))) for image_processing_class in self.image_processor_list: image_processor_dict = self.image_processor_dict.copy() for a_pixels, b_pixels in pixel_choices: image_processor_dict["min_pixels"] = min(a_pixels, b_pixels) image_processor_dict["max_pixels"] = max(a_pixels, b_pixels) image_processor = image_processing_class(**image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs() # Just checking that it doesn't raise an error image_processor(image_inputs, return_tensors="pt") @require_vision @require_torch def test_slow_fast_equivalence(self): dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")) if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) encoding_slow = image_processor_slow(dummy_image, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) self.assertEqual(encoding_slow.image_grid_thw.dtype, encoding_fast.image_grid_thw.dtype) self._assert_slow_fast_tensors_equivalence( encoding_slow.image_grid_thw.float(), encoding_fast.image_grid_thw.float() ) @require_vision @require_torch def test_slow_fast_equivalence_batched(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop: self.skipTest( reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors" ) dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) encoding_slow = image_processor_slow(dummy_images, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) self.assertEqual(encoding_slow.image_grid_thw.dtype, encoding_fast.image_grid_thw.dtype) self._assert_slow_fast_tensors_equivalence( encoding_slow.image_grid_thw.float(), encoding_fast.image_grid_thw.float() ) def test_get_num_patches_without_images(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={}) self.assertEqual(num_patches, 64) num_patches = image_processing.get_number_of_image_patches(height=200, width=50, images_kwargs={}) self.assertEqual(num_patches, 56) num_patches = image_processing.get_number_of_image_patches( height=100, width=100, images_kwargs={"patch_size": 28} ) self.assertEqual(num_patches, 16)