# 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 unittest import numpy as np import pytest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingTester, ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image class LlavaOnevisionImageProcessingTester(ImageProcessingTester): def __init__(self, **kwargs): # Random test inputs kwargs kwargs.setdefault("image_size", 20) # Image processor init kwargs kwargs.setdefault("size", {"height": 20, "width": 20}) super().__init__(**kwargs) @require_torch @require_vision class LlavaOnevisionImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processor_tester_class = LlavaOnevisionImageProcessingTester def test_call_pil(self): for image_processing_class in self.image_processing_classes.values(): # 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, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_numpy(self): for image_processing_class in self.image_processing_classes.values(): # 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, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_pytorch(self): for image_processing_class in self.image_processing_classes.values(): # 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, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) @unittest.skip( reason="LlavaOnevisionImageProcessor doesn't treat 4 channel PIL and numpy consistently yet" ) # FIXME raushan def test_call_numpy_4_channels(self): pass def test_nested_input(self): for image_processing_class in self.image_processing_classes.values(): 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 encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched as a nested list of images, where each sublist is one batch image_inputs_nested = [[image_input] for image_input in image_inputs] encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1522, 3, 20, 20) self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape) # Image processor should return same pixel values, independently of input format self.assertTrue((encoded_images_nested == encoded_images).all()) def test_multi_images(self): length = 384 scale_single, scale_multi = 2, 3 image_processor_dict = self.image_processor_tester.prepare_image_processor_dict() image_processor_dict["size"] = {"height": length, "width": length} # patch size for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**image_processor_dict) # Test batched as a nested list of images, where each sublist is one batch len_image_1 = length * scale_single image_inputs_1 = prepare_image_inputs( batch_size=1, min_resolution=0, # not used max_resolution=len_image_1, num_channels=3, equal_resolution=True, ) len_image_2 = length * scale_multi image_inputs_2 = prepare_image_inputs( batch_size=7, min_resolution=0, # not used max_resolution=len_image_2, num_channels=3, equal_resolution=True, ) image_inputs = [image_inputs_1, image_inputs_2] # Only single image should be patchified expected_num_patches = scale_single**2 + 1 # +1 for base image patch expected_output_image_shape = (8, expected_num_patches, 3, length, length) encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) @unittest.skip( reason="LlavaOnevisionImageProcessor doesn't compile (infinitely) when using class transforms" ) # FIXME yoni @pytest.mark.torch_compile_test def test_can_compile_fast_image_processor(self): pass def test_pad_for_patching(self): for backend_name, image_processing_class in self.image_processing_classes.items(): if backend_name == "torchvision": numpify = False torchify = True else: numpify = True torchify = False image_processing = image_processing_class(**self.image_processor_dict) # Create odd-sized images image_input = self.image_processor_tester.prepare_image_inputs( equal_resolution=True, numpify=numpify, torchify=torchify, )[0] self.assertIn(image_input.shape, [(3, 400, 400), (400, 400, 3)]) if numpify: image_input = image_input.transpose(2, 0, 1) # Test odd-width image_shape = (400, 601) encoded_images = image_processing._pad_for_patching(image_input, image_shape) self.assertEqual(encoded_images.shape[-2:], image_shape) # Test odd-height image_shape = (503, 400) encoded_images = image_processing._pad_for_patching(image_input, image_shape) self.assertEqual(encoded_images.shape[-2:], image_shape) def test_call_without_padding(self): for image_processing_class in self.image_processing_classes.values(): # 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) # Test not batched input encoded_images = image_processing(image_inputs[0], do_pad=False).pixel_values self.assertEqual(len(encoded_images), 1) # Test batched encoded_images = image_processing(image_inputs, do_pad=False).pixel_values self.assertEqual(len(encoded_images), len(image_inputs))