# coding=utf-8 # 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 from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD 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 ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import InstructBlipVideoImageProcessor class InstructBlipVideoProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=5, num_channels=3, image_size=24, min_resolution=30, max_resolution=80, do_resize=True, size=None, do_normalize=True, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD, do_convert_rgb=True, frames=4, ): super().__init__() size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb self.frames = frames def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_convert_rgb": self.do_convert_rgb, } def expected_output_image_shape(self, images): return self.frames, self.num_channels, self.size["height"], self.size["width"] 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, ) # let's simply copy the frames to fake a long video-clip if numpify or torchify: videos = [] for image in images: if numpify: video = image[None, ...].repeat(self.frames, 0) else: video = image[None, ...].repeat(self.frames, 1, 1, 1) videos.append(video) else: videos = [] for pil_image in images: videos.append([pil_image] * self.frames) return videos @require_torch @require_vision class InstructBlipVideoProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = InstructBlipVideoImageProcessor if is_vision_available() else None def setUp(self): super().setUp() self.image_processor_tester = InstructBlipVideoProcessingTester(self) @property # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) 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_convert_rgb")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) def test_call_pil(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors video_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for video in video_inputs: self.assertIsInstance(video[0], Image.Image) # Test not batched input (pass as `videos` arg to test that ImageProcessor can handle videos in absence of images!) encoded_videos = image_processing(images=video_inputs[0], return_tensors="pt").pixel_values expected_output_video_shape = (1, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape) # Test batched encoded_videos = image_processing(images=video_inputs, return_tensors="pt").pixel_values expected_output_video_shape = (5, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape) def test_call_numpy(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors video_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True) for video in video_inputs: self.assertIsInstance(video, np.ndarray) # Test not batched input (pass as `videos` arg to test that ImageProcessor can handle videos in absence of images!) encoded_videos = image_processing(images=video_inputs[0], return_tensors="pt").pixel_values expected_output_video_shape = (1, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape) # Test batched encoded_videos = image_processing(images=video_inputs, return_tensors="pt").pixel_values expected_output_video_shape = (5, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape) def test_call_pytorch(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors video_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) for video in video_inputs: self.assertIsInstance(video, torch.Tensor) # Test not batched input encoded_videos = image_processing(images=video_inputs[0], return_tensors="pt").pixel_values expected_output_video_shape = (1, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape) # Test batched encoded_videos = image_processing(images=video_inputs, return_tensors="pt").pixel_values expected_output_video_shape = (5, 4, 3, 18, 18) self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape)