# Copyright 2021 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 json import os import tempfile import unittest import numpy as np import pytest import requests from datasets import load_dataset from packaging import version from transformers import AutoImageProcessor from transformers.testing_utils import ( check_json_file_has_correct_format, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_torchvision_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 ImageGPTImageProcessor if is_torchvision_available(): from transformers import ImageGPTImageProcessorFast class ImageGPTImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, ): 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 def prepare_image_processor_dict(self): return { # here we create 2 clusters for the sake of simplicity "clusters": np.asarray( [ [0.8866443634033203, 0.6618829369544983, 0.3891746401786804], [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296], ] ), "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, } def expected_output_image_shape(self, images): return (self.size["height"] * self.size["width"],) def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return 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, ) @require_torch @require_vision class ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ImageGPTImageProcessor if is_vision_available() else None fast_image_processing_class = ImageGPTImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = ImageGPTImageProcessingTester(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, "clusters")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_normalize")) 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.size, {"height": 18, "width": 18}) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) def test_image_processor_to_json_string(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) obj = json.loads(image_processor.to_json_string()) for key, value in self.image_processor_dict.items(): if key == "clusters": self.assertTrue(np.array_equal(value, obj[key])) else: self.assertEqual(obj[key], value) def test_image_processor_to_json_file(self): for image_processing_class in self.image_processor_list: image_processor_first = image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "image_processor.json") image_processor_first.to_json_file(json_file_path) image_processor_second = image_processing_class.from_json_file(json_file_path).to_dict() image_processor_first = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, image_processor_second[key])) else: self.assertEqual(image_processor_first[key], value) def test_image_processor_from_and_save_pretrained(self): for image_processing_class in self.image_processor_list: image_processor_first = image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: image_processor_first.save_pretrained(tmpdirname) image_processor_second = image_processing_class.from_pretrained(tmpdirname).to_dict() image_processor_first = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, image_processor_second[key])) else: self.assertEqual(value, value) def test_image_processor_save_load_with_autoimageprocessor(self): for image_processing_class in self.image_processor_list: image_processor_first = image_processing_class(**self.image_processor_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = image_processor_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname) image_processor_first = image_processor_first.to_dict() image_processor_second = image_processor_second.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(value, image_processor_second[key])) else: self.assertEqual(value, value) @unittest.skip(reason="ImageGPT requires clusters at initialization") def test_init_without_params(self): pass # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input 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=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input 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=False, 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").input_ids expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) @unittest.skip(reason="ImageGPT assumes clusters for 3 channels") def test_call_numpy_4_channels(self): pass # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input 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=False, torchify=True) expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape), ) # For quantization-based processors, use absolute tolerance only to avoid infinity issues @require_vision @require_torch def test_slow_fast_equivalence(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") dummy_image = Image.open( requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw ) 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.input_ids.float(), encoding_fast.input_ids.float(), atol=1.0, rtol=0 ) @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.input_ids.float(), encoding_fast.input_ids.float(), atol=1.0, rtol=0 ) @slow @require_torch_accelerator @require_vision @pytest.mark.torch_compile_test def test_can_compile_fast_image_processor(self): if self.fast_image_processing_class is None: self.skipTest("Skipping compilation test as fast image processor is not defined") if version.parse(torch.__version__) < version.parse("2.3"): self.skipTest(reason="This test requires torch >= 2.3 to run.") torch.compiler.reset() input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8) image_processor = self.fast_image_processing_class(**self.image_processor_dict) output_eager = image_processor(input_image, device=torch_device, return_tensors="pt") image_processor = torch.compile(image_processor, mode="reduce-overhead") output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt") self._assert_slow_fast_tensors_equivalence( output_eager.input_ids.float(), output_compiled.input_ids.float(), atol=1.0, rtol=0 ) def prepare_images(): # we use revision="refs/pr/1" until the PR is merged # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") image1 = dataset[4]["image"] image2 = dataset[5]["image"] images = [image1, image2] return images @require_vision @require_torch class ImageGPTImageProcessorIntegrationTest(unittest.TestCase): @slow def test_image(self): image_processing = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") images = prepare_images() # test non-batched encoding = image_processing(images[0], return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (1, 1024)) expected_slice = [306, 191, 191] self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice) # test batched encoding = image_processing(images, return_tensors="pt") self.assertIsInstance(encoding.input_ids, torch.LongTensor) self.assertEqual(encoding.input_ids.shape, (2, 1024)) expected_slice = [303, 13, 13] self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)