# 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 io import unittest import httpx import numpy as np 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 if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import VitPoseImageProcessor if is_torchvision_available(): from transformers import VitPoseImageProcessorFast class VitPoseImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_affine_transform=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} 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_affine_transform = do_affine_transform self.size = size self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_image_processor_dict(self): return { "do_affine_transform": self.do_affine_transform, "size": self.size, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def expected_output_image_shape(self, images): return self.num_channels, 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 VitPoseImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = VitPoseImageProcessor if is_vision_available() else None fast_image_processing_class = VitPoseImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = VitPoseImageProcessingTester(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_affine_transform")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) 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": 20, "width": 20}) image_processor = image_processing_class.from_dict( self.image_processor_dict, size={"height": 42, "width": 42} ) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) 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 boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape)) # Test batched boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape) ) 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 boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape)) # Test batched boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape) ) 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) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] encoded_images = image_processing(image_inputs[0], boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (2, *expected_output_image_shape)) # Test batched boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size encoded_images = image_processing(image_inputs, boxes=boxes, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size * 2, *expected_output_image_shape) ) def test_call_numpy_4_channels(self): for image_processing_class in self.image_processor_list: # Test that can process images which have an arbitrary number of channels # Initialize image_processing image_processor = image_processing_class(**self.image_processor_dict) # create random numpy tensors self.image_processor_tester.num_channels = 4 image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) # Test not batched input boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] encoded_images = image_processor( image_inputs[0], boxes=boxes, return_tensors="pt", input_data_format="channels_last", image_mean=(0.0, 0.0, 0.0, 0.0), image_std=(1.0, 1.0, 1.0, 1.0), ).pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (len(boxes[0]), *expected_output_image_shape)) # Test batched boxes = [[[0, 0, 1, 1], [0.5, 0.5, 0.5, 0.5]]] * self.image_processor_tester.batch_size encoded_images = image_processor( image_inputs, boxes=boxes, return_tensors="pt", input_data_format="channels_last", image_mean=(0.0, 0.0, 0.0, 0.0), image_std=(1.0, 1.0, 1.0, 1.0), ).pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size * len(boxes[0]), *expected_output_image_shape), ) 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( io.BytesIO( httpx.get("http://images.cocodataset.org/val2017/000000039769.jpg", follow_redirects=True).content ) ) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) boxes = [[[0, 0, 1, 1]]] encoding_slow = image_processor_slow(dummy_image, boxes=boxes, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, boxes=boxes, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) 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") 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) boxes = [[[0, 0, 1, 1]]] * len(dummy_images) encoding_slow = image_processor_slow(dummy_images, boxes=boxes, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, boxes=boxes, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) def test_can_compile_fast_image_processor(self): from packaging import version from transformers.testing_utils import torch_device 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) boxes = [[[0, 0, 1, 1]]] output_eager = image_processor(input_image, boxes=boxes, device=torch_device, return_tensors="pt") image_processor = torch.compile(image_processor, mode="reduce-overhead") output_compiled = image_processor(input_image, boxes=boxes, device=torch_device, return_tensors="pt") self._assert_slow_fast_tensors_equivalence( output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5 )