# 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_torch_accelerator, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ( ImageProcessingTester, ImageProcessingTestMixin, load_coco_image, ) if is_torch_available(): import torch if is_vision_available(): from PIL import Image class VitPoseImageProcessingTester(ImageProcessingTester): def __init__(self, **kwargs): # Image processor init kwargs kwargs.setdefault("size", {"height": 20, "width": 20}) super().__init__(**kwargs) @require_torch @require_vision class VitPoseImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processor_tester_class = VitPoseImageProcessingTester def test_call_pil(self): for image_processing_class in self.image_processing_classes.values(): 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_processing_classes.values(): 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_processing_classes.values(): 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_processing_classes.values(): 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), ) self.image_processor_tester.num_channels = 3 @require_vision @require_torch def test_backends_equivalence(self): """VitPose requires boxes parameter for preprocessing.""" if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") dummy_image = load_coco_image("000000039769.jpg") boxes = [[[0, 0, 1, 1]]] encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor(dummy_image, boxes=boxes, return_tensors="pt") backend_names = list(encodings.keys()) reference_encoding = encodings[backend_names[0]].pixel_values for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].pixel_values) @require_vision @require_torch def test_backends_equivalence_batched(self): """VitPose requires boxes parameter for batched preprocessing.""" if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) boxes = [[[0, 0, 1, 1]]] * len(dummy_images) encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor(dummy_images, boxes=boxes, return_tensors="pt") backend_names = list(encodings.keys()) reference_encoding = encodings[backend_names[0]].pixel_values for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].pixel_values) @require_torch_accelerator @require_vision @pytest.mark.torch_compile_test def test_can_compile_torchvision_backend(self): """VitPose requires boxes parameter for preprocessing.""" from transformers.testing_utils import torch_device if "torchvision" not in self.image_processing_classes: self.skipTest("Skipping compilation test as torchvision backend is not available") torch.compiler.reset() input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8) image_processor = self.image_processing_classes["torchvision"](**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_tensors_equivalence( output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5 )