# Copyright 2024 The HuggingFace Team. All rights reserved. # # 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 unittest from transformers.image_utils import load_image from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torchvision, 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 from ...test_processing_common import url_to_local_path if is_vision_available(): from PIL import Image from transformers import RTDetrImageProcessor, RTDetrImageProcessorFast if is_torch_available(): import torch class RTDetrImageProcessingTester: def __init__( self, parent, batch_size=4, num_channels=3, do_resize=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=False, do_pad=False, return_tensors="pt", ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.do_resize = do_resize self.size = size if size is not None else {"height": 640, "width": 640} self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.do_pad = do_pad self.return_tensors = return_tensors def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_normalize": self.do_normalize, "do_pad": self.do_pad, "return_tensors": self.return_tensors, } def get_expected_values(self): return self.size["height"], self.size["width"] def expected_output_image_shape(self, images): height, width = self.get_expected_values() return self.num_channels, height, 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=30, max_resolution=400, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = RTDetrImageProcessor if is_vision_available() else None fast_image_processing_class = RTDetrImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = RTDetrImageProcessingTester(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_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "resample")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "return_tensors")) 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": 640, "width": 640}) def test_valid_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f: target = json.loads(f.read()) params = {"image_id": 39769, "annotations": target} for image_processing_class in self.image_processor_list: # encode them image_processing = image_processing_class.from_pretrained("PekingU/rtdetr_r50vd") # legal encodings (single image) _ = image_processing(images=image, annotations=params, return_tensors="pt") _ = image_processing(images=image, annotations=[params], return_tensors="pt") # legal encodings (batch of one image) _ = image_processing(images=[image], annotations=params, return_tensors="pt") _ = image_processing(images=[image], annotations=[params], return_tensors="pt") # legal encoding (batch of more than one image) n = 5 _ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt") # example of an illegal encoding (missing the 'image_id' key) with self.assertRaises(ValueError) as e: image_processing(images=image, annotations={"annotations": target}, return_tensors="pt") self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations")) # example of an illegal encoding (unequal lengths of images and annotations) with self.assertRaises(ValueError) as e: image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt") self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.") @slow def test_call_pytorch_with_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f: target = json.loads(f.read()) target = {"image_id": 39769, "annotations": target} for image_processing_class in self.image_processor_list: # encode them image_processing = image_processing_class.from_pretrained("PekingU/rtdetr_r50vd") encoding = image_processing(images=image, annotations=target, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 640, 640]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.5490, 0.5647, 0.5725]) torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4) # verify area expected_area = torch.tensor([2827.9883, 5403.4761, 235036.7344, 402070.2188, 71068.8281, 79601.2812]) torch.testing.assert_close(encoding["labels"][0]["area"], expected_area) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3) # verify image_id expected_image_id = torch.tensor([39769]) torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd) # verify class_labels expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) # verify orig_size expected_orig_size = torch.tensor([480, 640]) torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size) # verify size expected_size = torch.tensor([640, 640]) torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) @slow def test_image_processor_outputs(self): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) encoding = image_processing(images=image, return_tensors="pt") # verify pixel values: shape expected_shape = torch.Size([1, 3, 640, 640]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # verify pixel values: output values expected_slice = torch.tensor([0.5490196347236633, 0.5647059082984924, 0.572549045085907]) torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-5, atol=1e-5) def test_multiple_images_processor_outputs(self): images_urls = [ "http://images.cocodataset.org/val2017/000000000139.jpg", "http://images.cocodataset.org/val2017/000000000285.jpg", "http://images.cocodataset.org/val2017/000000000632.jpg", "http://images.cocodataset.org/val2017/000000000724.jpg", "http://images.cocodataset.org/val2017/000000000776.jpg", "http://images.cocodataset.org/val2017/000000000785.jpg", "http://images.cocodataset.org/val2017/000000000802.jpg", "http://images.cocodataset.org/val2017/000000000872.jpg", ] images = [] for url in images_urls: image = load_image(url_to_local_path(url)) images.append(image) for image_processing_class in self.image_processor_list: # apply image processing image_processing = image_processing_class(**self.image_processor_dict) encoding = image_processing(images=images, return_tensors="pt") # verify if pixel_values is part of the encoding self.assertIn("pixel_values", encoding) # verify pixel values: shape expected_shape = torch.Size([8, 3, 640, 640]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # verify pixel values: output values expected_slices = torch.tensor( [ [0.5333333611488342, 0.5568627715110779, 0.5647059082984924], [0.5372549295425415, 0.4705882668495178, 0.4274510145187378], [0.3960784673690796, 0.35686275362968445, 0.3686274588108063], [0.20784315466880798, 0.1882353127002716, 0.15294118225574493], [0.364705890417099, 0.364705890417099, 0.3686274588108063], [0.8078432083129883, 0.8078432083129883, 0.8078432083129883], [0.4431372880935669, 0.4431372880935669, 0.4431372880935669], [0.19607844948768616, 0.21176472306251526, 0.3607843220233917], ] ) torch.testing.assert_close(encoding["pixel_values"][:, 1, 0, :3], expected_slices, rtol=1e-5, atol=1e-5) @slow def test_batched_coco_detection_annotations(self): image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800)) with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f: target = json.loads(f.read()) annotations_0 = {"image_id": 39769, "annotations": target} annotations_1 = {"image_id": 39769, "annotations": target} # Adjust the bounding boxes for the resized image w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotations_1["annotations"])): coords = annotations_1["annotations"][i]["bbox"] new_bbox = [ coords[0] * w_1 / w_0, coords[1] * h_1 / h_0, coords[2] * w_1 / w_0, coords[3] * h_1 / h_0, ] annotations_1["annotations"][i]["bbox"] = new_bbox images = [image_0, image_1] annotations = [annotations_0, annotations_1] for image_processing_class in self.image_processor_list: image_processing = image_processing_class() encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, return_tensors="pt", # do_convert_annotations=True ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 640, 640 expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # Check the bounding boxes have been adjusted for padded images self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) expected_boxes_0 = torch.tensor( [ [0.6879, 0.4609, 0.0755, 0.3691], [0.2118, 0.3359, 0.2601, 0.1566], [0.5011, 0.5000, 0.9979, 1.0000], [0.5010, 0.5020, 0.9979, 0.9959], [0.3284, 0.5944, 0.5884, 0.8112], [0.8394, 0.5445, 0.3213, 0.9110], ] ) expected_boxes_1 = torch.tensor( [ [0.5503, 0.2765, 0.0604, 0.2215], [0.1695, 0.2016, 0.2080, 0.0940], [0.5006, 0.4933, 0.9977, 0.9865], [0.5008, 0.5002, 0.9983, 0.9955], [0.2627, 0.5456, 0.4707, 0.8646], [0.7715, 0.4115, 0.4570, 0.7161], ] ) torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3) torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3) # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height # format and not in the range [0, 1] encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, do_convert_annotations=False, return_tensors="pt", ) self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) # Convert to absolute coordinates unnormalized_boxes_0 = torch.vstack( [ expected_boxes_0[:, 0] * postprocessed_width, expected_boxes_0[:, 1] * postprocessed_height, expected_boxes_0[:, 2] * postprocessed_width, expected_boxes_0[:, 3] * postprocessed_height, ] ).T unnormalized_boxes_1 = torch.vstack( [ expected_boxes_1[:, 0] * postprocessed_width, expected_boxes_1[:, 1] * postprocessed_height, expected_boxes_1[:, 2] * postprocessed_width, expected_boxes_1[:, 3] * postprocessed_height, ] ).T # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max expected_boxes_0 = torch.vstack( [ unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, ] ).T expected_boxes_1 = torch.vstack( [ unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, ] ).T torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1) torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1) @slow @require_torch_accelerator @require_torchvision # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_fast_processor_equivalence_cpu_accelerator_coco_detection_annotations def test_fast_processor_equivalence_cpu_accelerator_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f: target = json.loads(f.read()) target = {"image_id": 39769, "annotations": target} processor = self.image_processor_list[1]() # 1. run processor on CPU encoding_cpu = processor(images=image, annotations=target, return_tensors="pt", device="cpu") # 2. run processor on accelerator encoding_gpu = processor(images=image, annotations=target, return_tensors="pt", device=torch_device) # verify pixel values self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape) self.assertTrue( torch.allclose( encoding_cpu["pixel_values"][0, 0, 0, :3], encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"), atol=1e-4, ) ) # verify area torch.testing.assert_close(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu")) # verify boxes self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape) self.assertTrue( torch.allclose( encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3 ) ) # verify image_id torch.testing.assert_close( encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu") ) # verify is_crowd torch.testing.assert_close( encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu") ) # verify class_labels self.assertTrue( torch.allclose( encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu") ) ) # verify orig_size torch.testing.assert_close( encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu") ) # verify size torch.testing.assert_close(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))