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| # coding=utf-8 | |
| # 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 pathlib | |
| import unittest | |
| import numpy as np | |
| from transformers.testing_utils import require_torch, require_vision, slow | |
| from transformers.utils import is_torch_available, is_vision_available | |
| from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs | |
| if is_torch_available(): | |
| import torch | |
| if is_vision_available(): | |
| from PIL import Image | |
| from transformers import DetrImageProcessor | |
| class DetrImageProcessingTester(unittest.TestCase): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=7, | |
| num_channels=3, | |
| min_resolution=30, | |
| max_resolution=400, | |
| do_resize=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], | |
| do_pad=True, | |
| ): | |
| # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p | |
| size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333} | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.num_channels = num_channels | |
| self.min_resolution = min_resolution | |
| self.max_resolution = max_resolution | |
| self.do_resize = do_resize | |
| 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 | |
| self.do_pad = do_pad | |
| 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, | |
| "image_mean": self.image_mean, | |
| "image_std": self.image_std, | |
| "do_pad": self.do_pad, | |
| } | |
| def get_expected_values(self, image_inputs, batched=False): | |
| """ | |
| This function computes the expected height and width when providing images to DetrImageProcessor, | |
| assuming do_resize is set to True with a scalar size. | |
| """ | |
| if not batched: | |
| image = image_inputs[0] | |
| if isinstance(image, Image.Image): | |
| w, h = image.size | |
| else: | |
| h, w = image.shape[1], image.shape[2] | |
| if w < h: | |
| expected_height = int(self.size["shortest_edge"] * h / w) | |
| expected_width = self.size["shortest_edge"] | |
| elif w > h: | |
| expected_height = self.size["shortest_edge"] | |
| expected_width = int(self.size["shortest_edge"] * w / h) | |
| else: | |
| expected_height = self.size["shortest_edge"] | |
| expected_width = self.size["shortest_edge"] | |
| else: | |
| expected_values = [] | |
| for image in image_inputs: | |
| expected_height, expected_width = self.get_expected_values([image]) | |
| expected_values.append((expected_height, expected_width)) | |
| expected_height = max(expected_values, key=lambda item: item[0])[0] | |
| expected_width = max(expected_values, key=lambda item: item[1])[1] | |
| return expected_height, expected_width | |
| class DetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): | |
| image_processing_class = DetrImageProcessor if is_vision_available() else None | |
| def setUp(self): | |
| self.image_processor_tester = DetrImageProcessingTester(self) | |
| 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, "image_mean")) | |
| self.assertTrue(hasattr(image_processing, "image_std")) | |
| self.assertTrue(hasattr(image_processing, "do_normalize")) | |
| self.assertTrue(hasattr(image_processing, "do_rescale")) | |
| self.assertTrue(hasattr(image_processing, "rescale_factor")) | |
| self.assertTrue(hasattr(image_processing, "do_resize")) | |
| self.assertTrue(hasattr(image_processing, "size")) | |
| self.assertTrue(hasattr(image_processing, "do_pad")) | |
| 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, {"shortest_edge": 18, "longest_edge": 1333}) | |
| self.assertEqual(image_processor.do_pad, True) | |
| image_processor = self.image_processing_class.from_dict( | |
| self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False | |
| ) | |
| self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) | |
| self.assertEqual(image_processor.do_pad, False) | |
| def test_batch_feature(self): | |
| pass | |
| def test_call_pil(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random PIL images | |
| image_inputs = prepare_image_inputs(self.image_processor_tester, 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").pixel_values | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| (1, self.image_processor_tester.num_channels, expected_height, expected_width), | |
| ) | |
| # Test batched | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True) | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |
| def test_call_numpy(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random numpy tensors | |
| image_inputs = prepare_image_inputs(self.image_processor_tester, 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").pixel_values | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| (1, self.image_processor_tester.num_channels, expected_height, expected_width), | |
| ) | |
| # Test batched | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |
| def test_call_pytorch(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random PyTorch tensors | |
| image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, torch.Tensor) | |
| # Test not batched input | |
| encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| (1, self.image_processor_tester.num_channels, expected_height, expected_width), | |
| ) | |
| # Test batched | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |
| def test_equivalence_pad_and_create_pixel_mask(self): | |
| # Initialize image_processings | |
| image_processing_1 = self.image_processing_class(**self.image_processor_dict) | |
| image_processing_2 = self.image_processing_class(do_resize=False, do_normalize=False, do_rescale=False) | |
| # create random PyTorch tensors | |
| image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, torch.Tensor) | |
| # Test whether the method "pad_and_return_pixel_mask" and calling the image processor return the same tensors | |
| encoded_images_with_method = image_processing_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt") | |
| encoded_images = image_processing_2(image_inputs, return_tensors="pt") | |
| self.assertTrue( | |
| torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4) | |
| ) | |
| self.assertTrue( | |
| torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4) | |
| ) | |
| 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", "r") as f: | |
| target = json.loads(f.read()) | |
| target = {"image_id": 39769, "annotations": target} | |
| # encode them | |
| image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") | |
| encoding = image_processing(images=image, annotations=target, return_tensors="pt") | |
| # verify pixel values | |
| expected_shape = torch.Size([1, 3, 800, 1066]) | |
| self.assertEqual(encoding["pixel_values"].shape, expected_shape) | |
| expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) | |
| self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) | |
| # verify area | |
| expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) | |
| self.assertTrue(torch.allclose(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]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) | |
| # verify image_id | |
| expected_image_id = torch.tensor([39769]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) | |
| # verify is_crowd | |
| expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) | |
| # verify class_labels | |
| expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) | |
| # verify orig_size | |
| expected_orig_size = torch.tensor([480, 640]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) | |
| # verify size | |
| expected_size = torch.tensor([800, 1066]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) | |
| def test_call_pytorch_with_coco_panoptic_annotations(self): | |
| # prepare image, target and masks_path | |
| image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
| with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: | |
| target = json.loads(f.read()) | |
| target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} | |
| masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") | |
| # encode them | |
| image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic") | |
| encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") | |
| # verify pixel values | |
| expected_shape = torch.Size([1, 3, 800, 1066]) | |
| self.assertEqual(encoding["pixel_values"].shape, expected_shape) | |
| expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) | |
| self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) | |
| # verify area | |
| expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) | |
| self.assertTrue(torch.allclose(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([0.2625, 0.5437, 0.4688, 0.8625]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) | |
| # verify image_id | |
| expected_image_id = torch.tensor([39769]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) | |
| # verify is_crowd | |
| expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) | |
| # verify class_labels | |
| expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) | |
| # verify masks | |
| expected_masks_sum = 822873 | |
| self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum) | |
| # verify orig_size | |
| expected_orig_size = torch.tensor([480, 640]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) | |
| # verify size | |
| expected_size = torch.tensor([800, 1066]) | |
| self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) | |