# 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 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_torchvision_available, is_vision_available from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput if is_vision_available(): from PIL import Image from transformers import GroundingDinoImageProcessor if is_torchvision_available(): from transformers import GroundingDinoImageProcessorFast class GroundingDinoImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_rescale=True, rescale_factor=1 / 255, 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_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad self.num_queries = 5 self.embed_dim = 5 # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_processor_dict with DeformableDetr->GroundingDino def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_pad": self.do_pad, } # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.get_expected_values with DeformableDetr->GroundingDino def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to GroundingDinoImageProcessor, 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 elif isinstance(image, np.ndarray): h, w = image.shape[0], image.shape[1] 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 # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.expected_output_image_shape with DeformableDetr->GroundingDino def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return self.num_channels, height, width def get_fake_grounding_dino_output(self): torch.manual_seed(42) return GroundingDinoObjectDetectionOutput( pred_boxes=torch.rand(self.batch_size, self.num_queries, 4), logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim), ) # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_inputs with DeformableDetr->GroundingDino 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 GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase): image_processing_class = GroundingDinoImageProcessor if is_vision_available() else None fast_image_processing_class = GroundingDinoImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = GroundingDinoImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_properties with DeformableDetr->GroundingDino 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, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "size")) # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_from_dict_with_kwargs with DeformableDetr->GroundingDino 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, {"shortest_edge": 18, "longest_edge": 1333}) self.assertEqual(image_processor.do_pad, True) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 1333}) def test_post_process_object_detection(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) outputs = self.image_processor_tester.get_fake_grounding_dino_output() results = image_processor.post_process_object_detection(outputs, threshold=0.0) self.assertEqual(len(results), self.image_processor_tester.batch_size) self.assertEqual(list(results[0].keys()), ["scores", "labels", "boxes"]) self.assertEqual(results[0]["boxes"].shape, (self.image_processor_tester.num_queries, 4)) self.assertEqual(results[0]["scores"].shape, (self.image_processor_tester.num_queries,)) expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220]) torch.testing.assert_close(results[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4) expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947]) torch.testing.assert_close(results[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4) @slow # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_detection_annotations with DeformableDetr->GroundingDino 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() 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]) 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([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) 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([800, 1066]) torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) @slow # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->GroundingDino 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 = 800, 1066 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( [ [0.4130, 0.2765, 0.0453, 0.2215], [0.1272, 0.2016, 0.1561, 0.0940], [0.3757, 0.4933, 0.7488, 0.9865], [0.3759, 0.5002, 0.7492, 0.9955], [0.1971, 0.5456, 0.3532, 0.8646], [0.5790, 0.4115, 0.3430, 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 the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # 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 # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_panoptic_annotations with DeformableDetr->GroundingDino 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") 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") for image_processing_class in self.image_processor_list: # encode them image_processing = image_processing_class(format="coco_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]) 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([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) 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([0.2625, 0.5437, 0.4688, 0.8625]) 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([17, 17, 63, 75, 75, 93]) torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels) # verify masks expected_masks_sum = 822873 relative_error = torch.abs(encoding["labels"][0]["masks"].sum() - expected_masks_sum) / expected_masks_sum self.assertTrue(relative_error < 1e-3) # 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([800, 1066]) torch.testing.assert_close(encoding["labels"][0]["size"], expected_size) @slow # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->GroundingDino def test_batched_coco_panoptic_annotations(self): # prepare image, target and masks_path 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_panoptic_annotations.txt") as f: target = json.loads(f.read()) annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotation_1["segments_info"])): coords = annotation_1["segments_info"][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, ] annotation_1["segments_info"][i]["bbox"] = new_bbox masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") images = [image_0, image_1] annotations = [annotation_0, annotation_1] for image_processing_class in self.image_processor_list: # encode them image_processing = image_processing_class(format="coco_panoptic") encoding = image_processing( images=images, annotations=annotations, masks_path=masks_path, return_tensors="pt", return_segmentation_masks=True, ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 800, 1066 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( [ [0.2625, 0.5437, 0.4688, 0.8625], [0.7719, 0.4104, 0.4531, 0.7125], [0.5000, 0.4927, 0.9969, 0.9854], [0.1688, 0.2000, 0.2063, 0.0917], [0.5492, 0.2760, 0.0578, 0.2187], [0.4992, 0.4990, 0.9984, 0.9979], ] ) expected_boxes_1 = torch.tensor( [ [0.1576, 0.3262, 0.2814, 0.5175], [0.4634, 0.2463, 0.2720, 0.4275], [0.3002, 0.2956, 0.5985, 0.5913], [0.1013, 0.1200, 0.1238, 0.0550], [0.3297, 0.1656, 0.0347, 0.1312], [0.2997, 0.2994, 0.5994, 0.5987], ] ) 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 the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # 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, masks_path=masks_path, 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) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->GroundingDino def test_max_width_max_height_resizing_and_pad_strategy(self): for image_processing_class in self.image_processor_list: image_1 = torch.ones([200, 100, 3], dtype=torch.uint8) # do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50 image_processor = image_processing_class( size={"max_height": 100, "max_width": 100}, do_pad=False, ) inputs = image_processor(images=[image_1], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50])) # do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100 image_processor = image_processing_class( size={"max_height": 300, "max_width": 100}, do_pad=False, ) inputs = image_processor(images=[image_1], return_tensors="pt") # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100 image_processor = image_processing_class( size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100} ) inputs = image_processor(images=[image_1], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100])) # do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100 image_processor = image_processing_class( size={"max_height": 300, "max_width": 100}, do_pad=True, pad_size={"height": 301, "width": 101}, ) inputs = image_processor(images=[image_1], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101])) ### Check for batch image_2 = torch.ones([100, 150, 3], dtype=torch.uint8) # do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100 image_processor = image_processing_class( size={"max_height": 150, "max_width": 100}, do_pad=True, pad_size={"height": 150, "width": 100}, ) inputs = image_processor(images=[image_1, image_2], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100])) def test_longest_edge_shortest_edge_resizing_strategy(self): image_1 = torch.ones([958, 653, 3], dtype=torch.uint8) # max size is set; width < height; # do_pad=False, longest_edge=640, shortest_edge=640, image=958x653 -> 640x436 image_processor = GroundingDinoImageProcessor( size={"longest_edge": 640, "shortest_edge": 640}, do_pad=False, ) inputs = image_processor(images=[image_1], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 640, 436])) image_2 = torch.ones([653, 958, 3], dtype=torch.uint8) # max size is set; height < width; # do_pad=False, longest_edge=640, shortest_edge=640, image=653x958 -> 436x640 image_processor = GroundingDinoImageProcessor( size={"longest_edge": 640, "shortest_edge": 640}, do_pad=False, ) inputs = image_processor(images=[image_2], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 436, 640])) image_3 = torch.ones([100, 120, 3], dtype=torch.uint8) # max size is set; width == size; height > max_size; # do_pad=False, longest_edge=118, shortest_edge=100, image=120x100 -> 118x98 image_processor = GroundingDinoImageProcessor( size={"longest_edge": 118, "shortest_edge": 100}, do_pad=False, ) inputs = image_processor(images=[image_3], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 98, 118])) image_4 = torch.ones([128, 50, 3], dtype=torch.uint8) # max size is set; height == size; width < max_size; # do_pad=False, longest_edge=256, shortest_edge=50, image=50x128 -> 50x128 image_processor = GroundingDinoImageProcessor( size={"longest_edge": 256, "shortest_edge": 50}, do_pad=False, ) inputs = image_processor(images=[image_4], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 128, 50])) image_5 = torch.ones([50, 50, 3], dtype=torch.uint8) # max size is set; height == width; width < max_size; # do_pad=False, longest_edge=117, shortest_edge=50, image=50x50 -> 50x50 image_processor = GroundingDinoImageProcessor( size={"longest_edge": 117, "shortest_edge": 50}, do_pad=False, ) inputs = image_processor(images=[image_5], return_tensors="pt") self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 50, 50]))