| import torchvision | |
| class CocoDetection(torchvision.datasets.CocoDetection): | |
| def init(self, img_folder, image_processor, ann_file): | |
| super().init(img_folder, ann_file) | |
| self.image_processor = image_processor | |
| def getitem(self, idx): | |
| # read in PIL image and target in COCO format | |
| img, target = super(CocoDetection, self).getitem(idx) | |
| # preprocess image and target: converting target to DETR format, | |
| # resizing + normalization of both image and target) | |
| image_id = self.ids[idx] | |
| target = {"image_id": image_id, "annotations": target} | |
| encoding = self.image_processor(images=img, annotations=target, return_tensors="pt") | |
| pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension | |
| target = encoding["labels"][0] # remove batch dimension | |
| return {"pixel_values": pixel_values, "labels": target} | |
| im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") | |
| path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"]) | |
| test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) | |
| Finally, load the metrics and run the evaluation. |