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| import itertools | |
| import json | |
| import os | |
| from detectron2.structures import Boxes, BoxMode, pairwise_iou | |
| from detectron2.utils.file_io import PathManager | |
| import numpy as np | |
| import pycocotools.mask as mask_util | |
| from detectron2.evaluation.coco_evaluation import COCOEvaluator | |
| from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco | |
| class GRiTCOCOEvaluator(COCOEvaluator): | |
| def process(self, inputs, outputs): | |
| for input, output in zip(inputs, outputs): | |
| prediction = {"image_id": input["image_id"]} | |
| if "instances" in output: | |
| instances = output["instances"].to(self._cpu_device) | |
| prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) | |
| if len(prediction) > 1: | |
| self._predictions.append(prediction) | |
| def _eval_predictions(self, predictions, img_ids=None): | |
| self._logger.info("Preparing results for COCO format ...") | |
| coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) | |
| tasks = self._tasks or self._tasks_from_predictions(coco_results) | |
| if self._output_dir: | |
| file_path = os.path.join(self._output_dir, "coco_instances_results.json") | |
| self._logger.info("Saving results to {}".format(file_path)) | |
| with PathManager.open(file_path, "w") as f: | |
| f.write(json.dumps(coco_results)) | |
| f.flush() | |
| if not self._do_evaluation: | |
| self._logger.info("Annotations are not available for evaluation.") | |
| return | |
| self._logger.info( | |
| "Evaluating predictions with {} COCO API...".format( | |
| "unofficial" if self._use_fast_impl else "official" | |
| ) | |
| ) | |
| coco_results = self.convert_classname_to_id(coco_results) | |
| for task in sorted(tasks): | |
| assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" | |
| coco_eval = ( | |
| _evaluate_predictions_on_coco( | |
| self._coco_api, | |
| coco_results, | |
| task, | |
| kpt_oks_sigmas=self._kpt_oks_sigmas, | |
| use_fast_impl=self._use_fast_impl, | |
| img_ids=img_ids, | |
| max_dets_per_image=self._max_dets_per_image, | |
| ) | |
| if len(coco_results) > 0 | |
| else None # cocoapi does not handle empty results very well | |
| ) | |
| res = self._derive_coco_results( | |
| coco_eval, task, class_names=self._metadata.get("thing_classes") | |
| ) | |
| self._results[task] = res | |
| def convert_classname_to_id(self, results): | |
| outputs = [] | |
| class_name_to_id = {} | |
| categories = sorted(self._coco_api.dataset['categories'], key=lambda x: x['id']) | |
| for cat in categories: | |
| class_name_to_id[cat['name']] = cat['id'] | |
| for pred in results: | |
| if pred['object_descriptions'] in class_name_to_id: | |
| pred['category_id'] = class_name_to_id[pred['object_descriptions']] | |
| del pred['object_descriptions'] | |
| outputs.append(pred) | |
| return outputs | |
| class GRiTVGEvaluator(COCOEvaluator): | |
| def process(self, inputs, outputs): | |
| for input, output in zip(inputs, outputs): | |
| assert input["image_id"] == int(input['file_name'].split('/')[-1].split('.')[0]) | |
| prediction = {"image_id": input["image_id"]} | |
| if "instances" in output: | |
| instances = output["instances"].to(self._cpu_device) | |
| prediction["instances"] = instances_to_coco_json(instances, input["image_id"], output_logits=True) | |
| h = input['height'] | |
| w = input['width'] | |
| scale = 720.0 / max(h, w) | |
| scaled_inst = [] | |
| for inst in prediction["instances"]: | |
| inst['bbox'][0] = inst['bbox'][0] * scale | |
| inst['bbox'][1] = inst['bbox'][1] * scale | |
| inst['bbox'][2] = inst['bbox'][2] * scale | |
| inst['bbox'][3] = inst['bbox'][3] * scale | |
| scaled_inst.append(inst) | |
| if len(scaled_inst) > 0: | |
| prediction["instances"] = scaled_inst | |
| if len(prediction) > 1: | |
| self._predictions.append(prediction) | |
| def _eval_predictions(self, predictions, img_ids=None): | |
| ''' | |
| This is only for saving the results to json file | |
| ''' | |
| self._logger.info("Preparing results for COCO format ...") | |
| coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) | |
| if self._output_dir: | |
| file_path = os.path.join(self._output_dir, "vg_instances_results.json") | |
| self._logger.info("Saving results to {}".format(file_path)) | |
| with PathManager.open(file_path, "w") as f: | |
| f.write(json.dumps(coco_results)) | |
| f.flush() | |
| def instances_to_coco_json(instances, img_id, output_logits=False): | |
| """ | |
| Add object_descriptions and logit (if applicable) to | |
| detectron2's instances_to_coco_json | |
| """ | |
| num_instance = len(instances) | |
| if num_instance == 0: | |
| return [] | |
| boxes = instances.pred_boxes.tensor.numpy() | |
| boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) | |
| boxes = boxes.tolist() | |
| scores = instances.scores.tolist() | |
| classes = instances.pred_classes.tolist() | |
| object_descriptions = instances.pred_object_descriptions.data | |
| if output_logits: | |
| logits = instances.logits.tolist() | |
| results = [] | |
| for k in range(num_instance): | |
| result = { | |
| "image_id": img_id, | |
| "category_id": classes[k], | |
| "bbox": boxes[k], | |
| "score": scores[k], | |
| 'object_descriptions': object_descriptions[k], | |
| } | |
| if output_logits: | |
| result["logit"] = logits[k] | |
| results.append(result) | |
| return results |