# Ultralytics YOLO 🚀, GPL-3.0 license import os from multiprocessing.pool import ThreadPool from pathlib import Path import hydra import numpy as np import torch import torch.nn.functional as F from ultralytics.yolo.utils import DEFAULT_CONFIG, NUM_THREADS, ops from ultralytics.yolo.utils.checks import check_requirements from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou from ultralytics.yolo.utils.plotting import output_to_target, plot_images from ..detect import DetectionValidator class SegmentationValidator(DetectionValidator): def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None): super().__init__(dataloader, save_dir, pbar, logger, args) self.args.task = "segment" self.metrics = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots) def preprocess(self, batch): batch = super().preprocess(batch) batch["masks"] = batch["masks"].to(self.device).float() return batch def init_metrics(self, model): head = model.model[-1] if self.training else model.model.model[-1] self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000)) self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO self.nc = head.nc self.nm = head.nm if hasattr(head, "nm") else 32 self.names = model.names self.metrics.names = self.names self.confusion_matrix = ConfusionMatrix(nc=self.nc) self.plot_masks = [] self.seen = 0 self.jdict = [] self.stats = [] if self.args.save_json: self.process = ops.process_mask_upsample # more accurate else: self.process = ops.process_mask # faster def get_desc(self): return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P", "R", "mAP50", "mAP50-95)") def postprocess(self, preds): p = ops.non_max_suppression(preds[0], self.args.conf, self.args.iou, labels=self.lb, multi_label=True, agnostic=self.args.single_cls, max_det=self.args.max_det, nm=self.nm) return p, preds[1][-1] def update_metrics(self, preds, batch): # Metrics for si, (pred, proto) in enumerate(zip(preds[0], preds[1])): idx = batch["batch_idx"] == si cls = batch["cls"][idx] bbox = batch["bboxes"][idx] nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions shape = batch["ori_shape"][si] correct_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init self.seen += 1 if npr == 0: if nl: self.stats.append((correct_masks, correct_bboxes, *torch.zeros( (2, 0), device=self.device), cls.squeeze(-1))) if self.args.plots: self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1)) continue # Masks midx = [si] if self.args.overlap_mask else idx gt_masks = batch["masks"][midx] pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:]) # Predictions if self.args.single_cls: pred[:, 5] = 0 predn = pred.clone() ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape, ratio_pad=batch["ratio_pad"][si]) # native-space pred # Evaluate if nl: tbox = ops.xywh2xyxy(bbox) # target boxes ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape, ratio_pad=batch["ratio_pad"][si]) # native-space labels labelsn = torch.cat((cls, tbox), 1) # native-space labels correct_bboxes = self._process_batch(predn, labelsn) # TODO: maybe remove these `self.` arguments as they already are member variable correct_masks = self._process_batch(predn, labelsn, pred_masks, gt_masks, overlap=self.args.overlap_mask, masks=True) if self.args.plots: self.confusion_matrix.process_batch(predn, labelsn) self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1))) # conf, pcls, tcls pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) if self.args.plots and self.batch_i < 3: self.plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot # Save if self.args.save_json: pred_masks = ops.scale_image(batch["img"][si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, ratio_pad=batch["ratio_pad"][si]) self.pred_to_json(predn, batch["im_file"][si], pred_masks) # if self.args.save_txt: # save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') def _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False): """ Return correct prediction matrix Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (array[M, 5]), class, x1, y1, x2, y2 Returns: correct (array[N, 10]), for 10 IoU levels """ if masks: if overlap: nl = len(labels) index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1 gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) gt_masks = torch.where(gt_masks == index, 1.0, 0.0) if gt_masks.shape[1:] != pred_masks.shape[1:]: gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0] gt_masks = gt_masks.gt_(0.5) iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) else: # boxes iou = box_iou(labels[:, 1:], detections[:, :4]) correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(self.iouv)): x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] # matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=detections.device) def plot_val_samples(self, batch, ni): plot_images(batch["img"], batch["batch_idx"], batch["cls"].squeeze(-1), batch["bboxes"], batch["masks"], paths=batch["im_file"], fname=self.save_dir / f"val_batch{ni}_labels.jpg", names=self.names) def plot_predictions(self, batch, preds, ni): plot_images(batch["img"], *output_to_target(preds[0], max_det=15), torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks, paths=batch["im_file"], fname=self.save_dir / f'val_batch{ni}_pred.jpg', names=self.names) # pred self.plot_masks.clear() def pred_to_json(self, predn, filename, pred_masks): # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} from pycocotools.mask import encode def single_encode(x): rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0] rle["counts"] = rle["counts"].decode("utf-8") return rle stem = Path(filename).stem image_id = int(stem) if stem.isnumeric() else stem box = ops.xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner pred_masks = np.transpose(pred_masks, (2, 0, 1)) with ThreadPool(NUM_THREADS) as pool: rles = pool.map(single_encode, pred_masks) for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): self.jdict.append({ 'image_id': image_id, 'category_id': self.class_map[int(p[5])], 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5), 'segmentation': rles[i]}) def eval_json(self, stats): if self.args.save_json and self.is_coco and len(self.jdict): anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations pred_json = self.save_dir / "predictions.json" # predictions self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...') try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb check_requirements('pycocotools>=2.0.6') from pycocotools.coco import COCO # noqa from pycocotools.cocoeval import COCOeval # noqa for x in anno_json, pred_json: assert x.is_file(), f"{x} file not found" anno = COCO(str(anno_json)) # init annotations api pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path) for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm')]): if self.is_coco: eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval eval.evaluate() eval.accumulate() eval.summarize() idx = i * 4 + 2 stats[self.metrics.keys[idx + 1]], stats[ self.metrics.keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50 except Exception as e: self.logger.warning(f'pycocotools unable to run: {e}') return stats @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) def val(cfg): cfg.data = cfg.data or "coco128-seg.yaml" validator = SegmentationValidator(args=cfg) validator(model=cfg.model) if __name__ == "__main__": val()