mirror of
https://github.com/THU-MIG/yolov10.git
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188 lines
8.3 KiB
Python
188 lines
8.3 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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import torch
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import LOGGER, ops
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from ultralytics.utils.metrics import OBBMetrics, batch_probiou
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from ultralytics.utils.plotting import output_to_rotated_target, plot_images
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class OBBValidator(DetectionValidator):
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"""
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A class extending the DetectionValidator class for validation based on an Oriented Bounding Box (OBB) model.
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Example:
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```python
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from ultralytics.models.yolo.obb import OBBValidator
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args = dict(model='yolov8n-obb.pt', data='coco8-seg.yaml')
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validator = OBBValidator(args=args)
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validator(model=args['model'])
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```
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"""
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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"""Initialize OBBValidator and set task to 'obb', metrics to OBBMetrics."""
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.args.task = 'obb'
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self.metrics = OBBMetrics(save_dir=self.save_dir, plot=True, on_plot=self.on_plot)
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def init_metrics(self, model):
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"""Initialize evaluation metrics for YOLO."""
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super().init_metrics(model)
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val = self.data.get(self.args.split, '') # validation path
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self.is_dota = isinstance(val, str) and 'DOTA' in val # is COCO
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def postprocess(self, preds):
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"""Apply Non-maximum suppression to prediction outputs."""
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return ops.non_max_suppression(preds,
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self.args.conf,
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self.args.iou,
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labels=self.lb,
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nc=self.nc,
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multi_label=True,
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agnostic=self.args.single_cls,
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max_det=self.args.max_det,
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rotated=True)
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def _process_batch(self, detections, gt_bboxes, gt_cls):
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"""
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Return correct prediction matrix.
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Args:
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detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
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Each detection is of the format: x1, y1, x2, y2, conf, class.
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labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
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Each label is of the format: class, x1, y1, x2, y2.
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Returns:
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(torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
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"""
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iou = batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
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return self.match_predictions(detections[:, 5], gt_cls, iou)
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def _prepare_batch(self, si, batch):
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idx = batch['batch_idx'] == si
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cls = batch['cls'][idx].squeeze(-1)
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bbox = batch['bboxes'][idx]
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ori_shape = batch['ori_shape'][si]
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imgsz = batch['img'].shape[2:]
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ratio_pad = batch['ratio_pad'][si]
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if len(cls):
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bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]]) # target boxes
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ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad, xywh=True) # native-space labels
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prepared_batch = dict(cls=cls, bbox=bbox, ori_shape=ori_shape, imgsz=imgsz, ratio_pad=ratio_pad)
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return prepared_batch
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def _prepare_pred(self, pred, pbatch):
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predn = pred.clone()
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ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'], ratio_pad=pbatch['ratio_pad'],
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xywh=True) # native-space pred
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return predn
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def plot_predictions(self, batch, preds, ni):
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"""Plots predicted bounding boxes on input images and saves the result."""
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plot_images(batch['img'],
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*output_to_rotated_target(preds, max_det=self.args.max_det),
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paths=batch['im_file'],
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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names=self.names,
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on_plot=self.on_plot) # pred
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def pred_to_json(self, predn, filename):
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"""Serialize YOLO predictions to COCO json format."""
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stem = Path(filename).stem
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image_id = int(stem) if stem.isnumeric() else stem
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rbox = torch.cat([predn[:, :4], predn[:, -1:]], dim=-1)
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poly = ops.xywhr2xyxyxyxy(rbox).view(-1, 8)
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for i, (r, b) in enumerate(zip(rbox.tolist(), poly.tolist())):
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self.jdict.append({
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'image_id': image_id,
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'category_id': self.class_map[int(predn[i, 5].item())],
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'score': round(predn[i, 4].item(), 5),
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'rbox': [round(x, 3) for x in r],
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'poly': [round(x, 3) for x in b]})
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def eval_json(self, stats):
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"""Evaluates YOLO output in JSON format and returns performance statistics."""
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if self.args.save_json and self.is_dota and len(self.jdict):
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import json
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import re
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from collections import defaultdict
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pred_json = self.save_dir / 'predictions.json' # predictions
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pred_txt = self.save_dir / 'predictions_txt' # predictions
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pred_txt.mkdir(parents=True, exist_ok=True)
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data = json.load(open(pred_json))
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# Save split results
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LOGGER.info(f'Saving predictions with DOTA format to {str(pred_txt)}...')
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for d in data:
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image_id = d['image_id']
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score = d['score']
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classname = self.names[d['category_id']].replace(' ', '-')
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lines = '{} {} {} {} {} {} {} {} {} {}\n'.format(
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image_id,
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score,
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d['poly'][0],
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d['poly'][1],
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d['poly'][2],
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d['poly'][3],
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d['poly'][4],
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d['poly'][5],
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d['poly'][6],
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d['poly'][7],
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)
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with open(str(pred_txt / f'Task1_{classname}') + '.txt', 'a') as f:
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f.writelines(lines)
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# Save merged results, this could result slightly lower map than using official merging script,
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# because of the probiou calculation.
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pred_merged_txt = self.save_dir / 'predictions_merged_txt' # predictions
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pred_merged_txt.mkdir(parents=True, exist_ok=True)
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merged_results = defaultdict(list)
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LOGGER.info(f'Saving merged predictions with DOTA format to {str(pred_merged_txt)}...')
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for d in data:
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image_id = d['image_id'].split('__')[0]
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pattern = re.compile(r'\d+___\d+')
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x, y = (int(c) for c in re.findall(pattern, d['image_id'])[0].split('___'))
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bbox, score, cls = d['rbox'], d['score'], d['category_id']
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bbox[0] += x
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bbox[1] += y
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bbox.extend([score, cls])
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merged_results[image_id].append(bbox)
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for image_id, bbox in merged_results.items():
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bbox = torch.tensor(bbox)
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max_wh = torch.max(bbox[:, :2]).item() * 2
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c = bbox[:, 6:7] * max_wh # classes
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scores = bbox[:, 5] # scores
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b = bbox[:, :5].clone()
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b[:, :2] += c
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# 0.3 could get results close to the ones from official merging script, even slightly better.
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i = ops.nms_rotated(b, scores, 0.3)
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bbox = bbox[i]
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b = ops.xywhr2xyxyxyxy(bbox[:, :5]).view(-1, 8)
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for x in torch.cat([b, bbox[:, 5:7]], dim=-1).tolist():
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classname = self.names[int(x[-1])].replace(' ', '-')
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poly = [round(i, 3) for i in x[:-2]]
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score = round(x[-2], 3)
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lines = '{} {} {} {} {} {} {} {} {} {}\n'.format(
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image_id,
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score,
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poly[0],
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poly[1],
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poly[2],
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poly[3],
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poly[4],
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poly[5],
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poly[6],
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poly[7],
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)
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with open(str(pred_merged_txt / f'Task1_{classname}') + '.txt', 'a') as f:
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f.writelines(lines)
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return stats
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