Update .pre-commit-config.yaml (#1026)
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Glenn Jocher
GitHub
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76 changed files with 928 additions and 935 deletions
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@ -28,13 +28,13 @@ class DetectionValidator(BaseValidator):
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self.niou = self.iouv.numel()
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def preprocess(self, batch):
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batch["img"] = batch["img"].to(self.device, non_blocking=True)
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batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
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for k in ["batch_idx", "cls", "bboxes"]:
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batch['img'] = batch['img'].to(self.device, non_blocking=True)
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batch['img'] = (batch['img'].half() if self.args.half else batch['img'].float()) / 255
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for k in ['batch_idx', 'cls', 'bboxes']:
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batch[k] = batch[k].to(self.device)
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nb = len(batch["img"])
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self.lb = [torch.cat([batch["cls"], batch["bboxes"]], dim=-1)[batch["batch_idx"] == i]
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nb = len(batch['img'])
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self.lb = [torch.cat([batch['cls'], batch['bboxes']], dim=-1)[batch['batch_idx'] == i]
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for i in range(nb)] if self.args.save_hybrid else [] # for autolabelling
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return batch
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@ -54,7 +54,7 @@ class DetectionValidator(BaseValidator):
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self.stats = []
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def get_desc(self):
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return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
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return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)')
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def postprocess(self, preds):
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preds = ops.non_max_suppression(preds,
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@ -69,11 +69,11 @@ class DetectionValidator(BaseValidator):
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def update_metrics(self, preds, batch):
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# Metrics
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for si, pred in enumerate(preds):
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idx = batch["batch_idx"] == si
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cls = batch["cls"][idx]
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bbox = batch["bboxes"][idx]
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idx = batch['batch_idx'] == si
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cls = batch['cls'][idx]
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bbox = batch['bboxes'][idx]
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nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions
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shape = batch["ori_shape"][si]
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shape = batch['ori_shape'][si]
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correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
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self.seen += 1
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@ -88,16 +88,16 @@ class DetectionValidator(BaseValidator):
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if self.args.single_cls:
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pred[:, 5] = 0
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predn = pred.clone()
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ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape,
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ratio_pad=batch["ratio_pad"][si]) # native-space pred
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ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape,
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ratio_pad=batch['ratio_pad'][si]) # native-space pred
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# Evaluate
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if nl:
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height, width = batch["img"].shape[2:]
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height, width = batch['img'].shape[2:]
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tbox = ops.xywh2xyxy(bbox) * torch.tensor(
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(width, height, width, height), device=self.device) # target boxes
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ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape,
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ratio_pad=batch["ratio_pad"][si]) # native-space labels
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ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape,
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ratio_pad=batch['ratio_pad'][si]) # native-space labels
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labelsn = torch.cat((cls, tbox), 1) # native-space labels
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correct_bboxes = self._process_batch(predn, labelsn)
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# TODO: maybe remove these `self.` arguments as they already are member variable
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@ -107,7 +107,7 @@ class DetectionValidator(BaseValidator):
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# Save
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if self.args.save_json:
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self.pred_to_json(predn, batch["im_file"][si])
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self.pred_to_json(predn, batch['im_file'][si])
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# if self.args.save_txt:
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# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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@ -120,7 +120,7 @@ class DetectionValidator(BaseValidator):
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def print_results(self):
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pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metrics.keys) # print format
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self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
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self.logger.info(pf % ('all', self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
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if self.nt_per_class.sum() == 0:
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self.logger.warning(
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f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
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@ -175,21 +175,21 @@ class DetectionValidator(BaseValidator):
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shuffle=False,
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seed=self.args.seed)[0] if self.args.v5loader else \
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build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, names=self.data['names'],
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mode="val")[0]
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mode='val')[0]
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def plot_val_samples(self, batch, ni):
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plot_images(batch["img"],
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batch["batch_idx"],
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batch["cls"].squeeze(-1),
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batch["bboxes"],
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paths=batch["im_file"],
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fname=self.save_dir / f"val_batch{ni}_labels.jpg",
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plot_images(batch['img'],
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batch['batch_idx'],
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batch['cls'].squeeze(-1),
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batch['bboxes'],
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paths=batch['im_file'],
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fname=self.save_dir / f'val_batch{ni}_labels.jpg',
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names=self.names)
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def plot_predictions(self, batch, preds, ni):
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plot_images(batch["img"],
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plot_images(batch['img'],
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*output_to_target(preds, max_det=15),
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paths=batch["im_file"],
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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) # pred
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@ -207,8 +207,8 @@ class DetectionValidator(BaseValidator):
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def eval_json(self, stats):
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if self.args.save_json and self.is_coco and len(self.jdict):
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anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations
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pred_json = self.save_dir / "predictions.json" # predictions
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anno_json = self.data['path'] / 'annotations/instances_val2017.json' # annotations
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pred_json = self.save_dir / 'predictions.json' # predictions
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self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
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try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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check_requirements('pycocotools>=2.0.6')
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@ -216,7 +216,7 @@ class DetectionValidator(BaseValidator):
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from pycocotools.cocoeval import COCOeval # noqa
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for x in anno_json, pred_json:
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assert x.is_file(), f"{x} file not found"
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assert x.is_file(), f'{x} file not found'
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anno = COCO(str(anno_json)) # init annotations api
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pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
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eval = COCOeval(anno, pred, 'bbox')
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@ -232,8 +232,8 @@ class DetectionValidator(BaseValidator):
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def val(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or "yolov8n.pt"
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data = cfg.data or "coco128.yaml"
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model = cfg.model or 'yolov8n.pt'
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data = cfg.data or 'coco128.yaml'
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args = dict(model=model, data=data)
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if use_python:
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@ -244,5 +244,5 @@ def val(cfg=DEFAULT_CFG, use_python=False):
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validator(model=args['model'])
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if __name__ == "__main__":
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if __name__ == '__main__':
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val()
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