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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@ -24,7 +24,7 @@ class SegmentationValidator(DetectionValidator):
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def preprocess(self, batch):
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batch = super().preprocess(batch)
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batch["masks"] = batch["masks"].to(self.device).float()
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batch['masks'] = batch['masks'].to(self.device).float()
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return batch
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def init_metrics(self, model):
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@ -37,8 +37,8 @@ class SegmentationValidator(DetectionValidator):
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self.process = ops.process_mask # faster
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def get_desc(self):
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return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
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"R", "mAP50", "mAP50-95)")
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return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P',
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'R', 'mAP50', 'mAP50-95)')
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def postprocess(self, preds):
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p = ops.non_max_suppression(preds[0],
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@ -55,11 +55,11 @@ class SegmentationValidator(DetectionValidator):
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def update_metrics(self, preds, batch):
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# Metrics
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for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
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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_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
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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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@ -74,23 +74,23 @@ class SegmentationValidator(DetectionValidator):
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# Masks
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midx = [si] if self.args.overlap_mask else idx
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gt_masks = batch["masks"][midx]
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pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:])
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gt_masks = batch['masks'][midx]
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pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch['img'][si].shape[1:])
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# Predictions
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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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@ -112,11 +112,11 @@ class SegmentationValidator(DetectionValidator):
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# Save
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if self.args.save_json:
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pred_masks = ops.scale_image(batch["img"][si].shape[1:],
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pred_masks = ops.scale_image(batch['img'][si].shape[1:],
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pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
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shape,
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ratio_pad=batch["ratio_pad"][si])
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self.pred_to_json(predn, batch["im_file"][si], pred_masks)
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ratio_pad=batch['ratio_pad'][si])
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self.pred_to_json(predn, batch['im_file'][si], pred_masks)
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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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@ -136,7 +136,7 @@ class SegmentationValidator(DetectionValidator):
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gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
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gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
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if gt_masks.shape[1:] != pred_masks.shape[1:]:
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gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
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gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0]
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gt_masks = gt_masks.gt_(0.5)
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iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
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else: # boxes
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@ -158,20 +158,20 @@ class SegmentationValidator(DetectionValidator):
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return torch.tensor(correct, dtype=torch.bool, device=detections.device)
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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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batch["masks"],
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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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batch['masks'],
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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[0], max_det=15),
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torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
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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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self.plot_masks.clear()
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@ -182,8 +182,8 @@ class SegmentationValidator(DetectionValidator):
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from pycocotools.mask import encode # noqa
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def single_encode(x):
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rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
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rle["counts"] = rle["counts"].decode("utf-8")
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rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0]
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rle['counts'] = rle['counts'].decode('utf-8')
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return rle
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stem = Path(filename).stem
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@ -203,8 +203,8 @@ class SegmentationValidator(DetectionValidator):
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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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@ -212,7 +212,7 @@ class SegmentationValidator(DetectionValidator):
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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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for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm')]):
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@ -231,8 +231,8 @@ class SegmentationValidator(DetectionValidator):
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def val(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or "yolov8n-seg.pt"
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data = cfg.data or "coco128-seg.yaml"
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model = cfg.model or 'yolov8n-seg.pt'
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data = cfg.data or 'coco128-seg.yaml'
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args = dict(model=model, data=data)
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if use_python:
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@ -243,5 +243,5 @@ 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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