ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -33,13 +33,13 @@ class SegmentationValidator(DetectionValidator):
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.plot_masks = None
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self.process = None
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self.args.task = 'segment'
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self.args.task = "segment"
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self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
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def preprocess(self, batch):
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"""Preprocesses batch by converting masks to float and sending to device."""
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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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@ -47,7 +47,7 @@ class SegmentationValidator(DetectionValidator):
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super().init_metrics(model)
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self.plot_masks = []
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if self.args.save_json:
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check_requirements('pycocotools>=2.0.6')
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check_requirements("pycocotools>=2.0.6")
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self.process = ops.process_mask_upsample # more accurate
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else:
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self.process = ops.process_mask # faster
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@ -55,33 +55,46 @@ class SegmentationValidator(DetectionValidator):
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def get_desc(self):
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"""Return a formatted description of evaluation metrics."""
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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) % (
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"Class",
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"Images",
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"Instances",
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"Box(P",
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"R",
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"mAP50",
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"mAP50-95)",
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"Mask(P",
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"R",
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"mAP50",
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"mAP50-95)",
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)
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def postprocess(self, preds):
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"""Post-processes YOLO predictions and returns output detections with proto."""
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p = ops.non_max_suppression(preds[0],
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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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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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nc=self.nc)
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p = ops.non_max_suppression(
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preds[0],
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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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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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nc=self.nc,
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)
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proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
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return p, proto
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def _prepare_batch(self, si, batch):
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"""Prepares a batch for training or inference by processing images and targets."""
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prepared_batch = super()._prepare_batch(si, batch)
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midx = [si] if self.args.overlap_mask else batch['batch_idx'] == si
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prepared_batch['masks'] = batch['masks'][midx]
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midx = [si] if self.args.overlap_mask else batch["batch_idx"] == si
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prepared_batch["masks"] = batch["masks"][midx]
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return prepared_batch
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def _prepare_pred(self, pred, pbatch, proto):
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"""Prepares a batch for training or inference by processing images and targets."""
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predn = super()._prepare_pred(pred, pbatch)
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pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch['imgsz'])
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pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch["imgsz"])
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return predn, pred_masks
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def update_metrics(self, preds, batch):
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@ -89,14 +102,16 @@ class SegmentationValidator(DetectionValidator):
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for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
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self.seen += 1
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npr = len(pred)
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stat = dict(conf=torch.zeros(0, device=self.device),
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pred_cls=torch.zeros(0, device=self.device),
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tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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tp_m=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device))
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stat = dict(
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conf=torch.zeros(0, device=self.device),
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pred_cls=torch.zeros(0, device=self.device),
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tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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tp_m=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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)
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pbatch = self._prepare_batch(si, batch)
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cls, bbox = pbatch.pop('cls'), pbatch.pop('bbox')
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cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
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nl = len(cls)
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stat['target_cls'] = cls
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stat["target_cls"] = cls
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if npr == 0:
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if nl:
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for k in self.stats.keys():
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@ -106,24 +121,20 @@ class SegmentationValidator(DetectionValidator):
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continue
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# Masks
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gt_masks = pbatch.pop('masks')
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gt_masks = pbatch.pop("masks")
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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_masks = self._prepare_pred(pred, pbatch, proto)
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stat['conf'] = predn[:, 4]
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stat['pred_cls'] = predn[:, 5]
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stat["conf"] = predn[:, 4]
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stat["pred_cls"] = predn[:, 5]
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# Evaluate
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if nl:
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stat['tp'] = self._process_batch(predn, bbox, cls)
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stat['tp_m'] = self._process_batch(predn,
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bbox,
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cls,
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pred_masks,
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gt_masks,
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self.args.overlap_mask,
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masks=True)
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stat["tp"] = self._process_batch(predn, bbox, cls)
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stat["tp_m"] = self._process_batch(
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predn, bbox, cls, pred_masks, gt_masks, self.args.overlap_mask, masks=True
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)
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if self.args.plots:
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self.confusion_matrix.process_batch(predn, bbox, cls)
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@ -136,10 +147,12 @@ 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(pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
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pbatch['ori_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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pred_masks = ops.scale_image(
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pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
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pbatch["ori_shape"],
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ratio_pad=batch["ratio_pad"][si],
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)
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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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@ -166,7 +179,7 @@ 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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@ -176,26 +189,29 @@ class SegmentationValidator(DetectionValidator):
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def plot_val_samples(self, batch, ni):
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"""Plots validation samples with bounding box labels."""
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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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masks=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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on_plot=self.on_plot)
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plot_images(
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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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masks=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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on_plot=self.on_plot,
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)
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def plot_predictions(self, batch, preds, ni):
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"""Plots batch predictions with masks and bounding boxes."""
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plot_images(
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batch['img'],
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batch["img"],
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*output_to_target(preds[0], max_det=15), # not set to self.args.max_det due to slow plotting speed
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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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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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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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on_plot=self.on_plot,
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) # pred
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self.plot_masks.clear()
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def pred_to_json(self, predn, filename, pred_masks):
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@ -205,8 +221,8 @@ class SegmentationValidator(DetectionValidator):
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def single_encode(x):
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"""Encode predicted masks as RLE and append results to jdict."""
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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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@ -217,37 +233,41 @@ class SegmentationValidator(DetectionValidator):
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with ThreadPool(NUM_THREADS) as pool:
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rles = pool.map(single_encode, pred_masks)
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for i, (p, b) in enumerate(zip(predn.tolist(), box.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(p[5])],
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'bbox': [round(x, 3) for x in b],
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'score': round(p[4], 5),
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'segmentation': rles[i]})
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self.jdict.append(
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{
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"image_id": image_id,
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"category_id": self.class_map[int(p[5])],
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"bbox": [round(x, 3) for x in b],
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"score": round(p[4], 5),
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"segmentation": rles[i],
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}
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)
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def eval_json(self, stats):
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"""Return COCO-style object detection evaluation metrics."""
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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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LOGGER.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
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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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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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check_requirements("pycocotools>=2.0.6")
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from pycocotools.coco import COCO # noqa
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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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for i, eval in enumerate([COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm")]):
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if self.is_coco:
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eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # im to eval
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eval.evaluate()
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eval.accumulate()
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eval.summarize()
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idx = i * 4 + 2
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stats[self.metrics.keys[idx + 1]], stats[
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self.metrics.keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50
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stats[self.metrics.keys[idx + 1]], stats[self.metrics.keys[idx]] = eval.stats[
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:2
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] # update mAP50-95 and mAP50
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except Exception as e:
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LOGGER.warning(f'pycocotools unable to run: {e}')
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LOGGER.warning(f"pycocotools unable to run: {e}")
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return stats
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