回传数据解析，兼容v5和v10
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jiajie555
277
ultralytics/models/yolo/segment/val.py
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277
ultralytics/models/yolo/segment/val.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from multiprocessing.pool import ThreadPool
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn.functional as F
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import LOGGER, NUM_THREADS, ops
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.metrics import SegmentMetrics, box_iou, mask_iou
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from ultralytics.utils.plotting import output_to_target, plot_images
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class SegmentationValidator(DetectionValidator):
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"""
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A class extending the DetectionValidator class for validation based on a segmentation model.
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Example:
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```python
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from ultralytics.models.yolo.segment import SegmentationValidator
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args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
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validator = SegmentationValidator(args=args)
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validator()
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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 SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
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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.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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return batch
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def init_metrics(self, model):
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"""Initialize metrics and select mask processing function based on save_json flag."""
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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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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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self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])
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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) % (
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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(
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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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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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return predn, pred_masks
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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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self.seen += 1
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npr = len(pred)
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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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nl = len(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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self.stats[k].append(stat[k])
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if self.args.plots:
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self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
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continue
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# 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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# 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(
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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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for k in self.stats.keys():
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self.stats[k].append(stat[k])
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pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
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if self.args.plots and self.batch_i < 3:
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self.plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
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# Save
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if self.args.save_json:
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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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def finalize_metrics(self, *args, **kwargs):
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"""Sets speed and confusion matrix for evaluation metrics."""
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self.metrics.speed = self.speed
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self.metrics.confusion_matrix = self.confusion_matrix
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def _process_batch(self, detections, gt_bboxes, gt_cls, pred_masks=None, gt_masks=None, overlap=False, masks=False):
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"""
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Return correct prediction matrix.
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Args:
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detections (array[N, 6]), x1, y1, x2, y2, conf, class
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labels (array[M, 5]), class, x1, y1, x2, y2
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Returns:
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correct (array[N, 10]), for 10 IoU levels
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"""
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if masks:
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if overlap:
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nl = len(gt_cls)
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index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
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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 = 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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iou = box_iou(gt_bboxes, detections[:, :4])
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return self.match_predictions(detections[:, 5], gt_cls, iou)
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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(
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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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*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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names=self.names,
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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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"""
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Save one JSON result.
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Examples:
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>>> result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
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"""
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from pycocotools.mask import encode # noqa
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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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return rle
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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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box = ops.xyxy2xywh(predn[:, :4]) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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pred_masks = np.transpose(pred_masks, (2, 0, 1))
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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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{
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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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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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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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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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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[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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return stats
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