回传数据解析，兼容v5和v10
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jiajie555
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utils/segment/metrics.py
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210
utils/segment/metrics.py
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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"""
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Model validation metrics
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"""
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import numpy as np
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from ..metrics import ap_per_class
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def fitness(x):
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# Model fitness as a weighted combination of metrics
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w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
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return (x[:, :8] * w).sum(1)
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def ap_per_class_box_and_mask(
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tp_m,
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tp_b,
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conf,
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pred_cls,
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target_cls,
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plot=False,
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save_dir='.',
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names=(),
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):
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"""
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Args:
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tp_b: tp of boxes.
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tp_m: tp of masks.
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other arguments see `func: ap_per_class`.
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"""
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results_boxes = ap_per_class(tp_b,
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conf,
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pred_cls,
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target_cls,
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plot=plot,
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save_dir=save_dir,
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names=names,
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prefix='Box')[2:]
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results_masks = ap_per_class(tp_m,
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conf,
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pred_cls,
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target_cls,
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plot=plot,
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save_dir=save_dir,
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names=names,
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prefix='Mask')[2:]
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results = {
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'boxes': {
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'p': results_boxes[0],
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'r': results_boxes[1],
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'ap': results_boxes[3],
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'f1': results_boxes[2],
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'ap_class': results_boxes[4]},
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'masks': {
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'p': results_masks[0],
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'r': results_masks[1],
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'ap': results_masks[3],
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'f1': results_masks[2],
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'ap_class': results_masks[4]}}
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return results
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class Metric:
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def __init__(self) -> None:
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self.p = [] # (nc, )
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self.r = [] # (nc, )
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self.f1 = [] # (nc, )
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self.all_ap = [] # (nc, 10)
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self.ap_class_index = [] # (nc, )
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@property
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def ap50(self):
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"""AP@0.5 of all classes.
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Return:
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(nc, ) or [].
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"""
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return self.all_ap[:, 0] if len(self.all_ap) else []
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@property
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def ap(self):
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"""AP@0.5:0.95
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Return:
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(nc, ) or [].
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"""
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return self.all_ap.mean(1) if len(self.all_ap) else []
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@property
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def mp(self):
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"""mean precision of all classes.
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Return:
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float.
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"""
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return self.p.mean() if len(self.p) else 0.0
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@property
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def mr(self):
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"""mean recall of all classes.
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Return:
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float.
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"""
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return self.r.mean() if len(self.r) else 0.0
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@property
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def map50(self):
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"""Mean AP@0.5 of all classes.
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Return:
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float.
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"""
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return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
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@property
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def map(self):
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"""Mean AP@0.5:0.95 of all classes.
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Return:
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float.
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"""
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return self.all_ap.mean() if len(self.all_ap) else 0.0
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def mean_results(self):
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"""Mean of results, return mp, mr, map50, map"""
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return (self.mp, self.mr, self.map50, self.map)
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def class_result(self, i):
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"""class-aware result, return p[i], r[i], ap50[i], ap[i]"""
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return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
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def get_maps(self, nc):
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maps = np.zeros(nc) + self.map
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for i, c in enumerate(self.ap_class_index):
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maps[c] = self.ap[i]
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return maps
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def update(self, results):
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"""
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Args:
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results: tuple(p, r, ap, f1, ap_class)
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"""
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p, r, all_ap, f1, ap_class_index = results
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self.p = p
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self.r = r
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self.all_ap = all_ap
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self.f1 = f1
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self.ap_class_index = ap_class_index
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class Metrics:
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"""Metric for boxes and masks."""
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def __init__(self) -> None:
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self.metric_box = Metric()
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self.metric_mask = Metric()
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def update(self, results):
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"""
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Args:
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results: Dict{'boxes': Dict{}, 'masks': Dict{}}
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"""
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self.metric_box.update(list(results['boxes'].values()))
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self.metric_mask.update(list(results['masks'].values()))
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def mean_results(self):
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return self.metric_box.mean_results() + self.metric_mask.mean_results()
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def class_result(self, i):
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return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
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def get_maps(self, nc):
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return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
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@property
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def ap_class_index(self):
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# boxes and masks have the same ap_class_index
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return self.metric_box.ap_class_index
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KEYS = [
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'train/box_loss',
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'train/seg_loss', # train loss
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'train/obj_loss',
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'train/cls_loss',
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'metrics/precision(B)',
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'metrics/recall(B)',
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'metrics/mAP_0.5(B)',
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'metrics/mAP_0.5:0.95(B)', # metrics
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'metrics/precision(M)',
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'metrics/recall(M)',
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'metrics/mAP_0.5(M)',
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'metrics/mAP_0.5:0.95(M)', # metrics
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'val/box_loss',
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'val/seg_loss', # val loss
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'val/obj_loss',
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'val/cls_loss',
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'x/lr0',
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'x/lr1',
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'x/lr2', ]
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BEST_KEYS = [
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'best/epoch',
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'best/precision(B)',
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'best/recall(B)',
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'best/mAP_0.5(B)',
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'best/mAP_0.5:0.95(B)',
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'best/precision(M)',
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'best/recall(M)',
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'best/mAP_0.5(M)',
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'best/mAP_0.5:0.95(M)', ]
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