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