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General cleanup (#69)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2022-12-08 08:28:13 -06:00 • committed by GitHub
parent 7ae45c6cc4
commit d63ee112d4
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13 changed files with 265 additions and 433 deletions
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149
ultralytics/yolo/utils/metrics.py
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@ -263,18 +263,6 @@ class ConfusionMatrix:
print(' '.join(map(str, self.matrix[i])))
def fitness_detection(x):
# Model fitness as a weighted combination of metrics
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (x[:, :4] * w).sum(1)
def fitness_segmentation(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 smooth(y, f=0.05):
# Box filter of fraction f
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
@ -422,55 +410,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
return tp, fp, p, r, f1, ap, unique_classes.astype(int)
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:]
results = {
"boxes": {
"p": results_boxes[0],
"r": results_boxes[1],
"f1": results_boxes[2],
"ap": results_boxes[3],
"ap_class": results_boxes[4]},
"masks": {
"p": results_masks[0],
"r": results_masks[1],
"f1": results_masks[2],
"ap": results_masks[3],
"ap_class": results_masks[4]}}
return results
class Metric:
def __init__(self) -> None:
@ -542,6 +481,11 @@ class Metric:
maps[c] = self.ap[i]
return maps
def fitness(self):
# Model fitness as a weighted combination of metrics
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (np.array(self.mean_results()) * w).sum()
def update(self, results):
"""
Args:
@ -555,20 +499,80 @@ class Metric:
self.ap_class_index = ap_class_index
class Metrics:
"""Metric for boxes and masks."""
class DetMetrics:
def __init__(self) -> None:
def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
self.save_dir = save_dir
self.plot = plot
self.names = names
self.metric = Metric()
def process(self, tp, conf, pred_cls, target_cls):
results = ap_per_class(tp, conf, pred_cls, target_cls, plot=self.plot, save_dir=self.save_dir,
names=self.names)[2:]
self.metric.update(results)
@property
def keys(self):
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)"]
def mean_results(self):
return self.metric.mean_results()
def class_result(self, i):
return self.metric.class_result(i)
def get_maps(self, nc):
return self.metric.get_maps(nc)
def fitness(self):
return self.metric.fitness()
@property
def ap_class_index(self):
return self.metric.ap_class_index
class SegmentMetrics:
def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
self.save_dir = save_dir
self.plot = plot
self.names = names
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 process(self, tp_m, tp_b, conf, pred_cls, target_cls):
results_mask = ap_per_class(tp_m,
conf,
pred_cls,
target_cls,
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix="Mask")[2:]
self.metric_mask.update(results_mask)
results_box = ap_per_class(tp_b,
conf,
pred_cls,
target_cls,
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix="Box")[2:]
self.metric_box.update(results_box)
@property
def keys(self):
return [
"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)"]
def mean_results(self):
return self.metric_box.mean_results() + self.metric_mask.mean_results()
@ -579,6 +583,9 @@ class Metrics:
def get_maps(self, nc):
return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
def fitness(self):
return self.metric_mask.fitness() + self.metric_box.fitness()
@property
def ap_class_index(self):
# boxes and masks have the same ap_class_index