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import numpy as np
import torch
from clipseg_eval.general_utils import log
from torch.nn import functional as nnf
class BaseMetric(object):
def __init__(
self, metric_names, pred_range=None, gt_index=0, pred_index=0, eval_intermediate=True, eval_validation=True
):
self._names = tuple(metric_names)
self._eval_intermediate = eval_intermediate
self._eval_validation = eval_validation
self._pred_range = pred_range
self._pred_index = pred_index
self._gt_index = gt_index
self.predictions = []
self.ground_truths = []
def eval_intermediate(self):
return self._eval_intermediate
def eval_validation(self):
return self._eval_validation
def names(self):
return self._names
def add(self, predictions, ground_truth):
raise NotImplementedError
def value(self):
raise NotImplementedError
def scores(self):
# similar to value but returns dict
value = self.value()
if isinstance(value, dict):
return value
else:
assert type(value) in {list, tuple}
return list(zip(self.names(), self.value()))
def _get_pred_gt(self, predictions, ground_truth):
pred = predictions[self._pred_index]
gt = ground_truth[self._gt_index]
if self._pred_range is not None:
pred = pred[:, self._pred_range[0] : self._pred_range[1]]
return pred, gt
class FixedIntervalMetrics(BaseMetric):
def __init__(
self, sigmoid=False, ignore_mask=False, resize_to=None, resize_pred=None, n_values=51, custom_threshold=None
):
super().__init__(
(
"ap",
"best_fgiou",
"best_miou",
"fgiou0.5",
"fgiou0.1",
"mean_iou_0p5",
"mean_iou_0p1",
"best_biniou",
"biniou_0.5",
"fgiou_thresh",
)
)
self.intersections = []
self.unions = []
# self.threshold = threshold
self.sigmoid = sigmoid
self.resize_to = resize_to
self.resize_pred = resize_pred # resize prediction to match ground truth
self.class_count = defaultdict(lambda: 0)
self.per_class = defaultdict(lambda: [0, 0])
self.ignore_mask = ignore_mask
self.custom_threshold = custom_threshold
self.scores_ap = []
self.scores_iou = []
self.gts, self.preds = [], []
self.classes = []
# [1:-1] ignores 0 and 1
self.threshold_values = np.linspace(0, 1, n_values)[1:-1]
self.metrics = dict(tp=[], fp=[], fn=[], tn=[])
def add(self, pred, gt):
pred_batch = pred[0].cpu()
if self.sigmoid:
pred_batch = torch.sigmoid(pred_batch)
gt_batch = gt[0].cpu()
mask_batch = (
gt[1] if len(gt) > 1 and not self.ignore_mask and gt[1].numel() > 0 else ([None] * len(pred_batch))
)
cls_batch = gt[2] if len(gt) > 2 else [None] * len(pred_batch)
if self.resize_to is not None:
gt_batch = nnf.interpolate(gt_batch, self.resize_to, mode="nearest")
pred_batch = nnf.interpolate(pred_batch, self.resize_to, mode="bilinear", align_corners=False)
if isinstance(cls_batch, torch.Tensor):
cls_batch = cls_batch.cpu().numpy().tolist()
assert len(gt_batch) == len(pred_batch) == len(cls_batch), (
f"{len(gt_batch)} {len(pred_batch)} {len(cls_batch)}"
)
for predictions, ground_truth, mask, cls in zip(pred_batch, gt_batch, mask_batch, cls_batch):
if self.resize_pred:
predictions = nnf.interpolate(
predictions.unsqueeze(0).float(),
size=ground_truth.size()[-2:],
mode="bilinear",
align_corners=True,
)
p = predictions.flatten()
g = ground_truth.flatten()
assert len(p) == len(g)
if mask is not None:
m = mask.flatten().bool()
p = p[m]
g = g[m]
p_sorted = p.sort()
p = p_sorted.values
g = g[p_sorted.indices]
tps, fps, fns, tns = [], [], [], []
for thresh in self.threshold_values:
valid = torch.where(p > thresh)[0]
if len(valid) > 0:
n = int(valid[0])
else:
n = len(g)
fn = int(g[:n].sum())
tp = int(g[n:].sum())
fns += [fn]
tns += [n - fn]
tps += [tp]
fps += [len(g) - n - tp]
self.metrics["tp"] += [tps]
self.metrics["fp"] += [fps]
self.metrics["fn"] += [fns]
self.metrics["tn"] += [tns]
self.classes += [cls.item() if isinstance(cls, torch.Tensor) else cls]
def value(self):
import time
t_start = time.time()
if set(self.classes) == set([None]):
all_classes = None
log.warning("classes were not provided, cannot compute mIoU")
else:
all_classes = set(int(c) for c in self.classes)
# log.info(f'compute metrics for {len(all_classes)} classes')
summed = {
k: [
sum([self.metrics[k][i][j] for i in range(len(self.metrics[k]))])
for j in range(len(self.threshold_values))
]
for k in self.metrics.keys()
}
if all_classes is not None:
assert len(self.classes) == len(self.metrics["tp"]) == len(self.metrics["fn"])
# group by class
metrics_by_class = {c: {k: [] for k in self.metrics.keys()} for c in all_classes}
for i in range(len(self.metrics["tp"])):
for k in self.metrics.keys():
metrics_by_class[self.classes[i]][k] += [self.metrics[k][i]]
# sum over all instances within the classes
summed_by_cls = {
k: {c: np.array(metrics_by_class[c][k]).sum(0).tolist() for c in all_classes}
for k in self.metrics.keys()
}
# Compute average precision
assert (np.array(summed["fp"]) + np.array(summed["tp"])).sum(), "no predictions is made"
# only consider values where a prediction is made
precisions = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j])
for j in range(len(self.threshold_values))
if summed["tp"][j] + summed["fp"][j] > 0
]
recalls = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fn"][j])
for j in range(len(self.threshold_values))
if summed["tp"][j] + summed["fp"][j] > 0
]
# remove duplicate recall-precision-pairs (and sort by recall value)
recalls, precisions = zip(*sorted(list(set(zip(recalls, precisions))), key=lambda x: x[0]))
from scipy.integrate import simps
ap = simps(precisions, recalls)
# Compute best IoU
fgiou_scores = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j] + summed["fn"][j])
for j in range(len(self.threshold_values))
]
biniou_scores = [
0.5 * (summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j] + summed["fn"][j]))
+ 0.5 * (summed["tn"][j] / (1 + summed["tn"][j] + summed["fn"][j] + summed["fp"][j]))
for j in range(len(self.threshold_values))
]
# index_0p5 = self.threshold_values.tolist().index(0.5)
# index_0p1 = self.threshold_values.tolist().index(0.1)
# index_0p2 = self.threshold_values.tolist().index(0.2)
# index_0p3 = self.threshold_values.tolist().index(0.3)
if self.custom_threshold is not None:
index_ct = self.threshold_values.tolist().index(self.custom_threshold)
if all_classes is not None:
# mean IoU
mean_ious = [
np.mean(
[
summed_by_cls["tp"][c][j]
/ (1 + summed_by_cls["tp"][c][j] + summed_by_cls["fp"][c][j] + summed_by_cls["fn"][c][j])
for c in all_classes
]
)
for j in range(len(self.threshold_values))
]
mean_iou_dict = {
"miou_best": max(mean_ious) if all_classes is not None else None,
# 'miou_0.5': mean_ious[index_0p5] if all_classes is not None else None,
# 'miou_0.1': mean_ious[index_0p1] if all_classes is not None else None,
# 'miou_0.2': mean_ious[index_0p2] if all_classes is not None else None,
# 'miou_0.3': mean_ious[index_0p3] if all_classes is not None else None,
"miou_best_t": self.threshold_values[np.argmax(mean_ious)],
"mean_iou_ct": (
mean_ious[index_ct] if all_classes is not None and self.custom_threshold is not None else None
),
"mean_iou_scores": mean_ious,
}
print(
f"metric computation on {(len(all_classes) if all_classes is not None else 'no')} classes took {time.time() - t_start:.1f}s"
)
return {
"ap": ap,
# fgiou
"fgiou_best": max(fgiou_scores),
# 'fgiou_0.5': fgiou_scores[index_0p5],
# 'fgiou_0.1': fgiou_scores[index_0p1],
# 'fgiou_0.2': fgiou_scores[index_0p2],
# 'fgiou_0.3': fgiou_scores[index_0p3],
"fgiou_best_t": self.threshold_values[np.argmax(fgiou_scores)],
# mean iou
# biniou
"biniou_best": max(biniou_scores),
# 'biniou_0.5': biniou_scores[index_0p5],
# 'biniou_0.1': biniou_scores[index_0p1],
# 'biniou_0.2': biniou_scores[index_0p2],
# 'biniou_0.3': biniou_scores[index_0p3],
"biniou_best_t": self.threshold_values[np.argmax(biniou_scores)],
# custom threshold
"fgiou_ct": fgiou_scores[index_ct] if self.custom_threshold is not None else None,
"biniou_ct": biniou_scores[index_ct] if self.custom_threshold is not None else None,
"ct": self.custom_threshold,
# statistics
"fgiou_scores": fgiou_scores,
"biniou_scores": biniou_scores,
"precision_recall_curve": sorted(list(set(zip(recalls, precisions)))),
"summed_statistics": summed,
"summed_by_cls_statistics": summed_by_cls,
**mean_iou_dict,
}
# ('ap', 'best_fgiou', 'best_miou', 'fgiou0.5', 'fgiou0.1', 'mean_iou_0p5', 'mean_iou_0p1', 'best_biniou', 'biniou_0.5', 'fgiou_thresh'
# return ap, best_fgiou, best_mean_iou, iou_0p5, iou_0p1, mean_iou_0p5, mean_iou_0p1, best_biniou, biniou0p5, best_fgiou_thresh, {'summed': summed, 'summed_by_cls': summed_by_cls}
class FixedIntervalMetricsWithMatching(FixedIntervalMetrics):
def __init__(
self, sigmoid=False, ignore_mask=False, resize_to=None, resize_pred=None, n_values=51, custom_threshold=None
):
super().__init__(sigmoid, ignore_mask, resize_to, resize_pred, n_values, custom_threshold)
self.threshold_values = np.array([0.5]) # np.linspace(0, 1, n_values)[1:-1]
self.metrics = dict(tp=[], fp=[], fn=[], tn=[])
def add(self, pred, gt):
pred_batch = pred[0].cpu()
if self.sigmoid:
pred_batch = torch.sigmoid(pred_batch)
gt_batch = gt[0].cpu()
mask_batch = (
gt[1] if len(gt) > 1 and not self.ignore_mask and gt[1].numel() > 0 else ([None] * len(pred_batch))
)
cls_batch = gt[2] if len(gt) > 2 else [None] * len(pred_batch)
if self.resize_to is not None:
gt_batch = nnf.interpolate(gt_batch, self.resize_to, mode="nearest")
pred_batch = nnf.interpolate(pred_batch, self.resize_to, mode="bilinear", align_corners=False)
if isinstance(cls_batch, torch.Tensor):
cls_batch = cls_batch.cpu().numpy().tolist()
assert len(gt_batch) == len(pred_batch) == len(cls_batch), (
f"{len(gt_batch)} {len(pred_batch)} {len(cls_batch)}"
)
for predictions, ground_truth, mask, cls in zip(pred_batch, gt_batch, mask_batch, cls_batch):
if self.resize_pred:
predictions = nnf.interpolate(
predictions.unsqueeze(0).float(),
size=ground_truth.size()[-2:],
mode="bilinear",
align_corners=True,
)
p = predictions.flatten()
g = ground_truth.flatten()
assert len(p) == len(g)
if mask is not None:
m = mask.flatten().bool()
p = p[m]
g = g[m]
p_sorted = p.sort()
p = p_sorted.values
g = g[p_sorted.indices]
tps, fps, fns, tns = [], [], [], []
for thresh in self.threshold_values:
valid = torch.where(p > thresh)[0]
if len(valid) > 0:
n = int(valid[0])
else:
n = len(g)
fn = int(g[:n].sum())
tp = int(g[n:].sum())
fns += [fn]
tns += [n - fn]
tps += [tp]
fps += [len(g) - n - tp]
self.metrics["tp"] += [tps]
self.metrics["fp"] += [fps]
self.metrics["fn"] += [fns]
self.metrics["tn"] += [tns]
self.classes += [cls.item() if isinstance(cls, torch.Tensor) else cls]
def value(self):
import time
t_start = time.time()
if set(self.classes) == set([None]):
all_classes = None
log.warning("classes were not provided, cannot compute mIoU")
else:
all_classes = set(int(c) for c in self.classes)
log.info(f"compute metrics for {len(all_classes)} classes")
summed = {
k: [
sum([self.metrics[k][i][j] for i in range(len(self.metrics[k]))])
for j in range(len(self.threshold_values))
]
for k in self.metrics.keys()
}
if all_classes is not None:
assert len(self.classes) == len(self.metrics["tp"]) == len(self.metrics["fn"])
# group by class
metrics_by_class = {c: {k: [] for k in self.metrics.keys()} for c in all_classes}
for i in range(len(self.metrics["tp"])):
for k in self.metrics.keys():
metrics_by_class[self.classes[i]][k] += [self.metrics[k][i]]
# # sum over all instances within the classes
# summed_by_cls = {
# k: {c: np.array(metrics_by_class[c][k]).sum(0).tolist() for c in all_classes}
# for k in self.metrics.keys()
# }
# Compute average precision
assert (np.array(summed["fp"]) + np.array(summed["tp"])).sum(), "no predictions is made"
# only consider values where a prediction is made
precisions = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j])
for j in range(len(self.threshold_values))
if summed["tp"][j] + summed["fp"][j] > 0
]
recalls = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fn"][j])
for j in range(len(self.threshold_values))
if summed["tp"][j] + summed["fp"][j] > 0
]
# remove duplicate recall-precision-pairs (and sort by recall value)
recalls, precisions = zip(*sorted(list(set(zip(recalls, precisions))), key=lambda x: x[0]))
from scipy.integrate import simps
ap = simps(precisions, recalls)
# Compute best IoU
fgiou_scores = [
summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j] + summed["fn"][j])
for j in range(len(self.threshold_values))
]
biniou_scores = [
0.5 * (summed["tp"][j] / (1 + summed["tp"][j] + summed["fp"][j] + summed["fn"][j]))
+ 0.5 * (summed["tn"][j] / (1 + summed["tn"][j] + summed["fn"][j] + summed["fp"][j]))
for j in range(len(self.threshold_values))
]
print(
f"metric computation on {(len(all_classes) if all_classes is not None else 'no')} classes took {time.time() - t_start:.1f}s"
)
return {
"ap": ap,
# fgiou
"fgiou_best": max(fgiou_scores),
"fgiou_best_t": self.threshold_values[np.argmax(fgiou_scores)],
# mean iou
# biniou
"biniou_best": max(biniou_scores),
"biniou_best_t": self.threshold_values[np.argmax(biniou_scores)],
# statistics
"fgiou_scores": fgiou_scores,
"biniou_scores": biniou_scores,
"precision_recall_curve": sorted(list(set(zip(recalls, precisions)))),
"summed_statistics": summed,
}
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