Spaces:
Sleeping
Sleeping
File size: 8,591 Bytes
8e0b903 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 |
import numpy as np
import torch
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, 0.1, 0.9]
return (x[:, :len(w)] * 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:]
results = {
"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]}}
return results
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
class Semantic_Metrics:
def __init__(self, nc, device):
self.nc = nc # number of classes
self.device = device
self.iou = []
self.c_bit_counts = torch.zeros(nc, dtype = torch.long).to(device)
self.c_intersection_counts = torch.zeros(nc, dtype = torch.long).to(device)
self.c_union_counts = torch.zeros(nc, dtype = torch.long).to(device)
def update(self, pred_masks, target_masks):
nb, nc, h, w = pred_masks.shape
device = pred_masks.device
for b in range(nb):
onehot_mask = pred_masks[b].to(device)
# convert predict mask to one hot
semantic_mask = torch.flatten(onehot_mask, start_dim = 1).permute(1, 0) # class x h x w -> (h x w) x class
max_idx = semantic_mask.argmax(1)
output_masks = (torch.zeros(semantic_mask.shape).to(self.device)).scatter(1, max_idx.unsqueeze(1), 1.0) # one hot: (h x w) x class
output_masks = torch.reshape(output_masks.permute(1, 0), (nc, h, w)) # (h x w) x class -> class x h x w
onehot_mask = output_masks.int()
for c in range(self.nc):
pred_mask = onehot_mask[c].to(device)
target_mask = target_masks[b, c].to(device)
# calculate IoU
intersection = (torch.logical_and(pred_mask, target_mask).sum()).item()
union = (torch.logical_or(pred_mask, target_mask).sum()).item()
iou = 0. if (0 == union) else (intersection / union)
# record class pixel counts, intersection counts, union counts
self.c_bit_counts[c] += target_mask.int().sum()
self.c_intersection_counts[c] += intersection
self.c_union_counts[c] += union
self.iou.append(iou)
def results(self):
# Mean IoU
miou = 0. if (0 == len(self.iou)) else np.sum(self.iou) / (len(self.iou) * self.nc)
# Frequency Weighted IoU
c_iou = self.c_intersection_counts / (self.c_union_counts + 1) # add smooth
# c_bit_counts = self.c_bit_counts.astype(int)
total_c_bit_counts = self.c_bit_counts.sum()
freq_ious = torch.zeros(1, dtype = torch.long).to(self.device) if (0 == total_c_bit_counts) else (self.c_bit_counts / total_c_bit_counts) * c_iou
fwiou = (freq_ious.sum()).item()
return (miou, fwiou)
def reset(self):
self.iou = []
self.c_bit_counts = torch.zeros(self.nc, dtype = torch.long).to(self.device)
self.c_intersection_counts = torch.zeros(self.nc, dtype = torch.long).to(self.device)
self.c_union_counts = torch.zeros(self.nc, dtype = torch.long).to(self.device)
KEYS = [
"train/box_loss",
"train/seg_loss", # train loss
"train/cls_loss",
"train/dfl_loss",
"train/fcl_loss",
"train/dic_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
"metrics/MIOUS(S)",
"metrics/FWIOUS(S)", # metrics
"val/box_loss",
"val/seg_loss", # val loss
"val/cls_loss",
"val/dfl_loss",
"val/fcl_loss",
"val/dic_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)",
"best/MIOUS(S)",
"best/FWIOUS(S)",]
|