Spaces:
Runtime error
Runtime error
File size: 10,083 Bytes
5b2fcab |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..general import xywh2xyxy
from ..loss import FocalLoss, smooth_BCE
from ..metrics import bbox_iou
from ..torch_utils import de_parallel
from .general import crop_mask
class ComputeLoss:
# Compute losses
def __init__(self, model, autobalance=False, overlap=False):
self.sort_obj_iou = False
self.overlap = overlap
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
self.device = device
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h["cls_pw"]], device=device)
)
BCEobj = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h["obj_pw"]], device=device)
)
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(
eps=h.get("label_smoothing", 0.0)
) # positive, negative BCE targets
# Focal loss
g = h["fl_gamma"] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
m = de_parallel(model).model[-1] # Detect() module
self.balance = {3: [4.0, 1.0, 0.4]}.get(
m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]
) # P3-P7
self.ssi = (
list(m.stride).index(16) if autobalance else 0
) # stride 16 index
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = (
BCEcls,
BCEobj,
1.0,
h,
autobalance,
)
self.na = m.na # number of anchors
self.nc = m.nc # number of classes
self.nl = m.nl # number of layers
self.nm = m.nm # number of masks
self.anchors = m.anchors
self.device = device
def __call__(self, preds, targets, masks): # predictions, targets, model
p, proto = preds
(
bs,
nm,
mask_h,
mask_w,
) = proto.shape # batch size, number of masks, mask height, mask width
lcls = torch.zeros(1, device=self.device)
lbox = torch.zeros(1, device=self.device)
lobj = torch.zeros(1, device=self.device)
lseg = torch.zeros(1, device=self.device)
tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(
p, targets
) # targets
# Losses
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros(
pi.shape[:4], dtype=pi.dtype, device=self.device
) # target obj
n = b.shape[0] # number of targets
if n:
pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split(
(2, 2, 1, self.nc, nm), 1
) # subset of predictions
# Box regression
pxy = pxy.sigmoid() * 2 - 0.5
pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box
iou = bbox_iou(
pbox, tbox[i], CIoU=True
).squeeze() # iou(prediction, target)
lbox += (1.0 - iou).mean() # iou loss
# Objectness
iou = iou.detach().clamp(0).type(tobj.dtype)
if self.sort_obj_iou:
j = iou.argsort()
b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
if self.gr < 1:
iou = (1.0 - self.gr) + self.gr * iou
tobj[b, a, gj, gi] = iou # iou ratio
# Classification
if self.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(
pcls, self.cn, device=self.device
) # targets
t[range(n), tcls[i]] = self.cp
lcls += self.BCEcls(pcls, t) # BCE
# Mask regression
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(
masks[None], (mask_h, mask_w), mode="nearest"
)[0]
marea = xywhn[i][:, 2:].prod(
1
) # mask width, height normalized
mxyxy = xywh2xyxy(
xywhn[i]
* torch.tensor(
[mask_w, mask_h, mask_w, mask_h], device=self.device
)
)
for bi in b.unique():
j = b == bi # matching index
if self.overlap:
mask_gti = torch.where(
masks[bi][None] == tidxs[i][j].view(-1, 1, 1),
1.0,
0.0,
)
else:
mask_gti = masks[tidxs[i]][j]
lseg += self.single_mask_loss(
mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]
)
obji = self.BCEobj(pi[..., 4], tobj)
lobj += obji * self.balance[i] # obj loss
if self.autobalance:
self.balance[i] = (
self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
)
if self.autobalance:
self.balance = [x / self.balance[self.ssi] for x in self.balance]
lbox *= self.hyp["box"]
lobj *= self.hyp["obj"]
lcls *= self.hyp["cls"]
lseg *= self.hyp["box"] / bs
loss = lbox + lobj + lcls + lseg
return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
# Mask loss for one image
pred_mask = (pred @ proto.view(self.nm, -1)).view(
-1, *proto.shape[1:]
) # (n,32) @ (32,80,80) -> (n,80,80)
loss = F.binary_cross_entropy_with_logits(
pred_mask, gt_mask, reduction="none"
)
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
def build_targets(self, p, targets):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
na, nt = self.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
gain = torch.ones(
8, device=self.device
) # normalized to gridspace gain
ai = (
torch.arange(na, device=self.device)
.float()
.view(na, 1)
.repeat(1, nt)
) # same as .repeat_interleave(nt)
if self.overlap:
batch = p[0].shape[0]
ti = []
for i in range(batch):
num = (
targets[:, 0] == i
).sum() # find number of targets of each image
ti.append(
torch.arange(num, device=self.device)
.float()
.view(1, num)
.repeat(na, 1)
+ 1
) # (na, num)
ti = torch.cat(ti, 1) # (na, nt)
else:
ti = (
torch.arange(nt, device=self.device)
.float()
.view(1, nt)
.repeat(na, 1)
)
targets = torch.cat(
(targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2
) # append anchor indices
g = 0.5 # bias
off = (
torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=self.device,
).float()
* g
) # offsets
for i in range(self.nl):
anchors, shape = self.anchors[i], p[i].shape
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain # shape(3,n,7)
if nt:
# Matches
r = t[..., 4:6] / anchors[:, None] # wh ratio
j = (
torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"]
) # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1 < g) & (gxy > 1)).T
l, m = ((gxi % 1 < g) & (gxi > 1)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
bc, gxy, gwh, at = t.chunk(
4, 1
) # (image, class), grid xy, grid wh, anchors
(a, tidx), (b, c) = (
at.long().T,
bc.long().T,
) # anchors, image, class
gij = (gxy - offsets).long()
gi, gj = gij.T # grid indices
# Append
indices.append(
(b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))
) # image, anchor, grid
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
tidxs.append(tidx)
xywhn.append(
torch.cat((gxy, gwh), 1) / gain[2:6]
) # xywh normalized
return tcls, tbox, indices, anch, tidxs, xywhn
|