Optimal Transport Assignment（OTA）是YOLOv5中的一个改进，它是一种更优的目标检测框架，可以在保证检测精度的同时，大幅提升检测速度。
话不多说，直接开始
1、首先在你的yolov5目录下面的找到loss.py文件
如下图：yolov5-master/utils/loss.py
2、在loss.py文件最后面加入这段代码，并保存。
import torch.nn.functional as F
from utils.metrics import box_iou
from utils.torch_utils import de_parallel
from utils.general import xywh2xyxy
class ComputeLossOTA:
# Compute losses
def __init__(self, model, autobalance=False):
super(ComputeLossOTA, self).__init__()
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
# 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)
det = de_parallel(model).model[-1] # Detect() module
self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
self.ssi = list(det.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
for k in 'na', 'nc', 'nl', 'anchors', 'stride':
setattr(self, k, getattr(det, k))
def __call__(self, p, targets, imgs): # predictions, targets, model
device = targets.device
lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p]
# Losses
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
n = b.shape[0] # number of targets
if n:
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
# Regression
grid = torch.stack([gi, gj], dim=1)
pxy = ps[:, :2].sigmoid() * 2. - 0.5
#pxy = ps[:, :2].sigmoid() * 3. - 1.
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box
selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
selected_tbox[:, :2] -= grid
iou = bbox_iou(pbox, selected_tbox, CIoU=True) # iou(prediction, target)
if type(iou) is tuple:
lbox += (iou[1].detach() * (1 - iou[0])).mean()
iou = iou[0]
else:
lbox += (1.0 - iou).mean() # iou loss
# Objectness
tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
# Classification
selected_tcls = targets[i][:, 1].long()
if self.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
t[range(n), selected_tcls] = self.cp
lcls += self.BCEcls(ps[:, 5:], t) # BCE
# Append targets to text file
# with open('targets.txt', 'a') as file:
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
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']
bs = tobj.shape[0] # batch size
loss = lbox + lobj + lcls
return loss * bs, torch.cat((lbox, lobj, lcls)).detach()
def build_targets(self, p, targets, imgs):
indices, anch = self.find_3_positive(p, targets)
device = torch.device(targets.device)
matching_bs = [[] for pp in p]
matching_as = [[] for pp in p]
matching_gjs = [[] for pp in p]
matching_gis = [[] for pp in p]
matching_targets = [[] for pp in p]
matching_anchs = [[] for pp in p]
nl = len(p)
for batch_idx in range(p[0].shape[0]):
b_idx = targets[:, 0]==batch_idx
this_target = targets[b_idx]
if this_target.shape[0] == 0:
continue
txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
txyxy = xywh2xyxy(txywh)
pxyxys = []
p_cls = []
p_obj = []
from_which_layer = []
all_b = []
all_a = []
all_gj = []
all_gi = []
all_anch = []
for i, pi in enumerate(p):
b, a, gj, gi = indices[i]
idx = (b == batch_idx)
b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
all_b.append(b)
all_a.append(a)
all_gj.append(gj)
all_gi.append(gi)
all_anch.append(anch[i][idx])
from_which_layer.append((torch.ones(size=(len(b),)) * i).to(device))
fg_pred = pi[b, a, gj, gi]
p_obj.append(fg_pred[:, 4:5])
p_cls.append(fg_pred[:, 5:])
grid = torch.stack([gi, gj], dim=1)
pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
#pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
pxywh = torch.cat([pxy, pwh], dim=-1)
pxyxy = xywh2xyxy(pxywh)
pxyxys.append(pxyxy)
pxyxys = torch.cat(pxyxys, dim=0)
if pxyxys.shape[0] == 0:
continue
p_obj = torch.cat(p_obj, dim=0)
p_cls = torch.cat(p_cls, dim=0)
from_which_layer = torch.cat(from_which_layer, dim=0)
all_b = torch.cat(all_b, dim=0)
all_a = torch.cat(all_a, dim=0)
all_gj = torch.cat(all_gj, dim=0)
all_gi = torch.cat(all_gi, dim=0)
all_anch = torch.cat(all_anch, dim=0)
pair_wise_iou = box_iou(txyxy, pxyxys)
pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
gt_cls_per_image = (
F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
.float()
.unsqueeze(1)
.repeat(1, pxyxys.shape[0], 1)
)
num_gt = this_target.shape[0]
cls_preds_ = (
p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
* p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
)
y = cls_preds_.sqrt_()
pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
).sum(-1)
del cls_preds_
cost = (
pair_wise_cls_loss
+ 3.0 * pair_wise_iou_loss
)
matching_matrix = torch.zeros_like(cost, device=device)
for gt_idx in range(num_gt):
_, pos_idx = torch.topk(
cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
)
matching_matrix[gt_idx][pos_idx] = 1.0
del top_k, dynamic_ks
anchor_matching_gt = matching_matrix.sum(0)
if (anchor_matching_gt > 1).sum() > 0:
_, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
matching_matrix[:, anchor_matching_gt > 1] *= 0.0
matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device)
matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
from_which_layer = from_which_layer[fg_mask_inboxes]
all_b = all_b[fg_mask_inboxes]
all_a = all_a[fg_mask_inboxes]
all_gj = all_gj[fg_mask_inboxes]
all_gi = all_gi[fg_mask_inboxes]
all_anch = all_anch[fg_mask_inboxes]
this_target = this_target[matched_gt_inds]
for i in range(nl):
layer_idx = from_which_layer == i
matching_bs[i].append(all_b[layer_idx])
matching_as[i].append(all_a[layer_idx])
matching_gjs[i].append(all_gj[layer_idx])
matching_gis[i].append(all_gi[layer_idx])
matching_targets[i].append(this_target[layer_idx])
matching_anchs[i].append(all_anch[layer_idx])
for i in range(nl):
if matching_targets[i] != []:
matching_bs[i] = torch.cat(matching_bs[i], dim=0)
matching_as[i] = torch.cat(matching_as[i], dim=0)
matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
matching_gis[i] = torch.cat(matching_gis[i], dim=0)
matching_targets[i] = torch.cat(matching_targets[i], dim=0)
matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
else:
matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
def find_3_positive(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
indices, anch = [], []
gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, 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=targets.device).float() * g # offsets
for i in range(self.nl):
anchors = self.anchors[i]
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain
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
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
anch.append(anchors[a]) # anchors
return indices, anch
3、第三步，找到train.py文件。
（1）将58行的ComputeLoss改成ComputeLossOTA(大家根据自己的代码行数看，有时候行数对不上，但找到对应代码就可以)
（2）再将258行的ComputeLoss改成ComputeLossOTA(大家根据自己的代码行数看，有时候行数对不上，但找到对应代码就可以)
（3）第三个要改的地方是313行的，加一个imgs。
上面就是train.py文件三个要改的地方
4、找到val.py
将214行加一个im，具体如图，之后要保存！
之后运行，结果涨了0.02！！！！！哈哈哈，涨点，确实涨了点啊啊啊！
