稷山做网站,广州市安全教育平台,企业网站轮播图,有哪些可以做兼职的翻译网站吗YOLO-OTA 第一步#xff1a;拉取 YOLOv5 的代码第二步#xff1a;添加 ComputeLossOTA 函数第二步#xff1a;修改 train 和 val 中损失函数为 ComputeLossOTA 函数1、在 train.py 中 首先添加 ComputeLossOTA 库。2、在 train.py 修改初始化的损失函数3、在 train.py 修改一… YOLO-OTA 第一步拉取 YOLOv5 的代码第二步添加 ComputeLossOTA 函数第二步修改 train 和 val 中损失函数为 ComputeLossOTA 函数1、在 train.py 中 首先添加 ComputeLossOTA 库。2、在 train.py 修改初始化的损失函数3、在 train.py 修改一些必要的参数4、修改一下 parser 参数方便控制是否使用 OTALOSS5、在 val.py 中修改一些必要的参数 开始训练训练 coco128 数据集训练 coco 数据集训练 CrowdHuman 数据集 第一步拉取 YOLOv5 的代码
git clone https://github.com/ultralytics/yolov5.git 第二步添加 ComputeLossOTA 函数
打开 utils/loss.py 文件向其中添加下面的部分
import torch.nn.functional as F
from utils.metrics import box_iou
from utils.torch_utils import de_parallel
from utils.general import xywh2xyxyclass ComputeLossOTA:# Compute lossesdef __init__(self, model, autobalanceFalse):super(ComputeLossOTA, self).__init__()device next(model.parameters()).device # get model deviceh model.hyp # hyperparameters# Define criteriaBCEcls nn.BCEWithLogitsLoss(pos_weighttorch.tensor([h[cls_pw]], devicedevice))BCEobj nn.BCEWithLogitsLoss(pos_weighttorch.tensor([h[obj_pw]], devicedevice))# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3self.cp, self.cn smooth_BCE(epsh.get(label_smoothing, 0.0)) # positive, negative BCE targets# Focal lossg h[fl_gamma] # focal loss gammaif g 0:BCEcls, BCEobj FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)det de_parallel(model).model[-1] # Detect() moduleself.balance {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7self.ssi list(det.stride).index(16) if autobalance else 0 # stride 16 indexself.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance BCEcls, BCEobj, 1.0, h, autobalancefor k in na, nc, nl, anchors, stride:setattr(self, k, getattr(det, k))def __call__(self, p, targets, imgs): # predictions, targets, model device targets.devicelcls, lbox, lobj torch.zeros(1, devicedevice), torch.zeros(1, devicedevice), torch.zeros(1, devicedevice)bs, as_, gjs, gis, targets, anchors self.build_targets(p, targets, imgs)pre_gen_gains [torch.tensor(pp.shape, devicedevice)[[3, 2, 3, 2]] for pp in p] # Lossesfor i, pi in enumerate(p): # layer index, layer predictionsb, a, gj, gi bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridxtobj torch.zeros_like(pi[..., 0], devicedevice) # target objn b.shape[0] # number of targetsif n:ps pi[b, a, gj, gi] # prediction subset corresponding to targets# Regressiongrid torch.stack([gi, gj], dim1)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 boxselected_tbox targets[i][:, 2:6] * pre_gen_gains[i]selected_tbox[:, :2] - gridiou bbox_iou(pbox, selected_tbox, CIoUTrue) # 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# Objectnesstobj[b, a, gj, gi] (1.0 - self.gr) self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio# Classificationselected_tcls targets[i][:, 1].long()if self.nc 1: # cls loss (only if multiple classes)t torch.full_like(ps[:, 5:], self.cn, devicedevice) # targetst[range(n), selected_tcls] self.cplcls 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 lossif 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 sizeloss lbox lobj lclsreturn 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_idxthis_target targets[b_idx]if this_target.shape[0] 0:continuetxywh 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], dim1)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, dim0)if pxyxys.shape[0] 0:continuep_obj torch.cat(p_obj, dim0)p_cls torch.cat(p_cls, dim0)from_which_layer torch.cat(from_which_layer, dim0)all_b torch.cat(all_b, dim0)all_a torch.cat(all_a, dim0)all_gj torch.cat(all_gj, dim0)all_gi torch.cat(all_gi, dim0)all_anch torch.cat(all_anch, dim0)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]), dim1)dynamic_ks torch.clamp(top_k.sum(1).int(), min1)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, reductionnone).sum(-1)del cls_preds_cost (pair_wise_cls_loss 3.0 * pair_wise_iou_loss)matching_matrix torch.zeros_like(cost, devicedevice)for gt_idx in range(num_gt):_, pos_idx torch.topk(cost[gt_idx], kdynamic_ks[gt_idx].item(), largestFalse)matching_matrix[gt_idx][pos_idx] 1.0del top_k, dynamic_ksanchor_matching_gt matching_matrix.sum(0)if (anchor_matching_gt 1).sum() 0:_, cost_argmin torch.min(cost[:, anchor_matching_gt 1], dim0)matching_matrix[:, anchor_matching_gt 1] * 0.0matching_matrix[cost_argmin, anchor_matching_gt 1] 1.0fg_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 imatching_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], dim0)matching_as[i] torch.cat(matching_as[i], dim0)matching_gjs[i] torch.cat(matching_gjs[i], dim0)matching_gis[i] torch.cat(matching_gis[i], dim0)matching_targets[i] torch.cat(matching_targets[i], dim0)matching_anchs[i] torch.cat(matching_anchs[i], dim0)else:matching_bs[i] torch.tensor([], devicecuda:0, dtypetorch.int64)matching_as[i] torch.tensor([], devicecuda:0, dtypetorch.int64)matching_gjs[i] torch.tensor([], devicecuda:0, dtypetorch.int64)matching_gis[i] torch.tensor([], devicecuda:0, dtypetorch.int64)matching_targets[i] torch.tensor([], devicecuda:0, dtypetorch.int64)matching_anchs[i] torch.tensor([], devicecuda:0, dtypetorch.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, targetsindices, anch [], []gain torch.ones(7, devicetargets.device).long() # normalized to gridspace gainai torch.arange(na, devicetargets.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 indicesg 0.5 # biasoff 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], devicetargets.device).float() * g # offsetsfor 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 anchorst targets * gainif nt:# Matchesr t[:, :, 4:6] / anchors[:, None] # wh ratioj 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# Offsetsgxy t[:, 2:4] # grid xygxi gain[[2, 3]] - gxy # inversej, k ((gxy % 1. g) (gxy 1.)).Tl, m ((gxi % 1. g) (gxi 1.)).Tj 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# Defineb, c t[:, :2].long().T # image, classgxy t[:, 2:4] # grid xygwh t[:, 4:6] # grid whgij (gxy - offsets).long()gi, gj gij.T # grid xy indices# Appenda t[:, 6].long() # anchor indicesindices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indicesanch.append(anchors[a]) # anchorsreturn indices, anch第二步修改 train 和 val 中损失函数为 ComputeLossOTA 函数
1、在 train.py 中 首先添加 ComputeLossOTA 库。
# 63 行
from utils.loss import ComputeLoss, ComputeLossOTA2、在 train.py 修改初始化的损失函数
# 263 行
if opt.losstype normloss:compute_loss ComputeLoss(model) # init loss class
elif opt.losstype otaloss:compute_loss ComputeLossOTA(model) # init loss class3、在 train.py 修改一些必要的参数
因为 OTA 需要图片为收入所以 ComputeLossOTA 和 ComputeLoss 相比需要添加 imgs 为输入。
# 319 行
if opt.losstype normloss:loss, loss_items compute_loss(pred, targets.to(device)) # loss scaled by batch_size
elif opt.losstype otaloss:loss, loss_items compute_loss(pred, targets.to(device), imgs) # loss scaled by batch_size4、修改一下 parser 参数方便控制是否使用 OTALOSS
parser.add_argument(--losstype, typestr, defaultnormloss, helpchoose loss type: loss otaloss)5、在 val.py 中修改一些必要的参数
# 212 行
# Loss
if compute_loss:# loss compute_loss(train_out, targets)[1] # box, obj, clsloss compute_loss(train_out, targets, im)[1] # box, obj, cls开始训练
下载的话权重去官网下载你需要的模型的权重。 如yolov5s.pt
训练 coco128 数据集
Usage - Single-GPU training:$ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights --cfg yolov5s.yaml --img 640 # from scratch
# 使用 OTA $ python train.py --data coco128.yaml --weights yolov5s.pt -losstype otaloss # from pretrained (recommended) -losstype otaloss$ python train.py --data coco128.yaml --weights --cfg yolov5s.yaml -losstype otaloss # from scratchUsage - Multi-GPU DDP training:$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3训练图片一个batch_size 如下 训练 coco 数据集
使用 coco 进行训练的命令如下
Usage - Single-GPU training:$ python train.py --data coco.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)$ python train.py --data coco.yaml --weights --cfg yolov5s.yaml --img 640 # from scratchUsage - Multi-GPU DDP training:$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3没有下载 coco 的话这个命令会自动下载。最好在coco数据集里面自己新建一个 coco.ymal内容可填写如下
# COCO 2017 dataset http://cocodataset.org# download command/URL (optional)
# download: bash ./scripts/get_coco.sh# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: /home/adr/Desktop/Code/Python/2D/datasets/coco/train2017.txt # 118287 images
val: /home/adr/Desktop/Code/Python/2D/datasets/coco/val2017.txt # 5000 images
test: /home/adr/Desktop/Code/Python/2D/datasets/coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794# number of classes
nc: 80# class names
names: [ person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light,fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow,elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee,skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard,tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple,sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch,potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone,microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear,hair drier, toothbrush ]这样使用其他 yolo 版本训练的时候只需要把这个 coco.yaml 的文件的绝对路径给它也行。
python train.py --data /home/adr/Desktop/Code/Python/2D/datasets/coco.yaml --weights --cfg yolov5s.yaml训练图片一个batch_size 如下
训练 CrowdHuman 数据集
1、下载 官网数据集下载
CrowdHuman dataset下载链接https://www.crowdhuman.org/download.html 把里面链接都进行下载。然后按照步骤二给出的连接即可。
2、转化为 coco 数据集格式
可以根据下面仓库的步骤进行 https://github.com/Shaohu-Li/YOLOv5-Tools
3、使用下面命令进行训练。
# 不使用预训练权重
python train.py --data /home/adr/datasets/CrowdHuman/crowdhuman.yaml --cfg yolov5s.yaml --img 640 --batch-size 32 --weights # 使用预训练权重
python train.py --data /home/adr/datasets/CrowdHuman/crowdhuman.yaml --cfg yolov5s.yaml --img 640 --batch-size 32 --weights yolov5s.pt训练图片一个batch_size 如下
本文参考 大神链接 B 站 https://space.bilibili.com/286900343
