YOLOv8 architecture updates from R&D branch (#88)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -5,9 +5,12 @@ import torch.nn as nn
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from ultralytics.yolo import v8
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
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from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE
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from ultralytics.yolo.utils.loss import BboxLoss
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from ultralytics.yolo.utils.metrics import smooth_BCE
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from ultralytics.yolo.utils.modeling.tasks import DetectionModel
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from ultralytics.yolo.utils.ops import xywh2xyxy
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from ultralytics.yolo.utils.plotting import plot_images, plot_results
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from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
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from ultralytics.yolo.utils.torch_utils import de_parallel
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@ -35,10 +38,7 @@ class DetectionTrainer(BaseTrainer):
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self.model.names = self.data["names"]
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def load_model(self, model_cfg=None, weights=None):
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model = DetectionModel(model_cfg or weights["model"].yaml,
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ch=3,
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nc=self.data["nc"],
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anchors=self.args.get("anchors"))
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model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
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if weights:
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model.load(weights)
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for _, v in model.named_parameters():
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@ -46,150 +46,14 @@ class DetectionTrainer(BaseTrainer):
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return model
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def get_validator(self):
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self.loss_names = 'box_loss', 'obj_loss', 'cls_loss'
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self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
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return v8.detect.DetectionValidator(self.test_loader,
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save_dir=self.save_dir,
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logger=self.console,
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args=self.args)
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def criterion(self, preds, batch):
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head = de_parallel(self.model).model[-1]
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sort_obj_iou = False
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autobalance = False
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# init losses
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.cls_pw], device=self.device))
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BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.obj_pw], device=self.device))
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# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
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cp, cn = smooth_BCE(eps=self.args.label_smoothing) # positive, negative BCE targets
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# Focal loss
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g = self.args.fl_gamma
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if self.args.fl_gamma > 0:
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BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
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balance = {3: [4.0, 1.0, 0.4]}.get(head.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
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ssi = list(head.stride).index(16) if autobalance else 0 # stride 16 index
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BCEcls, BCEobj, gr, autobalance = BCEcls, BCEobj, 1.0, autobalance
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def build_targets(p, targets):
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# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
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nonlocal head
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na, nt = head.na, targets.shape[0] # number of anchors, targets
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tcls, tbox, indices, anch = [], [], [], []
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gain = torch.ones(7, device=self.device) # normalized to gridspace gain
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ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)
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targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
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g = 0.5 # bias
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off = torch.tensor(
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[
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[0, 0],
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[1, 0],
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[0, 1],
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[-1, 0],
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[0, -1], # j,k,l,m
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# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
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],
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device=self.device).float() * g # offsets
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for i in range(head.nl):
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anchors, shape = head.anchors[i], p[i].shape
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gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
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# Match targets to anchors
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t = targets * gain # shape(3,n,7)
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if nt:
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# Matches
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r = t[..., 4:6] / anchors[:, None] # wh ratio
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j = torch.max(r, 1 / r).max(2)[0] < self.args.anchor_t # compare
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# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
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t = t[j] # filter
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# Offsets
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gxy = t[:, 2:4] # grid xy
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gxi = gain[[2, 3]] - gxy # inverse
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j, k = ((gxy % 1 < g) & (gxy > 1)).T
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l, m = ((gxi % 1 < g) & (gxi > 1)).T
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j = torch.stack((torch.ones_like(j), j, k, l, m))
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t = t.repeat((5, 1, 1))[j]
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offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
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else:
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t = targets[0]
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offsets = 0
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# Define
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bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
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a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
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gij = (gxy - offsets).long()
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gi, gj = gij.T # grid indices
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# Append
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indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
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tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
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anch.append(anchors[a]) # anchors
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tcls.append(c) # class
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return tcls, tbox, indices, anch
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if len(preds) == 2: # eval
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_, p = preds
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else: # len(3) train
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p = preds
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targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
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targets = targets.to(self.device)
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lcls = torch.zeros(1, device=self.device)
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lbox = torch.zeros(1, device=self.device)
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lobj = torch.zeros(1, device=self.device)
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tcls, tbox, indices, anchors = build_targets(p, targets)
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# Losses
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for i, pi in enumerate(p): # layer index, layer predictions
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b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
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tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
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bs = tobj.shape[0]
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n = b.shape[0] # number of targets
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if n:
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pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, head.nc), 1) # subset of predictions
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# Box regression
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pxy = pxy.sigmoid() * 2 - 0.5
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pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
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pbox = torch.cat((pxy, pwh), 1) # predicted box
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iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
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lbox += (1.0 - iou).mean() # iou loss
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# Objectness
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iou = iou.detach().clamp(0).type(tobj.dtype)
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if sort_obj_iou:
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j = iou.argsort()
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b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
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if gr < 1:
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iou = (1.0 - gr) + gr * iou
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tobj[b, a, gj, gi] = iou # iou ratio
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# Classification
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if head.nc > 1: # cls loss (only if multiple classes)
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t = torch.full_like(pcls, cn, device=self.device) # targets
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t[range(n), tcls[i]] = cp
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lcls += BCEcls(pcls, t) # BCE
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obji = BCEobj(pi[..., 4], tobj)
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lobj += obji * balance[i] # obj loss
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if autobalance:
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balance[i] = balance[i] * 0.9999 + 0.0001 / obji.detach().item()
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if autobalance:
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balance = [x / balance[ssi] for x in balance]
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lbox *= self.args.box
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lobj *= self.args.obj
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lcls *= self.args.cls
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loss = lbox + lobj + lcls
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return loss * bs, torch.cat((lbox, lobj, lcls)).detach()
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return Loss(self.model)(preds, batch)
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def label_loss_items(self, loss_items=None, prefix="train"):
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# We should just use named tensors here in future
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@ -212,10 +76,105 @@ class DetectionTrainer(BaseTrainer):
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plot_results(file=self.csv) # save results.png
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# Criterion class for computing training losses
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class Loss:
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def __init__(self, model):
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device = next(model.parameters()).device # get model device
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h = model.args # hyperparameters
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# Define criteria
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device), reduction='none')
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# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
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self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
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m = de_parallel(model).model[-1] # Detect() module
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self.BCEcls = BCEcls
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self.hyp = h
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self.stride = m.stride # model strides
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self.nc = m.nc # number of classes
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self.nl = m.nl # number of layers
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self.device = device
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self.use_dfl = m.reg_max > 1
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self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
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self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
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self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
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def preprocess(self, targets, batch_size, scale_tensor):
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if targets.shape[0] == 0:
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out = torch.zeros(batch_size, 0, 5, device=self.device)
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else:
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i = targets[:, 0] # image index
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_, counts = i.unique(return_counts=True)
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out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
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for j in range(batch_size):
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matches = i == j
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n = matches.sum()
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if n:
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out[j, :n] = targets[matches, 1:]
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out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
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return out
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def bbox_decode(self, anchor_points, pred_dist):
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if self.use_dfl:
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b, a, c = pred_dist.shape # batch, anchors, channels
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pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
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return dist2bbox(pred_dist, anchor_points, xywh=False)
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def __call__(self, preds, batch):
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loss = torch.zeros(3, device=self.device) # box, cls, dfl
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feats, pred_distri, pred_scores = preds if len(preds) == 3 else preds[1]
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pred_scores = pred_scores.permute(0, 2, 1).contiguous()
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pred_distri = pred_distri.permute(0, 2, 1).contiguous()
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dtype = pred_scores.dtype
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batch_size, grid_size = pred_scores.shape[:2]
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imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
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anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
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# targets
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targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
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targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
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gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
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mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
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# pboxes
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
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target_labels, target_bboxes, target_scores, fg_mask = self.assigner(
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pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
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anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
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target_bboxes /= stride_tensor
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target_scores_sum = target_scores.sum()
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# cls loss
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# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
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loss[1] = self.BCEcls(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
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# bbox loss
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if fg_mask.sum():
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loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
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target_scores_sum, fg_mask)
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loss[0] *= 7.5 # box gain
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loss[1] *= 0.5 # cls gain
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loss[2] *= 1.5 # dfl gain
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
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@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
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def train(cfg):
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cfg.model = cfg.model or "models/yolov5n.yaml"
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cfg.model = cfg.model or "models/yolov8n.yaml"
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cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
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cfg.imgsz = 160
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cfg.epochs = 5
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trainer = DetectionTrainer(cfg)
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trainer.train()
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@ -223,9 +182,9 @@ def train(cfg):
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if __name__ == "__main__":
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"""
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CLI usage:
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python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 imgsz=640
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python ultralytics/yolo/v8/detect/train.py model=yolov8n.yaml data=coco128 epochs=100 imgsz=640
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TODO:
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yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=100
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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"""
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train()
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@ -3,7 +3,6 @@ import os
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import hydra
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import numpy as np
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import torch
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import torch.nn.functional as F
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
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