import hydra import torch import torch.nn as nn from ultralytics.yolo import v8 from ultralytics.yolo.data import build_dataloader from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE from ultralytics.yolo.utils.modeling.tasks import DetectionModel from ultralytics.yolo.utils.plotting import plot_images, plot_results from ultralytics.yolo.utils.torch_utils import de_parallel # BaseTrainer python usage class DetectionTrainer(BaseTrainer): def get_dataloader(self, dataset_path, batch_size, mode="train", rank=0): # TODO: manage splits differently # calculate stride - check if model is initialized gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32) return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0] def preprocess_batch(self, batch): batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255 return batch def set_model_attributes(self): nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps) self.args.box *= 3 / nl # scale to layers self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers self.args.obj *= (self.args.img_size / 640) ** 2 * 3 / nl # scale to image size and layers self.model.nc = self.data["nc"] # attach number of classes to model self.model.args = self.args # attach hyperparameters to model # TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc self.model.names = self.data["names"] def load_model(self, model_cfg=None, weights=None): model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], anchors=self.args.get("anchors")) if weights: model.load(weights) for _, v in model.named_parameters(): v.requires_grad = True # train all layers return model def get_validator(self): return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, logger=self.console, args=self.args) def criterion(self, preds, batch): head = de_parallel(self.model).model[-1] sort_obj_iou = False autobalance = False # init losses BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.cls_pw], device=self.device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.obj_pw], device=self.device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 cp, cn = smooth_BCE(eps=self.args.label_smoothing) # positive, negative BCE targets # Focal loss g = self.args.fl_gamma if self.args.fl_gamma > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) balance = {3: [4.0, 1.0, 0.4]}.get(head.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 ssi = list(head.stride).index(16) if autobalance else 0 # stride 16 index BCEcls, BCEobj, gr, autobalance = BCEcls, BCEobj, 1.0, autobalance def build_targets(p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) nonlocal head na, nt = head.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch = [], [], [], [] gain = torch.ones(7, device=self.device) # normalized to gridspace gain ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, 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=self.device).float() * g # offsets for i in range(head.nl): anchors, shape = head.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.args.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, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors a, (b, c) = a.long().view(-1), 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 return tcls, tbox, indices, anch if len(preds) == 2: # eval _, p = preds else: # len(3) train p = preds targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1) targets = targets.to(self.device) lcls = torch.zeros(1, device=self.device) lbox = torch.zeros(1, device=self.device) lobj = torch.zeros(1, device=self.device) tcls, tbox, indices, anchors = build_targets(p, 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 bs = tobj.shape[0] n = b.shape[0] # number of targets if n: pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, head.nc), 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 sort_obj_iou: j = iou.argsort() b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] if gr < 1: iou = (1.0 - gr) + gr * iou tobj[b, a, gj, gi] = iou # iou ratio # Classification if head.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(pcls, cn, device=self.device) # targets t[range(n), tcls[i]] = cp lcls += BCEcls(pcls, t) # BCE obji = BCEobj(pi[..., 4], tobj) lobj += obji * balance[i] # obj loss if autobalance: balance[i] = balance[i] * 0.9999 + 0.0001 / obji.detach().item() if autobalance: balance = [x / balance[ssi] for x in balance] lbox *= self.args.box lobj *= self.args.obj lcls *= self.args.cls loss = lbox + lobj + lcls return loss * bs, torch.cat((lbox, lobj, lcls)).detach() # TODO: improve from API users perspective def label_loss_items(self, loss_items=None, prefix="train"): # We should just use named tensors here in future keys = [f"{prefix}/lbox", f"{prefix}/lobj", f"{prefix}/lcls"] return dict(zip(keys, loss_items)) if loss_items is not None else keys def progress_string(self): return ('\n' + '%11s' * 6) % \ ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Size') def plot_training_samples(self, batch, ni): images = batch["img"] cls = batch["cls"].squeeze(-1) bboxes = batch["bboxes"] paths = batch["im_file"] batch_idx = batch["batch_idx"] plot_images(images, batch_idx, cls, bboxes, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg") def plot_metrics(self): plot_results(file=self.csv) # save results.png @hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name) def train(cfg): cfg.model = cfg.model or "models/yolov5n.yaml" cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist") trainer = DetectionTrainer(cfg) trainer.train() if __name__ == "__main__": """ CLI usage: python ultralytics/yolo/v8/segment/train.py cfg=yolov5n-seg.yaml data=coco128-segments epochs=100 img_size=640 TODO: Direct cli support, i.e, yolov8 classify_train args.epochs 10 """ train()