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253 lines
11 KiB
253 lines
11 KiB
import hydra
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ultralytics.yolo import v8
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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.modeling.tasks import SegmentationModel
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from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy
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from ultralytics.yolo.utils.plotting import plot_images, plot_results
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from ultralytics.yolo.utils.torch_utils import de_parallel
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from ..detect import DetectionTrainer
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# BaseTrainer python usage
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class SegmentationTrainer(DetectionTrainer):
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def load_model(self, model_cfg=None, weights=None):
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model = SegmentationModel(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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if weights:
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model.load(weights)
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for _, v in model.named_parameters():
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v.requires_grad = True # train all layers
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return model
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def get_validator(self):
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self.loss_names = 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss'
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return v8.segment.SegmentationValidator(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 single_mask_loss(gt_mask, pred, proto, xyxy, area):
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# Mask loss for one image
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pred_mask = (pred @ proto.view(head.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)
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loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
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return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
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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, tidxs, xywhn = [], [], [], [], [], []
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gain = torch.ones(8, device=self.device) # normalized to gridspace gain
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ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1,
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nt) # same as .repeat_interleave(nt)
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if self.args.overlap_mask:
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batch = p[0].shape[0]
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ti = []
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for i in range(batch):
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num = (targets[:, 0] == i).sum() # find number of targets of each image
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ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
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ti = torch.cat(ti, 1) # (na, nt)
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else:
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ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
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targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., 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, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
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(a, tidx), (b, c) = at.long().T, 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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tidxs.append(tidx)
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xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
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return tcls, tbox, indices, anch, tidxs, xywhn
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if len(preds) == 2: # eval
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p, proto, = preds
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else: # len(3) train
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_, proto, 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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masks = batch["masks"]
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targets, masks = targets.to(self.device), masks.to(self.device).float()
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bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
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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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lseg = torch.zeros(1, device=self.device)
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tcls, tbox, indices, anchors, tidxs, xywhn = 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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n = b.shape[0] # number of targets
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if n:
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pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, head.nc, nm), 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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# Mask regression
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if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
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masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
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marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
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mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
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for bi in b.unique():
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j = b == bi # matching index
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if self.args.overlap_mask:
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mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
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else:
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mask_gti = masks[tidxs[i]][j]
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lseg += single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
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else:
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lseg += (proto * 0).sum()
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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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lseg *= self.args.box / bs
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loss = lbox + lobj + lcls + lseg
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return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
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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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keys = [f"{prefix}/{x}" for x in self.loss_names]
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return dict(zip(keys, loss_items)) if loss_items is not None else keys
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def progress_string(self):
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return ('\n' + '%11s' * 7) % \
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('Epoch', 'GPU_mem', *self.loss_names, 'Size')
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def plot_training_samples(self, batch, ni):
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images = batch["img"]
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masks = batch["masks"]
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cls = batch["cls"].squeeze(-1)
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bboxes = batch["bboxes"]
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paths = batch["im_file"]
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batch_idx = batch["batch_idx"]
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plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f"train_batch{ni}.jpg")
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def plot_metrics(self):
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plot_results(file=self.csv, segment=True) # save results.png
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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-seg.yaml"
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cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
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trainer = SegmentationTrainer(cfg)
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trainer.train()
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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/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 imgsz=640
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TODO:
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Direct cli support, i.e, yolov8 classify_train args.epochs 10
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"""
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train()
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