Integration of v8 segmentation (#107)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
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@ -576,11 +576,11 @@ class Detections:
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class Proto(nn.Module):
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# YOLOv5 mask Proto module for segmentation models
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# YOLOv8 mask Proto module for segmentation models
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def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
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super().__init__()
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self.cv1 = Conv(c1, c_, k=3)
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self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
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self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True) # nn.Upsample(scale_factor=2, mode='nearest')
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self.cv2 = Conv(c_, c_, k=3)
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self.cv3 = Conv(c_, c2)
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@ -628,16 +628,16 @@ class Detect(nn.Module):
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shape = x[0].shape # BCHW
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for i in range(self.nl):
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x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
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box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
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if self.training:
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return x, box, cls
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return x
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elif self.dynamic or self.shape != shape:
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self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
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self.shape = shape
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box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
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dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
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y = torch.cat((dbox, cls.sigmoid()), 1)
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return y if self.export else (y, (x, box, cls))
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return y if self.export else (y, x)
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def bias_init(self):
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# Initialize Detect() biases, WARNING: requires stride availability
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@ -651,19 +651,27 @@ class Detect(nn.Module):
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class Segment(Detect):
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# YOLOv5 Segment head for segmentation models
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def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=()):
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super().__init__(nc, anchors, ch)
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def __init__(self, nc=80, nm=32, npr=256, ch=()):
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super().__init__(nc, ch)
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self.nm = nm # number of masks
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self.npr = npr # number of protos
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self.no = 5 + nc + self.nm # number of outputs per anchor
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self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
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self.proto = Proto(ch[0], self.npr, self.nm) # protos
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self.detect = Detect.forward
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c4 = max(ch[0] // 4, self.nm)
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self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
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def forward(self, x):
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p = self.proto(x[0])
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mc = [] # mask coefficient
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for i in range(self.nl):
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mc.append(self.cv4[i](x[i]))
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mc = torch.cat([mi.view(p.shape[0], self.nm, -1) for mi in mc], 2)
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x = self.detect(self, x)
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return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
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if self.training:
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return x, mc, p
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return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
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class Classify(nn.Module):
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@ -101,7 +101,7 @@ class DetectionModel(BaseModel):
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if isinstance(m, (Detect, Segment)):
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s = 256 # 2x min stride
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m.inplace = self.inplace
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forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, Detect)) else self.forward(x)
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forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
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m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
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self.stride = m.stride
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m.bias_init() # only run once
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@ -163,8 +163,8 @@ class DetectionModel(BaseModel):
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class SegmentationModel(DetectionModel):
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# YOLOv5 segmentation model
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def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None):
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super().__init__(cfg, ch, nc)
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def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, verbose=True):
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super().__init__(cfg, ch, nc, verbose)
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class ClassificationModel(BaseModel):
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@ -300,7 +300,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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elif m in {Detect, Segment}:
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args.append([ch[x] for x in f])
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if m is Segment:
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args[3] = make_divisible(args[3] * gw, 8)
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args[2] = make_divisible(args[2] * gw, 8)
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else:
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c2 = ch[f]
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@ -0,0 +1 @@
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from . import v8
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@ -3,7 +3,7 @@
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# Task and Mode
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task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
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mode: "train" # choice=['train', 'val', 'infer']
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mode: "train" # choice=['train', 'val', 'predict']
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# Train settings -------------------------------------------------------------------------------------------------------
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model: null # i.e. yolov5s.pt, yolo.yaml
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@ -86,7 +86,8 @@ class TaskAlignedAssigner(nn.Module):
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if self.n_max_boxes == 0:
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device = gt_bboxes.device
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return (torch.full_like(pd_scores[..., 0], self.bg_idx).to(device), torch.zeros_like(pd_bboxes).to(device),
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torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device))
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torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device),
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torch.zeros_like(pd_scores[..., 0]).to(device))
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mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points,
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mask_gt)
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@ -103,7 +104,7 @@ class TaskAlignedAssigner(nn.Module):
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norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
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target_scores = target_scores * norm_align_metric
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return target_labels, target_bboxes, target_scores, fg_mask.bool()
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return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
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def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
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# get anchor_align metric, (b, max_num_obj, h*w)
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@ -146,9 +147,6 @@ class TaskAlignedAssigner(nn.Module):
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# (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
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is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2)
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# filter invalid bboxes
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# assigned topk should be unique, this is for dealing with empty labels
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# since empty labels will generate index `0` through `F.one_hot`
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# NOTE: but what if the topk_idxs include `0`?
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is_in_topk = torch.where(is_in_topk > 1, 0, is_in_topk)
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return is_in_topk.to(metrics.dtype)
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@ -9,11 +9,10 @@ from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
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from ultralytics.yolo.utils import colorstr
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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.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, strip_optimizer
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from ultralytics.yolo.utils.torch_utils import de_parallel
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# BaseTrainer python usage
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@ -78,7 +77,8 @@ class DetectionTrainer(BaseTrainer):
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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) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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return ('\n' + '%11s' *
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(4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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def plot_training_samples(self, batch, ni):
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plot_images(images=batch["img"],
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@ -100,15 +100,13 @@ class Loss:
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device = next(model.parameters()).device # get model device
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h = model.args # hyperparameters
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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 = model.model[-1] # Detect() module
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self.bce = nn.BCEWithLogitsLoss(reduction='none')
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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.no = m.no
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self.reg_max = m.reg_max
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self.device = device
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self.use_dfl = m.reg_max > 1
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@ -141,12 +139,15 @@ class Loss:
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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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feats = preds[1] if isinstance(preds, tuple) else preds
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pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
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(self.reg_max * 4, self.nc), 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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batch_size = pred_scores.shape[0]
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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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@ -159,7 +160,7 @@ class Loss:
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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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_, 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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42
ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml
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42
ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml
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@ -0,0 +1,42 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 1.00 # model depth multiple
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width_multiple: 1.00 # layer channel multiple
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# YOLOv8.0l backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C2f, [128, True]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C2f, [256, True]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 6, C2f, [512, True]],
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[-1, 1, Conv, [512, 3, 2]], # 7-P5/32
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[-1, 3, C2f, [512, True]],
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[-1, 1, SPPF, [512, 5]], # 9
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]
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# YOLOv8.0l head
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head:
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[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C2f, [512]], # 13
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C2f, [256]], # 17 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P4
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[-1, 3, C2f, [512]], # 20 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 9], 1, Concat, [1]], # cat head P5
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[-1, 3, C2f, [512]], # 23 (P5/32-large)
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[[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5)
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]
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42
ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml
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42
ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml
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@ -0,0 +1,42 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.67 # model depth multiple
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width_multiple: 0.75 # layer channel multiple
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# YOLOv8.0m backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C2f, [128, True]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C2f, [256, True]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 6, C2f, [512, True]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C2f, [768, True]],
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[-1, 1, SPPF, [768, 5]], # 9
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]
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# YOLOv8.0m head
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head:
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[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C2f, [512]], # 13
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C2f, [256]], # 17 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P4
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[-1, 3, C2f, [512]], # 20 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 9], 1, Concat, [1]], # cat head P5
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[-1, 3, C2f, [768]], # 23 (P5/32-large)
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[[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5)
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]
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@ -4,9 +4,8 @@
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.25 # layer channel multiple
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anchors: [[16,19], [55,65], [178,192]]
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# YOLOv8n v0.0 backbone
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# YOLOv8.0n backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
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@ -21,7 +20,7 @@ backbone:
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[-1, 1, SPPF, [1024, 5]], # 9
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]
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# YOLOv8n v0.0 head
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# YOLOv8.0n head
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head:
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[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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42
ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml
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42
ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml
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@ -0,0 +1,42 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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# YOLOv8.0s backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C2f, [128, True]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C2f, [256, True]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 6, C2f, [512, True]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C2f, [1024, True]],
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[-1, 1, SPPF, [1024, 5]], # 9
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]
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# YOLOv8.0s head
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head:
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[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C2f, [512]], # 13
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C2f, [256]], # 17 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P4
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[-1, 3, C2f, [512]], # 20 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 9], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C2f, [1024]], # 23 (P5/32-large)
|
||||
|
||||
[[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
42
ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml
Normal file
42
ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml
Normal file
@ -0,0 +1,42 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.00 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
|
||||
# YOLOv8.0x backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C2f, [128, True]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C2f, [256, True]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 6, C2f, [512, True]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C2f, [512, True]],
|
||||
[-1, 1, SPPF, [512, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv8.0x head
|
||||
head:
|
||||
[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C2f, [512]], # 13
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C2f, [256]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C2f, [512]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 9], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C2f, [512]], # 23 (P5/32-large)
|
||||
|
||||
[[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
@ -12,17 +12,14 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
masks = []
|
||||
if len(preds) == 2: # eval
|
||||
p, proto, = preds
|
||||
else: # len(3) train
|
||||
p, proto, _ = preds
|
||||
# TODO: filter by classes
|
||||
p = ops.non_max_suppression(p,
|
||||
p = ops.non_max_suppression(preds[0],
|
||||
self.args.conf_thres,
|
||||
self.args.iou_thres,
|
||||
agnostic=self.args.agnostic_nms,
|
||||
max_det=self.args.max_det,
|
||||
nm=32)
|
||||
proto = preds[1][-1]
|
||||
for i, pred in enumerate(p):
|
||||
shape = orig_img[i].shape if self.webcam else orig_img.shape
|
||||
if not len(pred):
|
||||
|
||||
@ -6,9 +6,10 @@ import torch.nn.functional as F
|
||||
from ultralytics.nn.tasks import SegmentationModel
|
||||
from ultralytics.yolo import v8
|
||||
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
||||
from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE
|
||||
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy
|
||||
from ultralytics.yolo.utils.loss import BboxLoss
|
||||
from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
|
||||
from ultralytics.yolo.utils.plotting import plot_images, plot_results
|
||||
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
|
||||
from ultralytics.yolo.utils.torch_utils import de_parallel
|
||||
|
||||
from ..detect import DetectionTrainer
|
||||
@ -31,188 +32,9 @@ class SegmentationTrainer(DetectionTrainer):
|
||||
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 single_mask_loss(gt_mask, pred, proto, xyxy, area):
|
||||
# Mask loss for one image
|
||||
pred_mask = (pred @ proto.view(head.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)
|
||||
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
|
||||
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
|
||||
|
||||
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, tidxs, xywhn = [], [], [], [], [], []
|
||||
gain = torch.ones(8, device=self.device) # normalized to gridspace gain
|
||||
ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1,
|
||||
nt) # same as .repeat_interleave(nt)
|
||||
if self.args.overlap_mask:
|
||||
batch = p[0].shape[0]
|
||||
ti = []
|
||||
for i in range(batch):
|
||||
num = (targets[:, 0] == i).sum() # find number of targets of each image
|
||||
ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
|
||||
ti = torch.cat(ti, 1) # (na, nt)
|
||||
else:
|
||||
ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
|
||||
targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., 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, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
|
||||
(a, tidx), (b, c) = at.long().T, 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
|
||||
tidxs.append(tidx)
|
||||
xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
|
||||
|
||||
return tcls, tbox, indices, anch, tidxs, xywhn
|
||||
|
||||
if len(preds) == 2: # eval
|
||||
p, proto, = preds
|
||||
else: # len(3) train
|
||||
_, proto, p = preds
|
||||
|
||||
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
|
||||
masks = batch["masks"]
|
||||
targets, masks = targets.to(self.device), masks.to(self.device).float()
|
||||
|
||||
bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
|
||||
lcls = torch.zeros(1, device=self.device)
|
||||
lbox = torch.zeros(1, device=self.device)
|
||||
lobj = torch.zeros(1, device=self.device)
|
||||
lseg = torch.zeros(1, device=self.device)
|
||||
tcls, tbox, indices, anchors, tidxs, xywhn = 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
|
||||
|
||||
n = b.shape[0] # number of targets
|
||||
if n:
|
||||
pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, head.nc, nm), 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
|
||||
|
||||
# Mask regression
|
||||
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
|
||||
masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
|
||||
marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
|
||||
mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
|
||||
for bi in b.unique():
|
||||
j = b == bi # matching index
|
||||
if self.args.overlap_mask:
|
||||
mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
|
||||
else:
|
||||
mask_gti = masks[tidxs[i]][j]
|
||||
lseg += single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
|
||||
else:
|
||||
lseg += (proto * 0).sum()
|
||||
|
||||
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
|
||||
lseg *= self.args.box / bs
|
||||
|
||||
loss = lbox + lobj + lcls + lseg
|
||||
return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
|
||||
|
||||
def label_loss_items(self, loss_items=None, prefix="train"):
|
||||
# We should just use named tensors here in future
|
||||
keys = [f"{prefix}/{x}" for x in self.loss_names]
|
||||
return dict(zip(keys, loss_items)) if loss_items is not None else keys
|
||||
|
||||
def progress_string(self):
|
||||
return ('\n' + '%11s' * 8) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
|
||||
if not hasattr(self, 'compute_loss'):
|
||||
self.compute_loss = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask)
|
||||
return self.compute_loss(preds, batch)
|
||||
|
||||
def plot_training_samples(self, batch, ni):
|
||||
images = batch["img"]
|
||||
@ -227,6 +49,129 @@ class SegmentationTrainer(DetectionTrainer):
|
||||
plot_results(file=self.csv, segment=True) # save results.png
|
||||
|
||||
|
||||
# Criterion class for computing training losses
|
||||
class SegLoss:
|
||||
|
||||
def __init__(self, model, overlap=True): # model must be de-paralleled
|
||||
|
||||
device = next(model.parameters()).device # get model device
|
||||
h = model.args # hyperparameters
|
||||
|
||||
m = model.model[-1] # Detect() module
|
||||
self.bce = nn.BCEWithLogitsLoss(reduction='none')
|
||||
self.hyp = h
|
||||
self.stride = m.stride # model strides
|
||||
self.nc = m.nc # number of classes
|
||||
self.no = m.no
|
||||
self.nm = m.nm # number of masks
|
||||
self.reg_max = m.reg_max
|
||||
self.overlap = overlap
|
||||
self.device = device
|
||||
|
||||
self.use_dfl = m.reg_max > 1
|
||||
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
|
||||
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
|
||||
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
|
||||
|
||||
def preprocess(self, targets, batch_size, scale_tensor):
|
||||
if targets.shape[0] == 0:
|
||||
out = torch.zeros(batch_size, 0, 5, device=self.device)
|
||||
else:
|
||||
i = targets[:, 0] # image index
|
||||
_, counts = i.unique(return_counts=True)
|
||||
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
|
||||
for j in range(batch_size):
|
||||
matches = i == j
|
||||
n = matches.sum()
|
||||
if n:
|
||||
out[j, :n] = targets[matches, 1:]
|
||||
out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
|
||||
return out
|
||||
|
||||
def bbox_decode(self, anchor_points, pred_dist):
|
||||
if self.use_dfl:
|
||||
b, a, c = pred_dist.shape # batch, anchors, channels
|
||||
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# 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)
|
||||
return dist2bbox(pred_dist, anchor_points, xywh=False)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
loss = torch.zeros(4, device=self.device) # box, cls, dfl
|
||||
feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
|
||||
batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
|
||||
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
|
||||
(self.reg_max * 4, self.nc), 1)
|
||||
|
||||
# b, grids, ..
|
||||
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
|
||||
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
|
||||
pred_masks = pred_masks.permute(0, 2, 1).contiguous()
|
||||
|
||||
dtype = pred_scores.dtype
|
||||
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
|
||||
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
|
||||
|
||||
# targets
|
||||
batch_idx = batch["batch_idx"].view(-1, 1)
|
||||
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
|
||||
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
|
||||
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
|
||||
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
|
||||
|
||||
masks = batch["masks"].to(self.device).float()
|
||||
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
|
||||
masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
|
||||
|
||||
# pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
|
||||
_, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
|
||||
pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
|
||||
anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
|
||||
|
||||
target_scores_sum = target_scores.sum()
|
||||
|
||||
# cls loss
|
||||
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
|
||||
loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
|
||||
|
||||
# bbox loss
|
||||
if fg_mask.sum():
|
||||
loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
|
||||
target_scores, target_scores_sum, fg_mask)
|
||||
for i in range(batch_size):
|
||||
if fg_mask[i].sum():
|
||||
mask_idx = target_gt_idx[i][fg_mask[i]] + 1
|
||||
if self.overlap:
|
||||
gt_mask = torch.where(masks[[i]] == mask_idx.view(-1, 1, 1), 1.0, 0.0)
|
||||
else:
|
||||
gt_mask = masks[batch_idx == i][mask_idx]
|
||||
xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
|
||||
marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
|
||||
mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
|
||||
loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy,
|
||||
marea) # seg loss
|
||||
# WARNING: Uncomment lines below in case of Multi-GPU DDP unused gradient errors
|
||||
# else:
|
||||
# loss[1] += proto.sum() * 0
|
||||
# else:
|
||||
# loss[1] += proto.sum() * 0
|
||||
|
||||
loss[0] *= 7.5 # box gain
|
||||
loss[1] *= 7.5 / batch_size # seg gain
|
||||
loss[2] *= 0.5 # cls gain
|
||||
loss[3] *= 1.5 # dfl gain
|
||||
|
||||
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
|
||||
|
||||
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
|
||||
# Mask loss for one image
|
||||
pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n, 32) @ (32,80,80) -> (n,80,80)
|
||||
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
|
||||
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
|
||||
|
||||
|
||||
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
|
||||
def train(cfg):
|
||||
cfg.model = cfg.model or "models/yolov8n-seg.yaml"
|
||||
|
||||
@ -66,7 +66,7 @@ class SegmentationValidator(DetectionValidator):
|
||||
agnostic=self.args.single_cls,
|
||||
max_det=self.args.max_det,
|
||||
nm=self.nm)
|
||||
return p, preds[1], preds[2]
|
||||
return p, preds[1][-1]
|
||||
|
||||
def update_metrics(self, preds, batch):
|
||||
# Metrics
|
||||
|
||||
Reference in New Issue
Block a user