# Ultralytics YOLO 🚀, GPL-3.0 license
from copy import copy
import torch
import torch.nn.functional as F
from ultralytics.nn.tasks import SegmentationModel
from ultralytics.yolo import v8
from ultralytics.yolo.utils import DEFAULT_CFG, RANK
from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.tal import make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel
from ultralytics.yolo.v8.detect.train import Loss
# BaseTrainer python usage
class SegmentationTrainer(v8.detect.DetectionTrainer):
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
if overrides is None:
overrides = {}
overrides['task'] = 'segment'
super().__init__(cfg, overrides, _callbacks)
def get_model(self, cfg=None, weights=None, verbose=True):
model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
if weights:
model.load(weights)
return model
def get_validator(self):
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
return v8.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
def criterion(self, preds, batch):
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']
masks = batch['masks']
cls = batch['cls'].squeeze(-1)
bboxes = batch['bboxes']
paths = batch['im_file']
batch_idx = batch['batch_idx']
plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f'train_batch{ni}.jpg')
def plot_metrics(self):
plot_results(file=self.csv, segment=True) # save results.png
# Criterion class for computing training losses
class SegLoss(Loss):
def __init__(self, model, overlap=True): # model must be de-paralleled
super().__init__(model)
self.nm = model.model[-1].nm # number of masks
self.overlap = overlap
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
try:
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)
except RuntimeError as e:
raise TypeError('ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n'
"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
"i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a "
"correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
# 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 = max(target_scores.sum(), 1)
# 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
if fg_mask.sum():
# bbox loss
loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
target_scores, target_scores_sum, fg_mask)
# masks loss
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]
for i in range(batch_size):
if fg_mask[i].sum():
mask_idx = target_gt_idx[i][fg_mask[i]]
if self.overlap:
gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
else:
gt_mask = masks[batch_idx.view(-1) == 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
# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
# WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.box / batch_size # seg gain
loss[2] *= self.hyp.cls # cls gain
loss[3] *= self.hyp.dfl # 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()
def train(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n-seg.pt'
data = cfg.data or 'coco128-seg.yaml' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = SegmentationTrainer(overrides=args)
trainer.train()
if __name__ == '__main__':
train()