import hydra
import torch
import torch.nn as nn
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.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
# BaseTrainer python usage
class SegmentationTrainer(DetectionTrainer):
def load_model(self, model_cfg=None, weights=None, verbose=True):
model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
if weights:
model.load(weights, verbose)
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,
logger=self.console,
args=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:
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"
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
trainer = SegmentationTrainer(cfg)
trainer.train()
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640
TODO:
Direct cli support, i.e, yolov8 classify_train args.epochs 10
"""
train()