yolov9-seg训练报错：RuntimeError: shape '[32, 65, -1]' is invalid for input of size 131712

[SXleader]

Logging results to runs\train\exp58
Starting training for 100 epochs...

  Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

0%| | 0/33 00:02
Traceback (most recent call last):
File "E:\study\yolov9-main\train.py", line 634, in
main(opt)
File "E:\study\yolov9-main\train.py", line 528, in main
train(opt.hyp, opt, device, callbacks)
File "E:\study\yolov9-main\train.py", line 304, in train
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
File "E:\study\yolov9-main\utils\loss_tal.py", line 178, in call
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
File "E:\study\yolov9-main\utils\loss_tal.py", line 178, in
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
RuntimeError: shape '[32, 65, -1]' is invalid for input of size 131712

在训练yolov9-seg时报错，其中train.py配置文件如下所示：
def parse_opt(known=False):
parser = argparse.ArgumentParser()
# parser.add_argument('--weights', type=str, default=ROOT / 'yolo.pt', help='initial weights path')
# parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
parser.add_argument('--weights', type=str, default='./yolov9-c-seg.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/segment/gelan-c-seg.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default=ROOT / './data/my_coco.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-high.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for autobatch')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=434, help='train, val image size (pixels)')

报错定位到了这个位置
    def __call__(self, p, targets, img=None, epoch=0):
    loss = torch.zeros(3, device=self.device)  # box, cls, dfl
    feats = p[1] if isinstance(p, tuple) else p[0]
    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)
 
 数据集为33张train，11张val，3通道tif文件格式，434*434大小，标签数据集为txt文件，有无大佬可以帮我解决报错