operands could not be broadcast together with shapes (8,256) (4,256,256)

hello，When I run,：python train.py --dataset pcontext --model deeplab --aux --backbone resnest200
I found the following error：

  File "/dahuafs/userdata/229288/00_deeplearning/01_pytorch/segEncoding/experiments/segmentation/train.py", line 238, in validation
    correct, labeled, inter, union = eval_batch(self.model, image, target)
  File "/dahuafs/userdata/229288/00_deeplearning/01_pytorch/segEncoding/experiments/segmentation/train.py", line 229, in eval_batch
    inter, union = utils.batch_intersection_union(pred.data, target, self.nclass)
  File "/dahuafs/userdata/229288/00_deeplearning/anaconda3/envs/scseg/lib/python3.7/site-packages/torch_encoding-1.2.2b20201023-py3.7-linux-x86_64.egg/encoding/utils/metrics.py", line 122, in batch_intersection_union
    predict = predict * (target > 0).astype(predict.dtype)
ValueError: operands could not be broadcast together with shapes (8,256) (4,256,256) 

        def eval_batch(model, image, target):
            outputs = model(image)
            outputs = gather(outputs, 0, dim=0)
            pred = outputs[0]
            target = target.cuda()
            correct, labeled = utils.batch_pix_accuracy(pred.data, target)
            inter, union = utils.batch_intersection_union(pred.data, target, self.nclass)
            return correct, labeled, inter, union

Is there something wrong with this code？

Start with the reproduced command and inspect validation/eval_batch in experiments/segmentation/train.py, then follow batch_intersection_union in encoding/utils/metrics.py. Compare the prediction and target shapes at the failing call and determine why validation passes incompatible inputs; done means the pcontext run completes this metric step without the broadcasting error.