Traceback (most recent call last):


 "test.py", line 33, in <module>

ret = client(query)


 "/home/tianlun/tools/ERNIE/ERNIE-develop/ernie/service/client.py", line 79, in __call__

ret, = super(ErnieClient, self).__call__(sen_ids, token_type_ids)


 "/home/tianlun/tools/ERNIE/ERNIE-develop/propeller/service/client.py", line 102, in __call__

raise RuntimeError('Client call failed')
RuntimeError: Client call failed

Traceback (most recent call last):


 "train.py", line 325, in <module>

main(args)


 "train.py", line 200, in main

train(segmentation_module, iterator_train, optimizers, history, epoch, args)


 "train.py", line 37, in train

loss, acc = segmentation_module(batch_data)


 "/scratch/olga/semantic-segmentation-pytorch/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/scratch/olga/SEM_SEG_ZIKUN/cars/code/image2image_models/semantic_seg_colorLabels/models/models.py", line 34, in forward

(pred, pred_deepsup) = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))


 "/scratch/olga/semantic-segmentation-pytorch/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/scratch/olga/SEM_SEG_ZIKUN/cars/code/image2image_models/semantic_seg_colorLabels/models/models.py", line 255, in forward

x = self.relu1(self.bn1(self.conv1(x)))


 "/scratch/olga/semantic-segmentation-pytorch/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__

result = self.forward(*input, **kwargs)


 "/scratch/olga/semantic-segmentation-pytorch/env/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 345, in forward

return self.conv2d_forward(input, self.weight)


 "/scratch/olga/semantic-segmentation-pytorch/env/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 342, in conv2d_forward

self.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same
