Multi-gpu training about graphcmr HOT 3 CLOSED

        # create GraphCNN
        self.graph_cnn = GraphCNN(self.mesh.adjmat,
                           self.mesh.ref_vertices.t(),
                           num_channels=self.options.num_channels,
                           num_layers=self.options.num_layers)
        self.graph_cnn = torch.nn.DataParallel(self.graph_cnn, device_ids=[0, 1]).to(self.device)
        # SMPL Parameter regressor
        self.smpl_param_regressor = SMPLParamRegressor()
        self.smpl_param_regressor = torch.nn.DataParallel(self.smpl_param_regressor, device_ids=[0, 1]).to(self.device)

Traceback (most recent call last):
  File "train.py", line 21, in <module>
    trainer.train()
  File "/home/GraphCMR/utils/base_trainer.py", line 65, in train
    out = self.train_step(batch)
  File "/home/GraphCMR/train/trainer.py", line 144, in train_step
    pred_vertices_sub, pred_camera = self.graph_cnn(images)
  File "/home/anaconda3/envs/gcnn/lib/python2.7/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/anaconda3/envs/gcnn/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 143, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/anaconda3/envs/gcnn/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/anaconda3/envs/gcnn/lib/python2.7/site-packages/torch/nn/parallel/parallel_apply.py", line 83, in parallel_apply
    raise output
RuntimeError: arguments are located on different GPUs at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:479