移动到GPU时,张量类型不匹配

Question

尝试将我的网络和张量移动到GPU时,我收到以下错误.我已经检查过网络参数是否已移动到GPU并检查每个批次的张量并移动它们(如果它们尚未在GPU上).但我仍然得到这个问题,说张量类型不匹配 - 一个是a torch.cuda.FloatTensor,另一个是torch.FloatTensor？有人能告诉我我做错了什么吗？谢谢.

我的代码:

class Train():
  def __init__(self, network, training, address):
    self.network    = network
    self.address    = address
    self.batch_size = training['batch_size']
    self.iterations = training['iterations']
    self.samples    = training['samples']
    self.data       = training['data']
    self.lr         = training['lr']
    self.noisy_lr   = training['nlr']
    self.cuda       = training['cuda']
    self.save       = training['save']
    self.scale      = training['scale']
    self.limit      = training['limit']
    self.replace    = training['strategy']
    self.optimizer  = torch.optim.Adam(self.network.parameters(), lr=self.lr)

  def tensor_to_Variable(self, t):
    if next(self.network.parameters()).is_cuda and not t.is_cuda:
        t = t.cuda()

    return Variable(t)

  def train(self):
    if self.cuda:
        self.network.cuda()
    dh = DataHandler(self.data)
    loss_fn = torch.nn.MSELoss()
    losses    = []
    validate  = []
    val_size  = 100
    val_diff  = 1
    total_val = float(val_size * self.batch_size)
    hypos     = []
    labels    = []

    # training loop
    for i in range(self.iterations):
        x, y = dh.get_batch(self.batch_size)
        x = self.tensor_to_Variable(x)
        y = self.tensor_to_Variable(y)

        self.optimizer.zero_grad()
        hypo = self.network(x)
        loss = loss_fn(hypo, y)
        loss.backward()
        self.optimizer.step()


class Feedforward(nn.Module):
   def __init__(self, topology):
    super(Feedforward, self).__init__()
    self.input_dim     = topology['features']
    self.num_hidden    = topology['hidden_layers']
    self.hidden_dim    = topology['hidden_dim']
    self.output_dim    = topology['output_dim']
    self.input_layer   = nn.Linear(self.input_dim, self.hidden_dim)
    self.hidden_layer  = nn.Linear(self.hidden_dim, self.hidden_dim)
    self.output_layer  = nn.Linear(self.hidden_dim, self.output_dim)
    self.dropout_layer = nn.Dropout(p=0.2)


def forward(self, x):
    batch_size = x.size()[0]
    feat_size  = x.size()[1]
    input_size = batch_size * feat_size

    self.input_layer = nn.Linear(input_size, self.hidden_dim)
    hidden = self.input_layer(x.view(1, input_size)).clamp(min=0)

    for _ in range(self.num_hidden):
        hidden = self.dropout_layer(F.relu(self.hidden_layer(hidden)))

    output_size = batch_size * self.output_dim
    self.output_layer = nn.Linear(self.hidden_dim, output_size)
    return self.output_layer(hidden).view(output_size)

错误:

Traceback (most recent call last):
  File "/media/project/train.py", line 78, in train
    hypo = self.network(x)

 * (torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (float beta, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (float beta, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
 * (float beta, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
      didn't match because some of the arguments have invalid types: (int, int, torch.cuda.FloatTensor, torch.FloatTensor)
 * (float beta, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
      didn't match because some of the arguments have invalid types: (int, int, torch.cuda.FloatTensor, torch.FloatTensor)

堆栈跟踪:

Traceback (most recent call last):
File "smpl.py", line 90, in <module>
main()
File "smpl.py", line 80, in main
trainer.train()
File "/media/mpl/temp/train.py", line 82, in train
hypo = self.network(x)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 206, in call
result = self.forward(input, **kwargs)
File "model/network.py", line 35, in forward
hidden = self.input_layer(x.view(1, input_size)).clamp(min=0)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 206, in call
result = self.forward(input, *kwargs)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/linear.py", line 54, in forward
return self.backend.Linear()(input, self.weight, self.bias)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/linear.py", line 10, in forward
output.addmm(0, 1, input, weight.t())
TypeError: addmm_ received an invalid combination of arguments - got (int, int, torch.cuda.FloatTensor, torch.FloatTensor), but expected one of: (torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
(torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2) (float beta, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
(float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2) (float beta, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
(float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2) (float beta, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
didn't match because some of the arguments have invalid types: (int, int, torch.cuda.FloatTensor, torch.FloatTensor)
* (float beta, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
didn't match because some of the arguments have invalid types: (int, int, torch.cuda.FloatTensor, torch.FloatTensor

Answer 1

发生这种情况是因为你重新初始化 self.input_layer你的forward()函数.

该调用self.network.cuda()将所有模型参数移动到cuda中.这意味着在创建FeedForward对象时初始化的任何和所有图层都将移动到cuda内存中.但是当你重新初始化 self.input_layer你的forward()函数时,你在cpu而不是gpu中初始化该层的参数.同样如此self.output_layer.