python - DataLoader 问题：RNN 模块权重不是单个连续内存块的一部分

Question

我正在尝试为时间序列数据构建 LSTM 模型。数据集的详细信息：
输入数据是时间序列，有 800 个主题，每个主题都有一个 60 行 200 列的二维数组数据。我将整个数据加载为形状的张量，[800,60,200]分类问题的标签是形状的[800,1]。我使用以下代码制作了一个数据字典：

class DataCurate(Dataset):
    def __init__(self, l1,l2, transform=None):
        self.l1=l1
        self.l2=l2
        self.transform=transform
    def __len__(self):
        return len(self.l1)
    def __getitem__(self, index):
        array=self.l1[index]
        label=self.l2[index]
        sample = {'time_data': array, 'labels': label}
        return sample

数据和标签在变量 x 和 y 中。我调用 data=Datacurate(x,y)

后来我使用代码为分类问题构建了 LSTM 模型：

class RNNModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
    super(RNNModel, self).__init__()
    self.hidden_size = hidden_size
    self.num_layers = num_layers
    self.lstm = nn.LSTM(input_size, hidden_size, num_layers,batch_first=True) 
    self.linear = nn.Linear(hidden_size, num_layers, bias=True) 
    self.softmax = nn.LogSoftmax()

    def forward(self, x)
        self.lstm.flatten_parameters()
        out_packed, state = self.lstm(x)  # RNN
        print("lstm output size: {out.size()}"+str(out_packed.size()))
        out = self.linear(out_packed[-1])  # linear transform
    print("linear output size {out.size()} "+str(out.size()))
    log_probs = F.log_softmax(out,dim=1)
    print("softmax output size {log_probs.size()}"+str(log_probs.size()))
        return log_probs

当我运行训练脚本时，这给了我一个错误：

UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
  out_packed, state = self.lstm(x)  # RNN
Traceback (most recent call last):
  File "main_2.py", line 100, in <module>
    output = model(train_inputs.transpose(0,1))
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/media/iab/disk_a/meghal/test/quickdraw_tutorial_dataset_v1/pytorch_RNN_examples/model.py", line 26, in forward
    out_packed, state = self.lstm(x)  # RNN
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/rnn.py", line 192, in forward
    output, hidden = func(input, self.all_weights, hx, batch_sizes)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/_functions/rnn.py", line 324, in forward
    return func(input, *fargs, **fkwargs)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/_functions/rnn.py", line 288, in forward
    dropout_ts)
RuntimeError: param_from.type() == param_to.type() ASSERT FAILED at /pytorch/aten/src/ATen/native/cudnn/RNN.cpp:491, please report a bug to PyTorch. parameter types mismatch

我不知道这意味着什么以及如何解决它。我对 LSTM 完全陌生。

标签： pythonlstmpytorchrnn