RNN的前向传播函数_result = _vf.lstm(input, hx, self._flat_weights, s

程序：

import torch as t
from torch import nn
from torch.autograd import Variable as V
 
t.manual_seed(1000)
 
# 输入 batch_size=3,序列长度都为2，序列中每个元素占4维
 
input=V(t.randn(2,3,4))
print(input)
 
# lstm 输入向量4维，3个隐藏元，1层
 
lstm=nn.LSTM(4,3,1)
 
# 初始状态：1层，batch_size=3,3个隐层元
 
h0=V(t.randn(1,3,3))
c0=V(t.randn(1,3,3))
 
out,(hn,cn)=lstm((input,h0),c0)
out,(hn,cn)=lstm(input,(h0,c0))
print(out)
print("hn=\n",hn)
print("cn=\n",cn)
 
out,hn=lstm(input,(h0,c0))
print(out)
print("hn=\n",hn)

这样写，out,(hn,cn)=lstm((input,h0),c0) 会报错：

/home/wangbin/anaconda3/envs/deep_learning/bin/python3.7 /home/wangbin/anaconda3/envs/deep_learning/project/main.py
Traceback (most recent call last):
  File "/home/wangbin/anaconda3/envs/deep_learning/project/main.py", line 21, in <module>
    out,(hn,cn)=lstm((input,h0),c0)
  File "/home/wangbin/anaconda3/envs/deep_learning/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/wangbin/anaconda3/envs/deep_learning/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 564, in forward
    max_batch_size = input.size(0) if self.batch_first else input.size(1)
AttributeError: 'tuple' object has no attribute 'size'
tensor([[[-0.5306, -1.1300, -0.6734, -0.7669],
         [-0.7029,  0.9896, -0.4482,  0.8927],
         [-0.6043,  1.0726,  1.0481,  1.0527]],
 
        [[-0.6424, -1.2234, -1.0794, -0.6037],
         [-0.7926, -0.1414, -1.0225, -0.0482],
         [ 0.6610, -0.8908,  1.4793, -0.3934]]])
 
Process finished with exit code 1

根据报错信息查看：

可以发现lstm的forward具有三个参数，self、input、hx，但是hx可以再分。

    def forward(self, input, hx=None):  # noqa: F811
        orig_input = input
        # xxx: isinstance check needs to be in conditional for TorchScript to compile
        if isinstance(orig_input, PackedSequence):
            input, batch_sizes, sorted_indices, unsorted_indices = input
            max_batch_size = batch_sizes[0]
            max_batch_size = int(max_batch_size)
        else:
            batch_sizes = None
            max_batch_size = input.size(0) if self.batch_first else input.size(1)
            sorted_indices = None
            unsorted_indices = None
 
        if hx is None:
            num_directions = 2 if self.bidirectional else 1
            zeros = torch.zeros(self.num_layers * num_directions,
                                max_batch_size, self.hidden_size,
                                dtype=input.dtype, device=input.device)
            hx = (zeros, zeros)
        else:
            # Each batch of the hidden state should match the input sequence that
            # the user believes he/she is passing in.
            hx = self.permute_hidden(hx, sorted_indices)
 
        self.check_forward_args(input, hx, batch_sizes)
        if batch_sizes is None:
            result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
                              self.dropout, self.training, self.bidirectional, self.batch_first)
        else:
            result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,
                              self.num_layers, self.dropout, self.training, self.bidirectional)
        output = result[0]
        hidden = result[1:]
        # xxx: isinstance check needs to be in conditional for TorchScript to compile
        if isinstance(orig_input, PackedSequence):
            output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
            return output_packed, self.permute_hidden(hidden, unsorted_indices)
        else:
            return output, self.permute_hidden(hidden, unsorted_indices)

如果这样写out,(hn,cn)=lstm(input,h0,c0) ，仍然报错：

/home/wangbin/anaconda3/envs/deep_learning/bin/python3.7 /home/wangbin/anaconda3/envs/deep_learning/project/main.py
tensor([[[-0.5306, -1.1300, -0.6734, -0.7669],
         [-0.7029,  0.9896, -0.4482,  0.8927],
         [-0.6043,  1.0726,  1.0481,  1.0527]],
 
        [[-0.6424, -1.2234, -1.0794, -0.6037],
         [-0.7926, -0.1414, -1.0225, -0.0482],
         [ 0.6610, -0.8908,  1.4793, -0.3934]]])
Traceback (most recent call last):
  File "/home/wangbin/anaconda3/envs/deep_learning/project/main.py", line 21, in <module>
    out,(hn,cn)=lstm(input,h0,c0)
  File "/home/wangbin/anaconda3/envs/deep_learning/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
TypeError: forward() takes from 2 to 3 positional arguments but 4 were given
 
Process finished with exit code 1

错误原因是self是人家的默认参数，虽然实参里面没写，但是形参里面已经存在，是默认传递的。