How to create a custom collate_fn function for batch with different number of elements

peony: <p>I am working on action recognition where my inputs are frames of a video. Each video have different number of frames and I wish to create my custom collate_fn function to deal with different number of frames each video have.</p>
<p>Here is my model:</p>
<pre><code class="lang-auto">
class MLP(nn.Module):
  
  def __init__(self, num_classes, rnn_layers, hidden_size, fc_size):
    super(MLP, self).__init__()
    self.hidden_size = hidden_size
    self.num_classes = num_classes
    self.fc_size = fc_size
    self.apply(self._init_weights)

    self.fc_pre= nn.Sequential(
                            nn.Linear(201, fc_size),
                            #nn.ReLU(),
                               #nn.Dropout(p=0.5),
                           # nn. Linear(750, 500),
                           # #nn.Dropout(p=0.5),
                           # #nn.ReLU(),
                           # nn.Linear(500, fc_size),
                           #nn.Dropout(p=0.5),
                           nn.ReLU())
    #Defines the number of features that define each element (time-stamp) of the input sequence
    self.rnn = nn.GRU(input_size = fc_size,
                hidden_size = hidden_size,
                num_layers = rnn_layers,
                batch_first = True)
    self.fc = nn.Linear(hidden_size, num_classes)

  
  #initialize hidden state for RNN  
  def init_hidden(self, num_layers, batch_size):
            return (torch.zeros(num_layers, batch_size, self.hidden_size).cuda(),
                    torch.zeros(num_layers, batch_size, self.hidden_size).cuda())
  
   #inital hidden state made of zeros
  def forward(self, inputs, hidden=None, steps=0):
        length = len(inputs)
        # print('inputs size')
        # print(inputs[3].size())
        #Input data: RNN should have 3 dimensions. (Batch Size, Sequence Length and Input Dimension (the number of expected features which is 201)
        fs = torch.zeros(inputs[0].size(0), length, self.rnn.input_size).cuda()
        
        for i in range(length):
            f = inputs[i]
            #flattens the tensor
            f = f.view(f.size(0), -1)
            
            #print(f.size())
            f = self.fc_pre(f)

            # stores the tensor f in the sha pe [fs.size(0), fs.size(2)] to each “row” in fs to prepare for learning the sequence in RNN
            fs[:, i, :] = f
            
            # print('fs')
            # print(fs.size())
       
        #outputs : batch size, seq legnth, hidden size
        outputs, hidden = self.rnn(fs, hidden)
        #print(outputs.size())
        
        #the training code applies crossentropyloss as criterion which also applies softmax to the output so I don't have to use softmax here
        outputs = self.fc(outputs)
        
        #print(outputs)
        #print(outputs.size())
        
        return outputs

  def _init_weights(self, module):
         if isinstance(module, nn.Linear):
             module.weight.data.uniform_(mean=0.0, std=1.0)
             if module.bias is not None:
                 module.bias.data.zero_()
</code></pre>
<p>I have tried to define my own collate_fn like this in my main:</p>
<pre><code class="lang-auto">def my_collate(batch):

    for classes, videos, frames in os.walk(traindir):  
        
        frames_list = pad_sequence(frames, batch_first=True, padding_value=0)
        return frames_list
</code></pre>
<p>but I get this error:</p>
<pre><code class="lang-auto">  return torch._C._nn.pad_sequence(sequences, batch_first, padding_value)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: received an empty list of sequences
</code></pre>
<p>Any insights or suggestions for me is appreciated. Thank you in advance</p>