Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

the padded data points, as we’ve already discussed.
We can do better than that: We can pack our mini-batches using a collate function.
Collate Function
The collate function takes a list of tuples (sampled from a dataset using its
__getitem__()) and collates them into a batch that’s being returned by the data
loader. It gives you the ability to manipulate the sampled data points in any way
you want to make them into a mini-batch.
In our case, we’d like to get all sequences (the first item in every tuple) and pack
them. Besides, we can get all labels (the second item in every tuple) and make them
into a tensor that’s in the correct shape for our binary classification task:
Data Preparation
1 def pack_collate(batch):
2 X = [item[0] for item in batch]
3 y = [item[1] for item in batch]
4 X_pack = rnn_utils.pack_sequence(X, enforce_sorted=False)
5
6 return X_pack, torch.as_tensor(y).view(-1, 1)
Let’s see the function in action by creating a dummy batch of two elements and
applying the function to it:
# list of tuples returned by the dataset
dummy_batch = [train_var_data[0], train_var_data[1]]
dummy_x, dummy_y = pack_collate(dummy_batch)
dummy_x
666 | Chapter 8: Sequences