Using a field representing real numbers with the iterator

I am trying to learn a regressor on text data and I use torchtext in all my other tasks but I see a problem in using it for this use case.

I define the field for targets as follows:

TARGETS = data.Field(
            sequential=False, tensor_type=torch.DoubleTensor, batch_first=True)
self.fields = [('targets', TARGETS), ('text', TEXT)]
self.train, self.val, self.test = data.TabularDataset.splits(
            path=self.path,
            train=self.train_suffix,
            validation=self.val_suffix,
            test=self.test_suffix,
            format=formatting,
            fields=self.fields)
TEXT.build_vocab(self.train)

I have a file that contains tab separate <values>\t<text>

When I make iterators out of it,

train_iter, val_iter, test_iter = data.Iterator.splits(
                (self.train, self.val, self.test),
                batch_sizes=(self.batch_size, self.test_batch_size,
                             self.test_batch_size),
                sort_key=lambda x: len(x.text),
                shuffle=True)
print(next(iter(train_iter)))

it gives me an error when getting the next batch:

AttributeError: ‘Field’ object has no attribute ‘vocab’

I know this is because I didn’t run .build_vocab for the TARGETS field. But why do I really need to do this? What if I just want to get real numbers and compute losses on them?

Any workaround is appreciated. If I am doing something wrong, please let me know too.

[nelson-liu]

Thanks for the issue.

Torchtext needs to convert the string number to an int or float somewhere down the line and it currently doesn’t do this. A quick fix would be to manually add a pipeline to the postprocessing argument that converts everything in the TARGETS field to int. With a slightly modified version of your code:

Edit: just noticed that your example uses doubles. changed my code accordingly

(tab separated file)

$ cat test.txt
1.1   test string
1.2   test string2
1.3   test string3

The following works on my machine in the meantime while we patch this:

In [1]: import torch

In [2]: from torchtext import data

In [3]: TEXT = data.Field(batch_first=True)

In [4]: TARGETS = data.Field(sequential=False, tensor_type=torch.DoubleTensor, batch_first=True, use_vocab=False, postprocessing=data.Pipeline(lambda x: float(x)))

In [5]: fields = [('targets', TARGETS), ('text', TEXT)]

In [6]: dataset = data.TabularDataset(path="test.txt", format="tsv", fields=fields)

In [7]: TEXT.build_vocab(dataset)

In [8]: train_iter = data.Iterator(dataset, batch_size=1, sort_key=lambda x: len(x.text), shuffle=True)

In [9]: batch = next(iter(train_iter))

In [10]: batch.targets
Out[10]: 
Variable containing:
 1.3000
[torch.cuda.DoubleTensor of size 1 (GPU 0)]

Hope that helps.

[greed2411]

for me the above one, didn’t work. if anyone is still wondering, change postprocessing=data.Pipeline(lambda x: float(x)) to preprocessing= lambda x: float(x) that made it work for me (pytorch 0.4 and torchtext 0.2.3)