CODEWITHSUNDEEP
pythonnlppytorchtorchtext
Bryan
Bryan

Reputation: 1519

How to make prediction from train Pytorch and PytorchText model?

General speaking, after I have successfully trained a text RNN model with Pytorch, using PytorchText to leverage data loading on an origin source, I would like to test with other data sets (a sort of blink test) that are from different sources but the same text format.

First I defined a class to handle the data loading.

class Dataset(object):
    def __init__(self, config):
        # init what I need

    def load_data(self, df: pd.DataFrame, *args):
        # implementation below
        # Data format like `(LABEL, TEXT)`

    def load_data_but_error(self, df: pd.DataFrame):
        # implementation below
        # Data format like `(TEXT)`

Here is the detail of load_data which I load data that trained successfully. 

TEXT = data.Field(sequential=True, tokenize=tokenizer, lower=True, fix_length=self.config.max_sen_len)
LABEL = data.Field(sequential=False, use_vocab=False)

datafields = [(label_col, LABEL), (data_col, TEXT)]

# split my data to train/test
train_df, test_df = train_test_split(df, test_size=0.33, random_state=random_state)

train_examples = [data.Example.fromlist(i, datafields) for i in train_df.values.tolist()]
train_data = data.Dataset(train_examples, datafields)

# split train to train/val
train_data, val_data = train_data.split(split_ratio=0.8)

# build vocab
TEXT.build_vocab(train_data, vectors=Vectors(w2v_file))
self.word_embeddings = TEXT.vocab.vectors
self.vocab = TEXT.vocab

test_examples = [data.Example.fromlist(i, datafields) for i in test_df.values.tolist()]
test_data = data.Dataset(test_examples, datafields)

self.train_iterator = data.BucketIterator(
            (train_data),
            batch_size=self.config.batch_size,
            sort_key=lambda x: len(x.title),
            repeat=False,
            shuffle=True)

self.val_iterator, self.test_iterator = data.BucketIterator.splits(
    (val_data, test_data),
    batch_size=self.config.batch_size,
    sort_key=lambda x: len(x.title),
    repeat=False,
    shuffle=False)

Next is my code (load_data_but_error) to load others source but causing error

TEXT = data.Field(sequential=True, tokenize=tokenizer, lower=True, fix_length=self.config.max_sen_len)
datafields = [('title', TEXT)]

examples = [data.Example.fromlist(i, datafields) for i in df.values.tolist()]
blink_test = data.Dataset(examples, datafields)

self.blink_test = data.BucketIterator(
    (blink_test),
    batch_size=self.config.batch_size,
    sort_key=lambda x: len(x.title),
    repeat=False,
    shuffle=True)

When I was executing code, I had an error AttributeError: 'Field' object has no attribute 'vocab' which has a question at here but it doesn't like my situation as here I had vocab from load_data and I want to use it for blink tests.

My question is what the correct way to load and feed new data with a trained PyTorch model for testing current model is?

Upvotes: 0

Views: 1508

Answers (2)

Anant Mittal
Anant Mittal

Reputation: 2123

Not really sure, but considering you have re-defined TEXT, you will have to explicitly create the vocab for your Field TEXT again. This can be done as follows:

TEXT.build_vocab(examples, min_freq = 2)

This particular statement adds the word from your data to the vocab only if it occurs at least two times in your data-set examples, you can change it as per your requirement.

You can read about build_vocab method at https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.Field.build_vocab.

Upvotes: 0

Bryan
Bryan

Reputation: 1519

What I need are

  1. to keep TEXT in load_data and reuse in load_data_but_error by assigning to class variables
  2. add train=True to object data.BucketIterator on load_data_but_error function

Upvotes: 1

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