I wonder what is the right way to use torchtext for inference.
Let's assume I've trained the model and dump all Fields with built vocabularies. It seems the next step is to use torchtext.data.Example to load one single example. Somehow I should numeralize it by using loaded Fields and create an Iterator.
I would appreciate any simple examples of using torchtext for inference.
For a trained model and vocabulary (which is part of the text field , you don't have to save the whole class) :
def read_vocab(path):
#read vocabulary pkl
import pickle
pkl_file = open(path, 'rb')
vocab = pickle.load(pkl_file)
pkl_file.close()
return vocab
def load_model_and_vocab():
import torch
import os.path
my_path = os.path.abspath(os.path.dirname(__file__))
vocab_path = os.path.join(my_path, vocab_file)
weights_path = os.path.join(my_path, WEIGHTS)
vocab = read_vocab(vocab_path)
model = classifier(vocab_size=len(vocab))
model.load_state_dict(torch.load(weights_path))
model.eval()
return model, vocab
def predict(model, vocab, sentence):
tokenized = [w.text.lower() for w in nlp(sentence)] # tokenize the sentence
indexed = [vocab.stoi[t] for t in tokenized] # convert to integer sequence
length = [len(indexed)] # compute no. of words
tensor = torch.LongTensor(indexed).to('cpu') # convert to tensor
tensor = tensor.unsqueeze(1).T # reshape in form of batch,no. of words
length_tensor = torch.LongTensor(length) # convert to tensor
prediction = model(tensor, length_tensor) # prediction
return round(1-prediction.item())
"classifier" is the class I defined for my model.
For saving the vocabulary pkl :
def save_vocab(vocab):
import pickle
output = open('vocab.pkl', 'wb')
pickle.dump(vocab, output)
output.close()
And for saving the model after training you can use :
torch.save(model.state_dict(), 'saved_weights.pt')
Tell me if it worked for you!
