fastNLP/reproduction/Char-aware_NLM/test.py

import os
from collections import namedtuple

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from utilities import *


def to_var(x):
    if torch.cuda.is_available():
        x = x.cuda()
    return Variable(x)


def test(net, data, opt):
    net.eval()
 
    test_input = torch.from_numpy(data.test_input)
    test_label = torch.from_numpy(data.test_label)

    num_seq = test_input.size()[0] // opt.lstm_seq_len
    test_input = test_input[:num_seq*opt.lstm_seq_len, :]
    # [num_seq, seq_len, max_word_len+2]
    test_input = test_input.view(-1, opt.lstm_seq_len, opt.max_word_len+2)

    criterion = nn.CrossEntropyLoss()

    loss_list = []
    num_hits = 0
    total = 0
    iterations = test_input.size()[0] // opt.lstm_batch_size
    test_generator = batch_generator(test_input, opt.lstm_batch_size)
    label_generator = batch_generator(test_label, opt.lstm_batch_size*opt.lstm_seq_len)

    hidden = (to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)), 
              to_var(torch.zeros(2, opt.lstm_batch_size, opt.word_embed_dim)))
    
    add_loss = 0.0 
    for t in range(iterations):
        batch_input = test_generator.__next__ ()
        batch_label = label_generator.__next__()
        
        net.zero_grad()
        hidden = [state.detach() for state in hidden]
        test_output, hidden = net(to_var(batch_input), hidden)
        
        test_loss = criterion(test_output, to_var(batch_label)).data
        loss_list.append(test_loss)
        add_loss += test_loss

    print("Test Loss={0:.4f}".format(float(add_loss) / iterations))
    print("Test PPL={0:.4f}".format(float(np.exp(add_loss / iterations))))


#############################################################

if __name__ == "__main__":

    word_embed_dim = 300
    char_embedding_dim = 15

    if os.path.exists("cache/prep.pt") is False:
        print("Cannot find prep.pt")

    objetcs = torch.load("cache/prep.pt")

    word_dict = objetcs["word_dict"]
    char_dict = objetcs["char_dict"]
    reverse_word_dict = objetcs["reverse_word_dict"]
    max_word_len = objetcs["max_word_len"]
    num_words = len(word_dict)

    print("word/char dictionary built. Start making inputs.")


    if os.path.exists("cache/data_sets.pt") is False:

        test_text = read_data("./tests.txt")
        test_set  = np.array(text2vec(test_text,  char_dict, max_word_len))

        # Labels are next-word index in word_dict with the same length as inputs
        test_label  = np.array([word_dict[w] for w in test_text[1:]] + [word_dict[test_text[-1]]])

        category = {"tests": test_set, "tlabel": test_label}
        torch.save(category, "cache/data_sets.pt") 
    else:
        data_sets = torch.load("cache/data_sets.pt")
        test_set = data_sets["tests"]
        test_label = data_sets["tlabel"]
        train_set = data_sets["tdata"]
        train_label = data_sets["trlabel"]


    DataTuple = namedtuple("DataTuple", "test_input test_label train_input train_label ")
    data = DataTuple( test_input=test_set,
                     test_label=test_label, train_label=train_label, train_input=train_set)

    print("Loaded data sets. Start building network.")



    USE_GPU = True
    cnn_batch_size = 700
    lstm_seq_len = 35 
    lstm_batch_size = 20
    

    net = torch.load("cache/net.pkl")
    
    Options = namedtuple("Options", [ "cnn_batch_size", "lstm_seq_len",
            "max_word_len", "lstm_batch_size", "word_embed_dim"])
    opt = Options(cnn_batch_size=lstm_seq_len*lstm_batch_size,
                  lstm_seq_len=lstm_seq_len,
                  max_word_len=max_word_len,
                  lstm_batch_size=lstm_batch_size,
                  word_embed_dim=word_embed_dim)


    print("Network built. Start testing.")

    test(net, data, opt)