python source code of eval

import argparse
import time
import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable

import data

from utils import batchify, get_batch, repackage_hidden

parser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='/u/zolnakon/repos/awd-lstm-lm/data/penn/',
                    help='location of the data corpus')
parser.add_argument('--bptt', type=int, default=70,
                    help='sequence length')
parser.add_argument('--cuda', action='store_false',
                    help='use CUDA')
randomhash = ''.join(str(time.time()).split('.'))
parser.add_argument('--model_path', type=str,  default='PTB.pt', #'/data/lisa/exp/zolnakon/last/40738.pt',
                    help='path to save the final model')
args = parser.parse_args()

###############################################################################
# Load data
###############################################################################

corpus = data.Corpus(args.data)

eval_batch_size = 10
test_batch_size = 1
train_data = batchify(corpus.train, eval_batch_size, args)
val_data = batchify(corpus.valid, eval_batch_size, args)
test_data = batchify(corpus.test, test_batch_size, args)

###############################################################################
# Evaluating code
###############################################################################

def evaluate(data_source, batch_size=10):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    total_loss = 0
    ntokens = len(corpus.dictionary)
    hidden = model.init_hidden(batch_size)
    for i in range(0, data_source.size(0) - 1, args.bptt):
        data, targets = get_batch(data_source, i, args, evaluation=True)
        output, hidden = model(data, hidden)
        output_flat = output.view(-1, ntokens)
        total_loss += len(data) * criterion(output_flat, targets).data
        hidden = repackage_hidden(hidden)
    return total_loss[0] / len(data_source)

# Load the best saved model.
with open(args.model_path, 'rb') as f:
    model = torch.load(f)

total_params = sum(x.size()[0] * x.size()[1] if len(x.size()) > 1 else x.size()[0] for x in model.parameters())
print('Model total parameters:', total_params)

criterion = nn.CrossEntropyLoss()
    
# Run on test data.
train_loss = evaluate(train_data)
val_loss = evaluate(val_data)
test_loss = evaluate(test_data, test_batch_size)
print('=' * 89)
print('| Evaluation | train ppl {:8.4f} | val ppl {:8.4f} | test ppl {:8.4f}'.format(
    math.exp(train_loss), math.exp(val_loss), math.exp(test_loss)))
print('=' * 89)