validation_log.py

# coding:utf8
from torch.autograd import Variable
import torch as t

from utils import AverageMeter, calculate_accuracy
import time


def val_epoch(epoch, data_loader, model, criterion, opt, logger):
    print('validation at epoch {}'.format(epoch))

    model.eval()

    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    accuracies = AverageMeter()

    end_time = time.time()

    for i, (inputs, targets) in enumerate(data_loader):
        data_time.update(time.time() - end_time)

        if opt.cuda:
            targets = targets.cuda(async=True)
        # inputs = Variable(inputs.cuda(), volatile=True)
        with t.no_grad():
            inputs = Variable(inputs.cuda())
            # targets = Variable(targets, volatile=True)
            targets = Variable(targets)
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            acc = calculate_accuracy(outputs, targets)

        losses.update(loss.item(), inputs.size(0))
        accuracies.update(acc, inputs.size(0))

        batch_time.update(time.time() - end_time)
        end_time = time.time()

        print('Epoch: [{0}][{1}/{2}]\t'
              'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
              'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
              'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
              'Acc {acc.val:.3f} ({acc.avg:.3f})'.format(
            epoch,
            i + 1,
            len(data_loader),
            batch_time=batch_time,
            data_time=data_time,
            loss=losses,
            acc=accuracies))

    logger.log({'epoch': epoch, 'loss': losses.avg, 'acc': accuracies.avg})

    return losses.avg