def evaluate(model, testloader):
model.eval()
# loss
top1 = AverageMeter()
top5 = AverageMeter()
for _, (inputs, targets) in enumerate(testloader):
# print(batch_idx)
targets = Variable(targets).cuda()
inputs = Variable(inputs).cuda()
logits, _, _ = model(inputs)
prec1, prec5 = Helper.accuracy(logits, targets, topk=(1, 5))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
sys.stdout.write('Acc@1: %.3f Acc@5: %.3f' % (top1.avg, top5.avg))
def evaluate_fgan_loss(inet, fnet, dataloader, nactors):
print('Evaluate Fusion Network')
inet.eval()
fnet.eval()
top1 = AverageMeter()
top5 = AverageMeter()
for batch_idx, (inputs, targets) in enumerate(dataloader):
z_missed, z_missed_g, target_missed, _ = batch_process(
inet, fnet, inputs, targets, nactors)
out_missed_g = inet.module.classifier(z_missed_g)
prec1, prec5 = Helper.accuracy(out_missed_g,
target_missed,
topk=(1, 5))
top1.update(prec1.item(), target_missed.size(0))
top5.update(prec5.item(), target_missed.size(0))
del z_missed, z_missed_g, target_missed, _
return top1.avg, top5.avg