classifier.py 文件源码

def score(self, devX, devy):
        self.model.eval()
        correct = 0
        if not isinstance(devX, torch.cuda.FloatTensor) or self.cudaEfficient:
            devX = torch.FloatTensor(devX).cuda()
            devy = torch.LongTensor(devy).cuda()
        for i in range(0, len(devX), self.batch_size):
            Xbatch = Variable(devX[i:i + self.batch_size], volatile=True)
            ybatch = Variable(devy[i:i + self.batch_size], volatile=True)
            if self.cudaEfficient:
                Xbatch = Xbatch.cuda()
                ybatch = ybatch.cuda()
            output = self.model(Xbatch)
            pred = output.data.max(1)[1]
            correct += pred.long().eq(ybatch.data.long()).sum()
        accuracy = 1.0*correct / len(devX)
        return accuracy