d477e70d888d8501cc0834c47f399a4f65a439d9,senteval/tools/relatedness.py,RelatednessPytorch,predict_proba,#RelatednessPytorch#,124

Before Change

    def predict_proba(self, devX):
        self.model.eval()
        probas = []
        for i in range(0, len(devX), self.batch_size):
            Xbatch = Variable(devX[i:i + self.batch_size], volatile=True)
            if len(probas) == 0:
                probas = self.model(Xbatch).data.cpu().numpy()
            else:
                probas = np.concatenate((probas,
                    self.model(Xbatch).data.cpu().numpy()), axis=0)
        return probas

After Change

    def predict_proba(self, devX):
        self.model.eval()
        probas = []
        with torch.no_grad():
            for i in range(0, len(devX), self.batch_size):
                Xbatch = devX[i:i + self.batch_size]
                if len(probas) == 0:
                    probas = self.model(Xbatch).data.cpu().numpy()
                else:
                    probas = np.concatenate((probas,
                        self.model(Xbatch).data.cpu().numpy()), axis=0)
        return probas

Instances