dc832101a57afe93706ce85cc90f476645be6d37,pycox/models/base.py,BaseModel,_predict_func_numpy,#BaseModel#,63

Before Change

        with torch.no_grad():
            dataset = NumpyTensorDataset(X)
            dataloader = DataLoaderSlice(dataset, batch_size)
            preds = [func(x.to(self.device)) for x in iter(dataloader)]
            preds = torch.cat(preds)
        if eval_:
            func.train()

        if return_numpy:
            return preds.numpy()
        return preds

    def save_model_weights(self, path, **kwargs):
        """Save the model weights.

After Change

        """
        dataset = NumpyTensorDataset(X)
        dataloader = DataLoaderSlice(dataset, batch_size)
        return self._predict_func_dataloader(func, dataloader, return_numpy, eval_)
        // if eval_:
        //     func.eval()
        // with torch.no_grad():
        //     dataset = NumpyTensorDataset(X)

Instances