执行评估时是否应将数据批处理移至CPU并从火炬张量(从火炬张量)转换为numpy数组.训练中的指标? [英] Should a data batch be moved to CPU and converted (from torch Tensor) to a numpy array when doing evaluation w.r.t. a metric during the training?
问题描述
我正在通过来自CS230斯坦福大学课程的吴安德(Andrew Ng)的教程，并在每次训练a>，通过计算指标进行评估.
I am going through Andrew Ng’s tutorial from the CS230 Stanford course, and in every epoch of the training, evaluation is performed by calculating the metrics.
但是在计算指标之前，他们将批次发送到CPU并将其转换为numpy数组(
But before calculating the metrics, they are sending the batches to CPU and converting them to numpy arrays (code here).
# extract data from torch Variable, move to cpu, convert to numpy arrays
output_batch = output_batch.data.cpu().numpy()
labels_batch = labels_batch.data.cpu().numpy()
# compute all metrics on this batch
summary_batch = {metric: metrics[metric](output_batch, labels_batch) for metric in metrics}
我的问题是:他们为什么这样做?他们为什么不只是计算指标(已完成
My question is: why do they do that? Why don’t they just calculate the metrics (which is done here) on GPU using torch methods (e.g. torch.sum as opposed to np.sum)?
我认为GPU到CPU的传输会减慢速度，因此应该有一个很好的理由吗?
I would think that GPU to CPU transfers would slow things down, so there should be a very good reason for doing them?
我是PyTorch的新手，所以我可能会丢失一些东西.
I am new to PyTorch so I might be missing something.
推荐答案
如果我错了，请纠正我.将数据发送回CPU可以减少GPU负载，即使在进入下一个循环周期时更换了内存.此外，我相信转换为 numpy 的好处是可以释放内存，因为您正在从计算图中分离数据.您最终要操纵 labels_batch.cpu().numpy()一个固定的数组与 labels_batch 的张量，该张量通过链接的 backward_fn 回调.
Correct me if I'm wrong. Sending back the data to the CPU allows to reduce the GPU load even though memory is replaced when entering the following loop cycle. Futhermore, I believe converting to numpy has the advantage of freeing memory since you are detaching your data from the calculation graph. You end up manipulating labels_batch.cpu().numpy() a fixed array vs labels_batch a tensor attached to the entire network through linked backward_fn callbacks.
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