将下面代码中的numpy操作转换为纯pytorch函数：boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) landms = landms * 640 landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > args.confidence_threshold)[:] # ) boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS order = scores.argsort()[::-1][:args.top_k] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)

boxes = boxes.cpu()
scores = conf.squeeze(0).data.cpu()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, cfg[variance])
landms = landms * 640
landms = landms.cpu()

相关问题

将下面代码中的numpy操作转换为纯pytorch函数：landms = landms * 640 landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > args.confidence_threshold)[:] # ) boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS order = scores.argsort()[::-1][:args.top_k] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)

landms = landms * 640
landms = landms.cpu().numpy()

转换为纯pytorch函数:
landms = landms * 640
landms = landms.detach().numpy()

将下列numpy代码转化为纯pytorch代码： inds = np.where(scores > args.confidence_threshold)[:] boxes = boxes[inds] landms = landms[inds] scores = scores[inds]

请使用 PyTorch 的 mask 和 index_select 函数来替换 numpy 中的 where 和索引选择。

import torch

# 将 scores 转化为 PyTorch tensor
scores = torch.tensor(scores)

# 创建一个 mask，用来筛选出 scores 大于 args.confidence_threshold 的元素
mask = (scores > args.confidence_threshold)

# 使用 mask 和 index_select 函数来筛选出 boxes、landms 和 scores
boxes = boxes[mask]
landms = landms[mask]
scores = scores[mask]