【代码学习】voxel 或者 pillar，稀疏张量转稠密张量的代码理解，理解了很久

本文主要是介绍【代码学习】voxel 或者 pillar，稀疏张量转稠密张量的代码理解，理解了很久，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

需要 feature 和对应的坐标 coords

debug：转置，不然维度不匹配！
在这里插入图片描述
对应的代码，向量化应该比 for循环快

    def voxel_indexing(self, sparse_features, coords): # sparse_features: [N, C], coords:[N, 4]dim = sparse_features.shape[-1]dense_feature = Variable(torch.zeros(dim, cfg.batch_size, cfg.seq_num, cfg.X, cfg.Y)).to(cfg.device) # N = batch_size# print(f"dense_feature.shape = {dense_feature.shape}")dense_feature[:, coords[:,0], coords[:,1], coords[:,2], coords[:,3]]= sparse_features.t()# batch_canvas = []# batch_size = cfg.N# for batch_id in range(batch_size):#     canvas = torch.zeros(dim, cfg.W, cfg.H, cfg.D, dtype=sparse_features.dtype, device=sparse_features.device)#     batch_mask = (coords[:, 0] == batch_id)#     # print(f"batch_id = {batch_id}")#     # print(f"{batch_mask}")  #     this_coords = coords[batch_mask, :]#     # print(f"====> len(this_coords) = {len(this_coords)}")#     voxels = sparse_features[batch_mask, :]#     voxels = voxels.t()#     # print(f"voxels.t().shape = {voxels.shape}")#     canvas[:, this_coords[:, 1], this_coords[:, 2], this_coords[:, 3]] = voxels#     batch_canvas.append(canvas)# batch_canvas = torch.stack(batch_canvas, 0)# print(f"batch_canvas.shape = {batch_canvas.shape}")# return dense_featurereturn dense_feature.transpose(0, 1).contiguous()# return batch_canvas