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！
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
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