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| # Copyright (C) 2024-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # | |
| # -------------------------------------------------------- | |
| # utilitary functions for DUSt3R | |
| # -------------------------------------------------------- | |
| import numpy as np | |
| import torch | |
| def todevice(batch, device, callback=None, non_blocking=False): | |
| ''' Transfer some variables to another device (i.e. GPU, CPU:torch, CPU:numpy). | |
| batch: list, tuple, dict of tensors or other things | |
| device: pytorch device or 'numpy' | |
| callback: function that would be called on every sub-elements. | |
| ''' | |
| if callback: | |
| batch = callback(batch) | |
| if isinstance(batch, dict): | |
| return {k: todevice(v, device) for k, v in batch.items()} | |
| if isinstance(batch, (tuple, list)): | |
| return type(batch)(todevice(x, device) for x in batch) | |
| x = batch | |
| if device == 'numpy': | |
| if isinstance(x, torch.Tensor): | |
| x = x.detach().cpu().numpy() | |
| elif x is not None: | |
| if isinstance(x, np.ndarray): | |
| x = torch.from_numpy(x) | |
| if torch.is_tensor(x): | |
| x = x.to(device, non_blocking=non_blocking) | |
| return x | |
| to_device = todevice # alias | |
| def to_numpy(x): return todevice(x, 'numpy') | |
| def to_cpu(x): return todevice(x, 'cpu') | |
| def to_cuda(x): return todevice(x, 'cuda') | |
| def collate_with_cat(whatever, lists=False): | |
| if isinstance(whatever, dict): | |
| return {k: collate_with_cat(vals, lists=lists) for k, vals in whatever.items()} | |
| elif isinstance(whatever, (tuple, list)): | |
| if len(whatever) == 0: | |
| return whatever | |
| elem = whatever[0] | |
| T = type(whatever) | |
| if elem is None: | |
| return None | |
| if isinstance(elem, (bool, float, int, str)): | |
| return whatever | |
| if isinstance(elem, tuple): | |
| return T(collate_with_cat(x, lists=lists) for x in zip(*whatever)) | |
| if isinstance(elem, dict): | |
| return {k: collate_with_cat([e[k] for e in whatever], lists=lists) for k in elem} | |
| if isinstance(elem, torch.Tensor): | |
| return listify(whatever) if lists else torch.cat(whatever) | |
| if isinstance(elem, np.ndarray): | |
| return listify(whatever) if lists else torch.cat([torch.from_numpy(x) for x in whatever]) | |
| # otherwise, we just chain lists | |
| return sum(whatever, T()) | |
| def listify(elems): | |
| return [x for e in elems for x in e] | |