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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # | |
| # This source code is licensed under the Apache License, Version 2.0 | |
| # found in the LICENSE file in the root directory of this source tree. | |
| from abc import ABCMeta, abstractmethod | |
| from collections import OrderedDict | |
| import torch | |
| import torch.distributed as dist | |
| from mmcv.runner import BaseModule, auto_fp16 | |
| class BaseDepther(BaseModule, metaclass=ABCMeta): | |
| """Base class for depther.""" | |
| def __init__(self, init_cfg=None): | |
| super(BaseDepther, self).__init__(init_cfg) | |
| self.fp16_enabled = False | |
| def with_neck(self): | |
| """bool: whether the depther has neck""" | |
| return hasattr(self, "neck") and self.neck is not None | |
| def with_auxiliary_head(self): | |
| """bool: whether the depther has auxiliary head""" | |
| return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None | |
| def with_decode_head(self): | |
| """bool: whether the depther has decode head""" | |
| return hasattr(self, "decode_head") and self.decode_head is not None | |
| def extract_feat(self, imgs): | |
| """Placeholder for extract features from images.""" | |
| pass | |
| def encode_decode(self, img, img_metas): | |
| """Placeholder for encode images with backbone and decode into a | |
| semantic depth map of the same size as input.""" | |
| pass | |
| def forward_train(self, imgs, img_metas, **kwargs): | |
| """Placeholder for Forward function for training.""" | |
| pass | |
| def simple_test(self, img, img_meta, **kwargs): | |
| """Placeholder for single image test.""" | |
| pass | |
| def aug_test(self, imgs, img_metas, **kwargs): | |
| """Placeholder for augmentation test.""" | |
| pass | |
| def forward_test(self, imgs, img_metas, **kwargs): | |
| """ | |
| Args: | |
| imgs (List[Tensor]): the outer list indicates test-time | |
| augmentations and inner Tensor should have a shape NxCxHxW, | |
| which contains all images in the batch. | |
| img_metas (List[List[dict]]): the outer list indicates test-time | |
| augs (multiscale, flip, etc.) and the inner list indicates | |
| images in a batch. | |
| """ | |
| for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]: | |
| if not isinstance(var, list): | |
| raise TypeError(f"{name} must be a list, but got " f"{type(var)}") | |
| num_augs = len(imgs) | |
| if num_augs != len(img_metas): | |
| raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})") | |
| # all images in the same aug batch all of the same ori_shape and pad | |
| # shape | |
| for img_meta in img_metas: | |
| ori_shapes = [_["ori_shape"] for _ in img_meta] | |
| assert all(shape == ori_shapes[0] for shape in ori_shapes) | |
| img_shapes = [_["img_shape"] for _ in img_meta] | |
| assert all(shape == img_shapes[0] for shape in img_shapes) | |
| pad_shapes = [_["pad_shape"] for _ in img_meta] | |
| assert all(shape == pad_shapes[0] for shape in pad_shapes) | |
| if num_augs == 1: | |
| return self.simple_test(imgs[0], img_metas[0], **kwargs) | |
| else: | |
| return self.aug_test(imgs, img_metas, **kwargs) | |
| def forward(self, img, img_metas, return_loss=True, **kwargs): | |
| """Calls either :func:`forward_train` or :func:`forward_test` depending | |
| on whether ``return_loss`` is ``True``. | |
| Note this setting will change the expected inputs. When | |
| ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor | |
| and List[dict]), and when ``resturn_loss=False``, img and img_meta | |
| should be double nested (i.e. List[Tensor], List[List[dict]]), with | |
| the outer list indicating test time augmentations. | |
| """ | |
| if return_loss: | |
| return self.forward_train(img, img_metas, **kwargs) | |
| else: | |
| return self.forward_test(img, img_metas, **kwargs) | |
| def train_step(self, data_batch, optimizer, **kwargs): | |
| """The iteration step during training. | |
| This method defines an iteration step during training, except for the | |
| back propagation and optimizer updating, which are done in an optimizer | |
| hook. Note that in some complicated cases or models, the whole process | |
| including back propagation and optimizer updating is also defined in | |
| this method, such as GAN. | |
| Args: | |
| data (dict): The output of dataloader. | |
| optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of | |
| runner is passed to ``train_step()``. This argument is unused | |
| and reserved. | |
| Returns: | |
| dict: It should contain at least 3 keys: ``loss``, ``log_vars``, | |
| ``num_samples``. | |
| ``loss`` is a tensor for back propagation, which can be a | |
| weighted sum of multiple losses. | |
| ``log_vars`` contains all the variables to be sent to the | |
| logger. | |
| ``num_samples`` indicates the batch size (when the model is | |
| DDP, it means the batch size on each GPU), which is used for | |
| averaging the logs. | |
| """ | |
| losses = self(**data_batch) | |
| # split losses and images | |
| real_losses = {} | |
| log_imgs = {} | |
| for k, v in losses.items(): | |
| if "img" in k: | |
| log_imgs[k] = v | |
| else: | |
| real_losses[k] = v | |
| loss, log_vars = self._parse_losses(real_losses) | |
| outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs) | |
| return outputs | |
| def val_step(self, data_batch, **kwargs): | |
| """The iteration step during validation. | |
| This method shares the same signature as :func:`train_step`, but used | |
| during val epochs. Note that the evaluation after training epochs is | |
| not implemented with this method, but an evaluation hook. | |
| """ | |
| output = self(**data_batch, **kwargs) | |
| return output | |
| def _parse_losses(losses): | |
| """Parse the raw outputs (losses) of the network. | |
| Args: | |
| losses (dict): Raw output of the network, which usually contain | |
| losses and other necessary information. | |
| Returns: | |
| tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor | |
| which may be a weighted sum of all losses, log_vars contains | |
| all the variables to be sent to the logger. | |
| """ | |
| log_vars = OrderedDict() | |
| for loss_name, loss_value in losses.items(): | |
| if isinstance(loss_value, torch.Tensor): | |
| log_vars[loss_name] = loss_value.mean() | |
| elif isinstance(loss_value, list): | |
| log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) | |
| else: | |
| raise TypeError(f"{loss_name} is not a tensor or list of tensors") | |
| loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key) | |
| log_vars["loss"] = loss | |
| for loss_name, loss_value in log_vars.items(): | |
| # reduce loss when distributed training | |
| if dist.is_available() and dist.is_initialized(): | |
| loss_value = loss_value.data.clone() | |
| dist.all_reduce(loss_value.div_(dist.get_world_size())) | |
| log_vars[loss_name] = loss_value.item() | |
| return loss, log_vars | |