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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
@property
def with_neck(self):
"""bool: whether the depther has neck"""
return hasattr(self, "neck") and self.neck is not None
@property
def with_auxiliary_head(self):
"""bool: whether the depther has auxiliary head"""
return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None
@property
def with_decode_head(self):
"""bool: whether the depther has decode head"""
return hasattr(self, "decode_head") and self.decode_head is not None
@abstractmethod
def extract_feat(self, imgs):
"""Placeholder for extract features from images."""
pass
@abstractmethod
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
@abstractmethod
def forward_train(self, imgs, img_metas, **kwargs):
"""Placeholder for Forward function for training."""
pass
@abstractmethod
def simple_test(self, img, img_meta, **kwargs):
"""Placeholder for single image test."""
pass
@abstractmethod
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)
@auto_fp16(apply_to=("img",))
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
@staticmethod
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
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