mmtrack.models.vid.base 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule, auto_fp16
from mmtrack.utils import get_root_logger
[文档]class BaseVideoDetector(BaseModule, metaclass=ABCMeta):
"""Base class for video object detector.
Args:
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self, init_cfg):
super(BaseVideoDetector, self).__init__(init_cfg)
self.logger = get_root_logger()
self.fp16_enabled = False
[文档] def freeze_module(self, module):
"""Freeze module during training."""
if isinstance(module, str):
modules = [module]
else:
if not (isinstance(module, list) or isinstance(module, tuple)):
raise TypeError('module must be a str or a list.')
else:
modules = module
for module in modules:
m = getattr(self, module)
m.eval()
for param in m.parameters():
param.requires_grad = False
@property
def with_detector(self):
"""bool: whether the framework has a detector"""
return hasattr(self, 'detector') and self.detector is not None
@property
def with_motion(self):
"""bool: whether the framework has a motion model"""
return hasattr(self, 'motion') and self.motion is not None
@property
def with_aggregator(self):
"""bool: whether the framework has a aggregator"""
return hasattr(self, 'aggregator') and self.aggregator is not None
[文档] @abstractmethod
def forward_train(self,
imgs,
img_metas,
ref_img=None,
ref_img_metas=None,
**kwargs):
"""
Args:
img (Tensor): of shape (N, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
img_metas (list[dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
ref_img (Tensor): of shape (N, R, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
R denotes there is #R reference images for each input image.
ref_img_metas (list[list[dict]]): The first list only has one
element. The second list contains reference image information
dict where each dict has: 'img_shape', 'scale_factor', 'flip',
and may also contain 'filename', 'ori_shape', 'pad_shape', and
'img_norm_cfg'. For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
"""
pass
@abstractmethod
def simple_test(self, img, img_metas, **kwargs):
pass
[文档] def aug_test(self, imgs, img_metas, **kwargs):
"""Test function with test time augmentation."""
pass
[文档] def forward_test(self,
imgs,
img_metas,
ref_img=None,
ref_img_metas=None,
**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.
ref_img (list[Tensor] | None): The list only contains one Tensor
of shape (1, N, C, H, W) encoding input reference images.
Typically these should be mean centered and std scaled. N
denotes the number for reference images. There may be no
reference images in some cases.
ref_img_metas (list[list[list[dict]]] | None): The first and
second list only has one element. The third list contains
image information dict where each dict has: 'img_shape',
'scale_factor', 'flip', and may also contain 'filename',
'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on
the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`. There
may be no reference images in some cases.
"""
if isinstance(imgs, torch.Tensor):
imgs = [imgs]
elif not isinstance(imgs, list):
raise TypeError(
f'imgs must be a list or tensor, but got {type(imgs)}')
assert isinstance(img_metas, list)
if isinstance(img_metas[0], dict):
img_metas = [img_metas]
elif not isinstance(img_metas[0], list):
raise TypeError(
'img_metas must be a List[List[dict]] or List[dict]')
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)})')
if num_augs == 1:
# proposals (List[List[Tensor]]): the outer list indicates
# test-time augs (multiscale, flip, etc.) and the inner list
# indicates images in a batch.
# The Tensor should have a shape Px4, where P is the number of
# proposals.
if 'proposals' in kwargs:
kwargs['proposals'] = kwargs['proposals'][0]
return self.simple_test(
imgs[0],
img_metas[0],
ref_img=ref_img,
ref_img_metas=ref_img_metas,
**kwargs)
else:
assert imgs[0].size(0) == 1, 'aug test does not support ' \
'inference with batch size ' \
f'{imgs[0].size(0)}'
# TODO: support test augmentation for predefined proposals
assert 'proposals' not in kwargs
return self.aug_test(
imgs,
img_metas,
ref_img=ref_img,
ref_img_metas=ref_img_metas,
**kwargs)
[文档] @auto_fp16(apply_to=('img', 'ref_img'))
def forward(self,
img,
img_metas,
ref_img=None,
ref_img_metas=None,
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,
ref_img=ref_img,
ref_img_metas=ref_img_metas,
**kwargs)
else:
return self.forward_test(
img,
img_metas,
ref_img=ref_img,
ref_img_metas=ref_img_metas,
**kwargs)
def _parse_losses(self, 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
[文档] def train_step(self, data, optimizer):
"""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)
loss, log_vars = self._parse_losses(losses)
outputs = dict(
loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
return outputs
[文档] def val_step(self, data, optimizer):
"""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.
"""
losses = self(**data)
loss, log_vars = self._parse_losses(losses)
outputs = dict(
loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
return outputs
[文档] def show_result(self,
img,
result,
score_thr=0.3,
bbox_color='green',
text_color='green',
thickness=1,
font_scale=0.5,
win_name='',
show=False,
wait_time=0,
out_file=None):
"""Draw `result` over `img`.
Args:
img (str or Tensor): The image to be displayed.
result (dict): The results to draw over `img` det_bboxes or
(det_bboxes, det_masks). The value of key 'det_bboxes'
is list with length num_classes, and each element in list
is ndarray with shape(n, 5)
in [tl_x, tl_y, br_x, br_y, score] format.
score_thr (float, optional): Minimum score of bboxes to be shown.
Default: 0.3.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
text_color (str or tuple or :obj:`Color`): Color of texts.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
Default: 0.
show (bool): Whether to show the image.
Default: False.
out_file (str or None): The filename to write the image.
Default: None.
Returns:
img (Tensor): Only if not `show` or `out_file`
"""
# TODO: make it support tracking
img = mmcv.imread(img)
img = img.copy()
assert isinstance(result, dict)
bbox_results = result.get('det_bboxes', None)
mask_results = result.get('det_masks', None)
if isinstance(mask_results, tuple):
mask_results = mask_results[0] # ms rcnn
bboxes = np.vstack(bbox_results)
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_results)
]
labels = np.concatenate(labels)
# draw segmentation masks
if mask_results is not None and len(labels) > 0: # non empty
masks = mmcv.concat_list(mask_results)
inds = np.where(bboxes[:, -1] > score_thr)[0]
np.random.seed(42)
color_masks = [
np.random.randint(0, 256, (1, 3), dtype=np.uint8)
for _ in range(max(labels) + 1)
]
for i in inds:
i = int(i)
color_mask = color_masks[labels[i]]
mask = masks[i].astype(bool)
img[mask] = img[mask] * 0.5 + color_mask * 0.5
# if out_file specified, do not show image in window
if out_file is not None:
show = False
# draw bounding boxes
mmcv.imshow_det_bboxes(
img,
bboxes,
labels,
class_names=self.CLASSES,
score_thr=score_thr,
bbox_color=bbox_color,
text_color=text_color,
thickness=thickness,
font_scale=font_scale,
win_name=win_name,
show=show,
wait_time=wait_time,
out_file=out_file)
if not (show or out_file):
return img