Source code for mmdet.models.dense_heads.autoassign_head
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Sequence, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Scale
from mmengine.model import bias_init_with_prob, normal_init
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
from ..task_modules.prior_generators import MlvlPointGenerator
from ..utils import levels_to_images, multi_apply
from .fcos_head import FCOSHead
EPS = 1e-12
class CenterPrior(nn.Module):
"""Center Weighting module to adjust the category-specific prior
distributions.
Args:
force_topk (bool): When no point falls into gt_bbox, forcibly
select the k points closest to the center to calculate
the center prior. Defaults to False.
topk (int): The number of points used to calculate the
center prior when no point falls in gt_bbox. Only work when
force_topk if True. Defaults to 9.
num_classes (int): The class number of dataset. Defaults to 80.
strides (Sequence[int]): The stride of each input feature map.
Defaults to (8, 16, 32, 64, 128).
"""
def __init__(
self,
force_topk: bool = False,
topk: int = 9,
num_classes: int = 80,
strides: Sequence[int] = (8, 16, 32, 64, 128)
) -> None:
super().__init__()
self.mean = nn.Parameter(torch.zeros(num_classes, 2))
self.sigma = nn.Parameter(torch.ones(num_classes, 2))
self.strides = strides
self.force_topk = force_topk
self.topk = topk
def forward(self, anchor_points_list: List[Tensor],
gt_instances: InstanceData,
inside_gt_bbox_mask: Tensor) -> Tuple[Tensor, Tensor]:
"""Get the center prior of each point on the feature map for each
instance.
Args:
anchor_points_list (list[Tensor]): list of coordinate
of points on feature map. Each with shape
(num_points, 2).
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It should includes ``bboxes`` and ``labels``
attributes.
inside_gt_bbox_mask (Tensor): Tensor of bool type,
with shape of (num_points, num_gt), each
value is used to mark whether this point falls
within a certain gt.
Returns:
tuple[Tensor, Tensor]:
- center_prior_weights(Tensor): Float tensor with shape of \
(num_points, num_gt). Each value represents the center \
weighting coefficient.
- inside_gt_bbox_mask (Tensor): Tensor of bool type, with shape \
of (num_points, num_gt), each value is used to mark whether this \
point falls within a certain gt or is the topk nearest points for \
a specific gt_bbox.
"""
gt_bboxes = gt_instances.bboxes
labels = gt_instances.labels
inside_gt_bbox_mask = inside_gt_bbox_mask.clone()
num_gts = len(labels)
num_points = sum([len(item) for item in anchor_points_list])
if num_gts == 0:
return gt_bboxes.new_zeros(num_points,
num_gts), inside_gt_bbox_mask
center_prior_list = []
for slvl_points, stride in zip(anchor_points_list, self.strides):
# slvl_points: points from single level in FPN, has shape (h*w, 2)
# single_level_points has shape (h*w, num_gt, 2)
single_level_points = slvl_points[:, None, :].expand(
(slvl_points.size(0), len(gt_bboxes), 2))
gt_center_x = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2)
gt_center_y = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2)
gt_center = torch.stack((gt_center_x, gt_center_y), dim=1)
gt_center = gt_center[None]
# instance_center has shape (1, num_gt, 2)
instance_center = self.mean[labels][None]
# instance_sigma has shape (1, num_gt, 2)
instance_sigma = self.sigma[labels][None]
# distance has shape (num_points, num_gt, 2)
distance = (((single_level_points - gt_center) / float(stride) -
instance_center)**2)
center_prior = torch.exp(-distance /
(2 * instance_sigma**2)).prod(dim=-1)
center_prior_list.append(center_prior)
center_prior_weights = torch.cat(center_prior_list, dim=0)
if self.force_topk:
gt_inds_no_points_inside = torch.nonzero(
inside_gt_bbox_mask.sum(0) == 0).reshape(-1)
if gt_inds_no_points_inside.numel():
topk_center_index = \
center_prior_weights[:, gt_inds_no_points_inside].topk(
self.topk,
dim=0)[1]
temp_mask = inside_gt_bbox_mask[:, gt_inds_no_points_inside]
inside_gt_bbox_mask[:, gt_inds_no_points_inside] = \
torch.scatter(temp_mask,
dim=0,
index=topk_center_index,
src=torch.ones_like(
topk_center_index,
dtype=torch.bool))
center_prior_weights[~inside_gt_bbox_mask] = 0
return center_prior_weights, inside_gt_bbox_mask
[docs]@MODELS.register_module()
class AutoAssignHead(FCOSHead):
"""AutoAssignHead head used in AutoAssign.
More details can be found in the `paper
<https://arxiv.org/abs/2007.03496>`_ .
Args:
force_topk (bool): Used in center prior initialization to
handle extremely small gt. Default is False.
topk (int): The number of points used to calculate the
center prior when no point falls in gt_bbox. Only work when
force_topk if True. Defaults to 9.
pos_loss_weight (float): The loss weight of positive loss
and with default value 0.25.
neg_loss_weight (float): The loss weight of negative loss
and with default value 0.75.
center_loss_weight (float): The loss weight of center prior
loss and with default value 0.75.
"""
def __init__(self,
*args,
force_topk: bool = False,
topk: int = 9,
pos_loss_weight: float = 0.25,
neg_loss_weight: float = 0.75,
center_loss_weight: float = 0.75,
**kwargs) -> None:
super().__init__(*args, conv_bias=True, **kwargs)
self.center_prior = CenterPrior(
force_topk=force_topk,
topk=topk,
num_classes=self.num_classes,
strides=self.strides)
self.pos_loss_weight = pos_loss_weight
self.neg_loss_weight = neg_loss_weight
self.center_loss_weight = center_loss_weight
self.prior_generator = MlvlPointGenerator(self.strides, offset=0)
[docs] def init_weights(self) -> None:
"""Initialize weights of the head.
In particular, we have special initialization for classified conv's and
regression conv's bias
"""
super(AutoAssignHead, self).init_weights()
bias_cls = bias_init_with_prob(0.02)
normal_init(self.conv_cls, std=0.01, bias=bias_cls)
normal_init(self.conv_reg, std=0.01, bias=4.0)
[docs] def forward_single(self, x: Tensor, scale: Scale,
stride: int) -> Tuple[Tensor, Tensor, Tensor]:
"""Forward features of a single scale level.
Args:
x (Tensor): FPN feature maps of the specified stride.
scale (:obj:`mmcv.cnn.Scale`): Learnable scale module to resize
the bbox prediction.
stride (int): The corresponding stride for feature maps, only
used to normalize the bbox prediction when self.norm_on_bbox
is True.
Returns:
tuple[Tensor, Tensor, Tensor]: scores for each class, bbox
predictions and centerness predictions of input feature maps.
"""
cls_score, bbox_pred, cls_feat, reg_feat = super(
FCOSHead, self).forward_single(x)
centerness = self.conv_centerness(reg_feat)
# scale the bbox_pred of different level
# float to avoid overflow when enabling FP16
bbox_pred = scale(bbox_pred).float()
# bbox_pred needed for gradient computation has been modified
# by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
# F.relu(bbox_pred) with bbox_pred.clamp(min=0)
bbox_pred = bbox_pred.clamp(min=0)
bbox_pred *= stride
return cls_score, bbox_pred, centerness
[docs] def get_pos_loss_single(self, cls_score: Tensor, objectness: Tensor,
reg_loss: Tensor, gt_instances: InstanceData,
center_prior_weights: Tensor) -> Tuple[Tensor]:
"""Calculate the positive loss of all points in gt_bboxes.
Args:
cls_score (Tensor): All category scores for each point on
the feature map. The shape is (num_points, num_class).
objectness (Tensor): Foreground probability of all points,
has shape (num_points, 1).
reg_loss (Tensor): The regression loss of each gt_bbox and each
prediction box, has shape of (num_points, num_gt).
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It should includes ``bboxes`` and ``labels``
attributes.
center_prior_weights (Tensor): Float tensor with shape
of (num_points, num_gt). Each value represents
the center weighting coefficient.
Returns:
tuple[Tensor]:
- pos_loss (Tensor): The positive loss of all points in the \
gt_bboxes.
"""
gt_labels = gt_instances.labels
# p_loc: localization confidence
p_loc = torch.exp(-reg_loss)
# p_cls: classification confidence
p_cls = (cls_score * objectness)[:, gt_labels]
# p_pos: joint confidence indicator
p_pos = p_cls * p_loc
# 3 is a hyper-parameter to control the contributions of high and
# low confidence locations towards positive losses.
confidence_weight = torch.exp(p_pos * 3)
p_pos_weight = (confidence_weight * center_prior_weights) / (
(confidence_weight * center_prior_weights).sum(
0, keepdim=True)).clamp(min=EPS)
reweighted_p_pos = (p_pos * p_pos_weight).sum(0)
pos_loss = F.binary_cross_entropy(
reweighted_p_pos,
torch.ones_like(reweighted_p_pos),
reduction='none')
pos_loss = pos_loss.sum() * self.pos_loss_weight
return pos_loss,
[docs] def get_neg_loss_single(self, cls_score: Tensor, objectness: Tensor,
gt_instances: InstanceData, ious: Tensor,
inside_gt_bbox_mask: Tensor) -> Tuple[Tensor]:
"""Calculate the negative loss of all points in feature map.
Args:
cls_score (Tensor): All category scores for each point on
the feature map. The shape is (num_points, num_class).
objectness (Tensor): Foreground probability of all points
and is shape of (num_points, 1).
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It should includes ``bboxes`` and ``labels``
attributes.
ious (Tensor): Float tensor with shape of (num_points, num_gt).
Each value represent the iou of pred_bbox and gt_bboxes.
inside_gt_bbox_mask (Tensor): Tensor of bool type,
with shape of (num_points, num_gt), each
value is used to mark whether this point falls
within a certain gt.
Returns:
tuple[Tensor]:
- neg_loss (Tensor): The negative loss of all points in the \
feature map.
"""
gt_labels = gt_instances.labels
num_gts = len(gt_labels)
joint_conf = (cls_score * objectness)
p_neg_weight = torch.ones_like(joint_conf)
if num_gts > 0:
# the order of dinmension would affect the value of
# p_neg_weight, we strictly follow the original
# implementation.
inside_gt_bbox_mask = inside_gt_bbox_mask.permute(1, 0)
ious = ious.permute(1, 0)
foreground_idxs = torch.nonzero(inside_gt_bbox_mask, as_tuple=True)
temp_weight = (1 / (1 - ious[foreground_idxs]).clamp_(EPS))
def normalize(x):
return (x - x.min() + EPS) / (x.max() - x.min() + EPS)
for instance_idx in range(num_gts):
idxs = foreground_idxs[0] == instance_idx
if idxs.any():
temp_weight[idxs] = normalize(temp_weight[idxs])
p_neg_weight[foreground_idxs[1],
gt_labels[foreground_idxs[0]]] = 1 - temp_weight
logits = (joint_conf * p_neg_weight)
neg_loss = (
logits**2 * F.binary_cross_entropy(
logits, torch.zeros_like(logits), reduction='none'))
neg_loss = neg_loss.sum() * self.neg_loss_weight
return neg_loss,
[docs] def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
objectnesses: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None
) -> Dict[str, Tensor]:
"""Calculate the loss based on the features extracted by the detection
head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is
num_points * num_classes.
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is
num_points * 4.
objectnesses (list[Tensor]): objectness for each scale level, each
is a 4D-tensor, the channel number is num_points * 1.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
all_num_gt = sum([len(item) for item in batch_gt_instances])
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
all_level_points = self.prior_generator.grid_priors(
featmap_sizes,
dtype=bbox_preds[0].dtype,
device=bbox_preds[0].device)
inside_gt_bbox_mask_list, bbox_targets_list = self.get_targets(
all_level_points, batch_gt_instances)
center_prior_weight_list = []
temp_inside_gt_bbox_mask_list = []
for gt_instances, inside_gt_bbox_mask in zip(batch_gt_instances,
inside_gt_bbox_mask_list):
center_prior_weight, inside_gt_bbox_mask = \
self.center_prior(all_level_points, gt_instances,
inside_gt_bbox_mask)
center_prior_weight_list.append(center_prior_weight)
temp_inside_gt_bbox_mask_list.append(inside_gt_bbox_mask)
inside_gt_bbox_mask_list = temp_inside_gt_bbox_mask_list
mlvl_points = torch.cat(all_level_points, dim=0)
bbox_preds = levels_to_images(bbox_preds)
cls_scores = levels_to_images(cls_scores)
objectnesses = levels_to_images(objectnesses)
reg_loss_list = []
ious_list = []
num_points = len(mlvl_points)
for bbox_pred, encoded_targets, inside_gt_bbox_mask in zip(
bbox_preds, bbox_targets_list, inside_gt_bbox_mask_list):
temp_num_gt = encoded_targets.size(1)
expand_mlvl_points = mlvl_points[:, None, :].expand(
num_points, temp_num_gt, 2).reshape(-1, 2)
encoded_targets = encoded_targets.reshape(-1, 4)
expand_bbox_pred = bbox_pred[:, None, :].expand(
num_points, temp_num_gt, 4).reshape(-1, 4)
decoded_bbox_preds = self.bbox_coder.decode(
expand_mlvl_points, expand_bbox_pred)
decoded_target_preds = self.bbox_coder.decode(
expand_mlvl_points, encoded_targets)
with torch.no_grad():
ious = bbox_overlaps(
decoded_bbox_preds, decoded_target_preds, is_aligned=True)
ious = ious.reshape(num_points, temp_num_gt)
if temp_num_gt:
ious = ious.max(
dim=-1, keepdim=True).values.repeat(1, temp_num_gt)
else:
ious = ious.new_zeros(num_points, temp_num_gt)
ious[~inside_gt_bbox_mask] = 0
ious_list.append(ious)
loss_bbox = self.loss_bbox(
decoded_bbox_preds,
decoded_target_preds,
weight=None,
reduction_override='none')
reg_loss_list.append(loss_bbox.reshape(num_points, temp_num_gt))
cls_scores = [item.sigmoid() for item in cls_scores]
objectnesses = [item.sigmoid() for item in objectnesses]
pos_loss_list, = multi_apply(self.get_pos_loss_single, cls_scores,
objectnesses, reg_loss_list,
batch_gt_instances,
center_prior_weight_list)
pos_avg_factor = reduce_mean(
bbox_pred.new_tensor(all_num_gt)).clamp_(min=1)
pos_loss = sum(pos_loss_list) / pos_avg_factor
neg_loss_list, = multi_apply(self.get_neg_loss_single, cls_scores,
objectnesses, batch_gt_instances,
ious_list, inside_gt_bbox_mask_list)
neg_avg_factor = sum(item.data.sum()
for item in center_prior_weight_list)
neg_avg_factor = reduce_mean(neg_avg_factor).clamp_(min=1)
neg_loss = sum(neg_loss_list) / neg_avg_factor
center_loss = []
for i in range(len(batch_img_metas)):
if inside_gt_bbox_mask_list[i].any():
center_loss.append(
len(batch_gt_instances[i]) /
center_prior_weight_list[i].sum().clamp_(min=EPS))
# when width or height of gt_bbox is smaller than stride of p3
else:
center_loss.append(center_prior_weight_list[i].sum() * 0)
center_loss = torch.stack(center_loss).mean() * self.center_loss_weight
# avoid dead lock in DDP
if all_num_gt == 0:
pos_loss = bbox_preds[0].sum() * 0
dummy_center_prior_loss = self.center_prior.mean.sum(
) * 0 + self.center_prior.sigma.sum() * 0
center_loss = objectnesses[0].sum() * 0 + dummy_center_prior_loss
loss = dict(
loss_pos=pos_loss, loss_neg=neg_loss, loss_center=center_loss)
return loss
[docs] def get_targets(
self, points: List[Tensor], batch_gt_instances: InstanceList
) -> Tuple[List[Tensor], List[Tensor]]:
"""Compute regression targets and each point inside or outside gt_bbox
in multiple images.
Args:
points (list[Tensor]): Points of all fpn level, each has shape
(num_points, 2).
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
Returns:
tuple(list[Tensor], list[Tensor]):
- inside_gt_bbox_mask_list (list[Tensor]): Each Tensor is with \
bool type and shape of (num_points, num_gt), each value is used \
to mark whether this point falls within a certain gt.
- concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
level. Each tensor has shape (num_points, num_gt, 4).
"""
concat_points = torch.cat(points, dim=0)
# the number of points per img, per lvl
inside_gt_bbox_mask_list, bbox_targets_list = multi_apply(
self._get_targets_single, batch_gt_instances, points=concat_points)
return inside_gt_bbox_mask_list, bbox_targets_list
def _get_targets_single(self, gt_instances: InstanceData,
points: Tensor) -> Tuple[Tensor, Tensor]:
"""Compute regression targets and each point inside or outside gt_bbox
for a single image.
Args:
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It should includes ``bboxes`` and ``labels``
attributes.
points (Tensor): Points of all fpn level, has shape
(num_points, 2).
Returns:
tuple[Tensor, Tensor]: Containing the following Tensors:
- inside_gt_bbox_mask (Tensor): Bool tensor with shape \
(num_points, num_gt), each value is used to mark whether this \
point falls within a certain gt.
- bbox_targets (Tensor): BBox targets of each points with each \
gt_bboxes, has shape (num_points, num_gt, 4).
"""
gt_bboxes = gt_instances.bboxes
num_points = points.size(0)
num_gts = gt_bboxes.size(0)
gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
xs, ys = points[:, 0], points[:, 1]
xs = xs[:, None]
ys = ys[:, None]
left = xs - gt_bboxes[..., 0]
right = gt_bboxes[..., 2] - xs
top = ys - gt_bboxes[..., 1]
bottom = gt_bboxes[..., 3] - ys
bbox_targets = torch.stack((left, top, right, bottom), -1)
if num_gts:
inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0
else:
inside_gt_bbox_mask = bbox_targets.new_zeros((num_points, num_gts),
dtype=torch.bool)
return inside_gt_bbox_mask, bbox_targets