# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish, build_norm_layer
from mmengine.model import bias_init_with_prob
from torch import Tensor
from mmdet.models.dense_heads.anchor_head import AnchorHead
from mmdet.models.utils import images_to_levels, multi_apply
from mmdet.registry import MODELS
from mmdet.structures.bbox import cat_boxes, get_box_tensor
from mmdet.utils import (InstanceList, OptConfigType, OptInstanceList,
OptMultiConfig, reduce_mean)
from .utils import DepthWiseConvBlock
@MODELS.register_module()
class EfficientDetSepBNHead(AnchorHead):
"""EfficientDetHead with separate BN.
num_classes (int): Number of categories num_ins (int): Number of the input
feature map. in_channels (int): Number of channels in the input feature
map. feat_channels (int): Number of hidden channels. stacked_convs (int):
Number of repetitions of conv norm_cfg (dict): Config dict for
normalization layer. anchor_generator (dict): Config dict for anchor
generator bbox_coder (dict): Config of bounding box coder. loss_cls (dict):
Config of classification loss. loss_bbox (dict): Config of localization
loss. train_cfg (dict): Training config of anchor head. test_cfg (dict):
Testing config of anchor head. init_cfg (dict or list[dict], optional):
Initialization config dict.
"""
def __init__(self,
num_classes: int,
num_ins: int,
in_channels: int,
feat_channels: int,
stacked_convs: int = 3,
norm_cfg: OptConfigType = dict(
type='BN', momentum=1e-2, eps=1e-3),
init_cfg: OptMultiConfig = None,
**kwargs) -> None:
self.num_ins = num_ins
self.stacked_convs = stacked_convs
self.norm_cfg = norm_cfg
super().__init__(
num_classes=num_classes,
in_channels=in_channels,
feat_channels=feat_channels,
init_cfg=init_cfg,
**kwargs)
def _init_layers(self) -> None:
"""Initialize layers of the head."""
self.reg_conv_list = nn.ModuleList()
self.cls_conv_list = nn.ModuleList()
for i in range(self.stacked_convs):
channels = self.in_channels if i == 0 else self.feat_channels
self.reg_conv_list.append(
DepthWiseConvBlock(
channels, self.feat_channels, apply_norm=False))
self.cls_conv_list.append(
DepthWiseConvBlock(
channels, self.feat_channels, apply_norm=False))
self.reg_bn_list = nn.ModuleList([
nn.ModuleList([
build_norm_layer(
self.norm_cfg, num_features=self.feat_channels)[1]
for j in range(self.num_ins)
]) for i in range(self.stacked_convs)
])
self.cls_bn_list = nn.ModuleList([
nn.ModuleList([
build_norm_layer(
self.norm_cfg, num_features=self.feat_channels)[1]
for j in range(self.num_ins)
]) for i in range(self.stacked_convs)
])
self.cls_header = DepthWiseConvBlock(
self.in_channels,
self.num_base_priors * self.cls_out_channels,
apply_norm=False)
self.reg_header = DepthWiseConvBlock(
self.in_channels, self.num_base_priors * 4, apply_norm=False)
self.swish = Swish()
def init_weights(self) -> None:
"""Initialize weights of the head."""
for m in self.reg_conv_list:
nn.init.constant_(m.pointwise_conv.bias, 0.0)
for m in self.cls_conv_list:
nn.init.constant_(m.pointwise_conv.bias, 0.0)
bias_cls = bias_init_with_prob(0.01)
nn.init.constant_(self.cls_header.pointwise_conv.bias, bias_cls)
nn.init.constant_(self.reg_header.pointwise_conv.bias, 0.0)
def forward_single_bbox(self, feat: Tensor, level_id: int,
i: int) -> Tensor:
conv_op = self.reg_conv_list[i]
bn = self.reg_bn_list[i][level_id]
feat = conv_op(feat)
feat = bn(feat)
feat = self.swish(feat)
return feat
def forward_single_cls(self, feat: Tensor, level_id: int,
i: int) -> Tensor:
conv_op = self.cls_conv_list[i]
bn = self.cls_bn_list[i][level_id]
feat = conv_op(feat)
feat = bn(feat)
feat = self.swish(feat)
return feat
def forward(self, feats: Tuple[Tensor]) -> tuple:
cls_scores = []
bbox_preds = []
for level_id in range(self.num_ins):
feat = feats[level_id]
for i in range(self.stacked_convs):
feat = self.forward_single_bbox(feat, level_id, i)
bbox_pred = self.reg_header(feat)
bbox_preds.append(bbox_pred)
for level_id in range(self.num_ins):
feat = feats[level_id]
for i in range(self.stacked_convs):
feat = self.forward_single_cls(feat, level_id, i)
cls_score = self.cls_header(feat)
cls_scores.append(cls_score)
return cls_scores, bbox_preds
def loss_by_feat(
self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> dict:
"""Calculate the loss based on the features extracted by the detection
head.
Args:
cls_scores (list[Tensor]): Box scores for each scale level
has shape (N, num_anchors * num_classes, H, W).
bbox_preds (list[Tensor]): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W).
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: A dictionary of loss components.
"""
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.prior_generator.num_levels
device = cls_scores[0].device
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, batch_img_metas, device=device)
cls_reg_targets = self.get_targets(
anchor_list,
valid_flag_list,
batch_gt_instances,
batch_img_metas,
batch_gt_instances_ignore=batch_gt_instances_ignore)
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
avg_factor) = cls_reg_targets
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# concat all level anchors and flags to a single tensor
concat_anchor_list = []
for i in range(len(anchor_list)):
concat_anchor_list.append(cat_boxes(anchor_list[i]))
all_anchor_list = images_to_levels(concat_anchor_list,
num_level_anchors)
avg_factor = reduce_mean(
torch.tensor(avg_factor, dtype=torch.float, device=device)).item()
avg_factor = max(avg_factor, 1.0)
losses_cls, losses_bbox = multi_apply(
self.loss_by_feat_single,
cls_scores,
bbox_preds,
all_anchor_list,
labels_list,
label_weights_list,
bbox_targets_list,
bbox_weights_list,
avg_factor=avg_factor)
return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
def loss_by_feat_single(self, cls_score: Tensor, bbox_pred: Tensor,
anchors: Tensor, labels: Tensor,
label_weights: Tensor, bbox_targets: Tensor,
bbox_weights: Tensor, avg_factor: int) -> tuple:
"""Calculate the loss of a single scale level based on the features
extracted by the detection head.
Args:
cls_score (Tensor): Box scores for each scale level
Has shape (N, num_anchors * num_classes, H, W).
bbox_pred (Tensor): Box energies / deltas for each scale
level with shape (N, num_anchors * 4, H, W).
anchors (Tensor): Box reference for each scale level with shape
(N, num_total_anchors, 4).
labels (Tensor): Labels of each anchors with shape
(N, num_total_anchors).
label_weights (Tensor): Label weights of each anchor with shape
(N, num_total_anchors)
bbox_targets (Tensor): BBox regression targets of each anchor
weight shape (N, num_total_anchors, 4).
bbox_weights (Tensor): BBox regression loss weights of each anchor
with shape (N, num_total_anchors, 4).
avg_factor (int): Average factor that is used to average the loss.
Returns:
tuple: loss components.
"""
# classification loss
labels = labels.reshape(-1)
label_weights = label_weights.reshape(-1)
cls_score = cls_score.permute(0, 2, 3,
1).reshape(-1, self.cls_out_channels)
loss_cls = self.loss_cls(
cls_score, labels, label_weights, avg_factor=avg_factor)
# regression loss
target_dim = bbox_targets.size(-1)
bbox_targets = bbox_targets.reshape(-1, target_dim)
bbox_weights = bbox_weights.reshape(-1, target_dim)
bbox_pred = bbox_pred.permute(0, 2, 3,
1).reshape(-1,
self.bbox_coder.encode_size)
if self.reg_decoded_bbox:
# When the regression loss (e.g. `IouLoss`, `GIouLoss`)
# is applied directly on the decoded bounding boxes, it
# decodes the already encoded coordinates to absolute format.
anchors = anchors.reshape(-1, anchors.size(-1))
bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
bbox_pred = get_box_tensor(bbox_pred)
loss_bbox = self.loss_bbox(
bbox_pred, bbox_targets, bbox_weights, avg_factor=avg_factor * 4)
return loss_cls, loss_bbox