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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.ops import batched_nms
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures.bbox import (cat_boxes, empty_box_as, get_box_tensor,
get_box_wh, scale_boxes)
from mmdet.utils import InstanceList, MultiConfig, OptInstanceList
from .anchor_head import AnchorHead
@MODELS.register_module()
class RPNHead(AnchorHead):
"""Implementation of RPN head.
Args:
in_channels (int): Number of channels in the input feature map.
num_classes (int): Number of categories excluding the background
category. Defaults to 1.
init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or \
list[dict]): Initialization config dict.
num_convs (int): Number of convolution layers in the head.
Defaults to 1.
""" # noqa: W605
def __init__(self,
in_channels: int,
num_classes: int = 1,
init_cfg: MultiConfig = dict(
type='Normal', layer='Conv2d', std=0.01),
num_convs: int = 1,
**kwargs) -> None:
self.num_convs = num_convs
assert num_classes == 1
super().__init__(
num_classes=num_classes,
in_channels=in_channels,
init_cfg=init_cfg,
**kwargs)
def _init_layers(self) -> None:
"""Initialize layers of the head."""
if self.num_convs > 1:
rpn_convs = []
for i in range(self.num_convs):
if i == 0:
in_channels = self.in_channels
else:
in_channels = self.feat_channels
# use ``inplace=False`` to avoid error: one of the variables
# needed for gradient computation has been modified by an
# inplace operation.
rpn_convs.append(
ConvModule(
in_channels,
self.feat_channels,
3,
padding=1,
inplace=False))
self.rpn_conv = nn.Sequential(*rpn_convs)
else:
self.rpn_conv = nn.Conv2d(
self.in_channels, self.feat_channels, 3, padding=1)
self.rpn_cls = nn.Conv2d(self.feat_channels,
self.num_base_priors * self.cls_out_channels,
1)
reg_dim = self.bbox_coder.encode_size
self.rpn_reg = nn.Conv2d(self.feat_channels,
self.num_base_priors * reg_dim, 1)
def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Forward feature of a single scale level.
Args:
x (Tensor): Features of a single scale level.
Returns:
tuple:
cls_score (Tensor): Cls scores for a single scale level \
the channels number is num_base_priors * num_classes.
bbox_pred (Tensor): Box energies / deltas for a single scale \
level, the channels number is num_base_priors * 4.
"""
x = self.rpn_conv(x)
x = F.relu(x)
rpn_cls_score = self.rpn_cls(x)
rpn_bbox_pred = self.rpn_reg(x)
return rpn_cls_score, rpn_bbox_pred
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.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
losses = super().loss_by_feat(
cls_scores,
bbox_preds,
batch_gt_instances,
batch_img_metas,
batch_gt_instances_ignore=batch_gt_instances_ignore)
return dict(
loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox'])
def _predict_by_feat_single(self,
cls_score_list: List[Tensor],
bbox_pred_list: List[Tensor],
score_factor_list: List[Tensor],
mlvl_priors: List[Tensor],
img_meta: dict,
cfg: ConfigDict,
rescale: bool = False,
with_nms: bool = True) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
levels of a single image, each item has shape
(num_priors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas from
all scale levels of a single image, each item has shape
(num_priors * 4, H, W).
score_factor_list (list[Tensor]): Be compatible with
BaseDenseHead. Not used in RPNHead.
mlvl_priors (list[Tensor]): Each element in the list is
the priors of a single level in feature pyramid. In all
anchor-based methods, it has shape (num_priors, 4). In
all anchor-free methods, it has shape (num_priors, 2)
when `with_stride=True`, otherwise it still has shape
(num_priors, 4).
img_meta (dict): Image meta info.
cfg (ConfigDict, optional): Test / postprocessing configuration,
if None, test_cfg would be used.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
:obj:`InstanceData`: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
cfg = self.test_cfg if cfg is None else cfg
cfg = copy.deepcopy(cfg)
img_shape = img_meta['img_shape']
nms_pre = cfg.get('nms_pre', -1)
mlvl_bbox_preds = []
mlvl_valid_priors = []
mlvl_scores = []
level_ids = []
for level_idx, (cls_score, bbox_pred, priors) in \
enumerate(zip(cls_score_list, bbox_pred_list,
mlvl_priors)):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
reg_dim = self.bbox_coder.encode_size
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, reg_dim)
cls_score = cls_score.permute(1, 2,
0).reshape(-1, self.cls_out_channels)
if self.use_sigmoid_cls:
scores = cls_score.sigmoid()
else:
# remind that we set FG labels to [0] since mmdet v2.0
# BG cat_id: 1
scores = cls_score.softmax(-1)[:, :-1]
scores = torch.squeeze(scores)
if 0 < nms_pre < scores.shape[0]:
# sort is faster than topk
# _, topk_inds = scores.topk(cfg.nms_pre)
ranked_scores, rank_inds = scores.sort(descending=True)
topk_inds = rank_inds[:nms_pre]
scores = ranked_scores[:nms_pre]
bbox_pred = bbox_pred[topk_inds, :]
priors = priors[topk_inds]
mlvl_bbox_preds.append(bbox_pred)
mlvl_valid_priors.append(priors)
mlvl_scores.append(scores)
# use level id to implement the separate level nms
level_ids.append(
scores.new_full((scores.size(0), ),
level_idx,
dtype=torch.long))
bbox_pred = torch.cat(mlvl_bbox_preds)
priors = cat_boxes(mlvl_valid_priors)
bboxes = self.bbox_coder.decode(priors, bbox_pred, max_shape=img_shape)
results = InstanceData()
results.bboxes = bboxes
results.scores = torch.cat(mlvl_scores)
results.level_ids = torch.cat(level_ids)
return self._bbox_post_process(
results=results, cfg=cfg, rescale=rescale, img_meta=img_meta)
def _bbox_post_process(self,
results: InstanceData,
cfg: ConfigDict,
rescale: bool = False,
with_nms: bool = True,
img_meta: Optional[dict] = None) -> InstanceData:
"""bbox post-processing method.
The boxes would be rescaled to the original image scale and do
the nms operation.
Args:
results (:obj:`InstaceData`): Detection instance results,
each item has shape (num_bboxes, ).
cfg (ConfigDict): Test / postprocessing configuration.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
with_nms (bool): If True, do nms before return boxes.
Default to True.
img_meta (dict, optional): Image meta info. Defaults to None.
Returns:
:obj:`InstanceData`: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
assert with_nms, '`with_nms` must be True in RPNHead'
if rescale:
assert img_meta.get('scale_factor') is not None
scale_factor = [1 / s for s in img_meta['scale_factor']]
results.bboxes = scale_boxes(results.bboxes, scale_factor)
# filter small size bboxes
if cfg.get('min_bbox_size', -1) >= 0:
w, h = get_box_wh(results.bboxes)
valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
if not valid_mask.all():
results = results[valid_mask]
if results.bboxes.numel() > 0:
bboxes = get_box_tensor(results.bboxes)
det_bboxes, keep_idxs = batched_nms(bboxes, results.scores,
results.level_ids, cfg.nms)
results = results[keep_idxs]
# some nms would reweight the score, such as softnms
results.scores = det_bboxes[:, -1]
results = results[:cfg.max_per_img]
# TODO: This would unreasonably show the 0th class label
# in visualization
results.labels = results.scores.new_zeros(
len(results), dtype=torch.long)
del results.level_ids
else:
# To avoid some potential error
results_ = InstanceData()
results_.bboxes = empty_box_as(results.bboxes)
results_.scores = results.scores.new_zeros(0)
results_.labels = results.scores.new_zeros(0)
results = results_
return results
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