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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from mmcv.ops import batched_nms
from mmengine.config import ConfigDict
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.utils import (ConfigType, InstanceList, OptConfigType,
OptInstanceList, OptMultiConfig)
from ..utils import (gaussian_radius, gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, multi_apply,
transpose_and_gather_feat)
from .base_dense_head import BaseDenseHead
@MODELS.register_module()
class CenterNetHead(BaseDenseHead):
"""Objects as Points Head. CenterHead use center_point to indicate object's
position. Paper link <https://arxiv.org/abs/1904.07850>
Args:
in_channels (int): Number of channel in the input feature map.
feat_channels (int): Number of channel in the intermediate feature map.
num_classes (int): Number of categories excluding the background
category.
loss_center_heatmap (:obj:`ConfigDict` or dict): Config of center
heatmap loss. Defaults to
dict(type='GaussianFocalLoss', loss_weight=1.0)
loss_wh (:obj:`ConfigDict` or dict): Config of wh loss. Defaults to
dict(type='L1Loss', loss_weight=0.1).
loss_offset (:obj:`ConfigDict` or dict): Config of offset loss.
Defaults to dict(type='L1Loss', loss_weight=1.0).
train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
Useless in CenterNet, but we keep this variable for
SingleStageDetector.
test_cfg (:obj:`ConfigDict` or dict, optional): Testing config
of CenterNet.
init_cfg (:obj:`ConfigDict` or dict or list[dict] or
list[:obj:`ConfigDict`], optional): Initialization
config dict.
"""
def __init__(self,
in_channels: int,
feat_channels: int,
num_classes: int,
loss_center_heatmap: ConfigType = dict(
type='GaussianFocalLoss', loss_weight=1.0),
loss_wh: ConfigType = dict(type='L1Loss', loss_weight=0.1),
loss_offset: ConfigType = dict(
type='L1Loss', loss_weight=1.0),
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None) -> None:
super().__init__(init_cfg=init_cfg)
self.num_classes = num_classes
self.heatmap_head = self._build_head(in_channels, feat_channels,
num_classes)
self.wh_head = self._build_head(in_channels, feat_channels, 2)
self.offset_head = self._build_head(in_channels, feat_channels, 2)
self.loss_center_heatmap = MODELS.build(loss_center_heatmap)
self.loss_wh = MODELS.build(loss_wh)
self.loss_offset = MODELS.build(loss_offset)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.fp16_enabled = False
def _build_head(self, in_channels: int, feat_channels: int,
out_channels: int) -> nn.Sequential:
"""Build head for each branch."""
layer = nn.Sequential(
nn.Conv2d(in_channels, feat_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(feat_channels, out_channels, kernel_size=1))
return layer
def init_weights(self) -> None:
"""Initialize weights of the head."""
bias_init = bias_init_with_prob(0.1)
self.heatmap_head[-1].bias.data.fill_(bias_init)
for head in [self.wh_head, self.offset_head]:
for m in head.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
def forward(self, x: Tuple[Tensor, ...]) -> Tuple[List[Tensor]]:
"""Forward features. Notice CenterNet head does not use FPN.
Args:
x (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
center_heatmap_preds (list[Tensor]): center predict heatmaps for
all levels, the channels number is num_classes.
wh_preds (list[Tensor]): wh predicts for all levels, the channels
number is 2.
offset_preds (list[Tensor]): offset predicts for all levels, the
channels number is 2.
"""
return multi_apply(self.forward_single, x)
def forward_single(self, x: Tensor) -> Tuple[Tensor, ...]:
"""Forward feature of a single level.
Args:
x (Tensor): Feature of a single level.
Returns:
center_heatmap_pred (Tensor): center predict heatmaps, the
channels number is num_classes.
wh_pred (Tensor): wh predicts, the channels number is 2.
offset_pred (Tensor): offset predicts, the channels number is 2.
"""
center_heatmap_pred = self.heatmap_head(x).sigmoid()
wh_pred = self.wh_head(x)
offset_pred = self.offset_head(x)
return center_heatmap_pred, wh_pred, offset_pred
def loss_by_feat(
self,
center_heatmap_preds: List[Tensor],
wh_preds: List[Tensor],
offset_preds: List[Tensor],
batch_gt_instances: InstanceList,
batch_img_metas: List[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> dict:
"""Compute losses of the head.
Args:
center_heatmap_preds (list[Tensor]): center predict heatmaps for
all levels with shape (B, num_classes, H, W).
wh_preds (list[Tensor]): wh predicts for all levels with
shape (B, 2, H, W).
offset_preds (list[Tensor]): offset predicts for all levels
with shape (B, 2, 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[str, Tensor]: which has components below:
- loss_center_heatmap (Tensor): loss of center heatmap.
- loss_wh (Tensor): loss of hw heatmap
- loss_offset (Tensor): loss of offset heatmap.
"""
assert len(center_heatmap_preds) == len(wh_preds) == len(
offset_preds) == 1
center_heatmap_pred = center_heatmap_preds[0]
wh_pred = wh_preds[0]
offset_pred = offset_preds[0]
gt_bboxes = [
gt_instances.bboxes for gt_instances in batch_gt_instances
]
gt_labels = [
gt_instances.labels for gt_instances in batch_gt_instances
]
img_shape = batch_img_metas[0]['batch_input_shape']
target_result, avg_factor = self.get_targets(gt_bboxes, gt_labels,
center_heatmap_pred.shape,
img_shape)
center_heatmap_target = target_result['center_heatmap_target']
wh_target = target_result['wh_target']
offset_target = target_result['offset_target']
wh_offset_target_weight = target_result['wh_offset_target_weight']
# Since the channel of wh_target and offset_target is 2, the avg_factor
# of loss_center_heatmap is always 1/2 of loss_wh and loss_offset.
loss_center_heatmap = self.loss_center_heatmap(
center_heatmap_pred, center_heatmap_target, avg_factor=avg_factor)
loss_wh = self.loss_wh(
wh_pred,
wh_target,
wh_offset_target_weight,
avg_factor=avg_factor * 2)
loss_offset = self.loss_offset(
offset_pred,
offset_target,
wh_offset_target_weight,
avg_factor=avg_factor * 2)
return dict(
loss_center_heatmap=loss_center_heatmap,
loss_wh=loss_wh,
loss_offset=loss_offset)
def get_targets(self, gt_bboxes: List[Tensor], gt_labels: List[Tensor],
feat_shape: tuple, img_shape: tuple) -> Tuple[dict, int]:
"""Compute regression and classification targets in multiple images.
Args:
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box.
feat_shape (tuple): feature map shape with value [B, _, H, W]
img_shape (tuple): image shape.
Returns:
tuple[dict, float]: The float value is mean avg_factor, the dict
has components below:
- center_heatmap_target (Tensor): targets of center heatmap, \
shape (B, num_classes, H, W).
- wh_target (Tensor): targets of wh predict, shape \
(B, 2, H, W).
- offset_target (Tensor): targets of offset predict, shape \
(B, 2, H, W).
- wh_offset_target_weight (Tensor): weights of wh and offset \
predict, shape (B, 2, H, W).
"""
img_h, img_w = img_shape[:2]
bs, _, feat_h, feat_w = feat_shape
width_ratio = float(feat_w / img_w)
height_ratio = float(feat_h / img_h)
center_heatmap_target = gt_bboxes[-1].new_zeros(
[bs, self.num_classes, feat_h, feat_w])
wh_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w])
offset_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w])
wh_offset_target_weight = gt_bboxes[-1].new_zeros(
[bs, 2, feat_h, feat_w])
for batch_id in range(bs):
gt_bbox = gt_bboxes[batch_id]
gt_label = gt_labels[batch_id]
center_x = (gt_bbox[:, [0]] + gt_bbox[:, [2]]) * width_ratio / 2
center_y = (gt_bbox[:, [1]] + gt_bbox[:, [3]]) * height_ratio / 2
gt_centers = torch.cat((center_x, center_y), dim=1)
for j, ct in enumerate(gt_centers):
ctx_int, cty_int = ct.int()
ctx, cty = ct
scale_box_h = (gt_bbox[j][3] - gt_bbox[j][1]) * height_ratio
scale_box_w = (gt_bbox[j][2] - gt_bbox[j][0]) * width_ratio
radius = gaussian_radius([scale_box_h, scale_box_w],
min_overlap=0.3)
radius = max(0, int(radius))
ind = gt_label[j]
gen_gaussian_target(center_heatmap_target[batch_id, ind],
[ctx_int, cty_int], radius)
wh_target[batch_id, 0, cty_int, ctx_int] = scale_box_w
wh_target[batch_id, 1, cty_int, ctx_int] = scale_box_h
offset_target[batch_id, 0, cty_int, ctx_int] = ctx - ctx_int
offset_target[batch_id, 1, cty_int, ctx_int] = cty - cty_int
wh_offset_target_weight[batch_id, :, cty_int, ctx_int] = 1
avg_factor = max(1, center_heatmap_target.eq(1).sum())
target_result = dict(
center_heatmap_target=center_heatmap_target,
wh_target=wh_target,
offset_target=offset_target,
wh_offset_target_weight=wh_offset_target_weight)
return target_result, avg_factor
def predict_by_feat(self,
center_heatmap_preds: List[Tensor],
wh_preds: List[Tensor],
offset_preds: List[Tensor],
batch_img_metas: Optional[List[dict]] = None,
rescale: bool = True,
with_nms: bool = False) -> InstanceList:
"""Transform network output for a batch into bbox predictions.
Args:
center_heatmap_preds (list[Tensor]): Center predict heatmaps for
all levels with shape (B, num_classes, H, W).
wh_preds (list[Tensor]): WH predicts for all levels with
shape (B, 2, H, W).
offset_preds (list[Tensor]): Offset predicts for all levels
with shape (B, 2, H, W).
batch_img_metas (list[dict], optional): Batch image meta info.
Defaults to None.
rescale (bool): If True, return boxes in original image space.
Defaults to True.
with_nms (bool): If True, do nms before return boxes.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Instance segmentation
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 len(center_heatmap_preds) == len(wh_preds) == len(
offset_preds) == 1
result_list = []
for img_id in range(len(batch_img_metas)):
result_list.append(
self._predict_by_feat_single(
center_heatmap_preds[0][img_id:img_id + 1, ...],
wh_preds[0][img_id:img_id + 1, ...],
offset_preds[0][img_id:img_id + 1, ...],
batch_img_metas[img_id],
rescale=rescale,
with_nms=with_nms))
return result_list
def _predict_by_feat_single(self,
center_heatmap_pred: Tensor,
wh_pred: Tensor,
offset_pred: Tensor,
img_meta: dict,
rescale: bool = True,
with_nms: bool = False) -> InstanceData:
"""Transform outputs of a single image into bbox results.
Args:
center_heatmap_pred (Tensor): Center heatmap for current level with
shape (1, num_classes, H, W).
wh_pred (Tensor): WH heatmap for current level with shape
(1, num_classes, H, W).
offset_pred (Tensor): Offset for current level with shape
(1, corner_offset_channels, H, W).
img_meta (dict): Meta information of current image, e.g.,
image size, scaling factor, etc.
rescale (bool): If True, return boxes in original image space.
Defaults to True.
with_nms (bool): If True, do nms before return boxes.
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).
"""
batch_det_bboxes, batch_labels = self._decode_heatmap(
center_heatmap_pred,
wh_pred,
offset_pred,
img_meta['batch_input_shape'],
k=self.test_cfg.topk,
kernel=self.test_cfg.local_maximum_kernel)
det_bboxes = batch_det_bboxes.view([-1, 5])
det_labels = batch_labels.view(-1)
batch_border = det_bboxes.new_tensor(img_meta['border'])[...,
[2, 0, 2, 0]]
det_bboxes[..., :4] -= batch_border
if rescale and 'scale_factor' in img_meta:
det_bboxes[..., :4] /= det_bboxes.new_tensor(
img_meta['scale_factor']).repeat((1, 2))
if with_nms:
det_bboxes, det_labels = self._bboxes_nms(det_bboxes, det_labels,
self.test_cfg)
results = InstanceData()
results.bboxes = det_bboxes[..., :4]
results.scores = det_bboxes[..., 4]
results.labels = det_labels
return results
def _decode_heatmap(self,
center_heatmap_pred: Tensor,
wh_pred: Tensor,
offset_pred: Tensor,
img_shape: tuple,
k: int = 100,
kernel: int = 3) -> Tuple[Tensor, Tensor]:
"""Transform outputs into detections raw bbox prediction.
Args:
center_heatmap_pred (Tensor): center predict heatmap,
shape (B, num_classes, H, W).
wh_pred (Tensor): wh predict, shape (B, 2, H, W).
offset_pred (Tensor): offset predict, shape (B, 2, H, W).
img_shape (tuple): image shape in hw format.
k (int): Get top k center keypoints from heatmap. Defaults to 100.
kernel (int): Max pooling kernel for extract local maximum pixels.
Defaults to 3.
Returns:
tuple[Tensor]: Decoded output of CenterNetHead, containing
the following Tensors:
- batch_bboxes (Tensor): Coords of each box with shape (B, k, 5)
- batch_topk_labels (Tensor): Categories of each box with \
shape (B, k)
"""
height, width = center_heatmap_pred.shape[2:]
inp_h, inp_w = img_shape
center_heatmap_pred = get_local_maximum(
center_heatmap_pred, kernel=kernel)
*batch_dets, topk_ys, topk_xs = get_topk_from_heatmap(
center_heatmap_pred, k=k)
batch_scores, batch_index, batch_topk_labels = batch_dets
wh = transpose_and_gather_feat(wh_pred, batch_index)
offset = transpose_and_gather_feat(offset_pred, batch_index)
topk_xs = topk_xs + offset[..., 0]
topk_ys = topk_ys + offset[..., 1]
tl_x = (topk_xs - wh[..., 0] / 2) * (inp_w / width)
tl_y = (topk_ys - wh[..., 1] / 2) * (inp_h / height)
br_x = (topk_xs + wh[..., 0] / 2) * (inp_w / width)
br_y = (topk_ys + wh[..., 1] / 2) * (inp_h / height)
batch_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=2)
batch_bboxes = torch.cat((batch_bboxes, batch_scores[..., None]),
dim=-1)
return batch_bboxes, batch_topk_labels
def _bboxes_nms(self, bboxes: Tensor, labels: Tensor,
cfg: ConfigDict) -> Tuple[Tensor, Tensor]:
"""bboxes nms."""
if labels.numel() > 0:
max_num = cfg.max_per_img
bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:,
-1].contiguous(),
labels, cfg.nms)
if max_num > 0:
bboxes = bboxes[:max_num]
labels = labels[keep][:max_num]
return bboxes, labels
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