import torch
import torch.nn as nn
from mmcv.cnn import bias_init_with_prob, normal_init
from mmcv.ops import batched_nms
from mmcv.runner import force_fp32
from mmdet.core import multi_apply
from mmdet.models import HEADS, build_loss
from mmdet.models.utils import gaussian_radius, gen_gaussian_target
from ..utils.gaussian_target import (get_local_maximum, get_topk_from_heatmap,
transpose_and_gather_feat)
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin
[docs]@HEADS.register_module()
class CenterNetHead(BaseDenseHead, BBoxTestMixin):
"""Objects as Points Head. CenterHead use center_point to indicate object's
position. Paper link <https://arxiv.org/abs/1904.07850>
Args:
in_channel (int): Number of channel in the input feature map.
feat_channel (int): Number of channel in the intermediate feature map.
num_classes (int): Number of categories excluding the background
category.
loss_center_heatmap (dict | None): Config of center heatmap loss.
Default: GaussianFocalLoss.
loss_wh (dict | None): Config of wh loss. Default: L1Loss.
loss_offset (dict | None): Config of offset loss. Default: L1Loss.
train_cfg (dict | None): Training config. Useless in CenterNet,
but we keep this variable for SingleStageDetector. Default: None.
test_cfg (dict | None): Testing config of CenterNet. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
in_channel,
feat_channel,
num_classes,
loss_center_heatmap=dict(
type='GaussianFocalLoss', loss_weight=1.0),
loss_wh=dict(type='L1Loss', loss_weight=0.1),
loss_offset=dict(type='L1Loss', loss_weight=1.0),
train_cfg=None,
test_cfg=None,
init_cfg=None):
super(CenterNetHead, self).__init__(init_cfg)
self.num_classes = num_classes
self.heatmap_head = self._build_head(in_channel, feat_channel,
num_classes)
self.wh_head = self._build_head(in_channel, feat_channel, 2)
self.offset_head = self._build_head(in_channel, feat_channel, 2)
self.loss_center_heatmap = build_loss(loss_center_heatmap)
self.loss_wh = build_loss(loss_wh)
self.loss_offset = build_loss(loss_offset)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.fp16_enabled = False
def _build_head(self, in_channel, feat_channel, out_channel):
"""Build head for each branch."""
layer = nn.Sequential(
nn.Conv2d(in_channel, feat_channel, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(feat_channel, out_channel, kernel_size=1))
return layer
[docs] def init_weights(self):
"""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)
[docs] def forward(self, feats):
"""Forward features. Notice CenterNet head does not use FPN.
Args:
feats (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, feats)
[docs] def forward_single(self, feat):
"""Forward feature of a single level.
Args:
feat (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(feat).sigmoid()
wh_pred = self.wh_head(feat)
offset_pred = self.offset_head(feat)
return center_heatmap_pred, wh_pred, offset_pred
[docs] @force_fp32(apply_to=('center_heatmap_preds', 'wh_preds', 'offset_preds'))
def loss(self,
center_heatmap_preds,
wh_preds,
offset_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
"""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).
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.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
boxes can be ignored when computing the loss. Default: 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]
target_result, avg_factor = self.get_targets(gt_bboxes, gt_labels,
center_heatmap_pred.shape,
img_metas[0]['pad_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)
[docs] def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape):
"""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 (list[int]): feature map shape with value [B, _, H, W]
img_shape (list[int]): image shape in [h, w] format.
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
[docs] def get_bboxes(self,
center_heatmap_preds,
wh_preds,
offset_preds,
img_metas,
rescale=True,
with_nms=False):
"""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).
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
rescale (bool): If True, return boxes in original image space.
Default: True.
with_nms (bool): If True, do nms before return boxes.
Default: False.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where 5 represent
(tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
The shape of the second tensor in the tuple is (n,), and
each element represents the class label of the corresponding
box.
"""
assert len(center_heatmap_preds) == len(wh_preds) == len(
offset_preds) == 1
scale_factors = [img_meta['scale_factor'] for img_meta in img_metas]
border_pixs = [img_meta['border'] for img_meta in img_metas]
batch_det_bboxes, batch_labels = self.decode_heatmap(
center_heatmap_preds[0],
wh_preds[0],
offset_preds[0],
img_metas[0]['batch_input_shape'],
k=self.test_cfg.topk,
kernel=self.test_cfg.local_maximum_kernel)
batch_border = batch_det_bboxes.new_tensor(
border_pixs)[:, [2, 0, 2, 0]].unsqueeze(1)
batch_det_bboxes[..., :4] -= batch_border
if rescale:
batch_det_bboxes[..., :4] /= batch_det_bboxes.new_tensor(
scale_factors).unsqueeze(1)
if with_nms:
det_results = []
for (det_bboxes, det_labels) in zip(batch_det_bboxes,
batch_labels):
det_bbox, det_label = self._bboxes_nms(det_bboxes, det_labels,
self.test_cfg)
det_results.append(tuple([det_bbox, det_label]))
else:
det_results = [
tuple(bs) for bs in zip(batch_det_bboxes, batch_labels)
]
return det_results
[docs] def decode_heatmap(self,
center_heatmap_pred,
wh_pred,
offset_pred,
img_shape,
k=100,
kernel=3):
"""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 (list[int]): image shape in [h, w] format.
k (int): Get top k center keypoints from heatmap. Default 100.
kernel (int): Max pooling kernel for extract local maximum pixels.
Default 3.
Returns:
tuple[torch.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, labels, cfg):
if labels.numel() == 0:
return bboxes, labels
out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels,
cfg.nms_cfg)
out_labels = labels[keep]
if len(out_bboxes) > 0:
idx = torch.argsort(out_bboxes[:, -1], descending=True)
idx = idx[:cfg.max_per_img]
out_bboxes = out_bboxes[idx]
out_labels = out_labels[idx]
return out_bboxes, out_labels