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- # Copyright (c) OpenMMLab. All rights reserved.
- 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
-
-
- @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
-
- 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)
-
- 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)
-
- 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
-
- @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)
-
- 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
-
- @force_fp32(apply_to=('center_heatmap_preds', 'wh_preds', 'offset_preds'))
- 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
- result_list = []
- for img_id in range(len(img_metas)):
- result_list.append(
- self._get_bboxes_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, ...],
- img_metas[img_id],
- rescale=rescale,
- with_nms=with_nms))
- return result_list
-
- def _get_bboxes_single(self,
- center_heatmap_pred,
- wh_pred,
- offset_pred,
- img_meta,
- rescale=False,
- with_nms=True):
- """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.
- Default: False.
- with_nms (bool): If True, do nms before return boxes.
- Default: True.
-
- Returns:
- tuple[Tensor, Tensor]: 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.
- """
- 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:
- det_bboxes[..., :4] /= det_bboxes.new_tensor(
- img_meta['scale_factor'])
-
- if with_nms:
- det_bboxes, det_labels = self._bboxes_nms(det_bboxes, det_labels,
- self.test_cfg)
- return det_bboxes, det_labels
-
- 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:
- 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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