|
- # Copyright (c) OpenMMLab. All rights reserved.
- import numpy as np
- import torch
- import torch.nn as nn
- from mmcv.cnn import ConvModule
- from mmcv.ops import DeformConv2d
-
- from mmdet.core import (build_assigner, build_sampler, images_to_levels,
- multi_apply, unmap)
- from mmdet.core.anchor.point_generator import MlvlPointGenerator
- from mmdet.core.utils import filter_scores_and_topk
- from ..builder import HEADS, build_loss
- from .anchor_free_head import AnchorFreeHead
-
-
- @HEADS.register_module()
- class RepPointsHead(AnchorFreeHead):
- """RepPoint head.
-
- Args:
- point_feat_channels (int): Number of channels of points features.
- gradient_mul (float): The multiplier to gradients from
- points refinement and recognition.
- point_strides (Iterable): points strides.
- point_base_scale (int): bbox scale for assigning labels.
- loss_cls (dict): Config of classification loss.
- loss_bbox_init (dict): Config of initial points loss.
- loss_bbox_refine (dict): Config of points loss in refinement.
- use_grid_points (bool): If we use bounding box representation, the
- reppoints is represented as grid points on the bounding box.
- center_init (bool): Whether to use center point assignment.
- transform_method (str): The methods to transform RepPoints to bbox.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """ # noqa: W605
-
- def __init__(self,
- num_classes,
- in_channels,
- point_feat_channels=256,
- num_points=9,
- gradient_mul=0.1,
- point_strides=[8, 16, 32, 64, 128],
- point_base_scale=4,
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox_init=dict(
- type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5),
- loss_bbox_refine=dict(
- type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
- use_grid_points=False,
- center_init=True,
- transform_method='moment',
- moment_mul=0.01,
- init_cfg=dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='reppoints_cls_out',
- std=0.01,
- bias_prob=0.01)),
- **kwargs):
- self.num_points = num_points
- self.point_feat_channels = point_feat_channels
- self.use_grid_points = use_grid_points
- self.center_init = center_init
-
- # we use deform conv to extract points features
- self.dcn_kernel = int(np.sqrt(num_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- assert self.dcn_kernel * self.dcn_kernel == num_points, \
- 'The points number should be a square number.'
- assert self.dcn_kernel % 2 == 1, \
- 'The points number should be an odd square number.'
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super().__init__(
- num_classes,
- in_channels,
- loss_cls=loss_cls,
- init_cfg=init_cfg,
- **kwargs)
-
- self.gradient_mul = gradient_mul
- self.point_base_scale = point_base_scale
- self.point_strides = point_strides
- self.prior_generator = MlvlPointGenerator(
- self.point_strides, offset=0.)
-
- self.sampling = loss_cls['type'] not in ['FocalLoss']
- if self.train_cfg:
- self.init_assigner = build_assigner(self.train_cfg.init.assigner)
- self.refine_assigner = build_assigner(
- self.train_cfg.refine.assigner)
- # use PseudoSampler when sampling is False
- if self.sampling and hasattr(self.train_cfg, 'sampler'):
- sampler_cfg = self.train_cfg.sampler
- else:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
- self.transform_method = transform_method
- if self.transform_method == 'moment':
- self.moment_transfer = nn.Parameter(
- data=torch.zeros(2), requires_grad=True)
- self.moment_mul = moment_mul
-
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- if self.use_sigmoid_cls:
- self.cls_out_channels = self.num_classes
- else:
- self.cls_out_channels = self.num_classes + 1
- self.loss_bbox_init = build_loss(loss_bbox_init)
- self.loss_bbox_refine = build_loss(loss_bbox_refine)
-
- def _init_layers(self):
- """Initialize layers of the head."""
- self.relu = nn.ReLU(inplace=True)
- self.cls_convs = nn.ModuleList()
- self.reg_convs = nn.ModuleList()
- for i in range(self.stacked_convs):
- chn = self.in_channels if i == 0 else self.feat_channels
- self.cls_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.reg_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points
- self.reppoints_cls_conv = DeformConv2d(self.feat_channels,
- self.point_feat_channels,
- self.dcn_kernel, 1,
- self.dcn_pad)
- self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,
- self.cls_out_channels, 1, 1, 0)
- self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,
- self.point_feat_channels, 3,
- 1, 1)
- self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,
- pts_out_dim, 1, 1, 0)
- self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,
- self.point_feat_channels,
- self.dcn_kernel, 1,
- self.dcn_pad)
- self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,
- pts_out_dim, 1, 1, 0)
-
- def points2bbox(self, pts, y_first=True):
- """Converting the points set into bounding box.
-
- :param pts: the input points sets (fields), each points
- set (fields) is represented as 2n scalar.
- :param y_first: if y_first=True, the point set is represented as
- [y1, x1, y2, x2 ... yn, xn], otherwise the point set is
- represented as [x1, y1, x2, y2 ... xn, yn].
- :return: each points set is converting to a bbox [x1, y1, x2, y2].
- """
- pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:])
- pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1,
- ...]
- pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0,
- ...]
- if self.transform_method == 'minmax':
- bbox_left = pts_x.min(dim=1, keepdim=True)[0]
- bbox_right = pts_x.max(dim=1, keepdim=True)[0]
- bbox_up = pts_y.min(dim=1, keepdim=True)[0]
- bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
- bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
- dim=1)
- elif self.transform_method == 'partial_minmax':
- pts_y = pts_y[:, :4, ...]
- pts_x = pts_x[:, :4, ...]
- bbox_left = pts_x.min(dim=1, keepdim=True)[0]
- bbox_right = pts_x.max(dim=1, keepdim=True)[0]
- bbox_up = pts_y.min(dim=1, keepdim=True)[0]
- bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
- bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
- dim=1)
- elif self.transform_method == 'moment':
- pts_y_mean = pts_y.mean(dim=1, keepdim=True)
- pts_x_mean = pts_x.mean(dim=1, keepdim=True)
- pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True)
- pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True)
- moment_transfer = (self.moment_transfer * self.moment_mul) + (
- self.moment_transfer.detach() * (1 - self.moment_mul))
- moment_width_transfer = moment_transfer[0]
- moment_height_transfer = moment_transfer[1]
- half_width = pts_x_std * torch.exp(moment_width_transfer)
- half_height = pts_y_std * torch.exp(moment_height_transfer)
- bbox = torch.cat([
- pts_x_mean - half_width, pts_y_mean - half_height,
- pts_x_mean + half_width, pts_y_mean + half_height
- ],
- dim=1)
- else:
- raise NotImplementedError
- return bbox
-
- def gen_grid_from_reg(self, reg, previous_boxes):
- """Base on the previous bboxes and regression values, we compute the
- regressed bboxes and generate the grids on the bboxes.
-
- :param reg: the regression value to previous bboxes.
- :param previous_boxes: previous bboxes.
- :return: generate grids on the regressed bboxes.
- """
- b, _, h, w = reg.shape
- bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2.
- bwh = (previous_boxes[:, 2:, ...] -
- previous_boxes[:, :2, ...]).clamp(min=1e-6)
- grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp(
- reg[:, 2:, ...])
- grid_wh = bwh * torch.exp(reg[:, 2:, ...])
- grid_left = grid_topleft[:, [0], ...]
- grid_top = grid_topleft[:, [1], ...]
- grid_width = grid_wh[:, [0], ...]
- grid_height = grid_wh[:, [1], ...]
- intervel = torch.linspace(0., 1., self.dcn_kernel).view(
- 1, self.dcn_kernel, 1, 1).type_as(reg)
- grid_x = grid_left + grid_width * intervel
- grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1)
- grid_x = grid_x.view(b, -1, h, w)
- grid_y = grid_top + grid_height * intervel
- grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1)
- grid_y = grid_y.view(b, -1, h, w)
- grid_yx = torch.stack([grid_y, grid_x], dim=2)
- grid_yx = grid_yx.view(b, -1, h, w)
- regressed_bbox = torch.cat([
- grid_left, grid_top, grid_left + grid_width, grid_top + grid_height
- ], 1)
- return grid_yx, regressed_bbox
-
- def forward(self, feats):
- return multi_apply(self.forward_single, feats)
-
- def forward_single(self, x):
- """Forward feature map of a single FPN level."""
- dcn_base_offset = self.dcn_base_offset.type_as(x)
- # If we use center_init, the initial reppoints is from center points.
- # If we use bounding bbox representation, the initial reppoints is
- # from regular grid placed on a pre-defined bbox.
- if self.use_grid_points or not self.center_init:
- scale = self.point_base_scale / 2
- points_init = dcn_base_offset / dcn_base_offset.max() * scale
- bbox_init = x.new_tensor([-scale, -scale, scale,
- scale]).view(1, 4, 1, 1)
- else:
- points_init = 0
- cls_feat = x
- pts_feat = x
- for cls_conv in self.cls_convs:
- cls_feat = cls_conv(cls_feat)
- for reg_conv in self.reg_convs:
- pts_feat = reg_conv(pts_feat)
- # initialize reppoints
- pts_out_init = self.reppoints_pts_init_out(
- self.relu(self.reppoints_pts_init_conv(pts_feat)))
- if self.use_grid_points:
- pts_out_init, bbox_out_init = self.gen_grid_from_reg(
- pts_out_init, bbox_init.detach())
- else:
- pts_out_init = pts_out_init + points_init
- # refine and classify reppoints
- pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach(
- ) + self.gradient_mul * pts_out_init
- dcn_offset = pts_out_init_grad_mul - dcn_base_offset
- cls_out = self.reppoints_cls_out(
- self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset)))
- pts_out_refine = self.reppoints_pts_refine_out(
- self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset)))
- if self.use_grid_points:
- pts_out_refine, bbox_out_refine = self.gen_grid_from_reg(
- pts_out_refine, bbox_out_init.detach())
- else:
- pts_out_refine = pts_out_refine + pts_out_init.detach()
-
- if self.training:
- return cls_out, pts_out_init, pts_out_refine
- else:
- return cls_out, self.points2bbox(pts_out_refine)
-
- def get_points(self, featmap_sizes, img_metas, device):
- """Get points according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- img_metas (list[dict]): Image meta info.
-
- Returns:
- tuple: points of each image, valid flags of each image
- """
- num_imgs = len(img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # points center for one time
- multi_level_points = self.prior_generator.grid_priors(
- featmap_sizes, device=device, with_stride=True)
- points_list = [[point.clone() for point in multi_level_points]
- for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level grids
- valid_flag_list = []
- for img_id, img_meta in enumerate(img_metas):
- multi_level_flags = self.prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'])
- valid_flag_list.append(multi_level_flags)
-
- return points_list, valid_flag_list
-
- def centers_to_bboxes(self, point_list):
- """Get bboxes according to center points.
-
- Only used in :class:`MaxIoUAssigner`.
- """
- bbox_list = []
- for i_img, point in enumerate(point_list):
- bbox = []
- for i_lvl in range(len(self.point_strides)):
- scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5
- bbox_shift = torch.Tensor([-scale, -scale, scale,
- scale]).view(1, 4).type_as(point[0])
- bbox_center = torch.cat(
- [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1)
- bbox.append(bbox_center + bbox_shift)
- bbox_list.append(bbox)
- return bbox_list
-
- def offset_to_pts(self, center_list, pred_list):
- """Change from point offset to point coordinate."""
- pts_list = []
- for i_lvl in range(len(self.point_strides)):
- pts_lvl = []
- for i_img in range(len(center_list)):
- pts_center = center_list[i_img][i_lvl][:, :2].repeat(
- 1, self.num_points)
- pts_shift = pred_list[i_lvl][i_img]
- yx_pts_shift = pts_shift.permute(1, 2, 0).view(
- -1, 2 * self.num_points)
- y_pts_shift = yx_pts_shift[..., 0::2]
- x_pts_shift = yx_pts_shift[..., 1::2]
- xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1)
- xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1)
- pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center
- pts_lvl.append(pts)
- pts_lvl = torch.stack(pts_lvl, 0)
- pts_list.append(pts_lvl)
- return pts_list
-
- def _point_target_single(self,
- flat_proposals,
- valid_flags,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- stage='init',
- unmap_outputs=True):
- inside_flags = valid_flags
- if not inside_flags.any():
- return (None, ) * 7
- # assign gt and sample proposals
- proposals = flat_proposals[inside_flags, :]
-
- if stage == 'init':
- assigner = self.init_assigner
- pos_weight = self.train_cfg.init.pos_weight
- else:
- assigner = self.refine_assigner
- pos_weight = self.train_cfg.refine.pos_weight
- assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore,
- None if self.sampling else gt_labels)
- sampling_result = self.sampler.sample(assign_result, proposals,
- gt_bboxes)
-
- num_valid_proposals = proposals.shape[0]
- bbox_gt = proposals.new_zeros([num_valid_proposals, 4])
- pos_proposals = torch.zeros_like(proposals)
- proposals_weights = proposals.new_zeros([num_valid_proposals, 4])
- labels = proposals.new_full((num_valid_proposals, ),
- self.num_classes,
- dtype=torch.long)
- label_weights = proposals.new_zeros(
- num_valid_proposals, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- pos_gt_bboxes = sampling_result.pos_gt_bboxes
- bbox_gt[pos_inds, :] = pos_gt_bboxes
- pos_proposals[pos_inds, :] = proposals[pos_inds, :]
- proposals_weights[pos_inds, :] = 1.0
- if gt_labels is None:
- # Only rpn gives gt_labels as None
- # Foreground is the first class
- labels[pos_inds] = 0
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- # map up to original set of proposals
- if unmap_outputs:
- num_total_proposals = flat_proposals.size(0)
- labels = unmap(labels, num_total_proposals, inside_flags)
- label_weights = unmap(label_weights, num_total_proposals,
- inside_flags)
- bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags)
- pos_proposals = unmap(pos_proposals, num_total_proposals,
- inside_flags)
- proposals_weights = unmap(proposals_weights, num_total_proposals,
- inside_flags)
-
- return (labels, label_weights, bbox_gt, pos_proposals,
- proposals_weights, pos_inds, neg_inds)
-
- def get_targets(self,
- proposals_list,
- valid_flag_list,
- gt_bboxes_list,
- img_metas,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- stage='init',
- label_channels=1,
- unmap_outputs=True):
- """Compute corresponding GT box and classification targets for
- proposals.
-
- Args:
- proposals_list (list[list]): Multi level points/bboxes of each
- image.
- valid_flag_list (list[list]): Multi level valid flags of each
- image.
- gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
- ignored.
- gt_bboxes_list (list[Tensor]): Ground truth labels of each box.
- stage (str): `init` or `refine`. Generate target for init stage or
- refine stage
- label_channels (int): Channel of label.
- unmap_outputs (bool): Whether to map outputs back to the original
- set of anchors.
-
- Returns:
- tuple:
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights_list (list[Tensor]): Label weights of each level. # noqa: E501
- - bbox_gt_list (list[Tensor]): Ground truth bbox of each level.
- - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level. # noqa: E501
- - proposal_weights_list (list[Tensor]): Proposal weights of each level. # noqa: E501
- - num_total_pos (int): Number of positive samples in all images. # noqa: E501
- - num_total_neg (int): Number of negative samples in all images. # noqa: E501
- """
- assert stage in ['init', 'refine']
- num_imgs = len(img_metas)
- assert len(proposals_list) == len(valid_flag_list) == num_imgs
-
- # points number of multi levels
- num_level_proposals = [points.size(0) for points in proposals_list[0]]
-
- # concat all level points and flags to a single tensor
- for i in range(num_imgs):
- assert len(proposals_list[i]) == len(valid_flag_list[i])
- proposals_list[i] = torch.cat(proposals_list[i])
- valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_labels, all_label_weights, all_bbox_gt, all_proposals,
- all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply(
- self._point_target_single,
- proposals_list,
- valid_flag_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- stage=stage,
- unmap_outputs=unmap_outputs)
- # no valid points
- if any([labels is None for labels in all_labels]):
- return None
- # sampled points of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- labels_list = images_to_levels(all_labels, num_level_proposals)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_proposals)
- bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals)
- proposals_list = images_to_levels(all_proposals, num_level_proposals)
- proposal_weights_list = images_to_levels(all_proposal_weights,
- num_level_proposals)
- return (labels_list, label_weights_list, bbox_gt_list, proposals_list,
- proposal_weights_list, num_total_pos, num_total_neg)
-
- def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels,
- label_weights, bbox_gt_init, bbox_weights_init,
- bbox_gt_refine, bbox_weights_refine, stride,
- num_total_samples_init, num_total_samples_refine):
- # 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)
- cls_score = cls_score.contiguous()
- loss_cls = self.loss_cls(
- cls_score,
- labels,
- label_weights,
- avg_factor=num_total_samples_refine)
-
- # points loss
- bbox_gt_init = bbox_gt_init.reshape(-1, 4)
- bbox_weights_init = bbox_weights_init.reshape(-1, 4)
- bbox_pred_init = self.points2bbox(
- pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False)
- bbox_gt_refine = bbox_gt_refine.reshape(-1, 4)
- bbox_weights_refine = bbox_weights_refine.reshape(-1, 4)
- bbox_pred_refine = self.points2bbox(
- pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False)
- normalize_term = self.point_base_scale * stride
- loss_pts_init = self.loss_bbox_init(
- bbox_pred_init / normalize_term,
- bbox_gt_init / normalize_term,
- bbox_weights_init,
- avg_factor=num_total_samples_init)
- loss_pts_refine = self.loss_bbox_refine(
- bbox_pred_refine / normalize_term,
- bbox_gt_refine / normalize_term,
- bbox_weights_refine,
- avg_factor=num_total_samples_refine)
- return loss_cls, loss_pts_init, loss_pts_refine
-
- def loss(self,
- cls_scores,
- pts_preds_init,
- pts_preds_refine,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- device = cls_scores[0].device
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
- # target for initial stage
- center_list, valid_flag_list = self.get_points(featmap_sizes,
- img_metas, device)
- pts_coordinate_preds_init = self.offset_to_pts(center_list,
- pts_preds_init)
- if self.train_cfg.init.assigner['type'] == 'PointAssigner':
- # Assign target for center list
- candidate_list = center_list
- else:
- # transform center list to bbox list and
- # assign target for bbox list
- bbox_list = self.centers_to_bboxes(center_list)
- candidate_list = bbox_list
- cls_reg_targets_init = self.get_targets(
- candidate_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- stage='init',
- label_channels=label_channels)
- (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init,
- num_total_pos_init, num_total_neg_init) = cls_reg_targets_init
- num_total_samples_init = (
- num_total_pos_init +
- num_total_neg_init if self.sampling else num_total_pos_init)
-
- # target for refinement stage
- center_list, valid_flag_list = self.get_points(featmap_sizes,
- img_metas, device)
- pts_coordinate_preds_refine = self.offset_to_pts(
- center_list, pts_preds_refine)
- bbox_list = []
- for i_img, center in enumerate(center_list):
- bbox = []
- for i_lvl in range(len(pts_preds_refine)):
- bbox_preds_init = self.points2bbox(
- pts_preds_init[i_lvl].detach())
- bbox_shift = bbox_preds_init * self.point_strides[i_lvl]
- bbox_center = torch.cat(
- [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1)
- bbox.append(bbox_center +
- bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4))
- bbox_list.append(bbox)
- cls_reg_targets_refine = self.get_targets(
- bbox_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- stage='refine',
- label_channels=label_channels)
- (labels_list, label_weights_list, bbox_gt_list_refine,
- candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine,
- num_total_neg_refine) = cls_reg_targets_refine
- num_total_samples_refine = (
- num_total_pos_refine +
- num_total_neg_refine if self.sampling else num_total_pos_refine)
-
- # compute loss
- losses_cls, losses_pts_init, losses_pts_refine = multi_apply(
- self.loss_single,
- cls_scores,
- pts_coordinate_preds_init,
- pts_coordinate_preds_refine,
- labels_list,
- label_weights_list,
- bbox_gt_list_init,
- bbox_weights_list_init,
- bbox_gt_list_refine,
- bbox_weights_list_refine,
- self.point_strides,
- num_total_samples_init=num_total_samples_init,
- num_total_samples_refine=num_total_samples_refine)
- loss_dict_all = {
- 'loss_cls': losses_cls,
- 'loss_pts_init': losses_pts_init,
- 'loss_pts_refine': losses_pts_refine
- }
- return loss_dict_all
-
- # Same as base_dense_head/_get_bboxes_single except self._bbox_decode
- def _get_bboxes_single(self,
- cls_score_list,
- bbox_pred_list,
- score_factor_list,
- mlvl_priors,
- img_meta,
- cfg,
- rescale=False,
- with_nms=True,
- **kwargs):
- """Transform outputs of a single image into bbox predictions.
-
- 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]): Score factor from all scale
- levels of a single image. RepPoints head does not need
- this value.
- mlvl_priors (list[Tensor]): Each element in the list is
- the priors of a single level in feature pyramid, has shape
- (num_priors, 2).
- img_meta (dict): Image meta info.
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- 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]: Results of detected bboxes and labels. If with_nms
- is False and mlvl_score_factor is None, return mlvl_bboxes and
- mlvl_scores, else return mlvl_bboxes, mlvl_scores and
- mlvl_score_factor. Usually with_nms is False is used for aug
- test. If with_nms is True, then return the following format
-
- - det_bboxes (Tensor): Predicted bboxes with shape \
- [num_bboxes, 5], where the first 4 columns are bounding \
- box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
- column are scores between 0 and 1.
- - det_labels (Tensor): Predicted labels of the corresponding \
- box with shape [num_bboxes].
- """
- cfg = self.test_cfg if cfg is None else cfg
- assert len(cls_score_list) == len(bbox_pred_list)
- img_shape = img_meta['img_shape']
- nms_pre = cfg.get('nms_pre', -1)
-
- mlvl_bboxes = []
- mlvl_scores = []
- mlvl_labels = []
- 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:]
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-
- cls_score = cls_score.permute(1, 2,
- 0).reshape(-1, self.cls_out_channels)
- if self.use_sigmoid_cls:
- scores = cls_score.sigmoid()
- else:
- scores = cls_score.softmax(-1)[:, :-1]
-
- # After https://github.com/open-mmlab/mmdetection/pull/6268/,
- # this operation keeps fewer bboxes under the same `nms_pre`.
- # There is no difference in performance for most models. If you
- # find a slight drop in performance, you can set a larger
- # `nms_pre` than before.
- results = filter_scores_and_topk(
- scores, cfg.score_thr, nms_pre,
- dict(bbox_pred=bbox_pred, priors=priors))
- scores, labels, _, filtered_results = results
-
- bbox_pred = filtered_results['bbox_pred']
- priors = filtered_results['priors']
-
- bboxes = self._bbox_decode(priors, bbox_pred,
- self.point_strides[level_idx],
- img_shape)
-
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_labels.append(labels)
-
- return self._bbox_post_process(
- mlvl_scores,
- mlvl_labels,
- mlvl_bboxes,
- img_meta['scale_factor'],
- cfg,
- rescale=rescale,
- with_nms=with_nms)
-
- def _bbox_decode(self, points, bbox_pred, stride, max_shape):
- bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1)
- bboxes = bbox_pred * stride + bbox_pos_center
- x1 = bboxes[:, 0].clamp(min=0, max=max_shape[1])
- y1 = bboxes[:, 1].clamp(min=0, max=max_shape[0])
- x2 = bboxes[:, 2].clamp(min=0, max=max_shape[1])
- y2 = bboxes[:, 3].clamp(min=0, max=max_shape[0])
- decoded_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
- return decoded_bboxes
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