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| # 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 | |
| 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 | |