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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Dict, List, Sequence, Tuple | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from mmcv.cnn import ConvModule | |
| from mmcv.ops import DeformConv2d | |
| from mmengine.config import ConfigDict | |
| from mmengine.structures import InstanceData | |
| from torch import Tensor | |
| from mmdet.registry import MODELS, TASK_UTILS | |
| from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptInstanceList | |
| from ..task_modules.prior_generators import MlvlPointGenerator | |
| from ..task_modules.samplers import PseudoSampler | |
| from ..utils import (filter_scores_and_topk, images_to_levels, multi_apply, | |
| unmap) | |
| from .anchor_free_head import AnchorFreeHead | |
| class RepPointsHead(AnchorFreeHead): | |
| """RepPoint head. | |
| Args: | |
| num_classes (int): Number of categories excluding the background | |
| category. | |
| in_channels (int): Number of channels in the input feature map. | |
| point_feat_channels (int): Number of channels of points features. | |
| num_points (int): Number of points. | |
| gradient_mul (float): The multiplier to gradients from | |
| points refinement and recognition. | |
| point_strides (Sequence[int]): points strides. | |
| point_base_scale (int): bbox scale for assigning labels. | |
| loss_cls (:obj:`ConfigDict` or dict): Config of classification loss. | |
| loss_bbox_init (:obj:`ConfigDict` or dict): Config of initial points | |
| loss. | |
| loss_bbox_refine (:obj:`ConfigDict` or 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 (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \ | |
| dict]): Initialization config dict. | |
| """ # noqa: W605 | |
| def __init__(self, | |
| num_classes: int, | |
| in_channels: int, | |
| point_feat_channels: int = 256, | |
| num_points: int = 9, | |
| gradient_mul: float = 0.1, | |
| point_strides: Sequence[int] = [8, 16, 32, 64, 128], | |
| point_base_scale: int = 4, | |
| loss_cls: ConfigType = dict( | |
| type='FocalLoss', | |
| use_sigmoid=True, | |
| gamma=2.0, | |
| alpha=0.25, | |
| loss_weight=1.0), | |
| loss_bbox_init: ConfigType = dict( | |
| type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5), | |
| loss_bbox_refine: ConfigType = dict( | |
| type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), | |
| use_grid_points: bool = False, | |
| center_init: bool = True, | |
| transform_method: str = 'moment', | |
| moment_mul: float = 0.01, | |
| init_cfg: MultiConfig = dict( | |
| type='Normal', | |
| layer='Conv2d', | |
| std=0.01, | |
| override=dict( | |
| type='Normal', | |
| name='reppoints_cls_out', | |
| std=0.01, | |
| bias_prob=0.01)), | |
| **kwargs) -> None: | |
| 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=num_classes, | |
| in_channels=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.) | |
| if self.train_cfg: | |
| self.init_assigner = TASK_UTILS.build( | |
| self.train_cfg['init']['assigner']) | |
| self.refine_assigner = TASK_UTILS.build( | |
| self.train_cfg['refine']['assigner']) | |
| if self.train_cfg.get('sampler', None) is not None: | |
| self.sampler = TASK_UTILS.build( | |
| self.train_cfg['sampler'], default_args=dict(context=self)) | |
| else: | |
| self.sampler = PseudoSampler(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 = MODELS.build(loss_bbox_init) | |
| self.loss_bbox_refine = MODELS.build(loss_bbox_refine) | |
| def _init_layers(self) -> None: | |
| """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: Tensor, y_first: bool = True) -> Tensor: | |
| """Converting the points set into bounding box. | |
| Args: | |
| pts (Tensor): the input points sets (fields), each points | |
| set (fields) is represented as 2n scalar. | |
| y_first (bool): 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]. Defaults to True. | |
| Returns: | |
| Tensor: 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: Tensor, | |
| previous_boxes: Tensor) -> Tuple[Tensor]: | |
| """Base on the previous bboxes and regression values, we compute the | |
| regressed bboxes and generate the grids on the bboxes. | |
| Args: | |
| reg (Tensor): the regression value to previous bboxes. | |
| previous_boxes (Tensor): previous bboxes. | |
| Returns: | |
| Tuple[Tensor]: 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: Tuple[Tensor]) -> Tuple[Tensor]: | |
| return multi_apply(self.forward_single, feats) | |
| def forward_single(self, x: Tensor) -> Tuple[Tensor]: | |
| """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: List[Tuple[int]], | |
| batch_img_metas: List[dict], device: str) -> tuple: | |
| """Get points according to feature map sizes. | |
| Args: | |
| featmap_sizes (list[tuple]): Multi-level feature map sizes. | |
| batch_img_metas (list[dict]): Image meta info. | |
| Returns: | |
| tuple: points of each image, valid flags of each image | |
| """ | |
| num_imgs = len(batch_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(batch_img_metas): | |
| multi_level_flags = self.prior_generator.valid_flags( | |
| featmap_sizes, img_meta['pad_shape'], device=device) | |
| valid_flag_list.append(multi_level_flags) | |
| return points_list, valid_flag_list | |
| def centers_to_bboxes(self, point_list: List[Tensor]) -> List[Tensor]: | |
| """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: List[Tensor], | |
| pred_list: List[Tensor]) -> List[Tensor]: | |
| """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 _get_targets_single(self, | |
| flat_proposals: Tensor, | |
| valid_flags: Tensor, | |
| gt_instances: InstanceData, | |
| gt_instances_ignore: InstanceData, | |
| stage: str = 'init', | |
| unmap_outputs: bool = True) -> tuple: | |
| """Compute corresponding GT box and classification targets for | |
| proposals. | |
| Args: | |
| flat_proposals (Tensor): Multi level points of a image. | |
| valid_flags (Tensor): Multi level valid flags of a image. | |
| gt_instances (InstanceData): It usually includes ``bboxes`` and | |
| ``labels`` attributes. | |
| gt_instances_ignore (InstanceData): It includes ``bboxes`` | |
| attribute data that is ignored during training and testing. | |
| stage (str): 'init' or 'refine'. Generate target for | |
| init stage or refine stage. Defaults to 'init'. | |
| unmap_outputs (bool): Whether to map outputs back to | |
| the original set of anchors. Defaults to True. | |
| Returns: | |
| tuple: | |
| - labels (Tensor): Labels of each level. | |
| - label_weights (Tensor): Label weights of each level. | |
| - bbox_targets (Tensor): BBox targets of each level. | |
| - bbox_weights (Tensor): BBox weights of each level. | |
| - pos_inds (Tensor): positive samples indexes. | |
| - neg_inds (Tensor): negative samples indexes. | |
| - sampling_result (:obj:`SamplingResult`): Sampling results. | |
| """ | |
| inside_flags = valid_flags | |
| if not inside_flags.any(): | |
| raise ValueError( | |
| 'There is no valid proposal inside the image boundary. Please ' | |
| 'check the image size.') | |
| # assign gt and sample proposals | |
| proposals = flat_proposals[inside_flags, :] | |
| pred_instances = InstanceData(priors=proposals) | |
| 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(pred_instances, gt_instances, | |
| gt_instances_ignore) | |
| sampling_result = self.sampler.sample(assign_result, pred_instances, | |
| gt_instances) | |
| 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: | |
| bbox_gt[pos_inds, :] = sampling_result.pos_gt_bboxes | |
| pos_proposals[pos_inds, :] = proposals[pos_inds, :] | |
| proposals_weights[pos_inds, :] = 1.0 | |
| labels[pos_inds] = sampling_result.pos_gt_labels | |
| 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, | |
| fill=self.num_classes) # fill bg label | |
| 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, sampling_result) | |
| def get_targets(self, | |
| proposals_list: List[Tensor], | |
| valid_flag_list: List[Tensor], | |
| batch_gt_instances: InstanceList, | |
| batch_img_metas: List[dict], | |
| batch_gt_instances_ignore: OptInstanceList = None, | |
| stage: str = 'init', | |
| unmap_outputs: bool = True, | |
| return_sampling_results: bool = False) -> tuple: | |
| """Compute corresponding GT box and classification targets for | |
| proposals. | |
| Args: | |
| proposals_list (list[Tensor]): Multi level points/bboxes of each | |
| image. | |
| valid_flag_list (list[Tensor]): Multi level valid flags of each | |
| image. | |
| 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. | |
| stage (str): 'init' or 'refine'. Generate target for init stage or | |
| refine stage. | |
| unmap_outputs (bool): Whether to map outputs back to the original | |
| set of anchors. | |
| return_sampling_results (bool): Whether to return the sampling | |
| results. Defaults to False. | |
| Returns: | |
| tuple: | |
| - labels_list (list[Tensor]): Labels of each level. | |
| - label_weights_list (list[Tensor]): Label weights of each | |
| level. | |
| - bbox_gt_list (list[Tensor]): Ground truth bbox of each level. | |
| - proposals_list (list[Tensor]): Proposals(points/bboxes) of | |
| each level. | |
| - proposal_weights_list (list[Tensor]): Proposal weights of | |
| each level. | |
| - avg_factor (int): Average factor that is used to average | |
| the loss. When using sampling method, avg_factor is usually | |
| the sum of positive and negative priors. When using | |
| `PseudoSampler`, `avg_factor` is usually equal to the number | |
| of positive priors. | |
| """ | |
| assert stage in ['init', 'refine'] | |
| num_imgs = len(batch_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]) | |
| if batch_gt_instances_ignore is None: | |
| batch_gt_instances_ignore = [None] * num_imgs | |
| (all_labels, all_label_weights, all_bbox_gt, all_proposals, | |
| all_proposal_weights, pos_inds_list, neg_inds_list, | |
| sampling_results_list) = multi_apply( | |
| self._get_targets_single, | |
| proposals_list, | |
| valid_flag_list, | |
| batch_gt_instances, | |
| batch_gt_instances_ignore, | |
| stage=stage, | |
| unmap_outputs=unmap_outputs) | |
| # sampled points of all images | |
| avg_refactor = sum( | |
| [results.avg_factor for results in sampling_results_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) | |
| res = (labels_list, label_weights_list, bbox_gt_list, proposals_list, | |
| proposal_weights_list, avg_refactor) | |
| if return_sampling_results: | |
| res = res + (sampling_results_list, ) | |
| return res | |
| def loss_by_feat_single(self, cls_score: Tensor, pts_pred_init: Tensor, | |
| pts_pred_refine: Tensor, labels: Tensor, | |
| label_weights, bbox_gt_init: Tensor, | |
| bbox_weights_init: Tensor, bbox_gt_refine: Tensor, | |
| bbox_weights_refine: Tensor, stride: int, | |
| avg_factor_init: int, | |
| avg_factor_refine: int) -> Tuple[Tensor]: | |
| """Calculate the loss of a single scale level based on the features | |
| extracted by the detection head. | |
| Args: | |
| cls_score (Tensor): Box scores for each scale level | |
| Has shape (N, num_classes, h_i, w_i). | |
| pts_pred_init (Tensor): Points of shape | |
| (batch_size, h_i * w_i, num_points * 2). | |
| pts_pred_refine (Tensor): Points refined of shape | |
| (batch_size, h_i * w_i, num_points * 2). | |
| labels (Tensor): Ground truth class indices with shape | |
| (batch_size, h_i * w_i). | |
| label_weights (Tensor): Label weights of shape | |
| (batch_size, h_i * w_i). | |
| bbox_gt_init (Tensor): BBox regression targets in the init stage | |
| of shape (batch_size, h_i * w_i, 4). | |
| bbox_weights_init (Tensor): BBox regression loss weights in the | |
| init stage of shape (batch_size, h_i * w_i, 4). | |
| bbox_gt_refine (Tensor): BBox regression targets in the refine | |
| stage of shape (batch_size, h_i * w_i, 4). | |
| bbox_weights_refine (Tensor): BBox regression loss weights in the | |
| refine stage of shape (batch_size, h_i * w_i, 4). | |
| stride (int): Point stride. | |
| avg_factor_init (int): Average factor that is used to average | |
| the loss in the init stage. | |
| avg_factor_refine (int): Average factor that is used to average | |
| the loss in the refine stage. | |
| Returns: | |
| Tuple[Tensor]: loss components. | |
| """ | |
| # 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=avg_factor_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=avg_factor_init) | |
| loss_pts_refine = self.loss_bbox_refine( | |
| bbox_pred_refine / normalize_term, | |
| bbox_gt_refine / normalize_term, | |
| bbox_weights_refine, | |
| avg_factor=avg_factor_refine) | |
| return loss_cls, loss_pts_init, loss_pts_refine | |
| def loss_by_feat( | |
| self, | |
| cls_scores: List[Tensor], | |
| pts_preds_init: List[Tensor], | |
| pts_preds_refine: List[Tensor], | |
| batch_gt_instances: InstanceList, | |
| batch_img_metas: List[dict], | |
| batch_gt_instances_ignore: OptInstanceList = None | |
| ) -> Dict[str, Tensor]: | |
| """Calculate the loss based on the features extracted by the detection | |
| head. | |
| Args: | |
| cls_scores (list[Tensor]): Box scores for each scale level, | |
| each is a 4D-tensor, of shape (batch_size, num_classes, h, w). | |
| pts_preds_init (list[Tensor]): Points for each scale level, each is | |
| a 3D-tensor, of shape (batch_size, h_i * w_i, num_points * 2). | |
| pts_preds_refine (list[Tensor]): Points refined for each scale | |
| level, each is a 3D-tensor, of shape | |
| (batch_size, h_i * w_i, num_points * 2). | |
| 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]: A dictionary of loss components. | |
| """ | |
| featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] | |
| device = cls_scores[0].device | |
| # target for initial stage | |
| center_list, valid_flag_list = self.get_points(featmap_sizes, | |
| batch_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( | |
| proposals_list=candidate_list, | |
| valid_flag_list=valid_flag_list, | |
| batch_gt_instances=batch_gt_instances, | |
| batch_img_metas=batch_img_metas, | |
| batch_gt_instances_ignore=batch_gt_instances_ignore, | |
| stage='init', | |
| return_sampling_results=False) | |
| (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init, | |
| avg_factor_init) = cls_reg_targets_init | |
| # target for refinement stage | |
| center_list, valid_flag_list = self.get_points(featmap_sizes, | |
| batch_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( | |
| proposals_list=bbox_list, | |
| valid_flag_list=valid_flag_list, | |
| batch_gt_instances=batch_gt_instances, | |
| batch_img_metas=batch_img_metas, | |
| batch_gt_instances_ignore=batch_gt_instances_ignore, | |
| stage='refine', | |
| return_sampling_results=False) | |
| (labels_list, label_weights_list, bbox_gt_list_refine, | |
| candidate_list_refine, bbox_weights_list_refine, | |
| avg_factor_refine) = cls_reg_targets_refine | |
| # compute loss | |
| losses_cls, losses_pts_init, losses_pts_refine = multi_apply( | |
| self.loss_by_feat_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, | |
| avg_factor_init=avg_factor_init, | |
| avg_factor_refine=avg_factor_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 _predict_by_feat_single(self, | |
| cls_score_list: List[Tensor], | |
| bbox_pred_list: List[Tensor], | |
| score_factor_list: List[Tensor], | |
| mlvl_priors: List[Tensor], | |
| img_meta: dict, | |
| cfg: ConfigDict, | |
| rescale: bool = False, | |
| with_nms: bool = True) -> InstanceData: | |
| """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 (:obj:`ConfigDict`): Test / postprocessing configuration, | |
| if None, test_cfg would be used. | |
| rescale (bool): If True, return boxes in original image space. | |
| Defaults to False. | |
| with_nms (bool): If True, do nms before return boxes. | |
| Defaults to True. | |
| 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). | |
| """ | |
| 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) | |
| results = InstanceData() | |
| results.bboxes = torch.cat(mlvl_bboxes) | |
| results.scores = torch.cat(mlvl_scores) | |
| results.labels = torch.cat(mlvl_labels) | |
| return self._bbox_post_process( | |
| results=results, | |
| cfg=cfg, | |
| rescale=rescale, | |
| with_nms=with_nms, | |
| img_meta=img_meta) | |
| def _bbox_decode(self, points: Tensor, bbox_pred: Tensor, stride: int, | |
| max_shape: Tuple[int, int]) -> Tensor: | |
| """Decode the prediction to bounding box. | |
| Args: | |
| points (Tensor): shape (h_i * w_i, 2). | |
| bbox_pred (Tensor): shape (h_i * w_i, 4). | |
| stride (int): Stride for bbox_pred in different level. | |
| max_shape (Tuple[int, int]): image shape. | |
| Returns: | |
| Tensor: Bounding boxes decoded. | |
| """ | |
| 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 | |