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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import List, Optional, Sequence, Tuple | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from mmcv.cnn import ConvModule | |
| from mmengine.config import ConfigDict | |
| from mmengine.structures import InstanceData | |
| from torch import Tensor | |
| from mmdet.models.layers import multiclass_nms | |
| from mmdet.models.losses import accuracy | |
| from mmdet.models.task_modules import SamplingResult | |
| from mmdet.models.utils import multi_apply | |
| from mmdet.registry import MODELS, TASK_UTILS | |
| from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptMultiConfig | |
| from .bbox_head import BBoxHead | |
| class SABLHead(BBoxHead): | |
| """Side-Aware Boundary Localization (SABL) for RoI-Head. | |
| Side-Aware features are extracted by conv layers | |
| with an attention mechanism. | |
| Boundary Localization with Bucketing and Bucketing Guided Rescoring | |
| are implemented in BucketingBBoxCoder. | |
| Please refer to https://arxiv.org/abs/1912.04260 for more details. | |
| Args: | |
| cls_in_channels (int): Input channels of cls RoI feature. \ | |
| Defaults to 256. | |
| reg_in_channels (int): Input channels of reg RoI feature. \ | |
| Defaults to 256. | |
| roi_feat_size (int): Size of RoI features. Defaults to 7. | |
| reg_feat_up_ratio (int): Upsample ratio of reg features. \ | |
| Defaults to 2. | |
| reg_pre_kernel (int): Kernel of 2D conv layers before \ | |
| attention pooling. Defaults to 3. | |
| reg_post_kernel (int): Kernel of 1D conv layers after \ | |
| attention pooling. Defaults to 3. | |
| reg_pre_num (int): Number of pre convs. Defaults to 2. | |
| reg_post_num (int): Number of post convs. Defaults to 1. | |
| num_classes (int): Number of classes in dataset. Defaults to 80. | |
| cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024. | |
| reg_offset_out_channels (int): Hidden and output channel \ | |
| of reg offset branch. Defaults to 256. | |
| reg_cls_out_channels (int): Hidden and output channel \ | |
| of reg cls branch. Defaults to 256. | |
| num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1. | |
| num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0. | |
| reg_class_agnostic (bool): Class agnostic regression or not. \ | |
| Defaults to True. | |
| norm_cfg (dict): Config of norm layers. Defaults to None. | |
| bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'. | |
| loss_cls (dict): Config of classification loss. | |
| loss_bbox_cls (dict): Config of classification loss for bbox branch. | |
| loss_bbox_reg (dict): Config of regression loss for bbox branch. | |
| init_cfg (dict or list[dict], optional): Initialization config dict. | |
| Defaults to None. | |
| """ | |
| def __init__(self, | |
| num_classes: int, | |
| cls_in_channels: int = 256, | |
| reg_in_channels: int = 256, | |
| roi_feat_size: int = 7, | |
| reg_feat_up_ratio: int = 2, | |
| reg_pre_kernel: int = 3, | |
| reg_post_kernel: int = 3, | |
| reg_pre_num: int = 2, | |
| reg_post_num: int = 1, | |
| cls_out_channels: int = 1024, | |
| reg_offset_out_channels: int = 256, | |
| reg_cls_out_channels: int = 256, | |
| num_cls_fcs: int = 1, | |
| num_reg_fcs: int = 0, | |
| reg_class_agnostic: bool = True, | |
| norm_cfg: OptConfigType = None, | |
| bbox_coder: ConfigType = dict( | |
| type='BucketingBBoxCoder', | |
| num_buckets=14, | |
| scale_factor=1.7), | |
| loss_cls: ConfigType = dict( | |
| type='CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0), | |
| loss_bbox_cls: ConfigType = dict( | |
| type='CrossEntropyLoss', | |
| use_sigmoid=True, | |
| loss_weight=1.0), | |
| loss_bbox_reg: ConfigType = dict( | |
| type='SmoothL1Loss', beta=0.1, loss_weight=1.0), | |
| init_cfg: OptMultiConfig = None) -> None: | |
| super(BBoxHead, self).__init__(init_cfg=init_cfg) | |
| self.cls_in_channels = cls_in_channels | |
| self.reg_in_channels = reg_in_channels | |
| self.roi_feat_size = roi_feat_size | |
| self.reg_feat_up_ratio = int(reg_feat_up_ratio) | |
| self.num_buckets = bbox_coder['num_buckets'] | |
| assert self.reg_feat_up_ratio // 2 >= 1 | |
| self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio | |
| assert self.up_reg_feat_size == bbox_coder['num_buckets'] | |
| self.reg_pre_kernel = reg_pre_kernel | |
| self.reg_post_kernel = reg_post_kernel | |
| self.reg_pre_num = reg_pre_num | |
| self.reg_post_num = reg_post_num | |
| self.num_classes = num_classes | |
| self.cls_out_channels = cls_out_channels | |
| self.reg_offset_out_channels = reg_offset_out_channels | |
| self.reg_cls_out_channels = reg_cls_out_channels | |
| self.num_cls_fcs = num_cls_fcs | |
| self.num_reg_fcs = num_reg_fcs | |
| self.reg_class_agnostic = reg_class_agnostic | |
| assert self.reg_class_agnostic | |
| self.norm_cfg = norm_cfg | |
| self.bbox_coder = TASK_UTILS.build(bbox_coder) | |
| self.loss_cls = MODELS.build(loss_cls) | |
| self.loss_bbox_cls = MODELS.build(loss_bbox_cls) | |
| self.loss_bbox_reg = MODELS.build(loss_bbox_reg) | |
| self.cls_fcs = self._add_fc_branch(self.num_cls_fcs, | |
| self.cls_in_channels, | |
| self.roi_feat_size, | |
| self.cls_out_channels) | |
| self.side_num = int(np.ceil(self.num_buckets / 2)) | |
| if self.reg_feat_up_ratio > 1: | |
| self.upsample_x = nn.ConvTranspose1d( | |
| reg_in_channels, | |
| reg_in_channels, | |
| self.reg_feat_up_ratio, | |
| stride=self.reg_feat_up_ratio) | |
| self.upsample_y = nn.ConvTranspose1d( | |
| reg_in_channels, | |
| reg_in_channels, | |
| self.reg_feat_up_ratio, | |
| stride=self.reg_feat_up_ratio) | |
| self.reg_pre_convs = nn.ModuleList() | |
| for i in range(self.reg_pre_num): | |
| reg_pre_conv = ConvModule( | |
| reg_in_channels, | |
| reg_in_channels, | |
| kernel_size=reg_pre_kernel, | |
| padding=reg_pre_kernel // 2, | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU')) | |
| self.reg_pre_convs.append(reg_pre_conv) | |
| self.reg_post_conv_xs = nn.ModuleList() | |
| for i in range(self.reg_post_num): | |
| reg_post_conv_x = ConvModule( | |
| reg_in_channels, | |
| reg_in_channels, | |
| kernel_size=(1, reg_post_kernel), | |
| padding=(0, reg_post_kernel // 2), | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU')) | |
| self.reg_post_conv_xs.append(reg_post_conv_x) | |
| self.reg_post_conv_ys = nn.ModuleList() | |
| for i in range(self.reg_post_num): | |
| reg_post_conv_y = ConvModule( | |
| reg_in_channels, | |
| reg_in_channels, | |
| kernel_size=(reg_post_kernel, 1), | |
| padding=(reg_post_kernel // 2, 0), | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU')) | |
| self.reg_post_conv_ys.append(reg_post_conv_y) | |
| self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1) | |
| self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1) | |
| self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs, | |
| self.reg_in_channels, 1, | |
| self.reg_cls_out_channels) | |
| self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs, | |
| self.reg_in_channels, 1, | |
| self.reg_offset_out_channels) | |
| self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1) | |
| self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1) | |
| if init_cfg is None: | |
| self.init_cfg = [ | |
| dict( | |
| type='Xavier', | |
| layer='Linear', | |
| distribution='uniform', | |
| override=[ | |
| dict(type='Normal', name='reg_conv_att_x', std=0.01), | |
| dict(type='Normal', name='reg_conv_att_y', std=0.01), | |
| dict(type='Normal', name='fc_reg_cls', std=0.01), | |
| dict(type='Normal', name='fc_cls', std=0.01), | |
| dict(type='Normal', name='fc_reg_offset', std=0.001) | |
| ]) | |
| ] | |
| if self.reg_feat_up_ratio > 1: | |
| self.init_cfg += [ | |
| dict( | |
| type='Kaiming', | |
| distribution='normal', | |
| override=[ | |
| dict(name='upsample_x'), | |
| dict(name='upsample_y') | |
| ]) | |
| ] | |
| def _add_fc_branch(self, num_branch_fcs: int, in_channels: int, | |
| roi_feat_size: int, | |
| fc_out_channels: int) -> nn.ModuleList: | |
| """build fc layers.""" | |
| in_channels = in_channels * roi_feat_size * roi_feat_size | |
| branch_fcs = nn.ModuleList() | |
| for i in range(num_branch_fcs): | |
| fc_in_channels = (in_channels if i == 0 else fc_out_channels) | |
| branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels)) | |
| return branch_fcs | |
| def cls_forward(self, cls_x: Tensor) -> Tensor: | |
| """forward of classification fc layers.""" | |
| cls_x = cls_x.view(cls_x.size(0), -1) | |
| for fc in self.cls_fcs: | |
| cls_x = self.relu(fc(cls_x)) | |
| cls_score = self.fc_cls(cls_x) | |
| return cls_score | |
| def attention_pool(self, reg_x: Tensor) -> tuple: | |
| """Extract direction-specific features fx and fy with attention | |
| methanism.""" | |
| reg_fx = reg_x | |
| reg_fy = reg_x | |
| reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid() | |
| reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid() | |
| reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2) | |
| reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3) | |
| reg_fx = (reg_fx * reg_fx_att).sum(dim=2) | |
| reg_fy = (reg_fy * reg_fy_att).sum(dim=3) | |
| return reg_fx, reg_fy | |
| def side_aware_feature_extractor(self, reg_x: Tensor) -> tuple: | |
| """Refine and extract side-aware features without split them.""" | |
| for reg_pre_conv in self.reg_pre_convs: | |
| reg_x = reg_pre_conv(reg_x) | |
| reg_fx, reg_fy = self.attention_pool(reg_x) | |
| if self.reg_post_num > 0: | |
| reg_fx = reg_fx.unsqueeze(2) | |
| reg_fy = reg_fy.unsqueeze(3) | |
| for i in range(self.reg_post_num): | |
| reg_fx = self.reg_post_conv_xs[i](reg_fx) | |
| reg_fy = self.reg_post_conv_ys[i](reg_fy) | |
| reg_fx = reg_fx.squeeze(2) | |
| reg_fy = reg_fy.squeeze(3) | |
| if self.reg_feat_up_ratio > 1: | |
| reg_fx = self.relu(self.upsample_x(reg_fx)) | |
| reg_fy = self.relu(self.upsample_y(reg_fy)) | |
| reg_fx = torch.transpose(reg_fx, 1, 2) | |
| reg_fy = torch.transpose(reg_fy, 1, 2) | |
| return reg_fx.contiguous(), reg_fy.contiguous() | |
| def reg_pred(self, x: Tensor, offset_fcs: nn.ModuleList, | |
| cls_fcs: nn.ModuleList) -> tuple: | |
| """Predict bucketing estimation (cls_pred) and fine regression (offset | |
| pred) with side-aware features.""" | |
| x_offset = x.view(-1, self.reg_in_channels) | |
| x_cls = x.view(-1, self.reg_in_channels) | |
| for fc in offset_fcs: | |
| x_offset = self.relu(fc(x_offset)) | |
| for fc in cls_fcs: | |
| x_cls = self.relu(fc(x_cls)) | |
| offset_pred = self.fc_reg_offset(x_offset) | |
| cls_pred = self.fc_reg_cls(x_cls) | |
| offset_pred = offset_pred.view(x.size(0), -1) | |
| cls_pred = cls_pred.view(x.size(0), -1) | |
| return offset_pred, cls_pred | |
| def side_aware_split(self, feat: Tensor) -> Tensor: | |
| """Split side-aware features aligned with orders of bucketing | |
| targets.""" | |
| l_end = int(np.ceil(self.up_reg_feat_size / 2)) | |
| r_start = int(np.floor(self.up_reg_feat_size / 2)) | |
| feat_fl = feat[:, :l_end] | |
| feat_fr = feat[:, r_start:].flip(dims=(1, )) | |
| feat_fl = feat_fl.contiguous() | |
| feat_fr = feat_fr.contiguous() | |
| feat = torch.cat([feat_fl, feat_fr], dim=-1) | |
| return feat | |
| def bbox_pred_split(self, bbox_pred: tuple, | |
| num_proposals_per_img: Sequence[int]) -> tuple: | |
| """Split batch bbox prediction back to each image.""" | |
| bucket_cls_preds, bucket_offset_preds = bbox_pred | |
| bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0) | |
| bucket_offset_preds = bucket_offset_preds.split( | |
| num_proposals_per_img, 0) | |
| bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds)) | |
| return bbox_pred | |
| def reg_forward(self, reg_x: Tensor) -> tuple: | |
| """forward of regression branch.""" | |
| outs = self.side_aware_feature_extractor(reg_x) | |
| edge_offset_preds = [] | |
| edge_cls_preds = [] | |
| reg_fx = outs[0] | |
| reg_fy = outs[1] | |
| offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs, | |
| self.reg_cls_fcs) | |
| offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs, | |
| self.reg_cls_fcs) | |
| offset_pred_x = self.side_aware_split(offset_pred_x) | |
| offset_pred_y = self.side_aware_split(offset_pred_y) | |
| cls_pred_x = self.side_aware_split(cls_pred_x) | |
| cls_pred_y = self.side_aware_split(cls_pred_y) | |
| edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1) | |
| edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1) | |
| return edge_cls_preds, edge_offset_preds | |
| def forward(self, x: Tensor) -> tuple: | |
| """Forward features from the upstream network.""" | |
| bbox_pred = self.reg_forward(x) | |
| cls_score = self.cls_forward(x) | |
| return cls_score, bbox_pred | |
| def get_targets(self, | |
| sampling_results: List[SamplingResult], | |
| rcnn_train_cfg: ConfigDict, | |
| concat: bool = True) -> tuple: | |
| """Calculate the ground truth for all samples in a batch according to | |
| the sampling_results.""" | |
| pos_proposals = [res.pos_bboxes for res in sampling_results] | |
| neg_proposals = [res.neg_bboxes for res in sampling_results] | |
| pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results] | |
| pos_gt_labels = [res.pos_gt_labels for res in sampling_results] | |
| cls_reg_targets = self.bucket_target( | |
| pos_proposals, | |
| neg_proposals, | |
| pos_gt_bboxes, | |
| pos_gt_labels, | |
| rcnn_train_cfg, | |
| concat=concat) | |
| (labels, label_weights, bucket_cls_targets, bucket_cls_weights, | |
| bucket_offset_targets, bucket_offset_weights) = cls_reg_targets | |
| return (labels, label_weights, (bucket_cls_targets, | |
| bucket_offset_targets), | |
| (bucket_cls_weights, bucket_offset_weights)) | |
| def bucket_target(self, | |
| pos_proposals_list: list, | |
| neg_proposals_list: list, | |
| pos_gt_bboxes_list: list, | |
| pos_gt_labels_list: list, | |
| rcnn_train_cfg: ConfigDict, | |
| concat: bool = True) -> tuple: | |
| """Compute bucketing estimation targets and fine regression targets for | |
| a batch of images.""" | |
| (labels, label_weights, bucket_cls_targets, bucket_cls_weights, | |
| bucket_offset_targets, bucket_offset_weights) = multi_apply( | |
| self._bucket_target_single, | |
| pos_proposals_list, | |
| neg_proposals_list, | |
| pos_gt_bboxes_list, | |
| pos_gt_labels_list, | |
| cfg=rcnn_train_cfg) | |
| if concat: | |
| labels = torch.cat(labels, 0) | |
| label_weights = torch.cat(label_weights, 0) | |
| bucket_cls_targets = torch.cat(bucket_cls_targets, 0) | |
| bucket_cls_weights = torch.cat(bucket_cls_weights, 0) | |
| bucket_offset_targets = torch.cat(bucket_offset_targets, 0) | |
| bucket_offset_weights = torch.cat(bucket_offset_weights, 0) | |
| return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, | |
| bucket_offset_targets, bucket_offset_weights) | |
| def _bucket_target_single(self, pos_proposals: Tensor, | |
| neg_proposals: Tensor, pos_gt_bboxes: Tensor, | |
| pos_gt_labels: Tensor, cfg: ConfigDict) -> tuple: | |
| """Compute bucketing estimation targets and fine regression targets for | |
| a single image. | |
| Args: | |
| pos_proposals (Tensor): positive proposals of a single image, | |
| Shape (n_pos, 4) | |
| neg_proposals (Tensor): negative proposals of a single image, | |
| Shape (n_neg, 4). | |
| pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals | |
| of a single image, Shape (n_pos, 4). | |
| pos_gt_labels (Tensor): gt labels assigned to positive proposals | |
| of a single image, Shape (n_pos, ). | |
| cfg (dict): Config of calculating targets | |
| Returns: | |
| tuple: | |
| - labels (Tensor): Labels in a single image. Shape (n,). | |
| - label_weights (Tensor): Label weights in a single image. | |
| Shape (n,) | |
| - bucket_cls_targets (Tensor): Bucket cls targets in | |
| a single image. Shape (n, num_buckets*2). | |
| - bucket_cls_weights (Tensor): Bucket cls weights in | |
| a single image. Shape (n, num_buckets*2). | |
| - bucket_offset_targets (Tensor): Bucket offset targets | |
| in a single image. Shape (n, num_buckets*2). | |
| - bucket_offset_targets (Tensor): Bucket offset weights | |
| in a single image. Shape (n, num_buckets*2). | |
| """ | |
| num_pos = pos_proposals.size(0) | |
| num_neg = neg_proposals.size(0) | |
| num_samples = num_pos + num_neg | |
| labels = pos_gt_bboxes.new_full((num_samples, ), | |
| self.num_classes, | |
| dtype=torch.long) | |
| label_weights = pos_proposals.new_zeros(num_samples) | |
| bucket_cls_targets = pos_proposals.new_zeros(num_samples, | |
| 4 * self.side_num) | |
| bucket_cls_weights = pos_proposals.new_zeros(num_samples, | |
| 4 * self.side_num) | |
| bucket_offset_targets = pos_proposals.new_zeros( | |
| num_samples, 4 * self.side_num) | |
| bucket_offset_weights = pos_proposals.new_zeros( | |
| num_samples, 4 * self.side_num) | |
| if num_pos > 0: | |
| labels[:num_pos] = pos_gt_labels | |
| label_weights[:num_pos] = 1.0 | |
| (pos_bucket_offset_targets, pos_bucket_offset_weights, | |
| pos_bucket_cls_targets, | |
| pos_bucket_cls_weights) = self.bbox_coder.encode( | |
| pos_proposals, pos_gt_bboxes) | |
| bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets | |
| bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights | |
| bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets | |
| bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights | |
| if num_neg > 0: | |
| label_weights[-num_neg:] = 1.0 | |
| return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, | |
| bucket_offset_targets, bucket_offset_weights) | |
| def loss(self, | |
| cls_score: Tensor, | |
| bbox_pred: Tuple[Tensor, Tensor], | |
| rois: Tensor, | |
| labels: Tensor, | |
| label_weights: Tensor, | |
| bbox_targets: Tuple[Tensor, Tensor], | |
| bbox_weights: Tuple[Tensor, Tensor], | |
| reduction_override: Optional[str] = None) -> dict: | |
| """Calculate the loss based on the network predictions and targets. | |
| Args: | |
| cls_score (Tensor): Classification prediction | |
| results of all class, has shape | |
| (batch_size * num_proposals_single_image, num_classes) | |
| bbox_pred (Tensor): A tuple of regression prediction results | |
| containing `bucket_cls_preds and` `bucket_offset_preds`. | |
| rois (Tensor): RoIs with the shape | |
| (batch_size * num_proposals_single_image, 5) where the first | |
| column indicates batch id of each RoI. | |
| labels (Tensor): Gt_labels for all proposals in a batch, has | |
| shape (batch_size * num_proposals_single_image, ). | |
| label_weights (Tensor): Labels_weights for all proposals in a | |
| batch, has shape (batch_size * num_proposals_single_image, ). | |
| bbox_targets (Tuple[Tensor, Tensor]): A tuple of regression target | |
| containing `bucket_cls_targets` and `bucket_offset_targets`. | |
| the last dimension 4 represents [tl_x, tl_y, br_x, br_y]. | |
| bbox_weights (Tuple[Tensor, Tensor]): A tuple of regression | |
| weights containing `bucket_cls_weights` and | |
| `bucket_offset_weights`. | |
| reduction_override (str, optional): The reduction | |
| method used to override the original reduction | |
| method of the loss. Options are "none", | |
| "mean" and "sum". Defaults to None, | |
| Returns: | |
| dict: A dictionary of loss. | |
| """ | |
| losses = dict() | |
| if cls_score is not None: | |
| avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) | |
| losses['loss_cls'] = self.loss_cls( | |
| cls_score, | |
| labels, | |
| label_weights, | |
| avg_factor=avg_factor, | |
| reduction_override=reduction_override) | |
| losses['acc'] = accuracy(cls_score, labels) | |
| if bbox_pred is not None: | |
| bucket_cls_preds, bucket_offset_preds = bbox_pred | |
| bucket_cls_targets, bucket_offset_targets = bbox_targets | |
| bucket_cls_weights, bucket_offset_weights = bbox_weights | |
| # edge cls | |
| bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num) | |
| bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num) | |
| bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num) | |
| losses['loss_bbox_cls'] = self.loss_bbox_cls( | |
| bucket_cls_preds, | |
| bucket_cls_targets, | |
| bucket_cls_weights, | |
| avg_factor=bucket_cls_targets.size(0), | |
| reduction_override=reduction_override) | |
| losses['loss_bbox_reg'] = self.loss_bbox_reg( | |
| bucket_offset_preds, | |
| bucket_offset_targets, | |
| bucket_offset_weights, | |
| avg_factor=bucket_offset_targets.size(0), | |
| reduction_override=reduction_override) | |
| return losses | |
| def _predict_by_feat_single( | |
| self, | |
| roi: Tensor, | |
| cls_score: Tensor, | |
| bbox_pred: Tuple[Tensor, Tensor], | |
| img_meta: dict, | |
| rescale: bool = False, | |
| rcnn_test_cfg: Optional[ConfigDict] = None) -> InstanceData: | |
| """Transform a single image's features extracted from the head into | |
| bbox results. | |
| Args: | |
| roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5). | |
| last dimension 5 arrange as (batch_index, x1, y1, x2, y2). | |
| cls_score (Tensor): Box scores, has shape | |
| (num_boxes, num_classes + 1). | |
| bbox_pred (Tuple[Tensor, Tensor]): Box cls preds and offset preds. | |
| img_meta (dict): image information. | |
| rescale (bool): If True, return boxes in original image space. | |
| Defaults to False. | |
| rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. | |
| Defaults to None | |
| Returns: | |
| :obj:`InstanceData`: Detection results of each image | |
| 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). | |
| """ | |
| results = InstanceData() | |
| if isinstance(cls_score, list): | |
| cls_score = sum(cls_score) / float(len(cls_score)) | |
| scores = F.softmax(cls_score, dim=1) if cls_score is not None else None | |
| img_shape = img_meta['img_shape'] | |
| if bbox_pred is not None: | |
| bboxes, confidences = self.bbox_coder.decode( | |
| roi[:, 1:], bbox_pred, img_shape) | |
| else: | |
| bboxes = roi[:, 1:].clone() | |
| confidences = None | |
| if img_shape is not None: | |
| bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1) | |
| bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1) | |
| if rescale and bboxes.size(0) > 0: | |
| assert img_meta.get('scale_factor') is not None | |
| scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat( | |
| (1, 2)) | |
| bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view( | |
| bboxes.size()[0], -1) | |
| if rcnn_test_cfg is None: | |
| results.bboxes = bboxes | |
| results.scores = scores | |
| else: | |
| det_bboxes, det_labels = multiclass_nms( | |
| bboxes, | |
| scores, | |
| rcnn_test_cfg.score_thr, | |
| rcnn_test_cfg.nms, | |
| rcnn_test_cfg.max_per_img, | |
| score_factors=confidences) | |
| results.bboxes = det_bboxes[:, :4] | |
| results.scores = det_bboxes[:, -1] | |
| results.labels = det_labels | |
| return results | |
| def refine_bboxes(self, sampling_results: List[SamplingResult], | |
| bbox_results: dict, | |
| batch_img_metas: List[dict]) -> InstanceList: | |
| """Refine bboxes during training. | |
| Args: | |
| sampling_results (List[:obj:`SamplingResult`]): Sampling results. | |
| bbox_results (dict): Usually is a dictionary with keys: | |
| - `cls_score` (Tensor): Classification scores. | |
| - `bbox_pred` (Tensor): Box energies / deltas. | |
| - `rois` (Tensor): RoIs with the shape (n, 5) where the first | |
| column indicates batch id of each RoI. | |
| - `bbox_targets` (tuple): Ground truth for proposals in a | |
| single image. Containing the following list of Tensors: | |
| (labels, label_weights, bbox_targets, bbox_weights) | |
| batch_img_metas (List[dict]): List of image information. | |
| Returns: | |
| list[:obj:`InstanceData`]: Refined bboxes of each image. | |
| """ | |
| pos_is_gts = [res.pos_is_gt for res in sampling_results] | |
| # bbox_targets is a tuple | |
| labels = bbox_results['bbox_targets'][0] | |
| cls_scores = bbox_results['cls_score'] | |
| rois = bbox_results['rois'] | |
| bbox_preds = bbox_results['bbox_pred'] | |
| if cls_scores.numel() == 0: | |
| return None | |
| labels = torch.where(labels == self.num_classes, | |
| cls_scores[:, :-1].argmax(1), labels) | |
| img_ids = rois[:, 0].long().unique(sorted=True) | |
| assert img_ids.numel() <= len(batch_img_metas) | |
| results_list = [] | |
| for i in range(len(batch_img_metas)): | |
| inds = torch.nonzero( | |
| rois[:, 0] == i, as_tuple=False).squeeze(dim=1) | |
| num_rois = inds.numel() | |
| bboxes_ = rois[inds, 1:] | |
| label_ = labels[inds] | |
| edge_cls_preds, edge_offset_preds = bbox_preds | |
| edge_cls_preds_ = edge_cls_preds[inds] | |
| edge_offset_preds_ = edge_offset_preds[inds] | |
| bbox_pred_ = (edge_cls_preds_, edge_offset_preds_) | |
| img_meta_ = batch_img_metas[i] | |
| pos_is_gts_ = pos_is_gts[i] | |
| bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, | |
| img_meta_) | |
| # filter gt bboxes | |
| pos_keep = 1 - pos_is_gts_ | |
| keep_inds = pos_is_gts_.new_ones(num_rois) | |
| keep_inds[:len(pos_is_gts_)] = pos_keep | |
| results = InstanceData(bboxes=bboxes[keep_inds.type(torch.bool)]) | |
| results_list.append(results) | |
| return results_list | |
| def regress_by_class(self, rois: Tensor, label: Tensor, bbox_pred: tuple, | |
| img_meta: dict) -> Tensor: | |
| """Regress the bbox for the predicted class. Used in Cascade R-CNN. | |
| Args: | |
| rois (Tensor): shape (n, 4) or (n, 5) | |
| label (Tensor): shape (n, ) | |
| bbox_pred (Tuple[Tensor]): shape [(n, num_buckets *2), \ | |
| (n, num_buckets *2)] | |
| img_meta (dict): Image meta info. | |
| Returns: | |
| Tensor: Regressed bboxes, the same shape as input rois. | |
| """ | |
| assert rois.size(1) == 4 or rois.size(1) == 5 | |
| if rois.size(1) == 4: | |
| new_rois, _ = self.bbox_coder.decode(rois, bbox_pred, | |
| img_meta['img_shape']) | |
| else: | |
| bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred, | |
| img_meta['img_shape']) | |
| new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) | |
| return new_rois | |