Source code for mmdet.models.dense_heads.ssd_head

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import force_fp32

from mmdet.core import (build_anchor_generator, build_assigner,
                        build_bbox_coder, build_sampler, multi_apply)
from ..builder import HEADS
from ..losses import smooth_l1_loss
from .anchor_head import AnchorHead

# TODO: add loss evaluator for SSD
[docs]@HEADS.register_module() class SSDHead(AnchorHead): """SSD head used in Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. stacked_convs (int): Number of conv layers in cls and reg tower. Default: 0. feat_channels (int): Number of hidden channels when stacked_convs > 0. Default: 256. use_depthwise (bool): Whether to use DepthwiseSeparableConv. Default: False. conv_cfg (dict): Dictionary to construct and config conv layer. Default: None. norm_cfg (dict): Dictionary to construct and config norm layer. Default: None. act_cfg (dict): Dictionary to construct and config activation layer. Default: None. anchor_generator (dict): Config dict for anchor generator bbox_coder (dict): Config of bounding box coder. reg_decoded_bbox (bool): If true, the regression loss would be applied directly on decoded bounding boxes, converting both the predicted boxes and regression targets to absolute coordinates format. Default False. It should be `True` when using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. train_cfg (dict): Training config of anchor head. test_cfg (dict): Testing config of anchor head. init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 def __init__(self, num_classes=80, in_channels=(512, 1024, 512, 256, 256, 256), stacked_convs=0, feat_channels=256, use_depthwise=False, conv_cfg=None, norm_cfg=None, act_cfg=None, anchor_generator=dict( type='SSDAnchorGenerator', scale_major=False, input_size=300, strides=[8, 16, 32, 64, 100, 300], ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]), basesize_ratio_range=(0.1, 0.9)), bbox_coder=dict( type='DeltaXYWHBBoxCoder', clip_border=True, target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0], ), reg_decoded_bbox=False, train_cfg=None, test_cfg=None, init_cfg=dict( type='Xavier', layer='Conv2d', distribution='uniform', bias=0)): super(AnchorHead, self).__init__(init_cfg) self.num_classes = num_classes self.in_channels = in_channels self.stacked_convs = stacked_convs self.feat_channels = feat_channels self.use_depthwise = use_depthwise self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.act_cfg = act_cfg self.cls_out_channels = num_classes + 1 # add background class self.anchor_generator = build_anchor_generator(anchor_generator) self.num_anchors = self.anchor_generator.num_base_anchors self._init_layers() self.bbox_coder = build_bbox_coder(bbox_coder) self.reg_decoded_bbox = reg_decoded_bbox self.use_sigmoid_cls = False self.cls_focal_loss = False self.train_cfg = train_cfg self.test_cfg = test_cfg # set sampling=False for archor_target self.sampling = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) # SSD sampling=False so use PseudoSampler sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) self.fp16_enabled = False def _init_layers(self): """Initialize layers of the head.""" self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() # TODO: Use registry to choose ConvModule type conv = DepthwiseSeparableConvModule \ if self.use_depthwise else ConvModule for channel, num_anchors in zip(self.in_channels, self.num_anchors): cls_layers = [] reg_layers = [] in_channel = channel # build stacked conv tower, not used in default ssd for i in range(self.stacked_convs): cls_layers.append( conv( in_channel, self.feat_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)) reg_layers.append( conv( in_channel, self.feat_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)) in_channel = self.feat_channels # SSD-Lite head if self.use_depthwise: cls_layers.append( ConvModule( in_channel, in_channel, 3, padding=1, groups=in_channel, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)) reg_layers.append( ConvModule( in_channel, in_channel, 3, padding=1, groups=in_channel, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg)) cls_layers.append( nn.Conv2d( in_channel, num_anchors * self.cls_out_channels, kernel_size=1 if self.use_depthwise else 3, padding=0 if self.use_depthwise else 1)) reg_layers.append( nn.Conv2d( in_channel, num_anchors * 4, kernel_size=1 if self.use_depthwise else 3, padding=0 if self.use_depthwise else 1)) self.cls_convs.append(nn.Sequential(*cls_layers)) self.reg_convs.append(nn.Sequential(*reg_layers))
[docs] def forward(self, feats): """Forward features from the upstream network. Args: feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. Returns: tuple: cls_scores (list[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_anchors * num_classes. bbox_preds (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_anchors * 4. """ cls_scores = [] bbox_preds = [] for feat, reg_conv, cls_conv in zip(feats, self.reg_convs, self.cls_convs): cls_scores.append(cls_conv(feat)) bbox_preds.append(reg_conv(feat)) return cls_scores, bbox_preds
[docs] def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, bbox_targets, bbox_weights, num_total_samples): """Compute loss of a single image. Args: cls_score (Tensor): Box scores for eachimage Has shape (num_total_anchors, num_classes). bbox_pred (Tensor): Box energies / deltas for each image level with shape (num_total_anchors, 4). anchors (Tensor): Box reference for each scale level with shape (num_total_anchors, 4). labels (Tensor): Labels of each anchors with shape (num_total_anchors,). label_weights (Tensor): Label weights of each anchor with shape (num_total_anchors,) bbox_targets (Tensor): BBox regression targets of each anchor wight shape (num_total_anchors, 4). bbox_weights (Tensor): BBox regression loss weights of each anchor with shape (num_total_anchors, 4). num_total_samples (int): If sampling, num total samples equal to the number of total anchors; Otherwise, it is the number of positive anchors. Returns: dict[str, Tensor]: A dictionary of loss components. """ loss_cls_all = F.cross_entropy( cls_score, labels, reduction='none') * label_weights # FG cat_id: [0, num_classes -1], BG cat_id: num_classes pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero( as_tuple=False).reshape(-1) neg_inds = (labels == self.num_classes).nonzero( as_tuple=False).view(-1) num_pos_samples = pos_inds.size(0) num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples if num_neg_samples > neg_inds.size(0): num_neg_samples = neg_inds.size(0) topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) loss_cls_pos = loss_cls_all[pos_inds].sum() loss_cls_neg = topk_loss_cls_neg.sum() loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples if self.reg_decoded_bbox: # When the regression loss (e.g. `IouLoss`, `GIouLoss`) # is applied directly on the decoded bounding boxes, it # decodes the already encoded coordinates to absolute format. bbox_pred = self.bbox_coder.decode(anchor, bbox_pred) loss_bbox = smooth_l1_loss( bbox_pred, bbox_targets, bbox_weights, beta=self.train_cfg.smoothl1_beta, avg_factor=num_total_samples) return loss_cls[None], loss_bbox
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None): """Compute losses of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (N, num_anchors * num_classes, H, W) bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (N, num_anchors * 4, H, W) gt_bboxes (list[Tensor]): each item are the truth boxes for each image in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): class indices corresponding to each box img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes_ignore (None | list[Tensor]): specify which bounding boxes can be ignored when computing the loss. Returns: dict[str, Tensor]: A dictionary of loss components. """ featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == self.anchor_generator.num_levels device = cls_scores[0].device anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, img_metas, device=device) cls_reg_targets = self.get_targets( anchor_list, valid_flag_list, gt_bboxes, img_metas, gt_bboxes_ignore_list=gt_bboxes_ignore, gt_labels_list=gt_labels, label_channels=1, unmap_outputs=False) if cls_reg_targets is None: return None (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets num_images = len(img_metas) all_cls_scores =[ s.permute(0, 2, 3, 1).reshape( num_images, -1, self.cls_out_channels) for s in cls_scores ], 1) all_labels =, -1).view(num_images, -1) all_label_weights =, -1).view(num_images, -1) all_bbox_preds =[ b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) for b in bbox_preds ], -2) all_bbox_targets =, -2).view(num_images, -1, 4) all_bbox_weights =, -2).view(num_images, -1, 4) # concat all level anchors to a single tensor all_anchors = [] for i in range(num_images): all_anchors.append([i])) # check NaN and Inf assert torch.isfinite(all_cls_scores).all().item(), \ 'classification scores become infinite or NaN!' assert torch.isfinite(all_bbox_preds).all().item(), \ 'bbox predications become infinite or NaN!' losses_cls, losses_bbox = multi_apply( self.loss_single, all_cls_scores, all_bbox_preds, all_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, num_total_samples=num_total_pos) return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)