Source code for mmdet.models.dense_heads.gfl_head

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

from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
                        build_assigner, build_sampler, distance2bbox,
                        images_to_levels, multi_apply, multiclass_nms,
                        reduce_mean, unmap)
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead

class Integral(nn.Module):
    """A fixed layer for calculating integral result from distribution.

    This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
    P(y_i) denotes the softmax vector that represents the discrete distribution
    y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}

        reg_max (int): The maximal value of the discrete set. Default: 16. You
            may want to reset it according to your new dataset or related

    def __init__(self, reg_max=16):
        super(Integral, self).__init__()
        self.reg_max = reg_max
                             torch.linspace(0, self.reg_max, self.reg_max + 1))

    def forward(self, x):
        """Forward feature from the regression head to get integral result of
        bounding box location.

            x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
                n is self.reg_max.

            x (Tensor): Integral result of box locations, i.e., distance
                offsets from the box center in four directions, shape (N, 4).
        x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
        x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
        return x

[docs]@HEADS.register_module() class GFLHead(AnchorHead): """Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. GFL head structure is similar with ATSS, however GFL uses 1) joint representation for classification and localization quality, and 2) flexible General distribution for bounding box locations, which are supervised by Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively 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: 4. conv_cfg (dict): dictionary to construct and config conv layer. Default: None. norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='GN', num_groups=32, requires_grad=True). loss_qfl (dict): Config of Quality Focal Loss (QFL). reg_max (int): Max value of integral set :math: `{0, ..., reg_max}` in QFL setting. Default: 16. init_cfg (dict or list[dict], optional): Initialization config dict. Example: >>> self = GFLHead(11, 7) >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] >>> cls_quality_score, bbox_pred = self.forward(feats) >>> assert len(cls_quality_score) == len(self.scales) """ def __init__(self, num_classes, in_channels, stacked_convs=4, conv_cfg=None, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, init_cfg=dict( type='Normal', layer='Conv2d', std=0.01, override=dict( type='Normal', name='gfl_cls', std=0.01, bias_prob=0.01)), **kwargs): self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.reg_max = reg_max super(GFLHead, self).__init__( num_classes, in_channels, init_cfg=init_cfg, **kwargs) 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.integral = Integral(self.reg_max) self.loss_dfl = build_loss(loss_dfl) 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)) assert self.num_anchors == 1, 'anchor free version' self.gfl_cls = nn.Conv2d( self.feat_channels, self.cls_out_channels, 3, padding=1) self.gfl_reg = nn.Conv2d( self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1) self.scales = nn.ModuleList( [Scale(1.0) for _ in self.anchor_generator.strides])
[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: Usually a tuple of classification scores and bbox prediction cls_scores (list[Tensor]): Classification and quality (IoU) joint scores for all scale levels, each is a 4D-tensor, the channel number is num_classes. bbox_preds (list[Tensor]): Box distribution logits for all scale levels, each is a 4D-tensor, the channel number is 4*(n+1), n is max value of integral set. """ return multi_apply(self.forward_single, feats, self.scales)
[docs] def forward_single(self, x, scale): """Forward feature of a single scale level. Args: x (Tensor): Features of a single scale level. scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize the bbox prediction. Returns: tuple: cls_score (Tensor): Cls and quality joint scores for a single scale level the channel number is num_classes. bbox_pred (Tensor): Box distribution logits for a single scale level, the channel number is 4*(n+1), n is max value of integral set. """ cls_feat = x reg_feat = x for cls_conv in self.cls_convs: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) cls_score = self.gfl_cls(cls_feat) bbox_pred = scale(self.gfl_reg(reg_feat)).float() return cls_score, bbox_pred
[docs] def anchor_center(self, anchors): """Get anchor centers from anchors. Args: anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format. Returns: Tensor: Anchor centers with shape (N, 2), "xy" format. """ anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2 anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2 return torch.stack([anchors_cx, anchors_cy], dim=-1)
[docs] def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, bbox_targets, stride, num_total_samples): """Compute loss of a single scale level. Args: anchors (Tensor): Box reference for each scale level with shape (N, num_total_anchors, 4). cls_score (Tensor): Cls and quality joint scores for each scale level has shape (N, num_classes, H, W). bbox_pred (Tensor): Box distribution logits for each scale level with shape (N, 4*(n+1), H, W), n is max value of integral set. labels (Tensor): Labels of each anchors with shape (N, num_total_anchors). label_weights (Tensor): Label weights of each anchor with shape (N, num_total_anchors) bbox_targets (Tensor): BBox regression targets of each anchor wight shape (N, num_total_anchors, 4). stride (tuple): Stride in this scale level. num_total_samples (int): Number of positive samples that is reduced over all GPUs. Returns: dict[str, Tensor]: A dictionary of loss components. """ assert stride[0] == stride[1], 'h stride is not equal to w stride!' anchors = anchors.reshape(-1, 4) cls_score = cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4 * (self.reg_max + 1)) bbox_targets = bbox_targets.reshape(-1, 4) labels = labels.reshape(-1) label_weights = label_weights.reshape(-1) # FG cat_id: [0, num_classes -1], BG cat_id: num_classes bg_class_ind = self.num_classes pos_inds = ((labels >= 0) & (labels < bg_class_ind)).nonzero().squeeze(1) score = label_weights.new_zeros(labels.shape) if len(pos_inds) > 0: pos_bbox_targets = bbox_targets[pos_inds] pos_bbox_pred = bbox_pred[pos_inds] pos_anchors = anchors[pos_inds] pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0] weight_targets = cls_score.detach().sigmoid() weight_targets = weight_targets.max(dim=1)[0][pos_inds] pos_bbox_pred_corners = self.integral(pos_bbox_pred) pos_decode_bbox_pred = distance2bbox(pos_anchor_centers, pos_bbox_pred_corners) pos_decode_bbox_targets = pos_bbox_targets / stride[0] score[pos_inds] = bbox_overlaps( pos_decode_bbox_pred.detach(), pos_decode_bbox_targets, is_aligned=True) pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1) target_corners = bbox2distance(pos_anchor_centers, pos_decode_bbox_targets, self.reg_max).reshape(-1) # regression loss loss_bbox = self.loss_bbox( pos_decode_bbox_pred, pos_decode_bbox_targets, weight=weight_targets, avg_factor=1.0) # dfl loss loss_dfl = self.loss_dfl( pred_corners, target_corners, weight=weight_targets[:, None].expand(-1, 4).reshape(-1), avg_factor=4.0) else: loss_bbox = bbox_pred.sum() * 0 loss_dfl = bbox_pred.sum() * 0 weight_targets = bbox_pred.new_tensor(0) # cls (qfl) loss loss_cls = self.loss_cls( cls_score, (labels, score), weight=label_weights, avg_factor=num_total_samples) return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
[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]): Cls and quality scores for each scale level has shape (N, num_classes, H, W). bbox_preds (list[Tensor]): Box distribution logits for each scale level with shape (N, 4*(n+1), H, W), n is max value of integral set. gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) 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 (list[Tensor] | None): 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) label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 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=label_channels) if cls_reg_targets is None: return None (anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets num_total_samples = reduce_mean( torch.tensor(num_total_pos, dtype=torch.float, device=device)).item() num_total_samples = max(num_total_samples, 1.0) losses_cls, losses_bbox, losses_dfl,\ avg_factor = multi_apply( self.loss_single, anchor_list, cls_scores, bbox_preds, labels_list, label_weights_list, bbox_targets_list, self.anchor_generator.strides, num_total_samples=num_total_samples) avg_factor = sum(avg_factor) avg_factor = reduce_mean(avg_factor).clamp_(min=1).item() losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox)) losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl)) return dict( loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
def _get_bboxes(self, cls_scores, bbox_preds, mlvl_anchors, img_shapes, scale_factors, cfg, rescale=False, with_nms=True): """Transform outputs for a single batch item into labeled boxes. Args: cls_scores (list[Tensor]): Box scores for a single scale level has shape (N, num_classes, H, W). bbox_preds (list[Tensor]): Box distribution logits for a single scale level with shape (N, 4*(n+1), H, W), n is max value of integral set. mlvl_anchors (list[Tensor]): Box reference for a single scale level with shape (num_total_anchors, 4). img_shapes (list[tuple[int]]): Shape of the input image, list[(height, width, 3)]. scale_factors (list[ndarray]): Scale factor of the image arange as (w_scale, h_scale, w_scale, h_scale). cfg (mmcv.Config | None): 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: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. The first item is an (n, 5) tensor, where 5 represent (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. The shape of the second tensor in the tuple is (n,), and each element represents the class label of the corresponding box. """ cfg = self.test_cfg if cfg is None else cfg assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) batch_size = cls_scores[0].shape[0] mlvl_bboxes = [] mlvl_scores = [] for cls_score, bbox_pred, stride, anchors in zip( cls_scores, bbox_preds, self.anchor_generator.strides, mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] assert stride[0] == stride[1] scores = cls_score.permute(0, 2, 3, 1).reshape( batch_size, -1, self.cls_out_channels).sigmoid() bbox_pred = bbox_pred.permute(0, 2, 3, 1) bbox_pred = self.integral(bbox_pred) * stride[0] bbox_pred = bbox_pred.reshape(batch_size, -1, 4) nms_pre = cfg.get('nms_pre', -1) if nms_pre > 0 and scores.shape[1] > nms_pre: max_scores, _ = scores.max(-1) _, topk_inds = max_scores.topk(nms_pre) batch_inds = torch.arange(batch_size).view( -1, 1).expand_as(topk_inds).long() anchors = anchors[topk_inds, :] bbox_pred = bbox_pred[batch_inds, topk_inds, :] scores = scores[batch_inds, topk_inds, :] else: anchors = anchors.expand_as(bbox_pred) bboxes = distance2bbox( self.anchor_center(anchors), bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) batch_mlvl_bboxes =, dim=1) if rescale: batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( scale_factors).unsqueeze(1) batch_mlvl_scores =, dim=1) # Add a dummy background class to the backend when using sigmoid # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class padding = batch_mlvl_scores.new_zeros(batch_size, batch_mlvl_scores.shape[1], 1) batch_mlvl_scores =[batch_mlvl_scores, padding], dim=-1) if with_nms: det_results = [] for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, batch_mlvl_scores): det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, cfg.score_thr, cfg.nms, cfg.max_per_img) det_results.append(tuple([det_bbox, det_label])) else: det_results = [ tuple(mlvl_bs) for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) ] return det_results
[docs] def get_targets(self, anchor_list, valid_flag_list, gt_bboxes_list, img_metas, gt_bboxes_ignore_list=None, gt_labels_list=None, label_channels=1, unmap_outputs=True): """Get targets for GFL head. This method is almost the same as `AnchorHead.get_targets()`. Besides returning the targets as the parent method does, it also returns the anchors as the first element of the returned tuple. """ num_imgs = len(img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] num_level_anchors_list = [num_level_anchors] * num_imgs # concat all level anchors and flags to a single tensor for i in range(num_imgs): assert len(anchor_list[i]) == len(valid_flag_list[i]) anchor_list[i] =[i]) valid_flag_list[i] =[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_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( self._get_target_single, anchor_list, valid_flag_list, num_level_anchors_list, gt_bboxes_list, gt_bboxes_ignore_list, gt_labels_list, img_metas, label_channels=label_channels, unmap_outputs=unmap_outputs) # no valid anchors if any([labels is None for labels in all_labels]): return None # sampled anchors 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]) # split targets to a list w.r.t. multiple levels anchors_list = images_to_levels(all_anchors, num_level_anchors) labels_list = images_to_levels(all_labels, num_level_anchors) label_weights_list = images_to_levels(all_label_weights, num_level_anchors) bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors) bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors) return (anchors_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg)
def _get_target_single(self, flat_anchors, valid_flags, num_level_anchors, gt_bboxes, gt_bboxes_ignore, gt_labels, img_meta, label_channels=1, unmap_outputs=True): """Compute regression, classification targets for anchors in a single image. Args: flat_anchors (Tensor): Multi-level anchors of the image, which are concatenated into a single tensor of shape (num_anchors, 4) valid_flags (Tensor): Multi level valid flags of the image, which are concatenated into a single tensor of shape (num_anchors,). num_level_anchors Tensor): Number of anchors of each scale level. gt_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). gt_labels (Tensor): Ground truth labels of each box, shape (num_gts,). img_meta (dict): Meta info of the image. label_channels (int): Channel of label. unmap_outputs (bool): Whether to map outputs back to the original set of anchors. Returns: tuple: N is the number of total anchors in the image. anchors (Tensor): All anchors in the image with shape (N, 4). labels (Tensor): Labels of all anchors in the image with shape (N,). label_weights (Tensor): Label weights of all anchor in the image with shape (N,). bbox_targets (Tensor): BBox targets of all anchors in the image with shape (N, 4). bbox_weights (Tensor): BBox weights of all anchors in the image with shape (N, 4). pos_inds (Tensor): Indices of positive anchor with shape (num_pos,). neg_inds (Tensor): Indices of negative anchor with shape (num_neg,). """ inside_flags = anchor_inside_flags(flat_anchors, valid_flags, img_meta['img_shape'][:2], self.train_cfg.allowed_border) if not inside_flags.any(): return (None, ) * 7 # assign gt and sample anchors anchors = flat_anchors[inside_flags, :] num_level_anchors_inside = self.get_num_level_anchors_inside( num_level_anchors, inside_flags) assign_result = self.assigner.assign(anchors, num_level_anchors_inside, gt_bboxes, gt_bboxes_ignore, gt_labels) sampling_result = self.sampler.sample(assign_result, anchors, gt_bboxes) num_valid_anchors = anchors.shape[0] bbox_targets = torch.zeros_like(anchors) bbox_weights = torch.zeros_like(anchors) labels = anchors.new_full((num_valid_anchors, ), self.num_classes, dtype=torch.long) label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: pos_bbox_targets = sampling_result.pos_gt_bboxes bbox_targets[pos_inds, :] = pos_bbox_targets bbox_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 self.train_cfg.pos_weight <= 0: label_weights[pos_inds] = 1.0 else: label_weights[pos_inds] = self.train_cfg.pos_weight if len(neg_inds) > 0: label_weights[neg_inds] = 1.0 # map up to original set of anchors if unmap_outputs: num_total_anchors = flat_anchors.size(0) anchors = unmap(anchors, num_total_anchors, inside_flags) labels = unmap( labels, num_total_anchors, inside_flags, fill=self.num_classes) label_weights = unmap(label_weights, num_total_anchors, inside_flags) bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) return (anchors, labels, label_weights, bbox_targets, bbox_weights, pos_inds, neg_inds) def get_num_level_anchors_inside(self, num_level_anchors, inside_flags): split_inside_flags = torch.split(inside_flags, num_level_anchors) num_level_anchors_inside = [ int(flags.sum()) for flags in split_inside_flags ] return num_level_anchors_inside