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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- import torch.nn as nn
- from mmcv.cnn import ConvModule, Scale
- from mmcv.runner import force_fp32
-
- from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
- images_to_levels, multi_apply, reduce_mean, unmap)
- from ..builder import HEADS, build_loss
- from .anchor_head import AnchorHead
-
-
- @HEADS.register_module()
- class ATSSHead(AnchorHead):
- """Bridging the Gap Between Anchor-based and Anchor-free Detection via
- Adaptive Training Sample Selection.
-
- ATSS head structure is similar with FCOS, however ATSS use anchor boxes
- and assign label by Adaptive Training Sample Selection instead max-iou.
-
- https://arxiv.org/abs/1912.02424
- """
-
- def __init__(self,
- num_classes,
- in_channels,
- stacked_convs=4,
- conv_cfg=None,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
- reg_decoded_bbox=True,
- loss_centerness=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0),
- init_cfg=dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='atss_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs):
- self.stacked_convs = stacked_convs
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- super(ATSSHead, self).__init__(
- num_classes,
- in_channels,
- reg_decoded_bbox=reg_decoded_bbox,
- 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.loss_centerness = build_loss(loss_centerness)
-
- 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))
- self.atss_cls = nn.Conv2d(
- self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- 3,
- padding=1)
- self.atss_reg = nn.Conv2d(
- self.feat_channels, self.num_base_priors * 4, 3, padding=1)
- self.atss_centerness = nn.Conv2d(
- self.feat_channels, self.num_base_priors * 1, 3, padding=1)
- self.scales = nn.ModuleList(
- [Scale(1.0) for _ in self.prior_generator.strides])
-
- 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 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.
- """
- return multi_apply(self.forward_single, feats, self.scales)
-
- 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 scores for a single scale level
- the channels number is num_anchors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for a single scale
- level, the channels number is num_anchors * 4.
- centerness (Tensor): Centerness for a single scale level, the
- channel number is (N, num_anchors * 1, H, W).
- """
- 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.atss_cls(cls_feat)
- # we just follow atss, not apply exp in bbox_pred
- bbox_pred = scale(self.atss_reg(reg_feat)).float()
- centerness = self.atss_centerness(reg_feat)
- return cls_score, bbox_pred, centerness
-
- def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
- label_weights, bbox_targets, num_total_samples):
- """Compute loss of a single scale level.
-
- Args:
- cls_score (Tensor): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W).
- bbox_pred (Tensor): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W).
- anchors (Tensor): Box reference for each scale level with shape
- (N, num_total_anchors, 4).
- 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
- weight shape (N, num_total_anchors, 4).
- num_total_samples (int): Number os positive samples that is
- reduced over all GPUs.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
-
- anchors = anchors.reshape(-1, 4)
-
- b = cls_score.shape[0]
- c = cls_score.shape[1]
- h = cls_score.shape[2]
- w = cls_score.shape[3]
-
- cls_score = cls_score.permute(0, 2, 3, 1).reshape(
- -1, self.cls_out_channels).contiguous()
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
- bbox_targets = bbox_targets.reshape(-1, 4)
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
-
- # classification loss
- loss_cls, loss_batch = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- loss_batch = loss_batch.reshape(b, h, w, c)
- loss_batch = loss_batch.sum(3).sum(2).sum(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)
-
- 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_centerness = centerness[pos_inds]
-
- centerness_targets = self.centerness_target(
- pos_anchors, pos_bbox_targets)
- pos_decode_bbox_pred = self.bbox_coder.decode(
- pos_anchors, pos_bbox_pred)
-
- # regression loss
- loss_bbox = self.loss_bbox(
- pos_decode_bbox_pred,
- pos_bbox_targets,
- weight=centerness_targets,
- avg_factor=1.0)
-
- # centerness loss
- loss_centerness = self.loss_centerness(
- pos_centerness,
- centerness_targets,
- avg_factor=num_total_samples)
-
- else:
- loss_bbox = bbox_pred.sum() * 0
- loss_centerness = centerness.sum() * 0
- centerness_targets = bbox_targets.new_tensor(0.)
-
- return loss_cls, loss_batch, loss_bbox, loss_centerness, centerness_targets.sum()
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
- def loss(self,
- cls_scores,
- bbox_preds,
- centernesses,
- 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)
- centernesses (list[Tensor]): Centerness for each scale
- level with shape (N, num_anchors * 1, H, W)
- 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.prior_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, loss_batch, losses_bbox, loss_centerness,\
- bbox_avg_factor = multi_apply(
- self.loss_single,
- anchor_list,
- cls_scores,
- bbox_preds,
- centernesses,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- num_total_samples=num_total_samples)
-
- bbox_avg_factor = sum(bbox_avg_factor)
- bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item()
- losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
- return dict(
- loss_cls=losses_cls,
- loss_bbox=losses_bbox,
- loss_centerness=loss_centerness,
- loss_batch=loss_batch)
-
- def centerness_target(self, anchors, gts):
- # only calculate pos centerness targets, otherwise there may be nan
- anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
- anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
- l_ = anchors_cx - gts[:, 0]
- t_ = anchors_cy - gts[:, 1]
- r_ = gts[:, 2] - anchors_cx
- b_ = gts[:, 3] - anchors_cy
-
- left_right = torch.stack([l_, r_], dim=1)
- top_bottom = torch.stack([t_, b_], dim=1)
- centerness = torch.sqrt(
- (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
- (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
- assert not torch.isnan(centerness).any()
- return centerness
-
- 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 ATSS 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] = torch.cat(anchor_list[i])
- valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_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.
- 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)+len(neg_inds)<num_valid_anchors:
- print("error")
- if len(pos_inds) > 0:
- if self.reg_decoded_bbox:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- else:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_bboxes, 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 since v2.5.0
- 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
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