import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
force_fp32, images_to_levels, multi_apply,
multiclass_nms, unmap)
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
def reduce_mean(tensor):
if not (dist.is_available() and dist.is_initialized()):
return tensor
tensor = tensor.clone()
dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
return tensor
[docs]@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),
loss_centerness=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0),
**kwargs):
self.stacked_convs = stacked_convs
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
super(ATSSHead, self).__init__(num_classes, in_channels, **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):
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_anchors * 4, 3, padding=1)
self.atss_centerness = nn.Conv2d(
self.feat_channels, self.num_anchors * 1, 3, padding=1)
self.scales = nn.ModuleList(
[Scale(1.0) for _ in self.anchor_generator.strides])
def init_weights(self):
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.atss_cls, std=0.01, bias=bias_cls)
normal_init(self.atss_reg, std=0.01)
normal_init(self.atss_centerness, std=0.01)
[docs] def forward(self, feats):
return multi_apply(self.forward_single, feats, self.scales)
def forward_single(self, x, scale):
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):
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)
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 = self.loss_cls(
cls_score, labels, label_weights, avg_factor=num_total_samples)
# 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)
pos_decode_bbox_targets = self.bbox_coder.decode(
pos_anchors, pos_bbox_targets)
# regression loss
loss_bbox = self.loss_bbox(
pos_decode_bbox_pred,
pos_decode_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 = torch.tensor(0).cuda()
return loss_cls, 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):
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).cuda()).item()
num_total_samples = max(num_total_samples, 1.0)
losses_cls, 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).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)
def centerness_target(self, anchors, bbox_targets):
# only calculate pos centerness targets, otherwise there may be nan
gts = self.bbox_coder.decode(anchors, bbox_targets)
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
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
def get_bboxes(self,
cls_scores,
bbox_preds,
centernesses,
img_metas,
cfg=None,
rescale=False):
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_scores) == len(bbox_preds)
num_levels = len(cls_scores)
device = cls_scores[0].device
featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
mlvl_anchors = self.anchor_generator.grid_anchors(
featmap_sizes, device=device)
result_list = []
for img_id in range(len(img_metas)):
cls_score_list = [
cls_scores[i][img_id].detach() for i in range(num_levels)
]
bbox_pred_list = [
bbox_preds[i][img_id].detach() for i in range(num_levels)
]
centerness_pred_list = [
centernesses[i][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
centerness_pred_list,
mlvl_anchors, img_shape,
scale_factor, cfg, rescale)
result_list.append(proposals)
return result_list
def _get_bboxes_single(self,
cls_scores,
bbox_preds,
centernesses,
mlvl_anchors,
img_shape,
scale_factor,
cfg,
rescale=False):
assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
mlvl_bboxes = []
mlvl_scores = []
mlvl_centerness = []
for cls_score, bbox_pred, centerness, anchors in zip(
cls_scores, bbox_preds, centernesses, mlvl_anchors):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
centerness = centerness.permute(1, 2, 0).reshape(-1).sigmoid()
nms_pre = cfg.get('nms_pre', -1)
if nms_pre > 0 and scores.shape[0] > nms_pre:
max_scores, _ = (scores * centerness[:, None]).max(dim=1)
_, topk_inds = max_scores.topk(nms_pre)
anchors = anchors[topk_inds, :]
bbox_pred = bbox_pred[topk_inds, :]
scores = scores[topk_inds, :]
centerness = centerness[topk_inds]
bboxes = self.bbox_coder.decode(
anchors, bbox_pred, max_shape=img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_centerness.append(centerness)
mlvl_bboxes = torch.cat(mlvl_bboxes)
if rescale:
mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
mlvl_scores = torch.cat(mlvl_scores)
# 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 = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
mlvl_centerness = torch.cat(mlvl_centerness)
det_bboxes, det_labels = multiclass_nms(
mlvl_bboxes,
mlvl_scores,
cfg.score_thr,
cfg.nms,
cfg.max_per_img,
score_factors=mlvl_centerness)
return det_bboxes, det_labels
[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 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):
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, ) * 6
# 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.background_label,
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 = 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:
labels[pos_inds] = 1
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