Source code for mmdet.models.dense_heads.fovea_head
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import DeformConv2d
from mmcv.runner import BaseModule
from mmdet.core import multi_apply
from mmdet.core.utils import filter_scores_and_topk
from ..builder import HEADS
from .anchor_free_head import AnchorFreeHead
INF = 1e8
class FeatureAlign(BaseModule):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deform_groups=4,
init_cfg=dict(
type='Normal',
layer='Conv2d',
std=0.1,
override=dict(
type='Normal', name='conv_adaption', std=0.01))):
super(FeatureAlign, self).__init__(init_cfg)
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
4, deform_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deform_groups=deform_groups)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, shape):
offset = self.conv_offset(shape)
x = self.relu(self.conv_adaption(x, offset))
return x
[docs]@HEADS.register_module()
class FoveaHead(AnchorFreeHead):
"""FoveaBox: Beyond Anchor-based Object Detector
https://arxiv.org/abs/1904.03797
"""
def __init__(self,
num_classes,
in_channels,
base_edge_list=(16, 32, 64, 128, 256),
scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128,
512)),
sigma=0.4,
with_deform=False,
deform_groups=4,
init_cfg=dict(
type='Normal',
layer='Conv2d',
std=0.01,
override=dict(
type='Normal',
name='conv_cls',
std=0.01,
bias_prob=0.01)),
**kwargs):
self.base_edge_list = base_edge_list
self.scale_ranges = scale_ranges
self.sigma = sigma
self.with_deform = with_deform
self.deform_groups = deform_groups
super().__init__(num_classes, in_channels, init_cfg=init_cfg, **kwargs)
def _init_layers(self):
# box branch
super()._init_reg_convs()
self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
# cls branch
if not self.with_deform:
super()._init_cls_convs()
self.conv_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
else:
self.cls_convs = nn.ModuleList()
self.cls_convs.append(
ConvModule(
self.feat_channels, (self.feat_channels * 4),
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.cls_convs.append(
ConvModule((self.feat_channels * 4), (self.feat_channels * 4),
1,
stride=1,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
bias=self.norm_cfg is None))
self.feature_adaption = FeatureAlign(
self.feat_channels,
self.feat_channels,
kernel_size=3,
deform_groups=self.deform_groups)
self.conv_cls = nn.Conv2d(
int(self.feat_channels * 4),
self.cls_out_channels,
3,
padding=1)
[docs] def forward_single(self, x):
cls_feat = x
reg_feat = x
for reg_layer in self.reg_convs:
reg_feat = reg_layer(reg_feat)
bbox_pred = self.conv_reg(reg_feat)
if self.with_deform:
cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
cls_score = self.conv_cls(cls_feat)
return cls_score, bbox_pred
[docs] def loss(self,
cls_scores,
bbox_preds,
gt_bbox_list,
gt_label_list,
img_metas,
gt_bboxes_ignore=None):
assert len(cls_scores) == len(bbox_preds)
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
points = self.prior_generator.grid_priors(
featmap_sizes,
dtype=bbox_preds[0].dtype,
device=bbox_preds[0].device)
num_imgs = cls_scores[0].size(0)
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
for bbox_pred in bbox_preds
]
flatten_cls_scores = torch.cat(flatten_cls_scores)
flatten_bbox_preds = torch.cat(flatten_bbox_preds)
flatten_labels, flatten_bbox_targets = self.get_targets(
gt_bbox_list, gt_label_list, featmap_sizes, points)
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
pos_inds = ((flatten_labels >= 0)
& (flatten_labels < self.num_classes)).nonzero().view(-1)
num_pos = len(pos_inds)
loss_cls = self.loss_cls(
flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs)
if num_pos > 0:
pos_bbox_preds = flatten_bbox_preds[pos_inds]
pos_bbox_targets = flatten_bbox_targets[pos_inds]
pos_weights = pos_bbox_targets.new_zeros(
pos_bbox_targets.size()) + 1.0
loss_bbox = self.loss_bbox(
pos_bbox_preds,
pos_bbox_targets,
pos_weights,
avg_factor=num_pos)
else:
loss_bbox = torch.tensor(
0,
dtype=flatten_bbox_preds.dtype,
device=flatten_bbox_preds.device)
return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
[docs] def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points):
label_list, bbox_target_list = multi_apply(
self._get_target_single,
gt_bbox_list,
gt_label_list,
featmap_size_list=featmap_sizes,
point_list=points)
flatten_labels = [
torch.cat([
labels_level_img.flatten() for labels_level_img in labels_level
]) for labels_level in zip(*label_list)
]
flatten_bbox_targets = [
torch.cat([
bbox_targets_level_img.reshape(-1, 4)
for bbox_targets_level_img in bbox_targets_level
]) for bbox_targets_level in zip(*bbox_target_list)
]
flatten_labels = torch.cat(flatten_labels)
flatten_bbox_targets = torch.cat(flatten_bbox_targets)
return flatten_labels, flatten_bbox_targets
def _get_target_single(self,
gt_bboxes_raw,
gt_labels_raw,
featmap_size_list=None,
point_list=None):
gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
label_list = []
bbox_target_list = []
# for each pyramid, find the cls and box target
for base_len, (lower_bound, upper_bound), stride, featmap_size, \
points in zip(self.base_edge_list, self.scale_ranges,
self.strides, featmap_size_list, point_list):
# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
points = points.view(*featmap_size, 2)
x, y = points[..., 0], points[..., 1]
labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes
bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1],
4) + 1
# scale assignment
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero().flatten()
if len(hit_indices) == 0:
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
continue
_, hit_index_order = torch.sort(-gt_areas[hit_indices])
hit_indices = hit_indices[hit_index_order]
gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride
gt_labels = gt_labels_raw[hit_indices]
half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0])
half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1])
# valid fovea area: left, right, top, down
pos_left = torch.ceil(
gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long(). \
clamp(0, featmap_size[1] - 1)
pos_right = torch.floor(
gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long(). \
clamp(0, featmap_size[1] - 1)
pos_top = torch.ceil(
gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long(). \
clamp(0, featmap_size[0] - 1)
pos_down = torch.floor(
gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long(). \
clamp(0, featmap_size[0] - 1)
for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \
zip(pos_left, pos_top, pos_right, pos_down, gt_labels,
gt_bboxes_raw[hit_indices, :]):
labels[py1:py2 + 1, px1:px2 + 1] = label
bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \
(x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \
(y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \
(gt_x2 - x[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \
(gt_y2 - y[py1:py2 + 1, px1:px2 + 1]) / base_len
bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.)
label_list.append(labels)
bbox_target_list.append(torch.log(bbox_targets))
return label_list, bbox_target_list
# Same as base_dense_head/_get_bboxes_single except self._bbox_decode
def _get_bboxes_single(self,
cls_score_list,
bbox_pred_list,
score_factor_list,
mlvl_priors,
img_meta,
cfg,
rescale=False,
with_nms=True,
**kwargs):
"""Transform outputs of a single image into bbox predictions.
Args:
cls_score_list (list[Tensor]): Box scores from all scale
levels of a single image, each item has shape
(num_priors * num_classes, H, W).
bbox_pred_list (list[Tensor]): Box energies / deltas from
all scale levels of a single image, each item has shape
(num_priors * 4, H, W).
score_factor_list (list[Tensor]): Score factor from all scale
levels of a single image. Fovea head does not need this value.
mlvl_priors (list[Tensor]): Each element in the list is
the priors of a single level in feature pyramid, has shape
(num_priors, 2).
img_meta (dict): Image meta info.
cfg (mmcv.Config): 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:
tuple[Tensor]: Results of detected bboxes and labels. If with_nms
is False and mlvl_score_factor is None, return mlvl_bboxes and
mlvl_scores, else return mlvl_bboxes, mlvl_scores and
mlvl_score_factor. Usually with_nms is False is used for aug
test. If with_nms is True, then return the following format
- det_bboxes (Tensor): Predicted bboxes with shape \
[num_bboxes, 5], where the first 4 columns are bounding \
box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
column are scores between 0 and 1.
- det_labels (Tensor): Predicted labels of the corresponding \
box with shape [num_bboxes].
"""
cfg = self.test_cfg if cfg is None else cfg
assert len(cls_score_list) == len(bbox_pred_list)
img_shape = img_meta['img_shape']
nms_pre = cfg.get('nms_pre', -1)
mlvl_bboxes = []
mlvl_scores = []
mlvl_labels = []
for level_idx, (cls_score, bbox_pred, stride, base_len, priors) in \
enumerate(zip(cls_score_list, bbox_pred_list, self.strides,
self.base_edge_list, mlvl_priors)):
assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
scores = cls_score.permute(1, 2, 0).reshape(
-1, self.cls_out_channels).sigmoid()
# After https://github.com/open-mmlab/mmdetection/pull/6268/,
# this operation keeps fewer bboxes under the same `nms_pre`.
# There is no difference in performance for most models. If you
# find a slight drop in performance, you can set a larger
# `nms_pre` than before.
results = filter_scores_and_topk(
scores, cfg.score_thr, nms_pre,
dict(bbox_pred=bbox_pred, priors=priors))
scores, labels, _, filtered_results = results
bbox_pred = filtered_results['bbox_pred']
priors = filtered_results['priors']
bboxes = self._bbox_decode(priors, bbox_pred, base_len, img_shape)
mlvl_bboxes.append(bboxes)
mlvl_scores.append(scores)
mlvl_labels.append(labels)
return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
img_meta['scale_factor'], cfg, rescale,
with_nms)
def _bbox_decode(self, priors, bbox_pred, base_len, max_shape):
bbox_pred = bbox_pred.exp()
y = priors[:, 1]
x = priors[:, 0]
x1 = (x - base_len * bbox_pred[:, 0]). \
clamp(min=0, max=max_shape[1] - 1)
y1 = (y - base_len * bbox_pred[:, 1]). \
clamp(min=0, max=max_shape[0] - 1)
x2 = (x + base_len * bbox_pred[:, 2]). \
clamp(min=0, max=max_shape[1] - 1)
y2 = (y + base_len * bbox_pred[:, 3]). \
clamp(min=0, max=max_shape[0] - 1)
decoded_bboxes = torch.stack([x1, y1, x2, y2], -1)
return decoded_bboxes
def _get_points_single(self, *args, **kwargs):
"""Get points according to feature map size.
This function will be deprecated soon.
"""
warnings.warn(
'`_get_points_single` in `FoveaHead` will be '
'deprecated soon, we support a multi level point generator now'
'you can get points of a single level feature map '
'with `self.prior_generator.single_level_grid_priors` ')
y, x = super()._get_points_single(*args, **kwargs)
return y + 0.5, x + 0.5