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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.models.dense_heads.fcos_head import FCOSHead
from mmdet.registry import MODELS
from mmdet.utils import OptMultiConfig
@MODELS.register_module()
class NASFCOSHead(FCOSHead):
"""Anchor-free head used in `NASFCOS <https://arxiv.org/abs/1906.04423>`_.
It is quite similar with FCOS head, except for the searched structure of
classification branch and bbox regression branch, where a structure of
"dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
strides (Sequence[int] or Sequence[Tuple[int, int]]): Strides of points
in multiple feature levels. Defaults to (4, 8, 16, 32, 64).
regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
level points.
center_sampling (bool): If true, use center sampling.
Defaults to False.
center_sample_radius (float): Radius of center sampling.
Defaults to 1.5.
norm_on_bbox (bool): If true, normalize the regression targets with
FPN strides. Defaults to False.
centerness_on_reg (bool): If true, position centerness on the
regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
Defaults to False.
conv_bias (bool or str): If specified as `auto`, it will be decided by
the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
None, otherwise False. Defaults to "auto".
loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
loss_centerness (:obj:`ConfigDict`, or dict): Config of centerness
loss.
norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
config norm layer. Defaults to
``norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)``.
init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
dict], opitonal): Initialization config dict.
""" # noqa: E501
def __init__(self,
*args,
init_cfg: OptMultiConfig = None,
**kwargs) -> None:
if init_cfg is None:
init_cfg = [
dict(type='Caffe2Xavier', layer=['ConvModule', 'Conv2d']),
dict(
type='Normal',
std=0.01,
override=[
dict(name='conv_reg'),
dict(name='conv_centerness'),
dict(
name='conv_cls',
type='Normal',
std=0.01,
bias_prob=0.01)
]),
]
super().__init__(*args, init_cfg=init_cfg, **kwargs)
def _init_layers(self) -> None:
"""Initialize layers of the head."""
dconv3x3_config = dict(
type='DCNv2',
kernel_size=3,
use_bias=True,
deform_groups=2,
padding=1)
conv3x3_config = dict(type='Conv', kernel_size=3, padding=1)
conv1x1_config = dict(type='Conv', kernel_size=1)
self.arch_config = [
dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config
]
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i, op_ in enumerate(self.arch_config):
op = copy.deepcopy(op_)
chn = self.in_channels if i == 0 else self.feat_channels
assert isinstance(op, dict)
use_bias = op.pop('use_bias', False)
padding = op.pop('padding', 0)
kernel_size = op.pop('kernel_size')
module = ConvModule(
chn,
self.feat_channels,
kernel_size,
stride=1,
padding=padding,
norm_cfg=self.norm_cfg,
bias=use_bias,
conv_cfg=op)
self.cls_convs.append(copy.deepcopy(module))
self.reg_convs.append(copy.deepcopy(module))
self.conv_cls = nn.Conv2d(
self.feat_channels, self.cls_out_channels, 3, padding=1)
self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)
self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
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