|
|
|
import torch |
|
import torch.nn as nn |
|
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule |
|
from mmengine.model import BaseModule |
|
|
|
from mmdet.registry import MODELS |
|
|
|
|
|
@MODELS.register_module() |
|
class SSDNeck(BaseModule): |
|
"""Extra layers of SSD backbone to generate multi-scale feature maps. |
|
|
|
Args: |
|
in_channels (Sequence[int]): Number of input channels per scale. |
|
out_channels (Sequence[int]): Number of output channels per scale. |
|
level_strides (Sequence[int]): Stride of 3x3 conv per level. |
|
level_paddings (Sequence[int]): Padding size of 3x3 conv per level. |
|
l2_norm_scale (float|None): L2 normalization layer init scale. |
|
If None, not use L2 normalization on the first input feature. |
|
last_kernel_size (int): Kernel size of the last conv layer. |
|
Default: 3. |
|
use_depthwise (bool): Whether to use DepthwiseSeparableConv. |
|
Default: False. |
|
conv_cfg (dict): Config dict for convolution layer. Default: None. |
|
norm_cfg (dict): Dictionary to construct and config norm layer. |
|
Default: None. |
|
act_cfg (dict): Config dict for activation layer. |
|
Default: dict(type='ReLU'). |
|
init_cfg (dict or list[dict], optional): Initialization config dict. |
|
""" |
|
|
|
def __init__(self, |
|
in_channels, |
|
out_channels, |
|
level_strides, |
|
level_paddings, |
|
l2_norm_scale=20., |
|
last_kernel_size=3, |
|
use_depthwise=False, |
|
conv_cfg=None, |
|
norm_cfg=None, |
|
act_cfg=dict(type='ReLU'), |
|
init_cfg=[ |
|
dict( |
|
type='Xavier', distribution='uniform', |
|
layer='Conv2d'), |
|
dict(type='Constant', val=1, layer='BatchNorm2d'), |
|
]): |
|
super(SSDNeck, self).__init__(init_cfg) |
|
assert len(out_channels) > len(in_channels) |
|
assert len(out_channels) - len(in_channels) == len(level_strides) |
|
assert len(level_strides) == len(level_paddings) |
|
assert in_channels == out_channels[:len(in_channels)] |
|
|
|
if l2_norm_scale: |
|
self.l2_norm = L2Norm(in_channels[0], l2_norm_scale) |
|
self.init_cfg += [ |
|
dict( |
|
type='Constant', |
|
val=self.l2_norm.scale, |
|
override=dict(name='l2_norm')) |
|
] |
|
|
|
self.extra_layers = nn.ModuleList() |
|
extra_layer_channels = out_channels[len(in_channels):] |
|
second_conv = DepthwiseSeparableConvModule if \ |
|
use_depthwise else ConvModule |
|
|
|
for i, (out_channel, stride, padding) in enumerate( |
|
zip(extra_layer_channels, level_strides, level_paddings)): |
|
kernel_size = last_kernel_size \ |
|
if i == len(extra_layer_channels) - 1 else 3 |
|
per_lvl_convs = nn.Sequential( |
|
ConvModule( |
|
out_channels[len(in_channels) - 1 + i], |
|
out_channel // 2, |
|
1, |
|
conv_cfg=conv_cfg, |
|
norm_cfg=norm_cfg, |
|
act_cfg=act_cfg), |
|
second_conv( |
|
out_channel // 2, |
|
out_channel, |
|
kernel_size, |
|
stride=stride, |
|
padding=padding, |
|
conv_cfg=conv_cfg, |
|
norm_cfg=norm_cfg, |
|
act_cfg=act_cfg)) |
|
self.extra_layers.append(per_lvl_convs) |
|
|
|
def forward(self, inputs): |
|
"""Forward function.""" |
|
outs = [feat for feat in inputs] |
|
if hasattr(self, 'l2_norm'): |
|
outs[0] = self.l2_norm(outs[0]) |
|
|
|
feat = outs[-1] |
|
for layer in self.extra_layers: |
|
feat = layer(feat) |
|
outs.append(feat) |
|
return tuple(outs) |
|
|
|
|
|
class L2Norm(nn.Module): |
|
|
|
def __init__(self, n_dims, scale=20., eps=1e-10): |
|
"""L2 normalization layer. |
|
|
|
Args: |
|
n_dims (int): Number of dimensions to be normalized |
|
scale (float, optional): Defaults to 20.. |
|
eps (float, optional): Used to avoid division by zero. |
|
Defaults to 1e-10. |
|
""" |
|
super(L2Norm, self).__init__() |
|
self.n_dims = n_dims |
|
self.weight = nn.Parameter(torch.Tensor(self.n_dims)) |
|
self.eps = eps |
|
self.scale = scale |
|
|
|
def forward(self, x): |
|
"""Forward function.""" |
|
|
|
x_float = x.float() |
|
norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps |
|
return (self.weight[None, :, None, None].float().expand_as(x_float) * |
|
x_float / norm).type_as(x) |
|
|
