| |
| 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) |
|
|
