| |
| import torch.nn as nn |
| from mmcv.cnn import ConvModule, is_norm |
| from mmengine.model import caffe2_xavier_init, constant_init, normal_init |
| from torch.nn import BatchNorm2d |
|
|
| from mmdet.registry import MODELS |
|
|
|
|
| class Bottleneck(nn.Module): |
| """Bottleneck block for DilatedEncoder used in `YOLOF. |
| |
| <https://arxiv.org/abs/2103.09460>`. |
| |
| The Bottleneck contains three ConvLayers and one residual connection. |
| |
| Args: |
| in_channels (int): The number of input channels. |
| mid_channels (int): The number of middle output channels. |
| dilation (int): Dilation rate. |
| norm_cfg (dict): Dictionary to construct and config norm layer. |
| """ |
|
|
| def __init__(self, |
| in_channels, |
| mid_channels, |
| dilation, |
| norm_cfg=dict(type='BN', requires_grad=True)): |
| super(Bottleneck, self).__init__() |
| self.conv1 = ConvModule( |
| in_channels, mid_channels, 1, norm_cfg=norm_cfg) |
| self.conv2 = ConvModule( |
| mid_channels, |
| mid_channels, |
| 3, |
| padding=dilation, |
| dilation=dilation, |
| norm_cfg=norm_cfg) |
| self.conv3 = ConvModule( |
| mid_channels, in_channels, 1, norm_cfg=norm_cfg) |
|
|
| def forward(self, x): |
| identity = x |
| out = self.conv1(x) |
| out = self.conv2(out) |
| out = self.conv3(out) |
| out = out + identity |
| return out |
|
|
|
|
| @MODELS.register_module() |
| class DilatedEncoder(nn.Module): |
| """Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`. |
| |
| This module contains two types of components: |
| - the original FPN lateral convolution layer and fpn convolution layer, |
| which are 1x1 conv + 3x3 conv |
| - the dilated residual block |
| |
| Args: |
| in_channels (int): The number of input channels. |
| out_channels (int): The number of output channels. |
| block_mid_channels (int): The number of middle block output channels |
| num_residual_blocks (int): The number of residual blocks. |
| block_dilations (list): The list of residual blocks dilation. |
| """ |
|
|
| def __init__(self, in_channels, out_channels, block_mid_channels, |
| num_residual_blocks, block_dilations): |
| super(DilatedEncoder, self).__init__() |
| self.in_channels = in_channels |
| self.out_channels = out_channels |
| self.block_mid_channels = block_mid_channels |
| self.num_residual_blocks = num_residual_blocks |
| self.block_dilations = block_dilations |
| self._init_layers() |
|
|
| def _init_layers(self): |
| self.lateral_conv = nn.Conv2d( |
| self.in_channels, self.out_channels, kernel_size=1) |
| self.lateral_norm = BatchNorm2d(self.out_channels) |
| self.fpn_conv = nn.Conv2d( |
| self.out_channels, self.out_channels, kernel_size=3, padding=1) |
| self.fpn_norm = BatchNorm2d(self.out_channels) |
| encoder_blocks = [] |
| for i in range(self.num_residual_blocks): |
| dilation = self.block_dilations[i] |
| encoder_blocks.append( |
| Bottleneck( |
| self.out_channels, |
| self.block_mid_channels, |
| dilation=dilation)) |
| self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks) |
|
|
| def init_weights(self): |
| caffe2_xavier_init(self.lateral_conv) |
| caffe2_xavier_init(self.fpn_conv) |
| for m in [self.lateral_norm, self.fpn_norm]: |
| constant_init(m, 1) |
| for m in self.dilated_encoder_blocks.modules(): |
| if isinstance(m, nn.Conv2d): |
| normal_init(m, mean=0, std=0.01) |
| if is_norm(m): |
| constant_init(m, 1) |
|
|
| def forward(self, feature): |
| out = self.lateral_norm(self.lateral_conv(feature[-1])) |
| out = self.fpn_norm(self.fpn_conv(out)) |
| return self.dilated_encoder_blocks(out), |
|
|
