Source code for mmdet.models.necks.dilated_encoder

import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
from torch.nn import BatchNorm2d

from ..builder import NECKS

class Bottleneck(nn.Module):
    """Bottleneck block for DilatedEncoder used in `YOLOF.


    The Bottleneck contains three ConvLayers and one residual connection.

        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,
                 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(
        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

[docs]@NECKS.register_module() class DilatedEncoder(nn.Module): """Dilated Encoder for YOLOF <>`. 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. """ def __init__(self, in_channels, out_channels, block_mid_channels, num_residual_blocks): 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 = [2, 4, 6, 8] 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)
[docs] 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),