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import torch.nn as nn |
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from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm, |
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normal_init) |
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from torch.nn import BatchNorm2d |
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from ..builder import NECKS |
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class Bottleneck(nn.Module): |
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"""Bottleneck block for DilatedEncoder used in `YOLOF. |
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<https://arxiv.org/abs/2103.09460>`. |
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The Bottleneck contains three ConvLayers and one residual connection. |
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Args: |
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in_channels (int): The number of input channels. |
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mid_channels (int): The number of middle output channels. |
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dilation (int): Dilation rate. |
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norm_cfg (dict): Dictionary to construct and config norm layer. |
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""" |
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def __init__(self, |
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in_channels, |
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mid_channels, |
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dilation, |
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norm_cfg=dict(type='BN', requires_grad=True)): |
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super(Bottleneck, self).__init__() |
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self.conv1 = ConvModule( |
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in_channels, mid_channels, 1, norm_cfg=norm_cfg) |
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self.conv2 = ConvModule( |
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mid_channels, |
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mid_channels, |
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3, |
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padding=dilation, |
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dilation=dilation, |
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norm_cfg=norm_cfg) |
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self.conv3 = ConvModule( |
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mid_channels, in_channels, 1, norm_cfg=norm_cfg) |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = self.conv2(out) |
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out = self.conv3(out) |
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out = out + identity |
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return out |
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@NECKS.register_module() |
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class DilatedEncoder(nn.Module): |
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"""Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`. |
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This module contains two types of components: |
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- the original FPN lateral convolution layer and fpn convolution layer, |
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which are 1x1 conv + 3x3 conv |
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- the dilated residual block |
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Args: |
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in_channels (int): The number of input channels. |
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out_channels (int): The number of output channels. |
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block_mid_channels (int): The number of middle block output channels |
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num_residual_blocks (int): The number of residual blocks. |
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""" |
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def __init__(self, in_channels, out_channels, block_mid_channels, |
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num_residual_blocks): |
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super(DilatedEncoder, self).__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.block_mid_channels = block_mid_channels |
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self.num_residual_blocks = num_residual_blocks |
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self.block_dilations = [2, 4, 6, 8] |
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self._init_layers() |
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def _init_layers(self): |
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self.lateral_conv = nn.Conv2d( |
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self.in_channels, self.out_channels, kernel_size=1) |
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self.lateral_norm = BatchNorm2d(self.out_channels) |
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self.fpn_conv = nn.Conv2d( |
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self.out_channels, self.out_channels, kernel_size=3, padding=1) |
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self.fpn_norm = BatchNorm2d(self.out_channels) |
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encoder_blocks = [] |
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for i in range(self.num_residual_blocks): |
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dilation = self.block_dilations[i] |
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encoder_blocks.append( |
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Bottleneck( |
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self.out_channels, |
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self.block_mid_channels, |
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dilation=dilation)) |
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self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks) |
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def init_weights(self): |
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caffe2_xavier_init(self.lateral_conv) |
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caffe2_xavier_init(self.fpn_conv) |
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for m in [self.lateral_norm, self.fpn_norm]: |
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constant_init(m, 1) |
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for m in self.dilated_encoder_blocks.modules(): |
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if isinstance(m, nn.Conv2d): |
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normal_init(m, mean=0, std=0.01) |
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if is_norm(m): |
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constant_init(m, 1) |
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def forward(self, feature): |
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out = self.lateral_norm(self.lateral_conv(feature[-1])) |
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out = self.fpn_norm(self.fpn_conv(out)) |
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return self.dilated_encoder_blocks(out), |
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