FPEM_FFM.py

# -*- coding: utf-8 -*-
# @Time    : 2019/9/13 10:29
# @Author  : zhoujun
import torch
import torch.nn.functional as F
from torch import nn

from models.basic import ConvBnRelu


class FPEM_FFM(nn.Module):
    def __init__(self, in_channels, inner_channels=128, fpem_repeat=2, **kwargs):
        """
        PANnet
        :param in_channels: 基础网络输出的维度
        """
        super().__init__()
        self.conv_out = inner_channels
        inplace = True
        # reduce layers
        self.reduce_conv_c2 = ConvBnRelu(in_channels[0], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c3 = ConvBnRelu(in_channels[1], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c4 = ConvBnRelu(in_channels[2], inner_channels, kernel_size=1, inplace=inplace)
        self.reduce_conv_c5 = ConvBnRelu(in_channels[3], inner_channels, kernel_size=1, inplace=inplace)
        self.fpems = nn.ModuleList()
        for i in range(fpem_repeat):
            self.fpems.append(FPEM(self.conv_out))
        self.out_channels = self.conv_out * 4

    def forward(self, x):
        c2, c3, c4, c5 = x
        # reduce channel
        c2 = self.reduce_conv_c2(c2)
        c3 = self.reduce_conv_c3(c3)
        c4 = self.reduce_conv_c4(c4)
        c5 = self.reduce_conv_c5(c5)

        # FPEM
        for i, fpem in enumerate(self.fpems):
            c2, c3, c4, c5 = fpem(c2, c3, c4, c5)
            if i == 0:
                c2_ffm = c2
                c3_ffm = c3
                c4_ffm = c4
                c5_ffm = c5
            else:
                c2_ffm += c2
                c3_ffm += c3
                c4_ffm += c4
                c5_ffm += c5

        # FFM
        c5 = F.interpolate(c5_ffm, c2_ffm.size()[-2:])
        c4 = F.interpolate(c4_ffm, c2_ffm.size()[-2:])
        c3 = F.interpolate(c3_ffm, c2_ffm.size()[-2:])
        Fy = torch.cat([c2_ffm, c3, c4, c5], dim=1)
        return Fy


class FPEM(nn.Module):
    def __init__(self, in_channels=128):
        super().__init__()
        self.up_add1 = SeparableConv2d(in_channels, in_channels, 1)
        self.up_add2 = SeparableConv2d(in_channels, in_channels, 1)
        self.up_add3 = SeparableConv2d(in_channels, in_channels, 1)
        self.down_add1 = SeparableConv2d(in_channels, in_channels, 2)
        self.down_add2 = SeparableConv2d(in_channels, in_channels, 2)
        self.down_add3 = SeparableConv2d(in_channels, in_channels, 2)

    def forward(self, c2, c3, c4, c5):
        # up阶段
        c4 = self.up_add1(self._upsample_add(c5, c4))
        c3 = self.up_add2(self._upsample_add(c4, c3))
        c2 = self.up_add3(self._upsample_add(c3, c2))

        # down 阶段
        c3 = self.down_add1(self._upsample_add(c3, c2))
        c4 = self.down_add2(self._upsample_add(c4, c3))
        c5 = self.down_add3(self._upsample_add(c5, c4))
        return c2, c3, c4, c5

    def _upsample_add(self, x, y):
        return F.interpolate(x, size=y.size()[2:]) + y


class SeparableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(SeparableConv2d, self).__init__()

        self.depthwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, padding=1,
                                        stride=stride, groups=in_channels)
        self.pointwise_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.pointwise_conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x