比較清晰FPN的PyTorch實現

想學習一下具體FPN(特徵金字塔)的結構具體實現，比較了一些相關代碼，發現以下這個比較清楚–

簡單結構圖

傳送門：

'''FPN in PyTorch.
See the paper "Feature Pyramid Networks for Object Detection" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.autograd import Variable


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class FPN(nn.Module):
    def __init__(self, block, num_blocks):
        super(FPN, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        # Bottom-up layers
        self.layer1 = self._make_layer(block,  64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        # Top layer
        self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)  # Reduce channels

        # Smooth layers
        self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)

        # Lateral layers
        self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer2 = nn.Conv2d( 512, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer3 = nn.Conv2d( 256, 256, kernel_size=1, stride=1, padding=0)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def _upsample_add(self, x, y):
        '''Upsample and add two feature maps.

        Args:
          x: (Variable) top feature map to be upsampled.
          y: (Variable) lateral feature map.

        Returns:
          (Variable) added feature map.

        Note in PyTorch, when input size is odd, the upsampled feature map
        with `F.upsample(..., scale_factor=2, mode='nearest')`
        maybe not equal to the lateral feature map size.

        e.g.
        original input size: [N,_,15,15] ->
        conv2d feature map size: [N,_,8,8] ->
        upsampled feature map size: [N,_,16,16]

        So we choose bilinear upsample which supports arbitrary output sizes.
        '''
        _,_,H,W = y.size()
        return F.upsample(x, size=(H,W), mode='bilinear') + y

    def forward(self, x):
        # Bottom-up
        c1 = F.relu(self.bn1(self.conv1(x)))
        c1 = F.max_pool2d(c1, kernel_size=3, stride=2, padding=1)
        print(f'c1:{c1.shape}')
        c2 = self.layer1(c1)
        print(f'c2:{c2.shape}')  

        c3 = self.layer2(c2)
        print(f'c3:{c3.shape}') 
        c4 = self.layer3(c3)
        print(f'c4:{c4.shape}') 
        c5 = self.layer4(c4)
        print(f'c5:{c5.shape}') 

        # Top-down
        p5 = self.toplayer(c5)
        print(f'p5:{p5.shape}') 
        p4 = self._upsample_add(p5, self.latlayer1(c4))
        print(f'latlayer1(c4):{self.latlayer1(c4).shape}, p4:{p4.shape}')

        p3 = self._upsample_add(p4, self.latlayer2(c3))
        print(f'latlayer1(c3):{self.latlayer2(c3).shape}, p3:{p3.shape}')

        p2 = self._upsample_add(p3, self.latlayer3(c2))
        print(f'latlayer1(c2):{self.latlayer3(c2).shape}, p2:{p2.shape}')

        # Smooth
        p4 = self.smooth1(p4)
        p3 = self.smooth2(p3)
        p2 = self.smooth3(p2)
        return p2, p3, p4, p5

def FPN101():
    # return FPN(Bottleneck, [2,4,23,3])
    return FPN(Bottleneck, [2,2,2,2])

def test():
    net = FPN101()
    fms = net(Variable(torch.randn(1,3,600,900)))
    for fm in fms:
        print(fm.size())

test()

輸出結果：

c1:torch.Size([1, 64, 150, 225])
c2:torch.Size([1, 256, 150, 225])
c3:torch.Size([1, 512, 75, 113])
c4:torch.Size([1, 1024, 38, 57])
c5:torch.Size([1, 2048, 19, 29])
p5:torch.Size([1, 256, 19, 29])
latlayer1(c4):torch.Size([1, 256, 38, 57]), p4:torch.Size([1, 256, 38, 57])
latlayer1(c3):torch.Size([1, 256, 75, 113]), p3:torch.Size([1, 256, 75, 113])
latlayer1(c2):torch.Size([1, 256, 150, 225]), p2:torch.Size([1, 256, 150, 225])

# p2, p3, p4, p5
torch.Size([1, 256, 150, 225])
torch.Size([1, 256, 75, 113])
torch.Size([1, 256, 38, 57])
torch.Size([1, 256, 19, 29])