pytorchトレーニングデータおよびテストすべてのコード(6)ネットワーク

ResNet 101とASPP

model = ResNet(nInputChannels, Bottleneck, [3, 4, 23, 3], os, pretrained=pretrained)

nInputChannels=3，os=16， Bottleneck ：class Bottleneck(nn.Module)

まず、Bottleneckネットワークを見てみましょう.

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, rate=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               dilation=rate, padding=rate, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True) #  ， 
        self.downsample = downsample
        self.stride = stride
        self.rate = rate

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

これはbottleneckで、入力はxで、出力はoutput+residualです.この2つのtensorのshapeは同じであってこそ加算を許可することができ,入力したshapeが出力のshapeに等しくなければ必ずdownsampleが存在し,shapeの変化を行う.すべてのボリュームにbiasは追加されず、すべての入力はrelu関数を経て数値が変更され、出力と同じになります.
ここでのボリュームサイズ計算は,[n+2 p-r(k-1)+1]/s+1を再確認する.
resnet 101ネットワークを見てみると、6つの関数が入っています.次は1つ説明します.

class ResNet(nn.Module):

    def __init__(self, nInputChannels, block, layers, os=16, pretrained=False):
        pass
    def _make_layer(self, block, planes, blocks, stride=1, rate=1):
        pass
    def _make_MG_unit(self, block, planes, blocks=[1,2,4], stride=1, rate=1):
        pass
    def forward(self, input):
        pass
    def _init_weight(self):
        pass
    def _load_pretrained_model(self):
        pass

最初の関数:nInputChannels=3,layers=[3,4,23,3]すなわちresnet 101のblockの層数参照論文https://blog.csdn.net/lanran2/article/details/79057994

    def __init__(self, nInputChannels, block, layers, os=16, pretrained=False):
        self.inplanes = 64
        super(ResNet, self).__init__()
        if os == 16:
            strides = [1, 2, 2, 1]
            rates = [1, 1, 1, 2]
            blocks = [1, 2, 4]
        elif os == 8:
            strides = [1, 2, 1, 1]
            rates = [1, 1, 2, 2]
            blocks = [1, 2, 1]
        else:
            raise NotImplementedError

        # Modules
        self.conv1 = nn.Conv2d(nInputChannels, 64, kernel_size=7, stride=2, padding=3,
                                bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], rate=rates[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], rate=rates[1])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], rate=rates[2])
        self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], rate=rates[3])

        self._init_weight()

        if pretrained:
            self._load_pretrained_model()

ここでmaxpool層を定義すると、出力h,wの計算方式とボリュームの計算方式は同じであり、チャネルは入力と同じである.

self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

次に、

self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], rate=rates[0])

関数_make_Layer():blockはネットワークブロック、planesは特徴図の層数がチャネル数、blocksはネットワークブロックの数、ここでのblock.expansion=4

    def _make_layer(self, block, planes, blocks, stride=1, rate=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, rate, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

次に、

self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], rate=rates[3])

関数_make_MG_unit:異なるrateを結合することを定義します.

    def _make_MG_unit(self, block, planes, blocks=[1,2,4], stride=1, rate=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, rate=blocks[0]*rate, downsample=downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, len(blocks)):
            layers.append(block(self.inplanes, planes, stride=1, rate=blocks[i]*rate))

        return nn.Sequential(*layers)

次は

self._init_weight()

モデルのすべてのパラメータを初期化するには

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                # m.weight.data.normal_(0, math.sqrt(2. / n))
                torch.nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

次の

        if pretrained:
            self._load_pretrained_model()

関数_load_pretrained_modelは、ネットワークが必ずしも同じではないが、同じパラメータがロードされると定義している.

    def _load_pretrained_model(self):
        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth')
        model_dict = {}
        state_dict = self.state_dict()
        for k, v in pretrain_dict.items():
            if k in state_dict:
                model_dict[k] = v
        state_dict.update(model_dict)
        self.load_state_dict(state_dict)

次にforward関数を見ます.

    def forward(self, input):
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        low_level_feat = x
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x, low_level_feat

これはネットワーク層の構造です.
次の説明ASPPネットワーク