I got a modified resnet, it change the latest AVGPool2d layer and remove latest Linear layer. when AVGPool2d's parameter stride not setting , it will be setting with kernel_size.
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=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,
                               padding=1, 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

    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


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 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])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7)  # official is `nn.AvgPool2d(7, stride=1)`
        # self.fc = nn.Linear(512 * block.expansion, num_classes)

        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))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

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

        return nn.Sequential(*layers)

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

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        # x = self.fc(x)

        return x

then, I found a simple way to reconstruct the resnet. just remove latest 2 layer and add a new AvgPool2d with stride is None.
class ResNet50MoveLinear(nn.Module):
    def __init__(self):
        """Load the pretrained ResNet-50 and replace top fc layer."""
        super(ResNet50MoveLinear, self).__init__()
        resnet = models.resnet50(pretrained=True)
        modules = list(resnet.children())[:-2]  # delete the last fc layer.
        self.resnet = nn.Sequential(*modules)
        self.avgpool = nn.AvgPool2d(7)
        # self.linear = nn.Linear(resnet.fc.in_features, embed_size)
        # self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)

    def forward(self, images):
        """Extract feature vectors from input images."""
        # with torch.no_grad():
        #     features = self.resnet(images)
        # features = features.reshape(features.size(0), -1)
        # features = self.bn(self.linear(features))

        features = self.resnet(images)  # need gradient
        features = self.avgpool(features)
        features = features.view(features.size(0), -1)  # reshape
        return features

I think these 2 methods will be same and will get a similar results, but second method get a worse results(accuracy: 0.918 -> 0.895). and I can’t find the difference.
I would greatly appreciate for some suggestions, thanks!