AttributeError: 'ResNet' object has no attribute 'get_features'

1

Hello
i defined a method inside a class but i don’t know why i’m getting the following error
AttributeError: ‘ResNet’ object has no attribute ‘get_features’

this is my class:

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet, self).__init__()
        resnet = resnet50(pretrained=True)
        self.backbone = nn.Sequential(
            resnet.conv1,
            resnet.bn1,
            resnet.relu,
            resnet.maxpool,
            resnet.layer1,  # res_conv2
            resnet.layer2,  # res_conv3
            resnet.layer3,  # res_conv4
        )
        self.res_part = nn.Sequential(
            Bottleneck(1024, 512, stride=1, downsample=nn.Sequential(
                nn.Conv2d(1024, 2048, kernel_size=1, stride=1, bias=False),
                nn.BatchNorm2d(2048),
            )),
            Bottleneck(2048, 512),
            Bottleneck(2048, 512),
        )
        self.res_part.load_state_dict(resnet.layer4.state_dict())
        reduction = nn.Sequential(
            nn.Conv2d(2048, 512, 1), 
            nn.BatchNorm2d(512), 
            nn.ReLU()
        )

        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.global_softmax = nn.Linear(512, num_classes) 
        self.global_softmax.apply(weights_init_kaiming)
        self.global_reduction = copy.deepcopy(reduction)
        self.global_reduction.apply(weights_init_kaiming)
    def forward(self, x):
        """
        :param x: input image tensor of (N, C, H, W)
        :return: (prediction, triplet_losses, softmax_losses)
        """
        x = self.backbone(x)
        x = self.res_part(x)


        #global branch
        glob = self.global_avgpool(x)
        global_triplet_feature = self.global_reduction(glob).squeeze()
        global_softmax_class = self.global_softmax(global_triplet_feature)

        return global_softmax_class

    def get_features(self):
        return nn.Sequential(
            self.backbone,
            self.res_part,
        )
2

The get_features method is currently not used in your code and seems to work if I call it directly:

model = ResNet()
model.get_features()
3

my code works with other Classes, i added this new class because i want to use a specific resnet50

4

And you should be able to add this method to your custom module.
However, it seems to be working if I’m using your code and call the method directly.
What kind of error are you seeing?

5

i only get this error:

AttributeError: ‘ResNet’ object has no attribute ‘get_features’

6

It still works for me after removing all undefined classes and functions:

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet, self).__init__()
        resnet = models.resnet50(pretrained=True)
        self.backbone = nn.Sequential(
            resnet.conv1,
            resnet.bn1,
            resnet.relu,
            resnet.maxpool,
            resnet.layer1,  # res_conv2
            resnet.layer2,  # res_conv3
            resnet.layer3,  # res_conv4
        )
        reduction = nn.Sequential(
            nn.Conv2d(2048, 512, 1), 
            nn.BatchNorm2d(512), 
            nn.ReLU()
        )

        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.global_softmax = nn.Linear(512, num_classes) 
        self.global_reduction = copy.deepcopy(reduction)

    def forward(self, x):
        """
        :param x: input image tensor of (N, C, H, W)
        :return: (prediction, triplet_losses, softmax_losses)
        """
        x = self.backbone(x)
        #global branch
        glob = self.global_avgpool(x)
        global_triplet_feature = self.global_reduction(glob).squeeze()
        global_softmax_class = self.global_softmax(global_triplet_feature)

        return global_softmax_class

    def get_features(self):
        return nn.Sequential(
            self.backbone,
        )


model = ResNet()
model.get_features()
> Sequential(
  (0): Sequential(
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
...
7

here where i need to call get_features and it’s not working

self.features = getattr(resnet, net)(pretrained=pretrained).get_features()
9

problem solved, thank you

10

How did you solved the issue?