botnet：only the case of equal height and width is supported?

[xiaodaodao123]

what should do when the size of feature map is 256*128

[BIGBALLON]

You can slightly modify the input of the model and the corresponding configuration file

[xiaodaodao123]

resnet = resnet50(pretrained)
layer4 = BoTStack(dim=1024, fmap_size=(16, 8), stride=1, rel_pos_emb=True)
backbone = list(resnet.children())
self.base = nn.Sequential(*backbone[:-3], layer4,)

i did this, there must be some mistakes

[BIGBALLON]

The output feature map of ( *backbone[:-3],) is 14x14 if input image size is 224x224.

[xiaodaodao123]

the input image size is 256x128

[BIGBALLON]

Thank you for reminding me!!
Fixed the issue, please check #10

python botnet.py 
torch.Size([16, 1000])

def test_botnet50():
    x = torch.ones(16, 3, 224, 224).cuda()
    model = botnet50().cuda()
    y = model(x)
    print(y.shape)


def test_backbone():
    x = torch.ones(16, 3, 256, 128).cuda()
    resnet = resnet50()
    layer = BoTStack(dim=1024, fmap_size=(16, 8), stride=1, rel_pos_emb=True)
    backbone = list(resnet.children())
    model = nn.Sequential(
        *backbone[:-3],
        layer,
        nn.AdaptiveAvgPool2d((1, 1)),
        nn.Flatten(1),
        nn.Linear(2048, 1000),
    ).cuda()

    y = model(x)
    print(y.shape)


if __name__ == "__main__":
    test_backbone()