= xse_resnext18()
tst = torch.randn(64, 3, 128, 128)
x = tst(x) y
XResnet
Resnet from bags of tricks paper
init_cnn
init_cnn (m)
XResNet
XResNet (block, expansion, layers, p=0.0, c_in=3, n_out=1000, stem_szs=(32,32,64), widen=1.0, sa=False, act_cls=<class'torch.nn.modules.activation.ReLU'>, ndim=2, ks=3, stride=2, groups=1, reduction=None, nh1=None, nh2=None, dw=False, g2=1, sym=False, norm_type=<NormType.Batch:1>, pool=<functionAvgPoolat0x7ff4cf81d6c0>, pool_first=True, padding=None, bias=None, bn_1st=True, transpose=False, init='auto', xtra=None, bias_std=0.01, dilation:Union[int,Tuple[int,int]]=1, padding_mode:str='zeros', device=None, dtype=None)
A sequential container.
xresnet50_deeper
xresnet50_deeper (pretrained=False, **kwargs)
xresnet34_deeper
xresnet34_deeper (pretrained=False, **kwargs)
xresnet18_deeper
xresnet18_deeper (pretrained=False, **kwargs)
xresnet50_deep
xresnet50_deep (pretrained=False, **kwargs)
xresnet34_deep
xresnet34_deep (pretrained=False, **kwargs)
xresnet18_deep
xresnet18_deep (pretrained=False, **kwargs)
xresnet152
xresnet152 (pretrained=False, **kwargs)
xresnet101
xresnet101 (pretrained=False, **kwargs)
xresnet50
xresnet50 (pretrained=False, **kwargs)
xresnet34
xresnet34 (pretrained=False, **kwargs)
xresnet18
xresnet18 (pretrained=False, **kwargs)
xse_resnext50_deeper
xse_resnext50_deeper (n_out=1000, pretrained=False, **kwargs)
xse_resnext34_deeper
xse_resnext34_deeper (n_out=1000, pretrained=False, **kwargs)
xse_resnext18_deeper
xse_resnext18_deeper (n_out=1000, pretrained=False, **kwargs)
xse_resnext50_deep
xse_resnext50_deep (n_out=1000, pretrained=False, **kwargs)
xse_resnext34_deep
xse_resnext34_deep (n_out=1000, pretrained=False, **kwargs)
xse_resnext18_deep
xse_resnext18_deep (n_out=1000, pretrained=False, **kwargs)
xsenet154
xsenet154 (n_out=1000, pretrained=False, **kwargs)
xse_resnet152
xse_resnet152 (n_out=1000, pretrained=False, **kwargs)
xresnext101
xresnext101 (n_out=1000, pretrained=False, **kwargs)
xse_resnext101
xse_resnext101 (n_out=1000, pretrained=False, **kwargs)
xse_resnet101
xse_resnet101 (n_out=1000, pretrained=False, **kwargs)
xresnext50
xresnext50 (n_out=1000, pretrained=False, **kwargs)
xse_resnext50
xse_resnext50 (n_out=1000, pretrained=False, **kwargs)
xse_resnet50
xse_resnet50 (n_out=1000, pretrained=False, **kwargs)
xresnext34
xresnext34 (n_out=1000, pretrained=False, **kwargs)
xse_resnext34
xse_resnext34 (n_out=1000, pretrained=False, **kwargs)
xse_resnet34
xse_resnet34 (n_out=1000, pretrained=False, **kwargs)
xresnext18
xresnext18 (n_out=1000, pretrained=False, **kwargs)
xse_resnext18
xse_resnext18 (n_out=1000, pretrained=False, **kwargs)
xse_resnet18
xse_resnet18 (n_out=1000, pretrained=False, **kwargs)
= xresnext18()
tst = torch.randn(64, 3, 128, 128)
x = tst(x) y
= xse_resnet50()
tst = torch.randn(8, 3, 64, 64)
x = tst(x) y
= xresnet18(ndim=1, c_in=1, ks=15)
tst = torch.randn(64, 1, 128)
x = tst(x) y
= xresnext50(ndim=1, c_in=2, ks=31, stride=4)
tst = torch.randn(8, 2, 128)
x = tst(x) y
= xresnet18(ndim=3, c_in=3, ks=3)
tst = torch.randn(8, 3, 32, 32, 32)
x = tst(x) y