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)
tst = xse_resnext18()
x = torch.randn(64, 3, 128, 128)
y = tst(x)
tst = xresnext18()
x = torch.randn(64, 3, 128, 128)
y = tst(x)
tst = xse_resnet50()
x = torch.randn(8, 3, 64, 64)
y = tst(x)
tst = xresnet18(ndim=1, c_in=1, ks=15)
x = torch.randn(64, 1, 128)
y = tst(x)
tst = xresnext50(ndim=1, c_in=2, ks=31, stride=4)
x = torch.randn(8, 2, 128)
y = tst(x)
tst = xresnet18(ndim=3, c_in=3, ks=3)
x = torch.randn(8, 3, 32, 32, 32)
y = tst(x)