XResnet

Resnet from bags of tricks paper

init_cnn

 init_cnn (m)

source

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=<function
          AvgPool>, 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.

Modules will be added to it in the order they are passed in the constructor. Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.

The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).

What’s the difference between a Sequential and a :class:torch.nn.ModuleList? A ModuleList is exactly what it sounds like–a list for storing [Module](https://docs.fast.ai/torch_core.html#module) s! On the other hand, the layers in a Sequential are connected in a cascading way.

Example::

# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))*

source

xresnet50_deeper

 xresnet50_deeper (pretrained=False, **kwargs)

source

xresnet34_deeper

 xresnet34_deeper (pretrained=False, **kwargs)

source

xresnet18_deeper

 xresnet18_deeper (pretrained=False, **kwargs)

source

xresnet50_deep

 xresnet50_deep (pretrained=False, **kwargs)

source

xresnet34_deep

 xresnet34_deep (pretrained=False, **kwargs)

source

xresnet18_deep

 xresnet18_deep (pretrained=False, **kwargs)

source

xresnet152

 xresnet152 (pretrained=False, **kwargs)

source

xresnet101

 xresnet101 (pretrained=False, **kwargs)

source

xresnet50

 xresnet50 (pretrained=False, **kwargs)

source

xresnet34

 xresnet34 (pretrained=False, **kwargs)

source

xresnet18

 xresnet18 (pretrained=False, **kwargs)

source

xse_resnext50_deeper

 xse_resnext50_deeper (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext34_deeper

 xse_resnext34_deeper (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext18_deeper

 xse_resnext18_deeper (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext50_deep

 xse_resnext50_deep (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext34_deep

 xse_resnext34_deep (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext18_deep

 xse_resnext18_deep (n_out=1000, pretrained=False, **kwargs)

source

xsenet154

 xsenet154 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnet152

 xse_resnet152 (n_out=1000, pretrained=False, **kwargs)

source

xresnext101

 xresnext101 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext101

 xse_resnext101 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnet101

 xse_resnet101 (n_out=1000, pretrained=False, **kwargs)

source

xresnext50

 xresnext50 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext50

 xse_resnext50 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnet50

 xse_resnet50 (n_out=1000, pretrained=False, **kwargs)

source

xresnext34

 xresnext34 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext34

 xse_resnext34 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnet34

 xse_resnet34 (n_out=1000, pretrained=False, **kwargs)

source

xresnext18

 xresnext18 (n_out=1000, pretrained=False, **kwargs)

source

xse_resnext18

 xse_resnext18 (n_out=1000, pretrained=False, **kwargs)

source

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)