XResnet

Resnet from bags of tricks paper

init_cnn

def init_cnn(
    m
):

Call self as a function.

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XResNet

def XResNet(
    block, expansion, layers, p:float=0.0, c_in:int=3, n_out:int=1000, stem_szs:tuple=(32, 32, 64), widen:float=1.0,
    sa:bool=False, act_cls:type=ReLU, ndim:int=2, ks:int=3, stride:int=2, groups:int=1, reduction:NoneType=None,
    nh1:NoneType=None, nh2:NoneType=None, dw:bool=False, g2:int=1, sym:bool=False,
    norm_type:NormType=<NormType.Batch: 1>, pool:function=AvgPool, pool_first:bool=True, padding:NoneType=None,
    bias:NoneType=None, bn_1st:bool=True, transpose:bool=False, init:str='auto', xtra:NoneType=None,
    bias_std:float=0.01, dilation:Union=1, padding_mode:Literal='zeros', device:NoneType=None, dtype:NoneType=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()),
        ]
    )
)

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xresnet50_deeper

def xresnet50_deeper(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet34_deeper

def xresnet34_deeper(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet18_deeper

def xresnet18_deeper(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet50_deep

def xresnet50_deep(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet34_deep

def xresnet34_deep(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet18_deep

def xresnet18_deep(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet152

def xresnet152(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet101

def xresnet101(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet50

def xresnet50(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet34

def xresnet34(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnet18

def xresnet18(
    pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext50_deeper

def xse_resnext50_deeper(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext34_deeper

def xse_resnext34_deeper(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext18_deeper

def xse_resnext18_deeper(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext50_deep

def xse_resnext50_deep(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext34_deep

def xse_resnext34_deep(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext18_deep

def xse_resnext18_deep(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xsenet154

def xsenet154(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnet152

def xse_resnet152(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnext101

def xresnext101(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext101

def xse_resnext101(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnet101

def xse_resnet101(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnext50

def xresnext50(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext50

def xse_resnext50(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnet50

def xse_resnet50(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnext34

def xresnext34(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext34

def xse_resnext34(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnet34

def xse_resnet34(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xresnext18

def xresnext18(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnext18

def xse_resnext18(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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xse_resnet18

def xse_resnet18(
    n_out:int=1000, pretrained:bool=False, **kwargs
):

Call self as a function.

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)