jactorch.nn.cnn.layers — Jacinle documentation

class Conv1dLayer[source]#

Bases: ConvNDLayerBase

__add__(other)#

Return type:: Sequential

__init__(in_channels, out_channels, kernel_size, stride=1, padding_mode='default', padding=0, border_mode='zeros', dilation=1, groups=1, batch_norm=None, dropout=None, bias=None, activation=None)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

__mul__(other)#

Parameters:: other (int)
Return type:: Sequential

append(module)#

Append a given module to the end.

Parameters:: module (nn.Module) – module to append
Return type:: Sequential

extend(sequential)#

Return type:: Sequential

forward(input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

insert(index, module)#

Parameters:

index (int)
module (Module)

Return type:

Sequential

pop(key)#

Parameters:: key (int | slice)
Return type:: Module

reset_parameters()#

property input_dim#

property output_dim#

class Conv2dLayer[source]#

Bases: ConvNDLayerBase

__add__(other)#

Return type:: Sequential

__init__(in_channels, out_channels, kernel_size, stride=1, padding_mode='default', padding=0, border_mode='zeros', dilation=1, groups=1, batch_norm=None, dropout=None, bias=None, activation=None)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

__mul__(other)#

Parameters:: other (int)
Return type:: Sequential

append(module)#

Append a given module to the end.

Parameters:: module (nn.Module) – module to append
Return type:: Sequential

extend(sequential)#

Return type:: Sequential

forward(input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

insert(index, module)#

Parameters:

index (int)
module (Module)

Return type:

Sequential

pop(key)#

Parameters:: key (int | slice)
Return type:: Module

reset_parameters()#

property input_dim#

property output_dim#

class Conv3dLayer[source]#

Bases: ConvNDLayerBase

__add__(other)#

Return type:: Sequential

__init__(in_channels, out_channels, kernel_size, stride=1, padding_mode='default', padding=0, border_mode='zeros', dilation=1, groups=1, batch_norm=None, dropout=None, bias=None, activation=None)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

__mul__(other)#

Parameters:: other (int)
Return type:: Sequential

append(module)#

Append a given module to the end.

Parameters:: module (nn.Module) – module to append
Return type:: Sequential

extend(sequential)#

Return type:: Sequential

forward(input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

insert(index, module)#

Parameters:

index (int)
module (Module)

Return type:

Sequential

pop(key)#

Parameters:: key (int | slice)
Return type:: Module

reset_parameters()#

property input_dim#

property output_dim#

class ConvNDLayerBase[source]#

Bases: Sequential

__add__(other)#

Return type:: Sequential

__init__(in_channels, out_channels, kernel_size, stride=1, padding_mode='default', padding=0, border_mode='zeros', dilation=1, groups=1, batch_norm=None, dropout=None, bias=None, activation=None)[source]#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

__mul__(other)#

Parameters:: other (int)
Return type:: Sequential

append(module)#

Append a given module to the end.

Parameters:: module (nn.Module) – module to append
Return type:: Sequential

extend(sequential)#

Return type:: Sequential

forward(input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

insert(index, module)#

Parameters:

index (int)
module (Module)

Return type:

Sequential

pop(key)#

Parameters:: key (int | slice)
Return type:: Module

reset_parameters()[source]#

property input_dim#

property output_dim#

class Deconv1dLayer[source]#

Bases: _DeconvLayerBase

__init__(in_channels, out_channels, kernel_size, stride=None, padding_mode='same', padding=0, border_mode=None, dilation=1, groups=1, output_size=None, scale_factor=None, resize_mode='nearest', batch_norm=None, dropout=None, bias=None, activation=None, algo='resizeconv')#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(input, output_size=None)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()#

property input_dim#

property output_dim#

class Deconv2dLayer[source]#

Bases: _DeconvLayerBase

__init__(in_channels, out_channels, kernel_size, stride=None, padding_mode='same', padding=0, border_mode=None, dilation=1, groups=1, output_size=None, scale_factor=None, resize_mode='nearest', batch_norm=None, dropout=None, bias=None, activation=None, algo='resizeconv')#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(input, output_size=None)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()#

property input_dim#

property output_dim#

class Deconv3dLayer[source]#

Bases: _DeconvLayerBase

__init__(in_channels, out_channels, kernel_size, stride=None, padding_mode='same', padding=0, border_mode=None, dilation=1, groups=1, output_size=None, scale_factor=None, resize_mode='nearest', batch_norm=None, dropout=None, bias=None, activation=None, algo='resizeconv')#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(input, output_size=None)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()#

property input_dim#

property output_dim#

class DeconvAlgo[source]#

Bases: JacEnum

__new__(value)#

classmethod assert_valid(value)#: Assert if the value is a valid choice.

classmethod choice_names()#: Returns the list of the name of all possible choices.

classmethod choice_objs()#: Returns the list of the object of all possible choices.

classmethod choice_values()#: Returns the list of the value of all possible choices.

classmethod from_string(value)#

Parameters:: value (str | JacEnum)
Return type:: JacEnum

classmethod is_valid(value)#: Check if the value is a valid choice.

classmethod type_name()#: Return the type name of the enum.

CONVTRANSPOSE = 'convtranspose'#

RESIZECONV = 'resizeconv'#

class LinearLayer[source]#

Bases: Sequential

__add__(other)#

Return type:: Sequential

__init__(in_features, out_features, batch_norm=None, dropout=None, bias=None, activation=None)[source]#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

__mul__(other)#

Parameters:: other (int)
Return type:: Sequential

append(module)#

Append a given module to the end.

Parameters:: module (nn.Module) – module to append
Return type:: Sequential

extend(sequential)#

Return type:: Sequential

forward(input)#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

insert(index, module)#

Parameters:

index (int)
module (Module)

Return type:

Sequential

pop(key)#

Parameters:: key (int | slice)
Return type:: Module

reset_parameters()[source]#

property input_dim#

property output_dim#

class MLPLayer[source]#

Bases: Module

__init__(input_dim, output_dim, hidden_dims, batch_norm=None, dropout=None, activation='relu', flatten=True, last_activation=False)[source]#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(input)[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

reset_parameters()[source]#

`Conv1dLayer`
`Conv2dLayer`
`Conv3dLayer`
`ConvNDLayerBase`
`Deconv1dLayer`
`Deconv2dLayer`
`Deconv3dLayer`
`DeconvAlgo`
`LinearLayer`
`MLPLayer`

jactorch.nn.cnn.layers#