Documentation
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Index ¶
- type BatchNorm2dModule
- type Conv2dModule
- type ConvTranspose2dModule
- type FunctionalModule
- type IModule
- type LinearModule
- type Module
- func (m *Module) Apply(function func(IModule))
- func (m *Module) Buffers() []torch.Tensor
- func (m *Module) Init(outer IModule)
- func (m *Module) IsTraining() bool
- func (m *Module) Name() string
- func (m *Module) NamedBuffers() map[string]torch.Tensor
- func (m *Module) NamedParameters() map[string]torch.Tensor
- func (m *Module) Outer() IModule
- func (m *Module) Parameters() []torch.Tensor
- func (m *Module) SetStateDict(sd map[string]torch.Tensor) error
- func (m *Module) StateDict() map[string]torch.Tensor
- func (m *Module) To(device torch.Device, dtype ...int8)
- func (m *Module) Train(on bool)
- type SequentialModule
- type Visitor
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type BatchNorm2dModule ¶
type BatchNorm2dModule struct {
Module
NumFeatures int64
Eps float64
Momentum float64
Affine bool
TrackRunningStats bool
Weight torch.Tensor
Bias torch.Tensor
RunningMean torch.Tensor `gotorch:"buffer"`
RunningVar torch.Tensor `gotorch:"buffer"`
}
BatchNorm2dModule torch.nn.BatchNorm2d
func BatchNorm2d ¶
func BatchNorm2d(numFeatures int64, eps, momentum float64, affine, trackRunningStats bool) *BatchNorm2dModule
BatchNorm2d creates a `BatchNorm2dModule` instance
type Conv2dModule ¶
type Conv2dModule struct {
Module
InChannels int64
OutChannels int64
KernelSize int64
Stride int64
Padding int64
Dilation int64
Groups int64
PaddingMode string
Weight torch.Tensor
Bias torch.Tensor
}
Conv2dModule applies convolution over a 2D input.
type ConvTranspose2dModule ¶
type ConvTranspose2dModule struct {
Module
InChannels int64
OutChannels int64
KernelSize int64
Stride int64
Padding int64
OutPadding int64
Groups int64
Dilation int64
PaddingMode string
Weight torch.Tensor
Bias torch.Tensor
}
ConvTranspose2dModule corresponds to torch.nn.ConvTranspose2d
func ConvTranspose2d ¶
func ConvTranspose2d(inChannels, outChannels, kernelSize, stride, padding, outPadding, groups int64, bias bool, dilation int64, paddingMode string) *ConvTranspose2dModule
ConvTranspose2d torch.nn.conv_transpose2d TODO(qijun): only support zero padding mode only support symmetry kernel/stride/padding/dilation not support output_size when forwarding
type FunctionalModule ¶
FunctionalModule wraps a function in a `Module`.
func Functional ¶
func Functional(f func(torch.Tensor) torch.Tensor) *FunctionalModule
Functional returns a new `Functional` instance.
type IModule ¶
type IModule interface {
// Train corresponds to torch.nn.Module.train(bool). It effects only
// certain modules like Dropout and BatchNorm.
Train(on bool)
// IsTraining returns true if the module is in training mode
IsTraining() bool
// To corresponds to torch.nn.Module.to(). It recursively casts all
// parameters to the given `dtype` and `device`.
To(device torch.Device, dtype ...int8)
// StateDict mimics torch.nn.Module.state_dict()
StateDict() map[string]torch.Tensor
// SetStateDict mimics torch.nn.Module.set_state_dict()
SetStateDict(sd map[string]torch.Tensor) error
// Apply function recursively to each module
Apply(f func(IModule))
// Name returns module type name
Name() string
}
IModule is the interface of `Module`s
type LinearModule ¶
type LinearModule struct {
Module
InFeatures int64
OutFeatures int64
Weight torch.Tensor
Bias torch.Tensor
}
LinearModule applies a linear transformation with optional bias.
func Linear ¶
func Linear(in, out int64, bias bool) *LinearModule
Linear creates a `Linear` instance
type Module ¶
type Module struct {
// contains filtered or unexported fields
}
Module contains default implementation of `Module`s
func (*Module) Init ¶
Init initializes a `Module`, using a `Module` that's not `Init`ed will panic Example:
type MyModel struct {
Module
}
func NewMyModule() *MyModel {
r := &MyModel{}
r.Init(r)
return r
}
func (*Module) IsTraining ¶
IsTraining returns true if the module is in training mode
func (*Module) NamedBuffers ¶
NamedBuffers returns parameters (recursively) that are not trainable, with their names
func (*Module) NamedParameters ¶
NamedParameters returns trainable parameters (recursively) with their names
func (*Module) Parameters ¶
Parameters returns trainable parameters (recursively)
func (*Module) SetStateDict ¶
SetStateDict sets the module's all tensor fields to values in sd.
func (*Module) StateDict ¶
StateDict mimics torch.Module.state_dict(), which returns parameters and buffers with their (unique) names.
type SequentialModule ¶
SequentialModule is a list of `Module`s that acts as a `Module` itself.
func Sequential ¶
func Sequential(modules ...IModule) *SequentialModule
Sequential returns a new `SequentialModule` instance.
func (*SequentialModule) Forward ¶
func (s *SequentialModule) Forward(inputs ...interface{}) interface{}
Forward feeds `inputs` to the first module and then chains outputs to inputs, returning the last output.
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