Struct AvgPool2d 

pub struct AvgPool2d {
    pub stride: [usize; 2],
    pub kernel_size: [usize; 2],
    pub padding: PaddingConfig2d,
    pub count_include_pad: bool,
    pub ceil_mode: bool,
}

Applies a 2D avg pooling over input tensors.

Should be created with AvgPool2dConfig.

Remarks

The zero-padding values will be included in the calculation of the average. This means that the zeros are counted as legitimate values, and they contribute to the denominator when calculating the average. This is equivalent to torch.nn.AvgPool2d with count_include_pad=True.

Fields

stride: [usize; 2]

Stride of the pooling.

kernel_size: [usize; 2]

Size of the kernel.

padding: PaddingConfig2d

Padding configuration.

count_include_pad: bool

If the padding is counted in the denominator when computing the average.

ceil_mode: bool

If true, use ceiling instead of floor for output size calculation.

Implementations

impl AvgPool2d

pub fn forward(&self, input: Tensor<4>) -> Tensor<4>

Applies the forward pass on the input tensor.

See avg_pool2d for more information.

Shapes

• input: [batch_size, channels, height_in, width_in]
• output: [batch_size, channels, height_out, width_out]

Trait Implementations

impl Clone for AvgPool2d

fn clone(&self) -> AvgPool2d

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AvgPool2d

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Display for AvgPool2d

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Module for AvgPool2d

fn visit<V>(&self, _visitor: &mut V)

where V: ModuleVisitor,

Visit each parameter and module-owned control value with a visitor.

fn map<M>(self, _mapper: &mut M) -> AvgPool2d

where M: ModuleMapper,

Map each parameter and module-owned control value with a mapper.

fn to_device(self, _: &Device) -> AvgPool2d

Move the module and all of its sub-modules to the given device.

fn fork(self, _: &Device) -> AvgPool2d

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device>) -> Vec<Device>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn materialize(self) -> AvgPool2d

Fold parameter reparameterizations into their effective values and remove them.

fn valid(&self) -> AvgPool2d

Returns a validation snapshot without autodiff or active training flags.

fn train(self) -> AvgPool2d

Enables autodiff and restores configured training state throughout the module.

fn devices(&self) -> Vec<Device>

Return all the devices found in the underneath module tree without duplicates.

fn set_require_grad(self, require_grad: bool) -> Self

Set whether every floating-point tensor parameter in the module tree requires gradients.

fn set_require_grad_group(self, group: ParamGroup, require_grad: bool) -> Self

Set whether matched floating-point tensor parameters require gradients.

fn no_grad(self) -> Self

Disable gradients for every floating-point tensor parameter in the module tree.

fn freeze(self) -> Self

Freeze the whole module tree.

fn unfreeze(self) -> Self

Unfreeze the whole module tree.

fn freeze_group(self, group: ParamGroup) -> Self

Freeze every module-owned value in the given group, leaving the rest of the module untouched.

fn unfreeze_group(self, group: ParamGroup) -> Self

Unfreeze every module-owned value in the given group, leaving the rest of the module untouched.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules, without initializing the ones not initialized yet.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

fn quantize_weights_group( self, quantizer: &mut Quantizer, group: ParamGroup, ) -> Self

Quantize the weights of the given parameter group.

fn apply_reparameterization<R>(self, reparameterizer: R) -> Self

where Self: Sized, R: Reparameterizer,

Attach reparameterizations using the given Reparameterizer.

fn apply_lora(self, lora: Lora) -> Self

where Self: Sized,

Attach LoRA adapters to the module’s 2-D weights, freezing all base tensor parameters.

fn apply_qlora(self, qlora: QLora) -> Self

where Self: Sized,

Apply QLoRA to the module: quantize the (frozen) base tensor parameters and attach trainable LoRA adapters to 2-D weights.

fn into_record(self) -> ModuleRecord

where Self: Sized,

Collect this module’s tensor parameters into a ModuleRecord.

fn into_record_group(self, group: ParamGroup) -> ModuleRecord

where Self: Sized,

Collect the tensor parameters group names into a ModuleRecord.

fn try_load_record(self, record: ModuleRecord) -> Result<Self, RecordError>

where Self: Sized,

Apply a ModuleRecord to this module, returning the loaded module.

fn load_record(self, record: ModuleRecord) -> Self

where Self: Sized,

Apply a ModuleRecord to this module, consuming and returning it.

fn save_file<P>(self, path: P) -> Result<(), RecordError>

where P: AsRef<Path>, Self: Sized,

Save this module’s parameters to a burnpack file on disk.

fn load_file<P>(self, path: P) -> Self

where P: AsRef<Path>, Self: Sized,

Load this module’s parameters from a burnpack file on disk, returning the loaded module.

fn try_load_file<P>(self, path: P) -> Result<Self, RecordError>

where P: AsRef<Path>, Self: Sized,

Fallible variant of load_file.

impl ModuleDisplay for AvgPool2d

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, content: Content) -> Option<Content>

Custom attributes for the module.

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

impl ModuleDisplayDefault for AvgPool2d

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.