Trait burn::tensor::ops::ModuleOps

pub trait ModuleOps<B>where
    B: Backend,{
Show 45 methods    // Required methods
    fn conv2d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn deform_conv2d(
        x: <B as Backend>::FloatTensorPrimitive,
        offset: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        mask: Option<<B as Backend>::FloatTensorPrimitive>,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: DeformConvOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn deform_conv2d_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        offset: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        mask: Option<<B as Backend>::FloatTensorPrimitive>,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: DeformConvOptions<2>,
    ) -> DeformConv2dBackward<B>;
    fn conv3d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn conv_transpose2d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvTransposeOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn conv_transpose3d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvTransposeOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn avg_pool2d(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        count_include_pad: bool,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn avg_pool2d_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        grad: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        count_include_pad: bool,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn adaptive_avg_pool2d(
        x: <B as Backend>::FloatTensorPrimitive,
        output_size: [usize; 2],
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn adaptive_avg_pool2d_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn max_pool2d(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        dilation: [usize; 2],
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn max_pool2d_with_indices(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        dilation: [usize; 2],
    ) -> MaxPool2dWithIndices<B>;
    fn max_pool2d_with_indices_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        dilation: [usize; 2],
        output_grad: <B as Backend>::FloatTensorPrimitive,
        indices: <B as Backend>::IntTensorPrimitive,
    ) -> MaxPool2dBackward<B>;
    fn interpolate(
        x: <B as Backend>::FloatTensorPrimitive,
        output_size: [usize; 2],
        options: InterpolateOptions,
    ) -> <B as Backend>::FloatTensorPrimitive;
    fn interpolate_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        grad: <B as Backend>::FloatTensorPrimitive,
        output_size: [usize; 2],
        options: InterpolateOptions,
    ) -> <B as Backend>::FloatTensorPrimitive;

    // Provided methods
    fn embedding(
        weights: <B as Backend>::FloatTensorPrimitive,
        indices: <B as Backend>::IntTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn embedding_backward(
        weights: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        indices: <B as Backend>::IntTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv1d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv1d_x_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv1d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv1d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv2d_x_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv2d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv2d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv3d_x_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv3d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv3d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose1d(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        bias: Option<<B as Backend>::FloatTensorPrimitive>,
        options: ConvTransposeOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose1d_x_backward(
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose1d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<1>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose1d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose2d_x_backward(
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose2d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<2>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose2d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose3d_x_backward(
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose3d_weight_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        weight: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        options: ConvTransposeOptions<3>,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn conv_transpose3d_bias_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        bias: <B as Backend>::FloatTensorPrimitive,
        output_grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn unfold4d(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: [usize; 2],
        options: UnfoldOptions,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn avg_pool1d(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        count_include_pad: bool,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn avg_pool1d_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        grad: <B as Backend>::FloatTensorPrimitive,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        count_include_pad: bool,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn adaptive_avg_pool1d(
        x: <B as Backend>::FloatTensorPrimitive,
        output_size: usize,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn adaptive_avg_pool1d_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        grad: <B as Backend>::FloatTensorPrimitive,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn max_pool1d(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        dilation: usize,
    ) -> <B as Backend>::FloatTensorPrimitive { ... }
    fn max_pool1d_with_indices(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        dilation: usize,
    ) -> MaxPool1dWithIndices<B> { ... }
    fn max_pool1d_with_indices_backward(
        x: <B as Backend>::FloatTensorPrimitive,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        dilation: usize,
        output_grad: <B as Backend>::FloatTensorPrimitive,
        indices: <B as Backend>::IntTensorPrimitive,
    ) -> MaxPool1dBackward<B> { ... }
}

Expand description

Module operations trait.

Required Methods§

fn conv2d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvOptions<2>, ) -> ::FloatTensorPrimitive

Two dimensional convolution.

§Shapes

x: [batch_size, channels_in, height, width], weight: [channels_out, channels_in, kernel_size_1, kernel_size_2], bias: [channels_out],

fn deform_conv2d( x: ::FloatTensorPrimitive, offset: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, mask: Option<::FloatTensorPrimitive>, bias: Option<::FloatTensorPrimitive>, options: DeformConvOptions<2>, ) -> ::FloatTensorPrimitive

Two dimensional deformable convolution.

§Shapes

x: [batch_size, channels_in, height, width], weight: [channels_out, channels_in, kernel_size_1, kernel_size_2], bias: [channels_out],

fn deform_conv2d_backward( x: ::FloatTensorPrimitive, offset: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, mask: Option<::FloatTensorPrimitive>, bias: Option<::FloatTensorPrimitive>, output_grad: ::FloatTensorPrimitive, options: DeformConvOptions<2>, ) -> DeformConv2dBackward

Backward pass for the deform_conv2d operation.

fn conv3d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvOptions<3>, ) -> ::FloatTensorPrimitive

Three dimensional convolution.

§Shapes

x: [batch_size, channels_in, depth, height, width], weight: [channels_out, channels_in, kernel_size_1, kernel_size_2, kernel_size_3], bias: [channels_out],

fn conv_transpose2d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvTransposeOptions<2>, ) -> ::FloatTensorPrimitive

Two dimensional transposed convolution.

§Shapes

x: [batch_size, channels_in, height, width], weight: [channels_in, channels_out, kernel_size_1, kernel_size_2], bias: [channels_out],

fn conv_transpose3d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvTransposeOptions<3>, ) -> ::FloatTensorPrimitive

Three dimensional transposed convolution.

§Shapes

x: [batch_size, channels_in, height, width], weight: [channels_in, channels_out, kernel_size_1, kernel_size_2, kernel_size_3], bias: [channels_out],

fn avg_pool2d( x: ::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> ::FloatTensorPrimitive

Two dimensional avg pooling.

§Shapes

x: [batch_size, channels, height, width],

fn avg_pool2d_backward( x: ::FloatTensorPrimitive, grad: ::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> ::FloatTensorPrimitive

Backward pass for the avg pooling 2d operation.

fn adaptive_avg_pool2d( x: ::FloatTensorPrimitive, output_size: [usize; 2], ) -> ::FloatTensorPrimitive

Two dimensional adaptive avg pooling.

§Shapes

x: [batch_size, channels, height, width],

fn adaptive_avg_pool2d_backward( x: ::FloatTensorPrimitive, grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the adaptive avg pooling 2d operation.

fn max_pool2d( x: ::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> ::FloatTensorPrimitive

Two dimensional max pooling.

§Shapes

x: [batch_size, channels, height, width],

fn max_pool2d_with_indices( x: ::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> MaxPool2dWithIndices

Two dimensional max pooling with indices.

§Shapes

x: [batch_size, channels, height, width],

fn max_pool2d_with_indices_backward( x: ::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: ::FloatTensorPrimitive, indices: ::IntTensorPrimitive, ) -> MaxPool2dBackward

Backward pass for the max pooling 2d operation.

fn interpolate( x: ::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> ::FloatTensorPrimitive

Down/up samples the input.

§Shapes

x: [batch_size, channels, height, width],

fn interpolate_backward( x: ::FloatTensorPrimitive, grad: ::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> ::FloatTensorPrimitive

Backward pass for the interpolate operation.

Provided Methods§

fn embedding( weights: ::FloatTensorPrimitive, indices: ::IntTensorPrimitive, ) -> ::FloatTensorPrimitive

Embedding operation.

§Arguments

weights - The embedding weights.
indices - The indices tensor.

§Returns

The output tensor.

fn embedding_backward( weights: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, indices: ::IntTensorPrimitive, ) -> ::FloatTensorPrimitive

Embedding backward operation.

§Arguments

weights - The embedding weights.
output_grad - The output gradient.
indices - The indices tensor.

§Returns

The gradient.

fn conv1d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvOptions<1>, ) -> ::FloatTensorPrimitive

One dimensional convolution.

§Shapes

x: [batch_size, channels_in, length], weight: [channels_out, channels_in, kernel_size], bias: [channels_out],

fn conv1d_x_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<1>, ) -> ::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for x.

fn conv1d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<1>, ) -> ::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for weight.

fn conv1d_bias_backward( x: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for bias.

fn conv2d_x_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<2>, ) -> ::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for x.

fn conv2d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<2>, ) -> ::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for weight.

fn conv2d_bias_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for bias.

fn conv3d_x_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<3>, ) -> ::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for x.

fn conv3d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvOptions<3>, ) -> ::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for weight.

fn conv3d_bias_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for bias.

fn conv_transpose1d( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, bias: Option<::FloatTensorPrimitive>, options: ConvTransposeOptions<1>, ) -> ::FloatTensorPrimitive

One dimensional transposed convolution.

§Shapes

x: [batch_size, channels_in, length], weight: [channels_in, channels_out, length], bias: [channels_out],

fn conv_transpose1d_x_backward( weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for x.

fn conv_transpose1d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for weight.

fn conv_transpose1d_bias_backward( x: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for bias.

fn conv_transpose2d_x_backward( weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for x.

fn conv_transpose2d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for weight.

fn conv_transpose2d_bias_backward( x: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for bias.

fn conv_transpose3d_x_backward( weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for x.

fn conv_transpose3d_weight_backward( x: ::FloatTensorPrimitive, weight: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for weight.

fn conv_transpose3d_bias_backward( x: ::FloatTensorPrimitive, bias: ::FloatTensorPrimitive, output_grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for bias.

fn unfold4d( x: ::FloatTensorPrimitive, kernel_size: [usize; 2], options: UnfoldOptions, ) -> ::FloatTensorPrimitive

Four-dimensional unfolding.

§Shapes

x: [batch_size, channels_in, height, width], returns: [batch_size, channels_in * kernel_size_1 * kernel_size_2, number of blocks],

fn avg_pool1d( x: ::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> ::FloatTensorPrimitive

One dimensional avg pooling.

§Shapes

x: [batch_size, channels, length],

fn avg_pool1d_backward( x: ::FloatTensorPrimitive, grad: ::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> ::FloatTensorPrimitive

Backward pass for the avg pooling 1d operation.

fn adaptive_avg_pool1d( x: ::FloatTensorPrimitive, output_size: usize, ) -> ::FloatTensorPrimitive

One dimensional adaptive avg pooling.

§Shapes

x: [batch_size, channels, length],

fn adaptive_avg_pool1d_backward( x: ::FloatTensorPrimitive, grad: ::FloatTensorPrimitive, ) -> ::FloatTensorPrimitive

Backward pass for the adaptive avg pooling 1d operation.

fn max_pool1d( x: ::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> ::FloatTensorPrimitive

One dimensional max pooling.

§Shapes

x: [batch_size, channels, length],

fn max_pool1d_with_indices( x: ::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> MaxPool1dWithIndices

One dimensional max pooling with indices.

§Shapes

x: [batch_size, channels, height, width],

fn max_pool1d_with_indices_backward( x: ::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, output_grad: ::FloatTensorPrimitive, indices: ::IntTensorPrimitive, ) -> MaxPool1dBackward

Backward pass for the max pooling 1d operation.

Object Safety§

This trait is not object safe.

Implementations on Foreign Types§

§

impl ModuleOps<Fusion> for Fusion
where B: FusionBackend,

§

fn conv1d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvOptions<1>, ) -> <Fusion as Backend>::FloatTensorPrimitive

§

fn conv2d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvOptions<2>, ) -> <Fusion as Backend>::FloatTensorPrimitive

§

fn deform_conv2d( x: <Fusion as Backend>::FloatTensorPrimitive, offset: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, mask: Option<<Fusion as Backend>::FloatTensorPrimitive>, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: DeformConvOptions<2>, ) -> <Fusion as Backend>::FloatTensorPrimitive

§

fn deform_conv2d_backward( x: <Fusion as Backend>::FloatTensorPrimitive, offset: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, mask: Option<<Fusion as Backend>::FloatTensorPrimitive>, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, output_grad: <Fusion as Backend>::FloatTensorPrimitive, options: DeformConvOptions<2>, ) -> DeformConv2dBackward<Fusion>

§

fn conv3d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvOptions<3>, ) -> <Fusion as Backend>::FloatTensorPrimitive

§

fn conv_transpose1d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<1>, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn conv_transpose2d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<2>, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn conv_transpose3d( x: <Fusion as Backend>::FloatTensorPrimitive, weight: <Fusion as Backend>::FloatTensorPrimitive, bias: Option<<Fusion as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<3>, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn avg_pool1d( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn avg_pool2d( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn avg_pool1d_backward( x: <Fusion as Backend>::FloatTensorPrimitive, grad: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn avg_pool2d_backward( x: <Fusion as Backend>::FloatTensorPrimitive, grad: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn max_pool1d( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn max_pool2d( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn max_pool1d_with_indices( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> MaxPool1dWithIndices<Fusion>

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fn max_pool2d_with_indices( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> MaxPool2dWithIndices<Fusion>

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fn max_pool1d_with_indices_backward( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, output_grad: <Fusion as Backend>::FloatTensorPrimitive, indices: <Fusion as Backend>::IntTensorPrimitive, ) -> MaxPool1dBackward<Fusion>

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fn max_pool2d_with_indices_backward( x: <Fusion as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: <Fusion as Backend>::FloatTensorPrimitive, indices: <Fusion as Backend>::IntTensorPrimitive, ) -> MaxPool2dBackward<Fusion>

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fn adaptive_avg_pool1d( x: <Fusion as Backend>::FloatTensorPrimitive, output_size: usize, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn adaptive_avg_pool2d( x: <Fusion as Backend>::FloatTensorPrimitive, output_size: [usize; 2], ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn adaptive_avg_pool1d_backward( x: <Fusion as Backend>::FloatTensorPrimitive, grad: <Fusion as Backend>::FloatTensorPrimitive, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn adaptive_avg_pool2d_backward( x: <Fusion as Backend>::FloatTensorPrimitive, grad: <Fusion as Backend>::FloatTensorPrimitive, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn interpolate( x: <Fusion as Backend>::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> <Fusion as Backend>::FloatTensorPrimitive

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fn interpolate_backward( x: <Fusion as Backend>::FloatTensorPrimitive, grad: <Fusion as Backend>::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> <Fusion as Backend>::FloatTensorPrimitive

Implementors§

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impl<B, C> ModuleOps<Autodiff<B, C>> for Autodiff<B, C>
where B: Backend, C: CheckpointStrategy,

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impl<E, I, Q> ModuleOps<NdArray<E, I, Q>> for NdArray<E, I, Q>
where E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement,

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impl<E, Q> ModuleOps<LibTorch<E, Q>> for LibTorch<E, Q>
where E: TchElement, Q: QuantElement,

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impl<F, I> ModuleOps<Candle<F, I>> for Candle<F, I>
where F: FloatCandleElement, I: IntCandleElement,

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Trait burn::tensor::ops::ModuleOpsCopy item path

Required Methods§

fn conv2d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv3d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv_transpose2d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv_transpose3d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

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fn avg_pool2d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <B as Backend>::FloatTensorPrimitive

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fn avg_pool2d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <B as Backend>::FloatTensorPrimitive

fn adaptive_avg_pool2d( x: <B as Backend>::FloatTensorPrimitive, output_size: [usize; 2], ) -> <B as Backend>::FloatTensorPrimitive

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fn adaptive_avg_pool2d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn max_pool2d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> <B as Backend>::FloatTensorPrimitive

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fn max_pool2d_with_indices( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> MaxPool2dWithIndices<B>

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fn max_pool2d_with_indices_backward( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> MaxPool2dBackward<B>

fn interpolate( x: <B as Backend>::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> <B as Backend>::FloatTensorPrimitive

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fn interpolate_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, output_size: [usize; 2], options: InterpolateOptions, ) -> <B as Backend>::FloatTensorPrimitive

Provided Methods§

fn embedding( weights: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

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fn embedding_backward( weights: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv1d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv1d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv1d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv1d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn conv2d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv2d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv2d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn conv3d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv3d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv3d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose1d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

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fn conv_transpose1d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose1d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose1d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose2d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose2d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose2d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose3d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose3d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

fn conv_transpose3d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn unfold4d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], options: UnfoldOptions, ) -> <B as Backend>::FloatTensorPrimitive

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fn avg_pool1d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <B as Backend>::FloatTensorPrimitive

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fn avg_pool1d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <B as Backend>::FloatTensorPrimitive

fn adaptive_avg_pool1d( x: <B as Backend>::FloatTensorPrimitive, output_size: usize, ) -> <B as Backend>::FloatTensorPrimitive

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fn adaptive_avg_pool1d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

fn max_pool1d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> <B as Backend>::FloatTensorPrimitive

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fn max_pool1d_with_indices( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> MaxPool1dWithIndices<B>

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fn max_pool1d_with_indices_backward( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, output_grad: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> MaxPool1dBackward<B>

Object Safety§

Implementations on Foreign Types§

impl<B> ModuleOps<Fusion<B>> for Fusion<B>where B: FusionBackend,

fn conv1d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvOptions<1>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn conv2d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvOptions<2>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn conv3d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvOptions<3>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn conv_transpose1d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<1>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn conv_transpose2d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<2>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn conv_transpose3d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, weight: <Fusion<B> as Backend>::FloatTensorPrimitive, bias: Option<<Fusion<B> as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<3>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn avg_pool1d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn avg_pool2d( x: <Fusion<B> as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn avg_pool1d_backward( x: <Fusion<B> as Backend>::FloatTensorPrimitive, grad: <Fusion<B> as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

fn avg_pool2d_backward( x: <Fusion<B> as Backend>::FloatTensorPrimitive, grad: <Fusion<B> as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive

Trait burn::tensor::ops::ModuleOps

impl<B> ModuleOps<Fusion<B>> for Fusion<B>
where B: FusionBackend,

impl<B, C> ModuleOps<Autodiff<B, C>> for Autodiff<B, C>
where B: Backend, C: CheckpointStrategy,

impl<E, I, Q> ModuleOps<NdArray<E, I, Q>> for NdArray<E, I, Q>
where E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement,

impl<E, Q> ModuleOps<LibTorch<E, Q>> for LibTorch<E, Q>
where E: TchElement, Q: QuantElement,

impl<F, I> ModuleOps<Candle<F, I>> for Candle<F, I>
where F: FloatCandleElement, I: IntCandleElement,

impl<R, F, I> ModuleOps<JitBackend<R, F, I>> for JitBackend<R, F, I>
where R: JitRuntime, F: FloatElement, I: IntElement,