burn::tensor::ops

Trait ModuleOps

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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.

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so 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

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impl<R> ModuleOps<BackendRouter<R>> for BackendRouter<R>
where R: RunnerChannel,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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>

Dyn Compatibility§

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 ModuleOps

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

impl<R> ModuleOps<BackendRouter<R>> for BackendRouter<R>
where R: RunnerChannel,

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