Trait burn_core::tensor::ops::ModuleOps

pub trait ModuleOps<B>where
    B: Backend,{
    // Required methods
    fn embedding(
        weights: <B as Backend>::TensorPrimitive<2>,
        indexes: <B as Backend>::IntTensorPrimitive<2>
    ) -> <B as Backend>::TensorPrimitive<3>;
    fn embedding_backward(
        weights: <B as Backend>::TensorPrimitive<2>,
        output: <B as Backend>::TensorPrimitive<3>,
        indexes: <B as Backend>::IntTensorPrimitive<2>
    ) -> <B as Backend>::TensorPrimitive<2>;
    fn conv2d(
        x: <B as Backend>::TensorPrimitive<4>,
        weight: <B as Backend>::TensorPrimitive<4>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        options: ConvOptions<2>
    ) -> <B as Backend>::TensorPrimitive<4>;
    fn conv_transpose2d(
        x: <B as Backend>::TensorPrimitive<4>,
        weight: <B as Backend>::TensorPrimitive<4>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        options: ConvTransposeOptions<2>
    ) -> <B as Backend>::TensorPrimitive<4>;
    fn avg_pool2d(
        x: <B as Backend>::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2]
    ) -> <B as Backend>::TensorPrimitive<4>;
    fn avg_pool2d_backward(
        x: <B as Backend>::TensorPrimitive<4>,
        grad: <B as Backend>::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2]
    ) -> <B as Backend>::TensorPrimitive<4>;
    fn max_pool2d(
        x: <B as Backend>::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2]
    ) -> <B as Backend>::TensorPrimitive<4>;
    fn max_pool2d_with_indexes(
        x: <B as Backend>::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2]
    ) -> MaxPool2dWithIndexes<B>;
    fn max_pool2d_with_indexes_backward(
        x: <B as Backend>::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        output_grad: <B as Backend>::TensorPrimitive<4>,
        indexes: <B as Backend>::IntTensorPrimitive<4>
    ) -> MaxPool2dBackward<B>;

    // Provided methods
    fn conv2d_backward(
        x: <B as Backend>::TensorPrimitive<4>,
        weight: <B as Backend>::TensorPrimitive<4>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        output_grad: <B as Backend>::TensorPrimitive<4>,
        options: ConvOptions<2>
    ) -> Conv2dBackward<B> { ... }
    fn conv1d(
        x: <B as Backend>::TensorPrimitive<3>,
        weight: <B as Backend>::TensorPrimitive<3>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        options: ConvOptions<1>
    ) -> <B as Backend>::TensorPrimitive<3> { ... }
    fn conv_transpose1d(
        x: <B as Backend>::TensorPrimitive<3>,
        weight: <B as Backend>::TensorPrimitive<3>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        options: ConvTransposeOptions<1>
    ) -> <B as Backend>::TensorPrimitive<3> { ... }
    fn conv1d_backward(
        x: <B as Backend>::TensorPrimitive<3>,
        weight: <B as Backend>::TensorPrimitive<3>,
        bias: Option<<B as Backend>::TensorPrimitive<1>>,
        output_grad: <B as Backend>::TensorPrimitive<3>,
        options: ConvOptions<1>
    ) -> Conv1dBackward<B> { ... }
}

Required Methods

fn embedding( weights: <B as Backend>::TensorPrimitive<2>, indexes: <B as Backend>::IntTensorPrimitive<2> ) -> <B as Backend>::TensorPrimitive<3>

fn embedding_backward( weights: <B as Backend>::TensorPrimitive<2>, output: <B as Backend>::TensorPrimitive<3>, indexes: <B as Backend>::IntTensorPrimitive<2> ) -> <B as Backend>::TensorPrimitive<2>

fn conv2d( x: <B as Backend>::TensorPrimitive<4>, weight: <B as Backend>::TensorPrimitive<4>, bias: Option<<B as Backend>::TensorPrimitive<1>>, options: ConvOptions<2> ) -> <B as Backend>::TensorPrimitive<4>

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 conv_transpose2d( x: <B as Backend>::TensorPrimitive<4>, weight: <B as Backend>::TensorPrimitive<4>, bias: Option<<B as Backend>::TensorPrimitive<1>>, options: ConvTransposeOptions<2> ) -> <B as Backend>::TensorPrimitive<4>

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 avg_pool2d( x: <B as Backend>::TensorPrimitive<4>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2] ) -> <B as Backend>::TensorPrimitive<4>

Two dimensional avg pooling.

Shapes

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

fn avg_pool2d_backward( x: <B as Backend>::TensorPrimitive<4>, grad: <B as Backend>::TensorPrimitive<4>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2] ) -> <B as Backend>::TensorPrimitive<4>

Backward pass for the avg pooling 2d operation.

fn max_pool2d( x: <B as Backend>::TensorPrimitive<4>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2] ) -> <B as Backend>::TensorPrimitive<4>

Two dimensional max pooling.

Shapes

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

fn max_pool2d_with_indexes( x: <B as Backend>::TensorPrimitive<4>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2] ) -> MaxPool2dWithIndexes<B>

Two dimensional max pooling with indexes.

Shapes

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

fn max_pool2d_with_indexes_backward( x: <B as Backend>::TensorPrimitive<4>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], output_grad: <B as Backend>::TensorPrimitive<4>, indexes: <B as Backend>::IntTensorPrimitive<4> ) -> MaxPool2dBackward<B>

Backward pass for the max pooling 2d operation.

Provided Methods

fn conv2d_backward( x: <B as Backend>::TensorPrimitive<4>, weight: <B as Backend>::TensorPrimitive<4>, bias: Option<<B as Backend>::TensorPrimitive<1>>, output_grad: <B as Backend>::TensorPrimitive<4>, options: ConvOptions<2> ) -> Conv2dBackward<B>

Backward pass for the conv2d operation.

fn conv1d( x: <B as Backend>::TensorPrimitive<3>, weight: <B as Backend>::TensorPrimitive<3>, bias: Option<<B as Backend>::TensorPrimitive<1>>, options: ConvOptions<1> ) -> <B as Backend>::TensorPrimitive<3>

One dimensional convolution.

Shapes

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

fn conv_transpose1d( x: <B as Backend>::TensorPrimitive<3>, weight: <B as Backend>::TensorPrimitive<3>, bias: Option<<B as Backend>::TensorPrimitive<1>>, options: ConvTransposeOptions<1> ) -> <B as Backend>::TensorPrimitive<3>

One dimensional transposed convolution.

Shapes

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

fn conv1d_backward( x: <B as Backend>::TensorPrimitive<3>, weight: <B as Backend>::TensorPrimitive<3>, bias: Option<<B as Backend>::TensorPrimitive<1>>, output_grad: <B as Backend>::TensorPrimitive<3>, options: ConvOptions<1> ) -> Conv1dBackward<B>

Backward pass for the conv1d operation.

Implementors