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DocumentationFunctionalSpacial Opsconv1d_op

conv1d_op

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Structs

Struct: Conv1d

Namespace for 1D convolution operations.

Fields

Methods

compute_shape(mut curr: ArrayShape, args: List[ArrayShape])
Computes the shape of an array after a 1-dimensional convolution operation.
Args

  • curr: ArrayShape

  • args: List[ArrayShape]

__call__(mut curr: Array, args: List[Array])
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Args

  • curr: Array

  • args: List[Array]

vjp(primals: List[Array], grad: Array, out: Array) -> List[Array]
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Args

  • primals: List[Array]

  • grad: Array

  • out: Array

Returns
  • List[Array]
jvp(primals: List[Array], tangents: List[Array]) -> Array
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Args

  • primals: List[Array]

  • tangents: List[Array]

Returns
  • Array
fwd(arg0: Array, kernel: Array, bias: Array, stride: Int, padding: Int, dilation: Int, groups: Int) -> Array
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Args

  • arg0: Array

  • kernel: Array

  • bias: Array

  • stride: Int

  • padding: Int

  • dilation: Int

  • groups: Int

Returns
  • Array

Functions

conv1d

conv1d(arg0: Array, kernel: Array, bias: Array, stride: Int, padding: Int, dilation: Int, groups: Int) -> Array
Applies a 1D convolution over an input signal composed of several input planes.
Args

  • arg0: Array Input tensor of shape (batch_size, in_channels, length).

  • kernel: Array Convolution kernel of shape (out_channels, in_channels // groups, kernel_size).

  • bias: Array Bias tensor of shape (out_channels).

  • stride: Int Stride of the convolution.

  • padding: Int Zero-padding added to both sides of the input.

  • dilation: Int Spacing between kernel elements.

  • groups: Int Number of blocked connections from input channels to output channels.

Returns
  • Array - Output tensor of shape (batch_size, out_channels, output_length).
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