1D Convolution

Torch-style 1D convolution in NCW layout: x is [batch, in_channels, width], weight is [out_channels, in_channels / groups, kW], output is [batch, out_channels, out_w]. Symmetric zero padding.

Usage

nv_conv1d(
  x,
  weight,
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  groups = 1L,
  precision = "highest"
)

Arguments

x

(arrayish)
[N, C_in, W]. Can be any data type; x and weight are promoted to a common data type. An R value assumes the other operand's data type, and materializes at its default data type when that has none either.

weight

(arrayish)
[C_out, C_in / groups, kW]. Promoted together with x – see x.

stride, padding, dilation

(integer())
Length 1.

groups

(integer(1))
Grouped/depthwise convolution.

precision

(character(1))
"highest", "high" or "default".

Value

(arrayish)
Has the operands' common data type, and shape [N, C_out, out_W].

See also

nv_conv2d(), nv_conv3d(), prim_convolution().

Examples

# one batch, one channel, width 5, convolved with a width-3 kernel
x <- nv_array(1:5, shape = c(1, 1, 5), dtype = "f32")
weight <- nv_array(c(1, 0, -1), shape = c(1, 1, 3), dtype = "f32")
nv_conv1d(x, weight)
#> AnvlArray
#> (1,.,.) =
#>  -2 -2 -2
#> [ CPUf32{1,1,3} ] 

# `padding = 1` keeps the input width, `stride = 2` visits every other
# window position
nv_conv1d(x, weight, padding = 1L)
#> AnvlArray
#> (1,.,.) =
#>  -2 -2 -2 -2  4
#> [ CPUf32{1,1,5} ] 
nv_conv1d(x, weight, stride = 2L)
#> AnvlArray
#> (1,.,.) =
#>  -2 -2
#> [ CPUf32{1,1,2} ]