tests/testthat/test-nn-conv.R

In torch: Tensors and Neural Networks with 'GPU' Acceleration

test_that("conv3d", {
  input <- torch_randn(20, 16, 10, 5, 10)

  m <- nn_conv3d(16, 33, 3, stride = 2)
  o <- m(input)

  expect_tensor_shape(o, c(20, 33, 4, 2, 4))

  m <- nn_conv3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(4, 2, 0))
  o <- m(input)

  expect_tensor_shape(o, c(20, 33, 8, 5, 9))
})

test_that("conv2d", {
  input <- torch_randn(1, 3, 13, 13)
  conv <- nn_conv2d(3, 10, kernel_size = 3, padding = "same")
  out <- conv(input)

  expect_tensor_shape(out, c(1, 10, 13, 13))
})

test_that("nn_conv_transpose1d", {
  m <- nn_conv_transpose1d(32, 16, 2)
  input <- torch_randn(10, 32, 2)
  output <- m(input)

  expect_tensor_shape(output, c(10, 16, 3))
})

test_that("nn_conv_transpose2d", {
  input <- torch_randn(20, 16, 10, 10)
  m <- nn_conv_transpose2d(16, 33, 3, stride = 2)

  output <- m(input)
  expect_tensor_shape(output, c(20, 33, 21, 21))

  m <- nn_conv_transpose2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2))
  output <- m(input)
  expect_tensor_shape(output, c(20, 33, 13, 10))

  # exact output size can be also specified as an argument
  input <- torch_randn(1, 16, 12, 12)
  downsample <- nn_conv2d(16, 16, 3, stride = 2, padding = 1)
  upsample <- nn_conv_transpose2d(16, 16, 3, stride = 2, padding = 1)
  h <- downsample(input)
  h$size()
  output <- upsample(h, output_size = input$size())

  expect_equal(output$size(), input$size())
})

test_that("nn_conv_transpose3d", {
  input <- torch_randn(20, 16, 10, 5, 10)

  # With square kernels and equal stride
  m <- nn_conv_transpose3d(16, 33, 3, stride = 2)
  output <- m(input)
  expect_tensor_shape(output, c(20L, 33L, 21L, 11L, 21L))

  # non-square kernels and unequal stride and with padding
  m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(0, 4, 2))
  output <- m(input)

  expect_tensor_shape(output, c(20L, 33L, 21L, 1L, 7L))
})