src/test/scripts/applications/nn/component/conv2d.dml

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source("scripts/nn/layers/conv2d.dml") as conv2d
source("src/test/scripts/applications/nn/conv2d_simple.dml") as conv2d_simple
source("scripts/nn/layers/conv2d_builtin.dml") as conv2d_builtin
source("src/test/scripts/applications/nn/util.dml") as test_util

conv2d = function() {
  /*
   * Test for the 2D convolution functions.
   */
  print("Testing the 2D convolution functions.")

  # Generate data
  N = 2  # num examples
  C = 3  # num channels
  Hin = 5  # input height
  Win = 5  # input width
  F = 2  # num filters
  Hf = 3  # filter height
  Wf = 3  # filter width
  stride = 1
  pad = 1
  X = rand(rows=N, cols=C*Hin*Win, pdf="normal")

  # Create layer
  [W, b] = conv2d::init(F, C, Hf, Wf, -1)

  # Forward
  [out, Hout, Wout] = conv2d::forward(X, W, b, C, Hin, Win, Hf, Wf, stride, stride, pad, pad)
  [out_simple, Hout_simple, Wout_simple] = conv2d_simple::forward(X, W, b, C, Hin, Win, Hf, Wf,
                                                                  stride, stride, pad, pad)
  [out_builtin, Hout_builtin, Wout_builtin] = conv2d_builtin::forward(X, W, b, C, Hin, Win, Hf, Wf,
                                                                      stride, stride, pad, pad)

  # Equivalency check
  out = matrix(out, rows=1, cols=N*F*Hout*Wout)
  out_simple = matrix(out_simple, rows=1, cols=N*F*Hout*Wout)
  out_builtin = matrix(out_builtin, rows=1, cols=N*F*Hout*Wout)
  for (i in 1:length(out)) {
    rel_error = test_util::check_rel_error(as.scalar(out[1,i]),
                                           as.scalar(out_simple[1,i]), 1e-10, 1e-12)
    rel_error = test_util::check_rel_error(as.scalar(out[1,i]),
                                           as.scalar(out_builtin[1,i]), 1e-10, 1e-12)
  }
}

conv2d()