src/test/scripts/applications/nn/component/conv2d_depthwise.dml

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source("scripts/nn/layers/conv2d_depthwise.dml") as conv2d_depthwise
source("src/test/scripts/applications/nn/util.dml") as test_util

conv2d_depthwise = function() {
  /*
   * Test for the 2D depthwise convolution function.
   */
  print("Testing the 2D depthwise convolution function.")

  # Generate data
  N = 2  # num examples
  C = 2  # num channels
  Hin = 3  # input height
  Win = 3  # input width
  M = 2  # num filters per input channel (i.e. depth multiplier)
  Hf = 3  # filter height
  Wf = 3  # filter width
  stride = 1
  pad = 1
  X = matrix(seq(1,N*C*Hin*Win), rows=N, cols=C*Hin*Win) / (N*C*Hin*Win) * 2 - 1  # normalized

  # Create layer
  W = matrix(seq(1,C*M*Hf*Wf), rows=C, cols=M*Hf*Wf) / (C*M*Hf*Wf) * 2 - 1  # normalized
  b = matrix(seq(1,C*M), rows=C*M, cols=1) / (C*M)^2  # non-zero & non-one

  # Forward
  [out, Hout, Wout] = conv2d_depthwise::forward(X, W, b, Hin, Win, M, Hf, Wf, stride, stride,
                                                pad, pad)

  # Equivalency check
  target = matrix("2.13040113  3.20447516  2.16743827
                   3.30324078  4.94212961  3.30324078
                   2.16743827  3.20447516  2.13040113

                   0.52623457  0.85030866  0.67438275
                   1.11574078  1.75462961  1.2824074
                   0.89660496  1.35030866  0.97067899

                  -0.30015433 -0.42052469 -0.15200615
                  -0.15509261 -0.1828704   0.01157404
                   0.07021603  0.07947529  0.1442901

                  -0.90432101 -1.27469134 -0.64506173
                  -0.8425926  -1.12037039 -0.50925928
                  -0.20061731 -0.2746914  -0.01543214


                  -0.31404325 -0.62885809 -0.49922845
                  -0.86342597 -1.55787039 -1.19675934
                  -0.94367278 -1.62885797 -1.20293212

                   0.0817901   0.01697529  0.00771603
                  -0.05092596 -0.2453704  -0.21759261
                  -0.21450615 -0.48302469 -0.36265433

                   1.25540125  1.74614203  1.1813271
                   1.67824078  2.31712961  1.51157403
                   0.95910496  1.24614203  0.81095684

                   2.65123463  3.8919754   2.68827152
                   3.99074078  5.87962961  3.99074078
                   2.68827152  3.8919754   2.65123463", rows=N, cols=C*M*Hout*Wout)

  for (i in 1:nrow(out)) {
    for(j in 1:ncol(out)) {
      rel_error = test_util::check_rel_error(as.scalar(out[i,j]),
                                             as.scalar(target[i,j]), 1e-3, 1e-4)
    }
  }
}

conv2d_depthwise()