src/test/scripts/applications/nn/component/batch_norm2d.dml

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source("scripts/nn/layers/batch_norm2d.dml") as batch_norm2d
source("src/test/scripts/applications/nn/util.dml") as test_util

print("Testing the 2D (spatial) batch normalization function.")

# Generate data
N = 2  # Number of examples
C = 3  # num channels
Hin = 4  # input height
Win = 5  # input width
mode = 'train'  # execution mode
mu = 0.9  # momentum of moving averages
eps = 1e-5  # smoothing term
X = matrix("70  29 23 55 72
            42  98 68 48 39
            34  73 44  6 40
            74  18 18 53 53

            63  85 72 61 72
            32  36 23 29 63
             9  43 43 49 43
            31  43 89 94 50

            62  12 32 41 87
            25  48 99 52 61
            12  83 60 55 34
            30  42 68 88 51


            67  59 62 67 84
             8  76 24 19 57
            10  89 63 72  2
            59  56 16 15 70

            32  69 55 39 93
            84  36  4 30 40
            70 100 36 76 59
            69  15 40 24 34

            51  67 11 13 32
            66  85 55 85 38
            32  35 17 83 34
            55  58 52  0 99", rows=N, cols=C*Hin*Win)

# Create layer
[gamma, beta, ema_mean, ema_var] = batch_norm2d::init(C)

# Forward
[out, ema_mean_upd, ema_var_upd, cache_mean, cache_var] =
      batch_norm2d::forward(X, gamma, beta, C, Hin, Win, mode, ema_mean, ema_var, mu, eps)

# Equivalency check
target = matrix("0.86215019 -0.76679718 -1.00517964  0.26619387  0.94161105
                -0.25030172  1.97460198  0.78268933 -0.01191914 -0.36949289
                -0.56814504  0.98134136 -0.17084086 -1.68059683 -0.32976246
                 1.02107191 -1.20383179 -1.20383179  0.18673301  0.18673301

                 0.50426388  1.41921711  0.87856293  0.42108631  0.87856293
                -0.78498828 -0.61863315 -1.15928721 -0.90975463  0.50426388
                -1.74153018 -0.32751167 -0.32751167 -0.07797909 -0.32751167
                -0.82657707 -0.32751167  1.58557224  1.79351616 -0.0363903

                 0.4607178  -1.49978399 -0.71558321 -0.36269283  1.44096887
                -0.99005347 -0.08822262  1.91148913  0.06861746  0.42150795
                -1.49978399  1.28412855  0.38229787  0.18624771 -0.63716316
                -0.79400325 -0.32348287  0.69597805  1.48017895  0.0294075


                 0.74295878  0.42511559  0.54430676  0.74295878  1.41837597
                -1.60113597  1.10053277 -0.96544927 -1.16410136  0.34565473
                -1.52167511  1.61702824  0.5840373   0.94161105 -1.83951855
                 0.42511559  0.30592418 -1.28329265 -1.32302308  0.86215019

                -0.78498828  0.75379658  0.17155361 -0.4938668   1.75192738
                 1.37762833 -0.61863315 -1.9494741  -0.86816585 -0.45227802
                 0.79538536  2.04304862 -0.61863315  1.04491806  0.33790874
                 0.75379658 -1.49199748 -0.45227802 -1.11769855 -0.70181072

                 0.0294075   0.65676796 -1.53899395 -1.46057391 -0.71558321
                 0.61755812  1.36254871  0.18624771  1.36254871 -0.48032296
                -0.71558321 -0.59795308 -1.30373383  1.28412855 -0.63716316
                 0.18624771  0.30387771  0.06861746 -1.97030437  1.91148913",
                rows=1, cols=N*C*Hin*Win)
out = matrix(out, rows=1, cols=N*C*Hin*Win)
for (i in 1:length(out)) {
  rel_error = test_util::check_rel_error(as.scalar(out[1,i]),
                                         as.scalar(target[1,i]), 1e-3, 1e-4)
}