Method test_batch_norm

test/python/test_mkldnn.py:124–186

(self)

        self.assertListEqual([2, 1, 3, 3], list(dx.shape()))

    def test_batch_norm(self):
        x_shape = [2, 2]
        x = singa_wrap.Tensor(x_shape)
        x.CopyFloatDataFromHostPtr([1, 2, 3, 4])

        dy_shape = [2, 2]
        dy = singa_wrap.Tensor(dy_shape)
        dy.CopyFloatDataFromHostPtr([4, 3, 2, 1])

        scale_shape = [2]
        scale = singa_wrap.Tensor(scale_shape)
        scale.CopyFloatDataFromHostPtr([1, 1])

        bias_shape = [2]
        bias = singa_wrap.Tensor(bias_shape)
        bias.CopyFloatDataFromHostPtr([0, 0])

        mean_shape = [2]
        mean = singa_wrap.Tensor(mean_shape)
        mean.CopyFloatDataFromHostPtr([1, 2])
        var = singa_wrap.Tensor(mean_shape)
        var.CopyFloatDataFromHostPtr([1, 2])

        handle = singa_wrap.BatchNormHandle(0.9, x)

        # 2D Forward Inference
        y = singa_wrap.CpuBatchNormForwardInference(handle, x, scale, bias,
                                                    mean, var)
        self.assertListEqual([2, 2], list(y.shape()))

        # 2D Forward Training
        (y, mean_updated, var_updated) = singa_wrap.CpuBatchNormForwardTraining(
            handle, x, scale, bias, mean, var)
        self.assertListEqual([2, 2], list(y.shape()))
        self.assertListEqual([2], list(mean_updated.shape()))
        self.assertListEqual([2], list(var_updated.shape()))

        # 2D Backward dx
        (dx, dscale,
         dbias) = singa_wrap.CpuBatchNormBackwardx(handle, y, dy, x, scale,
                                                   bias, mean_updated,
                                                   var_updated)
        self.assertListEqual([2, 2], list(dx.shape()))
        self.assertListEqual([2], list(dscale.shape()))
        self.assertListEqual([2], list(dbias.shape()))

        # 4D Forward Inference

        x2_shape = [1, 2, 4, 4]
        x2 = singa_wrap.Tensor(x2_shape)
        x2.CopyFloatDataFromHostPtr([
            0.0736655, 0.0459045, 0.0779517, 0.0771059, 0.0586862, 0.0561263,
            0.0708457, 0.0977273, 0.0405025, -0.170897, 0.0208982, 0.136865,
            -0.0367905, -0.0618205, -0.0103908, -0.0522777, -0.122161,
            -0.025427, -0.0718576, -0.185941, 0.0166533, 0.178679, -0.0576606,
            -0.137817, 0.150676, 0.153442, -0.0929899, -0.148675, -0.112459,
            -0.106284, -0.103074, -0.0668811
        ])