IVGCVSW-2038 + IVGCVSW-2039 + IVGCVSW-2040 Add NHWC support to the Float32 and UInt8

BatchNormalization workloads

 * Enabled NHWC support in RefBatchNormalizationFloat32Workload
 * Added NHWC unit tests for both FP32 and U8
 * Refactored the existing unit tests

Change-Id: I6aa18f1dcc0666b80a17a7ed229cf53607bae147

1 files changed, 148 insertions, 4 deletions

diff --git a/src/backends/test/LayerTests.cpp b/src/backends/test/LayerTests.cpp
index c28a1d46ad..1faacacb5c 100755
--- a/src/backends/test/LayerTests.cpp
+++ b/src/backends/test/LayerTests.cpp
@@ -5338,14 +5338,158 @@ LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory&
 
 LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory)
 {
-    auto ret = BatchNormTestImpl<float>(workloadFactory, 0.f, 0);
-    return ret;
+    // BatchSize: 1
+    // Channels: 2
+    // Height: 3
+    // Width: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+         1.f, 4.f,
+         4.f, 2.f,
+         1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+         1.f, 1.f,
+         4.f, 1.f,
+        -2.f, 4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+        1.f, 4.f,
+        4.f, 2.f,
+        1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+        3.f, 3.f,
+        4.f, 3.f,
+        2.f, 4.f
+    };
+
+    return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+                                    0.f, 0, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    // BatchSize: 1
+    // Height: 3
+    // Width: 2
+    // Channels: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f,  1.f,
+        4.f,  1.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f,  4.f,
+        2.f,  1.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, -2.f,
+        6.f,  4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f, 3.f,
+        4.f, 3.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f, 4.f,
+        2.f, 3.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, 2.f,
+        6.f, 4.f
+    };
+
+    return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+                                    0.f, 0, armnn::DataLayout::NHWC);
 }
 
 LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory)
 {
-    auto ret = BatchNormTestImpl<uint8_t>(workloadFactory, 1.f/20.f, 50);
-    return ret;
+    // BatchSize: 1
+    // Channels: 2
+    // Height: 3
+    // Width: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+         1.f, 4.f,
+         4.f, 2.f,
+         1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+         1.f, 1.f,
+         4.f, 1.f,
+        -2.f, 4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Channel 0, Height (3) x Width (2)
+        1.f, 4.f,
+        4.f, 2.f,
+        1.f, 6.f,
+
+        // Batch 0, Channel 1, Height (3) x Width (2)
+        3.f, 3.f,
+        4.f, 3.f,
+        2.f, 4.f
+    };
+
+    return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+                                      1.f/20.f, 50, armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory)
+{
+    // BatchSize: 1
+    // Height: 3
+    // Width: 2
+    // Channels: 2
+
+    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
+    std::vector<float> inputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f,  1.f,
+        4.f,  1.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f,  4.f,
+        2.f,  1.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, -2.f,
+        6.f,  4.f
+    };
+    std::vector<float> expectedOutputValues
+    {
+        // Batch 0, Height 0, Width (2) x Channel (2)
+        1.f, 3.f,
+        4.f, 3.f,
+
+        // Batch 0, Height 1, Width (2) x Channel (2)
+        4.f, 4.f,
+        2.f, 3.f,
+
+        // Batch 0, Height 2, Width (2) x Channel (2)
+        1.f, 2.f,
+        6.f, 4.f
+    };
+
+    return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
+                                      1.f/20.f, 50, armnn::DataLayout::NHWC);
 }
 
 LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory)