COMPMID-494: Port BatchNormalizationLayer to new validation

Change-Id: Ief5334dd1cf571d977acf4ce9e5f580c5c9ab433
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/88158
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 167 insertions, 0 deletions

diff --git a/tests/validation/fixtures/BatchNormalizationLayerFixture.h b/tests/validation/fixtures/BatchNormalizationLayerFixture.h
new file mode 100644
index 0000000000..f4772a8dd9
--- /dev/null
+++ b/tests/validation/fixtures/BatchNormalizationLayerFixture.h
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/CPP/BatchNormalizationLayer.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class BatchNormalizationLayerValidationFixedPointFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape0, TensorShape shape1, float epsilon, DataType dt, int fractional_bits)
+    {
+        _fractional_bits = fractional_bits;
+        _data_type       = dt;
+        _target          = compute_target(shape0, shape1, epsilon, dt, fractional_bits);
+        _reference       = compute_reference(shape0, shape1, epsilon, dt, fractional_bits);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&src_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor)
+    {
+        if(is_data_type_float(_data_type))
+        {
+            float min_bound = 0.f;
+            float max_bound = 0.f;
+            std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<T>();
+            std::uniform_real_distribution<> distribution(min_bound, max_bound);
+            std::uniform_real_distribution<> distribution_var(0, max_bound);
+            library->fill(src_tensor, distribution, 0);
+            library->fill(mean_tensor, distribution, 1);
+            library->fill(var_tensor, distribution_var, 0);
+            library->fill(beta_tensor, distribution, 3);
+            library->fill(gamma_tensor, distribution, 4);
+        }
+        else
+        {
+            int min_bound = 0;
+            int max_bound = 0;
+            std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<T>(_fractional_bits);
+            std::uniform_int_distribution<> distribution(min_bound, max_bound);
+            std::uniform_int_distribution<> distribution_var(0, max_bound);
+            library->fill(src_tensor, distribution, 0);
+            library->fill(mean_tensor, distribution, 1);
+            library->fill(var_tensor, distribution_var, 0);
+            library->fill(beta_tensor, distribution, 3);
+            library->fill(gamma_tensor, distribution, 4);
+        }
+    }
+
+    TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, float epsilon, DataType dt, int fixed_point_position)
+    {
+        // Create tensors
+        TensorType src   = create_tensor<TensorType>(shape0, dt, 1, fixed_point_position);
+        TensorType dst   = create_tensor<TensorType>(shape0, dt, 1, fixed_point_position);
+        TensorType mean  = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position);
+        TensorType var   = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position);
+        TensorType beta  = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position);
+        TensorType gamma = create_tensor<TensorType>(shape1, dt, 1, fixed_point_position);
+
+        // Create and configure function
+        FunctionType norm;
+        norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon);
+
+        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(var.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(beta.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        src.allocator()->allocate();
+        dst.allocator()->allocate();
+        mean.allocator()->allocate();
+        var.allocator()->allocate();
+        beta.allocator()->allocate();
+        gamma.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!var.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!beta.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(AccessorType(src), AccessorType(mean), AccessorType(var), AccessorType(beta), AccessorType(gamma));
+
+        // Compute function
+        norm.run();
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, float epsilon, DataType dt, int fixed_point_position)
+    {
+        // Create reference
+        SimpleTensor<T> ref_src{ shape0, dt, 1, fixed_point_position };
+        SimpleTensor<T> ref_mean{ shape1, dt, 1, fixed_point_position };
+        SimpleTensor<T> ref_var{ shape1, dt, 1, fixed_point_position };
+        SimpleTensor<T> ref_beta{ shape1, dt, 1, fixed_point_position };
+        SimpleTensor<T> ref_gamma{ shape1, dt, 1, fixed_point_position };
+
+        // Fill reference
+        fill(ref_src, ref_mean, ref_var, ref_beta, ref_gamma);
+
+        return reference::batch_normalization_layer(ref_src, ref_mean, ref_var, ref_beta, ref_gamma, epsilon, fixed_point_position);
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+    int             _fractional_bits{};
+    DataType        _data_type{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class BatchNormalizationLayerValidationFixture : public BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape0, TensorShape shape1, float epsilon, DataType dt)
+    {
+        BatchNormalizationLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, epsilon, dt, 0);
+    }
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FIXTURE */