From 06b184ac568dc974986bae680957c4477f8ef6ca Mon Sep 17 00:00:00 2001
From: Gian Marco Iodice <gianmarco.iodice@arm.com>
Date: Tue, 29 Aug 2017 16:05:25 +0100
Subject: COMPMID-439 - Refactored NEQuantizationLayer and NEQuantizationLayer
 in order to support 3D input tensors

Change-Id: I03eac2108a30bed56d40dfd52e75577a35d492e0
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85783
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
---
 .../fixtures/DequantizationLayerFixture.h          | 106 ++++++++++++++-------
 1 file changed, 70 insertions(+), 36 deletions(-)

(limited to 'tests/validation/fixtures')

diff --git a/tests/validation/fixtures/DequantizationLayerFixture.h b/tests/validation/fixtures/DequantizationLayerFixture.h
index 7543eb2d2e..28d43cf754 100644
--- a/tests/validation/fixtures/DequantizationLayerFixture.h
+++ b/tests/validation/fixtures/DequantizationLayerFixture.h
@@ -49,11 +49,8 @@ public:
     template <typename...>
     void setup(TensorShape shape, DataType data_type)
     {
-        // Initialize random min and max values
-        rand_min_max(&_min, &_max);
-
-        _target    = compute_target(shape, data_type, _min, _max);
-        _reference = compute_reference(shape, data_type, _min, _max);
+        _target    = compute_target(shape, data_type);
+        _reference = compute_reference(shape, data_type);
     }
 
 protected:
@@ -63,28 +60,80 @@ protected:
         library->fill_tensor_uniform(tensor, 0);
     }
 
-    TensorType compute_target(const TensorShape &shape, DataType data_type, float min, float max)
+    template <typename U>
+    void fill_min_max(U &&tensor)
+    {
+        std::mt19937                          gen(library->seed());
+        std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
+
+        Window window;
+
+        window.set(0, Window::Dimension(0, tensor.shape()[0], 2));
+
+        for(unsigned int d = 1; d < tensor.shape().num_dimensions(); ++d)
+        {
+            window.set(d, Window::Dimension(0, tensor.shape()[d], 1));
+        }
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const float n1 = distribution(gen);
+            const float n2 = distribution(gen);
+
+            float min = 0.0f;
+            float max = 0.0f;
+
+            if(n1 < n2)
+            {
+                min = n1;
+                max = n2;
+            }
+            else
+            {
+                min = n2;
+                max = n1;
+            }
+
+            auto out_ptr = reinterpret_cast<float *>(tensor(id));
+            out_ptr[0]   = min;
+            out_ptr[1]   = max;
+        });
+    }
+
+    TensorType compute_target(const TensorShape &shape, DataType data_type)
     {
+        TensorShape shape_min_max = shape;
+        shape_min_max.set(Window::DimX, 2);
+
+        // Remove Y and Z dimensions and keep the batches
+        shape_min_max.remove_dimension(1);
+        shape_min_max.remove_dimension(1);
+
         // Create tensors
-        TensorType src = create_tensor<TensorType>(shape, data_type);
-        TensorType dst = create_tensor<TensorType>(shape, DataType::F32);
+        TensorType src     = create_tensor<TensorType>(shape, data_type);
+        TensorType dst     = create_tensor<TensorType>(shape, DataType::F32);
+        TensorType min_max = create_tensor<TensorType>(shape_min_max, DataType::F32);
 
         // Create and configure function
         FunctionType dequantization_layer;
-        dequantization_layer.configure(&src, &dst, &min, &max);
+        dequantization_layer.configure(&src, &dst, &min_max);
 
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(min_max.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();
+        min_max.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(!min_max.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(src));
+        fill_min_max(AccessorType(min_max));
 
         // Compute function
         dequantization_layer.run();
@@ -92,43 +141,28 @@ protected:
         return dst;
     }
 
-    SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type, float min, float max)
+    SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type)
     {
+        TensorShape shape_min_max = shape;
+        shape_min_max.set(Window::DimX, 2);
+
+        // Remove Y and Z dimensions and keep the batches
+        shape_min_max.remove_dimension(1);
+        shape_min_max.remove_dimension(1);
+
         // Create reference
-        SimpleTensor<T> src{ shape, data_type };
+        SimpleTensor<T>     src{ shape, data_type };
+        SimpleTensor<float> min_max{ shape_min_max, data_type };
 
         // Fill reference
         fill(src);
+        fill_min_max(min_max);
 
-        return reference::dequantization_layer<T>(src, min, max);
-    }
-
-    /** Generate random constant values to be used as min and max for dequantization.
-     */
-    void rand_min_max(float *min, float *max)
-    {
-        std::mt19937                          gen(library->seed());
-        std::uniform_real_distribution<float> distribution(-10000.0, 10000.0);
-
-        const float n1 = distribution(gen);
-        const float n2 = distribution(gen);
-
-        if(n1 < n2)
-        {
-            *min = n1;
-            *max = n2;
-        }
-        else
-        {
-            *min = n2;
-            *max = n1;
-        }
+        return reference::dequantization_layer<T>(src, min_max);
     }
 
     TensorType          _target{};
     SimpleTensor<float> _reference{};
-    float               _min = 0.f;
-    float               _max = 0.f;
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-- 
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