From 574775c7fa78a094bbeb7f9f87aca832936884e2 Mon Sep 17 00:00:00 2001
From: Georgios Pinitas <georgios.pinitas@arm.com>
Date: Mon, 18 Feb 2019 20:08:02 +0000
Subject: COMPMID-1937: Adds support for DequantizationLayer for NEON/CL.

Change-Id: I4b73edd176a277294e0e42e642460bc61210778a
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/744
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
---
 .../fixtures/DequantizationLayerFixture.h          | 87 ++++------------------
 1 file changed, 13 insertions(+), 74 deletions(-)

(limited to 'tests/validation/fixtures')

diff --git a/tests/validation/fixtures/DequantizationLayerFixture.h b/tests/validation/fixtures/DequantizationLayerFixture.h
index 0bf3522cd6..2e3712dff2 100644
--- a/tests/validation/fixtures/DequantizationLayerFixture.h
+++ b/tests/validation/fixtures/DequantizationLayerFixture.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -47,10 +47,10 @@ class DequantizationValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(TensorShape shape, DataType data_type)
+    void setup(TensorShape shape, DataType data_type, QuantizationInfo qinfo)
     {
-        _target    = compute_target(shape, data_type);
-        _reference = compute_reference(shape, data_type);
+        _target    = compute_target(shape, data_type, qinfo);
+        _reference = compute_reference(shape, data_type, qinfo);
     }
 
 protected:
@@ -60,80 +60,28 @@ protected:
         library->fill_tensor_uniform(tensor, 0);
     }
 
-    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)
+    TensorType compute_target(const TensorShape &shape, DataType data_type, QuantizationInfo qinfo)
     {
-        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 min_max = create_tensor<TensorType>(shape_min_max, DataType::F32);
+        TensorType src = create_tensor<TensorType>(shape, DataType::QASYMM8, 1, qinfo);
+        TensorType dst = create_tensor<TensorType>(shape, data_type);
 
         // Create and configure function
         FunctionType dequantization_layer;
-        dequantization_layer.configure(&src, &dst, &min_max);
+        dequantization_layer.configure(&src, &dst);
 
         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();
@@ -141,28 +89,19 @@ protected:
         return dst;
     }
 
-    SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type)
+    SimpleTensor<T> compute_reference(const TensorShape &shape, DataType data_type, QuantizationInfo qinfo)
     {
-        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<float> min_max{ shape_min_max, data_type };
+        SimpleTensor<uint8_t> src{ shape, DataType::QASYMM8, 1, qinfo };
 
         // Fill reference
         fill(src);
-        fill_min_max(min_max);
 
-        return reference::dequantization_layer<T>(src, min_max);
+        return reference::dequantization_layer<T>(src);
     }
 
-    TensorType          _target{};
-    SimpleTensor<float> _reference{};
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
 };
 } // namespace validation
 } // namespace test
-- 
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