COMPMID-1586: Add support for NHWC CLDeconvolutionLayer

COMPMID-1651: Fix QASYMM8 CLDeconvolutionLayer

This patch also extends the range of values used for testing Convolution and
Deconvolution to cover quantized [-1.0f, 1.0f].

Change-Id: I8b280669db67bb3ec25bf5d411c8f5954f5b0dab
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149869
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>

4 files changed, 56 insertions, 36 deletions

diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index 3b420eac09..795b9de6cd 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -77,7 +77,8 @@ protected:
         {
             case DataType::QASYMM8:
             {
-                std::uniform_int_distribution<uint8_t> distribution(0, 3);
+                std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
+                std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
                 library->fill(tensor, distribution, i);
                 break;
             }
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index d3a7be74b0..85c7ed5604 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -23,6 +23,7 @@
  */
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/Globals.h"
 #include "tests/IAccessor.h"
@@ -39,6 +40,8 @@ namespace test
 {
 namespace validation
 {
+using namespace arm_compute::misc::shape_calculator;
+
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DeconvolutionLayerFixtureBase : public framework::Fixture
 {
@@ -48,12 +51,15 @@ public:
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
-               const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
+               const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
-        _data_type = data_type;
+        _data_type         = data_type;
+        _bias_data_type    = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+        _data_layout       = data_layout;
+        _quantization_info = quantization_info;
 
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
+        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, inner_border);
+        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border);
     }
 
 protected:
@@ -64,7 +70,8 @@ protected:
         {
             case DataType::QASYMM8:
             {
-                std::uniform_int_distribution<uint8_t> distribution(0, 3);
+                std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
+                std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
                 library->fill(tensor, distribution, i);
                 break;
             }
@@ -86,14 +93,21 @@ protected:
         }
     }
 
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
-                              const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border, DataType data_type, QuantizationInfo quantization_info)
+    TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
+                              const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &inner_border)
     {
+        if(_data_layout == DataLayout::NHWC)
+        {
+            permute(input_shape, PermutationVector(2U, 0U, 1U));
+            permute(weights_shape, PermutationVector(2U, 0U, 1U));
+            permute(output_shape, PermutationVector(2U, 0U, 1U));
+        }
+
         // Create tensors
-        TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, quantization_info);
-        TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, quantization_info);
-        TensorType bias    = create_tensor<TensorType>(bias_shape, is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type, 1, quantization_info);
-        TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info);
+        TensorType src     = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout);
+        TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _quantization_info, _data_layout);
+        TensorType bias    = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout);
+        TensorType dst     = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info, _data_layout);
 
         // Create and configure function
         FunctionType conv;
@@ -127,12 +141,12 @@ protected:
     }
 
     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
-                                      const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border, DataType data_type, QuantizationInfo quantization_info)
+                                      const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> inner_border)
     {
         // Create reference
-        SimpleTensor<T>     src{ input_shape, data_type, 1, quantization_info };
-        SimpleTensor<T>     weights{ weights_shape, data_type, 1, quantization_info };
-        SimpleTensor<TBias> bias{ bias_shape, data_type, 1, quantization_info };
+        SimpleTensor<T>     src{ input_shape, _data_type, 1, _quantization_info };
+        SimpleTensor<T>     weights{ weights_shape, _data_type, 1, _quantization_info };
+        SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info };
 
         // Fill reference
         fill(src, 0);
@@ -142,9 +156,12 @@ protected:
         return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, inner_border);
     }
 
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-    DataType        _data_type{};
+    TensorType       _target{};
+    SimpleTensor<T>  _reference{};
+    DataType         _data_type{};
+    DataType         _bias_data_type{};
+    DataLayout       _data_layout{};
+    QuantizationInfo _quantization_info{};
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
@@ -153,16 +170,18 @@ class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<Tens
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
-               unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type)
+               unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, DataLayout data_layout)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         const std::pair<unsigned int, unsigned int> inner_border(inner_border_right, inner_border_top);
-        auto        out_dim      = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
-        TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
-        DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, QuantizationInfo());
+        auto        out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
+        TensorInfo  input_info(input_shape, 1, data_type);
+        TensorInfo  weights_info(weights_shape, 1, data_type);
+        TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
+        DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, data_layout, QuantizationInfo());
     }
 };
 
@@ -172,16 +191,18 @@ class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixture
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
-               unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info)
+               unsigned int inner_border_right, unsigned int inner_border_top, unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         const std::pair<unsigned int, unsigned int> inner_border(inner_border_right, inner_border_top);
-        auto        out_dim      = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
-        TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
-        DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, quantization_info);
+        auto        out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, sx, sy);
+        TensorInfo  input_info(input_shape, 1, data_type, quantization_info);
+        TensorInfo  weights_info(weights_shape, 1, data_type, quantization_info);
+        TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
+        DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, inner_border, data_type, data_layout, quantization_info);
     }
 };
 
diff --git a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
index 3bb935e49f..93e4e64830 100644
--- a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
+++ b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
@@ -68,11 +68,10 @@ protected:
         }
         else if(is_data_type_quantized_asymmetric(_data_type))
         {
-            const QuantizationInfo          quant_info = src_tensor.quantization_info();
-            const int                       min_bound  = quant_info.quantize(-1.f, RoundingPolicy::TO_NEAREST_UP);
-            const int                       max_bound  = quant_info.quantize(1.f, RoundingPolicy::TO_NEAREST_UP);
-            std::uniform_int_distribution<> distribution(min_bound, max_bound);
-            std::uniform_int_distribution<> distribution_std(quant_info.quantize(0.1f, RoundingPolicy::TO_NEAREST_UP), max_bound);
+            const QuantizationInfo quant_info = src_tensor.quantization_info();
+            std::pair<int, int> bounds = get_quantized_bounds(quant_info, -1.f, 1.0f);
+            std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
+            std::uniform_int_distribution<> distribution_std(quant_info.quantize(0.1f, RoundingPolicy::TO_NEAREST_UP), bounds.second);
             library->fill(src_tensor, distribution, 0);
             library->fill(mean_tensor, distribution, 1);
             library->fill(std_tensor, distribution_std, 2);
diff --git a/tests/validation/fixtures/ReduceMeanFixture.h b/tests/validation/fixtures/ReduceMeanFixture.h
index 6debd4a038..8692213641 100644
--- a/tests/validation/fixtures/ReduceMeanFixture.h
+++ b/tests/validation/fixtures/ReduceMeanFixture.h
@@ -32,6 +32,7 @@
 #include "tests/IAccessor.h"
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
 #include "tests/validation/reference/ReductionOperation.h"
 #include "tests/validation/reference/ReshapeLayer.h"
 
@@ -63,10 +64,8 @@ protected:
         }
         else
         {
-            const QuantizationInfo          quant_info = tensor.quantization_info();
-            const int                       min_bound  = quant_info.quantize(-1.f, RoundingPolicy::TO_NEAREST_UP);
-            const int                       max_bound  = quant_info.quantize(1.f, RoundingPolicy::TO_NEAREST_UP);
-            std::uniform_int_distribution<> distribution(min_bound, max_bound);
+            std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
+            std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
 
             library->fill(tensor, distribution, 0);
         }