COMPMID-1915: Deconvolution doesn't work when inner_dimension_top != 0 or inner_dimension_right != 0

Change-Id: Ia0533cfb34878fc81e929eb405c49e46609d26b8
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/616
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

10 files changed, 137 insertions, 60 deletions

diff --git a/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h
index 7a58c5acef..2963156d11 100644
--- a/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -85,11 +85,13 @@ public:
     CLDeconvolutionLayer &operator=(CLDeconvolutionLayer &&) = default;
     /** Set the input, weights, biases and output tensors.
      *
+     * @note This method will be deprecated in the next release.
+     *
      * @param[in,out] input              Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
      * @param[in]     weights            The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in]     bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
      * @param[out]    output             Output tensor. The output has the same number of dimensions as the @p input.
-     * @param[in]     info               Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     * @param[in]     info               Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
      * @param[in]     inner_border_right The number of zeros added to right edge of the input.
      * @param[in]     inner_border_top   The number of zeros added to top edge of the input.
      * @param[in]     weights_info       (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
@@ -99,11 +101,13 @@ public:
                    unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo());
     /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer
      *
+     * @note This method will be deprecated in the next release.
+     *
      * @param[in] input              Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
      * @param[in] weights            The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in] bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
      * @param[in] output             Output tensor info. The output has the same number of dimensions as the @p input.
-     * @param[in] info               Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     * @param[in] info               Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
      * @param[in] inner_border_right The number of zeros added to right edge of the input.
      * @param[in] inner_border_top   The number of zeros added to top edge of the input.
      * @param[in] weights_info       (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
@@ -113,6 +117,30 @@ public:
     static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
                            unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo());
 
+    /** Set the input, weights, biases and output tensors.
+     *
+     * @param[in,out] input        Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
+     * @param[in]     weights      The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in]     bias         (Optional) The biases have one dimension. Data type supported: Same as @p input.
+     * @param[out]    output       Output tensor. The output has the same number of dimensions as the @p input.
+     * @param[in]     info         Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
+     * @param[in]     weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
+     *
+     */
+    void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());
+    /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer
+     *
+     * @param[in] input        Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
+     * @param[in] weights      The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in] bias         (Optional) The biases have one dimension. Data type supported: Same as @p input.
+     * @param[in] output       Output tensor info. The output has the same number of dimensions as the @p input.
+     * @param[in] info         Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo.
+     * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());
+
     // Inherited methods overridden:
     void run() override;
     void prepare() override;
diff --git a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
index 277945d617..dad5d81b14 100644
--- a/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -88,6 +88,8 @@ public:
     virtual ~NEDeconvolutionLayer() = default;
     /** Set the input, weights, biases and output tensors.
      *
+     * @note This method will be deprecated in the next release.
+     *
      * @param[in,out] input              Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
      * @param[in]     weights            The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in]     bias               Optional, ignored if NULL. The biases have one dimension. Data type supported: Same as @p input.
@@ -101,6 +103,8 @@ public:
                    unsigned int inner_border_right, unsigned int inner_border_top);
     /** Static function to check if given info will lead to a valid configuration of @ref NEDeconvolutionLayer
      *
+     * @note This method will be deprecated in the next release.
+     *
      * @param[in] input              Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
      * @param[in] weights            The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in] bias               (Optional) The biases have one dimension. Data type supported: Same as @p input.
@@ -114,6 +118,28 @@ public:
     static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info,
                            unsigned int inner_border_right, unsigned int inner_border_top);
 
+    /** Set the input, weights, biases and output tensors.
+     *
+     * @param[in,out] input   Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
+     * @param[in]     weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in]     bias    Optional, ignored if NULL. The biases have one dimension. Data type supported: Same as @p input.
+     * @param[out]    output  Output tensor. The output has the same number of dimensions as the @p input.
+     * @param[in]     info    Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     *
+     */
+    void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEDeconvolutionLayer
+     *
+     * @param[in] input   Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: F32.
+     * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in] bias    (Optional) The biases have one dimension. Data type supported: Same as @p input.
+     * @param[in] output  Output tensor info. The output has the same number of dimensions as the @p input.
+     * @param[in] info    Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info);
+
     // Inherited methods overridden:
     void run() override;
     void prepare() override;
diff --git a/docs/00_introduction.dox b/docs/00_introduction.dox
index 97b70f9fa9..aeb8a3d44c 100644
--- a/docs/00_introduction.dox
+++ b/docs/00_introduction.dox
@@ -238,6 +238,10 @@ If there is more than one release in a month then an extra sequential number is
 
 @subsection S2_2_changelog Changelog
 
+v19.02 Public major release
+ - Deprecated functions/interfaces
+    - Usage of inner_border_right and inner_border_top has been deprecated in @ref CLDeconvolutionLayer and @ref NEDeconvolutionLayer
+
 v18.11 Public major release
  - Various bug fixes.
  - Various optimisations.
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index e07feb226a..9da02c10ad 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -158,6 +158,18 @@ void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const
     _scaled_output.allocator()->allocate();
 }
 
+void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+                                     const WeightsInfo &weights_info)
+{
+    configure(input, weights, bias, output, info, 0, 0, weights_info);
+}
+
+Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+                                      const WeightsInfo &weights_info)
+{
+    return CLDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0, weights_info);
+}
+
 void CLDeconvolutionLayer::run()
 {
     prepare();
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
index 6887a0a8cd..44d7197a65 100644
--- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -145,6 +145,15 @@ void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, con
     _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info);
     _scaled_output.allocator()->allocate();
 }
+Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info)
+{
+    return NEDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0);
+}
+
+void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info)
+{
+    configure(input, weights, bias, output, info, 0, 0);
+}
 
 void NEDeconvolutionLayer::run()
 {
diff --git a/tests/validation/CL/DeconvolutionLayer.cpp b/tests/validation/CL/DeconvolutionLayer.cpp
index 46a229bb39..31852c8eb6 100644
--- a/tests/validation/CL/DeconvolutionLayer.cpp
+++ b/tests/validation/CL/DeconvolutionLayer.cpp
@@ -50,16 +50,16 @@ constexpr AbsoluteTolerance<float>  tolerance_qasymm8(0.0);               /**< T
 constexpr float                     tolerance_num = 0.07f;                /**< Tolerance number */
 
 const auto data4x4 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 3)
-                     * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data3x3 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 2)
-                     * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data3x3_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadX", 0, 2)
-                               * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                               * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data1x1 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 1)
-                     * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data_layouts_dataset = framework::dataset::make("DataLayout", { DataLayout::NCHW });
 } // namespace
@@ -74,12 +74,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                             TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),   // Invalid weights shape
                                             TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),  // Invalid bias shape
                                             TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink
+                                            TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32), // Inner border different from 0
                                             TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
                                           }),
     framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
                                             TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
                                             TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32),
                                             TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
                                               TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
                                           })),
     framework::dataset::make("BiasInfo",  { TensorInfo(TensorShape(1U), 1, DataType::F16),
@@ -88,32 +90,36 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                             TensorInfo(TensorShape(1U), 1, DataType::F32),
                                             TensorInfo(TensorShape(4U), 1, DataType::F32),
                                             TensorInfo(TensorShape(4U), 1, DataType::S32),
+                                            TensorInfo(TensorShape(4U), 1, DataType::S32),
                                           })),
     framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
                                             TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
                                             TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32),
                                             TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32),
                                             TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
                                           })),
     framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
                                                 PadStrideInfo(1, 1, 0, 0),
                                                 PadStrideInfo(1, 1, 0, 0),
                                                 PadStrideInfo(1, 1, 1, 1),
                                                 PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
                                            })),
-    framework::dataset::make("ax",          {   1U,
-                                                1U,
-                                                1U,
+    framework::dataset::make("ax",          {   0U,
+                                                0U,
+                                                0U,
                                                 0U,
                                                 0U,
                                             })),
-   framework::dataset::make("ay",           {   1U,
-                                                1U,
-                                                1U,
+   framework::dataset::make("ay",           {   0U,
                                                 0U,
                                                 0U,
+                                                0U,
+                                                1U,
+                                                0U,
                                             })),
-    framework::dataset::make("Expected", { false, false, false, false, true })),
+    framework::dataset::make("Expected", { false, false, false, false, false, true })),
     input_info, weights_info, bias_info, output_info, pad_info, ax, ay, expected)
 {
     bool is_valid = bool(CLDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_info, ax, ay));
diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp
index 9a3ed30241..4a05535e09 100644
--- a/tests/validation/NEON/DeconvolutionLayer.cpp
+++ b/tests/validation/NEON/DeconvolutionLayer.cpp
@@ -46,16 +46,16 @@ namespace
 constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */
 
 const auto data4x4 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 3)
-                     * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 3) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data3x3 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 2)
-                     * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data3x3_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadX", 0, 2)
-                               * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                               * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data1x1 = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 4) * framework::dataset::make("StrideY", 1, 4) * framework::dataset::make("PadX", 0, 1)
-                     * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("ax", 0) * framework::dataset::make("ay", 0) * framework::dataset::make("NumKernels", { 3 });
+                     * framework::dataset::make("PadY", 0, 1) * framework::dataset::make("NumKernels", { 3 });
 
 const auto data_layouts_dataset = framework::dataset::make("DataLayout", { DataLayout::NCHW });
 } // namespace
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
index 85c7ed5604..8f15f04f0e 100644
--- a/tests/validation/fixtures/DeconvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -51,15 +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, DataLayout data_layout, QuantizationInfo quantization_info)
+               DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
         _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);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, inner_border);
+        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info);
+        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info);
     }
 
 protected:
@@ -94,7 +94,7 @@ protected:
     }
 
     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)
+                              const PadStrideInfo &info)
     {
         if(_data_layout == DataLayout::NHWC)
         {
@@ -111,7 +111,7 @@ protected:
 
         // Create and configure function
         FunctionType conv;
-        conv.configure(&src, &weights, &bias, &dst, info, inner_border.first, inner_border.second);
+        conv.configure(&src, &weights, &bias, &dst, info);
 
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -141,7 +141,7 @@ 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)
+                                      const PadStrideInfo &info)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, _data_type, 1, _quantization_info };
@@ -153,7 +153,7 @@ protected:
         fill(weights, 1);
         fill(bias, 2);
 
-        return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, inner_border);
+        return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info);
     }
 
     TensorType       _target{};
@@ -170,18 +170,17 @@ 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, DataLayout data_layout)
+               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);
-        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());
+        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, data_type, data_layout, QuantizationInfo());
     }
 };
 
@@ -191,18 +190,17 @@ 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, DataLayout data_layout, QuantizationInfo quantization_info)
+               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);
-        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);
+        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, data_type, data_layout, quantization_info);
     }
 };
 
diff --git a/tests/validation/reference/DeconvolutionLayer.cpp b/tests/validation/reference/DeconvolutionLayer.cpp
index 5ca3b44baa..916792479f 100644
--- a/tests/validation/reference/DeconvolutionLayer.cpp
+++ b/tests/validation/reference/DeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -35,7 +35,7 @@ namespace reference
 {
 template <typename T, typename TB>
 SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape,
-                                    const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a)
+                                    const PadStrideInfo &info)
 {
     // Create reference
     const int stride_x           = info.stride().first;
@@ -45,8 +45,8 @@ SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTens
     const int weights_upper_dims = weights.shape().total_size() / (weights_width * weights_height);
 
     // Find the upsampled dimensions
-    unsigned int out_x = (src.shape().x() - 1) * stride_x + a.first + 1;
-    unsigned int out_y = (src.shape().y() - 1) * stride_y + a.second + 1;
+    unsigned int out_x = (src.shape().x() - 1) * stride_x + 1;
+    unsigned int out_y = (src.shape().y() - 1) * stride_y + 1;
 
     // Find the padding needed for the convolution with stride 1 in order to match output shape
     unsigned int padx = output_shape.x() - (out_x - weights_width + 1);
@@ -64,13 +64,8 @@ SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTens
     const int width_scaled  = scaled.shape().x();
     const int height_scaled = scaled.shape().y();
     const int num_2d_slices = src.shape().total_size() / (width_in * height_in);
-    const int ax            = a.first;  // The number of zeros added to right edge of the input.
-    const int ay            = a.second; // The number of zeros added to top edge of the input.
     ARM_COMPUTE_ERROR_ON(info.pad().first > (weights.shape().x() - 1));
 
-    ARM_COMPUTE_ERROR_ON_MSG(ax > stride_x - 1, "ax must be smaller than stride_x");
-    ARM_COMPUTE_ERROR_ON_MSG(ay > stride_y - 1, "ay must be smaller than stride_y");
-
     if(src.data_type() == DataType::QASYMM8)
     {
         const uint8_t quantized_zero = src.quantization_info().offset;
@@ -100,9 +95,9 @@ SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTens
         const int offset_slice_in  = slice * width_in * height_in;
         const int offset_slice_out = slice * width_scaled * height_scaled;
         const int start_x          = padx / 2;
-        const int start_y          = ay + pady / 2;
+        const int start_y          = pady / 2;
         const int end_y            = height_scaled - pady / 2;
-        const int end_x            = width_scaled - ax - padx / 2;
+        const int end_x            = width_scaled - padx / 2;
 
         for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
         {
@@ -120,11 +115,11 @@ SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTens
 }
 
 template SimpleTensor<uint8_t> deconvolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
-                                                   const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a);
+                                                   const PadStrideInfo &info);
 template SimpleTensor<float> deconvolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
-                                                 const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a);
+                                                 const PadStrideInfo &info);
 template SimpleTensor<half> deconvolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape,
-                                                const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a);
+                                                const PadStrideInfo &info);
 } // namespace reference
 } // namespace validation
 } // namespace test
diff --git a/tests/validation/reference/DeconvolutionLayer.h b/tests/validation/reference/DeconvolutionLayer.h
index 95fb416b30..21583e3b12 100644
--- a/tests/validation/reference/DeconvolutionLayer.h
+++ b/tests/validation/reference/DeconvolutionLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,8 +46,7 @@ namespace reference
  *
  */
 template <typename T, typename TB>
-SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info,
-                                    const std::pair<unsigned int, unsigned int> &a);
+SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info);
 } // namespace reference
 } // namespace validation
 } // namespace test