COMPMID-1492 Create tests/validate_examples/graph_depthwise_convolution

Add new validate graph example and unify common example code

Change-Id: Ibfd7ae2067ad805d6c82d953fe3febfbea961149
Signed-off-by: John Kesapides <john.kesapides@arm.com>
Reviewed-on: https://review.mlplatform.org/c/825
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

8 files changed, 1376 insertions, 950 deletions

diff --git a/arm_compute/graph/TypeLoader.h b/arm_compute/graph/TypeLoader.h
index dcdc1736a7..41f382ad1d 100644
--- a/arm_compute/graph/TypeLoader.h
+++ b/arm_compute/graph/TypeLoader.h
@@ -123,6 +123,30 @@ inline ::std::istream &operator>>(::std::istream &stream, ConvolutionMethod &tar
     target = Convolution_method_from_name(value);
     return stream;
 }
+
+/** Converts a string to a strong types enumeration @ref DepthwiseConvolutionMethod
+ *
+ * @param[in] name String to convert
+ *
+ * @return Converted Target enumeration
+ */
+DepthwiseConvolutionMethod depthwise_convolution_method_from_name(const std::string &name);
+
+/** Input Stream operator for @ref DepthwiseConvolutionMethod
+ *
+ * @param[in]  stream Stream to parse
+ * @param[out] target Output target
+ *
+ * @return Updated stream
+ */
+inline ::std::istream &operator>>(::std::istream &stream, DepthwiseConvolutionMethod &target)
+{
+    std::string value;
+    stream >> value;
+    target = depthwise_convolution_method_from_name(value);
+    return stream;
+}
+
 } // namespace graph
 } // namespace arm_compute
 #endif /* __ARM_COMPUTE_GRAPH_TYPE_LOADER_H__ */
diff --git a/src/graph/TypeLoader.cpp b/src/graph/TypeLoader.cpp
index 0c1ce25b92..b63672b39b 100644
--- a/src/graph/TypeLoader.cpp
+++ b/src/graph/TypeLoader.cpp
@@ -125,5 +125,30 @@ ConvolutionMethod Convolution_method_from_name(const std::string &name)
     }
 #endif /* ARM_COMPUTE_EXCEPTIONS_DISABLED */
 }
+
+DepthwiseConvolutionMethod depthwise_convolution_method_from_name(const std::string &name)
+{
+    static const std::map<std::string, DepthwiseConvolutionMethod> methods =
+    {
+        { "default", DepthwiseConvolutionMethod::Default },
+        { "gemv", DepthwiseConvolutionMethod::GEMV },
+        { "optimized3x3", DepthwiseConvolutionMethod::Optimized3x3 },
+    };
+
+#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
+    try
+    {
+#endif /* ARM_COMPUTE_EXCEPTIONS_DISABLED */
+        return methods.at(arm_compute::utility::tolower(name));
+
+#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
+    }
+    catch(const std::out_of_range &)
+    {
+        throw std::invalid_argument(name);
+    }
+#endif /* ARM_COMPUTE_EXCEPTIONS_DISABLED */
+}
+
 } // namespace graph
 } // namespace arm_compute
diff --git a/tests/validate_examples/graph_convolution.cpp b/tests/validate_examples/graph_convolution.cpp
index 4f5ab0dc08..acc1e69544 100644
--- a/tests/validate_examples/graph_convolution.cpp
+++ b/tests/validate_examples/graph_convolution.cpp
@@ -35,6 +35,7 @@
 #include "utils/Utils.h"
 
 #include "ValidateExample.h"
+#include "graph_validate_utils.h"
 
 #include <utility>
 
@@ -45,161 +46,9 @@ using namespace arm_compute::graph;
 using namespace arm_compute;
 using namespace arm_compute::test;
 using namespace arm_compute::test::validation;
-namespace
-{
-/*Available Padding modes */
-enum class PaddingMode
-{
-    Valid,
-    Same,
-    Manual
-};
 
-/** Stream Input operator for the PaddingMode type
- *
- * @param[in]  stream Input stream.
- * @param[out] Mode   Convolution parameters to output
- *
- * @return input stream.
- */
-inline ::std::istream &operator>>(::std::istream &stream, PaddingMode &Mode)
-{
-    static const std::map<std::string, PaddingMode> modes =
-    {
-        { "valid", PaddingMode::Valid },
-        { "same", PaddingMode::Same },
-        { "manual", PaddingMode::Manual }
-    };
-    std::string value;
-    stream >> value;
-    try
-    {
-        Mode = modes.at(arm_compute::utility::tolower(value));
-    }
-    catch(const std::out_of_range &)
-    {
-        throw std::invalid_argument(value);
-    }
-
-    return stream;
-}
-
-/** Formatted output of the PaddingMode type
- *
- * @param[out] os   Output stream.
- * @param[in]  Mode PaddingMode to output
- *
- * @return Modified output stream.
- */
-inline ::std::ostream &operator<<(::std::ostream &os, PaddingMode Mode)
-{
-    switch(Mode)
-    {
-        case PaddingMode::Valid:
-            os << "Valid";
-            break;
-        case PaddingMode::Same:
-            os << "Same";
-            break;
-        case PaddingMode::Manual:
-            os << "Manual";
-            break;
-        default:
-            throw std::invalid_argument("Unsupported padding mode format");
-    }
-
-    return os;
-}
-/** Structure holding all the input tensor graph parameters */
-struct TensorParams
-{
-    int              width{ 0 };
-    int              height{ 0 };
-    int              fm{ 0 };
-    int              batch{ 0 };
-    QuantizationInfo quant_info{ 1.0f, 0 };
-    std::string      npy{};
-    uint64_t         range_low{ 0 };
-    uint64_t         range_high{ 16 };
-};
-/** Structure holding all the verification graph parameters */
-struct VerificationParams
-{
-    float absolute_tolerance{ -1.f };
-    float relative_tolerance{ -1.f };
-    float tolerance_number{ -1.f };
-};
-
-/** Structure holding all the common graph parameters */
-struct FrameworkParams
-{
-    bool                       help{ false };
-    int                        threads{ 0 };
-    arm_compute::graph::Target target{ arm_compute::graph::Target::NEON };
-};
-
-/** Structure holding all the Convolution layer graph parameters */
-struct ConvolutionParams
-{
-    arm_compute::DataType                 data_type{ DataType::F32 };
-    arm_compute::DataLayout               data_layout{ DataLayout::NCHW };
-    arm_compute::graph::ConvolutionMethod convolution_method{ arm_compute::graph::ConvolutionMethod::Default };
-
-    /** Padding graph parameters */
-    int         padding_top{ 0 };
-    int         padding_bottom{ 0 };
-    int         padding_left{ 0 };
-    int         padding_right{ 0 };
-    int         padding_stride_x{ 0 };
-    int         padding_stride_y{ 0 };
-    PaddingMode padding_mode{ PaddingMode::Valid };
-    struct
-    {
-        struct
-        {
-            int X{ 0 };
-            int Y{ 0 };
-        } stride{};
-        PaddingMode mode{ PaddingMode::Valid };
-    } padding{};
-};
-
-/** Structure holding all the graph Example parameters */
-struct ExampleParams
-{
-    FrameworkParams    common_params{};
-    TensorParams       input{};
-    TensorParams       weights{};
-    TensorParams       bias{};
-    TensorParams       output{};
-    VerificationParams verification{};
-    ConvolutionParams  convolution{};
-};
-
-/** Formatted output of the ConvolutionParams type
- *
- * @param[out] os            Output stream.
- * @param[in]  common_params Convolution parameters to output
- *
- * @return Modified output stream.
- */
-::std::ostream &operator<<(::std::ostream &os, const ExampleParams &common_params)
+namespace
 {
-    os << "Threads : " << common_params.common_params.threads << std::endl;
-    os << "Target : " << common_params.common_params.target << std::endl;
-    os << "Data type : " << common_params.convolution.data_type << std::endl;
-    os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
-       << std::endl;
-    os << "Weight dimensions(X,Y, Channels(same as input), OFM) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << "," <<
-       common_params.weights.fm << ")" << std::endl;
-    os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," <<
-       common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y <<
-       ")" << std::endl;
-    os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl;
-    os << "Convolution Method: " << common_params.convolution.convolution_method << std::endl;
-    return os;
-}
-
 /** Convolution command line options used to configure the graph examples
  *
  * (Similar to common options)
@@ -210,11 +59,12 @@ struct ExampleParams
  * CommonOptions options( parser );
  * parser.parse(argc, argv);
  */
-class ConvolutionOptions final
+class ConvolutionOptions final : public CommonGraphValidateOptions
 {
 public:
     explicit ConvolutionOptions(CommandLineParser &parser) noexcept
-        : width(parser.add_option<SimpleOption<int>>("width", 9)),
+        : CommonGraphValidateOptions(parser),
+          width(parser.add_option<SimpleOption<int>>("width", 9)),
           height(parser.add_option<SimpleOption<int>>("height", 9)),
           channels(parser.add_option<SimpleOption<int>>("channels", 1)),
           batch(parser.add_option<SimpleOption<int>>("batch", 1)),
@@ -227,16 +77,9 @@ public:
           padding_right(parser.add_option<SimpleOption<int>>("padding_right", 0)),
           stride_x(parser.add_option<SimpleOption<int>>("stride_x", 1)),
           stride_y(parser.add_option<SimpleOption<int>>("stride_y", 1)),
-          help(parser.add_option<ToggleOption>("help")),
-          threads(parser.add_option<SimpleOption<int>>("threads")),
-          target(),
-          data_type(),
           padding_mode(),
           conv_mode(),
           data_layout(),
-          absolute_tolerance(parser.add_option<SimpleOption<float>>("abs_tolerance", -1.0f)),
-          relative_tolerance(parser.add_option<SimpleOption<float>>("rel_tolerance", -1.0f)),
-          tolerance_number(parser.add_option<SimpleOption<float>>("tolerance_num", -1.0f)),
           scale(parser.add_option<SimpleOption<float>>("scale", 1.0f)),
           offset(parser.add_option<SimpleOption<int>>("offset", 0)),
           weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
@@ -252,24 +95,10 @@ public:
           weights_npy(parser.add_option<SimpleOption<std::string>>("weights_npy")),
           bias_npy(parser.add_option<SimpleOption<std::string>>("bias_image"))
     {
-        const std::set<PaddingMode> available_padding_modes
-        {
-            PaddingMode::Valid,
-            PaddingMode::Same
-        };
-
-        const std::set<arm_compute::graph::Target> supported_targets
+        const std::set<ConvolutionPaddingMode> available_padding_modes
         {
-            Target::NEON,
-            Target::CL,
-            Target::GC,
-        };
-
-        const std::set<arm_compute::DataType> supported_data_types
-        {
-            DataType::F16,
-            DataType::F32,
-            DataType::QASYMM8,
+            ConvolutionPaddingMode::Valid,
+            ConvolutionPaddingMode::Same
         };
 
         const std::set<arm_compute::graph::ConvolutionMethod> supported_convolution_methods
@@ -286,14 +115,10 @@ public:
             DataLayout::NCHW,
         };
 
-        padding_mode = parser.add_option<EnumOption<PaddingMode>>("padding_mode", available_padding_modes, PaddingMode::Valid);
-        target       = parser.add_option<EnumOption<Target>>("target", supported_targets, Target::NEON);
-        data_type    = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
+        padding_mode = parser.add_option<EnumOption<ConvolutionPaddingMode>>("padding_mode", available_padding_modes, ConvolutionPaddingMode::Valid);
         conv_mode    = parser.add_option<EnumOption<arm_compute::graph::ConvolutionMethod>>("convolution_method", supported_convolution_methods, arm_compute::graph::ConvolutionMethod::Default);
         data_layout  = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC);
 
-        target->set_help("Target to execute on");
-        data_type->set_help("Data type to use");
         padding_mode->set_help("Set padding mode");
         help->set_help("Show this help message");
         width->set_help("Set Input dimension width");
@@ -310,10 +135,6 @@ public:
         stride_x->set_help("Set padding stride x");
         stride_y->set_help("Set padding stride y");
         conv_mode->set_help("Set convolution method");
-        data_layout->set_help("Data layout to use");
-        absolute_tolerance->set_help("Absolute tolerance used for verification");
-        relative_tolerance->set_help("Absolute tolerance used for verification");
-        tolerance_number->set_help("Absolute tolerance used for verification");
         scale->set_help("Quantization scale from QASYMM8");
         offset->set_help("Quantization offset from QASYMM8");
         weights_scale->set_help("Quantization scale from QASYMM8");
@@ -328,6 +149,69 @@ public:
         weights_range_high->set_help("Lower bound for input randomization range");
     }
 
+    /** Fill out the supplied parameters with user supplied parameters
+     *
+     * @param[out] os            Output stream.
+     * @param[in]  common_params Example parameters to output
+     *
+     * @return None.
+     */
+    void consume_parameters(ExampleParams &common_params)
+    {
+        common_params.input.width             = width->value();
+        common_params.input.height            = height->value();
+        common_params.input.fm                = channels->value();
+        common_params.input.batch             = batch->value();
+        common_params.input.quant_info.scale  = scale->value();
+        common_params.input.quant_info.offset = offset->value();
+        common_params.input.npy               = input_npy->value();
+        common_params.input.range_low         = input_range_low->value();
+        common_params.input.range_high        = input_range_high->value();
+
+        common_params.weights.width             = weights_width->value();
+        common_params.weights.height            = weights_height->value();
+        common_params.weights.fm                = OFM->value();
+        common_params.weights.npy               = weights_npy->value();
+        common_params.weights.quant_info.scale  = weights_scale->value();
+        common_params.weights.quant_info.offset = weights_offset->value();
+        common_params.weights.range_low         = weights_range_low->value();
+        common_params.weights.range_high        = weights_range_high->value();
+
+        common_params.bias.npy = bias_npy->value();
+
+        common_params.output.quant_info.scale  = output_scale->value();
+        common_params.output.quant_info.offset = output_offset->value();
+        common_params.output.npy               = output_npy->value();
+
+        common_params.convolution.padding_mode     = padding_mode->value();
+        common_params.convolution.padding_top      = padding_top->value();
+        common_params.convolution.padding_bottom   = padding_bottom->value();
+        common_params.convolution.padding_left     = padding_left->value();
+        common_params.convolution.padding_right    = padding_right->value();
+        common_params.convolution.padding_stride_x = stride_x->value();
+        common_params.convolution.padding_stride_y = stride_y->value();
+
+        common_params.data_type          = data_type->value();
+        common_params.data_layout        = data_layout->value();
+        common_params.convolution_method = conv_mode->value();
+    }
+
+    void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
+    {
+        os << "Threads : " << common_params.common_params.threads << std::endl;
+        os << "Target : " << common_params.common_params.target << std::endl;
+        os << "Data type : " << common_params.data_type << std::endl;
+        os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
+           << std::endl;
+        os << "Weight dimensions(X,Y, Channels(same as input), OFM) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << "," <<
+           common_params.weights.fm << ")" << std::endl;
+        os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," <<
+           common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y <<
+           ")" << std::endl;
+        os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl;
+        os << "Convolution Method: " << common_params.convolution_method << std::endl;
+    }
+
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     ConvolutionOptions(const ConvolutionOptions &) = delete;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
@@ -337,7 +221,7 @@ public:
     /** Allow instances of this class to be moved */
     ConvolutionOptions &operator=(ConvolutionOptions &&) noexcept(true) = default;
     /** Default destructor */
-    ~ConvolutionOptions() = default;
+    ~ConvolutionOptions() override = default;
 
     SimpleOption<int>                                 *width;              /**< Input width */
     SimpleOption<int>                                 *height;             /**< Input height */
@@ -352,16 +236,9 @@ public:
     SimpleOption<int>                                 *padding_right;      /**< Padding right */
     SimpleOption<int>                                 *stride_x;           /**< Padding stride x */
     SimpleOption<int>                                 *stride_y;           /**< Padding stride y */
-    ToggleOption                                      *help;               /**< show help message */
-    SimpleOption<int>                                 *threads;            /**< Number of threads option */
-    EnumOption<arm_compute::graph::Target>            *target;             /**< Graph execution target */
-    EnumOption<arm_compute::DataType>                 *data_type;          /**< Graph data type */
-    EnumOption<PaddingMode>                           *padding_mode;       /**< Padding mode */
+    EnumOption<ConvolutionPaddingMode>                *padding_mode;       /**< Padding mode */
     EnumOption<arm_compute::graph::ConvolutionMethod> *conv_mode;          /**< Convolution method */
     EnumOption<arm_compute::DataLayout>               *data_layout;        /**< Graph data layout */
-    SimpleOption<float>                               *absolute_tolerance; /**< Absolute tolerance used in verification */
-    SimpleOption<float>                               *relative_tolerance; /**< Relative tolerance used in verification */
-    SimpleOption<float>                               *tolerance_number;   /**< Tolerance number used in verification */
     SimpleOption<float>                               *scale;              /**< Input Quantization scale from QASYMM8 */
     SimpleOption<int>                                 *offset;             /**< Input Quantization offset from QASYMM8 */
     SimpleOption<float>                               *weights_scale;      /**< Weights Quantization scale from QASYMM8 */
@@ -379,227 +256,26 @@ public:
     SimpleOption<std::string> *bias_npy;    /**< Use bias .npy image */
 };
 
-/** Consumes the convolution graph options and creates a structure containing any information
- *
- * @param[in] options Options to consume
- *
- * @return Convolutionparams structure containing the common graph parameters
- */
-ExampleParams consume_covolution_graph_parameters(ConvolutionOptions &options)
-{
-    ExampleParams common_params;
-
-    common_params.common_params.help    = options.help->is_set() ? options.help->value() : false;
-    common_params.common_params.threads = options.threads->value();
-    common_params.common_params.target  = options.target->value();
-
-    common_params.input.width             = options.width->value();
-    common_params.input.height            = options.height->value();
-    common_params.input.fm                = options.channels->value();
-    common_params.input.batch             = options.batch->value();
-    common_params.input.quant_info.scale  = options.scale->value();
-    common_params.input.quant_info.offset = options.offset->value();
-    common_params.input.npy               = options.input_npy->value();
-    common_params.input.range_low         = options.input_range_low->value();
-    common_params.input.range_high        = options.input_range_high->value();
-
-    common_params.weights.width             = options.weights_width->value();
-    common_params.weights.height            = options.weights_height->value();
-    common_params.weights.fm                = options.OFM->value();
-    common_params.weights.npy               = options.weights_npy->value();
-    common_params.weights.quant_info.scale  = options.weights_scale->value();
-    common_params.weights.quant_info.offset = options.weights_offset->value();
-    common_params.weights.range_low         = options.weights_range_low->value();
-    common_params.weights.range_high        = options.weights_range_high->value();
-
-    common_params.bias.npy = options.bias_npy->value();
-
-    common_params.output.quant_info.scale  = options.output_scale->value();
-    common_params.output.quant_info.offset = options.output_offset->value();
-    common_params.output.npy               = options.output_npy->value();
-
-    common_params.convolution.padding_mode       = options.padding_mode->value();
-    common_params.convolution.padding_top        = options.padding_top->value();
-    common_params.convolution.padding_bottom     = options.padding_bottom->value();
-    common_params.convolution.padding_left       = options.padding_left->value();
-    common_params.convolution.padding_right      = options.padding_right->value();
-    common_params.convolution.padding_stride_x   = options.stride_x->value();
-    common_params.convolution.padding_stride_y   = options.stride_y->value();
-    common_params.convolution.convolution_method = options.conv_mode->value();
-    common_params.convolution.data_type          = options.data_type->value();
-    common_params.convolution.data_layout        = options.data_layout->value();
-
-    common_params.verification.absolute_tolerance = options.absolute_tolerance->value();
-    common_params.verification.relative_tolerance = options.relative_tolerance->value();
-    common_params.verification.tolerance_number   = options.tolerance_number->value();
-
-    return common_params;
-}
-
-/** Calculate stride information.
- *
- * Depending on the selected padding mode create the desired PadStrideInfo
- *
- * @param[in] params Convolution parameters supplied by the user.
- *
- * @return PadStrideInfo with the correct padding mode.
- */
-inline PadStrideInfo calculate_convolution_padding(ExampleParams params)
-{
-    switch(params.convolution.padding_mode)
-    {
-        case PaddingMode::Manual:
-        {
-            return PadStrideInfo(params.convolution.padding_stride_x, params.convolution.padding_stride_y, params.convolution.padding_left, params.convolution.padding_right, params.convolution.padding_top,
-                                 params.convolution.padding_bottom, DimensionRoundingType::FLOOR);
-        }
-        case PaddingMode::Valid:
-        {
-            return PadStrideInfo();
-        }
-        case PaddingMode::Same:
-        {
-            return arm_compute::calculate_same_pad(TensorShape(params.input.width, params.input.height), TensorShape(params.weights.width, params.weights.height),
-                                                   PadStrideInfo(params.convolution.padding_stride_x,
-                                                                 params.convolution.padding_stride_y));
-        }
-        default:
-            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
-    }
-}
-
 /** ConvolutionLayer Graph example validation accessor class */
 template <typename D>
-class ConvolutionVerifyAccessor final : public graph::ITensorAccessor
+class ConvolutionVerifyAccessor final : public VerifyAccessor<D>
 {
-public:
+    using BaseClassType = VerifyAccessor<D>;
+    using BaseClassType::BaseClassType;
+    using BaseClassType::_params;
     using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
 
-    /** Constructor
-     *
-     * @param[in] params Convolution parameters
-     */
-    explicit ConvolutionVerifyAccessor(ExampleParams &params)
-        : _params(std::move(params))
+    SimpleTensor<D> reference(SimpleTensor<D> &src, SimpleTensor<D> &weights, SimpleTensor<TBias> &bias, const TensorShape &output_shape) override
     {
-    }
+        // Calculate padding information
+        const PadStrideInfo padding_info = calculate_convolution_padding(_params);
 
-    // Inherited methods overriden:
-    bool access_tensor(ITensor &tensor) override
-    {
-        if(_params.output.npy.empty())
-        {
-            const RelativeTolerance<float> rel_tolerance(relative_tolenace(_params.verification.relative_tolerance));  /**< Relative tolerance */
-            const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance(_params.verification.absolute_tolerance)); /**< Absolute tolerance */
-            const float                    tolerance_num(tolerance_number(_params.verification.tolerance_number));     /**< Tolerance number */
-
-            //Create Input tensors
-            SimpleTensor<D>     src{ TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch), _params.convolution.data_type, 1, _params.input.quant_info };
-            SimpleTensor<D>     weights{ TensorShape(_params.weights.width, _params.weights.height, _params.weights.fm), _params.convolution.data_type, 1, _params.weights.quant_info };
-            SimpleTensor<TBias> bias{ TensorShape(_params.input.height), _params.convolution.data_type, 1, _params.input.quant_info };
-
-            //Fill the tenors with random values
-            fill_tensor<D>(src, 0, static_cast<D>(_params.input.range_low), static_cast<D>(_params.input.range_high));
-            fill_tensor<D>(weights, 1, static_cast<D>(_params.weights.range_low), static_cast<D>(_params.weights.range_high));
-            fill_tensor<TBias>(bias, 2, static_cast<TBias>(_params.input.range_low), static_cast<TBias>(_params.input.range_high));
-
-            // Calculate padding information
-            const PadStrideInfo padding_info = calculate_convolution_padding(_params);
-
-            //Calculate reference
-            SimpleTensor<D> output = reference::convolution_layer<D>(src, weights, bias, permute_shape(tensor.info()->tensor_shape(), _params.convolution.data_layout, DataLayout::NCHW), padding_info, Size2D(1,
-                                                                     1),
-                                                                     1,
-                                                                     _params.output.quant_info);
-
-            arm_compute::test::validation::validate(Accessor(tensor), output, rel_tolerance, tolerance_num, abs_tolerance);
-        }
-        else
-        {
-            //The user provided a reference file use an npy accessor to validate
-            NumPyAccessor(_params.output.npy, tensor.info()->tensor_shape(), tensor.info()->data_type()).access_tensor(tensor);
-        }
-        return false;
+        //Calculate reference
+        return reference::convolution_layer<D>(src, weights, bias, output_shape, padding_info, Size2D(1, 1),
+                                               1, _params.output.quant_info);
     }
 
-private:
-    /** Fill tensor with Random values.
-     *
-     * Validate the given tensor against the reference result.
-     *
-     * @param[out] tensor The tensor we want to file
-     * @param[in]  seed   seed for the randomization function
-     * @param[in]  low    lower bound for random values
-     * @param[in]  high   upper bound for random values
-     *
-     * @return None.
-     */
-    template <typename T>
-    void fill_tensor(arm_compute::test::SimpleTensor<T> &tensor, std::random_device::result_type seed, T low, T high)
-    {
-        std::mt19937 gen(seed);
-        switch(tensor.data_type())
-        {
-            case arm_compute::DataType::QASYMM8:
-            {
-                uint8_t qasymm8_low  = tensor.quantization_info().quantize(low, RoundingPolicy::TO_NEAREST_UP);
-                uint8_t qasymm8_high = tensor.quantization_info().quantize(high, RoundingPolicy::TO_NEAREST_UP);
-
-                std::uniform_int_distribution<uint8_t> distribution(qasymm8_low, qasymm8_high);
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = tensor.quantization_info().quantize(distribution(gen), RoundingPolicy::TO_NEAREST_UP);
-                }
-
-                break;
-            }
-            case arm_compute::DataType::S32:
-            {
-                std::uniform_int_distribution<int32_t> distribution(static_cast<int32_t>(low), static_cast<uint32_t>(high));
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = distribution(gen);
-                }
-
-                break;
-            }
-
-            case arm_compute::DataType::F16:
-            {
-                std::uniform_real_distribution<float> distribution(static_cast<half>(low), static_cast<half>(high));
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = static_cast<half>(distribution(gen));
-                }
-                break;
-            }
-            case arm_compute::DataType::F32:
-            {
-                std::uniform_real_distribution<float> distribution(static_cast<float>(low), static_cast<float>(high));
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = distribution(gen);
-                }
-
-                break;
-            }
-            default:
-                ARM_COMPUTE_ERROR("NOT SUPPORTED!");
-        }
-    }
-    /** Select relative tolerance.
-     *
-     * Select relative tolerance if not supplied by user.
-     *
-     * @param[in] user_value supplied relative tolerance. -1 designates no user input
-     *
-     * @return Appropriate relative tolerance.
-     */
-    float relative_tolenace(float user_value)
+    float relative_tolerance() override
     {
         const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
         {
@@ -618,32 +294,20 @@ private:
                 }
             }
         };
-        if(user_value == -1)
+
+        if(_params.convolution_method == arm_compute::graph::ConvolutionMethod::Winograd
+           && _params.data_type == DataType::F32
+           && _params.common_params.target == arm_compute::graph::Target::NEON)
         {
-            if(_params.convolution.convolution_method == arm_compute::graph::ConvolutionMethod::Winograd
-               && _params.convolution.data_type == DataType::F32
-               && _params.common_params.target == arm_compute::graph::Target::NEON)
-            {
-                return 0.05f;
-            }
-            else
-            {
-                return relative_tolerance.at(_params.common_params.target).at(_params.convolution.data_type);
-            }
+            return 0.05f;
+        }
+        else
+        {
+            return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
         }
-
-        return user_value;
     }
 
-    /** Select absolute tolerance.
-     *
-     * Select absolute tolerance if not supplied by user.
-     *
-     * @param[in] user_value supplied absolute tolerance. -1 designates no user input
-     *
-     * @return Appropriate absolute tolerance.
-     */
-    float absolute_tolerance(float user_value)
+    float absolute_tolerance() override
     {
         const std::map<Target, const std::map<DataType, float>> absolute_tolerance
         {
@@ -663,21 +327,10 @@ private:
             }
         };
 
-        if(user_value == -1)
-        {
-            return absolute_tolerance.at(_params.common_params.target).at(_params.convolution.data_type);
-        }
-        return user_value;
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
     }
-    /** Select tolerance number.
-     *
-     * Select tolerance number if not supplied by user.
-     *
-     * @param[in] user_value supplied tolerance number. -1 designates no user input
-     *
-     * @return Appropriate tolerance number.
-     */
-    float tolerance_number(float user_value)
+
+    float tolerance_number() override
     {
         const std::map<Target, const std::map<DataType, float>> absolute_tolerance
         {
@@ -697,133 +350,38 @@ private:
             }
         };
 
-        if(user_value == -1)
-        {
-            return absolute_tolerance.at(_params.common_params.target).at(_params.convolution.data_type);
-        }
-        return user_value;
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
     }
-
-    ExampleParams _params;
 };
 
-/** Generates appropriate convolution verify accessor
- *
- * @param[in] params User supplied parameters for convolution.
- *
- * @return A convolution verify accessor for the requested datatype.
- */
-inline std::unique_ptr<graph::ITensorAccessor> get_convolution_verify_accessor(ExampleParams params)
-{
-    switch(params.convolution.data_type)
-    {
-        case DataType::QASYMM8:
-        {
-            return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<uint8_t>>(
-                       params);
-        }
-        case DataType::F16:
-        {
-            return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<half>>(
-                       params);
-        }
-        case DataType::F32:
-        {
-            return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<float>>(
-                       params);
-        }
-        default:
-            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
-    }
-}
-/** Generates appropriate accessor according to the specified graph parameters
- *
- * @param[in] graph_parameters Graph parameters
- * @param[in] lower            Lower random values bound
- * @param[in] upper            Upper random values bound
- * @param[in] seed             Random generator seed
- *
- * @return An appropriate tensor accessor
- */
-inline std::unique_ptr<graph::ITensorAccessor> get_accessor(const TensorParams &tensor, PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0)
-{
-    if(!tensor.npy.empty())
-    {
-        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(tensor.npy);
-    }
-    else
-    {
-        return arm_compute::support::cpp14::make_unique<RandomAccessor>(lower, upper, seed);
-    }
-}
 } // namespace
 
-class GraphConvolutionValidateExample final : public ValidateExample
+class GraphConvolutionValidateExample final : public GraphValidateExample<ConvolutionLayer, ConvolutionOptions, ConvolutionVerifyAccessor>
 {
+    using GraphValidateExample::graph;
+
 public:
     GraphConvolutionValidateExample()
-        : graph(0, "Convolution Graph example")
+        : GraphValidateExample("Convolution Graph example")
     {
     }
-    bool do_setup(int argc, char **argv) override
-    {
-        CommandLineParser parser;
-
-        ConvolutionOptions Options(parser);
 
-        parser.parse(argc, argv);
-
-        ExampleParams params = consume_covolution_graph_parameters(Options);
-
-        if(params.common_params.help)
-        {
-            parser.print_help(argv[0]);
-            return false;
-        }
+    ConvolutionLayer GraphFunctionLayer(ExampleParams &params) override
+    {
+        const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
+        const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
 
-        std::cout << params << std::endl;
+        const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
+        const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
 
         // Calculate padding information
         const PadStrideInfo padding_info = calculate_convolution_padding(params);
 
-        // Create input descriptor
-        const TensorShape input_shape      = permute_shape(TensorShape(params.input.width, params.input.height, params.input.fm, params.input.batch), DataLayout::NCHW, params.convolution.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(input_shape, params.convolution.data_type, params.input.quant_info, params.convolution.data_layout);
-
-        const PixelValue lower = PixelValue(params.input.range_low, params.convolution.data_type, params.input.quant_info);
-        const PixelValue upper = PixelValue(params.input.range_high, params.convolution.data_type, params.input.quant_info);
-
-        const PixelValue weights_lower = PixelValue(params.weights.range_low, params.convolution.data_type, params.weights.quant_info);
-        const PixelValue weights_upper = PixelValue(params.weights.range_high, params.convolution.data_type, params.weights.quant_info);
-
-        graph << params.common_params.target
-              << params.convolution.convolution_method
-              << InputLayer(input_descriptor, get_accessor(params.input, lower, upper, 0))
-              << ConvolutionLayer(params.weights.width, params.weights.height, params.weights.fm,
-                                  get_accessor(params.weights, weights_lower, weights_upper, 1),
-                                  get_accessor(params.bias, lower, upper, 2),
-                                  padding_info, 1, params.weights.quant_info, params.output.quant_info)
-              << OutputLayer(get_convolution_verify_accessor(params));
-
-        GraphConfig config;
-        config.num_threads = params.common_params.threads;
-
-        graph.finalize(params.common_params.target, config);
-
-        return true;
-    }
-
-    void do_run() override
-    {
-        graph.run();
-    }
-
-    void do_teardown() override
-    {
+        return ConvolutionLayer(params.weights.width, params.weights.height, params.weights.fm,
+                                get_accessor(params.weights, weights_lower, weights_upper, 1),
+                                get_accessor(params.bias, lower, upper, 2),
+                                padding_info, 1, params.weights.quant_info, params.output.quant_info);
     }
-
-private:
-    Stream graph;
 };
 
 /** Main program for Graph Convolution test
diff --git a/tests/validate_examples/graph_depthwiseconvolution.cpp b/tests/validate_examples/graph_depthwiseconvolution.cpp
new file mode 100644
index 0000000000..cdad404dfa
--- /dev/null
+++ b/tests/validate_examples/graph_depthwiseconvolution.cpp
@@ -0,0 +1,396 @@
+/*
+ * Copyright (c) 2019 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.
+ */
+#include "arm_compute/graph.h"
+
+#include "support/ToolchainSupport.h"
+
+#include "tests/NEON/Accessor.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/reference/DepthwiseConvolutionLayer.h"
+#include "tests/validation/reference/Permute.h"
+
+#include "utils/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include "ValidateExample.h"
+#include "graph_validate_utils.h"
+
+#include <utility>
+
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+using namespace arm_compute::graph;
+using namespace arm_compute;
+using namespace arm_compute::test;
+using namespace arm_compute::test::validation;
+
+namespace
+{
+/** Depthwise Convolution command line options used to configure the graph examples
+ *
+ * (Similar to common options)
+ * The options in this object get populated when "parse()" is called on the parser used to construct it.
+ * The expected workflow is:
+ *
+ * CommandLineParser parser;
+ * CommonOptions options( parser );
+ * parser.parse(argc, argv);
+ */
+class DepthConvolutionOptions final : public CommonGraphValidateOptions
+{
+public:
+    explicit DepthConvolutionOptions(CommandLineParser &parser) noexcept
+        : CommonGraphValidateOptions(parser),
+          width(parser.add_option<SimpleOption<int>>("width", 9)),
+          height(parser.add_option<SimpleOption<int>>("height", 9)),
+          channels(parser.add_option<SimpleOption<int>>("channels", 1)),
+          batch(parser.add_option<SimpleOption<int>>("batch", 1)),
+          weights_width(parser.add_option<SimpleOption<int>>("weights_width", 3)),
+          weights_height(parser.add_option<SimpleOption<int>>("weights_height", 3)),
+          padding_top(parser.add_option<SimpleOption<int>>("padding_top", 0)),
+          padding_left(parser.add_option<SimpleOption<int>>("padding_left", 0)),
+          padding_bottom(parser.add_option<SimpleOption<int>>("padding_bottom", 0)),
+          padding_right(parser.add_option<SimpleOption<int>>("padding_right", 0)),
+          stride_x(parser.add_option<SimpleOption<int>>("stride_x", 1)),
+          stride_y(parser.add_option<SimpleOption<int>>("stride_y", 1)),
+          padding_mode(),
+          conv_mode(),
+          depth_multiplier(parser.add_option<SimpleOption<int>>("depth_multiplier", 1)),
+          data_layout(),
+          scale(parser.add_option<SimpleOption<float>>("scale", 1.0f)),
+          offset(parser.add_option<SimpleOption<int>>("offset", 0)),
+          weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
+          weights_offset(parser.add_option<SimpleOption<int>>("weights_offset", 0)),
+          output_scale(parser.add_option<SimpleOption<float>>("output_scale", 1.0f)),
+          output_offset(parser.add_option<SimpleOption<int>>("output_offset", 0)),
+          input_range_low(parser.add_option<SimpleOption<uint64_t>>("input_range_low")),
+          input_range_high(parser.add_option<SimpleOption<uint64_t>>("input_range_high")),
+          weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")),
+          weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high")),
+          input_npy(parser.add_option<SimpleOption<std::string>>("input_image")),
+          output_npy(parser.add_option<SimpleOption<std::string>>("reference_image")),
+          weights_npy(parser.add_option<SimpleOption<std::string>>("weights_npy")),
+          bias_npy(parser.add_option<SimpleOption<std::string>>("bias_image"))
+    {
+        const std::set<ConvolutionPaddingMode> available_padding_modes
+        {
+            ConvolutionPaddingMode::Valid,
+            ConvolutionPaddingMode::Same
+        };
+
+        const std::set<arm_compute::graph::DepthwiseConvolutionMethod> supported_convolution_methods
+        {
+            arm_compute::graph::DepthwiseConvolutionMethod::Default,
+            arm_compute::graph::DepthwiseConvolutionMethod::GEMV,
+            arm_compute::graph::DepthwiseConvolutionMethod::Optimized3x3,
+        };
+
+        const std::set<DataLayout> supported_data_layouts
+        {
+            DataLayout::NHWC,
+            DataLayout::NCHW,
+        };
+
+        padding_mode = parser.add_option<EnumOption<ConvolutionPaddingMode>>("padding_mode", available_padding_modes, ConvolutionPaddingMode::Valid);
+        conv_mode    = parser.add_option<EnumOption<arm_compute::graph::DepthwiseConvolutionMethod>>("convolution_method", supported_convolution_methods,
+                                                                                                     arm_compute::graph::DepthwiseConvolutionMethod::Default);
+        data_layout = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC);
+
+        padding_mode->set_help("Set padding mode");
+        width->set_help("Set Input dimension width");
+        height->set_help("Set Input dimension height");
+        channels->set_help("Set Input dimension channels");
+        batch->set_help("Set Input dimension batch");
+        weights_width->set_help("Set weights_dimensions width");
+        weights_height->set_help("Set weights_dimensions height");
+        padding_top->set_help("Set padding top");
+        padding_bottom->set_help("Set padding bottom");
+        padding_left->set_help("Set padding left");
+        padding_right->set_help("Set padding right");
+        stride_x->set_help("Set padding stride x");
+        stride_y->set_help("Set padding stride y");
+        conv_mode->set_help("Set convolution method");
+        data_layout->set_help("Data layout to use");
+        scale->set_help("Quantization scale from QASYMM8");
+        offset->set_help("Quantization offset from QASYMM8");
+        output_scale->set_help("Quantization scale from QASYMM8");
+        output_offset->set_help("Quantization offset from QASYMM8");
+        input_npy->set_help("Use input .npy instead");
+        output_npy->set_help("Use .npy as a reference");
+        input_range_low->set_help("Lower bound for input randomization range");
+        input_range_high->set_help("Lower bound for input randomization range");
+        weights_scale->set_help("Quantization scale from QASYMM8");
+        weights_offset->set_help("Quantization offset from QASYMM8");
+        weights_range_low->set_help("Lower bound for input randomization range");
+        weights_range_high->set_help("Lower bound for input randomization range");
+        depth_multiplier->set_help("Depth multiplier");
+    }
+
+    /** Fill out the supplied parameters with user supplied parameters
+     *
+     * @param[out] os            Output stream.
+     * @param[in]  common_params Example parameters to output
+     *
+     * @return None.
+     */
+    void consume_parameters(ExampleParams &common_params)
+    {
+        common_params.input.width             = width->value();
+        common_params.input.height            = height->value();
+        common_params.input.fm                = channels->value();
+        common_params.input.batch             = batch->value();
+        common_params.input.quant_info.scale  = scale->value();
+        common_params.input.quant_info.offset = offset->value();
+        common_params.input.npy               = input_npy->value();
+        common_params.input.range_low         = input_range_low->value();
+        common_params.input.range_high        = input_range_high->value();
+
+        common_params.weights.width             = weights_width->value();
+        common_params.weights.height            = weights_height->value();
+        common_params.weights.npy               = weights_npy->value();
+        common_params.weights.range_low         = weights_range_low->value();
+        common_params.weights.range_high        = weights_range_high->value();
+        common_params.weights.quant_info.scale  = weights_scale->value();
+        common_params.weights.quant_info.offset = weights_offset->value();
+
+        common_params.bias.npy = bias_npy->value();
+
+        common_params.output.quant_info.scale  = output_scale->value();
+        common_params.output.quant_info.offset = output_offset->value();
+        common_params.output.npy               = output_npy->value();
+
+        common_params.convolution.padding_mode     = padding_mode->value();
+        common_params.convolution.padding_top      = padding_top->value();
+        common_params.convolution.padding_bottom   = padding_bottom->value();
+        common_params.convolution.padding_left     = padding_left->value();
+        common_params.convolution.padding_right    = padding_right->value();
+        common_params.convolution.padding_stride_x = stride_x->value();
+        common_params.convolution.padding_stride_y = stride_y->value();
+        common_params.convolution.depth_multiplier = depth_multiplier->value();
+
+        common_params.data_type                = data_type->value();
+        common_params.data_layout              = data_layout->value();
+        common_params.depth_convolution_method = conv_mode->value();
+    }
+
+    void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
+    {
+        os << "Threads : " << common_params.common_params.threads << std::endl;
+        os << "Target : " << common_params.common_params.target << std::endl;
+        os << "Data type : " << common_params.data_type << std::endl;
+        os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
+           << std::endl;
+        os << "Weight dimensions(X,Y, Channels(same as input)) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << ","
+           << ")" << std::endl;
+        os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," <<
+           common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y <<
+           ")" << std::endl;
+        os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl;
+        os << "Convolution Method: " << common_params.depth_convolution_method << std::endl;
+        os << "Depth multiplier: " << common_params.convolution.depth_multiplier;
+    }
+
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    DepthConvolutionOptions(const DepthConvolutionOptions &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    DepthConvolutionOptions &operator=(const DepthConvolutionOptions &) = delete;
+    /** Allow instances of this class to be moved */
+    DepthConvolutionOptions(DepthConvolutionOptions &&) noexcept(true) = default;
+    /** Allow instances of this class to be moved */
+    DepthConvolutionOptions &operator=(DepthConvolutionOptions &&) noexcept(true) = default;
+    /** Default destructor */
+    ~DepthConvolutionOptions() override = default;
+
+    SimpleOption<int>                                          *width;              /**< Input width */
+    SimpleOption<int>                                          *height;             /**< Input height */
+    SimpleOption<int>                                          *channels;           /**< Input channels */
+    SimpleOption<int>                                          *batch;              /**< Input batch */
+    SimpleOption<int>                                          *weights_width;      /**< weights width */
+    SimpleOption<int>                                          *weights_height;     /**< weights height */
+    SimpleOption<int>                                          *padding_top;        /**< Padding top */
+    SimpleOption<int>                                          *padding_left;       /**< Padding left */
+    SimpleOption<int>                                          *padding_bottom;     /**< Padding bottom */
+    SimpleOption<int>                                          *padding_right;      /**< Padding right */
+    SimpleOption<int>                                          *stride_x;           /**< Padding stride x */
+    SimpleOption<int>                                          *stride_y;           /**< Padding stride y */
+    EnumOption<ConvolutionPaddingMode>                         *padding_mode;       /**< Padding mode */
+    EnumOption<arm_compute::graph::DepthwiseConvolutionMethod> *conv_mode;          /**< Convolution method */
+    SimpleOption<int>                                          *depth_multiplier;   /**< Depth multiplier */
+    EnumOption<arm_compute::DataLayout>                        *data_layout;        /**< Graph data layout */
+    SimpleOption<float>                                        *scale;              /**< Input Quantization scale from QASYMM8 */
+    SimpleOption<int>                                          *offset;             /**< Input Quantization offset from QASYMM8 */
+    SimpleOption<float>                                        *weights_scale;      /**< Weights Quantization scale from QASYMM8 */
+    SimpleOption<int>                                          *weights_offset;     /**< Weights Quantization offset from QASYMM8 */
+    SimpleOption<float>                                        *output_scale;       /**< Output Quantization scale from QASYMM8 */
+    SimpleOption<int>                                          *output_offset;      /**< Output Quantization offset from QASYMM8 */
+    SimpleOption<uint64_t>                                     *input_range_low;    /**< Lower bound for input randomization range */
+    SimpleOption<uint64_t>                                     *input_range_high;   /**< Upper bound for input randomization range */
+    SimpleOption<uint64_t>                                     *weights_range_low;  /**< Lower bound for weights randomization range */
+    SimpleOption<uint64_t>                                     *weights_range_high; /**< Upper bound for weights randomization range */
+
+    SimpleOption<std::string> *input_npy;   /**< Use input .npy image */
+    SimpleOption<std::string> *output_npy;  /**< Use output .npy image to verify*/
+    SimpleOption<std::string> *weights_npy; /**< Use weights .npy image */
+    SimpleOption<std::string> *bias_npy;    /**< Use bias .npy image */
+};
+
+/** DepthwiseConvolutionLayer Graph example validation accessor class */
+template <typename D>
+class DepthConvolutionVerifyAccessor final : public VerifyAccessor<D>
+{
+public:
+    using BaseClassType = VerifyAccessor<D>;
+    using BaseClassType::BaseClassType;
+    using BaseClassType::_params;
+    using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
+
+public:
+    SimpleTensor<D> reference(SimpleTensor<D> &src, SimpleTensor<D> &weights, SimpleTensor<TBias> &bias, const TensorShape &output_shape) override
+    {
+        // Calculate padding information
+        const PadStrideInfo padding_info = calculate_convolution_padding(_params);
+
+        //Calculate reference
+        return reference::depthwise_convolution<D>(src, weights, bias, output_shape, padding_info,
+                                                   _params.convolution.depth_multiplier,
+                                                   Size2D(1U, 1U),
+                                                   _params.output.quant_info);
+    }
+
+    float relative_tolerance() override
+    {
+        const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
+        {
+            {
+                arm_compute::graph::Target::CL,
+                {   { DataType::F16, 0.01f },
+                    { DataType::F32, 0.01f },
+                    { DataType::QASYMM8, 0.0f }
+                }
+            },
+            {
+                arm_compute::graph::Target::NEON,
+                {   { DataType::F16, 0.01f },
+                    { DataType::F32, 0.01f },
+                    { DataType::QASYMM8, 1.0f }
+                }
+            }
+        };
+
+        return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
+    }
+
+    float absolute_tolerance() override
+    {
+        const std::map<Target, const std::map<DataType, float>> absolute_tolerance
+        {
+            {
+                Target::CL,
+                {   { DataType::F16, 0.0f },
+                    { DataType::F32, 0.0000f },
+                    { DataType::QASYMM8, 0.0f }
+                }
+            },
+            {
+                Target::NEON,
+                {   { DataType::F16, 0.2f },
+                    { DataType::F32, 0.002f },
+                    { DataType::QASYMM8, 0.0f }
+                }
+            }
+        };
+
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
+    }
+
+    float tolerance_number() override
+    {
+        const std::map<Target, const std::map<DataType, float>> absolute_tolerance
+        {
+            {
+                Target::CL,
+                {   { DataType::F16, 0.05f },
+                    { DataType::F32, 0.00f },
+                    { DataType::QASYMM8, 0.0f }
+                }
+            },
+            {
+                Target::NEON,
+                {   { DataType::F16, 0.05f },
+                    { DataType::F32, 0.0f },
+                    { DataType::QASYMM8, 0.0f }
+                }
+            }
+        };
+
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
+    }
+};
+
+} // namespace
+
+class GraphDepthwiseConvolutionValidateExample final : public GraphValidateExample<DepthwiseConvolutionLayer, DepthConvolutionOptions, DepthConvolutionVerifyAccessor>
+{
+    using GraphValidateExample::graph;
+
+public:
+    GraphDepthwiseConvolutionValidateExample()
+        : GraphValidateExample("DepthWiseConvolution Graph example")
+    {
+    }
+
+    DepthwiseConvolutionLayer GraphFunctionLayer(ExampleParams &params) override
+    {
+        const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
+        const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
+
+        const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
+        const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
+
+        // Calculate padding information
+        const PadStrideInfo padding_info = calculate_convolution_padding(params);
+
+        return DepthwiseConvolutionLayer(params.weights.width, params.weights.height,
+                                         get_accessor(params.weights, weights_lower, weights_upper, 1),
+                                         get_accessor(params.bias, lower, upper, 2),
+                                         padding_info, params.convolution.depth_multiplier, params.weights.quant_info, params.output.quant_info);
+    }
+};
+
+/** Main program for Graph Depthwise Convolution test
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( Input dimensions [width, height, channels, batch]
+ *                             Weights dimensions [width, height, channels]
+ *                             Padding [top,bottom,left,right, Stride x, Stride y, mode [Valid / Same / Manual] )
+ *                             Convolution Method[ Default/GEMV/Optimized3x3]
+ *                             Verification[tolerance_number,absolute_tolerance,relative_tolerance] )
+ *
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphDepthwiseConvolutionValidateExample>(argc, argv);
+}
diff --git a/tests/validate_examples/graph_fully_connected.cpp b/tests/validate_examples/graph_fully_connected.cpp
index e4f51175f0..085518c865 100644
--- a/tests/validate_examples/graph_fully_connected.cpp
+++ b/tests/validate_examples/graph_fully_connected.cpp
@@ -35,6 +35,7 @@
 #include "utils/Utils.h"
 
 #include "ValidateExample.h"
+#include "graph_validate_utils.h"
 
 #include <utility>
 
@@ -45,77 +46,10 @@ using namespace arm_compute::graph;
 using namespace arm_compute;
 using namespace arm_compute::test;
 using namespace arm_compute::test::validation;
-namespace
-{
-/** Structure holding all the input tensor graph parameters */
-struct TensorParams
-{
-    int              width{ 1 };
-    int              height{ 1 };
-    int              fm{ 1 };
-    int              batch{ 1 };
-    QuantizationInfo quant_info{ 1.0f, 0 };
-    uint64_t         range_low{ 0 };
-    uint64_t         range_high{ 16 };
-};
-/** Structure holding all the verification graph parameters */
-struct VerificationParams
-{
-    float absolute_tolerance{ -1.f };
-    float relative_tolerance{ -1.f };
-    float tolerance_number{ -1.f };
-};
-
-/** Structure holding all the common graph parameters */
-struct FrameworkParams
-{
-    bool                       help{ false };
-    int                        threads{ 0 };
-    arm_compute::graph::Target target{ arm_compute::graph::Target::NEON };
-};
 
-/** Structure holding all the fully_connected layer graph parameters */
-struct FullyConnectedParams
-{
-    arm_compute::DataType   data_type{ DataType::F32 };
-    arm_compute::DataLayout data_layout{ DataLayout::NCHW };
-    FullyConnectedLayerInfo info{};
-    int                     num_outputs{ 1 };
-};
-
-/** Structure holding all the graph Example parameters */
-struct ExampleParams
-{
-    FrameworkParams      common_params{};
-    TensorParams         input{};
-    TensorParams         weights{};
-    TensorParams         output{};
-    VerificationParams   verification{};
-    FullyConnectedParams fully_connected{};
-};
-
-/** Formatted output of the fully_connectedParams type
- *
- * @param[out] os            Output stream.
- * @param[in]  common_params fully_connected parameters to output
- *
- * @return Modified output stream.
- */
-::std::ostream &operator<<(::std::ostream &os, const ExampleParams &common_params)
+namespace
 {
-    std::string false_str = std::string("false");
-    std::string true_str  = std::string("true");
-
-    os << "Threads : " << common_params.common_params.threads << std::endl;
-    os << "Target : " << common_params.common_params.target << std::endl;
-    os << "Data type : " << common_params.fully_connected.data_type << std::endl;
-    os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
-       << std::endl;
-    os << "Number of outputs : " << common_params.fully_connected.num_outputs << std::endl;
-    return os;
-}
-
-/** fully_connected command line options used to configure the graph examples
+/** Fully connected command line options used to configure the graph examples
  *
  * (Similar to common options)
  * The options in this object get populated when "parse()" is called on the parser used to construct it.
@@ -125,19 +59,13 @@ struct ExampleParams
  * CommonOptions options( parser );
  * parser.parse(argc, argv);
  */
-class FullyConnectedOptions final
+class FullyConnectedOptions final : public CommonGraphValidateOptions
 {
 public:
     explicit FullyConnectedOptions(CommandLineParser &parser) noexcept
-        : width(parser.add_option<SimpleOption<int>>("width", 3)),
+        : CommonGraphValidateOptions(parser),
+          width(parser.add_option<SimpleOption<int>>("width", 3)),
           batch(parser.add_option<SimpleOption<int>>("batch", 1)),
-          help(parser.add_option<ToggleOption>("help")),
-          threads(parser.add_option<SimpleOption<int>>("threads")),
-          target(),
-          data_type(),
-          absolute_tolerance(parser.add_option<SimpleOption<float>>("abs_tolerance", -1.0f)),
-          relative_tolerance(parser.add_option<SimpleOption<float>>("rel_tolerance", -1.0f)),
-          tolerance_number(parser.add_option<SimpleOption<float>>("tolerance_num", -1.0f)),
           input_scale(parser.add_option<SimpleOption<float>>("input_scale", 1.0f)),
           input_offset(parser.add_option<SimpleOption<int>>("input_offset", 0)),
           weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
@@ -150,31 +78,8 @@ public:
           weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")),
           weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high"))
     {
-        const std::set<arm_compute::graph::Target> supported_targets
-        {
-            Target::NEON,
-            Target::CL,
-            Target::GC,
-        };
-
-        const std::set<arm_compute::DataType> supported_data_types
-        {
-            DataType::F16,
-            DataType::F32,
-            DataType::QASYMM8,
-        };
-
-        target    = parser.add_option<EnumOption<Target>>("target", supported_targets, Target::NEON);
-        data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
-
-        target->set_help("Target to execute on");
-        data_type->set_help("Data type to use");
-        help->set_help("Show this help message");
         width->set_help("Set Input dimension width");
         batch->set_help("Set Input dimension batch");
-        absolute_tolerance->set_help("Absolute tolerance used for verification");
-        relative_tolerance->set_help("Absolute tolerance used for verification");
-        tolerance_number->set_help("Absolute tolerance used for verification");
         input_scale->set_help("Quantization scale from QASYMM8");
         input_offset->set_help("Quantization offset from QASYMM8");
         weights_scale->set_help("Quantization scale from QASYMM8");
@@ -188,6 +93,44 @@ public:
         weights_range_high->set_help("Lower bound for input randomization range");
     }
 
+    /** Fill out the supplied parameters with user supplied parameters
+     *
+     * @param[out] os            Output stream.
+     * @param[in]  common_params Example parameters to output
+     *
+     * @return None.
+     */
+    void consume_parameters(ExampleParams &common_params)
+    {
+        common_params.input.width             = width->value();
+        common_params.input.batch             = batch->value();
+        common_params.input.quant_info.scale  = input_scale->value();
+        common_params.input.quant_info.offset = input_offset->value();
+        common_params.input.range_low         = input_range_low->value();
+        common_params.input.range_high        = input_range_high->value();
+
+        common_params.weights.quant_info.scale  = weights_scale->value();
+        common_params.weights.quant_info.offset = weights_offset->value();
+        common_params.weights.range_low         = weights_range_low->value();
+        common_params.weights.range_high        = weights_range_high->value();
+
+        common_params.output.quant_info.scale  = output_scale->value();
+        common_params.output.quant_info.offset = output_offset->value();
+
+        common_params.data_type                   = data_type->value();
+        common_params.fully_connected.num_outputs = num_outputs->value();
+    }
+
+    void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
+    {
+        os << "Threads : " << common_params.common_params.threads << std::endl;
+        os << "Target : " << common_params.common_params.target << std::endl;
+        os << "Data type : " << common_params.data_type << std::endl;
+        os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
+           << std::endl;
+        os << "Number of outputs : " << common_params.fully_connected.num_outputs << std::endl;
+    }
+
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     FullyConnectedOptions(const FullyConnectedOptions &) = delete;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
@@ -197,95 +140,41 @@ public:
     /** Allow instances of this class to be moved */
     FullyConnectedOptions &operator=(FullyConnectedOptions &&) noexcept(true) = default;
     /** Default destructor */
-    ~FullyConnectedOptions() = default;
-
-    SimpleOption<int>                      *width;              /**< Input width */
-    SimpleOption<int>                      *batch;              /**< Input batch */
-    ToggleOption                           *help;               /**< show help message */
-    SimpleOption<int>                      *threads;            /**< Number of threads option */
-    EnumOption<arm_compute::graph::Target> *target;             /**< Graph execution target */
-    EnumOption<arm_compute::DataType>      *data_type;          /**< Graph data type */
-    SimpleOption<float>                    *absolute_tolerance; /**< Absolute tolerance used in verification */
-    SimpleOption<float>                    *relative_tolerance; /**< Relative tolerance used in verification */
-    SimpleOption<float>                    *tolerance_number;   /**< Tolerance number used in verification */
-    SimpleOption<float>                    *input_scale;        /**< Input Quantization scale from QASSYMM8 */
-    SimpleOption<int>                      *input_offset;       /**< Input Quantization offset from QASSYMM8 */
-    SimpleOption<float>                    *weights_scale;      /**< Weights Quantization scale from QASSYMM8 */
-    SimpleOption<int>                      *weights_offset;     /**< Weights Quantization offset from QASSYMM8 */
-    SimpleOption<float>                    *output_scale;       /**< Output Quantization scale from QASSYMM8 */
-    SimpleOption<int>                      *output_offset;      /**< Output Quantization offset from QASSYMM8 */
-    SimpleOption<int>                      *num_outputs;        /**< Number of outputs. */
-    SimpleOption<uint64_t>                 *input_range_low;    /**< Lower bound for input randomization range */
-    SimpleOption<uint64_t>                 *input_range_high;   /**< Upper bound for input randomization range */
-    SimpleOption<uint64_t>                 *weights_range_low;  /**< Lower bound for weights randomization range */
-    SimpleOption<uint64_t>                 *weights_range_high; /**< Upper bound for weights randomization range */
+    ~FullyConnectedOptions() override = default;
+
+    SimpleOption<int>      *width;              /**< Input width */
+    SimpleOption<int>      *batch;              /**< Input batch */
+    SimpleOption<float>    *input_scale;        /**< Input Quantization scale from QASSYMM8 */
+    SimpleOption<int>      *input_offset;       /**< Input Quantization offset from QASSYMM8 */
+    SimpleOption<float>    *weights_scale;      /**< Weights Quantization scale from QASSYMM8 */
+    SimpleOption<int>      *weights_offset;     /**< Weights Quantization offset from QASSYMM8 */
+    SimpleOption<float>    *output_scale;       /**< Output Quantization scale from QASSYMM8 */
+    SimpleOption<int>      *output_offset;      /**< Output Quantization offset from QASSYMM8 */
+    SimpleOption<int>      *num_outputs;        /**< Number of outputs. */
+    SimpleOption<uint64_t> *input_range_low;    /**< Lower bound for input randomization range */
+    SimpleOption<uint64_t> *input_range_high;   /**< Upper bound for input randomization range */
+    SimpleOption<uint64_t> *weights_range_low;  /**< Lower bound for weights randomization range */
+    SimpleOption<uint64_t> *weights_range_high; /**< Upper bound for weights randomization range */
 };
 
-/** Consumes the fully_connected graph options and creates a structure containing any information
- *
- * @param[in] options Options to consume
- *
- * @return fully_connectedparams structure containing the common graph parameters
- */
-ExampleParams consume_fully_connected_graph_parameters(FullyConnectedOptions &options)
-{
-    ExampleParams common_params;
-
-    common_params.common_params.help    = options.help->is_set() ? options.help->value() : false;
-    common_params.common_params.threads = options.threads->value();
-    common_params.common_params.target  = options.target->value();
-
-    common_params.input.width             = options.width->value();
-    common_params.input.batch             = options.batch->value();
-    common_params.input.quant_info.scale  = options.input_scale->value();
-    common_params.input.quant_info.offset = options.input_offset->value();
-    common_params.input.range_low         = options.input_range_low->value();
-    common_params.input.range_high        = options.input_range_high->value();
-
-    common_params.weights.quant_info.scale  = options.weights_scale->value();
-    common_params.weights.quant_info.offset = options.weights_offset->value();
-    common_params.weights.range_low         = options.weights_range_low->value();
-    common_params.weights.range_high        = options.weights_range_high->value();
-
-    common_params.output.quant_info.scale  = options.output_scale->value();
-    common_params.output.quant_info.offset = options.output_offset->value();
-
-    common_params.fully_connected.data_type   = options.data_type->value();
-    common_params.fully_connected.num_outputs = options.num_outputs->value();
-
-    common_params.verification.absolute_tolerance = options.absolute_tolerance->value();
-    common_params.verification.relative_tolerance = options.relative_tolerance->value();
-    common_params.verification.tolerance_number   = options.tolerance_number->value();
-
-    return common_params;
-}
-
-/** fully_connectedLayer Graph example validation accessor class */
+/** Fully Connected Layer Graph example validation accessor class */
 template <typename D>
-class FullyConnectedVerifyAccessor final : public graph::ITensorAccessor
+class FullyConnectedVerifyAccessor final : public VerifyAccessor<D>
 {
-public:
+    using BaseClassType = VerifyAccessor<D>;
+    using BaseClassType::BaseClassType;
+    using BaseClassType::_params;
     using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
 
-    /** Constructor
-     *
-     * @param[in] params fully_connected parameters
-     */
-    explicit FullyConnectedVerifyAccessor(ExampleParams &params)
-        : _params(params)
-    {
-    }
-
-    // Inherited methods overridden:
-    bool access_tensor(ITensor &tensor) override
+    // Inherited methods overriden:
+    void create_tensors(arm_compute::test::SimpleTensor<D>     &src,
+                        arm_compute::test::SimpleTensor<D>     &weights,
+                        arm_compute::test::SimpleTensor<TBias> &bias,
+                        ITensor                                &tensor) override
     {
-        const RelativeTolerance<float> rel_tolerance(relative_tolenace(_params.verification.relative_tolerance));  /**< Relative tolerance */
-        const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance(_params.verification.absolute_tolerance)); /**< Absolute tolerance */
-        const float                    tolerance_num(tolerance_number(_params.verification.tolerance_number));     /**< Tolerance number */
-
         // Calculate Tensor shapes for verification
         const TensorShape      input_shape        = TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch);
-        const TensorDescriptor input_descriptor   = TensorDescriptor(input_shape, _params.fully_connected.data_type, _params.input.quant_info);
+        const TensorDescriptor input_descriptor   = TensorDescriptor(input_shape, _params.data_type, _params.input.quant_info);
         const TensorDescriptor weights_descriptor = FullyConnectedLayerNode::compute_weights_descriptor(input_descriptor,
                                                                                                         _params.fully_connected.num_outputs,
                                                                                                         _params.fully_connected.info,
@@ -293,101 +182,31 @@ public:
         const TensorDescriptor output_desciptor = FullyConnectedLayerNode::compute_output_descriptor(input_descriptor, _params.fully_connected.num_outputs, _params.output.quant_info);
 
         //Create Input tensors
-        SimpleTensor<D>     src{ input_descriptor.shape, _params.fully_connected.data_type, 1, input_descriptor.quant_info };
-        SimpleTensor<D>     weights{ weights_descriptor.shape, _params.fully_connected.data_type, 1, weights_descriptor.quant_info };
-        SimpleTensor<TBias> bias{ TensorShape(tensor.info()->tensor_shape().x()), _params.fully_connected.data_type, 1, _params.input.quant_info };
-
-        //Fill the tensors with random values
-        fill_tensor<D>(src, 0, static_cast<D>(_params.input.range_low), static_cast<D>(_params.input.range_high));
-        fill_tensor<D>(weights, 1, static_cast<D>(_params.weights.range_low), static_cast<D>(_params.weights.range_high));
-        fill_tensor<TBias>(bias, 2, static_cast<TBias>(_params.input.range_low), static_cast<TBias>(_params.input.range_high));
-
-        //Calculate reference
-        SimpleTensor<D> output = reference::fully_connected_layer<D>(src, weights, bias, output_desciptor.shape, _params.output.quant_info);
-
-        arm_compute::test::validation::validate(Accessor(tensor), output, rel_tolerance, tolerance_num, abs_tolerance);
-
-        return false;
+        src     = SimpleTensor<D> { input_descriptor.shape, _params.data_type, 1, input_descriptor.quant_info };
+        weights = SimpleTensor<D> { weights_descriptor.shape, _params.data_type, 1, weights_descriptor.quant_info };
+        bias    = SimpleTensor<TBias> { TensorShape(tensor.info()->tensor_shape().x()), _params.data_type, 1, _params.input.quant_info };
     }
 
-private:
-    /** Fill tensor with Random values.
-     *
-     * Validate the given tensor against the reference result.
-     *
-     * @param[out] tensor The tensor we want to file
-     * @param[in]  seed   seed for the randomization function
-     * @param[in]  low    lower bound for random values
-     * @param[in]  high   upper bound for random values
-     *
-     * @return None.
-     */
-    template <typename T>
-    void fill_tensor(arm_compute::test::SimpleTensor<T> &tensor, std::random_device::result_type seed, T low, T high)
+    TensorShape output_shape(ITensor &tensor) override
     {
-        std::mt19937 gen(seed);
-        switch(tensor.data_type())
-        {
-            case arm_compute::DataType::QASYMM8:
-            {
-                const uint8_t qasymm8_low  = tensor.quantization_info().quantize(low, RoundingPolicy::TO_NEAREST_UP);
-                const uint8_t qasymm8_high = tensor.quantization_info().quantize(high, RoundingPolicy::TO_NEAREST_UP);
+        ARM_COMPUTE_UNUSED(tensor);
 
-                std::uniform_int_distribution<uint8_t> distribution(qasymm8_low, qasymm8_high);
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = tensor.quantization_info().quantize(distribution(gen), RoundingPolicy::TO_NEAREST_UP);
-                }
-
-                break;
-            }
-            case arm_compute::DataType::S32:
-            {
-                std::uniform_int_distribution<int32_t> distribution(static_cast<int32_t>(low), static_cast<uint32_t>(high));
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = distribution(gen);
-                }
-
-                break;
-            }
-
-            case arm_compute::DataType::F16:
-            {
-                std::uniform_real_distribution<float> distribution(static_cast<half>(low), static_cast<half>(high));
-
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = static_cast<half>(distribution(gen));
-                }
-                break;
-            }
-            case arm_compute::DataType::F32:
-            {
-                std::uniform_real_distribution<float> distribution(static_cast<float>(low), static_cast<float>(high));
+        const TensorShape      input_shape      = TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch);
+        const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, _params.data_type, _params.input.quant_info);
+        const TensorDescriptor output_desciptor = FullyConnectedLayerNode::compute_output_descriptor(input_descriptor, _params.fully_connected.num_outputs, _params.output.quant_info);
 
-                for(int i = 0; i < tensor.num_elements(); ++i)
-                {
-                    tensor[i] = distribution(gen);
-                }
+        return output_desciptor.shape;
+    }
 
-                break;
-            }
-            default:
-                ARM_COMPUTE_ERROR("NOT SUPPORTED!");
-        }
+    arm_compute::test::SimpleTensor<D> reference(arm_compute::test::SimpleTensor<D>     &src,
+                                                 arm_compute::test::SimpleTensor<D>     &weights,
+                                                 arm_compute::test::SimpleTensor<TBias> &bias,
+                                                 const arm_compute::TensorShape         &output_shape) override
+    {
+        return reference::fully_connected_layer<D>(src, weights, bias, output_shape, _params.output.quant_info);
     }
-    /** Select relative tolerance.
-     *
-     * Select relative tolerance if not supplied by user.
-     *
-     * @param[in] user_value supplied relative tolerance. -1 designates no user input
-     *
-     * @return Appropriate relative tolerance.
-     */
-    float relative_tolenace(float user_value)
+
+    float relative_tolerance() override
     {
         const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
         {
@@ -406,23 +225,11 @@ private:
                 }
             }
         };
-        if(user_value == -1)
-        {
-            return relative_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
-        }
 
-        return user_value;
+        return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
     }
 
-    /** Select absolute tolerance.
-     *
-     * Select absolute tolerance if not supplied by user.
-     *
-     * @param[in] user_value supplied absolute tolerance. -1 designates no user input
-     *
-     * @return Appropriate absolute tolerance.
-     */
-    float absolute_tolerance(float user_value)
+    float absolute_tolerance() override
     {
         const std::map<Target, const std::map<DataType, float>> absolute_tolerance
         {
@@ -442,21 +249,10 @@ private:
             }
         };
 
-        if(user_value == -1)
-        {
-            return absolute_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
-        }
-        return user_value;
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
     }
-    /** Select tolerance number.
-     *
-     * Select tolerance number if not supplied by user.
-     *
-     * @param[in] user_value supplied tolerance number. -1 designates no user input
-     *
-     * @return Appropriate tolerance number.
-     */
-    float tolerance_number(float user_value)
+
+    float tolerance_number() override
     {
         const std::map<Target, const std::map<DataType, float>> absolute_tolerance
         {
@@ -476,110 +272,35 @@ private:
             }
         };
 
-        if(user_value == -1)
-        {
-            return absolute_tolerance.at(_params.common_params.target).at(_params.fully_connected.data_type);
-        }
-        return user_value;
+        return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
     }
-
-    ExampleParams _params;
 };
 
-/** Generates appropriate fully_connected verify accessor
- *
- * @param[in] params User supplied parameters for fully_connected.
- *
- * @return A fully_connected verify accessor for the requested datatype.
- */
-inline std::unique_ptr<graph::ITensorAccessor> get_fully_connected_verify_accessor(ExampleParams params)
-{
-    switch(params.fully_connected.data_type)
-    {
-        case DataType::QASYMM8:
-        {
-            return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<uint8_t>>(
-                       params);
-        }
-        case DataType::F16:
-        {
-            return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<half>>(
-                       params);
-        }
-        case DataType::F32:
-        {
-            return arm_compute::support::cpp14::make_unique<FullyConnectedVerifyAccessor<float>>(
-                       params);
-        }
-        default:
-            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
-    }
-}
-
 } // namespace
 
-class Graphfully_connectedValidateExample final : public ValidateExample
+class GraphFullyConnectedValidateExample final : public GraphValidateExample<FullyConnectedLayer, FullyConnectedOptions, FullyConnectedVerifyAccessor>
 {
+    using GraphValidateExample::graph;
+
 public:
-    Graphfully_connectedValidateExample()
-        : graph(0, "fully_connected Graph example")
-    {
-    }
-    bool do_setup(int argc, char **argv) override
+    GraphFullyConnectedValidateExample()
+        : GraphValidateExample("Fully_connected Graph example")
     {
-        CommandLineParser parser;
-
-        FullyConnectedOptions Options(parser);
-
-        parser.parse(argc, argv);
-
-        ExampleParams params = consume_fully_connected_graph_parameters(Options);
-
-        if(params.common_params.help)
-        {
-            parser.print_help(argv[0]);
-            return false;
-        }
-
-        std::cout << params << std::endl;
-
-        // Create input descriptor
-        const TensorShape      input_shape      = TensorShape(params.input.width, params.input.height, params.input.fm, params.input.batch);
-        const TensorDescriptor input_descriptor = TensorDescriptor(input_shape, params.fully_connected.data_type, params.input.quant_info, params.fully_connected.data_layout);
-
-        const PixelValue lower = PixelValue(params.input.range_low, params.fully_connected.data_type, params.input.quant_info);
-        const PixelValue upper = PixelValue(params.input.range_high, params.fully_connected.data_type, params.input.quant_info);
-
-        const PixelValue weights_lower = PixelValue(params.weights.range_low, params.fully_connected.data_type, params.weights.quant_info);
-        const PixelValue weights_upper = PixelValue(params.weights.range_high, params.fully_connected.data_type, params.weights.quant_info);
-
-        graph << params.common_params.target
-              << InputLayer(input_descriptor, get_random_accessor(lower, upper, 0))
-              << FullyConnectedLayer(params.fully_connected.num_outputs,
-                                     get_random_accessor(weights_lower, weights_upper, 1),
-                                     get_random_accessor(lower, upper, 2),
-                                     params.fully_connected.info, params.weights.quant_info, params.output.quant_info)
-              << OutputLayer(get_fully_connected_verify_accessor(params));
-
-        GraphConfig config;
-        config.num_threads = params.common_params.threads;
-
-        graph.finalize(params.common_params.target, config);
-
-        return true;
     }
 
-    void do_run() override
+    FullyConnectedLayer GraphFunctionLayer(ExampleParams &params) override
     {
-        graph.run();
-    }
+        const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
+        const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
 
-    void do_teardown() override
-    {
-    }
+        const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
+        const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
 
-private:
-    Stream graph;
+        return FullyConnectedLayer(params.fully_connected.num_outputs,
+                                   get_random_accessor(weights_lower, weights_upper, 1),
+                                   get_random_accessor(lower, upper, 2),
+                                   params.fully_connected.info, params.weights.quant_info, params.output.quant_info);
+    }
 };
 
 /** Main program for Graph fully_connected test
@@ -592,5 +313,5 @@ private:
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example<Graphfully_connectedValidateExample>(argc, argv);
+    return arm_compute::utils::run_example<GraphFullyConnectedValidateExample>(argc, argv);
 }
diff --git a/tests/validate_examples/graph_validate_utils.h b/tests/validate_examples/graph_validate_utils.h
new file mode 100644
index 0000000000..485d3c1409
--- /dev/null
+++ b/tests/validate_examples/graph_validate_utils.h
@@ -0,0 +1,695 @@
+/*
+ * Copyright (c) 2019 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 __GRAPH_VALIDATE_UTILS_H__
+#define __GRAPH_VALIDATE_UTILS_H__
+
+#include "arm_compute/graph.h"
+
+#include "ValidateExample.h"
+#include "utils/command_line/CommandLineParser.h"
+
+namespace arm_compute
+{
+namespace utils
+{
+/*Available Padding modes */
+enum class ConvolutionPaddingMode
+{
+    Valid,
+    Same,
+    Manual
+};
+
+/** Stream Input operator for the ConvolutionPaddingMode type
+ *
+ * @param[in]  stream Input stream.
+ * @param[out] Mode   Convolution parameters to output
+ *
+ * @return input stream.
+ */
+inline ::std::istream &operator>>(::std::istream &stream, ConvolutionPaddingMode &Mode)
+{
+    static const std::map<std::string, ConvolutionPaddingMode> modes =
+    {
+        { "valid", ConvolutionPaddingMode::Valid },
+        { "same", ConvolutionPaddingMode::Same },
+        { "manual", ConvolutionPaddingMode::Manual }
+    };
+    std::string value;
+    stream >> value;
+#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
+    try
+    {
+#endif /* ARM_COMPUTE_EXCEPTIONS_DISABLED */
+        Mode = modes.at(arm_compute::utility::tolower(value));
+#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
+    }
+    catch(const std::out_of_range &)
+    {
+        throw std::invalid_argument(value);
+    }
+#endif /* ARM_COMPUTE_EXCEPTIONS_DISABLED */
+
+    return stream;
+}
+
+/** Formatted output of the ConvolutionPaddingMode type
+ *
+ * @param[out] os   Output stream.
+ * @param[in]  Mode ConvolutionPaddingMode to output
+ *
+ * @return Modified output stream.
+ */
+inline ::std::ostream &operator<<(::std::ostream &os, ConvolutionPaddingMode Mode)
+{
+    switch(Mode)
+    {
+        case ConvolutionPaddingMode::Valid:
+            os << "Valid";
+            break;
+        case ConvolutionPaddingMode::Same:
+            os << "Same";
+            break;
+        case ConvolutionPaddingMode::Manual:
+            os << "Manual";
+            break;
+        default:
+            throw std::invalid_argument("Unsupported padding mode format");
+    }
+
+    return os;
+}
+
+/** Structure holding all the input tensor graph parameters */
+struct TensorParams
+{
+    int              width{ 1 };
+    int              height{ 1 };
+    int              fm{ 1 };
+    int              batch{ 1 };
+    QuantizationInfo quant_info{ 1.0f, 0 };
+    std::string      npy{};
+    uint64_t         range_low{ 0 };
+    uint64_t         range_high{ 16 };
+};
+
+/** Structure holding all the verification graph parameters */
+struct VerificationParams
+{
+    float absolute_tolerance{ -1.f };
+    float relative_tolerance{ -1.f };
+    float tolerance_number{ -1.f };
+};
+
+/** Structure holding all the common graph parameters */
+struct FrameworkParams
+{
+    bool                       help{ false };
+    int                        threads{ 0 };
+    arm_compute::graph::Target target{ arm_compute::graph::Target::NEON };
+};
+
+/** Structure holding all the graph Example parameters */
+struct CommonParams
+{
+    FrameworkParams       common_params{};
+    TensorParams          input{};
+    TensorParams          weights{};
+    TensorParams          bias{};
+    TensorParams          output{};
+    VerificationParams    verification{};
+    arm_compute::DataType data_type{ DataType::F32 };
+};
+
+/** Structure holding all the Convolution layer graph parameters */
+struct ConvolutionParams
+{
+    int depth_multiplier{ 1 };
+    /** Padding graph parameters */
+    int                    padding_top{ 0 };
+    int                    padding_bottom{ 0 };
+    int                    padding_left{ 0 };
+    int                    padding_right{ 0 };
+    int                    padding_stride_x{ 0 };
+    int                    padding_stride_y{ 0 };
+    ConvolutionPaddingMode padding_mode{ ConvolutionPaddingMode::Valid };
+    struct
+    {
+        struct
+        {
+            int X{ 0 };
+            int Y{ 0 };
+        } stride{};
+        ConvolutionPaddingMode mode{ ConvolutionPaddingMode::Valid };
+    } padding{};
+};
+
+/** Structure holding all the fully_connected layer graph parameters */
+struct FullyConnectedParams
+{
+    FullyConnectedLayerInfo info{};
+    int                     num_outputs{ 1 };
+};
+
+/** Structure holding all the graph Example parameters */
+struct ExampleParams : public CommonParams
+{
+    FullyConnectedParams                           fully_connected{};
+    ConvolutionParams                              convolution{};
+    arm_compute::graph::DepthwiseConvolutionMethod depth_convolution_method{ arm_compute::graph::DepthwiseConvolutionMethod::Default };
+    arm_compute::graph::ConvolutionMethod          convolution_method{ arm_compute::graph::ConvolutionMethod::Default };
+    arm_compute::DataLayout                        data_layout{ DataLayout::NCHW };
+};
+
+/** Calculate stride information.
+ *
+ * Depending on the selected padding mode create the desired PadStrideInfo
+ *
+ * @param[in] params Convolution parameters supplied by the user.
+ *
+ * @return PadStrideInfo with the correct padding mode.
+ */
+inline PadStrideInfo calculate_convolution_padding(ExampleParams params)
+{
+    switch(params.convolution.padding_mode)
+    {
+        case ConvolutionPaddingMode::Manual:
+        {
+            return PadStrideInfo(params.convolution.padding_stride_x, params.convolution.padding_stride_y, params.convolution.padding_left, params.convolution.padding_right, params.convolution.padding_top,
+                                 params.convolution.padding_bottom, DimensionRoundingType::FLOOR);
+        }
+        case ConvolutionPaddingMode::Valid:
+        {
+            return PadStrideInfo();
+        }
+        case ConvolutionPaddingMode::Same:
+        {
+            return arm_compute::calculate_same_pad(TensorShape(params.input.width, params.input.height), TensorShape(params.weights.width, params.weights.height),
+                                                   PadStrideInfo(params.convolution.padding_stride_x,
+                                                                 params.convolution.padding_stride_y));
+        }
+        default:
+            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+}
+/** CommonGraphValidateOptions command line options used to configure the graph examples
+ *
+ * (Similar to common options)
+ * The options in this object get populated when "parse()" is called on the parser used to construct it.
+ * The expected workflow is:
+ *
+ * CommandLineParser parser;
+ * CommonOptions options( parser );
+ * parser.parse(argc, argv);
+ */
+class CommonGraphValidateOptions
+{
+public:
+    explicit CommonGraphValidateOptions(CommandLineParser &parser) noexcept
+        : help(parser.add_option<ToggleOption>("help")),
+          threads(parser.add_option<SimpleOption<int>>("threads")),
+          target(),
+          data_type(),
+          absolute_tolerance(parser.add_option<SimpleOption<float>>("abs_tolerance", -1.0f)),
+          relative_tolerance(parser.add_option<SimpleOption<float>>("rel_tolerance", -1.0f)),
+          tolerance_number(parser.add_option<SimpleOption<float>>("tolerance_num", -1.0f))
+    {
+        const std::set<arm_compute::graph::Target> supported_targets
+        {
+            arm_compute::graph::Target::NEON,
+            arm_compute::graph::Target::CL,
+            arm_compute::graph::Target::GC,
+        };
+
+        const std::set<arm_compute::DataType> supported_data_types
+        {
+            DataType::F16,
+            DataType::F32,
+            DataType::QASYMM8,
+        };
+
+        target    = parser.add_option<EnumOption<arm_compute::graph::Target>>("target", supported_targets, arm_compute::graph::Target::NEON);
+        data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32);
+
+        target->set_help("Target to execute on");
+        data_type->set_help("Data type to use");
+        help->set_help("Show this help message");
+        absolute_tolerance->set_help("Absolute tolerance used for verification");
+        relative_tolerance->set_help("Absolute tolerance used for verification");
+        tolerance_number->set_help("Absolute tolerance used for verification");
+        ;
+    }
+
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CommonGraphValidateOptions(const CommonGraphValidateOptions &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CommonGraphValidateOptions &operator=(const CommonGraphValidateOptions &) = delete;
+    /** Allow instances of this class to be moved */
+    CommonGraphValidateOptions(CommonGraphValidateOptions &&) noexcept(true) = default;
+    /** Allow instances of this class to be moved */
+    CommonGraphValidateOptions &operator=(CommonGraphValidateOptions &&) noexcept(true) = default;
+    /** Default destructor */
+    virtual ~CommonGraphValidateOptions() = default;
+
+    void consume_common_parameters(CommonParams &common_params)
+    {
+        common_params.common_params.help    = help->is_set() ? help->value() : false;
+        common_params.common_params.threads = threads->value();
+        common_params.common_params.target  = target->value();
+
+        common_params.verification.absolute_tolerance = absolute_tolerance->value();
+        common_params.verification.relative_tolerance = relative_tolerance->value();
+        common_params.verification.tolerance_number   = tolerance_number->value();
+    }
+
+    /** Formatted output of the ExampleParams type
+     *
+     * @param[out] os            Output stream.
+     * @param[in]  common_params Example parameters to output
+     *
+     * @return None.
+     */
+    virtual void print_parameters(::std::ostream &os, const ExampleParams &common_params)
+    {
+        os << "Threads : " << common_params.common_params.threads << std::endl;
+        os << "Target : " << common_params.common_params.target << std::endl;
+        os << "Data type : " << common_params.data_type << std::endl;
+    }
+
+    ToggleOption                           *help;               /**< show help message */
+    SimpleOption<int>                      *threads;            /**< Number of threads option */
+    EnumOption<arm_compute::graph::Target> *target;             /**< Graph execution target */
+    EnumOption<arm_compute::DataType>      *data_type;          /**< Graph data type */
+    SimpleOption<float>                    *absolute_tolerance; /**< Absolute tolerance used in verification */
+    SimpleOption<float>                    *relative_tolerance; /**< Relative tolerance used in verification */
+    SimpleOption<float>                    *tolerance_number;   /**< Tolerance number used in verification */
+};
+
+/** Consumes the consume_common_graph_parameters graph options and creates a structure containing any information
+ *
+ * @param[in]  options       Options to consume
+ * @param[out] common_params params structure to consume.
+ *
+ * @return consume_common_graph_parameters structure containing the common graph parameters
+ */
+void consume_common_graph_parameters(CommonGraphValidateOptions &options, CommonParams &common_params)
+{
+    common_params.common_params.help    = options.help->is_set() ? options.help->value() : false;
+    common_params.common_params.threads = options.threads->value();
+    common_params.common_params.target  = options.target->value();
+
+    common_params.verification.absolute_tolerance = options.absolute_tolerance->value();
+    common_params.verification.relative_tolerance = options.relative_tolerance->value();
+    common_params.verification.tolerance_number   = options.tolerance_number->value();
+}
+
+/** Generates appropriate accessor according to the specified graph parameters
+ *
+ * @param[in] tensor Tensor parameters
+ * @param[in] lower  Lower random values bound
+ * @param[in] upper  Upper random values bound
+ * @param[in] seed   Random generator seed
+ *
+ * @return An appropriate tensor accessor
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_accessor(const TensorParams &tensor, PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0)
+{
+    if(!tensor.npy.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<arm_compute::graph_utils::NumPyBinLoader>(tensor.npy);
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<arm_compute::graph_utils::RandomAccessor>(lower, upper, seed);
+    }
+}
+
+/** Graph example validation accessor class */
+template <typename D>
+class VerifyAccessor : public graph::ITensorAccessor
+{
+public:
+    using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
+    /** Constructor
+     *
+     * @param[in] params Convolution parameters
+     */
+    explicit VerifyAccessor(ExampleParams &params)
+        : _params(std::move(params))
+    {
+    }
+    // Inherited methods overriden:
+    bool access_tensor(ITensor &tensor) override
+    {
+        if(_params.output.npy.empty())
+        {
+            arm_compute::test::SimpleTensor<D>     src;
+            arm_compute::test::SimpleTensor<D>     weights;
+            arm_compute::test::SimpleTensor<TBias> bias;
+
+            //Create Input tensors
+            create_tensors(src, weights, bias, tensor);
+
+            //Fill the tensors with random values
+            fill_tensor(src, 0, static_cast<D>(_params.input.range_low), static_cast<D>(_params.input.range_high));
+            fill_tensor(weights, 1, static_cast<D>(_params.weights.range_low), static_cast<D>(_params.weights.range_high));
+            fill_tensor(bias, 2, static_cast<TBias>(_params.input.range_low), static_cast<TBias>(_params.input.range_high));
+
+            arm_compute::test::SimpleTensor<D> output = reference(src, weights, bias, output_shape(tensor));
+
+            validate(tensor, output);
+        }
+        else
+        {
+            //The user provided a reference file use an npy accessor to validate
+            arm_compute::graph_utils::NumPyAccessor(_params.output.npy, tensor.info()->tensor_shape(), tensor.info()->data_type()).access_tensor(tensor);
+        }
+        return false;
+    }
+
+    /** Create reference tensors.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[out] src     The tensor with the source data.
+     * @param[out] weights The tensor with the weigths data.
+     * @param[out] bias    The tensor with the bias data.
+     * @param[in]  tensor  Tensor result of the actual operation passed into the Accessor.
+     *
+     * @return None.
+     */
+    virtual void create_tensors(arm_compute::test::SimpleTensor<D>     &src,
+                                arm_compute::test::SimpleTensor<D>     &weights,
+                                arm_compute::test::SimpleTensor<TBias> &bias,
+                                ITensor                                &tensor)
+    {
+        //Create Input tensors
+        src     = arm_compute::test::SimpleTensor<D> { TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch), _params.data_type, 1, _params.input.quant_info };
+        weights = arm_compute::test::SimpleTensor<D> { TensorShape(_params.weights.width, _params.weights.height, _params.weights.fm), _params.data_type, 1, _params.weights.quant_info };
+        bias    = arm_compute::test::SimpleTensor<TBias> { TensorShape(_params.input.height), _params.data_type, 1, _params.input.quant_info };
+    }
+
+    /** Calculate reference output tensor shape.
+     *
+     * @param[in] tensor Tensor result of the actual operation passed into the Accessor.
+     *
+     * @return output tensor shape.
+     */
+    virtual TensorShape output_shape(ITensor &tensor)
+    {
+        return arm_compute::graph_utils::permute_shape(tensor.info()->tensor_shape(), _params.data_layout, DataLayout::NCHW);
+    }
+
+    /** Calculate reference tensor.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[in] src          The tensor with the source data.
+     * @param[in] weights      The tensor with the weigths data.
+     * @param[in] bias         The tensor with the bias data.
+     * @param[in] output_shape Shape of the output tensor.
+     *
+     * @return Tensor with the reference output.
+     */
+    virtual arm_compute::test::SimpleTensor<D> reference(arm_compute::test::SimpleTensor<D>     &src,
+                                                         arm_compute::test::SimpleTensor<D>     &weights,
+                                                         arm_compute::test::SimpleTensor<TBias> &bias,
+                                                         const arm_compute::TensorShape         &output_shape) = 0;
+
+    /** Fill QASYMM tensor with Random values.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[out] tensor The tensor we want to file
+     * @param[in]  seed   seed for the randomization function
+     * @param[in]  low    lower bound for random values
+     * @param[in]  high   upper bound for random values
+     *
+     * @return None.
+     */
+    void fill_tensor(arm_compute::test::SimpleTensor<uint8_t> &tensor, std::random_device::result_type seed, uint8_t low, uint8_t high)
+    {
+        ARM_COMPUTE_ERROR_ON(tensor.data_type() != arm_compute::DataType::QASYMM8);
+
+        std::mt19937 gen(seed);
+
+        uint8_t qasymm8_low  = tensor.quantization_info().quantize(low, RoundingPolicy::TO_NEAREST_UP);
+        uint8_t qasymm8_high = tensor.quantization_info().quantize(high, RoundingPolicy::TO_NEAREST_UP);
+
+        std::uniform_int_distribution<uint8_t> distribution(qasymm8_low, qasymm8_high);
+
+        for(int i = 0; i < tensor.num_elements(); ++i)
+        {
+            tensor[i] = tensor.quantization_info().quantize(distribution(gen), RoundingPolicy::TO_NEAREST_UP);
+        }
+    }
+    /** Fill S32 tensor with Random values.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[out] tensor The tensor we want to file
+     * @param[in]  seed   seed for the randomization function
+     * @param[in]  low    lower bound for random values
+     * @param[in]  high   upper bound for random values
+     *
+     * @return None.
+     */
+    void fill_tensor(arm_compute::test::SimpleTensor<int32_t> &tensor, std::random_device::result_type seed, int32_t low, int32_t high)
+    {
+        std::mt19937                           gen(seed);
+        std::uniform_int_distribution<int32_t> distribution(static_cast<int32_t>(low), static_cast<uint32_t>(high));
+
+        for(int i = 0; i < tensor.num_elements(); ++i)
+        {
+            tensor[i] = distribution(gen);
+        }
+    }
+    /** Fill F32 tensor with Random values.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[out] tensor The tensor we want to file
+     * @param[in]  seed   seed for the randomization function
+     * @param[in]  low    lower bound for random values
+     * @param[in]  high   upper bound for random values
+     *
+     * @return None.
+     */
+    void fill_tensor(arm_compute::test::SimpleTensor<float> &tensor, std::random_device::result_type seed, float low, float high)
+    {
+        ARM_COMPUTE_ERROR_ON(tensor.data_type() != arm_compute::DataType::F32);
+        std::mt19937                          gen(seed);
+        std::uniform_real_distribution<float> distribution(low, high);
+
+        for(int i = 0; i < tensor.num_elements(); ++i)
+        {
+            tensor[i] = distribution(gen);
+        }
+    }
+    /** Fill F16 tensor with Random values.
+     *
+     * Validate the given tensor against the reference result.
+     *
+     * @param[out] tensor The tensor we want to file
+     * @param[in]  seed   seed for the randomization function
+     * @param[in]  low    lower bound for random values
+     * @param[in]  high   upper bound for random values
+     *
+     * @return None.
+     */
+    void fill_tensor(arm_compute::test::SimpleTensor<half> &tensor, std::random_device::result_type seed, half low, half high)
+    {
+        ARM_COMPUTE_ERROR_ON(tensor.data_type() != arm_compute::DataType::F16);
+        std::mt19937                          gen(seed);
+        std::uniform_real_distribution<float> distribution(static_cast<half>(low), static_cast<half>(high));
+
+        for(int i = 0; i < tensor.num_elements(); ++i)
+        {
+            tensor[i] = static_cast<half>(distribution(gen));
+        }
+    }
+
+    /** Select relative tolerance.
+     *
+     * Select relative tolerance if not supplied by user.
+     *
+     * @return Appropriate relative tolerance.
+     */
+    virtual float relative_tolerance() = 0;
+
+    /** Select absolute tolerance.
+     *
+     * Select absolute tolerance if not supplied by user.
+     *
+     * @return Appropriate absolute tolerance.
+     */
+    virtual float absolute_tolerance() = 0;
+
+    /** Select tolerance number.
+     *
+     * Select tolerance number if not supplied by user.
+     *
+     * @return Appropriate tolerance number.
+     */
+    virtual float tolerance_number() = 0;
+
+    /** Validate the output versus the reference.
+     *
+     * @param[in] tensor Tensor result of the actual operation passed into the Accessor.
+     * @param[in] output Tensor result of the reference implementation.
+     *
+     * @return None.
+     */
+    void validate(ITensor &tensor, arm_compute::test::SimpleTensor<D> output)
+    {
+        float user_relative_tolerance = _params.verification.relative_tolerance;
+        float user_absolute_tolerance = _params.verification.absolute_tolerance;
+        float user_tolerance_num      = _params.verification.tolerance_number;
+        /* If no user input was provided override with defaults. */
+        if(user_relative_tolerance == -1)
+        {
+            user_relative_tolerance = relative_tolerance();
+        }
+
+        if(user_absolute_tolerance == -1)
+        {
+            user_absolute_tolerance = absolute_tolerance();
+        }
+
+        if(user_tolerance_num == -1)
+        {
+            user_tolerance_num = tolerance_number();
+        }
+
+        const arm_compute::test::validation::RelativeTolerance<float> rel_tolerance(user_relative_tolerance); /**< Relative tolerance */
+        const arm_compute::test::validation::AbsoluteTolerance<float> abs_tolerance(user_absolute_tolerance); /**< Absolute tolerance */
+        const float                                                   tolerance_num(user_tolerance_num);      /**< Tolerance number */
+
+        arm_compute::test::validation::validate(arm_compute::test::Accessor(tensor), output, rel_tolerance, tolerance_num, abs_tolerance);
+    }
+
+    ExampleParams _params;
+};
+
+/** Generates appropriate convolution verify accessor
+ *
+ * @param[in] params User supplied parameters for convolution.
+ *
+ * @return A convolution verify accessor for the requested datatype.
+ */
+template <template <typename D> class VerifyAccessorT>
+inline std::unique_ptr<graph::ITensorAccessor> get_verify_accessor(ExampleParams params)
+{
+    switch(params.data_type)
+    {
+        case DataType::QASYMM8:
+        {
+            return arm_compute::support::cpp14::make_unique<VerifyAccessorT<uint8_t>>(
+                       params);
+        }
+        case DataType::F16:
+        {
+            return arm_compute::support::cpp14::make_unique<VerifyAccessorT<half>>(
+                       params);
+        }
+        case DataType::F32:
+        {
+            return arm_compute::support::cpp14::make_unique<VerifyAccessorT<float>>(
+                       params);
+        }
+        default:
+            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+}
+
+template <typename LayerT, typename OptionsT, template <typename D> class VerifyAccessorT>
+class GraphValidateExample : public ValidateExample
+{
+public:
+    GraphValidateExample(std::string name)
+        : graph(0, name)
+    {
+    }
+
+    virtual LayerT GraphFunctionLayer(ExampleParams &params) = 0;
+
+    bool do_setup(int argc, char **argv) override
+    {
+        CommandLineParser parser;
+
+        OptionsT Options(parser);
+
+        parser.parse(argc, argv);
+
+        ExampleParams params;
+
+        Options.consume_common_parameters(params);
+        Options.consume_parameters(params);
+
+        if(params.common_params.help)
+        {
+            parser.print_help(argv[0]);
+            return false;
+        }
+
+        Options.print_parameters(std::cout, params);
+        // Create input descriptor
+        const TensorShape input_shape = arm_compute::graph_utils::permute_shape(TensorShape(params.input.width, params.input.height, params.input.fm, params.input.batch),
+                                                                                DataLayout::NCHW, params.data_layout);
+        arm_compute::graph::TensorDescriptor input_descriptor = arm_compute::graph::TensorDescriptor(input_shape, params.data_type, params.input.quant_info, params.data_layout);
+
+        const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
+        const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
+
+        graph << params.common_params.target
+              << params.convolution_method
+              << params.depth_convolution_method
+              << arm_compute::graph::frontend::InputLayer(input_descriptor, get_accessor(params.input, lower, upper, 0))
+              << GraphFunctionLayer(params)
+              << arm_compute::graph::frontend::OutputLayer(get_verify_accessor<VerifyAccessorT>(params));
+
+        arm_compute::graph::GraphConfig config;
+        config.num_threads = params.common_params.threads;
+
+        graph.finalize(params.common_params.target, config);
+
+        return true;
+    }
+
+    void do_run() override
+    {
+        graph.run();
+    }
+
+    void do_teardown() override
+    {
+    }
+
+    arm_compute::graph::frontend::Stream graph;
+};
+
+} // graph_validate_utils
+} // arm_compute
+#endif //__GRAPH_VALIDATE_UTILS_H__
diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
index f27610afb8..122dbd4d98 100644
--- a/tests/validation/reference/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
@@ -50,8 +50,10 @@ namespace reference
  */
 template <typename T, typename TB>
 SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info,
-                                      unsigned int depth_multiplier, const Size2D &dilation)
+                                      unsigned int depth_multiplier, const Size2D &dilation, QuantizationInfo out_quant_info)
 {
+    ARM_COMPUTE_UNUSED(out_quant_info);
+
     SimpleTensor<T> dst{ dst_shape, src.data_type(), 1 };
 
     // Compute reference
@@ -119,9 +121,14 @@ SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTe
 
 template <>
 SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape,
-                                            const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
+                                            const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, QuantizationInfo out_quant_info)
 {
-    SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.quantization_info() };
+    // if no explicit quantization has been set you the same as src
+    if(out_quant_info == QuantizationInfo(0.0f, 0))
+    {
+        out_quant_info = src.quantization_info();
+    }
+    SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, out_quant_info };
 
     // Create reference
     const int   input_offset   = -src.quantization_info().offset;
@@ -206,10 +213,10 @@ SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, co
 }
 
 template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape,
-                                                   const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation);
+                                                   const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, QuantizationInfo out_quant_info);
 
 template SimpleTensor<half> depthwise_convolution(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &biases, const TensorShape &dst_shape,
-                                                  const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation);
+                                                  const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, QuantizationInfo out_quant_info);
 } // namespace reference
 } // namespace validation
 } // namespace test
diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.h b/tests/validation/reference/DepthwiseConvolutionLayer.h
index 2146611d13..ac70de02ca 100644
--- a/tests/validation/reference/DepthwiseConvolutionLayer.h
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.h
@@ -37,7 +37,7 @@ namespace reference
 {
 template <typename T, typename TB>
 SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info,
-                                      unsigned int depth_multiplier, const Size2D &dilation = Size2D(1U, 1U));
+                                      unsigned int depth_multiplier, const Size2D &dilation = Size2D(1U, 1U), QuantizationInfo out_quant_info = QuantizationInfo(0.0f, 0));
 } // namespace reference
 } // namespace validation
 } // namespace test