COMPMID-617 Add validation to NEConvolutionLayer

Change-Id: I796a13e6ea672e274aaa8234ee0689828fec7292
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111348
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Ioan-Cristian Szabo <ioan-cristian.szabo@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

6 files changed, 433 insertions, 125 deletions

diff --git a/arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h b/arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h
index aff2671441..9923c31018 100644
--- a/arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h
+++ b/arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h
@@ -61,6 +61,15 @@ public:
      * @param[in]  alpha  Weight of the matrix product
      */
     void configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMMatrixMultiplyKernel
+     *
+     * @param[in] input0 Input tensor containing the Matrix A. Data types supported: QS8/QS16/F16/F32
+     * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0
+     * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
diff --git a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
index 5b37d6cc62..84ec736d03 100644
--- a/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
+++ b/arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h
@@ -77,6 +77,17 @@ public:
      * @param[out] output The output tensor. Data types supported: Same as @p input
      */
     void configure(const ITensor *input, const ITensor *bias, ITensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEWeightsReshapeKernel
+     *
+     * @param[in] input  The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
+     *                   and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM,  num_patches] if unshared. Data types supported: QS8/QS16/F16/F32
+     * @param[in] biases The shared biases tensor to append.  Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
+     *                   dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input
+     * @param[in] output The output tensor. Should be a 2D Tensor. Data types supported: Same as @p input
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
index 893dfa0f9d..a79f21f3ac 100644
--- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
@@ -62,6 +62,17 @@ public:
      *                          Data types supported: Same as @p weights.
      */
     void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
+     *
+     * @param[in] weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QS16/F16/F32.
+     * @param[in] biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+     * @param[in] output       Destination tensor. Data types supported: Same as @p weights.
+     * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
+     *                         Data types supported: Same as @p weights.
+     *
+     * @return an error status
+     */
+    static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW);
 
     // Inherited methods overridden:
     void run() override;
@@ -101,6 +112,23 @@ public:
      *                          tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
      */
     void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo());
+    /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayer
+     *
+     * @param[in] input        Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
+     *                         while every optional dimension from 4 and above represent a batch of inputs.
+     *                         Data types supported: QS8/QS16/F16/F32.
+     * @param[in] weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
+     * @param[in] biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
+     * @param[in] output       Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     *                         Data types supported: Same as @p input.
+     * @param[in] conv_info    Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
+     *                         tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                           const WeightsInfo &weights_info = WeightsInfo());
 
     // Inherited methods overridden:
     void run() override;
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
index a583c1dfd4..aa5e2dd0dd 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
@@ -1408,36 +1408,36 @@ void matrix_matrix_multiply_qs16(const ITensor *input0, const ITensor *input1, I
     },
     ina, inb, out);
 }
-} // namespace
-
-NEGEMMMatrixMultiplyKernel::NEGEMMMatrixMultiplyKernel()
-    : _input0(nullptr), _input1(nullptr), _output(nullptr), _alpha(1.0f)
-{
-}
 
-void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha)
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8, DataType::QS16);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8, DataType::QS16);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+    ARM_COMPUTE_UNUSED(input0);
+    ARM_COMPUTE_UNUSED(input1);
+    ARM_COMPUTE_UNUSED(output);
 
-    if(output->info()->dimension(1) == 1)
+    if(output->dimension(1) == 1)
     {
-        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+        ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
     }
 
-    _input0 = input0;
-    _input1 = input1;
-    _output = output;
-    _alpha  = alpha;
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
+{
+    Window win            = Window();
+    bool   window_changed = false;
 
     unsigned int       num_elems_processed_per_iteration_x = 0;
     const unsigned int num_elems_processed_per_iteration_y = 4;
 
     // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
-    if((output->info()->dimension(1) == 1))
+    if((output->dimension(1) == 1))
     {
-        switch(input0->info()->data_type())
+        switch(input0->data_type())
         {
             case DataType::F32:
             {
@@ -1469,24 +1469,22 @@ void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor
         }
 
         // Configure kernel window
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x));
 
-        AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
+        AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x);
 
-        update_window_and_padding(win,
-                                  AccessWindowStatic(input0->info(), 0, 0, input0->info()->tensor_shape().x(), 1),
-                                  AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
-                                  output_access);
+        window_changed = update_window_and_padding(win,
+                                                   AccessWindowStatic(input0, 0, 0, input0->tensor_shape().x(), 1),
+                                                   AccessWindowHorizontal(input1, 0, num_elems_processed_per_iteration_x),
+                                                   output_access);
 
         Coordinates coord;
-        coord.set_num_dimensions(output->info()->num_dimensions());
-        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
-
-        INEKernel::configure(win);
+        coord.set_num_dimensions(output->num_dimensions());
+        output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
     }
     else
     {
-        switch(input0->info()->data_type())
+        switch(input0->data_type())
         {
             case DataType::F32:
             {
@@ -1518,19 +1516,51 @@ void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor
         }
 
         // Configure kernel window
-        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
 
-        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
 
-        update_window_and_padding(win,
-                                  AccessWindowRectangle(input0->info(), 0, 0, 4, 1, 1.f, 0.25f),
-                                  AccessWindowStatic(input1->info(), 0, 0, input1->info()->tensor_shape().x(), ceil_to_multiple(input1->info()->tensor_shape().y(), 4)),
-                                  output_access);
+        window_changed = update_window_and_padding(win,
+                                                   AccessWindowRectangle(input0, 0, 0, 4, 1, 1.f, 0.25f),
+                                                   AccessWindowStatic(input1, 0, 0, input1->tensor_shape().x(), ceil_to_multiple(input1->tensor_shape().y(), 4)),
+                                                   output_access);
 
-        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
-
-        INEKernel::configure(win);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
     }
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+NEGEMMMatrixMultiplyKernel::NEGEMMMatrixMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr), _alpha(1.0f)
+{
+}
+
+void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
+
+    _input0 = input0;
+    _input1 = input1;
+    _output = output;
+    _alpha  = alpha;
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
+}
+
+Status NEGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first);
+
+    return Status{};
 }
 
 void NEGEMMMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
index d52e88c37a..794c179277 100644
--- a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
+++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
@@ -86,6 +86,57 @@ void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output,
     },
     in);
 }
+
+TensorShape get_output_shape(const ITensorInfo *input, bool has_bias)
+{
+    TensorShape output_shape{ input->tensor_shape() };
+
+    output_shape.collapse(3);
+    const size_t tmp_dim = output_shape[0];
+    output_shape.set(0, output_shape[1]);
+    output_shape.set(1, tmp_dim + (has_bias ? 1 : 0));
+
+    return output_shape;
+}
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->num_dimensions() != 1));
+        ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->num_dimensions() != 2));
+        ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->dimension(0) != input->tensor_shape()[3]));
+        ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->dimension(0) != input->tensor_shape()[3] || biases->dimension(1) != input->tensor_shape()[4]));
+    }
+
+    // Checks performed when output is configured
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input, biases != nullptr));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+    Window window = calculate_max_window(*input, Steps());
+    window.set(Window::DimX, Window::Dimension(0, input->dimension(0), input->dimension(0)));
+    window.set(Window::DimY, Window::Dimension(0, input->dimension(1), input->dimension(1)));
+    window.set(Window::DimZ, Window::Dimension(0, input->dimension(2), input->dimension(2)));
+
+    // The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+    return std::make_pair(Status{}, window);
+}
 } // namespace
 
 NEWeightsReshapeKernel::NEWeightsReshapeKernel()
@@ -95,35 +146,15 @@ NEWeightsReshapeKernel::NEWeightsReshapeKernel()
 
 void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
-
-    const int          fixed_point_position = input->info()->fixed_point_position();
-    const DataType     dt                   = input->info()->data_type();
-    const TensorShape &input_shape          = input->info()->tensor_shape();
-    TensorShape        output_shape{ input_shape };
-    output_shape.collapse(3);
-
-    const size_t tmp_dim = output_shape[0];
-    output_shape.set(0, output_shape[1]);
-    output_shape.set(1, tmp_dim + (bias != nullptr ? 1 : 0));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output tensor auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), output_shape, 1, dt, fixed_point_position);
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(get_output_shape(input->info(), (bias != nullptr))));
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
-
-    if(bias != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
-        ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (bias->info()->num_dimensions() != 1));
-        ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 5) && (bias->info()->num_dimensions() != 2));
-        ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (bias->info()->dimension(0) != input->info()->tensor_shape()[3]));
-        ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 5) && (bias->info()->dimension(0) != input->info()->tensor_shape()[3] || bias->info()->dimension(1) != input->info()->tensor_shape()[4]));
-    }
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
+                                                  (bias != nullptr) ? bias->info() : nullptr,
+                                                  output->info()));
 
     _input  = input;
     _bias   = bias;
@@ -154,15 +185,17 @@ void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias
     }
 
     // Configure kernel
-    Window window = calculate_max_window(*input->info(), Steps());
-    window.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
-    window.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
-    window.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
+    auto win_config = validate_and_configure_window(input->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
+}
 
-    // The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
-    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+Status NEWeightsReshapeKernel::validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, biases, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
 
-    INEKernel::configure(window);
+    return Status{};
 }
 
 void NEWeightsReshapeKernel::run(const Window &window, const ThreadInfo &info)
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 5ca8eb8179..8f7d940fca 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -44,6 +44,16 @@ namespace arm_compute
 
 namespace arm_compute
 {
+namespace
+{
+TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool has_bias)
+{
+    const unsigned int mat_weights_cols = weights->dimension(3);
+    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+    return TensorShape(mat_weights_cols, mat_weights_rows);
+}
+} // namespace
+
 NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
 {
@@ -51,18 +61,12 @@ NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_p
 
 void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
-
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
+    // Perform validation step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
+                                                                          (biases != nullptr) ? biases->info() : nullptr,
+                                                                          output->info(),
+                                                                          transpose1xW));
 
     // Check if bias are present, if yes they will be embedded to the weights matrix
     const bool _has_bias = (biases != nullptr);
@@ -72,10 +76,7 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I
     if(transpose1xW)
     {
         // Create tensor to store the reshaped weights
-        const unsigned int mat_weights_cols = weights->info()->dimension(3);
-        const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
-        TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
-        TensorInfo         info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
+        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), _has_bias));
 
         _weights_reshaped.allocator()->init(info_wr);
         _memory_group.manage(&_weights_reshaped);
@@ -91,6 +92,46 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I
     }
 }
 
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool has_bias = (biases != nullptr);
+
+    // Checks performed when biases are present
+    if(has_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(transpose1xW)
+    {
+        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, has_bias));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
+    }
+
+    return Status{};
+}
+
 void NEConvolutionLayerReshapeWeights::run()
 {
     _memory_group.acquire();
@@ -105,50 +146,89 @@ void NEConvolutionLayerReshapeWeights::run()
     _memory_group.release();
 }
 
-NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
-      _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+namespace
 {
+TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has_bias, bool is_fully_connected_convolution)
+{
+    unsigned int mat_weights_cols = weights->dimension(3);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+
+    if(is_fully_connected_convolution)
+    {
+        // Create tensor to store the reshaped weights
+        return TensorShape(mat_weights_cols, mat_weights_rows);
+    }
+    else
+    {
+        // Create tensor to store transposed weights
+        const float transpose_width = 16.0f / weights->element_size();
+        return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+    }
 }
 
-void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
+                                      bool &has_bias,
+                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+                                      unsigned int &conv_w, unsigned int &conv_h)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
-    ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
 
     if(biases != nullptr)
     {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
-        ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    const DataType dt                   = input->info()->data_type();
-    const int      fixed_point_position = input->info()->fixed_point_position();
+    dt                   = input->data_type();
+    has_bias             = (biases != nullptr);
+    are_weights_reshaped = weights_info.are_reshaped();
+    kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
+    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+    mat_weights_cols     = weights->dimension(3);
+    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
 
-    _has_bias             = (biases != nullptr);
-    _are_weights_reshaped = weights_info.are_reshaped();
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
 
-    // Get parameters from conv_info
-    unsigned int stride_x = 0;
-    unsigned int stride_y = 0;
-    std::tie(stride_x, stride_y) = conv_info.stride();
+    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
 
-    // Get convolved dimensions
-    unsigned int conv_w = 0;
-    unsigned int conv_h = 0;
+    return Status{};
+}
+} // namespace
 
-    const unsigned int kernel_width  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
-    const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
-    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
-                                                 conv_info);
+NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
+      _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+{
+}
+
+void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
 
-    // Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels
-    _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    DataType     dt{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _has_bias, _are_weights_reshaped,
+                                                   kernel_width, kernel_height,
+                                                   _is_fully_connected_convolution,
+                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+
+    ARM_COMPUTE_ERROR_THROW_ON(status);
+
+    const unsigned int fixed_point_position = input->info()->fixed_point_position();
 
 #if defined(__arm__)
     if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
@@ -162,9 +242,6 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     }
 #endif /* defined(__arm__) || defined(__aarch64__) */
 
-    unsigned int mat_weights_cols = weights->info()->dimension(3);
-    unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
-
     // Reshape weights if needed
     if(_mm_optimised_kernel != nullptr)
     {
@@ -230,7 +307,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     shape_im2col.set(0, mat_input_cols);
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
-    _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
+    _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
     _memory_group.manage(&_input_im2col_reshaped);
 
     // Create tensor (interleave) to prepare input tensor for GEMM
@@ -239,7 +316,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
         TensorShape shape_interleaved(shape_im2col);
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-        _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
+        _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
         _memory_group.manage(&_input_interleaved_reshaped);
     }
 
@@ -247,7 +324,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
-    _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
+    _gemm_output.allocator()->init(_input_im2col_reshaped.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm));
     _memory_group.manage(&_gemm_output);
 
     // Configure kernels
@@ -296,8 +373,6 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
     _gemm_output.allocator()->allocate();
 
-    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
-
     // Allocate intermediate tensor
     if(!_are_weights_reshaped)
     {
@@ -305,6 +380,128 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     }
 }
 
+Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                    const WeightsInfo &weights_info)
+{
+    DataType     dt{};
+    bool         has_bias{};
+    bool         are_weights_reshaped{};
+    bool         is_fully_connected_convolution{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, has_bias, are_weights_reshaped, kernel_width, kernel_height,
+                                                   is_fully_connected_convolution, mat_weights_cols, mat_weights_rows,
+                                                   conv_w, conv_h);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(status);
+
+    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
+    bool                         optimised_kernel = false;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(optimised_kernel)
+    {
+        if(are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+    else
+    {
+        if(are_weights_reshaped)
+        {
+            const unsigned int transpose_width = 16 / input->element_size();
+            mat_weights_cols                   = weights_info.num_kernels();
+            mat_weights_rows                   = weights->dimension(0) / transpose_width + (has_bias ? 1 : 0);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(is_fully_connected_convolution)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+
+    // Validate im2col
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+    TensorShape        shape_im2col   = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, has_bias));
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(im2_col_info.tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
+
+    // Validate GEMM interleave and multiply
+    if(!is_fully_connected_convolution)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+    }
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    return Status{};
+}
+
 void NEConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)