COMPMID-1329: Add support for GenerateProposals operator in CL

Change-Id: Ib0798cc17496b7817f5b5769b25d98913a33a69d

19 files changed, 1606 insertions, 24 deletions

diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h
index 12700192e6..cc76231345 100644
--- a/arm_compute/core/CL/CLKernels.h
+++ b/arm_compute/core/CL/CLKernels.h
@@ -83,6 +83,7 @@
 #include "arm_compute/core/CL/kernels/CLGaussian3x3Kernel.h"
 #include "arm_compute/core/CL/kernels/CLGaussian5x5Kernel.h"
 #include "arm_compute/core/CL/kernels/CLGaussianPyramidKernel.h"
+#include "arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
 #include "arm_compute/core/CL/kernels/CLHOGDescriptorKernel.h"
 #include "arm_compute/core/CL/kernels/CLHOGDetectorKernel.h"
 #include "arm_compute/core/CL/kernels/CLHarrisCornersKernel.h"
diff --git a/arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h b/arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h
new file mode 100644
index 0000000000..6c883348a5
--- /dev/null
+++ b/arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2018 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 __ARM_COMPUTE_CLGENERATEPROPOSALSLAYERKERNEL_H__
+#define __ARM_COMPUTE_CLGENERATEPROPOSALSLAYERKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Interface for Compute All Anchors kernel */
+class CLComputeAllAnchorsKernel : public ICLKernel
+{
+public:
+    /** Default constructor */
+    CLComputeAllAnchorsKernel();
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLComputeAllAnchorsKernel(const CLComputeAllAnchorsKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLComputeAllAnchorsKernel &operator=(const CLComputeAllAnchorsKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    CLComputeAllAnchorsKernel(CLComputeAllAnchorsKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    CLComputeAllAnchorsKernel &operator=(CLComputeAllAnchorsKernel &&) = default;
+    /** Default destructor */
+    ~CLComputeAllAnchorsKernel() = default;
+
+    /** Set the input and output tensors.
+     *
+     * @param[in]  anchors     Source tensor. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: F16/F32
+     * @param[out] all_anchors Destination tensor. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input
+     * @param[in]  info        Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo
+     *
+     */
+    void configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLComputeAllAnchorsKernel
+     *
+     * @param[in] anchors     Source tensor info. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: F16/F32
+     * @param[in] all_anchors Destination tensor info. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input
+     * @param[in] info        Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo
+     *
+     * @return a Status
+     */
+    static Status validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info);
+
+    // Inherited methods overridden:
+    void run(const Window &window, cl::CommandQueue &queue) override;
+
+private:
+    const ICLTensor *_anchors;
+    ICLTensor       *_all_anchors;
+};
+} // arm_compute
+#endif // __ARM_COMPUTE_CLGENERATEPROSPOSALSLAYERKERNEL_H__
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 03f195f7da..1c9571463b 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -595,13 +595,17 @@ public:
      * @param[in] soft_nms_method          (Optional) Soft NMS method
      * @param[in] soft_nms_sigma           (Optional) Soft NMS sigma value
      * @param[in] soft_nms_min_score_thres (Optional) Soft NMS minimum score threshold
+     * @param[in] suppress_size            (Optional) Filter out boxes based on their size. Defaults to false
+     * @param[in] min_size                 (Optional) Smaller boxes than min_size will be filtered out. Defaults to 1
+     * @param[in] im_width                 (Optional) Boxes whose centers (on the x axis) is beyond im_width will be filtered. Defaults to 1
+     * @param[in] im_height                (Optional) Boxes whose centers (on the y axis) is beyond im_height will be filtered. Defaults to 1
      */
     BoxNMSLimitInfo(float score_thresh = 0.05f, float nms = 0.3f,
                     int detections = 100, bool soft_nms_enabled = false,
                     NMSType soft_nms_method = NMSType::LINEAR,
-                    float soft_nms_sigma = 0.5f, float soft_nms_min_score_thres = 0.001f)
+                    float soft_nms_sigma = 0.5f, float soft_nms_min_score_thres = 0.001f, bool suppress_size = false, float min_size = 1.0f, float im_width = 1.0f, float im_height = 1.0f)
         : _score_thresh(score_thresh), _nms(nms), _detections_per_im(detections), _soft_nms_enabled(soft_nms_enabled), _soft_nms_method(soft_nms_method), _soft_nms_sigma(soft_nms_sigma),
-          _soft_nms_min_score_thres(soft_nms_min_score_thres)
+          _soft_nms_min_score_thres(soft_nms_min_score_thres), _suppress_size(suppress_size), _min_size(min_size), _im_width(im_width), _im_height(im_height)
     {
     }
     /** Get the score threshold */
@@ -639,6 +643,26 @@ public:
     {
         return _soft_nms_min_score_thres;
     }
+    /** Get if NMS will suppress boxes based on their size/position */
+    bool suppress_size() const
+    {
+        return _suppress_size;
+    }
+    /** Get size suppression threshold */
+    float min_size() const
+    {
+        return _min_size;
+    }
+    /** Get image width (NMS may suppress boxes whose center sits beyond the image width) */
+    float im_width() const
+    {
+        return _im_width;
+    }
+    /** Get image height (NMS may suppress boxes whose center sits beyond the image height) */
+    float im_height() const
+    {
+        return _im_height;
+    }
 
 private:
     float   _score_thresh;
@@ -648,6 +672,10 @@ private:
     NMSType _soft_nms_method;
     float   _soft_nms_sigma;
     float   _soft_nms_min_score_thres;
+    bool    _suppress_size;
+    float   _min_size;
+    float   _im_width;
+    float   _im_height;
 };
 
 /** Padding and stride information class */
@@ -1049,6 +1077,137 @@ private:
     unsigned int _sampling_ratio;
 };
 
+/** Generate Proposals Information class */
+class GenerateProposalsInfo
+{
+public:
+    /** Constructor
+     *
+     * @param[in] im_width       Width of the original image
+     * @param[in] im_height      Height of the original image
+     * @param[in] im_scale       Scale applied to the original image
+     * @param[in] spatial_scale  (Optional)Scale applied to the feature map. Defaults to 1.0
+     * @param[in] pre_nms_topN   (Optional)Number of the best scores to be selected from the transformations. Defaults to 6000.
+     * @param[in] post_nms_topN  (Optional)Number of the best scores to be selected from the NMS operation. Defaults to 300.
+     * @param[in] nms_thres      (Optional)NMS overlap threshold. Defaults to 0.7.
+     * @param[in] min_size       (Optional)Size used to validate the anchors produced. Defaults to 16.
+     * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region of interest). Defaults to 4.
+     */
+    GenerateProposalsInfo(float im_width, float im_height, float im_scale, float spatial_scale = 1.0, int pre_nms_topN = 6000, int post_nms_topN = 300, float nms_thres = 0.7, float min_size = 16.0,
+                          size_t values_per_roi = 4)
+        : _im_height(im_height), _im_width(im_width), _im_scale(im_scale), _spatial_scale(spatial_scale), _pre_nms_topN(pre_nms_topN), _post_nms_topN(post_nms_topN), _nms_thres(nms_thres),
+          _min_size(min_size), _values_per_roi(values_per_roi)
+    {
+    }
+
+    /* Get the original height */
+    float im_height() const
+    {
+        return _im_height;
+    }
+    /* Get the original width */
+    float im_width() const
+    {
+        return _im_width;
+    }
+    /* Get the image scale */
+    float im_scale() const
+    {
+        return _im_scale;
+    }
+    /* Get the value of how many best scores to select (before NMS) */
+    int pre_nms_topN() const
+    {
+        return _pre_nms_topN;
+    }
+    /* Get the value of how many best scores to select (after NMS) */
+    int post_nms_topN() const
+    {
+        return _post_nms_topN;
+    }
+    /* Get the NMS overlap threshold */
+    float nms_thres() const
+    {
+        return _nms_thres;
+    }
+    /* Get the minimal size */
+    float min_size() const
+    {
+        return _min_size;
+    }
+    /* Get the spatial scale to be applied to the feature maps */
+    float spatial_scale() const
+    {
+        return _spatial_scale;
+    }
+    /* Get the values used to represent a ROI(Region of interest)*/
+    size_t values_per_roi() const
+    {
+        return _values_per_roi;
+    }
+
+private:
+    float  _im_height;
+    float  _im_width;
+    float  _im_scale;
+    float  _spatial_scale;
+    int    _pre_nms_topN;
+    int    _post_nms_topN;
+    float  _nms_thres;
+    float  _min_size;
+    size_t _values_per_roi;
+};
+
+/** ComputeAnchors information class */
+class ComputeAnchorsInfo
+{
+public:
+    /** Constructor
+     *
+     * @param[in] feat_width     Feature map width
+     * @param[in] feat_height    Feature map height
+     * @param[in] spatial_scale  Feature map scale
+     * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region Of Interest). Defaults to 4
+     */
+    ComputeAnchorsInfo(float feat_width, float feat_height, float spatial_scale, size_t values_per_roi = 4)
+        : _feat_height(feat_height),
+          _feat_width(feat_width),
+          _spatial_scale(spatial_scale),
+          _values_per_roi(values_per_roi)
+    {
+    }
+
+    /* Get the height of the feature map */
+    float feat_height() const
+    {
+        return _feat_height;
+    }
+
+    /* Get the width of the feature map */
+    float feat_width() const
+    {
+        return _feat_width;
+    }
+
+    /* Get the scale of the feature map */
+    float spatial_scale() const
+    {
+        return _spatial_scale;
+    }
+
+    /* Get the values used to represent a ROI(Region Of Interest)*/
+    size_t values_per_roi() const
+    {
+        return _values_per_roi;
+    }
+
+private:
+    float  _feat_height;
+    float  _feat_width;
+    float  _spatial_scale;
+    size_t _values_per_roi;
+};
+
 /** Bounding Box Transform information class */
 class BoundingBoxTransformInfo
 {
diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h
index 694e818788..9d4aa5b6a2 100644
--- a/arm_compute/runtime/CL/CLFunctions.h
+++ b/arm_compute/runtime/CL/CLFunctions.h
@@ -44,6 +44,7 @@
 #include "arm_compute/runtime/CL/functions/CLChannelExtract.h"
 #include "arm_compute/runtime/CL/functions/CLChannelShuffleLayer.h"
 #include "arm_compute/runtime/CL/functions/CLColorConvert.h"
+#include "arm_compute/runtime/CL/functions/CLComputeAllAnchors.h"
 #include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
 #include "arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h"
 #include "arm_compute/runtime/CL/functions/CLConvolution.h"
@@ -76,6 +77,7 @@
 #include "arm_compute/runtime/CL/functions/CLGaussian3x3.h"
 #include "arm_compute/runtime/CL/functions/CLGaussian5x5.h"
 #include "arm_compute/runtime/CL/functions/CLGaussianPyramid.h"
+#include "arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h"
 #include "arm_compute/runtime/CL/functions/CLHOGDescriptor.h"
 #include "arm_compute/runtime/CL/functions/CLHOGDetector.h"
 #include "arm_compute/runtime/CL/functions/CLHOGGradient.h"
diff --git a/arm_compute/runtime/CL/functions/CLComputeAllAnchors.h b/arm_compute/runtime/CL/functions/CLComputeAllAnchors.h
new file mode 100644
index 0000000000..6c6da791a1
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLComputeAllAnchors.h
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2018 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 __ARM_COMPUTE_CLCOMPUTEALLANCHORS_H__
+#define __ARM_COMPUTE_CLCOMPUTEALLANCHORS_H__
+
+#include "arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
+#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to run @ref CLComputeAllAnchorsKernel.
+ *
+ * This function calls the following OpenCL kernels:
+ * -# @ref CLComputeAllAnchorsKernel
+ */
+class CLComputeAllAnchors : public ICLSimpleFunction
+{
+public:
+    /** Set the input and output tensors.
+     *
+     * @param[in]  anchors     Source tensor. Original set of anchors of size (4, A) where A is the number of anchors. Data types supported: F16/F32
+     * @param[out] all_anchors Destination tensor. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input
+     * @param[in]  info        Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo
+     *
+     */
+    void configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLComputeAllAnchorsKernel
+     *
+     * @param[in] anchors     Source tensor info. Original set of anchors of size (4, A) where A is the number of anchors. Data types supported: F16/F32
+     * @param[in] all_anchors Destination tensor info. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input
+     * @param[in] info        Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo
+     *
+     * @return a Status
+     */
+    static Status validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info);
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLCOMPUTEALLANCOHORS_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h b/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h
new file mode 100644
index 0000000000..343229fe10
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2018 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 __ARM_COMPUTE_CLGENERATEPROPOSALSLAYER_H__
+#define __ARM_COMPUTE_CLGENERATEPROPOSALSLAYER_H__
+#include "arm_compute/core/CL/kernels/CLBoundingBoxTransformKernel.h"
+#include "arm_compute/core/CL/kernels/CLCopyKernel.h"
+#include "arm_compute/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
+#include "arm_compute/core/CL/kernels/CLMemsetKernel.h"
+#include "arm_compute/core/CL/kernels/CLPermuteKernel.h"
+#include "arm_compute/core/CL/kernels/CLReshapeLayerKernel.h"
+#include "arm_compute/core/CL/kernels/CLStridedSliceKernel.h"
+#include "arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLMemoryGroup.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
+#include "arm_compute/runtime/IFunction.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to generate proposals for a RPN (Region Proposal Network)
+ *
+ * This function calls the following OpenCL kernels:
+ * -# @ref CLComputeAllAnchors
+ * -# @ref CLPermute x 2
+ * -# @ref CLReshapeLayer x 2
+ * -# @ref CLStridedSlice x 3
+ * -# @ref CLBoundingBoxTransform
+ * -# @ref CLCopyKernel
+ * -# @ref CLMemsetKernel
+ * And the following CPP kernels:
+ * -# @ref CPPBoxWithNonMaximaSuppressionLimit
+ */
+class CLGenerateProposalsLayer : public IFunction
+{
+public:
+    /** Default constructor
+     *
+     * @param[in] memory_manager (Optional) Memory manager.
+     */
+    CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLGenerateProposalsLayer(const CLGenerateProposalsLayer &) = delete;
+    /** Default move constructor */
+    CLGenerateProposalsLayer(CLGenerateProposalsLayer &&) = default;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLGenerateProposalsLayer &operator=(const CLGenerateProposalsLayer &) = delete;
+    /** Default move assignment operator */
+    CLGenerateProposalsLayer &operator=(CLGenerateProposalsLayer &&) = default;
+
+    /** Set the input and output tensors.
+     *
+     * @param[in]  scores              Scores from convolution layer of size (W, H, A), where H and W are the height and width of the feature map, and A is the number of anchors. Data types supported: F16/F32
+     * @param[in]  deltas              Bounding box deltas from convolution layer of size (W, H, 4*A). Data types supported: Same as @p scores
+     * @param[in]  anchors             Anchors tensor of size (4, A). Data types supported: Same as @p input
+     * @param[out] proposals           Box proposals output tensor of size (5, W*H*A). Data types supported: Same as @p input
+     * @param[out] scores_out          Box scores output tensor of size (W*H*A). Data types supported: Same as @p input
+     * @param[out] num_valid_proposals Scalar output tensor which says which of the first proposals are valid. Data types supported: U32
+     * @param[in]  info                Contains GenerateProposals operation information described in @ref GenerateProposalsInfo
+     *
+     * @note Only single image prediction is supported. Height and Width (and scale) of the image will be contained in the @ref GenerateProposalsInfo struct.
+     * @note Proposals contains all the proposals. Of those, only the first num_valid_proposals are valid.
+     */
+    void configure(const ICLTensor *scores, const ICLTensor *deltas, const ICLTensor *anchors, ICLTensor *proposals, ICLTensor *scores_out, ICLTensor *num_valid_proposals,
+                   const GenerateProposalsInfo &info);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLGenerateProposalsLayer
+     *
+     * @param[in] scores              Scores info from convolution layer of size (W, H, A), where H and W are the height and width of the feature map, and A is the number of anchors. Data types supported: F16/F32
+     * @param[in] deltas              Bounding box deltas info from convolution layer of size (W, H, 4*A). Data types supported: Same as @p scores
+     * @param[in] anchors             Anchors tensor info of size (4, A). Data types supported: Same as @p input
+     * @param[in] proposals           Box proposals info  output tensor of size (5, W*H*A). Data types supported: Data types supported: U32
+     * @param[in] scores_out          Box scores output tensor info of size (W*H*A). Data types supported: Same as @p input
+     * @param[in] num_valid_proposals Scalar output tensor info which says which of the first proposals are valid. Data types supported: Same as @p input
+     * @param[in] info                Contains GenerateProposals operation information described in @ref GenerateProposalsInfo
+     *
+     * @return a Status
+     */
+    static Status validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out,
+                           const ITensorInfo           *num_valid_proposals,
+                           const GenerateProposalsInfo &info);
+
+    // Inherited methods overridden:
+    void run() override;
+
+private:
+    // Memory group manager
+    CLMemoryGroup _memory_group;
+
+    // OpenCL kernels
+    CLPermuteKernel              _permute_deltas_kernel;
+    CLReshapeLayerKernel         _flatten_deltas_kernel;
+    CLPermuteKernel              _permute_scores_kernel;
+    CLReshapeLayerKernel         _flatten_scores_kernel;
+    CLComputeAllAnchorsKernel    _compute_anchors_kernel;
+    CLBoundingBoxTransformKernel _bounding_box_kernel;
+    CLMemsetKernel               _memset_kernel;
+    CLCopyKernel                 _padded_copy_kernel;
+
+    // CPP kernels
+    CPPBoxWithNonMaximaSuppressionLimitKernel _cpp_nms_kernel;
+
+    // Temporary tensors
+    CLTensor _deltas_permuted;
+    CLTensor _deltas_flattened;
+    CLTensor _scores_permuted;
+    CLTensor _scores_flattened;
+    CLTensor _all_anchors;
+    CLTensor _all_proposals;
+    CLTensor _keeps_nms_unused;
+    CLTensor _classes_nms_unused;
+    CLTensor _proposals_4_roi_values;
+
+    // Output tensor pointers
+    ICLTensor *_num_valid_proposals;
+    ICLTensor *_scores_out;
+
+    /** Internal function to run the CPP BoxWithNMS kernel */
+    void run_cpp_nms_kernel();
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLGENERATEPROPOSALSLAYER_H__ */
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index ccc9aec0d8..fde9608949 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -275,6 +275,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "gemmlowp_output_stage_quantize_down", "gemmlowp.cl" },
     { "gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl" },
     { "gemmlowp_output_stage_quantize_down_float", "gemmlowp.cl" },
+    { "generate_proposals_compute_all_anchors", "generate_proposals.cl" },
     { "harris_score_3x3", "harris_corners.cl" },
     { "harris_score_5x5", "harris_corners.cl" },
     { "harris_score_7x7", "harris_corners.cl" },
@@ -655,6 +656,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/gemv.clembed"
     },
     {
+        "generate_proposals.cl",
+#include "./cl_kernels/generate_proposals.clembed"
+    },
+    {
         "harris_corners.cl",
 #include "./cl_kernels/harris_corners.clembed"
     },
diff --git a/src/core/CL/cl_kernels/bounding_box_transform.cl b/src/core/CL/cl_kernels/bounding_box_transform.cl
index d33018847e..14a0fadc2f 100644
--- a/src/core/CL/cl_kernels/bounding_box_transform.cl
+++ b/src/core/CL/cl_kernels/bounding_box_transform.cl
@@ -28,11 +28,11 @@
 /** Perform a padded copy of input tensor to the output tensor. Padding values are defined at compile time
  *
  * @attention The following variables must be passed at compile time:
- * -# -DDATA_TYPE = Tensor data type. Supported data types: F16/F32
+ * -# -DDATA_TYPE= Tensor data type. Supported data types: F16/F32
  * -# -DWEIGHT{X,Y,W,H}= Weights [wx, wy, ww, wh] for the deltas
  * -# -DIMG_WIDTH= Original image width
  * -# -DIMG_HEIGHT= Original image height
- * -# -DBOX_FIELDS=Number of fields that are used to represent a box in boxes
+ * -# -DBOX_FIELDS= Number of fields that are used to represent a box in boxes
  *
  * @param[in]  boxes_ptr                                Pointer to the boxes tensor. Supported data types: F16/F32
  * @param[in]  boxes_stride_x                           Stride of the boxes tensor in X dimension (in bytes)
@@ -97,7 +97,7 @@ __kernel void bounding_box_transform(
 
     // Useful vector constant definitions
     const VEC_DATA_TYPE(DATA_TYPE, 4)
-    max_values = (VEC_DATA_TYPE(DATA_TYPE, 4))(IMG_WIDTH, IMG_HEIGHT, IMG_WIDTH, IMG_HEIGHT);
+    max_values = (VEC_DATA_TYPE(DATA_TYPE, 4))(IMG_WIDTH - 1, IMG_HEIGHT - 1, IMG_WIDTH - 1, IMG_HEIGHT - 1);
     const VEC_DATA_TYPE(DATA_TYPE, 4)
     sign = (VEC_DATA_TYPE(DATA_TYPE, 4))(-1, -1, 1, 1);
     const VEC_DATA_TYPE(DATA_TYPE, 4)
diff --git a/src/core/CL/cl_kernels/generate_proposals.cl b/src/core/CL/cl_kernels/generate_proposals.cl
new file mode 100644
index 0000000000..bc6f4b5e17
--- /dev/null
+++ b/src/core/CL/cl_kernels/generate_proposals.cl
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2018 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 "helpers.h"
+
+/** Generate all the region of interests based on the image size and the anchors passed in. For each element (x,y) of the
+ * grid, it will generate NUM_ANCHORS rois, given by shifting the grid position to match the anchor.
+ *
+ * @attention The following variables must be passed at compile time:
+ * -# -DDATA_TYPE= Tensor data type. Supported data types: F16/F32
+ * -# -DHEIGHT= Height of the feature map on which this kernel is applied
+ * -# -DWIDTH= Width of the feature map on which this kernel is applied
+ * -# -DNUM_ANCHORS= Number of anchors to be used to generate the rois per each pixel
+ * -# -DSTRIDE= Stride to be applied at each different pixel position (i.e., x_range = (1:WIDTH)*STRIDE and y_range = (1:HEIGHT)*STRIDE
+ * -# -DNUM_ROI_FIELDS= Number of fields used to represent a roi
+ *
+ * @param[in]  anchors_ptr                           Pointer to the anchors tensor. Supported data types: F16/F32
+ * @param[in]  anchors_stride_x                      Stride of the anchors tensor in X dimension (in bytes)
+ * @param[in]  anchors_step_x                        anchors_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  anchors_stride_y                      Stride of the anchors tensor in Y dimension (in bytes)
+ * @param[in]  anchors_step_y                        anchors_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  anchors_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  anchors_step_z                        anchors_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  anchors_offset_first_element_in_bytes The offset of the first element in the boxes tensor
+ * @param[out] rois_ptr                              Pointer to the rois. Supported data types: same as @p in_ptr
+ * @param[out] rois_stride_x                         Stride of the rois in X dimension (in bytes)
+ * @param[out] rois_step_x                           pred_boxes_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[out] rois_stride_y                         Stride of the rois in Y dimension (in bytes)
+ * @param[out] rois_step_y                           pred_boxes_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[out] rois_stride_z                         Stride of the rois in Z dimension (in bytes)
+ * @param[out] rois_step_z                           pred_boxes_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[out] rois_offset_first_element_in_bytes    The offset of the first element in the rois
+ */
+#if defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)
+__kernel void generate_proposals_compute_all_anchors(
+    VECTOR_DECLARATION(anchors),
+    VECTOR_DECLARATION(rois))
+{
+    Vector anchors = CONVERT_TO_VECTOR_STRUCT_NO_STEP(anchors);
+    Vector rois    = CONVERT_TO_VECTOR_STRUCT(rois);
+
+    const size_t idx = get_global_id(0);
+    // Find the index of the anchor
+    const size_t anchor_idx = idx % NUM_ANCHORS;
+
+    // Find which shift is this thread using
+    const size_t shift_idx = idx / NUM_ANCHORS;
+
+    // Compute the shift on the X and Y direction (the shift depends exclusively by the index thread id)
+    const DATA_TYPE
+    shift_x = (DATA_TYPE)(shift_idx % WIDTH) * STRIDE;
+    const DATA_TYPE
+    shift_y = (DATA_TYPE)(shift_idx / WIDTH) * STRIDE;
+
+    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+    shift = (VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS))(shift_x, shift_y, shift_x, shift_y);
+
+    // Read the given anchor
+    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+    anchor = vload4(0, (__global DATA_TYPE *)vector_offset(&anchors, anchor_idx * NUM_ROI_FIELDS));
+
+    // Apply the shift to the anchor
+    const VEC_DATA_TYPE(DATA_TYPE, NUM_ROI_FIELDS)
+    shifted_anchor = anchor + shift;
+
+    vstore4(shifted_anchor, 0, (__global DATA_TYPE *)rois.ptr);
+}
+#endif //defined(DATA_TYPE) && defined(WIDTH) && defined(HEIGHT) && defined(NUM_ANCHORS) && defined(STRIDE) && defined(NUM_ROI_FIELDS)
diff --git a/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
new file mode 100644
index 0000000000..5d100a4c1e
--- /dev/null
+++ b/src/core/CL/kernels/CLGenerateProposalsLayerKernel.cpp
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2018 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/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLArray.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Window.h"
+
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(anchors, all_anchors);
+    ARM_COMPUTE_RETURN_ERROR_ON(anchors->dimension(0) != info.values_per_roi());
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(anchors, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(anchors->num_dimensions() > 2);
+    if(all_anchors->total_size() > 0)
+    {
+        size_t feature_height = info.feat_height();
+        size_t feature_width  = info.feat_width();
+        size_t num_anchors    = anchors->dimension(1);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(all_anchors, anchors);
+        ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->num_dimensions() > 2);
+        ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(0) != info.values_per_roi());
+        ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(1) != feature_height * feature_width * num_anchors);
+    }
+    return Status{};
+}
+} // namespace
+
+CLComputeAllAnchorsKernel::CLComputeAllAnchorsKernel()
+    : _anchors(nullptr), _all_anchors(nullptr)
+{
+}
+
+void CLComputeAllAnchorsKernel::configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(anchors, all_anchors);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(anchors->info(), all_anchors->info(), info));
+
+    // Metadata
+    const size_t   num_anchors = anchors->info()->dimension(1);
+    const DataType data_type   = anchors->info()->data_type();
+    const float    width       = info.feat_width();
+    const float    height      = info.feat_height();
+
+    // Initialize the output if empty
+    const TensorShape output_shape(info.values_per_roi(), width * height * num_anchors);
+    auto_init_if_empty(*all_anchors->info(), output_shape, 1, data_type);
+
+    // Set instance variables
+    _anchors     = anchors;
+    _all_anchors = all_anchors;
+
+    // Set build options
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+    build_opts.add_option("-DWIDTH=" + float_to_string_with_full_precision(width));
+    build_opts.add_option("-DHEIGHT=" + float_to_string_with_full_precision(height));
+    build_opts.add_option("-DSTRIDE=" + float_to_string_with_full_precision(1.f / info.spatial_scale()));
+    build_opts.add_option("-DNUM_ANCHORS=" + support::cpp11::to_string(num_anchors));
+    build_opts.add_option("-DNUM_ROI_FIELDS=" + support::cpp11::to_string(info.values_per_roi()));
+
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("generate_proposals_compute_all_anchors", build_opts.options()));
+
+    // The tensor all_anchors can be interpreted as an array of structs (each structs has values_per_roi fields).
+    // This means we don't need to pad on the X dimension, as we know in advance how many fields
+    // compose the struct.
+    Window win = calculate_max_window(*all_anchors->info(), Steps(info.values_per_roi()));
+    ICLKernel::configure_internal(win);
+}
+
+Status CLComputeAllAnchorsKernel::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(anchors, all_anchors, info));
+    return Status{};
+}
+
+void CLComputeAllAnchorsKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    // Collapse everything on the first dimension
+    Window collapsed = window.collapse(ICLKernel::window(), Window::DimX);
+
+    // Set arguments
+    unsigned int idx = 0;
+    add_1D_tensor_argument(idx, _anchors, collapsed);
+    add_1D_tensor_argument(idx, _all_anchors, collapsed);
+
+    // Note that we don't need to loop over the slices, as we are launching exactly
+    // as many threads as all the anchors generated
+    enqueue(queue, *this, collapsed);
+}
+} // namespace arm_compute
diff --git a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
index 89413fcca4..2b9934cfa8 100644
--- a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
+++ b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
@@ -54,7 +54,7 @@ std::vector<int> SoftNMS(const ITensor *proposals, std::vector<std::vector<T>> &
         areas[i] = (x2[i] - x1[i] + 1.0) * (y2[i] - y1[i] + 1.0);
     }
 
-    // Note: Soft NMS scores have already been initialize with input scores
+    // Note: Soft NMS scores have already been initialized with input scores
 
     while(!inds.empty())
     {
@@ -150,17 +150,21 @@ std::vector<int> NonMaximaSuppression(const ITensor *proposals, std::vector<int>
 
         for(unsigned int j = 0; j < sorted_indices_temp.size(); ++j)
         {
-            const auto xx1 = std::max(x1[sorted_indices_temp.at(j)], x1[i]);
-            const auto yy1 = std::max(y1[sorted_indices_temp.at(j)], y1[i]);
-            const auto xx2 = std::min(x2[sorted_indices_temp.at(j)], x2[i]);
-            const auto yy2 = std::min(y2[sorted_indices_temp.at(j)], y2[i]);
-
-            const auto w     = std::max((xx2 - xx1 + 1.f), 0.f);
-            const auto h     = std::max((yy2 - yy1 + 1.f), 0.f);
-            const auto inter = w * h;
-            const auto ovr   = inter / (areas[i] + areas[sorted_indices_temp.at(j)] - inter);
-
-            if(ovr <= info.nms())
+            const float xx1 = std::max(x1[sorted_indices_temp.at(j)], x1[i]);
+            const float yy1 = std::max(y1[sorted_indices_temp.at(j)], y1[i]);
+            const float xx2 = std::min(x2[sorted_indices_temp.at(j)], x2[i]);
+            const float yy2 = std::min(y2[sorted_indices_temp.at(j)], y2[i]);
+
+            const float w     = std::max((xx2 - xx1 + 1.f), 0.f);
+            const float h     = std::max((yy2 - yy1 + 1.f), 0.f);
+            const float inter = w * h;
+            const float ovr   = inter / (areas[i] + areas[sorted_indices_temp.at(j)] - inter);
+            const float ctr_x = xx1 + (w / 2);
+            const float ctr_y = yy1 + (h / 2);
+
+            // If suppress_size is specified, filter the boxes based on their size and position
+            const bool keep_size = !info.suppress_size() || (w >= info.min_size() && h >= info.min_size() && ctr_x < info.im_width() && ctr_y < info.im_height());
+            if(ovr <= info.nms() && keep_size)
             {
                 new_indices.push_back(j);
             }
@@ -214,8 +218,9 @@ void CPPBoxWithNonMaximaSuppressionLimitKernel::run_nmslimit()
     for(int b = 0; b < batch_size; ++b)
     {
         const int num_boxes = _batch_splits_in == nullptr ? 1 : static_cast<int>(*reinterpret_cast<T *>(_batch_splits_in->ptr_to_element(Coordinates(b))));
-        // Skip first class
-        for(int j = 1; j < num_classes; ++j)
+        // Skip first class if there is more than 1 except if the number of classes is 1.
+        const int j_start = (num_classes == 1 ? 0 : 1);
+        for(int j = j_start; j < num_classes; ++j)
         {
             std::vector<T>   cur_scores(scores_count);
             std::vector<int> inds;
@@ -290,7 +295,7 @@ void CPPBoxWithNonMaximaSuppressionLimitKernel::run_nmslimit()
 
         // Write results
         int cur_out_idx = 0;
-        for(int j = 1; j < num_classes; ++j)
+        for(int j = j_start; j < num_classes; ++j)
         {
             auto     &cur_keep        = keeps[j];
             auto      cur_out_scores  = reinterpret_cast<T *>(_scores_out->ptr_to_element(Coordinates(cur_start_idx + cur_out_idx)));
diff --git a/src/runtime/CL/functions/CLComputeAllAnchors.cpp b/src/runtime/CL/functions/CLComputeAllAnchors.cpp
new file mode 100644
index 0000000000..409d3c9e91
--- /dev/null
+++ b/src/runtime/CL/functions/CLComputeAllAnchors.cpp
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2018 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/runtime/CL/functions/CLComputeAllAnchors.h"
+
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+void CLComputeAllAnchors::configure(const ICLTensor *anchors, ICLTensor *all_anchors, const ComputeAnchorsInfo &info)
+{
+    // Configure ComputeAllAnchors kernel
+    auto k = arm_compute::support::cpp14::make_unique<CLComputeAllAnchorsKernel>();
+    k->configure(anchors, all_anchors, info);
+    _kernel = std::move(k);
+}
+
+Status CLComputeAllAnchors::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
+{
+    return CLComputeAllAnchorsKernel::validate(anchors, all_anchors, info);
+}
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
new file mode 100644
index 0000000000..80ed0e55a4
--- /dev/null
+++ b/src/runtime/CL/functions/CLGenerateProposalsLayer.cpp
@@ -0,0 +1,251 @@
+/*
+ * Copyright (c) 2018 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/runtime/CL/functions/CLGenerateProposalsLayer.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Types.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+CLGenerateProposalsLayer::CLGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)),
+      _permute_deltas_kernel(),
+      _flatten_deltas_kernel(),
+      _permute_scores_kernel(),
+      _flatten_scores_kernel(),
+      _compute_anchors_kernel(),
+      _bounding_box_kernel(),
+      _memset_kernel(),
+      _padded_copy_kernel(),
+      _cpp_nms_kernel(),
+      _deltas_permuted(),
+      _deltas_flattened(),
+      _scores_permuted(),
+      _scores_flattened(),
+      _all_anchors(),
+      _all_proposals(),
+      _keeps_nms_unused(),
+      _classes_nms_unused(),
+      _proposals_4_roi_values(),
+      _num_valid_proposals(nullptr),
+      _scores_out(nullptr)
+{
+}
+
+void CLGenerateProposalsLayer::configure(const ICLTensor *scores, const ICLTensor *deltas, const ICLTensor *anchors, ICLTensor *proposals, ICLTensor *scores_out, ICLTensor *num_valid_proposals,
+                                         const GenerateProposalsInfo &info)
+{
+    const DataType data_type         = deltas->info()->data_type();
+    const int      num_anchors       = scores->info()->dimension(2);
+    const int      feat_width        = scores->info()->dimension(0);
+    const int      feat_height       = scores->info()->dimension(1);
+    const int      total_num_anchors = num_anchors * feat_width * feat_height;
+    const int      pre_nms_topN      = info.pre_nms_topN();
+    const int      post_nms_topN     = info.post_nms_topN();
+    const size_t   values_per_roi    = info.values_per_roi();
+
+    // Compute all the anchors
+    _memory_group.manage(&_all_anchors);
+    _compute_anchors_kernel.configure(anchors, &_all_anchors, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()));
+
+    const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors);
+    _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, data_type));
+
+    // Permute and reshape deltas
+    _memory_group.manage(&_deltas_permuted);
+    _memory_group.manage(&_deltas_flattened);
+    _permute_deltas_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 });
+    _flatten_deltas_kernel.configure(&_deltas_permuted, &_deltas_flattened);
+    _deltas_permuted.allocator()->allocate();
+
+    const TensorShape flatten_shape_scores(1, total_num_anchors);
+    _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, data_type));
+
+    // Permute and reshape scores
+    _memory_group.manage(&_scores_permuted);
+    _memory_group.manage(&_scores_flattened);
+    _permute_scores_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 });
+    _flatten_scores_kernel.configure(&_scores_permuted, &_scores_flattened);
+    _scores_permuted.allocator()->allocate();
+
+    // Bounding box transform
+    _memory_group.manage(&_all_proposals);
+    BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
+    _bounding_box_kernel.configure(&_all_anchors, &_all_proposals, &_deltas_flattened, bbox_info);
+    _deltas_flattened.allocator()->allocate();
+    _all_anchors.allocator()->allocate();
+
+    // The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort)
+    // that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation.
+    // Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input)
+    // and the filtering
+    const int   scores_nms_size = std::min<int>(std::min<int>(post_nms_topN, pre_nms_topN), total_num_anchors);
+    const float min_size_scaled = info.min_size() * info.im_scale();
+    _memory_group.manage(&_classes_nms_unused);
+    _memory_group.manage(&_keeps_nms_unused);
+
+    // Note that NMS needs outputs preinitialized.
+    auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, data_type);
+    auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type);
+    auto_init_if_empty(*num_valid_proposals->info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type);
+
+    // Initialize temporaries (unused) outputs
+    _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(1, 1), 1, data_type));
+    _keeps_nms_unused.allocator()->init(*scores_out->info());
+
+    // Save the output (to map and unmap them at run)
+    _scores_out          = scores_out;
+    _num_valid_proposals = num_valid_proposals;
+
+    _memory_group.manage(&_proposals_4_roi_values);
+    _cpp_nms_kernel.configure(&_scores_flattened, &_all_proposals, nullptr, scores_out, &_proposals_4_roi_values, &_classes_nms_unused, nullptr, &_keeps_nms_unused, num_valid_proposals,
+                              BoxNMSLimitInfo(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f, true, min_size_scaled, info.im_width(), info.im_height()));
+    _keeps_nms_unused.allocator()->allocate();
+    _classes_nms_unused.allocator()->allocate();
+    _all_proposals.allocator()->allocate();
+    _scores_flattened.allocator()->allocate();
+
+    // Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images
+    _padded_copy_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
+    _proposals_4_roi_values.allocator()->allocate();
+
+    _memset_kernel.configure(proposals, PixelValue());
+}
+
+Status CLGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out,
+                                          const ITensorInfo *num_valid_proposals, const GenerateProposalsInfo &info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(proposals, scores_out, num_valid_proposals);
+
+    const int num_anchors       = scores->dimension(2);
+    const int feat_width        = scores->dimension(0);
+    const int feat_height       = scores->dimension(1);
+    const int num_images        = scores->dimension(3);
+    const int total_num_anchors = num_anchors * feat_width * feat_height;
+    const int values_per_roi    = info.values_per_roi();
+
+    ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);
+
+    TensorInfo all_anchors_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLComputeAllAnchorsKernel::validate(anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())));
+
+    TensorInfo deltas_permuted_info = deltas->clone()->set_tensor_shape(TensorShape(values_per_roi * num_anchors, feat_width, feat_height)).set_is_resizable(true);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 }));
+
+    TensorInfo deltas_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info));
+
+    TensorInfo scores_permuted_info = scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteKernel::validate(scores, &scores_permuted_info, PermutationVector{ 2, 0, 1 }));
+
+    TensorInfo scores_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
+    TensorInfo proposals_4_roi_values(proposals->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info, BoundingBoxTransformInfo(info.im_width(), info.im_height(),
+                                                                       1.f)));
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } }));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(proposals, PixelValue()));
+
+    if(num_valid_proposals->total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->dimension(0) > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(num_valid_proposals, DataType::U32);
+    }
+
+    if(proposals->total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(proposals->num_dimensions() > 2);
+        ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, deltas);
+    }
+
+    if(scores_out->total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(scores_out->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(scores_out->dimension(0) != size_t(total_num_anchors));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores_out, scores);
+    }
+
+    return Status{};
+}
+
+void CLGenerateProposalsLayer::run_cpp_nms_kernel()
+{
+    // Map inputs
+    _scores_flattened.map(true);
+    _all_proposals.map(true);
+
+    // Map outputs
+    _scores_out->map(CLScheduler::get().queue(), true);
+    _proposals_4_roi_values.map(CLScheduler::get().queue(), true);
+    _num_valid_proposals->map(CLScheduler::get().queue(), true);
+    _keeps_nms_unused.map(true);
+    _classes_nms_unused.map(true);
+
+    // Run nms
+    CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX);
+
+    // Unmap outputs
+    _keeps_nms_unused.unmap();
+    _classes_nms_unused.unmap();
+    _scores_out->unmap(CLScheduler::get().queue());
+    _proposals_4_roi_values.unmap(CLScheduler::get().queue());
+    _num_valid_proposals->unmap(CLScheduler::get().queue());
+
+    // Unmap inputs
+    _scores_flattened.unmap();
+    _all_proposals.unmap();
+}
+
+void CLGenerateProposalsLayer::run()
+{
+    // Acquire all the temporaries
+    _memory_group.acquire();
+
+    // Compute all the anchors
+    CLScheduler::get().enqueue(_compute_anchors_kernel, false);
+
+    // Transpose and reshape the inputs
+    CLScheduler::get().enqueue(_permute_deltas_kernel, false);
+    CLScheduler::get().enqueue(_flatten_deltas_kernel, false);
+    CLScheduler::get().enqueue(_permute_scores_kernel, false);
+    CLScheduler::get().enqueue(_flatten_scores_kernel, false);
+
+    // Build the boxes
+    CLScheduler::get().enqueue(_bounding_box_kernel, false);
+    // Non maxima suppression
+    run_cpp_nms_kernel();
+    // Add dummy batch indexes
+    CLScheduler::get().enqueue(_memset_kernel, true);
+    CLScheduler::get().enqueue(_padded_copy_kernel, true);
+
+    // Release all the temporaries
+    _memory_group.release();
+}
+} // namespace arm_compute
diff --git a/tests/validation/CL/GenerateProposalsLayer.cpp b/tests/validation/CL/GenerateProposalsLayer.cpp
new file mode 100644
index 0000000000..28cdc71ae6
--- /dev/null
+++ b/tests/validation/CL/GenerateProposalsLayer.cpp
@@ -0,0 +1,334 @@
+/*
+ * Copyright (c) 2018 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/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CL/functions/CLComputeAllAnchors.h"
+#include "arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h"
+#include "arm_compute/runtime/CL/functions/CLSlice.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/CL/CLArrayAccessor.h"
+#include "tests/Globals.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/ComputeAllAnchorsFixture.h"
+#include "utils/TypePrinter.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+template <typename U, typename T>
+inline void fill_tensor(U &&tensor, const std::vector<T> &v)
+{
+    std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
+}
+
+const auto ComputeAllInfoDataset = framework::dataset::make("ComputeAllInfo",
+{
+    ComputeAnchorsInfo(10U, 10U, 1. / 16.f),
+    ComputeAnchorsInfo(100U, 1U, 1. / 2.f),
+    ComputeAnchorsInfo(100U, 1U, 1. / 4.f),
+    ComputeAnchorsInfo(100U, 100U, 1. / 4.f),
+
+});
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(GenerateProposals)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
+               framework::dataset::make("scores", { TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F32),
+                                                    TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Mismatching types
+                                                    TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong deltas (number of transformation non multiple of 4)
+                                                    TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong anchors (number of values per roi != 5)
+                                                    TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16)}), // Output tensor num_valid_proposals not scalar
+               framework::dataset::make("deltas",{ TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
+                                                   TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
+                                                   TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
+                                                   TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
+                                                   TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32)})),
+               framework::dataset::make("anchors", { TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
+                                                     TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
+                                                     TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
+                                                     TensorInfo(TensorShape(5U, 9U), 1, DataType::F32),
+                                                     TensorInfo(TensorShape(4U, 9U), 1, DataType::F32)})),
+               framework::dataset::make("proposals", { TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
+                                                       TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
+                                                       TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
+                                                       TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
+                                                       TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32)})),
+               framework::dataset::make("scores_out", { TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
+                                                        TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
+                                                        TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
+                                                        TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
+                                                        TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32)})),
+               framework::dataset::make("num_valid_proposals", { TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
+                                                                 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
+                                                                 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
+                                                                 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
+                                                                 TensorInfo(TensorShape(1U, 10U), 1, DataType::U32)})),
+               framework::dataset::make("generate_proposals_info", { GenerateProposalsInfo(10.f, 10.f, 1.f),
+                                                                     GenerateProposalsInfo(10.f, 10.f, 1.f),
+                                                                     GenerateProposalsInfo(10.f, 10.f, 1.f),
+                                                                     GenerateProposalsInfo(10.f, 10.f, 1.f),
+                                                                     GenerateProposalsInfo(10.f, 10.f, 1.f)})),
+               framework::dataset::make("Expected", { true, false, false, false, false })),
+        scores, deltas, anchors, proposals, scores_out, num_valid_proposals, generate_proposals_info, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLGenerateProposalsLayer::validate(&scores.clone()->set_is_resizable(true),
+                                                          &deltas.clone()->set_is_resizable(true),
+                                                          &anchors.clone()->set_is_resizable(true),
+                                                          &proposals.clone()->set_is_resizable(true),
+                                                          &scores_out.clone()->set_is_resizable(true),
+                                                          &num_valid_proposals.clone()->set_is_resizable(true),
+                                                          generate_proposals_info)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLComputeAllAnchorsFixture = ComputeAllAnchorsFixture<CLTensor, CLAccessor, CLComputeAllAnchors, T>;
+
+TEST_SUITE(Float)
+TEST_SUITE(FP32)
+DATA_TEST_CASE(IntegrationTestCaseAllAnchors, framework::DatasetMode::ALL, framework::dataset::make("DataType", { DataType::F32 }),
+               data_type)
+{
+    const int values_per_roi = 4;
+    const int num_anchors    = 3;
+    const int feature_height = 4;
+    const int feature_width  = 3;
+
+    SimpleTensor<float> anchors_expected(TensorShape(values_per_roi, feature_width * feature_height * num_anchors), DataType::F32);
+    fill_tensor(anchors_expected, std::vector<float> { -38, -16, 53, 31, -84, -40, 99, 55, -176, -88, 191, 103,
+                                                       -22, -16, 69, 31, -68, -40, 115, 55, -160, -88, 207, 103,
+                                                       -6, -16, 85, 31, -52, -40, 131, 55, -144, -88, 223, 103, -38,
+                                                       0, 53, 47, -84, -24, 99, 71,
+                                                       -176, -72, 191, 119, -22, 0, 69, 47, -68, -24, 115, 71, -160, -72, 207,
+                                                       119, -6, 0, 85, 47, -52, -24, 131, 71, -144, -72, 223, 119, -38, 16, 53,
+                                                       63, -84, -8, 99, 87, -176, -56, 191, 135, -22, 16, 69, 63, -68, -8, 115,
+                                                       87, -160, -56, 207, 135, -6, 16, 85, 63, -52, -8, 131, 87, -144, -56, 223,
+                                                       135, -38, 32, 53, 79, -84, 8, 99, 103, -176, -40, 191, 151, -22, 32, 69,
+                                                       79, -68, 8, 115, 103, -160, -40, 207, 151, -6, 32, 85, 79, -52, 8, 131,
+                                                       103, -144, -40, 223, 151
+                                                     });
+
+    CLTensor all_anchors;
+    CLTensor anchors = create_tensor<CLTensor>(TensorShape(4, num_anchors), data_type);
+
+    // Create and configure function
+    CLComputeAllAnchors compute_anchors;
+    compute_anchors.configure(&anchors, &all_anchors, ComputeAnchorsInfo(feature_width, feature_height, 1. / 16.0));
+    anchors.allocator()->allocate();
+    all_anchors.allocator()->allocate();
+
+    fill_tensor(CLAccessor(anchors), std::vector<float> { -38, -16, 53, 31,
+                                                          -84, -40, 99, 55,
+                                                          -176, -88, 191, 103
+                                                        });
+    // Compute function
+    compute_anchors.run();
+    validate(CLAccessor(all_anchors), anchors_expected);
+}
+
+DATA_TEST_CASE(IntegrationTestCaseGenerateProposals, framework::DatasetMode::ALL, framework::dataset::make("DataType", { DataType::F32 }),
+               data_type)
+{
+    const int values_per_roi = 4;
+    const int num_anchors    = 2;
+    const int feature_height = 4;
+    const int feature_width  = 5;
+
+    std::vector<float> scores_vector
+    {
+        5.44218998e-03f, 1.19207997e-03f, 1.12379994e-03f, 1.17181998e-03f,
+        1.20544003e-03f, 6.17993006e-04f, 1.05261997e-05f, 8.91025957e-06f,
+        9.29536981e-09f, 6.09605013e-05f, 4.72735002e-04f, 1.13482002e-10f,
+        1.50015003e-05f, 4.45032993e-06f, 3.21612994e-08f, 8.02662980e-04f,
+        1.40488002e-04f, 3.12508007e-07f, 3.02616991e-06f, 1.97759000e-08f,
+        2.66913995e-02f, 5.26766013e-03f, 5.05053019e-03f, 5.62100019e-03f,
+        5.37420018e-03f, 5.26280981e-03f, 2.48894998e-04f, 1.06842002e-04f,
+        3.92931997e-06f, 1.79388002e-03f, 4.79440019e-03f, 3.41609990e-07f,
+        5.20430971e-04f, 3.34090000e-05f, 2.19159006e-07f, 2.28786003e-03f,
+        5.16703985e-05f, 4.04523007e-06f, 1.79227004e-06f, 5.32449000e-08f
+    };
+
+    std::vector<float> bbx_vector
+    {
+        -1.65040009e-02f, -1.84051003e-02f, -1.85930002e-02f, -2.08263006e-02f,
+        -1.83814000e-02f, -2.89172009e-02f, -3.89706008e-02f, -7.52277970e-02f,
+        -1.54091999e-01f, -2.55433004e-02f, -1.77490003e-02f, -1.10340998e-01f,
+        -4.20190990e-02f, -2.71421000e-02f, 6.89801015e-03f, 5.71171008e-02f,
+        -1.75665006e-01f, 2.30021998e-02f, 3.08554992e-02f, -1.39333997e-02f,
+        3.40579003e-01f, 3.91070992e-01f, 3.91624004e-01f, 3.92527014e-01f,
+        3.91445011e-01f, 3.79328012e-01f, 4.26631987e-01f, 3.64892989e-01f,
+        2.76894987e-01f, 5.13985991e-01f, 3.79999995e-01f, 1.80457994e-01f,
+        4.37402993e-01f, 4.18545991e-01f, 2.51549989e-01f, 4.48318988e-01f,
+        1.68564007e-01f, 4.65440989e-01f, 4.21891987e-01f, 4.45928007e-01f,
+        3.27155995e-03f, 3.71480011e-03f, 3.60032008e-03f, 4.27092984e-03f,
+        3.74579988e-03f, 5.95752988e-03f, -3.14473989e-03f, 3.52022005e-03f,
+        -1.88564006e-02f, 1.65188999e-03f, 1.73791999e-03f, -3.56074013e-02f,
+        -1.66615995e-04f, 3.14146001e-03f, -1.11830998e-02f, -5.35363983e-03f,
+        6.49790000e-03f, -9.27671045e-03f, -2.83346009e-02f, -1.61233004e-02f,
+        -2.15505004e-01f, -2.19910994e-01f, -2.20872998e-01f, -2.12831005e-01f,
+        -2.19145000e-01f, -2.27687001e-01f, -3.43973994e-01f, -2.75869995e-01f,
+        -3.19516987e-01f, -2.50418007e-01f, -2.48537004e-01f, -5.08224010e-01f,
+        -2.28724003e-01f, -2.82402009e-01f, -3.75815988e-01f, -2.86352992e-01f,
+        -5.28333001e-02f, -4.43836004e-01f, -4.55134988e-01f, -4.34897989e-01f,
+        -5.65053988e-03f, -9.25739005e-04f, -1.06790999e-03f, -2.37016007e-03f,
+        -9.71166010e-04f, -8.90910998e-03f, -1.17592998e-02f, -2.08992008e-02f,
+        -4.94231991e-02f, 6.63906988e-03f, 3.20469006e-03f, -6.44695014e-02f,
+        -3.11607006e-03f, 2.02738005e-03f, 1.48096997e-02f, 4.39785011e-02f,
+        -8.28424022e-02f, 3.62076014e-02f, 2.71668993e-02f, 1.38250999e-02f,
+        6.76669031e-02f, 1.03252999e-01f, 1.03255004e-01f, 9.89722982e-02f,
+        1.03646003e-01f, 4.79663983e-02f, 1.11014001e-01f, 9.31736007e-02f,
+        1.15768999e-01f, 1.04014002e-01f, -8.90677981e-03f, 1.13103002e-01f,
+        1.33085996e-01f, 1.25405997e-01f, 1.50051996e-01f, -1.13038003e-01f,
+        7.01059997e-02f, 1.79651007e-01f, 1.41055003e-01f, 1.62841007e-01f,
+        -1.00247003e-02f, -8.17587040e-03f, -8.32176022e-03f, -8.90108012e-03f,
+        -8.13035015e-03f, -1.77263003e-02f, -3.69572006e-02f, -3.51580009e-02f,
+        -5.92143014e-02f, -1.80795006e-02f, -5.46086021e-03f, -4.10550982e-02f,
+        -1.83081999e-02f, -2.15411000e-02f, -1.17953997e-02f, 3.33894007e-02f,
+        -5.29635996e-02f, -6.97528012e-03f, -3.15250992e-03f, -3.27355005e-02f,
+        1.29676998e-01f, 1.16080999e-01f, 1.15947001e-01f, 1.21797003e-01f,
+        1.16089001e-01f, 1.44875005e-01f, 1.15617000e-01f, 1.31586999e-01f,
+        1.74735002e-02f, 1.21973999e-01f, 1.31596997e-01f, 2.48907991e-02f,
+        6.18605018e-02f, 1.12855002e-01f, -6.99798986e-02f, 9.58312973e-02f,
+        1.53593004e-01f, -8.75087008e-02f, -4.92327996e-02f, -3.32239009e-02f
+    };
+
+    std::vector<float> anchors_vector{ -38, -16, 53, 31,
+                                       -120, -120, 135, 135 };
+
+    SimpleTensor<float> proposals_expected(TensorShape(5, 9), DataType::F32);
+    fill_tensor(proposals_expected, std::vector<float> { 0, 0, 0, 79, 59,
+                                                         0, 0, 5.0005703f, 52.63237f, 43.69501495f,
+                                                         0, 24.13628387f, 7.51243401f, 79, 46.06628418f,
+                                                         0, 0, 7.50924301f, 68.47792816f, 46.03357315f,
+                                                         0, 0, 23.09477997f, 51.61448669f, 59,
+                                                         0, 0, 39.52141571f, 52.44710541f, 59,
+                                                         0, 23.57396317f, 29.98791885f, 79, 59,
+                                                         0, 0, 41.90219116f, 79, 59,
+                                                         0, 0, 23.30098343f, 79, 59
+                                                       });
+
+    SimpleTensor<float> scores_expected(TensorShape(9), DataType::F32);
+    fill_tensor(scores_expected, std::vector<float>
+    {
+        2.66913995e-02f,
+        5.44218998e-03f,
+        1.20544003e-03f,
+        1.19207997e-03f,
+        6.17993006e-04f,
+        4.72735002e-04f,
+        6.09605013e-05f,
+        1.50015003e-05f,
+        8.91025957e-06f
+    });
+
+    // Inputs
+    CLTensor scores      = create_tensor<CLTensor>(TensorShape(feature_width, feature_height, num_anchors), data_type);
+    CLTensor bbox_deltas = create_tensor<CLTensor>(TensorShape(feature_width, feature_height, values_per_roi * num_anchors), data_type);
+    CLTensor anchors     = create_tensor<CLTensor>(TensorShape(values_per_roi, num_anchors), data_type);
+
+    // Outputs
+    CLTensor proposals;
+    CLTensor num_valid_proposals;
+    CLTensor scores_out;
+    num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::F32));
+
+    CLGenerateProposalsLayer generate_proposals;
+    generate_proposals.configure(&scores, &bbox_deltas, &anchors, &proposals, &scores_out, &num_valid_proposals,
+                                 GenerateProposalsInfo(80, 60, 0.166667f, 1 / 16.0, 6000, 300, 0.7f, 16.0f));
+
+    // Allocate memory for input/output tensors
+    scores.allocator()->allocate();
+    bbox_deltas.allocator()->allocate();
+    anchors.allocator()->allocate();
+    proposals.allocator()->allocate();
+    num_valid_proposals.allocator()->allocate();
+    scores_out.allocator()->allocate();
+
+    // Fill inputs
+    fill_tensor(CLAccessor(scores), scores_vector);
+    fill_tensor(CLAccessor(bbox_deltas), bbx_vector);
+    fill_tensor(CLAccessor(anchors), anchors_vector);
+
+    // Run operator
+    generate_proposals.run();
+
+    // Gather num_valid_proposals
+    num_valid_proposals.map();
+    const float N = *reinterpret_cast<float *>(num_valid_proposals.ptr_to_element(Coordinates(0, 0)));
+    num_valid_proposals.unmap();
+
+    // Select the first N entries of the proposals
+    CLTensor proposals_final;
+    CLSlice  select_proposals;
+    select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, size_t(N)));
+    proposals_final.allocator()->allocate();
+    select_proposals.run();
+
+    // Select the first N entries of the proposals
+    CLTensor scores_final;
+    CLSlice  select_scores;
+    select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(size_t(N)));
+    scores_final.allocator()->allocate();
+    select_scores.run();
+
+    // Validate the output
+    validate(CLAccessor(proposals_final), proposals_expected);
+    validate(CLAccessor(scores_final), scores_expected);
+}
+
+FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsFixture<float>, framework::DatasetMode::ALL,
+                       combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsFixture<half>, framework::DatasetMode::ALL,
+                       combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F16 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // Float
+
+TEST_SUITE_END() // GenerateProposals
+TEST_SUITE_END() // CL
+
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/ComputeAllAnchorsFixture.h b/tests/validation/fixtures/ComputeAllAnchorsFixture.h
new file mode 100644
index 0000000000..f82cac4fe6
--- /dev/null
+++ b/tests/validation/fixtures/ComputeAllAnchorsFixture.h
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2018 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 ARM_COMPUTE_TEST_COMPUTEALLANCHORS_FIXTURE
+#define ARM_COMPUTE_TEST_COMPUTEALLANCHORS_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ComputeAllAnchors.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ComputeAllAnchorsFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(size_t num_anchors, const ComputeAnchorsInfo &info, DataType data_type)
+    {
+        _target    = compute_target(num_anchors, data_type, info);
+        _reference = compute_reference(num_anchors, data_type, info);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor)
+    {
+        library->fill_tensor_uniform(tensor, 0, T(0), T(100));
+    }
+
+    TensorType compute_target(size_t num_anchors, DataType data_type, const ComputeAnchorsInfo &info)
+    {
+        // Create tensors
+        TensorShape anchors_shape(4, num_anchors);
+        TensorType  anchors = create_tensor<TensorType>(anchors_shape, data_type);
+        TensorType  all_anchors;
+
+        // Create and configure function
+        FunctionType compute_all_anchors;
+        compute_all_anchors.configure(&anchors, &all_anchors, info);
+
+        ARM_COMPUTE_EXPECT(all_anchors.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        all_anchors.allocator()->allocate();
+        anchors.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!all_anchors.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(CLAccessor(anchors));
+
+        // Compute function
+        compute_all_anchors.run();
+
+        return all_anchors;
+    }
+
+    SimpleTensor<T> compute_reference(size_t                    num_anchors,
+                                      DataType                  data_type,
+                                      const ComputeAnchorsInfo &info)
+    {
+        // Create reference tensor
+        SimpleTensor<T> anchors(TensorShape(4, num_anchors), data_type);
+
+        // Fill reference tensor
+        fill(anchors);
+        return reference::compute_all_anchors(anchors, info);
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_COMPUTEALLANCHORS_FIXTURE */
diff --git a/tests/validation/reference/BoundingBoxTransform.cpp b/tests/validation/reference/BoundingBoxTransform.cpp
index 6ac512e749..9918ff68c5 100644
--- a/tests/validation/reference/BoundingBoxTransform.cpp
+++ b/tests/validation/reference/BoundingBoxTransform.cpp
@@ -84,10 +84,10 @@ SimpleTensor<T> bounding_box_transform(const SimpleTensor<T> &boxes, const Simpl
             const T pred_h     = T(std::exp(dh)) * height;
 
             // Store the prediction into the output tensor
-            pred_boxes_ptr[start_delta]     = scale * utility::clamp<T>(pred_ctr_x - T(0.5) * pred_w, T(0), T(img_w));
-            pred_boxes_ptr[start_delta + 1] = scale * utility::clamp<T>(pred_ctr_y - T(0.5) * pred_h, T(0), T(img_h));
-            pred_boxes_ptr[start_delta + 2] = scale * utility::clamp<T>(pred_ctr_x + T(0.5) * pred_w, T(0), T(img_w));
-            pred_boxes_ptr[start_delta + 3] = scale * utility::clamp<T>(pred_ctr_y + T(0.5) * pred_h, T(0), T(img_h));
+            pred_boxes_ptr[start_delta]     = scale * utility::clamp<T>(pred_ctr_x - T(0.5) * pred_w, T(0), T(img_w - 1));
+            pred_boxes_ptr[start_delta + 1] = scale * utility::clamp<T>(pred_ctr_y - T(0.5) * pred_h, T(0), T(img_h - 1));
+            pred_boxes_ptr[start_delta + 2] = scale * utility::clamp<T>(pred_ctr_x + T(0.5) * pred_w, T(0), T(img_w - 1));
+            pred_boxes_ptr[start_delta + 3] = scale * utility::clamp<T>(pred_ctr_y + T(0.5) * pred_h, T(0), T(img_h - 1));
         }
     }
     return pred_boxes;
diff --git a/tests/validation/reference/ComputeAllAnchors.cpp b/tests/validation/reference/ComputeAllAnchors.cpp
new file mode 100644
index 0000000000..48f4767fae
--- /dev/null
+++ b/tests/validation/reference/ComputeAllAnchors.cpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2018 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 "ComputeAllAnchors.h"
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/misc/Utility.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T>
+SimpleTensor<T> compute_all_anchors(const SimpleTensor<T> &anchors, const ComputeAnchorsInfo &info)
+{
+    const int   num_anchors = anchors.shape()[1];
+    const auto  width       = int(info.feat_width());
+    const auto  height      = int(info.feat_height());
+    const float stride      = 1. / info.spatial_scale();
+
+    SimpleTensor<T> all_anchors(TensorShape(4, width * height * num_anchors), anchors.data_type());
+    const T        *anchors_ptr     = anchors.data();
+    T              *all_anchors_ptr = all_anchors.data();
+
+    // Iterate over the input grid and anchors
+    for(int y = 0; y < height; y++)
+    {
+        for(int x = 0; x < width; x++)
+        {
+            for(int a = 0; a < num_anchors; a++)
+            {
+                const T      shift_x   = T(x) * T(stride);
+                const T      shift_y   = T(y) * T(stride);
+                const size_t anchor_id = a + x * num_anchors + y * width * num_anchors;
+                // x1
+                all_anchors_ptr[anchor_id * 4] = anchors_ptr[4 * a] + shift_x;
+                // y1
+                all_anchors_ptr[anchor_id * 4 + 1] = anchors_ptr[4 * a + 1] + shift_y;
+                // x2
+                all_anchors_ptr[anchor_id * 4 + 2] = anchors_ptr[4 * a + 2] + shift_x;
+                // y2
+                all_anchors_ptr[anchor_id * 4 + 3] = anchors_ptr[4 * a + 3] + shift_y;
+            }
+        }
+    }
+    return all_anchors;
+}
+template SimpleTensor<float> compute_all_anchors(const SimpleTensor<float> &anchors, const ComputeAnchorsInfo &info);
+template SimpleTensor<half> compute_all_anchors(const SimpleTensor<half> &anchors, const ComputeAnchorsInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/reference/ComputeAllAnchors.h b/tests/validation/reference/ComputeAllAnchors.h
new file mode 100644
index 0000000000..b21bf3cc7e
--- /dev/null
+++ b/tests/validation/reference/ComputeAllAnchors.h
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2018 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 __ARM_COMPUTE_TEST_COMPUTEALLANCHORS_H__
+#define __ARM_COMPUTE_TEST_COMPUTEALLANCHORS_H__
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T>
+SimpleTensor<T> compute_all_anchors(const SimpleTensor<T> &anchors, const ComputeAnchorsInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_COMPUTEALLANCHORS_H__ */
diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h
index df16cba9b5..58162000a6 100644
--- a/utils/TypePrinter.h
+++ b/utils/TypePrinter.h
@@ -261,6 +261,58 @@ inline std::string to_string(const BoundingBoxTransformInfo &bbox_info)
     return str.str();
 }
 
+/** Formatted output of the ComputeAnchorsInfo type.
+ *
+ * @param[out] os           Output stream.
+ * @param[in]  anchors_info Type to output.
+ *
+ * @return Modified output stream.
+ */
+inline ::std::ostream &operator<<(::std::ostream &os, const ComputeAnchorsInfo &anchors_info)
+{
+    os << "(" << anchors_info.feat_width() << "x" << anchors_info.feat_height() << ")~" << anchors_info.spatial_scale();
+    return os;
+}
+
+/** Formatted output of the ComputeAnchorsInfo type.
+ *
+ * @param[in] anchors_info Type to output.
+ *
+ * @return Formatted string.
+ */
+inline std::string to_string(const ComputeAnchorsInfo &anchors_info)
+{
+    std::stringstream str;
+    str << anchors_info;
+    return str.str();
+}
+
+/** Formatted output of the GenerateProposalsInfo type.
+ *
+ * @param[out] os             Output stream.
+ * @param[in]  proposals_info Type to output.
+ *
+ * @return Modified output stream.
+ */
+inline ::std::ostream &operator<<(::std::ostream &os, const GenerateProposalsInfo &proposals_info)
+{
+    os << "(" << proposals_info.im_width() << "x" << proposals_info.im_height() << ")~" << proposals_info.im_scale();
+    return os;
+}
+
+/** Formatted output of the GenerateProposalsInfo type.
+ *
+ * @param[in] proposals_info Type to output.
+ *
+ * @return Formatted string.
+ */
+inline std::string to_string(const GenerateProposalsInfo &proposals_info)
+{
+    std::stringstream str;
+    str << proposals_info;
+    return str.str();
+}
+
 /** Formatted output of the QuantizationInfo type.
  *
  * @param[out] os                Output stream.