diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index ccc9aec..fde9608 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -275,6 +275,7 @@
     { "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 @@
 #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 d330188..14a0fad 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 @@
 
     // 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 0000000..bc6f4b5
--- /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 0000000..5d100a4
--- /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 89413fc..2b9934c 100644
--- a/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
+++ b/src/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.cpp
@@ -54,7 +54,7 @@
         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 @@
 
         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 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 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);
+            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(ovr <= info.nms())
+            // 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 @@
     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 @@
 
         // 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 0000000..409d3c9
--- /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 0000000..80ed0e5
--- /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