COMPMID-1330: Add support for NormalizePlanarYUV operator in CL
Change-Id: Id0754b9e2bc3ef7ff2c4c21c3b89709588c41bd3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146637
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 0cc6e32..4af2b09 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -308,6 +308,8 @@
{ "non_max_suppression", "nonmax.cl" },
{ "normalization_layer_cross_map", "normalization_layer.cl" },
{ "normalization_layer_in_map", "normalization_layer.cl" },
+ { "normalize_planar_yuv_layer_nchw", "normalize_planar_yuv_layer.cl" },
+ { "normalize_planar_yuv_layer_nhwc", "normalize_planar_yuv_layer.cl" },
{ "NV12_to_IYUV_bt709", "color_convert.cl" },
{ "NV12_to_RGB888_bt709", "color_convert.cl" },
{ "NV12_to_RGBA8888_bt709", "color_convert.cl" },
@@ -674,6 +676,10 @@
#include "./cl_kernels/normalization_layer.clembed"
},
{
+ "normalize_planar_yuv_layer.cl",
+#include "./cl_kernels/normalize_planar_yuv_layer.clembed"
+ },
+ {
"batchnormalization_layer.cl",
#include "./cl_kernels/batchnormalization_layer.clembed"
},
diff --git a/src/core/CL/cl_kernels/normalize_planar_yuv_layer.cl b/src/core/CL/cl_kernels/normalize_planar_yuv_layer.cl
new file mode 100644
index 0000000..dc66524
--- /dev/null
+++ b/src/core/CL/cl_kernels/normalize_planar_yuv_layer.cl
@@ -0,0 +1,134 @@
+/*
+ * 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"
+
+#if defined(DATA_TYPE) && defined(VEC_SIZE)
+
+#define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+
+/** Apply normalize_planar_yuv layer on tensors with NCHW format.
+ *
+ * @note Data type should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE e.g. -DVEC_SIZE=8
+ * @note The depth of the input tensor should be given as a preprocessor argument using -DNUM_CHANNELS e.g. -DNUM_CHANNELS=8
+ *
+ * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32
+ * @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes)
+ * @param[in] src_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the first source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the first source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p src_ptr
+ * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
+ * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
+ * @param[in] std_ptr Pointer to the std tensor. Supported data types: same as @p src_ptr
+ * @param[in] std_stride_x Stride of the std tensor in X dimension (in bytes)
+ * @param[in] std_step_x std_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] std_offset_first_element_in_bytes The offset of the first element in the var source tensor
+ */
+__kernel void normalize_planar_yuv_layer_nchw(TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ VECTOR_DECLARATION(mean),
+ VECTOR_DECLARATION(std))
+{
+ Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+ Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+ Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
+ Vector std = CONVERT_TO_VECTOR_STRUCT(std);
+
+ const uint current_slice = get_global_id(2) % NUM_CHANNELS;
+
+ const DATA_TYPE curr_mean = *((__global DATA_TYPE *)(mean.ptr + current_slice * mean.stride_x));
+ const DATA_TYPE curr_std = *((__global DATA_TYPE *)(std.ptr + current_slice * std.stride_x));
+
+ TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)src.ptr);
+ TYPE res = (data - curr_mean) / curr_std;
+
+ VSTORE(VEC_SIZE)
+ (res, 0, (__global DATA_TYPE *)dst.ptr);
+}
+
+/** Apply normalize_planar_yuv layer on tensors with NHWC format.
+ *
+ * @note Data type should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE e.g. -DVEC_SIZE=8
+ *
+ * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32
+ * @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes)
+ * @param[in] src_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the first source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the first source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p src_ptr
+ * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
+ * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
+ * @param[in] std_ptr Pointer to the std tensor. Supported data types: same as @p src_ptr
+ * @param[in] std_stride_x Stride of the std tensor in X dimension (in bytes)
+ * @param[in] std_step_x std_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] std_offset_first_element_in_bytes The offset of the first element in the var source tensor
+ */
+__kernel void normalize_planar_yuv_layer_nhwc(TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ VECTOR_DECLARATION(mean),
+ VECTOR_DECLARATION(std))
+{
+ Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+ Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+ Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
+ Vector std = CONVERT_TO_VECTOR_STRUCT(std);
+
+ const uint current_slice = get_global_id(0);
+
+ const TYPE curr_mean = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(mean.ptr + current_slice * VEC_SIZE * mean.stride_x));
+ const TYPE curr_std = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(std.ptr + current_slice * VEC_SIZE * std.stride_x));
+
+ TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)src.ptr);
+ TYPE res = (data - curr_mean) / curr_std;
+
+ VSTORE(VEC_SIZE)
+ (res, 0, (__global DATA_TYPE *)dst.ptr);
+}
+#endif // defined(DATA_TYPE) && defined(VEC_SIZE)
diff --git a/src/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.cpp b/src/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.cpp
new file mode 100644
index 0000000..31451ef
--- /dev/null
+++ b/src/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.cpp
@@ -0,0 +1,173 @@
+/*
+ * 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/CLNormalizePlanarYUVLayerKernel.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/ICLTensor.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"
+
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, std);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, std);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(mean->num_dimensions() > 1, "mean and std must be vectors");
+
+ const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(channel_idx) != mean->dimension(0));
+
+ // Checks performed when output is configured
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *mean, ITensorInfo *std)
+{
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output, *input->clone());
+
+ const unsigned int num_elems_processed_per_iteration = 16 / input->element_size();
+
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+
+ bool window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, input->valid_region());
+
+ if(input->data_layout() == DataLayout::NHWC)
+ {
+ AccessWindowHorizontal mean_access(mean, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal std_access(std, 0, num_elems_processed_per_iteration);
+ window_changed = window_changed || update_window_and_padding(win, mean_access, std_access);
+ }
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
+CLNormalizePlanarYUVLayerKernel::CLNormalizePlanarYUVLayerKernel()
+ : _input(nullptr), _output(nullptr), _mean(nullptr), _std(nullptr)
+{
+}
+
+void CLNormalizePlanarYUVLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, mean, std);
+
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), *input->info()->clone());
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), mean->info(), std->info()));
+
+ _input = input;
+ _output = output;
+ _mean = mean;
+ _std = std;
+
+ const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
+ const unsigned int channel_idx = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
+
+ // Set build options
+ CLBuildOptions build_opts;
+ build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
+ build_opts.add_option(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
+ build_opts.add_option(("-DNUM_CHANNELS=" + support::cpp11::to_string(input->info()->dimension(channel_idx))));
+
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("normalize_planar_yuv_layer_" + lower_string(string_from_data_layout(input->info()->data_layout())), build_opts.options()));
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), output->info(), mean->info(), std->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure_internal(win_config.second);
+
+ // Set config_id for enabling LWS tuning
+ _config_id = "normalize_planar_yuv_layer_";
+ _config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
+ _config_id += "_";
+ _config_id += lower_string(string_from_data_type(input->info()->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(input->info()->dimension(0));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(input->info()->dimension(1));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(input->info()->dimension(2));
+}
+
+Status CLNormalizePlanarYUVLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, std));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), mean->clone().get(), std->clone().get()).first);
+
+ return Status{};
+}
+
+void CLNormalizePlanarYUVLayerKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+ Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
+ Window slice = collapsed.first_slice_window_3D();
+
+ Window slice_in = collapsed.first_slice_window_1D();
+ slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+
+ unsigned int idx = 2 * num_arguments_per_3D_tensor();
+ add_1D_tensor_argument(idx, _mean, slice_in);
+ add_1D_tensor_argument(idx, _std, slice_in);
+
+ do
+ {
+ idx = 0;
+ add_3D_tensor_argument(idx, _input, slice);
+ add_3D_tensor_argument(idx, _output, slice);
+ enqueue(queue, *this, slice, lws_hint());
+ }
+ while(collapsed.slide_window_slice_3D(slice));
+}
diff --git a/src/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.cpp
index fac2902..03463b2 100644
--- a/src/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.cpp
@@ -36,26 +36,75 @@
using namespace arm_compute;
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, std);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, std);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(mean->num_dimensions() > 1, "mean and std must be vectors");
+
+ const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(channel_idx) != mean->dimension(0));
+
+ // Checks performed when output is configured
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *mean, ITensorInfo *std)
+{
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output, *input->clone());
+
+ const unsigned int num_elems_processed_per_iteration = 4;
+
+ // Configure kernel window
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+ const int mean_padding = ceil_to_multiple(mean->dimension(0), num_elems_processed_per_iteration) - mean->dimension(0);
+ const int std_padding = ceil_to_multiple(std->dimension(0), num_elems_processed_per_iteration) - std->dimension(0);
+ AccessWindowStatic mean_access(mean, 0, 0, mean->dimension(0) + mean_padding, mean->dimension(1));
+ AccessWindowStatic std_access(std, 0, 0, std->dimension(0) + std_padding, std->dimension(1));
+
+ const bool window_changed = update_window_and_padding(win, input_access, output_access, mean_access, std_access);
+ output_access.set_valid_region(win, input->valid_region());
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
GCNormalizePlanarYUVLayerKernel::GCNormalizePlanarYUVLayerKernel()
- : _input(nullptr), _output(nullptr), _mean(nullptr), _sd(nullptr)
+ : _input(nullptr), _output(nullptr), _mean(nullptr), _std(nullptr)
{
}
-void GCNormalizePlanarYUVLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *sd)
+void GCNormalizePlanarYUVLayerKernel::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *std)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16);
- ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, mean, sd);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, sd);
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != mean->info()->dimension(0));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, mean, std);
+
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), *input->info()->clone());
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), mean->info(), std->info()));
_input = input;
_output = output;
_mean = mean;
- _sd = sd;
-
- const unsigned int num_elems_processed_per_iteration = 4;
+ _std = std;
// Set build options
std::set<std::string> build_opts;
@@ -67,19 +116,17 @@
_kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel("normalize_planar_yuv_layer", build_opts));
// Configure kernel window
- Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+ auto win_config = validate_and_configure_window(input->info(), output->info(), mean->info(), std->info());
+ ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
- AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
- const int mean_padding = ceil_to_multiple(mean->info()->dimension(0), num_elems_processed_per_iteration) - mean->info()->dimension(0);
- const int sd_padding = ceil_to_multiple(sd->info()->dimension(0), num_elems_processed_per_iteration) - sd->info()->dimension(0);
- AccessWindowStatic mean_access(mean->info(), 0, 0, mean->info()->dimension(0) + mean_padding, mean->info()->dimension(1));
- AccessWindowStatic sd_access(sd->info(), 0, 0, sd->info()->dimension(0) + sd_padding, sd->info()->dimension(1));
+ IGCKernel::configure(std::get<1>(win_config));
+}
- update_window_and_padding(win, input_access, output_access, mean_access, sd_access);
- output_access.set_valid_region(win, input->info()->valid_region());
-
- IGCKernel::configure(win);
+Status GCNormalizePlanarYUVLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, std));
+ ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), mean->clone().get(), std->clone().get())));
+ return Status{};
}
void GCNormalizePlanarYUVLayerKernel::run(const Window &window)
@@ -100,7 +147,7 @@
unsigned int idx = 2 * num_arguments_per_3D_tensor();
add_1D_tensor_argument(idx, _mean, 3, slice_in);
- add_1D_tensor_argument(idx, _sd, 4, slice_in);
+ add_1D_tensor_argument(idx, _std, 4, slice_in);
slice_in = window.first_slice_window_3D();
diff --git a/src/runtime/CL/functions/CLNormalizePlanarYUVLayer.cpp b/src/runtime/CL/functions/CLNormalizePlanarYUVLayer.cpp
new file mode 100644
index 0000000..11d70e3
--- /dev/null
+++ b/src/runtime/CL/functions/CLNormalizePlanarYUVLayer.cpp
@@ -0,0 +1,55 @@
+/*
+ * 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/CLNormalizePlanarYUVLayer.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+namespace arm_compute
+{
+CLNormalizePlanarYUVLayer::CLNormalizePlanarYUVLayer()
+ : _norm_kernel()
+{
+}
+
+void CLNormalizePlanarYUVLayer::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std)
+{
+ _norm_kernel.configure(input, output, mean, std);
+}
+
+Status CLNormalizePlanarYUVLayer::validate(const ITensorInfo *input, const ITensorInfo *output,
+ const ITensorInfo *mean, const ITensorInfo *std)
+{
+ return CLNormalizePlanarYUVLayerKernel::validate(input, output, mean, std);
+}
+
+void CLNormalizePlanarYUVLayer::run()
+{
+ CLScheduler::get().enqueue(_norm_kernel, true);
+}
+} // namespace arm_compute
diff --git a/src/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.cpp
index 5fb971c..19fdc3d 100755
--- a/src/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.cpp
+++ b/src/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -37,9 +37,15 @@
{
}
-void GCNormalizePlanarYUVLayer::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *sd)
+void GCNormalizePlanarYUVLayer::configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *std)
{
- _norm_kernel.configure(input, output, mean, sd);
+ _norm_kernel.configure(input, output, mean, std);
+}
+
+Status GCNormalizePlanarYUVLayer::validate(const ITensorInfo *input, const ITensorInfo *output,
+ const ITensorInfo *mean, const ITensorInfo *std)
+{
+ return GCNormalizePlanarYUVLayerKernel::validate(input, output, mean, std);
}
void GCNormalizePlanarYUVLayer::run()