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/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h
index da2d316..4750031 100644
--- a/arm_compute/core/CL/CLKernels.h
+++ b/arm_compute/core/CL/CLKernels.h
@@ -96,6 +96,7 @@
#include "arm_compute/core/CL/kernels/CLNonLinearFilterKernel.h"
#include "arm_compute/core/CL/kernels/CLNonMaximaSuppression3x3Kernel.h"
#include "arm_compute/core/CL/kernels/CLNormalizationLayerKernel.h"
+#include "arm_compute/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.h"
#include "arm_compute/core/CL/kernels/CLPermuteKernel.h"
#include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"
#include "arm_compute/core/CL/kernels/CLPoolingLayerKernel.h"
diff --git a/arm_compute/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.h b/arm_compute/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.h
new file mode 100644
index 0000000..5418d31
--- /dev/null
+++ b/arm_compute/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.h
@@ -0,0 +1,83 @@
+/*
+ * 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_CLNORMALIZEPLANARYUVLAYERKERNEL_H__
+#define __ARM_COMPUTE_CLNORMALIZEPLANARYUVLAYERKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Interface for the NormalizePlanarYUV layer kernel. */
+class CLNormalizePlanarYUVLayerKernel : public ICLKernel
+{
+public:
+ /** Constructor */
+ CLNormalizePlanarYUVLayerKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLNormalizePlanarYUVLayerKernel(const CLNormalizePlanarYUVLayerKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLNormalizePlanarYUVLayerKernel &operator=(const CLNormalizePlanarYUVLayerKernel &) = delete;
+ /** Default Move Constructor. */
+ CLNormalizePlanarYUVLayerKernel(CLNormalizePlanarYUVLayerKernel &&) = default;
+ /** Default move assignment operator */
+ CLNormalizePlanarYUVLayerKernel &operator=(CLNormalizePlanarYUVLayerKernel &&) = default;
+ /** Default destructor */
+ ~CLNormalizePlanarYUVLayerKernel() = default;
+
+ /** Set the input and output tensors.
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, channels].
+ * Data types supported: F16/F32.
+ * @param[out] output Destination tensor. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor. 1 dimension with size equal to the number of input channels.
+ * Data types supported: same as @p input
+ */
+ void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std);
+ /** Static function to check if given info will lead to a valid configuration of @ref CLNormalizePlanarYUVLayerKernel
+ *
+ * @param[in] input Source tensor info. 3 lower dimensions represent a single input with dimensions [width, height, channels].
+ * Data types supported: F16/F32.
+ * @param[out] output Destination tensor info. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor info. 1 dimension with size equal to the number of input channels.
+ * Data types supported: same as @p input
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std);
+
+ // Inherited methods overridden:
+ void run(const Window &window, cl::CommandQueue &queue) override;
+
+private:
+ const ICLTensor *_input;
+ ICLTensor *_output;
+ const ICLTensor *_mean;
+ const ICLTensor *_std;
+};
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_CLNORMALIZEPLANARYUVLAYERKERNEL_H__ */
diff --git a/arm_compute/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.h b/arm_compute/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.h
index 0d785ca..7ffe5b2 100644
--- a/arm_compute/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.h
+++ b/arm_compute/core/GLES_COMPUTE/kernels/GCNormalizePlanarYUVLayerKernel.h
@@ -50,14 +50,26 @@
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, channels].
* Data types supported: F16.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] sd Standard deviation values tensor. 1 dimension with size equal to the feature maps [FM].
- * Data types supported: Same as @p input
+ * @param[out] output Destination tensor. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor. 1 dimension with size equal to the feature maps [FM].
+ * Data types supported: same as @p input
*/
- void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *sd);
+ void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *std);
+ /** Static function to check if given info will lead to a valid configuration of @ref GCNormalizePlanarYUVLayerKernel
+ *
+ * @param[in] input Source tensor info. 3 lower dimensions represent a single input with dimensions [width, height, channels].
+ * Data types supported: F16.
+ * @param[out] output Destination tensor info. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor info. 1 dimension with size equal to the number of input channels.
+ * Data types supported: same as @p input
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std);
// Inherited methods overridden:
void run(const Window &window) override;
@@ -66,7 +78,7 @@
const IGCTensor *_input;
IGCTensor *_output;
const IGCTensor *_mean;
- const IGCTensor *_sd;
+ const IGCTensor *_std;
};
}
#endif /*__ARM_COMPUTE_GCNORMALIZEPLANARYUVLAYERKERNEL_H__ */
diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h
index d2bfdfd..02a4dab 100644
--- a/arm_compute/runtime/CL/CLFunctions.h
+++ b/arm_compute/runtime/CL/CLFunctions.h
@@ -93,6 +93,7 @@
#include "arm_compute/runtime/CL/functions/CLNonLinearFilter.h"
#include "arm_compute/runtime/CL/functions/CLNonMaximaSuppression3x3.h"
#include "arm_compute/runtime/CL/functions/CLNormalizationLayer.h"
+#include "arm_compute/runtime/CL/functions/CLNormalizePlanarYUVLayer.h"
#include "arm_compute/runtime/CL/functions/CLOpticalFlow.h"
#include "arm_compute/runtime/CL/functions/CLPermute.h"
#include "arm_compute/runtime/CL/functions/CLPhase.h"
diff --git a/arm_compute/runtime/CL/functions/CLNormalizePlanarYUVLayer.h b/arm_compute/runtime/CL/functions/CLNormalizePlanarYUVLayer.h
new file mode 100644
index 0000000..85f7d93
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLNormalizePlanarYUVLayer.h
@@ -0,0 +1,75 @@
+/*
+ * 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_CLNORMALIZEPLANARYUVLAYER_H__
+#define __ARM_COMPUTE_CLNORMALIZEPLANARYUVLAYER_H__
+
+#include "arm_compute/runtime/IFunction.h"
+
+#include "arm_compute/core/CL/kernels/CLNormalizePlanarYUVLayerKernel.h"
+#include "arm_compute/core/Types.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to run @ref CLNormalizePlanarYUVLayerKernel
+ *
+ * @note The function simulates a NormalizePlanarYUV layer.
+ */
+class CLNormalizePlanarYUVLayer : public IFunction
+{
+public:
+ /** Default constructor */
+ CLNormalizePlanarYUVLayer();
+ /** Set the input and output tensors.
+ *
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, channels].
+ * Data types supported: F16/F32.
+ * @param[out] output Destinationfeature tensor. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the number of input channels. Data types supported: Same as @p input
+ * @param[in] std Standard deviation values tensor. 1 dimension with size equal to the number of input channels.
+ * Data types supported: Same as @p input
+ */
+ void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *mean, const ICLTensor *std);
+ /** Static function to check if given info will lead to a valid configuration of @ref CLNormalizePlanarYUVLayer
+ *
+ * @param[in] input Source tensor info. 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * Data types supported: F16/F32.
+ * @param[out] output Destination tensor info. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the number of input channels. Data types supported: Same as @p input
+ * @param[in] std Standard deviation values tensor info. 1 dimension with size equal to the number of input channels.
+ * Data types supported: Same as @p input
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std);
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ CLNormalizePlanarYUVLayerKernel _norm_kernel; /**< NormalizePlanarYUV layer kernel to run */
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLNORMALIZEPLANARYUVLAYER_H__ */
diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.h
index 2862eeb..d6cf4d0 100644
--- a/arm_compute/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.h
+++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCNormalizePlanarYUVLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -44,14 +44,26 @@
GCNormalizePlanarYUVLayer();
/** Set the input and output tensors.
*
- * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, FM].
+ * @param[in] input Source tensor. 3 lower dimensions represent a single input with dimensions [width, height, channels].
* Data types supported: F16.
- * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input
- * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
- * @param[in] sd Standard deviation values tensor. 1 dimension with size equal to the feature maps [FM].
- * Data types supported: Same as @p input
+ * @param[out] output Destination tensor. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor. 1 dimension with size equal to the number of input channels.
+ * Data types supported: same as @p input
*/
- void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *sd);
+ void configure(const IGCTensor *input, IGCTensor *output, const IGCTensor *mean, const IGCTensor *std);
+ /** Static function to check if given info will lead to a valid configuration of @ref CLNormalizePlanarYUVLayer
+ *
+ * @param[in] input Source tensor info. 3 lower dimensions represent a single input with dimensions [width, height, channels].
+ * Data types supported: F16/F32.
+ * @param[out] output Destination tensor info. Data type supported: same as @p input
+ * @param[in] mean Mean values tensor info. 1 dimension with size equal to the number of input channels. Data types supported: same as @p input
+ * @param[in] std Standard deviation values tensor info. 1 dimension with size equal to the number of input channels.
+ * Data types supported: same as @p input
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *std);
// Inherited methods overridden:
void run() override;
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()
diff --git a/tests/datasets/NormalizePlanarYUVLayerDataset.h b/tests/datasets/NormalizePlanarYUVLayerDataset.h
index 2d71a56..1a97e68 100644
--- a/tests/datasets/NormalizePlanarYUVLayerDataset.h
+++ b/tests/datasets/NormalizePlanarYUVLayerDataset.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -55,7 +55,7 @@
description << "In=" << *_tensor_it << ":";
description << "Out=" << *_tensor_it << ":";
description << "Mean=" << *_param_it << ":";
- description << "Sd=" << *_param_it << ":";
+ description << "Std=" << *_param_it << ":";
return description.str();
}
diff --git a/tests/datasets/RandomNormalizePlanarYUVLayerDataset.h b/tests/datasets/RandomNormalizePlanarYUVLayerDataset.h
index 5693004..56eb604 100644
--- a/tests/datasets/RandomNormalizePlanarYUVLayerDataset.h
+++ b/tests/datasets/RandomNormalizePlanarYUVLayerDataset.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -46,6 +46,8 @@
add_config(TensorShape(21U, 11U, 12U, 1U), TensorShape(12U));
add_config(TensorShape(7U, 3U, 6U, 1U), TensorShape(6U));
add_config(TensorShape(7U, 2U, 3U, 1U), TensorShape(3U));
+ add_config(TensorShape(7U, 2U, 3U, 3U), TensorShape(3U));
+ add_config(TensorShape(21U, 11U, 12U, 3U), TensorShape(12U));
}
};
} // namespace datasets
diff --git a/tests/validation/CL/NormalizePlanarYUVLayer.cpp b/tests/validation/CL/NormalizePlanarYUVLayer.cpp
new file mode 100644
index 0000000..aa1a00e
--- /dev/null
+++ b/tests/validation/CL/NormalizePlanarYUVLayer.cpp
@@ -0,0 +1,142 @@
+/*
+ * 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/Types.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLNormalizePlanarYUVLayer.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/RandomNormalizePlanarYUVLayerDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+constexpr RelativeTolerance<float> tolerance_f16(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
+constexpr RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(NormalizePlanarYUVLayer)
+
+template <typename T>
+using CLNormalizePlanarYUVLayerFixture = NormalizePlanarYUVLayerValidationFixture<CLTensor, CLAccessor, CLNormalizePlanarYUVLayer, T>;
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::RandomNormalizePlanarYUVLayerDataset(), framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
+ shape0, shape1, dt, data_layout)
+{
+ TensorShape src_dst_shapes = shape0;
+ if(data_layout == DataLayout::NHWC)
+ {
+ permute(src_dst_shapes, PermutationVector(2U, 0U, 1U));
+ }
+
+ // Create tensors
+ CLTensor src = create_tensor<CLTensor>(src_dst_shapes, dt, 1, QuantizationInfo(), data_layout);
+ CLTensor dst = create_tensor<CLTensor>(src_dst_shapes, dt, 1, QuantizationInfo(), data_layout);
+ CLTensor mean = create_tensor<CLTensor>(shape1, dt, 1);
+ CLTensor sd = create_tensor<CLTensor>(shape1, dt, 1);
+
+ // Create and Configure function
+ CLNormalizePlanarYUVLayer norm;
+ norm.configure(&src, &dst, &mean, &sd);
+
+ // Validate valid region
+ const ValidRegion valid_region = shape_to_valid_region(src_dst_shapes);
+ validate(dst.info()->valid_region(), valid_region);
+}
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data types
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Window shrink
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8), // Unsupported data type
+ TensorInfo(TensorShape(32U, 16U, 8U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16), // Mismatching mean and sd shapes
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
+ }),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+ TensorInfo(TensorShape(32U, 16U, 8U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32),
+ })),
+ framework::dataset::make("MSTDInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::U8),
+ TensorInfo(TensorShape(8U), 1, DataType::F16),
+ TensorInfo(TensorShape(6U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ })),
+ framework::dataset::make("Expected", { false, false, false, true, false, false })),
+ input_info, output_info, msd_info, expected)
+{
+ const auto &mean_info = msd_info;
+ const auto &sd_info = msd_info;
+ bool has_error = bool(CLNormalizePlanarYUVLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &mean_info.clone()->set_is_resizable(false), &sd_info.clone()->set_is_resizable(false)));
+ ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+TEST_SUITE(Float)
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(Random, CLNormalizePlanarYUVLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomNormalizePlanarYUVLayerDataset(),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(Random, CLNormalizePlanarYUVLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomNormalizePlanarYUVLayerDataset(),
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/GLES_COMPUTE/NormalizePlanarYUVLayer.cpp b/tests/validation/GLES_COMPUTE/NormalizePlanarYUVLayer.cpp
index e06b19c..540a2be 100644
--- a/tests/validation/GLES_COMPUTE/NormalizePlanarYUVLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/NormalizePlanarYUVLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -70,10 +70,46 @@
validate(dst.info()->valid_region(), valid_region);
}
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Mismatching data types
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Window shrink
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8), // Unsupported data type
+ TensorInfo(TensorShape(32U, 16U, 8U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16), // Mismatching mean and sd shapes
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), // Mismatching shapes
+ }),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+ TensorInfo(TensorShape(32U, 16U, 8U), 1, DataType::F16),
+ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16),
+ TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F16),
+ })),
+ framework::dataset::make("MSTDInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::U8),
+ TensorInfo(TensorShape(8U), 1, DataType::F16),
+ TensorInfo(TensorShape(6U), 1, DataType::F16),
+ TensorInfo(TensorShape(2U), 1, DataType::F16),
+ })),
+ framework::dataset::make("Expected", { false, false, false, true, false, false })),
+ input_info, output_info, msd_info, expected)
+{
+ const auto &mean_info = msd_info;
+ const auto &sd_info = msd_info;
+ bool has_error = bool(GCNormalizePlanarYUVLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &mean_info.clone()->set_is_resizable(false), &sd_info.clone()->set_is_resizable(false)));
+ ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(Random, GCNormalizePlanarYUVLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(datasets::RandomNormalizePlanarYUVLayerDataset(),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(Random, GCNormalizePlanarYUVLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomNormalizePlanarYUVLayerDataset(),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_f16, 0);
diff --git a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
index 09905cf..cc73e53 100644
--- a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
+++ b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
@@ -45,16 +45,16 @@
{
public:
template <typename...>
- void setup(TensorShape shape0, TensorShape shape1, DataType dt)
+ void setup(TensorShape shape0, TensorShape shape1, DataType dt, DataLayout data_layout)
{
_data_type = dt;
- _target = compute_target(shape0, shape1, dt);
+ _target = compute_target(shape0, shape1, dt, data_layout);
_reference = compute_reference(shape0, shape1, dt);
}
protected:
template <typename U>
- void fill(U &&src_tensor, U &&mean_tensor, U &&sd_tensor)
+ void fill(U &&src_tensor, U &&mean_tensor, U &&std_tensor)
{
if(is_data_type_float(_data_type))
{
@@ -62,43 +62,48 @@
float max_bound = 0.f;
std::tie(min_bound, max_bound) = get_normalize_planar_yuv_layer_test_bounds<T>();
std::uniform_real_distribution<> distribution(min_bound, max_bound);
- std::uniform_real_distribution<> distribution_sd(0.1, max_bound);
+ std::uniform_real_distribution<> distribution_std(0.1, max_bound);
library->fill(src_tensor, distribution, 0);
library->fill(mean_tensor, distribution, 1);
- library->fill(sd_tensor, distribution_sd, 2);
+ library->fill(std_tensor, distribution_std, 2);
}
}
- TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, DataType dt)
+ TensorType compute_target(TensorShape shape0, const TensorShape &shape1, DataType dt, DataLayout data_layout)
{
+ if(data_layout == DataLayout::NHWC)
+ {
+ permute(shape0, PermutationVector(2U, 0U, 1U));
+ }
+
// Create tensors
- TensorType src = create_tensor<TensorType>(shape0, dt, 1);
- TensorType dst = create_tensor<TensorType>(shape0, dt, 1);
+ TensorType src = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
+ TensorType dst = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
TensorType mean = create_tensor<TensorType>(shape1, dt, 1);
- TensorType sd = create_tensor<TensorType>(shape1, dt, 1);
+ TensorType std = create_tensor<TensorType>(shape1, dt, 1);
// Create and configure function
FunctionType norm;
- norm.configure(&src, &dst, &mean, &sd);
+ norm.configure(&src, &dst, &mean, &std);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(sd.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(std.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
mean.allocator()->allocate();
- sd.allocator()->allocate();
+ std.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT(!sd.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!std.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
- fill(AccessorType(src), AccessorType(mean), AccessorType(sd));
+ fill(AccessorType(src), AccessorType(mean), AccessorType(std));
// Compute function
norm.run();
@@ -111,12 +116,12 @@
// Create reference
SimpleTensor<T> ref_src{ shape0, dt, 1 };
SimpleTensor<T> ref_mean{ shape1, dt, 1 };
- SimpleTensor<T> ref_sd{ shape1, dt, 1 };
+ SimpleTensor<T> ref_std{ shape1, dt, 1 };
// Fill reference
- fill(ref_src, ref_mean, ref_sd);
+ fill(ref_src, ref_mean, ref_std);
- return reference::normalize_planar_yuv_layer(ref_src, ref_mean, ref_sd);
+ return reference::normalize_planar_yuv_layer(ref_src, ref_mean, ref_std);
}
TensorType _target{};
diff --git a/tests/validation/reference/NormalizePlanarYUVLayer.cpp b/tests/validation/reference/NormalizePlanarYUVLayer.cpp
index 2442943..afb8992 100644
--- a/tests/validation/reference/NormalizePlanarYUVLayer.cpp
+++ b/tests/validation/reference/NormalizePlanarYUVLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -35,7 +35,7 @@
{
// NormalizePlanarYUV Layer for floating point type
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type *>
-SimpleTensor<T> normalize_planar_yuv_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &sd)
+SimpleTensor<T> normalize_planar_yuv_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &std)
{
SimpleTensor<T> result(src.shape(), src.data_type());
@@ -53,7 +53,7 @@
for(int l = 0; l < cols; ++l)
{
const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
- result[pos] = (src[pos] - mean[i]) / sd[i];
+ result[pos] = (src[pos] - mean[i]) / std[i];
}
}
}
@@ -61,8 +61,8 @@
return result;
}
-template SimpleTensor<half> normalize_planar_yuv_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &mean, const SimpleTensor<half> &sd);
-
+template SimpleTensor<half> normalize_planar_yuv_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &mean, const SimpleTensor<half> &std);
+template SimpleTensor<float> normalize_planar_yuv_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &mean, const SimpleTensor<float> &std);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/NormalizePlanarYUVLayer.h b/tests/validation/reference/NormalizePlanarYUVLayer.h
index c8740a3..41ce486 100644
--- a/tests/validation/reference/NormalizePlanarYUVLayer.h
+++ b/tests/validation/reference/NormalizePlanarYUVLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -36,7 +36,7 @@
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
-SimpleTensor<T> normalize_planar_yuv_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &sd);
+SimpleTensor<T> normalize_planar_yuv_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &std);
} // namespace reference
} // namespace validation
} // namespace test