COMPMID-1048 Add NHWC data format support to Winograd input transform 4x4_3x3
https://confluence.arm.com/display/MLENG/Winograd+Input+Transform%3A+NCHW+vs+NHWC+on+OpenCL
Change-Id: Iac35a54389266701b7d8f5434a7a37df85b7b187
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/133315
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
index b92ff2f..58e8291 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -49,6 +49,7 @@
* @note Winograd input transform supports the following configurations:
* F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
* Strides: only unit strides
+ * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3)
*
* @param[in] input The input tensor to transform. Data types supported: F32
* @param[in] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
@@ -60,6 +61,7 @@
* @note Winograd input transform supports the following configurations:
* F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
* Strides: only unit strides
+ * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3)
*
* @param[in] input The input tensor to transform. Data types supported: F32
* @param[in] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 9666702..f64cf9d 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -250,11 +250,15 @@
const Size2D output_tile_size = winograd_info.output_tile_size;
const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);
- // Compute height
- const unsigned int num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
- const unsigned int num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
+ const size_t idx_w = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL);
- const unsigned int width = input.tensor_shape()[get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)];
+ // Compute height
+ const unsigned int num_tiles_x = std::ceil((input.tensor_shape()[idx_w] - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
+ const unsigned int num_tiles_y = std::ceil((input.tensor_shape()[idx_h] - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
+
+ const unsigned int width = input.tensor_shape()[idx_c];
const unsigned int height = num_tiles_x * num_tiles_y;
const unsigned int depth = input_tile_size.area();
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 0b2f414..75d4feb 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -377,6 +377,7 @@
{ "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" },
{ "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" },
{ "winograd_input_transform_4x4_3x3_stepz1_nchw", "winograd.cl" },
+ { "winograd_input_transform_4x4_3x3_stepz1_nhwc", "winograd.cl" },
{ "winograd_output_transform_2x2_3x3_nchw", "winograd.cl" },
{ "winograd_output_transform_4x4_3x3_nchw", "winograd.cl" },
{ "winograd_output_transform_4x4_5x5_nchw", "winograd.cl" },
diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl
index c7ca8f6..383a3a7 100644
--- a/src/core/CL/cl_kernels/winograd.cl
+++ b/src/core/CL/cl_kernels/winograd.cl
@@ -25,7 +25,7 @@
#if defined(SRC_DIM_Z)
-/** This OpenCL kernel performs Winograd filter transform 3x3 when the data format is NCHW and the output tile is 2x2
+/** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NCHW and the output tile is 2x2
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
*
@@ -117,7 +117,7 @@
*(__global float *)(dst_addr + 15 * dst_stride_z) = out3.s3;
}
-/** This OpenCL kernel performs Winograd filter transform 3x3 when the data format is NCHW and the output tile is 4x4
+/** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NCHW and the output tile is 4x4
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
*
@@ -255,7 +255,7 @@
*(__global float *)(dst_addr + 35 * dst_stride_z) = out5.s5;
}
-/** This OpenCL kernel performs Winograd filter transform 3x3 when the data format is NHWC and the output tile is 4x4
+/** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NHWC and the output tile is 4x4
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
*
@@ -404,7 +404,7 @@
*(__global float *)(dst_addr + 34 * dst_stride_z) = out54;
*(__global float *)(dst_addr + 35 * dst_stride_z) = out55;
}
-/** This OpenCL kernel performs Winograd filter transform 5x5 when the data format is NCHW and the output tile is 4x4
+/** This OpenCL kernel performs Winograd filter transform 5x5 when the data layout is NCHW and the output tile is 4x4
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
*
@@ -857,7 +857,7 @@
vstore2(out33, 0, (__global float *)(dst_addr + 15 * dst_stride_z));
}
-/** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data format is NCHW
+/** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data layout is NCHW
*
* @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
* @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
@@ -1116,6 +1116,348 @@
dst_addr += dst_plane_stride;
}
+#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
+/** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data layout is NHWC
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_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 dst_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 dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_4x4_3x3_stepz1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+ int z = get_global_id(2);
+
+ __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * src_stride_x;
+
+ // Clamp coordinates. This clamp is valid for all rows
+ int4 y_coord0 = (int4)(y * 4) + (int4)(0, 1, 2, 3) - (int4)PAD_LEFT;
+ int2 y_coord1 = (int2)(y * 4) + (int2)(4, 5) - (int2)PAD_LEFT;
+ y_coord0 = clamp(y_coord0, -1, SRC_DIM_1);
+ y_coord1 = clamp(y_coord1, -1, SRC_DIM_1);
+
+ // Row4
+ int z_coord = (z * 4) - PAD_TOP + 4;
+
+ // If z < 0, set y to -1
+ int4 valid_y0 = select(y_coord0, -1, (int4)z_coord < 0);
+ int2 valid_y1 = select(y_coord1, -1, (int2)z_coord < 0);
+ // If z >= SRC_DIM_2, set y to SRC_DIM_2
+ valid_y0 = select(valid_y0, SRC_DIM_1, (int4)z_coord >= SRC_DIM_2);
+ valid_y1 = select(valid_y1, SRC_DIM_1, (int2)z_coord >= SRC_DIM_2);
+
+ // Clamp z coordinate
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ float d40 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d41 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d42 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d43 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d44 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d45 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ float k0 = d44;
+ float k1 = d44;
+ float k2 = d44;
+ float k3 = d44;
+ float k4 = d44;
+ float k5 = (float)0.0f;
+
+ k0 += 4.0f * d40 - 5.0f * d42;
+ k1 += -4.0f * d41 - 4.0f * d42 + d43;
+ k2 += 4.0f * d41 - 4.0f * d42 - d43;
+ k3 += -2.0f * d41 + 2.0f * d43 - d42;
+ k4 += 2.0f * d41 - 2.0f * d43 - d42;
+ k5 += 4.0f * d41 - 5.0f * d43 + d45;
+
+ // Row0
+ z_coord = (z * 4) - PAD_TOP + 0;
+
+#if PAD_TOP != 0
+ valid_y0 = select(y_coord0, -1, (int4)z_coord < 0);
+ valid_y1 = select(y_coord1, -1, (int2)z_coord < 0);
+ valid_y0 = select(valid_y0, SRC_DIM_1, (int4)z_coord >= SRC_DIM_2);
+ valid_y1 = select(valid_y1, SRC_DIM_1, (int2)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+#else // PAD_TOP != 0
+ valid_y0 = y_coord0;
+ valid_y1 = y_coord1;
+#endif // if PAD_TOP == 0, we cannot read out of bound
+
+ float d00 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d01 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d02 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d03 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d04 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d05 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row2
+ z_coord = (z * 4) - PAD_TOP + 2;
+ valid_y0 = select(y_coord0, -1, (int4)z_coord < 0);
+ valid_y1 = select(y_coord1, -1, (int2)z_coord < 0);
+ valid_y0 = select(valid_y0, SRC_DIM_1, (int4)z_coord >= SRC_DIM_2);
+ valid_y1 = select(valid_y1, SRC_DIM_1, (int2)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ float d20 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d21 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d22 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d23 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d24 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d25 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Compute destination address
+ __global float *dst_addr = (__global float *)(dst_ptr + dst_offset_first_element_in_bytes + x * dst_stride_x + (y + z * (int)NUM_TILES_X) * dst_stride_y);
+
+ uint dst_plane_stride = dst_stride_z / sizeof(float);
+
+ float out0 = k0;
+ float out1 = k1;
+ float out2 = k2;
+ float out3 = k3;
+ float out4 = k4;
+ float out5 = k5;
+ float out6 = k0;
+ float out7 = k1;
+ float out8 = k2;
+ float out9 = k3;
+ float out10 = k4;
+ float out11 = k5;
+ float out12 = k0;
+ float out13 = k1;
+ float out14 = k2;
+ float out15 = k3;
+ float out16 = k4;
+ float out17 = k5;
+ float out18 = k0;
+ float out19 = k1;
+ float out20 = k2;
+ float out21 = k3;
+ float out22 = k4;
+ float out23 = k5;
+ float out24 = k0;
+ float out25 = k1;
+ float out26 = k2;
+ float out27 = k3;
+ float out28 = k4;
+ float out29 = k5;
+
+ // Channels [0, 5]: [out00, out01, out02, out03, out04, out05]
+ out0 += 16.0f * d00 - 20.0f * d02 - 20.0f * d20 + 25.0f * d22 + 4.0f * d04 - 5.0f * d24;
+ out1 += -16.0f * d01 - 16.0f * d02 + 4.0f * d03 + 20.0f * d21 + 20.0f * d22 - 5.0f * d23 + 4.0f * d04 - 5.0f * d24;
+ out2 += 16.0f * d01 - 16.0f * d02 - 4.0f * d03 - 20.0f * d21 + 20.0f * d22 + 5.0f * d23 + 4.0f * d04 - 5.0f * d24;
+ out3 += -8.0f * d01 - 4.0f * d02 + 8.0f * d03 + 10.0f * d21 + 5.0f * d22 - 10.0f * d23 + 4.0f * d04 - 5.0f * d24;
+ out4 += 8.0f * d01 - 4.0f * d02 - 8.0f * d03 - 10.0f * d21 + 5.0f * d22 + 10.0f * d23 + 4.0f * d04 - 5.0f * d24;
+ out5 += 16.0f * d01 - 20.0f * d03 - 20.0f * d21 + 4.0f * d05 + 25.0f * d23 - 5.0f * d25;
+
+ *((__global float *)dst_addr) = out0;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out1;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out2;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out3;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out4;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out5;
+ dst_addr += dst_plane_stride;
+
+ // Row1
+ z_coord = (z * 4) - PAD_TOP + 1;
+ // Row1 can never be out of bounds
+ valid_y0 = y_coord0;
+ valid_y1 = y_coord1;
+
+ float d10 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d11 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d12 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d13 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d14 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d15 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row3
+ z_coord = (z * 4) - PAD_TOP + 3;
+ valid_y0 = select(y_coord0, -1, (int4)z_coord < 0);
+ valid_y1 = select(y_coord1, -1, (int2)z_coord < 0);
+ valid_y0 = select(valid_y0, SRC_DIM_1, (int4)z_coord >= SRC_DIM_2);
+ valid_y1 = select(valid_y1, SRC_DIM_1, (int2)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ float d30 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d31 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d32 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d33 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d34 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d35 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Compute common parts for the channels between [6, 29]
+ // Channels [6, 11]: [out10, out11, out12, out13, out14, out15]
+ // Channels [12, 17]: [out20, out21, out22, out23, out24, out25]
+ float part0 = -16.0f * d20 + 20.0f * d22 - 4.0f * d24;
+ float part1 = 16.0f * d10 - 20.0f * d12 + 4.0f * d14 - 4.0f * d30 + 5.0f * d32 - d34;
+ float part2 = 16.0f * d22 - 4.0f * d24;
+ float part3 = 16.0f * d21 - 4.0f * d23;
+ float part4 = 16.0f * d12 - 4.0f * d14 - 4.0f * d32 + d34;
+ float part5 = 16.0f * d11 - 4.0f * d13 - 4.0f * d31 + d33;
+ float part6 = 4.0f * d22 - 4.0f * d24;
+ float part7 = 8.0f * d11 - 8.0f * d13 - 2.0f * d31 + 2.0f * d33;
+ float part8 = 4.0f * d12 - 4.0f * d14 - d32 + d34;
+ float part9 = 8.0f * d21 - 8.0f * d23;
+ float part10 = -16.0f * d21 + 20.0f * d23 - 4.0f * d25;
+ float part11 = -16.0f * d11 + 20.0f * d13 - 4.0f * d15 + 4.0f * d31 - 5.0f * d33 + d35;
+
+ // Channels [18, 23]: [out30, out31, out32, out33, out34, out35]
+ // Channels [24, 29]: [out40, out41, out42, out43, out44, out45]
+ float part12 = 8.0f * d10 - 10.0f * d12 + 2.0f * d14 - 8.0f * d30 + 10.0f * d32 - 2.0f * d34;
+ float part13 = part0 * 0.25f; // -4.0f * d20 + 5.0f * d22 - d24
+ float part14 = part2 * 0.25f; // 4.0f * d22 - d24
+ float part15 = 8.0f * d11 - 2.0f * d13 - 8.0f * d31 + 2.0f * d33;
+ float part16 = 8.0f * d12 - 2.0f * d14 - 8.0f * d32 + 2.0f * d34;
+ float part17 = part3 * 0.25f; // 4.0f * d21 - d23
+ float part18 = part6 * 0.25f; // d22 - d24
+ float part19 = 4.0f * d11 - 4.0f * d13 - 4.0f * d31 + 4.0f * d33;
+ float part20 = 2.0f * d12 - 2.0f * d14 - 2.0f * d32 + 2.0f * d34;
+ float part21 = part9 * 0.25f; // 2.0f * (d21 - d23)
+ float part22 = part10 * 0.25f; // - 4.0f * d21 + 5.0f * d23 - d25
+ float part23 = part11 * 0.5f + 6.0f * d31 - 7.5f * d33 + 1.5f * d35; // - 8.0f * d11 + 10.0f * d13 - 2.0f * d15 + 8.0f * d31 - 10.0f * d33 + 2.0f * d35;
+
+ out6 += part0 - part1;
+ out12 += part0 + part1;
+ out7 += part2 + part3 + part4 + part5;
+ out8 += part2 - part3 + part4 - part5;
+ out13 += part2 + part3 - part4 - part5;
+ out14 += part2 - part3 - part4 + part5;
+ out9 += part6 + part7 + part8 + part9;
+ out10 += part6 - part7 + part8 - part9;
+ out15 += part6 - part7 - part8 + part9;
+ out16 += part6 + part7 - part8 - part9;
+ out11 += part10 + part11;
+ out17 += part10 - part11;
+
+ out18 += part13 - part12;
+ out24 += part13 + part12;
+ out19 += part14 + part15 + part16 + part17;
+ out20 += part14 - part15 + part16 - part17;
+ out25 += part14 - part15 - part16 + part17;
+ out26 += part14 + part15 - part16 - part17;
+ out21 += part18 + part19 + part20 + part21;
+ out22 += part18 - part19 + part20 - part21;
+ out27 += part18 - part19 - part20 + part21;
+ out28 += part18 + part19 - part20 - part21;
+ out23 += part22 + part23;
+ out29 += part22 - part23;
+
+ *((__global float *)dst_addr) = out6;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out7;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out8;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out9;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out10;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out11;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out12;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out13;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out14;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out15;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out16;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out17;
+ dst_addr += dst_plane_stride;
+
+ *((__global float *)dst_addr) = out18;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out19;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out20;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out21;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out22;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out23;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out24;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out25;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out26;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out27;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out28;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out29;
+ dst_addr += dst_plane_stride;
+
+ // Row5
+ z_coord = (z * 4) - PAD_TOP + 5;
+ valid_y0 = select(y_coord0, -1, (int4)z_coord < 0);
+ valid_y1 = select(y_coord1, -1, (int2)z_coord < 0);
+ valid_y0 = select(valid_y0, SRC_DIM_1, (int4)z_coord >= SRC_DIM_2);
+ valid_y1 = select(valid_y1, SRC_DIM_1, (int2)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ float d50 = *(__global float *)(src_addr + valid_y0.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d51 = *(__global float *)(src_addr + valid_y0.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ float d52 = *(__global float *)(src_addr + valid_y0.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ float d53 = *(__global float *)(src_addr + valid_y0.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ float d54 = *(__global float *)(src_addr + valid_y1.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ float d55 = *(__global float *)(src_addr + valid_y1.s1 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Channels [30, 35]
+ out0 = 16.0f * d10 - 20.0f * d12 - 20.0f * d30 + 25.0f * d32 + 4.0f * d50 - 5.0f * d52 + d54 + 4.0f * d14 - 5.0f * d34;
+ out1 = -16.0f * d11 - 16.0f * d12 + 4.0f * d13 + 20.0f * d31 + 20.0f * d32 - 5.0f * d33 - 4.0f * d51 - 4.0f * d52 + d53 + d54 + 4.0f * d14 - 5.0f * d34;
+ out2 = 16.0f * d11 - 16.0f * d12 - 4.0f * d13 - 20.0f * d31 + 20.0f * d32 + 5.0f * d33 + 4.0f * d51 - 4.0f * d52 - d53 + d54 + 4.0f * d14 - 5.0f * d34;
+ out3 = -8.0f * d11 - 4.0f * d12 + 8.0f * d13 + 10.0f * d31 - 10.0f * d33 + 5.0f * d32 - 2.0f * d51 + 2.0f * d53 - d52 + d54 + 4.0f * d14 - 5.0f * d34;
+ out4 = 8.0f * d11 - 4.0f * d12 - 8.0f * d13 - 10.0f * d31 + 5.0f * d32 + 10.0f * d33 + 2.0f * d51 - 2.0f * d53 - d52 + d54 + 4.0f * d14 - 5.0f * d34;
+ out5 = 16.0f * d11 - 20.0f * d13 + 4.0f * d15 - 20.0f * d31 + 25.0f * d33 - 5.0f * d35 + 4.0f * d51 - 5.0f * d53 + d55;
+
+ *((__global float *)dst_addr) = out0;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out1;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out2;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out3;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out4;
+ dst_addr += dst_plane_stride;
+ *((__global float *)dst_addr) = out5;
+ dst_addr += dst_plane_stride;
+}
+
+#endif /* defined(SRC_DIM_1) && defined(SRC_DIM_2) */
+
#define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact) \
({ \
comm_fact.s0 = tmp.s2 - 4.25f * tmp.s4 + tmp.s6; \
@@ -1287,7 +1629,7 @@
#endif // defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP)
#if defined(NUM_TILES_X)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2, the filter size 3x3 and the data format is NCHW
+/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2, the filter size 3x3 and the data layout is NCHW
*
* @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
*
@@ -1389,7 +1731,7 @@
vstore2((float2)(out10, out11), 0, (__global float *)(dst_addr + 1 * dst_stride_y));
}
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data format is NCHW
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data layout is NCHW
*
* @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
*
@@ -1566,7 +1908,7 @@
vstore4((float4)(out30, out31, out32, out33), 0, (__global float *)(dst_addr + 3 * dst_stride_y));
}
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data format is NHWC
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data layout is NHWC
*
* @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
*
@@ -1774,7 +2116,7 @@
col.s3 = comm_fact.s0 + 8.f * comm_fact.s1 + comm_fact.s2 + d7; \
})
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data format is NCHW
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data layout is NCHW
*
* @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
*
diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
index febd22b..e73ac7d 100644
--- a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
@@ -40,13 +40,13 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW);
const PadStrideInfo conv_info = winograd_info.convolution_info;
const Size2D output_tile_size = winograd_info.output_tile_size;
const Size2D kernel_size = winograd_info.kernel_size;
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd input transform only supports 3x3 and 5x5 kernels");
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::NHWC && (output_tile_size != Size2D(4U, 4U) || kernel_size != Size2D(3U, 3U)));
ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U)
&& output_tile_size != Size2D(4U, 4U),
"Winograd input transform only supports 2x2 or 4x4 output tile for 3x3 kernels");
@@ -75,12 +75,28 @@
const Size2D output_tile_size = winograd_info.output_tile_size;
const Size2D kernel_size = winograd_info.kernel_size;
- const unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1;
- const unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1;
+ unsigned int num_elems_read_per_iteration_x = 0;
+ unsigned int num_elems_read_per_iteration_y = 0;
+ unsigned int pad_left = 0;
+ unsigned int pad_top = 0;
+
+ if(input->data_layout() == DataLayout::NCHW)
+ {
+ num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1;
+ num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1;
+ pad_left = conv_info.pad_left();
+ pad_top = conv_info.pad_top();
+ }
+ else
+ {
+ num_elems_read_per_iteration_x = 1;
+ num_elems_read_per_iteration_y = output_tile_size.width + kernel_size.width - 1;
+ pad_top = 1;
+ }
Window win = calculate_max_window(*input, Steps(1, 1));
- AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y);
+ AccessWindowRectangle input_access(input, -pad_left, -pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y);
bool window_changed = update_window_and_padding(win, input_access);
@@ -108,17 +124,27 @@
const Size2D output_tile_size = winograd_info.output_tile_size;
const Size2D kernel_size = winograd_info.kernel_size;
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+
// Compute number of elements to process in the X and Y direction
- const int num_elements_x = input->info()->dimension(0) - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
- const int num_elements_y = input->info()->dimension(1) - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom();
+ const int num_elements_x = input->info()->dimension(idx_w) - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
+ const int num_elements_y = input->info()->dimension(idx_h) - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom();
- // Check if we need to extend the right or bottom border
- const unsigned int extra_border_right = ((num_elements_x % output_tile_size.width) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.width - 1);
- const unsigned int extra_border_bottom = ((num_elements_y % output_tile_size.height) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.height - 1);
+ _input = input;
+ _output = output;
+ if(input->info()->data_layout() == DataLayout::NCHW)
+ {
+ // Check if we need to extend the right or bottom border
+ const unsigned int extra_border_right = ((num_elements_x % output_tile_size.width) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.width - 1);
+ const unsigned int extra_border_bottom = ((num_elements_y % output_tile_size.height) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.height - 1);
- _input = input;
- _output = output;
- _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right() + extra_border_right, conv_info.pad_bottom() + extra_border_bottom, conv_info.pad_left());
+ _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right() + extra_border_right, conv_info.pad_bottom() + extra_border_bottom, conv_info.pad_left());
+ }
+ else
+ {
+ _border_size = BorderSize(1U, 0U, 1U, 0);
+ }
_num_tiles_x = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width));
_num_tiles_y = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height));
@@ -134,6 +160,12 @@
build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
+ if(input->info()->data_layout() == DataLayout::NHWC)
+ {
+ build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
+ build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2)));
+ }
+
// Create kernel
std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();
@@ -148,7 +180,7 @@
// Append stepz and data layout
kernel_name += "_stepz";
kernel_name += support::cpp11::to_string(_step_z);
- kernel_name += "_nchw";
+ kernel_name += "_" + lower_string(string_from_data_layout(input->info()->data_layout()));
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
@@ -183,12 +215,16 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
- Window slice = window.first_slice_window_3D();
- slice.set(Window::DimX, Window::Dimension(0, _num_tiles_x, 1));
- slice.set(Window::DimY, Window::Dimension(0, _num_tiles_y, 1));
+ const size_t idx_w = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::CHANNEL);
- ARM_COMPUTE_ERROR_ON(((slice.z().end() - slice.z().start()) % _step_z) != 0);
- slice.set(Window::DimZ, Window::Dimension(slice.z().start(), slice.z().end(), _step_z));
+ Window slice = window.first_slice_window_3D();
+ slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1));
+ slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1));
+
+ ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0);
+ slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z));
do
{
diff --git a/tests/datasets/WinogradInputTransformDataset.h b/tests/datasets/WinogradInputTransformDataset.h
index 59fb2ad..e365f96 100644
--- a/tests/datasets/WinogradInputTransformDataset.h
+++ b/tests/datasets/WinogradInputTransformDataset.h
@@ -97,12 +97,11 @@
std::vector<WinogradInfo> _infos{};
};
-class SmallWinogradInputTransformDataset final : public WinogradInputTransformDataset
+class SmallWinogradInputTransformDataset2x2_3x3 final : public WinogradInputTransformDataset
{
public:
- SmallWinogradInputTransformDataset()
+ SmallWinogradInputTransformDataset2x2_3x3()
{
- // (2x2, 3x3)
add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
@@ -110,8 +109,14 @@
add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+ }
+};
- // (4x4, 3x3)
+class SmallWinogradInputTransformDataset4x4_3x3 final : public WinogradInputTransformDataset
+{
+public:
+ SmallWinogradInputTransformDataset4x4_3x3()
+ {
add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
@@ -119,8 +124,14 @@
add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+ }
+};
- // (4x4, 5x5)
+class SmallWinogradInputTransformDataset4x4_5x5 final : public WinogradInputTransformDataset
+{
+public:
+ SmallWinogradInputTransformDataset4x4_5x5()
+ {
add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
@@ -131,24 +142,35 @@
}
};
-class LargeWinogradInputTransformDataset final : public WinogradInputTransformDataset
+class LargeWinogradInputTransformDataset2x2_3x3 final : public WinogradInputTransformDataset
{
public:
- LargeWinogradInputTransformDataset()
+ LargeWinogradInputTransformDataset2x2_3x3()
{
- // (2x2, 3x3)
add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ }
+};
- // (4x4, 3x3)
+class LargeWinogradInputTransformDataset4x4_3x3 final : public WinogradInputTransformDataset
+{
+public:
+ LargeWinogradInputTransformDataset4x4_3x3()
+ {
add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ }
+};
- // (4x4, 5x5)
+class LargeWinogradInputTransformDataset4x4_5x5 final : public WinogradInputTransformDataset
+{
+public:
+ LargeWinogradInputTransformDataset4x4_5x5()
+ {
add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 4565c2d..59fe25d 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -53,6 +53,10 @@
{
constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
constexpr AbsoluteTolerance<float> tolerance_convolution_layer_f32(0.1f);
+const auto SmallWinogradInputTransformDataset = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x2_3x3(),
+ framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), datasets::SmallWinogradInputTransformDataset4x4_5x5()));
+const auto LargeWinogradInputTransformDataset = framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(),
+ framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), datasets::LargeWinogradInputTransformDataset4x4_5x5()));
} // namespace
using namespace arm_compute::misc::shape_calculator;
@@ -102,7 +106,7 @@
using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, float>;
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallWinogradInputTransformDataset(), datasets::LargeWinogradInputTransformDataset()),
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDataset, LargeWinogradInputTransformDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("DataType", { DataType::F32 })),
shape_in, winograd_info, data_layout, data_type)
@@ -123,15 +127,19 @@
winograd_input_transform.configure(&in, &out, winograd_info);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallWinogradInputTransformDataset(),
- framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(framework::dataset::concat(combine(SmallWinogradInputTransformDataset,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ combine(datasets::SmallWinogradInputTransformDataset4x4_3x3(),
+ framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradInputTransformDataset(),
- framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(framework::dataset::concat(combine(LargeWinogradInputTransformDataset,
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ combine(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
+ framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
framework::dataset::make("DataType", { DataType::F32 })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index f40f3d2..07795c2 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -314,10 +314,15 @@
}
}
- TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
+ TensorType compute_target(TensorShape input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
{
+ if(data_layout == DataLayout::NHWC)
+ {
+ permute(input_shape, PermutationVector(2U, 0U, 1U));
+ }
+
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
- TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
+ TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo());
// Create and configure function
FunctionType transf;
@@ -345,7 +350,7 @@
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout };
+ SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo() };
// Fill reference
fill(src, 0, -1.f, 1.f);