COMPMID-1037 Add support for F(4x4, 5x5) in CLWinogradInputTransformKernel

Change-Id: Iac26936f46d0f7cdd9d2f8393b0092cd5a223c45
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127675
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

6 files changed, 199 insertions, 4 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index a7b39c20e2..8405754f52 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -357,6 +357,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "winograd_filter_transform_2x2_3x3_nchw", "winograd.cl" },
     { "winograd_filter_transform_4x4_3x3_nchw", "winograd.cl" },
     { "winograd_filter_transform_4x4_5x5_nchw", "winograd.cl" },
+    { "winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd.cl" },
     { "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" },
     { "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" },
     { "winograd_output_transform_2x2_3x3_nchw", "winograd.cl" },
diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl
index 03ff377d52..9932119003 100644
--- a/src/core/CL/cl_kernels/winograd.cl
+++ b/src/core/CL/cl_kernels/winograd.cl
@@ -707,6 +707,175 @@ __kernel void winograd_input_transform_2x2_3x3_stepz2_nchw(
     vstore2(out32, 0, (__global float *)(dst_addr + 14 * dst_stride_z));
     vstore2(out33, 0, (__global float *)(dst_addr + 15 * dst_stride_z));
 }
+
+#define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact)                     \
+    ({                                                              \
+        comm_fact.s0 = tmp.s2 - 4.25f * tmp.s4 + tmp.s6;            \
+        comm_fact.s1 = tmp.s1 - 4.25f * tmp.s3 + tmp.s5;            \
+        comm_fact.s2 = 2.5f * tmp.s3;                               \
+        comm_fact.s3 = 0.5f * tmp.s1 + 2.f * tmp.s5 - comm_fact.s2; \
+        comm_fact.s4 = 0.25f * tmp.s2 - 1.25f * tmp.s4 + tmp.s6;    \
+        comm_fact.s5 = 4.f * tmp.s2 + tmp.s6 - 5.f * tmp.s4;        \
+        comm_fact.s6 = 2.f * tmp.s1 + 0.5f * tmp.s5 - comm_fact.s2; \
+        \
+        out.s0 = tmp.s0 - tmp.s6 + 5.25f * tmp.s4 - 5.25f * tmp.s2; \
+        out.s1 = comm_fact.s0 + comm_fact.s1;                       \
+        out.s2 = comm_fact.s0 - comm_fact.s1;                       \
+        out.s3 = comm_fact.s3 + comm_fact.s4;                       \
+        out.s4 = comm_fact.s4 - comm_fact.s3;                       \
+        out.s5 = comm_fact.s5 + comm_fact.s6;                       \
+        out.s6 = comm_fact.s5 - comm_fact.s6;                       \
+        out.s7 = tmp.s7 - tmp.s1 + 5.25f * tmp.s3 - 5.25f * tmp.s5; \
+    })
+
+/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4
+ *
+ * @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_5x5_stepz1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    int x = get_global_id(0);
+    int y = get_global_id(1);
+    int z = get_global_id(2);
+
+    // Compute input address
+    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 4 * src_stride_x + y * 4 * src_stride_y + z * src_stride_z;
+
+    src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y);
+
+    // Load 8x8 input tile
+    const float8 in_row0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));
+    const float8 in_row1 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));
+    const float8 in_row2 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));
+    const float8 in_row3 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y));
+    const float8 in_row4 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y));
+    const float8 in_row5 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));
+    const float8 in_row6 = vload8(0, (__global float *)(src_addr + 6 * src_stride_y));
+    const float8 in_row7 = vload8(0, (__global float *)(src_addr + 7 * src_stride_y));
+
+    // Calculate common factors for intermediate tensor
+    float8 comm_fact0 = in_row2 + in_row6 - 4.25f * in_row4;
+    float8 comm_fact1 = in_row1 + in_row5 - 4.25f * in_row3;
+    float8 comm_fact2 = 0.25f * in_row2 - 1.25f * in_row4 + in_row6;
+
+    // Calculate intermediate tensor and reuse common factor vectors
+    const float8 tmp0 = in_row0 - in_row6 + 5.25f * in_row4 - 5.25f * in_row2;
+    const float8 tmp1 = comm_fact0 + comm_fact1;
+    const float8 tmp2 = comm_fact0 - comm_fact1;
+
+    comm_fact0 = 2.5f * in_row3;
+    comm_fact1 = 0.5f * in_row1 - comm_fact0 + 2.f * in_row5;
+
+    const float8 tmp3 = comm_fact1 + comm_fact2;
+    const float8 tmp4 = comm_fact2 - comm_fact1;
+
+    comm_fact1 = 2.f * in_row1 - comm_fact0 + 0.5f * in_row5;
+    comm_fact2 = 4.f * in_row2 - 5.f * in_row4 + in_row6;
+
+    const float8 tmp5 = comm_fact1 + comm_fact2;
+    const float8 tmp6 = comm_fact2 - comm_fact1;
+    const float8 tmp7 = in_row7 - in_row1 + 5.25f * in_row3 - 5.25f * in_row5;
+
+    // Calculate output rows (reuse comm_fact0 vector)
+    float8 out0, out1, out2, out3, out4, out5, out6, out7;
+
+    OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out1, tmp1, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out2, tmp2, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out3, tmp3, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out4, tmp4, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out5, tmp5, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out6, tmp6, comm_fact0);
+    OUTPUT_ROW_4x4_5x5(out7, tmp7, comm_fact0);
+
+    // Store values across the 64 channels
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y;
+
+    *((__global float *)(dst_addr + 0 * dst_stride_z))  = out0.s0;
+    *((__global float *)(dst_addr + 1 * dst_stride_z))  = out0.s1;
+    *((__global float *)(dst_addr + 2 * dst_stride_z))  = out0.s2;
+    *((__global float *)(dst_addr + 3 * dst_stride_z))  = out0.s3;
+    *((__global float *)(dst_addr + 4 * dst_stride_z))  = out0.s4;
+    *((__global float *)(dst_addr + 5 * dst_stride_z))  = out0.s5;
+    *((__global float *)(dst_addr + 6 * dst_stride_z))  = out0.s6;
+    *((__global float *)(dst_addr + 7 * dst_stride_z))  = out0.s7;
+    *((__global float *)(dst_addr + 8 * dst_stride_z))  = out1.s0;
+    *((__global float *)(dst_addr + 9 * dst_stride_z))  = out1.s1;
+    *((__global float *)(dst_addr + 10 * dst_stride_z)) = out1.s2;
+    *((__global float *)(dst_addr + 11 * dst_stride_z)) = out1.s3;
+    *((__global float *)(dst_addr + 12 * dst_stride_z)) = out1.s4;
+    *((__global float *)(dst_addr + 13 * dst_stride_z)) = out1.s5;
+    *((__global float *)(dst_addr + 14 * dst_stride_z)) = out1.s6;
+    *((__global float *)(dst_addr + 15 * dst_stride_z)) = out1.s7;
+    *((__global float *)(dst_addr + 16 * dst_stride_z)) = out2.s0;
+    *((__global float *)(dst_addr + 17 * dst_stride_z)) = out2.s1;
+    *((__global float *)(dst_addr + 18 * dst_stride_z)) = out2.s2;
+    *((__global float *)(dst_addr + 19 * dst_stride_z)) = out2.s3;
+    *((__global float *)(dst_addr + 20 * dst_stride_z)) = out2.s4;
+    *((__global float *)(dst_addr + 21 * dst_stride_z)) = out2.s5;
+    *((__global float *)(dst_addr + 22 * dst_stride_z)) = out2.s6;
+    *((__global float *)(dst_addr + 23 * dst_stride_z)) = out2.s7;
+    *((__global float *)(dst_addr + 24 * dst_stride_z)) = out3.s0;
+    *((__global float *)(dst_addr + 25 * dst_stride_z)) = out3.s1;
+    *((__global float *)(dst_addr + 26 * dst_stride_z)) = out3.s2;
+    *((__global float *)(dst_addr + 27 * dst_stride_z)) = out3.s3;
+    *((__global float *)(dst_addr + 28 * dst_stride_z)) = out3.s4;
+    *((__global float *)(dst_addr + 29 * dst_stride_z)) = out3.s5;
+    *((__global float *)(dst_addr + 30 * dst_stride_z)) = out3.s6;
+    *((__global float *)(dst_addr + 31 * dst_stride_z)) = out3.s7;
+    *((__global float *)(dst_addr + 32 * dst_stride_z)) = out4.s0;
+    *((__global float *)(dst_addr + 33 * dst_stride_z)) = out4.s1;
+    *((__global float *)(dst_addr + 34 * dst_stride_z)) = out4.s2;
+    *((__global float *)(dst_addr + 35 * dst_stride_z)) = out4.s3;
+    *((__global float *)(dst_addr + 36 * dst_stride_z)) = out4.s4;
+    *((__global float *)(dst_addr + 37 * dst_stride_z)) = out4.s5;
+    *((__global float *)(dst_addr + 38 * dst_stride_z)) = out4.s6;
+    *((__global float *)(dst_addr + 39 * dst_stride_z)) = out4.s7;
+    *((__global float *)(dst_addr + 40 * dst_stride_z)) = out5.s0;
+    *((__global float *)(dst_addr + 41 * dst_stride_z)) = out5.s1;
+    *((__global float *)(dst_addr + 42 * dst_stride_z)) = out5.s2;
+    *((__global float *)(dst_addr + 43 * dst_stride_z)) = out5.s3;
+    *((__global float *)(dst_addr + 44 * dst_stride_z)) = out5.s4;
+    *((__global float *)(dst_addr + 45 * dst_stride_z)) = out5.s5;
+    *((__global float *)(dst_addr + 46 * dst_stride_z)) = out5.s6;
+    *((__global float *)(dst_addr + 47 * dst_stride_z)) = out5.s7;
+    *((__global float *)(dst_addr + 48 * dst_stride_z)) = out6.s0;
+    *((__global float *)(dst_addr + 49 * dst_stride_z)) = out6.s1;
+    *((__global float *)(dst_addr + 50 * dst_stride_z)) = out6.s2;
+    *((__global float *)(dst_addr + 51 * dst_stride_z)) = out6.s3;
+    *((__global float *)(dst_addr + 52 * dst_stride_z)) = out6.s4;
+    *((__global float *)(dst_addr + 53 * dst_stride_z)) = out6.s5;
+    *((__global float *)(dst_addr + 54 * dst_stride_z)) = out6.s6;
+    *((__global float *)(dst_addr + 55 * dst_stride_z)) = out6.s7;
+    *((__global float *)(dst_addr + 56 * dst_stride_z)) = out7.s0;
+    *((__global float *)(dst_addr + 57 * dst_stride_z)) = out7.s1;
+    *((__global float *)(dst_addr + 58 * dst_stride_z)) = out7.s2;
+    *((__global float *)(dst_addr + 59 * dst_stride_z)) = out7.s3;
+    *((__global float *)(dst_addr + 60 * dst_stride_z)) = out7.s4;
+    *((__global float *)(dst_addr + 61 * dst_stride_z)) = out7.s5;
+    *((__global float *)(dst_addr + 62 * dst_stride_z)) = out7.s6;
+    *((__global float *)(dst_addr + 63 * dst_stride_z)) = out7.s7;
+}
 #endif // defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP)
 
 #if defined(NUM_TILES_X)
diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
index df7ffe83a0..a47590d20f 100644
--- a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
@@ -46,8 +46,9 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
     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), "Winograd input transform only supports 3x3 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_tile_size != Size2D(2U, 2U), "Winograd input transform only supports 2x2 output tile size");
+    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_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U), "Winograd input transform only supports 2x2 output tile for 3x3 kernels");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd input transform only supports 4x4 output tile for 5x5 kernels");
     ARM_COMPUTE_UNUSED(conv_info);
     ARM_COMPUTE_UNUSED(output_tile_size);
     ARM_COMPUTE_UNUSED(kernel_size);
diff --git a/tests/datasets/WinogradInputTransformDataset.h b/tests/datasets/WinogradInputTransformDataset.h
index 625daa0e6e..cbe63645de 100644
--- a/tests/datasets/WinogradInputTransformDataset.h
+++ b/tests/datasets/WinogradInputTransformDataset.h
@@ -109,6 +109,13 @@ public:
         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));
+        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));
+        add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
     }
 };
 
@@ -121,6 +128,10 @@ public:
         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));
+        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));
+        add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
     }
 };
 } // namespace datasets
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 08810eaf58..4bfe4c8f42 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -126,14 +126,14 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::Dat
                                                                                                                      framework::dataset::make("DataLayout", { DataLayout::NCHW })),
                                                                                                              framework::dataset::make("DataType", { DataType::F32 })))
 {
-    validate(CLAccessor(_target), _reference);
+    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 })),
                                                                                                            framework::dataset::make("DataType", { DataType::F32 })))
 {
-    validate(CLAccessor(_target), _reference);
+    validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 TEST_SUITE_END() // InputTransform
 
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 757a06d847..fa6e372cde 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -62,6 +62,18 @@ void initialize_matrix_transform(SimpleTensor<T> &src, const Size2D &output_tile
         0.0f, 4.0f, 0.0f, -5.0f, 0.0f, 1.0f,
     };
 
+    static const float imatrix4x4_5x5[] =
+    {
+        1.f, 0.f, -21.f / 4.f, 0.f, 21.f / 4.f, 0.f, -1.f, 0.f,
+        0.f, 1.f, 1.f, -17.f / 4.f, -17.f / 4.f, 1.f, 1.f, 0.f,
+        0.f, -1.f, 1.f, 17.f / 4.f, -17.f / 4.f, -1.f, 1.f, 0.f,
+        0.f, 1.f / 2.f, 1.f / 4.f, -5.f / 2.f, -5.f / 4.f, 2.f, 1.f, 0.f,
+        0.f, -1.f / 2.f, 1.f / 4.f, 5.f / 2.f, -5.f / 4.f, -2.f, 1.f, 0.f,
+        0.f, 2.f, 4.f, -5.f / 2.f, -5.f, 1.f / 2.f, 1.f, 0.f,
+        0.f, -2.f, 4.f, 5.f / 2.f, -5.f, -1.f / 2.f, 1.f, 0.f,
+        0.f, -1.f, 0.f, 21.f / 4.f, 0.f, -21.f / 4.f, 0.f, 1.f
+    };
+
     // ------------------------------------------
 
     // Winograd filter transform matrices
@@ -122,6 +134,7 @@ void initialize_matrix_transform(SimpleTensor<T> &src, const Size2D &output_tile
     {
         { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::INPUT), imatrix4x4_5x5 },
         { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5 },