COMPMID-1298: Fuse ReLu activation in CLWinogradOutputTransform
Change-Id: I9e6e43a5839d04c2e4b4552c05446efb0a5074cf
Reviewed-on: https://review.mlplatform.org/232
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index 3bbbb58..bdb230d 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -63,8 +63,10 @@
* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
* @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
* @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation.
*/
- void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info);
+ void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
*
* @note Winograd output transform supports the following configurations for NCWH data layout
@@ -82,10 +84,11 @@
* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
* @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
* @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation @ref ActivationLayerInfo. Only RELU, BOUNDED_RELU, LU_BOUNDED_RELU, LEAKY_RELU and SOFT_RELU supported.
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
index 395f595..f11eb2a 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
@@ -108,13 +108,11 @@
CLWinogradInputTransform _input_transform;
CLWinogradFilterTransformKernel _filter_transform;
CLWinogradOutputTransformKernel _output_transform;
- CLActivationLayer _activationlayer_function;
CLTensor _input0;
CLTensor _input1;
CLTensor _batched_mm_output;
const ICLTensor *_original_weights;
bool _is_prepared;
- bool _is_activationlayer_enabled;
};
-}
+} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H__ */
diff --git a/src/core/CL/cl_kernels/activation_helpers.h b/src/core/CL/cl_kernels/activation_helpers.h
index dfab082..9d4af84 100644
--- a/src/core/CL/cl_kernels/activation_helpers.h
+++ b/src/core/CL/cl_kernels/activation_helpers.h
@@ -70,7 +70,7 @@
// Soft RELU Activation
inline TYPE srelu_op(TYPE x)
{
- return LOG_OP(ADD_OP((TYPE)CONST_ONE, EXP_OP(x)));
+ return CONVERT(LOG_OP(ADD_OP((VEC_DATA_TYPE(float, VEC_SIZE))CONST_ONE, EXP_OP(CONVERT(x, VEC_DATA_TYPE(float, VEC_SIZE))))), TYPE);
}
// Absolute Activation
inline TYPE abs_op(TYPE x)
diff --git a/src/core/CL/cl_kernels/helpers.h b/src/core/CL/cl_kernels/helpers.h
index 7ee97d9..180bd50 100644
--- a/src/core/CL/cl_kernels/helpers.h
+++ b/src/core/CL/cl_kernels/helpers.h
@@ -50,6 +50,9 @@
#define VSTORE_STR(size) vstore##size
#define VSTORE(size) VSTORE_STR(size)
+#define float1 float
+#define half1 half
+
#define VEC_DATA_TYPE_STR(type, size) type##size
#define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size)
diff --git a/src/core/CL/cl_kernels/winograd_output_transform.cl b/src/core/CL/cl_kernels/winograd_output_transform.cl
index f52b027..e979978 100644
--- a/src/core/CL/cl_kernels/winograd_output_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_output_transform.cl
@@ -23,7 +23,15 @@
*/
#include "helpers.h"
+#if defined(FUSED_ACTIVATION)
+#include "activation_layer.cl"
+#define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x)
+#else /* defined(FUSED_ACTIVATION) */
+#define ACTIVATION_FUNC(x) (x)
+#endif /* defined(FUSED_ACTIVATION) */
+
#if defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2/2x1 or 1x2, the filter size 3x3/3x1 or 1x3 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
@@ -32,6 +40,10 @@
* @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
* @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
* @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half.
+ * @note It is possible to select the activation function to apply using -DFUSED_ACTIVATION e.g. -DFUSED_ACTIVATION=relu
+ * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively.
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. Accepted values are -DVEC_SIZE=2 (for output_tile_size 2x2, 2x1, 1x2) and -DVEC_SIZE=4 (for output_tile_size 4x4, 4x1, 1x4)
+ * @note Select data type should be given too with -DSELECT_DATA_TYPE e.g -DSELECT_DATA_TYPE=int
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
@@ -86,6 +98,7 @@
float out00 = d00 + d01 + d02;
float out01 = d01 - d02 - d03;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 4 * src_stride_z));
DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 5 * src_stride_z));
DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z));
@@ -150,10 +163,12 @@
// Store the output tile
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = (DATA_TYPE)out00;
- *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = (DATA_TYPE)out01;
+ const const VEC_DATA_TYPE(DATA_TYPE, 2)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2)));
+ *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(out00, out01), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+ vstore2(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2))), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -162,11 +177,12 @@
out10 += (DATA_TYPE)b;
out11 += (DATA_TYPE)b;
#endif // defined(HAS_BIAS)
-
- vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))((DATA_TYPE)out10, (DATA_TYPE)out11), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+ vstore2(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out10, out11), VEC_DATA_TYPE(DATA_TYPE, 2))), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
+#if defined(VEC_SIZE) && VEC_SIZE == 4
/** 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
@@ -230,6 +246,7 @@
float out02 = d01 + d02 + 4.0f * d03 + 4.0f * d04;
float out03 = d01 - d02 + 8.0f * d03 - 8.0f * d04 + d05;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z));
DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 7 * src_stride_z));
DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z));
@@ -351,12 +368,14 @@
// Store the output tile
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = (DATA_TYPE)out00;
- *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = (DATA_TYPE)out01;
- *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = (DATA_TYPE)out02;
- *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = (DATA_TYPE)out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
+ *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2;
+ *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out00, (DATA_TYPE)out01, (DATA_TYPE)out02, (DATA_TYPE)out03), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -377,9 +396,9 @@
out32 += (float)b;
out33 += (float)b;
#endif // defined(HAS_BIAS)
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out10, (DATA_TYPE)out11, (DATA_TYPE)out12, (DATA_TYPE)out13), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out20, (DATA_TYPE)out21, (DATA_TYPE)out22, (DATA_TYPE)out23), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out30, (DATA_TYPE)out31, (DATA_TYPE)out32, (DATA_TYPE)out33), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out10, out11, out12, out13), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out20, out21, out22, out23), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out30, out31, out32, out33), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
@@ -579,25 +598,29 @@
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#if defined(SRC_DEPTH)
int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w);
-#else /* defined(SRC_DEPTH) */
+#else /* defined(SRC_DEPTH) */
int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z);
-#endif /* defined(SRC_DEPTH) */
+#endif /* defined(SRC_DEPTH) */
offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, (int4)dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
// Store the 1x4 output tile
- *((__global DATA_TYPE *)(dst_ptr + offset.s0)) = (DATA_TYPE)out00;
- *((__global DATA_TYPE *)(dst_ptr + offset.s1)) = (DATA_TYPE)out01;
- *((__global DATA_TYPE *)(dst_ptr + offset.s2)) = (DATA_TYPE)out02;
- *((__global DATA_TYPE *)(dst_ptr + offset.s3)) = (DATA_TYPE)out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *((__global DATA_TYPE *)(dst_ptr + offset.s0)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + offset.s1)) = out0_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + offset.s2)) = out0_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + offset.s3)) = out0_dt.s3;
#elif defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
// Store the 4x1 output tile
int offset = dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
int mult_y = min(dst_size - offset, 1);
- *((__global DATA_TYPE *)(dst_ptr + mult_y * 0 * dst_stride_y + offset)) = (DATA_TYPE)out00;
- *((__global DATA_TYPE *)(dst_ptr + mult_y * 1 * dst_stride_y + offset)) = (DATA_TYPE)out01;
- *((__global DATA_TYPE *)(dst_ptr + mult_y * 2 * dst_stride_y + offset)) = (DATA_TYPE)out02;
- *((__global DATA_TYPE *)(dst_ptr + mult_y * 3 * dst_stride_y + offset)) = (DATA_TYPE)out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *((__global DATA_TYPE *)(dst_ptr + mult_y * 0 * dst_stride_y + offset)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y * 1 * dst_stride_y + offset)) = out0_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y * 2 * dst_stride_y + offset)) = out0_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y * 3 * dst_stride_y + offset)) = out0_dt.s3;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
// Get output address
#if defined(SRC_DEPTH)
@@ -609,22 +632,30 @@
int4 mult_y = min((int4)dst_size - offset, (int4)1); // If out of bound, we don't want to increase dst_stride_y, so we set the multiplier to 0. It will be 1 otherwise.
// Store the 4x4 output tile
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * dst_stride_y + offset.s0)) = (DATA_TYPE)out00;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s0)) = (DATA_TYPE)out01;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s0)) = (DATA_TYPE)out02;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s0)) = (DATA_TYPE)out03;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * dst_stride_y + offset.s1)) = (DATA_TYPE)out10;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * dst_stride_y + offset.s1)) = (DATA_TYPE)out11;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * dst_stride_y + offset.s1)) = (DATA_TYPE)out12;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * dst_stride_y + offset.s1)) = (DATA_TYPE)out13;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * dst_stride_y + offset.s2)) = (DATA_TYPE)out20;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * dst_stride_y + offset.s2)) = (DATA_TYPE)out21;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * dst_stride_y + offset.s2)) = (DATA_TYPE)out22;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * dst_stride_y + offset.s2)) = (DATA_TYPE)out23;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * dst_stride_y + offset.s3)) = (DATA_TYPE)out30;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * dst_stride_y + offset.s3)) = (DATA_TYPE)out31;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * dst_stride_y + offset.s3)) = (DATA_TYPE)out32;
- *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * dst_stride_y + offset.s3)) = (DATA_TYPE)out33;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out1_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out10, out11, out12, out13), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out2_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out20, out21, out22, out23), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out3_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out30, out31, out32, out33), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * dst_stride_y + offset.s0)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s0)) = out0_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s0)) = out0_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s0)) = out0_dt.s3;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * dst_stride_y + offset.s1)) = out1_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * dst_stride_y + offset.s1)) = out1_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * dst_stride_y + offset.s1)) = out1_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * dst_stride_y + offset.s1)) = out1_dt.s3;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * dst_stride_y + offset.s2)) = out2_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * dst_stride_y + offset.s2)) = out2_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * dst_stride_y + offset.s2)) = out2_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * dst_stride_y + offset.s2)) = out2_dt.s3;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * dst_stride_y + offset.s3)) = out3_dt.s0;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * dst_stride_y + offset.s3)) = out3_dt.s1;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * dst_stride_y + offset.s3)) = out3_dt.s2;
+ *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * dst_stride_y + offset.s3)) = out3_dt.s3;
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
}
@@ -690,6 +721,7 @@
Tensor4D src = CONVERT_TO_TENSOR4D_STRUCT(src, SRC_DEPTH);
const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0);
#else /* defined(SRC_DEPTH) */
+
Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0);
#endif /* defined(SRC_DEPTH) */
@@ -706,6 +738,7 @@
#if defined(SRC_DEPTH)
__global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w;
#else /* defined(SRC_DEPTH) */
+
__global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
#endif /* defined(SRC_DEPTH) */
@@ -740,15 +773,18 @@
// Store the output tile
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out00;
- *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out01;
- *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out02;
- *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+ *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
+ *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2;
+ *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))(out00, out01, out02, out03), 0, (__global DATA_TYPE *)(dst_addr));
+ vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr));
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z));
DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 9 * src_stride_z));
DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 10 * src_stride_z));
@@ -859,10 +895,10 @@
#endif // defined(HAS_BIAS)
// Store the output tile
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s0, (DATA_TYPE)out_col1.s0, (DATA_TYPE)out_col2.s0, (DATA_TYPE)out_col3.s0), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s1, (DATA_TYPE)out_col1.s1, (DATA_TYPE)out_col2.s1, (DATA_TYPE)out_col3.s1), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s2, (DATA_TYPE)out_col1.s2, (DATA_TYPE)out_col2.s2, (DATA_TYPE)out_col3.s2), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
- vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s3, (DATA_TYPE)out_col1.s3, (DATA_TYPE)out_col2.s3, (DATA_TYPE)out_col3.s3), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s0, out_col1.s0, out_col2.s0, out_col3.s0)), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s1, out_col1.s1, out_col2.s1, out_col3.s1)), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s2, out_col1.s2, out_col2.s2, out_col3.s2)), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
+ vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s3, out_col1.s3, out_col2.s3, out_col3.s3)), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
@@ -960,18 +996,21 @@
#endif /* defined(SRC_DEPTH) */
offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, (int4)dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
- *(__global DATA_TYPE *)(dst_ptr + offset.s0) = (DATA_TYPE)out00;
- *(__global DATA_TYPE *)(dst_ptr + offset.s1) = (DATA_TYPE)out01;
- *(__global DATA_TYPE *)(dst_ptr + offset.s2) = (DATA_TYPE)out02;
- *(__global DATA_TYPE *)(dst_ptr + offset.s3) = (DATA_TYPE)out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *(__global DATA_TYPE *)(dst_ptr + offset.s0) = out0_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + offset.s1) = out0_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + offset.s2) = out0_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + offset.s3) = out0_dt.s3;
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
// Get output address
int offset = dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
-
- *(__global DATA_TYPE *)(dst_ptr + 0 * dst_stride_y + offset) = (DATA_TYPE)out00;
- *(__global DATA_TYPE *)(dst_ptr + 1 * dst_stride_y + offset) = (DATA_TYPE)out01;
- *(__global DATA_TYPE *)(dst_ptr + 2 * dst_stride_y + offset) = (DATA_TYPE)out02;
- *(__global DATA_TYPE *)(dst_ptr + 3 * dst_stride_y + offset) = (DATA_TYPE)out03;
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+ *(__global DATA_TYPE *)(dst_ptr + 0 * dst_stride_y + offset) = out0_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + 1 * dst_stride_y + offset) = out0_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + 2 * dst_stride_y + offset) = out0_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + 3 * dst_stride_y + offset) = out0_dt.s3;
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -1094,26 +1133,37 @@
int4 mult_y = min((int4)dst_size - offset, (int4)1); // If out of bound, we don't want to increase dst_stride_y, so we set the multiplier to 0. It will be 1 otherwise.
// Store the output tile
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col0.s0;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col1.s0;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col2.s0;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col3.s0;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col0.s1;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col1.s1;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col2.s1;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col3.s1;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col0.s2;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col1.s2;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col2.s2;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col3.s2;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col0.s3;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col1.s3;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col2.s3;
- *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col3.s3;
+ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+ out_col0_dt = ACTIVATION_FUNC(CONVERT(out_col0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+ out_col1_dt = ACTIVATION_FUNC(CONVERT(out_col1, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+ out_col2_dt = ACTIVATION_FUNC(CONVERT(out_col2, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+ out_col3_dt = ACTIVATION_FUNC(CONVERT(out_col3, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * (int)dst_stride_y + offset.s0) = out_col0_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * (int)dst_stride_y + offset.s0) = out_col1_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * (int)dst_stride_y + offset.s0) = out_col2_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * (int)dst_stride_y + offset.s0) = out_col3_dt.s0;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * (int)dst_stride_y + offset.s1) = out_col0_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * (int)dst_stride_y + offset.s1) = out_col1_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * (int)dst_stride_y + offset.s1) = out_col2_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * (int)dst_stride_y + offset.s1) = out_col3_dt.s1;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * (int)dst_stride_y + offset.s2) = out_col0_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * (int)dst_stride_y + offset.s2) = out_col1_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * (int)dst_stride_y + offset.s2) = out_col2_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * (int)dst_stride_y + offset.s2) = out_col3_dt.s2;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * (int)dst_stride_y + offset.s3) = out_col0_dt.s3;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * (int)dst_stride_y + offset.s3) = out_col1_dt.s3;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * (int)dst_stride_y + offset.s3) = out_col2_dt.s3;
+ *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * (int)dst_stride_y + offset.s3) = out_col3_dt.s3;
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
/** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 3x1 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
@@ -1181,7 +1231,9 @@
#endif // defined(HAS_BIAS)
);
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
+#if defined(VEC_SIZE) && VEC_SIZE == 4
/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 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
@@ -1449,9 +1501,11 @@
#endif // defined(HAS_BIAS)
dst_size);
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
/** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x3 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
@@ -1519,7 +1573,9 @@
#endif // defined(HAS_BIAS)
);
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
+#if defined(VEC_SIZE) && VEC_SIZE == 4
/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 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
@@ -1787,5 +1843,6 @@
#endif // defined(HAS_BIAS)
dst_size);
}
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
index 7f1afe0..84b5ea2 100644
--- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
@@ -46,8 +46,18 @@
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ if(act_info.enabled())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((input->data_type() == DataType::QASYMM8) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+ && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+ && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
+ && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC),
+ "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported");
+ }
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
@@ -133,14 +143,14 @@
{
}
-void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info)
+void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_output_transform_shape(*input->info(), winograd_info)));
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info, act_info));
_input = input;
_bias = bias;
@@ -161,6 +171,21 @@
// Set build options
CLBuildOptions build_opts;
+ build_opts.add_option_if(act_info.enabled(), "-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
+ build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
+ build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
+
+ if((output_tile_size.x() == 2) || (output_tile_size.x() == 1 && output_tile_size.y() == 2))
+ {
+ build_opts.add_option("-DVEC_SIZE=2");
+ }
+ else if((output_tile_size.x() == 4) || (output_tile_size.x() == 1 && output_tile_size.y() == 4))
+ {
+ build_opts.add_option("-DVEC_SIZE=4");
+ }
+
+ build_opts.add_option_if(act_info.enabled(), "-DSELECT_DATA_TYPE=" + get_cl_select_type_from_data_type(input->info()->data_type()));
+
build_opts.add_option_if(_bias != nullptr, std::string("-DHAS_BIAS"));
build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(num_tiles.width));
build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
@@ -195,9 +220,9 @@
_config_id += lower_string(string_from_data_layout(winograd_info.output_data_layout));
}
-Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
+Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info, act_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), winograd_info.output_tile_size).first);
return Status{};
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 1abcb67..069196e 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -84,8 +84,8 @@
} // namespace
CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _activationlayer_function(), _input0(), _input1(), _batched_mm_output(),
- _original_weights(nullptr), _is_prepared(false), _is_activationlayer_enabled(false)
+ : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr),
+ _is_prepared(false)
{
}
@@ -133,14 +133,7 @@
(input->info()->data_type() == DataType::F16)));
// Configure output transform
- _output_transform.configure(&_batched_mm_output, biases, output, winograd_info);
-
- // Configure activation layer
- _is_activationlayer_enabled = act_info.enabled();
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.configure(output, nullptr, act_info);
- }
+ _output_transform.configure(&_batched_mm_output, biases, output, winograd_info, act_info);
// Allocate temporary tensors
_input0.allocator()->allocate();
@@ -216,11 +209,6 @@
// Run output transform
CLScheduler::get().enqueue(_output_transform);
- if(_is_activationlayer_enabled)
- {
- _activationlayer_function.run();
- }
-
_memory_group.release();
}
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index f7f06b7..efa049f 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -139,6 +139,17 @@
const auto LargeWinogradOutputTransformDatasetNCHW = datasets::LargeWinogradOutputTransformDatasetNCHW();
const auto LargeWinogradOutputTransformDatasetNHWC = datasets::LargeWinogradOutputTransformDatasetNHWC();
+
+//Activation Functions
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU)
+});
} // namespace
using namespace arm_compute::misc::shape_calculator;
@@ -562,16 +573,18 @@
}
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
- combine(SmallWinogradOutputTransformDatasetNCHW,
- framework::dataset::make("DataType", { DataType::F16 })))
+ combine(combine(SmallWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
- combine(LargeWinogradOutputTransformDatasetNCHW,
- framework::dataset::make("DataType", { DataType::F16 })))
+ combine(combine(LargeWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -579,16 +592,18 @@
TEST_SUITE_END() // FP16
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
- combine(SmallWinogradOutputTransformDatasetNCHW,
- framework::dataset::make("DataType", { DataType::F32 })))
+ combine(combine(SmallWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
- combine(LargeWinogradOutputTransformDatasetNCHW,
- framework::dataset::make("DataType", { DataType::F32 })))
+ combine(combine(LargeWinogradOutputTransformDatasetNCHW,
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -618,16 +633,18 @@
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
- combine(SmallWinogradOutputTransformDatasetNHWC,
- framework::dataset::make("DataType", { DataType::F16 })))
+ combine(combine(SmallWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
- combine(LargeWinogradOutputTransformDatasetNHWC,
- framework::dataset::make("DataType", { DataType::F16 })))
+ combine(combine(LargeWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F16 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -635,16 +652,18 @@
TEST_SUITE_END() // FP16
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
- combine(SmallWinogradOutputTransformDatasetNHWC,
- framework::dataset::make("DataType", { DataType::F32 })))
+ combine(combine(SmallWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
- combine(LargeWinogradOutputTransformDatasetNHWC,
- framework::dataset::make("DataType", { DataType::F32 })))
+ combine(combine(LargeWinogradOutputTransformDatasetNHWC,
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -702,7 +721,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -712,7 +731,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -724,7 +743,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -734,7 +753,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -746,7 +765,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -756,7 +775,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -768,7 +787,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset ),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -779,7 +798,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset ),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -792,7 +811,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -803,7 +822,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -816,7 +835,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -827,7 +846,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -845,7 +864,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -855,7 +874,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -867,7 +886,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -877,7 +896,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -889,7 +908,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -899,7 +918,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
@@ -911,7 +930,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -922,7 +941,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -935,7 +954,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -946,7 +965,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -959,7 +978,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
@@ -970,7 +989,7 @@
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index 9c9e634..8f34654 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -494,10 +494,10 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type)
+ void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info = ActivationLayerInfo())
{
- _target = compute_target(input_shape, winograd_info, data_type);
- _reference = compute_reference(input_shape, winograd_info, data_type);
+ _target = compute_target(input_shape, winograd_info, data_type, act_info);
+ _reference = compute_reference(input_shape, winograd_info, data_type, act_info);
}
protected:
@@ -522,7 +522,7 @@
}
}
- TensorType compute_target(const TensorShape &input_shape, const WinogradInfo &winograd_info, DataType data_type)
+ TensorType compute_target(const TensorShape &input_shape, const WinogradInfo &winograd_info, DataType data_type, ActivationLayerInfo act_info)
{
TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
@@ -533,7 +533,7 @@
// Create and configure function
FunctionType output_transform;
- output_transform.configure(&src, &bias, &dst, winograd_info);
+ output_transform.configure(&src, &bias, &dst, winograd_info, act_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -557,7 +557,7 @@
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &input_shape, WinogradInfo winograd_info, DataType data_type)
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info)
{
winograd_info.output_data_layout = DataLayout::NCHW;
TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
@@ -570,7 +570,9 @@
fill(src, 0, -1.f, 1.f);
fill(bias, 1, -1.f, 1.f);
- return reference::winograd_output_transform<T>(src, bias, output_shape, winograd_info);
+ const SimpleTensor<T> winograd_output = reference::winograd_output_transform<T>(src, bias, output_shape, winograd_info);
+
+ return (act_info.enabled()) ? reference::activation_layer<T>(winograd_output, act_info) : winograd_output;
}
TensorType _target{};