COMPMID-2048: Add support for dilation in NEDepthwiseConvolution.
Change-Id: If9941e770779fbf918ba5ff0573da9378078b969
Signed-off-by: Usama Arif <usama.arif@arm.com>
Reviewed-on: https://review.mlplatform.org/c/999
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h b/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
index 87ca4da..c0381cb 100644
--- a/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
+++ b/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
@@ -58,21 +58,25 @@
* @param[out] output Destination tensor. Data type supported: Same as @p input.
* @param[in] conv_info Padding and stride information to use for the convolution.
* @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+ *
*/
- void configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1);
+ void configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U));
/** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer3x3Kernel
*
* @note Supported data layouts: NCHW and NHWC
*
- * @param[in] input Source tensor. DataType supported: QASYMM8/F16/F32.
- * @param[in] weights Weights tensor. This is a 3D tensor with dimensions [3, 3, IFM] for NCHW or [IFM, 3, 3] if NHWC data layout. Data type supported: Same as @p input.
- * @param[in] output Destination tensor. Data type supported: Same as @p input.
+ * @param[in] input Source tensor info. DataType supported: QASYMM8/F16/F32.
+ * @param[in] weights Weights tensor info. This is a 3D tensor with dimensions [3, 3, IFM] for NCHW or [IFM, 3, 3] if NHWC data layout. Data type supported: Same as @p input.
+ * @param[in] output Destination tensor info. Data type supported: Same as @p input.
* @param[in] conv_info Padding and stride information to use for the convolution.
* @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1,
+ const Size2D &dilation = Size2D(1U, 1U));
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
@@ -86,6 +90,7 @@
PadStrideInfo _conv_info;
unsigned int _num_elems_written_per_iteration;
unsigned int _depth_multiplier;
+ Size2D _dilation;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEDEPTHWISECONVOLUTIONKERNEL3x3_H__ */
diff --git a/arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h b/arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h
index de67136..3e123b4 100644
--- a/arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h
+++ b/arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -62,23 +62,27 @@
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] has_bias Boolean that specifies if the depthwise convolution has bias.
* @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*/
- void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1);
+ void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1,
+ const Size2D &dilation = Size2D(1U, 1U));
/** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseIm2ColKernel
*
- * @param[in] input The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+ * @param[in] input The input tensor info to convert. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
- * @param[in] output The output tensor. First 3 lower dimensions represent a transform of each 3D input,
+ * @param[in] output The output tensor info. First 3 lower dimensions represent a transform of each 3D input,
* while every dimension above 3 represents a batch. Data types supported: Same as @p input
* @param[in] kernel_dims The kernel dimensions (width and height).
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] has_bias Boolean that specifies if the depthwise convolution has bias.
* @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1,
+ const Size2D &dilation = Size2D(1U, 1U));
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
@@ -104,6 +108,7 @@
PadStrideInfo _conv_info;
bool _has_bias;
unsigned int _depth_multiplier;
+ Size2D _dilation;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_NEDEPTHWISEIM2COLKERNEL_H__ */
diff --git a/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h b/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h
index e6dc43a..3547d2d 100644
--- a/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h
+++ b/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -79,6 +79,125 @@
return r;
}
+/** Perform a 3x3 convolution for 4 consecutive elements on float32 when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset (Optional) Input quantization offset.
+ *
+ */
+inline float32x4_t single_convolve_3x3_dilation(const float *in_top, const float *in_mid, const float *in_low,
+ const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+
+ const float32x4x3_t vtop =
+ {
+ {
+ vld1q_f32(in_top),
+ vld1q_f32(in_top + dilation_x),
+ vld1q_f32(in_top + 2 * dilation_x)
+ }
+ };
+ const float32x4x3_t vmid =
+ {
+ {
+ vld1q_f32(in_mid),
+ vld1q_f32(in_mid + dilation_x),
+ vld1q_f32(in_mid + 2 * dilation_x)
+ }
+ };
+ const float32x4x3_t vlow =
+ {
+ {
+ vld1q_f32(in_low),
+ vld1q_f32(in_low + dilation_x),
+ vld1q_f32(in_low + 2 * dilation_x)
+ }
+ };
+ float32x4_t out = vmulq_f32(vtop.val[0], m0.val[0]);
+ out = vmlaq_f32(out, vtop.val[1], m0.val[1]);
+ out = vmlaq_f32(out, vtop.val[2], m0.val[2]);
+
+ out = vmlaq_f32(out, vmid.val[0], m1.val[0]);
+ out = vmlaq_f32(out, vmid.val[1], m1.val[1]);
+ out = vmlaq_f32(out, vmid.val[2], m1.val[2]);
+
+ out = vmlaq_f32(out, vlow.val[0], m2.val[0]);
+ out = vmlaq_f32(out, vlow.val[1], m2.val[1]);
+ out = vmlaq_f32(out, vlow.val[2], m2.val[2]);
+
+ return out;
+}
+
+/** Perform a 3x3 convolution for 8 consecutive elements on float32 when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset (Optional) Input quantization offset.
+ *
+ */
+template <unsigned int stridex>
+float32x4x2_t convolve_3x3_dilation(const float *in_top, const float *in_mid, const float *in_low,
+ const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2,
+ const size_t dilation_x, int input_offset = 0);
+
+template <>
+inline float32x4x2_t convolve_3x3_dilation<1>(const float *in_top, const float *in_mid, const float *in_low,
+ const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+
+ const float32x4x2_t out =
+ {
+ {
+ single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset),
+ single_convolve_3x3_dilation(in_top + 4, in_mid + 4, in_low + 4, m0, m1, m2, dilation_x, input_offset)
+ }
+ };
+
+ return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3_dilation<2>(const float *in_top, const float *in_mid, const float *in_low,
+ const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+
+ float32x4x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+ out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1);
+ out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2);
+ out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3);
+ return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3_dilation<3>(const float *in_top, const float *in_mid, const float *in_low,
+ const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+
+ float32x4x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+ ;
+ out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1);
+ return out;
+}
+
/** Perform a convolve3x3 on float32.
*
* @param[in] in_top Pointer to the first row of the input.
@@ -183,6 +302,143 @@
return out;
}
+/** Perform a 3x3 convolution for 4 consecutive elements on uint8_t when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset Input quantization offset.
+ *
+ */
+inline int32x4_t single_convolve_3x3_dilation(const uint8_t *in_top, const uint8_t *in_mid, const uint8_t *in_low,
+ const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2,
+ size_t dilation_x, int input_offset)
+{
+ const int32x4_t v_input_offset = vdupq_n_s32(input_offset);
+
+ const uint8x8x3_t vtop =
+ {
+ {
+ vld1_u8(in_top),
+ vld1_u8(in_top + dilation_x),
+ vld1_u8(in_top + 2 * dilation_x)
+ }
+ };
+ const uint8x8x3_t vmid =
+ {
+ {
+ vld1_u8(in_mid),
+ vld1_u8(in_mid + dilation_x),
+ vld1_u8(in_mid + 2 * dilation_x)
+ }
+ };
+ const uint8x8x3_t vlow =
+ {
+ {
+ vld1_u8(in_low),
+ vld1_u8(in_low + dilation_x),
+ vld1_u8(in_low + 2 * dilation_x)
+ }
+ };
+
+ const int32x4x3_t vtop_s32 =
+ {
+ {
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vtop.val[0])))), //convert from uint8x8 to uint16x8, to uint16x4(lower or bottom half) to int16x4 to int32x4
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vtop.val[1])))),
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vtop.val[2])))),
+ }
+ };
+ const int32x4x3_t vmid_s32 =
+ {
+ {
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vmid.val[0])))),
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vmid.val[1])))),
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vmid.val[2])))),
+ }
+ };
+ const int32x4x3_t vlow_s32 =
+ {
+ {
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vlow.val[0])))),
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vlow.val[1])))),
+ vaddw_s16(v_input_offset, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(vlow.val[2])))),
+ }
+ };
+
+ int32x4_t out = vmulq_s32(vtop_s32.val[0], m0.val[0]);
+ out = vmlaq_s32(out, vtop_s32.val[1], m0.val[1]);
+ out = vmlaq_s32(out, vtop_s32.val[2], m0.val[2]);
+
+ out = vmlaq_s32(out, vmid_s32.val[0], m1.val[0]);
+ out = vmlaq_s32(out, vmid_s32.val[1], m1.val[1]);
+ out = vmlaq_s32(out, vmid_s32.val[2], m1.val[2]);
+
+ out = vmlaq_s32(out, vlow_s32.val[0], m2.val[0]);
+ out = vmlaq_s32(out, vlow_s32.val[1], m2.val[1]);
+ out = vmlaq_s32(out, vlow_s32.val[2], m2.val[2]);
+
+ return out;
+}
+
+/** Perform a 3x3 convolution for 4 consecutive elements on uint8_t when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset Input quantization offset.
+ *
+ */
+template <unsigned int stridex>
+int32x4x2_t convolve_3x3_dilation(const uint8_t *in_top, const uint8_t *in_mid, const uint8_t *in_low,
+ const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2,
+ const size_t dilation_x, int input_offset);
+
+template <>
+inline int32x4x2_t convolve_3x3_dilation<1>(const uint8_t *in_top, const uint8_t *in_mid, const uint8_t *in_low, const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ const int32x4x2_t out =
+ {
+ {
+ single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset),
+ single_convolve_3x3_dilation(in_top + 4, in_mid + 4, in_low + 4, m0, m1, m2, dilation_x, input_offset)
+ }
+ };
+ return out;
+}
+
+template <>
+inline int32x4x2_t convolve_3x3_dilation<2>(const uint8_t *in_top, const uint8_t *in_mid, const uint8_t *in_low,
+ const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ int32x4x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+
+ out.val[0] = vsetq_lane_s32(vgetq_lane_s32(out.val[0], 2), out.val[0], 1);
+ out.val[0] = vsetq_lane_s32(vgetq_lane_s32(out.val[1], 0), out.val[0], 2);
+ out.val[0] = vsetq_lane_s32(vgetq_lane_s32(out.val[1], 2), out.val[0], 3);
+ return out;
+}
+
+template <>
+inline int32x4x2_t convolve_3x3_dilation<3>(const uint8_t *in_top, const uint8_t *in_mid, const uint8_t *in_low,
+ const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ int32x4x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+ out.val[0] = vsetq_lane_s32(vgetq_lane_s32(out.val[0], 3), out.val[0], 1);
+ return out;
+}
+
/** Perform a convolve3x3 on uint8_t
*
* @param[in] in_top Pointer to the first row of the input.
@@ -390,6 +646,124 @@
return r;
}
+/** Perform a 3x3 convolution for 8 consecutive elements on float16 when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset (Optional)Input quantization offset.
+ *
+ */
+inline float16x8_t single_convolve_3x3_dilation(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low,
+ const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
+ const size_t dilation_x, int input_offset = 0)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+ const float16x8x3_t vtop =
+ {
+ {
+ vld1q_f16(in_top),
+ vld1q_f16(in_top + dilation_x),
+ vld1q_f16(in_top + 2 * dilation_x)
+ }
+ };
+ const float16x8x3_t vmid =
+ {
+ {
+ vld1q_f16(in_mid),
+ vld1q_f16(in_mid + dilation_x),
+ vld1q_f16(in_mid + 2 * dilation_x)
+ }
+ };
+ const float16x8x3_t vlow =
+ {
+ {
+ vld1q_f16(in_low),
+ vld1q_f16(in_low + dilation_x),
+ vld1q_f16(in_low + 2 * dilation_x)
+ }
+ };
+ float16x8_t out = vmulq_f16(vtop.val[0], m0.val[0]);
+ out = vaddq_f16(out, vmulq_f16(vtop.val[1], m0.val[1]));
+ out = vaddq_f16(out, vmulq_f16(vtop.val[2], m0.val[2]));
+
+ out = vaddq_f16(out, vmulq_f16(vmid.val[0], m1.val[0]));
+ out = vaddq_f16(out, vmulq_f16(vmid.val[1], m1.val[1]));
+ out = vaddq_f16(out, vmulq_f16(vmid.val[2], m1.val[2]));
+
+ out = vaddq_f16(out, vmulq_f16(vlow.val[0], m2.val[0]));
+ out = vaddq_f16(out, vmulq_f16(vlow.val[1], m2.val[1]));
+ out = vaddq_f16(out, vmulq_f16(vlow.val[2], m2.val[2]));
+
+ return out;
+}
+
+/** Perform a 3x3 convolution for 16 consecutive elements on float16 when dilation.x() or dilation.y() is not 1.
+ *
+ * @param[in] in_top Pointer to the first row of the input.
+ * @param[in] in_mid Pointer to the second row of the input.
+ * @param[in] in_low Pointer to the third row of the input.
+ * @param[in] m0 First row of the filter.
+ * @param[in] m1 Second row of the filter.
+ * @param[in] m2 Third row of the filter.
+ * @param[in] dilation_x Dilation, in elements across x.
+ * @param[in] input_offset (Optional)Input quantization offset.
+ *
+ */
+template <unsigned int stridex>
+float16x8x2_t convolve_3x3_dilation(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low,
+ const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
+ const size_t dilation_x, int input_offset = 0);
+
+template <>
+inline float16x8x2_t convolve_3x3_dilation<1>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low,
+ const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ const float16x8x2_t out =
+ {
+ {
+ single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset),
+ single_convolve_3x3_dilation(in_top + 8, in_mid + 8, in_low + 8, m0, m1, m2, dilation_x, input_offset)
+ }
+ };
+ return out;
+}
+
+template <>
+inline float16x8x2_t convolve_3x3_dilation<2>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low,
+ const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+ float16x8x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 2), out.val[0], 1);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 4), out.val[0], 2);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 6), out.val[0], 3);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 0), out.val[0], 4);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 2), out.val[0], 5);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 4), out.val[0], 6);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 6), out.val[0], 7);
+ return out;
+}
+
+template <>
+inline float16x8x2_t convolve_3x3_dilation<3>(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low,
+ const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2,
+ const size_t dilation_x, int input_offset)
+{
+ ARM_COMPUTE_UNUSED(input_offset);
+ float16x8x2_t out = convolve_3x3_dilation<1>(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 6), out.val[0], 2);
+ out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 1), out.val[0], 3);
+ return out;
+}
+
/** Perform a convolve3x3 on float16.
*
* @param[in] in_top Pointer to the first row of the input.