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authorUsama Arif <usama.arif@arm.com>2019-04-12 10:29:17 +0100
committerPablo Marquez <pablo.tello@arm.com>2019-04-24 12:31:21 +0000
commit881f2ded860fc1db23810076b699c4492556c376 (patch)
tree6237444ceaf2d5098e0b546693b8c18955963012
parent557d4aece64b2ed422ec853dbc2b7a4949ea56ca (diff)
downloadComputeLibrary-881f2ded860fc1db23810076b699c4492556c376.tar.gz
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>
-rw-r--r--arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h15
-rw-r--r--arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h15
-rw-r--r--arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h376
-rw-r--r--arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h12
-rw-r--r--arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h3
-rw-r--r--src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp106
-rw-r--r--src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp27
-rw-r--r--src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp11
-rw-r--r--src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp55
-rw-r--r--src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp6
-rw-r--r--tests/validation/NEON/DepthwiseConvolutionLayer.cpp188
11 files changed, 718 insertions, 96 deletions
diff --git a/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h b/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
index 87ca4da05b..c0381cb8d7 100644
--- a/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
+++ b/arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h
@@ -58,21 +58,25 @@ public:
* @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 @@ private:
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 de671361d6..3e123b4839 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 @@ public:
* @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 @@ private:
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 e6dc43a47b..3547d2d110 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 @@ inline int32x4x3_t load_matrix_row(const uint8_t *ptr, int weights_offset = 0)
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 @@ inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, c
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 @@ inline float16x8x3_t load_matrix_row(const float16_t *ptr, int weights_offset =
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.
diff --git a/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h
index c60233664d..396e2368c3 100644
--- a/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h
@@ -73,7 +73,7 @@ public:
* @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] act_info (Optional) Activation layer information in case of a fused activation.
- * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*/
void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));
@@ -88,7 +88,7 @@ public:
* @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] act_info (Optional) Activation layer information in case of a fused activation.
- * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*
* @return a status
*/
@@ -110,9 +110,11 @@ private:
* @param[in] conv_info Padding and stride information to use for the convolution.
* @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
* @param[in] act_info Activation layer information in case of a fused activation.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+ *
*/
void configure_generic(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier, const ActivationLayerInfo &act_info);
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation = Size2D(1U, 1U));
/** Configure the kernels/functions for the optimized pipeline.
*
* @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
@@ -186,7 +188,7 @@ public:
* @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] act_info (Optional) Activation layer information in case of a fused activation.
- * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*/
void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));
@@ -201,7 +203,7 @@ public:
* @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] act_info (Optional) Activation layer information in case of a fused activation.
- * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*
* @return a status
*/
diff --git a/arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h b/arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h
index df8f29d2c7..7d2cff7315 100644
--- a/arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h
+++ b/arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h
@@ -92,10 +92,11 @@ public:
* @param[in] weights Weights tensor info.
* @param[in] conv_info Convolution layer metadata.
* @param[in] depth_multiplier (Optional) Depth multiplier to be used.
+ * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
*
* @return True if the assembly kernel could be used else false. Note that transformations of input/output could be needed.
*/
- static bool is_optimized_supported(const ITensorInfo *input, const ITensorInfo *weights, PadStrideInfo conv_info, unsigned int depth_multiplier = 1);
+ static bool is_optimized_supported(const ITensorInfo *input, const ITensorInfo *weights, PadStrideInfo conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1, 1));
// Inherited methods overridden:
void run() override;
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
index ec672e0fc4..fdafc2da90 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -49,7 +49,7 @@ class convolver_3x3
{
public:
static void convolve(const Window &window, unsigned int num_elems_written_per_iteration,
- const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+ const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
{
const int input_offset = -input->info()->quantization_info().offset;
const int weights_offset = -weights->info()->quantization_info().offset;
@@ -57,6 +57,7 @@ public:
const int input_stride_x = input->info()->strides_in_bytes().x();
const int input_stride_y = input->info()->strides_in_bytes().y();
const int input_stride_z = input->info()->strides_in_bytes().z();
+ const int input_stride_w = input->info()->strides_in_bytes()[3];
const int output_stride_y = output->info()->strides_in_bytes().y();
const int kernel_stride_y = weights->info()->strides_in_bytes().y();
const int kernel_stride_z = weights->info()->strides_in_bytes().z();
@@ -74,9 +75,10 @@ public:
// setup input window for the iterator
Window window_in = window;
- // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0
+ // Iteration of input is taken care of in execute_window_loop
window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+ window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
Window window_k = calculate_max_window(*weights->info(), Steps(1u));
@@ -91,7 +93,7 @@ public:
int ih = 0;
int oh = 0;
- const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y - (id.z() - id.z() / depth_multiplier) * input_stride_z;
+ const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y + (id.z() / depth_multiplier) * input_stride_z + input_stride_w * id[3];
const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z;
const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base);
@@ -104,45 +106,54 @@ public:
for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
{
auto in_top = reinterpret_cast<const T1 *>(input_ptr + (ih + 0) * input_stride_y);
- auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + 1) * input_stride_y);
- auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2) * input_stride_y);
- auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y);
+ auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + dilation.y()) * input_stride_y);
+ auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2 * dilation.y()) * input_stride_y); //uint8
+ auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y); //int32
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_top += delta_input, in_mid += delta_input, in_low += delta_input,
p_out += num_elems_written_per_iteration)
{
- auto vres = detail::convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset);
- detail::store_results<stridex>(p_out, vres);
+ if(dilation == Size2D(1U, 1U))
+ {
+ auto vres = detail::convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset);
+ detail::store_results<stridex>(p_out, vres);
+ }
+ else
+ {
+ auto vres = detail::convolve_3x3_dilation<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, dilation.x(), input_offset);
+ detail::store_results<stridex>(p_out, vres);
+ }
}
}
},
- in, out);
+ out);
}
};
template <typename T1, typename T2>
inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration,
- const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+ const ITensor *input, const ITensor *weights, ITensor *output,
+ const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
{
const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
switch(conv_stride_x)
{
case 1:
- convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier);
+ convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation);
break;
case 2:
- convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier);
+ convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation);
break;
case 3:
- convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier);
+ convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation);
break;
default:
ARM_COMPUTE_ERROR("Not implemented");
}
}
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
@@ -157,7 +168,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
if(output->total_size() != 0)
{
- const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+ const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
if(is_data_type_quantized_asymmetric(input->data_type()))
@@ -173,13 +184,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+ const Size2D &dilation)
{
Window win;
bool window_changed = false;
// Get convolved dimensions
- const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+ const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
// Output auto inizialitation if not yet initialized
@@ -197,15 +209,16 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
switch(input->data_type())
{
case DataType::QASYMM8:
- num_elems_read_per_iteration = 16;
+ num_elems_read_per_iteration = 16 + 15 * (dilation.x() - 1);
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- num_elems_read_per_iteration = 24;
+ num_elems_written_per_iteration = 32 >> conv_stride_x;
+ num_elems_read_per_iteration = 24 + 23 * (dilation.x() - 1);
break;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F32:
- num_elems_read_per_iteration = 12;
+ num_elems_read_per_iteration = 12 + 11 * (dilation.x() - 1);
break;
default:
ARM_COMPUTE_ERROR("Data type not supported.");
@@ -214,7 +227,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
// Configure kernel window
win = calculate_max_window(*output, Steps(num_elems_written_per_iteration));
- AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3, conv_stride_x, conv_stride_y);
+ AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3 + 2 * (dilation.y() - 1), conv_stride_x, conv_stride_y);
AccessWindowStatic weights_access(weights, 0, 0, 3, 3);
AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration);
@@ -227,7 +240,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
} // namespace
NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel()
- : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1)
+ : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1), _dilation()
{
}
@@ -236,28 +249,41 @@ BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const
return _border_size;
}
-void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+ const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier));
-
- _input = input;
- _output = output;
- _weights = weights;
- _conv_info = conv_info;
- _depth_multiplier = depth_multiplier;
- _num_elems_written_per_iteration = 16 >> _conv_info.stride().first;
- _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left());
-
- auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, dilation));
+
+ _input = input;
+ _output = output;
+ _weights = weights;
+ _conv_info = conv_info;
+ _depth_multiplier = depth_multiplier;
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ case DataType::F32:
+ _num_elems_written_per_iteration = 16 >> _conv_info.stride().first;
+ break;
+ case DataType::F16:
+ _num_elems_written_per_iteration = 32 >> _conv_info.stride().first;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Data type not supported.");
+ }
+ _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left());
+ _dilation = dilation;
+ auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, dilation);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
INEKernel::configure(win_config.second);
}
-Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+ const Size2D &dilation)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier).first);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, dilation).first);
return Status{};
}
@@ -272,14 +298,14 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const Threa
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier);
+ convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation);
break;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F32:
- convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier);
+ convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation);
break;
case DataType::QASYMM8:
- convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier);
+ convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation);
break;
default:
ARM_COMPUTE_ERROR("Not implemented");
diff --git a/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp b/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
index 62373e348b..88f8b31a35 100644
--- a/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
@@ -38,7 +38,8 @@ using namespace arm_compute;
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output,
+ const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, const Size2D &dilation)
{
ARM_COMPUTE_UNUSED(conv_info);
//Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input) is not needed here as this kernel doesn't use NEON FP16 instructions.
@@ -48,6 +49,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || dilation.y() < 1);
return Status{};
}
@@ -84,7 +86,7 @@ void NEDepthwiseIm2ColKernel::run_generic(const Window &window)
Iterator out(_output, window_out);
const int full_length = input_w + pad_left + pad_right;
- const int max_initial_x = stride_x * (((full_length - _kernel_dims.width) / stride_x) + 1);
+ const int max_initial_x = stride_x * (((full_length - (_kernel_dims.width + (_kernel_dims.width - 1) * (_dilation.x() - 1))) / stride_x) + 1);
// Define pad value
auto zero = static_cast<T>(0);
@@ -103,12 +105,12 @@ void NEDepthwiseIm2ColKernel::run_generic(const Window &window)
// Get pointers
const uint8_t *const input_ptr = in.ptr() + id.z() / _depth_multiplier * input_stride_z;
auto output_ptr = reinterpret_cast<T *>(out.ptr());
- const int height = src_y + _kernel_dims.height;
- const int width = src_x + _kernel_dims.width;
+ const int height = src_y + (_kernel_dims.height + (_kernel_dims.height - 1) * (_dilation.y() - 1));
+ const int width = src_x + (_kernel_dims.width + (_kernel_dims.width - 1) * (_dilation.x() - 1));
- for(int y = src_y; y < height; ++y)
+ for(int y = src_y; y < height; y += _dilation.y())
{
- for(int x = src_x; x < width; ++x, ++output_ptr)
+ for(int x = src_x; x < width; x += _dilation.x(), ++output_ptr)
{
if(x < 0 || x >= input_w || y < 0 || y >= input_h)
{
@@ -130,15 +132,16 @@ void NEDepthwiseIm2ColKernel::run_generic(const Window &window)
}
NEDepthwiseIm2ColKernel::NEDepthwiseIm2ColKernel()
- : _func(nullptr), _input(nullptr), _output(nullptr), _kernel_dims(), _conv_info(), _has_bias(), _depth_multiplier(1)
+ : _func(nullptr), _input(nullptr), _output(nullptr), _kernel_dims(), _conv_info(), _has_bias(), _depth_multiplier(1), _dilation()
{
}
-void NEDepthwiseIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
+void NEDepthwiseIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier,
+ const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, depth_multiplier));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, depth_multiplier, dilation));
_input = input;
_output = output;
@@ -146,6 +149,7 @@ void NEDepthwiseIm2ColKernel::configure(const ITensor *input, ITensor *output, c
_conv_info = conv_info;
_has_bias = has_bias;
_depth_multiplier = depth_multiplier;
+ _dilation = dilation;
// Configure kernel window
Window win = calculate_max_window(*input->info(), Steps());
@@ -172,10 +176,11 @@ void NEDepthwiseIm2ColKernel::configure(const ITensor *input, ITensor *output, c
INEKernel::configure(win);
}
-Status NEDepthwiseIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
+Status NEDepthwiseIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier,
+ const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, depth_multiplier));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, depth_multiplier, dilation));
return Status{};
}
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 23c3f81edc..8211104bda 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -49,6 +49,14 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ // idx_w and idx_h only used for validation
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_UNUSED(idx_w);
+ ARM_COMPUTE_UNUSED(idx_h);
+
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
@@ -239,6 +247,9 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w
const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
+
const bool can_run_optimised_3x3_kernel = (weights->info()->dimension(idx_w) == 3) && (weights->info()->dimension(idx_h) == 3);
if(bool(can_run_optimised_3x3_kernel))
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index 4c602b3640..5133756993 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -51,7 +51,8 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor
ITensor *output,
const PadStrideInfo &conv_info,
unsigned int depth_multiplier,
- const ActivationLayerInfo &act_info)
+ const ActivationLayerInfo &act_info,
+ const Size2D &dilation)
{
ARM_COMPUTE_UNUSED(act_info);
@@ -87,8 +88,8 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor
_permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
_permuted_weights.info()->set_data_layout(DataLayout::NCHW);
- // Configure optimized depthwise
- _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier);
+ // Configure depthwise
+ _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier, dilation);
// Configure border handler
_border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
@@ -99,7 +100,7 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor
else
{
// Configure depthwise convolution kernel
- _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier);
+ _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier, dilation);
// Configure border handler
_border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
@@ -185,18 +186,25 @@ void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input,
const ActivationLayerInfo &act_info,
const Size2D &dilation)
{
- ARM_COMPUTE_ERROR_ON(dilation.x() != 1 || dilation.y() != 1);
- ARM_COMPUTE_UNUSED(dilation);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ // idx_w and idx_h only used for validation
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_UNUSED(idx_w);
+ ARM_COMPUTE_UNUSED(idx_h);
+
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
+
_original_weights = weights;
_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
_has_bias = biases != nullptr;
_is_optimized = NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input->info(),
weights->info(),
conv_info,
- depth_multiplier);
+ depth_multiplier, dilation);
_is_nchw = input->info()->data_layout() == DataLayout::NCHW;
_permute = _is_optimized == _is_nchw;
_is_prepared = false;
@@ -209,7 +217,7 @@ void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input,
}
else
{
- configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info);
+ configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation);
}
// Configure activation
@@ -230,7 +238,11 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
- ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() != 1 || dilation.y() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() < 1 || dilation.y() < 1);
+ const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
if(biases != nullptr)
{
@@ -239,7 +251,7 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx));
}
- if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier))
+ if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier, dilation))
{
const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
@@ -356,12 +368,17 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
{
const unsigned int channel_idx = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
ARM_COMPUTE_UNUSED(channel_idx);
- ARM_COMPUTE_ERROR_ON(dilation.x() != 1 || dilation.y() != 1);
- ARM_COMPUTE_UNUSED(dilation);
-
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_ERROR_ON((input->info()->dimension(channel_idx) * depth_multiplier) != weights->info()->dimension(channel_idx));
+ // idx_w and idx_h only used for validation
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_UNUSED(idx_w);
+ ARM_COMPUTE_UNUSED(idx_h);
+
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
_is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
@@ -392,7 +409,7 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
bool append_bias = (biases != nullptr) && !_is_quantized;
// Calculate output shape
- TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
+ TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation);
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
@@ -420,7 +437,7 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
shape_im2col.set(1, conv_size);
shape_im2col.set(2, weights_z);
_input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
- _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier);
+ _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation);
// Weights reshape configuration
const TensorShape shape_weights_reshape(patch_size, weights_z);
@@ -491,11 +508,13 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
- ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() != 1 || dilation.y() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() < 1 || dilation.y() < 1);
const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) + (weights->dimension(width_idx) - 1) * (dilation.x() - 1) > input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(height_idx) + (weights->dimension(height_idx) - 1) * (dilation.y() - 1) > input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom());
// Clone output to use auto init
auto output_clone = output->clone();
@@ -522,7 +541,7 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe
const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
const bool append_bias = (biases != nullptr) && !is_quantized;
- TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+ TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation);
const size_t weights_w = weights_to_use->dimension(0);
const size_t weights_h = weights_to_use->dimension(1);
const size_t weights_z = weights_to_use->dimension(2);
@@ -549,7 +568,7 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe
shape_im2col.set(1, conv_size);
shape_im2col.set(2, weights_z);
TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
- ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation));
// Weights reshape configuration
const TensorShape shape_weights_reshape(patch_size, weights_z);
diff --git a/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp
index d9b2bff810..049bf66689 100644
--- a/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp
+++ b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp
@@ -254,7 +254,8 @@ Status NEDepthwiseConvolutionAssemblyDispatch::validate(const ITensorInfo
bool NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(const ITensorInfo *input,
const ITensorInfo *weights,
PadStrideInfo conv_info,
- unsigned int depth_multiplier)
+ unsigned int depth_multiplier,
+ const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
@@ -290,8 +291,9 @@ bool NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(const ITenso
bool is_same_padding = (pad_top == same_pad.pad_top()) && (pad_right == same_pad.pad_right()) && (pad_bottom == same_pad.pad_bottom()) && (pad_left == same_pad.pad_left());
bool is_valid_padding = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0);
bool supported_padding = is_same_padding || is_valid_padding;
+ bool is_dilation_1 = dilation.x() == 1 && dilation.y() == 1;
- return is_data_type_valid && weights_supported && supported_strides && supported_padding && (depth_multiplier == 1);
+ return is_data_type_valid && weights_supported && supported_strides && supported_padding && (depth_multiplier == 1) && is_dilation_1;
}
void NEDepthwiseConvolutionAssemblyDispatch::run()
diff --git a/tests/validation/NEON/DepthwiseConvolutionLayer.cpp b/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
index 6c0f590241..b61393f9ea 100644
--- a/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/NEON/DepthwiseConvolutionLayer.cpp
@@ -29,6 +29,7 @@
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/DepthwiseConvolutionLayerDataset.h"
+#include "tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
@@ -60,7 +61,7 @@ TEST_SUITE(DepthwiseConvLayer)
// *INDENT-OFF*
// clang-format off
-DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching data type input/weights
TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32), // Mismatching input feature maps
TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Unsupported weights dimensions
@@ -69,6 +70,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases size
TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases dimensions
TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // patch size bigger than input width
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // dilation < 1
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
}),
framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
@@ -80,6 +83,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F32),
})),
framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
TensorInfo(TensorShape(2U), 1, DataType::F32),
@@ -90,6 +95,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(2U), 1, DataType::F32),
TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
+ TensorInfo(TensorShape(2U), 1, DataType::F32),
})),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
@@ -100,6 +107,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
@@ -110,6 +119,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
})),
framework::dataset::make("DepthMultiplier", { 1,
1,
@@ -120,21 +131,38 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip
1,
1,
1,
+ 1,
+ 1,
})),
- framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, true })),
- input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected)
+ framework::dataset::make("Dilation", { Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(25U, 1U),
+ Size2D(0U, 1U),
+ Size2D(1U, 1U),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
+ input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier,dilation, expected)
{
- bool is_valid = bool(NEDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier));
+ bool is_valid = bool(NEDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false),
+ &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier, ActivationLayerInfo(), dilation));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
-DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32), // Mismatching input feature maps
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching depth multiplier
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
+ TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32), // patch size bigger than input width
+ TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32), // dilation < 1
TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8),
}),
@@ -145,6 +173,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip
TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8),
})),
framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32),
@@ -154,6 +184,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip
TensorInfo(TensorShape(2U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(2U), 1, DataType::F32),
TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
+ TensorInfo(TensorShape(16U), 1, DataType::F32),
TensorInfo(TensorShape(24U), 1, DataType::S32),
})),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
@@ -163,6 +195,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip
TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32),
+ TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
@@ -172,6 +206,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
+ PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 1, 0),
})),
framework::dataset::make("DepthMultiplier", { 1,
@@ -181,12 +217,25 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip
1,
1,
2,
+ 2,
+ 2,
3,
})),
- framework::dataset::make("Expected", { false, false, false, false, false, false, true, true })),
- input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected)
+ framework::dataset::make("Dilation", { Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ Size2D(25U, 1U),
+ Size2D(0U, 1U),
+ Size2D(1U, 1U),
+ Size2D(1U, 1U),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false,false, false, true, true })),
+ input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier,dilation, expected)
{
- bool is_valid = bool(NEDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier));
+ bool is_valid = bool(NEDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier, ActivationLayerInfo(), dilation));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -213,6 +262,25 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture<float>, fram
{
validate(Accessor(_target), _reference, tolerance_f32);
}
+
+TEST_SUITE(Dilation)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // Dilation
TEST_SUITE_END() // Generic
TEST_SUITE(W3x3)
@@ -234,6 +302,26 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture3x3<float>, f
{
validate(Accessor(_target), _reference, tolerance_f32);
}
+TEST_SUITE(Dilation)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+
+TEST_SUITE_END() // Dilation
+
FIXTURE_DATA_TEST_CASE(RunOptimizedSmall, NEDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallOptimizedDepthwiseConvolutionLayerDataset3x3(),
framework::dataset::make("DepthMultiplier", 1)),
@@ -276,6 +364,26 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture<half>, frame
{
validate(Accessor(_target), _reference, tolerance_f16, tolerance_num);
}
+
+TEST_SUITE(Dilation)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f16, tolerance_num);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f16, tolerance_num);
+}
+TEST_SUITE_END() // Dilation
+
TEST_SUITE_END() // Generic
TEST_SUITE(W3x3)
template <typename T>
@@ -296,6 +404,28 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture3x3<half>, fr
{
validate(Accessor(_target), _reference, tolerance_f16);
}
+
+TEST_SUITE(Dilation)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerFixture3x3<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerFixture3x3<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_f16);
+}
+
+TEST_SUITE_END() // Dilation
+
FIXTURE_DATA_TEST_CASE(RunOptimizedSmall, NEDepthwiseConvolutionLayerFixture3x3<half>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallOptimizedDepthwiseConvolutionLayerDataset3x3(),
framework::dataset::make("DepthMultiplier", 1)),
@@ -337,6 +467,27 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerQuantizedFixture<uin
{
validate(Accessor(_target), _reference, tolerance_qasymm8);
}
+
+TEST_SUITE(Dilation)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+TEST_SUITE_END() //Dilation
TEST_SUITE_END() // Generic
TEST_SUITE(W3x3)
FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::PRECOMMIT,
@@ -376,6 +527,27 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerQuantizedFixture3x3<
{
validate(Accessor(_target), _reference, tolerance_qasymm8);
}
+
+TEST_SUITE(Dilation)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), depth_multipliers),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerQuantizedFixture3x3<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+ validate(Accessor(_target), _reference, tolerance_qasymm8);
+}
+TEST_SUITE_END() // Dilation
TEST_SUITE_END() // W3x3
TEST_SUITE_END() // QASYMM8
TEST_SUITE_END() // Quantized