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| author | joshua <joshua@joshuayun.com> | 2023-12-30 23:54:31 -0500 |
|---|---|---|
| committer | joshua <joshua@joshuayun.com> | 2023-12-30 23:54:31 -0500 |
| commit | 86608c6770cf08c138a2bdab5855072f64be09ef (patch) | |
| tree | 494a61b3ef37e76f9235a0d10f5c93d97290a35f /Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c | |
| download | sdr-software-master.tar.gz | |
Diffstat (limited to 'Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c')
| -rw-r--r-- | Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c | 460 |
1 files changed, 460 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c b/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c new file mode 100644 index 0000000..71fb771 --- /dev/null +++ b/Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c @@ -0,0 +1,460 @@ +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_pool_q7_HWC.c
+ * Description: Pooling function implementations
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief A few utility functions used by pooling functions
+ *
+ *
+ */
+
+static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale)
+{
+ int i;
+
+ for (i = 0; i < length; i++)
+ {
+ target[i] = (q7_t) (buffer[i] / scale);
+ }
+}
+
+static void compare_and_replace_if_larger_q7(q7_t * base, // base data
+ const q7_t * target, // compare target
+ const uint16_t length // data length
+ )
+{
+ q7_t *pIn = base;
+ const q7_t *pCom = target;
+ union arm_nnword in;
+ union arm_nnword com;
+ uint16_t cnt = length >> 2;
+
+ while (cnt > 0u)
+ {
+ in.word = *__SIMD32(pIn);
+ com.word = *__SIMD32(pCom)++;
+
+ // if version
+ if (com.bytes[0] > in.bytes[0])
+ in.bytes[0] = com.bytes[0];
+ if (com.bytes[1] > in.bytes[1])
+ in.bytes[1] = com.bytes[1];
+ if (com.bytes[2] > in.bytes[2])
+ in.bytes[2] = com.bytes[2];
+ if (com.bytes[3] > in.bytes[3])
+ in.bytes[3] = com.bytes[3];
+
+ *__SIMD32(pIn)++ = in.word;
+
+ cnt--;
+ }
+
+ cnt = length & 0x3;
+ while (cnt > 0u)
+ {
+ if (*pCom > *pIn)
+ {
+ *pIn = *pCom;
+ }
+ pIn++;
+ pCom++;
+ cnt--;
+ }
+}
+
+static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length)
+{
+ q15_t *pCnt = base;
+ q7_t *pV = target;
+ q31_t v1, v2, vo1, vo2;
+ uint16_t cnt = length >> 2;
+ q31_t in;
+
+ while (cnt > 0u)
+ {
+ q31_t value = *__SIMD32(pV)++;
+ v1 = __SXTB16(__ROR(value, 8));
+ v2 = __SXTB16(value);
+#ifndef ARM_MATH_BIG_ENDIAN
+
+ vo2 = __PKHTB(v1, v2, 16);
+ vo1 = __PKHBT(v2, v1, 16);
+
+#else
+
+ vo1 = __PKHTB(v1, v2, 16);
+ vo2 = __PKHBT(v2, v1, 16);
+
+#endif
+
+ in = *__SIMD32(pCnt);
+ *__SIMD32(pCnt)++ = __QADD16(vo1, in);
+
+ in = *__SIMD32(pCnt);
+ *__SIMD32(pCnt)++ = __QADD16(vo2, in);
+
+ cnt--;
+ }
+ cnt = length & 0x3;
+ while (cnt > 0u)
+ {
+ *pCnt++ += *pV++;
+ cnt--;
+ }
+}
+
+#endif // ARM_MATH_DSP
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Pooling
+ * @{
+ */
+
+ /**
+ * @brief Q7 max pooling function
+ * @param[in, out] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 0
+ *
+ * The pooling function is implemented as split x-pooling then
+ * y-pooling.
+ *
+ * This pooling function is input-destructive. Input data is undefined
+ * after calling this function.
+ *
+ */
+
+void
+arm_maxpool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ int16_t i_x, i_y;
+
+ /* first does the pooling along x axis */
+ for (i_y = 0; i_y < dim_im_in; i_y++)
+ {
+
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ /* for each output pixel */
+ q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+ q7_t *win_start;
+ q7_t *win_stop;
+ if (i_x * stride - padding < 0)
+ {
+ win_start = target;
+ } else
+ {
+ win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+ }
+
+ if (i_x * stride - padding + dim_kernel >= dim_im_in)
+ {
+ win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+ } else
+ {
+ win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+ }
+
+ /* first step is to copy over initial data */
+ /* arm_copy_q7(win_start, target, ch_im_in); */
+ memmove(target, win_start, ch_im_in);
+
+ /* start the max operation from the second part */
+ win_start += ch_im_in;
+ for (; win_start < win_stop; win_start += ch_im_in)
+ {
+ compare_and_replace_if_larger_q7(target, win_start, ch_im_in);
+ }
+ }
+ }
+
+ /* then does the pooling along y axis */
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+
+ /* for each output row */
+ q7_t *target = Im_out + i_y * dim_im_out * ch_im_in;
+ q7_t *row_start;
+ q7_t *row_end;
+ /* setting the starting row */
+ if (i_y * stride - padding < 0)
+ {
+ row_start = Im_in;
+ } else
+ {
+ row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+ }
+ /* setting the stopping row */
+ if (i_y * stride - padding + dim_kernel >= dim_im_in)
+ {
+ row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+ } else
+ {
+ row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+ }
+
+ /* copy over the first row */
+ /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */
+ memmove(target, row_start, dim_im_out * ch_im_in);
+
+ /* move over to next row */
+ row_start += ch_im_in * dim_im_in;
+
+ for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+ {
+ compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in);
+ }
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int max = -129;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+ {
+ max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ }
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+ /**
+ * @brief Q7 average pooling function
+ * @param[in,out] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*dim_im_out*ch_im_in
+ *
+ * The pooling function is implemented as split x-pooling then
+ * y-pooling.
+ *
+ * This pooling function is input-destructive. Input data is undefined
+ * after calling this function.
+ *
+ */
+
+void
+arm_avepool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+ /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+ q15_t *buffer = (q15_t *) bufferA;
+ int16_t i_x, i_y;
+ int16_t count = 0;
+
+ /* first does the pooling along x axis */
+ for (i_y = 0; i_y < dim_im_in; i_y++)
+ {
+
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ /* for each output pixel */
+ q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+ q7_t *win_start;
+ q7_t *win_stop;
+ if (i_x * stride - padding < 0)
+ {
+ win_start = target;
+ } else
+ {
+ win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+ }
+
+ if (i_x * stride - padding + dim_kernel >= dim_im_in)
+ {
+ win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+ } else
+ {
+ win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+ }
+
+ /* first step is to copy over initial data */
+ arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in);
+ count = 1;
+
+ /* start the max operation from the second part */
+ win_start += ch_im_in;
+ for (; win_start < win_stop; win_start += ch_im_in)
+ {
+ accumulate_q7_to_q15(buffer, win_start, ch_im_in);
+ count++;
+ }
+ buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count);
+ }
+ }
+
+ /* then does the pooling along y axis */
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ /* for each output row */
+ q7_t *target = Im_out + i_y * dim_im_out * ch_im_in;
+ q7_t *row_start;
+ q7_t *row_end;
+ /* setting the starting row */
+ if (i_y * stride - padding < 0)
+ {
+ row_start = Im_in;
+ } else
+ {
+ row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+ }
+ /* setting the stopping row */
+ if (i_y * stride - padding + dim_kernel >= dim_im_in)
+ {
+ row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+ } else
+ {
+ row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+ }
+
+ /* copy over the first row */
+ arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in);
+ count = 1;
+
+ /* move over to next row */
+ row_start += ch_im_in * dim_im_in;
+
+ for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+ {
+ accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in);
+ count++;
+ }
+ buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count);
+ }
+
+#else
+ /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+ int16_t i_ch_in, i_x, i_y;
+ int16_t k_x, k_y;
+
+ for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+ {
+ for (i_y = 0; i_y < dim_im_out; i_y++)
+ {
+ for (i_x = 0; i_x < dim_im_out; i_x++)
+ {
+ int sum = 0;
+ int count = 0;
+ for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+ {
+ for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+ {
+ if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+ {
+ sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+ count++;
+ }
+ }
+ }
+ Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+ }
+ }
+ }
+
+#endif /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Pooling group
+ */
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