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authorrihab kouki <rihab.kouki@st.com>2020-07-28 11:24:49 +0100
committerrihab kouki <rihab.kouki@st.com>2020-07-28 11:24:49 +0100
commit96d6da4e252b06dcfdc041e7df23e86161c33007 (patch)
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parent9f95ff5b6ba01db09552b84a0ab79607060a2666 (diff)
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Official ARM version: v5.6.0HEADmaster
Diffstat (limited to 'NN/Source/ConvolutionFunctions')
-rw-r--r--NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c239
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+/*
+ * Copyright (C) 2010-2019 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_depthwise_conv_u8_basic_ver1.c
+ * Description: u8 depthwise convolution function
+ *
+ * $Date: June, 2019
+ * $Revision: V.0.8.0
+ *
+ * Target : Cortex-M cores with DSP extension
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+#include <stdint.h>
+#include <stdio.h>
+
+#define DILATION_X (1)
+#define DILATION_Y (1)
+
+/**
+ * @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief uint8 depthwise convolution function with asymmetric quantization for even number of channel multiplier
+ * and input channels. Unless specified otherwise, arguments are mandatory. Both square and non-square inputs
+ * are accepted.
+ *
+ * @param[in] input Pointer to input tensor
+ * @param[in] input_x Width of input tensor
+ * @param[in] input_y Height of input tensor
+ * @param[in] input_ch Channels in input tensor
+ * @param[in] kernel Pointer to kernel weights
+ * @param[in] kernel_x Width of kernel
+ * @param[in] kernel_y Height of kernel
+ * @param[in] ch_mult Number of channel multiplier
+ * @param[in] pad_x Padding sizes x
+ * @param[in] pad_y Padding sizes y
+ * @param[in] stride_x Convolution stride along the width
+ * @param[in] stride_y Convolution stride along the height
+ * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement.
+ * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement.
+ * @param[in] bias Pointer to optional bias values. If no bias is
+ * availble, NULL is expected
+ * @param[in] input_offset Input tensor zero offset
+ * @param[in] filter_offset Kernel tensor zero offset
+ * @param[in] output_offset Output tensor zero offset
+ * @param[in,out] output Pointer to output tensor
+ * @param[in] output_x Width of output tensor
+ * @param[in] output_y Height of output tensor
+ * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in] out_shift Amount of right-shift for output
+ * @param[in] out_mult Output multiplier for requantization
+ * @return The function returns one of the following
+ * <code>ARM_MATH_SIZE_MISMATCH</code> - Not supported dimension of tensors
+ * <code>ARM_MATH_SUCCESS</code> - Successful operation
+ * <code>ARM_MATH_ARGUMENT_ERROR</code> - Implementation not available
+ *
+ * <b> Input constraints</b>
+ * ch_mult is multiple of 2
+ * kernel_x is multiple of 2
+ *
+ */
+
+arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input,
+ const uint16_t input_x,
+ const uint16_t input_y,
+ const uint16_t input_ch,
+ const uint8_t *kernel,
+ const uint16_t kernel_x,
+ const uint16_t kernel_y,
+ const int16_t ch_mult,
+ const int16_t pad_x,
+ const int16_t pad_y,
+ const int16_t stride_x,
+ const int16_t stride_y,
+ const int16_t dilation_x,
+ const int16_t dilation_y,
+ const int32_t *bias,
+ const int32_t input_offset,
+ const int32_t filter_offset,
+ const int32_t output_offset,
+ uint8_t *output,
+ const uint16_t output_x,
+ const uint16_t output_y,
+ const int32_t output_activation_min,
+ const int32_t output_activation_max,
+ const int32_t out_shift,
+ const int32_t out_mult)
+{
+ arm_status status = ARM_MATH_SUCCESS;
+ #if defined (ARM_MATH_DSP)
+ int i_out = 0;
+ (void)dilation_x;
+ (void)dilation_y;
+
+ const int32_t input_offset_pkd = (input_offset & 0xFFFF) | (input_offset & 0xFFFF) << 16;
+ const int32_t kernel_offset_pkd = (filter_offset & 0xFFFF) | (filter_offset & 0xFFFF) << 16;
+
+ if (0 != ch_mult % 2 || 0 != kernel_x % 2)
+ {
+ return ARM_MATH_SIZE_MISMATCH;
+ }
+
+ for (int i_out_y = 0; i_out_y < output_y; i_out_y++)
+ {
+ const int16_t base_idx_y = (i_out_y * stride_y) - pad_y;
+ for (int i_out_x = 0; i_out_x < output_x; i_out_x++)
+ {
+ const int16_t base_idx_x = (i_out_x * stride_x) - pad_x;
+ for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
+ {
+ for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult += 2)
+ {
+ const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult;
+
+ int32_t acc_0 = 0;
+ int32_t acc_1 = 0;
+ if (NULL != bias)
+ {
+ acc_0 = bias[idx_out_ch];
+ acc_1 = bias[idx_out_ch + 1];
+ }
+
+ for (int i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
+ {
+ const int32_t idx_y = base_idx_y + DILATION_Y * i_ker_y;
+ const int32_t y_in_range = (idx_y >= 0) && (idx_y < input_y);
+
+ for (int i_ker_x = 0; i_ker_x < kernel_x; i_ker_x += 2)
+ {
+ if (1 == y_in_range)
+ {
+ const int32_t idx_x = base_idx_x + DILATION_X * i_ker_x;
+ const int32_t idx_x1 = base_idx_x + DILATION_X * (i_ker_x + 1);
+ /* Range check for first input */
+ if (idx_x >= 0 && idx_x < input_x)
+ {
+ const int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch;
+
+ const int32_t ker_idx_0 =
+ (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch;
+ const int32_t ker_idx_1 = ker_idx_0 + input_ch * ch_mult;
+
+ int32_t input_pkd = input[idx_0] | (input[idx_0 + input_ch] << 16);
+ int32_t kernel_pkd = kernel[ker_idx_0] | (kernel[ker_idx_1] << 16);
+
+ input_pkd = __SADD16(input_pkd, input_offset_pkd);
+ kernel_pkd = __SADD16(kernel_pkd, kernel_offset_pkd);
+ /* Range check for second input */
+ if (idx_x1 >= input_x)
+ {
+ input_pkd &= 0xFFFF;
+ }
+ acc_0 = __SMLAD(input_pkd, kernel_pkd, acc_0);
+
+ kernel_pkd = kernel[ker_idx_0 + 1] | (kernel[ker_idx_1 + 1] << 16);
+ kernel_pkd = __SADD16(kernel_pkd, kernel_offset_pkd);
+ acc_1 = __SMLAD(input_pkd, kernel_pkd, acc_1);
+ }
+ }
+ }
+ }
+
+ /* Requantize and clamp output to provided range */
+ acc_0 = arm_nn_divide_by_power_of_two(arm_nn_sat_doubling_high_mult(
+ acc_0 * (1 << LEFT_SHIFT(out_shift)), out_mult),
+ RIGHT_SHIFT(out_shift));
+
+ acc_0 += output_offset;
+
+ if (output_activation_min > acc_0)
+ {
+ acc_0 = output_activation_min;
+ }
+
+ if (acc_0 > output_activation_max)
+ {
+ acc_0 = output_activation_max;
+ }
+ output[i_out++] = acc_0;
+
+ /* Requantize and clamp output to provided range */
+ acc_1 = arm_nn_divide_by_power_of_two(arm_nn_sat_doubling_high_mult(
+ acc_1 * (1 << LEFT_SHIFT(out_shift)), out_mult),
+ RIGHT_SHIFT(out_shift));
+ acc_1 += output_offset;
+
+ if (output_activation_min > acc_1)
+ {
+ acc_1 = output_activation_min;
+ }
+
+ if (acc_1 > output_activation_max)
+ {
+ acc_1 = output_activation_max;
+ }
+ output[i_out++] = acc_1;
+ }
+ }
+ }
+ }
+#else
+ /* No available implementation. */
+ status = ARM_MATH_ARGUMENT_ERROR;
+#endif
+ return status;
+}
+
+/**
+ * @} end of NNConv group
+ */
+
+