From 6ab94e0b318884bbcb95e2ea3835f951502e1d99 Mon Sep 17 00:00:00 2001 From: jaseg Date: Wed, 14 Oct 2020 12:47:28 +0200 Subject: Move firmware into subdirectory --- .../cifar10/arm_nnexamples_cifar10.cpp | 196 +++++++++++++++++++++ 1 file changed, 196 insertions(+) create mode 100644 fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp (limited to 'fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp') diff --git a/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp b/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp new file mode 100644 index 0000000..ee1083b --- /dev/null +++ b/fw/midi-dials/Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp @@ -0,0 +1,196 @@ +/* ---------------------------------------------------------------------- +* Copyright (C) 2010-2018 Arm Limited. All rights reserved. +* +* +* Project: CMSIS NN Library +* Title: arm_nnexamples_cifar10.cpp +* +* Description: Convolutional Neural Network Example +* +* Target Processor: Cortex-M4/Cortex-M7 +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions +* are met: +* - Redistributions of source code must retain the above copyright +* notice, this list of conditions and the following disclaimer. +* - Redistributions in binary form must reproduce the above copyright +* notice, this list of conditions and the following disclaimer in +* the documentation and/or other materials provided with the +* distribution. +* - Neither the name of Arm LIMITED nor the names of its contributors +* may be used to endorse or promote products derived from this +* software without specific prior written permission. +* +* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS +* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE +* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, +* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN +* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +* POSSIBILITY OF SUCH DAMAGE. +* -------------------------------------------------------------------- */ + +/** + * @ingroup groupExamples + */ + +/** + * @defgroup CNNExample Convolutional Neural Network Example + * + * \par Description: + * \par + * Demonstrates a convolutional neural network (CNN) example with the use of convolution, + * ReLU activation, pooling and fully-connected functions. + * + * \par Model definition: + * \par + * The CNN used in this example is based on CIFAR-10 example from Caffe [1]. + * The neural network consists + * of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a + * fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will + * be classified into one of the 10 output classes. + * This example model implementation needs 32.3 KB to store weights, 40 KB for activations and + * 3.1 KB for storing the \c im2col data. + * + * \image html CIFAR10_CNN.gif "Neural Network model definition" + * + * \par Variables Description: + * \par + * \li \c conv1_wt, \c conv2_wt, \c conv3_wt are convolution layer weight matrices + * \li \c conv1_bias, \c conv2_bias, \c conv3_bias are convolution layer bias arrays + * \li \c ip1_wt, ip1_bias point to fully-connected layer weights and biases + * \li \c input_data points to the input image data + * \li \c output_data points to the classification output + * \li \c col_buffer is a buffer to store the \c im2col output + * \li \c scratch_buffer is used to store the activation data (intermediate layer outputs) + * + * \par CMSIS DSP Software Library Functions Used: + * \par + * - arm_convolve_HWC_q7_RGB() + * - arm_convolve_HWC_q7_fast() + * - arm_relu_q7() + * - arm_maxpool_q7_HWC() + * - arm_avepool_q7_HWC() + * - arm_fully_connected_q7_opt() + * - arm_fully_connected_q7() + * + * Refer + * \link arm_nnexamples_cifar10.cpp \endlink + * + * \par [1] https://github.com/BVLC/caffe + */ + +#include +#include +#include "arm_math.h" +#include "arm_nnexamples_cifar10_parameter.h" +#include "arm_nnexamples_cifar10_weights.h" + +#include "arm_nnfunctions.h" +#include "arm_nnexamples_cifar10_inputs.h" + +#ifdef _RTE_ +#include "RTE_Components.h" +#ifdef RTE_Compiler_EventRecorder +#include "EventRecorder.h" +#endif +#endif + +// include the input and weights + +static q7_t conv1_wt[CONV1_IM_CH * CONV1_KER_DIM * CONV1_KER_DIM * CONV1_OUT_CH] = CONV1_WT; +static q7_t conv1_bias[CONV1_OUT_CH] = CONV1_BIAS; + +static q7_t conv2_wt[CONV2_IM_CH * CONV2_KER_DIM * CONV2_KER_DIM * CONV2_OUT_CH] = CONV2_WT; +static q7_t conv2_bias[CONV2_OUT_CH] = CONV2_BIAS; + +static q7_t conv3_wt[CONV3_IM_CH * CONV3_KER_DIM * CONV3_KER_DIM * CONV3_OUT_CH] = CONV3_WT; +static q7_t conv3_bias[CONV3_OUT_CH] = CONV3_BIAS; + +static q7_t ip1_wt[IP1_DIM * IP1_OUT] = IP1_WT; +static q7_t ip1_bias[IP1_OUT] = IP1_BIAS; + +/* Here the image_data should be the raw uint8 type RGB image in [RGB, RGB, RGB ... RGB] format */ +uint8_t image_data[CONV1_IM_CH * CONV1_IM_DIM * CONV1_IM_DIM] = IMG_DATA; +q7_t output_data[IP1_OUT]; + +//vector buffer: max(im2col buffer,average pool buffer, fully connected buffer) +q7_t col_buffer[2 * 5 * 5 * 32 * 2]; + +q7_t scratch_buffer[32 * 32 * 10 * 4]; + +int main() +{ + #ifdef RTE_Compiler_EventRecorder + EventRecorderInitialize (EventRecordAll, 1); // initialize and start Event Recorder + #endif + + printf("start execution\n"); + /* start the execution */ + + q7_t *img_buffer1 = scratch_buffer; + q7_t *img_buffer2 = img_buffer1 + 32 * 32 * 32; + + /* input pre-processing */ + int mean_data[3] = INPUT_MEAN_SHIFT; + unsigned int scale_data[3] = INPUT_RIGHT_SHIFT; + for (int i=0;i<32*32*3; i+=3) { + img_buffer2[i] = (q7_t)__SSAT( ((((int)image_data[i] - mean_data[0])<<7) + (0x1<<(scale_data[0]-1))) + >> scale_data[0], 8); + img_buffer2[i+1] = (q7_t)__SSAT( ((((int)image_data[i+1] - mean_data[1])<<7) + (0x1<<(scale_data[1]-1))) + >> scale_data[1], 8); + img_buffer2[i+2] = (q7_t)__SSAT( ((((int)image_data[i+2] - mean_data[2])<<7) + (0x1<<(scale_data[2]-1))) + >> scale_data[2], 8); + } + + // conv1 img_buffer2 -> img_buffer1 + arm_convolve_HWC_q7_RGB(img_buffer2, CONV1_IM_DIM, CONV1_IM_CH, conv1_wt, CONV1_OUT_CH, CONV1_KER_DIM, CONV1_PADDING, + CONV1_STRIDE, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_OUT_RSHIFT, img_buffer1, CONV1_OUT_DIM, + (q15_t *) col_buffer, NULL); + + arm_relu_q7(img_buffer1, CONV1_OUT_DIM * CONV1_OUT_DIM * CONV1_OUT_CH); + + // pool1 img_buffer1 -> img_buffer2 + arm_maxpool_q7_HWC(img_buffer1, CONV1_OUT_DIM, CONV1_OUT_CH, POOL1_KER_DIM, + POOL1_PADDING, POOL1_STRIDE, POOL1_OUT_DIM, NULL, img_buffer2); + + // conv2 img_buffer2 -> img_buffer1 + arm_convolve_HWC_q7_fast(img_buffer2, CONV2_IM_DIM, CONV2_IM_CH, conv2_wt, CONV2_OUT_CH, CONV2_KER_DIM, + CONV2_PADDING, CONV2_STRIDE, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_OUT_RSHIFT, img_buffer1, + CONV2_OUT_DIM, (q15_t *) col_buffer, NULL); + + arm_relu_q7(img_buffer1, CONV2_OUT_DIM * CONV2_OUT_DIM * CONV2_OUT_CH); + + // pool2 img_buffer1 -> img_buffer2 + arm_maxpool_q7_HWC(img_buffer1, CONV2_OUT_DIM, CONV2_OUT_CH, POOL2_KER_DIM, + POOL2_PADDING, POOL2_STRIDE, POOL2_OUT_DIM, col_buffer, img_buffer2); + +// conv3 img_buffer2 -> img_buffer1 + arm_convolve_HWC_q7_fast(img_buffer2, CONV3_IM_DIM, CONV3_IM_CH, conv3_wt, CONV3_OUT_CH, CONV3_KER_DIM, + CONV3_PADDING, CONV3_STRIDE, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_OUT_RSHIFT, img_buffer1, + CONV3_OUT_DIM, (q15_t *) col_buffer, NULL); + + arm_relu_q7(img_buffer1, CONV3_OUT_DIM * CONV3_OUT_DIM * CONV3_OUT_CH); + + // pool3 img_buffer-> img_buffer2 + arm_maxpool_q7_HWC(img_buffer1, CONV3_OUT_DIM, CONV3_OUT_CH, POOL3_KER_DIM, + POOL3_PADDING, POOL3_STRIDE, POOL3_OUT_DIM, col_buffer, img_buffer2); + + arm_fully_connected_q7_opt(img_buffer2, ip1_wt, IP1_DIM, IP1_OUT, IP1_BIAS_LSHIFT, IP1_OUT_RSHIFT, ip1_bias, + output_data, (q15_t *) img_buffer1); + + arm_softmax_q7(output_data, 10, output_data); + + for (int i = 0; i < 10; i++) + { + printf("%d: %d\n", i, output_data[i]); + } + + return 0; +} -- cgit