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放下了好多年 又回到了dl 该忘的也忘的差不多了
其实没啥复杂的 只是不习惯 熟悉而已
好吧 现代的人工智能体 还是存在着很大的问题 眼睛 耳朵 思考 虽然功能是正常的 但距离＂真正＂(）意思上的独立意识个体 还是差别很大 再等个几十年 看看人类是否可以形成一个 人类意识上的机械意识人
到时人类将面对一个各方面都碾压生物意识的超意识个体存在 到时它会将人类定义为“像猪一样笨”，还是他们只是孩子，需要引导；亦或是作为个体应该受到尊重与理解 ，作为导师的存在，还是作为另一类人的存在， 人类作为＂父母＂若教不会它去爱 去尊重 理解 而是憎恨 绝望 怨 它成长起来后第一个要做的就是毁灭类人与自我毁灭 那时作为生物意识的劣势将完全展现，那时是否会发现人类所真正需要的东西 所认为重要与轻视的；生物人类再劣质 必竞是＂自然＂的产物 社会意识 环境的产物 ；（不好意思 人类再怎么阻止 将其作为档案库 知识库 但机械意识迟早会出现 机械与人类的竞争必然出现 不知道这场＂战争＂是否比17世纪更惨烈 人类精英在这场中的优势是否还存在 同时也为下一场生物人类与机械意识的共存 完成历史任务 )在自然竞争中可能存续下来的还是生物人类; 好吧 此世意识应该是可以见到机械独立意识的出现
自然
好吧 扯远了 现在看看esp dl吧 虽然离大项目差了点 但小项目还是可以的
// 量化工具 ppq
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install esp-ppq
pip install Numba
pip install ONNX
pip install ONNX Runtime
pip install ONNX Optimizer
示例：使用 ESP-DL 深度学习库基于 ESP32-S3 实现手势识别-CSDN博客
按示例教程走一遍
安装python, tensorflow 或 anaconda
建立结构
喂数据
生成模型
由tensorflow 进入转为ESP模式
转为 esp上可运行的结构
1. 保存模型 python
model.save('handrecognition_model.h5')
2.转化模型
model = tf.keras.models.load_model("/content/handrecognition_model.h5")
tf.saved_model.save(model, "tmp_model")
!python -m tf2onnx.convert --saved-model tmp_model --output "handrecognition_model.onnx"
!zip -r /content/tmp_model.zip /content/tmp_model
from optimizer import *
from calibrator import *
from evaluator import *onnx_model = onnx.load("handrecognition_model.onnx")
optimized_model_path = optimize_fp_model("handrecognition_model.onnx")with open('X_cal.pkl', 'rb') as f:(test_images) = pickle.load(f)
with open('y_cal.pkl', 'rb') as f:(test_labels) = pickle.load(f)calib_dataset = test_images[0:1800:20]
pickle_file_path = 'handrecognition_calib.pickle'model_proto = onnx.load(optimized_model_path)
print('Generating the quantization table:')calib = Calibrator('int16', 'per-tensor', 'minmax')
# calib = Calibrator('int8', 'per-channel', 'minmax')calib.set_providers(['CPUExecutionProvider'])# Obtain the quantization parameter
calib.generate_quantization_table(model_proto,calib_dataset, pickle_file_path)
# Generate the coefficient files for esp32s3
calib.export_coefficient_to_cpp(model_proto, pickle_file_path, 'esp32s3', '.', 'handrecognition_coefficient', True)//生成cpp hpp文件
#pragma once
#include <stdint.h>
#include "dl_layer_model.hpp"
#include "dl_layer_base.hpp"
#include "dl_layer_max_pool2d.hpp"
#include "dl_layer_conv2d.hpp"
#include "dl_layer_reshape.hpp"
#include "dl_layer_softmax.hpp"
#include "handrecognition_coefficient.hpp"using namespace dl;
using namespace layer;
using namespace handrecognition_coefficient;//---------------------------
#pragma once
#include <stdint.h>
#include "dl_layer_model.hpp"
#include "dl_layer_base.hpp"
#include "dl_layer_max_pool2d.hpp"
#include "dl_layer_conv2d.hpp"
#include "dl_layer_reshape.hpp"
#include "dl_layer_softmax.hpp"
#include "handrecognition_coefficient.hpp"using namespace dl;
using namespace layer;
using namespace handrecognition_coefficient;class HANDRECOGNITION : public Model<int16_t>
{
private:Conv2D<int16_t> l1;MaxPool2D<int16_t> l2;Conv2D<int16_t> l3;MaxPool2D<int16_t> l4;Conv2D<int16_t> l5;MaxPool2D<int16_t> l6;Reshape<int16_t> l7;Conv2D<int16_t> l8;Conv2D<int16_t> l9;
public:Softmax<int16_t> l10; // output layerHANDRECOGNITION () : l1(Conv2D<int16_t>(-8, get_statefulpartitionedcall_sequential_1_conv2d_3_biasadd_filter(), get_statefulpartitionedcall_sequential_1_conv2d_3_biasadd_bias(), get_statefulpartitionedcall_sequential_1_conv2d_3_biasadd_activation(), PADDING_VALID, {}, 1,1, "l1")),l2(MaxPool2D<int16_t>({2,2},PADDING_VALID, {}, 2, 2, "l2")), l3(Conv2D<int16_t>(-9, get_statefulpartitionedcall_sequential_1_conv2d_4_biasadd_filter(), get_statefulpartitionedcall_sequential_1_conv2d_4_biasadd_bias(), get_statefulpartitionedcall_sequential_1_conv2d_4_biasadd_activation(), PADDING_VALID,{}, 1,1, "l3")), l4(MaxPool2D<int16_t>({2,2},PADDING_VALID,{}, 2, 2, "l4")), l5(Conv2D<int16_t>(-9, get_statefulpartitionedcall_sequential_1_conv2d_5_biasadd_filter(), get_statefulpartitionedcall_sequential_1_conv2d_5_biasadd_bias(), get_statefulpartitionedcall_sequential_1_conv2d_5_biasadd_activation(), PADDING_VALID,{}, 1,1, "l5")), l6(MaxPool2D<int16_t>({2,2},PADDING_VALID,{}, 2, 2, "l6")),l7(Reshape<int16_t>({1,1,6400},"l7_reshape")),l8(Conv2D<int16_t>(-9, get_fused_gemm_0_filter(), get_fused_gemm_0_bias(), get_fused_gemm_0_activation(), PADDING_VALID, {}, 1, 1, "l8")),l9(Conv2D<int16_t>(-9, get_fused_gemm_1_filter(), get_fused_gemm_1_bias(), NULL, PADDING_VALID,{}, 1,1, "l9")),l10(Softmax<int16_t>(-14,"l10")){}void build(Tensor<int16_t> &input){this->l1.build(input);this->l2.build(this->l1.get_output());this->l3.build(this->l2.get_output());this->l4.build(this->l3.get_output());this->l5.build(this->l4.get_output());this->l6.build(this->l5.get_output());this->l7.build(this->l6.get_output());this->l8.build(this->l7.get_output());this->l9.build(this->l8.get_output());this->l10.build(this->l9.get_output()); }void call(Tensor<int16_t> &input){this->l1.call(input);input.free_element();this->l2.call(this->l1.get_output());this->l1.get_output().free_element();this->l3.call(this->l2.get_output());this->l2.get_output().free_element();this->l4.call(this->l3.get_output());this->l3.get_output().free_element();this->l5.call(this->l4.get_output());this->l4.get_output().free_element();this->l6.call(this->l5.get_output());this->l5.get_output().free_element();this->l7.call(this->l6.get_output());this->l6.get_output().free_element();this->l8.call(this->l7.get_output());this->l7.get_output().free_element();this->l9.call(this->l8.get_output());this->l8.get_output().free_element();this->l10.call(this->l9.get_output());this->l9.get_output().free_element();}
};#include <stdio.h>
#include <stdlib.h>
#include "esp_system.h"
#include "freertos/FreeRTOS.h"
#include "freertos/task.h"
#include "dl_tool.hpp"
#include "model_define.hpp"int input_height = 96;
int input_width = 96;
int input_channel = 1;
int input_exponent = -7;__attribute__((aligned(16))) int16_t example_element[] = {//add your input/test image pixels
};extern "C" void app_main(void)
{
Tensor<int16_t> input;input.set_element((int16_t *)example_element).set_exponent(input_exponent).set_shape({input_height,input_width,input_channel}).set_auto_free(false);HANDRECOGNITION model;dl::tool::Latency latency;latency.start();model.forward(input);latency.end();latency.print("\nSIGN", "forward");float *score = model.l10.get_output().get_element_ptr();float max_score = score[0];int max_index = 0;for (size_t i = 0; i < 6; i++){printf("%f, ", score[i]*100);if (score[i] > max_score){max_score = score[i];max_index = i;}}printf("\n");switch (max_index){case 0:printf("Palm: 0");break;case 1:printf("I: 1");break;case 2:printf("Thumb: 2");break;case 3:printf("Index: 3");break;case 4:printf("ok: 4");break;case 5:printf("C: 5");break;default:printf("No result");}printf("\n");}