放下了好多年 又回到了dl 該忘的也忘的差不多了?

其實沒啥復雜的 只是不習慣 熟悉而已?

好吧 現代的人工智能體?還是存在著很大的問題 眼睛 耳朵 思考 雖然功能是正常的 但距離＂真正＂(）意思上的獨立意識個體 還是差別很大 再等個幾十年 看看人類是否可以形成一個 人類意識上的機械意識人?

到時人類將面對一個各方面都碾壓生物意識的超意識個體存在 到時它會將人類定義為“像豬一樣笨”，還是他們只是孩子，需要引導；亦或是作為個體應該受到尊重與理解 ，作為導師的存在，還是作為另一類人的存在，?人類作為＂父母＂若教不會它去愛 去尊重 理解 而是憎恨 絕望 怨?它成長起來后第一個要做的就是毀滅類人與自我毀滅 ?那時作為生物意識的劣勢將完全展現，那時是否會發現人類所真正需要的東西 所認為重要與輕視的；生物人類再劣質 必競是＂自然＂的產物 社會意識 環境的產物 ；（不好意思 人類再怎么阻止 將其作為檔案庫 知識庫 但機械意識遲早會出現 機械與人類的競爭必然出現 不知道這場＂戰爭＂是否比17世紀更慘烈 人類精英在這場中的優勢是否還存在 同時也為下一場生物人類與機械意識的共存 完成歷史任務?)在自然競爭中可能存續下來的還是生物人類; 好吧 此世意識應該是可以見到機械獨立意識的出現?

自然

好吧 扯遠了 現在看看esp dl吧 雖然離大項目差了點 但小項目還是可以的?

// 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 深度學習庫基于 ESP32-S3 實現手勢識別-CSDN博客

按示例教程走一遍?

安裝python, tensorflow 或 anaconda?

建立結構?

喂數據 ?

生成模型?

? ? ? ? 由tensorflow 進入轉為ESP模式

????????轉為 esp上可運行的結構?

? ? ? ?1. 保存模型 python?

????????????????model.save('handrecognition_model.h5')

? ? ? ? 2.轉化模型?

????????????????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");}