tiandiao123

#include <future>
#include <iostream>
using namespace std;


int factorial(int N){
    if(N == 1){
        return 1;
    }

tiandiao123

#include <iostream>
#include <thread>
#include <future>
#include <deque>
using namespace std;


std::deque<std::packaged_task<int()>> task_q;
std::mutex mu;
std::condition_variable cond;

tiandiao123

#include <string>
#include <iostream>
#include <future>
#include <thread>

using namespace std;

void TestFuture(promise<string> p){
    this_thread::sleep_for(3s);
    cout << "begin setting value ... " << endl;

tiandiao123

#include <iostream>
#include <thread>
#include <mutex>

using namespace std;


void SystemInit(){
    cout << "hello world! "<< endl;
}

tiandiao123

#include <thread>
#include <iostream>
using namespace std;

class MyThread
{
public:
    void Main(){
        cout << "my thread main fuction" << name << " : " << age << endl;
    }

tiandiao123

# modified codes from https://www.bilibili.com/video/BV1oa411b7c9/?spm_id_from=333.999.0.0&vd_source=0e3bf657889554538e4fce27455e8e66
import os
import asyncio
import time

async def print_info(waiting_time, info):
    await asyncio.sleep(waiting_time)
    return "{} --- {}".format(waiting_time, info)

tiandiao123

# the following codes are modified from https://www.bilibili.com/video/BV155411V7tj/?spm_id_from=333.337.search-card.all.click&vd_source=0e3bf657889554538e4fce27455e8e66
import os
import asyncio
import time

class GatherApples:
    def __init__(self):
        self.apples = [1]

    async def ask_for_apple(self):

tiandiao123

import tensorflow as tf


saved_model_dir = "/Users/cuiqingli123/Workspace/torch_op_exp/mobile_net_v3_small"
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

tiandiao123

import tensorflow as tf
import numpy as np

def representative_dataset():
    for _ in range(100):
      data = np.random.rand(1, 224, 224, 3)
      yield [data.astype(np.float32)]


saved_model_dir = "/Users/cuiqingli123/Workspace/torch_op_exp/mobile_net_v3_small"

tiandiao123

import tensorflow as tf


saved_model_dir = "/Users/cuiqingli123/Workspace/torch_op_exp/mobile_net_v3_small"
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the model.