SuoXC

import filecmp
import os
import pathlib
import shutil


def no_change(src_file, dest_file):
    equal = filecmp.cmp(src_file.resolve(), dest_file.resolve(), False)
    return equal

SuoXC

import os
import zipfile


def recursive_unzip(zip_file, target_dir):
    print("export: ", zip_file)

    if not os.path.exists(target_dir):
        os.makedirs(target_dir)

SuoXC

import random
import time
from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED

from memory_profiler import profile

random.seed(10)
total_iterations = 1000

SuoXC

import os
import importlib

# 自动注册当前文件夹下所有模块
module_dir = os.path.dirname(__file__)

for i in os.listdir(module_dir):
    module_name = os.path.basename(i).split('.')[0]
    importlib.import_module('.' + module_name, __package__)
    

SuoXC

pattern="*.log" #change to your file pattern
# need to install jq
function filter_function(){
    file=$1
    cat $file |jq -r 'select(.userid==100) |[.userid, .name, .age, .type] |@csv'
}

files=$(ls $pattern)
for file in $files ; do
    echo "current_file:$file"

SuoXC

<IfModule mod_rewrite.c>
    <IfModule mod_negotiation.c>
        Options -MultiViews
    </IfModule>

    RewriteEngine On

    # Redirect Trailing Slashes...
    RewriteRule ^(.*)/$ /$1 [L,R=301]

SuoXC

import tensorflow as tf
def gradient_panalty(real, fake, discriminator, alpha, gp_lambda=10):
    # alpha = tf.placeholder(shape=[None, 1, 1, 1], dtype=tf.float32)
    # alpha = tf.random_uniform(shape=[batch_size, 1, 1, 1], minval=0., maxval=1.)
    interpolated = real + alpha * (fake - real)
    logit = discriminator(interpolated)
    grad = tf.gradients(logit, interpolated)[0]  # gradient of D(interpolated)
    grad_norm = tf.norm(tf.layers.flatten(grad), axis=1)  # l2 norm
    gp = gp_lambda * tf.reduce_mean(tf.square(grad_norm - 1.))
    return gp

SuoXC

import tensorflow as tf

def export_submodel(sess, inputs, outputs, output_dir):
    output_nodes = list(inputs.values()) + list(outputs.values())
    output_node_names = [node.name.replace(":0", "") for node in output_nodes]
    graph = tf.get_default_graph()
    graph_def = graph.as_graph_def()
    # sess = tf.get_default_session()
    output_graph_def = tf.graph_util.convert_variables_to_constants(
        sess,

SuoXC

from queue import Queue
from threading import Thread


class AsyncQueue:
    def __init__(self, generator, cache_size=10):
        self._q = Queue(maxsize=cache_size)
        self._generator = generator
        self._input_thread = Thread(target=self.append_to_queue)
        self._input_thread.setDaemon(True)

SuoXC

enable-rpc=true
rpc-allow-origin-all=true
rpc-listen-all=true
#rpc-secret=xiaocong
dir=/home/suo/下载/aria2


#是否启用https加密，启用之后要设置公钥,私钥的文件路径
#rpc-secure=true
#启用加密设置公钥