serve/update_skill.py

import numpy as np
import json
from trueskill import TrueSkill
import paramiko
import io, os
import sys
from serve.constants import SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD, SSH_SKILL
trueskill_env = TrueSkill()
sys.path.append('../')
from model.models import IMAGE_GENERATION_MODELS

ssh_skill_client = None
sftp_skill_client = None

def create_ssh_skill_client(server, port, user, password):
    global ssh_skill_client, sftp_skill_client
    ssh_skill_client = paramiko.SSHClient()
    ssh_skill_client.load_system_host_keys()
    ssh_skill_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
    ssh_skill_client.connect(server, port, user, password)

    transport = ssh_skill_client.get_transport()
    transport.set_keepalive(60)

    sftp_skill_client = ssh_skill_client.open_sftp()
def is_connected():
    global ssh_skill_client, sftp_skill_client
    if ssh_skill_client is None or sftp_skill_client is None:
        return False
    # 检查SSH连接是否正常
    if not ssh_skill_client.get_transport().is_active():
        return False
    # 检查SFTP连接是否正常
    try:
        sftp_skill_client.listdir('.')  # 尝试列出根目录
    except Exception as e:
        print(f"Error checking SFTP connection: {e}")
        return False
    return True
def ucb_score(trueskill_diff, t, n):
    exploration_term = np.sqrt((2 * np.log(t + 1e-5)) / (n + 1e-5))
    ucb = -trueskill_diff + 1.0 * exploration_term
    return ucb

def update_trueskill(ratings, ranks):
    new_ratings = trueskill_env.rate(ratings, ranks)
    return new_ratings

def serialize_rating(rating):
    return {'mu': rating.mu, 'sigma': rating.sigma}

def deserialize_rating(rating_dict):
    return trueskill_env.Rating(mu=rating_dict['mu'], sigma=rating_dict['sigma'])


def save_json_via_sftp(ratings, comparison_counts, total_comparisons):
    global sftp_skill_client
    if not is_connected():
        create_ssh_skill_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
    data = {
        'ratings': [serialize_rating(r) for r in ratings],
        'comparison_counts': comparison_counts.tolist(),
        'total_comparisons': total_comparisons
    }  
    json_data = json.dumps(data)
    with sftp_skill_client.open(SSH_SKILL, 'w') as f:
        f.write(json_data)

def load_json_via_sftp():
    global sftp_skill_client
    if not is_connected():
        create_ssh_skill_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
    with sftp_skill_client.open(SSH_SKILL, 'r') as f:
        data = json.load(f)
    ratings = [deserialize_rating(r) for r in data['ratings']]
    comparison_counts = np.array(data['comparison_counts'])
    total_comparisons = data['total_comparisons']
    return ratings, comparison_counts, total_comparisons


def update_skill(rank, model_names, k_group=4):

    ratings, comparison_counts, total_comparisons = load_json_via_sftp()

    # group = Model_ID.group
    group = []
    for model_name in model_names:
        group.append(IMAGE_GENERATION_MODELS.index(model_name))
    print(group)

    pairwise_comparisons = [(i, j) for i in range(len(group)) for j in range(i+1, len(group))]
    for player1, player2 in pairwise_comparisons:
        if rank[player1] < rank[player2]:
            ranks = [0, 1]
            updated_ratings = update_trueskill([[ratings[group[player1]]], [ratings[group[player2]]]], ranks)
            ratings[group[player1]], ratings[group[player2]] = updated_ratings[0][0], updated_ratings[1][0]
        elif rank[player1] > rank[player2]:
            ranks = [1, 0]
            updated_ratings = update_trueskill([[ratings[group[player1]]], [ratings[group[player2]]]], ranks)
            ratings[group[player1]], ratings[group[player2]] = updated_ratings[0][0], updated_ratings[1][0]
            
        comparison_counts[group[player1], group[player2]] += 1
        comparison_counts[group[player2], group[player1]] += 1
    
    total_comparisons += 1

    save_json_via_sftp(ratings, comparison_counts, total_comparisons)

    from model.matchmaker import RunningPivot
    if group[0] in RunningPivot.running_pivot:
        RunningPivot.running_pivot.remove(group[0])