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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: XiaShan
@Contact: [email protected]
@Time: 2024/4/17 20:57
"""
import os, torch, random
from tqdm import tqdm, trange
import torch.nn as nn
import torch.optim as optim
from data import load_ESOL
from model import Graphormer
from parameter import parse_args, IOStream, table_printer
def train(args, IO, train_loader, num_node_features, num_edge_features):
# 使用GPU or CPU
device = torch.device('cpu' if args.gpu_index < 0 else 'cuda:{}'.format(args.gpu_index))
if args.gpu_index < 0:
IO.cprint('Using CPU')
else:
IO.cprint('Using GPU: {}'.format(args.gpu_index))
torch.cuda.manual_seed(args.seed) # 设置PyTorch GPU随机种子
# 加载模型及参数量统计
model = Graphormer(args, num_node_features, num_edge_features).to(device)
IO.cprint(str(model))
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
IO.cprint('Model Parameter: {}'.format(total_params))
# 优化器
optimizer = optim.AdamW(model.parameters(), lr=args.lr)
IO.cprint('Using AdamW')
# 损失函数
criterion = nn.L1Loss(reduction="sum")
epochs = trange(args.epochs, leave=True, desc="Epochs")
for epoch in epochs:
#################
### Train ###
#################
model.train() # 训练模式
train_loss = 0.0 # 一个epoch，所有样本损失总和
for i, data in tqdm(enumerate(train_loader), total=len(train_loader), desc="Train_Loader"):
data = data.to(device)
optimizer.zero_grad()
outputs = model(data)
loss = criterion(outputs, data.y)
loss.backward()
nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=1.0) # 剪裁可迭代参数的梯度范数，防止梯度爆炸
optimizer.step()
train_loss += loss.item()
IO.cprint('Epoch #{:03d}, Train_Loss: {:.6f}'.format(epoch, train_loss / len(train_loader.dataset)))
torch.save(model, 'outputs/%s/model.pth' % args.exp_name)
IO.cprint('The current best model is saved in: {}'.format('******** outputs/%s/model.pth *********' % args.exp_name))
def test(args, IO, test_loader):
"""测试模型"""
device = torch.device('cpu' if args.gpu_index < 0 else 'cuda:{}'.format(args.gpu_index))
# 输出内容保存在之前的训练日志里
IO.cprint('')
IO.cprint('********** TEST START **********')
IO.cprint('Reload Best Model')
IO.cprint('The current best model is saved in: {}'.format('******** outputs/%s/model.pth *********' % args.exp_name))
model = torch.load('outputs/%s/model.pth' % args.exp_name).to(device)
model = model.eval() # 创建一个新的评估模式的模型对象，不覆盖原模型
################
### Test ###
################
test_loss = 0.0
# 损失函数
criterion = nn.L1Loss(reduction="sum")
for i, data in tqdm(enumerate(test_loader), total=len(test_loader), desc="Test_Loader"):
data = data.to(device)
with torch.no_grad():
outputs = model(data)
loss = criterion(outputs, data.y)
test_loss += loss.item()
IO.cprint('TEST :: Test_Loss: {:.6f}'.format(test_loss / len(test_loader.dataset)))
def exp_init():
"""实验初始化"""
if not os.path.exists('outputs'):
os.mkdir('outputs')
if not os.path.exists('outputs/' + args.exp_name):
os.mkdir('outputs/' + args.exp_name)
# 跟踪执行脚本，windows下使用copy命令，且使用双引号
# os.system(f"copy main.py outputs\\{args.exp_name}\\main.py.backup")
# os.system(f"copy data.py outputs\\{args.exp_name}\\data.py.backup")
# os.system(f"copy layer.py outputs\\{args.exp_name}\\layer.py.backup")
# os.system(f"copy model.py outputs\\{args.exp_name}\\model.py.backup")
# os.system(f"copy parameter.py outputs\\{args.exp_name}\\parameter.py.backup")
os.system('cp main.py outputs' + '/' + args.exp_name + '/' + 'main.py.backup')
os.system('cp data.py outputs' + '/' + args.exp_name + '/' + 'data.py.backup')
os.system('cp layer.py outputs' + '/' + args.exp_name + '/' + 'layer.py.backup')
os.system('cp model.py outputs' + '/' + args.exp_name + '/' + 'model.py.backup')
os.system('cp parameter.py outputs' + '/' + args.exp_name + '/' + 'parameter.py.backup')
if __name__ == '__main__':
args = parse_args()
random.seed(args.seed) # 设置Python随机种子
torch.manual_seed(args.seed) # 设置PyTorch随机种子
exp_init()
IO = IOStream('outputs/' + args.exp_name + '/run.log')
IO.cprint(str(table_printer(args))) # 参数可视化
train_loader, test_loader, num_node_features, num_edge_features = load_ESOL(args)
train(args, IO, train_loader, num_node_features, num_edge_features)
test(args, IO, test_loader)