/
finetune_dynamic_plugin.py
137 lines (109 loc) · 5.71 KB
/
finetune_dynamic_plugin.py
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import sys
sys.path.append('./')
from os import path
from utils.parse_args import args
from utils.dataloader import EdgeListData
from tqdm import tqdm
# import dgl
import random
import numpy as np
import torch
from utils.logger import Logger, log_exceptions
from modules.dynamicGNN.trainer_roland import Trainer
import importlib
import setproctitle
import pandas as pd
import datetime
setproctitle.setproctitle('GraphPro')
modules_class = 'modules.dynamicGNN.plugin_dynamic.'+args.pre_model+"_dynamic"
def import_pretrained_model():
module = importlib.import_module('modules.plugins.'+args.pre_model)
return getattr(module, args.pre_model)
def import_finetune_model():
module = importlib.import_module(modules_class)
return getattr(module, args.pre_model+"_"+args.f_model)
def init_seed(seed):
random.seed(seed)
np.random.seed(seed)
# dgl.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
init_seed(args.seed)
logger = Logger(args)
pretrain_data = path.join(args.data_path, "pretrain.txt")
pretrain_val_data = path.join(args.data_path, "pretrain_val.txt")
finetune_data = path.join(args.data_path, "fine_tune.txt")
test_data_num = 8 if args.data_path.split('/')[-1] == 'amazon' else 4
logger.log(f"test_data_num: {test_data_num}")
test_datas = [path.join(args.data_path, f"test_{i}.txt") for i in range(1, test_data_num+1)]
all_data = [pretrain_data, pretrain_val_data, finetune_data, *test_datas]
recalls, ndcgs = [], []
if args.f_model == 'roland':
@log_exceptions
def run():
pretrain_dataset = EdgeListData(pretrain_data, pretrain_val_data)
# LightGCN
pretrain_model = import_pretrained_model()(pretrain_dataset, phase='vanilla').to(args.device)
pretrain_model.load_state_dict(torch.load(args.pre_model_path), strict=False)
pretrain_model.eval()
meta_model_sd = pretrain_model.state_dict()
for num_stage in range(1,len(test_datas)+1):
test_data_idx = num_stage + 2
ft_data_idx = test_data_idx - 1
finetune_dataset = EdgeListData(train_file=all_data[ft_data_idx], test_file=all_data[test_data_idx], phase='finetune', pre_dataset=pretrain_dataset, has_time=True, user_hist_files=all_data[:ft_data_idx])
if num_stage == 1:
model = import_finetune_model()(finetune_dataset, pretrain_model, meta_model=pretrain_model).to(args.device)
if num_stage > 1:
# update model for next stage
model = import_finetune_model()(finetune_dataset, meta_model=updated_model).to(args.device)
print(model)
logger.info(f"ROLAND Learning Stage {num_stage}, test data: {all_data[test_data_idx]}, incremental train data: {all_data[ft_data_idx]}")
trainer = Trainer(finetune_dataset, logger)
best_perform = trainer.train_finetune(model)
recalls.append(best_perform['recall'][0])
ndcgs.append(best_perform['ndcg'][0])
# update meta model
# reload the best model
model.load_state_dict(torch.load(trainer.save_path))
model.meta_model = None
updated_model = model.update_meta_model(model, meta_model_sd)
meta_model_sd = updated_model.state_dict()
# update exp time for saving new model
args.exp_time = datetime.datetime.now().strftime('%b-%d-%Y_%H-%M-%S')
logger.info(f"recalls: {recalls} \n ndcgs: {ndcgs} \n avg. recall: {np.round(np.mean(recalls), 4)}, avg. ndcg: {np.round(np.mean(ndcgs), 4)}")
run()
elif args.f_model in ['evolveGCN_H', 'evolveGCN_O']:
@log_exceptions
def run():
pretrain_dataset = EdgeListData(pretrain_data, pretrain_val_data)
# LightGCN
pretrain_model = import_pretrained_model()(pretrain_dataset, phase='vanilla').to(args.device)
pretrain_model.load_state_dict(torch.load(args.pre_model_path), strict=False)
pretrain_model.eval()
for num_stage in range(1,len(test_datas)+1):
test_data_idx = num_stage + 2
ft_data_idx = test_data_idx - 1
finetune_dataset = EdgeListData(train_file=all_data[ft_data_idx], test_file=all_data[test_data_idx], phase='finetune', pre_dataset=pretrain_dataset, has_time=True, user_hist_files=all_data[:ft_data_idx])
if num_stage == 1:
last_emb = torch.concat(pretrain_model.generate(), dim=0)
model = import_finetune_model()(finetune_dataset, pretrain_model, last_emb).to(args.device)
if num_stage > 1:
# update model for next stage
model = import_finetune_model()(finetune_dataset, last_emb=last_emb).to(args.device)
print(model)
logger.info(f"EvolveGCN Learning Stage {num_stage}, test data: {all_data[test_data_idx]}, incremental train data: {all_data[ft_data_idx]}")
trainer = Trainer(finetune_dataset, logger)
best_perform = trainer.train_finetune(model)
recalls.append(best_perform['recall'][0])
ndcgs.append(best_perform['ndcg'][0])
if args.f_model == 'evolveGCN_H':
last_emb = torch.concat(model.generate(), dim=0)
elif args.f_model == 'evolveGCN_O':
last_emb = torch.concat([model.user_embedding, model.item_embedding], dim=0)
# update exp time for saving new model
args.exp_time = datetime.datetime.now().strftime('%b-%d-%Y_%H-%M-%S')
logger.info(f"recalls: {recalls} \n ndcgs: {ndcgs} \n avg. recall: {np.round(np.mean(recalls), 4)}, avg. ndcg: {np.round(np.mean(ndcgs), 4)}")
run()