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53ebf66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | import os,sys
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import argparse
parser = argparse.ArgumentParser('the main to train model')
parser.add_argument('--config_file',type=str,required=True)
parser.add_argument('--cuda',type=int,default=None,required=False)
parser.add_argument("--kfold_index",type=int,default=None,required=False)
args = parser.parse_args()
cuda_id = args.cuda
os.environ["CUDA_VISIBLE_DEVICES"] = str(cuda_id)
import time
import torch
import utils
from torch import optim
import numpy as np
from models import Modules,reader,train_val
from models.ScheduleOptimizer import ScheduledOptim,scheduleoptim_dict_str
from models.popen import Auto_popen
from models.loss import Dynamic_Task_Priority,Dynamic_Weight_Averaging
POPEN = Auto_popen(args.config_file)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_num_interop_threads(4)
POPEN.cuda_id = device
POPEN.kfold_index = args.kfold_index
if POPEN.kfold_cv:
if args.kfold_index is None:
raise NotImplementedError("please specify the kfold index to perform K fold cross validation")
POPEN.vae_log_path = POPEN.vae_log_path.replace(".log","_cv%d.log"%args.kfold_index)
#POPEN.vae_pth_path = POPEN.vae_pth_path.replace(".pth","_cv%d.pth"%args.kfold_index)
# Run name
if POPEN.run_name is None:
run_name = POPEN.model_type + time.strftime("__%Y_%m_%d_%H:%M")
else:
run_name = POPEN.run_name
# log dir
logger = utils.setup_logs(POPEN.vae_log_path)
logger.info(f" ==============<<< device used: {device}:{cuda_id} >>>============== ")
# built model dir or check resume
POPEN.check_experiment(logger)
# |=====================================|
# |=========== setup part ==========|
# |=====================================|
# read data
train_loader,val_loader,test_loader = reader.get_dataloader(POPEN)
# =========== setup model ===========
# train_iter = iter(train_loader)
# X,Y = next(train_iter)
# -- pretrain --
if POPEN.pretrain_pth is not None:
# load pretran model
pretrain_popen = Auto_popen(os.path.join(utils.script_dir, POPEN.pretrain_pth))
pretrain_model = pretrain_popen.Model_Class(*pretrain_popen.model_args)
if not os.path.exists(pretrain_popen.vae_pth_path):
if type(args.kfold_index) == int:
pretrain_popen.kfold_index = args.kfold_index
pretrain_model = utils.load_model(pretrain_popen,pretrain_model,logger)
if POPEN.Model_Class == pretrain_popen.Model_Class:
# if not POPEN.Resumable:
# # we only load pre-train for the first time
# # later we can resume
model = pretrain_model.to(device)
del pretrain_model
elif POPEN.modual_to_fix is not None:
# POPEN.model_type != pretrain_popen.model_type
model = POPEN.Model_Class(*POPEN.model_args)
for modual in POPEN.modual_to_fix:
if modual in dir(pretrain_model):
eval(f'model.{modual}').load_state_dict(
eval(f'model.{modual}').state_dict()
)
state_dict = {'epoch': 0,
'validation_acc': 0,
'state_dict': model.to('cpu'),
'validation_loss': np.inf}
shared_pretrain_pth = POPEN.vae_pth_path.replace(f"_cv{args.kfold_index}", '')
if not os.path.exists(shared_pretrain_pth):
utils.snapshot(shared_pretrain_pth, state_dict)
utils.snapshot(POPEN.vae_pth_path, state_dict)
model = torch.load(POPEN.vae_pth_path, map_location=torch.device('cpu'))['state_dict']
model = model.to(device)
# -- end2end --
elif POPEN.model_type == "CrossStitch_Model":
backbone = {}
for t in POPEN.tasks:
task_popen = Auto_popen(POPEN.backbone_config[t])
task_model = task_popen.Model_Class(*task_popen.model_args)
task_model = utils.load_model(task_popen,task_model,logger)
backbone[t] = task_model.to(device)
POPEN.model_args = [backbone] + POPEN.model_args
model = POPEN.Model_Class(*POPEN.model_args).to(device)
else:
Model_Class = POPEN.Model_Class # DL_models.LSTM_AE
model = Model_Class(*POPEN.model_args).to(device)
if POPEN.Resumable:
model = utils.load_model(POPEN, model, logger)
# =========== fix parameters ===========
if isinstance(POPEN.modual_to_fix, list):
for modual in POPEN.modual_to_fix:
model = utils.fix_parameter(model,modual)
logger.info(' \t \t ==============<<< %s part is fixed>>>============== \t \t \n'%POPEN.modual_to_fix)
# =========== set optimizer ===========
if POPEN.optimizer == 'Schedule':
optimizer = ScheduledOptim(optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
betas=(0.9, 0.98),
eps=1e-09,
weight_decay=1e-4,
amsgrad=True),
n_warmup_steps=20)
elif type(POPEN.optimizer) == dict:
optimizer = eval(scheduleoptim_dict_str.format(**POPEN.optimizer))
else:
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=POPEN.lr,
betas=(0.9, 0.98),
eps=1e-09,
weight_decay=POPEN.l2)
if POPEN.loss_schema == 'DTP':
POPEN.loss_schedualer = Dynamic_Task_Priority(POPEN.tasks,POPEN.gamma,POPEN.chimerla_weight)
elif POPEN.loss_schema == 'DWA':
POPEN.loss_schedualer = Dynamic_Weight_Averaging(POPEN.tasks,POPEN.tau,POPEN.chimerla_weight)
# =========== resume ===========
best_loss = np.inf
best_acc = 0
best_epoch = 0
previous_epoch = 0
if POPEN.Resumable:
previous_epoch,best_loss,best_acc = utils.resume(POPEN, optimizer,logger)
# |=====================================|
# |========== training part ==========|
# |=====================================|
for epoch in range(POPEN.max_epoch-previous_epoch+1):
epoch += previous_epoch
# ----------| train |----------
logger.info("===============================| epoch {} |===============================".format(epoch))
train_val.train(dataloader=train_loader,model=model,optimizer=optimizer,popen=POPEN,epoch=epoch)
# -----------| validate |-----------
if epoch % POPEN.config_dict['setp_to_check'] == 0:
logger.info("===============================| start validation |===============================")
val_total_loss,val_avg_acc = train_val.validate(val_loader,model,popen=POPEN,epoch=epoch)
_,_ = train_val.validate(test_loader,model,popen=POPEN,epoch=epoch)
DICT ={"ran_epoch":epoch,"n_current_steps":optimizer.n_current_steps,"delta":optimizer.delta} if type(optimizer) == ScheduledOptim else {"ran_epoch":epoch}
POPEN.update_ini_file(DICT,logger)
# -----------| compare the result |-----------
if (best_loss > val_total_loss): #| (best_acc < val_avg_acc):
# update best performance
best_loss = min(best_loss,val_total_loss)
best_acc = max(best_acc,val_avg_acc)
best_epoch = epoch
# save
utils.snapshot(POPEN.vae_pth_path, {
'epoch': epoch + 1,
'validation_acc': val_avg_acc,
'state_dict': model.to('cpu'),
'validation_loss': val_total_loss,
# 'optimizer': optimizer.state_dict(),
})
# update the popen
POPEN.update_ini_file({'run_name':run_name,
"ran_epoch":epoch,
"best_acc":best_acc,
"cuda_id":cuda_id},
logger)
elif (epoch - best_epoch >= 30)&((type(optimizer) == ScheduledOptim)):
optimizer.increase_delta()
elif (epoch - best_epoch >= 60)&(epoch > POPEN.max_epoch/2):
# at the late phase of training
logger.info("<<<<<<<<<<< Early Stopping >>>>>>>>>>")
break |