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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=str,default=0,required=False)
parser.add_argument("--kfold_index",type=int,default=1,required=False)
args = parser.parse_args()
cuda_id = args.cuda if args.cuda is not None else utils.get_config_cuda(args.config_file)
os.environ["CUDA_VISIBLE_DEVICES"] = str(cuda_id)
import time
import torch
import copy
import utils
from torch import optim
import numpy as np
from models import 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')
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 {device}{cuda_id} |=========================== ")
# built model dir or check resume
POPEN.check_experiment(logger)
# |=====================================|
# |=========== setup part ==========|
# |=====================================|
# read data
loader_set = {}
n_covar_dict = {}
base_path = ['cycle_train_val.csv', 'cycle_test.csv']
base_csv = 'cycle_MTL_transfer.csv'
for task in POPEN.cycle_set:
if (task in ['MPA_U', 'MPA_H', 'MPA_V', 'SubMPA_H']):
datapopen = Auto_popen('log/Backbone/RL_hard_share/3M/schedule_lr.ini')
datapopen.split_like = [path.replace('cycle', task) for path in base_path]
datapopen.kfold_index = args.kfold_index
n_covar_dict[task] = datapopen.n_covar
elif (task in ['RP_293T', 'RP_muscle', 'RP_PC3']):
datapopen = Auto_popen('log/Backbone/RL_hard_share/3R/schedule_MTL.ini')
datapopen.csv_path = base_csv.replace("cycle",task)
datapopen.kfold_index = args.kfold_index
datapopen.pad_to = POPEN.pad_to
datapopen.aux_task_columns = POPEN.aux_task_columns
datapopen.other_input_columns = POPEN.other_input_columns
datapopen.n_covar = POPEN.n_covar
elif (task in ['pcr3', '293']):
datapopen = Auto_popen('log/Backbone/RL_hard_share/karollus_RPs/rp_cycle.ini')
datapopen.csv_path = base_csv.replace("cycle",task)
datapopen.kfold_index = args.kfold_index
datapopen.aux_task_columns = POPEN.aux_task_columns
datapopen.other_input_columns = POPEN.other_input_columns
datapopen.pad_to = POPEN.pad_to
datapopen.n_covar = POPEN.n_covar
datapopen.shuffle = False
loader_set[task] = reader.get_dataloader(datapopen)
POPEN.n_covar = n_covar_dict
POPEN.get_model_config() # update model config
# =========== setup model ===========
# train_iter = iter(train_loader)
# X,Y = next(train_iter)
# -- pretrain --
if POPEN.pretrain_pth is not None:
# load pretran model
logger.info("===============================| pretrain |===============================")
logger.info(f" {POPEN.pretrain_pth}")
pretrain_popen = Auto_popen(os.path.join(utils.script_dir, POPEN.pretrain_pth))
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 = torch.load(pretrain_popen.vae_pth_path, map_location=torch.device('cpu'))['state_dict']
if POPEN.model_type == pretrain_popen.model_type:
# 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
if (POPEN.cycle_set != pretrain_popen.cycle_set):
model.all_tasks = POPEN.cycle_set
model.tower = torch.nn.ModuleDict(
{POPEN.cycle_set[i] : model.tower[t] for i, t in enumerate(pretrain_popen.cycle_set)}
)
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': 0}
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'))
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)
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)
model = model.to(device)
logger.info(' \t \t ==============| %s 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
epoch = 0
if POPEN.Resumable:
previous_epoch,best_loss,best_acc = utils.resume(POPEN, optimizer,logger)
# |=====================================|
# |========== training part ==========|
# |=====================================|
epoch += previous_epoch
# -----------| validate |-----------
logger.info("===============================| test |===============================")
verbose_dict = train_val.cycle_validate(loader_set,model,optimizer,popen=POPEN,epoch=epoch, which_set=2)
# matching task performance influence what to save
if np.any(['r2' in key for key in verbose_dict.keys()]):
val_avg_acc = np.mean([values for key, values in verbose_dict.items() if 'r2' in key])
acc_dict = {f"cv{args.kfold_index}_{key}":values for key, values in verbose_dict.items() if 'r2' in key}
else:
val_avg_acc = np.mean([values for key, values in verbose_dict.items() if 'acc' in key])
acc_dict = {}
val_total_loss = verbose_dict['Total']
# 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)
# POPEN.update_ini_file(acc_dict,logger) |