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train.py
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import data_loader
import models
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import util
import torch.nn.functional as F
from args import TrainArgParser
from evaluator import ModelEvaluator
from logger import TrainLogger
from saver import ModelSaver
import warnings
import torch, gc
warnings.filterwarnings("ignore")
#
def train(args):
if args.ckpt_path and not args.use_pretrained:
model, ckpt_info = ModelSaver.load_model(args.ckpt_path, args.gpu_ids)
args.start_epoch = ckpt_info['epoch'] + 1
else:
model_fn = models.__dict__[args.model]
model = model_fn(will_train=True,**vars(args))
if args.use_pretrained:
model.load_pretrained(args.ckpt_path, args.gpu_ids)
model = nn.DataParallel(model, args.gpu_ids)
model = model.to(args.device)
model.train()
# optimizer scheduler
if args.use_pretrained or args.fine_tune:
parameters = model.module.fine_tuning_parameters(args.fine_tuning_boundary, args.fine_tuning_lr)
else:
parameters = model.parameters()
optimizer = util.get_optimizer(parameters, args)
lr_scheduler = util.get_scheduler(optimizer, args)
if args.ckpt_path and not args.use_pretrained and not args.fine_tune:
ModelSaver.load_optimizer(args.ckpt_path, optimizer, lr_scheduler)
# logger evaluator saver
cls_loss_fn = util.get_loss_fn(is_classification=True, dataset=args.dataset, size_average=False)
data_loader_fn = data_loader.__dict__[args.data_loader]
train_loader = data_loader_fn(args, phase='train', is_training=True)
logger = TrainLogger(args, len(train_loader.dataset), train_loader.dataset.pixel_dict)
eval_loaders = [data_loader_fn(args, phase='val', is_training=False)]
evaluator = ModelEvaluator(args.do_classify, args.dataset, eval_loaders, logger,
args.agg_method, args.num_visuals, args.max_eval, args.epochs_per_eval)
saver = ModelSaver(args.save_dir, args.epochs_per_save, args.max_ckpts, args.best_ckpt_metric, args.maximize_metric)
# train
while not logger.is_finished_training():
logger.start_epoch()
for inputs, target_dict in train_loader:
logger.start_iter()
with torch.set_grad_enabled(True):
inputs.to(args.device)
# MVCS2
cls_logits = model.forward(inputs)
cls_targets = target_dict['is_abnormal']
cls_loss = cls_loss_fn(cls_logits, cls_targets.to(args.device))
loss = cls_loss.mean()
logger.log_iter(inputs, cls_logits, target_dict, cls_loss.mean(), optimizer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
logger.end_iter()
util.step_scheduler(lr_scheduler, global_step=logger.global_step)
metrics, curves = evaluator.evaluate(model, args.device, logger.epoch)
saver.save(logger.epoch, model, optimizer, lr_scheduler, args.device,
metric_val=metrics.get(args.best_ckpt_metric, None))
logger.end_epoch(metrics, curves)
util.step_scheduler(lr_scheduler, metrics, epoch=logger.epoch, best_ckpt_metric=args.best_ckpt_metric)
#
def train_with_table(args,table):
if args.ckpt_path and not args.use_pretrained:
model, ckpt_info = ModelSaver.load_model(args.ckpt_path, args.gpu_ids)
args.start_epoch = ckpt_info['epoch'] + 1
else:
model_fn = models.__dict__[args.model]
model = model_fn(**vars(args))
if args.use_pretrained:
model.load_pretrained(args.ckpt_path, args.gpu_ids)
model = nn.DataParallel(model, args.gpu_ids)
model = model.to(args.device)
model.train()
# Get optimizer and scheduler
if args.use_pretrained or args.fine_tune:
parameters = model.module.fine_tuning_parameters(args.fine_tuning_boundary, args.fine_tuning_lr)
else:
parameters = model.parameters()
optimizer = util.get_optimizer(parameters, args)
lr_scheduler = util.get_scheduler(optimizer, args)
if args.ckpt_path and not args.use_pretrained and not args.fine_tune:
ModelSaver.load_optimizer(args.ckpt_path, optimizer, lr_scheduler)
# Get logger, evaluator, saver
cls_loss_fn = util.get_loss_fn(is_classification=True, dataset=args.dataset, size_average=False)
data_loader_fn = data_loader.__dict__[args.data_loader]
train_loader = data_loader_fn(args, phase='train', is_training=True)
logger = TrainLogger(args, len(train_loader.dataset), train_loader.dataset.pixel_dict)
eval_loaders = [data_loader_fn(args, phase='val', is_training=False)]
evaluator = ModelEvaluator(args.do_classify, args.dataset, eval_loaders, logger,
args.agg_method, args.num_visuals, args.max_eval, args.epochs_per_eval)
saver = ModelSaver(args.save_dir, args.epochs_per_save, args.max_ckpts, args.best_ckpt_metric, args.maximize_metric)
# Train model
while not logger.is_finished_training():
logger.start_epoch()
for inputs, target_dict in train_loader:
logger.start_iter()
# prepare table data
ids = [int(item) for item in target_dict['study_num']]
table_data=[]
for i in range(len(target_dict['study_num'])):
table_data.append(torch.tensor(np.array(table[table['idx']==ids[i]].iloc[:,4:]),dtype=torch.float32))
table_data = torch.stack(table_data).squeeze(1)
# end for table data
with torch.set_grad_enabled(True):
inputs.to(args.device)
table_data.to(args.device)
cls_logits = model.forward(inputs,table_data)
cls_targets = target_dict['is_abnormal']
cls_loss = cls_loss_fn(cls_logits, cls_targets.to(args.device))
loss = cls_loss.mean()
logger.log_iter(inputs, cls_logits, target_dict, cls_loss.mean(), optimizer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
logger.end_iter()
util.step_scheduler(lr_scheduler, global_step=logger.global_step)
metrics, curves = evaluator.evaluate(model, args.device, logger.epoch)
saver.save(logger.epoch, model, optimizer, lr_scheduler, args.device,
metric_val=metrics.get(args.best_ckpt_metric, None))
logger.end_epoch(metrics, curves)
util.step_scheduler(lr_scheduler, metrics, epoch=logger.epoch, best_ckpt_metric=args.best_ckpt_metric)
if __name__ == '__main__':
table_data = pd.read_csv('/mntcephfs/lab_data/wangcm/wzp/ehr/ehr_nosub_1.csv')
util.set_spawn_enabled()
parser = TrainArgParser()
args_ = parser.parse_args()
# train(args_)
train_with_table(args_,table_data)