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trainer_decompose.py
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trainer_decompose.py
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import os
import time
from datetime import datetime
import cv2
import torch
import numpy as np
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torchvision.transforms.functional as TF
from loguru import logger
from torch.utils import tensorboard
from tqdm import tqdm
from utils.helpers import dir_exists, get_instance, remove_files, double_threshold_iteration, get_optimizer_instance
from utils.metrics import AverageMeter, get_metrics, get_metrics, count_connect_component
import ttach as tta
from PIL import Image
np.set_printoptions(threshold=np.inf)
class Trainer:
def __init__(self, model, CFG=None, loss=None, train_loader=None, val_loader=None):
self.CFG = CFG
if self.CFG.amp is True:
self.scaler = torch.cuda.amp.GradScaler(enabled=True)
self.loss = loss
self.model = nn.DataParallel(model.cuda())
self.train_loader = train_loader
self.val_loader = val_loader
self.optimizer = get_optimizer_instance(
torch.optim, "optimizer", CFG, self.model.parameters())
self.lr_scheduler = get_instance(
torch.optim.lr_scheduler, "lr_scheduler", CFG, self.optimizer)
start_time = datetime.now().strftime('%y%m%d%H%M%S')
self.checkpoint_dir = os.path.join(
CFG.save_dir, self.CFG['model']['type'], start_time)
self.writer = tensorboard.SummaryWriter(self.checkpoint_dir)
dir_exists(self.checkpoint_dir)
cudnn.benchmark = True
def train(self):
for epoch in range(1, self.CFG.epochs + 1):
self._train_epoch(epoch)
if self.val_loader is not None and epoch % self.CFG.val_per_epochs == 0:
results = self._valid_epoch(epoch)
logger.info(f'## Info for epoch {epoch} ## ')
for k, v in results.items():
logger.info(f'{str(k):15s}: {v}')
if epoch % self.CFG.save_period == 0:
self._save_checkpoint(epoch)
def _train_epoch(self, epoch):
self.model.train()
wrt_mode = 'train'
self._reset_metrics()
tbar = tqdm(self.train_loader, ncols=160)
tic = time.time()
for img, gt, dgt, cgt in tbar:
self.data_time.update(time.time() - tic)
img = img.cuda(non_blocking=True)
gt = gt.cuda(non_blocking=True)
dgt = dgt.cuda(non_blocking=True)
cgt = cgt.cuda(non_blocking=True)
self.optimizer.zero_grad()
#amp是加速训练用的
if self.CFG.amp is True:
with torch.cuda.amp.autocast(enabled=True):
# pre, _, _, _ = self.model(img)
pre_d, pre_c, pre = self.model(img)
loss = self.loss(pre_d, dgt) + self.loss(pre_c, cgt) + self.loss(pre, gt)
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
else:
pre = self.model(img)
loss = self.loss(pre, dgt)
loss.backward()
self.optimizer.step()
self.total_loss.update(loss.item())
self.batch_time.update(time.time() - tic)
self._metrics_update(
*get_metrics(pre, gt, threshold=self.CFG.threshold).values())
tbar.set_description(
'TRAIN ({}) | Loss: {:.4f} | AUC {:.4f} F1 {:.4f} Acc {:.4f} Sen {:.4f} Spe {:.4f} Pre {:.4f} IOU {:.4f} |B {:.2f} D {:.2f} |'.format(
epoch, self.total_loss.average, *self._metrics_ave().values(), self.batch_time.average, self.data_time.average))
self.writer.add_scalar(
f'{wrt_mode}/loss', self.total_loss.average, epoch)
for k, v in list(self._metrics_ave().items())[:-1]:
self.writer.add_scalar(f'{wrt_mode}/{k}', v, epoch)
for i, opt_group in enumerate(self.optimizer.param_groups):
self.writer.add_scalar(
f'{wrt_mode}/Learning_rate_{i}', opt_group['lr'], epoch)
self.lr_scheduler.step()
def _valid_epoch(self, epoch):
logger.info('\n###### EVALUATION ######')
self.model.eval()
wrt_mode = 'val'
self._reset_metrics()
tbar = tqdm(self.val_loader, ncols=160)
with torch.no_grad():
for img, gt, dgt, cgt in tbar:
img = img.cuda(non_blocking=True)
gt = gt.cuda(non_blocking=True)
dgt = dgt.cuda(non_blocking=True)
cgt = cgt.cuda(non_blocking=True)
if self.CFG.amp is True:
with torch.cuda.amp.autocast(enabled=True):
pre_d, pre_c, predict = self.model(img)
# loss = self.loss(pre_d, dgt) + self.loss(pre_c, cgt) + self.loss(pre, gt)
loss = self.loss(predict, gt)
else:
predict = self.model(img)
loss = self.loss(predict, gt)
self.total_loss.update(loss.item())
self._metrics_update(
*get_metrics(predict, gt, threshold=self.CFG.threshold).values())
tbar.set_description(
'EVAL ({}) | Loss: {:.4f} | AUC {:.4f} F1 {:.4f} Acc {:.4f} Sen {:.4f} Spe {:.4f} Pre {:.4f} IOU {:.4f} |'.format(
epoch, self.total_loss.average, *self._metrics_ave().values()))
self.writer.add_scalar(
f'{wrt_mode}/loss', self.total_loss.average, epoch)
self.writer.add_scalar(
f'{wrt_mode}/loss', self.total_loss.average, epoch)
for k, v in list(self._metrics_ave().items())[:-1]:
self.writer.add_scalar(f'{wrt_mode}/{k}', v, epoch)
log = {
'val_loss': self.total_loss.average,
**self._metrics_ave()
}
return log
def _save_checkpoint(self, epoch):
state = {
'arch': type(self.model).__name__,
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'config': self.CFG
}
filename = os.path.join(self.checkpoint_dir,
f'checkpoint-epoch{epoch}.pth')
logger.info(f'Saving a checkpoint: {filename} ...')
torch.save(state, filename)
return filename
def _reset_metrics(self):
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.total_loss = AverageMeter()
self.auc = AverageMeter()
self.f1 = AverageMeter()
self.acc = AverageMeter()
self.sen = AverageMeter()
self.spe = AverageMeter()
self.pre = AverageMeter()
self.iou = AverageMeter()
self.CCC = AverageMeter()
def _metrics_update(self, auc, f1, acc, sen, spe, pre, iou):
# def _metrics_update(self, f1, acc, sen, spe, pre, iou):
self.auc.update(auc)
self.f1.update(f1)
self.acc.update(acc)
self.sen.update(sen)
self.spe.update(spe)
self.pre.update(pre)
self.iou.update(iou)
def _metrics_ave(self):
return {
"AUC": self.auc.average,
"F1": self.f1.average,
"Acc": self.acc.average,
"Sen": self.sen.average,
"Spe": self.spe.average,
"pre": self.pre.average,
"IOU": self.iou.average
}