File size: 3,449 Bytes
aa24fe8 | 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 | import os
import torch
from tqdm import tqdm
from utils.utils import get_lr
def fit_one_epoch(model, train_util, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, cuda, fp16, scaler, save_period, save_dir):
total_loss = 0
rpn_loc_loss = 0
rpn_cls_loss = 0
roi_loc_loss = 0
roi_cls_loss = 0
val_loss = 0
print('Start Train')
with tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen):
if iteration >= epoch_step:
break
images, boxes, labels = batch[0], batch[1], batch[2]
with torch.no_grad():
if cuda:
images = images.cuda()
rpn_loc, rpn_cls, roi_loc, roi_cls, total = train_util.train_step(images, boxes, labels, 1, fp16, scaler)
total_loss += total.item()
rpn_loc_loss += rpn_loc.item()
rpn_cls_loss += rpn_cls.item()
roi_loc_loss += roi_loc.item()
roi_cls_loss += roi_cls.item()
pbar.set_postfix(**{'total_loss' : total_loss / (iteration + 1),
'rpn_loc' : rpn_loc_loss / (iteration + 1),
'rpn_cls' : rpn_cls_loss / (iteration + 1),
'roi_loc' : roi_loc_loss / (iteration + 1),
'roi_cls' : roi_cls_loss / (iteration + 1),
'lr' : get_lr(optimizer)})
pbar.update(1)
print('Finish Train')
print('Start Validation')
with tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen_val):
if iteration >= epoch_step_val:
break
images, boxes, labels = batch[0], batch[1], batch[2]
with torch.no_grad():
if cuda:
images = images.cuda()
train_util.optimizer.zero_grad()
_, _, _, _, val_total = train_util.forward(images, boxes, labels, 1)
val_loss += val_total.item()
pbar.set_postfix(**{'val_loss' : val_loss / (iteration + 1)})
pbar.update(1)
print('Finish Validation')
loss_history.append_loss(epoch + 1, total_loss / epoch_step, val_loss / epoch_step_val)
eval_callback.on_epoch_end(epoch + 1)
print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
print('Total Loss: %.3f || Val Loss: %.3f ' % (total_loss / epoch_step, val_loss / epoch_step_val))
#-----------------------------------------------#
# 保存权值
#-----------------------------------------------#
if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch:
torch.save(model.state_dict(), os.path.join(save_dir, 'ep%03d-loss%.3f-val_loss%.3f.pth' % (epoch + 1, total_loss / epoch_step, val_loss / epoch_step_val)))
if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss):
print('Save best model to best_epoch_weights.pth')
torch.save(model.state_dict(), os.path.join(save_dir, "best_epoch_weights.pth"))
torch.save(model.state_dict(), os.path.join(save_dir, "last_epoch_weights.pth")) |