Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

cleanup

This commit is contained in:
Glenn Jocher 2020-04-29 12:00:30 -07:00
parent 9f88f5cc21
commit d62d68929c
1 changed files with 7 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
train.py
View File

@ -22,7 +22,7 @@ last = wdir + 'last.pt'
best = wdir + 'best.pt' best = wdir + 'best.pt'
results_file = 'results.txt' results_file = 'results.txt'
# Hyperparameters https://github.com/ultralytics/yolov3/issues/310 # Hyperparameters
hyp = {'giou': 3.54, # giou loss gain hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain 'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight 'cls_pw': 1.0, # cls BCELoss positive_weight
@ -315,13 +315,13 @@ def train():
single_cls=opt.single_cls, single_cls=opt.single_cls,
dataloader=testloader) dataloader=testloader)
# Write epoch results # Write
with open(results_file, 'a') as f: with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls) f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket: if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name)) os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))
# Write Tensorboard results # Tensorboard
if tb_writer: if tb_writer:
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss', tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1', 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
@ -334,34 +334,25 @@ def train():
if fi > best_fitness: if fi > best_fitness:
best_fitness = fi best_fitness = fi
# Save training results # Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve) save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save: if save:
with open(results_file, 'r') as f: with open(results_file, 'r') as f: # create checkpoint
# Create checkpoint
chkpt = {'epoch': epoch, chkpt = {'epoch': epoch,
'best_fitness': best_fitness, 'best_fitness': best_fitness,
'training_results': f.read(), 'training_results': f.read(),
'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(), 'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()} 'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last checkpoint # Save last, best and delete
torch.save(chkpt, last) torch.save(chkpt, last)
# Save best checkpoint
if (best_fitness == fi) and not final_epoch: if (best_fitness == fi) and not final_epoch:
torch.save(chkpt, best) torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt del chkpt
# end epoch ---------------------------------------------------------------------------------------------------- # end epoch ----------------------------------------------------------------------------------------------------
# end training # end training
n = opt.name n = opt.name
if len(n): if len(n):
n = '_' + n if not n.isnumeric() else n n = '_' + n if not n.isnumeric() else n
@ -378,7 +369,6 @@ def train():
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache() torch.cuda.empty_cache()
return results return results