def snapshot_segdis(model, discriminator, valoader, epoch, best_miou,
best_eiou, snapshot_dir, prefix):
# miou = val(model,valoader)
miou, eiou = val_e(model, valoader)
snapshot = {'epoch': epoch, 'state_dict': model.state_dict(), 'miou': miou}
snapshot_dis = {
'epoch': epoch,
'state_dict': discriminator.state_dict(),
'miou': miou
}
if miou > best_miou:
best_miou = miou
torch.save(
snapshot,
os.path.join(snapshot_dir, '{}_maxmiou.pth.tar'.format(prefix)))
torch.save(
snapshot_dis,
os.path.join(snapshot_dir,
'{}_dis_maxmiou.pth.tar'.format(prefix)))
if eiou > best_eiou:
best_eiou = eiou
torch.save(
snapshot,
os.path.join(snapshot_dir, '{}_maxeiou.pth.tar'.format(prefix)))
print(
"[{}] Curr mIoU: {:0.4f} Curr eIoU: {:0.4f} Best mIoU: {:0.4f} Best eIoU: {:0.4f}"
.format(epoch, miou, eiou, best_miou, best_eiou))
return best_miou, best_eiou
def snapshote(model, valoader, epoch, best_miou, best_eiou, snapshot_dir,
prefix):
miou, eiou = val_e(model, valoader)
# eiou = val_sigmoid(model,valoader)
# eiou = -2
snapshot = {
'epoch': epoch,
'state_dict': model.state_dict(),
'miou': miou,
'eiou': eiou
}
if miou > best_miou:
best_miou = miou
torch.save(
snapshot,
os.path.join(snapshot_dir, '{}_maxmiou.pth.tar'.format(prefix)))
if eiou > best_eiou:
best_eiou = eiou
torch.save(
snapshot,
os.path.join(snapshot_dir, '{}_maxeiou.pth.tar'.format(prefix)))
print(
"[{}] Curr mIoU: {:0.4f} Curr eIoU: {:0.4f} Best mIoU: {:0.4f} Best eIoU: {:0.4f}"
.format(epoch, miou, eiou, best_miou, best_eiou))
return best_miou, best_eiou