def to_json(self, log_dir, filename):
utils.check_dir(log_dir)
json_file = os.path.join(log_dir,filename)
var_dict = copy.copy(vars(self))
var_dict.pop('meters')
#for key in ('viz', 'viz_dict'):
# if key in list(var_dict.keys()):
# var_dict.pop(key)
with open(json_file, 'w') as f:
json.dump(var_dict, f, cls=utils.NpEncoder)
def save_checkpoint(state, is_best, log_dir, filename='checkpoint.pth.tar'):
utils.check_dir(log_dir)
filename = os.path.join(log_dir, filename)
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, os.path.join(log_dir, 'model_best.pth.tar'))
fn = os.path.join(log_dir, 'checkpoint_epoch{}.pth.tar')
torch.save(state, fn.format(state['epoch']))
if (state['epoch'] - 1) % 5 != 0:
#remove intermediate saved models, e.g. non-modulo 5 ones
if os.path.exists(fn.format(state['epoch'] - 1)):
os.remove(fn.format(state['epoch'] - 1))
state['exp_logger'].to_json(log_dir=log_dir, filename='logger.json')
def init_output_env(_config, root_dir, log_dir, path_dataset):
utils.check_dir(os.path.join(root_dir, 'runs'))
utils.check_dir(log_dir)
utils.check_dir(path_dataset)
with open(os.path.join(log_dir, 'config.json'), 'w') as f:
json.dump(_config, f)
def init_output_env(_config, root_dir, log_dir, path_dataset):
utils.check_dir(os.path.join(root_dir, "runs"))
utils.check_dir(log_dir)
utils.check_dir(path_dataset)
with open(os.path.join(log_dir, "config.json"), "w") as f:
json.dump(_config, f)