def init_optimizer(optimizer: optim.Optimizer, configs):
if not configs.resume_optim:
return
else:
print("Resuming optimizer from %s" % configs.ockpt_file)
o = torch.load(configs.ockpt_file)['optim']
optimizer.load_state_dict(o)
def update_optimizer_params(optimizer: Optimizer, new_state) -> Optimizer:
optim_state = optimizer.state_dict()
if "params" in new_state["param_groups"][0].keys():
del new_state["param_groups"][0]["params"]
optim_state["param_groups"][0].update(new_state["param_groups"][0])
optimizer.load_state_dict(optim_state)
return optimizer
def load_optim(optimizer: Optimizer, checkpoint_path: str,
device: torch.device) -> Optimizer:
"""
Load optimizer to continuer training
Args:
optimizer : initialized optimizer
checkpoint_path: path to the checkpoint
device : device to send optimizer to (must be the same as in the model)
Note: must be called after initializing the model
Output: optimizer with the loaded state
"""
checkpoint = torch.load(checkpoint_path)
optimizer.load_state_dict(checkpoint["optimizer"])
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(device)
for param_group in optimizer.param_groups:
print("learning_rate: {}".format(param_group["lr"]))
print("Loaded optimizer {} state from {}".format(optimizer,
checkpoint_path))
return optimizer
def load(self, model: Module, optimizer: Optimizer) -> dict:
"""
Loads a PyTorch model from a TileDB array.
:param model: Pytorch Module. A defined PyTorch model.
:param optimizer: PyTorch Optimizer. A defined PyTorch optimizer.
:return: Dict. A dictionary with attributes other than model or optimizer
state_dict.
"""
model_array = tiledb.open(self.uri)
model_array_results = model_array[:]
schema = model_array.schema
model_state_dict = pickle.loads(
model_array_results["model_state_dict"].item(0))
optimizer_state_dict = pickle.loads(
model_array_results["optimizer_state_dict"].item(0))
# Load model's state and optimizer dictionaries
model.load_state_dict(model_state_dict)
optimizer.load_state_dict(optimizer_state_dict)
# Get the rest of the attributes
out_dict = {}
for idx in range(schema.nattr):
attr_name = schema.attr(idx).name
if (schema.attr(idx).name != "model_state_dict"
and schema.attr(idx).name != "optimizer_state_dict"):
out_dict[attr_name] = pickle.loads(
model_array_results[attr_name].item(0))
return out_dict
def Load(self, filename, model: nn.Module, optimizer: optim.Optimizer, scheduler: optim.lr_scheduler):
checkpoint = th.load(os.path.join(self.directory, filename))
model.load_state_dict(checkpoint["model_dict"])
optimizer.load_state_dict(checkpoint["optimizer_dict"])
scheduler.load_state_dict(checkpoint["scheduler_dict"])
return model, optimizer, scheduler
def load_state(net: Connect4Network,
optimizer: optim.Optimizer,
scheduler,
args: AlphaZeroArgs,
iteration: np.int,
new_optim_state=True) -> np.int:
""" Loads saved model and optimizer states if exists
@param net: Neural Network object
@param optimizer: pytorch optimizer
@param scheduler: pytorch scheduler
@param args: AlphaZeroArgs
@param iteration: current iteration
@param new_optim_state:
@return:
"""
checkpoint_path = util.get_model_file_path(args.neural_net_name, iteration)
start_epoch, checkpoint = 0, None
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
if checkpoint is not None:
if len(checkpoint) == 1 or new_optim_state:
net.load_state_dict(checkpoint['state_dict'])
print("Loaded checkpoint model {checkpoint_path}")
else:
start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
print(
f"Loaded checkpoint model {checkpoint_path}, and optimizer, scheduler."
)
return start_epoch
def load_checkpoint(checkpoint_path: str, model: nn.Module, optimizer: optim.Optimizer=None):
if not os.path.exists(checkpoint_path):
raise ValueError('missing checkpoint file {}'.format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint.pop('state_dict'))
if optimizer is not None:
optimizer.load_state_dict(checkpoint.pop('optimizer'))
return checkpoint
def load_checkpoint(filename: str, model: nn.Module,
optimizer: optim.Optimizer):
if os.path.isfile(filename):
checkpoint = torch.load(filename)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
return start_epoch, model, optim
def load_optimizer(optimizer: Optimizer, path: str):
"""
Load optimizer state for resuming training
:param optimizer:
:param path:
"""
optimizer.load_state_dict(torch.load(path))
print("Optimizer state loaded.")
def load(self, path_to_checkpoint: str, optimizer: Optimizer = None, scheduler: _LRScheduler = None) -> 'Model':
checkpoint = torch.load(path_to_checkpoint)
self.load_state_dict(checkpoint['state_dict'])
step = checkpoint['step']
if optimizer is not None:
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if scheduler is not None:
scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
return step
def resume(weights, optimizer: optim.Optimizer, ema: ModelEMA, device,
results):
start_epoch = 0
pretrained = weights.endswith(".pth")
if pretrained:
checkpoint = torch.load(weights, map_location=device)
if (ckpt_optim := checkpoint.get("optimizer", None)) is not None:
optimizer.load_state_dict(ckpt_optim)
if ema and (ckpt_ema := checkpoint.get("ema", False)):
ema.ema.load_state_dict(ckpt_ema.state_dict())
ema.updates = ckpt_ema["updates"]
def resume_checkpoint(config: Config,
model: Module,
optimizer: Optimizer = None) -> int:
"""
resume training process data from config.logs which generated by make_checkpoint()
:return number of last epoch
"""
last_epoch = -1
temp_weight_path = config.temp_weight_path
temp_optim_path = config.temp_optim_path
if os.path.exists(config.train_record_file):
try:
with open(config.train_record_file, 'r') as f:
last = f.readlines()[-1]
import json
info = json.loads(last)
last_epoch = int(info["epoch"])
last_init = str(info["init"])
if not os.path.exists(temp_weight_path):
temp_weight_path = temp_weight_path.replace(
config.init_time, last_init)
if not os.path.exists(temp_optim_path):
temp_optim_path = temp_optim_path.replace(
config.init_time, last_init)
print("Continue train from last epoch %d" % last_epoch)
except:
warn("Rename invalid train record file from {} to {}".format(
config.train_record_file,
config.train_record_file + '.badfile'))
warn("Can't get last_epoch value, {} will be returned".format(
last_epoch))
os.rename(config.train_record_file,
config.train_record_file + '.badfile')
if os.path.exists(temp_weight_path):
try:
model.load_state_dict(load(temp_weight_path))
print("Resumed weight checkpoint from {}".format(temp_weight_path))
except:
warn("Move invalid temp {} weights file from {} to {}".format(
type(model), temp_weight_path, temp_weight_path + '.badfile'))
os.rename(temp_weight_path, temp_weight_path + '.badfile')
if optimizer is not None and os.path.exists(temp_optim_path):
try:
optimizer.load_state_dict(load(temp_optim_path))
print(
"Resumed optimizer checkpoint from {}".format(temp_optim_path))
except:
warn("Move invalid temp {} weights file from {} to {}".format(
type(optimizer), temp_optim_path,
temp_optim_path + '.badfile'))
os.rename(temp_optim_path, temp_optim_path + '.badfile')
return last_epoch
def load_checkpoint(model: nn.Module, optimizer: optim.Optimizer,
filepath: str) -> Tuple[int, float]:
"""Loads model and optimizer state from the provided .model file."""
if not os.path.exists(filepath):
raise ValueError(f'Filepath: {filepath} does not exist!')
logging.info(f'Loading checkpoint: {filepath}...')
checkpoint = torch.load(filepath)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
return (checkpoint['epoch'], checkpoint['loss'])
def resume_training(checkpoint_path, model, model_optim: optim.Optimizer,
proxy_optim: optim.Optimizer):
try:
checkpoint = torch.load(checkpoint_path)
model.module.load_state_dict(checkpoint['model'])
model_optim.load_state_dict(checkpoint['model_optim'])
proxy_optim.load_state_dict(checkpoint['proxy_optim'])
except BaseException as err:
print("? Failed to load models. Quitting...")
print(err)
quit()
return model, model_optim, proxy_optim, checkpoint['epoch']
def restore_snapshot(model: nn.Module, optimizer: Optimizer,
snapshot_file: str):
checkpoint = torch.load(snapshot_file)
start_epoch = checkpoint['epoch'] + 1
best_loss = checkpoint['loss']
model.load_state_dict(checkpoint['model'])
if optimizer is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
train_history = pd.DataFrame.from_dict(checkpoint['train_history'])
return start_epoch, train_history, best_loss
def load_state(model: nn.Module, optimizer: Optimizer, path: pathlib.Path,
callbacks: List[BaseCallback]):
""" Loads model, optimizer and epoch states from path """
checkpoint = torch.load(
str(path), map_location={'cuda:0': f'cuda:{get_local_rank()}'})
if isinstance(model, DistributedDataParallel):
model.module.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
for callback in callbacks:
callback.on_checkpoint_load(checkpoint)
logging.info(f'Loaded checkpoint from {str(path)}')
return checkpoint['epoch']
def _load_checkpoint(model: nn.Module, optimizer: optim.Optimizer,
config: Config):
best_statistic = 0
start = datetime.now()
if os.path.isfile(ct.L2R_TRAIN_PROGRESS.format(config.name)):
with open(ct.L2R_MODEL.format(config.name), 'rb') as file:
checkpoint = torch.load(file, map_location=ct.DEVICE)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
model.epochs_trained = checkpoint['epoch']
best_statistic = checkpoint['best_statistic']
helpers.log(
f'Loaded checkpoint from {ct.L2R_MODEL.format(config.name)} in {datetime.now() - start}.'
)
return best_statistic
def load_checkpoint(self,
model: nn.Module,
optimizer: optim.Optimizer = None,
file_name: str = 'pytorch_model.pt'):
filepath = os.path.join(self.directory, file_name)
checkpoint = th.load(filepath)
model.load_state_dict(checkpoint['model_dict'])
if optimizer is not None:
optimizer.load_state_dict(checkpoint['optimizer_dict'])
hyperparam_dict = {
k: v
for k, v in checkpoint.items()
if k != 'model_dict' or k != 'optimizer_dict'
}
return model, optimizer, hyperparam_dict
def load_latest_model_parameters(
model: torch.nn.Module,
*,
optimiser: Optimizer = None,
model_name: str,
model_directory: Path,
) -> Tuple[Union[torch.nn.Module, Tuple[torch.nn.Module, Optimizer]], bool]:
"""
inplace but returns model
:param optimiser:
:param model:
:type model:
:param model_directory:
:param model_name:
:return:"""
if model:
model_path = model_directory / model_name
list_of_files = list(model_path.glob(f"*{parameter_extension}"))
if len(list_of_files) == 0:
print(
f"Found no previous models with extension {parameter_extension} in {model_path}"
)
else:
latest_model_parameter_file = max(list_of_files,
key=os.path.getctime)
print(
f"loading previous model parameters: {latest_model_parameter_file}"
)
model.load_state_dict(torch.load(str(latest_model_parameter_file)))
if optimiser:
opt_st_d_file = latest_model_parameter_file.with_suffix(
optimiser_extension)
if opt_st_d_file.exists():
optimiser.load_state_dict(torch.load(str(opt_st_d_file)))
print(f"loading previous optimiser state: {opt_st_d_file}")
return (model, optimiser), True
else:
return model, True
if optimiser:
return (model, optimiser), False
return model, False
def load_training_model(model: nn.Module,
optimizer: optim.Optimizer,
model_name="model",
path_state=PATH_STATE,
device=None):
if device is None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
state_file = model_name + STATE_EXT
state_file = os.path.join(path_state, state_file)
checkpoint = torch.load(state_file)
model.load_state_dict(checkpoint[DEFAULT_STATE_DICT_MODEL])
optimizer.load_state_dict(checkpoint[DEFAULT_STATE_DICT_OPT])
epoch = checkpoint['epoch']
model.to(device)
optimizer.zero_grad()
model.train()
return model, optimizer, epoch
pass
def resume(
resume_path: str, model: nn.Module, optimizer: optim.Optimizer
) -> Tuple[int, nn.Module, optim.Optimizer, float]:
assert os.path.exists(resume_path), "there is no checkpoint at the result folder"
print("loading checkpoint {}".format(resume_path))
checkpoint = torch.load(resume_path, map_location=lambda storage, loc: storage)
begin_epoch = checkpoint["epoch"]
best_loss = checkpoint["best_loss"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
print("training will start from {} epoch".format(begin_epoch))
return begin_epoch, model, optimizer, best_loss
def load_state_dict(self, state_dict):
r"""Loads the optimizer state.
Args:
state_dict (dict): SWA optimizer state. Should be an object returned
from a call to `state_dict`.
"""
swa_state_dict = {
"state": state_dict["swa_state"],
"param_groups": state_dict["param_groups"],
}
opt_state_dict = {
"state": state_dict["opt_state"],
"param_groups": state_dict["param_groups"],
}
PT_Optimizer.load_state_dict(self, swa_state_dict)
self.optimizer.load_state_dict(opt_state_dict)
self.opt_state = self.optimizer.state
def _restore(
mdl: nn.Module, optimizer: optim.Optimizer,
scheduler: optim.lr_scheduler._LRScheduler, ckpt_loc: str
) -> t.Tuple[nn.Module, optim.Optimizer, optim.lr_scheduler._LRScheduler, int,
float]:
"""Restore model training state
Args:
mdl (nn.Module):
The randomly initialized model
optimizer (optim.Optimizer):
The optimizer
scheduler (optim.lr_scheduler._LRScheduler):
The scheduler for learning rate
ckpt_loc (str):
Location to store model checkpoints
Returns:
t.Tuple[nn.Module,
optim.Optimizer,
optim.lr_scheduler._LRScheduler,
int, float]:
The restored status
"""
# Restore model checkpoint
mdl.load_state_dict(torch.load(os.path.join(ckpt_loc, 'mdl.ckpt')))
optimizer.load_state_dict(
torch.load(os.path.join(ckpt_loc, 'optimizer.ckpt')))
scheduler.load_state_dict(
torch.load(os.path.join(ckpt_loc, 'scheduler.ckpt')))
# Restore timer and step counter
with open(os.path.join(ckpt_loc, 'log.out')) as f:
records = f.readlines()
if records[-1] != 'Training finished\n':
final_record = records[-1]
else:
final_record = records[-2]
global_counter, t_final = final_record.split('\t')[:2]
global_counter = int(global_counter)
t_final = float(t_final)
t0 = time.time() - t_final * 60
return mdl, optimizer, scheduler, global_counter, t0
def resume(
result_path: str,
model: nn.Module,
optimizer: optim.Optimizer,
) -> Tuple[Any]:
resume_path = os.path.join(result_path, "checkpoint.pth")
print("loading checkpoint {}".format(resume_path))
checkpoint = torch.load(resume_path, map_location=lambda storage, loc: storage)
begin_epoch = checkpoint["epoch"]
best_loss = checkpoint["best_loss"]
model.load_state_dict(checkpoint["state_dict"])
# confirm whether the optimizer matches that of checkpoints
optimizer.load_state_dict(checkpoint["optimizer"])
return begin_epoch, model, optimizer, best_loss
def load_checkpoint_reconstructions(
self,
model: nn.Module,
optimizer: optim.Optimizer = None,
file_name: str = "pytorch_model.pt",
):
filepath = os.path.join(self.directory, file_name)
checkpoint = th.load(filepath)
model.load_state_dict(checkpoint["model_dict"])
if optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer_dict"])
hyperparam_dict = {
k: v
for k, v in checkpoint.items()
if k != "model_dict" or k != "optimizer_dict"
}
return model, optimizer, hyperparam_dict
def restore(self, module: nn.Module, optimizer: optim.Optimizer) -> Union[Tuple[None, None], Tuple[int, float]]:
# getting files in the checkpoints folder
files = self._get_files_in_dir()
# if there are no files, return False
if not files:
return None, None
# getting the latest checkpoint file
checkpoint_file = files[0]
# loading the data
data = torch.load(checkpoint_file)
# loading data to module and optimizer
module.load_state_dict(data['state_dict'])
optimizer.load_state_dict(data['optimizer'])
# returning frame and epsilon
return data['frame'], data['epsilon']
def restore_snapshot(model: torch.nn.Module, optimizer: Optimizer, snapshot_file: str, multi_gpu=False):
checkpoint = torch.load(snapshot_file)
start_epoch = checkpoint['epoch'] + 1
best_loss = checkpoint['loss']
try:
model.load_state_dict(checkpoint['model'])
except RuntimeError:
model.load_state_dict(checkpoint['model'], strict=False)
print('Loaded model with strict=False mode')
try:
if optimizer is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
except:
print('Optimizer state not loaded')
train_history = pd.DataFrame.from_dict(checkpoint['train_history'])
return start_epoch, train_history, best_loss
def _load_checkpoint(self, model: nn.Module, optimizer: optim.Optimizer,
state):
# from .. import __version__
checkpoint = torch.load(
os.path.join(self.save_directory, self.filename))
# checks
if checkpoint['check_model_class'] != str(model.__class__):
raise TypeError("Models do not match: %s and %s" %
(checkpoint['check_model_class'], model.__class__))
if checkpoint['check_optimizer_class'] != str(optimizer.__class__):
raise TypeError(
"Optimizers do not match: %s and %s" %
(checkpoint['check_optimizer_class'], optimizer.__class__))
# checkpoint['check_trainer_version'] # TODO
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
state.set(checkpoint['trainer_state'])
def load( # type: ignore
self,
*,
timestamp: Optional[Timestamp] = None,
model: torch.nn.Module,
optimizer: Optimizer,
) -> Optional[Mapping[str, Any]]:
"""
Load a PyTorch model from a TileDB array.
:param timestamp: Range of timestamps to load fragments of the array which live
in the specified time range.
:param model: A defined PyTorch model.
:param optimizer: A defined PyTorch optimizer.
:return: A dictionary with attributes other than model or optimizer state_dict.
"""
# TODO: Change timestamp when issue in core is resolved
model_array = tiledb.open(self.uri, ctx=self.ctx, timestamp=timestamp)
model_array_results = model_array[:]
schema = model_array.schema
model_state_dict = pickle.loads(
model_array_results["model_state_dict"].item(0))
optimizer_state_dict = pickle.loads(
model_array_results["optimizer_state_dict"].item(0))
# Load model's state and optimizer dictionaries
model.load_state_dict(model_state_dict)
optimizer.load_state_dict(optimizer_state_dict)
# Get the rest of the attributes
out_dict = {}
for idx in range(schema.nattr):
attr_name = schema.attr(idx).name
if (schema.attr(idx).name != "model_state_dict"
and schema.attr(idx).name != "optimizer_state_dict"):
out_dict[attr_name] = pickle.loads(
model_array_results[attr_name].item(0))
return out_dict
def load_checkpoint(
self,
model_G: nn.Module,
model_D: nn.Module,
optimizer_G: optim.Optimizer = None,
optimizer_R: optim.Optimizer = None,
optimizer_D: optim.Optimizer = None,
file_name: str = "pytorch_model.pt",
):
filepath = os.path.join(self.directory, file_name)
checkpoint = th.load(filepath)
model_G.load_state_dict(checkpoint["model_G_dict"])
model_D.load_state_dict(checkpoint["model_D_dict"])
if optimizer_G is not None:
optimizer_G.load_state_dict(checkpoint["optimizer_G_dict"])
optimizer_R.load_state_dict(checkpoint["optimizer_R_dict"])
optimizer_D.load_state_dict(checkpoint["optimizer_D_dict"])
hyperparam_dict = {
k: v
for k, v in checkpoint.items()
if k != "model_G_dict" or k != "model_D_dict"
or k != "optimizer_G_dict" or k != "optimizer_D_dict"
}
return model_G, model_D, optimizer_G, optimizer_R, optimizer_D, hyperparam_dict
