def max_ckpt_in_folder(self, path, name_key='ckpt_'):
files = gfile.listdir(str(path))
files = [x for x in files if name_key in x]
if len(files) == 0:
return 0
ckpt_vs = []
for name in files:
name = name.split(name_key)[-1]
name = re.sub('[^0-9]', '', name)
ckpt_vs.append(int(name))
return max(ckpt_vs)
def restore_hpc_weights_if_needed(self, model: LightningModule):
"""If there is a set of hpc weights, use as signal to restore model."""
did_restore = False
# look for hpc weights
folderpath = str(self.weights_save_path)
if gfile.exists(folderpath):
files = gfile.listdir(folderpath)
hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]
# if hpc weights exist restore model
if len(hpc_weight_paths) > 0:
self.hpc_load(folderpath, self.on_gpu)
did_restore = True
return did_restore
def _get_next_version(self):
root_dir = os.path.join(self.save_dir, self.name)
if not gfile.isdir(root_dir):
log.warning('Missing logger folder: %s', root_dir)
return 0
existing_versions = []
for d in gfile.listdir(root_dir):
if gfile.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
existing_versions.append(int(d.split("_")[1]))
if len(existing_versions) == 0:
return 0
return max(existing_versions) + 1
def __init__(self,
filepath: Optional[str] = None,
monitor: str = 'val_loss',
verbose: bool = False,
save_last: bool = False,
save_top_k: int = 1,
save_weights_only: bool = False,
mode: str = 'auto',
period: int = 1,
prefix: str = ''):
super().__init__()
if (filepath):
filepath = str(
filepath
) # the tests pass in a py.path.local but we want a str
if save_top_k > 0 and filepath is not None and gfile.isdir(
filepath) and len(gfile.listdir(filepath)) > 0:
rank_zero_warn(
f"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0."
"All files in this directory will be deleted when a checkpoint is saved!"
)
self._rank = 0
self.monitor = monitor
self.verbose = verbose
if filepath is None: # will be determined by trainer at runtime
self.dirpath, self.filename = None, None
else:
if gfile.isdir(filepath):
self.dirpath, self.filename = filepath, '{epoch}'
else:
if not is_remote_path(filepath): # dont normalize remote paths
filepath = os.path.realpath(filepath)
self.dirpath, self.filename = os.path.split(filepath)
makedirs(self.dirpath) # calls with exist_ok
self.save_last = save_last
self.save_top_k = save_top_k
self.save_weights_only = save_weights_only
self.period = period
self.epoch_last_check = None
self.prefix = prefix
self.best_k_models = {}
# {filename: monitor}
self.kth_best_model_path = ''
self.best_model_score = 0
self.best_model_path = ''
self.save_function = None
self.warned_result_obj = False
torch_inf = torch.tensor(np.Inf)
mode_dict = {
'min': (torch_inf, 'min'),
'max': (-torch_inf, 'max'),
'auto': (-torch_inf, 'max') if 'acc' in self.monitor
or self.monitor.startswith('fmeasure') else (torch_inf, 'min'),
}
if mode not in mode_dict:
rank_zero_warn(
f'ModelCheckpoint mode {mode} is unknown, '
f'fallback to auto mode.', RuntimeWarning)
mode = 'auto'
self.kth_value, self.mode = mode_dict[mode]