def _read_latest_config_files(self, run_path_pairs):
"""Reads and returns the projector config files in every run
directory."""
"""If no specific config exists, use the default config provided in
the root directory."""
default_config_fpath = os.path.join(self.logdir,
metadata.PROJECTOR_FILENAME)
default_config = ProjectorConfig()
if tf.io.gfile.exists(default_config_fpath):
with tf.io.gfile.GFile(default_config_fpath, "r") as f:
file_content = f.read()
text_format.Merge(file_content, default_config)
# Relative metadata paths do not work with subdirs, so convert
# any metadata paths to absolute paths.
for embedding in default_config.embeddings:
embedding.metadata_path = _rel_to_abs_asset_path(
embedding.metadata_path, default_config_fpath)
configs = {}
config_fpaths = {}
for run_name, assets_dir in run_path_pairs:
config = ProjectorConfig()
config_fpath = os.path.join(assets_dir,
metadata.PROJECTOR_FILENAME)
if tf.io.gfile.exists(config_fpath):
with tf.io.gfile.GFile(config_fpath, "r") as f:
file_content = f.read()
text_format.Merge(file_content, config)
elif tf.io.gfile.exists(default_config_fpath):
config = default_config
has_tensor_files = False
for embedding in config.embeddings:
if embedding.tensor_path:
if not embedding.tensor_name:
embedding.tensor_name = os.path.basename(
embedding.tensor_path)
has_tensor_files = True
break
if not config.model_checkpoint_path:
# See if you can find a checkpoint file in the logdir.
logdir = _assets_dir_to_logdir(assets_dir)
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path and not has_tensor_files:
continue
if ckpt_path:
config.model_checkpoint_path = ckpt_path
# Sanity check for the checkpoint file existing.
if (config.model_checkpoint_path and _using_tf() and
not tf.io.gfile.glob(config.model_checkpoint_path + "*")):
logger.warn(
'Checkpoint file "%s" not found',
config.model_checkpoint_path,
)
continue
configs[run_name] = config
config_fpaths[run_name] = config_fpath
return configs, config_fpaths
def _read_latest_config_files(self, run_path_pairs):
"""Reads and returns the projector config files in every run
directory."""
configs = {}
config_fpaths = {}
for run_name, assets_dir in run_path_pairs:
config = ProjectorConfig()
config_fpath = os.path.join(assets_dir, metadata.PROJECTOR_FILENAME)
if tf.io.gfile.exists(config_fpath):
with tf.io.gfile.GFile(config_fpath, "r") as f:
file_content = f.read()
text_format.Merge(file_content, config)
has_tensor_files = False
for embedding in config.embeddings:
if embedding.tensor_path:
if not embedding.tensor_name:
embedding.tensor_name = os.path.basename(
embedding.tensor_path
)
has_tensor_files = True
break
if not config.model_checkpoint_path:
# See if you can find a checkpoint file in the logdir.
logdir = _assets_dir_to_logdir(assets_dir)
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path and not has_tensor_files:
continue
if ckpt_path:
config.model_checkpoint_path = ckpt_path
# Sanity check for the checkpoint file existing.
if (
config.model_checkpoint_path
and _using_tf()
and not tf.io.gfile.glob(config.model_checkpoint_path + "*")
):
logger.warn(
'Checkpoint file "%s" not found',
config.model_checkpoint_path,
)
continue
configs[run_name] = config
config_fpaths[run_name] = config_fpath
return configs, config_fpaths
def _read_latest_config_files(self, run_path_pairs):
"""Reads and returns the projector config files in every run directory."""
configs = {}
config_fpaths = {}
for run_name, assets_dir in run_path_pairs:
config = ProjectorConfig()
config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
if tf.gfile.Exists(config_fpath):
with tf.gfile.GFile(config_fpath, 'r') as f:
file_content = f.read()
text_format.Merge(file_content, config)
has_tensor_files = False
for embedding in config.embeddings:
if embedding.tensor_path:
if not embedding.tensor_name:
embedding.tensor_name = os.path.basename(embedding.tensor_path)
has_tensor_files = True
break
if not config.model_checkpoint_path:
# See if you can find a checkpoint file in the logdir.
logdir = _assets_dir_to_logdir(assets_dir)
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path and not has_tensor_files:
continue
if ckpt_path:
config.model_checkpoint_path = ckpt_path
# Sanity check for the checkpoint file.
if (config.model_checkpoint_path and
not tf.train.checkpoint_exists(config.model_checkpoint_path)):
tf.logging.warning('Checkpoint file "%s" not found',
config.model_checkpoint_path)
continue
configs[run_name] = config
config_fpaths[run_name] = config_fpath
return configs, config_fpaths