|
|
"""Get configurable parameters.""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import logging |
|
|
from collections.abc import Sequence |
|
|
from pathlib import Path |
|
|
from typing import Any, cast |
|
|
|
|
|
from jsonargparse import Namespace |
|
|
from jsonargparse import Path as JSONArgparsePath |
|
|
from omegaconf import DictConfig, ListConfig, OmegaConf |
|
|
|
|
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
|
|
|
def _convert_nested_path_to_str(config: Any) -> Any: |
|
|
"""Goes over the dictionary and converts all path values to str.""" |
|
|
if isinstance(config, dict): |
|
|
for key, value in config.items(): |
|
|
config[key] = _convert_nested_path_to_str(value) |
|
|
elif isinstance(config, list): |
|
|
for i, item in enumerate(config): |
|
|
config[i] = _convert_nested_path_to_str(item) |
|
|
elif isinstance(config, Path | JSONArgparsePath): |
|
|
config = str(config) |
|
|
return config |
|
|
|
|
|
|
|
|
def to_yaml(config: Namespace | ListConfig | DictConfig) -> str: |
|
|
"""Convert the config to a yaml string. |
|
|
|
|
|
Args: |
|
|
config (Namespace | ListConfig | DictConfig): Config |
|
|
|
|
|
Returns: |
|
|
str: YAML string |
|
|
""" |
|
|
_config = config.clone() if isinstance(config, Namespace) else config.copy() |
|
|
if isinstance(_config, Namespace): |
|
|
_config = _config.as_dict() |
|
|
_config = _convert_nested_path_to_str(_config) |
|
|
return OmegaConf.to_yaml(_config) |
|
|
|
|
|
|
|
|
def to_tuple(input_size: int | ListConfig) -> tuple[int, int]: |
|
|
"""Convert int or list to a tuple. |
|
|
|
|
|
Args: |
|
|
input_size (int | ListConfig): input_size |
|
|
|
|
|
Example: |
|
|
>>> to_tuple(256) |
|
|
(256, 256) |
|
|
>>> to_tuple([256, 256]) |
|
|
(256, 256) |
|
|
|
|
|
Raises: |
|
|
ValueError: Unsupported value type. |
|
|
|
|
|
Returns: |
|
|
tuple[int, int]: Tuple of input_size |
|
|
""" |
|
|
ret_val: tuple[int, int] |
|
|
if isinstance(input_size, int): |
|
|
ret_val = cast(tuple[int, int], (input_size,) * 2) |
|
|
elif isinstance(input_size, ListConfig | Sequence): |
|
|
if len(input_size) != 2: |
|
|
msg = "Expected a single integer or tuple of length 2 for width and height." |
|
|
raise ValueError(msg) |
|
|
|
|
|
ret_val = cast(tuple[int, int], tuple(input_size)) |
|
|
else: |
|
|
msg = f"Expected either int or ListConfig, got {type(input_size)}" |
|
|
raise TypeError(msg) |
|
|
return ret_val |
|
|
|
|
|
|
|
|
def update_config(config: DictConfig | ListConfig | Namespace) -> DictConfig | ListConfig | Namespace: |
|
|
"""Update config. |
|
|
|
|
|
Args: |
|
|
config: Configurable parameters. |
|
|
|
|
|
Returns: |
|
|
DictConfig | ListConfig | Namespace: Updated config. |
|
|
""" |
|
|
_show_warnings(config) |
|
|
|
|
|
return _update_nncf_config(config) |
|
|
|
|
|
|
|
|
def _update_nncf_config(config: DictConfig | ListConfig) -> DictConfig | ListConfig: |
|
|
"""Set the NNCF input size based on the value of the crop_size parameter in the configurable parameters object. |
|
|
|
|
|
Args: |
|
|
config (DictConfig | ListConfig): Configurable parameters of the current run. |
|
|
|
|
|
Returns: |
|
|
DictConfig | ListConfig: Updated configurable parameters in DictConfig object. |
|
|
""" |
|
|
if "optimization" in config and "nncf" in config.optimization: |
|
|
if "input_info" not in config.optimization.nncf: |
|
|
config.optimization.nncf["input_info"] = {"sample_size": None} |
|
|
config.optimization.nncf.input_info.sample_size = [1, 3, 10, 10] |
|
|
if config.optimization.nncf.apply and "update_config" in config.optimization.nncf: |
|
|
return OmegaConf.merge(config, config.optimization.nncf.update_config) |
|
|
return config |
|
|
|
|
|
|
|
|
def _show_warnings(config: DictConfig | ListConfig | Namespace) -> None: |
|
|
"""Show warnings if any based on the configuration settings. |
|
|
|
|
|
Args: |
|
|
config (DictConfig | ListConfig | Namespace): Configurable parameters for the current run. |
|
|
""" |
|
|
if "clip_length_in_frames" in config.data and config.data.init_args.clip_length_in_frames > 1: |
|
|
logger.warning( |
|
|
"Anomalib's models and visualizer are currently not compatible with video datasets with a clip length > 1. " |
|
|
"Custom changes to these modules will be needed to prevent errors and/or unpredictable behaviour.", |
|
|
) |
|
|
if ( |
|
|
"devices" in config.trainer |
|
|
and (config.trainer.devices is None or config.trainer.devices != 1) |
|
|
and config.trainer.accelerator != "cpu" |
|
|
): |
|
|
logger.warning("Anomalib currently does not support multi-gpu training. Setting devices to 1.") |
|
|
config.trainer.devices = 1 |
|
|
|
