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"""Get configurable parameters."""
# Copyright (C) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
# TODO: This would require a new design.
# TODO: https://jira.devtools.intel.com/browse/IAAALD-149
from pathlib import Path
from typing import List, Optional, Union
from warnings import warn
from omegaconf import DictConfig, ListConfig, OmegaConf
def update_input_size_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
"""Update config with image size as tuple, effective input size and tiling stride.
Convert integer image size parameters into tuples, calculate the effective input size based on image size
and crop size, and set tiling stride if undefined.
Args:
config (Union[DictConfig, ListConfig]): Configurable parameters object
Returns:
Union[DictConfig, ListConfig]: Configurable parameters with updated values
"""
# handle image size
if isinstance(config.dataset.image_size, int):
config.dataset.image_size = (config.dataset.image_size,) * 2
config.model.input_size = config.dataset.image_size
if "tiling" in config.dataset.keys() and config.dataset.tiling.apply:
if isinstance(config.dataset.tiling.tile_size, int):
config.dataset.tiling.tile_size = (config.dataset.tiling.tile_size,) * 2
if config.dataset.tiling.stride is None:
config.dataset.tiling.stride = config.dataset.tiling.tile_size
return config
def update_nncf_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
"""Set the NNCF input size based on the value of the crop_size parameter in the configurable parameters object.
Args:
config (Union[DictConfig, ListConfig]): Configurable parameters of the current run.
Returns:
Union[DictConfig, ListConfig]: Updated configurable parameters in DictConfig object.
"""
crop_size = config.dataset.image_size
sample_size = (crop_size, crop_size) if isinstance(crop_size, int) else crop_size
if "optimization" in config.keys():
if "nncf" in config.optimization.keys():
config.optimization.nncf.input_info.sample_size = [1, 3, *sample_size]
if config.optimization.nncf.apply:
if "update_config" in config.optimization.nncf:
return OmegaConf.merge(config, config.optimization.nncf.update_config)
return config
def update_multi_gpu_training_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
"""Updates the config to change learning rate based on number of gpus assigned.
Current behaviour is to ensure only ddp accelerator is used.
Args:
config (Union[DictConfig, ListConfig]): Configurable parameters for the current run
Raises:
ValueError: If unsupported accelerator is passed
Returns:
Union[DictConfig, ListConfig]: Updated config
"""
# validate accelerator
if config.trainer.accelerator is not None:
if config.trainer.accelerator.lower() != "ddp":
if config.trainer.accelerator.lower() in ("dp", "ddp_spawn", "ddp2"):
warn(
f"Using accelerator {config.trainer.accelerator.lower()} is discouraged. "
f"Please use one of [null, ddp]. Setting accelerator to ddp"
)
config.trainer.accelerator = "ddp"
else:
raise ValueError(
f"Unsupported accelerator found: {config.trainer.accelerator}. Should be one of [null, ddp]"
)
# Increase learning rate
# since pytorch averages the gradient over devices, the idea is to
# increase the learning rate by the number of devices
if "lr" in config.model:
# Number of GPUs can either be passed as gpus: 2 or gpus: [0,1]
n_gpus: Union[int, List] = 1
if "trainer" in config and "gpus" in config.trainer:
n_gpus = config.trainer.gpus
lr_scaler = n_gpus if isinstance(n_gpus, int) else len(n_gpus)
config.model.lr = config.model.lr * lr_scaler
return config
def get_configurable_parameters(
model_name: Optional[str] = None,
config_path: Optional[Union[Path, str]] = None,
weight_file: Optional[str] = None,
config_filename: Optional[str] = "config",
config_file_extension: Optional[str] = "yaml",
) -> Union[DictConfig, ListConfig]:
"""Get configurable parameters.
Args:
model_name: Optional[str]: (Default value = None)
config_path: Optional[Union[Path, str]]: (Default value = None)
weight_file: Path to the weight file
config_filename: Optional[str]: (Default value = "config")
config_file_extension: Optional[str]: (Default value = "yaml")
Returns:
Union[DictConfig, ListConfig]: Configurable parameters in DictConfig object.
"""
if model_name is None and config_path is None:
raise ValueError(
"Both model_name and model config path cannot be None! "
"Please provide a model name or path to a config file!"
)
if config_path is None:
config_path = Path(f"anomalib/models/{model_name}/{config_filename}.{config_file_extension}")
config = OmegaConf.load(config_path)
# Dataset Configs
if "format" not in config.dataset.keys():
config.dataset.format = "mvtec"
config = update_input_size_config(config)
# Project Configs
project_path = Path(config.project.path) / config.model.name / config.dataset.name
if config.dataset.format.lower() in ("btech", "mvtec"):
project_path = project_path / config.dataset.category
(project_path / "weights").mkdir(parents=True, exist_ok=True)
(project_path / "images").mkdir(parents=True, exist_ok=True)
config.project.path = str(project_path)
# loggers should write to results/model/dataset/category/ folder
config.trainer.default_root_dir = str(project_path)
if weight_file:
config.model.weight_file = weight_file
config = update_nncf_config(config)
# thresholding
if "pixel_default" not in config.model.threshold.keys():
config.model.threshold.pixel_default = config.model.threshold.image_default
return config
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