| |
| |
| """ |
| Created on Tue Jul 23 14:35:48 2019 |
| |
| @author: aditya |
| """ |
|
|
| r"""This module provides package-wide configuration management.""" |
| from typing import Any, List |
|
|
| from yacs.config import CfgNode as CN |
|
|
|
|
| class Config(object): |
| r""" |
| A collection of all the required configuration parameters. This class is a nested dict-like |
| structure, with nested keys accessible as attributes. It contains sensible default values for |
| all the parameters, which may be overriden by (first) through a YAML file and (second) through |
| a list of attributes and values. |
| |
| Extended Summary |
| ---------------- |
| This class definition contains default values corresponding to ``joint_training`` phase, as it |
| is the final training phase and uses almost all the configuration parameters. Modification of |
| any parameter after instantiating this class is not possible, so you must override required |
| parameter values in either through ``config_yaml`` file or ``config_override`` list. |
| |
| Parameters |
| ---------- |
| config_yaml: str |
| Path to a YAML file containing configuration parameters to override. |
| config_override: List[Any], optional (default= []) |
| A list of sequential attributes and values of parameters to override. This happens after |
| overriding from YAML file. |
| |
| Examples |
| -------- |
| Let a YAML file named "config.yaml" specify these parameters to override:: |
| |
| ALPHA: 1000.0 |
| BETA: 0.5 |
| |
| >>> _C = Config("config.yaml", ["OPTIM.BATCH_SIZE", 2048, "BETA", 0.7]) |
| >>> _C.ALPHA # default: 100.0 |
| 1000.0 |
| >>> _C.BATCH_SIZE # default: 256 |
| 2048 |
| >>> _C.BETA # default: 0.1 |
| 0.7 |
| |
| Attributes |
| ---------- |
| """ |
|
|
| def __init__(self, config_yaml: str, config_override: List[Any] = []): |
|
|
| self._C = CN() |
| self._C.GPU = [0] |
| self._C.VERBOSE = False |
|
|
| self._C.MODEL = CN() |
| self._C.MODEL.MODE = 'global' |
| self._C.MODEL.SESSION = 'ps128_bs1' |
|
|
| self._C.OPTIM = CN() |
| self._C.OPTIM.BATCH_SIZE = 1 |
| self._C.OPTIM.NUM_EPOCHS = 100 |
| self._C.OPTIM.NEPOCH_DECAY = [100] |
| self._C.OPTIM.LR_INITIAL = 0.0002 |
| self._C.OPTIM.LR_MIN = 0.0002 |
| self._C.OPTIM.BETA1 = 0.5 |
|
|
| self._C.TRAINING = CN() |
| self._C.TRAINING.VAL_AFTER_EVERY = 3 |
| self._C.TRAINING.RESUME = False |
| self._C.TRAINING.SAVE_IMAGES = False |
| self._C.TRAINING.TRAIN_DIR = 'images_dir/train' |
| self._C.TRAINING.VAL_DIR = 'images_dir/val' |
| self._C.TRAINING.SAVE_DIR = 'checkpoints' |
| self._C.TRAINING.TRAIN_PS = 64 |
| self._C.TRAINING.VAL_PS = 64 |
|
|
| self._C.RCD = CN() |
| self._C.RCD.stage = 17 |
| self._C.RCD.num_M = 32 |
| self._C.RCD.num_Z = 32 |
| self._C.RCD.lr = 1e-3 |
| self._C.RCD.optimizer = 'ADAM' |
| self._C.RCD.epsilon = 1e-8 |
| self._C.RCD.weight_decay = 0 |
| self._C.RCD.beta1 = 0.9 |
| self._C.RCD.beta2 = 0.999 |
| self._C.RCD.dir = './pretrained_models' |
| self._C.RCD.scale = 2 |
| self._C.RCD.cpu = 'store_true' |
| self._C.RCD.n_GPUs = 1 |
| self._C.RCD.chop= 'store_true' |
| self._C.RCD.self_ensemble = 'store_true' |
| self._C.RCD.precision = 'single' |
| self._C.RCD.save_models = 'store_true' |
| self._C.RCD.model = 'mask' |
| self._C.RCD.pre_train = './pretrained_models' |
| self._C.RCD.resume = 0 |
|
|
| |
| self._C.merge_from_file(config_yaml) |
| self._C.merge_from_list(config_override) |
|
|
| |
| self._C.freeze() |
|
|
| def dump(self, file_path: str): |
| r"""Save config at the specified file path. |
| |
| Parameters |
| ---------- |
| file_path: str |
| (YAML) path to save config at. |
| """ |
| self._C.dump(stream=open(file_path, "w")) |
|
|
| def __getattr__(self, attr: str): |
| return self._C.__getattr__(attr) |
|
|
| def __repr__(self): |
| return self._C.__repr__() |
|
|
