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4336727 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
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
# Override parameter values from YAML file first, then from override list.
self._C.merge_from_file(config_yaml)
self._C.merge_from_list(config_override)
# Make an instantiated object of this class immutable.
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__()
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