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config.py
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config.py
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# encoding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path as osp
import sys
import time
import numpy as np
from easydict import EasyDict as edict
import argparse
import torch.utils.model_zoo as model_zoo
C = edict()
config = C
cfg = C
C.seed = 12345
"""please config ROOT_dir and user when u first using"""
C.repo_name = 'TapLab'
C.abs_dir = osp.realpath(".")
C.this_dir = C.abs_dir.split(osp.sep)[-1]
C.root_dir = C.abs_dir[:C.abs_dir.index(C.repo_name) + len(C.repo_name)]
C.log_dir = osp.abspath(osp.join(C.root_dir, 'log', C.this_dir))
C.log_dir_link = osp.join(C.abs_dir, 'log')
C.snapshot_dir = osp.abspath(osp.join(C.log_dir, "snapshot"))
exp_time = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())
C.log_file = C.log_dir + '/log_' + exp_time + '.log'
C.link_log_file = C.log_file + '/log_last.log'
C.val_log_file = C.log_dir + '/val_' + exp_time + '.log'
C.link_val_log_file = C.log_dir + '/val_last.log'
"""Data Dir and Weight Dir"""
C.dataset_path = "datasets/cityscapes/input/"
C.img_root_folder = C.dataset_path
C.gt_root_folder = C.dataset_path
C.train_source = osp.join(C.dataset_path, "../list/cityscapes_train_list.txt")
C.eval_source = osp.join(C.dataset_path, "../list/cityscapes_val_list.txt")
C.test_source = osp.join(C.dataset_path, "../list/cityscapes_val_list.txt")
C.is_test = False
"""Path Config"""
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
add_path(osp.join(C.root_dir, 'furnace'))
from utils.pyt_utils import model_urls
"""Image Config"""
C.num_classes = 19
C.background = -1
C.image_mean = np.array([0.485, 0.456, 0.406]) # 0.485, 0.456, 0.406
C.image_std = np.array([0.229, 0.224, 0.225])
C.target_size = 1024
C.image_height = 1024
C.image_width = 1024
C.num_train_imgs = 2975
C.num_eval_imgs = 500
""" Settings for network, this would be different for each kind of model"""
C.fix_bias = True
C.fix_bn = False
C.sync_bn = True
C.bn_eps = 1e-5
C.bn_momentum = 0.1
C.pretrained_model = "pretrained/resnet18_v1.pth"
"""Train Config"""
C.lr = 1e-2
C.lr_power = 0.9
C.momentum = 0.9
C.weight_decay = 5e-4
C.batch_size = 5 # 5 * C.num_gpu
C.nepochs = 100 * 5 # * 5(scales)
C.niters_per_epoch = 3000 // C.batch_size # 2975
C.num_workers = 8
C.train_scale_array = [0.75, 1, 1.25, 1.5, 1.75, 2.0]
#C.ngpus = 3
"""Eval Config"""
C.eval_iter = 30
C.eval_stride_rate = 5 / 6
C.eval_scale_array = [1, ] # multi scales: 0.5, 0.75, 1, 1.25, 1.5, 1.75
C.eval_flip = False # True if use the ms_flip strategy
C.eval_base_size = 1024
C.eval_crop_size = 1024
"""Display Config"""
C.snapshot_iter = 50
C.record_info_iter = 20
C.display_iter = 50
def open_tensorboard():
pass
if __name__ == '__main__':
print(config.epoch_num)
parser = argparse.ArgumentParser()
parser.add_argument(
'-tb', '--tensorboard', default=False, action='store_true')
args = parser.parse_args()
if args.tensorboard:
open_tensorboard()