148 lines
8.7 KiB
Python
148 lines
8.7 KiB
Python
import argparse
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import os
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from util import util
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import torch
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import models
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import data
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class BaseOptions():
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"""This class defines options used during both training and test time.
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It also implements several helper functions such as parsing, printing, and saving the options.
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It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
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"""
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def __init__(self):
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"""Reset the class; indicates the class hasn't been initailized"""
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self.initialized = False
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def initialize(self, parser):
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"""Define the common options that are used in both training and test."""
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# basic parameters
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parser.add_argument('--dataroot', type=str, default='./LEVIR-CD', help='path to images (should have subfolders A, B, label)')
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parser.add_argument('--val_dataroot', type=str, default='./LEVIR-CD', help='path to images in the val phase (should have subfolders A, B, label)')
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parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
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parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
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parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
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# model parameters
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parser.add_argument('--model', type=str, default='CDF0', help='chooses which model to use. [CDF0 | CDFA]')
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parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB ')
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parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB')
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parser.add_argument('--arch', type=str, default='mynet3', help='feature extractor architecture | mynet3')
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parser.add_argument('--f_c', type=int, default=64, help='feature extractor channel num')
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parser.add_argument('--n_class', type=int, default=2, help='# of output pred channels: 2 for num of classes')
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parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
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parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
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parser.add_argument('--SA_mode', type=str, default='BAM', help='choose self attention mode for change detection, | ori |1 | 2 |pyramid, ...')
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# dataset parameters
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parser.add_argument('--dataset_mode', type=str, default='changedetection', help='chooses how datasets are loaded. [changedetection | concat | list | json]')
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parser.add_argument('--val_dataset_mode', type=str, default='changedetection', help='chooses how datasets are loaded. [changedetection | concat| list | json]')
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parser.add_argument('--dataset_type', type=str, default='CD_LEVIR', help='chooses which datasets too load. [LEVIR | WHU ]')
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parser.add_argument('--val_dataset_type', type=str, default='CD_LEVIR', help='chooses which datasets too load. [LEVIR | WHU ]')
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parser.add_argument('--split', type=str, default='train', help='chooses wihch list-file to open when use listDataset. [train | val | test]')
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parser.add_argument('--val_split', type=str, default='val', help='chooses wihch list-file to open when use listDataset. [train | val | test]')
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parser.add_argument('--json_name', type=str, default='train_val_test', help='input the json name which contain the file names of images of different phase')
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parser.add_argument('--val_json_name', type=str, default='train_val_test', help='input the json name which contain the file names of images of different phase')
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parser.add_argument('--ds', type=int, default='1', help='self attention module downsample rate')
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parser.add_argument('--angle', type=int, default=0, help='rotate angle')
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parser.add_argument('--istest', type=bool, default=False, help='True for the case without label')
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parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
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parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
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parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
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parser.add_argument('--load_size', type=int, default=286, help='scale images to this size')
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parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
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parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
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parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | none]')
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parser.add_argument('--no_flip', type=bool, default=True, help='if specified, do not flip(left-right) the images for data augmentation')
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parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
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# additional parameters
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parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
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parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
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parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
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parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
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self.initialized = True
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return parser
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def gather_options(self):
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"""Initialize our parser with basic options(only once).
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Add additional model-specific and dataset-specific options.
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These options are defined in the <modify_commandline_options> function
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in model and dataset classes.
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"""
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if not self.initialized: # check if it has been initialized
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser = self.initialize(parser)
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# get the basic options
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opt, _ = parser.parse_known_args()
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# modify model-related parser options
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model_name = opt.model
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model_option_setter = models.get_option_setter(model_name)
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parser = model_option_setter(parser, self.isTrain)
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opt, _ = parser.parse_known_args() # parse again with new defaults
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# modify dataset-related parser options
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dataset_name = opt.dataset_mode
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if dataset_name != 'concat':
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dataset_option_setter = data.get_option_setter(dataset_name)
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parser = dataset_option_setter(parser, self.isTrain)
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# save and return the parser
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self.parser = parser
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return parser.parse_args()
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def print_options(self, opt):
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"""Print and save options
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It will print both current options and default values(if different).
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It will save options into a text file / [checkpoints_dir] / opt.txt
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"""
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message = ''
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message += '----------------- Options ---------------\n'
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for k, v in sorted(vars(opt).items()):
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comment = ''
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default = self.parser.get_default(k)
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if v != default:
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comment = '\t[default: %s]' % str(default)
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message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
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message += '----------------- End -------------------'
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print(message)
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# save to the disk
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expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
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util.mkdirs(expr_dir)
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file_name = os.path.join(expr_dir, 'opt.txt')
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with open(file_name, 'wt') as opt_file:
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opt_file.write(message)
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opt_file.write('\n')
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def parse(self):
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"""Parse our options, create checkpoints directory suffix, and set up gpu device."""
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opt = self.gather_options()
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opt.isTrain = self.isTrain # train or test
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# process opt.suffix
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if opt.suffix:
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suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
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opt.name = opt.name + suffix
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self.print_options(opt)
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# set gpu ids
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str_ids = opt.gpu_ids.split(',')
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opt.gpu_ids = []
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for str_id in str_ids:
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id = int(str_id)
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if id >= 0:
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opt.gpu_ids.append(id)
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if len(opt.gpu_ids) > 0:
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torch.cuda.set_device(opt.gpu_ids[0])
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self.opt = opt
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return self.opt
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