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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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class BaseOptions(): |
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def __init__(self): |
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self.parser = argparse.ArgumentParser() |
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self.initialized = False |
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def initialize(self): |
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self.parser.add_argument('--name', type=str, default='people', help='name of the experiment. It decides where to store samples and models') |
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self.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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self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') |
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self.parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization') |
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self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator') |
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self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit") |
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self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose') |
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self.parser.add_argument('--fp16', action='store_true', default=False, help='train with AMP') |
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self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training') |
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self.parser.add_argument('--isTrain', type=bool, default=True, help='local rank for distributed training') |
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self.parser.add_argument('--batchSize', type=int, default=8, help='input batch size') |
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self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size') |
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self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') |
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self.parser.add_argument('--label_nc', type=int, default=0, help='# of input label channels') |
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self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') |
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self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') |
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self.parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/') |
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self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]') |
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self.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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self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') |
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self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data') |
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self.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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self.parser.add_argument('--display_winsize', type=int, default=512, help='display window size') |
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self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed') |
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self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG') |
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self.parser.add_argument('--latent_size', type=int, default=512, help='latent size of Adain layer') |
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self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') |
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self.parser.add_argument('--n_downsample_global', type=int, default=3, help='number of downsampling layers in netG') |
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self.parser.add_argument('--n_blocks_global', type=int, default=6, help='number of residual blocks in the global generator network') |
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self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network') |
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self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use') |
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self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer') |
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self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input') |
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self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input') |
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self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input') |
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self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features') |
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self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps') |
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self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') |
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self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer') |
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self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features') |
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self.parser.add_argument('--image_size', type=int, default=224, help='number of clusters for features') |
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self.parser.add_argument('--norm_G', type=str, default='spectralspadesyncbatch3x3', help='instance normalization or batch normalization') |
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self.parser.add_argument('--semantic_nc', type=int, default=3, help='number of clusters for features') |
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self.initialized = True |
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def parse(self, save=True): |
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if not self.initialized: |
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self.initialize() |
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self.opt = self.parser.parse_args() |
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self.opt.isTrain = self.isTrain |
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str_ids = self.opt.gpu_ids.split(',') |
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self.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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self.opt.gpu_ids.append(id) |
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if len(self.opt.gpu_ids) > 0: |
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torch.cuda.set_device(self.opt.gpu_ids[0]) |
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args = vars(self.opt) |
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print('------------ Options -------------') |
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for k, v in sorted(args.items()): |
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print('%s: %s' % (str(k), str(v))) |
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print('-------------- End ----------------') |
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if self.opt.isTrain: |
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expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) |
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util.mkdirs(expr_dir) |
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if save and not self.opt.continue_train: |
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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('------------ Options -------------\n') |
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for k, v in sorted(args.items()): |
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opt_file.write('%s: %s\n' % (str(k), str(v))) |
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opt_file.write('-------------- End ----------------\n') |
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return self.opt |
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