|
import argparse |
|
import torch |
|
|
|
class BaseOptions(): |
|
def __init__(self): |
|
self.parser = argparse.ArgumentParser() |
|
self.initialized = False |
|
|
|
def initialize(self): |
|
self.parser.add_argument('--name', type=str, default='demo', help='name of the experiment. It decides where to store samples and models') |
|
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') |
|
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') |
|
self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator') |
|
self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit") |
|
self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose') |
|
|
|
self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size') |
|
self.parser.add_argument('--loadSize', type=int, default=512, help='scale images to this size') |
|
self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') |
|
self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') |
|
self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') |
|
|
|
self.parser.add_argument('--dataroot', type=str, |
|
default='/home/sh0089/sen/fashion/') |
|
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]') |
|
self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') |
|
self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') |
|
self.parser.add_argument('--nThreads', default=1, type=int, help='# threads for loading data') |
|
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.') |
|
|
|
self.parser.add_argument('--display_winsize', type=int, default=512, help='display window size') |
|
self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed') |
|
|
|
self.initialized = True |
|
|
|
def parse(self, save=True): |
|
if not self.initialized: |
|
self.initialize() |
|
self.opt = self.parser.parse_args() |
|
self.opt.isTrain = self.isTrain |
|
|
|
str_ids = self.opt.gpu_ids.split(',') |
|
self.opt.gpu_ids = [] |
|
for str_id in str_ids: |
|
id = int(str_id) |
|
if id >= 0: |
|
self.opt.gpu_ids.append(id) |
|
|
|
if len(self.opt.gpu_ids) > 0: |
|
torch.cuda.set_device(self.opt.gpu_ids[0]) |
|
|
|
args = vars(self.opt) |
|
|
|
print('------------ Options -------------') |
|
for k, v in sorted(args.items()): |
|
print('%s: %s' % (str(k), str(v))) |
|
print('-------------- End ----------------') |
|
|
|
return self.opt |
|
|
