295 строки
11 KiB
Python
295 строки
11 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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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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import models
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import data
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import pickle
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class BaseOptions:
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def __init__(self):
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self.initialized = False
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def initialize(self, parser):
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# experiment specifics
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parser.add_argument(
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"--name",
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type=str,
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default="label2coco",
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help="name of the experiment. It decides where to store samples and models",
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)
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parser.add_argument(
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"--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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)
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parser.add_argument(
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"--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here"
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)
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parser.add_argument("--model", type=str, default="pix2pix", help="which model to use")
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parser.add_argument(
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"--norm_G",
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type=str,
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default="spectralinstance",
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help="instance normalization or batch normalization",
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)
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parser.add_argument(
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"--norm_D",
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type=str,
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default="spectralinstance",
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help="instance normalization or batch normalization",
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)
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parser.add_argument(
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"--norm_E",
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type=str,
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default="spectralinstance",
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help="instance normalization or batch normalization",
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)
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parser.add_argument("--phase", type=str, default="train", help="train, val, test, etc")
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# input/output sizes
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parser.add_argument("--batchSize", type=int, default=1, help="input batch size")
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parser.add_argument(
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"--preprocess_mode",
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type=str,
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default="scale_width_and_crop",
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help="scaling and cropping of images at load time.",
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choices=(
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"resize_and_crop",
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"crop",
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"scale_width",
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"scale_width_and_crop",
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"scale_shortside",
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"scale_shortside_and_crop",
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"fixed",
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"none",
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"resize",
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),
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)
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parser.add_argument(
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"--load_size",
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type=int,
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default=1024,
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help="Scale images to this size. The final image will be cropped to --crop_size.",
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)
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parser.add_argument(
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"--crop_size",
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type=int,
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default=512,
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help="Crop to the width of crop_size (after initially scaling the images to load_size.)",
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)
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parser.add_argument(
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"--aspect_ratio",
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type=float,
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default=1.0,
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help="The ratio width/height. The final height of the load image will be crop_size/aspect_ratio",
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)
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parser.add_argument(
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"--label_nc",
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type=int,
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default=182,
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help="# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.",
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)
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parser.add_argument(
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"--contain_dontcare_label",
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action="store_true",
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help="if the label map contains dontcare label (dontcare=255)",
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)
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parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels")
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# for setting inputs
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parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/")
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parser.add_argument("--dataset_mode", type=str, default="coco")
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parser.add_argument(
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"--serial_batches",
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action="store_true",
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help="if true, takes images in order to make batches, otherwise takes them randomly",
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)
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parser.add_argument(
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"--no_flip",
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action="store_true",
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help="if specified, do not flip the images for data argumentation",
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)
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parser.add_argument("--nThreads", default=0, type=int, help="# threads for loading data")
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parser.add_argument(
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"--max_dataset_size",
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type=int,
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default=sys.maxsize,
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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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)
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parser.add_argument(
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"--load_from_opt_file",
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action="store_true",
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help="load the options from checkpoints and use that as default",
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)
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parser.add_argument(
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"--cache_filelist_write",
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action="store_true",
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help="saves the current filelist into a text file, so that it loads faster",
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)
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parser.add_argument(
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"--cache_filelist_read", action="store_true", help="reads from the file list cache"
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)
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# for displays
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parser.add_argument("--display_winsize", type=int, default=400, help="display window size")
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# for generator
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parser.add_argument(
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"--netG", type=str, default="spade", help="selects model to use for netG (pix2pixhd | spade)"
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)
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parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer")
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parser.add_argument(
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"--init_type",
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type=str,
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default="xavier",
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help="network initialization [normal|xavier|kaiming|orthogonal]",
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)
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parser.add_argument(
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"--init_variance", type=float, default=0.02, help="variance of the initialization distribution"
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)
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parser.add_argument("--z_dim", type=int, default=256, help="dimension of the latent z vector")
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parser.add_argument(
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"--no_parsing_map", action="store_true", help="During training, we do not use the parsing map"
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)
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# for instance-wise features
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parser.add_argument(
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"--no_instance", action="store_true", help="if specified, do *not* add instance map as input"
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)
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parser.add_argument(
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"--nef", type=int, default=16, help="# of encoder filters in the first conv layer"
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)
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parser.add_argument("--use_vae", action="store_true", help="enable training with an image encoder.")
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parser.add_argument(
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"--tensorboard_log", action="store_true", help="use tensorboard to record the resutls"
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)
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# parser.add_argument('--img_dir',)
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parser.add_argument(
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"--old_face_folder", type=str, default="", help="The folder name of input old face"
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)
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parser.add_argument(
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"--old_face_label_folder", type=str, default="", help="The folder name of input old face label"
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)
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parser.add_argument("--injection_layer", type=str, default="all", help="")
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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 parser with basic options
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if not self.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, unknown = 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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# modify dataset-related parser options
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# dataset_mode = opt.dataset_mode
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# dataset_option_setter = data.get_option_setter(dataset_mode)
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# parser = dataset_option_setter(parser, self.isTrain)
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opt, unknown = parser.parse_known_args()
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# if there is opt_file, load it.
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# The previous default options will be overwritten
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if opt.load_from_opt_file:
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parser = self.update_options_from_file(parser, opt)
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opt = parser.parse_args()
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self.parser = parser
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return opt
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def print_options(self, opt):
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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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def option_file_path(self, opt, makedir=False):
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expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
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if makedir:
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util.mkdirs(expr_dir)
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file_name = os.path.join(expr_dir, "opt")
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return file_name
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def save_options(self, opt):
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file_name = self.option_file_path(opt, makedir=True)
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with open(file_name + ".txt", "wt") as opt_file:
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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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opt_file.write("{:>25}: {:<30}{}\n".format(str(k), str(v), comment))
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with open(file_name + ".pkl", "wb") as opt_file:
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pickle.dump(opt, opt_file)
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def update_options_from_file(self, parser, opt):
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new_opt = self.load_options(opt)
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for k, v in sorted(vars(opt).items()):
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if hasattr(new_opt, k) and v != getattr(new_opt, k):
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new_val = getattr(new_opt, k)
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parser.set_defaults(**{k: new_val})
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return parser
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def load_options(self, opt):
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file_name = self.option_file_path(opt, makedir=False)
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new_opt = pickle.load(open(file_name + ".pkl", "rb"))
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return new_opt
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def parse(self, save=False):
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opt = self.gather_options()
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opt.isTrain = self.isTrain # train or test
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opt.contain_dontcare_label = False
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self.print_options(opt)
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if opt.isTrain:
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self.save_options(opt)
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# Set semantic_nc based on the option.
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# This will be convenient in many places
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opt.semantic_nc = (
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opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1)
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)
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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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int_id = int(str_id)
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if int_id >= 0:
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opt.gpu_ids.append(int_id)
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if len(opt.gpu_ids) > 0:
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print("The main GPU is ")
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print(opt.gpu_ids[0])
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torch.cuda.set_device(opt.gpu_ids[0])
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assert (
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len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0
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), "Batch size %d is wrong. It must be a multiple of # GPUs %d." % (opt.batchSize, len(opt.gpu_ids))
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self.opt = opt
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return self.opt
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