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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
import os
from util import util
import torch
class BaseOptions:
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
# experiment specifics
self.parser.add_argument(
"--name",
type=str,
default="label2city",
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(
"--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here"
) ## note: to add this param when using philly
# self.parser.add_argument('--project_dir', type=str, default='./', help='the project is saved here') ################### This is necessary for philly
self.parser.add_argument(
"--outputs_dir", type=str, default="./outputs", help="models are saved here"
) ## note: to add this param when using philly Please end with '/'
self.parser.add_argument("--model", type=str, default="pix2pixHD", help="which model to use")
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")
# input/output sizes
self.parser.add_argument("--batchSize", type=int, default=1, help="input batch size")
self.parser.add_argument("--loadSize", type=int, default=1024, 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("--label_nc", type=int, default=35, help="# of input label channels")
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")
# for setting inputs
self.parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/")
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=2, 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.",
)
# for displays
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",
)
# for generator
self.parser.add_argument("--netG", type=str, default="global", help="selects model to use for netG")
self.parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer")
self.parser.add_argument("--k_size", type=int, default=3, help="# kernel size conv layer")
self.parser.add_argument("--use_v2", action="store_true", help="use DCDCv2")
self.parser.add_argument("--mc", type=int, default=1024, help="# max channel")
self.parser.add_argument("--start_r", type=int, default=3, help="start layer to use resblock")
self.parser.add_argument(
"--n_downsample_global", type=int, default=4, help="number of downsampling layers in netG"
)
self.parser.add_argument(
"--n_blocks_global",
type=int,
default=9,
help="number of residual blocks in the global generator network",
)
self.parser.add_argument(
"--n_blocks_local",
type=int,
default=3,
help="number of residual blocks in the local enhancer network",
)
self.parser.add_argument(
"--n_local_enhancers", type=int, default=1, help="number of local enhancers to use"
)
self.parser.add_argument(
"--niter_fix_global",
type=int,
default=0,
help="number of epochs that we only train the outmost local enhancer",
)
self.parser.add_argument(
"--load_pretrain",
type=str,
default="",
help="load the pretrained model from the specified location",
)
# for instance-wise features
self.parser.add_argument(
"--no_instance", action="store_true", help="if specified, do *not* add instance map as input"
)
self.parser.add_argument(
"--instance_feat",
action="store_true",
help="if specified, add encoded instance features as input",
)
self.parser.add_argument(
"--label_feat", action="store_true", help="if specified, add encoded label features as input"
)
self.parser.add_argument("--feat_num", type=int, default=3, help="vector length for encoded features")
self.parser.add_argument(
"--load_features", action="store_true", help="if specified, load precomputed feature maps"
)
self.parser.add_argument(
"--n_downsample_E", type=int, default=4, help="# of downsampling layers in encoder"
)
self.parser.add_argument(
"--nef", type=int, default=16, help="# of encoder filters in the first conv layer"
)
self.parser.add_argument("--n_clusters", type=int, default=10, help="number of clusters for features")
# diy
self.parser.add_argument("--self_gen", action="store_true", help="self generate")
self.parser.add_argument(
"--mapping_n_block", type=int, default=3, help="number of resblock in mapping"
)
self.parser.add_argument("--map_mc", type=int, default=64, help="max channel of mapping")
self.parser.add_argument("--kl", type=float, default=0, help="KL Loss")
self.parser.add_argument(
"--load_pretrainA",
type=str,
default="",
help="load the pretrained model from the specified location",
)
self.parser.add_argument(
"--load_pretrainB",
type=str,
default="",
help="load the pretrained model from the specified location",
)
self.parser.add_argument("--feat_gan", action="store_true")
self.parser.add_argument("--no_cgan", action="store_true")
self.parser.add_argument("--map_unet", action="store_true")
self.parser.add_argument("--map_densenet", action="store_true")
self.parser.add_argument("--fcn", action="store_true")
self.parser.add_argument("--is_image", action="store_true", help="train image recon only pair data")
self.parser.add_argument("--label_unpair", action="store_true")
self.parser.add_argument("--mapping_unpair", action="store_true")
self.parser.add_argument("--unpair_w", type=float, default=1.0)
self.parser.add_argument("--pair_num", type=int, default=-1)
self.parser.add_argument("--Gan_w", type=float, default=1)
self.parser.add_argument("--feat_dim", type=int, default=-1)
self.parser.add_argument("--abalation_vae_len", type=int, default=-1)
######################### useless, just to cooperate with docker
self.parser.add_argument("--gpu", type=str)
self.parser.add_argument("--dataDir", type=str)
self.parser.add_argument("--modelDir", type=str)
self.parser.add_argument("--logDir", type=str)
self.parser.add_argument("--data_dir", type=str)
self.parser.add_argument("--use_skip_model", action="store_true")
self.parser.add_argument("--use_segmentation_model", action="store_true")
self.parser.add_argument("--spatio_size", type=int, default=64)
self.parser.add_argument("--test_random_crop", action="store_true")
##########################
self.parser.add_argument("--contain_scratch_L", action="store_true")
self.parser.add_argument(
"--mask_dilation", type=int, default=0
) ## Don't change the input, only dilation the mask
self.parser.add_argument(
"--irregular_mask", type=str, default="", help="This is the root of the mask"
)
self.parser.add_argument(
"--mapping_net_dilation",
type=int,
default=1,
help="This parameter is the dilation size of the translation net",
)
self.parser.add_argument(
"--VOC", type=str, default="VOC_RGB_JPEGImages.bigfile", help="The root of VOC dataset"
)
self.parser.add_argument("--non_local", type=str, default="", help="which non_local setting")
self.parser.add_argument(
"--NL_fusion_method",
type=str,
default="add",
help="how to fuse the origin feature and nl feature",
)
self.parser.add_argument(
"--NL_use_mask", action="store_true", help="If use mask while using Non-local mapping model"
)
self.parser.add_argument(
"--correlation_renormalize",
action="store_true",
help="Since after mask out the correlation matrix(which is softmaxed), the sum is not 1 any more, enable this param to re-weight",
)
self.parser.add_argument("--Smooth_L1", action="store_true", help="Use L1 Loss in image level")
self.parser.add_argument(
"--face_restore_setting", type=int, default=1, help="This is for the aligned face restoration"
)
self.parser.add_argument("--face_clean_url", type=str, default="")
self.parser.add_argument("--syn_input_url", type=str, default="")
self.parser.add_argument("--syn_gt_url", type=str, default="")
self.parser.add_argument(
"--test_on_synthetic",
action="store_true",
help="If you want to test on the synthetic data, enable this parameter",
)
self.parser.add_argument("--use_SN", action="store_true", help="Add SN to every parametric layer")
self.parser.add_argument(
"--use_two_stage_mapping", action="store_true", help="choose the model which uses two stage"
)
self.parser.add_argument("--L1_weight", type=float, default=10.0)
self.parser.add_argument("--softmax_temperature", type=float, default=1.0)
self.parser.add_argument(
"--patch_similarity",
action="store_true",
help="Enable this denotes using 3*3 patch to calculate similarity",
)
self.parser.add_argument(
"--use_self",
action="store_true",
help="Enable this denotes that while constructing the new feature maps, using original feature (diagonal == 1)",
)
self.parser.add_argument("--use_own_dataset", action="store_true")
self.parser.add_argument(
"--test_hole_two_folders",
action="store_true",
help="Enable this parameter means test the restoration with inpainting given twp folders which are mask and old respectively",
)
self.parser.add_argument(
"--no_hole",
action="store_true",
help="While test the full_model on non_scratch data, do not add random mask into the real old photos",
) ## Only for testing
self.parser.add_argument(
"--random_hole",
action="store_true",
help="While training the full model, 50% probability add hole",
)
self.parser.add_argument("--NL_res", action="store_true", help="NL+Resdual Block")
self.parser.add_argument("--image_L1", action="store_true", help="Image level loss: L1")
self.parser.add_argument(
"--hole_image_no_mask",
action="store_true",
help="while testing, give hole image but not give the mask",
)
self.parser.add_argument(
"--down_sample_degradation",
action="store_true",
help="down_sample the image only, corresponds to [down_sample_face]",
)
self.parser.add_argument(
"--norm_G", type=str, default="spectralinstance", help="The norm type of Generator"
)
self.parser.add_argument(
"--init_G",
type=str,
default="xavier",
help="normal|xavier|xavier_uniform|kaiming|orthogonal|none",
)
self.parser.add_argument("--use_new_G", action="store_true")
self.parser.add_argument("--use_new_D", action="store_true")
self.parser.add_argument(
"--only_voc", action="store_true", help="test the trianed celebA face model using VOC face"
)
self.parser.add_argument(
"--cosin_similarity",
action="store_true",
help="For non-local, using cosin to calculate the similarity",
)
self.parser.add_argument(
"--downsample_mode",
type=str,
default="nearest",
help="For partial non-local, choose how to downsample the mask",
)
self.parser.add_argument("--mapping_exp",type=int,default=0,help='Default 0: original PNL|1: Multi-Scale Patch Attention')
self.parser.add_argument("--inference_optimize",action='store_true',help='optimize the memory cost')
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 # train or test
str_ids = self.opt.gpu_ids.split(",")
self.opt.gpu_ids = []
for str_id in str_ids:
int_id = int(str_id)
if int_id >= 0:
self.opt.gpu_ids.append(int_id)
# set gpu ids
if len(self.opt.gpu_ids) > 0:
# pass
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 ----------------')
# save to the disk
expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
util.mkdirs(expr_dir)
if save and not self.opt.continue_train:
file_name = os.path.join(expr_dir, "opt.txt")
with open(file_name, "wt") as opt_file:
opt_file.write("------------ Options -------------\n")
for k, v in sorted(args.items()):
opt_file.write("%s: %s\n" % (str(k), str(v)))
opt_file.write("-------------- End ----------------\n")
return self.opt
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