test model

[cqray1990]

when i run test.py the result as follows:

it didn't work to erase the watermark

[cqray1990]

[cqray1990]

and i use pix2pix model to test,

----------------- Options ---------------
add_contrast: False
aspect_ratio: 1.0
batch_size: 1
checkpoints_dir: ./pytorch-CycleGAN-and-pix2pix/models_data [default: ./models_data]
crop_size: 512 [default: 256]
dataroot: ./data_temp/test_example_20_02_24_09_58_24/chusai_data_for_watermark_remove [default: None]
dataset_mode: aligned
direction: AtoB
display_winsize: 256
epoch: latest
eval: False
gpu_ids: 0
init_gain: 0.02
init_type: normal
input_nc: 1
isTrain: False [default: None]
load_iter: 0 [default: 0]
load_size: 512 [default: 256]
max_dataset_size: inf
model: pix2pix [default: test]
n_layers_D: 3
name: chusai_watermask_remover_model [default: experiment_name]
ndf: 64
netD: basic
netG: unet_256
ngf: 64
no_dropout: False
no_flip: False
norm: batch
ntest: inf
num_test: 3 [default: 50]
num_threads: 4
output_nc: 1
phase: test
preprocess: resize_and_crop
results_dir: ./data_temp/test_example_20_02_24_09_58_24/gan_result_chu_dir [default: ./results/]
serial_batches: False
suffix:
verbose: False
----------------- End -------------------
@Mingtzge

[Mingtzge]

the image fake_B is the result of erasing the watermark

[cqray1990]

the image fake_B is the result of erasing the watermark

but real_A doesnot have watermark,

[cqray1990]

but real_A doesnot have watermark,
@Mingtzge

[Mingtzge]

Hi, can you provide the input parameters when running "test.py"?

[cqray1990]

@Mingtzge
def initialize(self, parser):
"""Define the common options that are used in both training and test."""

basic parameters

parser.add_argument('--dataroot', default="/2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement-master/data_temp/test_example_20_01_20_12_16_08/data_cut_twist", help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--checkpoints_dir', type=str, default='./models_data', help='models are saved here')

model parameters

parser.add_argument('--model', type=str, default='pix2pix', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
parser.add_argument('--input_nc', type=int, default=1, help='# of input image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--output_nc', type=int, default=1, help='# of output image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')

dataset parameters

parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
parser.add_argument('--add_contrast', action='store_true', help='if true, constrast and brightness change dynamical')
parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
parser.add_argument('--load_size', type=int, default=256, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
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.')
parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')

additional parameters

parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
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]')
parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
self.initialized = True
return parser

class TestOptions(BaseOptions):
"""This class includes test options.

It also includes shared options defined in BaseOptions.
"""

def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
# Dropout and Batchnorm has different behavioir during training and test.
parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')
# rewrite devalue values
parser.set_defaults(model='pix2pix')
# To avoid cropping, the load_size should be the same as crop_size
parser.set_defaults(load_size=parser.get_default('crop_size'))
self.isTrain = False
return parser