from visdom import Visdom
viz = Visdom()
assert viz.check_connection()
viz.close()
opt = TrainOptions().parse()
save_opt(opt)
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
total_steps = 0
sparse_c_loss_points, sparse_c_loss_avr_points = [], []
win_sparse_C = viz.line(X=torch.zeros((1, )),
Y=torch.zeros((1, )),
name="win_sparse_C")
for epoch in range(1, opt.epoch + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
from models.cycle_gan_model import CycleGANModel
from utilSet.visualizer import Visualizer
from config import TestOptions
from data.dataset import DataLoader
opt = TestOptions().parse()
opt.nThreads = 1
opt.batchSize = 1
opt.serial_batches = True
opt.no_flip = True
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
root_dir = os.path.join(opt.result_root_dir, opt.variable)
web_dir = os.path.join(root_dir, opt.variable_value, opt.phase)
webpage = html.HTML(web_dir,
'Experiment =GAN2C, Phase = test, Epoch = latest')
for i, data in enumerate(dataset):
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
print('process image... %s' % img_path)
from utilSet.visualizer import Visualizer
from config import TestOptions
from data.dataset import DataLoader
import ntpath
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
root_dir = os.path.join(opt.result_root_dir, opt.variable)
web_dir = os.path.join(root_dir, opt.variable_value, opt.phase)
webpage = html.HTML(web_dir,
'Experiment = GAN2C, Phase = test, Epoch = latest')
# test
for i, data in enumerate(dataset):
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
# -*- coding:utf-8 -*-
import time
from config import TrainOptions
from models.cycle_gan_model import CycleGANModel
from utilSet.visualizer import Visualizer, save_opt
from data.dataset import DataLoader
opt = TrainOptions().parse()
save_opt(opt)
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
total_steps = 0
for epoch in range(1, opt.epoch + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
visualizer.reset()
total_steps += 1
epoch_iter += 1
model.set_input(data)
model.optimize_parameters()