opt_gan.skip = 1
opt_gan.use_norm = 1
opt_gan.use_wgan = 0
opt_gan.self_attention = True
opt_gan.times_residual = True
opt_gan.instance_norm = 0
opt_gan.resize_or_crop = "no"
opt_gan.which_epoch = "200"
opt_gan.nThreads = 1 # test code only supports nThreads = 1
opt_gan.batchSize = 1 # test code only supports batchSize = 1
opt_gan.serial_batches = True # no shuffle
opt_gan.no_flip = True # no flip
data_loader = CreateDataLoader(opt_gan)
dataset = data_loader.load_data()
gan = create_model(opt_gan)
# gan.eval()
visualizer = Visualizer(opt_gan)
# create website
web_dir = os.path.join("./ablation/", opt_gan.name,
'%s_%s' % (opt_gan.phase, opt_gan.which_epoch))
webpage = html.HTML(
web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' %
(opt_gan.name, opt_gan.phase, opt_gan.which_epoch))
######################################################
##### BDD Seg. #################################################
import bdd.encoding.utils as utils_seg
from bdd.encoding.parallel import DataParallelModel
from bdd.encoding.models import get_segmentation_model
def get_config(config):
import yaml
with open(config, 'r') as stream:
return yaml.load(stream)
opt = TrainOptions().parse()
config = get_config(opt.config)
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
model = create_model(opt)
visualizer = Visualizer(opt)
total_steps = 0
##### BDD Seg. #################################################
import bdd.encoding.utils as utils_seg
from bdd.encoding.nn import SegmentationLosses, BatchNorm2d
from bdd.encoding.nn import SegmentationMultiLosses
from utils.focal_loss import FocalLoss
from bdd.encoding.parallel import DataParallelModel, DataParallelCriterion
from bdd.encoding.models import get_segmentation_model
from bdd.experiments.segmentation.option import Options
opt_seg = Options() #.parse()
opt_seg.dataset = "cityscapes"