def __init__(self, opt):
self.opt = opt
self.create_dataset()
self.n_samples = len(self.dataset)
self.end2end_model = ProgressiveSegEnd2EndModel(opt, self.n_samples)
#end2end model
if len(opt.gpu_ids) > 0:
self.end2end_model = DataParallelWithCallback(
self.end2end_model, device_ids=opt.gpu_ids)
self.end2end_model_on_one_gpu = self.end2end_model.module
else:
self.end2end_model_on_one_gpu = self.end2end_model
self.progressive_model = self.end2end_model_on_one_gpu.progressive_model
self.pix2pix_model = self.end2end_model_on_one_gpu.pix2pix_model
self.pix2pix_model.optimizer_G, self.pix2pix_model.optimizer_D = \
self.pix2pix_model.create_optimizers(opt)
self.optimizer_D2 = self.end2end_model_on_one_gpu.create_optimizers()
self.phase = "stabilize"
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
self.inception_model = InceptionV3([block_idx])
self.inception_model.cuda()
def __init__(self,opt):
self.dataset = self.create_dataset(opt)
n_samples = len(self.dataset)
self.progressive_model = ProgressiveModel(opt, n_samples)
self.opt = opt
if len(opt.gpu_ids) > 0:
self.progressive_model = DataParallelWithCallback(self.progressive_model,
device_ids=opt.gpu_ids)
self.progressive_model_on_one_gpu = self.progressive_model.module
else:
self.progressive_model_on_one_gpu = self.progressive_model
def __init__(self, opt):
self.opt = opt
self.pix2pix_model = Pix2PixModel(opt)
if len(opt.gpu_ids) > 0:
self.pix2pix_model = DataParallelWithCallback(
self.pix2pix_model, device_ids=opt.gpu_ids)
self.pix2pix_model_on_one_gpu = self.pix2pix_model.module
else:
self.pix2pix_model_on_one_gpu = self.pix2pix_model
self.generated = None
if opt.isTrain:
self.optimizer_G, self.optimizer_D = \
self.pix2pix_model_on_one_gpu.create_optimizers(opt)
self.old_lr = opt.lr