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