def loadModels(self, ): try: generator_name = util.fcnt_load(self.sheckpoints_dir, "pix2pix_generator", "pth") print(generator_name) self.generator = torch.load( generator_name, map_location=lambda storage, loc: storage.cuda( convertDevice(self.gpu_ids[0]))) self.generator.setSetting(self.setting) except: print("Checkpoint directory or files could not be found." + "New directory {} will be created.".format( self.sheckpoints_dir)) import traceback traceback.print_exc() return False if self.setting['data']['base']['isTrain']: try: discriminator_name = util.fcnt_load(self.sheckpoints_dir, "pix2pix_discriminator", "pth") print(discriminator_name) self.discriminator = torch.load(discriminator_name).to( self.gpu_ids[0]) self.discriminator.setSetting(self.setting) except: print("Checkpoint directory or files could not be found." + "New directory {} will be created.".format( self.sheckpoints_dir)) import traceback traceback.print_exc() return False return True
def loadModels(self, ): import aimaker.models.model_factory as mf model_factory = mf.ModelFactory(self.setting) generator_name = self.setting['controllers']['pix2pix'][ 'generatorModel'] discriminator_name = self.setting['controllers']['pix2pix'][ 'discriminatorModel'] try: generator_path = util.fcnt_load(self.sheckpoints_dir, "pix2pix_generator", "pth") self.generator = self._loadModel(generator_path, self.gpu_ids[0]) except: import traceback traceback.print_exc() self.generator = model_factory.create(generator_name).to( self.gpu_ids[0]) try: discriminator_path = util.fcnt_load(self.sheckpoints_dir, "pix2pix_discriminator", "pth") self.discriminator = self._loadModel(discriminator_path, self.gpu_ids[0]) except: import traceback traceback.print_exc() self.discriminator = model_factory.create(discriminator_name).to( self.gpu_ids[0]) return True
def loadModels(self,): try: self.generator = torch.load(util.fcnt_load(self.checkpoints_dir, "starGAN_generator", "pth")).cuda(self.gpu_ids[0]) self.discriminator = torch.load(util.fcnt_load(self.checkpoints_dir, "starGAN_discriminator", "pth")).cuda(self.gpu_ids[0]) self.generator.setConfig(self.config) self.discriminator.setConfig(self.config) return True except: print("Checkpoint directory or files could not be found."+ "New directory {} will be created.".format(self.checkpoints_dir)) return False
def loadModels(self, ): try: generator_model = util.fcnt_load(self.checkpoints_dir, "voxel_flow_generator", "pth") discriminator_model = util.fcnt_load(self.checkpoints_dir, "voxel_flow_discriminator", "pth") self.generator = torch.load(generator_model).cuda(self.gpu_ids[0]) self.discriminator = torch.load(discriminator_model).cuda( self.gpu_ids[0]) self.generator.setConfig(self.config) self.discriminator.setConfig(self.config) except: print("Checkpoint directory or files could not be found." + "New directory {} will be created.".format( self.checkpoints_dir))