def _make_batch_generator(self): # data load and construct batch generator self.logger.info("Creating dataset...") trainset_list = [] for i in range(len(self.cfg.trainset)): trainset_list.append(eval(self.cfg.trainset[i])("train")) trainset_loader = DatasetLoader(trainset_list, True, transforms.Compose([\ transforms.ToTensor(), transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)]\ )) batch_generator = DataLoader(dataset=trainset_loader, batch_size=self.cfg.num_gpus * self.cfg.batch_size, shuffle=True, num_workers=self.cfg.num_thread, pin_memory=True) self.joint_num = trainset_loader.joint_num[0] self.itr_per_epoch = math.ceil(trainset_loader.__len__() / cfg.num_gpus / cfg.batch_size) self.batch_generator = batch_generator
def _make_batch_generator(self): # data load and construct batch generator self.logger.info("Creating dataset...") testset = eval(self.cfg.testset)("test") testset_loader = DatasetLoader(testset, False, transforms.Compose([\ transforms.ToTensor(), transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)]\ )) batch_generator = DataLoader(dataset=testset_loader, batch_size=self.cfg.num_gpus * self.cfg.test_batch_size, shuffle=False, num_workers=self.cfg.num_thread, pin_memory=True) self.testset = testset self.joint_num = testset_loader.joint_num self.skeleton = testset_loader.skeleton self.flip_pairs = testset.flip_pairs self.tot_sample_num = testset_loader.__len__() self.batch_generator = batch_generator