会在c++打印出来,但当前接口无法返回Author dancingpipi commented Aug 14, 2020 会在c++打印出来,但当前接口无法返回 难受,所以想获得,只能用pyreader或者dataloader接口吗?Member zhhsplendid commented Aug 16, 2020 嗯,当前是的zhhsplendid closed this as completed Aug 16, 2020 Sign up for free to join...
DataLoader(train, batch_size=batch_size, shuffle=False, sampler=train_sampler,num_workers=2) # validation data loader (per patient) val = LiverDataSet(directory=val_folder, context=context) val_data_list = [] patients = val.getPatients() for key in patients.keys(): samples = patients[key...
data_path ='data/modelnet40_normal_resampled/'train_dataset = ModelNetDataLoader(root=data_path, args=args, split='train')#Dataset负责整理数据test_dataset = ModelNetDataLoader(root=data_path, args=args, split='test')#num_workers决定了有几个进程来处理data loadingtrainDataLoader = torch.utils.d...
from torch.utils.data import TensorDataset import torch from torch.utils.data import DataLoader a = torch.tensor([[11, 22, 33], [44, 55, 66], [77, 88, 99], [11, 22, 33], [44, 55, 66], [77, 88, 99], [11, 22, 33], [44, 55, 66], [77, 88, 99], [11, 22, 33...
(1)Ids(numpy格式):15行:对数据集中每个数据都做jieba分词,返回Ids,len,label(convert_example函数) (2)batch:21行:数据集打乱分批次管理 (3)数据对齐并多线程处理:26行:,在每个数据Ids,len,label三者按pad,stack,stack结构组成(匿名函数),使用paddle.io.DataLoader接口多线程异步加载数据。 In [8] from func...
但是,在语法上,返回一个 tuple 可以省略括号,而多个变量可以同时接收一个 tuple,按位置赋给对应的值,所以,Python的函数返回多值其实就是返回一个 tuple,但写起来更方便。...:x和n,这两个参数都是位置参数,调用函数时,传入的两个值按照位置顺序依次赋给参数x和n。......
train_set, train_loader, train_sampler = build_dataloader( dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, @@ -125,47 +149,52 @@ def main(): logger=logger, training=True, merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch, total_epochs=args.epochs total_ep...
train_cfg=dict(by_epoch=True,max_epochs=220)runner=Runner(model,work_dir='runs/gan/',train_dataloader=train_dataloader,train_cfg=train_cfg,optim_wrapper=opt_wrapper_dict)runner.train() 到这里,我们就完成了一个 GAN 的训练,通过下面的代码可以查看刚才训练的 GAN 生成的结果。
dataloader = DataLoader(dataset, batch_size=ops.batch_size, num_workers=ops.num_workers, shuffle=True, pin_memory=False, drop_last = True) # 优化器设计 optimizer_SGD = optim.SGD(model_.parameters(), lr=ops.init_lr, momentum=ops.momentum, weight_decay=ops.weight_decay)# 优化器初始化 opt...
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