def train_loop(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集数据总量 for number, (x, y) in enumerate(dataloader): # number迭代次数,每次迭代输入batch=64的张量(64,1,28,28) # 计算预测和误差 pred = model(x) loss = loss_fn(pred, y) # 反向传播 optim...
在batcher之前就是item sampler, 之后就是batchsampler 需要注意的是 其实batch processing和item processing一样 (可以用torchdata.datapipes.iter.Mapper构建preprocess). 故此dataloader1之后的流程便不再讨论 3. Worker, timeout: 都涉及到worker, 见dataloader2的解析 4.Dropout 主要是DDP的东西, 也在dataloader2 ...
数据处理工具箱概述 Pytorch涉及数据处理(数据装载,数据预处理,数据增强等)主要工具包及相互关系如图所示: 图的左边是torch.utils.data工具包,主要包括以下4个类: Dataset:是一个抽象类,其他数据集要继承这个类,并且重写其中两个方法(getitem, len)。 DataLoader:定义一个新的迭代器,实现批量(batch)读取,打乱数据(...
DataLoader(dataset2, **test_kwargs) my_auto_wrap_policy = functools.partial( size_based_auto_wrap_policy, min_num_params=100 ) torch.cuda.set_device(rank) init_start_event = torch.cuda.Event(enable_timing=True) init_end_event = torch.cuda.Event(enable_timing=True) model = Net().to(...
> self.w: self._goto_next_row() return slice(top, bottom, 1), slice(left, right, 1) def __iter__(self): return self def _goto_next_row(self): self._i += self.si self._j = 0 def crop_patches(dataloader, ori_size, window_size, stride): """ 将`dataloader`中的数据裁块...
Expand Down Expand Up @@ -102,6 +104,7 @@ def calculate_correct_answers(name, model, dataloader, num_micro_batches = args.orig_global_batch_size // micro_batch_size_times_data_parallel def loss_func(output_predictions, labels, output_tensor): args = get_args() logits = output_tensor...
train_dataloader = dict( batch_size=2, num_workers=2, dataset=dict(dataset=dict(pipeline=train_pipeline, metainfo=metainfo))) test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo)) eval_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo)) lr =...
作为联合发起方之一,拓数派携手1024基金会共同推进 AI4AI(AI for All Initiative) 公益普及。结对{大模型/编程}实践营 是 AI4AI 雏鹰计划(AI for Young Eagle)的亮点项目,为零基础学生群体量身定制,体现了 AI4AI 的核心理念——让 AI 从精英专属走向大众普及。本项目由拓数派大模型数据计算系统 PieDataCS ...
test_dataloader = DataLoader(tokenized_datasets['test'], batch_size=batch_size, shuffle=True) vocab_size = tokenizer.vocab_size encoder = TransformerEncoder(vocab_size, d_model, num_layers, num_heads, d_ff, dropout, max_sequence_length=sequence_length) ...
labels[idx]) } return sample # Create an instance of the dataset dataset = MyDataset(data) # Create DataLoader dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True) def weights_init(m): if isinstance(m, nn.Linear): init.xavier_uniform_(m.weight) if m....