解释idx和batch_x在for循环中的赋值过程: 当使用enumerate(dataloader)时,每次迭代都会从dataloader中获取一个元素(通常是一个批次的数据),并将其与当前迭代的索引一起赋值给idx和batch_x。 idx是当前的迭代索引(从0开始),而batch_x是当前批次的数据。 描述for idx, batch_
程序停在了 for step,data in enumerate(loader),下面是部分bug信息 Traceback (most recent call last): ... File ".../torch/utils/data/dataloader.py", line 206, in __next__ idx, batch = self.data_queue.get() File "/usr/lib/python2.7/multiprocessing/queues.py", line 378, in get ret...
learning_rate = float(data_options['learning_rate']) momentum = float(net_options['momentum']) decay = float(net_options['decay']) steps = [float(step) for step in data_options['steps'].split(',')] scales = [float(scale) for scale in data_options['scales'].split(',')] # Trai...
dataB = data[:, modB].float().to(device2) L1 = modelA(dataA) @@ -666,7 +666,7 @@ def test(): errors = [] for batch_idx, data in enumerate(train_loader): # Preparing the batch data = data.permute(0, -1, 1, 2, 3) if dim == 3 else data.permute(0, -1, 1, 2)...
load_state_dict(state_dict) # Iterate over the data for batch_idx, batch in enumerate(dataloader): # Store the state every 1000 batches if batch_idx % 1000 == 0: torch.save(dataloader.state_dict(), "dataloader_state.pt")✅ LLM Pre-training LitData is highly optimized for LLM pre...
`enumerate` 函数返回一个包含索引和值对的迭代器。 3. `(val, idx) for (idx, val) in enumerate(membership_mat[i])` 是一个生成器表达式,它遍历 `enumerate` 返回的迭代器,将每个隶属度值和它的索引作为一个元组 `(val, idx)`。 4. `max(...)` 用于找到具有最大隶属度值的元组。`max` 函数...
# Reduce the number of batches to match the total number of batches in the data loader # if num_batches exceeds the number of batches in the data loader num_batches = min(num_batches, len(data_loader)) for i, (input_batch, target_batch) in enumerate(data_loader): ...
label = self.dataset.loc[idx, "label"] sample = {"text": text, "label": label} # 返回一个 dict return sample # 加载训练集 sentiment_train_set = SentimentDataset(data_path + "sentiment.train.data") sentiment_train_loader = DataLoader(sentiment_train_set, batch_size=batch_size, shuffle...
在一篇教程中，我看到有人使用torch.utils.data.DataLoader来完成这项任务，所以我更改了代码，改为使用...
() for idx, country_name in enumerate(self.country_list, 0): country_dict[country_name] = idx return country_dict # 获取国家数量 def getCountriesNum(self): return self.country_num # 根据索引,返回国家的字符串 def idx2country(self, index): return self.country_list[index] def __getitem...