我现在正在使用Pytorch运行Python程序。我使用自己的数据集,而不是torch.data.dataset
。我从特征提取中提取的泡菜文件中下载数据。但是出现以下错误:
Traceback (most recent call last):
File "C:\Users\hp\Downloads\efficient_densenet_pytorch-master\demo-emotion.py", line 326, in <module>
fire.Fire(demo)
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\fire\core.py", line 138, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\fire\core.py", line 468, in _Fire
target=component.__name__)
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\fire\core.py", line 672, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "C:\Users\hp\Downloads\efficient_densenet_pytorch-master\demo-emotion.py", line 304, in demo
train(model,train_set1, valid_set=valid_set, test_set=test1, save=save, n_epochs=n_epochs,batch_size=batch_size,seed=seed)
File "C:\Users\hp\Downloads\efficient_densenet_pytorch-master\demo-emotion.py", line 172, in train
n_epochs=n_epochs,
File "C:\Users\hp\Downloads\efficient_densenet_pytorch-master\demo-emotion.py", line 37, in train_epoch
loader=np.asarray(list(loader))
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\hp\Anaconda3\envs\tf-gpu\lib\site-packages\torch\utils\data\dataset.py", line 257, in __getitem__
return self.dataset[self.indices[idx]]
TypeError: 'DataLoader' object is not subscriptable
代码是:
train_set1 = Owndata()
train1, test1 = train_set1 .get_splits()
# prepare data loaders
train_dl = torch.utils.data.DataLoader(train1, batch_size=32, shuffle=True)
test_dl =torch.utils.data.DataLoader(test1, batch_size=1024, shuffle=False)
test_set1 = Owndata()
'''print('test_set# ',test_set)'''
if valid_size:
valid_set = Owndata()
indices = torch.randperm(len(train_set1))
train_indices = indices[:len(indices) - valid_size]
valid_indices = indices[len(indices) - valid_size:]
train_set1 = torch.utils.data.Subset(train_dl, train_indices)
valid_set = torch.utils.data.Subset(valid_set, valid_indices)
else:
valid_set = None
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=10,
small_inputs=True,
efficient=efficient,
)
train(model,train_set1, valid_set=valid_set, test_set=test1, save=save, n_epochs=n_epochs, batch_size=batch_size, seed=seed)
感谢您的帮助!提前谢谢!
不是这行给您一个错误,因为它是您未显示的最后一个train
函数。
您正在混淆两件事:
torch.utils.data.Dataset
对象是可索引的(例如,dataset[5]
可以正常工作)。这是一个简单的对象,它定义了如何获取单个(通常是单个)数据样本。torch.utils.data.DataLoader
-不可索引,只能迭代,通常从Dataset
以上返回一批数据。可以使用num_workers
并行工作。这就是您要索引的内容,而您应该为此使用dataset
。请参阅PyTorch documentation about data,以更好地了解它们的工作原理。