我有一个包含子文件夹(类(的文件夹，每个子文件夹中都有图像。
data
|_ classe1
|_ image1
|_ image2
|_ classe2
|_ ...
我的目标是创建一个数据集(训练+测试集(，用pytorch-resnet训练我的模型。我有一个错误，我不知道如何解决它，因为我不真正理解DataLoader的结构，所以我尝试了这个：
我有这个：
dataset = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['data']}
batch_size = 32
validation_split = .3
shuffle_dataset = True
random_seed= 42
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset :
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=valid_sampler)
dataloaders_dict = {'train': train_loader, 'val': validation_loader}
但当我尝试运行我的模型时，我会出现以下错误：
Epoch 0/99
----------
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-79-8c30eb5e6a01> in <module>()
3
4 # Train and evaluate
----> 5 model_ft, hist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs, is_inception=False)
4 frames
<ipython-input-56-9421c2d39473> in train_model(model, dataloaders, criterion, optimizer, num_epochs, is_inception)
22
23 # Iterate over data.
---> 24 for inputs, labels in dataloaders[phase]:
25 inputs = inputs.to(device)
26 labels = labels.to(device)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
401 def _next_data(self):
402 index = self._next_index() # may raise StopIteration
--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
404 if self._pin_memory:
405 data = _utils.pin_memory.pin_memory(data)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
KeyError: 0
有什么建议吗？是否检测到任何错误？
问题很可能来自第一行，其中dataset实际上是一个包含一个元素的dict(pytorch数据集(。这会更好：
x = 'data'
dataset = datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
我假设data_transforms['data']是预期类型的转换(如这里详细描述的(。
当pytorch试图从您的"；数据集"；(dict(，它只包含一个元素。
顺便说一句，我认为pytorch提供了torch.utils.data.random_split`功能，这样您就不必自己进行训练/测试拆分。你可能想查一下。