Pytorch - TypeError：'torch.Size' 对象不能解释为整数

Question

嗨，我正在训练 PyTorch 模型并出现此错误：

----> 5 for i, data in enumerate(trainloader, 0):

TypeError: 'torch.Size' object cannot be interpreted as an integer

不确定这个错误是什么意思。

你可以在这里找到我的代码：

model.train()
for epoch in range(10):
    running_loss = 0

    for i, data in enumerate(trainloader, 0):

        inputs, labels = data

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if i % 2000 == 0:
          print (loss.item())
        running_loss += loss.item()
        if i % 1000 == 0:
            print ('[%d, %5d] loss: %.3f' % (epoch, i, running_loss/ 1000))
            running_loss = 0

torch.save(model, 'FeatureNet.pkl')

---

更新

这是 DataLoader 的代码块。我正在使用自定义的数据加载器和数据集，它们x是大小为 (1025, 16) 的图片，并且y是用于分类的单热编码向量。

x_train.shape = (1100, 1025, 16)

y_train.shape = (1100, 10)

clean_dir = '/home/tk/Documents/clean/' 
mix_dir = '/home/tk/Documents/mix/' 
clean_label_dir = '/home/tk/Documents/clean_labels/' 
mix_label_dir = '/home/tk/Documents/mix_labels/' 

class MSourceDataSet(Dataset):

    def __init__(self, clean_dir, mix_dir, clean_label_dir, mix_label_dir):

        with open(clean_dir + 'clean0.json') as f:
            clean0 = torch.Tensor(json.load(f))

        with open(mix_dir + 'mix0.json') as f:
            mix0 = torch.Tensor(json.load(f))

        with open(clean_label_dir + 'clean_label0.json') as f:
            clean_label0 = torch.Tensor(json.load(f))


        with open(mix_label_dir + 'mix_label0.json') as f:
            mix_label0 = torch.Tensor(json.load(f))


        self.spec = torch.cat([clean0, mix0], 0)
        self.label = torch.cat([clean_label0, mix_label0], 0)

    def __len__(self):
        return self.spec.shape


    def __getitem__(self, index): 

        spec = self.spec[index]
        label = self.label[index]
        return spec, label

获取项目

a, b = trainset.__getitem__(1000)
print (a.shape)
print (b.shape)

a torch.Size([1025, 16]).形状= ; b.形状 =torch.Size([10])

错误信息

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-3bd71e5c00e1> in <module>()
      3     running_loss = 0
      4 
----> 5     for i, data in enumerate(trainloader, 0):
      6 
      7         inputs, labels = data

~/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
    311     def __next__(self):
    312         if self.num_workers == 0:  # same-process loading
--> 313             indices = next(self.sample_iter)  # may raise StopIteration
    314             batch = self.collate_fn([self.dataset[i] for i in indices])
    315             if self.pin_memory:

~/anaconda3/lib/python3.7/site-packages/torch/utils/data/sampler.py in __iter__(self)
    136     def __iter__(self):
    137         batch = []
--> 138         for idx in self.sampler:
    139             batch.append(idx)
    140             if len(batch) == self.batch_size:

~/anaconda3/lib/python3.7/site-packages/torch/utils/data/sampler.py in __iter__(self)
     32 
     33     def __iter__(self):
---> 34         return iter(range(len(self.data_source)))
     35 
     36     def __len__(self):

TypeError: 'torch.Size' object cannot be interpreted as an integer

Answer 1

你的问题是__len__功能。您不能使用shapeas 返回值。

下面是一个示例：

import torch
class Foo:
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return self.data.shape

myFoo = Foo(data=torch.rand(10, 20))
print(len(myFoo))

将引发完全相同的错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-285-e97aace2f622> in <module>
      7 
      8 myFoo = Foo(data=torch.rand(10, 20))
----> 9 print(len(myFoo))

TypeError: 'torch.Size' object cannot be interpreted as an integer

由于形状代表一个torch.Size元组：

print(myFoo.data.shape)

输出：

torch.Size([10, 20])

因此，您必须决定要移交给哪个维度__len__，例如第一个维度：

import torch
class Foo:
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return self.data.shape[0] # choosing first dimension for len

myFoo = Foo(data=torch.rand(10, 20))
print(len(myFoo))
# prints 10

工作正常并返回10。当然，您也可以选择输入的任何其他维度，但您必须选择一个。

因此，在您的代码中，您MSourceDataSet必须将__len__函数更改为例如：

def __len__(self):
    return self.spec.shape[0] # as said of course you can also choose other dimensions

这应该可以解决您的问题。