你好,我正在训练一个PyTorch模型时遇到了以下错误:
----> 5 for i, data in enumerate(trainloader, 0):
TypeError: 'torch.Size' 对象无法被解释为整数
不确定这个错误的含义。
您可以在此处找到我的代码:
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
以下是DataLoader的代码块。我正在使用自定义的dataloader和datasets,其中x
是大小为(1025, 16)的图片,y
是用于分类的独热编码向量。
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
getitem
a, b = trainset.__getitem__(1000)
print (a.shape)
print (b.shape)
a.shape = torch.Size([1025, 16])
;
b.shape = 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