I am using nn.CrossEntropyLoss() in as my optimizer in a model that I am developing. The problem that I am having is that the model outputs a vector of size (batchsize, #classes) when it is supposed to output a (batchsize) vector.
Isn’t CrossEntropyLoss supposed to apply LogSoftmax?
Here’s my Dataset:
class DatasetPlus(Dataset):
def __init__(self, root_img, root_data, width, hight, transform=None):
self.root_img = root_img
self.root_data = root_data
self.width = width
self.hight = hight
self.transform = transform
# labels are stored in a csv file
self.labels = pd.read_csv(self.root_data)
self.imgs = [image for image in sorted(
os.listdir(self.root_img)) if image[-4:] == '.jpg']
self.len = len(self.imgs)
def __len__(self):
return self.len
def __getitem__(self, idx):
img_name = self.imgs[idx]
img_path = os.path.join(self.root_img, img_name)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)
img = cv2.resize(img, (self.width, self.hight), cv2.INTER_AREA)
img = np.array(img) / 255.0
if self.transform is not None:
img = self.transform(img)
img_id = int(img_name[6:-4])
label = self.labels.where(self.labels['ID'] == img_id)['Label'].dropna().to_numpy()[0]
label = torch.tensor(label, dtype=torch.float32)
return img, label
Here is my model:
class Net(nn.Module):
def __init__(self, h, w):
super().__init__()
nw = (((w - 4) // 2) -4) // 2
nh = (((h - 4) // 2) -4) // 2
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * nh * nw, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 3)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = (self.fc3(x))
return x
Here’s my training code:
model = Net(224, 224)
trainloader = DataLoader(ds, batch_size=4, shuffle=True)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)
def train_model(epochs):
for epoch in range(epochs):
losses = 0.0
for i, data in enumerate(trainloader, 0):
optimizer.zero_grad()
img, label = data
yhat = model(img)
loss = criterion(yhat, label)
loss.backward()
optimizer.step()
losses += loss.item()
# if i % 5 == 99:
print(f'[{epoch + 1}, {i + 1:5d}] loss: {losses:.3f}')
losses = 0.0
train_model(5)
I have explained the problem but here’s the error anyways:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[9], line 1
----> 1 train_model(5)
Cell In[8], line 13, in train_model(epochs)
11 print(yhat.size())
12 print(label.size())
---> 13 loss = criterion(yhat, label)
14 loss.backward()
15 optimizer.step()
File c:\Users\Yasamin\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Users\Yasamin\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\loss.py:720, in BCEWithLogitsLoss.forward(self, input, target)
719 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 720 return F.binary_cross_entropy_with_logits(input, target,
721 self.weight,
722 pos_weight=self.pos_weight,
723 reduction=self.reduction)
File c:\Users\Yasamin\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\functional.py:3160, in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
3157 reduction_enum = _Reduction.get_enum(reduction)
3159 if not (target.size() == input.size()):
-> 3160 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
3162 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([4])) must be the same as input size (torch.Size([4, 3]))
And finally, these are the outputs and the labels that raise this error:
yhat=
tensor([[ 0.0097, 0.0184, -0.1236],
[ 0.0020, 0.0135, -0.1324],
[ 0.0095, 0.0136, -0.1261],
[ 0.0027, 0.0176, -0.1285]], grad_fn=<AddmmBackward0>)
torch.Size([4, 3])
label=
tensor([2., 1., 0., 2.])
torch.Size([4])
