HI, Here is my code and I have some errors.
I don’t know why the ‘y’ is dict.
Thank you for your help in advnace 
training_data = datasets.VOCDetection(
root="/data/",
image_set="train",
#download=True,
transform=transforms.Compose([transforms.Resize((1000,600)), transforms.ToTensor()])
)
test_data = datasets.VOCDetection(
root="/data/",
image_set="val",
#download=True,
transform=transforms.Compose([transforms.Resize((1000,600)), transforms.ToTensor()])
)
train_dataloader = DataLoader(training_data, batch_size=2)
test_dataloader = DataLoader(test_data, batch_size=2)
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.conv1 = nn.Conv2d(3,6,5)
self.conv2 = nn.Conv2d(6,16,5)
#self.conv1.in_channels = 1
self.fc1 = nn.Linear(580944, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2,2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x # output
model = MyNet()
learning_rate = 1e-3
batch_size = 64
epochs = 5
loss_fn = nn.CrossEntropyLoss()
# optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# Define training and test
def train_loop(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
for batch, (X, y) in enumerate(dataloader):
# calculate prediction and loss
pred = model(X)
#y = torch.as_tensor(y)
#y = torch.Tensor(y)
loss = loss_fn(pred, y) # -->> this line occurs error
# backward propagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss : {loss:>7f} [{current:>5d}/{size:>5d}]")
def test_loop(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in dataloader:
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= num_batches
correct /= size
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
# reset
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# training
epochs = 10
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train_loop(train_dataloader, model, loss_fn, optimizer)
test_loop(test_dataloader, model, loss_fn)
Here is my error:
File "model.py", line 105, in train_loop
loss = loss_fn(pred, y)
TypeError: cross_entropy_loss(): argument 'target' (position 2) must be Tensor, not dict