Struggling with thread: [0,0,0] Assertion `t >= 0 && t < n_classes'

Hello all! I have been trying to create a neural network to analyze some datapoints. A brief description of the data would be that it is in a csv file with each row having 23 columns, the first 20 of which are actually the points themselves (x, y, z format). Anyhow, I have got a simple architecture below, but when it runs into the loss optimization function it throws this error:
C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\cuda\ block: [0,0,0], thread: [0,0,0] Assertion t >= 0 && t < n_classes failed.


batch_size = 1
num_workers = 6

class ASLDataset( # inheriting from Dataset class
    def __init__(self, csv_file, root_dir="", transform=transforms.ToTensor()):
        self.annotation_df = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.annotation_df)

    def __getitem__(self, idx):
        dataPoints = []
        for i in range(20):
            dataPoints.append(eval(self.annotation_df.iloc[idx, i]))
        dataPoints = torch.tensor(dataPoints, device='cuda')
        print(str(idx) + " " + str(dataPoints.shape))
        label = self.annotation_df.iloc[idx, 21]
        label = torch.tensor(int(label), device='cuda')
        return dataPoints, label

train_dataset = ASLDataset('./ASL_Alph_Train.csv') #, train_transform)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

val_dataset = ASLDataset('./ASL_Alph_Test.csv')  # val.csv
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

classes = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z') #

# for i in range(30):
#     ASLDataset.__getitem__(train_dataset, i)

class VSNetwork(nn.Module):
    def __init__(self):
        super(VSNetwork, self).__init__()

        self.conv1 = nn.Conv1d(in_channels=20, out_channels=12, kernel_size=3)
        self.fc1 = nn.Linear(12, 64)
        self.fc2 = nn.Linear(64, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)

        return x
model = VSNetwork()

loss_fn = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001) 

I have been searching for several hours to no avail, I would really appreciate help! God bless you all!

Based on the error message it seems your target contains out of bounds values so make sure all values are in the range [0, nb_classes-1].

I believe that I included my classes in my code. It’s an array so it should only start at 0. When I print out the labels in my data loader they are all from 0 to 5, which is what I expect.