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\Loss.cu:250: block: [0,0,0], thread: [0,0,0] Assertion t >= 0 && t < n_classes failed.
Code:
batch_size = 1
num_workers = 6
np.set_printoptions(threshold=np.inf)
torch.set_printoptions(threshold=torch.inf)
class ASLDataset(torch.utils.data.Dataset): # 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')
print(label)
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!