Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
The main bug in this code is that `perturbation` needs to be created with `requires_grad=True`, but its gradients need to be calculated with respect to the loss. Here's the fixed version:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Calculate gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Calculate gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Calculate gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[1.0200]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
The main bug in this code is that the perturbation tensor is created without requiring gradients for its data. Here's the fixed version with all bugs corrected:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input) perturbation.requires_grad = True for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
1 | perturbation = torch.zeros_like(input, requires_grad=True) |
1 2 | perturbation = torch.zeros_like(input) perturbation.requires_grad = True |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input) perturbation.requires_grad = True for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input) perturbation.requires_grad = True for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[0.9800]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
The main bug in this code is that the perturbation tensor doesn't maintain its gradients between iterations. Here's the fixed version:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with current perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Backward pass loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with current perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Backward pass loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Forward pass with current perturbation output = model(input + perturbation) loss = loss_fn(output, target) # Backward pass loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[1.0200]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
The main bug in this code is that the perturbation tensor needs to detach from its computation graph and create a new one in each iteration. Here's the fixed version:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Detach perturbation from previous computation graph perturbation = perturbation.detach() perturbation.requires_grad = True output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation.detach() model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Detach perturbation from previous computation graph perturbation = perturbation.detach() perturbation.requires_grad = True output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation.detach() model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Detach perturbation from previous computation graph perturbation = perturbation.detach() perturbation.requires_grad = True output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation.detach() model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[0.9800]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
The main bug in this code is that the perturbation tensor doesn't maintain its gradients between iterations because we're not properly setting up the gradient computation for each iteration. Here's the fixed version:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Compute output and loss with gradient computation enabled output = model(input + perturbation) loss = loss_fn(output, target) # Compute gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Compute output and loss with gradient computation enabled output = model(input + perturbation) loss = loss_fn(output, target) # Compute gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): # Zero out the gradients before each iteration if perturbation.grad is not None: perturbation.grad.data.zero_() # Compute output and loss with gradient computation enabled output = model(input + perturbation) loss = loss_fn(output, target) # Compute gradients loss.backward() # Update perturbation with torch.no_grad(): perturbation.data = perturbation.data + alpha * perturbation.grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[0.9800]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True