cleverhans.torch.utils.clip_eta will cause GPU memory leaking for its in-place operation when param norm==2
def clip_eta(eta, norm, eps):
"""
PyTorch implementation of the clip_eta in utils_tf.
:param eta: Tensor
:param norm: np.inf, 1, or 2
:param eps: float
"""
if norm not in [np.inf, 1, 2]:
raise ValueError("norm must be np.inf, 1, or 2.")
avoid_zero_div = torch.tensor(1e-12, dtype=eta.dtype, device=eta.device)
reduc_ind = list(range(1, len(eta.size())))
if norm == np.inf:
eta = torch.clamp(eta, -eps, eps)
else:
if norm == 1:
raise NotImplementedError("L1 clip is not implemented.")
norm = torch.max(
avoid_zero_div, torch.sum(torch.abs(eta), dim=reduc_ind, keepdim=True)
)
elif norm == 2:
norm = torch.sqrt(
torch.max(
avoid_zero_div, torch.sum(eta ** 2, dim=reduc_ind, keepdim=True)
)
)
factor = torch.min(
torch.tensor(1.0, dtype=eta.dtype, device=eta.device), eps / norm
)
# eta *= factor # this line used in-place operation and causes memory leaking. When i call this function in a for loop, the allocated memory keeps increasing and result in Out-of-Memory ,you would better change it as the line below
return eta * factor
return eta
cleverhans.torch.utils.clip_eta will cause GPU memory leaking for its in-place operation when param norm==2