Torch C++是一个用于检查NAN的接口,它是PyTorch深度学习框架的C++版本。它提供了一种方便的方式来检测和处理在计算过程中出现的非数值(NaN)情况。 在深度学习中,NaN通常表示计算...
logprob_cluster = cat.log_prob(cluster_S.detach()).view(batch_size,1) check_nan(logprob_cluster) z = cluster_S n_evals =40x1 = np.linspace(-9,205, n_evals) x = torch.from_numpy(x1).view(n_evals,1).float().cuda() z = z.repeat(n_evals,1) cluster_H = cluster_H.repeat(...
check一下你的input是否nan 如果不是，是不是因为backward完之后，导致pool的输入变成了nan ...
然后尝试将BN层在FP32精度下进行运算，不过仍然出现了nan；
Consistency check (5 runs): Run 1: N: 100, Result: nan N: 10, Result: inf N: 100, Result: nan Run 2: N: 100, Result: nan N: 10, Result: inf N: 100, Result: nan Run 3: N: 100, Result: nan N: 10, Result: inf ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - CUDA error: an illegal memory access was encountered when use TORCH_NCCL_NAN_CHECK. · pytorch/pytorch@a4be5cb
def check_training_status(self): """Checks if loss is admissible. If not, training is stopped after max_nans consecutive inadmissible loss loss corresponds to the training loss of the model. `max_nans` is the maximum number of consecutive NaNs after which a ValueError will be """ loss_...
randn(16, 8).to(device) mock_out = self.gen(mock_in) # check the shapes of all the three: self.assertEqual(mock_out.shape, (16, 3, 256, 256)) self.assertEqual(torch.isnan(mock_out).sum().item(), 0) self.assertEqual(torch.isinf(mock_out).sum().item(), 0) ...
_amp_foreach_non_finite_check_and_unscale_ _amp_update_scale _assert _autograd_functions _baddbmm_mkl_ _batch_norm_impl_index _bmm _cast_Byte _cast_Char _cast_Double _cast_Float _cast_Half _cast_Int _cast_Long _cast_Short _cat _choose_qparams_per_tensor _classes _compute_linear_...
self.logger.warning("{}: Output '{}' contains NaN or Inf. Stop training".format(self.__class__.__name__, output) ) engine.terminate() 开发者ID:pytorch,项目名称:ignite,代码行数:20,代码来源:terminate_on_nan.py 示例15: _check_gradients ...