(loss_test) # Check if any gpu has NaN loss if rank == 0: iterator.set_description(f"Loss: {loss_test.item()}") if torch.isnan(loss_test): raise ValueError("NaN loss.") scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() optimizer.zero_grad() cleanup() if __...
# 需要导入模块: import torch [as 别名]# 或者: from torch importisfinite[as 别名]defdetect_nan_tensors(self, loss: Tensor)->None:model = self.get_model()# check if loss is nanifnottorch.isfinite(loss).all():raiseValueError('The loss returned in `training_step` is nan or inf.')#...
def _cast_and_nan_check_input(self, x, weight = None): """Convert input ``x`` to a tensor and check for Nans.""" # 将输入“x”转换为张量并检查 Nans。 with profiler.record_function("process x"): if not isinstance(x, torch.Tensor): x = torch.as_tensor(x, dtype=self.dtype,...
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 ...
nan_to_num nan_to_num_ nanmedian nanquantile nansum narrow narrow_copy native_batch_norm native_group_norm native_layer_norm native_norm ne neg neg_ negative negative_ nextafter nn no_grad nonzero norm norm_except_dim normal not_equal nuclear_norm numel nvtoolsext_dll_path ones ones_like ...
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(n_evals,1) xz = torch.cat([z,x], dim=1) ...
For CPU tensors, this method is currently only available with MKL. Usetorch.backends.mkl.is_available()to check if MKL is installed. Parameters input(Tensor) – the input tensor of at leastsignal_ndim+ 1dimensions signal_ndim(int) – the number of dimensions in each signal.signal_ndimcan ...
withprofiler.record_function("cast_and_nan_check"): value,weight=self._cast_and_nan_check_input(value, weight) ifvalue.numel() ==0: return withprofiler.record_function("update value"): self.mean_value+= (value*weight).sum() withprofiler.record_function("update weight"): ...
softmax(new_logits, dim=1) if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break if just_prob: return probs #with torch.no_grad(): # add eps for unexpected torch error # probs = nn.functional.softmax(new_logits...
roll(self.periods, dims=1) if self.periods > 0: shift[:, 0:self.periods] = np.nan else: shift[:, self.periods:] = np.nan return shift Example #22Source File: utils.py From Adversarial-Continual-Learning with MIT License 5 votes def read_sn3_pascalvincent_tensor(path, strict=True...