allclose(torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08])) False >>> torch.allclose(torch.tensor([10000., 1e-08]), torch.tensor([10000.1, 1e-09])) True >>> torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')])) False >>> ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importallclose[as 别名]deftest_permuted_global_pool():N_1, N_2 =4,6x = torch.randn(N_1 + N_2,4) batch = torch.cat([torch.zeros(N_1), torch.ones(N_2)]).to(torch.long) perm = torch.randperm(N_1 + N_2) px =...
1e-08]))False>>>torch.allclose(torch.tensor([10000.,1e-08]), torch.tensor([10000.1,1e-09]))True>>>torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan')]))False>>>torch.allclose(torch.tensor([1.0, float('nan')]), torch.tensor([1.0, float('nan...
print(torch.sum(b, dim=0, keepdim=True)) # torch.cumsum()根据指定的维度计算累加和 # 按照行计算累加和 print(torch.cumsum(b, dim=1)) # 按照列计算累加和 print(torch.cumsum(b, dim=0)) # torch.median()根据指定的维度计算中位数 # 计算每行的中位数 print(torch.median(b, dim=1, keep...
本文将围绕torch.allclose()方法展开讨论,并重点关注其中的绝对容差和相对容差概念。文章分为五个部分:引言、torch.allclose()方法、绝对容差与相对容差的概念、在torch.allclose()中使用绝对容差和相对容差的注意事项以及结论与总结。 1.3 目的 本文旨在深入介绍torch.allclose()方法以及其中涉及到的绝对容差和相对容差概...
torch.stack() torch.zeros() torch.manual_seed() torch.save() torch.zeros_like() torch.Tensor() torch.randn() torch.nn() torch.cat() torch.ones() torch.from_numpy() torch.nn.Sequential() torch.nn.Module() torch.nn.Linear() torch.no_grad() torch.tensor() ...
🐛 Describe the bug I encountered an unexpected behavior when computing the mean of different slices of a tensor and comparing them using torch.allclose(). Despite torch.allclose() returning True, indicating that the slices are numericall...
Because torch.allclose returns a bool rather than a Tensor. cc @Chillee @samdow @kshitij12345 @janeyx99
# compute ONNX Runtime output prediction ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)} ort_outs = ort_session.run(None, ort_inputs) # compare ONNX Runtime and PyTorch results np.testing.assert_allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-...
numpy.allclose(a,b,rtol=1e-05,atol=1e-08,equal_nan=False)[source] Returns True if two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (rtol* abs(b)) and the absolute differenceatolare added togethe...