x1,x2,x3 = torch.split(torch.index_select(verts,0, faces[:,0]) - torch.index_select(verts,0, faces[:,1]),1, dim =1) y1,y2,y3 = torch.split(torch.index_select(verts,0, faces[:,1]) - torch.index_select(verts,0, faces[:,2]),1, dim =1) a = (x2*y3 - x3*y2)**...
index_select has several implementations since it can be used in multiple manners, it is a very flexible operator. In your case, when the selected dim is -1, it is not performant. We have a fast path when the tensor.dim() == 1...
h = torch.index_select(self.weight, 4, self.copyNodes) # And now we add the zero padding if Nin < self.N: zeroPad = torch.zeros(B, F, self.N-Nin).type(x.dtype).to(x.device) x = torch.cat((x, zeroPad), dim = 2) # Compute the filter output u = NVGF(self.h, self....
torch.index_select() 和 torch.masked_select() index必须是一个torch.long的数据类型 张量变换torch.reshape() 和 torch.transpose() 重组形状,注意的一点就是重组后,数据量不能变 torch.squeeze() 和 torch.unsqueeze() 如果本来就不为1的维度,就算你指定它,那也压缩不了。 张量运算 Pytorch中文文档最新发布...
Tensor.select_scatter: lambda self, src, dim, index: -1, Tensor.share_memory_: lambda self: -1, Tensor.short: lambda self, memory_format=torch.preserve_format: -1, Tensor.size: lambda self: -1, Tensor.slice_scatter: lambda self, src, dim=0, start=None, end=None, step=1: -1, ...
ratios[i]))) indices.insert(1, len(indices)) base_anchors = torch.index_select(base_anchors, 0, torch.LongTensor(indices)) multi_level_base_anchors.append(base_anchors) return multi_level_base_anchors Example #13Source File: test_assigner.py From mmdetection with Apache License 2.0 6 votes...
torch.index_select() 和torch.masked_select() index必须是一个torch.long的数据类型 张量变换torch.reshape() 和torch.transpose() 重组形状,注意的一点就是重组后,数据量不能变torch.squeeze() 和torch.unsqueeze() 如果本来就不为1的维度,就算你指定它,那也压缩不了。 张量运算 ...
使用python定位到最深的实现为IndexIVF类的merge_from方法。这个函数很可能是 faiss 库中的底层实现,通过 swig 工具将 C/C++ 代码包装成 Python 接口。 再TEST宏处进入测试 #include <gtest/gtest.h> // https://github1s.com/facebookresearch/faiss/blob/HEAD/tests/test_merge.cpp#L14 // now use ondis...
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it will be treated as a concatenation of multiple bags (sequences). offsets is required to be a 1D tensor containing the starting index positions of each bag in input. Therefore, for offsets of shape (B), input will be viewed as having B bags. Empty bags (i.e., having 0-length) ...