函数作用
计算两个tensor的矩阵乘法，torch.bmma,b),tensor a 的size为b,h,w),tensor b的size为b,w,m) 也就是说两个tensor的第一维是相等的，然后第一个数组的第三维和第二个数组的第二维度要求一样，对于剩下的则不做要求，输出维度 （b,h,m）
代码示例
>>> c=torch.randn2,5))
>>> printc)
tensor[[ 1.0559, -0.3533, 0.5194, 0.9526, -0.2483],[-0.1293, 0.4809, -0.5268, -0.3673, 0.0666]])
>>> d=torch.reshapec,5,2))
>>> printd)
tensor[[ 1.0559, -0.3533],[ 0.5194, 0.9526],[-0.2483, -0.1293],[ 0.4809, -0.5268],[-0.3673, 0.0666]])
>>> e=torch.bmmc,d)
Traceback most recent call last):File "<stdin>", line 1, in <module>
RuntimeError: Dimension out of range expected to be in range of [-2, 1], but got 2)
当tensor维度为2时会报错！
>>> ccc=torch.randn1,2,2,5))
>>> ddd=torch.randn1,2,5,2))
>>> e=torch.bmmccc,ddd)
Traceback most recent call last):File "<stdin>", line 1, in <module>
RuntimeError: invalid argument 1: expected 3D tensor, got 4D at /opt/conda/conda-bld/pytorch_1535490206202/work/aten/src/TH/generic/THTensorMath.cpp:2304
维度为4时也会报错！
>>> cc=torch.randn2,2,5))
>>>printcc)
tensor[[[ 1.4873, -0.7482, -0.6734, -0.9682, 1.2869],[ 0.0550, -0.4461, -0.1102, -0.0797, -0.8349]],[[-0.6872, 1.1920, -0.9732, 0.4580, 0.7901],[ 0.3035, 0.2022, 0.8815, 0.9982, -1.1892]]])
>>>dd=torch.reshapecc,2,5,2))
>>> printdd)
tensor[[[ 1.4873, -0.7482],[-0.6734, -0.9682],[ 1.2869, 0.0550],[-0.4461, -0.1102],[-0.0797, -0.8349]],[[-0.6872, 1.1920],[-0.9732, 0.4580],[ 0.7901, 0.3035],[ 0.2022, 0.8815],[ 0.9982, -1.1892]]])
>>>e=torch.bmmcc,dd)
>>> printe)
tensor[[[ 2.1787, -1.3931],[ 0.3425, 1.0906]],[[-0.5754, -1.1045],[-0.6941, 3.0161]]])>>> e.size)
torch.Size[2, 2, 2])正确！