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I got two arrays :
A
B
Array A contains a batch of RGB images, with shape:
[batch, Width, Height, 3]
whereas Array B contains coefficients needed for a "transformation-like" operation on images, with shape:
[batch, 4, 4, 3]
To put it simply, the operation for a single image is a multiplication that outputs an environment map (normalMap * Coefficients).
The output I want should hold shape:
[batch, Width, Height, 3]
I tried using torch.bmm but failed. Is this possible somehow?
537
PyTorch bmm is used for matrix multiplication in batches where the scenario involves that the matrices to be multiplied have the size of 3 dimensions that is x, y, and z and the dimension of the first dimension for matrices to be multiplied should be the same.
You can only multiply two matrices if their dimensions are compatible , which means the number of columns in the first matrix is the same as the number of rows in the second matrix.
For matrix multiplication in PyTorch, use torch.mm() . Numpy's np. dot() in contrast is more flexible; it computes the inner product for 1D arrays and performs matrix multiplication for 2D arrays.
In order for matrix multiplication to be defined, the number of columns in the first matrix must be equal to the number of rows in the second matrix. To find A B AB AB , we take the dot product of a row in A and a column in B.
I think you need to calculate that PyTorch works with
BxCxHxW : number of mini-batches, channels, height, width
format, and also use matmul, since bmm works with tensors or ndim/dim/rank =3.
I know you may find this online, but for any case:
batch1 = torch.randn(10, 3, 20, 10)
batch2 = torch.randn(10, 3, 10, 30)
res = torch.matmul(batch1, batch2)
res.size() # torch.Size([10, 3, 20, 30])
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