CODEWITHSUNDEEP
pythonpytorchmatrix-multiplication
singa1994
singa1994

Reputation: 817

Matrix multiplication (element-wise) from numpy to Pytorch

I got two numpy arrays (image and and environment map), 

MatA
MatB

Both with shapes (256, 512, 3)

When I did the multiplication (element-wise) with numpy:

prod = np.multiply(MatA,MatB)

I got the wanted result (visualize via Pillow when turning back to Image)

But when I did it using pytorch, I got a really strange result(not even close to the aforementioned).

I did it with the following code:

MatATensor = transforms.ToTensor()(MatA)
MatBTensor = transforms.ToTensor()(MatB)

prodTensor = MatATensor * MatBTensor

For some reasons, the shape for both MatATensor and MatBtensor is 

torch.Size([3, 256, 512])

Same for the prodTensor too. When I tried to reshape to (256,512,3), I got an error.

Is there a way to get the same result?

I am new to pytorch, so any help would be appreciated.

Upvotes: 3

Views: 2528

Answers (2)

ndrwnaguib
ndrwnaguib

Reputation: 6115

I suggest you use torch.from_numpy, which will easily convert your ndarrays to torch tensors. As in:

In[1]: MatA = np.random.rand(256, 512, 3)
In[2]: MatB = np.random.rand(256, 512, 3)

In[3]: MatA_torch = torch.from_numpy(MatA)
In[4]: MatB_torch = torch.from_numpy(MatB)

In[5]: mul_np = np.multiply(MatA, MatB)
In[6]: mul_torch = MatA_torch * MatB_torch

In[7]: torch.equal(torch.from_numpy(mul_np), mul_torch)
Out[7]: True

In[8]: mul_torch.shape
Out[8]: torch.Size([256, 512, 3])

If you want it back to numpy, just do:

mul_torch.numpy()

Upvotes: 1

Shai
Shai

Reputation: 114796

If you read the documentation of transforms.ToTensor() you'll see this transformation does not only convert a numpy array to torch.FloatTensor, but also transpose its dimensions from HxWx3 to 3xHxW.
To "undo" this you'll need to

 prodasNp = (prodTensor.permute(2, 0, 1) * 255).to(torch.uint8).numpy()

See permute for more information.

Upvotes: 2

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