%matplotlib inline Tensors behave almost exactly the same way in PyTorch as they do in Torch.
Create a tensor of size (5 x 7) with uninitialized memory:
importtorcha=torch.FloatTensor(5,7)Initialize a tensor randomized with a normal distribution with mean=0, var=1:
a=torch.randn(5,7)print(a)print(a.size())``torch.Size`` is in fact a tuple, so it supports the same operations
The first difference is that ALL operations on the tensor that operate in-place on it will have an _ postfix. For example, add is the out-of-place version, and add_ is the in-place version.
a.fill_(3.5)# a has now been filled with the value 3.5b=a.add(4.0)# a is still filled with 3.5# new tensor b is returned with values 3.5 + 4.0 = 7.5print(a,b)Some operations like narrow do not have in-place versions, and hence, .narrow_ does not exist. Similarly, some operations like fill_ do not have an out-of-place version, so .fill does not exist.
Another difference is that Tensors are zero-indexed. (In lua, tensors are one-indexed)
b=a[0,3]# select 1st row, 4th column from aTensors can be also indexed with Python's slicing
b=a[:,3:5]# selects all rows, 4th column and 5th column from aThe next small difference is that all functions are now NOT camelCase anymore. For example indexAdd is now called index_add_
x=torch.ones(5,5)print(x)z=torch.Tensor(5,2)z[:,0]=10z[:,1]=100print(z)x.index_add_(1,torch.LongTensor([4,0]),z)print(x)Converting a torch Tensor to a numpy array and vice versa is a breeze. The torch Tensor and numpy array will share their underlying memory locations, and changing one will change the other.
Converting torch Tensor to numpy Array ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
a=torch.ones(5)print(a)b=a.numpy()print(b)a.add_(1)print(a)print(b)# see how the numpy array changed in valueConverting numpy Array to torch Tensor ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
importnumpyasnpa=np.ones(5)b=torch.from_numpy(a)np.add(a,1,out=a)print(a)print(b)# see how changing the np array changed the torch Tensor automaticallyAll the Tensors on the CPU except a CharTensor support converting to NumPy and back.
CUDA Tensors are nice and easy in pytorch, and transfering a CUDA tensor from the CPU to GPU will retain its underlying type.
# let us run this cell only if CUDA is availableiftorch.cuda.is_available():# creates a LongTensor and transfers it# to GPU as torch.cuda.LongTensora=torch.LongTensor(10).fill_(3).cuda()print(type(a))b=a.cpu()# transfers it to CPU, back to# being a torch.LongTensor