Deep-Learning-with-PyTorch

54 CHAPTER 3 It starts with a tensor
4
3
STORAGE
1
2
5
1
“START AT 0
2 ROWS
3 COLS”
TENSORS
(REFERENCING
THE SAME
STORAGE)
“START AT 0
3 ROWS
2 COLS”
4
5
2
1 5 3 2 1 ...
1
3
1
WHERE THE
NUMBERS
ACTUALlY
ARE
Figure 3.4
Tensors are views of a Storage instance.
storage as the points tensor—just not all of it, and with different dimensionality (1D
versus 2D). The underlying memory is allocated only once, however, so creating alternate
tensor-views of the data can be done quickly regardless of the size of the data
managed by the Storage instance.
3.7.1 Indexing into storage
Let’s see how indexing into the storage works in practice with our 2D points. The storage
for a given tensor is accessible using the .storage property:
# In[17]:
points = torch.tensor([[4.0, 1.0], [5.0, 3.0], [2.0, 1.0]])
points.storage()
# Out[17]:
4.0
1.0
5.0
3.0
2.0
1.0
[torch.FloatStorage of size 6]
Even though the tensor reports itself as having three rows and two columns, the storage
under the hood is a contiguous array of size 6. In this sense, the tensor just knows
how to translate a pair of indices into a location in the storage.
We can also index into a storage manually. For instance:
# In[18]:
points_storage = points.storage()
points_storage[0]
# Out[18]:
4.0