view() reshapes the tensor without copying memory, similar to numpy's reshape().
Given a tensor a with 16 elements:
import torch
a = torch.range(1, 16)
To reshape this tensor to make it a 4 x 4 tensor, use:
a = a.view(4, 4)
Now a will be a 4 x 4 tensor. Note that after the reshape the total number of elements need to remain the same. Reshaping the tensor a to a 3 x 5 tensor would not be appropriate.
What is the meaning of parameter -1?
If there is any situation that you don't know how many rows you want but are sure of the number of columns, then you can specify this with a -1. (Note that you can extend this to tensors with more dimensions. Only one of the axis value can be -1). This is a way of telling the library: "give me a tensor that has these many columns and you compute the appropriate number of rows that is necessary to make this happen".
This can be seen in this model definition code. After the line x = self.pool(F.relu(self.conv2(x))) in the forward function, you will have a 16 depth feature map. You have to flatten this to give it to the fully connected layer. So you tell PyTorch to reshape the tensor you obtained to have specific number of columns and tell it to decide the number of rows by itself.
view() reshapes the tensor without copying memory, similar to numpy's reshape().
Given a tensor a with 16 elements:
import torch
a = torch.range(1, 16)
To reshape this tensor to make it a 4 x 4 tensor, use:
a = a.view(4, 4)
Now a will be a 4 x 4 tensor. Note that after the reshape the total number of elements need to remain the same. Reshaping the tensor a to a 3 x 5 tensor would not be appropriate.
What is the meaning of parameter -1?
If there is any situation that you don't know how many rows you want but are sure of the number of columns, then you can specify this with a -1. (Note that you can extend this to tensors with more dimensions. Only one of the axis value can be -1). This is a way of telling the library: "give me a tensor that has these many columns and you compute the appropriate number of rows that is necessary to make this happen".
This can be seen in this model definition code. After the line x = self.pool(F.relu(self.conv2(x))) in the forward function, you will have a 16 depth feature map. You have to flatten this to give it to the fully connected layer. So you tell PyTorch to reshape the tensor you obtained to have specific number of columns and tell it to decide the number of rows by itself.
view() reshapes a tensor by 'stretching' or 'squeezing' its elements into the shape you specify:
How does view() work?
First let's look at what a tensor is under the hood:
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Tensor and its underlying storage |
e.g. the right-hand tensor (shape (3,2)) can be computed from the left-hand one with t2 = t1.view(3,2) |
Here you see PyTorch makes a tensor by converting an underlying block of contiguous memory into a matrix-like object by adding a shape and stride attribute:
shapestates how long each dimension isstridestates how many steps you need to take in memory til you reach the next element in each dimension
view(dim1,dim2,...)returns a view of the same underlying information, but reshaped to a tensor of shapedim1 x dim2 x ...(by modifying theshapeandstrideattributes).
Note this implicitly assumes that the new and old dimensions have the same product (i.e. the old and new tensor have the same volume).
PyTorch -1
-1 is a PyTorch alias for "infer this dimension given the others have all been specified" (i.e. the quotient of the original product by the new product). It is a convention taken from numpy.reshape().
Hence t1.view(3,2) in our example would be equivalent to t1.view(3,-1) or t1.view(-1,2).