torch.squeeze () of dimensional data compression, remove dimension is a dimension 1, a default is to delete all of the dimensions 1.
You can also dim the specified location, delete the specified location is the number of dimensions of a dimension.
torch.unsqueeze () data dimensions
import torch x = torch.zeros(3, 2, 4, 1, 2, 1) # dimension of 3*2*4*1*2 print(x.size()) # torch.Size([3, 2, 4, 1, 2, 1]) print(x.shape) # torch.Size([3, 2, 4, 1, 2, 1]) y = torch.squeeze(x) # Returns a tensor with all the dimensions of input of size 1 removed. print(y.size()) # torch.Size([3, 2, 4, 2]) print(y.shape) z = torch.unsqueeze(y, dim=0) # Add a dimension of 1 in the 0th position print(z.size()) # torch.Size([1, 3, 2, 4, 2]) print(z.shape) z = torch.unsqueeze(y, dim=1) # Add a dimension of 1 in the 1st position print(z.size()) # torch.Size([3, 1, 2, 4, 2]) print(z.shape) z = torch.unsqueeze(y, dim=2) # Add a dimension of 1 in the 2nd position print(z.size()) # torch.Size([3, 2, 1, 4, 2]) print(z.shape) y = torch.squeeze(x, dim=0) # remove the 0th position of 1 (no 1) print('dim=0', y.size()) # torch.Size([3, 2, 4, 1, 2, 1]) print('dim=0', y.shape) y = torch.squeeze(x, dim=1) # remove the 1st position of 1 (no 1) print('dim=1', y.size()) # torch.Size([3, 2, 4, 1, 2, 1]) print('dim=1', y.shape) y = torch.squeeze(x, dim=2) # remove the 2nd position of 1 (no 1) print('dim=2', y.size()) # torch.Size([3, 2, 4, 1, 2]) print('dim=2', y.shape) y = torch.squeeze(x, dim=3) # remove the 3rd position of 1 (yes) print('dim=3', y.size()) # torch.Size([3, 2, 4, 2, 1]) print('dim=3', y.shape) y = torch.squeeze(x, dim=4) # remove the 4th position of 1 (no 1) print('dim=4', y.size()) # torch.Size([3, 2, 4, 1, 2, 1]) print('dim=4', y.shape) y = torch.squeeze(x, dim=5) # remove the 5th position of 1 (yes) print('dim=5', y.size()) # torch.Size([3, 2, 4, 1, 2]) print('dim=5', y.shape) y = torch.squeeze(x, dim=6) # RuntimeError: Dimension out of range (expected to be in range of [-6, 5], but got 6) print('dim=6', y.size()) print('dim=6', y.shape)
expansion. Need to dim the specified position to a specified position plus the dimension is a dimension.