Some common data type conversion methods in pytorch are similar to list and NP Conversion method of ndarray

0. Guide pack

import torch
import numpy as np
import PIL

1.tensor Type conversion

#  Set up Torch Default type 
torch.set_default_tensor_type(torch.FloatTensor)
# CPU GPU #  data type 
# torch.FloatTensor torch.cuda.FloatTensor # 32 Bit floating point 
# torch.DoubleTensor torch.cuda.DoubleTensor # 64 Bit floating point 
# torch.cuda.HalfTensor # 16 Bit floating point 

#  Type conversion 
tensor = torch.rand(1,3,224,224)
tensor = tensor.float()     	#  To float type , other :half() int() double() char() byte() short() long()
tensor = tensor.cuda()      	#  To GPU object 
tensor = tensor.cpu()       	#  To CPU object 
# type_as() Method 
tensor1 = torch.rand(2,3)
tensor2 = torch.IntTensor(2,3)
tensor3 = tensor1.type_as(tensor2)  #  take tensor1 To tensor2 The type of 
# print(tensor1,tensor2,tensor3)

2.tensor and list transformation

'''tensor and list transformation '''
tensor1 = torch.ones([1,5])     #  Create a 1x5 Unit tensor 
list1 = tensor1.tolist()        # tensor -> list
tensor1 = torch.tensor(list1)   # list -> tensor

3.tensor and np.ndarray transformation

'''tensor and np.ndarray transformation '''
ndarray = tensor.numpy()            #  Be careful GPU The object must be converted to CPU object 
tensor = torch.from_numpy(ndarray)  #  Note that it is generally converted to floating-point objects 

4. Images tensor And images ndarray transformation ,tensor -> cv2

''' Images tensor And images ndarray transformation ,tensor -> cv2'''
# tensor The image is dimensional, and the information is [N, C, H, W]
ndarray = np.random.random((3,224,224))
tensor = torch.tensor(ndarray).permute(2,0,1)       #ndarray turn tensor Note that the dimension of the image is HWC->CHW
img = tensor.permute(1,2,0).cpu().numpy()           # On the contrary, the same is true 

5.cv2(ndarray) And PIL Conversion between

'''PIL.Image and np.ndarray transformation , But cv2 And PIL Conversion between '''
path = '1.jpg'    # Pass in your own image address here 
ndarray = np.asarray(PIL.Image.open(path))          #Image turn ndarray
img = PIL.Image.fromarray(ndarray.astype(np.uint8)) #ndarray turn Image