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How to convert a NumPy ndarray to a PyTorch Tensor and vice versa?
A PyTorch tensor is like numpy.ndarray. The difference between these two is that a tensor utilizes the GPUs to accelerate numeric computation. We convert a numpy.ndarray to a PyTorch tensor using the function torch.from_numpy(). And a tensor is converted to numpy.ndarray using the .numpy() method.
Steps
Import the required libraries. Here, the required libraries are torch and numpy.
Create a numpy.ndarray or a PyTorch tensor.
Convert the numpy.ndarray to a PyTorch tensor using torch.from_numpy() function or convert the PyTorch tensor to numpy.ndarray using the .numpy() method.
Finally, print the converted tensor or numpy.ndarray.
Example 1
The following Python program converts a numpy.ndarray to a PyTorch tensor.
# import the libraries
import torch
import numpy as np
# Create a numpy.ndarray "a"
a = np.array([[1,2,3],[2,1,3],[2,3,5],[5,6,4]])
print("a:\n", a)
print("Type of a :\n", type(a))
# Convert the numpy.ndarray to tensor
t = torch.from_numpy(a)
print("t:\n", t)
print("Type after conversion:\n", type(t))Output
When you run the above code, it will produce the following output
a: [[1 2 3] [2 1 3] [2 3 5] [5 6 4]] Type of a : <class 'numpy.ndarray'> t: tensor([[1, 2, 3], [2, 1, 3], [2, 3, 5], [5, 6, 4]], dtype=torch.int32) Type after conversion: <class 'torch.Tensor'>
Example 2
The following Python program converts a PyTorch tensor to a numpy.ndarray.
# import the libraries
import torch
import numpy
# Create a tensor "t"
t = torch.Tensor([[1,2,3],[2,1,3],[2,3,5],[5,6,4]])
print("t:\n", t)
print("Type of t :\n", type(t))
# Convert the tensor to numpy.ndarray
a = t.numpy()
print("a:\n", a)
print("Type after conversion:\n", type(a))Output
When you run the above code, it will produce the following output
t: tensor([[1., 2., 3.], [2., 1., 3.], [2., 3., 5.], [5., 6., 4.]]) Type of t : <class 'torch.Tensor'> a: [[1. 2. 3.] [2. 1. 3.] [2. 3. 5.] [5. 6. 4.]] Type after conversion: <class 'numpy.ndarray'>
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