PyTorch.py

import torch
import numpy as np

x = torch.empty(5,3)
print(x)

x = torch.rand(5,3)
print(x)

x = torch.zeros(5,3,dtype=torch.long)
print(x)

x = torch.tensor([5.5,3])

print(x)

x = x.new_ones(5,3,dtype=torch.double)

print(x)

x = torch.randn_like(x, dtype=torch.float)

print(x)

print(x.size())

y = torch.rand(5, 3)

print(x + y)

print(torch.add(x, y))

result = torch.empty(5, 3)
torch.add(x, y, out = result)

print(result)
# _ means in place
y.add_(x)
print(y)

print(x[:,1])

x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)
print(x.size(), y.size(), z.size())

x = torch.randn(1)
print(x)
print(x.item())


a = torch.ones(5)
print(a)

b = a.numpy()
print(b)

a.add_(1)
print(a)
print(b)

a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)

x = torch.randn(1)

if torch.cuda.is_available():
    device = torch.device("cuda")
    y = torch.ones_like(x, device = device)
    x = x.to(device)
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))