63 lines
1.3 KiB
Python
Executable File
63 lines
1.3 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import print_function
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import torch
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x = torch.empty(5, 3)
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x = torch.rand(5, 3)
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x = torch.zeros(5, 3, dtype=torch.long)
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x = torch.tensor([5.5, 3])
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x = x.new_ones(5, 3, dtype=torch.double)
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print(x)
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x = torch.randn_like(x, dtype=torch.float)
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print(x.size())
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y = torch.rand(5, 3)
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print(x+y)
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print(torch.add(x, y))
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result = torch.empty(5, 3)
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torch.add(x, y, out=result)
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print(result)
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print(x[:, 1])
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x = torch.randn(4, 4)
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y = x.view(16)
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z = x.view(-1, 8) # the size -1 is inferred from other dimensions
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print(x.size(), y.size(), z.size())
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x = torch.randn(1)
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print(x)
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print(x.item())
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a = torch.ones(5)
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print(a)
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b = a.numpy()
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print(b)
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a.add_(1)
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print(a)
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print(b)
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import numpy as np
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a = np.ones(5)
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b = torch.from_numpy(a)
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np.add(a, 1, out=a)
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print(a)
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print(b)
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print(torch.cuda.is_available())
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# let us run this cell only if CUDA is available
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# We will use ``torch.device`` objects to move tensors in and out of GPU
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if torch.cuda.is_available():
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device = torch.device("cuda") # a CUDA device object
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y = torch.ones_like(x, device=device) # directly create a tensor on GPU
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x = x.to(device) # or just use strings ``.to("cuda")``
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z = x + y
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print(z)
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print(z.to("cpu", torch.double)) # ``.to`` can also change dtype together!
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else:
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print("you don't have cuda")
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