166 lines
2.5 KiB
Python
166 lines
2.5 KiB
Python
import torch
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# empty, zeros, ones of different sizes, specify datatype
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print('empty, zeros, ones of different sizes, specify datatype')
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x = torch.empty(1)
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print(x)
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x = torch.empty(3)
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print(x)
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x = torch.zeros(2, 3)
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print(x)
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x = torch.ones(2,3,4)
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print(x)
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x = torch.ones(2, 5, dtype=torch.float64)
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print(x.dtype)
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print()
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# from data
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print('from data')
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x = torch.tensor([2.5, 0.1])
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print(x)
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print()
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#basic ops
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print('basic ops')
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x = torch.rand(2,2)
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y = torch.rand(2,2)
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print('add')
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z1 = x + y
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z2 = torch.add(x,y)
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print(x)
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print(y)
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print(z1)
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print(z2)
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# in place addition
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x.add_(y)
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print(x)
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print('sub')
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z1 = x - y
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z2 = torch.sub(x,y)
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print(x)
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print(y)
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print(z1)
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print(z2)
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# in place addition
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x.sub_(y)
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print(x)
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print('mul')
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z1 = x * y
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z2 = torch.mul(x,y)
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print(x)
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print(y)
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print(z1)
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print(z2)
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# in place addition
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x.mul_(y)
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print(x)
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print('div')
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z1 = x / y
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z2 = torch.div(x,y)
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print(x)
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print(y)
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print(z1)
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print(z2)
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# in place addition
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x.div_(y)
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print(x)
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print()
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#slicing
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print('slicing, item')
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x = torch.rand(2,3,2)
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print(x)
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print(x[:,2,:])
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print(x[1, 2, 1])
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print(x[1, 2, 1].item()) # for single element tensors only
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print()
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# reshaping
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print('reshaping')
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x = torch.rand(4,4)
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print(x)
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y = x.view(16)
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print(y)
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y = x.view(-1, 8)
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print(y)
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y = x.view(2, -1)
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print(y)
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# y = x.view(3, -1) # fails 'shape is invalid'
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# print(y)
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print()
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# transposing
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print('transposing')
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x = torch.rand(2, 3)
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print(x.size())
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x = torch.transpose(x, 0, 1)
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print(x.size())
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x = torch.t(x)
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print(x.size())
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x = torch.rand(2, 3, 4)
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print(f'Original: {x.size()}')
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x = torch.transpose(x, 0, 1)
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print(f'01: {x.size()}')
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x = torch.rand(2, 3, 4)
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x = torch.transpose(x, 1, 2)
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print(f'12: {x.size()}')
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x = torch.rand(2, 3, 4)
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x = torch.transpose(x, 0, 2)
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print(f'02: {x.size()}')
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print()
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# numpy
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import numpy as np
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print('numpy')
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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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print(type(b))
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a.add_(1) # vectors/tensors share same memory
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print(a)
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print(b)
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c = np.ones(5)
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print(c)
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d = torch.from_numpy(c)
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print(d)
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e = d.to(dtype=torch.float32) # e has its own memory
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print(e)
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c += 1 # c and d share same memory
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print(c)
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print(d)
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print(e)
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# device
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print('device')
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if torch.cuda.is_available():
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device = torch.device('cuda')
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else:
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device = torch.device('cpu')
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print(device)
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x = torch.ones(5, device=device)
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y = torch.ones(5)
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y = y.to(device)
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z = x+y
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print(z)
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z = z.to('cpu') #move tensor back to cpu for conversion into numopy vector
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print(z)
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a = z.numpy()
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print(a)
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x = torch.ones(5, requires_grad=True) # enable grad for autograd
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print(x) |