Given an number and an encoding length, how can I convert the number to its binary representation as a tensor?
Eg, given the number 6 and width 8, how can I obtain the tensor:
(0, 0, 0, 0, 0, 1, 1, 0)
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Related: Convert binary to list of digits PythonTom Hale– Tom Hale2019-05-01 09:01:01 +00:00Commented May 1, 2019 at 9:01
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4 Answers
def binary(x, bits):
mask = 2**torch.arange(bits).to(x.device, x.dtype)
return x.unsqueeze(-1).bitwise_and(mask).ne(0).byte()
If you wanna reverse the order of bits, use it with torch.arange(bits-1,-1,-1) instead.
Tiana's answer was a good one. BTW, to convert Tiana's 2-base result back to 10-base numbers, one can do like this:
import torch
import numpy as np
def dec2bin(x, bits):
# mask = 2 ** torch.arange(bits).to(x.device, x.dtype)
mask = 2 ** torch.arange(bits - 1, -1, -1).to(x.device, x.dtype)
return x.unsqueeze(-1).bitwise_and(mask).ne(0).float()
def bin2dec(b, bits):
mask = 2 ** torch.arange(bits - 1, -1, -1).to(b.device, b.dtype)
return torch.sum(mask * b, -1)
if __name__ == '__main__':
NUM_BITS = 7
d = torch.randint(0, 16, (3, 6))
b = dec2bin(d, NUM_BITS)
# print(d)
# print(b)
# print(b.shape)
# print("num of total bits: {}".format(np.prod(b.shape)))
d_rec = bin2dec(b, NUM_BITS)
# print(d_rec)
print(abs(d - d_rec).max()) # should be 0.
4 Comments
Tom Hale
Why
.float() and not .byte() as per the referenced answer?
Tom Hanks
Yes, in general cases you can use
.byte() here. I used .float() because I needed to pass the results to a third-party evaluating program, which required the bits in float format. Anyway, all the numbers were still either 0 or 1.
etham
Good answer, but actually nothing to deal with decimal... Input tensor 'x' is already in binary, the function puts its bits into separate tensors rather than convert its base!
Jackson H
For large integers (64 bits), this gives "RuntimeError: Overflow when unpacking long". Can anyone suggest a solution?
If the input is unsigned bytes and the output width is 8 bits:
>>> binary = np.unpackbits(np.array([0xaa, 0xf0], dtype=np.uint8))
>>> print(torch.tensor(binary))
tensor([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0], dtype=torch.uint8)
Note that unpackbits() only operates with np.uint8.
Comments
def decimal_to_binary_tensor(value, width=0):
string = format(value, '0{}b'.format(width))
binary = [0 if c == '0' else 1 for c in string]
return torch.tensor(binary, dtype=torch.uint8)
Examples:
>>> print(decimal_to_binary_tensor(6, width=8))
tensor([0, 0, 0, 0, 0, 1, 1, 0], dtype=torch.uint8)
>>> print(decimal_to_binary_tensor(6))
tensor([1, 1, 0], dtype=torch.uint8)