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2025ML-project-neural_compr.../integer_discrete_flows/coding/rans.py
2025-11-07 12:54:36 +01:00

67 lines
2.2 KiB
Python

"""
Closely based on https://github.com/rygorous/ryg_rans/blob/master/rans64.h
ROUGH GUIDE:
We use the pythonic names 'append' and 'pop' for encoding and decoding
respectively. The compressed state 'x' is an immutable stack, implemented using
a cons list.
x: the current stack-like state of the encoder/decoder.
precision: the natural numbers are divided into ranges of size 2^precision.
start & freq: start indicates the beginning of the range in [0, 2^precision-1]
that the current symbol is represented by. freq is the length of the range.
freq is chosen such that p(symbol) ~= freq/2^precision.
"""
import numpy as np
from functools import reduce
rans_l = 1 << 31 # the lower bound of the normalisation interval
tail_bits = (1 << 32) - 1
x_init = (rans_l, ())
def append(x, start, freq, precision):
"""Encodes a symbol with range [start, start + freq). All frequencies are
assumed to sum to "1 << precision", and the resulting bits get written to
x."""
if x[0] >= ((rans_l >> precision) << 32) * freq:
x = (x[0] >> 32, (x[0] & tail_bits, x[1]))
return ((x[0] // freq) << precision) + (x[0] % freq) + start, x[1]
def pop(x_, precision):
"""Advances in the bit stream by "popping" a single symbol with range start
"start" and frequency "freq"."""
cf = x_[0] & ((1 << precision) - 1)
def pop(start, freq):
x = freq * (x_[0] >> precision) + cf - start, x_[1]
return ((x[0] << 32) | x[1][0], x[1][1]) if x[0] < rans_l else x
return cf, pop
def append_symbol(statfun, precision):
def append_(x, symbol):
start, freq = statfun(symbol)
return append(x, start, freq, precision)
return append_
def pop_symbol(statfun, precision):
def pop_(x):
cf, pop_fun = pop(x, precision)
symbol, (start, freq) = statfun(cf)
return pop_fun(start, freq), symbol
return pop_
def flatten(x):
"""Flatten a rans state x into a 1d numpy array."""
out, x = [x[0] >> 32, x[0]], x[1]
while x:
x_head, x = x
out.append(x_head)
return np.asarray(out, dtype=np.uint32)
def unflatten(arr):
"""Unflatten a 1d numpy array into a rans state."""
return (int(arr[0]) << 32 | int(arr[1]),
reduce(lambda tl, hd: (int(hd), tl), reversed(arr[2:]), ()))