/
visualizer_utils.py
90 lines (70 loc) · 2.41 KB
/
visualizer_utils.py
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from logging import getLogger
import numpy # NOQA
from chainer import cuda
def red_blue_cmap(x):
"""Red to Blue color map
Args:
x (float): value between -1 ~ 1, represents normalized saliency score
Returns (tuple): tuple of 3 float values representing R, G, B.
"""
if x > 0:
# Red for positive value
# x=0 -> 1, 1, 1 (white)
# x=1 -> 1, 0, 0 (red)
return 1., 1. - x, 1. - x
else:
# Blue for negative value
x *= -1
return 1. - x, 1. - x, 1.
def min_max_scaler(saliency, logger=None):
"""Normalize saliency to value 0~1
Args:
saliency (numpy.ndarray or cupy.ndarray): saliency array
logger:
Returns (numpy.ndarray or cupy.ndarray): normalized saliency array
"""
xp = cuda.get_array_module(saliency)
maxv = xp.max(saliency)
minv = xp.min(saliency)
if maxv == minv:
logger = logger or getLogger(__name__)
logger.info('All saliency value is 0')
saliency = xp.zeros_like(saliency)
else:
saliency = (saliency - minv) / (maxv - minv)
return saliency
def abs_max_scaler(saliency, logger=None):
"""Normalize saliency to value -1~1
Args:
saliency (numpy.ndarray or cupy.ndarray): saliency array
logger:
Returns (numpy.ndarray or cupy.ndarray): normalized saliency array
"""
xp = cuda.get_array_module(saliency)
maxv = xp.max(xp.abs(saliency))
if maxv <= 0:
logger = logger or getLogger(__name__)
logger.info('All saliency value is 0')
return xp.zeros_like(saliency)
else:
return saliency / maxv
def normalize_scaler(saliency, axis=None, logger=None):
"""Normalize saliency to be sum=1
Args:
saliency (numpy.ndarray or cupy.ndarray): saliency array.
axis (int): axis to take sum for normalization.
logger:
Returns (numpy.ndarray or cupy.ndarray): normalized saliency array
"""
xp = cuda.get_array_module(saliency)
if xp.sum(saliency < 0) > 0:
logger = logger or getLogger(__name__)
logger.warning('saliency array contains negative number, '
'which is unexpected!')
vsum = xp.sum(xp.abs(saliency), axis=axis, keepdims=True)
if vsum <= 0:
logger = logger or getLogger(__name__)
logger.info('All saliency value is 0')
return xp.zeros_like(saliency)
else:
return saliency / vsum