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utils.py
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utils.py
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import numpy as np
import scipy as sp
import scipy.ndimage as ndimage
from config import DefaultCofig as cfg
def progress_bar(rate, len=30):
'''
:param rate: progress rate
:param len: length of bar
:return:[============>.................]
'''
cur_len = int(rate * len)
if cur_len == 0:
bar = '[..............................]'
elif cur_len < len:
bar = '[' + ('=' * cur_len) + '>' + ('.' * (len - cur_len)) + ']'
else:
bar = '[==============================]'
return bar
def eta_format(eta):
if eta > 3600:
eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, eta % 60)
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
return eta_format
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
cfg.lr = cfg.original_lr
for i in range(len(cfg.steps)):
scale = cfg.scales[i] if i < len(cfg.scales) else 1
if epoch >= cfg.steps[i]:
cfg.lr = cfg.lr * scale
if epoch == cfg.steps[i]:
break
else:
break
for param_group in optimizer.param_groups:
param_group['lr'] = cfg.lr
def gaussian_filter_density(shape, points):
density = np.zeros(shape, dtype=np.float32)
if len(points) == 0:
return density
tree = sp.spatial.KDTree(points.copy(), leafsize=2048)
distances, locations = tree.query(points, k=4)
for i, (x, y) in enumerate(points):
pt2d = np.zeros(shape, dtype=np.float32)
if y == pt2d.shape[0]:
y -= 1
if x == pt2d.shape[1]:
x -= 1
pt2d[y, x] = 1.
if len(points) > 3:
sigma = (distances[i][1] + distances[i][2] + distances[i][3]) * 0.1
elif len(points) > 2:
sigma = (distances[i][1] + distances[i][2]) * 0.1
elif len(points) > 1:
sigma = (distances[i][1]) * 0.1
else:
sigma = np.average(np.array(shape)) / 2. / 2.
density += ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')
return density