def yolov2_image_coordinate(t_xy, t_wh, biases):
import numpy as np
from nnabla.parameter import pop_parameter, set_parameter
h, w = t_xy.shape[-2:]
xs = pop_parameter('xs')
ys = pop_parameter('ys')
if xs is None or (h != xs.shape[-1]):
xs = nn.Variable.from_numpy_array(np.arange(w).reshape(1, 1, 1, -1))
xs.need_grad = False
set_parameter('xs', xs)
if ys is None or (h != ys.shape[-2]):
ys = nn.Variable.from_numpy_array(np.arange(h).reshape(1, 1, -1, 1))
ys.need_grad = False
set_parameter('ys', ys)
t_x, t_y = F.split(t_xy, axis=2)
oshape = list(t_x.shape)
oshape.insert(2, 1)
t_x = F.reshape((t_x + xs) / w, oshape)
t_y = F.reshape((t_y + ys) / h, oshape)
pop_parameter('biases')
biases = biases.reshape(1, biases.shape[0], biases.shape[1], 1,
1) / np.array([w, h]).reshape(1, 1, 2, 1, 1)
b = nn.Variable.from_numpy_array(biases)
b.need_grad = False
set_parameter('biases', b)
t_wh = t_wh * b
return t_x, t_y, t_wh
def test_get_set_pop_parameter():
import nnabla as nn
from nnabla.parameter import set_parameter, pop_parameter, get_parameter
nn.clear_parameters()
x = nn.Variable((2, 3, 4, 5))
key = 'a/b/c'
set_parameter(key, x)
x2 = get_parameter(key)
assert x is x2
x3 = pop_parameter(key)
assert x is x3
x4 = get_parameter(key)
assert x4 is None
