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from itertools import product as product
import numpy as np
from math import ceil
class PriorBox(object):
def __init__(self, cfg, image_size=None):
super(PriorBox, self).__init__()
self.min_sizes = cfg["min_sizes"]
self.steps = cfg["steps"]
self.clip = cfg["clip"]
self.image_size = image_size
self.feature_maps = [
[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)]
for step in self.steps
]
self.name = "s"
def forward(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [
x * self.steps[k] / self.image_size[1] for x in [j + 0.5]
]
dense_cy = [
y * self.steps[k] / self.image_size[0] for y in [i + 0.5]
]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
output = np.array(anchors).reshape(-1, 4)
if self.clip:
output = np.clip(output, 0, 1)
return output
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