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