| import torch |
| from itertools import product as product |
| import numpy as np |
| from math import ceil |
|
|
|
|
| class PriorBox(object): |
| def __init__(self, cfg, image_size=None, phase='train'): |
| 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] |
|
|
| 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] |
| if min_size == 32: |
| dense_cx = [x*self.steps[k]/self.image_size[1] for x in [j+0, j+0.25, j+0.5, j+0.75]] |
| dense_cy = [y*self.steps[k]/self.image_size[0] for y in [i+0, i+0.25, i+0.5, i+0.75]] |
| for cy, cx in product(dense_cy, dense_cx): |
| anchors += [cx, cy, s_kx, s_ky] |
| elif min_size == 64: |
| dense_cx = [x*self.steps[k]/self.image_size[1] for x in [j+0, j+0.5]] |
| dense_cy = [y*self.steps[k]/self.image_size[0] for y in [i+0, i+0.5]] |
| for cy, cx in product(dense_cy, dense_cx): |
| anchors += [cx, cy, s_kx, s_ky] |
| else: |
| cx = (j + 0.5) * self.steps[k] / self.image_size[1] |
| cy = (i + 0.5) * self.steps[k] / self.image_size[0] |
| anchors += [cx, cy, s_kx, s_ky] |
| |
| output = torch.Tensor(anchors).view(-1, 4) |
| if self.clip: |
| output.clamp_(max=1, min=0) |
| return output |
|
|
