def forward(self):
mean = []
# TODO merge these
if self.version == 'v2':
for k, f in enumerate(self.feature_maps):
for i, j in product(range(f), repeat=2):
f_k = self.image_size / self.steps[k]
# unit center x,y
cx = (j + 0.5) / f_k
cy = (i + 0.5) / f_k
# aspect_ratio: 1
# rel size: min_size
s_k = self.min_sizes[k]/self.image_size
mean += [cx, cy, s_k, s_k]
# aspect_ratio: 1
# rel size: sqrt(s_k * s_(k+1))
s_k_prime = sqrt(s_k * (self.max_sizes[k]/self.image_size))
mean += [cx, cy, s_k_prime, s_k_prime]
# rest of aspect ratios
for ar in self.aspect_ratios[k]:
mean += [cx, cy, s_k*sqrt(ar), s_k/sqrt(ar)]
mean += [cx, cy, s_k/sqrt(ar), s_k*sqrt(ar)]
else:
# original version generation of prior (default) boxes
for i, k in enumerate(self.feature_maps):
step_x = step_y = self.image_size/k
for h, w in product(range(k), repeat=2):
c_x = ((w+0.5) * step_x)
c_y = ((h+0.5) * step_y)
c_w = c_h = self.min_sizes[i] / 2
s_k = self.image_size # 300
# aspect_ratio: 1,
# size: min_size
mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k,
(c_x+c_w)/s_k, (c_y+c_h)/s_k]
if self.max_sizes[i] > 0:
# aspect_ratio: 1
# size: sqrt(min_size * max_size)/2
c_w = c_h = sqrt(self.min_sizes[i] *
self.max_sizes[i])/2
mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k,
(c_x+c_w)/s_k, (c_y+c_h)/s_k]
# rest of prior boxes
for ar in self.aspect_ratios[i]:
if not (abs(ar-1) < 1e-6):
c_w = self.min_sizes[i] * sqrt(ar)/2
c_h = self.min_sizes[i] / sqrt(ar)/2
mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k,
(c_x+c_w)/s_k, (c_y+c_h)/s_k]
# back to torch land
output = torch.Tensor(mean).view(-1, 4)
if self.clip:
output.clamp_(max=1, min=0)
return output
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