SSD - Pytorch

The purpose of the document is study 

class PriorBox(object):
  """ Compute priorbox coordinates in center-offset form for each source feature map
  """
  def __init__(self, cfg):
    super(PriorBox, self).__init__()
    self.image_size = cfg['min_dim'] #300
    self.num_priors = len(cfg['aspect_ratios']) #6
    self.variance = cfg['variance'] or [0.1] 
    self.feature_maps = cfg['feature_maps'] #[38, 19, 10, 5, 3, 1]
    self.min_sizes = cfg['min_sizes']  #[30, 60, 111, 162, 213, 264]
    self.max_sizes = cfg['max_sizes'] #[60, 111, 162, 213, 264, 315]
    self.steps = cfg['steps'] #[8, 16, 32, 64 , 100, 300]
    self.aspect_ratios = cfg['aspect_ratios'] #[[2],[2,3],[2,3],[2,3],[2],[2]]
    self.clip = cfg['clip'] #True
    self.version = cfg['name'] #VOC
    for v in self.variance:
      if v<=0:
        raIse ValueError("variances must be greater than 0")
  def forward(self):
    mean=[]
    for k,f in enumerate(self.feature_maps): 
      for i, j in product(range(f), repeat=2): #get feature map eg)36*36
        f_k = self.image_size/self.steps[k] 
        # 300/[8 16 32 64 100 300]=[37.5  18.75  9.375 4.6875 3 1]
        # 37.5
        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
        #[30, 60, 111, 162, 213, 264]/300=[0.1 0.2 0.37 0.54 0.71 0.88]
        mean += [cx, cy, s_k, s_k]

        #aspect_ration : 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
        #feature map : 36 aspect ratio: 2
        #featrue map : 36 aspect ratio : 2,3[2],[2,3],[2,3],[2,3],[2],[2]]
          mean += [cx, cy, s_k*sqrt(ar), s_k/sqrt(ar)]
        for ar in self.aspect_ratios[k]: # #[
          mean += [cx, cy, s_k/sqrt(ar), s_k*sqrt(ar)] #(centerx, centery, width, height) #나중에 계싼 필요 할거 같은데..?

        output = torch.Tensor(mean).view(-1,4)
        if self.clip:
          output.clamp_(max=1,mim=0) #좌표 안이여서 가능한 것 같음
        return output          

 

output: center x, center y, scaled width, scaled height

 

 

 

---

 

 

pytorch._clamp

pytorch

 

a=[]
a+=[1,2,3,4]
a+=[5,6,7,8]
output = torch.Tensor(a).view(-1,4)
print(output.clamp_(min=1,max=3))

Output

tensor([[1., 2., 3., 3.],
        [3., 3., 3., 3.]])

 

[참조]

github:https://github.com/amdegroot/ssd.pytorch

pytorch documnet: https://pytorch.org/docs/