def __call__(self) -> torch.Tensor:
    anchors: List[float] = []
    for (k, f) in enumerate(self.feature_maps):
        min_sizes: List[int] = self.min_sizes[k]
        for i in range(f[0]):
            for j in 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])
                    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, device=self.device, dtype=self.dtype).view((- 1), 4)
    if self.clip:
        output = output.clamp(max=1, min=0)
    return output
def __call__(self) -> torch.Tensor:
    anchors: List[float] = []
    for (k, f) in enumerate(self.feature_maps):
        min_sizes: List[int] = self.min_sizes[k]
        for i in range(f[0]):
            for j in 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])
                    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, device=self.device, dtype=self.dtype).view((- 1), 4)
    if self.clip:
        output = output.clamp(max=1, min=0)
    return output