Circular Smooth Label Encoder. Args: angle_targets (Tensor): Angle offset for each scale level Has shape (num_anchors * H * W, 1) Returns: list[Tensor]: The csl encoding of angle offset for each scale level. Has shape (num_anc
(self, angle_targets)
| 38 | self.coding_len = int(self.angle_range // omega) |
| 39 | |
| 40 | def encode(self, angle_targets): |
| 41 | """Circular Smooth Label Encoder. |
| 42 | |
| 43 | Args: |
| 44 | angle_targets (Tensor): Angle offset for each scale level |
| 45 | Has shape (num_anchors * H * W, 1) |
| 46 | |
| 47 | Returns: |
| 48 | list[Tensor]: The csl encoding of angle offset for each |
| 49 | scale level. Has shape (num_anchors * H * W, coding_len) |
| 50 | """ |
| 51 | |
| 52 | # radius to degree |
| 53 | angle_targets_deg = angle_targets * (180 / math.pi) |
| 54 | # empty label |
| 55 | smooth_label = torch.zeros_like(angle_targets).repeat( |
| 56 | 1, self.coding_len) |
| 57 | angle_targets_deg = (angle_targets_deg + |
| 58 | self.angle_offset) / self.omega |
| 59 | # Float to Int |
| 60 | angle_targets_long = angle_targets_deg.long() |
| 61 | |
| 62 | if self.window == 'pulse': |
| 63 | radius_range = angle_targets_long % self.coding_len |
| 64 | smooth_value = 1.0 |
| 65 | elif self.window == 'rect': |
| 66 | base_radius_range = torch.arange( |
| 67 | -self.radius, self.radius, device=angle_targets_long.device) |
| 68 | radius_range = (base_radius_range + |
| 69 | angle_targets_long) % self.coding_len |
| 70 | smooth_value = 1.0 |
| 71 | elif self.window == 'triangle': |
| 72 | base_radius_range = torch.arange( |
| 73 | -self.radius, self.radius, device=angle_targets_long.device) |
| 74 | radius_range = (base_radius_range + |
| 75 | angle_targets_long) % self.coding_len |
| 76 | smooth_value = 1.0 - torch.abs( |
| 77 | (1 / self.radius) * base_radius_range) |
| 78 | |
| 79 | elif self.window == 'gaussian': |
| 80 | base_radius_range = torch.arange( |
| 81 | -self.angle_range // 2, |
| 82 | self.angle_range // 2, |
| 83 | device=angle_targets_long.device) |
| 84 | |
| 85 | radius_range = (base_radius_range + |
| 86 | angle_targets_long) % self.coding_len |
| 87 | smooth_value = torch.exp(-torch.pow(base_radius_range, 2) / |
| 88 | (2 * self.radius**2)) |
| 89 | |
| 90 | else: |
| 91 | raise NotImplementedError |
| 92 | |
| 93 | if isinstance(smooth_value, torch.Tensor): |
| 94 | smooth_value = smooth_value.unsqueeze(0).repeat( |
| 95 | smooth_label.size(0), 1) |
| 96 | |
| 97 | return smooth_label.scatter(1, radius_range, smooth_value) |
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