This repo has two purposes.
- generate anchors for one-stage detection models' prediction
- assign targets to the anchors for obtaining the loss
Anchor-based detection models have been mainly developed over four series.
- Faster R-CNN
- YOLO
- RetinaNet
- SSD
Each anchor-based detection model series has a different manners of creating anchor priors, creating anchors, and combining them with the model's regression predictions. Despite slight variations from model to model, models included in a series basically share the same manners.
Now let's see the examples of how Anchor-Maker gives its anchors to models 🔥
img_size = 416
anchor_sizes = [
[(10, 13), (16, 30), (33, 23)],
[(30, 61), (62, 45), (59, 119)],
[(116, 90), (156, 198), (373, 326)]
]
strides = [8, 16, 32]
anchors = yolo_anchors(img_size, anchor_sizes, strides)
for i, s in enumerate(strides):
pred[i][..., :2] = torch.sigmoid(pred[i][..., :2]) * s + anchors[i][..., :2]
pred[i][..., 2:4] = torch.exp(pred[i][..., 2:4]) * anchors[i][..., 2:]Here, we should note that in YOLO, unlike the others, anchors and model predictions are a list of tensors on each stride, not tensors flattened over given strides.
img_size = 608
anchor_sizes = [32, 64, 128, 256, 512]
anchor_scales = [1, 2**(1/3), 2**(2/3)]
aspect_ratios = [(2**(1/2), 2**(-1/2)), (1, 1), (2**(-1/2), 2**(1/2))]
strides = [8, 16, 32, 64, 128]
anchors = retinanet_anchors(img_size, anchor_sizes, anchor_scales, aspect_ratios, strides)
pred[..., :2] = anchors[..., :2] + (pred[..., :2] * anchors[..., 2:])
pred[..., 2:4] = torch.exp(pred[..., 2:4]) * anchors[..., 2:]img_size = 300
anchor_sizes = [21, 45, 99, 153, 207, 261]
upper_sizes = [45, 99, 153, 207, 261, 315]
strides = [8, 16, 32, 64, 100, 300]
num_anchors = [4, 6, 6, 6, 4, 4]
anchors = ssd_anchors(img_size, anchor_sizes, upper_sizes, strides, num_anchors)
pred[..., :2] = anchors[..., :2] + (center_variance * pred[..., :2] * anchors[..., 2:])
pred[..., 2:4] = torch.exp(size_variance * pred[..., 2:4]) * anchors[..., 2:]It is very necessary to map the predictions and target labels for obtaining the detection models' loss. Anchor-Assigner assigns targets to the anchors, and then return indices of target-assigned anchors and lables of assigned targets.
This implementation can be commonly applied to all series of detection models.
assigner = retinanet_assigner(0.5, 0.4)
assigns = assigner(labels, anchors)
for pred, assign in zip(preds, assigns):
fore_idx, fore_label = assign['foreground']
back_idx, _ = assign['background']
cls_loss = self.focal_loss(pred[..., 4:], fore_idx, back_idx, fore_label[..., 4:])
reg_loss = self.smooothL1_loss(pred[..., :4], anchors, fore_idx, fore_label[..., :4])BSD 3-Clause License Copyright (c) 2022